Adler, G., R. Duval, D. Fürceri, S. Celik, and M. Püplawski-Ribeirü (2017): “Gone with the Headwinds: Global Productivity,” IMF Staff Discussion Notes 17/04, International Monetary Fund.
Admati, A. R., P. M. DeMarzo, M. F. Hellwig, and P. Pfleiderer (2017): “The Leverage Ratchet Effect,” Forthcoming in the Journal of Finance.
Aghion, P., P. Askenazy, N. Berman, G. Cette, and L. Eymard (2012): “Credit Constraints And The Cyclicality Of R&D Investment: Evidence From France,” Journal of the European Economic Association, 10(5), 1001−1024.
Andrews, D., e. Criscuolo, and P. N. Gal (2015): “Frontier Firms, Technology Diffusion and Public Policy: Micro Evidence from OECD Countries,” OECD Productivity Working Papers 2, OECD Publishing.
Bartelsman, E. J., and Z. Wolf (2017): “Measuring Productivity Dispersion,” Tinbergen Institute Discussion Papers 17-033/VI, Tinbergen Institute.
Chudik, A., K. Mohaddes, M. H. Pesaran, and M. Raissi (2013): “Debt, Inflation and Growth Robust Estimation of Long-Run Effects in Dynamic Panel Data Models,” Globalization and Monetary Policy Institute Working Paper 162, Federal Reserve Bank of Dallas.
Chudik, A., K. Mohaddes, M. H. Pesaran, and M. Raissi (2016): “Long-Run Effects in Large Heterogeneous Panel Data Models with Cross- Sectionally Correlated Errors,” in Essays in Honor of Aman Ullah, vol. 36 of Advances in Econometrics, pp. 85−135. Emerald Publishing Ltd.
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)| false ( Chudik, A., K. Mohaddes, M. H. Pesaran, and M. Raissi 2016): “ Long-Run Effects in Large Heterogeneous Panel Data Models with Cross- Sectionally Correlated Errors,” in Essays in Honor of Aman Ullah, vol. 36of Advances in Econometrics, pp. 85− 135. Emerald Publishing Ltd.
Chudik, A., K. Mohaddes, M. H. Pesaran, and M. Raissi (2017): “Is There a Debt-Threshold Effect on Output Growth?,” The Review of Economics and Statistics, 99(1), 135−150.
Chudik, A., and M. H. Pesaran (2015): “Common Correlated Effects Estimation of Heterogeneous Dynamic Panel Data Models with Weakly Exogenous Regressors,” Journal of Econometrics, 188(2), 393−420.
Chudik, A., M. H. Pesaran, and J.-C. Yang (2016): “Half-Panel Jackknife Fixed Effects Estimation of Panels with Weakly Exogenous Regressors,” Globalization and Monetary Policy Institute Working Paper 281, Federal Reserve Bank of Dallas.
Coricelli, F., N. Driffield, S. Pal, and I. Roland (2012): “When Does Leverage Hurt Productivity Growth? A Firm-Level Analysis,” Journal of International Money and Finance, 31(6), 1674−1694.
Gal, P. (2013): “Measuring Total Factor Productivity at the Firm Level using OECD-ORBIS,” OECD Economics Department Working Papers 1049, OECD Publishing.
Gopinath, G., S. Kalemli-Ozcan, L. Karabarbounis, and C. Villegas-Sanchez (2017): “Capital Allocation and Productivity in South Europe,” The Quarterly Journal of Economics, p. qjx024.
Gordon, R. J. (2012): “Is U.S. Economic Growth Over? Faltering Innovation Confronts the Six Headwinds,” NBER Working Papers 18315, National Bureau of Economic Research, Inc.
Grossman, S., and O. Hart (1982): “Corporate Financial Structure and Managerial Incentives,” in The Economics ofInformation and Uncertainty, pp. 107−140. National Bureau of Economic Research, Inc.
Heider, F., and A. Ljungqvist (2015): “As Certain as Debt and Taxes: Estimating the Tax Sensitivity of Leverage from State Tax Changes,” Journal of Financial Economics, 118(3), 684712.
Hsieh, C-T., and P. Klenow (2009): “Misallocation and Manufacturing TFP in China and India,” The Quarterly Journal of Economics, 124(4), 1403−1448.
Jarmuzek, M., and R. Rozenov (2017): “Excessive Private Sector Leverage and Its Drivers; Evidence from Advanced Economies,” IMF Working Papers 17/72, International Monetary Fund.
Jensen, M. C. (1986): “Agency Costs of Free Cash Flow, Corporate Finance, and Takeovers,” American Economic Review, 76(2), 323−329.
Kalemli-Ozcan, S., L. Laeven, and D. Moreno (2015): “Debt Overhang in Europe: Evidence from Firm-Bank-Sovereign Linkages,” University of Maryland.
Kalemli-Ozcan, S., B. Sorensen, C. Villegas-Sanchez, v. Volosovych, and S. Yesiltas (2015): “How to Construct Nationally Representative Firm Level Data from the ORBIS Global Database,” NBER Working Papers 21558, National Bureau of Economic Research, Inc.
Kobayashi, K., and D. Shirai (2017): “Debt-Ridden Borrowers and Economic Slowdown,” CIGS Working Paper Series 17-002E, The Canon Institute for Global Studies.
Lang, L., E. Ofek, and R. M. Stulz (1996): “Leverage, Investment, and Firm Growth,” Journal of Financial Economics, 40(1), 3−29.
