Local Average Treatment Effect Research Articles

The heterogeneous impact of college education on happiness by gender

AbstractObjectiveThis study examines the heterogeneous impacts of college education on happiness by gender.MethodsTo yield unbiased impact estimates, we take advantage of a natural experiment known as the graduation quota program, which suddenly and massively expanded the opportunities to attend college in the early 1980s in South Korea. Using whether the birth cohorts were exposed to the graduation quota program as an instrumental variable (IV), this study estimates the longer‐term effects of college education on happiness by gender.ResultsThe estimated local average treatment effect by the IV analysis indicates that men who were induced to attend college by the graduation quota program became happier. However, there was no noticeable impact on women's happiness.ConclusionsConsidering that the subjective happiness in our study was measured in their late 30s and 40s, the impact of college on happiness seems to be long‐lasting for men but not for women.

Efficient and Robust Estimation of the Generalized LATE Model

This article studies the estimation of causal parameters in the generalized local average treatment effect (GLATE) model, which expands upon the traditional LATE model to include multivalued treatments. We derive the efficient influence function (EIF) and the semiparametric efficiency bound (SPEB) for two types of causal parameters: the local average structural function (LASF) and the local average structural function for the treated (LASFT). The moment conditions generated by the EIF satisfy two robustness properties: double robustness and Neyman orthogonality. Based on the robust moment conditions, we propose the double/debiased machine learning (DML) estimator for estimating the LASF. The DML estimator is well-suited for high dimensional settings. We also propose null-restricted inference methods that are robust against weak identification issues. As an empirical application of these methods, we examine the potential health outcome across different types of health insurance plans using data from the Oregon Health Insurance Experiment.

Computer Assisted Learning and Academic Performance in Rural Taiwan

The objective of the current study is to examine the impact of an in-school computer-assisted learning (CAL) intervention on the math achievement of rural students in Taiwan, including a marginalized subgroup of rural students called Xinzhumin, and the factors associated with this impact. In order to achieve this, we conducted a cluster randomized controlled trial involving 1,840 fourth- and fifth-grade students at 95 schools in four relatively poor counties and municipalities of Taiwan during the spring semester of 2019. While the Intention-To-Treat (ITT) analysis found that the CAL intervention had no significant impacts on student math achievement, the Local Average Treatment Effect (LATE) analysis revealed significant associations with the math performance of the most active 20% of students in the treatment group. LATE estimates suggest that using CAL for more than 20 minutes per week for ten weeks corresponds to higher math test scores, both in general (0.16 SD–0.22 SD), and for Xinzhumin students specifically (0.3 SD–0.34 SD). Teacher-level characteristics were associated with compliance rates.

The Finite Sample Performance of Instrumental Variable-Based Estimators of the Local Average Treatment Effect When Controlling for Covariates

AbstractThis paper investigates the finite sample performance of a range of parametric, semi-parametric, and non-parametric instrumental variable estimators when controlling for a fixed set of covariates to evaluate the local average treatment effect. Our simulation designs are based on empirical labor market data from the US and vary in several dimensions, including effect heterogeneity, instrument selectivity, instrument strength, outcome distribution, and sample size. Among the estimators and simulations considered, non-parametric estimation based on the random forest (a machine learner controlling for covariates in a data-driven way) performs competitive in terms of the average coverage rates of the (bootstrap-based) 95% confidence intervals, while also being relatively precise. Non-parametric kernel regression as well as certain versions of semi-parametric radius matching on the propensity score, pair matching on the covariates, and inverse probability weighting also have a decent coverage, but are less precise than the random forest-based method. In terms of the average root mean squared error of LATE estimation, kernel regression performs best, closely followed by the random forest method, which has the lowest average absolute bias.

The effects of land direct payment program on farm income and production practices

AbstractMany countries of the world have implemented subsidy programs to maintain a minimum amount of farm production especially for rice. Agricultural support programs usually cause significant fiscal burdens on government budgets. These programs can be generally categorized into decoupled and coupled programs, and one longstanding research interest of previous literature is whether these programs are decoupled or coupled with farm production practices. In Taiwan, a unique land direct payment (LDP) program to rice farms was implemented to ease severe financial burden of the price support program with government purchase. In contrast to production control programs imposing quantity restrictions on farm production, this program is voluntary for rice farms, and participants are paid at a fixed rate per hectare of farmland. This paper examines the impacts of this program on farm income and farm production practices in Taiwan. The causal effect of the program is identified based on the exogenous rollout of the policy across multiple times and areas. We construct a dataset linking nationally representative rice farm surveys over several years and administrative records on program recipients to estimate the local average treatment effect of the program. We find that the effect is 4% of farm income. In addition, program participants rely less on price support programs and produce less rice and use less fertilizer, pesticide, and machinery. Although the LDP reduces rice production, we find that it causes overall government spending on the price support and direct payment programs to increase by 8.7% after the LDP.

