# Signaling Specific Skills and the Labor Market of College Graduates

Matias Busso, Sebastián Montaño, Juan Muñoz-Morales

► To cite this version:

Matias Busso, Sebastián Montaño, Juan Muñoz-Morales. Signaling Specific Skills and the Labor Market of College Graduates. Review of Economics and Statistics, 2025, pp.1-46. 10.1162/REST.a.1635 . hal-05490569

HAL Id: hal-05490569

https://hal.science/hal-05490569v1

Submitted on 5 Feb 2026

**HAL** is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
# Signaling Specific Skills and the Labor Market of College Graduates

Matías Busso, Sebastián Montaño, and Juan Muñoz-Morales*

October 8, 2025

## Abstract

Using administrative data and a regression discontinuity design, we study the labor market effects of a government-sponsored award given to top-performing students on Colombia's national college exit exam. The award signals field-specific skills, leading recipients to earn seven to ten percent more than comparable peers without the signal. The benefits are concentrated among graduates from lower-reputation institutions, who enter the market with weaker signals and gain access to better job matches and higher-paying firms. These returns persist for up to five years, driven by an upward shift in the intercept of the wage-experience profile of those with weaker signals.

**Keywords:** Signaling, skills, wage returns, awards, college reputation, Colombia

**JEL classifications:** J20, J24, J31, J44, O15, D80.

<footnote>*Busso: Inter-American Development Bank (mbusso@iadb.org). Montaño: Universidad de Los Andes (s.montano42@uniandes.edu.co). Muñoz: IESEG School of Management, Univ. Lille, CNRS, UMR 9221- LEM-Lille Économie Management, F-59000 Lille, France (j.munoz@ieseg.fr). For helpful discussion and comments, we thank seminar participants at EAFIT University (10/21), Central Bank of Colombia (10/21), University of Maryland (10/21), Paris School of Economics (09/22), Copenhagen Business School (10/22), IFLAME (04/22), SOLE (05/22), JMA (05/22), RIDGE (05/22), EEA (08/22), LACEA-LAMES (11/22), NEUDC (11/22), LEER (04/23), EALE (09/23), World Bank (10/23), CUNEF (01/24), Aix-Marseille School of Economics (01/24), George Washington University (03/24), and NBER Economics of Education (05/24). We are especially grateful to Evan Riehl, Nolan Pope, Sergio Urzua, Sebastian Galiani, Juliana Londoño-Vélez, David Margolis, Stefano Caria, Sergio Ocampo, Roberto Hsu Rocha, William LeRoy, Sebastian Calonico, three anonymous referees, and the editor for their constructive suggestions. Kyunglin Park, Julian Martinez Correa, and Juan Francisco Santos Rojas provided excellent research assistance. The first version of the paper was finished by 09/2021. This paper was previously circulated under the title "The Signaling Effect of Academic Distinctions on the Career of Young Professionals." The authors declare that they have no relevant or material financial interests that relate to the research described in this paper. The opinions expressed in this document are those of the authors and do not necessarily reflect the views of the Inter-American Development Bank, its Board of Directors, and the countries they represent. Errors are our own. Data and replication instructions are provided through Dataverse at: https://doi.org/10.7910/DVN/5RYXKR</footnote>
# 1 Introduction

Employers make job and wage offers under conditions of asymmetric information, as they typically do not observe the full set of skills that candidates possess (Spence, 1973, 1974). To mitigate this information gap, firms rely on various tools when evaluating job applicants. Academic degrees, the reputation of the institutions granting those degrees, and diploma characteristics have all been shown to reduce information frictions by providing signals of worker ability and offering firms useful screening devices.<fnref n="1" /> In this paper, we show that even in a high-skilled labor market –where workers have access to several signaling mechanisms– a salient signal on specific skills (i.e., those acquired through a college major’s curriculum) has a positive and persistent information value. Workers who are able to leverage this signal achieve higher earnings and better job matches, particularly those who enter the labor market with weaker initial signals, such as graduates from lower-reputation institutions.

The signal studied in this paper is an award given to college seniors for their performance in field-specific evaluations. In Colombia, college students who are about to graduate –and to enter the labor market– are assessed using a college exit exam that evaluates skills *specific* to their field of study. The exam also includes a *core* component evaluating general cognitive skills such as reading and English proficiency. Test takers with exceptional performance in the field-specific component of the test receive a salient and well-publicized national distinction award.<fnref n="2" /> The college exit exam is taken by graduates of every college. Thus, the national distinction award signals high skills irrespective of the college attended by a student.

We exploit the discontinuity in the assignment of the national distinction award to implement a regression discontinuity design that examines the causal effect of obtain-

<footnote n="1">¹For articles that study the returns to academic degrees see: Hungerford and Solon (1987), Kane and Rouse (1995), Jaeger and Page (1996), Tyler, Murnane and Willett (2000), Clark and Martorell (2014), and Jepsen, Mueser and Troske (2016). For articles that estimate returns to college reputation, see: MacLeod et al. (2017), Barrera and Bayona (2019), and Bordon and Braga (2020). For articles estimating the returns to diploma characteristics (e.g., Latin Honors), see: Khoo and Ost (2018) and Freier, Schumann and Siedler (2015).</footnote>

<footnote n="2">²Typically, graduates include the award in their resumes, and colleges frequently highlight students who have received the award as a sign of their education quality and reputation.</footnote>
ing the award on recipients' initial earnings and firms' hiring decisions. Our design compares otherwise identical students (i.e., with similar average characteristics and skills) with and without the award, to estimate the labor-market returns of the signal. We use longitudinal labor market data linked to college records and the universe of test scores from high school and college exit exams. We focus on the universe of students who took the college exit exam between 2006 and 2009, identifying those who received the national distinction through publicly available lists of all award recipients.

The award increases recipients’ initial earnings by 7 to 10 percent –equivalent to an additional year of education in Colombia. Our estimates are robust to alternative estimation strategies and different outcome measures. We provide evidence that our results are not driven by either the manipulation of the running variable or selective attrition.

The signal yields a higher wage return for individuals who enter the labor market with weaker initial credentials. To guide the analysis, we introduce a stylized conceptual framework that emphasizes the role of signaling in a labor market where graduates come from colleges and fields of study with heterogeneous reputations. We find that the award generates greater earnings gains of 16 percent for graduates of *non-elite* institutions, while it does not significantly benefit graduates from *elite* ones.<fnref n="3" /> The magnitude of these returns is substantial—enabling awardees from lower-reputation colleges to earn wages comparable to those of graduates from elite institutions. Conditional on college reputation, the signal has stronger effects for students whose parents did not attend college, those who graduated from institutions with weaker labor market linkages, and for women.

We explore why the signal improves the labor market outcomes of graduates from non-elite colleges. We proceed in three steps. First, we argue that the signal conveys

<footnote n="3">³Throughout the manuscript, we use the terms (non-)elite and (low-)high-reputation colleges, schools, or institutions interchangeably. We operationalize this classification in Section 6, using the QS University Rankings to group colleges into three tiers: top 5 schools (i.e., elite, high-reputation institutions), schools ranked 6 to 20, and schools ranked below the top 20 (i.e., non-elite, low-reputation institutions).</footnote>
information about workers' specific skills at labor market entry. We build an index that assesses the match between the worker's field of study and their industry of employment. The signal on students' field-specific skills increases the likelihood of employment in firms whose industry aligns with their field of study, with the effect largely driven by graduates from lower-reputation institutions. Importantly, the return to the signal is higher for specific skills that are less transferable across industries. Second, we show that it is the informational content about the student's specific skills, rather than the mere fact that the student has a signal to use in the market, that seems to drive positive earnings returns. We find that the earnings returns associated with a different signal, one indicating general skills, are small and statistically insignificant. Third, we present evidence consistent with the idea that the estimated effects are unlikely to be due to differences in general skills or additional investments in formal education. This allows us to interpret the earnings returns of the national distinction award as those that accrue exclusively from the signaling content of the award (and not from differences in skills or further educational investments). A corollary of these results is that the signal seems to improve the allocation of talent in the economy.

The effect of the signal persists for up to five years after labor market entry. This persistence may reflect an upward shift in the wage-experience profile, driven either through wage gains at the initial employer or through further transitions to higher-paying firms. To explore this, we construct firm-level wage percentile measures by ranking firms (within narrowly defined industries) according to the average wages they pay their employees. In the absence of the award, individuals near the discontinuity begin their careers at firms in the 72nd percentile of the wage-paying distribution. Graduates from elite institutions are more likely to secure initial positions at higher-paying firms—around the 75th percentile—compared to their counterparts from non-elite colleges, who typically start at firms in the 66th percentile (a statistically significant difference). We find that receiving the national distinction award increases this percentile rank by 0.03 points for graduates from elite colleges and by 0.06 points for those from non-elite colleges. Among job switchers, awardees of all colleges con-
tinue to find jobs at similarly high-paying firms. This suggests that the signal induces a lasting upward shift in the intercept of the wage–experience profile, driven in part by graduates from non-elite colleges. This results in a persistent earnings gap between awardees and comparable students without the signal.

Ever since Spence (1973, 1974) established a theory of signaling and screening in the labor market, multiple empirical studies have estimated the effects of education signals and separate them from the human capital content usually attached to them. One set of studies has analyzed the effects of obtaining a diploma by measuring the size of the so-called “sheepskin effect”, which refers to the economic return of completing a degree, among otherwise similarly educated individuals who graduated from high school (Tyler, Murnane and Willett, 2000; Jepsen, Mueser and Troske, 2016; Clark and Martorell, 2014) or college (Hungerford and Solon, 1987; Kane and Rouse, 1995; Jaeger and Page, 1996) compared to those who had not graduated. In a study focusing on Colombia, Arteaga (2018) shows that a reform that decreased the content of human capital in an elite university led to a reduction in earnings after graduation.

Our paper is also closely related to a recent and growing literature that analyzes the labor-market effects of introducing signals about workers’ skills in the job-matching process. This literature provides *experimental* evidence showing a positive effect of signaling general cognitive skills (such as numeracy, linguistic abilities, or abstract reasoning) and non-cognitive abilities (such as grit, creativity, or trustworthiness) on current and future labor market outcomes of unskilled workers in low-information settings (Abebe et al., 2021; Bassi and Nansamba, 2022; Carranza et al., 2022; Pallais, 2014; Heller and Kessler, 2021).

Several other related studies have shown not only that diplomas are labor market signals but that their *characteristics* matter as well for labor market performance. First, the reputation of the institution granting the diploma plays an informational role when students enter the labor market and it is therefore positively correlated with college graduates’ earnings (MacLeod et al., 2017; Barrera and Bayona, 2019; Bordon and Braga, 2020; Ge, Isaac and Miller, 2022). Second, the students’ within-university
ranking also has a positive wage return (e.g., Khoo and Ost (2018); Freier, Schumann and Siedler (2015); Feng and Graetz (2017) analyze the effect of Latin honors).<fnref n="4" /> Third, the GPA and grades used as signals have positive effects in the first years after graduation (Hansen, Hvidman and Sievertsen, 2024; Tan, 2023; Pietro, 2017; Landaud et al., 2024).<fnref n="5" />

Our results also contribute to the literature on elite colleges and career trajectories (Zimmerman, 2019; Machado, Reyes and Riehl, 2025; Chetty, Deming and Friedman, 2023). We find that, in the absence of the signal, graduates from low-reputation colleges are less likely to secure employment at high-paying firms, consistent with evidence from the United States (Weinstein, 2025). However, access to a high-precision signal helps mitigate this disadvantage by enabling these students to access similar opportunities, thereby broadening access to high-paying jobs and contributing to a more inclusive labor market.

Finally, our paper also relates to a growing literature on occupational licensing used as signals. Previous studies have demonstrated that the imposition of license requirements for specific occupations can reduce labor market supply and, consequently, lead to increased wages (Kleiner and Soltas, 2023; Kleiner and Krueger, 2013; Gittleman, Klee and Kleiner, 2018; Blair and Chung, 2019, 2021; Chung, 2022; Blair and Fisher, 2022). However, in addition to restricting labor supply, occupational licensing allows workers to signal their skills to the market and this process seems to favor disadvantaged populations (Blair and Chung, 2022).

