Pith. sign in

REVIEW 3 major objections 5 minor 41 references

Medicaid doula coverage cuts low birth weight for Black mothers by about half a percentage point where it has had time to work, while the average effect across all mothers is zero.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

T0 review · grok-4.5

2026-07-10 18:33 UTC pith:UTTO7RPC

load-bearing objection Solid early DiD on Medicaid doula mandates: average null is clean, Black early-cohort drop is coherent but rests on five selected states and is only marginal under proper few-cluster inference. the 3 major comments →

arxiv 2607.07770 v1 pith:UTTO7RPC submitted 2026-07-08 econ.GN q-fin.EC

Helping Hands, Healthier Infants: The Effect of Medicaid Doula Coverage Mandates on Birth Outcomes

classification econ.GN q-fin.EC
keywords doula careMedicaidlow birth weightracial disparities in infant healthdifference-in-differencesmaternal health policy
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

U.S. states have been adding Medicaid coverage for doulas—non-clinical birth companions—hoping to improve infant health and shrink the large Black–white gap in low birth weight. This paper evaluates those mandates with the staggered 2021–2024 rollout, 32 million births, and a new measure of the state doula workforce, identifying from policy timing rather than from which mothers choose a doula. On average there is no detectable change in low birth weight. The effect concentrates among Black mothers in the earliest-adopting states, where the rate falls by roughly half a percentage point (about 5 percent of baseline), pre-trends are flat, the gain grows with years of exposure, and mass shifts out of the low-weight range into the normal range. Coverage roughly doubles registered doula supply, and that induced supply is associated with lower Black low birth weight. The author presents the findings as early, directionally clear evidence that the policy works where it has had time to operate and where need is greatest, not as a finished causal claim, because most mandates are still too recent for the data.

Core claim

Where Medicaid doula coverage has operated longest, low birth weight among Black mothers falls by about 0.52 percentage points on a roughly 10 percent base, with flat pre-trends, an effect that grows with exposure, and a coherent upward shift across the birth-weight distribution; the average effect for all mothers is a precise zero, and few-cluster inference places the Black result near conventional two-sided significance.

What carries the argument

Staggered difference-in-differences on state Medicaid doula coverage timing (two-way fixed effects and Callaway–Sant’Anna group-time estimates), with coverage used as an instrument for registered doula supply per 10,000 births.

Load-bearing premise

Without the mandates, low-birth-weight paths for Black mothers in the five earliest-adopting states would have continued to track those in never-treated states.

What would settle it

When 2025–2026 natality data add meaningful post-period years for the large 2024–2025 adopter wave, the Black-mother low-birth-weight decline in longer-exposed states should remain or strengthen; if it vanishes or pre-trends reappear under the same design, the central claim fails.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper evaluates staggered Medicaid doula coverage mandates (2021–2024) using CDC WONDER natality (32.1 million births, 2016–2024) and a newly constructed NPPES-based state–year doula workforce measure. Identification comes from policy timing in a DiD design (TWFE and Callaway–Sant’Anna), not from comparing doula users to non-users. The average effect on low birth weight is a precise zero. The main claim is heterogeneity: among Black mothers in the five earliest-adopting states with meaningful post exposure (NJ, MD, NV, RI, VA), LBW falls by about 0.52 pp (~5% of baseline), with flat pre-trends, an effect that grows with exposure, and a coherent upward shift across birth-weight bins. Valid few-cluster inference (wild cluster bootstrap, randomization inference) places the two-sided p near 0.10. Coverage roughly doubles registered doula supply (first-stage F ≈ 21–35); 2SLS links the induced supply increase to lower Black LBW, imprecisely. The author frames the result as credible early evidence constrained by power, not a finished causal claim.

