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UNIVERSITY 
An Empirical Analysis of Inclusive Growth in Kenya: Measure'!lent, Determinants and 
Policy Recommendations 
Wakhu, Ronald Brian 
081160 
Submitted in partial fulfillment 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 
June 2017 
This Research Project is available for Library use on the understanding that it is copyright 
material and that no quotation from the Research Project may be published without proper 
acknowledgement. 
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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 pee~ submitted for examination with my approval as .the 
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Strathmore Institute of Mathematical Sciences 
Strathmore University 
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Abstract 
This study exammes inclusive growth in Kenya. The call for inclusive growth has been 
broadcasted the world over by policy makers. However, there seems to be very little 
understanding and very small steps towards the achievement of significant inclusive growth. A 
unified measure of Inclusive growth is estimated by integrating economic growth performance 
(to capture growth) and income distribution properties (to capture equity).This allows us to 
capture inequality as well as economic growth. The unified measure is then regressed against a 
set of variables that are used in growth and inequality analysis in order to identify the 
dete1minants of inclusive growth. 
Results indicate that inclusive growth in Kenya from 1978-2005 had large shocks in the 
1990s,but largely remained non-inclusive before 1992 and inclusive briefly after 2002 till 
2005.0f the variables used in analyzing the determinants of inclusive growth, only trade 
openness and financial deepening are found to be statistically significant, positively and 
negatively respectively. 
Key Words: Inclusive Growth, inequality, income distribution, economic growth 
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Acknowledgements 
I would like to express my sincere gratitude to Dr. Godfrey Madigu my supervisor for the 
continuous support and advice in writing this paper. I would also like to thank Professor 
Nathaniel Agola of Doshisha University for his helpful insights on discussing inclusive growth. 
Lastly I would like to thank the staff of the IMF offices in Kenya whose research inspired me to 
Wlite this paper. 
Table of Contents 
Abstrac/ ..... .... ..................... ....... .................. ........ .......................... .. .. ... ............ .......... ........... ... .... ........... ... .. . 2 
INTRODUCTION ..... .......... ... .. ... ............. ... .. .. .. ... ... .... ... .... ... ... ..... .. .. .. ..... ... .. .. ........... ... ......... .......... .... 5 
1. 1 Background of the Study .... ........ .... ..... ......... .. ..... .. .. .... .. .... .... ... .... ..... .. ...... ... .......... .......... .. .. ......... 5 
1.1. 1 What is Inclusive Growth? ...... ... ...... ........ ... .. .. ... ........... ... ..... .... ...... ..... .......... ..... .... .... ... ....... 5 
1. 1.2 Limitations of GD P Growth as a Measure of Economic Performance .. ......... ..... .... ..... .. .. .. .. 6 
1.2 Proble1n Statement .. ... .. ... .... ..... ... .. .. .... ..... ..... ... .. ... .. .. .... .... ..... ... ..... ... .. .... .. .. ... ... .... ... .. .. .. .. .... ..... ... . 7 
1.3 Research Questions .... ..... ........ ...... ........ ....... .... .... ... ...... ................ .. ... ..... .......... ................... ......... 7 
1.4 Research Objectives ... ..... ... .. .. ............ .. ...... ....... ..... .. ............. ... .. ......... ... .... .... .. .. ..... ... ..... ... ... .... ... . 8 
1.5 Significance of the Research .. .. ...... ...... ................ ............... ........................ ... ............. .... .... ... ....... 8 
2 LITERATURE REVIEW ... .... ....... ........ ........... ........ .............. ... ...... .. ... ....... ... .. ......... ...... ........ .. ... ........ 9 
2. 1 Pro-Poor Growth ...... ..... .................. .... .... ....... ...... ....... ........... ...... ............. .. ............. ............ ......... 9 
2.2 Measuring Inclusive Growth .. .. .... .. ..... ... ............ ................ ........... ............. ..... ... ... ...... ..... ........ .. . 10 
2.3 Sources of Inclusive Growth ............. .............. ................ .................. .... ................. ........ ....... ...... 12 
2.4 Conceptual Framework ... .. .... .. .......... .... ... ........ .... ... ..... .. ....... .. ... ..... ...... .. ........ ..... ...... ....... ... ....... 12 
3 METHODOLOGY ... ... ... ........ ..... .. ...... ...... .... .. .. ........ ....... ........ .. ....... .......... .... ... ... .. ......... .. .. ... .... ... .... 14 
3.1 Research Design ........... .... .... .. ... .... ... ... ... .... ..... ... .. .... ..... ........ .. ........ .. ....... ... .. ............ ... .. .... .. ... .... 14 
3.2 Data Types and Sources .... .... .... ......... .... ... ............. .... .... .. ..... .. ... .... .. ......... .... ..... ... .. ..... .. ... .. ... .... . 14 
3.3 Description of Independent Variables ...... .......... .... ...... .... ... .... .... ........ .. ... .. .. ................ .. .. .... .. ..... 15 
3.4 Dealing with Missing Data .. ... ....... .... ......... .... ........... .. .... ........ .. ....... ...... .. .. .. ....... ... ......... .... ... .. .. 15 
