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Researchers increasingly use aggregate relational data to learn about the size and distribution of survey respondents’ weak-tie personal networks. Aggregate relational data are collected by asking questions about respondents’ connectedness to many different groups (e.g., “How many teachers do you know?”). This approach can be powerful, but to use aggregate relational data, researchers must locate external information about the size of each group from a census or administrative records (e.g., the number of teachers in the population). This need for external information makes aggregate relational data difficult or impossible to collect in many settings. Here, the authors show that relatively simple modifications can overcome this need for external data, significantly increasing the flexibility of the method and weakening key assumptions required by the associated estimators. The key idea is to estimate the size of these groups from the sample of survey respondents, rather than relying on external sources of information. These methods are appropriate for using a sample survey to study the size and distribution of weak-tie network connections. They can also be used as part of the network scale-up method to estimate the size of hidden populations. The authors illustrate this approach with two empirical studies: a large simulation study and original household survey data collected in Hanoi, Vietnam.

This study checks the hypothesis that sustainable well-being is a determinant factor of fertility through the application of a multiversal method based on the assumptions of Vibration of Effects (VoE) model of multiversal sampling on the Italian Equitable and Sustain-able Well-being (BES) dataset. For 48 social indicators of well-being, adopting the Province as statistical unit, the estimate of linear effect has been systematically sampled 1,024 times. This multiversal sampling allows to estimate the component of the hypothetical variability of the estimate of the effect size that depends on the theoretical choices of the analysis. This component is commonly underestimated, hidden, or ignored in regular multivariate analysis or classical bootstrapping. Results do not allow to claim that any of the 48 social indicators is a robust linear predictor of fertility. Indicators associated with high statistical significance are also at risk of misleading the causal relationship. Implications for policy-making and the future direction of the research on BES are discussed in the conclusive remarks.

Multiverse analysis is a paradigm for estimation of the uncertainty regarding the veracity of a scientific claim, through a systemic not random sampling of a massive set of specifications of a model, which is the multiverse. Specifications, once fit on a sample, result in statistics. Observation of the variability of result statistics across groups of specifications is considered useful for checking the robustness of the claim or for better understanding its premises. However, the assumptions behind theseprocedures are not explicit and not always univocal: generally, the proprieties of a multiversal sample hold uniformly only for non-parametric assumptions. A new formal categorisation of the analytical choices in modelling is proposed. It helps to make the assumption of the multiverse more transparent and to check the parametric assumption. These theories are applied to the panel dataset. The analytical process is documented from the design of the hypothesis to the computation of the distribution of estimates for the same generalised linear effect. The analysis highlights the sensitivity of the model to the estimation of fixed covariates in the panel and how these results are so sensitive to this decision to twist the estimates of the linear effect. In the conclusion, the theory of multiversal sampling is related to the debate on how to weigh a multiverse.

This manuscript is a comprehensive historical and theoretical examination of the development of ‘multiversal methods’ as a response to the replication crisis. Multiversal methods are statistical procedures designed to assess the uncertainty arising from analyst-driven decisions in inferential models based on statistical regressions. The replication crisis is a surge in discovering that many studies fail to replicate the findings of previous studies. Replication crisis has raised concerns about the reliability and credibility of scientific research, particularly in social sciences and medicine. Section I provides a non-technical overview of the design of causal inference based on statistical regressions. Furtherly, it outlines and comments on the procedures to compute multiversal statistics. Section II presents the historical and social context within occurred key epistemological innovations contributing to the development of the theories behind multiversal methods. The section argues why and what these advancements drew from the epistemology of misinformation (‘bullshit epistemology’) for a sense of urgency for remedies to some enduring issues in scientific production: publication bias and p-hacking. Section III is a comment over two relevant works within paradigm of Open Science, to outline the limitations and challenges of this framework.

We expect that consensus meetings, where researchers come together to discuss their theoretical viewpoints, prioritize the factors they agree are important to study, standardize their measures, and determine a smallest effect size of interest, will prove to be a more efficient solution to the lack of coordination and integration of claims in science than integrative experiments.

In recent decades, the relationship between the average length of life for those who die in the first year of life—the life table quantity a10—and the level of infant mortality, on which its calculation is often based, has broken down. The very low levels of infant mortality in the developed world correspond to a range of a10 quantities. We illustrate the competing effect of falling mortality and reduction in preterm births on a10 through two populations with very different levels of premature birth—infants born to non-Hispanic White mothers and infants born to non-Hispanic Black mothers in the United States—using linked birth and infant death cohort data. Through simulation, we further demonstrate that falling mortality reduces a10, while a reduction in premature births increases it. We use these observations to motivate the formulation of a new approximation formula for a10 in low-mortality contexts, which aims to incorporate differences in preterm birth through a proxy measure—the ratio of infant to under-five mortality. Models are built and tested using data from the Human Mortality Database. Model results and validation show that the newly proposed model outperforms existing alternatives.

We assess if asking victims about the places where crimes happen leads to estimates of “crime in place” with better measurement properties. We analyze data from the Barcelona Victimization Survey (2015–2020) aggregated in 73 neighborhoods using longitudinal quasi-simplex models and criterion validity to estimate the quality of four types of survey-based measures of crime. The distribution of survey-based offense location estimates, as opposed to victim residence estimates, is highly similar to police-recorded crime statistics, and there is little trade off in terms of the reliability and validity of offense location and victim residence measures. Estimates of crimes reported to the police show a better validity, but their reliability is lower and capture fewer crimes.

