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In this paper we review the available summary measures for the magnitude of socio-economic inequalities in health. Measures which have been used differ in a number of important respects, including (1) the measurement of "relative" or "absolute" differences; (2) the measurement of an "effect" of lower socio-economic status, or of the "total impact"...
Contexts in source publication
Context 1
... conditions potentially pose a major statistical challenge for identification. Table 4 presents some summary information on the presence of comorbordities in our full sample. 34 Column 1 of Table 4 displays the number of individuals (and marginal distribution) in each wave who smoke or have been diagnosed with either AD, HD, ADHD, obesity or depression. ...
Context 2
... 4 presents some summary information on the presence of comorbordities in our full sample. 34 Column 1 of Table 4 displays the number of individuals (and marginal distribution) in each wave who smoke or have been diagnosed with either AD, HD, ADHD, obesity or depression. Across each row we present the number of individuals (and conditional frequency) who also engage in smoking or suffer other poor health outcomes. ...
Context 3
... demonstrate the robustness of our results, Appendix Tables 4 presents results for the male and female subsample that correspond to their preferred instruments sets using stepwise estimation on those subsamples. While the first stage properties in Appendix Table 4 are improved, a eyeball test confirms that there are no important statistical differences between these estimates and those using the instruments set constructed for the full sample with health vector 1 in Table 7. ...
Context 4
... demonstrate the robustness of our results, Appendix Tables 4 presents results for the male and female subsample that correspond to their preferred instruments sets using stepwise estimation on those subsamples. While the first stage properties in Appendix Table 4 are improved, a eyeball test confirms that there are no important statistical differences between these estimates and those using the instruments set constructed for the full sample with health vector 1 in Table 7. Similarly, combining the separate instrument sets for males and females and estimating the system of equations for the full sample yields no observable differences. ...
Similar publications
In this paper we review the available summary measures for the magnitude of socio-economic inequalities in health. Measures which have been used differ in a number of important respects, including (1) the measurement of "relative" or "absolute" differences; (2) the measurement of an "effect" of lower socio-economic status, or of the "total impact"...
Citations
... These relationships endured after adjusting for a suite of sociodemographic factors. These findings are in line with a literature which studies links between mental health and educational outcomes [24][25][26]56 , across a range of data types and study designs 27,28 . This study provides timely evidence for the New Zealand context, where inequalities in mental health and education have been documented 6,21 , but there is less evidence studying the connection between these two important variables. ...
... However, a comprehensive set of sociodemographic variables was used to gauge the confounding effects and attempt to mitigate this impact. Moreover, a link between mental health conditions and educational outcomes has also been observed across studies using alternative study designs which rely on different assumptions, such as Mendelian randomisation 27,28 , and family fixed effect models 9 , which provides supporting evidence for the veracity of this association. Still, even if these differences are not causal effects-that is, only due to mental health-or indeed more likely, some mix of correlation and causation, it remains important to document healthrelated inequalities in educational outcomes. ...
Young people experiencing mental health conditions are vulnerable to poorer educational outcomes for many reasons, including: social exclusion, stigma, and limited in-school support. Using a near-complete New Zealand population administrative database, this prospective cohort study aimed to quantify differences in educational attainment (at ages 15–16 years) and school suspensions (over ages 13–16 years), between those with and without a prior mental health condition. The data included five student cohorts, each starting secondary school from 2013 to 2017 respectively (N = 272,901). Both internalising and externalising mental health conditions were examined. Overall, 6.8% had a mental health condition. Using adjusted modified Poisson regression analyses, those with prior mental health conditions exhibited lower rates of attainment (IRR 0.87, 95% CI 0.86–0.88) and higher rates of school suspensions (IRR 1.63, 95% CI 1.57–1.70) by age 15–16 years. Associations were stronger among those exhibiting behavioural conditions, compared to emotional conditions, in line with previous literature. These findings highlight the importance of support for young people experiencing mental health conditions at this crucial juncture in their educational pathway. While mental health conditions increase the likelihood of poorer educational outcomes, deleterious outcomes were not a necessary sequalae. In this study, most participants with mental health conditions had successful educational outcomes.
... However, there may also be reverse causation, health problems that emerge in early life influencing educational attainment. In addition, education and health may be jointly determined by other factors (e.g., Ding et al. 2009). For example, socioeconomic status in childhood may affect educational attainment as well as health in later life (e.g., Haas 2008;Pakpahan, Hoffmann, and Kröger 2017). ...
