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The upsurge in inequality among citizens will most likely result in serious repercussions, which can stop economic growth in a country. Unequal economic distribution can result to critical and unwanted consequences, such as the upsurge in crime, conflict, corruption, moral decadence and social unrest. Such social challenges can hamper the country’s development, harmony, and social stability. The rise in crime rates is one of the unwanted consequences of the rising inequality in the region.

Understanding how inequality contributes to the increase in the crime rate is fundamental. Economic and social efficiency cost of the rise in poverty and inequality that transcends through high criminality can be significantly large. Costs that come up when dealing with the crime such as costs incurred through the legal system, courts, and prisons, policing incurrences, private security and healthcare can be very large. It is important to note that not only can rise in inequality and crime can increase costs to the legal system and the society at large but contribute to serious economic problems. (How? Evidence?)

Various studies explore the relationship between economic inequality and rise in crime rate. Zhu and Li found that the disgruntled citizens with unequal opportunities are likely to consider themselves deserted hence easily engage in criminal activities to earn a living. Moreover, Songman Kang argues, in his article, that concentration of poverty in a country is a function of inequality in the economy, which contributes significantly to the always rising criminal activities in every nation. This study explores inequality and crime rate using China as an example.

Literature Review

An expansive pool of literature across various disciplines inclusive of economics, criminology, and sociology explore the relationship between inequality and crime. Various theories assume varying perspectives that depict the relationship between crime and inequality in economies. Such a theory is anomie, which proposes that the collapse in values and social norms can potentially lead to the rise in crime rates. Additionally, the strain theory postulates that crimes can emerge where there is a deficiency in lawful means to acquire shared social goals for the disadvantaged majority in the society. (give citations for the anomie and strain theories)

Negative perceptions of improvement in the economy via lawful activities could also result into the decay in the moral dilemma related to lawbreaking. As reported by Zhu and Li, China has a magnanimous level of economic inequality. The authors find out that income inequality in the country comes up with the prevailing unequal distribution of opportunities. They further argue that providing equal opportunities to all citizens can help in policy planning and that it is important for developing countries to encourage inclusive growth as a pertinent ingredient in the building of a harmonious and peaceful society. The authors built a cost-benefit model using series data from the year 1981 to 1999, hence investigating a U-shaped relationship between crime rates and income inequality to further explore this issue.

Hypothesis and Model

According to Zhu and Li, the hypothesis is that inequality contributes to high crime rates in every society. The gap in economy between the underprivileged and the privileged expounds on why many criminals make use of illegitimate methods to gain wealth to achieve elevated social status and riches. Unequal distribution of resources would further contribute to increased criminal activities that augment tension between the varying classes of people, resulting in aggression, robbery, political instability, juvenile delinquency, unemployment, assassinations, and massacres. Apparently, the disappointed people will usually feel rejected, neglected, humiliated and lopsided, which pushes them to participate in criminal activities to survive the hard-economic times. Inequality among citizens in any economy is a great determinant of criminal activities. There is a close correlation between economic inequality and violent crimes due to economic segregation in the society.

Method

To investigate the relationship between crime and inequality, data is gathered from government statistics reporting crime rates from different regions. Further, poverty indexes of every region, and GDP and unemployment rate which also contribute to the crime rate will be used to compare and evaluate the sets of data collected for a conclusion on the topic. To be specific, China has been used as the country of interest to confirm the relationship between inequality and the prevalent crime rate. Because the country has an inequality rate, through Gini Index, this is considered high in terms of inequality standards as shown in the graph below over 40 in average.

Sources: Economic Policy Reforms 2017: Going for Growth - © OECD 2017

The Gini index measures the extent to which the distribution of disposable income among households deviates from perfect equal distribution. A value of zero represents perfect equality and a value of 100 extremely inequality.

Gini was highest in 2007 – 2009 during the Great depression. It was above 49 during his period. Although it is declining, it is above 45 which is still very high. The comparison between Gini and the average crime rate for China since 2003 – to – 2016 is as shown of the graph below.

Source: http://data.stats.gov.cn/english/easyquery.htm?cn=C01

From the above graph, it is evident that the approved arrests on various crime committed on average are very high are compared to the Gini index (The crime rate and the Gini index cannot be compared with each other). In 2016, Gini was almost 46 whereas the average crime rate was around 130,000 per unit. Therefore, when the Gini decrease, crime rates increase and vice versa.

