metadata: authors: 0: author: Vijil,Mariana
1: author: Wagner,Laurent
2: author: Woldemichael,Martha Tesfaye
count: World
docna: 0: docna: Import Uncertainty and Export Dynamics
docty: Policy Research Working Paper
subtopic: International Trade and Trade Rules,Common Carriers Industry,Food & Beverage Industry,Plastics & Rubber Industry,Textiles, Apparel & Leather Industry,Pulp & Paper Industry,Construction Industry,Business Cycles and Stabilization Policies,General Manufacturing,Employment and Unemployment,Transport Services,Business Environment
teratopic: Macroeconomics and Economic Growth,Private Sector Development,Industry,Social Protections and Labor,International Economics and Trade,Transport
abstracts: cdata!: A supply chain is only as strong as its
            weakest link. Firms are constantly managing uncertainties,
            including unexpected delays in the provision of a critical
            input that can slow down or halt the production process,
            possibly making the manufacturer miss a delivery deadline.
            As most exporters are also importers of intermediate goods,
            supply chain unreliability related to import processing
            times at the border could impact downstream export dynamics.
            Exploiting a rich data set built on firm-level information
            for 48 developing countries over 2006-14, this paper relies
            on the Poisson pseudo-maximum likelihood estimator to
            investigate how unpredictability in border clearance times
            for imports affects manufacturing firms' entry, exit,
            and survival in export markets. The analysis finds that
            uncertainty in the time to clear imported inputs impacts
            neither the entry nor the exit rate, but translates into
            lower survival rates for new exporters, reducing the number
            of firms that continue to serve the foreign market beyond
            their first year of entry. This effect grows larger over
            time, owing to rising reputational costs to input-importing
            exporters, and is mainly driven by South-North trade,
            possibly reflecting the time-sensitivity of buyers in
            developed countries. The results also reveal heterogeneous
            effects across export industries, as well as the mediating
            role of sunk costs of entry in foreign markets, which
            attenuate the negative effect of uncertainty on survival
            rates, as firms delay exiting the export market. Most
            importantly, the measure of uncertainty displays a
            distinctive effect on export performance, as neither the
            mean nor the median time to import impacts survival.
repnme: repnme: Import Uncertainty and Export Dynamics
display_title: Import Uncertainty and Export Dynamics
listing_relative_url: /research/2019/03/30929008/import-uncertainty-export-dynamics
topicv3: Investment Climate m1326293 405,Manufacturing Sectors m1326521 1281,Transport m1326661 135,Trade m1326924 698,Enterprise Surveys m1504072 2762
publishtoextweb_dt: 2019-03-25T00:00:00Z
historic_topic: Macroeconomics and Economic Growth,Private Sector Development,Industry,Social Protections and Labor,International Economics and Trade,Transport
no_of_pages: 43
author_keys: 0: author_key: 1257901
1: author_key: 1042859
2: author_key: 1405106
model_extractions: Policy Research Working Paper 8793 Import Uncertainty and Export Dynamics _Mariana Vijil Laurent Wagner Martha Tesfaye Woldemichael_ Finance, Competitiveness and Innovation Global Practice March 2019
Policy Research Working Paper 8793

# **Abstract**

A supply chain is only as strong as its weakest link. Firms are constantly managing uncertainties, including unexpected delays in the provision of a critical input that can slow down or halt the production process, possibly making the manufacturer miss a delivery deadline. As most exporters are also importers of intermediate goods, supply chain unreliability related to import processing times at the border could impact downstream export dynamics. Exploiting a rich data set built on firm-level information for 48 developing countries over 2006–14, this paper relies on the Poisson pseudo-maximum likelihood estimator to investigate how unpredictability in border clearance times for imports affects manufacturing firms’ entry, exit, and survival in export markets. The analysis finds that uncertainty in the time to clear imported inputs impacts neither the entry nor the exit rate, but translates into lower survival rates for new exporters, reducing the number of firms that continue to serve the foreign market beyond their first year of entry. This effect grows larger over time, owing to rising reputational costs to input-importing exporters, and is mainly driven by South-North trade, possibly reflecting the time-sensitivity of buyers in developed countries. The results also reveal heterogeneous effects across export industries, as well as the mediating role of sunk costs of entry in foreign markets, which attenuate the negative effect of uncertainty on survival rates, as firms delay exiting the export market. Most importantly, the measure of uncertainty displays a distinctive effect on export performance, as neither the mean nor the median time to import impacts survival.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice. 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/research. The authors may be contacted at mvijil@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
# Import Uncertainty and Export Dynamics<sup>_∗_</sup>

Mariana Vijil<sup>_†_</sup> Laurent Wagner<sup>_‡_</sup> Martha Tesfaye Woldemichael<sup>_§_</sup>

## **JEL Classification** : F14, L25, O1.

**Keywords** : export dynamics, import uncertainty, supply chain unreliability, Customs, trade costs, firms.

> _∗_ We are grateful to Ana Margarida Fernandes and Hibret Belete Maemir for providing access to the Exporter Dynamics Database. We thank Jean-Fran ̧cois Arvis, Cyril Chalendard, Jaime de Melo, Enrique Fanta, Ana Margarida Fernandes, Pierre Mandon, Zafer Mustafaoglu, Martha Denisse Pierola, Patrick Plane, Mary-Fran ̧coise Renard, Shawn Tan as well as participants of the AFR External Sector Network Seminar at the IMF for their useful comments and discussions. All errors remain our own. The findings expressed in this paper are those of the authors and do not necessarily represent the views of the Ferdi, IMF or World Bank.

> _†_ World Bank. Email: `mvijil@worldbank.org`

> _‡_ FERDI. Email: `laurent.wagner@ferdi.fr`

> _§_ IMF and CERDI, Universit ́e Clermont Auvergne. Email: `mwoldemichael2@imf.org`
# **1 Introduction**

The business literature has extensively analyzed how delays and unpredictable delivery times influence firms’ supply chain management decisions (Arvis, Carruthers, Smith, and Willoughby, 2011; Kunaka and Carruthers, 2014). With the development of lean retailing and just-in-time manufacturing practices, together with the increased fragmentation of production across the world,<sup>1</sup> timeliness and reliability have become the watchwords of firms involved in supply-chain trade. Import and export activities have grown increasingly intertwined, with foreign inputs typically imported and incorporated in the production of final goods or transformed and exported to other countries where they may further enter as intermediate inputs in the value-added exported to third countries (Koopman, Powers, Wang, and Wei, 2010; Koopman, Wang, and Wei, 2014). Against this background, unexpected factors such as late shipment arrivals, including truck breakdown-related transport delays, or uncertainty over the time required to clear the consignment after it has reached destination, owing for instance to excessive formalities and physical inspections by border authorities, can lead to postponed delivery of key inputs, thereby disrupting the supply chain. Such uncertainty carries reputational costs for the importing firm, undermining its ability to meet buyer quality requirements and delivery deadlines, and exposing it to the risk of losing sales contracts, market shares and valued customer relationships. Unexpected delays in delivery and their potential snowball effect across the supply chain (Harrigan and Venables, 2006) prompt importers to adopt costly hedging strategies, such as switching to more reliable but expensive transportation modes (Hummels, 2001) or maintaining safety stocks. Evidence suggests that inventory holding to guard against uncertainty generates costs that can vary from 15 percent of the cost of goods per year to as much as 50 percent; reaching on average 27 percent of the goods value in the case of the US (Clark, Kozlova, and Schaur, 2016).

Several studies have formally quantified the effect of timeliness on trade volumes. Based on disaggregated import data for the US, Hummels (2001) finds that each day in transit reduces the probability that the US imports manufactured goods from a given country by 1.5 percent and is equivalent to an ad valorem tariff rate of 0.8 percent, a figure updated to range between 0.6 and 2.3 percent in Hummels and Schaur (2013). Using similar data, Clark, Kozlova, and Schaur (2016) show that unexpected delays in the arrival date of shipments at ports in the US, captured by the difference between the actual and expected dates of arrival, reduce imports by 1 to 2 percent. Time costs also impede exports both at the extensive and intensive margins. Using a sample of 98 countries, Djankov, Freund, and Pham (2010) argue that each additional day required to move products from the factory gate and onto the ship reduces the volume of exports by more than 1 percent, while Li and Wilson (2009) find that it decreases the probability to export and the share of exports in total sales in developing countries. Freund and Rocha (2011) convey a similar message in the case of African countries and find inland transit delays to matter more than those related to documentation processing and Customs clearance. Likewise, Volpe Martincus, Carballo, and Graziano (2015) show that a 10 percent increase in Customs-driven delays in the time to export leads to a 3.8 percent decline in firms’ exports in Uruguay, with additional negative consequences for export

> 1World Bank Group; IDE-JETRO; OECD; UIBE; World Trade Organization (2017), _Global Value Chain Development Report 2017: Measuring and Analyzing the Impact of GVCs on Economic Development._ Washington, DC: World Bank.

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market diversification. Most of these studies concur to say that the negative effects of delays on trade are particularly exacerbated for time-sensitive goods such as perishable agricultural products or parts and components (Hummels and Schaur, 2013). As a result, countries with better ability to export on time are found to be more likely to develop comparative advantage in time-sensitive industries and to export intermediate inputs than final goods (Gamberoni, Lanz, and Piermartini, 2010).

In this paper, we argue that uncertainty in the time required to complete Customs procedures and other border processes and formalities for imported inputs affect importing firms’ export dynamics. A large body of literature has documented the positive impact of foreign intermediate goods or input tariff liberalization on productivity by providing firms access to a larger array of intermediate inputs - the variety channel - and to the foreign technology embodied in imported inputs, through the so-called quality channel.<sup>2</sup> Since only the most productive firms self-select into exporting (Melitz, 2003), imports of intermediate inputs matter for export performance. A number of papers have also evidenced the direct relationship between imports and export outcomes. For instance, van der Marel (2017) shows that non-tariff barriers hindering the import of intermediate manufactured products reduce total trade values, the average export per firm and the number of exporting firms. Using data on Argentinian firms, Bas (2012) finds that input tariff liberalization raises the probability of firm entry into export markets, while Bas and Strauss-Kahn (2014) evidence a positive impact on the number of products exported by French manufacturing firms. By the same token, input-importing firms in South Africa (Edwards, Sanfilippo, and Sundaram, 2017) and China (Feng, Li, and Swenson, 2016) exhibit both higher export volume and scope. In addition to supporting the export diversification of importing firms at the intensive and extensive margins, foreign inputs also have a bearing on how long they operate in export markets. Lopez (2006) argues that importing intermediate inputs increases the probability of export survival of Chilean firms, whereas Wagner (2013) finds that importing firms in Germany are less likely to exit the export market. Access to foreign inputs also seems to enhance the quality of the export bundle (Kugler and Verhoogen, 2009, 2012; Manova and Zhang, 2012; Fan, Li, and Yeaple, 2015; Bas and Strauss-Kahn, 2015; Bastos, Silva, and Verhoogen, 2018). In line with these studies, Pierola, Fernandes, and Farole (2018) show that Peruvian firms that use imported intermediate inputs not only export and grow more, but they also export high-quality goods and are more geographically diversified.

