Propagation of positive effects of postdisaster policies through supply chains: Evidence from the Great East Japan Earthquake and Tsunami

Kashiwagi, Yuzuka; Todo, Yasuyuki Working Paper Propagation of positive effects of postdisaster policies through supply chains: Evidence from the Great East Japan Earthquake and Tsunami ADB Economics Working Paper Series, No. 604 Provided in Cooperation with: Asian Development Bank (ADB), Manila Suggested Citation: Kashiwagi, Yuzuka; Todo, Yasuyuki (2020) : Propagation of positive effects of postdisaster policies through supply chains: Evidence from the Great East Japan Earthquake and Tsunami, ADB Economics Working Paper Series, No. 604, Asian Development Bank (ADB), Manila, https://doi.org/10.22617/WPS200003-2 This Version is available at: https://hdl.handle.net/10419/230359 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/3.0/igo/ ASIAN DEVELOPMENT BANK ADB ECONOMICS WORKING PAPER SERIES NO. 604 January 2020 PROPAGATION OF POSITIVE EFFECTS OF POSTDISASTER POLICIES THROUGH SUPPLY CHAINS EVIDENCE FROM THE GREAT EAST JAPAN EARTHQUAKE AND TSUNAMI Yuzuka Kashiwagi and Yasuyuki Todo ASIAN DEVELOPMENT BANK ADB Economics Working Paper Series Propagation of Positive Effects of Postdisaster Policies through Supply Chains: Evidence from the Great East Japan Earthquake and Tsunami Yuzuka Kashiwagi and Yasuyuki Todo No. 604 | January 2020 Yuzuka Kashiwagi ([email protected]) is a research fellow of Japan Society for the Promotion of Science and a doctoral candidate of the Graduate School of Economics, Waseda University. Yasuyuki Todo ([email protected]) is a professor in the Faculty of Political Science and Economics, Waseda University, and a faculty fellow at the Research Institute of Economy, Trade and Industry. This paper was prepared as background material for the Asian Development Outlook (ADO) 2019 theme chapter on “Strengthening Disaster Resilience.” The authors would like to thank Benno Ferrarini, Thomas McDermott, Ilan Noy, Yasuyuki Sawada, and participants at the ADO workshop for their useful comments. Creative Commons Attribution 3.0 IGO license (CC BY 3.0 IGO) © 2020 Asian Development Bank 6 ADB Avenue, Mandaluyong City, 1550 Metro Manila, Philippines Tel +63 2 8632 4444; Fax +63 2 8636 2444 www.adb.org Some rights reserved. Published in 2020. ISSN 2313-6537 (print), 2313-6545 (electronic) Publication Stock No. WPS200003-2 DOI: http://dx.doi.org/10.22617/WPS200003-2 The views expressed in this publication are those of the authors and do not necessarily reflect the views and policies ofthe Asian Development Bank (ADB) or its Board of Governors or the governments they represent. ADB does not guarantee the accuracy of the data included in this publication and accepts no responsibility for any consequence of their use. The mention of specific companies or products of manufacturers does not imply that they are endorsed or recommended by ADB in preference to others of a similar nature that are not mentioned. By making any designation of or reference to a particular territory or geographic area, or by using the term “country” inthis document, ADB does not intend to make any judgments as to the legal or other status of any territory or area. This work is available under the Creative Commons Attribution 3.0 IGO license (CC BY 3.0 IGO) https://creativecommons.org/licenses/by/3.0/igo/. By using the content of this publication, you agree to be bound bytheterms of this license. For attribution, translations, adaptations, and permissions, please read the provisions andterms of use at https://www.adb.org/terms-use#openaccess. This CC license does not apply to non-ADB copyright materials in this publication. If the material is attributed toanother source, please contact the copyright owner or publisher of that source for permission to reproduce it. ADB cannot be held liable for any claims that arise as a result of your use of the material. Please contact [email protected] if you have questions or comments with respect to content, or if you wish toobtain copyright permission for your intended use that does not fall within these terms, or for permission to use theADB logo. Corrigenda to ADB publications may be found at http://www.adb.org/publications/corrigenda. Note: In this publication, “$” refers to United States dollars. The ADB Economics Working Paper Series presents data, information, and/or findings from ongoing research and studies to encourage exchange of ideas and to elicit comment and feedback about development issues in Asia and the Pacific. Since papers in this series are intended for quick and easy dissemination, the content may or may not be fully edited and may later be modified for final publication. CONTENTS TABLES AND FIGURE iv ABSTRACT v I. INTRODUCTION 1 II.GROUP SUBSIDIES AFTER THE GREAT EAST JAPAN EARTHQUAKE AND TSUNAMI 1 III.DATA 3 A. Data source 3 B. Identification of the Disaster Areas and Subsidized Firms 3 C. Construction of Variables and Samples 5 D. Descriptive Statistics 6 IV. EMPIRICAL METHODOLOGIES 8 A. Estimation of Direct Effects 8 B. Estimation of Indirect Effects through Supply Chains 9 V. RESULTS ON DIRECT EFFECTS 10 A. Logit Estimations and Balancing Tests 10 B. Analysis of Covariance Estimations of Direct Effects 12 C. Distinguishing between Small and Medium-Sized Enterprises 13 VI.RESULTS ON INDIRECT EFFECTS 14 VII. DISCUSSION AND CONCLUSIONS16 REFERENCES 19 TABLES