Africa’s Wars of Liberation: Impact and Economic Recovery
Mathurin Z. Somé
Department of Economics,
University of California – Santa Barbara
Fall 2013
Abstract
Do the ways in which Africa became independent from colonial rules have persistent
economic effects? To examine this question, I use the Synthetic Control Method to jointly
examine the impact of Africa’s independence on growth, and its economic recovery in post
colonial period. Before independence, I find that annual per capita income for countries that
became independent through armed conflicts (Angola, Guinea-Bissau, Kenya, Mozambique, and
Zimbabwe) is 7 percentage points lower than their counterparts with peaceful independence. This
income gap increased to 10 percentage points at the time of independence, suggesting a
worsening of state freedoms. The income deficit remained constant at 10 percentage points for
about a decade after independence. However, the gap narrowed over time, then dissipated after 15
years as both groups' per capita incomes converge to a similar growth pattern. That is, the results
suggest that independence via conflict has no persistent effect on today's economic performance
in Africa.
J.E.L codes: C2, C3, F62, D4
Keywords: Economic Growth, Africa, Wars of Liberation, Synthetic Control Method
1
I. Introduction
The majority of Sub-Saharan African countries became independent from Europe
during the 1960-1970s. While some countries gained independence through peaceful
agreement with their former European colonizers, others did so through violent and
armed conflict. Decades after achieving self-dependence, Africa's economic performance
is still poor (Easterly and Levine, 1997; Collier and Gunning, 1999; Rodrik, 1997).
Empirical evidence suggests that part of the current underdevelopment of Africa
is attributable to the continent's history, linking the current poor economic performance to
its colonial legacy. For instance, Easterly and Levine (1997) showed that ethnic
fragmentation as a result of the random partition of Africa under colonial rules at the
Berlin conference in 1884-85, explains an important part of the region's current
underdevelopment. The work of Acemoglu, Johnson, and Robinson (2001) in a crosscountry growth regression suggests that places where disease environment was hostile to
European colonial soldiers, they set up extractive institutions, which are persistent in
post-independent Africa, undermining current economic development in the former
colonies. Dell (2008), exploiting geographic and contemporary household survey data, as
well as data from historic record, utilized regression discontinuity estimation strategy to
establish long-term adverse effects on economic development in Peru and Bolivia, two
Spanish colonies.
The Wars of Liberation in this paper refers to an anti-colonial struggle for
independence, which has characterized African countries during the Cold War era. They
2
represent a historic event under colonial rules, which have been pointed out as one of the
determinants for poor economic performance in post-independent Africa (Michailof et
al., 2002). However, they have not been investigated systematically. There are reasons to
believe that the Wars of Liberation from colonial rules may have negative influence on
growth. Wars are known to be costly and destructive (Collier, 1999; Collier et al. 2003;
Knight, Loayza and Villanueva, 1996; Soarès, 2006), disruptive (Glick and Taylor, 2005;
Blomberg and Hess, 2006), and may have long-term negative influence on economic
performance (Koubi, 2005; Bates et al., 2007).
This paper examines the effect of Africa's independence via wars, on its
subsequent economic performance. I form two categories of countries. One that
transitioned to independence via armed conflict under colonial rules, and the other that
became independent through peaceful agreement with former European colonizers. Using
the Penn World Table, Version 7 spanning the years 1950-2009, I first consider the
residuals from regressing per capita incomes on year fixed-effects with an intercept, to
adjust for group trends. Next, I rescale regular calendar years into time since
independence (TSI) so that the time of independence is 0 for all countries. From the
``treatment'' group of five countries (Angola, Guinea-Bissau, Kenya, Zimbabwe, and
Mozambique), I create one single unit, the conflict group, by aggregating countries'
economic characteristics. I construct annual country-level panel data for 18 countries,
including the conflict group I apply the Synthetic Control Method proposed by Abadie
and Gardeazabal (2003) to produce a relevant counterfactual per-capita income from 17
countries that achieved peaceful independence.
3
I estimate the effect of the Wars of Liberation as the difference between the actual
income per capita and its synthetic version in the absence of conflict. Relative to the
synthetic counterpart without conflict, I find that annual per capita income gap between
the two categories of countries is about 7 percentage points prior to independence. This
suggests that annual per capita income for the conflict group would have been 7% larger,
had it achieved peaceful independence. I also find that as countries moved closer towards
independence, annual per capita income gap rose to about 10 percentage points,
suggesting a worsening of the wars of independence. This gap remained constant at 10
percentage points within a decade following independence, then narrowed progressively,
which gap closing after about 15 years of independence as both per capita incomes
converge to the same level, and remained relatively flat. That is, the effect of the Wars of
Liberation from colonial rules dissipated in the long-run following independence. The
paper provides evidence showing that this particular historic event under colonial rules
has no persistent effect on subsequent economic development in Sub-Saharan Africa. I
use randomization methods for inference, which requires estimating a placebo
``treatment'' effect for each country from the ``control'' group, applying the Synthetic
Control Method as done for the ``treatment'' group. This is an inferential technique that
produces the conventional p-values as the t-statistic. I perform a robustness check, which
indicates a steady estimation pattern over time.
To further understand the economic channels through which economic
performance was affected by the Wars of Liberation, I explore three economic
mechanisms based on the work of Blomberg, Hess, and Orphanides (2004). In doing so, I
4
apply the Synthetic Control Method to produce the relevant counterfactual investment
share of real GDP per capita, the share of government expenditures, and countries'
openness to trade. A decline in investment during wars (and theoretically, an increase
after conflicts), an increase in government spending, and decrease/increase in openness to
trade are expected. The evidence suggests that higher government spending and disrupted
trade flows explain a substantial part of the economic downturn. However, the result
indicates that the economic catch-up in the conflict group comes from higher investment
in post-independent Africa. Additionally, I examine whether the results are driven by any
particular country in the conflict group. In order to do that, I iteratively remove one
country at a time from the five-country ``treatment'' group. I compute the aggregate
economic features for the four remaining countries in the ``treatment'' group, and then
apply the Synthetic Control Method. In doing so, I find that the outcome is not
substantially driven by any particular country.
