The Effects of Birth Weight Over the Life Cycle

The Eﬀects of Birth Weight over the Life Cycle

Prashant Bharadwaj, Petter Lundborg and Dan-Olof Rooth∗

August 2013

Abstract

This paper provides evidence on the role of birth weight in determining income, so-

cial beneﬁts take up and mortality at each point over the life cycle. A unique dataset

on nearly all Swedish male twins born between 1926-1958 containing information on

birth weight is linked with administrative records spanning nearly entire life time la-

bor market participation. We ﬁnd that birth weight and income are positively linked

for large parts of the life cycle, although there is some evidence of a fade out after

age 50. Our results indicate that lower birth weight children are more likely to avail

of social beneﬁt programs such as unemployment insurance and welfare. Finally, our

point estimates suggest that birth weight might matter for adult mortality; however,

these estimates are not always statistically signiﬁcant.

∗Bharadwaj: UC San Diego ([email protected]), Lundborg: Lund University, Rooth: Linneaus University. Much thanks to Julie Cullen and participants at the NBER Cohort Studies Meeting for comments and to Matt Gibson for superb research assistance.

1 1 Introduction

Economic research in the last decade has shown considerable consensus on the importance of early childhood health in determining various short and long term outcomes (Almond and Currie 2011, Heckman 2007). Birth outcomes, and in particular birth weight has been shown to matter for educational achievement in school, completed years of education, IQ and labor market outcomes such as employment and earnings. Not only have the economic outcomes examined been numerous, but the settings just as varied, with studies using data from developed countries like the United States, Canada, and Norway to middle income countries like Chile, China and Taiwan and even in less developed countries like South Africa.1 However, despite the rich number of studies and geographical settings, data limitations have largely prevented an examination of the role of birth weight in determining various outcomes such as income, take up of various social assistance programs and adult mortality at each point in the life cycle.2

One of the key reasons to examine the role of birth weight over the life cycle is to answer questions about the persistence of health inequalities at birth. For example, the income diﬀerences due to birth weight found by Black, Devereux, and Salvanes (2007) are measured around age 30; while this is an important result, it is important to examine whether these diﬀerences persist or fade with age. The recent literature on in utero assaults (see Almond (2006), Almond and Mazumder (2011) and Roseboom et al (2001)) has found persistent long run impacts of harm to the fetal environment.3 Our paper adds to this important literature

1A sample of these studies in Economics include: Almond, Chay, and Lee (2005), Royer (2009), Black, Devereux, and Salvanes (2007), Behrman and Rosenzweig (2004), Oreopoulos, Stabile, Walld, and Roos (2008), Bharadwaj, Eberhard, and Neilson (2011), Lin and Liu (2009), Cooper and Sandler (1997) and more recently Figlio, Guryan, Karbownik, and Roth (2013). The literature in medicine and public health on the links between birth weight and short and long term outcomes is also equally large. Relevant to our study however, it appears that this literature has not been able to examine birth weight and old age outcomes within a causal framework. 2While other birth outcomes such as gestational age are extremely important in this context, we only examine birth weight in this study since a natural experiment providing us with arguably exogenous variation in gestational age is not possible in this setting. 3There are many more papers that look at long run eﬀects of in utero assaults. A small sampling of other papers of interest in this area are: Almond, Edlund, and Palme (2009), Mazumder, Almond, Park,

2 by addressing the long term eﬀects over the life cycle of low birth weight. Given the richness of the data (on average, for every individual we have 35 years of labor market data), we are able to examine the extent to which lower birth weight children ever catch up to their heavier counterparts. The dynamics of the birth weight eﬀect using repeated observations are under explored in this literature.

By examining the relationship between birth weight and social assistance4 we provide yet another way of quantifying the long term beneﬁts of programs that are aimed at improving birth outcomes (for example see Bitler and Currie (2005)). Recent work has suggested that social assistance programs not directly intended to improve early childhood health appear to have important spillovers (Hoynes, Schanzenbach, and Almond 2012); hence, understanding whether better infant health itself leads to lower social assistance take up is important. We shed light on three important questions in this context: (a) does the eﬀect of birth weight on income vary over the life cycle, (b) does birth weight matter for the take up of social assistance programs like unemployment insurance and welfare and (c) does birth weight matter for adult mortality?

