Abstract
In this article, we consider the relationship between crime and attitudes toward privatization in Ukraine. In our theory, crime is a source of vulnerability that undermines citizens’ support for privatization. However, the success of privatization may also depend on the ability of the government to control crime. To discern these relationships, we first demonstrate that higher rates of crime are associated with less support for privatization, as our theory suggests. To address the possibility that institutional weaknesses during privatization affect the ability to control crime, we use Soviet institutional legacies, specifically industrialization, as an instrumental variable to assess the causal impact of crime on attitudes toward privatization. Soviet-led industrialization contributed to rapid economic growth, but at the cost of declining social and family structures. The instrumental variables analysis suggests crime causes a decline in support for privatization. This evidence suggests controlling crime should be part of Ukraine’s reconstruction effort in the wake of Russia’s unprovoked invasion in February 2022.
Introduction
A puzzle of Ukraine’s transition is why support for privatization has varied substantially in its regions (Brik and Shestakovskyi, 2020). The issue of establishing support for privatization remains significant during the Russo-Ukraine War. The reason is that pro-market reforms remain a significant goal of policy and institutional reform in Ukraine. Continued progress in pro-market reforms is significant in part because these reforms will provide an economic foundation for the successful reconstruction of Ukraine.
In this article, we add to the literature on post-communist privatization by considering the relationship between crime against people and property and support for privatization. Our main theoretical argument is that both crime and privatization involve vulnerabilities and that in the absence of credible control of crime, individuals will be less likely to support privatization. Our empirical evidence approach begins by providing robust descriptive evidence that higher crime correlates with lower levels of support for privatization. Since privatization may be causing crime, we use Soviet industrialization as an instrumental variable for crime. Using this approach, we provide causal evidence that crime indeed undermines support for privatization.
This article addresses a long-standing question in the post-communist privatization literature, which is widespread opposition to privatization despite its apparent critical role in promoting prosperity and well-being (Denisova et al, 2009; Denisova and Zhuravskaya, 2012). This is certainly the case in Ukraine, where support for privatization was initially high as the Soviet Union collapsed before declining. More significantly for this article, there is substantial regional variation in support for privatization. This regional variation, specifically greater support for privatization in western regions of the country, is one of the critical puzzles of Ukraine’s economic transition (Brik and Shestakovskyi, 2020).
Our main contribution to the public choice and new institutional economics analysis of privatization is to offer a novel perspective on the link between crime and support for privatization. Institutionalists in the public choice tradition have long been concerned with Soviet and post-Soviet economic performance, especially challenges arising from soft budget constraints (Kornai, 1992; Boettke and Candela, 2021) and the link between command planning institutions and totalitarianism (Lavoie, 1985; Boettke, 1990; Gregory, 2004; 2009) Another challenge is lack of a supportive “market culture,” one that creates stickiness in transplanting capitalist institutions to countries formerly occupied by the Soviet Union (Berkowitz et al, 2001; Pejovich, 2003; 2012; Boettke et al, 2008). More generally, this literature clarifies the incentive and information problems under Soviet-style socialism (Boettke, 2001; Boettke and Leeson, 2004; Boettke et al, 2005). More recently, this research considers the challenges of de jure and de facto property protection. According to the property rights perspective, effective post-communist property rights depend on the credibility of commitment to property rights (Weimer, 1997). This commitment depends on effective political institutions. In post-Soviet contexts in Russia and other former Soviet colonies, a challenge immediately after the collapse of the Soviet Union and in the subsequent transition to capitalism has been the unbridled political influence of individuals and organizations with an interest in incomplete property rights protection (Sonin, 2003; Guriev and Sonin, 2009; Gans-Morse, 2017a; 2017b). One of the consequences is that illegal markets remain a significant threat, one that is in some ways worse than under the Soviet system: whereas corruption under the Soviet system was a reflection of inefficient institutions (greasing the wheel), the current problem of corruption reflects political capitalism, or greasing the palms of politicians and bureaucrats (Boettke et al, 2023).
A presumption in the aforementioned research is that criminality, as well as the character of criminality, reflects institutions, including deficits in the rule of law. For example, the lack of credible commitment to property contributes to an environment where criminality is how property is ultimately allocated. While this perspective is certainly persuasive, as rule of law no doubt influences criminal behavior, crime is also likely to influence citizens’ support for privatization. Our theory draws on sociological perspectives on crime, as well as the legal institutionalism of institutional economics. The intuition of our theory is that privatization, while significant for economic prosperity, creates its own vulnerabilities. The inability to control crime also suggests that the government may be less able to manage the process of privatization effectively, as a market economy depends on effective legal rules, including rules that address and limit criminal activity. Intuitively, people who experience more vulnerability from crime will be less willing to accept privatization, as both signify vulnerability.
Although our theory suggests crime explains privatization, there are reasonable arguments, including those introduced earlier, that crime is an outcome of privatization processes. To address causality, we leverage insights from the literature on the persistence of Soviet institutions. Previous research in this tradition has considered the long-run impact of Communist Party membership for corruption (Obydenkova and Libman, 2015), as well as how party members influence prospects for democracy (Lankina et al, 2016).
Another aspect of Soviet institutions was the way in which planners in the Soviet system promoted development in occupied countries. The Soviet system was reasonability effective in mobilizing capital and labor. It was much less effective in promoting economic growth through technological progress. Technology is now recognized as perhaps the fundamental explanation for prosperity. Mobilization of capital and labor also had collateral harms besides failing to promote innovation. The Soviet approach to industrialization involved the substantial rearrangement and ultimately weakening of family and social structures, which has been associated with increases in crime. Since the crime rate in different regions may differ depending on their level of industrialization and urbanization, the Soviet role in encouraging industrialization serves as a natural experiment to examine the causal effect of crime on attitudes.
Leveraging these insights, we conceptualize Soviet legacies as a natural experiment. While Soviet policies were not random, they were externally imposed, much like any colonial institutions, many of which are described as natural experiments (Acemoglu et al, 2001; Diamond and Robinson, 2010; Acemoglu and Robinson, 2012).
Our evidence that more crime is associated with lower levels of support for privatization affirms the conventional wisdom that the east of Ukraine is more violent and subject to criminality than the west. Our analysis of Soviet industrialization as an instrumental variable lends support to the idea that colonially imposed institutions impact crime and, from a longer-run perspective, undermine support for privatization.
Our research has important implications for Ukraine’s reconstruction. A significant challenge for reconstruction is ensuring continued support for privatization, as many enterprises and land remains state owned. Continued success in privatization is critical to ensuring a robust economic foundation for reconstruction. Our analysis suggests that control of crime may be a way to improve support for privatization and that reconstruction efforts should consider crime control as one of the components of economic reform in Ukraine’s reconstruction plan.
