IDENTIFYING PREFERENCES FOR EQUAL

COLLEGE ACCESS, INCOME, Y

INCOME EQUALITY

Bernardo Lara

(Autor correspondiente)

School of Business and

Ciencias económicas

Universidad de Talca

Santiago, Chile

blara@utalca.cl

Kenneth A. Shores

(Autor correspondiente)

Health and Human

Desarrollo

Universidad Estatal de Pensilvania

University Park, Pensilvania 16802

kshores@psu.edu

Abstracto
Revealed preferences for equal college access may be due to
beliefs that equal access increases societal income or income
equality. To isolate preferences for those goods, we implement
an online discrete choice experiment using social statistics gener-
ated from true variation among commuting zones. encontramos que,
ceteris paribus, the average income that individuals are willing to
sacriﬁce is (1) $4,984 to increase higher education enrollment by 1 standard deviation (14 por ciento); (2) $1,168 to decrease rich/poor
gaps in higher education enrollment by 1 standard deviation (8
por ciento); y (3) $2,900 to decrease the 90/10 income inequal- ity ratio by 1 standard deviation (1.66). Además, we ﬁnd that political aﬃliation is an important moderator of preferences for equality. While both Democrats and Republicans are willing to trade over $4,000 to increase higher education enrollment by 1
standard deviation, Democrats are willing to sacriﬁce nearly three
times more income to decrease either rich/poor gaps in higher
education enrollment or the 90/10 income inequality ratio by 1
standard deviation.

https://doi.org/10.1162/edfp_a_00271

270

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Bernardo Lara and Kenneth A. Shores

INTRODUCCIÓN

1 .
Suppose the government found itself with an unexpected budget surplus, and policy
makers consider three policies for spending this surplus. The ﬁrst policy considered is
intended to reduce college attendance gaps between high- and low-income individuals,
which could be accomplished by expanding ﬁnancial aid for low-income students (Dy-
Narciso 2003) while holding admissions rates constant. The second policy is intended to
decrease income inequality, which could be accomplished with an unconditional cash
transfer to low-income individuals.1 The third policy is intended to increase income for
everyone, which could be accomplished by a uniform tax rebate. In this stylized exam-
por ejemplo, the policy maker faces a decision between increasing equality of college access,
equality in income, or average income.

Supposing the social planner knows the actual costs and eﬀects for each of the poli-
cíes, two additional pieces of information are needed to determine which of the policies
should be pursued. Primero, the policy maker needs to know how much individuals value
each of the societal variables. Segundo, to make comparisons across diﬀerent social vari-
ables, the policy maker needs common units of measurement. With this information,
it would then be possible to quantify how much societal income individuals would be
willing to spend to improve each social value.

en este documento, we are concerned with individual preferences for equality of college
access, and how those preferences relate to preferences for other societal variables, en-
cluding income and income equality. Traditionally, data about preferences for distribu-
tions of social variables have been collected from opinion surveys, such as the General
Social Survey in the United States and the World Values Survey at the international
nivel. Mientras tanto, the academic community has focused mostly on understanding
preferences for equality in income and has not, a nuestro conocimiento, considered multi-
dimensional preferences for distributions of other variables, such as access to higher
education (Clark and D’Ambrosio 2015).

Information regarding individual preferences for multiple social variables is not
easily obtained from traditional opinion surveys, because of omitted variable bias. Primero,
preferences for equal college access can be confounded by preferences for either eﬃ-
ciency or equality in income. Por ejemplo, an individual who is interested in improving
college access for low-income students may believe that increased access has positive
spillovers on both eﬃciency and income equality, and is for those reasons desirable and
not desirable per se. Segundo, individuals make unobserved assumptions about the so-
cietal costs that a preferred distribution of college access or income would require. Re-
spondents may prefer equal income distributions, all else constant, but because they
believe that equality distorts incentives, they also expect societal costs to be large and,
por lo tanto, their revealed preferences for equal income will appear attenuated (Piketty
1995).

To recover preferences, we implement a survey-based discrete choice experiment
(DCE) that identiﬁes social preferences for equal access to higher education, eﬃciency,
and income equality. Survey respondents are asked to select between one of two so-
cieties. For each society a respondent sees, we randomly assign the values of four

Imbens, Frotar, and Sacerdote (2001) ﬁnd increases in unearned transfers have small eﬀects on earned income,
particularly among individuals with low earnings.

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271

Preference for Equal College Access and Income

societal statistics: average median family income (societal income), the ratio of aver-
age income of the 10 percent richest to the 10 percent poorest (90/10 income ratio or
income inequality), the enrollment rate in higher education (average education), y
the diﬀerence in higher education enrollment rates between children from families in
the 90th and 10th income percentiles (opportunity for higher education). Variation for
these statistics is derived from true variation among commuting zones in the United
Estados, using Census data and the education mobility data from Chetty et al. (2014).
Because societal statistics are randomly assigned, we avoid biases due to beliefs about
the relations among societal values or about the costs of equality. With these data, nosotros
obtain measurements of how much average household income individuals are willing
to sacriﬁce to improve other social values, thus providing a common metric for making
comparisons across diﬀerent domains.

encontramos que (1) individuals are willing to decrease average income by $4,984 to in- crease enrollment in higher education by 1 standard deviation (Dakota del Sur) (14 por ciento); (2) the average individual is willing to exchange $1,168 of average income to decrease gaps in
college enrollment by 1 Dakota del Sur (8 por ciento); y (3) the average individual is willing to ex-
cambiar $2,900 of average income to decrease the 90/10 income inequality ratio by 1 Dakota del Sur (1.66). Además, we evaluate “Rawlsian trades”—so named because of the distribu- tive priority Rawls gives to equality of opportunity over income equality in his theory— and ﬁnd the average individual is willing to increase gaps in college access by 2.49 SDs to reduce the 90/10 income ratio by 1 Dakota del Sur. We identify meaningful diﬀerences based on political aﬃliation. Although right- leaning voters care less about inequality (Kuziemko et al. 2015), this preference may be due to beliefs about societal costs and not inequality per se. Además, we know little about whether preferences for equality in college access and income correlate with political aﬃliation. We ﬁnd that Republicans have nearly lexicographic preferences for average income, meaning they are unwilling to trade any units of income for equality in either dimension. De este modo, Republicans are not equality averse because of perceived costs but because societal income is the most important social variable in their social welfare functions. Hacemos, sin embargo, ﬁnd overlap among partisans, as both Democrats and Republicans are willing to trade meaningful quantities of average income (encima $4,000) to increase enrollment in higher education by 1 Dakota del Sur (14 por ciento). Estos resultados
sugerir, between parties, there is an overlapping consensus with respect to increasing
average levels of education and a large chasm with respect to equalizing educational
opportunities or income.

