HOMICIDE IN TRANSITION ECONOMIES: HISTORY WRITTEN IN BLOOD Emanuel Vila March 2015

Acknowledgement I would like to dedicate this academic research paper to my father Florian Vila who was assassinated on the 9th of April 2003. The headlines of this event shook the Albanian country and communities throughout the world, but I am proud that even to this day people take the hat off to his name. I wouldnt be where I am today had it not been for him, therefore contributing towards a better understanding of criminal activity in post-communist economies using established economic theory has a personal significance for me. Additionally I would like to thank my tutor Daniel Howdon who helped me with the econometric models and overall direction the project, as well as my professor Daphne Athanasouli who guided me through the theories of transition economies.

1. Abstract 2. .Introduction 3. Literature Review 4. Further Analysis 5. ..Data and Methodology 6. ..Results, Discussion and Conclusion 7. Appendix 8. ... Bibliography

1. ABSTRACT The collapse of communism in Eastern Europe in 1989 and Soviet Union in 1991 are watershed events in world history. The demise of communism was cherished by many and a wave of democratization to replace the highly inefficient command economy followed soon after. Twenty-five years later the side effects of the transition process that was initiated in the early 90s, characterised by immense suffering and pain in already fragile societies, are clearly visible. One such side effect was the immediate spike in criminal homicide rates prominent in almost every region that underwent this process. Was this a coincidence? What can we attribute this increase in criminal activity to? What impact did political and economic reforms have? What impact did cultural and demographic factors such as war and ethnic tensions have? I employ panel data analysis to answer these questions and handpick the most suitable control variables that explain homicide rates in transition economies, in order to build upon an already existing framework for such analysis. My findings indicate that civil wars, where present, led to an inevitable increase in crime activity by desensitising the general population and perpetuating a culture of violence. Mass privatisation, a proxy of shock therapy applied to rapidly transform the economy also led to an undeniably large increase in homicide rates. This highlights the tradeoff between superior future economic performance and present human costs that policy makers were faced with. Trade liberalisation however, while it decreased criminal activity in the countries in the Commonwealth of Independent States (CIS) region such as Russia, Ukraine and Kazakhstan due to the reduction in black market activities, it promoted criminal activity in Central Eastern Europe (CEE) as globalization offered more opportunities to exploit. Democracy, income inequality, ethnic tensions, inflation, GDP growth and alcohol consumption prove statistically significant for different countries within different regions, albeit to a different extent. Health expenditure on the other hand, a proxy for poverty proves insignificant for neither the CEE, nor the Baltic and CIS region. 2. INTRODUCTION At the beginning of 1989 the continent of Europe, dominated by the largest country on earth that no longer exists, the Union of Soviet Socialist Republics (USSR), embraced an economic system labeled as a command

economy. The aim to centralize the economic power follows from the belief that economic coordination by markets is inefficient and welfare is best maximized by centralized planning and administration (Mickiewicz 2010). However, command economies were characterised by highly bureaucratic and extensive administrative structures that led to higher costs and distortion of information, a lack of economic efficiency derived from overproduction to meet the quotas set by the government and no profit motives for people as all economic parameters are decided by the planners. Ultimately the system was incompetent and it was the factors above, in addition to exaggerated military spending and public dissatisfaction that led to the political revolutions that started in mid-1989 in Central and Eastern Europe and Central Asia, a period of history otherwise known as the post-communist transition. Such transition affected the life of the populations in the countries involved in many significant respects, a key aspect of which is population health. (Safaei 2012) found that the countries of Central and Eastern Europe that had gone under immense political and socioeconomic restructuring after the collapse of communism in the 90s experienced sharp spikes in mortality rates. This notion that transition had an immediate and largely adverse impact on health is further reinforced by (J. Figueras et al. 2004), yet the casual link between transition and increased mortality is widely debated by literature. (M. Bobak et al 2007) identifies income inequality and corruption as the source of poor health outcomes, (Cockerham et al. 2002) and (Cockerham et al. 2006) found that people who favour political ideologies such as pro-socialism demonstrate less activity toward achieving health than antisocialists, (Stuckler et. al 2009) discovered that economic reforms such as mass privatization in Eastern Europe and Soviet union were associated with a 12.8% increase in mortality rates, while (Mckee and Shkolnikov 2001; Zatonski and Willett 2005) blame the life style factors such as the high prevalence of smoking, alcohol consumption and poor diet. Additionally, (Figueras et al. 2004) found increased mortality to be related to morbidity due to poorly organised health systems, while (Franco et al. 2004; Besley and Kudamatsu2006) have drawn attention to lack of political culture and democracy, low levels of which were a symptom in a majority of countries that underwent this grueling process. Yet, it is the work of (Leichter 1991) and (Winston et al. 1999) that have inspired this dissertation, whose work centers on the connection between violence, as a result of disrespect for order and state control as an explanatory factor for the increase in death rates in transition economies. During the period of transition, countries of the former Soviet Union such as Russia faced a multitude of challenges related to crime, law and justice. This included drafting a new criminal code (Solomon 2005), corruption among the political elite (Coulloudon 1997; Wedel 2001), a police system with budget shortfalls and widespread corruption (Beck and Lee 2002), and consequently a judiciary distrusted by citizens (Huskey 1997). Equally, during the same time period of transition, the personal safety of the citizens of Eastern Europe were threatened by rising levels of criminal activity (Lotspeich, 1995; Savelsberg 1995; Zvekic 1998).

25 years after transition began, the assassination of Boris Nemtsov on the 27th of February 2015 is yet another bleak reminder of the still existing prospects of crime in post-communist countries like Russia, as it leaves liberals in fear of a new wave of violent repression. On March 7th (The Economist, 2015) quoted that This postcard murder, referring to a symbolic image of Nemtsovs memorial procession with the cupolas of St Basils church in the background, marks the return of Russias campaign of political violence from Ukraine to the homeland. Thus understanding crime using economic theory, or criminal homicide as I refer to it on this paper, the act of a human causing the death of another, using many forms including accidental or purposeful murder, has a profound professional significance. On a social level, literature finds serious negative effects due to crime as well as fear of crime, particularly on psychological health (MORRALL et al. 2010), on childrens cognitive performance and further development (Sharkey 2010) and on the risks it poses on families way of life as a sudden and uninvited intrusion in their lives that changes their meaning of existence, as they struggle to deal with the major transformations and challenges, that ultimately lead to them having a higher probability of developing sustained and dysfunctional psychological problems. (Thompson et al. 1998) On an economic level, homicide was estimated to decrease life expectancy by nearly 5 years(Redelings et al. 2010), which is a measure of well-being in society, while also acting like a tax on the entire economy: [as] it discourages domestic and foreign direct investments, it reduces firms' competitiveness, and reallocates resources creating uncertainty and inefficiency.(Detotto and Edoardo 2010) The rest of the chapter is organised as follows. On section 3 I will present a literature review of homicide studies in transition, on section 4 I further analyse the dependent and independent variables involved in the study , in section 5 I discus the econometric model used to empirically asses my model and present the data, the results of which I discus and conclude in section 6. 2. LITERATURE REVIEW In 2009, (Stamatel 2009) used pooled time-series analyses of data from nine countries from 1990 to 2003 In East-Central Europe, to examine whether correlates of cross-national homicide variation tested with data from highly developed, predominantly Western Nations could also explain homicide rates in transition economies. His work built upon the four structural and cultural contexts that have been identified as correlates of cross-regional homicide in other countries by (Gartner 1990); namely the economic context (distribution of economic resources), integrative context (integration of social networks and Institutions of social control), demographic context (the composition and activities of the population) and culture context (exposure to violent and legitimate violence). Stamatel concluded that within the economic

