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 lifeenvironments on well-being of infants’ and children’s survival andchildhood outcomes, and into adulthood (see Almond et al. 2019for a survey)→ large returns to early investments to improve childhoodenvironments

• pareto-improvement through early targeting of redistributiveinvestments?

c©Royal Holloway

The “Heckman curve”

c©Royal Holloway

Early life interventions

• A large fraction of research focused on the role of educationinterventions and on cognitive outcomes→ Head Start, Carolina Abecedarian Project, Perry preschoolprograms,... (see Heckman and many others)

• in parallel, a large literature on health and nutrition conditions inutero 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 Gruber1996a; 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 cashtransfers)

• pollution/sanitation/weatherc©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 inthe 1970s and 1980s

• those treated then are now around age 30-40, so impacts oncompleted education, earnings and other adult outcomes can beanalysed → this has led to a surge in studies examininglonger-run impacts of such policies

• prior work used small survey data (PSID), often with a limited setof 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 stock2. a choice (enters the utility function)3. produced by the individual

Intuition:

• health is a durable capital stock that yields healthy time asservice 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 = φtHt is consumption of health services (or healthy time) Ht :stock of health at tφt : 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 − δtHt

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 otherconsumables according to the following production functions:

It = It(Mt ,THt ;E )

Zt = Zt(Xt ,Tt ;E )

M,X: endogenous goods inputsTH,T: endogenous time inputsE: 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∑t=0

ptMt + qtXt(1 + r)t

=n∑

t=0

ωtTWt(1 + r)t

+ A0 Budget constraint (1)

p,q: pricesTW: hrs of workω: wageA0 : initial assetsr: interest rate

TWt + THt + Tt + TLt = Ω Time constraint (2)

TL: time lost through illnessΩ : total time

c©Royal Holloway

The Grossman model

Substituting into BC:

n∑t=0

ptMt + qtXt + ωt (THt + Tt + TLt)

(1 + r)t=

n∑t=0

ωtΩ

(1 + r)t+ A0 (3)

Assumptions:

∂TLt∂Ht

< 0 (4)

TLt = Ω− ht (5)

c©Royal Holloway

The Grossman model - equilibrium conditions

πt−1(1 + r)t−1︸ ︷︷ ︸PDV of MHC

=

ωtGt(1+r)t +

(1−δt)ωt+1Gt+1(1+r)t+1

+ ...+ (1−δt)...(1−δn−1)ωnGn(1+r)n

+Uhtλ Gt + ...+ (1− δt) ... (1− δn−1)Uhnλ Gn︸ ︷︷ ︸

PDV of MHB

(6)

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

πt−1 =pt−1

∂It−1/∂Mt−1=

ωt−1∂It−1/∂THt−1

(7)

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(1 + r)t

+Uhtλ︸ ︷︷ ︸

discounted marginal value of of health capital

· Gt︸︷︷︸MP of health capital

(8)

which depends on

• λ: MU of wealth• discounted wage rate (value of a unit increase in market time)• Uht : MU of healthy time ∂U∂ht• Gt : MP of health stock in healthy time production ∂ht∂Ht = −

∂TLt∂Ht

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)

Gt

[ωt +

Uhtλ

(1 + r)t]

= πt−1(r − π̃t−1 + δt) (9)

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 anextended model may also be self-care or own private medicalexpenses) 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 investmentin 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 rentalprice of health goes up (is constant), so health investmentdecreases (remains unchanged)

• yet, health stock depreciates quicker, hence while health stockgoes 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 (higherproductivity), 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 orstochastic future earnings

2. Individual heterogeneity:

• depreciation rates• initial health stocks• productivity in producing health• preference

3. Differential mortality: role of genetics, early (in utero) healthenvironments,...

c©Royal Holloway

https://www.nber.org/papers/w7078.pdf

Possible extensions

4. Health production function:

• Multiple inputs: private vs. public health care, out-of pocketexpenditure, health lifestyle...

• Joint production: there may be joint production of healthy timeand consumption (e.g. vegetable cons., sports,...)

• constant returns to scale in health production: some lack of healthinvestment may be irreversible, marginal productivity of healthinvestment may be decreasing in age...

c©Royal Holloway

Possible extensions

5. Perfect foresight:→ Over (under-) investment into health due to differentinformation set about health risks, and benefits of healthinvestments→ diagnosis process or doctor visits may be informative abouthealth stock and the health production function → learning aboutreturns to health investments

6. Rationality: no role for bounded rationality→ people may be perfectly informed but find it hard to adjusttheir behaviour→ time inconsistency: hyperbolic discounting where futurebenefits 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 careor other health-relevant expenses (cigarettes and unhealthy foods,medical care and health-enhancing consumer goods, diseaseprevention,...)

• 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, vaccinationinformation,...)

• some routine interventions like free prevention, health check offers• behavioural interventions (habit formation, ...)

