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Exploring the nexus of growth, inequality and fragility in Africa

An AERC research project on growth in fragile states in Africa

Nicholas Ngepah1

Ruth Ngepah2

1 Associate Professor of Economics, University of Johannesburg, Executive Director, African Institute for Inclusive

Growth (AIIG) nnnbal@yahoo.fr

2 Masters Research Student, University of Pretoria, nda_ruth@yahoo.fr

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Abstract

This work constitutes an attempt to compare qualitative and quantitative approaches in the

examination of the nexus of inequality, growth and fragility in Africa. The econometric

modelling compares a battery of methods such as probit, ROLS and 3SLS to draw inference in

panel data for 49 African countries. The key findings suggest that economic growth

accompanied by extreme inequality tends to increase political fragility. All measures of fragility

are significantly associated with lower levels of economic growth. All inequalities reduce growth

but gender inequality has the strongest growth reducing impact followed by extreme inequality in

the political fragility models and average inequality in both political fragility and FSI models. The

findings mainly suggest that the key channel through which many factors militate to perpetuate

fragility and low levels of economic growth is extreme inequality. Having a relatively balanced

gender mix in a country’s labour force may tend to cool down the flares of discontentment and

hence reduce the possibility of socio-economic unrest. Extreme inequality reduction, women

empowerment and measures to support female labour force participation in fragile states should

be the focus of policy efforts in countries marked by high fragility and low levels of economic

performance. Such measures should focus on the most ethnically, linguistically and religiously

polarised countries of Africa with low levels of human capital development and high inequality

in its distribution.

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1. Introduction

African Economies have registered robust economic growth since the last decade, averaging 5%

per annum (Martins 2013), which is significantly higher than the average population growth rate

of 2.9%3. About a third of African economies have been growing at the rate of at least 6% per

annum (World-Bank 2013). Currently, twenty-one countries in Sub-Saharan African (SSA)

countries have gained the ranks of middle income countries (MIC), with ten more projected to

join by 2025 (Devarajan & Fengler 2012). The potential for further growth in Africa still remains

enormous, with vast productive natural resources including abundant land in a period of high

and increasing food prices, eminent demographic dividends, agglomeration economies from

increasing urbanisation, more revenue streams expected from mineral exploitation (Christiaensen

et al. 2013).

However, the degree to which the economic growth in Africa reaches all sectors of the society is

a key challenge. There is strong concern that the high economic growth has not been beneficial

to the majority of African population (McKay 2013). The growth has not translated into poverty

reduction at commensurate rate when compared with other regions of the developing world.

This, despite a marked improvements recorded in the SSA’s human development indicators4.

World-Bank (2013) identifies persistently high inequality as the underlying reason for the slow

pace of poverty reduction amidst high and robust economic growth in Africa. At the same time,

a number of countries in fragility are caught up in a vicious cycle of violence, chronic poverty,

inequality and exclusion from the gains of growth. OECD (2009) defines a fragile state as that

which lacks political will and/or capacity to provide basic functions needed for poverty reduction, development and

to safeguard the security and human rights of their populations (OECD, 2009: p76).

Cilliers & Sisk (2013) identify up to 26 countries in Africa in the category of ‘more fragile’ states,

characterised by a much slower trajectory to long-term peace and development. The shear

proportion of “more fragile’ states in Africa poses a significant risk in the African renaissance

rhetoric and the underlying realities. Cilliers and Sisk (2013) also group the drivers of fragility

into four dimensions: poor and weak governance; high levels of conflict and violence; high levels

of inequality and economic exclusion; and poverty. These imply that the robust growth story in

Africa is potentially in jeopardy. These causes (especially the socio-economic ones) are also

potential consequences of fragility. By implication, growth, inequality and fragility have

potentially endogenous complex relationships worth exploring in greater depth and jointly. While

fragility has been wildly recognised as an issue of significant importance, little has been done, not

only in terms of its determinants but its relationship to growth and the biggest threat to

sustainable growth in Africa – inequality. This gives the underlying importance of the objective

of this study.

This paper probes the nexus of growth- inequality-fragility relationship within the context of the

robust growth in some African countries captured within endogenous econometric models. We

start with an exploration of the underlying fundamentals of growth, inequality and fragility in a

qualitative review together with trends in the data. We then move on to undertake an

3 Average for 2005-2010, from World Development Indicators (WDI). 4 Up to 70% primary enrolment rates in 2010, 60% adult literacy, falling child mortality from 175/1000 to 125/1000 between 1990 and 2010 (International Labour Organization 2013).

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econometric analysis that is consistent with endogeneity issues that are likely to characterise these

variables.

2. Growth and inequality underpinnings by fragility status

There are two indicators of fragility commonly used. One referred to as the Fragile State Index

(FSI) from Fund For Peace (FFP). This indicator comprises twelve sub indicators: four social,

two economic and six political. The other measurement of fragility is the Country Policy and

Institutional Assessment (CPIA) of the World Bank. It is constructed using a set of sixteen

criteria categorised into four clusters. These clusters respectively relate to economic

management, structural policies, social inclusion and equity, and public sector management and

institutions.

For the purpose of this work, the economic components of both indicators have to be excluded

due to the fact that our analysis involves economic growth and inequality. For the same reason

both measures also have to be purged of inequality components. For the CPIA, we exclude

economic management and social inclusion policies which strongly include economic

performance and inequality. The resultant indicator therefore lays emphasis on the political

elements of fragility including the regulatory environment. The FSI for the purpose of this work

excludes economic and social sub indicators, laying emphasis therefore on security and political

effectiveness and legitimacy. These indicators are used comparatively in the empirical section. In

the descriptive section, we use the fund for peace classification to dichotomize states by their

respective fragility status.

There is a marked difference between fragile and non-fragile states in terms of growth, inequality

and other underlying factors. This section explores these differences starting from economic

growth and inequality levels and following on with possible underlying factors such as human

capital accumulation, fiscal capital, economic participation and dependency burden. Because

gender issues surface strongly in development outcomes and especially in fragile states, this

section also looks at key elements of human capital accumulation and labour market in the

gender perspective.

2.1. Comparative levels of growth and inequality

The real income levels of fragile states whether considered in overall or per capita terms are

significantly lower than the non-fragile counterparts in Africa. Table 1A explores the levels of

inequality and economic performance comparing non-fragile states to fragile ones. Table 1B

compares key economic and inequality indicators for pre-2000 and post-2000 periods for African

fragile and non-fragile states. Economic growth is also relatively weak in fragile states with

average per annum growth of 3.71% and 1.17% for real GDP and real per capita GDP

respectively. Generally, the economic structure of developing countries in Africa is characterised

by high content of services and agriculture. There is a relatively higher content of agriculture

value additions in fragile economies compared to non-fragile ones. Notwithstanding, most of the

economic growth in fragile states have come from manufacturing and industry, growing above

4% per annum on average. Services and agriculture grow at below 3% on average for the period

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under consideration. The recent decade and the half starting from the year 2000 correspond to

the period of robust economic growth in Africa. During that period, growth in real GDP per

capita moved from 1.8 to 2.72. Per capita growth rate moved from -0.13 to 1.57 in fragile states

(Table 1B).

In painting the picture of the distributional levels of economic growth, inequality is disaggregated

by within and between income classes along the income distribution spectrum. Gender inequality

is captured by disparities in labour force participation. Fragile states seem to exhibit relatively

lower levels of average inequality and inequality within the poor compared to non-fragile states

of Africa. Inequality among those at the top end of income distribution is relatively higher in

African fragile states. Extreme inequality (between the rich and poor) is peculiarly high in African

fragile states. Consequently, the palmer ratio which measures the gap between the incomes of the

bottom 40% and top 10% of the population is significantly higher in African fragile states. It is

even higher than the fragile states’ average for all developing countries. On average, inequality

has slowed down from pre 2000 to post 2000. However, this masks a great dispersion in

inequality levels. Ngepah (2016) shows that extreme inequality of which palmer ratio is one type,

significantly reduces economic growth in Africa. African fragile states though exhibiting

relatively milder average inequality are characterised by higher levels of the bad types of

inequality.

Gender disparities in labour market participation are markedly higher in African fragile states. It

is also higher than all fragile states average globally. This suggests that the situation of women

may be particularly precarious in African fragile states. Fragility and conflicts interact with other

forms of inequality to undermine girls’ and women’s empowerment. There is a strong evidence

of a negative relationship between conflicts and gender relations (O’Connell 2011).

Literature has established that inequality is both a significant underlying cause and consequence

of fragility. Baker (2014) in an analysis of correlates of fragility, using the failed state index

identifies inequality as the key correlates of fragility with far stronger relationship than poverty

and economic decline. The study also finds gender inequality to have significantly high levels of

correlation with fragility status.

