Category Archives: Field Experiments

“What Determines Productivity,” C. Syverson (2011)

Chad Syverson, along with Nick Bloom, John van Reenen, Pete Klenow and many others, has been at the forefront of a really interesting new strand of the economics literature: persistent differences in productivity. Syverson looked at productivity differences within 4-digit SIC industries in the US (quite narrow industries like “Greeting Cards” or “Industrial Sealants”) a number of years back, and found that in the average industry, the 90-10 ratio of total factor productivity plants was almost 2. That is, the top decile plant in the average industry produced twice as much output as the bottom decline plant, using exactly the same inputs! Hsieh and Klenow did a similar exercise in China and India and found even starker productivity differences, largely due a big left-tail of very low productivity firms. This basic result is robust to different measures of productivity, and to different techniques for identifying differences; you can make assumptions which let you recover a Solow residual directly, or run a regression (adjusting for differences in labor and capital quality, or not), or look at deviations like firms having higher marginal productivity of labor than the wage rate, etc. In the paper discussed in the post, Syverson summarizes the theoretical and empirical literature on persistent productivity differences.

Why aren’t low productivity firms swept from the market? We know from theory that if entry is allowed, potentially infinite and instantaneous, then no firm can remain which is less productive than the entrants. This suggests that persistence of inefficient firms must result from either limits on entry, limits on expansion by efficient firms, or non-immediate efficiency because of learning-by-doing or similar (a famous study by Benkard of a Lockwood airplane showed that a plant could produce a plane with half the labor hours after producing 30, and half again after producing 100). Why don’t inefficient firms already in the market adopt best practices? This is related to the long literature on diffusion, which Syverson doesn’t cover in much detail, but essentially it is not obvious to a firm whether a “good” management practice at another firm is actually good or not. Everett Rogers, in his famous “Diffusion of Innovations” book, refers to a great example of this from Peru in the 1950s. A public health consultant was sent for two years to a small village, and tried to convince the locals to boil their water before drinking it. The water was terribly polluted and the health consequences of not boiling were incredible. After two years, only five percent of the town adopted the “innovation” of boiling. Some didn’t adopt because it was too hard, many didn’t adopt because of a local belief system that suggested only the already-sick ought drink boiled water, some didn’t adopt because they didn’t trust the experience of the advisor, et cetera. Diffusion is difficult.

Ok, so given that we have inefficient firms, what is the source of the inefficiency? It is difficult to decompose all of the effects. Learning-by-doing is absolutely relevant in many industries – we have plenty of evidence on this count. Nick Bloom and coauthors seem to suggest that management practices play a huge role. They have shown clear correlation between “best practice” management and high TFP across firms, and a recent randomized field experiment in India (discussed before on this site) showed massive impacts on productivity from management improvements. Regulation and labor/capital distortions also appear to play quite a big role. On this topic, James Schmitz wrote a very interesting paper, published in 2005 in the JPE, on iron ore producers. TFP in Great Lakes ore had been more or less constant for many decades, with very little entry or foreign competition until the 1980s. Once Brazil began exporting ore to the US, labor productivity doubled within a handful of years, and capital and total factor productivity also soared. A main driver of the change was more flexible workplace rules.

Final version in 2011 JEP (IDEAS version). Syverson was at Kellogg recently presenting a new paper of his, with an all-star cast of coauthors, on the medical market. It’s well worth reading. Medical productivity is similarly heterogeneous, and since the medical sector is coming up on 20% of GDP, the sources of inefficiency in medicine are particularly important!


“Inefficient Hiring in Entry-Level Labor Markets,” A. Pallais (2012)

It’s job market season again. I’m just back from a winter trip in Central Europe (though, being an economist, I skipped the castles and cathedrals, instead going to Schumpeter’s favorite Viennese hiking trail and von Neumann’s boyhood home in Budapest) and have a lot of papers to post about, but given that Pallais’ paper is from 2011’s job market, I should clear it off the docket. Her paper was, I thought, a clever use of a field experiment (and I freely admit by bias in favor of theoretically sound field experiments rather than laboratory exercises when considering empirical quantities).

Here’s the basic theoretical problem. There are a bunch of candidates for a job, some young and some old. The old workers have had their productivity revealed to some extent by their past job experience. For young workers, employers can only see a very noisy signal of their productivity. It involves a small cost to hire workers; they must be trained, etc. In equilibrium, firms will hire young workers who have expected productivity above the firm’s cost. Is this socially efficient? No, because of a simple information externality. The social planner would hire all young workers whose productivity plus the value of information revealed during their young tenure is above the firm’s cost. That is, private firms will not take into account that their hiring of a worker creates a positive externality from information that allows for better worker-firm matches in future periods. If yound workers could pay firms to work for them, then this might fix the problem to some extent, though in general such arrangements are not legal (though on this point, see my comment in the final paragraph). Perhaps this might explain the high levels of unemployment among the young, and the fact that absence from the labor market for young workers at the start of their career is particularly damaging?

How important is this? It’s tough in a lot of real world data to separate the benefits to workers of having their underlying revealed by early job experience from workers upgrading their skills during their first job. It is also tough to see the general equilibrium effects: if the government assists some young workers in getting hired, does this lead to less unemployment among young workers in future periods or do these assisted workers simply crowd out others that would have been hired in the absence of the intervention? Pallais uses an online job market similar to mechanical turk. Basically, on the site you can hire workers to perform small tasks like data entry. They request a wage and you can hire them or not. Previous hires are public, as are optional ratings and comments by the employers. Empirical data on past interventions is somewhat ambiguous.

Pallais hires a huge number of workers to do data entry. She randomly divides the applicants into three groups: those she doesn’t hire, those she hires and gives only minimal feedback, and those she hires and provides detailed comments. The task is ten hours of simple data entry with no training, so it’s tough to imagine anyone would infer the workers’ underlying human capital has improved. Other employers can see that Pallais has made a hire as soon as the contract begins, but the comments are added later; there is no effect on workers’ job offers until after the comments appear. And the effect appears substantial. Just being hired and getting a brief comment has a small impact on worker’s future wages and employment. A longer, positive comment has what looks like an enormous impact on the worker’s future employment and wages. Though the treatment does lower wages received by other people on data entry jobs by increasing the supply of certified workers, the overall increase in welfare from more hiring of young workers trumps the lower wages.

Interesting, but two comments. First, for some reason the draft of this paper I read seems to suggest some sort of idea that this sorting is good for workers, if only between the lines. But it needn’t be so! A simple model: all firms are identical, and have cost .4 of hiring a worker. Workers have skills drawn from a uniform [0,1] distribution. No signals are received in the first period. Therefore, all workers have expected skill .5, and all are hired at wage .1 (by the no profit condition in a competitive labor demand market). After the first hiring, skill level is completely revealed. Therefore, only 60% of workers are hired in the second period, at a wage equal to their skill minus .4. A policy that ex-ante would have revealed the skill of young workers would have decreased employment among young workers by 40 percent! Note that this would be the efficient outcome, so a social planner who cares about total welfare would still want to reveal the skill, even though the social planner who cares only about employment would not do so.

Second, to the extent that skill revelation is important, young workers with private information about their skills ought self-select. Those who believe themselves to be high type should choose jobs which frequently throw off public signals about their underlying quality (i.e., firms that promote good young folks quickly, industries like sales with easily verifiable output, etc.). Those who believe themselves low type should select into jobs without such signals. If everyone is rational and knows their own type, you can see some unraveling will happen here. What has the empirical career concerns literature learned about such selection?

November 2011 working paper (No IDEAS version). I see on her CV that this paper is currently R&Red at AER.

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