Entrenched Autocracy or New Democracy: Which Is Better for Business?

KYKLOS, Vol. 67 – August 2014 – No. 3, 398–419

Entrenched Autocracy or New Democracy: Which Is Better for Business? Kanybek Nur-tegin*

I. INTRODUCTION In the past three-and-a-half decades there has been a significant shift towards democratic governance. According to the Center for Systemic Peace (CSP), the number of autocracies has plummeted, while the number of democracies has increased dramatically. CSP’s chart depicting global changes in governance for 1800–2011 is shown in Figure 1. This trend towards democracy appears to have accelerated over the past few years in many parts of the world. Popular uprisings have occurred in the countries of the former Soviet Union, some leading to the overthrow of long-standing autocrats, including the Rose Revolution in Georgia in 2003, the Orange Revolution in Ukraine in 2004 with a new uprising and government overthrow in 2014, and the Tulip Revolution in Kyrgyzstan in 2005 with another uprising and government overthrow in 2010. Decades-long autocracies in the Middle East and North Africa were toppled or were forced to make significant political concessions during the 2010–2012 Arab Spring. Considerable political changes occurred in East and Southeast Asia, including Nepal, Thailand, and Myanmar. An unprecedented number of former heads of states and prime ministers are being charged with corruption and even jailed, according to recent media reports1. The latest case involves Pakistan’s Supreme Court’s order to arrest the country’s sitting prime minister Raja Pervez Ashraf 2. *

Associate Professor of Economics, Wilkes Honors College, Florida Atlantic University, USA. Contact information: 5353 Parkside Drive, Jupiter, FL 33458; [email protected]; 1-561-799-8650. 1. Recent examples include ex-president of Taiwan Lee Teng-hui (http://www.nytimes.com/2011/07/01/ world/ asia/01taiwan.html?_r=0), ex-president of the Philippines Gloria Macapagal-Arroyo of the Philippines (http://www.cnn.com/2012/10/04/world/asia/philippines-ex-president-arrested/index.html), ex-president of Mongolia Nambar Enkhbayar (http://www.aljazeera.com/news/asia-pacific/2012/08/ 20128393741765865.html), ex-president of Peru Alan Garcia (http://www.peruthisweek.com/news -2436-Former-President-Alan-Garcia-to-face-corruption-charges-in-Congress/), and ex-president of Argentina Fernando de la Rua (http://www.dw.de/argentine-ex-president-appears-on-corruption -charges/a-16168626). 2. http://www.nytimes.com/2013/01/16/world/asia/pakistan-high-court-orders-arrest-of-prime-minister .html?hp

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ENTRENCHED AUTOCRACY OR NEW DEMOCRACY Figure 1

Global Trends in Governance, 1800–2011

Source: Center for Systemic Peace; http://www.systemicpeace.org/polity/polity1.htm.

This fast-paced global advent of democracy can generally be considered a welcome phenomenon as it brings the promise of substantial political improvements in the lives of millions of people. Amartya Sen (1999, 6) has argued that ‘. . . political freedom is a part of human freedom in general, and exercising civil and political rights is a crucial part of good lives of individuals as social beings’. However, democratic revolutions often have a negative side effect: the downfall of a longstanding autocracy generally leads to a period of political instability. The question raised in this paper is whether a nation benefits economically from a transition to a democratic form of governance. The comparative economic merits of democracy versus autocracy have been thoroughly researched. Numerous studies also point to the negative economic impact of political instability. An important and novel argument put forward in this paper is that a comparison of the economic advantages of alternative regime types should be carried out by taking into account the stability of the regimes under consideration. A number of arguments have been put forward as to why democracy may be a superior form of governance for promoting economic growth. Drury et al. (2006, 124) note that ‘democracy allows for the eviction of bad leaders’. Kurzman et al. (2002, 4) argue that ‘dictatorship, however benevolent, © 2014 John Wiley & Sons Ltd.

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undermines the rule of law needed for routine economic activity’ and that in a democracy ‘legal and electoral limits on arbitrary power give individuals the security to plan for their economic futures’. Doucouliagos and Ulubasoglu (2008) maintain that citizens are more motivated to work and invest when they are free to choose how to allocate their resources and have access to uncontrolled information, both of which are more likely in a democracy. In addition, cronyism and corruption, which are negatively correlated with growth, tend to be greater in countries ruled by autocrats (Bueno de Mesquita et al., 2001; Nur-tegin and Czap, 2012). Democracies are also better in fostering entrepreneurship and innovation (Bhagwati, 2002) and even diffusion of telecommunications technology (Holkeboer and Vreeland, 2013). The positive effect of education on economic growth has also been found to be stronger in more democratic countries (Armellini, 2012). Dictatorships have also been argued to have economic advantages. Sen (1999) reminds us about ‘the Lee hypothesis’, named after Lee Kuan Yew, Singapore’s long-serving former Prime Minister, who suggested that discipline is more important than democracy for a country’s development. The Lee hypothesis became widely known partly due to the growth success of relatively authoritarian East and South-East Asian economies, including Singapore, South Korea, and more recently China. As noted by Nelson and Singh (1998), democracy has even been seen as an inefficient luxury that only rich countries can afford. Bhagwati (2002) notes that democracy may entail inefficiencies due to rent-seeking and warns of ‘a danger of what Jonathan Rauch has called demosclerosis: the paralysis of gridlock afflicting a lobbying-infested democracy’ (p. 155). Peev and Mueller (2012) find that greater democratization among the republics of the former Soviet Union is associated with not only greater economic freedoms that tend to advance growth but also with larger public sectors and public deficits that tend to retard growth. Doucouliagos and Ulubasoglu (2008) list other advantages of autocracies over democracies given in the literature, including a better ability to quickly mobilize resources, suppress conflicts, and force unpopular but growth-promoting measures. Empirically, there is no overwhelming evidence in favor of either of the two types of regime. Two meta-analyses confirm this. Kurzman et al. (2002) examine 47 empirical papers on the relationship between democracy and growth, which included 19 studies that found the relationship to be positive, six studies where the relationship was negative, and ten studies that found the link to be statistically insignificant. Doucouliagos and Ulubasoglu (2008, 62) review ‘483 regression estimates from 84 published democracy-growth studies’ and report that ‘15% of the estimates are negative and statistically significant, 21% of the estimates are negative and statistically insignificant, 37% of the estimates are positive and statistically insignificant, and 27% of the estimates are positive and statistically significant’.

