Anthropometrics is a research program that explores the extent to which economic processes affect human biological processes using height and weight as markers. This agenda differs from health economics in the sense that instead of studying diseases or longevity, macro manifestations of well-being, it focuses on cellular-level processes that determine the extent to which the organism thrives in its socio-economic and epidemiological environment. Thus, anthropometric indicators are used as a proxy measure for the biological standard of living as complements to conventional measures based on monetary units.
Using physical stature as a marker, we enabled the profession to learn about the well-being of children and youth for whom market-generated monetary data are not abundant even in contemporary societies. It is now clear that economic transformations such as the onset of the Industrial Revolution and modern economic growth were accompanied by negative externalities that were hitherto unknown. Moreover, there is plenty of evidence to indicate that the Welfare States of Western and Northern Europe take better care of the biological needs of their citizens than the market-oriented health-care system of the United States.
Obesity has reached pandemic proportions in the United States affecting 40% of the population. It is fostered by a sedentary and harried lifestyle, by the diminution in self-control, the spread of labor-saving technologies, and the rise of instant gratification characteristic of post-industrial society. The spread of television and a fast-food culture in the 1950s were watershed developments in this regard that accelerated the process. Obesity poses a serious health risk including heart disease, stroke, diabetes, and some types of cancer and its cost reaches $150 billion per annum in the United States or about $1,400 per capita. We conclude that the economy influences not only mortality and health but reaches bone-deep into the cellular level of the human organism. In other words, the economy is inextricably intertwined with human biological processes.
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Anthropometrics: The Intersection of Economics and Human Biology
John Komlos
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The Economics of Early Interventions Aimed at Child Development
Samuel Berlinski and Marcos Vera-Hernández
A set of policies is at the center of the agenda on early childhood development: parenting programs, childcare regulation and subsidies, cash and in-kind transfers, and parental leave policies. Incentives are embedded in these policies, and households react to them differently. They also have varying effects on child development, both in developed and developing countries. We have learned much about the impact of these policies in the past 20 years. We know that parenting programs can enhance child development, that centre based care might increase female labor force participation and child development, that parental leave policies beyond three months don’t cause improvement in children outcomes, and that the effects of transfers depend much on their design. In this review, we focus on the incentives embedded in these policies, and how they interact with the context and decision makers to understand the heterogeneity of effects and the mechanisms through which these policies work. We conclude by identifying areas of future research.
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The Growth of Health Spending in the United States From 1776 to 2026
Thomas E. Getzen
During the 18th and 19th centuries, medical spending in the United States rose slowly, on average about .25% faster than gross domestic product (GDP), and varied widely between rural and urban regions. Accumulating scientific advances caused spending to accelerate by 1910. From 1930 to 1955, rapid per-capita income growth accommodated major medical expansion while keeping the health share of GDP almost constant. During the 1950s and 1960s, prosperity and investment in research, the workforce, and hospitals caused a rapid surge in spending and consolidated a truly national health system. Excess growth rates (above GDP growth) were above +5% per year from 1966 to 1970, which would have doubled the health-sector share in fifteen years had it not moderated, falling under +3% in the 1980s, +2% in 1990s, and +1.5% since 2005. The question of when national health expenditure growth can be brought into line with GDP and made sustainable for the long run is still open. A review of historical data over three centuries forces confrontation with issues regarding what to include and how long events continue to effect national health accounting and policy. Empirical analysis at a national scale over multiple decades fails to support a position that many of the commonly discussed variables (obesity, aging, mortality rates, coinsurance) do cause significant shifts in expenditure trends. What does become clear is that there are long and variable lags before macroeconomic and technological events affect spending: three to six years for business cycles and multiple decades for major recessions, scientific discoveries, and organizational change. Health-financing mechanisms, such as employer-based health insurance, Medicare, and the Affordable Care Act (Obamacare) are seen to be both cause and effect, taking years to develop and affecting spending for decades to come.
