1

Community-level effect of the maternal health voucher program on out-of-pocket spending

on delivery services at private health facilities in Uganda

Francis Obare and Ben Bellows

Introduction

In many countries, high out-of-pocket spending on healthcare services prevents some people

from seeking care and can result in financial catastrophe and impoverishment for others (van

Damme et al 2004; Whitehead et al. 2001; Xu et al. 2003, 2007). The problem is particularly

pronounced in low-income countries characterized by weak healthcare systems and high out-of-

pocket payments (Leive and Xu 2008; Su et al. 2006; World Bank 2005). Healthcare financing

strategies that combine demand-side subsidies with supply-side incentives have the potential of

protecting individuals in low-income countries from financial catastrophe and impoverishment

arising from out-of-pocket expenditures on healthcare (Bhatia and Gorter 2007; Jacobs and Price

2006; Peters et al. 2008; Ranson 2002; Xu et al. 2007). Reproductive health vouchers is one such

approach that aims to reduce the financial barriers to accessing healthcare for the poor, stimulate

client demand for services and give the clients the purchasing power to seek care from the full

range of available providers (Bhatia and Gorter 2007; Cave 2001; Gorter et al. 2003; Janisch and

Potts 2005). Reduction in financial barriers is achieved through subsidizing the cost of services,

transport costs to accredited providers, or both.

However, the effectiveness of voucher programs in reducing out-of-pocket spending for

beneficiaries depends on whether both service and transport costs are subsidized as well as other

factors such as service availability in a given locality and community perceptions about the

quality of care. For instance, distance to care has been found to be a major determinant of uptake

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of health care services in developing countries (Feikin et al. 2009; Schoeps et al. 2011). In the

context of a voucher program, to the extent that transport costs to contracted providers remain

higher than service costs in cases where the program subsidizes only the latter, out-of-pocket

spending on transport would still be a barrier to service utilization for clients. In addition,

perceptions about the quality of available services are a key determinant of service uptake in

developing countries (Kyomuhendo 2003; Stekelenburg et al. 2004; World Bank 2005). It is

therefore likely that even with a voucher program in place, clients might continue paying out-of-

pocket at facilities that offer better services if they perceive the quality of care offered by

accredited providers to be poor.

This paper uses two rounds of household survey data to examine the community-level

effect of the maternal health voucher program on out-of-pocket spending on delivery services at

private health facilities in Uganda. It specifically examines the differences in changes in the

likelihood of paying for delivery at a private facility and the amount paid over time among

women from villages that were exposed to the voucher program and those from villages that

were not exposed to the program. The expectation is that as more women from villages that were

exposed to the program bought and used the voucher, the lower the average out-of-pocket

spending will be compared to those villages that were not exposed. The increased likelihood of

finding women who did not pay for delivery or who paid lower amounts in exposed villages

should, in turn, be consistent with increased uptake of the voucher over time. In contrast, given

the absence of the voucher subsidy in non-exposed villages, there should be no major change in

the pool of women who did not pay or in the amount paid for delivery at a private facility at the

community level.

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Healthcare expenditure in Uganda

There has been a steady rise in the percentage of gross domestic product (GDP) spent on

healthcare in Uganda over the past one and half decades from 5.5% in 1996 to 9.5% in 2011

(Table 1). During the same period, government spending on healthcare as percentage of general

government expenditure has remained stable at between 10% and 11%. In addition, the

percentage of GDP spent on healthcare in the country has remained constantly higher than that of

her neighbours, Kenya and Tanzania. Moreover, government spending on healthcare as

percentage of general government expenditure has remained constantly higher than that of Kenya

and comparable to that of Tanzania over the years (Table 1).

<Insert Table 1 about here>

In terms of contribution to the total healthcare expenditure, the government’s share

steadily declined from 31% in 1996 to 20% in 2008 before increasing to 26% in 2011 (Figure 1).

Contributions from private sources, on the other hand, steadily increased from 20% in 1996 to

34% in 2002 before stabilizing at between 26% and 27% thereafter. Over the same period, out-

of-pocket expenditure comprised the largest share of healthcare spending in the country.

