The Environmental Consequences of Rural and Urban Population Change: An Exploratory Spatial Panel Study of Forest Cover in the Southern United States, 2001–2006

This exploratory study examines the effects of rural and urban population change on forest cover at the local level across the southern United States. Using county‐level data from the National Land Cover Database and other U.S. government sources, we regressed the total area of forest cover on rural and urban population size in spatial panel models with two‐way fixed effects. When we controlled for several other factors, including the number of forestry operations at the county level, regression results indicate that urban change had no effect, but rural population size was positively related to total forest area, and this effect was most pronounced in and around Georgia. Thus, in areas of the southern United States, rural growth was associated with afforestation, not deforestation. We speculate on how this unusual finding contributes to the debate between ecological modernization and urban political economy implicated in previous cross‐national research.     

Supplementing a survey with respondent Twitter data to measure e-cigarette information exposure

ABSTRACT

Social media data are increasingly used by researchers to gain insights on individuals’ behaviors and opinions. Platforms like Twitter provide access to individuals’ postings, networks of friends and followers, and the content to which they are exposed. This article presents the methods and results of an exploratory study to supplement survey data with respondents’ Twitter postings, networks of Twitter friends and followers, and information to which they were exposed about e-cigarettes. Twitter use is important to consider in e-cigarette research and other topics influenced by online information sharing and exposure. Further, Twitter metadata provide direct measures of user’s friends and followers as opposed to survey self-reports. We find that Twitter metadata provide similar information to survey questions on Twitter network size without inducing recall error or other measurement issues. Using sentiment coding and machine learning methods, we find Twitter can elucidate on topics difficult to measure via surveys such as online expressed opinions and network composition. We present and discuss models predicting whether respondents’ tweet positively about e-cigarettes using survey and Twitter data, finding the combined data to provide broader measures than either source alone.     

The impact of US tariffs against China on US imports: Evidence for trade diversion?

We provide evidence on the existence of short-run trade diversion effects towards third countries as a consequence of tariff shocks. We exploit sudden policy changes in the context of the trade dispute between the United States and China in 2018. Based on a data set covering monthly product-level information on US imports from 30 countries for the period 2016 until May 2019, we employ a difference-in-differences estimation framework. Doing so, we can show a strong negative direct effect of US tariffs on US imports from China, but do not find evidence for significant short-run trade diversion effects towards third countries.     

Parental job loss and children's educational attainment in black and white middle-class families

Objectives. We aim to understand why blacks are significantly less likely than whites to perpetuate their middle-class status across generations. To do so, we focus on the potentially different associations between parental job loss and youth's educational attainment in black and white middle-class families.Methods. We use data from the Panel Study of Income Dynamics (PSID), following those children “born” into the survey between 1968 and 1979 and followed through age 21. We conduct multivariate regression analyses to test the association between parental job loss during childhood and youth's educational attainment by age 21.Results. We find that parental job loss is associated with a lesser likelihood of obtaining any postsecondary education for all offspring, but that the association for blacks is almost three times as strong. A substantial share of the differential impact of job loss on black and white middle-class youth is explained by race differences in household wealth, long-run measures of family income, and, especially, parental experience of long-term unemployment.Conclusions. These findings highlight the fragile economic foundation of the black middle class and suggest that intergenerational persistence of class status in this population may be highly dependent on the avoidance of common economic shocks.     

A Better Method for Analyzing the Costs and Benefits of Fundraising at Universities

This article develops a new methodology for a more comprehensive and useful analysis of the costs and benefits of fundraising, as well as the total costs and net benefits associated with development efforts in general. This approach does a better job of linking the timing of return of fundraising efforts and measuring the actual return on investments in fundraising (as opposed to the reported return) than the widely used guidelines from the Council for Advancement and Support of Education and the National Association of College and University Business Officers (CASE/NACUBO) (1990). The implications are a better methodology for practitioners that they can use to enhance internal decision making and a better methodology for boards to use in evaluating performance and accountability. Recently, other large, public universities have begun using several of these concepts in the assessment of their development offices (Rooney, 1998).     

Estimation in an empirical bayes model for longitudinal and cross‐sectionally clustered binary data

Some studies generate data that can be grouped into clusters in more than one way. Consider for instance a smoking prevention study in which responses on smoking status are collected over several years in a cohort of students from a number of different schools. This yields longitudinal data, also cross‐sectionaliy clustered in schools. The authors present a model for analyzing binary data of this type, combining generalized estimating equations and estimation of random effects to address the longitudinal and cross‐sectional dependence, respectively. The estimation procedure for this model is discussed, as are the results of a simulation study used to investigate the properties of its estimates. An illustration using data from a smoking prevention trial is given.