Debt, Human Capital, and the Allocation of Talent*
Titan Alon

Natalie Cox

Minki Kim

Arlene Wong

Preliminary Draft
September 2023

Abstract
We empirically and theoretically analyze the impact of household assets and liabilities (namely student debt) on labor market outcomes. Exploiting exogenous variation in the composition of college funding, we find that more student debt leads
to higher initial earnings but lower returns to experience. Initial occupation choiceout of college plays an important role in driving the results. To explain the data, wedevelop and calibrate a quantitative model in which occupation choice and lifecycle
human capital accumulation interact with credit constraints. Intertemporal distortions arising from credit limits cause households to dis-invest in human capital andswitch to occupations with more front-loaded compensation schemes as alternativemodes of consumption smoothing. Using the model, we analyze the aggregate productivity and welfare consequences of federal extended repayment and student debt
forgiveness programs. The results show that while extended repayment policies always produce gains, the benefits of student debt forgiveness are non-monotonic dueto the distortionary effects of redistributive taxation. Moreover, although the fraction of households induced to switch occupations is small, they account for almost
half the aggregate gains in labor productivity as relaxed credit constraints induce
workers to flow from high amenity to human capital intensive occupations.

Keywords: Student debt, occupation choice, wage profiles, credit constraints, misallocation of talent, higher education.

* We thank seminar participants at Stanford, Princeton, MIT Sloan, NYU, UCSD, USC, Dartmouth,
Wharton, the Urban Institute, NBER SI, and the Fed Minneapolis for their valuable comments and suggestions. We thank Marcelo Ferreira, Cristoforo Pizzimenti, and Clemens Lehner for outstanding research
assistance.

1

Introduction

Total outstanding student loan debt reached $1.57 trillion in 2022, surpassing auto loans
and credit card debt to become the second largest household financial liability after
home mortgages.1 On the one hand, the increase in student debt represents a policy success in which subsidized federal loans alleviated credit frictions to help broaden accessto higher education. On the other hand, the rise in student debt may prevent many indebted students from fully realizing the benefits of a college education by exacerbating
subsequent credit constraints after graduation. Surveys of non-delinquent borrowers
from federal loan programs suggest such concerns are well founded, with 34% of borrowers reporting their student loans resulted in more hardship than anticipated; 54%
reported they would borrow less if they could repeat college; and nearly one-fifth reported “significantly changing career plans because of student loan burdens”.2
This paper provides a theoretical and empirical analysis of the welfare and productivity
consequences of rising student debt. It focuses in particular on student debt’s effecton aggregate labor markets and the early career outcomes of college graduates. We
show that students graduating with more student debt have higher initial earnings, but
lower returns to experience in the first ten years of their career. Nearly half the effectis mediated by initial occupation choice after graduation, with more indebted students
selecting into occupations with more front loaded compensation schemes. We develop aquantitative heterogeneous-agent, incomplete markets model to rationalize the findings
and study their implications for federal student loan policies. The results show thatbudget-neutral reforms which alter the principal or duration on outstanding federal
student loans can increase aggregate welfare and labor productivity. The gains arise
from reducing credit constraints that inhibit human capital accumulation on the job andgive rise to a misallocation of talent by reducing the efficiency of occupational sorting.
While our findings apply more broadly to the interaction of credit market frictions andlabor markets, there are several advantages to focusing in particular on the impact ofstudent debt. First, student loan debt is among the largest and fastest growing forms ofhousehold credit. Second, it is primarily incurred early in life, before individual labormarket experiences diverge, making its effects on lifecycle outcomes easier to measureand isolate. Figure 1 shows that student debt accounts for virtually all debt held by
young workers and a substantial portion of the the variation in their net worth. Third,
1
2

See Federal Reserve Bank of New York (2022) for details on the composition of household debt.
See Baum and O’Malley (2003) for details on the complete questionnaire and survey methodology.

1

Figure 1: Net Worth and Student Debt of Young Workers, Ages 22-25.
160

Net worthStudent debt outstanding120

Thousand $

80

40

0
-8

-15

-7

-12

-7

-29

-40
-51

-80
p5

p10

p25
p50
p75
Percentile of net worth

p90

p95

Notes: Source data from 2018 Survey of Consumer Finance. Student debt defined
as total value of aggregate loan balance of education-purpose expenses. Sample isrestricted to BA degree holders between age 22 and 25.

student debt is largely non-dischargeable, allowing us to largely abstract from strategic delinquency and bankruptcy considerations. Empirically, households are much lesslikely to become seriously delinquent (90-days or more) on their student debt than onother forms of credit, such as credit card debt or auto loans.3 Finally, the federal government plays an out-sized role in student debt markets, accounting for 92.7% of the total
outstanding student loan debt.4 Changes in federal loan policies can therefore generate aggregate variation in household debt, providing a natural laboratory to study thecausal effects on labor market outcomes. The dominant role of the federal government
also means that there is broad scope for policy improvements to deliver substantial aggregate welfare and productivity gains.
3

In 2022Q3, only 1.04% of outstanding student debt became seriously delinquent, the lowest rate
among all major categories of household credit except mortgages and home equity. See Federal Reserve
Bank of New York (2022).
4
Student loan debt held by the federal government is composed overwhelmingly of Direct Loans,which account for $1.4 trillion of the total. The remaining balance is made up mostly of Title IV loans
issued through the FFEL Program and federal Perkins loans. See Consumer Financial Protection Bureau(2022) for further details.

2

Employing panel microdata from the National Longitudinal Survey of Youth (NLSY97),
we provide empirical evidence on the effect of student debt on early career labor market
outcomes. We instrument student debt levels using variation in the share of grant funding within a college and across cohorts. Constructing the instrument requires accessing
restricted-use NLSY97 identifiers for the participants’ educational institutions to merge
in annual data on college-level loans and grants from the National Center of Education
Statics (NCES). The empirical result show that an additional $1000 of student debt increases initial earnings by 3.14%, but reduces the returns to experience by 1.37%. The
effect on initial earnings corresponds to an additional $508 in annual earnings for every$1000 in student debt, in line with similar estimates in the literature.5 The effects of student debt on the returns to experience are statistically significant and also sizable giventhat the average annual earnings growth of individuals between the ages of 25 to 30
are estimated to be 7.75% (Guvenen et al. 2021). Finally, nearly half the estimated effecton earnings is mediated by initial occupation choice after graduation, as more indebted
students select into occupations with more front loaded compensation schemes.
To understand the data, we develop a dynamic model of lifecycle human capital accumulation and occupation choice in the presence of credit market frictions. When credit
constraints bind, household discounting of future income streams is greater than prevailing market interest rates. The resulting intertemporal distortions lead households
to dis-invest in human capital accumulation as an alternative form of consumption
smoothing, reducing lifetime earnings and aggregate labor productivity. A novel featureof the model is that these adjustments can occur not only through reduced investment
on the job, but also through changes in occupation choice. Indebted households disproportionately select into occupations with more front loaded compensation schemes,
even when their abilities make them a better match for other types of work. The result isa misallocation of talent that can compound the effects of student loan debt on aggregate
labor productivity.
To study the implications for federal policies, we embed these mechanisms into a quantitative heterogeneous agent, incomplete markets model that can be taken to the data.
Individuals are born with heterogeneous family assets and occupation-specific abilities.
They endogenously incur student debt when deciding whether to attend college, accounting for any selection effects that may be in the data. After graduation, households
5

For instance, see Rothstein and Rouse (2011), Chapman (2015), and Luo and Mongey (2019) for comparable estimates of the impact of student debt on initial earnings.

3

choose an occupation based on their innate abilities and financial assets, taking prevailing wages as given. Earnings evolve endogenously over their lifecycle as a consequenceof costly investments in human capital and idiosyncratic labor market shocks. Households are also subject to progressive taxation, face idiosyncratic human capital shocks,
receive retirement benefits which depend on their earnings, and face realistic student
debt repayment provisions and borrowing constraints. The calibrated model matches
the aggregate earnings profile and the joint distribution of family assets, student debt,
and college matriculation. It also replicates the employment shares, initial earnings, andreturns to experience in the 18 detailed occupation groups households choose between.
Using the quantitative model, we measure the aggregate welfare and productivity consequences of budget-neutral changes to the repayment duration or principal of federal
student loans. The first exercise computes the effect of 2 and 5 year extensions to thestandard federal repayment program. In each case, the interest rate on outstanding federal loans is recomputed to ensure that the net present value of individual student debt
does not change. The model predicts these extensions would meaningfully boost welfare and labor productivity, with the gains increasing in the program’s duration. For
instance, a 5 year budget-neutral extension to the standard federal repayment program
would increase aggregate labor productivity by 0.57% and consumption equivalent welfare by 0.45%, with benefits concentrated among low wealth households. The results
also show that induced occupation switchers contribute negligibly to aggregate welfare gains, but account for one-third of the rise in aggregate labor productivity despite
representing only 0.41% of the treated population. Decompositions of the aggregate
productivity increase reveals that the out-sized contribution of induced-switchers is theresult of a systematic pattern in the direction of occupation switching; the policy induces
workers to flow predominantly from low-skill service jobs into more human capital intensive occupations, mainly Sales, Engineering, Math and Computer Science.
The second set of exercises examines the effect of reducing the principal of outstanding
federal student loan debt via student debt forgiveness. It computes the aggregate anddistributional effects of student debt forgiveness capped at the 10k, 50k, and 100k thresholds. Given the empirical distribution of student debt, the 100k-cap program effectively
amounts to complete debt forgiveness. To ensure the program is budget-neutral, thedebt forgiveness is funded by income taxes which are distributed across householdswith the same proportionality as the overall U.S. tax system.
The results show that the impact of debt forgiveness policies is non-monotonic in the4

size of the program, with middle-sized programs performing the best. The 50k-capped
program yields a modest 0.02% rise in consumption equivalent welfare and a 0.20% increase in aggregate labor productivity. In contrast, the 100k-capped program yielded
only a 0.09% increase in labor productivity and a decline in consumption equivalent
welfare, while the small 10k program lead to aggregate declines in both welfare andproductivity. The non-monotonicity arises from the countervailing effects of debt forgiveness and the distortionary taxation which funds them. The small 10k program isinsufficient to substantially alleviate credit constraints, despite still requiring a substantial rise in tax revenue. The 100k program virtually eliminates student debt, but requires
large increases in distortionary taxation that reduce the benefits by discouraging humancapital accumulation on their own. The model suggests that middle-sized programs
which are large enough to alleviate credit constraints, but no so large as to substantially
increase distortionary taxes, deliver the best returns. Overall, the results suggest that
extended repayment duration policies are a better tool than student debt forgiveness.
While both alleviate credit constraints, the former does not require distortionary taxes
to support transfers across households.

