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The effect of family income on birth weight

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Master project by Genevieve Jeffrey, Yi-Ting Kuo, Laura López and Stella Veazey. Barcelona GSE Master's in Economics of Public Policy

Published in: Economy & Finance
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The effect of family income on birth weight

  1. 1. The effect of family income on birth weight Genevieve Jeffrey, Yi-Ting Kuo, Laura López and Stella Veazey 1
  2. 2. Introduction 2 Outline ● Introduction ● Literature review ● Identification strategies: ○ EITC expansion - difference-in-differences ○ Synthetic control group to examine income variation in Alaska ● Methodology ● Results ● Policy implications and conclusions
  3. 3. 3 1. Introduction ● The question we want to answer: How does income affect children’s health outcomes over the lifetime?
  4. 4. 1. Introduction 4 Since the literature points to birth weight as a key indicator of children’s future health outcomes as well as income and education, we chose to study Treatment During Gestation
  5. 5. 1. Introduction 5 To get a more complete picture, we wanted to estimate the impact on 1. The high impact sample (at the poverty threshold) 2. The whole population: Do the effects persist for the whole distribution?
  6. 6. Low birth weight as an outcome of poverty: ● Determinants of low birth weight (<2,500 grams): prenatal care, smoking, alcohol and drug use, stress, nutrition, maternal weight gain during pregnancy (Cramer 1995) Low birth weight as a cause of potential outcomes in the future of the child: ● Subsequent physical health and cognitive and emotional problems that can persist through childhood and adolescence (Brooks-Gunn & Duncan 1997) 6 2. Literature review
  7. 7. 2. Literature review Policy intervention 7 Early-childhood health (i.e. birth weight) plays an important role in life chances and, ultimately, the reproduction of inequality over generations. Hence, “policies that increase birth weights by reducing poverty among pregnant women may therefore generate long-term benefits.” (Strully et al. 2010, p.535)
  8. 8. 2. Literature review Income and health outcomes 8 Benzeval et al. (2013)distinguish between three main types of pathway potentially linking income to health outcomes: a. Materialist arguments - Investment theory: afford certain goods that affect children’s health outcomes b. Psychosocial mechanisms - Family stress model c. Behavioral factors: unhealthy behaviors, i.e. EITC and smoking Strully et. al. (2010) Same effect for the whole population? Diminishing marginal returns vs. Adler et al. (1994) establish that “the positive relationship between income and health is not limited to the lower end of the income distribution, but persists throughout.”
  9. 9. I. Earned Income Tax Credit Difference-in-Difference 9
  10. 10. The Earned Income Tax Credit (EITC) ● A refundable transfer to lower income working families through the tax system ● One of the most important and impactful mechanisms to fight poverty : Lifts 6 million people out of poverty (Short (2011)) 10 Income, the EITC & Infant Health Referencing a study by Hoynes, Miller and Simon
  11. 11. Main research question: The impact of Income on 1. Birth Weight 2. Incidence of Low Birth Weight Secondary Points of Analysis: 1. Mechanisms through which the intervention affects the outcome: How Mother’s behavior during pregnancy is affected 11 Income, the EITC & Infant Health Referencing a study by Hoynes, Miller and Simon
  12. 12. The Earned Income Tax Credit (EITC) The EITC increases income through: 1. Tax Credit 2. Creating incentives to earn an income Research Design: 1. Takes advantage of OBRA1993 a tax reform of the EITC that results in an exogenous change in income 12 Income, the EITC & Infant Health Referencing a study by Hoynes, Miller and Simon
  13. 13. 13 Income, the EITC & Infant Health Referencing a study by Hoynes, Miller and Simon Tax reform expansions differ by family size 1. No Children : $347 2. 1 Child : $725 3. 2 >= Children:$2160 These are changes in the maximum credit available to each group
  14. 14. 14 Income, the EITC & Infant Health Referencing a study by Hoynes, Miller and Simon Data Variables of Interest: Birth Weight, Gender, Birth Order, State, Birth Month, Age, Race, Mother [Race, Education, Marital Status] Data Set: US Vital Statistics Natality Data [1983-1999] Variables of Interest: Family after-tax income, employment and health insurance Data Set: March Current Population Survey (CPS) with NBER’s TAXSIM to assign income tax and amount of EITC *Assume complete take up rate
