-tendereÒ3ocio %conomicÒ0roµle april 2017 · table of content acronyms 1.0 introduction 2.0...

Mtendere Socio-Economic Profile April 2017 1

Mtendere Socio-Economic Profile April 20172


MCA-ZambiaStand No. 1200Twikatane Road, Off Addis Ababa DriveLusaka, ZAMBIATel: +260 211 376900-24Fax: +260 211 258175Mailing address: P.O. Box 51290, Lusaka, Zambia.

DisclaimerThis document presents findings from the household survey and property listing census carried out in Kwamwena and Ndeke-Vorna Valley. The survey and the listing census were managed and implemented directly by MCA-Zambia, with the analysis contracted out to a consultant. Views presented in this report are therefore those of the consultant.

AcknowledgementMillennium Challenge Account Zambia (MCA-Zambia) acknowledges the efforts of all those who contributed to the successful conduct of this study and report. In particular, MCA-Zambia recognizes the efforts of Consultant Dr. Emmanuel Maliti for leading the analysis and writing of this report. Special thanks are extended to the team from MCA-Zambia department of Monitoring, Evaluations and Economics (Mr. Claude Kasonka, Mr. Patrick Chilumba, Mr. Samuel Mwanangombe and Mr. Floyd Mwansa) for designing and coordinating the study as well as drafting the preliminary findings. We also thank the team from Communications and Outreach Dr. John Kunda, Musonda Chibamba and Mr. Henry Sakala for their editorial work on the final version of this report.

We are also grateful to all the respondents from Kwamwena and Ndeke-Vorna Valley who gave their time to participate in the study.

Thank you.

Supported By The American People

i


APL

ATP

BoZ

CBE

FPL

FSD

GRZ

HHs

LCC

LWSSD

LWSC

MCA

MCC

MFI

OECD

POU

SEP

UNDP

UNICEF

USA

WC

WDI

WHO

Absolute Poverty Line

Ability to Pay

Bank of Zambia

Community Based Enterprise

Food Poverty Line

Financial Sector Deepening

Government of the Republic of Zambia

Households

Lusaka City Council

Lusaka Water Supply Sanitation and Drainage

Membrane Filtration Technique

Millennium Challenge Account

Millennium Challenge Corporation

Micro-Finance Institutions

Economic Co-operation and Development

Point-of-Use

Social Economic Profiling

United Nations Development Programme

United Nations Children’s Fund

United States of America

Water Closet

World Development Indicators

World Health Organisation

Acronyms

ii


Table of content

Acronyms1.0 Introduction2.0 Methodology

3.0 Socio-economic Status

4.0 Sources of Drinking Water

5.0 Sanitation

6.0 Rent and Improvement

7.0 Property Development

2.1 Data collection methods2.2 Data analysis methods2.3 Data presentaion methods

3.1 Age distribution3.2 Education levels3.3 HH headship3.4 HH size3.5 HH income3.6 Poverty status3.7 Socio-economic differences between landlords and tenants

4.1 Sources of drinking water in Mtendere4.2 Connection to various water supply systems4.3 Paying for water (for all sources excl. those with taps on the plot)4.4 Awareness of LWSC intention to install meters4.5 Ability to pay for water services

5.1 Presence of toilets5.2 Types of toilets5.3 Satisfaction with the toilet facilities5.4 Preferences for toilet types5.5 Sharing of toilet facilities5.6 Quality of the toilet facilities5.7 Disposal of solid waste5.8 Disposal of sanitary pads5.9 Disposal of diapers5.10 Sanitation ladder

6.1 Rentals6.2 Preference on improvements

7.1 Landlords living in the same plot with tenants7.2 Construction of separate WC for each tenant7.3 Property improvements in the past 2 years7.4 Decommissioning of pit latrines7.5 Use of plots7.6 Willingness to connect to the sewer system7.7 Regression analysis7.8 Results from the regression analysis

ii8999911111314151621222525272931333434353940424343444545474750525252545961626365

iii


8.0 Financial Inclusion

9.0 Public Awareness of MCA

10.0 Conclusion

ReferencesAnnex

9.1 Public awareness of MCA-Zambia as an entity9.2 Public awareness of MCA-Zambia activities9.3 Public awareness of funding sources

8.1 Borrowing behaviour8.2 Borrowed amount, security and repayment behaviour8.3 Bank accounts8.4 Regression analysis8.5 Savings

68686971727476767777798086

iv


List of FiguresFigure 1: Distribution of program’s resources Figure 2: Overall age structure in MtendereFigure 3: Gender differences in age structure Figure 4: Histogram of age of HH heads Figure 5: Education level of HH headsFigure 6: Gender differences in education (%) Figure 7: Histogram of average HH size Figure 8: Histogram of average no. children Figure 9: Histogram of average no. adults Figure 10: Income categories (% of HHs) Figure 11: Histogram of average HH income Figure 12: Histogram of food expenditure Figure 13: Histogram of non-food expendFigure 14: Distribution of HHs across sources of income Figure 15: % of landlords and tenants Figure 16: Distribution of tertiary educated heads of HHs Figure 17: Improved vs inferior sources Figure 18: Main sources of water (% of HHs) Figure 19: % of HHs connected to various water supply systems Figure 20: Sources of water connectionFigure 21: Paying for water (for HHs lacking tap water) Figure 22: Payment arrangements for shared water connection Figure 23: HHs awareness on LWSC intent to install pre-paid meters Figure 24: Reasons against prepaid meters Figure 25: Presence of toilet Figure 26: HHs lacking toilet facility rely on neighbours’ facilities Figure 27: Types of toilets in Mtendere Figure 28: The higher the income the higher the use of waterborne/flush toilets (% of HHs) -- 32Figure 29: The higher the education the higher the use of waterborne/flush toilets (% of HHs)32Figure 30: The higher the HH size the higher the use of waterborne/flush toilets (% of HHs) -- 32Figure 31: Use of waterborne/flush toilets and sources of income Figure 32: Reasons for being unsatisfied with the existing toilet facilitiesFigure 33: Methods of disposing solid wasteFigure 34: Methods of disposing sanitary pads in MtendereFigure 35: Methods of disposing diapers in Mtendere Figure 36: Sanitation ladder Figure 37: Differences in estimates between tenants and landlords (in Kwacha)Figure 38: Histogram of rent payment by tenants (residential properties) Figure 39: Histogram of rent payment by tenants (commercial properties) Figure 40: Histogram of rent collected by landlords (residential properties) Figure 41: Histogram of rent collected by landlords (commercial properties) Figure 42: Improvements tenants would like to see Figure 43: Plans to construct separate WC for each tenant Figure 44: Willingness to construct another WC Figure 45: Reasons against constructing another WCFigure 46: Types of improvement carried out in the past 2 years Figure 47: Improvement done by landlords living with tenants Figure 48: Improvement done by landlords not living with tenants Figure 49: Sources of funding for the improvements Figure 50: Own sources of funding and education Figure 51: Histogram of values of improvements Figure 52: Histogram of the costs of constructing a toilet Figure 53: Willingness to decommission pit latrines Figure 54: Reasons against decommissioning of the existing pit latrines Figure 55: Use of plots

v

8121212131415151618181919202223252527282831313234343637373738404444454647484849495153535455555656575859596061


Figure 56: Plots acquisitionFigure 57: Willingness to connect to the sewer networkFigure 58: % of HHs to have borrowed money Figure 59: HHs income and % of borrowersFigure 60: Loan security (% of HHs) Figure 61: Types of security (% of HHs) Figure 62: Comparative statistics on bank account ownership Figure 63: % of HHs saving in the bank Figure 64: Savings and sources of income (% of HHs) Figure 65: Public awareness of MCA-ZambiaFigure 66: Public understanding of MCA-Zambia activitiesFigure 67: Awareness of funding sources

List of TablesTable 1: HHs monthly income in Kwacha Table 2: Monthly expenditure in Kwacha Table 3: Poverty headcounts in Mtendere Table 4: landlords/tenants and monthly expenditure Table 5: Differences in bank account ownership Table 6: Water bill per day and month (in Kwacha) Table 7: Results from computing ATP Table 8: Satisfaction levels by toilet types (grouped types of toilets) (% of HHs) Table 9: Willingness to spend on the preferred toilet facility (in Kwacha) Table 10: Value of the improvements Table 11: Costs incurred to construct diverse types of toilets Table 12: Logistic regression results of the willingness to connect to the sewer system Table 13: Borrowed amount Table 14: Logistic regression results of HHs ownership of bank accounts

vi

626368707070727475767677

1617212424303339415858656973


1.0 Introduction

The United States of America (USA), through the Millennium Challenge Corporation (MCC) and the Government of the Republic of Zambia (GRZ) are implementing the Lusaka Water

Supply, Sanitation, and Drainage (LWSSD) project under the Millennium Challenge Compact agreement (“the Compact”) that aim at assisting Zambia to advance economic growth and reduce poverty. The LWSSD Project is composed of two main activities namely 1) infrastructure activity (US$ 291.7 million), and 2) institutional strengthening activity (US$ 19.7 million) (percentage distribution of funding resources is presented in Figure 1). The two activities are made up of multiple sub-activities.1

Two field-based data collection exercises were commissioned relating to a new sewer system in Mtendere suburb and the 191-km networked water distribution lines in Kwamwena and Ndeke-Vorna Valley. The Mtendere sewer system is programmed to benefit approximately 90,000 people with landlords being expected to connect to the network. Households (HHs) in Kwamwena and Ndeke-Vorna Valley are also expected to connect to the new water distribution network once works have been completed. The two data collection exercises which are expected to inform decision making processes are primarily titled 1) the Mtendere Social Economic Profile (SEP) and; 2) the Kwamwena and Ndeke-Vorna Valley Water Supply Situation Analysis. This document reports findings from the analysis of data collected from Mtendere.Under the Mtendere SEP, a census of 22,895 HHs was undertaken. The purpose of analysing the census data is to 1) determine, access to WASH and estimate willingness and ability of the HHs residing in Mtendere to pay for Water Closets (WCs) 2) establish income and expenditure patterns of the Mtendere HHs 3) using an appropriate model, estimate poverty and vulnerability levels in Mtendere 4) identify the demand for sanitation and factors that may affect sanitation uptake in Mtendere.

This report presents results of the analysis of the data from the Mtendere census. The analysis intends to inform potential interventions aiming at increasing HHs connection to the new sewer network.

M&E

2%11%

6%

82%

Institutional stregthening

Program administration & audit

Infrastucture activity

Figure 1: Distribution of programme’s resources


2.0 Methodology2.1 Data collection methods

2.1 Data collection methods

A census of 22,895 HHs was carried out in Mtendere collecting a range of information from socio-economic status of tenants and landlords to HHs access to water and sanitation services. The

census dataset comprises of about 170 variables including variables that came from the field data and variables that were generated through manipulating field data. Despite repeated visits, about 1000 HHs could not be found in their place of residence and therefore missed out on the interviews. None of the observations was dropped during the data cleaning exercise.

Multiple methods of data analysis were applied to generate a diverse amount of information. Categorical variables are summarised through frequencies and percentages, with continuous variables summarised by means, standard deviation and frequencies. The process of generating descriptive statistics served as an additional quality assurance and quality control measure by identifying anomalies, outliers, and improbable values.

Possible associations among categorical variables were investigated using cross-tabulation and the respective chi square tests. The null hypothesis is the absence of systematic association between variables against an alternative hypothesis (that is, highly unlikely for the relationship to have occurred by chance). The chi-square statistic only implies association rather than one variable maintaining a causal effect on the other. Regression analysis, in particular, the logit models are applied to examine the relationship between HHs’ willingness to connect and socioeconomic and other characteristics of the HHs. Specific types of other data analysis methods are presented in various parts of the report. It is worth mentioning in advance that this study adopts the economists approach of estimating a demand function for sanitation by applying a regression analysis of socio-economic variations of the HHs willingness to pay for a service (for the case of Mtendere, it is willingness to connect to the new sewer system. See an extensive methodological discussion on the approaches for estimating demand for water and sanitation from Parry-Jones, 1999).

Multiple methods of data analysis were applied including descriptive statistics and regression analysis.

2.3 Data presentation methodsResults are presented in a variety of tables and graphical summaries that are sufficient to convey key messages from the census data. Visualization helps readers understand and remember large amount of information. In most cases, the study identifies and assesses


Data are presented in summarised tables and graphs. Whenever possible, the data are benchmarked against national averages.

socio-economic factors influencing choices and behaviors of the HHs residing in Mtendere.

As much as possible, descriptive statistics are benchmarked against national and regional averages. Varieties of national level benchmark data are utilised. Benchmarks from other countries are sourced from datasets managed by the multilaterals. For time-constrained readers, the report presents key findings and take away messages as standalone short paragraphs on the left-hand side of each subsection.


3.0 Socio-economic Status

The age of HH heads ranges from 16 years to a maximum of 97 years with the mean value of 39 years (median value of 38 years and standard deviation of 12 years). The standard deviation

implies that each age point in dataset sits from an average distance of 12 statistical data points from the mean. Majority of HH heads are in their productive age where more than 60 per cent of the HH heads are clustered in the age categories 25-34 and 35-44 years (Figure 2).2 The age structure in Mtendere is in line with literature on informal settlements which has Through cross-tabulation chi square tests were used to investigate consistently found that tenants of the informal rental housing tend to be young (see for instance, Coccato, 1996).

Female heads of HHs are on average older (41 years: n = 6,070) than male heads of HHs (38 years: n = 16,805) and this difference is statistically significant (p-value = 0.000) (that is, the difference is unlikely to have occurred by chance). As a result, the proportion of female heads of HHs in the oldest age category of 54 years+ (17 per cent) is more than twice the proportion of male heads of HHs in the same category (8 per cent). Further differences by the sex of HH heads are illustrated in Figure 3.

Literature such as Minh et al (2013) found the rate for willingness to pay for improved water and sanitation facility is lowest among the old age groups. In other words, the young aged individuals are relatively more willing to pay for improved water and sanitation facilities. The reason is, as people get older they tend to be more economically conservatives and their willingness to pay for goods and services decreases.

The histogram of age distribution, that is, Figure 4 confirms the relatively young age of the HH heads in Mtendere - age values are skewed to the left (skewness3 value of 1.1). Hence, if results from Minh et al (2013) holds in Zambia, then HHs in Mtendere will most likely have high rates of willingness to pay for improved sanitation.4

3.1 Age distribution

With large proportion of relatively young HH heads, the willingness to pay for improved sanitation services is likely to be high.


Figure 2: Age structure of HH heads in Mtendere

Figure 3: Differences in age structure by the sex of HH heads

Figure 4: Histogram of age of HH heads


Figure 5: Education level of HH heads

Four levels of education attainment were investigated; from ‘never been to school’, ‘primary’ and ‘secondary’ to the highest level of ‘tertiary’ education.5,6 Figure 5 shows that only 3 per cent of HH heads have never been to school implying a high literacy level in Mtendere. The 3 per cent illiteracy rate is significantly lower than the national average in urban areas of 8 per cent (GRZ, 2014).7 The corresponding literacy level is therefore standing at 94 per cent with the largest proportion of HH heads clustered at secondary education (54 per cent), followed at a distance by primary educated HH heads (25 per cent). HH heads with tertiary level education stands at 15 per cent (which is nearly twice the national average rate of 9.6 per cent for males and 6.5 per cent for females in urban areas, GRZ, 2014).

Levels of education also differ by the sex of HH heads. Specifically, the proportion of female headed HHs that are uneducated is more than 3 times (7 per cent) that of male headed heads of HHs (2 per cent) and this difference is statistically significant (p-value 0.000). Correspondingly, the proportion of educated male HH heads is higher than that of female HH heads. For example, the proportion of male HH heads with secondary education stands at 58 per cent against 44 per cent of female heads of HHs (Figure 6).

Literature provides scientific evidence that education matters for the willingness to pay for improved water and sanitation services (see for instance Seraj, 2008).8 Hence at the observed literacy rate, it is more likely for Mtendere’s residents, majority of whom are educated, to comprehend the consequence of poor sanitation and such understanding should raise the likelihood of improving the existing facilities.