Looking at the behavior of the average crime rates and the GDP per capita, average crime rates slightly decreases when GDP per capital rises though not significant as shown on the figure below. When GDP per capita was at it lowest level in 2007 during the Great Depression, crime rates was significantly high though it ranges between 120,000 to 130,000 since 2006 to date.

The average crime rates tend to follow a normal (How is it normal? What would be abnormal?) trend with the rate of unemployment in urban areas. This is indicated on the trend graph below.

The disposable income among the major towns in China is higher as compared to the rural setting. In urban area, disposal income per capita is almost the same for all the regions as shown in the graph. On the other hand, disposal income is completely different in rural settings from both regions. Northern region is a more than 70% of the disposal income in rural area whereas western region shows a disposable income that ranges between 60 and 50%. This clearly depicts how income inequality is evidenced in China’s major regions.

Source: http://www.oecd-ilibrary.org

On the other hand, the relevant economic theory for this study is the neoclassical theory of demand because I think committing a crime is a kind of way to gain profit but which is wrong and distort, especially the criminal activity related to money. Major factors to consider include profit maximization, the supply of money, and contrasting demands among others. Also, following various methods proposed by other researchers, the model that I shall use in the study is given by the formula below. (This paragraph is confused. It hardly seems to discuss the neoclassical theory of demand. You need to clarify this and explain the economics you want to include correctly).

Ycrime (it)=βx Xit +Vi + Vt + ϵit. This is not the model you estimate on the next page

Having proposed the above formula, YCRIMEit represents the crime rate at time t for county i. βx represents the k x 1 vector for the Xit coefficients. Vi represents the fixed effect for county i, Vt represents the time and ϵit represents the independent errors.

Numerous studies have investigated the determinants of crime rates by making use of regression analysis. Nevertheless, the study of what factors determine crime rates is blinded by various econometric challenges such as heteroskedasticity, and endogeneity. Regarding endogeneity, including deterrence variable within the research model may result in endogeneity. It can be postulated that increased expenditure on deterrence can result in a decrease in the crime rate. Though, increased crime can result in increased expenditure. Moreover, decreased criminality can result in a decrease in inequality. The Chinese government has kick-started some campaigns to fight crime. Such campaigns have been launched in the nation, and this could result in a heave in crime rates (Zilian, and Jiangli).

The regression analysis of the average crime rates vs. Gini index and unemployment rates is as shown below:

image7.emf

_cons 134643.2 185296.8 0.73 0.483 -273192.4 542478.8

UnemploymentrateinUrbanareas -34162.67 37299.92 -0.92 0.379 -116259.2 47933.9

GiniCoefficient 3068.626 3722.885 0.82 0.427 -5125.388 11262.64

AverageCrimeRate Coef. Std. Err. t P>|t| [95% Conf. Interval]

Total 1.6077e+09 13 123665560 Root MSE = 11527

Adj R-squared = -0.0744

Residual 1.4615e+09 11 132865833 R-squared = 0.0909

Model 146128112 2 73064056.2 Prob > F = 0.5921

F(2, 11) = 0.55

Source SS df MS Number of obs = 14

. regress AverageCrimeRate GiniCoefficient UnemploymentrateinUrbanareas

Considering the regression output above, Gini coefficient and the unemployment rate has a significance influence on the crime rates in China. The P – values are above 0.05 hence the null hypothesis that inequality contributes to crime rates is thus ascertained.

Data

From OECD library, the Gini Index data on the inequality in the income distribution in China from 2003-2016 reveals a shocking trend. Using the Gini Index, the China's Gini Coefficient in 2016 was 46.5 (0.465) points, which confirms significant economic and social inequality in the country as alleged in the hypothesis.

Year	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016
Gini Index	48.7	48.4	49.1	49	48.1	47.7	47.4	47.3	46.9	46.2	46.5

Furthermore, Statistica puts the number of crime in China to be at 6.43 million annually in average. Though the data is not present for all the years the set of data is unbalanced. It is noteworthy that various studies have affirmed that the official statistics of crime in the country are based on them. Publications by the government are indispensable when studying crime rates, though one should take caution when interpreting results. (Are these observations applicable to YOUR regression?)

