MNC foreign investment and industrial disasters: The moderating role of technological,safety management,and philanthropic capabilities

Research Summary: We investigate how industrial disasters can discourage FDI and how MNCs' technological, safety management, and philanthropic capabilities can moderate these effects. Using two unique panel data sets of entry and expansion of U.S. wholly‐owned manufacturing subsidiaries overseas, we found that industrial disasters are associated with reduced foreign entry of wholly‐owned subsidiaries in the disaster industry, but not for all firms in the host country experiencing the disaster. We also found that MNCs' technological, safety management, and philanthropic capabilities can, in some cases, positively moderate the negative relationships between industrial disasters and the foreign entry and expansion of wholly‐owned subsidiaries. Additionally, three‐way interactions with government stability suggest that technological and safety management capabilities substitute government stability in managing industrial disasters, while philanthropic capability complements government stability. Managerial Summary: How can MNCs' technological, safety management, and philanthropic capabilities overcome the effects of industrial disasters such as chemical spills and explosions in host countries? Our results show that industrial disasters are associated with reduced foreign entry of wholly‐owned subsidiaries in the industry in which the industrial disaster occurs, but not for other firms operating in the country experiencing the disaster. However, an MNC's technological capability can, in general, lower the negative consequences of industrial disasters in both the entry and expansion of its wholly‐owned subsidiaries. Regarding the institutional quality of a host country, the results imply that MNCs should develop philanthropic capability when the government stability of the host country is strong, and develop technological and safety management capabilities when the government stability is weak.     

Using partial identification methods to estimate the effect of violence against women on their children’s health outcomes

A growing literature in economics seeks to estimate the costs of violence against women by examining, for example, its impact on the health outcomes of their children. However, it is difficult to assign a causal interpretation to these nonexperimental studies due to the presence of unobservable characteristics affecting violence and health outcomes simultaneously. The lack of credible instrumental variables applicable in several countries further limits our knowledge. I address this gap by using new partial identification methods to estimate the relative size of the unobservables needed to eliminate the estimated effects in nonexperimental studies. I also expand the external validity of the analysis by using data from five standardized nationally representative household surveys in Latin America. Consistent with previous studies, cross-sectional estimates show large negative associations between violence against women and an array of child health outcomes. However, when accounting for omitted variable bias, at best, two-thirds of the estimates remain robust and they are concentrated on the outcomes with the largest cross-sectional estimated impacts.     

A Pearson's test for symmetry with an application to the Spanish business cycle

In this paper, we look for asymmetries in the Spanish business cycle. To that end, we firstly propose an easy nonparametric testing procedure to test for symmetry based on a Pearson's chi-squared statistic, which we call P-test. Then, we test for two popular forms of asymmetry, deepness and steepness, using a battery of nonparametric tests. In addition, we analyse possible complementarities between the tests used in this paper, and we compute p-value adjustments for multiple tests.     

Identifying crime generators and spatially overlapping high-risk areas through a nonlinear model: A comparison between three cities of the Valencian region (Spain)

The behavior and spatial distribution of crime events can be explained through the characterization of an area in terms of its demography, socioeconomy, and built environment. In particular, recent studies on the incidence of crime in a city have focused on the identification of features of the built environment (specific places or facilities) that may increase crime risk within a certain radius. However, it is hard to identify environmental characteristics that consistently explain crime occurrence across cities and crime types. This article focuses on the assessment of the effect that certain types of places have on the incidence of property crime, robbery, and vandalism in three cities of the Valencian region (Spain): Alicante, Castellon, and Valencia. A nonlinear effects model is used to identify such places and to construct a risk map over the three cities considering the three crime types under research. The results obtained suggest that there are remarkable differences across cities and crime types in terms of the types of places associated with crime outcomes. The identification of high-risk areas allows verifying that crime is highly concentrated, and also that there is a high level of spatial overlap between the high-risk areas corresponding to different crime types.     

Extending the SAM with Social and Environmental Indicators: An Application to Bolivia

Extending economic accounts with sets of social and environmental indicators is a first step towards a more integrated analysis of aspects of sustainability problems. In this article, therefore, a proposal is made to nest social and environmental indicators into an existing economic accounting framework. The Social Accounting Matrix (SAM) is taken as a basis, because of its flexibility regarding extensions with non-monetary data addressing social and environmental concerns. The main thrust of the paper is methodological. From the discussion of methodological issues and the application to the SAM for Bolivia for the year 1989, it is concluded that it is, in principle, feasible and relatively simple to extend the SAM with the two sets of indicators. However, additional data will have to be collected to be able to address, more adequately, the problems of sustainability.     

Where to vacation? An agent-based approach to modelling tourist decision-making process

Agent-based models (ABMs) are becoming more relevant in social simulation due to the potential to model complex phenomena that emerge from individual interactions. In tourism research, complexity is a subject of growing interest and researchers start to analyse the tourism system as a complex phenomenon. However, there is little application of ABMs as a tool to explore and predict tourism patterns. The purpose of the paper is to develop an ABM that increases knowledge in tourism research by (i) considering the complexity of tourism phenomenon, (ii) providing tools to explore the complex relations between system components and (iii) giving insights on the functioning of the system and the tourist decision-making process. A theoretical ABM is developed to improve knowledge on tourist decision-making in the selection of a destination to vacation. Tourists’ behaviour, such as individual motivation, and social network influence in the vacation decision-making process are hereby discussed.     

Gay tourism in a sun and beach destination

Gay tourism is seen as an attractive business opportunity for many destinations. However, there is a lack of research at identifying the resources necessary for success in this type of tourism. This work is aimed at filling this gap by using the premises of the resource based view and transfers them to the analysis of territories to identify the valuable resources that are required for a sun and beach destination to attract gay tourists. To this end, this study has focused on a gay tourism sub niche, tourists lodged in gay-exclusive resorts in Gran Canaria. In order to confirm the validity of this approach, the relationship between the satisfaction of gay tourists and the condition of the valuable resources was studied by means of a robust statistical new method, namely Bayesian model averaging. That method permits the inclusion of uncertainty in the theoretical models that determine destination competitiveness, thus reducing many of the problems that arise in the application of the more conventional statistical methods in this type of analysis.     

How do services of owner-occupied housing affect income inequality and redistribution?

This paper aims at analyzing the redistributive impact that the inclusion of the imputed rental market value of owner-occupied housing would have if used for quantifying the ability to pay rather than imputation based on cadastral values. We consider the Spanish personal income tax as reference, due to the differential treatment that it provides for imputed income from owner-occupied housing, together with the exceptionally high percentages of home ownership in Spain. By means of micro-simulation we explore the consequences of alternative possibilities for dealing with implicit income from owner-occupied housing.     

A theory of socially inefficient patent holdout

This paper proposes a framework to analyze holdout in patent licensing negotiations. We show that when the validity of a patent is probabilistic, a potential downstream user has incentives to shun to pay the price offered by a patent holder to license the technology and risk being brought to court. These incentives are exacerbated when jurisdictions are local, and the downstream producer can approach courts sequentially. The informational spillovers across trials imply that this firm often finds optimal to go to court aiming to invalidate the patent in a jurisdiction due to the knock-on effect on future jurisdictions. This process results in excessive litigation compared to when the jurisdiction is global. The distortions from sequential litigation are likely to be aggravated when final competition is accounted for or when patent injunctions are not allowed.