Although there was a steady decline in the percentage of total healthcare expenditure from out-

of-pocket payments from 49% in 1996 to 37% in 2002, by 2005 it was back to the 1996 level and

even increased to more than half in 2008 (Figure 1). The increase in the percentage of total

healthcare expenditure from out-of-pocket payments after 2002 occurred despite the government

abolishing user fees at public health centres and hospitals in 2001 (Orem et al. 2011). However,

the policy allowed hospitals to operate a dual system with a private wing for those who could

afford and a free wing for those who could not afford to pay (Orem et al. 2011). The trends in

4

out-of-pocket payments after 2002 suggest that households may still have opted to pay for the

services under the dual system.

<Insert Figure 1 about here>

Uganda maternal health voucher program

The maternal health voucher program in Uganda was implemented between 2008 and 2012 with

funding from the German Development Bank (KfW) and the Global Partnership on Output-

Based Aid (GPOBA-World Bank). The program was first launched in six districts in late 2008

(Mbarara, Ibanda, Isingiro, Kiruhura, Kamwenge, and Bushenyi) before being rolled out to 14

other districts in the south-west of the country. The voucher, also known as HealthyBaby, cost

clients Uganda Shillings (UGSh.) 3,000 (approximately US $1.50) and subsidized safe

motherhood services including antenatal care, delivery and postnatal care services to

economically disadvantaged women. The service package specifically included four antenatal

care visits, institutional delivery (normal or Caesarean if needed), transport in cases of

emergency, transfer to a referral facility and treatment and management of complications as well

as postnatal care for up to six weeks. Community-based voucher distributors were responsible

for targeting poor pregnant women using a district-customized poverty grading tool consisting of

eight items on household assets and amenities, expenditure or income, and access to health

services. Women scoring between zero (the minimum) and 12 points qualified for the voucher.

The program used multiple marketing campaigns to increase awareness about the voucher and

the importance of delivering at a health facility including extensive campaigns on radio, market

day visits, community film nights, and sponsoring events such as local concerts.

5

Vouchers were redeemed for services from accredited providers that comprised private

for- or not-for-profit health facilities that offered basic or comprehensive emergency obstetric

care, hence the focus on private facilities in this paper. A private consulting firm was contracted

to map providers before joint selection and accreditation by both the voucher management

agency and the consultant. The mapping exercise involved identifying suitable private providers,

making site visits, and carrying out clinical audits. Facilities were accredited based on minimum

standards of care (Class A) as well as on accessibility of services especially in remote areas

(Class B). It was expected that providers accredited as a Class B facility would use income from

the program to upgrade the facilities and improve quality of care over time. Such facilities were

linked with referral centers for effective care. The program was implemented through private

providers because of the government policy of offering services in public health facilities at no

fee. However, to the extent that such a policy has affected the quality of services in public

facilities, it is likely that clients may turn to private providers where out-of-pocket spending on

services is high. Marie Stopes International-Uganda (MSI-U) was the voucher management

agency responsible for accrediting providers, distributing the vouchers, ensuring quality,

verifying and processing claims, and controlling fraud.

A key feature of the program’s design that has implications for evaluating its community-

level effect is that there was no random assignment of facilities to the scheme while vouchers

were also not randomly assigned to clients or to villages. The evaluation therefore used a quasi-

experimental design with baseline and follow-up surveys in intervention and comparison sites.

The comparison group was, however, determined post-hoc based on the absence of a voucher

user in the village at follow-up as an indication that such villages were not targeted by the

distributors (hereafter referred to as non-exposed villages). In contrast, intervention villages were

6

taken to be those where a voucher user was present at follow-up as indicative of the villages that

were targeted by the voucher distributors (hereafter referred to as exposed villages).