Small area population forecasts are widely used by government and business for a variety of planning, research and policy purposes, and often influence major investment decisions. Yet, the toolbox of small area population forecasting methods and techniques is modest relative to that for national and large subnational regional forecasting. In this paper, we assess the current state of small area population forecasting, and suggest areas for further research. The paper provides a review of the literature on small area population forecasting methods published over the period 2001–2020. The key themes covered by the review are extrapolative and comparative methods, simplified cohort-component methods, model averaging and combining, incorporating socioeconomic variables and spatial relationships, ‘downscaling’ and disaggregation approaches, linking population with housing, estimating and projecting small area component input data, microsimulation, machine learning, and forecast uncertainty. Several avenues for further research are then suggested, including more work on model averaging and combining, developing new forecasting methods for situations which current models cannot handle, quantifying uncertainty, exploring methodologies such as machine learning and spatial statistics, creating user-friendly tools for practitioners, and understanding more about how forecasts are used. Supplementary Information The online version contains supplementary material available at 10.1007/s11113-021-09671-6.

The death of a child affects the well-being of parents and families worldwide, but little is known about the scale of this phenomenon. Using a novel methodology from formal demography applied to data from the 2019 Revision of the United Nations World Population Prospects, we provide the first global overview of parental bereavement, its magnitude, prevalence, and distribution over age for the 1950–2000 annual birth cohorts of women. We project that the global burden of parental bereavement will be 1.6 times lower for women born in 2000 than for women born in 1955. Accounting for compositional effects, we anticipate the largest improvements in regions of the Global South, where offspring mortality continues to be a common life event. This study quantifies an unprecedented shift in the timing of parental bereavement from reproductive to retirement ages. Women in the 1985 cohort and subsequent cohorts will be more likely to lose an adult child after age 65 than to lose a young child before age 50, reversing a long-standing global trend. “Child death” will increasingly come to mean the death of adult offspring. We project persisting regional inequalities in offspring mortality and in the availability of children in later life, a particular concern for parents dependent on support from their children after retirement. Nevertheless, our analyses suggest a progressive narrowing of the historical gap between the Global North and South in the near future. These developments have profound implications for demographic theory and highlight the need for policies to support bereaved older parents.

Interest in the study of gene–environment interaction has recently grown due to the sudden availability of molecular genetic data—in particular, polygenic scores—in many long-running longitudinal studies. Identifying and estimating statistical interactions comes with several analytic and inferential challenges; these challenges are heightened when used to integrate observational genomic and social science data. We articulate some of these key challenges, provide new perspectives on the study of gene–environment interactions, and end by offering some practical guidance for conducting research in this area. Given the sudden availability of well-powered polygenic scores, we anticipate a substantial increase in research testing for interaction between such scores and environments. The issues we discuss, if not properly addressed, may impact the enduring scientific value of gene–environment interaction studies.

Forthcoming in American Behavioral Scientist. The structured inequalities built into our sociotechnical institutions shape access to knowledge. During the COVID-19 pandemic, knowledge acquisition was shaped by news sources, class, and race. Through analysis of nationally representative data using logistic models, this study reveals how the use of different news sources differentially shapes access to accurate knowledge about COVID-19 topics for different demographics. Those who rely on informal and local news sources have the largest knowledge gaps about these topics, while those who seek information from national or international news outlets and politicians have the most accurate knowledge. Race and class influence knowledge of government operations, public health, and science of COVID-19. In particular, Black people, people with less education, and those with lower incomes are significantly less likely to have accurate knowledge about COVID-19, all else equal. These findings have implications for knowledge dissemination that impacts public health, as well as for how news media target different audiences in an increasingly fractured landscape.

Exponential random graph models (ERGM) have been widely applied in the social sciences in the past ten years. However, diagnostics for ERGM have lagged behind their use. Collinearity-type problems can emerge without detection when fitting ERGM, skewing coefficients, biasing standard errors, and yielding inconsistent model estimates. This article provides a method to detect multicollinearity in ERGM. It outlines the problem and provides a method to calculate the variance inflation factor from ERGM parameters. It then evaluates the method with a Monte Carlo simulation, fitting 216,000 ERGMs and calculating the variance inflation factors for each model. The distribution of variance inflation factors is analyzed using multilevel regression to determine what network characteristics lend themselves to collinearity-type problems. The relationship between variance inflation factors and unstable standard errors (a standard sign of collinearity) is also examined. The method is shown to effectively detect multicollinearity and guidelines for interpretation are discussed.

This report is a high-level summary analysis of the 2017 GitHub Open Source Survey dataset, presenting frequency counts, proportions, and frequency or proportion bar plots for every question asked in the survey., Comment: 58 pages

This short data visualisation and accompanying perspective explains recent trends and outlines three reasons to be even more optimistic about the future of Machine Learning in the academic Social Sciences.

