Purpose Economic hardship and crime is always a debatable issue in the political economy literature. Some authors define poverty leads to crime some are completely opposite. The purpose of this paper is to find out the impact of poverty on crime in the USA. Design/methodology/approach Using time series data of USA over the period from 1965 to 2016, this study applies autoregressive distributed lag approach to identify the effect of poverty on crime. Findings The outcomes confirm a positive co-integrating relationship between poverty and property crime. It can be argued that poverty ultimately leads property crime in long run in the USA. However, unemployment and GDP exhibit neither long-run nor short-run relationship with property crime and they are not cointegrated for the calculated period. Research limitations/implications The subject of this paper helps to explain and analyze the nexus between poverty and crime in the USA. Practical implications Government and policymakers should focus more on poverty rather than unemployment alone to control property crime. Originality/value This study attempts to identify the consequences of economic hardship and poverty on the crime in the advanced economy like USA.

The objective of this study is to examine the impact of sectoral shift on the stock return of Bangladesh. This study employs auto-regressive distributive lag (ARDL) approach using the weekly data of various sectoral indices of Bangladesh over the period from May 1999 to September 2016. The findings tend to indicate that there has possible sectoral portfolio diversification in the market and ‘general product industry' is the most exogenous and profitable sector from the rest. This study is one of the first attempts of the sectoral analysis and its impact on the stock return with the reference to Bangladesh. Furthermore, this study can be a benchmark for the policymakers of emerging economies to find the impact of economic transformation in the stock returns of the equity markets.

This study aims to explore the impact of financial development, technological development on poverty for Asian and Non-Asian countries. In order to test the proposed model, five years' data from 80 countries over the year 2008 to 2014 have been collected. This study uses the Pooled OLS regression and found that poverty has a significant relationship with inflation and unemployment. However, technological advancement has found negatively and statistically significant with poverty. Furthermore, this study found that the Asian region has poverty reduction effect in comparing other regions when technological advancement speeds up.

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