Project Evaluation from Application to Econometric Theory: A Qualitative Explanation of Difference in Difference (DiD) Approach

Muhammad Ateeq-Ur-Rehman

doi:10.11648/j.ijdsa.20170301.12

Abstract

The main objective of this study is to show the importance of the Difference in Difference (DiD) method and its applicability in the field of human and social sciences. The DiD method is one of the famous tools in econometrics to investigate the causal effect of the policy before and after treatment or policy. Why difference in difference method is most important in these days? Because the traditional methods requires more instructions as compare to DiD method which is easier and applicable without randomization of the data. The difference is compared with treated and non-treated group in two time’s period model with the same unit of data. The first difference removes the time-invariant factors while Difference in Difference removes the time-variant factors of the model and the remaining statistic shows the original impact of the treatment or policy.

Highlights

The main objective of the development policies are to change the economic outcomes injecting different economic shocks to the economy
Why difference in difference method is most important in these days? Because the traditional methods requires complex procedures as compare to Difference in Difference (DiD) method which is easier and applicable without randomization of the data
The difference in difference method is one of the famous tools in econometrics to investigate the causal effect of the policy

Summary

Introduction

The main objective of the development policies are to change the economic outcomes injecting different economic shocks to the economy. Performance-based policy plays an important role in the economy and economic differences after policy shocks can be measured through econometric tools. Differencein-Difference approach measures the average difference of two comparable groups before and after policy implementation while the policy is considered effective if comparative group difference is decreased. In the situation of nonrandom and discontinuous data type, the difference in difference method can be more applicable and favorable to generate the required results [1]. Different four groups are taken into analysis and three of those are not treated while the one treated or enrolled group is analyzed to investigate the policy effect [2]. The required average outcomes cannot be achieved without treatment effect. The controlled (untreated) and treated groups are analyzed critically according to pre and post-treatment period. Difference in difference approach measures the efficiency and affectivity of the policy in repeated crosssection panel data type [3]

Objectives

Methods

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Conclusion