Category: Data Source

The cost of regulatory compliance in the U.S.

by  Tyler Cowen in

We quantify firms’ compliance costs of regulation from 2002 to 2014 in terms of their labor input expenditure to comply with government rules, a primary component of regulatory compliance spending for large portions of the U.S. economy. Detailed establishment-level occupation data, in combination with occupation-specific task information, allow us to recover the share of an establishment’s wage bill owing to employees engaged in regulatory compliance. Regulatory costs account on average for 1.34 percent of the total wage bill of a firm, but vary substantially across and within industries, and have increased over time. We investigate the returns to scale in regulatory compliance and find an inverted-U shape, with the percentage regulatory spending peaking for an establishment size of around 500 employees. Finally, we develop an instrumental variable methodology for decoupling the role of regulatory requirements from that of enforcement in driving firms’ compliance costs.

That is from a new NBER working paper from Francesco Trebbi and Miao Ben Zhang.  Keep in mind those are the costs of compliance narrowly interpreted, not the costs of regulation overall.  And they do not consider the longer-term innovation costs from “having to turn the firm over to the lawyers.”

Child care sentences to ponder

by  Tyler Cowen in

There is to date little or no evidence of beneficial effects of longer parental leave (or fathers’ quotas) on maternal participation and earnings. In most cases longer leave delays mothers’ return to work, without long-lasting consequences on their careers. More generous childcare funding instead encourages female participation whenever subsidized childcare replaces maternal childcare.

That is from a new NBER working paper by Stefania Albanesi, Claudia Olivetti, and Barbara Petrongolo.  Drawn from data across 24 countries.

Detective Wanted

by  Alex Tabarrok in

Nat Friedman is seeking a full-time solo technical leader to go on a modern day Indiana Jones-style treasure hunt. You will be responsible for starting and running a crowdsourced effort to crack an archaeological puzzle of great historical significance. Success would be global news, could rewrite large chunks of history, and is guaranteed to be a story you will tell your grandchildren.

This is a full-time position for a 3-6 month period (which is about how long we think it will take to crack the puzzle, or at least to set it on a course to be solved). Pay range is $120-250k/yr. Think of this as an adventurous interlude between your more lucrative commercial gigs.

You will act as a mini-CTO, making appropriate technical decisions, staying responsive, and allocating time and resources effectively. This role will require highly effective communication, the ability to make complex code understandable, the ability to write clear technical documentation, the ability to foster and grow an online community, coupled with solid software engineering knowledge.

The ideal candidate will have experience in creating, managing, maintaining, and contributing to open source software projects. A background in working with custom software and data pipelines for scientific research is desirable. Comfort with PyTorch, C++, and OpenCV is a big plus.

More here.

What determines graduate admissions for economics?

by  Tyler Cowen in

We introduce a model of the admissions process based upon standard agency theory and explore its implications with economics PhD admissions data from 2013-2019. We show that a subjective score that aggregates subjective ratings and recommendation letter features plays a more important role in determining admissions than an objective score based upon graduate record exam (GRE) scores. Subjective evaluations by references who write multiple letters are not only more influential than those of references who write one letter, but they are also more informative. Since multiple-letter references are also more highly ranked economists, this implies that there is a constraint on the supply of high-quality references. Moreover, we find that both the subjective and objective scores are correlated with job placement at a top economics department after the completion of the PhD. These indicators of individual achievement have a smaller effect than an undergraduate degree from an Ivy Plus school (i.e., Ivy League + Stanford, MIT, Duke, and Chicago). In the self-selected pool of applicants, Ivy Plus graduates are twice as likely to be admitted to a top 10 graduate program and are much more likely to obtain an assistant professor position at a top 10 program upon PhD completion. Given that Ivy Plus students must pass a stringent selection process to gain admission to their undergraduate program, we cannot reject the hypothesis that admission committees use information efficiently and fairly. However, this also implies that there may be a return to attending a selective undergraduate program in order to be pooled with highly skilled individuals.

That is from a new paper by Jessica Bai, Matthew Esche, W. Bentley MacLeod & Yifan Shi.

Why businesses fail

by  Tyler Cowen in

This paper is about micro-enterprises in Brazil, by Priscila de Oliviera:

Micro firms in low and middle income countries often have low profitability and do not grow over time. Several business training programs have tried to improve management and business practices, with limited effects. We run a field experiment with micro-entrepreneurs in Brazil (N=742) to study the under-adoption of improved business practices, and shed light on the constraints and behavioral biases that may hinder their adoption. We randomly offer entrepreneurs reminders and micro-incentives of either 20 BRL (4 USD) or 40 BRL (8 USD) to implement record keeping or marketing for three consecutive months, following a business training program. Compared to traditional business training, reminders and micro-incentives significantly increase adoption of marketing (13.2 p.p.) and record keeping (19.2 p.p.), with positive effects on firm survival and investment over four months. Our findings, together with additional survey evidence, suggest that behavioral biases inhibit the adoption of improved practices, and are consistent with inattention as a key driver of under-adoption. In addition, our survey evidence on information avoidance points to it as a limiting factor to the adoption of record keeping, but not marketing activities. Taken together, the results suggest that behavioral biases affect firm decisions, with significant impact on firm survival.