Our primary result is that U.S. residents are willing to exchange meaningful
amounts of average income for other social variables, including overall levels of ed-
ucation (which is often viewed purely as a vehicle for increasing economic growth)
and reductions in inequality. Segundo, our results help clarify some confusion about
the relation between access to higher education and equality of income. When consid-
ered in isolation, individuals may indicate greater preferences for college access rela-
tive to equal income; sin embargo, our results indicate that some of this rank-ordering is
attributable to omitted variable bias. When respondents consider societal variables si-
multaneously, they are willing to pay over twice as much for equivalent reductions of
income inequality relative to college enrollment inequality. This implies that if there is
a public policy choice between a tax credit to reduce income inequality by 1 SD or an

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Bernardo Lara and Kenneth A. Shores

education intervention to reduce college enrollment gaps by 1 Dakota del Sur, all else constant, el
preferred policy choice would be the tax credit.

The next section reviews the most relevant background literature, y sección 3
provides a theoretical and empirical justiﬁcation for the focus on college access. Sección
4 details the experiment that was implemented. Sección 5 describes the data and the
econometric methodology, y sección 6 provides and discusses the results.

2 . B AC K G RO U N D L I T E R AT U R E
En general, academic research has focused on preferences for income equality and not
equal educational opportunity. Clark and D’Ambrosio (2015) classify research about
preferences for income equality into two ﬁelds: comparative and normative. En el
comparative case, individuals think of themselves as the relevant reference group and
consider whether their place in a speciﬁc distribution of income is better or worse than
alternative distributions. In the normative case, the relevant reference group is an ideal
standard; por lo tanto, individuals consider whether a distribution of income is better or
worse relative to the standard and not with respect to their own position.

Our paper is most closely related to the normative case. In this branch of research
there are two approaches. The ﬁrst one estimates empirical correlations between a soci-
ety’s level of income equality and its members’ observed level of well-being. Contextual
factors—such as credit constraints (Benabou 2000), observed social mobility (Piketty
1995; Alesina, Stantcheva, and Teso 2018), and expected social mobility (Benabou and
Ok 2001; Alesina and La Ferrara 2005)—can then be used to explain preferences for
distributions of income. D’Ambrosio and Clark (2015) provide a summary of such re-
search and show that results diﬀer depending on the data source, country of analysis,
and the inequality metric used. The heterogeneity in results is not surprising, given
that diﬀerent groups (p.ej., socioeconomic, political) residing in diﬀerent contexts have
diﬀerent beliefs about the relevance of income inequality (Grosfeld and Senik 2010).

Benjamin et al. (2012) caution against the use of willingness-to-pay (WTP) Estadísticas
based on assessments of subjective well-being. The reason is that respondents under-
state the importance of money in measures of subjective well-being relative to when
they are presented with choice sets. When presented with choice sets (even hypotheti-
cal ones), respondents systematically weight income gains more highly than when they
are asked whether an equivalent income gain will improve their well-being. These re-
sults suggest that forced choice experiments may be a superior way to elicit WTP for
other social variables.

The second approach uses experiments to estimate individuals’ WTP for equality.
To separate respondent preferences for equality from their beliefs about the costs of
equality, Johansson-Stenman, Carlsson, and Daruvala (2002) provide individuals with
hypothetical societies for their future grandchildren and randomly set a uniform distri-
bution of income. They ﬁnd high levels of inequality aversion in their sample. Similarmente,
Amiel and Cowell (1999) and Pirttilä and Uusitalo (2010) use a leaky bucket experiment,
which imposes a societal cost to redistribute income, and ﬁnd a wide range of inequality
aversion.

Inequality aversion varies among political partisans. En efecto, research has pro-
vided considerable evidence that liberals and conservatives have what appear to be

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273

Preference for Equal College Access and Income

fundamental diﬀerences in preferences for income equality. Data from the General
Social Survey show that Democrats are twice as likely as Republicans to favor govern-
mental action to remedy inequality.2 Data from the Pew Research Center show that
Republicans are twice as likely as Democrats to say that a person is rich because of his
or her own eﬀorts and nearly three times as likely to say that a person is poor because
of lack of eﬀort.3

Researchers have also shown that individuals respond to information diﬀerently
based on political identiﬁcation. Kuziemko et al. (2015) randomly provide accurate
information about levels of inequality in the United States to a sample of respon-
dents through Amazon’s Mechanical Turk (MTurk) interface, and ﬁnd this information
changes how much individuals care about inequality but does not change support for
redistribution policies. They also show that liberals care more about inequality over-
todo, and the eﬀect of presenting information to them is larger. Alesina, Stantcheva, y
Teso (2018) provide individuals with accurate information about social mobility, y
ﬁnd that liberal respondents increase their support for redistribution when presented
pessimistic data about mobility, whereas conservative respondents are inelastic to in-
formación. To our knowledge, empirical research regarding variation in inequality aver-
sion between political partisans has not addressed whether this variation is explained
by beliefs about costs or preferences for equality.

Finalmente, Lü (2013) tests whether educational opportunity mediates inequality aver-
sión. The author deﬁnes educational opportunity as the diﬀerence in the rate of col-
lege enrollment between individuals in high- and low-income districts. The relative
diﬀerences in college attendance are randomly assigned, and income diﬀerences are
held constant. Respondents then report whether they believe the income diﬀerences
between the two districts are too large. Lü ﬁnds that as access to higher education be-
comes more equal, respondents are less likely to report that the income diﬀerences are
too large.

Our study ﬁlls two gaps in the literature. Primero, we obtain estimates for how much
average income individuals are willing to trade for equal access to higher education and
income jointly. Eso es, respondents make decisions that require trade-oﬀs between av-
erage income, income equality, and equal access to higher education. Our model con-
verts preferences for these latter variables into a common WTP metric; we ﬁnd that
preferences for equal income dominate preferences for equal access to higher educa-
ción. Segundo, we show that preferences for equal access to higher education and equal
income diﬀer by political aﬃliation, beyond diﬀerences in beliefs about costs. Republi-
can voters’ WTP to reduce inequalities in income or access to higher education is close
to zero.

3 . T H E O RY
Our goal is to distinguish preferences for equal access to higher education from prefer-
ences for society’s overall level of income, average education, and income equality. Nosotros

2. See www.apnorc.org/projects/Pages/HTML%20Reports/inequality-trends-in-americans-attitudes0317-6562

.aspx.

3. Ver https://www.pewresearch.org/fact-tank/2017/05/02/why-people-are-rich-and-poor-republicans-and-dem

ocrats-have-very-diﬀerent-views/.

