The Determinants of Income Inequality and the Relationship to Crime
Adeleye, Ngozi Bosede Postgraduate Student, Department of Economics University of Sussex, UK
[email protected]
Dissertation submitted in partial fulfilment of: MSc degree in International Economics University of Sussex, UK September, 2014
Acknowledgement I am deeply grateful to my supervisor, Dr. Dimitra Petropoulou for her professional guidance throughout the course of this dissertation. Her contributions and painstaking thoroughness are simply astounding; and my immense gratitude to Professor Barry Reilly for qualitative discussions at various stages of this research that further improved my grasp of econometrics. Needless to say, any remaining errors are mine.
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Abstract The goal of this thesis is to study the determinants of income inequality and to establish the relationship between income inequality and crime (which in this dissertation is limited to homicide rates per 100,000 population). Theoretical expectations on the relationship between income inequality and homicide rates are very strong and so are the empirical findings. A panel dataset covering 137 countries from 2000 – 2012 and four econometric methodologies – OLS, fixed effects, 2-stage fixed effects and 2-step difference GMM – are adopted to test for the determinants of income inequality, the link between inequality and homicide rate and to test for criminal inertia in the data.
On the determinants of income inequality, I find evidence that income inequality is strongly associated with GDP per capita, the rule of law index, secondary education and unemployment rate. Although, this was not the focus of this research, it is important to state here that amongst other determinants of income inequality, the significance of the rule of law index was constant and consistent in all the different methodologies used.
On the relationship between income inequality and homicide rate, I find Gini index to be a strong determinant of homicide rate. Empirical results find that the death penalty is not a crime deterring factor but rather that with good governance, the rate of homicide can be greatly reduced both in the short-run and long-run. Also, not surprising is the fact that factors determining income inequality also determine homicide rates.
The results of my dynamic model support the hypothesis that criminal inertia is important in the study of crime rates as the lagged homicide rate is positively associated with homicide rate. On the basis of my work it seems clear that other factors determining income inequality and homicide rates should, whenever possible, be studied separately.
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Contents List of Tables and Figures 1
Introduction
2
Related Literature
2.1
The Gini Index and Determinants
2.2
Income Inequality, Crime Rate and Dynamics
3
Methodology
3.1
Pooled OLS Estimation
3.2
Fixed Effects/2SFE Estimation
3.3
2-Step Difference GMM
4
Data and Descriptive Statistics
4.1
Data Collection, Selection and Missing Values
4.2
Variables and Data Sources 4.2.1
Dependent Variables
4.2.2
Independent Variables 4.2.2.1 Economic Variables 4.2.2.2 Deterrence Variable 4.2.2.3 Demographic Variables 4.2.2.4 Institutional Variables 4.2.2.5 Interaction Terms 4.2.2.6 Persistence Variable 4.2.2.7 Regional Dummy Variables
4.3
Descriptive Statistics
5
Results
5.1
Determinants of Income Inequality
.
5.1.1
Pooled OLS
5.1.2
Comparison of results: OLS, FE and 2SFE
5.1.3
Interaction Terms
5.2
Income Inequality and Homicide Rate: OLS and 2SFE
5.3
Crime Persistence: 2-Step Difference GMM
6
Conclusion
References
Appendix A 3
List of Tables 1
Variables, Description and Sources
2
Summary Statistics
3
Correlation Coefficients
4
Ranking of Countries
5
Gini Index: Pooled OLS Results
6
Gini Index: Comparison of Results
7
Gini Index: Pooled OLS Results with Interaction Terms
8
Homicide Rate: Comparison of Results
9
Homicide Rate: 2-Step Difference GMM Results
10
List of Countries by Regions and Income Groups
11
Full Correlation Matrix
12
Gini Index: Pooled OLS Results (Level)
13
Gini Index: Pooled OLS Results (with Corruption Index)
14
Gini Index: Hausman Test
15
Homicide Rate: 2-Step Difference GMM (Stata Output)
16
Gini Index: Pooled OLS Results: Regions
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Homicide Rate: Pooled OLS Results: Regions
List of Figures 1
Distribution of Average Gini Index by Countries (2000 – 2012)
2
Distribution of Average Homicide Rate by Countries (2000 – 2012)
3
Cross-Country Distribution of Gini Index and Homicide Rate (2000 – 2012)
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1
Introduction
This thesis differs by employing the use of more indicators with a wider geographical scope and a broader focus on potential determinants of income inequality. It is thus the hope that contribution is made to existing literature by empirically examining a wide range of potential determinants of income inequality and its influence on homicide rates globally1. It is important to make a clear distinction between the concepts of inequality and poverty. Income inequality solely covers the degree of income disparities between people of a given population, thus making inequality a relative term. Poverty, on the other hand, has to be defined in absolute terms by some sort of poverty line that, however, varies across countries.
Even though poor countries are associated with high levels of income inequality and crime rates, and that while inequality is a very broad concept, which covers disparities in many social and economic respects, it is not the intention of this thesis to address the issue of poverty reduction as such, but merely investigate factors of income inequality. The wording “income inequality” and “inequality” is used interchangeably throughout the thesis, unless something else is specifically stated.
Generally, high levels of income inequality have historically persisted across the globe, with the most skewed income distributions found in the other 6 regions aside Europe and Central Asia (ECA)2 – a region that has 9 out of the 10 lowest-Gini-indexed countries in the world. From the data, Latin America (LAC) has the highest regional average Gini index of 0.482 followed by South Asia (SA) with 0.438 and Europe with the lowest average index of 0.31. There is no doubt that income inequality is a deeply rooted and multifaceted problem, with both moral and economic aspects, which is why the topic spurs a continuous global debate.
Much of the income inequality literature focuses on the relationship between income inequality and economic growth, but a different strand of the literature takes a step back and instead looks at the determinants of income inequality and its offshoots, one of which is increase in crime rates. The foundation of this empirical analysis is a balanced panel 1
Data shows that countries with low Gini index have low homicide rates. The 10 lowest Gini-indexed countries on average for the 13-year period covered are: Mauritius (0.19), Norway (0.237), Denmark (0.237), Sweden (0.246), Slovenia (0.249), Finland (0.257), Iceland (0.257), Czech Republic (0.259), Netherlands (0.263) and Belgium (0.263). 2
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dataset that includes yearly observations from 2000 - 2012 for 137 countries (across 7 regions)3 and delineated along 4 income groups – high income, upper middle income, lower middle income and low income countries.
For such a large dataset (of which the initial plan was to have 214 countries), it is unarguable that data availability posed a very serious challenge. With the exception of Europe, most countries lacked data on the Gini index and other key indicators. As a consequence of that, the newly updated inequality dataset from Solt (2009), which provides Gini coefficients from across countries and time, constitutes the backbone of this thesis. The income inequality data is then complimented with additional data from World Bank‟s World Development Indicators (WDI) to create an extensive, comprehensive and up-to-date cross-country panel dataset. This makes the application of advanced panel data techniques possible, in order to investigate the effect of potential factors on income inequality and the link to crime rates.
The remainder of the thesis is structured as follows: Section 2 reviews existing literatures on income inequality and crime rates. Section 3 presents the econometric methodologies adopted. Section 4 details the data and descriptive statistics. Section 5 discusses the results while Section 6 concludes.
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Related Literature
Income inequality is a global issue that has been on the front burner of international fora in their efforts to reduce the widening gap between the rich and the poor. Several indicators of inequality have been identified with econometricians arriving at mixed conclusions either as a result of the estimation techniques used, the focus of the research or the type of data employed. Section 2.1 briefly reviews the Gini index and its determinants. Section 2.2, without any claim to be exhaustive, discusses the theoretical and empirical literature on the relationship between income inequality and crime rates and the dynamics of crime.
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The regions are: East Asia and Pacific (EAP), Europe and Central Asia (ECA), Middle East and North Africa (ME), North America (NA), Latin America and the Caribbean (LAC), South Asia (SA) and SubSaharan Africa (SSA). See Appendix A1 for comprehensive list of countries.
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2.1
The Gini Index and Determinants
Barro (2000), in his famous paper „Inequality and Growth in a Panel of Countries‟ gave evidence from a broad panel of countries. The study shows little overall relation between income inequality and rates of growth and investment. Using the 3-Stage Least Squares (3SLS) estimation technique on a sample of over 100 countries from 1960 - 1995, he found that higher inequality tends to retard growth in poor countries and encourage growth in richer places. He further confirmed that the full relationship between an indicator of inequality, such as a Gini coefficient, and the level of per capita GDP is described by an inverted-U curve named after Kuznets (1955) which empirically states that inequality first rises and later falls as the economy develops.