context, the rate of homicides is negatively related to GDP per capita and not significantly related to income inequality, within the demographic context its positively related to ethnic diversity and population density but negatively related to the percentage of young people between 15 and 25 years old and within the integrative context, its not significantly related to divorce rates, a measure of intra-group cohesion [that] was anticipated to correspond to higher homicide rates. He discovered that homicides were also positively related to political violence within the culture context and additionally proved a correlation between lower homicides and successful economic reforms and higher levels of democracy, as a result of a countrys ability to recover quickly from the disruption of the end of the communist regime and to institute a new social order. Although the two later factors are a subcategory of the economic and integration context, I prefer to single them out into the economic reforms and political reforms context respectively, as they are quite unique features for transition economies. (LaFree and Tseloni 2006) reinforced the idea based on the conflict perspective, which predicts that violent crime rates will increase along with the brutalizing effects of the market economies that so far have universally accompanied democratization, as he found "homicide rates in transitional democratic regimes were significantly higher by an estimated average of 54.4%". Nevertheless different academics use different explanatory variables within the four contexts outlined above to try and explain homicide rates in transition economies. For example (Favarin 2013) finds that according to her OLS model, a shift from transition stage to a more solid democratic stage the Balkan countries, Bulgaria and Romania reduced their homicide rates by 40.6 %, but (LaFree and Tseloni 2006) concludes that that during the second half of the twentieth century homicide rates gradually increased for full democracies while the average homicide rate for a hypothetical country of average percentage of population fifteen to twenty-four years old and average prosperity under autocracy is effectively zero". Nevertheless democracy facilitates a neutral stance towards ethnically diverse countries, thereby indirectly leading to a decrease in harassment, violence and ultimately murder, as emphasized by (Ashforth 2005) who quotes that differences of religion and culture ought to be treated as private matters to which government should remain blind. Other academics have focused on the economic context as means of explaining homicide rates, such as (Pridemore and Kim 2007) who finds that regions with greater increases in unemployment experienced greater increases in homicide rates while regions that privatized experienced smaller increases in homicide rates. Equally (Prasad 2012) finds a negative correlation between trade liberalization and crime in transitioning India. While (Stamatel 2009) concluded that for CEE countries income inequality was insignificant for homicide rates, other literature by (Neopolitan 1996; LaFree 1999; Messner and Rosenfeld 1997) directly contradicts this. (Ouimet 2012) also finds that the strong bivariate relationship between GNI and homicide (0.59) holds in a multivariate analysis for all countries but he limits this finding only for the subsample of medium to high Human Development Index (HDI)

nations, thereby saying that inequality doesnt explain homicide rates for low HDI countries. Nevertheless according to the data from World Health Organisation, none of the countries in my study are ranked as countries with low HDI. According to (nationsonline.org) the country with the highest HDI that classifies as low HDI is Pakistan at an HDI of 0.497. The lowest HDI reached by the countries in my sample was by Kyrgyzstan at an HDI level of 0.58 in year 2000 as demonstrated in graph 1. Graph.1a UNDP Human Development Index HDI The demographics context has also been under the scope with regard to homicide rates, as (Kikuchi 2010) explores the spatial and temporal dimensions of crime in neighborhoods and finds that racial homogeneity of the target neighborhood is most important when offending along with co-offenders and (Frhling et al. 2003) deduces that rising crime levels in Latin America are attributable to ethnic diversity. When identifying the main factors that lead to increased homicides in transition economies, its important not to not leave important variables out of the equation, therefore violating one of the classical assumption of Ordinary Least Squares (OLS) regression theory (that the explanatory variables are independent of the error term) and hence skewing the results due to omitted variable bias. As a result I will control for all variables that might have caused a spike in homicide rates during the post-communist transition period. For example (Ouimet 2012) uses Gross National income (GNI), Gini coefficient and excess infant mortality which is a proxy for poverty, to account for the economic context. To control for the demographic context he factors in the portion of 15-29 year olds and percentages of ethnicities as part of the population, to control for the culture context he includes a violent conflict dummy, and to account for the political context he uses a full democracy dummy and an authoritarian regime dummy. On the other hand, (Pridemore and Kim 2007) use the ratio of the income received by the top 20% relative to the bottom 20% wage earners to account for inequality and therefore the economic context, the rate of heavy drinking per 100,000 of deaths ,

percentage of population living in cities with more than 100,000 residents and percentage of population aged 25-44 to account for the demographic context, and the rate per 1000 residents enrolled in college and voter turnout as percentage of registered voters who voted in the 2000 Russian Presidential election to account for the integration context. In order to ensure my model is as relevant and robust as possible, I will examine all different control variables used by different academics within each of the six categories examined so far. Crime rates increased during the period of transition, albeit at different rates for different countries. To the authors knowledge no empirical study distinguishes between the explanatory variables that might significant in the CEE, Baltic or CIS countries separately. What might explain the increase in homicide for one set of countries might not significantly correlate with the increase in crimes in another. I aim to bridge that gap by handpicking the best control variables for the set of countries involved in my study. Below I have devised a table including all the categories discussed above (see Table. 1), with all respective control variables for which either lack of data isnt a problem or I believe to be worth testing based on supportive literature. In the next section, I will examine each control variable individually, as I provide the data and conduct a comprehensive visual analysis to help me devise the ultimate econometrics model that explains homicide trends in post-communist countries. Economic Context Integrative context Real GDP per Capita GDP growth Inflation Unemployment Income Inequality

Voter Turnout Health Expenditure Demographic context Culture Context

Percentage of youth Concentration of population Ethnic diversity Alcohol Consumption

War/ Political violence Divorce Rates Political Reform Context Economic Reform Contex

Democracy Scores

Mass Privatisation Trade Liberalisation Table. 1 Control variables that might explain homicide trends 4. FURTHER ANALYSIS

Homicide Rates are the focus of my study. Using data from the World health organisation (WHO) I have plotted the graph below for the 19 countries of interest. This should give a clear snapshot of countries homicide rates trajectory following the period of transition in the 90s.

