Corporate Resilience to Banking Crises: The Roles of Trust and Trade Credit

Ross Levine, Chen Lin, and Wensi Xie (2016 NBER working paper)

Outline

  • Regress outcome on trust, crisis, and trust*crisis (difference-in-differences)
    • outcome ∈ {use of trade credit, profitability, employment}
    • trust is the extent to which people in a country trust each other.
      • Following La Porta, Lopez-de-Silanes, Shleifer, and Vishny (1997a) and Guiso, Sapienza, and Zingales (2008), they use responses to the World Value Survey, in which participants are asked whether they trust other people.
    • crisis equals 1 for the year in which a banking crisis starts and for each of the two following years (follow Laeven and Valencia (2012) in dating crises).
    • Data covers 3,600 manufacturing firms in 34 countries from 1990-2011, and comes from Worldscope.
  • Examine whether results are stronger in industries that rely more on short-term liquidity.
    • Define industry short-term liquidity needs as the ratio of inventory to sales, using U.S. firms only (data from Compustat).
      • the ratio of inventory to sales is supposed to capture the proportion of working capital that is financed by ongoing sales.
    • control for severity of crisis, maturity of country financial markets, macro-economic conditions, quality of government institutions, legal shareholder and creditor protections, etc.

Takeaways

  • Firms’ outcomes are better (firms are more resilient) in high-trust countries, since they can rely on informal trade credit financing when the banking channel is in crisis.  In another words, firms who have good relationships with their suppliers (and maybe customers) can rely on them in bad times.
  • The effect is stronger for firms whose operations imply greater short-term liquidity needs.

Production Chains

David K. Levine (2012 Review of Economic Dynamics)

Summary

  • In economies with greater specialization of agents, production chains are necessarily longer.
  • If shocks to (failures of) agents are randomly distributed, the longer chains have a greater probability of failure.
  • If shocks are correlated, the existence of chains where no agents fail is more likely, and so chains will be longer; however, these longer chains are more sensitive to changes in the probability of the failure of any single agent.
  • Shocks that are concentrated within production chains can be less costly than shocks that hit multiple chains, even if their first-order impacts are identical.
    • Consider an economy with two production chains, composed of equal sized firms. Let a shock be an instance where a firm fails, causing its production chain to shut down.  A shock that hits two firms in production chain 1 (and shuts it down) is less costly than a shock that hits one firm in each chain (shutting down both chains).
    • In an economy with 3 chains, a shocks that hits two chains can be transferred so that it affects only chain if the chains’ inputs are substitutes (see Table 1)
      • There are three auto manufacturing chains, using three specialized firms each that produce tires, pistons, and other parts.
      • Shock A hits all three firms in the production chain for Jaguars.  This chain shuts down, but the chains producing BMWs and Toyotas still operate.
      • Shock B hits the tire producer for Jaguar, the piston producer for BMW, and the other parts producer for Toyota.  However, if these parts can be substituted across firms, then Jaguar’s piston and other parts producers can be reassigned to the BMW and Toyota chains so that, as with shock A, only one chain shuts down.
      • Shock C hits all three tire producers.  All three chains shut down.
  • The author models the correlation of shocks as the probability r≥0 that a firm is in a chain where all firms are failures.  Given that a firm is not in such a chain, it fails with independent probability p.

Takeaways

  1. With specialization, correlation of shocks in production chains leads to higher expected output, higher welfare, longer production chains, and greater sensitivity to shocks.
    1. This greater sensitivity is the “price we pay” for the higher productivity.
  2. Correlation of shocks within production chains is less costly than correlation of shocks across chains, especially when inputs in one process can be substitutes for inputs in another.

Input Specificity and the Propagation of Idiosyncratic Shocks in Production Networks

Barrot, Jean-Noel, and Julien Sauvagnat, QJE (2004).

Question:

Do firm-level shocks propagate in production networks?

Methods:

  • Use natural disasters as the exogenous shock to suppliers
  • Compare firms whose suppliers are in disaster areas with firms who do not have any suppliers in disaster areas.
  • Use three measures of asset specificity:
    • Rauch’s (1999) classification of the extent to which goods are traded on markets
    • Supplier’s R&D expenditures
    • Supplier’s number of patents
  • Regress customer change in sales when a specific supplier suffers a shock
  • Regress (non-disaster) supplier change in sales when a customer has another specific supplier that suffers a shock.
  • Control for firm characteristics, number of suppliers, and fiscal-quarter fixed effects.
  • Take appropriate consideration for
    • Customers who are affected by the same disaster as their supplier
      • exclude observations where customer and supplier are less than 300 miles apart
    • the fact that customers may select their suppliers based on natural disaster risk
      • this would bias against finding any results
    • customers lose sales through some other channel – e.g., if their own customer base is in the same area as the supplier
      • find that there are not results when the customer-supplier link is inactive

Data:

  • Data on non-financial firms with headquarters in the U.S. between 1978-2013
  • Firm characteristics and location (county) from Compustat
  • Regulation SFAS 131 requires firms to report the names of customers accounting for more than 10% of segment sales (starting in 1978)
    • Supply chain from Compustat Segment data – match customer names to Compustat by string-matching and by hand
  • Disaster date and location data from SHELDUS (Spatial Hazard and Loss Database for the U.S.) at the University of South Carolina
  • Data on supplier specificity
    • Recreate Rauch’s (1999) measure of input specificity based on SIC-code
    • R&D data from Compustat
    • patent data from Kogan, et al. (2012), retrieved in turn from Google patents

Findings:

  • When a supplier’s location is hit by a natural disaster, customers’ sales growth falls by 2 to 3 percent and their equity value falls by 1%.
    • This effect is not observed when the supplier and customer do not have an active relationship at the time of disaster.
    • This effect is only observed when the supplier supplies a specific input.
    • $1 of lost supplier sales leads to $2.4 of lost customer sales – shocks propagate.
  • Suppliers who are not hit by a disaster, but which have a customer who has another supplier that is in a disaster zone, are also negatively affected.

Comments:

  • This paper looks at the effect of supplier shocks on customers and on other suppliers.  The authors also look at the impact of customer shocks on suppliers, but not at the effect of customer shocks on specific suppliers, which they should have done in a paper about asset specificity.  The position this as a supply-side story, but they do not fairly consider the demand-side.
  • Something else the authors could do to support their story is to look at inventory.  The basic story is that when a specific supplier is hit by a disaster, the supply of a specific input falls, so the (customer) firm has no choice but to cut production, leading to lost sales.  By effectively using sales as a measure of production, they assume that (1) inventory levels are fixed and (2) the relative sales price of the customer’s final good is fixed.  If, as I would expect, customers deplete inventory to make up for lost production, then the affect of the supplier shock is even bigger than this paper shows.
  • The authors probably want to look at lost sales to see the effect of shocks on firms’ bottom line.  If pre-shock inventories were optimal, than any decrease in inventory has a cost associated with it, as well.  My point is that there may be, and probably are, other consequences beyond lost sales.  How do the lost sales compare in magnitude to the declines in market cap for these companies?