Table 1A: Inequality and economic performance: Africa versus the rest of the developing world

All developing countries Africa

Non-fragile Fragile Non-fragile Fragile

Inequality within:

Average (Gini) 46.54 43.55 42.37 41.50

Palma Ratio 2.76 3.13 3.56 3.60

The poor (q1/d1) 5.44 4.48 5.26 4.51

Middle class (q4/q5) 1.56 1.54 1.50 1.51

Rich (q5/q4) 2.77 2.69 2.45 2.53

Inequality between:

Rich and poor (q5/q1) 13.47 12.75 10.95 11.76

Rich and middleclass (q5/q3) 3.33 3.25 2.72 2.92

Middleclass and poor (q3/q1) 2.85 2.79 2.72 2.72

Gender inequality in labour force participation 1.68 1.29 1.71 1.91***

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Value added in:

Agriculture 21.85 35.73 20.41 31.57

Industry 28.24 19.88 29.84 20.76

Manufacturing 13.15 10.70 16.39 9.74

Services 45.71 40.53 46.21 42.97

Growth in value added in:

Agriculture 3.35 2.18 2.86 2.12

Industry 5.34 4.60 4.68 4.24

Manufacturing 4.84 2.74 4.57 7.86

Services 4.77 3.24 4.95 2.84

Real GDP growth 9.07 3.61 5.64 3.71

Real GDP per capita growth 8.57 0.79 4.34 1.17

Real GDP 2.52E+10 5.44E+09 8.23E+10 8.08E+09

Real GDP per capita 2.57E+07 5.75E+08 5.52E+09 3.91E+08

Source: Authors’ computation using data from WDI

In the light of Table 1A, there is corroboration with previous findings in the sense that gender

inequality is highest in fragile states. However, the comparative inequality levels of fragile state in

Africa seem to suggest that not all inequalities impact fragility in the same way. This idea will be

further explored in an econometric analysis in section three below.

Table 1B: Pre- and Post-2000 comparisons of inequality5 and economic performance

African non-fragile African fragile

Pre-2000 Post-2000 Pre-2000 Post-2000

Average (Gini) 48.14 44.30 48.45 39.20

Palma Ratio 3.68 3.06 3.99 2.17

The poor (q1/d1) 5.76 4.38 4.48 4.45

Middle class (q4/q5) 1.60 1.51 1.60 1.47

Rich (q5/q4) 2.90 2.56 2.92 2.39

Rich and poor (q5/q1) 15.15 10.70 15.71 9.13

Rich and middleclass (q5/q3) 3.39 3.12 3.48 2.38

Middleclass and poor (q3/q1) 3.04 2.53 3.04 2.48

Gender inequality in labour force participation 1.88 1.57 1.31 1.29

Real GDP growth 4.43 5.0 2.66 4.42

Real GDP per capita growth 1.84 2.72 -0.13 1.57

Real GDP 2.20E+10 2.90E+10 4.40E+09 6.30E+09

Real GDP per capita 2.70E+07 2.40E+07 7.50E+08 4.20E+08

Source: Authors’ computation using data from WDI

5 South Africa is a country with the highest inequality in the world. Cognisant of this, we excluded South Africa

from the sample, however, this exclusion did not significantly alter the mean of the non-fragile group. It changed from a Gini coefficient of 48.14 to 47.76 in the pre-2000 period, and 44.30 to 43.62 in the post-2000 period. We therefore do not think that the exclusion of South Africa from the sample is warranted, especially given the sparing inequality data points in the African sample.

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2.2. Human Capital accumulation

Table 2 presents the situation of human capital in fragile and non-fragile states comparing Africa

with all other developing regions. Fragile states are worse off in terms of all dimensions of

human capital, though in Africa even non-fragile states are worse off than overall fragile state in

developing countries. An individual is on average expected to live only about 50.5 years in

African fragile states compared to about 56 years in non-fragile states. In fragile states, 98.5 of

every thousand children die before their first birthday compared to 76 per thousand for non-

fragile counterparts. The picture of health human capital shows that African fragile states are

much worse off than fragile states in the rest of the world.

Table 2: The state of human capital: Africa versus the rest of the developing world

All developing countries Africa

Non-fragile Fragile Non-fragile Fragile

Life expectancy at birth 63.22 55.65 55.85 50.49***

Infant mortality rate 55.73 79.26 76.14 98.45***

Share of pop with no education 27.97 55.59 43.74 59.24***

Share of pop with primary 36.34 25.32 33.50 23.49***

Share of pop with secondary 29.60 16.85 20.44 15.75***

Share of pop with tertiary 6.09 2.25 2.31 1.54***

Average years of education 5.63 3.25 4.18 2.98***

Source: Authors’ computation using data from Barro-lee dataset and WDI

Note: *,**,*** denote 10%, 5% and 1% significant levels for t-test of mean difference between fragile and non-fragile

groups.

The situation of educational human capital shows fragile states as worse of also. Up to 59% of

the populations in fragile states have no education. 23.5% of fragile states population in Africa

have primary education compared to 33% for non-fragile states. Clearly, the inroads made

towards basic educational improvements in light of the Millennium Development Goals

(MDGs) have not gone far enough in the fragile states of Africa. The importance of carrying

educational human capital beyond primary to secondary and tertiary levels has been underscored

by Ngepah (2016) in light of the new Sustainable Development Goals (SDG). He shows that

educational human capital distribution (measured in terms of the ratio of primary and no

education population to population with secondary and tertiary education) is a key driver of

extreme inequality in Africa. In this respect, given that in African countries including fragile

states, the services sector, which is more semi-skilled, and skilled labour intensive, has

increasingly become the principal component of production. Its share in total output stands at

43% compared to agriculture which is 32% (Table 1). With current skewness in skills

distribution, this means that inequality might worsen in the near future. Although in general, the

situation of human capital has improved in both fragile and non-fragile states from pre-2000 to

post-2000, the gap between fragile and non-fragile states of Africa post-2000 remains

significantly high (Figure 1).

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Figure 1: Pre- and Post-2000 comparisons of human capital endowments

The services sector is consequently employing most young Africans in both fragile and non-

fragile states. This shift from agriculture to services requires a corresponding upward movement

of human capital which is clearly marginal in fragile states, with the population share of 15.8%

having secondary education, and only 1.5% with tertiary education. The average years of

schooling in fragile states population is just about 3. The importance of education in economic

growth, which is the key pillar of poverty reduction (Dollar & Kraay 2002) has long been

established. One year increase in the average years of education in a population increases the

level of output per capita by 3 to 6 percent in the long term, and would lead to a percentage

point faster growth if the effects on productivity are accounted (van der Ploeg & Veugelers

2008). Clearly, fragile states are far behind in terms of health and educational human capital. This

obviously plays a significant role in the very low level of economic performance and will certainly

in the future stifle economic growth and exacerbate inequality. Pritchett (2001) presents an

interesting discussion on the heterogeneous effects of education on output. Of Importance to

this argument are the additional beneficial effects, other than economic output raising that he

highlights. Such benefits include lowering of child mortality, and enhancing of cognitive skills

which might bring indirect but long term effects on output and development.

5.3

9.0

5.1

3.2

1.5

0.2

5.9 5.7

3.3 3.6

2.8

0.3

4.7

11.8

6.9

2.0

1.0 0.1

5.3

8.2

5.0

2.7 2.1

0.2

0.0

2.0

4.0

6.0

8.0

10.0

12.0

Life expectancyat birth

Infant mortalityrate

Share of popwith no

education

Share of popwith primary

Share of popwith secondary

Share of popwith tertiary

Non-Fragile Pre-2000 Non-Fragile Post-2000

Fragile Pre-2000 Fragile Post-2000

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Therefore, major interventions have to be undertaken in the area of building both health and

educational human capital in fragile states of Africa. It is worth noting that overall, non-fragile

African countries are not far off from fragile states in the rest of the world. In this respect, Africa

may still be a fragile continent worthy of major interventions in its human capital development.

2.3. Inputs, economic participation and dependency burden

Table 3 highlights the differences between fragile and non-fragile countries in Africa and the rest

of developing countries with respect to key economic inputs in terms of human and physical

capital, dependency burden, labour market participation, and natural resource rent. Increasingly,

emphasis is being placed on the role of human capital distribution in inequality and also the role

of inequality in underdevelopment and possibly, fragility (Ngepah 2016). Human capital

distribution measured as the share of population with secondary and tertiary education over that

with primary and no education, is shown in Table 3 to be quite low (0.09) in fragile African

states. Non-fragile states of Africa are not any better compared to the rest of the developing

world. Overall, African labour force is still in the no education and primary education category.

The apparent shifting of investment and production to relatively higher skills sector like services

and manufacturing implies that the situation of human capital distribution will inevitably result in

more inequality with increasing production. Given the possible connection between inequality

and fragility, this situation will likely jeopardize Africa’s development unless efforts are made to

increase the share of secondary and higher education in African populations. It is emphasised

here that although the MDG objective 2 of universal primary education has laid a good

foundation and greatly improved access to primary education (UNECA 2015), it has neither

improved primary completion rate nor the distribution of human capital, as few people are still

concentrated at high skill and high education level of the distribution, while majority are low

skilled.

The level of investment per GDP is comparable across all developing countries of Africa and the

world with fragile countries not showing any distinctive difference. Investment per unit GDP

ranges on average from 20.13 to 22, with African countries marginally higher. This suggests that

both fragile and non-fragile countries are equally attracting investments. It is also apparent from

the structure of the economy and growth in the value added of the different sectors that new

investments might be going primarily to the manufacturing, industry and services. Comparing

the situation of physical capital with that of human capital clearly reveals a mismatch between

investments in sectoral development and the skills required to bring about an even distribution

of the fruits of the resulting economic growth. Therefore, it is not surprising that the recent

substantial economic growth in Africa in the past one the half decades have still left inequality

persistently high, and consequently not been beneficial to the majority of African population

(McKay, 2013; Ngepah, 2016 and World Bank, 2013).

