Growth, History, or Institutions: What Explains State...

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Growth, History, or Institutions: What Explains State Fragility in Sub-Saharan Africa?

Graziella Bertocchi University of Modena and Reggio Emilia, CEPR, CHILD and IZA Andrea Guerzoni University of Modena and Reggio Emilia

March 2012

Corresponding author: [email protected]

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Abstract

This paper explores the empirical determinants of state fragility in sub-Saharan Africa over the

1992–2007 period. Our dataset includes those sub-Saharan countries for which we have information

on the distribution by quintiles of the World Bank Country Policy and Institutional Assessment

(CPIA) ratings. We evaluate the potential influence on fragility of a wide range of economic,

institutional, and historical variables. Among economic factors, we consider per-capita GDP, both

in levels and growth rates, investment, natural resources, and schooling. We also consider economic

policy variables such as government expenditures, trade openness, and inflation. Demographic

forces are accounted for through the fertility rate, life expectancy, and the youth bulge. Institutional

factors are captured by measures of ethnic fractionalization, civil liberties, revolutions, and

conflicts, as well as governance indicators. Moreover, we select historical variables that reflect the

colonial experience of the region, namely the national identity of the colonizers and the political

status during the colonial period. Finally, we account for geographic factors such as latitude, access

to sea, and the presence of fragile neighbors. Our central findings is that institutions are the main

determinants of fragility: even after controlling for reverse causality and omitted variable bias, the

probability for a country to be fragile increases with restrictions of civil liberties and with the

number of revolutions. Before controlling for endogeneity, economic factors such as per-capita

GDP growth and investment show some explanatory power, but economic prosperity displays a

contradictory net impact since growth reduces fragility while investment facilitates it. Moreover,

instrumental variables estimates show that per-capita GDP growth is no longer a significant factor.

Colonial variables display a marginal residual influence: after controlling for all other factors

former colonies are actually associated with a lower probability of being fragile.

Keywords

state fragility, Africa, institutions, colonial history

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Introduction

The concept of state fragility (from now on, fragility) has recently reached center stage in the debate

on economic development, and in particular on the growth prospects of sub-Saharan Africa (SSA).

The concept of fragility has been associated with various combinations of the following

dysfunctions: inability to provide basic services and meet vital needs, unstable and weak

governance, a persistent condition of extreme poverty, lack of territorial control, and high

propensity to conflict and civil war. The crucial relevance of fragility for SSA countries is

motivated by the fact that they are overrepresented among fragile states, as shown by the European

Report on Development (2009), which is entirely devoted to the problem of fragility in Africa.

Likewise, the Global Report 2009, prepared by Marshall & Cole (2009) within by the Polity IV

Project, underscores the continuing malaise affecting the area. Moreover, since the condition of

fragility may jeopardize aid flows, the poorest countries of the region run the risk of being excluded

from international intervention.

Several studies have examined the influence of the condition of fragility on development, either

through its direct impact on income and growth, or indirectly through aid allocation. The purpose of

the present paper is instead to investigate the potential determinants of fragility, taking into account

the complex pattern of interrelationships among a wide range of factors. We focus our attention on

SSA, for several reasons. The first reason is that, as previously explained, this issue is particularly

important for policy intervention in this region. The second reason is that fragility has proven such a

multi-faceted issue that by concentrating on a specific area we can reach more meaningful

conclusions and more useful policy implications. Indeed, by restricting the attention to SSA, we can

highlight its specific economic, social, geographic, and political characteristics, which are

sufficiently heterogeneous despite a common history of colonization, ethnic conflict, natural

resources abundance, and other factors that may trigger fragility.

In selecting our measure of fragility, i.e., our dependent variable, we refer to the Country Policy

and Institutional Assessment (CPIA) ratings prepared annually by the World Bank (WB).

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According to the OECD Development Assistance Committee (DAC), fragile states are defined as

those countries in the bottom two CPIA quintiles (as well as those which are not rated). Since CPIA

ratings are publicly available only since 2005, in our empirical investigation we employ information

on their distribution by quintiles compiled by the OECD-DAC, which has been available since

1999.

The variables which we include in our investigation as regressors, since they are potentially

relevant for Africa’s economic and institutional performance, and therefore also for fragility in the

region, are chosen among those which have been found relevant within the empirical literature on

growth and institutional development. Moreover, in selecting our covariates we also take into

account the insights coming from non-quantitative studies in the fields of political sciences and

applied development. On the basis of these contributions, we develop a conceptual framework that

justifies our selection of the following three sets of determinants. The first set includes economic

variables: namely, measures of the level of development and economic activity, policy-related

variables, and demographic factors. The second set of variables captures the quality of institutions,

i.e., of a potentially very large set of non economic or demographic factors that we try to capture

with measures of repression, conflict, acute political instability leading to revolutions, and ethnic

fragmentation. We also explore the explanatory power of the peculiar history of the region, which

has been marked by the colonization experience. Therefore, we also select a third set of variables

that reflects the history of African colonization. Most of the variables are taken from Bertocchi &

Canova (2002), who assemble a dataset for Africa in order to investigate the role of colonization

within a growth regressions approach drawing on Barro (1991).

Our results can be summarized as follows. Over the 1992–2007 period, institutions are the key

determinants of fragility: the probability for a country to be fragile increases with restrictions of

civil liberties and with the number of revolutions. At first glance, economic determinants such as

per-capita GDP growth and investment show some explanatory power, but economic prosperity

displays a contradictory net impact, since growth reduces fragility while investment facilitates it.

Moreover, the former loses significance once we control for endogeneity. Economic policies and

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demographics are insignificant, and so are natural resources, despite their central role within the

debate on development in SSA. Other institutional variables such as conflicts and ethnic

fractionalization display some non-robust significance, depending on the chosen specification.

Finally, we find only a mild residual impact of colonial variables.

The rest of the paper is organized as follows. We first present the related literature. Second, we

develop a conceptual framework that highlights the potential determinants of fragility and generates

testable implications. Third, we detail our definition of fragility and describe our dataset. Fourth, we

present our empirical findings. Finally we conclude and suggest directions for future research.