... They emphasised that the mere inclusion of health status -where child nutrition usually serves as an intermediate indicator -on the right-hand side of the child schooling model may introduce endogeneity and thus cannot guarantee a causal interpretation on the estimated coefficient. Without the context of climate shocks and gender-disaggregation, Ding et al. (2006) and Aturupane et al. (2013) address this issue and estimate the contemporaneous causal effect of child health on academic performance by making use of an instrumental variable approach. They, however, implicitly assumed that the effect of child health on education is the same for all children regardless of gender and underlying socio-cultural and environmental conditions. ...
This study examines the impact of drought on child health and schooling outcomes and investigates the contemporaneous relationship between these two main building blocks of human capital. We merge child-level longitudinal data from the Ethiopia Rural Socioeconomic Survey (ERSS) with geo-referenced climate data. Our findings from within-child variation estimators reveal that drought has a detrimental impact on the highest grade completed of female children. We show that the negative effect of drought on a female child's completed years of formal schooling is channelled, albeit not entirely, through ill health. Our result is robust to using recursive bivariate estimation with exclusion restriction to correct for biases associated with the endogeneity of child health due to time-varying heterogeneities. Gender bias in the household explains why the direct and mediated schooling effects of drought are concentrated only on female children. We find that households respond to drought-induced income shocks by decreasing the allocation of resources for the medical treatment of an ill female child. Moreover, households also increase the use of female child labour for non-agricultural activities, which is consistent with a disproportionate increase in school absenteeism of older girls during drought. We discuss how gender-responsive policy design and implementation may help alleviate gender inequality in human development in the face of climate change.
... Depression is associated with cognitive decline or cognitive impairment (e.g., Gilchrist and Creed, 1994;Rock et al., 2014;Starkstein et al., 1992), which can impede one's learning process. Thus, depression is commonly found to reduce educational achievement (e.g., Crystal et al., 1994;Ding et al., 2006;Owens et al., 2012). For example, in China, a two-wave study of 540 sixthgrade children randomly sampled in Shanghai showed that prior depression negatively predicted subsequent educational achievement reported by teachers (Chen and Li, 2000). ...
Research on depression and education usually focuses on a unidirectional relationship. This paper proposes a reciprocal relationship, simultaneously estimating the effects of depression on education and of education on depression. China, which has the world's largest education system, is used as a case study. This paper applies structural equation modeling to three datasets: the China Family Panel Studies, the China Education Panel Survey, and the Gansu Survey of Children and Families. Analyses reveal a reciprocal and negative relationship between depression and educational outcomes. Specifically, early depression reduces later educational achievement, and higher educational achievement also lowers the level of subsequent depression by resulting in less peers' unfriendliness, less pressure from parents' expectations, and less teachers' criticism. More time spent on studies is not associated with higher educational achievement but significantly increases the level of depression. Children from lower SES families bear more pressure and spend more time on studies, which does not correspond to higher educational achievement but rather to higher levels of depression. In the long term, prior depression lowers educational attainment and, after controlling for prior depression, lower educational attainment is also associated with higher levels of subsequent depression. This paper shows that the lower achievers, not the high achievers, bear the major psychological burden of the education system's quest to produce high achievement. This situation reinforces these students' educational disadvantage.
... vii See Angrist and Pischke (2010) for more discussion, and comments by Keane (2010) and Sims (2010) that provide a critique of this shift. viii This paper was first presented at a conference in 2003, and an early version appears as a NBER working paper (Ding, Lehrer, Rosenquist, & Audrain-McGovern (2006)). ...
... In particular, individuals who hold intersectional identities, and their families, rely on the supports these programs provide. Additionally, research shows: 1. that enhanced educational outcomes can improve health and social conditions (Hahn & Truman, 2015) and 2. the negative impact that poor public health factors can have on vulnerable learners (Cheong & Massey, 2018;Ding, Lehrer, Rosenquist, & Audrain-McGovern, 2006;Jensen, 2009). Yet, there is a lack of discourse on how school-based changes regarding family supports could improve public health and social outcomes. ...
... Therefore, this study aims to explain the relationship of health and cleanliness between childhood and adulthood on educational outcomes. There are numerous body of literature on the relationship between health and education, such as Grossman [2] and more recently by other authors including Cutler and Lleras-Muney [3]; Ding et al. [4]; Gan and Gong [5], health and education relationship can be explained in three ways: ...
As a key indicator in Sustainable Development Goals, good health and well-being, also clean water and sanitation is studied in this research. In Indonesia where infrastructure development is still advancing, the difference of those infrastructure when people are in childhood and adulthood could be very different. It is also interrelated to their health status. Early health is considered as important, if not more, to current health as in early years there are primary development happened to an individual, whether it is internal or external. Health is also linked to productivity such as educational outcomes. This study aims to explain the relationship of health and cleanliness between childhood and adulthood on educational outcomes. Using IFLS 5 data and multinomial logistic, the result is while cleanliness and infrastructure in childhood is found to be more important for higher education, current health and early health is just as strong.