Drawing on these two strands of literature on the trade-effects of border clearance times and the role of imported inputs in export outcomes, this paper investigates how upstream uncertainty faced by firms in the time to clear imported inputs at the border shapes downstream export dynamics. Our interest is twofold. First, we explore whether unpredictability in the time required to accomplish Customs and other border processes and formalities for imported inputs deters manufacturing firms from entering new export markets. Second, we examine whether it influences the export survival of firms already serving foreign markets based on the export exit rate of incumbents and entrants, and the share of firms that are still in operation in the foreign market one, two or three years after they start exporting. In doing so, we make several contributions to the existing literature. First, we argue that predictability in imported inputs delivery times is more important than actual observed times, and depart from the cross-country

> 2See for instance Schor (2004); Amiti and Konings (2007); Kasahara and Rodrigue (2008); Topalova and Khandelwal (2011); Halpern, Koren, and Szeidl (2015) and Bas, Johansson, Murtin, and Nicoletti (2016).

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studies using single-value, average-type country-level indicators of time-related trade costs such as the Doing Business Indicators. Instead, we compute a novel measure of import uncertainty taking advantage of the time-varying within-country distribution of Customs clearance times across manufacturing firms sourced from the World Bank Enterprise Surveys (WBES), thereby accounting for the heterogeneity of border times at the country-sector-firm-year level (Volpe Martincus, 2016). Specifically, we consider the within-country-sector-year interquartile range of the days elapsed between the time of arrival of imported inputs at the destination port of entry and the time of release from Customs, for a sample of 48 developing countries over 2006-2014.

Second, by matching this dataset with information on firm export dynamics at the origin-destinationsector-year level obtained from the World Bank’s Exporter Dynamics Database (EDD), we are able to shed light on how uncertainty in the time to import affects the decision of (i) non-exporters to enter foreign markets; (ii) both new and incumbent exporters to exit foreign markets, and (iii) firms to keep on exporting one, two and three years after the date they started serving the foreign market. Therefore, our paper emphasizes the granular origins of aggregate trade performance by exploiting firm-level information to derive country-sector level indicators of export dynamics. Export outcomes at the macro-level are driven by the activities of individual firms operating in different sectors across the country, with sometimes only a handful of them - dubbed export superstars - accounting for the lion’s share of total exports (Freund and Pierola, 2015).

By constructing a new measure of uncertainty in border clearance times for imports, we quantify a new source of trade costs. Traditionally captured by a wide range of variables including standard gravity-type measures such as bilateral distance, tariffs, contiguity, common language and participation in trade agreements, trade costs also relate to a country’s legal and regulatory framework, as well as the availability of quality infrastructure and other trade facilitation measures aimed for instance at reducing administrative red tape. Abundant research highlights the sizeable impact of trade costs on aggregate export volumes,<sup>3</sup> the intensive and extensive margins of trade<sup>4</sup> and export survival<sup>5</sup> on the one hand, and their large growth- and welfare-implications on the other (Anderson and van Wincoop, 2004).<sup>6</sup> Our results suggest that uncertainty in the time required to clear imported inputs at the border impacts neither the entry nor the exit rate of manufacturing firms in developing countries, but translates into lower survival rates for new exporters, with a smaller number of manufacturing firms continuing to serve the foreign market beyond their first year of entry. Interestingly, this effect grows larger over time as input-importing exporters bear the increasing reputational costs associated with missed delivery deadlines, and appears to be driven by South-North trade, possibly reflecting the time-sensitivity of buyers in developed countries. We also find that the export dynamics-effects of import uncertainty are

> 3See for example Limao (2001); Coulibaly and Fontagn ́e (2006); Blonigen and Wilson (2008) and Volpe Martincus, Carballo, and Cusolito (2017).

> 4See for instance Debaere and Mostashari (2010); Shepherd (2010); Dennis and Shepherd (2011); Albornoz, Calvo Pardo, Corcos, and Ornelas (2012); Regolo (2013); Feenstra and Ma (2014) and Beverelli, Neumueller, and Teh (2015).

> 5See for instance Brenton, Saborowski, and von Uexkull (2010); Brenton, Cadot, and Pierola (2012); Fugazza and Molina (2016); Cadot, Iacovone, Pierola, and Rauch (2013); Araujo, Mion, and Ornelas (2016) and Carr`ere and Strauss-Kahn (2017). 6Since the seminal work of Romer and Frankel (1999) who show that trade increases income, the literature has burgeoned with studies aimed at identifying the positive relationship between trade and economic growth. Winters (2004) provides an extensive overview of the literature.

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heterogeneous across export industries. Finally, sunk costs of entry in foreign markets attenuate the negative effect of import uncertainty on survival rates as firms delay exiting the export market. Our results show that neither the mean nor the median time to import impacts survival while our measure of uncertainty captured by the interquartile range leads to highly significant results with regard to export survival. This powerful finding highlights the distinctive effect of uncertainty on export performance. Our findings are robust to the inclusion of other measures of domestic trade costs associated with the import process, including cumbersome formalities to import, ease of access to finance and corrupt practices at the border, confirming the distinctive impact of our measure of import uncertainty on firm export dynamics. Our results suggest that developing countries seeking to promote the survival of newly-exporting firms in foreign markets should consider undertaking policies targeted at reducing the uncertainty these firms face when importing their production inputs.

The remainder of the paper is organized as follows. Section 2 describes the import process, introduces the dataset and provides stylized facts. Section 3 specifies the econometric model and presents the results. Section 4 discusses the quality of our measure of import uncertainty as well as endogeneity concerns. Section 5 concludes.

# **2 Data**

## **2.1 Import Uncertainty**

Following harmonization efforts promoted by international organizations, including the World Customs Organization (WCO) and the Word Trade Organization (WTO), the import process from the moment the cargo arrives at the border post until it is cleared by Customs is relatively standard across countries (Figure 1). This standardization was key to reducing the complexity of the process, as close to 100 steps must be performed by multiple public and private stakeholders, with each step being a potential source of delay.<sup>7</sup> Taking the maritime transport case as an example,<sup>8</sup> the first step is the arrival of the vessel at the port of entry. At this stage, congestion at port terminals can lead to berthing delays with vessels waiting hours or sometimes even days in the queue, and with delays being charged by the shipping company to the importer through higher shipping rates. After berthing, port operators unload the vessel and place its cargo in a shipyard or warehouse while the importing firm or its representative, the Customs broker, verifies the cargo content (e.g., looking for possible inconsistencies); and prepares Customs documents, import licenses, Sanitary and Phytosanitary (SPS) certificates and permits if applicable,<sup>9</sup> filled in electronic or paper form depending on Customs and other border control agencies’ use of Information Technology (IT) systems. In many countries, SPS agencies and Customs require separate declarations, with significant data gathering and task duplication for the importer or its delegate that can be a source of delays. Insufficient container placement capacity or gantry cranes, poor efficiency of port operators during cargo movement or the low celerity with which the importer or the Customs broker

> 7 See for instance the Colombia time release study.

> 8The import process remains similar for other transport modes.

> 9Import licenses are required for goods subject to import quotas, import prohibition (e.g. arms, chemicals) or sanitary and phytosanitary measures (e.g., agricultural goods, food, forestry, cosmetics, pharmaceuticals, etc.).

5
<!-- Start of picture text --> Goods Arrival to Unloading Preparation Customs<br>Point of Entry and and Clearance<br>(port, airport, Warehousing Customs<br>border post) Declaration<br>Time to Import: from Goods Arrival to Customs Clearance<br>Stakeholders - Other Government ' | - Customs & Other :<br>Involved in the ‘Shippingtpping CCompany ' || AgenciesPolice) (SPS, TBT, '' || Border- Bank Agencies 'H<br>Import Process ' . ' | - Importer ' | - Importer :<br>i Port Authority ' | - Customs Broker | - Customs Broker '<br>' ' | - Logistics Operator H | - Logistics Operator H<br><!-- End of picture text -->
sometimes multiple times depending on the number of agency inspections, with direct costs charged to the importer and higher risks of deterioration for perishable goods. As in previous steps, the quality of the port physical infrastructure and efficiency of port operators in moving the cargo, as well as their limited use of IT systems to efficiently allocate containers, can also generate delays (even for non-intrusive inspection methods based on x-rays). In addition, delays can occur as the importer or its representative must be physically present during any physical inspection. Also, rent-seeking practices, including informal payments to obtain a modification of the Customs classification or valuation of the imported good, or to limit or altogether avoid sample testing can occur at this stage considering the multiple stakeholders involved in the clearance process. In a final step,<sup>14</sup> Customs clear the shipment once all border control agencies have provided their clearance and the importer has paid any outstanding balance, marking the end of the import process as defined in this paper. The released shipment can then be picked up by the importing firm either immediately or after some time if the firm decides to take advantage of port storage facilities or if it faces liquidity constraints to pay port fees.

We rely on the World Bank Enterprise Surveys (WBES) database for the period 2006-2016 to construct our measure of uncertainty in import clearance times, which covers unpredictable delays arising at any stage of the import process as discussed above, from vessel berthing to Customs clearance.<sup>15</sup> The WBES database provides quantitative and qualitative information on the characteristics of firms operating in the manufacturing and services sectors in developing countries; as well as the business environment they face (e.g. access to finance, regulations, taxes and trade costs). Surveys are administered to nationally representative samples of formal firms with at least five workers. We use the standardized version of the database which compiles only those surveys following the WBES Global methodology, yielding information that is comparable across countries and years. We restrict our analysis to manufacturing firms identified through ISIC codes 15 to 36.<sup>16</sup> Since in most low-income countries, only a very limited number of firms operate in some manufacturing sectors defined at the 2-digit ISIC level, we aggregate the 22 ISIC manufacturing divisions into five sectors of interest (Table A1 in the Appendix): (i) manufacture of food, beverage and tobacco products (ISIC codes 15-16); (ii) manufacture of textile and leather-related products (ISIC codes 17-19); (iii) manufacture of wood-related products (ISIC codes 20-22); (iv) manufacture of minerals, metals and chemicals (ISIC codes 23-28); and (v) manufacture of advanced products (ISIC codes 29-36).<sup>17</sup> We end up with a sample of 16,475 firms whose selected characteristics are displayed in Table 1.<sup>18</sup> We find that 30 percent of manufacturing firms are direct exporters and among these, 74 percent import the inputs that enter their production process. In contrast, only one-third of firms that exclusively sell in the domestic market are also direct importers, comforting us in our approach. In

> 14For example, in the orange or red channel the inspector assesses the appropriate value and taxes and duties to be paid.