Related literature

Within the literature on international relations and development, there are very few studies that

focus on the determinants of fragility. Vallings & Moreno-Torres (2005) present a discussion of the

main issues and argue that institutions are the central drivers of fragility, since any other factor

commonly associated with fragility is itself linked to weak institutions. For instance, while poverty

is certainly associated with fragility, not all poor areas turn out to be fragile, since fragility occurs

only when poverty is combined with the presence of a weak state that cannot manage effectively the

causes and consequences of poverty itself. On the other hand, the empirical study conducted by

Carment, Samy & Prest (2008) over a cross-sectional sample of world countries finds that per-

capita income is the main driver of fragility, with higher income being associated with lower

fragility. These contradictory conclusions emerging from the literature are one of the motivations

for the present investigation.

Our work is also related to several streams of research within the economic literature. First, while

our focus is to investigate the determinants of fragility, a number of authors have tried to identify

the effect of fragility on economic outcomes. In a growth regression framework, Bertocchi &

Guerzoni (2011) find that for SSA a conventional definition of fragility is not a significant covariate

once standard regressors are accounted for, while only an extreme measure of fragility exerts a

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detrimental effect on growth. For a comparable sample, Baliamoune-Lutz (2009) shows that the

impact of fragility on per-capita income interacts with several other factors: in fragile countries,

beyond a threshold level trade openness may actually be harmful to income, while small

improvements in political institutions can have adverse effects. Burnside & Dollar (2000) provide

evidence that aid is most effective in developing countries with sound institutions and policies, even

if this conclusion is challenged, among others, by Hansen & Tarp (2001) and Dalgaard, Hansen &

Tarp (2004). For a world sample of fragile countries, McGillivray & Feeny (2008) find that, while

growth would have been slower in the absence of aid, at the same time these countries can only

efficiently absorb a fraction of the aid flows. Chauvet & Collier (2008) analyze the preconditions

for sustained policy turnarounds in failing states and show that financial aid can prolong state

failure, while aid through technical assistance can shorten it. Overall, a clear impact of fragility on

economic outcomes has proved hard to assess. One possible explanation for the absence of a clear

causal link running from fragility to economic development is the endogeneity of fragility, or else

the presence of a common third factor that determines both outcomes. In our investigation, we

explicitly take into account this potential endogeneity.

A broader stream of research has tried to uncover the economic and non-economic determinants

of a wide range of institutions. The connection with this literature comes from the fact that in the

definition of fragility institutions play a crucial role. North (1981) has become the standard

reference for the idea that institutions shape economic outcomes and are in turn affected by them.

Engerman & Sokoloff (1997) show how factor endowments shape economic and political

institutions through history. Acemoglu, Johnson & Robinson (2004) distinguish between economic

and political institutions, while Acemoglu & Johnson (2005) unbundle the relative importance of

property-rights vs. contracting institutions.1 Our work acknowledges the potential relevance of

different kinds of institutions.

Finally, our contribution is indebted to the research line on colonial influence. La Porta et al.

(1998) have focused on the legal systems inherited by the colonies, Bertocchi & Canova (2002) on

1 See also Bertocchi (2006, 2011) and references therein.

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the nationality of the colonizers, Hall & Jones (1999) on the extent to which the colonizers'

languages of are spoken today. Together with Landes (1998) and North, Summerhill & Weingast

(1998), these contributions tend to agree on the conclusion that former British colonies inherited

better institutions than the former colonies of France, Spain and Portugal.2 Moreover, Acemoglu,

Johnson, & Robinson (2001) develop a theory of institutional development which emphasizes the

environmental conditions in the colonies, and in particular settler mortality (employed as an

instrument for current institutions). Our investigation builds on these earlier contributions.

The determinants of fragility

While a theory of the determinants of fragility has not yet been developed, in this section we

summarize the main explanations of its emergence which have been advanced within a widely

interdisciplinary, often non-quantitative literature. Our main goal is to extract a set of hypotheses

that can guide our empirical investigation, generate testable implications, and offer an interpretation

of the resulting evidence.

An early approach to the analysis of fragility, pioneered by international organizations such as

the WB and the OECD, linked it to the level of development. Within this policy-oriented debate,

standard measures of economic success, such as income and growth, were evaluated together with

indicators of the quality of government intervention in the realms of fiscal, monetary, and trade

policy. More recently, public policies focused on human capital, both in terms of health and

education, were also added to this list. As discussed at length in Carment, Prest & Samy (2009),

Collier & O’Connell (2007), and in extended literature summarized in the European Report on

Development (2009), over time the perception that economic progress and sound policies can

guarantee a state’s strength has been shaken. It has been acknowledged that, especially in SSA, the

beneficial influence of economic factors is by no means mechanical, since relatively wealthy

2 However, in Blanton, Mason & Athow (2001) a British legacy is more frequently associated with ethnic conflict.

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societies have often been plagued by corruption and predation activities that have generated

instability and dysfunctions, and thus lead to fragility. The availability of natural resources can also

play a counterproductive role, since it can lead to the well known curse, especially when associated

with high level of societal stratification and/or demographic pressure.3 In such situations, even

commitment to economic reform can bring about, perversely, turbulence and instability. Thus, the

lesson to be learnt from the approach that has stressed economic explanations of fragility is that the

beneficial effect of positive economic outcomes is not necessarily warranted, which calls for careful

analysis of the channels at work.

A parallel approach has focused on the consequences of institutional instability and conflict for a

state legitimacy and strength (Collier et al., 2003). Ndulu et al. (2007) explore the political economy

of Africa in the post-war period and examine several possible kinds of instability-inducing

conditions. Internal conflicts are often linked to ethnic and/or political struggle and can lead to

repression, human rights limitation, violence, and sudden changes in regimes. External conflicts are

conducive to wars, border instability, and refugee flows. Paradoxically, conflicts can be particularly

disruptive when there is more wealth to be appropriated by competing entities, and/or when

demographic pressure is particularly intense (Collier & Hoeffler, 1998), so that complex

interactions between economic and non-economic factors are often present and the impact on

fragility of economic factors may change as institutional factors do. For instance, high income

growth and/or investment activity may reduce fragility under good institutions but increase it under

bad ones. Even institutions per se may have a non-linear impact. For example, the impact of civil

liberties on fragility is a priori ambiguous: while on the one hand autocracies intrinsically feed

fragility, on the other at least in principle very liberal democracies may also prove to be vulnerable

to political and economic disorder. Moreover, revolutions and other episodes of acute political

unrest are likely to represent a threat to stability, but in principle they could also represent a reaction

3 See Ross (2003) on the link between natural resources and civil war.

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to dysfunctions and thus a prelude to a new, more stable order.4 The direction of the links between

these factors and fragility therefore remains an empirical issue.