3.2 Education levels

At the literacy rate of 97%, it is more likely for Mtendere residents to comprehend the consequences of poor sanitation, which in turn should raise the willingness to invest in improved sanitation.


Same as the national average in urban areas, at the time of the census, 74 per cent of HHs in Mtendere were male headed and the remaining 26 female headed.9 Also, it is more likely for male heads of HHs to be married (85 per cent) than female HH heads (21 per cent). Census data shows that, the lower rate of married female HH heads give rise to higher rate of female HH heads who are divorced, widowed and separated (20 per cent, 34 per cent and 8 per cent respectively). Such rates are higher than for the male headed HHs were only 7 per cent are divorced, 10 per cent are widowed and 3 per cent are separated.

The role of gender on preferences for sanitation facilities remains contradictory in the literature. On one hand, as revealed by the 2006 United Nations Development Programme’s (UNDP) Human Development Report ‘… women consistently give higher value for cost scores to toilets than do men’ (UNDP, 2006, p. 120). On the other hand, findings from studies such as Sejar (2008) and Minh et al (2013) are contrary to UNDP’s conclusion. Sejar (2008), for instance, showed that HHs headed by a female member would opt for status quo rather than spending on improved sanitation. That finding is in line with Minh et al (2013) who found that male heads of HHs were more willing to pay for improved sanitation than female heads. Such contradictory findings point to the importance of context matters when it comes to the role of gender in access to improved sanitation facilities.

3.3 HH headship

Same as the national average in urban areas, 74% of HHs are male headed. Irrespective of HH headship, evidences from literature indicate that context matters on whether the sex of HH heads influences improved sanitation facilities.

Figure 6: Differences in education by the sex of HH heads (%)


Figure 7: Histogram of HH sizes

Figure 8: Histogram of no. of children

3.4 HH sizeThe size of HHs in Mtendere averages 4.6 individuals per HH ranging from 1 to 5 individuals (a standard deviation of 2). The HH size is close to the national urban average of 4.8 individuals (GRZ, 2014). There are no differences between the HH sizes of the male and female headed HHs. Figure 7 shows that the distribution of average values of HH size in Mtendere is slightly skewed to the left with skewness value of 1 and kurtosis of 5.10 The high value of kurtosis (above the reference standard of 3 - a Kurtosis value for a normal distribution) indicates the presence of some extreme values influencing the variance.HHs in Mtendere are composed of an average of 3 adults. Again, the histogram of average number of adults per HHs is slightly skewed to the left with skewness value of 2 and a high kurtosis value of 12 - presence of extreme values influencing the variance (Figure 9). The average number of children per HH is 2 skewing to the left (skewness value of 1 and kurtosis of 5- Figure 8). Consistent with literature on demographic economics in the developing world, the HHs size in Mtendere tend to be larger for less educated HH heads and this relationship is statistically significant (p-value 0.000).

The average HH size in Mtendere is 5 individuals (same as the national average in urban areas). The HH size is on average composed of 3 adults and 2 children.


The common hypothesis in the literature on demand functions is that due to income constraint, poorer HHs will have lower demand for goods including improved sanitation. As HHs tend to understate their income than overstate their expenditure (Johnson, McKay and Round, 1990; Deaton, 1997), this study follows best practices by adopting monthly expenditure data as a proxy for the HHs monthly income.In the presence of large standard deviation, we use the median values as representatives of the average income.11 Table 1 shows that average HHs income in Mtendere suburb is K1838 with statistically significant difference (p-value 0.000) between income of the male headed HH (K1893) and female headed HHs (K1665) (Table 1). The observed monthly income is far below the national mean monthly income of urban HHs which stands at K3,152 (GRZ, 2014).12 The distribution of income values is highly skewed to the left (Figure 11) as evidenced by the skewness value of 4 and the variance is largely affected by the presence of extreme values (as evidenced by a large kurtosis value of 24). The high volatility of income values between HHs is further evidenced by wide range of the minimum income of K104 to a maximum income of K26,638.

3.5 HH income

Figure 9: Histogram of no. of adults

Table 1: HHs monthly income (in Kwacha)

Overall

Overall

Mediam

Standard deviation

Range (min, max)

n

Skewness

Kurtosis

2329

1838

1866

(104, 26638)

22895

4

24

2114

1665

1707

(104, 241182)

6070

4

26

2407

1893

1913

(213, 26638)

16805

4

23

Male Female


On average HHs in Mtendere suburb spend K600 on food and K1145 on non-food items each month (Table 2), with both figures below the national mean expenditure of urban based HHs (K930 and K1750 for food and non-food expenditure respectively) (GRZ, 2014). The histograms of the mean values of food and non-food expenditure (Figure 12 and 13) are highly skewed to the left with a skewness value of 2 (food expenditure) and 5 (non-food expenditure) (Figure 12 and 13). Moreover, the extreme values influence the variances of the distribution more so for non-food expenditure (kurtosis value of 36) than food expenditure (kurtosis of 11). Food items absorb the largest share of income at 34 per cent similar to the national average as presented in the 2015 Living Conditions Monitoring Survey (GRZ, 2015). Such high share of food expenditure makes Mtendere’s residents increasingly vulnerable to poverty in the event of high volatility of food prices.

Table 2 shows that the second most expensive items are rental and school expenses whose income shares stand at 12 per cent each. Thereafter are the transport and ‘other expenses’ each absorbing 9 per cent of HHs monthly income. The census instruments did not collect data on HHs expenditure on sanitation. It is therefore most likely that respondents lumped such expenses under the category ‘other expenses’.

Table 2: HHs monthly expenditure (in Kwacha)

MeanObservations

Food

Alcohol

Electricity

Energy

Rent

Medical

School

Transport

Water

Airtime

Cloth

Other expenditure

781

33

81

102

375

37

283

211

49

90

80

211

22895

22895

22895

22895

22895

22895

22895

22895

22895

22895

22895

22895

(20,5000)

(0,3500)

(0,1000)

(0,500)

(0,5000)

(0,3000)

(0,15000)

(0,3000)

(0,5000)

(0,1000)

(0,5000)

(0,20000)

34%

1%

3%

4%

16%

2%

12%

9%

2%

4%

3%

9%

600

0

50

90

350

0

0

138

30

50

0

125

609

162

99

57

370

164

813

246

86

130

225

457

Median StandardDeviation

Range %

Figure 10 shows the distribution of HHs in Mtendere across income levels. The largest proportion of HHs are in the lower income categories, a state that is further confirmed by income values being highly skewed to the left (Figure 11). Specifically, 80 per cent of HHs in Mtendere suburb are earning below K3,000 per month against, for instance, 7 per cent earning above K5,000. x2 statistics reveal that high-income


Only 2% of HH heads ‘do not receive income’. It is therefore more likely for the remaining 88% (who are income earners) to demand a good or services (e.g. sanitation facilities) than non-income earners.

Figure 10: Income categories (% of HHs)

Figure 11: Histogram of HH incomes

earners are most likely to be educated, living in large HHs, married, and are either employed or engaged in business activities (annex 1-5).


Figure 12: Histogram of food expenditures

Figure 13: Histogram of non-food expenditures

The dominant source of income in Mtendere suburb is employment13 (58 per cent of HH heads) more so for male HH heads (61 per cent) than female HH heads (49 per cent) (p-value = 0.000) (Figure 14). Business activities follows next by generating income for 29 per cent of HHs. Less than 10 per cent of HHs rely on the remaining sources of income (rentals and farming).


58% of HH heads depend on employment

Figure 14: Distribution of HHs across sources of income

Overall

Male HH heads

Female HH heads


Despite limited involvement in farming (only 102 out of 22, 895 HH heads or 0.45 per cent), the HHs earning from farming are better off (6 per cent are in the highest income category (earning K7,001+)) against 3 per cent of HHs engaging in employment and businesses. It is most likely that there are HHs in Mtendere who are engaged in medium to large scale farming undertakings.

HHs are expected to pay for connection to the new sewer system as well as for the construction of improved toilet facilities. It is therefore imperative to be aware of the income-poverty headcounts of the censused HHs. This study uses the international poverty lines as recently revised by the World Bank where extreme poverty is defined as living on a per capita income of less than $1.90 per day. Moderate poverty is defined as living on between $1.90 and $3.10 per capita per day (World Bank, 2016). The international poverty line is a monetary measure of poverty and ‘the money’ aspect is relevant for the LWSSD project which intends to determine the affordability and the willingness of HHs in Mtendere to pay (a monetary aspect) for connection to the sewer.

Table 3 presents the poverty rates in Mtendere calculated using the international poverty lines. A total of 6365 out of the 22895 HHs in Mtendere are extremely monetary poor (28 per cent). The extreme poverty incidence in Mtendere is more than twice the global average of 11 per cent, but far less than the average of 41 per cent in the Sub-Saharan Africa (World Bank, 2016). About 43 per cent of Mtendere HHs are nonpoor which is equivalent to 9859 HHs out of the censused 22895 HHs. The moderate poverty incidence stands at 29 per cent.

It is worth mentioning that, the national poverty line would have been a far more appropriate cut-off point determine poverty status in the study area. However, in the absence of census data14 that are consistent with the ones used to establish the national poverty line, this study opted for the international poverty lines as cut-off points. The figures in Table 3 cannot therefore be benchmarked against the national poverty lines which used different methodological approach (see GRZ, 2015).

Using the international poverty line as a benchmark, the extreme poverty rate in Mtendere stands at 28%. About 29% are moderately poor with the remaining 43% as nonpoor.

3.6 Poverty status

Table 3: Poverty headcounts in Mtendere

Extremely Poor

Moderately Poor

NonpoorTotal

28

29

43

100

6,365

6,671

9,859

22,895

No. of People Proportion


3.7 Socio-economic differences between landlords and tenantsMtendere is an occupancy-mix environment, inhabited by landlords and tenanted dwellings. Such difference is necessary to highlight give the power differences between the two groups. For instance, tenants as landlords may have the same sanitation needs but their interest and the means of achieving the needs differ (Eales and Schaub-Jones, 2005). Tenants do not own the houses they live in, they pay rent and depend on landlords (as agents of provision) to provide sanitation facilities making them less interested to invest on improvements (see such discussion in Eales and Schaub-Jones, 2005).

Over 74 per cent of habitants in Mtendere suburb are tenants (16,847 HHs) with the remaining 26 per cent (6,048 HHs) as landlords (Figure 15). High proportion of tenants is what Gunter (2014) consider as a sign of difficulties to penetrate the housing market as a homeowner. Hence, in Mtendere, three out of every HHs living in rental accommodation in Mtendere will have limited incentives to invest in amenities such as better sanitation. Skewness of residency status towards tenants in urban informal settlements is common elsewhere (see for instance, studies in Kenya [Gulyani and Talukdar, 2008], Senegal [Scott, Cotton, and Khan, 2013], Lesotho and Mozambique (Eales and Schaub-Jones, 2005 and Schaub-Jones, 2005).

The average number of tenants per plot is 3 with a large standard deviation of 2 and a wide range of a minimum value of 0 to a maximum value of 28 [the censused questionnaire requested tenants to state the number of other tenants living in the same plot to which the respondent live].

74% of HHs in Mtendere are tenants. As tenants, they do not own the houses they live in, and depend on landlords (as agents of provision) to provide sanitation facilities making them less interested to invest on improvements.

Figure 15: % of landlords and tenants


Tenants are better educated than landlords (Figure 16) and they have more dependents on average (3 children) than landlords (2 children). These differences are statistically significant (p-value 0.000). The education difference is enormous at the tertiary level, where 73 per cent of tertiary educated HH heads are tenants and the remaining 27 per cent are landlords. The same difference emerges at the secondary education level (p-value 0.000). Better education makes tenants as more likely to better internalize IEC sanitation messages than landlords.

On average, landlords are 12 year older (48 years) than tenants (36 years). Literature such as Whittington (1993) have found that the older a person is, the less willing he/she is to pay for goods and services. Hence, the relative age difference between landlords and tenants signals possible difficulties to convince landlords to invest in improve sanitation. In such an environment, the existing marketing literature advocates that the marketing of goods and services to conservative buyers (relatively aged individuals) to be sensitive to the behavior of such group, by being 1) as clear and exciting as possible on the value of good 2) framing the value of good in terms of its lifetime value 3) conservative consumers prefer to conclude purchase in one package rather than in multiple packages 4) use of reassuring words.

Using the median income, landlords are earning more than tenants (K1910 versus K1820) (Table 4). A snapshot regressions between income and age of the landlords and tenants reveal that 1) age is negatively associated with income for the case of landlords and 2) it is positively associated with income for the case of tenants. That is, as landlords get older their income decline, unlike tenants whose income

Age matters for the willingness to pay; the older a person is, the more conservative he/she is on spending. On average landlords are older than tenants which in relative terms make landlords more unwilling to pay for improved services.

Landlords are relatively earning slightly more than tenants. Personal finance is therefore most likely not to be among the constraints to invest in improved sanitation facilities.

Figure 16: Distribution of tertiary educated HH heads


The proportion of landlords with bank accounts stands at 53% against 47% of tenants, with both rates being higher than the national average in urban areas of 35%.

Table 4: Landlords/tenants and monthly income

Mean

Median

Standard deviation

Range (min, max)

n

Skewness

Kurtosis

2256

1820

1676

(104, 26638)

16847

3

24

2532

1910

2303

(213, 25900)

6048

3

19

Landlords Proportion

rise with age. One possible explanation is the relative older age of landlords (48 years) than tenants (36 years). At the time of the census the landlords were much closer to the retirement age range (55+) which is mostly characterized by declining income, unlike tenants who were clustered in the productive age groups that are characterized by increasing income.

Sources of income also differ between landlords and tenants. The overall proportion of HH heads depending on employment as a source of income is 58 per cent, more so for tenants (64 per cent) than landlords (42 per cent). As expected, the proportion of landlords depending on rentals as source of income is by far higher (20 per cent) than the proportion of tenants in the same income category (less than 1 per cent). However, the proportion of HHs depending on business is similar in both groups (30 per cent for landlords against 28 per cent for tenants).

Financial inclusion is one of the hot topics in the development field today. In Mtendere suburb, the proportion of landlords with bank accounts stands at 53 per cent against 47 per cent of tenants (Table 5), with both rates being higher than the 35 per cent national average in urban areas (Financial Sector Deepening [FSD-Zambia] and BoZ, 2015). However, the two groups have similar proportions when it comes to borrowing and loan repayments. About 29 per cent of landlords have borrowed in the past two years against 27 per cent of tenants. On the side of repayment, repayment rate on the side of landlords stands at 89 per cent against 82 per cent of tenants. The repayment data are self-reporting, and it is therefore necessary to be cautious as borrowers tend to overestimate repayment and underreporting defaults.

Table 5: Differences in bank account ownership

Have bank account

Do not have bank accounts

47%

53%

100%

53%

47%

100%Total

Landlords Proportion

Pearson chi2(1) = 58.7305 p-value = 0.000


4.0 Sources of Drinking Water4.1 Sources of drinking water in MtendereIn line with the existing literature, this study categories water sources in Mtendere between ‘improved’ and ‘inferior’ sources. The former refers to sources that are superior to the traditional, unprotected ones (inferior sources). They include HH tap water connections, boreholes, protected dug wells, protected springs, or rainwater collection (WHO and UNICEF, 2004). Following such definition, 82 per cent of HHs in Mtendere are in the category of ‘improved’ sources of water (Figure 17).

Figure 18 illustrates the proportion of HHs across itemised sources of drinking water. Most HHs in Mtendere (67 per cent) have access to tap water (40 per cent of which are within their plots and the remaining 26 per cent located outside their plots). The 33 per cent of HHs without tap water are therefore not benefiting from possible subsidization of

Overall, 82% of HHs in Mtendere have access to ‘improved water sources’. Tap water serves 67% of HHs, 40% of which are using taps located within their plots.