Our paper contributes to the literature in four distinct ways. First, we provide an estimation of returns to signaling based on a national policy that offers comparable information across graduates from all colleges within a given field. Unlike Latin honors or other college-specific attributes, the signal studied in this paper is based on a uni-

<footnote n="4">⁴A number of studies have also documented the positive effects of awards on workers’ productivity (Neckermann, Cueni and Frey, 2014; Chan et al., 2014). That is, outside an education setting.</footnote>

<footnote n="5">⁵Some earlier literature has also indicated that awards (along with scores) can have motivational effects, prompting students to alter their behavior by influencing crucial decisions related to educational investments (Avery et al., 2018; Beuchert, Eriksen and Krægpøth, 2020; Owen, 2010; Papay, Murnane and Willett, 2016; Smith, Hurwitz and Avery, 2017). We investigate this phenomenon and find no evidence that recipients of the national distinction awards change their educational choices when compared to the counterfactual group.</footnote>
versal ranking of students' field-specific skills, measured among a nationwide cohort of graduates who take the test in the same year. The signal allows employers to accurately compare workers across schools, and therefore provides students who graduate from low-reputation programs with a means to demonstrate their productivity among peers in other schools. Our results, in general, suggest that the experimental estimates carry over to more general settings and that high-skilled students who lack the ability to properly signal their skills benefit from having access to alternative signaling mechanisms.

Second, we show that signals are valued in the labor market even in the context of high-skilled workers where the information provided by diplomas, college reputation, and Latin Honors is already available. Even though one might expect that the information asymmetry between job applicants and employers would be fairly small in the cases of college graduates, we nonetheless find sizable earnings impacts of the signal for those in these groups.

Third, the national distinction award signals a set of skills that are specific to the field of study, which are less transferable across industries than cognitive and non-cognitive skills. Our results provide evidence of the existence of information frictions when employers try to screen candidates based on the skills acquired during college. These frictions are not lessened by other signals such as college reputation and grades, suggesting that better information about college graduates' skills is valuable in the job-matching process.

Finally, we are able to follow workers for five years after the signal was introduced to show that returns to signaling do not fade out over time. Signals usually have non-persistent effects that fade out during the first years of tenure because employers learn and update beliefs about workers (Farber and Gibbons, 1996; Khoo and Ost, 2018). We show that this is not always the case. Some labor market frictions, such as on-the-job learning and lack of efficient job-mobility, may be also at play implying that the returns to signals might not be transitory and may last across several years in the labor market.
## 2 Setting and Institutional Background

The higher education system in Colombia includes public and private institutions (referred to as colleges in this paper) that offer programs in different fields of study. Two types of programs are offered: technical programs, with a length of two or three years, and professional programs, designed to be completed in four to five years. We focus on professional programs equivalent to a bachelor's degree in the United States. Admissions are decentralized, with students applying for admission to specific college programs (or majors). Programs may have different admission requirements across and within colleges. A key component of students' applications is the high school exit exam performance, which all students must take to enroll in college. Programs and colleges are heterogeneous in terms of their selectivity, the quality of the education they provide, their tuition fees, and, as a result, their perceived reputation (MacLeod et al., 2017; Camacho, Messina and Uribe, 2017).

In 2003, the government introduced a college exit exam, known as *Saber Pro*, to assess the skill levels of new graduates and the quality of the instruction provided by all colleges and programs in the country. Students can take the exam after completing three-quarters of their program's coursework, but most students take it within one year before their graduation term. Exam results matter for colleges because test scores are used to create nationwide rankings, which constitute public information and can determine a college's ability to attract good students. Therefore, many schools provide internal incentives and tools to prepare and motivate students to perform well in the exam. Test scores also matter for students because there are several benefits for high-achieving test-takers, such as scholarships, remission of graduation fees, and student loan forgiveness.

The college exit exam is comprised of two components. First, a *core component* that assesses general abilities across fields by testing reading comprehension and English proficiency. The reading section examines the capacity to read analytically, understand college-level written material, identify different perspectives, and make judgments. Students answer 15 multiple-choice questions based on two reading passages, one
adapted from an academic journal and the other from the news media. The English section, on the other hand, focuses on testing the ability to communicate in written English effectively. It includes 45 questions divided into seven parts requiring knowledge of different vocabularies.

Second, the college exit exam includes a *specific component* which measures students' expertise in their program's field of study. Depending on the field, students take between four and twelve sub-tests on subjects deemed fundamental for their future careers as professionals in each area. For instance, students enrolled in economics are evaluated through four sub-tests, in microeconomics, macroeconomics, econometrics, and economic history; while physics students are tested in electromagnetism, electrodynamics, thermodynamics, quantum physics, and classical, quantum, and statistical mechanics. Questions are designed by experts in each field and follow well-defined standards to compare test scores across years.

Due to data availability, our analysis focuses on the 2006–2009 period, when executive decrees made the college exit exam mandatory for graduation and held institutions accountable for ensuring student participation. However, changes to the exam's legal framework during this time weakened the Ministry of Education's ability to enforce mandatory participation. We estimate that approximately 80 percent of graduating seniors took the exam during this period, and nearly all of them sat for the field-specific test corresponding to their major. Although participation was not universal, raising potential concerns about nonrandom selection into the exam, we provide evidence that participation is not systematically related to student demographics, pre-college test scores, or college reputation.<fnref n="6" /> Furthermore, as discussed in Section ??, our identification strategy relies on the assumption that the national distinction award is assigned as good as randomly among students with scores near the cutoff.

Students who obtain a score among the top ten scores of the *field-specific* compo-

<footnote n="6">⁶Appendix A.1 presents evidence on the legal basis for the mandatory nature of the Saber Pro exam. It also documents that most students took the exam and that the likelihood of participation is uncorrelated with student or diploma characteristics, such as college reputation. Additionally, the appendix outlines the exam's core and field-specific components, demonstrating a strong correspondence between students' fields of study and the specific test components to which they were assigned.</footnote>
nent are given a national distinction award every year. (This means that in any given field-year combination, there can be more than 10 awardees if multiple students share the same score among the top ten scores). The annual public announcement of the top scorers is broadly publicized. Recipients receive public recognition throughout national news media and in a ceremony held by the Ministry of Education to hand out certificates. Universities also maintain a public list of awardees on their websites to advertise the quality of their programs and, in turn, to attract the best students and boost their demand.

The national distinction award is a signal for the labor market about students' specific skills relative to all other students in the country. Because it is based on a standardized test, students are ranked nationwide within their fields of specialization (independently of the college they attended). In that sense, the national distinction award provides information different from the one given by graduating with honors from college (which only allows for within-college comparisons). The distinction award is a signal that employers and students actively use in the labor market. Employers can easily find award recipients through the media, on college websites, or in job candidates' resumes. Whereas the national distinction award is a signal actively used in the labor market, the actual test score on the specific component of the exam is likely not used because it is neither readily available to students nor would it be easy to interpret by employers.<fnref n="7" />

## 3 Data

Our sample of analysis consists of 198,742 students enrolled in four- and five-year programs who took the exit exam between 2006 and 2009, and we have earnings information. Using individual-level identifiers, we combine four data sources: 1) ad-

<footnote n="7">⁷Appendix A.2 provides further description of the award. It shows that more popular fields tend to have more awardees because of the assignment rule. It also shows that awardees come from public and private universities, spanning the complete school reputation ranking. The appendix also provides evidence, based on online profiles of a small subset of awardees, that students who received the award use it as a signal for the labor market. It also provides evidence suggesting that students who did not receive the award are unlikely to use the test score as a signal.</footnote>
ministrative records of the universe of college exit exams, both the core exam and the specific components; 2) among these students, who were eligible to receive the award based exclusively on the field-specific component of the exam, we identified all award recipients from publicly available records published online; 3) administrative records of the universe of students who ever registered to a higher education institution in Colombia –including information about the institution in which students enrolled, the field of study the student selected, the student's high school exit exam scores, and some sociodemographic information; 4) administrative social security records from 2008 to 2016.<fnref n="8" /> The records include monthly earnings in the formal sector (measured in the latest observed month between the second and third quarters of every year).<fnref n="9" /> Our main outcome of interest is the first observed labor earnings after graduation, although we consider alternative measures for robustness.<fnref n="10" />

In our data, about 57 percent of college graduates are women. They are, on average, 26 years old and classified as belonging to the lower-middle class of households.<fnref n="11" /> Most graduates in our sample are first-generation college students: Only a third have a mother who graduated from a two- or four-year college. Most students attend a private college, most of which are considered low-reputation institutions. We observe overall test scores for 45 field-specific exams, which we group into six areas of study: Health (10 fields), Engineering (10 fields), Agricultural Sciences (6 fields), Social Sciences and Humanities (9 fields), Business and Economics (3 fields), and Math and Natural Sciences (7 fields).<fnref n="12" />

<footnote n="8">⁸The college exit exam comes from the *Saber Pro* data set. The lists of award recipients are gathered from http://www2.icfesinteractivo.gov.co/result_ecaes/sniee_ins_mej.htm. The higher education records come from “Sistema para la Prevención de la Deserción de la Educación Superior” (SPADIES). The administrative social security records come from “Observatorio Laboral para La Educación” (OLE).</footnote>

<footnote n="9">⁹We lack labor-market information for those individuals out of the labor force, unemployed, or working in the economy's informal sector. In Colombia, 75 percent of workers with a college education are employed in the formal sector.</footnote>

<footnote n="10">¹⁰We provide specific details about the data, variables, and the estimating sample in Appendix B.</footnote>

<footnote n="11">¹¹Households in Colombia are classified into six socioeconomic strata that are used to target social programs and different public subsidies. The strata range from one (very low) to six (very high).</footnote>

<footnote n="12">¹²Appendix Table B.2 provides descriptive statistics of our main estimation sample.</footnote>
# 4 Empirical Strategy

We use a sharp regression discontinuity design to estimate the causal effect of winning the national distinction award on labor market outcomes. Let $D_{ijt} = 1(Score_{ijt} \ge c_{jt})$ be an indicator variable that assigns a value of one if student $i$, enrolled in field of study $j$ and taking the exam at year $t$, obtains a score in the field-specific component above a threshold $c_{jt}$ and, thus, is awarded the distinction.<fnref n="13" /> Additionally, we define the (running) variable $Z_{ijt}$ as:

$$Z_{ijt} = (Score_{ijt} - c_{jt})/\sigma_{jt},$$

where $\sigma_{jt}$ represents the standard deviation of the specific exit college exam score computed for students in the field of study $j$ taking the exam in year $t$.<fnref n="14" />

Using these measures, we estimate the parameters of the following equation:

$$Y_{ijs} = \alpha + \beta Z_{ijt} + \delta D_{ijt} + \tau(Z_{ijt} \times D_{ijt}) + X'_{i}\gamma + \varepsilon_{ijs}, \quad (1)$$

where $Y_{ijs}$ represents a student $i$'s outcome in year $s > t$. Our main outcome of interest is the log of first observed earnings after graduation (i.e., earnings observed at an early stage of the career of college graduates). However, our results are robust to alternative earnings measures as discussed below. Our parameter of interest, $\delta$, is estimated as:

$$\delta(c_{jt}) = \lim_{c \downarrow c_{jt}} E[Y_{ijs} | D_{ijt} = 1, Score_{ijt} = c, X_i] - \lim_{c \uparrow c_{jt}} E[Y_{ijs} | D_{ijt} = 0, Score_{ijt} = c, X_i].$$

Equation (1) represents the reduced-form approach of a sharp regression discontinu-

<footnote n="13">¹³We do not have information to observe $c_{jt}$ directly. Still, we can easily compute it by finding the minimum score among the award recipients for every program and test edition.</footnote>

<footnote n="14">¹⁴Alternatively, we could have used the ranking in the exam for each field-year as a running variable. This can prevent that, if the distribution of $Z_{ijt}$ is sparse around the threshold, we end up inadvertently dropping some field-years of the estimation. It has, though, two disadvantages. First, the difference between two rank points might represent different achievement gaps in the college exit exam depending on where in the distribution of test scores this difference is computed. On the contrary, the difference between $Z_{ijt}$ and $Z'_{ijt}$ is always interpreted as a difference in test scores measured in standard deviation. Second, using a continuous running variable allows us to more easily rely on existing methods of bandwidth selection, which are not available for discrete running variables. We provide results using the test score ranking as a running variable in our robustness analysis.</footnote>
nuity design. We present estimates for different bandwidths and use local polynomial regressions of different orders (Imbens and Lemieux, 2008). We consider bandwidths computed by minimizing mean square errors (MSE) as well as coverage error expansion bandwidths (CE) as suggested by Calonico, Cattaneo and Farrell (2020).

