metadata: authors: 0: author: Ghose,Devaki
1: author: Montfaucon,Angella Faith Lapukeni
count: Indonesia
docna: 0: docna: Firms in Global Value Chains during Covid-19
            : Evidence from Indonesia
docty: Policy Research Working Paper
theme: Global value chains,Mitigation,Trade Facilitation,Economic Policy,Trade Policy,Environment and Natural Resource Management,Regional Integration,Enterprise Development,Private Sector Development,Climate change,Trade,Adaptation,Trade Logistics
abstracts: cdata!: Using detailed monthly firm-level trade
            data from Indonesia from February 2019 to June 2021, this
            paper shows that firm-level exports were overall more
            resilient than imports during Covid-19. Firms that
            participated in global value chains were more resilient to
            the Covid-19 shock beyond the immediate short-run compared
            to firms that did not. However, among global value chain
            firms, those that faced certain types of non-tariff measures
            on their import products, notably port of entry
            restrictions, on average faced larger reductions in export
            quantities and number of transactions compared to firms that
            did not face such restrictions, consistent with the evidence
            of major port congestion during Covid-19. Therefore,
            although international connectedness could be a source of
            vulnerability to global shocks in the immediate short run,
            policies that enable firms to be more globally engaged
            through global value chains could enhance resilience.
            Relatedly, tackling measures such as port of entry
            restrictions can ensure fast and efficient port and customs
            procedures, especially during periods of high port
            congestion, as global value chain trade requires goods to
            cross borders many times.
display_title: Firms in Global Value Chains during
            Covid-19 : Evidence from Indonesia
listing_relative_url: /research/2023/06/34109682/firms-global-value-chains-during-covid-19-evidence-indonesia
topicv3: Trade Competitiveness and Diversification
            m1326471 765,Non-Tariff Measures m1326926 778,Trade Linkages, Spillovers and
            Connectivity m1504235 2601,Coronavirus (COVID-19) m1939478 4828
publishtoextweb_dt: 2023-06-30T00:00:00Z
dois: 10.1596/1813-9450-10514
author_keys: 0: author_key: 1947054
1: author_key: 1819671
model_extractions: Policy Research Working Paper 10514 Firms in Global Value Chains during Covid-19 Evidence from Indonesia _Devaki Ghose Angella Faith Montfaucon_ Macroeconomics, Trade and Investment Global Practice & Development Research Group June 2023
Policy Research Working Paper 10514

# **Abstract**

Using detailed monthly firm-level trade data from Indonesia from February 2019 to June 2021, this paper shows that firm-level exports were overall more resilient than imports during Covid-19. Firms that participated in global value chains were more resilient to the Covid-19 shock beyond the immediate short-run compared to firms that did not. However, among global value chain firms, those that faced certain types of non-tariff measures on their import products, notably port of entry restrictions, on average faced larger reductions in export quantities and number of transactions compared to firms that did not face such restrictions, consistent with the evidence of major port congestion during Covid-19. Therefore, although international connectedness could be a source of vulnerability to global shocks in the immediate short run, policies that enable firms to be more globally engaged through global value chains could enhance resilience. Relatedly, tackling measures such as port of entry restrictions can ensure fast and efficient port and customs procedures, especially during periods of high port congestion, as global value chain trade requires goods to cross borders many times.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice and the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at amontfaucon@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Firms in Global Value Chains during Covid-19: Evidence from Indonesia

Devaki Ghose<sup>*</sup> Angella Faith Montfaucon<sup>†</sup> World Bank World Bank

## _JEL Classification_ : F13, F14, F42

_Keywords_ : Trade policy, global value chains, resilience, non-tariff measures

- Development Economics Research Group, The World Bank, dghose@worldbank.org.

> †Macroeconomics, Trade and Investment Global Practice, World Bank. amontfaucon@worldbank.org. The authors would like to thank Bayu Agnimaruto for playing a key role in updating the Indonesia non-tariff measures data, Chalisa Kallayanamitra and Jana Mirjam Silberring for excellent research assistance, and Csilla Lakatos and Ana Margarida Fernandes for helpful discussions. This paper has partially benefited from financial support from the Umbrella Facility for Trade trust fund, financed by the governments of the Netherlands, Norway, Sweden, Switzerland, and the United Kingdom. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors and do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors or the countries they represent. All errors are our responsibility.
# **1 Introduction**

Since the Covid-19 pandemic started in early 2020 there has been an increasing interest in understanding firms’ resilience to economic shocks. Since firms are increasingly operating within global value chains (GVCs), it is of particular interest to scholars and policymakers to understand the vulnerabilities that come along with this interconnectedness in order to better prepare for future shocks. Covid-19 disrupted many GVCs and led to supply shortages in different sectors all around the world (Cerdeiro and Komaromi, 2020). Additionally, the pandemic revealed structural impediments in the trade sector that could be addressed with the right policy changes. However, determining whether participation in GVCs is beneficial or detrimental for firms during a global crisis situation remains a difficult endeavor, as illustrated by the fact that previous studies reveal heterogeneous effects between different firms (Cirera et al., 2021), sectors (Espitia et al., 2022), countries (Bruhn et al., 2021) and outcome measures (Constantinescu et al., 2022).

In this paper, we analyze how the pandemic affected firm-level imports and exports in Indonesia as well as quantify the role of non-tariff measures on imports, using detailed monthly firm-level trade data from February 2019 to June 2021. We measure the effect of the pandemic on a variety of outcomes, including firm-level export and import values, volumes, number of transactions, and number of products. To measure the effects of NTMs on firm-level trade, we compare various firmlevel import and export outcomes before and after Covid-19 for firms exposed to NTMs compared to firms that were not, including firm-, HS-8 product-, and partner country fixed effects. We find evidence that certain types of NTMs, such as the port of entry restrictions on imports, negatively affected firm-level exports during the pandemic.

In the second step, we compare the impact of the pandemic on firms that participated in GVCs with those that did not. GVC firms are defined as firms that both imported and exported in the Covid-19 pre-period (February 2019-November 2019). All other firms are categorized as nonGVC firms. Since in GVCs, tariffs and non-tariff barriers are effectively a tax on exports (because imports are essential for exports), to further understand the effect of import policies, we differentiate between GVC firms that faced non-tariff measures (NTMs) versus those that did not. To assess the level of NTMs faced by a firm, we rely on data from the World Bank Jakarta NTM database. This data improves on other NTM data by UNCTAD-ERIA, as it is at a higher frequency (monthly), more updated (up to December 2021), and has a time dimension (panel). This makes it appropriate for the purposes of this analysis because we can capture changes in NTMs real-time during the pandemic as opposed to annual data.

Indonesia makes an interesting case for multiple reasons. High levels of raw material exports have led to high forward participation in GVCs, but this has been on a downward trend in recent years (Figure 1). The backward participation is still in its infancy, with low level of foreign value 2
<!-- Start of picture text --> Backward participation in GVCs: Foreign value added share of gross exports<br>eo 35<br>Fe2<br>g 30<br>5 2s<br>e<br>20<br>-<br>15<br>10<br>0<br>PPMSPP9 o® SM MPHPPDdS DN DSPH PGPDD7D?@ DTM_od WD?& PGDPDSH MS _ PGDPDBD A SMQWSD NS ADWh?Mag _ A®_ AdNPW_AD _ AOggAN AD<br>—Indonesia ——Peers —— Aspirational Peers<br><!-- End of picture text --> <!-- Start of picture text --> Forward participation in GVCs: Domestic value added in foreign exports as a<br>share of 3ross exportsP'<br>2 3535<br>2<br>ia<br>“55 25<br>2015 OO<br>10<br>0<br>PHMDPD3 oS MPMAP?Sd SD_ SHBPOD DDD?DN HPWHDD @ odDD?& WPDPOS DPDBDGH A GyQPSD WWNS AD A® WWAS LSAD AO AY osAD<br>—Indonesia ——Peers ——Aspirational Peers<br><!-- End of picture text -->
Che et al., 2020; Hayakawa and Mukunoki, 2021a).