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Bernardo Lara and Kenneth A. Shores

operationalize equal access to higher education as the relative diﬀerence in the proba-
bilities that individuals from diﬀerent parental income percentiles (the 10th and 90th
percentiles) attend college. Under certain conditions, such a deﬁnition of equal access
converges with the traditional notion of fair equality of opportunity articulated by Rawls
in Theory of Justice and in political philosophy more broadly (Arneson 1999; Brighouse
and Swift 2008; Rawls 2009). This conception of access is also widely used in empiri-
cal applications. Por ejemplo, along with income mobility, Chetty et al. (2014) measure
equality of opportunity as the probability of college attendance conditional on parental
income.

Debate about whether or not public policy should promote equal access to higher
education or income equality is salient in both public policy and political philosophy.
Tuition-free higher education was a prominently featured campaign issue during the
Democratic primaries of 2016. As of April 2016, a Gallup survey of 2,024 adults found
eso 47 percent supported tuition-free higher education, and less reliable polling data
indicate this support has grown.4

Mientras tanto, educational attainment is associated with increased earnings and lower
unemployment. As of 2016, the unemployment rate for those with a bachelor’s degree
era 2.6 por ciento, comparado con 5.2 percent for those with a high school diploma. Me-
dian weekly earnings were 1.67 times higher for these same groups.5 A common pol-
icy proposal is to provide subsidies to low-income students to attend college. Dinarski
(2002) estimates that a $1,000 subsidy increases college attendance by 4 por ciento. As of August 2019, the federal expenditures on Pell Grants is $28.2 billion (College Board
2019). Estimates of the population costs required to close the college attendance rate
gap are not easily obtained.

In political philosophy, the origin of the debate can be traced back to Rawls’s (2009)
relative ranking of the two principles of distributive justice: fair equality of opportunity
and the diﬀerence principle. For our purposes, we can think of the diﬀerence principle
as any preferred distribution of income, such as equality, and the fair equality principle
as ensuring equal access to higher education. In the Rawlsian schema, the diﬀerence
principle is lexically subordinate to the fair equality principle, meaning that the condi-
tions of fair equality are to be satisﬁed before attention is paid to the diﬀerence principle.
De este modo, for Rawls, it is allowable to trade equality of income for educational opportunity.
Against this view, Arneson (1999) has argued that equal opportunity principles have
a meritocratic bias. Eso es, equal opportunity principles that eliminate barriers based
on social class (and other characteristics) leave open barriers based on ability. Porque
discrimination based on ability has no greater moral justiﬁcation than discrimination
on the basis of social class, equal opportunity principles need to be given either lower
distributive priority or discarded. Such a concern is easily applied to higher education
subsidies, as those would favor the skilled. Other philosophers have oﬀered various
reasons to promote equal opportunity. Each argument has a common feature, cual
is to identify a beneﬁt promoted by opportunity that is of greater value than the “con-
sumption interest” (taylor 2004, pag. 337) promoted by distributing shares of income. Para
Shields (2015), the beneﬁt is autonomy; for Shiﬀrin (2003), the beneﬁt is democratic

4. See Gallup (2016) and Bankrate (2016), respectivamente.
5. See Bureau of Labor Statistics Employment Projections (https://www.bls.gov/emp/).

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275

Preference for Equal College Access and Income

equality; and for Taylor (2004), the beneﬁt is self-realization. Despite the ongoing dis-
agreement among political theorists, A NOSOTROS. residents, and policy makers, our analysis is
the ﬁrst to conduct an empirical test to determine whether individuals prioritize equal-
ity of access to higher education or income equality.

4 . E X P E R I M E N TA L D E S I G N
Empirical Problem: Omitted Variable Bias
Typical opinion surveys ask respondents the extent to which they agree with various so-
cial objectives. Por ejemplo, the General Social Survey 2016 asks participants to rate the
priority the government should give to reducing income inequality. Because individuals
might have diﬀerent beliefs about the costs and mechanisms required to produce dif-
ferent social objectives, it is diﬃcult to interpret the answers to these surveys as proper
measures of social preferences.

To see how diﬀerences in individual beliefs can aﬀect survey results, considerar un
simple survey where individuals are asked if they support a governmental action to
improve a social variable X . We can characterize individuals as having two random
parameters that inﬂuence their answer:

(1) The society’s income αb that the respondent believes to be traded oﬀ in order to

achieve X , y

(2) The society’s income αt that the respondent is willing to trade oﬀ to achieve X .

Given those parameters, the respondent is only willing to support X if she believes
the income αb needed to produce X is less than the income αt she is willing to trade. A
illustrate the omitted bias problem, assume that αb and αt are independently distributed
following exponential distributions of parameters βb and βt, respectively.6 The expected
value of an exponential distribution is its distributional parameter and the expected
support for the policy reported in the simple survey would be equivalent to:
(cid:2) αt

(cid:2) ∞

mi[Support for X | βt, βb] =

F (αt, αb | βt, βb)dαb dαt =

(1)

βt
βt + βb

Notice the expected support for X is a function of both beliefs and preferences. En
hecho, we obtain diﬀerent results depending on βb. If βb = βt then the expected support
will be 0.5. En cambio, if βb → 0 (no income sacriﬁce for X ) then individuals will have
perfect support. Finalmente, if βb → ∞ then support approaches zero.

De este modo, unobserved beliefs (βb ) about costs can bias results of simple opinion sur-
veys. Además, these surveys do not provide the amount of income that respondents
are willing to trade (βb ) for X . Through randomization, our survey improves upon
simple surveys by imposing the costs needed to produce societal variables. Aleatorio-
ization therefore allows identiﬁcation of unbiased estimates of βt, or the respondents’
willingness to support X .

6. An exponential distribution of parameter β has a probability density function f (X|b ) = 1

β e−x/β .

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Bernardo Lara and Kenneth A. Shores

Discrete Choice Experiment
We use a DCE to randomly assign societal values, along four dimensions, to two dif-
ferent hypothetical future societies.7 Between these two societies, respondents must
decide which one is preferable.8 The four dimensions isolated are (1) societal income;
(2) income inequality; (3) average education; y (4) equal access to higher education.
The survey experiment consists of two sections. en el primero, respondents are pre-
sented with descriptive information about the four societal variables and asked a series
of diagnostic questions to determine whether they understand the data. Regardless of
whether respondents answer the diagnostic questions correctly, the survey tells them
the correct answer.9

In the second section, respondents are given information about contemporary U.S.
statistics in each of these dimensions. Respondents are then asked to choose between
two hypothetical future societies, A and B, in which values for each of the four variables
are randomly assigned to each society. Por ejemplo, societies A and B may both be
assigned the same level of income but society A has high levels of income inequality
whereas society B has large gaps in college access. Respondents choose which bundle
of randomly assigned values is optimal, according to their own welfare criteria.

