Right wing politicians look more alike than those on the left, and voters use this information cue when they know little about candidates

How do voters make decisions when they know almost nothing about a political candidate, including their party affiliation? In new research, Raluca L. Pahontu and Stavros Poupakis argue that when a candidate’s party isn’t known, voters often use an information shortcut – the candidate’s appearance. They find that voters are more likely to select candidates that look more like other elected officials from their preferred political party, and that this effect is stronger for Republicans than for to Democrats

Asynchronous fieldwork in cross-country surveys: an application to physical activity

Multi-country surveys often aim at cross-country comparisons. A common quality standard is conducting these surveys within a common fieldwork period, across all participating countries. However, the rate the target sample is achieved within that fieldwork period in each country varies substantially. Thus, the distribution of the interview month often varies substantially in the final sample. This may lead to biased estimates of cross-country differences if the variable of interest exhibit a non-constant trend over time. We demonstrate the implications of such an asynchronous fieldwork, using physical activity measured in the European Social Survey Round 7 collected between September 2014 and January 2015. Accounting for fieldwork month, we present a set of different post-estimation predictions. Physical activity varies across interview month, with countries with more observations during autumn were upward-biased, compared to countries with more observations during winter. Our results demonstrate how comparisons between countries are affected when interview month is omitted, and how accounting for interview month in the analysis is an easy way to mitigate this problem

Attendance, Weight Loss, and Participation in a Behavioural Diabetes Prevention Programme

Background Weight 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. Method Prospective 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. Results The 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. Conclusion Most 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

Severe prenatal shocks and adolescent health: Evidence from the Dutch Hunger Winter

This paper investigates health impacts at the end of adolescence 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 first 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 obese at age 18. We then find that this effect is partly moderated by warfare exposure and a reduction in energy-adjusted protein intake. Lastly, 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

Regression with an imputed dependent variable

Published online: 29 September 2022Researchers 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)

Factors associated with women's healthcare decision-making during and after pregnancy in urban slums in Mumbai, India: a cross-sectional analysis

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

Exploring the Associations between Early Childhood Development Outcomes and Ecological Country-Level Factors across Low- and Middle-Income Countries.

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

Cohort Profile : The DynaHEALTH consortium - a European consortium for a life-course bio-psychosocial model of healthy ageing of glucose homeostasis

Peer reviewe

Three Essays in Applied Microeconometrics

Chapter 1 develops a specification test for a single index binary outcome model in semi-parametric estimation. The semiparametric estimator examined does not rely on any distributional assumption, but it still relies on the single-index assumption. The violation of this assumption creates a source of heteroscedasticity. I extend a set of attractive LM statistics, constructed using auxiliary regressions for the case of logit and probit models, to the semiparametric environment. I derive its asymptotic distribution and show that is has well-behaved finite properties in a Monte Carlo experiment. An empirical example is also provided. Chapter 2 proposes a novel estimation strategy that accounts for asynchronous fieldwork, often found in multi-country surveys. The resulting biases are substantial and this is likely to provide misleading cross-country comparisons. I highlight the importance of accounting for the heterogeneity induced by seasonality in the context of regression modelling in order to obtain unbiased comparisons. This is illustrated with a comparison between a synchronous national survey and an asynchronous cross-national one. Chapter 3, joint work with Thomas Crossley and Peter Levell, proposes a novel estimator useful for data combination. Researchers are often interested in the relationship between two variables, with no available data set containing both. For example, surveys on income and wealth are often missing consumption data. A common strategy is to use proxies for the dependent variable that are common to both surveys to impute the dependent variable into the data set containing the independent variable. We consider the consequences of estimating a regression with an imputed dependent variable, and how those consequences depend on the imputation procedure adopted. We show that an often used procedure is biased, and offer both a correction and refinements that improve precision. We illustrate these with a Monte Carlo study and an empirical application

Political Networks across the Globe

Political networks are an important feature of the political and economic landscape of countries. Despite their ubiquity and significance, information on such networks has proven hard to collect due to a pervasive lack of transparency. However, with the advent of big data and artificial intelligence, major financial services institutions are now actively collating publicly available information on politically exposed persons and their networks. In this study, we use one such data set to show how network characteristics vary across political systems. We provide results from more than 150 countries and show how the format of the network tends to reflect the extent of democratisation of each country. We also outline further avenues for research using such data