Long-run impacts of early life health interventions - Center for Economic Studies (CES)
←
→
Page content transcription
If your browser does not render page correctly, please read the page content below
Long-run impacts of early life health interventions Melanie Lührmann Royal Holloway, University of London and IFS September 16, 2020 c Royal Holloway
Early life interventions • A large literature documents large effects of early life environments on well-being of infants’ and children’s survival and childhood outcomes, and into adulthood (see Almond et al. 2019 for a survey) → large returns to early investments to improve childhood environments • pareto-improvement through early targeting of redistributive investments? c Royal Holloway
Early life interventions • A large fraction of research focused on the role of education interventions and on cognitive outcomes → Head Start, Carolina Abecedarian Project, Perry preschool programs,... (see Heckman and many others) • in parallel, a large literature on health and nutrition conditions in utero establishes large returns to prenatal programs ... (e.g. Currie and Gruber 1996b) ...in terms of infancy survival ...education ...childhood health • emerging body of research on conditions in the infancy period (Bütikofer et al, 2019; Hoynes et al. 2016; Currie and Gruber 1996a; Hjort et al. 2017; Bhalotra and Venkataramani 2015) c Royal Holloway
Early life interventions - types Type of interventions (or shocks): • education/cognition (parental time investment, stimuli, play, childcare policies,...) • nutrition/malnutrition (hunger, famine, food supplementation, school meals, breastfeeding, SNAP (food stamps) and similar programs) • healthcare/disease • universal healthcare or healthcare for the poor (Medicaid, NHS) • infectious disease outbreaks (diarrhea, tuberculosis, pandemics) • new drugs or treatments that improve infant and childhood health(e.g. deworming drugs, penicillin) • welfare systems (e.g. EITC, maternity leave, conditional cash transfers) • pollution/sanitation/weather c Royal Holloway
Early life interventions - stage by stage Fast growing literature on the (contemporaneous and long-run) impact of interventions and shocks • in utero • during infancy (i.e. in the first year of life) • during preschool years c Royal Holloway
Long run impacts of early life shocks or interventions? Why do we need movement in the data frontier? • How long do the impacts of these interventions last? • requires interventions that are “old enough” so we can follow treated cohorts over time • many large US education and welfare experiments happened in the 1970s and 1980s • those treated then are now around age 30-40, so impacts on completed education, earnings and other adult outcomes can be analysed → this has led to a surge in studies examining longer-run impacts of such policies • prior work used small survey data (PSID), often with a limited set of available outcomes c Royal Holloway
Long run health impacts of early life interventions? • health and mortality impacts tend to manifest later • severe health shocks tend to be more prevalent from about age 50 • need about 6-7 decades of data and large samples for adequate statistical power Figure: Mortality rates by age, UK, cohorts born 1944 to 1955 c Royal Holloway
A seminal model of health capital - Grossman (1972) Components: • it’s an old seminal paper, but... • it is a useful conceptual framework for studying • ... most aspects of the demand for health • ... understanding sources of health inequalities • ... income and price impacts on the demand for health • ... the design of public health programmes, interventions c Royal Holloway
The Grossman model Components: • human capital model of the demand for health • health is 1. a stock 2. a choice (enters the utility function) 3. produced by the individual Intuition: • health is a durable capital stock that yields healthy time as service flow • stock depreciates with age and increases with investment • health investments crowd out time for other activities, i.e. market work and leisure, and other consumption c Royal Holloway
