The Democratic Window of Opportunity

Abstract

We show that drought-induced changes in the intensity of riots lead to moves toward democracy in sub-Saharan Africa and that these changes are often a result of concessions made as a result of the riots. This provides evidence that low-intensity conflict can have a substantial short-run impact on democratic change and supports the “window of opportunity” hypothesis: droughts lead to an increase in the threat of conflict, and incumbents often respond by making democratic concessions.

Keywords

riots drought transitions democracy autocracy

What determines a country’s political institutions, and in particular, the extent to which they are democratic?¹ An important set of explanations has focused on the idea that conflict, or the possibility of conflict, induces leaders to promote institutional change. Tilly (1990), Besley and Persson (2008, 2009), and Dincecco and Prado (2012) argue that conflict, and in particular wars between countries, created the setting for Western European nations to build institutions that would enable the enforcement of contracts and collection of taxes. Conflict also plays an important role in Acemoglu and Robinson’s (2000, 2001, 2006) theory of democratization; they emphasize how the threat of conflict, in the form of a revolution, induces autocrats to make democratic concessions in an attempt to defuse that threat. In their theory revolutions are more likely in times of economic hardship, so negative economic shocks open a “window of opportunity” that can lead to a peaceful transition toward democracy.

In this article we test the hypothesis that low-intensity conflict (riots) leads to democratic reform, and that this often happens through concessions made in response to the riots. The events in Togo in 1991 illustrate this mechanism. As Piot (2010, 31) explains, “[i]n Togo in summer 1991, after months of clashes on the streets between dissidents and the military, Eyadéma capitulated to calls for a national conference to discuss steps toward democratization.” Meredith (2005, 397) concurs that the riots put pressure on President Eyadéma: “[a]fter months of strikes, demonstrations and violence, Eyadéma agreed in April 1991 to allow opposition parties to operate and in July yielded to demands for a national conference.” The concessions were partial, however, “[a]t the end of the national conference in 1991, Eyadéma cleverly headed off an attempt by the political opposition to strip him of power…but agreed to hold presidential elections” (Piot 2010, 33). Fearing electoral fraud, the opposition boycotted the election and allowed Eyadéma to win with 96 percent of the vote (Piot 2010, 33).

The main difficulty in testing whether conflict opens a “window of opportunity” is that riots are rarely exogenous: there might be problems of reverse causality because the expectation of political change might itself lead to riots, and there might be unobservable omitted variables that cause both riots and political change. We address these technical problems by using droughts to create an instrumental variable (IV) for riots, as there is a considerable amount of case study evidence that shows that droughts often trigger social unrest in poor countries (see, e.g., Walton and Seddon 1994).²

To test the hypothesis that riots lead to democratic change, we focus on sub-Saharan Africa in the period between 1990 and 2007. This choice is motivated by the large number of democratic (and antidemocratic) transitions that took place in that part of world during the sample period and by the fact that drought is a particularly relevant instrument for this subset of countries.³ We use data on democratic change from the Polity IV project (Marshall and Jaggers, 2010) and geographical data on riots from the Social Conflict in Africa Database (Hendrix and Salehyan 2011).

Our IV estimates show a strong first stage relationship between drought and riots and represent empirical evidence that droughts lead to an increase in riots. We then find a significant second stage relationship between (instrumented) riots and democratic change. The magnitude of the effect is substantial; for example, some of our estimates suggest that the probability of a democratic change in the average country in the sample increases by 16.7 percentage points (from a baseline probability of 5.7 percent) as a consequence of the impact a drought has on riots. Naturally, finding that riots lead to changes in democracy does not show that this is because governments feel threatened and make concessions, as suggested by the theory and the events in Togo discussed previously. To address this concern, we restrict our attention to instances of democratic change that can be explicitly associated with concessions made by the incumbent government.⁴ This provides strong evidence that riots can lead to democratic change because incumbent governments are induced to make democratic concessions. This channel is different from that emphasized in the previous literature (e.g., Burke and Leigh 2010; Brückner and Ciccone 2011), where democratic change happens when its opportunity cost is low, as measured by gross domestic product (GDP) per capita.

These results provide new evidence that low-intensity conflict can lead to democratic change over relatively short periods. These results are consistent with the causal mechanism underlying Acemoglu and Robinson’s (2000, 2001, 2006) theory of political transitions, where the threat of a revolution induces autocratic rulers to make democratic concessions. Rioting and popular protest represent a threat to autocratic rulers and may signal that the democratic “window of opportunity” is open. This might be because riots could unintentionally degenerate into a regime challenge (e.g., if rioters storm the presidential palace) or because the political opposition might use them for this purpose (e.g., to try to start a revolution). In either case, the incumbent government might react to this increased threat by making democratic concessions.