3.5 Data Analysis .. .......... ... ......... ... .. ... .... ........ .. ... .... ... .. ... .. ........ ... ... ...... ........... .... ..... ....... .... ....... ..... 15 
3.5.1 Measuring Inclusive Growth ......... .... ....... ..... .. ...... .... ..... ............... ... .... ..... ..... ....... ... ...... ... .. 15 
3.5.2 Sources of Inclusive Growth ... .. .. ...... ..... ...... ....... .. ..... ........ ....... ...................... .... .. .. ............. 18 
4 RESULTS AND DISCUSSIONS ... ... ... ... ... ... .... ..... ...... ..... ... ...... .. ....... ....... ...... ..... .... .. ...... .... .. .. .. ... .... l 9 
4.1 Measurement of Inclusive Growth .. .... .... ....... ..... ...... ....... .. ..... ...... ...... ... .. .... ........ ... ... ... .... .. ...... .. 19 
4 .2 Determinants of Inclusive Growth .. .... ..... ...... ... .......... .... .. ..... .. .. .. ... ...... ... ...... .... .. .. ... .......... .. .... .. 20 
4.2.1 Unit Root Test for Stationarity ... ... ....... .. .. .. ... .. .. .... ... . ~ .. .... ..... ... ............... ..... ....... .... ............ 20 
4.2.2 The Generalized Method of Moments Resu lts ............... ........... ... ....... .... .... .. ..... .. ... .... ..... .. . 21 
5 CONCLUSIONS AND POLICY RECOMMENDATIONS .......... .... .................. .. .... .. .......... .. ......... . 24 
6 APPENDICES ..... ....... ......... ....... .... ....... ....... ............ ........... .............................................................. . 25 
7 BIBLIOGRAPHY .................... ............ ................. ......... ................... .......... ....... ......... ...... .. ................ 26 
1 INTRODUCTION 
1.1 Background of the Study 
The pursuit of consistent, high and equitable growth in economies is central to policy-makers 
decisions and lies at the core of development economics. Kenya has performed well 
economically in the past decade, attributed to high public and private investment (M'Amanja & 
Monisey, 2006) and significant developments in the service sector (The World Bank Group, 
2016). This year, Kenya is projected to have an average growth rate of 5.5% (The World Bank, 
20 17), which is one of the highest in the region. 
Despite this, Kenya continues to lag behind in standard of living and quality of life measures. 
Kenya is ranked poorly in terms ofhwnan development; being placed 146th out of201 countries, 
using the Human Development Index 1 (United Nations Development Program, 20 16) .Inequality 
is also rife within the country, with a GIN! coefficient2 of 0.425 (United Nations Development 
Program, 20 16), the 48111 most unequal country in the world. Available estimates show that 
povetty and inequality statistics have remained stagnant over time, even with high economic 
growth (Kenya National Bmeau of Statistics, 2005, 1997). 
Creating a more equitable society and expanding social participation in the process and benefits 
of economic growth is one of the biggest policy challenges that world leaders face (Sammans, 
Blanke, Corrigan, & Drzeniek, 2015). The benefits of growth should be shared with a majority of 
the population, i.e. growth should be broad based and inclusive. Inclusive growth is a key 
element of sustainable and effective economic growth (Berg & Ostry, 2011) 
1.1.1 What is Inclusive Growth? 
The pmsuit of inclusive growth has been discussed widely by world leaders and policy makers. 
Despite the consensus that inclusive growth is vital and urgent, there is surptisingly little clarity 
on exactly what it is, with different institutions and scholars offering varying definitions and 
descriptions. 
1 The HOI (Human Development Index} is a composite index of four indicators. Its components are to reflect three 
major dimensions of human development: longevity, knowledge and access to resources. (Noorbakhsh, 1998} 
2 The GIN I coefficient is a measure of social inequality. It attempts to measure how income is distributed in the 
society .The closer the measure is to one, the more unequal the country is. (Gini, 1909} 
Ianchovichina and Lundstrom of the World Bank (2009) define describe inclusive growth as 
referring to both the pace and pattern of growth, which are considered interlinked and therefore 
need to be addressed together. Ranieri and Ramos (2015) describe inclusive growth as a concept 
that advances equitable opportunities for economic participants during economic growth with 
benefits incmTed by every section of society. It has also been defined as growth that is 
accompanied by lower income inequality, so that the increment of income accrues 
disproportionately to those with lower incomes, that is it is not associated with an increase in 
inequality (Rauniyar & Kanbur, 2010). 
However, even with the lack of a single definition, there is evidently convergence in the core 
elements of inclusive growth. These elements include; 
• Consideration for both pace and pattern of growth. 
• Growth that qat only lifts people out of poverty, but also reduces inequality. 
• Social gains and opportunities such as education and health are also focused on. 
A definition that encompasses all the elements above and hence best defines inclusive growth is 
that by the OECD. The OECD postulates that inclusive growth is economic growth that creates 
opportunity for all segments of the population and distributes the dividends of increased 
prosperity, both in monetary and non-monetary terms (2014). 
The proposal that not only the pace, but also the pattem of economic growth is vital for 
sustainable economic growth is in line with the findings of the Growth Report (2008), which was 
put together by policy makers from around the world. 
