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Dr. John Haltiwanger, University of Maryland

Dr. John Haltiwanger, University of Maryland

Gerri C. LeBow Hall
3220 Market Street
Rm. 406
Philadelphia, PA 19104
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Abstract. A large literature on misallocation and productivity has arisen in recent years, with
Hsieh and Klenow (2009; hereafter HK) as its standard empirical framework. The framework’s usefulness and theoretical founding make it a valuable starting point for analyzing misallocations. However, we show that the empirical lynchpin of this approach can be very sensitive to model
misspecification. The condition in the HK model that maps from observed production behaviors to the misallocative wedges/distortions holds in a single theoretical case, with strict assumptions required on both the demand and supply sides. We demonstrate that applying the HK methodology when there is any deviation from these assumptions will mean that the “distortions” recovered from the data may not be signs of inefficiency. Rather, they may simply reflect demand shifts or movements of the firm along its marginal cost curve, quite possibly in directions related to higher profits for the business. The framework may then not just spuriously identify inefficiencies; it might be more likely to do so precisely for businesses better in some fundamental way than their competitors. Empirical tests in our data, which allow us to separate price and quantity and as such directly test the model’s assumptions, suggest the framework’s necessary conditions do not hold. We empirically investigate two of the possible sources of departures from the HK assumptions and implications and find support for both. We also find that measures of distortions that emerge from this approach are in fact strongly positively related with survival, suggesting they embody favorable profit conditions for the business. At the same time, however, once we condition on demand and supply fundamentals, the distortion measure becomes inversely related with survival. This suggests the measure may contain a distortionary component, but it is empirically swamped by other factors.


Misallocation Measures: The Distortion that Ate the Residual

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