Data

Data were collected in 2008 at the start of the voucher program and in 2010-2011, roughly two

years after the program started. The target populations in both surveys were women aged

between 15-49 years who had a pregnancy or birth during the past 12 months preceding the

interview date. The surveys were conducted in six districts (Mbarara, Ibanda, Isingiro, Kiruhura,

Kamwenge, and Bushenyi) where the maternal health voucher program was first launched before

being rolled out to other districts. A two-stage cluster sample design was used. First, geographic

information system (GIS) data obtained from the Uganda Bureau of Statistics (UBOS) were used

to identify parishes between 5 to 10 kilometres of 13 health facilities that were initially

contracted to provide services to voucher clients and within three kilometres of a major road in

the six districts. Parishes were then randomly selected from among those within the stipulated

distance to the facilities. In the second stage, villages were randomly selected from the sampled

parishes. A total of 97 villages were sampled in the 2008 survey. At follow-up, 133 villages were

sampled. Of these, 68 had been included in the 2008 survey while 65 were sampled from within

five kilometers of the contracted facilities in order to maximize the possibility of getting

respondents who had likely used the vouchers. This approach was adopted because the 2010

voucher claims data showed very high concentration of clients around the contracted facilities.

Two visits were made to each village. The purpose of the first visit was to seek the

cooperation of the local leader, generate a list of households where a pregnancy or birth occurred

in the past 12 months, and take coordinates to ensure that all villages were within the prescribed

7

geographical location. The survey was then administered during the second visit to all women

living in the households that met the inclusion criteria. In both surveys, respondents provided

information on household assets and amenities, health-related household arrangements, food

security, household expenditures on goods and services, individual background characteristics

(age, education level, religious affiliation, and marital and employment status), general health

status and health care utilization, childbearing experiences and intentions, family planning

knowledge and use, trust and social cohesion in the community, and awareness, use and

perceptions about vouchers. In 2008, women were asked detailed questions about two most

recent births while in the follow-up survey, women provided detailed information on all births in

the five years before the survey. Written informed consent was obtained from the participants in

both surveys. The Institutional Review Boards of the Population Council and Mbarara University

granted ethical clearance for the study.

This paper uses information on respondents who were sampled from villages that were

included in both surveys and who had a birth within two years preceding the interview date. Out

of 2,266 women who participated in the 2008 survey, 1,592 were from villages that were

included in the subsequent survey, 81% of these (1,284) reported a birth in the last two years

preceding the interview date, and 275 delivered their most recent babies at a private health

facility. In the follow-up survey, 2,313 women were interviewed, 753 were from villages that

were included in the previous survey, 73% of these (549) had a birth within two years preceding

the interview date, and 161 delivered their most recent babies at a private facility. Table 2

presents the distribution of women from villages that were included in both surveys according to

survey round and exposure to the voucher program. There was no significant difference in the

distribution of women from exposed and non-exposed villages in the 2008 survey by the

8

background characteristics considered. In the follow-up survey, the only significant difference

between women from exposed and non-exposed villages was with respect to poverty status. Over

time, there was no significant difference in the distribution of women in the 2008 and 2010-2011

surveys by the background characteristics.

<Insert Table 2 about here>

Analysis

The community-level effect of the voucher program on the likelihood of paying out-of-pocket

and on the amount paid for delivery services at private health facilities is determined by

difference-in-differences estimation, that is, the difference in changes over time between women

from villages that were exposed to the voucher program and those from villages that were not

(Gertler et al. 2011). Analysis is in three parts. The first part entails comparison of changes in

proportions of women who delivered their most recent babies at a private facility and paid for the

services as well as the average and median amounts paid over time (in Uganda Shillings) in

exposed and non-exposed villages. In the second part of the analysis, a parish-level fixed-effects

logit model is estimated to examine changes in the likelihood of paying for delivery at a private

health facility over time in exposed and non-exposed villages. The model includes an interaction

term between survey year and whether the village was exposed to the voucher program and is

specified as follows:

jijijijijijij XXXXXit ...*)(log 21322110 (1)

The parameter X1 in Equation (1) is the indicator for survey year, X2 is the indicator for

village-level exposure to the voucher program, Xij is the vector of other covariates included in the

9

model for birth i from parish j, and β is the associated vector of fixed parameters. The parameter

α0 represents the likelihood of paying for delivery at a private facility among women from non-

exposed villages at baseline (in 2008); α1 is the change in the likelihood of paying for delivery

among women from non-exposed villages between baseline and follow-up; α2 is the difference in

the likelihood of paying for delivery between women from exposed and non-exposed villages at

baseline; α3 represents the difference in the changes in the likelihood of paying for delivery

between women from exposed and non-exposed villages over time (difference-in-differences

estimate); and j are the unobserved characteristics of births from villages in the same parish that

might be correlated with the outcome of interest. The results are presented as odds ratios.

