This paper reviews research using longitudinal microdata (LMDs) to study productivity movements and the factors behind productivity growth. The research explores productivity dispersion across firms, persistence of productivity differentials, the consequences of entry and exit, and the role of resource reallocation in aggregate productivity growth. It also identifies factors correlated with productivity growth, such as managerial ability, technology use, human capital, and regulation. The more advanced literature addresses the causality between these factors and productivity growth. The paper discusses the use of LMDs, particularly the Longitudinal Research Database (LRD), which is a large panel dataset of U.S. manufacturing plants. It highlights the importance of LMDs in filling the gap between two main subfields in productivity research: growth accounting and evolutionary models of productivity growth. The research shows that productivity is highly dispersed across firms, with some firms being substantially more productive than others. A large portion of aggregate productivity growth is attributed to resource reallocation. The paper also discusses the challenges in quantifying the importance of various factors behind productivity growth, such as changes in the regulatory environment or technology. The paper reviews studies using LMDs to examine the factors underlying productivity growth, including technology, human capital, and international exposure to competition. It also discusses the limitations of LMDs in productivity research, such as issues related to frequency, coverage, sampling, and missing variables. The paper highlights the importance of microdata in understanding productivity dispersion and evolution, and how these findings relate to more aggregate results. The paper also discusses stylized facts on productivity dispersion and evolution, including the degree of heterogeneity across establishments and firms in productivity in nearly all industries. It reviews models of productivity evolution, including the Jovanovic model and the Ericson and Pakes model, and discusses the results of these models. The paper also discusses the effects of entry and exit on productivity, and the cyclicality of productivity growth. Overall, the paper emphasizes the importance of microdata in understanding productivity growth and its relation to more aggregate results.This paper reviews research using longitudinal microdata (LMDs) to study productivity movements and the factors behind productivity growth. The research explores productivity dispersion across firms, persistence of productivity differentials, the consequences of entry and exit, and the role of resource reallocation in aggregate productivity growth. It also identifies factors correlated with productivity growth, such as managerial ability, technology use, human capital, and regulation. The more advanced literature addresses the causality between these factors and productivity growth. The paper discusses the use of LMDs, particularly the Longitudinal Research Database (LRD), which is a large panel dataset of U.S. manufacturing plants. It highlights the importance of LMDs in filling the gap between two main subfields in productivity research: growth accounting and evolutionary models of productivity growth. The research shows that productivity is highly dispersed across firms, with some firms being substantially more productive than others. A large portion of aggregate productivity growth is attributed to resource reallocation. The paper also discusses the challenges in quantifying the importance of various factors behind productivity growth, such as changes in the regulatory environment or technology. The paper reviews studies using LMDs to examine the factors underlying productivity growth, including technology, human capital, and international exposure to competition. It also discusses the limitations of LMDs in productivity research, such as issues related to frequency, coverage, sampling, and missing variables. The paper highlights the importance of microdata in understanding productivity dispersion and evolution, and how these findings relate to more aggregate results. The paper also discusses stylized facts on productivity dispersion and evolution, including the degree of heterogeneity across establishments and firms in productivity in nearly all industries. It reviews models of productivity evolution, including the Jovanovic model and the Ericson and Pakes model, and discusses the results of these models. The paper also discusses the effects of entry and exit on productivity, and the cyclicality of productivity growth. Overall, the paper emphasizes the importance of microdata in understanding productivity growth and its relation to more aggregate results.