## 9. The Role of Financial Constraints ## Source details **Canonical URL:** [9. The Role of Financial Constraints](https://www.imf.org/-/media/files/publications/wp/2017/wp1767.pdf) ## Other formats - [Markdown version](/-/media/files/publications/wp/2017/wp1767.pdf.md) - [Structured JSON version](/-/media/files/publications/wp/2017/wp1767.pdf.json) --- ### Introduction and research question - Real business investment in Italy declined by over 12 percent during the 2010–2012 period. - Research question: Do bank-specific loan supply shocks during the European sovereign debt crisis causally reduce firms’ productivity in Italy, and through which channels? - Identification strategy: exploit heterogeneous pre-crisis syndicated-loan exposure of Italian banks to borrowers in financially-distressed euro area countries (Greece, Ireland, Portugal, Spain — “GIPS”), combined with the Amiti and Weinstein (2016) factor decomposition to separate credit supply from credit demand. ### Data, samples, and exposure construction - Data sources: - Syndicated loan data (Dealogic) 2005–2016; matched to firms (Orbis) and banks (SNL). - Samples: - Annual loan-level data 2005–2016: 47,205 firm-bank-year observations covering 39 banks and 814 firms. - Quarterly syndicated-loan sample 2010Q1–2012Q2: 12,238 firm-bank-quarter observations by 32 banks to 592 firms. - Firm-year aggregation (Orbis annual firm data): 1,132 firm-year observations including 316 firms. - Exposure measures: - exposure_b;09: bank b’s 2009 share of loans to borrowers in GIPS (recorded at 2009 level). - exposure_f;09: borrower-weighted share of lending from banks with higher exposure to GIPS. ### Key summary statistics (preserved exactly as presented) - Loans/Assets (mean): 0.18 - Bank Exposure (mean): 0.04 - Bank Total Assets (mean): 365 (million) - Bank Capital/Assets (mean): 0.07 - Bank Liquid Assets/Total Assets (mean): 0.26 - Bank Deposits/Assets (mean): 0.39 - Bank ROA (mean): -0.56 (%) - Bank NPL (mean): 0.11 (%) - Firm Exposure (mean): 0.04 - Investment (mean): 0.05; Investment (median): -0.01 - Employment (mean): 2885 - Capital-Labor Ratio (mean): 4.19 - Labor Productivity (mean): 0.22 - Revenue TFP (mean): 2.19 - Firm Total Assets (mean): 1.39 (million) - Firm Debt/Assets (mean): 0.44 - Observations: loan-level 12,238; firm-year 1,132 ### Empirical strategy (overview) - Loan-level regression (Equation 2): l_f,b,t = β_l · exposure_b;09 + bank controls (2009) + firm and time fixed effects. - Firm-level regressions (Equation 4): LT_f,t = β_fq · exposure_f;09 + firm controls + firm and quarter fixed effects; LT are loan growth, weighted-average spread (bps), weighted-average maturity (years). - Firm-year regressions (Equation 5): y_f,t = β_fa · exposure_f;09 + firm controls + firm and industry*time fixed effects; y includes labor productivity, TFP, investment, employment, capital-to-labor ratio. - Exogeneity check: exposure variable strongly negatively correlated with Amiti and Weinstein supply factors and uncorrelated with demand factors (Table 3). ### Main findings — bank lending channel (quarterly loan-level) - Banks with higher pre-crisis exposure to borrowers in GIPS reduced loan supply to Italian firms significantly (Table 4). - Pooled OLS coefficient examples: exposure coefficients range from -0.779 (simple pooled) to -0.012*** (specifications with firm*time or industry*time fixed effects and bank controls). - Moving a bank from the 10th to the 90th percentile in exposure reduces its loan supply by -2.8%. - Interpretation example: average and median syndicated loans in sample are 77.4 and 25.2 million Euros; a -2.8% reduction implies a decrease of 2.2 million Euros for the average loan. ### Main findings — financing substitutability (quarterly firm-level) - Firms could not fully substitute away from affected banks: - A one standard deviation increase in borrower exposure reduces loan growth by 21% (0.9 standard deviations). - Exposure increased average spread by 59.092 basis points. - No statistically significant