Significance. If the Black early-cohort result holds, the paper supplies the first policy-timing quasi-experimental evaluation of statewide Medicaid doula mandates and shows that benefits concentrate where baseline risk is highest—consistent with Peet et al. (2022), Sonchak (2015), and the midwifery-workforce literature. That contribution is policy-relevant given rapid mandate adoption and persistent Black–white LBW gaps. Strengths include transparent use of Callaway–Sant’Anna, event studies, wild-cluster bootstrap and randomization inference for few treated clusters, leave-one-state-out and gestational-lag checks, bin-by-bin distributional estimates, and a first-stage workforce measure with F well above weak-instrument thresholds. The average null is precisely estimated and honestly reported. The binding limit is statistical power and the small number of early-treated states, which the paper itself emphasizes.

major comments (3)
  1. [§5.2–5.3, Tables 3–4, Figure 7] §5.2–5.3, Table 3 (Black early-cohort ATT = −0.52), Table 4, and Figure 7 (left): the sole non-null load-bearing claim rests on five positively selected early adopters. Valid few-cluster inference already yields two-sided p ≈ 0.10. Given SES/Medicaid selection (Table 2) and the geographic mismatch with high-burden states (Figures 2–3), the parallel-trends assumption for Black LBW cannot be secured against coincident state maternal-health policies. The paper should report an honest-DiD / partial-identification sensitivity analysis (Rambachan–Roth style, already flagged in §6) for the Black early-cohort path, and/or a continuous intensity measure (e.g., reimbursement generosity), so that the claim is not left resting only on a flat pre-trend p = 0.48 and leave-one-out stability.
  2. [§4.3, §5.4, Table 5, §6] §4.3 Eq. (2), §5.4 Table 5, and §6: the 2SLS exclusion restriction—that mandates affect Black LBW only through NPPES doula supply—is untested and is the same identifying assumption as the reduced form. The paper correctly notes that NPPES may capture registration for billing rather than new entry and that other concurrent policies are not absorbed by state and year FE. Present the 2SLS strictly as a mechanism check (as the text already leans toward doing) and avoid language that treats the IV coefficient as an independent causal estimate of supply on LBW until registration vs. entry can be separated or competing channels are more tightly bounded.
  3. [§3.1–3.2, §5.2] §3.1–3.2 and §5.2: the early-cohort definition (five states with at least two post-period years) and the mid-year treatment coding rule are free parameters that select the sample in which the Black result appears. The all-cohort CS average for Black mothers is −0.23 pp and insignificant. Report a pre-specified or systematically varied set of cohort/exposure cutoffs (and the corresponding few-cluster inference) so that the −0.52 pp figure is not an artifact of the two-post-year threshold, and clarify how many post years each of the five states actually contributes.
minor comments (5)
  1. [Abstract, §1, References] Abstract and §1 cite “Peet (2022)” / “Peet et al. (2022)” for the heterogeneity logic; the reference list has Peet et al. (2022) on WIC and Peet et al. (2024) on doulas. Align in-text citations with the intended paper.
  2. [Figures 5, 7] Figure 5 vs. Figure 7: the main text mixes TWFE and Callaway–Sant’Anna event studies for Black mothers; state clearly in each caption which estimator is plotted and keep the primary dynamic figure CS throughout §5.2.
  3. [Table 1, §3.2] Table 1 notes CDC cell suppression; for Black LBW in small states this can induce composition changes when aggregating. A short robustness note dropping suppressed cells or small states would help.
  4. [Appendix Figure 12, §6] Appendix Figure 12 (left) shows flat prenatal visits, gestation, and smoking for Black mothers—presented as a puzzle for the mechanism. A sentence in §5 or §6 on why LBW moves while these channels do not would tighten interpretation.
  5. [Tables, References] Minor typos and formatting: “T able” spacing in table titles; “Cl ement” in de Chaisemartin reference; ensure arXiv and journal citation keys are consistent.

