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Natural disasters and firm resilience in Italian industrial districts - Giulio Cainelli, Andrea Fracasso, Giuseppe Vittucci Marzetti

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Presentation of Giuseppe Vittucci Marzetti, Department of Sociology and Social Research, University of Milano-Bicocca, Italy at the third meeting of the Spatial productivity Lab of the OECD Trento Centre held on 7 February 2019.

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Natural disasters and firm resilience in Italian industrial districts - Giulio Cainelli, Andrea Fracasso, Giuseppe Vittucci Marzetti

  1. 1. Aim and theoretical framework Data and empirical methodology Estimation results Closing remarks Natural disasters and firm resilience in Italian industrial districts Giulio Cainelli,1 Andrea Fracasso,2 Giuseppe Vittucci Marzetti3 OECD SPL, Trento February 7, 2019 1Dpt. of Economics and Management, University of Padova, email: giulio.cainelli@unipd.it 2Department of Economics and Management and School of International Studies, University of Trento, email: andrea.fracasso@unitn.it 3Department of Sociology and Social Research, University of Milano-Bicocca, email: giuseppe.vittucci@unimib.it Cainelli, Fracasso, Vittucci Marzetti Natural disasters and firm resilience in Italian industrial districts 1/29
  2. 2. Aim and theoretical framework Data and empirical methodology Estimation results Closing remarks Layout 1 Aim and theoretical framework Aim and background Literature review: natural disasters and economic performance Theoretical background: industrial districts and resilience On natural disasters as natural experiments 2 Data and empirical methodology The 2012 Emilia-Romagna earthquake Data Research questions Empirical methodology 3 Estimation results Average impact of the earthquake Industrial district effect 4 Closing remarks Cainelli, Fracasso, Vittucci Marzetti Natural disasters and firm resilience in Italian industrial districts 2/29
  3. 3. Aim and theoretical framework Data and empirical methodology Estimation results Closing remarks Aim and background Literature review: natural disasters and economic performance Theoretical background: industrial districts and resilience On natural disasters as natural experiments Aim and background The impact of natural disasters on economic performance and growth has recently become an object of research. Underlying idea and implicit assumptions: most natural events are unpredictable and “random”; natural events are exogenous shocks that can be used as natural experiments to test economic hypotheses; highly localized events impact mostly firms’ production and not final demand, facilitating identification. This work falls within this strand of the literature: use a large sample of firms in Emilia-Romagna in the period 2010-2013, around the time of a localized earthquake sequence of severe intensity (May 2012) assess whether the location of firms within an industrial district mitigates or exacerbates the impact of the disaster on their activity and performance. Cainelli, Fracasso, Vittucci Marzetti Natural disasters and firm resilience in Italian industrial districts 3/29
  4. 4. Aim and theoretical framework Data and empirical methodology Estimation results Closing remarks Aim and background Literature review: natural disasters and economic performance Theoretical background: industrial districts and resilience On natural disasters as natural experiments Natural disasters and economic performance Most contributions focusing on the relationship between natural disasters and economic performance are cross-country and use macroeconomic data (Cavallo & Noy, 2009; Lazzaroni & van Bergeijk; 2014). These analysis do not allow to detect how local conditions and individual factors interact with the shocks (Barone & Mocetti, 2014). Only few studies investigate firms’ performance after a localized major supply shock by using firm-level data. Cainelli, Fracasso, Vittucci Marzetti Natural disasters and firm resilience in Italian industrial districts 4/29
