Elder poverty in an ageing world: Conditions of social vulnerability and low income for women in rich and middle-income nations

Social vulnerability for older persons, especially older women, due to insufficient income in retirement and earlier in life and low market earnings may be attributable to many sources, both demographic and economic, in our globalizing world. This paper examines the problems of population ageing, low incomes, and social spending on the elderly in comparative perspective, with a focus on older women in several rich and middle-income nations. We examine the United States, Canada, and a set of European nations using the LIS (Luxembourg Income Study) database, and three middle-income nations, focusing on Taiwan, China, and Mexico, around the turn of the century. In particular, we address what happens to older women as they outlive their husbands and have fewer claims on pensions and retirement wealth and maintain fewer productive capacities in the paid labor force. Issues that arise include the implications of policies relating to taxation, social spending, and transfers, as well as - of course - gender differentials in labor force participation, lifetime savings, and pre- and post-retirement incomes. Many older women, especially those in middle-income countries, also often share living arrangements with their adult children. We assess the net effects of existing policies on poverty and low income and wealth. While best practices may be identified, each nation must create its own set of mutually supportive policies that provide protection against global economic forces while at the same time encouraging self-reliance and efficient behavior, especially in the savings market. We conclude that policy can make a difference in outcomes, as shown for instance by the low cost but highly target effective Canadian efforts in fighting elder poverty, and by the Australian superannuation retirement income system. However, the developing economies of Mexico, China, and even Taiwan evolve from a tradition that does not yet support Western-style social insurance programs. In these nations, intergenerational co-residence is liable to be the key feature of antipoverty policy for elders in the coming decades

Income poverty and income support for minority and immigrant children in rich countries

The Luxembourg Income Study (LIS) and the databases underlying the European Statistics on Income and Living Conditions (EU-SILC) allow estimates of the extent to which immigrant and nonimmigrant children are poor across a wide range of rich nations. These data also allow estimates of the effects of social transfers that reduce poverty amongst all families with children. For all of the fourteen countries in the combined sample, children in migrant families have greater market-income poverty rates and greater disposable income poverty rates than do children in native-born families by a factor of about 2 to 1. Still, safety nets are important for all such families. For instance, before transfers, more than half of children in migrant families in France and Sweden are in poverty; however, after transfers, these rates are more than halved in these nations for both migrant and native-born children. In contrast, in the United States (US) the antipoverty effect of social transfers for both native and migrant families is negligible, because net transfers overall are insignificant in comparison with other rich countries. Thus the differences in benefits across countries, for both migrants and natives, are greater than are the differences within countries for these same groups. If the United States is to do better in fighting child poverty and realizing the economic and social potential of all of its children, it needs to expand its efforts on behalf of both immigrant and native children

Synthetic Control Groups: An introduction to key concepts, recent extensions, and a hands-on application