Empirical examination of these relationships is complicated by the myriad factors that aﬀect both birth outcomes and the economic outcomes of interest. Hence, while there are many studies in epidemiology that address the link between birth weight and adult mortality, these studies largely compute correlations rather than causal estimates.5 Particularly in the instance of examining birth weight and mortality, we know from studies like Almond, Chay, and Lee (2005) that ordinary least squares estimates tend to be severely overestimated. The predominant solution to this endogeneity problem in Economics has been to use twins based

Crimmins, and Finch (2010), Lin and Liu (2012) and Van Ewijk (2011). 4Although in the Swedish context the term “assistance” refers exclusively to welfare, we use it inter- changeably with “beneﬁts” to imply take up of unemployment insurance, welfare etc 5A recent meta-analysis of the relationship between birth weight and adult mortality found that birth weight mattered for mortality, and particularly so for mortality due to cardiovascular reasons (Risnes et al 2011). However, none of the studies discussed in this meta-analysis can claim to get close to causal estimates. However, the upside to these studies is that they analyze large population based samples, whereas studies in Economics (and this paper included) examine this question using twins who constitute a smaller fraction of overall births.

3 estimates, taking as random the variation in birth weight within twin pairs (Almond, Chay, and Lee 2005, Royer 2009, Black, Devereux, and Salvanes 2007, Behrman and Rosenzweig 2004). We follow the same strategy in this paper. To answer the three main questions we posed earlier, we need outcomes collected over a very long period of time to allow us to observe mortality outcomes and life time income, but also a data set that also allows us to use empirical strategies such as twins ﬁxed eﬀects to obtain arguably causal estimates.

Our unique data allows us to do precisely this. Our database contains birth records matched with mortality and yearly income records (including income by source) at the individual level for a vast majority of all twins born between 1926-1958 in Sweden. The average age of these twins by 2010 (the last year for which we have mortality records) is 68; the oldest alive twin pairs are 84 and the youngest alive twin pairs are 54. Since life expectancy in 2010 was around 80 for the entire population (presumably it was less for the cohorts we examine), we expect a moderate level of mortality in our sample; indeed we observe approximately 11% mortality in this sample.6

We find that twins fixed effects of birth weight on adult mortality to be economically meaningful, but not consistently statistically significant. Particularly between the ages of 70-80, our estimates suggest that birth weight might play a role, but we have very few observations in these age ranges to make more precise statements. Our results vary from that of Almond, Chay, and Lee (2005) in an important dimension in that our OLS estimates tend to understate the effect of birth weight on mortality.

In line with Black, Devereux, and Salvanes (2007) and others who have examined the relationship between birth weight and income, we find birth weight to have a positive and statistically significant effect on income. We find this effect to be persistent over large parts of the life cycle, suggesting that lower birth weight twins do not catch up to their heavier counterparts, except after age 50. The effect of birth weight on income towards the end of

6In addition to examining mortality at later ages, our extensive data will allow us to examine causes of death as well. For the purposes of this draft, we do not examine causes of death.

4 the life cycle is small and statistically insigniﬁcant. In terms of social assistance program take up, our results show that lower birth weight twins are more likely to avail of assistance programs like unemployment insurance, welfare and sickness/disability pay. The take up of welfare occurs early in the life cycle and fades in later ages. Take up of unemployment insurance is also positively related to low birth weight. This relationship appears largely stable across the life cycle.

Another benefit of observing long periods of income data is that we can explicitly account for any life cycle bias that might otherwise arise in estimates obtained from data observed on individuals at a given age. For example, Black, Devereux, and Salvanes (2007) examine the relationship between birth weight and income for people in the age range of 25-35 years, Behrman and Rosenzweig (2004) use income as observed around age 45 and only for women and Oreopoulos, Stabile, Walld, and Roos (2008) examine the take up of social assistance between the ages of 18-22. If life cycle bias as in Haider and Solon (2006) is an important factor in these studies, then the estimates obtained from such studies need not be reflective of life time income patterns.7 Our paper is, to the best of our knowledge, the first to provide a complete picture of the causal returns to birth weight by examining income, mortality and social assistance take up at each point in the life cycle.

2 Empirical Strategy

The empirical strategy used in this paper is a straightforward application of the traditional twins fixed effects approach used by various authors. What is novel is that we use repeated observations over time to obtain a “birth weight effect” at different ages. Our baseline

7A recent paper by Bhuller, Mogstad, and Salvanes (2011) suggests that life cycle bias in returns to education is minimized around age 32 in Norway. Hence, the paper by Black, Devereux, and Salvanes (2007) is fairly consistent with life cycle earnings estimates. Speciﬁc to our context in Sweden, B¨ohlmarkand Lindquist (2006) show that life cycle bias is an issue when analyzing Swedish earnings data.

