longtable

# Scientific Talent Leaks Out of Funding Gaps111This paper previously
circulated under the title _The Effect of Funding Delays on the Research
Workforce: Evidence from Tax Records_. This paper uses data from the U.S.
Census Bureau. Any views expressed are those of the authors and not those of
the U.S. Census Bureau. The Census Bureau’s Disclosure Review Board and
Disclosure Avoidance Officers have reviewed this data product for unauthorized
disclosure of confidential information and have approved the disclosure
avoidance practices applied to this release. (DRB Approval Number: CBDRB-
FY21-CES009-002, CBDRB-FY22-CES008-003, CBDRB-FY22-CES007-008, CBDRB-
FY23-CES008-002, CBDRB-FY23-0333). We are grateful to Enrico Berkes, Valerie
Bostwick, Matt Clancy, Holden Diethorn, Lorenz Ekerdt, Ina Ganguli, Donna
Ginther, Cheryl Grim, Ian Hutchins, Danielle Li, Kyle Myers, Bruce Weinberg,
and numerous seminar and conference participants for their invaluable
feedback. We are also indebted to the NIH Principal Investigators who
graciously allowed us to interview them.

Wei Yang Tham222Harvard Business School & Laboratory for Innovation Science at
Harvard<EMAIL_ADDRESS>Joseph Staudt333US Census Bureau,
<EMAIL_ADDRESS>Elisabeth Ruth Perlman444US Census Bureau,
<EMAIL_ADDRESS>Perlman would like to dedicate this paper to a
soft money supported scientist, whose year-to-year funding uncertainty and
grant non-renewal has impacted their own life. Stephanie D. Cheng555Edgeworth
Economics

###### Abstract

We study how delays in NIH grant funding affect the career outcomes of
research personnel. Using comprehensive earnings and tax records linked to
university transaction data along with a difference-in-differences design, we
find that a funding interruption of more than 30 days has a substantial effect
on job placements for personnel who work in labs with a single NIH R01
research grant, including a 3 percentage point (40%) increase in the
probability of not working in the US. Incorporating information from the full
2020 Decennial Census and data on publications, we find that about half of
those induced into nonemployment appear to permanently leave the US and are
90% less likely to publish in a given year, with even larger impacts for
trainees (postdocs and graduate students). Among personnel who continue to
work in the US, we find that interrupted personnel earn 20% less than their
continuously-funded peers, with the largest declines concentrated among
trainees and other non-faculty personnel (such as staff and undergraduates).
Overall, funding delays account for about 5% of US nonemployment in our data,
indicating that they have a meaningful effect on the scientific labor force at
the national level.

> “My current job started 8 years ago when my boss told me he had 6 months of
> guaranteed funding. I worked for him full-time for 4 years, my salary
> cobbled together from a half-dozen grants over that time…[W]hile my skills
> are undoubtedly valuable to a research lab, it is incredibly difficult for
> someone like me to find a stable job because of the funding issues…” -
> Anonymous lab technician/manager [Guzey, 2019]

##  1 Introduction

The heavy dependence of university research on federal funding is not
accidental. In the aftermath of “the scientist’s war,” Vannevar Bush, director
of the WWII Office of Scientific Research and Development, laid out a vision
for US science. Bush’s 1945 report, “Science: The Endless Frontier” called for
the creation of a federal science agency that would provide “stability of
funds so that long-range programs may be undertaken.”666Guided by the
importance of basic research to the war effort, Bush sought to “strengthen”
universities, which he viewed as essential to the production of basic research
because they were “least under pressure for immediate, tangible results.” See
Stephan [2013] and Gross and Sampat [2023]. The grant system that resulted,
however, exhibits instabilities that harm individual scientists and ultimately
science itself [Alberts et al., 2014].

Today, federal funding routinely exposes researchers to grant uncertainty
through, for example, the 1998–2003 boom-bust cycle of the National Institutes
of Health (NIH) [Freeman and Van Reenen, 2009] and the contentious federal
budgeting process with accompanying threats of government shutdowns.777The
unpredictability of when a given year’s budget will be passed is such a
regular occurrence that it might even be considered a permanent feature of the
scientific funding landscape. For example, the National Institute of Allergy
and Infectious Diseases (NIAID), which accounted for 14% of the NIH’s budget
in FY 2020, explicitly addresses this issue in an online guide to the grant
application process, stating that it is “assiduous about issuing awards using
funds from the [continuing resolution].” Moreover, the process of applying for
and renewing grants can itself be lengthy and unpredictable, ranging between 8
and 20 months for NIH grants [Fikes, 2018, DrugMonkey, 2009, 2016, Mervis and
Marshall, 1996].888The National Institute of Allergy and Infectious Diseases
(NIAID) guide to grant timelines says that “[i]f your application succeeds on
the first try, it typically takes between 8 and 20 months after the due date
to get an award”. Specifically, 8–20 months refers to the time between
application submission to the arrival of funds.

In addition, the COVID-19 pandemic highlighted that funding delays are more
than a bureaucratic nuisance; they can hinder scientific progress when it is
urgently needed [Williams, 2023, Collison et al., 2021]. However, we lack
systematic evidence on how delays might affect the careers of people supported
by grant funding, which is crucial for policy because these research personnel
have acquired scarce human capital, at both high personal and social cost, and
play a critical role in advancing knowledge and technology. Furthermore, the
impacts of funding delays today may feed into the expectations of potential
scientific workers and deter talent from entering the research workforce.

The impacts of funding delays on the scientific workforce are difficult to
study because they are often caused by aggregate shocks (i.e., all researchers
are affected by the federal budgeting process). We address this challenge by
isolating lab-level variation in funding delays arising from a particular
institutional feature of the NIH’s most common research funding mechanism, the
R01 grant. R01s are usually granted for four to five years, after which a
Principal Investigator (PI) can apply for renewal after each term ends.
999Receiving an R01 is generally regarded as necessary for establishing an
independent research lab in the biomedical sciences [Faherty, 2022]. However,
even if the R01 is _successfully renewed_ , there can be delays in the
disbursement of its funds. Confining our analysis to _successfully renewed_
R01s, we measure funding delays using publicly available administrative data
on NIH grants (ExPORTER), identifying treated labs that experience a delay
(“interrupted” labs) and compare them to similar control labs whose funding
was not delayed (“continuously-funded” labs).101010Renewal is not guaranteed –
the success rate of renewal applications in a given year is about 20%. Using a
delay of more than 30 days as a baseline definition, over 20% of these
successfully renewed grants (between 2005-2018) experience an
interruption.111111Our choice of 30 calendar days is meant to approximate a
month – grants are usually funded on the first of the month, so the arrival of
new grant funding can be thought of as occurring on a monthly basis. We also
present results separately for “short” (30 to 90 days) and “long” (90 days or
more) delays, which confirm that our results are not sensitive with respect to
the exact definition of an interruption.

We next link our sample of successfully renewed R01s to university
administrative data on grant transactions (UMETRICS), allowing us to observe
the individual personnel (e.g., faculty, postdocs, grad students, etc.)
supported by these grants. These personnel are then linked to their career
outcomes using the universe of confidential W-2 and 1040 Schedule C (ILBD) tax
records as well as unemployment insurance earnings records (LEHD), which
provide their complete earnings and employment history in the US for
2005-2018. We also link these lab personnel to a variety of additional data,
including PubMed publications, comprehensive administrative data on
demographic characteristics (age, gender, race, and ethnicity) as well as the
full 2000, 2010, and 2020 Decennial Censuses.

Using a difference-in-differences design, we compare the career outcomes of
personnel in labs with an interrupted R01 to those in labs with only
continuously-funded R01s. Our estimation procedure combines “stacking” by
cohorts of grants set to expire (but ultimately successfully renewed) in a
given year [Baker et al., 2022, Cengiz et al., 2019] and the estimator from
Callaway and Sant’Anna [2020]. We first examine how funding interruptions
affect the job placements of lab personnel. After an interruption, personnel
in labs supported by a single R01 are immediately 3 percentage points (pp)
more likely to become nonemployed in the US (i.e., they do not appear in our
comprehensive tax and earnings data), an almost 40% increase.121212The term
“nonemployed” has been used before in the economics literature (e.g., Murphy
and Topel [1997] and Hornstein et al. [2014]) to draw a distinction from the
more common term “unemployed”, which refers to people who are not working and
actively looking for a job. We do not observe whether nonemployed personnel
are actively looking for a job, and thus cannot observe whether they are
unemployed.,131313Section 4.2 describes how we use mean changes in outcomes
for continuously-funded personnel to convert our estimates into percentage
increases. This US nonemployment effect persists for at least five years and,
for context, is about one-third of the motherhood nonemployment effect for
Ph.D.s in the biological sciences [Cheng, 2021]. There is a corresponding
decrease in employment probability in US industry, government, or the non-
profit sector (hereafter referred to as “industry”).

These employment effects are concentrated among trainees (graduate students
and postdocs) and the US-born, who are 6.1 pp and 3.5 pp more likely to enter
US nonemployment, a 60% increase for both subsamples. For both groups, these
changes are almost entirely driven by departures from universities. In
contrast, we do not find an effect for any job placement outcomes of faculty
and estimate that foreign-born personnel are half as likely as their US-born
counterparts to be induced into US nonemployement. Thus, long term and
university supported contracts appear to insulate faculty from the
consequences of interruptions, while the temporary status of trainees ensures
they bear the brunt. Meanwhile, the relative attachment of foreign-born
personnel to university employment (and to their original university) may
reflect less flexibility in altering career plans due to visa constraints or a
stronger preference for staying in the US [Ganguli and Gaul’e, 2019].

The seemingly permanent increase, for interrupted single-R01 personnel, in the
likelihood of US nonemployment raises the question of what these highly
employable individuals are doing.141414The Survey of Doctorate Recipients
consistently finds that the unemployment rate among surveyed individuals is
about 1.5%, several times lower than the overall unemployment rate. For
2001-2013, see here. For 2015-2021, see here. Some may remain in the US, truly
not working, but others may have left the US and therefore the scope of our
US-based tax data. To distinguish these groups, we compute the share of the
nonemployment effect attributable to personnel who are absent from the full
2020 Decennial Census (and therefore likely to be living outside the US),
finding that about half have left the US entirely.151515We also examine
personnel presence in the US over time using the 2000, 2010, and 2020
Decennial Censuses, which also suggests that interruptions push single-R01
personnel out of the US (Appendix Table A2). For trainees, the results are
starker, with 70% leaving the US, likely reflecting the mobility of a
relatively young population with less attachment to the US labor market and a
greater willingness to find a job abroad. Breaking out by place of birth, we
find that while only 30% of the US-born induced to nonemployment left the US,
nearly all of the foreign-born left, possibly reflecting a combination of push
(e.g., immigration restrictions) and pull (e.g., support networks in their
home countries) factors.

Since interruptions appear to encourage the exit of scientific personnel from
the US, it is natural to ask whether these personnel leave the scientific
enterprise altogether or whether they continue to produce scientific output
elsewhere. Though our administrative earnings and Decennial data are
comprehensive, they end at the US border, so we use publications to track the
scientific output of personnel across the world. Computing the share of the
nonemployment increase attributable to personnel who publish, we find that the
vast majority (about 87%) of single-R01 personnel publish less actively after
an interruption. Once again, the results are most dramatic for trainees, with
96% publishing less actively. By comparison, nearly all faculty induced to
nonemployment continue to publish at similar rates after an interruption.

In contrast to single-R01 personnel, personnel in labs supported by multiple
R01s experience precisely estimated zero post-interruption changes for all
employment outcomes. The importance of a funding cushion accords with the
intuitions of PIs we interviewed and with evidence that, until the grant is
renewed, interruptions lead to a spending collapse for single-R01 labs and
only modest spending decreases for multiple-R01 labs [Tham, 2023].161616See
Appendix Figure A1. Since time-varying confounders likely affect all labs
similarly, this cushion phenomenon for multiple-R01 labs increases the
credibility that our results for single-R01 labs are, indeed, driven by
funding interruptions.171717This is similar to a placebo test, as ex ante we
do not know that the effects of interruptions on the multiple-R01 sample are
necessarily zero. See Section B. We interpret this as evidence for an
intuitive mechanism: interruptions severely constrain the NIH funding of PIs
with a single R01 and other sources of funding (e.g., university-provided
bridge funding) cannot compensate, leaving them unable to pay the salaries of
their lab personnel.

