health microinsurance education project evaluation ... 2 final baseline... · freedom from hunger,...

Innovations for Poverty Action | Osu PMB 57 | Accra, Ghana | 021 774570 | www.poverty-action.org 1

Innovations for Poverty Action

Health Microinsurance Education Project Evaluation Northern Region, Ghana

Final Baseline Report

September 2011

Annex 2 – Final Baseline Report Freedom from Hunger R2-125


Contents 1. Executive Summary ............................................................................................................ 4

2. Introduction ....................................................................................................................... 7

3. Background ........................................................................................................................ 7

Health Insurance in Ghana ..................................................................................................... 7

4. Description of Study ......................................................................................................... 10

Health Microinsurance Education ....................................................................................... 10

Partnership Roles ................................................................................................................. 11

Evaluation Design ................................................................................................................. 12

Data Collection and Analysis ................................................................................................ 14

5. Baseline Survey Results .................................................................................................... 16

Respondent Demographics .................................................................................................. 16

Respondent Household Demographics ............................................................................... 18

Initial Health Insurance Registration and Enrollment Levels ............................................... 21

Use of insurance for well-patient visits ............................................................................... 29

Health insurance status of household members ................................................................. 29

Demographic Characteristics and Insurance Registration and Enrollment ......................... 32

Attributes Related to Adult Registration and Enrollment ............................................... 32

Attributes Related to Child Registration and Enrollment ................................................ 36

Household Position and Adult Registration and Enrollment ........................................... 38

Household Position and Child Registration and Enrollment ........................................... 39

Attributes Related to Household Registration Rates ....................................................... 40

Living Conditions of SAT Clients ........................................................................................... 42

Income Generating Activities ............................................................................................... 45

Household Consumption ..................................................................................................... 49

Household Assets ................................................................................................................. 50

Food Security and Shocks .................................................................................................... 51

Perceptions of Current Financial Situation .......................................................................... 54

Perceptions of Current Health ............................................................................................. 55

Insurance Registration and Financial Attributes .................................................................. 56

Subjective Expectations and Risk Aversion .......................................................................... 64

Knowledge and Attitudes about Health Insurance .............................................................. 70

Knowledge and Attitudes and Registration and Enrollment Status .................................... 73

Registration/Enrollment and Knowledge ........................................................................ 73

Registration/Enrollment and Attitudes ........................................................................... 73


Health Events ....................................................................................................................... 74

Incidence of Health Events .............................................................................................. 74

Impact of Health Events ................................................................................................... 75

Responses to Health Events ............................................................................................. 76

Insurance Registration and Enrollment and Health Events ............................................. 77

Severity of Health Events and Registration and Enrollment ........................................... 79

Treatment of Health Events ............................................................................................. 80

Well-Patient Visits and Insurance Registration and Enrollment ..................................... 83

6. Timeline Going Forward ................................................................................................... 84

7. Conclusion ........................................................................................................................ 85

8. Appendix A: Stratification ................................................................................................ 88

9. Appendix B: Enrollment Status Extrapolation ................................................................. 90

10. Appendix C: Currency In Ghana .................................................................................... 92

11. Appendix D: Full Regression Results ............................................................................. 93


1. Executive Summary

National health insurance that provides a comprehensive set of health care services has been available to the formal and informal sectors in Ghana since 2003. However, coverage is far from universal, especially in rural areas. Freedom from Hunger, Sinapi Aba Trust (SAT) and Innovations for Poverty Action have formed a partnership to create, implement, and evaluate a program to educate microfinance clients in Ghana’s Northern Region about health insurance provided through the National Health Insurance Scheme (NHIS) of Ghana. The evaluation will assess if education has a positive effect on insurance take-up and retention, and the impact of insurance on use of health services, health spending, and financial security. Designed as a randomized control trial, the evaluation will compare outcomes for microfinance clients randomly assigned to receive the education treatment to outcomes for clients randomly assigned to the control group. In October 2010, Innovations for Poverty Action completed the baseline survey of all clients in the sample. This report summarizes results and analysis from the baseline data for some key questions in the study, including data on client demographics, insurance enrollment status, food security, financial shocks, respondent assessment of current health, and knowledge and attitudes towards insurance, incidences of injury or illness, and relationships between insurance registration and health and financial outcomes.

The main findings from the baseline data are as follows:

SAT client registration and enrollment: About 70 percent of clients report having registered in the NHIS at some point. However, many clients who have registered are likely not to be currently enrolled and eligible to receive covered health services. Based on reported insurance status and extrapolations from cross-checking of data obtained from visual inspection of insurance cards and reports of use of insurance to access health services, we estimate that at the time of the baseline survey that only 32.6% of the clients interviewed were actively enrolled (with premium payment current) and eligible for covered health care services. We further estimate that another 9.9% may be in the required initial three-month waiting period and therefore will become eligible for benefits within the next three months. This leaves 57.4% of the sample who are not actively enrolled in the NHIS at the time of the baseline survey.

These estimates are in-line with data from other sources about enrollment of informal sector adults in the NHIS. Although NHIS data indicates that 29.4% of informal sector adults are registered1, there is no data available for how many of these are actively enrolled and therefore eligible for covered services. Data from the Ghana Demographic and Household Survey (2008) indicates that 39% of women and 29% of men were covered by the NHIS2. Clients’ Household Insurance Registration and Enrollment: Sinapi Aba Trust clients’ household members are registered at just under the same rate as the clients themselves, with 66 percent of household members reported as registered. About 12 percent of households have no one registered, and 36 percent had all household members registered. Children in SAT households are slightly more likely to be currently enrolled, and less likely to

1 NHIS Annual Report 2009

2 Ghana Demographic and Household Survey – September 2008


be expired, probably because their younger age makes it more likely that they have been registered recently. Client household members are enrolled also at just under the same rate as the clients themselves. We estimate that about 29% of all family members are enrolled in insurance. Demographic Characteristics and Registration: Women are more likely to be registered and enrolled for insurance than men. Individuals with any level of education are more likely to be registered than those with no education at all, and there was no relationship between education and enrollment. Household Finances: Sinapi Aba Trust clients in our sample reported a wide range in their sales from their microenterprise business. About 17 percent reported no weekly sales, likely because their businesses were dormant at the time of the survey. About 43 percent have sales under 50 GHC per week (a little over $30), but some respondents reported sales of over 1000 GHC per week (a little over $600). About half of SAT households have income from farming; the average annual value of the harvest, excluding outliers, was 642 GHC. Earnings from other income sources, excluding outliers, averaged 21 GHC per week.

Respondents were asked to report consumption for key categories, including food, transportation, and communication. Average annual per capita consumption per household member for these categories was about 450 GHC, or $300. It should be noted that the consumption recorded in the survey did not include all possible types of consumption—for example, spending on clothes and school fees were not included—so SAT clients are likely living on substantially more per year than 450 GHC. Shocks: A large number of clients are susceptible to both hunger and to income shocks. Nearly half of all clients reported hunger or food insecurity event in the past month. Sixteen percent reported having to take a child out of school because the household suddenly did not have enough money; 37 percent reported having to sell valuables for the same reason. Insurance and Finances and Shocks: None of the income measures were significantly correlated with insurance registration rates or current enrollment rates. Consumption was significantly associated with higher insurance registration rates, but the size of the relationship was very small. The lack of a large, significant relationship between income or consumption and insurance registration may suggest that the cost of the insurance premium is not a large barrier to enrollment for SAT clients.

Insurance registration was significantly correlated with a lower probability that a household would experience hunger. Higher insurance registration rates were also associated with lower probability of pulling a child out of school for financial reasons. Current enrollment was not significantly correlated with probability of experiencing a food insecurity event or pulling a child out of school. Subjective Expectations: Eliciting estimates of the probability of illness from respondents proved to be challengin. When asked to estimate the relative risk of getting sick at different frequencies, many respondents gave answers that were not logically consistent, which raises questions about the validity of the data from these questions. On average, respondents reported a 73 percent chance of getting sick at least once in the next month,


and an 84 percent chance of getting sick at least once in the next year. There was no significant relationship between the respondents’ estimated likelihood of getting sick and the probability of being registered for health insurance. Insurance Knowledge and Attitudes: Most SAT clients have some basic knowledge about health insurance and have positive views of it. A large majority of clients report talking to other members of their household about insurance. The most common reason given by clients for not having health insurance was that they had not gotten around to registering, but that they intended to at some point. A large number also said that the premium was too expensive. For those clients who had a health event and did not use insurance to pay for the health event, none of them indicated it was because they did not know how. Respondents with more knowledge about insurance and more positive attitudes towards insurance were more likely to be registered in insurance, as were their households. There was no significant relationship with enrollment status. Health Events: About 52 percent of households and 11 percent of individuals experienced a health event in the month prior to the survey. Among individuals who experienced a health event, almost all of them received treatment. There was no detectable relationship between insurance registration status and probability of getting treatment, but this is unsurprising because the lack of variation in probability of getting treatment makes it difficult to find relationships at a significant level.

Being currently enrolled in insurance was positively correlated with the likelihood of having a health event; this was true of being registered as well, but the correlation size was smaller. Type of Treatment Sought: While registration and enrollment were not significantly related to the likelihood of getting treatment, they had a significant relationship to what type of treatment was sought. The two most common health service providers that SAT clients consulted when faced with a health event were doctors and chemical (drug) sellers. Chemical sellers can sell drugs directly to clients that would often require a prescription in countries like the United States. These consultations are free, fast, and easy, but insurance will only cover the cost of a medicine if a patient gets a prescription for it from a doctor. As a result, we would expect to see those without insurance going directly to a chemical seller and foregoing the cost of a doctor consult, and those with insurance more likely to consult a doctor so that medicines are covered. In fact, this is what our data suggests with a large and significant relationship between insurance registration and enrollment and use of doctors: being registered in insurance was associated with a 17 percentage point increase in the probability of consulting a doctor, and a 10 percentage point decrease in the probability of consulting a chemical seller; being currently enrolled increased the relationship in both cases. Individuals who were enrolled in insurance were more likely to have attended a well patient visit in the past month.


2. Introduction Although Ghana introduced a national health insurance program in 2003, enrollment rates remain low, particularly in rural areas. In 2010, Freedom from Hunger entered into a partnership with Sinapi Aba Trust (SAT), a Ghanaian microfinance institute, and Innovations for Poverty Action, an NGO specializing in impact evaluation, to design, implement and evaluate a program to teach microfinance clients about health insurance. The key questions of this evaluation are to determine whether the program increases up-take of insurance, and how insurance enrollment affects use of health services, health spending, and indicators of financial security. The project is receiving funding from the International Labor Organization. The baseline survey for this project was conducted in September and October of 2010, just before the education program began implementation. The survey questioned five members of each of the 300 microfinance groups included in the sample. Data entry for the survey was completed in February of 2011. This report is a preliminary analysis of the baseline data, answering priority questions about the characteristics of the clients in the sample, including insurance enrollment status, prevalence of food insecurity, susceptibility to shocks, respondents’ rating of their own current health, and knowledge and attitudes toward insurance.

3. Background

Health Insurance in Ghana

Ghana introduced a national health insurance program in 2003. Under the program, individuals in the informal sector can register for health insurance by paying an insurance premium and registration fee, and after a three month period, receive a comprehensive set of covered health services for no fee3. Pregnant women, children under age 18 (of registered parents) and persons age 70 and older are not required to pay the annual premium, but may need to pay a one-time registration fee. The health services covered by the NHIS are laid out in the minimum basic benefits package (see Appendix C). The list is fairly extensive and purports to cover 95% of all health problems reported in Ghanaian healthcare facilities (see Box 1). A prescribed medicines list is also delineated. Expensive, highly specialized care such as dialysis for chronic renal failure, and organ transplants are not covered by the NHIS. Neither are ARVs for the treatment of HIV/AIDS, as these drugs are supplied by a separate government program (www.nhis.gov.gh). There is a notable emphasis on female reproductive health in the benefits package. Benefits for maternity care include antenatal care, caesarean sections, and postnatal care for up to

3 National Health Insurance Authority Report – 2009

http://www.nhis.gov.gh/


six months after birth. Treatment for breast and cervical cancer are included in the package, although treatment for other cancers is not. While the program has dramatically increased access to health care services, there are still a large number of Ghanaians, particularly among the poorest, who are not registered in the health insurance program. The Ghanaian National Health Insurance Authority (NHIA) estimates that 62 percent of the population is registered with 48 percent actually having current enrollment. A controversial report published by Oxfam suggests that this is likely much lower and that insurance enrollment rates could be as low as 18 percent.4 The insurance program is run at the district level by local National Health Insurance Scheme (NHIS) offices, and overseen at the regional and national level by the NHIA. The NHIS districts have operated largely as independent franchises, with discretion to set their own registration fees and other policies, but reform of the health insurance program is a current topic of political debate, and it appears that NHIA has made some attempts to take a larger role in coordinating policies across NHIS offices. While NHIS offices can set their own registration fees, which usually range from 2-5 GHC (1.32-3.30 USD), NHIA sets annual premiums. Premiums are in theory based on the individual’s ability to pay. In practice, determining ability to pay is challenging. In the Northern region, with the exception of government employees, few people pay more than the lowest rate, which is set at 7.20 GHC (4.75 USD) per annum. In addition, some districts reported charging premiums out of line with the 7.20 GHC set by the NHIA. Because fees (and sometime premiums) vary by NHIS office, the total cost of registering for insurance varies as well, but is typically around 10-14 GHC (6.60-9.25 USD) for adults. See Table 1 for a list of premiums and fees charged by the NHIS districts serving the project program participants. Children under 18 are exempt from the premium payment, but usually must pay the registration fee. Table 1: Insurance Premiums and Fees Reported by NHIS Districts Serving Clients of the Tamale, Bole, Salaga and Walewale SAT Branches

NHIS District Registration fee for adult

Premium for adult

Total cost of registration for adult

Tolon GHC 3.00 GHC 7.20 GHC 10.20 Savelugu GHC 4.00 GHC 7.20 GHC 11.20 Tamale GHC 3.00 GHC 7.20 GHC 10.20 West Manprusi GHC 4.00 GHC 10.00 GHC 14.00 Bole GHC 5.00 GHC 8.00 GHC 13.00

East Gonja GHC 3.00 GHC 7.20 GHC 10.20

AVERAGE GHC 3.67 GHC 7.80 GHC 11.47

4 Oxfam Internationa 2011. “Achieving a Shared Goal: Free Universal Health Care in Ghana”.

http://www.oxfam.org/en/policy/achieving-shared-goal-ghana-healthcare March 9, 2011.

http://www.oxfam.org/en/policy/achieving-shared-goal-ghana-healthcare


Once a person registers with NHIS and pays applicable fees and the annual premium, there is a three month waiting period before he or she can use the insurance to access health care services. At the end of that period, the individual is supposed to receive a health insurance card from NHIS. In many cases, the card arrives late. If that happens, the individual can sometimes obtain a temporary card from NHIS which can be used to access health services. The insurance remains in effect for 1 year, at which point the individual must re-enroll. After the expiration date, the individual has a 3 month grace period in which he or she can re-enroll, and can continue to receive services without interruption. If the individual fails to re-enroll within that grace period, formal NHIS policy is that the individual must go through another 3-month waiting period. In reality, no NHIS offices serving the SAT clients in our sample enforce this rule at this time5; rather, they allow individuals to access care immediately after re-enrolling, even if the policy has expired. (If the insurance has been expired for more than one year, clients must usually pay the premium for every year that they have missed in order to use insurance immediately.) The expiration date is printed on the NHIS card, but the onus falls on the client to remember to re-enroll; this poses a particular challenge for illiterate clients who cannot read the expiration date on the card, and who may not understand that they need to pay once a year. When a client’s insurance expires at the end of one year, the client is still considered to be “registered” with NHIS—their information is stored in NHIS databases, and if they re-enroll, they do not receive a new insurance card. In order to be considered “enrolled” or “active”, however, the client must be current on the premium payment. If the client fails to pay the annual premium, the client may be termed “unenrolled”, “inactive” or “expired”. NHIS offices report that it is re-enrollment that is a particular challenge. While registration rates have increased, many of the registered individuals fail to re-enroll each year. For example, the Tolon NHIS office, which serves a rural area near the city of Tamale in Northern Ghana6, estimates that about half of the population in its district is registered and has a current policy, but another 30 percent has registered but not renewed their insurance, allowing it to expire. This is consistent with our findings where 70% of the respondents report being registered for insurance, but only about 32.6% of the total could be either confirmed as currently enrolled (premiums current) from visual inspection of the insurance card, or through extrapolation based on their reported use and ways of paying for health services.

5 Some NHIS directors have reported that the National Health Insurance Authority has directed them to begin

enforcing this rule as of October 2011. IPA has received reports of similar efforts by the NHIA in the past, and no change in policy has resulted. 6 Some of SAT’s groups served by its Tamale branch are located in the areas served by the Tolon NHIS office.

People may register at any NHIS office, so the Tolon NHIS office possibly serves some people living within the city of Tamale as well.


There are a number of potential barriers to registration and enrollment in the health insurance program. Individuals may not know about the program, or may not understand how insurance works, what is covered, or may not know how to go about registering. Some individuals may also be unable to afford the premium. While a 10-14 GHC payment is not a particularly high amount even in Ghana, a large family may find it a challenge to put together the money to cover every adult household member under age 70. Individuals may also believe that insurance is not a good value for them, that they will not need health services, or that the quality of services available does not justify paying for insurance. Lastly, individuals may have every desire and intention to register, but simply do not get around to doing it.

4. Description of Study

Health Microinsurance Education

If knowledge about Ghana’s health insurance program or about insurance in general is a barrier to registration or re-enrollment, education may be an effective means of increasing insurance uptake and increasing access to health care services. Education could also be effective in increasing interest in the program and pushing those who want to register but have not yet done so. The Health Microinsurance Education (HME) project aims to provide education about health insurance to clients of the microfinance institution Sinapi Aba Trust in Northern Ghana. The education sessions are given at meetings of the clients’ microfinance groups. Four types of education modules are being tested; the material covered is the same for each module, but delivery differs. The four versions are as follows:

Registered and Currently Enrolled 52%

Unregistered 18%

Registered but Expired

(Unenrolled) 30%

Figure 2: Insurance Take-up in Tolon, Ghana

Source: Tolon NHIS


Technical Learning Conversations (TLCs)—six sessions, about 30 minutes each, administered every two weeks.

Technical Learning Conversations (TLCs)——six sessions, about 30 minutes each, administered every two weeks, plus an additional 30 minute session one year later reminding clients they must re-enroll to prevent their insurance from expiring.

Consolidated Sessions—one session, about two hours long, administered once.

Consolidated Sessions—one session, about two hours long, administered once, plus an additional 30 minute session one year later reminding clients they must re-enroll to prevent their insurance from expiring.

The education sessions are delivered by the loan officers who serve the microfinance clients. After completing the education program, the clients have the opportunity to meet with representatives from NHIS to register or re-enroll in health insurance. The education modules will be evaluated to assess their impact on take-up rates of national health insurance. The education sessions began in October 2010. Although originally scheduled to end in early January 2011, challenges with scheduling meetings with groups meant that the delivery of the education was often delayed, with education for some groups extending through March.

Partnership Roles

The Health Microinsurance Education evaluation is a collaboration of three organizations. The health insurance education modules have been designed by Freedom from Hunger (FFH), a U.S.-based NGO. FFH is providing education materials, training, reimbursement of related costs for training, and technical support to Sinapi Aba Trust (SAT), a Ghanaian microfinance institute, which is implementing the education modules with its clients. The education program is being evaluated, using a randomized control trial, by Innovations for Poverty Action (IPA), a U.S.-based research NGO. IPA has worked closely with FFH and SAT to design and plan a program implementation design that adheres to the randomized design. In addition, with the guidance of academic researchers Raymond Guiteras PhD of University of Maryland and Harounan Kazianga PhD of Oklahoma State University, IPA is designing and conducting the data collection surveys that will be used to determine program effect on client health insurance knowledge, health insurance take-up rates, and reported use of and spending for health services. FFH and SAT are assisting in planning the evaluation and designing the data collection tools. SAT is also assisting with data collection activities. Freedom from Hunger has designed the education materials and provided training to SAT branch managers and credit officers. SAT is providing the education to the MFI members. IPA has done limited monitoring of the program implementation. SAT is also coordinating


with local National Health Insurance Scheme districts to invite insurance marketers to visit all of the client groups in the sample and offer the clients insurance.

Evaluation Design

A randomized control trial (RCT) is a type of impact evaluation that randomly assigns some individuals to participate in a program (the treatment group), and some individuals to not participate (the control group), and compare the outcomes for the two groups. Randomized control trials have the advantage that, with a large enough sample, the treatment and control groups are statistically identical; on average the only difference between them is that one group gets the treatment and one does not. Therefore, any differences in outcomes can be attributed with certainty to the treatment. The HME Project Evaluation is employing an RCT to answer the following research questions:

1. What is the impact of the education on consumer knowledge about health microinsurance?

2. What is the impact of education on consumer decision making about whether to enroll or not in an available health insurance scheme?

3. Does education affect the ability to appropriately use insurance to access and use covered services?

4. Does education influence decision making about re-enrollment in the insurance scheme? (in this case after one year)

5. What types of individuals (demographic and economic characteristics) are more likely to take-up the National Health Insurance Scheme? For which types of people does the education have the greatest effect with respect to take-up, re-enrollment, and appropriate use of services?

6. Which type of education is more effective: education delivered in short sessions every two weeks, or education delivered in one long session in terms of take-up, usage, and re-enrollment?

7. What is the impact of a refresher training (provided one year after the initial education) on re-enrollment in the population?

8. What is the impact of health insurance on household financial stability and well-being? How does health insurance affect the way households access and finance health treatment? Without health insurance, how do households manage health carexpenses? Do they draw on savings? Do they sacrifice consumption? Do they sell assets? (Endline and qualitative studies)


9. What do households perceive as the benefits of health insurance and what is the impact of the education on these perceptions? (Qualitative study)

10. What factors influenced their decision to enroll or not enroll in the health insurance and did education affect these? (Qualitative study)

11. What do they consider to be significant barriers to adoption, and is this different for groups that received education? (Qualitative) study)

In order to answer these questions, the HME Project evaluation uses four treatment groups, one for each of the four education approaches, and one control group. Since education sessions are given to an entire credit group at once, randomization is done at the level of the credit group; that is, all clients in the same credit group will be assigned to the same treatment group or to the control group. The sample for the evaluation comprises active credit groups in four SAT branches in the Northern Region: the Tamale branch, the Walewale branch, the Salaga branch, and the Bole branch. Active credit groups are those that are currently meeting on a regular basis. Credit groups may become inactive if they stop working with SAT entirely, choose not to take loans during a particular loan cycle, or if they fall behind on their payments. Active credit groups were identified by taking lists of groups from SAT, and conducting a census interview with each of the groups. The census interview ascertained that the group was actually active, collected basic information about the group members including enrollment status, and recorded contact information so that the group could be contacted for future survey interviews. The sample size was set at a total 300 credit groups, to ensure enough power to measure the impact of the intervention. In order to have 300 groups, groups from four different SAT branches were included in the sample. In Walewale, Salaga and Bole, 60 active groups were selected for inclusion in the sample from among the branches’ active groups. The Tamale branch has the largest number of credit groups, so the sample from Tamale was twice the size as the samples in the other branches, with 120 active groups included in the sample. In Salaga and Bole, more than 60 active groups were identified; in those cases, the 60 groups to be included in the sample were selected randomly from the total number of groups. . Five members in each credit group were randomly selected to be surveyed7. After the credit groups to be included in the sample were selected, the credit groups in the sample were randomly assigned to treatment and control groups. Of the sample credit groups, 40 percent were assigned to the control group, while 15 percent were assigned to each of the four treatment groups. Assignment was random, stratified on branch, urban or rural, and high or low enrollment, based on information collected through a census of credit groups. Stratification means that randomization is done separately for each of the

7 Credit groups that had fewer than 5 members were randomly paired with another credit group with fewer

than 5 members to create a new “credit group” with at least 5 members. These pairs are treated as one credit group in the research design; both credit groups assigned to the pair are placed in the same treatment group or in the control group.


combinations stratification variables. Enrollment was defined as being current on an NHIS insurance policy. For further detail on the stratification groups, see Appendix A. Table 3. Criteria for Assigning Stratification Groups Based on Enrollment

Group Enrollment Criteria Assignment

A plurality of the group was not currently enrolled Low enrollment

A plurality of the group was currently enrolled High enrollment

A plurality of the group did not know enrollment status or had no data reported Unknown enrollment

A tie between the share "unknown" and the share "low" and/or "high" Unknown enrollment

A tie between the share "low" and the share "high" Low enrollment

Data Collection and Analysis

The impact of the program will be assessed using data from several sources. The primary sources of data are an extensive baseline survey conducted with clients before the beginning of the education sessions and one shorter take-up survey conducted after the education sessions and NHIS marketer visits. An endline study, which would be a repeat of the baseline survey one year later, is also planned. The baseline survey was administered from September 2010 to November 2010. The survey was administered at each respondent’s home, unless the respondent requested an alternative location, such as the place of business or SAT microfinance group meeting location. The survey was conducted with just over 1500 respondents. (The additional respondents over 1500 come from additional groups that were included in the survey in case one of the original groups became inactive or otherwise could not be included.) The survey was conducted in paper form, by enumerators hired and trained by IPA. The survey was 40 pages long and took approximately 1-2 hours to complete. The survey included the following sections: Cover page and household list with personally identifying information Section A: Household roster, demographics and insurance status Section B: Household finances and living conditions Section C: Subjective expectations about health and risk Section D: Health events and use of health services Section E: Knowledge and attitudes about insurance Section F: Social ties to other SAT groups Section G: Location Section H: Biometrics The biometrics section was often completed separately from the rest of the survey, at a time when the children in the household were at home. Data entry for the baseline survey


was done by IPA’s in-house data entry team in Accra, using double-double data entry. Data entry was conducted from January 2011 to February 2011. The take-up survey and endline will be conducted with the same respondents as the baseline. The take-up survey, conducted in July 2011, was much shorter than the baseline survey, with a length of about 5 pages. It was conducted at SAT microfinance group meetings by surveyors hired and trained by IPA. The survey covered the enrollment information in the Household Roster section of the baseline survey. The endline survey will be conducted in early 2012, after reminder sessions are completed with half of the groups that received initial education and clients are offered the opportunity to register. Data entry will be done by IPA’s in-house Accra data entry team. Data was also collected to assess directly the education’s impact on client knowledge of insurance through knowledge tests. The knowledge tests were made up of Section E from the baseline survey. The knowledge tests were administered by the SAT loan officer doing the education sessions after the last education session was completed. The last knowledge tests were administered in March 2011 and delivered to IPA in April. IPA conducted data entry and preliminary analysis of these data, then provided the data and a report to FFH. We will also seek to use SAT’s repayment records as another source of data. By comparing information such as size of loans taken and repayment rates for the respondents, the impact of having insurance on repayment of microcredit loans can be assessed. These data will be compiled by SAT and shared with IPA for analysis. We also hoped to collect data from the NHIS marketer visits. IPA provided SAT with forms listing the respondents in each group they were to visit. The forms ask the NHIS marketer to record whether the respondent registered, chose not to register, or was already enrolled. The forms were intended to help monitor which groups the NHIS marketers visited, but the data collected can also be used to check the accuracy of the respondents’ self-reported enrollment status in the endline survey. While some of these forms were returned to IPA by SAT, it appears that the forms were not widely used during the NHIS visits. The first round of analysis of the data collected in the baseline survey was contained in a preliminary baseline analysis report completed in April 2011. The preliminary baseline analysis covered a few key variables that could be quickly cleaned and analyzed following data entry. This report contains a more comprehensive baseline analysis, covering all the topics in the baseline survey. Analyses of the results will be completed and included in reports following the two take-up surveys. These analyses will incorporate data from the baseline as well, and will answer the key questions about the efficacy of the education program outlined earlier in this section. As of the writing of this report, the data from the first uptake survey has been entered by the IPA data entry team, and is ready for cleaning and analysis. Upon completion of this analysis, the results will be presented in a preliminary results report, scheduled to be completed by October.


