metadata: authors: 0: author: Fernandes,Ana Margarida
1: author: Ferro,Esteban
2: author: Wilson,John S.
count: World
docna: 0: docna: Product standards and firms’ export decisions
docty: Policy Research Working Paper
subtopic: International Trade and Trade Rules,Pest Management,Legal Reform,Social Policy,Legislation,Foreign Trade Promotion and Regulation,Trade Technology and Productivity,Trade Law,Regulatory Regimes,Standards and Technical Regulations,Judicial System Reform,Legal Products,Trade and Standards,Pulp & Paper Industry,Common Carriers Industry,Plastics & Rubber Industry,Food & Beverage Industry,Textiles, Apparel & Leather Industry,Business Cycles and Stabilization Policies,Construction Industry,General Manufacturing
teratopic: Macroeconomics and Economic Growth,Social Development,Law and Development,Governance,Industry,Science and Technology Development,Agriculture,Public Sector Development,International Economics and Trade
abstracts: cdata!: The paper estimates the effect of
            product standards on firms’ export decisions using two novel
            datasets. The first covers all exporting firms in 42
            developing countries. The second covers pesticide standards
            for 243 agricultural and food products in 63 importing
            countries over 2006–12. The analysis shows that product
            standards significantly affect foreign market access. More
            restrictive standards in the importing country, relative to
            the exporting country, lower firms’ probability of exporting
            as well as their export values and quantities. The relative
            restrictiveness of standards also deters exporting firms
            from entering new markets and leads to higher exit rates
            from those markets. Moreover, firm characteristics mediate
            the effect of product standards on firms’ export decisions.
            Smaller exporters are more negatively affected in their
            market entry and exit decisions by the relative stringency
            of standards than larger exporters. Positive network effects
            of exporters from the same country may help reduce the
            burden of importing countries’ standards on firms’ decisions
            to enter new markets.
repnme: repnme: Product standards and firms’ export decisions
display_title: Product standards and firms’ export decisions
listing_relative_url: /research/2015/06/24657369/product-standards-firms’-export-decisions
topicv3: Manufacturing Sectors m1326521 1281,Trade m1326924 698,International Standards and Technical
            Regulations m1326925 768,Food Safety Standards, Audits and
            Certification m1327375 1052,Pesticides m1504403 2914
publishtoextweb_dt: 2015-06-18T00:00:00Z
historic_topic: Macroeconomics and Economic Growth,Social Development,Law and Development,Governance,Industry,Science and Technology Development,Agriculture,Public Sector Development,International Economics and Trade
no_of_pages: 42
author_keys: 0: author_key: 658876
1: author_key: 1078137
2: author_key: 454584
model_extractions: WPS7315 Policy Research Working Paper 7315 Product Standards and Firms’ Export Decisions _Ana M. Fernandes Esteban Ferro John S. Wilson_ Development Research Group Trade and International Integration Team June 2015
Policy Research Working Paper 7315

# **Abstract**

The paper estimates the effect of product standards on firms’ export decisions using two novel datasets. The first covers all exporting firms in 42 developing countries. The second covers pesticide standards for 243 agricultural and food products in 63 importing countries over 2006–12. The analysis shows that product standards significantly affect foreign market access. More restrictive standards in the importing country, relative to the exporting country, lower firms’ probability of exporting as well as their export values and quantities. The relative restrictiveness of standards also deters exporting firms from entering new markets and leads to higher exit rates from those markets. Moreover, firm characteristics mediate the effect of product standards on firms’ export decisions. Smaller exporters are more negatively affected in their market entry and exit decisions by the relative stringency of standards than larger exporters. Positive network effects of exporters from the same country may help reduce the burden of importing countries’ standards on firms’ decisions to enter new markets.

This paper is a product of the Trade and International Integration Team, Development Research Group. 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://econ.worldbank.org. The authors may be contacted at afernandes@worldbank.org and eferro@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
# **Product Standards and Firms’ Export Decisions**<sup></sup>

**Ana M. Fernandes**<sup>a</sup> **Esteban Ferro**<sup>b</sup> **John S. Wilson**<sup>c</sup>

The World Bank The World Bank The Center for Global Enterprise

**Keywords:** Exporter dynamics, Entry, Exit, Intensive margin, Extensive margin, Non-tariff measures, Product standards.

