--- title: "Leaving School VA on the Table: Ainsworth, Dehejia, Pop-Eleches & Urquiola (2023)" description: >- Distilled: Romanian households leave roughly one standard deviation of school value added unexploited when choosing high school tracks; both incomplete information and preferences for curricular focus and peer quality contribute, with preferences explaining 83 percent of the gap that would remain after full information correction. An information RCT raises value added by 0.12 SD for low-achieving students (out of 1 SD potential); a rank-ordered logit and counterfactual simulation decompose the residual. American Economic Review 2023, AEA open access. Seven core results with source locators, datasets used, the model, and the method. sidebar: label: Ainsworth et al. 2023 order: 1 tags: [paper-summary, school-choice, household-finance, information-economics, discrete-choice, panel-regression, open-access, peer-reviewed, unreplicated, data:romania-moe-admissions] paper: authors: Robert Ainsworth, Rajeev Dehejia, Cristian Pop-Eleches, Miguel Urquiola authorList: - { family: Ainsworth, given: Robert, affiliation: University of Florida } - { family: Dehejia, given: Rajeev, orcid: "0000-0002-0927-429X", affiliation: New York University } - { family: Pop-Eleches, given: Cristian, orcid: "0000-0002-3451-3555", affiliation: Columbia University } - { family: Urquiola, given: Miguel, orcid: "0000-0001-7075-9547", affiliation: Columbia University } year: 2023 venue: American Economic Review 113(4), April 2023, 1049-1082 venueShort: AER 2023 doi: 10.1257/aer.20210949 jel: codes: [D12, D83, I21, I28] assignedBy: claude-sonnet-4-6 date: 2026-06-24 topics: - School Choice and Performance - Housing Market and Economics - Gender, Labor, and Family Dynamics dataAccess: proprietary-confidential outcome: - academic value added of chosen high school track - probability of passing the baccalaureate exam - household preference rankings for school tracks outcomeClass: [household-finance] license: "open access (AEA open access policy for 2023 publications; Crossref returned no license block 2026-06-24; specific CC variant not confirmed in this session)" licenseShort: AEA open access access: open machineAccess: "open (AEA website, 2026-06-24; freely accessible under AEA 2023 open access policy)" redistribution: extract-only resultsCount: 7 citedByCount: 32 methods: role: both family: reduced-form-causal buildsFrom: [randomized-survey-experiment, panel-regression, regression-discontinuity-design] identification: randomized contributionType: [new-fact, new-data, measurement] mechanisms: [information-asymmetry] introducesData: true scope: region: Romania assetClass: Romanian public high school tracks period: 2004-01..2019-12 frequency: annual dataType: [administrative, survey, experimental] granularity: [individual] n: "2,162,736 students (administrative data, 2004-2017 and 2019); 3,898 students in 194 middle schools, 48 towns (survey/experiment, 2019)" findings: - ref: R1 outcome: academic value added of chosen high school track metric: sd-effect value: "mean percentile rank of chosen track = 67.1 among feasible tracks; potential gain from switching to highest-VA option = 1.01 SD = 12 pp baccalaureate pass probability (2019 SD)" direction: negative vsBenchmark: below the maximum VA available in the feasible choice set by ~1 SD - ref: R2 outcome: academic value added of chosen high school track metric: correlation value: "overall VA-selectivity correlation = 0.562; most selective third coefficient = -0.243 (SE 0.024)" direction: mixed vsBenchmark: positive for least and moderately selective tracks; negative for most selective third - ref: R3 outcome: household beliefs about school value added metric: r-squared value: "mean absolute error of VA beliefs = 1.1 within-town quintiles; R-squared of household VA scores on true VA quintile = 0.17 (vs 0.33 for selectivity)" direction: negative vsBenchmark: households roughly 2x more accurate on selectivity than on VA - ref: R4 outcome: academic value added of chosen high school track metric: sd-effect value: "all students: 0.048 SD (SE 0.025, sig. 10%); low-achieving: 0.121 SD (SE 0.049, sig. 5%); high-achieving: -0.002 SD (insig.)" direction: positive vsBenchmark: information treatment vs control group, regression (1) - ref: R5 outcome: academic value added of chosen high school track metric: sd-effect value: "low-achieving students ineligible for both top baseline choices: 0.184 SD (SE 0.065, sig. 1%) = 2.21 pp baccalaureate pass probability" direction: positive vsBenchmark: near-zero effect for students admitted