The article is organized as follows. The second section introduces the puzzle of privatization in Ukraine, including variation in regional support for privatization. The third section develops our theory of crime, Soviet institutional legacies, and privatization. The fourth and fifth sections present the empirical analysis of the relationship between attitudes toward privatization and crime, as well as the results of instrumental variable analysis, where we use Soviet institutional legacies as an instrument for crime. The sixth section concludes with a policy discussion of how knowledge of the link between crime and privatization support informs Ukraine’s ongoing reconstruction.
The puzzle of uneven privatization in Ukraine
When the Soviet Union collapsed, the context was one of severely dysfunctional institutions. Massive inefficiency, perverse political incentives, and shortages characterized Soviet economies. Privatization of state-owned assets, especially land and business, promised to improve efficiency and in the process create a framework for wealth creation.
Things did not turn out as expected. One problem was that the J-curve theory—the expected decline in the standard of living followed by gradual improvements in the economy—proved overly optimistic: many countries experienced a rapid decline of their economy after the collapse of the Soviet Union but ended up stuck in a low-level equilibrium with lower growth than the Soviet period, as well as persistent problems with economic inequality (Hellman, 1998). Part of the problem was massive graft, with the state essentially selling off its assets for almost nothing and the emergence of widespread corruption (Solnick, 1998).
These challenges were present in Ukraine. When the Soviet Union collapsed, there was great hope for Ukraine, the Soviet Union’s second-largest economy. To get an idea of the optimism surrounding Ukraine, in 1990, Deutsche Bank predicted that the Ukrainian economy would grow fastest among the former Soviet republics due to such advantages as its educated population, proximity to Europe, balance of agriculture and industry, and significant natural resources (Starr and Dawisha, 2016). There was also substantial support for reform: according to monitoring surveys conducted at the beginning of independence, in 1992, 63.5 percent of Ukrainians were positive about the privatization of land (only 13.9 percent were negative), 56.2 percent were positive about the privatization of small enterprises (13.6 percent were negative), and 25.1 percent were positive about the privatization of large enterprises (31.6 percent were negative). However, by the end of the 1990s, among the post-Soviet countries, the citizens of Ukraine had the worst assessment of their economic and political systems (Rovelli and Zaiceva, 2011).
The decline in overall support for privatization in Ukraine is, along with the previously mentioned regional variation, a key puzzle of Ukraine’s post-communist reform process. One explanation for declining support for privatization involves the political institutions that emerged immediately in the wake of communist rule, in particular, what has been called “crony capitalism.” “Crony capitalism” refers to systems in which political power and economic opportunities are distributed not according to the principle of meritocracy and democratic equality in rights but according to the possibilities of individual citizens to influence state institutions and use them in their own interests (Aligica and Tarko, 2014; 2015). Ukraine would appear to be a textbook case of crony capitalism. In the early 1990s, privatization was blocked by the communist elite and the then managers of state enterprises, who were called “red directors.” Many of them used their control over state enterprises to enrich themselves and “nationalize” losses, by which they meant for the state to subsidize the losses—something that is common under capitalism but also contrary to market reform. Then, the lion’s share of the surviving state enterprises passed into their hands due to opaque privatization. In any case, a broad class of entrepreneurs and new economic opportunities for most of the population did not arise, while budgetary resources were depleted by ineffective and predatory policies.
Crony capitalism is an institutional problem. Other explanations focus on the symptoms of crony capitalism. Distributional perspectives focus on how the experience with inflation and unemployment during transition—more generally, the extent of the macroeconomic shocks—influenced support for privatization (Hayo, 1999). The idea here is that inherited economic structure and vulnerability to the transition and subsequent shocks can affect attitudes (Guriev and Ananyev, 2015).
The unfairness of the process and citizens’ dissatisfaction with the subsequent inequality of wealth and opportunity is a compelling explanation for declining support for pro-market reforms. The transitional crisis turned out to be very protracted and painful, the key industrial assets of Ukrainian Soviet Socialist Republic (SSR) were never distributed in favor of the most efficient owners, and Ukraine did not become a market democracy. The transition was accompanied by hyperinflation, which destroyed the savings of the least protected groups of the population. Ukrainian gross domestic product (GDP) declined by 63 percent from 1991 to 1998. By the mid-2000s, Ukraine had the worst economic growth rates among the 15 post-Soviet republics, surpassed only by Moldova. The average growth of the Ukrainian economy during 1990–2004 was –2.8 percent (Frye, 2010). Massive unemployment did not help the situation. Although official estimates of unemployment were around 3 percent, the International Labor Organization gave a score of 9.8 percent, while individual surveys and surveys of enterprises showed figures that reached 14–15 percent. In some regions, the levels of hidden unemployment reached 58 percent (Foglesong and Solomon, 2001). Many of the official or hidden unemployed turned to the shadow economy, which reached 60 percent of GDP by 1996. Thus, the fall in real living conditions is another possible factor erasing support for market reforms. The collapse of the economy and institutions could provoke another defining factor in support of reforms. Along these lines, in Ukraine, the 2009 recession appeared to contribute to greater declines in support for market reforms and democracy in the east, where the crisis was more severe (De Haas et al, 2016).
Each of these factors has been offered as an explanation for low or declining support for privatization. Inequality of opportunity, which was extreme in post-Soviet countries immediately after transition and remains high today (Milanovic, 1999; Gimpelson and Treisman, 2015), is considered a significant explanation for policy preferences. Another is how “unfair” inequality (inequality due to differences in opportunities) (EBRD, 2016) influences reform support for privatization. The inability to avoid corruption and interest group influence is also implicated as an explanation for opposition to market-oriented reforms (Denisova, 2016).
What has to an extent been left out of these explanations for the puzzle of declining privatization support is crime and the ongoing consequences of Soviet institutional legacies. Massive poverty, the expansion of shadow activity, the collapse of social safety nets, and the inability of legal institutions to protect people have made the transition excessively criminogenic. In opinion polls in the 1990s, unemployment, uncontrolled inflation, and rising crime were among the top three social threats that worried the population. Throughout the 1990s, the rise in crime was one of the biggest fears for 60–70 percent of the population, falling to 43 percent only in 2010 (Kupets et al, 2013). In addition, research on Soviet legacies has shown how the specific way that the Soviet Union promoted industrialization and industrial cities may have had consequences for crime. The remainder of the article explores these possibilities, starting with a theory of how crime relates to privatization.
The theoretical link between crime and privatization
Crime reflects and is a source of vulnerability, much like a macroeconomic problem. This could lead people to oppose markets, which involve more vulnerability to others. High rates of crime also suggest that market reforms would be less likely to work effectively in the presence of lawlessness. A rational individual might prefer control of crime before supporting privatization, or they may simply believe that unless there are reductions in crime, they would not prefer the added uncertainty of markets.