Figure 17: Improved vs inferior water sources

Figure 18: Main sources of water (% of HHs)


the service. The 40 per cent ‘within plots’ tap water is one percentage point higher than the national average in urban areas of 41 per cent (GRZ, 2014). Other improved sources such as boreholes and communal tap/kiosk serve 15 and 18 per cent of Mtendere residents respectively. Only 1 per cent of the HHs are served by the inferior sources of shallow wells and streams. Inferior sources come together with health risks and time-cost (Hutton and Haller, 2004).

This section also attempts to determine underlying factors associated with the observed sources of water. The proportion of female headed HHs depending on tap water – whether within or outside the plot is 72 per cent against 64 per cent of male headed HHs (p-value = 0.000). Female headed HHs are also relatively more dependent on tap water from neighbours (30 per cent) than male headed HHs (26 per cent). In the absence of specific indicator tracking in-house responsibilities and time spent for fetching water, the fact that large proportion of women fetch water from neighbours indirectly indicates the burden for fetching water to be more on the women side than men.

Sources of drinking water is also associated with education, sources of income, income, and HH size (all associations with p-value = 0.000). HHs with tap water are most likely to be highly educated and belonging to relatively higher income categories. For instance, 76 per cent of HHs with a head educated at the tertiary level use tap water against 62 per cent of HHs with a head with no schooling experience. Despite the differences, both proportions are above the 50 per cent mark – implying that irrespective of education levels more than half of HHs in Mtendere use tap water. Considerable differences appear when the analysis shifts from tap water to the inferior sources. For example, the communal water is a relatively common source for uneducated HHs (24 per cent) than educated HHs at tertiary level (9 per cent).

Significant differences emerge when tap water is separated between taps within and outside plots. More specific, low income HHs seem to rely on tap water outside their plots whereas unlike the high-income earners who are largely use water from taps located within their plots. As a way of an example, the proportion of low income earners (earning less than K1000) relying on tap water outside their plots is 10 percentage points higher (34 per cent) than HHs relying on tap water inside their plot (24 per cent). The reverse is true for high income earners (HHs earning above K9000). In that income category, the larger proportion of HHs (58 per cent) are drawing water from taps located in their plots against 17 per cent who are being served by taps outside their plots.Statistical tests also reveal that HHs using tap water installed in their plots are most likely to be earning from rentals and farming, and are living in large HHs. Specifically, the proportion of HHs who have installed tap water in their plots is higher for HHs earning from

Despite most HH heads engaging in business and employment, the two sources of income do not lead to higher rates of tap water installation relative to other income sources (rentals and farming).


Only 37% of HHs are connected to various water supply system. The main connection type is LWSC’s piped water system (88%) followed at a distance by piped water from water trusts and boreholes.

rentals (53 per cent) and farming (51 per cent) than HHs earning from employment (40 per cent) and business (39 per cent). Thus, despite majority of HH heads in Mtendere being employed (58 per cent) and engaging in business (29 per cent) (section 3.5), these two sources of income do not lead to higher rates of tap water installation relative to the other income sources. HHs earning from these sources are more likely to depend on communal tap/kiosks than HHs earning from rentals and farming. Bottled drinking water is a rare commodity irrespective of the socio-economic characteristics.

The census also enquired the level of HHs connections to the existing water supply network (by water connection reference is made to piped-boreholes; piped-water trusts; piped-LWSC and piped-others). A total of 8,577 out of the censused 22,895 HHs (37 per cent) are connected to various water supply system (Figure 19).

The dominant connection type is LWSC’s piped water system, which is accessed by more than 88 per cent of HHs that have been connected (7,511 out of 8,570) (Figure 20). The second connection type is from water trusts supplying water to 12 per cent of the housing units. Half of the 7,511 housing units connected by LWSC piped water are metered (51 per cent).

4.2 Connection to various water supply systems

Figure 19: Connection to water supply systems (% of HHs)


LWSC metered facilities have been disconnected to housing units hosting 128 HHs. An interesting aspect is that, disconnected housing units are most likely to be occupied by HHs in the highest income bracket (earning K9000+) (2.4 per cent) than HHs in the lower income bracket (1.4 per cent for those earning between K1000-K3000). However, this association may be purely by chance rather than a systematic variation (p-value>0.05).

The willingness of the landlords and tenants to install prepaid meters varies systematically (p-value = 0.002). Despite the overall high rates of willingness to install prepaid meters (standing at 81 per cent), tenants are more open to the idea of prepaid meters (81 per cent) than landlords (79 per cent). Moreover, it is found that landlords living with tenants are less likely (76 per cent) to install prepaid meters than landlords detached from tenants (84 per cent). It is possible that

Willingness to install prepaid meters is higher for tenants than for landlords. Landlords living with tenants are less likely to install prepaid meters than landlords living with tenants.

Figure 20: Types of water connection

Figure 21: Paying for water (HHs lacking tap water)


This section tracks payment behaviour for HHs lacking tap water in their plots (reference is made to HHs using water from neighbours’ tap, boreholes inside or outside the plots; shallow well, stream or communal tap/kiosk). Majority of HHs lacking tap water in their plots are paying money to access water from other sources (93 per cent) (Figure 21). That means only 6 per cent of the censused non-tap owners do not pay for water (896 out of 13,733 surveyed non-tap owners). A high proportion of HHs paying for water indicates that HHs in informal settlements are customers too, and their willingness to pay for improved facilities might be high.

HHs paying for water spend an average of K4 each day with a high standard deviation of K10 (Table 6). A standard deviation above the mean implies that the data is over dispersed and heavy-tailed. The mean is therefore biased by “outliers” (influenced by extreme values) becoming unreliable representation of the data. In such case the appropriate measure is the median which stands at K2.5. The average monthly bill was estimated at K52 with a standard deviation of K58, again indicating over dispersed census data (the median value of monthly water bill is K30) (Table 6). Histograms of the daily and monthly bill (annex 7) confirm highly left skewed distribution, more so for the daily water bill (skewness value of 16) than monthly water bill (skewness value of 4). The variances of both set of values are also affected by extreme values, more so for the daily bills (kurtosis value of 351) than monthly bills (kurtosis value of 29).

Standard deviation for the monthly bill for high income earners (e.g. HHs earning K9000+) is considerably higher than the corresponding figure for lower income earners (e.g. HHs earning less than K1000). We hypothesise that this is due to wealthier individuals being relatively freer to choose from a greater variety of lifestyles (causing volatility in water consumption) whereas time limitations and the physical strain involved, for instance, in carrying water to a dwelling (for less

landlords living with tenants are not paying equal share of the water bills, a situation that could change with the installation of meters.The choices of payment arrangements for water services is also influenced by whether landlords live with tenants. The cross-tabulation exercise shows that landlords living with tenants are most likely to lump water bills in the rentals (75 per cent) than the other options such as sharing water bill with tenants (22 per cent) (p-value = 0.000). Lumping water bills in the rentals could be a strategic move of the landlords that aim at 1) minimising the burden of collecting and managing water bills from multiple tenants 2) shunning away from contributing to the water bills.

93% of non-tap owners pay for water from other sources. It is therefore more likely for them to pay for water once improved facilities are extended to their plots. HHs spend K2.5 (daily) and K30 (monthly) on water with high deviations from the mean.

4.3 Paying for water (for all sources excl. those with taps on the plot)


wealthy individuals) would place an upper bound on the level of water consumption by the relatively poor.

A quick regression analysis suggests a positive linear relationship between the amount spent on water and HHs income, and the relationship is significant with the overall p-value = 0.000 (i.e. the relationship is highly unlikely to have occurred by chance). However, the adjusted R2 was 0.0291, implying that only 2.9 per cent of the variations in the data can be explained by HHs income (other variables are also important in determining the level of spending on water).

HHs lacking tap water and pay for other water sources are most likely to be male, living in large HHs, less educated and low income, employed and engaging in business activities. Whereas 6 per cent of primary educated HHs lacking tap water are NOT paying for water, the proportion rises to 11 per cent for tertiary level educated HHs. In other words, the likelihood of paying for water is much higher for low income HHs relative to high income HHs. A qualitative survey could probably disclose reasons behind such differences.

HHs who do not pay for water are most likely to be in the rentals category (11 per cent) and those who do not receive income (14 per cent) than HHs in the rest of the other sources of income (employment and business). The habit of rental earners of not paying for water might be explained by their tendency to lump water bills with rentals (35 per cent of landlords tend to lump water bills with rentals). Figure 22 illustrates the common water payment arrangements for shared facilities. The leading practice is sharing the bill among HHs every month (52 per cent of HHs). Lumping the water bills with rentals is second as practiced by 35 per cent of HHs. ‘Other’ means of payment arrangements make up 10 per cent of the HHs (such means refer to currently not paying, still discussing payment arrangement etc.)

Table 6: Water bill (in Kwacha)

Water bill per day

Water bill per month

10

58

10

58

2.5

30

3124

4566

(0,250)

(0,840)

n Mean Median SD Range


As part of LWSC’s continued efforts to improve service delivery, the company is embarking on a pre-paid metering project for its customers. The census data show that more than 6 out of 10 HHs in Mtendere (63 per cent) are aware of such program (Figure 23). The remaining 4 out of 10 HHs (37 per cent) remain unaware of LWSC intention (equivalent to 7,560 out of 20,221 HHs). HHs awareness of the LWSC intention differs systematically with socio-economic characteristics of HH heads from the sex of the HH heads (p-value = 0.003) to education (p-value = 0.000). For example, HHs that are aware of LWSC roll out plans are most likely to be well educated (68 per cent of tertiary educated HHs) than HHs who have never attended schooling (56 per cent). With regards to differences by the sex of HH heads, the level of awareness is marginally higher for female headed HHs (65 per cent) than 62 per cent for male headed HHs.

4.4 Awareness of LWSC intention to install meters

HHs awareness of LWSC’s resolve to install prepaid meters stands at 63%. The rate of willingness to accept prepaid meters is 81%.

Figure 22: Payment arrangements for shared water connections

Figure 23: Awareness of LWSC intent to install pre-paid meters (% of HHs)


The willingness of HH heads in Mtendere to install prepaid meters stands at 81 per cent, more so for male headed HHs (82 per cent) than female headed HHs (77 per cent) (p-value = 0.000). We hypothesise the following reasons for the observed difference 1) as the larger proportion of female headed HHs depend on neighbours’ tap (see section 4.1); they might be concerned that neighbours will refuse sharing water once the said meters are installed 2) women are earning relatively less than men, and they might be concerned of increasing costs resulting from the installation of the said meters. Willingness to install prepaid meters is also significantly associated with HHs income (p-value = 0.000) but not with education (p-value = 0.077). As ones move towards high-income categories the willingness to install prepaid meter declines. For instance, the proportion of HH heads who are willing to have prepaid meters and are in the high-income category (earning K9000+) stands at 79 percent which is 5 percentage lower than the proportion of HH heads earning less than K1000 (84 per cent). This is a surprising finding, especially in the wake of the observation in the next paragraph that the proportion of HHs constrained by the cost of prepaid meters declines as ones moves from lower to higher income categories.

The leading reason against the installation of prepaid meters (for HHs unwilling to install such meters) is ‘cost’ as cited by 80 per cent of the HH heads (Figure 24). As expected, the proportion of HHs constrained by the cost of prepaid meters (those who mentioned ‘expensive’) declines as ones moves from lower to higher income categories. For example, the proportion of low income earners (earning less than K1000) who are unwilling to have prepaid meters stands at 81 per

Despite overwhelming willingness to install prepaid meters, the wealthier HHs are relatively less willing compared to less wealthy HHs.

Figure 24: Reasons against prepaid meters


Following DANCEE (2002), the Ability to Pay (ATP) for water services is assessed by the following measures:

Measure 1: Using the World Bank’s cut off point of 4 per cent to benchmark the income share of water expenditure. The lower the income share of water expenditure, the higher the ATP.

Measure 2: The income share of food expenditure. An affordability problem is likely to occur if food expenditure exceeds 70 per cent of the HHs disposal income

Measure 3: Residual income share of water expenditure. Residual income is obtained by subtracting food expenditure from HHs total income.

Table 7 displays results from applying the three ATP measures. Using the established benchmarks, the t HHs residing in Mtendere have high ATP for water services. For instance, the income share of water expenditure is only 2 per cent which is far below the World Bank’s benchmark of 4 per cent (measure 1). Even when the HHs income is a residual, the 3 per cent residual income share of water expenditure remains below the World Bank’s benchmark of 4 per cent.

When benchmarked against established standards, the ability of HHs in Mtendere to pay for improved water services is high.

4.5 Ability to pay for water services

cent, declining to 67 per cent for HHs earning K9000+. Irrespective of the income levels the rates of unwillingness because of the cost factor remain high. Thus, any concerns on the costs will need to be addressed prior to the rolling out the meters.

Table 7: Results from computing ATP

Measure 1

Measure 2

Measure 3

4 per cent

70 per cent

4 per cent

2 per cent

34 per cent

3 per cent

High ATP

High ATP

High ATP

ATP factor Benchmarks Results


5.0 Sanitation5.1 Presence of toiletsThe presence of a toilet in the censused properties was nearly universal (97 per cent) (Figure 25). Statistical tests also confirm that HHs with toilets in the plots are most likely to be better educated (p-value = 0.000), clustered in the higher income category (p-value = 0.000), living in large HH size (p-value = 0.021) and are mostly earning from rentals and farming activities (p-value = 0.000). The presence of toilets increases from 96 per cent of HH heads with no education to 99 per cent of HH heads educated at the tertiary level. Likewise, the proportion rises from 91 per cent of HHs earning less than K1000 per month (lowest income category) to 99 of HHs earning above K9000 (highest income category).

Figure 25: Presence of toilet in the properties

Figure 26: HHs lacking toilet facility rely on neighbours’ facilities


5.2 Types of toilets

The 3 per cent of HHs (equivalent to 782 HHs) whose properties lack toilet facilities are mostly using neighbours’ toilets (96 per cent of such HHs) (Figure 26). The use of communal (including toilet facilities in the nearby market) is uncommon (less than 1 per cent of HHs lacking toilets in their properties make use of such facilities). Only one of the censused HHs (0.13 per cent) claimed to be practising open defecation. Open defecation is not readily spoken about in many societies, resulting into understated prevalence of such practices. About 4 per cent of HHs lacking toilets in their properties cited ‘other methods’ which refer to, for example, the use of toilets found in nearby churches.

‘Pit latrine with slab’ is the common toilet facility in Mtendere serving 3 out of every 4 HHs (76 per cent equivalent to 17,400 HHs) (Figure 27). The literature on sanitation indicates that the standard average production of faeces and urine is 0.5 litres per capita per day (Whittington, et al 1993). In the presence of 17,400 HHs using pit latrines, a total of 1305 m3 of human waste are discharged into the pit latrines each month.

The use of pit latrines is relatively more prevalent for tenants (78 per cent) than landlords (71 per cent). Landlords are more into using waterborne or flush toilets (whether inside or outside houses) (13 per cent) than tenants (5 per cent). Landlords have therefore mostly invested in better toilets for themselves than for tenants (see the discussion on such bias investment behavior in Eales and Schaub-Jones, 2005).

Statistical tests confirm that the type of toilets is significantly associated with several socio-economic variables including education, sources of income, income, and HH size (all associations have p-values = 0.000). Whereas 3 per cent of HHs with no education use waterborne and flush toilet (inside the house), the proportion is more than doubled to 17 per cent for HHs with tertiary educated HH heads. Correspondingly, the proportion of HHs using pit latrines declines as ones move towards higher levels of education (Figure 29). Specifically, 81 per cent of HHs whose heads have never been to school are using pit latrine against 60 per cent for tertiary educated HH heads. Both proportions are significantly high implying that the overall use of pit latrines is widespread irrespective of the levels of education.

HHs living in properties lacking toilets predominantly rely on neighbours’ facilities (96% of such HHs).