to their most-preferred baseline choices - ref: R6 outcome: academic value added of chosen high school track metric: sd-effect value: "fully correcting beliefs raises VA by 0.13-0.20 SD for low-achievers (17-25% of potential) and 0.10-0.23 SD for high-achievers (11-24%) across four model specifications" direction: positive vsBenchmark: "17-25% of the 1.01 SD potential gain; preferences account for remaining 75-83%" - ref: R7 outcome: household preference rankings for school tracks metric: coefficient value: "rank-ordered logit: curricular focus = 0.931 (SE 0.071); peer quality = 0.344 (SE 0.069); VA-pass bacc. = 0.337 (SE 0.082); location = 0.276 (SE 0.069); all sig. at 1%; R-squared = 0.33" direction: negative vsBenchmark: curricular focus preference ~3x the VA-pass coefficient; 83% of unexploited VA under accurate beliefs is due to preferences (Table 15, col. 5) resultType: new-finding relatesTo: - { cite: "Abdulkadiroğlu, Pathak, and Walters (2020)", doi: '10.1257/aer.20172040', relation: tests, note: "extends their NYC parents-value-VA question to Romania with full feasible-choice-set observation and VA measured for all options" } - { cite: "Hastings and Weinstein (2008)", doi: '10.1162/qjec.2008.123.4.1373', relation: extends, note: "extends their QJE school-choice information experiment to a value-added framing and adds the preference decomposition channel" } - { cite: "Angrist, Hull, Pathak, and Walters (2017)", doi: '10.1257/aer.p20171111', relation: builds-on, note: "adapts their RD approach to validate value added estimates against causal effects at track admissions cutoffs" } - { cite: "Kapor, Neilson, and Zimmerman (2020)", doi: '10.1257/aer.20170129', relation: builds-on, note: "related model of heterogeneous beliefs in a centralized school choice market" } - { cite: "Fack, Grenet, and He (2019)", doi: '10.1257/aer.20151422', relation: builds-on, note: "preference estimation in a centralized serial dictatorship mechanism; this paper adds an information experiment and a VA simulation" } openQuestions: - "Whether information interventions change only students' information sets or also their preferences, with different implications for student well-being and schooling outcomes (p. 1081)." - "Whether larger or more sustained information delivery would produce larger effects on track assignments and value added (p. 1080)." - "What general equilibrium incentive effects on schools would arise if households more strongly demanded higher value added, given that schools attracting VA-seeking households would have incentives to invest in academic quality rather than selectivity or peer composition (Section VI, p. 1080)." replicationCode: url: https://doi.org/10.3886/E181263V1 status: available extraction: - { by: "paper-distiller (claude-sonnet-4-6)", date: "2026-06-24", role: extracted, note: "Full text read (pp. 1049-1082); seven results extracted from source PDF. Not human-verified. Not reproduced. Replication data available at https://doi.org/10.3886/E181263V1 but not run here." } - by: paper-verifier (claude-sonnet-4-6) date: 2026-06-24 role: verified note: "Locators and reported magnitudes re-checked against source PDF; all 7 core-result rows confirmed (Tables 3-8, 13-15). Two fixes applied: (1) JEL code I28 added to frontmatter (PDF abstract lists D12, D83, I21, I28); (2) X_i covariate list in eq. (1) corrected from three items to two per footnote 27 (VA of baseline-assigned track and indicator for ranked-feasible-track; the wiki had listed the same covariate twice under different names)." licenceVerification: - { source: "Crossref REST API works/10.1257/aer.20210949", checked: "2026-06-24", by: "paper-distiller (claude-sonnet-4-6)", found: "license[] returned as empty array; no explicit CC block recorded in Crossref; AEA open access policy (2022+) applies to 2023 publications but specific CC variant not confirmed in this session" } rightsSignalConflict: false --- **What this is.