AND FIGURE TABLES 1 Definition of Small and Medium-Sized Enterprises 6 2 Summary Statistics 7 3 Logit Estimations 10 4 Balancing Tests 11 5 Direct Effect of the Subsidies: All Firms 12 6 Direct Effect of the Subsidies: Comparison between Small and Medium Firms 13 7 Indirect Effect of the Subsidies within the Region: All Firms 14 8 Indirect Effect of the Subsidies within the Region: Small Firms 15 9 Indirect Effect of the Subsidies beyond the Region: All Firms 15 10 Indirect Effect of the Subsidies beyond the Region: Small Firms 16 FIGURE Disaster Areas and Disaster-Hit Prefectures 4 ABSTRACT This study evaluates the impact of “group subsidies,” a policy intervention to repair and reinstall damaged capital goods and facilities of small and medium-sized enterprises after the Great East Japan earthquake and tsunami. In addition to their direct effect on firms that received the subsidies, we estimate their indirect effect on firms that did not receive the subsidies but were linked with recipient firms through supply chains. Employing a propensity score matching and analysis of variance approach, we find a positive effect of the subsidies on small recipient firms’ postdisaster sales and employment. We also find a positive indirect effect of the group subsidies on firms in disaster-hit prefectures that did not receive any group subsidy but were linked through supply chains with a recipient firm. Our results indicate the propagation of postdisaster policy effects through supply chains, which are often ignored in the academic literature and the policymaking arena. Keywords: natural disasters, postdisaster policy, propagation, supply chains JEL code: H20, L14 I.  INTRODUCTION When a natural disaster hits a region, the economic shock propagates to regions that are not directly hit by the disaster through the disruption of supply chains. Customers of firms directly hit by the disaster may shrink production due to lack of material, parts, or components, whereas their suppliers may mirror them due to lack of demand. The recent emerging literature on this issue found econometric evidence of such propagation, using firm-level data and supply chain information for the United States (Barrot and Sauvagnat 2016); Japan (Carvalho et al. 2016); and the world (Kashiwagi, Todo, and Matous 2018). Some other studies, such as Hallegatte (2012); Henriet, Hallegatte, and Tabourier (2012); and Inoue and Todo (2017, 2018), took another approach by using simulation analysis on an agent-based model, confirming the substantial indirect effect of disasters due to propagation. This issue has become more concerning as the frequency and severity of natural disasters is projected to increase due to climate change (Milly et al. 2002) and evolving seismic trends (Beroza 2012). Following a natural disaster, the government and other institutions often implement policy interventions, such as subsidies and financial reliefs, to repair or reinstall damaged capital stocks and maintain employment, to alleviate its negative effect at the firm level. A few studies have examined the direct effect of such interventions on the recovery of private firms. Notably, De Mel, McKenzie, and Woodruff (2012) examined the effect of relief aid and access to capital on the recovery of microenterprises in Sri Lanka after the massive tsunami in 2004, using a randomized experiment. They find a positive effect of the interventions, particularly, on profits and revenues of retailors, but not on those of firms in the manufacturing and other service sectors. However, to the best of the authors' knowledge, no study has examined the indirect effect of postdisaster policy interventions on the performance of firms not directly hit by a disaster but linked with directly hit firms. To fill the research gap and to contribute to postdisaster policies, this study estimates direct and indirect effects of the policy intervention, or the “group subsidies,” to repair and reinstall fixed assets of small and medium-sized enterprises (SMEs) damaged by the Great East Japan earthquake in 2011 and the subsequent tsunami. We utilize comprehensive firm-level data for more than 1 million Japanese firms, containing information on approximately 5 million supply chain links among them. To avoid biases due to self-selection of recipient firms of the subsidies and unobservable factors, we employ a propensity score matching (PSM) estimation combined with an analysis of covariance (ANCOVA) approach of McKenzie (2012). To preview our results, we find a direct positive and statistically significant effect of the subsidies on recipients’ sales and employment, particularly, those small in size; and an indirect positive effect on sales of firms in the earthquake-hit region that were linked with recipients of the subsidies. Therefore, our results suggest that postdisaster subsidies to reconstruct the damaged production facilities of small enterprises can effectively facilitate their recovery and that the positive effect propagates to firms through supply chains.  