Examining the Wealth Trends in Kombewa, Kenya

A Thesis Submitted in Partial Fulfillment of the Requirements of the Renée Crown University Honors Program at

Syracuse University

Alizée McLorg

Candidate for Bachelor of Science Degree and Renée Crown University Honors

Spring 2020

Honors Thesis in Public Health

Thesis Project Advisor: Dr. David Larsen, Associate Professor of Public Health

Thesis Project Reader: Dr. Bhavneet Walia, Assistant Professor of Public Health

Honors Director: Dr. Danielle Smith, Director of Renée Crown Honors Program

Date: 4/19/2019

Abstract

The primary purpose of this study is to understand wealth trends in a rural Kenyan community. Understanding wealth trends is important for understanding health outcomes, overall well-being and for informing economic and health policy. Using the Kombewa Health and Demographic Surveillance System, 20,370 households were assessed between 2011-2018. Data on household materials, assets, education and mortality were used. Three indices were developed to quantify wealth: principal component analysis, multiple correspondence analysis and the multidimensional poverty index. Wealth quintiles and levels of deprivation relating to socioeconomic status were then created and analyzed over time. The first two indices demonstrate an increase in wealth during the assessment period with the percentage of households in the wealthiest quintile increasing from 19% to 23%. The multidimensional poverty index, however, shows no change in socioeconomic status over time. Among other factors, lack of sanitation and improved water seems to be the main justification. Our results indicate that households are accumulating assets, but their increased accumulation is not translating to changes in living conditions known to improve health. Hence, while houses are getting wealthier, they are not necessarily getting healthier.

Keywords: Health and Demographic Surveillance System (HDSS), Kenya, low and middle income countries (LMIC), multidimensional poverty index (MPI), multiple correspondence analysis (MCA), principal component analysis (PCA), socioeconomic status (SES), wealth index

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Executive Summary

Measuring wealth is essential for understanding the success of a community and to

predict health outcomes. Socioeconomic status (SES), defined by the American Psychological

Association (APA, 2007) is the social standing of an individual or group. Examinations of SES

often reveals inequities of access to resources and correlated health outcomes. Although in high

income countries, income is a common measurement of SES, many low and middle income

countries (LMIC) do not have data on household income. Therefore, several methods for

measuring SES in LMIC were developed, including asset-based wealth indices.

Using the Kombewa Health and Demographic Surveillance System, three wealth indices

were created to examine wealth and SES trends. The Kombewa HDSS is situated in rural

Western Kenya and has been collecting demographic data since 2011. Our analysis included

households that had at least two available housing characteristic data points. A total of 20,370

households between 2011-2018 were included. Two wealth indices and one SES index were

created using the HDSS: principal component analysis (PCA), multiple correspondence analysis

(MCA) and multidimensional poverty index (MPI). PCA and MCA only used housing

characteristic data and measured wealth while the MPI also incorporated education and mortality

to measure multidimensional wealth or SES.

After examining the change in quintiles over time, PCA and MCA indices demonstrated a

slight increase in wealth. During the first time period, 22% of households were in the poorest

quintile. In the last time period, only 16% of households were in the poorest quintile. PCA and

MCA ranked most of the households in the same quintile with a correlation coefficient of 0.971.

However, MPI results demonstrated that the SES of households did not change over time. About

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70% of households were considered non-deprived for all time periods. Deprivations in education

and mortality remained stagnant while access to sanitation and water decreased over time.

Our results suggest that although asset accumulation and improved housing material has

increased over time, sanitation and water decreased. The decrease in sanitation may be attributed

to lack of coordination among organizations responsible for the water in Kombewa. Sanitation

and water access are associated with health outcomes so while houses are getting wealthier, they

many not necessarily be getting healthier. These changes must come from more comprehensive

policy approaches because improved well-being and health of the community is co-dependent on

the government and household decision-making.

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Table of Contents

Contents

Abstract..............................................................................................................................2

Executive Summary...........................................................................................................3

Table of Contents...............................................................................................................5

Acknowledgements............................................................................................................6

Chapter 1: Introduction....................................................................................................7

Introduction....................................................................................................................7

Literature Review..........................................................................................................8Creation of Wealth Indices:..........................................................................................9Principal Component Analysis (PCA):......................................................................11Multiple Correspondence Analysis (MCA):..............................................................11Multidimensional Poverty Index:...............................................................................12

Chapter 2: Methods.........................................................................................................14

Methods.........................................................................................................................14Study design...............................................................................................................14Setting.........................................................................................................................14Participants.................................................................................................................15Variables.....................................................................................................................15Data sources...............................................................................................................15Statistical methods......................................................................................................16Bias.............................................................................................................................18