Overall, interruptions account for about 5% of the US nonemployment among
research personnel within our sample.181818This is calculated by multiplying
our estimated effect of an interruption on US nonemployment (3 pp) by the
proportion of treated personnel (approx. 20%) and then dividing this ratio by
the proportion of all personnel who are nonemployed in the US at the end of
our sample period (12.5%). That is: $(0.03*0.2)/0.125=0.048$. By comparison, a
different sort of administrative delay – green card delays – account for about
7.4% of departures from the US in a representative sample of US doctorates
[Kahn and MacGarvie, 2020].191919Appendix Section D.3 details how we calculate
this number. This indicates that policies to reduce or eliminate funding
delays can have a meaningful effect on retaining scientific talent within the
US.

Losing highly-trained research personnel may be detrimental to the scientific
enterprise, but it is unclear whether interruptions damage the careers of
individual scientists. If interruptions push personnel into higher-paying
private sector jobs, their earnings could rise relative to continuously-funded
peers who remain at universities. Instead, we find that, after an
interruption, the relative earnings of single-R01 personnel decline by
20%.202020Since our data do not include earnings outside the US, we obtain our
main earnings estimates from a restricted sample that only contains personnel
who are “fully attached” to the US labor market after an interruption, i.e.,
they are employed in the US every year post-interruption. A causal
interpretation of these earnings estimates requires additional assumptions
about post-treatment selection into the sample. We discuss these in further
detail in Section 5.4.1. We also find that job mobility within academia
increases, though not in industry. We view the combination of an earnings
decline and higher mobility as prima facie evidence that interruptions not
only prematurely push personnel out of their university, but also lead to
worse job matches and job instability. As with job placement outcomes,
interruptions have precisely estimated zero effects on the earnings of
multiple-R01 personnel, again suggesting that these individuals are shielded
from the consequences of funding delays.

In a final set of results, we probe our definition of an interruption by
examining whether impacts vary by delay length. We find that longer
interruptions (greater than 90 days) do not lead to stronger effects,
suggesting that universities/PIs are unable to effectively bridge even
relatively short funding gaps.212121An interrupted lab is unlikely to know a
priori if or when their grant will eventually be renewed. Labs face
considerable uncertainty at the time of funding expiration.

As with any difference-in-differences approach, the plausibility of our
estimates hinges on a parallel trends assumption, and there are several
reasons to believe this is plausible in our setting. First, raw means and
event studies suggest that interrupted and continuously-funded personnel trend
similarly prior to grant expiry. Second, though not necessary for parallel
trends to hold, balance statistics suggest that interrupted and continuously-
funded labs are similar across a variety of pre-treatment observables,
including demographic characteristics (gender, race, ethnicity, and place of
birth), occupational composition, and research production. Third, as
suggested, the effects of interruptions are confined to groups that, ex ante,
we would expect to be most vulnerable to funding delays – specifically, non-
faculty in labs supported by a single R01. Finally, all of our main estimates
are robust to controlling for the number of resubmissions an R01 renewal
application went through before approval, which is a measure of perceived
quality.

To gain further insight into the causes and consequences of funding
interruptions, we interviewed six PIs who have experienced these challenges. A
recurring theme was constant worry about funding stability, with one PI noting
that this “can be very stressful, in general it is hard to plan research in
advance, when getting most of your funding in installments and uncertainty.”
This is especially true for PIs running single-R01 labs, with one stating that
“If you have only one R01 grant, then you are really exposed to the vagaries
of the funding cycle.” The PIs agreed that when funding was tight, personnel,
as the largest grant expense, were often first on the chopping block, leading
to departures of lab personnel like technicians and postdocs. Notably, one PI
recounted an instance where a technician, compelled to search for a new job
due to funding uncertainty, had already moved on to a new position by the time
the lab’s funding was eventually secured.

Our work lies at the intersection of several strands of literature in labor
economics and the economics of innovation. There are parallels between the
funding interruptions studied in this paper and a rich literature on the
impact of adverse events on the labor market [Oyer, 2006, Oreopoulos et al.,
2012, Rothstein, 2021, Huckfeldt, 2022]. Our work differs in that it studies a
unique but important labor market, complementing work on how scientific
careers can be meaningfully affected by early events [Azoulay et al., 2021,
Hill, 2019]. To the extent that research generates positive externalities, a
better understanding of this market is important not only for worker welfare
but also knowledge production. Moreover, acquiring the human capital necessary
to enter the research workforce is individually and societally (particularly
through government investments) costly and labor market outcomes today may
influence the expectations of potential future researchers.

A combination of theoretical and empirical work in the economics of innovation
suggests that failure-tolerant incentive schemes which provide long-term
stability can induce more risk-taking and exploration among innovators and
scientists [Manso, 2011, Ederer and Manso, 2013, Azoulay et al., 2011, Myers
and Tham, 2023].222222Although the extent to and conditions under which this
applies in science remains unclear. In the context of science, anecdotal
evidence suggests that one mechanism through which stability might lead to
more innovation is that it enables scientists to make longer-term plans with
respect to hiring personnel [Fikes, 2018, Fagen, 2016]. By highlighting the
impact of grant instability on the entire research workforce (not just faculty
or research outputs), this paper helps to build a fuller picture of the
interaction between grant funding and the research workforce behind knowledge
production.232323See Baruffaldi and Gaessler [2021] for work on the role of
physical capital in research production and Babina et al. [2023] on how
funding constraints can influence the type of research outputs produced.

Our work also relates to the literature on high-skilled immigration,
particularly work studying the impact of policy on worker placement across
sectors and borders (e.g. Diethorn [2022], Kahn and MacGarvie [2020], Amuedo-
Dorantes and Furtado [2019], Amuedo-Dorantes et al. [2019]). A key difference
is that we do not study effects of an immigration policy per se (e.g., changes
in H-1B caps, OPT extension). However, because our population of interest is
mobile and has a high proportion of non-US citizens, any disruptions to the
labor market naturally intersect with immigration issues.

The rest of the paper is organized into the following sections, which the
reader can jump to by clicking on the following links: Background, Data,
Estimation, Results, Conclusion.

##  2 Background

###  2.1 NIH Funding and R01 Grants

The NIH is responsible for an annual budget of $30-40 billion, most of which
is disbursed through research grants. The R01 is the largest grant mechanism
through which the NIH funds extramural research. It is designed to provide
enough funding to establish an independent research career.242424In interviews
with NIH Principal Investigators, all of them stated that there are no or few
good substitutes for getting an R01 grant. An R01 project period lasts for 4-5
years, after which it must be renewed in order to receive additional funding
for a subsequent project period.252525They can also be shorter (1-3 years),
but this is uncommon. Thus, the same _project_ can last for multiple _project
periods_.

Principal Investigators (PIs) generally want to maintain R01 funding for as
long as possible, so it is expected that as their current project period ends
they will apply to renew their project for another 4-5 year project
period.262626R01 renewal is sometimes even listed as a criterion for receiving
tenure (e.g., The Ohio State University College of Medicine [2020]). In order
to avoid lapses in funding between two project periods, PIs usually start to
apply for renewal about a year before a project period ends, balancing the
need to have made sufficient progress on their project while allowing time to
prepare the renewal application itself as well as time to revise and then
resubmit an application that is rejected.

The focus of our paper is on the effects of temporary interruptions, so we
exclusively analyze projects that are _successfully renewed_ at least once and
so span multiple project periods. Though all grants in our sample are
eventually successfully renewed, some will experience a lapse in funding
between two project periods (i.e., are interrupted) and others will be
continously-funded. Of course, some projects are not renewed upon expiry, and
these are not used in our analysis.

###  2.2 Where do Funding Delays Come From?

Funding delays can arise for several reasons. First, the US federal budgeting
process is often fraught and rarely in place by the beginning of the fiscal
year [Saturno et al., 2023]. This introduces uncertainty into that year’s NIH
budget and limits funding to grants that are high priority, delaying decisions
on others until there is more clarity about its budget for the fiscal year.

Figure 1 shows the relationship between the date in a fiscal year (1998-2018)
when the federal budget was passed and the average start date of NIH grant
budgets. When a grant is "Ongoing" (e.g., in the third year of a 5-year
grant), there is no relationship. But for grants that had to be competed for
(i.e., "New" or "Renewed" grants), budgets tend to start later in the fiscal
year if the federal budget was passed later.

Second, for any given application, the review process itself may end up being
lengthy. For example, if an application is unsuccessful on its first attempt,
the PI must revise and resubmit their proposal, adding time to the approval
process. Indeed, we estimate that, among R01 grants that are eventually
successfully renewed, each additional resubmission is associated with a 12 pp
increase in the probability of being interrupted (i.e., there are more than 30
days between expiry and renewal, per the definition we use in our main
analysis). As described later, we use resubmissions to control for (perceived)
quality and isolate variation in interruption status for grants with the same
number of resubmissions.

Figure 1: Each point represents a Fiscal Year (FY) from FY1998 to FY2018, and
shows the average date on which NIH grants that FY were funded, plotted
against the month the US federal budget was passed for that FY. “Ongoing”
grants were already approved in previous FYs, while “New” and “Renewed” grants
are newly approved in the current FY.

###  2.3 Why Interruptions May Affect Employee Outcomes

When an interruption occurs, a lab must decrease spending. Since payroll
typically comprises the bulk of a grant’s expenditures, the PI’s ability to
continue supporting personnel is circumscribed, especially if the interrupted
R01 is the lab’s only funding source.272727In the sample of labs from Tham
[2023, Section “Descriptive Statistics”] , the median single-R01 lab spent
$13,900 on labor payments and $900 on vendor payments in the month one year
prior to R01 expiry. Beyond funding lapses per se, even the _potential_ of an
interruption may affect lab personnel through uncertainty over if or when
funding will arrive. This uncertainty can create ambiguity surrounding the
future employment prospects of personnel, possibly driving some to leave their
current lab positions before funding actually runs out. For example, one PI we
interviewed described the university’s union contract requiring them to warn
personnel six months in advance if funding had not yet been secured. Thus,
personnel in a lab that receives late notice about the success of its renewal
application may alter their career plans even if a break in funding does not
ultimately occur.282828Uncertainty is also a function of the score a renewal
application received in peer review. That is, the better the score, the more
confident a PI will be that their R01 will be funded. This implies that our
estimates, which identify the impacts of funding delays per se, are likely
smaller than the combined impacts of both uncertainty and funding lapses.

To better understand how PIs perceive and respond to both actual and potential
interruptions, we conducted six interviews with PIs who were identified via
public grant data as having had an interrupted R01. Every PI we interviewed
expressed that funding lapses were a constant worry, even in non-renewal years
(one PI simply noted they were “always” worried). They stressed the importance
of trying to get additional grants (preferably another R01) as a buffer
against a potential lapse in funding, so that the salaries of personnel could
be shifted to a different grant if necessary.

They also all spoke about the unpredictability of grant scoring,
unpredictability of timing, and lack of communication from the NIH. One PI
noted, “There is a graveyard of grants inside NIH, for every one funded grant,
five or six are never funded. Some of my ideas that got funded were sort of
lousy, while some very good ideas were not. About 10% of the grants I write
are funded.”

In the event of an interruption, PIs expressed strong aversion to losing
personnel as that would be the most disruptive to the functioning of the lab.
However, without other grants to compensate, it is difficult to avoid cutting
payroll – the largest grant expense – in the midst of a funding
interruption.292929The only other major expense PIs brought up was animal
models (e.g., mice). Non-graduate students are particularly vulnerable to
funding interruptions.303030Graduate students are thought to be less
vulnerable because universities or departments have commitments to fund their
training (e.g., they can be shifted to teaching positions even if grant money
is unavailable), although this may be less so for graduate students who are in
the later stages of their program. Several PIs expressed regret over having
lost “really good people” due to interruptions. One PI commented that, “for me
it is like surfing, we have to stay in the front of the wave, and if you get
behind it quickly circles down, you don’t have people and can’t produce data.”

Thinking about the fate of those who leave, one of the PIs noted that “people
who are good get picked up by other labs.” PIs also mentioned that they tried
to time their hiring of people with grant funding cycles, so that postdocs and
graduate students would find it natural to leave the lab around the time of a
potential funding lapse. Another PI suggested that lab support staff not
engaged in the publication process would be first to be let go.

Since interruptions are quite common (about 20% of the R01s in our sample
experience a lapse in funding exceeding 30 days), personnel and research
institutions (including the NIH) are well-aware of their possibility and may
have developed ways of mitigating their disruptive effects. For example, a
PI’s home institution may provide bridge funding while a PI waits for delayed
funding to arrive. However, the university’s willingness to provide support
may depend on the belief that external sources will eventually (and preferably
quickly) support the lab. While the PIs we interviewed acknowledged that there
might be options for bridge funding, these amounts were likely to be small and
unlikely to be enough to avoid losing personnel. This accords with remarks
from a grants administrator (see Appendix of Tham [2023]) who noted that
bridge funding was more likely to be granted for one-time purchases such as
equipment rather than ongoing expenses like personnel compensation.