## **JEL Classification codes** : F14, Q17, O13, L15.

>  We are grateful to Jose-Daniel Reyes as well as participants at the 2014 European Trade Study Group conference and the World Bank DECTI brown bag for comments. Research for this paper has been supported in part by the World Bank’s Multidonor Trust Fund for Trade and Development and through the Strategic Research Partnership on Economic Development. We also acknowledge the generous financial support from the World Bank research support budget and the Knowledge for Change Program (KCP), a trust funded partnership in support of research and data collection on poverty reduction and sustainable development housed in the office of the Chief Economist of the World Bank. The findings expressed in this paper are those of the authors and do not necessarily represent the views of the World Bank.

> a Senior Economist, Trade and International Integration Unit, Development Research Group, World Bank (afernandes@worldbank.org). b Economic Consultant, Trade and International Integration Unit, Development Research Group, World Bank (eferro@worldbank.org).

> c Fellow, The Center for Global Enterprise, New York, New York. (jwilson@thecge.net).
# **1. Introduction**

While the substantial declines in tariffs over recent decades under World Trade Organization (WTO) negotiations and in the context of preferential trade agreements have fostered growth in world trade, countries have been tempted to resort to alternative ways to protect their domestic markets. The growing use of non-tariff measures (NTMs), generally in the form of import regulations, by both developed and developing countries has resulted in a trade policy environment that is less transparent and less well understood.<sup>1</sup> Anecdotal evidence suggests that NTMs restrict market access, particularly for smaller exporters in developing countries. But the effects of NTMs on trade are difficult to assess due to the breadth of regulations covered as well as their non-measurability.

In this paper, we provide rigorous econometric evidence on the effect of product standards—a type of NTM—on trade using firm-level data. We estimate the effect of standards on pesticide residue limits for agricultural and food products imposed by importing countries on firms’ decisions to export, enter or exit a product-destination market, as well as their export values and quantities. To do so we combine two novel datasets, one covering all exporting firms in 42 developing countries and one covering pesticide standards for 243 agricultural products in 63 importing countries over the 2006-2012 period. Agro-food products are an important component of the export portfolio of the developing countries in our sample, accounting on average for 20 percent of their total exports, and play a critical role for the development of poor rural areas in most developing countries.<sup>2</sup> Our main findings are as follows. First, product standards affect foreign market access in that more restrictive product standards in the importing country relative to the exporting country reduce significantly firms’ probability of exporting as well as their export values and quantities. Second, our evidence shows that the relative restrictiveness of importing countries’ standards deters exporting firms from entering new product-destination markets and leads to higher exit rates from product-destination markets (once firm size is controlled for). Third, firm characteristics mediate the effect of product standards on firms’ export decisions. Smaller

> 1 NTMs are a key topic of negotiation under new trade agreements such as the ongoing Transatlantic Trade and Investment Partnership (TTIP) negotiations between the United States (US) and European Union (EU). Since tariffs in the EU and US are low, any substantial impact of such an agreement on trade flows for signatories and third markets will be driven by changes in NTMs.

> 2 The average masks heterogeneity across countries in the share that agro-food products represent in total exports, ranging from 50-60 percent for Kenya, Nicaragua, and Uganda to 5 percent or less for Bangladesh, Botswana, Cambodia, and Mexico.

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exporters are more negatively affected in their market entry and exit decisions by the relative stringency of standards than larger exporters. The presence of firms from the same country exporting the same product to a given destination – network effects – reduces the negative effect of the relative restrictiveness of destination country standards on firms’ decisions to enter a new destination market.