The Grossman model - utility Two goods: healthy time ht , other consumption Zt Intertemporal utility function U = U(ht , Zt ) where ht = φt Ht is consumption of health services (or healthy time) Ht : stock of health at t φt : service flow per per unit of health stock health at t c Royal Holloway
The Grossman model - investment Net investment in health in t is Ht+1 − Ht = It − δt Ht Assumption: δt is exogenous but increasing in age c Royal Holloway
The Grossman model - production Individuals use time (and input goods) to produce health and other consumables according to the following production functions: It = It (Mt , THt ; E ) Zt = Zt (Xt , Tt ; E ) M,X: endogenous goods inputs TH,T: endogenous time inputs E: consumer’s exogenous stock of knowledge (education) Note: there is no joint production using the same inputs here (e.g. vegetables may be M or X, and both affect I and Z) c Royal Holloway
The Grossman model - constraints n n X pt Mt + qt Xt X ωt TWt t = + A0 Budget constraint (1) (1 + r ) (1 + r )t t=0 t=0 p,q: prices TW: hrs of work ω: wage A0 : initial assets r: interest rate TWt + THt + Tt + TLt = Ω Time constraint (2) TL: time lost through illness Ω: total time c Royal Holloway
The Grossman model Substituting into BC: n n X pt Mt + qt Xt + ωt (THt + Tt + TLt ) X ωt Ω = + A0 (3) (1 + r )t (1 + r )t t=0 t=0 Assumptions: ∂TLt
The Grossman model - equilibrium conditions ωt Gt (1−δt )ωt+1 Gt+1 (1−δt )...(1−δn−1 )ωn Gn (1+r )t + (1+r )t+1 + ... + (1+r )n πt−1 = (6) (1 + r )t−1 + Uh Uhn | {z } λ Gt + ... + (1 − δt ) ... (1 − δn−1 ) λ Gn t PDV of MHC | {z } PDV of MHB c Royal Holloway
PDV of MC of gross investment: depends on ... • the interest rate r • MC of gross investment, πt−1 , which is a function pt−1 ωt−1 πt−1 = = (7) ∂It−1 /∂Mt−1 ∂It−1 /∂THt−1 of • the price p of health inputs M • the MP of input in the production of health, or, alternatively, • the price of the time input TH, ω • and the MP of TH into production of H c Royal Holloway
PDV of marginal health benefit The marginal benefit of gross health investment in t: ωt + Uht · Gt (8) (1 + r )t λ |{z} | {z } MP of health capital discounted marginal value of of health capital which depends on • λ: MU of wealth • discounted wage rate (value of a unit increase in market time) ∂U • Uht : MU of healthy time ∂h t ∂ht • Gt : MP of health stock in healthy time production ∂Ht = − ∂TL ∂Ht t c Royal Holloway
Interpretation • Equation 6 determines optimal gross investment in t-1 • Equation 7: cost is minimised when the relative price of both inputs (time, goods) equals the ratio of marginal productivities Note: AC of gross investment is constant and equal to MC due to • homogeneous production functions • prices that do not depend on the stock (or on age) c Royal Holloway
Optimal health stock in t Optimal investment (not discounted) Uht t Gt ωt + (1 + r ) = πt−1 (r − πg t−1 + δt ) (9) λ must equal rental (or user) cost of health capital, which depends on • interest rate • depreciation rate • percentage rate of change in marginal cost between period t - 1 and period t ≈ 0 c Royal Holloway
Model predictions Reduction in price of medical care p • substitute medical care for other health inputs (here: time; in an extended model may also be self-care or own private medical expenses) due to change in relative prices (SE) • hold more health capital (IE) Increase in wages (incomes) ω • increases opportunity cost of time, induces lower time investment in health stock (SE) • hold more health capital (IE) • raises return on a healthy day → increases health capital c Royal Holloway
Model predictions Increase in age (here equal to t) • if depreciation rate is (constant) increases in age, then rental price of health goes up (is constant), so health investment decreases (remains unchanged) • yet, health stock depreciates quicker, hence while health stock goes down, health investments may not (in fact, empirically, health expenditure increases in age) c Royal Holloway
Model predictions Increase in educational attainment Under the assumption that more educated people are better at producing of health capital (higher productivity), i.e. • they are better able to determine high-yield health investments (prevention, timing of doctor visits, types of treatments) • they have a larger health stock • but not clear whether they invest more • (education also affects wages) c Royal Holloway