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ENTRENCHED AUTOCRACY OR NEW DEMOCRACY

There is a strand of literature that argues that democracy is better for economic performance, but its superiority over autocracy is evident only in the long run, when democracy is stable and mature. According to Xu and Li (2008, 183), ‘the effect of political freedom on promoting economic growth is realized and detectable at later stages of social and economic development’. They further note that their ‘estimates . . . are robust under all models and specifications’ (p. 186). Kurzman et al. (2002) report similar results. Papaioannou and Siourounis (2008, 1523) cite a number of papers that ‘provide positive within correlations between democracy and long run growth’ and report that their empirical results support Friedrich Hayek’s (1960) remark that ‘the benefits of democracy will show themselves only in the long run, while its more immediate achievements may well be inferior to those of other forms of government’. Thus, a country that ousts its autocrat (e.g. Egypt or Libya) or transitions to democracy in some other way (e.g. former Soviet Union) may have to wait a few decades to see the economic advantages of democracy. In the meantime, these countries may experience a period of political instability as transition to democracy is not an immediate or a smooth process. In nations previously ruled by autocrats for centuries, the new democratic form of governance will require deep cultural shifts and considerable institutional adjustments. The unstable political climate has been shown to have a strong detrimental effect on the economy (e.g. see Chong and Gradstein, 2009). According to Alesina et al. (1996) and Kurzman et al. (2002), political instability disrupts production, discourages long-term planning, and drives away investors. Because the time that it takes for the new democratic regime to mature may be quite long, encompassing a considerable portion of a person’s life, a pertinent question for people in autocratically governed countries is whether democracy really is worth pursuing over preserving the autocracy despite the instability that may ensue. This question can be posed in terms of real-world examples: Which Egypt and Tunisia made their people better off, the pre- or post-revolution ones? Or, how do post-revolution Kyrgyzstan and Ukraine compare to their neighbors, Kazakhstan and Belarus, respectively, which have been ruled by unchanging autocrats for two decades? The purpose of this paper is to provide an answer to these questions from an empirical standpoint. It is worth emphasizing here what this paper does not do: it does not compare autocracies to mature democracies, such as the United States or Switzerland. This paper is different from the existing studies in several important ways. First, I make the comparison of the economic environment in newly established democracies to that in entrenched autocracies by directly and explicitly accounting for the regime’s political stability, and my results indicate that this interaction between regime and stability is important. Second, the interaction index in this paper is constructed using continuous measures of instability and power concentration, unlike in most other analyses where one or both of these variables are © 2014 John Wiley & Sons Ltd.

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simple zero-one dummies. Third, unlike most other empirical studies that rely on country-level data, this paper analyzes firm-level variations based on a global survey of tens of thousands of business enterprises conducted by the World Bank and its partners. Chong and Gradstein (2009) analyze a related issue of volatility and economic growth and observe that ‘much of the existing research relies on cross country analysis . . .’ (p. 2). Fourth, my dependent variable and many control variables do not rely on aggregate statistics reported by national statistical agencies, but are derived from direct answers to standardized survey questions. Given the large number of countries included in my analysis – ninety-four – there is likely to be a great deal of variation in the methods and quality of data collection among countries. In contrast, the World Bank surveys are carefully standardized and its ‘sampling criteria . . . [are] very precise’ (Chong and Gradstein, 2009). Fifth, unlike much of the existing research that proxy economic activity by GDP per capita (which, among other things, does not take into account the size of the underground economy), the dependent variable in this paper is constructed on the basis of direct evaluations of the business environment by firm owners and managers. Survey data, of course, have their own drawbacks, most notably their reliance on the subjective responses of the participants. But as Chong and Gradstein (2009, 2) note, ‘it is precisely a subjective assessment . . . that matters for firm decision making, and ultimately, growth’. II. DATA AND ESTIMATION The main purpose of this paper is to see how businesses fare in politically stable countries governed by a strong autocrat compared to those in countries that are more democratic but less stable. The source of data for the dependent variable is Enterprise Surveys, a firm-level worldwide survey of tens of thousands of business enterprises conducted by the World Bank and its partners. The survey includes several questions designed to gauge respondents’ assessments of their business environment. One such set of questions is replicated in Table 1. The questions in Table 1 were used in the forming of the first of two alternative measures of the dependent variable, Business Obstacles. This measure was created by summing the responses to these questions for each cross-sectional unit i, i.e. Business Obstaclesi [1] = j30ai + j30bi + j30ci + j30fi + h30i. A small proportion of observations with ‘Do Not Know’ or ‘Does Not Apply’ entries for any component of the sum was dropped. For robustness, a different set of Enterprise Surveys questions was used to create the second measure of the dependent variable. These questions are listed in Table 2. This alternative proxy for the dependent variable, Business Obstacles [2], was obtained by summing the scores for questions c30a through l30b. The Regime-Stability Index is the main independent variable of interest. It was designed to reflect the differences in regime types and political stability among

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ENTRENCHED AUTOCRACY OR NEW DEMOCRACY Table 1 Survey questions used in construction of the dependent variable Business Obstaclesi [1] As I list some of many factors that can affect the current operations of a business, please look at this card and tell me if you think that each factor is No Obstacle, a Minor Obstacle, a Major Obstacle, or a Very Severe Obstacle to the current operations of this establishment.* No obstacle

Minor obstacle

Moderate obstacle

Major obstacle

Very severe obstacle

Do Not Know

Does Not Apply

0 0

1 1

2 2

3 3

4 4

-9 -9

-7 -7

0

1

2

3

4

-9

-7

0 0

1 1

2 2

3 3

4 4

-9 -9

-7 -7

j30a. Tax rates j30b. Tax administration j30c. Business licensing and permits j30f. Corruption h30. Courts

*The questionnaire also includes part j30e (political stability), dropped here for obvious reasons.

Table 2 Survey questions used in construction of the dependent variable Business Obstaclesi [2] Is [insert factor] No Obstacle, a Minor Obstacle, a Major Obstacle, or a Very Severe Obstacle to the current operations of this establishment? No Minor Moderate Major Very severe Do Not Does Not obstacle obstacle obstacle obstacle obstacle Know Apply c30a. Electricity d30a. Transportation of goods, supplies & inputs d30b. Customs & trade regulations e30. Practices of competitors in the informal sector i30. Street crime, theft and disorder k30. Access to financing l30a. Labor regulations l30b. Inadequately educated workforce

0 0

1 1

2 2

3 3

4 4

-9 -9

-7 -7

0

1

2

3

4

-9

-7

0

1

2

3

4

-9

-7

0

1

2

3

4

-9

-7

0

1

2

3

4

-9

-7

0

1

2

3

4

-9

-7

0

1

2

3

4

-9

-7

countries. In construction of this variable, all countries in the sample were arranged along a regime type – regime stability continuum, which increases as we move from countries with greater political freedoms and greater instability towards more stable but more autocratic states. As does any other index (e.g. the Consumer Price Index or the Corruption Perceptions Index), the © 2014 John Wiley & Sons Ltd.