She is currently on the job market from UC Berkeley.  There should be many more papers on this kind of topic!

A Big and Embarrassing Challenge to DSGE Models

by  Alex Tabarrok in

Dynamic stochastic general equilibrium (DSGE) models are the leading models in macroeconomics. The earlier DSGE models were Real Business Cycle models and they were criticized by Keynesian economists like Solow, Summers and Krugman because of their non-Keynesian assumptions and conclusions but as DSGE models incorporated more and more Keynesian elements this critique began to lose its bite and many young macroeconomists began to feel that the old guard just weren’t up to the new techniques. Critiques of the assumptions remain but the typical answer has been to change assumption and incorporate more realistic institutions into the model. Thus, most new work today is done using a variant of this type of model by macroeconomists of all political stripes and schools.

Now along comes two statisticians, Daniel J. McDonald and the acerbic Cosma Rohilla Shalizi. McDonald and Shalizi subject the now standard Smet-Wouters DSGE model to some very basic statistical tests. First, they simulate the model and then ask how well can the model predict its own simulation? That is, when we know the true model of the economy how well can the DSGE discover the true parameters? [The authors suggest such tests haven’t been done before but that doesn’t seem correct, e.g. Table 1 here. Updated, AT] Not well at all.

If we take our estimated model and simulate several centuries of data from it, all in the stationary regime, and then re-estimate the model from the simulation, the results are disturbing. Forecasting error remains dismal and shrinks very slowly with the size of the data. Much the same is true of parameter estimates, with the important exception that many of the parameter estimates seem to be stuck around values which differ from the ones used to generate the data. These ill-behaved parameters include not just shock variances and autocorrelations, but also the “deep” ones whose presence is supposed to distinguish a micro-founded DSGE from mere time-series analysis or reduced-form regressions. All this happens in simulations where the model specification is correct, where the parameters are constant, and where the estimation can make use of centuries of stationary data, far more than will ever be available for the actual macroeconomy.

Now that is bad enough but I suppose one might argue that this is telling us something important about the world. Maybe the model is fine, it’s just a sad fact that we can’t uncover the true parameters even when we know the true model. Maybe but it gets worse. Much worse.

McDonald and Shalizi then swap variables and feed the model wages as if it were output and consumption as if it were wages and so forth. Now this should surely distort the model completely and produce nonsense. Right?

If we randomly re-label the macroeconomic time series and feed them into the DSGE, the results are no more comforting. Much of the time we get a model which predicts the (permuted) data better than the model predicts the unpermuted data. Even if one disdains forecasting as end in itself, it is hard to see how this is at all compatible with a model capturing something — anything — essential about the structure of the economy. Perhaps even more disturbing, many of the parameters of the model are essentially unchanged under permutation, including “deep” parameters supposedly representing tastes, technologies and institutions.

Oh boy. Imagine if you were trying to predict the motion of the planets but you accidentally substituted the mass of Jupiter for Venus and discovered that your model predicted better than the one fed the correct data. I have nothing against these models in principle and I will be interested in what the macroeconomists have to say, as this isn’t my field, but I can’t see any reason why this should happen in a good model. Embarrassing.

Addendum: Note that the statistical failure of the DSGE models does not imply that the reduced-form, toy models that say Paul Krugman favors are any better than DSGE in terms of “forecasting” or “predictions”–the two classes of models simply don’t compete on that level–but it does imply that the greater “rigor” of the DSGE models isn’t buying us anything and the rigor may be impeding understanding–rigor mortis as we used to say.

Addendum 2: Note that I said challenge. It goes without saying but I will say it anyway, the authors could have made mistakes. It should be easy to test these strategies in other DSGE models.

Redistribution and credit card debt

by  Tyler Cowen in

Is Chase Sapphire a Pareto improvement, or does it also involve redistribution?  Here is a new paper on that topic:

We study credit card rewards as an ideal laboratory to quantify the cross-subsidy from naive to sophisticated consumers in retail financial markets. Using granular data on the near universe of credit card accounts in the United States, we find that sophisticated consumers profit from reward credit cards at the expense of naive consumers who lose money both in absolute terms and relative to classic cards. We estimate an aggregate annual cross-subsidy of $15.5 billion. Notably, our results are not driven by income—while sophisticated high-income consumers benefit the most, naive high-income consumers pay the most. Banks lure consumers into the use of reward cards by offering lower interest rates than on comparable classic cards and bank profits are highest for borrowers in the middle of the credit score distribution. We show that credit card rewards transfer wealth from less to more educated, from poorer to richer, from rural to urban, and from high to low minority areas, thereby widening existing spatial disparities.