Chapter 3: Results and Discussion.................................................................................19

Results...........................................................................................................................19PCA and MCA...........................................................................................................19MPI.............................................................................................................................20

Discussion......................................................................................................................20Key results..................................................................................................................20Interpretation..............................................................................................................21Conclusion..................................................................................................................22

Reference List...................................................................................................................23

Appendices........................................................................................................................26

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Acknowledgements

This thesis would not be possible without the support of several people. First, thank you

to Naomi Shanguhyia for supporting my project, for being excited with me and for connecting

me with her friend Gladys, who made Kenya feel a bit more like home. Thank you to the entire

HDSS team, Winnie Al-Abdneger, Ken Omolo and Peter Sifuna, for welcoming me to Kombewa

and always taking time to explain. Their support and friendship made this thesis possible. Thank

you to Dr. Andrea Shaw, for supervising and supporting my experience in Kenya and for her

continued mentorship. Thank you to my reader, Dr. Bhavneet Walia, who helped contextualize

this project and helped me better understand and develop an interest in health economics. Thank

you to my parents, who supported my ambitious ideas, for listening to me and for always being

there; I am beyond lucky to have them. Finally, thank you to my advisor, Dr. David Larsen, who

helped me reflect on how to make this thesis meaningful at every step. His guidance helped me

build genuine relationships, further develop my interest in global health and develop tangible

research skills. His mentoring has been substantial to my academic and professional growth and I

feel very fortunate to have had his support. Thank you all for your time and effort in helping me

complete this project.

A special thanks to the Renée Crown Honors Program for financially supporting this project

through the Crown-Wise Fund.

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Chapter 1: Introduction Introduction

An estimated 736 million people lived in extreme poverty in 2015, with over half of them

living in sub-Saharan Africa (United Nations, 2019; World Bank, 2019). Poverty decreases

access to health care, decreases opportunity for educational attainment and increases food

insecurity (Akinboade & Adeyefa, 2018; Ferguson, Bovaird Mph, & Mueller, 2007; Peña &

Bacallao, 2002; Peters et al., 2008). Additionally, living in poor housing conditions associated

with poverty is an exposure to poor health (Tusting et al., 2020). Poverty therefore acts in two

ways by decreasing access to positive health factors and by exposing and increasing risk of poor

health such as infant and child mortality (Phelan, Link, & Tehranifar, 2010; Taylor-Robinson et

al., 2019). When examining global wealth trends, it was found that global wealth increased by

66% from 1995- 2014 (Lange, Wodon, & Carey, 2018). However, global wealth held by low

and middle income countries (LMIC) only grew 5% during the same time period (Lange et al.,

2018). Furthermore, many LMICs have experienced large population growth, thus when

examining wealth trends per capita, many LMICs actually declined. Specifically, Kenya’s total

wealth per capita decreased by 5% between 1995-2014 (Lange et al., 2018). Because poverty

disproportionately affects LMICs, measuring the wealth trends of communities in LMICs is

essential for understanding and improving health outcomes (World Bank, 2000). We used a

longitudinal dataset that provided information on housing materials, assets, education and

mortality to examine the wealth and SES trends in a rural Kenyan community during a seven

year period (2011-2018).

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Literature Review

In an effort to understand the importance of measuring household wealth, and how to do

so in LMIC, substantial literature was reviewed. The following articles examine the importance

of measuring wealth and propose various methodologies for doing so in LMIC settings.

It has been clearly demonstrated that there is a positive relationship between

socioeconomic status (SES) and health outcomes (Bruder, 2002; Feinstein, 1993; Haacker &

Birungi, 2018; Yoshikawa, Aber, & Beardslee, 2012). This phenomenon is clearly evidenced in

Kenya, which is considered a LMIC. A study conducted in Kenya analyzed the impact of

poverty on childhood disability risk factors (T Mugoya & Mutua, 2015). Secondary data analysis

conducted using Kenyan Demographic Health Survey (DHS) data, specifically analyzed data on

women with children under five years old and the associated children’s data. Logistic regression

analyses were conducted to examine the association between SES and childhood disability

factors and the results demonstrated that urban women from low SES were approximately ten

times more likely to have never received antenatal care. This study demonstrates how poverty

can decrease access to health care and increase risk factors for child disability. The correlation

between SES and health outcomes is prevalent in many LMIC and further emphasizes the

importance of managing the SES of communities in order to influence health outcomes

positively.

Living in poverty serves as an exposing factor for poor health outcomes. Tusting et al.

