Intergenerational health correlations: Is it genes or is it income?

Transcription

1 Intergenerational health correlations: Is it genes or is it income? Ana Llena-Nozal a* Maarten Lindeboom b Bas van der Klaauw c January 2006 Very preliminary and incomplete Abstract: This paper investigates how much of health is transmitted across generations through genetics and how much through behavior and socio-economic status (SES). Low SES children start adulthood with lower education and health and this may constrain their economic position in adulthood. The intergenerational correlation of income may be linked to health but it is not clear what mechanisms are driving this. Low SES parents may face financial constraints that lower parental investment, they may have lower parenting skills, or they may have worse health outcomes that constraint economic opportunities and these may be transmitted across generations. Both unobserved heterogeneity and parental health need to be accounted for in order to disentangle the correlation. We use a cohort study, the National Child Development Study, which follows individuals since their birth in 1958 until their 40s. We exploit the richness of the data and incorporate information on twins, adoptees and the cohort members own children to disentangle the nature and nurture components. Our findings suggest that parental income significantly reduces the risk of ill health in children. Individuals not having a natural father are more prone to poor health but experience a similar mitigating impact of income. Income appears to no longer be significant when we control for parental health and time investment. On the other hand, when financial difficulties are used to measure poverty, the effects persist, even after controlling for individual unobserved heterogeneity. a: Free University Amsterdam & Tinbergen Institute. b. Free University Amsterdam, Tinbergen Institute, HEB, IZA & Netspar. * Correspondence: Department of Economics and Business Administration, Free University of Amsterdam, De Boelelaan 1105, 1081 HV Amsterdam, The Netherlands. allenanozal@econ.vu.nl

2 c. Free University Amsterdam, Tinbergen Institute, Scholar & CEPR.

3 1. Introduction There exists a strong positive association between health and socio-economic status (SES) at adult ages. Whilst there is agreement on the strength of the association between health and SES, little is known about the underlying mechanisms. Causality can run both ways: from poor health to lower income or from low income to poorer health. Many studies point out that the gradient in health status has its antecedents in childhood and in recent years there has been a growing literature exploring the association between SES and health in childhood. Looking at child s health instead of adult health has the additional advantage that it rules out problems of reverse causality. Indeed, in Western countries, children do not contribute to family income and one can therefore focus solely on the adverse effects of poverty on health. There are several reasons why parental income might be associated with offspring s health. One possibility is that parents from a lower socio-economic background suffer from financial constraints and invest less in their children's education, nutrition and environment. Financial constraints might results in parents from lower incomes purchasing less health care or other goods affecting child s health. Parental decisions such as prenatal care and nutrition can be affected by parental income and wealth and may have a strong influence in the health of their children even as they enter adulthood. In addition, lower income can influence health through the effects of the environment such as neighborhood and housing conditions. Secondly, health behavior is correlated with income and worse health habits (drinking, smoking and exercise) might be transmitted across generations. Finally, health problems might be genetically transmitted across generations. In particular, some studies have shown the association between parental mortality from cardiovascular disease and offspring s birth weight (Davey Smith et al., 1997) and between parental diabetes and offspring s birth weight (Hypponen et al., 2003). Genetic endowments might also explain why some individuals are both healthier and wealthier. Both unobserved heterogeneity and parental health need to be accounted for in order to disentangle the correlation. Recent research has looked at the possible transmission mechanisms between income and child s health. Most studies point out that: 1) Permanent income appears to be more important than current income; 2) it is the level rather than the changes in income which matter; and 3) that it is persistent poverty rather than transitory or occasional poverty which matters (Benzeval & Judge, 2001). Case, Lubotsky and Paxson (CLP-2002) suggest that the relationship can be partly

4 explained by the fact that children in higher-income households experience less chronic conditions and their parents manage those conditions better. Higher income children have higher health stocks for any given chronic conditions and low-income children have more adverse effects from poor health at birth. They also find that the income effect for children living with birth and non-birth parents is not significantly different. Nevertheless, controlling for health at birth, parental health, health insurance and maternal labor supply does not completely eliminate the effect of income and do not account for the income gradient in childhood health. Currie et al. (2004) replicate the analysis with pooled data from the UK (HSE) and find that the size of the gradient is significantly smaller and does not increase with child age. Using additional data on nutrition and lifestyle, they find that consumption of vegetables and parental exercise are important but do not reduce the income gradient in child heath. On the other hand, family income is not important in determining health measured by blood tests results. Currie & Stabile (2003) pursue this analysis further using panel data and find that children from both low and high SES recover similarly from past health shocks. Their analysis shows that the gradient occurs because low SES children receive more shocks. Most studies on child health and income do not control for unobserved heterogeneity and parental health. The methods used have generally looked at the association between parental income and child health, controlling for a set of variables which attempt to capture the child s health endowment and as much as possible the parents own health and the parents production of child health. Doyle et al. (2005) question to what extent the income (or education/ses) effects on child s health are the result of a spurious correlation due to the correlation with some unobservable variables. The study by Kebede (2003) uses a fixed-effects specification to remove the bias caused by the correlation between income and the unobservables. Kebede (2003) examines the determinants of child health in rural Ethiopia and finds no significant correlation between children s health and per capita expenditures. Parental health is highly significant and appears to influence child health through genetic rather than behavioral factors. On the other hand, Burgess et al. (2005) question whether the use of fixed-effects for children is appropriate since the individual effects at early ages might not be fixed. They use a cohort data from the UK and find that, controlling for maternal health and parental choice of health inputs in early childhood, there is almost no effect of income on child health. Their results also suggest that the transmission mechanism from income to child health operates through maternal health (in particular mental health) rather than through health related behaviors. Doyle et al. (2005) identify the effect of parental education on child health using the exogenous variation in schooling caused by the raising of the minimum school leaving age (for those born after September 1957) and