5 speciﬁcation is as follows:

Yijt = βtBWij + µj + γtXij + ijt (1)

Where Yijt is log income of individual i, belonging to twin pair j at time t; Xij are time invariant individual specific characteristics like education or sex. The coefficient of interest is βt which is the birth weight effect at time t. The above equation is estimated at different ages (t’s) providing us with the life cycle pattern of the birth weight effect. The key empirical strategy is the inclusion of the twin pair fixed effect (µj). Including this fixed effect controls for all correlates that might be relevant for birth weight formation as well as income, such as family background, parental income during pregnancy, nutritional intake of the mother etc. To see this more clearly, we can express Equation 1 in terms of within twin differences (where the other twin is i0):

Yijt − Yi0jt = βt(BWij − BWi0j) + γt(Xij − Xi0j) + (ijt − i0jt) (2)

The operating assumption for βt to be interpreted as the birth weight eﬀect at a given point in time t is that BWij − BWi0j is uncorrelated with ijt − i0jt. This is a rather strict assumption if we believe for example that parental investments in early childhood are part of what determines incomes as adults and that such investments vary by birth weight.

The question of whether parents react to birth weight or initial endowments is a subject of recent research8 (for a recent review see Almond and Mazumder (2012)). In the framework of Bharadwaj, Eberhard, and Neilson (2011) and Conti, Heckman, Yi, and Zhang (2010) (both these papers explicitly use a framework that involves twins), the bias in OLS estimates arise largely from parental investments compensating or reinforcing initial health diﬀerences. Un- der their framework, OLS estimates will tend to overstate initial diﬀerences if parents act in

8Some papers that tackle this issue are: Aizer and Cunha (2012), Conti, Heckman, Yi, and Zhang (2010), Adhvaryu and Nyshadham (2011), and Bharadwaj, Eberhard, and Neilson (2011).

6 reinforcing ways, and understate the role of initial differences if parents act in compensating ways. Perfectly equal treatment of twins by parents is a likely an unrealistic assumption. To the extent that parents in developed countries like Sweden might act in more compensatory ways, we interpret our estimates of the long run effects of birth weight that arise despite parents’ best efforts to compensate.9

3 Data

The data on birth characteristics comes from a part of the Swedish registry called BIRTH. BIRTH is part of a project where the twin registry in Sweden went to get the birth records of all twins born in Sweden between 1926-1958. These birth records were kept in paper form at local delivery archives around Sweden. Collecting and preserving birth information is/was enforced by law and people at the twin registry set out to digitize them. Our sample only includes twins that survived up to 1972. In 1972, there was a large twin survey conducted for the twin cohorts born 1926-1958. Unfortunately we do not have access to the universe of twins used to obtain the sample interviewed in 1972. Hence we are unable to construct weights or assess attrition in any systematic manner. The information we have on twins interviewed in 1972 include information on birth weight, sex, year of birth and year of death (if after 1972).10

Information on individual years of schooling are obtained from the education register (utbild- ningsregistret, UREG) from 1990 or 2007, where years of schooling has been imputed based

9Another underlying aspect of twins fixed effects estimates is the role of sibling peer effects or how one twin can affect the other twin’s choices. We recognize this as something that might be occurring in the background, but we are not aware of papers that explicitly address this issue or how this might drive our results. 10Since we only capture twins where both were alive as of 1972, we expect to find fewer twins from the 1930’s as compared to twins from the 1950’s. As a fraction of overall live births we certainly capture fewer twins than perhaps expected from earlier cohorts. The fraction of twins (compared to total live births) observed in the data stabilizes around 1940. Unfortunately, since we do not observe the original universe of twins, it is difficult the assess the degree to which the earlier cohorts suffer from selective survival. To ensure our results are not being driven by selective survival, we present analysis restricting data to cohorts born after 1940 as a robustness check in Appendix Table 1.

7 on obtained degree. For the overwhelming majority of people, we thus have register-based information on schooling. If the person died before 1990, schooling information was taken from the census of 1970.

The data from the twin registry was matched with data from the education register, as well as income records maintained at the individual level. The income records we have access to start in 1968 and end in 2007 and are present at the yearly level. The income records come from the equivalent of W2 records in the United States, in that the income is reported by employers and is not based on self reports. For people with multiple jobs, only the total income is reported. This same data source also provides information income by source, such as unemployment beneﬁts, sickness pay and welfare pay. All of our income data is adjusted by 2007 CPI measures to make them comparable.11

While we have income data from 1968-2007, there have been some changes in what is included in our main income measure. Diﬀerent income sources (labor income, income from social safety nets etc) exist in the register because they are taxable. Fortunately, starting in 1974, most income sources, including the various social beneﬁts, were taxable and are included separately in the data. Between 1968-1974 we only have access to income from labor market participation and self employment.12

