The present study intends to scrutinize the asymmetrical influence of economic growth, industrial production, CPI (consumer price index) and oil price on the trade deficit for the People’s Republic of China’s economy. The Toda–Yamamoto causality, non-linear ARDL method, and quarterly data for 1995Q1 to 2021Q4 have been utilized to investigate the results. The estimated results confirm the uni-directional causality and presence of non-linear co-integration among variables under discussion. However, bound test analysis also reveals the long-run asymmetrical association among TD (trade deficit), IP (industrial production), oil price, and GDP growth, but not the CPI (consumer price index). Further, long-run asymmetrical outcomes highlight that a decrease (increase) in industrial production and an increase (decrease) in oil price and GDP growth rate increase (decrease) the trade deficit. Short-run asymmetrical outcomes reveal a similar trend to the long run, but the impact of all variables in the short run is insignificant, which means that linkages between the trade deficit and the explanatory variables are a long-run phenomenon in People’s Republic of China. Thus, in terms of policy, to reduce the trade deficit, it is necessary to focus on attaining standardized GDP growth, increasing industrial-sector production using advanced technology, and replacing oil-using energy sources with green technology (solar panels, wind farm energy).

The basic objective of the existing study was to inspect the triangular association between economic growth, poverty, and income disparity in farming and non-farming communities across agro-climatic zones in Punjab province, Pakistan. The cross-sectional Household Integrated Economic Survey (HIES) data and Poverty Equivalent Growth Rate (PEGR) methodology were applied from 2001–2002 to 2015–2016. Outcomes of the study found that in a short period, 2001–2002 to 2004–2005; 2004–2005 to 2005–2006; 2005–2006 to 2007–2008; 2007–2008 to 2010–2011; 2010–2011 to 2011–2012; 2011–2012 to 2013–2014; and 2013–2014 to 2015–2016, economic growth has presented hybrid (pro-poor and anti-poor) pattern across both communities of all agro-climatic zones of Punjab province in different periods. In the longer period of 2001–2002 to 2015–2016, economic growth has been pro-poor across both communities of all the zones apart from zone III (Cotton-Wheat Punjab); there is an anti-poor pattern of economic growth. Results for the decomposition of change in poverty further indicate that economic growth is a dominant factor in reducing poverty for all investigated zone. Moreover, a positive redistribution component reduces the beneficial impacts of economic growth for the poor more than for non-poor, that ultimately makes economic development patterns anti-poor in zone III. In the present study, we proposed two-fold policy implications. First, improve the living standard of households in each agro-climatic zone by increasing their incomes. Second, develop a precise taxation system that helps to reduce income disparities among upper-pro to lower-income groups.

Purpose: The purpose of this paper is to measure the unidimensional and multidimensional inequality in the case of Pakistan and compare their results at the provincial as well as regional (urban and rural areas) level. The authors collected data from Pakistan Social and Living Standard Measurement and Household Integrated Economic Survey for fiscal years of 1998–1999 and 2013–2014. Design/methodology/approach: The authors used Gini coefficient for unidimensional inequality and multidimensional indexing approach of Araar (2009) for multidimensional inequality. Findings: The findings predicted that unidimensional inequality is relatively high in the urban area due to uneven dissemination of income, but multidimensional inequality is quite high in rural areas because of higher disparities among all dimensions. At the provincial level, Punjab has relatively high-income inequality followed by Sindh, KPK and Baluchistan. Originality/value: This study is a pioneering effort to compare two time periods to explore unidimensional and multidimensional inequality in all provinces of Pakistan and their representative rural-urban regions by applying Araar and Duclos’s (2009) approach. Further, this study opens some new insights for policy makers.