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Trade shocks and credit reallocation. (2019). Hassan, Fadi ; Federico, Stefano ; Rappoport, Veronica.
In: LSE Research Online Documents on Economics.
RePEc:ehl:lserod:103422.

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  27. Figure 6: Possible confounding factor: cross-border capital flows -100,000 200,000 300,000 400,000 500,000 600,000 700,000 800,000 900,000 1,000,000 1998-Q2 1999-Q1 1999-Q4 2000-Q3 2001-Q2 2002-Q1 2002-Q4 2003-Q3 2004-Q2 2005-Q1 2005-Q4 2006-Q3 2007-Q2 2008-Q1 2008-Q4 2009-Q3 2010-Q2 2011-Q1 2011-Q4 2012-Q3 2013-Q2 2014-Q1 2014-Q4 2015-Q3 2016-Q2 2017-Q1 Millions of Euro Cross-Border Liabili4es, Banking Sector (BIS) Italy Note: The figure reports the evolution of the total outstanding liabilities of Italian banks towards foreign counterparts. Data: Bank of International Settlement.
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  36. In columns (2) and (4) this is instrumented with the variable ExposureOC −i,b, where bank exposure is defined using imports from China of other advanced countries, as defined in Equation 6. Bank controls include bank characteristics pre-2001 interacted with a post-2001 dummy, these are log-assets, share of NPLs, core-funding ratio, and the capital ratio. All regressions include firm-year fixed effects, firm-bank dummies, and a dummy that captures if a firm operates in a sector in which the bank specializes its lending activities. Standard errors are double clustered at the bank and sector level. ***significant at the 1% level, ** significant at the 5% level, * significant at the 10% level.
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  52. Since our baseline specification where bank exposure is defined using a median cutoff between treated and control sectors does not easily lend to a visual representation of the identifying variation, we define bank exposure using a continuous measure for sector exposure to competition from China (as in Table A1). The figure plots binned scatterplots of industry-level outcome and treatment residuals against a continuous measure of sector exposure to competition from China (as in Borusyak et al. (2018)). Outcome and treatment residuals are obtained from a regression which includes the same controls as in the baseline specification and are then averaged for the pre- and post-2001 periods; the difference between pre- and post-2001 average is then taken for both outcome and treatment residuals. The lower panel of figure A1 replicates the same visual analysis excluding outliers (i.e. sectors in the bin with the largest value of industry China shock).
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  53. Table 17: Bank relevance and exposure Market share (pre-2002) Average exposure to China Private banks 0.69 0.39 (0.19) Foreign banks 0.05 0.41 (0.27) Cooperative banks 0.23 0.42 (0.13) Mutual banks 0.03 0.34 (0.22) Note: The table reports the market shares and the average exposure to China of different type of banks: standard private banks, foreign banks operating in Italy, cooperative banks, and mutual banks.
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  54. Table A5: Baseline results: First differences Dependent : ln Cibt (1) (2) (3) (4) OLS 2SLS OLS 2SLS ExposureIT −i,b-0.077*** -0.093*** (0.007) (0.014) ExposureIT −i,b × Controli-0.073*** -0.08*** (0.011) (0.024) ExposureIT −i,b × Treatedi-0.081*** -0.102*** (0.01) (0.016) Bank-firm specialization X X X X Bank controls X X X X Firm-time F.E. X X X X Firm-bank F.E. X X X X Instrument: ExposureOC −i,b × Postt First stage ExposureOC −i,b 0.61*** (0.02) ExposureOC −i,b × Controli 0.55*** (0.03) ExposureOC −i,b × Treatedi 0.67*** (0.02) AR-Wald test, F 12.1 7.3 Observations 188,664 188,664 188,664 188,664 Adj.R2 0.19 0.41 0.19 0.41 Note: The table reports the coefficients of the baseline specification in Equation 7 and 8. The dependent variable is the change in the log of outstanding credit between bank b and firm i between the average of 1998-2001 and that of 2002-2007, ∆ ln Cib.
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  55. Table A7 reports shift-share IV coefficients that are obtained from a weighted IV regression at the industry level, as in Borusyak et al. (2018)). Standard errors allow for clustering at the level of four-digit sector and are valid in the framework of Adao, Kolesar and Morales (2018) (Table A7). Figure A1 provides a visual representation of the identifying variation at the industry-level.
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  56. The dependent variable is the log of outstanding credit between bank b and firm i in year t, ln Cibt. We split the sample between firms that are in sector whose exposure to China competition is above median (treated firms) and below median (control firms), as defined in Equation 1. The variable ExposureIT −s,b is instrumented with ExposureOC −s,b, where bank exposure is defined using imports from China of other advanced economies as in Equation 6). Bank controls include bank characteristics pre2001 interacted with a post-2001 dummy, these are log-assets, share of NPLs, corefunding ratio, and the capital ratio. All regressions include firm-year fixed effects, firm-bank dummies, and a dummy that captures if a firm operates in a sector in which the bank specializes its lending activities. Standard errors are double clustered at the bank and sector level. ***significant at the 1% level, ** significant at the 5% level, * significant at the 10% level.
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  57. The variable ExposureIT −i,b captures bank exposure to China’s entrance in the WTO, as defined in Equation 3 and it instrumented with the variable ExposureOC −i,b, where bank exposure is defined using imports from China of other advanced countries, as defined in Equation 6. Other bank controls include bank characteristics pre-2001 interacted with a post-2001 dummy, these are log-assets, share of NPLs, core-funding ratio, and the capital ratio. All regressions include firm-year fixed effects, firm-bank dummies, and a dummy that captures if a firm operates in a sector in which the bank specializes its lending activities. Standard errors are double clustered at the bank and sector level. ***significant at the 1% level, ** significant at the 5% level, * significant at the 10% level.
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  58. These are i) the core funding (deposits), ii) domestic interbank lending, iii) foreign funding, iv) core capital. All variables are expressed as a share of bank overall liabilities. The variable ExposureIT b captures bank exposure to China’s entrance in the WTO, as defined in Equation 3 and this is instrumented with the variable ExposureOC b , where bank exposure is defined using imports from China of other advanced countries, as defined in Equation 6. Other bank controls include bank characteristics pre-2001 interacted with a post-2001 dummy, these are log-assets, core-funding ratio, and the capital ratio. All regressions include bank fixed effects and year dummies. Standard errors are clustered at the bank level. ***significant at the 1% level, ** significant at the 5% level, * significant at the 10% level.
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  59. Topalova, P. (2010), ‘Factor immobility and regional impacts of trade liberalization: Evidence on poverty from india’, American Economic Journal: Applied Economics 2(4), 1–41.