The main income measure we use in this paper is a measure that includes all sources of income. Formally, the definition of “total income” from Statistics Sweden is: Total labour income consists of income from work and income from self-employment. Income from work includes, besides wage income, income from pensions, sickness benefits, and other taxable benefits. So it means that the income measure before and after 1974 are not strictly comparable, as the pre-1974 period excludes benefits. Even though many benefits became taxable

11For the cohorts we examine, there does not appear to be much by way of changing variances in income over time. 12Recent work by Björklund,Jäntti, and Lindquist (2009) suggests that this change in definition is not of primary concern for our analysis since benefits constituted less than 5 percent of total earnings in years prior to 1974. In any case, we can reproduce our main analysis restricting the sample to years after 1974 and the basic pattern of results remains unchanged.

8 in 1974, most are not separately shown until somewhat later years. Pensions are shown from 1974, unemployment beneﬁts are not separately shown until 1978 and sickness insurance beneﬁts are shown separately from 1981 onwards.13

It should be noted that it seems common to include social safety net income in the total income measure in papers using Swedish data. It is also important to understand that most benefits are related to having worked and to ones earnings. Sickness benefits and unemployment benefits are related to ones labor income and typically 80-90 percent of ones labor earnings are paid out, although with a cap at some level, which has varied over time. So in some sense, labor income plus benefits paid out will better reflect ones contracted labor earnings that year compared to a measure that only includes labor income. The income data was matched to all twins who were contacted and alive during the 1972-73 survey. We lose less than 1% of the data due to matching issues across the vital statistics and income registries. For the purposes of this paper, we focus on labor market outcomes of men to avoid some of the empirical realities of selective participation by women, especially during the time period we examine.14 As a result, to make the discussion streamlined, we also just focus on men for the results on mortality.15 Table 1 provides the essential statistics on our sample.

13Sickness benefits reporting underwent a large change in 1992. Starting in 1992, only sickness benefits where sick leave was taken for more than 2 weeks were reported in the tax registry. Hence, we separate our analysis for sickness pay to examine the periods 1981-1991 and 1992-2005. 14The evolution of the female employment rate since the 1960s reflects the entrance of women into the Swedish welfare state. At the beginning of the 1960s about 50 percent of the women in working ages were employed, a number that had increased to about 80 percent thirty years later. 15Results for females are available upon request.

9 4 Results

4.1 Mortality

Figure 1 shows the relationship between birth weight and adult survival (for males) to diﬀerent ages. The ﬁgures plot βt as estimated from Equation 1. In the case of mortality the estimation conditions on survival up to age t − 10. In other words, these graphs answer the question, “What is the probability of observing someone at age t conditional on that person being alive at t − 10?”

As opposed to the second panel in Figure 1 which is the OLS counterpart, the first panel shows larger but largely insignificant results on mortality. For ages 70+ the relationship between birth weight and survival appears particularly large and positive. However, we have very few twin pairs who survive to these ages and our estimates are quite imprecise. Tables 2 and 3 confirm that the relationship between birth weight and adult mortality is negative and significant only for a few age groups, typically between age 70-80. Various panels of Table 2 show that examining twin pairs with different birth weight differences does not increase precision. These differences reflect the 25th (128 grams), 50th (280 grams) and the 75th (500 grams) percentiles of the distribution of within twin birth weight differences. Medically, twins are considered “discordant” if their birth weight differs by more than 10%. From these tables, it is important to note that the point estimates are economically meaningful. It is likely that the lack of significance is simply a result of having very few twin pairs to examine among much older age groups. For example, estimates from Table 3 suggest that those born less than 2000 grams are 7 percent less likely to survive to age 70 conditional on surviving to age 60. Given that average conditional survival in this age group is around 88%, the size of the birth weight effect is meaningful.

Lower panels in Table 3 show largely similar results using unconditional survival at diﬀerent ages as the dependent variable. These estimates answer the question, “What is the proba-

10 bility of observing someone at age t?” While the twins fixed effects results from Table 2 and 3 are not statistically significant in many specification, the difference in OLS and twins fixed effects estimates from Figure 1 are worth noting. The fact that twins fixed effects estimates are larger than OLS for adult mortality is in contrast to the pattern found in Almond, Chay and Lee (2004), where OLS estimates are much larger than twins estimates for neonatal and infant mortality. The contrast is interesting if we believe that twins fixed effects estimates of relationship between birth weight and neonatal or infant mortality reflect “pure” birth weight effects not complicated by differential investments or experiences within pairs; on the other hand, we might believe that long run mortality is more susceptible to differential investments or experiences within twin pairs.