  5. 5. Aim and theoretical framework Data and empirical methodology Estimation results Closing remarks Aim and background Literature review: natural disasters and economic performance Theoretical background: industrial districts and resilience On natural disasters as natural experiments Firm-level studies on the impact of natural disasters Cole et al. (2013) use plant-level Japanese data to estimate the impact of 1995 Kobe earthquake on firms’ survival. Highly damaged firms face higher risk of exit. In surviving firms: value added and employment lower during the reconstruction and higher afterwards; productivity always higher. Mel et al. (2012) investigate business recovery in Sri Lanka after the 2004 tsunami: Affected firms lag behind unaffected comparable firms. Direct aid helps recovery, more in services than in manufacturing. Fabling et al. (2014) analyze the impact of the Canterbury earthquakes in 2010-2011 in New Zealand: Pre-shock profitability increases survival probability. Coelli & Manasse (2014) investigate the impact of the floods in Veneto in 2010: After recovery, affected firms perform better than unaffected. Aid transfers in the aftermath significantly contribute to the recovery. Cainelli, Fracasso, Vittucci Marzetti Natural disasters and firm resilience in Italian industrial districts 5/29
  6. 6. Aim and theoretical framework Data and empirical methodology Estimation results Closing remarks Aim and background Literature review: natural disasters and economic performance Theoretical background: industrial districts and resilience On natural disasters as natural experiments Firm-level studies on the impact of natural disasters Hayakawa et al. (2015) analyze how the 2011 flood in Thailand affected the procurement patterns at Japanese affiliates of MNCs: Natural disasters do not have persistent effects. Adjustments among suppliers by MNCs depend on ex-ante knowledge of alternative sources. Todo et al. (2013) and Tokui et al. (2017) investigate the role of supply chains in firms’ recovery after the 2011 Great East Japan earthquake: Supply chains have two opposite effects: make recovery harder because of higher vulnerability to network disruption; facilitate recovery through support from trading partners, easier search for new partners, and agglomeration economies. evidence is that the positive effects exceed the negative ones. Cole et al. (2015) analyze the effect of clustering on survival in the 1995 Kobe earthquake in Japan: Firms’ location in clusters reduces survival probabilities. It does not impact much on firms’ performance after the shock. Cainelli, Fracasso, Vittucci Marzetti Natural disasters and firm resilience in Italian industrial districts 6/29
  7. 7. Aim and theoretical framework Data and empirical methodology Estimation results Closing remarks Aim and background Literature review: natural disasters and economic performance Theoretical background: industrial districts and resilience On natural disasters as natural experiments Theoretical background: industrial districts and resilience While Industrial Districts (IDs) generate positive externalities, at a theoretical level it is not clear whether they strengthen or weaken firms’ vulnerability to large negative supply shocks: + higher resilience for: Agglomeration externalities: higher productivity, profitability, survival rates in good times. Risk sharing via interlinking transactions (Dei Ottati, 1994; Cainelli, Montresor & Vittucci Marzetti, 2012). Fiscal stimulus and external aid