(2020) suggests that adequate housing is essential to human well-being and positive health. A

cross-sectional analysis of 33 African countries was done to examine the association between

housing conditions and disease among children. The results demonstrated that improved housing

reduced the odds of malaria infection, stunting, diarrhea and anemia by up to 18%. This study

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reveals the close relationship between housing conditions and health and the importance of

implementing improved housing material, water and sanitation to improve health outcomes.

Additionally, more research on the wealth of communities and how it influences public

health is needed to inform better policy changes. Baum (2005) suggests that more concentration

on the dynamics and distribution of wealth is necessary to formulate policies and adequately

address social inequalities. Baum (2005) argues that public health research has been lacking

when examining the impacts of wealth, especially wealth distribution within countries. If more

public health research was devoted to understanding the distribution of wealth within

communities, impactful policies could be implemented in an effort to improve wealth and health

trends.

Creation of Wealth Indices:

In high income countries, SES is often measured by household income, education or

occupation (APA, 2007). In the United States for example, the Census Bureau evaluates total

family income and sets thresholds for poverty based on family size and consumption (Census

Bureau United States, 2019). This metric is standard throughout the United States and gives a

general understanding of the wealth of the population. This standard metric, however, is not

representative in LMIC. Accurate household income is not available because many individuals

work in an informal sector without income taxation, their income varies or they are self-

employed (Rutstein & Johnson, 2004). One common alternative to measurement of income is

measurement of household consumption.

The Global Consumption Database, managed by The World Bank, collects data about

household consumption in sectors of food, housing, energy and is a comprehensive proxy for

household SES (World Bank, 2019). The Theory of the Consumption Function, stated by

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(Friedman, 1957), is that household consumption expenditure is a highly dependable and stable

function of current income, therefore serving as an accurate proxy for income and SES.

Consumption data, when available, is a smooth and less-variable measure of wealth (Deaton &

Zaidi, 2002). However, consumption data is also difficult to measure in LMICs because of the

large number of informal transactions and lack of detailed expenditure accounts(Deaton & Zaidi,

2002). Therefore, a widely acceptable and common method of measuring wealth is an asset-

based index. Asset-based indices are created from household materials and durable assets which

are collected through household surveys (Rutstein & Johnson, 2004). The basis of creating a

wealth index from household characteristics assumes that if a household has more and better

characteristics (including assets), they are wealthier. After collection of the household

characteristics, analyses are performed, and households are divided into wealth quintiles.

Significant research has been conducted to determine the validity and accuracy of using such

proxies as a determinate for wealth and have demonstrated positive results (Howe, Hargreaves,

& Huttly, 2008). Additionally, measuring SES can be done by using an asset-based index in

conjunction with other demographic data such as education and mortality.

The Kombewa Health and Demographic Surveillance System (HDSS) nearly replicates

the household asset index used by the Demographic and Health Survey and therefore collects

data on housing materials, durable assets and other demographics. Because this is the available

data, asset based wealth indices were created to determine the wealth trends of the Kombewa

community and a multidimensional measurement of wealth was used to asses SES. Through the

literature, three common methods of wealth and SES indices were identified: principal

component analysis (PCA), multiple correspondence (MCA) and multidimensional poverty

index (MPI).

10

Principal Component Analysis (PCA):

PCA is a very common method used for creating a wealth index and has been used by the

DHS program, UNICEF, The World Bank and more (Rutstein & Johnson, 2004). PCA uses

household material and asset data to rank households into quintiles. Many studies have used

PCA to measure wealth in LMIC and the process has been described in detail by the DHS

Program (Hargreaves et al., 2007; Harttgen & Vollmer, 2013; Nundy et al., 2011; Wittenberg &

Leibbrandt, 2017).

The philosophy of the DHS Wealth Index is that wealth is an unobserved variable where

a household’s relative wealth position is associated with several indicator variables. The

indicator variables are thought to be correlated to a household’s economic status, meaning that a

household that has more indicator variables (assets) is wealthier. The creation of a wealth index

was initiated because income and consumption data are often unavailable or inaccurate in LMIC.

An important distinction about the PCA wealth index is that it is an economic measurement and

not a socioeconomic measurement because it only analyzes assets and doesn’t measure

education, occupation or other measurements of SES. PCA is one of the most common methods

used for creating a wealth index because of its relative simplicity and its direct relationship to

DHS.

Multiple Correspondence Analysis (MCA):

MCA is a statistical method similar to PCA but does not assume the variables are linear

and can use categorical variables (Ezzrari & Verme, 2012). Several studies have determined the

validity of MCA as a measurement of poverty and it is starting to be utilized more (Asselin &

Anh, 2008; Booysen, van der Berg, Burger, Maltitz, & Rand, 2008; Hruschka, Hadley, &

Hackman, 2017).