We highlight two additional features of the DCE. Primero, because asking respondents
multiple questions is more cost eﬀective than repeatedly introducing the survey to new
respondents, we give them four versions of the choice experiment, in which societal
values are randomly assigned for each new question. Standard errors are therefore
clustered at the respondent level. Segundo, to minimize primacy and recency eﬀects,
the four societal attributes were presented in a randomized order across respondents
(Hainmueller, Hopkins, and Yamamoto 2014).

Social Welfare Variables Construction
Respondents are presented with information about a society’s overall level of income
and human capital development, as well as levels of income and equality of access to
higher education. These variables are constructed based on means and SDs from U.S.
commuting zones (CZs) using Chetty et al. (2014) data available on the Equality-of-
Opportunity.org Web site. Respondents are asked to choose values that conform to dif-
ferent combinations of CZ-level family income per capita, income inequality, level of
higher education, and educational mobility. Eﬀectively, respondents are randomly as-
signed CZ descriptive characteristics and are asked which bundle of descriptive statis-
tics is most desirable.

7. Although respondents may still consider the social status of their children, it is not clear they should be fully
veiled. Primero, what constitutes a veiled experiment is ambiguous and preferences vary by the speciﬁcation (Amiel,
Cowell, and Gaertner 2009). Segundo, there is evidence that nonveiled respondents have greater justice concerns
than veiled respondents (Herne and Suojanen 2004; Traub et al. 2005).

8. Discrete choice experiments are a method for studying social preferences for discrete outcomes and are widely

used in diﬀerent research areas (see Vossler, Doyon, and Rondeau 2012 for a summary).

9. For the diagnostic questions about income equality and equal college access statistics, 79.4 y 61.2 por ciento
of respondents answered correctly, respectivamente, y 71.1 percent of respondents answered a ﬁnal diagnostic
question correctly that asked to identify the diﬀerence between two societies in a simulation of the survey.
Screen shots of the survey platform are available in a separate online appendix that can be accessed on Education
Finance and Policy’s Web site at https://www.mitpressjournals.org/doi/suppl/10.1162/edfp_a_00271.

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277

Preference for Equal College Access and Income

Mesa 1. Discrete Choice Experiment, Randomization Values Actual

Variable

Income per capita

Inequality income

Percent college educated

Inequality higher education

Mean − 1 Dakota del Sur

Mean − 0.5 Dakota del Sur

Significar

Significar + 0.5 Dakota del Sur

Significar + 1 Dakota del Sur

$36,000 $39,000

$42,000 $45,000

$48,000 8 14% 46% 8.8 21% 50% 9.6 28% 54% 10.5 35% 59% 11.3 42% 63% Notas: Descriptive statistics for the four societal variables randomly assigned to respondents. All values taken from Chetty et al. (2014) from the Equality-of-Opportunity.org project. Mean corresponds to national mean and variation is based on the estimated between-commuting zone standard deviation. SD = standard deviation. The statistics presented to respondents are household income per capita, the per- centage of persons aged 25 years and above with at least a bachelor’s degree, el 90/10 income inequality ratio, and the percent of children from the 90th income percentile who attended a four-year college program by age 21 años, minus the percent of children from the 10th percentile. To generate the values to be presented, we take values for each variable at the national level and set those as midpoints. For variation, we calculate the CZ-level SDs using comparable statistics from the Chetty et al. (2014) datos. We then add/subtract one-half and one times the respective SDs to the average values. Allá- delantero, lowest/highest values are the average minus/plus one times the SD, for a total of ﬁve values per variable. For purposes of easier interpretation, we modify the values slightly by rounding. Mesa 1 shows the ﬁnal set of variables values that are assigned to respondents.10 5 . DATA A N D M E T H O D S Data Data for the survey are collected using Amazon’s MTurk interface, with the sample drawn from persons living in the United States. Actualmente, MTurk is an established on- line platform that can be used to carry out social and survey experiments. Por ejemplo, Berinsky, Huber, and Lenz (2012) show that MTurk samples are more representative than in-person convenience samples and less representative than nationally represen- tative probability samples used by ﬁrms like YouGov. En tono rimbombante, Berinsky, Huber, and Lenz are able to replicate multiple attitudinal experiments previously conducted, with nationally representative sampling designs, using MTurk data. Además, Kuziemko et al. (2015) ﬁnd that the unweighted MTurk sample for their study was as representative of U.S. Census data as unweighted samples from a nationally representative sample of U.S. adults contacted by Columbia Broadcasting Company. Finalmente, Levay, Freese, and Druckman (2016) ﬁnd that diﬀerences in political attitudes between the population- based American National Election Studies and an MTurk sample can be substantially reduced once one includes controls for demographic variables. Chandler, Mueller, and Paolacci (2014) raise three concerns regarding the use of MTurk data. Primero, respondents may participate multiple times on the same survey; segundo- ond, respondent performance on diagnostic items, such as cognitive reﬂection tasks, may be inﬂated due to conceptually related experiments; tercero, researchers may utilize 10. Additional details about these data and the construction of these variables are available in the online appendix. 278 l D o w n o a d e desde h t t p : / / directo . mi t . / F / e d u e d p a r t i c e – pdlf / / / / 1 5 2 2 7 0 1 6 9 3 4 8 0 e d p _ a _ 0 0 2 7 1 pd . / f f b y g u e s t t o n 0 8 septiembre 2 0 2 3 Bernardo Lara and Kenneth A. Shores Table 2. Estadísticas descriptivas: (1) Analytic MTurk Sample, (2) 2010 A NOSOTROS. Census, y (3) Kuziemko et al. (2015) Variable Sex Female Male Race/Ethnicity Black Other White Age, years 18—29 30—44 45—64 65 or older Educational attainment Associate’s or two-year college degree Did not finish high school Four-year college degree Graduate or professional degree High school diploma or equivalent Some college, no degree Technical or vocational school after high school Party affiliation Democrat Republican MTurk Sample 2010 A NOSOTROS. Census Kuziemko et al. (2015) Frequency Percentage Percentage Percentage 420 576 72 123 799 358 445 164 31 95 5 384 121 109 252 32 592 306 42.17 57.83 7.24 12.37 80.38 35.87 44.59 16.43 3.11 9.52 0.5 38.47 12.12 10.92 25.25 3.21 59.3 30.6 50.8 49.2 12.6 17.7 63.7 57.2 42.8 7.8 7.6 77.8 13.0 (18 a 24 años) 35.41 (sample mean) 35.0 (25 a 44 años) 34.8 (45 a 64 años) 17.1 (65+ años) 5.52 11.6 19.49 11.19 28.95 19.1 4.04 44.8 44.3 43.3 (at least college) 67.5 Notas: This table compares descriptive statistics for the analytic MTurk sample, el 2010 A NOSOTROS. Census, and the larger MTurk sample obtained in Kuziemko et al. (2015). Statistics on political affiliation are taken from Gallup 2019 (año 2010). post hoc data cleaning. Our survey is designed to mitigate these threats. Primero, although our survey was administered in two waves, we used JavaScript to pre-screen and exit respondents if their unique WorkerID appeared in the second wave. Segundo, the di- agnostic items we use to ensure attention and comprehension are task-speciﬁc to the survey instrument and not generic cognitive reﬂection tasks. Finalmente, all respondents who completed the survey were included in the main analysis; no post hoc data clean- ing was conducted. The survey was posted in two waves on MTurk, 5 January and 12 Enero 2017. We collected complete responses from 999 MTurk participants, at a rate of $0.75 per re-
sponse.11 Table 2 shows descriptive statistics for survey participants, comparable U.S.
Census data for 2010, and the Kuziemko et al. (2015) MTurk sample (norte = 3,741).