More recently, the attention has shifted toward the connection between a state's colonial history

and its current fragility. This theme has been especially relevant for Africa. Historically, nowhere

else was colonization so far-reaching as in the African experience that began at the end of the 19th

century (Bertocchi & Canova, 2002). Indeed there is a shared perception that fragility, as well as

other related dysfunctions such as corruption and ethnic conflict, might find its roots in the legacy

of colonization: the European Development Report (2009) illustrates the central role of colonization

for state formation in this region, by stressing its artificial character following decolonization, the

extractive nature of colonial domination, the political and economic dependence from the

metropolitan power, and the exacerbation of ethnic fragmentation. The Polity IV Project also has

studied the importance of bearing a colonial past for fragility. However, the hypothesis that fragility

is determined by colonial history has not yet been tested econometrically.

While the above contributions are mostly based on informal reasoning, Besley & Persson (2009,

2010) formalize a model of state capacity, a concept which largely overlaps with that of fragility.

Indeed their goal is to provide a unifying framework for thinking about a range of issues that are

common to our previous discussion, such as the risk of external or internal conflict, the degree of

political instability, and economic dependence on natural resources. Through a set a propositions

and extensions, they identify a number of key causal links. They argue that state capacity increases

with the general level of development and wealth, while decreases with policy distortions and the

proportion of national income generated by natural resources. It also increases with the demand for

public goods, which in turn can be induced by different factors such as welfare expenses and

external threats. State capacity is reinforced by political stability and weakened by conflict and

4 See Skocpol (1979) and Goldstone (1991) for a socio-political analysis of revolutions.

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ethnic tension. The model also predicts that reverse causation can affect the above channels, thus

leaving to the empirics a final answer.5

Based on the above considerations, we can operationalize our empirical strategy as follows. We

explore the empirical determinants of fragility in SSA by selecting a large number of variables that

reflect the above described literature. We select three sets of variables that we respectively label as

economic, institutional, and colonial. Our economic variables can in turn be classified according to

three subsets. First, we identify standard measures of development and economic activity: namely,

the level of per-capita income, its growth rate, private investment, a measure of natural resources,

and investment in human capital through schooling. Second, we introduce measures of the quality

of policies, such as trade openness, the rate of inflation, and the size of government. Third, we

capture demographic considerations with life expectancy, the fertility rate, and the youth bulge.6

Our institutional variables include common measures of civil rights repression, sudden regime

change associated with revolutions, armed conflict and ethnic fragmentation, which should proxy

for the complexity of the institutional environment.7 Among colonial variables, which capture the

specific history of Africa, we select the political status of the countries involved and the national

identity of the colonizer. Operationally, based on the previous discussion is it reasonable to expect

fragility to be more likely whenever economic performances and institutional quality are worse, as

well as in association with a colonial past. Taking into account the warnings previously mentioned,

we also investigate the possible interactions among the above covariates, as well as their potential

endogeneity and codetermination. Our choice of a large number of regressors should minimize the

omitted variable bias which is typical in comparable investigations. The potential two-way

relationship between fragility and some of the regressors is directly addressed by means of

5 Acemoglu (2010) develops a dynamic political-economy model of state capacity, while Andrimihaja, Cinyabuguma & Shantayanan (2011) present a model of fragility for Africa. 6 See Urdal (2006) on the link between youth bulges and conflict. 7 We also consider additional indicators, among which the Worldwide Governance Indicators (see Kaufman, Kraay & Mastruzzi, 2009).

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instrumental variables estimation. More details on the definition and sources of all the variables are

provided in the next section, which describes our dataset.

Data

The concept of fragility is an elusive one. Failure, vulnerability, and weakness have often been used

as synonymous of fragility, by the Fund for Peace, the United States Agency for International

Development (USAID), and the Brookings Institution, respectively. Fragility itself has been defined

in several different manners by various international organizations. For example, the United

Kingdom Department for International Development (DfID) defines as fragile those states where

the government cannot or will not deliver core functions to its people. The fact that multiple

definitions of fragility have been proposed stems from its intrinsic multi-dimensional nature, which

has been acknowledged both in academic and policy-oriented work.8

Since one of our goals is to generate useful directions for policy, for our empirical investigation

we select a definition of fragility which is particularly relevant for policy, because of its direct

influence on the concessional lending and grants assigned by the WB through the International

Development Association (IDA).9 According to the WB, fragile states are defined as low-income

countries scoring 3.2 and below (over a 1-6 range) on the CPIA ratings. The OECD-DAC defines as

fragile states those countries in the bottom two CPIA quintiles, as well as those which are not

rated.10 Since CPIA ratings have been publicly available only since 2005, for the purposes of our

empirical investigation we use the OECD-DAC information about the distribution of the IDA)

member countries by CPIA quintiles, which is available from 1999 until 2007.

8 See Stewart & Brown (2009) and Carment, Prest & Samy (2009). In policy terms, the DfID (2005) and USAID (2005) have both suggested a two-dimensional definition of fragility. 9 IDA is the part of the WB that helps the world’s poorest countries. It currently represents one of the largest sources of assistance for the world’s 79 poorest countries, 39 of which are in Africa. 10 Related indexes are the Failed State Index, the Index of State Weakness, the indicator of Failed & Fragile States, and the Fragility States Index, respectively published by the Fund for Peace, the Brookings Institution, Country Indicators for Foreign Policy, and Polity IV. Fosu & O’Connell (2006) introduce the fragility-related concept of state breakdown, a condition involving civil wars and acute political instability.

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CPIA ratings are prepared annually by WB staff and are intended to capture the quality of a

country’s policies and institutional arrangements, with a focus on the key elements that are within

the country’s control, rather than on outcomes (such as growth rates) that are influenced by

elements outside the country’s control. Scores are assigned on the basis of 16 criteria (20 until

2003) which are grouped in four equally weighted clusters: Economic Management, Structural

Policies, Policies for Social Inclusion and Equity, and Public Sector Management and Institutions.