effect on weighted-average maturity. - Conclusion: contraction in loan volume by affected banks passed through to aggregate firm borrowing and borrowing costs in the syndicated loan market. ### Main findings — productivity and channels (firm-year level) - Productivity effects (Table 6): - A one standard deviation increase in exposure reduces labor productivity by 10.9%. - A one standard deviation increase in exposure reduces total factor productivity by 5.8%. - Coefficients reported: exposure_f;09 = -0.109*** for LP and -0.058* for TFP. - Channels (Table 7): - Investment: exposure_f;09 = -0.087** (investment declines significantly). - Employment: exposure_f;09 = -1.008 (negative but not statistically significant). - Capital-to-labor ratio: exposure_f;09 = -0.110* (negative and statistically significant). - Interpretation: firms reduce investment more than employment, leading to sub-optimal capital-labor ratios (labor adjustment frictions / labor-hoarding), reducing productivity. - Heterogeneous effects: - Capital intensity (Table 8): - High K/L: exposure_f;09 = -0.214** on labor productivity and -0.123** on TFP. - Low K/L: exposure coefficients smaller and not statistically significant. - Financial constraints: - Investment impact stronger for financially constrained firms: exposure_f;09 = -0.094** for constrained vs -0.088 for unconstrained. - Employment effects not statistically significant for either group. ### Interpretation, model, and mechanism - Transmission: shock to banks’ asset side (rising borrowing costs and defaults for GIPS borrowers) reduced bank loan supply to Italian firms — a supply shock orthogonal to Italian firms’ demand. - Firms could not fully substitute funding; credit rationing lowered investment and caused inefficient input mixes due to labor adjustment frictions, thereby reducing productivity. - Model (Appendix A sketch): - Firms face asymmetric labor adjustment costs ½ϵ ( (l_t − l_{t−1})^2 / l_{t−1} ), with ϵ = 0 if l_t > l_{t−1} and ϵ > 0 otherwise. - Borrowing constraint b_{t+1} ≤ θ k_{t+1} and production y = A k^β l^{1−β}. - Capital-to-labor ratio: k / l = β (δ + r) / (w − ϵ Δ̄ l_t)^{1−β}; the wedge from adjustment costs lowers capital intensity and TFP. - Revenue productivity TFPR_t and labor productivity LP_t are reduced by the labor wedge (Equations (14) and (15)). ### Policy implications (conclusions) - Increase flexibility in allocation of labor within firms to limit the impact of credit-supply shocks on productivity — the “Jobs Act” is expected to yield benefits gradually over the long term. - Accelerate financial-sector repair to improve banking-system resilience: stricter supervisory oversight and faster reduction of high nonperforming loans. *Source: IMF Working Paper — Chapter 9: The Role of Financial Constraints (as provided in the source content).* ### References ............................................................................................................. ### References ### Document structure and appendices - References ............................................................................................................................... 23 - Appendix A ............................................................................................................................. 25 - Appendix B ............................................................................................................................. 26 ### Figures listed in the unit - 1. Labor Productivity and Real Investment ............................................................................... 4 - 2. TFP Distributions ................................................................................................................ 11 - 3. Firm Borrowing Costs in Financially-Distressed Euro Area Countries ............................. 12 - 4. Distribution of Borrower and Lender Relationships ........................................................... 