Strathmore 
UNIVERSITY 
INCOME DISTRIBUTION AND HOUSEHOLD DEBT; lVIACROECONOlVIICS OF 
KEEPING UP WITH THE JONESES. 
LANG' AT LYNN CHEMTAI 
082026 
Submitted in partial ful1i1Jment of the requirements for the Degree of 
Bachelor of Business Science in Financial Economics at Strathmore University 
Strathmore Institute of Mathematical Sciences 
Strathmore University 
Nairobi, Kenya 
February 2018. 
DECLARATION 
I declare that this work has not been previously submitted and approved for the award of a 
degree by this or any other University. To the best of my knowledge and belief, the Research 
Project contains no material previously published or written by another person except where due 
reference is made in the Research Project itself. 
© No part of this Research Project may be reproduced without the permission of the author and 
Strathmore University 
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This Research Project has been submitted for examination with my approval as the Supervisor. 
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Strathmore Institute of Mathematical Sciences 
Stratlunore University 
ABSTRACT. 
The purpose of this study is to examine the factors that affect household debt. The variable of 
interest is income inequality and its effect on household debt. The paper uses the generalized 
method of moments (GMM) method to estimate the relationship between household debt and 
income distribution. Time series data is used for the period between 1980-2015. The study also 
examines other factors that affect household debt such as lending rates, growth in income rates, 
unemployment rates and gross domestic product per capita rates. The theoretical framework 
focuses on the permanent income hypothesis and consumption smoothing which explains 
consumer spending throughout his lifetime and we can infer consumer borrowing from this. The 
main hypothesis of the study is that income distribution affects household debt to a large extent 
because of the effect of keeping up with the J oneses on individuals. ~ost of the research done in 
this area focuses on developed countries and not much has been done on developing countries 
such as Kenya. The fmdings in this study can be used by policy makers to better understand the 
impact of household debt on the economy. 
KEY WORDS: Household debt, income distribution, income inequality. income 
distribution, keeping up with the Joneses. 
ii 
Table of Contents 
CHAPTER ONE; INTRODUCTION .......... ... .................................................... ... ............................... 1 
1.1 Background ... ............................ .... .. ........... ... ....... ... ... ..................... .. ... .. ... ............. ...................... 1 
1.2 Problem statement. ............ .......................................................................................................... 3 
1.3 Research question .................................... .................................................................................... 4 
1.4 Research objective ....................................................................................................................... 4 
1.5 Research hypotheses ........................... .. ........................................................ .......... ..................... 4 
1.6 Significance and contribution of the research ............................................................................. 4 
CHAPTER TWO; LITERATURE REVIEW ................................................... ...... .......... .. ................. 5 
2.1 Theoretical literature review . ............... ............ .......... ......... ...... .. .... .. ...................................... .... 5 
2.1.1 Permanent income theory ..................................................................................................... 5 
2.1.2 Life cycle hypothesis .......... ...... .......... ...... ........ ........... .... ....................................................... 5 
2.1.3 Consumption smoothing ................................................... ....... ............................................. 6 
2.1.4 Theoretical approaches to income inequality ............................................................... ........ 6 
2.2 Empirical literature review ................ ............................................................ ............................. 7 
2.2.1 Income distribution and household debt. .................... ...................... ...... .. ........................... 7 
2.2.2 Household debt and the economy ......................................................... ...... .......... .............. 11 
2.3 Summary of the empirical literature review ............................................. .. .............................. 12 
2.4 Research gap ... ... ...... ............ ...... .... ........................................................... .... ............................. 12 
CHAPTER THREE; METHODOLOGY ........ ..... ...... ... ... .... ............ ..... .. .... ... ....... .. .... .. ..... ....... ... ...... 13 
3.1 Introduction ... ........ ....... .............. ... ........................................................... ............. ...... .............. 13 
3.2 Research design ................... ......... .......... .... ... ........ ......................................... .. ... ....................... 13 
3.3 Population and sample of study ........................ .............. ........... ... ............................. .... ............ 13 
3.4 Model specification and estimation ................. ............ ...................... .... ........ ............... ............. 13 
3.4.1 Theoretical underpinning .............................................................. ..................................... 13 
3.4.2 Justification of model. .... ................. ... ........ ... .. .............. ...... ........ .. .. ......... .... ..... ........ ... ..... .. 13 
3.4.5 Variables ................... .. .. .. .... ......... .... ................ ... .. .. .. .......................... ........ ..... ............. .. ..... 14 
3.4.6 En1pirical model. .................. ... ....... ...... ......... ........ ....... ............. .. ........................................ 15 
3.5. Data Analysis ....... ................... ................... .... .... ... ... ... ........ ...................................................... 16 
CHAPTER FOUR; RESULTS AND DISCUSSIONS ....................................... ...... .......................... . 17 
4.1. Introduction .............. ... ................. ..... .............................. ..... .. ................ ...... ... ...... ................... 17 
Ill 
4.2. Descriptive statistics .. ...... ... .... .. .................................. .. ........ ..... ... .. ... ...... .. ....... ..... .... .. .. .. ....... ... 17 
4.3. Regression results ... ...... .... ..... .......... .... .. ............. .. ...... ........... ... ........ ..... ..... .. .. ........ ...... . · ........... 18 
4.4 Diagnostic checks . .. ..... ............ ..... ... .... ....................... .................. .... ..... .. .... ....... ....... ........... ...... 19 
4.4.1 Sargan-Hansen tests .. ....... ...... .. ... ...... ..... .. ...... ..... .. ... ................................. ... ... .. .. ...... ..... .. ... 19 
CHAPTER 5; CONCLUSIONS AND RECOM.lVIENDATIONS .. .... .. ...... .... ..... .. .... .... ... ..... .... ..... ..... 20 
5.1. Conclusion ............... ..... ............... .. ..... ....... ... .. .. ........ ...... ......... .. ..... ..... .. ..... .. ...... .... ..... ...... ........ 20 
5.2. Policy recommendations ... .... ......... ....... ..... ..... ...................... ....... .... .... .................... ... .... .. ... .... . 20 
5.3. Areas of further research ...... ....... .. .... ..... ..... ... ..... ... .. ........ ................... ... .... .. .. ...... ... ... .. .. .... .. ... . 21 
5.4. Limitations .... ... .... .......... ... ....... .... .............. ... ... ..... ........ .......... ...... ..... ..... ..... ... ....... ..... ... ........... 21 
References .... ...... .. ... ... ... ...... ..... ... .... .. ... ... .... .... ... ...... ... .......... .... ... .... ... ... : .. .. ....................... ....... .. ....... .. 22 
iv 
CHAPTER ONE; INTRODUCTION. 