The ratings reflect a variety of indicators, observations, and judgments based on country

knowledge, originated in the WB or elsewhere, and on relevant publicly available indicators.

For our purposes, to refer to the CPIA ratings offers three advantages. First, the ratings have a

crucial practical relevance, since as previously mentioned they significantly influence aid allocation

according to a specific formula. Second, information on their distribution by quintiles is now

available for a relatively extended time period (1999–2007). Third, because of their design, they do

not reflect mechanically any of the variables we employ as regressors, so that they can safely be

employed to define our dependent variable.

Despite the above considerations, it should be pointed out that the CPIA ratings are not free of

criticism. Doubts have been cast by various parts both about the methodology of the assessments

and the confidence with which they should be used as a basis for aid allocation. A common

criticism is that they implicitly rely on a uniform model of what matters in development (see, for

example Kanbur, 2005). Another is that transparency and accountability could be addressed more

accurately. It has also been argued that lack of reliable information may prevent objective

assessments by WB staff. The WB Independent Evaluation Group (2010) has recently reviewed the

ratings and proposed recommendations for revision.

We construct a dataset including those 41 sub-Saharan countries for which we have information

on the CPIA ratings distribution by quintiles. For these countries, we define a fragility dummy

variable, which takes value 1 if a country belongs to the bottom two CPIA quintiles or if it is not

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rated, 0 otherwise.11 The variables which we include as regressors are the following.12 Among

economic factors, we consider per-capita GDP, both in levels and growth rates, and investment. We

proxy for natural resources with diamond deposits.13 Human capital is measured with primary

school enrollment. Among policy variables we consider government expenditures, trade openness,

and inflation. To account for demographic pressure, we select the fertility rate, controlling at the

same time for life expectancy.14 We also include the proportion of males aged 14 to 24 over

population as a proxy for the youth bulge. To gauge the explanatory power of conflict and

institutions, we include the following variables: the index of ethnic fractionalization first introduced

by Easterly & Levine (1997), the Freedom House index of civil liberties, the number of

revolutions,15 and the number of years under an armed conflict.16 Even though these variables can

only be viewed as proxies of the complex institutional environment, they do capture potentially

different facets of the institutional environment since, for instance, not all conflicts, even merely

internal ones, involve revolutions. The former are often long-lasting, chronic experiences, while the

latter reflect acute episodes. Furthermore, a revolution may occur at different levels of civil

liberties. We also consider alternatives such as the index of political rights and three of the

Worldwide Governance Indicators, namely government effectiveness, rule of law, and voice and

accountability. Next, we select our controls for colonial history as a set of dummies for the identity

11 We also consider as an alternative dependent variable an extreme definition of fragility that includes only countries in the very bottom CPIA quintile. See Bertocchi & Guerzoni (2010). 12 Most of the variables are from Bertocchi & Canova (2002). 13 Data are from the UCDP/PRIO Geographical and Resource Datasets and described in Gleditsch et al. (2002). We also collect data from Collier & O’Connell (2007), who compile a list of resource-rich countries, i.e., of countries that satisfy the following three conditions: rents from energy, minerals and forests exceed 5% of GNI; a forward-moving average of these rents exceeds 10% of GNI; the share of primary commodities in exports exceeds 20% for at least a five-year period. 14 The literature on fragility often relies on the UN Human Development Index (see Carment, Samy & Prest, 2008 and United Nations, 2009), which includes three components: education, wealth, and health, respectively measured by years of schooling, gross national income per-capita and life expectancy. Therefore, all the components of the index are de facto controlled for within our dataset. 15 Data are from Banks (2011), where revolutions are defined as illegal or forced change in the top governmental elite, any attempt at such a change, or any successful or unsuccessful armed rebellion whose aim is independence from the central government. 16 The source is again the UCDP/PRIO Armed Conflict Dataset. We also construct separate variables for internal, external, and mixed conflicts.

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of the colonizers, i.e., Britain, France and Portugal, respectively, and a dummy capturing the

political status during the colonial period.17 Finally, as in Sachs and Warner (1997), our

investigation also accounts for the potential role of geography,18 as captured by latitude (as a proxy

for climate, and hence productivity, disease, etc.), access to sea, and information about the

occurrence of fragility in neighboring countries.19

For the empirical investigation, the available data are organized as a panel that covers the 1992–

2007 period and is composed of two cross-sections, over 1992–99 and 2000–07. The dependent

variable is the fragility dummy in 1999 and 2007, i.e., in the final year of each cross-section. For

those regressors for which we have yearly information we consider their average value in 1992–99

and 2000–07.20

This approach is meant to maximize the length of the period of observation, given the limited

range of the information on fragility. By organizing the available data in this way, we are able to

extend the investigation back to 1992, even though information on fragility is only available from

1999. Moreover, by treating as dependent variable fragility in the final year of each subperiods, we

are able to mitigate the reverse causality problem running from fragility itself to the regressors.

The pairwise correlation coefficients show that fragility is highly correlated with civil liberties

restrictions (0.50) and revolutions (0.44), less so with conflicts (0.26), while the correlation with

economic variables is much lower (e.g., -0.14 with per-capita GDP growth). The correlation

between fragility and the Worldwide Governance Indicators is very high but far from perfect

(ranging between -0.58 and -0.71). We also find a high but not perfect correlation of revolutions

with conflicts (0.60) and with civil liberties restrictions (0.43). This pattern of correlation suggests

that each indicator captures different facets of the environment and justifies our broad choice of

diverse institutional indicators.

17 The dummy takes value 2 for colonies, 1 for dependencies, and 0 for independent countries. 18 Bleaney & Dimico (2010) show that geography influences not only per-capita income, but also institutional quality. 19 Iqbal & Starr (2008) explore the potential effect of bad neighbours. 20 The exceptions are the proxy for the youth bulge, which is measured with the initial proportion of males aged 14 to 24 over population, i.e., in 1992 and 2000, and for conflicts, for which we enter the number of years under conflict during each subperiod.

