nep-eff New Economics Papers
on Efficiency and Productivity
Issue of 2018‒01‒15
five papers chosen by

  1. Innovation, Reallocation, and Growth By Acemoglu, Daron; Akcigit, Ufuk; Alp, Harun; Bloom, Nicholas; Kerr, William R.
  2. Predictably Unequal? The Effects of Machine Learning on Credit Markets By Fuster, Andreas; Goldsmith-Pinkham, Paul; Ramadorai, Tarun; Walther, Ansgar
  3. Productivity and Misallocation in General Equilibrium By Baqaee, David Rezza; Farhi, Emmanuel
  4. Winning Connections? Special Interests and the Sale of Failed Banks By Igan, Deniz; Lambert, Thomas; Wagner, Wolf; Zhang, Quxian
  5. Structural Change within the Service Sector and the Future of Baumol's Disease By Duernecker, Georg; Herrendorf, Berthold; Valentinyi, Akos

  1. By: Acemoglu, Daron; Akcigit, Ufuk; Alp, Harun; Bloom, Nicholas; Kerr, William R.
    Abstract: We build a model of firm-level innovation, productivity growth and reallocation featuring endogenous entry and exit. A new and central economic force is the selection between high- and low-type firms, which differ in terms of their innovative capacity. We estimate the parameters of the model using US Census micro data on firm-level output, R&D and patenting. The model provides a good fit to the dynamics of firm entry and exit, output and R&D. Taxing the continued operation of incumbents can lead to sizable gains (of the order of 1.4% improvement in welfare) by encouraging exit of less productive firms and freeing up skilled labor to be used for R&D by high-type incumbents. Subsidies to the R&D of incumbents do not achieve this objective because they encourage the survival and expansion of low-type firms.
    Keywords: Entry; growth; industrial policy; Innovation; R&D; reallocation; selection
    JEL: E2 L1 O31 O32 O33
    Date: 2017–11
  2. By: Fuster, Andreas; Goldsmith-Pinkham, Paul; Ramadorai, Tarun; Walther, Ansgar
    Abstract: Recent innovations in statistical technology, including in evaluating creditworthiness, have sparked concerns about impacts on the fairness of outcomes across categories such as race and gender. We build a simple equilibrium model of credit provision in which to evaluate such impacts. We find that as statistical technology changes, the effects on disparity depend on a combination of the changes in the functional form used to evaluate creditworthiness using underlying borrower characteristics and the cross-category distribution of these characteristics. Employing detailed data on US mortgages and applications, we predict default using a number of popular machine learning techniques, and embed these techniques in our equilibrium model to analyze both extensive margin (exclusion) and intensive margin (rates) impacts on disparity. We propose a basic measure of cross-category disparity, and find that the machine learning models perform worse on this measure than logit models, especially on the intensive margin. We discuss the implications of our findings for mortgage policy.
    Keywords: credit access; Machine Learning; Mortgages; statistical discrimination
    JEL: G21 G28 R30
    Date: 2017–11
  3. By: Baqaee, David Rezza; Farhi, Emmanuel
    Abstract: We provide a general non-parametric formula for aggregating microeconomic shocks in general equilibrium economies with distortions such as taxes, markups, frictions to resource reallocation, and nominal rigidities. We show that the macroeconomic impact of a shock can be boiled down into two components: its ``pure'' technology effect; and its effect on allocative efficiency arising from the associated reallocation of resources, which can be measured via changes in factor income shares. We also derive a formula showing how these two components are determined by structural microeconomic parameters such as elasticities of substitution, returns to scale, factor mobility, and network linkages. Overall, our results generalize those of Solow (1957) and Hulten (1978) to economies with distortions. To demonstrate their empirical relevance, we pursue different applications, focusing on markup distortions. For example, we operationalize our non-parametric results and show that improvements in allocative efficiency account for about 50% of measured TFP growth over the period 1997-2015. We also implement our structural results and conclude that eliminating markups would raise TFP by about 40%, increasing the economywide cost of monopoly distortions by two orders of magnitude compared to the famous 0.1% estimates of Harberger (1954).
    Date: 2017–11
  4. By: Igan, Deniz; Lambert, Thomas; Wagner, Wolf; Zhang, Quxian
    Abstract: We study how lobbying affects the resolution of failed banks, using a sample of FDIC auctions between 2007 and 2014. We show that bidding banks that lobby regulators have a higher probability of winning an auction. In addition, the FDIC incurs higher costs in such auctions, amounting to 16.4 percent of the total resolution losses. We also find that lobbying winners have worse operating and stock market performance than their non-lobbying counterparts, suggesting that lobbying results in a less efficient allocation of failed banks. Our results provide new insights into the bank resolution process and the role of special interests.
    Keywords: bank resolution; failed banks; financial crisis; Lobbying; rent seeking
    JEL: D72 E65 G18 G21
    Date: 2017–11
  5. By: Duernecker, Georg; Herrendorf, Berthold; Valentinyi, Akos
    Abstract: Structural change reduces aggregate productivity growth when it leads to the reallocation of production to industries with low productivity growth. We document that this so-called Baumol's disease considerably reduced productivity growth in the postwar U.S. We capture the effect of Baumol's disease on productivity growth with a novel model of structural change that disaggregates the service sector into services with low and high productivity growth. The data imply that the two service subsectors are substitutes. We find that the substitutability limits how important services with low productivity growth may become, which bounds the future effect of Baumol's disease.
    Keywords: Baumol's Disease; Productivity Growth Slowdown; Service Sector; structural change
    JEL: O41 O47 O51
    Date: 2017–11

General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.