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BackgroundWeight loss in diabetes prevention programmes has been shown to be associated with participants’ age, socio-economic status, and ethnicity. However, little is known about how these differences relate to attendance and whether such differences can be mediated by other potentially modifiable factors. Differential effectiveness across these factors may exacerbate health inequalities.MethodProspective analysis of participant data collected by one provider of the standardised national NHS diabetes prevention programme in England. Mediation analysis was performed via a structural equation model to examine whether the number of attended sessions mediated the associations of age, socio-economic status, and ethnicity with follow-up weight. The group-level factor of number of attended sessions was examined using multiple linear regression as a benchmark; multilevel linear regression using three levels (venue, coach, and group); and fixed effects regression to account for venue-specific and coach-specific characteristics.ResultsThe associations of age, socio-economic status, and ethnicity with follow-up weight were all mediated by the number of attended sessions. Group size was associated with attendance in an inverted ‘U’ shape, and the number of days between referral and group start was negatively associated with attendance. Time of day, day of the week, and the number of past groups led by the coach were not associated with attendance.ConclusionMost of the differences in weight loss initially attributed to socio-demographic factors are mediated by the attendance of the diabetes prevention programme. Therefore, targeted efforts to improve uptake and adherence to such programmes may help alleviate inequalities.

Sample selectivity is a recurrent problem in public health programmes and poses serious challenges to their evaluation. Traditional approaches to handle sample selection tend to rely on restrictive assumptions. The aim of this paper is to illustrate a copula-based selection model to handle sample selection in the evaluation of public health programmes. Motivated by a public health programme to promote physical activity in Leeds (England), we describe the assumptions underlying the copula selection, and its relative advantages compared with commonly used approaches to handle sample selection, such as inverse probability weighting and Heckman’s selection model. We illustrate the methods in the Leeds Let’s Get Active programme and show the implications of method choice for estimating the effect on individual’s physical activity. The programme was associated with increased physical activity overall, but the magnitude of its effect differed according to adjustment method. The copula selection model led to a similar effect to the Heckman’s approach but with relatively narrower 95% confidence intervals. These results remained relatively similar when different model specifications and alternative distributional assumptions were considered. The copula selection model can address important limitations of traditional approaches to address sample selection, such as the Heckman model, and should be considered in the evaluation of public health programmes, where sample selection is likely to be present.

This paper investigates impacts, mechanisms and selection effects of prenatal exposure to multiple shocks, by exploiting the unique natural experiment of the Dutch Hunger Winter. At the end of World War II, a famine occurred abruptly in the Western Netherlands (November 1944 - May 1945), pushing the previously and subsequently well-nourished Dutch population to the brink of starvation. We link high-quality military recruits data with objective health measurements for the cohorts born in the years surrounding WWII with newly digitised historical records on calories and nutrient composition of the war rations, daily temperature, and warfare deaths. Using difference-in-differences and triple differences research designs, we show that the cohorts exposed to the Dutch Hunger Winter since early gestation have a higher Body Mass Index and an increased probability of being overweight at age 18, and that this effect is partly accounted for by warfare exposure and a reduction in energy-adjusted protein intake. Moreover, we account for selective mortality using a copula-based approach and newly-digitised data on survival rates, and find evidence of both selection and scarring effects. These results emphasise the complexity of the mechanisms at play in studying the consequences of early conditions.

This paper investigates impacts, mechanisms and selection effects of prenatal exposure to multiple shocks, by exploiting the unique natural experiment of the Dutch Hunger Winter. At the end of World War II, a famine occurred abruptly in the Western Netherlands (November 1944 - May 1945), pushing the previously and subsequently well-nourished Dutch population to the brink of starvation. We link high-quality military recruits data with objective health measurements for the cohorts born in the years surrounding WWII with newly digitised historical records on calories and nutrient composition of the war rations, daily temperature, and warfare deaths. Using difference-in-differences and triple differences research designs, we show that the cohorts exposed to the Dutch Hunger Winter since early gestation have a higher Body Mass Index and an increased probability of being overweight at age 18, and that this effect is partly accounted for by warfare exposure and a reduction in energy-adjusted protein intake. Moreover, we account for selective mortality using a copula-based approach and newly-digitised data on survival rates, and find evidence of both selection and scarring effects. These results emphasise the complexity of the mechanisms at play in studying the consequences of early conditions.

