Türkiye’de dolandırıcılık suçunu etkileyen sosyo-ekonomik faktörlerin panel ARDL analizi İle incelenmesi

Öz

Bu çalışmada, 2011-2020 yılları arasında Türkiye'deki dolandırıcılık suçlarının sosyo-ekonomik faktörlerle ilişkisi incelenmektedir. Çalışma, Türkiye İBBS-2 İstatistiki Bölge Birimleri Sınıflamasına göre, işsizlik, enflasyon, kişi başına düşen milli gelir, alınan göç, altyapı yatırımları ve nüfus yoğunluğu gibi değişkenlerin dolandırıcılık suç oranlarına etkilerini analiz etmektedir. Analiz yöntemi olarak Panel ARDL analizi kullanılmıştır, çünkü bu yöntem farklı düzeylerde durağanlığa sahip değişkenler arasındaki uzun dönemli ilişkileri incelemeye olanak sağlamaktadır.. Bulgulara göre, PMG ARDL uzun dönem analiz sonuçları literatüre daha uygundur. Literatürün aksine, işsiz sayısındaki artışın dolandırıcılık suçunu azaltıcı bir etkiye sahip olduğu tespit edilmiştir. Çalışmanın bulguları, işsizlik oranlarındaki artışın dolandırıcılığı artırmadığını, aksine belirli ekonomik koşullarda bireyleri daha fazla yasal ve güvenli gelir yollarına yönlendirdiğini ortaya koymaktadır. Bununla birlikte, enflasyondaki artışlar, altyapı yatırım miktarı, alınan göç ve nüfus yoğunluğu dolandırıcılık suçunu artırmaktadır. Özellikle, enflasyonun bireylerin satın alma gücünü olumsuz yönde etkileyerek, ekonomik zorlukları artırması; göç, kentleşme ve altyapı yatırımlarındaki artışların ise suç oranlarını yükselten başlıca faktörler olarak ortaya çıkması dikkat çekmektedir. Ayrıca, okuma yazma bilmeyenlerin sayısının artması nitekli suç olan dolandırıcılık suçunu azaltıcı bir etkiye sahiptir. Bu bulgular, sosyo-ekonomik faktörlerin dolandırıcılık suçuyla olan ilişkisini daha iyi anlamamıza katkı sağlamaktadır.

Anahtar Kelimeler

• Enflasyon
• Göç
• PANEL ARDL
• İşsiz Sayısı

Investigation of socio-economic factors affecting fraud crime in Türkiye by panel ARDL analysis

Abstract

This study analyses the relationship between socio-economic factors and fraud offences in Turkiye between 2011 and 2020. The study analyses the effects of variables such as unemployment, inflation, national income per capita, migration, infrastructure investments and population density on fraud crime rates according to Türkiye NUTS-2 Classification of Statistical Regional Units. Panel ARDL analysis is used as the analysis method because this method allows to examine the long-run relationships between variables with different levels of stationarity. According to the findings, PMG ARDL long-run analysis results are more in line with the literature. Contrary to the literature, an increase in the number of unemployed is found to have a decreasing effect on fraud offences. The findings of the study reveal that the increase in unemployment rates does not increase fraud, on the contrary, it directs individuals to more legal and secure income paths under certain economic conditions. However, increases in inflation, the amount of infrastructure investment, migration and population density increase fraud offences. In particular, it is noteworthy that inflation increases economic difficulties by negatively affecting the purchasing power of individuals, while increases in migration, urbanisation and infrastructure investments are the main factors that increase crime rates. Moreover, the increase in the number of illiterate people has a decreasing effect on the crime of fraud, which is a qualitative crime. These findings contribute to a better understanding of the relationship between socio-economic factors and fraud offences.

Keywords

• Inflation
• Migration
• PANEL ARDL
• Number of Unemployed

Kaynakça

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42. Gillani, S., Khan, R. E. A. & Rashid Gill, A. (2011). Unemployment and property crimes in Pakistan. Asian Economic and Financial Review, 1(3), 124-133.
43. Gordon, David M. (1971). Problems in Political Economy: An Urban Perspective. Lexington, MA: Heath.
44. Metz, N., & Burdina, M. (2018). Neighbourhood income inequality and property crime. Urban Studies, 55(1), 133-150. https://doi.org/10.1177/0042098016643914
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47. Orrenius, P., & Zavodny, M. (2015). Undocumented immigration and human trafficking. Handbook of the economics of international migration, 1, 659-716. https://doi.org/10.1016/B978-0-444-53764-5.00013-X
48. Pare, P. P. P. (2006). Income inequality and crime across nations reexamined.
49. Pesaran, M. H. (2006). A simple panel unit root test in the presence of cross section dependence (pp. 1–64). Cambridge University & usc. https://doi.org/10.1002/jae.951
50. Pesaran, M. H., Ullah, A. & Yamagata, T. (2008). A Bias-Adjusted LM Test of Error Cross-Section Independence. Econometrics Journal, 11(1), 105-127. https://doi.org/10.1111/j.1368-423X.2007.00227.x
51. Shahid, M., Ahmad, K., Inayat, M. A., & Bhatti, M. K. (2024). Socio-Economic Determinants of Property Crime Across the Districts of Punjab: Highlighting the Role of Law Enforcement Agencies of Pakistan. Bulletin of Business and Economics (BBE), 13(2), 22-36. https://doi.org/10.61506/01.00289
52. Sirohi, N., & Misra, G. (2024). Vulnerability of individuals to economic crime and the role of financial literacy in its prevention: Evidence from India. Crime, Law and Social Change, 1-32. https://doi.org/10.1007/s10611-024-10138-w
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