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KANYBEK NUR-TEGIN Table 3. Alternative Regime-Stability Index Formulas RSI [1]

1 ⎛ ⎞ Autocracy ⋅ ⎜ ⎝ 1 + ∑Tt =1 (rct ) ⎟⎠

RSI [2]

1 ⎛ ⎞ Autocracy ⋅ ⎜ ⎝ 1 + ∑Tt =1 (t ⋅ rct ) ⎟⎠

RSI [3] RSI [4]

Autocracy · (duration of current regime)

RSI [5]

⎛ 1 + duration of current regime ⎞ Autocracy ⋅ ⎜ ⎟⎠ ⎝ 1 + ∑Tt =1 (t ⋅ rct )

⎛ 1 + duration of current regime ⎞ Autocracy ⋅ ⎜ ⎟⎠ ⎝ 1 + ∑Tt =1 (rct )

A Regime-Stability Index (RSI) is the independent variable of interest that consists of two components: Autocracy and Stability.

Regime-Stability Index suffers from a certain degree of arbitrariness. To partially alleviate this problem I examine the sensitivity of my results to several alternative ways of measuring the regime type – regime stability variation among countries by creating five versions of the Regime-Stability Index, referred to as RSI [1] – [5] hereafter. Each version of the index multiplicatively interacts a measure of concentration of political power, Autocracy, with various measures of political stability: Autocracy × Stability. Both components of this index are based on the latest data provided by the Polity IV Project of the Center for Systemic Peace. The formulas for each version of the index are given in Table 3. In Table 3, rct is a regime change dummy variable that is equal to one for every year a revolution, coup d’état or other regime change that occurs within a given time period. As mentioned above, all Regime-Stability Indices increase with both greater concentration of power and greater stability of the regime: Autocracy and duration of current regime are in the numerator, while the sum of regime changes is in the denominator. Possible divisions by zero are avoided by adding 1 to the denominator. The duration of current regime is measured in years. In RSI [2] and RSI [5], more recent regime changes are weighted more heavily by means of multiplying by t, which ranges from 1 to the last year in a given period, T. Further details on the construction of the Regime-Stability Indices are provided in the Appendix. Table 4 shows RSI scores for each country in my final sample. A quick examination of these scores shows that relatively democratic, but unstable countries, such as Ivory Coast, Liberia, Montenegro, and Kyrgyzstan, are located at the lower end of the spectrum, while more stable and more autocratic countries like Vietnam, Swaziland, Uzbekistan, and Chad are clustered at the opposite end. The rest of the independent variables can be categorized as either firm-level or country-level controls. The data for all firm-level control variables come from

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ENTRENCHED AUTOCRACY OR NEW DEMOCRACY Table 4 Regime-StabilityIndices by Country Country Albania Angola Argentina Armenia Azerbaijan Bangladesh Belarus Benin Bhutan Bolivia Botswana Brazil Bulgaria Burkina Faso Burundi Cameroon Chad Chile Colombia Congo Braz. Congo, DR. Costa Rica Croatia Czech R. Dominican R Ecuador El Salvador Eritrea Estonia Gabon Gambia Georgia Ghana Guatemala Guinea Guinea-B. Guyana Honduras Hungary Indonesia Ivory Coast Jamaica Kazakhstan Kenya Kyrgyzstan Laos Latvia

RSI [1] RSI [2] RSI [3] RSI [4] RSI [5] 0.50 1.86 3.00 1.50 3.60 5.00 6.00 4.00 2.67 4.00 3.00 3.00 2.00 5.50 0.42 7.50 6.50 1.00 4.00 5.00 0.38 1.00 0.67 1.50 1.50 3.00 3.00 9.00 0.67 15.00 4.00 5.00 0.75 1.50 6.00 0.71 2.50 4.00 1.00 1.00 1.10 2.00 17.00 1.00 2.67 18.00 3.00

0.15 0.59 3.00 0.35 1.64 5.00 1.80 4.00 0.25 4.00 3.00 3.00 2.00 1.00 0.06 7.50 6.50 1.00 4.00 1.88 0.05 1.00 0.11 1.00 0.50 0.60 3.00 6.00 0.11 15.00 1.07 5.00 0.18 0.50 2.40 0.09 2.50 4.00 1.00 0.19 0.09 2.00 17.00 0.17 0.27 18.00 3.00

18 156 78 60 234 75 198 68 0 108 129 69 36 77 0 240 208 20 208 165 18 90 12 45 39 54 75 270 16 255 128 80 15 39 120 0 85 108 18 27 0 100 289 12 16 594 51

5.00 24.14 81.00 16.50 50.40 80.00 72.00 72.00 2.67 112.00 132.00 72.00 38.00 44.00 0.42 127.50 110.50 21.00 212.00 60.00 1.50 91.00 4.67 24.00 21.00 30.00 78.00 144.00 6.00 270.00 36.00 85.00 4.50 21.00 66.00 0.71 45.00 112.00 19.00 10.00 1.10 102.00 306.00 5.00 8.00 612.00 54.00

1.54 7.68 81.00 3.88 22.91 80.00 21.60 72.00 0.25 112.00 132.00 72.00 38.00 8.00 0.06 127.50 110.50 21.00 212.00 22.50 0.20 91.00 0.78 16.00 7.00 6.00 78.00 96.00 1.00 270.00 9.60 85.00 1.06 7.00 26.40 0.09 45.00 112.00 19.00 1.88 0.09 102.00 306.00 0.83 0.80 612.00 54.00

Country Lesotho Liberia Lithuania Macedonia Madagascar Malawi Mali Mauritania Mauritius Mexico Moldova Mongolia Montenegro Mozambique Namibia Nepal Nicaragua Niger Pakistan Panama Paraguay Peru Philippines Poland Romania Russia Rwanda Senegal Serbia Sierra Leone Slovak R. Slovenia South Africa Swaziland Tajikistan Tanzania Togo Trinidad Turkey Uganda Ukraine Uruguay Uzbekistan Venezuela Vietnam Yemen Zambia

RSI [1] RSI [2] RSI [3] RSI [4] RSI [5] 0.43 0.45 1.00 1.00 1.00 1.67 2.00 8.00 1.00 0.60 3.00 0.50 1.00 3.00 5.00 1.67 2.00 1.25 8.00 2.00 1.50 0.40 3.00 1.00 1.00 2.33 7.00 1.50 1.50 0.50 0.50 1.00 0.50 20.00 3.50 6.00 5.00 1.00 4.00 4.00 4.00 1.00 20.00 7.00 18.00 4.33 2.00