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A study conducted in Western Kenya used DHS to create wealth indices over time

(Amek et al., 2015). The wealth indices were created by three methods: PCA, polychoric PCA

and MCA. The results concluded that MCA was the best method because the standardized

weights for variables were higher and had the highest variation. MCA was highly and

statistically significant with PCA and placed 93% of the households in the same quintile. This

study is substantial because it created three different wealth indices to understand the strengths

and weaknesses of each measurement and informed our decision to use PCA and MCA as

methods for creating wealth indices. Although it is not used as often, MCA can be an excellent

method of measuring economic status.

Multidimensional Poverty Index:

Unlike PCA and MCA, MPI is a method used to measure SES, as opposed to wealth. The

MPI identifies people’s deprivations based on three equally weighted categories: education,

health and living standards (Alkire,S. & Santos, 2010). The MPI was developed by the Oxford

Poverty and Human Development Initiative to be a comprehensive measurement of SES that

identifies people who are deprived and non-deprived (Alkire,S. & Santos, 2010). The MPI

became a popular method of measuring poverty because economic experts Anand and Sen

declared that “the need for a multidimensional view of poverty and deprivation guides the search

for an adequate indicator of poverty” (Anand & Sen, 1997). This pushed researchers to develop a

more comprehensive way of measuring poverty that included indicators beyond household

assets.

A study conducted in seven areas in sub-Saharan Africa used MPI to measure SES and

relate SES to mortality. For creation of the wealth index, (Coates et al., 2019) modified the MPI

by removing the health indicator and only including education and household assets. They then

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split households into deprivation groups and the results showed a positive association between

SES and mortality (Coates et al., 2019). This study demonstrates the utility of using MPI as a

measure of SES and examines poverty from multiple lenses other than wealth. Although

different from PCA and MCA, MPI can provide novel insights to the overall well-being of

populations.

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Figure 1: Location of Kombewa HDSS area

Chapter 2: MethodsMethodsStudy design

The analysis in this paper is based on household material and asset data collected by the

Kombewa HDSS. We analyzed change in wealth over time.

Setting

The HDSS operates out of the United States Army Medical Research Unit- Kenya, a

research site that collaborates with the Walter Reed Army Institute of Research (WRAIR) and

Kenya Medical Research Institute (KEMRI) (Sifuna et al., 2014). The Kombewa HDSS is

located in rural Western Kenya, in Kisumu County. It covers about 369 km2and stretches along

Lake Victoria, about 40 km west of Kisumu city (Sifuna et al., 2014). The area has a total of 37

sub-locations and 357 villages and the HDSS research site is located at the heart of the HDSS

area.

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Participants

The HDSS monitored over 200,00 individuals from 46,045 households between 2011-

2018. After deleting the households that did not have more than one observation over time,

20,370 households remained, and all analyses were done on these households. We created three

time variables: T1, T2 and T3. Every household in our analysis has data for T1 and T2. During

T3, only 4413 households had available data.

Variables

As our primary outcome, we assessed how wealth changed over time. Specifically, we

looked at the change in proportion of households in quintiles and deprivations over time. As a

secondary outcome we examined SES and how specific assets changed.

Data sources

The Kombewa HDSS was established in 2007 to conduct clinical trials, nested studies

and disease surveillance (Sifuna et al., 2014). From 2008-2010, household structures were

mapped with GPI coordinates and basic demographic data from households was collected in

preparation for survey collection the following year. In 2011, baseline census surveys were

conducted and have been conducted every six months since, including, births, in and out

migration, pregnancy, morbidity and mortality. Demographic information such as education,

marital status and occupation and household characteristic data are collected every two years.

The PCA and MCA wealth indices are based on household characteristics and the MPI is based

on household characteristics as well as educational and mortality data for the Kombewa

population.

The household indicators used in the creation of wealth indices were housing materials

(source of water, type of sanitation, floor material, roof material, wall material, source of

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cooking fuel and electricity) and durable assets (car, motorcycle, bicycle, tin lamp, refrigerator,

television, radio, solar panel, hi fi stereo, electric iron, fan, cellphone, sofa set, table, flash light,

kerosene lamp, kerosene stove and motorboat). Data on livestock was also collected but

collection only started in 2014, therefore livestock was not included in our analyses. Table 1

explains all the indicators used for PCA and MCA. Table 2 details the definitions of improved

and unimproved sources for housing materials. The MPI consists of three main indicators:

health, education and standard of living. We used a modified version of the MPI and only used

child mortality (<18) as opposed to child mortality and nutrition. The Kombewa HDSS does not

collect data on nutrition and therefore was not available. The remaining indicators: education and

standard of living, followed MPI guidelines. Educational deprivations were calculated based on

years of schooling and child attendance in primary education, and standard of living was

calculated using housing characteristic and asset data. Table 3 explains all the MPI indicators

and their respective weights.