We first consider the theoretical link between economic vulnerability and crime. The anomie theory of the US classical sociologist Robert K. Merton suggests that criminality arises from the inability of people to achieve their goals by socially accepted means. Faced with the impossibility of satisfying their needs in legal ways, especially when the law itself becomes the subject of decay, a person turns to other methods (Merton, 1938). Ukrainian sociologists have developed a school of thought rooted in the theories of Durkheim and Merton regarding social anomie (Golovakha and Panina, 1994; 2001; Golovakha et al, 2020). Based on regular surveys, they have created an index of social anomie that reflects individuals’ skepticism about the significance of social trust and their belief that their social peers are likely to be morally fraudulent. The argument presented in this scholarship is that the socioeconomic transition drastically altered social integration, as previous social norms disappeared and new norms have not been fully established. In this context, people are likely to experience social disorientation. Thus, our reference to the theory of anomie is grounded not only in literature but also in empirical evidence within Ukrainian society.
Crime in post-Soviet Ukraine can be seen as a reaction to social strain and mirrors the broader problems of a transitional society. The late Soviet and post-Soviet periods were accompanied by a perfect set of factors producing social strain. The sharp impoverishment of the population in parallel with the enrichment of narrow politically connected groups created massive unfair inequality. At the same time, very few had access to legal ways of obtaining wealth. Such transitional problems as unemployment, poverty, and the growth of shadow activities could play a vital role in the overall growth of crime (Fajnzylber et al, 1998; 2002).
Classical works in the social sciences well explain the sharp rise in crime in the post-Soviet period. The institutions of transitional countries were supposed to maintain law and order. The welfare state had to support the least protected from deprivation and poverty. The emergence of new economic opportunities resulting from healthy liberalization was supposed to keep people’s faith in the system and market ideas. Yet, none of this worked in Ukraine during the transition. However, gaps remain regarding significant regional differences in crime.
Many works on criminology, economics, and sociology show that the growth of crime and urbanization go together. Reasons include a high population density and increased clashes between people (Ladbrook, 1988), higher inequality (Soares, 2004), more resources to redistribute (Glaeser and Sacerdote, 1999; Buonanno, 2003), a large proportion of young people and migrants, and less interpersonal cohesion (Baumer and Wolff, 2014; World Bank, 2017).
Nor should we overlook the possibility that urbanization can directly influence people’s views and could therefore be responsible for the different economic and political views of the eastern and southern regions. Rapid urbanization can be accompanied by the disintegration of a common identity, the traditional family, and communal ties. This, in turn, may mean a decrease in social capital and people’s ability to cooperate, which are the basis for democracy and the market economy (Putnam, 2000; Steinhardt and Delhey, 2020).
When linked to privatization, the mechanisms just outlined mean that there is a dual vulnerability between crime and privatization. Much of the analysis of privatization focuses on its discontents. While there are also significant benefits, crime is likely to have a similar effect on attitudes toward income inequality, unequal opportunities, or inflation: people who are in a vulnerable situation are less willing to accept the vulnerability that is associated with privatization.
There is also an institutional reason to link crime to privatization. The link we see here is through ability to control crime and perceptions that the process of privatization can be effectively managed. According to legal institutionalism, markets function because of the rule of law (Hodgson, 2009; Deakin et al, 2017). Rule of law is what makes capitalism coherent. It is an idea that goes back to Adam Smith, who recognized that a good capitalist order depends on the appropriate “constitutional framework” (Brennan and Buchanan, 1985), and is also present in the work of “old” institutionalists, such as John Commons (1924), and among Austrian economists (Hayek, 1973). This provides a complementary rationale: since crime suggests weaker institutions to manage privatization, legal institutionalism suggests that people may rationally oppose privatization because they believe it will not be managed effectively, using crime as a reasonable way to formulate this belief.
This leads us to our hypothesis, which is that crime will undermine support for privatization. We also expect that Soviet institutions are related to crime. There are substantial differences in crime levels between the western and eastern regions. According to Iavorskyi (2011), economic development as such has little or no effect on crime. More critical are sociodemographic parameters, such as the high concentration of the population in urban areas, the effectiveness of the police, the level of education, and the death rate, which may reflect other socioeconomic problems of the regions. As we will show in the following, the economic structure of Ukrainian regions, the level of their urbanization, and the level of crime are well correlated. The reasons for this are rooted in the Soviet (and pre-Soviet) history of Ukraine. Industrialization went hand in hand with the movement of people to cities, and it was the industrial regions that were more susceptible to crime. Thus, the more industrialized cities of southeastern Ukraine were also the most urbanized and were associated with higher crime in both the Soviet and post-Soviet periods. The rise in crime in Soviet Ukraine began in the 1970s. From 1972 to 1989, in the Ukrainian SSR, the share of citizens living in urban areas grew from 56 to 66 percent. During the same time, levels of crime more than doubled (Foglesong and Solomon, 2001). The explanation for this rate of crime reflects the factors outlined earlier: industrialization, urbanization, and crime go together. In the empirical section, we further clarify these linkages in the post-Soviet setting.
Data
We rely on two main data sources. The first data set comprise the results of the annual national monitoring surveys for 1992–2018. It covers Ukrainian citizens’ political, economic, and social views and attitudes, as well as a large set of demographic indicators, such as residence patterns. The data were collected by the Institute of Sociology of the National Academy of Sciences of Ukraine (IS NASU) every year or every two years, namely, 1992, 1994–2006 2008, 2010, 2012, 2014, 2016, and 2018. The data are available and representative for all Ukraine regions, except for the Autonomous Republic of Crimea and the occupied part of Donetsk and Luhansk regions since 2014.
From this data set, we use variables on the attitudes of Ukrainian citizens toward the privatization of land, large enterprises, and small enterprises. These attitudes toward privatization are our main dependent variables in the analysis. We use other variables from this data set about the identity and set of economic and political views of Ukrainians to find relationships between real residence factors and citizens’ attitudes toward privatization. The survey also provides demographic data on the age structure of the population of the regions, the structure of the residence, the income of the population, and confidence in political leaders during 1992–2018. Residence variables are used to construct instrumental variables on the proportion of each region’s urban and rural population.
To integrate with other socioeconomic variables, these data are aggregated by region and year. Thus, in categorical variables, we use the proportions of citizens of certain regions in specific years who have particular views and attitudes. For numeric or continuous variables, we use the regional average for a specific year.
The second source is state statistics, mainly from regional statistical compilations (reports) by the State Statistics Service of Ukraine or provided through requests for access to public information. Data for the following variables are available for the relevant years: life expectancy by region (1992–2018), number of registered crimes by region (1995–2018), nominal gross regional product per capita (1996, 2000–18), population by region for calculating other variables in per capita terms (1992–2018), and Gini index by region (2000–18). To form two other instrumental variables about the economic specialization of regions, we use the share of citizens employed in industry (2000–18) and the percentage of citizens employed in agricultural production (2000–18).