4.2 Income

For labor market outcomes we examine only male twin pairs as is standard in the labor literature facing this empirical reality at a time when female labor force participation was much lower and hence, highly selective. Figure 2 shows the estimates of the relationship between log birth weight and log income (we measure total income, including income from benefits) at each point in the life cycle using both fixed effects and OLS estimation methodologies. Since we create similar graphs for income and social assistance take up, it is useful to describe in detail how these graphs were created. The graphs plot coefficients and standard errors from regressions that use OLS or fixed effects depending on the graph. The coefficients can be interpreted as 3 year moving averages since at each age, we use 3 years of data to smooth the graphs. To be precise, the coefficient shown for age 50 uses data from ages 50-53, the coefficient for age 51 uses data from ages 51-54 and so on. These regressions control for calendar year of observation and nothing else.

In general, higher birth weight leads to higher income earlier in the life cycle, but there seems to be a decline in the birth weight eﬀect at older ages. Table 4 shows that around age

11 30, a 10% increase in birth weight (approximately 250 grams) can lead to a 1% increase in total income. These estimates are quite similar to the ones in Black, Devereux, and Salvanes (2007). This birth weight effect seems to last until around age 40-50 after which it appears to decline. Table 5a explores non-linearity in the birth weight-income relationship. While LBW appears to lead to lower incomes, the estimates are quite imprecise. While most coefficients in Table 5a are not significant, the general pattern suggests some non-linearity in the birth weight - income relationship. The coefficients on very low birth weight (VLBW) tend to be larger than the effects for higher thresholds, although the coefficients are rarely statistically significant. Table 5b explores this relationship by samples where within twin birth weight differences are at certain levels of discordancy. This table shows similar patterns to Table 4.

The fact that the birth weight effect disappears after age 50 is perhaps unexpected if we believe that initial labor market differences tend to persist. Many studies have found that labor market conditions at the time of entry are incredibly important for long term outcomes (see for example, Kahn (2010) and Beaudry and DiNardo (1991)). The declining result is not a feature of the sample itself changing since the results from the balanced panel for ages 30-55 in Table 4 show a remarkable decline in the birth weight effect by age 45 and nearly disappearing by age 50 and 55. However, examining just the results for income earned from labor market activity shows some persistence in the effect of birth weight even though some of the effects are not significant at later ages. Since the difference in labor and total income comes from social benefits (for the most part), this indicates perhaps the role of social assistance in decreasing what appears to be a more persistent gap in labor market outcomes. Finally, part of the declining result could also be the inclusion of diverse cohorts in the estimating sample. Appendix Table 1 shows that the effects appear to decline more for cohorts born 1938-1947 than they do for cohorts born 1948-1957.

12 4.3 Social Beneﬁts

Figure 3 shows the effects of birth weight on the take up of unemployment insurance (UI) and welfare. In both cases, it appears that lower birth weight within the twins fixed effects framework is related to greater take up of social assistance programs. In the case of welfare, we see these effects earlier in the life cycle, which seem to disappear in later ages. Table 6a explores some of these relationships, where the dependent variable is the fraction of time spent earning income from social assistance sources (we examine these in 10 year intervals). Table 6a suggests, for example that a 10% decrease in birth weight is associated with a 0.006 increase in the fraction of time between the ages of 35-45 spent on sickness pay. Given a base of 0.22, this suggests an increase of nearly 3%. Likewise, between the ages of 45-55, a 10% decrease in birth weight can lead to an increase in unemployment insurance take up of 0.005 - which translates to a 5.6% increase in the fraction of time spent on UI.

Table 6b is similar in spirit, but the dependent variable is defined as 1 if a person is ever observed taking UI or welfare payments at a given age. Examining earlier ages suggests that lower birth weight children are more likely to be welfare recipients early on in the life cycle, in line with the results in Figure 3. A 10% increase in birth weight results in a 6% decrease in the take up on welfare at age 30. While the effects for UI take up in Table 5b are not always statistically significant at conventional levels, quite a few coefficients are just barely shy of significance at the 10% level. The one clearly significant effect at age 50 suggests that a 10% increase in birth weight will decrease UI take up by around 5%.