Background Understanding factors associated with women's healthcare decision-making during and after pregnancy is important. While there is considerable evidence related to general determinants of women’s decision-making abilities or agency, there is little evidence on factors associated with women's decision-making abilities or agency with regards to health care (henceforth, health agency), especially for antenatal and postnatal care. We assessed women’s health agency during and after pregnancy in slums in Mumbai, India, and examined factors associated with increased participation in healthcare decisions. Methods Cross-sectional data were collected from 2,630 women who gave birth and lived in 48 slums in Mumbai. A health agency module was developed to assess participation in healthcare decision-making during and after pregnancy. Linear regression analysis was used to examine factors associated with increased health agency. Results Around two-thirds of women made decisions about perinatal care by themselves or jointly with their husband, leaving about one-third outside the decision-making process. Participation increased with age, secondary and higher education, and paid employment, but decreased with age at marriage and household size. The strongest associations were with age and household size, each accounting for about a 0.2 standard deviation difference in health agency score for each one standard deviation change (although in different directions). Similar differences were observed for those in paid employment compared to those who were not, and for those with higher education compared to those with no schooling. Conclusion Exclusion of women from maternal healthcare decision-making threatens the effectiveness of health interventions. Factors such as age, employment, education, and household size need to be considered when designing health interventions targeting new mothers living in challenging conditions, such as urban slums in low- and middle-income countries.

Discrimination affects hiring, mating and voting decisions. Whilst discrimination in elections mainly relates to gender or race, we introduce a novel source of discrimination: candidate resemblance. When candidates' partisanship is not known, voters select those that resemble most elected co-partisans. Using a machine learning algorithm for face comparison, we find a stronger resemblance effect for Republicans compared to Democrats in the US. This happens because Republicans have a higher within-party facial resemblance than Democrats, even when accounting for gender and race. We find a similar pattern in the UK, where Conservative MPs are more similar looking to each other than Labour. Using a survey experiment, we find that Tory voters reward resemblance, while there is no similar effect for Labour. We estimate that facial dissimilarity decreases the candidate's re-election probability by 5-14 percentage points. The results are consistent with an interpretation of this behaviour as a form of statistical discrimination.

Sample selectivity is a recurrent problem in public health programmes and poses serious challenges to their evaluation. Traditional approaches to handle sample selection tend to rely on restrictive assumptions. The aim of this paper is to illustrate a copula-based selection model to handle sample selection in the evaluation of public health programmes. Motivated by a public health programme to promote physical activity in Leeds (England), we describe the assumptions underlying the copula selection, and its relative advantages compared with commonly used approaches to handle sample selection, such as inverse probability weighting and Heckman's selection model. We illustrate the methods in the Leeds Let's Get Active programme and show the implications of method choice for estimating the effect on individual's physical activity. The programme was associated with increased physical activity overall, but the magnitude of its effect differed according to adjustment method. The copula selection model led to a similar effect to the Heckman's approach but with relatively narrower 95% confidence intervals. These results remained relatively similar when different model specifications and alternative distributional assumptions were considered. The copula selection model can address important limitations of traditional approaches to address sample selection, such as the Heckman model, and should be considered in the evaluation of public health programmes, where sample selection is likely to be present.

This paper provides a comprehensive assessment of the short-term impact of the COVID-19 pandemic on businesses worldwide with a focus on developing countries. The results are based on a novel data set collected by the World Bank Group and several partner institutions in 51 countries covering more than 100,000 businesses. The paper provides several stylized facts. First, the COVID-19 shock has been severe and widespread across firms, with persistent negative impact on sales. Second, the employment adjustment has operated mostly along the intensive margin (that is leave of absence and reduction in hours), with a small share of firms laying off workers. Third, smaller firms are disproportionately facing greater financial constraints. Fourth, firms are increasingly relying on digital solutions as a response to the shock. Fifth, there is great uncertainty about the future, especially among firms that have experienced a larger drop in sales, which is associated with job losses. These findings provide a better understanding of the magnitude and distribution of the shock, the main channels affecting businesses, and how firms are adjusting. The paper concludes by discussing some avenues for future research.