As the main contribution, this paper documents that the Wars of Liberation from
colonial rules in Africa have no persistent adverse effects on subsequent economic
development after 15 years of independence. Additionally, the study herein contributes to
the growing body of literature on conflict and income growth by providing a quantitative
understanding of income differences within Africa during the countries' transition to
independence. The paper also contributes to the literature by providing insights from
history in how a country's colonial experience is linked to its current economic
performance.
The analysis in this paper has historical and economic significance. Empirical
studies have shown that historic events in Africa are important for understanding its
5
current economic (under)development, linking the continent's colonial experience to its
subsequent economic performance. For example, Acemoglu, Johnson and Robinson
(2001, 2002) have studied the impact of European legacies on Africa's current economic
development. They have stressed the role of institutional settings in shaping Africa's
economic underdevelopment today. Also, Banerjee and Iyer (2005) compared districts
where revenue was historically collected directly by the British authorities, to districts
where revenue was collected directly by domestic landlords. Using the British conquer
date as an instrumental variable for revenue collection in colonial India, they found that
districts where there was no landlords systems display higher level of health, education,
and agricultural technology investments relative to landlord systems. Other relevant
colonial legacy works include (Nunn, 2008; Grier, 1999; Manning, 1990). This paper is
also related to studies of conflict and income growth (Blomberg, Hess, and Orphanides,
2004; Collier, 1999; Sumarto and Vothknecht, 2009), and to conflicts and economic
recovery (Blattman and Miguel, 2010; David and Weinstein, 2002).
Abadie and Gardeazabal (2003) proposed a different approach to policy analysis,
or an intervention of interest at the aggregate level with very little error (Wooldridge,
2007). They examined the effects of terrorist conflict on economic growth in the Basque
country using the Synthetic Control Method. They found that per capita income in the
Basque country would have been 10 percentage points higher, had it not experienced
terrorist conflict. The idea behind the methodology used in this paper is that, comparing a
combination of countries that experienced peaceful independence to a single country that
became independent through wars is better than comparing just one country that gained
peaceful independence to a single one that became independent through conflict. This
6
econometric technique has the advantage of providing a time series estimates of the
impact of independence via wars, not just the differences in the mean estimates between
two periods. The Synthetic Control Method does not require a large dataset. It is ideal for
macro interventions, and provides safeguard against extrapolation.
The rest of the paper is organized as follows: Section II gives a brief background
of Africa's independence and also discusses the related literature. In Section III, I give a
brief description of the Synthetic Control Method. Section IV presents a detailed
description of the data and sample. Section V presents the empirical specification.
Section VI discusses the regression results and section VII concludes.
II. Background
2.1. Route to Africa's Independence
Independence movements in Africa began to form when colonial rulers
introduced indirect rules to traditional authorities, about more than half a century after the
Berlin Conference in 1884-1885. The outcome of the conference is the artificial partition
of Africa by European colonialists, known as ``The Scramble for Africa''. The
movements toward independence gained momentum with strong involvement of
educated African political leaders who joined from Europe, combined with some external
shocks such as the WW II (Bates et al., 2007; Walshe, 1972).1
By the 1950s, Northern African countries began to gain independence. Libya was
granted independence in 1949 by the United Nations, Egypt and Sudan in 1952, Tunisia
1
Britain, France, Germany, Spain, Italy, Belgium, and Portugal. The ``Scramble for Africa'' is also
identified as the ``rush for African territories.
7
in 1956, and Algeria in 1962. While most countries became independent between 19601965 in the Western and Central Africa, the Eastern and Southern part of Africa were still
under colonial control due to the presence of mineral resources. At the end of the 1960s,
six African countries remained colonies, from which five were in Southern Africa:
Angola (Portugal/settler), Mozambique (Portugal/settler), Namibia (South Africa/settler),
South Africa (settler) and Zimbabwe (British/settler). The small Portuguese colony of
Guinea-Bissau and Cape Verde in West Africa was the sixth colony.2 While transition to
independence was non-violent in some countries that became independent by the mid
1960s, for other countries such as Angola, Mozambique, Kenya, Namibia, Eritrea,
Zimbabwe, Guinea Bissau, Cameroon, and Madagascar, the process was fierce and
turned into a long-lasting conflict. Independence struggles were long, drawn-out issues in
these countries.
2.2. Other Related Literature
Numerous studies have given attention to the role of historic events in explaining
countries' economic (under)development. Using pooled data of 63 ex-colonial states,
Grier (1999) showed that colonies that were held for a longer period of time than other
countries tend to perform better, on average, after independence.3 In a cross-country
growth framework, Acemoglu et al. (2001) showed that places where disease
environment was hostile to colonial soldiers, European colonizers would set up extractive
institutions that allow a colonizer to transfer resources from a colony with minim loss of
2
The reason for this is that Guinea-Bissau and Cape Verde constituted a short-lived confederacy after
independence under the leadership of .A.I.G.C, a political Party of Independence (Antonio Martins, 2010),
Liberia and Ethiopia are considered non-colonized countries in Africa (Bertocchi and Canova , 2002).
3
Olsson (2007), also Freyer and Sacerdote (2006), find similar results with a dataset of 80 small
islands from the Atlantic, Pacific and Indian oceans.
8
life due to disease. Acemoglu et al. (2001) documented that those extractive institutions
are persistent in post-independent Africa, thus, impeding growth. In the same line of
research, Dell (2008), exploiting data from historic record, contemporary household
information, and geographic data, uses a regression discontinuity estimation strategy to
establish that the mita forced labor system established by the Spanish in Peru and Bolivia
between 1573 and 1812, has a long-term negative influence on household consumption.
Dell (2008) documented that in the former mita district, household consumption level is,
on average, 32% lower than that of households in former non-mita districts. She argues
that this significant gap is a consequence of low levels of education, and less developed
road networks. This suggests that colonial legacy such as institutions, can have persistent
impacts on countries economic development.
The work of Nunn (2008), which exploited the number of slaves exported from
African countries in each century spanning the years 1400-1900, showed that African
countries that are the poorest today, are the ones that suffered the most loss of slaves.