Bounding program benefits when participation is misreported

Instrumental variables (IV) are commonly used to estimate treatment effects in case of noncompliance. However, program participation is often misreported in survey data and standard techniques are not sufficient to point identify and consistently estimate the effects of interest. In this paper, we show that the identifiable IV estimand that ignores treatment misclassification is a weighted average of local average treatment effects with weights that can also be negative. This is troublesome because it may fail to deliver a correct causal interpretation, and this is true even if all the weights are non-negative. Therefore, we provide three IV strategies to bound the program benefits when both noncompliance and misreporting are present. We demonstrate the gain of identification power achieved by leveraging multiple exogenous variations when discrete or multiple-discrete IVs are available. At last, we use our new Stata command, ivbounds, to study the benefits of participating in the 401(k) pension plan on savings.

Double robustness for complier parameters and a semi-parametric test for complier characteristics

Summary We propose a semi-parametric test to evaluate (a) whether different instruments induce subpopulations of compliers with the same observable characteristics, on average; and (b) whether compliers have observable characteristics that are the same as the full population, treated subpopulation, or untreated subpopulation, on average. The test is a flexible robustness check for the external validity of instruments. To justify the test, we characterise the doubly robust moment for Abadie’s class of complier parameters, and we analyse a machine learning update to weighting that we call the automatic $\kappa$ weight. We use the test to reinterpret Angrist and Evans' different local average treatment effect estimates obtained using different instrumental variables.

New evidence on the relationship between mental and physical health

Past research has documented that mental health has a causal impact on physical health. This represents a local average treatment effect, but policymakers are primarily interested in mental health changes for the entire population, not just those exposed to an arbitrary exogenous shock. The need for reliable quantitative evidence presents a notable empirical challenge. To fill this research void, we use a recent method proposed by Oster (2019) to assess the reliability of the instrumental-variable estimates. Our findings support the soundness of the instrumental-variable estimates in the present context, meaning that researchers can have a good degree of confidence in using such estimates for policy formulation.

Student Success Coaching Is Best Delivered Early and Often

This research investigates the effects of a success coaching program by examining the timing of the first appointment and the frequency of additional coaching. Using a fuzzy regression discontinuity approach, we provide Local Average Treatment Effects of the program when students make appointments early in the semester. Students with appointments scheduled before discontinuity had an average of two additional meetings compared to students who waited to make their first appointment. We find each coaching session yields an effect of about 0.2–0.3 grade points and approximately 0.8 additional credits earned. This article contributes to understanding of the timing and frequency effects for success coaching and recommends programs aim to have first interactions with students happen before the sixth week of classes and offer four to six appointments per semester for maximum impact.

Inference under covariate-adaptive randomization with imperfect compliance

This paper studies inference in a randomized controlled trial (RCT) with covariate-adaptive randomization (CAR) and imperfect compliance of a binary treatment. In this context, we study inference on the local average treatment effect (LATE), i.e., the average treatment effect conditional on individuals that always comply with the assigned treatment. As in Bugni et al. (2018, 2019), CAR refers to randomization schemes that first stratify according to baseline covariates and then assign treatment status so as to achieve “balance” within each stratum. In contrast to these papers, however, we allow participants of the RCT to endogenously decide to comply or not with the assigned treatment status.We study the properties of an estimator of the LATE derived from a “fully saturated” instrumental variable (IV) linear regression, i.e., a linear regression of the outcome on all indicators for all strata and their interaction with the treatment decision, with the latter instrumented with the treatment assignment. We show that the proposed LATE estimator is asymptotically normal, and we characterize its asymptotic variance in terms of primitives of the problem. We provide consistent estimators of the standard errors and asymptotically exact hypothesis tests. In the special case when the target proportion of units assigned to each treatment does not vary across strata, we can also consider two other estimators of the LATE, including the one based on the “strata fixed effects” IV linear regression, i.e., a linear regression of the outcome on indicators for all strata and the treatment decision, with the latter instrumented with the treatment assignment.Our characterization of the asymptotic variance of the LATE estimators in terms of the primitives of the problem allows us to understand the influence of the parameters of the RCT. We use this to propose strategies to minimize their asymptotic variance in a hypothetical RCT based on data from a pilot study. We illustrate the practical relevance of these results using a simulation study and an empirical application based on Dupas et al. (2018).