Agnello et al. (2012), used the fixed effects (FE) estimator technique on a panel of 62 countries from 1973–2005 and analysed the impact of financial reforms on income inequality. They found that the removal of bureaucratic controls towards directed credit and excessively high reserve requirements in addition to improvements in the securities market greatly reduce inequality. The result corroborates the findings of the other political economy literatures, which emphasizes that access to credit and banking sector reforms reduces inequality (Beck et al., 2007; Demirguc-Kunt and Levine, 2009).
Empirical works on trade openness as a determinant of income inequality is broad in addition to varying conclusions. The trade hypotheses has several variants. First, is that greater openness reduces aggregate inequality in all countries (Ravallion, 2001; White and Anderson, 2001; Dollar and Kraay, 2002) a position disagreed by Barro (2000), Odedokun and Round (2001) and Atif et al. (2012) who opined that openness has little income equalising effect particularly for developing countries. The second hypotheses is more conflicting in that greater openness does reduce inequality in less developed countries (Calderón and Chong, 2001) but Barro (2000); Ravallion (2001); Ravallion (2014) found that the impact of openness on inequality falls as GDP per capita increases.
Chatterjee and Turnovsky (2012), examined the impact of government‟s provision of public goods such as infrastructure which represents an important mechanism through which income can be redistributed across societal strata. They concluded that public capital is both an engine of growth and a determinant of the distributions of wealth, income, and welfare. Government public investment increases wealth inequality over 7
time, regardless of its financing. The time path of income inequality is, however, highly sensitive to financing policies, and is often characterized by sharp inter-temporal tradeoffs, with income inequality declining in the short run but increasing in the long run.
Using pooled Gini index data for 119 countries from 1993 - 2002, Choi (2006) found that income inequality, as defined by the Gini index, increases as FDI stocks (a percentage of GDP) increases. Increases in per capita GDP and real per capita GDP growth rate reduce income inequality in a country, whereas an increase in GDP deteriorates income distribution (van der Hoeven, 2010; Huhta, 2012; Østergaard, 2013). Furthermore, countries in Latin America and the Caribbean proved to have a less equal income distribution compared to countries in Europe.
Gupta et al. (2002), used cross-country data and discussed the different channels through which corruption may affect income inequality and poverty; and also that policies that reduce corruption will most likely reduce inequality and poverty as well. Empirical evidence supports that corruption distorts government‟s role in resource allocation; reduces the level of social services available to the poor; reduces growth and investment; skews expenditure away from operations and maintenance and towards public investment (Lin, 2007; Li and Yu, 2014). Overall, high rising corruption increases income inequality and poverty. Thus, the adverse distributional consequences of corruption can be mitigated by: a) sound management of natural resources; b) broad-based labourintensive growth; c) efficient spending on education and health; d) effective targeting of social reforms; e) efficient access to education as all these policies have equalising effect on income.
On education, and based on a panel that covers both developing and OECD countries from 1960 - 1990, De Gregorio and Lee (2002); Perugini and Martino (2008) found that average years of schooling, and other educational factors, contribute positively to a more equal distribution of income. They showed the empirical evidence linking education to income distribution. Barro (2000)‟s conclusion on education is varying with primary and secondary education reducing income inequality and tertiary education further widening the inequality gap. Some other studies found that primary and secondary education are equalising variables while higher education further widens the inequality gap (Lochner, 2004; Lochner and Moretti, 2004; Lo Prete, 2013) 8
One of the key demographic factors in terms of income inequality is the population growth rate, as it is generally argued that poor people tend to have more children. Also, it is plausible to find that a bigger rural population and more people employed in agriculture is associated with lower income inequality as the distribution of income is even among that social class. Odedokun and Round (2001); Sahn and Stifel (2003) have opposing views regarding the effect of urbanization. Across a sample of African countries they found that there is a clear gap in living standards between rural areas and urban areas, with rural areas lagging behind, but they also found that inequality in living standards tends to be worse in rural areas than in urban areas.
Herzer and Nunnenkamp (2012), examined the long-run effect of foreign aid on income inequality for 21 recipient countries using panel co-integration techniques to control for omitted variable and endogeneity bias. They found that foreign aid exerts an inequality increasing effect on income distribution. For reasons being that, donors would have to allocate aids in line with their rhetoric on pro-poor growth, by targeting the most needy and deserving. At the same time, the authorities in the recipient countries would have to ensure that aids actually reach the poor. The following arguments suggest that both conditions tend to be violated once it is taken into account that foreign donors are not purely altruistic and local authorities have incentives to divert aid funds for personal benefit. This view is supported by Justesen and Bjørnskov (2012), who found that foreign aid inflows in democracies tend to increase income inequality. Interestingly enough, such effect is not found to be significant in autocracies.
Religion is another determinant having mixed conclusions regarding its impact on income inequality. Empirical evidence supports the argument that more time spent on religious activities tend to be an economic drag measured by market output (GDP) (Lipford and Tollison, 2003; Mangeloja, 2005; McCleary and Barro, 2006). However, Elgin et al. (2013) had opposing views. They found that religion has both equality-reducing and equalityincreasing effects. Also, in high-income countries, religion has a positive effect on income (Barro and McCleary, 2003) while the opposite holds true in low-income countries (Bettendorf and Dijkgraaf, 2010).
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There are several other potential determinants of income inequality that have been studied throughout the literature and, with good reasons, not all are covered in this literature review. In addition, it is reasonable to expect that some factors can have causal interrelationships, not accounted for here, and hereby exert indirect effects on income inequality. However, the inconsistent findings in much of the existing literature simply reveal the complexity of the subject.
2.2 Income Inequality, Crime Rate and Dynamics Too much inequality may generate economic inefficiency and slow economic development. Can this occur through the influence of inequality on crime and violence? The answer seems to be yes: crime and violence may result from excessive relative poverty and inequality, along with more sociological factors, and have sizable economic and social cost. This section reviews theoretical arguments and the limited empirical evidence on the relationship between inequality and intentional homicides. The analysis here involves the potential effect of inequality on private and collective violence and the substantial economic loss these social ills may cause. Although debatable, there seems to be evidence of such a direct link. But much less work has been done on the more elementary stage of this process - that is, situations where inequality or poverty produce private rather than collective violence, essentially through crime and the development of illegal activity. This lack of analysis seems somewhat surprising given that some of the early work on the economics of crime in industrial countries emphasized the role of poverty and inequality in explaining spatial differences in criminality (Ehrlich, 1973).
Literatures on the relationship between income inequality and violent crimes such as homicides is still very sparse compared to other types of criminal acts like property crime, theft, burglary, robbery, fraud, embezzlement, forgery, shoplifting and so on. However, the link between inequality and crime in general has been stressed by the three main theories of crime: Becker (1968) economic theory of crime; the social disorganization theory of Shaw and McKay (1942) and Merton (1938). Central to these theories is the undenying fact that income inequality is criminogenic4. In the economic theory of crime, individuals allocate time between market and criminal activities by comparing the expected returns from each, and taking account of the likelihood and severity of punishment. In these models, income inequality leads to crime by placing low-income 4
A situation or system likely to cause criminal behaviour.
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individuals who have low returns from market activities in proximity to high-income individuals who have things that are worth taking. The majority of the studies focusing on the relationship between the income distribution and criminality conclude in favour of a detrimental effect of income inequality on criminal behaviours.
The rationale behind these findings might be based on economic considerations, that is, income inequality increases the gain derived from a criminal act and/or on a sentiment of frustration of the less well-off, individuals when they compare their situation with respect to the wealthier ones. The Gini coefficient is the measure of economic inequality employed in most empirical papers. However, some studies use additional inequality indices (ratio of income of the richest to the poorest quintile, proportion of the population with an income below a certain value) to check the robustness of their findings (Fajnzylber et al., 2002a; Nilsson, 2004; Brush, 2007).