We contribute to two sets of literature. The first covers studies on how firms’ participation in GVCs played a role in the impact of the Covid-19 pandemic. A growing body of literature has analyzed the effects that the pandemic had on different firms (Ayres and Raveendranathan, 2021; Cirera et al., 2021; Bruhn et al., 2021; Bricongne et al., 2012; Brucal et al., 2021; de Lucio et al., 2022) and the role of GVCs as a potential transmitter of the Covid-19 induced consequences (Bonadio et al., 2021; Egger and Zhu, 2022; Baqaee and Farhi, 2020; Gerschel et al., 2020; Heise et al., 2020; Chakrabati et al., 2021; Lafrogne-Joussier et al., 2022; Rose et al., 2021; Arenas et al., 2022). Most scholars agree that the initial impact was stronger on international firms (Constantinescu et al., 2022; Pimenta et al., 2021; Borino et al., 2021) and characteristics like high imports or connections to China made firms worse off (Lafrogne-Joussier et al., 2022; Meier et al., 2020). However, authors also argue that internationally trading firms were more resilient and recovered faster (Constantinescu et al., 2022; Borino et al., 2021; Hyun et al., 2020; de Lucio et al., 2022). Our paper contributes to this literature by providing novel evidence of various performance indicators of GVC firms compared to non-GVC firms following Covid-19 up to a year and a half into the pandemic, especially since GVC data is usually available with a lag.

Second, we contribute to the literature on the role of trade policy when such a shock takes place, particularly non-tariff measures. Until now, only a few studies have focused on Covid-19 and trade policy, mainly looking at the trade policy responses to the pandemic (Baldwin and Evenett, 2020). We contribute to this literature by focusing on pre-existing trade policy measures and how they amplify the effect of the pandemic on GVC firms. Our use of a novel data set for NTMs, an updated version of that used in Cal`ı et al. (2022), makes such an exercise possible.

The rest of the paper is organized as follows: Section 2 describes the data used, Section 3 analyzes the trends prior to and during the Covid-19 crisis, Section 4 outlines the empirical strategy and results from the regression analysis and Section 5 concludes.

# **2 Data**

The trade data is obtained from Panjiva, which is a private data provider collecting daily highfrequency customs data. We obtain the daily firm-to-firm import and export data for Indonesia from February 2019 to June 2021. The data is checked and cleaned rigorously with respect to firm identities, data completeness, quantity units, product codes, and partner countries, as detailed in Appendix subsection A1. Further, we validated the data quality by comparing the annual aggregates with the World Integrated Trade Solution (WITS) and found that Panjiva’s exports and imports data in 2019 captured about 94% and 79% of WITS, respectively. A summary of the firm-level data is given in Table A1.

4
With the data, we compute firm-level export and import values, volumes, prices, number of transactions, export destinations, import origin countries, and number of products. The average price calculation at the firm-level accounts for the fact that different firms produce different products. So, for every disaggregated product (8-digit HS code), we compute the average price per unit across all firms and all destinations in that month. For every firm, we compute a ratio most disaggregated produce-wise: the numerator is the price that the firm charges for that HS-8 product and the denominator is the average price of that product. Then, we compute a weighted firm-month average price per unit of these products in the firm’s export basket.

We define GVC firms as firms that imported and exported at least once in the Covid-19 preperiod (February 2019-November 2019). Non-GVC firms are either pure importers or pure exporters. There are 8,290 GVC firms (23.11%), 22,065 pure importers (61.52%), and 5,511 pure exporters (15.37%) in our data.

The data shows that 80.4 percent of export value for Indonesia was generated by GVC firms before Covid (i.e., before March 2020) and 82.1 percent after Covid. Overall, about 81.5 percent of Indonesia’s exports were generated by GVC firms in the sample period. These figures are slightly higher than what was reported in other studies where about two-thirds of export values were generated by importing-exporters (Cal`ı et al., 2022).

The data on non-tariff measures (NTMs) is sourced from the World Bank NTM database for Indonesia.<sup>1</sup> This dataset improves on existing data from UNCTAD-ERIA through more frequent updates, a wider variety of sources, and intensive forward and backward tracing that enables the creation of panel data. The data is hand collected through extensive regulatory checks, careful coding processes using the UNCTAD 3-digit MAST classification, and updated annually, making it one of the most up-to-date data on NTMs that exist, and ensuring only active regulations remain and inactive regulations are timely identified. As trade policy changes take place within the year, with over 13 government institutions issuing regulations and over 600 regulations in the data, this database allows for a timely assessment of policies, and changes to those policies, and more relevant in this paper, is sufficiently up to date for the analysis. While the data spans from January 2008 to December 2021, we use the 2019-2021 time span for the analysis to match with the trade data.

Figure 2 shows the monthly variation of incidences of NTMs in Indonesia both for products (a) and trade value (b) for all NTMs. While this gives some indication that there is some variation on a monthly basis and that the incidence has increased in 2021, it is too broad to draw meaningful interpretations. Most NTMs are designed to achieve public policy objectives, however, some NTMs can potentially affect trade. Even when this is not their main objective, such NTMs can hurt domestic producers by making imports more expensive, increasing business costs, and reducing productivity,

> 1Available at https://datacatalog.worldbank.org/search/dataset/0063543/indonesia_ nontariff_measures

5
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# **4 Regression Analysis and Results**

In this section, by using event study and differences in difference designs, we empirically investigate how Covid-19 affected export outcomes for a) all importers and exporters, b) firms that participate in GVCs compared to firms that do not participate in GVCs and c) firms that face NTMs in the pre-period compared to firms that did not face NTMs in the pre-period.

## **4.1 All Importers and Exporters**

We do the following event study to analyze the impact of the Covid-19 shock on firm-level imports and exports. We also investigate whether the effects differ depending on whether firms participate in GVCs or not, that is, run equation 1 separately for GVC versus non-GVC firms in section 4.2.

_Yit,pc_ : outcome variable for firm _i_ selling HS-8 product _p_ to country _c_ at year-month _t_ . We use the inverse hyperbolic sign transformation of the dependent variable so that zero observations are not dropped out. We control for firm, HS-8 product, and partner country fixed effects. Standard errors are clustered at the 8-digit product level. The period of our analysis is February 2019-June 2021 and the frequency is monthly. The time period in our analysis is limited by the availability of high-frequency firm-level data from Panjiva. The omitted month is March 2020, when the WHO characterized Covid-19 as a pandemic. We plot the _βt_ coefficients associated with the outcome variables for each month.

### **Fact 1: Exports were more resilient to the Covid-19 shock than imports (Figures 19, 20,**

### **21).**

This is both on the intensive margin (export volumes Figure 19) and extensive margin (export transactions Figure 20 as well as the number of products exported Figure 21). Even though we observe a decline in imports and exports in our results, the drop in imports for all three measures (Figures 19b, 20b, 21b) is more significant - both in terms of magnitude and duration - than for exports (Figures 19a, 20a, 21a).