> 15The WBES database was accessed in August 2017.

> 16The survey question allowing to identify input-importing firms applies to manufacturing firms only, naturally dictating our sample choice. The Enterprise Surveys classify firms based on the International Standard Industrial Classification of All Economic Activities (ISIC), Revision 3.1.

> 17Recycling (code 37) is excluded from the analysis due to poor data coverage in the World Bank Enterprise Surveys and unavailability in the Exporter Dynamics Database (see Section 2.2).

> 18This figure was arrived at by restricting the sample to those firms that reported positive direct exports as a share of total sales and that also provided an indication on whether they import material inputs or supplies. Only manufacturing firms located in the 48 developing countries retained in the econometric analysis are considered (see Section 2.2 and Table A1 in the Appendix).

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line with the literature, exporting firms present specific characteristics that distinguish them from their non-exporting counterparts (see for example the discussion by Bernard and Jensen (2007)). In particular, they tend to be older, larger and more productive, and this is also broadly verified across all five export industries.

Table 1: Selected Characteristics of Firms by Exporter/Importer Status and by Export Industry

||||Exporters|||Non-Exporte|rs|
|---|---|---|---|---|---|---|---|
||All Sample|All Exporters|Importing<br>Exporters|Non-Importing<br>Exporters|All Non-<br>Exporters|Importing<br>Non-Exporters|Non-Importing<br>Non-Exporters|
|||**Aggre**|**gate Manufa**|**cturing Industry**||||
|Number of frms|16,475|5,407|4,349|1,058|11,068|4,551|6,517|
|_% of sample_|100%|30.0%|73.7%|26.3%|70.0%|35.6%|64.4%|
|Age (years)|19|23|23|20|17|20|16|
|Size (nb. of employees)|114|246|287|130|58|103|33|
|TFP|1.75|1.91|1.94|1.84|1.68|1.85|1.58|
||**M**|**anufacture of **|**Food, Bever**|**age and Tobacco**|**Products**|||
|Number of frms|3,471|959|768|191|2,512|886|1,626|
|_% of sample_|100%|22.94%|80.8%|19.2%|77.1%|38.7%|61.3%|
|Age (years)|23|31|32|27|21|22|21|
|Size (nb. of employees)|144|258|286|138|110|206|49|
|TFP|157|186|193|156|149|160|140|
||.<br>**M**|.<br>**anufacture of **|.<br> **Textile and **|.<br> **Leather-Related **|.<br> **Products**|.|.|
|Number of frms|4,489|1,896|1,468|428|2,593|846|1,747|
|_% of sample_|100%|38.6%|68.6%|31.4%|61.5%|27.7%|72.3%|
|Age (years)|16|18|19|16|15|18|14|
|Size (nb. of employees)|174|347|424|181|65|131|39|
|TFP|1.63|1.70|1.62|1.86|1.60|1.67|1.57|
|||**Manufac**|**ture of Woo**|**d-Related Produ**|**cts**|||
|Number of frms|1,224|265|200|65|959|399|560|
|_% of sample_|100%|21.7%|66.1%|33.9%|78.3%|30.7%|69.3%|
|Age (years)|20|24|23|26|19|22|17|
|Size (nb. of employees)|48|87|101|57|37|78|20|
|TFP|1.75|2.15|2.27|1.87|1.62|1.90|1.51|
|||**Manufacture**|**of Minerals**|**, Metals and Ch**|**emicals**|||
|Number of frms|5,232|1,697|1,469|228|3,535|1,818|1,717|
|_% of sample_|100%|31.6%|78.7%|21.3%|68.4%|44.2%|55.8%|
|Age (years)|20|24|24|23|18|20|17|
|Size (nb. of employees)|103|213|242|107|52|77|33|
|TFP|1.94|2.05|2.12|1.74|1.88|2.01|1.77|
|||**Manuf**|**acture of A**|**dvanced Product**|**s**|||
|Number of frms|2,059|590|444|146|1,469|602|867|
|_% of sample_|100%|24.5%|73.2%|26.4%|75.5%|33.2%|66.8%|
|Age (years)|17|23|24|20|15|19|13|
|Size (nb. of employees)|58|141|167|66|31|50|22|
|TFP|1.69|1.85|1.77|2.05|1.64|1.90|1.51|

Notes: Authors’ calculations based on the World Bank Enterprise Surveys. Descriptive statistics computed using survey weights, except for the number of firms. Sample restricted to firms that reported a positive or null number to question d3c _”What percentage of this establishment’s sales were direct exports?”_ and to those that replied yes or no to question d13 _”Were any of the material inputs or supplies purchased [...] imported directly?”_ of the Manufacturing Module. Included firms are those located in the 48 developing countries retained in the empirical analysis (see Section 2.2 and Table A1 in the Appendix). _Age_ is the number of years since the firm began operations; _Size_ is the number of permanent, full-time employees; _TFP_ stands for total factor productivity.

The explanatory variable of interest is captured by the interquartile range of the average number of days to clear imported material inputs or supplies through Customs and other border control agencies, computed over a representative population of manufacturing firms at the country-sector-year level.<sup>19</sup> We

> 19More precisely, question D.14 from the Manufacturing Module of the WBES reads _“In [the last] fiscal year, when this_

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<!-- Start of picture text --> sas| |——_i PS —_<br>MNA| | —— i i <}>———_——_——<br>Lac] }+}——_OHR<br>Eca| Hi—_—_—<br>EAP] | > ——<br>SSA| | —— i Po,<br>0 5 10 15 20 25 30<br>Uncertainty in the Time to Import (Days)<br><!-- End of picture text -->
uncertainty. Figure 2 thus implies that for an importer operating in a particular sector and in a specific region, for example Latin America and the Caribbean, the effective clearance time might be significantly longer than suggested by a simple average. Cross-country disparities are further investigated in Figure A1 in the Appendix. We find evidence of significant differences in the distribution of import clearance times, with countries such as Albania or Romania displaying close-to-zero interquartile ranges whereas 50 percent of Ivorian importing firms reported experiencing 13 to 60 days of delay in clearing imported inputs through Customs. Consistent with Volpe Martincus (2016) who argues that using distributions rather than point estimates allows improvements to the measurement of border clearance times given the heterogeneity of border times across observations, these stylized facts clearly suggest that relying on a single, average-type measure of time to import is not appropriate to fully grasp the uncertainty that firms face when importing their inputs.

# **2.2 Export Dynamics**

We match the WBES with a rich array of variables drawn from the Exporter Dynamics Database (EDD). The latter contains measures of the degree of product and market export diversification, firm dynamics in terms of entry, exit and survival in export markets, as well as the average unit prices of the products they export over the period 1997-2014 (Fernandes, Freund, and Pierola, 2016). Our analysis focuses on the export dynamics of manufacturing firms at the country-destination-sector-year level.<sup>21</sup> More specifically, we investigate whether uncertainty in import clearance times affects firm entry, exit as well as first-, second- and third-year survival rates in export markets. Out of the 72 developing countries originally present in EDD, 48 were successfully matched with the WBES. Since the latter are only conducted every four to six years and data collection takes over a year, we allow matching EDD data with up to three years backward or forward relative to the WBES to enhance the size of the final dataset. Table A2 in the Appendix displays the list of countries included in the analysis along with the corresponding time adjustments, if applicable.

Figure 3 presents preliminary evidence of the relationship between uncertainty in the duration of the import process and firms’ export dynamics in developing countries. Once country, destination, sector and year fixed-effects are accounted for, increases in import uncertainty appear to be significantly associated with higher exit rates of exporters and lower probability of entering exporters to survive past the first year of exports, thereby hinting at a potential adverse effect on firms’ exporting status. However, Figure 3 also suggests a positive correlation between import uncertainty and firm entry rates in export markets. Taken together, these scatter plots seem suggestive of export experimentation and failure leading to a substantial turnover rate or churning as firms enter and exit export markets, yielding low survival rates (Brenton, Saborowski, and von Uexkull, 2010; Brenton, Cadot, and Pierola, 2012). We hypothesize that such dynamics are driven by uncertainty in the time to import. Specifically, we conjecture that unpredictability in border clearance times for key inputs disrupts the production process, making the importing exporter likely to miss delivery deadlines. If such incident happens frequently, exporting firms risk losing their credibility as reliable suppliers, and time-sensitive buyers may decide to terminate

> 21We thank Ana Margarida Fernandes and Hibret Belete Maemir for kindly computing the EDD statistics at the countrydestination-sector-year level.

10
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# **3 Empirical Methodology**

## **3.1 Econometric Model**

We formally explore the relationship between uncertainty in import clearance times and firm export dynamics by estimating the following baseline model: The dependent variable _FirmDynamicsijkt_ is the entry, exit or survival rate of manufacturing firms in sector _k_ of country _i_ and exporting to destination _j_ . _Uncertaintyikt_ is the interquartile range of the time to clear imports at the border, and varies across time by exporting country _i_ and sector _k_ . We include vector _Xikt_ to control for time-varying exporting firm characteristics taken from the WBES and averaged at the country-sector-year level. It comprises (i) firm export intensity defined as direct exports as a fraction of total sales, (ii) import penetration, captured by the proportion of firms that import material inputs or supplies, (iii) the number of years since the firm first started to export as a proxy for average export experience, (iv) the natural logarithm of the number of permanent, full-time employees as a proxy for firm size, and (v) firm total factor productivity (TFP). Consistent with the literature on export dynamics, _Wij_ is a vector of standard time-invariant bilateral gravity variables such as contiguity, distance, and a binary variable for colonial relationship taken from CEPII. _EIAijt_ is a time-variant dummy provided at Jeffrey Bergstrand’s website and indicating whether the pair is involved in an economic integration agreement. _Tariffijkt_ refers to bilateral applied tariffs derived from WITS.<sup>22</sup> We also include vectors _Fit_ and _Pit_ to control for country-specific characteristics such as GDP per capita and the Doing Business indicator of the cost of procedures to start a business as a share of GNI per capita drawn from CEPII to account for the level of development and the average quality of the business environment, respectively. Finally, _Oi_ , _Dj_ , _Sk_ and _Tt_ refer to exporter-, importer-, sector- and year-specific effects respectively.

Table A3 in the Appendix provides a detailed description of the variables, while associated summary statistics are presented in Table 2. Given the preponderance of zeros for the dependent variables of interest, the baseline model is estimated by Poisson Pseudo-Maximum-Likelihood (PPML) following Santos Silva and Tenreyro (2006, 2010). Standard errors are clustered by exporter-importer pair.