Fajnzylber et al. (2002a), tested for the relationship between income inequality, homicide rates and robbery rates. The authors used the Gini index as the inequality indicator with a panel dataset for 40 countries from 1970 - 1994. The econometric methodology employed was the dynamic specification system GMM estimator to deal with the endogeneity of inequality across countries. They found that income inequality increases both homicide and robbery rates. This position had earlier been established by Krohn (1976); Messner (1983); Krahn et al. (1986) who agree that the Gini index is the best predictor of homicide rates.
In a related study, Fajnzylber et al. (2002b), used a combination of inequality indicators such as the Gini index, ratio of income of the richest to the poorest quintile, index of income polarization on the same set of 40 countries from 1970-1994 and deployed the GMM System estimator. They found that crime rates and inequality are positively associated. Thus, strongly agreeing with the earlier findings of Witt et al. (1999) that there is a strong and positive link between inequality and murder/non-negligent manslaughter.
Using the fixed effects estimation technique while controlling for country-specific time trends, Nilsson (2004), did a study on Sweden counties from 1973- 2000. The indicators of income inequality used in the study were the proportion of the population with an income below 10%, 20% or 40% of the median income (PR), Gini index, 90th/10th 11
percentile and the crime categories were split into overall crime rate and 3 property crimecategories: burglary, auto theft and robbery. He found that PR exerts a positive influence on property crime but not on assault and that other inequality measures are not related to criminal behaviours.
Brush (2007), did a study on US counties for two periods (1990 - 2000) to test the relationship between inequality, overall crime rates, violent crimes and property crimes. The inequality indicators were the Gini index, poverty percentile and percent of population with an income over $ 100,000. The methodology was cross-sectional estimates based on 2000 data and first-differences estimates based on within county variations. His results were mixed. On the cross sectional estimates, he found that there is a positive relationship between inequality and reported crime rates, both for violent and property crimes and from the first difference estimates, inequality is found to be negatively and not significantly associated with criminality.
Choe (2008), in a study of US states and Columbia district from 1995-2004 tested for the relationship between the Gini index and different types of crimes - overall violent crime, murder, rape, robbery, assault, overall property crime, burglary, larceny, and motor. The econometric methods used were the fixed effects estimation technique to control for state specific-fixed effects (decided on the basis of the Hausman test) and the Arellano Bond GMM procedure to control for crime dynamics while taking the endogeneity of covariates taken into account. From the fixed effects estimates, he found that overall violent and property crimes are positively influenced by inequality. Among violent crimes, only rape is associated with inequality and for the property crimes, burglary is related significantly to inequality. From the Arellano Bond (GMM) estimates inequality is positively and significantly related to robbery and burglary.
Kelly (2000), used cross-sectional estimates and controlling for urban-specific fixed effects, studied urban counties in the USA in 1991 to establish the relationship between income inequality and violent and property crimes. He found that inequality is not related to property crime but increases violent crime and that the lower is the 25th percentile wage, the higher is the probability to observe criminal activities.
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3
Methodology
There remains no general consensus on how best to model income inequality or the incidence of crime, so an inclusive approach is taken in this study. To address the research questions of this dissertation I use four estimation methodologies – Pooled Ordinary Least Squares (OLS), Fixed Effects/2-Stage Fixed Effects and 2-Step Difference Arellano-Bond General Methods of Moments (GMM) models. The empirical study has two components. The first set of regressions (Table 5 - 7) using pooled OLS and Fixed Effects estimation methods attempt to identify the relevant determinants of inequality, as measured by the Gini index, first with a baseline model and then supplementary inclusion of additional variables. The second set of regressions (Table 8 and 9) using pooled OLS, 2-Stage Fixed Effects and Dynamic Panel modelling test for the impact of inequality on homicide rates, alongside other possible determinants, and also test for criminal hysteresis in the data5. These models reflect estimation techniques widely employed by economists working with similar datasets, each of which are discussed in-depth below.
3.1
Pooled OLS Estimation
OLS analysis captures not only the variation of what emerges through time or space, but the variation of these two dimensions simultaneously. Rather than testing a crosssectional model for all countries at one point in time or testing a time series model for one country using time series data, a pooled model is tested for all countries through time (Pennings et al., 2006). Given these advantages, pooled analysis has become central in quantitative studies of comparative economics. An accumulating body of research has utilized pooled models to provide answers to classical questions of the discipline (Alvarez et al., 1991; Hicks and Swank, 1992).
In order to correct for the possible existence of heteroscedasticity and to remove the effect of outliers, I estimate these models using robust and homoscedasticity-consistent standard errors, as proposed by White (1980) . Moreover, the log-transformation of the dependent variable can serve to mitigate problems of heteroscedasticity of the error term and reduce the impact of outliers in the data.
The baseline pooled OLS linear model is given as:
5
Data is discussed extensively in Chapter 4.
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[1]
,
where,
denotes the unobserved random error term,
denotes natural logarithm of
the dependent variable (which could either be the natural logarithm of Gini index or natural logarithm of homicide rate), time, 1, 2…..T, coefficients;
, vector of observed time-variant factors and their regression , vector of observed time-invariant factors and their regression
coefficients and
3.2
, the constant term; i,, countries, 1, 2……..N; t,
denotes unobserved country-specific effects
Fixed Effects and 2-Stage Fixed Effects Estimation
In the case where
is expected to correlate with one or more of the explanatory
variables in the above model, when In this case and since
,
, the fixed effects model is needed.
has to be removed prior to estimation or it will result in biased estimates is country-specific and assumed to be fixed over time, the effect can be
“differenced” away. This transformation removes the country-specific effects
and the
time-invariant variables . I obtain ̅
̅
̅
and this reduces to: ̅̅̅
(
̅)
̅
[2]
where: denotes a time dummy to control for temporal variation in the dependent variable; thus, the application of OLS to [2] provides unbiased and consistent estimates for the covariates of interest.
However, the advantage of eliminating unobserved heterogeneity comes at a price. One of the drawbacks of the FE model is its inability to estimate the effect of time-invariant variables. A far less recognised issue is its inefficiency in estimating the effect of rarely changing variables, that is, variables with near-zero, within variance, such as governance and institutions. While we can estimate a model with slowly changing independent variables, the fixed effect absorbs most of the explanatory power of these, with point 14
estimates giving the appearance that such variables are substantively and statistically insignificant (Plumper and Troeger, 2007; Greene, 2011). An inefficient estimation is not merely a nuisance leading to somewhat higher standard errors. Inefficiency leads to highly unreliable point estimates (for slowly changing variables) and may lead to incorrect inferences. Therefore, the inefficiency of the FE model in estimating variables with zero or low within variance needs to be taken seriously (Chatelain and Kirsten, 2010).
To overcome this limitation, I propose an alternative method6 that permits estimation of coefficients of time-invariant variables that is more efficient than under FE panel estimation. This alternative, potentially superior methodology, is referred to as „„2-stage fixed effects‟‟ (2SFE) estimation, because the estimator decomposes the unit FE into the component explained by the time-invariant or the rarely changing variables, and another unexplained component. As its name suggests, this technique involves two stages of estimation. Stage 1 involves FE estimation of the static baseline model consisting of timevarying variables only, from which estimates of time-varying variables and countryspecific effects, effects‟‟
̂ are generated. Note that at this juncture the „„estimated country
̂ do not equal the country effects
in the original model. Rather, these
estimated effects include all time-invariant variables, the overall constant term, and the mean effects of the time-varying variables, , or, in other words, ̅ ̂
where,
̅
̅
[3]
is the pooled-OLS estimate in [2]. This ̂ includes the unobserved country-
specific effects as well as the observed units specific effects residuals ̅ , and the time-varying variables
, the unit means of the
̅ , whereas
only accounts for
unobservable country-specific effects. Stage 2 involves regressing the estimated country-specific effects, ̂ from stage 1, on the observed time-invariant and rarely-changing variables, the using pooled OLS, to obtain the unexplained component regressing the estimated country-specific effect on the ̂
.
variables, (see equation [4]), (that is, the residual from
variables). [4]
6
The 2SFE is used solely to estimate the homicide model due to the presence of a time-invariant variable, dtpen, which will be omitted if the FE estimation technique is used. It is also used as a robustness check in the Gini model to reveal some deficiencies of the FE estimator.