flowing faster towards IDs, insofar they have vantage positions in terms of signaling, lobbying and political connections (Brioschi et al., 2002; Brusco, 1982; Brusco et al., 1996; Cainelli & Zoboli, 2004; Noy, 2009). – lower resilience for: Shock transmission via supply chains (Henriet et al., 2012, Carvalho et al., 2014). Higher reliance on local public goods. Localized lending relationships and risk sharing mechanisms, increasing the probability of mass defaults (Cainelli, Montresor & Vittucci Marzetti, 2012). Cainelli, Fracasso, Vittucci Marzetti Natural disasters and firm resilience in Italian industrial districts 7/29
  8. 8. Aim and theoretical framework Data and empirical methodology Estimation results Closing remarks Aim and background Literature review: natural disasters and economic performance Theoretical background: industrial districts and resilience On natural disasters as natural experiments Research question and analysis As the theory does not tell whether IDs make firms more or less resilient to disruptive supply shocks, the overall effect is an empirical issue. By using firm-level data in 2010-2013 for a large sample of firms in Emilia-Romagna (hit by an earthquake sequence in May 2012), we estimate the effect of natural disasters on affected firms and the differential impact on those located within/outside IDs. Cole et al. (2015) is the closest paper in spirit. Main differences besides the natural disaster We focus on IDs rather than clusters; We apply different techniques and estimation methods. Cainelli, Fracasso, Vittucci Marzetti Natural disasters and firm resilience in Italian industrial districts 8/29
  9. 9. Aim and theoretical framework Data and empirical methodology Estimation results Closing remarks Aim and background Literature review: natural disasters and economic performance Theoretical background: industrial districts and resilience On natural disasters as natural experiments Natural disasters as natural experiments: methodological issues We argue that natural disasters cannot be treated as “exogenous” in econometric terms and that they do not give rise to “natural experiments” cause: The spatial distribution of firms is correlated with characteristics at the firm level that affect firms’ performance and resilience. The unconditional probability of a firm being “treated” (hit by the shock) is not “as good as random”, for it is correlated with such characteristics. Example: Due to specialization economies, firms in the same sector tend to be spatially concentrated. When an earthquake hits a region, the “treatment group” (the firms hit) and the “control group” (firms not hit) are systematically different, at least in terms of the sector they belong to. This calls for caution and analytical tools to address the issue. Cainelli, Fracasso, Vittucci Marzetti Natural disasters and firm resilience in Italian industrial districts 9/29
  10. 10. Aim and theoretical framework Data and empirical methodology Estimation results Closing remarks The 2012 Emilia-Romagna earthquake Data Research questions Empirical methodology Emilia-Romagna earthquake in 2012 Part of the Emilia-Romagna region (North-East of Italy) hit by a sequence of major earthquakes between May 20 and June 6 2012. Widespread structural damages: historical buildings collapsed, and warehouses and factories partially or totally destroyed. This natural disaster: has not yet been covered in the literature; is interesting for in the region: the industrial density is high; there are several Industrial Districts (IDs). Source: Wikimedia. Cainelli, Fracasso, Vittucci Marzetti Natural disasters and firm resilience in Italian industrial districts 10/29