Related Literature. Our findings contribute to the literature examining how credit
market frictions effect labor market outcomes. Recent contributions have shown thataccess to consumer credit can effect household job search behavior with aggregate implications for the efficiency of worker sorting and business cycle volatility (Herkenhoff,
Phillips, and Cohen-Cole 2016; Herkenhoff 2019). This paper focuses in particular on
student debt and its effect on the early career outcomes of college graduates. Our work
complements research on credit frictions in the financing of higher education (Lochnerand Monge-Naranjo 2012; Lochner, Stinebrickner, and Suleymanoglu 2021) by investigating how student debt subsequently effects labor market outcomes after graduation.
Empirically, this paper presents new evidence on how student debt effects household
earnings profiles and occupation choice. It contributes to a growing literature documenting the effect of student debt on household lifecycle outcomes, such as homeownership, marriage, fertility, and attending graduate school (Goodman, Isen, and Yannelis
2018; Chakrabarti et al. 2020). In particular, we provide additional evidence of how debt
effects lifecycle earnings through distortions to occupation choice (Rothstein and Rouse
2011; Luo and Mongey 2019; Herkenhoff, Phillips, and Cohen-Cole 2021).
Our analysis employs a dynamic stochastic heterogeneous agent model of lifecycle earn5

ings with incomplete markets. Following the literature, we analyze the effect of policy
reforms by calibrating the model with microdata on household assets, student debt, education, and labor market outcomes (Ionescu 2009; Huggett, Ventura, and Yaron 2011;
Abbott et al. 2019; Fu, Lin, and Tanaka 2021). A novel feature of our framework is
the inclusion of 18 distinct occupations which, based in part on the endogenous sorting of workers, exhibit different earnings and returns to experience. In this sense, ourpaper is most similar to Luo and Mongey (2019) who develop a quantitative model of
how student debt effects household earnings and occupation choice. We build on theircontribution by showing how student debt reduces the returns to experience by incentivizing households to sort into occupations with front loaded compensation schemes.
As a result, while Luo and Mongey (2019) find that reducing student debt leads workers to sort into occupations with greater amenity value, we find that workers flow into
occupations with greater scope for earnings growth. Taken together, our results suggest that greater scope for human capital accumulation on the job may be one particular
non-wage amenity driving the sorting patterns they uncover.
The remainder of the paper is organized as follows. Section 2 presents a simple model
to show how credit constraints lead to intertemporal distortions that can reduce humancapital investment and give rise to a misallocation of talent. Section 3 presents empiricalevidence on the effect of student debt lifecycle earnings profiles and occupation choice.
Section 4 describes the quantitative model, calibration strategy, and reports the model
fit. Section 5 presents the computational results. Section 6 concludes.

2

An Illustrative Model

This section introduces a simple model of human capital accumulation and occupation
choice in the presence of credit constraints. When constraints bind, households disinvest in human capital accumulation as an alternative mode of consumption smoothing. Section 2.1 describes the intertemporal distortions to human capital accumulationon the job. Section 2.2 shows how credit constraints also effect aggregate human capitalby distorting occupation choice, pushing households towards occupations with front
loaded compensation schemes over those which are best matched to their abilities.
Households live for two periods, young (y) and old (o), and choose consumption, savings, an occupation, and how much to invest in human capital accumulation. Eachhousehold is endowed with one unit of time in each period and begins life with initialassets ay . The problem of a household in occupation k is given by6Vk = max u(cy ) + βu(co )cy ,co ,ao ,ssubject to
cy = wk (1 − s) + ay − aoco = wk h(θik , s) + (1 + r)ao
ao ≥ −ā , s ∈ [0, 1]
where h(s, θ) is the human capital in adulthood of a household with talent θi who invested s time in human capital accumulation. Human capital during youth is normalized to one. The human capital technology h(s, θ) is an increasing and concave functionof time invested,
∂ 2h
∂h
> 0,
<0
∂s
∂s2
To interpret θik as an index of individual talent which makes it easier for individual i toaccumulation human capital in occupation k, we impose that
∂h
> 0,
∂θ

∂ ∂h
> 0,
∂θ ∂s

∂ ∂ 2h<0∂θ ∂s2

Most widely used models of human capital accumulation satisfy these criteria, suchas Ben-Porath (1967) and Mincer (1974). Households realize their occupation specificabilities Θ = {θk } as soon as they enter the labor market and, taking wages wk as given,choose the occupation which maximizes their discounted lifetime utility. Formally, theoptimal occupation choice isk ∗ = argmax { V1 , V2 , ..., VK }The credit constraints appear in the parameter ā which limits the amount households
can borrow against future income. The key economic friction is that households cannot
collateralize their human capital in order to loosen present day borrowing constraints.
2.1

Intertemporal Distortions to Human CapitalIn the absence of frictions, households invest in human capital accumulation until the
marginal return of further investment equals the return on physical capital. The optimal
7

investment in human capital s∗ is given by,∂h(θik , s∗ )
=1+r
∂s

(1)

When borrowing constraints bind, households discount future income streams relative
to the present at a rate that is greater than the market interest rate 1 + r. As a result,households dis-invest in human capital as an alternative form of consumption smoothing. When credit constraints bind, households invest in human capital accumulation
until the marginal return equals the shadow interest rate, 1 + rc , so that∂h(θik , sc )>1+r∂sConsequently, credit constraints lead households to invest less in human capital accumulation, sc < s∗ , leading to higher initial earnings
wk (1 − sc ) > wk (1 − s∗ )but lower returns to experienceh( · , sc )h( · , s∗ )<(1 − sc )(1 − s∗ )Moreover, total lifecycle human capital accumulation is reduced for workers facing
credit constraints, leading to a reduction in aggregate labor productivity alongside changes
in the structure of lifecycle earnings.
2.2

The Misallocation of Talent

In addition to reducing investment on the job, households can also respond to credit
constraints by switching occupations. Limited ability to borrow against future income
leads some households to switch away from occupations that offer opportunities forhuman capital accumulation on the job to those with more front-loaded compensation
schemes. As a result, occupation choice will depend on initial household assets, giving
rise to a misallocation of talent as some workers select into occupations for which theirabilities are not optimally matched.
A key condition for credit constraints to give rise to a misallocation of talent is heterogeneity in occupational wages wk . The variation in wages provides a trade-off in the8level and steepness of the lifecycle earnings profile when moving across occupations ina manner similar to the within occupation trade-offs offered by training on the job, s, reviewed in section 2.1. In particular, with heterogeneity in wk , each occupation will haveits own cutoff θ̄k such that all workers with talent θk > θ̄k will be constrained. In otherwords, it is again the high ability individuals that will be most effected by the credit
constraints. Intuitively, the cutoff θ̄k corresponds to the ability level at which individuals expect sufficiently rapid earnings growth during their career that they would wantto borrow beyond the limit ā. Formally, the cutoff can be expressed implicitly by,1β[ay + wk (1 − s∗ )] −wk h(s∗ , θ̄k ) = −ā1+β(1 + β)(1 + r)(2)

where s∗ is optimal human capital investment on the job, as defined in equation (1),and the whole left hand side corresponds to optimal asset holdings a∗ in the absenceof constraints. The expression shows how the cutoffs depend on both initial household
assets, ay , and vary across occupations with wk . Consistent with economic intuition, thecutoffs in all occupations are increasing in initial household assets ay , so that it is highly
talented individuals from poor households that are most effected by the constraints.
To illustrate the misallocation of talent, it is useful to consider a simple parametric casewith log utility and human capital technology h(s, θ) = θ1−α sα , which satisfies theassumptions made above. Suppose households can choose between two occupations
where, without loss of generality, occupation k = 2 offers higher wages, so w2 > w1 . In
the absence of binding credit constraints, households select the occupation which offers
them the highest present discounted value of lifetime earnings. Given a realization ofoccupation specific abilities, the condition reduces to choosing occupation 1 if

θ1 >w2 − w1 w2
+
· θ2
κw1
w1

(3)

h α
i
1
 1−α
1
1
where κ = 1+rα 1−α − α 1−α > 0. The condition in (3) is economically intuitive: itis optimal to choose the occupation offering a lower wage provided one has sufficiently
high ability to generate greater overall lifetime earnings.
In contrast, we can consider an individual with high level of debt (or sufficiently low
assets) such that they are constrained in both occupations. In this case, they value not
9only discounted lifetime earnings, but also the timing of that income. Given a realizationof occupation specific abilities, an individual chooses occupation 1 if
θ1 >w 2 + ayw 1 + ay1+αβ
 (1−α)β