Our benchmark specification also considers a vector of control variables, $X_i$ (Calonico et al., 2019) to further ensure comparability between award recipients and non-recipients. This vector includes age, gender, socioeconomic status, mother's education level, test scores from the high school exit exam, and test scores from the core component of the college exit exam. In addition, the vector includes a set of six study areas, × year fixed effects; this vector captures differences across the different test editions and controls for variation across programs because the cutoffs are field-specific. Standard errors are clustered by area of study and test year.

# 5 Results

Before estimating the effect of the distinction award on labor market outcomes, we provide evidence that our identification strategy satisfies the internal validity requirements of a regression discontinuity design.<fnref n="15" /> First, manipulation of the running variable appears very unlikely: cutoffs were not known in advance, varied across years and fields, and scores are smooth around the threshold. Formal density tests confirm this, as we fail to reject the null of no manipulation in more than 90 percent of field-year exams. Although 11 out of 112 tests (9.8 percent) reject smoothness, no field exhibits systematic evidence of manipulation across years, and our results remain unchanged when those cases are excluded. Second, students just above and below the cutoff seem to be comparable in pre-determined characteristics: they have similar distributions of gender, age, parental education, socioeconomic status, and type of institution. They also show no discontinuities in proxies for ability, such as high school test scores and English proficiency. Only reading scores show a small imbalance, but including the

<footnote n="15">¹⁵See Appendix C for more details.</footnote>
full vector of general skills as controls does not affect our estimates. We also find no evidence that students from elite universities are more likely to cluster at the threshold. Third, sample selection is unlikely to be a concern, as awardees and non-awardees are equally likely to graduate from college and appear in the Social Security earnings data. The timing of graduation and of first observed earnings is also balanced around the cutoff.

## 5.1 Effect of the National Distinction Award on Earnings

*Main results.* We use equation (1) to estimate the effect of receiving the national distinction award on the first observed earnings of college graduates.

Figure 1a plots the causal effect measured by the discontinuity observed between recipients and non-recipients around the normalized cutoff of zero. Recipients are shown to the right of the cutoff. The positive slope of the curve captures the fact that students who perform better on the specific skills part of the college exit exam tend to earn higher earnings after graduation. There is also a positive and statistically significant premium on earnings from being awarded the national distinction of around 8 percent.

In principle, the sample's composition could have affected this estimate due to pooling students taking their field-specific exams in different years. Figure 1b plots the results of estimating the discontinuity on the log of first observed earnings after graduation conditional on several variables including initial and general skills, baseline sociodemographic variables, and areas of study × test year fixed effects, as specified in equation (1). Results remain the same. The slope of the curve, however, shrinks, which suggests that the control variables absorb most of the predictive power of the field-specific test score on earnings.

Table 1 shows the complete set of results –again, using the first observed earnings after graduation as the outcome variable. Our preferred specification, shown in Column 3, controls for the area of study by year of the exam, a set of baseline sociodemographic characteristics, and a rich set of measures of skills. To facilitate the comparison
son of estimates across columns, we fix the bandwidth to the MSE-optimal bandwidth determined for the specification shown in Column 3. The effect of the national distinction award on early-career earnings ranges from 7 to 10 percent. This magnitude is meaningful, particularly given that awardees and non-awardees have similar average academic performance, suggesting that the returns reflect a pure signaling effect rather than differences in underlying ability. The estimated effect is similar to the 11 percent return to an additional year of schooling in Colombia (Tenjo et al., 2017), and smaller than the 13 percent return found by Arteaga (2018) for a policy that reduced the coursework required for college graduation. It lies within the range of signaling effects found by Hansen, Hvidman and Sievertsen (2024), who estimate that a one-standard-deviation increase in GPA raises earnings by between 3.8 and 20 percent, depending on the specification.

*Robustness.* Regression discontinuity estimates might be sensitive to the choice of tuning parameters. Figure 2 provides formal estimates of equation (1) using alternative bandwidths and local polynomial regressions of a different order. The bottom of the figure describes the specifications, which vary in three dimensions. First, we vary the control variables. We present estimations with no controls, with field-year fixed effects, conditioning on test score measures, and with the full set of individual-level controls (labeled “covariates”). Second, we vary the order of the polynomial. We present estimates using a local linear regression or a local quadratic regression. Third, we present estimates obtained using MSE bandwidths or CE bandwidths.<fnref n="16" />

We observe very stable point estimates between roughly a 7 to 12 percent increase in earnings for the national award recipients. We provide additional robustness checks in Appendix D. Specifically, we show that the estimated effect is robust to: (i) a large set of bandwidths; (ii) alternative definitions of the outcome variable that take into

<footnote n="16">¹⁶Note that CE bandwidths are commonly smaller than MSE bandwidths, which are widely used in regression discontinuity applications. As mentioned by Calonico, Cattaneo and Farrell (2020), estimates based on MSE bandwidths require robust-biased-corrected methods to make a valid statistical inference, although confidence intervals would remain suboptimal regarding coverage error. CE bandwidths correct such a lack of optimality by yielding inference-optimal choices.</footnote>
consideration that students might take different amounts of time to enter the formal labor market after graduation (we thus estimate the effect of the signal on the log earnings one year after graduation and on the average log earnings between the ages of 23 and 28); (iii) removing the 2006 cohort of students for which we only observe wages after two years of graduation; (iv) dropping smaller fields from the estimation and fields with less than 10 awardees (to assess whether awardees from smaller fields are different from those of more popular fields); and (v) using the ranking of the field-specific scores as running variable instead of the score; and (vi) removing from the sample the few fields-years in which we reject the null hypothesis of no manipulation of the running variable.<fnref n="17" />

# 6 Who benefits more from the signal?

To guide the discussion of the positive effects of the national field-specific award on earnings, we first present a conceptual framework that highlights potential channels that might be operating in the labor market.

## 6.1 Labor-Market Valuation of Signals

Employers value workers' specific skills but do not directly observe them. Instead, when making hiring and wage-offer decisions of college graduates, they largely rely on one signal: The reputation of the college from which students graduated (Deming et al., 2016; MacLeod et al., 2017; Zimmerman, 2019; Barrera and Bayona, 2019; Bordon and Braga, 2020). The national distinction award introduces a second signal about people's specific skills.

*Students Skills.* Following MacLeod et al. (2017), consider a continuum *I* of students endowed with pre-college skills $\alpha_i \sim N(0, 1)$ and initial wealth $W_i^0 \sim N(0, 1)$, for each

<footnote n="17">¹⁷In Appendix Figure A.6 we show that the estimated effects of the award on earnings are not statistically significant, almost anywhere else in the test score distribution. The national distinction award is given to approximately the top one percent of test takers. We expect that the difference exists only between awardees and non-awardees. Indeed, we do not observe any other jump across the distribution.</footnote>
$i \in I$. $\alpha_i$ is not directly observable. Instead, a proxy measure is a high school exit exam,

$$T_i = \alpha_i + \varepsilon_i^{\alpha},$$

which is a function of the pre-college skills and a random variable, $\varepsilon_i^{\alpha} \sim N(0, \frac{1}{\rho^{\alpha}})$, where $\rho^{\alpha}$ is a precision term.

Colleges are either *elite*, denoted by $s^+$, or *non-elite*, denoted by $s^-$, and admit applicants based on their high school exit test scores and ability to pay for college-specific tuition. This implies that the probability of attending each type of college is given by,

$$P[i \in s^+] = P[T_i > \bar{T} | W_i^0 > \bar{W}_s] \\ P[i \in s^-] = P[T_i \le \bar{T}] + \underbrace{P[T_i > \bar{T} | W_i^0 \le \bar{W}_s]}_{\text{Income-constrained}}$$

where $\bar{W}_s$ is the tuition cost of college $s$ and $\bar{T}$ is the minimum high school test-score threshold for admission. This leads to colleges having a student body of different initial skills, where only highly skilled students who have the means to pay for tuition attend elite colleges, whereas students in non-elite colleges are a combination of students who are either lower-skilled or income-constrained (e.g., those from less advantaged backgrounds).<fnref n="18" /> As a consequence, students in elite colleges are less diverse in terms of initial abilities compared to students in non-elite ones (i.e., $\text{Var}(\alpha_{i \in s^+}) < \text{Var}(\alpha_{i \in s^-})$).

After college graduation, students' skills include additional attributes that are heterogeneous and depend on the college $s$ they attended and their field of specialization $f$. We assume that college inputs increase students' skills, and the improvement varies depending on the field of study. This captures the fact that some colleges might be better in specific fields. The post-college level of skills is:

$$\theta_i = \alpha_i + v_{sf},$$

<footnote n="18">¹⁸We assume that everyone attends college. In our setting, students from high-income families are more likely to attend elite colleges (suggesting that credit constraints might be at play in our setting).</footnote>
where $v_{sf}$ corresponds to a college-specific field attribute.

*Signals for the labor market.* Employers care about post-college skills of college graduates, which are not observable. However, college's and field's labor market *reputation* is a signal about the level of skills of their students.<fnref n="19" /> Formally, this is captured by:

$$R_{sf} = \theta_{i \in sf} + \epsilon^{R_{sf}},$$

where $\theta_{i \in sf}$ represents the skills of graduates from college s and field f, and $\epsilon^{R_{sf}} \sim N(0, \frac{1}{\rho^{R_{sf}}})$ is an error term.<fnref n="20" /> The precision of the field-college reputation as a signal is determined by $\frac{1}{\rho^{R_{sf}}}$, which varies depending if the combination of college and field is elite or not (i.e., $\rho^{R_{sf}} \in \{\rho^{R^+}, \rho^{R^-}\}$). Note that these precision terms depend on the variance in the initial ability of students enrolled in that field and college. This implies that elite field-colleges' reputation is more precise than non-elite's, $\rho^{R^+} > \rho^{R^-}$, because the variance of initial ability among students in elite field-colleges is smaller.

The national distinction award is a second signal in the labor market. It is revealed for those who obtain the national distinction award ($a_{if}$), which is based on the specific component of the college exit exam, such that:

$$a_{if} = \begin{cases} 1 & \text{if } \theta_i + \epsilon^a > k_f \\ 0 & \text{otherwise,} \end{cases}$$

where $k_f$ is an unknown threshold used to assign the national distinction award in field f and $\epsilon^a \sim N(0, \frac{1}{\rho^{a}})$. The distinction not only reveals information about the school-specific field skills, $v_{sf}$, but also information about the pre-college ability $\alpha_i$. The threshold for winning the national distinction award, $k_f$, is high enough to select a small number of high-skilled graduates, making it a stronger signal about the post-

<footnote n="19">¹⁹MacLeod et al. (2017) distinguish between college reputation and college labor market reputation, which is a clear distinction given their setting. We simplify the setting by restricting our framework to college labor market reputation.</footnote>

<footnote n="20">²⁰Our measure of reputation reflects employers' perceptions of graduate quality, as well as other factors that may improve job search outcomes for graduates of high-reputation field-colleges. For example, the tendency of high-paying firms to recruit more actively at high-reputation institutions, a well-documented pattern in the United States (Weinstein, 2025).</footnote>
college skills. This implies that the national distinction award is a more precise signal than college reputation (i.e., $\rho^a > \rho^{R^+} > \rho^{R^-}$).