  11. 11. Aim and theoretical framework Data and empirical methodology Estimation results Closing remarks The 2012 Emilia-Romagna earthquake Data Research questions Empirical methodology Data Bureau van Dijk financial information on about 26,000 firms (manufacturing and KIBS) located in Emilia-Romagna during the period 2010-2013 Industrial district Earthquake Total No Yes No 14,937 1,886 16,823 Yes 6,940 2,522 9,462 Total 21,877 4,408 26,285 Dependent variables: Turnover. Tangibles. Bank debt/sales ratio. Value Added (VA). Production value. Return On Equity (ROE): Net Income/Equity %. Return On Sales (ROS): EBIT/Net Sales %. Cainelli, Fracasso, Vittucci Marzetti Natural disasters and firm resilience in Italian industrial districts 11/29
  12. 12. Aim and theoretical framework Data and empirical methodology Estimation results Closing remarks The 2012 Emilia-Romagna earthquake Data Research questions Empirical methodology Distribution by sector ATECO 2-digit no earthquake - no district earthquake - no district no earthquake - district earthquake - district Total No. Col % Row % No. Col % Row % No. Col % Row % No. Col % Row % No. Col % 10 542 3.6 50.7 52 2.8 4.9 411 5.9 38.4 65 2.6 6.1 1070 4.1 11 36 0.2 42.4 4 0.2 4.7 34 0.5 40.0 11 0.4 12.9 85 0.3 13 39 0.3 18.8 14 0.7 6.7 45 0.6 21.6 110 4.4 52.9 208 0.8 14 173 1.2 28.3 72 3.8 11.8 111 1.6 18.2 255 10.1 41.7 611 2.3 15 99 0.7 63.5 9 0.5 5.8 40 0.6 25.6 8 0.3 5.1 156 0.6 16 147 1.0 55.7 23 1.2 8.7 62 0.9 23.5 32 1.3 12.1 264 1.0 17 80 0.5 48.5 13 0.7 7.9 46 0.7 27.9 26 1.0 15.8 165 0.6 18 234 1.6 59.8 22 1.2 5.6 85 1.2 21.7 50 2.0 12.8 391 1.5 19 2 0.0 25.0 0 0.0 0.0 4 0.1 50.0 2 0.1 25.0 8 0.0 20 174 1.2 58.8 32 1.7 10.8 77 1.1 26.0 13 0.5 4.4 296 1.1 21 17 0.1 48.6 6 0.3 17.1 6 0.1 17.1 6 0.2 17.1 35 0.1 22 226 1.5 46.0 40 2.1 8.1 142 2.0 28.9 83 3.3 16.9 491 1.9 23 368 2.5 67.3 17 0.9 3.1 136 2.0 24.9 26 1.0 4.8 547 2.1 24 63 0.4 46.3 21 1.1 15.4 39 0.6 28.7 13 0.5 9.6 136 0.5 25 1392 9.3 50.3 259 13.7 9.4 752 10.8 27.2 366 14.5 13.2 2769 10.5 26 196 1.3 49.5 40 2.1 10.1 123 1.8 31.1 37 1.5 9.3 396 1.5 27 268 1.8 51.8 57 3.0 11.0 139 2.0 26.9 53 2.1 10.3 517 2.0 28 1184 7.9 52.7 195 10.3 8.7 614 8.8 27.3 254 10.1 11.3 2247 8.5 29 94 0.6 46.8 19 1.0 9.5 61 0.9 30.3 27 1.1 13.4 201 0.8 30 81 0.5 70.4 8 0.4 7.0 25 0.4 21.7 1 0.0 0.9 115 0.4 31 135 0.9 46.4 14 0.7 4.8 126 1.8 43.3 16 0.6 5.5 291 1.1 32 210 1.4 61.4 23 1.2 6.7 70 1.0 20.5 39 1.5 11.4 342 1.3 62 683 4.6 62.1 78 4.1 7.1 272 3.9 24.7 67 2.7 6.1 1100 4.2 63 549 3.7 63.2 50 2.7 5.8 215 3.1 24.8 54 2.1 6.2 868 3.3 68 5053 33.8 60.2 540 28.6 6.4 2124 30.6 25.3 671 26.6 8.0 8388 31.9 69 323 2.2 64.0 27 1.4 5.3 141 2.0 27.9 14 0.6 2.8 505 1.9 70 997 6.7 65.9 81 4.3 5.4 360 5.2 23.8 74 2.9 4.9 1512 5.8 71 580 3.9 60.2 69 3.7 7.2 263 3.8 27.3 51 2.0 5.3 963 3.7 72 105 0.7 62.1 16 0.8 9.5 42 0.6 24.9 6 0.2 3.6 169 0.6 73 340 2.3 62.7 36 1.9 6.6 145 2.1 26.8 21 0.8 3.9 542 2.1 74 546 3.7 60.9 49 2.6 5.5 230 3.3 25.7 71 2.8 7.9 896 3.4 Total 14937 100.0 56.8 1886 100.0 7.2 6940 100.0 26.4 2522 100.0 9.6 26285 100.0 Cainelli, Fracasso, Vittucci Marzetti Natural disasters and firm resilience in Italian industrial districts 12/29
  13. 13. Aim and theoretical framework Data and empirical methodology Estimation results Closing remarks The 2012 Emilia-Romagna earthquake Data Research questions Empirical methodology Distribution by Province Province no earthquake - no district earthquake - no district no earthquake - district earthquake - district Total No. Col % Row % No. Col % Row % No. Col % Row % No. Col % Row % No. Col % Bologna 5931 39.7 85.6 940 49.8 13.6 2 0.0 0.0 57 2.3 0.8 6930 26.4 Ferrara 201 1.3 17.2 840 44.5 72.0 75 1.1 6.4 51 2.0 4.4 1167 4.4 Forl`ı-Cesena 964 6.5 51.0 0 0.0 0.0 925 13.3 49.0 0 0.0 0.0 1889 7.2 Modena 1391 9.3 24.6 0 0.0 0.0 2498 36.0 44.1 1773 70.3 31.3 5662 21.5 Parma 2686 18.0 93.8 0 0.0 0.0 177 2.6 6.2 0 0.0 0.0 2863 10.9 Piacenza 1076 7.2 85.3 0 0.0 0.0 185 2.7 14.7 0 0.0 0.0 1261 4.8 Ravenna 867 5.8 52.6 0 0.0 0.0 781 11.3 47.4 0 0.0 0.0 1648 6.3 Reggio nell’Emilia 298 2.0 9.1 106 5.6 3.2 2244 32.3 68.2 641 25.4 19.5 3289 12.5 Rimini 1523 10.2 96.6 0 0.0 0.0 53 0.8 3.4 0 0.0 0.0 1576 6.0 Total 14937 100.0 56.8 1886 100.0 7.2 6940 100.0 26.4 2522 100.0 9.6 26285 100.0 Cainelli, Fracasso, Vittucci Marzetti Natural disasters and firm resilience in Italian industrial districts 13/29