·

w2
· θ2
w1

(4)

Comparing the occupational sorting rules in equation (3) to (4), one can see that there
are individuals Θ = {θ1 , θ2 } who would choose occupation 1 in the absence of credit
constraints, but choose occupation 2 if they were financially constrained by debt or insufficient assets.6 The extent to which these distortions to occupation choice effect aggregate outcomes depends on the joint distribution of assets, ay , and talent, Θ, in the
population. The more highly talented individuals are encumbered with large debts, thegreater will be the effect on aggregate labor productivity.
Figure 2 illustrates the misallocation of talent by plotting how occupational sorting depends on the presence of credit constraints. For a given initial assets ay , it shows how
individuals sort into each occupation as a function of their talents. The cutoffs θ̄1 andθ̄2 are defined as in (2), indicating the regions where credit constraints bind in each occupation.7 The shaded region represents the population of workers who switch fromoccupation 1 to occupation 2 in the presence of credit constraints. The lower border ofthe region corresponds to the optimal occupation sorting rule in the absence of credit
constraints expressed in (3). The low ability population, those with θ1 < θ̄1 and θ2 < θ̄2 ,continue to choose occupations according to this rule, since they are sufficiently untalented that credit constraints do not effect them. The high ability population, those with
θ1 > θ̄1 and θ2 > θ̄2 , are most affected by the constraints and sort according to equation(4). Since w2 > w1 , there is an additional population whose occupation choices are distorted because they become constrained in occupation 1 θ1 > θ̄1 but not occupation 2,
θ2 < θ̄2 , and hence switch to the latter.
Moving to populations with higher assets, the cutoffs θ̄ increase and the upper bor6

To see this more directly, note that the occupation sorting condition in (4) is a line passing throughthe origin with slope greater than w2 /w1 . This implies that that the slope of the sorting rule in Θ space is
steeper when individuals are constrained than when they are not constrained.
7
Given the parametric example being considered, equation (2) can be solved explicitly so that
1



ay
θ̄k =
1+
1
α
wk
α 1−α + α 1−αβ(1 + r) 1−α10Figure 2: Misallocation of Talent

Notes: This figure illustrates how the misallocation of talent depends on individual’s abilities, for a given
initial asset ay . Occupation 2 is assumed to offer higher wages, w2 > w1 .

der of the misallocation region shifts inward toward the optimal sorting rule, shrinking
the region where credit constraints distort occupation choices. Conversely, moving tohouseholds with lower assets leads the θ̄ cutoffs to shrink and the misallocation region
to expand, resulting in greater distortions to occupation choice. As a result, the aggregate effect of the credit frictions will depend on the joint distribution of assets a andtalents Θ in the population.
This joint distribution will also determine the marginal effect of policies like student
debt forgiveness, since it will determine on the mass of workers near the threshold of
misallocation region. As discussed in section 2.1, policies which alleviate credit constraints will boost human capital accumulation and alter the lifecycle earnings profile
by reducing the shadow interest rate. For the population near the misallocation region,
alleviating credit constraints can also induce occupation switching that leads to discreteadjustments in both productivity and lifecycle earnings. Figure 3 illustrates the effect,
showing how initial earnings, returns to experience, and occupation choice of a constrained household responds to reductions in their debt. Initial reductions in household
debt lead to increased human capital accumulation, reduce initial earnings and boost11

Figure 3: Household Assets and Occupation Switching

Notes: This figure illustrates the effects of reducing an individual household’s debt level on lifetime utility,
initial earnings, and returns to experience. The simulation corresponds to an individual who is has higher
ability in occupation 1, θ1 > θ2 , while occupation 2 offers the higher wages, w2 > w1 .

ing returns to experience on the job. Eventually, household debt falls sufficiently far thatthe household passes out of the misallocaiton region and switches from occupation 2 to
occupation 1, eschewing the higher initial earnings for greater lifetime earnings. The
occupation switch leads to a large, discrete jump in earnings and returns to experience.
After the switch, further debt reductions continues to effect human capital accumulation within the occupation, albeit with slightly larger effects in the more human capitalintensive occupation.
The illustration shows how endogenous occupation choice can mediate part of the impact of credit constraints on aggregate labor productivity and household earnings. Assessing the true aggregate contribution of these channels therefore requires measuring
the joint distribution of assets and talent in the relevant population which will together
determine the within and between occupation effects of credit frictions, as well as how
they respond to policy interventions. We turn to addressing these challenges of measurement in the following sections, both empirically by exploiting plausibly exogenous
variations in student debt as well as structurally through the calibration of a larger scalequantitative model which embeds these core mechanisms.
12

3

The Empirical Evidence

In this section, we provide reduced-form empirical evidence that individuals’ earnings
trajectories change in the presence of student debt, and that at least part of this effectis explained by occupational choice. We use panel data from the NLSY 1997 and an
instrumental variables design to first estimate the impact of student debt on both initial
earnings and returns to experience.
Our findings suggest that individuals with high student debt are forced to trade off between current and future income early in life – initial earnings are higher for those withstudent debt, while returns to experience are lower. Moreover, this trade-off appears
to depend significantly on their first occupation and industry choice upon graduation.
When we include occupation and industry fixed effects in the IV regression, they explain
almost half of our estimated effects.
To further understand why occupational choice matters so much for our estimates, we
conduct a supplementary analysis to test whether earnings trajectories vary considerably across occupations. We use the Current Population Survey to construct ageearnings profiles for each occupation in order to investigate how differential sorting
into occupations potentially drives our earnings results. In the raw data, we indeed finda strong negative correlation between initial earnings and returns to experience acrossoccupations. These empirical findings support the set-up of our theoretical model ofoccupational choice, student debt and human capital accumulation, and motivate ourquantitative analysis.
3.1

Data

Our empirical analysis draws from several data sources. The primary dataset is the
NLSY 1997, an individual-level panel dataset that contains information on higher education, student debt, and labor outcomes. It follows individuals from 1997 through 2015.
Summary statistics are provided in Table 6 in the appendix. We restrict our analysis toindividuals whose highest level of education is a bachelors degree.8
Using the NLSY, we instrument for student debt using variation in the share of grantfunding within college and across cohorts, and measure how incremental debt impacts
8

We have re-run our analysis while also including individuals with an Associates degree. While thefirst and second stage results are largely consistent with those we find on the BA-only population, we
refrain from making this our primary sample since BA and AA degree recipients make very different
human capital investment decisions in college and have markedly different observed occupational choices
post-degree.

13

labor market decisions and lifetime earnings trajectories. To construct our instrument,
we have accessed restricted-use data that identifies NLSY participants’ educational institution. Using the college identifier, we then merge in information from the National
Center for Education Statistics (NCES) on the amount of loans and amount of grants
used at that given college in a given year.
Supplementary analysis of occupation-specific age-earnings profiles uses the Current
Population Survey. While the CPS does not contain information on student debt, it
does contain comprehensive earnings information across the spectrum of occupations,industries, and ages.
3.2

Instrumental Variable Design

To estimate the effect of student debt on intial earnings and returns to experience, we
consider the following equation:yit =α0 + Xit β| {z }

initial (log) earnings if no student debt

α2 SDit| {z }

α1 Expit| {z }++

returns to experience if no student debt+

effect of student debt on initial (log) earnings

α3 SDit × Expit
{z
}
|

+ϵit

(5)

effect of student debt on returns to experience

where yit is an outcome measure of individual i in year t for annual log earnings. The
variable SD denotes the level of student debt. The variable Exp denotes the years ofexperience. The variable Xit includes gender, race and cohort fixed effects.
We seek an unbiased and consistent estimate of α2 and α3 . The effect of student debt on
initial wages is measured by α2 . The effect of student debt on the returns to experienceis measured by α3 . There are potential challenges to estimating equation 5 using OLS.
For instance, we may be concerned that there is a correlation between the level of debt
an individual takes on and the unobservable quality or ability of an individual. The bias
can go either way. Individuals with high ability may expect to have higher future wage
growth. They may decide to borrow more today to smooth consumption over time,leading to an upward bias in α2 . On the other hand, debt may be positively selected.
For instance, low ability individuals may come from low income households, who areunable to provide parental support for their child’s education. This shows up as higher
borrowing for the low ability individual, leading to a downward bias in α2 and α3 .99These identification challenges to identifying a causal impact of student debt on earnings are also14

To address these identification challenges, we estimate the causal impact of student debt
on earnings using an school-cohort-level instrumental variable. Our instrument follows
that used in Luo and Mongey (2019) — it is defined as the share of grant funding, out of
all grant and federal student loan funding, issued by a college in a given year. Specifically, our instrument is defined as:

Zc(i),j =total grantsc(i),jtotal grantsc(i),j + total loansc(i),j(6)

The instrument utilizes the fact that students must fund their college tuition costs througha combination of parental funding, grants, work study aid, and student loans. Whileparental funding is specific and fixed at the student level, grant funding can vary significantly at the college-year level. As shown in Luo and Mongey (2019), variation in grantfunding is substantial both across and within institutions and years.
Intuitively, the instrument captures the fact that when colleges have less to award to
students in the form of grants, students must make up the remaining “gap” in funding using student loans. To meet the exogeneity assumption of a valid instrument, we
argue that yearly variation in the total amount of grant funding available at a college
is unrelated to the ability (or other unobserved characteristics) of any given student atthat college. However, to meet the relevance assumption, this variation in grant funding must also create a meaningful change in amount of student debt that students takeout. Table 10 in the appendix shows a strong first stage effect of shifts in the collegeyear grant share on individuals’ student debt. Importantly, the table also shows thatchanges in grant funding are compensated for entirely and exclusively by changes instudent debt, not other sources of funding. Total funding for college remains constantin response to one standard deviation increase in the college grant share. And while thelevel of student debt decreases almost one-for-one with the increase in grant funding,family and work study aid remain constant. This precise, isolated substitution is important, because it allows us to study the impact of an increase in student debt on future
earnings, absent of other confounding factors like more parental aid or increased work
study while in college.
highlighted by (Field 2009; Rothstein and Rouse 2011; Luo and Mongey 2019). These papers uses variation
in forgiveness of debt (Field 2009) and variation in grants (Luo and Mongey 2019) within a school acrosscohorts to instrument for student debt. Their identification comes from comparing outcomes of cohorts
within the same school, when cohorts within the school differ in terms of grants received.