0.06 0.07 1.00 0.17 0.27 0.83 2.00 1.07 1.00 0.16 3.00 0.50 0.13 1.50 5.00 0.19 2.00 0.33 1.78 2.00 1.50 0.09 3.00 1.00 0.33 0.64 3.50 0.30 0.19 0.07 0.33 1.00 0.29 20.00 0.93 2.40 3.75 1.00 4.00 0.71 4.00 1.00 20.00 0.74 18.00 3.25 0.38

18 10 17 12 40 70 68 0 40 36 51 16 4 72 75 10 38 45 112 40 51 16 63 17 24 56 154 18 6 24 15 17 24 640 126 120 225 47 96 0 64 24 320 0 972 208 30

3.00 1.36 18.00 7.00 11.00 25.00 36.00 8.00 41.00 7.80 54.00 8.50 3.00 39.00 80.00 5.00 40.00 12.50 64.00 42.00 27.00 3.60 66.00 18.00 13.00 21.00 84.00 10.50 4.50 3.50 8.00 18.00 6.50 660.00 35.00 66.00 80.00 48.00 100.00 4.00 68.00 25.00 340.00 7.00 990.00 73.67 12.00

0.44 0.20 18.00 1.17 2.93 12.50 36.00 1.07 41.00 2.05 54.00 8.50 0.38 19.50 80.00 0.56 40.00 3.33 14.22 42.00 27.00 0.78 66.00 18.00 4.33 5.73 42.00 2.10 0.56 0.49 5.33 18.00 3.71 660.00 9.33 26.40 60.00 48.00 100.00 0.71 68.00 25.00 340.00 0.74 990.00 55.25 2.25

The values in this table increase with more autocracy and greater stability.

Enterprise Surveys and include several industry dummies, the form of business ownership, whether or not the firm has female owners, the size of the firm proxied by the number of employees, years in operation, international quality certification, and an external auditor dummy. Country-level controls include a logarithm of GDP per capita, government’s share in GDP, openness to trade, and the degree of corruption. The first three of these variables were obtained from the World Development Indicators online © 2014 John Wiley & Sons Ltd.

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KANYBEK NUR-TEGIN Table 5 Summary Statistics* Name RSI [1] RSI [2] RSI [3] RSI [4] RSI [5] y [1]

Description

Standardized Regime-Stability Index [1] Standardized Regime-Stability Index [2] Standardized Regime-Stability Index [3] Standardized Regime-Stability Index [4] Standardized Regime-Stability Index [5] Standardized Business Obstacles [1] – dep. variable y [2] Standardized Business Obstacles [2] – dep. variable SoleProp Sole proprietorship, dummy variable Female Has female owners, dummy variable ISO Int’l quality certification, dummy variable YIO Years in operation, number of years Audit External audit in the last year, dummy variable Size Number of permanent, full-time employees Manufacturing Manufacturing sector dummy (ISIC-D) Construction Construction sector dummy (ISIC-F) W&Retail Wholesale and retail sector dummy (ISIC-G) Hotels Hotels sector dummy (ISIC-H) CPI Corruption (CPI, matches Enterprise Surveys years) WGI Corruption (WGI, 2009) LnGDPpc Log GDP per capita in PPP, mean of 2006–2010 Trade Imports + Exports, % of GDP, mean of 2006–2010 Govt Government Expenditures, % of GDP

Mean

Std. Dev.

Min

Max

0 0 0 0 0 0

1 1 1 1 1 1

-0.7493 -0.6271 -0.5872 -0.4830 -0.4429 -1.4906

3.9756 4.0879 5.4652 5.5411 5.5196 2.4317

0

1

-1.8110

3.1521

0.2678 0.4428 0.3130 0.4637 0.2327 0.4225 18.6945 16.89 0.5083 0.4999

0 0 0 0 0

1 1 1 310 1

0

64000

0.5619 0.0460 0.2675

0.4962 0 0.2095 0 0.4427 0

1 1 1

0.0512 3.3185

0.2204 0 1.2434 1.6

1 7.2

118.061 611.994

-0.3927 8.4328

0.6194 -1.4167 1.0201 5.7044

75.8517 33.8385 24.6149 13.9365

5.0852 5.3956

1.3710 10.1518 203.83 37.0160

* The summary statistics shown here are based on the first measure of the dependent variable, Business Obstacles [1]. The number of observations is between 36,766 and 39,381 firms; CPI ranges from 0 (highly corrupt) to 10 (very clean); and WGI ranges from -2.5 (more corrupt) to 2.5 (less corrupt).

database and were averaged over the period of five years (2006–2010, which correspond to the Enterprise Surveys years used in my sample) to reduce the impact of annual volatility. The degree of corruption was proxied by Transparency International’s Corruption Perceptions Index (CPI) as well as Control of Corruption variable from the Worldwide Governance Indicators (WGI). Additional details on all variables are provided in Table 5: Summary Statistics. For interpretational convenience, the Regime-Stability Indices and the dependent variable proxies were transformed into standard deviations from their respective means. Given the hierarchical nature of the dataset with thousands of firms in dozens of countries, individual observations are likely to be correlated among firms

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within each country. Including country-level control variables, such as GDP per capita and CPI, is not sufficient for handling this intracluster correlation. Ordinary least squares estimation of regression coefficients can lead to overstatement of precision of estimated coefficients even when intraclass correlation is small (Angrist and Pischke, 2009). Models with clustered data are usually estimated by one of three common approaches: OLS with cluster-adjusted standard errors, multilevel regressions, and random effects models. Arceneaux and Nickerson (2009) note that all of these three methods are structurally identical. Therefore, I estimate my main regressions using the random effects model given in equation (1):

Business Obstaclesig = a + β1RSI g + β 2 X g + β3 Z ig + ug + ε ig,

(1)

where Business Obstaclesig is the dependent variable for firm i in country g, RSIg is the Regime-Stability Index that varies only at the country level, Xg is a vector of control variables that also vary only at the country level, Zig is a vector of firm-specific characteristics, ug is a random component specific to country g, and εig is the idiosyncratic disturbance term. III. RESULTS The results of estimating equation (1) based on Business Obstacles [1] as the dependent variable and RSI [5] as the main regressor of interest are presented in Table 6. From the top row of Table 6 it is clear that the coefficient for the RegimeStability Index is negative, sizeable, and highly statistically significant. Thus, a movement of one standard deviation from the mean of the index from unestablished democracies towards entrenched autocracies is associated with a reduction in business obstacles, as perceived by firm managers, by about 0.13 standard deviations from the mean. That is, if democracy comes with instability, then the business environment appears to be worse compared to that in more politically stable but autocratic countries. This result is economically significant and it holds across many alternative specifications of the regression model. Briefly, Table 6 includes a number of other regressors with statistically and economically significant coefficients. For example, countries where the level of corruption is lower (higher CPI and WGI scores) have a better business environment. The estimated coefficients for both CPI and WGI are consistently negative and statistically and economically significant throughout all regressions. Sole proprietorships appear to encounter fewer obstacles in doing business than larger firms. This result may be due to the fact that due to their small size sole proprietorships find it easier to ‘hide’ from bureaucratic obstacles. Nur-tegin (2008) found a similar result in the context of tax evasion, where smaller firms © 2014 John Wiley & Sons Ltd.