Possible extensions: see here for details 1. Uncertainty: health insurance to smooth unexpected shocks → shocks could be introduced via stochastic depreciation rate or stochastic future earnings 2. Individual heterogeneity: • depreciation rates • initial health stocks • productivity in producing health • preference 3. Differential mortality: role of genetics, early (in utero) health environments,... c Royal Holloway
Possible extensions 4. Health production function: • Multiple inputs: private vs. public health care, out-of pocket expenditure, health lifestyle... • Joint production: there may be joint production of healthy time and consumption (e.g. vegetable cons., sports,...) • constant returns to scale in health production: some lack of health investment may be irreversible, marginal productivity of health investment may be decreasing in age... c Royal Holloway
Possible extensions 5. Perfect foresight: → Over (under-) investment into health due to different information set about health risks, and benefits of health investments → diagnosis process or doctor visits may be informative about health stock and the health production function → learning about returns to health investments 6. Rationality: no role for bounded rationality → people may be perfectly informed but find it hard to adjust their behaviour → time inconsistency: hyperbolic discounting where future benefits are weighted down in the short-run (present bias) → rational addiction models: Adda and Cornaglia (2010) → rational inattention, other behaviouristic biases? c Royal Holloway
Implications for research and policy? • Private health investment will depend on the price of medical care or other health-relevant expenses (cigarettes and unhealthy foods, medical care and health-enhancing consumer goods, disease prevention,...) • income growth is likely going to lead to better health Figure • if individuals do not have perfect information, then there may be scope for: • information interventions (5 a day campaign, vaccination information,...) • some routine interventions like free prevention, health check offers • behavioural interventions (habit formation, ...) • Timing may matter in health investments: role for childhood interventions c Royal Holloway
The Grossman model and early life health interventions or: How may early childhood health environments shape adult health? Early, more severe decumulation of health stock (than at older ages) or lack of reaching potential health stock • Infancy is a key development period → differential return to health investments (loss of stock due to shocks) in different periods? Depreciation rate • Early life illness may inhibit neurological development in infancy, accelerating aging process (Bhalotra and Venkataramani, 2013) → increase in depreciation rate throughout the life cycle • Biological embedding (Shonkoff et al., 2009) Immature “organism” adapts to key environmental characteristics, and retains initial programming, even when environment changes → irreversible change in health stock? c Royal Holloway
Important historic early life interventions Program Start year Impacts Education interventions Perry preschool 1970 Website Head Start 1965 Garces, Thomas, Currie (2002) Nutrition & health interventions Food Stamps (SNAP) 1962-75 Hoynes, Schanzenbach, Almond ( Medicaid intro 1970 Goodman-Bacon (2018, 2017) expansions 80s, 90s Brown et al. (2015) Wherry and Meyer (2016) Currie and Gruber (1996) Currie et al. (2008) NHS intro 1948 Luhrmann and Wilson (2020) Scandinavian Well-Child Programmes 1930s Bhalotra, Karlsson, Nilsson (2017 Bütikofer, Løken, Salvanes (2018 Hjort, Sølvsten, Wüst (2017), Wu European health systems and welfare programmes tend to be older than those in the US... c Royal Holloway
Typical identification strategies used in these studies • difference-in-difference model or regression discontinuity design • exploiting cohort-specific exposure to welfare programme or health intervention, combined with geographic variation from staggered rollout (in US states) • Example: long run impact of SNAP - a large US welfare programme - Hoynes et al (2016) difference-in-difference approach c Royal Holloway
