On September 6th, the expanded and extended
COVID-19-related unemployment benefits ended abruptly, and the idea of a “benefits
cliff” became clear for many Americans who had never experienced one before.

Benefits cliffs are created when income rises above
a legally imposed limit for continued receipt or, in the case of cash welfare
benefits, when eligibility expires. In the case of pandemic unemployment
insurance benefits, the cliff came for every recipient and very abruptly. Other
programs like childcare, housing, and Medicaid benefits tend to “taper,” but at
some point the beneficiary has to choose between earning more and maintaining
what is often a vital subsidy. Income-tested benefit cliffs, no matter how
gradual, tend to reduce work incentives at the margins.

On October 21st, I spoke with Dr. Alex Ruder, a principal adviser on benefits cliffs to the Atlanta Federal Reserve’s Community and Economic Development team. He specializes in workforce development and developed a Career Ladder Identifier and Financial Forecasting tool. Highlights from our conversation are below. You can find the full transcript here and listen to the audio podcast on Hardly Working.

The transcript below has been edited for length and
clarity.

Orrell: What’s the
origin story for Alexander Ruder, economist, and how you got there? What things
sort of drove your interest?

Ruder: Technically, I would be categorized as a
political scientist. If you categorize people by what their PhD is in. So,
obviously, I’m not doing political science work in my current job. But
nevertheless, political science, at least where I studied it, has significant
overlap with public policy and the methodologies that you need to study public
policy. So a good way to describe my work is as a public policy researcher.

I
had to go back to grad school at the University of Chicago. In the course of
studying public policy, I became very interested in economic development, and
specifically thinking about it from a regional perspective — what makes cities
competitive in attracting businesses and supporting businesses — basically
having a competitive infrastructure for business recruitment.

I did some internships in economic development. That
led me to look for a more advanced kind of graduate-level internship at the
time at the Department of Commerce and Economic Opportunity in Illinois. And it
turned out the division I got my graduate-level internship in was the Workforce
Development Division at the Illinois Department of Commerce.

But I also evaluated economic development and
workforce programs. And when you do evaluation in workforce, it really opens up
the hood, if you will, on issues that affect workforce development — local-versus-state
issues, data quality issues, challenges that areas have in implementing
workforce development programs. So I became very interested in just kind of how
these programs were structured and implemented because obviously you need
implementation success to have outcome success. My mentors from that job still
guide me today, which I value greatly. That was my initial entry into the
field.

You’ve done a lot of work on
benefits cliffs, and I’d like you to start off just defining for listeners what
they are and why they matter.

I
like to think about it as an effective marginal tax rate on income. And the
word effective means we’re not just looking at your payroll taxes and
your income tax, we’re looking at potential losses in financial resources more
broadly as your income goes up. That said, usually when we talk about benefits
cliffs, we mean the marginal losses for families on means-tested public
assistance when they reach an income threshold.

Like?

Like
food assistance through the Supplemental Nutrition and Assistance Program,
Children’s Health Insurance Program — Medicaid CHIP, Section 8 housing
subsidies. And there’s a long list of programs that have some means testing — which,
in other words, means that it has an income threshold.

Workforce
development programs are aimed at increasing income. As income goes up,
families are likely to lose public assistance. The benefits cliffs specifically
means that loss in public assistance makes you worse off economically than you
were before your income increased.

So you have developed
something called a Career Ladder Identifier and Financial Forecasting, or
CLIFF, tool. How does it work, and what do you know about its usefulness to
people?

So
a key dimension of labor market research is labor supply. What are the
incentives for people to move into employment, work more hours, or seek higher
wages? The question we were starting to ask is, “How can we bring this kind of
insight into the workforce development field to make it more useful to the
communities, particularly in the Southeast that we interact with at the Atlanta
Fed?” And that’s a lot of my job: taking research on benefits cliffs out into
the field.

So
what we started to do is take common career pathways that are being used as
models, and we saw that under certain conditions, not all conditions, the
economic return to career pathways can be inverted when benefit losses due to
higher incomes is taken into account.

Like you started on a career
pathway program, you end up making less? Is that what you mean?