Our findings are corroborated by Arenas et al. (2022), who compare the effects of domestic and external lockdowns on trade in the Philippines and also come to the conclusion that overall imports were more affected by the crisis than exports. When differentiating between the different lockdowns, the authors find that domestic lockdowns did not affect international trade, while external lockdowns affected imports and exports negatively. Arenas et al. (2022) also conclude that this decline was largely due to a drop in the number of products. This, in turn, aligns with the results we obtained, as illustrated by Figure 21, which shows a large drop in quantity around May 16
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<!-- Start of picture text --> N:TH!I 2::<br>A1 - To4g,, i; -||<br>1<br>2. 7 TT toe H INF<br>fAyy i .ii - 2s=2ss ATT |<br>$sec&aec&a&aa cl-roo-TTroo-TT-TT I11I11 ~ii TAKT11 d =Iii $s=2s |-|| - Ne-\WtTl.1-\WtTl.1Tl.11 711<br>©SoSo fl iliiii ANTrTr ot11 -iV t \/+ 1 L ia=Bx=BxBx |I1\\ 1rloayrloay 1 i 111111<br>x33 V/ 1 tyDD TL ESidid i 1 !<br>2 L 4 1 2 I A+<br>c= 1 i 2 at<br>2°SeSe 1 I!! L ar5.5. 1'' trinarina<br>i+ I i<br>1 1<br>a 1 © 1<br>FErFEreErreecEeEecEcEEEErETEEEEEEEEEEEerer ee EER Kerree eee FErFErErreecEeEEceEcEErEErFETEEEEEEEEEEE eee eee EER Kerree eee<br>ANDMPTMNORDDOKNHKEANTNMNORDDBOKNHKEANTHOKRODBABAGASGGDEEESSSSSCSCSCSCSCEweerSSOSOSCSOSCSOSOHSFTSTSOOSCSOSCSOSCSCOCOANNSSCSCOSCSSSSANANNNANNANNOCSONNANANANNANNSDSONNANNANANANANFErFEreErreecEeEecEcEEEErETEEEEEEEEEEEerBABAGASGGDEEESSSSSCSCSCSCSCEweerSSOSOSCSOSCSOSOHSFTSTSOOSCSOSCSOSCSCOCOANNSSCSCOSCSSSSANANNNANNANNOCSONNANANANNANNSDSONNANNANANANANFErFEreErreecEeEecEcEEEErETEEEEEEEEEEEerweerSSOSOSCSOSCSOSOHSFTSTSOOSCSOSCSOSCSCOCOANNSSCSCOSCSSSSANANNNANNANNOCSONNANANANNANNSDSONNANNANANANANFErFEreErreecEeEecEcEEEErETEEEEEEEEEEEerSSOSOSCSOSCSOSOHSFTSTSOOSCSOSCSOSCSCOCOANNSSCSCOSCSSSSANANNNANNANNOCSONNANANANNANNSDSONNANNANANANANFErFEreErreecEeEecEcEEEErETEEEEEEEEEEEerANANNNANNANNOCSONNANANANNANNSDSONNANNANANANANFErFEreErreecEeEecEcEEEErETEEEEEEEEEEEer DAODNQNNNNANANSSoanaNNANAAANANNANN ANNEER ANDMPTMNORADDOKNHKEANTNMNORDDBOKNHKEANTHOKRODBAGGGDASGGDEEESSSSSCSCSCSCSCEwereSSOSCSCOSCSOSOSFTSTSOOSCSOSCSOSCSCOANNSSCSCSOSCSSCSSANANNNANNANNOSONNANNANANANNSSONNANNANANANANFErFErErreecEeEEceEcEErEErFETEEEEEEEEEEE eeeBAGGGDASGGDEEESSSSSCSCSCSCSCEwereSSOSCSCOSCSOSOSFTSTSOOSCSOSCSOSCSCOANNSSCSCSOSCSSCSSANANNNANNANNOSONNANNANANANNSSONNANNANANANANFErFErErreecEeEEceEcEErEErFETEEEEEEEEEEE eeewereSSOSCSCOSCSOSOSFTSTSOOSCSOSCSOSCSCOANNSSCSCSOSCSSCSSANANNNANNANNOSONNANNANANANNSSONNANNANANANANFErFErErreecEeEEceEcEErEErFETEEEEEEEEEEE eeeSSOSCSCOSCSOSOSFTSTSOOSCSOSCSOSCSCOANNSSCSCSOSCSSCSSANANNNANNANNOSONNANNANANANNSSONNANNANANANANFErFErErreecEeEEceEcEErEErFETEEEEEEEEEEE eeeANANNNANNANNOSONNANNANANANNSSONNANNANANANANFErFErErreecEeEEceEcEErEErFETEEEEEEEEEEE eeeHr DBMONQNNNNNAANAAASSSANNNANANAANAYAaang DBMONQNNNNNAANAAASSSANNNANANAANAYAaangaang anaEER<br>Year-Month Year-Month<br>Data Source: panjiva Data Source: panjiva<br>=I<br>1” 1<br>1 - 1<br>2 g zt<br>ssS12|cS12|c|cc — 7N\_N\_ = 1ff ~ — y y t + fo}gBogBoBo T 7 /\AA I AXTM,TM, OT<br>5ZO£.|ZO£.|£.| vi I"—"—— he!== t7,7, vi f ba5cg5cgcgg 7 p WV AII<br>eo2082088 |/11 1 r \aa Any 1yy i Exgg 1 |ii<br>[o}£°=an 111!P 24[o}4[o}[o} = L 1I1I11 y<br>1oy 1<br><.1 1pod a roy<br>ANDMPTMNORDDOKNHKEANTNMNORDDBOKNHKEANTHOKRODBABAGASGGDEEESSSSSCSCSCSCSCEweerSSOSOSCSOSCSOSOHSFTSTSOOSCSOSCSOSCSCOCOANNSSCSCOSCSSSSANANNNANNANNOCSONNANANANNANNSDSONNANNANANANANFErFEreErreecEeEecEcEEEErETEEEEEEEEEEEerBABAGASGGDEEESSSSSCSCSCSCSCEweerSSOSOSCSOSCSOSOHSFTSTSOOSCSOSCSOSCSCOCOANNSSCSCOSCSSSSANANNNANNANNOCSONNANANANNANNSDSONNANNANANANANFErFEreErreecEeEecEcEEEErETEEEEEEEEEEEerweerSSOSOSCSOSCSOSOHSFTSTSOOSCSOSCSOSCSCOCOANNSSCSCOSCSSSSANANNNANNANNOCSONNANANANNANNSDSONNANNANANANANFErFEreErreecEeEecEcEEEErETEEEEEEEEEEEerSSOSOSCSOSCSOSOHSFTSTSOOSCSOSCSOSCSCOCOANNSSCSCOSCSSSSANANNNANNANNOCSONNANANANNANNSDSONNANNANANANANFErFEreErreecEeEecEcEEEErETEEEEEEEEEEEerANANNNANNANNOCSONNANANANNANNSDSONNANNANANANANFErFEreErreecEeEecEcEEEErETEEEEEEEEEEEer DAODNQNNNNANANSSoanaNNANAAANANNANN ee DAODNQNNNNANANSSoanaNNANAAANANNANN NNNEER Kerree eee ANDMPTMNORADDOKNHKEANTNMNORDDBOKNHKEANTHOKRODBAGGGDASGGDEEESSSSSCSCSCSCSCEwereSSOSCSCOSCSOSOSFTSTSOOSCSOSCSOSCSCOANNSSCSCSOSCSSCSSANANNNANNANNOSONNANNANANANNSSONNANNANANANANFErFErErreecEeEEceEcEErEErFETEEEEEEEEEEE eeeBAGGGDASGGDEEESSSSSCSCSCSCSCEwereSSOSCSCOSCSOSOSFTSTSOOSCSOSCSOSCSCOANNSSCSCSOSCSSCSSANANNNANNANNOSONNANNANANANNSSONNANNANANANANFErFErErreecEeEEceEcEErEErFETEEEEEEEEEEE eeewereSSOSCSCOSCSOSOSFTSTSOOSCSOSCSOSCSCOANNSSCSCSOSCSSCSSANANNNANNANNOSONNANNANANANNSSONNANNANANANANFErFErErreecEeEEceEcEErEErFETEEEEEEEEEEE eeeSSOSCSCOSCSOSOSFTSTSOOSCSOSCSOSCSCOANNSSCSCSOSCSSCSSANANNNANNANNOSONNANNANANANNSSONNANNANANANANFErFErErreecEeEEceEcEErEErFETEEEEEEEEEEE eeeANANNNANNANNOSONNANNANANANNSSONNANNANANANANFErFErErreecEeEEceEcEErEErFETEEEEEEEEEEE eeeHr DBMONQNNNNNAANAAASSSANNNANANAANAYAANA DBMONQNNNNNAANAAASSSANNNANANAANAYAANAANA Hr DBMONQNNNNNAANAAASSSANNNANANAANAYAANA DBMONQNNNNNAANAAASSSANNNANANAANAYAANAANA NNNEER Kerree eee<br>Year-Month Year-Month<br>Data Source: panjiva Data Source: panjiva<br><!-- End of picture text -->

<!-- Start of picture text --> 1 -<br>2<br>— — y y t +<br>ssS12|cS12|c|cc 7N\_N\_ = 1ff ~<br>5ZO£.|ZO£.|£.| vi I"—"—— he!== t7,7, vi f<br>eo2082088 |/11 1 r \aa Any 1yy i<br>an P<br>oy<br>1 pod<br>FErFEreErreecEeEecEcEEEErETEEEEEEEEEEEerer ee EER Kerree eee<br>ANDMPTMNORDDOKNHKEANTNMNORDDBOKNHKEANTHOKRODBABAGASGGDEEESSSSSCSCSCSCSCEweerSSOSOSCSOSCSOSOHSFTSTSOOSCSOSCSOSCSCOCOANNSSCSCOSCSSSSANANNNANNANNOCSONNANANANNANNSDSONNANNANANANANFErFEreErreecEeEecEcEEEErETEEEEEEEEEEEerBABAGASGGDEEESSSSSCSCSCSCSCEweerSSOSOSCSOSCSOSOHSFTSTSOOSCSOSCSOSCSCOCOANNSSCSCOSCSSSSANANNNANNANNOCSONNANANANNANNSDSONNANNANANANANFErFEreErreecEeEecEcEEEErETEEEEEEEEEEEerweerSSOSOSCSOSCSOSOHSFTSTSOOSCSOSCSOSCSCOCOANNSSCSCOSCSSSSANANNNANNANNOCSONNANANANNANNSDSONNANNANANANANFErFEreErreecEeEecEcEEEErETEEEEEEEEEEEerSSOSOSCSOSCSOSOHSFTSTSOOSCSOSCSOSCSCOCOANNSSCSCOSCSSSSANANNNANNANNOCSONNANANANNANNSDSONNANNANANANANFErFEreErreecEeEecEcEEEErETEEEEEEEEEEEerANANNNANNANNOCSONNANANANNANNSDSONNANNANANANANFErFEreErreecEeEecEcEEEErETEEEEEEEEEEEer DAODNQNNNNANANSSoanaNNANAAANANNANN NNNEER<br>Year-Month<br>Data Source: panjiva<br><!-- End of picture text -->

