Family, Work, Economy, or Social Policy: Examining Poverty among Children of Single Mothers in Affluent Democracies between 1985-2016

Children of single mothers face higher rates of poverty than children in two-parent households in practically every affluent democracy. While this difference is widely acknowledged, there is little consensus regarding the causes of their poverty and, as a result, little consensus on the best way to address poverty among these children. Explanations include both individual-level, structural, and political explanations in four areas: family structure, labor force activity, economic performance, and welfare generosity. Previous research, however, tends to focus on only one of these four aspects at a time. Using data from the Luxembourg Income Study and the Organisation for Economic Co-operation and Development, spanning a period of 31 years and 25 countries, I test each of these four explanations, examining the effects on children in single mother households separately (n=105,814) and children in both single mother households and children in two-parent households (n=668,549), conducting random intercept between-within logistic regression analysis. Individual-level measures of family structure and labor market activity affect child poverty generally in the expected way. Taking advantage of the longitudinal data at the country level, I focus on within-country change of the structural and political variables. Within-country economic performance is not significantly related to poverty, but welfare generosity, namely family allowances, significantly reduce the odds of poverty. Further, while the effects of family allowance spending are similar for children in both single mother and two parent households, they are stronger for the former than the latter. Yet, the disadvantage of living in a single mother household persists

Social Spending, Poverty, and Immigration: A Systematic Analysis of Welfare State Effectiveness and Nativity in 24 Upper- and Middle-Income Democracies

Previous research has highlighted the disadvantaged position immigrants often face in the economy, particularly when it comes to labor market outcomes such as employment or earnings. Extending this literature, the present study evaluates the economic exclusion of immigrants, conceptualized not as labor market outcomes but as relative poverty. This study examines the relationship between welfare generosity and immigrant poverty across rich western democracies and compares this relationship with that of native poverty. One publicly held belief is that immigrants disproportionately benefit from welfare generosity, while the literature on welfare chauvinism suggests greater social spending may not necessarily benefit immigrants. Furthermore, the effects may vary by spending and immigrant type. This study uses the Luxembourg Income Study to consider differences in the effects of welfare generosity on the odds an immigrant or native household is poor, how this effect varies by the type of spending, and how the effect changes depending on factors such as region of origin or citizenship status. Using four waves of data circa 2004 to 2014 across 24 upper- and middle-income democracies, the results show some support for welfare chauvinism and advantages to being an intra-EU immigrant and citizen immigrant

Assessing the Mental Health of Older Hispanic/Latinx Adults: Focus on the Impact of the Hispanic Cultural Value of Fatalismo on Depressive Symptomatology

Introduction: The impact of depression in older Hispanic/Latinx adults in the United States is poorly understood. The influence of the deeply embedded Hispanic/Latinx cultural value of fatalismo, referring to the belief that one’s future is predetermined and related to feelings of helplessness, has been found to negatively impact their psychological health [1, 2]. The present study explores the association between the Hispanic/Latinx cultural value of fatalismo and mental health. Objective: To contribute to the identification and understanding of psycho-social-cultural determinants of depression in the Hispanic/Latinx population. Methods: Using data from the Health and Retirement Study (HRS) dataset, a longitudinal panel study of U.S. adults over the age a 50 [3], multiple logistic regressions were used to investigate the association between demographic and psycho-social-cultural characteristics and depression. Depression was measured by a modified version of the Center for Epidemiological Studies Depression Scale (CES-D). The question “I often feel helpless in dealing with the problems in life” assessed fatalismo. Results: Results indicate that being Hispanic (OR=1.75; 95%CI 1.60-1.91), female gender (OR=1.5; 95%CI 1.43-1.64), living below the poverty level (OR=2.61; 95%CI 2.29-2.85), and feeling helpless (OR=4.27; 95%CI 3.80-4.79) were significant predictors of elevated depressive symptoms. When adjusting for race/ethnicity, gender, socioeconomic status, level of education, and age, the highest predictor of depressive symptoms was feeling helpless (OR=3.69; 95%CI 3.10-4.40). Discussion: Consistent with previous findings, the key determinants of depressive symptoms were race/ethnicity, gender, socioeconomic status, and feeling helpless. Conclusions: These findings provide evidence for the association between Hispanic/Latinx psycho-socio-cultural factors and mental health

Observing many researchers using the same data and hypothesis reveals a hidden universe of uncertainty

Significance Will different researchers converge on similar findings when analyzing the same data? Seventy-three independent research teams used identical cross-country survey data to test a prominent social science hypothesis: that more immigration will reduce public support for government provision of social policies. Instead of convergence, teams’ results varied greatly, ranging from large negative to large positive effects of immigration on social policy support. The choices made by the research teams in designing their statistical tests explain very little of this variation; a hidden universe of uncertainty remains. Considering this variation, scientists, especially those working with the complexities of human societies and behavior, should exercise humility and strive to better account for the uncertainty in their work. Abstract This study explores how researchers’ analytical choices affect the reliability of scientific findings. Most discussions of reliability problems in science focus on systematic biases. We broaden the lens to emphasize the idiosyncrasy of conscious and unconscious decisions that researchers make during data analysis. We coordinated 161 researchers in 73 research teams and observed their research decisions as they used the same data to independently test the same prominent social science hypothesis: that greater immigration reduces support for social policies among the public. In this typical case of social science research, research teams reported both widely diverging numerical findings and substantive conclusions despite identical start conditions. Researchers’ expertise, prior beliefs, and expectations barely predict the wide variation in research outcomes. More than 95% of the total variance in numerical results remains unexplained even after qualitative coding of all identifiable decisions in each team’s workflow. This reveals a universe of uncertainty that remains hidden when considering a single study in isolation. The idiosyncratic nature of how researchers’ results and conclusions varied is a previously underappreciated explanation for why many scientific hypotheses remain contested. These results call for greater epistemic humility and clarity in reporting scientific findings

The Crowdsourced Replication Initiative: Investigating Immigration and Social Policy Preferences. Executive Report.