1.1 Background. 
Debt can be classified into three categories; mortgage debt, revolving debt and non-revolving 
debt (Christen and Morgan, 2005). Mortgage debt is debt created by a mortgage and secured by 
mortgaged property. Revolving debt is the kind of debt offered by credit cards and is usually an 
easy way to get credit. Non-revolving debt, also known as installment debt, is debt that one 
typically repays in regular monthly instalments featuring as fixed amount such as car loans. 
Household debt in Kenya in 2015 was 34% of the country's Gross Domestic Product (GDP) 1 and 
has been on an upward trajectory creating a cause for concern. This continued increase in 
personal debt can be attributed to many factors, Munyoki and Okech (2012), such as interest 
rates and expectations of future increase in income. Ease of accessing credit' through facilities 
such as M-shwari in Kenya has also led to an increase in debt. In many developed countries such 
as The United States of America, buying goods on credit is the most popular means of payment 
and this is a trend that is being adopted in the Kenyan market. More and more people are taking 
up loans to fmance their daily expenditure creating a need to fmd out the main drivers of 
increased personal debt in Kenya (Munyoki and Okech, 2012). 
One of the main drivers of Household debt is income inequality (Ryoo and Yun, 2013). Income 
inequality is the extent to which income is distributed in an uneven manner among a population. 
Corrado Gini ( 1921 ), invented the Gini coefficient which measures the inequality among values 
of a frequency distribution for example levels of income. The measure varies between '0' 
reflecting complete equality an.d '1' indicating complete inequality. Pressman and Scott (2009) 
define the Gini coefficient as the ratio of income received by the top 10 percent of households' 
relative to the bottom 10 percent. It is a great measure of income inequalities but it is relatively 
insensitive to changes in income at the top of the distribution and it doesn't capture changing 
income 1ising concentration at the top of the distribution. The Gini coefficient in Kenya is 0.445 
reflecting a high level of inequalitY. Figure 1 below shows the Gini coefficient and household 
debt data in Kenya from 1976 to 1999. The trend shows that there is probable relationship 
between income inequality and household debt in Kenya. 
1 Source; World bank website, 2017. 
2 http:/ /i neq ualiti es.sid int.net/kenya/a bridged/gi ni-coefficient/ 
1 
Fast economic growth in economies widens income inequality (Yun, 2012). This is a possible 
explanation of the growth in income inequalities in Kenya. This increased income inequality 
contributes to increased consumer borrowing and this is because of conspicuous consumption 
(Christen and Morgan, 2005). 
Figure 1 Gini coefficient and household data 
Gi ni coefficient and househo ld data 
60 
55 
50 ,.,.-
i!S 
-------------------~ -----......~ 
.10 
35 
30 
25 
20 
1 ~ 
.• ::J 
10 
5 
0 
ye:.us 
Source; World bank and povcal.net data,2017 
Veblen (1991) states that conspicuous consumption of valuable goods is a means of reputability 
to the gentlemen of leisure. Conspicuous consumption is a phenomenon that has forced 
consumers with smaller incomes to use debt to keep up their consumption levels relative to 
consumers with larger incomes (Cillisten and Morgan, 2005). This concept is what is referred to 
as 'Keeping up with the Joneses' which is to always want to own the same expensive objects and 
do the same things as one's friends or neighbors because one is worried about seeming less 
important socially than they are (Ryoo and Yun, 2013). Therefore, one strives beyond their 
means to keep up socially and fmancially with others in one's social circle or neighborhood. 
Tsoukis (2007) further explains the aspects of 'keeping up' and 'catching up' with the joneses to 
have the same meaning as explained above. Households care about their relative position in the 
2 
economy, that is, they want to catch up with the Joneses and are willing to take a loan to do so 
(Konig and Grobl, 2014). This is a trend that can be seen in the Kenyan economy today. 
There are many drivers of personal borrowing. The main argument for the increase in personal 
debt stems from the permanent income hypothesis (Friedman, 1957). This explains that people 
smooth consumption in their early life because they expect larger incomes in their future. 
Therefore, they use debt for consumption expecting to earn more in future. The problem with 
this theory in the modem age is that people tend to borrow for luxury items and live beyond their 
means and are later unable to pay for their debts. A household cannot consume more than the 
sum of the present discounted value of its labor income and its current net worth (Christen and 
Morgan, 2005). 
The effect of consumer borrowing on economies has been viewed by many researchers to be a 
blind spot (Christen and Morgan, 2005; Tsoukis, 2007; Yun, 2012). This is because of its varied 
effects on the economy. On one hand, increase in consumer borrowing increases the money 
supply in the economy which boosts consumption and increases the Gross Domestic Product 
(GDP). On the other hand, an increase in money supplv increases inflation among other 
challenges such as personal distress when one is unable to repay a loan. A conclusive agreement 
is yet to be reached from an empirical perspective. This further creates the need to evaluate the 
factors that affect personal debt in Kenya and the effect on the economy. 
1.2 Problem statement. 
According to Christen and Morgan (2005) and Yun (2015), income inequality is the main factor 
that drives household debt. Household debt is important because it enables households to acquire 
goods and services when needed and pay for them later and this boosts consumption growth 
(Kapeller and Schutz, 2012). 