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Empirical results

Baseline specification

Because of the binary nature of the dependent variable, we run probit regressions. The marginal

effect of each regressor should therefore measure how the probability of being fragile is affected by

changes in the regressor itself. Among the regressors, we include the economic, institutional, and

historical variables previously discussed. In Table 1, column 1, we present our baseline

specification. We find that, among economic variables, the most significant one is GDP growth,

which exerts a negative impact on the probability to be a fragile country.21 Investment, on the other

hand, has a positive coefficient that may be attributed to the disruptive side effects of capital

accumulation.22 Natural resources display a positive coefficient, but it is insignificant, suggesting

that they do not affect the probability of being a fragile country once other covariates are controlled

for.23 Likewise, the remaining economic and demographic variables do not provide additional

explanatory power to the regression. In more detail, our measure for human capital, primary

schooling, displays a negative but insignificant coefficient. The impact of trade and inflation goes

into the expected direction (i.e., negative and positive, respectively), but is again not statistically

significant.24 Turning to institutional variables, we find that civil liberties and revolutions are highly

significant and with the expected sign, i.e., more restricted civil liberties25 and a higher frequency of

revolutions make fragility more likely. The interpretation is that autocracies tend to be associated

21 We explore the potential relevance of distributional considerations though the Gini coefficient. However, due to the reduced number of observations (38) the model cannot converge. See Gurr (1970) and Stewart (2008) on the link between inequalities and political violence. 22 An unreported variant of the same regression, which includes an interaction between investment and civil liberties, shows a negative effect for investment and a positive one for the interaction, suggesting that the impact of investment on fragility may turn positive only in countries with bad institutions, which are the majority in the sample. 23 The same insignificant impact is found for the alternative natural resources measure by Collier & O’Connell (2007) described in footnote 13. 24 Alternative measures of schooling, trade, and inflation yield similar results. 25 The square of civil liberties restrictions is omitted since insignificant, ruling out a non-linear impact.

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with fragility more often than democracies26 and that episodes of acute political unrest constitute a

further threat. The variable which accounts for armed conflicts is not significant27 and the same is

true for ethnic fractionalization.28 Fixed time effects are included in the form of a period dummy for

the second subperiod that shows a significantly positive (unreported) coefficient, pointing to an

intensification of fragility in the second subperiod. We include the period dummy in all subsequent

specifications.29,30 To sum up, these preliminary results show that both economic and institutional

factors tend to be associated with fragility. However, in the next sub-section we turn to instrumental

variables estimation in order to control for the potential two-way relationship between fragility and

our regressors.

____________

Table 1 in here ____________

Controlling for endogeneity

A provisional conclusion from column 1 of Table 1 is that both economic and institutional variables

play a relevant role in the determination of the condition of fragility. However, the results presented

so far need to be taken with caution, since our investigation is plagued by two major concerns,

reverse causality and omitted variable bias. Indeed, despite the large number of potential

determinants we consider, we still face the danger of having omitted crucial variables. Moreover,

our regressors may be endogenous to fragility. The two problems are actually linked, since the

26 An alternative specification including political rights, in place of civil liberties, yields similar results even though the impact of political rights is estimated less precisely. Isham, Kaufmann & Pritchett (1997) also find that civil liberties are more closely associated than political rights to the ability of governments to exercise authority. 27 Alternative variables which capture external and internal conflicts separately are similarly insignificant. We refer to Fearon & Laitin (2003) for further research on the intensity of conflicts. 28 Alternative measures of linguistic and religious fractionalization (see Alesina et al., 2003 and Alesina & La Ferrara, 2005) are also insignificant. 29 Due to our sample size, where the number of countries is much larger than the number of periods, we cannot run regressions with fixed effects at the country level, since they would induce a loss of degrees of freedom and the danger of multicollinearity. Moreover, in probit regressions country fixed effects do not produce consistent estimates. 30 Similar results obtain for an (unreported) alternative OLS specification as well as in a parsimonious specification where, to maximize the estimated sample size, we include only variables which are significant in the baseline specification.

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potential two-way relationship between fragility and some of the regressors may be driven by a

third factor which we have failed to include. However, in the discussion below we address each

question separately, starting with endogeneity.

While it is conceivable that fragility, rather than being caused by bad performances, may well be

the cause behind them, this potential problem is mitigated by the fact that our dependent variable is

constructed as the value of fragility in the final year of each cross-section, while the regressors

predate the dependent variable. This reduces the potential of reverse causality, even though it does

not rule it out under serial correlation in fragility. Nevertheless, we address this issue by running IV

probit regressions. We start by regarding as potentially endogenous the four variables that display a

significant impact in the initial probit specification of column 1, i.e., growth, investment, civil

liberties and revolution. For each variable, one by one, we preliminarily run a IV probit regression

where the selected variable is instrumented with its initial value at the beginning of each subperiod

(1992 and 2000, respectively). The rationale is that this procedure at least ensures that the values of

the regressors are determined prior to those of the dependent variable.31 For each resulting IV probit

regression, we run a Wald test for exogeneity. Results indicate that only the variable measuring

revolutions is endogenous, while growth, investment and civil liberties do pass the exogeneity test.

In other words, to use an instrument is justified only for revolutions. Thus, in column 2 of Table 1

we report an IV probit regression where the revolutions variable is instrumented with its initial

value. The resulting estimates show that civil liberties and revolutions retain their full significance,

while economic growth loses it entirely. A longer life expectancy now appears to be associated with

more fragility, albeit with marginal significance (at only 10%). Moreover, despite the previously

reported positive pairwise correlation between conflicts and fragility, we find that after controlling

for everything else more conflicts tend to reduce fragility.32 To conclude, after addressing

31 In our search for suitable instruments, following Acemoglu, Johnson & Robinson (2001) we also evaluate settler mortality which, however, proves to be a weak instrument for all our variables, possibly because of insufficient cross-country variation along the environmental dimension within SSA. 32 When conflicts are split between internal and external, only the former remain significant. This is not surprising given that in our sample most conflicts are internal.