5 grandparent s smoking histories as instrument for parental income. They find that the use of instruments eliminates the effects of parental income and education. In this paper we investigate how much of health is transmitted across generations through genetics and how much through pregnancy-related growth, parental and own health behavior and socio-economic status. We use the intergenerational information of a cohort study, the National Child Development Study, which follows individuals since their birth in 1958 until their 40s. We use the information on the parental background, health and behavior and income, and the individual s own information while exploiting the panel data nature of the data and the information on the cohort member s own children. This issue is particularly relevant for policy as it provides further evidence on whether increasing parental income through different benefits substantially contributes to children s outcomes. In addition, it throws light on the process of intergenerational mobility by examining what is the role of health transmission in the intergenerational correlation of earnings or to what extent is the transmission of social disadvantage merely a reflection of poor health transmitted across generations. 2. The model and empirical specification of the model 2.1 The Model We base our conceptual framework on the demand for health model developed by Grossman (1972, 1999) since it is the most relevant theoretical framework to explain an individual's health status. Health is defined as a durable capital stock that produces an output of healthy time (Grossman, 1972). The demand for health corresponds to two reasons: for consumption and for investment purposes. It represents a consumption commodity because sick days produce disutility. Health can be viewed as an investment commodity because it influences the time available for market and nonmarket activities. Indeed, an increase in the stock of health may increase economic resources through an increase in time available for work. Individuals are supposed to inherit an initial stock of health, which depreciates with age and increases with health investments. In this paper, we focus on how health is transmitted from the parents. In this case, health of offspring at birth might be due to a genetic component, but also partly the result of the optimizing behavior of parents. Parents make decisions in terms of how to allocate the resources in the households and invest in the child s human and health capital. The health outcome of children is thus a result of the parental maximization problem, where parents maximize the

6 expected value of an intertemporal utility function that has as arguments children's health, commodities and health inputs. The household's utility function in each period follows (Rosenzweig & Schultz, 1983; Rosenzweig & Wolpin, 1988): U U X it,z it,h it ;S where H is the health of the child, Z are health-related inputs (health behavior, health environment, use of medical services), X are other commodities. Utility may also be affected by household characteristics ( S such as education and age (representing life cycle position and preferences). Fertility decisions are taken as given and we ignore the problems of how decisions are taken in the household. Preferences are assumed to be intertemporally additive and the utility function is increasing and concave in its arguments (individuals are risk-averse). Households use certain inputs and transform them through a production technology into the health of their offspring. The production if child health is specified by the production function H h Z it, where represents family-specific health endowments known to the family but not controlled by them and child characteristics such as inherent healthiness/immunity. Child health is a function of such health inputs as nutrition, parental time and health care use, and of parental health productivity (i.e. the ability to translate those inputs into the production of child health). Production of health is also a function of past health. Parental decisions unknown to the researcher affect this process. This is the case, for instance, if parental behavior responds to unanticipated health outcomes as indicated by Rosenzweig & Wolpin (1988). The production of child health (the health technology or productivity) is thus influenced by the characteristics of the child, parental characteristics and the health environment. Income may affect the behavior of parents in the case of credit constraints as poor parents may not be able to invest optimally. On the other hand, the effect of income might also act through and impact on the quality of parenting. The budget constraint for the household is: F W t p t t where F is exogenous money income, p t are exogenous prices and W X Z. The reduced form demand function for the health outcome is: H p,f,.

