Thy Neighbor's Curse: Regional Instability and Economic Growth
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Journal of Economic Growth, 2: 279–304 (September 1997)
°
c 1997 Kluwer Academic Publishers, Boston.
Thy Neighbor’s Curse: Regional Instability
and Economic Growth
ALBERTO ADES
Goldman, Sachs & Co.
HAK B. CHUA
Malaysian Institute of Management
We show that regional instability, defined as political instability in neighboring countries, has a strong negative
effect on a country’s economic performance. The magnitude of this negative externality is similar in size to that
of an equivalent increase in domestic political instability. We also identify two main channels through which
regional instability lowers economic performance. First, regional instability disrupts trade flows. The shares
of merchandise and manufactured trade are lower in countries with high regional instability. Second, regional
instability leads to increased military outlays. Defense expenditures are higher in countries with high regional
instability. In contrast, the share of government expenditures allocated to education is lower in countries with
politically unstable neighbors. Our results suggest the existence of negative spillovers among politically unstable
neighboring countries. These adverse regional influences should be taken into account when projecting the future
economic performance of countries. The evidence presented also suggests that the gains from reducing regional
instability extend far beyond the welfare of the country experiencing political unrest. Policies directed at settling
current territorial disputes in a peaceful and orderly manner can have large beneficial effects for parties not directly
involved in the conflict.
Keywords: economic growth, political instability
JEL classification: O40
1. Introduction
One of the few undisputed stylized facts in the literature on the political economy of growth is
the strong interconnection between economic performance and political stability. Using the
number of political assassinations and the frequency of revolutions and coups as proxies for
political instability, Barro (1991) finds that these political variables are negatively correlated
with growth in a cross-section of countries. Using other measures of political instability,
Alesina et al. (1996) reach essentially the same conclusion. In a related line of research,
Cukierman et al. (1992) find that higher instability is positively correlated with inflation;
Ozler and Tabellini (1991) show that more instability leads to an increase in external debt
in developing countries; and Benhabib and Spiegel (1992) and Alesina and Perotti (1993)
find that sociopolitical instability reduces investment.
One theoretical reason underlying all these associations found in the data is based on the
negative incentive effects of political instability on most economic decisions. As far as280 ALBERTO ADES AND HAK B. CHUA growth is concerned, political instability introduces uncertainty into the economic environ- ment and might reduce, therefore, the incentives to save and invest by risk-averse economic agents.1 Another view sees political instability as an adverse influence on property rights, as argued by Barro (1991). But political instability can also have even more direct effects on economic outcomes. Major institutional disruptions and most civil wars often lead to the emigration of the most qualified section of the labor force, and the destruction of roads, ports, and other forms of public infrastructure necessary for production or trade with the outside world. There is no evidence to believe, however, that these disruptions are only suffered by the country experiencing political unrest. It is often the case that these effects spillover to other countries in the same region or even further. One classic example is offered by a group of African countries during the early 1980s. Being a landlocked country in Africa, Malawi began facing external transportation problems as civil unrest erupted in neighboring Mozambique. By the mid-1980s, the main external trading routes through Mozambique were fully closed. Shipping had to be rechanneled through Durban in South Africa, which was three to four times the distance of Malawi’s earlier trading routes. Already weakened by a persistent drought, the Malawian economy also had to deal with an influx of refugees from Mozambique, and a worsening of the security situation along the borders and external transportation routes (World Bank, 1992, pp. 322–326). Rwanda offered a similar case, having suffered the effects of political turmoil in neigh- boring Uganda and Tanzania. These countries had been engaged in a war from the second half of the 1970s until about 1985. The war had destroyed much of the transport networks, depleted the vehicle fleet, and spoiled the once-productive agricultural lands in Uganda. Rwanda, a small landlocked country located in a squeeze between Uganda and Tanzania, was invaded by Rwandese Tutsi refugees coming from Uganda. The government of Rwanda was able to repel the invasion, but sporadic fighting continued along the border. The trans- portation, trade, and tourism sectors in Rwanda were severely affected. On the fiscal front, the military situation required a substantial increase in security-related outlays, as reflected by the surge in imports of military equipment and corresponding declines in capital outlays in the national accounts (World Bank, 1992, pp. 465–470, 506–511, 542–548). Political instability in Uganda and Tanzania also spilled over to landlocked Burundi. Transportation costs from Burundi to the nearest Indian Ocean ports of Mombasa in Kenya and Dar Es Salaam in Tanzania remained high. Passage through these neighboring coun- tries was not reliable, with occasional disputes causing serious domestic shortages and disruptions in trade flows (World Bank, 1992, pp. 79–85). In the Middle East, the Gulf Crisis between August 1990 and February 1991 offers yet another example of how regional political shocks affect neighboring countries not directly involved in the conflict. The best example is provided by Jordan, who lost export markets in Iraq, Saudi Arabia, and Kuwait, and remittances from Jordanian workers in Kuwait and Saudi Arabia. Returning Jordanian workers required higher expenditures for education and health, worsening the fiscal deficit. Tourism and transport sector income fell. Gross domestic product declined by about 0.6 percent in 1990, a sharp reversal to the 8 percent growth rate projected before the dawn of the crisis (World Bank, 1992, pp. 286–291).2
THY NEIGHBOR’S CURSE: REGIONAL INSTABILITY AND ECONOMIC GROWTH 281 This article examines empirically the effects that regional political instability, defined as political instability in neighboring countries, has on economic performance in a cross- section of ninety-eight countries from 1960 to 1985. We also consider several possible channels whereby regional political instability might affect growth. We find that regional instability has strong negative effects on a country’s steady state per capita income. The magnitude of this negative externality is of similar size to the effect of an identical increase in domestic political instability. In addition, we find that there are two main channels through which regional instability hurts economic performance. First, regional instability disrupts trade flows. The shares of merchandise and manufactured trade are substantially lower in countries with high regional instability. Second, regional instability induces increased military outlays. This article should be seen as an effort to integrate two lines of theoretical and empirical work that have developed in recent years. First, there is the literature that examines the links between domestic political instability and economic growth. While there seems to be a general agreement that domestic political instability is negatively correlated with growth in per capita income, the main line of causality is still a matter of debate. Indeed, most of these studies have to deal with the issue of joint endogeneity of political instability and economic growth. The search for valid instruments has not proved an all too easy task. Because we focus on the effects of growth performance of regional as opposed to domestic instability, our findings are less subject to the criticisms mentioned above. From a theoretical point of view, it is easier to believe in the exogeneity of regional as opposed to domestic political instability. In this sense, our estimates can be seen as a “cleaner” measure of the effects of political instability on economic performance. A second line of research that we integrate is the one that stresses the importance of spillovers arising from geographical proximity. Rauch (1993) provides evidence of ex- ternalities from geographic concentration of human capital in cities; Glaeser et al. (1992) show that knowledge spillovers are strongest between, rather than within, industries in the same city; and Ades and Glaeser (1994) show that railroad density in nearby states had a positive influence on urbanization and manufacturing growth rates in the United States during the second half of the nineteenth century. Chua (1993) performs related tests in a cross-country setting and provides empirical support for the proposition that a country’s growth rate depends not only on domestic investment but also on the investment of its neigh- boring countries. This is taken as evidence of regional spillovers from human and physical capital between countries located in common geographical regions.3 This article essentially integrates both lines of research. We argue and show that regional political instability has strong negative effects on the economic performance of countries in the region. The structure of the article is as follows. Section 2 provides a brief description of the data set and summary statistics. Section 3 lays out the basic cross-country regressions relating regional instability to economic performance. Section 4 describes the two main channels through which regional instability affects economic performance. Section 4.1 examines the impact of regional instability on trade flows. Section 4.2 examines the impact of regional instability on defense spending. In Section 4.3, we decompose the total effect of regional instability into the direct, and the indirect effects operating through reduced trade and increased military outlays. Section 5 summarizes our main findings and concludes.