5. Baseline Survey Results The baseline data cover a large range of topics. The main questions we attempt to answer in the baseline data analysis are:

What are initial health insurance registration and enrollment rates for the SAT clients in the sample, and for their households?

What are the demographics of SAT clients? What demographic characteristics are associated with higher insurance registration and enrollment?

What are SAT clients’ financial outcomes like, including their living conditions, income, consumption, and assets?

What share of SAT clients face food insecurity or income shocks?

How do insurance registration rates and current enrollment status relate to financial outcomes, food insecurity, and shocks?

What are SAT clients’ knowledge and attitudes about insurance like? What reasons do SAT clients give for not having insurance?

How many SAT clients experience health events, and how do they deal with them? How do insurance registration rates and current enrollment status relate to health events and responses to health events?

The data used in the analysis cover 1,505 respondents. An additional five surveys were recorded, but the respondents declined to consent to be surveyed. Some of the 1,505 respondent surveys have missing information for some variables, but surveys missing data represent a small share of the total number of surveys for each variable. For some segments of the survey, respondents reported information about all individuals in their household, resulting in data for over 10,000 individuals.

Respondent Demographics

Table 4 shows that the vast majority of SAT clients in our sample (68 percent) are female. Table 4. Respondent Gender

Number Percent

Male 478 31.8%

Female 1027 68.2%

TOTAL 1505 100.0% Table 5 reports the number and percent of respondents who reported that they themselves were the household head, by gender. The male SAT clients in our sample almost always reported themselves to be the household head (93 percent). A much smaller but still considerable share of female SAT clients in our sample reported that they were the household head (26 percent).


Table 5. Respondent Status as Household Heads, By Gender

Number

HHH as a percent of respondents

Male 442 92.5%

Female 271 26.4%

TOTAL 713 47.4%

Table 6 demonstrates that most respondents have low levels of educational attainment and that there are large differences in education achieved by males and females. Nearly half of males (49 percent) and a majority of females (71 percent) have no schooling past primary or Koranic school. Forty percent of males and 25 percent of females have high school, vocational, or O Level education. Among males, 11 percent had achieved A level, training college, technical or professional, or tertiary level education, while among females, only 4 percent reached this level. Female respondents were twice as likely as male respondents to have no schooling at all. Table 6. Respondent Highest Level of Education, By Gender

Number of Male Respondents

Percent of Male Respondents

Number of Female Respondents

Percent of Female Respondents

None 117 24.5% 517 50.3% Primary 64 13.4% 156 15.2% Middle 108 22.6% 169 16.5%

Vocational/Computer 2 0.4% 12 1.2% O Level8 16 3.3% 12 1.2% High School 65 13.6% 64 6.2% A Level9 6 1.3% 7 0.7% Training College 15 3.1% 15 1.5% Tech/Prof 4 0.8% 4 0.4% Tertiary 26 5.4% 16 1.6%

Koranic 55 11.5% 55 5.4%

The most common status for both males and females was monogamous marriage, followed by polygamous marriage (Table 7). Although the number of respondents who reported they are separated, divorced or widowed was small for both genders, the share of women reporting to be in these categories was higher than the share of men.

8 O Level and A Level education is an alternative format for Senior High School. O Level qualification is the

equivalent of completing 2 years of Senior High School, or 4 years of secondary school. A Level qualification is earned with an additional 2 years of study, usually with specialization in a particular topic. O Levels and A Levels were phased out by the Ministry of Education in the early 1990s. 9 See above.


Table 7. Respondent Marital Status, By Gender

Number of Male Respondents

Percent of Male Respondents

Number of Female Respondents

Percent of Female Respondents

Monogamous marriage 285 59.6% 525 51.1%

Polygamous marriage 140 29.3% 346 33.7%

Consensual union 5 1.0% 1 0.1%

Separated 3 0.6% 15 1.5%

Divorced 4 0.8% 24 2.3%

Widowed 5 1.0% 73 7.1%

Never Married 36 7.5% 43 4.2%

The majority of respondents, 72 percent were Moslem; 17 percent were Christian (Table 8). A small number of respondents, 1 percent, reported following a traditional religion. One respondent reported a religion other than Christianity or Islam; this respondent reported having no religion. Table 8. Respondent Religion

Number Percent

Christian 398 26.5% Moslem 1088 72.3%

Traditional 17 1.1%

Other 1 0.1%

TOTAL 1504 100.0%

Respondent Household Demographics

Defining households can be somewhat challenging in Northern Ghana. Extended families often live together in compounds, which can comprise a group of structures, including multi-room houses and/or single-room huts, as well as communal areas for cooking, storage, keeping livestock, and growing kitchen gardens. The strength of the social and financial ties of the individuals living in the same compound can vary. In addition, some individuals, such as students or migratory workers, may live there some of the year. In our survey, households are defined as a group of people who live in the same compound and eat from the same pot, and live there for the majority of the year. The average SAT client household has just over 7 household members, split roughly evenly between adult members and children (Table 9).


Table 9. Respondent Household Size

Number

Average number of total household members 7.1 Average number of adults 3.7 Average number of children 3.4

Table 10 shows the average share of household adults who work for respondent’s households, as well as the share of all respondent household members who work10. In the average SAT client household, about 80 percent of adults are working. About 75 percent of all SAT adult household members work; larger households tend to have more non-working adult members than smaller households. Males and females work at the same rate; the difference between the share of adult household females who work and the share of adult household males who work is not statistically significant at the 95 percent confidence level. Table 10. Working Adults

Average share of household

Share of all respondent household members

Working Adult Females 82.6% 74.3% Working Adult Males 80.6% 76.2%

Table 11 shows the average share of household adults that work in various occupations for respondent’s households, as well as the share of all respondent household members who work in each occupation. These occupations represent each household member’s primary occupation; secondary occupations were not recorded. Household enterprises are by far the most common type of employment for respondents’ adult household members; this is unsurprising given that these numbers include the respondents themselves, and the respondents were selected from among SAT clients who take loans for household enterprises. Nearly 70% of households had at least one adult whose primary occupation was a household enterprise. This number may be underreported, as some types of household enterprises may have been recorded as “other”; however, since this question addresses only primary occupation, it is not a given that every household would have an adult whose primary occupation is running a household enterprise, since a respondent may consider his or her SAT enterprise to be a secondary occupation. About 30% of households have at least one adult whose primary occupation is agriculture.

10

These two measures differ because one looks at the average SAT client household, the other looks at the total population in these households. The first measure, while giving a better picture of the typical SAT client household, may under-represent the total number of people who fit a particular demographic characteristic; statistics will be skewed by the smaller SAT client households. Larger households are more likely to have non-working household members than smaller households. Therefore, when looking at the average SAT household, the number of people working will be larger than when calculating the share as a total of the entire population of SAT household members.


Table 11. Working Adults’ Primary Occupations



Average share of household adults who work in agriculture 11.6% 12.6%

Average share of household adults who work in a household enterprise 37.2% 32.3%

Average share of household adults who work as non-agricultural laborers 4.2% 4.1%

Average share of household adults who work as formal salaried employees 7.7% 6.7%

Average share of household adults who work as house help 0.1% 1.4%

Average share of household adults who do not work 19.7% 24.7%

Educational attainment among respondent households is quite low. Table 12 shows the average share of household adults who have never attended high school; on average, two-thirds of the adults in respondents’ households have never attended high school, and half of these have never had any formal schooling at all. As with respondents themselves, there are large gender discrepancies in educational attainment. Female household members are again much more likely to have no schooling, while male household members are more likely to have secondary or tertiary schooling. Table 12. Adults’ Education Levels



Share of all female household members

Share of all male household members

Adults who have no schooling 33.9% 34.4% 44.4% 23.2%

Adults whose highest level of schooling is primary 11.3% 10.7% 11.6% 9.8%

Adults whose highest level of schooling is middle/JSS 21.5% 22.0% 20.6% 23.7%

Adults whose highest level of schooling is Koranic 5.6% 5.7% 3.6% 8.1%

Adults whose highest level of schooling is high school/SSS 17.5% 18.5% 14.4% 23.1%

Adults whose highest level of schooling is other training 4.6% 4.4% 3.6% 5.3%

Adults whose highest level of schooling is tertiary 4.6% 4.3% 1.9% 6.9%


Table 13. Adults’ Marital Status

Table 14 shows school enrollment rates for school-age children in respondent households. Enrollment is fairly high, with over 90 percent of children age 5 to 12 enrolled. Male and female children are enrolled at equal rates; the difference in male and female school enrollment is not statistically significant at the 95% confidence level. Table 14. Children’s School Enrollment Rates

Initial Health Insurance Registration and Enrollment Levels

Determining the insurance status of a respondent proved challenging. After a person registers or enrolls with the NHIS for the first time and pays fees and the premium for one year, NHIS issues a membership card within three months, and this person is considered “registered” by the NHIS. Upon initial enrollment there is a three month waiting period before individuals become eligible to use their insurance to access covered services. The person will remain enrolled for one year from the date the insurance became active. To remain eligible for covered services, individuals must pay their annual premium each year. Failure to pay results in a loss of eligibility for covered services. Individuals who do not pay the annual premium may still be on the NHIS “registered” list but are not “actively” enrolled. . Based on anecdotal evidence from the field and from focus group interviews, we expected that not all respondents would know their insurance status. Confusion on this issue might come from several sources. First, a person might have registered, but not received a card from NHIS yet. Because NHIS cards are not issued until after the three month waiting period and then sometimes arrive late, if the person does not remember the date of registration, he or she may not know if the insurance has become active yet if the card has not been received. A more common source of confusion seemed to be expiration. All new



Average share of household adults who are married 66.5% 59.8% Average share of household adults who are in a polygamous marriage 22.4% 22.7%

Male Female TOTAL

Percent of Children in Respondent Households Age 5-12 Enrolled in School 93.3% 92.2% 92.8%


insurance cards are printed with five expiration dates each a year apart, which represent the dates that the person must re-enroll each year in order to remain enrolled for the next five years. Every time a person re-enrolls, the NHIS places a sticker over the old date, so that the most imminent date showing is the next expiration date. However, illiterate clients cannot read the dates on the card, and may not know or remember when they are supposed to re-enroll and whether their insurance is expired or not. To try to get as accurate of data as possible on respondent registration and enrollment status, the survey included several questions on registration, enrollment, and insurance use. In order to measure respondents’ NHIS registration and enrollment status, the survey begins by recording registration based on respondent-self reporting. Respondents who reported that they were registered were asked to produce cards for visual inspection by the enumerator who then checked the card for the next premium due date. Actual enrollment status (with current premium paid), which is more difficult for respondents to self-report than registration status, can be determined with certainty for most respondents who were able to present their insurance cards to an enumerator. All NHIS districts now use the five-date sticker system to keep track of client enrollment, an enumerator can determine whether the person is currently enrolled by looking at the earliest date not covered by a sticker; if that date has passed, the person’s insurance has expired and the person is not currently enrolled. Self-reported registration status was then also checked and adjusted by analysis of reported health events and use of insurance. Asking about recent health events, if insurance was used, and how the health service was paid for, provided additional information to further refine the estimate of actively enrolled respondents and also to get a sense of the number of respondents who do not know their registration or enrollment status. Self-reported Registration: Although an individual may have registered for the insurance at some point in the past, they are not actively enrolled and eligible to use covered health services unless they have paid the premium at some point in the past annual period. Table 15 shows the number and percent of SAT clients in our sample who report that they are registered in the National Health Insurance Scheme. Of the 1504 respondents with data, 70 percent reported being registered. Table 15. Respondent-Reported NHIS Registration Status

Respondents Percent

Registered 1051 69.9%

Not Registered 453 30.1%

WITH DATA 1504 100.0%

No data 1 TOTAL 1505

Table 16 shows the number and percentage of the clients who reported themselves as registered who indicated they had an insurance card.


Table 16. Insurance Card Status Of Respondents Reporting Insurance Enrollment

Respondents Percent

Have insurance card 947 90.6% Do not have insurance card 82 7.8%

Don't Know 16 1.5%

WITH DATA 1045 100.0%


If a respondent reported having an insurance card, the respondent was asked if he or she could show the card to the enumerator. Table 17 shows the responses to this request. If the respondent was unable or unwilling to show the card, the reason the respondent did not show the card was reported. Table 17. Respondents Willing and Able to Show Insurance Card to Enumerator

Respondents Percent

Showed card 502 53.1% Waiting for card from NHIS 50 5.3% Don't have access to card 339 35.8%

Other reason not to show 55 5.8%

WITH DATA 946 100.0%

No data 1 TOTAL 947

Of the 946 respondents who reported having an insurance card and who had data, 53 percent were able and willing to show their cards to the enumerator. The most common reason that respondents could not show their card was that the respondent did not have access to the card (35 percent). In most cases, looking at the insurance card enabled the enumerator to determine whether the respondent’s insurance was current or expired. Table 18 shows the respondent enrollment status as determined by the enumerator. Table 18. Respondent Enrollment Status, Based on Insurance Card

Respondents Percent

Expired 227 45.2% Current 270 53.8%

Don't Know 5 1.0%


No data 0 TOTAL 502


Of the 502 respondents who were able and willing to show their card, the enumerators were able to ascertain that the insurance was expired in 45 percent of cases and current in 54 percent of cases. Use of Insurance for Health Events: The Health Events section of the survey, which collected data on health events and use of health services, included data on use of health insurance. Data collected on health events, use of health services and use of health insurance was used to cross-check the reports of registration and enrollment and identify instances where the respondent may be confused about his or her health insurance registration or enrollment status. Table 19 shows the number and percent of respondents who reported having a health event in the past month. Health events include things such as a household member dying, giving birth, or experiencing an injury or illness that interfered with daily activities. Of 1505 respondents, 18 percent reported having at least one health event. Only one respondent reported experiencing more than one event; this respondent reported experiencing two health events. Table 19. Respondents Reporting a Health Event

Respondents Percent

No health events 1234 82.0%

At least 1 health event 271 18.0%

WITH DATA 1505 100.0%


Table 20 shows the number and percent of respondents who reported experiencing a health event and reported consulting a health service provider about the health event. Health service provider was defined broadly to include traditional healers, pharmacists, and midwives, as well as doctors, nurses and dentists. Table 20. Respondents Seeking Consultation for Health Events

Respondents Percent

No consultation 45 16.6%

Consultation 226 83.4%


No data 0 TOTAL 271

Of the 271 respondents who experienced a health event, 83 percent sought a consultation. The one respondent who reported two health events reported getting a consultation for each event. No respondents reported more than one consultation for a single health event.


Table 21 shows the number and percent of respondents who consulted a health service provider and used insurance to pay for the consultation. Of the 225 respondents with data who sought a consultation, 53 percent reported using insurance. The one respondent with two health events and two consultations reported using insurance for neither consultation. Table 21. Health Insurance Use Among Respondents Seeking Consultations for Health Events

Respondents Percent

Did not use insurance 106 47.1%

Used insurance 119 52.9%


No data 1 TOTAL 226

If a respondent’s reported use of insurance contradicts their reported insurance status, the respondent may be confused about his or her insurance status. There are two types of contradictions that we observed in this data. First, a respondent may have reported that he or she did not have insurance, but later reports using insurance to pay for health services. Second, a respondent may report that he or she has insurance, but later reports not using insurance when faced with a health event. Of the 225 respondents mentioned in Table 16 who sought a consultation, two reported having no insurance initially but later reported using insurance to pay for the consultation in the Health Events section; one of these reported use of insurance other than NHIS. Of the same 225, 58 reported having insurance, but later reported not using insurance to pay for the consultation. In total, 58 of the 225 respondents showed a possible contradiction in their reporting of their insurance status, while the remaining 166 were consistent. In some cases, there may be valid reasons why a respondent might have insurance, but not use it. For example, the respondent may have chosen to go to a service provider that does not accept insurance, or does not understand how to use the insurance with the provider. In other cases, the respondent may be confused about his or her insurance status. When asking about health events, respondents who had reported that they have health insurance were asked why they did not use insurance. For the 58 respondents who reported having insurance, but did not use health insurance when they experienced a health event, we examine the answers to the question about why insurance was not used. The responses are reported in column (1) of Table 22, categorized by reported registration status.


Table 22. Reasons Respondents Reported as Registered Did Not Use Insurance

Reason for not using insurance

(1) Respondents Reported as Registered

(2) Registered and had permanent card

(3) Registered, had permanent card, and showed card

(4) Registered, permanent, showed card, and card not expired

Isn't registered 1 1 0 0 Doesn't have card yet 11 7 0 0 Insurance was expired 21 21 9 8 Insurance doesn't cover treatment 3 2 1 1 Provider did not accept insurance 7 7 1 1 Thought would get better care with cash 2 2 1 0 Did not know how to use insurance 0 0 0 0 Other 7 6 1 0

Unknown (missing data) 6 5 4 1

TOTAL 58 51 17 11 Of the 58, only one respondent reported registering for insurance, but later gave not being registered as the reason for not using insurance. This, combined with the above finding that only two respondents reported using insurance while having earlier reported to not have insurance, suggests that only 1 percent of respondents who sought consultation were found to be confused by their registration status. On the other hand, confusion about enrollment status (i.e. being actively enrolled and eligible for covered services) is more common. Seven respondents reported being registered and having a permanent card but later said they did not use insurance because they did not have their cards when they faced a health event. It is possible that these respondents received their cards after the reported health event occurred, but no respondents who were actually able to present their cards to the enumerator reported not using insurance because they had not received their cards, suggesting that at least some of the discrepancy is due to confusion about having an insurance card and one’s actual enrollment status. To get a better idea of where confusion occurs, we look at the answers for increasingly narrow subsets of the 58, beginning with respondents who not only said they were registered, but also said they had an insurance card, reported in column (2). This drops respondents who said they were registered but did not have a card yet, so we would expect to see the number of respondents saying they did not use insurance because they did not have a card yet to drop between column (1) and column (2). This does indeed happen, although a number of respondents giving this answer still remain.


The next group we examine are those respondents who were registered and had a permanent card, and were able to show that card to the enumerator, reported in column (3). This drops respondents who said they had a card, but for whatever reason, did not show it to the enumerator. Once we look only at respondents who were able to show a card to enumerators, there seems to be a very low level of confusion: there are no respondents in this category who said they did not use insurance because they weren’t registered, or because they did not have a card yet. The most common reason was that they were not currently enrolled. This remains true even when we look only at respondents who were able to show a card to enumerators, and were found to be currently enrolled at the time of the survey, reported in column (4). This is not necessarily contradictory. Focus group interviews and interviews with health providers have provided several anecdotes of people who seek a consultation, discover their insurance is expired, and subsequently pay their premium to make their insurance current. No respondents reported specifically that they did not use insurance because of lack of knowledge. However, some of the reasons for not using insurance, like the treatment is not covered, the provider does not accept insurance, and the respondent thought they would get better treatment with cash, could be indicators of lack of knowledge about how to use the insurance to access services. Thirteen respondents who reported having insurance in the Household Roster section said they did not use it for other reasons, or had missing data when reporting health events. Table 23 lists the number and percent of respondents in each insurance status category as determined by respondent reporting that appears reasonably reliable or determined by an enumerator check of an insurance card. For respondents whose status cannot be reliably determined, data are extrapolated by applying percentages from other reliable data found in crosschecking other sections in the survey (for example, comparing percentages of expired cards of those presented to enumerators, and comparing self reports of registration to insurance use data when seeking healthcare).


Table 23. Extrapolations of Insurance Status

Number

Percent of total sample of 1504

Reported Unregistered 453 30.1%

Reported Registered 1051 69.9%

A. Reported registered who showed card 502

Reported Current 270 Reported Expired 227 Unknown 5 of which, extrapolated current 3 of which, extrapolated expired 2

B. Reported registered who did not show card, but reported having card 445

Reported Waiting for Card 50 Unknown 395 of which, extrapolated current 206 of which, extrapolated expired 173 of which, extrapolated waiting 16

C. Reported registered who did not show card and did not report having card 104

Reported Waiting for Card 82 Unknown 22 of which, extrapolated current 11 of which, extrapolated expired 9 of which, extrapolated waiting 2 A, B and C: All reported registered

All Current (Reported + Extrapolated) 490 32.6%

All Expired (Reported + Extrapolated) 411 27.4%

All Waiting (Reported + Extrapolated) 150 9.9%

TOTAL 1504

Based on reported insurance status and the extrapolations from cross-checking of data obtained from visual inspection of insurance cards and reports of use of insurance to access health services, we estimate that 70 percent of clients in the sample have registered at some point in time with the NHIS. However we also estimate that at the time of the baseline survey that only 32.6% of the clients interviewed were actively enrolled (with premium payment current) and eligible for covered health care services. We further estimate that another 9.9% may be in the required initial three-month waiting period and therefore will become eligible for benefits within the next three months. This leaves 57.4% of the sample who are not actively enrolled in the NHIS at the time of the baseline survey. For a full explanation of these extrapolations, please see Appendix B. Figure 24 summarizes the estimated enrollment status of SAT clients:


Figure 24: Estimate Enrollment Status of Sinapi Aba Trust Clients

Use of insurance for well-patient visits

A total of 118 respondents, or 7.8 percent of all respondents, reported going to a well-patient consultation. Examples of well-patient consultations include antenatal clinic visits, vaccinations, post-natal clinic visits, or annual check-ups. Of these 118 respondents, just over half used NHIS to pay for the consultation; about 2 percent used other insurance, and about 45 percent did not use insurance. The share of respondents using insurance for well-patient visits was comparable to the share of respondents who used insurance for consultations related to health events. Table 25. Use of insurance for well-patient visits

Respondents Percent

NHIS insurance 63 53.4% Other insurance 2 1.7% Did not use insurance 53 44.9%

TOTAL 118 100.0%

No data 0

Health insurance status of household members

Table 26 shows the share of respondents who reported different levels of insurance registration in their households. More than a third of respondents reported that all of their household members were registered for insurance. However, about a third of respondents reported that less than half of their household was registered for insurance, and 12 percent reported no one in their household was registered.