The authors are Sumit Agarwal, Andrea Presbitero, André F. Silva, and Carlo Wix. I guess I am going to continue using my Sapphire card, are you?

Via Arpit Gupta.

Further evidence on role models

by  Tyler Cowen in

Leveraging the Tennessee STAR class size experiment, we show that Black students randomly assigned to at least one Black teacher in grades K–3 are 9 percentage points (13 percent) more likely to graduate from high school and 6 percentage points (19 percent) more likely to enroll in college compared to their Black schoolmates who are not. Black teachers have no significant long-run effects on White students. Postsecondary education results are driven by two-year colleges and  concentrated among disadvantaged males. North Carolina administrative data yield similar findings, and analyses of mechanisms suggest role model effects may be one potential channel.

That is from a new AER paper by Seth Gershenson, Cassandra M. D. Hart, Joshua Hyman, Constance A. Lindsay and Nicholas W. Papageorge, “The Long-Run Impacts of Same-Race Teachers.”  Here are various ungated versions.  Just to be clear, I don’t consider this a justification for any particular set of policies.  I do see it as extra reason for the successful to be visible and to work hard!

Do students choose majors rationally?

by  Tyler Cowen in

And if not, what kind of errors do they make?

Students appear to stereotype majors, greatly exaggerating the likelihood that they lead to their most distinctive jobs (e.g., counselor for psychology, journalist for journalism, teacher for education). A stylized model of major choice suggests that stereotyping boosts demand for “risky” majors: ones with rare stereotypical careers and low-paying alternative jobs…The same model predicts—and the survey data confirm—that students also overestimate rare non-stereotypical careers and careers that are concentrated within particular majors. The model also generates predictions regarding role model effects, with students exaggerating the frequency of career-major combinations held by people they are personally close to.

That is all from the job market paper of John Conlon, Business Economics at Harvard University.

Interrupting Janet Yellen

by  Tyler Cowen in

How prevalent is gender bias among U.S. politicians? We analyze the transcripts of every congressional hearing attended by the chair of the U.S. Federal Reserve from 2001 to 2020 to provide a carefully identified effect of sexism, using Janet Yellen as a bundled treatment. We find that legislators who interacted with both Yellen and at least one other male Fed chair over this period interrupt Yellen more, and interact with her using more aggressive tones. Furthermore, we show that the increase in hostility experienced by Yellen relative to her immediate predecessor and successor are absent among those legislators with daughters. Our results point to the important role of societal biases bleeding into seemingly unrelated policy domains, underscoring the vulnerability of democratic accountability and oversight mechanisms to existing gender norms and societal biases.

That is from a new paper by James Bisbee, Nicolò Fraccaroli, and Andreas Kern.  The recurring strength of the daughter effect remains under-discussed in the social sciences!

All via the excellent Kevin Lewis.

Long Social Distancing

by  Tyler Cowen in

From Jose Maria Barrero, Nicholas Bloom, and Steven J. Davis:

More than ten percent of Americans with recent work experience say they will continue social distancing after the COVID-19 pandemic ends, and another 45 percent will do so in limited ways. We uncover this Long Social Distancing phenomenon in our monthly Survey of Working Arrangements and Attitudes. It is more common among older persons, women, the less educated, those who earn less, and in occupations and industries that require many face-to-face encounters. People who intend to continue social distancing have lower labor force participation – unconditionally, and conditional on demographics and other controls. Regression models that relate outcomes to intentions imply that Long Social Distancing reduced participation by 2.5 percentage points in the first half of 2022. Separate self-assessed causal effects imply a reduction of 2.0 percentage points. The impact on the earnings-weighted participation rate is smaller at about 1.4 percentage points. This drag on participation reduces potential output by nearly one percent and shrinks the college wage premium. Economic reasoning and evidence suggest that Long Social Distancing and its effects will persist for many months or years.

That is a new NBER working paper.

Sadly swept from the headlines

by  Tyler Cowen in

Cost estimate, we hardly knew ye:

Joe Biden’s plan to cancel up to $20,000 in student loan debt for federal aid borrowers is expected to cost about $400 billion, according to the Congressional Budget Office...

About 95% of borrowers meet the criteria for forgiveness and about 45% of borrowers will have their balances completely wiped out, the CBO said.

Here is the full story.