Table 3: Economic participation and resource rent

All developing countries Africa

Non-fragile Fragile Non-fragile Fragile

Human capital distribution 0.84 0.12 0.19 0.09

Gross fixed capital formation per GDP 21.43 20.13 21.96 20.42

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Natural Resource rents 8.87 14.06 11.35 11.91

Labour force participation (15-64) 67.81 66.80 68.61 74.44

Age dependency ratio 73.25 87.12 83.36 91.14

Age dependency ratio (old) 9.15 6.31 8.23 5.79

Age dependency ratio (Youth) 64.72 80.81 77.19 85.34

Natural resource dependency has been shown to be one of the root causes of underdevelopment

through various channels especially the so called Dutch diseases (Gylfason & Zoega 2006). It is

also a major culprit in the widening of income and wealth disparities in most natural resource

dependent developing countries (Fum & Hodler 2010; Ngepah, 2016). Recently, natural resource

rents have also featured as a cause of fragility. It is a prominent variable in models that seek to

understand the processes that make states fragile (Bjorvatn & Naghavi 2011; Feeny et al. 2013).

Congruent with literature, Table 3 shows that fragile states have significantly higher natural

resource rents in their GDP. In Africa, natural resource dependence is also high and there is only

a marginal difference of about half a point between fragile and non-fragile states. Ross (2008) has

established a strong empirical connection from natural resource (oil) dependency and gender

inequality dis-favouring women. He shows how oil production reduces women’s labour force

participation and consequently their political influence, perpetuating patriarchal norms, laws and

institutions. We therefore underscore the point that except natural resource wealth management

is improved through strong institutions, wider participation and benefit sharing; a good part of

Africa may remain under the threat of fragility.

Figure 2 : Pre- and Post-2000 comparisons of investment and dependency

The labour force participation of working age population does not show a very distinctive

difference between fragile and non-fragile developing countries. In Africa, fragile states have

even higher labour force participation rates than non-fragile states. However, behind this higher

rate of labour force participation, might be hidden a serious issue of quality of jobs. A report on

global employment trends by the International Labour Organization (ILO, 2013) has shown a

significant tendency of increases in informal employments relative to formal in many fragile

states. The report also shows that a good part of the employments in fragile states are informal,

2.1

8.8

0.7

8.2

2.3

7.7

1.0

7.1

1.5

9.2

0.6

8.6

2.2

9.0

0.6

8.4

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

10.0

GFCF per GDP Age dependency ratio Age dependency ratio(old)

Age dependency ratio(Youth)

Non-Fragile Pre-2000

Non-Fragile Post-2000

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precarious and of low quality. Despite the high labour force participation rate, the dependency

rate is significantly high in fragile states and much higher in African fragile states particularly.

More than 80% of the dependency burden comes from the youth. The worrisome fact is that

while the dependency burden has fallen slightly for non-fragile states (8.2 to 7.1), it has remained

almost the same for fragile states from pre- to post-2000. This youth bulge in fragile states may

jeopardize any prospects of stability and recovery unless dependency is also addressed.

2.4. Gender mainstreaming

Fragility puts females at a particularly disadvantaged socio-economic position. Overall,

irrespective of fragility status, there is a significant gender gap favouring the male population in

labour force participation. ILO (2013) shows that females participate more in vulnerable

employments and therefore participating in jobs of lower quality especially in fragile states.

Table 4: Gender differences

All developing countries Africa

Non-fragile Fragile Non-fragile Fragile

male female male female Male female male female

Labour force participation 80.08 54.86 77.83 54.93 79.40 57.19 80.91 66.97

Share of pop with no education 23.24 32.69 46.62 64.41 37.48 49.90 50.95 67.38 Share of pop with primary 38.20 34.46 29.17 21.59 36.82 30.34 26.71 20.33 Share of pop with secondary 32.07 27.29 21.25 12.55 23.08 17.99 20.16 11.42 Share of pop with tertiary 6.61 5.56 3.05 1.45 2.88 1.77 2.23 0.88

Average years of education 6.06 5.23 4.03 2.47 4.72 3.68 3.72 2.25

Human Capital spread 0.90 0.74 0.17 0.07 0.23 0.16 0.13 0.06

At the heart of the labour market gap and the vulnerability of employment lies a serious issue of

gender gap in human capital as suggested in Table 4. Above 60% of female population in fragile

states have no education compared to about 51% of males in Africa and 47% of males in all

developing countries. While 67% of females in African fragile states have no education, only

20% have primary education, 11% secondary education and less than 1% tertiary education.

Females in fragile states of Africa have on average 2.3 years of education compared with 3.7 for

males.

Fragile states seem therefore to be lacking along a number of key crucial socio-economic

dimension, including gender. These gaps in the socio-economic fundamentals in the fragile states

may interact in complex ways to cause not only fragility, but growth and inequality outcomes,

which is the subject matter of this work. In the next section, we take a formal econometric

approach to systematically investigate the nexus of growth, inequality and fragility with respect to

the factors explored above and many others that form the arguments of growth-inequality-

fragility relationship.

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3. Empirics of growth inequality and fragility

3.1. Theoretical underpinnings

There are three endogenous relationships in the nexus of growth-inequality-fragility. First is the

possibility that fragility may both be a cause and consequence of the economic growth process

and growth outcomes. Secondly, while fragility may tend to limit access to resources and fruits of

economic growth for some, and put others at a privilege position, leading to enhanced inequality,

inequality itself (both of access to inputs and resources and of outcomes) is a key determinants

of conflicts, socio-political unrest, low and less sustainable growth trajectory and hence fragility.

Finally, the relationship between growth and inequality is one of mutual causation.

In this section, we first explore the key sources of endogenous relationships among the key

variables before specifying appropriately defined frameworks of growth, inequality and fragility

based on theoretical and past empirical literature. Following these, we summarily define the

variables, their respective sources and the estimation techniques.

3.5.1. Potential sources of endogeneity

Although the poverty reducing impact of growth depends on progress in inequality, the

processes that generate growth and inequality are not mutually independent. The proposition of

the endogenous economic growth theory has resulted in renewed interest in the growth-

inequality relationship. The recent interest focuses rather on the corresponding endogenous

relationship between growth and inequality, different from the unidirectional Kuznets (1955)

type process. The focus of the review here is to establish the bidirectional relationship in order to

inform our approach for conceptual framework. The growth-inequality relationship also

intricately relate to fragility as discussed below.

From fragility to growth (and inequality) and vice-versa

The relationship between fragility and growth is a deep-seated one. At the heart of it are three

key concerns. The first one relates to the connection of fragility with poor and non-existent

delivery of basic services. Delivery of services is not only important for the development of

human capital necessary for an enhanced production system, but also supposes that even the

delivery and maintenance of the complementary facilities of infrastructure of all types may be

completely absent. In this context, the establishment of the private production system may be

totally hindered.

Secondly, one of the key consequences of neoclassical growth theory to the debate is that

fragility does not allow for sufficient levels of human and physical capital required to fuel

economic growth (Maier 2010). While low levels of capital accumulation perpetuates low growth,

the distortions and inefficiencies due to fragility can also hinder the adoption of new

technologies and constrain the possibility of a successful industrialisation process. Low levels of

resources do not also allow fragile states to be able to afford the costly but essential technology

adoption or upgrades to enhance productivity.

The third element consists of both formal and informal institutional constrains in fragile states

that hinder such from playing the role of supporting private sector development. Such

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constrains, in the view of new institutional economics, can increase transaction costs and reduce

or completely hinder the efforts of transformation of the production process, due essentially to

uncertainty. There is a clear empirical evidence pointing to the growth enhancing effects of

reforms of political institutions in Africa both at macro and micro levels (Bates et al. 2013).

The fact that the empirical determinants of fragility have included socio-economic aspects means

that the relationship of fragility and economic growth is mutually reinforcing. The link between

poverty and instability is a documented fact, in that not only does armed violence take place in

low-income countries (Cilliers and Sisk, 2013), there are more socio-political unrests in times of

significant economic down-turns. The deep relationship between fragility and the underlying

determinants of growth and development supposes a complex and bidirectional relationship

between fragility and economic growth.

Cilliers and Sisk (2013) in their discussion on long-term state fragility in Africa identify high

levels of income inequality and the related skewedness in allocation of benefits and resources

along ethnic/tribal and geographic entities, as key distinguishing characteristics of fragile states.

This identification agrees with the discussion of which socio-political tensions caused by such

inequality underlie low growth (Alesina & Perotti 1996), and now, also fragility. However,

inequality also sets the stage for adoption of distortionary policies and polarised power relations

which in turn fuel conflicts and reinforce fragility.

From growth to inequality and vice-versa

The work of Kuznets (1955) – which hypothesises that at the early stages of growth in

developing countries, inequality increases, and then starts to fall - has gained interest among

researchers (Oshima (1970); Ahluwalia (1976); Robinson (1976). Kuznets suggests labour market

imperfections, productivity differentials across economic sectors and the changing importance of

the various sectors in the economy as the main channels through which growth impacts

inequality. Stiglitz (1969) explained the same hypothesis within a neoclassical framework of

growth and distribution in which individual accumulation behaviour and changing factor rewards

(due to diminishing returns to capital) account for the U-shape in the evolution of inequality with

development. Institutions, social relations, culture etc., also tend to be modified by growth

through various ways6.