Circularity Check

0 steps flagged

No circularity: DiD/CS estimates and 2SLS first stage are identified from external policy timing and public data, not from quantities defined in terms of the LBW outcome.

full rationale

The paper’s load-bearing claims are reduced-form DiD/Callaway–Sant’Anna ATTs of Medicaid doula coverage on LBW (and subgroup LBW) and a first-stage effect of coverage on NPPES doula supply. Identification is the staggered timing of state mandates (hand-coded effective dates) against CDC WONDER birth aggregates and NPPES registry stocks; none of these objects is defined from the LBW series. Parallel-trends checks, wild-cluster bootstrap / randomization inference, leave-one-state-out, and the distributional bin shifts are diagnostics on the same external variation, not re-labelings of fitted parameters as predictions. Citations to Peet, Sonchak, Anderson, Bohren, etc. are used only for motivation and heterogeneity priors; they do not supply a uniqueness theorem or ansatz that forces the ATT. The 2SLS exclusion restriction is an untested assumption (correctness risk), not a definitional loop. Score 0 is therefore the honest finding.

Axiom & Free-Parameter Ledger

2 free parameters · 5 axioms · 0 invented entities

The central claim is an empirical DiD/IV estimate, not a derivation from axioms. Load-bearing premises are standard econometric identifying assumptions plus measurement choices for treatment timing and doula supply. No free parameters are fitted to produce the ATT; no new physical or theoretical entities are postulated. The ledger therefore lists the domain assumptions that, if false, would invalidate the causal reading.

free parameters (2)
  • Mid-year treatment coding rule = coverage effective on or before mid-year
    A state is coded treated in the first year coverage is effective on or before mid-year; this discrete timing choice affects which births count as exposed and is not estimated from data but chosen by the analyst.
  • Early-cohort definition (at least two post-period years) = NJ, MD, NV, RI, VA
    The headline Black-mother ATT is restricted to the five states with ≥2 post years; this sample cut is analyst-chosen and concentrates the result.
axioms (5)
  • domain assumption Parallel trends: absent coverage, treated and control states’ LBW paths (especially Black LBW) would have evolved in parallel.
    Stated in Section 4.2 as the core identifying assumption; supported by flat pre-trends for Black mothers only.
  • domain assumption No anticipatory or other coincident state-specific policies that affect Black LBW at the same time as doula adoption.
    Required for both reduced-form DiD and the IV exclusion restriction; discussed as untestable in Section 6.
  • domain assumption Coverage affects Black LBW only through measured doula supply (exclusion restriction for 2SLS).
    Equation (2) and Section 6; author flags that NPPES registration and other maternal-health initiatives could violate it.
  • standard math Staggered DiD estimators (TWFE and Callaway–Sant’Anna with not-yet-treated controls) recover the ATT under the stated assumptions.
    Standard modern DiD theory invoked in Section 4.1; not re-derived.
  • ad hoc to paper NPPES doula taxonomy stock is a valid (if noisy) measure of policy-relevant doula supply.
    Newly assembled measure; author notes it captures registration more than new entry or Medicaid-billed services (Section 3.4, 6).

pith-pipeline@v1.1.0-grok45 · 17334 in / 3527 out tokens · 70529 ms · 2026-07-10T18:33:15.456816+00:00 · methodology

0 comments
read the original abstract

Over the last decade a wave of U.S. states began reimbursing doula services through Medicaid, hoping to improve infant health and narrow stark racial gaps in birth outcomes. I evaluate these mandates using the staggered 2021-2024 rollout, a panel of 32.1 million births from CDC WONDER (2016-2024), and a newly assembled measure of the state doula workforce drawn from the national provider registry. Identification comes from the policy's timing rather than from comparing doula users to non-users, addressing the selection problem that limits the existing observational literature. On average I find no detectable effect on low birth weight (LBW). Consistent with the heterogeneity emphasized by Peet (2022) and the maternal-health-disparities literature, however, the effect concentrates among the group at greatest risk: Black mothers, for whom LBW falls by roughly half a percentage point (about 5% of the baseline) in the states with the longest exposure, with flat pre-trends and a coherent upward shift in the birth-weight distribution. The estimate is marginal once I use inference valid for few treated clusters, and the binding constraint is statistical power: most mandates took effect in 2024-2025, at or beyond the end of the data. A two-stage least squares analysis shows that coverage roughly doubles the doula workforce (first-stage F approximately 21-35), and that the induced increase in doula supply is associated with lower Black LBW, though imprecisely. I read the results as credible early evidence that doula mandates work where they have had time to operate and where the need is greatest, rather than as a finished causal claim.