Unregistered 30%

Expired 27%

Waiting 10%

Currently Enrolled

33%


Overall, 66.2 percent of all respondent household members (including respondents) were reported as being registered. Respondents were slightly more likely to be reported as registered than their household members (69.9 percent reported they were registered.) Table 26. Share of Respondents’ Household Members Registered for Insurance

Percent of Household Members Registered

Number of Households

Percent of Households

0% 181 12.02% 0-10% 12 0.80% 10-20% 63 4.18% 20-30% 53 3.52% 30-40% 49 3.25% 40-50% 122 8.10% 50-60% 35 2.32%

60-70% 130 8.63% 70-80% 105 6.97% 80-90% 184 12.22% 90-100% 30 1.99%

100% 542 35.99%

TOTAL 1506 100.00%

No data 0

Table 27 shows individual registration and enrollment status for all household members on which we collected data, including respondents, for adults and children. For individuals who did not show a card, enrollment status was extrapolated using the same methodology as with respondents (see Table 23.) Results here indicate that for all adults, although 33.3% reported being registered, that based on visual verification of the enrollment card and extrapolation of active enrollment using the same method as used for all respondents, that about 28.5% of the adults are currently enrolled, and that about 29.7% of children are currently enrolled. A slightly higher number of children are reported to be registered than adults. About 67 percent of adults were reported registered, while about 70 percent of children were reported to be registered. The fact that this difference is quite small is notable, since children do not have to pay a premium to register (although most NHIS branches charge a fee for the registration), while adults do. Given this, a larger difference in registration might have been expected. To register a child, generally at least one parent must be registered. However, as seen above, only 12 percent of households have no one registered, so lack of a registered adult cannot explain all of the unregistered children. This suggests that cost of the insurance premium is not the only barrier to insurance registration. Within those reported as registered, about 15 percent of adults were reported to be waiting to receive their permanent card from the NHIS. Of adults for whom the respondent had access to insurances cards, and showed the card to the surveyors, about half of them were currently enrolled and half of them had expired insurance policies, each totaling a little over


40 percent of those reported as registered. These proportions were applied to the individuals whose status could not be determined with certainty to estimate total numbers of individuals whose insurance card is current, expired, or not yet received from NHIS. As with registration, a slightly higher number of children were currently enrolled than adults, although again the difference was surprisingly small given that re-enrollment for children does not require a premium payment. Among children, the share waiting for their cards was slightly higher than for adults, at about 21 percent, while the share with expired cards was slightly lower, at about 37 percent. This outcome is sensible if one considers that because of their young age, children are more likely to have been registered recently than adults. Table 27. Extrapolations of Insurance Status, All Respondent Household Members

Adults Children

Number

Percent of total Number

Percent of total

Reported Unregistered 1807 33.3% 1474 29.7%

Reported Registered 3612 66.7% 3483 70.3%

A. Reported registered who showed card 1005

1086 Reported Current 500

573

Reported Expired 493

502 Unknown 12

11

of which, extrapolated current 6

6 of which, extrapolated expired 6

5

B. Reported registered who did not show card, but said they had a card 2233

1963

Reported Waiting for Card 151

209 Unknown 2082

1754

of which, extrapolated current 977

835 of which, extrapolated expired 964

732

of which, extrapolated waiting 141

187 C. Reported registered who did not show

card, and said they did not have a card 374

434 Reported Waiting for Card 243

314

Unknown 131

120 of which, extrapolated current 61

57

of which, extrapolated expired 61

50 of which, extrapolated waiting 9

13

A, B and C: All reported registered

All Current (Reported + Extrapolated) 1544 28% 1471 30% All Expired (Reported + Extrapolated) 1524 28% 1289 26%

All Waiting (Reported + Extrapolated) 544 10% 723 15%

TOTAL 5419

4957


Demographic Characteristics and Insurance Registration and Enrollment

Attributes Related to Adult Registration and Enrollment

For this analysis, we look at both registration and enrollment rates.

Registration: Registration means that a person has, sometime in the past 5 years, registered for health insurance, including paying one year’s premium and the associated fees. Most people seem to have accurate knowledge of whether or not they are registered for insurance. However, registration does not mean that a person is currently enrolled and can access services; the insurance may be expired if the person has not made a premium payment in the last year. In most cases, a person who is registered can make a premium payment and access care the same day. As a result, being registered means that a person will probably have access to care, provided they can pay any overdue premium. For this analysis, we use self-reported registration rates. Although there is some risk that registration status is mis-reported, the benefit of this approach is that these data are reported for almost every individual.

Enrollment: Enrollment means that a person has, sometime in the past 1 year, made a premium payment and can currently access covered services without paying anything additional. Many people appear to have very inaccurate knowledge of their enrollment status; they do not know whether their insurance is expired or current. The number of people who fail to re-enroll on time each year suggests that self-reported enrollment status is not very reliable. For this analysis, when considering enrolled individuals, we include only those who are “confirmed” enrolled; that is, a surveyor was shown a card for that individual and the expiration date on the card had not yet passed. The drawback to this approach is that surveyors were not able to look at every individual’s card, so a number of enrolled people whose cards were not seen were excluded from this group.

Registration

To determine what individual characteristics are associated with higher likelihood of being registered for insurance, we created a binomial variable for each respondent and his or her household members, equal to 1 if that person was reported as being registered in insurance, and equal to 0 otherwise. We regressed this variable on a number of dummy variables indicating various individual demographic characteristics. The results are reported in Table 28. Females are significantly more likely to be registered in insurance than males, by approximately 14 percentage points, on average. Older individuals are also significantly more likely to be registered than younger individuals. Those age 31 to 45 are on average 4 percentage points more likely to be registered than those 18 to 30; those over 45 are about 10 percentage points more likely to be registered than adults between the ages of 18 and 30.


Table 28: Predictors of Adult Individual Insurance Registration

Dependent Variable: Insurance Registration Status (1 if reported registered, 0 otherwise)

Coefficient Standard Error P-Value *Significant?

General: Female 0.14 0.01

0.00 *

Age 31 to 45 0.04 0.02

0.02 *

Over 45 0.10 0.02

0.00 *

Highest schooling is primary 0.06 0.02

0.00 *

Highest schooling is middle 0.13 0.02

0.00 *

Highest schooling is high school 0.22 0.02

0.00 *

Highest schooling is vocational 0.24 0.03

0.00 *

Highest schooling is tertiary 0.34 0.03

0.00 *

Highest schooling is koranic 0.13 0.03

0.00 *

Married 0.07 0.02

0.00 *

No Longer Married 0.05 0.03

0.15 Ethnicities:

Dagomba -0.04 0.02

0.07 Mamprusi -0.12 0.03

0.00 *

Gonja 0.08 0.03

0.00 *

Wala 0.06 0.05

0.27 Dagaare 0.10 0.05

0.07

Hausa -0.06 0.05

0.30 Vagla 0.18 0.06

0.00 *

Komkomba 0.12 0.04

0.00 *

Religion: Moslem 0.10 0.03

0.04 *

Christian 0.05 0.03

0.33 *

Geographical: Rural -0.06 0.02

0.00 *

Walewale 0.07 0.03

0.01 *

Bole 0.07 0.03

0.01 *

Salaga 0.08 0.03

0.00 *

constant 0.31 0.05 0.00 *

N = 5512

R Squared = 0.0785

Adjusted R Squared = 0.0743

Educational attainment is also a significant indicator of insurance registration status. Compared to those with no schooling, those who achieved primary-level schooling are 6 percentage points more likely to be registered; those who reach junior high school are 13 percentage points more likely to be registered; and those who attended high school are 22


percentage points more likely to be registered. Individuals with tertiary schooling are 36 percentage points more likely to be registered than those with no education at all. Less formal education also proved to be a positive indicator of registration; those with vocational schooling were 24 percentage points more likely to be registered than those with no school, and those who attended Koranic school were 13 percentage points more likely to be registered. Being married, on average, leads to a small but significant increase in the likelihood of beng registered in insurance; having once been married (and being currently divorced or widowed) has no significant relationship. Throughout this report, we include demographic variables that include ethnicity and religion, but the results for these variables should be treated with care. In most cases, variables representing ethnicity will behave as proxies for individuals who live in certain areas, participate in certain economic activities, and have access to certain resources, which may be related to registration. It is unlikely that ethnicity would have a direct causal relationship with registration, or with other variables we estimate regressions for, but we continue to include them because they are useful to serve as controls when examining other relationships. In this case, some ethnicities were found to be significantly more likely to be registered than others. However, we do not have enough information about the geographic, economic, and demographic variables associated with each ethnic group to draw conclusions about the relationship between ethnicity and insurance registration. Being Moslem was both associated with a significantly higher likelihood of being registered compared with following a traditional religion or “other” religion. (Very few individuals were identified as “other”.) Respondents reporting to be Moslem were 10 percentage points more likely to be registered than those following a traditional or other religion. Respondents who follow traditional religion may have lower registration rates because they might have lower demand for Western medicine if they use traditional medicine instead; alternatively, individuals attending religious services at churches or mosques may have more opportunities to register for insurance, as the NHIS sometimes goes to religious centers to register individuals for insurance. Geographical variables were also significant. Being located in a rural area was associated with a 6 percentage decline in the likelihood of being registered. Being located in Walewale, Bole, or Salaga was all positively associated with likelihood of being registered, compared with Tamale.

Enrollment

We next look at what demographic factors are significantly associated with higher likelihood that an individual was confirmed to be currently enrolled in health insurance. To be confirmed currently enrolled, the respondent had to show the surveyor an insurance card for that individual, confirming the person was indeed registered. The surveyor then used the expiration date on the card to determine whether that individual’s insurance was expired or current. For purposes of looking at the association of the demographic factors with enrolled


individuals, only those who could both provide a card and whose card was current were counted as confirmed to be currently enrolled. Full results are reported in Table A in Appendix D. In general, demographic factors were more closely associated with registration status than enrollment status. Demographic variables explained about 8 percent of variation in registration status, but only about 4 percent of variation in enrollment status. (It should be noted that in this context, we use the term “explain” to mean that the correlations between the demographic variables we are looking at and registration status can “predict” about 8 percent of the variation in registration status. However, this does not necessarily imply that these cause the variation in registration status.) There are several possible explanations for this. First, it could be that who is enrolled depends a lot just on chance. For example, if most people re-enroll when they get sick, then those who have been sick recently are more likely to be enrolled. If likelihood of getting sick doesn’t have a strong correlation with demographic characteristics, then there will be a lot of extra variation in the enrollment status variable that isn’t explained by demographics. Second, it could be that extra variation is introduced through the data collection methods. If who has access to their card is fairly random, then this will create random variation in the enrollment status variable that would not be explained by demographics. Lastly, it is possible that while demographics are highly correlated with registration status, other non-random variables that are not correlated with demographics are more important in explaining enrollment status. When looking at demographic traits associated with enrollment, fewer variables are significantly correlated with enrollment status than were correlated with registration status, and most of the correlation sizes are smaller. As with registration, women are more likely to be currently enrolled. The correlation size is about half of what it was for registration, with women 5 percentage points more likely to be currently enrolled than men, on average. Older adults were more likely to be currently enrolled than adults 18 to 30. No education variables were significantly correlated with enrollment status. This is surprising given that all of the education variables were significantly correlated with registration, with quite large correlation sizes in some cases. This could be explained by the theory that enrollment status is more random than registration status. Another possible explanation is that while education level is positively associated with registration, for respondents’ household members, it is also negatively associated with the respondent being able to show a surveyor an insurance card for that individual, because educated individuals are more likely to have control of their own cards. To test this theory, we regress a dummy variable equal to “1” if the individual’s card was shown to the surveyor, and “0” otherwise, on these same demographic variables, controlling for reported registration status. (Full results are reported in Error! Reference source not found. in Appendix D) We find that the theory holds true for those with the highest levels of education—those with tertiary or secondary education are significantly less


likely to have their cards shown to the surveyor by the respondent. However, there is no such significant relationship for those with lower levels of education, suggesting this effect cannot completely explain the disappearance of education as a significant determinant of enrollment status compared with registration status. Similarly, religious attributes were less strongly related to enrollment status than registration status as well. There was no significant relationship between being Moslem and enrollment status. Individuals identified as Christian were, on average, 6 percentage points more likely to be enrolled than those whose religion was traditional or other. The only geographic variable that was significantly related to enrollment was living in Walewale. Walewale residents were 9 percentage points more likely to be enrolled than residents of Tamale. There was no significant difference between residents of Bole or Salaga and Tamale, and residents of rural areas were no more or less likely to be enrolled than urban residents.

Attributes Related to Child Registration and Enrollment

Registration

We next look at predictors of insurance registration for children. Table 29 reports determinants of the probability that a child is reported as registered in insurance. Results were estimated for two models. The first includes children of all ages, and age is included as an independent variable. The second model adds school enrollment status as an independent variable. The second model includes only school-age children, and age is not included as an independent variable. Gender is not a statistically significant predictor of the likelihood a child will be registered. Children over age 7 are significantly more likely to be registered than younger children, with children in the older age group 6 percentage points more likely to be registered, on average. For children, religion is not a significant indicator of registration status at the 95 percent confidence level. However, at the 90 percent confidence level, being Christian was associated with fairly large decreases in the likelihood a child would be registered in both models: an 11 percentage point decline for the model for children of all ages, and a 14 percent point decline for the model for school-age children. Some ethnicities were significantly correlated with insurance registration for children, but many of these become insignificant when we consider school age children. Geographic variables were significant. Living in a rural area was associated with a 15 percentage point decrease in likelihood of registration for the model for children of all ages, and a 13 percentage point decrease in the model for school-age children. Children in Bole and Salaga were significantly more likely to be registered than children in Tamale. For school-age children, school enrollment status was a large and significant determinant of insurance registration status. A child enrolled in school was, on average, 28 percentage points more likely to be registered than a school-age child not enrolled in school.


Table 29: Predictors of Child Individual Insurance Registration


All Children School Age Children

Coefficient Standard Error

P-Value *Significant?

Coefficient

Standard Error


General:

Female -0.01 (0.01) 0.61 -0.01 (0.02) 0.63

Age 7 to 17 0.06 (0.01) 0.00*

Ethnicities:

Dagomba -0.14 (0.03) 0.00* -0.11 (0.03) 0.00*

Mamprusi -0.15 (0.03) 0.00* -0.14 (0.04) 0.00*

Gonja 0.06 (0.03) 0.04* 0.05 (0.04) 0.15

Wala -0.05 (0.06) 0.44 -0.02 (0.07) 0.81

Dagaare 0.02 (0.05) 0.69 -0.03 (0.07) 0.70

Hausa 0.03 (0.05) 0.51 0.05 (0.06) 0.44

Vagla 0.15 (0.07) 0.03* 0.14 (0.10) 0.16

Komkomba 0.08 (0.03) 0.01* 0.02 (0.04) 0.70

Religion:

Moslem 0.02 (0.06) 0.69 -0.04 (0.08) 0.58

Christian -0.11 (0.06) 0.07 -0.14 (0.08) 0.07

Geographical:

Rural -0.15 (0.02) 0.00* -0.13 (0.02) 0.00*

Walewale 0.02 (0.03) 0.56 0.00 (0.04) 0.93

Bole 0.12 (0.03) 0.00* 0.07 (0.04) 0.05

Salaga 0.15 (0.03) 0.00* 0.15 (0.04) 0.00*

School:

Enrolled

0.28 (0.03) 0.00*

constant 0.74 (0.06) 0.00* 0.60 (0.09) 0.00*

N = 4485 R Squared = 0.0811 Adjusted R Squared = 0.0779

N = 2555 R Squared = 0.0847 Adjusted R Squared = 0.0790

Enrollment

We next consider predictors of enrollment status for children. Full results are reported in Table C in Appendix D. Looking at children of all ages, children ages 7 to 17 are less likely to be currently enrolled than younger children, despite the fact that they are more likely to be registered. This is probably because although, because they are older, their parents have had more time to register them, it is also more likely that more time has elapsed since their registration, so their insurance is more likely to be expired. Gender is not significantly correlated with enrollment status, although some ethnicity variables are. Children reported as Moslem were about 11 percentage points more likely to


be enrolled than children reported to be traditional religion or other religion; being Christian was not statistically significant. Children living in Tamale were significantly less likely to be currently enrolled compared with children living in any other location. Living in a rural location was not significantly related to enrollment status, despite the fact that there was a large negative correlation between living in a rural location and being registered. Results are similar for school-age children (reported in Table D in Appendix D); however, it was notable that there was no significant relationship between enrollment in school and being currently enrolled in health insurance, despite the fact that there was a very large correlation between being enrolled in school and being reported as being registered for health insurance.

Household Position and Adult Registration and Enrollment

Registration

We next look at position in the household as a predictor of insurance registration status. To do this, we regress individual registration status on all of the variables in the previous models, plus dummy variables representing the individual’s relationship to the head of the household. The results for adults are reported in Table 30; coefficients, standard errors and P-values are only reported for the new household position variables. Table 30: Household Position and Adult Individual Insurance Registration




Household Position Head of Household 0.09 0.02

0.00 *

Spouse of Household Head 0.12 0.02

0.00 * Child of Household Head 0.03 0.02

0.12

N = 5380

R Squared = 0.0854


Spouses of household heads are most likely to be registered for insurance; on average, they are 13 percentage points more likely to be registered than household members who are neither the head of the household, spouse of the household or child of the household. Heads of households are also more likely to be registered than other household members. There was no significant difference between adult children of household heads and other household members.


Enrollment

When current enrollment is substituted as the dependent variable, results are nearly identical (Table 31). Household heads and spouses of household heads are significantly more likely to be enrolled. Table 31: Household Position and Adult Individual Insurance Enrollment

Dependent Variable: Insurance Enrollment Status (1 if reported enrolled, 0 otherwise)




0.00 *


0.00 * Child of Household Head 0.01 0.01

0.40

N = 5380

R Squared = 0.0521


Household Position and Child Registration and Enrollment

Registration

Results for all children are reported in Table 32. There is no significant difference between household heads and spouses of household heads who are under 18 compared with other household members. (There were very few individuals under 18 who were reported to be household heads or spouses of household heads: there were 16 household heads under 18 and 17 spouses of household heads under 18.) However, children of household heads remained significantly more likely to be registered than children more distantly related to the household head; a child of the household head was 5 percentage points more likely to be registered than the child of another household member. Table 32: Household Position and Child Individual Insurance Registration




Household Position Head of Household -0.21 0.45

0.65


0.49 Child of Household Head 0.05 0.02

0.00 *

N = 4406

R Squared = 0.0868



As with adults, results using enrollment status echo those using registration status as the dependent variable. Children of household heads are significantly more likely to be enrolled, by an average of 3 percentage points. Table 33: Household Position and Child Individual Insurance Enrollment

Dependent Variable: Insurance Enrollment Status (1 if reported enrolled, 0 otherwise)




0.88

Spouse of Household Head -0.13 0.33

0.69 Child of Household Head 0.03 0.01

0.00 *

N = 4406

R Squared = 0.0301


Attributes Related to Household Registration Rates

Registration

We next examine the household characteristics associated with higher household registration rates. To do this, we regress the share of household members reported to be registered on variables reflecting household demographic characteristics. The full results of this regression are reported in Table E in Appendix D. Higher numbers of some cohorts of gender and age were significant predictors of higher household registration rates. Each additional child of either gender age 7 to 17 was associated with a 3 percentage point increase in the share of the household reported as being registered for insurance, on average. Each female older than 45 was associated with a 6 percentage point increase in the household registration rate, on average. Young adult males were associated with lower household registration rates; each male 18 to 30 was associated with a 5 percentage point decrease in the household registration rate. Higher numbers of well-educated adults were significantly associated with higher household registration rates; higher numbers of adults with low educational attainment were associated with lower registration rates. Each additional adult with schooling at the high school, vocational, and tertiary level was associated with 4, 8 and 9 percentage point increases in the household registration rate, respectively. Each adult with no schooling was associated with a 4 percentage point decline in the household registration rate; each adult whose highest level of schooling was middle school was associated with a 9 percentage point decline in the household registration rate.


Ethnicity for each household was set equal to the reported ethnicity for the majority of household members. Only two ethnicities were significant indicators of household registration rates. Religion for the household, like ethnicity, was set equal to the religion reported for the majority of household members. Religion was not significant at the 95 percent confidence level. However, at the 90 percent confidence level, Moslem households, on average, had a household registration rate 12 percentage points higher than households whose religion was traditional, a fairly large difference. As with individual registration, this could be because those with traditional religions prefer to use traditional medicine as well, so have less reason to register, or it could be because those who go to churches and mosques have more opportunities to register due to occasional NHIS registration drives at religious institutions. Geographic indicators were also significant predictors of registration rates. Rural households had, on average, a household registration rate 5 percentage points lower than urban households. Households in Tamale had the lowest household registration rates; on average, household registration rates were 11 percentage points higher in Walewale, 13 percentage points higher in Bole, and 11 percentage points higher in Salaga compared with Tamale. Table F in Appendix D illustrates how these results can be interpreted using hypothetical households. The table describes demographic characteristics for a household, and then lists the share of household members predicted to be registered in that household, and the predicted number of household members that would be registered.

Enrollment

Many individuals who have registered for insurance are not currently enrolled; they have gone more than one year without paying a premium, and their insurance is expired. This means that before they can access health services using insurance, they must pay their overdue premium. (This is distinct from being unregistered in that before an unregistered person can access services, he or she must not only pay the premium but also wait 3 months for the policy to become active.) We repeat this regression using the percent of the household that is confirmed to be currently enrolled. Each individual in the household is considered confirmed to be currently enrolled if the respondent was able to present an insurance card for that individual that was not expired. Full results of this regression are reported in Table G in Appendix D. In general, individuals’ demographic attributes are not as closely associated with their confirmed enrollment status as they are with their self-reported registration status. There could be a couple reasons for this. First, current enrollment may simply have more noise in it. This could be true if many types of people, across all demographic groups, simply forget to re-enroll on time, making current enrollment status fairly random. Second, variation that is unassociated with demographic traits could have been introduced during the data


collection. Confirmed enrollment is based on the respondent showing the surveyor a current health insurance card. If the respondents who had access to their cards were random, this could introduce additional variation that would not be correlated with demographic traits. There is no significant relationship between the number of people in any gender and age cohort and the household enrollment rate. Additional adults with low levels of schooling—either no schooling or primary level schooling—are correlated with lower enrollment rates, as they were with lower registration rates. A couple of ethnicity variables are significant. The geographic variables are less significant than with registration. Living in a rural area was not significantly related to enrollment rate. Households in Walewale and Salaga still had significantly higher enrollment rates than those in Tamale, but unlike with registration, households in Bole did not have significantly higher enrollment rates than those in Tamale. When we control for whether or not the respondent was able to present a card for that individual (allowing us to confirm with certainty whether that individual was currently enrolled or not), only a couple demographic variables are significant. Being Christian or Moslem is positively associated with higher enrollment rates (this could again be due to more opportunity to register or re-enroll at the place of worship), and households in Walewale are still more likely to be enrolled than those in Tamale. Results of this regression are reported in Table H in Appendix D.

Living Conditions of SAT Clients

Table 34 shows the percent of respondent households who have homes made of different building materials. Housing styles in Northern Ghana vary in style according to region, available resources, and individuals’ preferences, but certain housing traits are associated with higher economic status. The most basic housing typically utilizes natural materials available locally: thatch for roofing; mud, wood or bricks for walls, and packed earth for flooring. Housing one step up incorporates sturdier, man-made materials: metal sheeting for the roof, and cement for the walls and floors. The highest tier housing may incorporate a wider variety of materials, including those common in Western architecture, such as tile or slate roofing, brick walls, and tiled or wooden floors. To learn about where SAT clients’ housing might fall in this spectrum, we look at the materials used for three building parts: the walls, roof, and floor. For each, the material recorded represents the primary material used to build that part. The most common material for walls was cement or concrete (59 percent), followed by mud or brick (39 percent). Very few households had homes with walls made of other materials. Almost 90 percent of households had roofs made primarily of metal, while 9 percent had roofs made of thatch. For flooring, 72 percent had concrete or cement, while 25 percent had earth floors.


Table 34. Housing Materials

Percent of SAT Households

Walls: Cement or concrete 59%

Mud or brick 39% Other 3%

Roof: Metal 89%

Thatch 9% Other 2%

Floor: Cement or concrete 72%

Earth 25%

Other 2%

Table 35 show the number of rooms in the house or compound for respondents’ households. The average number of rooms was 3.3; the largest reported number of rooms was 24. Most respondents—about 79 percent—have 4 or fewer rooms in their house. Table 35. Number of Rooms in House

Number of Rooms Percent of SAT Households

1 22% 2 26% 3 19% 4 12% 5 to 10 19% More than 10 2%

100%

Average number of rooms: 3.3

A minority of households—only 21 percent—have their own latrine or bathroom, while 79 percent do not (Table 36). Table 36. Households with Own Latrine

Percent of SAT

Households

Latrine in household 21% No latrine in household 79%


Table 37 shows the time it takes to walk to the nearest public transportation route from the respondent’s home. Public transportation includes buses, trotros, and taxis. In the Northern Region, households’ location relative to public transportation generally falls into three categories. First are those households who live in large towns and cities, where public transportation is readily available, there are multiple transportation routes, and few people need walk more than a few minutes to get to places where public transportation stops; available transport often includes buses, trotros, shared taxis that run routes, and private taxis for hire. The next category includes those who live close to major transportation routes; for example, in towns or villages along the main road between Tamale and Walewale. Transportation comes at regular intervals, and may include buses, trotros, and shared taxis. Most people can reach the route with a walk lasting perhaps 2 to 20 minutes. The final category comprises those who do not live near major transportation routes. Public transportation may come to their village only once a day, and it may be only a shared taxi. To reach routes where public transportation is readily available, people living in these communities may have to walk 30 minutes or more. Most respondents live very close to public transportation; 46 percent of respondents need walk no more than 5 minutes to get to a place where they can find public transportation. About 6% of respondents must walk more than 30 minutes; the largest reported time to walk to public transportation was 2 hours. Table 37. Time to Public Transportation

Percent of SAT Households

0 to 5 minutes 46% 6 to 10 minutes 21%

11 to 20 minutes 16% 21 to 30 minutes 10% 31 to 60 minutes 5% More than 60 minutes 1%

100%

Average time to transport: 13.3 minutes

Table 38 shows main sources of lighting for SAT respondent households. A large share of households (71 percent) have access to electricity. About 12 percent use kerosene lanterns, and another 9 percent use battery-powered lanterns. The remaining 8 percent use other sources of light, such as candles, flashlights, or solar powered lights. Table 38. Sources of Lighting

Percent of SAT

Households

Electricity 71% Kerosene lantern 12% Battery lantern 9%

Other 8%


Table 39 shows sources of water for SAT households. Just over half—56 percent—of households have access to piped water. About 14 percent get their water from private wells, 12 percent from public wells (either with or without a pump), and 10 percent from boreholes. The remaining 8 percent get water from other sources, including surface water or water delivered by tanker. Table 39. Sources of Water

Percent of SAT

Households

Piped water 56% Private well 14% Public well 12% Borehole 10%

Other 8%

Income Generating Activities

Sinapi Aba Trust clients receive loans for a specific business. Sinapi collects information about this business, including what the business is and how much money it makes, before granting the loan. In our questionnaire, we asked respondents to report how much sales revenue their Sinapi Aba Trust business makes in a typical week. Results are reported in Table 40. Table 40. SAT Business Sales Revenue in a Typical Week

Weekly SAT earnings Percent of Respondents

0 GHC 17% 1 to 10 GHC 12% 11 to 25 GHC 14% 26 to 50 GHC 17% 51 to 100 GHC 14% 101 to 200 GHC 9% 201 to 500 GHC 10% 500 to 1000 GHC 4%

More than 1000 GHC 3%

A notable share of respondents (17 percent) reported that they make 0 GHC (new Ghana cedis) in a typical week. While this is somewhat counterintuitive, it should be noted that some SAT groups included in the sample did not have an active loan with SAT at the time of the survey. Individuals and groups sometimes choose not to take loans during the lean season, when they don’t think they can make enough money from their business to repay the loan. This survey was conducted in September and October, before income from harvest has been fully collected, so it is not surprising that some SAT businesses would have been dormant during this period.