<!-- Start of picture text --> ” 1<br>1<br>g zt<br>fo}gBogBoBo T 7 /\AA I AXTM,TM, OT<br>ba5cg5cgcgg 7 p WV AII<br>Exgg 1 |ii<br>24[o}4[o}[o} = L 1I1I11 y<br>1<br>a roy<br>FErFErErreecEeEEceEcEErEErFETEEEEEEEEEEE eee eee EER Kerree eee<br>ANDMPTMNORADDOKNHKEANTNMNORDDBOKNHKEANTHOKRODBAGGGDASGGDEEESSSSSCSCSCSCSCEwereSSOSCSCOSCSOSOSFTSTSOOSCSOSCSOSCSCOANNSSCSCSOSCSSCSSANANNNANNANNOSONNANNANANANNSSONNANNANANANANFErFErErreecEeEEceEcEErEErFETEEEEEEEEEEE eeeBAGGGDASGGDEEESSSSSCSCSCSCSCEwereSSOSCSCOSCSOSOSFTSTSOOSCSOSCSOSCSCOANNSSCSCSOSCSSCSSANANNNANNANNOSONNANNANANANNSSONNANNANANANANFErFErErreecEeEEceEcEErEErFETEEEEEEEEEEE eeewereSSOSCSCOSCSOSOSFTSTSOOSCSOSCSOSCSCOANNSSCSCSOSCSSCSSANANNNANNANNOSONNANNANANANNSSONNANNANANANANFErFErErreecEeEEceEcEErEErFETEEEEEEEEEEE eeeSSOSCSCOSCSOSOSFTSTSOOSCSOSCSOSCSCOANNSSCSCSOSCSSCSSANANNNANNANNOSONNANNANANANNSSONNANNANANANANFErFErErreecEeEEceEcEErEErFETEEEEEEEEEEE eeeANANNNANNANNOSONNANNANANANNSSONNANNANANANANFErFErErreecEeEEceEcEErEErFETEEEEEEEEEEE eeeHr DBMONQNNNNNAANAAASSSANNNANANAANAYAANA DBMONQNNNNNAANAAASSSANNNANANAANAYAANAANA NNNEER<br>Year-Month<br>Data Source: panjiva<br><!-- End of picture text -->
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<!-- Start of picture text --> =enen i<br>fe]—— '11<br>S55 1f<br>3<br>5°EE<br>S i<br>2 !<br>=EWEW 1<br>a '<br>oO©© '''<br>Q I<br>=pay=BYTDS&25pay=BYTDS&25=BYTDS&25BYTDS&25TDS&25S&25&25255 ANDMPTMNORADDOKNHKEANTNMNORDDBOKNHKEANTHOKRODS6656666£5€FErFErErreecEeEEceEcEErEErFETEEEEEEEEEEEES5S5S5555SS5I!S6656666£5€FErFErErreecEeEEceEcEErEErFETEEEEEEEEEEEES5S5S5555SS5I!FErFErErreecEeEEceEcEErEErFETEEEEEEEEEEEES5S5S5555SS5I!ES5S5S5555SS5I!EEE EEEEEEEEE EEEEEEEEE EEE EEEEEEEEE EEEEEEEEE<br>2 S55S555S55 SS SS SSS SSSSSSSgagssssssgss SSSSSSSgagssssssgss<br>i=22 ANANNNANNANNOSONNANNANANANNSSONNANNANANANANANAANA NNN<br>> Year-Month<br>= _ Data Source: Source: panjiva<br><!-- End of picture text --> <!-- Start of picture text --> ©©<br>=EwEw i T =enen i<br>3saa | . T ~/'' Yo. — fe]—— '11<br>555 /|\ 7 A \ , |(| ANT S55 f1f<br>o, \ i A [\ 3<br>£ee |/ r AYtoto \/ \/.. 5°EE<br>Sw |7 I it L S i<br>RS 4) "] i 1 2 !<br>orEE iH 1 =EWEW 1<br>= ' a '<br>wo‘;OLOL iAII oO©© '''<br>i) I y Q I<br>1 rot!! I!!<br>pay=SoBeiiaS2B&25=SoBeiiaS2B&25SoBeiiaS2B&25BeiiaS2B&25iiaS2B&25S2B&252B&25&25255 ANDMPTMNORDDOKNHKEANTNMNORDDBOKNHKEANTHOKRODS66566666FErFEreErreecEeEecEcEEEErETEEEEEEEEEEE1S66566666FErFEreErreecEeEecEcEEEErETEEEEEEEEEEE1FErFEreErreecEeEecEcEEEErETEEEEEEEEEEE1FF ESS5S5S5555SS5rot!EEE EEEEEEEEE EEEEEEEEE =pay=BYTDS&25pay=BYTDS&25=BYTDS&25BYTDS&25TDS&25S&25&25255 ANDMPTMNORADDOKNHKEANTNMNORDDBOKNHKEANTHOKRODS6656666£5€FErFErErreecEeEEceEcEErEErFETEEEEEEEEEEEES5S5S5555SS5I!S6656666£5€FErFErErreecEeEEceEcEErEErFETEEEEEEEEEEEES5S5S5555SS5I!FErFErErreecEeEEceEcEErEErFETEEEEEEEEEEEES5S5S5555SS5I!ES5S5S5555SS5I!EEE EEEEEEEEE EEEEEEEEE<br>i= S55S555S55 SS SS SSS SSSSSSSgagssssssgss SSSSSSSgagssssssgss 2 S55S555S55 SS SS SSS SSSSSSSgagssssssgss SSSSSSSgagssssssgss<br>2 ANANNNANNANNOCSONNANANANNANNSDSONNANNANANANANANNANN NNN i=22 ANANNNANNANNOSONNANNANANANNSSONNANNANANANANANAANA NNN<br>> Year-Month > Year-Month<br>= Data Source: Source: panjiva = _ Data Source: Source: panjiva<br>22<br>=.. I!! rii Py 1HH - -<br>2 WA 2°<br>= ! Tr = I 7 Le<br><5_ago.5_ago._ago.ago.o. 1I'I'' -pTityt.- -- +yy Ly i" <5_&5_&_&& =!1\ T - 5<br>=aa 7 I'' fit11 1 oc== !<br>5 - -|| ~|| _TA!7!7! 2 ~_Yt11 5aa T "i + T 1II i<br>Ff==}s3vor><==}s3vor><=}s3vor><s3vor><|-T/1|Va11|Va1|Va1Va1 |-T/1|Va11|Va1|Va1Va1 L iV ANVWiV/yalioTVWiV/yalioTyalioTioTT UNVTTidI|1|VTTidI|1|TidI|1|I|1||1|1|| TTTLAY.LLAY.LL SSoqi4==SJatEesO===SJatEesO==SJatEesO=SJatEesO= 1|i7!|i7!7! *\iiuyLiiuyLuyLL i >AA k H ititpia1IIi\itpia1IIi\pia1IIi\1IIi\IIi\Ii\i\\ i<br>8 1 5 1<br>ANDMPTMNORDDOKNHKEANTNMNORDDBOKNHKEANTHOKNRODBAGAGDASGDEEESSSSSCSCSCSCSCEeeeSSOSOSCSOSCSOSOHSFTSTSOOSCSOSCSOSCSCONANNSSCSCSOSCSSCSSANANANNANNANNOCSONNANNANANNANNSDSONNANNANANANANFErFErErreeEEEEEEEEFETererBAGAGDASGDEEESSSSSCSCSCSCSCEeeeSSOSOSCSOSCSOSOHSFTSTSOOSCSOSCSOSCSCONANNSSCSCSOSCSSCSSANANANNANNANNOCSONNANNANANNANNSDSONNANNANANANANFErFErErreeEEEEEEEEFETerereeeSSOSOSCSOSCSOSOHSFTSTSOOSCSOSCSOSCSCONANNSSCSCSOSCSSCSSANANANNANNANNOCSONNANNANANNANNSDSONNANNANANANANFErFErErreeEEEEEEEEFETererSSOSOSCSOSCSOSOHSFTSTSOOSCSOSCSOSCSCONANNSSCSCSOSCSSCSSANANANNANNANNOCSONNANNANANNANNSDSONNANNANANANANFErFErErreeEEEEEEEEFETererANANANNANNANNOCSONNANNANANNANNSDSONNANNANANANANFErFErErreeEEEEEEEEFETerer DADNQNNNNANNAANSSoaaNNANAAAYNANAANA eer DADNQNNNNANNAANSSoaaNNANAAAYNANAANA ANN EEEEREEEEEEEEEKerre eee ANDMPTMNORDDOKNHKEANTNMNORDDBOKNHKEANTHOKNRODBAGAGDASGDEEESSSSSCSCSCSCSCETESTSSSOSOSCSOSCSOSOHSFTSTSOOSCSOSCSOSCSCONANNSSCSCSOSCSSCSSANANANNANNANNOCSONNANNANANNANNSDSONNANNANANANANFErFErErreeEEEEEEEEFETSEBAGAGDASGDEEESSSSSCSCSCSCSCETESTSSSOSOSCSOSCSOSOHSFTSTSOOSCSOSCSOSCSCONANNSSCSCSOSCSSCSSANANANNANNANNOCSONNANNANANNANNSDSONNANNANANANANFErFErErreeEEEEEEEEFETSETESTSSSOSOSCSOSCSOSOHSFTSTSOOSCSOSCSOSCSCONANNSSCSCSOSCSSCSSANANANNANNANNOCSONNANNANANNANNSDSONNANNANANANANFErFErErreeEEEEEEEEFETSESSOSOSCSOSCSOSOHSFTSTSOOSCSOSCSOSCSCONANNSSCSCSOSCSSCSSANANANNANNANNOCSONNANNANANNANNSDSONNANNANANANANFErFErErreeEEEEEEEEFETSEANANANNANNANNOCSONNANNANANNANNSDSONNANNANANANANFErFErErreeEEEEEEEEFETSEeT D®GDAAQAAANAAAASSSAANNANAANAYNANA D®GDAAQAAANAAAASSSAANNANAANAYNANAANA SST eT D®GDAAQAAANAAAASSSAANNANAANAYNANA D®GDAAQAAANAAAASSSAANNANAANAYNANAANA ANN EEEEREEEEEEEEEKerre eee<br>Year-Month Year-Month<br>Data Source: panjiva Data Source: panjiva<br><!-- End of picture text -->