##  3 Data

For our analysis, we need three key pieces of information: (1) which R01
grants were expiring but eventually _successfully renewed_ , (2) which
personnel were part of labs that depended on those R01s, and (3) the labor
market outcomes of those personnel. We obtain these data from: (1) ExPORTER –
a public database of NIH grants, (2) UMETRICS – administrative grant
transaction data from universities (including payments to personnel), and (3)
IRS/Census data including the universe of W-2 and 1040 Schedule C (1040-C) tax
records and the universe of unemployment insurance (UI) earnings records.
Together, these data allow us to identify personnel working in labs with a
successfully renewed R01 (but potentially experiencing a funding interruption)
and track their entire US employment and earnings history.

Figure 2: This diagram shows the process of linking R01 grants to personnel
and their labor market outcomes, starting with NIH ExPORTER data at the top
and ending with tax and unemployment insurance records stored at the US Census
Bureau.

We illustrate how the data come together with an example. Figure 2 provides a
graphical representation.

  1. 1.

Find R01 grants that were successfully renewed. In this example, we start with
an R01 grant with the code R01CA12345 (orange box in Figure 2).

  2. 2.

Find the PI of the R01. The PI of R01CA12345 is Dorothy Hodgkin.313131The
first British woman to win a Nobel Prize in the sciences.

  3. 3.

Find all of the PI’s grants. Find all grants administered by the PI in the 12
months prior to expiry of the focal R01. In this example, we find that in
addition to the focal R01 grant (R01CA12345), PI Hodgkin also had a smaller
R21 grant (R21AG98765).

  4. 4.

Find personnel paid by PI’s grants. Find all personnel paid by any of the
grants in the 12 months prior to expiry of the focal R01. In this case, PI
Hodgkin’s two grants were supporting a postdoc, graduate student, and a lab
technician. These four personnel (including PI Hodgkin herself) constitute
Hodgkins’s “lab”.

  5. 5.

Link personnel to labor market outcomes Merge personnel (including the PI)
with IRS/Census earnings and employment data. Link personnel with their
employers via the LEHD and W-2 data; employer characteristics come from the
LEHD and the LBD; university employers identified using IPEDS data. In this
case, the four personnel in the Hodgkins lab are linked to these IRS/Census
data sources.

The remainder of the section goes into more detail about these data linkages
as well as variable construction. Additional detail is also available in the
Data Appendix.

###  3.1 Grant and PI variables (ExPORTER)

We generate grant- and PI/lab-level variables using ExPORTER, publicly
available data on NIH grants provided by the NIH (details in Data Appendix
(ExPORTER)).323232https://exporter.nih.gov/

Identifying successfully renewed grants. We first use ExPORTER to identify
_sucessfully renewed_ NIH R01 grants – those that expire and are renewed
within the same fiscal year. Since we want to focus on the effects of
interruptions, we do not use NIH grants that expire, but are never renewed.

Measuring length of funding gaps and interruptions. This is measured as the
number of calendar days between the end of a project period and the beginning
of the next project period. As a baseline, we define an R01 as “interrupted”
if the gap is 30 or more days (approximating a month as grants are usually
disbursed on the first of the month) and as continuously-funded if the gap is
fewer than 30 days.333333In Section 5.5.2, we examine the effects of
interruptions defined by alternative funding gap lengths, finding that longer
interruptions (greater than 90 days) do not lead to stronger effects. Figure 3
shows that, among successfully renewed NIH R01 grants from fiscal years 2005
to 2018, about 20% were interrupted (Panel A) and the distribution of funding
gap lengths conditional on being interrupted is right-skewed with a median of
88 days (Panel B).

Figure 3: This figure shows the distribution of funding delays for renewed
R01s expiring in Fiscal Years 2005 to 2018. Panel A (top) shows the proportion
of R01 grants that are interrupted using our baseline definition of a 30-day
delay. Figure B (bottom) shows the distribution of funding delays conditional
on being interrupted.

PI grant portfolio (Number of R01s). After an interruption, PIs with multiple
R01s decrease spending by substantially less than PIs with a single R01 [Tham,
2023] and PIs we interviewed stressed the fragility of running a lab on only
one R01, suggesting that multiple-R01 personnel may be less affected by
funding interruptions. Thus, we divide our sample into “Single-R01” and
“Multiple-R01” labs by counting the number of R01s343434More precisely, we
define the size of the PI’s grant portfolio based on the number of
“R01-equivalent” grants, including the focal R01. The NIH Glossary states
“R01-equivalent grants are defined as activity codes DP1, DP2, DP5, R01, R37,
R56, RF1, RL1, U01 and R35 from select NIGMS and NHGRI program announcements
(PAs).” For brevity, we refer to this variable as the “Number of R01s” without
explicitly defining the other types of grants included. a PI has twelve months
before and after expiry.353535We include R01s awarded after the focal R01’s
expiration because these provide a funding cushion if they arrive promptly,
and given the time lag between grant application and receiving the funds, the
PI would have applied for these grants before the funding status of the focal
R01 is known (and so they are not caused by an interruption). Since post-
interruption spending remains relatively stable in multiple-R01 labs, we
should see muted effects for the personnel working in these labs.

###  3.2 Finding Lab Personnel (UMETRICS)

We identify personnel who are part of a PI’s lab by linking successfully
renewed R01 grants from ExPORTER to the UMETRICS database, which contains
information from 33 research-intensive universities representing about one-
third of US federal research expenditures [IRIS, 2019].363636We use the 2020
release of UMETRICS. All UMETRICS universities are classified as R1 (Doctoral
Universities – very high research activity) according to the Carnegie
Classification System and all rank in the top 20% of universities by federal
R&D expenditures (see Appendix Section E.2). UMETRICS is administrative
transaction-level data on _all_ spending from university research grants,
including payments from NIH R01 grants to personnel. For each of these
payments, we observe the transaction date and the occupation of the personnel
at the time of the payment.373737Personnel occupations are assigned by the
UMETRICS data team using information such as job titles, and they can change
over time (e.g. a post-doc may become a faculty member). Thus, we are able to
identify the individual research personnel that belong to each PI’s lab and
their occupation during the run-up to R01 expiry.383838For a given PI with a
successfully renewed R01, we identify all NIH grants they administered in the
12 months prior to the focal R01’s expiry. We define all personnel paid by any
of these grants during the 12 months prior to the focal-R01 expiry as part of
the PI’s lab.

###  3.3 Employment and Earnings Data

We use three sources of confidential tax/administrative data, available at the
US Census Bureau, to track the earnings of UMETRICS personnel: W-2 tax
records, the Longitudinal Employer-Household Dynamics (LEHD) database, and
1040 Schedule C (ILBD) tax records.393939UMETRICS personnel have been linked
within Census systems to a confidential person identifier using a
probabilistic matching process [Wagner et al., 2014]. This identifier allows
us to link UMETRICS personnel to a variety of comprehensive tax,
administrative, and survey data held by the US Census Bureau. We then use
confidential information on the universe of US firms from the Longitudinal
Business Database (LBD) and the LEHD to identify the characteristics of the
employers of these research personnel. Finally, we use public-use information
from the Integrated Postsecondary Education Data System (IPEDS) to identify
whether the personnel are employed at a university. Appendix Section E.1
provides more details on each of these datasets. With these data, we want to
understand how the job placement and earnings of research personnel are
affected by funding interruptions. To do so, we construct the variables below.

Sector indicators. We define three mutually exclusive indicators to represent
the sectors in which personnel can be employed in a given year:

  1. 1.

US university (or “academia”) – the personnel receives positive earnings from
an IPEDS university.

  2. 2.

US non-university (or “industry”) – the personnel _only_ receives positive
earnings from a non-IPEDS US employer.

  3. 3.

Nonemployed in the US (or simply “nonemployed”) – the personnel does not
receive earnings from an employer in W-2, LEHD, or ILBD data (complement of
categories (1) and (2)).

Earnings. We observe yearly earnings for each personnel from 2005 to 2018.
These are derived from a combination of W-2, LEHD, and 1040 Schedule C (ILBD)
earnings. We define a personnel’s total earnings in a given year as their
earnings from self-employment (ILBD) plus the maximum of their W-2 and LEHD
earnings. That is,
$earnings_{total}=earnings_{ilbd}+max\\{earnings_{W2},earnings_{lehd}\\}$.404040The
LEHD receives data from individual states unemployment insurance systems and
there are two gaps that are particularly important to this study: a)
Massachusetts data is not in the LEHD until 2011, and b) graduate student
stipends are not covered by unemployment insurance and thus not reflected in
LEHD data. The W-2 data fills these gaps.

Presence in the United States (Decennial Census). To help us distinguish
between personnel who are not working but are still present in the US and
those who leave the US altogether, we supplement our data with the Decennial
Censuses. Each Decennial Census aims to count all people residing in the US on
April 1st of the Census year, regardless of nationality, immigration status,
or labor force participation. Thus, being observed in the Decennial Census
indicates that a personnel was physically present in the US at the time of the
Census.414141An exception are Federally Affiliated Count Overseas Operation,
who are federal employees (and their dependents) stationed outside the US
(mostly military personnel).

###  3.4 Publication History

Our main outcomes measure the earnings and employment of lab personnel, but
these stop at the US border. Publications offer a measure of personnel’s
scientific activity that is observable no matter where they are employed. We
track the publishing activity of UMETRICS personnel by linking them to
publications in PubMed, a bibliographic database for biomedical research
produced by the US National Library of Medicine (NLM).424242This is done by
using an IRIS-provided link between UMETERICS personnel and PubMed
publications. The link is built by first disambiguating author names in PubMed
and then linking the disambiguated authors to UMETRICS personnel by name,
affiliation, ORCID (where available and necessary), email address, and
collaborator names.We have also run our analyses using an alternative match to
PubMed created by Enrico Berkes and used in Sattari et al. [2022].

###  3.5 Demographic Data

Given immigrants’ importance to US science, we explore whether funding delays
have heterogeneous effects by personnel place of birth. Place of birth is
available along with other demographic information from the Individual
Characteristics File (ICF), which is part of the data infrastructure of the
LEHD program [Vilhuber et al., 2014]. Information in the ICF is sourced from
the Social Security Administration (SSA) Numident and the Decennial Census.

###  3.6 Analysis Sample

Table 1: Unique Individuals in Single-R01 Labs by Occupation and Birthplace

| Personnel
---|---
Subsample | Count
All Personnel | 4,200
Occupation
Faculty | 900
Postdoc/Grad students | 1,300
Others | 2,000
Place of Birth
US-born | 2,700
Foreign-born | 1,400

  * •

This table shows the breakdown, by occupation and place of birth, of personnel
belonging to single-R01 labs. Due to rounding required by Census disclosure
avoidance rules, the summation across categories may not always equal the
total.

Table 2:  Covariate balance between interrupted and continuously-funded labs

|  |  |  |
---|---|---|---|---
Variable |  Difference in means |  Continuously Funded |  Interrupted |  P-value
Panel A: Single-R01 Labs
Lab size & composition
% Faculty |  -0.02 (0.02) |  0.33 |  0.31 |  0.36
% Postdoc/Grad Student |  0.01 (0.03) |  0.28 |  0.29 |  0.82
% Other Occ |  0.01 (0.03) |  0.39 |  0.40 |  0.60
Lab Size |  0.75 (1.18) |  7.72 |  8.47 |  0.52
% Female |  -0.02 |  0.46 |  0.44 |
% Asian |  0.02 |  0.27 |  0.29 |
% Black |  0.01 |  0.02 |  0.03 |
% White |  -0.03 |  0.67 |  0.65 |
% Hispanic |  -0.01 |  0.05 |  0.04 |
% US-Born |  0.01 |  0.56 |  0.57 |
Pubs & Funding
Pubs per year |  3.38 (1.74) |  9.05 |  12.43 |  0.05
NIH Funding (millions per year) |  -0.02 (0.04) |  0.50 |  0.48 |  0.65
Personnel Count |  |  667 |  182 |
Panel B: Multiple-R01 Labs
Lab size & composition
% Faculty |  0.01 (0.01) |  0.30 |  0.30 |  0.61
% Postdoc/Grad Student |  0.00 (0.01) |  0.29 |  0.29 |  0.96
% Other Occ |  -0.01 (0.01) |  0.41 |  0.41 |  0.68
Lab Size |  1.99 (1.56) |  14.96 |  16.95 |  0.20
Pubs & Funding
Pubs per year |  0.59 (1.74) |  19.16 |  19.75 |  0.74
NIH Funding (millions per year) |  -0.07 (0.06) |  1.18 |  1.11 |  0.24
Personnel Count |  |  1,313 |  417 |

  * •

This table shows differences in means, across lab characteristics, for
interrupted and continuously-funded labs at the time of R01 renewal, by
whether the labs have a single R01 (Panel A) or multiple R01s (Panel B). The
unit of observation is a PI/lab-by-R01 renewal. Publications and funding are
average publications and funding per year for the five years prior to R01
renewal. Due to Census disclosure avoidance restrictions, we only show the
means of demographic characteristics for single-R01 labs. Standard errors and
p-values are from a two-sided t-test.