1.1.2 Limitations of GDP Growth as a Measure of Economic Performance 
In discussing inclusive growth, it is important to highlight the shortfalls of GDP as a metric for 
economic development. Since independence, Kenyan economic policies have been geared 
towards economic growth rather than income distribution. The importance of inclusiveness and 
income inequality has kept a low profile in scholarly and policy discussions (Society for 
International Development, 2004). 
However, in recent times it has become evident that GDP is not a sufficient measure of economic 
development. Ivkovic analyzes GDP as a measure of well being and highlights one of the major 
problems as being methodological problems in calculating certain elements of the economy such 
as dishibution of income, health conditions and education (Ivkovic, 20 16) . . Even one of the 
pioneers of the GDP, Simon Kuznets acknowledged that GDP may only focus on the quantitative 
aspects of economic growth, and ignore the qualitative aspects (Kuznets, Inventive Activity: 
Problems of Definition and Measurement, 1962) 
With the apparent limitations of GDP, it is important for policy makers to discuss how equitable 
growth has been. While economic growth is very necessary to reduce poverty, it is not enough to 
reduce inequality, or even lift people rrom poverty. (Society for International Development, 
2004). 
1.2 Problem Statement 
As pointed out, there seems to be a general consensus that inclusive growth is good. As Lopez 
(2004) puts it, growth combined with more equity is better than growth alone and high inequality 
makes it harder to reduce poverty. However, the situation in Kenya based on government data 
(Kenya National Bureau of Statistics, 2005, 1997) and literature (Gakuru & Mathengi, 
2012),appears to contradict the inclusive growth principles and elements, growth is high but 
inequality and poverty is endemic. Although some research has been done on the areas of income 
growth and inequality, the studies are done independently and do not offer a unified measured of 
inclusive growth. Morrisey and M'Arnanja (2006) focus on the determinants of GDP growth in 
Kenya but leave out income distribution and equitable growth. The Society for International 
Development (20 13) carry out surveys that highlight inequalities in Kenya, but do not study 
income growth. 
This paper shall attempt to create a harmonized measure of inclusive growth that shall link both 
income growth and income dishibution. It shall also identify the sectors that promote inclusive 
growth and make policy reconunendations based on the findings of the analysis. 
1.3 Research Questions 
The research questions that shall aim to be answered in the research are; 
1. To what degree has growth been inclusive in Kenya? 
2. What are the main sectors that drive inclusive growth in Kenya? 
1.4 Research Objectives 
In line with the research questions, the objectives of my research are; 
1. Using econometric methods, measure the degree of inclusive growth in Kenya. 
2. Identify, using regression analysis, the key sectors that drive inclusive growth. 
3. Make policy recommendations based on the results of my analysis. 
1.5 Significance of the Research 
The study and analysis of inclusive growth is vital to policy makers and government officials. 
The Constitution of Kenya specifies in clause 201 that;"the public finance system is to promote 
an equitable society in that revenue raised nationally shall be shared equally between national 
and county government. Vision 2030,the development blueprint for Kenya, also highlights 
equitable growth as a goal for Kenya's economy (Government of Kenya, 2007).This makes a 
coherent measure of inclusive growth important for policy as it will enable them to assess 
progress that has been made thus far and make informed decisions on where and how to allocate 
resources. 
2 LITERATURE REVIEW 
In the third quarter of the 20th Century, most understanding of developing economics and growth 
theory centered on the work and literature of Simon Kuznets and Walt Rostow. Kuznets (1955) 
argued that economic growth in countries was accompanied by an initial increase in inequality, 
which past a turning point would lead to better living conditions for the poor. This growth model 
assumed that countries followed the same steps as those in Rostow's 5 stages of econorrilc 
growth (Rostow, 1956,1959).3 
This model of economic growth was only seen to work in developed countries, but did not hold 
in developing nations. Furthennore, empirical literature by Kanbur (2000) did not find any 
evidence of Kuznets' proposition. Work by FeiTeira, Leitte and Ravallion (2009) that examined 
statistical relationships between growth and inequality found an absence of cotTelation between 
growth rates and changes in inequality. Scholars and policy makers have therefore taken a keen 
interest in growth that is inclusive since the early 2000s.The recent flurry of media coverage 
regarding wealth inequalities has also fuelled the discussion on inclusive growth. 
2.1 Pro-Poor Growth 
In reviewing literature regarding measuring inclusive growth, understanding what 'pro-poor' 
growth is and how different scholars define it is necessary, as this will form the base of the 
various methodologies of measuring inclusive growth. Pro-poor growth can either be taken in its 
absolute definition, or relative definition. In the absolute definition, growth is considered pro-
poor as long as poor people benefit in absolute terms, as reflected in a pre-determined measure of 
poverty (Ravallion & Chen, 2003).In the relative definition, growth is considered pro poor if and 
only if the incomes of the poor rise faster than those of the population as a whole 
(Ianchovichina & Lw1dsh·om, 2009) .The definition of inclusive growth offered in chapter 1, 
(and by most scholars) is in line with the absolute definition of pro-poor growth. 