18

endogeneity we observe a predominance of institutions, over economic factors, as exogenous

determinants of fragility.33

An alternative definition of fragility

As previously explained, the concept itself of fragility is an elusive one. Therefore, as a preliminary

robustness check, in columns 3 and 4 of Table 1 we run analogues of the previous two regressions

for a different definition of the dependent variable, i.e., extreme fragility. Even under this more

severe definition, previous results are largely confirmed, with a predominance of institutional

factors especially after controlling for endogeneity. Moreover, ethnic fractionalization now emerges

as an additional determinant, with more fractionalization being associated with a higher probability

of extreme fragility. Since our results prove not to depend on the definition of fragility being used,

in the rest of the paper we focus on the more common OECD-DAC definition, which is also the

most relevant for policy.

Colonial history

Despite the fact that previous regressions already control for a large number of factors, in order to

address the potential issue of omitted variables we now expand the set of covariates even further to

include the role of history, and in particular colonial history. We consider a set of three dummies

capturing the national identity of the colonizers (namely, Britain, France, or Portugal), as well as a

dummy for political status which distinguishes among colonies, dependencies and independent

countries. Since preliminary checks confirm the endogeneity of revolutions even in this expanded

setting, we add each additional covariate, one by one, to the benchmark IV probit regression of

Table 1, column 2. Results from these extended specifications are presented in Table 2. We find

that the coefficient of the dummy for British colony (column 1) is negative, while French (column

2) and Portuguese (column 3) colonies display a positive coefficient. This suggests that having

being a British colony decreases the probability of being a fragile country, while the opposite occurs

33 Very similar results are obtained in a 2SLS specification.

19

for French and Portuguese colonies. The relatively beneficial impact of British colonial domination

confirms the findings of several other studies, including Bertocchi & Canova (2002).34 However,

none of the three variables is statistically significant. In column 4 political status displays a negative

and highly significant coefficient, which suggests that after controlling for all other factors former

colonies have a lower probability of being fragile. Other minor differences between this

specification and the benchmark can be attributed to the reduced sample size.35

We can summarize the main results as follows. First, our previous conclusions from Table 1

regarding economic and institutional variables are confirmed, with a predominant impact of

institutional ones. Second, the evidence supporting the view that the past colonization is crucial for

fragility is limited to the significance of the political status dummy which, however, suggests that

former colonies are now less likely to be fragile. Moreover, the identity of the colonizers and the

fact that a country was a settler colony do not emerge as significant factors for today’s fragility.

This may be due either to their limited variability within SSA, to a faded impact of colonial history,

or to the fact that the latter is already accounted for through standard regressors. In a similar vein,

Bhattacharyya (2009) also casts doubts on the ability of colonial legacy to explain African

underdevelopment.

___________

Table 2 in here ___________

Extended specifications Along the same lines of Table 2, in Table 3 we add a number of covariates reflecting other possible

channels of influence to our IV probit benchmark specification of column 2, Table 1. In columns 1

and 2 we add one by one two geographical variables, namely a country’s latitude and a dummy

reflecting the fact that a country is landlocked. They are both insignificant and leave previous

34 The impact of these covariates can be explained by the fact that British domination brought to its colonies a common law legal system, rather than the civil law system as under French and Portuguese domination (see La Porta et al., 1998). 35 An IV probit regression including settler mortality cannot be estimated because of a reduction of the number of observations to 48. In a 2SLS specification settler mortality is insignificant.

20

conclusions unaltered.36 In column 3 we gauge the potential relevance of the fact that a country has

a fragile country at its border. The impact is significant but negative.

Finally, in columns 4-6, we add one by one three of the Worldwide Governance Indicators, which

reflect a large number of specific and disaggregated individual variables measuring various

dimensions of governance. We select government effectiveness, rule of law, and voice and

accountability, which may be viewed as closer substitutes for the previously selected institutional

variables. To be noticed is that these variables are highly correlated with fragility (-0.71, -0.61, -

0.58, respectively), but at the same time they are also highly correlated with our institutional

variables: their correlation coefficients with civil liberties restrictions are -0.63, -0.70, and -0.92,

respectively, while with revolutions they are -0.54, -0.52, and -0.43. Unsurprisingly, the added

variables do not show any explanatory power while the significance of both revolutions and civil

liberties are hardly affected, with the exception of column 4 where civil liberties lose significance in

combination with voice and accountability. The effect of conflicts is instead not robust to these

additional controls. Other economic variables emerge as significant in some specifications, but

again not in a robust fashion. We can interpret these results as evidence that the governance

indicators capture factors that are already measured by our original institutional variables.

____________

Table 3 in here ___________

Summary and comparisons

Our empirical investigation points at institutions as the predominant cause of fragility. Purely

economic factors such as the level of income, its growth rate, or investment, display a non-robust

36 When each of these variables is added to a parsimonious specification, they both show a significantly positive impact. This suggests that there may be a link between geography and fragility that runs through a wide array of channels. As in Fearon & Laitin (2003) we also experiment with alternative geographic variables such as mountainousness and elevation. The models do not converge for these specifications but the variables are insignificant in parsimonious models.

21

impact on the dependent variable. Colonial history appears to be only marginally relevant for

fragility once we control for contemporary institutions.

Over a longer run perspective, Bertocchi & Guerzoni (2010) reach consistent findings for a cross-

sectional dataset including the same 41 SSA countries. An average measure of fragility over 1999–

2007 is regressed over average values of the regressors for 1960–1998. Over a world sample,

Vallings & Moreno-Torres (2005) also reach similar conclusions on the basis of Polity IV data on

political institutions. On the other hand, again for a world sample, Carment, Samy & Prest (2008)

find that a lower per-capita income level appears to be associated with higher fragility, while all

other potential determinants lose significance once reverse causality is controlled for. This

difference can be explained by several factors. First, they focus on 156 countries rather than on

SSA. Second, they employ different sample periods and estimation techniques. Namely, they rely

on a cross-sectional sample over the 1999–2005 period, while we employ a panel over 1999–2007.

Moreover, they use the indicator of Failed & Fragile States provided by Country Indicators for

Foreign Policy, rather that CPIA-based indicators we use. The fact that they report a positive

coefficient (i.e., a higher chance of fragility) for a SSA dummy suggests that our radically different

conclusions are mainly to be attributed to the specificity of the African region. Thus, a comparison

between the two studies highlights the fact that the peculiar characteristics of this region deserve

specific attention, since conclusions derived on the basis of a wider world sample would lead to

misleading implications for Africa.