II. GROUP SUBSIDIES AFTER THE GREAT EAST JAPAN EARTHQUAKE AND TSUNAMI Particularly, this study focuses on a policy intervention after the Great East Japan earthquake (hereafter, the earthquake) in March 2011, the Subsidies for the Recovery of Facilities of Groups of SMEs (Chusho Kigyo tou Gurupu Shisetsu tou Hukkyu Seibi Hojo Jigyo), known as the “group subsidies” (Gurupu Hojokin). The earthquake was of magnitude 9.0 and the fourth largest earthquake in the world 2 | ADB Economics Working Paper Series No. 604 since 1900. The death toll, including the missing persons, reached 18,880 (Cabinet Office of Japan 2012). Most of the human loss were caused by the tsunami. The epicenter was off the coast of the northeastern part of Japan, a relatively less developed region where many small and medium-sized suppliers in the automobile and electric machinery industries are located (Ministry of Economy, Trade, and Industry of Japan 2011). The direct loss of economic facilities including buildings, utilities, and social infrastructure was estimated to be 16.9 trillion yen (¥), or approximately $212 million using the exchange rate in 2011 (Cabinet Office of Japan 2012). 1 The government of Japan, through the SME agency under the Ministry of Economy, Trade, and Industry and prefecture governments, has been providing the group subsidies, henceforth referred to as “the subsidies,” to groups of SMEs in areas damaged by the earthquake—from north to south, the Hokkaido, Aomori, Iwate, Miyagi, Fukushima, Tochigi, Ibaraki, and Chiba Prefectures. Specifically, this subsidy program targets SMEs that form groups to recover from the damage of the earthquake and which play an important role in the employment and economic activities in the region. The program’s subsidies fulfill 75% (50% covered by the central government and 25% by the prefecture’s government) of the costs to repair or reinstall capital goods of SMEs destroyed by the earthquake and the subsequent tsunamis (Small and Medium Enterprise Agency of Japan 2011). A notable feature of this policy is that subsidies are provided not to individual firms but to groups of firms, such as those linked through supply chains and located in the same industrial park or the same commercial area. This policy measure was developed because subsidies were not supposed to be provided to individual firms for their own recovery from natural disasters but to groups of firms for regional recovery. Although the subsidies are provided primarily to SMEs, non-SMEs also receive the subsidies in some cases when they grouped with SMEs. The first round of the subsidies was announced in June 2011, 3 months after the earthquake, and granted in August 2011 (Small and Medium Enterprise Agency of Japan 2011). As of December 2018, more than 7 years after the earthquake, the program continues to provide subsidies to SMEs. The size of this policy is extremely large, with a total of ¥504 billion (approximately $4.5 billion) granted to 705 groups as group subsidies by 2018. For illustrative purposes, let us provide two examples from the first round of the subsidy program (Small and Medium Enterprise Agency of Japan 2012). In the first example, 17 firms in the electronics and precision machinery industries in the coastal areas of the Iwate Prefecture formed a group and received the subsidies; these firms were linked with supply chains and shared other business relationships. One of the recipient firms, whose five plants were completely destroyed by the tsunamis, received ¥700 million (about $6.3 million) to purchase production facilities for relocating the plant to a different location. Although this firm laid off all of 230 employees after the earthquake, it was projected to rehire 70 employees in 2012, owing to the subsidies. The second example is taken from the retail sector. Thirty retail shops in a shopping center in Iwate that were flooded by the tsunami and caught fire due to the earthquake formed a group to receive the subsidies of ¥670 million (about $6 million) to repair buildings and facilities. This subsidy facilitated the reopening of the shopping center in December 2011, 9 months after the earthquake. 1 The currency exchange rate applied was $1 = ¥111.95 (as of April 2019). Propagation of Positive Effects of Postdisaster Policies through Supply Chains | 9 firms within the same sector and same fiscal year-end month. We impose a common support—we drop firms whose propensity score is outside the overlap of the two distributions of participants and nonparticipants. Additionally, we set the caliper of the difference in the propensity score at 0.05, matching two firms only when the difference between their propensity scores is less than 5%. After matching, we check whether treatment firms (recipients of the subsidies) and matched controls are balanced in terms of pre-earthquake attributes, using t tests. Finally, using the matched sample, we run the following ordinary least squares estimations.    OQ OQ it it it i i Y Y Subsidy D ββ β δε =+ + + + (1) where it Y , it Subsidy , and i D denote an outcome variable, the dummy variable for receipt of the subsidy, and dummy variables of firm i in time t, respectively. We experiment with several sets of dummy variables, such as sector dummies, prefecture dummies, and fiscal year-end dummies. The time  t represents the pre-earthquake year or 2010,  t denotes the year of receipt of the subsidy or either 2011 or 2012, and  t denotes the postearthquake year or 2013. The outcome variables are the log of sales, number of workers, and sales per worker. Since we take a log of the outcome variables and incorporate the lagged outcome variables as an independent variable, following the ANCOVA of McKenzie (2012), we can rewrite equation (1) as:      OQ OQ  OQ it it it it it i