Literature on how inclusive growth can best be measured is fairly recent and many of the 
methodologies used still contain significant lapses and limitations. However, the work that has 
been done by various scholars and global organizations such as the World Bank will offer an 
3 Rostow's 5 stages of Economic Growth are Traditional Society followed by pre-conditions for take-off, take-off, 
maturity and finally high mass consumption 
important foundation for future research to be canied out. The majority of texts addressing the 
measurement and estimation of inclusive growth do not go beyond a conceptual exercise. 
2.2 Measuring Inclusive Growth 
Habito (2009)analyzes inclusive growth m Asian Developing countries, and identifies key 
factors that explain variation in patterns of inclusive growth. In his methodology, he follows a 
weak absolute pro-growth definition and looks at the povetty elasticity of growth. Habito also 
points out the weaknesses of the methodologies of many studies on growth and poverty; that is, 
they depend on regression models that fail to specify the identity that links the rate of economic 
growth. 
McKinley (2010) attempts to measure inclusive growth by constmcting a composite inclusive 
growth index, at the country level. He uses indicators such as growth, income distribution and 
inequality, productive employment, economic infrastmcture, gender equity, social protection and 
human capital. Using these he creates a weighted index constructed on a weighted average score, 
where an inclusive growth score of 1-3 signifies unsatisfactory inclusive growth, a score of 4-7 is 
satisfactory inclusive growth and a score of 8-10 shows a superior inclusive growth. The 
limitations of the methodology are that it requires value judgments and may therefore be 
subjective. Data availability is also a significant caveat to the methodology proposed by 
McKinley. It is also important to note that measming in isolation and simply adding up the 
contributions of a number of constituent elements does not necessarily generate a coherent 
measure of inclusiveness (Ranieri and Ramos, Inclusive Growth; Building up a Concept).This 
method of estimating inclusive growth is also wanting because the weighting system is done 
arbitrarily. 
Despite these limitations, McKinley's index has been used to measure inclusive growth in a 
variety of Asian countries. The reason for this may that McKinley's definition and therefore 
measurement of inclusive growth captures many key elements of equity and efficiency. These 
include good governance, basic social services, gender inequality and productive employment. 
Most other methodologies for measuring inclusive growth lack these elements and are only able 
to capture growth, inequality and/or growth or only two of these elements.(For example Son and 
Kakwani(2008) and Kray (2004)) 
Ranieri, Ramos and Lammens (2013), Following the definition of inclusive growth as a process 
that enhances benefit-sharing and participation ,postulate that inclusive growth can be measured 
based on 3 factors, income poverty, inequality( as a proxy for benefit sharing) and employment 
to population ratio, which is used as a proxy for the participation dimension of the mentioned 
definition. These three indjcators were deliberately given the same weight in the index in an 
attempt to attenuate the shortcomings of arbitratily determining the weight of each indicator (to 
avoid the shortcomings of McKinley (2010)) The model is applied to 43 countries to detennine 
how the inclusive their growth has been, as well as how it varies with GDP growth, which they 
describe as the 'inclusiveness of the growth process '. They however recognize that the tool can 
only determine a periodic snapshot of the evolution of inclusiveness in each country setting, not 
as a regular national-level monitoring tool. They analyze inclusive growth in 43 developing and 
frontier countries in 1996 and in 2006 based on availability of data. 
Their inclusiveness index is built through a min-max normalization of data on poverty, 
inequality, and the inverse of the employment-to-povetty ratio. It is constructed on a 0-1 scale, 
the indexes lower values represent better performance, that is the closer the index is to O,the 
more inclusive the country. Through their analysis and measurement of inclusive growth, they 
find that generally, growth was more inclusive in most countries in 2006 than they were in 1996. 
Houngbonon et al (2013) look at inclusive growth in A:fi:ica. They analyze inclusive growth from 
the angle of the incidence of growth on distribution of income across centiles, generations and 
place of residence; they refer these as the 'dimensions' of inclusive growth'. They then identify 
three stylized facts regarding inclusive growth. First, they determine that irrespective of the 
dimension considered, inclusive growth does not depend on the magnitude of growth. Secondly, 
growth stability is related to inclusive growth along the area dimension, that is, the place of 
residence. Lastly, they determine that the structure of growth is related to the dimension along 
which growth is inclusive. 
Their methodology involves tracking the change in household's expenditures over a year, 
according to the categories defined by the centiles, birth generations and places of residence. 
They therefore compute the average annual growth rate of the household expenditures for each 
category. 
The African Development Bank is also preparing an inclusive growth index. According to a 
briefing note the bank released in 2012. The index will be based on 8 pillars that were derived 
from its long term strategy pillars that are in learn with its medium term strategy pillars (2008-
2012).These pillars are; job creation, access to business opportunity, enhancement of regional 
integration, social protection and inclusion, improvement of agricultural technology, among 
others 
2.3 Sources of Inclusive Growth 
Empirical studies on the sources of inclusive growth are scarce. Specifically, No empirical study 
has been done analyzing the potential detetminants of inclusive growth in Kenya, and. what the 
relationship is with inclusive growth. 