Blanton, Mason and Athow (2001) stress that nations with French colonial legacies have
lower levels of entrepreneurial activity than nations with British legacies. In a crosscountry setting, Easterly and Levine (1997) used ethnic fragmentation as a result of the
European colonizers' legacy to document that heterogeneous preferences across
ethnically diversed societies are likely to hinder agreement on policy decisions and result
in lower public spending, hence slowdown economic growth.4
4
Other factors explaining income differences in Africa comprise geographic isolation. Works on this
matter include Collier (2006), Bloom and Sachs (1998), Acemoglu, Johnson and Robinson (2002), Hall and
Jones (1999). For climatic conditions, see Masters and McMillan (2001). Concerning culture and religion
see Barro and McCleary (2003). Bates et al (2007) investigate independence, political violence, and
growth.
9
Separately, a set of existing literature in the growth-conflict nexus focuses on the
costs of conflict. For instance, Collier (1999) uses a cross-section evidence for 92
countries spanning the years 1960-1989, to document that a 7-year civil conflict causes
national income to be about 15% lower than what it would have been in the absence of
conflict. In willingness-to-pay approach to account for the health incidence of wars in
Colombia, Soarès (2006) estimates that conflict reduced life expectancy at birth by 2.2
years and costs the country a 9.7% of its per capita GDP. Hess (2002) estimates the
economic welfare cost of conflict and finds that, on average, individuals who live in
countries that have experienced some conflict between 1960 and 1992 would be willing
to give up to about 8% of their current level of consumption to live in a peaceful
environment. Knight, Loayza and Villanueva (1996), estimate that civil conflict leads, on
average, to a permanent income loss of 2% of GDP. Fearon and Latin (2003) and Justino
(2007) focus on violent conflicts and their effect on household poverty.
Another set of studies focuses on all forms of terrorist conflicts. Blomberg, Hess,
and Orphanides (2004) find that the per capita GDP growth of a country that experienced
terrorist conflict in each of their sample period fell by about 1.6 percentage points over
the entire sample period. In the last part of their article, they also performed some panel
regressions regarding the incidence of terrorism on the share of investment and
government spending. They find that terrorist conflict increased government expenditures
share and crowds out investment. This indicates that conflict diverts government
spending away from growth-promoting sectors to military security and retards economic
growth directly through investment. Abadie and Gardeazabal (2003) find that economic
growth in the Basque country was 10 percentage points lower than what it would have
10
been in the absence of terrorist conflict. In a data set for 42 Asian countries spanning the
years 1970-2004, Gaibulloev and Sandler (2008) find that one additional terrorist conflict
per million persons leads to a 1.5% growth reduction.
Other studies examine the effects of civil wars on neighboring countries. Murdoch
and Sandler (2002, 2004) study the impact of neighboring conflict on growth using
contiguity matrices. They showed that civil wars reduce growth over an entire region of
neighboring countries. In response to Murdoch and Sandler's work, De Groot (2010),
using data for Africa, proposed a different contiguity matrix measurement and suggests
that civil conflict has two opposing effects. He finds that, like conflict countries
themselves, direct neighboring countries suffer from the negative effects of proximate
civil wars. However, he finds that there is positive spillover of conflict that affects noncontiguous countries, which is larger for countries that are closer to the conflict country.
Montalvo and Reynal-Querol (2007) investigate the impact of refugees from civil
conflicts on the incidence of malaria in refugee-host countries. They suggest that for
every 1,000 refugees; between 2000-2700 cases of malaria occur in the refugee-host
country.
Empirical literature has also stressed the causes of civil conflict and their
consequences on growth. For instance, Collier and Hoeffler (2004) argue that civil wars
and rebellions are explained where and when rebel groups have the opportunity to raise
revenues through the exploitation of natural resources, or when they can take advantage
of high levels of unemployment and poverty to recruit civilians to fight their
governments. Miguel, Satyanath and Sergenti (2004) have stressed the causes of conflicts
and their impact on growth in Africa using rainfall as an instrumental variable. Collier
11
and Hoeffler (2000) suggest that greed, rather than grievance, explains the prevalence of
conflicts in Africa. This argument has been proven to be inadequate empirically and
deficient theoretically based on collective-action and social movement theories (Athow,
Mason and Blanton, 2001).
Countries that have suffered from conflict need to recover their lost resources
(physical and human capital stocks, natural resources, economically, etc.). A much
scarcer empirical literature also analyzes post-conflict economic recovery. Organski and
Kugler (1977, 1980) investigate the effects of World War I and II on European countries'
economies and document that the effects dissipate in the long-run, on average within 1520 years with growth returning to its pre-conflict trends. Davis and Weinstein (2002) use
a dataset on Japanese regional population to study the impact of the Allied Japanese
bombing cities during World War II. They find that cities that suffered the largest
population declines due to bombing fully recovered within 15 years following the end of
the conflict. The work of Brakman, Garrtesen, and Shramm (2004) show similar results
using a dataset on the bombing of German cities during WWII, on after-war German
city's growth. Miguel and Roland (2006), Justino and Verwimp (2006), Davis and
Weinstein (2002); Elbadawi, Hegre and Milante (2008); Collier and Hoeffler (2006,
2002; 2004) focus some of their work on post-conflict economic recovery as well.
A group of studies investigate the incidence of conflict on trade and find that
conflict retards economic growth through trade distortions. For example, Blomberg and
Hess (2006) find that violent conflicts impede trade flows by 40 percentage points. Also,
Glick and Taylor (2005) find a strong negative impact of war on international trade.
Development Aid is also found to slow down growth in the presence of conflict. Collier
12
et al. (2003) suggest that conflict-torn countries face reduced growth from decrease in
Development Aid because donors worry that aid may be diverted to favor military actions
to the detriment of socially beneficial public investment.
Finally, a recent set of research has identified a negative relationship between
civil wars and educational attainments. For instance, Alderman et al. (2006) find that
children in Zimbabwe in the 1970s wars completed less grades of schooling while others
started school later than those that did not experience civil wars. Shemyakina (2006)
focuses on gender instead. Using data from Tajikistan, she found that females are more
vulnerable during conflict than males due to safety issues and low returns to females'
education. Alkresh and De Walque (2008) find contrasting results for females and males.