15

We also check whether variation in the college grant share changes other importanteducational outcomes which could confound our results, like the probability of college
completion or the ability and wealth distribution of enrolled students. These results
are shown in Table 11. Similar to Luo and Mongey (2019), we do not find a significant
evidence of our instrument impacting enrollment or student selection on observables.
In our second stage, in which we regress our instrumented student debt variable onearnings, we include fixed effects for college type – for example, private, public, forprofit, etc. While it would be ideal to include fixed effects for each individual college,
our small sample size does not allow this – we have very few instances in which morethan one student attended the same institution.
3.3

Estimated Impact of Student Debt on Initial Earnings and Returns to Experience

Using our instrument, we next investigate whether those who have subsequently higher
debt choose jobs with significantly different earnings profiles. Table 1 shows the instrumented regression coefficients for α2 and α3 for log earnings. The coefficient on α2 is positive, while the coefficient on α3 is negative. These coefficients imply that an individualwith more student debt has higher initial earnings upon graduation, but subsequently
lower returns to experience.
To interpret the magnitudes, the coefficients in column (I) imply that a additional $1K of
student debt increases initial earnings by 3.14%. For our data sample, this equates to an
additional $508 annual earnings upon graduation, for every $1K of additional student
debt. While we use a different sample from the existing literature, we arrive at estimateson the effects for initial earnings that are consistent with existing estimates.10
Our new empirical evidence is shown in columns (II). Specifically, we find that total
earnings grow by 1.37 ppts slower per year of experience respectively, for every $1K ofadditional student debt. These effects are statistically significant. The magnitudes arealso sizable, given the earnings of 25 − 30 year olds are estimated to grow at a rate of
7.75% on average each year (Guvenen et al. 2021).
We explore what factors explain the wage gap between those with and without student
debt by including different controls. The evidence suggests that a large part of the wage
gap between individuals that graduated with and without student debt is due to the se10

For instance, Luo and Mongey (2019), Rothstein and Rouse (2011), Chapman (2015).

16

Table 1: IV coefficients of student debt on initial earnings and returns to experience, withand without first occupation fixed effects

Effect of student debt ($000s) on:

Grants-based IV
(I)
(II)

(i) Log initial earnings(pvalue)

3.14%
0.08

1.56%
0.13

(ii) Mean returns to experience(pvalue)-1.37%
0.08-0.78%
0.01

Yes

Yes
Yes

Controls:
Ability, age, college-type, race, gender
Occupation FE & Occupation x Exp

Source of variation:

Across-cohort, within college-type(among all students)

Occupation FE & Occupation x Exp

No

Average initial earnings
Average returns to experience explained

Yes
50%
43%

Notes: The Table reports the instrumented estimates from regression 5 using the NLSY
data. Our IV utilizes changes in the college-year grant share, which in turn impacts theamount of student debt taken out by individual students. Our dependent variable is log
yearly earnings. See text for more details.

lection into different occupations upon graduation. The first occupation choice accounts
for almost half of the gap in wage profiles between the two groups.
To see this, Column (II) includes fixed effects for the first occupation that an individual
chooses upon graduation. We also include the interaction of the fixed effects with theyears of experience. The inclusion of these controls reduces the initial earnings gap
by 50% (α2 declines from 3.14 to 1.56). The marginal effect of student debt on returns
to experience declines by 43% (α3 declines from -1.37 to -0.78). These changes imply
that initial occupational choices can explain much of the difference in the subsequentearnings profiles between those with and without student debt. These results imply
17

student debt impacts the selection into different occupations upon graduation.
3.4

The Role of Occupational Choice and Other Mechanisms

Our instrumental variables analysis finds that additional student debt generates ageearnings profiles with initially higher earnings and lower returns to experience. Abouthalf of this effect is explained by occupational fixed effects, meaning that individuals
with student debt sort into professions that have a predictably flatter, front-loaded income trajectory. We provide some suggestive evidence in the data on potential sources
for the heterogeneity in occupational earnings trajectories, and then test for sorting ofindividuals with student debt into occupations that are characterized by a flatter profile.
For this analysis, we use the Annual Social and Economic Supplement (ASEC) of theCurrent Population Survey (CPS) from 2010-2018 to construct occupation and industry
specific age earnings profiles. These are cross-sectional profiles, meaning that the trajectory is constructed across individuals of different ages, rather than within person overtime. While this technique is sensitive to changes in cohort composition over time, it
allows us to look at longer trajectories and at more specific occupation groups. We pool
multiple cross-sections from the CPS in order to control for some cohort effects.
We use a quadratic specification to estimate occupation-specific earning trajectories. In
each 2-digit occupation category, we fit the regression specification earningsi = α +β1 agei + β2 age2i + Xi + ϵi to the cross-section. We restrict the sample to individuals
who are between 21-60 years old and have exactly a bachelors degree; we control forhousehold size, race, gender, state, and CPS cohort.
While we do not claim that these profiles are exogenous (e.g. unaffected by the sortingof individuals with different debt or ability levels into certain categories), they do help
explain why occupational fixed effects have such a large impact on our regression results. We find that there is considerable heterogeneity across occupations in the levelof earnings received upon graduation and returns to experience. If we summarize theage-earnings trajectories by their intercept and slope, the trade-off that individuals face
between front-loaded wages and lower growth becomes more apparent. As shown in
Figure 4 there is a statistically significant negative relationship between occupations’initial log earnings after graduation (y-axis), and their average yearly growth rate in thefirst 15 years (x-axis).
An individual who is constrained at age 23 by student debt may choose a career in18

Health Practice rather than in Law, since it provides higher initial wages that can beused to service the debt (see Figure 4). The debt constraint would thus induce themto choose a career with potentially lower expected net present value. For constrained
individuals, the earnings trajectory becomes an imperfect, costly means of transferringconsumption from the future to the present.
It is worth noting that these are equilibrium earnings trajectories, which encompass bothendogenous labor supply decisions and occupation-specific technological differences.
However, these differences in occupational earnings trajectories may stem from inherent technological differences. Performance, and consequently earnings, in some occupations may benefit more from on the job training. This will lead to a larger impact ofhuman capital investment on returns to experience, and thus steeper earnings profiles,
in particular occupations.
We next test whether individuals with higher student debt sort into occupations with aflatter, front-loaded earnings profile. We characterize occupation-specific earnings profiles using the annualized earnings growth rate between ages 23 and 48 from the CPS.
We then use our instrumental variables regression from the NLSY to test whether individuals with more debt choose occupations with lower growth rates. There is a statistically significant negative relationship between instrumented student debt and occupation specific growth rate of earnings. This confirms our earlier IV results, that foundinstrumented student debt had a positive effect on earnings and a negative effect on
returns to experience. A similar negative relationship exists for the OLS regression.
Using the OLS coefficients, we then predict the average debt level for individuals by
occupation. Figure 4 plots the predicted student debt levels by occupation using differently sized bubbles. Large bubbles have relatively high predicted student debt levels
compared to small bubbles. In line with our model predictions, high debt occupations
also have higher initial earnings and flatter profiles – i.e. they are concentrated in theupper left hand portion of the plot. This graph not only documents the slope-intercept
earnings trade-off across occupations, but also the sorting of constrained individuals
into front-loaded options.
3.5

Summary of Empirical Evidence

In summary, our empirical estimates find that exogenous increases in student debt leads
to higher initial earnings and lower returns to experience. Our evidence suggests that
19

min

phys. scientists

exec, admath &
CS

architec
ts

9.5

transport

sales

operato

rs

law

media

engineering

productionmanagement

health p

10.5
10Initial log(Earnings)

ractice

11

Figure 4: Initial earnings, earnings growth, and student debt by occupation

.035

.045

.055

.065

Annualized Log Earnings Growth.075

Size of marker indicates relative amount of predicted student debt

Notes: This figure plots the predicted student debt levels by occupation using differently sized bubbles.
Large bubbles have relatively high predicted student debt levels compared to small bubbles. In line with
our model predictions, high debt occupations also have higher initial earnings and flatter profiles – i.e.
they are concentrated in the upper left hand portion of the plot.

part of the effect comes from within occupation changes in earnings. The other partof the effect comes from changes in earnings resulting from the first-occupation choiceupon graduation. These empirical findings support the intuition behind our illustrativemodel, which explicitly models the human capital investment and occupational choicesof credit constrained individuals.