The final part of analysis involves the estimation of a fixed-effects negative binomial

model for the amount paid for delivery services at a private health facility. The model also

includes an interaction term between survey year and exposure to the voucher program and is

given by Equation (2):

jijij XXXXX ...*log 21322110 (2)

where λi is the amount paid by individual i from village j while the other parameters are similar

to those in Equation (1). The model also controls for similar characteristics as in Equation (1).

The results are presented as incidence-rate ratios. Table 3 presents the definitions and

measurement of the variables included in both models. The expectation is that as a result of the

voucher subsidy, there should be a greater decline in the proportion and the likelihood of women

paying as well as in the ratio of amounts paid for delivery at private facilities among those from

exposed compared to those from non-exposed villages over time.

<Insert Table 3 about here>

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Results

Changes in proportions, means and medians

Among women from villages that were exposed to the voucher program and who delivered their

most recent babies at a private health facility, the proportion that paid for the services declined

by 48 percentage points between 2008 and 2010/2011 (Table 4). In contrast, the proportion of

women from non-exposed villages that delivered their most recent babies at a private facility and

paid for the services declined by only 8 percentage points between the two surveys (Table 4).

The decline in the proportion of women that paid for delivery services at private facilities was

therefore greater in exposed compared to non-exposed villages by 40 percentage points.

Similarly, the average amount paid for delivery at private facilities declined by UGSh. 10,246

(equivalent US $5.10) in exposed villages compared to a decline of UGSh. 1,004 (equivalent US

$0.50) in non-exposed villages over time. The decline in the average amount paid was therefore

more than 10 times greater in exposed than in non-exposed villages. The median amount paid, on

the other hand, remained unchanged in exposed villages while it increased in non-exposed

villages (Table 4).

<Insert Table 3 about here>

Over the same period, the proportion of women from exposed villages that had ever used

the maternal health voucher increased from less than 1% in 2008 to 26% in 2010-2011 (not

shown). In contrast, as expected, none of the women from non-exposed villages had ever used

the voucher in either survey. Further analysis showed that at follow-up, the proportion of women

who paid for services at a private facility was significantly lower among those who had ever used

the voucher compared to those who had not (26% compared to 54% respectively; p<0.01). In

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addition, the average amount paid by voucher clients was significantly lower than that of non-

voucher clients (UGSh. 17,871 or US $8.90 compared to UGSh. 38,249 or US $ 19.10; p<0.05)

while the median amount paid was zero for voucher clients and UGSh. 30,000 (equivalent US

$15) for non-voucher clients.

Results from further analysis also showed that the use of the voucher at follow-up did not

significantly vary by all the maternal background characteristics considered in this paper except

poverty status. The proportion of women that had ever used the voucher was significantly higher

among those from the poorest forty percent households than among those from the other sixty

percent households (32% compared to 22%; p<0.05). It is, however, important to note that the

measure of poverty used in this analysis (household asset score based on principal component

analysis) is different from that used by the voucher management agency to identify beneficiaries

(poverty grading tool) given that the latter was not administered to survey respondents.

Odds ratios for paying for delivery

The results from the fixed-effects logit model predicting the likelihood of paying for delivery at a

private facility by exposure to the voucher program over time are presented in the second column

of Table 5. The estimate for the interaction term between survey year and exposure to the

program (difference-in-differences estimate) is statistically significant indicating that the decline

in the likelihood of paying for delivery services at a private facility was significantly greater in

exposed compared to non-exposed villages. The estimate for ever use of the voucher is also

consistent with the descriptive findings. In particular, women who had ever used the voucher

were significantly less likely to pay for delivery services compared to those who had not.