Blattman and Annan (2009) in a sample of 741 interviewed young Ugandan men, show
that children who have been kidnapped by the Lord's Resistance Army for some period of
time, have about 10% fewer years of schooling (ceteris paribus).
III. The Synthetic Control Method
Abadie and Gardeazabal (2003) proposed a data-driven econometric technique to
examine the effects of an event or policy intervention on an outcome of interest, by
focusing on a particular instance in which the magnitude of the event or intervention is
large relative to other determinants of the outcome. The Synthetic Control Method is a
counterfactual approach to estimate an event-free economic outcome of interest and
considers the gap between the counterfactual outcome and the actual outcome as the
effect attributable to the event. It generates weights to countries in the control group
13
based on the degree of similarities of their economic features to those of the ``treatment''
group. The counterfactual outcome is constructed as the weighted average of the outcome
of the control units, with weights chosen so that the resulting synthetic group best
generates the values of a set of predictors of the outcome of interest in the preintervention period. It is useful in comparative case studies between one or more units
(countries) affected by an event or intervention of interest, and one or more unaffected
units (or when they differ in the levels of exposure). In case more than one unit is
affected by the event, one could first aggregate the data from all units exposed to the
event. The Synthetic Control Method is a more generalized version of the Difference-inDifferences methodology in that it produces dynamic average time series effects, not just
the average differences in the means estimates. The use of this technique may be limited
by unmeasured factors affecting the capita incomes, as well as heterogeneity in the effect
of unobserved and observed factors. However, the matching of countries' economic
features in the pre-intervention period helps to control for those factors. By consequent,
endogeneity and selection bias are minimal concerns (Wooldridge, 2007).
IV. Data and Sample
The data are mainly extracted from the most widely used source for cross-country
comparison for level and growth rate GDP - The Penn World Table (PWT), which
measures of GDP involve extrapolation of levels in the benchmark year using National
Account growth rates. Recent studies have raised concerns about the Penn World Table
(PWT) data. Johnson et al. (2009) showed that the PWT estimates vary substantially
across different versions despite being derived from the same methodology and
14
underlying data. Specifically, Johnson et al. (2009) demonstrated that the PWT versions
6.1 and 6.2 show severe discrepancies. Another scholar such as Young (2012) also
elaborated extensively on the discrepancies of the PWT version 6. These inconsistencies
have led some scholars to explore other alternatives to GDP data such as Johnson et al.
(2009), and Young (2012). However, Young (2012) stressed that this issue has been
alleviated with the 2005 International Comparison Program (ICP) and that the updating
of the PWT data version 7 moved its level estimates systematically closer to his results.
Consequently, I use the annual updated PWT version 7 (Heston, Summers, and
Aten, 2013) for the period 1950-2009, which provides the outcome variable of interest
Yit , the per-capita real GDP in country i at time t (2005 constant prices: Chain Series), the
standard growth determinants in country i at time t, which include countries' openness to
trade, (Open)it; population size in thousands, (Pop)it; government expenditure share of
real GDP per capita, (G/GDP)it; investment share of real GDP per capita, (I/GDP)it.
Countries' years of independence are from Price (2003). Other growth determinants
extensively used in Africa's economic growth studies are religion data from Alesina et al.
(2003), language and ethnicity data from Easterly and Levine, (1997).
Next, I consider residual per capita income from the regression of per capita
income on year fixed-effects and a constant, to adjust for group trends. The residual per
capita incomes and the predictors for the ``treated'' group are aggregated to form the
conflict group. I create a rescaled time variable, time since independence (TSI). It
measures time relative to a country's independence so that the time of independence
corresponds to zero for all countries (second vertical line in figure 1). It is computed as
the difference between any subsequent year and a country's year of independence. Due to
15
data limitations, I obtain 13-year period data prior to independence, of which 3 represent
the pre-wars periods; and 32-year period data in the post-independence. Table 1 gives a
summary of the data along with their explications and sources. I consider the World
Bank's conceptualized subdivision of Sub-Saharan Africa and select the control group
based on data availability in the pre-independence period, which is key to this project.
Countries with no pre-independence data are discarded systematically. I created leads for
some countries that were short of a few data in the pre-independence period to construct a
balanced panel data. This procedure yields a sample of 17 countries that gained peaceful
independence: Botswana, Burundi, Cape Verde, Comoros, Djibouti, Lesotho, Malawi,
Mauritius, Nigeria, Rwanda, Sao Tomé and Principe, Seychelles, Somalia, South Africa,
Tanzania, Uganda, and Zambia. There are 8 countries that became independent via
conflict and constitute the treated group: Eritrea, Ethiopia, Kenya, Guinea-Bissau,
Angola, Mozambique, Namibia, and Zimbabwe. However, Eritrea and Namibia gained
recent independence, and do not have enough time series data in post independence
period. Ethiopia is said to be an independent country even before the invasion of Italy in
1935 (Bertocchi and Canova, 2002), and was discarded as well. This yields a group of
five countries (Angola, Guinea-Bissau, Kenya, Mozambique, and Zimbabwe).
V. Empirical Specification
The main point of this study is to examine whether independence in Africa has a
persistent impact on current per-capita income in the region. This paper uses a convex
combination of countries that experienced peaceful independence, to approximate the
16
economic characteristics of countries that became independent via Wars of Liberation
from Colonial rules. As a result, the synthetic control can be defined as the weighted
average of countries that did not experience conflict.
Suppose in this comparative case study we observe J + 1 countries. Let's assume
for simplicity that only ``one'' country (j = 1) experienced conflict before achieving
independence. This means that the remaining countries j = 2,... , J + 1 constitute potential
comparisons. The sample is a balanced longitudinal dataset where we can observe all
countries i at the same time t = 1,…,T. Suppose independence took place at time T 0 such
that T 0  1, T  , which means that there are T 0 pre-independence periods, and T 1 = T- T 0
post-independence periods where T is the last time period in the sample.
