Category Archives: Industrial Organization

“Costly Search and Consideration Sets in Storable Goods Markets,” T. Pires (2015)

Terrible news arrived today in our small community of economists: the bright young Portuguese economist and an old friend from my Northwestern days, Tiago Pires, passed away suddenly over the weekend. Tiago is a structural IO economist at the University of North Carolina-Chapel Hill who has written on demand estimation particularly in the face of search costs. Everyone who has met him can tell you that he was always in good spirits, and that he has been a true friend and useful sounding board for many of us. Friends tell me that Tiago had been making the rounds at the industrial organization conference IIOC just this week, and seemed to be in perfect health. To honor Tiago, let’s discuss Tiago’s job market paper from a couple years ago.

The basic idea, which runs through much of Tiago’s work, is that properly evaluating demand for products, and hence the effects of mergers or other IO policies, depends fundamentally on costly search. The basic idea is not new – it can be seen as far back as the great George Stigler’s 1961 paper on the economics of information – but the implications are still not fully drawn out.

Consider shopping for laundry detergent. Rare is the shopper who, like Honey Boo Boo’s family, searches for coupons and compares relative prices every week. Rather, most weeks you likely just show up at your usual store, perhaps glancing at the price of your usual detergent as you pass the aisle; there could be a great sale on some other detergent, but you’d never know it. As you start to run low on detergent at home, you’re more likely to actually stroll down the whole detergent aisle, perhaps checking the price of few more options. On occasion, the detergent makers sponsor an ad or a promotion at the end of the aisle, and you learn the price of that particular product cheaply. If the price is good and you know the price, you might buy some detergent, though not too much since the cost of searching in the future must be traded off against the cost of storing a bunch of detergent in your closet.

Tiago models shoppers who proceed exactly in that fashion: on the basis of how much detergent you have left, you search a handful of detergent prices, and you buy if the price is right. When you are almost out of detergent, you might search a bunch of prices. When you have quite a bit of detergent, you rationally only buy if you happen to see your usual favorite on sale. The data match the basics of the model: in particular, you are more likely to buy your “usual” brand when you have a lot of detergent left than when you are almost out, since it’s not worth bothering to search prices in the former case. This combination of costly search plus changing household “inventory” means that standard static analysis gives a very misleading portrait of what consumers do. First, elasticity estimates will be screwed up: if rivals shift their price up and down, and I don’t even notice the changes, you may think my demand is very inelastic, but really it’s just that I am not searching. Second, price promotions in conjunction with ads that lower search costs aren’t actually that useful for revenue or profit: the shopper would have eventually checked prices when their detergent stock ran low, and the ad just causes them to check prices early and buy if there is a good enough sale, stealing sales away from future shopping trips. Third, popular brands should do what they can to keep consumers from running low in their stock, such as making it obvious via packaging how much detergent is left, or trying to sell bigger packages. The reason is that only the consumer who is low on stock will bother to search the prices of competitors.

Tiago has used search costs is a number of other papers. With Guillermo Marshall, he studied how stores trade off convenience (being located near consumers, roughly) with quality (being a nice supermarket rather than a convenience store, roughly): as travel costs increase because of traffic or bad weather, you see more stores invest in increasing convenience rather than quality, in surprisingly big economic magnitudes. Terrible convenience stores in the ‘hood are partially driven by market frictions due to high transportation costs, not just differences in products demanded or in income! With Fernando Luco and Mahraz Parsanasab, he studies how the Internet has affected the film industry by changing both search costs for learning about what movies might be worth seeing, as well as changing the market structure of the film industry via Netflix, piracy and similar. Looking across countries, internet access improves film industry revenue and decreases market concentration as the internet becomes common, but broadband access has no such revenue effect, and actually makes market concentration worse as it becomes common. Here’s to Tiago’s memory, and to the continued study of markets using our most powerful tool: theoretically-sound models of structural choice combined with data about the operation of real markets!

The costly search paper described above can be found in its most recent working paper version here: November 2015 working paper (No RePEc IDEAS version).

“Firm Dynamics, Persistent Effects of Entry Conditions, and Business Cycles,” S. Moreira (2016)

Business cycle fluctuations have long run effects on a number of economic variables. For instance, if you enter the labor force during a recession, your wages are harmed for many years afterward. Many other economic parameters revert to trend, leaving a past recession just a blip on the horizon. Sara Moreira, a job candidate from the University of Chicago, investigates in her job market paper whether entrepreneurship changes induced by recessions persist in the long run.

New firm formation is procyclical: entrepreneurship fell roughly 20 percent during the recent recession. Looking back at the universe of private firms since the late 1970s, Moreira shows that this procyclicality is common, and that the firms that do form during recessions tend to be smaller than those which form during booms. Incredibly, this size gap persists for at least a decade after the firms are founded! At first glance, this is crazy: if my firm is founded during the 2001 recession, surely any effects from my founding days should have worn off after a decade of introducing new products, hiring new staff, finding new funding sources, etc. And yet Moreira finds this effect no matter how you slice the data, using overall recessions, industry-specific shocks, shocks based on tradable versus nontradable commodities, and so on, and it remains even when accounting for the autocorrelation of the business cycle. The effect is not small: the average firm born during a year with above trend growth is roughly 2 percent larger 10 years later than the average firm born during below trend growth years.

This gap is double surprising if you think about how firms are founded. Imagine we are in middle of a recession, and I am thinking of forming a new construction company. Bank loans are probably tough to get, I am unlikely to be flush with cash to start a new spinoff, I may worry about running out of liquidity before demand picks up, and so on. Because of these negative effects, you might reasonably believe that only very high quality ideas will lead to new firms during recessions, and hence the average firms born during recessions will be the very high quality, fast growing, firms of the future, whereas the average firms born during booms will be dry cleaners and sole proprietorships and local restaurants. And indeed this is the case! Moreira finds that firms born during recessions have high productivity, are more likely to be in high innovation sectors, and and less likely to be (low-productivity) sole proprietorships. We have a real mystery, then: how can firms born during a recession both be high quality and find it tough to grow?

Moreira considers two stories. It may be that adjustment costs matter, and firms born small because the environment is recessionary find it too costly to ramp up in size when the economy improves. Moreira finds no support for this idea: capital-intensive industries show the same patterns as industries using little capital.