Empirical works that lend support to this hypothesis made use of cross-country datasets from

1950s to 1970s (Adelman & Morris 1973; Ahluwalia, 1976b and Ram 1995). Ahluwalia (1976b)

estimates inequality as a function of log of per capita income and its square to capture the

quadratic effect in a cross section data, and confirms the existence of an inverted U-shaped

relationship. However, as more and better data became available, it was not verified for later

periods. Bruno et al. (1998) replicated the specifications and found no evidence of inverted U-

6E.g. People subsequently become politically more active, leading to change in the distribution of political power and

evolution of institutions (Justman & Gradstein 1999); transaction costs – which hinder institutional change –

becoming more affordable with economic growth (North 1990). Bourguignon (2004) observes that the process of

urbanisation that follows economic development occurs naturally with the evolution of social relations.

14

shape in latter cross-sections. Bourguignon & Morrison (1998) use unbalanced panel data for

developing countries and found that this hypothesis is not verified. Deininger & Squire (1996a)

use unbalanced panel with about ten year intervals. A simple pool regression of Gini with respect

to per capita income and its inverse give a significant inverted U-shape. However, decadal

differencing to account only for time changes gives an insignificant curvature. The introduction

of country fixed-effects causes the U-shape to disappear completely.

As Bourguignon (2004) remarks, all the above discussions do not imply that growth has no

significant impact on inequality, but rather much presence of country-specific factors in the

inequality impact of growth. This makes a country-specific study more interesting. Bourguignon

& Chakravarty (2003) suggest that indeed growth impacts inequality, a major contributing factor

being the difficulty of the poorest households to incorporate themselves into the labour market

in the advent of slow growth.

Three major ways through which inequality can impact growth are physical endowment (credit

constraints), human capital endowment and political economy. High credit constraints through

high interest rates to the poor imply that certain projects below a certain return threshold that

could only be undertaken by the poor fail to take-off. As such, redistribution will lead to higher

investment and/or higher return on capital (Bourguignon, 2004: 17). More formalised models

(like Galor & Zeira 1993; Banerjee & A. 1993; Aghion & Bolton 1997) put information

asymmetry at the centre of credit constraints. In these models, the evolution of inequality and

output is influenced by the limited choice by poor people (and possibly middle class) of

occupations and investment due to credit rationing. When the poor are thus prevented from

making productive investment (that would benefit them and the society), a low and inequitable

growth process can result. Besides, in a Keynesian economy where marginal rate of savings

increases with income, or with higher propensity to save from capital returns than labour returns,

those at the top end of the distribution may represent the main source of savings (Voitchovsky

2005).

In the view of a political economy approach, high inequality sets the stage for the adoption of

distortionary policies which adversely affects investment and generates political instability leading

to stifled growth (Bates 2015; Persson & Tabellini 1994). Alesina & Perotti (1996) have equally

argued that higher political instability can result from high inequality, the resulting uncertainty

then reduces investment levels. Rodrik (1996) has confirmed that divided societies with weak

institutions also witnessed the sharpest fall in post-1975 growth. This situation brought about a

weakness in their capacity to effectively respond to external shocks. This element does not only

affect growth, but directly impacts the fragility of the state as well.

Human capital endowment (education, skills and healthy life) is also important in the growth

effect of inequality. In situations where ability is rewarded, there is incentive for more effort, risk

taking and higher productivity, resulting in higher growth but with higher income inequality. In

such cases, talented individuals will tend to seize higher return to their skills. The resulting

concentration of talents and skills in the advanced technology upper income sector becomes

conducive for further innovation and growth (Hassler & Mora 2000). Such incentive can induce

greater effort in all parts of the distribution (Voitchovsky 2005). However, frustration in the

15

lower end of the distribution resulting from perceived unfairness (Akerlof & Yellen 1990) may

counteract the innovation gains.

Empirically, various authors have found negative impact of initial inequality on growth in

developed countries (Persson & Tabellini 1994), developing countries (Clarke 1995) and a

combination of both (Deininger & Squire 1996b). Schwambish et al. (2003) find that top end

inequality (measured by 90/50 percentile ratio) strongly and negatively impacts social

expenditures while the bottom end (captured by 50/10 percentile) show a small positive effect.

They suggest that high top end inequality reduces social solidarity, with the rich trying to pull out

of publicly funded programs such as health care and education, in preference to private

provision.

3.2. Empirical framework

The above discussions have highlighted the very strong endogenous relationships that potentially

exist among the key variables of interest. Cognisant of this, we propose that the first part of this

work will dwell heavily on simple and meticulous analysis of the descriptive statistics that may

underlie the variables. The work will therefore first explore patterns of growth within various

segments of the income distribution spectrum by fragility categories. In the same manner, we will

explore various factors that potentially determine growth, fragility and inequality within various

income segments and fragility status. We will finally analyse the correlations among the different

factors by different characteristics before transiting to more formal econometric models.

3.2.1. The growth model

We use five-year averages of panel data in an augmented Solow-type growth model following

from Voitchovsky (2005). Specifically, the five-year growth model is based on the following

form:

𝑦𝑖𝑡 − 𝑦𝑖𝑡−1 = 𝛼1𝑦𝑖𝑡−1 + 𝛼2𝐺𝑖𝑡−𝑗 + 𝛼3𝐹𝑖𝑡−𝑗 + 𝜔′𝑋𝑖𝑡−𝑗 + 𝑢𝑖𝑡 , (1)

where y is GDP per capita, 𝑡 and 𝑡 − 1 are time periods corresponding to observations that are

five years apart, 𝑋 is a vector of ‘exogenous’ control variables, 𝑖 is a country index, 𝜔′ is a vector

of coefficients, 𝐺 is a measure of inequality, F is a measure of fragility for country i at time t, 𝛼

are coefficients, j is an appropriate lag and 𝑢𝑖𝑡 is a composite term including and unobserved

country-specific effect, time-specific effect an error term.

According to Barro (2000), the neoclassical model underlying equation (1) explains a long-term

steady state level of income. As such, an enduring change in inequality (and other determinants

16

of growth) will affect growth rates only in the short-run. That is while the economy is still on the

convergence path to a new equilibrium. Due to the fact that the economy generally takes a long

time to reach a new steady state following a change in any of the determinants, the short-term

inequality effect on growth can actually last a good while.

The variables in the growth model are five year averages beginning from the year of inequality

data. This means that if our inequality observation is at 𝑡, then all the other variables are the

average from 𝑡 𝑡𝑜 𝑡 + 5. This approach takes care of the endogeneity between growth and

inequality as the reverse causation from growth to inequality would have been purged out.

3.2.2. The Inequality model

We equally use five-year averages of panel data similar to the one above to specify inequality.

Such specifications have been used in similar sense in recent past (see for example Figini, 1999;

Ngepah, 2016). Specifically, the five-year growth model is based on the following form:

𝐺𝑖𝑡 = 𝛽0 + 𝛽1𝐺𝑖𝑡−1 + 𝛽2∆𝑦𝑖𝑡−𝑗 + 𝛽3𝐹𝑖𝑡−𝑗 + 𝜑′𝑋𝑖𝑡−𝑗 + 𝜗𝑖𝑡 , (2)

where G is an inequality, 𝑡 and 𝑡 − 1 are time periods corresponding to observations that are

five years apart, 𝑋 is a vector of ‘exogenous’ control variables, 𝑖 is a country index, 𝜑′ is a vector

of coefficients, ∆𝑦 is growth in real GDP per capita, 𝛽 are coefficients and 𝜗𝑖𝑡 is a composite

term including and unobserved country-specific effect, time-specific effect an error term.

Contrary to the growth equation, our inequality measure in the inequality equation is taken at the

end of the period while the other variables are five year averages for the period ending with the

inequality observation.

3.2.3. Fragility specification

Our empirical specification of fragility is an adaptation from Feeny et al (2014) as follows:

𝐹𝑖𝑡 = 𝛿0 + 𝛿1𝐺𝑖𝑡−𝑗 + 𝛿2∆𝑦𝑖𝑡−𝑗 + 𝜋′𝑋𝑖𝑡−𝑗 + 𝜎𝑖𝑡 (3)

where F is a measure of fragility, G is an inequality, 𝑡 and 𝑡 − 1 are time periods corresponding

to observations that are five years apart, 𝑖 is a country index, ∆𝑦 is growth in real GDP per

capita, 𝜋 is a vector of coefficients of exogenous variables contained in X, 𝛿 are coefficients and

𝜎𝑖𝑡 is a composite term including and unobserved country-specific effect, time-specific effect an

error term.

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3.3. Variables and data

3.3.1. Endogenous variables

The three endogenous variables in the model are growth, inequality and fragility.

Income and income growth: Our measure of growth rate (𝑦𝑖𝑡 − 𝑦𝑖𝑡−1) is the growth in real

per capita GDP taken from the World Development indicators (WDI) database of the World

Bank (2015). The lagged variable (𝑦𝑖𝑡−1) is the average of real per capita GDP for the five year

preceding the five year period in consideration for the other variables.

Inequality measures: Our measure of inequality is the Gini index. The primary source of the

Gini coefficient is the WDI dataset. We also consider as a complementary source, the World

Income Inequality Database (WIID) of the UNU-WIDER, which appears richer due to diversity

of sources.