Despite the positive impact of household debt, its continued increase driven by income 
inequalities can be unsustainable in future (Tsoukis, 2007). Increased household debt ultimately 
leads to fmancial fragility and a higher likelihood of a financial crisis (Coibion, Gorodnichenko, 
K.udlyak and Mondragon, 2014). Other consequences of increased household debt include a 
decline in household savings rate, Aldo and Massimo (2009), and a further increase in income 
inequalities (Kapeller and Schutz, 2012). This has created the need to examine the factors that 
drive household debt in Kenya. 
3 
This study is further informed by the fact that studies done in this field have been done mostly in 
developed countries such as The United States of America, (Christen and Morgan, 2005; Zezza, 
201 0; Pressman and Scott, 2009) creating the need to conduct the study in a developing country 
because the macroeconomic dynamics of the two economies differ. As a result of this, there is 
need to take a closer look at household debt in Kenya and its root drivers. 
1.3 Research question. 
o Is there a relationship between income inequalities and household debt in Kenya? 
1.4 Research objective. 
• To determine the relationship between income inequalities and household debt in 
Kenya. 
1.5 Research hypotheses. 
• There is a positive relationship between income inequalities and household debt. 
1.6 Significance and contribution of the research. 
This study investigates the impact of income inequalities on household debt and its impact on the 
Kenyan economy. The significance of this research is to inform policy on various aspects of 
household debt and income inequality. Sustainable growth path cannot be achieved unless action 
is taken to revert to an income distribution which allows for sustainable growth in the living 
standards of the median household. Focus should be directed on formulating policy which 
promotes economic growth with wide spread effects across all income level especially those who 
are at the lower quartile. There is need for policies that would help stabilize financial systems 
and prevent instability while dealing with income inequality. This study seeks to contribute to 
existing literature by focusing on a developing country, Kenya, because most of the r.esearch 
done in this area has been done on developed countries such as The United States of Ame1ica. 
The results of this study will be able to be compared with results of other studies done in 
developed countries with different macroeconomic dynamics. 
4 
CHAPTER TWO; LITERATURE REVIEW. 
2.1 Theoretical literature review. 
2.1.1 Permanent income theory. 
The permanent income hypothesis was advanced by Milton Friedman ( 1957). He explains that 
households spend money at a level consistent with their expected long-term average income. 
Households will only save if their current income is higher than the anticipated level of 
permanent income and borrow when their income is lower than their anticipated level of 
consumption. This implies that changes in consumer behavior are not predictable and depend on 
individual expectations. Its implications on economic policies are that even an increase in 
income in the economy influenced by economic policies may not result in increased consumer 
spending until workers adjust their expectations about future incomes. The main insight of the 
model is that households' smooth consumption levels during their lifespan. 
This model, however, focuses on demand-side determinants of household borrowing and treats 
the supply of funds as perfectly elastic. Households face liquidity constraints and the structure of 
the lending market have a significant effect on the extent of household borrowing (Christen and 
Morgan, 2005). 
2.1.2 Life cycle hypothesis. 
This theory was advanced by Modigliani and Brumberg in 1954. The life-cycle hypothesis 
suggests that individuals plan their consumption and savings behavior over their life-cycle. They 
intend to even out their consumption in the best possible manner over their entire lifetimes, doing 
so by accumulating when they earn and dis-saving when they are retired. Their saving and 
spending behavior adjusts so that their consumption does not fluctuate across the stage of their 
life. It assumes that the household has unlimited ability to borrow, there is no risk of default 
associated with holding debt, the cost of borrowing does not vary, and households have perfect 
foresight about their future non-fluctuating earnings. These assumptions are not true in reality. 
Tobin (1967) advanced the theory and suggested that in the early stages of a life-cycle, a 
household might borrow up to levels that would make its current net worth negative to afford the 
costs of living. According to Tobin's variant of the life-cycle model, it is optimal for younger 
individuals to borrow and attain a higher level of consumption than one allowed by their income. 
The idea is that younger households would expect their productivity and eamings to increase in 
5 
the future. As the household ages, its income exceeds its consumption requirements, resulting in 
an increasingly positive net worth. The increased income that comes with middle age will allow 
the household to pay off its early life's debts and accumulate savings for retirement as it would 
in the standard version of the model. 
2.1.3 Consumption smoothing. 
Consumption smoothing refers to the way in which people try to optimize their lifetime standard 
of living by ensuring a proper balance of spending and saving during the different phases of their 
life3. Some households may choose to borrow and over spend while putting off savings to enjoy 
a higher standard of living. 
Household debt occurs mostly due to consumption smoothing. For households to achieve 
maximum utility, they rearrange their income flows over their whole life to smooth consumption. 
This helps agents to smoothen their consumption when there is unsteady and erratic income 
flow. Households would tend to borrow to fund current consumption in periods when income is 
low, relative to average income over their lifetime, with a view to then repaying the loans in 
periods when income will be high, relative to average lifetime income (Aldo and Massimo, 
2009). 
2.1.4 Theoretical approaches to income inequality. 
Income inequality theories can be divided into two main traditions, quantitative and structural 
theories. (Guidetti and Rehbein, 2014) . The quantitative tradition is linked to the development of 
economics and has focused on relation between inequality and growth. A free market for goods 
and labour leads to an increasing division of labour and thereby to economic growth. The overall 
product is distributed among the population so that everyone profits from this growth. However, 
the product is distributed not equally but proportionately and this leads to income inequalities in 
the economy. The main argument in this theory is that inequality exists and persists because 
different social groups have unequal access to socially relevant resources and power. This 
unequal distribution persists because each generation passes on its resources to the next, so that 
power and resources remain in the family remain with wealthy families. The structural approach 
deals with the basis of class divisions in the society. In unequal societies, only some segments of 
3 https:/ /www .cffc.org. nz/assets/Docu ments/Reti rem ent -I ncome-Positi on-Paper -6-Lifeti me-Consumption-
Smoothing-2012.pdf 
6 
society perform interaction with the world as labour, while others reap the profits without having 
to perform labour. Against this background, inequality is merely the surface of the invisible 
structure, which consists in the unequal distribution of capital and labour. Both theories explain 
the existence of income inequalities in economies. 