In an era of mass migration, social scientists, populist parties and social movements raise concerns over the future of immigration-destination societies. What impacts does this have on policy and social solidarity? Comparative cross-national research, relying mostly on secondary data, has findings in different directions. There is a threat of selective model reporting and lack of replicability. The heterogeneity of countries obscures attempts to clearly define data-generating models. P-hacking and HARKing lurk among standard research practices in this area.This project employs crowdsourcing to address these issues. It draws on replication, deliberation, meta-analysis and harnessing the power of many minds at once. The Crowdsourced Replication Initiative carries two main goals, (a) to better investigate the linkage between immigration and social policy preferences across countries, and (b) to develop crowdsourcing as a social science method. The Executive Report provides short reviews of the area of social policy preferences and immigration, and the methods and impetus behind crowdsourcing plus a description of the entire project. Three main areas of findings will appear in three papers, that are registered as PAPs or in process

The Crowdsourced Replication Initiative: Investigating Immigration and Social Policy Preferences. Executive Report

Breznau N, Rinke EM, Wuttke A, et al. The Crowdsourced Replication Initiative: Investigating Immigration and Social Policy Preferences. Executive Report. 2019

Observing many researchers using the same data and hypothesis reveals a hidden universe of uncertainty.

This study explores how researchers' analytical choices affect the reliability of scientific findings. Most discussions of reliability problems in science focus on systematic biases. We broaden the lens to emphasize the idiosyncrasy of conscious and unconscious decisions that researchers make during data analysis. We coordinated 161 researchers in 73 research teams and observed their research decisions as they used the same data to independently test the same prominent social science hypothesis: that greater immigration reduces support for social policies among the public. In this typical case of social science research, research teams reported both widely diverging numerical findings and substantive conclusions despite identical start conditions. Researchers' expertise, prior beliefs, and expectations barely predict the wide variation in research outcomes. More than 95% of the total variance in numerical results remains unexplained even after qualitative coding of all identifiable decisions in each team's workflow. This reveals a universe of uncertainty that remains hidden when considering a single study in isolation. The idiosyncratic nature of how researchers' results and conclusions varied is a previously underappreciated explanation for why many scientific hypotheses remain contested. These results call for greater epistemic humility and clarity in reporting scientific findings

Observing many researchers using the same data and hypothesis reveals a hidden universe of uncertainty

This study explores how researchers' analytical choices affect the reliability of scientific findings. Most discussions of reliability problems in science focus on systematic biases. We broaden the lens to emphasize the idiosyncrasy of conscious and unconscious decisions that researchers make during data analysis. We coordinated 161 researchers in 73 research teams and observed their research decisions as they used the same data to independently test the same prominent social science hypothesis: that greater immigration reduces support for social policies among the public. In this typical case of social science research, research teams reported both widely diverging numerical findings and substantive conclusions despite identical start conditions. Researchers' expertise, prior beliefs, and expectations barely predict the wide variation in research outcomes. More than 95% of the total variance in numerical results remains unexplained even after qualitative coding of all identifiable decisions in each team's workflow. This reveals a universe of uncertainty that remains hidden when considering a single study in isolation. The idiosyncratic nature of how researchers' results and conclusions varied is a previously underappreciated explanation for why many scientific hypotheses remain contested. These results call for greater epistemic humility and clarity in reporting scientific findings

Observing Many Researchers Using the Same Data and Hypothesis Reveals a Hidden Universe of Uncertainty

Breznau N, Rinke EM, Wuttke A, et al. Observing Many Researchers Using the Same Data and Hypothesis Reveals a Hidden Universe of Uncertainty. 2021.This study explores how researchers’ analytical choices affect the reliability of scientific findings. Most discussions of reliability problems in science focus on systematic biases. We broaden the lens to include conscious and unconscious decisions that researchers make during data analysis and that may lead to diverging results. We coordinated 161 researchers in 73 research teams and observed their research decisions as they used the same data to independently test the same prominent social science hypothesis: that greater immigration reduces support for social policies among the public. In this typical case of research based on secondary data, we find that research teams reported widely diverging numerical findings and substantive conclusions despite identical start conditions. Researchers’ expertise, prior beliefs, and expectations barely predicted the wide variation in research outcomes. More than 90% of the total variance in numerical results remained unexplained even after accounting for research decisions identified via qualitative coding of each team’s workflow. This reveals a universe of uncertainty that is hidden when considering a single study in isolation. The idiosyncratic nature of how researchers’ results and conclusions varied is a new explanation for why many scientific hypotheses remain contested. It calls for greater humility and clarity in reporting scientific findings