SNAP, formerly food stamps programme • 40.3 million recipients in 20 million households (2018) • average monthly benefit of USD 252 per household • delivered in vouchers that can be used in grocery stores • means testing: requires gross monthly income below 130% of poverty line • third largest US welfare programme in terms of expenditure (after Medicaid and EITC) c Royal Holloway
What is the link between SNAP and health? • SNAP is a conditional cash transfer programme • it conditions on the transfer being spent on food • healthy nutrition is emerging as a key factor in early life interventions • cash and conditional cash transfer programmes have been extensively used to buffer individual shocks during the COVID-19 pandemic • e.g. SNAP • voucher system to compensate for (unavailable) free school meals in the UK (affects 1.3 million children) • direct payout of missed school meals in the US: about 120 USD per month and child (affects 30 million students who receive free or reduced price school meals) c Royal Holloway
Challenges to identification in SNAP • universal programme • federally administered (little variation in generosity across states) • few reforms • negative selection: typically receive SNAP when adverse shock hits c Royal Holloway
Hoynes et al. (2016): staggered rollout of FSP Also used in Bailey et al. (2020) c Royal Holloway
Hoynes et al. (2016): Staggered rollout of FSP c Royal Holloway
Hoynes et al. (2016): difference-in-difference approach Compare adult outcomes for those with early childhood exposure to FSP in their county of birth to those born earlier (and therefore without childhood FSP exposure) yibc = α + δTc,b + Xibc β + ηc + λb + γt + θs · b + ρZc60 · b + ibc (10) where T : childhood FSP exposure (share of months FS available between conception and age 5 in birth county) b: cohort c: geography (here:county) s: state c Royal Holloway
Hoynes et al. (2016): identifying assumptions • exogeneous introduction of FSP across counties → empirically: control for trends in the observable determinants of FSP adoption by including interactions between characteristics of the county of birth and linear trends in year of birth CB60g · c • common trend assumption: no competing welfare programs rolled out with similar staggering → control for county of birth characteristics (community health centers, hospitals and hospital beds per capita, and non-FSP government transfers per capita), measured as averages over the first five years of life. c Royal Holloway
Hoynes et al (2009): impact of childhood safety net on adult outcomes • examine change in economic resources available in utero and during childhood (up to age 5) • Food Stamp Program, rolled out across counties in the U.S. between 1961 and 1975. • Data: PSID (incl. county of birth information) • 3000 nationally representative hhs + 1900 low income and minority hhs • combine with USDA annual reports on county FSP caseloads per county and year to construct childhood FSP exposure (share of time between conception and age 5 that FSP is available in birth county) • oldest individuals can be followed up to age 53 • control for county characteristics • good earnings, income and education information and some health information (summarised in metabolic health index) c Royal Holloway
FSP exposure - timing effects? • Does the timing matter? Are returns of SNAP different depending on when benefits were received between age 0 and 5? c Royal Holloway
Hoynes et al (2009): findings • childhood outcomes (Hoynes and Schanzenbach, 2009) • introduction of FSP increased householdsspending on food • increase in economic resources rather than nutrition programme • pregnancies exposed to FSP three months prior to birth yielded deliveries with increased birth weight • largest gains at the lowest birth weights; larger impacts for African American mothers • adult outcomes • food stamp program has effects decades after initial exposure • greater exposure to FSP before age 4-5 significantly reduces the incidence of adult metabolic syndrome (obesity, high blood pressure, and diabetes) • for women, an increase in economic self-sufficiency c Royal Holloway