An additional 43 percent of clients report earning less than 50 GHC per week in sales revenue. The data do not reflect about the profitability of the business, but businesses with these levels of revenue are likely not to translate into large income for the SAT clients. Table 41 lists annual earnings from a business earning 50 GHC per week, given different profit margins. Since most SAT businesses are small retail businesses, selling products many other small businesses sell, they are likely to be operating in competitive environments where profit margins are small. Table 41. Annual Earnings for 50 GHC/week Business for Different Profit Margins

Profit Margin Annual Income, GHC Annual Income, USD

5% Profit 130.00 $86.67 10% Profit 260.00 $173.33

25% Profit 650.00 $433.33 50% Profit 1,300.00 $866.67

100% Profit 2,600.00 $1,733.33

About 3 percent of respondents reported earning more than 1000 GHC in a typical week. Some of these data are likely to be incorrect, due to confusion over currency conversion (see Appendix C). However, it is possible that some of the businesses have large revenues, as some Sinapi businesses receive loans that are quite high, in the range of 1000 GHC. The average weekly earnings from respondents’ Sinapi Aba Trust businesses was 184 GHC per week; the highest reported earning was 7000 GHC per week. When reported earnings over 1000 GHC per week (a total of 55 observations) are excluded on the grounds that they are errors or outliers, the average is only 96 GHC per week. In the Northern Region, it is common for households to farm even if they have other income generating activities. Nearly half of SAT clients report that their household engages in farming. Of those who do engage in farming, most have small to medium size plots. The largest reported farm size was 30 acres. Table 42 shows farming activity for SAT respondents, by plot size. Table 42. SAT Households’ Farming Activities

Acres Farmed Percent of Respondents

Household Doesn't Farm 51%

1 Acre or less 7% 2 to 5 Acres 29% 5 to 10 Acres 10%

More than 10 Acres 3%


Ownership of land in Ghana can be complex. In urban areas, concept of land ownership is aligned with holding legal title to land. However, in rural areas, which include most farm land, “ownership” of land is determined by traditional rights to land. For residential property, living on a piece of land for a long time is sufficient to establish traditional rights to it. However, to gain traditional rights to farmland, a farmer must get permission to farm that piece of land from the local chief. Once the chief has granted this permission, the individual will consider himself the “owner” of that land. Among households that farm, most of them own all of the land they farm, according to the Ghanaian standards of ownership described above. About 22 percent of households do not own any of the land they farm, and about 4 percent own some of the land they farm. Table 43. Farmland Ownership

Ownership Status Percent of Respondents

Household owns all the land it farms 74% Household owns some of the land it farms 4% Household owns none of the land it farms 22%

To estimate income from farming activities, respondents were asked to estimate the quantity and value of their harvest from the previous season, by crop type. Major crops for the northern region were included, and there was a category for “other” to capture additional crops. Quantities were measured using local metrics; for example, yams were measured in units of 100 tubers, and maize was measured in number of “bags” representing the standard-sized bags that are used for transporting and selling maize. To determine value, the respondent was asked to estimate potential earnings for each crop type, that is, what the household would have earned if it had sold all of their harvest. (In reality, farming households often sell some of their harvest, and keep some for household consumption.) Farming income was estimated by aggregating the respondents’ estimates of potential earnings for every crop type. Results are reported in Table 44. It should be noted that unlike revenues from SAT businesses, which were reported on a weekly basis, these numbers represent estimates of annual earnings from farming. Table 44. Potential Earnings From Farming At Last Harvest Annual Harvest Earnings

Percent of Respondents

0 GHC 57% 1 to 100 GHC 6% 101 to 250 GHC 11%

251 to 500 GHC 10% 501 to 1000 GHC 8% 1001 to 5000 GHC 7%



A little over half (57 percent) of households had no income from farming, roughly in line with the 51 percent of households reporting they do not farm. Similarly, as with households reporting to farm less than one acre, only a small number of households (6 percent) reported very small (under 100 GHC) incomes from farming. Most respondents with farm income (about 21 percent of all respondents) had estimated farming income between 100 and 500 GHC, though a sizeable 16 percent had estimated farming incomes exceeding 500 GHS. Among households with farming income, the average estimated potential gross income from the last harvest was 777 GHC. Four respondents had estimated farming incomes exceeding 10,000 GHC. When these four data points are excluded as outliers, the average falls to 642 GHC. Households with very large estimated potential earnings may have large commercial farming operations. It is also possible that respondents, including those with very high incomes estimates, are overestimating the value of the harvest they consumed in their own household. The household consumption component of total potential farm income is likely more difficult to estimate, since the household never sees that value in the form of transaction payments. Respondents were also asked to estimate weekly household earnings from sources other than their SAT business and farming (Table 45). About 62 percent reported no other earnings. About 13 percent reported earning 10 GHC or less per week from other sources; about 20 percent reported earning somewhere between 10 and 100 GHC per week. The average reported earnings from other income sources was 151 GHC. The largest reported weekly earnings from other income sources was 100,000 GHC. This is almost certainly a currency conversion error. When reported earnings of over 1000 GHC are excluded (a total of 7 observations) on the grounds that they are either errors or outliers, the average reported earnings from other income sources is only 21 GHC. Table 45. Weekly Reported Earnings from Other Activities

Weekly other earnings Percent of Respondents

0 GHC 62% 1 to 10 GHC 13% 11 to 25 GHC 7% 26 to 50 GHC 8% 51 to 100 GHC 5% 101 to 200 GHC 2% 201 to 500 GHC 1%



Household Consumption

Respondents were asked to report consumption for a number of product and spending categories. While the categories were not comprehensive, we attempted to include items that were representative of major expenditure categories for households in Northern Ghana. Categories were also selected based on frequency of purchases. In order to improve recall accuracy, categories were selected for which people normally buy things on a weekly or monthly basis. Less frequent expenditures, such as clothing or school fees, were excluded. Respondents were asked to estimate their consumption over the past week for 8 spending categories. Results are reported in Table 46. For each category, respondents were asked to estimate both how much of that item they bought, and to estimate the value of what they used from their own production. For maize and rice, respondents were asked to report their consumption over the past month, since many households buy these items on a monthly, not weekly, basis. The responses were divided by four to convert them to weekly consumption estimates. Table 46. Average Household Spending by Category

Average Household Consumption (GHC)

Category: Purchased Own Production

Yam 6.05 4.52 Maize* 3.72 1.16 Rice* 2.94 0.18 Soup Ingredients 8.06 0.44 Meat 6.36 0.90

Transportation 8.39 0.07 Communication 4.95 0.02

Wood and Charcoal 4.93 1.00

*reported on a monthly basis and adjusted to weekly level

Respondents were also asked to directly estimate their total spending on food items over the past week. This enables us to compare the respondents’ overall estimate to the sum of their estimates for individual items, to get a sense of the consistency and accuracy of their responses. Table 47 reports average household consumption for each of the two estimation approaches. There is no statistically significant difference in the means for each of the two approaches. Table 47. Average Food Expenditures, by Estimation Approach

Estimate Average Household Consumption (GHC)

Overall Estimate of Food Consumption 33.90

Sum of Food Category Estimates 34.30


We next examine how closely the sum of food category estimates predicts the overall estimate for individual respondents. We regress the overall estimate on the sum of the food category estimates. Results are reported in Table 48. The relationship is fairly close. An additional estimated cedi in spending in the itemized categories implies an increase of 0.70 cedis in the overall estimate. Table 48. Relationship between Sum of Itemized Categories and Overall Estimate

Dependent variable: Overall Estimate of Food Consumption

Coefficient Standard Error P-Value

Sum of Food Category Estimates 0.69 0.03 0.00

Constant 10.00 1.51 0.00

N= 1511 R-Squared = 0.2586 Adj. R-Squared = 0.2581

Although the estimates using itemized categories and an overall estimate are quite close, it is likely that both methods underestimate actual expenditures on food. The categories in the itemized expenditure section are by no means exhaustive, suggesting that total expenditure is underestimated. Overall estimates also have a tendency to underestimate total expenditure, as respondents often forget expenditures in their recall. When the non-food items for which data were collected—transportation, communication and wood and charcoal-- are included, average household expenditure is 53.80 GHC (Table 49). This translates into annual average expenditures of 2791 GHC per household for the items for which we collected data, or 446 GHC per household member, on average. Table 49. Summary Household Consumption for Measured Categories

Average Household Consumption (GHC)

Average Household Consumption (USD)

Total estimated weekly consumption, weekly 53.70 $35.80 Total estimated weekly consumption, annually 2791.33 $1,860.89 Total estimated weekly consumption per household member, annually 446.39 $297.59

Household Assets

Table 50 shows household ownership of selected assets. Of the assets on which data were collected, cell phones were the most widely owned by SAT respondent households, with about 89 percent of respondents reporting that their household owned a working cell phone. The least commonly owned asset was a car or other private passenger vehicle; only about 9 percent of respondents reported that their household owned one.


Table 50. Household Ownership of Selected Assets

Percent of Households who…

Asset Own in working condition Own non-working Do not own

Stove 11% 3% 86% Iron 48% 2% 50% Motorcycle 34% 2% 64% Bicycle 63% 5% 33% Car 9% 1% 90% Cell Phone 89% 1% 10% Radio 67% 3% 30% Cassette Player 49% 7% 44%

CD Player 45% 3% 52%

Livestock are an important asset in Northern Ghana. Table 51 shows the share of households reporting to own different categories of livestock. The most common livestock owned by SAT respondent households was poultry; about 32 percent of households own at least one chicken, guinea fowl, or duck. Only about 7 percent of households own any cattle, the most valuable type of common livestock. Although fish farming exists in the regions included in the sample, only two households reported owning any fish as livestock. Table 51. Household Ownership of Livestock

Percent of Households who…

Livestock Own 10 or fewer

Own more than 10 Do not own

For those who own, average number owned

Cattle 6% 1% 93% 6.4

Sheep 15% 3% 82% 7.0 Goats 22% 3% 75% 5.9 Poultry 19% 13% 68% 25.3 Fish 0% 0% 100% 7.0

Other livestock 4% 1% 95% 18.6

Food Security and Shocks

Figure 52 shows the percent of respondents who answered positively to each of five questions about food security. An answer of “yes” indicates food insecurity.


Figure 52. Percent of Respondents Reporting Food Insecurity

Respondents who answered positively about experiencing food insecurity for a given question were asked how frequently they experience that type of food security. Table 53 shows the percent of respondents reporting food insecurity events for the first four food security questions, along with the frequency of the food insecurity events for those respondents who said they had experienced them. The percent of respondents indicating that they experienced each type of food insecurity rarely, sometime, or often was very similar for the four types of food insecurity. The first three questions in Table 53 comprise a metric for examining household hunger developed by Deitchler, Ballard, Swindale and Coates.11 For each of the three questions, the metric assigns a score of 0 if the respondent reports the event has never taken place in the past month. The metric assigns a score of 1 if it has taken place “rarely” or “sometimes”, and a score of 2 if it has taken place “often”. The scores for the three questions are then summed to create a food quantity security score for that respondent, where the maximum score is 6. Scores of 1-2 indicate little to no household hunger, scores of 3-4 indicate moderate household hunger, and scores of 5-6 indicate severe household hunger. Figure 54 shows the distribution of scores for respondents. Severe household hunger is rare, affecting only a few households. Two-thirds of households did not report a single incident of hunger in the past month; 21 percent were found to have little to no household hunger; 12 percent were found to have moderate household hunger, and 0.5 percent were found to have severe household hunger. Although still a significant

11

Dietchler, Megan, Terri Ballard, Anne Swindale, and Jennifer Coates. “Introducing a Simple Measure of Household Hunger for Cross Cultural Use”. USAID. Technical Note 12. February 2011.

31%

19%

15%

8%

31%

0% 5% 10% 15% 20% 25% 30% 35%

In the past four weeks, did it happen that there wasno food to eat of any kind in your house, because

of lack of resources to get food?

In the past 4 weeks, did you or any householdmember go to sleep hungry because there was not

enough food?

In the past 4 weeks, did you or any adult householdmember go a whole day and night without eating

anything because there was not enough food?

In the past 4 weeks, did any child in your householdgo a whole day and night without eating anything

because there was not enough food?

In the past 7 days, did you or any other member ofthe household eat some meals without meat or fish

because there was not enough money for food?


level of hunger, there are fewer children experiencing hunger, which is to be expected as research has shown that mothers will often buffer their children from food insecurity by altering their own dietary intake first. 12 Table 53. Frequency of Hunger Events

Question % Yes Of Yes, %

Rarely Of Yes, %

Sometimes Of Yes, %

Often

In the past four weeks, did it happen that there was no food to eat of any kind in your house, because of lack of resources to get food? 31% 11% 83% 6%

In the past 4 weeks, did you or any household member go to sleep hungry because there was not enough food? 19% 15% 82% 4%

In the past 4 weeks, did you or any adult household member go a whole day and night without eating anything because there was not enough food? 15% 15% 81% 4%

In the past 4 weeks, did any child in your household go a whole day and night without eating anything because there was not enough food? 8% 14% 83% 3%

Figure 54. Number of Respondents, by Hunger Score

Table 55 reports the percent of respondents who reported taking a child out of school or selling values due to not having enough money. Selling valuables was a common response. Valuables were broadly defined, including everything from traditional stores of value such as livestock or jewelry, to business assets, to food one would not normally sell.

12

McIntyre L., Glanville NT, Raine KD, Dayle JB, Anderson B, Battaglia N. Do low income lone mothers compromise their nutrition to feed their children? CMAJ 2003; 168: 686-91.

998

199 110

174

8 3 5 0

200

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0 1 2 3 4 5 6

Hunger Score (Higher Score = More Household Hunger)


Table 55: Households Experiencing Income Shocks

Percent of households reporting taking a child out of school 16%

Percent of households reporting selling valuables 37%

Perceptions of Current Financial Situation

Figure 56 shows the share of respondents who reported that they worry that their household will not have enough money to buy the things they normally buy, broken down by how frequently they reporting worrying about this. Almost 90 percent of respondents report worrying that they will not have enough money for their household’s normal expenditures sometimes, often or always; of this, 23 percent reported they worry “always”, suggesting financial concerns are a large source of worry for many SAT clients. Figure 56. Financial Worries

In addition to asking respondents how frequently they worry about their finances, we also asked them to rank their current financial situation. The ranking metric was designed to be accessible to respondents who were not number literate. The respondents were shown a picture of a ladder with 10 rungs. The respondents were told that the top of the ladder (rung 10) represented a very good financial situation, while the bottom of the ladder (rung 1) represented a very bad financial situation. The respondents were asked to point to the spot on the ladder that represented their household’s current financial situation. The surveyors then recorded the rung number that corresponded with where the respondents pointed on the ladder. The respondents’ reported rankings are shown in Figure 57. Responses were skewed towards the lower end of the ladder. The average ranking was 4.6, and 47 percent of respondents ranked their household’s financial situation at 4 or below.

23%

7%

58%

5%

6%

0% 10% 20% 30% 40% 50% 60% 70%

Always

Often

Sometimes

Rarely

Never

How often do you worry that your household will not have enough money to buy the things you buy normally?


Figure 57. Perception of Financial Situation

Perceptions of Current Health

Respondents were also asked to rate their own health using an image of a ladder with ten rungs, where the first rung represented very bad health, and the tenth rung represented very good health. Figure 58 represents the number of respondents who rated their health at each level, from 1 to 10. The mean rating was 6.7, while the most common rating was 5. Figure 58. Number of Respondents Rating their Health at Levels 1-10

6%

10%

14%

18%

25%

11%

6% 5%

2% 3%

0%

5%

10%

15%

20%

25%

30%

1 2 3 4 5 6 7 8 9 10

On a ladder from 1 to 10 where 10 is a good: How would you classify your household's fianancial situation these days?

Improving Financial Rating


Insurance Registration and Financial Attributes

We next look at correlations between household registration and enrollment rates and household financial attributes. We cannot detect causal relationships with these data, but we can theorize about possible causal effects, and see whether the relationships we see in the data are consistent with what we would expect if these causal effects existed. First, we hypothesize that households with more financial means are more likely to be registered and enrolled for insurance, because they are better able to afford the insurance premium. If this is true, we would expect that households with higher incomes and higher consumption expenditures would be more likely to have higher registration and enrollment rates. It should be noted that a correlation between these variables and registration and enrollment rates could have alternate explanations. Causality may run the other way, as having insurance may improve health outcomes, allowing people to work more and earn more money. In addition, both higher income and consumption and higher registration/enrollment rates could be attributable to a third variable. For example, smarter or better educated respondents may be both more successful financially and more likely to register for insurance.

Registration and Household Income and Consumption

We start by looking at registration regression results. Table 59 shows regression results for three different single variable regressions. In particular, the household registration rate was regressed on either weekly income from the respondent’s SAT business, annual income from the farm harvest, and weekly income from other sources besides the SAT business and farming. We also regress the household registration rate on our measure of annual consumption per household measure. (Note that this is not a comprehensive measure of

18 45

63

94

302

189 196 204

155

237

0

50

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350

1 2 3 4 5 6 7 8 9 10

Improving Health Rating


household consumption, but rather reflects only household consumption for the expenditure categories covered in our survey.) None of the income variables are significantly related to the household insurance registration rate. While this could indicate that household income is not correlated with insurance registration, the lack of relationship could also be due to the limits of our income measures. Revenues from the SAT business cover account only for sales volume, but do not take into account profitability, so actual household income is uncertain. Farm income is the most comprehensively measured, since it is based on estimates for earnings from individual crops, but more than half of all respondents line in non-farming households. The measure of income from sources other than the SAT business and farming may have limited accuracy because of the broadness of this category. More specifically, it is easy for respondents to accidentally omit income sources. Examples of easily forgotten income sources might include earnings from seasonal labor that they are not currently engaged in, or income that family members earn and spend without first contributing to the household’s communal resource pool. Table 59. Regression Results: Income and Household Registration Rates

Regression Coefficient Standard Error P-Value N R-Squared

(1) Share of household that is registered on sales revenue from SAT business 3.88E-7 3.18E-07 0.22 1505 0.0010

(2) Share of household that is registered on income from farming 3.77E-06 4.59E-06 0.41 1505 0.0004

(3) Share of household that is registered on income from other sources 4.35E-08 5.96E-08 0.47 1505 0.0004

(4) Share of household that is registered on annual household consumption per household member 0.00008 0.00002 0.00* 1505 0.0099

Annual consumption per household member is positively and significantly correlated with the household registration rate. However, the size of the correlation is not large: 100 GHC in additional consumption per household member is associated with a 0.5 percentage point increase in the household registration rate. Also, the R-squared associated with this regression is only 0.005, meaning that differences in annual consumption per household member explain only 0.5 percent of the variation in registration rates.


The lack of a statistically significant correlation between income and registration rates and the small size of the coefficient for consumption as a predictor of insurance registration each suggest that there is not a strong relationship between financial resources and insurance registration for our sample. If this is correct, then it is possible that the cost of the insurance premium is not an important constraint to registration for our sample. It should be noted that even if there is little or no relationship between household wealth and registration rates in our sample, such a relationship still might not hold for people in Northern Ghana on the whole. To qualify for a Sinapi Aba Trust loan, clients must demonstrate that they have a business that can support repayment of their loan, which implies that our sample is perhaps comprised entirely of people who are wealthy enough to have no trouble paying the registration fee. This does not necessarily hold for other segments of the Northern Region’s population.

Enrollment and Household Income and Consumption

We next look at the relationship between the same financial measures and household enrollment rate. Results are reported in Table 60. There is no significant correlation between our income or consumption measures and household enrollment rate, despite the fact that maintaining current enrollment requires more financial resources than simply registering once. This suggests that the cost of paying premiums is not a significant barrier to either registering for insurance or maintaining current enrollment. Table 60. Regression Results: Income and Household Enrollment Rates

Regression Coefficient Standard Error P-Value N R-Squared

(1) Share of household that is enrolled on sales revenue from SAT business -3.62E-08 1.93E-07 0.85 1505 0.0000

(2) Share of household that is enrolled on income from farming -1.25E-07 2.78E-06 0.41 1505 0.0000

(3) Share of household that is enrolled on income from other sources -2.47E-08 3.62E-08 0.50 1505 0.0003

(4) Share of household that is enrolled on annual household consumption per household member 0.00002 0.00001 0.12 1505 0.0016


Household Food Security

Next, we hypothesize that higher health insurance registration rates can lead to improved financial outcomes, including a lower susceptibility to shocks, because households would be protected from large medical expenditures. If this is true, we would expect to see less food insecurity, fewer children recently from school, and fewer asset sales among households with higher insurance registration rates. We start by looking at determinants of food security events. As previously described, we ask five questions about different types of food insecurity events. For each the respondent is asked to say whether the event happens often, sometimes, rarely, or never. For each event, the respondent was assigned a value of “1” if the even happened often or sometimes, and “0” if it happened rarely or never. These indicator values were then regressed on the household’s insurance registration rate and a number of household demographic characteristics. If the hypothesis that insurance registration leads to better food security is true, then we would expect to see a positive correlation between food security outcomes and registration, controlling for other factors. The results from all five regressions are reported in Table I in Appendix D. Household insurance registration rates were significantly positively correlated with food security according to four of the five events. A household where all members are registered for insurance was 14 percentage points less likely to report often or sometimes having no food in the house than a household where no members were registered for insurance, 8 percentage points less likely to report household members often or sometimes going to bed hungry, 9 percentage points less likely to report adults often or sometimes going 24 hours without food, and 7 percentage points less likely to report often or sometimes not eating meat due to financial constraints. We include demographic variables in our regression as controls. Some household demographic variables were also significantly correlated with higher likelihoods of food insecurity events. Additional household members in certain age cohorts had low but significant correlations with some food insecurity events: an additional female child under 6 was associated with a 2 percentage point decline in the chance a household reported often or sometimes having a child go 24 hours without food, and an additional male aged 7 to 17 was associated with a 2 percentage point increase in the chance a household reported often or sometimes having someone go to bed hungry. Although these variables were statistically significant, the results for these variables should be interpreted with caution. First, the size of the coefficient estimates is not large. Second, ten gender and age cohorts were included as independent variables for five different dependent variables, resulting in 50 separate coefficient estimates. At a 95% confidence level, a significant result should be found just by chance for one in twenty estimated coefficients. For our 50 estimated coefficients, significance at the 95 percent confidence level was found twice, in line with what might be expected by chance. The fact that significance was only found for one dependent variable for each cohort further means that it is difficult to attribute much meaning to these results.