Y Y Y Subsidy X ββ β δε −=+− + ++ (2) Therefore, we essentially estimate the effect of the subsidies on the growth rate of sales, employment, and sales per worker, considering the fixed effects included in—and the convergence represented by—  OQ it Y. When we estimate equation (2), assuming  β = , or conduct DID estimations, we obtain similar results. Therefore, we rely on the ANCOVA approach. In the benchmark estimations, we use all SMEs in the disaster areas. Subsequently, we focus on either small or medium firms to examine whether firm size affects the effect of the subsidies. B. Estimation of Indirect Effects through Supply Chains Next, we examine whether the positive effect of the subsidies propagates through supply chains. When firms had suppliers or customers that were directly damaged by the earthquake and tsunami, they may have been subjected to the indirect negative effects of the earthquake because of the disruption of supply chains, as found in the literature (Barrot and Sauvagnat 2016; Carvalho et al. 2016; Kashiwagi, Todo, and Matous 2018). However, if their damaged suppliers or customers were supported by the subsidies and thus recovered more quickly than otherwise, the indirect effect on the firms may have been smaller than when their suppliers or customers did not receive any subsidy. To estimate this indirect effect, we adopt the PSM–ANCOVA approach, similar to that in the previous section. In this examination, we deal with the following two samples—one comprising firms in the four prefectures severely hit by the earthquake—from north to south, Aomori, Iwate, Miyagi, and Fukushima—to examine the propagation of the effect of the subsidies within the region, and the other comprising firms outside the four prefectures to examine distant propagation. In each case, we utilize firms that did not receive the subsidy but were linked with firms in the disaster areas through supply chains and examine whether the performance of firms linked with subsidized firms is better than that with nonsubsidized firms. 10 | ADB Economics Working Paper Series No. 604 Specifically, we first run a logit regression to estimate determinants of links of firms with any recipient of the subsidies, given any possible link with a firm in the officially defined disaster areas, for each of the four sectors defined in section V.A. Second, using the propensity score from the logit estimations, we match each firm linked with any recipient to another firm linked with any nonrecipient within the same sector and the same fiscal year-end month. Subsequently, we run the following ordinary least squares estimation:     OQ OQ it it it i i Y Y LinkSubsidy D ββ β δε =+ + + + (3) Where LinkSubsidy is the dummy variable for any supply chain link with a recipient of the subsidies. Other variables are the same as in equation (1). In practice, we distinguish between links with recipient suppliers and customers to examine the presence of downstream (from subsidized suppliers to their customers) and upstream (from subsidized customers to their suppliers) propagation of positive effects of the subsidies.  V. RESULTS ON DIRECT EFFECTS A. Logit Estimations and Balancing Tests We start with the benchmark results for the direct effect of the subsidies on recipient firms, following the procedure explained in section IV.A. First, we run logit estimation for firms in the disaster areas in each of the four sectors. The results shown in Table 3 indicate that some of the covariates significantly affect the receipt of the subsidies. Caliendo and Kopeinig (2005) argued that the choice of the covariates significantly affect PSM estimates and suggested to exclude covariates that do not affect the treatment significantly. We experiment with various sets of covariates, for example, by dropping insignificant covariates and including squared terms, and confirm that the PSM estimates do not change significantly. Because we run similar logit estimations using several different samples later, we use the same set of covariates in any logit estimation to avoid an arbitrary choice of covariates in each estimation. Each of the covariates used is significantly correlated with the treatment variable in at least several estimations. Table 3: Logit Estimations Dependent variable: Receipt of the group subsidies in 2011 or 2012 (1) (2) (3) (4) Sales (log) 0.493*** 0.141 0.0906 0.204* (0.135) (0.117) (0.102) (0.110) Number of workers (log) 0.0833 0.244* 0.477*** 0.214** (0.138) (0.125) (0.116) (0.108) Sales growth (2009–2010) 0.124 0.0275 –0.567 –0.702** (0.349) (0.201) (0.390) (0.350) Firm age (log) 0.835*** 0.573*** 0.641*** 0.835*** (0.194) (0.159) (0.156) (0.167) President’s age (log) –0.507 –0.180 –0.241 –0.237 (0.499) (0.359) (0.374) (0.452) Number of plants –0.00816 0.582*** 0.162* 0.202 (0.107) (0.170) (0.0937) continued on next page Propagation of Positive Effects of Postdisaster Policies through Supply Chains | 11  (1) (2) (3) (4) Credit evaluation index –0.0160 0.0144 –0.0190 –0.0422** (0.0209) (0.0163) (0.0204) (0.0181) Number of suppliers (plus 1 and logged) 0.0508 0.136 0.00383 0.135 (0.138) (0.108) (0.0731) (0.0943) Number of customers (plus 1 and logged) –0.346* –0.0193 0.0991 –0.112 (0.178) (0.114) (0.146) (0.145) Tsunami dummy 2.583*** 2.529*** 2.622*** 2.487*** (0.224) (0.149) (0.181) (0.220) Evacuation dummy 1.820** 2.731*** 3.081*** (0.836) (0.289) (0.804) Number of damaged SMEs within 1 km –0.0358 –0.202*** –0.0480 –0.312*** (0.0670) (0.0500) (0.0664) (0.0611) Prefecture dummies YES YES YES YES Number of observations 1,103 4,006 2,438 2,010 Pseudo R 2 0.239 0.254 0.223 0.214 km = kilometer, SME = small and medium-sized enterprise. Notes: Robust standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Source: Authors’ own based on Tokyo Shoko Research. 