Our final sample consists of about 4,200 research personnel belonging to 600
single-R01 labs and about 13,500 personnel belonging to 1,200 multiple-R01
labs. Using information from UMETRICS (Section 3.2), we define three
occupations: faculty, trainees (postdocs and graduate students), and “others”
(which includes occupations such as staff, research scientists, and
undergrads). Using information from the ICF (Section 3.5), we classify
personnel by whether they are born in the US (“US-born”) or not (“foreign-
born”).434343Although “non-US-born” is more accurate, we use the term
“foreign-born” for easier reading. Table 1 shows that about 21% of lab
personnel are faculty, 31% are postdocs or graduate students, and the
remaining 48% have other occupations. About two-thirds of lab personnel were
born in the US, with the remaining third born elsewhere.

Table 2 compares interrupted and continuously-funded labs across a range of
characteristics, including lab size and composition, number of publications
five years prior to R01 renewal, and amount of funding five years prior to R01
renewal. There are no statistically significant differences, and the only
substantive difference is that interrupted single-R01 labs produce about three
more publications per year than their continuously-funded counterparts.
However, to the extent that publications measure lab “quality”, this
difference indicates that lower quality labs do not select into being
interrupted. Though not necessary for the parallel trends assumption to hold,
the similarity of these pre-expiry baseline characteristics shows that
interrupted and continuously-funded personnel are working in otherwise
comparable labs and increases the plausibility that they would have been on
parallel paths if not for the interruption.

##  4 Estimation

###  4.1 Stacked Difference-in-Differences

We use a difference-in-differences (DiD) design to estimate the effect of
funding interruptions on personnel. Our strategy involves three steps:

  1. 1.

Stack the data by event-year (i.e., the focal R01’s expiration year).

  2. 2.

Identify continuously-funded personnel within each expiration year cohort that
have not experienced an interruption in a two-year window around that expiry
year (i.e., identify “clean controls”).

  3. 3.

Estimate average treatment effects (ATTs) and event studies using a modified
version of the Callaway and Sant’Anna [2020] (CS) estimator that compares
interrupted and “clean” continuously-funded personnel _within the same R01
expiry year_.

In a typical staggered DiD setting, the CS estimator estimates disaggregated
“group-time” treatment effects, where groups are defined by time of
treatment.444444These group-time effects can then be aggregated as desired
(e.g., as a static treatment effect or by time relative to treatment for an
event study). In that setting, control units are not assigned to groups
because they do not have a defined counterfactual treatment period.454545In
many applications it is difficult to define a counterfactual treatment time.
For instance, the year in which a state might have but did not pass a minimum
wage increase. In contrast, in our setting, since every R01 has a clear
expiration year, all personnel (whether interrupted or continuously-funded)
have a well-defined treatment period. Thus, in Step (1), take advantage of
this data structure by stacking personnel by R01 expiry year cohorts, forming
a cohort-by-personnel-by-time panel dataset. Each cohort can be thought of as
a separate DiD/event study with a single treatment period. With this
structure, we are more likely to compare personnel in labs with projects and
budgets that are at similar stages in their lifecycle.464646For instance,
personnel may be less likely to leave their job at the beginning of an R01
than at the end of an R01, so using them as control units will overstate the
effect of an interruption.

After stacking, we limit control units (continuously-funded personnel) within
a given expiry-year cohort to those that are “clean” – that is, they are not
treated in a time window of interest. Specifically, in Step (2) we require
that, to be included in an expiry-year cohort, control personnel must not be
treated two years before or after the expiration year of the focal
R01.474747For instance, in the cohort with expiration year 2001, control
(continuously-funded) personnel must not have been treated in any year from
1999 to 2003. This would not be the case if personnel are in a lab with two
R01s that are expiring in consecutive years, 2001 and 2002. The first R01 is
continuously-funded in 2001, but the second R01 is interrupted in 2002.
Without any restrictions, these personnel would be controls in the 2001 cohort
but treated in 2002.

With our stacked data structure, it is common to use a modified two-way fixed
effects estimator with unit-cohort fixed effects and time-cohort fixed effects
[Baker et al., 2022]. In Step (3), we instead use the CS estimator because it
allows for transparent and flexible aggregation of the group-time treatment
estimates.484848CS aggregates treatment effects by group size. Two-way fixed
effects implicitly uses OLS weights which is more efficient at the cost of
bias [Baker et al., 2022].

As discussed in Section 3.1, evidence on lab spending494949We show in Tham
[2023] that funding interruptions substantially reduce grant expenditures in
single-R01 labs but only modestly reduces spending (in log points) in
multiple-R01 labs (see Appendix Figure A1). and PI interviews suggest that
personnel can easily be moved between a lab’s grants. Thus, personnel working
in labs supported by multiple R01s may be cushioned from the effects of a
funding interruption. This motivates us to estimate the effects of funding
interruptions on career outcomes separately for personnel in multiple- and
single-R01 labs.

###  4.2 Benchmarking Estimates

As discussed in Section 3.3, many of our outcome variables are indicators
(e.g., sector of employment), and so our estimates reveal percentage point
(pp) changes after an interruption. To better understand the magnitude of
these estimates, we benchmark them against the absolute change in the mean of
the corresponding outcome for the control group (i.e., personnel in
continuously-funded labs), just before treatment ($\bar{y}^{c}_{-1}$) and five
years post-treatment ($\bar{y}^{c}_{5}$). That is,

$y_{benchmark}=|\bar{y}^{c}_{5}-\bar{y}^{c}_{-1}|.$

This change gives us a sense of how an outcome would have evolved in the
absence of an interruption. We then report the size of an estimate (either an
event study coefficient or an aggregated average treatment effect (ATT)),
denoted $\hat{\beta}$, as a percentage of $y_{benchmark}$. That is,

$\text{Effect size}=\frac{\hat{\beta}}{y_{benchmark}}.$

This reveals the size of an interruption impact on an outcome relative to the
underlying change of that outcome.

###  4.3 Identification

We rely on two main assumptions to identify the average treatment effect on
the treated (ATT) of an interruption on the career outcomes of personnel: (1)
parallel trends and (2) no anticipation.

Parallel Trends. The parallel trends assumption requires that the average
outcome among the treated and comparison populations would have followed
parallel trends in the absence of treatment. In our context, this means that
the employment and earnings outcomes for interrupted and continuously-funded
personnel would have evolved in parallel if the funding interruption had not
occurred.

The parallel trends assumption allows treatment to be non-random based on
characteristics that affect the _level_ of the outcome but requires the
treatment be mean independent of characteristics that affect the _trend_ of
the outcome. For instance, highly organized PIs may select into the
continuously-funded control group because they are more likely to submit their
grant renewal paperwork on time and avoid a funding interruption. Their high
level of organization may also affect employee outcomes (e.g., by ensuring
that postdocs and graduate students are regularly publishing in a timely
manner). However, as long as unobservable PI organizational skills affect
employee outcomes in the same way both before and after the treatment, it does
not violate the parallel trends assumption.

Though the parallel trends assumption cannot be tested, there are several
reasons it is plausible in the setting of R01 interruptions. First, neither
raw means (Figure 5) nor event studies (Figures 4 and 6) show evidence of
diverging trends prior to grant expiration. Second, balance statistics (Table
2) suggest that interrupted and continuously-funded labs are quite similar
across a variety of characteristics, including demographics (gender, race,
ethnicity, and place of birth), occupational composition, and research
production. Third, the effects of interruptions are concentrated among non-
faculty in labs supported by a single R01, which is who, ex ante, we would
expect to bear the brunt of funding delays. Moreover, if time-varying
unobserved confounders were driving our results, they presumably affect
multiple-R01 labs in ways that are similar to how they affect single-R01 labs.
However, in contrast to single-R01 labs, we find little evidence that the
employment outcomes of personnel in multiple-R01 labs are impacted by
interruptions.

No or Limited Anticipation. The no anticipation assumption requires zero
treatment effect prior to the treatment actually taking place. The limited
anticipation assumption relaxes this requirement if anticipation occurs at a
fixed length of time before the treatment. In this case, the treatment period
can be redefined as the point when units are aware of treatment. We do not see
any differences between treated and control outcomes before treatment,
suggesting that personnel and their PIs do not differentially anticipate
interruptions more than a year in advance (our data are at a yearly
frequency).

##  5 Results

###  5.1 Sector placement

In this section, we consider whether funding delays affect personnel’s sector
of employment. We construct three mutually exclusive outcomes based on the
observed Employer Identification Numbers (EIN) in personnel’s W-2s and the
LEHD. In a given year, an individual can be (1) not employed in the US
(“nonemployed”), (2) employed at a US university (“academia”), (3) employed in
the US but not at a university (“industry”).

Event Studies. Figure 4 shows event studies for these three employment
outcomes, estimated using the stacked difference-in-differences method
described in Section 4.1. The estimates are plotted for years -5 to 5, where
year 0 is the year of R01 grant expiry.

Figure 4: This figure shows event studies of the effects of interruptions on
three mutually exclusive employment outcomes: 1) nonemployed in the US (Panel
A), 2) employed at a US university (Panel B), or 3) employed in US industry
(Panel C). They are obtained using our modified Callaway and Sant’Anna [2020]
estimator. The expiration of a lab’s grant takes place at year 0, and the
estimated interruption effects range from 5 years before to 5 years after
expiry. The left column is for personnel in a single-R01 lab and the right
column is for personnel in a multiple-R01 lab. Standard errors are
bootstrapped and clustered at the expiring-R01-level. Standard errors not
available for Figure C for Census disclosure avoidance reasons. The data
underlying this graph can be found in Table A4.

The left column of graphs show estimates for personnel in single-R01 labs.
After an interruption, there is an immediate and persistent 3 pp increase in
nonemployment. Personnel also shift sectors within the US. There is an
immediate 5 pp decrease in the probability of being at a US university, but
after two years continuously-funded personnel “catch up” in their university
departures (as seen in the raw means below), so the two groups are equally
likely to be working at a US university. Correspondingly, interrupted
personnel are initially 2 pp more likely to be in US industry, but become 2 pp
less likely after two years.

Overall, these patterns suggest the following. First, the level shift in
nonemployment indicates a one-time move out of the US labor force for
interrupted personnel. Second, the initial decrease and then recovery in US
university employment suggests a shift in the timing of departures – that is,
interruptions induced personnel to leave the university sector earlier than
they otherwise would have – but did not change the long-term stock of
personnel at US universities.

In contrast, for personnel in multiple-R01 labs (right column of graphs),
interruptions have no effect on any of the three employment outcomes. This
suggests that multiple funding sources insulate lab personnel from the
consequences of an interruption.505050See Appendix Section B for a direct
comparison of the two interrupted groups.

Raw Means. To unpack the mechanics underlying our event studies, Figure 5
displays the raw fractions of personnel that fall into each employment
category (separately for interrupted and continuously-funded labs). The left
and right columns of graphs display means for single- and multiple-R01
personnel, respectively.

Figure 5: This figure shows the average probability that a personnel is in one
of three mutually exclusive employment categories: 1) nonemployed in the US
(Panel A), 2) employed at a US university (Panel B), or 3) employed in US
industry (Panel C). These probabilities are calculated from five years before
R01 grant expiration to five years after. Figures in the left column are for
personnel in a single-R01 lab and figures in the right column are for
personnel in a multiple-R01 lab. Both groups are split into continuously-
funded and interrupted labs. The data underlying this graph can be found in
Table A5.

