Modeling the contemporaneous relationship in a continuous variable panel model

Continuous-variable panel models are widely used in social and business research to assess the relationships between variables over time given measurements at several waves on a single sample of individuals. Emphasis is often placed on estimating and testing the cross-effects which are the regression coefficients indicating the lagged effect of one variable on another. For the simple two-variable model we consider the problem of incorpomating the contemporaneous relationship between the variables into the model. Three extensions of the “independent regressions” model are considered. Their similarities and differences are examined. The use of the models is illustrated by examining data on the attitudes toward the criminal justice system and capital punishment for a panel of petit jurors.     

Cross‐Movement Coalition Formation: Bridging the Labor‐Environment Divide*

Relations between the labor and environmental movements exist within a complex web of clashing interests, electoral politics, and attempts to form enduring blue‐green coalitions. Unions and other labor organizations are often portrayed as solely interested in economic growth. Environmental organizations are often seen as solely interested in preserving the natural world at the expense of economic growth, thus creating a direct conflict between the interests of labor and environmental organizations. Despite these perceived differences, efforts to bridge the divide between the two movements are increasingly common. This article examines the formation of a collective identity shared by workers and environmentalists participating in the coalition. I develop this argument through an ethnographic analysis of the formation of a blue‐green coalition, the Alliance for a Healthy Tomorrow, using in‐depth interviews, observations, and content analyses. I demonstrate how coalition leaders and bridge brokers work to bridge and consolidate the identities of labor and environmental groups to campaign for environmental health regulatory changes in Massachusetts.     

Fertility assimilation of immigrants: Evidence from count data models

This study applies count data estimation techniques to investigate the fertility adjustment of immigrants in the destination country. Data on completed fertility are taken from the 1996 wave of the German Socioeconomic Panel (GSOEP). While the economic literature stresses the role of prices and incomes as determinants of fertility, the demographic literature discusses whether assimilation or disruption effects dominate immigrants' fertility after migration. We find evidence in favor of the assimilation model according to which immigrant fertility converges to native levels over time. In addition, we confirm the negative impact of female human capital on fertility outcomes. Received: 7 January 1999/Accepted: 10 August 1999     

The hidden majority/minority consensus: Minorities show similar preference patterns of immigrant support as the majority population

The acceptance of new arrivals has become an important topic regarding the social cohesion of the receiving countries. However, previous studies focused only on the native population's drivers of attitudes towards immigrants, disregarding that immigrant-origin inhabitants now form a considerable part of the population. To test whether the drivers for the willingness to support immigrants are the same for natives and immigrants and their descendants, we rely on a vignette study conducted in a representative German online panel (N = 3149) which contains an overrepresentation of immigrant-origin respondents. We presented participants with three vignettes of potential immigrants, varying, amongst other factors, economic prospects, safe and war-ridden countries of origin (to capture deservingness), as well as religious identity. While we find that minority members are generally slightly more welcoming towards immigrants than majority members, at their core are the same factors that drive attitudes to immigrants in both groups: economic cost, cultural similarity, and deservingness. However, we observe differences at the margins: Immigrant-origin respondents take into account economic prospects to a lesser degree than majority members do, and by trend, they are less likely to distinguish between immigrants from war-ridden and safe countries of origin. Furthermore, we can show that the preference for immigrants with the same religious identities not only occurs among majority members but also among minority members.     

THE EFFECT OF LEGALIZING RETAIL MARIJUANA ON HOUSING VALUES: EVIDENCE FROM COLORADO

Does legalizing retail marijuana generate more benefits than costs? This paper provides a first step toward addressing that question by measuring the benefits and costs that are capitalized into housing values. We exploit the time‐series and cross‐sectional variations in the adoption of Colorado's municipality retail marijuana laws (RMLs) and examine the effect on housing values with a difference‐in‐differences strategy. Our estimates show that the legalization leads to an average 6% increase in housing values, indicating that the capitalized benefits outweigh the costs. In addition, we find suggestive evidence that this relatively large housing value appreciation is likely due to RMLs inducing strong housing demand while having no discernible effect on housing supply. Finally, we show that the effect of RMLs is heterogeneous across locations and property types. (JEL K20, R28)     

Treatment policy estimands for recurrent event data using data collected after cessation of randomised treatment

In the past, many clinical trials have withdrawn subjects from the study when they prematurely stopped their randomised treatment and have therefore only collected ‘on‐treatment’ data. Thus, analyses addressing a treatment policy estimand have been restricted to imputing missing data under assumptions drawn from these data only. Many confirmatory trials are now continuing to collect data from subjects in a study even after they have prematurely discontinued study treatment as this event is irrelevant for the purposes of a treatment policy estimand. However, despite efforts to keep subjects in a trial, some will still choose to withdraw. Recent publications for sensitivity analyses of recurrent event data have focused on the reference‐based imputation methods commonly applied to continuous outcomes, where imputation for the missing data for one treatment arm is based on the observed outcomes in another arm. However, the existence of data from subjects who have prematurely discontinued treatment but remained in the study has now raised the opportunity to use this ‘off‐treatment’ data to impute the missing data for subjects who withdraw, potentially allowing more plausible assumptions for the missing post‐study‐withdrawal data than reference‐based approaches. In this paper, we introduce a new imputation method for recurrent event data in which the missing post‐study‐withdrawal event rate for a particular subject is assumed to reflect that observed from subjects during the off‐treatment period. The method is illustrated in a trial in chronic obstructive pulmonary disease (COPD) where the primary endpoint was the rate of exacerbations, analysed using a negative binomial model.     

Economics reasoning by econometrics or artificial intelligence

Currently, the mechanization of economic forecasting relies on econometric models with obvious limitations. Artificial Intelligence holds out several new possibilities. Will these innovations limit themselves to correcting existing defects, thereby improving the performance of established models, or will they bring about new approaches in modelling and in economic theory itself? Although this paper in no way pretends to answer this question, it will touch upon it as several avenues of research are outlined.     

Nonparametric multiple comparison procedures for unbalanced two-way layouts

In this paper, we consider nonparametric multiple comparison procedures for unbalanced two-way factorial designs under a pure nonparametric framework. For multiple comparisons of treatments versus a control concerning the main effects or the simple factor effects, the limiting distribution of the associated rank statistics is proven to satisfy the multivariate totally positive of order two condition. Hence, asymptotically the proposed Hochberg procedure strongly controls the familywise type I error rate for the simultaneous testing of the individual hypotheses. In addition, we propose to employ Shaffer's modified version of Holm's stepdown procedure to perform simultaneous tests on all pairwise comparisons regarding the main or simple factor effects and to perform simultaneous tests on all interaction effects. The logical constraints in the corresponding hypothesis families are utilized to sharpen the rejective thresholds and improve the power of the tests.     

Weighted Logrank Permutation Tests for Randomly Right Censored Life Science Data

In biomedical research, weighted logrank tests are frequently applied to compare two samples of randomly right censored survival times. We address the question how to combine a number of weighted logrank statistics to achieve good power of the corresponding survival test for a whole linear space or cone of alternatives, which are given by hazard rates. This leads to a new class of semiparametric projection tests that are motivated by likelihood ratio tests for an asymptotic model. We show that these tests can be carried out as permutation tests and discuss their asymptotic properties. A simulation study together with the analysis of a classical data set illustrates the advantages.