Green productivity divergence and factor endowments: Evidence from the Yellow River Basin, China

Using panel data for the Yellow River Basin (YRB) of China from 2006 to 2017, we investigate cross-city conventional total factor productivity (TFP) and green TFP convergence, and the moderating effects of relative factor endowments on TFP growth. Allowing for cross-city and cross-time variation in the production function, we estimate TFP across cities using a nonlinear varying coefficient model, and decompose it into various input-embedded and input-free productivity components based on the new growth accounting, covering all growth-driving channels. This paper then employs a conditional convergence framework to examine whether convergence occurs, through which channels, and the effects of relative factor endowments on them. Empirical results show that lagging cities fail to achieve TFP catch-up, and that the divergence of capital-embedded and labor-embedded productivity instead triggers a widening of the cross-city TFP gap. Part of the cause of this increase in these gaps is that cities with relatively high capital deepening and capital-to-energy ratio are experiencing rapid TFP growth by driving the quality of capital and labor. Nor have these effects been altered in examining environmentally constrained or green TFP convergence.

The Differential Time-Varying Effect Model (DTVEM): A tool for diagnosing and modeling time lags in intensive longitudinal data

With the recent growth in intensive longitudinal designs and the corresponding demand for methods to analyze such data, there has never been a more pressing need for user-friendly analytic tools that can identify and estimate optimal time lags in intensive longitudinal data. The available standard exploratory methods to identify optimal time lags within univariate and multivariate multiple-subject time series are greatly underpowered at the group (i.e., population) level. We describe a hybrid exploratory-confirmatory tool, referred to herein as the Differential Time-Varying Effect Model (DTVEM), which features a convenient user-accessible function to identify optimal time lags and estimate these lags within a state-space framework. Data from an empirical ecological momentary assessment study are then used to demonstrate the utility of the proposed tool in identifying the optimal time lag for studying the linkages between nervousness and heart rate in a group of undergraduate students. Using a simulation study, we illustrate the effectiveness of DTVEM in identifying optimal lag structures in multiple-subject time-series data with missingness, as well as its strengths and limitations as a hybrid exploratory-confirmatory approach, relative to other existing approaches.

Greenhouse Emissions and Productivity Growth

In this paper, we examine the effect of emissions, as measured by carbon dioxide (CO2), on economic growth among a set of OECD countries during the period 1981–1998. We examine the relationship between total factor productivity (TFP) growth and emissions using a semiparametric smooth coefficient model that allow us to directly estimate the output elasticity of emissions. The results indicate that there exists a monotonically-increasing relationship between emissions and TFP growth. The output elasticity of CO2 emissions is small with an average sample value of 0.07. In addition, we find an average contribution of CO2 emissions to productivity growth of about 0.063 percent for the period 1981–1998.

Time-varying effect modeling with longitudinal data truncated by death: conditional models, interpretations, and inference

Recent studies found that infection-related hospitalization was associated with increased risk of cardiovascular (CV) events, such as myocardial infarction and stroke in the dialysis population. In this work, we develop time-varying effects modeling tools in order to examine the CV outcome risk trajectories during the time periods before and after an initial infection-related hospitalization. For this, we propose partly conditional and fully conditional partially linear generalized varying coefficient models (PL-GVCMs) for modeling time-varying effects in longitudinal data with substantial follow-up truncation by death. Unconditional models that implicitly target an immortal population is not a relevant target of inference in applications involving a population with high mortality, like the dialysis population. A partly conditional model characterizes the outcome trajectory for the dynamic cohort of survivors, where each point in the longitudinal trajectory represents a snapshot of the population relationships among subjects who are alive at that time point. In contrast, a fully conditional approach models the time-varying effects of the population stratified by the actual time of death, where the mean response characterizes individual trends in each cohort stratum. We compare and contrast partly and fully conditional PL-GVCMs in our aforementioned application using hospitalization data from the United States Renal Data System. For inference, we develop generalized likelihood ratio tests. Simulation studies examine the efficacy of estimation and inference procedures.