Our article is related to a large literature that examines the relationship between international conflict and institutions. Tilly (1990), Hoffman and Rosenthal (1997), and O’Brien (2011) argue that Western European institutions for tax collection were created as a result of the need to pay for the costs of war. More recently, Besley and Persson (2008, 2009, 2010, 2011) have developed a theory of institutional development where conflict plays a central role. Dincecco, Federico, and Vindigni (2011) and Dincecco and Prado (2012) provide empirical support for the link between conflict, fiscal capacity, and institutional change. Our article contributes to this literature but differs in that it focuses on Africa (while most of the literature focuses on Western Europe), looks at a more recent historical period, examines the short-term rather than long-term impact of conflict on institutional change, and emphasizes the role of the threat of internal conflict instead of the threat of international wars.

This article also relates to a small but growing empirical literature on the threat of revolution and democratization. Przeworski (2009) uses data on riots, demonstrations, and strikes to study the correlation between franchise extensions and the threat of revolution in a broad world sample starting in 1918. Aidt and Jensen (2014) take a longer historical perspective and use the international diffusion of information about revolutionary events in Europe between 1820 and 1938 to study the causal link between revolutionary threats and suffrage reform. Both studies find evidence supporting Acemoglu and Robinson’s (2000) theory of democratization. None of these articles, however, explores the association between temporary economic shocks, riots, and democratization.⁵ Burke and Leigh (2010) use information about antigovernment protests reported in the New York Times to study the link between riots and democratic change in a world sample of countries, but cannot properly identify the effect. Using more detailed riots data for Africa and an IV approach, we can identify the causal impact riots have on democratic change.

Finally, our article is also related to the large literature on adverse economic conditions and political change (e.g., Burke and Leigh 2010; Brückner and Ciccone 2011) and on economic shocks and civil conflict (e.g., Collier and Hoeffler 1998, 2004; Miguel, Satyanath, and Sergenti 2004; Jensen and Gleditsch 2009; Brückner and Ciccone 2010). Brückner and Ciccone (2011) and Burke and Leigh (2010) use rainfall shocks to identify when and where the democratic “window of opportunity” might be open. Brückner and Ciccone (2011) establish a causal link between negative rainfall shocks and democratic change in sub-Saharan Africa, suggesting that this happens because rainfall shocks reduce real GDP per capita, which in turn reduces the opportunity cost of contesting power; Barron, Miguel, and Satyanath (2013) discuss the robustness of these results. Burke and Leigh (2010) study a broader sample of countries and find results similar to those in Brückner and Ciccone (2011).⁶ Miguel, Satyanath, and Sergenti (2004) use rainfall as an instrument for economic growth to establish a causal link between (the lack of) economic growth and civil conflict.⁷

The rest of this article is organized as follows: The second section discusses the theoretical framework. The third section presents the data, while the fourth section lays out our empirical strategy. The fifth section presents and discusses our main results, and the sixth section presents our conclusions.

Theoretical Framework

The idea that the threat of social conflict is linked in a causal way to democratic change has a long history in political economy, and has recently gained renewed currency through the work of Acemoglu and Robinson (2000, 2001, 2006) and Boix (2003). The formalization of this idea in the work by Acemoglu and Robinson emphasizes the notion of a “window of opportunity” for democratic reform. Their theory starts from the premise that incumbent rulers are unwilling to share power with other groups because this compromises their policy objectives. Consequently, democratic reform only happens in situations where opposition groups pose a threat to the status quo that is seen as credible by the incumbents. In some cases the incumbents perceive a need to act preemptively in order to avoid radical political change. Sharing power through democratic reform is, of course, only one alternative among many open to the incumbents. Repression or policy concessions are often sufficient and typically preferable, but sometimes more durable institutional change is required as the lesser of multiple evils. A key element in this theory is that institutional reforms are durable, so that they cannot be easily reversed once the threat that triggered them has subsided. History contains numerous instances in which reforms were undertaken under threat but were subsequently rolled back; the “Arab Spring” is perhaps the most recent example. These instances do not undermine the logic of the theory; what matters is that at the time they were made, the concessions were considered of sufficient duration to dissuade opposition groups from overthrowing the regime. Acemoglu and Robinson (2001) develop this logic in a formal model where democratic concessions can be reversed; in practice what makes democratic change more durable than other types of concessions is that it involves a change in institutions. This does not make it impossible to revert to the exante status quo, but it does increase the cost of doing so.