4.4 Measurement Error

Since we are using birth weight data from a time when measurement methods were perhaps less reﬁned, it is likely that birth weight and subsequently birth weight diﬀerences are measured with more error for the earlier cohorts than for later cohorts. While we have no direct

13 way of assessing the extent to which measurement error plays a role, we can use rounding as a proxy for poor measurement. We do this by examining the fraction of births that are rounded to 50 gram intervals in Figure 4. The ﬁrst panel in Figure 4 shows very clearly that the number of children where birth weight was a multiple of 50 has reduced considerably between 1930-1950. This is likely due to improvements in birth weight measurements over time. In addition, the second panel of Figure 4 shows that in the early 1930’s a much larger fraction of twins were noted to have the same birth weight. This number falls from around 6-8% in the early 1930’s to around 2% in the 1940’s. While we use the full sample of birth for the most part, in Appendix Table 1, we replicate our main results for the younger cohorts for robustness. In general we ﬁnd results from younger cohorts to be larger and more precisely estimated, suggesting that the results for older cohorts are understated due to measurement error.

5 Conclusion

This paper adds to the emerging literature on the long lasting impacts of birth outcomes by examining the relationship between birth weight and adult mortality, income and social assistance take up over the life cycle. We find that birth weight is a predictor of income and social assistance in adulthood, although most of the effects appear concentrated in early adulthood. At age 50+, most of the birth weight effects seem to fade.

Our results are important in highlighting how inequalities at birth play out over the life cycle and support the idea that part of the health-income gradient in adulthood can be explained by health in childhood (Case, Lubotsky, and Paxson 2001). The extent to which policies or parental investments can remedy initial deficiencies is an important question for researchers to tackle. A first step towards this goal is to understand whether initial differences have long lasting impacts and then to examine intermediary channels that might

14 drive the long run results. We recognize that differences in early childhood can lead to differences in the medium/long term for various reasons and pinning down which one of these intermediary channels best explains the long run results is beyond the scope of this paper. We therefore view our results as arising from a combination of health deficiencies at birth and subsequent behavioral responses that might be aimed at mitigating or exacerbating these initial differences.

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Roseboom, T., J. Van Der Meulen, A. Ravelli, C. Osmond, D. Barker, and O. Bleker (2001): “Eﬀects of prenatal exposure to the Dutch famine on adult disease in later life: an overview,” Twin research, 4(5), 293–298.

Royer, H. (2009): “Separated at girth: Estimating the long-run and intergenerational Eﬀects of Birthweight using Twins,” American Economic Journal - Applied Economics.

Van Ewijk, R. (2011): “Long-term health eﬀects on the next generation of Ramadan fasting during pregnancy,” Journal of health economics, 30(6), 1246–1260.

19 Figure 1:

Figure 2:

20 Figure 3:

Figure 4:

21 Table 1: Summary statistics

Mean (where Std Dev applicable) Total sample size 35,316

% All Male pairs 36.88 % All Female pairs 37.93 % Mixed sex pairs 25.2

Among all male-male twin pairs (13,024 observations): Years of schooling 10.84 3.06 Birth weight difference (in grams) 337.8 278.94 Age in 1972 28.03 8.59 % Dead within sample period 14.56 Average age of death 59.44 13.29 Average maximum yearly income over sample period (SEK)* 446,107 627,324 Retirement by age 60 (observed for 6,775 observations) 17.65 38.12

*deflated using 2007 CPI measure Table 2: Birth Weight and Adult Mortality

Conditional survival Survival to Age conditional on surviving at Age-10 All twin pairs 40 50 60 70 80

All male twin pairs Log Birth Weight 0.00207 0.00999 0.0226 0.110* 0.119 (0.0126) (0.0153) (0.0264) (0.0662) (0.206)

Observations 8,283 11,385 8,188 3,135 684 Mean of dependent variable 0.991 0.980 0.957 0.881 0.724

All Male Twins with BW diff > 125 Log Birth Weight 0.00111 0.00648 0.0192 0.120* 0.111 (0.0130) (0.0151) (0.0268) (0.0660) (0.211)

Observations 6,248 8,458 6,078 2,284 532 Mean of dependent variable 0.991 0.981 0.956 0.885 0.729

All Male Twins with BW diff > 280 Log Birth Weight -9.11e-07 0.00462 0.0136 0.126* 0.0425 (0.0142) (0.0161) (0.0279) (0.0672) (0.214)

Observations 4,158 5,635 4,081 1,558 368 Mean of dependent variable 0.989 0.980 0.956 0.881 0.742

All Male Twins with BW diff > 500 Log Birth Weight 0.00248 0.00865 0.0280 0.0924 0.0971 (0.0173) (0.0198) (0.0308) (0.0742) (0.220)

Observations 2,017 2,750 2,044 814 214 Mean of dependent variable 0.988 0.976 0.956 0.883 0.752 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Notes: All coefficients are twins fixed effect estimates. Dependent variable is binary. Table 3: Birth Weight and Adult Mortality between ages 70-80