Followup paper: Bailey et al. (2020) • move to large linked dataset of survey-administrative data (> 17 million households) • Social security data linked with census records • examine a comprehensive set of outcomes such as human capital, disability, mortality, incarceration • aggregate to birth county x birth year x survey year cells (partially also by race and sex) • but: loose information on socio-economic status (education, poverty) and shorter time horizon (up to age 33) • take into account impact of complementary welfare programs (EITC, Community Health Centers, WIC) c Royal Holloway
Bailey et al. (2020) - econometric specification a=17 X ycbt = ηc +δs(c)b +γt +Xcbt β+Zc60 bρ+ πa ·1[b−FSc = a]+cbt a=−5[a6=10] (11) where ηc : birthcounty FE δs(c)b : birth state x year FE Xcbt : cohort-county-year FE (all at birth) Zc60 b: 1960 county characteristics x linear birth cohort FSc : year FSP was first available in county c a: age when FSP was first introduced πa : event time coefficients, ranging from 5 years before birth to age 17 (age 10 omitted category) c Royal Holloway
Bailey et al. (2020) - hypotheses • If no pre-trends: pi should not be statistically significant for a < −1 (conception) • If earlier investment have larger returns, then π̂a should be largest in utero and early childhood (a=-1 to 5) • Estimate spline function: ycbt =ηc + δs(c)b + γt + Xcbt β + Zc60 bρ + ω1 1[b − FSc < −1] · (b − FSc ) | {z } FS pre-conception (pre-trends) + ω2 1[−1 ≤ b − FSc < 6] · (b − FSc ) | {z } FS in utero & early childhood (12) + ω3 1[7 ≤ b − FSc < 11] · (b − FSc ) | {z } FS age 6-11 + ω4 1[12 < b − FSc ] · (b − FSc ) +cbt | {z } FS age 12-17 c Royal Holloway
Robustness checks • test for pre-trends (see above) • county adoption timing voluntary =? endogenous? • balancing test • birth county-corth year controls (population, mortality rates, complementary welfare programme rollout) • flexible Xcbt terms (birth cohort-county-year FE (all at birth) • pre-trends c Royal Holloway
Bailey et al. (2020) - does the timing of FSP receipt matter? c Royal Holloway
Bailey et al. (2020) - magnitude of results Implies: 5yr + IU exposure → 0.009 SD increase in composite index similar results in spine model: 5.75 years x 0.0017=0.0098 c Royal Holloway
Bailey et al. (2020) - a few additional results • 7% TOT impact on earnings • 0.06 SD in human capital index • 11% reduction in mortality • Largest impacts on human capital, esp. years of schooling and attending college • ...concentrate among whites, particularly males • survival gains concentrated among non-whites • reductions in incarceration among non-whites (only) c Royal Holloway
Bütikofer et al. (2019): long-run impact of infant health care centers • treatment: well-child visits include physical examination and information on adequate nutrition (breastfeeding) • DiD; similar in method to Hoynes et al. • use the variation in exposure to infant health care services driven by mother and child health care center openings, and the scope of the services provided • exploit the rollout of newly established mother and child health care centers across municipalities over time. c Royal Holloway
Bütikofer et al. (2019): difference-in-difference approach • DiD; similar in method to Hoynes et al. • use the variation in exposure to infant health care services driven by mother and child health care center openings, and the scope of the services provided • exploit the rollout of newly established mother and child health care centers across municipalities over time. • data: Norwegian registry data, combined with historic data on center rollout • health data: Cohort of Norway (CONOR) data and the National Health Screening Service’s Age 40 Program data c Royal Holloway
Bütikofer et al. (2019): robustness • similar identifying assumptions • test whether municipality characteristics predict center opening • use sibling fixed effects to show that results are not driven by selective migration into municipalities with early centers c Royal Holloway
Bütikofer et al. (2019): findings • access to mother and child health care centers in the first year of life increased • completed years of schooling by 0.15 years • earnings by two percent. • effects were stronger for children from a low socioeconomic background • 10 percent reduction in the persistence of educational attainment across generations. • positive effects on adult height and fewer health risks at age 40 • immediate effect: access to well-child visits decreased infant mortality from diarrhea whereas infant mortality from pneumonia, tuberculosis, or congenital malformations are not affected • mechanism: better nutrition c Royal Holloway