  14. 14. Aim and theoretical framework Data and empirical methodology Estimation results Closing remarks The 2012 Emilia-Romagna earthquake Data Research questions Empirical methodology Research questions 1 What was the average effect of the earthquake sequence on firms’ performance in the short- and medium-term? 2 How did the location in an ID mediate the impact of the earthquake on firms’ performance? Cainelli, Fracasso, Vittucci Marzetti Natural disasters and firm resilience in Italian industrial districts 14/29
  15. 15. Aim and theoretical framework Data and empirical methodology Estimation results Closing remarks The 2012 Emilia-Romagna earthquake Data Research questions Empirical methodology Estimating the average impact of the earthquake To estimate the average effect of the earthquake on firms’ performance we employ two alternative approaches: Difference-In-Differences (DID). Propensity Score Matching (PSM) in: levels; first-differences. Cainelli, Fracasso, Vittucci Marzetti Natural disasters and firm resilience in Italian industrial districts 15/29
  16. 16. Aim and theoretical framework Data and empirical methodology Estimation results Closing remarks The 2012 Emilia-Romagna earthquake Data Research questions Empirical methodology Estimating the average impact of the earthquake: DID The DID approach compares the change in the performance of firms located in an area hit by the earthquake with that of firms placed in areas not affected by the disaster, after controlling for a number of firm- and area-specific characteristics. The impact of the earthquake is captured by estimating either: yit = ai + β0tt + β1ei + β2ei tt + uit (1) or: ∆yi = δ0 + δ1ei + δ2Xi + νi (2) yit is the performance variable of interest (turnover, tangibles, debt/sales, VA, production, ROE, ROS) for the firm i in period t; tt is a time dummy equal to 1 for the period after the earthquake and 0 otherwise (t ∈ {0, 1} is the pre/post earthquake period); ei is the earthquake dummy, equal to 1 if the firm is located in an area hit by the earthquake; Xi is a vector of firm-level controls (sector, ID dummy, year of incorporation). Cainelli, Fracasso, Vittucci Marzetti Natural disasters and firm resilience in Italian industrial districts 16/29
  17. 17. Aim and theoretical framework Data and empirical methodology Estimation results Closing remarks The 2012 Emilia-Romagna earthquake Data Research questions Empirical methodology Estimating the average impact of the earthquake: DID Equation (1): Fixed-Effects (FE) is a consistent estimator. The parameter of interest is the coefficient attached to the interacting term ei tt (β2). Equation (2): The model is in first-differences and includes firm-specific controls possibly affecting the rate of change. OLS is a consistent estimator. The parameter of interest is the coefficient attached to the dummy ei (δ1). Cainelli, Fracasso, Vittucci Marzetti Natural disasters and firm resilience in Italian industrial districts 17/29
