metadata: authors: 0: author: Bluhm,Richard
1: author: de Crombrugghe,Denis
2: author: Szirmai,Adam
count: Africa,World
docna: 0: docna: Do Weak Institutions Prolong Crises ? On the
            Identification, Characteristics, and Duration of Declines
            During Economic Slumps
docty: Policy Research Working Paper
subtopic: Economic Growth,Economic Theory & Research,Industrial Economics,International Trade and Trade Rules,Armed Conflict,Youth and Governance,Social Analysis,National Governance,Quality of Life & Leisure,Government Policies,Fiscal & Monetary Policy,Consumption
teratopic: Industry,International Economics and Trade,Conflict and Development,Macroeconomics and Economic Growth,Public Sector Development,Social Development,Governance
abstracts: cdata!: This paper studies periods of prolonged
            contractions in output per capita in a sample of 145
            countries from 1950 to 2014. Economic slumps are defined as
            abrupt interruptions of a period of growth by several regime
            switches. Slumps start with a sharp contraction along with a
            trend break, which is followed by another switch when growth
            stabilizes again. The paper then analyzes the correlates of
            these slumps, focusing on the length and depth of the
            contraction, from the beginning of the slump to its trough.
            The results establish three new stylized facts: (i) weak
            political institutions predate crises whereas political
            reforms tend to follow them, (ii) the length and depth of
            economic declines are robustly correlated with executive
            constraints and ethnic heterogeneity, and (iii) there is a
            robust interaction between these two variables, suggesting
            that institutions constraining leaders are important for
            stabilizing growth. This is particularly relevant for
            Sub-Saharan Africa, where politics are often ethnic and
            decision makers are comparatively unconstrained.
repnme: repnme: Do Weak Institutions Prolong Crises ? On
            the Identification, Characteristics, and Duration of
            Declines During Economic Slumps
display_title: Do Weak Institutions Prolong Crises
            On the Identification, Characteristics, and Duration of
            Declines During Economic Slumps
listing_relative_url: /research/2020/01/31738420/weak-institutions-prolong-crises-identification-characteristics-duration-declines-during-economic-slumps
topicv3: Economic Growth m1326241 471,Political Economy of Reform m1326263 836,Trade m1326924 698,Political Violence and War m1327310 2462,Macroeconomic and Structural Policies
            m1327380 439
publishtoextweb_dt: 2020-01-29T00:00:00Z
historic_topic: Industry,International Economics and Trade,Conflict and Development,Macroeconomics and Economic Growth,Public Sector Development,Social Development,Governance
origu: DEC-Knowledge and Strategy (DECKS)
no_of_pages: 71
author_keys: 0: author_key: 1212356
model_extractions: Policy Research Working Paper 9127 Do Weak Institutions Prolong Crises?

On the Identification, Characteristics, and Duration of Declines During Economic Slumps _Richard Bluhm Denis de Crombrugghe Adam Szirmai_ Development Economics Knowledge and Strategy Team January 2020
Policy Research Working Paper 9127

# **Abstract**

This paper studies periods of prolonged contractions in output per capita in a sample of 145 countries from 1950 to 2014. Economic slumps are defined as abrupt interruptions of a period of growth by several regime switches. Slumps start with a sharp contraction along with a trend break, which is followed by another switch when growth stabilizes again. The paper then analyzes the correlates of these slumps, focusing on the length and depth of the contraction, from the beginning of the slump to its trough. The results establish three new stylized facts: (i) weak political institutions predate crises whereas political reforms tend to follow them, (ii) the length and depth of economic declines are robustly correlated with executive constraints and ethnic heterogeneity, and (iii) there is a robust interaction between these two variables, suggesting that institutions constraining leaders are important for stabilizing growth. This is particularly relevant for Sub-Saharan Africa, where politics are often ethnic and decision makers are comparatively unconstrained.

This paper is a product of the Knowledge and Strategy Team, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at bluhm@mak.uni-hannover.de.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Do Weak Institutions Prolong Crises?

_On the identification, characteristics, and duration of declines during economic slumps_ Richard Bluhm Denis de Crombrugghe Adam Szirmai _Keywords:_ economic slumps, crisis duration, political institutions, structural breaks, ethnic fractionalization, development

## _JEL codes:_ O43, O11, C41, F43

Richard Bluhm (corresponding author) is an assistant professor at Leibniz University Hannover, Germany; email: _bluhm@mak.uni-hannover.de_ . Denis de Crombrugghe is an associate professor at Maastricht University, the Netherlands; email: _d.decrombrugghe@maastrichtuniversity.nl_ . Adam Szirmai is a professorial fellow at UNU-MERIT and professor at Maastricht University, the Netherlands; email: _szirmai@merit.unu.edu_ . This paper has been presented at ABCDE, AFD Paris, DIAL, EEA-ESEM, ETH Zurich, Duke Fuqua, Hertie School/DIE Workshop, Ifo Workshop on Political Economy, Nederlandse Economendag, NIPF-DEA, Silvaplana Workshop in Political Economy, and Warwick Workshop on Economic Growth. We have greatly benefited from comments by Oded Galor, Martin Gassebner, Melanie Krause, Nicolas Meisel, Thomas Roca, Ajay Shah, Jan-Egbert Sturm, Kaj Thomsson, and Bart Verspagen. We would also like to thank Aart Kraay and three anonymous referees for their valuable suggestions and help in improving the paper. We gratefully acknowledge financial support from the Agence Fran ̧caise de D ́eveloppement (AFD).
# **1 Introduction**

Modern economic growth since the 1950s has been far from steady. Every growth ‘miracle’ is easily matched by a spectacular growth collapse. For example, the East Asian miracle was interrupted by the Asian financial crisis; several African nations went from ‘up and coming’ in the 1950s to long periods of stagnation between 1973 and 2000; and Latin America, long characterized by political turmoil and economic volatility, experienced a lost decade in the 1980s. Many economic slumps in developing countries over this period were considerably deeper and longer than even the ‘Great Depression’ of the 1930s. There is also a long list of relatively short-lived advanced economy crises. What can we learn from such abrupt reversals of growth, and why do declines last so much longer in some countries than in others?

The instability of growth is of great concern in economics. It has been suggested that it not only affects output in the short run (e.g. Ramey and Ramey, 1995), but that curbing volatility also plays an important role in sustaining long-run growth (e.g. Broadberry and Wallis, 2017). A growing literature on trend breaks has confirmed that growth is often not steady but instead characterized by switching among growth regimes (e.g. Ben-David and Papell, 1995, Jerzmanowski, 2006, Jones and Olken, 2008, Kerekes, 2012, Papell and Prodan, 2014, Szirmai and Foster-McGregor, 2017). This perspective offers new stylized facts. For example, positive growth is relatively easy to ignite (Hausmann et al., 2005) but much harder to sustain (Berg et al., 2012). Unstable growth has serious welfare implications (Pritchett et al., 2016). However, the implications of negative regime switches are only beginning to be explored. Long-lasting slumps can nullify decades of positive growth, with little hope that lost potential output is ever fully recouped (Cerra and Saxena, 2008), and they seem to be driven by different factors than growth accelerations (Jones and Olken, 2008, Hausmann et al., 2008).

A potential explanation for why some declines last so much longer than others is that their duration is driven by the prevailing structure of institutions. Institutions create specific political and economic incentives, solve or worsen coordination failures and define the set of 2
feasible policies. Seminal contributions to this literature link stronger institutions with higher _levels_ of GDP per capita (Acemoglu et al., 2001). Others have shown that strong institutions and political stability bring about reduced output volatility (e.g. Acemoglu et al., 2003, Mobarak, 2005). However, there is still a lack of evidence convincingly linking institutions to short-run growth, just as there is an ongoing discussion about the relative importance of first improving political institutions versus prioritizing macroeconomic policies (Acemoglu et al., 2003, Fat ́as and Mihov, 2013).

This paper focuses on three aspects in particular. First, we propose a strictly statistical characterization of an economic slump: a positive growth regime is interrupted by a sharp downward shift and a trend break, followed by a second trend break when growth resumes (as in Papell and Prodan, 2012, 2014). A key difference to earlier approaches is that this superimposes the desired break structure _ex ante_ , rather than classifying episodes _ex post_ . We demonstrate the effectiveness of this approach by identifying many historical episodes following such a pattern in the post-1950 world. Second, we conduct an event analysis which reveals that political institutions are weaker than normal just before the start of a large economic crisis, but that the crisis itself may trigger positive institutional change. This change happens across all indicators of political institutions and shows that institutions are not as static as is often thought. Third, we single out the decline phase of a slump which is defined as the time from the first break until the empirical trough and analyze its duration. We identify constraints on the executive, ethnic heterogeneity, and an interaction between these two variables as robust correlates of the duration of the decline phase. Furthermore, those same factors seem able to explain the total depth of the decline, but exclusively through the duration and not through the speed of contraction.

Our findings support the view that the length of the decline phase depends on the political system’s ability to quickly react to a negative shock with coordinated policies in order to avert outright social conflict. A large body of political economy theory asserts that the ability of resilient political institutions to internalize social conflict is essential to development (e.g.

3
Acemoglu and Robinson, 2006, North et al., 2009, Besley and Persson, 2011). Some of these theories specifically argue that weakly institutionalized societies are especially prone to collapses because declining rents during a negative shock undermine the prevailing political arrangements (e.g. North et al., 2009). In fact, Broadberry and Wallis (2017) decompose several hundreds of years of economic growth and come to the conclusion that a reduction in the frequency and rate of shrinking are an integral part of forging ahead. They suggest that this is related to a transition from ‘identity rules’ among powerful groups to a system of impersonal rule for all. A key feature of societies based on identity rules is that they place few constraints on the ruling elite which creates a commitment problem towards other less powerful groups. Weak institutions thus bring with them the seeds of increased vulnerability to crises, as well as potentially much longer and deeper declines once crises occur. Similar mechanisms are suggested in the literature on institutions and macroeconomic volatility (Acemoglu et al., 2003, Mobarak, 2005).