282 ALBERTO ADES AND HAK B. CHUA 2. The Data 2.1. Construction of the Data Set Our data set was constructed from several different sources. The data on population, per capita GDP, primary and secondary school enrollment rates, government consumption, government defense expenditures, the share of investment in GDP, the number of revolutions and coups, and the number of political assassinations are all from Barro and Wolf (1989). The data on merchandise and manufactured trade are from the World Bank’s World Tables. Also, we use data on land area, which were taken from the 1986 edition of the FAO Production Yearbook. The definitions of these variables can be found in Appendix A. The full data set covers 118 countries over the sample period 1960 to 1985. Due to missing observations, and unless otherwise specified, we restrict the empirical analysis to a sample of ninety-eight countries, which is the same large sample used in Barro (1991). The full list of 118 countries is shown in Appendix B. 2.2. An Index of Regional Instability Barro (1991) uses the number of revolutions and coups per year averaged over 1960 to 1985 (or subsample) as an index of political instability. This variable (abbreviated as REV) is interpreted as an adverse influence on property rights, which therefore affects investment and economic performance negatively. We construct from this variable several indexes of regional political instability. Our theory of regions emphasizes the importance of geographical proximity. This is the same approach as the one followed by De Long and Summers (1991), who focus on distance between capital cities, and Glaeser et al. (1992), who look at intellectual spillovers between industries in the same city. Of course, regions could be defined on other counts. Potential candidates might include language, colonial linkages, a customs union, or even a common currency area. There are certainly many historical examples where the effects of political instability have extended far beyond geographically proximate countries. The cases of the Spanish Civil War (which led to a large migration of political refugees to Latin America) and of Nazi Germany (which led to a mass emigration of Jewish intellectuals to the United States) come to mind immediately.4 We believe, however, that these spillovers are most pronounced among countries within a same geographical region. The classification that we use for a region is that of bordering countries, as in Chua (1993). Such a definition prevents any subjective selectivity into what countries a certain region should include.5 Other classifications that emphasize distance between capital cities as in De Long and Summers (1991) or that include in a country’s region its main trading partners as in Elliot (1993) have received weak or no support from the data. For each country, our main regional instability index is constructed by averaging the number of revolutions and coups per year of all the countries in the defined region and in our data set of 118 countries, with the exclusion of the country itself. A country’s relevant
THY NEIGHBOR’S CURSE: REGIONAL INSTABILITY AND ECONOMIC GROWTH 283
Table 1. Countries ranked in terms of domestic instability and regional instability.
Highest REV Index Highest REGREV Index
Bolivia 1.15 Chile (Bolivia, Argentina) 0.78
Mozambique 1.00 Paraguay (Bolivia, Argentina) 0.73
Argentina .092 Swaziland (Mozambique) 0.52
Syria 0.79 Uruguay (Argentina) 0.52
Iraq 0.78 Madagascar (Mozambique) 0.50
Sudan 0.74 Turkey (Iraq, Syria) 0.50
Angola 0.73 Togo (Benin, Ghana) 0.50
Ethiopia 0.73 CAR (Sudan, Chad, Zaire) 0.49
Chad 0.67 Kenya (Sudan, Ethiopia, Uganda) 0.49
Bangladesh 0.62 Zambia (Mozambique, Angola, Zaire) 0.48
Note: Countries in parentheses represent neighboring countries with high domestic political
instability.
region is defined to be its neighboring countries. In other words, the formula that we use to
calculate country j’s regional instability is
1X n
REGREVj = REVi ,
n i=1
where n is the number of neighboring countries bordering the respective country and R E Vi
is the number of revolutions and coups per year for neighboring country i from 1960 to
1985 or subsample. Unless otherwise specified, all our references to regional instability
apply to this index.
We also constructed various other indices of regional instability to examine whether our
results are sensitive to the proxy used. Namely, we used the sum (REGREVSUM), the
maximum (REGREVMAX), and GDP and population-weighted averages of REV (WRE-
GREVY and WREGREVN, respectively) in neighboring countries as measures of regional
political instability.
2.3. Description of the Data
Table 1 shows the ten countries with highest values of domestic and regional instability.
Most of these are either in Africa or Latin America, and none of them belongs to the OECD.
Bolivia, a country whose average length of presidential tenure during the last century does
not exceed twelve months, ranks highest in terms of domestic instability. On the other
hand, a relatively stable country like Chile ranks first in terms of regional instability due to
its proximity to Argentina and Bolivia.
