- Æ i,2010 + η i . One observation is one firm (cross section). Province×Industry fixed effects are included. F
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- à ∆θi,t + à − 1 à ∆Éq i,t = Èi + Èp,s,t + γ Æi,t + ηi,t (30) Equation (30) highlights that an empirical investigation based on data on revenues rather than quantities presents both challenges and opportunities. The main challenge is to provide evidence that the results are not driven by correlation between output demand (or other local competitive conditions)50 and credit supply factors: evidence provided in section 5.1 and 6 are reassuring on this regard. At the same time, we have the opportunity to take into account other sources of productivity increase, besides technical efficiency (Hall, 2011). These encompass improvements in quality of the product offered and access to new markets or new niches that may result in an increase in markups.
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- Ã is estimated from self-reported elasticity of demand, as in Pozzi & Schivardi (2016).
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- After an M&A episode, acquired banks generally reduce (in the short-run) their supply of credit to pre-existing borrowers (Buono & Formai, 2013). Following this intuition, we estimate the model: Æi,t = Æi + Æt,p,s + γ MAi,t + ηi,t where Æi,t is the credit supply shock experienced by firm i and MAi,t is the share of i ’s previous period lenders which are being acquired by another financial institution. Results are presented in column (4) of Table A.10: credit supply is negatively affected by lenders’ M&A episodes. The collection of results presented in this section, being consistent with the relevant literature, provide additional support to our measure of credit supply shocks.
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- As in appendix B.1, we follow Pozzi & Schivardi (2016) and estimate elasticity of demand from INVIND self-reported elasticities.52 Results are reported in Table A.4. The effect of credit supply shocks on revenues productivity is significantly stronger in industries with higher elasticity of demand. Demand elasticity can be correlated with many technological or economic factors. Therefore, this empirical finding should be interpreted with extreme caution. Nonetheless, Table A.4 suggests that the effect of credit supply 52 We assume each two digit industry has a single elasticity and take the median value among all the responses. We drop responses implying negative values of Ã. We use both 2007 and 1996 survey waves. on technical efficiency is likely larger than the effect on market appeal, at least in manufacturing.
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- C Additional Materials on Credit Supply and Productivity Growth C.1 TFP, TFPR and TFPQ - cont’d We follow De Loecker (2011) and consider a firm producing quantity Qi,t of a single differentiated good, at price Pi,t, and facing a CES demand function. Let its production function be a CobbDouglas. Quantity produced (supply) is Qi,t = exp{Éq i,t + f (li,t, ki,t, mi,t, βq )} = exp{Éq i,t + βq l li,t + βq k ki,t + βq m mi,t} Quantity sold (demand) is: Qi,t =
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- During the Europe sovereign debt crisis, the spread on bonds issued by Italian government (and other southern European countries) increased sharply. Investors responded by acquiring more of these assets, “crowding out†credit to private non-financial corporation (Broner et al. , 2014). Therefore, for each year between 2010 and 2013, we estimate the model: Æb,t = Æt + γ ∆ Sovereign Assets b,t + ηb,t where Sovereign Assets b,t is the share of sovereign bonds on the total assets of bank b. Results are presented in column (3) of of Table A.10: banks increasing more their exposure to sovereign debt decreased their credit supply to corporate borrowers.