<!-- Start of picture text --> =.. I!! rii<br>2 WA<br>= ! Tr<br>-- Ly i"<br>- +yy<br><5_ago.5_ago._ago.ago.o. 1I'I'' -pTityt.-<br>=aa 7 I'' fit11 1<br>5 - -|| ~|| _TA!7!7! 2 ~_Yt11<br>3vor><vor><><< 1 L V ANVWiV/yalioTVWiV/yalioTyalioTioTT TTTLAY.LLAY.LL<br>Ff==}s3vor><==}s3vor><=}s3vor><s3vor><|-T/1|Va11|Va1|Va1Va1 iV UNVTTidI|1|VTTidI|1|TidI|1|I|1||1|1||<br>8 1<br>FErFErErreeEEEEEEEEFETerererer eer EEEEREEEEEEEEEKerreEEREEEEEEEEEKerreEEEEEEEEEKerreKerree eee<br>ANDMPTMNORDDOKNHKEANTNMNORDDBOKNHKEANTHOKNRODBAGAGDASGDEEESSSSSCSCSCSCSCEeeeSSOSOSCSOSCSOSOHSFTSTSOOSCSOSCSOSCSCONANNSSCSCSOSCSSCSSANANANNANNANNOCSONNANNANANNANNSDSONNANNANANANANFErFErErreeEEEEEEEEFETererBAGAGDASGDEEESSSSSCSCSCSCSCEeeeSSOSOSCSOSCSOSOHSFTSTSOOSCSOSCSOSCSCONANNSSCSCSOSCSSCSSANANANNANNANNOCSONNANNANANNANNSDSONNANNANANANANFErFErErreeEEEEEEEEFETerereeeSSOSOSCSOSCSOSOHSFTSTSOOSCSOSCSOSCSCONANNSSCSCSOSCSSCSSANANANNANNANNOCSONNANNANANNANNSDSONNANNANANANANFErFErErreeEEEEEEEEFETererSSOSOSCSOSCSOSOHSFTSTSOOSCSOSCSOSCSCONANNSSCSCSOSCSSCSSANANANNANNANNOCSONNANNANANNANNSDSONNANNANANANANFErFErErreeEEEEEEEEFETererANANANNANNANNOCSONNANNANANNANNSDSONNANNANANANANFErFErErreeEEEEEEEEFETerer DADNQNNNNANNAANSSoaaNNANAAAYNANAANA ANN EEEEREEEEEEEEEKerre<br>Year-Month<br>Data Source: panjiva<br><!-- End of picture text -->

<!-- Start of picture text --> Py 1HH - -<br>2°<br>= I 7 Le<br>1\\ 5<br>T<br><5_&5_&_&& =!1\ -<br>oc== !<br>5aa T "i + T 1II i<br>atEesO=EesO=sO== 1|i7!|i7!7! *\iiuyLiiuyLuyLL >AA k H i<br>SSoqi4==SJatEesO===SJatEesO==SJatEesO=SJatEesO= i ititpia1IIi\itpia1IIi\pia1IIi\1IIi\IIi\Ii\i\\<br>5 1<br>FErFErErreeEEEEEEEEFETSESE SST EEEEREEEEEEEEEKerreEEREEEEEEEEEKerreEEEEEEEEEKerreKerree eee<br>ANDMPTMNORDDOKNHKEANTNMNORDDBOKNHKEANTHOKNRODBAGAGDASGDEEESSSSSCSCSCSCSCETESTSSSOSOSCSOSCSOSOHSFTSTSOOSCSOSCSOSCSCONANNSSCSCSOSCSSCSSANANANNANNANNOCSONNANNANANNANNSDSONNANNANANANANFErFErErreeEEEEEEEEFETSEBAGAGDASGDEEESSSSSCSCSCSCSCETESTSSSOSOSCSOSCSOSOHSFTSTSOOSCSOSCSOSCSCONANNSSCSCSOSCSSCSSANANANNANNANNOCSONNANNANANNANNSDSONNANNANANANANFErFErErreeEEEEEEEEFETSETESTSSSOSOSCSOSCSOSOHSFTSTSOOSCSOSCSOSCSCONANNSSCSCSOSCSSCSSANANANNANNANNOCSONNANNANANNANNSDSONNANNANANANANFErFErErreeEEEEEEEEFETSESSOSOSCSOSCSOSOHSFTSTSOOSCSOSCSOSCSCONANNSSCSCSOSCSSCSSANANANNANNANNOCSONNANNANANNANNSDSONNANNANANANANFErFErErreeEEEEEEEEFETSEANANANNANNANNOCSONNANNANANNANNSDSONNANNANANANANFErFErErreeEEEEEEEEFETSEeT D®GDAAQAAANAAAASSSAANNANAANAYNANA D®GDAAQAAANAAAASSSAANNANAANAYNANAANA ANN EEEEREEEEEEEEEKerre<br>Year-Month<br>Data Source: panjiva<br><!-- End of picture text -->
<!-- Start of picture text --> 1 Ps<br>28 y r<br>sc ~ - I - ,<br>82a2aa ~VAyVAyy \. AA-TI-TI ||!|! t I v /\\\<br>5BA£BioeSoFBA£BioeSoF£BioeSoFBioeSoFeSoFoF ii L U7ii 1 1 7Yr!'ra1Yr!'ra1'ra1ra11 h Lo Y11V11V1VV 1<br>2 1<br>FSee \1<br>3° IiYiY<br>L Ig11<br>2 '<br>ANDMPTMORDDOKNHKEANTNMNORDDBOKNHKEANTHOKRODBABAGDASGDEEESSGSSSCSCSCSCSCEweerSSOSOSCSOSCSOSOSFTSTSOOSCSOSCSOSCOCOANNSSCSCSOSCSSCSSANANANNANNANNOSONNANNANANNANNSSONNANNNANANANFErFErerreeceEeceEcErEErFETEEEEEEEEEEEerBABAGDASGDEEESSGSSSCSCSCSCSCEweerSSOSOSCSOSCSOSOSFTSTSOOSCSOSCSOSCOCOANNSSCSCSOSCSSCSSANANANNANNANNOSONNANNANANNANNSSONNANNNANANANFErFErerreeceEeceEcErEErFETEEEEEEEEEEEerweerSSOSOSCSOSCSOSOSFTSTSOOSCSOSCSOSCOCOANNSSCSCSOSCSSCSSANANANNANNANNOSONNANNANANNANNSSONNANNNANANANFErFErerreeceEeceEcErEErFETEEEEEEEEEEEerSSOSOSCSOSCSOSOSFTSTSOOSCSOSCSOSCOCOANNSSCSCSOSCSSCSSANANANNANNANNOSONNANNANANNANNSSONNANNNANANANFErFErerreeceEeceEcErEErFETEEEEEEEEEEEerANANANNANNANNOSONNANNANANNANNSSONNANNNANANANFErFErerreeceEeceEcErEErFETEEEEEEEEEEEer DADNQNNNNANNANNSSoanANNANAAAYNANNANN ee DADNQNNNNANNANNSSoanANNANAAAYNANNANN ANNEER Keeree eee<br>Year-Month<br>Data Source: panjiva<br><!-- End of picture text -->