> 22WITS uses the concept of effectively applied tariff which is defined as the lowest available tariff. If a preferential tariff exists, it is used as the effectively applied tariff. Otherwise, the Most-Favored Nation (MFN) applied tariff is used.

12
Table 2: Summary Statistics

||Mean|Median|Std. Dev.|Min.|Max.|Obs.|
|---|---|---|---|---|---|---|
|Firm entry rate (%)|0.597|0.574|0.306|0|1|14,476|
|Firm exit rate (%)|0.524|0.500|0.292|0|1|12,744|
|Entrant 1<sup>st </sup>year survival rate (%)|0.286|0.250|0.294|0|1|12,654|
|Entrant 2<sup>nd </sup>year survival rate (%)|0.151|0|0.228|0|1|11,343|
|Entrant 3<sup>rd </sup>year survival rate (%)|0.110|0|0.194|0|1|5,966|
|Log import uncertainty|2.195|2.303|0.845|0|3.970|14,476|
|Log time to import (days)|2.422|2.466|0.527|0.916|3.713|14,476|
|Log median time to import (days)|2.035|1.946|0.653|0.693|4.094|14,476|
|Common border|0.052|0|0.222|0|1|14,476|
|Colony|0.008|0|0.087|0|1|14,476|
|Log distance|8.573|8.819|0.901|5.089|9.886|14,476|
|Trade agreement|0.536|1|0.499|0|1|14,476|
|Log tarifs|1.311|1.281|1.249|0|5.972|14,476|
|Log exporter GDP cap.|8.086|8.383|1.062|5.840|9.673|14,476|
|Log importer GDP cap.|8.975|9.077|1.532|5.065|11.64|14,476|
|Log exporter entry cost (% GNI)|3.190|3.437|1.063|0.405|5.308|14,476|
|Log importer entry cost (% GNI)|2.300|2.389|1.669|-2.303|7.181|14,476|
|Log documents to import|1.947|1.946|0.333|1.099|3.045|14,390|
|Log LPI timeliness|1.178|1.203|0.135|0.756|1.379|13,338|
|High-income destination|0.716|1|0.451|0|1|14,476|
|Sector 1|0.223|0|0.416|0|1|14,476|
|Sector 2|0.194|0|0.396|0|1|14,476|
|Sector 3|0.115|0|0.318|0|1|14,476|
|Sector 4|0.264|0|0.441|0|1|14,476|
|Sector 5|0.204|0|0.403|0|1|14,476|
|Log export uncertainty|1.555|1.609|0.960|0|3.738|11,888|
|Log time to export (days)|1.879|1.852|0.569|0.693|3.689|13,836|
|Log median time to export (days)|1.607|1.386|0.647|0.693|3.689|13,836|
|**Panel A: All Firms**|||||||
|Log direct exports (% sales)|1.675|1.761|1.141|-3.818|3.838|14,476|
|Log direct imports (%)|-0.744|-0.634|0.504|-2.711|0|14,476|
|Log experience|2.457|2.510|0.414|0.348|3.570|14,476|
|Log nb. of employees|4.041|3.955|0.649|2.180|6.073|14,476|
|Log TFP|0.495|0.511|0.228|-0.780|1.155|14,476|
|Log collateral (%)|-0.323|-0.220|0.340|-1.871|0|14,456|
|Log no loan application (%)|-1.076|-0.950|0.679|-4.343|-0.033|14,405|
|Log bribes (% annual sales)|0.737|0.475|0.724|0|3.024|14,098|
|Log licensing obstacle (%)|-2.203|-2.066|0.924|-6.335|-0.114|13,469|
|**Panel B: Exporting Firms**|||||||
|Log direct exports (% sales)|3.448|3.478|0.610|0.692|4.605|14,476|
|Log direct imports (%)|-0.303|-0.189|0.388|-2.549|0|14,274|
|Log experience|2.497|2.550|0.450|0.423|3.871|14,410|
|Log nb. of employees|4.890|4.930|0.899|1.609|7.436|14,463|
|Log TFP|0.572|0.618|0.313|-0.667|1.433|13,798|
|Log collateral (%)|-0.287|-0.154|0.400|-3.042|0|13,475|
|Log no loan application (%)|-1.254|-1.151|0.850|-3.836|0|11,203|
|Log bribes (% annual sales)|0.502|0.187|0.699|0|3.857|13,148|
|Log licensing obstacle (%)|-2.079|-1.976|1.031|-5.105|0|10,835|

Notes: Authors’ calculations based on WBES, CEPII, Jeffrey Bergtrand’s website, WITS, Doing Business and LPI. Descriptive statistics computed from the sample of Column 1 of Table 3. Variables from WBES calculated over the entire population of firms (Panel A) or the sample of exporting firms only (Panel B). The description and source of variables are provided in Table A3 in the Appendix.

13
# **3.2 Results**

Table 3 presents PPML estimates of the baseline model for each dependent variable of interest. Two remarkable results stand out. First, uncertainty in the number of days required to clear imports at the border impacts neither the entry nor the exit rate of manufacturing firms in developing countries (Columns 1 and 2). However, it reduces the survival rate of new exporters by resulting in a lower number of entrant firms that continue to serve the foreign market beyond their first year of entry (Columns 3-5). As the effect is significant only with respect to the export survival of entrants as opposed to the exit rate which pertains to both entrants and incumbents, i.e. any firm that stops serving the foreign market in _t_ despite having exported in _t −_ 1, regardless of the date of entry, these results suggest that newlyexporting firms are more affected by the detrimental impact of uncertainty than incumbents. Second, the adverse effect on export survival is economically meaningful and grows larger over time, with a doubling of import uncertainty leading to a decrease in the export survival rate of entrants of 4 percent the first year, 6 percent the second and 7 percent the third. In other words, if uncertainty in border clearance times for imports fell from 25 days (90<sup>th</sup> percentile) to 3 days (10<sup>th</sup> percentile), new exporters would see their probability of continuing to serve foreign markets one, two and three years after they started operations improve by 8.5, 12.5 and 15.3 percent respectively, _ceteris paribus_ .<sup>23</sup> We explain these results in light of our initial assumption (Section 2.1) according to which unpredictability in border clearance times for key inputs disrupts the production process, making the importing exporter likely to miss delivery deadlines, and hence lose sales contracts with time-sensitive buyers. The detrimental effect is felt by young exporters that started operating in foreign markets one, two and three years ago, with import uncertainty taking an increasing toll on entrants’ export activities with time. This entails that some export relationships are extremely short-lived, time-sensitive buyers ending them as immediately as the very first delivery deadline is missed. This is typically the case for markets such as low-end garments featuring little product differentiation and where buyers can easily switch suppliers owing to low search costs (Brenton, Cadot, and Pierola, 2012). But even when a young exporter succeeds to survive more than a year past its entry date, it continues to incur costs associated with the loss of credibility as a reliable supplier. We argue that these reputational costs accumulate over time whenever the exporter experiences postponed delivery of its imported inputs, which erodes the “patience” and “confidence” of its customers, up to a level that ultimately leads them to terminate the business relationship.

Control variables broadly display the expected sign. The average number of years since firms started exporting in the sector is positively associated with the survival rate of new exporters, suggesting that the latter benefit from lower asymmetries of information associated with the collection of information on market conditions and business opportunities in international markets, possibly through the work performed by production associations or export promotion agencies. For instance, following evidence from Lederman, Olarreaga, and Zavala (2016) for a panel of Latin American firms between 2006 and 2010, export promotion agencies improve firm entry and survival in export markets. This is also in

> 23These magnitudes are obtained as the product of the difference between the 90th and 10th percentile of import uncertainty and the corresponding coefficient in Columns 3-5 of Table 3.

14
Table 3: Import Uncertainty and Export Dynamics: Core Results

||Firm Entry<br>Rate<br>(1)|Firm Exit<br>Rate<br>(2)|First yr.<br>Survival<br>(3)|Second yr.<br>Survival<br>(4)|Third yr.<br>Survival<br>(5)|
|---|---|---|---|---|---|
|Log import uncertainty|0.000<br>|0.000<br>|-0.040***<br>|-0.059***<br>|-0.072**<br>|
||(0.006)|(0.007)|(0.014)|(0.021)|(0.036)|
|Log direct imports (%)|-0.014|-0.004|-0.026|-0.020|0.061|
||(0.011)|(0.015)|(0.031)|(0.053)|(0.093)|
|Log direct exports (% sales)|-0.027***|-0.032***|0.105***|0.113***|0.088|
||(0.005)|(0.013)|(0.028)|(0.040)|(0.062)|
|Log experience|0.001|-0.004|0.115***|0.157***|0.126|
||(0.013)|(0.016)|(0.035)|(0.058)|(0.104)|
|Log nb. of employees|-0.020*|-0.027***|0.081***|0.117***|0.132**|
||(0.010)|(0.009)|(0.019)|(0.029)|(0.057)|
|Log TFP|0.043*|-0.045**|0.175***|0.308***|0.269***|
||(0.024)|(0.019)|(0.039)|(0.063)|(0.100)|
|Common border|-0.033|-0.040|0.065*|0.068|-0.020|
||(0.022)|(0.027)|(0.039)|(0.059)|(0.089)|
|Colony|-0.079*|-0.019|0.148**|0.219*|0.080|
||(0.041)|(0.041)|(0.073)|(0.112)|(0.283)|
|Log distance|0.110***|0.037***|-0.071***|-0.109***|-0.150***|
||(0.009)|(0.010)|(0.018)|(0.029)|(0.051)|
|Trade agreement|-0.063***|-0.035**|0.098***|0.162***|0.194**|
||(0.016)|(0.018)|(0.033)|(0.051)|(0.082)|
|Log tarifs|0.008|0.012*|-0.027**|-0.028*|-0.030|
||(0.005)|(0.006)|(0.011)|(0.017)|(0.027)|
|Log exporter GDP cap.|-0.075|0.056|-0.042|-0.182|2.706*|
||(0.097)|(0.117)|(0.181)|(0.260)|(1.640)|
|Log importer GDP cap.|0.061|0.004|0.122|0.136|0.264|
||(0.041)|(0.049)|(0.098)|(0.152)|(0.229)|
|Log exporter entry cost (% GNI)|-0.021|-0.078*|-0.141*|-0.327***|0.155|
||(0.036)|(0.043)|(0.084)|(0.121)|(0.400)|
|Log importer entry cost (% GNI)|0.013|-0.013|-0.011|-0.021|-0.115|
||(0.014)|(0.018)|(0.035)|(0.054)|(0.083)|
|Constant|-1.036|-1.151|-1.232|-0.556|-25.937*|
||(0.863)|(1.032)|(1.642)|(2.424)|(14.823)|
|Observations|14,476|12,001|11,834|10,755|5,680|
|R<sup>2</sup>|0.182|0.183|0.106|0.100|0.123|
|Country Fixed Efects|Yes|Yes|Yes|Yes|Yes|
|Destination Fixed Efects|Yes|Yes|Yes|Yes|Yes|
|Sector Fixed Efects|Yes|Yes|Yes|Yes|Yes|
|Year Fixed Efects|Yes|Yes|Yes|Yes|Yes|

Notes: PPML estimates. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Robust standard errors clustered on exporter-importer pair in parentheses. Log direct imports (%), Log direct exports (% sales), Log experience, Log nb. of employees and Log TFP computed over the sample of exporting firms, except in Column 1. The description and source of variables are provided in Table A3 in the Appendix.

