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Romain Duval and Gee Hee Hong are with the International Monetary Fund. Yannick Timmer is with Trinity College Dublin. We are extremely thankful to John Bluedorn, Giovanni Dell’Ariccia, Xavier Freixas, Fadi Hassan, Jesper Linde, Sebnem Kalemli-Ozcan, Maria Soledad Martinez Peria, Benjamin Moll, Maurice Obstfeld and participants at IMF and Brookings seminars for helpful comments and discussions. The views expressed in the paper are solely those of the authors and do not necessarily represent the views of the institutions to which the authors are affiliated.
One advantage of comparing the five years after versus the five years before the crisis is that we allow for a dynamic TFP response instead of restricting it to be contemporaneous. Papers by Mian and Sufi (2014) and Khawja and Mian (2008) are other examples of recent examples of approaches that collapse the data around events. See Betrand et al. (2004) for a discussion of differences-in-differences strategies.
See Eurostat (2008) for further information on the categorization and correspondence with other sector classifications (http://ec.europa.eu/eurostat/documents/3859598/5902521/KS-RA-07-015-EN.PDF).
Results are robust to considering the principal component instead.
Building on the identification of production function as proposed by Olley and Pakes (1996) and Levinsohn and Petrin (2003), Wooldridge (2009) provides a single-equation instrumental variable approach to control for the correlation between factor inputs (intermediates) and unobserved firm productivity by proxying the latter with a function of observed firm characteristics that reflect a firm’s reaction to productivity changes. The use of revenue productivity implies that firm-specific price variations within each sector affect our productivity estimates. While these can, all else equal, reflect quality changes, they can also reflect market power of the firm. If more resilent firms increased prices since the crisis, this would mechanically result in relatively higher measured productivity growth for these firms. However, since Gilchrist et al. (2017) show that financially constrained firms raised prices during the financial crisis, our results would be if anything, downward biased. See also Syverson (2011) for a discussion of these pros and cons of using revenue-based productivity.
Since we only focus on within-firm TFP growth and the summary statistics are not weighted, the numbers are very large.
These results are quantitatively and statistically robust to controlling additionally for each firms’ average rollover risk over the years 2003-2007. This further shows that our results are not driven by the fact that firms that had balance sheet vulnerabilities on the eve of the financial crisis were intrinsically weak firms that were structurally forced to raise short-term debt as a result.
All results below are robust to considering the change in the average bank CDS spread over a narrower window, namely between the week before and the week after the collapse of Lehman Brothers (sourced from Datastream again). This is because the exogenous spike in CDS spreads seen in the aftermath of the Lehman was much larger than any other changes that took place during the year 2008, and as a result correlates strongly with the change in the average CDS between the first and second halves of 2008 we use in the regressions presented in this paper. In addition, the results are robust to using the principal component of the change in bank CDS spreads, rather than their simple average, for each country.
The difference in coefficients on the direct effects of vulnerabilities between Tables 2 and 3 partly is due to the fact that the coefficient in Table 2 captures the impact in the average firm (not necessarily in the average country) while the coefficient in Table 3 captures the impact in the average firm in the average country.
The results are also robust along several other dimensions. For instance, the results hold with other measures of financial vulnerabilities (i.e. interest coverage ratio), with different windows of pre-crisis period and with other measures of credit conditions at the country level (based on national survey of senior loan officers on credit supply conditions). The results are not reported here, but are available upon request.