The number and relative strength of groups with whom the ruling elite needs to coordinate play a significant role in identity rule societies (Bluhm and Thomsson, 2015). This is especially important in sub-Saharan Africa where power is typically concentrated in the executive branch and cabinet posts are distributed across ethnic groups in line with their population shares (Francois et al., 2015). Ethnic heterogeneity itself has been linked to a variety of coordination failures leading to inadequate policies, low provision of public goods and conflict. At the same time, greater diversity can be beneficial and may be necessary to reap the advantages of skill complementarities in highly diversified economies (Alesina and Ferrara, 2005). The negative effects of diversity may also become muted as “richer societies have developed institutional features that allow them to better cope with the conflict element intrinsic in diversity” (Alesina and Ferrara, 2005, p. 763). One of our key findings is that in the context of economic declines the (negative) effects of ethnic heterogeneity depend on certain political institutions and _vice versa_ .

Few macroeconomic policy indicators are correlated with the length of economic declines 4
once we account for constraints on the executive, ethnic heterogeneity and their interaction. This does not imply that macroeconomic policy is unimportant. In fact, our analysis suggests that controlling inflation could reduce the duration of declines. However, the mechanism we have in mind is more general: even if sound policy responses are available, coordination failures, rent seeking and power struggles combined with ethnic cleavages lead to substantially longer declines in heterogeneous countries with weak institutions.

The paper is structured as follows. Section 2 outlines the identification of slumps and defines the decline phase. Section 3 briefly discusses the data and characteristics of the estimated slumps, and reports the results of the event study. Section 4 analyzes the duration of the decline phase and discusses the results. Section 5 concludes.

# **2 Identifying slumps**

## **Restricted structural breaks**

Beginning with Pritchett’s (2000) classification of post-World War II growth experiences into ‘Hills, Plateaus, Mountains, and Plains’, a growing literature employs tests of structural stability to identify and subsequently analyze the growth episodes of interest.<sup>1</sup> Standard structural break approaches (e.g. Bai and Perron, 2003) work well for identifying growth spurts but perform poorly when it comes to identifying growth collapses. A wellknown advantage of these methods is that they allow us to distinguish abrupt regime changes from ordinary ‘year-to-year’ business cycle fluctuations. By definition, a structural break requires a departure from an ongoing growth process to be statistically significant at some reasonable level. This often discounts apparently large movements in economic activity which are actually in line with the observed fluctuations of a particular country, but can also classify smaller fluctuations in tranquil times as important.<sup>2</sup> An unappreciated weakness of standard

> 1See, for example, Hausmann et al. (2005), Jones and Olken (2008), Berg et al. (2012), or Pritchett et al.

> (2016). The Markov-switching models in Jerzmanowski (2006) and Kerekes (2012) are an exception.

> 2By contrast, uniform economic criteria would treat all series as if they were generated by the same

5
structural break approaches, at least in our setting, is that they leave the particular type of structural change unspecified. As a result, they often do not identify the theoretically desired pattern of breaks but instead record all significant changes which must then be classified _ex post_ .

To improve the identification of what we interchangeably refer to as deep recessions, slumps, or growth collapses, Papell and Prodan (2012, 2014) propose a _two-break model with parameter restrictions_ . They demonstrate that this modified structural change approach consistently identifies well-known slumps, such as the Great Depression in the United States. Their key innovation is to impose features of the desired pattern directly instead of searching for _unrestricted_ structural changes first. Their two-break model accounts for three growth regimes (a pre-slump regime, a contraction-recovery regime, and a post-slump regime) and places sign restrictions on the estimated coefficients to ensure the breaks occur in the desired direction.<sup>3</sup> Whereas they focus on the question whether growth in a few developed countries eventually returns to its pre-slump trend path, we apply a variant of their method to identify slumps in a large sample of countries over the period from 1950 to 2014.

The restricted structural change approach has another notable advantage: it can take into account meaningful restrictions on the growth process before and after the desired episode. For instance, a slump always interrupts a period of positive growth according to our definition. To see why these restrictions are useful, consider a sharp upward break in economic growth followed quickly by a deep recession. If the unrestricted Bai and Perron (2003) approach detects the upward break (‘upbreak’) first, it is likely to miss the downward break (‘downbreak’) which follows too soon. The appropriately restricted approach would dismiss the upbreak and detect the downbreak instead. Conversely, in the case of a double dip recession where the second dip is much deeper, the unrestricted approach might register

> process. Such criteria can therefore not discriminate among multiple plausible starting points or assess whether an episode truly constitutes a departure from the previous growth regime.

> 3Since this approach is a version of Bai’s (1999) sequential likelihood ratio test, the number of slumps— which is not known in advance—can then be estimated by recursively applying the model on ever smaller sub-samples until all breaks in the GDP per capita series have been found. Note that Papell and Prodan (2012) also refer to these episodes as ‘economic slumps’.

6
only the second downbreak, whereas the restricted approach would locate the starting date of the slump at the first downturn, where the positive trend is interrupted. This is not just pure conjecture. Supplementary Appendix S3 (available at _The World Bank Economic Review_ website) discusses the results obtained from identifying economic slumps by ‘inverting’ the episodes of sustained growth from Berg et al. (2012), who apply a variant of the Bai-Perron approach, and compares them to those identified by our two-break model.

Based on this discussion, we define slumps according to three criteria. First, a slump is a _departure from a previously positive trend_ . Second, a slump must begin with _negative growth in the first year_ . Third, all slumps should be significant _regime switches_ and not just ordinary business cycle fluctuations. What precisely constitutes a significant regime switch will vary and depend on the country’s own, idiosyncratic growth process—a feature which we argue is desirable. We focus on growth in GDP per capita, since we are primarily interested in the welfare consequences of slumps and not in aggregate output _per se_ (although we also report results for aggregate output).

We capture these criteria in the following partial structural change model: where _yt_ is the log of GDP per capita at time _t_ , _β_ is a time trend, _γ_ 0 is the coefficient on an intercept break occurring together with a trend change ( _γ_ 1) after the first break at time _tb_ 1, _γ_ 2 is a second trend change occurring after the second break at time _tb_ 2, **1** ( _·_ ) is an indicator function selecting the regime, _p_ is the optimal lag order determined by the Bayesian information criterion (BIC) to parametrically adjust for the presence of serial correlation, and _{εt}_ is a martingale difference sequence.

Eq. (1) formalizes the notion that the evolution of GDP per capita around a slump is a simple function of time split into three different growth regimes: (1) a _pre-slump regime_ from the beginning of the time series of a country until time _tb_ 1, (2) a _slump-recovery regime_ 7
lasting from time _tb_ 1 + 1 to time _tb_ 2, and (3) a _post-slump regime_ from time _tb_ 2 + 1 onwards. The second break ( _tb_ 2) is necessary to allow the return to the historical growth path after the recovery phase, or to some new relatively steady state.

The location of the breakpoints is endogenous. We impose _two restrictions_ to make sure we only select breaks meeting our definition of slumps. First, we require _β >_ 0, so that growth must be positive in the years before a slump begins. Second, we also impose the condition that _γ_ 0 _<_ 0, so that a slump always starts with a drop in the intercept. The intercept shift implies that there is an instantaneous drop at the start of the slump. The other coefficients are left unrestricted, allowing the model to catch slumps of various shapes and durations, even if they are unfinished (e.g., a decline starting at _tb_ 1 and lasting until the end of the time series).<sup>4</sup> This approach can be extended to permit other plausible structures, such as three-break models (including, e.g., a pre-slump regime, a contraction, a recovery and a post-slump regime). However, allowing for more than two breaks seems to offer few additional benefits while adding layers of complexity.<sup>5</sup> Following Papell and Prodan (2012), we implement the sequential break search algorithm as follows. First, we fit the structural change model specified in eq. (1) for all possible combinations of _tb_ 1 and _tb_ 2. We always exclude 5% of the observations at the beginning and end of the sample to avoid registering spurious breaks. Second, we compute the sup- _W_ test statistic, that is, the supremum of a Wald test of the null hypothesis of no structural change (H0 : _γ_ 0 = _γ_ 1 = _γ_ 2 = 0) over all pairs of break dates implying estimates satisfying both restrictions. Third, we bootstrap the empirical distribution of the sup- _W_ statistic, since asymptotic break tests perform poorly in smaller samples (Antoshin et al., 2008, Prodan, 2008). If the bootstrap test rejects the null hypothesis at the desired significance level, we

> 4An alternative approach would be to impose the restriction _β_ + _γ_ 1 + _γ_ 2 _>_ 0, so that growth must be positive after the second break. A case like Madagascar in Figure 1d would then not qualify as a slump.

> 5We have experimented with three-break models using different parameter restrictions. This often identified exactly the same episodes as the simpler two-break model without providing a better estimate of the starting date. Moreover, with annual data, the contraction phase alone is often too brief to allow the observation of two separate breaks. Single break models with parameter restrictions are an alternative but then require more ex post classification in return (see the comparison in Supplementary Appendix S3).

8
record the break pair (<sup>�</sup> _tb_ 1 _,_<sup>�</sup> _tb_ 2) and split the sample into a series running until the first break and a series starting just after the second break. We always report two sets of results, for a nominal size of 10% and 20% respectively, so as to arbitrate between committing type I and type II errors. Using the 20% threshold increases the risk of detecting more false positives and, hence, primarily serves as a robustness check. The process starts again on each subsample until the bootstrap test fails to reject the null hypothesis of no breaks or the sample gets too small ( _T ≤_ 20).<sup>6</sup> We do not attempt to characterize all types of breaks an economy can experience. Broken trends blur the conceptual distinction between unit-root and trend-stationary series, and are compatible with various models of aggregate output. Our approach is flexible since it allows for multiple breaks and for different growth regimes occurring before, during and after slumps. Although we assume that there is some structure in the growth process, it need not be generated by neoclassical steady-state growth, endogenous growth, or any other specific model of economic growth.