Let Y Uit represents the per-capita GDP of country i at time t in the absence of
conflict, for i = 1,…J+1, and t = 1,...,T. Define Y itE to be the per-capita GDP that would
be observed in period 1, T  for country i at time t if country i experiences conflict while
transitioning to independence. As in Abadie and Gardeazabal (2003), the paper assumes
the two outcomes are similar prior to the start of the wars. That is, the relation Y Uit = Y itE
holds before conflict. This is a key assumption made in this paper, which is
hypothetically non-testable due to data limitations. It would have been ideal to have data
available in pre-conflict period in order to compare the two outcomes. The intuition of
the matching is that only countries that are alike in both observed and unobserved
characteristics should produce identical trajectories of the per capita income over
extended periods of time. Moreover, the Wars of Liberation in the conflict countries were
self-contained (Bates et al., 2007). As a result, the model will rely on the assumption of
17
no interference between countries' per-capita incomes. That is, the per capita GDP of
countries with peaceful independence are not affected by conflicts, ruling out spill-over
effects. However, if conflict had negative spillover effect on the per capita income of the
countries included in the synthetic control, then the synthetic control would
underestimate the counterfactual per capita income. If conflict had positive spillover
effect in the economies of countries included in the control group, the model would
produce an overestimate of the counterfactual income.
Let  it  YitE  YitU denotes the effect of conflict of independence for country i at
time t, and let D it be an indicator that takes value 1 if country i experienced conflict in its
independence process at time t. The observed outcome for country i at time t is given by
the following equation:
Yit  YitU   it Dit .
Since only country “one” experienced conflict in period
(1)
1, T0 
and the effect can be
observed before and/or after independence period T 0 , D it can be defined as follows:
1 if i = 1 for t  1, T ,
Dit 
0 Otherwise
18
In this setup, the aim is to estimate ( 11 ,... 1T0 ) before independence and ( 1(T0 1) ,...1T )
in post-independence period for country i=1. The effect of conflict for all t can be defined
as:
1t  Y1t  Y1Ut
Since we can observe Y 1Et , to estimate  1t we just need to estimate the counterfactual
outcome Y 1Ut . As in Abadie et al. (2003 and 2010), I consider the following factor model:
YitU   t   t Z i  ti   it
(2)
where  t is an unknown common factor with constant factor loadings across countries,
Z i an h×1 vector of observed covariates,  t is a 1× h vector of unknown parameters, t
is a 1× g vector of unobserved common factors,  i is a g × 1 vector of unknown factor
loadings, and  it are the unobserved error terms at the country level with mean zero.
Equation (2) can be converted into the difference-in-differences (or fixed-effects)
model provided that we allow t being constant for all t. That is, the difference-indifferences (DID) model allows the presence of unobserved confounders but restrict the
effect of those confounders to remain invariant over time, so that they are eliminated
when taking time differences. It would have been convenient to use the DID estimation
given the before and after outcome analysis between the two groups, and the presence of
event. The DID circumvents endogeneity issues that typically arise when making
comparisons between heterogeneous units (Bertrand, Duflo, and Mullainathan, 2004).
However, the endogenous nature of independence via wars, will raise concerns about the
19
validity of the DID estimation. Other problems with the DID relate to the standard errors
of the estimates because they are most often generated from using Ordinary Least
Squares (OLS) in panel data on units with treatment and control groups for long time
series before and after intervention. Other studies employ cross-country panel regression
approach, using instrumental variable approach to account for endogeneity that may
arise. However, instruments are not only difficult to find, but are mostly controversial.
In contrast to the difference-in-differences (or fixed-effects), the synthetic control
model presented here allows for the unobserved confounders to vary over time and thus,
taking time differences does not eliminate the unobserved confounders  i . More
importantly, the Synthetic Control Method allows tracing the estimates dynamically,
which is what this paper aims at determining. Equation (2) also satisfies the usual
assumptions in standard econometric regression approach. A synthetic control such that
the following two equalities are satisfied:
J 1
w Z
j 2
*
j
j
 Z1
J 1
and
w 
j 2
*
j
j
 1
(3)
provides an unbiased estimator for Y 1Ut . Since the  j are unobserved, choosing the
synthetic control that satisfies the above equations is not feasible. However, as
demonstrated in Abadie and Gardeazabal (2003), the factor model in equation (2) implies
that a synthetic control can fit Z 1 and a long set of pre-independence outcomes
20
(Y11,..., Y1T0 ) only as long as it fits Z 1 and  j so that equation (3) holds approximately.5 We
want to find a J×1 vector of weights W  (w2 ,..., wJ 1 ) such that w j  0 for j = 2,...,J+1,
and sum up to 1.6 Each particular value w j represents a potential synthetic control,
meaning, a particular weighted average of the control group of countries. The vector W
indexes each synthetic control of the per-capita GDP value so that the following equation
is satisfied:
5
The synthetic control can provide useful unbiased estimates in more general contexts, for example in
an auto-regressive model with time-varying coefficient, even if data for only a single pretreatment period
are available.
6
Detailed methodology about the weights construction is as follows: Let W = ( ( w j ,..., wJ 1 )' be a $(J ×1)
vector of positive weights w j
 0 , j = 2,...J+1that sum to 1. Each value of w represents a weight of the
available control group and thus, represents a synthetic control. The GDP per capita
1,...,T for the group of countries affected by conflict, and
j = 2,...J+1. Let the
Y1t is observed for t =
Y jt for the unaffected group, where
T0 1 vector K = (k1 ,..., kT0 )' define a linear combination of pre-independence period
T0
outcome:
Yi K   k sYis . For instance, when the ki ' s equal zero for t  1,T0  1 and kT0  1 , then
s 1
the per capita GDP value in the period immediately prior to independence
if for all
t  1, T0 
1
1
kt  , then Yi K 
T0
T0
(TSI  1) is Yi K  YiT0 . Now,
T0
Y
s 1
is
, which is the simple average of the per capita GDP
for the pre- independence periods. Suppose there are
m such linear combinations K1 ,..., K m . Let
X 1  (Z1, Y1 K1 ,..., Y1 Km ) be a k × 1 vector of pre-independence characteristics for the affected group,
k  h  m . Let the k × J matrix X 2 be defined similarly and contains the same variables for the
K
K
unaffected group. For example, the j  th column of X 2 is ( Z j , Y1 1 ,..., Y1 m ) . One obvious choice
for
Y1 K1 , Y2K2 ,..., Yi Km is Yi K1  Yi1 , Yi K2  Yi 2 ,…, Yi
KT0
 YiT0 . That is, the values of the GDP per capita
for all available pre-independence periods. Equation (4) can hold exactly only if the convex hull
CH   (Y21,...,Y2T0 , Z 2 ),..., (Y( J 1)T0 , Z J 1 )  contains (Y11,Y12 ,...,Y1T0 , Z1) . However, the synthetic
control is selected so that equation (4) holds exactly in the data. Researchers should refrain from using the
synthetic control method when (Y11, Y12 ,..., Y1T0 , Z1) falls far from the convex hull of CH .