<Insert Table 5 about here>

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Incidence-rate ratios for amount paid for delivery

The last column of Table 5 presents the results from the fixed-effects negative binomial model

predicting the amount paid for delivery at private facilities by exposure to the voucher program

over time. Again, the estimate for the interaction term between survey year and exposure to the

program is statistically significant indicating that the decline in the average amount paid for

delivery was significantly greater in exposed than in non-exposed villages. Similarly, women

who had ever used the voucher were significantly less likely to pay more for delivery compared

to those who had not used the voucher.

Discussion and conclusion

The major finding of this paper is that over time, there was a significantly greater decline in the

likelihood of women paying as well as in the average amount paid for delivery at private

facilities in villages that were exposed to the maternal health voucher program in Uganda

compared to villages that were not. These changes were also consistent with increased uptake of

the voucher over time in villages that were exposed to the program. Moreover, at follow-up, the

proportion of women that paid for delivery at a private facility was significantly lower among

those who had ever used the voucher compared to those who had not. Similarly, the average

amount paid by voucher clients was significantly lower than that of non-voucher clients. Ideally,

voucher clients should not pay anything for services that are subsidized by the program.

However, such payments may be occasioned by providers’ lack of understanding of the benefit

package or if there are services that are not subsidized by the program. Evidence from Kenya, for

instance, shows that voucher clients seeking maternal health care services reported paying

additional amounts for services that providers regarded as not being part of the benefit package

13

(Reproductive Health Vouchers Evaluation Team 2012). Nonetheless, the significantly greater

decline in out-of-pocket expenditure at private facilities for women from exposed villages over

and above the decline that was noted in non-exposed villages in Uganda is consistent with

increased uptake of the voucher over time and is therefore attributable to the program.

As noted earlier, there was an upward trend in out-of-pocket expenditure on healthcare

services in Uganda even after the abolition of user fees in public health centres and hospitals

(Orem et al. 2011; World Health Organization 2013). Available evidence suggests that although

removal of user fees increases service uptake, it may have a negative impact on the quality of

care (Lagarde and Palmer 2008). Since the policy abolishing user fees allowed hospitals to

operate a dual system for paying and non-paying clients, the trend in out-of-pocket payments

suggests that quality of care might have been an issue with the free services; hence most

households might have opted to pay. Unlike abolition of user fees, vouchers aim to improve

service delivery through explicit performance-based contracting with service providers based on

set minimum standards of care as well as through stimulating competition for voucher clients

(Bhatia and Gorter 2007; Gorter et al. 2003). In addition, vouchers not only empower clients to

seek services but also generate revenue for health facilities which can be used to improve service

quality.

The above findings might, however, be influenced by the study’s limitations. First, there

was no random assignment of facilities, villages or clients to the voucher program. It could

therefore be argued that any differences between exposed and non-exposed villages could be due

to unobserved differences in respondent characteristics. There was, however, no observed

significant difference in the distribution of respondents by background characteristics at baseline

while at follow-up, respondents only significantly differed according to one of the eight

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background characteristics considered. Second, the identification of respondents from within

specific geographical distances to contracted facilities may lead to under- or over-representation

of voucher users depending on how spread they are from the facilities. This could, in turn, result

in under- or over-estimation of the community-level impact of the program on out-of-pocket

expenditure on delivery services at private facilities. Under-estimation may result from under-

representation of voucher clients while over-estimation may arise if voucher clients were over-

represented in the sampled areas. Third, the effect of the program could be undermined by

periods of low voucher sales especially during the first year of the program. This was partly due

to deliberate efforts to avoid selling the voucher to women in their third trimester of pregnancy

as they could not make the stipulated number of antenatal care visits to allow for full

reimbursement to providers.

Despite the limitations, the findings of this paper suggest that the maternal health voucher

program in Uganda significantly contributed to reductions in out-of-pocket expenditure for

delivery services at private health facilities in the regions where it was implemented. This is

consistent with the view in the literature that demand-side subsidies combined with supply-side

incentives have the potential to protect economically disadvantaged individuals from financial

catastrophe and impoverishment arising from out-of-pocket expenditures on health care services

(Bhatia and Gorter 2007; Jacobs and Price 2006; Peters et al. 2008; Ranson 2002; Xu et al.