7 Reduced form expressions relate the impact of exogenous factors on a variable representing child s health. To examine the relationship between health outcomes and health inputs, hybrid or quasi-structural equations are used. The reason is the lack of available data. H Z m, p l,f, where Y m corresponds to one input and the other are the determinants of all other inputs. Nevertheless, "hybrid" functions give biased estimates according to Rosenzweig and Schultz (1983) because they are unable to differentiate between the properties of the health production function and the characteristics of the household preferences Empirical specification In this paper we estimate a household health production function using information on several health and morbidity indicators and a set of determinants such as parental characteristics and parental health behavior. Direct estimation of the production function will most likely lead to biased coefficient estimates. The reason for the bias is that a mother may have information regarding her health endowment that may influence her choice of inputs (health behavior for instance), leading to an endogeneity of certain health inputs. The error term, containing household heterogeneity is likely to be correlated with the health outcome-- thus OLS estimates will be inconsistent. Indeed, the observed association between the variables and the measures of the child s health will overstate the consequences for the child s health. Consistent estimates of the health production can be obtained estimating a structural demand system identifying the underlying preference parameters. Given the absence of all prices and household expenditures, Rosenzweig and Schultz (1983) suggest that a two-stage least squares. They estimate first the demand equations for behavioral input variables which are then used for the second-stage estimates of the health production parameters. In our paper, we combine different approaches to obtain as much information as possible on the relative importance of factors that contribute to the intergenerational transmission of health. Firstly, we will examine the association between income and child health at birth and during childhood and adolescence. We will exploit the richness of our dataset and introduce several measures of parental health and behavior in an attempt to capture as much heterogeneity as possible. We will also exploit the panel data nature of the data to estimate individual fixed-effects for an indicator

8 for chronic illnesses. Secondly, we will look at three generations health outcomes since the dataset contains information on the children of the cohort members and examine the effects of grandparents and parents factors on the cohort member s children while exploiting the variation among siblings. Thirdly, we will use data on twins to difference out any correlation attributable to genetics. Identical twins possess identical genetic endowments and share the same pre-natal environment. Differences in the health of their children will be informative. Finally, we will use adoptees to try to get a causal relation between health behavior and income. Children are randomly placed with adoptive parents and thus the relationship between parental health and children s health cannot reflect genetic factors Fixed-effects The indicator for the offspring health is regressed on time-variant covariates and the fixed-effects are estimated. We will then regress the estimated individual fixed-effects on a series of variables representing prenatal inputs, parental behavior in early life/perinatal care inputs, parental health productivity, and a set of own characteristics. We also take into account birth order and number of siblings since it has been found to affect parental choice of other inputs since parents have to make decisions about the distribution of those inputs across siblings. The fixed-effects model controls for sample selection due to fertility and mortality selection. We are well aware that for children individual characteristics (particularly in health) might not be fixed but rather develop over time. Differencing might not therefore remove all the individual fixed effects. We nevertheless perform the estimation as a comparison method with the OLS estimates; where the health variable of the child at the different ages is regressed on a set of parental characteristics (see part 3) Siblings fixed-effects (for twins also) We use differences in parental behaviors between births to estimate a so-called sibling fixedeffects model. The sibling fixed-effect model is estimated by taking deviations of health from the family mean. For this, we need data on health outcomes of siblings and parental behavior to estimate the effects of parental behavior, the variance of health endowments and the variance in measurement errors for each outcome. The sibling fixed-effects model requires that the heterogeneity parameter is constant across births. The reduced form health outcome of a child is: H = X i ij + X j + ε ij

9 Where H i is the child s health measure, X ij are the k variables on individual I in household j and X j are the k2 household variables (e.g. parental characteristics) and ij is the error term. The error term is the sum of a household specific component (equal across siblings) and a personspecific component. The person-specific component represents unobservable personal characteristics such as health endowments. ij i ij The household-specific term reflects unobserved variation in the health environment of households, the infant health technology and household preferences (Pitt, Rosenzweig, 1990) OLS for adoptees 3. Data The National Child Development Study (NCDS) is a longitudinal study of 17,000 babies born in Great Britain in the week of 3-9 March NCDS data are available for secondary analysis from The Data Archive at the University of Essex. The study started as the Perinatal Mortality Survey (PMS) and surveyed the economic and obstetric factors associated with stillbirth and infant mortality. Since the first wave, cohort members have been traced on six other occasions to monitor their physical, educational and social circumstances. The waves were carried out in 1965 (age 7), 1969 (age 11), 1974 (age 16), 1981 (age 23), 1991 (age 33) and 1999 (age 42). The first three surveys were augmented with immigrants born in the same week, but no attempt to include immigrants was made since In addition to the main sweeps, information about the public examinations was obtained from the schools in For the birth survey, information was gathered from the mother and the medical records. For the surveys during childhood and adolescence, interviews were carried out with parents, teachers, and the school health service, while ability tests were administered. The subsequent surveys included information on employment and income, health and health behavior, citizenship and values, relationships, parenting and housing, education and training of the respondents.