4

The Quantitative ModelIn this section, we construct and calibrate a quantitative heterogeneous agent, incomplete markets model with occupation choice and on-the-job human capital investment.
Each period corresponds to five years. Life begins at age 18 when individuals are endowed with initial assets (a), realize their occupation specific talents (θk ), and decide
whether or not to go to college subject to a secondary school taste shock (ζ). Individuals
20who decide to go to college may endogenously incur student debt d if they have insufficient assets to cover tuition. At age 23, graduates enter the labor market and choose an
occupation k ∈ {1, 2, . . . , K} to maximize their expected lifetime income.
k ∗ = argmax { V1 , V2 , ..., VK }Working life continues until retirement at age 63; all households die at age 78. Households can be identified by their assets (a), human capital (h), student debt (d), occupation (k), employment status (z), and age (t). The problem of an employed working age
household can be expressed recursively,
Vk (a, h, d, e, t) = max′ u(c) + βE [Vk (a′ , h′ , d′ , z ′ , t + 1)]
c,s,a

subject to
c + a′ = T (wk (1 − s)hk ) + (1 + r)a − ϕ(a, h, d, z, t)h′k = exp(z ′ )H(s, h, θ)d′ = (1 + rd )d − ϕ(a, h, d, z, t)a′ ≥ −ā , s ∈ [0, 1]
1−ρwhere households have CRRA preferences u(c) = c 1−ρ−1 . As in Huggett, Ventura, andYaron (2011), human capital accumulation is risky and subject to log-normal idiosyncratic shocks, z ′ , with a Ben-Porath (1967) learning technology given by
H(s, h, θ) = θik (shk )α + (1 − δ)hk
where h prevailing human capital, θ is individual talent, and s is the time invested intraining. The functions T (wk (1 − s)hk ) and ϕ(a, h, d, z, t) represent the tax system andstudent debt repayment rule, respectively, which we explain further below.
Student Debt and College Matriculation Households endogenously incur student
debt when making the decision whether or not to go to college. Individuals make theirmatriculation decision at age 18–the beginning of life in the model. High school graduates solve the same lifecycle consumption-savings problem as college graduates above,21

but make no occupation choice and have no student debt. We also abstract from earnings heterogeneity among high school graduates, allowing high school talent to be thesame for all graduates and calibrating it to match the lifecycle income growth of highschool graduates, as in Lee and Seshadri (2019). Households optimally enroll in college
if the lifetime utility of doing so, given their chosen college occupation k ∗ , surpasses
what they would get as a high school graduate. The college matriculation decision also
includes a high school taste shock ζ to capture unobserved heterogeneity and smoothout the optimal enrollment policy.
If individuals decide to attend college (e.g. Vk∗ > Vhs ζ), then they will incur student debt
if their initial assets are insufficient to cover the cost of college tuition net of any grants
or family assistance they receive. Formally, letting τ denote tuition net of any grants orfamily assistance, household debt is given by0d=min{ 0 , a − x · τ }0

if Vk∗ < Vhs ζ
if Vk∗ > Vhs ζ

where we note that the value of college Vk∗ (a, h, d, z, t), and hence the optimal college
matriculation decision, implicitly depends on the amount of college debt households
would take on. To capture the variety of individual circumstances determining accessto college grants and family assistance, we assume that the out-of-pocket net tuitionindividuals must pay to attend college depends stochastically on family background.
Parameter x captures extensive probability of having student debt; with probability 1−x,
an individual is able to attend college without incurring any out-of-pocket expenses.
With probability x, individuals receive an out-of-pocket net-tuition cost τ of attending
college. In calibrating the model, we allow probability x to depend on family assets, andconditional on facing out-of-pocket expenses, we assume net tuition costs are stochastic
and jointly log-normal in the population such that,
 
 


2
a
µ
σ ρaτ
 0  ∼ ln N  a  ,  a

2
τ
µτ
ρaτ στ
We choose the parameters governing x and τ , which determine the net-tuition offers
households receive, to match the post-matriculation realizations of student debt by family
background. The correlation between family assets, a, and the extensive margin of debt,
22

x, is implemented via a copula. The flexible parameterization allows us to capture theendogeneity of student debt and patterns of selection into college that we observe in thedata. Finally, we note that even after receiving grants and financial aid, some households
may still have insufficient assets to pay for college and so are unable to matriculate evenif very talented. These households highlight the key credit market friction that motivates
government education loans and grants in the first place: individuals cannot borrowagainst their future human capital.
Student Debt Repayment Households begin repaying student loans after they graduate college. The payments a household makes to service its student loan debt dependon its outstanding balance d, time to maturity T̄ − t, household assets, potential earnings, and employment status z. The repayment function ψ(a, h, d, z, t) summarizes how
repayments depend on individual circumstances.
In normal circumstances, a household with sufficient financial resources will make payments ρ(d, t) to amortize its student loan over a repayment period t < T̄ , as in Luo andMongey (2019), given byrddρ(d, t) =1 − (1 + rd )−(T̄ −t+1)


Due to the stochastic human capital risk, it is possible that households find themselves
unable to make their student loan payments. This may happen if a household has experienced long sequences of negative shocks which result in human capital depreciation.
Consistent with “undue hardship” provisions of student debt repayment programs, we
capture the effect of these circumstances on debt repayment through the function ξ(z)such that the repayment function is given by,

ϕ(a, h, d, z, t) = minnρ(d, t) , ξ(z)(a + w(1 − s)h)oso that households are never forced to make student debt payments in excess of ξ(z)of their households net worth (e.g. income and assets). In other words, households
become delinquent on their student loan debt if their annual payments are in excess of
ξ(e) fraction of their total net worth.
Finally, while economic hardship can allow households to defer debt repayments andaccrue interest on their outstanding balance, we do not allow debt deferral to continue
23

beyond the maturity ceiling T̄ . In the final period of debt maturity households must pay
off their total outstanding balance or default. Households do not make any student debt
payments beyond the maturity period, e.g. ϕ(a, h, d, z, t) = 0 for all t > T̄ .

The Tax System T(y) is a function which represents the prevailing tax system andtransforms gross household income y into after-tax income. In particular, T(y) takes theform of a step function
T(y) = (1 − τ (y)) · y
where τ (y) represent the effective marginal tax rates for the tax bracket of individuals
with income y. The brackets and marginal rates τ (y) are chosen to match the effective
tax rates estimated by the Congressional Budget Office (CBO) displayed in Figure 12.
Accurately modelling the effective marginal tax rates is quantitatively important sincethese will influence household incentives to raise their income by attending college andinvesting in human capital (Saez, Slemrod, and Giertz 2012; Jones 2019). Furthermore,
several of our policy counterfactuals are redistributive (e.g. debt forgiveness) and we
use tax system T(y) to quantitatively match the progressivity of tax incidence required
to finance these programs.

Retirement and Retirement Benefits. Households retire deterministically at age 63
and continue to make consumption and savings decisions until mortality at age 78. Retired households fund consumption out of savings and retirement benefits π(ŷ), which
they begin receiving after retiring. Formally, retired households solve

VR (a, π) = max
u(c) + βVR (a′ , π)′c,asubject to
c + a′ = e + (1 + r)aa′ ≥ −ā

Following Huggett, Ventura, and Yaron (2011), we assume that the social security system pays everyone a common retirement transfer, denoted as e. We set this number to24

$20,447.78 per year, which is the average annual social security benefit as documented
in the US Social Security Administration in 2023.

Production Technology and Equilibrium Wages. Given the focus on occupation choice,
we endogenize occupational wage rates with an aggregate production technology that
employs both high school and differentiated college labor. In particular, we employ
a nested CES production technology which is common in other applied work (see for
instance, Jones (2014)). The technology is given by,
1σ+ Ac Hcσ ] σ
Y = A [Hhs

where σ parameterized the substitutability of high school and college labor, Hhs is total
high school effective labor, Ac captures college augmenting TFP – calibrated to match thecollege wage premium–and Hc is the total labor input of college graduates. The aggregate labor input of college graduates itself depends on the composition of employment
and occupation specific human capital such that,
# η1

"
Hc =

X

η
ak Hc,kkwhere Hc,k is total effective labor of college graduates in occupation k, ak is occupation
specific TFP chosen to match relative wages amongst college occupations, and η parameterizes the elasticity of substitution amongst college graduates.
4.1

Internal Calibration and Model Fit

The household risk preference parameter, the structural parameters of the human capital technology, and the interest rates on debt are set exogenously. The CRRA preference
parameters ρ is set equal to 2, consistent with standard values employed in the literature. While the population distribution of occupation specific talents θik are calibrated
internally, the structural parameters governing the human capital technology are set exogenously to be consistent with the literature. Following Huggett, Ventura, and Yaron(2011), α is set to 0.7 and the annual depreciation rate δ is set to 0.05 to match the insample end of working life decline in earnings. The annual exogenous interest rate on
household savings r equals 0.04 and the annual interest rate on student debt rd is set to0.042, consistent with the average federal student loan rate observed in the data.
25

Table 2: Internal Calibration Targets: Assets, Debt, and College Matriculation

Mean of log initial assets (population)
Variance of log initial assets (population)
Mean of log initial assets (college grads only)
Variance of log initial assets (college grads only)
Mean student debts ($)
Standard deviation of student debts ($)
Corr(Asset, Student Debt)
Fraction of sample with student debts (%)
Corr(Asset, Having Student Debt)
College completion rate by family background (%)- first asset quintile (Q1)
- second asset quintile (Q2)
- third asset quintile (Q3)
- fourth asset quintile (Q4)
- fifth asset quintile (Q5)
Variance of log earnings in the first 10 years
Ratio of assets, old-to-young workers

