This study aims to investigate the dynamic impact of socio-economic issues such as youth unemployment and income inequality on crime by comparing the findings from newly democratized African countries (South Africa and Namibia) to those of recently democratized European countries (Lithuania and the Ukraine) and newly democratized Asian countries (Kyrgyzstan) from 1994 to 2019. The study used the Bayesian vector autoregression approach with hierarchical priors. This model was chosen due to its ability to address the dynamics of several entities, such as the measurable defects where data quality is uncertain, or perhaps lacking as frequently happens. Prior selection in a Bayesian vector autoregression approach can help adjust for these flaws. According to the study's findings, an unexpected shock in unemployment and income inequality has a significant positive impact on crime; however, an unanticipated shock in government expenditure has a significant negative impact. The results are interesting, as we found that the impact of these socio-economic issues does not necessarily depend on the geographical area, as the results are similar for income inequality in both regions. This study serves as a wake-up call to the government that some policies (fiscal policy or income policy) are more appropriate for resolving concerns about inequality and unemployment than others. As a result, addressing these issues through policies that promote greater economic equality and ensure everyone has access to basic education, healthcare, and job training, could assist in reducing crime while promoting a more equal society.

The present reality about the Nigerian economy calls for investment and development in the non-oil sector. This becomes necessary as a result of fall in the oil price in the global market. This paper examined the Bayesian Vector Autoregression (BVAR) modeling and forecasting of the dynamic interrelationship between Economic growth and revenue from the oil and non-oil sectors in Nigeria. To achieve this, annual data on Gross Domestic Product (GDP), revenue from oil and non-oil sectors were collected from Central Bank of Nigeria (CBN) bulletin, the sample from 1981 to 2008 was used for analysis, while sample from 2009 to 2014 was used for model validation. Six (6) versions of Sims-Zha BVAR models were compared for out-of-sample forecast, the result revealed the superiority of the BVAR6 model over the other BVAR models. Lastly, evidence from the decomposition forecast errors revealed that revenue of oil sector contributed 7.69% to GDP while revenue from non-oil sector contributed 0.12% to GDP in Nigeria. This paper therefore recommended that the present government should encourage investment that is geared toward development in the non-oil sector, of which it has the capacity to improve the Economic growth of the Nigerian economy.