Statistical methods

All statistical analyses were conducted using R version 3.5.2, a programming software

used for statistical computing and graphics(R Core Team, 2018) . We used two methods to create

the wealth indices: PCA, MCA and used an MPI to quantify SES.

PCA is an explanatory data analysis tool that reduces the number of variables in a data set

into smaller dimensions and is a common statistical method used to create wealth indices. PCA

first transforms the initial set of n correlated variables into uncorrelated components (Vyas &

Kumaranayake, 2006). Each component is a linear weighted combination of the initial variables.

The weights from the first principal component are used as a dependent variable and a wealth

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score is calculated for each household to indicate wealth status (Vyas & Kumaranayake, 2006).

The PCA index score for household i is the linear combination:

Equation 1: y i=α 1 ( x1−x1 )

s1+

α 2 ( x2−x2 )s2

+⋯+α k ( xk−xk )

sk

where, xk and skare the mean and standard deviation of asset xk. α kis the weight assigned to each

asset, which remains the same for every household (Córdova, 2008).

MCA is also an explanatory data analysis tool that builds a matrix of uncorrelated linear

combinations to enhance the divergence in the original variables (Ezzrari & Verme, 2012). It can

also be used for qualitative variables, categorical variables and imposes less constraints on the

data (Engels et al., 2014). MCA gives more weight to indicators with smaller frequency and has

been shown to be more sensitive to deprivations than PCA (Ezzrari & Verme, 2012). The MCA

index score for household i is:

Equation 2: Zi=Ri1W 1+Ri2

W 2+…+R ijW j

where, Ri j is the response of household i to an asset j and W j is the MCA weight assigned to each

asset, which remains the same for every household (Ezzrari & Verme, 2012).

MPI is a measurement of SES instead of wealth and uses deprivation cutoffs to indicate

poverty. An n x d matrix, where n is the number of households and d is the number of

deprivations, is used. There are three main dimensions of poverty that are all equally weighted:

health, education and standard of living (Alkire,S. & Santos, 2010). Within the three categories,

there are typically ten indicators and a person is identified as multidimensionally poor if the sum

of all deprivations is 33% or greater (Alkire,S. & Santos, 2010). Because nutrition data was

missing from the HDSS, our analysis had a total of nine indicators. Each household is classified

as multidimensionally poor or non- poor based on their weighted total. This is represented by the

equation:

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Equation 3: C i=∑j=1

d=9

w j

where C i is the weighted score for household i, j is the first indicator and d is the 9th indicator. w j

represents the weight that is applied to each indicator j. Then, each household i is given a

poverty score, pk.

Equation 4: pk=1 if C i≥ 33 %pk=0 if C i<33 %

Bias

In these analyses, a large portion of data is missing. All 46,045 households collect

demographic data every year and collect household characteristic data every two years.

However, the inconsistency of household characteristic data collection resulted in missing data

and only 20,370 households were available to use for analyses. Additionally, because no

households had data for every year between 2011-2018, time frames (T1, T2 and T3) were

created. Altering the number of households and the time variable are potential biases in this

analysis.

Additionally, one potential bias for the MPI is the lack of nutrition data. The original MPI

guidelines include child mortality and nutrition as indicators for the health deprivation. Because

nutrition data was unavailable for the Kombewa HDSS, our health deprivation just included

child mortality. The child mortality indicator was weighted at 33.4% and a household is

considered multidimensionally poor at 33 %. Therefore, any household with a deprivation in the

health dimension will automatically be classified as multidimensionally poor, which can be a

potential bias.

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Chapter 3: Results and DiscussionResultsPCA and MCA

Table 4 shows the proportion of households owning particular asset items between the

three time periods: T1, T2, T3 as well as their PCA and MCA weights. During T1 and T2,

20,370 households were included and in T3, 4,413 households were included. The results show

that wealth increased over time.

PCA assigned the highest weight to owning a television followed by having improved

floor material and having electricity. It assigned the lowest weight to owning a motorboat, tin

lamp and table. MCA assigned the highest weight to owning a refrigerator followed by owning a

car and fan and the lowest weight to having an unimproved roof, owning no cellphone and no

radio. Generally, assets with higher weights represent higher wealth. The weights assigned by

PCA were greater than the weights assigned by MCA for every asset. Despite these differences,

the household wealth scores for PCA and MCA were highly and statistically significant (r =

0.956, p < .01). Additionally, PCA and MCA quintiles were highly and statistically significant

(r=0.971, p<.01) with a kappa statistic of .863.