Figures

Figures reproduced from arXiv: 2607.07770 by Farhad V. Farahani.

Figure 1
Figure 1. Figure 1: The Black–white low-birth-weight gap is large and widening, 2016–2024. Top: LBW rates by maternal race, with the gap shaded. Bottom: the gap in percentage points. This is the disparity the policy targets and the motivation for the paper’s focus on Black mothers. 2 Related Literature Do doulas work? The clinical foundation is the Cochrane review of Bohren et al. (2017), pooling 27 randomized trials of rough… view at source ↗
Figure 2
Figure 2. Figure 2: Left: treated (adopted within the 2016–2024 window) versus control states; Minnesota and Oregon (pre-2016 adopters) are dropped. Right: timing of adoption, lighter (earlier) to darker (more recent). Coverage is recent and back-loaded. 3.2 Birth data and outcomes The outcome data are the universe of U.S. births from CDC WONDER Natality, 2016– 2024, tabulated by state, year, maternal race, maternal education… view at source ↗
Figure 3
Figure 3. Figure 3: Left: Black–white LBW gap by state (grey = too few Black births for a reliable estimate). Right: overall LBW rate by state. Need is greatest in the Deep South—largely untreated. 6 [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Doula supply (births-weighted doulas per 10,000 births) by year, treated versus never￾treated states. The series diverge after coverage adoption begins in 2021. 4 Empirical Strategy 4.1 Difference-in-differences Let Yst denote a birth outcome (e.g. the LBW rate) in state s and year t. The baseline TWFE specification is Yst = β Dst + αs + λt + εst, (1) where Dst indicates that state s has active coverage in… view at source ↗
Figure 5
Figure 5. Figure 5: Event-study estimates relative to the year before adoption (95% CI). For all mothers (left) the path is flat throughout. For Black mothers (right) the pre-period is flat—supporting parallel trends—and the outcome declines after adoption. 9 [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Two-way fixed-effects DiD effects on LBW by subgroup (early-cohort ATT, 95% CI). Red points are undermined by a significant pre-trend; blue points have flat pre-trends. Black mothers are the one credible negative estimate. The corresponding Callaway–Sant’Anna event￾study graphs for every subgroup are in Appendix [PITH_FULL_IMAGE:figures/full_fig_p011_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Callaway–Sant’Anna estimates for Black mothers. Left: dynamic effect on LBW—flat pre-trends, growing with exposure. Right: effect across the birth-weight distribution—mass shifts out of the low-weight bins and into the normal range. A distributional view sharpens the interpretation. Rather than relying on the single 2,500g threshold, [PITH_FULL_IMAGE:figures/full_fig_p013_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Randomization inference: the observed Black-mother estimate (red line) lies in the left tail of 5,000 placebo estimates from random treatment assignments. 5.4 Mechanism: coverage expands the doula workforce Finally, the mechanism. The first stage is strong and clean: coverage raises the doula workforce by about 15 doulas per 10,000 births ( [PITH_FULL_IMAGE:figures/full_fig_p014_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Mechanism. Left: first stage—coverage expands the doula workforce. Right: OLS vs. 2SLS—instrumenting doula supply with coverage reveals a larger (marginal) negative effect on Black LBW [PITH_FULL_IMAGE:figures/full_fig_p015_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Left: LBW and VLBW by maternal race (pooled). Right: the socioeconomic gradient in LBW by maternal education [PITH_FULL_IMAGE:figures/full_fig_p022_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Left: staggered adoption (state × year). Right: LBW trends, treated vs. never-treated, all mothers and Black mothers. 21 [PITH_FULL_IMAGE:figures/full_fig_p022_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Left: effects across candidate channels for Black mothers (only LBW moves). Right: leave-one-state-out robustness of the Black-mother estimate [PITH_FULL_IMAGE:figures/full_fig_p023_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: TWFE estimates across the birth-weight distribution for Black mothers (early cohort), shown for comparison with the Callaway–Sant’Anna version in [PITH_FULL_IMAGE:figures/full_fig_p023_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Callaway–Sant’Anna event-study estimates on low birth weight for every subgroup (dynamic ATT relative to adoption, 95% CI; all treated cohorts vs. never-treated). The pre-period (left of the dashed line) tests parallel trends: it is flat for Black mothers, whereas other subgroups show pre-adoption movement, consistent with the pre-trend tests in [PITH_FULL_IMAGE:figures/full_fig_p024_14.png] view at source ↗