An important insight that follows from this theory is that the threat posed by opposition groups is not always credible, but when it is, a “window of opportunity” is open. Acemoglu and Robinson (2001), Burke and Leigh (2010), and Brückner and Ciccone (2011) associate the window of opportunity with temporary adverse economic shocks that lower the cost of contesting power, allowing opposition groups to credibly threaten to overthrow the incumbents. We build on this idea but add an important new dimension that is at the center of our empirical analysis. Acemoglu and Robinson (2000, 2001, 2006) assume that whether the window of opportunity is open is common knowledge to all parties. This assumption is challenged in the work by Andrews and Jackson (2005), among others, who point out that institutional reform typically takes place under conditions of extreme uncertainty. In essence, this means that nobody can be entirely certain that the window of opportunity is open, and at any given point in time, incumbents and opposition groups must act taking cues from events as they unfold. This insight, on one hand, allows us to conjecture a link between these cues and democratic change: we argue that riots which have not yet escalated into a full-blown regime challenge but which have the potential do so are important cues that may induce democratic concessions.⁸ On the other hand, allowing for uncertainty introduces the new and important possibility that riots may develop into full-blown revolutions or civil wars; this may happen, for example, if incumbents underestimate the threat and fail to act in time to avoid it.⁹

We argue that riots, although often triggered by adverse economic shocks (e.g., droughts), can affect regime transitions through channels other than the opportunity cost mechanism stressed in the previous literature (and captured by fluctuations in official GDP per capita data). For example, in poor countries with large informal sectors where a large fraction of the population lives near the subsistence level in the countryside, negative weather shocks can lead to large population movements that might trigger riots and induce democratic concessions.

In short, we can summarize our theory as follows: incumbents will only share power if they perceive that opposition groups pose a credible threat. They use cues from unfolding events to assess whether they need to act in order to preserve their power. One important cue is low-intensity social unrest (e.g., riots) triggered by droughts. Based on this logic, we hypothesize that riots induced by drought may result in democratic concessions even if GDP per capita stays constant. We interpret this as a more refined version of the “window of opportunity” theory of democratic change than the one tested elsewhere in the literature.

Data

Our data set combines information on democratic change, riots, droughts, and economic conditions for a sample of forty-one sub-Saharan African countries over the period 1990 to 2007.¹⁰ As many previous studies, we draw on the Polity IV database to measure changes in democracy (Marshall and Jaggers, 2010).¹¹ We use this database to construct three different measures of democratic change. The starting point for our first two measures is the variable regtrans. This variable indicates whether a regime transition has begun in a given year; it can take a number of different values, with the value repeated in every year of the transition. Following Burke and Leigh (2010), we only count transitions in the first year in which they occur, setting the regtrans values to zero in the later years. We then transform this adjusted version of regtrans into a binary variable that equals one if any change (pro- or antidemocratic) started in a given year, and zero otherwise. We call this new variable transition, binary. This variable tells us whether a transition has started and allows us to test whether riots have a short-run impact on democratic change but gives us no information about the direction or magnitude of the change.¹² We address this limitation by using a count version of transition, binary, which we call transition, count. It takes values in the set {−2,−1,0,1,2,3} as coded in the Polity IV database. Positive values denote pro-democratic changes (i.e., toward a higher Polity IV index score) of different magnitudes, while negative values denote antidemocratic changes; zero denotes no change. We also use detailed case study evidence we collected on the forty-three regime transitions recorded by the Polity IV database as having taken place in sub-Saharan Africa during our sample period. We use this information to identify those cases in which concessions by the incumbent government played a role in the transition (so that it was not simply the result of a coup or an election), which allows us to restrict our regressions to these cases only. This information was collected primarily from the Encyclopaedia Britannica, supplemented with information from other sources.¹³

Naturally, these measures involve a loss of information, both because they do not capture the transition period in full (as they look only at the first year of a transition) and because the magnitude of the change is not captured accurately. To account for this, we use the polity 2 variable from the Polity IV database, which is a version of the Polity IV index that has been corrected to allow for use in time-series analysis. However, periods of interregnum are coded as zero, which would lead us to mistakenly interpret instances in which a country with a negative polity 2 score falls into interregnum (and so its polity 2 score increases to 0) as democratic improvements. We avoid this problem by following Brückner and Ciccone (2011) and setting polity 2 equal to missing in all interregnum years and years immediately following an interregnum.

The data on riots are from the Social Conflict in Africa Database version 2.0, updated on July 12, 2011 (Hendrix and Salehyan 2011). This is a comprehensive database of protests, riots, strikes, and other social disturbances in Africa from 1990 to 2010, and it is constructed from Lexis-Nexis searches.¹⁴ The riots data include geographic coordinates, which we use to construct Figure 1.¹⁵ We include all riots, regardless of whether they eventually led to a civil war.¹⁶ We create the variable riots to capture the intensity of the protests; it is calculated by adding the duration (in days) of all riots that happened in a given year and in a given country. Different riots are counted individually even if they occurred on the same day. This coding has the advantage that both riots that last more days and days with more riots contribute more to the total.¹⁷

Figure 1.