Measures of fragility: there are differing versions of definition of fragility. For the purpose of

analytical engagement, we prefer (though not exclusively) the view of OECD (2009), in which

fragility refers to a situation in which a state lacks political will and/or capacity to provide basic functions

needed for poverty reduction, development and to safeguard the security and human rights of their populations

(OECD, 2009: p76). Two main sources of indicators of fragility can be found in literature. One

is the Failed State Index (FSI) computed by Fund for Peace. It comprises social, economic and

political indicators. The other is the Country Policy and Institutional Assessment (CPIA)

undertaken by the World Bank. It comprises elements of economic management; structural

policies; social inclusion and equity; and public sector management and institutions. For

robustness, we seek to use the indicators comparatively. We will also use a dummy classification

of 1 if a country is fragile and 0 if not.

Human capital variables generally measured in terms of education has two possible

candidates. The first is the use of enrolment ratios. However, this is an indicator of investment in

education rather than an outcome of education. Traditionally, the outcome variable used in

similar research is the average years of schooling in the population. The human capital variable

may not be entirely exogenous, given that the process of human capital accumulation can be

significantly influenced by growth, inequality and fragility, we pay more attention to descriptive

analyses here, while carefully considering possible appropriate lags for instruments. Educational

data is usually from the computation of Barro & Lee (2000). We will make use of this. However,

because it does not richly capture African countries, we will also use school enrolment rates for

primary and secondary from the WDI. Additional human capital variable is health related and

will be captured by infant mortality rates available from the WDI. Depending on consistency of

this data, which is generally scarce for countries, we will consider alternatives like adult mortality

or life expectancy at birth, also available from the WDI dataset.

3.3.2. Exogenous variables

The main exogenous determinants of fragility can be grouped into three: colonial identity,

disaster prevalence, classification as Small Island Developing State (SIDS), human capital7 (both

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education, measured in net primary enrolment ratio and health, often measured by infant

mortality rate), population (Feeny et al. 2013). The population variable is often taken as the log

of the size of the population, however, we think that the structure of the population in terms of

proportions of youth, adults and elderly would be more appropriate given the youth bulge and

youth unemployment in Africa. Interestingly, all these variables can be considered exogenous

variables in parsimonious sense in both the growth and inequality equations. To these, we also

include investments (Figini, 1999 and Voitchovsky, 2005) and openness.

The risk of omitted variable in our model is likely to be insignificant. Figini (1999) runs the

Ramsey test of omitted variables in inequality determination and finds no significant evidence of

omitted variable bias when human capital and investments are the only other determinants apart

from inequality measures included in growth regression. In an attempt to balance the risk

between multicolinearity bias and omitted variable bias, we stick to the basic model including

only investment and human capital and augment with the other exogenous variables described

above.

The investment variable is measured by the average share of gross fixed capital formation in GDP. It

is the five-year average from the year of inequality measure observed. The data is from the WDI

database.

Colonial identity is a dummy, taking the value of 1 if the country in question was colonised by a

given colonial master (Britain, France or Portugal) and 0 otherwise. The information for this

variable will be taken from the CIA World Factbook.

Disaster prevalence measures the number of disasters experience by a country within a given period.

The data can be sourced from the UCPD/PRIO Armed conflict dataset.

SIDS Status is a dummy, taking the value 1 if the country is classified by the United Nations as a

Small Island Developing State and 0 if not.

Openness measures total exposure to the rest of the world and is captured by total trade (imports

and exports) to GDP ratio. Both trade and GDP values will be taken from the World Bank’s

World Development Indicators (WDI) dataset.

Natural resource rents are the amount of natural resources in a unit of GDP. It will be measured by

the sum of all natural resources (oil, natural gas, coal, minerals and forest) values to GDP. The

data will be taken from the WDI.

3.3.3. Exogenous variables

Data were sourced from World Development Indicators (WDI), Uppsala conflict data program

at the department of peace and conflict research (UCDP/PRIO), Armed conflict dataset, the

International Monetary Fund (IMF), Barro and Lee dataset, World Income Inequality Database

(WIID) and International Labour Organization database (ILO).

Yearly data from 1960 to 2015 in an unbalanced panel are then aggregated to five years averages

to obtain a balanced panel. The data consist of 49 African and 77 other developing non-African

19

countries made of low income, lower middle income and upper middle income countries. Table

5 gives the list and description of the key variables used in this work.

Table 5: List of variables

Variable variables Data source

GDP Real GDP

CI_France Dummy variable taking 1 if a country is a former colonial of France and zero otherwise

CIA

CI_Bristish Dummy variable taking 1 if a country is a former colonial of Britain and zero otherwise

CIA

CI_Portugal Dummy variable taking 1 if a country is a former colonial of Portugal and zero otherwise

CIA

Ineq_av Average inequality (Gini coefficient) WDI and WIIDS

Ineq_gender Gender inequality (ratio of male to female labour force participation) ILO

GDPPC2 GDP per capita WDI

AVYRT Average Years of Total Schooling Male & Female Barro-Lee

IMR Mortality rate, infant (per 1,000 live births) WDI

NRR Total natural resources rents (% of GDP) WDI

SIDS Small Islands Developing States UNESCO

Ethnic Fractionalization Ethnic CGEH

language Fractionalization Language CGEH

Religion Fractionalization Religion CGEH

Ineq_extreme Extreme Inequality (Ratio of incomes accruing to 5th and 1st quintiles and 10th decile and 1st quintile)

WDI and WIIDS

Ineq_palma Palma Inequality (ratio of the income share of the top 10% of the population to that of the bottom 40%)

WDI and WIIDS

HCT Ineq HCT Inequality (People with tertiary and secondary education /people with primary and no education)

Barro-Lee

fragile Fragility FFP (fund for peace)

political Fragility CPIA the principal components of the political elements of the CPIA

WDI

FSI Fragile States Index purged of economic components FFP (fund for peace)

GFCF Gross fixed capital formation WDI

3.4. Estimation techniques

The panel data used in inequality-growth regression are unbalanced, due to the fact that the

underlying survey data from which inequality indices are generated are usually collect at different

points in time for different countries. The unbalanced nature of the data is even worse for

African countries. These countries have generally been under researched in the area of inequality

and growth at the cross country level.

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Baltagi et al (2002) have shown that where the unbalanced pattern is severe, maximum likelihood

estimator (mle) does better. Monte Carlo simulations have also revealed that the maximum

likelihood estimator for unbalanced panels performs well in situations where observations in the

data are missing at random. In such cases, the missing observations affect mainly the root mean

square errors, with t-tests on the slope parameters performing as good as the balanced panel

counterpart thereby making inference reliable (Pfaffermayr 2009).

Following this considerations, we propose to use a battery of estimation techniques

comparatively. First is a baseline pooled OLS model both in single equations and three-stage

least squares as an endogenous variant. Since most of the determinants of fragility are time-

invariant binary variables, we are unable to neither specify a fixed effect estimator nor a full

dynamic panel such as the GMM.

3.4.1. Single-equation estimation

The single equation models consist of a set of probit for fragility, robust OLS for inequality and

growth. Each model consists of four sub-models corresponding to average inequality, palmer

inequality, extreme inequality and gender inequality. The probit model estimates the probability

of being a fragile state. In this case, fragility is measured by a (0 and 1) binary variable. The OLS

estimation uses the failed state index fragility measure which is a continuous variable. In all the

models, we instrument for the endogenous variables (growth and inequality in fragility equation;

growth and fragility in inequality equation and fragility and inequality in growth equation) using

their respective one period lags. The following specifications are estimated in single equations.

(𝑔𝑟𝑜𝑤𝑡ℎ): 𝑦𝑖𝑡 = 𝛼0 + 𝛼1𝐼𝑛𝑒𝑞𝑖𝑡−1 + 𝛼2𝐹𝑖𝑡−1 + 𝜋𝑔′𝑋𝑖𝑡 + 𝜎1𝑖𝑡 (4)

(𝐼𝑛𝑒𝑞𝑢𝑎𝑙𝑖𝑡𝑦): 𝐼𝑛𝑒𝑞𝑖𝑡 = 𝛽0 + 𝛼1𝑦𝑖𝑡−1 + 𝛽2𝐹𝑖𝑡−1 + 𝜋𝑖𝑛𝑒𝑞′𝑋𝑖𝑡 + 𝜎2𝑖𝑡 (5)

(𝐹𝑟𝑎𝑔𝑖𝑙𝑖𝑡𝑦): 𝐹𝑖𝑡 = 𝛼0 + 𝛼1𝐼𝑛𝑒𝑞𝑖𝑡−1 + 𝛼2𝑦𝑖𝑡−1 + 𝜋𝐹′𝑋𝑖𝑡 + 𝜎3𝑖𝑡 (6)

After the single equation estimations, the test for endogeneity is undertaken using the cross

equation residuals in models that do not instrument for the suspected endogenous variables. The

test for endogeneity consists in running a simple OLS result for each of the possible endogenous

variables in their respective equations without instrumentations. Then the residuals are estimated

and included in a second step regression in the respective equations. The null hypothesis of

existence of endogeneity is confirmed if the residuals of the other potentially endogenous

variables are significant in the equation of the endogenous variable in question.

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3.4.2. Joint Estimation

Given that the test results suggest strong endogeneity, amongst the key variables (fragility,

inequality and growth) except for palmer ratio, we proceed with a full three stage least square

(3SLS) estimation. The 3SLS estimations make use of the fragile state index measures and

country policy and institutional assessment (CPIA) measures of the World Bank. In the CPIA

measure, we only pay attention to political fragility. The growth (4) and inequality (5) equations

are combined with the fragility framework (6) and estimated jointly.