Does higher education affect pro-environmental behavior? Evidence from household waste recycling in Greece

We examine the effect of higher education attainment on pro-environmental behavior focusing on household waste recycling. To address the endogeneity of higher education attainment, we exploit a set of reforms that increased opportunities for university studies in Greece, affecting cohorts graduating from high school in year 2000. We leverage the exogenous variation introduced by the school enrollment age cutoff and estimate the local average treatment effect of higher education employing a fuzzy regression discontinuity design, comparing educational attainment and recycling behavior between households that were just, and just not exposed to the reforms. We find little evidence that higher education increases the probability of recycling, and no evidence that the share of household waste recycled responds to higher educational attainment. Our results suggest that human capital accumulation alone may not deliver green behavior returns.

It is never too LATE: a new look at local average treatment effects with or without defiers

Summary In heterogeneous treatment effect models with endogeneity, identification of the local average treatment effect (LATE) typically relies on the availability of an exogenous instrument monotonically related to treatment participation. First, we demonstrate that a strictly weaker local monotonicity condition—invoked for specific potential outcome values rather than globally—identifies the LATEs on compliers and defiers. Second, we show that our identification results apply to subsets of compliers and defiers when imposing an even weaker local compliers-defiers assumption that allows for both types at any potential outcome value. We propose estimators that are potentially more efficient than two-stage least squares (2SLS) in finite samples, even in cases where 2SLS is consistent. Finally, we provide an empirical application to estimating returns to education using the quarter of birth instrument.

The Effect: An Introduction to Research Design and CausalityNickHuntington‐KleinChapman & Hall/CRC, 2022, xiv + 620 pages, $39.95, paperback. ISBN: 9781032125787

International Statistical ReviewEarly View Book Review The Effect: An Introduction to Research Design and Causality , Nick Huntington-Klein Chapman & Hall/CRC, 2022, xiv + 620 pages, $39.95, paperback. ISBN: 9781032125787 Brian W. Sloboda, Corresponding Author Brian W. Sloboda [email protected] Department of Economics and Finance, 1616 McCormick Drive, University of Maryland, Global Campus, Largo, 20774 Maryland, USASearch for more papers by this author Brian W. Sloboda, Corresponding Author Brian W. Sloboda [email protected] Department of Economics and Finance, 1616 McCormick Drive, University of Maryland, Global Campus, Largo, 20774 Maryland, USASearch for more papers by this author First published: 21 June 2023 https://doi.org/10.1111/insr.12547Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL No abstract is available for this article. References Angrist, J. D., and Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press. Angrist, J. D., and Pischke, J. S. (2014). Mastering' Metrics: The Path from Cause to Effect. Princeton: Princeton University Press. Athey, S., and Imbens, G. W. (2017). The State of Applied Econometrics: Causality and Policy Evaluation. Journal of Economic Perspectives, 31(2), 3– 32. Holland, P. W. (1986). Statistics and Causal Inference. Journal of the American Statistical Association, 81(396), 945– 960. Imbens, G. W., and Angrist, J. D. (1994). Identification and estimation of local average treatment effects. Econometrica: Journal of the Econometric Society, 467– 475 62. Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66, 688– 701. Early ViewOnline Version of Record before inclusion in an issue ReferencesRelatedInformation

Social Conflict and Outgroup Sentiment in South Korea: Evidence from the Yemeni Anti-Refugee Campaign

AbstractResearch on attitudes toward immigrants and refugees largely focuses on intergroup conflict and related threats imposed by outgroup members. This study shifts the analytic focus to intragroup conflict: a domestic struggle among natives over how to handle recently arrived refugees and on their perception of foreign workers in general and Muslims in particular. By exploiting an exogenous variation in the interview timing of a nationally representative survey conducted in South Korea, a “new immigration destination,” this study offers a causal estimate (local average treatment effect) of domestic societal conflict on outgroup attitudes. Results from regression discontinuity (RD) analysis show that in its aftermath—immediately following the completion of a controversial e-petition sponsored by the anti-refugee group demanding that the government extradite asylum seekers—the public opinion of Korean adults toward foreign workers and Muslims became more, not less, favorable. Heterogeneous treatment effects are also found across two respondent-level characteristics: cosmopolitan identity and relative deprivation. Specifically, the focal relationship is more pronounced among individuals who identify less with cosmopolitan citizenship and among those who are more relatively deprived.