It is noteworthy that since some years are missing from the sociological monitoring data, we consider the same years for Ukrstat socioeconomic data; that is to say, for each variable, data are taken only for 1992 and 1994–2006 and then every two years (2008, 2010, 2012, 2014, 2016, and 2018). Therefore, in the models’ estimates, the years from 2000 to 2006 are mainly taken continuously and then every second year until 2018.
Crime and privatization: an overview of the descriptive data
We begin by presenting data on attitudes toward privatization in Ukraine, as well as emphasizing that a key factor is that support for privatization appears to vary by regions. As Figure 1 shows, the dynamics of citizens’ attitudes toward privatization in post-Soviet Ukraine are unambiguous. The average proportion of those who support the privatization of land and large and small enterprises was largest at the beginning of transition (in 1992) but subsequently only declined, reaching a minimum during 2016–18 (see Figure 1).
Annual dynamics of the share of Ukrainian citizens who support or do not support privatization (average for the privatization of land and large and small enterprises)
Citation: Journal of Public Finance and Public Choice 39, 1; 10.1332/25156918Y2024D000000007
Source: Institute of Sociology of the National Academy of Sciences of Ukraine and State Statistics Service of Ukraine.The share of Ukrainians who do not support each of the three types of privatization has constantly been growing. The percentage of citizens with a positive attitude has been steadily declining (except for 1999–2004 and 2014, which indicates the possible influence of political factors and two revolutions, which could form positive expectations). These post-Soviet dynamics and sensitivity to current political events suggest that the primary reasons for the negative trend in support may originate not in the Soviet past but in factors from the post-Soviet period. The aforementioned transitional difficulties could affect the economic views of citizens.
More interesting is why these views behaved differently in different parts of the country. Even though average support for privatization declined in all regions, in the western regions, it decreased significantly less. During the years of independence, the country’s west has retained a substantially more positive attitude toward privatization than the central, southern, and, especially, eastern macro-regions. There is a clear tendency: the further we move to the southeast, the more negative the attitude toward privatization is. The west is a clear outlier (see Figure 2).
Share of citizens with positive (left panel) or negative (right panel) attitudes to privatization by region (average for the privatization of land and large and small enterprises)
Citation: Journal of Public Finance and Public Choice 39, 1; 10.1332/25156918Y2024D000000007
Source: Maps constructed by authors based on information from the Institute of Sociology of the National Academy of Sciences of Ukraine and State Statistics Service of Ukraine.Having a wide range of variables at the regional level, we can try to find what more tangible dimensions distinguish the different macro-regions of the country. Looking at the social data, we can see that similar patterns are repeated with average life expectancy by region and concerning the level of registered crime by region. As we move southeast, life expectancy declines, while per capita crime rates rise significantly (see Figure 3).
Average life expectancy for 1992–2018 (left panel) and crime per capita for 1995–2018 (right panel) by region
Citation: Journal of Public Finance and Public Choice 39, 1; 10.1332/25156918Y2024D000000007
Source: Maps constructed by authors based on information from the Institute of Sociology of the National Academy of Sciences of Ukraine and State Statistics Service of Ukraine.Long-term structural variables may have influenced real socioeconomic variables, such as life expectancy or crime rates, which may have determined the difference in economic views of the population in the post-Soviet period, specifically the structure of the residence and the structure of the regional economy. While the vast rise in post-Soviet crime rates may have been due to transitional difficulties, from the work of Foglesong and Solomon (2001), we know that the crime rate began to grow significantly back in the Ukrainian SSR during 1972–89 (see Figure 4). At that time, the country was experiencing a wave of urbanization, and during the same period, the average share of the urban population increased by 10 percent.
Number of registered crimes (Ukrainian SSR, 1972–91; Ukraine, 1991–2017)
Citation: Journal of Public Finance and Public Choice 39, 1; 10.1332/25156918Y2024D000000007
Source: Institute of Sociology of the National Academy of Sciences of Ukraine and State Statistics Service of Ukraine.In addition, the urbanization of Ukrainian regions is closely related to their economic structure, which depends on historical background. The Soviet economy was highly specialized in agricultural production and heavy industry. In contrast, the small-scale production of consumer goods and the service sector developed very weakly and had low shares in the economy (Lytvyn, 2011). This influenced the fact that more agricultural regions remained predominantly rural (see the left panels of Figure 5 and Figure 6). In comparison, more industrialized regions continued to urbanize (see the right panel of Figure 5 and Figure 6).
Average share of citizens living in rural (left panel) or urban areas (right panel) by region, 1992–2018
Citation: Journal of Public Finance and Public Choice 39, 1; 10.1332/25156918Y2024D000000007
Source: Maps constructed by authors based on information from the Institute of Sociology of the National Academy of Sciences of Ukraine and State Statistics Service of Ukraine.Share of citizens employed in agriculture (left panel) and industry (right panel) by region, 2000–18
Citation: Journal of Public Finance and Public Choice 39, 1; 10.1332/25156918Y2024D000000007
Source: Maps constructed by authors based on information from the Institute of Sociology of the National Academy of Sciences of Ukraine and State Statistics Service of Ukraine.This was especially typical for the southeast of Ukraine and partly for the central regions, where the share of citizens both employed in industrial production and living in cities is much higher. While moving to the west, Ukrainian regions are more rural, and a larger share of the population is engaged in agriculture.
Analyzing the data on attitudes, we see a significant difference between industrial and nonindustrial regions. We call “industrial regions” those where the share of industrial employment for 2000–18 exceeds the national average of 17 percent.
In industrial regions, the negative attitude toward privatization is on average 5.8 percentage points higher (42.3 percent for industrial; 36.6 percent for nonindustrial), while the positive attitude toward privatization is 5 percentage points lower. Industrial regions are also 6.7 percentage points more supportive of socialism and 4.5 percentage points less supportive of capitalism. They are also 3.1 percentage points more supportive of the planned economy and 2.5 percentage points less likely to support minimizing government intervention in the economy (see Table 1).
Summary statistics of key variables by regional economic specialization
| Specialization | ||||
|---|---|---|---|---|
| Industrial | Nonindustrial | Agricultural | Nonagricultural | |
| Privatization negative | 42.3% | 36.6% | 38.0% | 39.3% |
| Privatization positive | 31.0% | 36.0% | 33.9% | 34.7% |
| Socialism support | 25.8% | 19.1% | 21.6% | 21.4% |
| Capitalism support | 9.8% | 14.4% | 13.1% | 12.4% |
| Plan economy support | 31.0% | 28.0% | 29.5% | 28.4% |
| State role minimization support | 5.9% | 8.4% | 8.3% | 6.5% |
| Crime per capita | 12.2 | 8.8 | 8.8 | 11.5 |
| Life expectancy | 68.2 | 69.3 | 69.1 | 68.7 |
| Industrial employment share | 23.5% | 13.8% | 14.7% | 20.6% |
| Agricultural employment share | 15.2% | 25.4% | 27.8% | 13.8% |
| Share of urban residence | 71.1% | 55.2% | 54.9% | 70.1% |
| Share of rural residence | 29.7% | 46.4% | 47.0% | 30.6% |
Source: Institute of Sociology of the National Academy of Sciences of Ukraine and State Statistics Service of Ukraine.