Two possible joint regression techniques can be employed for the combined models. These are

two-stage (2sls) and three-stage (3sls) least square techniques. 2sls has been thought of as more

efficient than 3sls in small samples, particularly when cross-equation covariations are small. In

cases of large covariation, 3sls would have an edge even if the sample is small (Ngepah 2011;

Belsley 1988). We propose to take advantage of the benefits of full 3sls in our estimation. To make

the final choice of the right joint estimation method, we will perform the test of endogeneity and

the check the values of the cross-equation covariations, together with the final sample size.

The composite framework requires a statistical testing to determine if inequality may be strongly

endogenous with growth and fragility. This helps to specify appropriate systems of equations that

cater for endogeneity issues.

3.5. Econometric results and interpretation

The results of the single equations and 3SLS are presented in this section. We start by discussing

the outcome of the endogeneity test before reporting, interpreting and discussing the

econometric results.

3.5.1. Endogeneity test results.

The results of the endogeneity tests are reported in Table 6. The results suggest that except for

Palma inequality all other inequality measures are in a strong endogenous relationship with

growth and fragility (both for the failed state index and the CPIA measures of fragility). The test

results also show that growth and fragility are endogenous. Fragility and inequality are equally in

a strong endogenous relationship.

Table 6: Test for endongeinity using cross-equation residuals

Growth equation Inequality equation Fragility equation

FSI

Average inequality 1.2e13 (0.000) 7.4e15 (0.000) 275.94 (0.000) Extreme inequality 4.2e12 (0.000) 1.7e16 (0.000) 179.67 (0.000) Palma 7.4 e14 (0.000) 6.83 (0.000) 55.79 (0.000) Gender 1.9e13 (0.000) 1.9e16 (0.000) 815.74 (0.000)

CPIA

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Average inequality 4.7e16 (0.000) 8.7e14 (0.000) 38.13 (0.000) Extreme inequality 2.3e15 (0.000) 1.4e14 (0.000) 11.76 (0.000) Palma 10.0 (0.158) 11.68 (0.321) 13.41 (0.540) Gender 5.3e15 (0.000) 8.2e13 (0.000) 270.85 (0.000)

Based on these results, we instrument for the endogenous variables (fragility, growth and

inequality) using their respective one period lags in single equations regressions, and compare the

results with full 3sls estimates.

3.5.2. Estimation outputs

Given the complex relationships that may exist among the key variables of interest, our

emphases in estimation is to compare as may different models as we can. Hence, we first

estimate a probit model taking a binary dependent variable with values of 1 if a country is

classified by the Fund for peace and fragile and 0 otherwise. Following this, we run a

heteroscedasticity-consistent Ordinary Least Square (OLS) estimation, instrumenting the key

endogenous variables with their respective first lags. Finally, we undertake a full 3sls estimation

in order to fully take advantage of the cross equation co-variations.

All the estimation result outputs are presented in the appendix. Table A1 contains the probit

results, done within the different inequality (average inequality, Palma, extreme inequality and

Gender Inequality) sub-models. Table A2 gives the robust OLS results for inequality and growth,

within the four inequality sub-models. The 3SLS results are presented in Table A3. Both single

equation estimations and 3SLS estimations largely concur in terms of results. In interpretations,

we base our findings first on the probit results for the probability of being fragile and then on

the 3SLS estimations for fragility, inequality and growth, where we compare the failed state index

with the CPIA results. The interpretation of the results are grouped according to the key

endogenous variables of interest, i.e. inequality, growth and fragility.

3.5.3. The impact of inequality and growth on Fragility

The likelihood ratio and the underlying chi2 probability show that the probit model performs

well. The results suggest that growth in GDP has about zero effect on the probability of a

county being fragile. This is not surprising given that we have also controlled for a number of

key determinants of growth particularly human capital. While average inequality, extreme

inequality and gender inequality tend to reduce the probability of being fragile, the Palma ratio

strongly enhances the possibility of a country being fragile such that a one point increase in

Palma ratio increases the probability of being fragile by 0.79 points. The Palma ratio measures

the gap between the richest 10% and the poorest 40% in terms of their income shares. This

therefore suggests that as the poorer segment of the population view a few concentrating the

wealth of their nation to themselves, they may tend to be unhappy, threatening the socio-political

stability of the nation, leading to higher likelihood of fragility and in turn jeopardizing the

process of economic development.

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The only difference with these findings when compared with the 3SLS is that though the fragile

state index (FSI) results for inequality corroborates the probit findings, gender inequality in

labour market participation tends to increase fragility based on the CPIA measure. A point

increase in the gap between male and female in labour market participation increases CPIA

based political fragility by 0.9 points. Since the CPIA measure considers socio-political fragility

only, the suggestion here is that having a relatively balanced gender mix in a country’s labour

force may tend to cool down the flares of discontentment and hence reduce the possibility of

socio-economic unrest.

In the fragile state index model (FSI), a one percentage point increase in GDP per capita growth

reduces fragility by 1.4 points. This reduction is significant only in the model that includes

inequality. In the CPIA model, growth in per capita income tends to increase fragility in the

presence of extreme inequality. With extreme inequality, a one percentage point increase in

growth increases socio-political fragility by 0.3 points. Other factors associated with higher levels

of FSI are infant mortality rate, natural resource dependence and ethno-linguistic

fractionalization. Therefore countries in Africa with more fractionalized ethnic groups and

languages will tend to be more fragile according to the FSI measure. The CPIA measure suggests

that being a former British and Portuguese colony is associated with higher socio-political

fragility.

3.5.4. Fragility and inequality impacts of economic growth

All measures of fragility are significantly associated with lower levels of economic growth. The

coefficients vary from -0.054 in the presence of extreme inequality to -0.947 in the presence of

gender inequality in the FSI sub-models. The FSI model suggests that in the presence of gender

inequality a one point increase in fragility index reduces GDP growth by 0.947 percentage points.

When we consider political fragility using the CPIA approach, a one point increase in fragility

brings about up to four points decrease in growth in average inequality model, 2.4 percentage

points decrease in gender inequality model and 1.0 percentage point decrease in GDP growth.

The 3sls results are all consistent with those of the single equation estimations and suggest a

strong growth reducing impact of political fragility.

All inequalities reduce growth, with gender inequality showing the strongest impact followed by

extreme inequality in the CPIA-based model and average inequality in both models. This

confirms the view that average inequality, gender inequality and extreme inequality are bad for

economic growth. Some findings have emerged in recent literature, that upper end inequality is

good for growth (Voitchovsky, 2005; Cingano, 2014; Ngepah, 2016). However, we did not

control for these in this work. The CPIA-based models show consistent outcomes with respect

to other growth determinants such as investment, human capital, etc. Infant mortality rates and

natural resource dependence tend to reduce economic growth. However, when we control for

extreme inequality, natural resource dependence shows significant positive impacts on growth.

This suggests that in the presence of extreme inequality, natural resources bring about socio-

political polarization of the society which may militate to enhance fragility. In political fragility

models, being former colony of France is associated with lower economic growth while being a

former colony of Britain and Portugal is associated with significantly high growth. Ethno-

linguistic and religious fractionalizations tend to reduce growth. However, when one controls for

24

extreme inequality, being a former French colony and Ethno-linguistic and Fractionalization all

tend to reduce growth. These suggest that extreme inequality alone is the key channel through

which these factors reduce growth. The policy point of action should therefore pay attention to

measures for the reduction of extreme inequality (see Ngepah, 2016 for such policies).

3.5.5. Inequality impacts of Fragility and growth

The inequality estimates of the 3SLS suggest that past inequalities tend to lead to higher future

inequality for all inequalities along the income distribution spectrum. This suggests that all other

things being equal, highly unequal countries will tend to be more unequal in the future.

Economic growth tends to increase all inequalities considered in the models. Fragility reduces

average inequality but increases extreme and gender inequalities in both FSI and CPIA models. A

one point increase in fragility reduces the Gini coefficient by 0.5 percentage points for FSI and

one percentage point for CPIA. A one point increase in fragility increases the extreme inequality

gender gap in labour force participation (to the disadvantage of women) by 0.02 and 0.19 points

in the FSI model and 1.70 and 0.80 points in the CPI model respectively. These seem to suggest

two things. Firstly, few people take advantage of the nation’s resources in the presence of

fragility, leading to relatively divided societies that may in turn reinforce fragility. Secondly,

women may be more marginalised in fragile states and their labour market participation reduces

with increases in fragility. Extreme inequality reduction and women empowerment and measures

to support female labour force participation in fragile states should be called for. Consistent with

previous research ((Cornia, 2014 and Ngepah, 2016) is the fact that human capital distribution in

terms of the ratio of the share of population with secondary education and above to the share

with primary education and below, is one of the leading causes of inequality in Africa. Ethno-

linguistic fractionalization and religious fractionalization tend to reduce female labour force

participation as shown by their impacts on gender inequality.

4. Conclusion and policy implications

This work has investigated the nexus of inequality, growth and fragility in Africa. It has used a

qualitative approach by making sense out of changes in key indicators, comparing fragile and

non-fragile states in Africa and the rest of the developing world. It further complements the

analysis with econometric modelling in a battery of single equations (probit and ROLS) and 3SLS

that control for endogeneity.