It is interesting to note that the two sets of countries share no members in common. More-
over, as Table 2 shows, many countries have large differences between their domestic and
corresponding regional levels of political instability. Paraguay, a country of longlasting
presidents, shows the highest difference between its regional and domestic instability in-
dices. The opposite case is that of Mozambique, which ranks second in terms of domestic
instability but is bordered by relatively stable neighbors.284 ALBERTO ADES AND HAK B. CHUA Table 2. Countries ranked in terms of difference between domestic and regional instability. Low Domestic Instability High Domestic Instability High Regional Instability Low Regional Instability Paraguay (Bolivia, Argentina) 0.65 Mozambique (South Africa, Malawi, Zambia) −0.89 Chile (Bolivia, Argentina) 0.59 Bolivia (Paraguay, Brazil) −0.83 Uruguay (Argentina) 0.52 Argentina (Uruguay, Paraguay) −0.61 Swaziland (Mozambique) 0.46 Iraq (Kuwait, Saudi) −0.50 Kenya (Sudan, Ethiopia, Uganda) 0.44 Syria (Israel, Jordan) −.048 Cyprus (Turkey, Syria) 0.44 Surinam (Guyana, Brazil) −0.42 Zambia (Mozambique, Angola, Zaire) 0.43 Ecuador (Columbia) −0.41 Madagascar (Mozambique) 0.42 Korea (Japan) −0.40 Malawi (Mozambique) 0.42 Ethiopia (Kenya) −0.38 Kuwait 0.40 Tanzania (Malawi, Kenya, Zambia) −0.36 Note: The index is constructed by simply calculating the difference between the regional instability index REGREV and the domestic instability index REV. Countries in parentheses represent the influential neigh- boring countries. 3. Regional Instability and Economic Growth In this section, we describe our basic regression results concerning the effects of regional instability and other variables on per capita GDP growth. Except for the first regression of Table 5b (which concerns nonisland countries only), all of our results are always based on the same ninety-eight-country sample. Table 3 shows summary statistics for our main economic variables and the indices of domestic and regional political instability. Table 4 shows correlations for these same vari- ables. In the raw data, all of our indicators of regional instability are negatively correlated with growth, initial GDP, schooling, and investment. On the other hand, the share of de- fense expenditures in GDP is positively correlated with all REGREV, REGREVMAX, and REGREVSUM, while its correlation with domestic instability is almost nonexistent. Also, all the indicators of regional instability are strongly correlated among themselves. One of the most striking statistics in Table 4 is the fairly low correlation between the index of domestic instability and the various indexes of regional instability. This low cor- relation between domestic and regional instability, which is also highlighted by Figure 1, suggests that the index of regional instability can provide additional information not previ- ously captured by the domestic instability variable. An additional advantage is that from a theoretical point of view it is quite reasonable to treat regional instability as an exogenous shock. The exogenous nature of this variable justifies putting it on the right-hand side of a regression with less argument about causality. This advantage stands in contrast with the endogeneity problems that Alesina and Perotti (1993) try to disentangle in interpreting the negative correlation between domestic political instability and investment. Table 5a summarizes the importance of regional instability as a predictor of economic growth in cross-section growth regressions with the standard benchmark variables as in Barro (1991) (see Appendix A for a definition of these variables). Regression (1) reports the benchmark regression with the index of domestic political instability included (REV). Regression (2) adds the index of regional instability to the first regression. The coefficient
THY NEIGHBOR’S CURSE: REGIONAL INSTABILITY AND ECONOMIC GROWTH 285
Table 3. Summary statistics.
Standard
Variable Mean Deviation Maximum Minimum
GR6085 0.022 0.019 0.074 −0.017
GDP60 1.917 1.813 7.38 0.208
PRIM60 0.785 0.312 1.44 0.05
INV 0.190 0.078 0.369 0.042
DEF 0.030 0.034 0.275 0.000
REV 0.217 0.253 1.150 0.000
REGREV 0.235 0.183 0.783 0.000
REGREVSUM 0.782 0.845 3.310 0.000
REGREVMAX 0.419 0.359 1.150 0.000
Note: REV is the number of revolutions and coups per year averaged
over the period 1960 to 1985. REGREV is the average of REV in
neighboring countries. REGREVSUM and REGREVMAX correspond
to the sum and maximum of the REV variable in neighboring countries.
Table 4. Correlation matrix.
GR6085 GDP60 PRIM60 INV DEF REV REGREV REGREVMAX REGREVSUM
GR6085 1.00
GDP60 0.09 1.00
PRIM60 0.46 0.65 1.0
INV 0.52 0.52 0.66 1.0
DEF 0.02 0.00 -0.014 0.16 1.0
REV −0.38 −0.37 −0.37 −0.45 −0.03 1.0
REGREV −0.35 −0.50 −0.47 −0.30 0.18 0.27 1.0
REGREVMAX −0.36 −0.42 −0.34 −0.26 0.25 0.34 0.89 1.0
REGREVSUM −0.42 −0.42 −0.39 −0.27 0.20 0.35 0.81 0.85 1.0
on the index of regional instability (REGREV) is about −0.023 (s.e. = 0.008), indicating
that an increase of one per decade in the average annual number of revolutions and coups in
neighboring countries reduces steady state per capita GDP 14.4%.6 One interesting result is
the fact that the addition of the regional instability index REGREV has almost no effect on
the magnitude of the coefficient on the domestic instability index REV. This is consistent
with the observed low correlation between the index of domestic and regional instability,
suggesting that the regional instability index adds new information not previously captured
by the domestic instability index. Regression (3) reports the results obtained when the
data are weighted by the levels of per capita GDP in 1960. The coefficient on REGREV
becomes somewhat stronger.7 Column 4 reports the regression where the standard errors
for the coefficients are based on White’s (1980) heteroskedasticity-consistent covariance
matrix. As shown in the table, the standard errors show almost no difference with those
obtained by standard OLS estimation. Figure 2 plots the relationship between domestic
instability and growth. In Figure 3, we plot the relationship between regional instability
and economic growth, where both variables have been orthogonalized with respect to the
basic set of controls that is contained in regression (2). The negative correlation between286 ALBERTO ADES AND HAK B. CHUA Figure 1. Domestic vs. regional instability. regional instability and economic growth is just as apparent from the plot, which shows that the negative relationship is not simply driven by a few outliers. Column (5) in Table 5b shows that similar results hold when island countries are excluded from the sample. The regression of column (6) adds the average share of investment in GDP and secondary schooling rates in neighboring countries to our basic regression. The rationale for such inclusion is the possibility that regional instability is actually proxying for poor economic performance in neighboring countries. When these variables are included in the regression, the coefficient on the index of regional instability falls in size, suggesting that a portion of REGREV proxies for neighbors poor economic performance. Regression (7) shows that the index of regional instability also declines but remains significant when continent dummies are controlled for. Finally, to test whether the results are driven by a few outliers, regression (8) drops all the observations for which the residual in regression (2) exceeds two standard errors. The results indicate that the results of regression (2) are not driven by a few outliers. Table 6 includes other measures of regional instability in the standard growth regression. The results show that political instability in neighboring countries are an important factor and are robust to the use of different proxies for regional instability. As shown in columns (9) and (10) in Table 6, the coefficients on the sum and maximum number of revolutions
THY NEIGHBOR’S CURSE: REGIONAL INSTABILITY AND ECONOMIC GROWTH 287
Table 5a. Regional instability and economic growth.