Industrial and nonindustrial regions also differ in more intangible parameters. Thus, the average crime per capita in industrial regions for 1995–2018 is 12.2, and in nonindustrial regions, it is 8.8. Life expectancy differs by 1.1 years in favor of less industrialized regions. From the point of view of this study, the main difference between industrial and nonindustrial regions is urbanization (see Figure 7). The share of the urban population in industrial regions is 71.1 percent and in nonindustrial regions is 55.2 percent. The percentage of the rural population in industrial regions is 29.7 percent and in nonindustrial regions is 46.4 percent. In addition, if we look at agricultural regions in which the share of the population employed in agriculture during 2000–18 was more than 20 percent, then we see the opposite picture. The percentage of the urban population in agricultural regions is 54.9 percent and in the nonagricultural areas is 70.1 percent. The share of the rural population is 47.0 percent versus 30.6 percent.
Distributions of key explanatory variables in industrial and nonindustrial regions
Citation: Journal of Public Finance and Public Choice 39, 1; 10.1332/25156918Y2024D000000007
Source: Institute of Sociology of the National Academy of Sciences of Ukraine and State Statistics Service of Ukraine.This specificity of the patterns in residence and industrial structure between different regions of Ukraine provides a natural experiment that may allow for studying the causal effect of crime on attitudes. We use these patterns to construct instrumental variables for reported crime rates. Specifically, we will test the following four instrumental variables: (1) the share of the region’s citizens employed in agriculture; (2), the accompanying variable, the share of the region’s citizens living in rural areas (the first two variables have a strong negative linear relationship with the reported crime rate per capita); (3) the share of the region’s residents employed in industry; and (4), the accompanying variable, the share of the region’s residents living in cities (the second two variables strongly correlate with the crime rate, though the industrial employment variable has signs of a nonlinear relationship with crime, as Figure 8 shows). We also expect a high correlation between the level of urbanization and the economic structure of the regions, which can make some of the instruments excessive.
Potential instrumental variables
Citation: Journal of Public Finance and Public Choice 39, 1; 10.1332/25156918Y2024D000000007
Empirical model
Here, we consider the extent to which there is a statistical relationship between crime and attitudes toward privatization. We control for the effect of other variables that have been considered in the previous literature, including the impact of living standards on support for privatization, whether directly or indirectly reflected. To estimate the direct effects, we include an explanatory variable: the logarithm of GDP per capita. The expectation is that higher incomes will translate into more support for privatization. In one of the ordinary least squares (OLS) specifications, we also use the average regional incomes in dollar terms from the surveys (which have been available since 1992).
To estimate the indirect impact, we include a life expectancy variable that is highly dependent on the living conditions of people and their environment. We also include the average age of the population in the region, as we expect that regions with a higher proportion of elderly population may have lower support for reforms, either due to the more significant traumatic impact of the transition or due to the longer Soviet experience. Another essential variable is citizens’ confidence that there are political leaders in the country who can govern effectively. We also view this variable as a partial proxy for corruption and the ineffectiveness of governance, and we expect people who are disillusioned with politicians to be less likely to support the reforms they propose. Finally, we control for the effect of income inequality, as reflected by the regional Gini. Rising inequality is expected to negatively impact support for liberal economic policies. However, at the same time, this index may not reflect inequality of opportunity, which, according to the literature, influences economic attitudes (see Table 2).
Explanatory variables and their expected effects
| Explanatory variable | Expected effect |
|---|---|
| Registered crime per capita | Negative |
| Gross regional product per capita | Positive |
| Age | Negative |
| Life expectancy | Positive |
| Leaders: no | Negative |
| Gini | Negative |
Crime and privatization: OLS analysis
We first show the OLS results with data for 1992–2018 to cover the transition period of the 1990s (see Table 3). We regress the regional share of support for the three types of privatization on the log of reported crime rates and an array of controls. Dependent variables are: the percentage of positive and negative citizen attitudes about the privatization of large enterprises (see Models 1 and 2); the share of positive and negative citizen attitudes about the privatization of small enterprises (see Models 3 and 4); and the share of positive and negative citizen attitudes toward land privatization (see Models 5 and 6).
Privatization support by type (long OLS), 1992–2018
| Dependent variable | ||||||
|---|---|---|---|---|---|---|
| priv_land_pos | priv_land_neg | priv_large_pos | priv_large_neg | priv_small_pos | priv_small_neg | |
| 1 | 2 | 3 | 4 | 5 | 6 | |
| log(crime_pc) | –0.062*** (0.021) | 0.091*** (0.021) | –0.061*** (0.015) | 0.111*** (0.021) | 0.003 (0.022) | 0.030** (0.015) |
| log(inc_dol) | 0.004 (0.019) | –0.011 (0.018) | 0.027 (0.018) | –0.010 (0.021) | 0.038 (0.026) | –0.003 (0.014) |
| Age | –0.012*** (0.003) | 0.012*** (0.003) | –0.007*** (0.002) | 0.007** (0.003) | –0.012*** (0.003) | 0.010*** (0.002) |
| Life_Expect | 0.020*** (0.005) | –0.021*** (0.005) | 0.003 (0.003) | –0.013*** (0.004) | 0.019*** (0.004) | –0.019*** (0.003) |
| Leaders_no | –0.094* (0.057) | 0.020 (0.060) | –0.110*** (0.035) | 0.107* (0.062) | –0.074 (0.054) | 0.034 (0.049) |
| Constant | –0.191 (0.419) | 0.990** (0.428) | 0.421 (0.274) | 0.766* (0.394) | –0.331 (0.391) | 0.954*** (0.269) |
| Observations | 451 | 451 | 448 | 450 | 451 | 446 |
| R2 | 0.585 | 0.578 | 0.293 | 0.403 | 0.410 | 0.411 |
| Adjusted R2 | 0.563 | 0.557 | 0.257 | 0.373 | 0.379 | 0.380 |
| Residual std error | 0.109 (df = 428) | 0.114 (df = 428) | 0.075 (df = 425) | 0.112 (df = 427) | 0.105 (df = 428) | 0.087 (df = 423) |
| F Statistic | 27.375*** (df = 22; 428) | 26.670*** (df = 22; 428) | 8.023*** (df = 22; 425) | 13.119*** (df = 22; 427) | 13.509*** (df = 22; 428) | 13.415*** (df = 22; 423) |
Notes: The factor variable “Year” is omitted from the table. * p < 0.10, ** p < 0.05, *** p < 0.01.