Long-run Health and Mortality Eects of Exposure to Universal Health Care in Infancy Melanie Lührmann (Royal Holloway and IFS) and Tanya Wilson (University of Glasgow) Acknowledgement: British Academy/Leverhulme SG162230 & BA MF170399 1 /36
Disclaimer The permission of the Oce for National Statistics to use the Longitudinal Study is gratefully acknowledged, as is the help provided by sta of the Centre for Longitudinal Study Information & User Support (CeLSIUS). CeLSIUS is supported by the ESRC Census of Population Programme (Award Ref: ES/K000365/1). The authors alone are responsible for the interpretation of the data. This work contains statistical data from ONS which is Crown Copyright. The use of the ONS statistical data in this work does not imply the endorsement of the ONS in relation to the interpretation or analysis of the statistical data. This work uses research datasets which may not exactly reproduce National Statistics aggregates. 2 /36
Motivation Impact of infancy exposure to universal healthcare on mortality and health around ages 50-60 • Intervention: NHS introduction in 1948 • We digitised historical data sources to investigate the immediate impact of the NHS on infant survival • For longer-term outcomes we use a RD design enriched with geographical variation in medical services provision for identication. • impacts are estimated using large administrative datasets recording death and hospitalisation 3 /36
Related evidence: Medicaid introduction (1960s) and expansions (1980s-90s) • Short run: reductions of • perinatal (before birth and death < 7 days) and • neonatal (death < 28 days) mortality Goodman-Bacon (2018), Currie and Gruber (1996a,b) • Medium run: improvements in • childhood and adolescent health • educational attainment • better early labour market outcomes, higher tax receipts, lower welfare dependency Currie et al. (2008), Brown et al. (2015), Wherry and Meyer (2016) • Vietnam UHC led to signicant increase in utilization of public health services among eligible children (Vu 2019; Nguyen and Wang, 2012) 4 /36
Institutional Setting: Pre-NHS • Mainly private provision • National Insurance Act (1911) • rudimentary medical care provided to employed persons aged 16-70 with annual earnings below a threshold • Coverage did not extend to dependents • Limited access to free healthcare by LAs and vol. hospitals (under severe nancing problems by 1940s) 5 /36
Institutional setting: NHS • 1942: Beveridge report highlights social and health disparities in the UK • July 1948: introduction of universal healthcare via the National Health Service • Aims of the NHS: • equalisation of access to medical services • free at the point of use • access is based on clinical need, not ability to pay 6 /36
Institutional setting: NHS After fraught negotiations, family doctors (GPs) agreed to participate on 28th May 1948. Large-scale information campaign began June 1948 7 /36
Institutional setting: NHS Within 5 months 96% of population had signed up to the NHS: • 6th July: 35,757,997 people registered (84%) • 31st July: 38,669,195 (91%) • 30th Oct: 40,706,290 (95%) • 31st Dec: 41,466,755 (96%) By Sept, 18,165 out of 21,000 GPs had signed up (87%) 8 /36
Institutional setting: NHS Initially not accompanied by a large investment programme to boost resources (no new hospitals, no discontinuous expansion in doctors or nurses) • hospitals were centralised • doctors became independent contractors • local authorities continued to administer family health services 9 /36
Institutional setting: Distributional changes in services utilisation There can be little doubt that before the start of the new National Health Service many women [...] were deterred from seeking medical advice by economic reasons. Now that the nancial barrier has been removed, women [...] are able to consult their doctor more often than they did before. (Logan, 1950, Lancet) 10 /36 Source: Survey of Sickness, The Wellcome Library.