Riots in Africa, 1990–2007.

We use rainfall data from the Global Precipitation Climatology Project (GPCP) to identify countries and years with droughts between 1990 and 2007.¹⁸ We say that a country experienced a drought in a given year if its annual rainfall level was below the 20th percentile. We create a binary variable drought that equals one in country-years, where rainfall fell below this threshold, and zero otherwise.¹⁹ This is our main IV for riots.

Our measure of growth in per capita income, which we refer to as gdp per capita growth, is calculated using data on GDP per capita from the World Development Indicators (World Bank 2011). Deaton and Miller (1995) and Deaton (1999) show that decreases in commodity prices lead to slower (or negative) GDP growth in Africa. Brückner and Ciccone (2010) use monthly commodity price data for nineteen commodities to calculate an annual price and then use information in Deaton (1999) and the UN Commodity Trade Statistics Database to construct a commodity exports basket for each country. This then allows them to construct a country-specific index.²⁰ We follow them and use a growth version of this index, which we call commodity price index growth, as an instrument for gdp per capita growth in some of our specifications.^21,22

Figure 2 presents preliminary evidence of the relationship between riots and democratic change. Year 0 is defined as the year in which a transition begins (as recorded by transition, binary), and the axis to the left (right) of that point measures the time before (after) the start of the transition. The y-axis records the average across countries of the log of riots in that year. The graph clearly shows that riots increase in the run-up to a transition and that once a transition has begun, the average number of riots declines considerably.

Figure 2.

Pretransition riot activity.

Empirical Specification

Our main empirical specification is given by

t r a n s i t i o n_{i, t} = α + β ln (r i o t s)_{i, t - 1} + γ ln (g d p p e r c a p i t a g r o w t h)_{i, t - 1} + ι_{i} + θ_{t} + u_{i, t},

where $t r a n s i t i o n_{i, t}$ is one of the three measures of democratic change introduced earlier. We lag riots and gdp per capita growth by one year because it is unlikely that their effect on democratic change is immediate.²³ We include country $(ι_{i})$ and year $(θ_{t})$ effects, and we also include a lagged dependent variable in the specifications with polity2 as the dependent variable. This partial adjustment model allows us to estimate the short-run and long-run impact of riots on democratic change in a more flexible way.²⁴

We include gdp per capita growth in our regressions for three reasons. First, it is likely that fluctuations in recorded national income capture changes in the opportunity cost of a regime challenge, and so they might directly lead to preemptive democratization. This is the idea behind the specifications used by Brückner and Ciccone (2011). Second, including gdp per capita growth is necessary for drought to satisfy the exclusion restriction as an. If riots remain statistically significant in specifications that hold gdp per capita growth constant, then riots caused by drought affect democratic change through channels other than gdp per capita growth (e.g., through shocks to the informal sector, migration, etc.). Finally, including gdp per capita growth allows us to rule out that shocks lead to democratic change because they limit an autocrat’s ability to use public funds to “buy off” opposition groups (Haggard and Kaufman 1997) or because they create opportunities for peaceful constitutional exchange unrelated to the threat of conflict (Congleton 2007, 2011).

The main problem associated with the estimation of equation (1) is that riots and gdp per capita growth are likely to be endogenous, as a large number of factors, some of them unobservable, might impact on these variables and democratic change. To be able to make a causal claim, we must instrument for riots and gdp per capita growth, and we do so by using drought and commodity price index growth as instruments. For drought to be a relevant instrument, it needs to be strongly correlated with riots, and we later show that this is the case. To be valid, it must satisfy the independence condition and the exclusion restriction. The independence condition requires that the “treatment” assignment not be determined by the outcome. In our case, whether there is a drought cannot be a function of whether institutional change is about to take place (or not). Since drought (and rain more generally) is not determined by human activities or decisions, at least in the short run, this condition is satisfied. The exclusion restriction requires that the instrument not belong in the structural equation (1). Specifically, this means that drought should not affect democratic change through channels other than riots once we have controlled for country fixed effects, time effects, and gdp per capita growth. Analogously, for commodity price index growth to satisfy the independence condition, it must be that political change does not trigger changes in this index; in other words, that it does not affect the growth of international commodity prices. This is true since the countries in our data set are small players in international markets. The exclusion restriction requires that commodity price index growth only affects democratic change through its impact on gdp per capita growth (once we control for riots), which seems likely to be the case for small commodity exporters.

Main Empirical Results

We present our results in four tables. Table 1 shows ordinary least squares (OLS) and conditional logit estimates of the structural equation (1), as well as estimates of the reduced form where we associate the instrument directly with our measures of democratic change. Table 2 shows the main results from the IV estimation. Table 3 presents the results when we only consider transitions that involved concessions. Table 4 presents the results for a dynamic panel specification.