Alex Hollingsworth and Coady Wing are Assistant Professors in the School of Public and Environmental Affairs.Social scientists often look to policy change as a “natural experiment” that provides the opportunity to assess the causal effect of a policy treatment. For example, you might have data on an outcome both before and after an intervention for a “treated” unit and other "untreated" units. However, simply being untreated does not guarantee that those untreated units will serve as a valid control group for treated. Synthetic control methods use data on untreated units to produce a weighted control group that is more likely to serve as a valid control. These methods have become increasingly popular and can allow for causal inference in many settings where inference could not typically be done. This workshop introduces synthetic controls and will demonstrate a novel extension that exploits a machine-learning, data-driven approach that should be widely applicable to social scientists. In our work, we study the causal effects of Colorado’s recreational marijuana law on the sales of other legal psychoactive substances that might serve as complements or substitutes for marijuana. To do this we employ a novel extension of the synthetic control estimator. Synthetic control estimators are weighted combinations of untreated groups that are designed to serve as a control group. We extend the estimator most typical synthetic control estimator by incorporating a LASSO into the way the weights for the untreated groups are constructed. The data underlying our analysis come from a retail grocery store scanner database and DEA prescription drug monitoring data. We use detailed product codes to classify the sales of alcohol and tobacco products into a set of homogenous product categories. Then we construct a weekly state level time series for each alcohol and product category. In addition, we construct a weekly state-level time series for the sales of a large set of other product categories that are unlikely to be affected by the availability of legal recreational marijuana in any state. The alcohol and tobacco products in Colorado are potentially treated goods observed before and after Colorado legalized marijuana. The goal of our project is to estimate the counterfactual time series of psychoactive substance sales that would have prevailed in Colorado in the post-periods if the state had not legalized marijuana. The time series of the sales of alcohol, tobacco, and other products in other states represent a very large set of candidate comparison groups. The Synthetic Control Using Lasso (SCUL) approach is a machine-learning, data-driven way to comb through a very large set of candidate comparison time series, exclude a large number of candidates that are very different from the treated time series, and construct a weighted combination of a small number of candidates that closely resembles a target series. We use cross validation to choose the LASSO penalty parameter and to guard against overfitting the pre-treatment data. Constructing our synthetic control group using lasso has a few advantages over the traditional synthetic control estimator. The first is that the synthetic control can be constructed in a setting where there is a larger candidate set of control states and products than there are observations. This is a common occurrence in many "big data" settings. A second is that our estimator reduces researcher degrees of freedom by automating the model selection process. In general, the estimator allows for a comparison interrupted time series research design and should be broadly applicable to any research design where there are either a small number of treated units or where there are a larger number of candidate controls than observations. The results of our analysis suggest that Colorado’s recreational marijuana law did affect the sales of other legal psychoactive substances. Some products appear to be substitutes for legal marijuana and others seem to be complements. In particular, we find that the law reduced sales of hard liquor and malt liquor and increased sales of cases of light beer. The recreational marijuana law did not appear to affect sales of a many other alcohol and tobacco products. And it also did not appear to affect the volume of prescription opioid use in Colorado

National Policies Affect Poverty Rates in Rich Nations

The labour market alone cannot move struggling families out of poverty. The social welfare programs in a country have a great impact on the level of poverty among minority and native population.York's Knowledge Mobilization Unit provides services and funding for faculty, graduate students, and community organizations seeking to maximize the impact of academic research and expertise on public policy, social programming, and professional practice. It is supported by SSHRC and CIHR grants, and by the Office of the Vice-President Research & Innovation. [email protected] www.researchimpact.c

What can we learn about SARS-CoV-2 prevalence from testing and hospital data?

Measuring the prevalence of active SARS-CoV-2 infections is difficult because tests are conducted on a small and non-random segment of the population. But people admitted to the hospital for non-COVID reasons are tested at very high rates, even though they do not appear to be at elevated risk of infection. This sub-population may provide valuable evidence on prevalence in the general population. We estimate upper and lower bounds on the prevalence of the virus in the general population and the population of non-COVID hospital patients under weak assumptions on who gets tested, using Indiana data on hospital inpatient records linked to SARS-CoV-2 virological tests. The non-COVID hospital population is tested fifty times as often as the general population. By mid-June, we estimate that prevalence was between 0.01 and 4.1 percent in the general population and between 0.6 to 2.6 percent in the non-COVID hospital population. We provide and test conditions under which this non-COVID hospitalization bound is valid for the general population. The combination of clinical testing data and hospital records may contain much more information about the state of the epidemic than has been previously appreciated. The bounds we calculate for Indiana could be constructed at relatively low cost in many other states

Impacts of State Reopening Policy on Human Mobility

This study quantifies the effect of state reopening policies on daily mobility, travel, and mixing behavior during the COVID-19 pandemic. We harness cell device signal data to examine the effects of the timing and pace of reopening plans in different states. We quantify the increase in mobility patterns during the reopening phase by a broad range of cell-device-based metrics. Soon (four days) after reopening, we observe a 6% to 8% mobility increase. In addition, we find that temperature and precipitation are strongly associated with increased mobility across counties. The mobility measures that reflect visits to a greater variety of locations responds the most to reopening policies, while total time in vs. outside the house remains unchanged. The largest increases in mobility occur in states that were late adopters of closure measures, suggesting that closure policies may have represented more of a binding constraint in those states. Together, these four observations provide an assessment of the extent to which people in the U.S. are resuming movement and physical proximity as the COVID-19 pandemic continues