  15. 15. 15 Income, the EITC & Infant Health Referencing a study by Hoynes, Miller and Simon Research Design 1. Single Mothers - Account for 75% of EITC credit payments (Bitler, Hoynes, and Kuka 2013) 2. 18 and older - Below 18 are not eligible for the credit 3. Singleton Births- Multiple births have a systematically lower birth weight
  16. 16. 16 Income, the EITC & Infant Health Referencing a study by Hoynes, Miller and Simon Research Design 4. Treatment is determined by number of children prior to current pregnancy 5. Sensitive Development Stage determines EITC schedule assigned Identification Strategies 1. Primary Identification Strategy is parity-by-year 2. Fixed Effects - to control for demographic groups
  17. 17. 17 Income, the EITC & Infant Health Referencing a study by Hoynes, Miller and Simon Methodology Difference in Difference Analysis of the OBRA 1993 EITC Expansion ● Outcomes are observed for 2 groups, the treatment and control, for 2 time periods [before and after the treatment] ● To account for the permanent differences between the groups and time trend biases we estimate: Average gain in treatment - Average gain in control
  18. 18. 18 Income, the EITC & Infant Health Referencing a study by Hoynes, Miller and Simon Methodology The Difference-in-Difference estimate is: 1 = ( YB,2 - YB,1 ) - ( YA,2 - YA,1 )
  19. 19. 19 Income, the EITC & Infant Health Referencing a study by Hoynes, Miller and Simon Methodology First Specification ● First births are used as a control because they receive a considerably smaller childless credit ● Compare Parity2 [Treatment] to first births [Control]
  20. 20. 20 Income, the EITC & Infant Health Referencing a study by Hoynes, Miller and Simon Methodology Second Specification ● Because the two child EITC was expanded considerably more than the one child EITC we ● Include After x Parity2plus [Treatment] and first birth [Control] Third Specification ● Use After x Parity3plus (third and higher births) [Treatment] and second births [Control]
  21. 21. 21 Income, the EITC & Infant Health Referencing a study by Hoynes, Miller and Simon Methodology Estimate Ypjst = α + δAftert x Parity2plusp + βXst + γp +ηs +δt + Φj +εpjst p=parity, j=demographic group (education, age, race), s=state and t=effective tax year Xst controls for unemployment rate, welfare reform and Medicaid Include fixed effects for demographic group Φj , parity γp , state ηs and effective tax year δt
  22. 22. 22 Income, the EITC & Infant Health Referencing a study by Hoynes, Miller and Simon Methodology ● Also we weight by the number of births for a given (state, year, parity and demographic) group. ● Standard errors are clustered by state
  23. 23. 23 Main Outcomes And the High Impact Group High impact group: Single women between the ages of 18-45 with less than a high school education ● Nearly one-third of the women who work in low-wage jobs are mothers of dependent children, and nearly half of them are single mothers. Two main outcomes: for effective tax years 1991 - 1998 The percent low birth (<2500 grams) The percent low Apgar score (<5)
  24. 24. DIFFERENCE-IN-DIFFERENCES ESTIMATES OF OBRA93 ON MAIN OUTCOMES 24
  25. 25. DIFFERENCE-IN-DIFFERENCE ESTIMATES OF OBRA93 ON MAIN OUTCOMES 25
  26. 26. DIFFERENCE-IN-DIFFERENCE ESTIMATES OF OBRA93 ON OTHER OUTCOMES 26
  27. 27. Robustness Checks Assumption made: ● Tax credit is spent throughout the year → calculate the impact based on the gestational period and the sensitive development stage as defined above. Checking Results under varying assumptions: ● Assume that all the EITC tax credit is spent upon receipt in February. If an infant's time of gestation falls within February then that infant is treated. 27
  28. 28. Robustness Checks We estimate the low birth weight using the the maximum EITC credit specification with panel fixed effects presented in Table 6. 28 Difference in Differences estimates of OBRA1993 on Low Birth Weight (High Impact Group) Maximum Credit in February First Trimester Second Trimester Third Trimester -0.191 -0.318** -0.458*** (0.129) (0.134) (0.0953)
  29. 29. Robustness Checks ● We find our initial results to be robust to this sensitivity analysis with little variation, with all three trimesters experiencing a fall in the low birth weight. ● Only the first trimester is not significant but the direction of change is in line with our results. 29
  30. 30. Possible mechanism of Impact Income increases ↓ ● Less stress ● More access to prenatal care ↓ Higher birth weights 30
  31. 31. Conclusion ● EITC OBRA93 expansion had significant impacts on birth weight as well as prenatal behaviours. ● The intermediate outcomes of the expansion could increase average birth weight. ● We would like to know whether exogenous income increases have non-trivial impacts on birth weight for different sub-groups. 31
  32. 32. II. Alaska Permanent Fund Synthetic Control Method 32