2.2 Empirical literature review 
2.2.1 Income distribution and household debt. 
The most influential research done in this field, arguably, is the research done by Christen and 
Morgan (2005) in the US using OLS regression models in which the study shows that rising 
income inequality has substantially contributed to increased consumer borrowing. The study 
fmds that income inequality affects all components of total household debt but the impact is 
strongest on non-revolving debt (installment loans) which is used to fmance the purchase of 
consumer durables. The study argues and provides evidence that the income inequality effect on 
consumer borrowing is a result of conspicuous consumption. Rising income inequality has 
forced households with smaller income gains to use debt to keep up their consumption level 
relative to households with larger income gains. The study analyzes different types of debt 
separately and show the effect of income distribution on them. It also confirms the existence of a 
positive relationship between income inequality and household indebtedness beyond what is 
predicted by the standard life-cycle/permanent-income model. This is important as it involves 
theoretical literature in the model used. 
Another study done in the US that distinguished the long run and short run effects of income 
inequality was done by Iacoviello (2008). The study used data in the US from 1963-2003 using a 
modified stochastic growth model to establish the trend and cyclical behavior of household debt 
and the diverging patterns in consumption and wealth inequality over time. The study finds that 
short run changes in household debt can be explained by business cycle fluctuations (Iacoviello, 
2008). During recessions, people tend to bon·ow more to smooth out their consumption. At 
cyclical frequencies, household debt moves together with economic activity. At an aggregate 
level, changes in household debt can be explained by macroeconomic developments (Iacoviello, 
2008). As macroeconomic developments occur within a country, financial systems allocate 
resources better between those who have funds and those in need of them thus those who need 
credit can easily access them. This has a positive relationship with a countries economic activity. 
7 
Long run changes in household debt can be explained by the concurrent increase in income 
inequality (Iacoviello, 2008). Credit constraints also affect cyclicality of debt behavior. In good 
economic times, credit agents lend more and credit constrained borrowers can borrow more 
(Iacoviello, 2008). This research was important in distinguishing the short term and long-term 
factors that affect household debt. 
In the USA, the highest debt-to-income ratios are found at low and middle-income sections of 
the distribution (Aldo and Massimo, 2009). This debt is mostly credit card and non-bank debt 
which is highest among low income households. Rising household debt can be viewed as the 
outcome of persistent changes in income distribution and growing income inequalities (Aldo and 
Massimo, 2009). Households may go into debt to keep up with the consumption of their 
neighbors, some of whom have seen rising income levels with increases in income inequality. 
The state of unequal income distribution between workers and rentiers4 , for example, leads to 
consumption inequality which triggers the workers' desire to emulate rentiers. This raises the 
workers ' borrowing and stimulates aggregate demand. An increase in aggregate demand shifts 
income distribution in favor of rentiers which leads to the decline in the workers ' income share 
and their consumption. Households ability to extract equity from the value of their houses exerts 
positive impact on consumer expenditure and consumer borrowing (Aldo and Massimo, 2009). 
This is because the houses serve as collateral for borrowing and this increased indebtedness as 
households borrow more. In the U.S., the increase in income inequality experienced over the past 
25 years has not been accompanied by a corresponding increase in consumption inequality (Aldo 
and Massimo, 2009). Consumption rates rise even when there in income inequality because of 
the inelasticity of consumption with respect to reductions in households' real incomes, the 
availability of new goods and services, the drive for a continuous rise in the standard of living 
and imitation of the upper classes (Aldo and Massimo, 2009). 
Pressman and Scott (2009) use data from the Survey of Consumer finances from 1983-2005 to 
test the relationship between consumer debt and poverty and inequality in the US on different 
types of credit loans. The study shows that a possible reason of increased indebtedness is slow 
growth in real wages and real incomes. Income inequality growth would then spur work effort 
and an entrepreneurship spirit leading to more innovations to increase households' income 
4 A rentier is a person living on income from property or investments. 
8 
(Pressman and Scott, 2009). The study argues that income inequality contributes to economic 
growth and rising living standards but to know the true impact of greater inequalities on 
economic growth, health and crime, the measures of inequalities should be redone to take interest 
payments on consumer debt into account. This serves as the main contribution to this topic. 
The study done by Coibion et al (2014) suggests a hypothesis for the dramatic rise in household 
borrowing that preceded the financial crisis is that low-income households increased their 
demand for credit to fmance higher consumption expenditures to "keep up" with higher income 
households. Using household level data on debt accumulation during 2001-2012 using OLS 
estimation, they show that low-income households in high-inequality regions accumulated less 
debt relative to income than their counterparts in lower-inequality regions, which negates the 
hypothesis (Coibion, et al.,2014). The study finds that banks ultimately channel more credit 
toward lower-income applicants in low-inequality regions than high-inequality regions. Banks 
are most likely to channel relatively more credit to low-income applicants when the level of local 
inequality is low. Lower-income mortgage applicants in high inequality regions are rejected 
more frequently and pay higher mortgage rates than similar applicants in low-inequality regions. 
The study uses data over the course of the 2000s in the US and construct local income inequality 
measures which are important in accurately describing the income distribution of the region. 
Ryoo and Kim (2014) extend Kaldor's theory of income distribution to include workers' debt 
accumulation and their motive to emulate rentiers' consumption. Their results show that (i) the 
interaction between income distribution and emulation can produce instability and (ii) instability 
is more likely when the workers' emulation motive is strong and bankers' lending decisions are 
highly accommodating. This makes households to rely more on debt financing to maintain their 
lifestyles as the cost of living and expenses for essential goods and services such as housing 
prices, medical and education costs and inflation continue to increase over time (Ryoo and Kim, 
2014). Availability of credit leads to an expansion of credit supply and an increase in household 
debt. Household debt might not have grown to the unprecedented level it is now if not for the 
profound changes in the fmancial sector (Ryoo and Kim, 2014). The introduction of new 
financial products and practices has reduced lending standards and provided easier access to 
consumer loans. This is true especially in Kenya with the introduction of mobile money and 
9 
services such as Mshwari. Deterioration of lending standards securitization and financial 
deregulation has promoted the access to credit. Financial sector therefore plays an important role. 