The data in our sample are especially overrepresentative of white, the young,
college-educated, and Democratic individuals. Our data more closely resemble the
larger MTurk sample collected by Kuziemko et al. (2015). In their sample, women are

11. A sample size of 999 was deemed suﬃcient based on previous literature (de Bekker-Grob et al. 2015). Basado
on the number of choice tasks, atributos, and attribute levels, Orme (1998) recommends a sample size of 313.
Average completion time was 6 minutos 52 artículos de segunda clase; por lo tanto, the hourly rate was $6.54. l D o w n o a d e desde h t t p : / / directo . mi t . F / / e d u e d p a r t i c e – pdlf / / / / 1 5 2 2 7 0 1 6 9 3 4 8 0 e d p _ a _ 0 0 2 7 1 pdf . / f por invitado 0 8 septiembre 2 0 2 3 279 Preference for Equal College Access and Income overrepresented by the same amount men are overrepresented in our data.12 White par- ticipants constituted 78 percent of the Kuziemko et al. (2015) sample compared with 81 percent in our data. The average age of their respondents was 35 años, whereas our average age (based on the median values of the “binned” age data) es 36 años. Significar- mientras, 43 percent of their sample has at least a college degree, mientras 51 percent of our sample does. Finalmente, 68 percent of respondents in their sample voted for Barack Obama, mientras 66 percent of our sample either self-identify as Democrat or voted for a Democrat in the previous election. En general, these statistics conﬁrm that our data are not representative but are typical of MTurk respondents. In our main econometric speciﬁcations below, we weight the data to be representa- tive of the joint distribution of two variables most implicated in the research questions: educational attainment and political aﬃliation. Educational attainment is taken from the U.S. Census 2010, and political aﬃliation is taken from the 2010 Gallup poll.13 Be- cause party aﬃliation is not recorded in the U.S. Census, we estimate the joint distri- bution of these two variables using the raking method described by Deville, Särndal, and Sautory (1993) and implemented in Kolenikov (2017). Econometric Methods So far, we have deﬁned and motivated interest in four statistics. We now describe our econometric models for estimating how much respondents are willing to trade for these social variables. To estimate utility parameters, we use choice modeling methods. We ﬁrst estimate a nonparametric ordinary least squares (OLS) model to obtain raw esti- mates of respondent preferences for diﬀerent combinations of social welfare variables. We then model the data using a Cobb-Douglas utility function, allowing us to estimate the relevant tradeoﬀs, which can then be represented as indiﬀerence (or iso-welfare) curvas. The Cobb-Douglas model imposes additional functional form assumptions on the data; de este modo, the raw estimates from the OLS model provide information as to whether these assumptions are reasonable. (See Train [2003, páginas. 62–63] for additional discus- sion on the relationship between choice models and Cobb-Douglas equations.) In the nonparametric approach, we estimate the normalized level of utility as the probability that society X (independently of whether society A or society B is presented in the question) is chosen. The model includes interactions of indicator variables that correspond to combinations of societal values that a society could have. Por ejemplo, ﬁve levels of average family income and college attendance gaps were randomly as- signed to respondents. The interaction of these ﬁve variables results in twenty-ﬁve pa- rameter estimates. The following regression model formalizes the approach: 1i [X is chosen] = (cid:5) (cid:4) δ jk1X jk. . + 5(cid:3) 5(cid:3) j=1 k=1 5(cid:3) l=1 (cid:7) (cid:6) ρl1X . .yo. + 5(cid:3) m=1 (cid:7) (cid:6) σm1X . . .metro + εiX , (2) 12. Our sample has more male participants than other MTurk samples that have been evaluated (Berinsky, Huber, and Lenz 2012; Huﬀ and Tingley 2015). 13. The Gallup poll dichotomizes party aﬃliation by separating independents (acerca de 38 percent of the sampled respondents) into whether the respondent leans Republican or Democrat. We dichotomize political aﬃliation similarly. See Gallup (2019). 280 l D o w n o a d e desde h t t p : / / directo . mi t . / F / e d u e d p a r t i c e – pdlf / / / / 1 5 2 2 7 0 1 6 9 3 4 8 0 e d p _ a _ 0 0 2 7 1 pdf . / f por invitado 0 8 septiembre 2 0 2 3 Bernardo Lara and Kenneth A. Shores where 1i[X is chosen] is an indicator equal to 1 if society X is chosen by individual i and 0 de lo contrario. Mientras tanto, 1X jklm is an indicator equal to 1 (0 de lo contrario) if society X has j level of income, k level of income inequality, l level of average education, and m level of equal access to higher education. Por lo tanto, the coeﬃcients δ jk represent ﬁxed eﬀects for each combination of income and income inequality (of which there are twenty-ﬁve). Such ﬁxed eﬀect coeﬃcients are equivalent to utility values of each com- bination of income/income equality. The coeﬃcients ρl and σm capture the utility of each level of average education and equal access, respectivamente. In separate models, we exchange k income inequality with l average education or m equal access, which pro- vide combinations of the interactions of income/average education and income/equal access, respectivamente. The ﬁnal speciﬁcation replaces j level of income with m equal ac- impuesto, which provides the trade-oﬀ between equal income and equal access to higher education (es decir., “Rawlsian trades”). Finalmente, εiX is an individual error term related to heterogeneity in preferences for X . Because the choice sets are randomly assigned to individuals, mi[εiX ] = 0 y, por lo tanto, the OLS model is an unbiased estimator of the normalized utility levels (Hainmueller, Hopkins, and Yamamoto 2014). Although the econometric model (equation 2) is ﬂexible and provides interval-scaled estimates for diﬀerent combinations of societal values, it does not allow us to estimate an indiﬀerence curve, nor does it take advantage of the actual structure of the data generation process. Por lo tanto, our second methodological approach is the traditional choice model of McFadden (Train and McFadden 1978; McFadden, 1980). We begin by translating the societal preferences of an individual i for society A into a Cobb-Douglas utility function of the form: Ui (A) = α0 + αY ln (YA) + βY ln + αE ln (EA) + βE ln + εiA, (3) (cid:5) (cid:4) Y Ineq A (cid:5) (cid:4) E Ineq A where αY and αE are coeﬃcients corresponding to preferences for levels of income and average education, and βY and βE represent the negative preference for inequality of income and educational opportunity, respectively.14 We also include a constant α0 and an error εiA, which represents the individual heterogeneity in preferences for societies. As the survey asks individuals to choose between two societies, A and B, for soci- ety A to be chosen, it must be the case that Ui(A) − Ui(B) > 0. Given the functional assumption, this amounts to the following equation: αY ln (cid:9) (cid:8) YA YB + βY ln (cid:10) (cid:11) Y Ineq A Y Ineq B + αE ln (cid:9) (cid:8) EA EB + βE ln (cid:10) (cid:11) E Ineq A E Ineq B + ηAB i > 0, (4) i = εiA − εiB. There are four features of equation 4 to highlight. where the error term ηAB First, if we assume that each error εi follows a normal distribution, then ηAB i would also be normally distributed and, por lo tanto, the parameters can be estimated by a Probit Maximum Likelihood Estimator. Segundo, given that each pair of societies is randomly assigned across individuals, the estimates are unconfounded by preferences for equal college access and societal income. Tercero, because each society has the same set of fea- turas, there is not a constant in the model and, como consecuencia, we do not include one 14. A negative coeﬃcient on βE indicates disutility for higher levels of the 90/10 higher education attainment gap—that is, inequality of access to higher education. l D o w n o a d e desde h t t p : / / directo . mi t . F / / e d u e d p a r t i c e – pdlf / / / / 1 5 2 2 7 0 1 6 9 3 4 8 0 e d p _ a _ 0 0 2 7 1 pd . F / f por invitado 0 8 septiembre 2 0 2 3 281 Preference for Equal College Access and Income l D o w n o a d e d f r o m h t t p : / / directo . mi t . F / / e d u e d p a r t i c e – pdlf / / / / 1 5 2 2 7 0 1 6 9 3 4 8 0 e d p _ a _ 0 0 2 7 1 pdf / . f por invitado 0 8 septiembre 2 0 2 3 Notas: Each panel represents a pairwise trade among social variables. Shaded cell regions indicate strength of preference in standard deviation units for pairwise combinations of social variables. Black indicates greater utility; white indicates less utility. Utility estimates based on equation 2. Point estimates and standard errors shown in tables D.1, D.2, D.3, and D.4 in the online appendix. Cifra 1. Nonparametric Estimates Social Welfare Preferences, Contour Plots, Weighted Sample in our estimation. Cuatro, the Cobb-Douglas model imposes the functional form of de- creasing marginal returns to each variable, por lo tanto, the marginal rate of substitution (MRS) varies in the same proportion as the ratio between social statistics and the ratio of the utility parameters of each variable. 6 . R E S U LT S In this section we present results. Results from equation 2 allow us to plot the ordered preferences that respondents have for the social welfare variables, and results from equation 4 allow us to estimate MRS statistics and indiﬀerence curves. We then test for heterogeneous preferences based on political aﬃliation and educational attainment. Nonparametric Results We start with estimates of the preferences for each social value from equation 2. These results allow us to rank diﬀerent combinations of social statistics. Cifra 1 shows a con- tour that summarizes the interactions δ jl (income and education levels), δ jk (income and income inequality), δ jm (income and equal access), and δkm (income inequality and equal access). In each model, twenty-ﬁve estimates are available. Cells in white indicate that an assigned combination of societal values (p.ej., income $45,000 y