Objective In a recent study using data from 194 nations and 28 EU member states, Kleck (2021) reports that firearms are associated with the rate of suicides committed with firearms, but not non-firearm suicides or suicides overall. He concludes that firearm accessibility only influences suicide method. However, this conclusion is unjustified because the findings are internally inconsistent: if firearms only influence firearm suicide rates, they should be negatively associated with non-firearm suicides to reflect method substitution. I attempt to replicate Kleck’s results and, if there are differences, offer potential explanations. Method I reproduce Kleck’s analytical approach: ordinary least squares regression with log-transformed suicide rates as the dependent variable and crude firearm rates as the independent variable, weighted by the square root of the population. I also test ranked and z-scored firearm rates to account for outliers and check robustness of results, stratify analyses by various demographic subgroups, and include US state data. Results Firearms were positively associated with total suicide rates globally and within the US but not in the EU. Across all geographic units, firearms were associated with firearm suicides but not non-firearm suicides. Associations were robust to modifications of the firearm rate, though varied within subgroups. Firearms were negatively associated with non-firearm suicides among older persons and black Americans, indicating method substitution. Firearms were also negatively associated with total suicides among older persons in global analysis, which may be due to depletion of susceptibles at younger ages where firearms are more readily available. Conclusion Kleck’s findings failed to replicate. Therefore, I reject his conclusion that firearms do not influence suicide.

Objectives:A need for Open Research practices exists, yet there remains a lack of validated questionnaires to assess Open Research practices. The study aimed to develop a brief (

We argue that statistical practice in the social and behavioural sciences benefits from transparency, a fair acknowledgement of uncertainty and openness to alternative interpretations. Here, to promote such a practice, we recommend seven concrete statistical procedures: (1) visualizing data; (2) quantifying inferential uncertainty; (3) assessing data preprocessing choices; (4) reporting multiple models; (5) involving multiple analysts; (6) interpreting results modestly; and (7) sharing data and code. We discuss their benefits and limitations, and provide guidelines for adoption. Each of the seven procedures finds inspiration in Merton's ethos of science as reflected in the norms of communalism, universalism, disinterestedness and organized scepticism. We believe that these ethical considerations-as well as their statistical consequences-establish common ground among data analysts, despite continuing disagreements about the foundations of statistical inference.Wagenmakers and colleagues describe seven statistical procedures that increase transparency in data analysis. These procedures highlight common ground among data analysts from different schools and find inspiration in Merton's ethos of science.

Prediction is an underused tool in the social sciences, often for the wrong reasons. Many social scientists confuse prediction with unnecessarily complicated methods or with narrowly predicting the future. This is unfortunate. When we view prediction as the simple process of evaluating a model’s ability to approximate an outcome of interest, it becomes a more generally applicable and disarmingly simple technique. For all its simplicity, the value of prediction should not be underestimated. Prediction can address enduring sources of criticism plaguing the social sciences, like a lack of assessing a model’s ability to reflect the real world, or the use of overly simplistic models to capture social life. The author illustrates these benefits with empirical examples that merely skim the surface of the many and varied ways in which prediction can be applied, staking the claim that prediction is a truly illustrious “free lunch” that can greatly benefit social scientists in their empirical work.

While much progress has been made in understanding the demographic determinants of mortality in the United States using individual survey data and aggregate tabulations, the lack of population-level register data is a barrier to further advances in mortality research. With the release of Social Security application (SS-5), claim, and death records, the National Archives and Records Administration (NARA) has created a new administrative data resource for researchers studying mortality. We introduce the Berkeley Unified Numident Mortality Database (BUNMD), a cleaned and harmonized version of these records. This publicly available dataset provides researchers access to over 49 million individual-level mortality records with demographic covariates and fine geographic detail, allowing for high-resolution mortality research.

The role of external suppliers across statutory health insurance procurement processes varies widely and is a source of political contention throughout the modern world. We comprehensively examine the role of non-profit organisations in public health procurement within publicly funded healthcare which runs parallel to private provision in a 'two-tier' system. We build a unique 'Big Data' based pipeline which scrapes tens of thousands of heterogeneous accounting datasets from across a commissioning hierarchy. These datasets provide granular information on every element of procurement at the micro-level (where the value of a transaction is greater than twenty-five thousand pounds), mandated by transparency requirements introduced by David Cameron in 2010. We develop tools to scrape, parse, and reconcile suppliers with institutional registers. The processed dataset contains over four hundred and forty-five billion pounds worth of commissioning across over 1.9 million rows of clean data. Approximately 1% at each level of procurement comes from institutions listed on the Charity Commission for England and Wales: a number relatively consistent across time, despite contractual patterns. We show a slight regional variation and analyse the 'North-South' divide. Linking to the International Classification of Non-profit Organizations, we show involvement of multiple different types of charity, with more payments going to the 'Social Services' aggregate, but the highest cumulative values going to the 'Health' aggregate. We analyse the distribution across various sizes and ages, from grassroots to 'Super Major' non-profits, and analyse variation over time. We conclude with a re-evaluation of the effects of the controversial Health and Social Care Act of 2012 and the integration of the free market and volunteerism, otherwise known as the 'Big Society'.

