BIG DATA AND BIG CITIES: THE PROMISES AND LIMITATIONS OF IMPROVED MEASURES OF URBAN LIFE

New, “big data” sources allow measurement of city characteristics and outcome variables at higher collection frequencies and more granular geographic scales than ever before. However, big data will not solve large urban social science questions on its own. Big urban data has the most value for the study of cities when it allows measurement of the previously opaque, or when it can be coupled with exogenous shocks to people or place. We describe a number of new urban data sources and illustrate how they can be used to improve the study and function of cities. We first show how Google Street View images can be used to predict income in New York City, suggesting that similar imagery data can be used to map wealth and poverty in previously unmeasured areas of the developing world. We then discuss how survey techniques can be improved to better measure willingness to pay for urban amenities. Finally, we explain how Internet data is being used to improve the quality of city services. (JEL R1, C8, C18)     

Small area estimation: the EBLUP estimator based on spatially correlated random area effects

This paper deals with small area indirect estimators under area level random effect models when only area level data are available and the random effects are correlated. The performance of the Spatial Empirical Best Linear Unbiased Predictor (SEBLUP) is explored with a Monte Carlo simulation study on lattice data and it is applied to the results of the sample survey on Life Conditions in Tuscany (Italy). The mean squared error (MSE) problem is discussed illustrating the MSE estimator in comparison with the MSE of the empirical sampling distribution of SEBLUP estimator. A clear tendency in our empirical findings is that the introduction of spatially correlated random area effects reduce both the variance and the bias of the EBLUP estimator. Despite some residual bias, the coverage rate of our confidence intervals comes close to a nominal 95%.     

An individual treatment programme for sexually abused adult males: description and preliminary findings

Growing recognition of male sexual abuse and its potentially debilitating effects has underscored the need to develop effective treatment interventions for this population. The present study describes an individual treatment programme that was developed for adult males who have experienced childhood sexual abuse. The treatment programme focused on three areas related to sexual abuse, specifically feelings of self‐blame, anger and anxiety. The study also presents preliminary findings on treatment effects, using self‐report measures that five participants completed prior to treatment and at various assessments following treatment termination. Overall findings indicated improvements in behavioural self‐blame, anger, state anxiety and trait anxiety. Treatment did not appear to have an effect on characterological self‐blame over the long term. The study's findings are limited by the reliance on self‐report data and the absence of a comparison group. As such, our findings should be viewed as an initial contribution to the currently limited empirical data on treatment effects for sexually abused adult males. Copyright © 2005 John Wiley & Sons, Ltd.     

Threshold‐free estimation of functional antibody titers of a group B streptococcus opsonophagocytic killing assay

Opsonophagocytic killing assays (OPKA) are routinely used for the quantification of bactericidal antibodies in blood serum samples. Quantification of the OPKA readout, the titer, provides the basis for the statistical analysis of vaccine clinical trials having functional immune response endpoints. Traditional OPKA titers are defined as the maximum serum dilution yielding a predefined bacterial killing threshold value, and they are estimated by fitting a dose‐response model to the dilution‐killing curve. This paper illustrates a novel definition of titer, the threshold‐free titer, which preserves biological interpretability while not depending on any killing threshold or on a postulated shape of the dose‐response curve. These titers are shown to be more precise than the traditional threshold‐based titers when using simulated and experimental group B streptococcus OPKA experimental data. Also, titer linearity is shown to be not measurable when using threshold‐based titers, whereas it becomes measurable using threshold‐free titers. The biological interpretability and operational characteristics demonstrated here indicate that threshold‐free titers are an appropriate tool for the routine analysis of OPKA data. Copyright © 2015 John Wiley & Sons, Ltd.     

Spouses’ earnings association and inequality: A non-linear perspective

We analyze the association between spouses’ earnings taking account of non-linearities along both spouses’ distribution of earnings. We also document the non-linearity of the relationships between earnings and labor force participation, earnings and couple formation, and earnings and number of children. Using simulations, we then analyze how changes in spouses’ rank-dependence structure, labor force participation and couple formation contribute to the upsurge in inequality in the U.S between 1967 and 2018. We find that an increased tendency towards positive sorting contributed substantially to the rise in inequality only among dual-earner couples, while it contributed little to overall inequality across households. Temporal and distributional heterogeneity are important, as earnings association had a more substantial role in the bottom of the earnings distribution and in recent years. The decline in couple formation contributed substantially to the rise in inequality, while the increase in female labor force participation and the fertility decline had equalizing effects.

     

Endogenous fertility,endogenous lifetime and economic growth: the role of child policies

We examine the effects of child policies on both transitional dynamics and long-term demo-economic outcomes in an overlapping-generations neoclassical growth model à la Chakraborty (J Econ Theory 116(1):119–137, 2004) extended with endogenous fertility under the assumption of weak altruism towards children. The government invests in public health, and an individual’s survival probability at the end of youth depends on health expenditure. We show that multiple development regimes can exist. However, poverty or prosperity does not necessarily depend on the initial conditions, since they are the result of how a child policy is designed. A child tax, for example, can be used effectively to enable those economies that were entrapped in poverty to prosper. There is also a long-term welfare-maximising level of the child tax. We show that a child tax can be used to increase capital accumulation, escape from poverty and maximise long-term welfare also when (a) a public pay-as-you-go pension system is in place and (b) the government issues an amount of public debt. Interestingly, there also exists a couple child tax–health tax that can be used to find the second-best optimum optimorum. In addition, we show that results are robust to the inclusion of decisions regarding the child quantity–quality trade-off under the assumption of impure altruism. In particular, there exists a threshold value of the child tax below (resp. above) which child quality spending is unaffordable (resp. affordable) and different scenarios are in existence.     

A note on transformed likelihood approach in linear dynamic panel models

The transformed likelihood approach to estimation of fixed effects dynamic panel data models is shown to present very good inferential properties but it is not directly implemented in the most diffused statistical software. The present paper aims at showing how a simple model reformulation can be adopted to describe the problem in terms of classical linear mixed models. The transformed likelihood approach is based on the first differences data transformation, the following results derive from a convenient reformulation in terms of deviations from the first observations. Given the invariance to data transformation, the likelihood functions defined in the two cases coincide. Resulting in a classical random effect linear model form, the proposed approach significantly improves the number of available estimation procedures and provides a straightforward interpretation for the parameters. Moreover, the proposed model specification allows to consider all the estimation improvements typical of the random effects model literature. Simulation studies are conducted in order to study the robustness of the estimation method to mean stationarity violation.     

Studying the relationship between anxiety and school achievement: evidence from PISA data

Statistical Methods & Applications - Using the Programme for International Student Assessment (PISA) 2015 data for Italy, this paper offers a complete overview of the relationship between test...