Dependent Variable: (3) (4)
Growth of Real (1) (2) Weighted White-
GDP Per Capita, 1960–1985 OLS OLS GDP60 Correct
Obs 98 98 98 98
CONST 0.0170 0.0228 0.0207 0.0228
(0.0068) (0.0069) (0.0070) (0.0076)
LGDP60 −0.0150 −0.0159 −0.0131 −0.0159
(0.0028) (0.0027) (0.0030) (0.0028)
SEC60 0.0224 0.0175 0.0176 0.0175
(0.0109) (0.0107) (0.0158) (0.0099)
PRIM60 0.0324 0.0324 0.0331 0.0324
(0.0069) (0.0067) (0.0069) (0.0071)
PPIDEV −0.0176 −0.0183 −0.0160 −0.0183
(0.0057) (0.0055) (0.00396) (0.0050)
Gc /Y −0.1310 −0.1245 −0.0867 −0.1245
(0.0291) (0.0282) (0.0290) (0.029)
REV −0.0225 −0.0206 −0.0196 −0.0206
(0.0068) (0.0066) (0.0064) (0.0063)
REGREV −0.0230 −0.0349 −0.0230
(0.0083) (0.00921) (0.0088)
Adjusted R2 0.48 0.52 0.52 0.52
s.e.e. 0.0133 0.0128 0.0139 0.0128
Note: Standard errors are in parentheses. The regression in column (3) is based
on observations weighted by the levels of per capita GDP in 1960. Standard errors
for the regression in column (4) are based on White’s (1980) heteroskedasticity-
consistent covariance matrix.
and coups in neighboring countries are all negative and significant. An increase of 1 per
decade in REGREVSUM reduces steady state income by 2.9 percent, while an increase of
1 per decade in REGREVMAX reduces it by 7.5 percent.
The last two regressions in Table 6 report the results when the levels of domestic political
instability of neighboring countries are weighted by their shares of regional GDP and
population. The rationale for such procedure is easy to visualize and can be illustrated
with one simple example: one would expect the impact of increased political instability
in Canada on the United States to be much more significant than an equivalent increase of
political instability in Mexico. Columns (11) and (12) in Table 6 show that using weights
to calculate regional averages of political instability does not affect the basic result and has
no significant impact on the size of the coefficients.
To summarize the results of this section, we find that after controlling for the standard
determinants of growth, regional instability has a negative and sizable effect on steady
state per capita GDP in a cross-section of ninety-eight countries from 1960 to 1985. The288 ALBERTO ADES AND HAK B. CHUA Figure 2. Domestic instability vs. GDP growth 1960–1985. negative effect of regional instability on steady-state income is also robust to the inclusion of regional dummies, but the size drops when we control for measures of regional economic performance such as the average regional share of investment in GDP and the average regional secondary school enrollment rate. The effect of regional instability on steady-state income seems to be robust to the use of several different measures of domestic and regional political instability and do not seem to be driven by a few outliers. 4. Channels Through Which Regional Instability Lowers Growth Section 3 showed a strong negative influence of regional instability on steady-state per capita GDP. In this section, we examine some of the potential channels through which political instability in neighboring countries can affect economic performance. Two natural explanations are examined using cross-section data. The first hypothesis that we test is the potential disruption that regional instability can have on international trade flows. The second hypothesis is the impact that regional instability can have on forcing increases in domestic military expenditures. The redirection of resources toward defense and border security might come at the expense of otherwise more productive activities.
THY NEIGHBOR’S CURSE: REGIONAL INSTABILITY AND ECONOMIC GROWTH 289 Figure 3. Regional instability and economic growth. 4.1. Regional Instability and Trade At least since Adam Smith, economists have emphasized the importance of international trade for economic development. Access to international markets can enhance productivity and encourage specialization. Some casual evidence seems to reveal, however, that political instability in neighboring countries might block external trading routes and destroy transport networks. Passage through transit routes becomes unreliable, especially in situations where governments have lost control and lawlessness prevail. Such disruptions are especially acute for landlocked countries, which rely on transit routes through neighboring countries for coastal access. As we saw, this was the case for Malawi when civil unrest broke out in neighboring Mozambique, forcing shipping routes to be rechanneled through Durban in South Africa. Landlocked Burundi also faced high transports costs and unreliable passage through Tanzania in the middle of its political instability. Another illuminating story is that of Slovenia faced with a war between its neighbors Croatia and Serbia that erupted during the second half of 1992. Slovenia, although abstain- ing from the war, suffered a 15 percent drop in GNP between the outbreak of hostilities and the end of the year. Trade with the rest of former Yugoslavia collapsed, falling by 32 percent during the same period (Economist Intelligence Unit, 1992, Report 1, p. 26).
290 ALBERTO ADES AND HAK B. CHUA
Table 5b. Regional instability and economic growth (continued).
Dependent Variable: (5)
Growth of Real Nonisland (6) (7) (8)
GDP Per Capita, 1960–1985 Sample Full Sample Full Sample Robust
Obs 83 98 98 94
CONST 0.0250 0.0062 0.0275 0.0187
(0.0071) (0.0079) (0.0068) (0.0064)
LGDP60 −0.0144 −0.0177 −0.0152 −0.0172
(0.0028) (0.0027) (0.0028) (0.0025)
SEC60 0.0130 0.0093 0.0052 0.0189
(0.0120) (0.0103) (0.0109) (0.0095)
PRIM60 0.0292 0.0271 0.0312 0.0367
(0.0069) (0.0064) (0.0065) (0.0062)
PPIDEV −0.0186 −0.0183 −0.0169 −0.0173
(0.0052) (0.0052) (0.0052) (0.0049)
Gc /Y −0.1073 −0.1168 −0.1005 −0.1233
(0.0289) (0.0265) (0.0276) (0.0254)
REV −0.0207 −0.0196 −0.0195 −0.0182
(0.0064) (0.0062) (0.0065) (0.0060)
REGREV −0.0264 −0.0124 −0.0161 −0.0237
(0.0088) (0.0082) (0.0082) (0.0076)
REG SEC 0.0152
(0.0121)
REG INV 0.0784
(0.0340)
LAT AMER DUMMY −0.0092
(0.0037)
AFRICA DUMMY −0.0133
(0.0040)
Adjusted R2 0.52 0.58 0.57 0.60
s.e.e. 0.0121 0.0120 0.0121 0.0114
Note: Standard errors are in parentheses. Regression (8) drops all observations for which
the residual in regression (2) is larger than two standard errors.
Disruptions of the normal channels of trade can have severe effects on economic perfor-
mance. First, the shortage of food and basic necessities such as energy supplies can have
strong negative effects on the economy. To the extent that these basic necessities fit the
minimal input requirements of Leontief-type production functions, severe shortages in one
such necessity can cause a severe disruption to the whole production process.
Second, shortages or uncertainties about the availability of intermediate inputs in the
middle of these trade disruptions are also likely to force a consolidation of production intoTHY NEIGHBOR’S CURSE: REGIONAL INSTABILITY AND ECONOMIC GROWTH 291
Table 6. Other indexes of regional instability.
