Research Statement: Robert Ulbricht
June 2022*
My research covers, broadly speaking, three main topics: (i) the consequences of uncertainty for the business cycle, (ii) the macroeconomics of job-to-worker allocation, and (iii) the
long-run dynamics of political regimes. A recurring theme across these topics is the insight
that people face information frictions in their decisions to save and to work, to search for
jobs, to hire employees, to join a political revolution, or to contract agents. Methodologically,
most of my research is theory-based. I strive to distill complex realities into simple yet
insightful models. While abstracting from unnecessary detail, much of my work seeks rigorous
quantification of the core mechanism that is being studied. I summarize the three areas of
my research below (along with a fourth topic that is somewhat of an outlier).
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Uncertainty and the Business Cycle
The first strand of my research concerns the role of uncertainty for the business cycle.
In “Robust Predictions for DSGE Models with Incomplete Information” (AEJ:
Macroeconomics, forthcoming), my coauthor and I quantify the importance of expectational
shocks for explaining aggregate fluctuations. One challenge in doing so is that there are myriad
ways in which expectational shocks can propagate through an economy, and discriminating
between them usually requires taking a precise stand on the structure of information available
to agents. Unfortunately, there is no direct evidence to discriminate between the many
ex-ante plausible information structures (information is inherently hard to observe).
In our paper, we propose a simple workaround: Instead of taking a precise stand on the
information available to each inhabitant of an economy (consumers, firms, etc), we allow
the researcher to specify certain conservative bounds on their information (e.g., consumers
observe the price of their own consumption basket, everyone observes GDP with some lag,
etc). We then map these information-bounds into bounds on the dynamics of endogenous
equilibrium outcomes such as GDP, inflation, or consumption. Econometrically speaking, our
approach provides a “set identification” of the joint time series of macroeconomic variables
within a given economic environment.1
Our paper offers a general characterization theorem that applies to the majority of business
cycle models used in the literature. We then apply it to a specific business cycle model to
quantify how much of aggregate fluctuations can be accounted for by waves of optimism and
* An
up-to-date version of my CV and PDFs of all completed papers can be found on my website:
www.robertulbricht.com.
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In our language, an “economic environment” fully specifies the non-information aspects of an economy
(preferences, technologies, market structures), without taking a stand on the information that is available to
agents when they form their expectations.
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pessimism among consumers and firms. We find that if we allow for all types of uncertainty,
expectational shocks can account for up to 51% of the U.S. business cycle. However, this
hinges critically on firms being uncertain about idiosyncratic demand. If we shut down
idiosyncratic demand uncertainty, all other sources of uncertainty (including uncertainty
about idiosyncratic productivity and all aggregate variables) can together at most account
for 3% of the U.S. business cycle. In conclusion, expectational shocks are a strong contender
for being a primary driver of business cycles, but for them to fill that role it is essential
that—while making their production plans—firms face uncertainty about the idiosyncratic
demand that their products will receive.
In “Endogenous Uncertainty and Credit Crunches” (Review of Economic Studies,
R&R, 2nd round), my coauthor and I study the role of uncertainty about such idiosyncratic
factors2 as a source of slow recovery from financial crisis. The starting point is the realization
that when firms are unable to operate at potential (e.g., due to limited access to external
funds), less information about the quality of forgone projects and their potential demand is
being generated, increasing uncertainty. At the same time, the more uncertain lenders are
about a firms’ potential, the less likely the firm obtains funding. Jointly, these two forces
imply that an exogenous, but temporary, reduction in funding can morph into a persistent
spiral of increased uncertainty about a firm’s potential, heightened credit risk, and the firm
operating below potential.
