普及互联网使用有助于缩小农村内部收入差距吗？——来自CLDS村级数据的经验证据

由于互联网具有普惠性和亲贫性,个体使用互联网对其收入增长的促进作用在群体上表现为互联网使用普及的平均收入增长效应和收入差距缩小效应。采用“中国劳动力动态调查”（CLDS）2014年、2016年、2018年三期的数据,分析互联网使用普及对农村社区（村庄）平均家庭收入和家庭收入差距的影响,结果显示：村庄内使用互联网家庭的占比提高不仅能显著提升平均家庭收入水平,而且会显著降低家庭收入基尼系数,验证了互联网的普惠性和亲贫性;当普及程度小于0.4时互联网使用对家庭收入差距的影响不显著（具有随着互联网使用率提高由正转负的趋势）,当普及程度大于0.4后互联网使用对家庭收入差距具有显著的负向影响;互联网使用普及可以通过促进服务业发展、提升劳动力技能等群体改善效应缩小农村内部收入差距。因此,应当加快网络基础设施建设,推进互联网使用在农村地区的普及,着重提高弱势群体对互联网使用的可及性,推动互联网与农村应用场景的深度耦合,充分发挥互联网的普惠性和亲贫性功效,有效促进农村居民的收入水平提升和收入差距缩小。     

略论企业环境绩效评价指标体系

保护环境,控制环境成本,实现企业经济效益和社会环境效益的双赢是企业实现可持续发展的重要保证。分析环境管理绩效指标和环境操作绩效指标可以检测企业环境成本控制结果的好坏。标杆法和平衡计分卡法是企业环境绩效评价的两种有效方法。     

Labour market effects of retraining for the unemployed: the role of occupations

We analyse the impact of retraining for unemployed low-skilled job-seekers in Germany. This extensive training programme leads to a formal vocational degree, which is often required to access certain professions. Applying statistical matching methods, we find that on average, after a period with large lock-in effects, retraining strongly increases the employment probability of participants, especially women. We add to the literature by estimating the employment effects for different occupations for which participants are trained. Our results show that there are substantial differences depending on the occupation. As the composition of trained occupations differs for men and women, segregation could be a driver of higher employment effects for women. However, comparisons of men and women trained for the same occupations reveal that even after controlling for segregation, women profit more from retraining. While caseworkers are instructed to choose professions with high labour demand, other aspects, such as working conditions, preferences and aptitudes of participants, should be crucial in the choice of a suitable occupation. An individual assessment of the suitability of an occupation could therefore improve the overall effectiveness of the programme.     

Equity market neutral hedge funds and the stock market: an application of score-driven copula models

In this article, we study the time-varying market neutrality of equity market neutral hedge funds. We use data from the Hedge Fund Research? Equity Market Neutral Index (HFRX EH), which represents the performance of a portfolio of individual equity market neutral hedge funds. For each day, we measure different levels of association of the Standard and Poor’s 500 (S&;P 500) index and the HFRX EH. We use non-linear dynamic conditional score models of location, scale and copula that, to the best of our knowledge, have not yet been applied in the body of literature on hedge funds. We study whether the neutrality of the HFRX EH that is evidenced in the body of literature for the period of April 1993–April 2003 also holds for the following decade, for the period of May 2003–December 2016. We estimate different average levels of association for the pre-, during- and post-periods of the US financial crisis of 2008. We find that the association of the S&;P 500 and the HFRX EH, on average, is significantly positive for the pre- and post-periods of the financial crisis, and it is significantly negative for the period during the financial crisis.     

The negative consequences of school bullying on academic performance and mitigation through female teacher participation: evidence from Ghana

Exploiting data from Ghanaian schools’ eighth grade students collected in 2011, we estimate the causal effects of school bullying on academic achievement and gender-based mitigating approaches by using propensity score matching (PSM) and doubly robust (DR) estimator approach. We find that students victimized by bullying score at least 0.22 standard deviation lower than their peers in a standardized mathematics examination. Meanwhile, we document that the effect of bullying is significantly attenuated in the presence of female teachers in the classroom. These results hold through a set of robustness checks including placebo regressions and matching quality test. We explain the results through gender difference in teaching paradigm and conclude that a feminine management approach in class is required to reduce the effect of bullying.     

