## Democratic tipping points

Our political institutions today may have been shaped enduringly by seemingly random events at critical junctures in the past (e.g. Lipset and Rokkan 1967; Mahoney 2000, 2001; Capoccia and Kelemen 2007; Acemoglu and Robinson 2012). This elementary idea appears to fit important historical narratives.

But it is difficult to test empirically. It is hard to say whether political institutions are at a critical juncture in a specific country at a certain point in time. This makes it difficult to examine whether random events at specific critical junctures have persistent effects on political institutions. However, suppose one observes a group of countries subject to similar transitory shocks over a long enough period. If political institutions were at critical junctures in some of these countries during some of the time, these transitory shocks should lead to persistent changes in political institutions. This is the basic idea of the empirical examination of democratic tipping points we want to describe here. The group of countries is the world’s most agricultural countries since 1946, the transitory shocks are rainfall shocks, and the changes in political institutions are transitions from authoritarian regimes to democracy.

The world’s most agricultural countries are a logical place to examine whether transitory economic shocks can lead to persistent democratisation. In the 1960s, most of these countries were ruled by authoritarian regimes, but today many are democratic. Evidently, democratisation was a possibility in at least some of these countries during some of the time. Moreover, the economic weight of agriculture and the lack of irrigation make rainfall shocks an exogenous source of economic shocks in these countries.

A modern theory of political institutions predicting persistent effects of transitory shocks is that of Acemoglu and Robinson (2001, 2006). In their theory, the initially disenfranchised poor majority can contest the political power of a rich elite. As the opportunity cost of contesting power is lower following transitory negative economic shocks, such shocks can lead to democratisation. Although transitory, shocks may cause persistent democratisation depending on the constellation of economic and institutional preconditions—income inequality and the cost of coups for example. We refer to the constellations of preconditions where a transitory shock could lead to persistent democratisation as democratic tipping points.

The reason why the disenfranchised in Acemoglu and Robinson’s theory demand democratisation rather than policy concessions when transitory negative shocks put them into a (temporary) position to do so, is that democratisation is more difficult to reverse than policy concessions under an unchanged distribution of political power. Put differently, demands for democratisation are based on the expectation that, in some contingencies at least, democracy will prevail beyond the transitory events that back up democratisation demands. Hence, the persistence of democratisation following transitory shocks plays a key role in this theory of political institutions.

In Ciccone and Ismailov (2020), we examine the empirical evidence for democratic tipping points in the world’s most agricultural countries since 1946. We first show that the effect of rainfall on agricultural output is comparatively strong but transitory. A median rainfall shock—defined as the median year-on-year drop in rainfall starting at the median level of rainfall—causes a transitory drop in agricultural output of around 1%. We then go on to show that transitory rainfall shocks have persistent effects on political institutions. Following the median year-on-year negative rainfall shock, countries ruled by authoritarian regimes are more likely to democratise and 2-3 percentage points more likely to be democratic ten years later.

In this column, we focus on results using the dichotomous political regime classifications of Acemoglu et al. (2019), Przeworski et al. (2000) as updated by Cheibub et al. (2010) and Bjornskov and Rode (2020), and Geddes et al. (2014).

## The effect of median negative rainfall shock on the probability of short-term and longer-term Acemoglu et al. democratisation

Figure 1 illustrates the probability of democratisation following a median negative rainfall shock using the political regime classification of Acemoglu et al. (2019). The median negative rainfall shock is defined as the median year-on-year drop in rainfall starting at the median level of rainfall. The figure contains the point estimates (the dots) and the corresponding 90% confidence intervals. It can be seen that the median year-t negative rainfall shock increases the probability of democratisation between years t-1 and t by around 1.5 percentage points. The 90% confidence interval ranges from 0.1 percentage points to 2.9 percentage points. By year t+2, the median year-t negative rainfall shock increases the probability of democratisation by 2.5 percentage points. The 90% confidence interval goes from 0.7 percentage points to 4.3 percentage points. By year t+4, the increase in the probability of democratisation is above 3.5 percentage points, with a 90% confidence interval from 1.9 percentage points to 5.5 percentage points. And by year t+9, the increase in the probability of democratisation is above 3 percentage points, with a 90% confidence interval from 1.2 percentage points to 4.9 percentage points. Hence, transitory negative rainfall shocks lead to persistent democratisation according to the Acemoglu et al. political regime classification.

