Productivity growth decomposition with FE-IV approach: Rethinking Thai commercial banks after the financial crisis

To cope with the Asian Financial Crisis, Thai commercial banks have gone through a reconstructing period. This study aims to decompose the Total Factor Productivity growth (TFP) for Thai commercial banking industry with an output distance function. With an unbalanced panel dataset, we used the Fixed Effect (FE) model with Instrumental Variables (IV) to estimate the TFP growth empirically. We found the technical inefficiency change and scale effects were the two major contributors to the recent growth, while the input price effect of the premises and equipment was the major preventer of the growth. Moreover, the Thai commercial banking industry produced in decreasing return to scale, and the input–output allocation was not at the profit maximization optimum under the exogenous prices.     

Class Size,Teacher Hours and Educational Attainment*

We employ a regression‐discontinuity design to identify effects on educational attainment after compulsory school of class size and the number of pupils per weekly teacher hour using administrative rules as instruments. We use Danish administrative panel data. Average class size is 20, about the same as in the US and most European countries. Restricting the sample to observations close to the enrolment discontinuity points where the administrative rules have greatest predictive power, instrumental variables estimates are consistently negative. Estimates from the preferred specification are marginally significant and indicate modest effects in line with earlier studies. Estimates for subgroups are less precise, but they indicate larger effects for pupils from less advantaged backgrounds.     

The Limit Distribution of the Bootstrap for the Unit Root Test Statistic when the Residuals are Dependent

In this article, the unit root test for the AR(1) model with dependent residuals is considered. We adopt a bootstrap procedure to bootstrap the residuals with bootstrap sample size m less than the size n of the original sample. Under the assumptions that m → ∞ and m/n → 0, the convergence in probability of the bootstrap distribution function is established. Research supported by National Natural Science Foundation of China (No. 10471126)     

Moment-based estimation of smooth transition regression models with endogenous variables

Nonlinear regression models have been widely used in practice for a variety of time series and cross-section datasets. For purposes of analyzing univariate and multivariate time series data, in particular, smooth transition regression (STR) models have been shown to be very useful for representing and capturing asymmetric behavior. Most STR models have been applied to univariate processes, and have made a variety of assumptions, including stationary or cointegrated processes, uncorrelated, homoskedastic or conditionally heteroskedastic errors, and weakly exogenous regressors. Under the assumption of exogeneity, the standard method of estimation is nonlinear least squares. The primary purpose of this paper is to relax the assumption of weakly exogenous regressors and to discuss moment-based methods for estimating STR models. The paper analyzes the properties of the STR model with endogenous variables by providing a diagnostic test of linearity of the underlying process under endogeneity, developing an estimation procedure and a misspecification test for the STR model, presenting the results of Monte Carlo simulations to show the usefulness of the model and estimation method, and providing an empirical application for inflation rate targeting in Brazil. We show that STR models with endogenous variables can be specified and estimated by a straightforward application of existing results in the literature.     

精准扶贫过程中工具理性的越位和价值理性的回归——基于湖北省B村的调查

精准扶贫包含着以效率为核心的工具理性和以人文价值为核心的价值理性,以两种理性视角审视湖北省B村的扶贫现状,发现B村精准扶贫在实施过程中出现工具理性和价值理性的失衡,主要表现为政策形式偏离价值初衷、效率优先忽视公平诉求、技术思维淡化人文关怀等问题,工具理性僭越到价值理性之上。为了促进扶贫工作顺利开展和扶贫任务的圆满实现,必须要推进精准扶贫过程中科学与民主、效率与公平、扶贫与扶志、扶智的统一,来助力价值理性的回归。     

我国软件外包承接城市效率评价与驱动因素研究

就 业问题、环境污染以及从业人员研发能力下降已经成为制约软件外包承接城市发展的瓶颈问 题。文章运用DEA方法构建考虑非期望产出和非合意变量的软件外包承接效率评价模型,重 点比较不同模型下我国软件外包承接城市效率的差异,并引入Tobit回归分析软件外包承接 城市效率的驱动因素。研究发现：我国软件外包承接城市的投入产出组合在逐步优化,且东 部沿海城市的效率值明显优于中西部城市,其中大连市的效率值始终为1,处于生产的前沿 面。另外,人才竞争力对软件外包承接城市效率有显著的正向影响,信息技术基础设施竞争力与承接效率的关系呈显著负相关。     

Does computer penetration increase farmers’ income? An empirical study from China

The diffusion and adoption of modern information technology provide new chance for China to close urban-rural income gap. This paper uses China's provincial panel data from 2002 to 2013 to investigate the effect of computer penetration on rural residents' income. A public program aiming to connect every village with broadband Internet and other rural facilities provides plausibly exogenous variation in rural residents' availability and adoption of the broadband Internet, which is used to explore the instrument variable for rural computer penetration. The results show that rural computer penetration tends to increase rural residents' income over time, but the average effect remains limited. The dynamic panel threshold effects model, which allows for both the threshold variable and other covariates to be endogenous, is further used to explore the constraints of the income-increase effect of rural computer penetration. It shows that the effect is at least doubled over the average effect estimated from instrument variables method, once the digital divide causes are removed. Our findings have important implications for the government to increase rural residents' income and reduce urban-rural income gap by encouraging rural computer usage and removing the digital divide.     

Predictive financial models of the euro area: A new evaluation test

This paper investigates the predictive ability of financial variables for euro area growth. Our forecasts are built from univariate autoregressive and single equation models. Euro area aggregate forecasts are constructed both by employing aggregate variables and by aggregating country-specific forecasts. The forecast evaluation is based on a recently developed test for equal predictive ability between nested models. Employing a monthly dataset from the period between January 1988 and May 2005 and setting the out-of-sample period to be from 2001 onwards, we find that the single most powerful predictor on a country basis is the stock market returns, followed by money supply growth. However, for the euro area aggregate, the set of most powerful predictors includes interest rate variables as well. The forecasts from pooling individual country models outperform those from the aggregate itself for short run forecasts, while for longer horizons this pattern is reversed. Additional benefits are obtained when combining information from a range of variables or combining model forecasts.