Can production subsidies explain China's export performance? Evidence from firm-level data

This paper analyses the relationship between production subsidies and firms’ export performance using a very comprehensive and recent firm-level database and controlling for the endogeneity of subsidies. It documents robust evidence that production subsidies stimulate export activity at the intensive margin, although this effect is conditional on firm characteristics. In particular, the positive relationship between subsidies and the intensive margin of exports is strongest among profit-making firms, firms in capital-intensive industries, and those located in non-coastal regions. Compared to firm characteristics, the extent of heterogeneity across ownership structure (SOEs, collectives, and privately owned firms) proves to be relatively less important.

Red capitalists:political connections and firm performance in China

Using a unique firm level data, this paper analyses the role of political connections in the post-entry performance of private start-up companies in China. It documents robust evidence that political affiliation enhances firms' survival and growth prospects. But interestingly politically neutral start-ups enjoy faster productivity improvements conditional on survival. In addition, the benefits of political connections are largely confined to firms associated with local or top level governments, and they are more pronounced in capital-intensive industries. We conclude that the close association between the state and a segment of the business community is leading to sub-optimal resource allocation in the economy by interfering with the process of market selection.

On the comparative performance of socially responsible and Islamic mutual funds

This is the first study to provide comprehensive analyses of the relative performance of both socially responsible investment (SRI) and Islamic mutual funds. The analysis proceeds in two stages. In the first, the performance of the two categories of funds is measured using partial frontier methods. In the second stage, we use quantile regression techniques.By combining two variants of the Free Disposal Hull (FDH) methods (order-m and order-?) in the first stage of analysis and quantile regression in the second stage, we provide detailed analyses of the impact of different covariates across methods and across different quantiles. In spite of the differences in the screening criteria and portfolio management of both types of funds, variation in the performance is only found for some of the quantiles of the conditional distribution of mutual fund performance. We established that for the most inefficient funds the superior performance of SRI funds is significant. In contrast, for the best mutual funds this evidence vanished and even Islamic funds perform better than SRI.These results show the benefits of performing the analysis using quantile regression.

On the comparative performance of socially responsible and Islamic mutual funds

This is the first study to provide comprehensive analyses of the relative performance of both socially responsible investment (SRI) and Islamic mutual funds. The analysis proceeds in two stages. In the first, the performance of the two categories of funds is measured using partial frontier methods. In the second stage, we use quantile regression techniques. By combining two variants of the Free Disposal Hull (FDH) methods (order- m and order- α) in the first stage of analysis and quantile regression in the second stage, we provide detailed analyses of the impact of different covariates across methods and across different quantiles. In spite of the differences in the screening criteria and portfolio management of both types of funds, variation in the performance is only found for some of the quantiles of the conditional distribution of mutual fund performance. We established that for the most inefficient funds the superior performance of SRI funds is significant. In contrast, for the best mutual funds this evidence vanished and even Islamic funds perform better than SRI. These results show the benefits of performing the analysis using quantile regression. © 2013 Elsevier B.V.

Conditional Confidence Interval for the Scale Parameter of a Weibull Distribution

