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This study investigates the impact of monetary policies on stock markets based on a sample of five open countries with growing stock market over the period 2004 to 2014. Using a random effect model for the panel regression coupled with a panel vector error correction model to study the short term and long term relationship between the variables, the findings reveal a negative relation between interest rate and stock return and a direct link between money supply and stock return. The results confirm that both in the short run and long run monetary variables explain changes in stock return.

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Purpose This study aims to investigate the effect of immigration on housing prices in Australia both at the national and regional levels. Design/methodology/approach Data for eight Australian states on a quarterly basis from 2004–2017 is used. To study the possible dynamic and endogenous relationship between housing prices and immigration, a panel vector autoregressive error correction model (PVECM) is adopted. Findings Analysis of the results indicates that in the short run immigration positively and significantly affects housing prices, whereas in the long run no significant relationship was observed between the two variables. From the regional breakdown and analysis, it is discerned that in some states there is a significant and positive effect of immigration on residential real estate prices in the long run. Causality analysis confirms that the direction of causation is from immigration to housing prices. Practical implications The study illustrates that immigration and interstate migration, as well as high salaries, have been causing a rise in housing demand and subsequently housing prices. To monitor exceedingly high housing prices, local authorities should be controlling migration and salary levels. Originality/value Past research studies had highlighted the importance of native interstate migration in explaining the nexus between immigration – housing prices. In this study, it has been empirically verified how immigration has been affecting the locational decisions of natives and subsequently how this has been affecting housing prices.

Purpose This paper aims to examine the relationship between tourism development and income inequality, closely linked to the Sustainable Development Goals, for the case of a large sample of 83 countries (and subsamples) over the period 1990–2019. Design/methodology/approach This study uses rigorous dynamic panel data analysis, namely, a Panel Vector Autoregressive Error Correction model, which takes into account both dynamic and endogenous relationships in the tourism-inequality nexus. Findings The results provide strong support that tourism development has an income inequality reducing effect (albeit relatively small with a reported elasticity of 0.05). Subsamples analysis reveals that the impact of tourism on income inequality varies and is relatively larger in developing economies and those tourist-dependent economies, as compared to developed economies. In fact, it is reported that a 1% increase in tourism development reduces income inequality by 0.46% for developing and 0.56% for tourist-dependent economies as compared to only 0.02% in developed economies. It is further observed that tourism may affect income inequality indirectly via economic growth. Originality/value This paper attempts to supplement the dearth literature on the tourism-inequality nexus by analyzing subsamples from a large data set while also using a dynamic panel data framework. The potential indirect effect of tourism on inequality via the economic growth channel is also explored.