Most studies done the sources of inclusive growth are panel studies across a variety of nations 
for example, Barro and Lee (2000) shows that lower initial incomes , trade openness, fixed 
investment, moderate inflation and output volatility, and a better educated workforce have helped 
countries achieve more inclusive growth.FDI has a significantly positive impact on inclusive 
growth as in IMF (2007), while ICT in the total capital stock does not have a discernible impact. 
Interestingly, financial deepening, measured by the credit-to-GDP ratio, has a negative impact as 
in IMF (2007), but is not statistically significant. In addition to modemizing manufacturing, the 
globalization of services is increasingly a driver of economic growth in emerging markets (see 
Mishra, Lundstrom and Anand, 2011). 
In this paper, the variables that shall be used are the same one that is used by other scholars in 
the analysis of inclusive growth (as the ones mentioned above). These also happen to be the 
same variables used in growth and inequality analytics. However, it is important to note that data 
availability and the nature of the economy is also a factor to consider when selecting the 
variables. Further description of variables is offered in the third chapter of the paper. 
2.4 Conceptual Framework 
As discussed, the measurement of inclusive growth over the years has differed among different 
scholars. However, key elements in the measurement of inclusive growth can easily b identified 
in the methodologies used. These elements in the definition and therefore measurement of 
inclusive growth include; poverty, inequality, productive employment, gender inequality, 
participation, oppmtunity and benefits to growth. 
These measurements differ across countries and researchers use factors such as data availability 
and the prevailing economic conditions in a specific country to measure inclusive growth. 
In my study of inclusive growth, the diagram below features the most important elements to be 
captured 
Figure !Elements of Inclusive Growth 
To capture the above elements, the main data required is the rate of income growth (GDP per 
capita growth to be used as a proxy) and how income is distributed within Kenya. 
3 METHODOLOGY 
3.1 Research Design 
This study aims at establishing the degree of inclusive growth In Kenya and the specific sectors 
that yield affect inclusive growth (either positively or negatively) within the countly. In this 
regard the study will be· a time series study as I shall measure inclusive growth from 1978 till 
2005. 
The study is correlation as it studies the relationship between inclusive growth and other 
variables. The study is quantitative. Qnk1!1illtitla!flli.we metillr.@"lli eE!l]J~ m:fu~e:CC"tllYe liiileaiS1!lllieliiD:el!llts 
amtd tillte &t.clltriis,1ttccaill. III1l:amfu.e:tilillilll11i~~ m:JT mmm:effiiQ]l 2llli!'alllJfs:iis; mAf mta oolille:C!:tlecdl mr fu>y I!ID.'anmnJlnmllCllliiitrng p:re-
ex.i\s.1iiirmg slla!~ccaill mt!aJ tm.siiJrng ~~l!lrttaJ11ic:maill tle:<cllr.ill«JJllles. 
To measure inclusive growth, the study utilizes a social mobility function which takes into 
account both the income distribution and income growth, hence creating a unified measure. 
To determine the sources of inclusive growth, we use a Generalized Method of Moments 
approach. GMM is the best among method of moment's estimators when endogeneity is an issue. 
Endogeneity in this case may be as a result of simultaneity, that is, the explanatory variables are 
jointly determined with the dependent variable. It also does not require strong assumptions of the 
data generating process. 
3.2 Data Types and Sources 
The data used is secondary and collected from the World Bank database4.Data is also extracted 
from the Povcalnet database, which is an online analysis tool that helps to monitor global 
poverty ... This will enable us to study how inclusive growth has evolved over time, and also be a 
stepping stone and template for future research on the field. 
4 
www.worldbank.com 
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3.3 Description of Independent Variables 
The paper analyses whether variables used in growth and inequality analysis are proximate 
causes of inclusive growth. The variables that are used in the study are: 
Variable Description 
Trade Ope1mess Is described as the sum of exports and imports 
overGDP. 
Education Is captured by Government expenditure on 
education as a percentage of GDP 
Fiscal Policy Government consumption over GDP is used as a 
proxy for government spending 
Financial Deepening Is captured by domestic credit to private sector as a 
share ofGDP. 
Agticulture Value Added(%) This is the Value added on agricultural activities 
Foreign Direct Investment Investment made by a company in one countly in 
business interests in another country 
PopulatiOn IS the only control vanable m the estimatiOn model. 
3.4 Dealing with Missing Data 
The income distribution data for Kenya has gaps that need to be dealt with in order to carry out 
the analysis thoroughly. We use the most common method, linear interpolation due to its 
simplicity. However it is important to note that interpolation may lead to underestimation of 
inequality. 
3.5 Data Analysis 
3.5.1 Measuring Inclusive Growth 
Following Anand, Mishra and Peiris (2013), I estimate a unified measure for inclusive growth in 
Kenya by integrating their economic growth performance and income distribution outcomes. The 
microeconomic concept of a social mobility function at the macroeconomic level is utilized to 
measure inclusive growth that is closer to the absolute definition of pro-poor growth5. 