Table 1.

Structural and Reduced Form Regressions.

A. Structural Regressions: Riots and Democratic Change
	(1) Transition, binary_t	(2) Transition, count_t	(3) Polity2_t	(4) Transition, binary_t
	OLS	OLS	OLS	Conditional Logit
Riots_t−1, logs	0.014^†(0.007)	0.036**(0.015)	0.117(0.076)	0.260(0.162)
GDP per capita growth_t−1	−0.111(0.132)	0.239(0.373)	2.453(1.567)	−2.709(4.012)
Polity2_t−1			0.710***(0.032)
Country fixed effects	Y	Y	Y	Y
Year effects	Y	Y	Y	Y
Observations	624	625	597	442
B. Reduced Form: Drought and Democratic Change
	(1)	(2)	(3)
	Transition, binary_t	Transition, count_t	Polity2_t
	OLS	OLS	OLS
Instrument:
Drought_t−2	0.159***(0.056)	0.339**(0.149)	0.599 (0.511)
Controls:
GDP per capita growth_t−1	−0.153(0.128)	0.121(0.376)	2.434 (1.753)
Polity2_t−1			0.713*** (0.033)
Country fixed effects	Y	Y	Y
Year effects	Y	Y	Y
Observations	640	641	605

Notes: Standard errors are clustered by country and reported in parentheses. Column 4 in panel A reports conditional logit coefficients. ^†significance at the 10% level, **at the 5% level, ***at the 1% level.

Table 2.

2SLS, Riots, and Democratic Change.

	(1)	(2)	(3)	(4)
	Transition, binary _t	Transition, count _t	Transition, binary _t	Transition, count _t
	A. 2SLS
Riots _t ₋₁, logs (instrumented)	0.440	0.936	0.483	1.004
GDP per capita growth _t ₋₁ (instrumented)	−	−	−0.607	2.119
A-R Wald, F (p value)	[0.016]	[0.055]	[0.052]	[0.155]
A-R Wald, χ² (p value)	[0.007]	[0.035]	[0.027]	[0.109]
Stock-Wright, LM (p value)	[0.054]	[0.091]	[0.114]	[0.238]
GDP per capita growth _t ₋₁	Y	Y	−	−
Country fixed effects	Y	Y	Y	Y
Year effects	Y	Y	Y	Y
Observations	624	625	603	604
	B. First stage for riots _t ₋₁, logs
Drought _t ₋₂	0.380**(0.152)	0.378**(0.164)	0.349**(0.158)	0.346**(0.157)
Commodity price index growth _t ₋₁	−	−	0.043(0.515)	0.055(0.514)
GDP per capita growth _t ₋₁	Y	Y	−	−
Country fixed effects	Y	Y	Y	Y
Year effects	Y	Y	Y	Y

F-statistic for instrument; Angrist-Pischke F test when multiple instruments (p value in parentheses)	6.22(0.017)	6.15(0.018)	4.76(0.036)	4.75(0.036)
Observations	624	625	603	604
	C. First stage for GDP per capita growth _t ₋₁
Drought _t ₋₂			0.003(0.006)	0.003(0.006)
Commodity price index growth _t ₋₁			−0.039(0.036)	−0.039(0.036)
Country fixed effects			Y	Y
Year effects			Y	Y
Angrist-Pischke F test for multiple instruments (p value in parentheses)			1.15(0.290)	1.13(0.294)
Observations			603	604

Note: 2SLS = two-stage least-squares. In panel A we report p values for three significance tests in brackets; these are tests of the significance of the endogenous regressor/regressors in the structural equation, where the null is that it equals (they jointly equal) zero and that the overidentifying restrictions (where relevant) are valid. These tests are robust to weak instruments, and the versions we implement are robust to both heteroscedasticity and within-country correlation in the errors. In panels B and C, standard errors are clustered and reported in parentheses (). ^†Indicates significance at the 10 percent level. **Indicates significance at the 5 percent level. ***Indicates significance at the 1 percent level.

Table 3.

2SLS, Riots, and Democratic Change, Concessions Only.