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408


−0.4321 (−1.25) 39381 0.7182 0.1838

,

−0.1363 (−2.62) −0.0863 (−7.45) 0.0687 (7.16) −0.1763 (−3.87) 0.1165 (2.37)

t-statistics are in parentheses.

# of observations ‘within’ variance ‘between’ variance

Constant

Govt

Trade

ISO

Size

Female

YIO

WGI

Hotels

W&Retail

Construction

Manufacturing

LnGDPpc

CPI

Audit

SoleProp

RSI [5]

(1)

0.4353 (−1.28) 39381 0.7180 0.1776

−0.1363 (−2.66) −0.0838 (−7.22) 0.0680 (7.09) −0.1768 (−3.95) 0.1154 (2.39) 0.0166 (0.96) 0.0573 (2.23) 0.0189 (1.04) −0.0572 (−2.29)

(2)

−1.1827 (−2.95) 39381 0.7180 0.1727

0.1170 (2.55) 0.0167 (0.96) 0.0575 (2.23) 0.0188 (1.04) −0.0570 (−2.28) −0.3722 (−4.61)

−0.1263 (−2.53) −0.0838 (−7.21) 0.0682 (7.11)

(3)

−0.4169 (−1.30) 38937 0.7166 0.1597

0.0008 (2.84)

−0.0730 (−3.63)

0.0396 (1.88)

−0.1351 (−2.78) −0.0827 (−7.06) 0.0650 (6.68) −0.1758 (−4.14) 0.1130 (2.46)

(4)

−0.4205 (−1.28) 39381 0.7179 0.1670

0.0170 (1.74)

−0.0741 (−3.72)

0.0425 (2.03)

−0.1366 (−2.75) −0.0820 (−7.05) 0.0680 (7.09) −0.1766 (−4.07) 0.1148 (2.45)

(5)

−0.4209 (−1.24) 39214 0.7173 0.1785

−4e(−06) (−0.57)

−0.0740 (−3.71)

0.0408 (1.95)

−0.1363 (−2.65) −0.0849 (−7.30) 0.0680 (7.03) −0.1777 (−3.96) 0.1162 (2.39)

(6)

0.0011 (0.10)

−0.0730 (−3.66)

0.0417 (1.99)

−0.1362 (−2.67) −0.0833 (−7.14) 0.0677 (6.89) −0.1769 (−3.97) 0.1157 (2.40)

(7)

−0.4223 (−1.25) 39381 0.7180 0.1760

GLS estimates (random effects). Dependent variable: Business Obstacles [1]; main independent variable: RSI [5].

Table 6

−0.4002 (−1.18) 39381 0.7180 0.1756

−0.0009 (−0.73)

−0.0730 (−3.66)

0.0418 (2.00)

−0.1294 (−2.50) −0.0834 (−7.19) 0.0678 (7.07) −0.1738 (−3.89) 0.1216 (2.49)

(8)

−0.0047 (−0.57) −0.3570 (−1.00) 39381 0.7180 0.1764

−0.0730 (−3.66)

0.0418 (2.00)

−0.1394 (−2.71) −0.0835 (−7.19) 0.0679 (7.07) −0.1704 (−3.70) 0.1138 (2.35)

(9)

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were better able to become ‘invisible’. Somewhat related to this is the positive, albeit small, coefficient for YIO, the number of years in operation. The positive and significant coefficient for Audit indicates that firms whose financial statements were reviewed by an external auditor found the business environment to be less favorable than firms without any external audit. Further, the business environment was worse for firms whose main business was in construction and better for firms in the hotel business. Surprisingly, the business environment was rated as less favorable in countries with more per capita GDP. This effect may have to do with a possibility that firms in some countries tend to kvetch more on average3. Here, more kvetching appears to happen in countries with more income per capita, as indicated by the large and significant negative coefficient on lnGDPpc in the probit part of the regression reported in Table 9 below. The remaining coefficients were very small in magnitude and not statistically significant. As mentioned above, I have two proxies of the dependent variable, Business Obstacles [1] and [2] (also referred to as y [1] and y [2]), and five alternative measures of the Regime-Stability Index, RSI [1] – [5]. To examine the robustness of the results found above, nine additional regressions were estimated using these alternative measures of the regressand and the key regressor. The selection of other independent variables to be included into the latter regressions was made based on their significance in the regressions reported in Table 6. The results are summarized in Table 7. The top row of Table 7 shows that the negative sign, relative size, and statistical significance of the estimated coefficient for the Regime-Stability Index is indeed very robust across alternative measures. The coefficients for the rest of the variables remained mostly qualitatively unchanged, except for the coefficient for Construction and the constant term. I now address several issues that may cast some doubt on the results reported above. First, are these results driven primarily by the stability component and not the regime component of the Regime-Stability Index? As noted in the introductory section of this paper, political stability is an important determinant of economic activity. To investigate this possibility I specify a new regression where the two components of the index are identified separately, in addition to their interaction:

Business Obstaclesig = a + β1 Ag + β 2 Sg + β3 Ag ·Sg + β 4 X g + β 5 Z ig + ug + ε ig,

(2)

where Ag is the regime component of the RSI that measures Autocracy on the scale from 1 (strongly democratic) to 21 (strongly autocratic), Sg represents the 3.

Hellman et al. (2000), in their World Bank and EBRD paper on the first set of Enterprise Surveys (known at the time as the Business Environment and Enterprise Performance Surveys or BEEPS), caution against a potential for such bias.


409

410

RSI [2]

−0.0891 (−2.00) −0.0835 (−7.19) 0.0679 (7.08) −0.1802 (−3.89) 0.1154 (2.34) 0.0417 (2.00) −0.0729 (−3.66) −0.4021 (−1.17) 39381 0.7180 0.1828

RSI [1]

−0.1087 (−2.40) −0.0834 (−7.19) 0.0678 (7.07) −0.1935 (−4.10) 0.1151 (2.36) 0.0418 (2.00) −0.0728 (−3.65) −0.3514 (−1.03) 39381 0.7180 0.1806

t-statistics are in parentheses.