As in Ali and Son (2007), the generalized concentration curves can be presented in continuous 
time to be more amendable to econometric analysis. The population is arranged in the ascending 
order of their income. Let Yi is the average income of the bottom i percent of the population, 
where i varies from 0 to 100 and y is the mean income. Different values of i are plotted (curve 
AB in figure 1 below). Curve AB represents a social mobility curve discussed above. Since a 
5 See chapter 1 
higher curve implies greater social mobility, growth is inclusive if the social mobility curve 
moves upward at all points. However, there may be degrees of inclusive growth depending on: 
(i) How much the curve moves up (growth) 
(ii) How the distribution of income changes (equity). 
This feature of the social mobility curve is the basis of the integrated measure of inclusive 
growth. Thus, if two generalized concentration curves do not intersect, they could be ranked on 
social mobility i.e. inclusiveness of growth. 
<Cmmilutil!e 'lilnl:re .of 
~nn, ID_.;:Ii :9l'l~:!l 
Ii = D!DD 1(<tllhm! tire -mltire !Pqpiilnlimt 
Ii!;~ 
Figure 2: Social Mobilit)' Curve, som·ce, Anand, Mishra and Peiris (2013) 
A higher curve implies greater social mobility, growth is inclusive if the social mobility curve 
moves upward at all points. Social mobility curve (AlB) is more inclusive than the social 
mobility curve AB. Despite the fact that the average income is the same (Y) as the average 
income of the bottom segment of the society is higher. 
Anand, Mishra and Peiris use a social mobility index to calculate the area under the social 
mobility curve 
....,. _ rlOO_d 
y - Jo y i (1) 
A greater y implies a higher income. If y = y then this implies that the income of everyone is 
the same and hence income distribution is equitable. If y < y then this implies that the 
distJ.ibution of income is inequitable, signifying inequality. Using this index Ali and Son (2007) 
propose an income equity index (lEI), wwhich can be use.d to measure the degree of inequality. 
(i) = y~ 
y 
(2) 
Where a higher value of w represents a more equal society. A society is completely equal if 
w=1 
Making y subject of the formula; 
..... y = w * y (3) 
Therefore, for growth to be inclusive, it requires an increasing y 6 .As per the equation above, 
this can be done through (a) increasing y that is, average income through growth (b)increasing 
the income inequality index w and hence increasing equity or (c) a combination of the two. To 
fwther understand the dynamics of inclusive growth, we differentiate equation (3) above. 
dyis the change in the degree of growth inclusiveness. Growth becomes more inclusive if 
dy > O.The first tenn of the RHS of equation (4) represents the contribution of an incremental 
change in average income, while keeping the income distribution constant. The second term 
represents the contribution of a change in income distribution while keeping average income 
fixed. This has important policy in1plications which shall be discussed later. 
Inclusive growth depends on the sign and the magnitude of the two terms (dy, dw). . If both 
terms are positive, growth is unambiguously inclusive; similarly, if both terms are negative, 
growth is unambiguously non-inclusive. (Anand, Mishra, & Peiris, Inclusive Growth 
Revisited;Measurement and Determinants, 2013). 
6 In line with the pro-poor definition of inclusive growth 
However, there could be tradeoff between co and f. If the first term is positive but the second 
term is negative, higher social mobility is achieved at the expense of reduction in equity. 
Similarly, if the first term is negative but the second term is positive, then higher social mobility 
is achieved at the cost of contraction in average income. 
3.5.2 Sources oflnclusive Growth 
I then perf01m a regression based on the GMM estimation . The unified measure of inclusive 
growth (df) becomes my dependant variable while the variables described in section 3.3 are my 
independent variables .This is done to find the sources of inclusive growth. 
Where; 
FD: Financial Deepening 
TO: Trade Openness 
Edu: Education 
FP: Fiscal Policy 
FDL Foreign Direct Investment 
Agric: Agriculture 
Log P:Log Population 
4 RESULTS AND DISCUSSIONS 
4.1 Measurement of Inclusive Growth 
As discussed in the previous chapter, inclusive growth is measured for the years 1977-2005 by 
taking into account the social mobility index co and the income levels y and their respective 
changes ( dco, dy).The results generated show that the year to year change in inclusive growth 
shows great variability as mentioned before, growth is considered to be more inclusive if the 
inclusive growth measure is greater than O.Inclusive growth is calculated by 
10.00 
8.00 
6.00 
4.00 
2.00 
0.00 
-4.00 
-6.00 
Inclusive Growth 
Figure 3: The Trend of Inclusive Growth 1978-2005 
The variability in inclusive growth can be attributed to the great changes in income and income 
distribution from year to year. Great variability can particularly be seen in the 1990s.Three years 
patiicularly, from 1992-1999 stands out. Within these three years, data from the World Bank 
database shows that household income and expenditure greatly vary. In that regard, the 
variability of inclusive growth within this period can be attributed to the change in the income 
and expenditure, rather than the change in the distribution of income. 
The measure of inclusive growth also shows that growth was generally not inclusive in the years 
prior to 1992.It is however evident that Kenya exhibited significant inclusive growth towards the 
turn of the century and the years leading up to 2005,where inclusive growth then slowed down. 