We first see that, even unconditionally, the mean employment outcomes of
interrupted and continuously-funded personnel trend similarly prior to R01
expiry, which is again consistent with a parallel trends assumption. Second,
the post-expiry relative changes in these unconditional means closely mirror
the changes observed in the event studies of Figure 4; these raw means allow
us to see the separate dynamics of both interrupted and continuously-funded
personnel.515151For single-R01 personnel (left column of graphs), the fraction
of nonemployed interrupted personnel increases immediately (in year 0) and
persists until at least year 5. At the same time, a temporary initial increase
in interrupted personnel’s rate of exit from universities is later offset by a
relatively slower exit rate, allowing the interrupted to return to baseline.
Inversely, the interrupted initially enter industry more quickly, but a
relative slowdown later returns them to baseline. For multiple-R01 personnel
(right column of graphs), the employment outcomes of the interrupted and
continuously-funded are similar (in both levels and trends) before and after
R01 expiry. Finally, we observe that our three outcomes are either U- or
inverted U-shaped, which is largely driven by our sample requirement that
research personnel receive payments from a grant at their UMETRICS university
during the 12 months prior to R01 expiry (see Section 3.1).525252The
probability of being nonemployed in the US (Figure 5A) starts at 20%, declines
to 5% around grant expiry, and then increases to 15% five years later. The
probability of being at a US university Figure (5B) starts at 65% in year -5,
peaks at 90% in year 0, and declines to 60% in year 5. The probability of US
industry employment (Figure 5C) starts at 15%, declines to 5% around grant
expiry, then increases to over 20% five years later. See Table A5. In
particular, the inverted U-shape for university employment makes it clear that
the recovery visible in the left column event study of Figure 5B occurs
because interrupted personnel leave universities _earlier_ than the
continuously-funded – however, both groups flow out of universities after
grant expiry.

Overall, the raw means, like the event studies, clearly suggest that funding
interruptions to single-R01 labs substantially alter employment patterns for
the personnel of those labs, affecting entrance to nonemployment and the
_timing_ of departures from universities to industry.

Aggregated Static Effects. Table 3 presents the post-treatment estimates for
the three employment outcomes, aggregated over the entire five year post-
expiry period. Single-R01 personnel experience a 3 pp (37%)535353As noted in
Section 4.2, we benchmark our percentage point effects against the absolute
change in the mean of the corresponding outcome for continuously-funded
personnel, just before treatment and five years post-treatment. In this case,
4.2% of continuously-funded personnel are nonemployed one year prior to grant
expiry and this increased to 12.3% five years after expiry. Thus, we compute
the effect size as 0.03/—0.123 - 0.042— = 0.37. We perform a similar
calculation for other outcomes. increase in the probability of nonemployment,
which confirms the seemingly permanent exit of these individuals from the US
labor market. The catch-up visible in the event studies of Figure 4 (and in
the raw means of Figure 5) leads to a statistically insignificant drop in the
longer-term probability of employment in US academia, despite the sharp and
statistically significant initial drop. We again observe that the employment
outcomes of multiple-R01 personnel are unaffected by interruptions.

Table 3: Effect of Interruptions on Main Employment Outcomes

| Nonemployed | US University | US Industry
---|---|---|---
Subample | (1) | (2) | (3)
Panel A: Single-R01 Labs |  |  |
All Personnel | 0.0302*** | -0.0210 | -0.0092
| (0.0117) | (0.0184) | (0.0147)
Place of Birth |  |  |
Foreign-Born | 0.0183 | 0.0109 | -0.0292*
| (0.0231) | (0.0267) | (0.0158)
US-Born | 0.0350*** | -0.0354* | 0.0007
| (0.0111) | (0.0210) | (0.0195)
Occupation |  |  |
Faculty | 0.0030 | -0.0213 | 0.0216
| (0.0152) | (0.0178) | (0.0150)
Postdoc/Grad | 0.0610*** | -0.0537** | -0.0073
| (0.0235) | (0.0310) | (0.0240)
Other | 0.0262* | -0.0075 | -0.0187
| (0.0145) | (0.0287) | (0.0246)
Panel B: Multiple-R01 Labs |  |  |
All Personnel | -0.0093 | 0.0056 | 0.0037
| (0.0086) | (0.0133) | (0.0101)

  * •

This table shows aggregated estimates of the average treatment effects (ATTs)
of an interruption on three mutually exclusive employment outcomes: 1)
nonemployed in the US, 2) employed at a US university, or 3) employed in US
industry. They are obtained using our modified Callaway and Sant’Anna [2020]
estimator. The effects are aggregated over the 5 years after the expiration of
a lab’s grant. Panel A shows the results for personnel in a single-R01 lab and
Panel B shows the results for personnel in multiple-R01 labs. Standard errors
are bootstrapped and clustered at the expiring-R01-renewal level. *, **, and
*** indicate statistical significance at the 10%, 5%, and 1% levels.

Heterogeneity by Birthplace. The scientific workforce in the US is heavily
dependent on immigration. In our sample, slightly more than a third of lab
personnel were born outside the US (compared to 13% for the overall US
population),545454As estimated by the American Community Survey, see ACS Table
DP02, 2018 5-year estimates and ACS Table DP02, 2010 5-year estimates. and
visa restrictions may constrain their job choices. To better understand how
these restrictions interact with funding interruptions, we split the sample
into US-born personnel who face no visa-related work restrictions and
personnel born outside the US (“foreign-born”) who are likely to be on a
visa.555555Some of the foreign-born could be naturalized citizens or permanent
residents; we cannot observe this in our data.

Table 3 shows that the main employment effects are primarily concentrated
among the US-born. Post interruption, the single-R01 US-born are 3.5 pp (60%)
more likely to be nonemployed in the US. In contrast, the foreign-born are
only 1.8 pp (13.5%; statistically insignificant) more likely to be nonemployed
in the US. The large effect for the US-born is almost entirely driven by
departures from universities – there is a symmetric 3.5 pp (11.7%) decrease in
the probability of working at a US university and no change (0.1 pp) in the
probability of working in US industry. Thus, it is primarily the US-born that
are induced by funding interruptions to depart from US universities.

It is perhaps surprising that we find larger nonemployment effects for the US-
born.565656It is important to emphasize that the unconditional probability of
being nonemployed in the US is much higher for the foreign born (19% in year
5) than the US-born (9% in year 5).Foreign-born personnel on visas face
significant restrictions on both their ability to remain in the US without a
job, and on their ability to gain access to US employment from abroad, which
in some cases may lead them to have stronger preferences for a US
job.575757For example, Ganguli and Gaul’e [2019] find a stronger preference
among foreign-born graduate students in US PhD programs for a US-based postdoc
position. At the same time, universities have access to visa categories
unavailable to private employers, making it easier for foreign-born personnel
to remain in the US by working at a university. Consistent with this, the
point estimates in Table 3 for the foreign-born are positive for the
university employment outcome and negative for the industry employment
outcome. In contrast, the point estimates are reversed for the US-
born.585858Appendix Table A2 also hints at visa restrictions on movement,
showing the impacts of interruption on the probability of receiving positive
university earnings from a personnel’s own UMETRICS university or another
university. The point estimate on US-born personnel working at their own
university is negative ($-3.5$ pp), suggesting they are pushed to leave their
UMETRICS university. In contrast, the point estimate for interrupted foreign-
born personnel is positive (1.8 pp), suggesting that interruptions may lead to
less job mobility among foreign-born personnel. In addition, though the US-
born are more likely to be driven to nonemployment, _conditional_ on being
induced into nonemployment, the foreign-born are more likely to stay outside
the US (see Section 5.2 below).

Thus, the US-born again appear notably more mobile than their foreign-born
counterparts, able to more easily leave their current employment situation,
moving universities, sectors, or even become nonemployed in the US. Though the
choice of foreign-born to stay in academia rather than become nonemployed or
enter the non-university sector is not necessarily due to constraints imposed
by visa and work authorizations, overall our results suggest foreign-born
personnel face significant job mobility constraints.

Heterogeneity by Occupation. Contractual arrangements vary dramatically across
different types of personnel within a lab. Typically, the faculty PI is on a
permanent contract with the university595959There is variation even among
faculty contracts. In the biomedical sciences, faculty are often on “soft
money” contracts, where some part or even all of their salary is funded by
external grants, with no guarantee of salary from the university, even in
exchange for teaching. Other faculty, particularly in medical schools, might
be asked to raise their salaries either through grants or through clinical
work, that is, seeing patients in an associated hospital. and is supported by
university money, while postdocs are usually on temporary contracts (with no
renewal guarantee) and are supported by grant funding. Graduate students have
a finite horizon at the university, but typically enjoy employment guarantees
(in a teaching, if not a research position), though these commitments likely
vary across universities and may weaken as graduation approaches. Staff
contracts also vary, with some supported by grants and on year-to-year
contracts, and others on permanent university contracts. These contractual
differences suggest that funding interruptions may disproportionately impact
occupations with a less permanent relationship with the university.

Table 3 shows that the main employment effects for single-R01 labs are
primarily driven by trainees (graduate students and postdocs). After an
interruption, trainees in single-R01 labs are 6.1 pp (60%) more likely to be
nonemployed in the US. As with the US-born, this large effect is almost
entirely driven by departures of these trainees from universities. Indeed,
there is a 5.4 pp (15%) decrease in probability of working at a university and
no change (-0.7 pp) in the probability of working in industry. In contrast,
employment effects for faculty and other occupations are muted, consistent
with many of them being on permanent contracts.

###  5.2 Presence in the United States

The post-interruption increase in the probability of US nonemployment for
single-R01 personnel raises the question of what these individuals are doing,
since we have so far only been able to say what they are _not_ doing – namely
working for pay in the United States. These individuals may still be
physically present in the US, or they may have left the country entirely.
Since the scientific workforce is relatively mobile, leaving the US to find a
research job in another country is particularly plausible.

To address this, we use Decennial Censuses, which strive to enumerate every
person living in the United States and thus allow us to observe the actual
presence of personnel in the US, regardless of their employment status.
Specifically, we create two new outcome variables. First, an indicator taking
a value of one if a person is nonemployed in the US _and_ is _absent_ from the
2020 Decennial Census (zero otherwise). Second, an indicator taking a value of
one if a person is nonemployed in the US _and_ is _present_ in the 2020
Decennial Census (zero otherwise).

Table 4 presents our estimates for single-R01 labs. After an interruption,
personnel are 1.6 pp more likely to be nonemployed and absent from the 2020
Decennial Census and are 1.5 pp more likely to be nonemployed but present in
the 2020 Decennial Census. These estimates imply that slightly more than half
($1.6/(1.5+1.6)$) of the personnel induced to nonemployment by an interruption
leave the US permanently, while the rest either leave for a time and return by
mid-2020 or have been present in the US but not working. Thus, it appears that
a substantial fraction of personnel displaced by an interruption end up
leaving the US altogether.606060In Appendix Table A2, we use three consecutive
Censuses – 2000, 2010, and 2020 – to estimate the impact of an interruption on
the probability of personnel being present in each Census. We find that
single-R01 personnel are about 3 pp less likely to be observed in a Decennial
Census after an interruption.

Table 4: Effect of Interruptions on Presence in the 2020 Decennial Census for
Nonemployed Personnel from Single-R01 Labs

| Nonemployed | Nonemployed
---|---|---
| In 2020 Census | Not In 2020 Census
Subample | (1) | (2)
All Personnel | 0.0146 | 0.0156**
| (0.0096) | (0.0067)
Place of Birth |  |
Foreign-Born | -0.00887 | 0.0282
| (0.0127) | (0.0184)
US-Born | 0.0262** | 0.0106**
| (0.0114) | (0.0052)
Occupation |  |
Faculty | 0.0061 | -0.00208
| (0.0141) | (0.0177)
Postdoc/Grad | 0.0193 | 0.0432**
| (0.0178) | (0.0177)
Other | 0.015 | 0.0114
| (0.0126) | (0.0083)

  * •

This table shows, for single-R01 personnel, aggregated estimates of the
average treatment effects (ATTs) of an interruption on being nonemployed in
the US, split by whether or not a personnel is observed in the 2020 Decennial
Census. They are obtained using our modified Callaway and Sant’Anna [2020]
estimator. The effects are aggregated over the 5 years after the expiration of
a lab’s grant. Standard errors are bootstrapped and clustered at the
expiring-R01-level. *, **, and *** indicate statistical significance at the
10%, 5%, and 1% levels.

Breaking out the results by place of birth, Table 4 shows that about 70% of
US-born personnel induced to nonemployment are found in the 2020 Decennial
Census. In contrast, all of the foreign-born personnel induced to
nonemployment leave the US.616161Appendix Table A2 shows that single-R01
foreign-born personnel who experience a funding interruption are, if anything,
more likely to leave the US than the point estimate for nonemployment
suggests. Though interrupted US-born personnel are also less likely to be
present in a Decennial Census, the effect is about half the size of their
point estimate for nonemployment. Thus, even though interruptions have a
greater impact on the US-born’s entrance into nonemployment, _conditional_ on
being induced into nonemployment, the foreign-born are more likely to stay
outside the US, likely reflecting visa employment requirements or attachment
to their home countries (e.g., for family reasons).