28 ### Tables listed in the unit - 1. Loan Level Summary Statistics ........................................................................................... 10 - 2. Firm-Year Level Summary Statistics .................................................................................. 11 - 3. Retrieved Supply/Demand Factors and the Exposure Variable .......................................... 15 - 4. The Impact of Credit-Supply Shocks on Loans .................................................................. 16 - 5. Credit-Supply Shocks and Financing Substitutability ........................................................ 18 - 6. Credit Supply Shocks and Firm Productivity ...................................................................... 19 - 7. Credit Supply Shocks and Firm Productivity (Channels of Impact) ................................... 20 - 8. The Role of Capital Intensity .............................................................................................. 21 *Source: wp1767 - References (PDF), canonical URL provided by the source metadata.* ### 9. The Role of Financial Constraints ................................................................................... ### 9. The Role of Financial Constraints ### Introduction and research question - Real business investment in Italy declined by over 12 percent during the 2010–2012 period. - Research question: Do bank-specific loan supply shocks during the European sovereign debt crisis causally reduce firms’ productivity in Italy, and through which channels? - Identification strategy: exploit heterogeneous pre-crisis syndicated-loan exposure of Italian banks to borrowers in financially-distressed euro area countries (Greece, Ireland, Portugal, Spain — “GIPS”), combined with the Amiti and Weinstein (2016) factor decomposition to separate credit supply from credit demand. ### Data and exposure construction - Syndicated loan data (Dealogic) 2005–2016; matched to firms (Orbis) and banks (SNL). - Three samples: - Annual loan-level data 2005–2016: 47,205 firm-bank-year observations covering 39 banks and 814 firms. - Quarterly syndicated-loan sample 2010Q1–2012Q2: 12,238 firm-bank-quarter observations by 32 banks to 592 firms. - Firm-year aggregation (Orbis annual firm data): 1,132 firm-year observations including 316 firms. - Bank exposure measure (exposureb;09): bank b’s 2009 share of loans to borrowers in GIPS, recorded at pre-crisis 2009 level to avoid contemporaneous endogeneity. - Firm exposure (exposuref;09): borrower-weighted share of lending from banks with higher exposure to GIPS. ### Key summary statistics (from Tables 1 and 2 and text) - Loans/Assets (mean): 0.18 - Bank Exposure (mean): 0.04 - Bank Total Assets (mean): 365 (million) - Bank Capital/Assets (mean): 0.07 - Bank Liquid Assets/Total Assets (mean): 0.26 - Bank Deposits/Assets (mean): 0.39 - Bank ROA (mean): -0.56 (%) - Bank NPL (mean): 0.11 (%) - Firm Exposure (mean): 0.04 - Investment (mean): 0.05; Investment (median): -0.01 - Employment (mean): 2885 - Capital-Labor Ratio (mean): 4.19 - Labor Productivity (mean): 0.22 - Revenue TFP (mean): 2.19 - Firm Total Assets (mean): 1.39 (million) — table entry preserved as presented - Firm Debt/Assets (mean): 0.44 - Observations: loan-level 12,238; firm-year 1,132 ### Empirical strategy (overview) - Loan-level regression (Equation 2): l_f,b,t = β_l · exposure_b;09 + bank controls (2009) + firm and time fixed effects. - Firm-level regressions (Equation 4): LT_f,t = β_fq · exposure_f;09 + firm controls + firm and quarter fixed effects; LT are loan growth, weighted-average spread (bps), weighted-average maturity (years). - Firm-year regressions (Equation 5): y_f,t = β_fa · exposure_f;09 + firm controls + firm and industry*time fixed effects; y includes labor productivity, TFP, investment, employment, capital-to-labor ratio. - Exogeneity check: exposure variable strongly negatively correlated with Amiti and Weinstein supply factors and uncorrelated with demand factors (Table 3). ### Main findings — bank lending channel (quarterly loan-level) - Banks with higher pre-crisis exposure