Dependent Variable:
Growth of Real
GDP Per Capita 1960–1985 (9) (10) (11) (12)
Observations 98 98 98 98
CONST 0.0202 0.0217 0.0237 0.0241
(0.0068) (0.0069) (0.0067) (0.0068)
LGDP60 −0.0151 −0.0153 −0.0157 −0.0160
(0.0027) (0.0027) (0.0027) (0.0027)
SEC60 0.0172 0.0158 0.0181 0.0182
(0.0108) (0.0108) (0.0103) (0.0104)
PRIM60 0.0312 0.0330 0.0306 0.0307
(0.0067) (0.0067) (0.0065) (0.0066)
PPIDEV −0.0157 −0.0183 −0.0162 −0.0168
(0.0056) (0.0055) (0.0054) (0.0054)
Gc /Y −0.1219 −0.1258 −0.1214 −0.1239
(0.0286) (0.0282) (0.0275) (0.0276)
REV −0.0198 −0.0187 −0.0224 −0.0224
(0.0067) (0.0067) (0.0064) (0.0064)
REGREVSUM −0.0045
(0.0018)
REGREVMAX −0.0114
(0.0042)
WREGREVY -0.0239
(0.0067)
WREGREVN -0.0247
(0.0073)
Adjusted R2 0.51 0.52 0.54 0.54
s.e.e. 0.0129 0.0129 0.0125 0.0126
Note: Standard errors are in parentheses. In regression (11), instability of neigh-
bors is weighted by their share of regional GDP. In regression (12), instability
of neighbors is weighted by their share in regional population.
simple processes requiring less intermediate inputs and involving less specialization. This
kind of consolidation is natural if one considers the multiplicative O-Ring production func-
tion that Kremer (1993) proposes. Bottlenecks and trade restrictions become quantitatively
important with production functions that emphasize the importance of complementarities
between inputs. Uncertainties over input supplies could force firms to choose technologies
with simpler and smaller number of tasks.
Third, long phases of regional instability can also mean a severe disruption in communi-
cation channels with the rest of the world as communication networks become unreliable292 ALBERTO ADES AND HAK B. CHUA
Table 7. Regional instability and trade.
Average Share of Average Share of
Merchandise Trade in Manufactured Trade in
GDP, 1960–1985 GDP, 1960–1985
Dependent Variable (13) (14) (15) (16)
Obs 92 92 92 92
CONST 0.5360 0.6613 0.2195 0.2438
(0.1413) (0.1769) (0.0740) (0.0933)
LGDP60 −0.0934 −0.0905 −0.0418 −0.0523
(0.0689) (0.0728) (0.0360) (0.0384)
SEC60 −0.2743 −0.0358 −0.0377 0.0268)
(0.2688) (0.2741) (0.1407) (0.1446)
PRIM60 0.3160 0.3112 0.1529 0.1285
(0.1656) (0.1636) (0.0866) (0.0863)
REV −0.4191 −0.3936 −0.2106 −0.1974
(0.1529) (0.1470) (0.0800) (0.0776)
REGREV −0.5070 −0.5513 −0.2470 −0.2240
(0.2069) (0.2108) (0.1083) (0.1112)
LAND AREA −0.00003 −0.00002
(0.00002) (0.00001)
POP 1960 −0.0016 −0.0007
(0.0007) (0.0004)
REG INV −0.4536 −0.2191
(0.8670) (0.4573)
REG SEC −0.0938 0.1269
(0.3013) (0.1589)
Adjusted R2 0.14 0.21 0.15 0.21
s.e.e. 0.3111 0.2979 0.1628 0.1571
Note: Standard errors are in parentheses.
and transport costs become prohibitive. To the extent that growth depends on the facilitation
and exchange between entrepreneurs in different countries, regional instability can ham-
per the diffusion process of such knowledge transfers. We examine the negative influence
that regional instability has on trade by looking at cross-section data on merchandise and
manufactured trade flows.
The first regression in Table 7 reports results when the share of merchandise trade in
GDP is regressed on domestic and regional instability and other standard controls. Due
to missing observations, all the regressions in this section correspond to a subsample of
ninety-two countries. Both the domestic and regional instability indices enter negatively
and significantly. In addition, the coefficient on regional instability is quite large, indicating
that a rise in the average number of revolutions and coups by one in the region over a decadeTHY NEIGHBOR’S CURSE: REGIONAL INSTABILITY AND ECONOMIC GROWTH 293 (an increase in REGREV by 0.1) reduces the share of merchandise trade in GDP by about 5.1 percentage points. In comparison, a rise in the domestic number of revolutions and coups by one over a decade reduces the share of merchandise trade by about 4.2 percentage points. Following Pritchett (1991), regression (14) in Table 7 includes population in 1960 and land area as controls. We also add regional investment and schooling averages. None of these other variables seem to affect the size of the coefficient on regional instability or its significance. Regressions (15) and (16) in Table 7 reproduce (13) and (14) but substituting the share of merchandise trade for the share of manufactured trade as dependent variable. Again, both domestic and regional instability have negative and significant effects on trade. A comparison of the corresponding size of the coefficients on regional instability for the first and third regressions in Table 7 shows that an increase of REGREV by 1 per decade reduces the shares of merchandise and manufactured trade by about 0.17 standard deviations in both cases.8 4.2. Regional Instability and Defense Spending We investigate in this section the possible impact of regional instability on defense spending. Regional crises often force a substantial increase in military outlays to prevent the fighting from spreading across political boundaries and avoid the usual avalanche of refugees that comes with civil wars in neighboring countries. The most obvious examples of these effects during the period under examination were in the Middle East, where countries like Israel, Iran, Jordan, and Syria devoted more than 7 percent of GDP to defense. In Africa, during the late 1970s, the government of Rwanda raised security outlays when fighting between Uganda and Tanzania threatened to spread across the borders and refugees started flowing in from Uganda. The cross-sectional empirical literature relating military expenditures to economic growth has not uncovered a conclusive relationship between these two variables. The earlier work includes Benoit (1978) who found that countries with heavy defense spending generally had the highest growth rates of per capita GDP. Numerous further studies have been undertaken since then, mainly using cross-country data, but there seems to be no consensus on the impact of defense spending on economic performance. While we find that the share of defense spending in GDP enters negatively in a standard growth regression, it is not significant at the 5 percent level. More recently, Knight, Loayza, and Villanueva (1993) combine cross-section with time- series data and show that military spending has a significantly negative effect on growth. They argue that military expenditures not only crowd out private investment but also “create external diseconomies and misallocation of resources which affect the growth performance of the economy.” Defense expenditures are necessary for the protection of national property rights as Thompson (1974) and Barro (1991) argue. However, to the extent that geography and adverse external threats force outlays beyond normal levels, resources must be redirected from other productive activities. Such spending might be the optimal response in the face of such shocks, but it is likely to have adverse effects on investment and growth. We show
294 ALBERTO ADES AND HAK B. CHUA
Table 8. Regional instability and defense spending.