Alternatively, customers need to be acquired, and this acquisition process may generate persistence in firm size. Naturally, firms start small because it takes time to teach people about products and for demand to grow: a restaurant chain does not introduce 1000 restaurants in one go. If you start really small because of difficulty in getting funded, low demand, or any other reason, then in year 2 you have fewer existing customers and less knowledge about what consumers want. This causes you to grow slower in year 2, and hence in year 3, you remain smaller than firms that initially were large, and the effect persists every year thereafter. Moreira finds support for this effect: among other checks, industries whose products are more differentiated are the ones most likely to see persistence of size differences.

Taking this intuition to a Hopenhayn-style calibrated model, the data tells us the following. First, it is not guaranteed that recessions lead to smaller firms initially, since the selection of only high productivity ideas into entrepreneurship during recessions, and the problem of low demand, operate in opposite directions, but empirically the latter seems to dominate. Second, if the productivity distribution of new firms were identical during booms and recessions, the initial size difference between firms born during booms and recessions would be double what we actually observe, so the selection story does in fact moderate the effect of the business cycle on new firm size. Third, the average size gap does not close even though the effect of the initial demand shock, hence fewer customers in the first couple years and slower growth thereafter, begins to fade as many years go by. The reason is that idiosyncratic productivity is mean reverting, so the average (relatively low quality at birth) firm born during booms that doesn’t go out of business becomes more like an average overall firm, and the average (relatively high productivity at birth) firm born during recessions sees its relative productivity get worse. Therefore, the advantage recession-born firms get from being born with high quality firms fades, countering the fading harm of the size of these firms from the persistent demand channel. Fourth, the fact that high productivity firms born during recessions grow slowly due to the historic persistence of customer acquisition means that temporary recessions will still affect the job market many years later: the Great Recession, in Moreira’s calibration, will a decade later still be chewing up 600,000 jobs that firms from the 2008-2009 cohort would have employed. Really enjoyed this paper: it’s a great combination of forensic digging through the data, as well as theoretically well-founded rationalization of the patterns observed.

January 2016 working paper. Moreira also has interesting slides showing how to link the skilled wage premium to underlying industry-level elasticities in skilled and unskilled labor. She notes that as services become more important, where labor substitutability is more difficult, the effect of technological change on the wage premium will become more severe.

“Estimating Equilibrium in Health Insurance Exchanges,” P. Tebaldi (2016)

After a great visit to San Francisco for AEA and a couple weeks reading hundreds of papers while hiding out in the middle of the Pacific, it’s time to take a look at some of the more interesting job market papers this year. Though my home department isn’t directly hiring, I’m going to avoid commenting on work by candidates being flown out to Toronto in general, though a number of those are fantastic as well. I also now have a fourth year of data on job market “stars” which I’ll present in the next week or so.

Let’s start with a great structural IO paper by Pietro Tebaldi from Stanford. The Affordable Care Act in the United States essentially set up a version of universal health care that relies on subsidizing low income buyers, limiting prices via price caps and age rating limits (the elderly can only be charged a certain multiple of what the young are charged), and providing a centralized comparison system (“Bronze” or “Silver” or whatever plans essentially cover the same medical care, with only the provider and hospital bundle differing). The fundamental fact about American health care is less that it is a non-universal, privately-provided system than that it is enormously expensive: the US government, and this is almost impossible to believe until you look at the numbers, spends roughly the same percentage of GDP on health care as Canada, even though coverage is universal up north. Further, there is quite a bit of market power both on the insurer side, with a handful of insurers in any given market, and on the hospital side. Generally, there are only a handful of hospitals in any region, with the legacy of HMOs making many insurers very reluctant to drop hospitals from their network since customers will complain about losing “their” doctor. Because of these facts, a first-order concern for designing a government health care expansion must be controlling costs.

Tebaldi points out theoretically that the current ACA design inadvertently leads to high insurer markups. In nearly all oligopoly models, markup over costs depends on the price elasticity of demand: if buyers have inelastic demand in general, markups are high. In health care, intuitively young buyers are more price sensitive and have lower expected costs than older buyers; many young folks just won’t go to the doctor if it is too pricey to do so, and young folks are less likely to have a long-time doctor they require in their insurance network. Age rating which limits the maximum price difference between young and old buyers means that anything that leads to lower prices for the young will also lead to lower prices for the old. Hence, the more young people you get in the insurance pool, the lower the markups are, and hence the lower the cost becomes for everyone. The usual explanation for why you need young buyers is that they subsidize the old, high-cost buyers; the rationale here is that young buyers help even other young buyers by making aggregate demand more elastic and hence dragging down oligopoly insurer prices.

How can you get more young buyers in the pool while retaining their elastic demand? Give young buyers a fixed amount subsidy voucher that is bigger than what you give older buyers. Because buyers have low expected costs, they will only buy insurance if it cheap, hence will enter the insurance market if you subsidize them a lot. Once they enter, however, they remain very price sensitive. With lots of young folks as potential buyers, insurers will lower their prices for young buyers in order to attract them, which due to age rating also lowers prices for older buyers. It turns out that the government could lower the subsidy given to older buyers, so that total government subsidies fall, and yet out of pocket spending for the old would still be lower due to the price drop induced by the highly elastic young buyers entering the market.

Now that’s simply a theoretical result. Tebaldi also estimates what would happen in practice, using California data. Different regions of California have different age distributions. you can immediately see that prices are higher for young folks in regions where there are a lot of potential old buyers, and lower in regions with a fewer potential old buyers, for exactly the elasticity difference plus age rating reason given above. These regional differences permit identification of the demand curve, using age-income composition to instrument for price. The marginal costs of insurance companies are tougher to identify, but the essential idea just uses optimal pricing conditions as in Berry, Levinsohn and Pakes. The exact identification conditions are not at all straightforward in selection markets like insurance, since insurer marginal costs depend on who exactly their buyers are in addition to characteristics of the bundle of services they offer. The essential trick is that since insurers are pricing to set marginal revenue equal to marginal cost, the demand curve already estimated tells us whether most marginal customers are old or young in a given region, and hence we can back out what costs may be on the basis of pricing decisions across regions.

