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Research on behavioral functioning among Mexican-origin children primarily uses an individual-centered approach that ignores the residential context. In addition, most studies have been unable to consider an important measure of inequality for this population, legal status; and mental health of children with undocumented parents is underexplored. We address these gaps by investigating the influence of parental legal status and neighborhood characteristics on Mexican-origin children’s behavioral functioning using a multilevel approach.

We use data from the Los Angeles Family and Neighborhood Study and 2000 decennial census. Our primary focus is variation in internalizing and externalizing behavior problems among Mexican-origin youth (N = 2,535) with mothers who are undocumented, documented or naturalized citizens, or US-born using multilevel models.

The multilevel results show the importance of considering parental legal status. Mexican children of unauthorized mothers are more likely to exhibit internalizing and externalizing problems than all other groups of Mexican children. Furthermore, neighborhood-concentrated disadvantage is significantly associated with internalizing behavior problems, and neighborhood-concentrated affluence is significantly associated with externalizing behavior problems. In short, the results demonstrate the importance of considering both parental legal status and neighborhood contexts for understanding behavior problems of Mexican-origin children.

Our findings suggest that Mexican children’s mental health outcomes – measured by internalizing and externalizing behavior problems – vary significantly by parental legal status and neighborhood contexts. This study provides important nuances for public policy for health care prevention and interventions.

The dominance of road transport, both on passenger and freight movements, has reached alarming levels in what concerns their negative environmental impacts as well as societal and economic costs. To reverse this trend, a technology-driven approach and a behavioral change attitude need to be pursued. Promising results have been reported in Europe in the reduction of vehicle ownership, due to the introduction of an alternative transport mode known as car sharing. This work evaluates the contribution of car sharing to sustainable transport, based both in a technological shift and a potential behavioral change.

The state of the art on car sharing and policies presents the effects of these systems and how they have been promoted. As those effects can vary according to the geographical area, the users profile, and service characteristics, a worldwide analysis on car sharing systems covering more than 400 cities was performed. Average service indicators were quantified and characterization variables were accounted to those cities’ urban areas. Considering those normalized values, the authors performed an analysis of the car sharing system in Lisbon (Portugal). An initial assessment was made to estimate its current energy and environmental impacts. This outcome was then compared with the environmental and economic effects of using alternative vehicle technologies in car sharing. The results obtained enable a discussion of the more important variables for the success of the system and, consequently, to choose what policy instruments can help car sharing to succeed.

The results of the existing car sharing schemes reveal the positive contribution of car sharing to fill a “mobility gap” in sustainable transport. It works as a complement to other sustainable transport options and it impacts positively both society and car-sharers in terms of mobility costs, environmental, and energy implications. These results are more significant if a technology shift to electric mobility is promoted. Within the case study in Lisbon, the adoption of electric mobility would allow decreases up to 47% and 65% in energy consumption and CO₂ emissions, respectively. Moreover, the present value economic analysis revealed that, these systems will only be economically viable after approximately 7 years. A sensitivity analysis to the economic model was performed showing that the variables having higher influence were cost-related variables (reducing the break-even timeframe from 36% to 57%), such as vehicle purchase cost, insurance, maintenance and tax costs, and fuel cost.

Car sharing systems generally present social benefits to society as it leads to the reduction of car ownership, with all the positive effects that has on a lower demand for parking space, less congestion, reduced local pollutants and emissions. If the technology used by car sharing vehicles shifts from conventional to another type of technology, the effects both for society and car sharers are even more appealing from a social point of view. In the particular case study approached in the chapter, given the small scale of the car sharing network and low usage patterns, the local results have a low social impact at the city scale. A larger promotion of the system either with a more aggressive marketing campaign targeting specific population niches (e.g., environmentally conscious people), larger vehicle and parking availability, or better integration with the city’s public transport system could foster the deployment of the system, similarly to other cities.

Overall, the results obtained from this research work quantify the contribution of car sharing to sustainable transport and highlights the positive effects of promoting a technological shift. These facts reinforce the need for public policies to support the integration of car sharing within the city’s solutions to promote a more sustainable mobility. The successful deployment of car sharing systems can be influenced by policies targeting features such as allocation of parking, the fees and complementarity with public transport, signage and markings, and marketing of social and environmental benefits.

Despite the massive social benefits that the car has brought, it has become evident that the current mobility system is undermining the benefits it creates with substantial air quality problems, inactive lifestyles, deaths and injuries from accidents and major contributions to the global climate change challenge. The introduction of smart mobility innovations, in promising to challenge the existing regime of automobility may be a major policy opportunity, and also provide a source of new economic opportunity. However, it is far from clear that these opportunities will be recognized or, even where they are, realized due to the complexities of steering any transition in the mobility system.

This book sets out how we should understand the challenge of governing the smart mobility transition and, in this introductory chapter we set out the key arguments and contributions of each part of the book for addressing these challenges. The first section of the book focuses on how the role of the government is challenged by the growing network of actors and the new resource interdependencies that emerge from smart mobility. How these challenges come to be recognized and resolved is itself a critical part of the governance process as explored in the second section. The third section examines the changing context of governance and the capacity of the state to act to steer the transition. This allows us to identify, in our final concluding section, a set of critical topics for those researching and implementing the smart mobility revolution.