*Firms and wage setting.* Consider a continuum of J firms in the economy with productivity $p \sim N(0, 1)$. Every period a worker $i \in I$ and firm $j \in J$ meet determining a level of productivity,

$$y_{ijt} = \theta_i + p_j + \kappa y_{ij,t-1}. \quad (2)$$

Current productivity depends on the workers’ post-college skills, $\theta_i$, a base productivity level known by the employer, $p_j$, and its lagged value, implying that workers learn from previous experience and this on-the-job learning makes them more productive (i.e., $\kappa \in (0, 1)$). However, following MacLeod et al. (2017) and Farber and Gibbons (1996), prior productivity is not observable at period $t$, but instead employers observe signals $q_{it}$ (such as job interviews or references), with time-invariant precision $\rho^q$, such that:

$$q_{ijt} = y_{ij,t-1} + \varepsilon_t^q.$$

Therefore, at period $t$, employers have access to a time-changing vector of information, $\mathbb{I}_{it} = (R_s, a_{ij}, q_{i,0}, \dots, q_{i,t-1})$ (Farber and Gibbons, 1996). Using Bayesian learning and the assumption that signals follow a Gaussian distribution, employers use the information vector to compute an expected performance measure of worker $i$ at period $t$ of the form:

$$p_{it} = E[y_{it} | \mathbb{I}_{it}] = \beta_{a_s} a_i + \beta_{R_{sf}} R_{sf} + \beta_{q_s} \bar{q}_{it}, \quad (3)$$

where $\beta_{a_s} = \frac{\rho^a}{\rho^a+\rho^{R_{sf}}+t\rho^q}$, $\beta_{R_{sf}} = \frac{\rho^{R_{sf}}}{\rho^a+\rho^{R_{sf}}+t\rho^q}$, $\beta_{q_s} = \frac{t\rho^q}{\rho^a+\rho^{R_{sf}}+t\rho^q}$, and $\bar{q}_{it}$ is the average of the $q_{it}$ signals until $t-1$. Note that these coefficients vary by field and college of graduation since individuals from elite and non-elite field-colleges have different precision parameters $-\rho^{R_{sf}} \in \{\rho^{R^+}, \rho^{R^-}\}$.
Conditional on the signals, firms offer recent graduates an equilibrium entry wage equivalent to the expected performance measure and the base level of productivity, such that:

$$w_{ijsf0} = \beta_{a_s} a_i + \beta_{R_{sf}} R_{sf} + p_j. \quad (4)$$

Intuitively, Equation 4 implies that employers observe whether a graduate has received the award, as well as the field and college from which she graduated. Winning the award conveys a precise signal of ability, leading employers to place less weight on other observable characteristics. In contrast, the absence of the award results in a noisier signal, prompting employers to rely more heavily on other indicators—such as the reputation of the graduate’s field and college—to infer ability.

*Wage premia.* Following equation (4), wages vary depending on whether the student graduates from an elite or non-elite field-college and whether they receive the award or not. Therefore, the associated wage premia, $\delta_{sf}$, of the national distinction award also vary depending on the college and field of graduation, taking the form:

$$\delta_{sf} = E[w_{ijso}|a_i = 1] - E[w_{ijso}|a_i = 0] = \beta_{a_{sf}} = \frac{\rho^a}{\rho^a + \rho^{R_{sf}} + t\rho^q}.$$

These returns are then equivalent to $\delta^+ = \beta_{a^+}$ or $\delta^- = \beta_{a^-}$ for graduates of elite and non-elite colleges-by-fields, respectively. Since college reputation is more precise among elite colleges ($\rho^{R^+} > \rho^{R^-}$), the award’s wage premium is higher for graduates of non-elite colleges ($\delta^- > \delta^+$). This framework predicts that the national distinction award yields a higher return for college graduates with less precise reputation measures.
## 6.2 The Signal is Valuable for Worker's with Weaker Signals

We first assess whether the signal serves as a valuable screening device for inferring workers' skills, particularly for those who lack stronger signals upon entering the labor market (i.e., individuals who did not graduate from a high-reputation college).<sup>21</sup> We provide evidence by estimating equation (1) using our regression discontinuity design on subsamples of workers who graduated from universities with different reputations.<sup>22</sup> We rely on the QS University Rankings to classify colleges into top 5 schools (i.e., elite, high-reputation institutions), schools ranked 6 to 20, and schools below the top 20 (i.e., non-elite, low-reputation institutions). Column (1) of Table 2 shows our main results. Columns (2)-(4) show the results by subsample. We observe that students who graduated from the top-five universities do not benefit from the distinction compared to other graduates from the same schools. However, award recipients who graduated from universities with lower reputations experienced an increase in earnings of 15.8 percent, compared to observably similar students who graduated from comparable institutions but did not receive the award.

How do the returns to the signal compare to the returns of graduating from an elite college? We test this by estimating the regression discontinuity model in Equation (1) but modifying the subsamples. We compare earnings obtained by award winners in non-elite colleges with those earned by non-awardees in elite colleges, yielding an estimate of the magnitude of the return to the award comparable to the return of graduating from an elite college. We do this for award recipients graduating from colleges ranked 6 to 20, as well as from colleges not in the top 20. Columns (5)-(6) of Table 2 show the results. The earnings return for awardees who graduated from a

<footnote n="21"><sup>21</sup>As discussed below, the rankings are computed only at the college level, as we are unable to construct rankings by field within or across colleges. However, we believe that field-level reputations within Colombian colleges are sufficiently aligned with overall college reputation, such that college-level rankings capture much of the variation in field-level prestige. Importantly, the ranking we use is based on an external and objective source and does not rely on any of the information used in our estimation.</footnote>

<footnote n="22"><sup>22</sup>The results presented throughout Sections 7 and 8 allow the MSE-optimal bandwidth to vary depending on the sample and outcome of interest. Consequently, the effective sample used in the regression discontinuity estimates may differ from that used for the main results reported in Table 1. In Appendix E, we present estimates using a fixed bandwidth corresponding to the main estimation sample. The results are robust to both fixing the bandwidth and allowing it to vary.</footnote>
low-reputation college is equivalent to the return they would have obtained had they graduated from a high-reputation college (but without holding the award).

This evidence suggests that the national distinction award works as a signal in the labor market. It allows workers graduating from lower-reputation colleges to signal their high skills. This is consistent with the results of Deming et al. (2016), which, using a resume audit study design, found that college students who graduate from for-profit colleges are less likely to receive job callbacks than those graduating from non-selective public institutions. Our results also align with the existing experimental evidence, which documents that individuals from less favored educational backgrounds drive the positive effects of skills signaling on labor market outcomes (Abebe et al., 2021). Our theoretical framework suggests that, in the absence of the award, employers could make erroneous inferences about a young worker's skills based on observable group membership, specifically, college reputation. Instead, introducing a signal of skills given across colleges – such as the national distinction award– can help firms update their priors about highly skilled graduates from non-elite schools. This can explain the documented earnings premium for graduates from non-elite colleges relative to their peers. Our findings are similar to those of Carranza et al. (2022) and Pallais (2014) in that we provide evidence showing that job seekers, who lack ways to communicate their skills to employers, experience larger labor market returns to a signal on abilities.<fnref n="23" />

<footnote n="23">²³Graetz (2021) argues that if innate talent directly influences labor productivity, beyond the impact of knowledge acquired through formal education, then a positive regression discontinuity estimate of diplomas on wages can be considered as evidence of information frictions in the labor market. However, in his model, it remains challenging to identify the relative importance of acquired knowledge and innate talent in the production function. It is worth noting that this concern appears to be of lesser significance in our specific context for two key reasons. Firstly, observable characteristics, including various test score measures, are well-balanced, suggesting that unobserved innate talent is likely balanced around the discontinuity threshold. In other words, there is no evidence that individuals who received the award and those who narrowly missed it by a few test-score points possess significantly different levels of innate talent. Secondly, our robustness exercises indicate that our regression discontinuity estimates remain the same even when controlling for proxy measures of innate talent, such as a comprehensive set of pre-college test scores.</footnote>
## 6.3 Heterogeneity Beyond College Reputation

To better understand the effect of the signal for people with different background characteristics, we estimate the regression discontinuity model described in equation (1), conditioning on our measure of college reputation. We do this for the subsamples of students with different levels of parents' education, gender, and different access to job search networks. This measure captures the number of firms in a college-program's network. To compute it, we proceed as follows. First, we consider a firm *k* as part of college-program *j*'s network if the share of graduates from *j* working at *k* lies in the top quartile of the distribution of shares within *j*'s field. Second, we consider that a college-program *j* has a highly developed network if it ranks among the top quartile of programs in *j*'s field with the largest number of firms that belong to *j*'s network.<fnref n="24" />

Figure 3 plots the regression discontinuity estimates of the award for each group (described in the top part of the figure). Columns marked as (1) in the plot display the effect for the group of students who usually display lower earnings in the data and that, for the sake of simplicity, we label as “disadvantaged” (i.e., students with parents without college education, students with weaker college networks, and women), whereas columns marked as (2) display the effect within the group that can be ex-ante considered “advantaged” (i.e., students whose parents have college education, students with a stronger networks, and men). Being able to signal specific skills tends to benefit workers from a disadvantaged background. The signal has an earnings return of 15 percent for students whose parents do not have a college education, 11 percent for students with lower access to networks, and 14 percent for female workers. By contrast, we observe positive but not statistically significant effects for workers from more advantaged backgrounds.

Are the heterogeneous effects of signaling specific skills enough to close the earnings gap between workers from advantaged and disadvantaged backgrounds? Our data and research design do not allow us to answer this question unequivocally. We

<footnote n="24">²⁴We estimated equation (1) using the index of the college's network as the dependent variable and found no significant effect of winning the national distinction award.</footnote>
can use our results to provide some guidance, noting that doing so requires extrapo-
lating our local treatment effect estimates to a larger population.

We compute some back-of-the-envelope calculations analyzing hypothetical cases
in which the advantaged and/or disadvantaged groups can signal their skills. We
present the complete analysis in Appendix F. One important result to highlight is that
the ability to signal specific skills can potentially decrease the average earnings gaps
of those students born to parents with less/more years of schooling by three percent-
age points. A similar result is observed for the gender wage gap, which shrinks when
both men and women can signal their skills. These results are closely related to occu-
pational licenses' effects on reducing racial earnings gaps in the United States (Blair
and Chung, 2022).

This result implicitly suggests that selective college admission processes may be
inefficient. Students who are sufficiently skilled but are credit-constrained are less
likely to attend high-reputation universities. The distinction award can correct some
of the negative consequences of this inefficient allocation of students, but it has a lim-
ited scope. Policies that correct information frictions when students enter the labor
market could be accompanied by policies that tackle the problem before entering col-
lege. Londoño-Vélez, Rodríguez and Sánchez (2020) and Solis (2017) evaluate two
national policies in Colombia and Chile that provided financial aid to high-achieving
and low-income students to attend high-quality colleges. Their results suggest that the
policy closed the enrollment gap in access to college between low- and high-income
students. Furthermore, Londoño-Vélez et al. (2023) shows that, in the Colombian case,
the policy increased the earnings of financial aid recipients.

7 What do Signals Reveal about Weaker Applicants?

Several factors may explain why award recipients from low-reputation schools
tend to experience higher earnings. First, the signal may convey information about
workers' specific skills at labor market entry, counteracting the effect of graduating
from lower-reputation colleges; we examine this in Section 7.1. Second, the signal might capture returns unrelated to field-specific skills—for example, reflecting more general abilities such as problem-solving, critical thinking, socio-emotional skills, or aptitude for standardized testing. If this were the case, other similar awards not based on field-specific tests should also yield positive earnings effects. We provide evidence against this in Section 7.2. Third, the award may have motivated recipients to invest in additional human capital after graduation, thereby increasing their earnings; we rule out this channel in Section 7.3.

## 7.1 Specific-Skills Content of the Signal

If the signal conveys information about specific skills, it should facilitate better matching between award recipients in a given field and employers in sectors that demand those skills. Consistent with the conceptual framework and prior evidence, this effect is expected to be primarily driven by graduates of lower-reputation institutions. We therefore assess whether awardees from the field of study *f* are more likely to be employed in industries that require skills associated with the field *f*. For example, we can examine whether chemistry graduates are employed in pharmaceutical firms or whether veterinarians tend to work in industries related to animal care.

We construct an indicator variable equal to one if a graduate's field of study matches the industry code of their employer, and zero otherwise.<fnref n="25" /> We then estimate equation (1) using this indicator variable as the outcome. Column (1) of Table 3 shows the results.

We find that winning the national distinction award increases the likelihood of employment in industries that better align with the competencies of a graduate's field of study. Students from low-reputation colleges primarily drive this improvement in

<footnote n="25">²⁵To create this indicator variable, we evaluate whether the skills that a major or college program provides to its students match the description of the economic activity of an industry. For this purpose, we utilize the brochures that universities post online to advertise their majors. These brochures describe the economic sectors in which their graduates' abilities are best suited, and detail where their alumni are currently working (these brochures are commonly referred to as "alumni professional profiles"). Appendix G offers more details regarding the construction of this variable and shows that the results are robust when using alternative outcome measures.</footnote>
field-industry matching. As shown in columns (2)–(4) of Table 3, high-ability graduates from lower-reputation institutions experience the largest gains in job matching. This pattern helps explain why the earnings benefits of receiving the national distinction award are greatest among students from non-elite colleges. These results are consistent with a setting in which the information conveyed by the award regarding specific skills enables firms across industries to identify candidates with the precise qualifications required for the positions they seek to fill.