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

41 extracted references · 41 canonical work pages

  1. [1]

    Cochrane Database of Systematic Reviews , year=

    Bohren, Meghan A and Hofmeyr, G Justus and Sakala, Carol and Fukuzawa, Rieko K and Cuthbert, Anna , title=. Cochrane Database of Systematic Reviews , year=

  2. [2]

    American Journal of Public Health , year=

    Kozhimannil, Katy B and Hardeman, Rachel R and Attanasio, Laura B and Blauer-Peterson, Cori and O'Brien, Michelle , title=. American Journal of Public Health , year=

  3. [3]

    Birth , year=

    Kozhimannil, Katy B and Hardeman, Rachel R and Alarid-Escudero, Fernando and Vogelsang, Carrie A and Blauer-Peterson, Cori and Howell, Elizabeth A , title=. Birth , year=

  4. [4]

    American Journal of Public Health , year=

    Falconi, April M and Ramirez, Leah and Cobb, Rebecca and Levin, Carrie and Nguyen, Michelle and Inglis, Tiffany , title=. American Journal of Public Health , year=

  5. [5]

    Health Equity , year=

    Mottl-Santiago, Julie and Dukhovny, Dmitry and Cabral, Howard and Rodrigues, Dona and Spencer, Linda and Valle, Eduardo A and Feinberg, Emily , title=. Health Equity , year=

  6. [6]

    Health Economics , year=

    Peet, Evan D and Schultz, Dana and Tsui, Fuchiang , title=. Health Economics , year=

  7. [7]

    Health Economics , year=

    Peet, Evan D and Schultz, Dana and Lovejoy, Susan and Tsui, Fuchiang , title=. Health Economics , year=

  8. [8]

    Journal of Health Economics , year=

    Guldi, Melanie and Hamersma, Sarah , title=. Journal of Health Economics , year=

  9. [9]

    Journal of Health Economics , year=

    Cygan-Rehm, Kamila and Karbownik, Krzysztof , title=. Journal of Health Economics , year=

  10. [10]

    Journal of Health Economics , year=

    Noghanibehambari, Hamid and Fletcher, Jason , title=. Journal of Health Economics , year=

  11. [11]

    Journal of Health Economics , year=

    Sonchak, Lyudmyla , title=. Journal of Health Economics , year=

  12. [12]

    Journal of Political Economy , year=

    Anderson, D Mark and Brown, Ryan and Charles, Kerwin Kofi and Rees, Daniel I , title=. Journal of Political Economy , year=

  13. [13]

    Journal of Econometrics , year=

    Callaway, Brantly and Sant'Anna, Pedro H C , title=. Journal of Econometrics , year=

  14. [14]

    Journal of Econometrics , year=

    Sun, Liyang and Abraham, Sarah , title=. Journal of Econometrics , year=

  15. [15]

    Journal of Econometrics , year=

    Goodman-Bacon, Andrew , title=. Journal of Econometrics , year=

  16. [16]