	(1)	(2)	(3)	(4)
	Transition, binary _t	Transition, count _t	Transition, binary _t	Transition, count _t
	A. 2SLS
Riots _t ₋₁, logs (instrumented)	0.399	0.942	0.435	1.021
GDP per capita growth _t ₋₁ (instrumented)	−	−	−0.169	0.187
A-R Wald, F (p value)	[0.026]	[0.057]	[0.082]	[0.135]
A-R Wald, χ² (p value)	[0.014]	[0.037]	[0.048]	[0.092]
Stock-Wright, LM (p value)	[0.074]	[0.093]	[0.175]	[0.200]
GDP per capita growth _t ₋₁	Y	Y	−	−
Country fixed effects	Y	Y	Y	Y
Year effects	Y	Y	Y	Y
Observations	624	625	603	604
	B. First stage for riots _t ₋₁, logs
Drought _t ₋₂	0.380**(0.152)	0.378**(0.152)	0.349**(0.158)	0.346**(0.157)
Commodity price index growth _t ₋₁	−	−	0.043 (0.515)	0.055 (0.514)
GDP per capita growth _t ₋₁	Y	Y	−	−
Country fixed effects	Y	Y	Y	Y
Year effects	Y	Y	Y	Y
F-statistic for instrument; Angrist-Pischke F test when multiple instruments (p value in parentheses)	6.22(0.017)	6.15(0.018)	4.76(0.036)	4.75(0.036)
Observations	624	625	603	604
	C. First stage for GDP per capita growth _t ₋₁
Drought _t ₋₂			0.003(0.006)	0.003(0.006)
Commodity price index growth _t ₋₁			−0.039(0.036)	−0.039(0.036)
Country fixed effects			Y	Y
Year effects			Y	Y
Angrist-Pischke F test for multiple instruments (p value in parentheses)			1.15(0.290)	1.13(0.204)
Observations			603	604

Note: 2SLS = two-stage least-squares. In this table we only consider transitions where concessions were made by the incumbent; in all other cases, the dependent variable is set equal to 0. In panel A we report p values for three significance tests in brackets; these are tests of the significance of the endogenous regressor/regressors in the structural equation, where the null is that it equals (they jointly equal) zero and that the overidentifying restrictions (where relevant) are valid. These tests are robust to weak instruments, and the versions we implement are robust to both heteroscedasticity and within-country correlation in the errors. In panels B and C, standard errors are clustered and reported in parentheses ().

^†Indicates significance at the 10 percent level. **Indicates significance at the 5 percent level. ***Indicates significance at the 1 percent level.

Table 4.

Dynamic Specification with Polity2.

	(1)	(2)	(3)
	Polity2 _t	Polity2 _t	Polity2 _t
	Arellano-Bond GMM	Arellano-Bond GMM	LSDV
Riots _t ₋₁, logs	0.329(0.236)	0.492†(0.292)	0.131† (0.067)
GDP per capita growth _t ₋₁	3.513(2.477)	21.668(15.938)	2.851(1.828)
Polity2 _t ₋₁	0.916***(0.089)	0.975***(0.114)	0.803***(0.036)
Country fixed effect	Y	Y	Y
Year effect	Y	Y	Y
Arellano-Bond AR(1) test, p value	0.000	0.001	0.000
Arellano-Bond AR(2) test, p value	0.826	0.649	0.812
Excluded instrument(s):	Drought _t ₋₂	Drought _t ₋₂ Commodity price index growth _t ₋₁	−
Sargan overid test, p value	−	−	0.990
Hansen overid test, p value	0.983	0.571	−
Observations	546	529	546

Note: GMM = generalized method of moments. LSDV = least-squares dummy variable. The standard errors in the Arellano-Bond specification are consistent in the presence of any pattern of heteroscedasticity and autocorrelation. The Arellano-Bond result is based on using one lag in levels as instruments. The Arellano-Bond (1991) tests for autocorrelation test the null hypothesis of no first and second order serial autocorrelation in the errors against the alternative of AR(1) and AR(2), respectively. LSDV standard errors are bootstrapped with 100 repetitions (but the coefficient on riots stays significant with 10, 20, and 50 repetitions). The LSDV estimation uses Arellano-Bond and not system GMM. ^†Significance at the 10 percent level. **Significance at the 5 percent level. ***Significance at the 1 percent level.

Benchmark Estimates

As a benchmark, panel A of Table 1 reports OLS and conditional logit estimates of the structural equation (1). The outcome variable in column 1 is transition, binary, and we find that a one standard deviation increase in the log of riots leads to an increase in transition, binary of 0.024.²⁵ The mean value of this variable across countries and time is 0.057, so this is a substantial effect. In column 2, the outcome variable is transition, count, which makes a distinction between transitions to and away from democracy. The results are similar, and we conclude that riots are positively correlated with the likelihood of short-run democratic change. Column 3 shows a specification with polity2 in levels and a lagged dependent variable; the coefficient estimate on riots is positive but not significant.²⁶ Finally, column 4 shows the results from a fixed effects conditional logit estimation with transition, binary as the outcome variable. The coefficient on riots is positive but not significant.²⁷ All these correlations are consistent with the hypothesis that riots trigger democratic change.²⁸

Reduced Form Estimates

As we noted previously, endogeneity is a serious concern, and so the OLS (and logit) estimates cannot be given a causal interpretation. Before we report the IV estimates we present evidence on the reduced form, where we regress the measures of democratic change directly on drought.²⁹ This gives us an indication of whether the causal relationship we have hypothesized is present. Column 1 in panel B of Table 1 reports on the specification with transition, binary as the outcome variable and we find a positive and significant relationship with drought. Column 2 shows that drought is also significant in the regression using transition, count as the measure of democratic change. Column 3 reports the results for the partial adjustment model with polity2; drought is not significantly correlated with polity2, but the sign is as expected.