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2-Step Difference GMM7
3.3
This method is employed to test for crime persistence in the data, taking into account the possibility that crime may be serially correlated. This assumes a correlation between and
(here,
is the homicide rate). Thus [1] is modified as: [1‟]
,
where
is dependent variable, the natural logarithm of homicide rate,
the lag of the dependent variable, their coefficients and
denotes
denotes the vector of explanatory variables and
is the composite error term,
Several econometric problems may arise from estimating equation [1‟]. First, some explanatory variables are assumed to be endogenous because causality may run in both directions, thus, these regressors may be correlated with the error term. Second, timeinvariant country characteristics (fixed effects) may be correlated with the explanatory variables. The fixed effects are contained in the error term in equation [1‟], which consists of the unobserved country-specific effects,
and the observation-specific error,
Third, the presence of the lagged dependent variable
.
gives rise to autocorrelation,
and finally, the panel dataset has a short time dimension (T = 13) and a larger country dimension (N =137) that the Arellano-Bond estimator is designed for.
To solve these problems the difference GMM uses first-differences to transform equation [1‟] into Δ
ɸΔ
Δ
Δ
.
[5]
By transforming the regressors through first differencing the fixed country-specific effect is removed, as it does not vary with time. From [5] we get
[6] or, 7
Applicable only to the homicide model in testing crime persistence.
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[6‟]
-
Unobserved fixed effects no longer enter the equation as they are by assumption constant between periods. Also, the first-differenced lagged dependent variable is instrumented with its past levels, and now changes in criminal activity are assumed to be represented by equation [5].
The consistency of the parameters obtained by means of the GMM estimator depends crucially on the validity of the instruments. Therefore two specification tests suggested by Arellano and Bond (1991) and Arellano and Bover (1995) are considered. The first test is the Hansen test of over-identifying restrictions, which tests the null hypothesis of overall validity of the instruments used. Failure to reject this null hypothesis gives support to the choice of the instruments. We also report the test for serial correlation of the error term, which tests the null hypothesis that the differenced error term is first and second order serially correlated. Failure to reject the null hypothesis of no second-order serial correlation implies that the original error term is serially uncorrelated and the moment conditions are correctly specified (Roodman, 2014).
All three approaches reviewed in sections 3.1-3.3 are routinely employed by economists and together offer a comprehensive examination of the determination of income inequality and its relationship to crime, in addition to analysing crime persistency. Section 4 outlines the dependent and independent variables used, their sources and descriptive statistics.
4
Data and Descriptive Statistics
4.1
Data Collection, Selection and Missing Values
To obtain the desired dataset, it has been necessary to choose from already existing datasets and databases. There is no doubt that locating, compiling and grooming secondary data can be an enormous task. Even though data has become increasingly available for most indicators, it still important to be very critical towards the quality of the data being collated. Much of the data coming out of other regions, aside Europe, suffer from inconsistencies in the numbers and methodology, both across countries, within
17
countries and cross time (Meschi and Vivarelli, 2009; Solt, 2009; Lo Prete, 2013; Ravallion, 2014).
Acknowledging these profound problems, only data from accredited sources, of which most have been previously used in the literature, is included. In other words, the data collection process has been fundamental for the outcome of this thesis, in order to mitigate the problem of inaccurate data to the greatest possible extent. Ideally, the dataset will be a representative longitudinal sample across all regions containing prominent indicators believed to have influence on income inequality. The data collection and selection process involved combining similar datasets, while taking into consideration the units of measurements, thus yielding a balanced panel data (Dollar and Kraay, 2004; Østergaard, 2013).
Due to the problem of missing values, the initial sample size of 214 countries had to be scaled down to 137 countries8. Data priority was given to both the Gini index and homicide rates (as both are dependent variables in separate models), thus, countries without substantial data on either or both variables are dropped to minimise „holes‟ in the data and also to balance the „trade-off‟ between sample size, richness and power of the explanatory variables (Barro, 2000).
Finally, on a different note, it is hoped that the compiled dataset will serve as a secondary outcome of the thesis. Even though the dataset is mainly compiled for the purpose of this specific thesis, it may be useful in other respects as well.
4.2 Variables and Data Sources This section outlines the variables used in the analysis, the rationale for using them as well as related data sources. Unless otherwise stated, most variables were downloaded from www.worldbank.org World Development Indicators, these are reported in full in Table 1.
8
The dataset is globally represented covering all the 7 regions and across 4 income groups: Out of the 137 countries – 18 (EAP); 46 (ECA); 23 (LAC); 10 (ME); 2 (NA); 7 (SA) and 31 (SSA). Comprehensive list of countries and their classifications are in Table 10: Appendix A1.
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4.2.1 Dependent Variables There are two dependent variables, the natural logarithm of Gini index (lngini) and the natural logarithm of homicides rate (lnhom), both of which have been discussed extensively in the literature review. A total of fifteen explanatory variables are used across both models and their respective roles in each is further explained below. However, lngini is an argument in the homicide regression. The Gini coefficient is considered because it is a useful summary indicator of income inequality. So, in the homicide regression, I expect, a priori, a positive sign.
As annual data can be quite noisy, some of the variables are in growth rate form rather than annual values. The affected variables are GDP growth and urban population growth rate while the other variables are in their original form.
4.2.2 Independent Variables The independent variables are grouped into six: economic, deterrence, demographic, political variables, persistence and regional dummy variables. I add regressors from these groups sequentially, building my model specification. This is done for at least two reasons. First, to observe the impact of subsequently included variables on the baseline regression, and, second, because different missing values for particular regressors implies that including many of them simultaneously would reduce the number of usable data points. To further explain and understand the relationship the relationship among these indicators and their impact on the Gini index, four interaction terms are included.
4.2.2.1 Economic Variables Seven economic variables that are correlated with both dependent variables are included. These are the GDP growth rate (gdpgr), natural logarithm of GDP per capita (lngdppc), domestic credit (dcredit), trade (trade), oil rents (rents), government expenditure (cons_exp), and male unemployment rate (une_m).
Gdpgr, which is the annual percentage growth rate of the economy, is measured in constant local currency and proxies for the general level of prosperity in the countries. It is expected that as the economy grows, inequality falls; thus a negative coefficient is expected. The lngdppc, income per head, will be in both regressions testing for the Kuznets hypothesis which predicts that per capita income and its square should 19
respectively have positive and negative statistically significant coefficients in an inequality equation. On these basis, the first hypothesis tested is that income inequality grows as the regional level of development increases and thereafter declines. As a crime determinant, Krahn et al. (1986); Fajnzylber et al. (2002a) agree that per capita GDP reduces homicide rate. A priori signs expected.
Domestic credit, dcredit, which is measured as a percentage of GDP measures the volume of credit facilities from the financial sector. It is included to account for the impact of credit availability from the financial sector to private individuals. Empirical findings indicate that inequality gap is reduced with more access to credits. This variable will be used only in the Gini regression and a negative coefficient is anticipated.
A measure of trade openness, trade, which is the sum of imports and exports in a fiscal year as a percentage of GDP is included to test the impact of openness on income inequality. This variable is used only in the Gini regression and included to test for potential effects of globalization, which through Stolper-Samuelson effects can be hypothesised to potentially impact on inequality within countries. At the same time, if openness to trade is a stimulus of economic development, then it could go hand in hand with development of institutions, which might be more equalising. Empirical findings on the impact of openness on income inequality have been extensively reviewed in the literature, where some agree that trade is an unequalising variable, although different theoretical channels could give rise to different findings.
Oil rents, rents, which is a percentage of GDP, account for the contributions of natural resources to economic growth. This variable will be used only in the Gini regression as proxy for resource endowment. It is included to test if the abundance of natural resources influences income inequality. It goes without saying that natural resources have the potential to generate huge incomes for a country, but the question is, if the generated wealth trickles down to the lower members of society, or if it is kept exclusively by a small elite. Oil is unarguably becoming the most important resource in the world, as new reserves are being discovered progressively. It is expected that inequality should reduce in countries naturally-endowed. Since natural resources are relative to regions and countries, the expected sign is indeterminable.
20
Government expenditure, a percentage of GDP, constitutes a major source of income in poor households as well-targeted social expenditures are expected to lower income inequality. The cons_exp, variable will be used only in the Gini regression and its coefficients are expected to be negatively signed, following previous empirical studies.