As dependent variables, we use both positive and negative attitudes because they are not opposites of each other due to the third category of citizens who found it difficult to answer. It makes sense to give both categories because these shares differ not only for each type of privatization but also between types of privatization. For example, the percentage of those opposed to large-scale privatization has consistently exceeded the share of those who support it. In contrast, for the privatization of small enterprises, although support has constantly been falling, the average number of those who support it has always been higher than those who are against it.
In addition, in this model, instead of GDP per capita, we use the average individual income in dollar terms (obtained from surveys and aggregated at the regional level), which is available for all years from 1992 to 2018. We also exclude the Gini coefficient from this model, which is available only since 2000. Thus, this model estimates the impact of crime and other control variables on privatization support over 1992–2018.
The model shows the statistically significant impact of crime on support for privatization. The rise in crime is associated with a fall in the share of citizens who support the privatization of land and large enterprises and is associated with an increase in negative attitudes toward all three types of privatization, consistent with our main theoretical expectations.
There is also a highly statistically significant agnificant effect of the average age, with expected signs, and of life expectancy for attitudes toward all types of privatization, except for a positive attitude toward large privatization. The higher average age of residents is associated with less support for all types of privatization and with an increase in negative attitudes for all types. An increase in life expectancy is associated with a decrease in negative attitudes. Trust in leaders is significant in several models and has the expected effects of being associated with lower support for land privatization and large privatization.
We also estimate a shorter OLS model (2000–18) where we include the same variables that we will later use for the 2SLS method (see Table 4). In contrast to the first OLS, we include gross regional product (GRP) per capita and the Gini index.
Privatization support by type (short OLS), 2000–18
| Dependent variable | ||||||
|---|---|---|---|---|---|---|
| priv_land_pos | priv_land_neg | priv_large_pos | priv_large_neg | priv_small_pos | priv_small_neg | |
| 1 | 2 | 3 | 4 | 5 | 6 | |
| log(crime_pc) | –0.051 (0.031) | 0.078** (0.032) | –0.060*** (0.022) | 0.131*** (0.033) | 0.012 (0.030) | 0.033 (0.023) |
| log(GRP_pc) | –0.009 (0.021) | 0.013 (0.021) | 0.017 (0.016) | –0.008 (0.021) | 0.030 (0.020) | –0.007 (0.015) |
| Age | –0.011*** (0.004) | 0.013*** (0.003) | –0.009*** (0.002) | 0.010*** (0.003) | –0.011*** (0.003) | 0.010*** (0.003) |
| Life_expect | 0.020*** (0.006) | –0.021*** (0.006) | 0.001 (0.004) | –0.009 (0.006) | 0.016*** (0.005) | –0.016*** (0.004) |
| Leaders_no | –0.077 (0.070) | 0.064 (0.076) | –0.116*** (0.041) | 0.135* (0.079) | –0.146** (0.061) | 0.080 (0.059) |
| Gini | –0.186 (0.268) | –0.099 (0.262) | –0.048 (0.201) | –0.274 (0.297) | –0.185 (0.211) | 0.020 (0.169) |
| Constant | –0.210 (0.488) | 0.929* (0.498) | 0.574** (0.284) | 0.399 (0.480) | –0.211 (0.414) | 0.785** (0.316) |
| Observations | 306 | 306 | 305 | 305 | 306 | 301 |
| R2 | 0.570 | 0.554 | 0.273 | 0.374 | 0.402 | 0.379 |
| Adjusted R2 | 0.545 | 0.527 | 0.230 | 0.337 | 0.367 | 0.342 |
| Residual std error | 0.108 (df = 288) | 0.118 (df = 288) | 0.077 (df = 287) | 0.112 (df = 287) | 0.103 (df = 288) | 0.088 (df = 283) |
Notes: The factor variable “Year” is omitted from the table. * p < 0.10, ** p < 0.05, *** p < 0.01.
Crime and privatization: instrumental variables
OLS estimations cannot be interpreted causally because crime per capita and an omitted variable can jointly determine support for reforms. This problem can be addressed through the instrumental variable approach. Since the industrial structure of Ukraine was formed during the Soviet era and the level of urbanization was significantly tied to Soviet industrialization, it is plausibly exogenous to the change in post-Soviet support for privatization. Table 5 shows the first stage of the 2SLS assessment using the instrumental variables of urban residence and industrial employment.
First-stage estimates: determinants of crime (urban-industrial)
| Dependent variable | ||||||
|---|---|---|---|---|---|---|
| log(crime_pc) | ||||||
| 1 | 2 | 3 | 4 | 5 | 6 | |
| Place_resid_cities | 1.496*** (0.129) | 1.256*** (0.128) | 1.340*** (0.131) | 1.047*** (0.122) | 0.972*** (0.124) | 0.969*** (0.125) |
| Prom_empl_sh | 0.316 (0.306) | –0.180 (0.311) | –0.356 (0.315) | 0.181 (0.286) | 0.233 (0.284) | 0.242 (0.286) |
| log(GRP_pc) | 0.345*** (0.063) | 0.346*** (0.062) | 0.196*** (0.058) | 0.204*** (0.057) | 0.203*** (0.057) | |
| Age | 0.018** (0.007) | 0.004 (0.006) | 0.004 (0.006) | 0.004 (0.006) | ||
| Life_expect | –0.074*** (0.009) | –0.067*** (0.010) | –0.066*** (0.010) | |||
| Leaders_no | 0.326*** (0.120) | 0.324*** (0.120) | ||||
| Gini | 0.164 (0.503) | |||||
| Observations | 286 | 270 | 270 | 269 | 267 | 267 |
| R2 | 0.586 | 0.647 | 0.656 | 0.736 | 0.741 | 0.742 |
Notes: The factor variable “Year” is omitted from the table. * p < 0.10, ** p < 0.05, *** p < 0.01.
One issue is that it is not entirely plausible that the share of citizens in a region employed in agriculture, the share of citizens employed in industry, the share of citizens living in urban areas, and the share of citizens living in rural areas affect privatization only via crime rates. Rather, our emphasis is on Soviet industrial policies as occurring long before current attitudes toward privatization. The Soviet industrialization policies started half a century before the collapse of the Soviet Union. Hence, we see it as plausible that the effects of these changes are primarily on crime, not on current attitudes toward privatization. In any event, our primary goal is to present an association between crime and privatization, which we showed in the previous section.