Table 4 also breaks out the estimates by occupation, showing that trainees are
most likely to leave the US. Among postdocs and graduate students, 70% of the
nonemployment effect is associated with being absent from the 2020 Decennial
Census. In contrast, nearly all faculty and 60% of personnel in other
occupations that are induced to nonemployment by an interruption are present
in the 2020 Decennial Census. The larger effect for trainees likely reflects
the mobility of a relatively young population, who have less attachment to the
US labor market and are more willing to find a job abroad, and are thus
disproportionately pushed by interruptions out of the US scientific ecosystem.

###  5.3 Presence in Science: Publications

Although single-R01 personnel are more likely to leave the US after an
interruption, possibly to the detriment of US science, the social welfare loss
may be ameliorated if they are still working in science outside the US. We
examine this possibility – that personnel induced to leave the US by
interruptions are still involved in scientific research – by linking personnel
to publications in the PubMed database, which allow us to track scientific
activity beyond the US border. We then decompose the nonemployment effect by
publishing activity, which is analogous to the exercise, in Section 5.2, of
decomposing the effect by presence/absence in the 2020 Decennial Census.

To do this, we again create two new outcome variables. First, an indicator
taking a value of one if a person is nonemployed in the US _and_ has at least
one publication in a given year (zero otherwise). Second, an indicator taking
a value of one if a person is nonemployed in the US _and_ has no publications
in a given year (zero otherwise).

Table 5 displays our estimates for single-R01 labs. After an interruption,
personnel are 0.4 pp more likely to be nonemployed and publishing and 2.6 pp
more likely to be nonemployed and _not_ publishing. Thus, the vast majority –
87% ($2.6/(2.6+0.4)$) – of interrupted single-R01 personnel that are pushed
into nonemployment are also publishing less actively, suggesting that they are
less likely to be participating in the scientific enterprise.

Table 5: Effect of Interruptions on Publishing for Nonemployed Personnel from
Single-R01 Labs

| Nonemployed | Nonemployed
---|---|---
| Publishing | Not Publishing
Subample | (1) | (2)
All Personnel | 0.004 | 0.026**
| (0.006) | (0.009)
Place of Birth |  |
Foreign-Born | -0.007 | 0.025
| (0.011) | (0.018)
US-Born | 0.011* | 0.025***
| (0.006) | (0.010)
Occupation |  |
Faculty | 0.010 | -0.004
| (0.014) | (0.012)
Postdoc/Grad | 0.003 | 0.059***
| (0.013) | (0.021)
Other | 0.007 | 0.020*
| (0.007) | (0.012)

  * •

This table shows, for single-R01 personnel, aggregated estimates of the
average treatment effects (ATTs) of an interruption on being nonemployed in
the US, split by whether or not a personnel is observed with a publication in
PubMed. They are obtained using our modified Callaway and Sant’Anna [2020]
estimator. The effects are aggregated over the 5 years after the expiration of
a lab’s grant. Standard errors are bootstrapped and clustered at the
expiring-R01-level. *, **, and *** indicate statistical significance at the
10%, 5%, and 1% levels.

Looking at the breakdown by place of birth, we see that nearly all of the
foreign-born personnel pushed to nonemployment are less likely to publish.
Meanwhile, some US-born personnel induced into nonemployment do remain active
in science, with about 30% continuing to publish at similar rates after an
interruption.

The breakdown by occupation in the lab suggests that, once again, the results
are most dramatic for non-faculty. About 96% of graduate students and postdocs
and 76% of other lab personnel that become nonemployed after an interruption
are also less likely to publish. In contrast, nearly all faculty induced to
nonemployment continue to publish at a similar rate after an interruption.

Taken as a whole, the results of this section and Section 5.2 suggest that
about half of single-R01 personnel who become nonemployed after an
interruption leave the US and most (87%) reduce their publication rates. Thus,
not only do interruptions to single-R01 labs cause personnel to depart early
from universities, but they also push personnel to leave the US and to reduce
contributions to the scientific enterprise in the form of publications. The
size of this effect for trainees (graduate students and postdocs) is
particularly policy-relevant, suggesting that funding delays hurt the social
return to investments in human capital.626262In Appendix Table A3, we further
decompose the nonemployment effect for US-born and foreign-born personnel in
single-R01 labs, by both presence in the 2020 Decennial Census and publishing.
Among foreign-born personnel induced to nonemployment, nearly all are neither
in the 2020 Decennial Census nor publishing. Among the US-born induced to
nonemployment, the differences are less stark. About half remain in the US but
are less likely to publish.

###  5.4 Earnings and Job Mobility

So far, we have seen that interruptions prematurely push some single-R01
personnel out of universities, into nonemployment, out of the US, and out of
science altogether. Losing highly-trained research personnel is likely
detrimental to the scientific enterprise, but it is not clear how
interruptions affect other career outcomes. Thus, in this section, we further
examine the damage that funding delays can do to the careers of lab personnel
by estimating the impact of interruptions on earnings and job switching
behavior.

####  5.4.1 Earnings

The ex ante effects of interruptions on earnings are ambiguous. If
interruptions cause personnel to hastily depart from their current jobs at
universities, taking less well-fitting jobs and slowing their career
progression through academia, then earnings may decrease relative to the
counterfactual of belonging to a continuously-funded lab. For instance, if a
post-doc scrambles to find another job, their advancement to a tenure-track
faculty position may be delayed or derailed entirely, resulting in lower
earnings. Alternatively, if interruptions spur personnel to get private sector
jobs, their earnings may be higher than what they would have received by
staying in academia.

Event Studies. Figure 6A displays event studies, for research personnel in
both multiple- and single-R01 labs, using the arcsinh of total earnings as the
outcome. As with employment outcomes, interruptions have no effect on the
earnings of personnel in multiple-R01 labs. In contrast, personnel in
single-R01 labs experience a sharp earnings decline that reaches almost 40%
after 2 years and about 60% after 5 years.636363To interpret effect sizes with
an arcsinh outcome, we follow the suggestion in Bellemare and Wichman [2020]
that the small-sample bias correction in Kennedy [1981] suffices for most
applications when the untransformed variable is large enough (untransformed
mean greater than 10).  These estimates almost surely overstate the impact
because the sample includes personnel with zero earnings _in the US_. However,
as suggested by our analysis of nonemployment and presence in the 2020
Decennial Census (Section 5.2), many of these personnel probably receive
positive earnings outside the US, which we cannot observe using our US-based
administrative/tax data.

Figure 6: This figure shows event studies of the effects interruptions on the
arcsinh of total earnings. They are obtained using our modified Callaway and
Sant’Anna [2020] estimator. The expiration of a lab’s grant takes place at
time 0, and the estimated interruption effects range from 5 years before to 5
years after expiry. The green series is for personnel in a single-R01 lab and
the orange series is for personnel in a multiple-R01 lab. The top graph (Panel
A) is for the full sample of all research personnel and the bottom graph
(Panel B) is for the subsample of research personnel that have positive
earnings in all periods from one period before expiry to five years after
expiry (i.e., the fully attached subsample). Standard errors are bootstrapped
and clustered at the expiring-R01-level. The data underlying this graph can be
found in Table A6.

To obtain more plausible estimates of the impacts of interruptions on
earnings, we identify a subset of personnel who are “fully-attached” to the US
labor market, defined as having positive earnings in _all_ years -1 to 5.
Figure 6B shows that, as with the full sample, personnel in multiple-R01 labs
are mostly unaffected by an interruption. Personnel in single-R01 labs again
experience a sharp earnings decline after an interruption – 15% by year 1 and
38% by year 5. Thus, our earnings results are not completely driven by the
exit of research personnel from the US labor market; even conditional on
employment in the US, personnel from single-R01 labs experience substantial
and permanent earnings declines after a funding interruption.

Conditioning on positive earnings _after_ treatment raises concerns about
whether changes in sample composition bias our estimates. Our estimates
overstate the earnings impact if, for example, interruptions cause personnel
without US citizenship or permanent residency to select into the fully-
attached sample by accepting lower-paying jobs to remain in the US (rather
than taking higher paying jobs overseas). Our estimates will also be too large
(in magnitude) if the earnings impact of interruptions is smaller for
personnel who select _out_ of the fully-attached sample (e.g., if the jobs
they take outside the US are higher paying than their counterfactual US jobs).
On the other hand, our results may understate the earnings impact by excluding
personnel who are induced by interruptions to either permanent (e.g.,
retirement) or temporary (e.g., to care for children) nonemployment, thus
truly receiving zero earnings. Though we cannot definitively determine the
direction of the bias, we suspect that omitting personnel with true
nonemployment spells is likely to dominate other sources of selection into and
out of the fully-attached sample, in which case our estimate is a lower bound
on the true effect of interruptions on earnings.646464In Appendix Section D.2,
we perform a back-of-the-envelope calculation which suggests that, if the true
earnings effect is zero, then then among non-fully attached personnel,
interrupted personnel have to earn 1.2 log points (or over 200%) more than
continuously-funded personnel, which intuitively seems unlikely.

Aggregated Static Effects. Table 6 displays the aggregate post-treatment
effects for the full sample and the fully-attached subsample, broken out
separately for personnel in multiple- and single-R01 labs. For both samples,
the earnings of multiple-R01 personnel change minimally in response to an
interruption. For single-R01 personnel, however, consistent with the event
studies, there are large and statistically significant interruption effects,
with those in the full sample and fully-attached samples experiencing post-
interruption earnings declines of about 40% and 20% respectively.

Table 6: Effect of Interruptions on Arcsinh Earnings

| Arcsinh Earnings | Arcsinh Earnings
---|---|---
| No Attachment Restriction | Fully-Attached
Subample | (1) | (2)
Panel A: Single-R01 Labs |  |
All Personnel | -0.523*** | -0.227***
| (0.137) | (0.063)
Occupation |  |
Faculty | -0.138 | -0.047
| (0.156) | (0.089)
Postdoc/Grad | -0.743*** | -0.215*
| (0.277) | (0.131)
Other | -0.498*** | -0.402**
| (0.169) | (0.177)
Personnel Count | 4,200 | 3,400
Panel B: Multiple-R01 Labs |  |
All Personnel | 0.0493 | -0.059*
| (0.097) | (0.035)
Personnel Count | 13,500 | 11,000

  * •

This table shows aggregated estimates of the average treatment effects (ATTs)
of an interruption on the arcsine of total earnings. They are obtained using
our modified Callaway and Sant’Anna [2020] estimator. The effects are
aggregated over the 5 years after the expiration of a lab’s grant. Panel A
shows the results for personnel in a single-R01 lab and Panel B shows the
results for personel in multiple-R01 labs. Column (1) uses the full sample of
all research personnel and column (2) uses the subsample that have positive
earnings in all periods from one period before expiry to five years after
expiry (i.e., the fully attached subsample). Standard errors are bootstrapped
and clustered at the expiring-R01-level. *, **, and *** indicate statistical
significance at the 10%, 5%, and 1% levels.

Breaking the results out by occupation, we see that the effects are most
pronounced for non-faculty, with trainees (postdocs and grad students) and
other personnel (e.g., staff and undergrads) experiencing post-interruption
earnings declines of 20% and 34%. In contrast, and in tandem with small
employment effects, faculty experience modest (and statistically
insignificant) declines of 5%.656565For faculty, the sample with no attachment
restrictions may provide a better estimate of the earnings impact since
nonemployed faculty are overwhelmingly likely to be physically present in the
US in 2020 (see Table 4) and thus observed nonemployment are likely to be true
nonemployment spells rather than exits from the US. Thus, not only do non-
faculty bear the brunt of the displacement effects of interruptions, they also
suffer the largest long-term decline in earnings.666666The larger estimate for
“Other” occupations may be due to its heterogeneous composition in terms of
education levels and skills. The sample is more likely to consist of personnel
(e.g., administrative staff, undergraduates) who have less advanced degrees or
different skills than faculty or trainees, thus affecting their resiliency to
employment shocks [Hoynes et al., 2012, Davis et al., 2021].

Overall, we interpret the decline in earnings as evidence that the effect of
interruptions is not simply to shift the allocation of personnel across
sectors and borders, but to also disrupt their careers.