    Two-way fixed effects estimators with heterogeneous treatment effects , journal=

    de Chaisemartin, Cl. Two-way fixed effects estimators with heterogeneous treatment effects , journal=. 2020 , volume=

  17. [17]

    Review of Economics and Statistics , year=

    Cameron, A Colin and Gelbach, Jonah B and Miller, Douglas L , title=. Review of Economics and Statistics , year=

  18. [18]

    Journal of Applied Econometrics , year=

    MacKinnon, James G and Webb, Matthew D , title=. Journal of Applied Econometrics , year=

  19. [19]

    Review of Economic Studies , year=

    Rambachan, Ashesh and Roth, Jonathan , title=. Review of Economic Studies , year=

  20. [20]

    The Econometrics Journal , year=

    Chernozhukov, Victor and Chetverikov, Denis and Demirer, Mert and Duflo, Esther and Hansen, Christian and Newey, Whitney and Robins, James , title=. The Econometrics Journal , year=

  21. [21]

    Science , year=

    Aizer, Anna and Currie, Janet , title=. Science , year=

  22. [22]

    The intergenerational transmission of inequality: Maternal disadvantage and health at birth

    Anna Aizer and Janet Currie. The intergenerational transmission of inequality: Maternal disadvantage and health at birth. Science, 344 0 (6186): 0 856--861, 2014. doi:10.1126/science.1251872

  23. [23]

    Occupational licensing and maternal health: Evidence from early midwifery laws

    D Mark Anderson, Ryan Brown, Kerwin Kofi Charles, and Daniel I Rees. Occupational licensing and maternal health: Evidence from early midwifery laws. Journal of Political Economy, 128 0 (11): 0 4337--4383, 2020. doi:10.1086/710555

  24. [24]

    Continuous support for women during childbirth

    Meghan A Bohren, G Justus Hofmeyr, Carol Sakala, Rieko K Fukuzawa, and Anna Cuthbert. Continuous support for women during childbirth. Cochrane Database of Systematic Reviews, 0 (7): 0 CD003766, 2017. doi:10.1002/14651858.CD003766.pub6

  25. [25]

    Difference-in-differences with multiple time periods

    Brantly Callaway and Pedro H C Sant'Anna. Difference-in-differences with multiple time periods. Journal of Econometrics, 225 0 (2): 0 200--230, 2021. doi:10.1016/j.jeconom.2020.12.001

  26. [26]

    Bootstrap-based improvements for inference with clustered errors

    A Colin Cameron, Jonah B Gelbach, and Douglas L Miller. Bootstrap-based improvements for inference with clustered errors. Review of Economics and Statistics, 90 0 (3): 0 414--427, 2008. doi:10.1162/rest.90.3.414

  27. [27]

    The Econometrics Journal , author =

    Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey, and James Robins. Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21 0 (1): 0 C1--C68, 2018. doi:10.1111/ectj.12097

  28. [28]

    The effects of incentivizing early prenatal care on infant health

    Kamila Cygan-Rehm and Krzysztof Karbownik. The effects of incentivizing early prenatal care on infant health. Journal of Health Economics, 83: 0 102612, 2022. doi:10.1016/j.jhealeco.2022.102612

  29. [29]

    Two-way fixed effects estimators with heterogeneous treatment effects

    Cl \'e ment de Chaisemartin and Xavier D'Haultf uille. Two-way fixed effects estimators with heterogeneous treatment effects. American Economic Review, 110 0 (9): 0 2964--2996, 2020. doi:10.1257/aer.20181169

  30. [30]

    Role of doulas in improving maternal health and health equity among Medicaid enrollees, 2014--2023

    April M Falconi, Leah Ramirez, Rebecca Cobb, Carrie Levin, Michelle Nguyen, and Tiffany Inglis. Role of doulas in improving maternal health and health equity among Medicaid enrollees, 2014--2023. American Journal of Public Health, 114 0 (11): 0 1275--1285, 2024. doi:10.2105/AJPH.2024.307805

  31. [31]