70 71 72 73 74 75 76 77 78 79 80

Conditional on survival till year t-10 Log Birth Weight 0.110* 0.0603 0.0411 0.0584 0.0427 0.135 0.133 0.199 0.248* 0.0477 0.119 (0.0662) (0.0738) (0.0801) (0.0916) (0.104) (0.119) (0.126) (0.137) (0.148) (0.175) (0.206)

Observations 3,135 2,777 2,458 2,187 1,932 1,663 1,451 1,232 1,069 869 684 Mean of dependent variable 0.881 0.875 0.866 0.846 0.824 0.798 0.786 0.778 0.753 0.741 0.724

Unconditional survival Log Birth Weight 0.132* 0.123 0.145 0.103 0.0713 0.127 0.129 0.195 0.249* 0.187 0.244 (0.0746) (0.0825) (0.0886) (0.0964) (0.106) (0.118) (0.125) (0.132) (0.146) (0.164) (0.192)

Observations 3,373 3,015 2,691 2,412 2,149 1,878 1,659 1,431 1,245 1,042 831 Mean of dependent variable 0.819 0.806 0.791 0.767 0.741 0.707 0.688 0.669 0.647 0.618 0.596

Conditional on survival till year t-10 Less than 2000 grams -0.0725** -0.0805* -0.0325 -0.0380 -0.0421 -0.0813 -0.0641 -0.0306 -0.0224 0.0543 -0.0192 (0.0363) (0.0411) (0.0439) (0.0506) (0.0584) (0.0670) (0.0694) (0.0750) (0.0821) (0.102) (0.116)

Observations 3,135 2,777 2,458 2,187 1,932 1,663 1,451 1,232 1,069 869 684 Mean of dependent variable 0.881 0.875 0.866 0.846 0.824 0.798 0.786 0.778 0.753 0.741 0.724

Unconditional survival Less than 2000 grams -0.0628 -0.0798* -0.0899* -0.0869* -0.0806 -0.0996 -0.0874 -0.0659 -0.0556 -0.0448 -0.0844 (0.0412) (0.0454) (0.0475) (0.0528) (0.0594) (0.0663) (0.0686) (0.0736) (0.0816) (0.0959) (0.107)

Observations 3,373 3,015 2,691 2,412 2,149 1,878 1,659 1,431 1,245 1,042 831 Mean of dependent variable 0.819 0.806 0.791 0.767 0.741 0.707 0.688 0.669 0.647 0.618 0.596

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Notes: All coefficients are twins fixed effect estimates. Dependent variable is binary. Table 4: Log Birth Weight and Income at Different Ages - Twins FE

Dependent variable is log income Age 30 35 40 45 50 55 60 All Male Twins Log Birth Weight 0.108** 0.0953** 0.0809* 0.0715 0.0692 0.0325 0.0536 (0.0437) (0.0444) (0.0438) (0.0480) (0.0509) (0.0567) (0.0666)

Observations 14,477 15,929 17,171 17,083 16,902 14,148 10,671

Balanced sample between ages 30-55 Log Birth Weight 0.108** 0.0849* 0.0720 0.0399 0.00539 0.00357 (0.0479) (0.0466) (0.0484) (0.0531) (0.0605) (0.0664)

Observations 11,305 11,305 11,305 11,305 11,305 11,305

Dependent variable is log labor income Log Birth Weight 0.118*** 0.103** 0.0724 0.122** 0.0781 0.0814 0.0939 (0.0435) (0.0447) (0.0467) (0.0550) (0.0558) (0.0681) (0.0846)

Observations 10,696 11,740 12,692 12,591 12,414 10,366 7,783

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Notes: All coefficients are twin fixed effects and control for dummies for years of schooling. Dependent variable is log total income, including income from benefit payments. Table 5a: Birth Weight and Income at Different Ages - Non Linear Specs

Dependent variable is log income Age 30 35 40 45 50 55 60 All Male Twins Less than 1500 grams -0.0242 -0.0149 -0.0814 -0.144* -0.124 -0.0373 -0.0238 (0.0667) (0.0678) (0.0696) (0.0763) (0.0820) (0.0906) (0.110) Less than 2000 grams -0.0605** -0.0413 -0.0332 -0.0296 -0.0648** -0.0517 -0.0314 (0.0251) (0.0254) (0.0251) (0.0274) (0.0292) (0.0327) (0.0380) Less than 2500 grams -0.0346** -0.0271* -0.0190 -0.0140 -0.00931 0.00968 -0.0158 (0.0148) (0.0152) (0.0150) (0.0165) (0.0175) (0.0194) (0.0229) Observations 14,477 15,929 17,171 17,083 16,902 14,148 10,671