The most common and well-known meta-regression models work under the assumption that there is only one effect size estimate per study and that the estimates are independent. However, meta-analytic reviews of social science research often include multiple effect size estimates per primary study, leading to dependence in the estimates. Some meta-analyses also include multiple studies conducted by the same lab or investigator, creating another potential source of dependence. An increasingly popular method to handle dependence is robust variance estimation (RVE), but this method can result in inflated Type I error rates when the number of studies is small. Small-sample correction methods for RVE have been shown to control Type I error rates adequately but may be overly conservative, especially for tests of multiple-contrast hypotheses. We evaluated an alternative method for handling dependence, cluster wild bootstrapping, which has been examined in the econometrics literature but not in the context of meta-analysis. Results from two simulation studies indicate that cluster wild bootstrapping maintains adequate Type I error rates and provides more power than extant small sample correction methods, particularly for multiple-contrast hypothesis tests. We recommend using cluster wild bootstrapping to conduct hypothesis tests for meta-analyses with a small number of studies. We have also created an R package that implements such tests.

In multi-level and multi-layered foundations of gendered approaches for understanding the kinship system, son preferences, and male-skewed child sex ratios in India; patriarchy, and patrilineality have received greater attention than patrilocality. To fill this gap, in this study, we construct a measure of patrilocality and examine its association with skewed child sex ratios. We hypothesize that households practice sex selection and daughter discrimination because of patrilocal norms that dictate the later life co-residence between parents and sons. Our findings reveal that the child sex ratio, the sex ratio at birth, and the sex ratio at last birth are positively correlated with the patrilocality rates across states and districts of India. The relationship holds across the multiple robustness checks. Findings, although not surprising, emerge from the robust empirical analyses at a time when child sex ratios continue to worsen in India, notwithstanding the country’s socio-economic progress. We conclude that in the absence of strong social security measures and lack of preference for old-age homes amidst the accepted practice of patrilocality coupled with increasing lower fertility norms, the dependency on sons will continue and further lead to the continuation of sex selection in India.

[The article is written in Bahasa Malaysia] Reading habits are often associated with an increased cognitive level. However, book genre can also affect the cognitive level. This is because different book genres can be attributed to different type of knowledge and tendency towards a specific book genre can be viewed as a preference toward a specific type of knowledge. Research on Malaysian’s interest and the tendency towards book genre and type of knowledge can be investigated using the publication data of each book genre and its prices. The objective of this research is to analyze and compare the book publications and the average price of each book’s genre in Malaysia with Japan. Comparison was done based on book categorization genre strategy to 12 book genre. Based on Monte Carlo simulation method data, Malaysians are attracted to book genre on Language, literature, and science. While the Japanese are more interested in the book genre of social sciences and hobbies/art. The similarity between Malaysian and Japanese are that both societies also favors book genre of literature. However, it is possible that the themes of literary genre in Japan and Malaysia are very different. The result of this study can be used to identify the differences between Malaysian and Japanese reading habits as well as used for formulating strategies to changed Malaysian reading habits toward the style of Japanese reading habits.

Compliance with public health measures of adolescents who are often unfairly portrayed as spreaders of the Coronavirus is essential for controlling the pandemic. But does adolescents’ compliance develop independently from their parents? Using nationally representative longitudinal data and cross-lagged Structural Equation Panel Models I study compliance with social distancing measures of 6,754 triplets that comprise the adolescent child (age 19), their mother, and father during two national lockdowns in the UK. Results show that adolescents have the lowest and their mothers have the highest levels of compliance, and compliance generally drops over time. Moreover, parents, particularly mothers have significant influence on their adolescent child’s compliance. The child also has, albeit smaller effects on their parents’ compliance. Parental influence on adolescents’ compliance documented here thus redistributes some of the responsibility off adolescents towards all generations and calls for public health policies and campaigns that consider these family dynamics.

This brief note introduces the use of a range of responses rather than a single response for an opinion survey question. The methodological advantage of collecting this additional information is that it can help discern between ambiguity and ambivalence in situations where it is valuable to further investigate the nature of a respondent’s indecision.

Meta-analytic models for dependent effect sizes have grown increasingly sophisticated over the last few decades, which has created challenges for a priori power calculations. We introduce power approximations for tests of average effect sizes based upon several common approaches for handling dependent effect sizes. In a Monte Carlo simulation, we show that the new power formulas can accurately approximate the true power of meta-analytic models for dependent effect sizes. Lastly, we investigate the Type I error rate and power for several common models, finding that tests using robust variance estimation provide better Type I error calibration than tests with model-based variance estimation. We consider implications for practice with respect to selecting a working model and an inferential approach.

The application of deep learning methods to survey human development in remote areas with satellite imagery at high temporal frequency can significantly enhance our understanding of spatial and temporal patterns in human development. Current applications have focused their efforts in predicting a narrow set of asset-based measurements of human well-being within a limited group of African countries. Here, we leverage georeferenced village-level census data from across 30 percent of the landmass of India to train a deep-neural network that predicts 16 variables representing material conditions from annual composites of Landsat 7 imagery. The census-based model is used as a feature extractor to train another network that predicts an even larger set of developmental variables (over 90 variables) included in two rounds of the National Family Health Survey (NFHS) survey. The census-based model outperforms the current standard in the literature, night-time-luminosity-based models, as a feature extractor for several of these large set of variables. To extend the temporal scope of the models, we suggest a distribution-transformation procedure to estimate outcomes over time and space in India. Our procedure achieves levels of accuracy in the R-square of 0.92 to 0.60 for 21 development outcomes, 0.59 to 0.30 for 25 outcomes, and 0.29 to 0.00 for 28 outcomes, and 19 outcomes had negative R-square. Overall, the results show that combining satellite data with Indian Census data unlocks rich information for training deep learning models that track human development at an unprecedented geographical and temporal definition.