# **The duration of declines**

Before we can define the duration of the decline phase, we need to establish when a slump has finished. A slump starts with the first break year but does not necessarily end with the second break at _tb_ 1. As long as the level of GDP per capita preceding the slump has not been recovered, the economy is experiencing a loss of output. A slump is therefore not totally over until the level of GDP per capita has at least caught up again with its own past. If that point is reached within the sample, we define the recovery to have been completed in the first year _tc >_<sup>�</sup> _tb_ 1 where _ytc ≥ y_ � _tb_ 1.

Within a slump, we identify the trough separating the decline from the recovery phase, and then focus exclusively on the decline phase. This ‘to the bottom’ approach stands in contrast to the related literature which typically focuses on the entire duration of the slump 6 Supplementary Appendix S3 provides a formal description of the break search algorithm and the bootstrap.

9
(until the economy has fully recovered). Our rationale is that the decline and recovery processes are subject to very different dynamics and depend on different covariates. The decline phase is naturally delimited by two turning points, or switches in the growth regime. By contrast, the recovery phase ends when a previous income peak is reattained, which often occurs without a change in the growth regime.

The pre-slump level of GDP per capita is not always reached again within the sample period. In that case, the duration of the slump is censored. Even though GDP per capita may be recovering, we do not know how long it will take to restore the earlier peak. A provisional trough is then observed when _yt_ attains a minimum after<sup>�</sup> _tb_ 1. To cover all cases, we estimate the trough to have occurred at time: We denote the duration of the contraction phase—lasting from the initial break (<sup>�</sup> _tb_ 1) to the estimated trough (<sup>�</sup> _tmin_ )—by<sup>�</sup> _tD_ =<sup>�</sup> _tmin −_<sup>�</sup> _tb_ 1.

It is important to note that the end of the slump when output has recovered (year _tc_ ) does not in general coincide with the start of a new growth regime (year<sup>�</sup> _tb_ 2) and is used only as a device to identify the trough. In unfinished episodes it is even possible that the trough is dated after the estimated second break (see Figure 1d). For these unfinished spells, the true trough may lie in the future, that is, beyond the end of the sample period, and<sup>�</sup> _tD_ is only a lower bound for the duration of the contraction. The analysis will treat such spells as censored, thereby fully taking this qualification into account.<sup>7</sup> Figure 1 illustrates the diversity of slumps identified by this method. Panel (a) shows a finished slump in Nicaragua where trend growth is nearly unchanged after the second break.

> 7If the slump is still ongoing, the second break may have been placed at a point that maximizes the Wald statistic but does not correspond to the start of a new growth regime (see Figure 1d). In a small number of such cases, the sequential algorithm will detect the start of a “new” slump before the previous one has ended. These are not distinct slumps and they are not included in the sample.

10
– **Figure 1** Four types of slumps <!-- Start of picture text --> (a) finished & unchanged trend (b) finished & decelerated trend<br><!-- End of picture text --> <!-- Start of picture text --> Nicaragua (NIC) Switzerland (CHE)<br>1950 1955 1960 1965 1970 1975 1940 1960 1980 2000 2020<br>2<br>4<br>1.8<br>1.6<br>3.5<br>1.4<br>1.2 3<br><!-- End of picture text -->

- **(c)** finished & accelerated trend **(d)** unfinished & negative trend

<!-- Start of picture text --> Philippines (PHL) Madagascar (MDG)<br>1940 1960 1980 2000 2020 1960 1980 2000 2020<br>2 1<br>.8<br>1.5<br>.6<br>1<br>.4<br>.5 .2<br><!-- End of picture text --> _Notes:_ Models refitted using the estimated breaks<sup>�</sup> _tb_ 1 and<sup>�</sup> _tb_ 2 but without the optimal AR( _p_ ) terms to emphasize the trend breaks. The bold vertical lines are at<sup>�</sup> _tb_ 1 and<sup>�</sup> _tb_ 2, respectively. The dashed vertical line indicates<sup>�</sup> _tmin_ . _Source_ : Authors’ analysis based on data from the Penn World Tables version 9.0 (series _rgdpna/pop_ ).

The slump begins in 1958 and the trough is found in 1959. This is a relatively short-lived crisis before the ousting of the Somoza dictatorship ushers in a major slump (and civil war), lasting from 1977 to 1993. Panel (b) shows a finished slump in Switzerland where trend growth decelerates after the second break. In 1974, the Swiss economy is strongly affected by the oil crisis of the mid-1970s, leading to a 8.79% drop in GDP per capita within two years. After a strong recovery, Switzerland enters a low growth regime typical for the high-income economies in Western Europe of the 1980s and 1990s. Panel (c) shows a finished slump in the Philippines occurring in the early 1980s with an accelerated trend after the second break. The Philippines were hit by a combination of external shocks: the second oil crisis, declining export prices, and rising world interest rates, as well as internal shocks caused by a debt 11
overhang, bail outs and capital flight. The first break in 1983 coincides with the assassination of the opposition leader Benigno S. Aquino, the trough occurs in 1985, and the second break takes place in 2001—a few years before the formal end of the slump. Although the duration of the decline phase is only two years, GDP per capita contracted by 18.63% in this short period of time. Last but not least, panel (d) shows an unfinished slump with a continuing decline in Madagascar. Madagascar grew rapidly for over a decade following independence from France in 1960 but then experienced a dramatic and ongoing collapse from 1971 onward. This roughly coincides with the transition of power from Philibert Tsiranana—Madagascar’s first president—to Didier Ratsiraka who fully seized power in 1975 by means of a coup. By 2002, the average Malagasy was about 50% poorer than in 1971. Madagascar’s GDP per capita did not recover its pre-slump level over a period of 31 years. At the end of the observed period, the decline is still ongoing and the provisional trough coincides with the censoring cutoff in 2014.

# **3 Characteristics of slumps**

We apply the sequential algorithm to the entire Penn World Table (v9.0, series _rgdpna/pop_ ) yielding 57 or 77 slumps between 1950 and 2014, depending on how permissive we are towards type I errors.<sup>8</sup> Supplementary Appendix S2 lists all episodes and Supplementary Appendix S4 provides summary statistics.

We detect many well-known growth collapses and deep recessions. Most slumps begin between the 1970s and the early 1990s. Several downbreaks occur following the oil shock in 1973–1974 and another peak occurs between 1979 and 1981 during the second oil shock and debt crisis of the early 1980s. The period between the 1970s and early 1980s is generally marked by heightened volatility, as has been documented in a number of studies (Easterly

> 8 We run the algorithm on all the PWT (v9.0) countries with a population of at least one million and at least 20 years of data. However, we discard some episodes that are driven by positive breaks in the slope coefficient but fail the negative growth criterion due to the presence of the AR( _p_ ) terms. A simple rule is applied to these cases, requiring that an actual contraction occurs within the range of the two estimated breaks, otherwise there is no slump.

12
– **Table 1** Depth and duration, by income level and region

||Mean<br>Depth|Median<br>Depth|Mean<br>Duration|Median<br>Duration|Number<br>of Spells|Censored<br>Spells|Number of<br>Countries|
|---|---|---|---|---|---|---|---|
|||_Panel _|_(a) Structur_|_al breaks est_|_imated with _|_size = 0.10_||
|_Income Level_||||||||
|High Income (OECD)|-10.9|-11.1|2.4|2|7|1|29|
|High Income (Other)|-37.5|-46.0|10.0|11|4|2|12|
|Upper Middle Income|-26.3|-20.8|8.4|3|13|2|33|
|Lower Middle Income|-23.6|-19.0|6.3|2|17|2|39|
|Low Income|-31.9|-32.1|14.0|4|16|4|34|
|_Geographical Region_||||||||
|Africa|-30.9|-28.1|15.6|8|22|6|44|
|_Northern_|-16.1|-18.9|17.1|9|3|0|5|
|_Sub-Saharan_|-33.3|-36.2|4.5|2|19|6|39|
|Americas|-20.7|-18.7|4.9|3|12|1|23|
|Asia|-28.4|-22.6|4.4|1|14|3|43|
|Europe|-17.0|-14.5|2.9|2|9|1|35|
|_Total_|-26.0|-21.0|10.3|3|57|11|145|
|||_Panel _|_(b) Structur_|_al breaks est_|_imated with _|_size = 0.20_||
|_Income Level_||||||||
|High Income (OECD)|-7.5|-4.9|1.9|2|13|1|29|
|High Income (Other)|-37.5|-46.0|10.0|11|4|2|12|
|Upper Middle Income|-23.3|-18.9|7.3|2|17|2|33|
|Lower Middle Income|-23.5|-20.0|7.0|3|22|3|39|
|Low Income|-31.0|-36.2|14.1|5|21|5|34|
|_Geographical Region_||||||||
|Africa|-29.2|-28.0|13.9|8|29|7|44|
|_Northern_|-16.1|-18.9|14.9|5|3|0|5|
|_Sub-Saharan_|-30.7|-32.1|5.7|2|26|7|39|
|Americas|-22.5|-20.0|7.7|4|14|2|23|
|Asia|-24.5|-16.6|4.0|1|18|3|43|
|Europe|-13.1|-9.9|3.0|2|16|1|35|
|_Total_|-23.5|-18.9|9.4|3|77|13|145|

_Notes_ : Depth is defined as the percent decrease in GDP per capita at the trough relative to GDP per capita before the slump (not log difference). Mean and median duration are expressed in years. As a result of some spells being censored, both mean duration and depth are underestimated. The number of countries refers to countries with more than one million inhabitants and more than 20 observations of GDP per capita in a particular income group or region. _Source_ : Authors’ analysis based on data from the Penn World Tables version 9.0 (series _rgdpna/pop_ ).

et al., 1993, Rodrik, 1999, Pritchett, 2000, Jones and Olken, 2008). Several collapses follow the post-communist transitions of 1989–1990. We find no slumps starting in the period of the early and mid-2000s, but a handful of slumps begin in 2008.<sup>9</sup> Note that the global recession of 2008–2009 is still a bit too close to the end of the sample to permit reliable

> 9Supplementary Appendix S4 reports the annual frequency of starting dates for both samples.