282

Bernardo Lara and Kenneth A. Shores

90/10 income ratio 10.5) is less preferred. Darker shading indicates a stronger
preference.15

As expected, higher income per capita, higher levels of college enrollment, más bajo
income inequality, and more equal access to higher education are preferred, as indi-
cated by the black shading in the upper right quadrants and the white in the lower left
quadrants of each panel. These results demonstrate that respondents understood the
survey and were providing preferences that were correctly ordered.

More interestingly, we can observe which social statistics appear to be more rele-
vant to individuals. Because variables were generated based on observed SDs across
CZs in the United States, the shaded cell regions indicate strength of preference in SD
units. En general, individuals are willing to trade equivalent units of income for aver-
age education (figura 1, panel a), indicated by the uniformity along the diagonal from
the upper-left to the lower-right. Sin embargo, for income equality (figura 1, panel c) y
equal access to higher education (figura 1, panel b), preferences for income outweigh
equivalent preferences (in SD units) for equality (p.ej., $45,000 income and a 90/10 income ratio of 10.5 is preferred to $39,000 income and a 90/10 income ratio of 8.8).
En efecto, preferences for college access equality are nearly lexicographic, as increases in
estimated utility largely result from increases in societal income along the vertical axis.
Linear probability models are common estimators for DCEs, but they have limited
value if the objective is to recover the MRS (es decir., WTP) and to make comparisons across
variables. We now turn to results from equation 4, which provide the statistics of inter-
est but require parametric assumptions.

Parametric Results
Having displayed how bundles are ranked, we can now move on to direct estimation of
the indiﬀerence curve. We ﬁrst present direct estimates from equation 4 in panel A of
mesa 3. We display estimates from the unweighted and weighted data in columns 1 y
2, respectivamente.