Researchers are often interested in the relationship between two variables, with no single data set containing both. A common strategy is to use proxies for the dependent variable that are common to two surveys to impute the dependent variable into the data set containing the independent variable. We show that commonly employed regression or matching-based imputation procedures lead to inconsistent estimates. We offer an easily-implemented correction and correct asymptotic standard errors. We illustrate these with Monte Carlo experiments and empirical examples using data from the US Consumer Expenditure Survey (CE) and the Panel Study of Income Dynamics (PSID).

This study examines whether foreign direct investment inflows facilitate upgrading of export quality in host countries. The analysis focuses on the Russian Federation and uses customs data merged with firm-level information from Orbis. The results show a positive relationship between the quality of products exported by domestic firms and the presence of foreign affiliates in the upstream (input-supplying) industries. This relationship is present irrespective of export destination or foreign direct investment origin. The results are robust to using different proxies to measure product quality.

Background Home visiting programs constitute an important policy to support vulnerable families with young children. One of their principal aims is to improve infant-parent relationships, so a key measure of their effectiveness is based on observational measures of parent-children interactions. In the present study we provide novel evidence on the effectiveness of home visiting programs in improving mother-child interactions within a randomized controlled trial (RCT) of the Pro Kind program. A major goal of the Pro Kind program is to promote child development by strengthening the intuitive parenting skills of mothers. On this basis, the following research question is addressed in this paper: What is the impact of the Pro Kind home visitation program on the quality of mother-child interaction? Methods A randomly chosen subsample of the original sample was selected to participate on video recordings. This subsample of 109 mother-child dyads was videotaped during a 3-min typical play situation at the participants’ homes when the child was aged 25 months. We use a novel micro-coding system which allows us to examine how the intervention affected the dynamic feedback responses of both mothers and children in three key measures of behavior: orientation, positive contingency, and negative/lack of contingency. The analysis was conducted using a set of static probit models and dynamic cross-lagged panel probit models for each measure. Results The intervention significantly improved the interactions between girls and their mothers, by increasing the prevalence of orientation and positive contingency (and reducing that of negative/lack of contingency). This was achieved by increasing both the persistence of positive behaviors and also the probability of switching from negative to positive behaviors in the treatment group. Mixed impacts were detected for boys. Conclusions Overall, it can be said that the Pro Kind program has a positive impact on the quality of mother-daughter interaction. However, our findings might also influence the design and delivery of home visiting programs, to the extent that they suggest that more attention has to be devoted to the interactions between boys and their mothers. Furthermore, the results show the importance of careful dynamic modelling of interactions data from videotaped observations to have a more complete understanding of the effectiveness of home visiting programs.

Health Economics is a large and vibrant area of economics that has expanded rapidly over the past fifty years. This encyclopedia brings together leading scholars to provide the most comprehensive and up-to-date resource on health economics ever produced. In 85 peer-reviewed entries, readers of this work will find authoritative overviews all aspects of modern health economics research, organized around four broad themes: population health, global health, health systems, and evaluation. Students, scholars, researchers, and professionals seeking in-depth, foundational essays on both core and emerging topics will find comprehensive examinations of the vast and growing literature in health economics.