We can also compare the earning returns to the national distinction award across fields of study with different degrees of specialization. We calculate a specialization index that quantifies the level of transferability of skills by computing the number of industries (four-digit codes) where students in each field find jobs after graduation.<fnref n="26" /> For instance, we find that “Business” is the field of study demanded by the largest number of industries (387 in total). We interpret this as meaning that business students have a set of skills that are the most transferable across industries. On the other end of the spectrum, “Modern Languages” is used by 28 industries. We classify fields of study into two groups depending on whether they are above or below the median of this index. We estimate equation (1) in subsamples defined by these two groups. Columns (5)-(6) of Table 3 show the results.

The national distinction award yields positive earnings returns for students graduating from fields classified as more specialized according to this index. Still, it has little effect on graduates from fields with more transferable skills across industries. This pattern is consistent with a labor market in which firms in more specialized sectors use the signal provided by the national distinction award to identify candidates whose specific skills better match their needs.

<footnote n="26">²⁶We compute the number of four-digit industries in which graduates of each of the 41 fields of study are employed each year. We then compute the average number of industries that employed graduates of a given field from 2008 to 2016.</footnote>
## 7.2 Assessing the Value of the Signal Beyond Specific-Skills

The award may matter beyond signaling specific skills. It might be valued in-
trinsically, as a marker of general competence, or because it demonstrates the ability
to succeed in standardized assessments or shows motivation to perform well across
tasks. Alternatively, employers may simply prefer hiring individuals who have re-
ceived awards. If this hypothesis is correct, any award should have a positive wage
return in the labor market.

In 2010, an award was introduced for students with top scores in problem-solving, critical thinking, socio-emotional abilities, and English proficiency.<fnref n="27" /> We estimate a regression discontinuity model, similar to the one described in equation (1), to obtain an estimate of the earnings return to a core skills signal.<fnref n="28" /> Results are shown in Table 4. The small and not statistically significant effects of the signal on core skills on earnings contrast with the positive earnings return to a signal on specific skills. This suggests that it is the information about the field-specific skills of awardees that matters for the labor market.

## 7.3 Are There Effects on Human Capital Accumulation?

The earnings premium associated with the national distinction award, estimated
using equation (1), compares students with similar skill levels –as measured by their
general and specific high school and college exit exams scores. However, receiving
the award may have influenced students’ subsequent educational decisions. In partic-
ular, it could have been a motivational factor, prompting awardees to pursue further
education and accumulate additional skills. If so, part of the observed labor market re-
turn may reflect increased human capital rather than the signaling value of the award

<footnote n="27">²⁷Students taking these general-skills tests were enrolled in fields lacking a specific exam before 2010. Between 2003 and 2009, test-takers were only eligible to obtain a distinction in the *field-specific* component of the college exit exam.</footnote>

<footnote n="28">²⁸The information on the 2010 distinction award comes from publicly available records (available online). For this cohort of students, we observe test scores in the core component and whether or not they received a distinction award for their performance in that core component. We merge this information with the social security records described in Section 3. For 2010, we lack information about test scores related to the specific component of the college exit exam (which prevents us from estimating a regression discontinuity model like the one we can estimate for 2006-2009).</footnote>
alone.

Table 5 presents regression discontinuity estimates using multiple outcomes that measure skill accumulation. Column (1) presents the effect of the signal on the number of months taken to graduate since the person took the college exit exam. Column (2) shows the treatment effect on the total number of subjects as of graduation time. Columns (3) and (4) show impacts on the number of subjects a student registered and approved, respectively. Column (5) estimates the probability of graduating from a graduate program within five years of college graduation. The distinction award has no statistically significant effect on any of these outcomes. The bottom panels of the table disaggregate the results by college reputation, and we cannot reject the null hypothesis of no effect for any subgroup.

This result does not imply that skills are unrelated to earnings among recipients of the national distinction award. In fact, they are. A simple linear regression of earnings on an indicator for award receipt –without controlling for pre-award skills– yields an estimated premium of $\hat{\beta}_{ols} = 12.6\%$. This estimate reflects both the higher measured skills of awardees and the signaling value of the award itself, such that $\hat{\beta}_{ols} = \delta_{signal} + \delta_{skills}$ —where $\delta_{signal}$ is the signaling effect on earnings and $\delta_{skills}$ is the effect due to differences in skills. In contrast, our regression discontinuity design isolates the signaling effect, with an estimated return of $\hat{\delta}_{RD} = 8.6\%$. These estimates suggest that approximately 68% of the observed earnings premium ($\hat{\delta}_{RD}/\hat{\beta}_{ols} = 0.68$) is attributable to signaling, while the remainder reflects differences in skills.

# 8 Persistence of the Signal's Effect and Job-Ladders

So far, we have shown that the signal carries a positive return, particularly for graduates of low-reputation colleges. The evidence suggests that these returns are driven by the signal's informational content regarding workers' skills. We now examine whether these gains persist over time or fade as employers learn about workers' productivity through experience.
We estimate the parameter of interest in equation (1), using as the dependent variable the τ-th observed log of earnings after entry into the labor market. Figure 4 presents the results. The effect of winning the national distinction award does not fade, even after employers have had time to observe workers’ specific skills. Awardees earn approximately 8 percent more than comparable individuals, and this premium remains stable over at least the first five years in the labor market.<fnref n="29" />

Columns (1)–(3) of Table 6 report the estimated effect of the signal on earnings for all workers during the first three observed post-graduation periods, as well as separately by the ranking of the college attended. The wage gains associated with the award are persistent among students who entered the labor market with weaker signals, while those from elite institutions experience little benefit, both in the short and long run. This persistence may reflect an upward shift in the wage–experience profile, whether through wage increases at the initial firm or upon transitioning to higher-paying employers, with or without changes in the slope over time.

Column (4) shows that, on average, roughly half of the individuals in the control group—those who narrowly missed receiving the award—switch employers at least once within the first five years after graduation. Receiving the award does not significantly affect the likelihood of switching employers, regardless of the graduate's college reputation.

Columns (5)–(7) examine whether the award influences the type of firms where recipients are employed.<fnref n="30" /> The outcome in column (5) is a wage percentile ranking of the first employer ($\tau = 1$), reflecting the firm’s pay level based on the average wages of its employees.<fnref n="31" /> On average, individuals in the control group begin their careers at firms ranked in the 72th percentile of the wage ranking distribution. Graduates from elite

<footnote n="29">²⁹Because individuals can move in and out of formal employment over time, as a robustness check, we restrict the sample to individuals for whom we observe earnings at least three years after entering the market. Estimates using this 'balanced sample' are presented in Appendix Figure D.4. Our conclusion on the persistence of the effect holds.</footnote>

<footnote n="30">³⁰We lack firm identifiers for workers who exited into unemployment or informality or those observed only in 2008, for whom it is not possible to observe a subsequent employer.</footnote>

<footnote n="31">³¹We construct the wage percentile ranking by first calculating the average annual earnings that each firm pays its employees. Based on these yearly averages, we then compute each firm’s percentile rank within 62 two-digit industry codes. Finally, we take the average of each firm’s percentile rank across years. Award recipients are excluded to avoid endogeneity concerns.</footnote>
institutions are more likely to secure initial employment at higher-paying firms than those from non-elite colleges.<fnref n="32" /> However, the award has a positive and statistically significant effect on the likelihood of matching with a higher-paying firm—particularly for individuals entering the labor market with weaker signals.

These results relate to the literature on elite colleges and career trajectories (Zimmerman, 2019; Machado, Reyes and Riehl, 2025; Chetty, Deming and Friedman, 2023). Graduates from elite colleges without the signal are more likely to secure a first job at a high-paying firm than those from non-elite colleges without the signal. However, a high-precision signal appears to offset the advantage given by elite college networks and reputation, allowing graduates from lower-reputation programs to access similar opportunities. In doing so, it contributes to a diversification of access to high-paying jobs in the labor market.

Columns (6) and (7) assess whether, among individuals who change employers, the job trajectories of awardees differ from those of non-awardees. We compute the change in the wage percentile ranking between the first and second employer ($τ = 2$ in Column (6)) and between the first and third employer ($τ = 3$ in Column (7)). In both cases, we cannot reject the null hypothesis that the coefficients are zero. This suggests that the signal allows workers to initially match with higher-paying firms and to remain employed in similarly high-paying firms in subsequent moves. In other words, the signal appears to induce an upward shift in the intercept of the wage-experience profile, without affecting its slope—a parallel shift that may explain the persistence of the award's wage effects over the first five years after graduation.

Our results contrast with those of Khoo and Ost (2018) and Freier, Schumann and Siedler (2015), who find that the wage returns to graduating with honors dissipate three years after graduation. This could be explained by the different nature of the awards. Receiving an honors diploma depends on a within-program-college ranking,

<footnote n="32">³²The p-value for the difference in wage percentile ranking of first employers across graduates from top 5 colleges and those from colleges ranked below the top 20 is 0.000. These differences are consistent with the scenario described in Section 6.1, as well as evidence from the United States (Weinstein, 2025), in which high-paying firms may rely on observable reputation by recruiting more intensively at high-reputation colleges in the absence of the signal.</footnote>
which provides firms with a noisy signal of students' abilities. Such a ranking is a signal that mixes the student's abilities with the composition of the student body at his or her program and college. As firms learn about workers' specific skills, the value of a noisy signal given by the honors award diminishes. Employer-learning models predict that as employers learn about workers' unobserved skills/productivity the effects of signaling would dissipate over time (Farber and Gibbons, 1996; Altonji and Pierret, 2001). This learning process can potentially be accumulated even if workers change jobs as prospective employers either bid, by offering higher wages (Pinkston, 2009), or use job promotions as signals (DeVaro and Waldman, 2012).

The conceptual framework discussed in Section 6.1 suggests that workers' produc-
tivity in year *t* depends positively on the productivity in the previous year, implying
the potential existence of a (non-decreasing) wage experience-profile. This persistence
is consistent with career-development models in which workers acquire specific skills
as they accumulate on-the-job experience which, in turn, allows them to stay ahead on
the job ladder (Gibbons and Waldman, 1999*a,b*, 2006). This process might be more rel-
evant for skilled labor (Altonji, Kahn and Speer, 2016). Haltiwanger, Hyatt and McEn-
tarfer (2018) shows that in the reallocation of workers across firms, more educated
workers are more likely to work in more productive firms and less likely to separate
from them. Thus, having an early job in a more productive firm can put higher-ability
workers on a career path that allows them to have permanent earnings gains.<fnref n="33" />

9 Conclusion

This paper studies the labor market effects of signaling field-specific skills to po-
tential employers. The signal comes in the form of a salient and well-known national
distinction award given to the best student in each field (based on a mandatory exit
exam test score). We rely on administrative data and a regression discontinuity design

<footnote n="33"><sup>33</sup>The effects of getting off to a poor start also appear to linger. For example, evidence in the context of economic downturns has shown that college graduates who find their first job at low-paying firms with unattractive career opportunities have lower earnings even 10 or 15 years later (Beaudry and DiNardo, 1991; Oreopoulos, von Wachter and Heisz, 2012; Schwandt and von Wachter, 2019).</footnote>
to estimate that the signal has an earnings return of 7 to 10 percent.

We show that workers who graduated from lower-reputation colleges benefit the most from being able to signal their specific skills to employers. Implicitly, this result highlights that selective college admission processes may lead to inefficient allocations of students —especially for those who have limited financial resources to pursue higher education. Students who are sufficiently skilled but lack the necessary economic means are less likely to attend high-reputation universities. The national distinction award is a policy measure that is able to correct some of the negative consequences of this inefficient allocation of students, but it has a limited scope and therefore a limited capacity to correct all the potential negative consequences of educational mismatches. Information policies that correct information frictions when students enter the labor market could be accompanied by policies that tackle the problem before students enter college.

This positive return is observed even five years after graduation. The signal allows workers to find jobs in more higher-paying firms and sectors that better use their skills. We do not find evidence that the signal is associated with higher skill levels or with additional investments in education. These results suggest that policies that provide information about workers' skills are likely to improve the allocative efficiency of the economy by allowing high-skilled workers to find jobs where their talents are more productively used.

## References

* Abebe, Girum, A Stefano Caria, Marcel Fafchamps, Paolo Falco, Simon Franklin, and Simon Quinn. 2021. "Anonymity or Distance? Job Search and Labour Market Exclusion in a Growing African City." *The Review of Economic Studies*, 88(3): 1279–1310.