**Figure 1** Effect of median negative rainfall shock on the probability of short-term and longer-term Acemoglu et al. democratisation

*Note*: Effect of a median negative rainfall shock in year t on the probability of democratisation in a country that is an autocracy in year t-1. The dots are point estimates for the probability that the country is a democracy by year t (1Y Effect); by year t+2 (3Y Effect); by year t+4 (5Y Effect); and by year t+9 (10Y Effect). The bands give the 90% confidence intervals. The classification of democratic and autocratic regimes is based on Acemoglu et al. (2019). The median negative rainfall shock refers to a median year-on-year drop in rainfall starting at the median level of rainfall. The confidence bands are based on heteroskedastic- and autocorrelation-consistent (HAC) standard errors that are robust to both arbitrary heteroskedasticity and serial correlation.

## The effect of median negative rainfall shock on the probability of short-term and longer-term Przeworski et al. democratisation

Figure 2 illustrates the probability of democratisation following a median negative rainfall shock using the political regime classification of Przeworski et al. (2000) as updated by Cheibub et al. (2010) and Bjornskov and Rode (2020). It can be seen that the median year-t negative rainfall shock increases the probability of democratisation between years t-1 and t by around 1.5 percentage points. The 90% confidence interval ranges from 0.5 percentage points to 2.5 percentage points. By year t+2, the median year-t negative rainfall shock increases the probability of democratisation by 2.3 percentage points. The 90% confidence interval goes from 0.5 percentage points to 4.1 percentage points. By year t+4, the increase in the probability of democratisation is above 3 percentage points. And by year t+9, the increase in the probability of democratisation is around 2.5 percentage points, with a 90% confidence interval from 0.8 percentage points to 4.2 percentage points. Hence, transitory negative rainfall shocks lead to persistent democratisation according to the Przeworski et al. political regime classification.

**Figure 2** Effect of median negative rainfall shock on the probability of short-term and longer-term Przeworski et al. democratisation

*Note*: Effect of a median negative rainfall shock in year t on the probability of democratisation in a country that is an autocracy in year t-1. The dots are the point estimates for the probability that the country is a democracy by year t (1Y Effect); by year t+2 (3Y Effect); by year t+4 (5Y Effect); and by year t+9 (10Y Effect). The bands give the 90% confidence intervals. The classification of democratic and autocratic regimes is based on Przeworski et al. (2000) as updated by Cheibub et al. (2010) and Bjornskov and Rode (2020). The median negative rainfall shock refers to a median year-on-year drop in rainfall starting at the median level of rainfall. The confidence bands are based on heteroskedastic- and autocorrelation-consistent (HAC) standard errors that are robust to both arbitrary heteroskedasticity and serial correlation.

## The effect of median negative rainfall shock on the probability of short-term and longer-term Geddes et al. democratisation

Finally, Figure 3 illustrates the probability of democratisation following a median negative rainfall shock using the political regime classification of Geddes et al. (2014). Because Geddes et al. do not follow the convention (their own words) for the start dates of democratic regimes, we illustrate the strength of the effect of the median negative rainfall shock in year t on the probability of democratisation in two different ways. Our first approach relies on the original Geddes et al. data and hence their coding rule. The corresponding point estimates are the red dots in Figure 3. It can be seen that the median year-t negative rainfall shock increases the probability that an autocracy in year t-1 is a democracy in year t by around 0.8 percentage points. The 90% confidence interval ranges from -0.2 percentage points to 1.8 percentage points. The probability that the autocratic country is a democracy in year t+4 or in year t+9 is around 2.5 percentage points. The 90% confidence interval goes from around 0.6 percentage points to around 4.3 percentage points. Hence, transitory negative rainfall shocks lead to persistent democratisation according to the political regime classification of Geddes et al. Our second approach recodes the start dates of regimes in the Geddes et al. data according to the convention for the start dates of democratic regimes. The corresponding point estimates are the blue squares. In this case, the median year-t negative rainfall shock increases the probability of democratisation between years t-1 and t by around 1.5 percentage points. The 90% confidence interval ranges from 0.2 percentage points to 2.8 percentage points. The probability that the autocratic country is a democracy in year t+4 or in year t+9 is around 2 percentage points. The 90% confidence interval goes from 0.1 percentage points to 4 percentage points.