21
J 1
w Y
j
j 2
jt
J 1
J 1
J 1
J 1
j 2
j 2
j 2
j 2
  w j t   w j t Z j  t  w j j   w j  jt
Suppose that there exists some optimal weights (w2* ,..., w*J 1 ) such that:
J 1
w Y
j 2
*
j
j1
 Y11 ,
J 1
J 1
 w Y j 2  Y j 2 , …  w*jY jT0  Y1T0 and
j 2
*
j
j 2
J 1
w Z
j 2
*
j
j
 Z1
(4)
The estimated effect of independence via conflict is given by:

J 1
1t  Y1t   w*j Y jtU
j 2
Therefore, in the absence of independence via wars, the outcome of country i=1 for time
period t  1, T0  can be approximated by a synthetic control country (a weighted average
of the control countries) given by:

J 1
1t  Y1t   w*j Y jtU  0
j 2
The SCM solves a nested optimization problem to generate optimal weights w*j so that
the resulting synthetic control minimizes the root mean squared prediction error
(RMSPE) defined as follows:
 1
Arg min ( RMSPE )  arg min 
 T0
w*j  0
w*j  0

1
T0

t 1

 Y1t   w*jY jtU

j 2

J 1




2
2



Where each resulting weight w*j is a function of the predictors' weights v * obtained from
a symmetric, positive and semi-definite diagonal matrix V in the pre-independence
period. The weight v * designates the predictive power of an explanatory variable for the
22
per capita GDPs in the pre-independence period. A higher v * for instance means a higher
predictive power of the corresponding predictor. For every W * (V * ) , The SCM generates
the minimized root mean squared prediction error (RMSPE) for the per capita GDP in the
pre(post)-independence period.
The RMSPE measures the lack of fit between the GDP per capita for the conflict
group and its synthetic version. Although the weights are restricted to be positive and add
up to 1, they can be negative or larger than one, but will require extrapolation.7 For each
particular application we can calculate the magnitude of such discrepancy and one can
decide whether the characteristic of the affected group sufficiently resembled the
synthetic control. In the event that the fit is poor, the synthetic control method is not
recommended.
VI. Empirical Results
Table 1 shows the data description and sources. Table 2 displays the list of
countries in the sample, how they became independent, and the names of the Wars of
Liberation and their duration, and their colonial origins.
Table 3 gives a list the countries' weights. They are computed using equation (5).
I minimize the root mean squared prediction errors to obtain the weights so that the
resulting synthetic conflict group best produces the values of the GDP per capita and its
predictors during the pre-conflict period. I use the weights generated from the RMSPE
minimization to construct the synthetic conflict group as described in Section V. The
7
Extrapolation is an estimation of a value based on extending a known sequence of values or facts
beyond the area that is certainly known
23
synthetic conflict group is produced by Mauritius with the highest weight (0.346),
followed by South Africa (0.275), Uganda (0.175), Tanzania (0.137), Zambia (0.033),
Nigeria (0.022), Djibouti (0.005). Seychelles, Comoros, Cape Verde, Burundi, Lesotho,
Equatorial Guinea all have equal weights contribution of 0.001. The rest of the countries
in the sample have zero weight and do not contribute to the synthetic per capita income.
The weighted average per capita incomes of these 13 countries generates the
counterfactual of the conflict group.8
Table 4 compares the pre-conflict economic characteristics of the conflict group
to those of its synthetic counterpart, as well as their averaged values for the control
group. The synthetic outcomes approximate those of the pre-conflict economic values in
terms of log per capita income and its predictors for the conflict group better than the
average of the 17 countries in the sample. The synthetic conflict group is almost the same
in terms of log per capita GDP, Investment share, openness to trade, population,
Ethnicity, language, and Religion; a little less in terms of government expenditures,
possibly due to high spending in early conflict. The synthetic conflict group does not
resemble the average of the sample. This means that the average of the sample for the
control group provides an inadequate comparison unit for the conflict group than the
synthetic control method. Table 4 also shows that it is always possible to closely produce
the conflict group's economic attributes through a convex combination of a comparison
group without any extrapolation outside of the support of the data.
8
The weights can be regarded as the degrees of similarities in economic characteristics between the
conflict group and its synthetic version without conflict
24
Table 5 reports the root means square prediction error before and after
independence, as well as the ratio of the post-independence RMSPE over the preindependence RMSPE. This allows identifying the presence of some negative influence
of the Wars of Liberation on countries' per capita income. The presence of adverse effects
is characterized by the largest ratio value for the conflict group. Table 5 also shows that
there is no need to remove a country from the sample if its economic features cannot be
produced by the Synthetic Control Method. Countries in this category will show small
RMSPE as shown in the table.
Figure 1 summarizes the findings in this paper. The horizontal axis designates the
time since independence; the vertical axis indicates the log per capita income. The solid
line shows the per capita income for the conflict group while the dotted line shows the
per capita income level for the synthetic counterpart in the absence of wars. The area
prior to the first vertical line indicates the pre-wars income trends for both groups, which
are assumed to be identical. This is a key assumption made in this paper that is
hypothetically non-testable as there is no pre-conflict data available for verification.
Existence of pre-conflict data would have been ideal to verify this assumption. This
suggests that the first vertical line marks, on average, the beginning of the Wars of
Liberation. The second vertical line indicates the time of independence where (TSI = 0),
with the premise that the Wars of Liberation ended at that time. The entire left-hand side
of independence is the pre-independence period and the opposite side represents the postindependence period. The effects are observed before and after independence.