Enabling children to acquire an education is one of the most effective means to reduce inequality, poverty, and ill-health globally. While in normal times a government controls its educational policies, during times of macroeconomic instability, that control may shift to supporting international organizations, such as the International Monetary Fund (IMF). While much research has focused on which sectors has been affected by IMF policies, scholars have devoted little attention to the policy content of IMF interventions affecting the education sector and children’s education outcomes: denoted IMF-education policies. This article evaluates the extent which IMF-education policies exist in all programs and how these policies and IMF programs affect children’s likelihood of completing schools. While IMF-education policies have a small adverse effect yet statistically insignificant on children’s probability of completing school, these policies moderate effect heterogeneity for IMF programs. The effect of IMF programs (joint set of policies) adversely effect children’s chances of completing school by six percentage points. By analyzing how IMF-education policies but also how IMF programs affect the education sector in low- and middle-income countries, scholars will gain a deeper understanding of how such policies will likely affect downstream outcomes.

Despite established recommended standard definitions, measures, and methods by the UN Washington Group on Disability Statistics and the International Classification of Functioning, Disability and Health (ICF) to assess dimensions of disability, national censuses vary widely in the questions used to identify people with disabilities. Although many seek to conform ex-ante to ICF definitions, they also deviate from this basic framework in different ways. This complicates ex-post harmonization and standardization for cross-national comparisons of disabilityprevalence and outcomes influenced by disability status, such as labor market participation. Addressing these issues, this study uses IPUMS International Census microdata since 2,000 to examine disability mea- surement across 65 countries. We find that definitions, terminology, measurement, and instructions to both respondents and enumerators mat- ter for understanding disability prevalence cross-nationally. For instance, questions that included potentially stigmatizing language were associ- ated with lower rates of disability reporting, but questions that listed spe- cific limitations were associated with higher rates. Beyond disability, our findings also speak more broadly to ongoing challenges in survey har- monization for cross-national comparison.

Neonatal deaths (occurring within 28 days of birth) account for close to one-half of all deaths among children under age 5 worldwide. In most low- and middle-income countries, data on neonatal deaths come primarily from household surveys. We conducted a validation study of survey data on neonatal mortality in Guinea-Bissau (West Africa). We used records from an urban health and demographic surveillance system (HDSS) that monitors child survival prospectively as our reference data set. We selected a stratified sample of 599 women aged 15–49 among residents of the HDSS and collected the birth histories of 422 participants. We cross-tabulated survey and HDSS data. We used a mathematical model to investigate biases in survey estimates of neonatal mortality. Reporting errors in survey data might lead to estimates of the neonatal mortality rate that are too high, which may limit our ability to track progress toward global health objectives.

Police agencies globally are seeing an increase in reports of people going missing. These people are often vulnerable, and their safe and early return is a key factor in preventing them from coming to serious harm. One approach to quickly find missing people is to disseminate appeals for information using social media. Yet despite the popularity of twitter-based missing person appeals, presently little is known about how to best construct these messages to ensure they are shared far and wide. This paper aims to build an evidence-base for understanding how police accounts tweet appeals for information about missing persons, and how the public engage with these tweets by sharing them. We analyse 1,008 Tweets made by Greater Manchester Police between the period of 2011 and 2018 in order to investigate what features of the tweet, the twitter account, and the missing person are associated with levels of retweeting. We find that tweets with different choice of image, wording, sentiment, and hashtags vary in how much they are retweeted. Tweets that use custody images have lower retweets than Tweets with regular photos, while tweets asking the question “have you seen...?” and asking explicitly to be retweeted have more engagement in the form of retweets. These results highlight the need for conscientious, evidence-based crafting of missing appeals, and pave the way for further research into the causal mechanisms behind what affects engagement, to develop guidance for police forces worldwide.

As primary health care providers, family physicians play a critical role in Canada’s health care system by providing first-level contact for patients and acting as advocates for preventative medicine. Due to their essential role in population health, public health practitioners are concerned about temporal trends surrounding family physician visits. This study uses eight cycles of the Canadian Community Health Survey, a nationally representative survey, to conducing an age-period-cohort analysis with the bounding method, while controlling for sex and race. Most notably, we discover a declining cohort trend indicating that later cohorts are less likely to visit a family doctor over a 12-month period. These results suggest that health promotion policies surrounding family physician use should not only be targeted towards middle and older-aged adults who experience declining health, as the cohort effect implies that a portion of health care avoidance behaviour is molded in youth and young adulthood.

Demographic theory aims at explaining how population systems regulate themselves given available resources. Population ethics is concerned with demography in the sense that the analytical objects of interest are births, deaths, and populations. However, demographic theory which explores theoretically when, how and why populations grow, based on empirically observed patterns, has up until now played a minor role in population ethics. Similarly, debates about population dynamics among demographers have seldom been concerned with ideas and concepts in population ethics. In this manuscript, I will give a brief outline of how population size, population growth, and welfare mutually affect each other. Theories on the endogeneity between population size, population growth, and welfare will be referred to as demographic theory. I will give a particular focus on how population growth responds with respect to welfare, as welfare, utility, well-being, and happiness are important concepts in population ethics. A key concept in demographic theory is population homeostasis (the dynamics of a system which maintains a population at a steady population size, or growth rate), in particular resource dependent homeostasis. I will also discuss demographic theory in relation to historical and future demographic change. This working paper was later published in Oxford Handbook of Population Ethics, and is available at https://osf.io/preprints/socarxiv/at5pj/

Early evidence shows mixed effects of the COVID-19 pandemic on births in Europe. This study examines changes in births at the regional level in the four European countries that have been affected by the pandemic earlier and to a larger extent. It is also investigated the association between birth changes and some labour market characteristics, the pandemic impact in terms of COVID-deaths, and the share of population at risk of poverty. Results show considerable within-country heterogeneity in birth changes after the pandemic and that higher share of poverty, worse labour market performance, and higher excess mortality are associated with births decline.