The association between income and toilet types is a mirror image of the association between education and toilet types. The higher the HHs income the higher the use of waterborne/flush toilet (whether inside or outside the house), the less the use of pit latrines (Figure 28). Whereas more than half of HHs (52 per cent) in the highest income group (earning K9000+) are using waterborne and flush toilet, the proportion collapses to 6 per cent for HHs in the lowest income group (earning less than K1000) and 10 per cent of HHs earning between K1001-K3000. Correspondingly, the proportion of lowest income HHs using pit latrine stands at 87 per cent declining to 39 per cent for HHs in the highest income level. The poverty aspect is therefore mostly likely to be accounting to the likelihood of HHs using improved sanitation facility and this aspect is well covered in the literature (see for instance Mairena, 2008).

It is also more likely to find a HH using waterborne and flush toilet in the sources of income categories of farming (21 per cent) and rentals (18 per cent) than in the categories of employment (14 per cent) and business (12 per cent) (Figure 31). The type of toilets is also varying systematically with HH size, where the owners of waterborne/flush toilets are most likely to be living in larger HH size (Figure 30).

The higher the income and education levels, the higher the use of waterborne/flush toilets, the less the use pit latrines. Despite such differences, pit latrines are widely in use irrespective of HHs socio-economic characteristics.

Figure 27: Types of toilet


Figure 28: The higher the income the higher the use of waterborne/flush toilets (% of HHs)

Figure 29: The higher the education the higher the use of waterborne/flush toilets (% of HHs)

Figure 30: The higher the HH size the higher the use of waterborne/flush toilets (% of HHs)


Type of toilets also varies systematically with other aspects as well. Cross tabulation exercise shows that owners of waterborne/flush toilets are most likely to be connected to a water supply system, and surprisingly to also be unwilling to install prepaid meters (all associations have p-values = 0.000). Specifically, the proportion of HHs connected to a water supply system and use waterborne and flush toilet (inside the house) is 9 percentage points higher (12 per cent) than the proportion of HHs unconnected to any water supply system (3 per cent).

Furthermore, the willingness to install prepaid meters has some influence on the types of toilets being used by HHs in Mtendere (p-value = 0.000). The statistical tests surprisingly found that HHs who are unwilling to install prepaid meters (17 per cent) are most likely to install waterborne and flush toilets than HHs willing to connect to such meters (12 per cent). Concerns on pre-paid meters could therefore derail potential progress on HHs investment on waterborne and flush toilets.

Only 39 out of 3,677 toilets (1 per cent) are connected to the sewer system with most of the remaining toilets (94 per cent) flush to the septic tanks (this question was directed to HHs with waterborne and flush toilets, as well as to HHs with pour flush facilities). About 3 per cent of toilets flush to other end points cited by respondents as pit-latrines and ‘somewhere in the ground’.

HHs have concerns on the upcoming prepaid meters which in turn risks the potential increase in the use of waterborne and flush toilets. This aspect calls for sanitation campaigns to address whatever concerns HHs have on such meters.

Figure 31: Relationship between the use of water-borne/flush toilets and income sources (% of HHs)


The overall rate of being unsatisfied or very unsatisfied is slightly higher (48 per cent) than that of being satisfied or very satisfied (45 per cent). However, HHs’ responses should be interpreted with caution because expressing dissatisfaction may involve a loss of face leading to overstating the level of satisfaction (Whittington, D. et al, 1993). In other words, the ‘un-satisfaction’ levels might be higher than the stated 48 per cent.

Table 8 disaggregates the satisfaction levels by toilet types. About 73 per cent of owners of waterborne/flush toilets are either very satisfied or satisfied with their defecation place (a highest rate than the rest of the other defection places). On the other hand, 30 per cent of owners of the inferior toilet facilities are unsatisfied or very unsatisfied with their defecation place (reaching 54 per cent for pit latrine). Thus, the level of satisfaction rises with the quality of the facilities.

Cross tabulations show that female HH heads tend to have stronger feelings than male HH heads. Amongst pit latrine owners, a relatively larger proportion of female HH heads (55 per cent) are either unsatisfied or very unsatisfied with the current facility against 53 per cent of male HH heads.

The level of satisfaction varies systematically with HH size, where the higher the size of HHs the higher the level of satisfaction (p-value = 0.001). For instance, the proportion of small sized HHs (with less than 3 members) who are either ‘satisfied’ or ‘very satisfied’ with the relatively inferior toilet facilities stand at 36 per cent, increasing to 40 per cent for HHs with 9+ members. The observed positive relationship between HH size and satisfaction is explained by the fact that large HHs are most likely to be using improved toilet facilities (section 5.2).

Satisfaction with toilet facilities also correlates significantly with whether HHs are sharing such facilities (p-value = 0.000). More than half (54 per cent) of HHs sharing toilets are either ‘unsatisfied’ or ‘very unsatisfied’ against 45 per cent of HHs who do not share toilets.Bad smell/flies and lack of cleanness were the leading reasons for being unsatisfied with the exiting toilet facilities. The two reasons were

The level of being unsatisfied with the existing toilets is relatively higher (48%) than the rate of satisfaction (44%).

5.3 Satisfaction with the toilet facilities

Table 8: Satisfaction levels and types of toilet (% of HHs)

Very satisfied

Satisfied

Moderately satisfied

Unsatisfied

Very unsatisfied

Total

5

53

11

30

2

100

9

47

9

32

4

100

4

41

8

40

8

100

14

59

11

16

1

100

2

37

8

45

9

100

Overall Waterborne/flush toilets

Pour flush VIP andEcosan

Pit latrine


5.4 Preferences for toilet types

Majority of landlords prefer type III (88 per cent) (see pictures of the toilet types in annex 21), more so for landlords not living with tenants (92 per cent) than landlords living with tenants (86 per cent) (p-value = 0.000). Higher preferences for type III is an indication of high level of awareness of the existing improved sanitation technology in the market.

cited by 20 and 26 per cent of the censused subjects respectively (Figure 32). ‘Safety’ concerns (for example, toilet structures being unsafe for children especially at night) and ‘sharing’ followed next each with 17 per cent of the respondent HH heads.

Lack of sewer facility as a reason for being unsatisfied with the existing facilities was only raised by 26 out of 16120 HHs (0.2 per cent). The low rate does not necessarily imply sewer system is not among the development challenges but rather it could be an outcome of the HHs in Mtendere being unfamiliar with the sewer technology. Lack of water or erratic supply was mentioned by only 15 out of the 16120 HHs (0.1 per cent). With 76 per cent of HHs accustomed to pit latrines and the use of flush toilets marginalised to only 14 per cent of HHs, it is not surprising that lack of water is lowly ranked as a reason for being unsatisfied with existing toilet facilities. There are health benefits of addressing, in parallel, the lack of or erratic water supply and sanitation deficiencies. Literature give evidence that a combined improvement in water and sanitation facilities have the potential to reduce diarrheal diseases by more than 30 per cent (Fewtrell, L.; et al. 2005; Waddington and Snilstveit, 2009).

Figure 32: Reasons for being unsatisfied with the existing toilet facilities


Most landlords prefer toilet type III (88%). The average willingness to spend is 11 per cent of the HHs annual income and more than 10 times the national absolute poverty line.

The revealed preferences on toilet types are varying systematically with education level of the landlords (p-value = 0.001), sources of income (p-value = 0.000), income (p-value = 0.000) and HH size (p-value = 0.001). Because of its value in terms of prestige or social status, type III is highly preferred the higher the education level, whereas type I and II are mostly preferred by less educated landlords. As a way of an example, where 92 per cent of landlords educated at the tertiary level prefer type III, the proportion declines to 84 per cent for landlords who have never attended school. Moreover, the higher the income the higher the interest in type III. As 96 per cent of landlords in the highest income category (earning K9000+) are preferring type III, the proportion declines to 82 per cent for landlords in the lowest income category (earning less than K1000). The interest in type III also increases with HH size (annex 8). The habit of paying for water seems to have an influence on preferences on toilet type and this relationship is statistically significant (p-value = 0.000). For instance, the proportion of landlords who are paying for water and prefer type III is 11 percentage points higher than the proportion of landlords who are not spending money on water (p-value = 0.000).

Using the median values, landlords are willing to spend K3000 (Table 9) to construct their preferred facility, more so for male landlords (K3000) than female landlords (K2500). The average willingness to spend is 11 per cent the HHs annual income and more than 10 times the national absolute poverty line. Huge variations prevail in the proposed amounts coefficient of variation above 1 and a wide range of values from a minimum of K100 to a maximum value of K50000. The histogram of the mean values is largely skewed to the left (skewness value of 3) and the variance is largely influences by extreme values (kurtosis value of 23)

Table 9 also reveals that the willingness to pay is higher the higher the quality of toilets. It is an indication of public awareness of quality differences across the three types. Whereas on average HHs preferring type I are willing to spend K2000, it is K3000 for HHs aspiring to acquire type III. Censused subjects estimated an average period of 3 months needed to construct a waterborne toilet.

Table 9: Willingness to spend on the preferred toilet facility (in Kwacha)

Type I

Type II

Type III

Overall

2000

2000

3000

3000

4

3

4

3

42

184

1682

1912

3550

3654

4125

4072

(500, 30000)

(100, 25000)

(100, 50000)

(100, 50000)

18

11

24

23

5213

4005

4073

4094

Obs. Mean Median Skewness RangeKurtosis Kurtosis


5.5 Sharing of toilet facilities

The censused instrument also enquired on reasons against the construction of waterborne/flush toilet. About 10 per cent of HHs mentioned that they cannot afford water bills, with 3 and 4 per cent indicating lack of awareness of the benefits of a waterborne toilets and the need to continue using the existing facility (pit latrines) respectively. Differences of stated reasons by the sex of HH heads are statistically insignificant (p-value = 0.069).

It is not uncommon for HHs in Mtendere to share toilet facilities. This practice was confirmed by 81 per cent of the censused subjects. As 97 per cent of HHs have toilets in their properties (section 5.1), the observed 81 per cent of HHs sharing toilet facilities implies that structures in Mtendere are occupied by more than one HH. The average number of HHs sharing a single toilet is 4 (a standard deviation of 2) ranging from 0 (no sharing) to a maximum of 20 HHs. The challenge with the shared facilities is that they do not necessarily induce shared responsibility or responses to aspects such as cleanness. This is the primary reason behind the consideration of shared facilities as ‘unimproved’ type of toilets.

Sharing of toilet facilities is skewed towards less educated and less wealthy HHs (p-value = 0.000), living in small HHs (p-value = 0.000), being employed, engaging in business activities and earning from rentals (p-value = 0.001). It is more likely to find a HH sharing a toilet in the lower education category, for instance primary education (84 per cent) than in the higher tertiary level category (68 per cent). HHs sharing toilet facilities are also clustered in the lower income categories. The proportion of HHs sharing toilet facilities in the highest income category (earning above K9000) stands at 41 per cent, doubling to 83 per cent for HHs earning less than K1000. In terms of source of income, it is more likely to find a HH sharing toilet facility in the employment category (82 per cent), business (80 per cent), rentals (81 per cent) than in the farming category (70 per cent).Further cross-tabulations between toilet sharing and other aspects of water and sanitation offer additional insights. HHs sharing toilets are most likely to be tenants, unconnected to water supply system and unsatisfied with the existing toilet facility (p-value = 0.000). It is more likely to find a HH sharing toilet and have no water connection (88 per cent) than HHs with water connections (80 per cent). Moreover, the proportion of tenants sharing toilet facilities (90 per cent) is nearly twice that of landlords (57 per cent).

HHs sharing toilets are most likely tenants, unconnected to water supply systems, and unsatisfied with the existing toilet facility.


The quality of the toilet facilities was assessed by the types of construction materials (roofing, flooring and wall materials). On average 4 out of every 6 toilets in Mtendere (67 per cent) are roofed by better-quality materials (asbestos, iron sheets and tiles). The remaining 33 per cent used inferior materials (plastics, a mix of sacks, scrap metals, timber, cloth and situations where no roof exist).Concrete brinks are the dominant wall materials used to construct 90 per cent of the toilets in Mtendere. The remaining 10 per cent of the toilets used inferior materials including: pan brick, mud brick, burnt brick, poles, grass/straw, iron sheets, hardboard, sacks and plastics. About 91 per cent of toilets in Mtendere have been floored by concrete floor. The remaining 9 per cent of toilets are made of inferior flooring materials including mud, wood, blocks, metal sheet, iron bars, scrap metal, stones, mud, and sacks. Overall, toilets in Mtendere are constructed by better materials for walling, flooring and roofing. Toilet materials are therefore of less concern when compared to the type oftoilets facilities, the sharing aspects and lack of sewer system.

Toilets facilities are relatively better in an environment where landlords live in the same plot with tenants than when landlords are detached from tenants (p-value = 0.000). Specifically, the proportion of landlords living with tenants and sharing toilets with concrete floor, roofed by iron sheets and walled by concrete bricks is 6, 1 and 5 percentage points higher than the proportion of landlords not living with tenants.

Collection by Lusaka City Council (LCC)/private company/Community Based Enterprise (CBE) is the leading practice as cited by 58 per cent of HHs. Burying of waste follows next at a distance with 20 per cent. The third popular practice is by garbage bay/council bins as cited by 11 per cent of the censused HHs (Figure 33).HHs that are disposing solid waste through LCC/private company/CBE are most likely to the educated (p-value = 0.000) and in the higher income (p-value = 0.000). The rate of disposing solid waste through collection by LCC/private company/CBE stands at 67 per cent of HHs whose head has been educated at the tertiary level declining to 52 per cent for primary school educated HH heads. Similarly, about 70 per cent of HHs in the highest income category (earning K9000+) are disposing waste through collection by LCC/private company/CBE against 43 per cent of HHs earning less than K1000. The latter group relies on burying and burning for solid waste disposal.

5.6 Quality of the toilet facilities

5.7 Disposal of solid waste

Most materials for roofing, walling and flooring of toilets are relatively good. The key challenge remaining is the ‘types’ of toilets, the ‘sharing’ and the ‘absence’ of a sewer system.


Figure 33: Methods of disposing solid waste

Figure 34: Methods of disposing sanitary pads

Besides serving as toilets, the pit latrines are commonly used as dumping grounds for sanitary pads. That practice is upheld by 39 per cent of HHs, followed by ‘burning’ (35 per cent) (Figure 34). Increased decommissioning of pit latrine could be achieved if HHs in Mtendere are also provided with alternative means of disposing sanitary pads.About 11 per cent of HHs are dumping sanitary pads in drainage ditches/manholes/abandoned wells/streams in the backyard, and in no specific place. Only 10 per cent of HHs are disposing sanitary pads through LCC/private company/CBE. It is worth mentioning that survey subjects tend to overestimate the use of formal services and as such the use of LLC might be lower than being presented here.

5.8 Disposal of sanitary pads

Dumping in the pit latrines is the leading practice of disposing sanitary pads. Thus, decommissioning of pit latrines could be accelerated when HHs are offered alternative means of disposing sanitary pads.

Statistical tests reveal that it is more likely for tenants (11 per cent) to dispose sanitary pads through the collection by LCC/private company/CBE than landlords (9 per cent) (p-value = 0.000). The difference is marginal, but still a surprising outcome given that all along landlords (see for example section 5.2) have been more likely to engage in better sanitation practices than tenants. In terms of socio-economic variables, the use of LCC/private company/CBE for disposing sanitary pads is skewed towards better educated and wealthier HHs. That is, higher the education and income levels the higher the use of services from LCC/private company/CBE to dispose sanitary pads (p-value = 0.000).


5.9 Disposal of diapers

5.10 Sanitation ladder

The common practices of disposing diapers are similar to those being applied to sanitary pads. The collection by LCC/private company/CBE leads the way with 35 per cent of HHs (2426 out of 6859 HHs), followed by ‘burning’ as cited by 33 per cent of HH heads (Figure 35). The use of pit latrine as a disposal ground for diapers was mentioned by 16 per cent of the censused subjects (1074 out of 6859 HHs).

Similar to the case with sanitary pads, it is more likely for tenants (39 per cent) to use the services of LCC/private company/CBE to dispose diapers than landlords (31 per cent) (p-value = 0.000). The use of LCC/private company/CBE for disposing diapers is significantly associated with education and income, the higher the education and income levels the higher the use of services from LCC/private company/CBE (p-value = 0.000) (annex 9-10).