Figure 1 shows that in early start of the Wars, the gap between the two per capita
income trajectories for the two groups begin to widen. While the synthetic conflict group
25
's per-capita income takes an upward trend, that of the conflict group slopes downward,
then increase shortly before taking a slight dip toward independence, widening
substantially the income gap. This may suggest that conflict became more severe as
countries transitioned closer to independence. The gap remains constant for about a
decade after independence then narrows. This indicates that the effect of conflict is
decreasing over time. Although the conflict group per capita income begins to show
positive trend following independence, it remained below its counterfactual across
periods, then converges to the level of the counterfactual after about 15 years of
independence. After a decade and a half, the two income levels tend to converge to the
same income pattern and remain flat, suggesting a dissipating effect of conflict. In other
words, the results suggest that this specific historic event shows no persistent effect on
subsequent economic performance in Sub-Saharan Africa. The slope of the
counterfactual is almost the same before and after independence, indicating that there is
less variation across countries characteristics in the control group. This inhibits
heterogeneity issues that often arise in standard econometric regression approach,
minimizing standard errors problem. This also shows that independence per-say has no
influence on growth.
Figure 2 displays the trend of the gap estimates in both pre/post-independence
periods between the conflict group and its synthetic counterpart without conflict over
time. I estimate the effect of the wars of independence on the per-capita income as the
difference between the conflict group and its synthetic counterpart in both periods. The
estimated annual per capita income gap is about 7 percentage points in the preindependence period. This means that income per capita for the conflict group would
26
have been 7 percentage points larger, had it gained peaceful independence. However, in
the post-independence, the estimation shows that annual per capita income gap between
the two groups is about 10 percentage points. That is, income per capita for the conflict
group would have been 10 percentage points larger, had it become independent
peacefully. In dollar terms, I find that the conflict group suffered an annual $930 deficit
prior to independence over 10 years, and about an annual $920 income loss over 15 years
- an average annual total income loss of about $1850 over a 25-year period.9
Figures 5, 6, and 7 show the actual investment rate, share of government
spending, and openness to trade for the conflict group, respectively. In all three Figures,
the dotted curves represent the counterfactual versions of the same variables in the
absence of conflict.
6.1 Placebo Test

This section tests the significance of the estimates of  it . Are the results driven by
chance? Alternatively, how often will the results of the same magnitude be obtained if a
country other than the conflict group were randomly selected? To assess the validity of
the estimates, I perform a series of placebo studies.10 This placebo study appears under
different names in the literature. As a ``falsification test'', DiNardo and Pischke (1997)
used it as a technique to assess the effect of computers on wages distribution. Angrist and
Krueger (1999) used a similar technique under refutability, to study the effect of the
9
The computation in dollar terms is straight forward. Since the estimates are in log terms, take the
exponential and multiply them by $1000 (because they are in thousands). Then average them over 10 (or
15) years before (or after) independence
10
The placebo Code can be found by typing the command ``help synth" after Synth is installed in
STATA.
27
Mariel Boatlift on native unemployment in Miami, using other southern United States
cities as a comparison group. This is similar to permutation inference where the
distribution of a test statistic is computed under random permutations of the sample units'
assignment to the intervention and non-intervention groups. This is an inferential
technique and often produces the p-values as the t-statistic. The idea of the placebo test in
this paper is to apply the Synthetic Control Method to the control group as if they were
affected by conflicts, and compare the placebo estimates with those of the affected group.
This allows assessing whether the effect estimated by the synthetic control for the
affected group is large relative to the effects estimated for a randomly chosen group. This
is an exact inferential exercise in the sense that it is always possible to compute the exact
distribution of the estimated effect of the placebo interventions, regardless of whether the
data are individual or aggregate, the number of available comparison units, and time
periods. Under the assumption that conflict had an impact, the actual estimate is expected
to be greater relative to the distribution of the placebo estimates.
Conflict is artificially assigned in the data to each country in the sample of the
control group, shifting the conflict group to the set of countries unaffected by conflict. I
iteratively apply the Synthetic Control Method to each country to obtain estimates for
each country that did not experience conflict, as well as their respective root mean
squared prediction error (RMSPE). This allows comparing the estimated effect of conflict
on the conflict group to those of the placebo effect obtained for countries in the
comparison group. The effect of conflict on the conflict group's economic development
will be rated insignificant if the placebo studies provide effect of magnitude similar to or
larger than the one obtained for the conflict group. However, the placebo studies show
28
that the gap estimated for the conflict group is unusually large relative to those obtained
in placebo studies for each country that was unaffected by conflict in the sample, then the
analysis provides significant evidence of a negative impact of conflict on the conflict
group 's economic development.
In order to compare the estimates of the conflict group to those of placebo studies,
as in Abadie, Diamond and Hainmueller (2010), I compute the ratio of Post/preindependence RMSPE. The ratio measures the affinity between each country in the
sample and its synthetic counterpart before and after independence. This process prevents
the need to exclude countries with pre-independence per-capita income values that cannot
be reproduced by the synthetic control. If the effect of conflict were to be random, the
ratio of the Post/pre-independence RMSPE would be expected to be very large relative to
the placebo studies.11 Table 5 shows the ratios of post/pre-independence RMSPE for the
conflict group and those of the countries in the control group.
Figure 3 provides the distribution of the ratios. The conflict group's RMSPE ratio
clearly stands out in the data compared to the placebo distributions. For the conflict group
the post-independence gap is about 7.45 times larger than that of the pre-independence.
No other country in the data reached such a ratio. This implies that if one were to
randomly assign conflict to a country in the sample, the probability of estimating a gap of
the same magnitude as the conflict group is
1
 0.055. This number is close to the
18
conventional statistical significance level of 5%, giving evidence of the presence adverse
11
Note that a large Post-independence RMSPE does not indicate a greater effect of conflict if the
synthetic control does not closely reproduce the Log per-capita income prior to independence (it does not
indicate a large effect of conflict if the RMSPE in the pre-independence period is also large).
29
effects of the wars of independence on the countries' (Angola, Guinea-Bissau, Kenya,
Mozambique and Zimbabwe) per capita income.