`All models are wrong, but some are useful is an often-used mantra, particularly when a models ability to capture the full complexities of social life is questioned. However, an appropriate functional form is key to valid statistical inference, and under-estimating complexity can lead to biased results. Unfortunately, it is unclear a-priori what the appropriate complexity of a functional form should be. I propose to use methods from machine learning to identify the appropriate complexity of the functional form by i) generating an estimate of the fit potential of the outcome given a set of explanatory variables, ii) comparing this potential with the fit from the functional form originally hypothesized by the researcher, and iii) in case a lack of fit is identified, using recent advances in the field of explainable AI to generate understanding into the missing complexity. I illustrate the approach with a range of simulation and real-world examples.

The spread and transmission dynamics of directly transmitted airborne pathogens, such as SARS-CoV-2, are fundamentally determined by in-person contact patterns. Reliable quantitative estimates of contact patterns are critical to modeling and reducing the spread of directly transmitted infectious diseases. While national-level contact data are available in many countries, including the United States, local-level estimates of age-specific contact patterns are key since disease dynamics and public health policy vary by geography. However, collecting contact data for each state would require a very large sample and be prohibitively expensive. To overcome this challenge, we develop a flexible model to estimate age-specific contact patterns at the subnational level using national-level interpersonal contact data. Our model is based on dynamic multilevel regression with poststratification. We apply this approach to a national sample of interpersonal contact data collected by the Berkeley Interpersonal Contact Study (BICS). Results illustrate important state-level variation in levels and trends of contacts across the US.

Background. Obstetric provider coverage in rural Georgia has worsened, with nine rural labor and delivery units (LDUs) closing outside the Atlanta Metropolitan Statistical Area from 2012–2016. Georgia consistently has one of the highest maternal mortality rates in the nation and faces increased adverse health consequences from this decline in obstetric care.Objective. This study explores what factors may be associated with rural hospital LDU closures in Georgia from 2012–2016.Methods. This study describes differences between rural Georgia hospitals based on LDU closure status through a quantitative analysis of 2011 baseline regional, hospital, and patient data, and a qualitative analysis of newspaper articles addressing the closures.Results. LDUs that closed had higher proportions of Black female residents in their Primary Care Service Areas (PCSAs), of Black birthing patients, and of patients with Medicaid, self-pay or other government insurance; lower LDU birth volume; more women giving birth within their PCSA of residence; fewer obstetricians and obstetric provider equivalents per LDU; and fewer average annual births per obstetric provider. Qualitative results indicate financial distress primarily contributed to closures, but also suggest that low birth volume and obstetric provider shortage impacted closures.Conclusions for Practice. Rural LDU closure in Georgia has a disproportionate impact on Black and low-income women and may be prevented through funding maternity healthcare and addressing provider shortages.

The year 2020 has been characterized by the outbreak of covid19. The spread of the disease has had a severe impact in many aspects of our lives that go beyond our health. From economical to psychological or social contexts, covid19 caused a halt in our daily activities and forced us to adapt or develop new habits. The following study uses data retrieved from 800 groups in the platform meetup.com to study and compare the impact of the coronavirus in the social life of two vibrant but culturally different cities such as Washington DC and Barcelona. Results suggest that though groups have been able to maintain their activity, mostly through complementing offline with online events, communities have become more fragmented in 2020. The results also suggest that the halt in activity along with the fragmentation has been higher in Barcelona where a very strict lockdown policy was implemented and that Washington DC has been more able to effectively switch to online events after the outbreak of the pandemic.

BACKGROUND: Pakistan has a strong preference for boys over girls; previous evidence on sex preference is primarily reported at the postnatal stage in which the child mortality rate is higher for females than males. Prenatal sex discrimination in Pakistan, reflected in the inflated sex ratio at birth (SRB; ratio of male to female births) has been barely mentioned before this study.OBJECTIVE: We estimate the SRB and missing female births in Pakistan provinces from 1980 to 2020 and identify provinces with imbalanced SRB. We provide scenario-based projections of missing female births in provinces without the existing SRB inflation.METHODS: An extensive SRB database of 832,091 birth records was compiled from all available surveys and censuses. To synthesize different data sources and provide annual estimates and their associated uncertainties of SRBs across provinces, we adopted a Bayesian hierarchical time series model.RESULTS: As per our model, Balochistan has had SRB imbalance since 1980. The maximum SRB was estimated as 1.121 (95% credible interval [1.066; 1.142]) in 1997. Assuming different start year of SRB inflation process in provinces without existing imbalance, the largest female birth deficit is projected to be 76.2 thousand in Punjab in 2033 when the SRB inflation starts in 2021.CONTRIBUTION: This is the first study on estimating the SRB from 1980 to 2020 and providing scenario-based projections of missing female births up to 2050 by Pakistan province. We identified the Balochistan province with imbalanced SRB and demonstrated important disparities in the occurrence and quantity of female birth deficits before 2050.