The number of well educated adults was also correlated with the likelihood of having experienced a food insecurity event. An additional adult with no schooling was significantly associated with a 2 percentage point increase in the likelihood a household often or sometimes had an adult go 24 hours without food and a 3 percentage point increase in the likelihood of having a child go 24 hours without food. These amounts may seem small, but both of these are rare, high severity food insecurity events. Also, an additional adult with secondary schooling was associated with a 4 percentage point decrease in the likelihood a household reported not having any food in the house. Again, we included multiple variables representing different education levels for each of these five regressions, so we would expect a few of the dozens of estimated coefficients to be significantly different from zero just by chance. The variables for ethnicity and religion were significantly correlated with incidents of food insecurity only in a few instances. A household being majority Christian was associated with a higher likelihood of a child often or sometimes going 24 hours without food, but was not significant for any other food insecurity event. Geographic variables were also significant indicators of food insecurity events. Being located in a rural area was associated with a 6 percentage point increase in the probability that a household reported someone often or sometimes going to bed hungry and an 8 percentage point increase in the likelihood of going without meat (due to financial constraints). Being located in Walewale was associated with a 24 percentage point increase in the likelihood of often or sometimes having no food in the house, a 17 percentage point increase in the likelihood that a household often or sometimes had someone go to bed hungry and a 6 percentage point increase in the probability that the household often or sometimes had a child go 24 hours without food. Living in Salaga or Bole was significantly associated with a lower likelihood of reporting often or sometimes going without meat for financial reasons. In general, health insurance registration status and demographic variables did not explain a large amount of the variation in likelihood that a household would face a particular type of food insecurity event. The largest amount of variation in any of the food insecurity outcomes that these variables jointly explained was 10 percent, for the likelihood that a household was forced to forego the consumption of meat, which was one of the least severe measured food insecurity events. The smallest amount of variation in any of the food insecurity outcomes that these variables jointly explained was about 4 percent, for the likelihood that a household had a child go 24 hours without eating, which is considered to be the most severe event, because household normally try to shield children from foregoing food. This may suggest that for the households in our sample, the variables we considered are better at explaining whether a household has no food insecurity or some food insecurity, but are not as good at explaining which households have severe food insecurity; in other words, among households with food insecurity, looking at demographic traits, it is hard to guess which are at risk for severe hunger. When we replace household registration rate with household enrollment rate, we find that although registration was significantly associated with lower probability of food insecurity events, there was no significant relationship between higher current insurance enrollment


and probability of food insecurity events. (Coefficients on household enrollment rates for each type of food insecurity event are reported in Table J in Appendix D.) We next look at the relationship between registration and enrollment and the household’s hunger score. The hunger score is calculated as described in the section on health events; distribution of household hunger scores can be found in Figure 54. We regress the household hunger score on the household enrollment rate, household registration rate, our measure of annual household consumption, and a number of demographic variables. Results can be found in Table K in Appendix D. Registration rate is significantly correlated with lower hunger scores. Households with low registration rates were more likely to experience more extreme levels of hunger. A household with no members registered for insurance has, on average, a hunger score 0.35 points higher than a household where all members are registered. There was no significant relationship between enrollment and the hunger scores. This is consistent with lack of significant correlations between the food security and hunger questions and enrollment when these questions were assessed individually. Our measure of annual household consumption per capita was not significantly associated with the household hunger score. As with the previous regressions, some demographic variables were significantly correlated with the hunger score.

Household Financial Shocks

We next look at what determines certain types of responses to household shocks. The two types of responses we collected data on were the removal of children from school and the sale of assets or valuables (for financial reasons in both cases). For each type of event, the respondent was assigned a value of “1” if the respondent reported the event occurred in the past year, and “0” if not. These values were then regressed on the same variables as before: the household’s insurance registration rate and a number of household demographic characteristics. If the hypothesis that insurance registration leads to more protection from income shocks is true, one would expect that there would be a negative correlation between insurance registration rates and adverse responses to financial shocks, controlling for other factors. The full results from these regressions are reported in Table L in Appendix D. The household insurance registration rate was negatively correlated with the probability that the household had to pull a child out of school. In particular, a higher-than-average household registration rate was associated with a 7 percentage point decrease in the likelihood that a child was pulled out of school. Considering that only 16% of households reported pulling a child out of school, this estimate seems to reflect a strong relationship. The household insurance registration rate was not significantly related to the likelihood that the household reported selling assets or valuables for financial reasons. The number of school age children was, as expected, a significant indicator of higher likelihood of pulling children out of school. An additional school-aged girl in the household was associated with a 2 percentage point increase in the likelihood of pulling a child out of


school, while an additional school-aged boy was associated with a 3 percentage point increase in that same likelihood. Some adult gender and age cohorts were also associated with higher likelihood of pulling a child out of school. The only cohort variable significantly related to the selling valuables outcome was the number of males age 7 to 17. An additional male in this age group was associated with a 4 percentage point increase in the likelihood that the household felt compelled to sell valuables. The numbers of adults in the various educational categories were not significantly related to the likelihood a child had been pulled out of school. The number of adults whose highest level of education was middle school or Koranic school was associated with a higher likelihood of having had to sell assets. For each of the probability of pulling a child out of school and the probability of selling assets, only one ethnicity was found to have a significant relationship. Religion was not a significant indicator of either type of response to a shock. Some geographic variables were significantly associated with the two types of responses to shocks. Households in Salaga (Bole) were 9 (17) percentage points less likely to pull children out of school compared to households in Tamale. Rural households were 13 percentage points more likely to have sold assets than urban households, and households in Walewale were 20 percentage points more likely to have to sell off assets than households in Tamale. The household insurance registration rate and the demographic variables included in these regressions explain about 7.2 percent of the variation in likelihood a household will pull a child out of school and about 9.8 percent of the variation in likelihood a household sold valuables. When household enrollment rate is substituted for household registration rate as an independent variable, we find that there is no significant relationship between household enrollment rate and likelihood of pulling a child out of school or the likelihood of selling assets due to financial pressure. (Coefficients on household enrollment rates for each of these regressions are reported in Table M in Appendix D.)

Worry about Financial Matters

We next look at the determinants of respondents’ reported frequency of worry over financial matters. The outcome we consider here was constructed as follows: respondents who reported that they always or often worry that their household will not have enough money to buy the things they normally buy were assigned a value of “1”, while respondents who reported worrying sometimes, rarely or never about this were assigned a value of “0”. We consider the same independent variables as directly above. Full results are reported in Table N of Appendix D. The household insurance registration rate was not a significant predictor of whether or not a respondent reported frequently worrying that the household would not be able to buy the


things they normally buy. Some adult gender and age cohorts were positively associated with higher probability of reporting frequent financial worry. An additional adult householder with tertiary education was associated with a 6 percentage point decrease in the likelihood that a respondent reported frequent financial worry. This is unsurprising, as individuals with tertiary education are likely to have more access to stable jobs and financial resources and may have better financial management skills. Membership in just one ethnic group was significantly associated with likelihood of reporting frequent worry; religion was not significantly correlated with likelihood of worry. Among the geographic variables, only living in Walewale was significantly related to the outcome in question, with respondents of Walewale 17 percentage points more likely to report frequent financial worries compared to those in Tamale. When household enrollment rate is substituted for household registration rate among the independent variables, we find that as with registration rate, there is no significant correlation between household enrollment rate and respondent worry. The coefficient on enrollment rate for this regression is reported in Table O in Appendix D.

Self-Ranking of Financial Situation

Lastly, we consider respondents’ ranking of their own financial situation. Respondents were shown an image of a ladder with 10 rungs and asked to point to the rung whose height represented their financial situation (where higher rungs represented better situations). We regress this assessment of respondents’ own financial situations on the same set of variables as before plus measures of annual per capita household consumption. (This consumption data was omitted from the previous regressions because it was never a significant predictor for and it did not help explain variation in the various outcomes.) Full results are reported in Table P in Appendix D. The household insurance registration rate was not a significant determinant of respondents’ financial ladder rankings. Household consumption per capita, however, was, although the correlation size is small. An additional 100 GHC in consumption per household member is associated with a one-tenth of a rung increase on the financial ladder. The only age or educational cohort found to have a significant relationship with the respondent’s financial ladder ranking was the number of adults with tertiary education. Each additional adult with a tertiary education was associated with a three-tenths of a rung increase on the 10-point financial ladder. No ethnicity or religion variables were significantly associated with the respondent’s financial ladder ranking. Among the geographic variables, only living in Salaga is significantly associated with the financial ladder ranking: respondents in Salaga ranked their financial situation seven-tenths of a rung higher than respondents in Tamale.


When household enrollment rate was substituted for household registration rate as an independent variable, as with household registration rate, there was no significant relationship between household enrollment rate and how respondents ranked their financial situation. The coefficient on enrollment rate for this regression is reported in Table Q in Appendix D. To summarize the findings of this section, household insurance registration rate was significantly associated with lower likelihoods of food insecurity events and with a lower likelihood of having pulled a child out of school. The household registration rate was not significantly related with asset sales due to financial pressure, with less worry over financial matters, or with different financial outcomes overall (as measured by the ladder described above). Enrollment status was not significantly associated with financial outcomes. It should also be emphasized that although registration rates were related to the likelihood of facing food insecurity events and the likelihood of pulling children out of school, we do not have the data to determine whether these correlations reflect causal relationships. The research design of the randomized control trial, however, was intended to help us answer these questions. In particular, we hope to ask questions about food security, shocks and other financial outcomes in our endline survey, and we will be able to compare outcomes for respondents who were enrolled in the insurance program by virtue of having been earlier assigned one of the education treatments with outcomes for those who were not enrolled. Since treatment status was randomly assigned, we will be able to identify the causal effect of the (treatment-induced) insurance enrollment.

Subjective Expectations and Risk Aversion

Individuals’ perceptions of risk and risk preferences are likely to influence their decisions to purchase insurance. Individuals who believe they have a low risk of getting sick, or individuals who are more willing to take on risk, may have less reason to purchase insurance than individuals who believe they have a high risk of illness or injury, or individuals who are highly risk averse. Measuring individuals perceptions of risk can be challenging, particularly in the context of Northern Ghana, where many people have a low level of education. Many respondents have very limited concepts of probability, and some may be number illiterate. When conceptualizing the likelihood of an event, most people think of an event as certain to happen, certain not to happen, or uncertain. Quantifying the degree of uncertainty is often an unfamiliar concept. This is compounded by the fact that the vocabulary for describing probability is limited in many local languages. Cultural attitudes towards making predictions about future events also posed a challenge. Some individuals are superstitious about stating that bad things might happen to them, for fear that making the statement will make it more likely to happen. In other cases, individuals may be hesitant to make statements about likelihood because they feel such


knowledge is the domain of God. Lastly, some individuals may be hesitant to make predictions about likelihood because they associate such actions with witchcraft. The data collection methods used to estimate individuals perceptions about likelihood of illness were designed with the aim to be accessible to all respondents, including those who were number illiterate. Respondents were given a sheet of paper with 6 squares. Each square contained a number: “0”, “1”, “2”, “3”, “4”, and “5 or more”. The numbers represented the number of times a person might get sick. For respondents who were number illiterate, the squares contained dots representing those numbers, rather than the numeric character; for example, the square for “3” contained three dots. Respondents were given 12 tokens. Respondents were asked to distribute the tokens according to how likely they thought each number of incidences of sickness was; for example, if they thought it was more likely they would get sick twice than three times, they should put more tokens on the square representing “2” than on the square representing “3”. The surveyor recorded how many tokens the respondent placed on each square. To avoid some concerns about superstition, rather than ask directly how likely the respondent thought it was that he or she would get sick, the respondent was asked how many times he or she thought a person as healthy as him or herself might get sick. This circumvented the concern some respondents had that saying they themselves might get sick would make it more likely to happen. To address the concern about witchcraft, the tokens used were man-made items, in most cases bottle caps, because natural objects like stones or beans are more closely associated with witchcraft. Explaining this activity and administering it was very time consuming, and very challenging for some clients. Despite efforts to make the activity as non-threatening as possible, there were still some respondents who felt uncomfortable with it or became very frustrated with it. As with all questions, respondents always have the option of choosing not to answer, and surveyors were trained to be sensitive to their respondents’ comfort level, and allow them to move on to the next section when necessary. As a result, response rates on these questions were lower than for most other questions in the survey. Because of the difficulty of these questions, we limited the number of questions we asked. We asked the respondent to distribute tokens representing the likelihood someone as healthy as him or herself would get sick different numbers of times in the next month, and different numbers of times in the next year. We did not ask about likelihood of injury, or likelihood of someone in the household getting sick. Figure 61 shows respondents’ estimated probabilities of getting sick different numbers of times in the next month and next year, averaged across all respondents, based on the results from this activity. On average, respondents thought the chance of not getting sick at all in the next month was about 27 percent for a person as healthy as themselves. Respondents thought the chance of getting sick once in the next month was also about 27 percent. On average, respondents thought there was a 5 percent chance of getting sick 5 or more times in the next month.


Figure 61. Respondent’s Estimated Probability of Getting Sick at Different Frequencies Average across All Respondents

When asked to consider the chances of getting sick over the next year, respondents estimated higher probabilities for higher numbers of sicknesses compared with the chances of getting sick in the next month, as would be expected. On average respondents indicated they thought the chance of never getting sick was about 16 percent. On average, respondents thought the chance of getting sick once in the next year was 20 percent. Respondents thought the chance of getting sick 5 times or more over the next year was about 15 percent. Table 62 presents the same results, but rather than showing the average estimated probability for each frequency, it reports the average estimated probability of getting sick at that frequency or more. On average, respondents thought the probability of getting sick 1 or more times in the next month was about 73 percent, while the probability of getting sick 1 or more times in the next year was 85 percent. Table 62. Respondent’s Estimated Probability of Getting Sick, Average Across Respondents

In the next month In the next year

At least 1 time 73% 84% At least 2 times 45% 64% At least 3 times 26% 44% At least 4 times 13% 27%

At least 5 times 5% 15%

Comparing the results for different frequencies, as well as the results for the one month time frame and one year time frame, raises questions about the rationality of the responses. In particular, the estimated probabilities for getting sick multiple times in a month seem quite high, especially compared with the same estimates for a year. It is unlikely that respondents in Northern Ghana actually have a 26 percent chance of getting sick 3 or more

27% 27%

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0 1 2 3 4 5 or more

In Next Month In Next Year


times in a month. If they did, this would imply higher probabilities of getting sick 3, 4 or 5 or more times per year than the respondents actually estimated. Asking about the likelihood of getting sick for both the one-month timeframe and one-year timeframe allows us to check for rationality of answers by looking for blatantly irrational responses, for example, an estimate that the likelihood of getting sick at least once is higher for the next month than it is for the next month. Looking for instances in where the respondents estimates imply a higher chance of getting sick in the next month than in the next year, we find that about 30% of respondents answered these two questions in a way that was not rational when comparing the responses for the two time frames. This suggests that a fairly high number of respondents either did not understand the activity, or do not understand likelihood well enough to give consistent estimates of it. However, it is encouraging that the trends of the answers are generally consistent with what would be expected, with higher probabilities assigned to the higher frequencies of sickness for the one-year time frame, suggesting that even if not all respondents were consistent with their estimates for each frequency, there is a general understanding that likelihood of sickness is greater over the next year than the next month, and that getting sick many times in the next month is less likely than getting sick once or not at all. Respondents were also asked to estimate the probability they would go to the hospital if sick using a similar process. Respondents were given a sheet of paper with two squares, one for “yes” and one for “no”. Illiterate respondents were given a sheet with happy and sad faces in places of the text “yes” and “no”, respectively. They were asked to allocate 12 tokens between “yes” and “no” depending on the likelihood they would go to the hospital if sick, in two different circumstances. In the first circumstance, they were asked to imagine they had no health insurance. In the second circumstance, they were asked to imagine they did have health insurance. On average, respondents estimated a higher probability of going to the hospital for the circumstance in which they were asked to imagine they had insurance (Table 63). The average estimated probability of going to the hospital when sick was 86% when the respondent was asked to imagine a circumstance in which he or she had insurance. The average estimated probability for a circumstance in which the respondent did not have insurance was 71%. Table 63. Reported Probability of Going to Hospital When Sick, Average Across All Respondents

Average Estimated Probability

If you have insurance 86%

If you do not have insurance 71%

Although the average estimated probability was higher for the circumstance with insurance, not all respondents’ answers followed this pattern. Table ZZ reports the number of respondents whose estimated probability of going to the hospital was higher, lower and the


same with insurance as without insurance. A little over half—56 percent—of respondents gave a higher estimated probability of going to the hospital if sick in a circumstance where they had insurance compared with a circumstance where they did not have insurance. About 5 percent actually reported a lower probability of going to the hospital if they had insurance. The remainder, about 39 percent, gave the same estimated probability of going to the hospital in either circumstance. This means that the difference in average probability of going to the hospital in the two circumstances were driven by the 56 percent in the first category of Table 64, and a large share of respondent actually do not predict they would behave differently if they had insurance compared with if they did not. Table 64. Difference in Probability of Going to Hospital Depending on Insurance Status

Number Percentage

Higher chance of going to hospital if have insurance 833 56% Lower chance of going to hospital if have insurance 81 5%

No difference 573 39%

We next look at whether these expectations are associated with likelihood of having insurance. Since these expectations apply only to the respondent, we will look at the respondent’s registration rather than household registration rates. First, we test whether individuals who report they are certain they will get sick or certain they will not get sick have different likelihoods of registration than other respondents. For individuals who are certain they will not get sick, we create a dummy variable that equals “1” if the estimated probability of getting sick 0 times in the next month is 1, and “0” otherwise. For individuals who are certain they will get sick, we create a dummy variable that equals “1” if the estimated probability of getting sick 0 times in the next year is 0, and “0” otherwise. We regress insurance registration status on both dummy variables, in separate regressions. The results are reported in Table 65. Neither variable is a significant predictor of registration, meaning that there is no relationship between a respondent estimating he or she will get sick or not with certainty, and the likelihood that he or she is registered for insurance. Table 65. Estimated Probability of Illness and Insurance Registration, Where Respondent Indicates Certainty Independent Variable (Single Variable Regression) Coefficient

Standard Error

P-Value *Significant? R²

Probability of Never Getting Sick in a Month is 1 -0.03 0.05 0.55

0.0002

Probability of Never Getting Sick in a Year is 0 0.00 0.02 0.90

0.0000

We next look at whether estimating a higher likelihood of getting sick once is associated with higher likelihood of being registered for insurance. We regress registration status on each respondent’s estimated probability of getting sick at different frequencies. Results are reported in Table 66. No significant relationship was found between the estimated probabilities of sickness at different frequencies and registration status.


Table 66. Estimated Probability of Illness and Insurance Registration Independent Variable (Single Variable Regression) Coefficient

Standard Error P-Value *Significant?

R-Squared

Estimated probability of getting sick at least once in the next month 0.02 0.04 0.58

0.0003

Estimated probability of getting sick at least once in the next year 0.04 0.05 0.48

0.0005

Estimated probability of getting sick at least five times in the next month 0.05 0.12 0.70

0.0002

Estimated probability of getting sick at least five times in the next year -0.03 0.06 0.56

0.0004

We next look at respondents’ attitudes towards risk more generally, and how those attitudes are related to registration status. To get an idea of respondents’ risk aversion, we presented respondents with different hypothetical maize seed varieties. The hypothetical seed varieties were exactly the same, except that the amount of harvest produced in good and bad weather differed by variety. Table 67 describes the 5 seed options used for this question. Table 67. Risk Aversion Question Seed Options Seed Variety Good Weather Yield Bad Weather Yield

1 30 bags 30 bags

2 45 bags 27 bags

3 75 bags 15 bags

4 105 bags 6 bags

5 120 bags 0 bags

Starting with seed varieties 1 and 2, respondents were given a choice between two varieties at a time. They were told that there was equal chance of good weather and bad weather. If the respondent selected the riskier variety, in the first case seed variety 2, then the respondent was asked to choose between that variety and the next riskier variety. If the respondent chose the safer variety, the questioning ended. We assign each respondent a risk scale value equal to the seed variety the respondent ultimate said was their preferred variety. For example, if a respondent chose seed 2 over seed one, and then chose seed 2 over seed three, she would not be asked about seeds 4 and 5, and she would be assigned a risk scale value of 2. We regress the respondent registration status on the risk scale values. The results are reported in Table 68. When no other variables are included in the model, there is a positive


and significant relationship between the respondent’s risk scale value and probability that she is registered for insurance. Choosing a seed that is riskier than the preceding seed is associated with a 2 percentage point increase in the probability that the respondent is registered for insurance. This implies that respondents who are more risk averse are less likely to be registered. When demographic variables are included as controls, however, the relationship is no longer significant. This suggests that the relationship observed between higher preference for risk and insurance registration is likely driven by correlations between risk aversion and other variables of interest. For example, adults whose highest schooling is senior secondary school are both more likely to be registered and more likely to be less risk averse. Table 68. Risk Aversion and Registration


R-Squared

Risk Scale Value 0.02 0.01 0.01 * 0.0046 Risk Scale Value, Controlled for Demographics 0.00 0.01 0.84

0.0945

It should be noted that the size of the coefficient on the risk scale is fairly small. A respondent who chose the riskiest seed would be 8 percentage points more likely to be registered than a respondent who chose the safest seed. If higher risk aversion is associated with lower probability of insurance registration, one possible explanation is that individuals that do not have insurance tend to be more risk averse in other areas because of the lack of protection that insurance brings. Alternatively, individuals who are financially less secure may be both less likely to be enrolled and more likely to be risk averse.

Knowledge and Attitudes about Health Insurance

Respondents who had at least household member not registered in NHIS were asked why household members were not registered (Table 69). Multiple answers were allowed. The most common answer was that the household intended to register everyone, but had not gotten around to doing so. The second most common response was that the premium was too expensive. Very few had household members who were unregistered because they did not know about insurance or did not know how to register. Among respondents who selected “other” and specified an alternative, a common answer was “not interested in insurance”, with no given reason for the disinterest. Table 69. Reasons Reported for not Registering for Insurance

Number of respondents reporting

each reason


Didn't know about it 4

Don't know how to register 5 Too difficult to register 60 The premium is too expensive 316 Don't think will get sick 27 Services too far away 20 Services are not good 32 Intend to, but just haven't done it yet 403

Other 92

Respondents knowledge of health insurance before the education sessions was also tested. The knowledge test consisted of six true-or-false questions. Table 70 lists each question, the correct answer, and the percent of respondents who were able to give the correct answer. Table 70. Knowledge Test Correct Responses by Question

Correct Answer

Percent Respondents with

Correct Answer

T or F: After registering for insurance for the first time, I can use insurance to pay for health care immediately. F 56.58%

T or F: Transportation costs and lost work time are part of the costs of being sick. T 56.49%

T or F: I must re-enroll in insurance every year in order to access services using my insurance card. T 93.43%

T or F: There is a limit to how many times I can use my insurance each year. F 73.96%

T or F: People with health insurance must still pay the doctor or the hospital before they can get covered services. F 89.31%

T or F: If I do not use health services this year, I will get back the money I paid for insurance. F 95.28%

Table 71 reports the distribution of respondent scores on the knowledge test. Most respondents performed well. Of the respondents whose surveys had data for every


question on the knowledge test, 88 percent of respondents missed 2 or fewer questions on the test. Table 71. Respondent Performance on Knowledge Test

Number Correct

Number of Respondents Achieving that Score

Percent of Respondents Achieving that Score

0 14 0.9% 1 17 1.1% 2 34 2.3% 3 113 7.6% 4 375 25.4%

5 591 40.0%

6 335 22.7%

1479 100.0%

The Knowledge Section also asked respondents questions about their attitudes towards toward insurance. Table 72 shows the percent of respondents who responded “agree”, “disagree”, or “don’t know” to the two statements about health insurance. Responses suggest very favorable attitudes toward insurance. Table 72. Attitudes About Health Insurance

Agree Disagree Don't Know N

I would rather risk paying for health expenses cash and carry than pay for health insurance

6.1% 92.1% 1.8% 1501

Insurance is not a good value for the money 16.5% 74.7% 8.8% 1503

Of the respondents with data, 92 percent indicated they would rather pay for insurance than risk paying cash when they need health services, and 75 percent indicated that they though insurance was a good value for the cost. Table 73 shows the percent of respondents who report they have discussed insurance with at least one of their household members. A large majority of respondents, 87 percent, report discussing insurance with family. Table 73. Respondents Who Have Discussed Insurance with Household

Yes No N


Have you discussed health insurance with any of your household members? 87.0% 13.0% 1502

Knowledge and Attitudes and Registration and Enrollment Status

Registration/Enrollment and Knowledge

We examine the relationship between respondent’s knowledge of health insurance and probability that they are registered. We regress respondents’ registration status on the number of correct answers on the knowledge quiz (out of a total of 6) and a number of demographic control variables. Results are reported in

Table R in Appendix D. The score on the knowledge test is significantly correlated with registration status: an additional correct answer on the knowledge quiz was associated with an 8 percentage point increase in the probability of a respondent being registered. There was no significant relationship between knowledge and enrollment. The correlation between knowledge and registration rates also extended to the household as a whole. Each additional question the respondent got correct was associated with a 7 percentage point increase in the household registration rate. (Results reported in Table S in Appendix D. This relationship could be due to causation working in either direction. It may be that individuals who are already registered in insurance and whose households are registered have more exposure to the NHIS scheme, and hence learn more about it. It could also be that greater knowledge about health insurance leads individuals to register themselves and their families.

Registration/Enrollment and Attitudes

We next look at the relationship between respondent’s attitudes towards insurance and registration and enrollment status. There were two questions on the baseline that dealt directly with attitudes. Respondents were asked whether they agreed or disagreed with the statements “I would rather risk having to pay for health expenses using cash and carry than pay for health insurance” and “Health insurance is not a good value for the money.” In each case, a response of “disagree” indicated a more positive attitude towards insurance. We regress insurance registration on a variable equal to “1” if the respondent showed a positive attitude towards insurance, for each of the two questions, and on a number of demographic variables included as controls. Results are reported in Table T in Appendix D. A response of “Disagree” to the first question was positively associated with insurance registration. Respondents who answered “Disagree” were 12 percentage points more likely to be registered than respondents who said “Agree” or “Don’t know”. There was no


significant relationship between enrollment and answers to this question. There was also no significant relationship between responses to the second question and either registration or enrollment rates. Again, causality could run in either direction. It may be that respondents with more positive views of insurance are more likely to register for it. It could also be that respondents with insurance have good experiences with it, and therefore have more favorable attitudes towards it.