6.2 Robustness Check
Table 3 shows that the synthetic conflict group is constructed as a weighted
average of 13 countries: Mauritius (0.346), South Africa (0.275), Uganda (0.175),
Tanzania (0.137), Zambia (0.033), Nigeria (0.022), Djibouti (0.005), and Seychelles,
Comoros, Cape Verde, Burundi, Lesotho, Equatorial Guinea with equal weight
contribution of 0.001. To test the sensitivity of the results, I run a robustness check by
iteratively re-estimating the baseline model to construct the synthetic conflict group. In
each iteration, I remove countries with highest weight, one at a time that contributed to
the synthetic control group. The idea is to see if the estimates obtained above are
sensitive to the exclusion of any particular country from the sample. Figure 4 shows some
fairly robust estimates to the omission of a couple of the synthetic countries. In all cases,
the dynamic of the estimated effect of independence via conflict on the per capita income
is structurally informative.
6.3 Discussion
Relative to its synthetic version, the annual per capita GDP in the conflict group is
7 percentage points lower than what it would have been, had it achieved peaceful
independence. This suggests an adverse effect of the Wars of Liberation while they were
still ongoing in the region of Africa, consistent with the literature. The finding is
consistent with most research in the growth-conflict literature which focuses on the
30
economic costs of conflict (Collier, 1999; Hess, 2002; Soarès, 2006; Fearon and Latin,
2003; Justino, 2007). One could argue that the discrepancies in the per capita GDP before
independence may be caused by differences in income predictors between the conflict
group and its synthetic counterpart, or by other factors not shown in the sample. For
example, prior to independence, a higher industrial concentration in unaffected countries
such as South Africa, or in Tanzania, may cause these income differences in both periods.
However, it is believed that industrial revolution has by-passed Africa (Nwokeabia,
2009) and therefore cannot explain the per-capita income gap between the two categories
of countries.
Before independence, while conflict is ongoing, as shown in figure 1, the per
capita income for the conflict group decreases in early start of conflict. This could be
explained in Figure 5, which seems to indicate that investment rate is lower during that
same period, than what it would have been in the absence conflict - consistent with the
``crowding-out'' hypothesis in the literature. This is coupled with decreased trade flows as
materialized by its synthetic version (Figure 7), which is higher for the most part in the
conflict period. The rise in income for the conflict group (Figure 1) about five years prior
to independence coincides with the sudden spike in investment and government spending
(Figure 5 and 6). Per capita income for the conflict group slowly decreases towards
independence, widening the income gap. This coincides with a sharp decrease in the
share of investment, followed by larger government expenditures, destined to favor
military actions to the detriment of growth-promoting public and private sectors. This is
consistent with (Blomberg, Hess, and Orphanides, 2004). The overall slow income
31
growth is largely driven by substantial government spending and decreased investment
rate.
After independence, per capita income for the conflict group is trending upward,
albeit a decrease in openness to trade and government spending. This may be driven by
the rise in investment within the first decade of independence, consistent with Bates et al.
(2007) who argue that economic investment was followed by the retreat of the European
colonialists; or by the demographic collapse due to wars (Malthusian Law). Although
investment decreased shortly after the decade following independence, it increased again
sharply to level up with its synthetic version (Figure 5). However, it still remains below
its synthetic version without conflict for about 15 years. This could be explained by other
factors not included in the data, or by some observed factors.
There is almost zero income gap between the two groups of countries after 15
years. This is characterized by higher investment rate and more openness to trade. Figure
5 shows that after 15 years following independence, investment has increased
significantly. Both actual and counterfactual investment rates are similar and greater than
their pre-conflict trends, although they tend to decrease towards the end of the sample.
Figure 7 also shows that countries are more opened to trade since the actual openness to
trade is above its synthetic version without conflict.
The estimation results indicate that, relative to its synthetic version without
conflict, the per capita GDP in the conflict group would have been 7 percentage points
higher before achieving independence and about 10 percentage points after
independence. This shows that conflict is detrimental to growth, with varying effect in
magnitude - consistent with empirical and theoretical literature. After a decade of
32
independence, the gap narrows, then closes within after 15 years. This result is consistent
with Organski and Kugler (1980), who found that, due to intensive investment and
reconstruction efforts, countries that experienced bombings tend to return to their pregrowth trends 15-20 years after the wars have ended.12 This economic catch-up indicates
not only economic recovery, but also suggests that the way independence was achieved in
Sub-Saharan Africa does not matter in their subsequent economic performance as both
groups of countries display approximately similar income levels that remain relatively
flat. Other potential factors, whether observed and unobserved, may also explain this
economic catch-up. One potential argument for example may be the collapse of
authoritarian regimes and the rise of democracy in Africa in the mid-1990s along with
institutional qualities proposed by the work of Acemoglu et al., (2001, and 2002), Bates
(2002), World Bank (2004), which positively influences political and economic policies
as well as the quality of development outcome.
VII. Conclusion
Historic legacies have been shown to explain part of Africa's current
underdevelopment. The Wars of Liberation from colonial rules, one of the continent's
historic events, has often been identified as one of the causes of Africa's poor economic
development. However, they have not yet been investigated systematically. This paper
examines whether the way in which independence came about in Sub-Saharan African
countries, has persistent effects on the region's current economic performance. The
12
This result is also consistent with the work of Miguel and Roland (2005), Davis and Weinstein
(2002), who also find complete economic recovery in a two-decade period after conflict has ended
33
econometric results suggest that although the circumstances that surrounded
independence in Sub-Saharan Africa have adverse impacts on economic development
before and after independence, the effects are not persistent in today's economic
performance in the region. In fact, countries that became independent via the Wars of
Liberation from colonial rules tend to perform, on average, similarly like their
counterparts with peaceful independence. I use a convex combination of countries'
economic characteristics unaffected by conflict during their transition to independence, to
produce the counterfactual GDP per capita of those that experienced conflict in their
transition toward independence. I compute the effects as the difference of the two
outcomes. This technique is known as the Synthetic Control Method. I perform a
randomization test to show the validity of the estimates. I do a robustness check to
demonstrate that the estimates dynamic are robust to the omission of each individual
synthetic control.
34
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