** The core results, the model, and the method from the paper in condensed form: enough to know what it found and how. To replicate or extend, read the full source at [doi.org/10.1257/aer.20210949](https://doi.org/10.1257/aer.20210949). ## TL;DR Romanian households leave roughly one standard deviation of academic value added (VA) unexploited when choosing among high school tracks. Two candidate explanations are examined: households may lack information about which schools add the most academic value, or they may have genuine preferences for other school characteristics. The authors conduct a clustered information RCT - distributing VA rankings at baseline survey sessions - and find the treatment raises the VA of assigned tracks mostly for low-achieving students who were rejected by their top baseline choices (0.12 SD for all low-achieving students; 0.18 SD for the ineligible subgroup). A rank-ordered logit estimated on household preference rankings reveals that preferences for curricular focus and peer quality are far stronger than preferences for academic VA. Counterfactual simulations imply that fully correcting information would close only 17-25 percent of the VA gap for low-achieving students; preferences for other school traits account for 83 percent of the remaining gap. ## Core results Magnitudes and significance are as reported; \* / \*\* / \*\*\* = 10% / 5% / 1%. Locators point into the source PDF. | # | Result | Locator | Magnitude | |---|---|---|---| | R1 | Households choose tracks at the 67th percentile of VA in their feasible sets; the mean potential gain from switching to the highest-VA option is 1 standard deviation | Table 4, Panel B-C, p. 1062 | Mean percentile rank = 67.1 (all students); potential increase = 1.01 SD = 12 pp baccalaureate pass probability (2019 SD) | | R2 | VA and selectivity are positively correlated for less- and moderately-selective tracks but negatively correlated for the most selective third | Table 3, Figure 1, p. 1060 | Overall correlation = 0.562 (SE 0.005); most selective third: coefficient = -0.243 (SE 0.024) | | R3 | Households' VA beliefs are substantially inaccurate: scores are off by 1.1 within-town quintiles on average and explain only 17 percent of the variation in true VA | Tables 5-6, pp. 1063-1064 | Mean absolute error = 1.13 quintiles; R-squared on true VA quintile = 0.17 (VA); 0.33 (selectivity) | | R4 | The information treatment raises the VA of assigned tracks by 0.05 SD for all students (10%) and 0.12 SD for low-achieving students (5%); no significant effect for high-achieving students | Table 7, p. 1066 | All: 0.048 SD (SE 0.025)\*; low-achieving: 0.121 SD (SE 0.049)\*\*; high-achieving: -0.002 SD (insig.) | | R5 | Among low-achieving students ineligible for their two top baseline choices, the treatment raises VA by 0.18 SD = 2.21 pp baccalaureate probability (1%) | Table 8, p. 1067 | 0.184 SD (SE 0.065)\*\*\* for the ineligible-for-top-two subgroup; near-zero for those admitted to their most-preferred baseline choice | | R6 | Fully correcting beliefs raises VA by 0.13-0.20 SD for low-achievers (17-25% of potential) and 0.10-0.23 SD for high-achievers (11-24%), across four model specifications | Table 14, p. 1078 | Change in VA: 0.13-0.20 SD (low-achieving); 0.10-0.23 SD (high-achieving); share of potential increase 17-25% (low) and 11-24% (high) | | R7 | Households have much stronger preferences for curricular focus (beta = 0.93) than for academic VA (beta = 0.34) or peer quality (beta = 0.34); 83 percent of unexploited VA under accurate beliefs is due to preferences | Table 13, col. 1, p. 1074; Table 15, col. 5, p. 1079 | All rank-ordered logit coefficients sig. at 1%; R-squared = 0.33; 83% of VA left on table under accurate beliefs is attributable to preferences for curricular focus, peer quality, and other traits | **Overall (paper's conclusion).** Both information and preferences play a role in explaining why households leave VA on the table. Providing information raises the VA of tracks for some students, but only for low-achieving students who were rejected by their top choices. Even fully correcting information would leave most of the VA gap in place because households have strong preferences for curricular focus and peer quality that cause them to forego schools with high VA. Simply making VA information available is unlikely to close the gap. ## Theory / model The paper has no formal equilibrium model; it investigates two candidate explanations empirically. The preference structure is specified as a linear expected utility function over quality scores (p. 1073, eq. 4): $$ U_{ij} = \sum_{q} \beta_q \cdot s_{ij}^q + \epsilon_{ij} \tag{4} $$ where $$U_{ij}$$ is household $$i$$'s expected utility from track $$j$$, $$s_{ij}^q$$ is the household's baseline survey score for track $$j$$ on quality dimension $$q$$ (location, peer quality, VA on the baccalaureate, curricular focus, siblings and friends; scale 1 to 5), $$\beta_q$$ is the preference weight, and $$\epsilon_{ij}$$ follows a Type I extreme value distribution. The model assumes households rank tracks by expected utility and that the serial dictatorship mechanism is incentive compatible, so submitted rankings truthfully reveal preferences. Value added $$V_{jt}$$ is