13
break estimation (although we later vary all potentially important parameters influencing the detection likelihood).<sup>10</sup> Table 1 summarizes the distributions of depth, duration, and number of spells across income groups and continents. For this purpose, we define the depth of a decline as the percent decrease of GDP per capita at the trough relative to its pre-slump level (‘peaktrough ratio’). The spread of depth and duration is very large. We detect considerably longer declines and deeper slumps in low-income and middle-income countries than in high-income (OECD) countries, which is hardly surprising since the history of slumps directly affects the subsequent income category. The geographical distribution reveals interesting patterns. Africa, the Americas and Asia experience the deepest and longest declines. African slumps are striking in comparison to those in other regions. The _average_ slump in Africa is deeper and longer than the ‘Great Depression’ in the United States. Due to their long duration, the continent is home to the most censored (unfinished) spells. The four longest declines all occur in sub-Saharan Africa, even though the mean duration is only 4.5 years due to a high frequency of short but deep crises. Declines in North African countries were only half as deep as in sub-Saharan Africa, but the median slump lasted almost a decade.<sup>11</sup> Declines in Asia have been very deep as well but are generally much shorter.

The movement of other variables around the starting date of a slump is another interesting feature of their characteristics. We are especially interested in any evidence of institutional change occurring before, during or after a slump. To study this question descriptively, we employ an event methodology often used in the literature on currency and banking crises (e.g. Gourinchas and Obstfeld, 2012). The basic idea is to use dummy variables indicating the distance to the start of the slump as a means of detecting changes in the relative mean of each time-varying covariate.

> 10If a slump were to start in 2008, a minimum inter-break period of four years means that the next break could only be detected in 2012. Depending on the trimming parameter and the length of the current time series the algorithm may trim off one to three years at the end of the series, so that detecting a sequence of two breaks might not be feasible without a longer time series.

> 11Note that we observe only a handful of slumps (or less) in Northern Africa which is why we treat the African continent as a single group of countries in the remainder of the paper.

14
We run the following regression for each measure of political institutions: _xit_ = 10 � _s_ = _−_ 10<sup>_δ_</sup> _t,_<sup>�</sup> _tb_ 1+ _s_<sup>_βs_+</sup><sup>_μi_+</sup><sup>_εit_where</sup><sup>_δ_</sup> _t,_<sup>�</sup> _tb_ 1+ _s_<sup>istheKroneckerdeltawhichisequaltooneif</sup> _t_ =<sup>�</sup> _tb_ 1 + _s_ and zero otherwise, _βs_ are coefficients, _μi_ is an unobserved country effect and _εit_ is an idiosyncratic error term. We set _s ∈{−_ 10 _, . . . ,_ 0 _, . . . ,_ 10 _}_ , so that the result is a 21year window around the break date<sup>�</sup> _tb_ 1. The first year of the slump is _s_ = 1 corresponding to _t_ =<sup>�</sup> _tb_ 1 + 1. The standard errors are robust to heteroskedasticity and autocorrelation within both country and time clusters (Cameron et al., 2011). We plot the estimates of the coefficients (including 95% confidence bands) as they represent the conditional expectation of _xit_ at time _s_ relative to ‘normal’ times.<sup>12</sup> – **Figure 2** Institutions and politics <!-- Start of picture text --> Executive Constraints Executive Recruitment Political Competition<br>−10 −8 −6 −4 −2 0 2 4 6 8 10 −10 −8 −6 −4 −2 0 2 4 6 8 10 −10 −8 −6 −4 −2 0 2 4 6 8 10<br>s s s<br>Autocracy Democracy Polity Score<br>−10 −8 −6 −4 −2 0 2 4 6 8 10 −10 −8 −6 −4 −2 0 2 4 6 8 10 −10 −8 −6 −4 −2 0 2 4 6 8 10<br>s s s<br>1 1<br>0<br>0 0 −1<br>−1 −1 −2<br>−2 −2 −3<br>3 0 0<br>2 −1 −2<br>1 −2 −4<br>0<br>−3 −6<br><!-- End of picture text --> _Source_ : Authors’ analysis based on data from the Polity IV project and structural breaks estimated using the Penn World Tables version 9.0 (series _rgdpna/pop_ ).

All indicators describing a country’s political institutions from the Polity IV database are significantly lower (or higher) in the decade before a slump occurs and then gradually return to normal levels thereafter. Figure 2 shows that, ten years before a slump, countries tend to

> 12In this case, ‘normal’ refers to all observations other than the 21 years around the downbreak.

15
score about one point less on indexes of constraints on the executive or executive recruitment, about two points less (or more) on indexes of political competition, autocracy or democracy, and three to four points less on the combined Polity score.<sup>13</sup> These sizable differences vanish in the decade after the beginning of a slump. Eight years after the downbreak, we can no longer reject the null hypothesis that they take on values comparable to ‘normal’ times.

This remarkably consistent pattern suggests two stylized facts: ( _i_ ) slumps are preceded by weak institutions, and ( _ii_ ) abrupt negative growth creates room for political reform. Deep crises seem to increase the pressure on governments to pursue political change, illustrating the endogenous nature of reforms. Whereas it should be easier to bear the cost of reform in good times, it is often in bad times that the power balance shifts and the opposition to reform weakens. Another implication is that we have to rule out feedback from crises to institutions in the empirical analysis of slump duration.

Supplementary Appendix S6 reports the results of similar tests for an array of macroeconomic policy indicators. The general pattern is as expected: the size of the government increases during a slump, while prices and exchange rates react predictably. Other variables are trending over the entire period, but almost none exhibit such stark changes at the start of a slump as the pattern of institutional change documented here.

# **4 Explaining the duration of declines**

## **Empirical strategy**

We model the duration of declines in an ‘accelerated failure time’ (AFT) framework, where ‘failure time’ refers to time until the turnaround at<sup>�</sup> _tmin_ , that is, the end of the contraction phase. An AFT model is analogous to a classical log-linear regression model for duration and similarly easy to interpret. The hazard function and survival function are characterized

> 13These indexes have different ranges: ‘Executive Constraints’ ranges from 1 (unlimited authority) to 7 (parity or subordination), ‘Executive Recruitment’ from 1 (hereditary) to 8 (free and fair), ‘Political Competition’, ‘Autocracy’, and ‘Democracy’ from 1 to 10, and the ‘Polity Score’ from _−_ 10 to 10.

16
indirectly by the distribution of the error terms, for which a normal distribution is only one among several possibilities.

AFT models are estimated by Maximum Likelihood (ML) which can easily incorporate incomplete and censored spells. If a contraction is observed from start (<sup>�</sup> _tb_ 1) to finish (<sup>�</sup> _tmin_ ), its contribution to the likelihood is the probability of the recovery beginning at � � _tmin_ , conditionally on the contraction having lasted until _tmin_ . If the end of the contraction is not observed within the sample, then the spell is censored and only the so-called survival probability enters the likelihood. The difference between complete and incomplete spells is crucial. Persistent contractions are much more likely to be censored than brief contractions. Simply truncating unfinished spells biases their estimated duration downward. Dropping all unfinished spells is even worse, as persistent spells are then underrepresented and the sample biased towards short-lived contractions.

We have no strong theoretical prior concerning the error distribution or the implied shape of the hazard function. We may expect some countries to exit rather swiftly and others to take longer, but we leave open whether a prolonged decline phase will lead to a deterioration of fundamentals with a decreasing hazard, or whether the probability of a turnaround may be increasing over time as the contraction lasts.

Let<sup>�</sup> _t ≡ t −_<sup>�</sup> _tb_ 1 denote the time elapsed within a decline spell (‘spell time’). We specify the following regression equation for crisis durations in AFT form: where _INS_ 0 is a measure of institutions fixed at _t_ 0 _≡_<sup>�</sup> _tb_ 1, _ELF_ is a time-invariant measure of ethno-linguistic fractionalization, **x** 0 = ( _x_ 0 _,_ 1 _, x_ 0 _,_ 2 _, . . . , x_ 0 _,k_ )<sup>_′_</sup> is a _k ×_ 1 vector of controls fixed at _t_ 0 _≡_<sup>�</sup> _tb_ 1, usually including region fixed effects and a constant, and **z** _t_ = ( _zt,_ 1 _, zt,_ 2 _, . . . , zt,m_ )<sup>_′_</sup> is an _m ×_ 1 vector of time-varying controls assumed to be strictly exogenous. In the case of a log-normal model, _εt_ is distributed _N_ (0 _, σε_<sup>2).</sup> Although we start off with this log17
normal parameterization, we later test the robustness of our specification under different distributional assumptions.<sup>14</sup> Our main coefficients of interest are _β_ 1, _β_ 2 and _γ_ . We suppress the country-spell index to simplify the exposition.

The estimated coefficients are semi-elasticities of the expected duration with respect to the covariates, or elasticities if the covariates are in logs. They act as modifiers of the scale on which spell time is measured (which explains the term ‘accelerated’ failure time). A negative coefficient means that higher values of the covariate shorten the expected duration of the contraction phase and hasten recovery. Conversely, a positive coefficient means that higher values of the covariate delay recovery and prolong the expected duration of contraction (in effect ‘decelerating’ spell time).

The presence of the time-varying covariates **z** _t_ complicates estimation somewhat and becomes problematic if there is feedback from the spell duration to the covariates. In that case, the estimated coefficients are biased and the usual test statistics are invalid (Lancaster, 1990, Kalbfleisch and Prentice, 2002). We are mostly worried about the endogeneity of political institutions, given that the previous section highlights that they may respond endogenously to crises. We are less concerned about ethnic diversity, since it is time-constant in our data and historically predetermined.<sup>15</sup> The interaction term is also less of a concern. Interactions of an endogenous variable with an exogenous variable are identified under fairly mild conditions (see Bun and Harrison, 2014).<sup>16</sup> Therefore, to prevent feedback, we set potentially endogenous covariates at their last pre-slump value, corresponding to _t_ 0 =<sup>�</sup> _tb_ 1. In effect, we rely on the temporal ordering to identify these relationships. The estimates should nonetheless be interpreted as partial correlations rather than causal effects.<sup>17</sup> Unobserved

> 14For details, see Table S7.5 in Supplementary Appendix S7.