Thompson (20 16) looks at the consequence of rising concentration of income at the top of the 
distribution as increased borrowing. The paper uses an OLS regression for data form 1697-2013 
in the US. The following are his fmdings; When financial institutions use income inequality 
information in a region to help them target credit, it affects credit accessibility of the middle and 
low-income earners (Thomson, 20 16). Rising top incomes fuel increased housing consumption at 
the top, which in turn inspires debt-fmanced housing consumption further down the distribution. 
Rising disposable income at the top of the distribution could help to bid up the price of land and 
housing in affluent neighborhoods which further increases household debt to finance housing 
(Thomson, 2016). Low-income households in high-inequality areas accumulate less debt and 
have lower credit limits than their low-income counterparts in areas with lower inequality 
(Thomson, 2016). The study also fmds a positive relationship between income distribution and 
increased borrowing. This paper contributes to literature as it divides the distribution into 
quartiles and analyzes them separately. 
Loschiavo (2016) research question was on whether regional income inequality affect a 
household's likelihood of being indebted. This question is addressed by using survey data on 
Italian households from 2004-2012 through various regression models. The analysis shows that 
inequality in the regional income distribution has a negative effect on the probability of being 
indebted. In addition, richer households living in regions with greater income inequality have a 
greater likelihood of being indebted than similarly rich households residing in regions with low 
income inequality (and vice versa for poorer households) . This is because banks may use their 
local income inequality and a household's position in the income distribution to make inferences 
about an applicant's underlying default risk. The more unequal a region, the more the indebted 
households are concentrated among the richer ones (Loschiavo, 20 16). This study was in1portant 
as it introduced the importance of financial intermediaries in determining household debt. 
Wildauer (2016) investigates factors driving US household borrowing up to 2007 using a 
household debt accumulation function and regression. He contributes to literature by examining 
the impact of shifts in income distribution and asset prices on household borrowing. In the U.S 
the interaction of rising asset prices and the polarization of the income distribution explains a 
10 
large part of the increase in household borrowing before the crisis in 2008 (Wildauer, 20 16). The 
fmdings indicate that it is the interaction between the concentration of income at the top of the 
distribution and rising real estate prices which explains a large fraction of the increase in 
household borrowing prior to 2008. The research analyzed the role of distribution and asset 
prices together rather than separately which is a flaw as it is important in determining the 
relationship of the variables to one another. 
Soh, Chong and Chuah (20 16) sought to fmd out if the different types of household credit affect 
income growth and income inequality differently in Malaysia using different regression models 
for data from 1997-2015. The study fmds that different types of household credit matters. On 
income growth, both macro- and micro-analyses consistently find housing credit to be positively 
associated with future income growth, while consumption credit shows no significant evidence. 
On income inequality at the macro-level, housing credit in terms of total net disbursements is 
positively related to income inequality. However, the micro-level data, which investigate the 
proportion of households with housing loans, fmd a negative relationship with income inequality. 
This implies that financial inclusion that improves access to housing credit for more households 
would likely reduce income inequality and further accumulation of housing credit for existing 
borrowers may worsen income inequality given the likely concentration of housing wealth 
among richer households (Soh, Chong and Chuah, 2016). This is important to literature as it 
analyses macro and micro level data separately. 
In the research by Afzanizam et al (2017) the Generalized Methods of Moments (GMM) 
technique is employed for 55 countries and covers the period from 2000 to 2012. The results 
showed that there is a significant positive relationship between income gap and household debt. 
In addition, the level of indebtedness is deemed to be persistent throughout our sample countries, 
suggesting that households will remain in debt-trap. They conclude that household debt is highly 
influenced by macroeconomic stability, financial sector development, and government policies 
(Afzanizam, et al. , 2017). 
2.2.2 Household debt and the economy. 
A rise in inequality driven by an increase in the share of income going to those at the top of the 
income distribution induces the latter to save more which lowers interest rates and induces 
poorer households to borrow more. This ultimately leads to more financial fragility and a higher 
11 
likelihood of a financial crisis (Coibion, et al.,2014). In the USA, household debt outstanding as 
a share of GDP increased from about 45 per cent in 1975 to nearly 100 per cent in 2006 in the 
USA (Ryoo and Kim, 2014). This rapid accumulation of household debt was unsustainable 
which is ev~denced by the Great Recession. Prior to the crisis, fmancial markets allowed the 
private sector to fmance expenditure beyond real disposable income and provided a powerful 
channel for the globalization of the crisis (Zezza, 2010). 
The rise of inequality may affect macroeconomic evolution. If consumption grows less than 
proportionally with wealth, that is the rich consume relatively less with respect to wealth than the 
poor, then increasing inequality may result in insufficient aggregate demand (Russo, Riccetti and 
Gallegati, 2016). 
2.3 Summary of the empirical literature review. 
The main fmding in the empirical literature review is that income inequalities affect household 
borrowing (Ryoo and Kim, 2014; Afzanizam, et al. , 2017; Wildauer, 2016; Christen and 
Morgan, 2005). The studies have been conducted in developing countries and t:1e development 
of fmancial institutions has greatly impacted this fmding (Iacoviello, 2008). Wildauer (2016) 
finds that a redistribution of income towards the top reduces aggregate consumption but as long 
as the fmancial sector accommodates households ' demand for credit a positive relationship 
between income inequality and consumption is possible. Mathias and Krause fmd a positive 
correlation of consumer credit with consumption and labor, while there is a negative co-
movement between consumer credit and the real wage. The study shows that if real wage growth 
is slower than consumption growth then income inequality and household debt will continue to 
increase in the economy. 
2.4 Research gap. 
Most of the studies conducted have been done on developed countries and little research has 
been done on Developing or emerging economies. Research done on Kenya will contribute to 
related literature in a developing country and a comparison will be able to be drawn between 
results obtained in a developed and developing country. 
12 
CHAPTER THREE; METHODOLOGY. 
3.1 Introduction. 
This chapter presents the methodology for the study. The purpose of this paper was to find out 
the impact of income inequalities on household debt. The methodology follows the structure by 
(Afzanizam, et al., 2017). 