We test the instrumental variables with all possible combinations of control variables for a clearer picture of potential relationships. Across all specifications, the share of urban residence explains per capita crime and is associated with its rise. This instrument is a statistically and economically significant predictor of crime during 2000–18 across Ukrainian regions. Industrial employment is statistically insignificant and does not have independent explanatory power for the crime rate due to its strong correlation with urban residence (separately, the industrial employment variable is statistically and economically significant).
Table 6 presents the second-stage assessment, where the positive and negative attitudes of the residents of Ukrainian regions toward the three types of privatization are regressed on the explained crime rate per capita and a set of control variables. To evaluate the models, robust standard errors were used, and robustness checks were carried out. The weak instruments test did not reveal such in any of the six models. The Wu–Hausman test shows endogeneity for the first and second models (land privatization), justifying the need for an instrumental variable. At the same time, the Wu–Hausman test showed no endogeneity in Models 3, 4, and 6, which means that OLS assessments may also be valid. The coefficient on crime per capita is statistically and economically significant in four specifications, representing positive and negative attitudes toward land privatization and the privatization of large enterprises. The coefficient is statistically insignificant for the privatization of small enterprises. The first three models also show a statistically and economically significant impact of nominal GDP per capita on reform support. The coefficient has the expected sign, that is, it is associated with more substantial support for reforms.
Privatization support by type (instrument: urban residence)
| Dependent variable | ||||||
|---|---|---|---|---|---|---|
| priv_land_pos | priv_land_neg | priv_large_pos | priv_large_neg | priv_small_pos | priv_small_neg | |
| 1 | 2 | 3 | 4 | 5 | 6 | |
| log(crime_pc) | –0.245*** (0.083) | 0.203*** (0.073) | –0.112** (0.047) | 0.194** (0.076) | 0.071 (0.072) | –0.073 (0.070) |
| log(GRP_pc) | 0.097** (0.049) | –0.073* (0.042) | 0.054* (0.029) | –0.047 (0.047) | 0.038 (0.047) | 0.016 (0.039) |
| Age | –0.014*** (0.004) | 0.014*** (0.003) | –0.009*** (0.002) | 0.011*** (0.003) | –0.012*** (0.003) | 0.009*** (0.003) |
| Life_expect | 0.007 (0.010) | –0.014 (0.008) | –0.002 (0.005) | –0.004 (0.009) | 0.027*** (0.009) | –0.028*** (0.008) |
| Leaders_no | 0.027 (0.088) | 0.049 (0.084) | –0.101* (0.055) | 0.161* (0.086) | –0.162** (0.077) | 0.154** (0.073) |
| Gini | –0.249 (0.283) | –0.021 (0.229) | 0.113 (0.186) | –0.422 (0.270) | 0.060 (0.290) | –0.127 (0.194) |
| Constant | 0.451 (0.765) | 0.740 (0.674) | 0.572 (0.426) | 0.215 (0.726) | –1.192 (0.744) | 1.678*** (0.556) |
| Weak instruments | 0 | 0 | 0 | 0 | 0 | 0 |
| Wu–Hausman | 0 | 0.1 | 0.38 | 0.32 | 0.2 | 0.12 |
| Observations | 267 | 267 | 266 | 267 | 267 | 265 |
| R2 | 0.559 | 0.607 | 0.301 | 0.382 | 0.400 | 0.360 |
| Adjusted R2 | 0.529 | 0.580 | 0.253 | 0.340 | 0.359 | 0.316 |
| Residual std error | 0.108 (df = 249) | 0.108 (df = 249) | 0.071 (df = 248) | 0.110 (df = 249) | 0.101 (df = 249) | 0.089 (df = 247) |
Notes: The factor variable “Year” is omitted from the table. * p < 0.10, ** p < 0.05, *** p < 0.01.
In all models, the effect of average regional age is highly statistically significant. It is associated with a decrease in support for reforms and an increase in negative attitudes toward them. In Models 5 and 6 on small privatization, the coefficient of life expectancy is highly statistically significant, as well as the coefficient of the variable reflecting the average belief of the region’s residents that there are no effective leaders in the country. Increased life expectancy is associated with greater support for reform, and disappointment with leaders is associated with less. We also applied a second set of instrumental variables to explain crime rates through agricultural employment and rural residence. Table 7 shows the first stage of the 2SLS assessment using the instrumental variables of rural residence and agricultural employment.
First-stage estimates: determinants of crime per capita (rural-agricultural)
| Dependent variable | ||||||
|---|---|---|---|---|---|---|
| log(crime_pc) | ||||||
| 1 | 2 | 3 | 4 | 5 | 6 | |
| Place_resid_vil | –1.902*** (0.113) | –1.596*** (0.107) | –1.600*** (0.107) | –1.383*** (0.105) | –1.331*** (0.107) | –1.320*** (0.107) |
| Agro_empl_sh | 0.096*** (0.032) | 0.231*** (0.033) | 0.218*** (0.034) | 0.119*** (0.033) | 0.118*** (0.033) | 0.124*** (0.034) |
| log(grp_pc) | 0.490*** (0.056) | 0.473*** (0.057) | 0.288*** (0.059) | 0.295*** (0.059) | 0.303*** (0.059) | |
| Age | 0.010 (0.006) | 0.005 (0.006) | 0.005 (0.006) | 0.004 (0.006) | ||
| Life_expect | –0.063*** (0.009) | –0.059*** (0.009) | –0.057*** (0.009) | |||
| Leaders_no | 0.229** (0.106) | 0.215** (0.106) | ||||
| Gini | 0.600 (0.405) | |||||
| Observations | 322 | 299 | 299 | 297 | 295 | 295 |
| R2 | 0.623 | 0.716 | 0.719 | 0.772 | 0.774 | 0.775 |
Notes: The factor variable “Year” is omitted from the table. * p < 0.10, ** p < 0.05, *** p < 0.01.
Across all specifications, the share of rural residence explains per capita crime and is associated with its decline. This instrument is a statistically and economically significant predictor of crime during 2000–18 across Ukrainian regions. Agricultural employment is statistically insignificant and does not have independent explanatory power for the crime rate due to its strong correlation with urban residence (separately, the industrial employment variable is statistically and economically significant). Agricultural employment is also statistically (but to a much lesser extent economically) significant but has the opposite sign, being associated with increased crime. However, in a separate assessment of nonrural livelihoods, agricultural employment has a negative impact on crime. Due to the relatively small economic effect and its mixed nature, we include rural residence in the second-stage estimation.
Table 8 presents the second-stage assessment, where the positive and negative attitudes of the residents of Ukrainian regions toward the three types of privatization are regressed on the explained crime rate per capita and a set of control variables. To evaluate the models, robust standard errors were used, and robustness checks were carried out. The weak instruments test did not reveal such in any of the six models. The Wu–Hausman test shows endogeneity for the first and second models (land privatization), justifying the need for an instrumental variable. At the same time, the Wu–Hausman test showed no endogeneity in Models 3–6, which means that OLS assessments may also be valid. The coefficient on crime per capita is statistically and economically significant in four specifications, representing positive and negative attitudes toward land privatization and the privatization of large enterprises. As in the previous specification, the coefficient is statistically insignificant for the privatization of small enterprises.
