This study investigates the impact of democracy indices on the literacy rate. Panel Data of 134 Countries from 2007-2018 were collected from the website the World Bank and Gapminder. This study uses Ordinary Least Square (OLS), Pooled Ordinary Least Square (POLS), Driscoll-Kraay (DK), Second Stage Least Square (2SLS), Generalized Methods of Moments (GMM) methods. This research has found that political participation index and political culture index has a significant positive relationship with literacy rate in all the method. The functioning of the government index has a significant positive relationship and electoral process and the pluralism index has a significant negative relationship with literacy rate in all the methods except the GMM method. The civil liberties index has a significant negative relationship with literacy rate in POLS and in the other models, there is no significant relationship between the civil liberties index and literacy rate.

This study investigates the relationship of foreign direct investment (FDI) with major macroeconomic variables, explicitly gross domestic product (GDP), gross capital formation (GCF), agriculture, forestry and fishing (AFF), industry, import, export, inflation, and unemployment rate. Panel data from 205 countries from 1990 to 2018 were collected from the website of the World Bank. Robustness of the result has been ensured through the combined use of ordinary least squares (OLS), pooled ordinary least squares (POLS), Driscoll-Kraay (DK), two-stage least squares (2SLS) and generalized method of moments (GMM) models. This research has found that GDP and GCF had a significant positive relationship with FDI across all the models, while AFF and the unemployment rate had a significant negative and positive relationship with FDI in all models except the GMM model. Industry and import had a significant positive relationship with FDI in the POLS model, and export and inflation had no significant relationship with FDI in any model. Contribution/Originality:This study contributes to the existing literature through a utilitarian way to investigate the relationship of FDI with major macroeconomic variables globally. INTRODUCTIONThis paper analyzed the relationship between foreign direct investment (FDI) and macroeconomic variables including gross domestic product, gross capital formation, value addition of agriculture, forestry and fishing, value addition of industry including constructions, import of goods and services, export of goods and services, consumer price indices and unemployment rate. It is thought that there might be a link between foreign direct investments and these variables as all of these indicators, directly and indirectly, influence the gross domestic product of a country.Shiva and Agapi (2008) said foreign direct investment and trade are also seen as significant catalysts for developed countries' economic development. They introduced FDI as an important tool for the transition of technology from developed to emerging countries. Kueh, Puah, and Abu Mansor (2009) claimed that Malaysia's Asian Economic and Financial Review

The undeniable significance of production has prompted experts to explore further the competitive productivity of various nations across the globe. Despite the importance of global productivity competitiveness, prior studies have not included a comprehensive assessment of the multidimensional productivity index (MPI). Therefore, this study aims to achieve two objectives. First, it extends the scope of prior studies by integrating capital as an input alongside labor and energy consumption, based on 50 factors under 11 indices (including democracy, global competitiveness, and innovation index). Second, global competitive productivity convergence is reaffirmed and expanded. This study employed secondary panel data from 2007 to 2018, and 60,000 data points were obtained from 100 nations. The results reveal that the USA is the most productive country, followed by China, India, and Japan in the context of global competitive productivity. Regional productivity scores show that Asia has a superior productivity rank compared to Europe. However, Africa is performing worse than average. Unlike earlier studies, this study shows that macroeconomic, innovation and infrastructural variables mainly determine the MPI score. The main finding of this study is that there is no statistically significant difference in total factor productivity (TFP) among the developed, developing, and least developed countries. Also, there is no significant influence of regions or alliances on TFP across the countries, confirming the global convergence in competitive productivity. The novelty of this study is that certain statistical evidence accurately portrays global competitiveness in terms of productivity.

In the arena of economic analysis, the wealth of a nation is getting more and more attention to be a better indicator to evaluate the status of an economy. This paper had studied the aggregate household wealth of different nations of the world, 106 countries, for the year 2009-2018. During these years, only two countries of the world, China and the USA have managed to increase their wealth tremendously over the last decade while others experienced a slow pace in the growth of wealth. To satisfy the query of how efficient these countries were in maximizing their wealth, a stochastic frontier approach (SFA) has been used to predict the technical efficiency dependent variable and then generalized methods of moments (GMM) and other models have been used to find out the determinants of this efficiency. The study had come up with the result that land, labor, and capital mainly contributed to the output of wealth maximization while past year level of efficiency, export, and import played the main roles in determining the wealth maximizing efficiency status of a nation. It is found that there is a negative relationship between past-year efficiency with current years and the more a country imports, the less efficient the country is while the more it exports, the more efficient the country is in maximizing wealth.

Wealth maximization is still the principal objective of a corporation and income plays a pivotal role in this regard. Taking this to the country context, wealth maximization can be a more refined objective alongside GDP growth. Considering GDP as the key wealth maximizer for a nation, the present work was undertaken to determine cross-country wealth efficiency and its determinants based on GDP covariates. The relationship between aggregate net wealth and GDP of 106 different countries for a period of 2009 to 2018 were analyzed to estimate annual incremental wealth efficiency based on their GDP covariates using input-output stochastic frontier analysis (SFA). Further, the determinants of incremental wealth efficiency were identified using multiple regression models. The SFA analysis shows significant negative impact of GDP on wealth maximization efficiency, like the law of diminishing marginal return to scales advocates. With the increase of GDP of a country, its marginal efficiency in wealth maximization decreases though aggregate wealth increases. The robust regression models show that imports, broad money and exchange rate undermine the wealth efficiency of a country and country’s past efficiency positively influences the subsequent year’s efficiency. These findings are expected to open new horizons for policymakers in policy analyses. JEL classification numbers: E1, E2, F4 Keywords: Wealth Maximization, GDP, SFA, Technical Efficiency, GMM, Driscoll Kraay.