# of observations ‘within’ variance ‘b/n’ variance

Constant

Hotels

Construction

LnGDPpc

CPI

Audit

SoleProp

RSI [j]

y [1]

y [1]

−0.1334 (−2.59) −0.0834 (−7.19) 0.0679 (7.07) −0.1873 (−4.10) 0.1117 (2.32) 0.0417 (2.00) −0.0729 (−3.66) −0.3482 (−1.03) 39381 0.7180 0.1773

RSI [3]

y [1]

−0.1414 (−2.76) −0.0835 (−7.19) 0.0679 (7.07) −0.1799 (−4.03) 0.1152 (2.40) 0.0417 (1.99) −0.0730 (−3.66) −0.4066 (−1.21) 39381 0.7180 0.1755

RSI [4]

y [1]

−0.0999 (−1.85) −0.0886 (−7.25) 0.0812 (8.03) −0.1388 (−2.56) 0.0114 (0.20) −0.0385 (−1.74) −0.1824 (−8.76) 0.3725 (0.95) 36766 0.7397 0.2392

RSI [1]

y [2]

−0.0792 (−1.49) −0.0887 (−7.25) 0.0812 (8.03) −0.1262 (−2.37) 0.0112 (0.20) −0.0385 (−1.74) −0.1825 (−8.76) 0.3296 (0.83) 36766 0.7397 0.2419

RSI [2]

y [2]

−0.1313 (−2.15) −0.0886 (−7.25) 0.0812 (8.03) −0.1354 (−2.57) 0.0092 (0.17) −0.0385 (−1.74) −0.1825 (−8.77) 0.3759 (0.97) 36766 0.7397 0.2353

RSI [3]

y [2]

−0.1317 (−2.15) −0.0887 (−7.25) 0.0812 (8.03) −0.1271 (−2.46) 0.0117 (0.21) −0.0385 (−1.74) −0.1826 (−8.77) 0.3226 (0.83) 36766 0.7397 0.2351

RSI [4]

y [2]

−0.1235 (−2.02) −0.0887 (−7.25) 0.0812 (8.03) −0.1238 (−2.39) 0.0118 (0.21) −0.0385 (−1.74) −0.1826 (−8.77) 0.3101 (0.79) 36766 0.7397 0.2363

RSI [5]

y [2]

Robustnesscheck. GLS estimates (random effects) based on both proxies for the dependent variable and all versions of the Regime-Stability Index.

Table 7

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ENTRENCHED AUTOCRACY OR NEW DEMOCRACY Table 8 Individual effects of regime and stability and their interaction on business environment.

A S A·S SoleProp Audit CPI lnGDPpc Construction Hotels Constant

S [1]

S [2]

S [3]

S [4]

S [5]

−0.0997 −(2.15) 0.0442 (0.88) −0.1139 −(2.72) −0.0833 −(7.18) 0.0677 (7.05) −0.2133 −(4.35) 0.0830 (1.65) 0.0419 (2.01) −0.0729 −(3.66) −0.0237 −(0.06)

−0.0992 −(2.14) 0.0569 (1.16) −0.1068 −(2.57) −0.0832 −(7.17) 0.0677 (7.06) −0.2166 −(4.42) 0.0851 (1.73) 0.0420 (2.01) −0.0729 −(3.66) −0.0287 −(0.08)

−0.0904 −(1.98) 0.0133 (0.28) −0.1105 −(2.78) −0.0833 −(7.18) 0.0677 (7.05) −0.2059 −(4.27) 0.0912 (1.91) 0.0420 (2.01) −0.0731 −(3.67) −0.1150 −(0.32)

−0.0955 −(2.11) 0.0114 (0.23) −0.1198 −(3.08) −0.0834 −(7.18) 0.0677 (7.05) −0.2078 −(4.33) 0.0930 (1.96) 0.0420 (2.01) −0.0731 −(3.67) −0.1283 −(0.36)

−0.0957 −(2.11) 0.0124 (0.25) −0.1170 −(3.00) −0.0834 −(7.18) 0.0677 (7.05) −0.2086 −(4.33) 0.0945 (1.99) 0.0420 (2.01) −0.0730 −(3.67) −0.1375 −(0.38)

t-statistics are in parentheses. Method: GLS estimation, random effects. Dependent variable: Business Obstacles [1]. For all regressions in this table, the number of observations was 39,381.

Stability component (see the appendix for details), and Ag·Sg is the interaction term. The results of estimating equation (2) using Business Obstacles [1] and all five proxies for Stability are reported in Table 8, where the control variables are limited to those included in Table 74. As can be seen, the estimated coefficients for political stability, S, when A is low5, are not statistically significant and even have the wrong sign. What is more, when the interaction term was dropped from equation (2), the coefficient for Stability remained positive and not statistically significant for all of its five different measures (not shown). The negative coefficients and high t-statistics for the interaction term, on the other hand, are very similar to those for the RSI reported in Tables 6 and 7. These results indicate that political stability is not the driving component of the RSI. Stability’s effect on business activity manifests itself through its interaction with regime, whereby conditional on a given regime more stability is associated with a better business environment. The interaction term can also be interpreted to indicate that more autocracy is better for economic activity for a given level of political stability. 4.

5.

Using other sets of control variables does not change the signs and significance of the estimated coefficients for Ag, Sg, and Ag·Sg. Also note that the coefficients for control variables do not exhibit any notable changes. Strictly speaking, a correct interpretation of the coefficient for S must be made while holding A equal to zero. However, for the given sample A ranges from 1 to 20.


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Another potential cause for concern is a possible sample selection bias. Because we are dealing with survey data, it is conceivable that in more autocratic countries most recorded responses came from firms that tended to rate their business environment as relatively favorable, while most other firms declined to participate. The first row in Table 8 appears to conform with this possibility. In fact, sample selection is a fairly ubiquitous problem. Breen (1996, 35) notes that If we think hard enough, we can probably find some sort of selection process underlying any piece of social science data. A random sample of the adult population is actually only a random sample of those members of the population who are listed in the sampling frame; so if the sampling frame is, say, an electoral register, those adults who are not registered cannot be sampled.

