外商直接投资、集聚外部性与环境污染

本文在新经济地理和集聚外部性的综合理论框架下,利用2003-2013年中国275个地级市面板数据对外商直接投资、集聚外部性与环境污染之间的关系进行了实证研究。空间计量结果显示,我国城市污染排放强度存在显著的正向空间相关性,而且这种正向空间正相关性随着空间距离的增加呈现有规律的减弱趋势。在控制了FDI与集聚外部性的交互作用后,FDI引入明显降低了区域污染排放强度,Mar外部性和Jacobs外部性进一步增强了FDI的减排效应,但其影响效应随着地理距离的增加呈现出倒U型曲线特征。对于不同规模的城市而言,小城市更容易受益于专业化集聚带来的Mar外部性,大中型城市更容易分享多样化集聚带来的Jacobs外部性红利。     

Bayesian networks and the assessment of universities' value added

A broad literature focused on the effectiveness of tertiary education. In classical models, a performance indicator is regressed on a set of characteristics of the individuals and fixed effects at the institution level. The FE coefficients are interpreted as the pure value added of the universities. The innovative contribution of the present paper resides in the use of Bayesian network (BN) analysis to assess the effectiveness of tertiary education. The results of an empirical study focused on Italian universities are discussed, to present the use of BN as a decision support tool for policy-making purposes.     

Technology spillover and regional convergence process: a statistical analysis of the Italian case

Recent literature broadly highlight the importance of modelling technological innovation effects on economic growth. This paper develops a methodology that allows to measure technology contribution to economic convergence; the choice of a regional framework also allows to underline interregional knowledge transmission as a the major channel of technological progress. Moreover, the specification of a dynamic growth model enables to evaluate both the regional convergence and the effect of innovation on long-run labour productivity without resorting to any technology index measurement. We contribute to the methodological literature also by comparing different dynamic panel data estimation procedures and by detecting both the presence of small sample bias and the existence of a nearly unit root autoregressive process in labour productivity series. The results of an empirical analysis on Italian regions show how most of innovation resources derives from relevant spillover mechanisms. Furthermore, technology spillover intensity seems to be strongly affected by geography and productive structure of regions.Financial support by Fondazione Cassa di Risparmio di Rimini and Uniturim S.p.A. is gratefully acknowledged.     

High wage workers and low wage firms: negative assortative matching or limited mobility bias?

Summary.  In the empirical literature on assortative matching using linked employer–employee data, unobserved worker quality appears to be negatively correlated with unobserved firm quality. We show that this can be caused by standard estimation error. We develop formulae that show that the estimated correlation is biased downwards if there is true positive assortative matching and when any conditioning covariates are uncorrelated with the firm and worker fixed effects. We show that this bias is bigger the fewer movers there are in the data, which is 'limited mobility bias'. This result applies to any two-way (or higher) error components model that is estimated by fixed effects methods. We apply these bias corrections to a large German linked employer–employee data set. We find that, although the biases can be considerable, they are not sufficiently large to remove the negative correlation entirely.     

生产性服务业集聚与能源效率提升

本文从理论和实证上研究了生产性服务业集聚对能源效率的影响作用及其空间溢出效应。结果显示,生产性服务业集聚的Jacobs外部性不仅显著提升了本地区的能源效率,还对周边地区具有明显的空间溢出效应,而生产性服务业集聚的MAR外部性仅利于本地区的能源效率提升;生产性服务业集聚的MAR外部性和Jacobs外部性对东部地区的能源效率提升都存在显著的促进效应及空间溢出效应,而中部地区的能源效率提升仅受益于生产性服务业集聚的MAR外部性;特大城市、大城市生产性服务业集聚的Jacobs外部性对能源效率提升具有明显的空间溢出效应,而中小城市生产性服务业集聚的MAR外部性对本地区能源效率的提升作用更显著。     

异质性企业的空间选择与地区生产率差距研究

 本文在融入企业异质性的空间经济学理论框架下,采用中国工业企业数据库微观数据,运用分位数回归的方法,检验异质性企业的空间选择与产业集聚效应对地区(企业)生产率差距的影响程度。结果表明：（1）异质性企业的定位选择行为在中国显著存在,并且是影响地区（企业）生产率差距的另一重要的微观机制;（2）在中国,地区产业集聚并没有对本地企业生产率产生正向影响,即集聚效应并不明显。     

A new spatial shift-share decomposition for the regional growth analysis: a local study of the employment based on Italian Business Statistical Register

This paper presents a new shift-share model to decompose the employment growth rate of a territorial unit by taking into consideration the association between industry mix and firm legal status effects measured in its neighborhood. We explicitly consider this kind of association to go beyond the problem of asymmetry found in a previous work. The empirical application refers to the data collected in the Italian Business Statistical Register and, in particular, regional employment figures in Friuli Venezia Giulia (NUTS2 level), for 219 territorial units (LAU2 level) and 12 Local Labour Systems (LLS), for the years 2001 and 2004.     