  18. 18. Aim and theoretical framework Data and empirical methodology Estimation results Closing remarks The 2012 Emilia-Romagna earthquake Data Research questions Empirical methodology Estimating the average impact of the earthquake: PSM An alternative approach to quantify the average effect of the earthquake is the Propensity Score Matching (PSM). PSM controls for confounding factors in the estimation of the impact of the treatment by ensuring that the comparison is performed using treated and control units that are as similar as possible. Steps: The pre-treatment characteristics of the firms are summarized in a single variable (the propensity score) by means of a probit/logit estimation; Similar treated and control firms are matched; The average effect of the treatment on the treated is computed as the average difference between the values of the variable of interest for the treated and control firms in each pair of matched firms. This approach requires that the sample contains enough couples of treated and control units with the same propensity score. If industries are entirely concentrated in one area, no control group is actually available. The PSM can be applied to levels and first-differences. Cainelli, Fracasso, Vittucci Marzetti Natural disasters and firm resilience in Italian industrial districts 18/29
  19. 19. Aim and theoretical framework Data and empirical methodology Estimation results Closing remarks The 2012 Emilia-Romagna earthquake Data Research questions Empirical methodology Estimating the effect of industrial districts To analyze the possible influence of being in an ID on the impact of the earthquake, in Eq. (1) and (2) we add a district dummy with the associated interacting terms aimed at capturing differences in the average impact of a unique treatment, i.e. the earthquake, for district vs. non-district firms. yit = ci + γ0tt + γ1di tt + γ2ei tt + γ3ei di tt + it (3) or ∆yi = π0 + π1di + π2ei + π3ei di + π4Xi + νi (4) where di is a district dummy (equal to 1 if the firm is in a district). The parameters of interest are γ3 in Eq. (3) and π3 in Eq. (4). Cainelli, Fracasso, Vittucci Marzetti Natural disasters and firm resilience in Italian industrial districts 19/29
  20. 20. Aim and theoretical framework Data and empirical methodology Estimation results Closing remarks Average impact of the earthquake Industrial district effect Time series of mean turnover and VA for firms hit/not hit by the earthquake (a) Turnover (b) Value Added Cainelli, Fracasso, Vittucci Marzetti Natural disasters and firm resilience in Italian industrial districts 20/29
  21. 21. Aim and theoretical framework Data and empirical methodology Estimation results Closing remarks Average impact of the earthquake Industrial district effect Time series of mean tangibles and debt-sales ratio for firms hit and not hit by the earthquake (c) ln Tangibles (d) Bank debt/sales ratio Cainelli, Fracasso, Vittucci Marzetti Natural disasters and firm resilience in Italian industrial districts 21/29
  22. 22. Aim and theoretical framework Data and empirical methodology Estimation results Closing remarks Average impact of the earthquake Industrial district effect Average impact of the earthquake: estimation results PSM in levels Panel FE: 2010/11– First difference: 2010/11– Dependent variable 2012 2013 2012 2013 2012 2013 OLS PSM OLS PSM ln(turnover) -.0033 .0318 -.0281∗∗ -.0003 -.0225∗∗ -.0223∗ .0150 .0138 (.0362) (.0367) (.0118) (.0150) (.0114) (.0123) (.0145) (.0158) ln(tangibles) .0036 .0382 .0015 .0299∗ .0010 -.0004 .0301∗ .0295∗ (.0457) (.0463) (.0117) (.0158) (.0117) (.0126) (.0164) (.0175) debt/sales -.3758 -.0487 .8761∗∗∗ 1.107∗∗∗ .7413∗∗∗ .7469∗∗∗ 1.012∗∗∗ .9642∗∗∗ (.4747) (.4792) (.2646) (.3302) (.2807) (.2967) (.3403) (.3579) ln(value-added) -.0377 -.0210 -.0471∗∗∗ -.0094 -.0451∗∗∗ -.0442∗∗∗ -.0051 -.0065 (.0366) (.0374) (.0129) (.0151) (.0134) (.0142) (.0151) (.0161) ln(production) -.0117 .0301 -.0368∗∗∗ .0002 -.0282∗∗∗ -.0289∗∗∗ .0136 .0123 (.0354) (.0358) (.0111) (.0140) (.0108) (.0115) (.0137) (.0147) ROE .2943 1.056∗∗ -.8054 .1686 -.4791 -.3078 .3343 .4914 (.5623) (.5366) (.5366) (.5606) (.5739) (.5970) (.5753) (.6102) ROS -.8103∗∗∗ -.4231∗ -.6227∗∗∗ .0589 -.5348∗∗∗ -.5658∗∗∗ .0930 .0468 (.2483) (.2450) (.2148) (.2350) (.2311) (.2426) (.2457) (.2588) Robust standard errors in parenthesis. Controls for sector (4-digit), incorporation year and district dummy included in OLS (OLS regression) and PSM (Propensity Score Matching). Significance at: 1% ∗∗∗; 5% ∗∗; 10% ∗. Cainelli, Fracasso, Vittucci Marzetti Natural disasters and firm resilience in Italian industrial districts 22/29