In this study, there are many variables, such as education, inequality and employment while variables also include the incurrences for the judicial agency, court, security and so on. The focus of the study is chiefly on inequality, and these variables are important in the estimation. Inequality variables include the regional inequality in each county and the disparity between the urban and rural areas, which is determined as the consumption ratio in the urban regions to those in the rural regions.

Discussion

China and other developing countries fall into the category of transitional economies, and major changes have occurred in the economic systems after the adoption of open up policy in the economy. The progression from a central economic system to a free market system breeds numerous socio-economic challenges, and one of the daunting issues is the significant augmentation in crime rates. The cost of crime is very high. Additionally, the rise in criminal activities is sabotaging to growth in the economy and may bring about disarray among the populace which may breed political upheaval and social instability.

From the results of the study, regional inequality is correlated with crime rate positively, while education is correlated negatively with the crime rate. Various policy implications may be taken from this study. Foremost, the government ought to avail additional resources to combat regional inequality and urban-rural inequality. The government should provide education to the poor and ensure equal access to higher education for the disadvantaged majority. Eventually, the government ought to avail more employment opportunities in both urban and rural areas.

Bibliography

Barr, Caelainn. “Inequality index: where are the world's most unequal countries?” The Guardian. 26 Apr 2017. https://www.theguardian.com/inequality/datablog/2017/apr/26/inequality-index-where-are-the-worlds-most-unequal-countries

Kang, S. (2016). Inequality and crime revisited: Effects of local inequality and economic segregation on crime. Journal of Population Economics, 29(2), 593-626.

OECD (2017), OECD Economic Surveys: China 2017, OECD Publishing, Paris. http://dx.doi.org/10.1787/eco_surveys-chn-2017-en

Sachsida, A., Mendonça, M., Loureiro, J., & Gutierrez, C. (2010). Inequality and criminality revisited: Further evidence from Brazil. Empirical Economics, 39(1), 93-109.

Statistica “Crime and Penitentiary System in China - Statistics & Facts.” Satistica. https://www.statista.com/topics/2253/crime-and-penitentiary-system-in-china/.

The Urban-Rural Divide. “The increasing gap between the developing and the undeveloped creates a disconnect between the people of China” 2017. https://www.mtholyoke.edu/~koyam20m/Urbanruraldivide.html

Zhu, J., & Li, Z. (2017). Inequality and Crime in China. Frontiers of Economics in China, 12(2), 309.

�Caelainn Barr. “Inequality index: where are the world's most unequal countries?” The Guardian. 26 Apr 2017. https://www.theguardian.com/inequality/datablog/2017/apr/26/inequality-index-where-are-the-worlds-most-unequal-countries

�Sachsida, A., Mendonça, M., Loureiro, J., & Gutierrez, C. (2010). Inequality and criminality revisited: Further evidence from Brazil. Empirical Economics, 39(1), 93-109. https://link-springer-com.libproxy.temple.edu/content/pdf/10.1007%2Fs00181-009-0296-4.pdf

� Zhu, J., & Li, Z. (2017). Inequality and Crime in China. Frontiers of Economics in China, 12(2), 309. http://go.galegroup.com.libproxy.temple.edu/ps/i.do?&id=GALE|A524866915&v=2.1&u=temple_main&it=r&p=AONE&sw=w&authCount=1

� Songman Kang. "Inequality and Crime Revisited: Effects of Local Inequality and Economic Segregation on Crime." Journal of Population Economics 29, no. 2 (04, 2016): 593-626.

� Sachsida, A., Mendonça, M., Loureiro, J., & Gutierrez, C. (2010). Inequality and criminality revisited: Further evidence from Brazil. Empirical Economics, 39(1), 93-109. https://link-springer-com.libproxy.temple.edu/content/pdf/10.1007%2Fs00181-009-0296-4.pdf

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� Zhu, J., & Li, Z. (2017). Inequality and Crime in China. Frontiers of Economics in China, 12(2), 309.

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� Kang, S. (2016). Inequality and crime revisited: Effects of local inequality and economic segregation on crime. Journal of Population Economics, 29(2), 593-626. https://link-springer-com.libproxy.temple.edu/article/10.1007/s00148-015-0579-3

�Zhu, J., & Li, Z. (2017). Inequality and Crime in China. Frontiers of Economics in China, 12(2), 309.

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