外商直接投资、产业关联与技术外溢

本文利用1999-2007年170多万家制造业企业数据,从企业异质性视角,研究了FDI通过水平关联和垂直关联等渠道对内资企业的技术溢出效应.本文的研究不仅回答了我国制造业企业是否存在FDI技术外溢,而且还深入研究了技术外溢效应是如何发生的.研究结果表明:①从总体上看,外资企业主要通过前后向关联效应促进本地企业生产率的提高,在行业内,存在市场挤出效应.②与私营企业相比,外资并没有通过水平或垂直关联对国有企业产生正的技术外溢效应.内外资企业之间技术差距越小,越有利于FDI技术外溢的产生.③外资的异质性会影响FDI技术外溢的效果,出口型FDI对内资企业存在显著的水平挤出效应,外资控制权越高,技术外溢的效应越弱.     

A spatial analysis on Italian unemployment differences

Using spatial econometric models, this paper focuses attention on the spatial structure of provincial unemployment disparities of Italian provinces for the year 2003. On the basis of findings from the economic literature and of the available socio-economic data, various model specifications including supply- and demand-side variables are tested. Further we use ESDA analysis as equivalent to integration analysis on time series; therefore it is applied on each variable, dependent and independent, involved in the statistical model. The suggestions of ESDA lead us to the most adequate statistical model, which estimates indicate that there is a significant degree of neighbouring effect (i.e. positive spatial correlation) among labour markets at the provincial level in Italy; this effect is present notwithstanding we controlled for local characteristics. The unemployment shows a polarized spatial pattern that is strongly connected to labour demand and to a much lesser extent to the share of young population and economic structural composition.     

A practical approach for assessing the effect of grouping in hierarchical spatio-temporal models

Hierarchical spatio-temporal models allow for the consideration and estimation of many sources of variability. A general spatio-temporal model can be written as the sum of a spatio-temporal trend and a spatio-temporal random effect. When spatial locations are considered to be homogeneous with respect to some exogenous features, the groups of locations may share a common spatial domain. Differences between groups can be highlighted both in the large-scale, spatio-temporal component and in the spatio-temporal dependence structure. When these differences are not included in the model specification, model performance and spatio-temporal predictions may be weak. This paper proposes a method for evaluating and comparing models that progressively include group differences. Hierarchical modeling under a Bayesian perspective is followed, allowing flexible models and the statistical assessment of results based on posterior predictive distributions. This procedure is applied to tropospheric ozone data in the Italian Emilia–Romagna region for 2001, where 30 monitoring sites are classified according to environmental laws into two groups by their relative position with respect to traffic emissions.     

The transition from university to work: a multilevel approach to the analysis of the time to obtain the first job

The aim of the paper is to characterize the factors that determine the transition from university to work as well as to evaluate the effectiveness of universities and course programmes with respect to the labour market outcomes of their graduates. The study is focused on the analysis of the time to obtain the first job, taking into account the graduates' characteristics and the effects pertaining to course programmes and universities. For this a three-level discrete time survival model is used, where the logit of the hazard—conditionally on the random effects at course programme and university level—is a linear function of the covariates. The analysis is accomplished by using a large data set from a survey on job opportunities for the 1992 Italian graduates.     

中国企业出口贸易对劳动者工资的影响研究

随着经济全球化的发展,出口成为企业发展的主题,研究企业出口贸易对于劳动者工资的影响具有重要意义。基于Stolper-Samuelson定理(简称S-S定理)以及Melitz的异质企业模型,分析企业出口对员工工资收入的影响,利用中国工业企业数据库2005-2007年制造业企业数据实证检验出口对企业所支付人均工资的影响。研究认为:中国参与国际市场会带来劳动密集型企业产量增加,这会使中国企业的单位劳动所占资本提高,带来企业生产率水平的提高,进而会带动劳动者工资水平的提高。实证检验认为,企业出口与人均工资呈显著的正相关性,出口对企业工资具有促进作用。     

Spatial and non-spatial model-based protection procedures for the release of business microdata

In this paper we discuss methodology for the safe release of business microdata. In particular we extend the model-based protection procedure of Franconi and Stander (2002, The Statistician 51: 1–11) by allowing the model to take account of the spatial structure underlying the geographical information in the microdata. We discuss the use of the Gibbs sampler for performing the computations required by this spatial approach. We provide an empirical comparison of these non-spatial and spatial disclosure limitation methods based on the Italian sample from the Community Innovation Survey. We quantify the level of protection achieved for the released microdata and the error induced when various inferences are performed. We find that although the spatial method often induces higher inferential errors, it almost always provides more protection. Moreover the aggregated areas from the spatial procedure can be somewhat more spatially smooth, and hence possibly more meaningful, than those from the non-spatial approach. We discuss possible applications of these model-based protection procedures to more spatially extensive data sets.     