> 15The ethnic configuration of a country rarely changes as a short-run response to an economic decline, which is not to deny some tragic exceptions.

> 16Countries can have several recurrent slumps, but this is a minor concern in our application. To account for this dependence, we allow the error terms to be correlated across spells in the same country. This procedure assumes that the sequence of repeated spells does not matter. We show in the robustness section that our results hold when this assumption is relaxed.

> 17Supplementary Appendix S5 shows simple split sample plots where we show the duration of declines over constraints of the executive or ethnic heterogeneity for different quantiles of the other variable. The lines are crossing at some point, already suggesting the presence of an interaction effect in the raw data.

18
factors could still be determining political institutions and the duration of declines jointly, regardless of the proxies used in the regressions.

# **Results**

Our baseline specification models the duration of declines as a function of executive constraints, ethnic fractionalization, the log of initial GDP, and the US real interest rate. Constraints on the executive are our preferred proxy of political institutions for two reasons. First, the index is widely used in the empirical literature as a measure of institutional constraints placed on political actors and has already been linked to macroeconomic volatility (e.g. Acemoglu et al., 2003, Acemoglu and Johnson, 2005). Second, it is conceptually rooted in the economic theory of institutions, more so than any of the broader measures capturing wider aspects of the political regime (e.g. democracy or autocracy). Our proxy for the number and relative strength of other identity groups in society is ethnic fractionalization. This variable can be interpreted as the probability that two randomly chosen individuals in a country belong to different ethno-linguistic groups and is often used in the literature on conflict (e.g. Esteban and Ray, 2011). We use the finest level of linguistic disaggregation from Desmet et al. (2012). Controlling for initial GDP is important, as executive constraints are correlated with the level of development and both potentially determine the duration of declines. The US real interest rate serves as a proxy for ‘good’ or ‘bad’ times in the global economy (see Berg et al., 2012). This control too is important, since we cannot parameterize duration dependence and include a full set of time effects at the same time.

There are strong first-order effects of executive constraints and ethnic heterogeneity on the duration of declines. Column (1) to (3) in Table 2 present the corresponding results where we sequentially add region fixed effects and initial decade fixed effects to our base specification. Panel (a) reports the results for a false positive break detection rate of 10% and panel (b) increases this parameter to 20% in the hope of committing fewer type II errors. As mentioned earlier, this raises the possibility of detecting ‘false slumps’ which 19
– **Table 2** Correlates of slump duration

|||_Additive_|||_Interacte_|_d_|
|---|---|---|---|---|---|---|
||||_Fixed ef_|_ects on . . ._|||
||None<br>(1)|Region<br>(2)|Region + Time<br>(3)|None<br>(4)|Region<br>(5)|Region + Time<br>(6)|
|<sup>�</sup>||_Panel (a)_|_Structural break_|_s estimated wi_|_th size = 0.1_|_0_|
|Executive Constraints ( <br>_INS_0)<br>|-0.150**<br>(0.062)|-0.174***<br>(0.063)|-0.227***<br>(0.072)|-0.225***<br>(0.072)|-0.255***<br>(0.077)|-0.282***<br>(0.084)|
|Fractionalization ( <sup>�</sup><br>_ELF_15)|0.011**|0.016***|0.016***|0.014***|0.020***|0.019***|
||(0.005)|(0.005)|(0.005)|(0.005)|(0.005)|(0.005)|
|�<br>_INS_0_×_ <sup>�</sup><br>_ELF_15||||-0.004**<br>(0.002)|-0.004***<br>(0.001)|-0.003*<br>(0.002)|
|US Real Interest Rate|-0.139**|-0.134***|-0.122**|-0.118*|-0.118**|-0.111**|
||(0.061)|(0.052)|(0.056)|(0.060)|(0.051)|(0.054)|
|Initial log GDP|-0.149**|-0.017|-0.056|-0.157**|-0.029|-0.061|
||(0.076)|(0.091)|(0.078)|(0.072)|(0.089)|(0.078)|
|Exits|43|43|43|43|43|43|
|Spells|54|54|54|54|54|54|
|Years of Decline|383|383|383|383|383|383|
|Log-_L_|-76.214|-73.641|-68.075|-74.780|-71.978|-66.941|
|Pseudo-R<sup>2</sup>|0.098|0.129|0.195|0.115|0.149|0.208|
|||_Panel (b)_|_Structural break_|_s estimated wi_|_th size = 0.2_|_0_|
|Executive Constraints ( <sup>�</sup><br>_INS_0)|-0.179***|-0.177***|-0.167***|-0.237***|-0.239***|-0.215***|
||(0.055)|(0.059)|(0.055)|(0.058)|(0.062)|(0.060)|
|Fractionalization ( <sup>�</sup><br>_ELF_15)|0.007|0.011**|0.011**|0.009**|0.014***|0.013***|
||(0.004)|(0.005)|(0.005)|(0.005)|(0.005)|(0.005)|
|�<br>_INS_0_×_ <sup>�</sup><br>_ELF_15||||-0.004**|-0.003**|-0.003*|
|||||(0.002)|(0.001)|(0.002)|
|US Real Interest Rate|-0.073|-0.067*|-0.075|-0.059|-0.059|-0.071|
||(0.046)|(0.041)|(0.054)|(0.044)|(0.040)|(0.052)|
|Initial log GDP|-0.130*|-0.021|-0.065|-0.135**|-0.037|-0.075|
||(0.067)|(0.075)|(0.070)|(0.064)|(0.073)|(0.069)|
|Exits|61|61|61|61|61|61|
|Spells|74|74|74|74|74|74|
|Years of Decline|489|489|489|489|489|489|
|Log-_L_|-105.691|-102.837|-97.312|-104.042|-101.201|-96.122|
|Pseudo-R<sup>2</sup>|0.083|0.108|0.156|0.097|0.122|0.166|

_Notes_ : All models include a constant. The standard errors are clustered at the country level to account for repeated spells. Some countries are not included in the Polity IV data, so that we analyze at most 54 (out of 57) or 74 (out of 77) spells.<sup>_∗_</sup> _p <_ 0 _._ 1,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01 _Source_ : Authors’ analysis based on data from the Penn World Tables version 9.0 (series _rgdpna/pop_ ). Additional data come from the Polity IV project, Desmet et al. (2012), and the Federal Reserve Economic Data (FRED) database.

is why we consider it to be a robustness check and always focus our interpretation on the first set of results (unless indicated otherwise). The coefficients of executive constraints and fractionalization are significant in all estimations, apart from column (1) in panel (b) where the appropriate fixed effects are omitted and the coefficient on fractionalization turns marginally insignificant. An improvement in executive constraints by one standard deviation (2.28 points) is associated with a reduction in the duration of the decline phase of about 20
29–40%. A difference in fractionalization of one standard deviation (33.05 percentage points) corresponds to a difference in duration of about 30–41%. Neither the inclusion of region fixed effects nor the addition of initial decade fixed effects fundamentally change these results.

A key point of our paper is that these first few models miss a potentially important interaction effect. If greater ethnic diversity challenges the ability of political actors to take coordinated action, more cohesive institutions may help to overcome this vulnerability by internalizing these disputes and limiting the downside risks for the involved groups (see e.g. Rodrik, 1999, Bluhm and Thomsson, 2015). Hence, countries with a high degree of ethnic fractionalization may require strong institutions just to compensate. Conversely, countries with a greater degree of ethnic homogeneity may achieve a similar degree of social coordination with less developed institutions. This hypothesis is a less restrictive variant of the idea that there is a multiplicative effect between social conflict (broadly defined) and institutions in response to external shocks (Rodrik, 1999). Although such an effect has been proposed in the literature (e.g. Alesina and Ferrara, 2005), there is little empirical evidence of it, especially when it comes to the more precise channels through which political institutions can ‘mute’ the adverse effects of ethnic heterogeneity.

We find strong, albeit indirect, evidence suggesting that coordination matters when it comes to managing downturns. Columns (4) to (6) of Table 2 present the same specifications as before with the addition of an interaction between ethnic heterogeneity and constraints on the executive. In order to ease the interpretation, the institutions and fractionalization variables have been demeaned by subtracting their sample average from the observed values.<sup>18</sup> The demeaned variables are denoted _INS_<sup>�</sup> 0 and _ELF_<sup>�</sup> . The interaction effect is negative (as expected), significant at conventional levels in each regression, and remarkably stable. Since the earlier specifications are nested in these columns, testing the significance of

> 18This has the following effect. If either one of the two variables is at its mean, then the interaction term is zero. As a result, the coefficient of the institutions variable directly measures the effect of executive constraints at the average level of fractionalization, and vice versa. Away from the means, the coefficient on the interaction term becomes important. Note that the statistical significance of the interaction term is not affected, and that demeaning is irrelevant when the interaction term is absent.

21
the interaction basically amounts to testing that the interaction model fits the data better. Likelihood ratio tests also prefer the interaction model (at the 10% significance level) and the pseudo-R<sup>2</sup> s improve in every column.