• Timing may matter in health investments: role for childhoodinterventions

c©Royal Holloway

The Grossman model and early life health interventionsor: 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 healthinvestments (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 ImpactsEducation interventionsPerry preschool 1970 WebsiteHead Start 1965 Garces, Thomas, Currie (2002)

Nutrition & health interventionsFood Stamps (SNAP) 1962-75 Hoynes, Schanzenbach, Almond (2016)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), Wüst (2012)

European health systems and welfare programmes tend to be olderthan those in the US...

c©Royal Holloway

https://highscope.org/perry-preschool-project/https://www.aeaweb.org/articles?id=10.1257/00028280260344560https://pubs.aeaweb.org/doi/pdfplus/10.1257/aer.20130375https://www.journals.uchicago.edu/doi/pdfplus/10.1086/695528https://www.jstor.org/stable/2138939

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 fromstaggered rollout (in US states)

• Example: long run impact of SNAP - a large US welfareprogramme - 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 (afterMedicaid 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-19pandemic

• 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 freeor 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 toFSP in their county of birth to those born earlier (and thereforewithout childhood FSP exposure)

yibc = α+δTc,b +Xibcβ+ηc +λb +γt +θs ·b+ρZc60 ·b+�ibc (10)

whereT : childhood FSP exposure (share of months FS available betweenconception and age 5 in birth county)b: cohortc : 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 determinantsof FSP adoption by including interactions between characteristicsof the county of birth and linear trends in year of birth CB60g · c

• common trend assumption: no competing welfare programs rolledout with similar staggering→ control for county of birth characteristics (community healthcenters, hospitals and hospital beds per capita, and non-FSPgovernment transfers per capita), measured as averages over thefirst five years of life.

c©Royal Holloway

Hoynes et al (2009): impact of childhood safety net onadult outcomes

• examine change in economic resources available in utero andduring 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 (shareof time between conception and age 5 that FSP is available inbirth 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 differentdepending 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 bloodpressure, 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 (> 17million 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 (partiallyalso 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

ycbt = ηc+δs(c)b+γt+Xcbtβ+Zc60bρ+a=17∑

a=−5[a 6=10]

πa·1[b−FSc = a]+�cbt

(11)whereηc : birthcounty FEδs(c)b: birth state x year FEXcbt : cohort-county-year FE (all at birth)Zc60b: 1960 county characteristics x linear birth cohortFSc : year FSP was first available in county ca: age when FSP was first introducedπa: event time coefficients, ranging from 5 years before birth to age17 (age 10 omitted category)

c©Royal Holloway

Bailey et al. (2020) - hypotheses

• If no pre-trends: pi should not be statistically significant fora < −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 + Xcbtβ + Zc60bρ

+ ω11[b − FSc < −1] · (b − FSc)︸ ︷︷ ︸FS pre-conception (pre-trends)

+ ω21[−1 ≤ b − FSc < 6] · (b − FSc)︸ ︷︷ ︸FS in utero & early childhood

+ ω31[7 ≤ b − FSc < 11] · (b − FSc)︸ ︷︷ ︸FS age 6-11

+ ω41[12 < b − FSc ] · (b − FSc)︸ ︷︷ ︸FS age 12-17

+�cbt

(12)

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 receiptmatter?

c©Royal Holloway

Bailey et al. (2020) - magnitude of results

Implies: 5yr + IU exposure → 0.009 SD increase in composite indexsimilar 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 healthcare centers

• treatment: well-child visits include physical examination andinformation 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 ofthe services provided

• exploit the rollout of newly established mother and child healthcare 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 ofthe services provided

• exploit the rollout of newly established mother and child healthcare centers across municipalities over time.

• data: Norwegian registry data, combined with historic data oncenter rollout

• health data: Cohort of Norway (CONOR) data and the NationalHealth 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 oflife 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 infantmortality 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 E�ects of Exposure toUniversal 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 O�ce for National Statistics to use theLongitudinal Study is gratefully acknowledged, as is the help provided bysta� of the Centre for Longitudinal Study Information & User Support(CeLSIUS). CeLSIUS is supported by the ESRC Census of PopulationProgramme (Award Ref: ES/K000365/1). The authors alone areresponsible 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 theendorsement of the ONS in relation to the interpretation or analysis ofthe statistical data. This work uses research datasets which may notexactly reproduce National Statistics aggregates.

2 /36

Motivation

Impact of infancy exposure to universal healthcare on mortality andhealth around ages 50-60

• Intervention:NHS introduction in 1948

• We digitised historical data sources to investigate theimmediate impact of the NHS on infant survival

• For longer-term outcomes we use a RD design enriched withgeographical variation in medical services provision foridenti�cation.