Health Events

Incidence of Health Events

We next look at incidence of health events in respondents’ households. Respondents were asked to report all health events in the household in the past month that interfered with the person’s ability to complete daily tasks. Health events included deaths, giving birth, illness and injury. Respondents were given the following list as examples of health events: malaria, a broken bone, diabetes, asthma, or diarrhea. Just over half—about 52 percent—of respondents reported a health event in their household in the past month (Table 74). About 11 percent of individuals were reported as experiencing a health event. Table 74. Incidence of Health Events

Percent of Households 52% Percent of Individuals 11%

Health Events and Demographic Variables

We create a dummy variable for each individual in the data set that equals “1” for all individuals who experienced a health event, and “0” otherwise. We regress that variable on our set of demographic variables for individuals. Full results for individuals 18 and older are reported in Table U in Appendix D. Women are significantly more likely to experience a health event than men; women are, on average, 6 percentage points more likely to have had a health event in the past month than men. Age is also significantly correlated with probability of experiencing a health event. Generally, older individuals were more likely to experience a health event, but the relationship between age and experiencing a health event was not entirely linear; individuals with a very high age were actually less likely to experience a health event. To illustrate the relationship between age and health event likelihood, Table 75 shows the


probability for a married Dagomba woman with secondary school education living in rural Tamale, given various ages. Table 75. Age and Probability of Experiencing a Health Event in Adults Rural Tamale Moslem Dagomba married woman with secondary schooling

Age Probability of experiencing a health event

18 8.4% 30 11.9% 45 24.2% 55 27.8% 65 30.2%

70 17.2%

The only other variable that is significantly related to likelihood of a health event is the geographic variable for Walewale. Individuals in Walewale are 6 percentage points more likely to have experienced a health event than individuals in Tamale.

Impact of Health Events

Severity of Health Events

Most reported health events—about 63 percent—were moderate in severity, with the respondent reporting that they temporary prevent normal daily activities. About 32 percent were relatively mild, and caused only inconvenience. About 5 percent were severe, resulting in permanent disability or death. Table 76. Reported Severity of Health Events

Severity Number Share of total health events

Caused death 4 0.34% Caused permanent disability 51 4.31% Temporary inability to do daily tasks 743 62.86%

Inconvenience 384 32.49%

TOTAL 1182

Costs of Health Events

Health events also result in costs in terms of lost time and money for individuals. Table 77 reports the average number of days lost to the health event, and the average amount of income lost as a result of the health event. On average among respondents who reported lost days, each health event resulted in 16.8 days during which the health event prevented normal daily activities, and 17.7 days during which the event caused an inconvenience. Among individuals who reported data on lost income, an average of 33.60 GHC was lost in foregone wages for the person experiencing the health event. An average of an additional


29.30 GHC was lost in foregone wages for other members of the household, as a result of providing care to the individual experiencing the health event instead of going about their normal income generating activities. Table 77. Time Costs and Monetary Costs of Health Events, Average Across Individuals Reporting Costs

Average number of days event interfered with normal activities 16.8

Average number of days event caused inconvenience 17.7

Average amount of income lost by person with health event, Ghana cedis 33.60

Average amount of income lost by other household members, Ghana cedis 29.30

Responses to Health Events

We next look at responses to health events. Table 78 summarizes individuals’ responses to health events. Almost all respondents—about 97 percent—who experienced health events sought treatment. This may seem to be high, but there are several reasons why a high treatment rate would actually be expected. First, the respondents in this sample are clients of a Sinapi Aba Trust, which means that they have an income generating activity sufficient to qualify them for a microfinance loan. As such, the respondents are unlikely to be among the poorest of the population in the Northern Region. Second, since most of them are receiving microfinance loans, they have access to a liquidity cushion that can finance health expenses if the need arises. In addition, even for the most poor, social networks often function as a social safety net in emergencies. In Northern Ghana, individuals who are faced with sudden medical expenses often receive financial help from friends and family. A person who seems to have very few financial resources may actually have resources in the form of these social ties; it is unusual for a person to have no access to anyone with money to pay for medical fees. As a result, high treatment rates would not be unexpected even among populations that seem to be in bad economic situations. Lastly, reporting of health events may be biased such that health events for which treatment was sought were more likely to be reported, even if they were no more severe than events for which treatment was not sought. Respondents with relatively minor health events may be more likely to recognize those events as “health events” and remember them during the survey if they took the time to seek a consultation, whereas if respondents do not seek treatment, they may be more likely to try to ignore the problem or downplay it, and hence are less likely to think of it as a health events during the questionnaire. Table 78. Responses to Health Events

Number Percentage

Did not get treatment 39 3.3%

Got treatment 1147 96.7%


Among those who got treatment, went to:

Doctor 670 58%

Dentist 8 1%

Nurse 116 10%

Medical Assistant 49 4%

Midwife 2 0%

Pharmacist 38 3%

Drug/Chem Seller 117 10%

Traditional Healer 39 3%

Trained Traditional Birth Attendant 0 0%

Untrained Traditional Birth Attendant 0 0%

Spiritualist 1 0%

Don't know/no data 107 9%

Table 78 also reports, for those who sought treatment, what type of health service provider was consulted. The most common was a doctor; 58 percent of individuals reported seeking a consultation with a doctor for their health event. The next most common was a drug or chemical seller, with 10 percent of individuals seeking a consultation with this type of provider. It is worth noting that chemical sellers can sell medications directly to clients that would require a prescription from a doctor in countries such as the United States—however, the National Health Insurance Scheme will cover the cost of the drug only if the client has a prescription for it. Another 10 percent of individuals experiencing health events consulted a nurse. Small numbers of individuals consulted dentists, medical assistants, pharmacists, and traditional healers. (Services at the latter are not covered by insurance.)

Insurance Registration and Enrollment and Health Events

Registration

We next look at the relationship between insurance registration status and health events. We create a dummy variable that is equal to “1” if the individual experienced a health event, and “0” otherwise. We then regress this variable on the dummy for insurance registration status (which is equal to “1” if the individual is registered and “0” otherwise), enrollment status (equal to “1” if the individual is confirmed currently enrolled and “0” otherwise) and our demographic variables. The full results are reported in Table V in Appendix D. Health insurance registration is a significantly related to a higher likelihood of reporting a health event, but the correlation size is small. Being registered for insurance is correlated with a 2 percentage point increase in the likelihood that an individual reported experiencing a health event. There are a couple reasons why this might be the case. First, it might be that individuals who are more likely to experience a health event are aware that they are at higher risk, and


thus are more likely to register for insurance. Second, it may be that individuals with health insurance are more likely to seek treatment for less severe health events, and as discussed above, may be more likely to report these health events because the act of seeking treatment contributes to the respondents’ perception of the event as a health event.

Enrollment

Current health insurance enrollment had an even larger correlation with reporting a health event: being currently enrolled was associated with a 12 percentage point increase in the probability that an individual would have reported having had a health event in the past month. As with registration, it is possible that those who are most likely to experience a health event are aware of this, and are more conscientious about keeping their policies current. However, it is also possible that much of the relationship is due to causality in the other direction. It is common for individuals in Ghana to discover that their insurance is expired when they experience a health event and attempt to use their insurance to pay for treatment. In most cases, the individual (or someone on the individual’s behalf) then makes the back due premium payment, allowing the individual to then access health services. As a result, someone who has had a health event in the past month is likely to either have had a current policy at the time of the event, or to have re-enrolled at the time of the event, and therefore is more likely to be currently enrolled than someone who has not experienced a health event in the past month. This effect could be less likely to apply in the case of registration, because an individual who experiences a health event would have to wait 3 months after registering to access free care, and initial registration requires more time, so experiencing a health event is less likely to prompt an unregistered person to register than it is to prompt a person with expired insurance to re-enroll.

Other Demographic Variables

With insurance registration and enrollment included, a number of demographic variables included as controls remain significant. Being female is still significantly correlated with a higher probability of experiencing a health event. Being in the 7 to 17 age cohort and the 18 to 30 age cohort is associated with a lower probability of experiencing a health event; individuals in these age groups where 4 percentage points less likely to experience a health event than individuals in the youngest age cohort. Additional household adults of almost every education level were associated with lower probability of experiencing a health event. Each additional adult with either no schooling, or whose highest level of schooling was middle school or high school was associated with a 1 percentage point decline in the probability that an individual would experience a health event. An additional adult in the household whose highest level of schooling was Koranic was associated with a 2 percentage point declines in the probability of reporting a health event. One theory as to why additional household adults are associated with lower probability of health events might be that a larger number of adults leads to a safer, healthier environment that protects individuals from health events. However, conversely, it might be that a larger household creates more demands on individuals, and therefore individuals are


more likely to ignore or downplay a health event to go on meeting those demands, and as a result they are less likely to report the event as a health event than an individual in a smaller family whose health event got more attention. Only one ethnicity variable was significant; religion was not significantly associated with higher likelihood of reporting a health event. Living in Walewale continued to be a significant indicator of higher risk of experiencing a health event. Individuals in Walewale were 4 percentage points more likely to experience a health event in the past month compared with individuals living in Tamale.

Severity of Health Events and Registration and Enrollment

We next examine the relationship between registration and enrollment in health insurance, and the severity of reported health events. To do this, we consider only individuals who experienced a health event. We first create a dummy variable equal to “1” if the health event was moderate or severe; that is, if the individual with the health event was reported as having a health event that caused temporary inability to do daily activities, permanent disability, or death, and “0” if it only caused inconvenience. We regress this variable on registration and enrollment status, and on a number of control demographic variables. Results are reported in Table W of Appendix D. We find no significant relationship between either registration or enrollment status at the 95 percent confidence level. However, at the 90 percent confidence level, we find that being registered is associated with a 6 percentage point decline in the likelihood that a health event experienced by a person in our sample would be reported as moderate or severe. Previously we discussed the possibility that the association between being registered for insurance and having had a health event might be driven in part by individuals being more likely to report an incident as a health event if they treated it, and if they were more likely to get treatment if they had insurance. If this were the case, we would expect that individuals who are registered and enrolled in insurance would be more likely to have health events that would be classified as an inconvenience rather than severe health events. Our finding that being registered for a health insurance may be correlated with less severe health events is consistent with this theory. However, it is also possible that having insurance leads to earlier treatment, resulting in less severe health events. Some ethnicity and geographic variables are significant indicators of health event severity. These likely reflect variation in disease risk and health services availability in different areas. Of note, individuals in Walewale are less likely to report a health event as severe, while individuals in Bole and Salaga are more likely to do so. Walewale is the farthest north in the sample, and therefore more arid, while Bole and Salaga are farther south and more lush, so it is likely that malaria risk is greater in these areas. Bole and Salaga are also the most rural, and health services are the least accessible, so individuals in these locations may wait longer to seek treatment.


We next look at the relationship between enrollment and registration and the most severe outcomes for health events: permanent disability and death. For each, we create a dummy variable equal to “1” if that outcome happened, and “0” otherwise. We regress these variables on registration and enrollment status and a number of demographic control variables. Results are included in the same Table W. We find that individuals who are enrolled in insurance are significantly more likely to have experienced a health event resulting in permanent disability in the past month. There are several hypothesis that may account for this. First, individuals may enroll in insurance when they go to treat the event causing the disability. Second, individuals with conditions that result in permanent disability might be more likely to maintain current enrollment because they expect to use insurance for their condition in the future. There was no significant relationship between registration status and likelihood of reporting a health event that caused disability. We do find a significant relationship between health insurance registration and likelihood a health event results in death. A person who experienced a health event and was registered in insurance was 1 percentage point less likely to have that health event result in death than a person not registered in insurance. Given that the probability of a health event resulting in death was less than 0.5 percent for our sample, the size of this correlation is relatively large. Of the 4 health events that resulted in death, 3 of the individuals were not registered for insurance. There was no significant relationship between enrollment and likelihood of a health event resulting in death. There are a number of possible explanations for this correlation, and it should be interpreted with caution. It may be that those at the highest risk of death are also highly unlikely to register for insurance. The regression did control for a number of variables, including age and geographic area, but a number of other factors, such as working conditions and risk aversion, were not captured. These individuals may choose not to use modern medicine, or health services and health insurance may be very hard to access from where they live and work. It is also possible, however, that being registered in health insurance allows people to access health services more easily, resulting in earlier and better treatment and less risk of death due to a health event.

Treatment of Health Events

Likelihood of Treatment

We next look at the relationship between being registered for insurance, and getting treatment in the case of a health event. We create a dummy variable that is equal to “1” if an individual got treatment for his or her health event, and “0” if the individual did not. Individuals who did not experience a health event were excluded from the regression. We regress this variable on the dummy for registration status and on our demographic variables. Full results are reported in Table X in Appendix D. Almost no variables were significantly related to likelihood of getting treatment, including registration status. The only significant variable was the number of adults whose highest


level of schooling was vocational; each additional adult in the household in this educational category was associated with a 3 percentage point decline in the probability an individual experiencing a health event would get treatment. Given the number of variables, it is possible this variable appears significant by chance. When enrollment status is used in place of registration status, as with registration, there is no significant relationship between enrollment status and likelihood of getting treatment. The coefficient on enrollment status for this regression result is reported in Table Y in Appendix D. It is not surprising that we did not find many variables significantly correlated with treatment, because there was very little variation in the treatment variable; almost all individuals who experienced a health event got treatment.

Type of Treatment Sought

We examine the relationship between insurance status and the type of treatment sought. The two most common types of medical service providers consulted were doctors and drug/chemical sellers. As previously noted, chemical sellers are allowed to dispense many drugs that would normally require a doctor’s prescription in countries like the United States. For example, licensed chemical sellers can sell antibiotics and anti-malarial medications over the counter. A client can access these drugs more quickly by going directly to a chemical seller, and does not have to pay for a consultation with a doctor; consultations with chemical sellers are informal and typically cost nothing. However, the client must have a prescription for the drug in order to have the cost of the medication covered by health insurance. An additional issue is the quality of the care. A client who buys a medicine directly from a chemical shop with no prescription foregoes a physical examination, and most chemical sellers are not as highly trained as doctors. Given these distinctions between consulting a doctor and consulting a chemical seller, we hypothesize that respondents with health insurance would have more reason to visit a doctor, because they can access higher quality care at no cost and have the cost of their medicines covered by insurance. Those without insurance, on the other hand, would have more reason to visit a chemical shop, because it is easy, fast and costs less than consulting with a doctor. To test this, we create a dummy variable equal to “1” if an individual consulted a doctor for his or her health event, and “0” if the individual consulted another health service provider. We create a second dummy variable equal to “1” if an individual consulted a chemical seller for his or her health event, and “0” if the individual consulted another health service provider. Each of these dummy variables is in turn regressed on the dummy for health insurance registration status and on our demographic variables. Full results for each of these regressions are reported in Table Z in Appendix D. The relationship between insurance registration status and type of health service provider consulted is large and significant for both equations. Individuals who are registered in insurance are 17 percentage points more likely to consult a doctor when they experience a


health event than individuals who are not registered in insurance; individuals who are registered and currently enrolled are an additional 9 percentage points more likely to consult a doctor. At the same time, individuals who are registered in insurance are 10 percentage points less likely to consult a chemical seller when they experience a health event than individuals who are not registered for insurance, and individuals who are registered and currently enrolled are an additional 5 percentage points less likely to go to a chemical shop. Part of the relationship between insurance status and type of treatment may be due to the effect of individuals re-enrolling when they go for consultations with a doctor, as discussed with the correlation between insurance registration status and health events. In the case of enrollment status, this is a particularly compelling theory relative to the hypothesis that those who are enrolled are more likely to choose to go to a hospital, because many individuals are not aware of their enrollment status, although enrollment status could affect an individual’s ability to obtain a consultation once he or she has gone to that provider, if the individual finds his or her insurance is expired and for some reason cannot make a premium payment that day. However, this effect could be less for the case of treatment and registration status, because after registering, an individual must wait 3 months to access services, so visiting a doctor is more likely to prompt an individual to re-enroll than to register for the first time. If the relationship is not due to this effect, then these results would suggest that people who know that they are registered are more likely to seek out care from highly trained providers than those who are note registered. There is a significant relationship between likelihood of consulting a doctor and some age cohorts. Children ages 7 to 17 are 12 percentage points less likely to consult a doctor when experiencing a health event than children under 7, while adults 45 and over are 15 percentage points more likely to consult a doctor than individuals in the youngest cohort. This may be due to the type of health events faced by these different age groups. Children ages 7 to 17 may most commonly present with relatively routine health events, such as malaria or minor cuts, scrapes and bruises, which can be handled by nurses or medical assistants. Older adults, on the other hand, may be more likely to present with serious health conditions, such as diabetes, blood pressure problems, or heart disease, which would require the attention of a doctor. The age cohorts were not significantly related to the probability of an individual consulting a chemical seller. The results found limited relationship between number of adults with various levels of schooling in the household and the likelihood of consulting either type of health service provider. An additional adult with no schooling in the household was significantly correlated with a 2 percentage point decrease in the probability of an individual consulting a doctor when faced with a health event. An additional adult whose highest level of schooling was Koranic school was significantly correlated with a 4 percentage point increase in the probability that an individual would consult a chemical seller when faced with a health event. Some ethnicities were significantly associated with probability of consulting either type of health service provider. Religion was not significantly associated with the probability of consulting either type of provider.


Living in Bole or Salaga was significantly correlated with a lower likelihood of consulting a doctor compared with Tamale. Individuals in Bole were 16 percentage points less likely to consult a doctor when faced with a health event, and individuals in Salaga were 28 percentage points less likely to consult a doctor. There was no significant relationship between any of the geographic variables and the likelihood of an individual consulting a chemical seller. The negative relationship between an individual living in Bole and Salaga and likelihood of consulting a doctor may be related to the availability of medical services in these regions. Tamale has the most advanced medical services available in the Northern Region, and hosts multiple hospitals with doctors on staff, making it relatively easy for individuals to consult a doctor there. While Walewale has fewer medical resources, Tamale is in easy reach for most people living near Walewale; the drive between the two towns takes about an hour and public transportation is frequent. Bole and Salaga, on the other hand, have few medical resources, and transportation between these towns and the nearest cities with large medical facilities is difficult. Many communities are quite rural, and may be cut off from other towns and cities during the rainy season. These communities are likely to be served by small clinics, which rarely have doctors on staff, relying instead on community health nurses, medical assistants, and midwives. The relationship between registration and enrollment and higher likelihood of hospital visits and lower likelihood of chemical shop visits is robust even when severity of the health event is included as a control. This suggests that the difference in type of treatment pursued is not due to differences in the severity of health events between those registered or registered and enrolled and those who are not registered.

Well-Patient Visits and Insurance Registration and Enrollment

We next look at the relationship between registration and enrollment status and the likelihood that an individual will go for a consultation when he or she is not sick. These visits include antenatal care visits, immunizations, and check-ups. We create a dummy variable equal to “1” if the individual is reported as having gone to a well-patient visit, and “0” otherwise. We then regress this variable on registration status, enrollment status and a number of demographic variables. Results are reported in Table AA in Appendix D. There is no significant relationship between registration status and well-patient visits. There is a significant relationship between enrollment status and well-patient visits, though the size of the correlation is not large—being confirmed enrolled is associated with a 2 percentage point increase in the likelihood that an individual has gone for a well-patient visit in the past month. However, considering that very few individuals were reported as going for a well-patient visit, this small correlation is not unsurprising. The correlation may be due to individuals enrolling for insurance in advance of their well-patient visit. For example, a woman who goes to a clinic for antenatal care can be enrolled in insurance for free (and immediately) after being confirmed pregnant. This could result in high probability of being currently enrolled among those who have recently gone for well-patient visits. It could also be that those few people who are conscientious enough to go for well-patient


visits are also the most likely to pay attention to their enrollment status. Another possibility is that those who have insurance are then more likely to take advantage of well-patient visits, because they are free; in this case however, one would also expect those who are registered but not confirmed enrolled to also be more likely to attend well-patient visits. Only a few demographic variables are associated with higher likelihood of well-patient visits. Female and married individuals are more likely to have attended well-patient visits. Children age 7 to 17, and adults over 45, are slightly less likely to go for consultations when not sick. These demographics suggest that well-patient visits might be driven by provision of free services, in particular, antenatal clinics (married women are most likely to be pregnant).

Summary

To summarize the results in this section, individuals who are registered in insurance are slightly more likely to report having had a recent health event, and individuals whose enrollment is current are much more likely to report having had a health event than individuals who are not registered. Part of the latter correlation is likely due to individuals re-enrolling when they experience a health event, go for treatment, and find their insurance is expired. Nearly all individuals who experience health events get some type of treatment, regardless of whether they have insurance or not. However, individuals who are registered and currently enrolled are more likely to go to doctors (the highest trained health providers) than those who are not registered. Individuals who are unregistered are more likely to go to a chemical seller, who will sell drugs without charging for a consultation, than those who are registered. This suggests that those who are registered and enrolled may get higher quality care than those who are unregistered. All of these correlations should be interpreted with caution, as it is impossible to determine causation from these data. For example, it may be that insurance registration makes higher quality care more accessible, but it may also be that individuals who are more likely to want to access doctors are also more likely to register and re-enroll in insurance. It is our hope that the randomized control trial design of this project will enable us to examine the causal effect of insurance on the types of health care sought and the resulting health and financial outcomes of having access to that care.

6. Timeline Going Forward All education sessions were completed in early 2011. At the time of this report, the first round of post-education insurance uptake data has been collected by the IPA survey team. In addition to the uptake data collection, IPA worked with SAT to collect post-education knowledge surveys. SAT administered the knowledge surveys to individuals randomly selected by IPA, and the data from these surveys was entered by IPA. IPA provided the knowledge test survey data and some basic analysis of the data to FFH in August. A preliminary timeline for remaining project activities is as follows:


September – October 2011: Uptake Data Analysis January 2012: Training for reminder sessions February 2012: Reminder sessions implemented by SAT March 2012: NHIS marketer visits April 2012: Take-up Survey 2 May 2012: Take-up Survey data entry June 2012: Take-up Survey data analysis

7. Conclusion Analysis of data collected in our baseline survey suggests that Sinapi Aba Trust clients face a number of challenges in the areas of health and financial well-being. Many SAT clients have low levels of education, and 43 percent of them reported some type of food insecurity in the past month. They are susceptible to financial shocks; 16 percent reported removing a child from school because the household suddenly did not have enough money, while 37 percent reported selling valuables for that reason. SAT’s female clients likely face particular challenges. SAT female clients have lower levels of education than their male counterparts. At the same time, a relatively high number of SAT’s female clients—about a quarter—report they are the head of the household. While this could empowering for them, it also means that they are likely to bear a large share of the burden for their families’ health and financial well-being. A moderate number of SAT clients appear to be accessing health care using Ghana’s national health insurance scheme, but there is room for improvement. About 60% of SAT clients report they are registered in health insurance, but closer analysis that examined available insurance cards, and reports of use of insurance to access health services, enables us to estimate that only about 30% are actively enrolled in the insurance. Of clients who sought a health consultation in response to a health event, only 53 percent used insurance to pay for the consultation. SAT client household members have similar levels of insurance registration. A number of demographic and financial attributes are associated with higher likelihood of insurance registration. Women are more likely to be registered than men, and as one might expect, individuals with higher levels of education are more likely to be registered than individuals with no education. Individuals in rural areas are less likely to be registered, and of the four branch areas (Tamale, Walewale, Salaga, and Bole), individuals in Tamale are the least likely to be registered. Some of these attributes are also associated with being currently enrolled in insurance, but in general, active insurance enrollment is harder to predict. This may suggest that enrollment status is somewhat random; lack of significant results may also be due to additional variation in the enrollment status variable introduced as a result of the data collection methodology.


Higher income did not appear to be associated with higher likelihood of insurance registration or enrollment, at least for the income measures in our data. Higher consumption was correlated with higher likelihood of insurance registration, but the relationship was not very large. The lack of association between income or consumption and registration suggests that the cost of the insurance premium may not be a major barrier to registration. If it were, one would expect that those with the lowest income and consumption would be much less likely to be registered than those with higher income and consumption. It should be noted that this conclusion should not be generalized to populations outside of SAT clients, as SAT clients are, for several reasons, likely to have more financial resources than the poorest populations in Northern Ghana. SAT clients seem to have some basic knowledge about health insurance. Most were able to correctly answer a majority of questions on our knowledge test. In addition, no respondents reported not using insurance because they did not know how. SAT clients have positive attitudes towards insurance, and a large majority have discussed insurance with family members. Better knowledge about insurance and more favorable attitudes towards insurance was associated with higher registration rates, but it is not clear whether people register because they know about insurance and view it favorably, or if having insurance exposes people to information about it and makes favorable impressions about insurance. Although knowledge about insurance is already high among clients, and attitudes are generally favorable, the association between knowledge and attitudes and registration rates suggests that improving these among the remaining clients could have positive impacts on registration, depending on how causality runs. In addition, the large number of clients that fail to re-enroll on time suggests that education about re-enrollment requirements could be useful. It also suggests that the re-enrollment reminder sessions, scheduled to take place in early 2012, could be highly effective in increasing enrollment. Education could also serve to encourage clients to think and plan ahead regarding insurance use. A large number of clients without insurance reported that they planned to get it, but just had not done it yet. Education could be a prompt to remind them to do so. In addition, there were many clients who did not have access to their insurance cards. Education could encourage those clients to take charge of their own cards, making it easier for them to access health services, and making it more likely that they will keep track for their enrollment status. Being registered in insurance appears to be associated with better financial and health outcomes in some areas. Households with higher registration rates are less likely to face food insecurity events, even controlling for a number of other factors. Households with higher insurance registration rates are also less likely to pull a child out of school for financial reasons. The correlations between insurance registration and food security and responses to shocks may exist because households that are more stable financially are both better at mitigating financial shocks and more likely to register in insurance. It may also be because registration in insurance protects households from shocks resulting from costs from health events. Current enrollment status was not significantly related to food insecurity events or likelihood of pulling a child from school.