Another economic factor that affects inequality and crime rate is unemployment, une_m. It exists the general belief that unemployment is positively correlated with both income inequality and homicide rate. Since unemployment may reduce legal returns from work, inequality further widens. This variable will be in both regressions measuring the impact of the ratio of able-bodied males who are willing to work but find none. Positive and statistically significant coefficient is expected (Witte and Witt, 2001; Brush, 2007; Huhta, 2012)
4.2.2.2 Deterrence Variable The deterrence variable, death penalty (dtpen) obtained from www.deathpenaltyinfo.org is included to test if countries that uphold the death penalty have lower homicide rates. It takes the value 1 if countries have the death penalty, and 0 otherwise. However, it is evident that countries allowing the death penalty have higher homicide rates (Grogger, 1990; Hunt, 2004; Zimmerman, 2006). A positive and statistically significant coefficient is expected.
4.2.2.3 Demographic Variables Five demographic variables in the analysis are likely to be correlated with both inequality and homicide rate. These variables are the percentage of youth employed to population aged 15-24 years (m1524), the share of population living in urban areas (urbp), and education enrolment rates for primary (pry_educ), secondary (sec_educ) and tertiary (ter_educ) education. m1524, is used in both regressions. It is the percentage of employed youth to total population. Young men are said to be more prone to engage in criminal activities than the rest of the population, this means that the participation to crime is higher at the initial stage of adulthood (Bound and Freeman, 1991; Grogger, 2000). It also captures greater participation rates on the part of certain segments of the labour force e.g. young people. A high ratio indicates that a large proportion of youths are employed while a lower ratio can still be seen as a positive sign, especially for young people, if it is caused by an increase in their education. On crime rates, Krahn et al. 21
(1986) say that 15-19 year olds are best predictors of homicide rates, while young adults as percentage of population are weaker predictions. Negative coefficients are expected in both models.
I also consider the share of population living in urban areas as it is well documented that inequality falls as rural-urban migration takes place (Glaeser and Sacerdote, 2000; Rosenthal, 2008). This variable, urbp, will only be in the Gini regression and it is included to test for the influence of urbanisation on inequality. Empirical findings on the effect of increase in urban dwellers on income inequality are mixed. To some, increase in urbanisation imply increase in economic growth and therefore a fall in inequality as people move to the city from rural areas (Galor and Zeira, 1993; Eide and Showalter, 1999). I expect a negative coefficient.
The education variables, pry_educ sec_educ and ter_educ (of which ter_educ will not be considered in the homicide model), test the impact of education on income inequality and also the decision to engage in criminal activities through several channels. These are school enrolment ratios, all of which are expected to feature with negative coefficients although empirical literatures have mixed views as to the impact on income distribution. Furthermore, as noted by Witt et al. (1999); Lochner (2004) schooling generates benefits beyond the private return received by individuals. Finally, education also increases the cost associated with incarceration, since more educated individuals will experience greater losses in earnings while in jail. On a rational note, a higher educational attainment is expected to reduce inequality. Empirical findings on the effect on homicide rates is as much mixed. De Gregorio and Lee (2002) found no discernible effects of education variable on crime rates while Krahn et al. (1986) found that increased education reduces crime rates considerably.
4.2.2.4 Institutional Variables Two political variables are included; the rule of law index, rol, from World Bank‟s World Development Indicators and corruption index, corrupt from www.govindicators.org and Transparency International to test for the impact of good governance and corruption in the two models. The rule of law index ranges from -2.5 (weak governance) to 2.5 (strong governance) and it is expected that as a country improves on its index, inequality and homicide rates are expected to fall. Thus, negative coefficients are expected. However, 22
due to strong correlations between the rule of law index and corruption index, only the former will be used in both models to test the role of institutions (see Chong and Gradstein (2007) for a detailed study on the effect of institutional quality on different inequality indicators).
4.2.2.5 Interaction Terms To greatly expand understanding of the relationships among the variables particularly in the Gini model, and to further elucidate the channels through which they affect inequality, four interaction terms are included. They are rents*ME, trade*rol, lngdppc*rol, exp*rol. The Middle East region is the world‟s bedrock of oil reserves and to capture the effect of oil revenues on income inequality in that region, in addition to establishing if on average having higher oil rents is associated with greater income inequality, rents*ME, is included. To analyse the effect of institutions, rol, on income inequality, trade, lngdppc, and exp are interacted at different stages with this variable. It is hypothesised that equitable distribution of resources are prevalent in countries with good governance and such that uphold the rule of law. Ironically, equitable resource distribution, which reduces income inequality, are common in autocratic governments than in democratic governments. I expect some of these coefficients to be statistically significant but cannot predict the signs.
4.2.2.6 Persistence Variable For the persistence variable, lagged homicide rate, dynamics in crime is considered. In fact, past experience in criminal activity affects in several ways the decision to commit crime (Sah, 1991; Bourguignon, 1999; Neumayer, 2005). In other words, higher crime today is associated with higher crime tomorrow (i.e. persistence over time). Criminals can learn-by-doing and acquire an adequate criminal know-how level; this acquisition, in turn, makes the costs of carrying out criminal acts to decrease over time (Stack, 1984; Case and Katz, 1991). Convicted criminals have fewer opportunities of legal employment and a lower expected wage (Britt, 1997; Bourguignon, 1999; Choe, 2008; Bounanno and Montolio, 2009). These arguments strongly suggest the possibility of criminal hysteresis or inertia. I expect a positive coefficient.
23
4.2.2.7
Regional Dummy Variables
Seven regional dummies, EAP, ECA, LAC, ME, NA, SA and SSA, are added to control for region heterogeneities in the OLS equation. Each takes the value 1 if countries belong to that region, and 0 otherwise.
24
Table 1. No. 1 2
3 4 5 6 7
Variables, Description and Sources
Variable Name GINI Index (lngini) Intentional homicides (per 100,000 people) (lnhom)
Description of Variables Data Source(s) Index of 0 represents perfect equality, while an index of 1 Frederick Solt's Dataverse and implies perfect inequality. World Bank Open Data World Bank Open Data and Death purposely inflicted by another person per 100,000 United Nations Office on Drugs people and Crime (UNODC)
GDP per capita (current US$) (lngdppdc)
GDP per capita is gross domestic product divided by midyear population.
World Bank Open Data
GDP growth (annual %) (gdpgr) Domestic credit by financial sector (% of GDP) (dcredit) Trade (% of GDP) (trade) Oil rents (% of GDP) (rents)
Annual percentage growth rate of GDP at market prices based on constant local currency.
Same as above.
Financial resources provided to the private sector by financial corporations.
Same as above.
Trade is the sum of exports and imports of goods and services. Accounting for the contribution of natural resources to economic output. Rule of Law (proxy for good governance). Estimate on good governance ranges from -2.5 (very weak) to 2.5 (very strong).
Same as above. Same as above.
8
Rule of Law Index (rol)
9
Corruption Index (corrupt)
Reflects perceptions of the extent to which public power is exercised for private gain. Estimate of corruption (ranges from -2.5 (very corrupt) to 2.5 (least corrupt).
World Governance Indicators and Transparency International
10
Death Penalty (dtpen)
Dummy variable that takes the value 1 if the country retains the death penalty and 0 otherwise. Constructed by author
www.deathpenaltyinfo.org
Includes all government current expenditures for purchases of goods and services.
World Bank Open Data
This is the ratio of country's youth population that is employed.
Same as above.
Gross enrolment ratio is the ratio of total enrolment that officially corresponds to the primary level of education.
Same as above.
Gross enrolment ratio is the ratio of total enrolment that officially completes primary education.
Same as above.
Gross enrolment ratio is the ratio of total enrolment that officially completes secondary education.
Same as above.
Share of the labour force that is without work but available for and seeking employment.
Same as above.
11
12
13
14
15
16
General government consumption expenditure (% of GDP) (cons_exp) Male ages 15-24 years (% employed to total population) (m1524) School enrolment, primary, male (% gross) (pry_educ) School enrolment, secondary, male (% gross) (sec_educ) School enrolment, tertiary, male (% gross) (ter_educ) Unemployment, male (% of male labour force) (une_m)
Same as above.
17
Urban population (% of total) (urbp)
People living in urban areas as defined by national statistical offices.
Same as above.
18
rents*ME, trade*rol, lngdppc*rol, exp*rol
These are interaction terms included to measure impact on income inequality
Constructed by author.
19
EAP, Dummy for countries in East Asia & Pacific
Dummy variable that takes the value of 1 if the country is Constructed by author. located in East Asia and Pacific and 0 if otherwise.
20
ECA, Dummy for countries in Europe & Central Asia
Dummy variable that takes the value 1 if the country is located in Europe & Central Asia and 0 otherwise.