    Difference-in-differences with variation in treatment timing

    Andrew Goodman-Bacon. Difference-in-differences with variation in treatment timing. Journal of Econometrics, 225 0 (2): 0 254--277, 2021. doi:10.1016/j.jeconom.2021.03.014

  32. [32]

    The effects of pregnancy-related Medicaid expansions on maternal, infant, and child health

    Melanie Guldi and Sarah Hamersma. The effects of pregnancy-related Medicaid expansions on maternal, infant, and child health. Journal of Health Economics, 87: 0 102695, 2023. doi:10.1016/j.jhealeco.2022.102695

  33. [33]

    Doula care, birth outcomes, and costs among Medicaid beneficiaries

    Katy B Kozhimannil, Rachel R Hardeman, Laura B Attanasio, Cori Blauer-Peterson, and Michelle O'Brien. Doula care, birth outcomes, and costs among Medicaid beneficiaries. American Journal of Public Health, 103 0 (4): 0 e113--e121, 2013. doi:10.2105/AJPH.2012.301201

  34. [34]

    Modeling the cost-effectiveness of doula care associated with reductions in preterm birth and cesarean delivery

    Katy B Kozhimannil, Rachel R Hardeman, Fernando Alarid-Escudero, Carrie A Vogelsang, Cori Blauer-Peterson, and Elizabeth A Howell. Modeling the cost-effectiveness of doula care associated with reductions in preterm birth and cesarean delivery. Birth, 43 0 (1): 0 20--27, 2016. doi:10.1111/birt.12218

  35. [35]

    Wild bootstrap inference for wildly different cluster sizes

    James G MacKinnon and Matthew D Webb. Wild bootstrap inference for wildly different cluster sizes. Journal of Applied Econometrics, 32 0 (2): 0 233--254, 2017. doi:10.1002/jae.2508

  36. [36]

    Effectiveness of an enhanced community doula intervention in a safety net setting: A randomized controlled trial

    Julie Mottl-Santiago, Dmitry Dukhovny, Howard Cabral, Dona Rodrigues, Linda Spencer, Eduardo A Valle, and Emily Feinberg. Effectiveness of an enhanced community doula intervention in a safety net setting: A randomized controlled trial. Health Equity, 7 0 (1): 0 466--476, 2023. doi:10.1089/heq.2022.0200

  37. [37]

    Long-term health benefits of occupational licensing: Evidence from midwifery laws

    Hamid Noghanibehambari and Jason Fletcher. Long-term health benefits of occupational licensing: Evidence from midwifery laws. Journal of Health Economics, 92: 0 102807, 2023. doi:10.1016/j.jhealeco.2023.102807

  38. [38]

    Variation in the infant health effects of the women, infants, and children program by predicted risk using novel machine learning methods

    Evan D Peet, Dana Schultz, Susan Lovejoy, and Fuchiang Tsui. Variation in the infant health effects of the women, infants, and children program by predicted risk using novel machine learning methods. Health Economics, 31 0 (12): 0 2604--2615, 2022. doi:10.1002/hec.4617

  39. [39]

    The infant health effects of doulas: Leveraging big data and machine learning to inform cost-effective targeting

    Evan D Peet, Dana Schultz, and Fuchiang Tsui. The infant health effects of doulas: Leveraging big data and machine learning to inform cost-effective targeting. Health Economics, 33 0 (6): 0 1387--1411, 2024. doi:10.1002/hec.4821

  40. [40]

    A more credible approach to parallel trends

    Ashesh Rambachan and Jonathan Roth. A more credible approach to parallel trends. Review of Economic Studies, 90 0 (5): 0 2555--2591, 2023. doi:10.1093/restud/rdad018

  41. [41]

    Medicaid reimbursement, prenatal care and infant health

    Lyudmyla Sonchak. Medicaid reimbursement, prenatal care and infant health. Journal of Health Economics, 44: 0 10--24, 2015. doi:10.1016/j.jhealeco.2015.08.008