15
line with Carr`ere and Strauss-Kahn (2017) who show that experience raises the survival of developing countries’ exports to the OECD; and Albornoz, Calvo Pardo, Corcos, and Ornelas (2012) who underscore the importance of learning by exporting for survival. Sectors with larger and more productive firms are more likely to enjoy higher rates of export survival and lower exit rates.<sup>24</sup> TFP is also positively associated with firm entry rates, consistent with Melitz (2003) and Bernard, Eaton, Jensen, and Kortum (2003). Standard proxies of bilateral trade costs including tariffs, distance and limited participation in economic integration agreements lower export survival and increase exit rates. High costs of starting a business in the exporting country, which reflect the extent to which the business environment is conducive to firms’ activities, also deter the survival of entrants but reduce the exit rate.<sup>25</sup> Beyond these baseline estimates, we also investigate possible heterogeneity in the impact of unpredictable border clearance times for imports on export dynamics. First, we examine whether the adverse effect of import uncertainty on firm survival rates is more pronounced for exports to high-income countries. The rationale behind this assumption lies in the growing share of trade flows that belong to global value chains (GVCs). Multinational firms based in high-income countries outsource part of their manufacturing process, typically labor-intensive tasks, to suppliers in developing countries. Combined with the observation that importer-exporter relationships involving developed countries tend to rely more heavily on lean supply chain strategies (Volpe Martincus, 2016), this makes buyers in high-income countries particularly sensitive to delivery schedules, and hence more prone to terminate trade relationships with unreliable suppliers in developed countries. We argue that by raising importing firms’ probability of missing delivery deadlines, uncertainty in the time required to clear foreign inputs through Customs exerts a stronger effect on firm survival in high-income export markets. To test this prediction, we estimate the baseline regressions in two sub-samples distinguishing between high-income (South-North trade) and non-high-income (South-South trade) destinations. Tables 4 and 5 present the results. The impact of uncertainty in import clearance times on export survival is mainly driven by South-North trade (Table 4), as coefficients on the dependent variable of interest lose statistical significance in the sub-sample of South-South trade (Table 5).<sup>26</sup> In a context of global supply chains where time management is critical, this confirms the distinctive sensitivity of buyers in high-income countries to timeliness.

In the same vein, one would expect exports from time-sensitive industries to be more affected by unpredictable delays to clear imported inputs at the border. (Hummels, 2001, 2007) and Hummels and Schaur (2013) provide a classification of goods according to their time-sensitivity, based on trading firms’ choice between using expensive but fast air transportation versus inexpensive and slow sea shipping for their products. Time-sensitive goods include for instance perishable products likely to be spoiled in the

> 24Bilateral distance and the dummy for participation in economic integration agreements enter with a positive and negative sign, respectively in Column 1. This seems to relate to the importance of South-North trade and relatively limited intra-regional trade, hence leading firms from developing countries to enter mostly rich markets. It is also worth noting that Fernandes, Freund, and Pierola (2016) find a positive association between bilateral distance and firm export entry rate.

> 25Since the pool of firms exiting the export market in _t_ comprises firms that were identified as entrants or incumbents in _t −_ 1, business start-up costs seem to act as a sunk cost of entry into foreign markets that only the most productive firms - presumably the incumbents - can cover. The role of sunk costs in mediating the relationship between import uncertainty and export dynamics is also explored in this paper.

> 26To rule out the concern that results may be driven by sample size, we also carry out alternative regressions by augmenting the baseline model with a dummy equal to 1 if the destination is a high-income country and its interaction term with import uncertainty (results are available upon request). The message remains similar.

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Table 4: Import Uncertainty and Export Dynamics: South-North Trade

||Firm Entry<br>Rate<br>(1)|Firm Exit<br>Rate<br>(2)|First yr.<br>Survival<br>(3)|Second yr.<br>Survival<br>(4)|Third yr.<br>Survival<br>(5)|
|---|---|---|---|---|---|
|Log import uncertainty|0.009<br>|0.011<br>|-0.061***<br>|-0.085***<br>|-0.091**<br>|
||(0.007)|(0.008)|(0.016)|(0.024)|(0.041)|
|Log direct imports (%)|-0.017|-0.006|-0.026|-0.023|0.086|
||(0.014)|(0.018)|(0.037)|(0.058)|(0.104)|
|Log direct exports (% sales)|-0.026***|-0.027*|0.110***|0.121***|0.122*|
||(0.006)|(0.014)|(0.032)|(0.046)|(0.069)|
|Log experience|-0.015|-0.009|0.076*|0.103|0.129|
||(0.015)|(0.018)|(0.040)|(0.066)|(0.117)|
|Log nb. of employees|-0.012|-0.033***|0.101***|0.129***|0.119*|
||(0.012)|(0.010)|(0.021)|(0.032)|(0.062)|
|Log TFP|0.043|-0.072***|0.239***|0.379***|0.281***|
||(0.028)|(0.022)|(0.044)|(0.071)|(0.108)|
|Common border|-0.007|-0.035|0.019|-0.008|-0.104|
||(0.030)|(0.035)|(0.046)|(0.072)|(0.104)|
|Colony|-0.087**|-0.020|0.122|0.188*|0.077|
||(0.039)|(0.040)|(0.076)|(0.112)|(0.279)|
|Log distance|0.108***|0.031**|-0.067***|-0.092***|-0.149***|
||(0.010)|(0.012)|(0.020)|(0.032)|(0.055)|
|Trade agreement|-0.059***|-0.026|0.100***|0.217***|0.200**|
||(0.020)|(0.022)|(0.039)|(0.061)|(0.094)|
|Log tarifs|0.004|0.014*|-0.020|-0.015|-0.015|
||(0.007)|(0.008)|(0.012)|(0.019)|(0.030)|
|Log exporter GDP cap.|0.079|0.025|0.107|0.058|3.327*|
||(0.104)|(0.128)|(0.202)|(0.282)|(1.815)|
|Log importer GDP cap.|0.019|-0.072|0.186*|0.187|0.319|
||(0.047)|(0.055)|(0.111)|(0.166)|(0.244)|
|Log exporter entry cost (% GNI)|0.045|-0.016|-0.020|-0.205|0.281|
||(0.042)|(0.050)|(0.095)|(0.136)|(0.437)|
|Log importer entry cost (% GNI)|0.016|-0.010|0.010|0.005|-0.058|
||(0.016)|(0.020)|(0.037)|(0.059)|(0.093)|
|Constant|-2.378**|0.438|-3.986**|-3.898|-33.135**|
||(0.959)|(1.196)|(1.924)|(2.790)|(16.561)|
|Observations|10,363|8,693|8,513|7,857|4,290|
|R<sup>2</sup>|0.191|0.198|0.104|0.099|0.120|
|Country Fixed Efects|Yes|Yes|Yes|Yes|Yes|
|Destination Fixed Efects|Yes|Yes|Yes|Yes|Yes|
|Sector Fixed Efects|Yes|Yes|Yes|Yes|Yes|
|Year Fixed Efects|Yes|Yes|Yes|Yes|Yes|

Notes: PPML estimates. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Robust standard errors clustered on exporter-importer pair in parentheses. Log direct imports (%), Log direct exports (% sales), Log experience, Log nb. of employees and Log TFP computed over the sample of exporting firms, except in Column 1. The description and source of variables are provided in Table A3 in the Appendix.

17
Table 5: Import Uncertainty and Export Dynamics: South-South Trade

||Firm Entry<br>Rate<br>(1)|Firm Exit<br>Rate<br>(2)|First yr.<br>Survival<br>(3)|Second yr.<br>Survival<br>(4)|Third yr.<br>Survival<br>(5)|
|---|---|---|---|---|---|
|Log import uncertainty|-0.014<br>|-0.022<br>|0.020<br>|0.029<br>|-0.025<br>|
||(0.012)|(0.015)|(0.030)|(0.000)|(0.000)|
|Log direct imports (%)|0.004|0.008|-0.036|-0.061|-0.209|
||(0.022)|(0.030)|(0.059)|(0.000)|(0.000)|
|Log direct exports (% sales)|-0.030***|-0.048*|0.084|0.133|-0.060|
||(0.010)|(0.026)|(0.056)|(0.000)|(0.000)|
|Log experience|0.047*|0.012|0.308***|0.420|0.172|
||(0.024)|(0.034)|(0.070)|(0.000)|(0.000)|
|Log nb. of employees|-0.042**|-0.006|-0.017|0.033|0.178|
||(0.018)|(0.017)|(0.039)|(0.000)|(0.000)|
|Log TFP|0.040|0.008|0.012|0.082|0.323|
||(0.044)|(0.039)|(0.080)|(0.000)|(0.000)|
|Common border|-0.022|-0.011|0.089|0.125|0.311|
||(0.034)|(0.040)|(0.083)|(0.000)|(0.000)|
|Log distance|0.127***|0.059***|-0.115***|-0.205|-0.130|
||(0.016)|(0.022)|(0.044)|(0.000)|(0.000)|
|Trade agreement|-0.082***|-0.084***|0.176***|0.104|0.335|
||(0.026)|(0.031)|(0.064)|(0.000)|(0.000)|
|Log tarifs|0.015|0.014|-0.034|-0.057|-0.080|
||(0.009)|(0.012)|(0.023)|(0.000)|(0.000)|
|Log exporter GDP cap.|-0.493**|0.204|-0.412|-0.742|-0.796|
||(0.230)|(0.271)|(0.407)|(0.000)|(0.000)|
|Log importer GDP cap.|0.052|-0.015|-0.023|0.364|0.547|
||(0.094)|(0.120)|(0.236)|(0.000)|(0.000)|
|Log exporter entry cost (% GNI)|-0.194***|-0.193**|-0.473***|-0.683|-0.672|
||(0.072)|(0.090)|(0.170)|(0.000)|(0.000)|
|Log importer entry cost (% GNI)|0.012|-0.002|-0.071|-0.144|-0.581|
||(0.027)|(0.039)|(0.083)|(0.000)|(0.000)|
|Constant|2.851|-1.814|5.351|-8.910|-7.117|
||(2.065)|(2.419)|(3.763)|(0.000)|(0.000)|
|Observations|4,113|3,308|3,317|2,894|1,390|
|R<sup>2</sup>|0.205|0.192|0.139|0.131|0.168|
|Country Fixed Efects|Yes|Yes|Yes|Yes|Yes|
|Destination Fixed Efects|Yes|Yes|Yes|Yes|Yes|
|Sector Fixed Efects|Yes|Yes|Yes|Yes|Yes|
|Year Fixed Efects|Yes|Yes|Yes|Yes|Yes|

Notes: PPML estimates. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Robust standard errors clustered on exporter-importer pair in parentheses. Log direct imports (%), Log direct exports (% sales), Log experience, Log nb. of employees and Log TFP computed over the sample of exporting firms, except in Column 1. The dummy variable equal to 1 if the exporter and importer were in a colonial relationship post-1945 was dropped due to insufficient observations for which Colony = 1. The description and source of variables are provided in Table A3 in the Appendix.