That’s
right. Not every situation will have an extreme result. It’s building on a
model that assumes some form of career advancement over time. So when we looked
at this nursing career pathway over time and we contrasted the employment view
to this benefits cliff view, which we call net financial resources — a family’s
disposable income — you  could see that the
payoff to moving up a career pathway is actually not there for these families. The
implication is that these career pathways fundamentally may need to be
rethought to some extent if we think the justification of these career pathways
is increasing the standard of living as the worker moves up a career ladder.

So
that was the intellectual background. We’re just going around the Southeast and
around the country just talking about this finding and how we’ve developed this
methodology to look at benefits cliffs over time as a worker ages, because the
benefits cliffs workers face are not static. They change over time. What we
noticed was that employers, nonprofits that are doing the actual job coaching,
community organizations wanted to have this data localized, and they wanted it
in more of an actionable way. That is what gave rise to the CLIFF tool.

Can you give us some
examples of particularly good illustrations of how the tool is used?

The
tools are meant to facilitate three conversations. One is research for local
areas. Two is programmatic and policy change that would make the progression to
higher-paid jobs smoother for families. And then three is pilot program design.
So I’ve seen changes along all dimensions, not all of which are attributable to
our tool.

I’ve
seen some cases where people have looked at their internal policies and found
barriers to people coming back to get additional training to move up a career
path. You know, barriers such as needing to wait three years before you get
additional training dollars to move up a career pathway. And people said, “You
know what? I think we need to waive those because if people are wanting to
upskill, we should give them that opportunity.”

The
second dimension I’ve seen is people are implementing pilot programs to smooth
out the larger cliffs. The evidence of the effect of benefits cliffs on career
advancement is complex. So, it’s not that everyone’s just turning down jobs
because of their fear of losing benefits.

So what would the different
measure look like then?

If
a local organization wants to use this, there are two ways to use different
measures for self-sufficiency. One, you can use a measure to determine if
people meet eligibility requirements. Impact is the second way to measure. If
you’re evaluating performance as a gain to self-sufficiency, you want to look not
just at employment, not just wage gain, but whether recipients are moving
closer towards a local self-sufficiency target. The measure used impacts how
program performance is measured.

One of the main challenges
to smoothing benefit cliffs is that “extending the runway” inevitably means
increased cost to the taxpayer. We don’t eliminate the cliff so much as delay
it.

Thinking
about how to smooth out these programs, I mean, without a doubt, government
finances are going to play a complex role here. For any kind of large-scale
benefit mitigation strategy, you would probably have to look at — of course,
you would have to look at — what is the cost and the long-term gain in
terms of kind of a cost-benefit analysis.

Other
ways that you can think about doing it is to reverse the benefit pattern: make
the program less generous early on when wages are low, and use the saving to smooth
the cliff. That requires the policymaker to make a policy choice about where
need is greatest.

I wanted to get your
reflections before we wrap this up on the mother of all benefits cliffs that we
just passed through with the end-of-the-pandemic unemployment insurance
emergency programming.

I
don’t like to make, personally, pronouncements for or against until I have the
research I need to really make this decision. But I think you could safely say
that there is some disincentive effect of extended unemployment insurance.
There’s debate on how big it is.

You
can also take from that literature that extended unemployment benefits do
provide a financial cushion to families that are unable to work. And it smooths
consumption. So the questions I’m trying to answer right now in my own work
are, “Are the conditions that justify extended unemployment still there, that justified
it at the time?” So, clearly, that’s no longer an issue because it’s gone. So
the questions we have to begin to ask and look at very carefully are what the
data can tell us about why people are not working, why we have so many open
jobs, and why the labor supply is not picking up the way it should.

Where
I’m still open — and I think a lot of people are — is, what’s going to happen
with the subset of the population is that has the difficulties with childcare
access? That’s going to be a drag on employment likely. The health concerns — that’s
still going to play a role.

Then there’s going to be some people who use this as a transition period while they consider upskilling options because the opportunities do not look appealing anymore. I know a lot of people are thinking about that as well. So I think until we get answers to at least those three questions, we’re at least not going to be able to make an assessment about the end of extended unemployment.

The post Benefits cliffs: Highlights from my conversation with Alex Ruder appeared first on American Enterprise Institute – AEI.