3.2 Research design. 
The research employs a quantitative approach to test the research hypotheses. The study uses 
panel data for the period between 1980-2015. The variables used are household debt, growth in 
personal income rates, GDP per capita, lending rates, unemployment rates and credit ratio. Data 
is collected from secondary sources including the World bank, the IMF database and povcal.net 
database. A GMM model is used to test the impact of various variables to household debt. 
3.3 Population and sample of study. 
This study focuses on a developing country, Kenya. The data analyzed is for the years between 
1960-2015. 
3.4 Model specification and estimation. 
3.4.1 Theoretical underpinning. 
Generalized method of moments (GMM) is a general estimation principle. Estimators are 
derived from so-called moment conditions. Many estimators can be seen as special cases of 
GMM and a unifying framework for comparison. Maximum likelihood estimators have the 
smallest variance in the class of consistent and asymptotically nonnal estimators but there is 
need a full description of the distribution and correct specification. GMM is an alternative based 
on minimal assumptions. GMM estimation is often possible where a likelihood analysis is 
extremely difficult. There is need for only a partial specification of the model. Models used are 
for rational expectations. 
3.4.2 Justification of model. 
The GMM model was chosen for a variety of reasons. The GMM model when used as a 
combination of two differential models can reduce biases and imprecision associated with the 
difference estimator. Also, the GMM estimators can be applied in one and two step vmiants. The 
two-step estimator is asymptotically more efficient than the one-step estimator and this is useful 
13 
in achieving precise results. In this paper, we will use several variants of the GMr\.1 which is 
particularly important for the present study given the small size of the sample. 
3.4.5 Variables. 
Definition and measurement of variables. 
Figure 2 Variable definition 
VARIABLE 
HOUSEHOLD DEBT 
GINI COEFFICIENT 
CREDIT RATIO 
LENDING RATE 
GDP PER CAP IT A 
UNEMPLOYMENT RATES 
DEFINITION AND MEASUREMENT 
It is the dependent variable. It is defined as 
the amount of money that households owe 
financial institutions. 
It measures the extent of income gap between 
high and low-income level. Where the 
coefficient is rising, it means the income gap 
between the low and high-income earners are 
generally widening. It is expected that an 
increase in income inequalities will result in 
an increase in household debt. 
This is data on private sector credit to GDP 
ratio. It is expected that the credit ratio and 
household debt have a positive relationship. 
This represent the cost of borrowing 
whenever households decide to mcur 
liabilities which need to be paid together with 
the principle amount. It is expected that an 
increase in lending rates leads to a decrease in 
household debt. 
The GDP IS an indicator of a country's 
economic state. An increase in GDP per 
capita would cause an increase in household 
debt. 
This serves as a signal to development m 
14 
labour market. Lower unemployment rate will 
lead to higher household debt as labour 
market is in good condition. 
GROWTH IN PERSONAL INCOME It is expected that growth in personal income 
RATES rates leads to a greater expectation of future 
income growth rates and this leads to an 
increase in household debt. 
3.4.6 Empirical model. 
The model to be used is the generalized method of moments (GMM). 
The model to be estimated is; 
Whereby, 
Yit = Household debt as percentage of GDP 
Yic-1 =Household debt as percentage of GDP in the previous period 
and X' it will include: 
X1 = Gini coefficient 
X2 = Credit ratio 
X3 = GDP per capita 
~ = Lending rate 
Xs= Unemployment rate 
x6 = growth in personal income rates 
).lie= the error terms. 
Similarly, the equation can be written as: 
15 
where Y is household debt as percentage of GDP, x represents a set of controlling variables 
which affect household debt and E is the error term. 
3.5. Data Analysis. 
The data is analyzed usmg the Geometric method of moments (GMM) methodology. The 
regression equation used is highlighted in the section above. In the above equation, the lagged 
value of household debt is added to the regression equation so as to capture the sensitivity of 
current household debt to previous levels of debt. 
Endogeneity in the variables is caused by the simultaneity bias. To correct the simultaneity bias 
and correlation in the model, lagged regressors are used as instruments. This is because the lags 
of the explanatory variables are exogenous although they are weakly exogeneous. The 
shortcoming of this method of estimation is that using weak instruments could lead to biased 
parameter estimates in small samples and larger variance (Afzanizam, et al., 2017). 
Below are some of the moment conditions produced; 
E [Yi,t-s-(cir ci,t-t)] = 0 for s 2: 2; t = 3, .... ,T 
E [gini i,t-s-(cir ci,t-t)] = 0 for s 2: 2; t = 3, .... ,T 
E [Xi,t-s-(cir ci,t-t)] = 0 for s 2: 2; t = 3, .... ,T 
Grv1M estimators are applied in one and two step models. In this model, the two step GMJv1 
method is applied as it is asymptotically more efficient than the one step estimator. This is 
because in the two-step approach, the numbers of equations and parameters in the nonlinear step 
do not grow with the number of perfectly measured regressors which confers a computational 
simplicity (Erickson & Whited, 2002). Also, the two-step estimator uses optimal weight matrices 
where the moment conditions are weighted by a consistent estimate of their covariance matrix 
while the one step estimator use weighting matrices that are independent of estimated parameters 
(Afzanizam, et al., 2017). 
Diagnostic tests such as tests for stationarity and causality tests were carried out prior to the time 
series regression analysis. Sargan and Hansen tests were also carried out to test for over 
identification in the model. 
16 
J 
CHAPTER FOUR; RESULTS AND DISCUSSIONS. 
4.1. Introduction. 
This section highlights the results obtained from the data analysis. It also includes a summary of 
the descriptive analysis of all the variables that were used in the study. 
4.2. Descriptive statistics. 
Table 1 below highlights the descriptive statistics of the variables used in the study. The 
variables include, household d_ebt as a percentage of GDP, the Gini coefficient, credit ratio, 
lending rate, GDP per capita, unemployment rates and growth in personal income rates. 
Table 1; Descriptive statistics. 