– **Figure 3** Average partial effects <!-- Start of picture text --> (a) Fractionalization, size = 0.10 (b) Executive Constraints, size = 0.10<br>1 2 3 4 5 6 7 0 25 50 75 100<br>Executive Constraints (INS0) Fractionalization (ELF)<br>(c) Fractionalization, size = 0.20 (d) Executive Constraints, size = 0.20<br>1 2 3 4 5 6 7 0 25 50 75 100<br>Executive Constraints (INS0) Fractionalization (ELF)<br>1 .04 1 .2<br>)0<br>.03<br>.75 .75 0<br>.02<br>.5 .5 −.2<br>.01<br>Fraction of Data Fraction of Data<br>.25 .25 −.4<br>0<br>Partial Effect of Fractionalizaton (ELF)<br>Partial Effect of Executive Constraints (INS<br>0 −.01 0 −.6<br>1 .03 1 .2<br>)0<br>.75 .02 .75 0<br>.01<br>.5 .5 −.2<br>Fraction of Data Fraction of Data<br>0<br>.25 .25 −.4<br>Partial Effect of Fractionalizaton (ELF)<br>−.01 Partial Effect of Executive Constraints (INS<br>0 0 −.6<br><!-- End of picture text --> _Note(s)_ : Panels (a) and (b) illustrate the results for a nominal size of 10 percent. The break underlying panels (c) and (d) are estimated with a nominal size of 20 percent. The average partial effects shown in panels (a) to (d) are based on column (5) of Table 2 and are computed over the entire range of the variable on the horizontal axis while all other variables take on their respective realizations. The dashed lines are upper and lower 95 percent confidence limits. _Source_ : Authors’ analysis based on data from the Penn World Tables version 9.0 (series _rgdpna/pop_ ). Additional data come from the Polity IV project, Desmet et al. (2012), and the Federal Reserve Economic Data (FRED) database.

The estimated effects are both economically and statistically significant across a wide range of situations. Figure 3 plots the average partial effect with respect to one variable of the interaction term over representative values of the other, including a 95% confidence interval. The vertical axis on the right measures the average predicted semi-elasticity of the expected duration with respect to fractionalization or executive constraints. The effect 22
sizes can be interpreted just like coefficients. For example, when the executive constraints index is at unity (‘unlimited authority’), then a one percentage point higher fractionalization corresponds to a 2.5% longer duration of contractions (and a standard deviation rise in fractionalization when constraints are weak more than doubles the expected duration). In the background, Figure 3 also shows histograms of the sample. Executive constraints scores cover the entire range from 1 to 7, and ethno-linguistic fractionalization ranges from about zero (0.41) to near-total heterogeneity (96.5).

The predictions also cover a wide range of the observed duration. At the average score of executive constraints, a country with the highest (lowest) degree of ethnic heterogeneity is expected to decline for about 15.7 years (2.1 years). Hence, it would be difficult to understand the effects of institutions without taking fractionalization into account. Countries with stronger institutions can overcome the adverse effects of high fractionalization. At the 75<sup>_th_</sup> percentile of ethnic heterogeneity (= 91 _._ 9), a country with the highest (lowest) score of executive constraints is expected to decline for about 2.5 years (20 years). Or, as panel (b) shows, the partial effect of a unit change in executive constraints at perfect homogeneity is practically zero, while it peaks at about -31% at perfect heterogeneity. The estimates for the larger sample are very similar.

For our second set of main results, we investigate whether the effects of institutions and ethnic cleavages on the depth of slumps run solely through the duration of the decline phase or if they also affect the rate of contraction. This is an important distinction to make. We ultimately care about the _net present value of lost output_ (Pritchett et al., 2016) which is a function of these two variables. We use a simple decomposition of the depth of declines which abstracts from the potential income gains a country could experience in absence of a slump and simply takes pre-slump GDP per capita ( _yi_ 0) as the reference point. The depth of the decline in percent of pre-slump GDP per capita is the product of the estimated duration and the average growth rate during the contraction. Hence, we may define _g_ ̄ _i ≡_ � _yi,_ � _tmin − yi_ 0� _/_ (<sup>�</sup> _tmin − tb_ 1) _≡_ � _yi,_ � _tmin − yi_ 0� _/_<sup>�</sup> _tD_ as the average rate of decline 23
and<sup>�</sup> _tD ×_ ̄ _gi ≡ yi,_ � _tmin − yi_ 0 as the overall depth of the decline. Note that _g_ ̄ _i_ is negative by construction. We scale both outcomes by one hundred for readability.

– **Table 3** Average growth rate during decline and total depth

||||_Depende_|_nt variable_|||
|---|---|---|---|---|---|---|
|| ̄_gi_<br>(1)| ̄_gi_<br>(2)| ̄_gi_<br>(3)| ̃_tD ×_ ̄_gi_<br>(4)| ̃_tD ×_ ̄_gi_<br>(5)| ̃_tD ×_ ̄_gi_<br>(6)|
||_Pa_|_nel (a) Stru_|_ctural break_|_s estimated _|_with size = _|_0.10_|
|Executive Constraints ( <sup>�</sup><br>_INS_0)|0.227|0.627**|0.732***|4.151***|5.617***|6.275***|
||(0.190)|(0.238)|(0.217)|(1.486)|(1.351)|(1.581)|
|Fractionalization ( <sup>�</sup><br>_ELF_15)|0.007|-0.017|-0.017|-0.153**|-0.291***|-0.309***|
||(0.013)|(0.011)|(0.016)|(0.074)|(0.092)|(0.100)|
|�<br>_INS_0_×_ <sup>�</sup><br>_ELF_15|0.002|0.003|0.003|0.048|0.060**|0.053|
||(0.005)|(0.004)|(0.005)|(0.033)|(0.029)|(0.032)|
|US Real Interest Rate|0.082|0.015|0.220|2.773**|2.613**|0.805|
||(0.135)|(0.140)|(0.268)|(1.207)|(1.122)|(2.204)|
|Initial log GDP|0.158|0.413|0.436*|2.076|2.179|2.571|
||(0.261)|(0.310)|(0.240)|(1.482)|(1.796)|(1.766)|
|Region FE|NO|YES|YES|NO|YES|YES|
|Initial Decade FE|NO|NO|YES|NO|NO|YES|
|Spells|54|54|54|54|54|54|
|Log-_L_|-142.247|-137.284|-125.939|-243.640|-241.930|-236.354|
|Adjusted R<sup>2</sup>|-0.064|0.056|0.302|0.232|0.231|0.296|
||_Pa_|_nel (b) Stru_|_ctural break_|_s estimated _|_with size = _|_0.20_|
|Executive Constraints ( <sup>�</sup><br>_INS_0)|0.302*|0.513**|0.481**|4.309***|5.159***|5.016***|
||(0.157)|(0.197)|(0.188)|(1.230)|(1.234)|(1.302)|
|Fractionalization ( <sup>�</sup><br>_ELF_15)|0.002|-0.021**|-0.021*|-0.106*|-0.241***|-0.251***|
||(0.010)|(0.010)|(0.012)|(0.062)|(0.081)|(0.086)|
|�<br>_INS_0_×_ <sup>�</sup><br>_ELF_15|0.007*|0.009**|0.010**|0.061*|0.074**|0.065**|
||(0.004)|(0.004)|(0.004)|(0.032)|(0.030)|(0.032)|
|US Real Interest Rate|0.017|0.015|0.124|1.806*|1.924**|0.698|
||(0.110)|(0.112)|(0.225)|(0.960)|(0.931)|(1.855)|
|Initial log GDP|0.146|0.270|0.407|1.650|1.668|2.296|
||(0.218)|(0.268)|(0.247)|(1.303)|(1.531)|(1.591)|
|Region FE|NO|YES|YES|NO|YES|YES|
|Initial Decade FE|NO|NO|YES|NO|NO|YES|
|Spells|74|74|74|74|74|74|
|Log-_L_|-189.696|-186.990|-176.881|-331.143|-328.817|-324.852|
|Adjusted R<sup>2</sup>|0.001|0.029|0.200|0.222|0.235|0.256|

_Notes_ : All models include a constant. The standard errors are clustered at the country level to account for repeated spells.<sup>_∗_</sup> _p <_ 0 _._ 1,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01 _Source_ : Authors’ analysis based on data from the Penn World Tables version 9.0 (series _rgdpna/pop_ ). Additional data come from the Polity IV project, Desmet et al. (2012), and the Federal Reserve Economic Data (FRED) database.

We find only weak evidence of an effect of either executive constraints or fractionalization on the average rate of decline, but effects comparable to those from the duration regressions when we consider the overall depth of declines. Columns (1) and (2) in both panels of Table 3 report the results for the interacted model without and with region fixed effects. Column 24
(3) adds initial decade effects. Though the coefficient of institutions may be statistically significant, it is economically small (e.g. about 0.22–0.63 percentage points of _g_ ̄ _i_ in the case of a unit change in executive constraints away from the mean) and explains almost none of the variation on top of the regional (and decade) fixed effects. The remaining columns of Table 3 examine the overall depth of the slump. Columns (4) to (6) illustrate that we now recover the previously estimated effects, albeit with some loss of significance. (The coefficients have reversed signs since _g_ ̄ _i_ is negative.) Moreover, the interaction models explain 23–30% of the variation in depth, highlighting the relevance of the estimated effects.

Taken together, the estimates lead us to conclude that ( _i_ ) political institutions and ethnic heterogeneity are robustly correlated with the overall depth of slumps, and ( _ii_ ) these correlations run primarily through the duration of the decline phase.

One way to interpret this finding is through the lens of the delayed stabilizations literature (Alesina and Drazen, 1991). When (ethnic) groups engage in a ‘war of attrition’ over the burden of reform and are uncertain about how the reform will benefit all other groups (hence their willingness to bear the costs), then policy reform is delayed until the weakest group concedes. The expected time until stabilization is achieved increases with the number of groups involved in the decision-making process and the veto rights they possess, so that the adjustment speed depends on the political system (Spolaore, 2004). However, this interpretation does not explain the strong interaction effect between executive constraints and ethnic heterogeneity very well. A weakly constrained executive might actually reduce the duration of the decline phase in such models, if it implies that other groups possess less veto power.

Bluhm and Thomsson (2015) propose a different theory of how ethnic heterogeneity leads to delayed responses during crises. Groups facing a crisis have to decide on a policy response under uncertainty about post-crisis outcomes. When the executive is unconstrained, some groups have an incentive to delay cooperation as they fear boosting the strength of the independent executive and its power to expropriate them in the aftermath of a 25
crisis. Conversely, if institutions are sufficiently strong, the risk of expropriation practically disappears and only the uncertainty due to the crisis remains. There is still plenty of room for coordination failures to occur but the commitment problem that _always_ induces non-cooperative behavior disappears. If groups can fortify their position through blocking agreement on different policies (e.g. a nationalization or taking conditional loans), then such a mechanism generates the observed interaction. The commitment problem gets worse with increasing group diversity, but can be resolved by stronger constraints on the executive at all levels of heterogeneity.