Constructed by author.
21
LAC, Dummy for countries in Latin America & Caribbean
Dummy variable that takes the value 1 if the country is located in Latin America & Caribbean and 0 otherwise.
Constructed by author.
22
ME, Dummy for countries in Middle East & North Africa
Dummy variable that takes the value 1 if the country is located in Middle East & North Africa and 0 otherwise.
Constructed by author.
23
NA, Dummy for countries in North America
Dummy variable that takes the value 1 if the country is located in North America and 0 otherwise.
Constructed by author.
24
SA, Dummy for countries in South Asia
Dummy variable that takes the value 1 if the country is located in South Asia and 0 otherwise.
Constructed by author.
25
SSA, Dummy for countries in Sub-Saharan Africa
Dummy variable that takes the value 1 if the country is located in Sub-Saharan Africa and 0 otherwise.
Constructed by author.
25
4.3
Descriptive Analysis
The panel data set consist of 137 countries over a 13-year period giving a total crosssectional unit of 1781 observations. This section gives the summary statistics (Table 2), the correlation among the variables (Table 3) and the distribution of the average Gini index and average homicide rate across the countries (Figures 1 – 3).
Table 2.
Summary Statistics Variables
Mean
SD
Min
Max
0.395
0.098
0.154
0.753
Homicide Rate
8.280
11.917
0.000
91.610
GDP per capita
8.200
1.602
4.702
11.627
Trade (% GDP)
92.889
55.628
20.258
562.060
Govt. Cons. Exp. (% GDP)
16.083
9.518
3.460
164.696
Urban Population (% total pop.)
56.357
23.062
10.833
100.000
GDP Growth Rate
4.230
4.265
-18.000
34.500
67.455
60.733
-70.378
349.027
8.138
5.908
0.700
37.000
Oil rents (% GDP)
3.852
9.436
0.000
70.638
Death Penalty
0.248
0.432
0.000
1.000
Males Aged 15-24 years (% empl.)
45.460
14.568
13.500
79.200
Primary school enrolment ratio
104.604
12.977
35.315
171.220
Secondary school enrolment ratio
77.778
27.239
7.437
160.520
Tertiary school enrolment ratio
31.460
22.999
0.134
119.144
Rule of Law Index
0.018
0.984
-1.699
2.000
Corruption Index
0.048
1.028
-1.517
2.586
EAP
0.131
0.338
0.000
1.000
ECA
0.336
0.472
0.000
1.000
LAC
0.168
0.374
0.000
1.000
ME
0.073
0.260
0.000
1.000
NA
0.015
0.120
0.000
1.000
SA
0.051
0.220
0.000
1.000
SSA
0.226
0.419
0.000
1.000
N
1781
9
Gini Index
10
Domestic Credit (% of GDP)
11
Male Unemployment (% total) 12
Regions:
9
This is the 2002 Gini Index for Mauritius (SSA). The country’s index from 2000 – 2012 is between 0.1537 and 0.239 while its overall average Gini index is 0.1965 making it one of the least unequal countries in the world. Its homicide rate (per 100,000 people) is between 2 to 4.8 for the period under review. The highest Gini index of 0.752 is from Indonesia (EAP) in 2000. From 2000-2012, the country’s index rose from 0.3401 to 0.7526 and its homicide rate also increased in response from 0.44 to 9.4 per 100,000 people. 10 This is the 2011 homicides rate for Honduras (LAC) and it is the highest in the data. The bottom 33 countries in the homicide data are all Latin American countries with the exception of Hong Kong with a rate of 63.67 in 2000. 11 Negative credit was common among countries classified as ‘VHI’ (very high inequality). The reason for the negative lending could be attributable to the calling-in of exposures by financial institutions or due to toxic loans. 12 42 countries have ‘zero’ oil rents data
26
The simple correlation coefficients of the income distribution variables and homicide rate with other variables of interest are reported in Table 3. From the table, it can be seen that the income inequality is highly correlated with homicide rate, GDP per capita and the institutional variables. The high correlation between inequality and secondary enrolment rate is also noteworthy. A closer look at the correlation matrix, reveals that all the variables correlating with the Gini index also correlate with homicide rate indicating that factors that determine income inequality also, in most cases, determine homicide rate. Table 3.
Correlation Coefficients13
Variables
Gini Index
Homicide Rate
Gini Index
1.000
0.541
Homicide Rate
0.541
1.000
GDP per capita
-0.446
-0.453
Trade (% GDP)
-0.099
-0.229
Govt. Cons. Exp. (% GDP)
-0.420
-0.255
Urban Population (% total pop.)
-0.249
-0.273
GDP Growth Rate
0.131
0.145
Domestic Credit (% of GDP)
-0.295
-0.478
Male Unemployment (% total)
0.063
0.076
Oil rents (% GDP)
0.067
0.190
Death Penalty
0.236
0.083
Males Aged 15-24 years (% empl.)
0.183
0.196
Primary school enrolment ratio
0.321
0.127
Secondary school enrolment ratio
-0.507
-0.416
Tertiary school enrolment ratio
-0.405
-0.279
Rule of Law Index
-0.483
-0.579
Corruption Index
-0.445
-0.525
EAP
0.156
-0.194
ECA
-0.683
-0.360
LAC
0.482
0.551
ME
0.027
-0.182
NA
-0.037
-0.009
SA
0.104
-0.015
SSA
0.221
0.248
Regions:
The correlation among the variables reveal, for instance, that income per capita and secondary education are strong indicators of both income inequality and crime rates. Also, the rule of law index of -0.483 indicates that better enforcement of rules and good governance will ultimately reduce income inequality and will also reduce the crime rate (correlation with homicide rate is -0.579). The same analogy applies to the corruption 13
Full correlation matrix is in Table 11: Appendix A2.
27
index of -0.445, that is, as a country embarks on anti-corruption measures the inequality gap reduces and thus, the crime rate is expected to fall (correlation with homicide rate is 0.525). The positive correlation between male unemployment rate and the two dependent variables is weak. From the regions, it is revealed that ECA is negatively correlated with inequality and homicide rate while LAC show positive correlations with both variables.
Table 4 gives insights into the dataset. Countries with low inequality (mostly from Europe and Central Asia) also have on average low homicide rates for the period under review. Countries in Latin America and Africa exhibit high inequality and thus have higher homicide rates on average. Evidence from the data reveals that the bottom 30 countries on the homicide rate data (that is, those countries with very high rates) are from Latin America and the Caribbean with an average rate of 50 homicides per 100,000 population, and not unexpected these countries also have high Gini indices. European countries on the other hand, on average, have the lowest Gini indices and homicide rates. Table 4.
Ranking of Countries
Lowest (10) by Average Gini Index Country Name Region Group Index Mauritius SSA UMInc 0.197 Norway ECA HInc 0.237 Denmark ECA HInc 0.237 Sweden ECA HInc 0.246 Slovenia ECA HInc 0.249 Finland ECA HInc 0.257 Iceland ECA HInc 0.257 Czech Republic ECA HInc 0.259 Netherlands ECA HInc 0.263 Belgium ECA HInc 0.263
Highest (10) by Average Gini Index Country Name Region Group Index Panama LAC UMInc 0.529 Botswana SSA UMInc 0.531 Paraguay LAC LMInc 0.538 Lebanon ME UMInc 0.549 Honduras LAC LMInc 0.560 Colombia LAC UMInc 0.569 Seychelles SSA UMInc 0.576 South Africa SSA UMInc 0.590 Namibia SSA UMInc 0.618 Papua New Guinea EAP LMInc 0.647
Lowest (10) by Average Homicide Rate Country Name Region Group Rate Japan EAP HInc 0.514 Singapore EAP HInc 0.548 Austria ECA HInc 0.729 Iceland ECA HInc 0.774 Denmark ECA HInc 0.901 Switzerland ECA HInc 0.919 Norway ECA HInc 0.925 Malta ME HInc 0.960 Germany ECA HInc 0.995 Sweden ECA HInc 1.008
Highest (10) by Average Homicide Rate Country Name Region Group Rate Trinidad and Tobago LAC HInc 23.364 Angola SSA UMInc 25.020 Lesotho SSA LMInc 26.095 South Africa SSA UMInc 36.895 Guatemala LAC LMInc 38.152 Venezuela LAC UMInc 38.958 Colombia LAC UMInc 44.062 Jamaica LAC UMInc 46.198 Honduras LAC LMInc 55.435 El Salvador LAC LMInc 60.443
Note: World Bank classification of countries into income groups. HInc: high income; UMInc: upper middle income; LMInc: lower middle income; LInc: low income.
