پژوهش ها و چشم اندازهای اقتصادی

پژوهش ها و چشم اندازهای اقتصادی

بررسی رابطۀ بیکاری با جرم و جنایت در شهرستان‌های ایران: رهیافت اقتصادسنجی فضایی

نویسندگان
1 استادیار دانشکده اقتصاد و مدیریت، دانشگاه تبریز، تبریز، ایران
2 استاد اقتصاد، دانشکده اقتصاد و مدیریت، دانشگاه تبریز، تبریز، ایران
3 کارشناس ارشد اقتصاد، دانشکده اقتصاد و مدیریت، دانشگاه تبریز، تبریز، ایران
چکیده
جرم پدیـدهای چنـدوجهـی اسـت کـه همـواره مـورد توجه اقتصاددانان، جامعهشناسان، حقوقدانان و روان‌شناسان قرار گرفته است. از آنجایی‌که وقوع هر پدیده متأثر از عوامل مختلفی است، وقوع جرم نیز به ‏عنوان پدیدهای نامطلوب از این قاعده مستثنی نیست. عوامل مختلف اقتصادی، اجتماعی و سیاسی بر جرم و جنایت در جامعه تأثیر گذارند. در این میان یکی از مهم‌ترین عوامل اقتصادی مؤثر بر جرم، بیکاری است. در این راستا هدف اصلی مطالعۀ حاضر بررسی رابطۀ بیکاری با جرم و شناسایی عوامل مؤثر بر جرم و جنایت در شهرستان‏های ایران است. به این منظور با استفاده از داده‏ های 429 شهرستان ایران طی سال 1395 و با به‌کارگیری روش علیت فضایی رابطه بین دو متغیر بیکاری و شاخص جرم بررسی شد. نتایج حاصل از آزمون علیت فضایی بیانگر وجود رابطۀ علیت دوطرفه بین شاخص جرم و نرخ بیکاری است. به عبارت دیگر در دورۀ مورد بررسی جرم علت بیکاری بوده است و بیکاری نیز منجر به بروز و افزایش جرم شده است. در ادامه مدل جرم با لحاظ متغیر بیکاری و چندین متغیر کنترل با استفاده از رویکرد اقتصادسنجی فضایی مورد برآورد قرار گرفت. نتایج حاصل از برآورد مدل نشان‏ دهنده وجود اثرات سرریز فضایی است. به عبارت دیگر تغییر میزان جرم و جنایت در یک شهرستان دارای اثرات سرریز بر شهرستان‌های مجاور است. همچنین نتایج نشان ‏دهنده رابطه مثبت و معنادار متغیر بیکاری با جرم است. مطابق نتایج تحقیق، متغیرهای نرخ بیکاری، شاخص صنعتیشدن، نرخ شهرنشینی و نسبت طلاق به ازدواج مهم‌ترین متغیرهای مؤثر بر جرم و جنایت در شهرستانهای ایران هستند.
کلیدواژه‌ها

موضوعات


عنوان مقاله English

Unemployment and Crime in the Iranian Cities A Spatial Econometric Approach

نویسندگان English

Elham Nobahar 1
Seyed Kamal Sadeghi 2
Hadi kheirollahi zaki 3
1 Associate Professor of Economics, Faculty of Economics and Management, University of Tabriz, Tabriz, Iran
2 Professor of Economics, Faculty of Economics and Management, University of Tabriz, Tabriz, Iran
3 Master of Economics, Faculty of Economics and Management, University of Tabriz, Tabriz, Iran
چکیده English

Introduction

Crime is a multifaceted phenomenon that has always attracted the attention of economists, sociologists, lawyers and psychologists. Many experts and economic pioneers consider it necessary to achieve economic development to improve the level of security and reduce crime in the society. Since the occurrence of any phenomenon is affected by various factors, the occurrence of crime as an undesirable phenomenon is not excluded from this rule. Various economic, social and political factors affect crime in society. Identifying these factors can help a lot in the correct understanding and appropriate policy making in order to control and reduce the crime rate in the society. Meanwhile, one of the most important economic factors affecting crime is unemployment. Unemployment is one of the most important macroeconomic variables, which clearly affects many social phenomena, including crime. In this regard, the main goal of the present study is to investigate the relationship between unemployment and crime and to identify factors affecting crime in the Iranian cities. The statistical population of the current research is the cities of Iran and the time range under investigation is 2016.