2007). It is, however, worth noting that substantial reductions in out-of-pocket expenditure for

voucher beneficiaries can be achieved if the programs subsidize both transport and service costs

as is the case with the Bangladesh maternal health voucher scheme (Ahmed and Khan 2011; Hatt

et al. 2011). Findings from the Kenya voucher program, for instance, show that some women

who purchase the vouchers fail to use them because transportation costs to accredited health

15

facilities are higher than service costs at nearby non-contracted providers (Abuya et al. 2012;

Njuki et al. 2012). The other challenges that voucher programs need to overcome in order to

have substantial impact on health outcomes include proper targeting of clients, controlling for

potential fraud, ensuring efficient voucher uptake among beneficiaries who receive but do not

use the vouchers, and effectively monitoring and improving quality of care through practice

(Abuya et al. 2012; Bhatia and Gorter 2007).

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Table 1: Healthcare expenditure as percentage of gross domestic product and government spending on

healthcare as percentage of general government expenditure in Uganda, Kenya and Tanzania, 1996-2011

Year

Healthcare expenditure as percentage

of gross domestic product

(%)

Government spending on healthcare

as percentage of general government

expenditure (%)

Uganda Kenya Tanzania Uganda Kenya Tanzania

1996 5.5 4.1 3.4 9.8 7.3 10.2

1999 6.7 4.2 3.2 11.3 8.3 10.2

2002 7.5 4.5 3.4 9.7 8.3 11.1

2005 9.2 4.4 4.0 11.2 7.6 8.7

2008 8.8 4.2 5.4 9.5 6.1 16.0

2011 9.5 4.5 7.3 10.8 5.9 11.1 Source: World Health Organization (2013) Global Health Expenditure Database.

21

Table 2: Percent distribution of women by background characteristics according to survey year and

exposure to the voucher program

2008 survey 2010-2011 survey All sites

Characteristics

Exposed

villages

(%)

Non-

exposed

villages

(%)

Exposed

villages

(%)

Non-

exposed

villages

(%)

2008

survey

(%)

2010/11

survey

(%)

Current age (years) p=0.60 p=0.84 p=0.30

15-24 44.3 44.1 40.7 43.2 44.2 41.3

25-34 44.4 43.0 47.1 45.8 43.9 47.3

35 and above 11.3 12.9 11.6 11.1 11.9 11.4

Maternal age at last birth p=0.83 p=0.59 p=0.32

<20 years 13.3 13.1 14.7 12.1 13.3 14.1

20-29 years 64.2 63.1 64.8 68.4 63.8 65.7

30 years and above 22.5 23.8 20.4 19.5 22.9 20.2

Maternal education level p=0.51 p=0.29 p=0.07

No schooling/pre-unit 15.6 15.4 11.2 14.7 15.5 12.1

Primary 63.1 66.1 67.0 60.0 64.2 65.2

Secondary and above 21.2 18.5 21.7 24.7 20.2 22.4

Missing 0.1 0.0 0.2 0.5 0.1 0.3

Current marital status p=0.27 p=0.14 p=0.11

Never/formerly married 7.7 5.6 6.8 9.0 6.9 7.3

Married/living together 91.9 94.1 91.7 91.1 92.7 91.5

Missing 0.5 0.4 1.6 0.0 0.4 1.2

Place of residence p=0.28 p=0.32 p=0.71

Urban 11.4 9.6 9.6 12.1 10.7 10.2

Rural 88.6 90.4 90.4 87.9 89.3 89.8

Duration of residence p=0.80 p=0.65 p=0.17

Less than 5 years/visitor 33.3 35.0 37.1 39.0 33.9 37.6

5 years or more/always 66.2 64.5 62.5 61.1 65.6 62.4

Missing 0.5 0.5 0.4 0.0 0.5 0.3

Poverty status p=0.45 p<0.01 p=0.69

Poorest forty percent 37.8 39.7 40.5 29.0 38.4 37.6

Other sixty percent 62.3 60.3 59.5 71.1 61.6 62.4

Parity p=0.50 p=0.74 p=0.66

1-2 47.9 46.7 46.9 46.3 47.5 46.8

3-4 31.1 29.7 29.0 31.6 30.5 29.6

5 and above 21.1 23.6 24.2 22.1 22.0 23.6

Number of women 1,020 572 563 190 1,592 753 Percentages may not sum to exactly 100 in some cases due to rounding; p-values are from Chi-square tests for

differences between groups.