After estimating marginal costs and demand curves for insurance, Tebaldi can run a bunch of counterfactuals motivated by the theory discussed above. Replacing price-linked subsidies, where buyers get a subsidy linked to the second-lowest priced plan in their area, with vouchers, where buyers get a fixed voucher regardless of the prices set, essentially makes insurers act as if buyers are more price-elastic: raising insurance prices under price-linked subsidies will also raise the amount of the subsidy, and hence the price increase is not passed 1-for-1 to buyers. Tebaldi estimates insurance prices would fall $200 on average if the current price-linked subsidies were replaced with vouchers of an equivalent size. Since young buyers have elastic demand, coverage of young buyers increases as a result. The $200 fall in prices, then, results first from insurers realizing that all buyers are more sensitive to price changes when they hold vouchers rather than pay a semi-fixed amount determined by a subsidy, and second from the composition of buyers therefore including more elastic young buyers, lowering the optimal insurer markup. The harm, of course, is that vouchers do not guarantee year-to-year that subsidized buyers pay no more than a capped amount, since in general the government does not know every insurer’s cost curve and every buyer’s preferences.

Better still is to make vouchers depend on age. If young buyers get big vouchers, even more of them will buy insurance. This will drag down the price set by insurers since aggregate elasticity increases, and hence old buyers may be better off as well even if they see a reduction in their government subsidy. Tebaldi estimates that a $400 increase in subsidies for those under 45, and a $200 decrease for those over 45, will lead to 50% more young folks buying insurance, a 20% decrease in insurer markup, a post-subsidy price for those over 45 that is unchanged since their lower subsidy is made up for by lower insurer prices, and a 15% decrease in government spending since decreased subsidies for the old more than make up for increased subsidies for the young. There is such a thing as a free lunch!

Now, in practice, governments do not make changes around the margins like this. Certain aspects of the subsidy program are, crazily, set by law and not by bureaucrats. Note how political appearance and equilibrium effect differ in Tebaldi’s estimates: we decrease subsidies for the old and yet everyone including the old is better off due to indirect effects. Politicians, it goes without saying, do not win elections on the basis of indirect effects. A shame!

January 2016 working paper. The paper is quite concisely written, which I appreciate in our era of 80-page behemoths. If you are still reluctant to believe in the importance of insurer market power, Tebaldi and coauthors also have a paper in last year’s AER P&P showing in a really clean way the huge price differences in locations that have limited insurer competition. On the macro side, Tebaldi and network-extraordinaire Matt Jackson about deep recessions making a simple point. In labor search models, the better the boom times, the lower productivity workers you will settle for hiring. Thus, when negative economic shocks follow long expansions, they will lead to more unemployment, simply because there will be more relatively low productivity workers at every firm. Believable history-dependence in macro models is always a challenge, but this theory makes perfect sense.

Angus Deaton, 2015 Nobel Winner: A Prize for Structural Analysis?

Angus Deaton, the Scottish-born, Cambridge-trained Princeton economist, best known for his careful work on measuring the changes in wellbeing of the world’s poor, has won the 2015 Nobel Prize in economics. His data collection is fairly easy to understand, so I will leave larger discussion of exactly what he has found to the general news media; Deaton’s book “The Great Escape” provides a very nice summary of what he has found as well, and I think a fair reading of his development preferences are that he much prefers the currently en vogue idea of just giving cash to the poor and letting them spend it as they wish.

Essentially, when one carefully measures consumption, health, or generic characteristics of wellbeing, there has been tremendous improvement indeed in the state of the world’s poor. National statistics do not measure these ideas well, because developing countries do not tend to track data at the level of the individual. Indeed, even in the United States, we have only recently begun work on localized measures of the price level and hence the poverty rate. Deaton claims, as in his 2010 AEA Presidential Address (previously discussed briefly on two occasions on AFT), that many of the measures of global inequality and poverty used by the press are fundamentally flawed, largely because of the weak theoretical justification for how they link prices across regions and countries. Careful non-aggregate measures of consumption, health, and wellbeing, like those generated by Deaton, Tony Atkinson, Alwyn Young, Thomas Piketty and Emmanuel Saez, are essential for understanding how human welfare has changed over time and space, and is a deserving rationale for a Nobel.

The surprising thing about Deaton, however, is that despite his great data-collection work and his interest in development, he is famously hostile to the “randomista” trend which proposes that randomized control trials (RCT) or other suitable tools for internally valid causal inference are the best way of learning how to improve the lives of the world’s poor. This mode is most closely associated with the enormously influential J-PAL lab at MIT, and there is no field in economics where you are less likely to see traditional price theoretic ideas than modern studies of development. Deaton is very clear on his opinion: “Randomized controlled trials cannot automatically trump other evidence, they do not occupy any special place in some hierarchy of evidence, nor does it make sense to refer to them as “hard” while other methods are “soft”… [T]he analysis of projects needs to be refocused towards the investigation of potentially generalizable mechanisms that explain why and in what contexts projects can be expected to work.” I would argue that Deaton’s work is much closer to more traditional economic studies of development than to RCTs.

To understand this point of view, we need to go back to Deaton’s earliest work. Among Deaton’s most famous early papers was his well-known development of the Almost Ideal Demand System (AIDS) in 1980 with Muellbauer, a paper chosen as one of the 20 best published in the first 100 years of the AER. It has long been known that individual demand equations which come from utility maximization must satisfy certain properties. For example, a rational consumer’s demand for food should not depend on whether the consumer’s equivalent real salary is paid in American or Canadian dollars. These restrictions turn out to be useful in that if you want to know how demand for various products depend on changes in income, among many other questions, the restrictions of utility theory simplify estimation greatly by reducing the number of free parameters. The problem is in specifying a form for aggregate demand, such as how demand for cars depends on the incomes of all consumers and prices of other goods. It turns out that, in general, aggregate demand generated by utility-maximizing households does not satisfy the same restrictions as individual demand; you can’t simply assume that there is a “representative consumer” with some utility function and demand function equal to each individual agent. What form should we write for aggregate demand, and how congruent is that form with economic theory? Surely an important question if we want to estimate how a shift in taxes on some commodity, or a policy of giving some agricultural input to some farmers, is going to affect demand for output, its price, and hence welfare!