Both PCA and MCA demonstrate a slight increase of wealth over time. Notable

improvements include wall material, electricity and ownership of a cellphone. In T1, both indices

classified 22% of households in the first quintile and then decreased to 16% in T3. Similarly,

approximately 19% of households in T1 were classified in the fifth quintile and increased to

approximately 23% in T3. Table 5 and Table 6 detail the change in quintile over time for PCA

and MCA respectively. Figure 2 and Figure 3 represent the change in quintile in a stacked bar

plot. The poorest and wealthiest quintiles changed most drastically, and the third quintile

changed the least over time.

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Further examination of the housing materials showed that roof, floor and wall material

and access to electricity increased substantially over time. However, cooking fuel remained

stagnant and sanitation and water decreased significantly. Figure 4 represents the change in

housing material over time.

MPI

Although PCA and MCA scores demonstrated a total increase of wealth, MPI results

indicated that multidimensional wealth, or SES, is staying constant over time. In all time periods,

MPI indicated that approximately 63% of households were non-deprived and 37% were

deprived. Table 7 shows the proportion of households and their deprivations for specific

indicators between the three time periods. During T1 and T2, 20,336 households were included

and during T3, 4,409 were included. Electricity, housing and assets increased while education,

mortality, and cooking fuel did not change over time. However, access to improved sanitation

and water decreased. During T1, 36.5% of households were deprived in water access and

increased to 48.5 % of households during T3. Similarly, 97.7% of households were deprived in

sanitation during T1 and increased to 99.3% during T3. Table 8 details the change in deprivation

over time and Figure 5 represents the change in a stacked bar plot.

DiscussionKey results

Our study, using longitudinal household survey data, suggests that wealth slightly

increased over time in Kombewa, Kenya. However, SES, demonstrated by the MPI, remained

constant over time. The MPI differs from PCA and MCA in its measurement of SES and its

assignment of weights. Individual assets are categorized into one indicator and housing materials

are also categorized into one indicator. However, sanitation and water are their own indicators

meaning they hold more weight in relation to other indicators when compared with PCA and

20

MCA. Because of the larger weighting and the significant decrease in water accessibility, the

MPI is a representation of the decrease in sanitation over time. The results suggest that

ownership of most assets and improved housing material increased, but necessary structural

changes to water source and sanitation decreased. Hence, while households are getting wealthier,

they are not necessarily getting healthier.

Interpretation

The increase in assets such as electricity, refrigerator and cellphone is consistent with

other rural sub-Saharan African communities and suggests that wealth is increasing (Kabudula et

al., 2017). The increase of assets can build capacity for obtaining future assets and increasing

wealth (Chowa, Masa, & Sherraden, 2012). Additionally, the GDP of Kenya increased by

approximately 60% during the time period, indicating that the increase in wealth in Kombewa is

consistent with the trends of the country(The World Bank, 2019). Furthermore, the improvement

of housing material is linked with increased wealth. Finished housing material is associated with

positive health outcomes and improved SES so these improvements indicate that Kombewa is

heading in a positive direction (Morakinyo, Balogun, & Fagbamigbe, 2018; Tusting et al., 2020).

The Kombewa housing trends are consistent with other rural African communities (Tusting et

al., 2019).

Although wealth and SES in the form of housing material has improved, the decrease in

improved water source and toilet type indicates that structural change in sanitation needs to be

made. Although the new Kenyan constitution, created in 2010, assigned responsibility of water

and sanitation to county governments, local authorities have limited resources to implement

change and create sustainable water solutions. An analysis of the implementation of water in

Kisumu County showed that urban parts of Kisumu were given financial resources for water pipe

21

implementation, yet rural areas received no financial assistance (Karan Charles, Nyonje, Leonard

Ogweno, & Peter Anyang Nyongo, 2019). This could be a possible explanation for the decrease

in improved water source in Kombewa and demonstrates that Kisumu county needs to improve

resource allocation to its rural communities. In 2009, it was shown that only 58% of rural water

sources were functional(AMCOW Country Status Overview, 2011). This was also demonstrated

in the WHO report on sanitation where rural households increased their usage of unimproved

sanitation sources (WHO & UNICEF, 2017). These findings indicate that Kombewa is consistent

with other rural African communities but should indicate to governments and households that

improvements to water and sanitation are needed to increase SES and improve health outcomes.