10 3.1 Prenatal care and birth The PMS contains some information on the birth mother during the pregnancy with respect to her health input utilization, problems experienced during the pregnancy and background. The main measures of health at birth included are birthweight (in ounces), birthweight by gestational age and sex (in standard deviations) and an indicator of whether the child experienced an illness in the first week of life. Background information that might be related to child s health include mother s age, height and pre-pregnancy weight (in bands), mother s marital status, father s age and social class (based on occupation), grandparent s social class. Some information on maternal behavior during the pregnancy is also available. Prenatal care has been found in a number of studies (Rosenzweig & Schultz, 1982, Frank et al, 1992, Brown et al., 2001) to be relevant for birth outcomes and child health at birth. We therefore use the information on delay in seeking prenatal care (first week of mother s visit) and on the number of prenatal care visits of the mother. Maternal smoking during the pregnancy, an indicator of whether the mother was working during the pregnancy and in which week she stopped are also available. There is also information on multiple births and the NCDS contains a small sample of 438 twins. Previous obstetric records show whether the mother has had previous births, stillbirths and ectopic abortions. Finally, problems during the pregnancy such as pleclampsia, bleeding, toxemia and low hemoglobin levels are available. 3.2 Child health During childhood and adolescence parents are asked questions about their children s record of illnesses, psychological problems, accidents and hospitalizations. A medical examination is performed by a physician who records the child s specific medical problems. Using this information we develop several measures of child s health. The first one is a measure of morbidity based on the number of conditions the child has experienced at ages 7, 11 and 16. The conditions are categorized under 13 groups (see Power & Pecham and Appendix, 1987) and the group of infectious diseases is excluded from the morbidity index as most children experience them. The prevalence of conditions is as follows:

11 Table 1: Prevalence of conditions by age and sex Age 7 Age 11 Age 16 All Boys Girls All Boys Girls All Boys Girls Ear and Throat 35.76% 36.24% 35.26% 39.82% 39.24% 40.43% 32.43% 30.16% 34.78t% Other Acute 12.74% 13.51% 11.92% 3.99% 4.05% 3.93% 0.19% 0.23% 0.14% Acute 7.44% 6.62% 8.30% 3.61% 3.74% 3.47% 8.71% 8.48% 8.94% Recurrent Asthma & 18.32% 20.45% 16.06% 12.59% 14.70% 10.38% 15.40% 17.10% 13.59% Bronchitis Allergies 12.04% 12.55% 11.51% 17.74% 18.46% 16.97% 17.60% 17.07% 18.18% Chronic 3.99% 3.50% 4.09% 17.77% 18.78% 16.71% 17.43% 17.53% 17.34% Medical Chronic 8.85% 9.93% 7.70% 10.76% 11.95% 9.52% 8.81% 9.59% 8.02% Physical/mental handicap Chronic 4.52% 5.04% 3.98% 21.15% 22.79% 19.43% 14.93% 15.94% 13.85% sensory Injuries 19.58% 22.47% 16.54% 21.92% 24.10% 19.60% 44.22% 51.88% 36.15% Psychosocial 14.07% 15.74% 12.31% 12.12% 14.70% 9.40% 15.28% 16.26% 14.24% Psychosomatic 29.12% 28.55% 29.71% 24.41% 20.91% 28.10% 44.27% 35.31% 53.73% Other 11.67% 14.28% 8.90% 12.30% 12.71% 11.86% 24.02% 10.75% 38.26% We also use two other measures of health: whether the child was absent from school because of illness and whether the child has to be hospitalized Parental time inputs and productivity As a measure of parental health productivity, parental education in terms of years of schooling for both parents is available. We are also interested in parental investment in the child but not only in terms of resources but also in terms of time. We include whether the parents read to the child every week and whether the parents take the child for outings often. Breast-feeding is also included as another proxy of parental time resources. Mother s employment is also likely to limit time spent with the child, particularly before the child enters school. On the other hand, maternal employment is likely to be correlated with total income and resources. Likewise child health might have an impact on

12 maternal decision on whether to work or not and on mother s labor supply. Based on teacher s assessment, there is some information on the level of interested of parents in their child s schooling. Another indication of investment is the information on parental wishes about their offspring continuing their education. In wave 3, parents report their cigarette consumption and this can also be a proxy of parental attitudes towards health and health habits that can be transmitted. 3.4 Parental health The NCDS records parental weight and height when the child is age 11. This information can be transformed to obtain the Body mass Index (BMI) which is a measure of obesity. In addition, chronic conditions for the father, mother and/or relatives is recorded in all waves during childhood and adolescence. At age 7, the information is quite limited and it is only known whether a relative had a congenital heart condition, diabetes or convulsions. At ages 11, the father and the mother s chronic condition is categorized according to the following groups: Table 2: Parental chronic conditions Age 11 Age 16 Mother Father Mother Father Respiratory 12.55% 20.20% 14.86% 22.17% Psychiatry 26.16% 11.94% 25.00% 10.06% Subnormality 1.58% 0.20% 0.43% Urogenital 9.28% 2.29% 3.43% 2.29% Alimentary 6.33% 12.54% 5.57% 7.54% Locomotory 11.71% 16.42% 11.57% 19.20% Neurology 3.80% 5.17% 5.57% 4.91% Infectious 0.84% 0.80% 1.14% 1.83% Special 1.58% % 3.20% Cardiovascular 14.56% 19.90% 12.14% 19.31% Dermatological 1.58% 1.29% 1.43% 1.14% Other 10.02% 6.77% 15.29% 8.34%