There might exist is a possible relationship between the governance (for example shift to multi-
partism)and mega corruption scandals in Kenya in Kenya(for example the Goldenberg scandal, 
which exposed the inefficiency of Kenyan public institutions which cost Kenya approximately 
10% of its annual GDP and cut IMF aid worth $500M) and inclusive growth. It is however 
difficult to analyze and prove this statistically as data on Kenya's governance indicators is 
scarce. This possible relationship between governance and inclusive growth may account for the 
variability of inclusive growth in the 1990s.Growth was most inclusive in 1994,right after Kenya 
had embraced multi-partism, and sweeping reforms to enhance transparency, wealth distribution 
and income growth. This may account for the high degree of inclusivity in 1994,after decades of 
sub-standard governance policy, multi-partism and increased transparency may have improved 
inclusivity. 
Analysis on the possible factors May that lead to inclusive growth will therefore be done for 
variables that are commonly used in analyzing inequality and growth as described in the frrst 
chapter. 
4.2 Determinants of Inclusive Growth 
In this section we endeavor to find out what the causes of inclusive growth are. A GMM method of 
estimation is used. 
4.2.1 Unit Root Test for Stationarity ·-
Given the time series nature of our data, it was imperative to carry out estimation tests to be sure 
that our data is not non-stationary so that we avoid the problem of spmious regression results. 
Stationarity tests are therefore conducted for the series using the Augmented Dickens Fuller 
(ADF) test. The ADF assumes that the enor terms are independently and identically distributed. 
A time series data is said to be stationary if it's mean, vatiances and auto covariance remain the 
same no matter at what point we measure them. Unit root test for stationarity results the 
Augmented Dickens Fuller test is to test for stationarity in the data. The test indicates whether or 
not the variables are stationary. The null hypothesis is that of non-stationarity while the 
alternative hypothesis is that of stationarity. This study requires same order of stationary for the 
time senes data because it is pre-requisite m co-integration analysis and the Vector Error 
Correction Model. Results indicate that all the variables are integrated of order one I (1) 
Unit Root Test 
Variable 
At Level T-Statistic Probability 
Education -2.10 0.25 
FDI -6.02 0.00 
Financial Deepening -0.79 0.81 
Fiscal Policy -2.03 0.28 
GDPGrowth -4.66 0.00 
Trade Openness -1.07 0.08 
Inclusive Growth -4.71 0.00 
Value Added Agriculture -3.74 0.01 
First Ditf_erence 
Education -4.75 0.00 
FDI -6.19 0.00 
Financial Deepening -6.21 0.00 
Fiscal Policy -5.98 0.00 
GDPGrowth -10.84 0.00 
Inflation -5.97 0.00 
Trade Openness -5.32 0.00 
Inclusive Growth -5.51 0.00 
Value Added Agriculture -5.39 0.00 
. . Table 1Critical Value atl% 1s -3.63, 5% S1gmf1cance 1s -2.96, and 10% 1s -2.61 
4.2.2 The Generalized Method of Moments Results 
As discussed in the third chapter, we use a GMM approach to estimate the possible determinants 
of inclusive growth in Kenya, by examining whether factors used in growth and inequality 
analysis indeed have an effect on inclusive growth in the country. 
From our results in Table 2, we see that financial deepening and trade openness are the only 
significant variables (at a 95% confidence interval level).The rest of the variables relationship 
with inclusive growth is statistically insignificant. This illustrates that other factors may be 
proximate causes of inclusive growth other than those used in growth and inequality analytics, 
factors such as agricultural development, and governance (as discussed) 
Variable Coefficient Std.Error t-Statistic Prob 
c -75.60467 53.9456 -1.4015 0.1772 
Agriculture -0.076669 0.372023 -0.20609 0.8389 
Education -1.218438 1.327913 -0.91756 0.3704 
FDI -0.646664 1.87052 -0.34571 0.7334 
Financial Deepening -0.722041 0.285809 -2.52631 0.0206 
Fiscal Policy 0.243478 0.434513 0 .560347 0.5818 
GOP Growth -0.265346 0.140802 -1.88454 0.0749 
Trade Openness 0.305621 0.123209 1.913093 0.0227 
log Population 12.14762 6.34927 2.480513 0.00709 
Ta ble 2RegressJon Resu lts 
Financial Deepening 
The negative relationship between financial deepening and inclusive growth is consistent with 
that of fmancial deepening and inequality. Financial deepening could transpire at the intensive 
margin, expanding financial services for those who already enjoy access. As these tend to be 
established firms and high-income individuals it would worsen income inequality (Claessens and 
Perotti, 2007).This is evident in Kenya before the advent of microfinance, agency banking and 
mobile money, which was not popular for the years the data has covered. 
Education 
Education in particular stands out as a variable that would be expected to lead to inclusive 
growth, but this seems not to be the case. This non significant relationship can be attributed to 
the high rates of unemployment in Kenya. The results could also be as a result of the mismatch 
between the skills taught and skill set needed in the economy. If there is a mismatch between the 
two, there will predictably be no positive effect on the inclusive growth of the population. 
Furthermore, it is important to note that primary education was made free in Kenya in 2002, 
therefore the lower deciles of population may not have had access to education anyway, hence 
leading to the non-significant relationship observed. 
