The present study is designed to investigate that how Economic Growth (EG) of the South Asian region has affected the Environmental Degradation (ED) from 1980 to 2018. This study has used Newey and West (1987) robust standard errors approach to overcome the problem of autocorrelation and heteroskedasticity in panel data. The results of the statistical model confirmed the existence of the Inverted U-shaped Environmental Kuznets Curve (EKC). Furthermore, the results also confirmed that the use of energy is also deteriorating the environment significantly. One significant contribution of the study is to check the causality between EG and CO2 by applying a relatively new approach namely Granger non-causality test presented by Dumitrescu and Hurlin (2012). The results confirmed that economic growth is contributing towards more CO2 emissions. The study concluded that South Asian countries should use environment-friendly renewable energy sources to achieve a higher growth rate.

The study gauges the impact of refugee influx on the labor market in Pakistan. The impact of Afghan refugees is analyzed using three indicators of labor market namely, unemployment, formal employment and informal employment from years 1979 to 2022. For the purpose of analysis, ADF and PP tests are applied first for examining unit root problem. Variables are found having mixed order of integration so ARDL bound test for co-integration is used to derive long run and short run relationship between labor market indicators and refugee inflow. It is found that Afghan refugees have caused positive impact on the labor market of Pakistan in long run while in the short run, it increases unemployment rate. Expenditure on Education, CPI, GDP, Trade Openness and Gross Fixed Capital are significantly reducing Unemployment rate of Pakistan.  Expenditure on Education and Gross Fixed Capital are found to have positive effect while CPI and population Growth are reducing Formal Employment. Hence, it is concluded that refugees can serve as stimulus for investment and production in the country.  

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It is a fact that public expenditure has a strong association with industrial productivity. The industrial sector recorded slow growth of 5.43%, which adds 20.90% to the GDP of Pakistan (2017-2018). This study aims to find the effects of public expenditure on Total Factor Productivity (TFP) in the industrial sector of the country. The study constructed two different models. In the first model, the study used time series data from 1975 to 2018, and the growth of adjusted TFP was calculated by the growth accounting method. In the second model, the study collected data from 1977 to 2018 and checked the impact of government expenditure on the TFP growth in the industry. The unit root tests, Ordinary Least Square (OLS), and Vector Error Correction Model (VECM) were employed. The findings of the study revealed that public expenditures on education were significant and positively related to TFP growth in industries. Public expenditure on health, agriculture, and inflation had a significant and positive association with TFP growth in the industries. Foreign direct investment had a negative but significant impact on TFP growth. The results of the present study suggest that industrial productivity can be increased by increasing the expenditure on education and health.

Purpose of the study: This study is conducted to assess the success of the Female Stipend Program (FSP), started in the province Punjab, Pakistan under the Punjab Education Sector Reform Program in 2003. Methodology: Panel data on household-level collected from years 2016 to 2018 is used for the analysis. The impact of cash transfers (directed towards female students in selected districts of the province) on female school enrollment in public (elementary and high) schools is measured. Enrollment growth in public schools is used as a dependent variable whereas female stipend, the number of schools, student to teacher ratio, the population of the districts, and basic facilities available in public schools are taken as independent variables. The results are obtained by employing Linear Mixed Multilevel Modeling. Main findings: All the variables, except the population of districts, are having a highly significant impact on the enrollment rate in Punjab. Female school's stipend, number of schools in the district, and the accessibility to basic infrastructural facilities have an important impact on female enrollment rate while a high student to teacher ratio negatively contributes to female enrollment rate. Furthermore, districts, where a stipend program is implemented, have higher enrollments as compared to other districts in the province. Application of the study: Outcomes of the study indicate that cash transfer programs directed towards female school enrollment are very fruitful in the case of Punjab. Therefore, such programs should be started in other provinces of the country as well. Novelty/ Originality of the study: The present study contributes to the research gap by using the largest data set available for all 36 districts of the province. To further highlight major factors contributing to high female school enrollments, the study includes school infrastructure, the population of districts, student-teacher ratio, and availability of schools in the model.