We study this mechanism embedded into a neoclassical general equilibrium model, in
which firms are funded by a competitive banking sector. In this setting, the amplification and
internal persistence of financial disruptions carries over to aggregate financial shocks that hit
banks’ capacity to lend. Calibrated to U.S. data, we find that an exogenous financial shock
with a half-life of 4 quarters translates into an endogenous decline of output with a half-life
of 22 quarters. This discrepancy is caused entirely by the interaction between endogenous
uncertainty and financial frictions: when shutting down the former, the half-life of output
mimics the one of the exogenous financial shock. Besides offering an explanation for the
persistent decline in output and employment during financial crisis, the theory further explains
the contemporaneous rise in credit spreads and default rates, an increased cross-sectional
dispersion of firm sales, the increase in measured uncertainty, and high levels of disagreement
among forecasters.
In “Endogenous Second Moments: A Unified Approach to Fluctuations in Risk,
Dispersion, and Uncertainty” (Journal of Economic Theory, 2019), we study a related
question: namely, why several second moments—such as cross-sectional dispersion, risk,
volatility, or uncertainty—fluctuate (and co-move) over the business cycle. One possibility
would be exogenous shocks to these second moments. In our paper, we offer an alternative
interpretation, in which second moments fluctuate endogenously when the variables of interest
are concave (or convex) transformations of some (random) fundamental. Figure 1 illustrates
the mechanism for the simplest possible case where the fundamental X is exogenously “shifted
left”. For concave transforms g, this leads to an increase in variance of the transformed
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In this paper, uncertainty is about about firm-specific revenue-productivity. In line with the takeaway
from the paper above, this includes the idiosyncratic demand uncertainties that we have found to be key in
explaining aggregate fluctuations.
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g
fX1
fg(X1 )
fX2
fg(X2 )
Figure 1: Illustrating the mechanism in “Endogenous Second Moments: A Unified Approach to Fluctuations
in Risk, Dispersion, and Uncertainty”. The plot shows the pdf of a random variable X and its transform
g(X). For concave transforms g, a downward shift from X1 to X2 increases the variance of g(X).
variable g(X).
The methodological contribution of our paper is to provide precise conditions on the
nature of the fundamental shocks for which second moments are pro- or countercyclical
(going beyond the simple case of a translation). The applied contribution is to highlight one
important non-linearity that can explain the business cyclicality of several second moments,
casting doubt on the common practice of using them as evidence for second-moment shocks.
Specifically, we show that when the input-elasticity between capital and labor is strictly
below unity, log-employment and log-output become concave in a firm’s log-productivity,
while (under certain additional conditions) investment becomes convex. Empirically, this is
indeed the relevant case, with estimated input-elasticities ranging from 0.25 to 0.53.3 Hence,
first-moment shocks to the productivity distribution are very likely to endogenously induce
counter-cyclical fluctuations in the cross-sectional dispersion of employment and output, and
procyclical fluctuations in investment dispersion (all in line with the data). Moreover, if an
econometrician were to infer firms’ productivities using a Cobb-Douglas production function
(falsely assuming a unit elasticity), then the inferred productivity dispersion also becomes
counter-cyclical (even though the true productivity dispersion is constant). Calibrated to
U.S. data, these endogenous fluctuations account for a large share of the empirical cyclicalities,
suggesting caution when using the latter as evidence for second-moment shocks.
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These estimates are smaller than corresponding estimates for the input-elasticity at the macro level,
reflecting that unlike its counterpart at the macro level the micro-elasticity does not capture substitutions
across firms and sectors.
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Relatedly, in “Mismatch Cycles” (Journal of Political Economy, forthcoming), my
coauthors and I study the impact of another type of uncertainty on the business cyclicality of
mismatch between workers and jobs, and the consequences for labor productivity. I describe
the details of this paper below.
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The Macroeconomics of Job-to-Worker Allocation
In a second strand of my research, I study frictions that impede the allocation of workers to
jobs, and explore their implication for the individual worker and for the economy as a whole.