* Abowd, John M., Francis Kramarz, and David N. Margolis. 1999. "High Wage Workers and High Wage Firms." *Econometrica*, 67(2): 251–333.

* Altonji, Joseph G., and Charles R. Pierret. 2001. "Employer Learning and Statistical Discrimination." *The Quarterly Journal of Economics*, 116(1): 313–350.

* Altonji, Joseph G., Lisa B. Kahn, and Jamin D. Speer. 2016. "Cashier or Consultant? Entry Labor Market Conditions, Field of Study, and Career Success." *Journal of Labor Economics*, 34(S1): S361–S401.
**Arteaga, Carolina.** 2018. "The Effect of Human Capital on Earnings: Evidence from a Reform in Colombia's top University." *Journal of Public Economics*, 157: 212-225.

**Avery, Christopher, Oded Gurantz, Michael Hurwitz, and Jonathan Smith.** 2018. "Shifting College Majors in Response to Advanced Placement Exam Scores." *Journal of Human Resources*, 53(4): 918-956.

**Barrera, Felipe, and Hernando Bayona.** 2019. "Signaling or better human capital: Evidence from Colombia." *Economics of Education Review*, 70: 20-34.

**Bassi, Vittorio, and Aisha Nansamba.** 2022. "Screening and Signaling Non-Cognitive Skills: Experimental Evidence from Uganda." *The Economic Journal*, 132: 471-511.

**Beaudry, Paul, and John DiNardo.** 1991. "The Effect of Implicit Contracts on the Movement of Wages Over the Business Cycle: Evidence from Micro Data." *Journal of Political Economy*, 99(4): 665-688.

**Beuchert, Louise, Tine Louise Mundbjerg Eriksen, and Morten Visby Krægpøth.** 2020. "The impact of standardized test feedback in math: Exploiting a natural experiment in 3rd grade." *Economics of Education Review*, 77: 102017.

**Blair, Peter Q., and Bobby W. Chung.** 2019. "How Much of Barrier to Entry is Occupational Licensing?" *British Journal of Industrial Relations*, 57(4): 919-943.

**Blair, Peter Q., and Bobby W. Chung.** 2021. "A Model of Occupational Licensing and Statistical Discrimination." *AEA Papers and Proceedings*, 111: 201-05.

**Blair, Peter Q., and Bobby W. Chung.** 2022. "Job Market Signaling through Occupational Licensing." *The Review of Economics and Statistics*, 1-45.

**Blair, Peter Q, and Mischa Fisher.** 2022. "Does Occupational Licensing Reduce Value Creation on Digital Platforms?" National Bureau of Economic Research Working Paper 30388.

**Bordon, Paola, and Breno Braga.** 2020. "Employer learning, statistical discrimination and university prestige." *Economics of Education Review*, 77: 101995.

**Calonico, Sebastian, Matias Cattaneo, and Max Farrell.** 2020. "Optimal Bandwidth Choice for Robust Bias-corrected Inference in Regression Discontinuity Designs." *The Econometrics Journal*, 23: 192-210.

**Calonico, Sebastian, Matias Cattaneo, Max Farrell, and Roció Titiunik.** 2019. "Regression Discontinuity Designs Using Covariates." *Review of Economics and Statistics*, 101(3): 442-451.

**Camacho, Adriana, Julian Messina, and Juan P. Uribe.** 2017. "The Expansion of Higher Education in Colombia: Bad Students or Bad Programs?" *Documentos de Trabajo de Universidad de los Andes*, 015352.

**Carranza, Eliana, Robert Garlick, Kate Orkin, and Neil Rankin.** 2022. "Job Search and Hiring with Limited Information about Workseekers' Skills." *American Economic Review*, 112(11): 3547-83.

**Cattaneo, Matias, Michael Jansson, and Xinwei Ma.** 2020. "Simple Local Polynomial Density Estimators." *Journal of the American Statistical Association*, 115(531): 1449-1455.
Chan, Ho Fai, Bruno S. Frey, Jana Gallus, and Benno Torgler. 2014. "Academic honors and performance." *Labour Economics*, 31: 188–204.

Cheng, M, J Fan, and J Maroon. 1997. "On Automatic Boundary Corrections." *Annals of Statistics*, 25: 1691–1708.

Chetty, Raj, David J Deming, and John N Friedman. 2023. "Diversifying Society's Leaders? The Determinants and Causal Effects of Admission to Highly Selective Private Colleges." National Bureau of Economic Research Working Paper 31492.

Chung, Bobby W. 2022. "The costs and potential benefits of occupational licensing: A case of real estate license reform." *Labour Economics*, 76: 102172.

Clark, D., and P. Martorell. 2014. "The Signaling Value of a High School Diploma." *Journal of Political Economy*, 122(2): 282–318.

Deming, David J., Noam Yuchtman, Amira Abulafi, Claudia Goldin, and Lawrence F. Katz. 2016. "The Value of Postsecondary Credentials in the Labor Market: An Experimental Study." *American Economic Review*, 106(3): 778–806.

DeVaro, Jed, and Michael Waldman. 2012. "The Signaling Role of Promotions: Further Theory and Empirical Evidence." *Journal of Labor Economics*, 30(1): 91–147.

Diaz, Edgar. 2003. "Ley 749, Modelo Internacional." *El Tiempo*.

Farber, Henry S., and Robert Gibbons. 1996. "Learning and Wage Dynamics." *Quarterly Journal of Economics*, 111: 1007–1047.

Fedesarrollo. 2013. "Informe Mensual del Mercado Laboral."

Feng, Andy, and Georg Graetz. 2017. "A question of degree: The effects of degree class on labor market outcomes." *Economics of Education Review*, 61: 140–161.

Freier, Ronny, Mathias Schumann, and Thomas Siedler. 2015. "The Earnings Returns to Graduating with Honors - Evidence from Law Graduates." *Labour Economics*, 34: 39–50.

Ge, Suqin, Elliott Isaac, and Amalia Miller. 2022. "Elite Schools and Opting In: Effects of College Selectivity on Career and Family Outcomes." *Journal of Labor Economics*, 40(S1): S383–S427.

Gibbons, Robert, and Michael Waldman. 1999a. "Careers in Organizations: Theory and Evidences." In *Handbook of Labor Economics*. , ed. Orley Ashenfelter and David Card. North-Holland.

Gibbons, Robert, and Michael Waldman. 1999b. "A theory of Wage and Promotion Dynamics Inside Firms." *Quarterly Journal of Economics*, 114(4): 1321–1358.

Gibbons, Robert, and Michael Waldman. 2006. "Enriching a Theory of Wage and Promotion Dynamics inside Firms." *Journal of Labor Economics*, 24(1): 59–107.

Gittleman, Maury, Mark A. Klee, and Morris M. Kleiner. 2018. "Analyzing the Labor Market Outcomes of Occupational Licensing." *Industrial Relations: A Journal of Economy and Society*, 57(1): 57–100.

Graetz, Georg. 2021. "On the interpretation of diploma wage effects estimated by regression discontinuity designs." *Canadian Journal of Economics/Revue canadienne d'économique*, 54(1): 228–258.
Haltiwanger, John, Henry Hyatt, and Erika McEntarfer. 2018. "Who Moves Up the Job Ladder?" *Journal of Labor Economics*, 36(S1): S301-S336.

Hansen, Anne Toft, Ulrik Hvidman, and Hans Henrik Sievertsen. 2024. "Grades and employer learning." *Journal of Labor Economics*, 42(3): 659-682.

Heller, Sara B, and Judd B Kessler. 2021. "Information Frictions and Skill Signaling in the Youth Labor Market." National Bureau of Economic Research Working Paper 29579.

Hungerford, T., and G. Solon. 1987. "Sheepskin Effects in the Returns to Education." *Review of Economics and Statistics*, 69(1): 175-177.

Imbens, Guido, and Thomas Lemieux. 2008. "Regression discontinuity designs A guide to practice." *Journal of Econometrics*, 142: 615-635.

Jaeger, David, and Marianne Page. 1996. "Degrees Matter: New Evidence on Sheep-skin Effect in the Returns to Education." *Review of Economics and Statistics*, 78(4): 733-740.

Jepsen, C., P. Mueser, and K. Troske. 2016. "Labor Market Returns to the GED using Regression Discontinuity Analysis." *Journal of Political Economy*, 124(3): 621-649.

Kane, T.J., and C.E. Rouse. 1995. "Labor market returns to two- and four-year college." *American Economic Review*, 85(3): 665-674.

Khoo, Pauline, and Ben Ost. 2018. "The Effect of Graduating with Honors on Earnings." *Labour Economics*, 55: 149-162.

Kleiner, Morris M., and Alan B. Krueger. 2013. "Analyzing the Extent and Influence of Occupational Licensing on the Labor Market." *Journal of Labor Economics*, 31(2): S173-S202.

Kleiner, Morris M, and Evan J Soltas. 2023. "A Welfare Analysis of Occupational Licensing in U.S. States." *The Review of Economic Studies*. rdad015.

Landaud, Fanny, Éric Maurin, Barton Willage, and Alexander Willén. 2024. "The Value of a High School GPA." *The Review of Economics and Statistics*, 1-24.

Londoño-Vélez, Juliana, Catherine Rodríguez, and Fabio Sánchez. 2020. "Upstream and Downstream Impacts of College Merit-Based Financial Aid for Low-Income Students: Ser Pilo Paga in Colombia." *American Economic Journal: Economic Policy*, 12(2): 193-227.

Londoño-Vélez, Juliana, Catherine Rodríguez, Fabio Sánchez, and Luis Esteban Álvarez. 2023. "Financial Aid and Social Mobility: Evidence from Colombia's Ser Pilo Paga." *Mimeo*.

Machado, Cecilia, Germán Reyes, and Evan Riehl. 2025. "The Direct and Spillover Effects of Large-Scale Affirmative Action at an Elite Brazilian University." *Journal of Labor Economics*, 43(2): 391-431.

MacLeod, Bentley, Evan Riehl, Juan Saavedra, and Miguel Urquiola. 2017. "The big sort: College reputation and labor market outcomes." *American Economic Journal: Applied Economics*, 9(3): 223-261.

Neckermann, Susanne, Reto Cueni, and Bruno S. Frey. 2014. "Awards at work." *Labour Economics*, 31: 205-217.
**Oreopoulos, Philip, Till von Wachter, and Andrew Heisz.** 2012. "The Short- and Long-Term Career Effects of Graduating in a Recession." *American Economic Journal: Applied Economics*, 4(1): 1–29.

**Owen, Ann L.** 2010. "Grades, Gender, and Encouragement: A Regression Discontinuity Analysis." *The Journal of Economic Education*, 41(3): 217–234.

**Pallais, Amanda.** 2014. "Inefficient Hiring in Entry-Level Labor Markets." *American Economic Review*, 104(11): 3565–99.

**Papay, John P., Richard J. Murnane, and John B. Willett.** 2016. "The Impact of Test Score Labels on Human-Capital Investment Decisions." *The Journal of Human Resources*, 51(2): 357–388.

**Pietro, Giorgio Di.** 2017. "Degree classification and recent graduates' ability: Is there any signalling effect?" *Journal of Education and Work*, 30(5): 501–514.

**Pinkston, Joshua C.** 2009. "A Model of Asymmetric Employer Learning with Testable Implications." *The Review of Economic Studies*, 76(1): 367–394.

**Schwandt, Hannes, and Till von Wachter.** 2019. "Unlucky Cohorts: Estimating the Long-Term Effects of Entering the Labor Market in a Recession in Large Cross-Sectional Data Sets." *Journal of Labor Economics*, 37(S1): S161–98.

**Smith, Jonathan, Michael Hurwitz, and Christopher Avery.** 2017. "Giving College Credit Where It Is Due: Advanced Placement Exam Scores and College Outcomes." *Journal of Labor Economics*, 35(1): 67–147.

**Solis, Alex.** 2017. "Credit Access and College Enrollment." *Journal of Political Economy*, 125(2): 562–622.

**Spence, Michael.** 1973. "Job Market Signaling." *The Quarterly Journal of Economics*, 87(3): 355–374.

**Spence, Michael.** 1974. "Competitive and Optimal Responses to Signaling: An Analysis of Efficiency and Distribution." *Journal of Economic Theory*, 7(3): 296–332.