2011 and 2014. “Kigyo Data File.” Tokyo Shoko Research, Ltd. http://www.tsrnet.co.jp/service/product/data_approach/ (licensed to the Research Institute of Economy, Trade and Industry in 2011 and 2014). After matching using the propensity scores obtained from the logit estimations, we check whether the treatment group and the matched control group are balanced. Specifically, we conduct t tests to examine whether each of the covariates is systematically different between the two groups. Table 4 shows the results from the t tests. The mean of the most covariates is significantly different between the treatment and the control group before matching, suggesting that recipients of the subsidies were self-selected. However, after matching, we cannot reject the null hypothesis that the mean is the same between the two groups for all covariates at 5% significance level. Therefore, we conclude that matching is appropriately achieved.  Table 4: Balancing Tests Before Matching After Matching Mean t value p value Mean t value p value Variable Treated Control Treated Control Sales (log) 12.35 11.60 17.04 0.00 12.22 12.33 –1.72 0.09 Number of workers (log) 2.59 1.96 17.90 0.00 2.49 2.56 –1.20 0.23 Sales growth (2009–2010) –0.01 –0.02 0.80 0.42 –0.02 –0.02 –0.09 0.93 Firm age (log) 3.44 3.22 12.05 0.00 3.41 3.43 –0.71 0.48 President’s age (log) 4.08 4.08 1.20 0.23 4.08 4.08 0.37 0.71 Number of plants 0.46 0.20 14.37 0.00 0.39 0.40 –0.16 0.87 Credit evaluation index 50.70 49.13 10.41 0.00 50.45 50.59 –0.61 0.54 Table 3 continued continued on next page 12 | ADB Economics Working Paper Series No. 604 Before Matching After Matching Mean t value p value Mean t value p value Variable Treated Control Treated Control Number of suppliers (plus 1 and logged) 1.47 1.21 8.98 0.00 1.41 1.47 –1.25 0.21 Number of customers (plus 1 and logged) 1.66 1.39 10.85 0.00 1.60 1.64 –0.96 0.34 Tsunami dummy 0.43 0.07 38.78 0.00 0.32 0.31 0.26 0.80 Evacuation dummy 0.04 0.00 11.58 0.00 0.03 0.03 0.43 0.67 Number of damaged SMEs within 1 km 3.94 4.25 –7.07 0.00 3.96 4.01 –0.68 0.50 km = kilometer, SME = small and medium-sized enterprise. Notes: Robust standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Source: Authors’ own based on Tokyo Shoko Research. 2011 and 2014. “Kigyo Data File.” Tokyo Shoko Research, Ltd. http://www.tsrnet.co.jp/service/product/data_approach/ (licensed to the Research Institute of Economy, Trade and Industry in 2011 and 2014). B. Analysis of Covariance Estimations of Direct Effects Using the matched sample, we estimate equation (1) using no dummies, prefecture and sector dummies, or prefecture, sector, and fiscal year-end dummies. Our outcome variables are sales, number of workers, and sales per worker in 2013. As we take logs of all the outcome variables and use the outcome variable in 2010 in logs as an independent variable, we, essentially, estimate the effect of the subsidies on the growth rate of these variables. The results are shown in Table 5. Using any outcome variable, the different sets of dummy variables in the set of controls result in very similar size and significance of the coefficients. These results imply that the treatment group is adequately matched with the control group so that the treatment variable is not correlated with any characteristic specific to prefectures, sectors, or fiscal year-ends. We find that the receipt of the subsidies had no effect on any of the outcome variables. Table 5: Direct Effect of the Subsidies: All Firms (1) (2) (3) (4) (5) (6) (7) (8) (9) Outcome Sales in 2013 Employment in 2013 Sales per Worker in 2013 Subsidies 0.0323 0.0362 0.0362 0.00833 0.00862 0.00877 0.0164 0.0175 0.0174 (0.0293) (0.0274) (0.0274) (0.0183) (0.0181) (0.0181) (0.0272) (0.0264) (0.0264) Lagged outcome YES YES YES YES YES YES YES YES YES Prefecture FE NO YES YES NO YES YES NO YES YES Industry FE NO YES YES NO YES YES NO YES YES Fiscal year-end FE NO NO YES NO NO YES NO NO YES Observations 1,730 1,730 1,730 1,730 1,730 1,730 1,730 1,730 1,730 Adjusted R 2 0.836 0.856 0.856 0.896 0.899 0.899 0.585 0.609 0.609 FE = fixed effects. Notes: Robust standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Source: Authors’ own based on Tokyo Shoko Research. 2011 and 2014. “Kigyo Data File.” Tokyo Shoko Research, Ltd. http://www.tsrnet.co.jp/service/product/data_approach/ (licensed to the Research Institute of Economy, Trade and Industry in 2011 and 2014). Table 4 continued Propagation of Positive Effects of Postdisaster Policies through Supply Chains | 13 C. Distinguishing between Small and Medium-Sized Enterprises We further distinguish between SMEs, as defined in Table 1, and apply the same PSM–ANCOVA procedure as above. It must be noted that we run a logit estimation for the subsample of small or medium firms in the disaster areas and match firms within the same size category using the propensity scores from each category. Subsequently, although we do not show the results from the logit estimations or balancing tests for brevity of presentation, we confirm