According to Davidson and MacKinnon (1993), Heckman’s two-step model, heckit, can be used to test for a sample selection bias. This test is based on the statistical significance of the coefficient for the inverse Mills ratio, which is calculated in the selection (probit) stage of heckit and included as a regressor in the outcome stage6. There is an important difference between the selection process that may be found in my data and a typical selection process. In the latter, censoring occurs at one or sometimes both ends of the culprit variable that is arranged in an ascending or descending order, such as latently negative observations being recorded as zeros or data being bracketed between, say, zero and 100 (e.g. percent). In my Regime-Stability Indices, true unreported responses may be ‘hidden’ at various places along the RSI continuum, and it is unclear how this process would affect the distribution of the error term. Despite this reservation, I proceed with the estimation of heckit in order to examine potential differences with the results attained above. Heckman’s model necessitates the use of two additional variables: a selection variable and an exclusion restriction. The selection variable is the dependent variable in the selection stage of the model, which is used to determine whether or not the dependent variable in the second stage is observed. The exclusion restriction is recommended (see Breen, 1996) to avoid relying on the nonlinearity of the probit part as the sole source of identification of parameters in the second stage of the model7. In this paper, a zero-one selection variable was constructed based on Enterprise Surveys question A.16, which asks the interviewers to indicate whether or not the respondents’ answers were truthful. Enterprise Surveys question A.17, which asks whether the figures were taken from establishment’s records or were arbitrary and unreliable, was used in the creation of the exclusion dummy variable. The results of the two-step heckit estimation are presented in Table 9. 6. 7.

For details see Breen (1996). An exclusion restriction is a variable that affects the dependent variable in the selection part, but has no effect on the dependent variable in the outcome part.

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ENTRENCHED AUTOCRACY OR NEW DEMOCRACY Table 9 Sample-selectionbias check. Method: Heckman’s two-step model Dependent variable RSI [5] SoleProp Audit CPI LnGDPpc Construction Hotels Constant IMR A.17 dummy # of observations

Outcome equation y [1] −0.1997 (−33.87) −0.0962 (−6.44) −0.1440 (−10.75) −0.2773 (−34.04) 0.2300 (27.65) 0.0618 (2.30) −0.2101 (−7.87) −0.8570 (−15.64) −0.0833 (−8.69) – 27576

Selection equation A.16 dummy 0.7393 (14.38) −0.3287 (−10.09) 0.3510 (10.67) 0.2941 (15.09) −0.2406 (−12.80) 0.1500 (2.01) 0.5479 (6.15) 2.0687 (13.97) – 0.9929 (21.90) 28724

OLS y [1] −0.1639 (−45.91) −0.1760 (−13.11) −0.0724 (−5.98) −0.2271 (−34.50) 0.1838 (25.28) 0.0832 (3.32) −0.1214 (−5.19) −0.7493 (−14.68) – – 28724

All standard errors are heteroskedasticity-robust. z-statistics are in parentheses in the heckit model and t-statistics are in parentheses in the least squares model.

From Table 9 the coefficient for the inverse Mills ratio is statistically significant, which means that the possibility of the sample selection bias cannot be ruled out. However, despite the bias, these results are not in conflict with the findings in my main regressions, which are reported in Tables 6 and 7. In fact, the coefficient for the Regime-Stability Index in Table 9 is even more negative and more statistically significant. Ordinary least squares results are also provided in the rightmost column for comparison. Another potential problem is a possible endogeneity of the main independent variable, RSI. For example, it is reasonable to argue that a poor economic environment may trigger an uprising and lead to a regime change. Alesina et al. (1996) refer to a considerable body of literature where low growth has been shown to lead to political instability and coup d’états. However, there are good reasons to believe that the Regime-Stability Index is not endogenous. Recall that the dependent variable in this paper always corresponds to the last year of the survey conducted in a given country, which ranges from 2006 to 2010. The Regime-Stability Index, on the other hand, is constructed from data that spans a decade-and-a-half-long period going back to 1992 and ends in the year that precedes the year of the country’s dependent variable observation (see the Appendix). Therefore, endogeneity can be ruled out on the grounds that a present business environment cannot have an influence on the establishment of a © 2014 John Wiley & Sons Ltd.

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country’s regime, an event that has occurred in the past. It is also difficult to establish a direct causational link from the dependent variable to the political stability part of RSI, which is based on past regime changes or how long ago in the past the current regime was established. Finally, the Regime-Stability Index is a multiplicative interaction of variables measuring political stability and concentration of power, which makes it difficult to determine the direction of the systematic feedback and its strength, especially if the dependent variable affects one component positively and the other component negatively. Reverse causality is present in other independent variables, however. Specifically, the two proxies for corruption, CPI and WGI, are likely to be endogenous. Not only does corruption worsen the business environment, but also a bad business environment can foster corruption (e.g. a ‘grease-the-wheels’ type of corruption). What is more, corruption is included as an obstacle category in the construction of Business Obstacles [1]. However, Business Obstacles [2] does not include corruption, and the main results were robust to using this alternative dependent variable. Nevertheless, to address this problem more thoroughly, I could instrument for corruption using such variables as an index of ethnolinguistic fractionalization constructed for 226 countries by Desmet et al. (2009) or a dummy variable for the legal origin of a country’s Common Law or Commercial Code provided for 206 countries by La Porta et al. (1999). However, this would be a superfluous exercise as I am not interested in the coefficient for corruption itself, but include it only as a control variable in order to avoid an otherwise-likely omitted variable bias (see Pellegrini and Gerlagh, 2008, for a similar reasoning). One useful extension of this paper and a good additional check for robustness of the results would be to analyze the given issue using a difference-indifferences model based on micro-level data. However, this approach is more data-intensive and would require pre- and post-revolution data. Such data is unavailable at this time given that Enterprise Surveys were conducted in most countries only once and different geographical regions were covered in different years. However, Enterprise Surveys is a growing database with repeat surveys carried out in many countries, so a firm-level difference-in-differences method for comparative evaluation of the economic environment before and after a regime change may be possible in the near future. IV. CONCLUSION Democracy has many advantages over autocracy, especially when democracy is mature and the government is stable. However, transition from a deep-rooted tradition of autocratic governance to democracy is almost invariably associated with a period of political instability, which may last up to several decades and hurt business activity. Papaioannou and Siourounis (2008) – whose paper is the most closely related one to this study – graph time-demeaned real GDP per