Immediate eects: Infant mortality data We use data digitised from Registrar General's Statistical Review of England and Wales, and from Ministry of Health Annual Reports. Detailed population data on mortality in infancy by: • period 1943 to 1953 • county • subperiods of death (pre-, neo- and postneonatal death rates up to 1 year) • cause of death • marriage status of the mother (legitimacy) 11 /36
Immediate eects Pre-natal mortality and mortality at birth No evidence of a discontinuity in • maternal mortality • stillbirths • mortality around delivery (rst 30 minutes, rst day) → results not suggestive of improvements in ante-natal services → no NHS impact at delivery 12 /36
Immediate eects: Infant mortality data Reduction in infant mortality (17%) is predominantly driven by large declines in the neo-natal period... Source: Registrar General's Annual report 1940-1955, The Wellcome Library. by week 13 /36
Immediate eects: Infant mortality data .. due to prevention of deaths from acute conditions (pneumonia and diarrhea)... .. with lasting eects on human capital accumulation, employment and earnings (Bhalotra and Venkataramani, 2013, 2015) (a) Diarrhea (b) Pneumonia Source: Ministry of Health Annual Reports, The Wellcome Library. 14 /36
Immediate eects: Infant mortality data .. and concentrated among individuals of lower socio-economic status who prior to the NHS had low or no access to healthcare Source: Registrar General's Annual report 1940-1955, The Wellcome Library. 15 /36
Robustness of infant mortality results That the fall in infant mortality is associated with increased access to medical services via the NHS is consistent with Dykes (1950) • Case study of a large town in 1946 - nds strong SES gradient in infant mortality • Higher mortality related to delay in accessing medical care Examination of other factors inuencing infant mortality revealed no sharp discontinuity in: • breastfeeding practices • availability of vaccinations/food (rationing) Also investigated other potential drivers: • changes in birth trends/composition of births (by age/parity) • weather (`hard' winters) • Infant mortality trends in other countries 16 /36
Adult mortality data ONS Longitudinal Study • administrative data from ve successive linked censuses (1971-2011) • census panel is linked to death records up to 2015 with information on time and cause of death • approximate 1% sample of the population of England and Wales • data contains rich set of socio-economic characteristics • ...and location at birth combined with GBHD data on social class composition SES 17 /36
Identication strategy I method fuzzy RD design threshold birth in 1948 (UK Biobank: month and year of birth) window cohorts born between 1945 and 1951 fuzzy probability of an increase in pre- or postnatal care is a function of socio-economic status birth county FE capturing local economic conditions & healthcare infrastructure yicg = α + βCc + γ1 Tc + γ2 Tc LCic + δLCic + Xic0 η + µg + ic (1) 18 /36
Estimates of mortality rate, I Table: Estimates of mortality rates by ages 52 to 64 Mortality rate by age ... 52 54 56 58 60 62 64 Tc ∗ LCic -0.0173** -0.0223** -0.0187** -0.0249** -0.0279*** -0.0272** -0.0313*** (0.00763) (0.00874) (0.00875) (0.00998) (0.0100) (0.0104) (0.0112) Tc 0.00678* 0.00897** 0.00560 0.00697 0.0102* 0.00935* 0.00816 (0.00392) (0.00426) (0.00482) (0.00512) (0.00536) (0.00530) (0.00617) Observations 44,121 44,121 44,121 44,121 44,121 44,121 44,121 F-test for joint signicance of Tc LCic and Tc coecients p-value 0.0790* 0.0391** 0.1057 0.0509* 0.0244** 0.0347** 0.0262** Mean mortality rate prior to NHS inception, by social class LC 0.0488 0.0606 0.0730 0.0884 0.1029 0.1209 0.1421 HC 0.0306 0.0367 0.0462 0.0558 0.0657 0.0783 0.0899 Mortality reduction in percent (relative to mean), by social class LC -21.56 -22.00 (-17.95) -20.28 -17.20 -14.76 -16.28 HC 22.16 24.44 (12.12) (12.49) 15.53 11.94 (9.08) 19 /36
Geographical variation in medical services Identication strategy II • NHS: free healthcare in a rationed needs-based system → increased patient competition for healthcare • Recall: no supply change at NHS introduction, i.e. short-run xed resource • County-level per capita medical services mi determined by the fraction of population who could aord access pre-NHS • Higher county proportion of insured individuals (pre-NHS) → county medical services per capita in 1948 ↑ → proportion of new patients demanding healthcare ↓ • proxy proportion of insured through county-level social class composition 20 /36