  60. Utar, H. (2018), ‘Workers beneath the floodgates: Low-wage import competition and workers’ adjustment’, The Review of Economics and Statistics 100(4), 631–647.

  61. Wooldridge, J. M. (2009), ‘On estimating firm-level production functions using proxy variables to control for unobservables’, Economics Letters 104(3), 112–114.

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  45. Import exposure and human capital adjustment: Evidence from the U.S.. (2016). Lopresti, John ; Greenland, Andrew.
    In: Journal of International Economics.
    RePEc:eee:inecon:v:100:y:2016:i:c:p:50-60.

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  46. All We Need is Love? Trade-Adjustment, Inequality, and the Role of the Partner. (2016). Winkler, Erwin ; Huber, Katrin .
    In: SOEPpapers on Multidisciplinary Panel Data Research.
    RePEc:diw:diwsop:diw_sp873.

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  47. Redistribution of Local Demand Shocks through Firms Internal Networks. (2016). Mueller, Holger M ; Giroud, Xavier.
    In: CEPR Discussion Papers.
    RePEc:cpr:ceprdp:11384.

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  48. Econometric Analysis of Production Networks with Dominant Units. (2016). Yang, Cynthia Fan ; Pesaran, M.
    In: CESifo Working Paper Series.
    RePEc:ces:ceswps:_6141.

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  49. Econometric Analysis of Production Networks with Dominant Units. (2016). Pesaran, M ; Yang, Cynthia Fan.
    In: Cambridge Working Papers in Economics.
    RePEc:cam:camdae:1678.

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