Graph. 2 Real Gross Domestic Product, PPP$ per capita

ECONOMIC CONTEXT

Graph. 1b SDR, Homicide and Intentional Injury, 0-64, per 100,000

Economic strain can result from the general inadequacy of resources or the unequal distribution of resources. Particularly relevant for the East-Central European counties given the extent of economic restructuring that has been occurring during the post-communist transformations. This period of transition affected the well being of the citizens from these countries (Gros and Steinherr2004) a. GDP per Capita is probably the most common indicator of economic development and it has been used in a larger number of studies. (Bennet, (1991; LaFree and Tseloni (2006; Lin 2007). Furthermore, (Stamatel 2007) also found that found that homicide rates were negatively related to GDP per capita. Nevertheless there has been at least one case, Cuba, where a reduction of output (about 40 percent in 1989-94) did not translate into a mortality crisis: life expectancy in Cuba increased from 75 years in the late 1980s to 78 years in 2006 (Popov 2010). Using data from World health Organisation I plot the following graphs. Here we observe a negative relationship between the increase in Real GDP per Capita Purchase Power Parity adjusted against the rate of homicides. We also observe a distinction between two groups of countries whose as GDP per Capita increases, homicide and intentional injury rates decrease. This is observed in CEE countries like Albania, Romania, Macedonia, Slovenia, Slovakia, Hungary, Czech Republic to mention a few. For example, from 1990 to 2000, Czech Republic's GDP per Capital has increased by 26.2% from $12314.3 to $15546.4. For the same period of time, after a slight upsurge in homicide rates in mid 90's, homicide rates have decreased by 12.85% from 1.79 to 1.56 homicides and intentional injuries per 100,000 people. For the same year Romania's GDP increases by 9.37% from 5182.87 to 5668.39. Equally, It's homicide rates decrease by 36.35% from 5.31 to 3.38 homicides per 100,000 people. The other group of countries takes a different path, which nevertheless tells the same story. It is clear from the graph that as GDP per capita decreased, Homicide rates for Kazakhstan, Moldova, Russia, Ukraine and Georgia increased. For example from 1990 to 2000, Russia's GDP per capita decreased by 15% from $8002.65 to $6832.78, while homicide rates increased by an astounding 89.5% from 14.47 to 27.97 per 100,000 people. Likewise, for the same time period, Kazakhstan, Ukraine, Georgia and Moldova recorded a decrease in GDP per capita by 6.58%, 50.1% and 55.7% respectively, while they demonstrated increases in homicide rates by an average of 38.7%. The Baltic region countries of Latvia, Estonia and Lithuania religiously follow the same pattern. What stands out however is that in the CIS group, Kyrgyzstan and Armenia's GDP per capita decreased by 26.85% and 4.28%, yet homicide rates also decreased by 46.67% and 61.3% from 1990 to 2000. Potential explanations could be Armenias low ethnic mixture of only 1.8%, which is found by (Stamatel, 2009) to affect crime rates in transition economies, while Kyrgyzstan may have reduced homicide rates despite a decrease in GDP per capita due to low unemployment levels of around 2.9% to 3.1% during the 1996-2000 period, which was by far the lowest level of

unemployment out of our 19 country sample. Literature by (Popov 2010) confirms this correlation.

Graph. 3 Real Gross Domestic Product per Capita and Homicide

Rates It is clear in the scatter graph that although Russia, Ukraine, Kyrgyzstan, Latvia and Lithuania have a higher GDP per Capita than Macedonia and. Armenia, Albania, and Slovenia in 1993, their homicide rates exceed the other group's rates in 1994. I have measured these two variables with a one year gap of in order to take into account a small lag that lower deteriorating GDP per capita may have on homicide rates as people start to realise they are increasingly worse off. Nevertheless, the scatter graph suggests that other variables must be taken into account to give a fuller image of the model that explains homicide rates in transition economies and that GDP per Capita may not be statistically significant. b. GDP Growth has been found to have significant crime-reducing impact in the case of violent crime (Fajnzylber et al 2002). Findings of (Popov, 2010) reinforce the same notion after he concluded that due to transformational recession in the 1990s, output fell by 45 percent from 1989 to 1998, and the crime rate, murder rate, and suicide rate all sharply increased as well. Economic problems, especially economic slowdown, stagnation, deterioration, and collapse can greatly contribute to intra-state tensions and destabilization (Jourek 1999), which in turn places strains on existing social and political system that can consequently result in instability and internal conflict (Brown 1996). However one must be cautious when analysing the causality between crime and economic growth. As we discussed earlier crime can also affect economic growth as was the case for Italy in time span between 19792002. (Detotto and Edoardo 2010) c. Inflation will lead to people having less money as it directly reduces their savings. This deteriorates their living standards and may as a result impact their living standards. Inflation can explain more about families economic health and peoples daily lives (Serena 2013), as it is often

connected with economic inequality (Albanesi (2001); Thalassinos et al. 2012), which in turn is connected to higher crime levels. Using data from WHO I created the Graph. 4 and Graph. 5 below.

Graph. 4 Annual Average Rate of inflation (%)

Graph. 5 Inflation and Homicide Rats In graph. 4, Armenias inflation dwarfs that of every country's in 1992 and 1993 reaching average annual rates of inflation of 2118% and. 10997% respectively. It is perhaps no surprise that 1992 also symbolizes Armenias peak homicide rate of 26.55 crimes and intentional injuries per 100,000 people, beating the runner up Estonia at 21.02 by 26.3%. The second highest inflation around 1992-1993 period is Ukraine with inflation rates of 1650% and 3691% respectively .The homicide rates recorded in these two years are 12.21 and 14.02 per 100,000 people, and although these are big compared to CEE countries such as Slovakia (2.23;

2,29) and Slovenia (2,22; 1.36), they are the second smallest in the region, which is dominated by Russia at 31.37 homicides per 100,000 in 1992 and 33.19 homicides per 100,000 in 1993. However for the same period of time Russia also records a substantially high average annual rate of inflation at 1468%. When examining the countries with the highest inflation in this period of time, besides the countries discussed above, its easy to distinguish Kyrgyzstan, Kazakhstan and Latvia. These are countries that were ranked into the upper boundary of homicide rates in my visual analysis earlier. Graph.5 further reinforces this positive correlation between inflation, which according to literature characterizes inequality; homicide rates are run against inflation. According to the visual analysis I can predict that inflation is going to test significant for CIS and Baltic countries, but insignificant as an explanatory of homicides for CEE countries. Nevertheless, due to the extensive lack of data (for example Russia missing from 1996 to 2012, Bulgaria missing from 1999 onwards and data for Albania, Ukraine, Georgia and Kyrgyzstan being inconsistent at best), It will be difficult to run this model while ensuring it produces a robust model. d. Unemployment relates to the idea of stress factors affecting mortality. "Stress factors are associated with the transition to a market economy and are created by a rise in unemployment, labor mobility, migration, divorce, and income inequality" (Popov, 2010), who noted that Men in their 40s and 50s who lost their jobs (or had to move to another job or region) were the first candidates to die prematurely in the 1990s." But often scholars have argued that unemployment is connected to income inequality (Cardoso and Urani 1995), which in turn positively correlates to an increase in homicide rates. Employment and unemployment, indeed, seem to have an impact on the decrease or increase of crime (Steven and Winter-Ebmer 2002), however this paper treats seven types of crime, one of which is property crime and yields significant result when regressed against unemployment. The correlation between violent crime and unemployment is significantly weaker. Using data from WHO I plotted the following graph.