Methodology

In this study, the spatial causality test was used to investigate the relationship between unemployment and crime. The first step in investigating the spatial causality relationship between the studied variables is to perform the spatial independence test of the variables. In the second step, the existence of spatial dependence between variables is examined. If both the investigated variables have a spatial structure and there is a spatial dependence between the two variables, then the spatial causality test is performed in the third step. In this study, the spatial econometric approach has also been used to estimate the crime model in the cities of Iran. In this regard, the presence of spatial effects in the model has been tested using Moran's I test, and then the most appropriate spatial regression model has been selected and estimated based on the Lagrange coefficient (LM) test and the LR diagnostic tests. The software packages used in this study are Matlab 2023, GeoDa 1.16 and Stata 15.

Findings

The results of the spatial tests show that both crime index and unemployment rate have a spatial structure and the spatial dependence between these two variables was also confirmed, so in the third step, the spatial causality has been tested. The results of spatial causality test indicate the existence of a two-way causality relationship between the crime index and the unemployment rate. In other words, unemployment was the cause of crime during the period under investigation, and unemployment also led to the occurrence and increase of crime. According to the results of the spatial causality test, the crime model of the Iranian cities was developed in terms of the unemployment variable and several control variables. In order to estimate the model, the presence of spatial effects was first investigated using Moran's I test. The results of this test indicate the presence of spatial effects in the model. Also, based on the results of the Lagrange coefficient and likelihood ratio tests, the Spatial Durbin Model (SDM) was chosen as the most appropriate method for estimating the model. The results of the estimation of the crime model indicate that the spatial lag coefficient is positive and significant at a high level, which indicates the existence of spatial dependence in the model. The positiveness of this coefficient shows that an increase in crime in one city causes an increase in crime in neighboring cities. Also, according to the results of the research, the variables of unemployment, industrialization, urbanization and divorce rate are the most important variables affecting crime rate. The results show positive and significant relationships between unemployment, urbanization, divorce rate, and crime. The industrialization variable also has a negative and significant effect on crime. Also, the spillover effect of the unemployment variable is negative and significant. Based on the results, the higher the unemployment rate and the urbanization rate in the cities, the crime rate will also increase in those cities. On the other hand, as cities move toward industrialization and the number of industrial enterprises in them increases, the rate of crime will decrease more.

Discussion and conclusion

The findings reveal that, unemployment is one of the most important variables affecting crime in the Iranian cities. So, it is recommended that authorities pay special attention to sustainable policies regarding employment and its proportional distribution in cities. Considering excessive growth of urbanization and its detrimental impact on rampant crime rates, it is suggested that statesmen and policy makers create more facilities and pay special attention to rural areas to provide reverse migration in order to prevent occurrence of various crimes, which are happenning due to population increase especially in informal settlements of larger cities.