As expected, based on results from ﬁgure 1, increases in income and average educa-
tion have positive eﬀects on utility, and increases in the statistics measuring inequality
have negative signs. All point estimates are statistically signiﬁcant at p < .01. The estimates of the Cobb-Douglas parameters allow us to map the indiﬀerence curves, which are drawn using the utility levels at diﬀerent points of the y-axis. These parametric results mimic the contour ﬁgures generated from the nonparametric mod- els: Average education is more relevant than income inequality, and income inequality appears more relevant than equal access to higher education. These results indicate that independent improvement in income equality is preferred to equivalent (in SDs) inde- pendent improvement in educational equality, as shown by the fact that the indiﬀerence curve is steeper in ﬁgure 1, panel c, than in ﬁgure 1, panel b. Indeed, when compared directly in ﬁgure 1, panel d, we see that respondents are willing to trade approximately 2 SD units of equal access to higher education for 1 SD unit of income inequality. In ﬁgure 2, we graphically display the indiﬀerence curves that describe the trade- oﬀs individuals are willing to make between social values. Although these ﬁgures are 15. Estimated coeﬃcients and standard errors are shown in tables D.1, D.2, D.3, and D.4 in the online appendix. Results from the unweighted data are available in ﬁgure C.1 in the online appendix. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / / f e d u e d p a r t i c e - p d l f / / / / 1 5 2 2 7 0 1 6 9 3 4 8 0 e d p _ a _ 0 0 2 7 1 p d / . f f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 283 Preference for Equal College Access and Income Table 3. Cobb Douglas Results, Main Effects, and Marginal Rate of Substitution (MRS) Panel A: Probit Coefficient Estimates (cid:2) ln(income) (cid:2) ln(Inc. Inequality) (cid:2) ln(Educ.) (cid:2) ln(Educ. Inequality) Unweighted 4.280*** (0.206) −1.943*** (0.159) 1.061*** (0.056) −0.968*** (0.157) Panel B: Marginal Rate of Substitution MRSInequality Inc.,Income MRSInequality HE,Income MRSAvg. HE enrollment,Income MRSInequality Inc.,Inequality HE N −1.986*** (0.170) −0.176*** (0.029) 0.372*** (0.022) 11.294*** (1.910) 3,996 Weighted 4.340*** (0.262) −1.733*** (0.206) 1.030*** (0.064) −0.814*** (0.198) −1.747*** (0.217) −0.146*** (0.035) 0.356*** (0.026) 11.980*** (3.003) 3,996 Notes: Standard errors clustered by respondent in parentheses. MRS measured at the mean values. Probit coefficients based on equation 4. MRS estimates based on equation 5. Weighted estimates based on joint distributions of adult education and political affiliation using raking method of Deville, Särndal, and Sautory (1993) and implemented by Kolenikov (2017). HE = higher education. ***p < 0.01. informative, they do not give a statistic of the exact trade-oﬀs. For that purpose, we present the estimation results of equation 4 in panel B of table 3, which are the MRS (or WTP) statistics for certain social variables. The MRS can be easily recovered from the Cobb-Douglas utility as: MRSx,y = Coefficient x Coefficient y · y x (5) , where y is the average societal income; x is a vector of the other societal variables of interest (average education and the two inequality statistics). The ratio indicates how much respondents are willing to pay in social income for values of x. In the special Rawlsian trade-oﬀ, y is set to equal access, and x is equal income; this MRS statistic indicates how much respondents are willing to trade equal access for equal income. Therefore, if we assume the mean values of x and y provide a reasonable approximation to estimate the MRS,16 the WTP can be expressed as the average income individuals are willing to sacriﬁce.17 The ﬁndings indicate: 16. In other words, that the MRS is stable across diﬀerent values of x and y; based on the results from ﬁgure 2, this assumption seems reasonable. 17. Standard errors for the MRS statistics are calculated using the delta method. All results in the itemized list below are statistically signiﬁcant at p < .01. 284 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / f / e d u e d p a r t i c e - p d l f / / / / 1 5 2 2 7 0 1 6 9 3 4 8 0 e d p _ a _ 0 0 2 7 1 p d . / f f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 Bernardo Lara and Kenneth A. Shores l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / f / e d u e d p a r t i c e - p d l Notes: Each panel represents a pairwise trade among societal variables. Indifference curves derived from estimates from equation 4. Figure 2. Log Linear Estimates Social Welfare Preferences, Indifference Curves • • • • Individuals would be willing to decrease average income by $1,460 to reduce the gap in higher education from 54 percent to 44 percent. This implies that individuals would have a WTP of $1,168 for a 1 SD decrease in the higher education enrollment gap statistic. Individuals would be willing to decrease average income by $1,747 to decrease the 90/10 income inequality ratio from 9.6 to 8.6. This implies that individu- als would have a WTP of $2,900 for a 1 SD decrease in the income inequality statistic. Individuals would be willing to decrease average income by $3,560 to increase higher education enrollment from 28 percent to 38 percent. This implies that in- dividuals would have a WTP of $4,984 for a 1 SD increase in the average education statistic. Individuals would be willing to increase the higher education enrollment gap by 12 percent to decrease the 90/10 income ratio from 9.6 to 8.6. This implies that f / / / / 1 5 2 2 7 0 1 6 9 3 4 8 0 e d p _ a _ 0 0 2 7 1 p d . / f f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 285 Preference for Equal College Access and Income individuals would have a WTP of 2.49 SD of the higher education enrollment gap statistic for a 1 SD decrease in income inequality statistic. As shown, individuals are willing to sacriﬁce important amounts of income to im- prove other social parameters. Indeed, educational attainment, which is often encour- aged for its eﬀects on economic growth, is independently supported; individuals are willing to sacriﬁce social income for an educated population. In that sense, economic growth should not be the sole focus of policy, and public policy decisions that require trade-oﬀs between eﬃciency and other outcomes ought to be considered. These results are robust to concerns about respondent-survey interactions. First, as respondents are asked the same question four times, they may lose interest and anchor on familiar variables; however, we see little diﬀerence in responses between the ﬁrst and second two questions (tables D.5 and D.6 in the online appendix). Second, respon- dents may not comprehend the inequality statistics and favor the more familiar average income statistic. Individuals who respond correctly to the diagnostic questions express stronger WTP to reduce inequalities (tables D.7 and D.8 in the online appendix). In contrast to popular narratives about the special importance of the “American Dream” and its relation to equal access to higher education, our data reveal that in- dividuals care more about income equality than equal access to higher education. In traditional opinion surveys, revealed preferences for equal access to higher education may be inﬂated because respondents believe that reducing the gap in college access also reduces income inequality and/or increases average income. When we separate the preferences into the diﬀerent parts, our results suggest that the actual worth of equal access per se is relatively low, as respondents prefer income and equality of income over equal access to higher education. These data speak to contemporary debates about taxation and subsidies on the one hand (policies that aim to reduce income inequality at the potential cost of societal income), and free higher education and remedies for the achievement gap on the other (policies that aim to increase equal access at the po- tential cost of societal income). We have presented evidence that can guide policy when the choice is between improving college access for low-income students or delivering direct income subsidies to low-income families, all else constant. Survey respondents indicate they would support the latter, if the outcomes of the policies were known to them in advance. Heterogeneous Preferences We now turn to whether there is heterogeneity in the social preferences identiﬁed here. We identify heterogeneous eﬀects based on political aﬃliation and respondent educa- tional attainment. Both attributes are relevant for the variables included here. Diﬀer- ences in preferences for societal variables between right-leaning and left-leaning voters may be due to diﬀerences in beliefs about the costs of equality or in preferences for equality.18 Our survey design disentangles those competing explanations. Educational 18. Our survey asked participants two questions about their political aﬃliation: (1) if they self-identify as one of the major political parties, and (2) which political party they most recently voted for. We code as “right-leaning” a respondent who self-identiﬁed/voted Republican or Libertarian. We code as “left-leaning” a respondent who self-identiﬁed/voted Democratic or Green. Our identiﬁcation of political aﬃliation reduces the sample from 3,996 observations to 3,592. 