Safe at Last? Late Effects of a Mass Immunization Campaign on Households' Economic Insecurity

We study the effects of receiving immunization from COVID‐19 on households' economic insecurity. To provide causal estimates we use a fuzzy regression discontinuity design which takes advantage of the UK's immunization plan. The plan was primarily based on age, granting differential eligibility to proximate cohorts. Our estimated local average treatment effect indicates that the share of households who declared being economically insecure dropped by 41 percentage points among those who received the vaccine due to the eligibility criteria. Using a difference‐in‐discontinuity design we next document that immunization was more salient for women as well as for large households and those with children. Our results suggest that the mass immunization campaign against COVID‐19 had relevant short‐run economic effects, well beyond its expected impact on people's health.

Weather information and agricultural outcomes: Evidence from a pilot field experiment in Benin

Weather conditions are an important determinant of agricultural factor input, particularly labor allocation. The availability of weather forecasts can therefore lead to efficiency gains in the form of cost decreases and productivity increases. We test the practical feasibility, the uptake, and the effect of providing basic weather forecasts in the rainy season on the labor productivity of smallholder farmers. For this purpose, we conducted a Randomized Controlled Trial as a pilot with monthly data collections involving 331 farmers across six villages in northern Benin. We find that most farmers subscribe to the intervention and report satisfaction with the service. The impact estimates indicate positive and economically significant intention-to-treat and local average treatment effects in terms of reduced relative labor costs, increased yield and, in turn, increased labor productivity for maize and cotton cultivation. These benefits come at relatively low cost, suggesting that weather-related information via mobile phone outreach holds promise for helping smallholder farmers to better adapt to changing weather.

Age of Starting School, Academic Performance, and the Impact of Non‐Compliance: An Experiment within an Experiment, Evidence from Australia*

This paper estimates the difference in academic performance of the oldest (treated) and youngest (control) students in a given grade. We employ Queensland Department of Education school administration panel data for the population of 2008–2016 state school students. Academic performance is measured using standardised test scores (National Assessments Program in Numeracy and Literacy) and teacher‐assessed measures of performance and effort for individuals in grades 3, 5 and 7. The empirical analysis employs a regression discontinuity design based on birth date relative to administrative rules on age of school enrolment. The class‐assigning mechanism operates via a known cut‐off date and results in the oldest child in the grade being almost a year older than the youngest. However, lack of enrolment compliance implies the regression discontinuity design is fuzzy rather than sharp, identifying the local average treatment effect rather than the average treatment effect. In particular, given the one‐sided nature of non‐compliance, we employ the partially fuzzy estimator of Battistin and Rettore (Journal of Econometrics, 142, 715–30, 2008). Exploiting a 2008 enrolment cut‐off date reform, which delayed school starting age by 6 months, we find a large increase in enrolment compliance. This exogenous change in the enrolment age enables one to gauge the importance of enrolment non‐compliance in estimating the treatment effect of being older versus younger in cohort. We find that pre‐reform the treatment effect is small and generally statistically insignificant and post‐reform it is sizeable and statistically significant.

Instrumental variable estimation of weighted local average treatment effects

Instrumental variable estimation of weighted local average treatment effects

The rise of contract-intensive economic structures and democratic development: Are they related?

Does the rise of contract-intensive economic structures in an economy affect political norms and promote democracy? In this study we delineate how the economic structures of commercial contracting can yield the interests, values, and outlooks that favor the democratic rule of law for the enforcement of contracts. Analyses of most countries from 1950 to 2019 show that the increase in the intensity of contract flows has a significant impact on democracy. The results are robust across a range of specifications, measures, control variables, and with instrumental variable regression to check for reverse causality. A change from the tenth to ninetieth percentile in the institutionalization of commercial contracting corresponds with a substantial 58 to 105% increase in local average treatment effect (LATE) on democracy in the long run, depending on the measures examined.

The value of formal host-country education for the labour market position of refugees: Evidence from Austria

Refugees hosted across the developed world often work in low-quality jobs, regardless of education previously attained in their country of origin. In this paper, I analyse the long-term value of formal host-country education for refugees using the example of forcefully displaced Bosnians who arrived in Austria during the 1992-1995 Bosnian war. Deploying 22 years of Austrian microcensus data, I exploit the age at the time of forced migration as an instrument for the probability of receiving host-country instead of origin country education to recover local average treatment effects of education attained in Austria vis-A -vis Bosnia on labour market outcomes for refugees aged around schooling thresholds. These estimates show that attaining a formal degree in the host-country significantly reduces the probability of work below educational attainment and low-skill employment over the entire observation period. Income differences between Austrian and Bosnian degree holders are visible after more than two decades of stay in Austria. The discount on Bosnian education declines over time for men but not for women, suggesting that host-country degrees are particularly important to groups that faced cultural barriers to quality employment in their country of origin.