Let q(j)=D(p,c,e) say that the quantity of j consumed, in aggregate is a function of the price of all goods p and the total consumption (or average consumption) c, plus perhaps some random error e. This can be tough to estimate: if D(p,c,e)=Ap+e, where demand is just a linear function of relative prices, then we have a k-by-k matrix to estimate, where k is the number of goods. Worse, that demand function is also imposing an enormous restriction on what individual demand functions, and hence utility functions, look like, in a way that theory does not necessarily support. The AIDS of Deaton and Muellbauer combine the fact that Taylor expansions approximately linearize nonlinear functions and that individual demand can be aggregated even when heterogeneous across individuals if the restrictions of Muellbauer’s PIGLOG papers are satisfied to show a functional form for aggregate demand D which is consistent with aggregated individual rational behavior and which can sometimes be estimated via OLS. They use British data to argue that aggregate demand violates testable assumptions of the model and hence factors like credit constraints or price expectations are fundamental in explaining aggregate consumption.

This exercise brings up a number of first-order questions for a development economist. First, it shows clearly the problem with estimating aggregate demand as a purely linear function of prices and income, as if society were a single consumer. Second, it gives the importance of how we measure the overall price level in figuring out the effects of taxes and other policies. Third, it combines theory and data to convincingly suggest that models which estimate demand solely as a function of current prices and current income are necessarily going to give misleading results, even when demand is allowed to take on very general forms as in the AIDS model. A huge body of research since 1980 has investigated how we can better model demand in order to credibly evaluate demand-affecting policy. All of this is very different from how a certain strand of development economist today might investigate something like a subsidy. Rather than taking obversational data, these economists might look for a random or quasirandom experiment where such a subsidy was introduced, and estimate the “effect” of that subsidy directly on some quantity of interest, without concern for how exactly that subsidy generated the effect.

To see the difference between randomization and more structural approaches like AIDS, consider the following example from Deaton. You are asked to evaluate whether China should invest more in building railway stations if they wish to reduce poverty. Many economists trained in a manner influenced by the randomization movement would say, well, we can’t just regress the existence of a railway on a measure of city-by-city poverty. The existence of a railway station depends on both things we can control for (the population of a given city) and things we can’t control for (subjective belief that a town is “growing” when the railway is plopped there). Let’s find something that is correlated with rail station building but uncorrelated with the random component of how rail station building affects poverty: for instance, a city may lie on a geographically-accepted path between two large cities. If certain assumptions hold, it turns out that a two-stage “instrumental variable” approach can use that “quasi-experiment” to generate the LATE, or local average treatment effect. This effect is the average benefit of a railway station on poverty reduction, at the local margin of cities which are just induced by the instrument to build a railway station. Similar techniques, like difference-in-difference and randomized control trials, under slightly different assumptions can generate credible LATEs. In development work today, it is very common to see a paper where large portions are devoted to showing that the assumptions (often untestable) of a given causal inference model are likely to hold in a given setting, then finally claiming that the treatment effect of X on Y is Z. That LATEs can be identified outside of a purely randomized contexts is incredibly important and valuable, and the economists and statisticians who did the heavy statistical lifting on this so-called Rubin model will absolutely and justly win an Economics Nobel sometime soon.

However, this use of instrumental variables would surely seem strange to the old Cowles Commission folks: Deaton is correct that “econometric analysis has changed its focus over the years, away from the analysis of models derived from theory towards much looser specifications that are statistical representations of program evaluation. With this shift, instrumental variables have moved from being solutions to a well-defined problem of inference to being devices that induce quasi-randomization.” The traditional use of instrumental variables was that after writing down a theoretically justified model of behavior or aggregates, certain parameters – not treatment effects, but parameters of a model – are not identified. For instance, price and quantity transacted are determined by the intersection of aggregate supply and aggregate demand. Knowing, say, that price and quantity was (a,b) today, and is (c,d) tomorrow, does not let me figure out the shape of either the supply or demand curve. If price and quantity both rise, it may be that demand alone has increased pushing the demand curve to the right, or that demand has increased while the supply curve has also shifted to the right a small amount, or many other outcomes. An instrument that increases supply without changing demand, or vice versa, can be used to “identify” the supply and demand curves: an exogenous change in the price of oil will affect the price of gasoline without much of an effect on the demand curve, and hence we can examine price and quantity transacted before and after the oil supply shock to find the slope of supply and demand.

Note the difference between the supply and demand equation and the treatment effects use of instrumental variables. In the former case, we have a well-specified system of supply and demand, based on economic theory. Once the supply and demand curves are estimated, we can then perform all sorts of counterfactual and welfare analysis. In the latter case, we generate a treatment effect (really, a LATE), but we do not really know why we got the treatment effect we got. Are rail stations useful because they reduce price variance across cities, because they allow for increasing returns to scale in industry to be utilized, or some other reason? Once we know the “why”, we can ask questions like, is there a cheaper way to generate the same benefit? Is heterogeneity in the benefit important? Ought I expect the results from my quasiexperiment in place A and time B to still operate in place C and time D (a famous example being the drug Opren, which was very successful in RCTs but turned out to be particularly deadly when used widely by the elderly)? Worse, the whole idea of LATE is backwards. We traditionally choose a parameter of interest, which may or may not be a treatment effect, and then choose an estimation technique that can credible estimate that parameter. Quasirandom techniques instead start by specifying the estimation technique and then hunt for a quasirandom setting, or randomize appropriately by “dosing” some subjects and not others, in order to fit the assumptions necessary to generate a LATE. If is often the case that even policymakers do not care principally about the LATE, but rather they care about some measure of welfare impact which rarely is immediately interpretable even if the LATE is credibly known!

Given these problems, why are random and quasirandom techniques so heavily endorsed by the dominant branch of development? Again, let’s turn to Deaton: “There has also been frustration with the World Bank’s apparent failure to learn from its own projects, and its inability to provide a convincing argument that its past activities have enhanced economic growth and poverty reduction. Past development practice is seen as a succession of fads, with one supposed magic bullet replacing another—from planning to infrastructure to human capital to structural adjustment to health and social capital to the environment and back to infrastructure—a process that seems not to be guided by progressive learning.” This is to say, the conditions necessary to estimate theoretical models are so stringent that development economists have been writing noncredible models, estimating them, generating some fad of programs that is used in development for a few years until it turns out not to be silver bullet, then abandoning the fad for some new technique. Better, the randomistas argue, to forget about external validity for now, and instead just evaluate the LATEs on a program-by-program basis, iterating what types of programs we evaluate until we have a suitable list of interventions that we feel confident work. That is, development should operate like medicine.