Share of
Education in
Government
Share of Defense Spending in GDP Consumption
Dependent
Variable (17) (18) (19) (20)
Obs 98 98 98 98
CONST 0.0177 0.0047 0.0101 0.3510
(0.0148) (0.0131) (0.0127) (0.0440)
LGDP60 −0.0012 0.0016 −0.0017 0.0904
(0.0068) (0.0059) (0.0060) (0.0202)
SEC60 0.0456 0.0343 0.0406 −0.1264
(0.0273) (0.0239) (0.0245) (0.0813)
PRIM60 −0.0111 −0.0099 −0.0147 −0.0414
(0.0170) (0.0149) (0.0150) (0.0507)
REV −0.0064 0.0015 −0.0066 −0.0396
(0.0161) (0.0141) (0.0145) (0.0480)
REGREV 0.0495 0.0330 −0.1532
(0.0212) (0.0188) (0.0632)
REGDEF 0.5109
(0.0934)
REGREVMAX 0.0190
(0.0100)
REGDEFMAX 0.3421
(0.0745)
Adjusted R2 0.02 0.26 0.24 0.35
s.e.e. 0.0331 0.0289 0.0292 0.0986
Note: Standard errors are in parentheses.
in this section that regional instability has a strong positive influence on military spending,
even after controlling for the share of military spending by neighboring countries.
Table 8 summarizes the main empirical results linking defense spending to political
instability. First, we find that domestic political instability does not have a significant effect
on defense spending. Regression (17) shows that the coefficient on domestic instability is
negative, but insignificantly different from zero. Second, regional instability has a strong
positive impact on defense spending. These results seem to indicate that military outlays
respond more to outside rather than to inside threats. Regression (18) shows that the share
of defense spending in GDP is also strongly correlated with the corresponding share in
neighboring countries (REGDEF). Countries devoting large resources to military buildup
are likely to force a similar response among its neighbors, a reaction necessary to deterTHY NEIGHBOR’S CURSE: REGIONAL INSTABILITY AND ECONOMIC GROWTH 295 potential future military aggressions. Examples of this “ratchetting effect” abound among countries in the Middle East, between North and South Korea, and among Argentina, Chile, and Brazil during the 1970s and 1980s. Even after controlling for this effect, regional instability still shows a positive and significant impact on defense spending. That is, political crisis in neighboring countries that are not deliberate acts of aggression have a strong and positive impact on domestic defense spending. The magnitude of this coefficient suggests that an increase in the number of revolutions and coups by one over a decade in neighboring countries increases the share of defense spending in GDP by 0.3 percentage points. Regression (19) in Table 8 reports regression results where instead of regional averages the maximum is used as a proxy for regional instability and as the measure of regional military spending. The results are similar to those of the first two regressions. The last regression in Table 8 reports the results that we obtained when we regressed our standard variables on the share of total government consumption allocated to education. We found that regional instability has a negative impact on the share of government consumption allocated to education. By contrast, the domestic instability has no significant impact on educational spending in our cross-section of countries. 4.3. Interactions Between Regional Instability, Trade, and Defense The previous two subsections show that, in a cross-section of countries, regional instability is negatively correlated with the share of trade in GDP and positively correlated with the share of defense expenditures in GDP. In this section, we decompose the reduced-form effects of regional instability on growth into (i) direct effects and (ii) indirect effects operating through the correlation between regional instability and the shares of trade and defense spending in GDP.9 The first regression in Table 9 shows again our basic growth regression, now for a subsample of ninety-two countries.10 Regression (22) adds the shares of merchandise trade and defense expenditures in GDP to that regression. The size of the coefficient on regional instability is slightly smaller than the corresponding one in regression (2) once we control for trade and defense spending. The last two columns in the table show the regressions of the shares of merchandise trade and defense spending in GDP on our standard controls and on regional instability. The results in regression (24) differ marginally from those in regression (17) because of the slightly smaller sample used here. Table 10 shows the decomposition of the total effect of regional instability on steady- state per capita income into the direct and the indirect effects. The main indirect effect of regional instability on growth operates through reduced trade flows, accounting for 22.7 percent of the total effect. Increased defense outlays are quite small in size, accounting for 2.3 percent of the total effect. The remaining direct effect is, by far, the strongest of all and accounts for 75.0 percent of the total effect. This suggests that regional instability has also other important negative effects on growth not captured by the channels that we suggest.
296 ALBERTO ADES AND HAK B. CHUA
Table 9. Regressions of growth, trade, and defense.
Share of
Merchandise Share of
Per Capita GDP Growth Trade Defense
Dependent Variable (21) (22) (23) (24)
Obs 92 92 92 92
CONST 0.0203 0.0143 0.5360 0.0158
(0.0069) (0.0071) (0.1413) (0.0153)
LGDP60 −0.0166 −0.0155 −0.0934 −0.0014
(0.0028) (0.0028) (0.0689) (0.0075)
SEC60 0.0187 0.0230 −02743 0.0464
(0.0109) (0.0108) (0.2688) (0.0292)
PRIM60 0.0353 0.0310 0.3159 −0.0098
(0.0068) (0.0067) (0.1656) (0.0179)
PPIDEV −0.0180 0.0162
(0.0054) (0.0052)
Gc /Y −0.1210 −0.1263
(0.0277) (0.0270)
REV −0.0189 −0.0146 −0.4191 −0.0068
(0.0065) (0.0064) (0.1529) (0.0166)
REGREV −0.0276 −0.0205 −0.5070 0.0519
(0.0084) (0.0087) (0.2069) (0.0224)
Share of merchandise trade 0.0123
(0.0042)
Share of defense −0.0116
(0.0392)
Adjusted R2 0.55 0.58 0.14 0.02
s.e.e. 0.0125 0.0120 0.3110 0.0337
Note: Standard errors are in parentheses.
Table 10. Decomposition of the effects of regional instability on steady
state per capita GDP.
Estimated Effects for
Regional Instability on Steady Percentage of
Channel State Per Capita GDP Total
Direct 13.2% 75.0%
Merchandise trade 4.0 22.7
Defense 0.4 2.3
Total 17.6% 100%THY NEIGHBOR’S CURSE: REGIONAL INSTABILITY AND ECONOMIC GROWTH 297
5. Conclusion
This article shows that political instability in neighboring countries has a strong adverse
effect on economic performance.We also find that this effect is quantitatively important.
The impact on steady-state income is roughly equal to that of an equivalent increase in the
index of domestic instability. Our regional instability index indicates that an increase of 1
in the average annual number of revolutions and coups in neighboring countries per decade
reduces steady-state per capita income by about 17.6 percent. The empirical results are
quite robust to various different measures of regional instability.
We also find that there are two main channels through which regional instability affects
economic performance. First, regional instability disrupts trade flows. We show that the
shares of merchandise and manufactures trade in countries with high regional instability are
lower. Second, regional instability forces increases in military outlays, even after controlling
for the defense expenditures of neighboring countries. To the extent that these increased
military outlays crowd out resources from other more productive forms of government
expenditure, they will have a negative effect on economic performance.