Health insurance registration and enrollment is significantly correlated with how SAT clients address health events. While almost all individuals in the sample who had health events sought treatment regardless of insurance status, those with health insurance were much more likely to go to a doctor, the highest trained health service provider, and much less likely to go to a chemical seller, and the correlation was even larger for individuals who were both registered and currently enrolled. This suggests that one benefit of insurance may be that individuals with insurance are able to access higher quality health care, and have more incentive to go to consultations with qualified service providers who can make sure they get appropriate treatment. In addition, being currently enrolled in health insurance is positively associated with higher likelihood of attending a well-patient visit. It is unclear what accounts for this. It could be that individuals planning to go to well-patient visits re-enroll before doing so, or that individuals who are most conscientious about going to well patient visits are also the best at maintaining current enrollment. It could also be that those who are currently enrolled are more likely to go for well patient visits because there is no payment barrier. If the latter effect exists, then encouraging registration and enrollment could lead to more preventative care and better health outcomes, particularly for children and pregnant women, since child welfare clinics and antenatal clinics are forms of well patient that are readily available in Northern Ghana.


8. Appendix A: Stratification Stratification involves putting members of a sample into different groups, based on key traits of the sample members, and performing a separate randomization for each group. For example, in this study design, 40 percent of urban groups with high enrollment in Tamale are assigned to the control group, and 40 percent of rural groups with low enrollment in Salaga are assigned to the control group. Stratification increases power because it assures that, for instance, all of the groups in Bole are not assigned to the treatment group while all of the groups in Salaga are assigned to the control group by chance. Since each stratum is fairly small, it is not always possible to put exactly 40 percent of the stratum in the control group and 15 percent in each of the treatment groups. The number of credit groups assigned to each control and treatment groups is selected to be as close to those percentages as possible for each stratum while still ensuring that the total number of credit groups in each treatment group comes to 45 and the total number of credit groups in the control group comes out to 120. Table 4 reports the number of credit groups in each stratum that were assigned to the four treatment groups and the control group.


Table A1. Stratification Groups

Stratification Group

Groups in stratum

TLC, no Reminder (15%)

TLC, Reminder (15%)

Con, no Reminder (15%)

Con, Reminder (15%)

Control (40%)

Walewale

Low enrollment, Rural 10

1 1 1 1 6 Low enrollment, Urban 17

3 3 3 3 5

High enrollment, Rural 5

1 1 1 1 1 High enrollment, Urban 19

3 3 3 3 7

Unknown enrollment, Rural 0

0 0 0 0 0 Unknown enrollment, Urban 9 1 1 1 1 5

TOTAL 60

9 9 9 9 24

Salaga



1 1 1 1 1



1 1 1 1 1



TOTAL 60

9 9 9 9 24

Bole



1 1 1 1 2



3 3 3 3 9



TOTAL 60

9 9 9 9 24

Tamale



8 8 8 8 20



0 0 0 0 0

High enrollment, Urban 16

2 2 2 2 8 Unknown enrollment, Rural 7

1 1 1 1 3

Unknown enrollment, Urban 38 6 6 6 6 14

TOTAL 120

18 18 18 18 48

ALL BRANCHES 300 45 45 45 45 120

Percent of total

15% 15% 15% 15% 40%

9. Appendix B: Enrollment Status Extrapolation Each of the figures in Table 17 was calculated as follows: Reported Registered and Reported Unregistered: These measures record registration status without any adjustments and are the same data presented in Table 1 earlier in the analysis. Group A, Registered respondents who showed an insurance card to enumerators: The reported expired and reported current are taken directly from Table 4. The status of these respondents was determined by an enumerator examining the respondent’s insurance card, and as a result, these numbers are likely to be very accurate. The “unknown” are the number of respondents with “Don’t Know” recorded for this question, meaning the enumerator was unable to determine whether the card was current or expired. The number expired and enrolled within “unknown” is extrapolated using the proportion of expired and reported for those respondents for whom the enumerator was able to determine enrollment status. Group B, Registered respondents who did not show an insurance card, but reported having one: The reported waiting for card is taken directly from “Waiting for card from NHIS” in Table 3. The “unknown” are the respondents who reported “Don’t have access to card” or “Other reason not to show”, or for whom there was no data on this question. Respondents who “Showed card” are not included in Group B, because they are already captured in Group A. The number of “waiting” respondents within “unknown” was extrapolated using the share of clients who, in the Health Events section of the survey, reported they could not use insurance to pay for a health event because they were still waiting for their card, but who said they had an insurance card in the Household Roster section. The number of expired and current within “unknown” was extrapolated using the same proportion as for Group A, applied to the “unknown” respondents excluding those extrapolated to be “waiting”. Group C, Registered respondents who did not report having an insurance card: The reported waiting are taken directly from “Do not have insurance card” in Table 2. The “unknown” are those respondents who reported “Don’t know”, or for whom there was no


data for this question. Respondents who reported they “Have insurance card” are not included in Group C, because they are captured in Group B. The number of “waiting” respondents within “unknown” was extrapolated using the percent of respondents who reported they “Do not have insurance card” on this question, as a share of the respondents who knew whether they had a card or not. The number of current and expired respondents within “unknown” was extrapolated using the same proportions as in Groups A and B, applied to the “unknown” respondents excluding those extrapolated to be “waiting”. A, B and C, All Reported Registered: The figures in this section aggregate the total number of respondents in each category—Current, Expired, or Waiting—including those reported in each category and those extrapolated to be in each category. The percents represent the share of the total number of respondents for which there is registration data; the percents for these three categories sum to 70%, the share of clients who reported they are registered with NHIS.


10. Appendix C: Currency In Ghana Ghana’s currency was revalued and reissued in 2007. The new Ghana cedi (currency code GHS) was set equal to 10,000 old cedis (currency code GHC). Many Ghanaians, particularly in the Northern Region, still cite monetary amounts in the old currency. This can cause confusion when recording currency amounts in surveys. Even when surveyors are instructed to record all amounts in terms of new currency, it can be difficult to determine when someone is referring to old or new currency, and surveyors may make errors when converting, especially when amounts are large. Adding to the confusion, when citing old currency, many people use a short-form that cuts off the “thousand”. For example, 10,000 old cedis might be cited as “ten”. The Figure below shows examples of the three ways different currency amounts might be cited. Amount New Currency Old Currency Old Currency Short

0.50 GHS “Fifty pesewas” “Five thousand” “Five” 1 GHS “One Ghana cedi” “Ten thousand’ “Ten” 10 GHS “Ten Ghana cedis” “One hundred

thousand” “One hundred”

50 GHS “Fifty Ghana cedis” “Five hundred thousand”

“Five hundred”

100 GHS “One hundred Ghana cedis”

“One million”

1000 GHS “One thousand Ghana cedis”

“Ten million”


11. Appendix D: Full Regression Results Table A: Indicators of Individual Adult Insurance Enrollment Rates


General Female 0.05 0.01

0.00 *

Age 31 to 45 0.06 0.01

0.00 *

Over 45 0.07 0.01

0.00 *

Highest schooling is primary 0.00 0.01

0.77 Highest schooling is middle 0.01 0.01

0.46

Highest schooling is high school 0.01 0.01

0.55 Highest schooling is vocational 0.02 0.02

0.22

Highest schooling is tertiary 0.00 0.02

0.83 Highest schooling is koranic -0.02 0.02

0.35

Married 0.04 0.01

0.00 *

Divorced 0.05 0.02

0.02 *

Ethnicities Dagomba 0.01 0.01

0.35

Mamprusi -0.04 0.02

0.03 *

Gonja 0.07 0.02

0.00 *

Wala 0.04 0.03

0.25 Dagaare 0.04 0.03

0.23

Hausa 0.04 0.03

0.17 Vagla 0.02 0.04

0.60

Komkomba 0.00 0.02

0.89 Religion:

Moslem 0.03 0.03

0.27 Christian 0.06 0.03

0.04 *

Geographical: Rural 0.01 0.01

0.42

Walewale 0.09 0.02

0.00 *

Bole 0.01 0.02

0.60 Salaga 0.02 0.02

0.26

constant 0.22 0.02 0.00 *

N = 5512 R² = 0.0432

Adj R² = 0.0388


Table B: Indicators of Household Insurance Registration Rates

Dependent Variable: Household Registration Rate


Household composition, gender and age Number of female children 6 and under 0.01 0.01

0.28

Number of female children 7 to 17 0.03 0.01

0.01 *

Number of male children 6 and under 0.01 0.01

0.39 Number of male children 7 to 17 0.03 0.01

0.00 *

Number female adults 18 to 30 -0.01 0.01

0.23 Number female adults 30 to 45 0.02 0.02

0.29

Number female adults over 45 0.06 0.03

0.02 *

Number male adults 18 to 30 -0.05 0.01

0.00 *

Number male adults 30 to 45 0.00 0.02

0.90 Number male adults over 45 -0.02 0.02

0.47

Household composition, educational attainment Number of adults with no schooling -0.04 0.01

0.00 *

Number of adults whose highest schooling is primary -0.02 0.01

0.14 Number of adults whose highest schooling is middle -0.09 0.02

0.00 *

Number of adults whose highest schooling is high school 0.04 0.01

0.00 *

Number of adults whose highest schooling is vocational 0.09 0.02

0.00 *

Number of adults whose highest schooling is tertiary 0.08 0.02

0.00 *

Number of adults whose highest schooling is Koranic -0.01 0.02

0.70 Household majority ethnicity

Dagomba -0.02 0.03

0.63 Mamprusi -0.15 0.04

0.00 *

Gonja 0.03 0.04

0.45 Wala 0.03 0.07

0.70

Dagaare 0.11 0.07

0.11 Hausa -0.06 0.07

0.40

Vagla 0.06 0.07

0.40 Komkomba 0.12 0.05

0.03 *

Household majority religion Christian 0.02 0.06

0.80

Moslem 0.12 0.06

0.06 Household geographic attributes

Rural -0.05 0.03

0.04 *

Walewale 0.11 0.04

0.01 *

Bole 0.13 0.04

0.00 *

Salaga 0.11 0.04

0.00 *

Constant 0.51 0.07 0.00 *

N = 1493

R Squared = 0.1375



Table C: Indicators of Child Enrollment, All Ages


General Female -0.01 0.01

0.44

Age 7 to 17 -0.02 0.01

0.02 *

Ethnicities Dagomba -0.02 0.02

0.39

Mamprusi -0.11 0.02

0.00 *

Gonja 0.02 0.02

0.41 Wala -0.03 0.04

0.45

Dagaare 0.09 0.04

0.02 *

Hausa -0.12 0.04

0.00 *

Vagla 0.02 0.05

0.67 Komkomba 0.00 0.02

0.93

Religion: Christian 0.08 0.04

0.07

Moslem 0.11 0.04

0.01 *

Geographical: Rural 0.00 0.01

0.76

Walewale 0.12 0.02

0.00 *

Bole 0.04 0.02

0.05 *

Salaga 0.11 0.02

0.00 *

constant -0.01 0.05

0.86

N = 4485 R² = 0.0262 Adj R² = 0.0227


Table D: Indicators of Child Enrollment, School Age Children


Enrolled in School 0.00 0.02 0.93 General

Female -0.02 0.01 0.21 Ethnicities

Dagomba -0.01 0.03 0.79 Mamprusi -0.11 0.03 0.00 *

Gonja 0.01 0.03 0.68 Wala -0.02 0.05 0.68 Dagaare 0.08 0.05 0.09 Hausa -0.12 0.05 0.01 *

Vagla 0.00 0.07 0.99 Komkomba 0.01 0.03 0.66 Religion:

Christian 0.07 0.06 0.20 Moslem 0.12 0.06 0.04 *

Geographical: Rural 0.01 0.02 0.68

Walewale 0.12 0.03 0.00 *

Bole 0.05 0.03 0.06 Salaga 0.10 0.03 0.00 *

constant -0.04 0.06 0.57

N = 2555 R² = 0.0258 Adj R² = 0.0196


Table E: Indicators of Household Registration Rates


Household composition, gender and age

Number of female chldren 6 and under 0.01 0.01 0.28

Number of female chldren 7 to 17 0.03 0.01 0.01 *

Number of male chldren 6 and under 0.01 0.01 0.39

Number of male chldren 7 to 17 0.03 0.01 0.00 *

Number female adults 18 to 30 -0.01 0.01 0.23

Number female adults 30 to 45 0.02 0.02 0.29

Number female adults over 45 0.06 0.03 0.02 *

Number male adults 18 to 30 -0.05 0.01 0.00 *

Number male adults 30 to 45 0.00 0.02 0.90

Number male adults over 45 -0.02 0.02 0.47

Household composition, educational attainment

Number of adults with no schooling -0.04 0.01 0.00 *

Number of adults whose highest schooling is primary -0.02 0.01 0.14

Number of adults whose highest schooling is middle school -0.09 0.02 0.00 *

Number of adults whose highest schooling is high school 0.04 0.01 0.00 *

Number of adults whose highest schooling is vocational 0.09 0.02 0.00 *

Number of adults whose highest schooling is tertiary 0.08 0.02 0.00 *

Number of adults whose highest schooling is Koranic -0.01 0.02 0.70

Household majority ethnicity

Dagomba -0.02 0.03 0.63

Mamprusi -0.15 0.04 0.00 *

Gonja 0.03 0.04 0.45

Wala 0.03 0.07 0.70

Dagaare 0.11 0.07 0.11

Hausa -0.06 0.07 0.40

Vagla 0.06 0.07 0.40

Komkomba 0.12 0.05 0.03 *

Household majority religion

Christian 0.02 0.06 0.80

Moslem 0.12 0.06 0.06

Household geographic attributes

Rural -0.05 0.03 0.04 *

Walewale 0.11 0.04 0.01 *

Bole 0.13 0.04 0.00 *

Salaga 0.11 0.04 0.00 *

Constant 0.51 0.07 0.00 *


Table F. Hypothetical Predicted Household Registration Rates

Examples of Predicted Houshold Registration Rates

Total Household Members

Predicted Percent of Registered Household members

Predicted Number of Registered Household members

Family in Urban Tamale, Dagomba Christian: 35 yo male with tertiary education 32 yo female with middle school education 4 yo girl 10 yo boy

4 56% 2

Family in Urban Walewale, Mamprusi Moslem: 64 yo male with koranic schooling 42 yo male with primary school 36 yo female with no school 19 yo male with primary school 14 yo girl 11 yo boy 9 yo boy 6 yo girl

8 61% 5

Family in Rural Bole, Gonja Christian: 58 yo female no schooling 40 yo male with high school 35 yo female with primary school 14 yo boy 10 yo girl 8 yo girl 5 yo boy

7 78% 5

Family in Rural Salaga, Dagaare Moslem: 50 yo male with primary school 42 yo female with no school 22 yo female with vocational schooling 18 yo male with high school 16 yo boy 14 yo girl

6 92% 6

Table G: Indicators of Household Insurance Enrollment Rates

Dependent Variable: Household Enrollment Rate (number of confirmed enrolled household members over total number of household members)

Coefficient Standard

Error P-Value *Significant

Household composition, gender and age Number of female chldren 6 and under 0.01 0.01 0.13


Number of female chldren 7 to 17 0.00 0.01 0.46 Number of male chldren 6 and under 0.01 0.01 0.29 Number of male chldren 7 to 17 0.00 0.01 0.63 Number female adults 18 to 30 0.00 0.01 0.89 Number female adults 30 to 45 0.02 0.01 0.10 Number female adults over 45 0.02 0.02 0.15 Number male adults 18 to 30 -0.01 0.01 0.16 Number male adults 30 to 45 0.01 0.01 0.62 Number male adults over 45 -0.01 0.02 0.38 Household composition, educational attainment

Number of adults with no schooling -0.01 0.00 0.00 *

Number of adults whose highest schooling is primary -0.02 0.01 0.01 *

Number of adults whose highest schooling is middle school -0.01 0.01 0.38 Number of adults whose highest schooling is high school 0.00 0.01 0.71 Number of adults whose highest schooling is vocational 0.00 0.01 0.90 Number of adults whose highest schooling is tertiary -0.01 0.01 0.28 Number of adults whose highest schooling is Koranic -0.01 0.01 0.19 Household majority ethnicity

Dagomba 0.02 0.02 0.35 Mamprusi -0.06 0.02 0.02 *

Gonja 0.05 0.02 0.02 *

Wala 0.04 0.04 0.28

Dagaare 0.05 0.04 0.24 Hausa -0.01 0.04 0.83 Vagla -0.03 0.04 0.49 Komkomba 0.00 0.03 1.00 Household majority religion

Christian 0.07 0.04 0.05 Moslem 0.06 0.04 0.12 Household geographic attributes

Rural 0.01 0.02 0.52 Walewale 0.11 0.03 0.00 *

Bole 0.02 0.02 0.32 Salaga 0.05 0.02 0.02 *

Constant 0.02 0.04 0.66 N = 1493

R²= 0.0567 Adjusted R²= 0.0366

Table H: Indicators of Household Enrollment, Controlling for Card Access

Dependent Variable: Household Enrollment Rate (number of confirmed enrolled household members over total number of household members)


Was able to show card 0.53 0.01 0.00


Household composition, gender and age Number of female chldren 6 and under 0.01 0.00 0.15

Number of female chldren 7 to 17 0.00 0.00 0.85 Number of male chldren 6 and under 0.00 0.00 0.87 Number of male chldren 7 to 17 0.00 0.00 0.58 Number female adults 18 to 30 0.00 0.00 0.31 Number female adults 30 to 45 0.00 0.01 0.97 Number female adults over 45 0.00 0.01 0.95 Number male adults 18 to 30 0.00 0.00 0.57 Number male adults 30 to 45 0.00 0.01 0.97 Number male adults over 45 -0.01 0.01 0.42 Household composition, educational attainment

Number of adults with no schooling 0.00 0.00 0.50 Number of adults whose highest schooling is primary -0.01 0.01 0.31 Number of adults whose highest schooling is middle school -0.01 0.01 0.16 Number of adults whose highest schooling is high school 0.00 0.00 0.78 Number of adults whose highest schooling is vocational 0.01 0.01 0.39 Number of adults whose highest schooling is tertiary 0.00 0.01 0.77 Number of adults whose highest schooling is Koranic 0.00 0.01 0.97 Household majority ethnicity

Dagomba 0.00 0.01 0.97 Mamprusi -0.02 0.02 0.16 Gonja 0.02 0.02 0.28 Wala 0.04 0.03 0.18 Dagaare 0.04 0.03 0.13 Hausa -0.02 0.03 0.40 Vagla -0.04 0.03 0.19 Komkomba -0.03 0.02 0.19 Household majority religion

Christian 0.06 0.03 0.02 *

Moslem 0.06 0.03 0.02 *

Household geographic attributes Rural 0.00 0.01 0.96

Walewale 0.04 0.02 0.02 *

Bole 0.00 0.02 1.00 Salaga -0.01 0.02 0.70

Constant -0.06 0.03 0.03 *

N = 1493 R²= 0.0567 Adjusted R²= 0.0366

Table I: Indicators of Food Insecurity Events

(1) Household often or sometimes has no food in the

(2) Household often or sometimes has someone go to

(3) Household often or sometimes has adult go 24

(4) Household often or sometimes has child go 24

(5) Household often or sometimes goes without


house bed hungry hours without food

hours without food

meat

Coefficient (Standard Error)

*Significant at 95% Confidence Level

Percent of household members registered for insurance -0.14 (0.03)* -0.09 (0.03)* -0.09 (0.03)* -0.04*(0.02) -0.09 (0.03)*

Household Composition:

Number females 6 and under 0.02 (0.01) 0 (0.01) 0.01 (0.01) -0.02 (0.01)* 0.01 (0.01)

Number females 7 to 17 -0.01 (0.01) -0.01 (0.01) -0.01 (0.01) 0 (0.01) 0 (0.01)

Number males 6 and under 0 (0.01) 0.01 (0.01) 0 (0.01) 0 (0.01) 0.01 (0.01)

Number males 7 to 17 0.02 (0.01) 0.02 (0.01)* 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

Number females 18 to 30 0 (0.01) -0.01 (0.01) -0.01 (0.01) 0 (0.01) 0.02 (0.01)

Number females 31 to 45 0.01 (0.02) 0.01 (0.02) 0.01 (0.02) -0.01 (0.01) 0.03 (0.02)

Number females 45 plus 0.04 (0.03) 0.04 (0.03) 0 (0.02) 0 (0.02) 0.03 (0.03)

Number males 18 to 30 0.02 (0.01) 0.01 (0.01) 0 (0.01) 0 (0.01) 0.02 (0.01)

Number males 31 to 45 -0.03 (0.02) -0.02 (0.02) -0.01 (0.02) 0.02 (0.01) -0.04 (0.02)

Number males 45 plus -0.01 (0.03) 0.02 (0.03) 0.03 (0.02) 0.03 (0.02) -0.02 (0.03)

Number of adults with no schooling 0.01 (0.01) 0.01 (0.01) 0.02 (0.01)* 0.03 (0.01)* -0.03 (0.02)

Number of adults whose highest schooling is primary 0 (0.02) -0.01 (0.01) -0.01 (0.01) -0.01 (0.01) 0.00 (0.01)

Number of adults whose highest schooling is middle 0.02 (0.01) 0.01 (0.01) 0.02 (0.01) 0.01 (0.01) -0.01 (0.01)

Number of adults whose highest schooling is high school -0.04 (0.01)* -0.01 (0.01) -0.01 (0.01) -0.01 (0.01) -0.02 (0.01)

Number of adults whose highest schooling is vocational -0.03 (0.03) -0.03 (0.02) -0.02 (0.02) -0.01 (0.02) -0.02 (0.03)

Number of adults whose highest schooling is tertiary 0.01 (0.03) 0.01 (0.02) 0.02 (0.02) 0.01 (0.01) -0.03 (0.03)

Number of adults whose highest schooling is koranic -0.01 (0.02) 0.01 (0.02) 0.01 (0.02) 0.01 (0.01) 0.01 (0.02)

Household Ethnicity:

Dagomba 0.13 (0.04)* 0.06 (0.04) 0.06 (0.03) 0.03 (0.03) 0.14 (0.04)*

Mamprusi -0.01 (0.05) -0.02 (0.04) -0.01 (0.04) 0 (0.03) 0.04 (0.05)

Gonja 0.06 (0.05) 0.05 (0.04) 0.02 (0.04) 0 (0.03) -0.01 (0.05)

Wala 0.02 (0.08) 0.04 (0.07) -0.02 (0.06) -0.03 (0.05) -0.04 (0.09)

Dagaare 0.08 (0.08) 0.16 (0.07)* 0.15 (0.07)* 0.04 (0.05) 0.28 (0.09)*

Hausa 0.1 (0.09) 0.21 (0.07)* 0.19 (0.07)* 0.01 (0.05) 0.12 (0.09)

Vagla 0.09 (0.09) 0.07 (0.07) 0.04 (0.07) -0.02 (0.05) 0.01 (0.09)

Komkomba 0.2 (0.07)* 0.16 (0.06)* 0.21 (0.05)* 0.04 (0.04) 0.28 (0.07)*

Household Religion:

Christian -0.02 (0.08) 0.02 (0.07) 0.01 (0.06) 0.1 (0.05)* 0.09 (0.08)

Moslem -0.07 (0.08) -0.01 (0.07) 0 (0.06) 0.08 (0.05) 0.1 (0.08)


Geographic:

Rural 0.04 (0.03) 0.06 (0.03)* 0.01 (0.03) 0.01 (0.02) 0.08 (0.03)*

Walewale 0.24 (0.05)* 0.17 (0.04)* 0.08 (0.04) 0.06 (0.03)* -0.04 (0.05)

Bole -0.03 (0.05) -0.01 (0.04) 0 (0.04) 0.04 (0.03) -0.13 (0.05)*

Salaga -0.05 (0.05) -0.05 (0.04) -0.01 (0.04) 0.01 (0.03) -0.23 (0.05)*

_cons 0.28 (0.09)* 0.12 (0.07) 0.1 (0.07) -0.06 (0.05) 0.23 (0.08)*

N=1489 R²=0.0865 Ad. R²= 0.0664

N=1489 R²=0.0682 Ad. R²= 0.0478

N=1489 R²=0.0509 Ad. R²= 0.0301

N=1491 R²=0.0437 Ad. R²= 0.0227

N=1488 R²=0.1031 Ad. R²= 0.0833

Table J: Enrollment Rate and Food Insecurity Events

(1) Household often or sometimes has no food in the house

(2) Household often or sometimes has someone go to bed hungry

(3) Household often or sometimes has adult go 24 hours without food

(4) Household often or sometimes has child go 24 hours without food

(5) Household often or sometimes goes without meat



Percent of household members enrolled in insurance -0.01 (0.05) -0.08 (0.04) -0.09 (0.03)* -0.04 (0.03) 0.04 (0.05)

N=1489 R²=0.0752 Ad. R²= 0.0548

N=1489 R²=0.0633 Ad. R²= 0.0427

N=1489 R²=0.0431 Ad. R²= 0.0221

N=1491 R²=0.0426 Ad. R²= 0.0216

N=1488 R²=0.0989 Ad. R²= 0.0791

Table K. Indictors of Hunger Score

Coefficient Standard Error P-Value *Significant

Household Registration Rate -0.34 0.09 0.00 *


Household Enrollment Rate -0.02 0.14 0.89

Annual Consumption per Capita 0.00 0.00 0.08

Demographics

Number females 6 and under 0.00 0.04 0.93

Number females 7 to 17 -0.05 0.03 0.08

Number males 6 and under 0.01 0.03 0.68

Number males 7 to 17 0.04 0.03 0.16

Number females 18 to 30 0.00 0.03 0.98

Number females 31 to 45 0.11 0.06 0.05

Number females 45 plus 0.16 0.08 0.05 *

Number males 18 to 30 0.03 0.03 0.36

Number males 31 to 45 -0.06 0.06 0.29

Number males 45 plus 0.01 0.08 0.93

Number of adults with no schooling 0.03 0.03 0.28

Number of adults whose highest schooling is primary -0.01 0.04 0.81

Number of adults whose highest schooling is middle 0.05 0.03 0.09 Number of adults whose highest schooling is high school -0.07 0.03 0.05 Number of adults whose highest schooling is vocational -0.10 0.07 0.12