This ESRC-SDA funded project took a demographic approach using new metrics to studying population ageing. Key project findings mentioned in Policy Brief:• Until now, most notions of dependency are false.• As an average, the UK population is younger rather than older compared to 1950.• Old age dependency has declined rather than increased since 1980 as life expectancy at older ages and female labour force participation have increased.

This article develops a framework for studying homelessness, poverty and migration using Fuzzy Cognitive Modeling. The framework has been developed using data obtained from a survey of around 4000 respondents in five communities in northeastern Ontario, Canada. The intention was to develop a framework that could be applied in other communities where comparable sources of data are available. This framework can be used to perform sensitivity analysis as well as to develop index of poverty as discussed in this paper. The results suggest that this method provides a robust approach to analyze complex and multivariate datasets related to homelessness, poverty and migration with variables that not only have interdependencies but are also not crisp.

The Healthy Life Expectancy (HLE) in Brazil 2003 was estimated by Romero et al (2005) by using the Sullivan method and data from the World Health Survey carried out in Brazil in 2003. Here we use a Direct method to estimate the Healthy Life Years Lost (HLYL) and then the HLE. This is done after the analytic derivation of a more general model of survival-mortality and the estimation of a parameter bx related to the HLYL is followed by the formulation of a computer program providing results similar to those of the World Health Organization for the Healthy Life Expectancy (HALE) and the corresponding HLYL estimates. This program is an extension of classical life table including more columns to estimate the cumulative mortality, the average mortality, the person life years lost, and finally the HLYL parameter bx. Even more, a further extension of the Excel program based on the Sullivan method provides estimates of the Healthy Life Expectancy at every year of the lifespan.

In this article, we explore the last two decades of changes in the demography of grandparenthood in Italy, by means of a set of measures: the proportion of men and women becoming grandparents by age and time, the age at transition to grandparenthood and its crossing with a set of life events and the length of grandparenthood. We used data from the four waves of the Survey on Family and Social Subjects carried out by the Italian National Institute of Statistics in 1998, 2003, 2009 and 2016. Overall, the median age at which half of the population over 35 is made up of grandparents moved forward by at least 5 years during the two observed decades. The postponement of grandparenthood is evident in middle age: between 55 and 64 the ratio of grandparents to non-grandparents decreased significantly by about 10%. Overall, among people who had ever had children, the median age at the transition to grandparenthood advanced by three years from 1998 to 2016, both for men (59 to 62) and women (54 to 57). This difference is greater than that observed for age at parenthood and equal to the advantage gained in terms of life expectancy at age 60. Thus, despite increasing life expectancy, because of the postponement of grandparenthood, the shared period of life for grandparents and grandchildren has not increased, but rather remained stable.

Background. Digital technologies continue to facilitate drug trading. Televend was an innovative combination of multiple digital technologies, with its backend hosted on the darknet, while purchases were made through the messaging app Telegram. Here, we provide an initial characterisation of this nascent market. Methods. Televend and White House Market (WHM) were scraped (Jun–Jul 2021) and a global cross-sectional web survey of 15,513 drug buyers (Global Drug Survey; GDS) was conducted (Dec 2020–Mar 2021). Results. Televend was 10% of the size of WHM, the largest drug cryptomarket (4,515/44,830 listings per week). Both markets predominantly contained drug-related listings covering similar drug categories, with similar country of origin and destination. Very few GDS drug buyers reported use of Televend (0.73%). Most Televend buyers (68/114) reported buying cannabis, then cocaine (20), MDMA (17), and LSD (12). The Televend and darknet groups had similar demographic and drug use characteristics; whereas compared with app purchasers, older age increased the odds of Televend use (aRRR=1.06, p

This review systematizes the emerging literature for causal inference using deep neural networks under the potential outcomes framework. It provides an intuitive introduction on how deep learning can be used to estimate/predict heterogeneous treatment effects and extend causal inference to settings where confounding is non-linear, time varying, or encoded in text, networks, and images. To maximize accessibility, we also introduce prerequisite concepts from causal inference and deep learning. The survey differs from other treatments of deep learning and causal inference in its sharp focus on observational causal estimation, its extended exposition of key algorithms, and its detailed tutorials for implementing, training, and selecting among deep estimators in Tensorflow 2 available at github.com/kochbj/Deep-Learning-for-Causal-Inference.