<!-- Start of picture text --> 111<br>2 i<br>$s F I h<br>gC2S2SS _—i)i) T Ih T T I1'1'' T TT11 7T \ T<br>Fa5gorg85gorg8gorg8g88 7]ii=yii=y=yy T1!>T1!>T>TT '11 {=VttVttt 1i!—hV/,91i!—hV/,91—hV/,91V/,91911 -Ni/+|Ni/+||<br>oi. 1 AL 1<br>£82 \1 i 1<br>3s¢s¢ 'II<br>rot11<br>> L '<br>ANDMPTMNORDDOKNHKEANTNMNORDDBOKNHKEANTHORNODBAGAGDASGGDEEESSSSSCSCSCSCSCETESTSSSOSCSCOSCSOSOSFTSTSOOSCSOSCSOSCSCOANNSSCSCSCSCSSCSSANANNANANNANNOSONNANNANANNANNSDSONNANNANANANANFErFEreErreecEeEecEcEEEEFETEEEEEEEEEEESEBAGAGDASGGDEEESSSSSCSCSCSCSCETESTSSSOSCSCOSCSOSOSFTSTSOOSCSOSCSOSCSCOANNSSCSCSCSCSSCSSANANNANANNANNOSONNANNANANNANNSDSONNANNANANANANFErFEreErreecEeEecEcEEEEFETEEEEEEEEEEESETESTSSSOSCSCOSCSOSOSFTSTSOOSCSOSCSOSCSCOANNSSCSCSCSCSSCSSANANNANANNANNOSONNANNANANNANNSDSONNANNANANANANFErFEreErreecEeEecEcEEEEFETEEEEEEEEEEESESSOSCSCOSCSOSOSFTSTSOOSCSOSCSOSCSCOANNSSCSCSCSCSSCSSANANNANANNANNOSONNANNANANNANNSDSONNANNANANANANFErFEreErreecEeEecEcEEEEFETEEEEEEEEEEESEANANNANANNANNOSONNANNANANNANNSDSONNANNANANANANFErFEreErreecEeEecEcEEEEFETEEEEEEEEEEESEeT D®GDAAAANAAAAASSSANANANAAAYNANA D®GDAAAANAAAAASSSANANANAAAYNANAANA ST eT D®GDAAAANAAAAASSSANANANAAAYNANA D®GDAAAANAAAAASSSANANANAAAYNANAANA NNNEER reeree eee<br>Year-Month<br>Data Source: panjiva<br><!-- End of picture text -->