• impacts are estimated using large administrative datasetsrecording death and hospitalisation

3 /36

Related evidence: Medicaid introduction (1960s) andexpansions (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 dependencyCurrie et al. (2008), Brown et al. (2015), Wherry and Meyer(2016)

• Vietnam UHC led to signi�cant increase in utilization of publichealth services among eligible children (Vu 2019; Nguyen andWang, 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 disparitiesin the UK

• July 1948: introduction of universal healthcare via theNational 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 toparticipate 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 toboost resources (no new hospitals, no discontinuous expansion indoctors or nurses)

• hospitals were centralised• doctors became independent contractors• local authorities continued to administer family health services

9 /36

Institutional setting: Distributional changes in servicesutilisation

�There can be little doubt that before the start of the new NationalHealth Service many women [...] were deterred from seekingmedical advice by economic reasons. Now that the �nancial barrierhas been removed, women [...] are able to consult their doctormore often than they did before.� (Logan, 1950, Lancet)

Source: Survey of Sickness, The Wellcome Library.10 /36

Immediate e�ects: Infant mortality data

We use data digitised from Registrar General's Statistical Review ofEngland 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 e�ectsPre-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 e�ects: Infant mortality data

Reduction in infant mortality (17%) is predominantly driven bylarge declines in the neo-natal period...

Source: Registrar General's Annual report 1940-1955, The Wellcome Library. by week

13 /36

Immediate e�ects: Infant mortality data

.. due to prevention of deaths from acute conditions (pneumoniaand diarrhea)..... with lasting e�ects on human capital accumulation, employmentand earnings (Bhalotra and Venkataramani, 2013, 2015)

(a) Diarrhea (b) Pneumonia

Source: Ministry of Health Annual Reports, The Wellcome Library.

14 /36

Immediate e�ects: Infant mortality data

.. and concentrated among individuals of lower socio-economicstatus who prior to the NHS had low or no access to healthcare

Source: Registrar General's Annual report 1940-1955, The Wellcome Library.

.. resulting in a substantial narrowing of the SES infant mortalitygap

15 /36

Robustness of infant mortality results

That the fall in infant mortality is associated with increased accessto medical services via the NHS is consistent with Dykes (1950)

• Case study of a large town in 1946 - �nds strong SES gradientin infant mortality• Higher mortality related to delay in accessing medical care

Examination of other factors in�uencing infant mortality revealedno 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 2015with information on time and cause of death

• approximate 1% sample of the population of England andWales

• data contains rich set of socio-economic characteristics• ...and location at birth

combined with GBHD data on social class composition SES

17 /36

Identi�cation 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 afunction of socio-economic status

birth county FE capturing local economic conditions & healthcareinfrastructure

yicg = α+ βCc + γ1Tc + γ2TcLCic + δLCic + X′icη + 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 signi�cance of TcLCic and Tc coe�cientsp-value 0.0790* 0.0391** 0.1057 0.0509* 0.0244** 0.0347** 0.0262**

Mean mortality rate prior to NHS inception, by social classLC 0.0488 0.0606 0.0730 0.0884 0.1029 0.1209 0.1421HC 0.0306 0.0367 0.0462 0.0558 0.0657 0.0783 0.0899

Mortality reduction in percent (relative to mean), by social classLC -21.56 -22.00 (-17.95) -20.28 -17.20 -14.76 -16.28HC 22.16 24.44 (12.12) (12.49) 15.53 11.94 (9.08)

19 /36

Geographical variation in medical servicesIdenti�cation 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 thefraction of population who could a�ord 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 classcomposition

20 /36

Geographical variation in medical servicesEvidence

Source: The Hospital Surveys, HMSO; GBHD database.

21 /36

Geographical variation in medical servicesEvidence

Source: First General Practice Committee Report.

22 /36

Identi�cation strategy II

We proxy in�ow of new patients through county-level social classcomposition (proportion of insured):

yicg = α+ βCc + γ1Tc + γ2TcLCic

+γ3TcHIGHareag + γ4TcLCicHIGHareag

+γ5LCicHIGHareag + δLCic + ζHIGHareag

+X ′icη + �ic

(2)

HIGHareag : area with a high (upper tertile) proportion ofpreviously insured (→ low in�ow 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 signi�cance (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 classLC in HIGHarea -44.07 -39.83 -38.13 -42.04 -33.89 -32.96 -29.83LC in LOWarea -17.13 -19.60 -14.42 -16.19 -13.93 -11.01 -13.32HC in HIGHarea 0.61 11.92 -6.71 (1.61) 10.13 (9.00) 7.21HC 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 e�ects of patient in�ow on those with previousaccess 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 50-60years 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 servicesprior to the NHS.

• ...and larger reductions among lower SES individuals in areaswith more medical services per person.

However, evidence of adverse e�ect for those who would have hadaccess to healthcare without the NHS

• Survival gains for former group larger than mortality increasesof latter

26 /36

Implications for public policy

• Access to universal healthcare in infancy yields bene�ts acrossalmost the entire lifetime into older ages

• bene�ts 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 amongpatients

• This can lead to adverse e�ects (through access to fewermedical services in infancy) for those who had access under theprevious 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 matters2. health research benefits in particular from increasingly available

administrative data3. Europe’s welfare systems developed early4. 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 andmiddle childhood

6. mechanisms and life cycle pathway of impacts: what happens inthe “missing middle” years?

7. literature has mostly focused on shocks - shift towards publicpolicies (positive environment changes) that may help reduce earlylife inequalities

c©Royal Holloway

Mortality and income back

c©Royal Holloway

Shortpresentationforhealthcourse.pdfImmediate effects: Infant mortalityHealth

Robustness