**Figure 3** Effect of median negative rainfall shock on the probability of short-term and longer-term Geddes et al. democratisation

*Note*: Effect of a median negative rainfall shock in year t on the probability of democratisation in a country that is an autocracy in year t-1. The red dots are the point estimates for the probability that the country is a democracy by year t (1Y Effect); by year t+2 (3Y Effect); by year t+4 (5Y Effect); and by year t+9 (10Y Effect). The bands give the 90% confidence intervals. The classification of democratic and autocratic regimes is based on Geddes et al. (2014). The blue dots and corresponding 90% confidence bands are based on estimations with recoded Geddes et al. data using the conventional start date for regime transitions, see page 16 in the paper for details. The median negative rainfall shock refers to a median year-on-year drop in rainfall starting at the median level of rainfall. The confidence bands are based on heteroskedastic- and autocorrelation-consistent (HAC) standard errors that are robust to both arbitrary heteroskedasticity and serial correlation.

## Democratic change and agricultural output

The agricultural economics literature finds an inverted-U-shaped effect of rainfall on agricultural output (e.g. Schlenker and Lobell 2010, Lobell et al. 2011). Agricultural output rises with rainfall up to a relatively high level; beyond this level, additional rainfall lowers agricultural output.

The inverted-U-shaped effect of rainfall on agricultural output yields an opportunity to examine whether the effect of rainfall on democratisation we find is through agricultural output. In the theory of Acemoglu and Robinson (2001, 2006) we build on, transitorily lower output raises the probability of democratisation, and transitorily higher output lowers the probability of democratisation. The inverted-U-shaped effect of rainfall on agricultural output implies that when the level of rainfall is to the left of the peak of the inverted U, higher rainfall raises output. According to Acemoglu and Robinson’s theory, higher rainfall should therefore lower the probability of democratisation. But when the level of rainfall is to the right of the peak, higher rainfall lowers output and should, in theory, raise the probability of democratisation. As a result—if the effect of rainfall on democratisation is through agricultural output—the inverted-U-shaped effect of rainfall on agricultural output should translate into a U-shaped effect of rainfall on democratisation. That is, the shape of the effect of rainfall on the probability of democratisation should be the flipped image of the shape of the effect of rainfall on agricultural output.

As illustrated in Figure 4, we find this to be the case. The inverted-U-shaped solid black line is the effect of rainfall in year t on real agricultural output in year t and is measured on the left axis. The three different U-shaped coloured lines are the contemporary effects of rainfall on the probability of democratisation for the three different indicators we examine. Figure 4 also illustrates that the lowest point of the U-shaped effect of rainfall on the probability of democratisation turns out to be at a similar level of rainfall as the highest point of the inverted-U-shaped effect of rainfall on agricultural output. That is, rainfall shocks tend to produce the largest change in the probability of democratisation when their effect on agricultural output is largest.