We have something of an impasse here. Everyone agrees that on many questions theory is ambiguous in the absence of particular types of data, hence more and better data collection is important. Everyone agrees that many parameters of interest for policymaking require certain assumptions, some more justifiable than others. Deaton’s position is that the parameters of interest to economists by and large are not LATEs, and cannot be generated in a straightforward way from LATEs. Thus, following Nancy Cartwright’s delightful phrasing, if we are to “use” causes rather than just “hunt” for what they are, we have no choice but to specify the minimal economic model which is able to generate the parameters we care about from the data. Glen Weyl’s attempt to rehabilitate price theory and Raj Chetty’s sufficient statistics approach are both attempts to combine the credibility of random and quasirandom inference with the benefits of external validity and counterfactual analysis that model-based structural designs permit.

One way to read Deaton’s prize, then, is as an award for the idea that effective development requires theory if we even hope to compare welfare across space and time or to understand why policies like infrastructure improvements matter for welfare and hence whether their beneficial effects will remain when moved to a new context. It is a prize which argues against the idea that all theory does is propose hypotheses. For Deaton, going all the way back to his work with AIDS, theory serves three roles: proposing hypotheses, suggesting which data is worthwhile to collect, and permitting inference on the basis of that data. A secondary implication, very clear in Deaton’s writing, is that even though the “great escape” from poverty and want is real and continuing, that escape is almost entirely driven by effects which are unrelated to aid and which are uninfluenced by the type of small bore, partial equilibrium policies for which randomization is generally suitable. And, indeed, the best development economists very much understand this point. The problem is that the media, and less technically capable young economists, still hold the mistaken belief that they can infer everything they want to infer about “what works” solely using the “scientific” methods of random- and quasirandomization. For Deaton, results that are easy to understand and communicate, like the “dollar-a-day” poverty standard or an average treatment effect, are less virtuous than results which carefully situate numbers in the role most amenable to answering an exact policy question.

Let me leave you three side notes and some links to Deaton’s work. First, I can’t help but laugh at Deaton’s description of his early career in one of his famous “Notes from America”. Deaton, despite being a student of the 1984 Nobel laureate Richard Stone, graduated from Cambridge essentially unaware of how one ought publish in the big “American” journals like Econometrica and the AER. Cambridge had gone from being the absolute center of economic thought to something of a disconnected backwater, and Deaton, despite writing a paper that would win a prize as one of the best papers in Econometrica published in the late 1970s, had essentially no understanding of the norms of publishing in such a journal! When the history of modern economics is written, the rise of a handful of European programs and their role in reintegrating economics on both sides of the Atlantic will be fundamental. Second, Deaton’s prize should be seen as something of a callback to the ’84 prize to Stone and ’77 prize to Meade, two of the least known Nobel laureates. I don’t think it is an exaggeration to say that the majority of new PhDs from even the very best programs will have no idea who those two men are, or what they did. But as Deaton mentions, Stone in particular was one of the early “structural modelers” in that he was interested in estimating the so-called “deep” or behavioral parameters of economic models in a way that is absolutely universal today, as well as being a pioneer in the creation and collection of novel economic statistics whose value was proposed on the basis of economic theory. Quite a modern research program! Third, of the 19 papers in the AER “Top 20 of all time” whose authors were alive during the era of the economics Nobel, 14 have had at least one author win the prize. Should this be a cause for hope for the living outliers, Anne Krueger, Harold Demsetz, Stephen Ross, John Harris, Michael Todaro and Dale Jorgensen?

For those interested in Deaton’s work beyond what this short essay, his methodological essay, quoted often in this post, is here. The Nobel Prize technical summary, always a great and well-written read, can be found here.

“Buying Locally,” G. J. Mailath, A. Postlewaite & L. Samuelson (2015)

Arrangements where agents commit to buy only from selected vendors, even when there are more preferred products at better prices from other vendors, are common. Consider local currencies like “Ithaca Hours”, which can only be used at other participating stores and which are not generally convertible, or trading circles among co-ethnics even when trust or unobserved product quality is not important. The intuition people have for “buying locally” is to, in some sense, “keep the profits in the community”; that is, even if you don’t care at all about friendly local service or some other utility-enhancing aspect of the local store, you should still patronize it. The fruit vendor, should buy from the local bookstore even when her selection is subpar, and the book vendor should in turn patronize you even when fruits are cheaper at the supermarket.

At first blush, this seems odd to an economist. Why would people voluntarily buy something they don’t prefer? What Mailath and his coauthors show is that, actually, the noneconomist intuition is at least partially correct when individuals are both sellers and buyers. Here’s the idea. Let there be a local fruit vendor, a supermarket, a local bookstore and a chain bookstore. Since the two markets are not perfectly competitive, firms earn a positive rent with each sale. Assume that, tomorrow, the fruit vendor, the local book merchant, and each of the chain managers draw a random preference. Each food seller is equally likely to need a book sold by either the local or chain store, and likewise each bookstore employee is equally likely to need a piece of fruit sold either by the local vendor or the supermarket; you might think of these preferences as reflecting prices, or geographical distance, or product variety, etc. In equilibrium, prices of each book and each fruit are set equally, and each vendor expects to accrue half the sales.

Now imagine that the local bookstore owner and fruit vendor commit in advance not to patronize the other stores, regardless of which preference is drawn tomorrow. Assume for now that they also commit not to raise prices because of this agreement (this assumption will not be important, it turns out). Now the local stores expect to make 3/4 of all sales, since they still get the purchases of the chain managers with probability .5. Since the markup does not change, and there is a constant profit on each sale, then profits improve. And here is the sustainability part: as long as the harm from buying the “wrong product” is not too large, the benefit for the vendor-as-producer of selling more products exceeds the harm to the vendor-as-consumer of buying a less-than-optimal product.

That tradeoff can be made explicit, but the implication is quite general: as the number of firms you can buy at grows large, the benefit to belonging to a buy local arrangement falls. The harm of having to buy from a local producer is big because it is very unlikely the local producer is your first choice, and the price firms set in equilibrium falls because competition is stronger, hence there is less to gain for the vendor-as-producer from belonging to the buy local agreement. You will only see “buy local” style arrangements, like Ithaca Hours, or social shaming, in communities where vendors-as-consumers already purchase most of what they want from vendors-as-producers in the same potential buy local group.