<!-- Start of picture text --> ='=<br>11 -Ps 111<br>28 y r 2 i<br>sc ~ - I - , $s F I h<br>82a2aa ~VAyVAyy \. AA-TI-TI ||!|! t I v /\\\ gC2S2SS _—i)i) T Ih T T I1'1'' T TT11 7T \ T<br>5BA£BioeSoFBA£BioeSoF£BioeSoFBioeSoFeSoFoF ii L U7ii 1 1 7Yr!'ra1Yr!'ra1'ra1ra11 h Lo Y11V11V1VV 1 Fa5gorg85gorg8gorg8g88 7]ii=yii=y=yy T1!>T1!>T>TT '11 {=VttVttt 1i!—hV/,91i!—hV/,91—hV/,91V/,91911 -Ni/+|Ni/+||<br>2 1 oi. 1 AL 1<br>FSee 1\1 2£82 1\1 i 1<br>3° IiYiY 3s¢s¢ 'II<br>L Ig11 rot11<br>2 ' > L '<br>FErFErerreeceEeceEcErEErFETEEEEEEEEEEEerer ee EER Keeree eee FErFEreErreecEeEecEcEEEEFETEEEEEEEEEEESESE ST EER reeree eee<br>ANDMPTMORDDOKNHKEANTNMNORDDBOKNHKEANTHOKRODBABAGDASGDEEESSGSSSCSCSCSCSCEweerSSOSOSCSOSCSOSOSFTSTSOOSCSOSCSOSCOCOANNSSCSCSOSCSSCSSANANANNANNANNOSONNANNANANNANNSSONNANNNANANANFErFErerreeceEeceEcErEErFETEEEEEEEEEEEerBABAGDASGDEEESSGSSSCSCSCSCSCEweerSSOSOSCSOSCSOSOSFTSTSOOSCSOSCSOSCOCOANNSSCSCSOSCSSCSSANANANNANNANNOSONNANNANANNANNSSONNANNNANANANFErFErerreeceEeceEcErEErFETEEEEEEEEEEEerweerSSOSOSCSOSCSOSOSFTSTSOOSCSOSCSOSCOCOANNSSCSCSOSCSSCSSANANANNANNANNOSONNANNANANNANNSSONNANNNANANANFErFErerreeceEeceEcErEErFETEEEEEEEEEEEerSSOSOSCSOSCSOSOSFTSTSOOSCSOSCSOSCOCOANNSSCSCSOSCSSCSSANANANNANNANNOSONNANNANANNANNSSONNANNNANANANFErFErerreeceEeceEcErEErFETEEEEEEEEEEEerANANANNANNANNOSONNANNANANNANNSSONNANNNANANANFErFErerreeceEeceEcErEErFETEEEEEEEEEEEer DADNQNNNNANNANNSSoanANNANAAAYNANNANN ANNEER ANDMPTMNORDDOKNHKEANTNMNORDDBOKNHKEANTHORNODBAGAGDASGGDEEESSSSSCSCSCSCSCETESTSSSOSCSCOSCSOSOSFTSTSOOSCSOSCSOSCSCOANNSSCSCSCSCSSCSSANANNANANNANNOSONNANNANANNANNSDSONNANNANANANANFErFEreErreecEeEecEcEEEEFETEEEEEEEEEEESEBAGAGDASGGDEEESSSSSCSCSCSCSCETESTSSSOSCSCOSCSOSOSFTSTSOOSCSOSCSOSCSCOANNSSCSCSCSCSSCSSANANNANANNANNOSONNANNANANNANNSDSONNANNANANANANFErFEreErreecEeEecEcEEEEFETEEEEEEEEEEESETESTSSSOSCSCOSCSOSOSFTSTSOOSCSOSCSOSCSCOANNSSCSCSCSCSSCSSANANNANANNANNOSONNANNANANNANNSDSONNANNANANANANFErFEreErreecEeEecEcEEEEFETEEEEEEEEEEESESSOSCSCOSCSOSOSFTSTSOOSCSOSCSOSCSCOANNSSCSCSCSCSSCSSANANNANANNANNOSONNANNANANNANNSDSONNANNANANANANFErFEreErreecEeEecEcEEEEFETEEEEEEEEEEESEANANNANANNANNOSONNANNANANNANNSDSONNANNANANANANFErFEreErreecEeEecEcEEEEFETEEEEEEEEEEESEeT D®GDAAAANAAAAASSSANANANAAAYNANA D®GDAAAANAAAAASSSANANANAAAYNANAANA NNNEER<br>Year-Month Year-Month<br>Data Source: panjiva Data Source: panjiva<br>N t“|{ '11 + Hl'' TI11 ~<br>££<gu4T&5£<gu4T&5<gu4T&5gu4T&5&55 1|lrtyLt -ii itt7!77!77 1!1!11 |\\ y L/LKei+Kei++ ~_that1that11 VANToTLWA-oTLWA-LWA- ivfZue€&5Zue€&5e€&5€&5&55 |rt|lijitPit|lijitPitPit T'' T a1\1iTis Tytiid!! 4Aii NAL- _ ~Wh!\e-7Tty!Wh!\e-7Tty!\e-7Tty!-7Tty!ty! wt<br>aooityVi H| T i i 1 goryo11 TAIit T<br>><br>s 1 /i + | ti > i VN rit1 it 1T<br>i=SS L L i 4 ao11 i=== 1paipai | 1<br>= 0 L 111111 1 =S-S- 1|| 1itroditrodrod<br>bottoktok ! 1II<br>N ' N 1 '<br>ANDMPTMNORDDOKNHKEANTNMNORDDBOKNHKEANTHOKRODBABAGASGGDEEESSSSSCSCSCSCSCESLPSSSSOSOSCSOSCSOSOHSFTSTSOOSCSOSCSOSCSCOCOANNSSCSCOSCSSSSANANNNANNANNOCSONNANANANNANNSDSONNANNANANANANFErFEreErreecEeEecEcEEEErETEEEEEEEEEEESSBABAGASGGDEEESSSSSCSCSCSCSCESLPSSSSOSOSCSOSCSOSOHSFTSTSOOSCSOSCSOSCSCOCOANNSSCSCOSCSSSSANANNNANNANNOCSONNANANANNANNSDSONNANNANANANANFErFEreErreecEeEecEcEEEErETEEEEEEEEEEESSSLPSSSSOSOSCSOSCSOSOHSFTSTSOOSCSOSCSOSCSCOCOANNSSCSCOSCSSSSANANNNANNANNOCSONNANANANNANNSDSONNANNANANANANFErFEreErreecEeEecEcEEEErETEEEEEEEEEEESSSSOSOSCSOSCSOSOHSFTSTSOOSCSOSCSOSCSCOCOANNSSCSCOSCSSSSANANNNANNANNOCSONNANANANNANNSDSONNANNANANANANFErFEreErreecEeEecEcEEEErETEEEEEEEEEEESSANANNNANNANNOCSONNANANANNANNSDSONNANNANANANANFErFEreErreecEeEecEcEEEErETEEEEEEEEEEESSANNSSD GAAANAANANSNNNEERSONNANAAAANKerreSSD GAAANAANANSNNNEERSONNANAAAANKerre GAAANAANANSNNNEERSONNANAAAANKerreNNNEERSONNANAAAANKerre SL ANNSSD GAAANAANANSNNNEERSONNANAAAANKerreSSD GAAANAANANSNNNEERSONNANAAAANKerre GAAANAANANSNNNEERSONNANAAAANKerreNNNEERSONNANAAAANKerre eee ANDMPTMNORADDOKNHKEANTNMNORDDBOKNHKEANTHOKRODBAGGGDASGGDEEESSSSSCSCSCSCSCESererSSOSCSCOSCSOSOSFTSTSOOSCSOSCSOSCSCOANNSSCSCSOSCSSCSSANANNNANNANNOSONNANNANANANNSSONNANNANANANANFErFErErreecEeEEceEcEErEErFETEEEEEEEEEEErrrBAGGGDASGGDEEESSSSSCSCSCSCSCESererSSOSCSCOSCSOSOSFTSTSOOSCSOSCSOSCSCOANNSSCSCSOSCSSCSSANANNNANNANNOSONNANNANANANNSSONNANNANANANANFErFErErreecEeEEceEcEErEErFETEEEEEEEEEEErrrSererSSOSCSCOSCSOSOSFTSTSOOSCSOSCSOSCSCOANNSSCSCSOSCSSCSSANANNNANNANNOSONNANNANANANNSSONNANNANANANANFErFErErreecEeEEceEcEErEErFETEEEEEEEEEEErrrSSOSCSCOSCSOSOSFTSTSOOSCSOSCSOSCSCOANNSSCSCSOSCSSCSSANANNNANNANNOSONNANNANANANNSSONNANNANANANANFErFErErreecEeEEceEcEErEErFETEEEEEEEEEEErrrANANNNANNANNOSONNANNANANANNSSONNANNANANANANFErFErErreecEeEEceEcEErEErFETEEEEEEEEEEErrrGDIPGAAAAAAANAASSSANANANAAAANANAANA se GDIPGAAAAAAANAASSSANANANAAAANANAANA NNNEER Kerre eeee eee eee<br>Year-Month Year-Month<br>Data Source: panjiva Data Source: panjiva<br><!-- End of picture text -->

<!-- Start of picture text --> N { '11 +<br>|lrtyLtLt 1!1!11 y L/LKei+Kei++ ~_that1that11 -<br>££<gu4T&5£<gu4T&5<gu4T&5gu4T&5&55 1|lrtyLt -ii itt7!77!77 |\\ VANToTLWA-oTLWA-LWA-<br>s 1 /i + H| T i ti 1<br>i=SS L L i 4 ao11<br>= 0 L 111111 1<br>bottoktok<br>N '<br>FErFEreErreecEeEecEcEEEErETEEEEEEEEEEESSSS SL EERSONNANAAAANKerreSONNANAAAANKerreKerree eee<br>ANDMPTMNORDDOKNHKEANTNMNORDDBOKNHKEANTHOKRODBABAGASGGDEEESSSSSCSCSCSCSCESLPSSSSOSOSCSOSCSOSOHSFTSTSOOSCSOSCSOSCSCOCOANNSSCSCOSCSSSSANANNNANNANNOCSONNANANANNANNSDSONNANNANANANANFErFEreErreecEeEecEcEEEErETEEEEEEEEEEESSBABAGASGGDEEESSSSSCSCSCSCSCESLPSSSSOSOSCSOSCSOSOHSFTSTSOOSCSOSCSOSCSCOCOANNSSCSCOSCSSSSANANNNANNANNOCSONNANANANNANNSDSONNANNANANANANFErFEreErreecEeEecEcEEEErETEEEEEEEEEEESSSLPSSSSOSOSCSOSCSOSOHSFTSTSOOSCSOSCSOSCSCOCOANNSSCSCOSCSSSSANANNNANNANNOCSONNANANANNANNSDSONNANNANANANANFErFEreErreecEeEecEcEEEErETEEEEEEEEEEESSSSOSOSCSOSCSOSOHSFTSTSOOSCSOSCSOSCSCOCOANNSSCSCOSCSSSSANANNNANNANNOCSONNANANANNANNSDSONNANNANANANANFErFEreErreecEeEecEcEEEErETEEEEEEEEEEESSANANNNANNANNOCSONNANANANNANNSDSONNANNANANANANFErFEreErreecEeEecEcEEEErETEEEEEEEEEEESSANNSSD GAAANAANANSNNNEERSONNANAAAANKerreSSD GAAANAANANSNNNEERSONNANAAAANKerre GAAANAANANSNNNEERSONNANAAAANKerreNNNEERSONNANAAAANKerre<br>Year-Month<br>Data Source: panjiva<br><!-- End of picture text -->