18
event of delayed delivery, seasonal products such as garments and textiles which are subject to fashion cycles, and parts and components which are used as inputs in the production process. With this in mind, we check whether the effect of uncertainty in import clearance times on firm export dynamics varies across the five manufacturing sectors. We augment the baseline regressions with an export sector dummy and its interaction with import uncertainty. Table 6 summarizes the sector-specific marginal effects of import uncertainty on firm export entry, exit and survival rates along with their corresponding standard errors computed following Brambor, Clark, and Golder (2006).<sup>27</sup> For each dependent variable of interest, the marginal effect of import uncertainty is displayed for cases where the export sector dummy takes the value of one.

Table 6: Import Uncertainty and Export Dynamics: Sector-Specific Marginal Effects

||Firm Entry<br>Rate|Firm Exit<br>Rate|First yr.<br>Survival|Second yr.<br>Survival|Third yr.<br>Survival|
|---|---|---|---|---|---|
|Manufactureoffoodbeverageandtobaccoproducts|0.016|0.017|-0.082***|-0.104**|-0.068|
|,|(0.016)|(0.019)|(0.028)|(0.043)|(0.065)|
|Manufactureoftextileandleather-relatedroducts|0.014|0.000|0.002|-0.070*|-0.134**|
|p|(0.012)|(0.014)|(0.027)|(0.042)|(0.063)|
|Manufactureofwood-relatedroducts|-0.026*|0.024|-0.025|-0.042|-0.044|
|p|(0.015)|(0.017)|(0.034)|(0.047)|(0.094)|
|Manufactureofmineralsmetalsandchemicals|0.032***|0.011|-0.053**|-0.040|0.020|
|,|(0.011)|(0.013)|(0.025)|(0.037)|(0.068)|
|Manufactureofadvancedproducts|-0.030***|-0.034***|-0.039|-0.030|-0.155*|
||(0.009)|(0.012)|(0.029)|(0.047)|(0.086)|

Notes: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Coefficients are marginal effects for cases where the export sector dummy for each manufacturing industry takes the value of one. They are computed using parameter estimates from regressions of the baseline model augmented with an export sector dummy and its interaction with import uncertainty (full regression tables are available upon request). Standard errors in parentheses. Table A2 in the Appendix provides more detail on the aggregation of the five manufacturing sectors of interest.

We find that all export sectors exhibit some statistically significant effect of import uncertainty on firm export dynamics. The negative effect of unpredictability in import clearance times on the first and second-year survival rates of entrants in the food, beverage and tobacco industry reflects the timesensitivity of imported perishable goods that enter in the sector’s production process. Together with the practice of SPS agencies in developing countries that rarely adopt a risk management strategy for border control, implementing physical inspections of 100 percent of import shipments, this calls for broader trade facilitation reforms in support of the modernization of other border control agencies, in addition to Customs. Similarly, the adverse effect of import uncertainty on firm export survival rates is verified for the textile and garment industry, as well as manufacturing of ores and chemicals. Since most developing countries are exporters of agricultural raw materials and natural resources, promoting manufacturing activities based on the transformation of these products contributes to value addition and export upgrading, paving the way to industrialization. Likewise, several studies corroborate the historical role of the garment industry in creating jobs and helping countries move up the value chain. Consequently, our results suggest that by reducing export survival, uncertainty in import processing times is an impediment to developing countries’ diversification and structural transformation agenda. Last, and

> 27Full regression tables are available upon request.

19
remarkably, import uncertainty now displays a negative effect on both firm entry and exit rates, but only in the case of advanced products exports. We argue that high-technology products such as machinery, motor vehicles and precision instruments exhibit high sunk costs of export entry and exit given their sophistication, translating into lower survival rates only three years after the firm’s entry date into the foreign market.

Finally, we examine the role of sunk costs in mediating the relationship between import uncertainty and export dynamics. The theoretical literature on hysteresis in international trade shows that firms must incur a sunk cost of entry to start serving foreign markets, which generates hysteresis in aggregate trade flows by inducing a high persistence in export status, hence raising export survival.<sup>28</sup> In a seminal paper, Roberts and Tybout (1997) find that prior export experience raises the probability of exporting by 60 percentage points for Colombian firms. In other words, sunk costs of entry induce persistence in firms’ exporting status by making it hard to switch from non-exporter to exporter status and by entailing that those firms that have already incurred the sunk start-up costs to enter the foreign market are more likely to remain exporters.<sup>29</sup> Broadly speaking, sunk costs of entry pertain to the start-up costs faced by a firm that wants to export, such as the cost of identifying and informing potential buyers about its products, the cost of learning about the foreign market, including the prevailing regulations and standards, and the cost of setting up new distribution channels at destination (Melitz, 2003). Based on a simple model of exchange rate uncertainty, Brenton, Cadot, and Pierola (2012) confirm the positive relationship between sunk costs and export survival by showing that a firm’s option value of staying in the export market increases with sunk costs of reentry, even under the scenario of a negative exchange rate shock.

Against this background, we argue that the adverse impact of uncertainty in import clearance times on firms’ export survival rates should decline with increasing levels of sunk costs of reentry, leading exporting firms to wait before exiting the market as any future attempt of reentry would be costly. To test this prediction, we build on Helpman, Itskhoki, Muendler, and Redding (2017) and use the cost induced by procedures to start a business in the destination country, expressed as a share of GNI, as a proxy for the sunk costs absorbed by a firm when entering that foreign market. Table 7 presents the regressions including the interaction term between the cost of business start-up procedures at destination and import uncertainty at origin. Figure 4 shows how the total marginal effect of import uncertainty on the exit rate of incumbents and entrants, as well as the first-, second- and third-year survival rates of entrants varies across the range of possible values of entry costs at destination. Following Brambor, Clark, and Golder (2006) and Berry, Golder, and Milton (2012), the vertical axis on the right indicates the magnitude of the marginal effect, while the left axis is for the histogram depicting the distribution of observations in the sample across the range of costs associated with business start-up procedures in the importing country. We find that the survival-hindering impact of import uncertainty weakens and even turns insignificant as sunk costs of entry in the foreign market rise. These results support the assumption that high sunk costs of entry lead new exporters to “tough it out” and wait for better times instead of exiting the foreign market right away, despite recording lower margins due to unpredictable import

> 28See the seminal work by (Dixit, 1989a,b); Baldwin (1988); Baldwin and Krugman (1989); Krugman (1989); and Dixit and Pindyck (1994).

> 29The hysteresis effect associated with export participation has also been evidenced by Bernard and Wagner (2001) for German firms and Bernard and Jensen (2004) for US firms.

20
Table 7: Import Uncertainty and Export Dynamics: The Role of Sunk Costs

||Firm Entry<br>Rate<br>(1)|Firm Exit<br>Rate<br>(2)|First yr.<br>Survival<br>(3)|Second yr.<br>Survival<br>(4)|Third yr.<br>Survival<br>(5)|
|---|---|---|---|---|---|
|Log import uncertainty|0.005|0.020*|-0.066***|-0.103***|-0.093*|
||(0.009)|(0.011)|(0.021)|(0.032)|(0.056)|
|Log import uncertainty _×_<br>|-0.002<br>|-0.009**<br>|0.011*<br>|0.020**<br>|0.009<br>|
|Log importer entry cost (% GNI)|(0.003)|(0.004)|(0.007)|(0.010)|(0.017)|
|Log importer entry cost (% GNI)|0.017|0.006|-0.035|-0.063|-0.134|
||(0.015)|(0.019)|(0.037)|(0.058)|(0.089)|
|Log direct imports (%)|-0.014|-0.004|-0.026|-0.022|0.061|
||(0.011)|(0.015)|(0.031)|(0.053)|(0.093)|
|Log direct exports (% sales)|-0.027***|-0.032***|0.106***|0.114***|0.089|
||(0.005)|(0.013)|(0.028)|(0.040)|(0.062)|
|Log experience|0.001|-0.005|0.117***|0.161***|0.128|
||(0.013)|(0.016)|(0.035)|(0.058)|(0.104)|
|Log nb. of employees|-0.020*|-0.026***|0.080***|0.116***|0.132**|
||(0.010)|(0.009)|(0.019)|(0.029)|(0.057)|
|Log TFP|0.043*|-0.046**|0.176***|0.311***|0.269***|
||(0.024)|(0.019)|(0.039)<br>|(0.063)|(0.101)|
|Common border|-0.033|-0.040|0.065*|0.066|-0.022|
||(0.022)|(0.027)|(0.039)|(0.059)|(0.090)|
|Colony|-0.078*|-0.019|0.147**|0.217*|0.078|
||(0.041)|(0.041)|(0.073)|(0.112)|(0.284)|
|Log distance|0.110***|0.035***|-0.069***|-0.108***|-0.150***|
||(0.009)|(0.010)|(0.018)|(0.029)|(0.051)|
|Trade agreement|-0.064***|-0.036**|0.099***|0.163***|0.196**|
||(0.016)|(0.018)|(0.033)|(0.051)|(0.082)|
|Log tarifs|0.008|0.012*|-0.027**|-0.028*|-0.030|
||(0.005)|(0.006)|(0.011)|(0.017)|(0.027)|
|Log exporter GDP cap.|-0.074|0.055|-0.047|-0.190|2.719*|
||(0.097)|(0.117)|(0.181)|(0.261)|(1.638)|
|Log importer GDP cap.|0.061|0.004|0.125|0.143|0.263|
||(0.041)|(0.049)|(0.098)|(0.152)|(0.229)|
|Log exporter entry cost (% GNI)|-0.021|-0.078*|-0.141*|-0.327***|0.158|
||(0.036)|(0.043)|(0.084)|(0.121)|(0.400)|
|Constant|-1.046|-1.161|-1.180|-0.472|-26.023*|
||(0.863)|(1.033)|(1.641)|(2.428)|(14.805)|
|Observations<br>|14,476|12,001|11,834|10,755|5,680|
|R<sup>2</sup>|0.182|0.183|0.106|0.100|0.123|
|Country Fixed Efects|Yes|Yes|Yes|Yes|Yes|
|Destination Fixed Efects|Yes|Yes|Yes|Yes|Yes|
|Sector Fixed Efects|Yes|Yes|Yes|Yes|Yes|
|Year Fixed Efects|Yes|Yes|Yes|Yes|Yes|

Notes: PPML estimates. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Robust standard errors clustered on exporter-importer pair in parentheses. Log direct imports (%), Log direct exports (% sales), Log experience, Log nb. of employees and Log TFP computed over the sample of exporting firms, except in Column 1. The description and source of variables are provided in Table A3 in the Appendix.