Conclusion

The overall increase in wealth is a positive indication of the well-being of the households

in Kombewa. After creating three wealth indices: PCA, MCA and MPI, we concluded that

ownership of assets and improved housing materials increased. The PCA and MCA indices show

that the proportion of households in wealthier quintiles were greater in T3 than T1, indicating

that wealth increased slightly over time. The MPI, however, shows that SES did not change over

time. This can be explained by the decrease of access to improved sources of sanitation and

water. More structural changes need to be implemented to fully increase the SES, well-being and

health of the Kombewa community.

22

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Appendices

Table 1: PCA and MCA indicators and descriptions

Indicator DescriptionSource of drinking water Improved / Non-improvedType of toilet facility Improved / Non-improvedFloor material Improved/ Non-improvedRoof material Improved/ Non-improvedWall material Improved/ Non-improvedType of cooking fuel Improved/ Non-improvedElectricity Yes /NoMotorcycle Yes /NoBicycle Yes /NoTin lamp Yes /NoRefrigerator Yes /NoTelevision Yes /NoRadio Yes /NoSolar Panel Yes /NoHi Fi Stereo Yes /NoElectric Iron Yes /NoFan Yes /NoCell phone Yes /NoSofa Yes /NoTable Yes /NoFlashlight Yes /NoKerosene lamp Yes /NoKerosene stove Yes /NoMotorboat Yes /No

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Table 2: Definitions of improved and unimproved housing characteristics

Indicator Improved Non- improvedSource of drinking water Buying taps

Piped into compoundPiped to neighborsPiped at water kioskPublic tapBorehole River / streamProtected spring water

Buying tanksPublic well Unprotected wellUnprotected spring waterRainwaterTanker truckBuying water from river/ streamPond/ lake

Type of toilet facility Flush toilet (own and shared)VIP latrine (own and shared)Flush trench

Pit latrine (own and shared)No facility / bush

Floor material Wood planksPolished wood / vinyl / tilesCement

Mud / dung/ sand

Roof material Metal sheet/ tinIron sheet Tiles

Grass / thatchPlastic sheetCardboard sheetWood / timber

Wall material Wood / timberIron sheetCement mudTin/ metal sheet

MudObservation bricksCardboards sheetsCarton / plastics

Type of cooking fuel GasElectricity

Kerosene/ ParaffinCharcoalFirewoodAnimal wasteCrop residue / saw dust

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Dimensions of Poverty

Indicator Household is deprived if: Relative Weight

Education Years of schooling No household member aged 10 years or older has completed six year of schooling

16.7%

School attendance Any school aged- child is not attending school up to the age at which he/she would complete class 8.

16.7%

Health Child mortality Any child (less than 18) has died in the family

33.4 %

Standard of Living

Cooking fuel The household uses unimproved source 5.6%Sanitation The household uses unimproved source 5.6%Drinking water The household uses unimproved source 5.6%Electricity The household has no electricity 5.6%Housing Housing materials for at least one roof,

walls or floor are unimproved5.6%

Assets The household does not own more than one of these assets: radio, TV, telephone, bicycle, motorbike or refrigerator AND does not own a car

5.6%

Table 3: MPI indicators, descriptions and respective weights

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Table 4: Proportion of Assets and Respective PCA and MCA Weights Over Time

Assets Categories Percentage Of Assets Owned Weights

     T1(n = 20370)

T2 (n = 20370)

T3 (n = 4413) PCA Index MCA Index

Source of drinking water

Improved drinking water

63.5 56.4 51.6 0.120.00005058

Unimproved drinking water

36.5 43.6 48.4 -0.0000732

Toilet facility Improved toilet 3.4 0.6 0.7 0.25 0.00089Unimproved toilet 96.6 99.4 99.3 -0.00001706

Floor material Improved floor 32.1 46.2 51.9 0.58 0.0003435Unimproved floor 67.9 53.8 48.1 -0.0002329

Roof material Improved roof 83.4 93.6 96.7 0.27 0.0000456Unimproved roof 16.6 6.4 3.3 -0.0003809

Wall material Improved wall 18.3 29.0 34.0 0.32 0.0002717Unimproved wall 81.7 71.0 66.0 -0.00008893

- - -

Cooking fuel Improved cooking fuel

0.8 0.8 0.7 0.31 0.001674

Unimproved cooking fuel

99.2 99.2 99.3 -0.00001361

- - -Electricity Owns electricity 6.4 13.1 17.6 0.61 0.0008788

No electricity 93.6 86.9 82.4 -0.0001034- - -

Car Owns car 1.1 1.0 1.4 0.40 0.001864No car 98.9 99.0 98.6 -0.0000209

- - -Motorcycle Owns motorcycle 2.1 2.7 3.0 0.26 0.0007862

No motorcycle 97.9 97.3 97.0 -0.0000199- - -

Bicycle Owns bicycle 28.9 13.6 10.8 0.25 0.0002383No bicycle 71.1 86.4 89.2 -0.00006047