Conclusion

With a focus on SSA, we have explored the determinants of fragility by considering a wide array of

potential factors. Besides economic and institutional determinants, we have also considered the

unique role of the history and geography of the area. Our findings suggest that contemporary

institutions prevail as the central drivers of fragility in Africa.

22

Even if we find a limited role for colonial history, we cannot exclude that institutional variables

themselves may still be affected by long term factors that can even predate colonial dominations, as

suggested by Herbst (2000), who focuses on the underdevelopment of precolonial polities as an

explanation of the weakness of postcolonial states. Along the same lines, Bockstette, Chanda &

Putterman (2002) observe that state antiquity, a measure of the depth of experience with state-level

institutions, is positively correlated with institutional quality, while Gennaioli & Rainer (2007)

uncover for Africa a positive association between stronger precolonial political institutions and

public goods provision. Finally, Nunn (2008) finds that weakened and fragmented states may be the

result of the slave trades. Since these long term factors may represent an alternative driver of

current fragility, further empirical research is needed on this issue.

While the literature we have surveyed is mostly empirical, theoretical models of fragility have

recently been proposed, mainly with a focus on the related fiscal concept of state capacity. Building

on these contributions could yield a dynamic political-economy model of fragility, which can

explain not only the empirical links we have highlighted but also the complex process through

which the condition of fragility is reached, or abandoned. This is in our agenda for future research.

Replication data

The dataset, variable definitions and data sources are available at http://www.prio.no/jpr/datasets

and http://www.economia.unimore.it/Bertocchi_Graziella.

Acknowledgements

We thank the Associate Editor Magnus Öberg, Claire Vallings, another anonymous referee,

Arcangelo Dimico and Chiara Strozzi for helpful comments and Sara Colombini for excellent

research assistance.

Funding

Generous financial support from Fondazione Cassa Risparmio di Modena is gratefully

acknowledged.

23

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United Nations (2009) The Human Development Report. New York: Palgrave Macmillan. United States Agency for International Development (2005) Fragile States Strategy. Washington: USAID. Wan, DC Urdal, Henrik (2006) A clash of generations? Youth bulges and political violence. International Studies Quarterly 50 (3): 607–629. Vallings, Claire & Magüi Moreno-Torres (2005) Drivers of fragility: What makes states fragile? PRDE Working Paper No. 7. GRAZIELLA BERTOCCHI, b. 1956, PhD in Economics (University of Pennsylvania, 1988); Professor of Economics, University of Modena and Reggio Emilia (1993–); Assistant Professor of Economics, Brown University (1987–1994); CEPR and IZA Research Fellow; current main interests: Political Economy, Economic Dynamics and Growth, Macroeconomics; recent publications in European Economic Review, Journal of Economic Growth, Journal of Law and Economics, Economics & Politics.

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ANDREA GUERZONI, b. 1985, Master in Economics (University of Modena and Reggio Emilia, 2009); Research Associate, RECent, University of Modena and Reggio Emilia (2009–).

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Table 1. The determinants of fragility

Dependent variable: Fragility

Dependent variable: Extreme fragility

1 2 3 4 Regressor Probit IV Probita Probit IV Probita pcGDP (log) 0.5815

(0.7841) 0.5291

(0.6191) -1.584

(0.9688) -1.6719 (1.0350)

pcGDP growth -0.2716*** (0.0940)

-0.1510 (0.1039)

-.23306** (0.1004)

-0.0960 (0.0897)

Investment 0.1922** (0.0751)

0.1817** (0.0716)

0.2059*** (0.0703)

0.1828** (0.0716)

Natural resources 0.7528 (0.7906)

0.5505 (0.6657)

-0.0833 (0.8773)

-0.4505 (0.7566)

Primary enrollment

-0.0023 (0.0106)

-0.0008 (0.0104)

-0.0057 (0.0145)

-0.0012 (0.0138)

Government expenditures

0.0113 (0.0244)

0.0236 (0.0210)

0.0432 (0.0432)

0.0391 (0.0338)

Trade -0.00643 (0.0110)

-0.0173 (0.0112)

0.0038 (0.0132)

-0.0077 (0.0114)

Inflation 0.0001 (0.0010 )

0.0046 (0.0140)

0.0006 (0.0010)

-0.0002 (0.0007)

Life expectancy 0.0777 (0.0670)

0.1040* (0.0558)

-0.0576 (0.0646)

-0.0247 (0.0583)

Fertility rate (log) 4.1587 (3.2309)

2.7241 (2.8078)

-7.0437* (4.2409)

-7.8369 (4.8749)

Youth bulge -0.1738 (0.2026)

-0.1604 (0.1837)

-0.3173 (0.2651)

-0.1661 (0.2242)

Ethnic fractionalization

0.7087 (1.5312)

2.1651 (1.3983)

3.9310* (2.2167)

4.3443** (1.9026)

Civil liberties restrictions

1.6711*** (0.3621)

1.2454*** (0.4792)

1.5235*** (0.3686)

1.1969** (0.5284)

Revolutions 3.2850*** (0.9215)

5.025*** (0.7882)

3.0331*** (1.1282)

5.1118*** (0.9358)

Conflicts -0.1230 (0.1104)

-0.2552** (0.1032)

-0.1875 (0.1520)

-0.3262** (0.1327)

Pseudo R2 0.5637 - 0.6220 - Log pseudo likelihood

-21.9289 -21.10332 -15.9581 -16.0641

Wald exogeneity test P value

- 0.0574 - 0.0356

Observations 73 73 73 73 Panel dataset with fixed time effects. Probit coefficients. Robust standard errors in

parentheses. a Instrument: Initial values of Revolutions, i.e., in 1992 and 2000, respectively. *** significant at 1%, ** significant at 5%, * significant at 10%.