21
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clearance times. In addition, the negative and statistically significant coefficient on the interaction term in the firm exit rate regression suggests that this pattern is not exclusive to new exporters, as Figure 4 describes a diminishing positive effect of import uncertainty on the exit rate which turns insignificant and even becomes negative as the option-value of waiting rises with export entry costs at destination.

# **4 Robustness**

In this section, we provide additional regression estimates to ascertain the robustness of our baseline results. We start by providing evidence of the relevance of focusing on uncertainty in border clearance times as captured by the interquartile range of the time to import instead of using average observed times as is common in the literature. Accordingly, Table 8 compares the effect of our measure of import uncertainty to that of the average and median time to import.<sup>30</sup> If uncertainty only acts as a proxy for a more classic measure of trade costs, the results would be comparable across the three indicators. This is not the case. Neither the mean nor the median is significantly associated with export survival rates while the effect of the interquartile range remains highly significant. If anything, only the median time to import seems to exhibit some negative influence on export entry rates. As a result, the export dynamicseffects of uncertainty in the time required to import appear distinct from that of the average and median time to import. It is also worth mentioning that results obtained with the median time to import seem to do a better job at highlighting the pertinence of using the interquartile range than those derived from the mean time to import. This is unsurprising from a statistical standpoint as extreme values tend to influence more the mean than the median. As an illustration, the simple correlation between uncertainty and the mean time to import hovers around 73 percent in our sample, a much higher figure than the 53 percent found for the median time to import. With collinearity likely biasing the coefficients and standard errors of our measure of uncertainty, this in turn explains the lower significance of our estimates in Columns 6 and 8 relative to Columns 16 and 18.

The last panel of Table 8 (Columns 21-30) tests the robustness of the baseline results to the introduction of the median time to export and uncertainty in the time to export, similarly captured by the interquartile range of the number of days required to clear exports through Customs.<sup>31</sup> In doing so, we aim to demonstrate that the export-dynamics effects of uncertainty identified on the importing side are distinct from any effect that may arise on the exporting side. Neither the median nor the interquartile range influences survival rates while our core results remain broadly the same, albeit less statistically significant. However, we do find strong and significant negative effects of uncertainty in the time to export on export entry. This seems to suggest differentiated effects of uncertainty on exports and imports, with uncertainty in import times mattering more than uncertainty in export times for the survival of young exporters.

Next, Table 9 presents regression results obtained from the inclusion of additional variables which we suspect are correlated with both export dynamics and import uncertainty, therefore giving rise to omitted

> 30Full regression tables are available upon request.

> 31Full regression tables are available upon request.

23
Table 8: Import Uncertainty, Mean and Median Time to Import and Export Uncertainty

||Firm <br>Ra|Entry<br>te|Firm<br>R|Exit<br>ate|Entrant<br>Survi|First yr.<br>val Rate|Entrant <br>Survi|Second yr.<br>val Rate|Entrant <br>Surviv|Third yr.<br>al Rate|
|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|Log import uncertainty||0.015*||0.011||-0.044**||-0.054*||-0.022|
|||(0.008)||(0.010)||(0.020)||(0.032)||(0.062)|
|Log mean time to import|-0.012|-0.042**|-0.011|-0.031|-0.021|0.010|-0.056|-0.013|-0.080|-0.142|
||(0.010)|(0.018)|(0.013)|(0.023)|(0.023)|(0.042)|(0.037)|(0.069)|(0.071)|(0.142)|
||(11)|(12)|(13)|(14)|(15)|(16)|(17)|(18)|(19)|(20)|
|Log import uncertainty||0.009||0.009||-0.047***||-0.068***||-0.047|
|||(0.007)||(0.008)||(0.016)||(0.025)||(0.047)|
|Log median time to import|-0.015*|-0.030***|-0.013|-0.035**|-0.009|0.025|-0.007|0.037|-0.066|-0.073|
||(0.008)|(0.011)|(0.011)|(0.014)|(0.020)|(0.026)|(0.031)|(0.045)|(0.059)|(0.082)|
||(21)|(22)|(23)|(24)|(25)|(26)|(27)|(28)|(29)|(30)|
|Log import uncertainty||0.008||0.009||-0.030*||-0.024||-0.079*|
|||(0.008)||(0.009)||(0.017)||(0.026)||(0.042)|
|Log export uncertainty|-0.039***|-0.036***|-0.018**|-0.015|0.005|0.010|-0.009|-0.002|0.043|-0.005|
||(0.006)|(0.007)|(0.008)|(0.009)|(0.014)|(0.016)|(0.021)|(0.024)|(0.040)|(0.045)|
|Log median time to export|0.023**|0.011|0.009|-0.009|-0.004|0.012|0.004|0.026|0.048|0.180*|
||(0.010)|(0.011)|(0.013)|(0.015)|(0.024)|(0.027)|(0.035)|(0.040)|(0.079)|(0.093)|

Notes: PPML estimates. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Robust standard errors clustered on exporter-importer pair in parentheses. Control variables included but not reported. Full regression tables are available upon request. The description and source of variables are provided in Table A3 in the Appendix.

variable bias if not accounted for.<sup>32</sup> First, we consider the number of documents required to import taken from the Doing Business database. Since it covers the documents required for clearance by the entities involved in the import process, including government ministries, Customs authorities, port and container terminal authorities, health and technical control agencies as well as banks, it captures the extent to which importing can be difficult due to cumbersome administrative formalities. Since any misstep in furnishing required documents by the importer or its delegate Customs broker may cause delays that are not necessarily related to the efficiency of the border control process but rather to the characteristics of the importer itself, or the managerial quality and competencies of its broker, we expect this variable to influence import uncertainty, beyond its direct implications for export performance already documented in the literature.<sup>33</sup> In addition, failing to directly take into account broader indicators of trade costs might bias the results if they happen to be correlated with each other. Table 9 (Columns 1-5) evidences a negative association of the number of documents required to import with the entry rate and the thirdyear survival rate of entrants, but its introduction does not change our initial results. Second, following the same rationale, we include an indicator derived from the Logistics Performance Index (LPI) and measuring the timeliness of shipments in reaching their destination within the scheduled or expected delivery time. Again, our baseline estimates remain unchanged, while the LPI index influences neither the entry nor the survival rate of exporting firms (Columns 6-10). This result is meaningful for two reasons. First, the LPI index covers the timeliness of the whole import process, including transportation from the port of origin to the port of destination as well as road transportation to deliver the imported

> 32Full regressions are available upon request.

> 33See for instance Li and Wilson (2009); Djankov, Freund, and Pham (2010) and Gamberoni, Lanz, and Piermartini (2010) for studies relying on the Doing Business Indicators to show the adverse impact of delays on trade outcomes.

24
goods to the consignee’s warehouse once they have been cleared, blurring the specific role of ports and government border agencies in generating delays. Therefore, the fact that the coefficient on the LPI index never enters with a statistically significant sign contrary to the one on import uncertainty seems to suggest that the efficiency of Customs and other border agencies matters more than the performance of transportation companies. Second, to the best of our knowledge, this variable is the only available cross-country measure of uncertainty to date, and its statistical insignificance seems to imply that a more refined measure such as the one we use here may be necessary to fully grasp the implications of import uncertainty for export dynamics.

We also check whether our results are affected by the inclusion of proxies for access to finance. As mentioned in Section 2.1, liquidity-constrained firms are likely to face difficulties paying on time the duties and fees associated with the import process, thus leading to longer clearance times and higher uncertainty as measured by the interquartile range of the time to import. On the other hand, the micro-literature provides ample evidence of the detrimental impact of financial constraints on export dynamics.<sup>34</sup> If access to finance affects the ability of firms to enter export markets and survive while also influencing the time required to clear foreign inputs at the border, failing to properly control for its effect would yield biased estimates of the impact of import uncertainty on export dynamics. To address this concern, we consider two variables drawn from the WBES. To capture the burden imposed by loan requirements, we include the proportion of firms that had to provide collateral to obtain their most recent loan or line of credit. Alternatively, we use a broader indicator of the difficulties faced by firms to access the financing required to support their export activity, computed as the proportion of firms that needed a loan but did not apply for it owing to complex application procedures, unfavorable interest rates, excessively high collateral requirements, insufficient maturity and loan size, and anticipation of a negative decision by financial services providers. Results are displayed in Columns 11-20 of Table 9. Consistent with the literature, we find a detrimental impact of the proportion of loans requiring collateral on firm entry and first-year survival rates, with the coefficient on import uncertainty remaining unchanged across specifications. This is also broadly the case for the second variable capturing difficulties related to access to finance, which translate into depressed second- and third-year survival rates of entrants.

Furthermore, as described in Section 2.1, stages of the import process could be plagued by rent-seeking and corrupt practices, with Customs officials accepting informal payments for instance in exchange for modifying the classification or valuation of the imported good for tax purposes. Inspectors from other government entities such as SPS agencies involved in the clearance process could also engage in kickbacks and other illegal transactions that would ultimately influence the time required to clear foreign inputs at the border.<sup>35</sup> Accordingly, we check whether our results are immune to the inclusion of two indicators

> 34Using data for Chinese firms, Manova, Wei, and Zhang (2015) show that credit-constrained firms are less likely to penetrate foreign markets, in line with findings by Minetti and Zhu (2011) and Muˆuls (2015) for firms in Italy and Belgium respectively. Besides preventing firms from breaking into new markets, lack of access to finance also reduces export intensity (Bellone, Musso, Nesta, and Schiavo, 2010; Kiendrebeogo and Minea, 2016), and survival (Brenton, Cadot, and Pierola, 2012). For instance, Cadot, Iacovone, Pierola, and Rauch (2013) find that firms operating in a foreign market with limited presence of other firms from the same origin are less likely to obtain a credit from banks if they seek to ramp up production and exports, translating into lower survival in the foreign market.