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Notes: All coefficients are twin fixed effects and control for dummies for years of schooling. Dependent variable is log total income, including income from benefit payments. Table 5b: Birth Weight and Income at Different Ages by Birth Weight Differences within Twins

Age All Male Twins with BW diff > 125 30 35 40 45 50 55 60 Log Birth Weight 0.106** 0.0929** 0.0780* 0.0650 0.0623 0.0280 0.0546 (0.0448) (0.0452) (0.0434) (0.0488) (0.0518) (0.0561) (0.0687)

Observations 10,864 11,922 12,877 12,809 12,683 10,594 8,005

All Male Twins with BW diff > 280 Log Birth Weight 0.120*** 0.0998** 0.0902** 0.0887* 0.0841 0.0560 0.0813 (0.0464) (0.0470) (0.0449) (0.0500) (0.0519) (0.0559) (0.0712)

Observations 7,285 8,005 8,660 8,620 8,530 7,162 5,438

All Male Twins with BW diff > 500 Log Birth Weight 0.120** 0.151*** 0.0990* 0.115** 0.0987* 0.0183 0.0249 (0.0550) (0.0546) (0.0525) (0.0580) (0.0592) (0.0643) (0.0754)

Observations 3,599 3,973 4,341 4,322 4,281 3,573 2,777

All Male Twins: Age 35-45 45-55 55-65

Fraction of time spent receiving income from various social UI Sickness Pay Welfare UI Sickness Pay Welfare UI Sickness Pay Welfare programs

Log birth weight -0.0557* -0.0626* -0.0386 -0.0530** -0.0215 -0.00132 -0.0391 0.00629 -0.0119 (0.0297) (0.0347) (0.0298) (0.0226) (0.0241) (0.0188) (0.0255) (0.0282) (0.0201)

Observations 8,177 8,886 13,672 13,862 14,220 16,569 12,533 12,743 12,821 Mean of dependent variable 0.0938 0.227 0.138 0.0810 0.162 0.0562 0.0812 0.149 0.0364

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Notes: All coefficients are twins fixed effect estimates and control for schooling levels. Dependent variable is fraction of time (over a 10 year period) spent earning income from each source. Income sources are observed at the yearly level. For example, if a person was on welfare from at age 37 and 38, the dependent variable is 2/10=0.2.

Table 6b: Log Birth Weight and Social Assitance at Different Ages

Age UI Take Up 35 40 45 50 55 60

Log Birth Weight -0.0915 -0.0704 -0.0205 -0.0437 -0.0757* -0.0993** (0.0853) (0.0617) (0.0475) (0.0415) (0.0429) (0.0477)

Observations 2,602 5,993 9,583 12,525 11,837 9,698 Mean of dependent variable 0.135 0.174 0.159 0.140 0.131 0.143

Welfare Take Up 35 40 45 50 55 60

Log Birth Weight -0.150** -0.0392 -0.0844** -0.0217 -0.00703 0.0108 (0.0591) (0.0508) (0.0402) (0.0304) (0.0272) (0.0291)

Observations 8,943 12,198 14,319 15,667 14,224 10,720 Mean of dependent variable 0.278 0.249 0.155 0.0887 0.0595 0.0500

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Notes: All coefficients are twin fixed effect estimates. Dependent variable takes on a value of 1 if income from given social assistance source is received at given age. Appendix Table 1: Log Birth Weight and Income at Different Ages for Cohorts 1938-1957

Dependent variable is log income Age 30 35 40 45 50 All Male Twins Log Birth Weight 0.113** 0.0851* 0.108** 0.0585 0.0757 (0.0452) (0.0495) (0.0519) (0.0578) (0.0623)

Observations 13,119 13,071 13,006 12,910 12,763

1938-1947 30 35 40 45 50 All Male Twins Log Birth Weight 0.0743 -0.00914 0.0746 0.0499 -0.0136 (0.0673) (0.0654) (0.0674) (0.0776) (0.0829)

Observations 6,253 6,243 6,214 6,167 6,093

1948-1957 30 35 40 45 50 All Male Twins Log Birth Weight 0.147** 0.167** 0.132* 0.0561 0.157* (0.0605) (0.0738) (0.0785) (0.0854) (0.0926)

Observations 6,866 6,828 6,792 6,743 6,670

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Notes: All coefficients are twin fixed effects and control for dummies for years of schooling. Dependent variable is log total income, including income from benefit payments.