Spatial wage disparities: Sorting matters!

Spatial wage disparities can result from spatial differences in the skill composition of the workforce, in non-human endowments, and in local interactions. To distinguish between these explanations, we estimate a model of wage determination across local labour markets using a very large panel of French workers. We control for worker characteristics, worker fixed effects, industry fixed effects, and the characteristics of the local labour market. Our findings suggest that individual skills account for a large fraction of existing spatial wage disparities with strong evidence of spatial sorting by skills. Interaction effects are mostly driven by the local density of employment. Not controlling for worker heterogeneity leads to very biased estimates of interaction effects. Endowments only appear to play a small role.     

Random Effects Misspecification Can Have Severe Consequences for Random Effects Inference in Linear Mixed Models

There has been considerable and controversial research over the past two decades into how successfully random effects misspecification in mixed models (i.e. assuming normality for the random effects when the true distribution is non‐normal) can be diagnosed and what its impacts are on estimation and inference. However, much of this research has focused on fixed effects inference in generalised linear mixed models. In this article, motivated by the increasing number of applications of mixed models where interest is on the variance components, we study the effects of random effects misspecification on random effects inference in linear mixed models, for which there is considerably less literature. Our findings are surprising and contrary to general belief: for point estimation, maximum likelihood estimation of the variance components under misspecification is consistent, although in finite samples, both the bias and mean squared error can be substantial. For inference, we show through theory and simulation that under misspecification, standard likelihood ratio tests of truly non‐zero variance components can suffer from severely inflated type I errors, and confidence intervals for the variance components can exhibit considerable under coverage. Furthermore, neither of these problems vanish asymptotically with increasing the number of clusters or cluster size. These results have major implications for random effects inference, especially if the true random effects distribution is heavier tailed than the normal. Fortunately, simple graphical and goodness‐of‐fit measures of the random effects predictions appear to have reasonable power at detecting misspecification. We apply linear mixed models to a survey of more than 4 000 high school students within 100 schools and analyse how mathematics achievement scores vary with student attributes and across different schools. The application demonstrates the sensitivity of mixed model inference to the true but unknown random effects distribution.     

Transboundary pollution externalities: Think globally,act locally?

We analyze the implications of transboundary pollution externalities on environmental policymaking in a spatial setting, in which pollution diffuses across the global spatial economy independently of the specific location in which it is originally generated. This framework gives rise to a simple regional optimal pollution control problem allowing us to compare the global and local solutions in which, respectively, the transboundary externality is and is not taken into account in the determination of the optimal policy by individual local policymakers. We show that it is not obvious that transboundary externalities are a source of inefficiency per se since this is strictly related to the spatial features of the initial distribution of pollution. If the initial pollution distribution is spatially homogeneous then the local and global solutions will coincide and thus no efficiency loss will arise from transboundary externalities, but if it is spatially heterogeneous the local solution will be suboptimal and thus a global approach to environmental problems will be needed to achieve efficiency. From a normative perspective, in this latter (and most realistic) case we also quantify the amount of policy intervention needed at local level in order to achieve the globally desirable goal of pollution eradication in the long run. Our conclusions hold true in a number of different settings, including situations in which the spatial domain is either bounded or unbounded, and situations in which macroeconomic–environmental feedback effects are taken into account.     

浅谈广西防城金花茶自然保护区建设与地方经济发展的关系

金花茶是国家一级保护植物,建立防城金花茶自然保护区,是时代的、历史的要求。文章通过防城金花茶自然保护区建设及发展现状,阐明了搞好金花茶自然保护区建设在社会经济发展中的地位和作用,并指出了保护区的建设与地方经济协调发展中存在的问题,提出并论述了抓好保护区建设与地方经济协调发展的对策、措施和途径。     

Local fiscal multipliers and spillover effects: Evidence from Philippine regions

Fiscal policy in developed countries has been a rich topic since the Great Recession. However, research has remained limited for developing countries despite their similar use of fiscal policy and concerns about the efficiency of public spending. To help address this research gap, this paper provides a case study of multiplier effects of local government spending in regions in the Philippines as well as spillover effects of local government spending across regions. An instrumental variable based on the country’s intergovernmental transfer system is used to identify regional public spending in panel regressions. The local fiscal multiplier is estimated to be above one, where a 1-peso rise in spending by local government units in a region corresponds to a 1.2-peso rise in regional output. Multiplier effects are highest for capital expenditures and appear to be primarily driven by the services sector. Spillover effects are comparatively large, at around 1.8–2.0, highlighting the important role of domestic trade when stimulating regional economic activity.     

Improving footprint calculations of small open economies: combining local with multi-regional input–output tables

In a small, open and resource-poor economy, import and export dependency have an ever-growing impact on local policy decisions, which makes local (environmental) policy-makers increasingly depend on global data. This increases the interest in models that link local production and consumption data to global production, trade and environmental data. The recent increase in availability of global environmentally extended multi-regional input-output tables (EE-MRIO tables) provides an opportunity to link them with existing local environmentally extended input-output tables (EE-RIO tables). These combined tables make it possible (1) to analyse the links between local and global production and consumption and (2) to study global value chains, material use and environmental impacts simultaneously. However, estimations using input-output (I–O) analyses contain errors due to imperfect databases. In this article the magnitude of specification, aggregation and time errors are estimated and compared. The results show the need to combine local datasets with multi-regional ones and show that highest detailed (country and sector levels) as well as time series of I–O tables are the way forward for using I–O analyses in local policy-making. The paper provides guidance on trading off investments in model adoption and/or extension and the reliability of estimation results.     

A Tutorial on Palm Distributions for Spatial Point Processes

This tutorial provides an introduction to Palm distributions for spatial point processes. Initially, in the context of finite point processes, we give an explicit definition of Palm distributions in terms of their density functions. Then we review Palm distributions in the general case. Finally, we discuss some examples of Palm distributions for specific models and some applications.     

Influence analysis for Poisson inverse Gaussian regression models based on the EM algorithm

For Poisson inverse Gaussian regression models, it is very complicated to obtain the influence measures based on the traditional method, because the associated likelihood function involves intractable expressions, such as the modified Bessel function. In this paper, the EM algorithm is employed as a basis to derive diagnostic measures for the models by treating them as a mixed Poisson regression with the weights from the inverse Gaussian distributions. Several diagnostic measures are obtained in both case-deletion model and local influence analysis, based on the conditional expectation of the complete-data log-likelihood function in the EM algorithm. Two numerical examples are given to illustrate the results.     

论物业税开征对我国城市发展的影响

物业税属于地方税种,统一的物业税开征对于提高中国城市发展的效率和水平具有积极意义.我国的物业税体制改革应注意与地方公共服务的发展联系在一起,通过开征统一的物业税,构建公共财政框架,规范政府土地管理,改善城市公共服务,促进城市房地产业健康发展.     

Instrumental variable estimation based on grouped data

The paper considers the estimation of the coefficients of a single equation in the presence of dummy intruments. We derive pseudo ML and GMM estimators based on moment restrictions induced either by the structural form or by the reduced form of the model. The performance of the estimators is evaluated for the non-Gaussian case. We allow for heteroscedasticity. The asymptotic distributions are based on parameter sequences where the number of instruments increases at the same rate as the sample size. Relaxing the usual Gaussian assumption is shown to affect the normal asymptotic distributions. As a result also recently suggested new specification tests for the validity of instruments depend on Gaussianity. Monte Carlo simulations confirm the accuracy of the asymptotic approach.