  23. 23. Aim and theoretical framework Data and empirical methodology Estimation results Closing remarks Average impact of the earthquake Industrial district effect Average impact of the earthquake: main results Short-term (6-7 months) average impact of earthquake on activity and efficiency: statistically significant decrease of turnover, production value, value added (and ROS); increase in debt-sales ratios. No evidence of longer term effects (18 months), but for the slightly higher debt-sales ratio. Biased estimates in PSM in levels for systematic differences in pre-treatment levels between firms hit/not hit by the earthquake not accounted by the controls. Cainelli, Fracasso, Vittucci Marzetti Natural disasters and firm resilience in Italian industrial districts 23/29
  24. 24. Aim and theoretical framework Data and empirical methodology Estimation results Closing remarks Average impact of the earthquake Industrial district effect The effect of industrial districts: FE estimator Dependent variable Regressor Years: 2010/11- 2012 2013 ln(turnover) District dummy (d) · Time dummy (t) .0026 (.0104) -.0056 (.0136) Earthquake dummy (e) · Time dummy (t) -.0083 (.0175) .0186 (.0197) e · d · t -.0358 (.0241) -.0304 (.0286) e · t + e · d · t -.0441∗∗∗ (.0166) -.0118 (.0207) ln(tangibles) District dummy (d) · Time dummy (t) .0167 (.0106) .0155 (.0141) Earthquake dummy (e) · Time dummy (t) .0048 (.0154) .0322 (.0234) e · d · t -.0130 (.0226) -.0107 (.0324) e · t + e · d · t -.0082 (.0166) .0214 (.0223) debt/sales District dummy (d) · Time dummy (t) .0735 (.2424) .0583 (.2945) Earthquake dummy (e) · Time dummy (t) .1706 (.3697) .2849 (.4767) e · d · t 1.229∗∗ (.5424) 1.477∗∗ (.6715) e · t + e · d · t 1.400∗∗∗ (.3968) 1.761∗∗∗ (.4802) ln(value-added) District dummy (d) · Time dummy (t) .0044 (.0114) .0099 (.0134) Earthquake dummy (e) · Time dummy (t) -.0253 (.0185) .0046 (.0211) e · d · t -.0401 (.0261) -.0291 (.0296) e · t + e · d · t -.0655∗∗∗ (.0184) -.0245 (.0208) Robust standard errors in parenthesis. Significance at: 1% ∗∗∗; 5% ∗∗; 10% ∗. Cainelli, Fracasso, Vittucci Marzetti Natural disasters and firm resilience in Italian industrial districts 24/29
  25. 25. Aim and theoretical framework Data and empirical methodology Estimation results Closing remarks Average impact of the earthquake Industrial district effect The effect of industrial districts: FE estimator Dependent variable Regressor Years: 2010/11- 2012 2013 ln(production) District dummy (d) · Time dummy (t) .0019 (.0101) .0013 (.0128) Earthquake dummy (e) · Time dummy (t) -.0116 (.0149) .0204 (.0188) e · d · t -.0451∗∗ (.0210) -.0359 (.0268) e · t + e · d · t -.0566∗∗∗ (.0148) -.0155 (.0198) ROE District dummy (d) · Time dummy (t) .2851 (.4725) 1.046∗∗ (.4912) Earthquake dummy (e) · Time dummy (t) -.6541 (.7976) 1.038 (.7755) e · d · t -.1384 (1.103) -1.979∗ (1.111) e · t + e · d · t -.7925 (.7616) -.9412 (.7955) ROS District dummy (d) · Time dummy (t) .0037 (.1922) .0920 (.2112) Earthquake dummy (e) · Time dummy (t) -.4211 (.3218) .3739 (.3458) e · d · t -.3526 (.4426) -.5904 (.4693) e · t + e · d · t -.7737∗∗ (.3039) -.2166 (.3172) Robust standard errors in parenthesis. Significance at: 1% ∗∗∗; 5% ∗∗; 10% ∗. Cainelli, Fracasso, Vittucci Marzetti Natural disasters and firm resilience in Italian industrial districts 25/29
