SECTORAL FACTOR REALLOCATION AND PRODUCTIVITY GROWTH: RECENT TRENDS IN THE CHINESE ECONOMY

Based on the data of six major sectors and 13 industrial sectors of the Chinese economy, this study examines the impact of sectoral factor reallocation on productivity growth for the period 1986-2000. According to the results, the earlier post-reform high productivity growth was not sustained in more recent years. The overall performance of inter-sector reallocation was also disappointing. Limited improvements in productivity growth were observed for the industrial sectors as China beefed up reforms of state-owned enterprises in the late 1990s. This evidence highlights the huge potential gains for a developing economy like China to build sound market institutions in line with greater market openness and inter-sector factor mobility.The Chinese Economy, Total Factor Productivity, Inter-sector Reallocation

Treatment Effects on Ordinal Outcomes: Causal Estimands and Sharp Bounds

Assessing the causal effects of interventions on ordinal outcomes is an important objective of many educational and behavioral studies. Under the potential outcomes framework, we can define causal effects as comparisons between the potential outcomes under treatment and control. However, unfortunately, the average causal effect, often the parameter of interest, is difficult to interpret for ordinal outcomes. To address this challenge, we propose to use two causal parameters, which are defined as the probabilities that the treatment is beneficial and strictly beneficial for the experimental units. However, although well-defined for any outcomes and of particular interest for ordinal outcomes, the two aforementioned parameters depend on the association between the potential outcomes, and are therefore not identifiable from the observed data without additional assumptions. Echoing recent advances in the econometrics and biostatistics literature, we present the sharp bounds of the aforementioned causal parameters for ordinal outcomes, under fixed marginal distributions of the potential outcomes. Because the causal estimands and their corresponding sharp bounds are based on the potential outcomes themselves, the proposed framework can be flexibly incorporated into any chosen models of the potential outcomes, and are directly applicable to randomized experiments, unconfounded observational studies, and randomized experiments with noncompliance. We illustrate our methodology via numerical examples and three real-life applications related to educational and behavioral research.Comment: Accepted by the Journal of Education and Behavioral Statistic