Building on early animal studies, 20th-century researchers increasingly explored the fact that early events—ranging from conception to childhood—affect a child’s health trajectory in the long-term. By the 21st century, a wide body of research had emerged, incorporating the original fetal origins hypothesis into the developmental origins of health and disease. Evidence from Organization for Economic Cooperation and Development (OECD) countries suggests that health inequalities are strongly correlated with many dimensions of socioeconomic status, such as educational attainment, and that they tend to increase with age and carry stark intergenerational implications. Different economic theories have been developed to rationalize this evidence, with an overarching comprehensive framework still lacking. Existing models widely rely on human capital theory, which has given rise to separate dynamic models of adult and child health capital within a production function framework. A large body of empirical evidence has also found support for the developmental origins of inequalities in health. On the one hand, studies exploiting quasi-random exposure to adverse events have shown long-term physical and mental health impacts of exposure to early shocks, including pandemics or maternal illness, famine, malnutrition, stress, vitamin deficiencies, maltreatment, pollution, and economic recessions. On the other hand, studies from the 20th century have shown that early interventions of various content and delivery formats improve life course health. Further, given that the most socioeconomically disadvantaged groups show the greatest gains, such measures can potentially reduce health inequalities. However, studies of long-term impacts as well as the mechanisms via which shocks or policies affect health, and the dynamic interaction among them, are still lacking. Mapping the complexities of those early event dynamics is an important avenue for future research.

A poor start in life shapes children’s development over the life-course. Children from low- and middle-income countries (LMICs) are exposed to low levels of early stimulation, greater socioeconomic deprivation and persistent environmental and health challenges. Nevertheless, little is known about country-specific factors affecting early childhood development (ECD) in LMICs. Using data from 68 LMICs collected as part of the Multiple Indicator Cluster Surveys between 2010 and 2018, along with other publicly available data sources, we employed a multivariate linear regression analysis at a national level to assess the association between the average Early Childhood Development Index (ECDI) in children aged 3–5 and country-level ecological characteristics: early learning and nurturing care and socioeconomic and health indicators. Our results show that upper-middle-income country status, attendance at early childhood education (ECE) programs and the availability of books at home are positively associated with a higher ECDI. Conversely, the prevalence of low birthweight and high under-5 and maternal mortality are negatively associated with ECDI nationally. On average, LMICs with inadequate stimulation at home, higher mortality rates and without mandatory ECE programs are at greater risks of poorer ECDI. Investment in early-year interventions to improve nurturing care and ECD outcomes is essential for achieving Sustainable Development Goals.

This project received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 633595

Published online: 29 September 2022 Researchers are often interested in the relationship between two variables, with no single data set containing both. A common strategy is to use proxies for the dependent variable that are common to two surveys to impute the dependent variable into the data set containing the independent variable. We show that commonly employed regression or matching-based imputation procedures lead to inconsistent estimates. We offer a consistent and easily implemented two-step estimator, “rescaled regression prediction.” We derive the correct asymptotic standard errors for this estimator and demonstrate its relationship to alternative approaches. We illustrate with empirical examples using data from the US Consumer Expenditure Survey (CE) and the Panel Study of Income Dynamics (PSID).

OBJECTIVES:Implementation of a large-scale, child weight management program in low-income, ethnically diverse communities provided an important opportunity to evaluate its effectiveness under service level conditions (i.e. provision as a primary care child weight management service). METHODS:MEND 7-13 is a community-based, multi-component, childhood obesity intervention designed to improve dietary, physical activity and sedentary behaviors. It comprises twice weekly sessions for 10 consecutive weeks (35 contact hours) and is delivered to groups of children and accompanying parents/caregivers. The evaluation used an uncontrolled, repeated measures design. Overall, 3782 children with overweight or obesity attended 415 MEND 7-13 programs in eight US states, of whom 2482 children (65.6%) had complete data for change in zBMI. The intervention targeted low-income, ethnically diverse families. Changes in anthropometric, cardiovascular fitness and psychological outcomes were evaluated. A longitudinal multivariate imputation model was used to impute missing data. Peer effects analysis was conducted using the instrumental variables approach and group fixed effects. RESULTS:Mean changes in BMI and zBMI at 10 weeks were -0.49 kg/m2 (95% CI: -0.67, -0.31) and -0.06 (95% CI: -0.08, -0.05), respectively. Benefits were observed for cardiovascular fitness and psychological outcomes. Mean peer reduction in zBMI was associated with a reduction in participant zBMI in the instrumental variables model (B = 0.78, P = 0.04, 95% CI: 0.03, 1.53). Mean program attendance and retention were 73.9% and 88.5%, respectively. CONCLUSION:Implementing MEND 7-13 under service level conditions was associated with short-term improvements in anthropometric, fitness and psychological indices in a large sample of low-income, ethnically diverse children with overweight and obesity. A peer effect was quantified showing that benefits for an individual child were enhanced, if peers in the same group also performed well. To our knowledge, this is the first US study to evaluate outcomes of an up-scaled community-based, child weight management program and to show positive peer effects associated with participation in the intervention.

