Estimation in a generalization of bivariate probit models with dummy endogenous regressors

The purpose of this paper is to provide guidelines for empirical researchers who use a class of bivariate threshold crossing models with dummy endogenous variables. A common practice employed by the researchers is the specification of the joint distribution of unobservables as a bivariate normal distribution, which results in a bivariate probit model. To address the problem of misspecification in this practice, we propose an easy‐to‐implement semiparametric estimation framework with parametric copula and nonparametric marginal distributions. We establish asymptotic theory, including root‐n normality, for the sieve maximum likelihood estimators that can be used to conduct inference on the individual structural parameters and the average treatment effect (ATE). In order to show the practical relevance of the proposed framework, we conduct a sensitivity analysis via extensive Monte Carlo simulation exercises. The results suggest that estimates of the parameters, especially the ATE, are sensitive to parametric specification, while semiparametric estimation exhibits robustness to underlying data‐generating processes. We then provide an empirical illustration where we estimate the effect of health insurance on doctor visits. In this paper, we also show that the absence of excluded instruments may result in identification failure, in contrast to what some practitioners believe.     

An Empirical Investigation on the Transfer of Expatriates Within MNCs from a Knowledge Perspective

We study the use of expatriates in transferring knowledge within a multinational corporation (MNC). We argue that MNCs use expatriates to allocate knowledge between headquarters and its foreign affiliates. With data from MNCs headquartered in South Korea, we trace unobservable knowledge using observable labour mobility. Our empirical analysis shows that the use of expatriates increases as communication between South Korea and the host country becomes more costly. However, the extent to which the use of expatriates relates with communication costs decreases in the sectoral complexity.     

The prospect of decent work,decent industrial relations and decent social relations in China: towards a multi-level and multi-disciplinary approach

Abstract

Decent Work is a key initiative launched by the International Labour Organization in 1999. The initiative is to promote decent and productive employment with decent conditions of freedom, equality, security and human dignity. In reviewing academic literature on decent work, existing studies have been conducted primarily from a legal and political economic perspective. It is also largely situated outside any national industrial relations framework, both theoretically and practically. Decent work is an advocacy initiative of ILO, but the promotion of universal values embodied in the notion (e.g. equality, fairness, justice and dignity) needs to be tailored to specific societal contexts. Drawing on existing academic literature, this review article examines ideological, institutional and cultural distances between decent work and the reality of employment in China. It argues that achieving decent work requires an ideological transition of ‘traditional’ Chinese work ethics and a cultural transition from collectivism and altruism towards individualism and an emphasis on individual rights. This study also examines and highlights regulatory enforcement deficits and the inadequate role of the trade union in facilitating the advancement of decent work at various levels. Finally, the article argues that the study of decent work should be mainstreamed as an integral part of decent industrial relations and ultimately, decent social relations. It calls for a multi-level and multi-disciplinary approach to examining the historical, political, economic, ideological and cultural context of specific countries in fulfilling the ‘Decent Work’ agenda.     

Dynamic and non-neutral productivity effects of foreign ownership: A nonparametric approach

This paper studies two novel productivity characteristics of foreign acquisition on high-tech manufacturing firms: the dynamic and the non-Hicks-neutral effects. A dynamic productivity effect of foreign ownership arises when adoption of foreign technology and management practices takes time to fully realize. Furthermore, these dynamic adjustments may be capital or labor augmenting as adoption of advanced production technologies tends to have non-neutral productivity implications in developed countries. We propose and implement an econometric framework to estimate both effects using firm-level data from China's manufacturing sector. Our framework extends a nonparametric productivity framework, in which identification is achieved using a firm's first-order conditions and timing assumptions. We find strong evidence of dynamic and non-neutral effects from foreign ownership, with significant differences across investment sources. Investment from OECD sources is found to provide a long-term productivity boost for all but the largest recipients, while that from Hong Kong, Macau, and Taiwan does not raise performance. These findings have implications for China's declining labor share and for the rising domestic value-added content of its high-tech exports.     

Avoiding arbitrary exclusion restrictions using ratios of reduced-form estimates

We show how to obtain coherent structural-form (SF) exclusion restrictions using the reduced-form (RF) parameter ratios. It will be shown that an over-identified SF corresponds to a group of regressors sharing the same RF ratio value; those regressors should be excluded jointly from the SF. If there is no group structure, then the SF is just-identified; in this case, however, it is no longer clear which regressor should be excluded. Hence, just-identified SF’s are more arbitrary than over-identified SF’s in terms of exclusion restrictions. This is in stark contrast to the notion that the former is less arbitrary than the latter, because the former excludes fewer regressors. We formalize these points, and then suggest to find the number of modes in the estimated RF ratios as a way to find groups in the ratios. For this purpose, an informal graphical method using a kernel nonparametric method and a formal modality test are employed. An empirical example with selling price in a residential real estate market and duration on the market as two endogenous variables is provided. The authors are grateful to the editor and two anonymous referees for their comments     

Varying Coefficient Regression Models: A Review and New Developments

Varying coefficient regression models are known to be very useful tools for analysing the relation between a response and a group of covariates. Their structure and interpretability are similar to those for the traditional linear regression model, but they are more flexible because of the infinite dimensionality of the corresponding parameter spaces. The aims of this paper are to give an overview on the existing methodological and theoretical developments for varying coefficient models and to discuss their extensions with some new developments. The new developments enable us to use different amount of smoothing for estimating different component functions in the models. They are for a flexible form of varying coefficient models that requires smoothing across different covariates' spaces and are based on the smooth backfitting technique that is admitted as a powerful technique for fitting structural regression models and is also known to free us from the curse of dimensionality.     

Product Unbundling in the Travel Industry: The Economics of Airline Bag Fees

This paper provides theory and evidence on airline bag fees, offering insights into a real‐world case of product unbundling. The theory predicts that an airline's fares should fall when it introduces a bag fee, but that the full‐trip price (the bag fee plus the new fare) could either rise or fall. The empirical evidence presented in the paper provides strong confirmation of the first prediction. The data also suggest that the average fare falls by less than the bag fee itself so that the full price of a trip rises for passengers who choose to check bags.     

Cross redundancy and sensitivity in DEA models

Data envelopment analysis (DEA) measures the efficiency of each decision making unit (DMU) by maximizing the ratio of virtual output to virtual input with the constraint that the ratio does not exceed one for each DMU. In the case that one output variable has a linear dependence (conic dependence, to be precise) with the other output variables, it can be hypothesized that the addition or deletion of such an output variable would not change the efficiency estimates. This is also the case for input variables. However, in the case that a certain set of input and output variables is linearly dependent, the effect of such a dependency on DEA is not clear. In this paper, we call such a dependency a cross redundancy and examine the effect of a cross redundancy on DEA. We prove that the addition or deletion of a cross-redundant variable does not affect the efficiency estimates yielded by the CCR or BCC models. Furthermore, we present a sensitivity analysis to examine the effect of an imperfect cross redundancy on DEA by using accounting data obtained from United States exchange-listed companies.