One thing that isn’t explicit in the paper, perhaps because it is too trivial despite its importance, is how buy local arrangements affect welfare. Two possibilities exist. First, if in-group and out-of-group sellers have the same production costs, then “buy local” arrangements simply replace the producer surplus of out-of-group sellers with deadweight loss and some, perhaps minor, surplus for in group members. They are privately beneficial yet socially harmful. However, an intriguing possibility is that “buy local” arrangements may not harm social welfare at all, even if they are beneficial to in-group members. How is that? In-group members are pricing above marginal cost due to market power. A “buy local” agreement increases the quantity of sales they make. If the in-group member has lower costs than out of group members, the total surplus generated by shifting transactions to the in-group seller may be positive, even though there is some deadweight loss created when consumers do not buy their first choice good (in particular, this is true whenever the average willingness-to-pay differential for people who switch to the in-group seller once the buy local group is formed exceeds the average marginal cost differential between in-group and out-of-group sellers.)

May 2015 working paper (RePEc IDEAS version)

“Bonus Culture: Competitive Pay, Screening and Multitasking,” R. Benabou & J. Tirole (2014)

Empirically, bonus pay as a component of overall renumeration has become more common over time, especially in highly competitive industries which involve high levels of human capital; think of something like management of Fortune 500 firms, where the managers now have their salary determined globally rather than locally. This doesn’t strike most economists as a bad thing at first glance: as long as we are measuring productivity correctly, workers who are compensated based on their actual output will both exert the right amount of effort and have the incentive to improve their human capital.

In an intriguing new theoretical paper, however, Benabou and Tirole point out that many jobs involve multitasking, where workers can take hard-to-measure actions for intrinsic reasons (e.g., I put effort into teaching because I intrinsically care, not because academic promotion really hinges on being a good teacher) or take easy-to-measure actions for which there might be some kind of bonus pay. Many jobs also involve screening: I don’t know who is high quality and who is low quality, and although I would optimally pay people a bonus exactly equal to their cost of effort, I am unable to do so since I don’t know what that cost is. Multitasking and worker screening interact among competitive firms in a really interesting way, since how other firms incentivize their workers affects how workers will respond to my contract offers. Benabou and Tirole show that this interaction means that more competition in a sector, especially when there is a big gap between the quality of different workers, can actually harm social welfare even in the absence of any other sort of externality.

Here is the intuition. For multitasking reasons, when different things workers can do are substitutes, I don’t want to give big bonus payments for the observable output, since if I do the worker will put in too little effort on the intrinsically valuable task: if you pay a trader big bonuses for financial returns, she will not put as much effort into ensuring all the laws and regulations are followed. If there are other finance firms, though, they will make it known that, hey, we pay huge bonuses for high returns. As a result, workers will sort, with all of the high quality traders will move to the high bonus firm, leaving only the low quality traders at the firm with low bonuses. Bonuses are used not only to motivate workers, but also to differentially attract high quality workers when quality is otherwise tough to observe. There is a tradeoff, then: you can either have only low productivity workers but get the balance between hard-to-measure tasks and easy-to-measure tasks right, or you can retain some high quality workers with large bonuses that make those workers exert too little effort on hard-to-measure tasks. When the latter is more profitable, all firms inefficiently begin offering large, effort-distorting bonuses, something they wouldn’t do if they didn’t have to compete for workers.

How can we fix things? One easy method is with a bonus cap: if the bonus is capped at the monopsony optimal bonus, then no one can try to screen high quality workers away from other firms with a higher bonus. This isn’t as good as it sounds, however, because there are other ways to screen high quality workers (such as offering lower clawbacks if things go wrong) which introduce even worse distortions, hence bonus caps may simply cause less efficient methods to perform the same screening and same overincentivization of the easy-to-measure output.

When the individual rationality or incentive compatibility constraints in a mechanism design problem are determined in equilibrium, based on the mechanisms chosen by other firms, we sometimes called this a “competing mechanism”. It seems to me that there are quite a number of open questions concerning how to make these sorts of problems tractable; a talented young theorist looking for a fun summer project might find it profitable to investigate this as-yet small literature.

Beyond the theoretical result on screening plus multitasking, Tirole and Benabou also show that their results hold for market competition more general than just perfect competition versus monopsony. They do this through a generalized version of the Hotelling line which appears to have some nice analytic properties, at least compared to the usual search-theoretic models which you might want to use when discussing imperfect labor market competition.

Final copy (RePEc IDEAS version), forthcoming in the JPE.

“The Power of Communication,” D. Rahman (2014)

(Before getting to Rahman’s paper, a quick note on today’s Clark Medal, which went to Roland Fryer, an economist at Harvard who is best known for his work on the economics of education. Fryer is no question a superstar, and is unusual in leaving academia temporarily while still quite young to work for the city of New York on improving their education policy. His work is a bit outside my interests, so I will leave more competent commentary to better informed writers.

The one caveat I have, however, is the same one I gave last year: the AEA is making a huge mistake in essentially changing this prize from “Best Economist Under 40” to “Best Applied Microeconomist Under 40”. Of the past seven winners, the only one who isn’t obviously an applied microeconomist is Levin, and yet even he describes himself as “an applied economist with interests in industrial organization, market design and the economics of technology.” It’s not that Saez, Duflo, Levin, Finkelstein, Chetty, Gentzkow and Fryer are doing bad work – their research is all of very high quality and by no means “cute-onomics” – but simply that the type of research they do is a very small subset of what economists work on. This style of work is particularly associated with the two Cambridge schools, and it’s no surprise that all of the past seven winners either did their PhD or postdoc in Cambridge. Where are the macroeconomists, when Europe is facing unemployment rates upwards of 30% in some regions? Where are the finance and monetary folks, when we just suffered the worst global recession since the 1930s? Where are the growth economists, when we have just seen 20 years of incredible economic growth in the third world? Where are the historians? Where are the theorists, microeconomic and econometric, on whose backs the applied work winning the prizes are built? Something needs to change.)