Privatization support by type (instrument: rural residence)
| Dependent variable | ||||||
|---|---|---|---|---|---|---|
| priv_land_pos | priv_land_neg | priv_large_pos | priv_large_neg | priv_small_pos | priv_small_neg | |
| 1 | 2 | 3 | 4 | 5 | 6 | |
| log(crime_pc) | –0.299*** (0.080) | 0.260*** (0.074) | –0.125*** (0.043) | 0.203*** (0.076) | –0.040 (0.067) | 0.011 (0.054) |
| log(GRP_pc) | 0.128*** (0.043) | –0.093** (0.042) | 0.056** (0.028) | –0.040 (0.048) | 0.083** (0.038) | –0.008 (0.034) |
| Age | –0.012*** (0.004) | 0.014*** (0.004) | –0.009*** (0.002) | 0.011*** (0.003) | –0.012*** (0.003) | 0.010*** (0.003) |
| Life_expect | 0.001 (0.010) | –0.008 (0.009) | –0.004 (0.005) | –0.002 (0.009) | 0.015* (0.008) | –0.020*** (0.006) |
| Leaders_no | 0.047 (0.092) | –0.025 (0.086) | –0.085* (0.050) | 0.102 (0.086) | –0.122* (0.073) | 0.087 (0.068) |
| Gini | 0.124 (0.348) | –0.350 (0.326) | 0.040 (0.224) | –0.309 (0.330) | –0.018 (0.232) | 0.010 (0.191) |
| Constant | 0.556 (0.756) | 0.464 (0.707) | 0.718* (0.376) | –0.011 (0.652) | –0.490 (0.614) | 1.074*** (0.396) |
| Weak instruments | 0 | 0 | 0 | 0 | 0 | 0 |
| Wu–Hausman | 0 | 0 | 0.11 | 0.25 | 0.45 | 0.57 |
| Observations | 295 | 295 | 294 | 294 | 295 | 290 |
| R2 | 0.454 | 0.494 | 0.261 | 0.362 | 0.388 | 0.374 |
| Adjusted R2 | 0.420 | 0.463 | 0.215 | 0.323 | 0.351 | 0.335 |
| Residual std error | 0.122 (df = 277) | 0.126 (df = 277) | 0.079 (df = 276) | 0.114 (df = 276) | 0.104 (df = 277) | 0.089 (df = 272) |
Notes: The factor variable “Year” is omitted from the table. * p < 0.10, ** p < 0.05, *** p < 0.01.
The first two models on attitudes to land privatization also show a statistically and economically significant effect of GDP per capita. The growth of GDP is associated with an increase in support and a decrease in negative attitudes toward land privatization. Also, GDP shows significant effects on a positive attitude toward large and small privatization. In all models, as in the previous estimation, the effect of average regional age is highly statistically (but not economically) significant. It is associated with a decrease in support for reforms and an increase in negative attitudes toward them. In Model 6 on small privatization, the life expectancy coefficient is highly statistically significant (but less so economically). The coefficient on dissatisfaction with leaders is weakly statistically significant but has more considerable economic significance.
Conclusions and policy discussion
We have considered the relationship between the level of post-Soviet crime in the regions of Ukraine and the attitudes of citizens toward privatization. Our main finding is that crime has a causal impact on attitudes toward privatization: increases in crime reduce support for privatization. This relationship holds for the privatization of land and for the privatization of large enterprise.
Our proposed method of evaluating this recognizes that Soviet institutional practices contributed to crime. Crime increased under Soviet rule. The proposed mechanism is through Soviet planning within cities. We leveraged this variation, using Soviet-era “reforms” to discern the causal impact of crime. Since privatization and crime are endogenous, this method is a way to develop policy implications.
We then considered additional factors that contribute to support for privatization, including increasing income. This aligns with research which finds that macroeconomic experience influences support: prosperity may contribute to privatization. However, since there are also strong reasons to believe privatization is what causes growth, we caution about the causal impact. There is less of a concern with endogeneity with respect to the conclusions about leadership. Like previous studies, we find leadership matters for support for privatization.
There is a presumption that regional differences are significant in Ukraine. Narratives of “the west and the rest” capture differences on many margins: identity, economic attitudes, leadership and confidence on leaders. A significant institutional difference includes Soviet legacies and, due to this, crime. We have thus added to the understanding of regions by clarifying one source of differences, namely, Soviet urbanization and crime. The country’s central regions underwent greater industrialization during the Soviet years, making them both more urbanized and more criminal (especially in post-Soviet times, when the criminal redistribution of assets took place). This natural experiment allows us to conclude that the economic structure of regions, the structure of their residence, and the subsequent level of crime can wield explanatory power when it comes to the views of the population.
We conclude with the ongoing reconstruction effort as Ukraine continues to rebuild the country during the Russia–Ukraine War. In conflict and post-conflict settings, reconstruction includes substantial infrastructure investment. There are significant choices about what to focus on, as well as what constitutes “infrastructure.” One of the lessons for economic reform is that market institutions are a critical infrastructure for economic prosperity. Our finding that more growth contributes to support for privatization does not deny the impact of privatization on prosperity, only that the relationship is complicated and works in both directions. Rising lawlessness and falling living standards can convince citizens that these reforms should not be supported because their fruits will be distributed neither efficiently nor fairly, and the reform process itself can be accompanied by real dangers without citizens being able to protect themselves. Hence, crime reduction is not only good but also a way to improve support for privatization.
The hope is for a virtuous cycle of reconstruction: improvements in order, leading to reductions in crime, followed by increasing support for privatization. Over time, privatization is expected to contribute to more economic growth, which can then further reduce crime, in part by providing more resources to the public good of policing. There is a robust literature on the problems of getting policing institutions right. It may also be the case that more resources for policing leads to more problems with policing. Nonetheless, crime reduction is often described as a public good, and more revenue implies greater state capacity to provide the public good of crime reduction. Alongside constraints on government, which can undermine incentives for governments to provide public goods (Boettke and Candela, 2020), our analysis suggests that the stakes of successful crime reduction during reconstruction include increasing support for privatization.
Funding
The authors received no financial support for the research, authorship, and/or publication of this article.
Acknowledgments
Many thanks to Giampaolo Garzarelli, the editors of the special issue on Ukraine—Ilia Murtazashvili, Jennifer Brick Murtazashvili, and Tymofiy Mylovanov—and the referees for useful comments and suggestions. We thank Kyiv School of Economics for institutional support and our many colleagues for enabling us to continue to write and conduct research during the war.
Conflict of interest
The authors declare that there is no conflict of interest.
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