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capita growth against pre- and post-transition timeline and find that growth drops significantly before and during transition but rebounds to outpace the world average soon after a permanent transition to democracy. They arrive at this result using a fixed effects difference-in-differences model and country-level panel data. In contrast, the main model employed here is a random effects model and the data is at the level of individual firms. In addition, the question about the economic effect of democratization is posed somewhat differently in this paper: How do new democracies compare to a diametrically opposite set of countries with long-standing autocracies? The main finding in this paper is that within a relatively short time horizon – up to twenty years – the economic environment is better in stable autocracies than in new democracies. Post-regime-change political instability is one obvious reason for this result. Another important factor is the time necessary for the development of a full set of democratic institutions. In addition, this outcome may be partially due to a possible heterogeneity among autocrats, which, according to Alesina et al. (1996), includes not only kleptocratic but also technocratic dictators. Thus, countries with stable autocracies may have a better economic environment, if the world – or more narrowly, my sample – includes a high proportion of technocratic autocracies. My main finding contradicts some recent related studies, such as Rodrik and Wacziarg (2005, p.55), who report in their results that ‘democratization comes at no discernible cost in terms of growth, and with likely benefits in the form of short-term boost in growth and reduction in economic volatility’; and Papaioannou and Siourounis (2004, p. 27), who establish that ‘growth peaks and stabilizes at higher level’ as early as in the third year after democratization following a substantial initial drop. The sources of this disagreement may be the use of a different empirical method in this paper, including the explicit incorporation of the regime-stability interaction, and the use of micro-level data. To conclude, I am not counseling that autocratic governments should not become democratic. I simply show that there is an interim period when the economic environment of a new democracy compares unfavorably to that in a stable autocracy, and this interim period is longer than just a few years as supposed in previous studies (e.g. Papaioannou and Siourounis, 2008). As noted above, democracy has been shown to display its benefits in the longer run, when it has had enough time to mature and become fully stable. Furthermore, even in the short run, a transition to democracy can bring important indirect benefits (Doucouliagos and Ulubasoglu, 2008; Drury et al., 2006). As shown by Nur-tegin and Czap (2012), new democracies fare better than stable autocracies in terms of corruption, a key growth impediment. Finally, if the World’s autocrats are likely to be deposed sooner or later anyway, given the current global trend towards more representative institutions, and if democracy indeed is more advantageous in the long run, then an earlier transition appears to remain an attractive proposition for the nations that are still governed by a strong hand. © 2014 John Wiley & Sons Ltd.

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APPENDIX: CONSTRUCTION OF THE REGIME-STABILITY INDEX The Regime-Stability Indices in this paper are based on and similar to the indices developed by Nur-tegin and Czap (2012). However, there are two major differences. First, Nur-tegin and Czap calculate their indices for all countries for the same year (2009), which is appropriate for their case because the year of their dependent variable (corruption) does not vary by country. In this paper, the dependent variable is based on several questions from the World Bank’s Enterprise Surveys, which were conducted in different countries in different years. In the final dataset used here, the surveys were conducted according to the following schedule: Enterprise Survey Year 2006 2007 2008 2009 2010

Number of Countries in the Sample 10 10 6 43 25

To ensure that the year of collection of the dependent variable (Business Obstacles) is never before the end of the time period of the main independent variable (Regime-Stability Index), the latter is constructed such that its final year immediately precedes the year Enterprise Surveys were conducted in that country, instead of ending in the same year for all countries. Second, Nur-tegin and Czap focus only on two groups of countries: established autocracies and new democracies. That is, they filter away (i) unstable dictatorships with Autocracy (see below) scores exceeding 15 and at least one regime change in the last nine years and (ii) established democracies with Autocracy scores below 5 and no regime changes in the last 25 years. In the discussion of results in this paper, unstable dictatorships (which include Gambia, Mauritania, and Pakistan) and established democracies (which include Argentina, Botswana, Costa Rica, El Salvador, Jamaica, Mauritius, and Trinidad and Tobago) are retained in the sample because excluding these countries does not change the results qualitatively. In fact, because the absence of these countries makes the Regime-Stability Indices more pronounced, excluding these countries makes the magnitudes of the estimated coefficients bigger and even more statistically significant (results not shown). All five versions of the Regime-Stability Index are created by multiplicatively interacting an autocracy-democracy variable with a measure of the regime’s stability: Autocracy × Stability. The specific components are calculated as follows:

• The Autocracy portion in all indices is always the same and is created from the Polity2 variable obtained from the Polity IVTM Project dataset of the 416



•

•

Center for Systemic Peace (see Marshall et al., 2011). Polity2 is a revised combined Polity score that ranges from -10 (strongly autocratic) to +10 (strongly democratic) covering several decades for over 180 countries. The variable Autocracy is created by subtracting Polity2 scores from 11. Thus, concentration of power in Autocracy grows from 1 (strongly democratic) to a maximum of 21 (strongly autocratic). The total number of regime changes over a period of T years, ∑Tt =1 (rct ) , used in Formulas 1 and 4 in Table 3 is found on the basis of Polity IV’s regime durability variable, Durable. For a given year, Durable is the number of years since the most recent regime change, which implies that it is equal to zero for the years when regime changes occur. Regime change is defined by at least a three-point change in the Polity score within a period of three years. This change may occur due to an array of possible changes in Polity’s components, including competitiveness of executive recruitment, openness of executive recruitment, constraint on chief executive, and competitiveness of political participation. Thus, a regime could change due to a revolution, coup d’état, or a large enough change in up to three consecutive years in some or all of the above components of Polity. Therefore, a regime change need not necessarily be as cataclysmic as a revolution or a putsch, but can occur under the same executive as a result of a significant enough political transformation. However, it should be noted that such significant transformations rarely occur solely on the initiative of the presiding executive and are more likely to happen as a result of a considerable political pressure, such as a threat of a violent ouster. The number of regime changes is summed over the period from 1992 to the year for which the Regime-Stability Index is entered for the given country; that is, for the year that immediately precedes the Enterprise Survey year in that country. The year 1992 was chosen because my sample includes a large group of formerly socialist countries, which went through major political transformations in the 1989–1992 period. Thus, ∑Tt =1 (rct ) is a sum of all zeros in Durable during the period of time as defined immediately above for the given country. Formulas 2 and 5 include a modified sum of regime changes, ∑Tt =1 ( t ⋅ rct ) , where more recent regime changes are given greater weights. The weights are equal to the difference between the given year and 1991. For example, Albania had regime changes in 1992, 1996, and 1997, which makes the weighted sum of regime changes equal to (1992-1991)·1 + (1996-1991)·1 + (19971991)·1 = 1 + 5 + 6 = 12. Duration of current regime in Formulas 3, 4 and 5 is simply the value in Durable (i.e. the number of years the given regime has persisted) for the year of calculation of a country’s Regime-Stability Index. ○

○

○

•

•


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• The final step in the construction of the Regime-Stability Indices is the application of the formulas provided in Table 3 using the components derived according to the procedure described above.

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SUMMARY The recent downfall of autocracies worldwide, most notably in the Middle East, raises the question: Do the new, more democratic regimes hold a promise of creating a better economic and business environment? To answer this question, I analyze a new large-scale firm-level dataset for the post-regime-change time horizon of up to 20 years. The main finding is that during the post-revolution time period the business environment is likely to worsen under the new politically unstable regimes that replace stable autocracies.


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