... Finally, as the scientific literature is also now moving beyond only considering main genetic effects, it is worth pointing out that gene-gene interactions almost certainly do exist. 46 Indeed, both Ding et al. (2006Ding et al. ( , 2009 and Lehrer (2009b, 2011) consider such twoway interactions in their instrument set, but there is not much information even in the behavioral genetics literature on how and why these interactions operate. In other words, understanding the genetic architecture of a particular trait is one of the main goals and this challenge mirrors the steps required when labor economists create empirical models to understand the underlying data generation process. ...
The idea that genetic differences may explain a multitude of individual-level outcomes studied by economists is far from controversial. Since more datasets now contain measures of genetic variation, it is reasonable to postulate that incorporating genomic data in economic analyses will become more common. However, there remains much debate among academics as to, first, whether ignoring genetic differences in empirical analyses biases the resulting estimates. Second, several critics argue that since genetic characteristics are immutable, the incorporation of these variables into economic analysis will not yield much policy guidance. In this paper, we revisit these concerns and survey the main avenues by which empirically oriented economic researchers have utilized measures of genetic markers to improve our understanding of economic phenomena. We discuss the strengths, limitations, and potential of existing approaches and conclude by highlighting several prominent directions forward for future research.
JEL Classification: I12, J19, I26
... Os trabalhos apontam os efeitos do status nutricional, mensurado por indicadores antropométricos (Jamison, 1986;Moock & Leslie, 1986;Glewwe & Jacoby, 1995;Gomes-Neto et al., 1997;Crosnoe & Muller, 2004;D. C. Machado, 2008;Kaestner & Grossman, 2009), de fatores comportamentais (Pirie, Murray & Luepker, 1988;Ellickson, Tucker & Klein, 2001;Sigfusdottir, Kristjansson & Allegrante, 2007;Ding, Lehrer, Rosenquist & Audrain-McGovern, 2009;Kristjansson, Sigfusdottir & Allegrante, 2010;Carrell et al., 2011;Ponzo, 2013;Lindo et al., 2013) e outras medidas de saúde (Eide, Showalter & Goldhaber, 2010;Rees & Sabia, 2011;Rees & Sabia, 2014) sobre o nível de proficiência, atraso escolar, conclusão dos estudos e outros indicadores educacionais. ...
... Os resultados centrais corroboram a hipótese de que crianças expostas aos fatores de risco comportamentais à saúde possuem um maior atraso escolar. Considerando as evidências do presente estudo e a literatura especializada na área, como Renna (2007), D. C. Machado (2008), Ding et al. (2009), Eide et al. (2010 e Lindo et al. (2013), ações que envolvam o enfrentamento das DCNT, no tocante aos fatores de risco modificáveis, podem implicar não apenas melhorias na saúde física e psicológica, mas também numa melhoria do rendimento escolar. ...
This study aims to evaluate the role of exposure of students to behavioral risk factors to health - smoking, alcohol and overweight - in educational attainment in Brazil. We use microdata from the National Survey of School Health (Pesquisa Nacional de Saúde do Escolar-PENSE) 2012 and parametric and nonparametric techniques to estimate the effect of exposure to these factors in the indicator of delay in school progression of students in the 9th grade of elementary school. The main results indicate that exposure to risk factors has direct effect on delay in school progression. Furthermore, these effects are more intense for students with lower socioeconomic level. Then, the findings of this study suggest the importance of public policies that promote prevention of these risk factors among children, once the exposure to risk factors to health can generate repercussions not only in health but also in the educational component of human capital.
... Unfortunately, little is known about the psychological consequences of being stalked. It is important to resolve this issue because researchers have found that poor mental health lowers wages (Contoyannis and Rice, 2001;Goldsmith, Veum, and Darity, 1997), reduces educational attainment (Weili et al., 2006;Currie and Stabile, 2005;Kessler et al., 1995), and harms employment prospects (Currie and Madrian, 1999). ...
Objectives
This article offers new evidence on whether stalking damages the mental health of female victims. This study advances the literature by accounting for age of initial stalking victimization, mental health status prior to being stalked, and exposure to other forms of traumatic victimization. Methods
Using logistical analysis, we utilize data drawn from three large national data sets. ResultsWe find that being the victim of stalking as a young adult, ages 18–45, significantly increases the odds of initial onset of psychological distress; however, this is not the case for victims ages 12–17. Conclusions
Stalking has emerged as a deeply disturbing public issue because of its prevalence and the fear it creates in victims. Unfortunately, little is known about the psychological consequences of being stalked because the emerging literature typically is based on small, nonrandom samples. Our findings highlight the benefits of reducing stalking and the importance of supporting victims.
