Applied Statistical Modeling for Productivity and Efficiency Analysis in Pakistan's Industrial Firms

Abstract

In the industrial companies and more so in the developing economies like in Pakistan, the competitiveness and sustainable growth heavily depend on productivity and efficiency. Over the past few years, applied statistical modeling has become an effective method of analyzing the level of productivity, efficiency differentials, and performance drivers at the firm level. This paper will discuss how statistical models, such as regression analysis, stochastic frontier analysis and panel data techniques, have been used to analyze productivity and efficiency in the industrial sector in Pakistan. On the basis of firm level and sectorial evidence, the study evaluates how statistical modeling can facilitate informed decision-making, benchmarking performance, and policy formulation. The results indicate that statistical applications have a strong capability of offering perspectives about the efficiencies gaps, resource exploitation, and structural limitations on the productivity of industry in Pakistan. The research shows the significant role of analytical methods in order to enhance the performance and competitiveness of industry in the developing economies through the use of data.