The Sanitary and Phyto-Sanitary (SPS) standards considered in this paper are Maximum Residue Levels (MRLs) which restrict the maximum levels of residues from pesticides legally permitted on unprocessed food.<sup>3</sup> MRLs are mandatory regulations which condition market access in order to ensure that domestically-produced and imported unprocessed food is safe to eat.<sup>4</sup> Countries are quite heterogeneous in the products they regulate, the pesticides they regulate for each product, and the MRL they allow for a given product-pesticide pair.<sup>5</sup> In order to meet required MRLs, producers need to avoid using certain pesticides and determine the correct preharvest intervals, which often requires the use of more expensive inputs or specialized human capital. Non-compliance with an MRL can lead exporters to lose the full shipment value and be subject to additional monitoring and testing until multiple shipments successfully cross the border.<sup>6</sup> The contribution of our study is three-fold. First, ours is the first study that examines the relationship between explicit measures of product standards and firms’ export decisions for a large set of developing countries. Second, in contrast to most previous studies which rely on a count of standards, our use of MRLs for pesticides allows us to construct index measures that quantify the absolute stringency of these standards. Third, our study focuses on differences in the stringency of standards in the importing country versus the exporting (home) country. If the MRL is stricter in the importing country than in the exporting country, then firms may need to incur further production costs to meet the stricter MRL in the destination market. If the opposite

> 3 Residues from pesticides are very small traces of pesticide that sometimes remain on treated crops. 4 Once pesticides are demonstrated to be safe for consumers, MRLs are set by independent scientists, based on rigorous evaluation of each pesticide. MRLs act as an indicator of the correct use of pesticides and ensure compliance with legal requirements for low residues on unprocessed food.

> 5 The Agreement on the Application of Sanitary and Phytosanitary Measures approved by WTO members in the 1990s embodies the principle that multilateral trade rules allow countries to adopt measures to protect human, animal, or plant life or health, provided such measures do not discriminate or are not used as disguised protectionism. That Agreement sets out the basic rules for ensuring that each country’s food safety and animal and plant health laws and regulations are transparent, scientifically defensible, and fair.

> 6 Non-compliant exporters also have to pay the cost of shipment along with storage fees overseas while the complaint is processed, a fee to dispose of the product or re-export it, and potentially a violation fee. At the extreme, MRL violations lead to complete import bans for specific origin countries. For example in 2002 imports of frozen spinach from China were banned by Japan due to the finding of a pesticide (Chlorpyrifos) in excess of the allowable MRL.

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is true then the importing country’s MRL should not constitute a constraint on potential exports by firms in that exporting country. Considering the _relative_ stringency of standards in the importing country with respect to that in the exporting country adds variability to our standards measures and thus enables us to include in all our econometric specifications a stringent set of fixed effects to control for, among others, unobserved country-industry factors (such as domestic competition) that could bias the estimated effect of standards. Moreover, in some specifications we control also for unobserved time-varying exporting country, destination country, exporting country-product heterogeneity (via fixed effects) as a way to mitigate potential biases caused by omitted variables.

In the context of recent trade models with heterogeneous firms, following Melitz (2003), conforming to regulatory standards in an importing country constitutes a fixed entry cost to penetrate that market, as discussed by Bernard et al. (2011). But at the same time, conforming to regulatory standards may be part of the variable trade costs that need to be incurred every time the firm exports to that market, e.g., if more costly inputs need to be used to meet the standards. The models of Chaney (2008) and Bernard et al. (2011) examine the effects of destinationspecific or product-destination-specific fixed trade costs as well as destination-specific variable trade costs on the extensive and the intensive margins of firm exports. They show that variable trade costs affect both margins of firm exports, if those costs decline each existing exporter exports more and new firms enter the export market (since the threshold productivity level above which firms have positive profits from exporting decreases).<sup>7</sup> Fixed trade costs affect the extensive margin but they do not affect the intensive margin, i.e., the firm’s exports to that market (because existing exporters have already paid this cost). In a simpler model with a single firm making export decisions, Chen et al. (2008) allow compliance with standards to impose additional production costs on firms but also to possibly have a positive effect on demand (in terms of consumers’ willingness to pay for the products). The net effect of standards on a firm’s choice of optimal scale and export scope depends on the strength of the standards-induced increase in costs versus the strength of the standards-induced increase in demand.

Overall, our findings demonstrate a negative effect of the stringency of SPS standards on the extensive margin of firm exports, namely in terms of entry into new markets, as well as on

> 7 In these models a firm’s profitability of exports varies according to the destination market and it is more profitable for a firm to export to a market with high demand as well as low variable trade costs and low fixed costs. Therefore, only a subset of firms are able to export and that subset varies with the characteristics of the destination market.