کلیدواژه‌ها English

urban economics
Crime
Unemployment
Spatial Econometrics
the Iranian Cities
Abrishmi, Hamid., and Rezaei, Zakiye. (2014). Investigating the experimental effect of inflation on crimes in Iran. Majlis and Strategy, 22(83): 41-74. (in Persian).
Aliaga, J., Herrera, M., Leguía, D., Mur, J., Ruiz, M., & Villegas, H. (2011). Spatial causality. An application to the deforestation process in Bolivia. Investigaciones Regionales-Journal of Regional Research, (21), 183-198.
Ajide, F. M. (2021), Impact of economic condition on crime rate in Nigeria, The Journal of Developing Areas, 55(1).
Albu, C., Lobonţ, O., Moldovan, N., & Kuloglu, A. (2013). The criminal behaviour in Romanian socio-cultural contemporary context. Journal of Economic Computation and Economic Cybernetics Studies and Research, 47: 5–18
Armen, Seyed Aziz; Kafilli, Vahid; Farazmand, Hassan and Moltaft, Hossein (2016), economic factors affecting crime in Iran; Application of soft panel transfer, Economic Research Journal, 17 (66): 125-150. (in Persian).
Asgari, Ali and Nematullah Akbari (2008), Spatial Econometrics Methodology, Theory and Application, Isfahan University Quarterly, 1 and 2: 122-93. (in Persian)
Becker, G. S. (1968). Crime and punishment: An economic approach. Journal of political economy, 76(2), 169-217.
Bjerk, D. (2009). Thieves, thugs, and neighbourhood poverty. IZA Discussion Paper No. 4470.
Block, M. K., & Heineke, J. M. (1975). A labor theoretic analysis of the criminal choice. The American economic review, 65(3), 314-325.
Bonger, W. A. (1916). Criminality and economic conditions.‏
Brenner, M. H. (1976). Estimating the social costs of national economic policy: Implications for mental and physical health, and criminal aggression: A study prepared for the use of the joint economic committee, Congress of the United States (No. 5). US Government Printing Office.‏
Cantor, D., & Land, K. C. (1985). Unemployment and crime rates in the post-World War II United States: A theoretical and empirical analysis. American sociological review, 317-332.
Costantini, M., Meco, I., & Paradiso, A. (2018). Do inequality, unemployment and deterrence affect crime over the long run?. Regional Studies, 52(4), 558-571.
Choe, J. (2008). Income inequality and crime in the United States. Economics Letters, 101(1), 31-33.
Dadgar, Yadullah, Nazari, Ruhollah. (2012). Investigating the impact of the misery index on crime in Iran, biannual scientific journal of economic studies and policies, (24): 63-86. (in Persian)
Dadgar, Yadullah, Nazari, Ruhollah. (2015). Investigating crime in Iran using several economic models, Legal Research Quarterly, 19 (73): 5-78. (in Persian).
Dritsakis, N., & Gkanas, A. (2009), The effect of socio-economic determinants on crime rates: An empirical research in the case of Greece with cointegration analysis, International Journal of Economic Sciences and Applied Research, 2(2).
Ebrahimi, M., and Chakarzahi, A. (2014). The relationship between the crime rate and inflation and unemployment in Iran, Strategic Researches of Social Issues of Iran (Strategic Researches on Security and Social Order), 4(2 (consecutive 10)): 113-127. (in Persian)
Edmark, K. (2005). Unemployment and crime: Is there a connection?. Scandinavian Journal of Economics, 107(2), 353-373.
‏Ehrlich, I. (1973). Participation in illegitimate activities: A theoretical and empirical investigation. Journal of political Economy, 81(3), 521-565.
Elhorst, J. P. (2014), Spatial Econometric: from Cross-sectional Data to spatial Panels, SpringerBriefs, Heidelberg New York Dordrecht London: Springer.
Faaljo, Hamidreza; Molla Bahrami, Ahmed and Amiri, Hossein (2016), Non-linear study of various economic factors affecting the occurrence of crime in Iran, Assembly and Strategy, 24 (90): 101-124. (in Persian)