####  5.4.2 Job Mobility

In addition to earnings, we assess the career stability of personnel in the
wake of an interruption by examining their job switching behavior. In general,
job switching can be an important mechanism through which workers’ earnings
recover from adverse labor market events (e.g., Oreopoulos et al. [2012]).
However, this may not be true in the context of research personnel who wish to
remain in the academic sector post-interruption. For instance, a postdoc who
switches to a new postdoc position will forgo earnings from more lucrative
jobs [Cheng, 2023], in which case the combination of lower earnings and higher
job switching may be a sign of greater career instability.

Table 7: Effect of Interruptions on Job Changes for Fully-Attached Personnel
from Single-R01 Labs

New University |  | New Non-University
---|---|---
(1) |  | (2)
0.0370** |  | -0.0218
(0.0178) |  | (0.0189)

  * •

This table shows, for single-R01 personnel, aggregated estimates of the
average treatment effects (ATTs) of an interruption on the count of new
university and non-university EINs that pay a personnel in a given year. They
are obtained using our modified Callaway and Sant’Anna [2020] estimator. The
effects are aggregated over the 5 years after the expiration of a lab’s grant.
Standard errors are bootstrapped and clustered at the expiring-R01-level. *,
**, and *** indicate statistical significance at the 10%, 5%, and 1% levels.

To examine job switching behavior, we construct two variables that count the
numbers of _new_ academic and industry jobs a personel has in a given
year.676767More precisely, we count the number of EINs from which a personnel
received earnings in a given year but not the previous year. These variables
are constructed as a count of new jobs rather than a binary variable
indicating a new job for Census disclosure reasons. Since it is a count, the
percentage point interpretation is approximate. Table 7 shows difference-in-
difference estimates, for single-R01 personnel in the fully-attached
subsample, of an interruption’s impact on these job count variables. After an
interruption, personnel are 3.7 pp more likely to have a new university job in
a given year and no more likely to move into a new industry job. Thus, to the
extent that single-R01 personnel switch jobs after an interruption, they opt
for a new (presumably less desirable) academic job. In combination with the
earnings decline of the previous section, we interpret these job switching
results as evidence of interrupted personnel being pushed onto a less stable
academic career track rather than being pushed into higher-paying industry
jobs.

###  5.5 Robustness

####  5.5.1 Controlling for Resubmissions

A natural concern about the credibility of our estimates is whether
differences, across interrupted and continuously-funded labs, in project or PI
quality, lead to violations of the parallel trend assumption. Overall, we find
parallel trends quite plausible in our setting because raw means and event
studies suggest that interrupted and continuously-funded personnel trend
similarly prior to grant expiry (Section 5.1) and balance statistics suggest
that they are similar across a variety of pre-treatment observables (Section
3.6). Nevertheless, we probe the robustness of our main results by controlling
for the perceived quality of an R01 renewal application – specifically, the
number of resubmissions an R01 renewal application went through before
approval.686868We are grateful to Ian Hutchins for the suggestion.

Table 8:  Main Results, for Personnel from Single-R01 Labs, Controlling for
Resubmissions

|  | Controlling for
---|---|---
| No Controls | Resubmissions
| (1) | (2)
Dep. Variable |  |
Nonemployed | 0.03024*** | 0.03204***
| (0.01168) | (0.01068)
US University | -0.02104 | -0.02231
| (0.01843) | (0.02024)
US Industry | -0.009197 | -0.009729
| (0.0147) | (0.01796)
asinh(Earnings) | -0.2268*** | -0.1792***
| (0.06271) | (0.06271)

  * •

This table shows, for single-R01 personnel, aggregated estimates of the
average treatment effects (ATTs) of an interruption on three mutually
exclusive employment outcomes: 1) nonemployed in the US, 2) employed at a US
university, and 3) employed in US industry. It also shows the ATT for the
arcsinh of total earnings, which is estimated using subsample of research
personnel that have positive earnings in all periods from one period before
expiry to five years after expiry (i.e., the fully-attached subsample). They
are obtained using our modified Callaway and Sant’Anna [2020] estimator. The
effects are aggregated over the 5 years after the expiration of a lab’s grant.
Column (1) reproduces the main estimates from Table 3 and column (2) shows the
same estimates, but controlling for the number of resubmissions. Standard
errors are bootstrapped and clustered at the expiring-R01-level. *, **, and
*** indicate statistical significance at the 10%, 5%, and 1% levels.

In our context, a resubmission is an application for the renewal of R01
funding that follows an initial unsuccessful attempt. Thus, the number of
resubmissions is a coarser version of the score that the NIH awards a renewal
application.696969In 2009, the NIH went from allowing two resubmissions to one
resubmission (in rare cases, more resubmissions were possible under either
regime). Moreover, the need to resubmit an application can itself lead to a
funding interruption – indeed, we estimate that an additional resubmission is
associated with a 12 pp increase in the likelihood of interruption. Thus,
controlling for the number of resubmissions allows us estimate the effects of
interruptions within groups of labs that have similar levels of perceived
quality and similar probabilities of experiencing a resubmission-induced
interruption.

Table 8 shows that controlling for the number of resubmissions has a
negligible impact on our employment and earnings outcomes. Though we would
undoubtedly prefer a finer measure of renewal application quality (such as
confidential reviewer scores), these results suggest that the effects of
interruptions are not primarily driven by unobservable project/PI quality
differences.

####  5.5.2 Long and Short Funding Delays

Our analysis so far has not allowed treatment effects to vary by the duration
of an interruption to a lab’s R01 funding. However, longer interruptions may
lead to larger effects if, for example, PIs can find short-term support (e.g.,
university-provided bridge funding) for personnel until R01 funding arrives.
In this case, short delays can be weathered and the effects of interruptions
would be concentrated in labs that experience longer funding delays.

To examine whether interruption effects vary by delay length, we create two
treated samples – personnel in labs with “short” interruptions (delays of 30
to 90 days) and “long” interruptions (delays of 90 days or more).70707090 days
is approximately the median length of a funding interruption. We then obtain
estimates for each of these treatment groups, comparing them to our usual
continuously-funded control group but omitting the other treatment
group.717171We opt for two separate treatment groups because the CS estimator
does not accommodate continuous or discrete multi-valued treatments.

Table 9 shows these estimates for our main employment and earnings outcomes.
As with our baseline 30-day definition of an interruption, both short and long
interruptions have imprecise aggregate effects on the probabilities of being
at a university or in industry. For US nonemployment, the short interruptions
have a larger impact (4.5 pp) than long interruptions (1.1 pp, statistically
insignificant). Finally, for earnings, the impacts of short and long
interruptions are quite similar at 25% and 20%, respectively.

Therefore, both short and long funding delays impact the careers of single-R01
lab personnel, suggesting that, if alternative funding is available, its
ability to mitigate the effects of interruptions on personnel does not vary by
the length of the funding delay. Moreover, it appears that labs with
particularly long delays (i.e., labs with particularly intense treatments) do
not drive our results and that universities/PIs have trouble bridging even
relatively short funding gaps.

Table 9: Effect of Interruptions by Length of Interruption

| Nonemployed | US University | US Industry | asinh(Earnings)
---|---|---|---|---
| (1) | (2) | (3) | (4)
Interruption Length |  |  |  |
30-90 days | 0.0451*** | -0.0192 | -0.0259 | -0.253***
| (0.0160) | (0.0232) | (0.0228) | (0.0884)
>90 days | 0.0109 | -0.0234 | 0.0125 | -0.1952***
| (0.0149) | (0.0271) | (0.0169) | (0.0684)
Subsample | All | All | All | Fully Attached

  * •

This table shows, for single-R01 personnel, aggregated estimates of the
average treatment effects (ATTs) of an interruption on three mutually
exclusive employment outcomes: 1) nonemployed in the US, 2) employed at a US
university, and 3) employed in US industry. It also shows the ATT for the
arcsinh of total earnings, which is estimated using subsample of research
personnel that have positive earnings in all periods from one period before
expiry to five years after expiry (i.e., the fully-attached subsample). They
are obtained using our modified Callaway and Sant’Anna [2020] estimator. The
first row contains estimates for labs with ’short’ interruptions and the
second row contains estimates for labs with ’long’ interruptions. The effects
are aggregated over the 5 years after the expiration of a lab’s grant.
Standard errors are bootstrapped and clustered at the expiring-R01-level. *,
**, and *** indicate statistical significance at the 10%, 5%, and 1% levels.

##  6 Conclusion

In this paper, we study how delays in grant funding, or interruptions, affect
the careers of researchers – not only the PIs who run the lab, but also the
personnel they hire, including trainees (e.g., graduate students, post-docs)
and staff (e.g., research scientists, lab managers). Using a combination of
public-use NIH grant data and transaction-level data on grant expenditures, we
identify the research labs to which personnel belong and measure lab-level
variation in funding delays for the subset of R01 grants that are eventually
_successfully renewed_. By linking personnel to comprehensive US tax data,
Decennial Censuses, and publications, we then track the evolution of their
labor market outcomes after grant expiry.

Using a difference-in-differences design, we find that when the renewal of a
PI’s R01 is interrupted, their hired personnel are immediately and
persistently less likely to work in the US at least up to five years later
(the PIs themselves are unaffected). These effects are sizeable, amounting to
about one-third of the nonemployment effect of childbearing for mothers with a
Ph.D. in the biological sciences [Cheng, 2021]. Most of the interruption
effect is driven by personnel permanently leaving the US and reducing their
publishing activity. Those who remain employed in the US also earn
substantially less than their continuously-funded peers and seem to have less
stable jobs, as evidence by their job switching activity. These results are
concentrated among those less attached to universities – that is, graduate
students, postdocs, and lab staff – illustrating the susceptibility to
interruptions of personnel that are not on long-term, university-supported
contracts.

Compared to foreign-born personnel, the US-born are more responsive to funding
delays, being twice as likely to be induced into US nonemployment. This is
perhaps counterintuitive, however, the foreign-born often face work or study
requirements to remain in the US and barriers to reentry if they leave. These
restrictions may lead the foreign-born to be less responsive to funding set-
backs. Moreover, though interruptions are more likely to induce the US-born to
nonemployment, we find that, _conditional_ on becoming nonemployed, the
foreign-born are more likely to stay outside the US.

Among the constellation of policies and institutions affecting the labor
market outcomes of the research workforce, how important are renewal delays
for R01 grants? Based on our estimates, funding delays account for about 5% of
nonemployment among research personnel within our sample five years post-
interruption. This is comparable to the effect of green card delays on whether
US doctorates stay in the US – estimates from Kahn and MacGarvie [2020] imply
that being from a country affected by permanent residency visa caps (China or
India) accounts for 7.4% of departures from the US. Thus, eliminating funding
delays could have a meaningful effect on the retention of scientific talent
within the US.

The fact that we find non-trivial effects arising from a relatively modest
source of funding instability also suggests that larger sources of
instability, which are difficult to study because they typically stem from
aggregate shocks (e.g., the federal budgeting process) or otherwise impact
everyone (e.g., general uncertainty in the grant system), likely have even
larger impacts on the labor market outcomes of the research workforce. Thus,
our results are informative not only about the effects of funding delays per
se, but also about the effects of funding instability more generally.

Our finding that the effects of interruptions are confined to personnel in
labs supported by a single R01 further suggests that policies to more broadly
allocate NIH grants across many labs may have the unintended consequence of
also increasing funding instability for all labs [Reardon, 2007, Russo, 2008].
Even when application renewals are uncertain, labs can still engage in longer-
term planning if they are also supported by other non-expiring grants, which
becomes less likely if grants are spread more thinly across an increasing
number of labs.

More fundamentally, our results raise questions about the wisdom of tying
science funding to the yearly appropriations process. While some flexibility
may be necessary to respond to short-term needs or opportunities (e.g.,
emergencies such as the COVID-19 pandemic or unexpected scientific
breakthroughs), the long-term nature of science suggests that it may be
prudent to appropriate some portion of science agencies’ budgets for multiple
years at a time.

This leads to the question: what are candidate policy solutions for reducing
or eliminating funding delays (or at least mitigating their impact)? The
answer depends in part on the mechanisms driving the effects of funding
delays. One possible mechanism is that PIs do not receive sufficient notice or
information that enables them to find other temporary sources of funding (such
as bridge funding from their institution). In this case, providing PIs with
more and/or earlier information about their chances of renewal success would
help to mitigate the effects of interruptions. On the other hand, it may
simply be that such short-term funding options are scarce, such that even
giving PIs earlier notice would be of minimal help. If so, policymakers might
consider the provision of “insurance” or short-term liquidity to affected
labs. Alternatively, a more fundamental policy change would be to increase the
share of trainees funded by fellowships that are not tied to a specific PI.