IV Estimates

In columns 1 and 2 of Table 2 we present results where we instrument for riots using drought. In panel B of the table we report the first stage results where we regress riots on drought. In both columns drought at time t − 2 has a significant impact on riots at time t − 1. Panel A of the table presents the second stage results using the two-stage least-squares (2SLS) estimator. The outcome variables are transition, binary and transition, count (the results for polity2 are shown in Table 4). Since the F-statistics are below 10, we use weak-instrument robust inference p values (in square brackets) to assess significance. The point estimate on riots is positive and statistically significant in both columns 1 and 2. Taking the point estimates from column 1, a drought leads to an increase of 0.380 in the log of riots, while a one-unit increase in the log of riots leads to an increase of 0.440 in the probability of a transition. It then follows that a drought leads to an increase of 0.440 × 0.380 = 0.167, or 16.7 percentage points, in the probability of a transition. Since the average transition, binary value is 0.57, this is a considerable increase. The outcome variable in column 2 is transition, count, and again we find a positive and significant effect of riots on democratic change. The estimated coefficients are large, but this is not due to the relative weakness of the drought instrument; since the specifications are just-identified, the 2SLS estimate is median-unbiased (see Angrist and Pischke 2009, 213).

It is plausible that the impact of riots on democratic change is heterogeneous across countries. In this case, the IV estimate corresponds to an average causal response and should be interpreted as the impact on democratic change of riots that happen because a country has switched into a state of drought. In other words, the coefficient on riots captures the average democratic change that follows from riots that were triggered by drought. For example, the estimate in column 1 tells us that a drought at time t − 2 increases the log of riots at time t − 1 by 0.380, so that there is an increase of 16.7 percentage points in the probability of democratic change.

The average causal response is often uninformative because it is instrument-specific and captures only the impact on “compliers” whose treatment status changes as a result of a change in the instrument (Heckman 1997). In our case, however, the average causal response allows us to establish that the democratic change we observe is a consequence of the increase in riots that resulted from a drought. This proves particularly useful because it isolates the impact that comes from a change in drought status, allowing us to show that a specific shock, drought, can lead to an improvement in democracy because it increases the threat of social conflict, as captured by riots.

For our interpretation of the estimated IV coefficients as average causal responses to be valid, the monotonicity condition must hold (see Imbens and Angrist 1994; Angrist and Imbens 1995). This condition states that the impact of drought on the intensity of riots must always be in the same direction; that is riots cannot become less intense because there is a drought (relative to the counterfactual intensity of riots in the case of no drought). In essence, we need to rule out the possibility that droughts inhibit riots. This assumption cannot be tested, but we view it as being fairly uncontroversial.

In columns 3 and 4 of Table 2, we present IV results using drought and the commodities price index growth as instruments for riots and gdp per capita growth. Panel B of the table shows the first stage for riots, while panel C shows the first stage for gdp per capita growth. Panel A presents the second stage results using the 2SLS estimator. The F-statistics are again below 10, and so we use weak-instrument robust inference p values (in square brackets) to assess significance. Looking at the top row in column 3, we observe that the point estimate on riots in the specification with transition, binary is positive and statistically significant (according to two of the three p values). The outcome variable in column 4 is transition, count, and again we find a large positive effect of riots on democratic change, although it is no longer significant.

In Table 3 we repeat the regressions from Table 2 but we set transition, binary and transition, count to 0 in instances where the transition did not involve concessions by the incumbent. In short, transitions triggered by coups/revolutions or elections are not recorded as democratic changes in these specifications. If the results in Table 2 had been driven by transitions that were due to coups/revolutions or elections, then these results would not show up in Table 3. However, the results are very similar to those in Table 2; if anything, those in Table 3 are stronger. This shows that our results are indeed consistent with the “window of opportunity” hypothesis: riots lead to democractic change because they induce democratic concessions by the incumbent government.³⁰

The Partial Adjustment Model

Table 4 reports the results for the partial adjustment specification with polity2 as the dependent variable. We include a lagged dependent variable, which introduces what is known as the Nickell bias (see Nickell 1981). We address this problem by using the Arellano-Bond generalized method of moments (GMM) estimator (Arellano and Bond 1991).³¹ Column 1 reports the results from a specification where we use drought and lagged right-hand side variables as instruments for the differenced equation. We find the coefficient on riots not to be significant.³² In column 2, we repeat these regressions but treat gdp per capita growth as endogenous and include the commodity price index growth as an additional instrument in the Arellano-Bond estimation procedure. The coefficient on riots is significant at the 10 percent level.