28
Prior to regression analysis it is useful to explore at the distributions of income inequality and homicide rate by countries and the simple cross-correlation between them. Figure 1 plots the distribution of average Gini indices while Figure 2 plots the average homicide rates by countries. From Figure 3 it can be seen that countries with low inequality have lower rates of homicides and versa. Although, these figures are suggestive, further statistical analysis is required to examine their robustness and obtain orders of magnitude for the importance of other factors in explaining the movements in income inequality and homicide rates.
Figure1.
Distribution of Average Gini Index by Countries 25
23
22 20
Number of Countries
20
19 19
18
15
10
7 5
4
3
2
0 .2
.3 .4 .5 .6 Homicide Rate per 100'000 population
.7
The histogram plot mimics a normal distribution with majority of countries within the range of 0.25 and 0.5
Distribution of Average Homicide Rate per 100’000 population
Figure 2.
88
Number of Countries
80
60
40
25 20
8
7 2
3
2
2
0 0
20 40 Homicide Rate per 100'000 population
60
Plot shows left skewness with 90% of countries having an average homicide rate of 20 per 100‟00 population.
29
Figure 3.
Cross-Country Distribution of Gini Index and Homicide Rate .7
Papua New Guinea Namibia
Gini Index
.6
South Africa Seychelles Colombia Honduras Lebanon Paraguay Botswana Panama Bolivia Guatemala Nepal Zambia Ecuador Lesotho Indonesia Philippines Jamaica Chile Peru Dominican AngolaRepublic Cambodia Costa Rica El Salvador Rwanda China Argentina Swaziland Mexico Central African Republic Hong Kong SAR, China Iran, Islamic Rep. Malaysia Thailand Suriname India Maldives Nicaragua Uruguay Kenya Sri Lanka Jordan Bhutan Madagascar Lao Timor-Leste PDR Singapore Nigeria Mozambique The Venezuela, RB Georgia CoteBahamas, d'IvoireRico Puerto Turkey Uganda Russian Vietnam Macedonia, FYR Leone Federation Malawi Sierra Niger Pakistan Ghana Cameroon Morocco Guinea Senegal Chad Mauritania Mongolia St. Lucia and Tobago Tunisia Djibouti United States Fiji Trinidad Israel Moldova Mali Uzbekistan Latvia Benin Armenia Portugal United Kingdom Kyrgyz Republic Togo Bangladesh Tanzania Lithuania Estonia Italy Algeria Greece Egypt, Arab Rep. Spain New Tajikistan Zealand Australia Albania Korea, Rep. Kazakhstan Canada Romania Ethiopia Montenegro Ireland Bulgaria Poland Serbia Croatia Japan Switzerland France Azerbaijan Cyprus Germany Ukraine Belarus Hungary Malta Luxembourg Austria Slovak Republic Netherlands Belgium Czech Republic Iceland Finland Slovenia Sweden Denmark Norway
.5
.4
.3
.2
Mauritius 0
20 40 Homicide Rate per 100000 population
60
This shows positive correlation between the Gini index and homicide rate.
5
Results
The results arising from the regression analysis are reported and interpreted below. For parsimony, only passing reference is made to factors that are found not be statistically significant.
5.1
Determinants of income inequality
5.1.1 Pooled OLS14 The baseline specification, [7], comprises lngdppc, lngdppc2, trade, cons_exp, urbp and seven regional dummies (with Sub-Saharan Africa region as the base region).
[7]
Economic variables are then added sequentially to the baseline model. The specification with baseline and all economic variables is given by [8].
[8]
14
Refer to Table 12: Appendix A3 for Pooled OLS estimation of Gini Index (Levels)
30
Demographic variables are added sequentially to [8]. The specification with baseline, economic, and all demographic variables is given by [9].
[9]
Finally, [9] is further augmented to include institutional variables. The full model is given by [10].
[10]
Table 5 (columns 1 to 9) presents the estimated coefficients and their t-statistics from the full sample starting with the baseline model15. All estimated models display a relatively good fit to the data, with the R2 ranging from 0.56 (baseline model) to 0.68 (full model). The joint significance level of the model‟s independent variables from the F test is found to be statistically significant at 1% in all model specifications.
From the baseline model in column 1, a casual observation of the results reveals that the data supports the Kuznets hypothesis. There is a positive relationship between the level of economic development (per capita income) and inequality in the early stage of development while the relationship is reversed16 later on, giving rise to an inverted Ushape relationship. Both per capita income level and its square are statistically significant at the 1% level in all stages in the inequality equation. This implies that at the earlier stages of economic development, a 1 percent increase in per capita GDP is associated with an increase in the Gini index by 0.29 percent and as economic development further progresses a 1 percent rise in the square of per capita GDP is associated with a fall in the Gini index by 0.02 percent.
15 16
All estimation was performed using Stata 13. ̂ ̂ The turning point is at
31
With respect to the regions, at all stages of the model specifications, Europe (ECA) and Latin America (LAC) are both statistically significant at 1% level while the Middle East (ME) is mostly significant at 1% level. On average, income inequality is found to be lower in Europe by 20.27 percent, in the Middle East by 8.11 percent while higher in Latin America by 16.84 percent, relative to Sub-Saharan Africa17.
The coefficient for trade is positive and statistically significant at 1% at all stages of the regression. However, the economic impact and predicted effect on the Gini index is quite infinitesimal. Holding other variables fixed, a 1 percentage point increase in trade is estimated to increase the Gini index by 0.02 percent, on average, ceteris paribus. Supporting earlier empirical findings that openness has no income equalising effect (Barro, 2000; Dastidar, 2012).
17
The exact inequality differential among the regions is calculated using [exp ( ̂
32
.
Table 5. Pooled OLS Estimates (Dependent Variable: lnGini)
Independent Variables constant lngdppc lngdppc 2 trade cons_exp urbp
[1] Baseline Model -1.8466*** (-12.16) 0.2695*** (6.96) -0.0193*** (-8.91) 0.0002*** (3.19) 0.0007** (2.21) 0.0006* (1.70)
gdpgr
[2]
[3]
[4]
[5]
Economic Variables
[6]
[7]
Demographic Variables
-1.8344*** (-12.06) 0.2691*** (6.92) -0.0193*** (-8.89) 0.0002*** (3.15) 0.0006** (2.04) 0.0006* (1.76) -0.0012 (-1.15)
-1.8640*** (-11.57) 0.2796*** (6.72) -0.0204*** (-8.38) 0.0003*** (3.38) 0.0007** (2.39) 0.0006* (1.83) -0.0009 (-0.82) 0.0002* (1.89)
-1.4965*** (-8.78) 0.1690*** (3.75) -0.0130*** (-4.91) 0.0002** (2.55) 0.0001 (0.50) -0.0003 (-0.78) 0.0002 (0.20) 0.0003** (2.27) 0.0080*** (8.22)
-2.1766*** (-15.71) 0.3413*** (9.88) -0.0227*** (-10.85) 0.0004*** (5.68) -0.0008 (-0.75) -0.0009** (-2.00) -0.0004 (-0.32) 0.0006*** (4.77) 0.0094*** (11.24) 0.0002 (0.31)
-2.1209*** (-13.75) 0.3322*** (9.17) -0.0221*** (-10.11) 0.0004*** (5.24) -0.0008 (-0.74) -0.0009** (-2.03) -0.0004 (-0.31) 0.0006*** (4.75) 0.0090*** (9.79) 0.0002 (0.28) -0.0003 (-0.74)
-2.2695*** (-15.00) 0.3130*** (9.20) -0.0206*** (-10.07) 0.0003*** (3.08) -0.0003 (-0.29) -0.0008* (-1.83) -0.0006 (-0.53) 0.0006*** (4.78) 0.0087*** (9.49) -0.0001 (-0.18) -0.0007* (-1.95) 0.0027*** (4.93) -0.0009*** (-3.17) -0.0005* (-1.77)
0.0352 (1.61) -0.2249*** (-10.67) 0.1556*** (7.04) -0.0840*** (-3.24) 0.0152 (0.49) 0.0282 (1.10) 1695
0.0248 (1.04) -0.2255*** (-10.69) 0.1562*** (7.16) -0.0967*** (-3.60) 0.0140 (0.40) 0.0207 (0.79) 1670
0.0749*** (3.10) -0.2188*** (-11.17) 0.2184*** (10.18) -0.0589** (-2.16) 0.0165 (0.50) 0.0546** (2.03) 1649
0.0058 (0.27) -0.2888*** (-19.29) 0.1643*** (8.85) -0.1352*** (-5.95) -0.0585* (-1.85) 0.0102 (0.36) 1406
0.0064 (0.30) -0.2900*** (-19.34) 0.1675*** (8.53) -0.1356*** (-5.98) -0.0573* (-1.83) 0.0109 (0.39) 1406
0.0345 (1.39) -0.2471*** (-14.38) 0.1728*** (8.31) -0.1234*** (-5.21) -0.0213 (-0.70) 0.0206 (0.74) 1406
dcredit une_m rents m1524 pry_educ sec_educ ter_educ rol EAP ECA LAC ME NA SA No. of Obs.