Graph. 6 Unemployment rate (%) It's interesting to see that some countries, which depict a lower unemployment rate from 1990 - 1995 during the initial years of transition such as Russia, Estonia, Ukraine, Kyrgyzstan and Lithuania, are the countries that show the highest rates of homicide in for the same period of time. The explanation to this oddity can come from the fact that these are also the countries that were not prepared to fully restructure their labour markets in order to soften the pain of transition. As a result, this led to labour hoarding and inefficiencies, which affected future growth and development, which consequently would have affected homicide rates. (Mickiewicz 2010) quotes that jobs in the old industrial sectors may have been preserved by a combination of inadequate social welfare provision and industrial subsidies, typically of an implicit nature. That route turned to be self-defeating

Graph. 7 Unemployment and Homicide Rates

The very same relationship is captured in the regression of SDR, homicide to the rate of unemployment. This absolutely and categorically does not imply that an increase in unemployment would lead to a lower rate of homicides, it's rather a side effect of countries that were not prepared to restructure the labor markets and tried to keep employment high as we discussed above. As a result, I dont think unemployment is a good explanatory variable of homicide rates, therefore it will be omitted from my final model. d. Income Inequality has contributed to increases in post-communist violent crime rates as discovered not only by (Matutinovic1998: Savelsberg1995: Karstedt 2003), bur also (Cabinet Social Development Committee 2004) who quote that inequality generates fiscal costs on the wider community, such as through increased crime and health expenditure. As discussed in the literature review, (Ouimet 2012) finds that income inequality appears to be the most important factor in accounting for the variations in violence across countries that have a medium to high level of human development and other academics also agree when they hypothesize that income inequality, through its detrimental effects on social cohesion, would be related to an increase in violence worldwide, and in low and middle-income countries in particular (Wolf et al. 2014). This is in line with the general theory that inequality is detrimental to the well being of the society since besides corruption, income inequality is identified as the source of high mortality rates (Bobak et al 2007). Contrary to logic, an increase in inequality has not always been associated with a rise in criminal homicide rates, as (Chintrakam and Herzer 2012) examine the effect of income inequality on crime in the United States using a US state-level panel data set for the period 19652005 and find that inequality exerts a robust crime-reducing effect. Nevertheless, this result may not be surprising in a highly developed country like the US, where the lack of equality can be balanced by a more efficient welfare provision system and enforceable laws in place to deter crime from happening. On the other hand, (Stamatel 2009) found that while In developed countries, income inequality is correlated homicide, he found that found this variable to be statistically significant in only one of the eight time-series cross-section econometric models. Thus, homicide is not significantly related to income inequality in the CEE countries that he examined such as Bulgaria, Croatia, Czech Republic, Hungary, Macedonia, Poland, Romania, Slovakia and Slovenia. To gain a better understanding I data from the World Income Inequality Database and plotted the Gini coefficients for the countries in the sample.

Graph.8 Gini coefficients plotted over time For the purpose of descriptive analysis, I have divided the regions represented by the graph in 3 categories. First we have the countries that consistently fall in the lower boundary, with 0-33% Gini coefficient. These are countries such as Czech Republic, Slovakia, Slovenia, Hungary and Macedonia. Poland crosses the 33% inequality coefficient 33.7% in 1998 and continues in the middle region thereafter, while Romania follows the same upward trend by scoring 35.3% only three years later in 2001, however they consistently score below the 33% boundary in the first 8 years of transition. All these countries all fall in the lower region of intentional homicide scores, therefore underlining a potential correlation between low inequality and low homicide rates. The exception in this case is Albania. Although very little data exists for Albania, as highlighted by the non-consistent scatter plot, the data that does exist seems to imply Albania is one of the low boundary countries. From 1990 to 2004, which is the timeline for this constructed scatter graph, Albania scores under 33%, with 2005 being the first year it scratches the middle boundary at exactly 33%. This however should not disprove the correlation between inequality and correlation, because Albania was one of the countries that faced one of the most brutal and bloodiest civil wars in the region in 1997, hence skewing the homicide results. It's interesting to see that when I

18 23 28 33 38 43 48 53

1990 1992 1994 1996 1998 2000 2002 2004

Albania Bulgaria Czech Rep Macedonia Hungary Poland Romania Slovakia Slovenia Lithuania Latvia Estonia Russia Ukraine Armania Georgia Kazakhstan Kyrgyzstan Moldova

replaced the excessively large 41.3 Homicides per 100,000 people with the mean of homicide rates of previous and future years combined, Albania seems to be in the middle-boundary. Interestingly we find Baltic states like Latvia, and Estonia, as well as CIS countries like Kazakhstan in the Middle boundary region, which all report high homicide rates. Data for Kazakhstan is inconsistent prior to 2001 therefore it's difficult to know where it would have performed before that. Except Bulgaria which, scores in the lower boundary of homicide rates, all other countries scoring in between 33% and 38% Gini coefficients, fortify the theory that inequality can be positively correlated to homicide rates. It's little surprise that Russia, Kyrgyzstan, Ukraine and Moldova score above the 38% Gini coefficient, and are therefore categorized as high boundary scorers. Ukraine, which also falls under this category scores in highly in homicide and intentional injuries rate per 100,000 people, albeit lower than its CIS counterparts. Perhaps the most odd observation of all is Georgia, which has one of the lower homicide rates. I expect all three regions to test significant for their correlation between homicide rates and inequality coefficients. INTEGRATIVE CONTEXT The capacity of a nation to provide control and protection against violence should be greater the more prevalent and interdependent are individuals ties to social institutions and social institutions ties to each other (Gartner 1990). This argument is valid for post-communist countries because they radically altered traditional institutions such as families, churches, and schools, as the civil society adapted to the new political regimes.(Stamatel 2009) a. Voter turnout or the proportion voting for a specific candidate/party is often used as a measure of apathy or lack of trust (Putnam 1995); Villarreal 2002) This lack of trust that institutions can deal with enforcing law leads to an increase in crime. However voter turnout gradually decreasing over the years since transition began in CEE, CIS and the Baltic states in the doesnt necessarily explain reduced confidence in the system to regulate crime rates, but possibly the fact that citizens in post-communist states are exposed to increasingly numerous, heterogeneous, and conflicting messages regarding both the economy and politics. Moreover they also face a much broader set of political choices for which this information is relevant, therefore this may be the main contributor towards a decrease in election participation (Duch 2001). I gathered data from the International IDEA Voter Turnout Website, which contains the most comprehensive global collection of voter turnout statistics available. These statistics are regularly updated for national presidential and parliamentary elections since 1945, as well as European Parliament elections and presented for each country using both the

number of registered voters and voting age population (VAP) as indicators. See graph .9.

Graph .9 Voter Turnout as % of population Typically, voting functions are studied using time-series data (Fair 1978; Fair 1996; Norpoh et al. 1991) This approach is not possible in the transition countries of CEE, CIS and Baltic states because only a few elections have taken place since the fall of communism. However for the purpose of visual analysis, I will extrapolate the data for the years no elections were held so I can get a better cross-national image of voter turnout and try to identify patterns and correlations between homicide rates and voter turnout. See graph. 10 The main trend to be recognised is the overall decrease in voter turnout in the two decades since transition. In 1990 the average voter turnout was 74.82% for all the 19 countries in our sample, whereas by 2012 the average voter turnout is 59.1%. Since then overall homicides rates have decreased from an average of 6.75 homicides per 100,000 people to 3.7 homicides per 100,000. Nevertheless, the decrease in voter turnout does not have to necessarily associate with a loss of faith in institutions. As mentioned by (Duch 2001), according to the extant voting literature, political information plays a major role in shaping voter decisions (Fearon 1999; Zaller 1992). Given the inconsistency between voter turnout and homicide rates, as well as lack of data, I will omit voter turnout from my final model.

Graph. 10 Voter Turnout extrapolated b. Health expenditure is included in order to fill the gap left by the lack of data on education enrolment, education expenditure as well as police personnel and criminal justice system resources, which inherently represent the level of social development. In examining their connection to crime, health expenditure as % of GDP was included as a proxy for social development and good organization of the welfare state(Favarina 2013). Including this variable in the study provides an opportunity to control not only for economic and demographic aspects, but also for social characteristics of countries. Since there is some variation among the data, I have taken the average health expenditure for each country from 1995 to 2000 in order to categorise them accordingly. The countries with the lower averages of total spent as % of GDP on health are the ones with the higher average homicide rates for the same period of time. Below I examine data from WHO and plot Graph. 11 and 12.