13 Finally, when the cohort members are adults they are asked in wave 4 and 5 whether the father and the mother are still alive. 3.5 Parental income The information collected by the NCDS only contains one measure of family income when the child is 16. This might not be a reflection of living standards earlier in childhood nor of persistent poverty problems. For this reason, the data holders developed a measure of permanent income. Using grouped dependent variable techniques and variables representing parental education, occupation, age and region, they predicted permanent income. Because of the estimation technique, this variable is therefore correlated with other variables of interest. In addition, there are many missing cases because the following cases are excluded from the analysis: cases where occupational class is missing and cases where the children are not living with either natural parent at age 16. We therefore experiment with several measures of income including whether the family had serious financial difficulties, whether the child received free meals at school, and parental socioeconomic status based on occupation. 3.6 Background characteristics The dataset contains other additional information on the children s background such as the sex, number of siblings, birth order, region, and household composition, that is, whether the child had one parental figure or not and which one (natural, adopted, foster, etc). The NCDS includes a reduced number of children who have been adopted (around 200). Adoption is assumed to be random and children are placed with the adoption family 3 months after birth. This removes the potential bias that parents in better health select those children in better health for adoption. The NCDS adoptees are illegitimate children who are randomly placed for adoption. Because of this, we do have some information on both the birth mother and the adoptive parents. Because of the nature of the data we have some information on the birth mother and the adoptive parents, which is unusual and not often found in data. 3.7 Cohort member s children In addition, wave 5 includes information about the cohort members children. The sample consists of 4207 children, of which 23 are adopted. This includes some information about the pregnancy and the delivery (smoking, problems during labor, etc). The mother also reports information on the child s infectious illnesses (measles, chicken pox), hearing problems, speech

14 difficulties, as well as other conditions such as asthma, epilepsy, hay fever, eczema, migraine, diabetes and behavioral problems. Two assessment tests are given to the children to evaluate their reading and math abilities (Ppvt reading test and piat math test). They are nevertheless not the same as those previously administered to the cohort members. There is only information at one point in time for those children and it is done at a young age. In addition, there are issues of selection since older children will be typically those from teenage mothers. The data would only allow us to look at the correlation between parental illnesses and children s illnesses but we won t be able to disentangle the genetic part and the environmental part. Because of the children s age we cannot look at variables such as years of education and labor market outcomes correlation with the parents. 4. Results 4.1 Prenatal factors and health at birth Prenatal care shows to have a positive impact on the baby s weight. Maternal smoking is associated with lower birthweight. High socio-economic status or higher income appears to have a positive impact on birthweight. Mother s own physical characteristics such as height and weight are as expected highly correlated with the baby s own weight. Mother schooling appears to increase birthweight only when gestation time is not taken into account. Problems during the pregnancy such as bleeding and toxemia have a negative impact on health at birth. Obstetric history also appears to matter as indicated by the negative effect pas stillbirths and a too short interval between this birth and the previous one, while having more children increases the weight of the subsequent children. Nevertheless these results do not take into account the potential endogeneity of variables such as prenatal care choice, smoking, etc. Table 3: Health at birth and maternal care Estimated birthweight Log of estimated birthweight number of prenatal care visits (19.46)** (5.48)** (11.34)** (20.27)** (4.57)** (11.59)** Parity (9.01)** (8.01)** (6.44)** (7.64)** (6.67)** (5.97)** sex of child (14.66)** (17.08)** (12.67)** (12.71)** (15.75)** (12.32)** mother's age (5.01)** (1.35) (1.70) (4.77)** (0.77) (1.62)

15 Mother smoking medium Mother smoking heavy (9.06)** (8.05)** (7.21)** (8.32)** (7.37)** (7.09)** (12.20)** (12.08)** (9.91)** (11.08)** (11.13)** (9.93)** high ses (3.66)** (3.47)** (3.40)** (3.12)** Permanent income (4.14)** (4.28)** Week of 1s mother prenatal visit- 1st- 3 rd Week of 1s mother prenatal visit- 36 week Mother s weight in stones >=15st Mother s weight in stones: under 7 past stillbirths and neonatal deaths (4.17)** (2.91)** (0.23) (5.08)** (3.93)** (0.25) (3.75)** (1.78) (2.12)* (4.11)** (2.13)* (2.26)* (5.54)** (4.69)** (3.46)** (4.60)** (3.86)** (3.17)** (6.12)** (6.14)** (4.05)** (6.16)** (6.37)** (4.14)** (7.03)** (4.42)** (2.28)* (7.93)** (5.14)** (2.32)* Bleeding (6.28)** (4.21)** (0.86) (7.50)** (5.42)** (0.77) Toxaemia (6.23)** (5.93)** (5.04)** (6.65)** (6.44)** (5.57)** Interval between births <=1 year (4.39)** (0.61) (1.50) (4.89)** (0.98) (1.61) Scotland (3.87)** (5.12)** (3.20)** (3.11)** (4.46)** (3.05)** Mother stayed in school after minimum age height of mum in inches Gestationalperiod in days (3.40)** (1.33) (1.42) (3.82)** (1.61) (1.67) (10.85)** (12.67)** (8.93)** (8.72)** (10.70)** (8.36)** (62.12)** (73.36)** (58.08)** Constant (15.88)** (21.75)** (6.02)** (92.62)** (44.03)** Observations R-squared