In “Mismatch Cycles” (Journal of Political Economy, forthcoming), my coauthors and
I study how well workers in the U.S. are matched to jobs in the sense of how well their specific
skills line up with the specific skill requirements of their jobs. In the data, we see a lot of
“mismatch” between these. Some of it might emerge ex-post, due to skills or requirements
changing after a match is formed. But that doesn’t explain the prevalence of mismatch
right upon match formation. One explanation is that matching is random, in which case
mismatch is just an intrinsic property of labor markets. Our work explores another possibility
where mismatch emerges endogenously due to skill uncertainty (the paper provides direct
survey-based evidence in support of this).
To do so, we develop a tractable framework, in which workers are characterized by
multidimensional skill bundles and learn how skilled they are for specific tasks as they use
these specific skills in production. As workers revise their beliefs, they sort into jobs that
differ in both the type of skills they require (a “career” choice) and how intensely they make
use of them (pinning down “job rungs”). We estimate the framework using a combination
of worker-level data from the NLSY79 and occupation-level descriptors of job requirements
(O*NET).
At the worker-level, skill learning (prolonged by search frictions) creates mismatch that is
highly inert and carries over to highly inert job rungs and earning dynamics. Quantitatively,
the inertia is strong enough to explain an unemployment scar of 19 percent five years after
displacement, fully accounting for its empirical counterpart.
At the macro-level, the dynamics of mismatch are shaped by two opposing forces. On
the one hand, there is a cleansing of matches that are mismatched during recessions. On
the other hand, workers that are cleansed from the bottom job rungs have a high propensity
to look for new careers, resulting in sullying during recessions. The relative magnitude of
these two opposing effects depends on the size of a recession (cleansing is relatively larger the
larger a recession) and whether it is broad or concentrated to only a few sectors (sullying
is relatively larger the more concentrated). Calibrated to the “average” recession, we find
that cleansing dominates, which we independently confirm in the data. Through the lens
of the model, this corresponds to an aggregate increase in labor productivity of 0.4 percent
during recessions (but with a very heterogeneous incidence in the cross-section, ranging from
an increase in labor productivity by 1.5 percent for workers that have been continuously
employed for two years to a decline by 1 percent for new hires).
In “The Colocation Problem: Dual-Earner Job Search and Labor Market
Efficiency” (in progress), my coauthor and I study the role of another friction for the
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job-to-worker allocation, namely the fact that dual-earner couples need two jobs in one
location. Previously, this has been conjectured to discourage workers to search for jobs
that entail migration, forgoing potential earning gains (Mincer 1978). Closely related, when
search isn’t discouraged, the same friction has been argued to generate trailing spouses and
contribute to gender inequality (Venator 2021). The goal of our work is to quantify the
impact of this “colocation friction” on job mobility, migration rates, gender inequality, and
social welfare.
To do so, we develop a dual-earner framework with directed search and multiple job
applications across commuting zones. At the core of our study is a (fictitious) benchmark, in
which spouses can correlate their individual matching outcomes through the use of “correlated
submarkets”. E.g., if spouses search in perfectly correlated submarkets, then this ensures
that if one of them gets a job offer in a certain commuting zone, their spouse does so as well,
effectively shutting down the colocation friction. Using this benchmark as a “measurement
device”, we identify job searches for which the colocation friction is binding as job searches
where spouses would prefer to correlate their matching outcomes if given the choice.
In a general theorem, we provide exact conditions when this is the case. Intuitively, the
friction is binding whenever the costs of one spouse becoming a single-earner outweighs the
“hedging gains” from uncorrelated search. We identify four forces that contribute to the
friction becoming binding: (i) both spouses being well-matched in their current location, (ii)
steep job ladders/high returns on job experience, which raise the cost of becoming a trailing
spouse, (iii) large migration costs, (iv) large declines in matching rates from “narrowing” the
job search to a single location.
The quantitative exploration (in progress) leverages a novel indirect inference method,
exploiting a “super block-recursive” structure of our model. Specifically, we show that the
solution to the system of Hamilton–Jacobi–Bellman equations in our model neither depends
on the cross-sectional distribution, nor does it depend on certain location-specific preference
shocks. This allows us to sidestep one of the computationally most expensive steps of
our estimation procedure by directly inverting the ergodic distribution of the model over
commuting zones and matching it to its empirical counterpart (backing out the necessary
distribution of location-specific preferences shocks along the way).