<!-- Start of picture text --> Hl'' TI11 ~<br>T'' - _<br>1\1iTis\1iTis1iTisiTisis 4Aii NAL- ! wt<br>ivfZue€&5Zue€&5e€&5€&5&55 |rt|lijitPit|lijitPitPit T a1\1iTis Tytiid!! ~Wh!\e-7Tty!Wh!\e-7Tty!\e-7Tty!-7Tty!ty!<br>> i VN rit1 TAIit 1T<br>i=== 1paipai | 1<br>=S-S- 1|| 1itroditrodrod<br>! 1II<br>N 1 '<br>FErFErErreecEeEEceEcEErEErFETEEEEEEEEEEErrrrrr se EER Kerre eeee eee eee<br>ANDMPTMNORADDOKNHKEANTNMNORDDBOKNHKEANTHOKRODBAGGGDASGGDEEESSSSSCSCSCSCSCESererSSOSCSCOSCSOSOSFTSTSOOSCSOSCSOSCSCOANNSSCSCSOSCSSCSSANANNNANNANNOSONNANNANANANNSSONNANNANANANANFErFErErreecEeEEceEcEErEErFETEEEEEEEEEEErrrBAGGGDASGGDEEESSSSSCSCSCSCSCESererSSOSCSCOSCSOSOSFTSTSOOSCSOSCSOSCSCOANNSSCSCSOSCSSCSSANANNNANNANNOSONNANNANANANNSSONNANNANANANANFErFErErreecEeEEceEcEErEErFETEEEEEEEEEEErrrSererSSOSCSCOSCSOSOSFTSTSOOSCSOSCSOSCSCOANNSSCSCSOSCSSCSSANANNNANNANNOSONNANNANANANNSSONNANNANANANANFErFErErreecEeEEceEcEErEErFETEEEEEEEEEEErrrSSOSCSCOSCSOSOSFTSTSOOSCSOSCSOSCSCOANNSSCSCSOSCSSCSSANANNNANNANNOSONNANNANANANNSSONNANNANANANANFErFErErreecEeEEceEcEErEErFETEEEEEEEEEEErrrANANNNANNANNOSONNANNANANANNSSONNANNANANANANFErFErErreecEeEEceEcEErEErFETEEEEEEEEEEErrrGDIPGAAAAAAANAASSSANANANAAAANANAANA NNNEER<br>Year-Month<br>Data Source: panjiva<br><!-- End of picture text -->
<!-- Start of picture text --> o<br>=a ! 7<br>seo = | +<br>i= rt! [\N- or<br>5 A A | k<br>De = V AN<br>a=: + H =et 1 ' 1 myTr if Vyy<br>2 + i 1 \<br>ge | A I it<br>XOSe 1 I|<br>=aa - 141<br>oyoK 1 fi<br>a 1 Y<br>= bod<br>& !<br>ge 1 |<br>BS AWPTOQORLMPorAc aANYyHOERn Gor Ac aANntHENe|<br>a23 BHABAGDRBADBEEESSCSSCSCSOCOCSCEeeeFErFEreErreecEeEecEcEEEErETEEEEEEEEEEEr eee He DAVGDNNAANANWANASSOAANANAAANEEC ee eee eee<br>BE2 SSSSSooorAANAAAAANSSOAAAAAAAAANSSOOAANAAAAAAAANNeT SSSSSSOSSOANASSSSSOSOSONNN<br>> Year-Month<br>= Data Source: panjiva<br>Ss<br><!-- End of picture text -->

<!-- Start of picture text --> o<br>En !<br>fo '<br>i= T !<br>S A 1<br>on T '<br>ai= | h +! x 7<br>2 y _ | rt A<br>Sotf aN AN pN =<br>Pyx NY i y Y h A<br>=E ! 1 1 v<br>aroH i fI\ y p ¥L *<br>a 1<br>= !<br>& !<br>ga \<br>BS WPTQORMMPorAc aAgyHOER Gor Ac aNntHENe|<br>323BE2 BHABAGBDABADBEEESSCSSCSCSOCOCSCESererSSSSSooorAAAAAAAANSSOAAAAAAAAANSSOOAAAAAAAAAFErFErErreecEeEEceEcEErEErFETEEEEEEEEEEErere Tr B®ANQNRVRARANNAANSSCSCOANANANAAANANAeT SSOSSSSOSSOANASSSSSOSOOSONNNEEC ee eee eee<br>> Year-Month<br>= _ Data Source: panjiva<br>Ss<br><!-- End of picture text -->
<!-- Start of picture text --> = I1 7<br>o 1<br>5 i 7-7 (i<br>rags. I!I rrti~Ar!> ry Va<br>ryS=a T 1I Tetrt!iYiydil<br>5 -- 7 I -!it r Tt tTAidft 4<br>& ii! AlTO TTALYN TALT \ '<br>>£DoF4 i 1rtdIt4 v71I Nf =4 riitI Ty1 i<br>= |* + I<br>1 1<br>3 ‘<br>AMPMOROROK AK ANMTMONMBOrC AK ANTHONAD<br>BABBAGDGAGEEESGSGSSGSSGSGSGEererrSSSOSSOOHKTAANAAAAANSSOAAAAAAAAANSSOOAANAAAAAAAFErFEreErreecEeEecEcEEEErETEEEEEEEEEEEeer ANNOAOONNDVAANNAANNSOSoCONaNAANANAAYNHK OGOOOSCSOOOCAAASSSOSOSSOOSSANNEER er eeree eK<br>Year-Month<br>Data Source: panjiva<br><!-- End of picture text -->

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_βt_ thus denotes the dynamic treatment effect of firms exposed to certain NTMs compared to firms that are not exposed on the outcome _Yit,pc_ in year-month _t_ compared to the baseline month March 2020.

We control for firm, HS-8 product, year-month, and partner country fixed effects. Standard errors are clustered at the 8-digit product level. The period of our analysis is February 2019-June 2021 and the frequency is monthly. The time period in our analysis is limited by the availability of high-frequency firm-level data from Panjiva. The omitted month is March 2020, when the WHO characterized Covid-19 as a pandemic. We plot the _βt_ coefficients associated with the outcome variables for each month.

# **Fact 5: Firms subject to Port of entry restrictions (C3) saw a large fall in their export quantities and transactions following Covid-19 compared to those that did not face any restrictions (Figures 27a and b).**

This is consistent with the evidence of major port congestion during Covid-19, as the pandemic led to disruptions in maritime trade due to port terminal closures and transportation delays (UNCTAD, 2022).

We show that the results hold even if we change the definition of treated firms and define the treated firms as firms that face above the median level of port of entry restrictions (Figures A25 and A26 in appendix A2).

We do not find any systematic evidence that other NTMs negatively affected firms’ ability to export during the pandemic. The disproportionately large effects of the port of entry restrictions NTM is therefore likely related to port congestion. These results are consistent with those of Majune and Montfaucon (2023), who find that imports subject to port of entry restrictions and preshipment inspections were more negatively affected by Covid-19-related lockdown measures by trading partners.

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<!-- Start of picture text --> 3<br>aBeyx -' T 1!<br>z |. ~|' TT OI<br>Zuji -7 riot<br>gli| i 14boo. TT -AI<br>sot=z2a>ge * 1Li1H . + '!iWW L1 tetit it tatry ritmei \_TttVt|1<br>= me 1 it<br>2.xN 1i+<br>or<br>AMPMOROROK AK ANMTMONMBOrC AK ANTHONAD<br>BABBAGDGAGEEESGSGSSGSSGSGSGEweerSSSOSSOOHKTAANAAAAANSSOAAAAAAAAANSSOOAANAAAAAAAFErFEreErreecEeEecEcEEEErETEEEEEEEEEEEer ee DAODNQNNNNANANSSoanaNNANAAANANNHK OGOOOSCSOOOCAAASSSOSOSSOOSSANNEER er eeree eK<br>Year-Month<br>Data Source: panjiva<br><!-- End of picture text -->

<!-- Start of picture text --> 3<br>oO&gu !!<br>zs= 1 7 !<br><7 iy !<br>solt_g As wero Ae I PpT_TAG- it T<br>5So]2S38a faI THY1oaI VW1\ 1| VAtI1IiAI aeit 1 ata(on~ AVat 1toyiry<br>&id ry<br>=a5. 1boa<br>ao<br>BD ANPMORMROKAKANMT<br>BABBAGDAGDEEESGSGSSGSSGSGSGEwereSSSOSSOOHKTAAAAAAAANSSOAAAAAAAAANSSOOAAAAAAAAAFErFErErreecEeEEceEcEErEErFETEEEEEEEEEEE eee Hr DBMONQNNNNNAANAAASSSANNNANANAANAYAANAHK OGOOOSCSOOOANAASSSSOSGSOSSHON OBOrNNNEER AK ANTHONADer eee r eK<br>Year-Month<br>Data Source: panjiva<br><!-- End of picture text -->
The results imply that since GVC firms recovered much better from a crisis, being globally integrated may be beneficial for firms and the economy in the longer run. Policies to foster a business environment that enables firms to be more globally engaged through GVCs could be key, including tackling non-tariff measures such as the port of entry restrictions would ensure fast and efficient port and customs procedures since value chains require goods to cross borders many times. This is especially true during periods of high port congestion, as was the case during Covid-19.

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