16 4.2 Child health and parental income Our primary estimates for child health are presented in Table 4. Here we used the number of chronic illnesses at 3 different ages (corresponding to the 3 waves) as a measure of health. We also include another measure of health available at ages 11 and 16: whether the child was absent from school due to illness. The estimates for chronic illnesses are obtained using OLS while those for absence from school are obtained through a probit. We observe that the coefficient for the log of permanent parental income is negative and significant for all measures except at age 7. The size of the coefficient also increases with age, indicating that the income gradient becomes more pronounced with age, which is in line with the results found by Case et al. (2002) using US data but not with Currie et al. (2005) using British data. Compared to Case et al. (2002) our coefficient for income is smaller at ages 7 and 11 while being larger at age 16. The inclusion of additional controls such as family size and sex does not change primarily the results 1. Table 4: Health status and family income Number of chronic illness School absences due to illness Age 7 Age 11 Age 16 Age 11 Age 16 predicted permanent parental income (1.43) (2.26)* (5.02)** (5.90)** (11.34)** Constant (5.71)** (6.77)** (10.28)** (5.09)** (8.93)** Observations R-squared predicted permanent parental income Controls for Sex and Family size (1.82) (2.39)* (4.41)** (5.88)** (10.09)** Sex of child (5.87)** (3.93)** (7.12)** (5.52)** (5.94)** Constant (6.65)** (7.10)** (8.44)** (4.33)** (6.71)** Observations R-squared We do not include additional controls such as the age of the parents, the absence of the father or mother, parental education, occupation or region because the permanent income was predicted using those variables. For all tables in this section, we include only the relevant variables and/or those significant at least the 5% level.

17 We account next for the possibility that our results may be due to heterogeneity of health at birth. Indeed, as detailed by Case et al. (2002), children from a lower income family might be born with worse health and might take longer to recover, partly explaining why income matters for health at alter ages. We therefore include two indicators of health heterogeneity at birth. The first one is the child s birthweight and the second is an indicator of whether the child had an illness in the first week after birth. The results (in Table 5) indicate that health at birth is an important predictor of health during childhood and adolescence. Higher birthweight is associated with a lower number of chronic illnesses at ages 7 and 11. At age 16, the coefficient is insignificant, indicating that the effects of low birthweight might decrease with age. Birthweight does not appear to matter for school illnesses. An illness at birth increases the number of chronic illnesses at all ages. The coefficient does nevertheless decrease with age. We observe that the effects of parental income slightly decrease with the inclusion of health at birth but that the coefficient remains significant 2. We include another specification with the interaction between income and illness at birth in order to check whether poor birth health has larger adverse effects for low income children. This term is negative but only significant for children at age 11. Table 5: Health status and family income given health at birth Number of chronic illness predicted permanent parental income School absences due to illness Age 7 Age 11 Age 16 Age 11 Age (1.44) (2.15)* (4.13)** (5.88)** (10.00)** sex of child (6.01)** (4.19)** (8.50)** (5.58)** (5.74)** Estimated birthweight (2.33)* (2.76)** (1.62) (0.42) (0.79) Illness at birth (8.37)** (6.63)** (4.66)** (0.15) (1.19) Constant (6.70)** (7.36)** (8.37)** (4.17)** (6.72)** Observations R-squared Poor birth at health and income interactions predicted permanent parental income We also observe that the decrease in the impact of income associated with the controls for health at birth is much lower than the one obtained by Case et al (2002) where the coefficient becomes close to In this sense, our results are more in line with Currie et al. (2004) who use British data.

18 (1.24) (1.81) (3.86)** (5.68)** (9.75)** sex of child (5.98)** (4.14)** (8.53)** (5.60)** (5.75)** Estimated birthweight (2.33)* (2.76)** (1.63) (0.42) (0.81) Illness at birth (1.51) (2.24)* (1.69) (0.84) (0.96) Illness at birth*family income (1.21) (2.01)* (1.53) (0.85) (1.00) Constant (6.45)** (6.98)** (8.05)** (4.00)** (6.51)** Observations R-squared Health problems can also be transmitted through parents and poor parental health might be correlated with income. Our dataset contains some indication on parental chronic diseases. As mentioned in the data description, at age 7 we are unable to distinguish between which relative has the chronic illness but at ages 11 and 16 we do know whether it is the father or the father and which particular condition they have been diagnosed with. The results for this estimation are shown in Table 6. As expected, having a relative with a chronic condition has a negative impact on the child s health at all ages and for the different measures of health used. Mother s chronic illnesses appear to matter more than father s. Ina addition, including parental health significantly reduces the effect of parental income, which becomes insignificant for chronic illnesses. This is in contrasts with Case et al. (2002) and Currie et al. (2004) but similar to the findings of Burgess et al. (2004). On the other hand, the effect of income on school absence is slightly reduced but still significant. We interpret this as evidence that children s chronic conditions are genetically strongly influenced by parental conditions. We are nevertheless aware that parental health might be partly capturing the effect of income, although this does not appear to be the case for school absences. Table 6: Health status, family income, parental health Number of chronic illness predicted permanent parental income School absences due to illness Age 7 Age 11 Age 16 Age 11 Age (1.52) (1.06) (0.61) (5.12)** (7.84)** sex of child