Mean Median Standard .. mm1mum maxnnum 
Deviation 
Household debt (%of GDP) 32.49556 34.92687 8.5554 12.02212 45.38235 
Gini Coefficient 57.71 59 8.880283 38 70 
Credit Ratio (%of GDP) 21.20323 20.19755 5.752758 11.80493 34.37524 
Lending rate (%) 16.78479 14.90227 6.790862 9 36.24 
GDP per capita 444.3384 365.9748 347.9653 94.83526 1455.36 
Unemployment rates 10.68519 10.7 1.103736 8.1 12.2 
Growth in personal income 1.268005 1.709688 2.483197 -4.73578 5.230007 
rates 
From the table above, it is clear that Kenya exhibits high income inequality rates that is 
evidenced by the Gini coefficient with a mean of 57. 71. The large difference between the 
minimum and maximum values of the Gini coefficient show its immense fluctuation over the 
years. The Gini coefficient and household debt exhibit high standard deviation which further 
shows high fluctuations of the variables. The lending rate in Kenya also shows large variability 
with its highest value being 36.24% and the lowest value as 9%. The Government of Kenya in 
2016 introduced a cap value for lending in Kenya and this will eliminate that variability. This 
interest rate cap may lead to a decrease in the household debt in Kenya as many banks would not 
be willing to give loans with low interest rates. The unemployment rates in Kenya have remained 
17 
relatively stable over the past years with a mean value of 1 0.6%. This is a relatively large figure 
which can be explained by the growing labour force and lack of job opportunities. 
4.3. Regression results. 
The data was analyzed between 1980 and 2015 with omissions of seven years due to inadequacy 
of data. GMM model was used and the results are highlighted in table 2 below. 
Table 2. Regression Results. 
Variable Coefficient 
Household debt L1 0.4971 ** 
Gini coefficient 0.3329** 
Credit ratio 0.0972 
Unemployment -0.8973** 
GDP per Capita 0.1170 
Lending rate -0.0380** 
Income -0.3491 
Constant 8.6987 
The significance at 1%, 5% and 10% levels are denoted respectively by*,**,***. 
The coefficient of the lagged household debt was at 0.4971 and is statistically significant at 5%. 
For every 1% growth in the past debt level, there is a 0.4971% increase in the present debt level. 
This is consistent with the findings of Deep and Domanski (2002) which explains this in the 
concept of U.S credit cards. People are allowed to postpone the repayments of balances on their 
cards and he/she uses current income. The reason households default is due to usage of too many 
credit cards. 
The Gini index coefficient is 0.3329. This means that when the Gini index increase by 1 
increases household debt by 0.3329%. This is in line with the relationship explained by Christen 
and Morgan (2005) and Pressman and Scott (2009). This supports the hypothesis that income 
inequalities are a cause of household debt. Income disparities lead to increase in household 
indebtedness. 
Credit ratio is an insignificant variable. This contradicts the theory that financial development 
facilitates growth through higher spending by households. This is known as debt led growth. The 
18 
Gross domestic product and the growth m mcomes are also not statistically significant m 
explaining household debt. 
Unemployment rates are a significant variable in explaining indebtedness of households. It 
exhibits negative coefficient of 0.8973. This shows that a decline by 1% of unemployment rates 
lead to an increase in household debt by 0.8973%. Unemployment is an important factor in 
explaining indebtedness of households. A larger labour market results in more debt accumulated 
by households. The loan approval process is easier with employment hence more people take up 
more loans. 
The lending rate is also a significant value but it is only significant in the model chosen. The 
coefficient is -0.038. This means that a 1% decrease in the lending rate increases household debt 
by 0.038 . This shows that interest rate is a tool for managing debt levels of a country. This 
implies that monetary policy in an economy works well as a mechanism for reducing or 
increasing debt in an economy. This is in contrast to the findings of White (20 14) who suggested 
that monetary policy is guided by a flawed theory which ultimately led to the crisis in 2007. 
4.4 Diagnostic checks. 
4.4.1 Sargan-Hansen tests. 
The Sargan and Hansen tests are used to test for over- identification in a statistical model. 
Ho; overidentifying restrictions are valid. 
The p-value obtained was 0.0970 which shows we cannot reject the null hypothesis. Therefore, 
overidentifying restrictions are valid. 
19 
CHAPTER 5; CONCLUSIONS AND RECOMMENDATIONS. 
5.1. Conclusion. 
This paper aimed to determine the relationship between income inequalities and household debt 
in Kenya. The methodology used was the Generalized method of moments for data between 
1980 and 2015.Earlier studies show that household debt is mainly influenced by income 
inequalities. The paper analyzes this and also looks at other factors that could influence 
household debt. The fmdings show a positive relationship between income inequality and level 
of household debt in an economy. These results are consistent with the fmdings of other authors 
and show that a more uneven distribution of incomes in an economy, the greater the likelihood of 
an increase in debt accumulated by households in an economy. The fmdings show that the more 
unequally distributed incomes are, the more households borrow to keep up with the joneses. We 
can therefore conclude that widening of the income gap incentivizes households to increase 
leverage to make purchases and alleviate their status in social circles (Afzanizam, et al., 2017). 
It was also observed that when people enter into the loan market it is difficult to exit and many 
stay indebted for long periods of time. It was also shown that lending rates and unemployment 
rates are significant in determining the level of household debt in an economy. Lower 
unemployment and lending rates lead to an increase in household indebtedness. This is because 
with employment, more people have access to credit facilities and lower lending rates 
incentivizes people to borrow more as the cost of repayment is lower. 
5.2. Policy recommendations. 
Policies for a more inclusive growth are necessary in reducing household debt significantly. This 
involves policies that aim at investing in human capital and physical infrastructure to help in 
reducing income inequalities and lowering household borrowings. There is need to tackle the 
problem of rising income inequalities to reduce the household debt in an economy. Policies to 
combat this problem that do not involve government spending or subsidies should be sought. 
This is because such policies work in the short run but not in the long run. Governments should 
channel resources towards access to equal opportunities to all its citizens in an effort to reduce 
income inequalities. 