Methodologically, this strand of my research also includes “Optimal Delegated Search
with Adverse Selection and Moral Hazard” (Theoretical Economics, 2016). In this
work, I explore how to optimally delegate a stopping-problem à la McCall (1970) to an
agent (e.g., a recruiting agency) subject to two informational frictions: (i) “moral hazard”,
meaning that the agent’s search behavior is unobserved (or cannot be contracted upon); and
(ii) “adverse selection”, meaning in this case that the distribution of job candidates and
their skills itself is only known to the agent. In light of these frictions, the agent is naturally
inclined to search little and blame lackluster results on there only being bad job candidates
out there. I show that the second-best optimal way to address such gaming motives is to
offer the agent a menu of simple bonus contracts. Going beyond the literal application laid
out here, I argue that this can rationalize the ubiquitous use of bonus contracts in a variety
of situations that feature the dual-constraints of both moral hazard and adverse selection.
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Long-run Dynamics of Political Regimes
In “A Quantitative Theory of Political Transitions” (Review of Economic Studies,
2020), my coauthor and I document five empirical regularities about long-run regime dynamics,
and provide a quantitative framework of political transitions that accounts for these facts.
Specifically, the five regularities are:
1. The evolution of political systems is shaped by both revolts and democratic reforms,
with revolts being about three times as likely as reforms. Other modes of transition are
secondary.
2. Transition hazards are declining in regime maturity. Newly established regimes are
about three times as likely to be overthrown by a revolt and about six times as likely
to implement a democratic reform compared to regimes older than 10 years.
3. Transition hazards are inverse “J-shaped” in the inclusiveness of political systems:
political systems at the extremes of the autocracy–democracy spectrum have smaller
transition hazards than regimes near the center of the spectrum; full-scale democracies
are overall most stable.
4. Revolts establish autocratic regimes; reforms establish democracies. Political systems
near the center of the autocracy–democracy spectrum are unlikely to arise from either
mode of transition.
5. The distribution of regime types is bi-modal, with mass concentrated towards the
extremes of the autocracy–democracy spectrum.
We then develop a quantitative model of political transitions that accounts for all five
regularities. We demonstrate the quantitative potential by fitting the model to data on the
universe of political regimes existing between 1946 and 2010. The model matches the data
remarkably well. It is not only able to account for the above-listed regularities, but also
quantitatively replicates the shape of transition hazards, conditional outcome distributions,
and the stationary distribution of regime types (all untargeted).
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Dynamic Oligopoly Pricing
Finally, unrelated to the previous themes, “Dynamic Oligopoly Pricing: Evidence from
the Airline Industry” (International Journal of Industrial Organization, 2020) documents
regularities on the impact of competition on pricing dynamics. For this paper, my coauthor
and I implemented a webcrawler to collect data on the price path of airline ticket prices.
We use the data to shed light on how competition affects the scope of intertemporal price
differentiation.
We document three main findings. First, the rate at which prices increase towards the
scheduled departure date is significantly reduced in more competitive markets. Second, the
sensitivity of the intertemporal slope to competition increases in the heterogeneity of the
customer base. Third, ex-ante predictable advance purchase discounts account for 83 percent
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of within-flight dispersion in prices and for 17 percent of cross-market variation in pricing
dynamics.
While this is primarily an empirical paper, our findings hint towards intertemporal price
discrimination as a likely source of advance purchase discounts. Intuitively, monopoly airlines
discriminate against late booking customers with inelastic demand, but are restrained in
their ability to do so in more competitive environments. In line with this intuition, one would
expect that the intertemporal price gradient is particularly sensitive to competition when
there is a high potential to discriminate against late booking customers in the first place,
which is precisely what our findings regarding customer heterogeneity show.
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