Number of adults whose highest schooling is tertiary 0.02 0.06 0.72

Number of adults whose highest schooling is koranic 0.01 0.06 0.92


Dagomba 0.24 0.11 0.03 *

Mamprusi 0.01 0.13 0.96

Gonja 0.06 0.12 0.63

Wala -0.03 0.21 0.89

Dagaare 0.37 0.22 0.09

Hausa 0.50 0.22 0.02 *

Vagla 0.16 0.22 0.48

Komkomba 0.69 0.17 0.00 *

Household Religion:

Christian -0.01 0.20 0.98

Moslem -0.04 0.20 0.85

Geographic:

Rural 0.14 0.08 0.11

Walewale 0.60 0.14 0.00 *

Bole -0.02 0.13 0.86

Salaga -0.13 0.12 0.28

constant 0.63 0.22 0.01 *

N=1485 R²=0.1046 Adjusted R²=0.0836


Table L: Indicators of Responses to Shocks

(1) Household has pulled a school-age child out of school because they suddenly did not have enough money

(2) Household has sold assets they would not normally sell because they suddenly did not have enough money



Percent of household members registered for insurance -0.08 (0.03)* -0.04 (0.04)

Household Composition: Number females 6 and under 0.00 (0.01) -0.01 (0.02)

Number females 7 to 17 0.02 (0.01)* 0.00 (0.01)

Number males 6 and under -0.01 (0.01) 0.01 (0.01)

Number males 7 to 17 0.03 (0.01)* 0.04 (0.01)*



Number females 45 plus 0.02 (0.03) 0.07 (0.04)

Number males 18 to 30 0.01 (0.01) 0.02 (0.01)

Number males 31 to 45 -0.01 (0.02) 0.02 (0.02)

Number males 45 plus -0.01 (0.03) -0.01 (0.03)

Number of adults with no schooling 0.01 (0.01) 0.01 (0.01)

Number of adults whose highest schooling is primary 0.01 (0.01) 0.03 (0.02)

Number of adults whose highest schooling is middle 0.01 (0.01) 0.03 (0.01)*

Number of adults whose highest schooling is high school 0.01 (0.01) 0.02 (0.01)

Number of adults whose highest schooling is vocational 0 (0.02) 0.01 (0.03)

Number of adults whose highest schooling is tertiary -0.02 (0.02) 0 (0.03)

Number of adults whose highest schooling is koranic -0.02 (0.02) 0.05 (0.02)*

Household Ethnicity: Dagomba 0.05 (0.04) 0.08 (0.05)

Mamprusi 0.01 (0.04) 0.06 (0.05)

Gonja 0.11 (0.04)* 0.07 (0.05)

Wala 0.04 (0.07) 0.02 (0.09)

Dagaare 0.08 (0.07) 0.09 (0.09)

Hausa 0.13 (0.07) 0.19 (0.09)*

Vagla 0.13 (0.07) 0.09 (0.1)

Komkomba -0.04 (0.06) 0.12 (0.08)

Household Religion: Christian 0.06 (0.07) 0.04 (0.09)

Moslem 0.04 (0.07) 0.06 (0.09)

Geographic: Rural 0 (0.03) 0.13 (0.04)*

Walewale -0.01 (0.04) 0.2 (0.06)*

Bole -0.17 (0.04)* -0.01 (0.05)

Salaga -0.09 (0.04)* -0.01 (0.05)


_cons 0.08 (0.07) 0.06 (0.1)

N= 1491 N= 1492

R²=0.0737 R²=0.0981

Adj R²=0.0534 Adj R²=0.0783

Table M: Enrollment Rate and Shock Responses

(1) Household has pulled a school-age child out of school because they suddenly did not have enough money

(2) Household has sold assets they would not normally sell because they suddenly did not have enough money


*Significant at 95% Confidence Level Percent of household members enrolled in insurance -0.02 (0.04) 0.08 (0.06)

N= 1491 N= 1492

R²=0.0684 R²=0.0982

Adj R²=0.0479 Adj R²=0.0785


Table N. Indicators of Frequent Financial Worry

(1) Respondent always or often worries the household will not have enough money for the things they normally buy



Percent of household members registered for insurance -0.06 (0.03)


Number females 6 and under 0.00 (0.01)

Number females 7 to 17 0.00 (0.01)

Number males 6 and under 0.02 (0.01)

Number males 7 to 17 0.03 (0.01)*

Number females 18 to 30 -0.01 (0.01)

Number females 31 to 45 0.04 (0.02)

Number females 45 plus 0.01 (0.03)

Number males 18 to 30 0.03 (0.01)*

Number males 31 to 45 0.04 (0.02)

Number males 45 plus 0.02 (0.03)

Number of adults with no schooling 0.01 (0.01)

Number of adults whose highest schooling is primary 0.00 (0.02)

Number of adults whose highest schooling is middle -0.02 (0.01) Number of adults whose highest schooling is high school 0.00 (0.01) Number of adults whose highest schooling is vocational -0.01 (0.03)

Number of adults whose highest schooling is tertiary -0.06 (0.03)*

Number of adults whose highest schooling is koranic 0.00 (0.02)


Dagomba 0.10 (0.05)*

Mamprusi 0.03 (0.05)

Gonja 0.02 (0.05)

Wala 0.15 (0.09)

Dagaare -0.04 (0.09)

Hausa 0.03 (0.09)

Vagla 0.03 (0.09)

Komkomba 0.03 (0.07)

Household Religion:

Christian -0.10 (0.08)

Moslem -0.15 (0.09)

Geographic:

Rural 0.05 (0.04)

Walewale 0.17 (0.06)*

Bole 0.07 (0.05)

Salaga -0.04 (0.05)


_cons 0.31 (0.09)*

N= 1493

R²=0.0525

Adj R²=0.0317

Table O: Enrollment Rate and Financial Worry

(1) Respondent always or often worries the household will not have enough money for the things they normally buy



Percent of household members enrolled in insurance -0.05 (0.06)

N= 1493

R²=0.0535

Adj R²=0.0321


Table P: Indicators of Financial Ranking

(1) Respondent's Ladder Ranking of Financial Situation, 10 Highest 1 Lowest



Percent of household members registered for insurance 0.24 (0.16)

Annual Household Per Capita Consumption 0.001 (0.000)*


Number females 6 and under -0.11 (0.07)


Number males 6 and under 0.01 (0.07)

Number males 7 to 17 -0.1 (0.06)

Number females 18 to 30 0 (0.06)


Number females 45 plus -0.07 (0.15)

Number males 18 to 30 -0.08 (0.06)

Number males 31 to 45 0.1 (0.1)

Number males 45 plus 0.1 (0.15)

Number of adults with no schooling 0.03 (0.05)

Number of adults whose highest schooling is primary 0.08 (0.08)

Number of adults whose highest schooling is middle 0.01 (0.06)

Number of adults whose highest schooling is high school 0.09 (0.06)

Number of adults whose highest schooling is vocational 0.15 (0.13)

Number of adults whose highest schooling is tertiary 0.26 (0.12)*

Number of adults whose highest schooling is Koranic -0.16 (0.11)


Dagomba 0.14 (0.21)

Mamprusi 0.25 (0.24)

Gonja 0.03 (0.22)

Wala -0.17 (0.4)

Dagaare -0.09 (0.4)

Hausa 0.13 (0.41)

Vagla -0.08 (0.42)

Komkomba -0.06 (0.33)

Household Religion:

Christian 0.68 (0.37)

Moslem 0.6 (0.38)

Geographic:

Rural 0.15 (0.16)

Walewale -0.3 (0.26)

Bole 0.02 (0.24)

Salaga 0.68 (0.23)*

_cons 3.38 (0.42)*


N= 1493

R²=0.0664

Adj R²=0.0452

Table Q: Enrollment Rate and Financial Ranking

(1) Respondent's Ladder Ranking of Financial Situation, 10 Highest 1 Lowest



Percent of household members enrolled in insurance -0.31 (0.25)

N= 1493

R²=0.0658

Adj R²=0.0447


Table R. Registration and Enrollment and Knowledge About Insurance

(1) Registration (2) Enrollment


*Significant at 95% Level

Knowledge Score 0.08 (0.01)* 0.01 (0.01)

Registration Status 0.25 (0.02)*

General

Female 0.19 (0.03)* 0.02 (0.02)

Age 31 to 45 0.03 (0.03) 0.04 (0.02)

Over 45 0.12 (0.04)* 0.03 (0.03)

Highest schooling is primary 0.05 (0.03) -0.04 (0.03)

Highest schooling is middle 0.07 (0.03)* 0.00 (0.03)

Highest schooling is high school 0.13 (0.04)* 0.00 (0.03)

Highest schooling is vocational 0.15 (0.07)* 0.07 (0.05)

Highest schooling is tertiary 0.26 (0.07)* -0.06 (0.06)

Highest schooling is koranic -0.03 (0.05) -0.04 (0.04)

Married 0.06 (0.03) -0.04 (0.03)

Ethnicities

Dagomba -0.08 (0.04) 0.05 (0.04)

Mamprusi -0.16 (0.05)* -0.07 (0.04)

Gonja 0.05 (0.05) 0.1 (0.04)*

Wala 0.00 (0.09) 0.1 (0.08)

Dagaare 0.12 (0.09) 0.07 (0.07)

Hausa -0.04 (0.09) 0.11 (0.08)

Vagla -0.02 (0.1) -0.02 (0.08)

Komkomba -0.01 (0.07) -0.04 (0.06)

Religion:

Moslem 0.15 (0.11) 0.03 (0.09)

Christian 0.05 (0.1) 0.09 (0.09)

Geographical:

Rural -0.06 (0.03) 0.01 (0.03)

Walewale 0.02 (0.05) 0.2 (0.04)*

Bole 0.00 (0.05) 0.03 (0.04)

Salaga 0.04 (0.05) 0.07 (0.04)

Constant 0.01 (0.12)

N = 1491 N = 1491

R²= 0.1144 R²= 0.1292

Adjusted R²= 0.0993 Adjusted R²= 0.1137


Table S. Household Registration and Enrollment and Knowledge

(1) Registration (2) Enrollment

Coefficient (Standard Error) *Significant at 95% Level

Knowledge Score 0.07 (0.01)* 0.00 (0.00)

Annual Per Cap Consumption 0.00 (0.00)* 0.00 (0.00)

Household Registration Rate 0.18 (0.02)*

Household composition, gender and age

Number of female children 6 and under 0.00 (0.01) 0.01 (0.01)

Number of female children 7 to 17 0.02 (0.01)* 0.00 (0.01)

Number of male children 6 and under -0.01 (0.01) 0.01 (0.01)

Number of male children 7 to 17 0.02 (0.01)* 0.00 (0.01)

Number female adults 18 to 30 -0.02 (0.01) 0.00 (0.01)

Number female adults 30 to 45 0.01 (0.02) 0.02 (0.01)

Number female adults over 45 0.07 (0.02)* 0.02 (0.02)

Number male adults 18 to 30 -0.04 (0.01)* 0.00 (0.01)

Number male adults 30 to 45 -0.01 (0.02) 0.00 (0.01)

Number male adults over 45 -0.01 (0.02) -0.01 (0.02)

Household composition, educational attainment

Number of adults with no schooling -0.02 (0.01) -0.02 (0.01)*

Number of adults whose highest schooling is primary 0.00 (0.01) -0.02 (0.01)*

Number of adults whose highest schooling is middle school -0.04 (0.01)* -0.01 (0.00)

Number of adults whose highest schooling is high school 0.04 (0.01)* -0.01 (0.01)

Number of adults whose highest schooling is vocational 0.06 (0.02)* -0.01 (0.01)

Number of adults whose highest schooling is tertiary 0.07 (0.02)* -0.03 (0.01)*

Number of adults whose highest schooling is Koranic -0.01 (0.02) -0.01 (0.01)

Household majority ethnicity

Dagomba -0.03 (0.03) 0.03 (0.02)

Mamprusi -0.15 (0.04)* -0.03 (0.02)

Gonja 0.05 (0.04) 0.05 (0.02)*

Wala 0.01 (0.07) 0.04 (0.04)

Dagaare 0.07 (0.07) 0.03 (0.04)

Hausa -0.07 (0.07) 0.00 (0.04)

Vagla 0.06 (0.07) -0.04 (0.04)

Komkomba 0.11 (0.05)* -0.02 (0.03)

Household majority religion

Christian 0.02 (0.06) 0.07 (0.04)

Moslem 0.12 (0.06) 0.04 (0.04)

Household geographic attributes

Rural -0.06 (0.03)* 0.02 (0.02)

Walewale 0.07 (0.04) 0.09 (0.03)*

Bole 0.08 (0.04)* 0 (0.02)

Salaga 0.07 (0.04) 0.03 (0.02)


Constant 0.28 (0.07)* -0.08 (0.05)

N = 1493 N = 1493

R²= 0.1803 R²= 0.1305

Adjusted R²= 0.1618 Adjusted R²= 0.1102

Table T. Registration and Enrollment and Attitudes

(1) Registration

(2) Enrollment

(3) Registration

(4) Enrollment

I would rather risk paying cash and carry than pay for health insurance (disagreed) 0.12 (0.04)* 0.01 (0.04)

Insurance is not good value (disagreed) 0.05 (0.03)

-0.01 (0.02)

N = 1487 N = 1487 N = 1489 N = 1489

R²= 0.0821 R²= 0.1276 R²= 0.0791 R²= 0.1292

Adjusted R²= 0.0664




Note: Demographic variables were included as contols


Table U: Indicators of Health Events


Demographic Female 0.06 0.01 0.00 *

Age 0.01 0.00 0.00 *

Age squared -0.0001 0.0000 0.01 *

Highest schooling is primary 0.03 0.02 0.13 Highest schooling is middle 0.03 0.01 0.06 Highest schooling is secondary 0.00 0.02 0.88 Highest schooling is vocational -0.01 0.02 0.78 Highest schooling is tertiary 0.03 0.03 0.21 Highest schooling is Koranic -0.01 0.03 0.78 Married 0.01 0.01 0.29 Ethnicity

Dagomba 0.01 0.02 0.61 Mamprusi 0.02 0.02 0.43 Gonja 0.04 0.02 0.08 Wala 0.08 0.04 0.07 Dagaare 0.00 0.04 0.91 Hausa -0.06 0.04 0.15 Vagla -0.04 0.05 0.41 Komkomba 0.01 0.03 0.61 Religion

Moslem -0.04 0.05 0.35 Christian -0.03 0.04 0.50 Geographic

Rural 0.00 0.01 0.88 Walewale 0.06 0.02 0.01 *

Bole 0.00 0.02 0.90 Salaga -0.02 0.02 0.26

constant -0.05 0.06 0.37

N=3886

R²=0.0301

Adj. R²=0.0241


Table V: Registration Status and Probability of Reporting a Health Event


Enrolled 0.12 0.01 0.00 *

Registered 0.02 0.01 0.00 *

Demographics

Female 0.03 0.01 0.00 *

Age 7 to 17 -0.04 0.01 0.00 *

Age 18 to 30 -0.04 0.01 0.00 *

Age 31 to 45 -0.01 0.01 0.45

Over 45 0.00 0.02 0.79

Married -0.01 0.01 0.11

Household Composition

Number of adults in household with no schooling -0.01 0.00 0.00 *

Number of adults in household with highest schooling primary 0.00 0.00 0.74

Number of adults in household with highest schooling middle 0.00 0.00 0.09

Number of adults in household with highest schooling high school -0.01 0.00 0.00 *

Number of adults in household with highest schooling vocational -0.01 0.01 0.04 *

Number of adults in household with highest schooling tertiary 0.00 0.01 0.56

Number of adults in household with highest schooling Koranic -0.02 0.01 0.00 *

Ethnicity

Dagomba 0.00 0.01 0.84

Mamprusi 0.01 0.01 0.37

Gonja -0.02 0.01 0.19

Wala 0.01 0.03 0.81

Dagaare 0.05 0.02 0.03 *

Hausa -0.02 0.02 0.33

Vagla -0.02 0.03 0.51

Komkomba 0.03 0.02 0.13

Religion

Christian 0.01 0.02 0.58

Moslem 0.04 0.02 0.08

Geographic

Rural 0.01 0.01 0.18

Walewale 0.04 0.01 0.00 *

Bole 0.01 0.01 0.56

Salaga -0.02 0.01 0.16

_cons 0.09 0.02 0.00 *

N=10494 R²=0.0378 Adjusted R²=0.0351


Table W: Indicators of Health Event Severity

(1) More than inconvenience

(2) Resulted in Permanent Disability

(3) Resulted in Death



Enrolled 0.02 (0.03) 0.04 (0.02)* 0.01 (0.00)

Registered -0.06 (0.03) -0.02 (0.01) -0.01 (0.00)*

Demographics

Female -0.01 (0.03) 0.02 (0.01) 0.00 (0.00)

Age 7 to 17 -0.05 (0.04) 0.01 (0.02) 0.00 (0.00)

Age 18 to 30 0.06 (0.04) 0.01 (0.02) 0.00 (0.01)

Age 31 to 45 0.05 (0.05) 0.02 (0.02) -0.01 (0.01)*

Over 45 -0.02 (0.06) 0.03 (0.03) -0.01 (0.01)

Married 0.01 (0.04) 0.01 (0.02) 0.01 (0.00)*

Household Composition Number of adults in household with no schooling -0.03 (0.01)* 0.00 (0.00) 0.00 (0.00) Number of adults in household with highest schooling primary 0.01 (0.02) -0.01 (0.01) 0.00 (0.00) Number of adults in household with highest schooling middle 0.01 (0.01) 0.00 (0.01) 0.00 (0.00) Number of adults in household with highest schooling high school -0.02 (0.01) 0.01 (0.01)* 0.00 (0.00) Number of adults in household with highest schooling vocational 0.06 (0.03) 0.01 (0.01) 0.00 (0.00) Number of adults in household with highest schooling tertiary -0.02 (0.02) 0.00 (0.01) 0.00 (0.00) Number of adults in household with highest schooling Koranic -0.04 (0.03) -0.01 (0.01) 0.00 (0.00)

Ethnicity

Dagomba 0.08 (0.05) -0.02 (0.02) 0.01 (0.01)

Mamprusi 0.20 (0.05)* 0.01 (0.02) 0.00 (0.01)

Gonja 0.01 (0.06) 0.03 (0.03) 0.01 (0.01)

Wala 0.13 (0.10) 0.01 (0.05) 0.00 (0.01)

Dagaare -0.16 (0.09) 0.2 (0.04)* -0.01 (0.01)

Hausa -0.12 (0.11) 0.04 (0.05) 0.01 (0.01)

Vagla 0.12 (0.15) 0.19 (0.07)* 0.00 (0.02)

Komkomba -0.05 (0.08) 0.03 (0.04) 0.00 (0.01)

Religion

Christian -0.11 (0.11) -0.08 (0.05) 0.01 (0.01)

Moslem -0.11 (0.11) -0.04 (0.05) 0.00 (0.02)

Geographic

Rural 0.06 (0.04) 0.04 (0.02)* 0.00 (0.00)

Walewale -0.21 (0.06)* -0.05 (0.03)* 0.00 (0.01)

Bole 0.19 (0.06)* -0.01 (0.03) 0.00 (0.01)

Salaga 0.24 (0.06)* 0.03 (0.03) -0.01 (0.01)


constant 0.76 (0.12)* 0.05 (0.05) 0.01 (0.02)

N=1168 N=1168 N=1168

R²=0.1268 R²=0.0762 R²=0.0241

Adjusted R²=-0.1046




Table X: Indicators of Probability of Getting Treatment for a Health Event


Registered 0.02 0.01 0.19 Demographics

Female 0.00 0.01 0.81 Age 7 to 17 -0.01 0.01 0.56 Age 18 to 30 -0.02 0.02 0.13 Age 31 to 45 -0.01 0.02 0.58 Over 45 -0.04 0.02 0.08 Married 0.01 0.01 0.61 Household Composition

No. adults in household with no schooling 0.00 0.00 0.42 No. adults in household highest schooling primary 0.00 0.01 0.45 No. adults in household highest schooling middle 0.00 0.01 0.83 No. adults in household highest schooling high

school 0.00 0.01 0.50 No. adults in household highest schooling

vocational -0.03 0.01 0.02 *

No. adults in household highest schooling tertiary 0.01 0.01 0.40 No. adults in household highest schooling Koranic 0.01 0.01 0.34 Ethnicity

Dagomba 0.02 0.02 0.30 Mamprusi -0.01 0.02 0.63 Gonja 0.01 0.02 0.75 Wala -0.04 0.04 0.30 Dagaare -0.01 0.04 0.70 Hausa 0.02 0.04 0.67 Vagla 0.04 0.06 0.52 Komkomba 0.02 0.03 0.60 Religion

Christian 0.03 0.04 0.49 Moslem 0.02 0.04 0.58 Geographic

Rural -0.02 0.01 0.21 Walewale 0.03 0.02 0.12 Bole 0.00 0.02 0.91 Salaga 0.02 0.02 0.26

constant 0.93 0.05 0.00 *

N=1172 R²=0.0230 Adjusted R²=-0.0009


Table Y: Enrollment Status and Probability of Treatment


Enrolled 0.00 0.01 0.72

N=1172 R²=0.0216 Adjusted R²=-0.0023

Table Z: Indicators of Type of Treatment Sought

(1) Consulted a Doctor

(2) Consulted a Chemical Seller



Enrolled 0.09 (0.04)* -0.05 (0.02)*

Registered 0.17 (0.04)* -0.10 (0.02)*

Demographics

Female 0.03 (0.03) -0.02 (0.02)

Age 7 to 17 -0.12 (0.04)* 0.05 (0.03)

Age 18 to 30 0.04 (0.04) -0.02 (0.03)

Age 31 to 45 -0.01 (0.06) 0.00 (0.04)

Over 45 0.15 (0.07)* -0.03 (0.05)

Married -0.04 (0.04) -0.01 (0.03)


Number of adults in household with no schooling -0.02 (0.01)* 0.00 (0.01)

Number of adults in household with highest schooling primary -0.02 (0.02) 0.00 (0.01)

Number of adults in household with highest schooling middle -0.01 (0.01) -0.01 (0.01)

Number of adults in household with highest schooling high school 0.01 (0.01) 0.00 (0.01)

Number of adults in household with highest schooling vocational 0.05 (0.04) -0.02 (0.02)

Number of adults in household with highest schooling tertiary -0.01 (0.03) 0.00 (0.02)

Number of adults in household with highest schooling Koranic -0.04 (0.03) 0.04 (0.02)*

Ethnicity

Dagomba 0.09 (0.06) -0.01 (0.04)

Mamprusi 0.08 (0.06) -0.04 (0.04)

Gonja 0.18 (0.06)* -0.09 (0.04)*

Wala 0.24 (0.12)* -0.03 (0.08)

Dagaare 0.18 (0.10) -0.17 (0.07)*

Hausa 0.03 (0.13) 0.07 (0.09)

Vagla 0.09 (0.17) -0.07 (0.11)

Komkomba 0.11 (0.09) -0.14 (0.06)*

Religion

Christian -0.03 (0.12) 0.09 (0.08)

Moslem -0.01 (0.12) 0.07 (0.08)

Geographic

Rural 0.02 (0.04) 0.01 (0.03)

Walewale -0.05 (0.06) 0.05 (0.04)


Bole -0.16 (0.06)* 0.08 (0.04)

Salaga -0.28 (0.06)* 0.03 (0.04)

constant 0.58 (0.13)* 0.13 (0.09)

N=1029 N=1029

R²=0.1264 R²=0.0633

Adjusted R²=-0.1010 Adjusted R²=0.0361


Table AA: Indicators of Well-Patient Visits


Enrolled 0.02 0.01 0.00 *

Registered 0.00 0.00 0.82

Demographics

Female 0.02 0.00 0.00 *

Age 7 to 17 -0.02 0.00 0.00 *

Age 18 to 30 0.00 0.00 0.44

Age 31 to 45 0.01 0.01 0.38

Over 45 -0.02 0.01 0.01 *

Married 0.03 0.00 0.00 *


Number of adults in household with no schooling 0.00 0.00 0.00 *

No. of adults in household with highest schooling primary 0.00 0.00 0.03 *

No. of adults in household with highest schooling middle 0.00 0.00 0.07 No. of adults in household with highest schooling high school 0.00 0.00 0.55

No. of adults in household with highest schooling vocational 0.00 0.00 0.54

No. of adults in household with highest schooling tertiary 0.00 0.00 0.66

No. of adults in household with highest schooling Koranic 0.00 0.00 0.35

Ethnicity

Dagomba -0.01 0.01 0.07

Mamprusi -0.01 0.01 0.11

Gonja 0.00 0.01 0.92

Wala 0.01 0.01 0.63

Dagaare 0.01 0.01 0.60

Hausa 0.03 0.01 0.03 *

Vagla -0.01 0.02 0.54

Komkomba 0.01 0.01 0.25

Religion

Christian 0.01 0.01 0.39

Moslem 0.01 0.01 0.34

Geographic

Rural 0.00 0.00 0.95

Walewale 0.00 0.01 0.82

Bole 0.00 0.01 0.96

Salaga -0.01 0.01 0.08

_cons 0.020006 0.012743 0.116

N=10494 R²=0.0247 Adjusted R²=0.0220

health microinsurance education project evaluation ... 2 final baseline... · freedom from hunger,...

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