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Machine Learning in Policy Evaluation: New Tools for Causal Inference
Noémi Kreif and Karla DiazOrdaz
While machine learning (ML) methods have received a lot of attention in recent years, these methods are primarily for prediction. Empirical researchers conducting policy evaluations are, on the other hand, preoccupied with causal problems, trying to answer counterfactual questions: what would have happened in the absence of a policy? Because these counterfactuals can never be directly observed (described as the “fundamental problem of causal inference”) prediction tools from the ML literature cannot be readily used for causal inference. In the last decade, major innovations have taken place incorporating supervised ML tools into estimators for causal parameters such as the average treatment effect (ATE). This holds the promise of attenuating model misspecification issues, and increasing of transparency in model selection. One particularly mature strand of the literature include approaches that incorporate supervised ML approaches in the estimation of the ATE of a binary treatment, under the unconfoundedness and positivity assumptions (also known as exchangeability and overlap assumptions).
This article begins by reviewing popular supervised machine learning algorithms, including trees-based methods and the lasso, as well as ensembles, with a focus on the Super Learner. Then, some specific uses of machine learning for treatment effect estimation are introduced and illustrated, namely (1) to create balance among treated and control groups, (2) to estimate so-called nuisance models (e.g., the propensity score, or conditional expectations of the outcome) in semi-parametric estimators that target causal parameters (e.g., targeted maximum likelihood estimation or the double ML estimator), and (3) the use of machine learning for variable selection in situations with a high number of covariates.
Since there is no universal best estimator, whether parametric or data-adaptive, it is best practice to incorporate a semi-automated approach than can select the models best supported by the observed data, thus attenuating the reliance on subjective choices.
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Missing Women: A Review of Underlying Causes and Policy Responses
Aparajita Dasgupta and Anisha Sharma
One of the most egregious manifestations of gender bias is the phenomenon of “missing women.” The number of missing women is projected to increase to 150 million by 2035, as a result of prenatal sex selection and excess female mortality relative to men, and is reflected in male-biased sex ratios at all ages. The economics literature identifies several proximate causes of the deficit of females, including the widespread use of prenatal sex selection in many Asian countries, which has been fueled by the diffusion of ultrasound and other fetal sex-detection technology. The use of prenatal sex selection has become even more expansive with a decline in fertility, as parents with a preference for sons are less likely to achieve their desired sex composition of children at lower levels of fertility. Gender discrimination in investments in health and nutrition also leads to excess female mortality among children through multiple channels.
The deeper causes of son preference lie in the socioeconomic and cultural norms embedded in patriarchal societies, and recent literature in economics seeks to quantify the impact of these norms and customs on the sex ratio. Particularly important are the norms of patrilineality, in which property and assets are passed through the male line, and patrilocality, in which elderly parents coreside with their sons, whereas their daughters move to live with their husbands’ families after marriage. Another strand of the literature explores the hypothesis that the devaluing of women has roots in historical agricultural systems: Societies that have made little use of women’s labor are today the ones with the largest female deficits. Finally, economic development is often associated with a decline in son preference, but, in practice, many correlates of development, such as women’s education, income, and work status, have little impact on the sex ratio unless accompanied by more extensive social transformations.
A number of policies have been implemented by governments throughout the world to tackle this issue, including legislative bans on different forms of gender discrimination, financial incentives for families to compensate them for the perceived additional costs of having a daughter, and media and advocacy campaigns that seek to increase the inherent demand for daughters by shifting the norm of son preference. Quantitative evaluations of some of these policies find mixed results. Where policies are unable to address the root causes of son preference, they often simply deflect discrimination from the targeted margin to another margin, and in some cases, they even fail in their core objectives. On the other hand, the expansion of social safety nets has had a considerable impact in reducing the reliance of parents on their sons. Similarly, media and advocacy campaigns that aim to increase the perceived value of women have also shown promise, even if their progress appears slow. Analysis of the welfare consequences of such interventions suggests that governments must pay close attention to underlying sociocultural norms when designing policy.