20 30 40 50 60 70 80 90

100 110

1990 1995 2000 2005 2010

Albania Bulgaria Czech Rep Macedonia Hungary Poland Romania Slovakia Slovenia Lithuania Latvia Estonia Russia Ukraine Armania Georgia Kazakhstan Kyrgyzstan Moldova

Graph. 11 Total Health Expenditure as % of GDP

Graph .12 Health Expenditure and Homicide Rates

This negative correlation is further reinforced by looking at the scatter graph, where health expenditure X is plotted against homicide rates Y. The downward slope represents a decrease in homicide rates as health expenditure increases. For 1995 to 2000, Albania's average total spent on Health as % of GDP is 4.82% and reports 16.3 homicides per 100,00 people, Kazakhstan (4.5%) reports 19.77, Kyrgyzstan (5.47%) reports 11.95, Estonia (5.93%) reports 18.55, Russia (5.97%) reports 25.5. On the other hand, countries with high averages of total spent on health as % of GDP, such as Georgia (6.73%) records an average homicide rate of 2.06 per 100,000 people, Hungary (7.15%) records 2, Slovenia (7.79%)

records 1.67, and Macedonia(9%) records 2.31. These results seem to indicate a negative correlation between health expenditure and homicide rates. However, the lack of data prior to 1995 may make the variable statistically insignificant. DEMOGRAPHIC CONTEXT The demographic context builds on opportunity theory and captures the composition and dispersion of activities of a population, which affect the pool of potential offenders and the opportunities to commit crime (Stamatel, 2009). a. Youth in proportion to overall population is often considered by literature to depict a positive correlation to crime, as higher proportion of their population between the ages of fifteen and twenty-four have significantly higher homicide rates (LaFree and Tseloni 2006). It is in fact often reported that young people are more likely to commit violent crime compared to adults (Bennet 1991; Kikuchi 2010; Cole and Gramajo 2009). This is noted in Western Societies which demonstrate decreased levels of crime because as they are growing older, most crimes are committed by young men (Anonymous 2013). However Evidence from Russia Shows that both homicide offenders and victims are more likely to be more than 30 years old than younger (Chervyakov et al. 2002; Pridemore 2003). Data from East-Central Europe also shows that their homicide victims are older on average than in Western countries. Middle aged men in particular (Stamatel 2009). Nevertheless lack of data for the countries in my sample means I cannot include this control this variable into my final equation. b. Ethnic diversity on the other hand is easily measured as the percentage of ethnicities in relation to overall population. In his recent book, (Brown 1996) identified ethnic geography is the third structural perquisite for ethnic conflict. (Hipp 2011) tests and finds that higher levels of segregation in cities with high levels of racial/ethnic heterogeneity lead to particularly high overall levels of the types of crime studies. For example Kyrgyzstan has been the scene of one of the most serious outbreaks of ethnic violence in Soviet history. In one week, interethnic fighting left approximately 250 Kyrgyz and Uzbeks dead in the southern province of Osh, which lies at the eastern end of the Uzbek-dominated Ferghana Valley(HUSKEY 1997). Using data from the World Fact book I created the Table. 2 and Graph. 13.

Country

Ethnic mixture

% Albania 1,9 Bulgaria 13,1 Czech Rep 8,2 Macedonia 35,8 Hungary 7,7 Poland 1,4 Romania 10,7 Slovakia 10,5 Slovenia 4,9 Lithuania 14,7 Latvia 42,2 Estonia 29,7 Russia 22,3 Ukraine 22,2 Armenia 1,8 Georgia 16,2 Kazakhstan 36,9 Kyrgyzstan 48,9 Moldova 23,8

Table. 2 Ethnic Mixutres as % of Population The average ethnic mixture as % of population for the sample of CEE and Baltic countries included in the study is 16.44%. This pales in comparison to the ethnic mixture as % of population for CIS countries (28.68%). These results support the literature on the positive correlation between homicide rates and ethnic diversity, as we have seen CIS countries consistently reporting higher intentional homicide rates than the countries in the CEE region. Examining the Baltic region closer, we notice Latvia and Estonia recording 42.2% and 29.7% ethnic mixture respectively, which further reinforce the credibility of this correlation, as these are two of the highest scorers in homicide rates in the region. Macedonia in CEE on the other hand comes second in the region with an ethnic mixture of 35.8%, yet it has the second lowest homicide scores from the entire sample for years 1994 and 1995, scoring 1.42 and 1.67 respectively, surpassed only by Georgia at 0.22 and 0.02 for the same years. By the same token, Armenia has the second lowest ethnic mixture at 1.8%, outperformed only by Poland at 1.4%, yet in 1992 it records the highest homicide rates from our 19 country sample reporting as high as 26.55 homicides per 100,000 people. To counter this argument however, this peak in homicides if followed by a dramatic decrease of 68.6% in homicide rates to 8.33 in 1993, and continues to progressively improve by recording 5.65 in 1994, 5.34 in 1995, 3.57 in 1996 and 3.11 in 1997. It is also worth noting that in 2009, the last year for which original data from the World Health Organisation on Homicide rates for all the countries in our sample exist, Armenia elevates itself as the safest country with a homicide score of just 1.6, 88.7% lower than Russia and 72.7% lower than it's Baltic counterpart, Latvia.

Graph. 13 Ethnic Mixtures as % of Population c. Alcohol Consumption was studied by (Pridemore 2002) who found [its] consumption and family disruption to be positively and significantly associated with Russian regional homicide rates. Some even put forward the demographic echo theory, whereby the decrease in mortality between 1989 to 1994 was a mere echo of the decrease in alcohol consumption that occurred during Mikhail Gorbachevs anti-alcohol campaign of 1985-87. (Popov 2011). However Popov also suggested that alcohol consumption, although strongly correlated with the mortality rate, was most likely not the core cause but a symptom of the same stress factors that caused mortality. More specifically in terms of homicide rates, worldwide, alcohol consumption was associated with self-reported assault rates (Wolf et al. 2014) and homicide as we define it, is the the act of a human being causing the death of another human being. Dr. East points out that while every practical criminologist will attach some importance to the association of alcoholism in crime, It is easy to over-emphasize the connection (Nature 1939). In support of this this contention, he brings forward statistics from various prisons showing that familial or individual alcoholism is much less frequent cause of crime than was formerly supposed. Using data from WHO I plotted graph 14.

0 10 20 30 40 50 60 Ethnic mixture as % of population Albania Bulgaria Czech Rep Macedonia Hungary Poland Romania Slovakia Slovenia Lithuania Latvia Estonia Russia Ukraine Armania Georgia Kazakhstan Kyrgyzstan Moldova

Graph 14. Pure Alcohol Consumption, Litres per Capita, age 15+

Graph 15. Pure Alcohol Consumption and Homicide Rates. and graph. 15 respectively. We have countries in CEE like Romania, Czech Republic and Slovenia consuming 11.32, 13.72 and 13.36 liters per Capita in 1995, yet recording 4.12, 1.79 and 2.3 homicides per 100,000 people for the same year respectively. On the other hand, we have countries CIS and Baltic countries like Kyrgyzstan, Kazakhstan and Lithuania consuming 1.66, 4.91 and 6.09 liters per capita, yet reporting 16.94, 22.4 and 12.35 homicides per 100,000 people respectively. One possible explanation could be that alcohol consumption may be also positively correlated to GDP per Capita as alcohol may be expensive, and therefore the second group of countries analysed above all demonstrate decreasing GDP per Capita from 1990 to 1995, a characteristic that is associated with

higher crime rates. For example between 5 years since the transition period began in 1990, Kyrgyzstan GDP/Capita decreased by 44.9%, Kazakhstan's decreased by 28.4% and Lithuania's decreased by 33.3%. This explanation may be further validated when concluding that for the same time period, Romania, Czech Republic and Slovenia's GDPs/capita increased by 3.6%, 8.64% and 12.95% respectively.