that the treatment and the matched control group are balanced in any PSM estimation. The results can be made available by the authors upon request. We experiment with various sets of the dummy variables, as in section VI.B, and find that the results are essentially the same. Therefore, we only show the results using the full set of the prefecture, sector, and fiscal year-end month dummies. The results for small firms shown in columns (1)–(3) of Table 6 indicate that the subsidies to small firms had a positive and highly significant effect on sales and employment in 2013. Because both sales and employment increased, sales per capita of subsidized, small firms did not increase significantly when compared to nonsubsidized small firms. The effect is large because postearthquake sales for subsidized small firms is approximately 8% higher, and employment is 7% higher, than small nonsubsidized firms. By contrast, columns (4)–(6) of Table 6 indicate that the subsidies did not have a significant effect on either sales or employment of medium firms.  Table 6: Direct Effect of the Subsidies: Comparison between Small and Medium Firms (1) (2) (3) (4) (5) (6) Small Firms Medium Firms Outcome Sales in 2013 Employment in 2013 Sales per Worker in 2013 Sales in 2013 Employment in 2013 Sales per Worker in 2013 Subsidies 0.0833** 0.0694*** 0.0217 –0.0261 –0.00754 –0.0183 (0.0387) (0.0244) (0.0399) (0.0372) (0.0290) (0.0327) Lagged outcome YES YES YES YES YES YES Prefecture FE YES YES YES YES YES YES Industry FE YES YES YES YES YES YES Fiscal year-end FE YES YES YES YES YES YES Observations 878 878 878 734 734 734 Adjusted R 2 0.754 0.798 0.539 0.858 0.863 0.742 FE = fixed effects. Notes: Robust standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Source: Authors’ own based on Tokyo Shoko Research. 2011 and 2014. “Kigyo Data File.” Tokyo Shoko Research, Ltd. http://www.tsrnet.co.jp/service/product/data_approach/ (licensed to the Research Institute of Economy, Trade and Industry in 2011 and 2014).   These results clearly demonstrate that the group subsidies were effective to recover small firms damaged by the earthquake and tsunami, although the same subsidies had no clear impact on medium firms. The stark contrast between small and medium firms may reflect the fact that medium firms were more likely to receive other support, such as from business partners, than small firms, which had to rely on public support, such as the group subsidies. As a result, medium firms that did not receive the group subsidy may have recovered as quickly as medium firms that received the subsidies. 14 | ADB Economics Working Paper Series No. 604 VI. RESULTS ON INDIRECT EFFECTS Next, we estimate the indirect effect of the group subsidies on firms linked with subsidized firms in the disaster areas through supply chains. We follow the procedure in section V.B for the two subsamples of firms—one for the firms in the four disaster-hit prefectures, and the other for those outside the four prefectures. We confirm a balance between the treatment and the match control group in each estimation but do not show the results for brevity. First, we examine the propagation of the effect of the subsidies within the disaster-hit prefectures. In this estimation, we match each firm in the disaster-hit prefectures that did not receive the subsidy but was linked with a subsidized firm with another firm linked with a nonsubsidized firm in the disaster areas. It must be noted that the disaster areas are those officially defined to be severely hit by the earthquake and tsunami, whereas the disaster-hit prefectures are all areas in the four prefectures that include areas outside the officially defined disaster areas. We assume that supply chain links within the region are dense and strong and thus, indirect effects may be more prevalent within the region than outside the region. Table 7 shows the indirect effect of subsidized suppliers and customers. Column (1) indicates a positive and significant effect of any subsidized supplier of the focal firm without the subsidy on the firm’s sales in 2013. Precisely, postdisaster sales of firms linked with any subsidized supplier were 5.5% higher than sales of firms linked with any supplier that were located in the disaster areas but did not receive the group subsidy. Table 7: Indirect Effect of the Subsidies within the Region: All Firms (1) (2) (3) (4) (5) (6) Outcome Sales in 2013 Employment in 2013 Sales per Worker in 2013 Sales in 2013 Employment in 2013 Sales per Worker in 2013 Link with subsidized 0.0548*** 0.0132 0.0409** suppliers (0.0205) (0.0149) (0.0209) Link with subsidized 0.0188 0.0131 0.00646 customers (0.0161) (0.0115) (0.0163) Lagged outcome YES YES YES YES YES YES Prefecture FE YES YES YES YES YES YES Industry FE YES YES YES YES YES YES Fiscal year-end FE YES YES YES YES YES YES Observations 2,462 2,462 2,462 3,606 3,606 3,606 Adjusted R 2 0.868 0.874 0.688 0.886 0.902 0.693 FE = fixed effects. Notes: Robust standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Source: Authors’ own based on Tokyo Shoko Research. 2011 and 2014. “Kigyo Data File.” Tokyo Shoko Research, Ltd. http://www.tsrnet.co.jp/service/product/data_approach/ (licensed to the Research Institute of Economy, Trade and Industry in 2011 and 2014).  