Geographical variation in medical services Evidence Source: The Hospital Surveys, HMSO; GBHD database. 21 /36
Geographical variation in medical services Evidence Source: First General Practice Committee Report. 22 /36
Identication strategy II We proxy inow of new patients through county-level social class composition (proportion of insured): yicg = α + βCc + γ1 Tc + γ2 Tc LCic +γ3 Tc HIGHareag + γ4 Tc LCic HIGHareag (2) +γ5 LCic HIGHareag + δLCic + ζHIGHareag +Xic0 η + ic HIGHareag : area with a high (upper tertile) proportion of previously insured (→ low inow of new patients) 23 /36
Estimates of mortality rate, II Mortality rate by age ... 52 54 56 58 60 62 64 Tc ∗ LCic -0.0119 -0.0110 -0.0128 -0.0227* -0.0224 -0.0303** -0.0271 ∗ HIGHarea (0.0124) (0.0118) (0.0125) (0.0118) (0.0140) (0.0150) (0.0196) Tc ∗ LCic -0.0158** -0.0211** -0.0172* -0.0217** -0.0243** -0.0225** -0.0272** (0.00751) (0.00854) (0.00861) (0.0102) (0.0101) (0.0106) (0.0111) Tc ∗ HIGHarea -0.00825** -0.00598 -0.0108** -0.00763* -0.00453 -0.00344 -0.00254 (0.00318) (0.00361) (0.00520) (0.00441) (0.00428) (0.00473) (0.00529) Tc 0.00845** 0.0102** 0.00770 0.00852 0.0110** 0.0101* 0.00873 (0.00412) (0.00433) (0.00480) (0.00521) (0.00532) (0.00526) (0.00619) Observations 44,121 44,121 44,121 44,121 44,121 44,121 44,121 F-tests of joint signicance (p-values) LC in HIGHarea 0.0519* 0.0838* 0.0208** 0.0169** 0.0532* 0.0429** 0.0808* LC in LOWarea 0.0751* 0.0338** 0.1275 0.0988* 0.0397** 0.0700* 0.0534* HC in HIGHarea 0.0280** 0.0493** 0.0628* 0.1200 0.0943* 0.1488 0.3607 Mortality change in percent (relative to mean mortality rate), by area and social class LC in HIGHarea -44.07 -39.83 -38.13 -42.04 -33.89 -32.96 -29.83 LC in LOWarea -17.13 -19.60 -14.42 -16.19 -13.93 -11.01 -13.32 HC in HIGHarea 0.61 11.92 -6.71 (1.61) 10.13 (9.00) 7.21 HC in LOWarea 29.86 32.69 (18.08) (16.17) 17.43 13.43 9.60 24 /36
Estimates of mortality rate, II Higher mortality reductions • for low SES born in High SES areas • in High SES areas • amongst low SES ... but crowding out eects of patient inow on those with previous access to healthcare • that rise in the scarcity of available medical services 25 /36
Conclusion 1. Infancy access to UHC strongly reduces infant mortality (-17%) 2. Does it have a long-run impact on health and mortality 50-60 years after exposure? 3. Yes, evidence of mortality reduction (and, using Biobank data, reduction in the onset of cardiovascular disease) • ...among individuals with low or no access to medical services prior to the NHS. • ...and larger reductions among lower SES individuals in areas with more medical services per person. However, evidence of adverse eect for those who would have had access to healthcare without the NHS • Survival gains for former group larger than mortality increases of latter 26 /36
Implications for public policy • Access to universal healthcare in infancy yields benets across almost the entire lifetime into older ages • benets of early childhood interventions can be underestimated • informative for recent universal healthcare programmes (UN) • But.... • introducing a UHC system without accompanying investments in healthcare infrastructure increases competition among patients • This can lead to adverse eects (through access to fewer medical services in infancy) for those who had access under the previous system. 27 /36
Conclusions • childhood environments matter... • ...and their long-run effects are a productive field of research: 1. ample evidence that timing of redistributive interventions matters 2. health research benefits in particular from increasingly available administrative data 3. Europe’s welfare systems developed early 4. open questions around health capital accumulation (and its interaction with other forms of human capital) 5. emerging knowledge into long term effects ...and wether they can be predicted using indicators in early and middle childhood 6. mechanisms and life cycle pathway of impacts: what happens in the “missing middle” years? 7. literature has mostly focused on shocks - shift towards public policies (positive environment changes) that may help reduce early life inequalities c Royal Holloway
Mortality and income back c Royal Holloway
You can also read