Outlier Robust Small‐Area Estimation Under Spatial Correlation

Modern systems of official statistics require the estimation and publication of business statistics for disaggregated domains, for example, industry domains and geographical regions. Outlier robust methods have proven to be useful for small‐area estimation. Recently proposed outlier robust model‐based small‐area methods assume, however, uncorrelated random effects. Spatial dependencies, resulting from similar industry domains or geographic regions, often occur. In this paper, we propose an outlier robust small‐area methodology that allows for the presence of spatial correlation in the data. In particular, we present a robust predictive methodology that incorporates the potential spatial impact from other areas (domains) on the small area (domain) of interest. We further propose two parametric bootstrap methods for estimating the mean‐squared error. Simulations indicate that the proposed methodology may lead to efficiency gains. The paper concludes with an illustrative application by using business data for estimating average labour costs in Italian provinces.     

Conservative prior distributions for variance parameters in hierarchical models

Bayesian hierarchical models typically involve specifying prior distributions for one or more variance components. This is rather removed from the observed data, so specification based on expert knowledge can be difficult. While there are suggestions for “default” priors in the literature, often a conditionally conjugate inverse‐gamma specification is used, despite documented drawbacks of this choice. The authors suggest “conservative” prior distributions for variance components, which deliberately give more weight to smaller values. These are appropriate for investigators who are skeptical about the presence of variability in the second‐stage parameters (random effects) and want to particularly guard against inferring more structure than is really present. The suggested priors readily adapt to various hierarchical modelling settings, such as fitting smooth curves, modelling spatial variation and combining data from multiple sites.     

A log-linear regression model for the odd Weibull distribution with censored data

We introduce the log-odd Weibull regression model based on the odd Weibull distribution (Cooray, 2006). We derive some mathematical properties of the log-transformed distribution. The new regression model represents a parametric family of models that includes as sub-models some widely known regression models that can be applied to censored survival data. We employ a frequentist analysis and a parametric bootstrap for the parameters of the proposed model. We derive the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes and present some ways to assess global influence. Further, for different parameter settings, sample sizes and censoring percentages, some simulations are performed. In addition, the empirical distribution of some modified residuals are given and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be extended to a modified deviance residual in the proposed regression model applied to censored data. We define martingale and deviance residuals to check the model assumptions. The extended regression model is very useful for the analysis of real data.     

Lasso Regression Based on Empirical Mode Decomposition

The Hilbert–Huang transform uses the empirical mode decomposition (EMD) method to analyze nonlinear and nonstationary data. This method breaks a time series of data into several orthogonal sequences based on differences in frequency. These data components include the intrinsic mode functions (IMFs) and the final residue. Although IMFs have been used in the past as predictors for other variables, very little effort has been devoted to identifying the most effective predictors among IMFs. As lasso is a widely used method for feature selection within complex datasets, the main objective of this article is to present a lasso regression based on the EMD method for choosing decomposed components that exhibit the strongest effects. Both numerical experiments and empirical results show that the proposed modeling process can use time-frequency structure within data to reveal interactions between two variables. This allows for more accurate predictions concerning future events.     

A Revisit to the Behrens–Fisher Problem: Comparison of Five Test Methods

We revisit the well-known Behrens–Fisher problem and apply a newly developed ‘Computational Approach Test’ (CAT) to test the equality of two population means where the populations are assumed to be normal with unknown and possibly unequal variances. An advantage of the CAT is that it does not require the explicit knowledge of the sampling distribution of the test statistic. The CAT is then compared with three widely accepted tests—Welch–Satterthwaite test (WST), Cochran–Cox test (CCT), ‘Generalized p-value’ test (GPT)—and a recently suggested test based on the jackknife procedure, called Singh–Saxena–Srivastava test (SSST). Further, model robustness of these five tests are studied when the data actually came from t-distributions, but wrongly perceived as normal ones. Our detailed study based on a comprehensive simulation indicate some interesting results including the facts that the GPT is quite conservative, and the SSST is not as good as it has been claimed in the literature. To the best of our knowledge, the trends observed in our study have not been reported earlier in the existing literature.     

High dimensional variable selection with clustered data: an application of random multivariate survival forests for detection of outlier medical device components

In many medical studies patients are nested or clustered within doctor. With many explanatory variables, variable selection with clustered data can be challenging. We propose a method for variable selection based on random forest that addresses clustered data through stratified binary splits. Our motivating example involves the detection orthopedic device components from a large pool of candidates, where each patient belongs to a surgeon. Simulations compare the performance of survival forests grown using the stratified logrank statistic to conventional and robust logrank statistics, as well as a method to select variables using a threshold value based on a variable's empirical null distribution. The stratified logrank test performs superior to conventional and robust methods when data are generated to have cluster-specific effects, and when cluster sizes are sufficiently large, perform comparably to the splitting alternatives in the absence of cluster-specific effects. Thresholding was effective at distinguishing between important and unimportant variables.