As one example, Zambia’s decline from 1968 until 1994 can be understood through the lens of this coordination perspective and fits well with the stylized facts presented here. Zambia is very ethnically heterogeneous, had weak constraints on the political executive just after independence, and experienced one of the longest declines in our data set. The crisis began with an exogenous terms of trade shock when copper prices fell rapidly in the late 1960s (Fraser, 2010). Declining incomes sparked tensions between the Bemba-speaking Copperbelt and the government, so that violent riots had become a frequent sight (Posner, 2005). Then president Kenneth Kaunde reacted with nationalizations and the introduction of a single party government. Kaunde ethnically balanced his cabinet, which he hoped would curb tribalism (Larmer, 2008) but had the opposite effect (Posner, 2005). The crisis dragged on until mine workers and a former trade unionist brought down Kaunde’s government, reintroduced multi-party democracy, which enabled broad non-tribal coalitions, and ushered in an era of structural reforms. Copper prices eventually recovered and aided the recovery but the average Zambian was poorer in 2014 than at the start of the crisis. Supplementary Appendix S1 discusses the political economy of this case in more detail.

As mentioned in the introduction, the argument that impersonal rules and limits on executive power can help to mitigate the risk of deeper economic contractions also parallels recent historical work. Broadberry and Wallis (2017) trace historical improvements in the frequency and rate of shrinking and the subsequent stabilization of positive growth to the 26
replacement of identity rules by a system of impersonal rule. Societies based on identity rules place few constraints on the power of the elite vis-`a-vis the rest of society but grant some rule of law within elites. Most importantly, unconstrained power makes it difficult for rulers to credibly commit to rule-based dealings with less powerful groups. This is precisely how we think ethnic heterogeneity enters the picture. It reflects the presence of other identity groups, such as the Bemba miners in Zambia, with whom the ruling elite needs to coordinate (Bluhm and Thomsson, 2015).

# **Robustness**

Our main conclusions are unaffected by a variety of robustness checks, such as dropping influential observations, varying the national accounts data, and the inclusion of policy variables. We only present a selection of the most insightful perturbations here. A comprehensive battery of robustness checks can be found in Supplementary Appendix S7.

Table 4 takes the estimated breaks and data as given and focuses on unobserved heterogeneity and influential observations. Column (1) adds country-level random effects (frailties) and income-level fixed effects on top of region fixed effects. Column (2) drops former Soviet countries. Both perturbations do not affect our findings. Column (3) takes a more radical step and drops all of Africa. The estimated effect sizes are smaller and we lose precision on the interaction term. This loss of significance is hardly surprising. The African continent is home to some of the most ethnically diverse countries with the weakest institutions. Africa drives our results but also provides much of the variation needed to estimate the overall effect.<sup>19</sup> Column (4) deletes the censored spells. Now the estimated effect of political institutions is even smaller, and the interaction term becomes insignificant. A look into the underlying data reveals what is going on. Countries with completed spells

> 19We have also taken a less radical approach and interacted our three variables of interest with an Africa dummy. In every case, the added interactions are individually and jointly insignificant at the 5% level. We take this as further evidence that our results can be generalized to settings outside of Africa, such as Asia, where institutions are stronger and linguistic diversity is comparable. We thank an anonymous referee for pointing us towards this test.

27
have an average score of 3.1 on the index of executive constraints and an average degree of heterogeneity close to 55 out of 100. Countries with ongoing slumps score 1.54 in terms of executive constraints and have an average level of heterogeneity of about 77. The values refer to the smaller sample, but are not much different for the data underlying panel (b). This strongly suggests that it is the composition of the two groups that matters and not the fact of censoring. The whole point of duration analysis is that we can use censored observations without introducing bias. Note that if the slumps are defined in terms of GDP rather than GDP per capita, there are considerably fewer censored spells and our results hold in both samples without the censored data points.

– **Table 4** Robustness: heterogeneity, outliers and recurrent spells

||||_Current _|_modifcation_|||
|---|---|---|---|---|---|---|
||Country<br>RE<br>(1)|Without<br>Ex-Soviet<br>(2)|Without<br>Africa<br>(3)|Without<br>Censored<br>(4)|Single<br>Spells<br>(5)|Endogenous<br>Institutions<br>(6)|
||_P_|_anel (a) Stru_|_ctural break_|_s estimated _|_with size = _|_0.10_|
|Executive Constraints ( <sup>�</sup><br>_INS_0)<br>|-0.292***<br>(0.100)|-0.274***<br>(0.078)|-0.093*<br>(0.054)|-0.035<br>(0.051)|-0.256**<br>(0.105)|-0.213**<br>(0.087)|
|Fractionalization ( <sup>�</sup><br>_ELF_15)<br><br>|0.019***<br>(0.007)|0.021***<br>(0.005)|0.007**<br>(0.004)|0.009**<br>(0.004)|0.020***<br>(0.006)|0.018***<br>(0.006)|
|�<br>_INS_0_×_ <sup>�</sup><br>_ELF_15|-0.005**<br>(0.002)|-0.004***<br>(0.001)|-0.002*<br>(0.001)|-0.002<br>(0.001)|-0.005***<br>(0.002)|-0.005***<br>(0.002)|
|Region FE|YES|YES|YES|YES|YES|YES|
|Income Level FE|YES|NO|NO|NO|NO|NO|
|Exits|43|40|30|43|38|38|
|Spells|54|51|35|43|46|55|
||_P_|_anel (b) Stru_|_ctural break_|_s estimated _|_with size = _|_0.20_|
|Executive Constraints ( <sup>�</sup><br>_INS_0)<br>|-0.212***<br>(0.080)|-0.250***<br>(0.063)|-0.104**<br>(0.053)|-0.085*<br>(0.044)|-0.250***<br>(0.081)|-0.221***<br>(0.073)|
|Fractionalization ( <sup>�</sup><br>_ELF_15)|0.012**|0.014***|0.003|0.008**|0.012*|0.012**|
||(0.006)|(0.005)|(0.005)|(0.003)|(0.006)|(0.005)|
|�<br>_INS_0_×_ <sup>�</sup><br>_ELF_15|-0.004**<br>(0.002)|-0.004**<br>(0.001)|-0.002<br>(0.002)|-0.002**<br>(0.001)|-0.004**<br>(0.002)|-0.005***<br>(0.002)|
|Region FE|YES|YES|YES|YES|YES|YES|
|Income Level FE|YES|NO|NO|NO|NO|NO|
|Exits|61|57|42|61|52|54|
|Spells|74|70|48|61|61|75|

_Notes_ : All models include the US real interest rate, the log of initial GDP, and a constant. The standard errors are clustered at the country level to account for recurrent spells.<sup>_∗_</sup> _p <_ 0 _._ 1,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01 _Source_ : Authors’ analysis based on data from the Penn World Tables version 9.0 (series _rgdpna/pop_ ). Additional data come from the Polity IV project, Desmet et al. (2012), and the Federal Reserve Economic Data (FRED) database.

Until now, we have treated spells recurring in the same country as interchangeable.

28
Column (5) of Table 4 investigates whether this particular form of conditional independence is a reasonable assumption. It turns out that our findings are robust to excluding all spells other than the first, ruling out dependency among recurring slumps.

Column (6) allows for institutions to change endogenously during the decline phase (meaning we replace _INS_<sup>�</sup> 0 by _INS_<sup>�</sup> _t_ ). We showed earlier that constraints on the executive and a number of other institutional indicators improve in the aftermath of a crisis. So far, we have excluded this endogenous adjustment for two reasons: ( _i_ ) it could bias the estimation in favor of our hypothesis (if institutions both improve as a response and then shorten the remaining decline phase), and ( _ii_ ) elites might find it difficult to credibly promise to bind their hands and not renege later on (Acemoglu and Robinson, 2006). In any case, these concerns are not borne out empirically. The results remain quantitatively and qualitatively similar to our baseline.<sup>20</sup> A serious issue in the cross-country growth literature is the fragility of regression estimates with respect to different GDP series. Johnson et al. (2013) show that new renditions of the Penn World Tables (PWT) incorporating more recent PPP benchmarks can imply vastly different historical growth rates. Worse still, they point out that the findings of several prominent studies, including Ramey and Ramey (1995) and Hausmann et al. (2005) cited in the introduction, are not robust to different versions of the PWT.<sup>21</sup> In fact, this has prompted major changes in how the PWT data are prepared and made available to the public. Starting from version 8 different time series are reported for level and growth rate comparisons.<sup>22</sup> We take these concerns seriously and repeat the restricted structural break search using two very different data sets (in addition to the PWT 9). Specifically, we use the version 7 vintage of the PWT—before the computation of the chained series changed

> 20Another way of tackling this issue is to truncate the data and censor all spells lasting longer than a few, say five, years. Our results are robust to this type of truncation (at various points). We thank an anonymous referee for suggesting this test to us.

> 21 We are indebted to an anonymous referee for pointing this out to us.

> 22The PWT version 7 already offered a new series, _rdgpl_ 2, based on the growth rate of domestic absorption, as a response to the work of Johnson et al. (2013). We obtain very similar results when using this series instead of _rgdpch_ in the robustness checks.

29
fundamentally—and national accounts data from the World Development Indicators in local currencies. Each time we consider GDP per capita and GDP. We prefer GDP per capita throughout the paper since it is closer to a welfare relevant metric but expect to obtain similar results with GDP.