Trade Openness 
There exists a positive relationship of inclusive growth with trade openness. It is argued that 
trade openness brings many economic benefits, including increased technology transfer, transfer 
of skills, increased labour and total factor productivity and economic growth and development. 
Foreign Direct Investment: 
FDl has also not been statistically significant m determining inclusive growth. A plausible 
explanation for this is that FDI has not been targeted at areas that help people every sector of the 
society. An example is investments made in stock portfolios rather than infrastructure 
investments. 
Agriculture 
The lack of a positive significant relationship between agriculture and inclusive growth in Kenya 
is also an interesting result in the analysis of inclusive growth. In an ideal world, agriculture 
would be expected to be a catalyst in driving inclusive growth. However, a possible explanation 
to the non-significant relationship is that there is very minimal productivity. Although the value 
added in agriculture may be high, the productivity levels may still be too low to make any 
significant changes in inclusive growth. 
Fiscal Policy 
Fiscal policy is also found to have a non-significant relationship with inclusive growth. This may 
be as a result of inefficiency in enforcing fiscal policy, search that government services and 
resources do not reach every sector of society, and are rather com1pted or used inefficiently, 
hence preventing a positive relationship between the two variables. 
5 CONCLUSIONS AND POLICY RECOMMENDATIONS 
The results in the analysis of inclusive growth yielded interesting results. The regression results 
show that while financial deepening had a negative relationship with inclusive growth, this were 
in the years when agency, banking , micro-financing and mobile money were not prevalent in 
Kenya. Therefore, when finance is only accessible to the richest in the society, a negative trend 
in inclusive growth follows . However, since 2005, Kenya has made numerous strides in banking 
and finance that has benefited all sectors of society and lifted people out of poverty (Suri & Jack, 
20 16). Therefore, research and development in mobile money and government support and 
investing in microfinance and agency banking can be a possible boost to inclusive growth in the 
country. 
Next the trend of inclusive growth suggests that shocks in government institutions can be a 
significant determinant of inclusive growth. However, this can only be posed as a hypothesis in 
this paper and not proved using statistical analysis that indeed more efficiency in govermnent 
leads to growth being inclusive(purely based on a trend analysis).! argue that more transparency 
leads to greater inclusiveness while corruption and exposure of inefficiency in government leads 
to less inclusiveness in growth. 
While in recent years education has been made more accessible to the public (therefore possibly 
increasing inclusive growth), there still exists a mismatch between skills trained and skills 
needed in the market. It is therefore necessary that institutions that meet industry standards are 
set up and invested in, either by government or private ventures. It is also necessary to make 
agriculture create meaningful growth by investing in relevant technology even for small scale 
fanners that will enable them to produce higher quality agricultural produce, ready for wither 
local consumption or export. 
It is imperative to pursue not just growth, but consistent and inclusive growth. Results yielded 
show there 's still work to be done in various sectors in order to make them have a positive 
impact on inclusive growth. 
6 APPENDICES 
Definitions of Inclusive Growth 
.... 
~ 
.... QD = 
- ---= 
.=; l! 
"' 
.:il 
"' 
~ ~ 
-~ '"'"" il~ ~ <Q=> 
"' 
~ "'! ~ .., '!:: ~- :0 
'!:: = ...... :i !jJ .B 15 "' "' ;;: sm 0 "§ "i "'~ ~ ~ i "'- "" ~ :::1 '!!E "' -;;;;~ :i "' "'.32 ~5 .$ :;; ~ .., ~ :t: ~;; 15 .6 11= :iJ :E OJ ... 8. = !11 $ "'- 5: ~! """"" -;;;; ;;:; :;t ... 11 .,. ~E ~ ~~
"' = 
'<I .,_ 
- ·~ Q .. ~ ~ ,!J "' •,a) 
"' = = 
!P..lllRlllwn ;;rnti!Oh!!n (~ IM I I I I 
!!lhllllll~) I M IM I I I I I 
llllnl:!houllltiinll ;nndl ~Dm ([!liJ03i) I« M I I I I« I 
lfolllblto ((2!'JW,) I I I I I I 
~Wk.wlml JJlltJ ~Ill ((:2000) IM I I I« I I« I 
'YJI/hll!!!.n!'ltl~Mm ((.mnl)) I I I I I I I 
IK~I,~. .<!nil Son I(E'l!»!!) I« M I I I I I I I I 
Scm =tl ~I (!<lmlili) I« I I« I I I 
IIWr.mlr( l(i20011!) M II« I I I I I I 
1.1lll;nrril$tmll!l0Wi) II« I I I I I I I I I 
~,ll'~nmul !lilll!!'JI~) I I« I r« I I II« I I I I I I I Scm JJntli!UJ!l6l0nl «<lliD!lll I I I« I I I 
il(llmml ((:Ml'l'.l) r« I I I I I I I I I r« I I 
IIRDI.mi!,mr ;nn:lf ~(pZD!ll!!) r« X( I r« I IX( I« I I I« I I« 
I rflllr'(6""1·,,1fmmnn X( r« I« I« I« X( I« I« I I« I I« I 
Table 3 Source; Ramos, Ranieri and l am mens 
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