  26. 26. Aim and theoretical framework Data and empirical methodology Estimation results Closing remarks Average impact of the earthquake Industrial district effect The effect of industrial districts: FD Dependent variable Regressor Years: 2010/11- 2012 2013 ∆ ln(turnover) District dummy (d) -.0039 (.0109) -.0149 (.0138) Earthquake dummy (e) -.0094 (.0154) .0206 (.0200) e · d -.0254 (.0225) -.0109 (.0289) e + e · d -.0348∗∗ (.0166) .0100 (.0211) ∆ ln(tangibles) District dummy (d) .0103 (.0107) .0049 (.0142) Earthquake dummy (e) .0055 (.0155) .0334 (.0230) e · d -.0087 (.0228) -.0065 (.0322) e + e · d -.0033 (.0170) .0269 (.0229) ∆ debt/sales District dummy (d) .0025 (.2484) -.0100 (.3038) Earthquake dummy (e) .0711 (.3773) .1863 (.4548) e · d 1.336∗∗ (.5538) 1.668∗∗ (.6683) e + e · d 1.407∗∗∗ (.4103) 1.855∗∗∗ (.4982) ∆ ln(value added) District dummy (d) -.0026 (.0115) .0016 (.0136) Earthquake dummy (e) -.0263 (.0185) .0042 (.0212) e · d -.0366 (.0262) -.0236 (.0299) e + e · d -.0629∗∗∗ (.0190) -.0194 (.0216) Robust standard errors in parenthesis. Significance at: 1% ∗∗∗; 5% ∗∗; 10% ∗. Regressions include sector (4-digit) dummies and year of incorporation. Cainelli, Fracasso, Vittucci Marzetti Natural disasters and firm resilience in Italian industrial districts 26/29
  27. 27. Aim and theoretical framework Data and empirical methodology Estimation results Closing remarks Average impact of the earthquake Industrial district effect The effect of industrial districts: FD Dependent variable Regressor Years: 2010/11- 2012 2013 ∆ ln(production) District dummy (d) -.0020 (.0102) -.0054 (.0129) Earthquake dummy (e) -.0097 (.0150) .0230 (.0189) e · d -.0358∗ (.0213) -.0183 (.0270) e + e · d -.0455∗∗∗ (.0153) .0047 (.0196) ∆ ROE District dummy (d) -.3928 (.4786) .9712∗ (.4959) Earthquake dummy (e) -.5294 (.8098) 1.139 (.7810) e · d .0980 (1.123) -1.554 (1.123) e + e · d -.4314 (.7963) -.4155 (.8241) ∆ ROS District dummy (d) -.0418 (.1965) -.0097∗∗ (.2172) Earthquake dummy (e) -.3433 (.3264) .3435 (.3516) e · d -.3730 (.4508) -.4895 (.4797) e + e · d -.7163∗∗ (.3192) -.1460 (.3354) Robust standard errors in parenthesis. Significance at: 1% ∗∗∗; 5% ∗∗; 10% ∗. Regressions include sector (4-digit) dummies and year of incorporation. Cainelli, Fracasso, Vittucci Marzetti Natural disasters and firm resilience in Italian industrial districts 27/29
  28. 28. Aim and theoretical framework Data and empirical methodology Estimation results Closing remarks Average impact of the earthquake Industrial district effect The effect of industrial districts: main results Negative short-term impact of the earthquake on the activity and the efficiency (production, turnover, value added, and ROS) slightly higher for firms located in industrial districts. Impact of earthquake on firm indebtedness (debt/sales): positive and longer; larger for firms in IDs. Cainelli, Fracasso, Vittucci Marzetti Natural disasters and firm resilience in Italian industrial districts 28/29
  29. 29. Aim and theoretical framework Data and empirical methodology Estimation results Closing remarks Closing remarks Our analysis confirms previous findings that major supply shocks have limited and temporary (negative) effects on surviving companies. Indebtedness appears the tool through which firms preserve their activities. The location of firms within industrial districts weakens their response to localized exogenous shocks, but only in the short term. Caution with “causal interpretation”: not truly random shock if localization is very high in many industries; limited capacity to control for unobservables (for the “ID effect” in particular); ID channels not disentangled. Cainelli, Fracasso, Vittucci Marzetti Natural disasters and firm resilience in Italian industrial districts 29/29

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