– **Table 5** Robustness: different data sets for GDP and GDP per capita

||_GD_|_P per capita s_|_eries_|_G_|_DP series_||
|---|---|---|---|---|---|---|
||PWT7<br>_rgdpch_<br>(1)|PWT9<br>_rgdpna/pop_<br>(2)|WDI<br>_gdplcu/pop_<br>(3)|PWT7<br>_rgdpch × pop_<br>(4)|PWT9<br>_rgdpna_<br>(5)|WDI<br>_gdplcu_<br>(6)|
|||_Panel (a) Str_|_uctural breaks _|_estimated with s_|_ize = 0.10_||
|Executive Constraints ( <sup>�</sup><br>_INS_0)<br>|-0.209***<br>(0.064)|-0.255***<br>(0.077)|-0.170**<br>(0.069)|-0.181***<br>(0.046)|-0.163***<br>(0.045)|-0.161***<br>(0.051)|
|Fractionalization ( <sup>�</sup><br>_ELF_15)<br><br>|0.018***<br>(0.004)|0.020***<br>(0.005)|0.009*<br>(0.005)|0.011***<br>(0.003)|0.009***<br>(0.002)|0.008***<br>(0.002)|
|�<br>_INS_0_×_ <sup>�</sup><br>_ELF_15|-0.003**<br>(0.001)|-0.004***<br>(0.001)|-0.005**<br>(0.002)|-0.003***<br>(0.001)|-0.004***<br>(0.001)|-0.003***<br>(0.001)|
|Exits|48|43|47|59|63|53|
|Spells|58|54|59|62|64|56|
|||_Panel (b) Str_|_uctural breaks _|_estimated with s_|_ize = 0.20_||
|Executive Constraints ( <sup>�</sup><br>_INS_0)<br>|-0.197***<br>(0.048)|-0.239***<br>(0.062)|-0.175***<br>(0.060)|-0.122***<br>(0.036)|-0.109**<br>(0.046)|-0.142***<br>(0.042)|
|Fractionalization ( <sup>�</sup><br>_ELF_15)<br><br>|0.014***<br>(0.004)|0.014***<br>(0.005)|0.009**<br>(0.004)|0.008***<br>(0.003)|0.006**<br>(0.003)|0.007***<br>(0.002)|
|�<br>_INS_0_×_ <sup>�</sup><br>_ELF_15|-0.004***<br>(0.001)|-0.003**<br>(0.001)|-0.004***<br>(0.002)|-0.004***<br>(0.001)|-0.003***<br>(0.001)|-0.003***<br>(0.001)|
|Exits|67|61|61|72|76|75|
|Spells|79|74|75|75|77|78|

_Notes_ : All models include region FEs, the US real interest rate, the log of initial GDP, and a constant. The standard errors are clustered at the country level to account for repeated spells.<sup>_∗_</sup> _p <_ 0 _._ 1,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01 _Source_ : Authors’ analysis based on data from the Penn World Tables version 7.0, Penn World Tables version 9.0, and the World Development Indicators. Additional data come from the Polity IV project, Desmet et al. (2012), and the Federal Reserve Economic Data (FRED) database.

Our main findings are virtually unaffected by the choice of data sets and preference for a particular national income series. Table 5 takes our preferred specification, including the mean-centered interaction term and region fixed effects, but replaces the dependent variable with the duration estimated by running our break search algorithm on these different data sets. Columns (1) to (3) focus on GDP per capita, while columns (4) to (6) report results for GDP. As usual, we consider two type I error rates in panels (a) and (b). All results are qualitatively (and often even numerically) similar. Note that the underlying list of slumps 30
differs for each output series we consider, in part because the growth rates are computed differently and in part because these data sets have different temporal and geographical coverage.

– **Table 6** Robustness: policy variables

||||_Policy _|_variable_|||
|---|---|---|---|---|---|---|
||Policy<br>Volatility<br>(1)|Government<br>Size<br>(2)|Infation<br>(ln[1 +_δ_])<br>(3)|RER<br>Underval<br>(4)|Export<br>Share<br>(5)|Trade<br>Openness (d.f.)<br>(6)|
|||_Panel (a) St_|_ructural break_|_s estimated w_|_ith size = 0_|_.10_|
|Executive Constraints ( <sup>�</sup><br>_INS_0)<br>Fractionalization ( <sup>�</sup><br>_ELF_15)<br><br>|-0.255***<br>(0.075)<br>0.020***<br>(0.005)|-0.254***<br>(0.073)<br>0.020***<br>(0.005)|-0.235***<br>(0.073)<br>0.019***<br>(0.005)|-0.253***<br>(0.075)<br>0.020***<br>(0.005)|-0.239***<br>(0.079)<br>0.018***<br>(0.005)|-0.260***<br>(0.078)<br>0.018***<br>(0.005)|
|�<br>_INS_0_×_ <sup>�</sup><br>_ELF_15|-0.004***<br>(0.001)|-0.004***<br>(0.001)|-0.004***<br>(0.001)|-0.004***<br>(0.002)|-0.004***<br>(0.001)|-0.004***<br>(0.001)|
|Policy Variable|-0.011<br>(0.027)|-0.000<br>(0.012)|0.005***<br>(0.002)|0.142<br>(0.388)|0.019**<br>(0.009)|0.006<br>(0.006)|
|Exits|43|43|32|43|43|43|
|Spells|54|54|39|54|54|54|
|||_Panel (b) St_|_ructural break_|_s estimated w_|_ith size = 0_|_.20_|
|Executive Constraints ( <sup>�</sup><br>_INS_0)<br>|-0.243***<br>(0.062)|-0.241***<br>(0.062)|-0.221***<br>(0.060)|-0.238***<br>(0.062)|-0.238***<br>(0.062)|-0.250***<br>(0.067)|
|Fractionalization ( <sup>�</sup><br>_ELF_15)<br><br>|0.013***<br>(0.005)|0.014***<br>(0.005)|0.014**<br>(0.005)|0.013***<br>(0.005)|0.012**<br>(0.005)|0.012**<br>(0.005)|
|�<br>_INS_0_×_ <sup>�</sup><br>_ELF_15|-0.003**<br>(0.001)|-0.003**<br>(0.001)|-0.003**<br>(0.002)|-0.004**<br>(0.002)|-0.003**<br>(0.001)|-0.004**<br>(0.001)|
|Policy Variable|-0.013<br>(0.022)|0.003<br>(0.010)|0.005***<br>(0.001)|0.069<br>(0.295)|0.014**<br>(0.007)|0.005<br>(0.004)|
|Exits|61|61|45|61|61|61|
|Spells|74|74|54|74|74|74|

_Notes_ : All models include region FEs, the US real interest rate, the log of initial GDP, and a constant. The standard errors are clustered at the country level to account for repeated spells.<sup>_∗_</sup> _p <_ 0 _._ 1,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01 _Source_ : Authors’ analysis based on data from the Penn World Tables version 9.0 (series _rgdpna/pop_ ). The policy variables are computed using data from the PWT version 9.0, the World Development Indicators and the International Financial Statistics. Additional data come from the Polity IV project, Desmet et al. (2012), and the Federal Reserve Economic Data (FRED) database.

The relationships reported here survive many other sensible tests. They are robust to ( _i_ ) using different bootstrap techniques, ( _ii_ ) varying the interbreak period, ( _iii_ ) using longer time series, ( _iv_ ) adding other controls individually or in groups, ( _v_ ) changing the functional form, and ( _vi_ ) dropping individual countries one-by-one. Using different institutional indicators shows that—in line with theory—a narrow measure of constraints on the executive generates stronger interactions than broader measures of political institutions. All corresponding 31
results are reported in Supplementary Appendix S7.

Finally, we examine if accounting for macroeconomic policies changes the nature of our conclusions with respect to political institutions and heterogeneity.<sup>23</sup> Much of the earlier literature argues that (bad) policies are mostly a function of (weak) institutions. Fat ́as and Mihov (2013), however, show that the volatility of macroeconomic policy matters for growth, even within the _same_ set of institutions. Table 6 reports the results from adding several proxies for economic policies to our regression. As in Fat ́as and Mihov (2013), policy volatility is defined as the (rolling) standard deviation of the residuals from countrylevel regressions of the log-difference of real government consumption spending on the log difference of real GDP. Among the added indicators, only inflation and the export share turn out to be significant predictors of the duration of declines. Seriously analyzing the role of policy variables exceeds the scope of this paper, but it seems safe to conclude that such effects are not competing with our main results.

# **5 Concluding remarks**

Severe downward volatility is an ubiquitous phenomenon in the post-war period. While the literature has often stressed the role of positive growth spurts, our paper emphasizes that slumps can quickly undo the gains from growth and that, in some cases, the decline phase lasts very long. As it turns out, economic slumps are harder to identify than growth accelerations but also have a distinct pattern. We show that a restricted structural change approach directly incorporating this pattern works well as an inferential method for identifying economic slumps in a large sample of countries. We find a substantial number of slumps of varying length in developing and developed countries alike.

In the title of this paper we ask if weak institutions prolong crises—a question we believe can only be meaningfully answered with cross-country data. Our answer is yes, constraining leaders is beneficial for limiting the downside of negative shocks. However, our analysis

> 23We are indebted to an anonymous referee for pointing us in this direction.

32
suggests that there are, at least, two important qualifications to be made.

First, our event study illustrates that weak political institutions precede crises and positive institutional change occurs during and in the immediate aftermath of slumps. Our interpretation of this stylized fact is that, while good institutions may sustain growth, growth collapses can in turn contribute to endogenous institutional change. Severe economic crises seem to raise the pressure for institutional reform in a very broad sense.

Second, the length and depth of economic slumps is negatively correlated with constraints on the executive but the favorable effects of strengthening constraints are greater in ethnically heterogeneous societies. We take this as an indication that effective coordination and responses to slumps are hampered by the existence of many identity groups with whom the ruling group needs to coordinate. However, the coordination problems implied by ethnic heterogeneity are not immutable and can be overcome by strong impersonalized rules. We also provide evidence that these effects run primarily through the duration until the recovery starts and not through the pace of decline.

This interplay between weakly constrained leaders and group diversity is not well captured by current theories of policy reform and delay, which typically focus on information asymmetries or uncertainty about the benefits of reform. Our results are more in line with recent historical accounts which stress the emergence of impersonal rules, though without recognizing the role of ethnic or interest group diversity. Hence, a key reason to strengthen political institutions is the stabilization of growth, particularly in diverse countries and perhaps more so than the literature has emphasized so far.

33