Back to Business and (Re)employing Workers? Labor Market Activity During State COVID-19 Reopenings

We study the effect of state reopening policies on a large set of labor market indicators through May 2020 to: (1) understand the recent increase in employment using longitudinal as well as cross-sectional data, (2) assess the likely trajectory of reemployment going forward, and (3) investigate the strength of job matches that were disrupted by COVID-19. Estimates from event studies and difference-in-difference regressions suggest that some of the recent increases in employment activity, as measured by cellphone data on work-related mobility, internet searches related to employment, and new and continuing unemployment insurance claims, were likely related to state reopenings, often predating actual reopening dates somewhat. We provide suggestive evidence that increases in employment stem from people returning to their prior jobs: reopenings are only weakly related to job postings, and longitudinal CPS data show that large shares of the unemployed-on-layoff and employed-but-absent in April who transitioned to employment in May remain in the same industry or occupation. Longitudinal CPS estimates further show declines in reemployment probabilities with time away from work. Taken together, these estimates suggest that employment relationships are durable in the short run, but raise concerns that employment gains requiring new employment matches may not be as rapid.Weinberg gratefully acknowledges support from UL1 TR002733 and R24 HD058484

Three essays on voluntary HIV testing and the HIV epidemic

This dissertation examines voluntary HIV testing in three specific contexts. The first chapter evaluates the effects of written informed consent regulations on HIV testing rates using a difference-in-differences approach to analyze a natural experiment that occurred when New York State weakened its HIV testing written informed consent regulations. The study finds that streamlining the consent process in New York increased HIV testing rates about 1.7 percentage points, which is a large relative increase of approximately 31%. The second chapter uses methods from the econometric literature on partially identified models to estimate HIV prevalence from biometric survey data in which some fraction of respondents opt out of the HIV testing module and are not tested for HIV. The method is applied to biometric survey data collected in four African countries: Kenya, Ethiopia, Lesotho, and Zimbabwe. A key finding is that the estimates of HIV prevalence that are standard in the literature represent an optimistic interpretation of the data. The biometric survey data are consistent with a range of levels of HIV prevalence, and standard estimates typically are much closer to the lower end of this range than they are to the higher end. The third chapter reports reanalyzes data from a field experiment conducted in Malawi (Thornton, 2008). The experiment was intended to evaluate the effect of participation in voluntary counseling and testing (VCT) on condom purchasing. The experiment employed a non-standard design. Subjects were randomly assigned one of 27 differently valued VCT participation incentives that were meant to induce self-selection into treatment and control groups. The design makes it difficult to understand what treatment effects are uncovered by the data. The reanalysis uses statistical tools from the literature on treatment effect heterogeneity to clarify what the incentives experiment reveals about the causal effects of VCT. It also evaluates the strengths and weaknesses of the incentive based design. One interesting result is that VCT participation caused an increase in condom purchasing among the initial people induced to participate in VCT. But expansions of the program to cover more than about 65% of the population actually reduced condom purchasing among the new program entrants

Statistical Inference For Stacked Difference in Differences and Stacked Event Studies

Dr. Coady Wing is an Associate Professor in the O’Neill School of Public and Environmental Affairs. Wing’s primary research examines the way that occupational regulations shape the way that different types of workers are used to provide health services; he also studies the health and economic welfare of veterans. More broadly, Wing studies the methodological and substantive conditions under which quasi-experimental research designs appear to reproduce the results of randomized experiments. His research has been published in the Journal of Health Economics, Journal of Policy Analysis and Management, and American Journal of Public Health, among others