19 (5.57)** (4.09)** (9.02)** (5.00)** (5.39)** Family illnesses (7.27)** Father chronic illness Mother chronic illness (4.95)** (15.09)** (2.91)** (3.14)** (7.62)** (16.71)** (3.52)** (4.49)** Bmi father (4.26)** (3.18)** (1.17) (0.59) (0.52) Bmi mother (1.77) (2.02)* (1.28) (2.53)* (3.74)** Constant (6.52)** (5.64)** (2.88)** (2.84)** (3.78)** Observations R-squared We are also interested in the effects of parental time and parental health behavior on child s health. We present the results on Table 7. We observe again that adding parental behavior does reduce the impact of income on health and that the gradient disappears for the number of chronic illnesses. Income does still matter for school absences due to illness. Father s outing with the child significantly reduces the number of chronic illnesses at age 7 but not at 11. Parental over concern with schooling as assessed by the teacher appears to be positively correlated with the number of chronic illnesses. Surprisingly children whose parents were over concerned with their education and those whose parents showed little interest were less likely to miss school due to illnesses. Parental wishes for lower education are associated with lower child s health. Maternal smoking is also significantly correlated with poor health. Table 7: Health status, family income, parental behavior Number of chronic illness School absences due to illness Age 7 Age 11 Age 16 Age 11 Age 16 Predicted permanent parental income (1.21) (1.77) (1.49) (4.95)** (5.02)** sex of child Mother outings with the child Father outings with the child (6.10)** (3.06)** (8.74)** (4.55)** (5.89)** (1.68) (0.67) (4.95)** (3.52)** (1.34) (4.55)**

20 Mother interests in child s schooling Overconcerned Mother interests in child s schooling-very interested Mother interests in child s schooling little interest (2.82)** (4.07)** (4.95)** (1.44) (0.59) (4.55)** (1.91) (2.10)* (4.95)** Mother smokes (4.78)** (6.19)** Father smokes (0.28) (3.16)** Parents wish chid had left school (7.28)** (13.44)** Constant (5.98)** (6.12)** (4.82)** (3.93)** (1.38) Observations R-squared We likewise observe that the coefficient of income is highly reduced and becomes insignificant when including variables reflecting family shocks such as unemployment, separations from the mother and care placement. Table 9: Health status and shocks Number of chronic illness predicted permanent parental income School absences due to illness Age 7 Age 11 Age 16 Age 11 Age (1.41) (0.42) (0.74) (3.76)** (5.23)** sex of child (5.92)** (3.43)** (7.74)** (4.41)** (4.66)** Ever in care (0.91) (4.27)** (6.03)** (0.70) (2.07)* Separations from the mother 0.403

21 for more than a week Domestic problems Father unemployed Number of Weeks dad off work due to illness Number of Weeks dad off work due to unemployment Number of Weeks dad off work due to other causes (12.94)** (7.85)** (0.21) (4.71)** (6.05)** (3.06)** (5.21)** (2.06)* (0.26) (2.41)* (3.32)** (2.04)* (0.23) Constant (5.93)** (4.35)** (3.93)** (2.63)** (2.44)* Observations R-squared Finally, we compare different specifications for parental wealth: parental income, father s socioeconomic status (SES) at birth of the child and whether the family experienced financial difficulties 3. After inclusion of all the controls, neither parental income nor SES remain significant. Financial difficulties however are significant at all ages and have a much higher coefficient.

22 Table 10: Number of chronic illness and family income With permanent income With financial difficulties With SES Age 7 Age 11 Age 16 Age 7 Age 11 Age 16 Age 7 Age 11 Age 16 Predicted permanent parental income Financial difficulties Free meal at school Father SES High Father SES Low (0.91) (0.08) (1.34) (4.41)** (3.02)** (2.62)** (0.31) (1.96) (1.04) (1.12) (0.53) (0.06) (0.86) (1.26) sex of child (4.30)** (1.28) (7.23)** (4.60)** (1.70) (6.51)** (4.30)** (1.39) (6.28)** Estimated birthweight Illness at birth Family illnesses Father chronic illness Mother chronic illness (0.41) (0.62) (1.31) (0.51) (0.86) (1.84) (0.56) (0.79) (2.02)* (6.80)** (5.18)** (3.38)** (6.93)** (5.11)** (3.42)** (6.98)** (5.25)** (3.45)** (6.32)** (6.38)** (6.78)** (5.53)** (8.46)** (5.60)** (8.96)** (5.76)** (8.86)** (2.48)* (11.95)** (1.90) (12.06)** (2.27)* (12.26)** Bmi father (4.58)** (2.57)* (1.42) (4.47)** (2.60)** (1.15) (4.26)** (2.63)** (1.27) Bmi mother (2.14)* (1.10) (1.18) (2.05)* (0.45) (1.22) (1.72) (0.46) (1.35) Constant (5.08)** (3.52)** (0.97) (8.52)** (8.75)** (5.56)** (8.04)** (8.51)** (5.49)** Observations R-squared This table is an OLS regression including all variables from previous specifications: parental wealth, health at birth, parental health, parental behavior, and shocks. For simplicity, we only report the variables of the first three groups. Complete results are available upon request.