0.0330 (1.52) -0.2265*** (-10.74) 0.1557*** (7.06) -0.0846*** (-3.27) 0.0137 (0.44) 0.0349 (1.34) 1702
R-Squared 0.554 0.553 0.555 0.586 0.665 0.665 0.681 F Statistic 363.702 329.823 309.414 310.820 312.250 293.851 273.805 OLS Estimation Technique. t -statistics (in parentheses) are based on White heteroscedasticity-consistent std. errors. Statistical significance: *pchi2 = 0.0000 (V_b-V_B is not positive definite)
58
A6. Table 15.
2-Step Difference GMM: Homicide Rate
Dynamic panel-data estimation, two-step difference GMM Group variable: c_id Time variable : year Number of instruments = 63 F(19, 134) = 18.49 Prob > F = 0.000
Number of obs Number of groups Obs per group: min avg max Corrected Std. Err.
lnhom
Coef.
lnhom L1.
.3442766
.1279541
lngini lngdppc m1524 une_m rol pry_educ sec_educ yr3 yr4 yr5 yr6 yr7 yr8 yr9 yr10 yr11 yr12 yr13
.262096 -.1950641 -.0085507 -.0081188 -.3267133 -.0002951 .0006616 .1565328 .0892911 .1354418 .0914033 .0953453 .1013489 .1702363 .030932 -.0074259 .0032176 -.1401388
.1461425 .0769618 .0073576 .0087243 .1122335 .0026788 .0020037 .0470228 .0554717 .0568193 .0622068 .0649217 .0757854 .0828636 .0825547 .0871499 .093742 .0964451
t
= = = = =
1463 134 7 10.92 11
P>|t|
[95% Conf. Interval]
2.69
0.008
.0912058
.5973475
1.79 -2.53 -1.16 -0.93 -2.91 -0.11 0.33 3.33 1.61 2.38 1.47 1.47 1.34 2.05 0.37 -0.09 0.03 -1.45
0.075 0.012 0.247 0.354 0.004 0.912 0.742 0.001 0.110 0.019 0.144 0.144 0.183 0.042 0.708 0.932 0.973 0.149
-.0269484 -.3472811 -.0231028 -.025374 -.5486915 -.0055933 -.0033014 .0635299 -.0204223 .0230631 -.0316309 -.0330585 -.0485413 .0063465 -.1323469 -.1797932 -.1821877 -.3308904
.5511403 -.0428471 .0060014 .0091363 -.1047351 .0050032 .0046247 .2495356 .1990045 .2478205 .2144375 .2237492 .2512392 .334126 .1942109 .1649415 .1886229 .0506128
Instruments for orthogonal deviations equation Standard FOD.(m1524 lngini lngdppc une_m rol pry_educ sec_educ yr3 yr4 yr5 yr6 yr7 yr8 yr9 yr10 yr11 yr12 yr13) GMM-type (missing=0, separate instruments for each period unless collapsed) L(1/12).L3.lnhom Arellano-Bond test for AR(1) in first differences: z = Arellano-Bond test for AR(2) in first differences: z = Sargan test of (Not robust, Hansen test of (Robust, but
overid. restrictions: chi2(44) = 106.21 but not weakened by many instruments.) overid. restrictions: chi2(44) = 55.87 weakened by many instruments.)
59
-2.62 1.83
Pr > z = Pr > z =
0.009 0.067
Prob > chi2 =
0.000
Prob > chi2 =
0.108
A7. Table 16.
Pooled OLS Results for the Regions: Gini Index
Pooled OLS Estimates for the Regions (Dependent Variable: lnGini)
Independent Variables constant lngdppc lngdppc 2 trade cons_exp urbp gdpgr dcredit une_m rents m1524 pry_educ sec_educ ter_educ rol
Year dummies No. of Obs. R-Squared F Statistic
[1]
[2]
[3]
[4]
[5]
[6]
[7]
EAP
ECA
LAC
ME
NA
SA
SSA
-1.7695*** (-5.49) 0.2373*** (3.78) -0.0057 (-1.48) 0.0010*** (7.28) 0.0048 (1.30) -0.0055*** (-3.57) 0.0038 (1.17) -0.0007*** (-4.98) 0.0329*** (4.89) 0.0036 (0.81) 0.0034*** (2.84) -0.0032*** (-3.03) -0.0039*** (-6.67) -0.0010** (-2.10) -0.1088*** (-3.08) Yes 184 0.793 72.225
-1.4544*** (-6.57) 0.0795* (1.85) -0.0056** (-2.08) -0.0008*** (-5.55) -0.0074*** (-5.52) -0.0003 (-0.45) -0.0003 (-0.16) 0.0004*** (2.65) 0.0048*** (3.98) -0.0032*** (-2.80) -0.0009 (-1.64) 0.0021* (1.86) 0.0007 (1.45) -0.0009** (-2.28) -0.0378* (-1.78) Yes 582 0.413 18.298
-3.0975*** (-3.56) 0.6060*** (2.89) -0.0408*** (-3.19) -0.0001 (-0.44) 0.0090*** (2.68) 0.0016*** (3.18) 0.0015 (0.84) 0.0006 (1.58) -0.0033 (-1.00) -0.0033*** (-2.78) -0.0006 (-0.42) 0.0012 (0.99) -0.0017** (-2.50) -0.0003 (-0.63) -0.0026 (-0.14) Yes 239 0.506 9.801
0.3151 (0.23) -0.0134 (-0.04) -0.0118 (-0.61) -0.0023*** (-2.90) 0.0156*** (3.53) 0.0126*** (7.29) 0.0110*** (3.05) 0.0023*** (7.23) -0.0198*** (-3.73) 0.0102*** (5.81) -0.0180*** (-8.21) 0.0003 (0.18) -0.0090*** (-5.42) -0.0033** (-2.25) 0.0230 (0.55) Yes 108 0.905 63.221
-3.1504 (.) 0.0000 (.) -0.0188 (.) -0.0065 (.) -0.0135 (.) 0.0201 (.) -0.0030 (.) 0.0004 (.) 0.0179 (.) 0.0000 (.) 0.0153 (.) 0.0034 (.) 0.0140 (.) 0.0071 (.) 0.0000 (.) Yes 22 1.000 .
-4.0295 (-0.97) 0.5828 (0.49) -0.0355 (-0.42) -0.0014 (-0.49) 0.0092 (0.39) 0.0115 (0.75) -0.0229 (-1.26) -0.0065 (-1.62) -0.0418 (-0.93) -0.1207 (-0.70) 0.0085 (0.66) 0.0078 (1.60) 0.0015 (0.51) -0.0005 (-0.35) 0.3464* (2.00) Yes 65 0.702 8.568
-2.2779*** (-8.63) 0.3483*** (4.94) -0.0219*** (-4.37) 0.0013*** (3.80) 0.0097*** (7.45) 0.0013** (2.12) 0.0029** (2.38) 0.0004 (1.43) 0.0135*** (8.03) -0.0017** (-2.42) -0.0037*** (-8.36) -0.0011*** (-2.79) 0.0002 (0.72) -0.0017*** (-2.94) -0.0624*** (-3.74) Yes 206 0.884 81.835
OLS Estimation Technique. t -statistics (in parentheses) are based on White heteroscedasticity-consistent std. errors. Statistical significance: *p