Levinsohn, J., and A. Petrin (2003): “Estimating Production Functions Using Inputs to Control for Unobservables,” Review of Economic Studies, 70(2), 317−341.
Marchica, M., and R. Mura (2010): “Financial Flexibility, Investment Ability, and Firm Value: Evidence from Firms with Spare Debt Capacity,” Financial Management, 39(4), 1339−1365.
Midrigan, v., and D. Y. Xu (2014): “Finance and Misallocation: Evidence from Plant-Level Data,” American Economic Review, 104(2), 422−458.
Mohaddes, K., M. Raissi, and A. Weber (2017): “Can Italy Grow Out of its NPL Overhang? A Panel Threshold Analysis,” Economics Letters, 159(Supplement C), 185 - 189.
Moll, B. (2014): “Productivity Losses from Financial Frictions: Can Self-Financing Undo Capital Misallocation?,” American Economic Review, 104(10), 3186−3221.
Mura, A., F. Piras, and A. Valentincic (2016): “Do Asset Revaluations Signal Future Performance of Private Firms?,” Unpublished Manuscript.
Olley, G. S., and A. Pakes (1996): “The Dynamics of Productivity in the Telecommunications Equipment Industry,” Econometrica, 64(6), 1263−1297.
Pesaran, M. (2004): “General Diagnostic Tests for Cross Section Dependence in Panels,” Cambridge Working Papers in Economics 0435, Faculty of Economics, University of Cambridge.
Pesaran, M., and R. Smith (1995): “Estimating Long-Run Relationships from Dynamic Heterogeneous Panels,” Journal of Econometrics, 68(1), 79−113.
Reinhart, e. M., and K. S. Rogoff (2010): “Growth in a Time of Debt,” Working Paper 15639, National Bureau of Economic Research.
Restuccia, D., and R. Rogerson (2008): “Policy Distortions and Aggregate Productivity with Heterogeneous Plants,” Review ofEconomic Dynamics, 11(4), 707−720.
Wooldridge, J. M. (2009): “On Estimating Firm-Level Production Functions using Proxy Variables to Control for Unobservables,” Economics Letters, 104(3), 112−114.
We are grateful to Steve Bond, Ehsan Ebrahimy, Sebastian Doerr, Romain Duval, Rishi Goyal, Tryggvi Gudmundsson, Sebnem Kalemli-Ozcan, Kamiar Mohaddes, Anne-Charlotte Paret Onorato, and seminar participants at the European department of the IMF, as well as the Italian authorities for helpful comments.
University of Oxford.
Another example is Mohaddes, Raissi, and Weber (2017) who study the relationship between banks non-performing loans and economic growth in Italy.
Corporate debt overhangs could result in lower physical- and human-capital accumulation; and weaker-quality investment choices by firms (i.e. investing in lower-risk lower-return projects and less R&D spending), leading to slower embodied technical progress and lower TFP in the long term.
Coricelli, Driffield, Pal, and Roland (2012) show that TFP growth in Central and Eastern European countries tends to increase with leverage up to a threshold, beyond which additional borrowing lowers TFP growth.
Adler, Duval, Furceri, Celik, and Poplawski-Ribeiro (2017) argue that the processes of innovation and technological adoption which drive within firm productivity growth are facilitated by investments in human, physical and intangible capital.
Heider and Ljungqvist (2015) provide empirical support for the “leverage ratchet” mechanism, showing that US firms respond asymmetrically to changes in corporation tax. They find that firms increase debt when tax rates increase but do not respond when tax rates are cut.
Banks and insurance companies are excluded from the dataset.
Industry-level deflators are used as firm-level price data are not available. Dispersion in our productivity measures will therefore reflect within-industry dispersion in prices, as well as dispersion in the productive efficiency of firms (see Bartelsman and Wolf (2017) for further discussion of productivity measures which use industry-level deflators).
The detail of the liabilities side of the balance sheet varies for some companies in ORBIS. In particular, for some companies a decomposition of “Non-current liabilities” into “long-term debt” and “Other non-current liabilities” is not available consistently over time. We drop companies which do not have a breakdown between “long-term debt” and “Other non-current liabilities” in any year between 1999-2015. For companies which do not have a breakdown in every year, we impute “long-term debt” by linearly interpolating the share of long-term debt in “Non-current liabilities” between years in which the company reports a breakdown.
Our results in Section 6.3 are robust to the inclusion of these additional 490 firms. However, we cannot include them in our heterogeneous panel specifications owing to the firm-by-firm estimation of slope coefficients.
For a discussion of the short-run impact of high debt levels on productivity, see Adler, Duval, Furceri, Celik, and Poplawski-Ribeiro (2017).
Allowing the debt threshold to vary across firms would require a significantly longer time horizon for our firm panel than is currently available.
Note that long-run relationships do not provide any indication about direction of causality, but merely provide a statistical association between the variables in the long run. In fact, the causality can run both ways.
Similar histograms can be produced for the estimated short-term coefficients λi; βi, and ψi. For brevity, these results are not reported here but they are available upon request.
Note that a one standard deviation increase in corporate indebtedness corresponds to a 4, 6, 4, and 6 percent increase in indebtedness using debt to value added, debt to EBITDA, debt to assets, and long-term debt to value added, respectively.
The similarity between the baseline results which use the Wooldridge TFP measure and the results using the OLS approach is consistent with the high correlation between these measures reported in Gal (2013). The correlation between the Wooldridge measure and the Solow measure is weaker and so the difference in the magnitude of the long-run estimates is not altogether surprising.