Overall, our results contain two main lessons. First, operational details
matter in science funding [Williams, 2023, Duflo, 2017]. The yearly delay in
passing a federal budget and the possibility of funding interruptions are
issues that scientists and institutions are keenly aware of and try to prepare
for. As one PI noted, “this period of gaps can be very stressful, in general
it is hard to plan research in advance, when getting most of your funding in
installments and uncertainty.” Even within our sample of research-intensive
universities, the impact of funding interruptions on personnel is not trivial.

Second, not only are grants inputs into knowledge production, but also into
the production of future scientific human capital [Jiang et al., 2024]. The
benefits of policies and new funding models that reduce the occurrence of
funding disruptions may be understated if we focus on research output (e.g.,
publication-derived outcomes) without considering the impact on the people
working in these fields. Indeed, our data cannot tell us anything about the
personal effects of these uncertainties on the lives and well-being of lab
personnel. In addition, funding uncertainty and disruptions might shape the
beliefs of those who have yet to start (or are in the early stages of) their
scientific career, and therefore influence the composition of the future
scientific labor force. More work shedding light on the interaction of funding
certainty with expectations and career choice may be a fruitful line of future
work [Ganguli and Gaul’e, 2019, Aucejo et al., 2020].

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##  Appendix A Figures and Tables

Figure A1: This figure shows the main results from Tham [2023]. The figure
shows event-study estimates (with 95% confidence intervals) of the difference
in lab spending and number of employees between PIs of interrupted and
uninterrupted R01s. Details are available in Tham [2023].

TWFE With an Alternative Control Group

Figure A2: This figure shows the event studies using an alternative control
group, employees in labs with interrupted R01s and multiple R01s. The first
row shows event studies for the three mutually exclusive placement outcomes:
absent from US data, paid by a US university, and paid by a US non-university.
The second row shows the event study for arcsinh-transformed wages.

Table A1: Effect of Interruptions on Being at Original University or Different
University for Different Subsamples

| At Original |
---|---|---
| (UMETRICS) | At New
Subsample | University | University
All | -0.0211 | 0.0240
| (0.0215) | (0.0182)
Foreign-born | 0.0177 | 0.00705
| (0.0281) | (0.0224)
US-born | -0.0390 | 0.0325
| (0.0250) | (0.0228)
Faculty |  -0.0408** (0.0202) |  0.0110 (0.0209)
Postdoc/Grad |  -0.0775** (0.0331) |  0.0613 (0.0393)
Others |  0.0110 (0.0359) |  0.00830 (0.0188)

  * This table shows aggregated estimates of the average treatment effects (ATTs) of in our single-R01 sample of an interruption on the probability of being at a person’s original UMETRICS university and of being at any other university. These indicate whether: (1) the personnel receives positive earnings from _their own UMETRICS university_ (i.e., the university employing them at the time of R01 expiry) and (2) the personnel receives positive earnings from an IPEDS university other than their own. The first two rows are for all single-R01 employees, while the next two are for foreign-born employees, and the last two for US-born employees.The effects are aggregated over the 5 years after the expiration of a lab’s grant. The estimates are obtained using a modified Callaway and Sant’Anna [2020] estimator. Standard errors are bootstrapped and clustered at the interrupted R01 level. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels.

Table A2: Effect of Interruptions on Being Observed in Subsequent Decennial
Censuses

Subsample | In Census
---|---
All | -0.0294**
| (0.0149)
Foreign-born | -0.0390
| (0.0397)
US-born | -0.0157
| (0.0183)
Faculty | 0.0467
| (0.0317)
Postdoc/Grad | -0.0234
| (0.0393)
Other | -0.0432*
| (0.0246)

  * This table shows aggregated estimates of the average treatment effects (ATTs) of in our single-R01 sample of an interruption on the probability of being found in a Decennial Census (2000, 2010, and 2020). Note that this necessarily can only contain data for Decennial Census years.The first row is for all employees of single-R01 labs, while the following rows are for sub-samples of this group. The effects are aggregated over the 5 years after the expiration of a lab’s grant. The estimates are obtained using a modified Callaway and Sant’Anna [2020] estimator. Standard errors are bootstrapped and clustered at the interrupted R01 level. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels.

Table A3: Effect of Interruptions on Publishing and Presence in 2020 Decennial
Census if Absent From Tax Data

Subsample | Not Publishing | Not Publishing | Publishing | Publishing
---|---|---|---|---
| Not In 2020 Census | In 2020 Census | Not In 2020 Census | In 2020 Census
Foreign-born | 0.0357** | -0.00977 | -0.00774 | 0.000950
| (0.0173) | (0.0116) | (0.0116) | (0.00631)
US-born | 0.008823* | 0.01815** | 0.003393 | 0.009127
| (0.005007) | (0.008856) | (0.004069) | (0.005595)

  * This table shows aggregated estimates of the average treatment effects (ATTs) in our single-R01 sample of an interruption on the not being found in the administrative data, split by two things: whether or not a person is observed with a publication in MEDLINE, and whether or not a person is observed in the 2020 Decennial Census. For each group, the coefficients should sum to the overall estimate of not finding people in the administrative data. The first four rows are for all foreign-born people in single-R01 labs. The second four are for US-born people in single-R01 labs. The effects are aggregated over the 5 years after the expiration of a lab’s grant. The estimates are obtained using a modified Callaway and Sant’Anna [2020] estimator. Standard errors are bootstrapped and clustered at the interrupted R01 level. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels.

Table A4:  Event Studies for Sector of Employment | Single R01 | Multiple R01s
---|---|---
| Non- | US | US | Non- | US | US
| -employed | University | Industry | -employed | University | Industry
T-5 | 0.00322 | 0.00508 | -0.00830 | -0.00132 | 0.00772 | 0.00640
| (0.0153) | (0.0120) |  | (0.00623) | (0.00661) |
T-4 | -0.00415 | 0.00767 | -0.00353 | -0.000122 | 0.00404 | 0.003916
| (0.0139) | (0.0126) |  | (0.00606) | (0.00654) |
T-3 | -0.0125 | 0.0110 | 0.00153 | 0.00243 | -0.00169 | 0.000746
| (0.0118) | (0.0143) |  | (0.00630) | (0.00757) |
T-2 | 0.0101 | -0.0136 | 0.00353 | 0.000134 | 0.00496 | 0.00510
| (0.00921) | (0.0127) |  | (0.00685) | (0.00736) |
T-1 | -0.000914 | 0.00543 | -0.00451 | 0.00728 | -0.00996 | -0.00268
| (0.00954) | (0.0212) |  | (0.00558) | (0.00706) |
T+0 | 0.0290*** | -0.0489*** | 0.0199 | -0.00330 | -0.00276 | -0.00606
| (0.0100) | (0.0140) |  | (0.00536) | (0.0103) |
T+1 | 0.0313** | -0.0396* | 0.00831 | -0.00747 | 0.00184 | -0.00563
| (0.0125) | (0.0205) |  | (0.00763) | (0.0124) |
T+2 | 0.0249* | -0.00550 | -0.0194 | -0.00873 | 0.00534 | -0.00339
| (0.0139) | (0.0239) |  | (0.00924) | (0.0141) |
T+3 | 0.0274 | -0.00398 | -0.0234 | -0.00714 | -0.00512 | -0.01226
| (0.0168) | (0.0256) |  | (0.0122) | (0.0183) |
T+4 | 0.0379* | 0.00232 | -0.0402 | -0.0248* | 0.0187 | -0.00602
| (0.0203) | (0.0303) |  | (0.0137) | (0.0233) |
T+5 | 0.0342 | -0.00906 | -0.0251 | -0.0100 | 0.0276 | 0.0176
| (0.0239) | (0.0351) |  | (0.0177) | (0.0239) |
This table shows estimates underlying the event studies plotted in Figure 4 of
the effects of interruptions on three mutually exclusive employment outcomes:
1) nonemployed in the US , 2) employed at a US university, or 3) employed in
US industry. They are obtained using our modified Callaway and Sant’Anna
[2020] estimator. The expiration of a lab’s grant takes place at year 0, and
the estimated interruption effects range from 5 years before to 5 years after
expiry. The left column is for personnel in a single-R01 lab and the right
column is for personnel in a multiple-R01 lab. Standard errors are
bootstrapped and clustered at the expiring-R01-level. *, **, and *** indicate
statistical significance at the 10%, 5%, and 1% levels. Standard errors not
available for US Industry for Census disclosure avoidance reasons.
Table A5:  Raw Means: Probability by Sector A |  Nonemployed
---|---
| Single R01 | Multiple R01s
| Continuously |  |  | Continuously |  |
| Funded | Interrupted | Difference | Funded | Interrupted | Difference
T-5 | 21.14% | 21.06% | 0.08 | 20.68% | 20.73% | -0.05
T-4 | 16.19% | 15.74% | 0.45 | 17.25% | 16.61% | 0.64
T-3 | 11.59% | 10.30% | 1.29 | 13.59% | 13.11% | 0.48
T-2 | 8.42% | 7.50% | 0.92 | 9.30% | 8.63% | 0.67
T-1 | 4.19% | 3.41% | 0.78 | 5.07% | 4.95% | 0.12
T+0 | 4.13% | 5.80% | -1.67 | 5.01% | 4.54% | 0.47
T+1 | 7.05% | 9.08% | -2.03 | 8.44% | 7.37% | 1.07
T+2 | 9.34% | 10.65% | -1.31 | 10.53% | 10.12% | 0.41
T+3 | 10.80% | 12.21% | -1.41 | 12.31% | 12.15% | 0.16
T+4 | 11.95% | 13.67% | -1.72 | 13.99% | 12.35% | 1.64
T+5 | 12.26% | 13.77% | -1.51 | 15.62% | 14.50% | 1.12
B |  US University
| Single R01 | Multiple R01s
| Continuously |  |  | Continuously |  |
| Funded | Interrupted | Difference | Funded | Interrupted | Difference
T-5 | 62.91% | 64.04% | -1.13 | 66.51% | 65.30% | 1.21
T-4 | 69.22% | 72.10% | -2.88 | 71.26% | 71.20% | 0.06
T-3 | 76.02% | 79.11% | -3.09 | 76.90% | 76.83% | 0.07
T-2 | 82.90% | 85.58% | -2.68 | 83.88% | 84.22% | -0.34
T-1 | 91.01% | 93.92% | -2.91 | 91.37% | 90.92% | 0.45
T+0 | 90.77% | 88.67% | 2.10 | 90.53% | 90.07% | 0.46
T+1 | 81.58% | 80.46% | 1.12 | 80.59% | 81.67% | -1.08
T+2 | 74.75% | 76.88% | -2.13 | 73.28% | 74.07% | -0.79
T+3 | 68.73% | 71.37% | -2.64 | 67.69% | 67.96% | -0.27
T+4 | 63.53% | 67.09% | -3.56 | 62.77% | 65.26% | -2.49
T+5 | 60.23% | 63.02% | -2.79 | 57.50% | 61.77% | -4.27
C | US Industry
| Single R01 | Multiple R01s
| Continuously |  |  | Continuously |  |
| Funded | Interrupted | Difference | Funded | Interrupted | Difference
T-5 | 15.95% | 14.90% | 1.05 | 12.81% | 13.97% | -1.16
T-4 | 14.59% | 12.16% | 2.43 | 11.49% | 12.19% | -0.70
T-3 | 12.39% | 10.59% | 1.80 | 9.51% | 10.06% | -0.55
T-2 | 8.68% | 6.92% | 1.76 | 6.82% | 7.15% | -0.33
T-1 | 4.80% | 2.67% | 2.13 | 3.56% | 4.13% | -0.57
T+0 | 5.10% | 5.53% | -0.43 | 4.46% | 5.39% | -0.93
T+1 | 11.37% | 10.46% | 0.91 | 10.97% | 10.96% | 0.01
T+2 | 15.91% | 12.47% | 3.44 | 16.19% | 15.81% | 0.38
T+3 | 20.47% | 16.42% | 4.05 | 20.00% | 19.89% | 0.11
T+4 | 24.52% | 19.24% | 5.28 | 23.24% | 22.39% | 0.85
T+5 | 27.51% | 23.21% | 4.30 | 26.88% | 23.73% | 3.15
This table shows the number that are ploted in plotted in Figure 5: the
average probability that a personnel is in one of three mutually exclusive
employment categories: 1) nonemployed in the US (Panel A), 2) employed at a US
university (Panel B), or 3) employed in US industry (Panel C). These
probabilities are calculated from five years before R01 grant expiration to