**Tan, Brandon Joel.** 2023. "The Consequences of Letter Grades for Labor Market Outcomes and Student Behavior." *Journal of Labor Economics*, 41(3): 565–588.

**Tenjo, Jaime, Oriana Álvarez, Alejandro Gaviria Jaramillo, and María C. Jiménez.** 2017. "Evolution of Returns to Education in Colombia (1976-2014)." *Coyuntura Económica*, 47: 15–48.

**Tyler, John H., Richard J. Murnane, and John B. Willett.** 2000. "Estimating the Labor Market Signaling Value of the GED." *Quarterly Journal of Economics*, 115: 431–468.

**Weinstein, Russell.** 2025. "The Great Recession and the Widening Income Gap between Alumni of Elite and Less Selective Universities." *American Economic Journal: Economic Policy*, 17(1): 216–41.

**Zimmerman, Seth D.** 2019. "Elite Colleges and Upward Mobility to Top Jobs and Top Incomes." *American Economic Review*, 109(1): 1–47.
# Tables & Figures

## Tables

<table><caption>Table 1: Effect of National Distinction Award on Early-Career Earnings</caption><thead><tr><th></th><th colspan="6">Dependent Variable : Log Earnings</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td>National Award</td><td>0.095***<br>[0.029]</td><td>0.090***<br>[0.026]</td><td>0.086***<br>[0.027]</td><td>0.072***<br>[0.025]</td><td>0.071***<br>[0.025]</td><td>0.071***<br>[0.026]</td></tr><tr><td>Observations</td><td>198,742</td><td>198,742</td><td>198,742</td><td>198,742</td><td>198,742</td><td>198,742</td></tr><tr><td>Bandwidth</td><td>0.449</td><td>0.449</td><td>0.449</td><td>0.449</td><td>0.449</td><td>0.449</td></tr><tr><td>Control Obs.</td><td>3807</td><td>3807</td><td>3807</td><td>3807</td><td>3807</td><td>3807</td></tr><tr><td>Treatment Obs.</td><td>1538</td><td>1538</td><td>1538</td><td>1538</td><td>1538</td><td>1538</td></tr><tr><td>Area x Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td></td><td></td><td></td></tr><tr><td>Field x Year FE</td><td></td><td></td><td></td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Test Scores</td><td></td><td>Yes</td><td>Yes</td><td></td><td>Yes</td><td>Yes</td></tr><tr><td>Covariates</td><td></td><td></td><td>Yes</td><td></td><td></td><td>Yes</td></tr></tbody></table>

Notes. This table presents regression discontinuity estimates of the effect of the national distinction award on early-career earnings. The outcome variable is the log of early-career earnings, defined as the first observed earnings after graduating college. The running variable is the distance of scores (measured in standard deviations) from the cutoff for the national distinction award. Test scores (controls) include scores from the high school exit exam and scores from the core component tests (Reading and English Proficiency) of the college exit exam. Scores from the core component tests are not used by the exam authorities to confer the national distinction award. Covariates include age at test date, gender, socioeconomic status indicators, number of semesters in college, and mother's education indicators. Estimates use local linear regressions and an Epanechnikov kernel. The common bandwidth across columns corresponds to the MSE-optimal bandwidth computed using the specification in column (3). Robust standard errors are clustered at the area-year level and displayed in squared brackets. * $p<0.10$, ** $p<0.05$, *** $p<0.01$.
<table><caption>**Table 2: National Distinction Award and College Reputation**</caption><thead><tr><th rowspan="4"></th><th colspan="4">Dependent Variable : Log Earnings</th><th colspan="2">Cross-sample Comparison :</th></tr><tr><th rowspan="3">Full Sample</th><th colspan="3">College Ranking :</th><th colspan="2">Top 5 Non-awardees vs.</th></tr><tr><th rowspan="2">Top 5</th><th rowspan="2">Top 6-20</th><th rowspan="2">Below 20</th><th colspan="2">Top 6-20 Awardees</th></tr><tr><th>Top 6-20 Awardees</th><th>Below 20 Awardees</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td>National Award</td><td>0.086***<br>[0.027]</td><td>0.022<br>[0.044]</td><td>0.107<br>[0.076]</td><td>0.158**<br>[0.063]</td><td>0.083<br>[0.057]</td><td>-0.008<br>[0.055]</td></tr><tr><td>Observations</td><td>198,742</td><td>26,577</td><td>30,278</td><td>141,887</td><td>26,074</td><td>26,094</td></tr><tr><td>Bandwidth</td><td>0.449</td><td>0.411</td><td>0.324</td><td>0.290</td><td>0.292</td><td>0.321</td></tr><tr><td>Control Obs.</td><td>3807</td><td>1401</td><td>593</td><td>684</td><td>848</td><td>1003</td></tr><tr><td>Treatment Obs.</td><td>1538</td><td>668</td><td>342</td><td>317</td><td>324</td><td>340</td></tr><tr><td>Mean Control</td><td>14.17</td><td>14.22</td><td>14.22</td><td>14.06</td><td>14.22</td><td>14.23</td></tr></tbody></table>

*Notes.* The outcome variable is the log of early-career earnings, defined as the first observed earnings after graduating college. Column (1) replicates the main results (Column 3 of Table 1). Regression discontinuity estimates within samples defined by college ranking are displayed in columns (2) to (6). Columns (2) to (4) show estimates for students in schools within the same tier of the college ranking. Colleges are divided into three categories: top tier (schools in the top 5), middle tier (schools ranked 6th to 20th), and bottom tier (schools below the top 20). Columns (5) and (6) display estimates for award recipients in middle- and bottom-tier colleges with respect to non-recipients in top-tier schools. Estimates across columns control for area-year (of exam) fixed effects, test scores, and covariates. Test scores include scores from the high school exit exam and scores from the core component tests (Reading and English Proficiency) of the college exit exam. Covariates include age at test date, gender, socioeconomic status indicators, number of semesters in college, and mother's education indicators. Regression discontinuity estimates use local linear regressions, an Epanechnikov kernel, and MSE-optimal bandwidths. Robust standard errors are clustered at the area-year level and displayed in brackets. * *p*<0.10, ** *p*<0.05, *** *p*<0.01.
<table><caption>**Table 3: Effects on the Allocation of Skills**</caption><thead><tr><th rowspan="3"></th><th colspan="6">Dependent Variable :</th></tr><tr><th colspan="3">Field-Industry Match</th><th colspan="3">Log Earnings</th></tr><tr><th>Full Sample</th><th colspan="2">College Ranking :</th><th colspan="2">Type of Skills :</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td>National Award</td><td>0.049*<br>[0.026]</td><td>0.015<br>[0.041]</td><td>0.025<br>[0.071]</td><td>0.134*<br>[0.069]</td><td>0.082***<br>[0.031]</td><td>0.042<br>[0.060]</td></tr><tr><td>Observations</td><td>187,331</td><td>25,664</td><td>29,314</td><td>132,353</td><td>122,779</td><td>75,963</td></tr><tr><td>Bandwidth</td><td>0.385</td><td>0.475</td><td>0.395</td><td>0.297</td><td>0.519</td><td>0.357</td></tr><tr><td>Control Obs.</td><td>2916</td><td>1631</td><td>770</td><td>676</td><td>3828</td><td>540</td></tr><tr><td>Treatment Obs.</td><td>1333</td><td>702</td><td>363</td><td>297</td><td>1334</td><td>259</td></tr><tr><td>Mean Control</td><td>0.411</td><td>0.437</td><td>0.405</td><td>0.396</td><td>14.14</td><td>14.28</td></tr></tbody></table>

Notes. This table presents regression discontinuity estimates of the effect of the national distinction award on the likelihood of being employed in an industry related to the student's field of study (columns 1 to 4), and on early-career earnings by type of field (columns 5 and 6). The outcome variable in columns (1) to (4) is an indicator equal to one if a worker's industry matches the worker's field (college major). Details about the field-industry match indicator are presented in Appendix G. The outcome variable in columns (5) and (6) is the log of early-career earnings, defined as the first observed earnings after graduating college. Estimates across columns control for area-year (of exam) fixed effects, test scores, and covariates. Test scores include scores from the high school exit exam and scores from the core component tests (Reading and English Proficiency) of the college exit exam. Covariates include age at test date, gender, socioeconomic status indicators, number of semesters in college, and mother's education indicators. All estimates use local linear regressions, an Epanechnikov kernel, and MSE-optimal bandwidths. Robust standard errors are clustered at the area-year level and displayed in brackets. * *p*<0.10, ** *p*<0.05, *** *p*<0.01.
<table><caption>**Table 4:** Effect of Generic Skills Distinctions on Early-Career Earnings</caption><thead><tr><th rowspan="3"></th><th colspan="4">Dependent Variable : Log Earnings</th></tr><tr><th rowspan="2">Full Sample</th><th colspan="3">College Ranking :</th></tr><tr><th>Top 5</th><th>Top 6-20</th><th>Below 20</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>National Award</td><td>-0.010<br>[0.022]</td><td>0.051<br>[0.036]</td><td>0.006<br>[0.038]</td><td>-0.035<br>[0.027]</td></tr><tr><td>Observations</td><td>71,416</td><td>9,764</td><td>13,860</td><td>47,792</td></tr><tr><td>Bandwidth</td><td>0.435</td><td>0.634</td><td>0.488</td><td>0.371</td></tr><tr><td>Control Obs.</td><td>4773</td><td>1776</td><td>1660</td><td>2315</td></tr><tr><td>Treatment Obs.</td><td>4018</td><td>1064</td><td>1267</td><td>1613</td></tr><tr><td>Mean Control</td><td>14.10</td><td>14.09</td><td>14.21</td><td>14.05</td></tr></tbody></table>

*Notes.* This table shows estimates of the effect of distinctions awarded to top performers in four generic skills tests on early-career earnings. The outcome variable is the log of early-career earnings, defined as the first observed earnings after graduating from college. Estimates across columns control for area-year (of exam) fixed effects, test scores, and covariates. Test scores include scores from the high school exit exam and scores from the writing and reading comprehension tests of the college exit exam. Exam authorities do not grant distinctions to top scores in the writing and reading tests. Covariates include age at test date, gender, socioeconomic status indicators, number of semesters in college, and mother's education indicators. Estimates in column (1) use the full sample of colleges. Columns (2) to (4) separate the sample by college reputation into Top 5, Top 6-20, and Below Top 20 colleges. Robust standard errors are clustered at the area-year level and displayed in brackets. * *p*<0.10, ** *p*<0.05, *** *p*<0.01.
<table><caption>**Table 5:** Effects on Additional Accumulation of Skills</caption><thead><tr><th rowspan="3"></th><th colspan="5">Dependent Variable:</th></tr><tr><th rowspan="2">Months to<br>College<br>Grad. Date<br>(1)</th><th colspan="3">Number of Subjects by Graduation</th><th rowspan="2">Graduate<br>Education<br>(5)</th></tr><tr><th>Total<br>Registered<br>(2)</th><th colspan="2">Registered After Exam:</th></tr><tr><th></th><th></th><th></th><th>Total<br>(3)</th><th>Passed<br>(4)</th><th></th></tr></thead><tbody><tr><td colspan="6"><em>Full Sample</em></td></tr><tr><td>National Award</td><td>-0.001<br>[0.499]</td><td>-0.498<br>[0.949]</td><td>0.001<br>[0.270]</td><td>-0.005<br>[0.262]</td><td>0.017<br>[0.032]</td></tr><tr><td>Observations</td><td>198,742</td><td>146,764</td><td>146,764</td><td>146,764</td><td>198,742</td></tr><tr><td>Bandwidth</td><td>0.525</td><td>0.368</td><td>0.476</td><td>0.458</td><td>0.351</td></tr><tr><td>Mean Control</td><td>11.33</td><td>59.83</td><td>6.590</td><td>6.228</td><td>0.253</td></tr><tr><td colspan="6"><em>Top 5</em></td></tr><tr><td>National Award</td><td>-1.112<br>[0.760]</td><td>-0.031<br>[1.350]</td><td>0.149<br>[0.471]</td><td>0.109<br>[0.429]</td><td>0.062<br>[0.054]</td></tr><tr><td>Observations</td><td>26,577</td><td>22,753</td><td>22,753</td><td>22,753</td><td>26,577</td></tr><tr><td>Bandwidth</td><td>0.374</td><td>0.348</td><td>0.392</td><td>0.413</td><td>0.310</td></tr><tr><td>Mean Control</td><td>11.20</td><td>57.92</td><td>6.361</td><td>6.100</td><td>0.261</td></tr><tr><td colspan="6"><em>Top 6-20</em></td></tr><tr><td>National Award</td><td>1.628<br>[1.542]</td><td>-1.257<br>[2.242]</td><td>0.430<br>[0.674]</td><td>0.442<br>[0.699]</td><td>-0.065<br>[0.053]</td></tr><tr><td>Observations</td><td>30,278</td><td>25,372</td><td>25,372</td><td>25,372</td><td>30,278</td></tr><tr><td>Bandwidth</td><td>0.345</td><td>0.343</td><td>0.437</td><td>0.388</td><td>0.446</td></tr><tr><td>Mean Control</td><td>12.55</td><td>60.86</td><td>6.710</td><td>6.518</td><td>0.302</td></tr><tr><td colspan="6"><em>Below Top 20</em></td></tr><tr><td>National Award</td><td>-0.226<br>[1.377]</td><td>-0.303<br>[2.216]</td><td>-1.106<br>[0.873]</td><td>-1.119<br>[0.813]</td><td>0.065<br>[0.047]</td></tr><tr><td>Observations</td><td>141,887</td><td>98,639</td><td>98,639</td><td>98,639</td><td>141,887</td></tr><tr><td>Bandwidth</td><td>0.387</td><td>0.386</td><td>0.312</td><td>0.303</td><td>0.311</td></tr><tr><td>Mean Control</td><td>10.95</td><td>62.17</td><td>6.509</td><td>6.095</td><td>0.212</td></tr></tbody></table>