Our results provide evidence in favor of negative spillovers among politically unstable
neighboring countries. These adverse regional influences must be taken into account when
projecting the future economic performance of countries. The evidence presented suggests
that the gains from reducing regional instability extend beyond the welfare of the country
experiencing political unrest. In addition, policies directed at settling current territorial
disputes in a peaceful and orderly manner could have large beneficial effects for parties not
directly involved in the conflict.
Appendix A. Definition of Variables
GR6085 Average growth rate of real per capita GDP from 1960 to 1985
LGDP60 Logarithm of real per capita GDP in 1960
SEC60 Secondary school enrollment rate in 1960
PRIM60 Primary school enrollment rate in 1960
POP 1960 Population in millions in 1960
INV Average of the ratio of real domestic investment (private plus public)
to real GDP from 1960 to 1985
Gc /Y Average of the ratio of real government consumption (exclusive of
defense and education) to real GDP from 1960 to 1985.
PPIDEV Absolute value of the deviation of the PPP value of the investment
deflator (U.S. = 100) in 1960 from the sample mean
REV Number of revolutions and coups per year (1960 to 1985 or subsample)
REGREV Average number of revolutions and coups per year in neighboring coun-
tries (1960 to 1985 or subsample)298 ALBERTO ADES AND HAK B. CHUA
REGREVMAX The maximum number of revolutions and coups per year in neighboring
countries (1960 to 1985 or subsample)
REGREVSUM Sum of the number of revolutions and coups per year in neighboring
countries (1960 to 1985 or subsample)
WREGREVY Average number of revolution and coups per year in neighboring coun-
tries (1960 to 1985 or subsample) weighted by the neighbor’s share in
regional GDP in 1960
WREGREVN Average number of revolution and coups per year in neighboring coun-
tries (1960 to 1985 or subsample) weighted by the neighbor’s share in
regional population in 1960
REGDEF Average ratio of defense spending to GDP in neighboring countries
from 1960 to 1985
REGDEFMAX Maximum ratio of defense spending to GDP in neighboring countries
from 1960 to 1985
REGINV Average of INV in neighboring countries
REGSEC Average secondary school enrollment rate in neighboring countries
from 1960 to 1985
AFRICA Dummy variable for Sub-Saharan Africa
LAT AMER Dummy variable for Latin America
Appendix B. Region Classification: Bordering Countries (and Islands)
Note: Country ordering follows Barro and Wolf (1989). Data are not available for countries
in parentheses. (I) stands for “island country.”
1. Algeria Morocco, Mali, (Libya), Tunisia, Niger, Mauritania
2. Angola Zaire, Zambia, (Namibia), Congo
3. Benin Nigeria, Togo, Burkina Faso, Niger
4. Botswana South Africa, Zimbabwe, (Namibia)
5. Burundi Tanzania, Rwanda, Zaire
6. Cameroon Nigeria, Chad, Central African Republic, Congo, Gabon
7. Central African Republic Zaire, Chad, Sudan, Cameroon, Congo
8. Chad Sudan, Central African Republic, Niger, Cameroon,
(Libya), Nigeria
9. Congo Zaire, Gabon, Cameroon, Central African Republic,
Angola
10. Egypt Sudan, Israel, (Libya)
11. Ethiopia Sudan, Somalia, KenyaTHY NEIGHBOR’S CURSE: REGIONAL INSTABILITY AND ECONOMIC GROWTH 299
12. Gabon Congo, Cameroon
13. Gambia Senegal
14. Ghana Togo, Ivory Coast, Burkina Faso
15. Guinea Mali, Sierra Leonne, Ivory Coast, Liberia, Senegal, (Guinea
Bissau)
16. Ivory Coast Liberia, Ghana, Guinea, Burkina Faso, Mali
17. Kenya Uganda, Ethiopia, Tanzania, Somalia, Sudan
18. Lesotho South Africa
19. Liberia Guinea, Sierra Leonne, Ivory Coast
20. Madagascar (I) Mauritius, Mozambique
21. Malawi Mozambique, Zambia, Tanzania
22. Mali Mauritania, Algeria, Burkina, Guinea, Niger, Ivory Coast,
Senegal
23. Mauritania Mali, Senegal, Algeria, (West Sahara)
24. Mauritius (I) Madagascar
25. Morocco Algeria, (West Sahara)
26. Mozambique Malawi, Zimbabwe, Tanzania, South Africa, Zambia,
Swaziland
27. Niger Nigeria, Chad, Algeria, Mali, Burkina, Benin, (Libya)
28. Nigeria Cameroon, Niger, Benin, Chad
29. Rwanda Burundi, Zaire, Tanzania, Uganda
30. Senegal Mauritania, Gambia, Mali, Guinea, (Guinea-Bissau)
31. Sierra Leonne Guinea, Liberia
32. Somalia Ethiopia, Kenya, (Djibouti)
33. South Africa Botswana, (Namibia), Lesotho, Mozambique, Swaziland,
Zimbabwe
34. Sudan Ethiopia, Chad, Egypt, Central African Republic, Zaire,
Uganda, (Libya), Kenya
35. Swaziland South Africa, Mozambique
36. Tanzania Kenya, Mozambique, Malawi, Burundi, Uganda, Zambia,
Rwanda
37. Togo Ghana, Benin, Burkina Faso
38. Tunisia Algeria, (Libya)
39. Uganda Kenya, Zaire, Sudan, Tanzania, Rwanda
40. Zaire Angola, Congo, Zambia, Central African Republic,
Uganda, Sudan, Burundi, Rwanda
41. Zambia Zaire, Angola, Malawi, Zimbabwe, Mozambique, Tanza-
nia, (Namibia)
42. Zimbabwe Mozambique, Botswana, Zambia, South Africa
43. Bangladesh India, Burma
44. Burma Thailand, India, (Laos), Bangladesh, (China)
45. Hong Kong (I) (Taiwan), (China)
46. India Bangladesh, (China), Pakistan, Nepal, Burma, (Bhutan)
47. Iran Iraq, Pakistan, Turkey, (USSR), (Afghanistan)300 ALBERTO ADES AND HAK B. CHUA
48. Iraq Iran, Syria, Saudi Arabia, Turkey, Kuwait, Jordan
49. Israel Egypt, Jordan, Syria
50. Japan (I) South Korea, (China)
51. Jordan Saudi Arabia, Syria, Israel, Iraq
52. South Korea (North Korea), Japan
53. Kuwait Iraq, Saudi Arabia
54. Malaysia Indonesia, Thailand, Singapore, (Brunei)
55. Nepal India, (China)
56. Pakistan India, Iran, (China), (Afghanistan)
57. Philippines (I)Indonesia, (Brunei), (Vietnam)
58. Saudi Arabia Yemen, Jordan, Oman, United Arab Emir, Iraq, Kuwait, (Qatar)
59. Singapore Malaysia
60. Sri Lanka (I) India
61. Syria Turkey, Iraq, Jordan, Israel, (Lebanon)
62. Taiwan (I) Hong Kong, (China)
63. Thailand Malaysia, Burma, (Laos), (Cambodia)
64. Austria Fed Rep Germany, (Czechoslovakia), Italy, Switzerland, (Hungary),
(Yugoslavia), (Liechtenstein)
65. Belgium France, Netherlands, Federal Republic Germany, Luxembourg
66. Cyprus (I) Turkey, Syria, (Lebanon)
67. Denmark Fed Rep Germany
68. Finland Norway, Sweden, (USSR)
69. France Spain, Belgium, Switzerland, Italy, Federal Republic Germany, Lux-
embourg, (Monaco)