2000 Mathematics Subject Classification: 62F25, 62F03.

An agent-based model of team coordination and performance

This research is based on the premises that teams can be designed to optimize its performance, and appropriate team coordination is a significant factor to team outcome performance. Contingency theory argues that the effectiveness of a team depends on the right fit of the team design factors to the particular job at hand. Therefore, organizations need computational tools capable of predict the performance of different configurations of teams. This research created an agent-based model of teams called the Team Coordination Model (TCM). The TCM estimates the coordination load and performance of a team, based on its composition, coordination mechanisms, and job’s structural characteristics. The TCM can be used to determine the team’s design characteristics that most likely lead the team to achieve optimal performance. The TCM is implemented as an agent-based discrete-event simulation application built using JAVA and Cybele Pro agent architecture. The model implements the effect of individual team design factors on team processes, but the resulting performance emerges from the behavior of the agents. These team member agents use decision making, and explicit and implicit mechanisms to coordinate the job. The model validation included the comparison of the TCM’s results with statistics from a real team and with the results predicted by the team performance literature. An illustrative 26-1 fractional factorial experimental design demonstrates the application of the simulation model to the design of a team. The results from the ANOVA analysis have been used to recommend the combination of levels of the experimental factors that optimize the completion time for a team that runs sailboats races. This research main contribution to the team modeling literature is a model capable of simulating teams working on complex job environments. The TCM implements a stochastic job structure model capable of capturing some of the complexity not capture by current models. In a stochastic job structure, the tasks required to complete the job change during the team execution of the job. This research proposed three new types of dependencies between tasks required to model a job as a stochastic structure. These dependencies are conditional sequential, single-conditional sequential, and the merge dependencies.

Intergenerational effects of parental substance-related convictions and adult drug treatment court participation on children's school performance.

This study examined the intergenerational effects of parental conviction of a substance-related charge on children's academic performance and, conditional on a conviction, whether completion of an adult drug treatment court (DTC) program was associated with improved school performance. State administrative data from North Carolina courts, birth records, and school records were linked for 2005-2012. Math and reading end-of-grade test scores and absenteeism were examined for 5 groups of children, those with parents who: were not convicted on any criminal charge, were convicted on a substance-related charge and not referred by a court to a DTC, were referred to a DTC but did not enroll, enrolled in a DTC but did not complete, and completed a DTC program. Accounting for demographic and socioeconomic factors, the school performance of children whose parents were convicted of a substance-related offense was worse than that of children whose parents were not convicted on any charge. These differences were statistically significant but substantially reduced after controlling for socioeconomic characteristics; for example, mother's educational attainment. We found no evidence that parent participation in an adult DTC program led to improved school performance of their children. While the children of convicted parents fared worse on average, much--but not all--of this difference was attributed to socioeconomic factors, with the result that parental conviction remained a risk factor for poorer school performance. Even though adult DTCs have been shown to have other benefits, we could detect no intergenerational benefit in improved school performance of their children.

Exchange-Trade Funds Evaluation using Performance Measures Distribution

In this study we propose the use of the performance measure distribution rather than its punctual value to rank hedge funds. Generalized Sharpe Ratio and other similar measures that take into account the higher-order moments of portfolio return distributions are commonly used to evaluate hedge funds performance. The literature in this field has reported non-significant difference in ranking between performance measures that take, and those that do not take, into account higher moments of distribution. Our approach provides a much more powerful manner to differentiate between hedge funds performance. We use a non-semiparametric density based on Gram-Charlier expansions to forecast the conditional distribution of hedge fund returns and its corresponding performance measure distribution. Through a forecasting exercise we show the advantages of our technique in relation to using the more traditional punctual performance measures.

Joint Analysis of Multiple Algorithms and Performance Measures

There has been an increasing interest in the development of new methods using Pareto optimality to deal with multi-objective criteria (for example, accuracy and time complexity). Once one has developed an approach to a problem of interest, the problem is then how to compare it with the state of art. In machine learning, algorithms are typically evaluated by comparing their performance on different data sets by means of statistical tests. Standard tests used for this purpose are able to consider jointly neither performance measures nor multiple competitors at once. The aim of this paper is to resolve these issues by developing statistical procedures that are able to account for multiple competing measures at the same time and to compare multiple algorithms altogether. In particular, we develop two tests: a frequentist procedure based on the generalized likelihood-ratio test and a Bayesian procedure based on a multinomial-Dirichlet conjugate model. We further extend them by discovering conditional independences among measures to reduce the number of parameters of such models, as usually the number of studied cases is very reduced in such comparisons. Data from a comparison among general purpose classifiers is used to show a practical application of our tests.

Conditional mutual inclusive information enables accurate quantification of associations in gene regulatory networks

International audience