Notes. This table presents regression discontinuity estimates of the effect of the national distinction award on different measures of skills accumulation. The outcome variable in column (1) is the number of months from the date of the exam to the student's graduation date. In column (2), the outcome is the number of subjects in a student's academic history by the time they graduate. In columns (3) and (4), the outcomes correspond to the number of subjects a student registered for and successfully passed after taking the college exit exam. The outcome in columns (5) is an indicator equal to one if a student completes a graduate program within five years from the date of the exam. Estimates are presented in four panels: one for the full sample of colleges and three disaggregated by college reputation—Top 5, Top 6–20, and Below Top 20. Estimates in all columns control for area-year (of exam) fixed effects, test scores, and covariates. Test scores include scores from the high school exit exam and scores from the core component tests (Reading and English Proficiency) of the college exit exam. Covariates include age at test date, gender, socioeconomic status indicators, number of semesters in college, and mother's education indicators. All estimates use local linear regressions, an Epanechnikov kernel, and MSE-optimal bandwidths. Robust standard errors are clustered at the area-year level and displayed in brackets. * *p*<0.10, ** *p*<0.05, *** *p*<0.01.
<table><caption>**Table 6:** Effects on the Probability of Switching Jobs and Job Characteristics After Switching</caption><thead><tr><th rowspan="3"></th><th colspan="9">Dependent Variable :</th></tr><tr><th colspan="3">Log Earnings accross time, τ</th><th rowspan="2">Worker Switch Employers<br>Once or Twice</th><th colspan="3">Employer's Wage Premium Across Time, τ</th></tr><tr><th>τ = 1</th><th>τ = 2</th><th>τ = 3</th><th>First Employer</th><th>Δ Future Employers</th><th>τ = 1</th><th>τ = 2</th><th>τ = 3</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th></th><th></th></tr></thead><tbody><tr><td colspan="10"><em>Full Sample</em></td></tr><tr><td>National Award</td><td>0.086***<br>[0.027]</td><td>0.056*<br>[0.033]</td><td>0.093**<br>[0.042]</td><td>-0.013<br>[0.031]</td><td>0.034***<br>[0.012]</td><td>-0.009<br>[0.009]</td><td>-0.004<br>[0.012]</td><td></td><td></td></tr><tr><td>Observations</td><td>198,742</td><td>172,271</td><td>147,413</td><td>162,495</td><td>185,995</td><td>162,495</td><td>138,204</td><td></td><td></td></tr><tr><td>Bandwidth</td><td>0.449</td><td>0.449</td><td>0.449</td><td>0.449</td><td>0.449</td><td>0.449</td><td>0.449</td><td></td><td></td></tr><tr><td>Mean Control</td><td>14.17</td><td>14.33</td><td>14.47</td><td>0.482</td><td>0.715</td><td>0.030</td><td>0.048</td><td></td><td></td></tr><tr><td colspan="10"><em>Top 5</em></td></tr><tr><td>National Award</td><td>0.022<br>[0.044]</td><td>-0.001<br>[0.045]</td><td>0.065<br>[0.043]</td><td>-0.041<br>[0.052]</td><td>0.028*<br>[0.017]</td><td>-0.012<br>[0.017]</td><td>0.002<br>[0.017]</td><td></td><td></td></tr><tr><td>Observations</td><td>26,577</td><td>23,432</td><td>20,292</td><td>22,044</td><td>25,037</td><td>22,044</td><td>18,860</td><td></td><td></td></tr><tr><td>Bandwidth</td><td>0.411</td><td>0.411</td><td>0.411</td><td>0.411</td><td>0.411</td><td>0.411</td><td>0.411</td><td></td><td></td></tr><tr><td>Mean Control</td><td>14.22</td><td>14.39</td><td>14.53</td><td>0.483</td><td>0.751</td><td>0.029</td><td>0.043</td><td></td><td></td></tr><tr><td colspan="10"><em>Top 6-20</em></td></tr><tr><td>National Award</td><td>0.107<br>[0.076]</td><td>0.079<br>[0.089]</td><td>-0.004<br>[0.075]</td><td>-0.036<br>[0.064]</td><td>0.040*<br>[0.023]</td><td>-0.010<br>[0.021]</td><td>-0.008<br>[0.028]</td><td></td><td></td></tr><tr><td>Observations</td><td>30,278</td><td>26,700</td><td>23,003</td><td>25,218</td><td>28,641</td><td>25,218</td><td>21,603</td><td></td><td></td></tr><tr><td>Bandwidth</td><td>0.324</td><td>0.324</td><td>0.324</td><td>0.324</td><td>0.324</td><td>0.324</td><td>0.324</td><td></td><td></td></tr><tr><td>Mean Control</td><td>14.22</td><td>14.37</td><td>14.51</td><td>0.502</td><td>0.725</td><td>0.026</td><td>0.052</td><td></td><td></td></tr><tr><td colspan="10"><em>Below Top 20</em></td></tr><tr><td>National Award</td><td>0.158**<br>[0.063]</td><td>0.150**<br>[0.072]</td><td>0.191**<br>[0.096]</td><td>0.046<br>[0.062]</td><td>0.062**<br>[0.024]</td><td>0.013<br>[0.026]</td><td>0.019<br>[0.037]</td><td></td><td></td></tr><tr><td>Observations</td><td>141,887</td><td>122,139</td><td>104,118</td><td>115,233</td><td>132,317</td><td>115,233</td><td>97,741</td><td></td><td></td></tr><tr><td>Bandwidth</td><td>0.290</td><td>0.290</td><td>0.290</td><td>0.290</td><td>0.290</td><td>0.290</td><td>0.290</td><td></td><td></td></tr><tr><td>Mean Control</td><td>14.06</td><td>14.20</td><td>14.33</td><td>0.467</td><td>0.659</td><td>0.031</td><td>0.055</td><td></td><td></td></tr></tbody></table>

Notes. This table presents regression discontinuity estimates of the effect of the national distinction award on early career earnings (columns 1 to 3), the probability of switching employers (column 4) and on a measure of employer's productivity over time (columns 5 to 7) across levels of college reputation. In Columns (1) to (3) the outcome is the log of the first three observed earnings of a worker in an unbalanced sample. In column (4), the outcome is an indicator equal to one if the worker switches employers once or twice after they graduate from college. The outcome in column (5) corresponds to the productivity of the first observed employer. In columns (6) and (7), the outcome is the difference between the first employer's productivity and the second and third employer's. The measure of productivity in this table corresponds to the within-industry earnings percentile rank of firms. Estimates are presented in four panels: one for the full sample of colleges and three disaggregated by college reputation—Top 5, Top 6–20, and Below Top 20. Estimates across columns control for area-year (of exam) fixed effects, test scores, and covariates. Test scores include scores from the high school exit exam and scores from the core component tests (Reading and English Proficiency) of the college exit exam. Covariates include age at test date, gender, socioeconomic status indicators, number of semesters in college, and mother's education indicators. Estimates use local linear regressions and an Epanechnikov kernel. Bandwidths across columns are computed by minimizing the Mean Square Errors (MSE) of the linear specification. Robust standard errors are clustered at the area-year level and displayed in brackets. * *p*<0.10, ** *p*<0.05, *** *p*<0.01.
Figures

<figure><img src="image_2.png" /><figcaption>Figure 1: Effect of the National Distinction Award on Early-Career Earnings</figcaption></figure>

*Notes.* This figure plots early-career earnings as a function of the distance of scores (in standard deviations) from the cutoff for the national distinction award. Early-career earnings are defined as the first observed earnings after graduating from college. Panel (a) represents the regression discontinuity in log earnings without including any controls. Log earnings are rescaled by subtracting the mean to facilitate interpretation. Panel (b) represents the regression discontinuity in log earnings after controlling for area-year (of exam) fixed effects, test scores, and other covariates. Test scores include scores from the high school exit exam and scores from the core component tests (Reading and English Proficiency) of the college exit exam. Scores from the core component tests are not used by exam authorities to confer the national distinction award. Covariates include age at test date, gender, socioeconomic status indicators, number of semesters in college, and mother's education indicators. Plotted dots represent local averages of log earnings within equidistant bins of the running variable. A width of 0.2 is used to compute local averages. Solid lines represent local linear regressions using a bandwidth equal to 0.449 and an Epanechnikov kernel. 95 percent confidence intervals are displayed around the local regressions on both sides of the cutoff.
<figure><img src="image_3.png" /><figcaption>**Figure 2:** Robustness of the Effect of the National Distinction Award on Early-Career Earnings</figcaption></figure>

*Notes.* This figure shows that our estimates of the effect of the national distinction award on earnings are robust to changes in the tuning parameters of the research design and to different control variables. The outcome variable is the log of early-career earnings, defined as the first observed earnings after graduating college. Plotted dots represent regression discontinuity estimates using local polynomial regressions of different order and an Epanechnikov kernel. The MSE-optimal bandwidth used for each estimate is displayed at the bottom of the specification. The running variable is the distance of scores (measured in standard deviations) from the cutoff for the national distinction award. Test scores (controls) include scores from the high school exit exam and scores from the core component tests (Reading and English Proficiency) of the college exit exam. Covariates include age at test date, gender, socioeconomic status indicators, number of semesters in college, and mother's education indicators. 90 and 95 percent confidence intervals, based on robust standard errors clustered at the area-year level, are displayed around coefficients.
<figure><img src="image_4.png" /><figcaption>Figure 3: Heterogeneous Effects of the National Distinction Award</figcaption></figure>

*Notes.* The outcome variable is the log of early-career earnings, defined as the first observed earnings after graduating from college. Bars represent regression discontinuity estimates using local linear regressions, an Epanechnikov kernel, and MSE-optimal bandwidths. Estimates are computed within the subsample defined by the characteristic displayed at the top of each bar. All specifications control for area-year (of exam) fixed effects, college reputation, and a set of test scores, including scores from the high school exit exam and scores from the core component tests (Reading and English Proficiency) of the college exit exam. 95 percent confidence intervals, based on robust standard errors clustered at the area-year level, are displayed around estimates.
<figure><img src="image_5.png" /><figcaption>Figure 4: Persistence of the Effect on First τ Observed Earnings</figcaption></figure>

*Notes.* This figure presents regression discontinuity estimates of the effect of the national distinction award on earnings over time. Plotted dots correspond to independent regressions. The outcome variable is the log of the first five earnings observed after a student graduates from college. Coefficients are estimated using an “unbalanced” sample of all information available each time earnings are observed. Regressions control for area-year (of exam) fixed effects, test scores, and covariates. Test scores include scores from the high school exit exam and scores from the core component tests (Reading and English Proficiency) of the college exit exam. Covariates include age at test date, gender, socioeconomic status indicators, number of semesters in college, and mother’s education indicators. All estimates use local linear regressions, an Epanechnikov kernel, and MSE-optimal bandwidths. 90 and 95 percent confidence intervals, based on robust standard errors clustered at the area-yer level, are displayed around coefficients.