The ‘sanitation ladder’ is a renowned tool for analysing sanitation coverage at the HH level (WHO and UNICEF, 2008). The ‘ladder’ considers sanitation coverage as a three-step ladder: Step 1: HHs practising open defecation; Step 2: HHs using unimproved sanitation facilities (including shared facility15); and: Step 3: HHs using improved sanitation facilities. Unlike the preceding sections which focused on the distribution of the ‘types’ of toilets, this tool shows ways in which costs and health benefits varies depending on whether HHs are into improved or unimproved sanitation facilities. The ladder as presented by Figure 36 shows that only 19 per cent of the Mtendere are at the highest step of the ‘ladder’ (use of improved facilities). Because of sharing toilet facilities, the remaining 81 per cent are clustered in step 2 of the ladder (unimproved facilities)16.

It is worth mentioning that, the need to invest in improved sanitation is not only driven by majority of population in Mtendere using unimproved facilities but also from the risks of contamination of water sources due to close proximity between boreholes and pit latrines.

Disposing diapers using pit latrines is less prevalent. Only 16% of HHs dump diapers in the pits against 35% in the case of sanitary pads.

Because of sharing toilets, only 19% of the HHs in Mtendere are in the highest stage of the sanitation ladder.

Figure 35: Methods of disposing diapers


Figure 36: The sanitation ladder


6.0 Rent and Improvement

The census instrument requested both landlords and tenants to estimate the average monthly rent they collect/pay (for both residential and commercial structures). Landlords are collecting a monthly average of K1020 (median value of K900) for residential structures against K534 (median value of K500) that tenants claim to be paying each month. The median value of the amounts being collected by landlords is 4 times the national poverty line. It is likely that landlords are renting out more than one property and that could explain the differences between the amount they collect and the amount stated by tenants.

Similar differences were observed on the commercial structures, where landlords are collecting nearly twice (K773 and a median value of K600) the amount stated by tenants (K472 and a median value of K450) (Figure 37). The landlords’ estimate is 3 times the national poverty line. The observed high standard deviations in both cases indicate huge variations on rentals (whether being collected or paid). For instance, tenants’ estimates show that the rental fee being fetched from the largest payer (K3000) is more than 4 times the lowest rent (K70).

Histograms of the average rental payments and collections show that the distributions are highly skewed to the left (Figure 38-41). The skewness values of both residential and commercial rental payments is 4 with the variances of rental values for both types of properties being affected by extreme values, more so for residential rent payments (kurtosis value of 37) than for commercial properties (kurtosis value of 26). The rental collection values (by landlords) are also skewed

6.1 Rentals

On average landlords collect K900 and K450 as monthly rents for residential and commercial properties respectively. Both figures are far higher than the national poverty line. Rental income is poverty reducing.

Figure 37: Differences in rental estimates between tenants and landlords (in Kwacha)


to the left but less so than the rental payments (by tenants) with skewness values of 1 and 2 for residential and commercial properties respectively. Unlike the rental payment values (by landlords), the extreme values of rent collection (by landlords) are less affecting the variances (kurtosis values of 5 and 6 for residential and commercial structures respectively).

We also noticed high standard deviations for residential rentals at all levels of income (annex 11). Such deviations suggest 1) large differences in housing attributes and qualities in Mtendere 2) presence of small scale landlords renting one or two rooms, and the large-scale landlords offering for instance uni-family standalone structure.

Rents are skewed toward low values. The high standard deviations irrespective of income levels point to possible huge differences in housing attributes and qualities in Mtendere.

Figure 38: Histogram of rent payments by tenants (residential properties)

Figure 39: Histogram of rent payments by tenants (commercial properties)


The common rent payment schedule is monthly (99% of tenants). The schedule easy the financial burden to low income tenants, but limits the ability of landlords to make significant improvement to the Monthly payment is the dominant rent payment practise as confirmed by 99 per cent of the censused tenants. This model is not uncommon in similar settings in other countries (see for instance, the rental market in informal settlements in Johannesburg, South Africa as assessed by Gunter, 2014). While such payment schedule does not drain financial resources of the lowincome communities, it limits the financial capacity of landlords to undertake significant investment to their properties. Moreover, the monthly rental payment diminishes security of tenure making it more reliant on whether tenants are paying monthly rentals rather than being guided by lease agreement s (Gunter, 2014).

It is more likely to find aHH willing to installprepaid meters amongthose paying low rentthan HHs paying highrent. As their incomeshare of waterexpenditure is higher,the low rental HHs (lowincome earners) mightbe perceiving that theyare unfairly payingmore than high incomeearners.

Figure 41: Histogram of monthly rent beingcollected by landlords (commercial properties)

Figure 40: Histogram of monthly rentbeing collected by landlords (residential

properties)


Tenants were also asked “what improvements would you like to see on the plot?” The leading preference was ‘connection to sanitation services’ as stated by 40 per cent of the respondents, followed by 34 per cent who preferred ‘connection to water services’. The two preferences make a combined total of 74 per cent of the respondents (Figure 42). There is however possible response bias to this question. Cross-tabulation exercise reveals that the majority of HHs who are aware of MCA-Zambia’s primary activities did highly preferred connection to water and sanitation (41 per cent and 33 per cent respectively) and that association is statistically significant (p-value = 0.001). On the other side, it is also more likely for tenants to reveal the two preferences (water and sanitation) because the other pre-coded responses of the same questions (for example, extension) are more likely to be preferred by landlords rather than tenants.

Tenants paying relatively high rent are most likely to be male headed HHs, better educated and earning relatively higher monthly income (p-value = 0.000 in each case). As a way of an example, the proportion of HH heads educated at the tertiary level and incurring monthly rent above K1000 is 17 per cent which is more than four times the rate for the secondary educated HH heads (4 per cent) and nearly ten times that of primary school educated HH heads (2 per cent). High incomeearners are most likely residing in relatively better structures fetching high rental amounts. More than half of HHs (52 per cent) in the highest income category (earning K9000+) are in the highest rental categoryagainst 1 per cent of HHs earning between K1001- K3000 per month. There are also differences in the rental amount by the sex of HH heads. On average male tenants pay K20 monthly (K554) more than female tenants (K534). We hypothesise that the difference is due to relatively higher income on the side of male headed HHs than female headed HHs (section 3.5). Male headed HHs are therefore more likely to be staying in better houses (assuming that higher rent implies better houses).

The census data also reveals that it is more likely to find a HH willing to have prepaid meters among HHs paying low rent (low income earners) than HHs paying relatively high rent (high income earners). Specifically, the proportion of HHs eager to install prepaid meters and are in the lowest rental category (low income earners) is 88 per cent against 78 per cent of HHs in the highest rent category (high income earners) (p-value = 0.000). We hypothesise that this difference is due to low-income earners (whose income share of water expenditure is relatively higher-annex 12) perceiving that they are unfairly paying more than high income earners whose income share of water expenditure is relatively low.

40% of HHs prefer improvements on sanitation facilities, which in turn raises the likelihood of a successful campaign to improve sanitation in the area.

6.2 Preference on improvements


Tenants preferring connection to sanitation services are more likely to be less educated, whereas HHs preferring connection to water supply are more likely to be highly educated.

Other preferred improvements such as refurbishment, wall fence, and house completion had a score of less than 5 per cent. Preferences for plot improvement differ by the sex of the HH heads and such differences are statistically significant (p-value = 0.000). Specifically, the proportion of female headed HHs eager to connect to water services is 4 percentage higher (37 per cent) than the male headed HHs (33 per cent). Women’s higher interest on improved water facilities (relative to men) is well documented (see for instance, Mezgebo and Ewnetu, 2014). On the other hand, male HH heads in Mtendere are relatively keener on connecting to sanitation services (41 per cent) than female headed HHs (36 per cent).

Tenants in favour of connection to sanitation services are most likely to be less educated (46 per cent of HHs with no schooling experience) than better educated HHs (e.g. 31 per cent of HHs who have attained tertiary education) (p-value = 0.000). Possible reasons for this difference is that the better educated HH heads (and wealthy HHs) have relatively high access to better toilet facilities (section 5.2) and they will therefore be less keen on changes to the facilities they use. Less wealthy HHs are more into inferior facilities (section 5.2) and they will therefore be more likely to demand better facilities. Unlike the sanitation aspect, the better educated HHs are more likely to prefer connection to water services than relatively less educated. For instance, the proportion of HH heads with primary level education and prefering connection to water services is 30 per cent, rising to 35 per cent for tertiary educated HH heads. Less educated who are most likely to be in the lower income categories, could be more fearful of higher costs of water once connected.

Figure 42: Improvements tenants would like to see


Majority of property owners (that is, landlords) in Mtendere (60 per cent) are living in the same plots with tenants, and such practice is more skewed towards female landlords (67 per cent) than male landlords (56 per cent) (p-value = 0.000). Though we could not establish from the census data, other studies elsewhere found that most landlords living in the same plots with tenants are small-scale rentals (renting rooms rather as opposed to uni-family houses) (Edwards (1982) and such landlords share same socio-economic characteristics with tenants (Gilbert, 1991; 1993).

Landlords are most likely to invest in facilities that they themselves would benefit from than on facilities entirely to the advantages of tenants (Whittington, 1993). As such, tenants living with landlords become incidental beneficiaries of such behaviour of the landlords. As a way of an example, the cross-tabulation exercise reveals that it is more likely for landlords living with tenants to be connected to piped water from LWSC (87 per cent of such landlords, against 71 per cent of landlords not living with tenants), and using tap water as the main source of drinking water (45 per cent of such landlords against 30 per cent of landlords to live far away from tenants). All these differences are statistically significant (p-value = 0.000). Thus, the tendency of landlords living together with tenants emerges as an important factor for improved water facilities in the informal urban settlements.

However, further cross tabulations reveal that the possibilities of improved investments are likely to be taken away by the fact that landlords living with tenants are most likely to be less educated, in the low-income category and depend on rentals for a living (all associations with p-value 0.000) (annex 13-14). For example, whereas 58 per cent of landlords earning less than K1000 (low income earners) are living in the same plots with tenants, the proportion declines to 48 per cent for landlords earning K9000+ (high income earners). Whereas low income would limit the financial capacity to invest, low education levels would likely imply less knowledge on health benefit of improved facilities (see for instance, Augsburg and Rodríguez-Lesmes, 2015). We also observed from section 7.3 that landlords earning from rentals are relatively less likely to invest in improvements.

Landlords living with tenants are more likely to invest in relatively better facilities in the plot than landlords detached from tenants.

Most landlords (60%) live in the same plot with tenants. However, landlords living with tenants are less educated and are mostly clustered in the low-income categories. Such socio-economic characteristics constrain their likelihood of investing in improved amenities.

7.0 Property Development7.1 Landlords living in the same plot with tenants

7.2 Construction of separate WC for each tenantThe rate of willingness of landlords in Mtendere to construct a separate WC for each tenant stands at 54 per cent with the remaining 46 per cent being unwilling to do so (Figure 43). For landlords living with tenants, the rate is higher (55 per cent) than the rate for landlords


living away from tenants (25 per cent) (p-value = 0.000). Living with tenants is therefore emerging as a supporting condition for improved sanitation in low income informal settlements in Mtendere. It is also more likely for landlords living with tenants to improve the structures (50 per cent) than landlords not living with tenants (46 per cent) and this difference is statistically significant (p-value = 0.000).

The census instrument also enquired on whether landlords intend to construct another WC. This was confirmed by 65 per cent of landlords (Figure 44). Landlords that are willing to install a new WC are most likely to be living with tenants (p-value = 0.000), better educated (p-value = 0.006), living in large HHs (p-value = 0.016), and relatively less wealthy (p-value = 0.000) (annex 15 and 16). The latter is a surprising outcome with a probable reason being that most less wealthy landlords live with tenants (section 7.1) and they are therefore more likely to be sharing toilets. Moreover, as revealed in earlier sections, the low-

Figure 43: Willingness to construct separate WC for each tenant

Figure 44: Willingness to construct another WC

It is more likely for landlords living with tenants to construct a new WC, than landlords detached from tenants.


The two leading types of improvements were ‘extension’ and ‘refurbishment’ as cited by 42 per cent and 26 per cent of landlords respectively (Figure 46). ‘Completion of the structures’ followed next with 15 per cent. Only 9 per cent of landlords improved the existing water and sanitation facilities. The ‘extension’ types of improvement consisted of extension of bedrooms (28 per cent) and of bathroom/borehole (25 per cent). The former is an indication of increasing demand for residential facilities in Mtendere, in particular the ‘room rentals’ which is distinct from ‘uni-family house rentals’. As rational investors, landlords in Mtendere prefer to maximise returns by building rooms rather than latrines.

7.3 Property improvements in the past 2 years

income landlords (thus less educated landlords) are more likely to be unsatisfied with the current toilet facilities and thus why they are more likely to invest in ‘another’ WC. Further cross-tabulation highlights higher rate of willingness to construct another WC for relatively large HHs than for the small sized HHs (annex 16). Studies (for example, Jenkins, 1999) found that socio-economic and cost factors are vital but without strong drives for an improved facility (e.g. perception and attitude), HHs would be uninterested to change sanitation practices and facilities. Thus, sanitation sensitisation campaigns need to invest on latent aspects to induce influence on adoption.

The leading reason against constructing another WC was ‘affordability’ (HHs cannot afford to construct) as cited by 49 per cent of landlords (Figure 45). Absence of running water in the plot followed next as cited by 21 per cent of landlords and then by ‘others’ (for example, water logged area) and absence of sewer network which were mentioned by 10 and 17 per cent of landlords respectively. Ten per cent of landlords citing the absence of sewer as an impediment to constructing another WC is equivalent to 528 landlords which should results to 528 new WCs once the new sewer system is commissioned.

Figure 45: Reasons against constructing another WC

Water and sanitation were not among the leading improvements to have been carried out in the past 2 years. Landlords have mostly invested on house extensions indicating an increasing demand for residential facilities in Mtendere (renting of rooms).


In relatively terms landlords earning from rentals do not reinvest much of such income in plot improvement.

There are minor differences on the types of improvements to have been carried out by landlords living with tenants and those detached from tenants (Figure 47 and 48). For both cases, house extension was the leading type of improvement (42 per cent of landlords living in the same plot with tenants versus 41 per cent of landlords not living with tenants). Refurbishment and completion follow next (27 per cent vs 23 per cent) and (13 per cent vs 18 per cent) respectively. Only 10 and 9 per cent of landlords living with tenants and those detached from tenants respectively invested in improving water and sanitation facilities.

Figure 47: Improvements carried out by landlords living with tenants

Figure 46: Types of improvement carried out in the past 2 years


Figure 48: Improvements carried out by landlords not living with tenants

Figure 49: Sources of funding for the improvements

Most of the funding for improvements came from own resources (88 per cent) with the remaining funding from either family/friends or other sources (Figure 49). There are statistically significant differences on the sources of funding for improvement by the sex of HH heads (p-value = 0.000). Specifically, it is more likely for female landlords to improve their plots using funds from family and friends (14 per cent) than male landlords (7 per cent). Male landlords are relatively more reliant on own funds to finance improvements (91 per cent) than female landlords (84 per cent).

Sources of funding is also influenced by education, sources of income, and income levels, all demonstrating statistical significant association (p-value = 0.000). Better educated and wealthier landlords are more likely to self-finance plot improvement than low income and less educated landlords. The latter are mostly depending on friends and family (Figure 50). It is also more likely for landlords engaging in employment and business (92 per cent and 90 per cent respectively) to use own resources to finance plot improvement than for instance, landlords earning from rentals (84 per cent) and farming (88 per cent). These statistics show that rentals are not always reinvested in plot improvements.

Most funding for improvements were sourced from own resources. It is more likely for men to use own resources while it is more likely for women to use funds from family and friends.


The median value of improvement was K6000 equivalent to 22 per cent of the HHs annual income (Table 10). The values are volatility as evidenced by the overall standard deviation of K18337 which is much higher than the mean (each value of improvement sits on an average distance of K18337 from the mean). In such cases, the median values give better representation of the situations.