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- inCon i,2010 is a dummy taking value one iff firm i reports “difficulties to get external funds†as an important or somehow important obstacle to innovation. Number of PCs, export activity, R&D investments, and self-reported obstacle to innovation are taken from INVIND. Column (7): model is M S i,t = È + γ Æ i,t + η i,t . One observation is one firm observed for one or two years (cross section). M S i,t is firm i overall management score provided by the World Management Survey (Bloom &
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- inCon i,2010 Management unit of Capital Applications Growth Score (1) (2) (3) (4) (5) (6) (7) Æ i,t 0.808*** 0.238* 0.152* -1.629*** 2.166* (0.289) (0.128) (0.085) (0.594) (1.116) Æ i,t−1 0.0418** 1.759** (0.0197) (0.883) Model Panel Panel Panel Panel Panel Cross Section Cross Section Observations 3,632 517,165 13,522 5,991 13,249 506 183 R 2 0.968 0.757 0.562 0.872 0.843 0.421 0.020 Notes: Columns (1) to (5): model Y i,t = È i + È s,t,p + γ Æ i,t +
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- Productivity Growth 1995 2000 2005 2010 2015 year Figure A.7: Figures display evolution of Productivity (Cobb-Douglas, Value Added) for 1.5% random sample. Right panel shows residualized values after taking out FEs Figure A.8: Distribution of γ from equation ∆Éi,t = Èi +Èp,s,t +γ Æi,t +ηi,t. See section 5 for details. Distribution is computed from 50 (firm-level) bootstrapped sample. Industry level production function and firm level productivity growth is re-estimated for each bootstrapped sample. Estimates are all above zero (red vertical line) for all samples.
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- Productivity-enhancing activities can affect both terms. For instance a process innovation is more likely to increase Éq i,t while a product innovation should mainly affect θi,t, see Hall (2011) and Peters et al. (2017b). The main empirical specification (equation 9) of this paper can be re-written as: 1
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Standard errors (in parentheses) are clustered at firm level. *** p<0.01, ** p<0.05, * p<0.1 Previous volumes in this series No Title Author 710 March 2018 Exchange Rate Appreciations and Corporate Risk Taking Sebnem Kalemli-Ozcan, Xiaoxi Liu and Ilhyock Shim 709 March 2018 Does sovereign risk in local and foreign currency differ? Marlene Amstad, Frank Packer and Jimmy Shek 708 March 2018 Business Models and Dollar Funding of Global Banks Iñaki Aldasoro, Torsten Ehlers and Egemen Eren 707 March 2018 Global imbalances from a stock perspective.
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- Table A.7: Credit Supply Shock and Productivity Growth - Exclusion of small or large lenders VARIABLES Productivity Productivity Productivity Productivity (in delta Log) Exclude Small Large Small Large Banks Banks Banks Banks (1) (2) (3) (4) Manufacturing All Industries Æi,t 0.0176** 0.0335*** 0.0174*** 0.0188*** (0.00712) (0.00738) (0.00544) (0.00552) Observations 261,375 260,308 492,427 489,502 R-squared 0.164 0.172 0.197 0.211 Notes: ∆Éi,t = Èi + Ès,t,p + ηi,t. One observation is one firm for one year between 1998 and 2013 (unbalanced panel). Firm FEs and province×industry×year FEs are included. Singleton are dropped. Appendix C.4 details the classification of firms is “large bank†and “small bank†groups.
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- Table A.9: Exposure to Interbank in 2006 and growth of patent applications VARIABLES Patent Patent Patent Growth Growth Growth “Pre†[2003;2006] [2002;2006] [2002;2006] “Post†[2007;2009] [2007;2010] [2007;2009] (1) (2) (3) -1.463* -1.703** -1.738** (0.783) (0.834) (0.747) Observations 2,903 3,217 3,040 R-squared 0.058 0.054 0.057 Notes: Results of estimating model: PatentGrowthi = Èp + Ès + γINTBKi,2006 + ηi where PatentGrowthi = 2 P atentpost,i−P atentpre,i P atentpost,i+P atentpre,i . Patentpost,i (Patentpre,i) is the total number of patent applications made by firm i in the “pre†(“postâ€Â) period. The three columns are different because of the definition of “pre†and “post†periods.
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- The correction is exact if firms are monopolistic competitors, see De Loecker (2011).
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- The RHS variable Æi,t represents idiosyncratic shock to firm credit supply, and its construction is detailed in section 3.1. A 1% increase in Æi,t is the supply shock needed to increase the credit granted to firm i by 1%. The LHS variable is the first difference of productivity residual: ∆Éi,t = ∆yi,t − βk ∆ki,t − βl ∆li,t − βm ∆mi,t where y is log of net revenues, k is log of capital stock, l is labor (measured by log of wagebill) and m is log of intermediate inputs. Estimation of parameters β is described in section 3.2. Standard errors (in parentheses) are clustered at firm level. *** p<0.01, ** p<0.05, * p<0.1 Table A.8: Exposure to Interbank in 2006 and pre-2006 sensitivity to business cycle fluctuations.