Data

Model

10.50
2.47
11.10
2.19
17,78424,889
-0.16
67.0
-0.25

10.22
2.16
10.61
2.18
17,46824,173
-0.16
62.1
-0.22

18.2
22.8
26.7
37.7
50.7
0.39
5.76

24.4
26.2
29.5
35.4
51.2
0.39
5.79

The remaining parameters are simultaneously internally calibrated so that the model
endogenously generates a joint distribution of assets and student debt (as in Figure 1),college matriculation patterns, and occupational heterogeneity in earnings, returns toexperience, and worker sorting (as in Figure 4) that are consistent with the data.
The parameters {β, µa , σa , µτ , στ , ρaτ , µx , ρax , ϵ, σθ } primarily determine the joint distribution of household assets, student debt, college matriculation, and earnings inequality. Table 2 summarizes the 16 targets most closely associated with these 9 parameters.
These parameters are fit internally since student debt will be incurred endogenously
based on selection into college, which itself will depend on the parameters governing
expected payoffs in the labor market (see Table 3). Parameters µa and σa pin down the
distribution of initial assets in the population. Together with µτ , στ , ρaτ , these parameters determine the joint distribution of assets and student debt among the indebted,
conditional on selection into college. Similarly, parameters µx and ρax will determine
26

Table 3: Internal Calibration Targets: Occupational Heterogeneity

Occupation Group

Mean Log
Earnings ($)

Returns toExperience (%)

Employment
Share (%)

Data

Model

Data

Model

Data

Model

Executive Admin
9.95Management
9.93
Math and Computer Science 10.14
Architects and Engineers
10.09
Counslers
9.69
Teachers
9.83
Education
9.55
Sports
9.37
Media
9.60
Health Practitioners
10.29
Health Support
9.74
Food Services
9.76
Cleaning
9.44
Service Workers
9.38
Sales
9.73
Office & Admin
9.69
Maintenance
10.32
Transportation
9.20

9.93
9.91
10.12
10.07
9.66
9.82
9.56
9.35
9.56
10.27
9.74
9.74
9.46
9.39
9.75
9.69
10.33
9.16

12.06
13.28
11.34
11.20
10.44
8.41
4.23
18.09
11.60
4.28
8.23
2.81
10.85
7.80
13.44
9.49
3.49
18.25

11.83
12.85
11.17
11.07
10.65
8.47
3.89
18.49
11.68
4.49
8.09
2.87
9.84
7.43
12.77
9.43
3.09
18.83

7.35
8.75
4.56
6.12
5.69
14.17
1.61
3.44
2.90
4.03
2.20
5.31
1.29
3.17
11.11
15.30
1.07
1.93

7.14
8.88
4.26
5.93
5.95
14.16
1.73
2.99
3.03
4.06
2.20
5.34
1.29
3.10
12.29
14.71
1.06
1.88

Notes: This table summarizes the 54 calibrated parameters {wk , µk , νk }k=1,...,18 and their data counterparts. Employment share is calculated among college graduates, so they add up to 100%.

the extensive margin of college debt among graduates. Combined with the distributionof assets and student debt, parameter ϵ governing the high school taste shock will determine college matriculation patterns by family background. The common variance ofstudent ability, σθ , is set to match the variance of log earnings we observe in the data.
Finally, given lifecycle risk and the human capital technologies, the discount fact β determines the ratio of assets held by old-to-young workers.11 Table 7 summarizes the11

We used the ratio of liquid assets (excluding real estate) between under age 40 workers and age 55-69
workers in 2022 to calculate the data and model moments. Source data from the Distributional Financial
Accounts, Federal Reserve Board Governors.

27

calibrated parameters.
The parameters {wk , µk , νk }k=1,...,18 primarily determine that occupational heterogeneity
in mean earnings, returns to experience, and worker sorting across jobs. Table 3 displays the 54 data targets primarily associated with these parameters and the model fit.
The occupation-specific wage rates wk mainly control occupation k’s average earnings;
the mean population talent µk in occupation k most effects the returns to experience ofworkers who select into occupation k; and the occupation-specific amenity νk are chosen
to match the job choice probabilities, given the equilibrium occupation earnings profiles.
These parameters must be fit simultaneously with those in Table 2 since the model targets are interdependent. Labor market returns to a college education will determine
the willingness of households matriculate and take on student debt; at the same time,the distribution of debt among graduates will influence their subsequent investments inon-the-job training and occupational choice.

10.6

Figure 5: Model Fit: Occupational Heterogeneity and Student Debt

Sp
Sp ort
ns
or s
po
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Data

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Initial Log Real Earnings
9.4
9.8
10.2

M
a
M int
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H
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H ea
ea lt
ce
lthh PPr ra
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e

Model

0

.02

.04

.06
.08
.1
.12
.14
.16
Annualized Log Real Earnings Growth

.18

.2

Notes: This figure plots the mean log earnings and returns to experience for each occupationfrom the calibrated model along with their data counterparts. The size of the bubbles represent
the average amount of student debts among individual in that occupation.

Figure 5 plots the calibrated model’s fit of the relationship between student debt and28

occupational heterogeneity displayed originally in Figure 4. While the occupational
heterogeneity in mean earnings and returns to experience are targets of the calibration
process, the sorting of students with different levels of student debt across the occupations is not. Nevertheless, Figure 5 shows the model does a good job at replicatingthese patterns of sorting, consistent with the mechanism we study. The calibrated model
also does relatively well at replicating the response of the aggregate earnings profile tochanges in student debt, as summarized by the IV results in section 3. In particular,
simulating the effect of an exogenous increase in student debt among the populationof college graduates within the model predicts that a thousand dollar increase in debt
leads to a 2.48 percentage point rise of initial earnings and -0.28 percentage point dropin returns to experience. Though untargetted, the model’s predictions are close to our
IV results and within range presented in the literature.

5

Quantitative Results

GOING THROUGH MAJOR REVISION
This section uses the calibrated model to compute the aggregate welfare and productivity consequences of federal policies which aim to alleviate student debt burdens. The
analysis focuses in particular on two classes of policies: extended repayment programs
and student debt forgiveness.
5.1

Extended Repayment Programs

Table 4 reports the aggregate welfare and productivity effects of extending student loan
repayment periods. The current Federal Standard Repayment Plan requires students
repay loans in fixed monthly installments over the course of ten years.12 The results inTable 4 report the impact of extending the standard repayment period on federal loans
by 2 and 5 years. In each case, the interest rate on outstanding loans is adjusted sothat the net present value of individual liabilities do not change. In other words, theprogram has no net cost to the government and does not require any cross-household
redistribution; rather, the program redistributes costs within each individual’s lifecycle.
The model captures the extensive participation margin by allowing households to selectout of extended repayment programs and stay with the standard repayment plan.
12

While 10-years is standard repayment period on federal loans, in practice repayment periods often
vary with individual circumstances. For instance, students with Direct Consolidation Loans can face
repayment periods between 10 to 30 years.

29

Table 4: Welfare and Productivity Effects of Extended Repayment Programs∆ TFPR∆Welfare
Total

IncomeAmenity

Everyone
Switchers
Stayers

0.25%
1.80%
0.25%

0.45%
45.72%
0.25%-0.20%-43.92%0.41%
58.55%
0.22%% of job switchers% choosing longer repayment

0.38%
73.49%

0.66%
43.35%
0.45%-0.21%-41.37%0.57%
60.87%
0.37%

2 Year Elongated Repayment

5 Year Elongated Repayment
Everyone
Switchers
Stayers

0.45%
1.98%
0.45%

% of job switchers% choosing longer repayment

0.41%
84.68%

Notes: This table report the impacts of extending the standard 10-year repayment period to 12 and 15
years on welfare and aggregate TFPR. % of job switchers report the fraction of job switchers inducedby the policy, and % choosing longer repayment is the fraction of college graduates who prefer/choose
the longer repayment duration. Change in TFPR is defined as average of change in discounted lifetime
income across households

The results show that the extended repayment programs modestly increase welfare andlabor productivity, and that the effect is monotonically increasing in the duration of theextensions analyzed. Furthermore, while the number of workers induced to switch occupations was small, ranging from 0.38% - 0.41% of the population, they experienceby far the largest welfare and productivity gains from the program. While welfare increased by 0.25 - 0.45% for those who did not switch jobs, it grew by 1.80 - 1.98% for
occupation switchers. Despite these large effects, the aggregate welfare effects of the
program still closely mirror those of job stayers. In part, this is due to the fact that policy
induced job-switchers remain a small fraction of the overall population. It is also due tothe direction of job switches induced by policy. As the welfare decompositions in Table 4
show, the pattern of occupation switching was predominantly individuals leaving highamenity jobs for occupations which offered more scope for human capital accumulationand hence higher lifetime income. Due to the offsetting effects of income and amenities,
30

the net welfare consequences of this reallocation of workers remains modest despitelarge changes in the underlying sources of welfare.
While the occupation switching induced by the policy has a minimal effect on aggregate household welfare, it is responsible for nearly half of the gains in aggregate laborproductivity. By extending repayment periods, the policy change reduces the shadow
cost of borrowing which stimulates investment in human capital accumulation. Households switch from high amenity jobs to those with more scope for investment on the
job and hence greater returns to experience and higher lifetime earnings. Consistent
with theory in the preceding sections, high talented individuals will be most effectedby the constraints, making the productivity gains among induced switchers especiallylarge. Despite only representing a small share of the population, the productivity gains
among switchers nearly doubles the aggregate productivity gains from the policy relative
to those experienced by job stayers, from 0.22% - 0.37% to 0.41% -.57%. The results suggest that the impact of student debt and credit constraints on the occupation choice ofcollege graduates can have substantial economic effects on aggregate labor productivity.
Figure 6 accounts for the sources of aggregate gains in labor productivity across the occupations using a shift-share decomposition. It reports the total contribution of each
occupation to aggregate productivity, as well as its within-occupation and betweenoccupation components. Nearly one-third of the aggregate rise in productivity is dueto the reallocation of workers across occupations; the total between-occupation contribution is 0.17% while the within-occupation contribution is 0.40%.13 The decomposition
also shows that nearly all of the productivity gains are due to a small number of occupations: Sales, Management, Exec. Admin, Architects, Engineering, Math and Computer
Science. As repayment elongation policies alleviate credit constraints, many workers
switch to these occupations because they offer greater scope for human capital accumulation and hence higher lifetime earnings. The effect is evident in both the large between
and within contributions of these occupations to aggregate productivity. Not only do
repayment elongation policies lead more people to choose these occupations, they also
increase the intensity of human capital investments on the job within these professions.
Finally, even though the repayment extension policies are budget neutral and do not require cross-household redistribution, the policies nevertheless have distributions consequences. The distributional consequences are the result of the uneven way that student
13

In the case of the two-year debt elongation policy, the total within-occupation contribution is 0.24%
and the between-occupation contribution is 0.17%.

