Volume 13, Issue 1

This article explores the relationship between social protection and gender gaps in using enabling technology with the perspective of Sustainable Development Goals (SDGs), as both are SDG Indicators. A cross-country comparative examination of 38 countries shows that gender disparity in mobile phone ownership is significantly and negatively correlated with the overall mobile phone owning rate and the proportion of females owning a mobile phone in a country. The coverage of social insurance programmes in a country has very weak and negative association with gender disparity in mobile phone ownership, and the correlation is statistically insignificant. The cross-country variations in mobile phone ownership are significant if gender is not considered. The results suggest that the social protection systems in these countries may not be gender-sensitive enough. A number of practical implications are provided based on the empirical evidence, and improvements in future studies are also suggested since this article is an initial output of a series of follow-up research in the author’s plan.

How do we ensure a statewide voter registration database’s accuracy and integrity, especially when the database depends on aggregating decentralized, sub-state data with different list maintenance practices? We develop a Bayesian multivariate multilevel model to account for correlated patterns of change over time in multiple response variables, and label statewide anomalies using deviations from model predictions. We apply our model to California’s 22 million registered voters, using 25 snapshots from the 2020 presidential election. We estimate countywide change rates for multiple response variables such as changes in voter’s partisan affiliation and jointly model these changes. The model outperforms a simple interquartile range (IQR) detection when tested with synthetic data. This is a proof-of-concept that demonstrates the utility of the Bayesian methodology, as despite the heterogeneity in list maintenance practices, a principled, statistical approach is useful. At the county level, the total numbers of anomalies are positively correlated with the average election cost per registered voter between 2017 and 2019. Given the recent efforts to modernize and secure voter list maintenance procedures in the For the People Act of 2021 , we argue that checking whether counties or municipalities are behaving similarly at the state level is also an essential step in ensuring electoral integrity.

As public confidence in polling has been waning in the wake of recent elections (Narea, N. 2016. After 2016, Can we Ever Trust the Polls Again ? New Republic), many researchers have been seeking to diagnose the shortcomings in these data (Gelman, A., and J. Azari. 2017. “19 Things We Learned from the 2016 Election.” Statistics and Public Policy 4 (1): 1–10; Kennedy, C., M. Blumenthal, S. Clement, J. D. Clinton, C. Durand, C. Franklin, K. McGeeney, L. Miringoff, K. Olson, D. Rivers, L. Saad, G. E. Witt, and C. Wlezien. 2018. “An Evaluation of the 2016 Election Polls in the United States.” Public Opinion Quarterly 82 (1): 1–33; Mercer, A., C. Deane, and K. McGeeney. 2016. Why 2016 Election Polls Missed Their Mark. Pew Research Center . Also available at https://www.pewresearch.org/fact-tank/2016/11/09/why-2016-election-polls-missed-their-mark/). One conjecture stems from observed differences between polling results based on the methodological choice between live and non-live modes of survey administration (Enten, H. 2015. The Future of Polling May Depend on Donald Trumps Fate . FiveThirtyEight. Also available at https://fivethirtyeight.com/features/the-future-of-polling-may-depend-on-donald-trumps-fate/). While it has become commonplace to discuss “mode effect” on surveys, it reemerged in the political zeitgeist as the “Shy Trump” supporter hypothesis leading up to the 2016 U.S. Presidential Election (Edsall, T. B. 2016. How Many People Support Trump but Dont want to Admit it . New York Times). Motivated by the conflicting evidence for (Enns, P. K., J. Lagodny, and J. P. Schuldt. 2017. “Understanding the 2016 US Presidential Polls: The Importance of Hidden Trump Supporters.” Statistics, Politics, and Policy 8 (1): 41–63) and against (Coppock, A. 2017. “Did Shy Trump Supporters Bias the 2016 Polls? Evidence from a Nationally-Representative List Experiment.” Statistics, Politics, and Policy 8 (1): 29–40) this hypothesis, we built a complex statistical model that pools together results across multiple pollsters and throughout the election cycle while accounting for the nuances of these data. Specifically, we explored election data for the presence of mode effect using time series with a general additive mixed model (GAMM). We estimated mode effect at state and national levels to perform statistical mode adjustments, which we then compared to observed election results. In this paper, we utilized polling results from the United States Presidential Elections in 2016 (4208 polls) and 2020 (4133 polls). Using these data, we identified spatial trends and areas where mode effect was statistically significant at a 0.05 level. In summary, we make three contributions to the literature on mode effect adjustment in the poll aggregation setting. First, we present a straightforward and flexible statistical approach to estimating mode effect using time series data. In doing so, we help to bridge the gap between theory-focused statistical work and the social sciences. Second, we apply this method to two recent presidential elections, providing insight into the significance of mode effect. Third, we provide evidence for spatial mode effect trends suggesting regional voting behaviors that future scholars can explore.

Data from the Household Income and Labour Dynamics in Australia Survey is used to estimate distributions for the level of educational attainment for Australian indigenous and nonindigenous populations for the years 2001, 2006, 2014 and 2017. Bayesian inference is used to analyse how these ordinal categorical distributions have changed over time and to compare indigenous and nonindigenous distributions. Both the level of educational attainment and inequality in educational attainment are considered. To compare changes in levels over time, as well as inequality between the two populations, first order stochastic dominance and an index of educational poverty are used. To examine changes in inequality over time, two inequality indices and generalised Lorenz dominance are considered. Results are presented in terms of posterior densities for the indices and posterior probabilities for dominance for the dominance comparisons. We find some evidence of improvement over time, especially in the lower parts of the indigenous distribution and that inequality has significantly increased from 2001 to 2017.

Learning about the relationship between distance to landmarks and events and phenomena of interest is a multi-faceted problem, as it may require taking into account multiple dimensions, including: spatial position of landmarks, timing of events taking place over time, and attributes of occurrences and locations. Here I show that tree-based methods are well suited for the study of these questions as they allow exploring the relationship between proximity metrics and outcomes of interest in a non-parametric and data-driven manner. I illustrate the usefulness of tree-based methods vis-à-vis conventional regression methods by examining the association between: (i) distance to border crossings along the US-Mexico border and support for immigration reform, and (ii) distance to mass shootings and support for gun control.

Social inequality in the labor market leads to similarly unequal pension entitlements. From a life-course perspective, however, there are two components of inequality in the labor market: the degree of stability of employment until retirement, as well as the amount of gross income earned in periods of employment. The following analysis focuses on working-life and income trajectories of the birth cohorts 1935–1974 in East and West Germany until age 40. The results demonstrate a structural shift in the German labor market towards less stable employment in the first half of the working career. The labor market therefore offered increasingly less stable employment, with an especially stark negative trend for East Germans. Only West German women born after 1945 experienced a positive trend in employment stability. For employees of all birth cohorts, the analysis demonstrates that instability in the employment career has a strong negative effect on income and, subsequently, on pension wealth. The impact of income discrimination against those with less employment stability thus remained similar for the later-born despite the more widespread experience of employment interruptions.