Maryam Masnoon’s research while affiliated with Bahria University Karachi Campus and other places

Given contrasting evidence in the literature pertaining to the impact of Foreign Direct Investment on the host country’s economy, we take the case of Pakistan and test the said association for this nation. The data used for this study has spanned over the period of 1981 till 2010. Besides FDI, four other variables including Debt, Trade, Inflation and Domestic Investment have been included in the study, to regress upon GDP of this country. The methodology to test the impact of these variables on Pakistan’s economy has been limited to the least squares method. The co-integration of the variables has been ascertained through application of Augmented Dickey Fuller Test and is found to hold in the long run. Our findings indicate that Pakistan’s economic performance is negatively affected by foreign investment while its domestic investment has benefitted its economy. Moreover, the nation’s debt, trade and inflation have found to have negative impact on its GDP.

Capital structure of KSE listed pharmaceutical companies have been studied by taking leverage as the dependant variable. Regression is run by considering six explanatory variables that include firm's size, tangibility, growth, earnings volatility, profitability and tax rate. Further, this model is reduced to only four explanatory variables by removing tax rate and earning volatility to deal with the problem of multicollinearity. The proxies used in this study are total debt ratio for leverage, gross fixed assets for tangibility, effective tax rate for tax, natural log of sales for size, percentage in net profit margin for earnings volatility, sustainable growth rate for growth and operating profit margin for profitability. The time horizon selected for the study is from 2008-2011. It is found that growth and tangibility have positive relation while profitability and size have a negative relation with leverage. All 4 variables are found statistically significant at a significance level of 10%. VIF also comes out to be low, which indicates low multicollinearity in the model. The model explains 50% of the variation in leverage is significant at 1% significance level.