29

Table 2. The determinants of fragility: colonial history

Dependent variable: Fragility Regressor 1 2 3 4

pcGDP (log) 0.3803 (0.7273)

0.3267 (0.6907)

0.5336 (0.6613)

0.2822 (0.6746)

pcGDP growth -0.1619 (0.1155)

-0.1410 (0.1153 )

-0.1626 (0.1060)

-0.1372 (0.1281)

Investment 0.2028** (0.0845)

0.1880** (0.0774)

0.1931** (0.0807)

0.1653** (0.0784)

Natural resources

0.4107 (0.6569)

0.2792 (0.6561)

0.5921 (0.7272)

1.4603 (0.8954)

Primary enrollment -0.0005 (0.0107)

0.0003 (0.0109)

0.0005 (0.0106)

0.0070 (0.0103)


0.0208 (0.0236)

0.0275 (0.0238)

0.0182 (0.0235)

0.0089 (0.0238)

Trade -0.0163 (0.0127)

-0.0144 (0.0130)

-0.0181 (0.0118)

-0.0067 (0.0137)

Inflation 0.0166 (0.0138)

0.0153 (0.0146)

0.0002 (0.0028)

0.0022 (0.0130)

Life expectancy 0.0888 (0.0576)

0.0600 (0.0628)

0.1128** (0.0572)

0.1468** (0.0596)


1.2045 (2.8010)

2.8841 (2.9050)

5.6782* (3.1734

Youth bulge -0.1931 (0.1925)

-0.1858 (0.2026)

-0.1399 (0.1795)

-0.1148 (0.2048)


2.7982* (1.5739)

2.3978 (1.4614)

1.9521 (1.4371)

4.3566** (1.7901)


1.1854** (0.5014)

1.2322** (0.4947)

1.3421*** (0.5148)

1.0964* (0.5886)

Revolutions 5.3008*** (0.9393)

5.0920*** (0.8886)

4.9869*** (0.7498)

5.2787*** (0.7890)

Conflicts

-0.2952** (0.1168)

-0.2499** (0.1071)

-0.2238* (0.1146)

-0.2808** (0.1295)

British colony -0.8537 (0.5262)

French colony 0.9189 (0.5686)

Portuguese colony 0.5386 (0.7319)

Political status -1.2073*** (0.4402)

Log pseudo likelihood

-20.0454 -19.7173 -19.4297 -10.0439


0.0601 0.0470 0.0777 0.0214

Observations 73 73 73 67 Panel dataset. IV Probit coefficients. Robust standard errors in parentheses. Instrument: Initial values of Revolutions, i.e., in 1992 and 2000, respectively. *** significant at 1%, ** significant at 5%, * significant at 10%.

30

Table 3. The determinants of fragility: additional covariates

Dependent variable: Fragility Regressor 1 2 3 4 5 6 pcGDP (log) 0.5960

(0.6007) 0.5184

(0.6418) 1.3937* (0.8420)

0.4355 (0.4511)

-0.2828 (0.6341)

0.5716 (0.5476)

pcGDP growth -0.1429 (0.1061)

-0.1606 (0.1130)

-0.1435 (0.1115)

-0.1070 (0.0714)

-0.0940 (0.0720)

-0.1004 (0.0843)

Investment 0.1756** (0.0696)

0.2012** (0.0913)

0.2039** (0.0833)

0.1913*** (0.0707)

0.1378** (0.0619)

0.1902*** (0.0696)

Natural resources 0.5064 (0.6936)

0.5781 (0.7148)

0.5041 (0.6545)

0.7703* (0.4545)

1.1118 (0.6994)

0.0861 (0.5235)

Primary enrollment

-0.0033 (0.0121)

-0.0036 (0.0109)

-0.0122 (0.0129)

-0.0007 (0.0086)

-0.0078 (0.0129)

-0.0014 (0.0094)


0.0232 (0.0204)

0.0263 (0.0227 )

0.0380* (0.0205)

-0.0238 (0.0240)

0.0065 (0.0195)

0.0280 (0.0188)

Trade -0.0166 (0.0106 )

-0.0192 (0.0126)

-0.0245** (0.0099)

0.0134 (0.0127)

0.0018 (0.0157)

-0.0261** (0.0118)

Inflation 0.0044 (0.0138)

0.0053 (0.0155)

0.0100 (0.0145)

-0.0011** (0.0006)

0.0115 (0.0124)

-0.0008** (0.0003)

Life expectancy 0.1126* (0.0608)

0.0981* (0.0578)

0.0681 (0.0583)

0.1245*** (0.0452)

0.1208** (0.0555)

0.0855** (0.0415)


2.0659 (2.9391)

3.4158 (2.6149)

5.8303** (2.5938)

0.9782 (2.2810)

0.6442 (1.9830)

Youth bulge -0.1507 (0.1821)

-0.1997 (0.1901)

-0.1214 (0.1842)

0.2353 (0.1595)

-0.1081 (0.1703)

-0.1296 (0.1505)


2.3497 (1.5682 )

2.4327 (1.6600 )

4.3646*** (1.5840)

1.2033 (1.0823)

1.7559 (1.3918)

2.7590** (1.2053)


1.2506** (0.4911)

1.2633*** (0.4637)

1.1803** (0.4712)

0.9658** (0.4359)

0.8058*** (0.2939)

0.3967 (0.3880)

Revolutions 5.0501*** (0.8313)

5.067*** (0.9323)

5.7860*** (0.9458)

3.5613*** (0.4317)

3.7487*** (0.6881)

5.0323*** (0.7517)

Conflicts -0.2532** (0.1042)

-0.2447** (0.1074)

-0.1982** (0.0949)

-0.0591 (0.1089)

-0.1810* (0.1092)

-0.2772*** (0.0835)

Latitude -0.0115 (0.0341)

Landlocked -0.2094 (0.6503)

Fragile neighbor -1.6147** (0.7459)

Government effectiveness

-1.5805 (1.151)4

Rule of law 1.1675 (1.1463)

Voice and accountability

-1.3392 (0.8427)

Log pseudo likelihood

-20.7820 -19.7854 -19.1496 -7.1202 -14.2121 -16.6655


0.0673 0.0854 0.0082 0.0000 0.0382 0.0035

Observations 73 73 73 73 73 73

31

Panel dataset. IV Probit coefficients. Robust standard errors in parentheses. Instrument: Initial values of Revolutions, i.e., in 1992 and 2000, respectively. *** significant at 1%, ** significant at 5%, * significant at 10%.

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