Since, as we described in section V, the direct effect of the subsidies is significant only for small firms, we particularly examine the effect of a focal firm’s link with subsidized small firms on its Propagation of Positive Effects of Postdisaster Policies through Supply Chains | 15 postdisaster performance. The results in Table 8 demonstrate positive and significant indirect effects of links with subsidized small suppliers and customers on sales but not on employment. Table 8: Indirect Effect of the Subsidies within the Region: Small Firms (1) (2) (3) (4) (5) (6) Outcome Sales in 2013 Employment in 2013 Sales per Worker in 2013 Sales in 2013 Employment in 2013 Sales per Worker in 2013 Link with subsidized 0.0887** 0.00529 0.0862** small suppliers (0.0392) (0.0244) (0.0395) Link with subsidized 0.0748** 0.00155 0.0771** small customers (0.0373) (0.0258) (0.0369) Lagged outcome YES YES YES YES YES YES Prefecture FE YES YES YES YES YES YES Industry FE YES YES YES YES YES YES Fiscal year end FE YES YES YES YES YES YES Observations 654 654 654 748 748 748 Adjusted R 2 0.881 0.915 0.706 0.887 0.907 0.668 FE = fixed effects. Notes: Robust standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Source: Authors’ own based on Tokyo Shoko Research. 2011 and 2014. “Kigyo Data File.” Tokyo Shoko Research, Ltd. http://www.tsrnet.co.jp/service/product/data_approach/ (licensed to the Research Institute of Economy, Trade and Industry in 2011 and 2014).  Finally, we investigate the indirect effect of the group subsidies beyond the region, utilizing the sample of firms outside the four disaster-hit prefectures linked with firms in the disaster areas through supply chains. Tables 9 and 10 present the results using all SMEs and using only small firms, respectively. The results show no positive and significant indirect effect in any estimation.  Table 9: Indirect Effect of the Subsidies beyond the Region: All Firms (1) (2) (3) (4) (5) (6) Outcome Sales in 2013 Employment in 2013 Sales per Worker in 2013 Sales in 2013 Employment in 2013 Sales per Worker in 2013 Link with subsidized 0.00560 –0.0125 0.0187 suppliers (0.0254) (0.0269) (0.0254) Link with subsidized 0.00237 0.0193 –0.0172 customers (0.0231) (0.0213) (0.0252) Lagged outcome YES YES YES YES YES YES Prefecture FE YES YES YES YES YES YES Industry FE YES YES YES YES YES YES Fiscal year-end FE YES YES YES YES YES YES Observations 938 938 938 1,232 1,232 1,232 Adjusted R 2 0.967 0.938 0.878 0.969 0.956 0.822 Notes: FE = fixed effects, p = probability, R 2 = coefficient of determination. Robust standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Source: Authors’ own based on Tokyo Shoko Research. 2011 and 2014. “Kigyo Data File.” Tokyo Shoko Research, Ltd. http://www.tsrnet.co.jp/service/product/data_approach/ (licensed to the Research Institute of Economy, Trade and Industry in 2011 and 2014). 16 | ADB Economics Working Paper Series No. 604 Table 10: Indirect Effect of the Subsidies beyond the Region: Small Firms (1) (2) (3) (4) (5) (6) Outcome Sales in 2013 Employment in 2013 Sales per Worker in 2013 Sales in 2013 Employment in 2013 Sales per Worker in 2013 Link with subsidized –0.0724 –0.0573 –0.00969 small suppliers (0.0474) (0.0507) (0.0372) Link with subsidized 0.0340 0.00722 0.0348 small customers (0.0459) (0.0575) (0.0649) Lagged outcome YES YES YES YES YES YES Prefecture FE YES YES YES YES YES YES Industry FE YES YES YES YES YES YES Fiscal year end FE YES YES YES YES YES YES Observations 266 266 266 226 226 226 Adjusted R 2 0.971 0.953 0.922 0.975 0.946 0.788 FE = fixed effects. Notes: Robust standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Source: Authors’ own based on Tokyo Shoko Research. 2011 and 2014. “Kigyo Data File.” Tokyo Shoko Research, Ltd. http://www.tsrnet.co.jp/service/product/data_approach/ (licensed to the Research Institute of Economy, Trade and Industry in 2011 and 2014). VII. DISCUSSION AND CONCLUSIONS This study evaluates the impact of the “group subsidies” to repair and reinstall damaged capital goods and facilities of SMEs affected by the Great East Japan earthquake. Our innovation is that, in addition to their direct effect on firms that received the subsidies, we estimate their indirect effect on firms that did not receive the subsidies but were linked with recipient firms through supply chains. The indirect effect is worth investigating because many recent studies show that negative shocks of natural disasters propagate through supply chains (Barrot and Sauvagnat 2016; Carvalho et al. 2016; Kashiwagi, Todo, and Matous 2018). We employ a PSM–ANCOVA approach to correct for possible biases due to endogeneity and identify the average treatment effect on the treated. We find a positive effect of the subsidies on postdisaster sales and employment of small recipient firms that are defined as those with 20 employees or less in the manufacturing sector and five or less in the service sector (Table 1) when compared to those of small nonrecipient firms in disaster areas. However, the subsidies had no significant effect on medium firms. This contrast between small and medium firms may be attributed to the fact that medium firms were more likely to receive other supports from, for example, business partners including suppliers and customers, than small firms. Therefore, there is no significant difference in postdisaster performance between medium firms supported by the group subsidies and those not supported by the group subsidies but by other means. We also find a positive indirect