  33. 33. Background: Alaska Permanent Fund ● Established in 1976 and required: "at least 25 percent of all mineral lease rentals, royalties, royalty sale proceeds, federal mineral revenue-sharing payments and bonuses received by the state be placed in a permanent fund, the principal of which may only be used for income-producing investments." ● In 1980, enacted legislation to give residents a yearly payout ● Legal issue → 1st payout of $1000 given to all six-month residents in 1982 33
  34. 34. 34 Background: Alaska Permanent Fund
  35. 35. Methodology ● Comparative Case Study ● Synthetic control method developed by Abadie and Gardeazabal (2003) and Abadie, Diamond and Hainsmueller (2010) ○ Creates a counterfactual for “treated” region by weighting a group of regions from a “donor pool” that approximates the same trend in birth weights as the treated state in the pre-intervention period 35
  36. 36. Alaska vs. Rest of US 36 ● Alaska has a much higher mean birth weight and different trends over time than the rest of the US ● The rest of the US would not serve as an adequate control group
  37. 37. it = Yit I - Yit N ● For (i=1,...,J+1) where treated stated is i=1 at time t (t=1,...,T) ● T0 is the number of pre-intervention periods, 1≤T0 <T 37 effect of the policy for unit i at time t outcome observed in at time t in state i if exposed to the intervention in T0+1 through T outcome observed in the absence of the policy in state i at time t
  38. 38. 38 Yit = Yit N + it Dit ○ Dit indicates exposure to the intervention in t > T0 ● To identify ( 1T0+1 ,..., 1T ) → estimate Y1t N for t>T0 ● Assumption: no interference between units ● Anticipation effect
  39. 39. ● Factor model: ● t → unknown common factor with constant factor loadings across states ● Z → (r x 1) vector of observed predictors of birth weight not affected by the policy ● t → (1 x r) vector of unknown parameters ● t → (1 x F) vector of unobserved common factors ● i → (F x 1) vector of unknown factor loadings ● it → unobserved transitory shocks at state level with zero mean ● W is a (J x 1) vector of positive weights W=(w2 ,...,wJ+1 )’ that sum to 1 ○ Each value of W represents a potential synthetic control 39
  40. 40. Methodology ● If a set of weights (w*2,...,w*J+1) exists such that Abadie et al. prove that we can estimate 1t using ● If there is no set of weights such that the system of equations holds exactly, control is selected so it holds approximately 40
  41. 41. Methodology ● Let X1 be a (k x 1) vector of pre-intervention characteristics for Alaska and X0 be a (k x J) matrix for the unaffected states ● The vector of positive weights W* that sum to 1 is chosen to minimize some distance ||X1 -X0 W|| by considering: where V is a positive semidefinite matrix chosen to minimize the mean square prediction error 41
  42. 42. Data ● National Center for Health Statistics’ Vital Statistics Natality data 1970-1990 ○ birth weight, race of child, marital status, age and education of mother ● GDP data from Bureau of Economic Analysis ● Population data from US Census ● Cigarette Consumption from Orzechowski and Walker ● Compiled aggregate averages and proportions by state 42
  43. 43. Results I. Construction of synthetic control group II. Main specification III. Different subgroups IV. Placebo tests 43
  44. 44. Results: Construction of synthetic control group 44
  45. 45. Results: Construction of synthetic control group 45
  46. 46. 46 Results: Construction of synthetic control group
  47. 47. 47 RMSPE = 2.565841 Results: Main specification
  48. 48. 48 Results: High-impact subgroups
  49. 49. 49 Results: Low-impact subgroups
  50. 50. Results: Placebo tests 50 In-time placeboIn-space placebo
  51. 51. Discussion 51 Original Model The onset of the policy and the increase in birth weight coincided with a large increase in the proportion of GDP from oil and gas extraction. We cannot safely attribute the change in BW to the policy.
  52. 52. Discussion 52 Original Model Alaska’s GDP trend may be too unique to match well with a synthetic control group; we cannot separate the effect of the policy from the effect due to changes in the economy
  53. 53. Discussion 53 ● Could the policy have affected birth rates? If so, would babies on the margin bring down the birth weight average? ● We could not perform a synthetic control analysis for the variable birth rates due to data difficulties ● Here, we see Alaska’s birth rate declined after the policy along with a few of the other states in the synthetic group ● Hard to infer the effect on birth rates
  54. 54. Conclusion 54 ● Some evidence that the dividend influenced the average birth weight in Alaska, but overall results are inconclusive ● Cannot rule out that economy is driving the change ● Ideally we would also like to be able to match on welfare and Medicaid participation

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