CULTURE CONTEXT Capturing the underlying normative system of a society, can be a complicated construct that could include a myriad of factors, many of which are difficult to quantify cross-nationally (Kardstedt 2006), nevertheless a culture that perpetuates violence as a means of conflict resolution will lead to higher levels of homicide. (LaFree and Tseloni 2006) found that although fully democratic countries may enjoy the benefits of lower crime rates, some may be associated with higher crime rates due to social disorganization. a. War without a doubt has an effect on crime because exposure to officially sanctioned violence such as public executions or national civil wars, could encourage interpersonal violence through modeling or desensitization ( Stamatel, 2009). The dissolution of Yugoslavia was accompanied by civil war, while other East-Central European countries, such as Romania, Macedonia and Albania experienced brief periods of political violence related to ethnic conflict and economic strife. On the other hand however, ethnographers studying primitive society groups found some societies to be well acquainted with warfare, therefore they are relatively nonviolent (Diamond, 1997, Self-Brown et al. 2004). Further more results from (Ouimet, 2012) did not find evidence that war and civil conflict influenced the homicide rate. Using dummy variables to account for wars and civil conflicts, my model will incorporate the events represented in Table. 3.

Name of Conflict Date of Conflict Macedonian War versus the Albanian Liberation Army 22 January 2001 - 12 November 2001) The Albanian civil war 1997 The Lithuanian OMON assaults (December 1990 August 1991) The Armenian Nagorno-Karabakh War 1988-1994 The Georgian civil war (1991-1993), Czech republics Kosovo unrest (1994) The Romanian Transnistria War, in which Moldova and Russia were also involved (1992)

The Slovenian involvement in the Slovenian Independence war Bosnian War and Kosovo Conflict (1991), (1992-1995), (1999)

Russias First Chechen War, War of Dagestan, Second Chechen War and North Caucasus Insurgency (19941996), (1999), (19992009) and (2009)

The Kyrgyz Revolution of 2010 Table. 3 Conflicts in the Regions of Study b. Divorce rates and homicide rates are not significantly related. As a measure of intra-group cohesion, it was anticipated that high divorce rates would correspond with high homicide rates, as was the case with a sample of developed democracies(Stamatel, 2009). However, empirical studies on aggregate Russian data found no relationship between divorce and homicide (Sitckley and Makinen 2005; Pridemore et al. 2007). This could be because during communism, multi-generational households that could buffer the potential negative effects of divorce on adults and children provided support structures that maintained family cohesion. Besides, housing shortages may have limited the physical mobility of divorced couples, thereby encouraging family interactions regardless of martial status. As a result I will omit this control variable from the final model.

POLITICAL REFORM CONTEXT (Stamatel 2009) found that political reforms shaped the strength of the state, the rule of law and the ability of formal social control institutions to function legitimately and effectively. He concluded that progressive reforms towards democratisation and marketisation decreased homicide rates. (Kim and Pridemore 2005) also found a significant negative relationship between participation democratic elections and homicide rates. They noted that faith in political institutions decreases crime rates since it represents a level of trust and social cohesion, and this result is consistent with the multinational longitudinal analyses of (LaFree and Tseloni 2006), which found a curvilinear relationship between level of democracy and crime. There is a benefit in shifting from a transitional to a more democratic regime in post-communist countries. Data on the polity score and homicide rate from 1995 to 2011 were collected to conduct a fixed effect panel data analysis on the level of democracy and violent crime in the Balkan region, Bulgaria and Romania, confirming a negative association between the two variables (Favarin 2014). Democracies consider themselves systems of lawful power-sharing, whose actors are aware of the benefits of non-violence (Keane 2004), while anocratic regimes reflect an inherent quality of instability and are especially vulnerable to the onset of new political instability events, such as outbreaks of armed conflict, unexpected changes in leadership, or adverse regime changes (Marshall and Cole 2009). While extensive literature proves the correlation of democracy and homicides (LaFree and Drass 2002), some scholars provide a counter debate saying that the presence of contested authoritarian regimes,

strong secret police and efficient local police forces may explain in part why their homicide rate in Arab countries is relatively low, despite the large proportion of teenagers, economic challenges and high infant mortality rates that represent poverty (Hibou 2007; Sherry 1993). (Michnik 1998) found that dictatorships guarantee safe streets and the terror of the doorbell whereas in democracies the streets may be unsafe after dark. Besides, the presence of a system of disjunctive democracy that characterised transitional countries in Latin America was also a common pattern of the post-communist transition (Los 2003; Pridemore and Kim 2006). This effect occurs where the democratization of political institution does not correspond to the democratization of the criminal justice system and the criminal justice agencies continue to follow the old autocratic rules (Karstedt and Lafree 2006). Therefore this inefficient criminal justice system can be an explanation for a countrys high homicide rates, despite being ranked as a highly democratic state. Plotting the democracy scores attained from the Polity IV database we observe the following. (See Graph. 16) The lowest performers by far are Armenia, Kazakhstan and Kyrgyzstan, which have also some of the highest homicide rates from the sample of countries chosen to study. Armenia scores 7, which is above the democratic threshold of +6 until 1995 and then it is followed by a sharp dip from 1996 until 1998, with the last two years of this dip scoring at the other extreme of -6. Thereafter it improves, although it never reaches the pre 95's levels. While Armenia has one of the highest homicide rates, when examined closer, its surprising to see that the dip in democracy levels does not reflect in the intentional homicide rates. In fact, the peak homicides (26.55 per 100,000 people) is recorded in 1992, three years before the dip. Furthermore, even when the dip occurs in 1996, 1997 and 1998 the homicide rates of 3.57, 3.11 3.3 respectively, are well below the homicide rates in 1995 (5.34) when democracy scores were above the threshold. On the other hand Kazakhstan tells a different story. In the annual democracy data it scores -2 from 1990 to 1995, and then slips down to -4 in 1996 until 2003, where it dips once again to the extreme -6 and remains there until present. Equally, in 1995 it's homicide rates increase from by 12% from 19.98 to 22.4 and improve thereafter, until they reach the 2004-2005 region, where they once again increase by 6.6% from 15.59 to 16.63.

These results imply that democracy scores must be treated cautiously in regard to homicides, and further imply that a multitude of other factors have to be taken into account to give a better understanding of what explains the homicide model in post-communist countries. (LaFree and Tseloni 2006) found that while "fully democratic societies may enjoy many characteristics associated with low crime rates, they may also develop characteristics associated with crime rate, such as growing social disorganization.