286 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / f / e d u e d p a r t i c e - p d l f / / / / 1 5 2 2 7 0 1 6 9 3 4 8 0 e d p _ a _ 0 0 2 7 1 p d / f . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 Bernardo Lara and Kenneth A. Shores Table 4. Marginal Rate of Substitution (MRS), Respondent Political Affiliation Parameter Democrat Republican Democrat − Republican MRSInequality Inc.,Income MRSInequality HE,Income MRSAvg. HE enrollment,Income MRSInequality Inc.,Inequality HE −2.575*** (0.243) −0.237*** (0.040) 0.407*** (0.031) 10.888*** (1.858) −0.893*** (0.252) −0.082* (0.046) 0.294*** (0.032) 10.830* (6.327) N 2,368 1,224 −1.683*** (0.350) −0.154** (0.061) 0.113** (0.045) 0.058 (6.594) 3,592 Notes: Standard errors clustered by respondent in parentheses. MRS measured at the mean values. Probit coefficients based on equation 4 shown in online table D.9. MRS estimates based on equation 5. Standard errors for tests of significance among partisans calculated using the delta method. HE = higher education. ***p < 0.01; **p < 0.05; *p < 0.1. attainment is relevant because it both correlates with individual income and may inﬂu- ence the preferences for education variables.19 Results for political aﬃliation, showing important diﬀerences in the egalitarian preferences across political groups, are presented in table 4.20 The estimates show that, compared to Republicans, Democrats are willing to give up nearly three times the amount of average income for either of the equality measures. These diﬀerences in the WTP are statistically signiﬁcant at p < .01. Democrats also have a greater WTP for average educational attainment (p < .05); however, the magnitude of this diﬀer- ence is not large. Both groups are willing to sacriﬁce important amounts of income (over $4,000) to increase the average higher education enrollment by 1 SD (14 percent). This result suggests the presence of an overlapping consensus between parties with re- spect to increasing average levels of education—however, the parties are far apart with respect to equalizing income or educational opportunities. Finally, it is interesting to note that both groups give greater weight to income equality relative to access to higher education, despite having diﬀerent preferences for equalities of both kinds. Results based on educational attainment are presented in table 5.21 Respondents with college degrees have greater WTP for reductions in income inequality than those with some college education. Conversely, those with no college experience have greater WTP for reductions in income inequality than the college educated. Thus, WTP for income equality are not monotonic according to educational attainment. Meanwhile, WTP statistics for access to higher education are very similar for all educational groups. This ﬁnding is interesting because political aﬃliation inﬂuences preferences for both income equality and access to higher education, while educational attainment (a class status indicator) inﬂuences only preferences for income equality. If preferences for equal college access are class-insensitive, then it may be easier to obtain a consensus for policies promoting equal access to higher education, despite the fact that preferences 19. Educational attainment is coded as 0 for a four-year college degree or more; 1 for “some college”; 3 for a high school diploma or less. We exclude trade and vocational schools from the analysis. This reduces the sample to 3,484 observations. 20. Table D.9 in the online appendix displays model coeﬃcients. 21. Table D.10 in the online appendix displays model coeﬃcients. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / / f e d u e d p a r t i c e - p d l f / / / / 1 5 2 2 7 0 1 6 9 3 4 8 0 e d p _ a _ 0 0 2 7 1 p d . f / f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 287 Preference for Equal College Access and Income Table 5. Marginal Rate of Substitution (MRS), Respondent Level of Education Parameter MRSInequality Inc.,Income MRSInequality HE,Income MRSAvg. HE enrollment,Income MRSInequality Inc.,Inequality HE College or More −1.968*** (0.225) −0.194** (0.038) 0.392*** (0.030) 10.150*** (2.086) Some College −2.921*** (0.450) −0.209*** (0.072) 0.394*** (0.055) 13.991*** (4.696) Less than College −1.090*** (0.397) −0.206*** (0.068) 0.211*** (0.034) 5.280** (2.413) N 2,020 1,008 456 College − Some 0.952* (0.503) 0.015 (0.081) −0.002 (0.063) −3.841 (5.138) 3,028 College − Less −0.878* (0.457) 0.012 (0.078) 0.181*** (0.046) 4.870 (3.189) 2,476 Notes: Standard errors clustered by respondent in parentheses. MRS measured at the mean values. Probit coefficients based on equation 4 shown in online table D.10. MRS estimates based on equation 5. Standard errors for tests of significance among educational level calculated using the delta method. HE = higher education. ***p < 0.01; **p < 0.05; *p < 0.1. for equal access are weaker on average. This feature of access to higher education may be a second explanation (in addition to perceived spillover beneﬁts) for its prominence in U.S, society. Finally, college-educated respondents have greater WTP for levels of col- lege enrollment than those with no college education, but there is no diﬀerence when compared to those with some college experience. 7 . C O N C L U S I O N In this paper we have estimated social preferences for eﬃciency, educational attain- ment, income equality, and equal access to higher education. Not surprisingly, average income is an important aspect of respondents’ social welfare functions. More inter- estingly, respondents are willing to exchange societal income to increase levels of ed- ucational attainment (meaning that educational attainment is not desired purely for economic reasons) as well as both aspects of equality (meaning that respondents have distributive concerns). Moreover, respondents display a stronger independent prefer- ence for income equality relative to expanding access to college. This ﬁnding contra- dicts the traditional notion that equal access to higher education is more important than income equality in the United States. Quite possibly, college access is believed to have positive eﬀects on economic growth and income equality; for this reason, narrow- ing the income gap in college attendance has large popular support, despite its having relatively low independent value. Finally, we emphasize that the implemented discrete choice experiment has use- ful features that can be replicated in subsequent research. First, we use true variation in income, education, and inequality statistics. Second, by randomly assigning societal income, we impose a budget constraint, which provides a common metric for making comparisons across diﬀerent social variables. Third, we integrate diﬀerent dimensions of societal well-being into a common framework. Although discrete choice experiments are prevalent in political science and some subdisciplines of economics, they have not been used to identify the types of social preferences evaluated here. Consequently, ad- ditional research with diﬀerent samples and social statistics could provide a deeper 288 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / / f e d u e d p a r t i c e - p d l f / / / / 1 5 2 2 7 0 1 6 9 3 4 8 0 e d p _ a _ 0 0 2 7 1 p d / . f f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 Bernardo Lara and Kenneth A. Shores understanding of social preferences for eﬃciency, income equality, and other variants of equality of opportunity, in addition to other social concerns. ACKNOWLEDGMENTS We want to thank Randall Reback and Ilyana Kuziemko, as well as three anonymous referees, for their insightful comments. All errors are our own. REFERENCES Alesina, Alberto, and Eliana La Ferrara. 2005. Preferences for redistribution in the land of op- portunities. 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