The key findings are that:

While average inequality, extreme inequality and gender inequality tend to reduce the probability

of being fragile, the income gap between the poorest 40% and richest 10% of the population

(palma ratio) strongly enhances the possibility of a country being fragile. This therefore suggests

that as the poorer segment of the population view a few concentrating the wealth of their nation

to themselves, they may tend to be unhappy, threatening the socio-political stability of the

nation, leading to higher likelihood of fragility and in turn jeopardizing the process of economic

development. Having a relatively balanced gender mix in a country’s labour force may tend to

25

cool down the flares of discontentment and hence reduce the possibility of socio-economic

unrest.

Economic growth accompanied by extreme inequality tends to increase political fragility. Other

factors associated with higher FSI are infant mortality rate, natural resource dependence and

ethno-linguistic fractionalization. Therefore countries in Africa with more fractionalized ethnic

groups and languages will tend to be more fragile according to the FSI measure. The CPIA

measure suggests that being a former British and Portuguese colony is associated with higher

socio-political fragility.

All measures of fragility are significantly associated with lower levels of economic growth. All

inequalities reduce growth but gender inequality has the strongest growth reducing impact

followed by extreme inequality in the CPI based model and average inequality in both models. In

the presence of extreme inequality, natural resources bring about socio-political polarization of

the society which may militate to enhance fragility. In political fragility models, being former

colony of France is associated with lower economic growth while being a former colony of

Britain and Portugal is associated with significantly high growth. Ethno-linguistic and religious

fractionalizations tend to reduce growth. However, when one controls for extreme inequality,

being a former French colony and Ethno-linguistic and Fractionalization all tend to reduce

growth. These suggest that extreme inequality alone is the key channel through which these

factors reduce growth. The policy point of action should therefore pay attention to measures for

the reduction of extreme inequality (see Ngepah, 2016 for such policies).

All other things being equal, highly unequal countries will tend to be more unequal in the future.

Economic growth tends to increase all inequalities considered in the models. Fragility reduces

average inequality but increases extreme and gender inequalities in both FSI and CPIA models.

These seem to suggest two things. Firstly, few people take advantage of the nation’s resources in

the presence of fragility, leading to relatively divided societies that may in turn reinforce fragility.

Secondly, women may be more marginalised in fragile states and their labour market

participation reduces with increases in fragility. Extreme inequality reduction and women

empowerment and measures to support female labour force participation in fragile states should

be called for. Consistent with previous research (Cornia, 2014 and Ngepah, 2016) is the fact that

human capital distribution in terms of the ratio of the share of population with secondary

education and above to the share with primary education and below, is one of the leading causes

of inequality in Africa. Ethno-linguistic fractionalization and religious fractionalization tend to

reduce female labour force participation as shown by their impacts on gender inequality.

The caveat of this work is mainly with respect to data. Although good data has increasingly

become possible that make analysis like this one possible, the data is not yet adequate to be able

to allow for dynamic modelling in order to fully exploit information in the data especially with

respect to inequality. However, the data available so far has allowed us to robustly analyse the

issues in question and bring out reliable findings and policy implications.

26

Appendix

TableA1: Estimation output of probit models

Average Ineq Palma Ineq Extreme Ineq Gender Ineq

Gdp(-1) 0.000* (0.000)

0.003** (0.001)

0.000* (0.000)

0.000* (0.000)

Av.Ineq(-1) -0.052*** (0.016)

Palma (-1)

0.785** (0.282)

Ex-Ineq

-0.002* (0.013)

Gender Ineq -3.688*** (0.650)

Avyrt

0.153* (0.129)

1.852*** (0.582)

0.169* (0.143)

-1.480*** (0.348)

Imr

0.042*** (0.007)

0.121*** (0.029)

0.045*** (0.008)

0.045*** (0.011)

Nrr -0.022* (0.012)

-0.010* (0.072)

-0.007* (0.012)

-0.044*** (0.015)

Ci-France -2.884*** (0.630)

-14.796*** (4.320)

-2.242*** (0.546)

-5.956*** (1.298)

Ci-Bristish -6.092*** (0.914)

-35.820*** (9.680)

-6.268*** (0.866)

-11.633*** (1.970)

Ci-Portugal 0.000 0.000 0.000 0.000 Ethnic

0.529*** (0.088)

1.397*** (0.397)

0.340*** (0.075)

1.216*** (0.219)

Religion

-0.028* (0.102)

-0.374** (0.136)

0.047** (0.054)

0.299*** (0.093)

Language 0.113*** (0.024)

0.866*** (0.257)

0.117*** (0.026)

0.225*** (0.043)

Constant

-4.320*** (0.889)

-23.525 (5.592)

-6.224*** (1.014)

-0.675* (1.266)

No. Obs 719 362 513 533 LR Chi2(10) 516.05 295.35 357.90 441.93

Prob > Chi2 0.000 0.000 0.000 0.000 Pseudo R2 0.719 0.838 0.688 0.791

Note: the dependent variables are five year averages. *, **, *** denote significance at 10%, 5% and 1% levels

respectively; standard errors in parentheses.

Inequality categories are average inequality (Gini); Palma ratio (ratio of the incomes of the top 10% to the bottom

40% of the population); extreme inequality (ratio of the incomes of top 20% to bottom 20% of the population);

gender inequality (ratio of male-female labour force participation rates). IMR is Mortality rate, infant (per 1,000 live

births), NNR is Total natural resources rents (% of GDP); Religion, Language and Ethnic are fractionalizations

measure; CI (France, Britain, Portugal) represented a colonial identities; AVYRT is average years of total schooling

male and female.

Table A2: Estimation output of robust OLS models

Inequality Growth

Average Ineq Palma Ineq Extreme Ineq

Gender Ineq Average Ineq Palma Ineq Extreme Ineq

Gender Ineq

Fragile(-1) -4.513*** (1.141)

-0.357* (0.249)

-3.369*** (0.737)

0.232*** (0.029)

-0.783*** (0.223)

-0.515 (0.322)

-0.891*** (0.278)

-0.883*** (0.208)

Gdp(-1) 0.000*** (0.000)

0.000*** (0.000)

0.000*** (0.000)

0.000*** (0.000)

Av.Ineq(-1) -0.002

27

(0.008)

Hct Ineq 5.695*** (1.311)

3.676*** (0.362)

12.188*** (1.219)

-0.101*** (0.032)

Palma (-1) -0.070 (0.043)

Ex-Ineq

-0.009 (0.009)

Gender Ineq

-0.131 (0.103)

Gdppc2

0.000*** (0.000)

0.000*** (0.000)

0.000*** (0.000)

0.000** (0.000)

Imr

0.072*** (0.013)

0.020*** (0.003)

0.079*** (0.009)

-0.001* (0.000)

-0.022*** (0.003)

-0.013*** (0.004)

-0.023*** (0.003)

-0.026*** (0.003)

Nrr -0.136*** (0.031)

-0.052*** (0.014)

-0.037* (0.022)

-0.003*** (0.001)

0.012* (0.007)

0.013 (0.012)

0.008 (0.009)

0.022*** (0.007)

Sids

-2.790* (2.877)

-2.750*** (0.747)

-2.428* (2.331)

0.810*** (0.073)

-1.863*** (0.425)

-5.172*** (1.036)

-2.902*** (0.759)

-2.612*** (0.517)

Ci-France -1.947* (1.170)

0.178* (0.306)

0.533 (0.814)

0.385*** (0.030)

-0.913*** (0.275)

1.354*** (0.425)

-0.805** (0.331)

-1.849*** (0.273)

Ci-Bristish 1.556* (1.343)

0.006* (0.337)

-1.462 (0.935)

0.258*** (0.035)

-0.071 (0.289)

1.955*** (0.429)

-0.208 (0.357)

-0.920*** (0.266)

Ci-Portugal -3.851* (2.670)

-0.090* (0.556)

-3.730** (1.863)

0.094 (0.063)

2.345*** (0.476)

7.844*** (0.833)

3.346*** (0.750)

2.102*** (0.507)

Ethnic

0.210* (0.154)

-0.080* (0.038)

0.005 (0.108)

-0.021*** (0.004)

-0.047 (0.035)

-0.293*** (0.053)

-0.096* (0.045)

-0.046 (0.034)

Religion

0.181*** (0.052)

0.098*** (0.013)

0.402*** (0.047)

0.010*** (0.002)

-0.067*** (0.014)

0.009 (0.019)

-0.040* (0.019)

-0.068*** (0.015)

Language -0.017* (0.049)

0.053*** (0.011)

0.093*** (0.033)

-0.006*** (0.001)

0.025* (0.011)

0.040* (0.016)

0.036* (0.014)

0.037*** (0.011)

Constant

39.841*** (1.510)

0.441* (0.360)

0.635 (1.031)

1.147*** (0.040)

4.221*** (0.439)

1.924*** (0.504)

4.222*** (0.409)

5.239*** (0.381)

No. Obs 772 361 519 582 971 466 670 722

LR Chi2(10)

F( 13,758):38.22 F( 13, 347): 82.85

F( 13, 505):76.61

F( 13, 568):52.13

F( 11,959):22.53 F( 11, 454):18.76

F( 11,658):16.77 F( 11,710):30.52

Prob > F 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Note: the dependent variables are five year averages. *, **, *** denote significance at 10%, 5% and 1% levels

respectively; standard errors in parentheses.