Business and politicians' interaction is pervasive but has mostly been analyzed with a binary approach, i.e. either a firm is connected to a politician or not. Yet the network dimensions of such connections are ubiquitous. This paper uses use a unique data set for seven economies that documents politically exposed persons and their links to companies, political parties, and other individuals. The data set is used to identify networks of connections, including their scale and composition. The analysis finds that all country networks are integrated having a Big Island. They also tend to be marked by small-world properties of high clustering and short path length. Matching the data to firm-level information, the paper examines the association between being connected and firm-level attributes. The originality of the analysis is to identify how location in a network, including the extent of ties and centrality, is correlated with firm scale and performance. In a binary approach, such network characteristics are omitted and the scale and economic impact of politically connected business may be significantly mis/under-estimated. By comparing the results of the binary approach with the network approach, the paper also assesses the biases that result from ignoring network attributes.

Pension reform delays were a crucial part of the mechanism leading to the Greek crisis of 2009. The reason for these delays was frequently cited as the hostile stance of public opinion to any change in pensions. It is thus important to examine what lay behind these attitudes, and the reasons why these public objections had not been overcome. The international bibliography stresses that attitudes to pensions may be affected by the extent of understanding of pensions and that public opinion may in this way be open to persuasion. However, where there exists cross-subsidisation between occupational groups, selective knowledge may also operate defensively as a means of protecting the status quo. Thus there could be two dimensions of understanding, one helping and the other hindering change. A nation-wide sample undertaken in Greece by the authors in May 2009 attempts to disentangle the two influences, by drawing a distinction between general understanding, and particular awareness of one’s occupational pension situation. An ‘intransigence index’ is subjected to multivariate analysis isolating, inter alia, the two dimensions. As in other countries, greater understanding makes for a more conciliatory attitude. However, in sharp contrast, in Greece particular knowledge encourages more confrontational views, as part of a strategy defending pension privileges. This finding can be used to explain the role pension attitudes and path dependence played in generating the reform impasse which was ultimately responsible for the Greek crisis after 2009.

Home visiting programs constitute an important policy to support vulnerable families with young children. One of their principal aims is to improve infant-parent relationships, so a key measure of their effectiveness is based on observational measures of parent-children interactions. In the present study we provide novel evidence on the effectiveness of home visiting programs in improving mother-child interactions within a randomized controlled trial (RCT) of the Pro Kind program. A major goal of the Pro Kind program is to promote child development by strengthening the intuitive parenting skills of mothers. On this basis, the following research question is addressed in this paper: What is the impact of the Pro Kind home visitation program on the quality of mother-child interaction?A randomly chosen subsample of the original sample was selected to participate on video recordings. This subsample of 109 mother-child dyads was videotaped during a 3-min typical play situation at the participants' homes when the child was aged 25 months. We use a novel micro-coding system which allows us to examine how the intervention affected the dynamic feedback responses of both mothers and children in three key measures of behavior: orientation, positive contingency, and negative/lack of contingency. The analysis was conducted using a set of static probit models and dynamic cross-lagged panel probit models for each measure.The intervention significantly improved the interactions between girls and their mothers, by increasing the prevalence of orientation and positive contingency (and reducing that of negative/lack of contingency). This was achieved by increasing both the persistence of positive behaviors and also the probability of switching from negative to positive behaviors in the treatment group. Mixed impacts were detected for boys.Overall, it can be said that the Pro Kind program has a positive impact on the quality of mother-daughter interaction. However, our findings might also influence the design and delivery of home visiting programs, to the extent that they suggest that more attention has to be devoted to the interactions between boys and their mothers. Furthermore, the results show the importance of careful dynamic modelling of interactions data from videotaped observations to have a more complete understanding of the effectiveness of home visiting programs.