The Arellano-Bond GMM estimator is intended for cases in which n (i.e., countries) is large and t (i.e., years) is small. However, our panel has at most forty-one countries and eighteen years, so the Arellano-Bond estimates could be severely biased. An alternative is to use the bias-corrected least-squares dummy variable (LSDV) estimator (see Bruno 2005), which performs better than the Arellano-Bond GMM estimator when the number of cross-sectional units is small. The LSDV results are presented in column 3. The point estimate is somewhat smaller than with Arellano-Bond and significant at the 10 percent level. Focusing on the point estimate obtained with the LSDV estimator, a one standard deviation increase in the log of riots leads to a short-run increase of 1.744 × 0.131 = 0.229 points in the polity2 score, while the long-run impact is considerably larger and equal to 1.16 points.³³

Discussion of the Results

Taken together, our results suggest that riots trigger democratic change, providing new evidence for the causal mechanism that underlies Acemoglu and Robinson’s (2000, 2001, 2006) theory of political transitions: adverse economic shocks generate social unrest and riots, which in turn lead incumbent governments to make democratic concessions. Moreover, the size of the effect is large and of economic and political importance.

Acemoglu and Robinson’s (2000, 2001, 2006) theory of political transitions acknowledges that incumbents might use strategies other than concessions to avoid being overthrown.³⁴ For example, they may invest in repression or offer temporary transfers to those who pose a threat to their rule, thus eliminating their incentive to participate in a revolt.³⁵ In our setting, food subsidies could be used to mitigate the incentive to riot in the face of a drought, while international food aid could unintentionally serve the same function. It is not possible for us to control for these alternative strategies, and so they remain as potential sources of statistical bias in the estimation of β in equation (1). To establish the direction of this bias, let us consider the consequences of omitting spending on repression from equation (1).³⁶ The theory predicts that repression is negatively correlated with democratic change and positively correlated with riots. Consequently, failing to control for repression biases the estimate of β toward zero, which would work against us finding a coefficient that is significantly different from zero. This means that our estimate of the effect of riots on democratic change should be viewed as a lower bound on the true causal effect.

A related issue is that rain might discourage or stop riots; for example, travel may become more difficult and people may dislike getting wet. One might then think that drought is correlated with riots not because economic conditions provide a reason to protest but because the lack of rain makes riots possible (given some other underlying cause for the riots). However, this is unlikely to be a problem at an annual level of aggregation. Even in years with heavy rainfall, there will be periods without rain; riots could happen in those periods even if rain, in general, deters riots.

Conclusion

This article has shown that riots triggered by drought lead to democratic change. In doing so, it provides new evidence that conflict can lead to institutional change. While most work in this area has focused on international wars and their impact on institutional change over long periods of time, we have shown that low-intensity conflict can also lead to change in relatively short periods of time.

We have shown that droughts increase riots, which in turn lead to more democratic institutions. These results are driven by cases in which democratic concessions were made as a result of riots, and so they support the theory of democratic change formulated by Acemoglu and Robinson (2000, 2001, 2006). Although our focus on the recent experience of sub-Saharan Africa is somewhat specific, our study adds to a growing literature that finds evidence in support of this theory: Aidt and Jensen (2011) find evidence of this mechanism when they look at the international diffusion of information about revolutionary events in Europe in the period 1820 to 1938; Aidt and Franck (2015) establish a link between local riots and support for the Great Reform Act of 1832 in Britain; Przeworski (2009) studies the correlation between franchise extensions and the “threat of revolution” in a large sample of countries. Overall, this body of work provides compelling evidence in support of the central mechanism in Acemoglu and Robinson’s (2000, 2001, 2006) theory of political transitions, as many episodes of democratic change, both today and in the past, are the result of concessions made by governing elites in response to what they perceived to be threats to the established order.

Footnotes

Acknowledgment

We are grateful to Filipe Campante, Quoc-Anh Do, Markus Brückner, and Antonio Ciccone for sharing their data with us. We thank Pramila Krishnan, Jane Cooley Fruehwirth, Hamish Low, Julia Shvets, Hector Calvo Pardo, and seminar participants at Cambridge for helpful comments and suggestions.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes

Supplemental Material

The online [appendices/data supplements/etc] are available at .

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