Policies that aim at improving the wages of working citizens are paramount m reducing 
household debt in Kenya. This would ensure that citizens do not have to depend on debt to 
20 
finance their daily needs and lifestyles and therefore reducing the overall debt level in the 
country. 
5.3. Areas of further research. 
Further research aimed at fmding the optimal level of household indebtedness could would be 
useful in establishing the right amount of household debt in an economy. 
This study does not incorporate household debt decomposition. An assumption about how the 
money is spent is made in concluding the macroeconomics of keeping up with the joneses is at 
play. Household debt decomposition will produce more conclusive results and fmdings. 
5.4. Limitations. 
Jnconsistency in calculating Gini coefficient is a major limitation. Many sources of data provide 
different estimates for the Gini coefficients of countries which creates a major problem in 
estimation. This could also be a result of inconsistent data through time. 
The unavailability of data on debt decomposition is a major limitation of the study. The 
decomposition of what households spend money acquired through debt would provide better 
insight in determining the macroeconomics of keeping up with the joneses. 
21 
References 
Afzanizam, M., Rashida, A., Sarmidib, T., Hassan, A., Narc, S. , & Ghani, N. (2017). Does 
Income Gap Matter for Household Debt Accumulation? Institutions and Economies, 9(1), 1-19. 
Agarwal, S., Mikhed, V., & Scholnick, B. (2015). Does Inequality Cause Financial Distress? 
Evidence from Lottery Winners and Neighboring Bankruptcies. Unpublished paper. 
Aldo, B., & Massimo, P. (2009). Rising household debt: its causes and macroeconomic 
implications-a long-period analysis. Cambridge journal of economics, 33(1), 113-137. 
Barnett, R. C., Bhattacharya, J., & Bunzel, H. (20 1 0). Choosing to keep up with the joneses and 
income inequality. Economic theory, 45(3), 469-496. 
Christen , M., & Morgan, R. M. (2005). Keeping Up With the Joneses: Analyzing the Effect of 
Income Inequality on Consumer Borrowing. Quantitative marketing and economics, 3(1), 145-
173. 
Coibion, 0., Gorodnichenko, Y., Kudlyak, M., & Mondragon, J. (2014). Does Greater 
Inequality Lead to More Household Borrowing? New Evidence from Household Data (No. 
w19850). National Bureau of Economic Research. 
Erickson, T. , & Whited, T. M. (2002). Two-step GMM estimation of the errors-in-variables 
model using high-order moments. Econometric Theory, 18, 776-799. 
Daly, M., & May, W. (2006). Keeping up with the Joneses and staying Ahead of the Smiths; 
Evidence from Suicide data. Federal reserve bank of San Francisco. Working paper 2006-12. 
Friedman, M. (1957) . The permanent income hypothesis. Princeton University Press, 48(5), 990 
991. 
Gini, C. (1921). Measurement of inequality of incomes. The Economic Journal, 31(121), 124-
126. 
Guidetti, G. , & Rehbein (2014). Theoretical Approaches to Inequality m Economics and 
Sociology;A Preliminary Assessment. Transcience, 5(1), 1-15. 
22 
Iacoviello, M. (2008). Household debt and income inequality. Journal of Money, Credit and 
Banking, 40(5), 929-965. 
K, K. Y. (2012). Emulation and Consumer Debt: Implications of Keeping Up with the 
Joneses.Trinity college, Department of economics. Working paper 1208. 
Kapeller, J., & Schutz, B. (2012). Conspicuous consumption, inequality and debt:The nature of 
consumption-driven profit-led regimes.Metroeconomia, 66(1 ), 51-70. 
Konig, N., & Grobl, I. (2014). Catching up with the Joneses and Borrowing Constraints: An 
Agent-based Analysis of Household Debt (No. 4/2014) DEP (Socioeconomics) Discussion 
papers, Macroeconomics and Finance Series. 
Loschiavo, D. (2016). Household debt and income inequality: evidence from Italian survey data. 
Banca d'italia Eurosistema. 
Mathias, K., & Krause, C. (n.d.). (2014). Income Redistribution, Consumer Credit and Keeping 
up with the Riches. Unpublished paper. 
Munyoki, I., & Okech, T. C. (2012). Empirical Analysis ofPersonal Debt among the Youth in 
Kenya; The case of Graduate students in Kenyan Universities. International journal of academic 
research in economics and management science, 1(4), 2226-3624 
Pressman, S., & Scott, R. (2009). Consumer debt and the measurement of poverty and inequality 
in the us. Review of social economy, 67(2), 127-148. 
Russo, A., Riccetti, L., & Gallegati, M. (n.d.) (2016). Increasing Inequality, Consumer Credit 
and 
Financial Fragility rn an Agent Based Macroeconomic Model.Journal of Evolutionary 
Economics, 26(1), 25-47. 
Ryoo, S., & Kim, Y. K. (2014). Income distribution, consumer debt and keeping up with the 
joneses. Afetroeconomia, 65(4), 585-618. 
23 
Soh, J., Chong, A., & Chuah, K.-P. (2016). Household credit, growth and inequality in Malaysia: 
does the type of credit matter? Unpublished paper. 
Thomson, J. (2016). Do Rising Top Incomes Lead to Increased Borrowing in the Rest of the 
distribution? Finance and economics discussion series. 
Tsoukis, C. (2007). Keeping up with the Joneses, growth and distribution. Scottish journal of 
political economy, 54(4), 575-600. 
Veblen, T. (1991). The Theory ofthe Leisure Class. Oxford University Press. 
Wildauer, R. (20 16). Determinants of us household debt: new evidence from the scf . 
Unpublished paper. 
Yun, K. K. (2012). Emulation and Consumer Debt: Implications of Keeping Up with the 
Joneses. Trinity college, Department of Economics working paper, (1208). 
Zezza, G. (2010). Income distribution and borrowing; Tracking the U.S economy with a "New 
Cambridge" model. The Global Economic Crisis: New Perspectives on the Critique of 
Economic Theory and Policy. Routledge, London. 
24