70. Germany Austria, Netherlands, France, Switzerland, Belgium, Luxembourg,
Denmark, (Czechoslovakia)
71. Greece Turkey, (Bulgaria), (Albania), (Yugoslavia)
72. Iceland (I) Norway, United Kingdom
73. Ireland United Kingdom
74. Italy Switzerland, France, Austria, (Yugoslavia)
75. Luxembourg Belgium, Federal Republic Germany, France
76. Malta (I) Italy, Greece, (Libya), Egypt
77. Netherlands Federal Republic Germany, Belgium
78. Norway Sweden, Finland, (USSR)
79. Portugal Spain
80. Spain Portugal, France
81. Sweden Norway, Finland
82. Switzerland Italy, France, Federal Republic Germany, Austria
83. Turkey Syria, (USSR), Iran, Iraq, (Bulgaria), Greece
84. United Kingdom Ireland
85. Barbados (I) Trinidad and Tobago
86. Canada United StatesTHY NEIGHBOR’S CURSE: REGIONAL INSTABILITY AND ECONOMIC GROWTH 301
87. Costa Rica Panama
88. Dominican Republic Haiti
89. El Salvado Honduras, Guatemala
90. Guatemala Mexico, Honduras, El Salvador, (Belize)
91. Haiti Dominican Republic
92. Honduras Nicaragua, El Salvador, Guatemala
93. Jamaica (I) Haiti, (Cuba)
94. Mexico United States, Guatemala, (Belize)
95. Nicaragua Honduras, Costa Rica
96. Panama Costa Rica, Columbia
97. Tinidad and Tobago (I) Barbados, Venezuela
98. United States Canada, Mexico
99. Argentina Chile, Paraguay, Brazil, Bolivia, Uruguay
100. Bolivia Brazil, Peru, Chile, Argentina, Paraguay
101. Brazil Bolivia, Venezuela, Columbia, Peru, Paraguay, Argentina,
Uruguay, Suriname
102. Chile Argentina, Bolivia, Peru
103. Colombia Peru, Venezuela, Brazil, Ecuador, Panama
104. Ecuador Peru, Colombia
105. Guyana Brazil, Venezuela, Suriname
106. Paraguay Argentina, Brazil, Bolivia
107. Peru Colombia, Brazil, Ecuador, Bolivia, Chile
108. Suriname Guyana, Brazil, (French Guinea)
109. Uruguay Brazil, Argentina
110. Venezuela Brazil, Colombia, Guyana
111. Australia (I) New Zealand, Indonesia, Papua New Guinea
112. Fiji (I) Papua New Guinea, Australia, New Zealand
113. New Zealand (I) Australia, Fiji
114. Papua N.G. Indonesia
115. Burkina Faso Mali, Niger, Ivory Coast, Ghana, Benin, Togo
116. Oman Saudi Arabia, UAE, Yemen
117. Yemen Saudi Arabia, Oman
118. Indonesia Malaysia, Papua New Guinea
Acknowledgments
We are grateful to Alberto Alesina, Robert Barro, Edward Glaeser, Greg Mankiw, Jonathan
Morduch, Andrei Shleifer, and two anonymous referees for comments on an earlier draft of
this article. We also thank William Easterly, Sergio Rebelo, and especially Robert Barro for
providing us with the use of their datasets. Alberto Ades gratefully acknowledges financial
support from the National Science Foundation.302 ALBERTO ADES AND HAK B. CHUA
Notes
1. It is fair to say, however, that the existence of a precautionary motive for savings can make the effect of
uncertainty on investment and saving go in the opposite direction.
2. There are, however, cases in which countries may benefit from political unrest in neighboring countries. If
neighbors compete for a scarce pool of foreign capital or aid, political instability in one country can often lead
to a rival getting a larger share of that pool. If, however, foreign investors view the bad shock in one country
as a signal for the whole region, then countries in the whole region might well lose out on foreign capital or
aid. If neighbors are competing oligopolists of a good or resource, production disruptions in a rival country
can lead to an improvement in the terms of trade as well as an increased share of the export market for that
good. Neighboring countries may also benefit from the capital flight and migration of talented people that
often occur in countries with political turmoil. In the late 1960s and early 1970s, Brazil received a continuous
flow of Argentine scientists as a consequence of a crackdown on political opposition imposed by the Argentine
military regime. The brain drain from Yugoslavia in the midst of political instability and the hardships imposed
by global economic sanctions offered yet another case in which parties not involved in a conflict may actually
benefit from unrest in another country.
3. De Long and Summers (1991) investigate the possibility of spatial correlation in the residuals of cross-country
regressions but find no evidence for this hypothesis.
4. Other more recent examples are those of the civil war in Angola in the mid-1970s, which led to the expulsion
of about a half million Portuguese settlers. Also, in the 1990s, the United States received an influx of refugees
not just from neighboring Haiti but also from China.
5. A problem with this definition is the treatment of island countries, which do not have, strictly speaking,
bordering neighbors. For island countries, the nearest neighboring countries that lie across straits, channels,
or small bodies of water are used to define the relevant region. The relevant regions for each country under
this classification are summarized in Appendix B.
6. This elasticity is obtained dividing the coefficient on REGREV by 10 and dividing that again by th negative
of the coefficient on LGDP60.
7. Weighing the observations by land area leads to similar results.
8. We also examined whether regional instability has different effects on landlocked as opposed to nonlandlocked
countries. The rationale behind these regressions was the idea that regional instability might be especially
damaging for this last group as these countries must rely on their neighbors to gain access to seaport facilities.
When we split our sample into landlocked and nonlandlocked countries, we typically found that the coefficient
on regional instability was about double in size for the landlocked country sample. We also pooled both
samples and included a landlock dummy and an interaction term between regional instability and the landlock
dummy along with our standard controls. We found that this last variable had the right negative sign but
showed insignificantly. This should not come as a surprise given the small number of landlocked countries in
our sample.
9. The methodology that we use follows Murphy, Shleifer, and Vishny (1991).
10. The sample size drops to ninety-two observations due to the inclusion of trade data.
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