Farhamand, blossom; Safari, Babak and Mousavi, Vajiheh (2016), Spatial analysis of the impact of socio-economic factors on the occurrence of crimes in the provinces of Iran with an emphasis on immigration (2015-2016), Economic Research, 52 (118): 117-138. ( in Persian).
Fatás, A. & Summers, L. H. (2018), The permanent effects of fiscal consolidations, J. Int. Econ. 112.
Fleisher, B. M. (1963). The effect of unemployment on juvenile delinquency. Journal of Political Economy, 71(6), 543-555.‏
Fleisher, B. M. (1966), The effect of income on delinquency, The American Economic Review, 56(1/2): 118-137.
Florax, R. J., Folmer, H., & Rey, S. J. (2003). Specification searches in spatial econometrics: the relevance of Hendry’s methodology. Regional Science and Urban Economics, 33(5), 557-579.
Gudarzi Boroujerdi, Mohammadreza and Kamali, Mozhgan (2015), the effect of economic environment factors on preventing the occurrence or repetition of crime, Bushehr Police Science Quarterly, 9 (34), 43-63. (in Persian)
Hagigatt, Jafar, Akbar Mousavi, Seyyed Saleh. (2015). Advanced Applied Econometrics with JMulTi, Eview 10 and Stata 15.1 software, Tehran: Noor Alam Publications. (in Persian)
Han, L. (2010). Economic analyses of crime in England and Wales (Doctoral dissertation, University of Birmingham).‏
Heller, P. S. (2005), Understanding Fiscal Space. IMF Policy Discussion Paper
Kaldi, Alireza (1381). Crime diversion and prevention. Social Welfare Quarterly. 2 (3). 51-72. (in Persian)
Kassem, M., Ali, A., & Audi, M. (2019). Unemployment rate, population density and crime rate in Punjab (Pakistan): an empirical analysis. Bulletin of Business and Economics (BBE), 8(2): 92-104.
Kose, M. A., Kurlat, S., Ohnsorge, F. & Sugawara, N.(2018), A Cross-Country Database of Fiscal Space. SSRN Electron. J. doi:10.2139/ssrn.3013451.
Lee, L. F., & Yub, J. (2010). Estimation of spatial autoregressive panel data models with fixed effects, Journal of Econometrics. Journal of Econometrics, 154, 165.‏
LeSage, J., & Pace, R. K. (2009). Introduction to spatial econometrics. Chapman and Hall/CRC.
Lobonţ, O. R., Nicolescu, A. C., Moldovan, N. C., & Kuloğlu, A. (2017), The effect of socioeconomic factors on crime rates in Romania: a macro-level analysis, Economic research-Ekonomska istraživanja, 30(1): 91-111.
Madah, Majid. (2018), Analysis of the effect of poverty and income inequality on crime (theft) at the level of the country's provinces, Economic Research Journal, Year 11, Number 3: 303-324. (in Persian)
Madah, Majid (2018), study and analysis of the relationship between income inequality and crime rates in Iran, economic studies and policies, 7 (17): 75-90. (in Persian).
Mehrara, Mohsen, Mohammadian Nik-Pi, Ehsan. (2014). Economic investigation of crime and its interprovincial spillover effects in Iran: a spatial panel approach. Economic Modeling Scientific Quarterly, (29)9: 62-43. (in Persian)
Mehrgan, Nader and Fakher Saeed Gershasabi (2010), Income Inequality and Crime in Iran, Economic Research, 11(4): 109-125. (in Persian)
Mehrotra, N. R. et al. (2017), Debt Sustainability in a Low Interest Rate World. Hutchins Cent. Fisc. Monet. Policy
Memarzadeh, Ataale and Naqeibi, Mohammad (2018), Investigating the effect of violent crime on the accumulation of human capital in Iran's economy. Intelligence and Criminal Research Quarterly, 14: 71-92. (in Persian).
Merton, R. (1968), Social theory and social structure (2nd ed.), New York: The Free Press.
‏ Mur, J., Herrera, M., & Ruiz, M. (2011). Selecting the W Matrix. Parametric vs Nonparametric Approaches.‏