Inequality categories are average inequality (Gini); Palma ratio (ratio of the incomes of the top 10% to the bottom

40% of the population); extreme inequality (ratio of the incomes of top 20% to bottom 20% of the population);

gender inequality (ratio of male-female labour force participation rates). IMR is Mortality rate, infant (per 1,000 live

births), NNR is Total natural resources rents (% of GDP); Religion, Language and Ethnic are fractionalizations

measure; CI (France, Britain, Portugal) represented a colonial identities; AVYRT is average years of total schooling

male and female

Table A3: Estimation results for 3SLS

three stage FSI CPIA Based Fractionalization

Average Ineq Extreme Ineq Gender Ineq Average Ineq Extreme Ineq Gender Ineq

Growth Equations

GDP(-1)

0.000* (0.000)

-0.001*** (0.000)

0.000* (0.000)

0.001 (0.000)

-0.002*** (0.001)

Fragility

-0.261*** (0.077)

-0.054*** (0.018)

-0.947*** (0.288)

-4.070*** (0.844)

1.012*** (0.198)

-2.419*** (0.491)

Inequality

-0.083*** (0.027)

0.047 (0.032)

-5.967* (2.736)

-0.247*** (0.075)

-0.285*** (0.043)

-0.955 (0.909)

GFCF

-0.053*** (0.007)

0.137*** (0.025)

-0.016 (0.021)

0.121*** (0.033)

0.124*** (0.015)

0.142*** (0.039)

AVYRT -0.047 0.450*** -0.961* 0.357* 0.190* 0.248*

28

(0.120) (0.119) (0.553) (0.149) (0.102) (0.118) IMR

0.040** (0.014)

0.009 (0.009)

0.101** (0.036)

-0.074*** (0.019)

-0.025*** (0.007)

-0.050*** (0.017)

NRR

0.020 (0.016)

-0.036*** (0.011)

0.187* (0.073)

-0.130*** (0.034)

0.052*** (0.012)

-0.093*** (0.020)

SIDS

-5.780*** (1.712)

-11.315* 4.457

CI-France

-5.604*** (1.049)

-1.927*** (0.402)

-8.558*** (2.767)

-6.318*** (1.036)

1.190* (0.586)

-4.729*** (0.738)

CI-British

-3.001*** (0.777)

-1.755*** (0.471)

-5.070* (1.960)

1.915*** (0.714)

-3.751*** (0.553)

1.181* (0.648)

CI-Portugal

-5.469* (2.166)

-0.870 (0.954)

-21.030** (7.892)

5.915*** (1.464)

4.485*** (0.980)

ETHNIC

0.295*** (0.073)

0.202*** (0.049)

0.225 (0.209)

-0.586*** (0.170)

-0.113** (0.052)

-0.114 (0.092)

RELIGION

-0.189*** (0.040)

-0.088** (0.040)

-0.597*** (0.155)

-1.003*** (0.233)

1.300*** (0.213)

-0.828*** (0.172)

LANGUAGE

0.035 (0.024)

-0.062*** (0.018)

0.065 (0.077)

-0.161*** (0.039)

-0.010 (0.010)

-0.108*** (0.024)

CONSTANT

29.375*** (7.387)

3.002 (1.983)

95.393*** (29.720)

78.481*** (16.475)

-18.947*** (3.641)

39.103*** (7.904)

Inequality equations

Ineq(-1)

0.798*** (0.030)

0.852*** (0.051)

0.205 (0.128)

0.721*** (0.076)

GDPpc growth

0.193* (0.106)

0.042** (0.022)

0.185*** (0.064)

0.516 (0.736)

0.160*** (0.045)

0.151* (0.063)

Fragility

-0.447*** (0.093)

0.021* (0.014)

0.192*** (0.045)

-1.248*** (0.329)

1.708*** (0.273)

0.804*** (0.113)

HCT Ineq

1.892*** (0.566)

2.099** (0.937)

1.109*** (0.309)

4.996* (2.733)

0.457** (0.245)

0.312* (0.116)

GDPPC2

0.000* (0.000)

0.000 (0.000)

0.000*** (0.000)

0.000 (0.000)

0.000 (0.000)

0.000* (0.000)

IMR

0.086*** (0.017)

0.015 (0.014)

-0.037*** (0.008)

-0.254*** (0.056)

0.011 (0.015)

0.024*** (0.004)

NRR

0.049*** (0.016)

-0.016 (0.016)

-0.025*** (0.008)

-0.300*** (0.073)

-0.064*** (0.024)

0.034*** (0.007)

SIDS

-7.607*** (2.081)

-1.022 (1.545)

2.816*** (0.834)

CI-France

-5.445*** (1.356)

0.126 (0.830)

3.273*** (0.637)

-10.825*** (3.294)

-2.264*** (0.785)

1.546*** (0.277)

CI-British

-3.589*** (0.982)

-0.008 (0.737)

2.019*** (0.462)

8.300*** (2.057)

3.363*** (0.862)

-0.491*** (0.166)

CI-Portugal

-10.449*** (2.417)

4.393*** (1.092)

10.779*** (3.929)

-0.831*** (0.320)

ETHNIC

0.204** (0.094)

-0.015 (0.087)

-0.186*** (0.041)

-2.071*** (0.385)

-0.085 (0.108)

0.045* (0.025)

RELIGION

-0.052 (0.045)

0.130* (0.062)

0.089*** (0.025)

-1.836* (0.794)

-1.067*** (0.365)

0.235*** (0.069)

LANGUAGE

0.089*** (0.024)

-0.015 (0.030)

-0.051*** (0.011)

-0.401*** (0.099)

-0.047* (0.020)

0.027*** (0.008)

CONSTANT

43.297*** (8.719)

-2.100 (3.593)

-14.157*** (3.812)

224.748*** (36.515)

29.283*** (4.831)

-13.287*** (2.066)

Fragile state Index

FSI(-1) 0.927*** (0.053)

GDPpc growth

-0.475 (0.478)

-0.019 (0.390)

-1.404** (0.684)

-0.039 (0.092)

0.304* (0.126)

-0.126 (0.106)

Inequality

-0.490*** (0.055)

-0.071 (0.102)

-8.880** (3.874)

-0.067*** (0.012)

-0.139*** (0.026)

0.910*** (0.344)

IMR

0.197*** (0.029)

0.039 (0.025)

0.142** (0.052)

-0.023*** (0.004)

-0.002 (0.006)

-0.031*** (0.005)

NRR

0.095** (0.046)

0.020 (0.032)

0.188** (0.068)

-0.025*** (0.006)

-0.029*** (0.008)

-0.034*** (0.007)

SIDS

-21.159*** (5.195)

0.098 (3.305)

-16.369* (7.670)

0.000 (omitted)

0.000 (omitted)

0.000 (omitted)

CI-France

-14.776*** (2.078)

0.404 (1.708)

-8.310* (4.155)

-0.878** (0.324)

-0.847** (0.299)

-1.405*** (0.438)

CI-British -9.407*** 2.169 -5.751* 0.812*** 2.317*** 0.806***

29

(1.869) (1.610) (2.953) (0.205) (0.343) (0.247) CI-Portugal

-22.726*** (4.314)

0.000 (omitted)

-19.656*** (6.397)

0.945* (0.433)

0.000 (omitted)

1.006* (0.513)

ETHNIC

0.694*** (0.236)

0.016 (0.180)

0.076 (0.393)

-0.182*** (0.033)

0.031 (0.062)

-0.074* (0.040)

RELIGION

-0.357*** (0.115)

0.003 (0.144)

-0.775*** (0.155)

-0.162* (0.085)

-0.752*** (0.152)

-0.265*** (0.092)

LANGUAGE

0.158** (0.074)

0.029 (0.063)

-0.015 (0.135)

-0.037*** (0.009)

-0.020* (0.011)

-0.038*** (0.010)

CONSTANT

101.291*** (3.502)

2.998 (5.198)

100.975*** (8.850)

19.809*** (0.743)

16.711*** (0.714)

16.594*** (0.823)

Growth equation Growth Growth Growth Growth

Num Obs 225 86 225 158 81 166

R-sq -0.160 0.617 -16.102 -3.889 0.880 -1.306 chi2 117.870 141.060 47.430 93.200 439.760 135.550 P value 0.000 0.000 0.000 0.000 0.000 0.000 RMSE 2.260 0.950 8.6794 3.858 0.526 2.702

Inequality Average Extreme Gender Average Extreme Gender

R-sq 0.898 0.979 -3.366 -0.944 0.878 -2.589 chi2 4512.770 4005.150 76.810 232.100 939.870 108.260 P value 0.000 0.000 0.000 0.000 0.000 0.000 RMSE 3.684 1.556 1.305 9.428 1.396 0.843

Fragility FSI FSI FSI Frag-CPIA Frag-CPIA Frag-CPIA

R-sq 0.726 0.943 0.463 0.703 0.842 0.581 chi2 577.250 1425.190 229.140 380.910 384.260 263.110 P value 0.000 0.000 0.000 0.000 0.000 0.000 RMSE 7.730 3.426 10.815 0.840 0.652 0.995

Note: *, **, *** denote significance at 10%, 5% and 1% levels respectively; standard errors in parentheses.

The dependent variables are five year averages of inequality. Inequality categories are average inequality (Gini); Palma ratio (ratio of the incomes of the top 10% to the bottom 40% of the population); extreme inequality (ratio of the incomes of top 20% to bottom 20% of the population); gender inequality (ratio of male-female labour force participation rates). IMR is Mortality rate, infant (per 1,000 live births), NNR is Total natural resources rents (% of GDP); Religion, Language and Ethnic are fractionalizations measure; CI (France, British, and Portugal) represented a colonial identities; AVYRT is average years of total schooling male and female. All regressions include country and period dummies.

30

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