- - -Tin lamp Owns tin lamp 3.5 78.9 82.8 < 0.1 -0.00003722

No tin lamp 96.5 21.1 17.2 0.00003075- - -

Refrigerator Owns refrigerator 1.2 1.2 1.8 0.49 0.002116No refrigerator 98.8 98.8 98.2 -0.0000273

- - -

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Television Owns television 11.0 12.4 14.6 0.67 0.0008949No television 89.0 87.6 85.4 -0.0001217

- - -Radio Owns radio 70.2 72.9 74.5 0.42 0.0001275

No radio 29.8 27.1 25.5 -0.0003255- - -

Solar panel Owns solar panel 3.6 20.6 28.6 0.26 0.0003184No solar panel 96.4 79.4 71.4 -0.00005049

- - -Hi fi stereo Owns hi fi stereo 2.8 4.8 6.3 0.36 0.0008657

No hi fi stereo 97.2 95.2 93.7 -0.0000365- - -

Electric iron Owns electric iron 2.5 3.4 3.8 0.56 0.001568No electric iron 97.5 96.6 96.2 -0.00004889

- - -Fan Owns fan 0.8 0.7 1.8 0.35 0.001829

No fan 99.2 99.3 98.2 -0.00001594- - -

Cellphone Owns cellphone 69.1 82.7 81.6 0.39 0.0001063No cellphone 30.9 17.3 18.4 -0.000345

- - -Sofa Owns sofa 60.3 65.6 78.0 0.42 0.0001516

No sofa 39.7 34.4 22.0 -0.0002741- - -

Table Owns table 96.6 95.4 96.5 0.11 0.00001123No table 3.4 4.6 3.5 -0.0002716

- - -Flashlight Owns flashlight 43.2 28.9 27.8 0.39 0.000256

No flashlight 56.8 71.1 72.2 -0.0001394- - -

Kerosene lamp Owns kerosene lamp 52.6 32.4 20.9 0.37 0.00022001No kerosene lamp 47.4 67.6 79.1 -0.000149

- - -Kerosene stove Owns kerosene stove 28.3 15.3 17.4 0.45 0.0004208

No kerosene stove 71.7 84.7 82.6 -0.0001144- - -

Motorboat Owns motorboat 0.0 0.1 0.3 <0.1 0.00149No motorboat 100.0 99.9 99.7 -0.00000121

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Table 5: PCA Quintile Change

Quintile  T1

(n = 20370)  T2

(n = 20370)  T3

(n = 4413)1 22.1% 18.8% 16.1%2 19.9% 20.6% 17.8%3 19.8% 20.3% 19.9%4 19.1% 20.1% 23.3%5   19.1%   20.2%   22.8%

Table 6: MCA Quintile Change

Quintile  T1

(n = 20370)  T2

(n = 20370)  T3

(n = 4413)1 21.7% 19.2% 16.2%2 21.0% 19.6% 17.0%3 19.6% 20.4% 20.2%4 18.9% 20.4% 22.7%5   18.8%   20.3%   23.9%

Table 7: Percentage of asset deprivations for MPI

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Indicator Categories Percentage of households in deprivation categoryT1

(n = 20336)T2

(n = 20336)T3

(n = 4409)Years of schooling Deprived 20.6 20.6 19.7

Non-deprived 79.4 79.4 80.3

School attendance Deprived 6.3 6.3 8.1Non-deprived 93.7 93.7 91.9

Child mortality Deprived 4.7 4.7 4.5Non-deprived 95.3 95.3 95.5

Cooking fuel Deprived 96.8 97.7 98.0Non-deprived 3.2 2.3 2.0

Sanitation Deprived 97.7 99.6 99.3Non-deprived 2.3 0.4 0.7

Drinking water Deprived 36.5 43.6 48.5Non-deprived 63.5 56.4 51.5

Electricity Deprived 93.6 86.9 82.4Non-deprived 6.4 13.1 17.6

Housing Deprived 84.3 73.3 68.3Non-deprived 15.7 26.7 31.7

Assets Deprived 37.5 31.2 30.4Non-deprived 62.5 68.8 69.6

Table 8: MPI Deprivation over time

Figure 2: Stacked bar plot of PCA quintile change over time

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DeprivationT1

(n=20336)T2

(n=20336)T3

(n=4409)Deprived 63.4% 63.7% 63.0%Non-deprived   36.6%   36.3%   37.0%

Figure 3: Stacked bar plot of MCA quintile change over time

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Figure 4: Housing material change over time

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Figure 5: Stacked bar plot of MPI deprivation change over time

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