23 4.3 Fixed-effects estimation The effect of a change in financial difficulties since last wave appears to have an increase in the number of chronic conditions. Comparing the size of the coefficient with the OLS estimation, the panel data estimates are smaller. We also see that changes in the family composition matter for the appearance of chronic conditions. Indeed, no longer being with one s natural father raises the number of chronic conditions. Financial difficulties are instrumented by housing characteristics (house ownership, number of rooms, availability of indoor lavatory and hot water). The results show that using instruments highly increases the effects of financial difficulties on children s health. Table 11: Individual Fixed Effects Estimates for Health Individual FE IV FE Financial difficulties (4.38)** (4.74)** (4.90)** (4.48)** (3.44)** Number of children in the household (12.60)** (12.73)** (12.60)** (10.46)** Non natural mother (2.04)* (1.94) (1.48) Non natural father Chronic illness in the family (3.06)** (3.07)** (4.19)** (14.16)** (8.00)** Constant (291.88)** (73.55)** (73.02)** (71.33)** (34.67)** Observations R-squared In the second stage, the fixed effects estimated from the first stage are regressed on covariates representing parental health and behavior, and some individual characteristics. We observe that individual effects are lower for females and for those with higher birthweight. Father s year of education is negative and significant while mother s is positive. The opposite occurs with father s and mother s Body mass Index. Children who suffered from illnesses at birth and the mother experienced bleeding during the pregnancy have higher individual effects. Table 12: Regressions on Fixed Effects Variables FE model IV FE model sex of child (3.02)** (3.05)** (2.96)**

24 Parity (5.76)** (5.81)** (4.41)** Bleeding during pregnancy (9.37)** (9.45)** (9.52)** Toxemia (4.52)** (4.52)** (2.88)** Illness at birth (14.85)** (14.87)** (15.23)** Estimated birthweight (5.32)** (5.37)** (1.49) Bmi father (5.00)** (5.05)** (3.27)** Bmi mother Father s years of education Mother s years of education (8.30)** (8.35)** (5.57)** (3.12)** (3.16)** (0.15) (2.81)** (2.77)** (4.90)** Family illnesses (20.00)** Breastfed (4.53)** (4.46)** (5.72)** Maternal smoking during pregnancy (8.92)** (9.03)** (2.67)** bsag total score (16.35)** (16.27)** (6.89)** Constant (0.18) (0.38) (2.66)** Observations R-squared Cohort members and their children Table 13: Regression of children s Birthweight on parental birthweight and other parental characteristics With Birthweight With log of BW Sex (2.90)** (3.13)** (3.15)** (3.16)** (2.05)* (2.19)* (1.29) Birthweight (6.05)** (5.89)** (4.92)** (4.78)** (4.95)** (6.34)** (6.82)** Sex*BW (3.36)** (3.58)** (3.56)** (3.41)** (2.59)** (2.25)* (1.43) Sex of the child (2.68)** (3.22)** (3.43)** (3.19)** (4.78)** (2.48)* (4.05)** Birth order of

25 the child (6.49)** (8.68)** (8.99)** (9.51)** (8.25)** (8.67)** (4.97)** Age of parent (2.80)** (1.85) (1.59) (1.34) (1.25) (1.63) (1.44) Age squared (2.00)* (1.36) (1.25) (1.10) (0.72) (1.47) (1.02) O-levels (3.53)** (2.40)* (1.57) (1.44) (1.92) (1.89) A-levels (4.09)** (2.63)** (1.45) (1.41) (1.61) (1.71) Degree (4.73)** (3.12)** (1.84) (1.21) (1.66) (1.23) Unskilled (4.19)** (4.20)** (3.74)** (3.15)** (3.55)** (2.98)** Poor health (2.58)** (2.37)* (1.80) (2.14)* (1.51) Height at (8.31)** (8.15)** (8.93)** (7.73)** (8.36)** Missing height at 7 (8.15)** (7.96)** (8.84)** (7.55)** (8.32)** Psychosocial illness age 7 (2.86)** (2.90)** (2.44)* (3.00)** (2.52)* Sensory illness age 7 (2.22)* (2.24)* (1.62) (2.36)* (1.68) Mother smoked during pregnancy (7.36)** (8.19)** (7.58)** (8.00)** Child was a twin (12.85)** (10.05)** Child had an illness at birth (10.36)** (10.48)** Parent was a twin (3.70)** (4.06)** Child born early (22.73)** (22.51)** Child born late (12.85)** (11.67)** Constant (9.57)** (10.23)** (4.41)** (4.86)** (5.70)** (23.75)** (25.96)** Observations R-squared