Graph. 16 Democracy Scores from Polity IV database (Undemocratic Threshold -6/ Democratic Threshold +6)

ECONOMIC REFORM CONTEXT Although economic reforms did not contribute much in terms of overall variance explained by the Time-Series Cross-National Analysis by (Stamatel, 2009), it is nonetheless a significant predictor of homicide in transition economies who concluded that Economic reforms influenced citizens overall quality of life, their future prospects of well-being and the functioning of informal social control institutions, such as workplaces, families and educational systems, thus reducing crime. I want to focus on two economic reforms in particular; did mass privatization as the chosen

-6 -4 -2 0 2 4 6 8

10

1990 1995 2000 2005 2010

Albania Bulgaria Czech Rep Macedonia Hungary Poland Romania Slovakia Slovenia Lithuania Latvia Estonia Russia Ukraine Armania Georgia Kazakhstan Kyrgyzstan Moldova

method of large scale privatization influence crime rates and did trade liberalization have a direct impact on homicide rates. a.Mass Privatisations paper on overall health in transition economies by (Stuckler et.al 2009) was treated by media worldwide as a given fact. It was noted that mass privatisation programes were associated with a 12.8% increase in mortality rates, with the casual link between mass privatization and increased mortality being the increase in unemployment. This correlation was later disproved by (Erlea Gehlbachb 2010) who found the instantaneous effect of privatisation on mortality is implausible and controlled for differences across countries in long-term mortality trends, a common statistical method (indeed, one used by Stuckler and colleagues in other work. Additionally, (Gerry et al. 2010) found that the countries that undertook rapid (mass) privatisation took place near, at, or after the end of a period of sustained increase in male mortality trends in mortality, thus privatisers and non-privatisers do not much differ. In fact the latter paper found that mass privatization led to the reduction in mortality rates.

Country n=19

Year of Privatizati

on

Year of Peak

Mortality

CEE & Baltic States

Albania 1995 1997 Bulgaria 1993 1994 Czech Rep 1992 1994 Hungary 1990 1993 Macedonia 1993 2001 Latvia 1994 1993

Lithuania 1993 1994 Poland 1990 1992 Romania 1995 1992 Slovenia 1998 1991 Slovakia 1995 1997 Estonia 1993 1994

CIS States

Armenia 1992 1992 Georgia 1995 1998

Kazakhstan 1994 1995 Kyrgystan 1994 1994 Moldova 1994 1995 Russia 1992 1994 Ukraine 1995 1995

Table. 4 Large Scale Privatisation Among the CEE, Baltic and CIS States Mass privatization is associated with the quick implementation of all transition reforms at the same time. This is also known as shock therapy. The immediate changes would have left people little time to adapt, a side effect of which would have been eruption in violence and murders. In Table. 4 above it is clear that with the exception of Albania and Estonia,

the higher homicide and intentional injury rates per 100,000 people are reserved for countries such as Russian Federation, Kazakhstan, Latvia, Armenia, Lithuania, Ukraine and Kyrgyzstan, all of which implemented mass privatization or voucher-privatisation as their main method of privatisation. These are the countries highlighted in red. Since only Czech Republic is the only country in the CEE region that adopted the method of mass privatisation yet has one of the lowest homicide rates in the entire region, I will omit mass privatisation for CEE countries in my final model to ensure its as robust as possible. b. Trade Liberalisation promotes trade openness, discourages black markets, hence reducing smuggling, drugs, prostitution and other forms of illegal activities, a byproduct of which is violent crime (Prasad 2012). Furthermore (Stuckler 2009) uses it as a control variable to control for the effects of mass privatisation, while (Ghosh and Robertson 2012) show that, in general equilibrium, trade liberalisation can reduce expropriation activities and have a first-order effect on the gains from trade, thus reducing crime. (Collier and Gunning 1999; Russett and Oneal 2001; Books 2002; Andreas 2005) all present evidence that openness reduces conflict and document how the imposition of trade sanctions caused rising crimes in Yugoslavia. However, the model also admits the possibility that globalization causes an increase in crime, particularly for skilled-labor abundant countries (Ghosh and Robertson 2012). Rising social conflict has been attributed to trade liberalization episodes in several countries. For example, (Keen 2005) discusses the case of Sierra Leone, (Deraniyagala 2005) discusses Nepal, and Brysk 1997) discuss some examples of rising social conflict in the Latin American countries. 5. DATA AND METHODOLOGY I have decided to employ panel data analysis because it allows me to control for variables I cannot observe or measure like proportion of youth or education enrolment across countries that change over time. Some drawbacks of this are issues such as non-response in the case of micro panels or cross-country dependency in the case of macro panels (i.e. correlation between countries), but by using a panel data analysis with a random effect estimation and country dummies, I can take into account the country specific effects that are not constant over time, while avoiding the issues of correlation between countries. While a fixed effect estimation can control for unobserved time invariant factors such as culture, country specificities and religion (Favarin, 2013), there might be some time-variant variables that are omitted (Lin 2007). For example homicide rates can also affect democracy and economic growth, thus creating a problem of reverse causality. Below I have the three models for each group of countries:

1.

CEE_homicide_rates = x1GDPgrowthannual+ x2Gini_coeff + x3ethnic_mixt + x4alcohol + x5war + x6democracy + x7health_exp

+ x8trade_lib

2.

BALTIC_homicide_rates = x1mass_priv + x2GDPgrowthannual+ x3Gini_coeff + x4Infl + x5ethnic_mixt + x6alcohol + x7war +

x8democracy + x9health_exp x10trade_lib 3.

CIS_homicide_rates = x1mass_priv + x2GDPgrowthannual+ x3Gini_coeff + x4Infl + x5ethnic_mixt + x6alcohol + x7war +

x8democracy + x9health_exp x10trade_lib Since my initial results didnt exhibit consistency, I created three models for CEE region. In the second model I dropped inflation because its insignificance is consistent with the visual analysis performed earlier and on the third model I dropped GDP per Capital because its highly correlated with the Gini coefficient, besides not having a significant impact on homicide. This ensured my model was more robust as GDP growth became statistically significant. Before I present my results its essential to note how each control variable is measured. Control Variable Unit of measure Source of Data GDP per Capita Real gross domestic

product, PPP$ per capita World Health Organisation Database GDP annual growth Annual percentage growth

rate of GDP at market prices based on constant local currency. Aggregates are based on constant 2005 U.S. dollars

The World Bank Database Inequality A Gini coefficient of zero

expresses perfect equality, where all values are the same (for example, where everyone has the same income). A Gini coefficient of one (or 100%) expresses maximal inequality among values (for example, where only

Data from UNU-WIDER, World Income Inequality Database (WIID3.0b) September 2014

one person has all the income or consumption, and all others have none

Inflation Annual Average Rate of Inflation % World Health Organisation Database Ethnic mixture Proportion of ethnic minority habitants as % of total population The World Factbook Alcohol consumption Pure alcohol consumption, litres per capita, age 15+, World Health Organisation Database War A state of armed

conflict between autonomous organizations or groups or coalitions of such organizations.

The Correlates of War Project Democracy Variable coded form 1-10,

with higher numbers representing more institutionalised democracies. Vertical thresholds for Democracy (+6 and above) and Autocracy (-6 and below)

Annual level of democracy obtained by the Polity IV dataset.

Health Expenditure Total health expenditre as % of GDP, WHO estimates

World Health Organisation Database

Mass Privatisation The measurement scale for the indicators ranges from 1 to 4+, where 1 represents little or no change from a rigid centrally planned economy and 4+ represents the standards of an industrialised market economy.

European Bank for Reconstruction and Development

Trade Liberalisation The measurement scale for the indicators ranges from 1 to 4+, where 1 represents little or no change from a rigid centrally planned economy and 4+ represents the standards of an industrialised market economy.