a track-year effect on the probability of passing the baccalaureate exam, estimated via selection-on-observables (Rothstein 2010). The measures are validated against regression discontinuity estimates at track admissions cutoffs following Angrist, Hull, Pathak, and Walters (2017): school-specific RD cutoffs (the minimum transition score for admission) generate quasi-random variation in track attendance, and the paper shows that all VA measures closely match the resulting causal estimates. For cohorts without baccalaureate data (2015-2017, 2019), VA is extended via local linear forests (Athey et al. 2019), which explain almost 80 percent of the variation in true VA out of sample. The counterfactual comparison asks what track choices would look like if households had accurate VA beliefs. Two predicted VA values are compared for each student: - $$V_{i,IS}$$: weighted-average VA across the feasible set using the preference model with inaccurate (baseline survey) scores for VA - $$V_{i,AS}$$: the same but replacing inaccurate VA scores with within-town quintiles of measured VA The difference $$V_{i,AS} - V_{i,IS}$$ estimates the effect of accurate beliefs, holding preferences constant. **Identification.** Sections II-III are purely descriptive (selection-on-observables): they document the VA gap and the accuracy of households' beliefs without claiming causal identification. Section IV is identified by the clustered RCT (randomization at the middle school level, within matched pairs). Section V (preference estimation) is identified by within-town variation in quality scores and track choice under the rank-ordered logit. ## Method Three methodological components produce the paper's results. **Information experiment.** Middle schools were assigned to treatment or control by a matched-pair clustered randomization. At the end of the baseline survey session, all schools received a flyer with links to government admissions websites; treatment schools additionally received a ranking of the town's high school tracks by VA. The main treatment effect equation (eq. 1, p. 1065) is: $$ \text{std}(V_i) = \eta_0 + \eta_1 \cdot T_i + \eta_X' \cdot \mathbf{X}_i + \eta_i \tag{1} $$ where $$\text{std}(V_i)$$ is the standardized VA of student $$i$$'s assigned track, $$T_i$$ is the treatment indicator, and $$\mathbf{X}_i$$ includes the VA of the track to which the student would have been assigned based on the baseline preference ranking (equal to the VA of the highest-ranked feasible track, set to zero if no feasible track was ranked) and an indicator for whether the student ranked a feasible track. Standard errors are clustered by middle school treatment-control pairs (78 clusters). The treatment effect on the accuracy of VA beliefs is estimated via: $$ \left|\text{quint}(V_{jt}) - s_{ij,\text{fs}}^V\right| = \eta_0 + \eta_1 \cdot T_i + \eta_X' \cdot \mathbf{X}_{ij} + \eta_{ij} \tag{2} $$ where $$\text{quint}(V_{jt})$$ is the within-town quintile of measured VA and $$s_{ij,\text{fs}}^V$$ is the follow-up-survey quality score for track $$j$$ (p. 1069, eq. 2). The effect of treatment on the association between preference ranks and VA is estimated via: $$ \text{ppr}_{ij,\text{fs}} = \bigl(\delta_1 + \delta_2 \cdot T_i\bigr) \cdot \text{pr}(V_{jt}) + \bigl(\delta_{X,1} + \delta_{X,2} \cdot T_i\bigr)' \cdot \mathbf{X}_{ij} + \delta_{ij} \tag{3} $$ where $$\text{ppr}_{ij,\text{fs}}$$ is the follow-up-survey percentile preference rank of track $$j$$ and $$\text{pr}(V_{jt})$$ is the within-town VA percentile rank (p. 1070, eq. 3). The coefficient of interest is $$\delta_2$$, which measures how treatment changed the association between VA and preference ranks. **Rank-ordered logit for preference estimation.** Building on Fack, Grenet, and He (2019) for preference estimation in centralized choice mechanisms, and the heterogeneous-beliefs framework of Kapor, Neilson, and Zimmerman (2020), the preference weights $$\beta_q$$ are estimated by maximizing the log-likelihood of the top-two baseline track choices (eq. 5, p. 1073): $$ \Pr\!\left(r_{i1}, r_{i2} \mid \mathcal{J}_i, s_{ij}^q\right) = \prod_{l=1}^{2} \frac{\exp\!\left(\sum_q \hat{\beta}_q \cdot s_{il}^q\right)}{\sum_{b \in \mathcal{J}_i \setminus \{r_{i,m:m Ainsworth, Robert, Rajeev Dehejia, Cristian Pop-Eleches, and Miguel Urquiola. > "Why Do Households Leave School Value Added on the Table? The Roles of Information and Preferences." > *American Economic Review* 113, no. 4 (April 2023): 1049-1082. > DOI: 10.1257/aer.20210949. > Distilled extract only; redistribution of the verbatim PDF not authorized in this session.