to borrowers in GIPS reduced loan supply to Italian firms significantly (Table 4). - Pooled OLS coefficient examples: exposure coefficients range from -0.779 (simple pooled) to -0.012*** (specifications with firm*time or industry*time fixed effects and bank controls) — reported exactly as in source Table 4. - Moving a bank from the 10th to the 90th percentile in exposure reduces its loan supply by -2.8%. - Interpretation example: average and median syndicated loans in sample are 77.4 and 25.2 million Euros; a -2.8% reduction implies a decrease of 2.2 million Euros for the average loan. ### Main findings — financing substitutability (quarterly firm-level) - Firms could not fully substitute away from affected banks: - A one standard deviation increase in borrower exposure reduces loan growth by 21% (0.9 standard deviations) (Table 5). - Exposure increased average spread by 59.092 (reported exactly) basis points (Table 5, Column 4). - No statistically significant effect on weighted-average maturity (Table 5, Column 5). - Conclusion: contraction in loan volume by affected banks passed through to aggregate firm borrowing and borrowing costs in the syndicated loan market. ### Main findings — productivity and channels (firm-year level) - Productivity effects (Table 6): - Exposure reduces labor productivity and TFP. Estimates (Table 6, Columns (3)–(4)): - A one standard deviation increase in exposure reduces labor productivity by 10.9%. - A one standard deviation increase in exposure reduces total factor productivity by 5.8%. - Coefficients reported in Table 6: exposuref;09 = -0.109*** for LP and -0.058* for TFP (preserved). - Channels (Table 7): - Investment: exposuref;09 = -0.087** (investment declines significantly). - Employment: exposuref;09 = -1.008 (negative but not statistically significant in Column (2) of Table 7). - Capital-to-labor ratio: exposuref;09 = -0.110* (negative and statistically significant in Column (3)). - Interpretation: firms reduce investment more than employment, leading to sub-optimal capital-labor ratios (labor adjustment frictions / labor-hoarding), reducing productivity. - Heterogeneous effects: - Capital intensity (Table 8): high K/L firms show stronger negative productivity effects. Examples: - High K/L: exposuref;09 = -0.214** on labor productivity and -0.123** on TFP. - Low K/L: exposure coefficients smaller and not statistically significant in corresponding columns. - Financial constraints (Table 9): investment impact stronger for financially constrained firms (exposuref;09 = -0.094** for constrained vs -0.088 for unconstrained); employment effects not statistically significant for either group. ### Interpretation and mechanism - Transmission: shock to banks’ asset side (rising borrowing costs and defaults for GIPS borrowers) reduced bank loan supply to Italian firms — a supply shock orthogonal to Italian firms’ demand. - Firms could not fully substitute funding; credit rationing lowered investment and caused inefficient input mixes due to labor adjustment frictions, thereby reducing productivity. - Model (Appendix A sketch): convex labor-adjustment costs prevent optimal employment adjustment after a tightening in borrowing constraints, producing reduced capital-to-labor ratios and lower productivity. ### Policy implications (conclusions) - Increase flexibility in allocation of labor within firms to limit the impact of credit-supply shocks on productivity — the “Jobs Act” is expected to yield benefits gradually over the long term. - Accelerate financial-sector repair to improve banking-system resilience: stricter supervisory oversight and faster reduction of high nonperforming loans. _italic Source: IMF Working Paper — Chapter 9: The Role of Financial Constraints (as provided in the source content)._ ### REFERENCES ### REFERENCES ### References cited - Acharya, V. V., T. Eisert, C. Eufinger, and C. W. Hirsch (2016a). Real Effects of the Sovereign Debt Crisis in Europe: Evidence from Syndicated Loans. Available at SSRN 