The authors discuss the econometric underpinnings of Barro (2006)'s defense of the rare disaster model as a way to bring back an asset pricing model “into the right ballpark for explaining the equity-premium and related asset-market puzzles.” Arbitrarily low-probability economic disasters can restore the validity of model-implied moment conditions only if the amplitude of disasters may be arbitrary large in due proportion. The authors prove an impossibility theorem that in case of potentially unbounded disasters, there is no such thing as a population empirical likelihood (EL)-based model-implied probability distribution. That is, one cannot identify some belief distortions for which the EL-based implied probabilities in sample, as computed by Julliard and Ghosh (2012), could be a consistent estimator. This may lead to consider alternative statistical discrepancy measures to avoid the problem with EL. Indeed, the authors prove that, under sufficient integrability conditions, power divergence Cressie-Read measures with positive power coefficients properly define a unique population model-implied probability measure. However, when this computation is useful because the reference asset pricing model is misspecified, each power divergence will deliver different model-implied beliefs distortion. One way to provide economic underpinnings to the choice of a particular belief distortion is to see it as the endogenous result of investor's choice when optimizing a recursive multiple-priors utility a la Chen and Epstein (2002). Jeong et al. (2015)'s econometric study confirms that this way of accommodating ambiguity aversion may help to address the Equity Premium puzzle.

The authors propose a semiparametric approach for testing independence between two infinite-order cointegrated vector autoregressive series (IVAR(∞)). The procedures considered can be viewed as extensions of classical methods proposed by Haugh (1976, JASA) and Hong (1996b, Biometrika) for testing independence between stationary univariate time series. The tests are based on the residuals of long autoregressions, hence allowing for computational simplicity, weak assumptions on the form of the underlying process, and a direct interpretation of the results in terms of innovations (or shocks). The test statistics are standardized versions of the sum of weighted squares of residual cross-correlation matrices. The weights depend on a kernel function and a truncation parameter. Multivariate portmanteau statistics can be viewed as a special case of our procedure based on the truncated uniform kernel. The asymptotic distributions of the test statistics under the null hypothesis are derived, and consistency is established against fixed alternatives of serial cross-correlation of unknown form. A simulation study is presented which indicates that the proposed tests have good size and power properties in finite samples.

This chapter contributes to the growing global VAR (GVAR) literature by showing how global and national shocks can be identified within a GVAR framework. The usefulness of the proposed approach is illustrated in an application to the analysis of the interactions between public debt and real output growth in a multicountry setting, and the results are compared to those obtained from standard single country VAR analysis. We find that on average (across countries) global shocks explain about one-third of the long-horizon forecast error variance of output growth, and about one-fifth of the long-run variance of the rate of change of debt-to-GDP. Evidence on the degree of cross-sectional dependence in these variables and their innovations are exploited to identify the global shocks, and priors are used to identify the national shocks within a Bayesian framework. It is found that posterior median debt elasticity with respect to output is much larger when the rise in output is due to a fiscal policy shock, as compared to when the rise in output is due to a positive technology shock. The cross-country average of the median debt elasticity is 1.45 when the rise in output is due to a fiscal expansion as compared to 0.76 when the rise in output follows from a favorable output shock.

The authors propose to estimate a varying coefficient panel data model with different smoothing variables and fixed effects using a two-step approach. The pilot step estimates the varying coefficients by a series method. We then use the pilot estimates to perform a one-step backfitting through local linear kernel smoothing, which is shown to be oracle efficient in the sense of being asymptotically equivalent to the estimate knowing the other components of the varying coefficients. In both steps, the authors remove the fixed effects through properly constructed weights. The authors obtain the asymptotic properties of both the pilot and efficient estimators. The Monte Carlo simulations show that the proposed estimator performs well. The authors illustrate their applicability by estimating a varying coefficient production frontier using a panel data, without assuming distributions of the efficiency and error terms.

Research on weighting of measures often examines only one incentive at a time (usually bonus) and provide mixed findings regarding the relevance of non-financial performance (NFM) measures to evaluate and reward long-term time horizon employees. Using proprietary data from an auto dealership organization, we show that financial measures (FM) are weighted more for bonus than they are weighted for merit raise and promotion but NFM are weighted more than FM for merit raise and promotion. Thus, the temporal orientations of the measures and incentives seem to be aligned: the short-term (long-term) nature of FM (NFM) parallel’s the time horizon of the incentives. Next, our exploratory research questions find that for bonuses, both FM and NFM exert similar levels of significant and positive influence on junior and senior managers. But for promotions, the influence of FM is insignificant for both groups. In contrast, the influence of NFM on promotions is not only significant for both groups but is significantly greater for junior managers than it is for senior managers. That is, the evaluations of NFM for senior managers are less influential on their promotion than they are for junior managers suggesting that promotions for senior managers are often based on factors other than their formal performances.