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Population Issues in Welfare Economics, Ethics, and Policy Evaluation
Kevin Kuruc, Mark Budolfson, and Dean Spears
Nearly all large policy decisions influence not only the quality of life for existing individuals but also the number—and even identities—of yet-to-exist individuals. Accounting for these effects in a policy evaluation framework requires taking difficult stances on concepts such as the value of existence. These issues are at the heart of a literature that sits between welfare economics and philosophical population ethics. Despite the inherent challenges of these questions, this literature has produced theoretical insights and subsequent progress on variable-population welfare criteria. A surprisingly bounded set of coherent alternatives exists for practitioners once a set of uncontroversial axioms is adopted from the better-studied welfare criteria for cases where populations are assumed to be fixed. Although consensus has not yet been reached among these remaining alternatives, their recommendations often agree. The space has been sufficiently restricted and well explored that applications of the theoretical insights are possible and underway in earnest.
For reasons both theoretical and empirical, the applied literature studying optimal policy and its robustness to welfare criteria has documented a surprising degree of convergence between recommendations under quite different ethical stances on existence value. This convergence has appeared even in cases where population size itself is the choice variable. Although it may not always be the case that policy recommendations are invariant to population welfare criteria, tools have been developed that allow researchers to easily and transparently move between such criteria to study the robustness in their context of interest. The broader point is that the remaining theoretical uncertainties need not prevent population ethics from playing a role in policy evaluation—the tools are available for determining whether and which policies are broadly supported among a range of ethical views.
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Religiosity and Development
Jeanet Sinding Bentzen
Economics of religion is the application of economic methods to the study of causes and consequences of religion. Ever since Max Weber set forth his theory of the Protestant ethic, social scientists have compared socioeconomic differences across Protestants and Catholics, Muslims, and Christians, and more recently across different intensities of religiosity. Religiosity refers to an individual’s degree of religious attendance and strength of beliefs. Religiosity rises with a growing demand for religion resulting from adversity and insecurity or a surging supply of religion stemming from increasing numbers of religious organizations, for instance. Religiosity has fallen in some Western countries since the mid-20th century, but has strengthened in several other societies around the world. Religion is a multidimensional concept, and religiosity has multiple impacts on socioeconomic outcomes, depending on the dimension observed. Religion covers public religious activities such as church attendance, which involves exposure to religious doctrines and to fellow believers, potentially strengthening social capital and trust among believers. Religious doctrines teach belief in supernatural beings, but also social views on hard work, refraining from deviant activities, and adherence to traditional norms. These norms and social views are sometimes orthogonal to the general tendency of modernization, and religion may contribute to the rising polarization on social issues regarding abortion, LGBT rights, women, and immigration. These norms and social views are again potentially in conflict with science and innovation, incentivizing some religious authorities to curb scientific progress. Further, religion encompasses private religious activities such as prayer and the particular religious beliefs, which may provide comfort and buffering against stressful events. At the same time, rulers may exploit the existence of belief in higher powers for political purposes. Empirical research supports these predictions. Consequences of higher religiosity include more emphasis on traditional values such as traditional gender norms and attitudes against homosexuality, lower rates of technical education, restrictions on science and democracy, rising polarization and conflict, and lower average incomes. Positive consequences of religiosity include improved health and depression rates, crime reduction, increased happiness, higher prosociality among believers, and consumption and well-being levels that are less sensitive to shocks.
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Shared Prosperity: Concepts, Data, and Some Policy Examples
Francisco H. G. Ferreira, Emanuela Galasso, and Mario Negre
“Shared prosperity” is a common phrase in current development policy discourse. Its most widely used operational definition—the growth rate in the average income of the poorest 40% of a country’s population—is a truncated measure of change in social welfare. A related concept, the shared prosperity premium—the difference between the growth rate of the mean for the bottom 40% and the growth rate in the overall mean—is similarly analogous to a measure of change in inequality. This article reviews the relationship between these concepts and the more established ideas of social welfare, poverty, inequality, and mobility.
Household survey data can be used to shed light on recent progress in terms of this indicator globally. During 2008–2013, mean incomes for the poorest 40% rose in 60 of the 83 countries for which we have data. In 49 of them, accounting for 65% of the sampled population, it rose faster than overall average incomes, thus narrowing the income gap.
In the policy space, there are examples both of “pre-distribution” policies (which promote human capital investment among the poor) and “re-distribution” policies (such as targeted safety nets), which when well-designed have a sound empirical track record of both raising productivity and improving well-being among the poor.