Markov regime-switching Beta-t-EGARCH

We suggest a Markov regime-switching (MS) Beta-t-EGARCH (exponential generalized autoregressive conditional heteroscedasticity) model for U.S. stock returns. We compare the in-sample statistical performance of the MS Beta-t-EGARCH model with that of the single-regime Beta-t-EGARCH model. For both models we consider leverage effects for conditional volatility. We use data from the Standard Poor’s 500 (S&P 500) index and also a random sample that includes 50 components of the S&P 500. We study the outlier-discounting property of the single-regime Beta-t-EGARCH and MS Beta-t-EGARCH models. For the S&P 500, we show that for the MS Beta-t-EGARCH model extreme observations are discounted more for the low-volatility regime than for the high-volatility regime. The conditions of consistency and asymptotic normality of the maximum likelihood estimator are satisfied for both the single-regime and MS Beta-t-EGARCH models. All likelihood-based in-sample statistical performance metrics suggest that the MS Beta-t-EGARCH model is superior to the single-regime Beta-t-EGARCH model. We present an application to the out-of-sample density forecast performance of both models. The results show that the density forecast performance of the MS Beta-t-EGARCH model is superior to that of the single-regime Beta-t-EGARCH model.     

A specification test for the propensity score using its distribution conditional on participation

Propensity score matching has become a popular method for the estimation of average treatment effects. In empirical applications, researchers almost always impose a parametric model for the propensity score. This practice raises the possibility that the model for the propensity score is misspecified and therefore the propensity score matching estimator of the average treatment effect may be inconsistent. We show that the common practice of calculating estimates of the densities of the propensity score conditional on the participation decision provides a means for examining whether the propensity score is misspecified. In particular, we derive a restriction between the density of the propensity score among participants and the density among nonparticipants. We show that this restriction between the two conditional densities is equivalent to a particular orthogonality restriction and derive a formal test based upon it. The resulting test is shown via a simulation study to have dramatically greater power than competing tests for many alternatives. The principal disadvantage of this approach is loss of power against some alternatives.     

Choosing propensity score matching over regression adjustment for causal inference: when,why and how it makes sense

Summary

This study identified when regression adjustment fails to adjust adequately for differences in observed covariates and where propensity score matching is the only alternative.

Multivariate analysis might fail to adjust for observed confounders if:

• 1. The means of the propensity scores in the two groups are more than one-half a standard deviation apart unless distributions of the covariates in both groups are nearly symmetric, sample sizes of the two groups are approximately the same and distributions of the covariates in the two groups have similar variances;

• 2. The ratio of the propensity score variances in the two groups is significantly different from one;

• 3. The ratio of residual variances in the two groups after adjusting for the propensity score is significantly different from one.

Conducted retrospective analysis showed that the treatment effect would be an estimated $305 (or 26%) less if the misspecified outcome model had been chosen.     

Model uncertainty and asset return predictability: an application of Bayesian model averaging

We investigate model uncertainty associated with predictive regressions employed in asset return forecasting research. We use simple combination and Bayesian model averaging (BMA) techniques to compare the performance of these forecasting approaches in short-vs. long-run horizons of S&P500 monthly excess returns. Simple averaging involves an equally-weighted averaging of the forecasts from alternative combinations of factors used in the predictive regressions, whereas BMA involves computing the predictive probability that each model is the true model and uses these predictive probabilities as weights in combing the forecasts from different models. From a given set of multiple factors, we evaluate all possible pricing models to the extent, which they describe the data as dictated by the posterior model probabilities. We find that, while simple averaging compares quite favorably to forecasts derived from a random walk model with drift (using a 10-year out-of-sample iterative period), BMA outperforms simple averaging in longer compared to shorter forecast horizons. Moreover, we find further evidence of the latter when the predictive Bayesian model includes shorter, rather than longer lags of the predictive factors. An interesting outcome of this study tends to illustrate the power of BMA in suppressing model uncertainty through model as well as parameter shrinkage, especially when applied to longer predictive horizons.     

Entry to foreign markets and productivity: Evidence from a matched sample of Turkish manufacturing firms

We examine the effects of international trading activities of firms on creating productivity gains in Turkey by using a recent firm-level data set over the period 2003–2010. We establish treatment models and investigate the productivity improvements of firms through trade by using propensity score matching techniques together with difference-in-difference estimates. Three different groups of treatment are constructed: (1) firms that are involved only with import activities, (2) firms that are involved only with export activities and (3) firms that are involved with both export and import activities. The results of the study suggest that both exporting and importing have positive significant effects on total factor productivity (TFP) and labour productivity (LP) of firms. Importing is found to have a greater impact on productivity of firms compared to exporting. Further, two-way trade is found to have more significant effects than those of one-way trade on firm productivity. Finally, our results indicate that international trade has greater impact on LP rather than TFP of firms.