31

Figure 6: Repayment Elongation (5yr): Shift-Share Decomposition of Aggregate TFPR

Percentage Change in TFPR (%)

0.15

Total Contribution
Within Occupation
Across Occupation

0.10
0.05
0.00
-0.05

s
ch

or

er

ts
Te
a

at
h

M

Sp

ed

C
&

ia

S

t
M

M

Ex

an

ec

ag

,A

em

dm

en

in

n
at
io
uc

Ar

ch

ite

ct
s,

C

Ed

ou

En
g

ns

in

el

ee

or

rs

s

-0.10

Percentage Change in TFPR (%)

0.15

0.10

0.05

0.00

-0.05

rt
ns
Tr
a

ke
W
or

Se

rv

ic

e

po

rs

s
le
Sa

in
m

O
ffi
c

e

&

Ad

an
nt
en
ai

M

lth
ea
H

lth
ea
H

ce

t
pp
Su

ac
Pr

Se
d
Fo
o

or

e

ic
rv

in
an
le
C

tic

e

g

-0.10

Notes: This figures shows the shift-share decomposition of aggregate gains in labor productivity across occupations, for the five-year debt elongation policy. Black bars represent the total
gains in the labor productivity. Gray and red bars represent the across- and within-occupation
shares of the labor productivity gains, respectively.

32

0.00

Consumption-Equivalent Welfare Gain (%)
0.25
0.50
0.75
1.00
1.25
1.50

Figure 7: Five Year Extended Repayment
Debted Only

Quintile 1

Quintile 2

Quintile 3

Quintile 4

Everyone

Quintile 5

Net Asset (Asset-Student Debt) Quintile

Notes: This figures shows the distribution of welfare gains under the five-year
debt elongation policy across the household net asset distribution. Net asset isdefined as initial asset minus total amount of student debt at the initial period.
Black bars represent the welfare gains among individual who had positive student debts. Gray bars represent the welfare gains among everyone, including
individuals without student debts.

debt is distributed across households (recall Figure 1). Figure 7 plots the distributionof welfare gains under the five year extended repayment program across the household
wealth distribution. It shows that the welfare gains at the bottom of the wealth distribution are 3 to 4 times as large as the average. Consequently, alongside welfare andproductivity gains, extended repayment policies can also reduce economic inequality
even without explicit redistribution.
5.2

Debt Forgiveness Programs

Table 5 reports the aggregate welfare and productivity consequences of student debt
forgiveness policies. It reports the effect of forgiving outstanding student debt up to acap of 10k, 50k, and 100k; the final cap of 100k is essentially equivalent to complete student debt forgiveness. In contrast to extended repayment policies like those analyzed
in section 5.1, student debt forgiveness requires requires new net expenditures by thegovernment and hence redistribution across households. To be consistent with the pre33

vailing tax system, we assume that the costs associated with any debt forgiveness are
distributed across households as lump-sum tax liabilities with the same progressivity as
the existing income system.
The results in Table 5 show that the effect of student debt forgiveness on household welfare and productivity is non-linear in the size of the program. The smaller (10k) andlarger (100k) programs actually reduce aggregate household welfare, while the smaller
program also leads to a reduction in aggregate labor productivity. In contrast, the middle sized 50k program delivers modest welfare and labor productivity gains. The mixed
effects reflect the distortionary consequences of redistribution, since those who bear thecosts of taxation are not the same as those who benefit from student debt forgiveness. In
particular, higher income households bear most of the cost while the benefits accrue tolower income households (see Appendix Figures 14 and 15).
The net impact of each program is determined by the countervailing effects of relaxing
credit constraints for those who receive debt forgiveness and the added distortions fromtaxation required to fund the programs. The small 10k program leads to both modest
welfare and productivity losses because the amount of debt forgiven is too small to havea substantial effect on the human capital investments of the heavily indebted population, but it still results in substantial additional tax burdens for higher income earners. In
contrast, the median sized 50k program boosts aggregate welfare and productivity since
it is sufficiently large to alleviate credit constraints for much of the indebted population.
The large 100k debt forgiveness programs again yields negative household welfare. The
larger program helps a smaller population of constrained graduates at the margin, andleads to far greater tax burdens for higher income households, discouraging the pursuitof higher income through human capital accumulation.
As in the case of elongated repayment policies, the population of switchers play an important role in driving aggregate outcomes, especially in terms of productivity. The
share of the population switching occupations following debt forgiveness is also muchlarger than under repayment elongation policies. In part, this reflects the fact that forgiving debt provides a more drastic alleviation of credit constraints compared with extending the duration of repayments. In addition, debt forgiveness induces some occupation switching among higher income households as the increases in taxation necessary
to fund debt forgiveness discourages human capital investments at the top of the income distribution (Saez, Slemrod, and Giertz 2012; Jones 2019). Figure 8 shows how
the incidence of taxation to fund debt forgiveness effects the composition of occupation
34

Table 5: Welfare and Productivity Effects of Student Debt Forgiveness∆ TFPR∆Welfare
Total

IncomeAmenity-1.05%-3.30%-1.03%-0.70%
34.00%-1.03%-0.35%-37.30%-0.17%
50.65%-0.49%

0.02%
0.43%
0.01%

0.20%
10.08%
0.01%-0.18%-9.65%

0.20%
8.89%
0.07%

-0.18%
1.47%-0.21%

0.09%
13.84%-0.21%-0.27%-12.37%0.20%
14.15%-0.04%

10K cap student debt forgiveness
Everyone
Switchers (4.36% of treated)
Stayers
50K cap student debt forgiveness
Everyone
Switchers (6.61% of treated)
Stayers
100K cap student debt forgiveness
Everyone
Switchers.
Stayers(7.48% of treated)

Notes: This table report the effects of student debt forgiveness on welfare and aggregate TFPR. Fractionof job switchers induced by the policy reported in parentheses. Change in TFPR is defined as average ofchange in discounted lifetime income across households within each group.

switchers. Without any new taxes, the population of occupation switchers would decline monotonically with household net debt, as in the case of debt elongation policies.
Using flat taxes to fund the debt forgiveness would lead to many more occupations fromthe middle of the income distribution. Funding debt forgiveness through progressive
taxation, as in the benchmark results of Table 5, discourages human capital accumulation at the top of the income distribution and leads to occupational downgrading among
wealthier households.
Figure 9 displays a shift-share decomposition of the contribution of each occupation toaggregate labor productivity following a 50k-cap student debt forgiveness policy. Noteworthy is the fact that the relative contribution of each occupation has changed relative
to the decomposition of the debt elongation policies in Figure 6. Whereas under the
repayment elongation programs most switchers moved to higher earning occupations
with greater scope for human capital accumulation such as Management, Engineering,
Math, and Computer Science (recall Figure 6), most switching following debt forgive35

3.5

Figure 8: Occupation Switching by Net Assets under 30K Debt Forgiveness

0.0

0.5

Fraction of Job Switchers (%)
1.0
1.5
2.0
2.5
3.0

No TaxFlat Tax
Progressive Tax

Quintile 1

Quintile 2

Quintile 3

Quintile 4

Quintile 5

Net Asset (Asset-Student Debt) Quintile

Notes: This figure plots the fraction of job switchers within each of the five net
asset quintiles, under 30K-cap student debt forgiveness policy with different
ways of financing the policy.

ness is toward middle income occupations, such as Teachers, Office Administration,Media, and Counseling. As discussed above, these differences in the patterns of occupation switching reflect the countervailing forces of debt forgiveness and increased
taxation, which together predominantly effect households in the tails of the aggregate
wealth distribution and incentives them to move to the middle.
Partly as a consequence of these differential switching patterns, the composition ofwithin-occupation and between-occupation contributions to aggregate productivity arealso meaningfully different under the two policies. Under the 50k-capped program,within-occupation effects raise aggregate labor productivity by 0.21% while betweenoccupation effects lead to a -0.01% decline. Moreover, the composition of these effects
varies non-linearly across program sizes as the relative impact of debt forgiveness andincreased taxation varies, leading to different patterns of occupation switching. Under the small 10k-capped forgiveness program, within-occupation effects reduce aggregate productivity by -0.43% while between occupation effects raise it by 0.26%. Under
the 100k-capped program, which effectively amounts to forgiving nearly all outstand36

Figure 9: Debt Forgiveness (50k cap): Shift-Share Decomposition of Aggregate TFPR

Percentage Change in TFPR (%)

0.15

Total Contribution
Within Occupation
Across Occupation

0.10
0.05
0.00
-0.05

s
ch

or

er

ts
Te
a

at
h

M

Sp

ed

C
&

ia

S

t
M

M

Ex

an

ec

ag

,A

em

dm

en

in

n
at
io
uc

Ar

ch

ite

ct
s,

C

Ed

ou

En
g

ns

in

el

ee

or

rs

s

-0.10

Percentage Change in TFPR (%)

0.15

0.10

0.05

0.00

-0.05

rt
ns
Tr
a

ke
W
or

Se

rv

ic

e

po

rs

s
le
Sa

in
m

O
ffi
c

e

&

Ad

an
nt
en
ai

M

lth
ea
H

lth
ea
H

ce

t
pp
Su

ac
Pr

Se
d
Fo
o

or

e

ic
rv

in
an
le
C

tic

e

g

-0.10

Notes: This figures shows the shift-share decomposition of aggregate gains in labor productivity across occupations, for the 50K-cap debt forgiveness policy. Black bars represent the total
gains in the labor productivity. Gray and red bars represent the across- and within-occupation
shares of the labor productivity gains, respectively.