Enough bellyaching. Let’s take a look at Rahman’s clever paper, which might be thought as “when mediators are bad for society”; I’ll give you another paper shortly about “when mediators are good”. Rahman’s question is simple: can firms maintain collusion without observing what other firms produce? You might think this would be tricky if the realized price only imperfectly reflects total production. Let the market price p be a function of total industry production q plus an epsilon term. Optimally, we would jointly produce the monopoly quantity and split the rents. However, the epsilon term means that simply observing the market price doesn’t tell my firm whether the other firm cheated and produced too much.

What can be done? Green and Porter (1984), along with Abreu, Pearce and Stacchetti two years later, answered that collusion can be sustained: just let the equilibrium involve a price war if the market price drops below a threshold. Sannikov and Skrzypacz provided an important corollary, however: if prices can be monitored continuously, then collusion unravels. Essentially, if actions to increase production can be taken continuously, the price wars required to prevent cheating must be so frequent that join profit from sometimes colluding and sometimes fighting price wars is worse than joint profit than from just playing static Cournot.

Rahman’s trick saves collusion even when, as is surely realistic, cheaters can act in continuous time. Here is how it works. Let there be a mediator – an industry organization or similar – who can talk privately to each firm. Colluding firms alternate who is producing at any given time, with the one producing firm selling the monopoly level of output. The firms who are not supposed to produce at time t obviously have an incentive to cheat and produce a little bit anyway. Once in a while, however, the mediator tells the firm who is meant to produce in time t to produce a very large amount. If the price turns out high, the mediator gives the firm that was meant to produce a very large amount less time in the future to act as the monopolist, whereas if the price turns out low, the mediator gives that firm more monopolist time in the future. The latter condition is required to incentivize the producing firm to actually ramp up production when told to do so. Either a capacity constraint, or a condition on the demand function, is required to keep the producing firm from increasing production too much.

Note that if a nonproducing firm cheats and produce during periods you were meant to be producing 0, and the mediator happens to secretly ask the temporary monopolist firm to produce a large amount, you are just increasing the probability that the other firm gets to act as the monopolist in the future while you just get to produce zero. Even better, since the mediator only occasionally asks the producing firm to overproduce, and other firms don’t know when this time might be, the nonproducing firms are always wary of cheating. That is, the mediator’s ability to make private recommendations permits more scope for collusion than firms who only options are to punish based on continuously-changing public prices, because there are only rare yet unknown times when cheating could be detected. What’s worse for policymakers, the equilibrium here which involves occasional overproduction shows that such overproduction is being used to help maintain collusion, not to deviate from it; add overproduction to Green-Porter price wars as phenomena which look like collusion breaking down but are instead collusion being maintained.

Final working paper (RePEc IDEAS). Final version published in AER 2014. If you don’t care about proof details, the paper is actually a very quick read. Perhaps no surprise, but the results in this paper are very much related to those in Rahman’s excellent “Who will Monitor the Monitor?” which was discussed on this site four years ago.

“Dynamic Commercialization Strategies for Disruptive Technologies: Evidence from the Speech Recognition Industry,” M. Marx, J. Gans & D. Hsu (2014)

Disruption. You can’t read a book about the tech industry without Clayton Christensen’s Innovator’s Dilemma coming up. Jobs loved it. Bezos loved it. Economists – well, they were a bit more confused. Here’s the story at its most elemental: in many industries, radical technologies are introduced. They perform very poorly initially, and so are ignored by the incumbent. These technologies rapidly improve, however, and the previously ignored entrants go on to dominate the industry. The lesson many tech industry folks take from this is that you ought to “disrupt yourself”. If there is a technology that can harm your most profitable business, then you should be the one to develop it; take Amazon’s “Lab126” Kindle skunkworks as an example.

There are a couple problems with this strategy, however (well, many problems actually, but I’ll save the rest for Jill Lepore’s harsh but lucid takedown of the disruption concept which recently made waves in the New Yorker). First, it simply isn’t true that all innovative industries are swept by “gales of creative destruction” – consider automobiles or pharma or oil, where the major players are essentially all quite old. Gans, Hsu and Scott Stern pointed out in a RAND article many years ago that if the market for ideas worked well, you would expect entrants with good ideas to just sell to incumbents, since the total surplus would be higher (less duplication of sales assets and the like) and since rents captured by the incumbent would be higher (less product market competition). That is, there’s no particular reason that highly innovative industries require constant churn of industry leaders.

The second problem concerns disrupting oneself or waiting to see which technologies will last. Imagine it is costly to investigate potentially disruptive technologies for the incumbent. For instance, selling mp3s in 2002 would have cannibalized existing CD sales at a retailer with a large existing CD business. Early on, the potentially disruptive technology isn’t “that good”, hence it is not in and of itself that profitable. Eventually, some of these potentially disruptive technologies will reveal themselves to actually be great improvements on the status quo. If that is the case, then, why not just let the entrant make these improvements/drive down costs/learn about market demand, and then buy them once they reveal that the potentially disruptive product is actually great? Presumably the incumbent even by this time still retains its initial advantage in logistics, sales, brand, etc. By waiting and buying instead of disrupting yourself, you can still earn those high profits on the CD business in 2002 even if mp3s had turned out to be a flash in the pan.

This is roughly the intuition in a new paper by Matt Marx – you may know his work on non-compete agreements – Gans and Hsu. Matt has also collected a great dataset from industry journals on every firm that ever operated in automated speech recognition. Using this data, the authors show that a policy by entrants of initial competition followed by licensing or acquisition is particularly common when the entrants come in with a “disruptive technology”. You should see these strategies, where the entrant proves the value of their technology and the incumbent waits to acquire, in industries where ideas are not terribly appropriable (why buy if you can steal?) and entry is not terribly expensive (in an area like biotech, clinical trials and the like are too expensive for very small firms). I would add that you also need complementary assets to be relatively hard to replicate; if they aren’t, the incumbent may well wind up being acquired rather than the entrant should the new technology prove successful!

Final July 2014 working paper (RePEc IDEAS). The paper is forthcoming in Management Science.

“Upstream Innovation and Product Variety in the U.S. Home PC Market,” A. Eizenberg (2014)

Who benefits from innovation? The trivial answer would be that everyone weakly benefits, but since innovation can change the incentives of firms to offer different varieties of a product, heterogeneous tastes among buyers may imply that some types of innovation makes large groups of people worse off. Consider computers, a rapidly evolving technology. If Lenovo introduces a laptop with a faster processor, they may wish to discontinue production of a slower laptop, because offering both types flattens the demand curve for each, and hence lowers the profit-maximizing markup that can be charged for the better machine. This effect, combined with a fixed cost of maintaining a product line, may push firms to offer too little variety in equilibrium.