A histogram of average amount invested in improvements show that the distribution is strongly skewed to the left with a skewness value of 4 and a kurtosis value of 20 (Figure 51) confirming the presence of some extreme values affecting the variances of the distribution. A quick regression analysis was carried out suggesting a positive linear relationship between the amount spent on improvement and income of the landlords, and the relationship is significant (p-value = 0.000, that is the relationship is unlikely to have occurred by chance). However, the adjusted R2 was 0.0448, implying that only 4.5 per cent of the variations in the amount spent on improvement can be explained by income of the landlords (other variables are therefore important in determining the level of spending on improvements).

Systematic reviews of studies in water and sanitation consider cost as an important factor for improved facilities regardless of the technology (see for instance Hulland, 2015). Table 11 shows that the mean cost incurred to construct a toilet (which in some cases includes a bathroom) was K3611 (a median value of K3000). Male landlords have spent slightly more (K3749) than female landlords (K3378). The average amount spent on constructing a toilet is only 13 per cent of the HHs annual income. Figure 52 shows that the distributions of cost values is strongly skewed to the relatively low values (to the left) with a skewness value of 4 and a high kurtosis value of 20 (extreme values affecting the variance of the distribution).

Figure 50: Own sources of funding and education (% of HHs)


Erratic water supply is the leading reason against decommissioning. Such concern needs to be addressed to achieve high rates of latrine decommissioning.

Table 11 further disaggregates the costs for each toilet type. As expected the cost of pit latrine is lower than that of superior facilities (K3145 versus the cost of pour flush at K4183). However, the cost values do not vary much across toilet types which is contrary to the well-known differences in the quality of toilet types. An ideal approach to understand the costs of constructing a toilet is to make estimates from the supply side (collecting cost data from suppliers) rather than estimates from the demand side (self-reporting cost values by HHs).

Figure 51: Histogram of values of improvements

Table 10: Value of the improvements

Mean

Median

Std. Dev.

Range

n

12, 354

6000

18, 337

(100, 150000)

2139

12,403

6000

19,079

(100, 50000)

716

12, 329

6000

17, 959

(100, 150000)

1423

Overall RangeMale

Table 11: Costs incurred to construct the existing toilets

Waterborne/flush toilets

Pour flush

VIP and Ecosan

Pit latrine

Overall

3000

3000

4000

2500

3000

3302

3219

2433

2446

3,284

91

51

16

142

311

4082

4183

4144

3145

3612

15000

15000

9000

15000

15000

150

150

600

150

150

Obs. Mean Median SD MaxMin


An overwhelming 81 per cent of landlords are willing to decommission pit latrines once water borne toilets are constructed and connecting to the new sewer network (Figure 53). However, such willingness does not vary systematically with the sex of HH heads, income and education levels of the landlords (any difference based on such socio-economic characteristics is possibly by chance).

HHs that are willing to decommission pit latrines are most likely to install prepaid meters (p-value = 0.000). Specifically, the proportion of HHs that are willing to decommission pit latrine (82 per cent) and install a prepaid meter is higher than the proportion of HHs that are not willing to install a prepaid meter (75 per cent). This is an encouraging outcome as decommission will in turn increase the use of improved facilities such as waterborne/flush toilets whose efficiency depends on availability of water.

7.4 Decommissioning of pit latrines

Figure 53: Willingness to decommission pit latrines

Figure 52: Histogram of costs of constructing a toilet


The leading reason against decommissioning of the pit latrines is erratic water supply from the current sources and this reason was cited by 60 per cent of landlords (equivalent to 925 landlords) (Figure 54). The water supply system is therefore falling short of the present and future requirements to support potential increase in the use of improved sanitation facilities. The second ranked reason is the concern on possible overcrowding of the WCs once pit latrines are decommissioned (19 per cent of landlords). Other reasons such as the need to retain pit latrines as back up and the use of the latrines as disposal grounds for sanitary pads and diapers were mentioned by less than 10 per cent of the landlords.

The stated reasons are also significantly associated with education (p-value = 0.020) (the higher the education level the higher the proportion of landlords concerned with erratic water supply). Landlords who have never been to school and have cited erratic water supply as a reason against decommissioning is 45 per cent, rising to 71 per cent for landlords educated at the tertiary level. Possible explanation of such differences is that the better educated HHs have more experience with water connection (section 4.2) than less educated landlords and the latter are therefore underestimating the challenge of unreliability of water supply.

Figure 54: Reasons against decommissioning pit latrines

It is also more likely for better educated landlords (5 per cent of the secondary and tertiary educated landlords) to retain their pit latrines for throwing sanitary wastes, pads and diapers than the case with less educated landlords (1 per cent of primary educated landlords). We hypothesised the difference is due to better educated landlords being well aware of the difficulties of using the improved toilet facilities for disposing waste than the less educated landlords.

Better educated landlords are more likely than less educated landlords to retain pit latrines for throwing sanitary wastes, pads and diapers.


About 9 out of 10 plots (89 per cent) in Mtendere are utilised for residential use with the remaining 11 per cent commercial outlets (shops, saloon, barbershops, lodge, restaurants etc.) (Figure 55).

7.5 Use of plots

Figure 55: Use of plots

The use of plots is significantly associated with the income of landlords (p-value 0.021). As landlords move from lower income to higher income levels, the less they invest in residential assets and the more they are devoted to commercial structures (annex 17). It is also more likely for improvements to be made on plots being used for commercial purposes (54 per cent) than on residential plots (48 per cent) and this difference is statistically significant (p-value = 0.001). In other words, the priorities of landlords in improving facilities are skewed towards commercial rather than residential structures. This argument is further supported by the higher proportion of landlords who are willing to invest in another WC in commercial plots (71 per cent) than the proportion of landlords interested to invest in plots being used for residential purposes (64 per cent). Commercial properties generate higher income/rentals to the landlords than residential houses (see section 6.1), thus drawing more attention of the owners.

It is also not uncommon for landlords to convert some of the plots into combined use of commercial and residential purposes. Such multi-purpose plots account for 11 per cent of all plots in Mtendere (equivalent to 680 plots). The leading use on the commercial side is shops/ntemba occupying 3 out of 4 such plots (74 per cent). The rest of the other commercial uses make less than 10 per cent (saloon/

89% of plots in Mtendere are residential with the remaining 11 hosting commercial activities. It is more likely for improvements to be made on the commercial plots than on the residential plots.

The less educated landlords are also more concerned than the better educated landlords that, once installed, the WCs will be overcrowded. This difference could be due to the fact that, plots of less educated landlords are already congested, thus raising fear on possible overcrowding of WCs.


barbershops, , school/day care/tuition centres, and shebeen/bar/restaurant and lodge).

Plots in Mtendere were acquired through direct purchase from individuals, donation from council/Government, inheritance and other means (Figure 56). Though not specified in the census dataset, it is possible than one of the ‘other’ means is invasion of vacant land which has frequently been reported in urban areas of many developing countries. Direct purchase from individuals led the way in Mtendere with 61 per cent of the plots, followed at a distance by inheritance (19 per cent) and plots that were obtained from council/Government (18 per cent). The latter evidences the political dimension of access to urban land.

The means to which plots in Mtendere were acquired is significantly associated with improvements carried out in such plots (p-value = 0.000). For instance, it is more likely for a plot acquired from individuals to have undergone improvement in the past two year (51 per cent) than plots acquired through the other means, for instance, inheritance (48 per cent).

Landlords are more likely to invest in improvements including construction of another WC in commercial than in residential plots. Hence, residential plots risk lag behind when it comes to investment in water and sanitation facilities.

Figure 56: Methods used to acquire the existing plots

About 97 per cent of landlords in Mtendere are willing to connect to the new sewer system (Figure 57).17 HHs that are willing to connect to the sewer system are most likely to be better educated (p-value = 0.000), high-income earners (p-value = 0.000), and clustered in the employment category of sources of income (p-value = 0.029). Besides high rates of willingness to connect to the sewer system across all education and income levels, the higher the education and income levels, the higher the willingness to connect. The proportion of landlords who have never been to school and are willing to connect stands at 92 per cent rising to 97 per cent for landlords educated at the tertiary level. On the income side, the proportion of landlords at the lowest income level (earning less than K1000) and are willing to connect to the sewer system is 94 per cent rising to 96 per cent for landlords earning between K7001 and K9000.

7.6 Willingness to connect to the sewer system


Figure 57: Willingness to connect to the sewer network

Further cross-tabulations also reveals that the practice of paying for water has some influence on the willingness to connect to the sewer system (paying for water as a proxy HHs familiarity with the utility sector). Specifically, the proportion of landlords who are paying for water and are willing to connect to the sewer system is 98 per cent against 93 per cent for landlords who are not paying for water (p-value = 0.000).

It is more likely for landlords without waterborne/flush toilets to be willing to connect (97 per cent) than landlords using such toilets facilities (94 per cent). Therefore, the presence of inferior toilet facilities advances the interests to connect to the sewer network. The data also demonstrate that the more unsatisfied the landlords are with the existing toilet facilities, the more they are willing to connect to the sewer system. Specifically, the proportion of landlords who are very satisfied with existing toilet facilities and are willing to connect to the sewer system stands at 92 per cent, rising to 98 per cent for HHs who are very unsatisfied with the existing toilet facilities (p-value = 0.000). Thus, the ‘unsatisfied’ are the potential initial target to increase the rate of connections to the sewer.

7.7 Regression analysis

This section applies a regression analysis to investigate the willingness of the HHs in Mtendere to connect to the sewer system. The regression analysis simply identifies socio-economic characteristics and other factors affecting the willingness of HHs in Mtendere to connect to the sewer system.

Given the dichotomous nature of the dependent variable (willingness to connect), the analysis adopted a logit regression model. Let us

97% of landlords are willing to connect to the sewer system. HHs that are ‘unsatisfied’ with the existing toilet facilities have high rates of willingness to connect and are the potential initial targets to increase the rate of sewer connections.


Where the subscript i represents HHs, and β is a vector of parameters and µ is a normally distributed error term with mean 0 and variance 1. yi is not observable either, and we assume that it is distributed normally with the same mean and variance. There is a critical threshold that is 0, so that if yi* exceeds, then a HH will be willing to connect to the sewer system. In other words, the approach assumes that underlying the dichotomous variable is a continuous variable that determines the category of the observed dichotomous variable (Bollen, 2002). It is therefore possible to estimate the parameters of interest, β, to obtain information on yi*.

Where Z is a standard normal variable, and

is the cumulative distribution function of a

normal variable. Model (1) will be estimated by Maximum Likelihood. In discussing regression results reference is made to the effect that a characteristic has on the probability that a HH is willingness to connect (this is always relative to a base or reference category for each variable).

The dependent variable is dichotomous, that is, whether a HH is willing (=1) or unwilling to connect (=0). Independent variables are socio-economic characteristic of HHs including income, sex of HH heads, education levels, marital status, HH size, age of HH heads, roofing materials of the toilet, and whether HHs in Mtendere are willing to construct separate toilets for each tenant, pay for water and whether they are satisfied with the existing toilets. Paying for water is framed as a proxy for the HHs’ willingness to pay for a utility service which is hypothesised to positively correlate with the willingness to connect to a utility such as a sewer facility. Similar to Johnson and Nino-Zarazua (2011), the roofing materials of the toilets in Mtendere is adopted as a proxy for the poverty status. As connection to a sewer might involve some form of a cost to HHs, then the hypothesis is that the higher the use of modern roofs for the toilets (non-poor) the higher the willingness to connect to the sewer.

assume that willingness of the HHs to connect to the sewer system depends on a latent continuous (unobserved) variable y* which is determined by a set of exogenous variables, included in vector x’ so that:


The poverty rates computed in sub-section 3.6 were not included as independent variable as it will lead to the problem of multicollinearity (the correlation between poverty rates and income is 0.52). The use of both poverty rates and income would have created not only a simple multicollinearity concern but also a ‘structure’ multicollinearity – as the poverty rates were created out of the income data.

7.8 Results from the regression analysisTable 12 presents the regression results (the odds ratios) of the socio-economic correlates of the willingness of HHs in Mtendere to connect to the sewer system. An alternative hypothesis (a coefficient is statistically significant different from 0) is accepted for p <0.05 and p < 0.10. The overall model is found to be statistically significant (overall p-value = 0.000). That is, the relationship between the dependent variable and the independent variables is highly unlikely to have occurred by chance. However, only 13 per cent of the observed variations in the willingness to connect are explained by the selected independent variables and this suggests that other variables are also important in determining the rate of willingness to connect. For instance, Santos, et al (2011) found that attitudinal related variables (attitudes towards sanitation e.g. health protection, comfort, increased value and house modernisation, social image, prestige etc.) have higher explanatory power on the sanitation choice than HHs or individuals’ socio-economic and demographic characteristics and costs.

Table 12: Logistic regression results of the willingness to connect to the sewer system

Sex of heads

Male

Female

Age of HH heads

Below 16 years

17 - 24 years

25 - 34 years

35 - 44 years

45 - 54 years

Above 54 years

Marital status

Never married

Married

Separated

Divorced

Widowed

Co-habiting

Reference

1.35

Reference

0.98

Reference

1.14

0.65 - 2.81

0.73 - 1.31

0.86 - 1.52

Odd Ratios 95% CI


Despite the absence of a varieties of attitudinal variables, the purpose of this regression analysis is to quickly identify socio-economic and other factors affecting the willingness of HHs in Mtendere to connect to the new sewer system. Regression results are summarised as follows. Statistically significant correlates of HHs willingness to connect to the new sewer system include: (1) monthly income (the higher the HHs income the more likely for HHs connect to the sewer) (2) satisfaction with the existing toilet facilities (more likely for HHs who are satisfied with the existing toilet facilities to connect to the sewer) (3) paying for water (willingness to connect is higher for HHs who are currently

Willingness to connect is likely to be higher, the higher the income, the higher the satisfaction with the existing facilities and the higher the awareness of benefits of improved sanitation.

Education

Never attended

Primary

Secondary

Tertiary

Household size

0 - 2 people

3 - 5 people

6 - 8 people

9 and above

Month income

Below K2,000

K2,001 - K5,000

K5,001 - K7,000

K7,001 - K10,000

Above K10, 000

Roofing materials of the toilet

Better materials

inferior materials

Satisfaction with the existing toilets

Satisfied

unsatisfied

Paying for water

Paying

Not paying

Willingness to construct separate WC

for each tenant

Willing

Not willing

Constant

Reference

1.00

Reference

1.16

Reference

1.60

Reference

1.18

Reference

1.61

Reference

2.30

Reference

5.87

0.37

0.98 - 1.02

0.98 - 1.02

1.01 - 2.54*

0.66 - 211

1.22 - 2.13*

1.05 - 5.03*

2.72 - 12.67*

0.03 - 4.30

Significance at p <0.05* and p < 0.10**Prob > chi2 = 0.0000Pseudo R2 = 0.1333


paying for water services) (4) willingness to construct separate WCs for each tenants (more likely for HHs who are willing to construct separate WC for each tenants to connect to the new sewer system). Willingness to construct separate WCs for each tenant is considered as a proxy for the awareness of landlords on benefits (e.g. health benefits) of improved sanitation facilities.

Though it is more likely for male headed HHs (relative to female headed HHs) to have higher rates of willingness to connect, such differences came out as statistically insignificant. Other attributes such as age, HH size, marital status and education were also found to be insignificant (not different from 0).


8.0 Financial Inclusion8.1 Borrowing behaviourAbout 27 per cent (1 out of every 4 HH heads) have borrowed money in the last two years (Figure 58). The rate is 3 percentage points below the national average of 30 per cent (FSD Zambia and Bank of Zambia [BoZ], 2015).18 Contrary to other studies (see the WB’s Global Findex), there is no gender gap on borrowing in Mtendere. For example, the access rate of loans from commercial banks is 24 per cent for male HH heads against 25 per cent for female HH heads.