22

Table 3: Definition and measurement of variables used in multilevel analysis

Variable definition Measurement

Outcome variables

Paid for delivery at a private facility 0 = No;

1 = Yes

Amount paid for delivery Ranges from 0 to 750,000 Uganda Shillings

Covariates

Ever use of the maternal health voucher 0 = No;

1 = Yes

Survey year 0 = 2008 survey (first round)

1 = 2010-2011 survey (follow-up)

Exposure to the voucher program 0 = Not exposed

1 = Exposed

Maternal age at birth of last child Single years (ranges from 14 to 48 years)

Education level 0 = No schooling/pre-unit/primary;

1 = Secondary and above

Current marital status 0 = Never/formerly married;

1 = Married/living together

Current place of residence 0 = Urban;

1 = Rural

Duration of residence 0 = Less than 5 years/visitor;

1 = 5 years or more/always

Poverty statusa 0 = Other sixty percent;

1 = Poorest forty percent

Woman’s parity Ranges from 1 to 12 children

aBased on household assets and amenities.

23

Table 4: Distribution of women by whether they paid for delivery services at a private health facility and

the amount paid over time according to exposure to the voucher program

Exposed villages Non-exposed villages

Indicator

2008 survey

2010-2011

survey

2008 survey

2010-2011

survey

Paid for delivery at a private

health facility (%)

96.6

(N=176)

48.9

(N=141)

92.9

(N=99)

85.0

(N=20)

Amount paid (Uganda shillings) (N=174) (N=118) (N=95) (N=19)

Mean 39,701 29,455 41,315 40,311

Median 20,000 20,000 25,000 30,000 1 USD ≈ 2,000 Uganda shillings.

24

Table 5: Odds and incidence-rate ratios from the fixed-effects logit and negative binomial models for the

likelihood of paying and the amount paid for delivery at a private facility over time

Covariates

Paid for deliverya

(odds ratios)

Amount paidb

(incidence-rate ratios)

Survey year 1.34

(0.14 – 12.93)

0.84

(0.51 – 1.38)

Village exposed to program 1.00

(0.07 – 14.31)

1.12

(0.84 – 1.50)

Exposure to program × Survey year 0.02*

(0.01 – 0.44)

0.41**

(0.23 – 0.73)

Ever use of maternal health voucher 0.10**

(0.03 – 0.34)

0.11**

(0.06 – 0.21)

Maternal age at last birth 1.11

(0.97 – 1.26)

1.01

(0.98 – 1.04)

Highest education level 0.88

(0.32 – 2.44)

1.24*

(1.00 – 1.54)

Current marital status 7.77

(0.98 – 59.65)

2.03**

(1.20 – 3.43)

Current place of residence 2.54

(0.21 – 30.73)

0.68*

(0.48 – 0.95)

Duration of residence 4.24*

(1.30 – 13.77)

1.20

(0.96 – 1.49)

Poverty status 1.13

(0.40 – 3.23)

1.21

(0.97 – 1.50)

Parity 0.76

(0.51 – 1.15)

0.95

(0.87 – 1.04)

Number of women 312 398 aEstimates are based on Equation (1) in the text;

bEstimates are based on Equation (2) in the text; n/a: not applicable;

95% confidence intervals are in parentheses; *p<0.05; **p<0.01.

25

Figure 1: Healthcare expenditure in Uganda by source, 1996-2011

0

20

40

60

80

100

1996 1999 2002 2005 2008 2011

Per

cen

t

Government Private Out-of-pocket

Source: Computed by the authors from the World Health Organization (2013) Global Health Expenditure Database.