AbstractThis study examines how remittances impact economic growth, especially in labour-exporting countries, and what the possible outcomes of this inflow are in terms of poverty and income distribution. Further, this study displays the comparative analysis of the included economies (Pakistan, India, China, Bangladesh, and Indonesia). For this study, time series data from 2000 to 2024 are used for the selected variables such as remittances, GDP, inflation, HDI, and labour force participation. As per the data nature, Panel data is a perk to use in terms of capturing the outcomes of two data types at one station. At the very first step, this study designed the basic regression model to identify the dependent and independent variables. The next step is to identify the test type for the selected panel, whether to use a fixed effect or random effect model. As per data type, there are approximately similar economies, or maybe the Indonesian economy has slight differences in terms of economic regularities, so it is important to check if there is homogeneity or heterogeneity in the model. To identify the correct model between the Fixed Effects (FE) and Random Effects (RE) estimators within our panel data analysis, we use the Hausman specification test. The initial step is testing for the panel variables’ stationarity. Non-stationarity may result in spurious regressions, false statistical inferences, and unreliable hypothesis testing. Panel unit root tests are employed to test the stationarity of all the variables that have been incorporated in the model, and after checking the results, the ARDL technique is used to analyse the results. Results show that a 1% increase in remittances results in a long-run change of about 0.0308% in per capita GDP. This establishes a strong positive long-run link between economic growth and remittance inflows.
Hussain et al. (Wed,) studied this question.