The 27 per cent remains a relatively good rate considering that 84 per cent of adult Zambians prefer saving for something rather than borrowing to be able to obtain it (FSD Zambia & BoZ, 2015). In line with borrowing patterns elsewhere (see for instance, Demirguc-Kunt, 2015), friends are the leading sources of credit in Mtendere. About 2 out of every five HH heads (40 per cent) have borrowed from friends in the past two years. The next leading source is commercial banks which has served loans to 25 per cent of HHs in Mtendere. This rate is four times the national average of 6.3 per cent (FSD Zambia and BoZ).19 Family/relatives followed at a distance with 12 per cent of HH heads. A combined pool of friends, family and relatives sum up to 52 per cent of sources of credit in Mtendere.

27% of HH heads have borrowed money in last 2 years. The leading sources being friends (40%) and banks (25%). Surprisingly, there is no gender gap in access to loans.

Figure 58: % of HHs to have borrowed money

Figure 59: Relationship between HH income and bor-rowers (% of borrowers)


The median amount borrowed by HHs is K1000 which is far below the median cost incurred by HHs to construct their current toilets.

HHs who have borrowed money in the past two years are most likely to be well educated (p-value = 0.000), aged, and be clustered in the high-income categories (p-value = 0.000). For example, the proportion of HH heads educated at the tertiary level and had borrowed in the past two years is 12 percentage points higher (35 per cent) than HH heads with no schooling experience (23 per cent). Moreover, the proportion of HH heads having borrowed in the past two years and are in the lowest income category is 20 per cent, rising to 43 per cent for HH heads earning K9000+ (Figure 59). Annex 18 shows that the higher the age of HH heads the more the access to commercial banks loans the less the preference to borrow from informal sources (e.g. Kaloba, family and friends).

Further statistical analysis shows that besides high dependence of loans from friends across all education categories, the rate is lowest for HH heads with tertiary education (30 per cent) than in the rest of the other education levels (p-value = 0.000) (annex 19). Correspondingly, better educated HH heads depend on loans from commercial banks than less educated HH heads. Specifically, the proportion of HH heads educated at tertiary level and has a financial loan from commercial banks stands at 35 per cent against 23 per cent (secondary educated HH heads); 21 per cent (primary educated HH heads) and 23 per cent (HH heads with no schooling experience).

Whereas HH heads who are earning from rentals (40 per cent) are more likely to access loans from commercial banks, those engaging in farming (14 per cent) or do not earn any income (13 per cent) are most likely to be served by Kaloba.

It is more likely for highly educated and wealthier HH heads to borrow from banks. Less educated and relatively young HH heads are more likely to rely on informal sources such as friends.

8.2 Borrowed amount, security and repayment behaviourThe median amount20 borrowed by HHs in Mtendere in the past two years was K1000 (Table 13) which is far below the median cost of K3000 incurred by the HHs when constructing their toilets (see section 7.3). There are two implications 1) if lending is amongst the tools to spearhead the construction of improved toilets, then it is risky to offer credit above the K1000 2) the data on the borrowed amount and data on the repayment behavior could be utilized to construct a ‘credit rating’ to determine possible amount that can be loaned to individual HHs (GPS codes are available to trace individual HHs).

Table 13: Borrowed amount (in Kwacha)

Mean

Media

Standard deviation

Range (min, max)

n

5479

1000

13640

(10, 300000)

6084

5661

1000

14327

(10, 300000)

4486

4970

1000

11487

(10, 1500000)

1597

Overall Male Female


Repayment rates increase with education, age, and income. Hence, relatively young HH heads, less educated and less wealthy are riskier borrowers.

Most loans are not secured (63 per cent) (Figure 60), more so for loans that were obtained by male HH heads (63 per cent) than those given to female HH heads (60 per cent). With statistically significance value of p = 0.000, such differences signal possible gender bias in the credit market. Proof of employment and property are the most common forms of security as mentioned by 17 percent and 18 per cent of the HH heads (Figure 61). Property as a form of loan security is more common for female HH heads (20 per cent) than male HH heads (17 per cent). Thus, lenders are more likely to demand property as a loan security from female borrowers than from male borrowers.

The census instrument which was composed of self-reporting questions also investigated repayment behaviors of the self-declared financial borrowers. Repayment rates stands at 84 per cent, more so for male HH heads (85 per cent) than female HH heads (83 per cent). However, such difference is most likely to be by chance (p-value = 0.057). These data are self-reporting, and it is therefore necessary to be cautious as borrowers tend to overestimate repayment and underreporting defaults.

Figure 60: Loan security (% of HHs)

Figure 61: Types of security (% of HHs)


Landlords who entirely depend on rentals as a source of income are risker when it comes to loan repayment than HHs earning from other sources.

Repayment rates increase with education, age of HH heads, and income (p-value = 0.000 for each association). The census data set reveals that the proportions of HH heads with no schooling experience and have made timely loan repayment is 81 per cent, rising to 93 per cent for tertiary educated HH heads. Similarly, the proportion of HH heads earning less than K1000 and have repaid their loans is 73 per cent, rising to 92 per cent for HH heads earning K9000+. HHs who have repaid their loans on time are also most likely to be earning from farming and employment sectors (86 per cent repaid on time) than those earning from rentals (82 per cent). In relative terms the result implies that landlords who are entirely depending on rentals as a source of income are risker than HHs earning from other sources. As expected, the repayment rate for HH heads who do not receive any income is the lowest at 62 per cent. Positive correlation is also observed between age and repayment rate where older individuals are more likely to repay on time than younger individuals. Relatively younger HH heads are therefore riskier when it comes to repayment behavior.

Over the years, ownership of bank accounts has been the primary measure of the extent to which HHs are ‘financially included’. Nearly half of the HH heads in Mtendere have bank accounts (48 per cent: Figure 62) with male HH heads outpacing female HH heads by 9 per cent (51 per cent versus 42 per cent) (p-value = 0.000). The rate of bank account ownership in Mtendere is 13 percentage points higher than the national average in urban areas, 24 percentage points higher than the averages for Sub-Saharan African countries and higher than average ownership rates for most of the neighboring countries (FSD Zambia and BoZ; World Bank, 2017).

Similar to findings from other regions (see for instance, Demirguc-Kunt, et al 2015), ownership of bank accounts differs in important ways by characteristics such as education and income. For instance, the proportion of wealthier HH heads owning bank accounts (earning K9000+) in Mtendere stands at 89 per cent against 20 per cent of HH heads earning below K1000. More than half of HH heads earning their livelihood from employment (54 per cent) have bank accounts. It is not uncommon for salary payments to be processed through banks a practice that leads to high rates of accounts ownership by employees. The proportion of HH heads owning bank accounts do not exceed 50 per cent in the other sources of income (farming, rentals, businesses and HH heads not earning an income). Ownership of bank account is also common for HH heads who have never married (53 per cent) and as well as those who are married (51 per cent) against lower rates in the other marital status categories of separated, widowed divorced and co-habiting.

8.3 Bank accounts


Table 14 presents regression results of the socio-economic correlates of HHs ownership of bank account. An alternative hypothesis (a coefficient is statistically significant different from 0) is accepted for p <0.05 and p < 0.10. The overall result from the model is that it is highly unlikely for the relationship between the dependent variable and the independent variables to have occurred by chance (overall p-value = 0.000). However, only 15 per cent of the variation in ownership of bank account can be explained by the selected independent variables and this suggests other variables are also important in determining the extent to which HHs maintain bank accounts.

It is common for studies on inclusive finance to apply regression analysis when identifying determinants of financial inclusion (see for instance, Johnson, Malkamaki, and Niño-Zarazua, 2010). The dependent variable is dichotomous whether a HH own a bank account (Yes=1) or not (No=0). Given the dichotomous nature of the dependent variable, the analysis of the financial inclusion uses a logit regression model (see section 7.7 for statistical approach behind logit regression analysis). Independent variables are socio-economic characteristic of HHs including income levels, sources of income, sex of the HH heads, education levels, marital status, HH size, and age of HH heads, and floor materials of the toilet and whether respondents are landlords or tenants.

8.4 Regression analysis

Figure 62: Comparative statistics on bank account ownership

Source: World Bank’s WDI (except for Mtendere’s statistic, country level statistics are sourced from the World Bank’ World Development Indicators (WDI), FSD Zambia and Bank of Zambia, 2015)


Table 14: Results of a logistic regression of HHs ownership of bank accounts

Sex of HH headsMaleFemale

Age of HH headsBelow 16 years17 - 24 years25 - 34 years35 - 44 years45 - 54 yearsAbove 54 years

Marital statusNever marriedMarriedSeparatedDivorcedWidowedCo-habiting

EducationNever attendedPrimarySecondaryTertiary

Household size0 - 2 people3 - 5 people6 - 8 people9 and above

Month incomeBelow K2,000K2,001 - K5,000K5,001 - K7,000K7,001 - K10,000Above K10, 000

Tenancy StatusLandlordTenant

Toilet Roof MaterialsBetter materialsPoor materials

Constant

Reference1.12

Reference0.96

Reference0.84

Reference1.00

Reference0.95

Reference2.52

Reference1.51

Reference1.74

0.16

1.04 - 1.21*

0.93 - 0.99*

0.82 - 0.87*

0.99 - 1.00*

0.95 - 0.99*

2.41 - 2.64*

1.40 - 1.63*

1.631 - 1.85*

0.13 - 0.19*

Odd Ratios 95% CI

Significance at p <0.05* and p < 0.10**Prob > chi2 = 0.0000Pseudo R2 = 0.1079


Results from the regression analysis can be summarised as follows. After controlling for other variables, Table 14 shows that all variables in the empirical model are statistically significant: (1) male headed HHs are more likely to own bank accounts than female headed HHs (2) the older an individual is, the more likely that he/she will not be owning a bank account (3) small HHs are more likely to have bank accounts (4) marital status (unmarried HH heads are less likely to own bank account than HHs in the other marital status), (5) education (educated HH heads are more likely to own bank account than non-educated HHs), (6) income (the higher the HHs income the more likely for such HH heads to own bank accounts) (7) tenancy status (more likely for tenants to own bank account than landlords) (8) roofing materials of the toilet (ownership of bank accounts is higher for nonpoor than the poor).

A total of 5,005 HH heads out of 11,071 (45 per cent) are saving in bank accounts, more so for male HH heads (47 per cent) than female HH heads (41 per cent) (Figure 63) (p-value = 0.000). It is more likely for better educated HH heads to save (52 per cent of tertiary educated HH heads) than less educated HH heads (38 per cent of primary school educated HH heads) (p-value = 0.000). It is also more likely for high income earners to save relative to low income HHs. Whereas 44 per cent of HH heads in the lowest income category (earning less than K1000) are saving, the proportion is nearly twice for HH heads in the highest income category (62 per cent) (p-value = 0.000).Moreover, HH heads who are never married are more likely to save (52 per cent) than HH heads in the other categories of marital status (for example 36 per cent and 38 per cent of widowed and separated HH heads respectively) (p-value = 0.000). Sources of income also influences saving behaviours – with higher rates of saving for HH heads earning from business and employment (51 and 44 per cent respectively) than HH heads earning from rentals (38 per cent) and farming (31 per cent) (p-value = 0.000) (Figure 64).

8.5 Savings

45% of HH heads are saving in banks. It is more likely for better educated, wealthy, and never married individuals to save.

Figure 63: % of HHs saving in the bank


Figure 64: Relationship between income sources and income (% of HHs)


9.0 Public Awareness of MCA9.1 Public awareness of MCA-Zambia as an entityThe census also investigated the level of public awareness of the activities being pursued by MCA-Zambia. Close to 1 out of every 3 HH heads (30 per cent which is equivalent to 6,843 HH heads) have heard about MCA-Zambia (Figure 65). Public awareness was insignificantly associated with the sex of HH heads (p-value = 0.187) but significantly associated with income (p-value = 0.000). HH heads who have heard about MCA-Zambia are most likely to be in the higher income categories than in the lower categories. For instance, 54 per cent of HH heads earning above K9000 have heard about MCA-Zambia against 23 per cent of HHs earning monthly income of less than K1000.

Figure 65: Public awareness of MCA-Zambia (% of HHs)

Figure 66: Public understanding of MCA-Zambia activ-ities (% of HHs)


Only 30% of HHs have heard about MCA-Zambia, out of which 86% correctly understand MCA-Zambia’s core activity in Mtendere. Only 11% claimed to be aware of sources of finances for the MCA project.

9.2 Public awareness of MCA-Zambia activities

9.3 Public awareness of funding sources

Most censused subjects gave multiple answers when asked about MCA activities. Some of the subjects correctly stated water and sanitation, whereas others incorrectly mentioned activities such as roads. Therefore, the number of HHs who correctly stated the activities being implemented by MCA-Zambia was the sum of all respondents who have at least stated sanitation or water (even in cases where water or sanitation is mixed up with incorrect activities such as roads). Following this approach, an overwhelming 94 per cent (of the HH heads who have heard about MCA-Zambia) correctly comprehended MCA-Zambia intervention in Mtendere (Figure 66). A total of 329 out of the 6,704 (that is, 5 per cent) remained unaware of MCA-Zambia’s activities in Mtendere.

Public awareness on funding sources was the least known aspect. Only 11 per cent of HH heads (2,535 out of 22,895 HH heads) claimed to be aware of sources of finances for the MCA project (Figure 67). Awareness of funding sources is much higher for male HH heads (12 per cent) than female HH heads (9 per cent) and the difference is statistically significant (p-value = 0.000). We hypothesis that difference on awareness by gender is due to the pool of workers in infrastructure projects being male dominated and they therefore come into contact with several details associated with the project (including financing information).

About 72 per cent of the 2535 HH heads who claimed to know funding sources, cited the USA Government as the sole funding provider to the project. The proportion of HHs citing USA Government as the funding source was higher for male HH heads (74 per cent) than female HH

Figure 66: Public understanding of MCA-Zambia activ-ities (% of HHs)


heads (62 per cent) and such differences is statistically significant (p-value = 0.000). The possible explanation for the observed difference is the same as the one discussed in the preceding paragraph.It is worth mentioning that the GRZ is also making significant financial contribution to the COMPACT.21

Despite that, only 2.2 per cent of census subjects identified both the GRZ and USA as joint funders of the COMPACT. We hypothesised that the statement placed on the MCAs’ logo (that is, ‘supported by the American people’) is seen by the public as implying USA Government as the sole funder.


10.0 ConclusionFindings presented in this report will facilitate the sanitation marketing/improvement programs to characterise HHs in Mtendere into clusters with similar socio and non-socio-economic characteristics. The report shows that increased use of improved sanitation is shaped at difference scales by income status, tenancy status (whether a HH is a tenant or landlord), sources of income, levels of education, HH size, and other aspects such as whether landlords are sharing toilet facilities or living in the same plot with tenants.

Close to a half of the HHs in Mtendere are unsatisfied with the existing toilet facilities, a state that will most likely raise the demand for improved facilities. To further raise such demand, there is a need to address public concerns on the planned roll out of prepaid meters, as well as increased investments to improve the reliability of water supply, especially if the use of waterborne and flush toilets is to be encouraged. As most HHs are tied to pit latrines, it is therefore not surprising that, lack of water is currently not among the leading reasons for being unsatisfied with the existing toilets. However, it will come as a challenge in a situation of increasing shift from pit latrines to toilet facilities whose efficiency depends on reliable water supply.

Despite the absence of survey data to apply the Contingent Valuation Methods (CVM) in establishing willingness to pay for improved toilet facilities, connections to the sewer system and decommission of the pit latrines, still the binary responses to related survey questions signal potential high rates of willingness. However, for high rates of willingness to happen, Mtendere’s residents need to be presented with alternative means of disposing sanitary pads and diapers and be served with reliable water supply. The latter is a challenging aspect especially if providers of such alternative means are landlords who seem to be more interested in improving commercial rather than properties.

Despite the binary response to the questions on the willingness to construct ‘another’ WC and separate WC for each tenant was positive for more than 50 per cent of the respondents (54 per cent and 65 per cent stated ‘yes’ to the respective questions), the proportions of HHs unwilling to do either of the two (46 per cent unwilling to construct separate WCs for each tenant and 35 per cent unwilling to construct ‘another’ WC) remain a sizable number of landlords. Innovative marketing strategy and possible potential legal instruments are therefore paramount to convince landlords, (who are traditionally less interested to improve rented properties), to advance sanitation facilities for the benefit of their tenants. Nevertheless, any strategy must engage various stakeholders (local Government, community groups and targeted HHs) as a collective movement to reach the target of better sanitation in Mtendere.