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- The RHS variable IT BK i,2006 is the weighted average of firm’s i lenders’ liability on the interbank market over assets in 2006. The LHS variable is the first difference of Hicks-neutral productivity residual: ∆É i,t = ∆y i,t − ∆f (x i,t , β) where y is log of net revenues (or log of value added) and x is a set of inputs. Capital stock, labor, and (for the revenue case only) intermediate inputs are included in x. f (, β) is either a first (Cobb-Douglas) or second (Trans-Log) order polynomial in log inputs. Estimation of parameters β is described in section 3.2. Standard errors (in parentheses) are clustered at firm level. *** p<0.01, ** p<0.05, * p<0.1 . Table 8: Credit Supply Shock and Productivity-Enhancing Activities VARIABLES PCs per No. of Patents Patent R&D Export F
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- The RHS variable ITBKi,2006 is the weighted average of firm’s i lenders’ liability on the interbank market over assets in 2006. The LHS variable is the first difference of Hicks-neutral productivity residual: ∆Éi,t = ∆yi,t − ∆f(xi,t, β) where y is log of net revenues (or log of value added) and x is a set of inputs. Capital stock, labor, and (for the revenue case only) intermediate inputs are included in x. f(, β) is either a first (Cobb-Douglas) or second (Trans-Log) order polynomial in log inputs. Estimation of parameters β is described in section 3.2. Standard errors (in parentheses) are clustered at firm level. *** p<0.01, ** p<0.05, * p<0.1. Table 7: Exposure to Interbank Market and Productivity Growth -
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- They use a proxy for liquidity in 2007 (loans over deposits) to measure balance sheet strength.
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- To provide additional evidence that our measure of credit supply captures bank-level shocks, for each year t between 2007 and 2009 (credit crunch) we estimate the linear model: Æb,t = Æt + γ
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- VARIABLES Sensitivity Sensitivity Sensitivity Sensitivity to business cycle to business cycle to business cycle to business cycle (1) (2) (3) (4) All Industries Manufacturing ITBKi,2006 0.0767 0.0604 0.195-0.0292 (0.108) (0.0377) (0.225) (0.0464) Observations 34,004 34,004 17,759 17,759 R2 0.030 0.104 0.026 0.091 Output Value Added Revenues Value Added Revenues measure (delta log) (delta log) (delta log) (delta log) Notes: Model is βi = È + γ ITBKi,2006 + ηi. The RHS variable ITBKi,2006 is the weighted average of firm’s i lenders’ liability on the interbank market over assets in 2006. The LHS variable βi is the estimated parameter from the model ∆yi,t = αi + βi GDPgrt + i,t where ∆yi,t is the delta log of revenues or value added produced by firm i in year t < 2006 and GDPgrt is the growth rate of Italy’s GDP.
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Whited, Toni M, & Zhao, Jake. 2016. The misallocation of finance. Working paper. Figures 0 5 10 15 Density -.2 -.1 0 .1 Credit Supply Shock Figure 2: Histogram of credit supply shock. Normal distribution is superimposed. See section 3.1 for details. 0 2 4 6 8 Density -.5 0 .5 Productivity growth: CD - Rev 0 2 4 6 8 10 Density -.5 0 .5 Productivity growth: TL - Rev Figure 3: Histogram of productivity growth. Productivity is estimated as a residual from (log) revenues production function. Cobb-Douglas (top panel) or Trans-Log (bottom panel) functional form is assumed. Normal distribution is superimposed. See section 3.2 for details. -.05 0 .05 .1 T -3 T -2 T -1 T T + 1 T + 2 T + 3 CD - Rev TL - Rev -.05 0 .05 .1 .15 T -3 T -2 T -1 T T + 1 T + 2 T + 3