> 35Based on the “efficient grease” theory, one could argue that an importing firm may find it rational to extend bribes so as to reduce the red-tape it faces at the border and speed-up clearance of its foreign inputs through Customs. In other words,

25
Table 9: Import Uncertainty and Export Dynamics: Robustness Checks

||Firm Entry<br>Rate|Firm Exit<br>Rate|First yr.<br>Survival|Second yr.<br>Survival|Third yr.<br>Survival|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Log import uncertainty|-0.004|-0.003|-0.036**|-0.057***|-0.072**|
||(0.006)|(0.007)|(0.014)|(0.021)|(0.036)|
|Log documents to import|-0.082**|0.081|0.052|0.281|-0.526**|
||(0.041)|(0.053)|(0.116)|(0.178)|(0.238)|
||(6)|(7)|(8)|(9)|(10)|
|Log import uncertainty|0.007|0.000|-0.045***|-0.074***|-0.090**|
||(0.006)|(0.007)|(0.014)|(0.022)|(0.038)|
|Log LPI timeliness|0.047|-0.145|-0.033|-0.530|2.018|
||(0.133)|(0.168)|(0.319)|(0.488)|(1.480)|
||(11)|(12)|(13)|(14)|(15)|
|Log import uncertainty|-0.000|0.003|-0.041***|-0.060***|-0.079**|
||(0.006)|(0.008)|(0.015)|(0.022)|(0.036)|
|Log collateral (%)|-0.034**|0.016|-0.060*|0.000|-0.040|
||(0.016)|(0.016)|(0.031)|(0.045)|(0.070)|
||(16)|(17)|(18)|(19)|(20)|
|Log import uncertainty|0.000|0.001|-0.033**|-0.037|-0.080*|
||(0.006)|(0.009)|(0.016)|(0.026)|(0.045)|
|Log no loan application (%)|-0.003|0.002|-0.023|-0.068**|-0.108**|
||(0.008)|(0.009)|(0.018)|(0.028)|(0.051)|
||(21)|(22)|(23)|(24)|(25)|
|Log import uncertainty|0.004|-0.001|-0.039***|-0.061***|-0.051|
||(0.006)|(0.008)|(0.015)|(0.022)|(0.037)|
|Log bribes (% annual sales)|-0.008|0.020*|0.012|0.003|-0.006|
||(0.009)|(0.012)|(0.027)|(0.039)|(0.065)|
||(26)|(27)|(28)|(29)|(30)|
|Log import uncertainty|-0.001|-0.010|-0.033*|-0.045*|-0.105**|
||(0.006)|(0.010)|(0.017)|(0.025)|(0.045)|
|Log licensing obstacle (%)|-0.006|-0.008|-0.031**|-0.027|-0.073*|
||(0.007)|(0.007)|(0.013)|(0.019)|(0.043)|

Notes: PPML estimates. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Robust standard errors clustered on exporter-importer pair in parentheses. Control variables included but not reported. Full regression tables are available upon request. The description and source of variables are provided in Table A3 in the Appendix.

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taken from the WBES, namely (i) the share of annual sales spent in informal payments or gifts to public officials to “get things done” regarding Customs, taxes, licenses, regulations and services; and (ii) the proportion of firms identifying business licensing and permits as a major or very severe obstacle. The latter variable is a broad indicator of the quality of firms’ interaction with public officials, and reflects firms’ perception of the difficulty to obtain operating and import licenses. Columns 21-30 of Table 9 show that our core results remain robust after the sequential introduction of these two variables, apart from Column 25 where uncertainty no longer seems to affect the third-year export survival rate once bribery is accounted for. The proportion of sales revenue forgone because of rent-seeking practices is positively associated with firm exit rates, while the share of firms reporting the incidence of hurdles faced in connection with business and import licenses reduces the survival rate of entrants.

While the robustness checks reported in this section show that our core results do not seem to be influenced by omitted variables bias, one may still argue about the presence of endogeneity due to reverse causality. We rule out this concern for the following reasons. First, even though one could claim that higher firm entry or survival rates in foreign markets may translate into more intense export activity leading to Customs and other border agencies overflow, and ultimately delays in the time required to export, it is difficult to see how this would also directly affect import times. Indeed, extensive data verifications and physical inspections carried out by border agencies for imports are usually not relevant for most exports. Unless the country applies Customs export duties, which few countries do nowadays and only for a limited set of products, Customs agents seldom check export value or classification (McLinden, Fanta, Widdowson, and Doyle, 2011).<sup>36</sup> This also explains why WCO guidelines to measure release times focus on import transactions (World Customs Organization, 2011). Second, another potential reason for reverse causality is that as more firms enter the export market and survive, the private sector could push for Customs reforms to enhance efficacy and reduce delays. This is also unlikely as current evidence deplores the lack of coordination between the private sector and Customs and other border control authorities so much that Article 23.2 of the WTO Trade Facilitation Agreement urges signatories to “establish and/or maintain a national committee on trade facilitation or designate an existing mechanism to facilitate both domestic coordination and implementation of [its] provisions” where the participation of the business community is highly recommended (International Trade Center, 2015).

corruption would enhance efficiency by helping lower bureaucratic burden and delay (Leff, 1964 and Lui, 1985). However, Kaufmann and Wei (1999) show that firms with the ability to pay more bribes to corruption-prone officials are precisely the ones that suffer more from “bureaucratic harassment” and red tape. In addition to influencing border clearance times, rent-seeking has a direct bearing on firms’ export decision, as shown by Olney (2015) who finds that corruption reduces the likelihood that a firm exports directly while raising the probability of resorting to intermediaries to access foreign markets. More generally, the literature has emphasized the trade-effects of institutional quality (see for example Levchenko, 2007 and Nunn, 2007).

> 36Volpe Martincus, Carballo, and Graziano (2015) show that in Uruguay, some exports are subject to Customs physical inspections for the purpose of (i) collecting export taxes on a narrow set of products, (ii) controlling tax reimbursement claims and (iii) fighting illegal trade. The first reason holds only for a limited list of countries and for a narrow set of products. The second reason requires the exchange of information between Customs and Tax administrations, a best practice rarely in place in most developing countries. Inspections to fight illegal trade also cover a limited set of transactions.

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# **5 Conclusion**

As most exporters are also importers of intermediate goods and other inputs required in the production process, trade costs that constrain the capacity of firms to import are also likely to affect their export dynamics, especially in the context of rising GVCs. Using trade flows, Customs transactions and firmlevel data, several studies have so far provided evidence on how the reduction of import barriers can shape export performance and diversification patterns. This paper contributes to this literature by quantifying a new source of trade costs based on a novel measure of uncertainty in import clearance times and by exploring its impact on manufacturing firms’ export entry, exit and survival decisions. Using the PPML estimator on a sample of 48 developing countries over 2006-2014, we find that supply chain unreliability due to uncertainty in import clearance times impacts neither the entry nor the exit rate but translates into lower survival rates for entrants, reducing the number of firms that continue to serve the foreign market beyond their first year of entry. This effect grows larger over time with the accumulation of reputational costs to input-importing exporters and is mainly driven by South-North trade, possibly reflecting the time-sensitivity of importers in developed countries that tend to rely more heavily on lean supply-chain strategies. Results also reveal sectoral heterogeneity in the impact of import uncertainty, as well as the mediating role of sunk costs of entry in foreign markets that are found to attenuate the negative effect of uncertainty on export survival rates as firms delay exiting the export market. Most importantly, our measure of uncertainty displays a distinctive effect on export performance, as neither the mean nor the median time to import impacts survival.

Our findings suggest that developing countries seeking to promote the survival of newly-exporting firms in foreign markets should consider undertaking policies targeted at reducing the uncertainty these firms face when importing their production inputs. Predictability in border clearance times is key to a smooth running of the supply chain, allowing firms to deliver on time to time-sensitive foreign customers. Notwithstanding the contribution of firm-specific factors such as limited financial liquidity or willingness to pay bribes in influencing the duration of the import process, our results highlight the role of external factors related to trade facilitation and the investment climate that are outside firms’ control, hence calling for policy action. Specifically, given the lion’s share of Aid for Trade flows aimed at enhancing both hard and soft trade-related infrastructure and supporting border-related policies, our findings make the case for stepping up soft investments specifically designed to reduce supply chain unreliability due to unpredictable import clearance times.

First, efforts to address coordination failures among public and private actors involved in the movement of goods are key to lowering the dispersion of import times. For instance, incentivizing border control agencies, port operators and other transport and logistics stakeholders participating in the import process to adopt IT and electronically interconnect themselves, through a Single Electronic Window for instance, would facilitate collaboration and information sharing. This would in turn avoid duplication of requirements for importers, and should bring significant gains in reducing import time unreliability. An additional low-cost initiative that could successfully reduce import uncertainty is the use of IT systems for cargo tracking and tracing by port and road freight transport operators; and non-intrusive inspection mechanisms such as scanners by Customs. Second, effectively implementing the WTO Trade Facilitation 28
Agreement, especially provisions on advance rulings (Articles 3) and border agency and Customs cooperation (Articles 8 and 12) should significantly increase predictability and reduce the dispersion of border clearance times. Third, supporting the modernization of public border entities other than Customs, such as SPS agencies or the Police, would significantly contribute to lowering supply chain unreliability by shortening the import process as most import clearance delays usually originate from these agencies. In particular, incentivizing them to adopt risk management systems aimed at reducing physical inspections for low-risk consignments without compromising their mandate of protecting the domestic market from phytosanitary threats or illegal trade is key to enhancing predictability in import times. Fourth, incentivizing the expansion of the Authorized Economic Operator (AEO) Program could open a fast-track and predictable clearance channel for compliant firms. Inviting public agencies other than Customs to join is key for the program to be attractive to the private sector. Accepting a wide range of private stakeholders, including importers, exporters, Customs brokers and transport and logistics services providers that comply with a list of security, tax and Customs requirements is also key to facilitating the trade process without compromising its safety.<sup>37</sup> Finally, our findings call for a revised methodology to quantify time to trade by using transaction-level trade data. Nowadays IT systems adopted by public and private operators involved in the trading process, such as ASYCUDA for Customs, Navis for port operators and GPS devices for transport operators, allow moving away from perceptions-based indicators of time to trade, usually derived from expensive surveys administered to truckers, to produce transaction-based objective measures at low cost based on time stamps collected from these IT systems for any container in movement along the logistics supply chain, from port arrival to cargo delivery at the importer’s warehouse. Constructing measures of unreliability in times to trade using Big data opens new directions of research on trade costs and their impact on firm trade performance.

> 37In exchange, benefits for AEOs include lower clearance time, higher predictability and lower logistics costs due to reduced movement of cargo within logistics terminals.

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