AnnexAnnex 1: Relationship between HHs monthly income and education

Annex 3: Relationship between HHs monthly income and marital status

Annex 2: Relationship between HHs monthly income and HH size

Annex 4: Relationship between HHs monthly income and HH size

K0 - K1000

K10001 - K3000

K3001 - K5000

K5001 - K7000

K7001 - K9000

K9001 and above

K0 - K1000

K10001 - K3000

K3001 - K5000

K5001 - K7000

K7001 - K9000

K9001 and above

K0 - K1000

K10001 - K3000

K3001 - K5000

K5001 - K7000

K7001 - K9000

K9001 and above

K0 - K1000

K10001 - K3000

K3001 - K5000

K5001 - K7000

K7001 - K9000

K9001 and above

Total

Total

Total

Total

41.63

48.08

7.07

2.76

0.46

0

100

13.23

64.38

14.91

4.58

1.43

1.47

100

20.28

63.06

10.85

3.69

0.96

1.15

100

28.21

60.54

7.5

2.39

0.76

0.59

100

28.21

60.54

7.5

2.39

0.76

0.59

100

24.87

64.03

8.27

2

0.53

0.31

100

22.02

66.48

7.99

2.24

0.7

0.56

100

16.01

66.47

12.23

3.33

1.08

0.89

100

16.01

66.47

12.23

3.33

1.08

0.89

100

14.24

67.12

13.11

3.56

1.08

0.88

100

26.19

59.79

9.08

3.21

1

0.73

100

25.68

64.89

6.69

1.98

0.3

0.46

100

17.78

62.22

10

7.78

1.11

1.11

100

12.99

53.42

19.76

8.13

2.74

2.96

100

12.99

53.42

19.76

8.13

2.74

2.96

100

4.85

50.69

25.37

10.74

3.71

4.64

100

26.94

57.67

10.45

3.19

1.1

0.64

100

11.03

60.88

18.09

6.17

1.76

2.08

100

11.03

60.88

18.09

6.17

1.76

2.08

100

Never beento school

MarriedNever Married

0 - 2 people

0 - 2 people

Primary

Separated

3 - 5 people

3 - 5 people

Secondary

Divorced

I don’t Know

Co-habiting

9 and above

9 and above

Tertiary

Widowed

6 - 8 people

6 - 8 people

Pearson chi2(20) =2.5e+03 Pr = 0.000

Pearson chi2(25) = 537.7344 Pr = 0.000

Pearson chi2(15) =1.0e+03 Pr = 0.000

Pearson chi2(15) = 1.0e+03 Pr = 0.000


Annex 6: Cross-tabulation – water connections and education (% of HHs)

Annex 7: Histograms of water bills

Histogram of daily water bill

Histogram of monthly water bill

Annex 5: Relationship between HHs monthly income and marital status

Connected

Not connected

Total

K0 - K1000

K10001 - K3000

K3001 - K5000

K5001 - K7000

K7001 - K9000

K9001 and aboveTotal

91.89

8.11

100

13.23

64.38

14.91

4.58

1.43

1.47

100

20.28

63.06

10.85

3.69

0.96

1.15

100

92.63

7.37

100

22.02

66.48

7.99

2.24

0.7

0.56

100

94.77

5.23

100

26.19

59.79

9.08

3.21

1

0.73

100

89.67

10.33

100

17.78

62.22

10

7.78

1.11

1.11

100

94.5

5.5

100

26.94

57.67

10.45

3.19

1.1

0.64

100

Never beento school

MarriedNever Married

Primary

Separated

Secondary

Divorced

I don’t Know

Co-habiting

Tertiary

Widowed

Pearson chi2(4) = 21.0175 Pr = 0.000

Pearson chi2(25) = 537.7344 Pr = 0.000


Annex 8: Relationship between preferences for facility type and HH size

Type I

Type II

Type II

Total

4.83

11.72

83.45

100

4.11

8.89

86.99

99.99

2.88

9.32

87.8

100

1.68

8.48

89.99

99.99

0 - 2 3 - 5 8 and above6 - 8

Pearson chi2(6) = 22.2510 Pr = 0.001

Annex 9: Relationship between the use of LCC/private company/CBE to dispose diapers and education

Annex 10: Relationship between the use the services of LCC/private company/CBE to dispose diapers and monthly expenditure

Use LCC

Do not use LCC

Use LCC

Do not use LCC

30.77

69.23

0

31.74

68.26

0

36.76

63.24

0

46.2

53.8

100

41.23

58.77

100

41.23

58.77

100

32.67

67.33

0

40.86

59.14

100

44.58

55.42

0

50.56

49.44

100

Never beento school Primary Secondary I don’t Know

K9001 andabove

K7001 - K9000

K5001 - K9000

K3001 - K5000

K1001 - K3000

Tertiary

Pearson chi2(4) = 46.8243 Pr = 0.000

Pearson chi2(4) = 46.8243 Pr = 0.000

Table 11: Costs incurred to construct the existing toilets

Annex 12: Income share of water expenditure

K0 - K1000

K1001 - K3000

K3001 - K5000

K5001 - K7000

K7001 - K9000

K9001 and above

K0 - K1000

K1001 - K3000

K3001 - K5000

K5001 - K7000

K7001 - K9000

K9001 and above

144

234

383

456

575

758

1999

10720

2076

620

182

147

283

508

799

383

456

575

3504

6000

6000

3000

4000

6000

4.5

2.6

1.6

1.4

1.2

1

30

23

52

60

150

180

Obs. Mean SD Max

%

Min


Annex 16: Relationship between landlords intention to construct another WC and HH size

Yes

No

59.11

40.89

100

63.87

36.13

100

67.03

32.97

100

65.81

34.19

100

0 - 2 people 3 - 5 people 9 and above6 - 8 people

Pearson chi2(3) = 10.2752 Pr = 0.016

Annex 13: Relationship between landlords living with tenants and education

Annex 14: Relationship between HHs monthly landlords living with tenants and income

Annex 15: Relationship between landlords intentions to construct another WC and income (% of HHs)

Annex 17: Relationship between the use of the plot and monthly expenditure

Living with tenants

Not living with tenants

Total

Living with tenants

Not living with tenants

Total

Intend to construct

Do not intend

Total

Intend to construct

Do not intend

Total

58.76

41.24

100

66.03

33.97

100

66.88

33.13

100

52.22

47.78

100

64.26

35.74

100

85.08

14.92

100

56.99

47.01

100

66.74

33.26

100

88.03

11.97

100

58.39

37.45

100

66.7

33.3

100

88.43

11.57

100

58.39

41.61

100

59.29

40.71

100

91.19

8.81

100

57.64

42.36

100

48.12

51.24

100

47.48

52.52

100

57.26

42.74

100

87.14

12.86

100

58.18

41.82

100

59.8

40.2

100

87.27

12.73

100

Never beento school Primary Secondary I don’t Know

K9001 andabove

K9001 andabove

K9001 andabove

K7001 - K9000

K7001 - K9000

K7001 - K9000

K5001 - K9000

K5001 - K9000

K5001 - K7000

K3001 - K5000

K3001 - K5000

K3001 - K5000

K1001 - K3000

K1001 - K3000

K1001 - K3000

K0 - K1000

K0 - K1000

K0 - K1000

Tertiary

Pearson chi2(4) = 92.3086 Pr = 0.000

Pearson chi2(5) = 30.7971 Pr = 0.000

Pearson chi2(5) = 25.8459 Pr = 0.000

Pearson chi2(5) = 13.3143 Pr = 0.021


Annex 18: Relationship between sources of borrowed funds and age

Family/Relatives

Friend

Kaloba

Micro-credit organiztion

in community

Micro-credit organization

outside community

Commercial Bank

Workplace

Other (Specify)

Total

10

37

7

3

7

28

5

2

100

16

46

7

2

5

16

7

2

100

16

58

9

1

2

7

6

1

100

0

100

0

0

0

0

0

0

100

8

31

7

5

8

31

6

4

100

6

30

7

3

7

28

5

2

100

Above 54years

45 - 54years

35 - 44years

25 - 34years

17 - 24years

Below 16years

Pearson chi2(42) = 462.1112 Pr = 0.000Others include: neighbours, church, workmate, landlord, pension scheme, business partner, MTN mobile, building society, tenant, groceries on credit, vendor, and community based organisations

Annex 19: Relationship between sources of the borrowed money and education

Annex 20: Histogram of the borrowed amounts

Family/Relatives

Friend

Kaloba

Micro-credit

organization in

community

Mcro-credit

organization outside

community

Commercial Bank

Workplace

Other (Specify)

12

42

7

2

6

23

6

2

100

9

42

10

3

5

21

7

2

100

12

37

10

4

7

23

4

2

100

21

37

15

0

5

16

3

3

100

12

30

3

2

10

35

6

2

100

Never beento school Primary Secondary I don’t KnowTertiary

Pearson chi2(28) = 215.4012 Pr = 0.000


Type I Type II Type III

Annex 20: Histogram of the borrowed amounts


Reference

Augsburg, B. and P. Rodríguez-Lesmes (2015). Sanitation dynamics: toilet acquisition and its economic and social implications. IFS Working Paper W15/15. Institute for Fiscal Studies.

Bulmer, M. G. (1979). Principles of Statistics. Dover.

Coccato, M. (1996). Alternatives to home ownership: rental and shared sub-markets in infor-mal settlements. McGill University.

Deaton, A. (1997). The analysis of household surveys: a microeconometric approach to de-velopment policy. The John Hopkins University Press, Baltimore.

Demirguc-Kunt, A. et al (2015). The global findex database. measuring financial inclusion around the world. World Bank, Washington DC.

Eales, K. and D. Schaub-Jones (2005). Sanitation partnerships: landlord or tenant? the im-portance of rental relationships to poor community sanitation in 3 African countries. Building Partnerships for Development (BPD). London.

Edwards, M. (1982). Cities of tenants: renting among the urban poor in Latin America. In urbanization in contemporary latin america. Eds. Alan Gilbert, Jorge Hardoy and Ronaldo Ramirez, 128-158. John Wiley & Sons, Chichester.

Feyrer, J. (2005). Demographics and Productivity.

Fewtrell, L, et al. (2005). Water, sanitation, and hygiene interventions to reduce diarrhoea in less developed countries: a systematic review and meta-analysis. Lancet, 5, pp. 42-52.FSD Zambia and Bank of Zambia (2015). FinScope 2015. FSD-Zambia and Bank of Zambia. Lusaka.

Gilbert, A. (1991). Renting and the transition to owner occupation in Latin American Cities. Habitat International, 15(1/2), pp. 87-9.

Gilbert, A. (1993). In search of a home: rental housing in Latin America. London: UCL Press Limited.GRZ (2014). Zambia demographic and health survey 2013-14. Central Statistical Office. Lu-saka.Gulyani, S., D. Talukdar (2008). Slum real estate: the low-quality high-price puzzle in Nairobi’s slum rental market and its implications for theory and practice. World Development.

Gunter, A. (2014). Renting shacks: landlords and tenants in the informal housing sector in Johannesburg South Africa. Urbani izziv, 25, (special issue).

Waddington H, and B. Snilstveit (2009). Effectiveness and sustainability of water, sanitation, and hygiene interventions in interventions in combating diarrhoea. Journal of Development Effectiveness,1, pp. 295-335.


Westfall, P. (2014). Kurtosis as Peakedness. The American Statistician, 68(3), pp. 191–95.Whittington, D. et al (1993). Household sanitation in Kumasi, Ghana: adescription of current practices, attitudes, and perceptions. World Development, 21(5), pp. 735-48.

Whittington, D. et al (1993). Household demand for improved sanitation services in Kumasi, Ghana: A contingent valuation study. Water Resources Research, 29(6), pp. 1539-60.WHO and UNICEF (2004). Meeting the MDG drinking water and sanitation target: a mid-term assessment of progress; WHO, Geneva.

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End note1 Infrastructure activity consists of the following sub-activities: 1) core water network reha-bilitation 2) Chelston distribution line rehabilitation and expansion 3) Chelston and Kaunda square sewer sheds rehabilitation and expansion 4) central distribution line rehabilitation and expansion, and; 5) Bombay drain improvements (drainage). Sub-activities under the institu-tional strengthening activity are: 1) assistance to LWSC 2) assistance to LCC, and; 3) innova-tion grants for pro-poor services delivery.2 Literature on demographics and productivity found that people in their 40s are relatively more productive. The reason is that they embody a good balance of experience and creativi-ty (Feyrer, 2005)3 Skewness value of less than −1 or greater than +1 implies that the distribution is highly skewed (Bulmer, 1979). The amount of skewness therefore tells you how highly skewed the sample is: the bigger the number, the bigger the skew.4 Higher values indicate a higher, sharper peak; lower values indicate a lower, less distinct peak. Reference standard is a normal distribution, which has a kurtosis of 3; thus, excess kurtosis is simply kurtosis minus 3. A slightly different interpretation is provided by West-fall (2014) who indicates that “higher kurtosis means more of the variance is the result of infrequent extreme deviations, as opposed to frequent modestly sized deviations.” In other words, it’s the tails that mostly account for kurtosis, not the central peak.5 Some of the censused subjects opted for the pre-coded response of ‘don’t know’.6 The education variable refers to ‘completed’ levels (for instance, secondary educated HH heads are the ones who have ‘completed’ secondary education).7 The figure taken from DHS 2013-2014 refers to HH population while the unit of measure-ment for this study is the head of HHs.8 The effect of education on improved sanitation is mostly measured through studies on HHs willingness to pay for the improved sanitation using Contingent Valuation Method (CVM) and regression analysis of the bidding amounts9 The national average for urban areas comes from GRZ (2014).10 Despite such findings, other studies find the influence of age on the willingness to pay for improved sanitation as statistically not different from zero.11 The value of the standard deviation shows that each income point in dataset sits an aver-age distance of 1,866 statistical data points from the mean.12 This difference is possible as Mtendere is mostly a low-income side of urban areas where-as the national survey is a combination of data from both the nonpoor and poor urban areas.13 Employment refers to both formal and informal employment.14 Information lacking from the Mtendere census dataset is ages of the children that would have been used to generate the same equivalence scale used by the Government to com-pute the national poverty lines.15 Shared facilities are not recognised as improved sanitation due to challenges of mainte-nance as they are easily the avenues for the spread of diseases (Simiyu et al, 2017)16 The 81 per cent comes from section 5.5 where HHs were asked whether they share toi-let facilities. As 100 percent of HHs are using improved facilities (see section of 5.2– toilet types), then the proportion of HHs in the highest step of the ladder is simply obtained by subtracting 81 per cent from 100 per cent.17 The primary aim of the Mtendere census was to inform MCA-Zambia on the socio-eco-nomic status of HHs residing in the study area as well as the current situation of water and sanitation. It was therefore not a specialised and scientific study on the willingness to pay


for utility services. Such specialised studies require the field instrument to collect data that would have allowed the application of CVM or hedonic methods in determining the willing-ness to pay.18 The timeframe referred by the national average is the past one year.19 The national average is strictly referring to commercial banks and does not include borrow-ing from Micro-Finance Institutions (MFI). If the Mtendere census did not combine banks and non-banks financial institutions, then the national average can be the relevant benchmark for the census results from Mtendere.20 In the presence of extreme values of borrowed amount, the median gives a better repre-sentation of the data than the mean (the presence of extreme values influencing the mean is evidenced by the coefficient of variation greater than 1). The histogram of the distribution of borrowed is heavily skewed to the left with heavy tail of the extreme values (Annex 20).21 The exact contribution will be known at the end of the COMPACT as the current figures change due to changes in contingencies.

-tendereÒ3ocio %conomicÒ0roµle april 2017 · table of content acronyms 1.0 introduction 2.0...

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