Who Benefits from Universal Child Care? Estimating Marginal Returns to Early Child Care Attendance

Cornelißen, Thomas; Dustmann, Christian; Raute, Anna; Schönberg, Uta

doi:10.1086/699979

Cited by 245 publications

(206 citation statements)

References 77 publications

(93 reference statements)

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“…Felfe and Lalive () and Cornelissen et al . () for Germany; Gormley and Gayer () for the US state of Oklahoma; Fitzpatrick () for the US state of Georgia). There are few studies in Europe about the effect of universal childcare policies on recent cohorts of children, Cornelissen et al .…”

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confidence: 99%

Universal Pre‐school Education: The Case of Public Funding with Private Provision

Blanden

Bono

McNally

et al. 2016

The Economic Journal

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This article studies the effect of free pre-school education on child outcomes in primary school. We exploit the staggered implementation of free part-time pre-school for three year olds across Local Education Authorities in England in the early 2000s. The policy led to small improvements in attainment at age 5, with no apparent benefits by age 11. We argue that this is because the expansion of free places largely crowded out privately paid care, with small changes in total participation, and was achieved through an increase in private provision, where quality is lower on average than in the public sector.Over the last several decades, many governments around the world have sponsored the expansion of early education and childcare. There is a dual rationale behind these policies: to support maternal employment and to promote child development, particularly that of disadvantaged children. This article focuses on the UK, specifically England, 1 a relatively late adopter of universal early education compared to other European nations. The social and economic context prevailing in the UK in the early 2000s makes the British expansion stand out. First, maternal employment was high and supported by an established childcare market of private and public providers; second, the expansion relied entirely on the private sector to provide additional free places which were funded and regulated by the state.In this article, we evaluate the effect of the dramatic increase in free provision of early education and childcare for three year olds in England on early school outcomes.2 This policy was part of a general shift towards intervention in the early years through both universal and targeted programmes. From 2000, fully subsidised part-time nursery places (hereafter 'free places') were rolled out across England for all three year olds. Despite considerable state funds invested (£2 billion per year, National Audit Office, 2012), this is the first rigorous study attempting to evaluate the effects of this policy. There are studies that offer an evaluation of similar policies in other countries.

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confidence: 99%

Universal Pre‐school Education: The Case of Public Funding with Private Provision

Blanden

Bono

McNally

et al. 2016

The Economic Journal

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“…Consider the following restriction on potential outcomes:

E true2.4ex2.4ex[Y_{i} false(d false) false| U_{i} = u true2.4ex2.4ex] = α_{d} + \sum_{ℓ = 1}^{L} γ_{d ℓ} \times {true2.4ex2.4ex(J false(u false) - μ_{J} true2.4ex2.4ex)}^{ℓ}, d \in false{0, 1 false}, u \in false(0, 1 false) .

Polynomial models of this sort have been considered by, among others, Brinch, Mogstad, and Wiswall () and Cornelissen, Dustmann, Raute, and Schönberg (). Letting

λ_{1 ℓ} false(p false) = E false[{false(J false(U_{i} false) - μ_{J} false)}^{ℓ} false| U_{i} \leq p false]

and

λ_{0 ℓ} false(p false) = E false[{false(J false(U_{i} false) - μ_{J} false)}^{ℓ} false| U_{i} > p false]

, a two‐step control function estimator of the parameters of equation () is

false({\overset{α}{true}}_{d}, {\overset{γ}{true}}_{d 1}, \dots, {\overset{γ}{true}}_{d L} false) = normalarg \underset{α_{d}, γ_{d 1}, \dots, γ_{d L}}{normalmin} \sum_{i} 1 false{D_{i} = d false} true6.6ex6.6ex[Y_{i} - α_{d} - \sum ℓ

…”

Section: Multi‐valued Instrumentsmentioning

confidence: 99%

“…Polynomial models of this sort have been considered by, among others, Brinch, Mogstad, and Wiswall (2017) and Cornelissen, Dustmann, Raute, and Schönberg (2018). Letting…”

Section: Multi-valued Instrumentsmentioning

confidence: 99%

“…A standard approach to introducing covariates is to enter them additively into the potential outcomes model (see, e.g., Cornelissen, Dustmann, Raute, and Schönberg (2016), Kline and Walters (2016), and Brinch, Mogstad, and Wiswall (2017)). Suppose treatment choice is given by D i = 1{P(X i Z i ) ≥ U i } with U i independent of (X i Z i ), and assume…”

Section: Covariatesmentioning

confidence: 99%

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On Heckits, LATE, and Numerical Equivalence

Kline¹,

Walters²

2019

ECTA

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Structural econometric methods are often criticized for being sensitive to functional form assumptions. We study parametric estimators of the local average treatment effect (LATE) derived from a widely used class of latent threshold crossing models and show they yield LATE estimates algebraically equivalent to the instrumental variables (IV) estimator. Our leading example is Heckman's (1979) two‐step (“Heckit”) control function estimator which, with two‐sided non‐compliance, can be used to compute estimates of a variety of causal parameters. Equivalence with IV is established for a semiparametric family of control function estimators and shown to hold at interior solutions for a class of maximum likelihood estimators. Our results suggest differences between structural and IV estimates often stem from disagreements about the target parameter rather than from functional form assumptions per se. In cases where equivalence fails, reporting structural estimates of LATE alongside IV provides a simple means of assessing the credibility of structural extrapolation exercises.

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“…Partial identification of the M T E OO of interest is useful for two reasons. First and most importantly, bounds for the M T E OO can be used to shed light on the heterogeneity of treatment effects, allowing the researcher to understand who would benefit and who would lose from a specific treatment, as recently illustrated by Cornelissen et al (2018) and Bhuller et al (2019). This knowledge can be used to optimally design policies that incentivize to agents to take a treatment.…”

Section: Introductionmentioning

confidence: 99%

Sharp Bounds on the MTE With Sample Selection

Possebom

2019

SSRN Journal

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I analyze treatment effects in situations when agents endogenously select into the treatment group and into the observed sample. As a theoretical contribution, I propose pointwise sharp bounds for the marginal treatment effect (MTE) of interest within the always-observed subpopulation under monotonicity assumptions. Moreover, I impose an extra mean dominance assumption to tighten the previous bounds. I further discuss how to identify those bounds when the support of the propensity score is either continuous or discrete. Using these results, I estimate bounds for the MTE of the Job Corps Training Program on hourly wages for the always-employed subpopulation and find that it is decreasing in the likelihood of attending the program within the Non-Hispanic group. For example, the Average Treatment Effect on the Treated is between $.33 and $.99 while the Average Treatment Effect on the Untreated is between $.71 and $3.00.

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Who Benefits from Universal Child Care? Estimating Marginal Returns to Early Child Care Attendance

Cited by 245 publications

References 77 publications

Universal Pre‐school Education: The Case of Public Funding with Private Provision

Universal Pre‐school Education: The Case of Public Funding with Private Provision

On Heckits, LATE, and Numerical Equivalence

Sharp Bounds on the MTE With Sample Selection

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