2612855. - Acharya, V. V., T. Eisert, C. Eufinger, and C. W. Hirsch (2016b). Whatever It Takes: The Real Effects of Unconventional Monetary Policy. Available at SSRN 2740338. - Ackerberg, D., K. Caves, and G. Frazer (2006). Structural Identification of Production Functions. - Amiti, M. and D. E. Weinstein (2016). How Much Do Bank Shocks Affect Investment? Evidence from Matched Bank-Firm Loan Data. Journal of Political Economy. - Berton, F., S. Mocetti, A. Presbitero, and M. Richiardi (2017). Banks, Firms and Jobs. IMF Working Paper No. 17/38. - Bonaccorsi di Patti, E. and E. Sette (2016). Did the Securitization Market Freeze Affect Bank Lending During the Financial Crisis? Evidence from a Credit Register. Journal of Financial Intermediation 25, 54–76. - Borio, C. E., E. Kharroubi, C. Upper, and F. Zampolli (2016). Labour Reallocation and Productivity Dynamics: Financial Causes, Real Consequences. Bank for International Settlements Working Paper No. 534. - Bottero, M., S. Lenzu, and F. Mezzanotti (2015). Sovereign Debt Exposure and the Bank Lending Channel: Impact on Credit Supply and the Real Economy. Bank of Italy Temi di Discussione (Working Paper) No 1032. - Caballero, R. J., T. Hoshi, and A. K. Kashyap (2008, December). Zombie Lending and Depressed Restructuring in Japan. American Economic Review 98(5), 1943–77. - Cette, G., J. Lopez, and J. Mairesse (2016). Labour Market Regulations and Capital Intensity. NBER Working Paper No. 22603. - Chodorow-Reich, G. (2014). The Employment Effects of Credit Market Disruptions: Firm-level Evidence from the 2008–9 Financial Crisis. The Quarterly Journal of Economics 129(1), 1–59. - Cingano, F., F. Manaresi, and E. Sette (2016). Does Credit Crunch Investment Down? New Evidence on the Real Effects of the Bank-Lending Channel. Review of Financial Studies 29(10), 2737–2773. - Franklin, J., M. Rostom, and G. Thwaites (2015). The Banks that Said No: Banking Relationships, Credit Supply and Productivity in the United Kingdom. Bank of England Working Paper No. 557. - Gal, P. N. (2013). Measuring Total Factor Productivity at the Firm Level Using OECD-ORBIS. OECD Economics Department Working Papers No. 1049. - Jiménez, G., S. Ongena, J.-L. Peydró, and J. Saurina (2012). Credit Supply and Monetary Policy: Identifying the Bank Balance-Sheet Channel with Loan Applications. The American Economic Review 102(5), 2301–2326. - Khwaja, A. I. and A. Mian (2008). Tracing the Impact of Bank Liquidity Shocks: Evidence from an Emerging Market. The American Economic Review 98(4), 1413–1442. - Levinsohn, J. and A. Petrin (2003). Estimating Production Functions Using Inputs to Control for Unobservables. The Review of Economic Studies 70(2), 317–341. - Manaresi, F. and N. Pierri (2016). Credit Constraints and Firm Productivity: Evidence from Italy. - Peek, J. and E. S. Rosengren (2000). Collateral Damage: Effects of the Japanese Bank Crisis on Real Activity in the United States. American Economic Review 90(1), 30–45. - Syverson, C. (2011). What Determines Productivity? Journal of Economic Literature 49(2), 326–365. - Wooldridge, J. M. (2009). On Estimating Firm-level Production Functions Using Proxy Variables to Control for Unobservables. Economics Letters 104(3), 112–114. ### APPENDIX A — A model of asymmetric labor adjustment costs ### Model setup and constraints - Firms own capital stock k, borrow debt b at interest rate r, hire labor l on a competitive market at wage w, invest x, operate under monopolistic competition, and produce output y. - Demand is downward sloping and given by y^(−1) = ϕ. Capital depreciates at rate δ. Production function: y = A k^β l^(1−β). - Borrowing constraint: b_{t+1} ≤ θ k_{t+1}. Asymmetric labor adjustment costs: ½ϵ ( (l_t − l_{t−1})^2 / l_{t−1} ), where ϵ = 0 if l_t > l_{t−1} and ϵ > 0 otherwise. - For remainder of analysis set θ = 1 (a fall in θ is conceptually similar to a rise in r). ### Firm problem (objective) - Firms solve: max_{c,k',b',l} L = Σ_{t=0}^∞ β^t [ u(c) + λ [ (A k^β l^{1−β})^{(ϵ−1)/ϵ} − 1 ] / ((ϵ−1)/ϵ) − w l + b' − c − k' + (1−δ)k − (1+r)b − ϵ (l_t − l_{t−1})^2 / (2 l_{t−1}) ]. (Equation labeled (6) in source.) ### First-order conditions (as in source) - u'(c_t) = β^t. (7) - (ϵ−1)/ϵ β p_{t+1} y_{t+1} / k_{t+1} = δ + r_{t+1}. (8) - (ϵ−1)/ϵ (1−β) p_t y_t / l_t = w_t + ϵ [ (l_t − l_{t−1}) / l_{t−1} − (1 / (1 + r_{t+1})) (l_{t+1}^2 − l_t^2) / (2 l_t^2) ]. (9) ### Key definitions and implications - Define Δ̄ l_t ≡ [ (l_t − l_{t−1}) / l_{t−1} − (1 / (1+r_{t+1})) (l_{t+1}^2 − l_t^2) / (2 l_t^2) ]. - If l_t − l_{t−1} < 0 then Δ̄ l_t > 0 (note if l_{t+1} − l_t > 0 then ϵ = 0). Interpretation: labor adjustment costs act like a tax on labor; shedding workers imposes extra cost. ### Capital-to-labor ratio and productivity effects - Capital-to-labor ratio: k / l = β (δ + r) / (w − ϵ Δ̄ l_t)^{1−β}. (10) - If firms reduce labor, adjustment costs bias the capital-labor ratio downwards. - A rise in r induces firms to reduce capital and to reduce labor less; the wedge prevents sufficient labor reduction so firms operate with inefficiently low capital intensity. - Marginal revenue products: MRPK_t = (ϵ−1)/ϵ β p_t y_t / k_t = δ + r_t. (11) MRPL_t = (ϵ−1)/ϵ (1−β) p_t y_t / l_t = w_t − ϵ Δ̄ l_t. (12) - Revenue productivity (TFPR): TFPR_t = p_t y_t k_t^{−β} l_t^{−(1−β)} = [ (p_t y_t / k_t)^β (p_t y_t / l_t)^{1−β} ]^{ϵ/(ϵ−1)} = [ (δ + r_t)^β (w_t − ϵ Δ̄ l_t)^{1−β} ]^{ϵ/(ϵ−1)}. (14) - Conclusion: TFP is a function of the labor wedge. Adjustment costs prevent optimal reallocation between MRPL and MRPK and reduce firms’ TFP. - Labor productivity: LP_t = p_t y_t / l_t = [ (ϵ/(ϵ−1)) MRPL_t ]^{1/(1−β)}. (15) - The wedge also reduces firms’ labor productivity. ### APPENDIX B — Decomposing Loan Growth into Demand and Supply Factors ### Identification strategy - Starting identity (Khwaja and Mian (2008) framework): ΔL_{f,b,t} = α_{f,t} + ϕ_{b,t} + ε_{f,b,t}. (16) - ΔL_{f,b,t} = (L_{f,b,t} − L_{f,b,t−1}) / L_{f,b,t−1} is loan growth of firm f from bank b at time t. - α_{f,t} captures firm borrowing channel; ϕ_{b,t} denotes bank lending channel. - Bank b total loan growth D^B_{b,t} and firm f total loan growth D^F_{f,t} are weighted sums of micro ΔL_{f,b,t} with weights L_{f,b,t−1} / Σ_f L_{f,b,t−1} and L_{f,b,t−1} / Σ_b L_{f,b,t−1} respectively. - Amiti and Weinstein (2016) identification: recover α_{f,t} and ϕ_{b,t} by solving the system (up to a numeraire): D^B_{b,t} = Σ_f (L_{f,b,t−1} / Σ_f L_{f,b,t−1}) α_{f,t} + ϕ_{b,t}. (17) D^F_{f,t} = α_{f,t} + Σ_b (L_{f,b,t−1} / Σ_b L_{f,b,t−1}) ϕ_{b,t}. (18) ### Aggregate decomposition - Once α_{f,t} and ϕ_{b,t} are recovered, aggregate loan growth D_t can be decomposed as: D_t = (Ā_t + B̄_t) + W^B_{t−1} · (Â_{t} + 𝔅_{t}). (19) - (Ā_t + B̄_t) is the common factor affecting all banks and firms (median firm borrowing and bank supply conditions). - Â_t and 𝔅_t are vectors stacking deviations α_{f,t} and ϕ_{b,t} from Ā_t and B̄_t. - W^B_{t−1} is the share of bank b’s loan volume out of total lending by all banks in year t; 𝛽_{t−1} is a weighting matrix. ### Identification requirements and sample connectivity - Successful identification of α_{f,t} and ϕ_{b,t} requires at least two connections for each bank or firm. - Sample connectivity in this study: - Due to the syndicated nature of the loans market in the paper, 92.5% of firms have more than one bank relationship per quarter. - The average bank lends to several hundred firms at each quarter. - Focus is limited to observations satisfying the required number of connections; resulting loss in sample size is minimal. ### Figure 4 (distribution of relationships) - Figure 4 displays: - Top panel: number of borrowers per bank (x-axis categories: 1-25, 26-50, 51-100, 101-150, 151-200, 201-250, 251-300, >300; y-axis frequency labeled "Fraction of Banks"). - Bottom panel: number of banks lending to a firm (x-axis integer counts 1–15; y-axis frequency labeled "Fraction of Firms"). - Source: Authors’ calculations. *Source: wp1767 - REFERENCES (PDF chapter/section).* --- _Source: https://www.imf.org/-/media/files/publications/wp/2017/wp1767.pdf_