**Figure 4** Effect of rainfall on real agricultural output and on the probability of democratisation

*Note*: The inverted-U-shaped solid black line is the effect of rainfall in year t on real agricultural output in year t and is measured on the left axis. The U-shaped coloured lines are the effect of rainfall on the probability of democratisation between years t-1 and t (one year later). The three classifications of democratic and autocratic regimes used in the figure are those of Acemoglu et al. (2019) (blue solid line); Przeworski et al. (2000) (red dotted line), as updated by Cheibub et al. (2010) and Bjornskov and Rode (2020); and Geddes et al. (2014) (green dashed line). The effect of rainfall on the probability of democratisation is calculated using the effect of rainfall in year t in column (1) of Tables 2 and 3 in the paper respectively for the Acemoglu et al. and the Przeworski et al. democratisation indicator. For the Geddes et al. democratisation indicator, the effect of rainfall on the probability of democratisation is calculated using the effect of rainfall in year t-1 in column (5) of Table 3. This is because of Geddes et al.’s unconventional start date for democratic regime transitions; see page 16 for details. Real agricultural output is an index with the base period 2004-2006. Rainfall is measured in dm.

## Conclusion

The recent history of democratic (non-)transitions in the world’s most agricultural countries indicates that transitory events can have enduring effects on democratic institutions. When lower rainfall led to below-average agricultural output in these countries, countries ruled by authoritarian regimes were more likely to democratise and more likely to be democratic ten years later.

The shape of the effect of rainfall on the probability of democratisation indicates that the effect is through agricultural output. The agricultural economics literature finds an inverted-U-shaped effect of rainfall on agricultural output. In the theory of Acemoglu and Robinson (2001, 2006) we build on, transitorily lower output raises the probability of democratisation, and transitorily higher output lowers the probability of democratisation. Hence, the inverted-U-shaped effect of rainfall on agricultural output should translate into a U-shaped effect of rainfall on the probability of democratisation. We find this to be the case. Moreover, our results indicate that rainfall shocks tend to produce the largest change in the probability of democratisation when the estimated effect of rainfall on agricultural output is largest.

## References

Acemoglu, D and J A Robinson (2001), “A Theory of Political Transitions”, *American Economic Review* 91(4): 938–963.

Acemoglu, D and J A Robinson (2006), *Economic Origins of Dictatorship and Democracy*, Cambridge University Press.

Acemoglu, D, S Naidu, P Restrepo, and J A Robinson (2019), “Democracy Does Cause Growth”, *Journal of Political Economy* 127(1): 47–100.

Acemoglu, D and J A Robinson (2012), *Why Nations Fail: The Origins of Power, Prosperity, and Poverty*, Crown Publishing Group.

Bjornskov, C and M Rode (2020), “Regime Types and Regime Change: A New Dataset on Democracy, Coups, and Political Institutions”, *The Review of International Organizations* 15: 531–551.

Capoccia, G and R D Kelemen (2007), “The Study of Critical Junctures: Theory, Narrative, and Counterfactuals in Historical Institutionalism”, *World Politics* 59(3): 341–369.

Cheibub, J, J Gandhi and J R Vreeland (2010), “*Democracy and Dictatorship Revisited*”, Public Choice 143(1/2): 67–101.

Ciccone, A and A Ismailov (2020), “Democratic Tipping Points”, Working Paper.

Geddes, B, J Wright and E Frantz (2014), “Autocratic Breakdown and Regime Transitions: A New Data Set”, *Perspectives on Politics* 12(2): 313–331.

Lipset, S M and S Rokkan (1967), *Party Systems and Voter Alignments: Cross-National Perspectives* Volume 7, The Free Press.

Lobell, D B, W Schlenker and J Costa-Roberts (2011), “Climate Trends and Global Crop Production Since 1980”, *Science *333(6042): 616–620.

Mahoney, J (2000), “Path Dependence in Historical Sociology”, *Theory and Society* 29(4): 507-548.

Mahoney, J (2001), “Path-Dependent Explanations of Regime Change: Central America in Comparative Perspective”, *Studies in Comparative International Development *36(1): 111–141.

Przeworski, A, M E Alvarez, J A Cheibub and F Limongi (2000), *Democracy and Development: Political Institutions and Well-Being in the World, 1950–1990*, Cambridge University Press.

Przeworski, A and F Limongi (1997), “Modernization: Theories and Facts”, *World Politics* 49(2): 155–183.

Schlenker, W and D B Lobell (2010), “Robust Negative Impacts of Climate Change on African Agriculture”, *Environmental Research Letters* 5(1).

**Original Source**