This paper assesses the performance of regularized generalized least squares (RGLS) and reweighted least squares (RLS) methodologies in a confirmatory factor analysis model. Normal theory maximum likelihood (ML) and GLS statistics are based on large sample statistical theory. However, violation of asymptotic sample size is ubiquitous in real applications of structural equation modeling (SEM), and ML and GLS goodness-of-fit tests in SEM often make incorrect decisions on the true model. The novel methods RGLS and RLS aim to correct the over-rejection by ML and under-rejection by GLS. Proposed by Arruda and Bentler (2017), RGLS replaces a GLS weight matrix with a regularized one. Rediscovered by Hayakawa (2019), RLS replaces this weight matrix with one that derives from an ML function. Both of these methods outperform ML and GLS when samples are small, yet no studies have compared their relative performance. A confirmatory factor analysis Monte Carlo simulation study with normal and non-normal data was carried out to examine the statistical performance of these two methods at different sample sizes. Based on empirical rejection frequencies and empirical distributions of test statistics, we find that RLS and RGLS have equivalent performance when N≥70; whereas when N

We use large-scale diary data from a representative sample of Americans to proxy welfare at the level of individual activities. We make three contributions. First, we examine the association between individual activities and happiness, and show how this association has changed over time. Compared to 1985, domestic work and social care produce more happiness today. Watching TV produces less happiness today than it used to. Second, we combine activity-level data on happiness and time allocation to construct a measure of ‘time-weighted happiness’. We then analyse historical trends in this measure across population groups, particularly gender. We observe that, over the last 35 years, women’s time-weighted happiness has improved significantly relative to men’s. This trend is largely driven by gendered shifts in time allocation, rather than heterogenous trends in the enjoyability of individual activities. Our result is in stark contrast to previous work which showed a decline in women’s relative wellbeing. To explain this, our third contribution is to compare the determinants of life satisfaction – a global measure on which most previous work is built – with our measure of time-weighted happiness. Time-weighted happiness and life satisfaction turn out to only be weakly correlated. Moreover, although we obtain strong associations of income and employment status with life satisfaction, no such associations can be observed for time-weighted happiness. These findings highlight the importance of distinguishing between happiness as experienced in time and more global wellbeing measures. Finally, we verify the robustness our results by replicating them in data from the United Kingdom and show that our results are robust to alternative assumptions about how people use happiness scales.

Cite as:Charith, K., & Davydenko, A. (2021). Informational value of dividend initiations: Impact of cash dividends on share prices of manufacturing companies in Sri Lanka.International Journal of Economics and Finance, 13(3), 13-30. https://doi.org/10.5539/ijef.v13n3p13 Abstract The shareholder wealth consists of dividends and capital gains. The former is considered to be risk averse, whereas the latter is perceived to be risky. The risk return trade off in these two returns drives the investor preference. The objective of a for profit organization is to maximize shareholders’ wealth, however, disbursing dividends may not always be in the best interest of shareholders. Theoretically, retained earnings increase share prices as firms have more funds to be invested. The objective of the study is to measure the stimulus of cash dividends on share prices. We conduct empirical analysis based on data relating to companies listed on the Colombo Stock Exchange (CSE) under the manufacturing sector. As we show in our literature review, in order to reduce the risk of obtaining spurious results, this analysis requires the use of advanced modelling techniques allowing to model non stationarity of time series, as well the presence of control variables and lagged variables. The novelty of our study is in the use of advanced modelling and data visualisation techniques (including the ‘xdPlot’ dataviz framework recently proposed by the authors), especially in application to CSE data. We conduct a thorough exploratory data analysis (EDA) aiming to spot data anomalies and initiate appropriate data transformations. Given the results of EDA and the nature of the data available, we select the Arellano Bond estimator as the most adequate method for regression analysis. Market Price per share (MPS) termed as the dependent variable, whereas Dividend per Share (DPS) is viewed as the independent variable. The results validated theoretical literature such as signaling effect and bird in hand theory, but questioned some previous empirical studies. The study validated cash dividends as stimulus to investors given the positive relationship between DPS and MPS. Dataset:https://doi.org/10.6084/m9.figshare.13475901

This study revisits the regional and socio-economic pattern of masculinisation of Child Sex Ratios (CSR), Sex Ratio at Birth (SRB) and Sex Ratio at Last Birth (SRLB) by using the successive rounds of National Family Health Survey (NFHS). Although the masculinisation of CSR continues in many Indian states as well as in different socio-economic settings, a tremendous change in previously established patterns of CSR can be observed from the findings. District-level analysis presents an intra-state variation in CSR, SRB and SRLB, which helps in identifying the emerging ‘hotspots’ of the problem. The decline in preference for a son and rise in skewed CSR, SRB and SRLB invites the attention of researchers towards drawbacks in the method of measurement of preference for a son as people are now more aware of the legal consequences of sex-selective abortions and underreport the same.

Introduction: Despite evidence from numerous studies that document the association between risky sexual behaviors (RSBs) and behaviors driving them, few had investigated the trends of the association over time, which was the objective of this study.Methods: A total of 61,072 students from four Youth Risk Behavior Surveillance System (YRBS) survey years (1993, 2001, 2009, and 2017) were included. The relationship between five risky behaviors (suicide risk, smoking, binge drinking, marijuana use, and physical fight) and two indicators of RSBs (condom non-use and multiple sexual partners) was investigated. Interactions between survey years and risky behaviors were included in the models to test the strength of associations over time.Results: Compared to previous years, most associations between risky behaviors and RSBs were converging. However, there were no consistent decreasing changes in associations across time in marijuana, smoking, and RSBs relationships.Conclusion: Findings raise concerns about the potential impact of interventions directed toward adolescent’s substance use. However, longitudinal studies are needed to clarify directionality and make more specific practice and policy recommendations for adolescents’ smoking and marijuana use as proxies to reduce the growing burden of STIs among this population.Policy Implications: Despite the public push against adolescent’s substance use, our findings may suggest that the policies around tobacco and marijuana use have not been as effective on the adolescent population as intended.