effect of the group subsidies through supply chains within the four disaster-hit prefectures. In other words, sales of firms in the four prefectures that did not receive any group subsidy but were linked through supply chains with any firm in the officially defined disaster areas of the earthquake and tsunami are higher when any of their suppliers or customers received the subsidies than otherwise. Propagation of Positive Effects of Postdisaster Policies through Supply Chains | 17 By contrast, we find no indirect effect beyond the disaster-hit prefectures. This is possibly because firms outside the disaster-hit prefectures linked with any firm in the disaster-hit areas are larger than firms in the disaster-hit prefectures with such a link. The median number of workers for the former type, 38, is substantially larger than that for the latter, six, as only large and productive firms can reach distant partners. This logic is analogous to the fact that only large and productive firms can export, as found in the literature in international economics (Bernard and Jensen 2004, Melitz 2003). Large firms may not need to rely on public support by, for example, finding a substitute for damaged partners in the disaster areas. Accordingly, large firms linked with damaged partners without any subsidy may have recovered from the earthquake as quickly as those linked with damaged and subsidized partners. This is in line with the results of Barrot and Sauvagnat (2016) and Kashiwagi, Todo, and Matous (2018) who find an important role of substitution of partners in propagation. Overall, our results find positive and reasonably significant direct and indirect effects of the group subsidies on firm performance. Using the estimated effects of the subsidies, we conducted a simple simulation for cost–benefit analysis. The amount of the group subsidies provided to firms in the disaster-hit prefectures in 2011 and 2012 was ¥380 billion (about $3.4 billion), and thus the amount of the subsidies to small firms, in particular, is estimated by multiplying the total amount by the share of small firms in sales, taken from the TSR data, which is ¥31.8 billion (about $284 million). The direct benefits to recipients are estimated to be ¥57.8 billion (about $516 million) by the estimated effect (the coefficient in column [1] of Table 6) * total sales of small recipient firms in TSR / the share of recipient firms in the TSR data in the total number of recipient firms. The indirect benefits to suppliers of recipient firms are estimated to be ¥217 billion (about $1.9 billion) by estimated effect (the coefficient in column [1] of Table 8) * total sales of firms in the disaster-hit prefectures linked with small recipients. The indirect benefits to customers of recipients are estimated correspondingly. As a result, the total benefits amount to ¥299.1 billion (about $2.7 billion). This cost–benefit analysis highlights that the indirect effect through supply chains is larger than the direct effect and that the total benefit is substantially larger than the cost (although the administrative cost associated with this policy program is excluded). Our finding on the positive indirect effect of policies through supply chains would extend the existing literature, providing an important policy implication that such indirect effects should be incorporated when postdisaster policies are evaluated. Although previous studies have found that supply chains can be a channel of propagation of negative shocks by natural disasters, this study shows that they can also be a channel of propagation of positive policy effects, mitigating negative effects of disasters. The finding of the positive role of supply chains after natural disasters is in line with Todo, Nakajima, and Matous (2015) who find their similar positive role in facilitating economic recovery from disasters by facilitating support from business partners to firms damaged by disasters. These positive roles of supply chains should not be undervalued when we consider policies for recovery from natural disasters. However, our analysis also reveals that the group subsidies are not always effective. Particularly, we find that medium firms that did not receive the subsidy recovered as much as those that received the subsidy. This result should be interpreted with caution because we examined relatively long-term effects (effects 2–3 years after the earthquake), and ignored the immediate effects of the subsidies. However, this suggests that larger firms are more likely to receive support from other sources, such as supply chain partners, than the government. Hence, the government should be careful about providing postdisaster support to eligible SMEs to ensure the efficient use of public resources. 18 | ADB Economics Working Paper Series No. 604 As global value chains have expanded to many countries, including emerging and less developed countries (Baldwin 2016), these policy implications can be applicable to Asia, which has many SMEs that have integrated into global value chains and experiences a number of major disasters. Our analysis suggests that subsidies to firms to restore and reinstall capital goods can be quite effective to facilitate the recovery of disaster-hit regions, particularly when the region is a cluster of firms linked through supply chains, while the government may have to focus on micro and small firms as its target.