37

ing student debt, the within-occupation contribution to aggregate productivity is 0.12%
while the between-occupation contribution is 0.08%. While the source of productivity
gains varies student debt forgiveness programs, their cumulative contribution to aggregate labor productivity always appears less than repayment elongation policies whichsimilarly alleviate credit constraints without incurring the distortionary effects of redistributive taxation.

6

Conclusion

We empirically and theoretically examine how household assets affect individual careerdevelopment and aggregate labor market outcomes. Using panel microdata on the early
career development of recent college graduates, we document the relationship between
assets, debt, occupation choice, and the earnings lifecycle. Exploiting exogenous variation in student debt burdens following changes in the generosity of university tuition
grants, we find that those with more initial debt chose careers with higher initial earnings but lower returns to experience over the next 10-15 years. Initial occupation choice
mediates a substantial part of the measured effect of debt on the earnings lifecycle.
To understand the data and its implications, we develop a model in which credit constraints interact with human capital decisions. High debt burdens lead workers to distort labor market choices toward careers which offer more front-loaded compensation.
The adjustment process occurs both on the intensive margin, by reducing on-the-job
investment, and through an extensive margin adjustment in occupation choice. Calibrating the model to replicate key features of the microdata, we investigate the consequences of extended repayment and student debt forgiveness programs on lifecycle
earnings, occupation choice, welfare, and aggregate productivity.
Our counterfactual analysis suggests that both policies increase welfare and labor productivity by allowing households with large amounts of student debt to choose occupations better matched to their abilities. We find that although the fraction of household
who are induced to switch occupation by the policies is small, they drive the majorityof welfare and productivity gains. This means that the occupational choice channel isimportant to account for. While the repayment extension policy is universally welfare
improving, the distributional effects of debt forgiveness policies is only welfare improving under certain parameters. This is because debt forgiveness requires the government
to introduce distortionary taxation to fund the program.

38

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40

A

Tables and Figures Appendix

Table 6: Summary Statistics for NLSY Sample
All Individuals Used in IV Sample
Variable

Mean% Male% WhiteAge at BA
Year of BA
HH Networth in 1997
Avg. HH Income
Ability Quartile$ Student Loans

0.40
0.70
23.16
2006
138,384
69,8903.27
17,990

Variable% Male% WhiteAge at BA

Mean
0.38
0.67
23.26

Year of BA
HH Networth in 1997
Avg. HH Income
Ability Quartile$ Student Loans

Std. Dev.

Median

Min.

P25

0.49
0.46
2.73
3
134,91448,5520.86
25,203

0.00
0.00
0.00
1.00
0.00
0.00
22.00
19.00
21.00
2006
2001
2004
95,375250
33,00059,67630
36,253
4.00
1.00
3.00
11,500Conditional on Positive Student Debt
Std. Dev.
Median
Min.
P25
0.49
0.00
0.00
0.00
0.47
1.00
0.00
0.00
2.71
22.00
19.00
21.00

P75

Max.

1.00
1.00
24.00
2007
197,751
90,2544.00
25,750

1.00
1.00
34.00
2015
599,001
285,805
4.00
351,000

P75

Max.
1.00
1.00
34.00

1.00
1.00
24.00

2006

3

2006

2001

2004

2008

2015

116,10962,417
3.24
27,259115,873
40,4210.87
26,643

79,62055,2003.00
20,975

250
30
1.00
300

27,50034,0003.00
12,000

162,500
80,3504.00
35,000588,000
285,805
4.00
351,000

Notes: This table provides summary statistics for the NLSY97 sample that we use in our instrumental variables regression. The top panel includes all individuals in the sample, while the bottom panel
includes only those with positive student debt.

41

Figure 10: IV First Stage Estimates
Effect of 1sd (10ppt) increase incollege grant share on:

Total
Funding

Grants

Debt

Family Aid

Work Study
Aid

Tuitioncosts

Coefficient(pvalue)

-$160
0.95

$7,6700.00-$5,076
0.00

-$23
0.43

$197
0.09

-$863
0.56

Figure 11: IV Robustness Estimates
Effect of 1sd (10ppt) increase incollege grant share on:Years atcollegeCompletion
rate

1(Fulltime)

Coefficient(pvalue)

0.11
0.35

0.14%
0.28

0.01
0.21

42

Age starting
Ability
Parentalcollege(percentile) income
-0.02
0.71

1.72%
0.23

$1,1450.60

1(White)0.38
0.11

Table 7: Parameter Values
Category
Preferences
Human capital technology
Human capital shocks
Interest rates (annual)
Initial conditions
Extensive margin of student debt
Student debt repayment
High school taste
Occupational heterogeneity

Notation

Parameter value1−ρ
β, u(c) = c 1−ρ−1α(β, ρ) = (0.9855 , 2)H(s, h, θ) = θ(sh) + (1 − δ)h(δ, α) = (1 − (1 − 0.05)5 , 0.7)pz ∼ LogNormal(µz , σz )µz = 0.0, σz = (0.1512 ) ∗ 5(r, rd ) = (0.04, 0.042)d)!#!
" (r, r!
(µa , µτ , σa , στ , ρaτ )a0µa
σa2 ρaτ∼ ln N,2=(3.84, 3.60, 1.86, 1.73, −2.50)τµτρaτ στ
x ∼ Bernoulli(p)T̄ζ ∼ Frechet(ϵ)(K, σθ ){wk , µk , νk }k=1,...,1843

p = 0.8
T̄ = 2
ϵ = 10(K, σθ ) = (18, 0.52)
See Table 8

Table 8: Occupation-Specific Parameter Values

Occupation
Executive Admin
Management
Math and Computer Science
Architects and Engineers
Counslers
Teachers
Education
SportsMedia
Health Practitioners
Health Support
Food Services
Cleaning
Service Workers
Sales
Office & Admin
MaintenanceTransportation

44

k
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18

wk
5.397
5.387
5.984
5.848
4.343
4.332
2.922
3.828
4.277
5.192
4.145
3.127
3.748
3.078
4.897
4.119
5.152
3.519

µk
-1.529-1.429-1.523-1.516-1.714-1.593-1.897-1.419-1.792-1.773-1.880-1.793-1.923-1.814-1.483-1.585-1.970-1.530νk
0.03850.03760.03540.03650.04490.04490.05220.03960.04480.04040.04590.05140.04780.0516
0.04100.04610.0406
0.04190.0

5.0

Effective Tax Rate (%)
10.0 15.0 20.0 25.0

30.0

35.0

Figure 12: CBO Effective Tax Rates

0

200

400

600
800
Annual Income ($000)

1000

1200

1400

Notes: This figure plots effective tax rates by income categories. Effective tax
rate includes individual income taxes, social security taxes, corporate income
taxes, and exercise taxes. Source: Congressional Budget Office, Effective Federal
Tax Rates, 1979–2004 (December 2006), Table 1.

45

Figure 13: Validation: Aggregate Lifecycle Profile
11.5
11.3
11.1

Log Earnings

10.9
10.7
10.5
10.3
10.1
9.9
9.7
9.5
9.3
0

4

8
Model

12

16
20
24
Years of Experience

28

Guvenen et al. (2021 ECMA)

46

32

36

40

NLSY97

7

Figure 14: Welfare by Net Assets under 50k Debt Forgiveness
Everyone-3

Consumption-Equivalent Welfare Gain (%)-2 -1 0
1
2
3
4
5
6

Debted Only

Quintile 1

Quintile 2

Quintile 3

Quintile 4

Quintile 5

Net Asset (Asset-Student Debt) Quintile

Notes: This figures shows the distribution of welfare gains under the 50K-cap
debt forgiveness policy across the household net asset distribution. Net asset isdefined as initial asset minus total amount of student debt at the initial period.
Black bars represent the welfare gains among individual who had positive student debts. Gray bars represent the welfare gains among everyone, including
individuals without student debts.

47

35

Figure 15: Distribution of Costs and Benefits, 50k Debt Forgiveness

0

5

10

Thousand $
15
20

25

30

Amount Forgiven
Amount Paying

Quintile 1

Quintile 2

Quintile 3

Quintile 4

Quintile 5

Net Asset (Asset-Student Debt) Quintile

Notes: This figures shows the average amount of student debts forgiven andaverage amount of lump-sum taxes paid by the individuals within each net
asset quintile. Net asset is defined as initial asset minus total amount of student
debt at the initial period.

48