Mittal, M., Goyal, L. M., Sethi, J. K., & Hemanth, D. J. (2019), Monitoring the impact of economic crisis on crime in India using machine learning, Computational Economics, 53(4): 1467-1485.
Mohammadi Asl, Abbas. (1385). Juvenile delinquency and theories of social deviance. Tehran: Science. (in Persian)
Naqdi, Yazdan.; Kaghdzian, Soheila. and Lashkarizadeh, Mina. (2019), comparing the effects of inflation and unemployment on social security in Iran. Police Order and Security Research Journal, 13(3): 1-26. ( in Persian)
Noghani Dekht Bahmani, Mohsen and Mir Mohammad Tabar Seyed Ahmad (2014), Investigating economic factors affecting crime (a meta-analysis of research done in Iran), Strategic Researches on Security and Social Order of Iran, 4 (11): 85-102. (in Persian)
‏Nunley, J. M., Seals, R. A., & Zietz, J. (2011). The impact of macroeconomic conditions on property crime. Auburn Univ., Department of Economics.
Pan, M., Widner, B., & Enomoto, C. E. (2012). Growth and crime in contiguous states of Mexico. Review of Urban & Regional Development Studies, 24(1‐2), 51-64.
Pearl, J. (2009). Causal inference in statistics: An overview. Stat. Surv. 3, 96–146.
Phillips, M. B. (1991). A hedgehog proposal. Crime & Delinquency, 37(4), 555-574.‏
Porzecanski, A. C. (2018), Debunking the Relevance of the Debt-to-GDP Ratio. SSRN Electron. J. doi:10.2139/ssrn.3143244.
Raphael, S., & Sills M. (2006). Urban crime, race and the criminal justice system in the United States (Chapter thirty: 515–535).
Rennó Santos, M., Testa, A. & Weiss, D. B. (2021), Inflation and Cross-National Homicide: Assessing Nonlinear and Moderation Effects Across 65 Countries, 1965–2015. Int. Crim. Justice Rev. 31, 14.
Robert J. Barro & José F. (2008), Ursúa. Macroeconomic Crises since 1870, Brookings Pap. Econ. Act.
Rosenfeld, R., & Levin, A. (2016). Acquisitive crime and inflation in the United States: 1960–2012. Journal of quantitative criminology, 32, 427-447.‏.‏
Rozbeh, Reza (2017), Investigating the commission of a crime in the criminal system. Law-Yar magazine, 2(7): 97-110. (in Persian).
Sadeghi Hossein et al. (2004), analysis of economic factors affecting Berjaram in Iran, Economic Research, 68: 63-90. (in Persian)
Sadeghi, Hossein; Asgharpour, Hossein and Shaghaghi, Vahid (2004). Analysis of economic factors affecting crime in Iran, Economic Research Quarterly, (68): pp. 63-90. (in Persian)
Sadeghi, Hossein; Shaghaghi Shahri, Vahid and Asgharpour, Hossein (2004), Analysis of Economic Factors Affecting Crime in Iran, Economic Research, 68: 63-90. (in Persian).
‏Sjoquist, D. L. (1973). Property crime and economic behavior: Some empirical results. The american economic review, 63(3), 439-446.
Tang, C. F., & Lean, H. H. (2007). Will inflation increase crime rate? New evidence from bounds and modified Wald tests. Global Crime, 8(4), 311-323.
Teles, V. K. (2004). The effects of macroeconomic policies on crime. Economics Bulletin, 11(1), 1-9.‏
Torruam, J. T., & Abur, C. (2014). The relationship between unemployment, inflation and crime: An application of cointegration and causality analysis in Nigeria. Journal of Economics and Sustainable Development, 5(4): 131-137
Tsushima, Masahiro. (2000). Economic structure and crime: the case of Japan. The Journal of Socio-Economics, 25(4), 497-515.‏
Vega, S. H., & Elhorst, J. P. (2013). On spatial econometric models, spillover effects, and W. In 53rd ERSA Congress, Palermo, Italy,1-28.
Vieraitis, L. M., Kovandzic, T. V., & Marvell, T. B. (2007). The criminogenic effects of imprisonment: Evidence from state panel data, 1974–2002. Criminology & Public Policy, 6(3), 589-622.
Wu, D., & Wu, Z. (2012). Crime, inequality and unemployment in England and Wales. Applied economics, 44(29), 3765-3775.