As an empirical matter, however, things may well go the other direction. Spence’s famous product selection paper suggests that firms may produce too much variety, because they don’t take into account that part of the profit they earn from a new product is just cannibalization of other firm’s existing product lines. Is it possible to separate things out from data? Note that this question has two features that essentially require a structural setup: the variable of interest is “welfare”, a completely theoretical concept, and lots of the relevant numbers like product line fixed costs are unobservable to the econometrician, hence they must be backed out from other data via theory.

There are some nice IO tricks to get this done. Using a near-universe of laptop sales in the early 2000s, Eizenberg estimates heterogeneous household demand using standard BLP-style methods. Supply is tougher. He assumed that firms get a fixed cost per product line shock, then pick their product mix each quarter, then observe consumer demand, then finally play Nash-Bertrand differentiated product pricing. The problem is that the pricing game often has multiple equilibria (e.g., with two symmetric firms, one may offer a high-end product and the other a low-end one, or vice versa). Since the pricing game equilibria are going to be used to back out fixed costs, we are in a bit of a bind. Rather than select equilibria using some ad hoc approach (how would you even do so in the symmetric case just mentioned?), Eizenberg cleverly just partially identifies fixed costs as backed out from any possible pricing game equilibrium, using bounds in the style of Pakes, Porter, Ho and Ishii. This means that welfare effects are also only partially identified.

Throwing this model at the PC data shows that the mean consumer in the early 2000s wasn’t willing to pay any extra for a laptop, but there was a ton of heterogeneity in willingness to pay both for laptops and for faster speed on those laptops. Every year, the willingness to pay for a given computer fell $257 – technology was rapidly evolving and lots of substitute computers were constantly coming onto the market.

Eizenberg uses these estimates to investigate a particularly interesting counterfactual: what was the effect of the introduction of the lighter Pentium M mobile processor? As Pentium M was introduced, older Pentium III based laptops were, over time, no longer offered by the major notebook makers. The M raised predicted notebook sales by 5.8 to 23.8%, raised mean notebook price by $43 to $86, and lowered Pentium III share in the notebook market from 16-23% down to 7.7%. Here’s what’s especially interesting, though: total consumer surplus is higher with the M available, but all of the extra consumer surplus accrues to the 20% least price-sensitive buyers (as should be intuitive, since only those with high willingness-to-pay are buying cutting edge notebooks). What if a social planner had forced firms to keep offering the Pentium III models after the M was introduced? Net consumer plus producer surplus may have actually been positive, and the benefits would have especially accrued to those at the bottom end of the market!

Now, as a policy matter, we are (of course) not going to force firms to offer money-losing legacy products. But this result is worth keeping in mind anyway: because firms are concerned about pricing pressure, they may not be offering a socially optimal variety of products, and this may limit the “trickle-down” benefits of high tech products.

2011 working paper (No IDEAS version). Final version in ReStud 2014 (gated).

“Dynamic Constraints on the Distribution of Stochastic Choice: Drift Diffusion Implies Random Utility,” R. Webb (2013)

Neuroeconomics is a slightly odd field. It seems promising to “open up the black box” of choice using evidence from neuroscience, but despite this promise, I don’t see very many terribly interesting economic results. And perhaps this isn’t surprising – in general, economic models are deliberately abstract and do not hinge on the precise reason why decisions are made, so unsurprisingly neuro appears most successful in, e.g., selecting among behavioral models in specific circumstances.

Ryan Webb, a post-doc on the market this year, shows another really powerful use of neuroeconomic evidence: guiding our choices of the supposedly arbitrary parts of our models. Consider empirical models of random utility. Consumers make a discrete choice, such that the object chosen i is that which maximizes utility v(i). In the data, even the same consumer does not always make the same choice (I love my Chipotle burrito bowl, but I nonetheless will have a different lunch from time to time!). How, then, can we use the standard choice setup in empirical work? Add a random variable n(i) to the decision function, letting agents choose i which maximizes v(i)+n(i). As n will take different realizations, choice patterns can vary somewhat.

The question, though, is what distribution n(i) should take? Note that the probability i is chosen is just

P(v(i)+n(i)>=v(j)+n(j)) for all j

or

P(v(i)-v(j)>=n(i)-n(j)) for all j

If n are distributed independent normal, then the difference n(i)-n(j) is normal. If n are extreme value type I, the difference is logistic. Do either of those assumptions, or some alternative, make sense?

Webb shows that random utility is really just a reduced form of a well-established class of models in psychology called bounded accumulation models. Essentially, you receive a series of sensory inputs stochastically, the data adds up in your brain, and you make a decision according to some sort of stopping rule as the data accumulates in a drift diffusion. In a choice model, you might think for a bit, accumulating reasons to choose A or B, then stop at a fixed time T* and choose the object that, after the random drift, has the highest perceived “utility”. Alternatively, you might stop once the gap between the perceived utilities of different alternatives is high enough, or once one alternative has a sufficiently high perceived utility. It is fairly straightforward to show that this class of models all collapses to max v(i)+n(i), with differing implications for the distribution of n. Thus, neuroscience evidence about which types of bounded accumulation models appear most realistic can help choose among distributions of n for empirical random utility work.

How, exactly? Well, for any stopping rule, there is an implied distribution of stopping times T*. The reduced form errors n are then essentially the sample mean of random draws from an finite accretion process, and hence if the rule implies relatively short stopping times, n will be fat-tailed rather than normal. Also, consider letting the difference in underlying utility v(i)-v(j) be large. Then the stopping time under the accumulation models is relatively short, and hence the variance in the distribution of reduced form errors (again, essentially the sample mean of random draws) is relatively large. Hence, errors are heteroskedastic in the underlying v(i)-v(j). Webb gives additional results relating to the skew and correlation of n. He further shows that assuming independent normality or independent extreme value type I for the error terms can lead to mistaken inference, using a recent AER that tries to infer risk aversion parameters from choices among monetary lotteries. Quite interesting, even for a neuroecon skeptic!

2013 Working Paper (No IDEAS version).

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