The causal effect of divorce and income inequality on crime: Evidence from Azerbaijan

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Type of the article: Research Article

Abstract
Crime remains a significant socio-economic issue, shaped by social instability and economic inequality, and poses critical challenges for public administration and policymaking. In Azerbaijan, rising divorce rates and persistent income disparities have become prominent social concerns, with the former reflecting shifts in family structure and the latter captured by the Gini index as a measure of income inequality. This study explores the causal relationships between crime, income inequality, and divorce rates in Azerbaijan from 2000 to 2021, utilizing econometric methodologies. From a public administration perspective, the study provides empirical insights to support more effective and targeted interventions in crime prevention, social protection, and family policy. Methodologically, the Johansen cointegration test is applied to identify long-term equilibrium relationships among the variables, while the Toda-Yamamoto Granger causality test is employed to examine the directional causality. The cointegration analysis reveals stable long-term associations between crime, income inequality, and divorce, with trace statistics (32.172, 16.067, and 4.052) exceeding their respective critical values at the 5% significance level. Additionally, the Toda-Yamamoto test shows that income inequality significantly influences crime (χ² = 5.145, p = 0.023), with divorce exhibiting a stronger predictive relationship with crime (χ² = 7.071, p = 0.007). These findings underscore the necessity for integrated crime prevention strategies, emphasizing the role of public administration in designing and implementing coherent socio-economic policies.

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    • Figure 1. Trends in crime rates, divorce rates, and Gini index in Azerbaijan (2000–2021)
    • Figure 2. Scatter plots of crime rates, divorce rates, and the Gini index
    • Figure 3. Inverse roots of AR characteristic polynomial
    • Table 1. Data description
    • Table 2. Unit root test
    • Table 3. Optimal lag order selection
    • Table 4. VAR serial correlation LM test
    • Table 5. VAR diagnostics
    • Table 6. Johansen cointegration test
    • Table 7. Granger causality test with the Toda-Yamamoto approach
    • Conceptualization
      Mayis Gulaliyev, Shahla Huseynova
    • Data curation
      Mayis Gulaliyev
    • Investigation
      Mayis Gulaliyev, Shahla Huseynova
    • Methodology
      Mayis Gulaliyev
    • Project administration
      Mayis Gulaliyev
    • Resources
      Mayis Gulaliyev, Gunay Hasanova, Reyhan Azizova, Elmira Gojaeva
    • Software
      Mayis Gulaliyev
    • Supervision
      Mayis Gulaliyev, Shahla Huseynova, Reyhan Azizova, Elmira Gojaeva
    • Writing – original draft
      Mayis Gulaliyev
    • Formal Analysis
      Shahla Huseynova, Gunay Hasanova, Reyhan Azizova, Elmira Gojaeva
    • Validation
      Shahla Huseynova, Gunay Hasanova
    • Writing – review & editing
      Shahla Huseynova, Gunay Hasanova, Reyhan Azizova, Elmira Gojaeva
    • Visualization
      Gunay Hasanova
    • Funding acquisition
      Reyhan Azizova, Elmira Gojaeva
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