European Bank for Reconstruction and Development

Table. 5 Control Variables Units of Measure and Sources of Data

(CEE=1)

(BALTIC=1) ------------------------------------------------- Model 1 b/se ------------------------------------------------- GDP/growth (annual~) -0.030 (0.11) Gini_coeff 0.241 (0.18) Infl 0.111** (0.04) ethnic_mixt 0.200*** (0.04) alcohol 0.218 (0.22) war -0.174 (1.45) democracy -1.752* (0.70) health_exp -0.378 (0.87) trade_lib -6.094* (2.80) mass_priv 39.922** (14.93) country=10 35.048* LITHUANIA (14.73) country=11 37.417* LATVIA (15.77) country=12 37.617** ESTONIA (13.72) constant 0.000 (.) ------------------------------------------------- R2= 0.8161 *p

(CIS=1) --------------------------------------------------------- Model 1 b/se --------------------------------------------------------- GDP/growth (annual~) -0.127 (0.09) Gini_coeff -0.248 (0.12) Infl 0.008 (0.01) ethnic_mixt 0.206*** (0.04) alcohol 0.492*** (0.13) war 14.743*** (3.31) democracy 0.052 (0.15) health_exp -0.254 (0.53) trade_lib -3.712** (1.22) mass_priv 28.415*** (5.77) country=13 9.452* RUSSIA (3.90) country=14 -0.101* UKRAINE (2.58) country=15 -6.609* ARMANIA (4.11) country=16 -6.697** GEORGIA (2.12) country=17 5.572* KAZAKHSTAN (2.37) country=18 0.000 Kyrgyz Republic (.) country=19 0.000 Moldova (.) constant 0.000 (.) --------------------------------------------------------- R2= 0.8161 *p

6. RESULTS DISCUSSION AND CONCLUSION For the CEE region we consider the results of our final model 3. Empirical evidence suggests that GDP growth, alcohol consumption, democracy, inequality and war are statistically significant to the 99% confidence level. For each additional war in the region homicide rates increase by 3.539 per 100,000, while for each increase in a unit of democracy homicide rates decrease by 3.347 per 100,000. War was also found to increase homicide rates in the CIS region to an excessively large number of 14.743 per 100,000 for every war, while democracy levels tested insignificant. This is in line with the theory that despite highly developed democracies, countries plagued by internal conflicts and ethnic tensions, something that CIS countries tested positive towards, are likely to drown the benefits gained from highly developed institutions. On the other hand, with exception to the Lithuanian OMON assaults between 1990 and 1991, the Baltic states of Lithuania, Latvia and Estonia were more peaceful in comparison to the other two regions, therefore war tested insignificant. Further development of democracy on the other hand leads to a decrease in homicide rates, albeit to a smaller extent than in countries within the CEE region. This could be that despite generally more developed democracies than the CEE states, the Baltics were characterised by higher levels of ethnical fractionalization and although most fully democratic countries may enjoy benefits of low crime rates, some can be associated with higher crime rates due to social disorganization(LaFree et. al 2006). This is also evident in the CIS region where ethnic tensions test significantly to the 99% confidence level as an explanatory variable for homicide rates. Income inequality tests statistically significant as an explanatory variable for homicides in the CEE region in line with researchers who have argued that inequality in these countries has contributed to increases in post-communist violent crime rates (Matutinovic 1998; Savelsberg 1995; Karstedt 2003). In the Baltic region however, although the Gini coefficient is statistically insignificant, inflation which tests significant to the 99% confidence level, acts as a proxy for inequality (Albanesi, S. (2001, Thalassinos 2012). The countries in the CEE region on the other hand test insignificant for either the Gini coefficient or inflation and this is in line with the findings of (Stamatel 2009) who concluded homicide not [to be] significantly related to income inequality. The results of alcohol consumption differ by far the most among regions. In the CIS region each two liters of alcohol per capita consumed results in nearly 1 homicide more per 100,000 people. (Popov 2011) argued that alcohol consumption, although strongly correlated with the mortality rate, was most likely not the core cause but a symptom of the same stress factors

that caused mortality such as wars and internal conflicts. Thus in the absence of internal conflicts in the Baltic region, alcohol consumption tests insignificant. The same cant be said for the CEE region, since it was not only a war infested region, but a negative correlation between alcohol consumption and homicide rates is also noted. Health expenditure tested insignificant for all three regions and this could be due to the inconsistency of data prior to 1995, while on the other hand trade liberalisation tested significant for all three regions, although results suggest effect among regions. Liberalising trade reduced homicides in the CIS and Baltic region, however increased them in the CEE region. This goes against the theory that trade liberalization promotes trade openness, discourages black markets, reducing smuggling, drugs, prostitution and other forms of illegal activities, a byproduct of which is violent crime (Prasad 2012), as is the case for the CIS and Baltic region and lines up with the theory that globalization encourages an increase in crime, as there are more opportunities to exploit. (Ghosh 2012) While mass privatisation was omitted from the CEE countries model, it proves not only statistically significant for both Baltic and CIS regions, but also by far the biggest contributor to homicide rates. The decision to mass privatise led an increase of 39.9 homicides per 100,000 people in the Baltic states and 28.4 homicides per 100,000 people in the CIS region. So although economists like Jeffrey Sachs concluded that Czech Republic outperformed most of other countries(Sachs 1994) due to the rapid market reforms otherwise known as shock-therapy, a feature of which was mass privatisation, it has been found by literature to negatively affect inequality, unemployment and now homicide rates. This highlights the tradeoff between superior future economic performance and present human costs. My empirical models and control variable analysis has provided a wider framework for analysis criminal homicides in countries in transition economies. However the excessively large effects of mass privatisation on homicides suggests economic reforms such as governance restructuring, financial system reform, price liberalisation and stabilization should also be factored in to better control for the effects of shock therapy on homicide rates. Furthermore, other literature has shown that over 60% of the differences in the economic performance can in fact be explained by uneven initial conditions, such as the level of development and pre-transition disproportions in industrial structure and trade patterns (Popov 2000), therefore new control variables could be introduced in future econometric models.

7. APPENDIX LIST OF GRAPHS Graph.1a UNDP Human Development Index HDI Graph. 1b SDR, Homicide and Intentional Injury, 0-64, per 100,000 Graph. 2 Real Gross Domestic Product, PPP$ per capita Graph. 3 Real Gross Domestic Product per Capita and Homicide Rates Graph. 4 Annual Average Rate of inflation (%) Graph. 5 Inflation and Homicide Rats Graph. 6 Unemployment rate (%) Graph. 7 Unemployment and Homicide Rates Graph.8 GINI coefficients plotted over time Graph .9 Voter Turnout as % of population Graph. 10 Voter Turnout extrapolated Graph. 11 Total Health Expenditure as % of GDP Graph .12 Health Expenditure and Homicide Rates Graph. 13 Ethnic Mixtures as % of Population Graph 14. Pure Alcohol Consumption, Litres per Capita, age 15+ Graph 15. Pure Alcohol Consumption and Homicide Rates. Graph. 16 Democracy Scores from Polity IV database (Undemocratic Threshold -6/ Democratic Threshold +6) LIST OF TABLES Table. 1 Control variables that might explain homicide trends Table. 2 Ethnic Mixtures as % of Population Table. 3 Conflicts in the Regions of Study Table. 4 Large Scale Privatisation Among the CEE, Baltic and CIS States Table. 5 Control Variables Units of Measure and Sources of Data

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