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the intensive margin of firm exports. Also, our findings show that beyond average effects there is important heterogeneity across exporting firms. Thus, our findings are consistent with the idea that firms face not only a single fixed cost to export but also additional fixed costs to meet foreign standards in each destination market. Only those firms that are productive enough—size being a proxy for productivity—are able to cover those costs and enter a new foreign market.

From a policy point of view, SPS standards differ across countries and this heterogeneity is likely to remain in place since full global harmonization of such standards is not likely. At the multilateral level, the WTO SPS agreement has not been able to bring discipline for countries to adhere to the Codex Alimentarius standards and at the bilateral level SPS standards have only very recently begun to be discussed in the context of deep trade agreements (that include provisions on technical barriers to trade) but with little progress achieved to date. Thus, for a developing country that seeks to support its agricultural exporters in terms of their entry and survival in markets with more stringent SPS standards, the best strategy is for the government to help exporters meet foreign standards by creating the necessary testing facilities and customs clearance procedures, and by facilitating the imports of the necessary inputs to meet foreign standards.

The remainder of the paper proceeds as follows. Section 2 briefly reviews the existing literature on product standards. Section 3 describes the data while Section 4 presents the empirical framework. Section 5 discusses the main results and Section 6 focuses on the robustness tests. Section 7 concludes.

# **2. Brief Literature Review**

The effect of the two major types of NTMs - Sanitary and Phyto-Sanitary (SPS) and Technical Barriers to Trade (TBT) measures which address regulatory standards on agricultural and food products and manufacturing goods - on trade is ambiguous, particularly for developing countries. On the one hand, regulatory standards can impede trade flows by explicitly banning such flows or by imposing prohibitive costs of compliance—related to upgrading production systems, acquiring special types of processing and storage equipment, and implementing quality control procedures—for firms in developing countries that can undermine the competitiveness of their exports.<sup>8</sup> Survey evidence suggests that fixed costs of compliance with product standards

> 8 See Henson et al. (2000) on the challenges related to product standards faced by firms in developing countries.

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are important though recurring costs of compliance tend to be lower.<sup>9</sup> The inability to comply with standards is costly not only for individual firms but also for a country’s reputation as it can ultimately result in trade restrictions such as import bans for specific products from specific countries.<sup>10</sup> More broadly, regulatory standards can impede trade if used in a protectionist way, being more stringent than what scientists determine as acceptable. On the other hand, standards can act as trade facilitators by signaling that products are safe to the consumer (which is valuable under asymmetric information) and by providing the incentives for developing countries to modernize the supply chain structure in their export sectors (e.g., increasing investments in quality assurance), enabling them to improve their competitiveness while also strengthening the standards domestically. Maertens and Swinnen (2009) show that foreign standards acted as a catalyst for production upgrading in Senegal.

Most studies on product standards and trade examine how aggregate trade flow are affected by technical regulations in a gravity regression framework.<sup>11</sup> Disdier et al. (2008) show a trade-impeding effect from a higher number of SPS and TBT regulations imposed by OECD countries that significantly reduce exports by developing countries.<sup>12</sup> Anders and Caswell (2009) find a significantly negative impact of stricter food safety standards on U.S. seafood imports especially from developing countries, while Tran et al. (2011) find a significantly negative impact of a zero tolerance for a drug residue standard on crustacean imports by Canada, the EU15, Japan, and the U. S. from major Asian exporters. In contrast, Xiong and Beghin (2010) examine the effect of the tightening of an EU SPS standard on aflaxotin in 2002 and find that it had no effect on African exports of groundnuts, which were instead hampered by domestic supply constraints. Ferro et al. (2015)—who compiled the standards restrictiveness index based on data on MRLs of pesticides imposed by importing countries that we use—show that more restrictive standards are associated with a lower probability of observing a positive trade flow but do not affect trade volumes per se. They interpret this finding as indicating that meeting stringent

> 9 Using firm-level data from the World Bank Technical Barriers to Trade Survey for 16 developing countries, Maskus et al. (2005) show that fixed costs represent on average 425,000 U.S. dollars per firm (or 4.7 percent of value added) but the elasticity of firm variable production costs to standards and technical regulations is only in the 0.06%-0.13% range. Case study evidence for shrimp exports from Nicaragua shows that fixed costs to comply with quality and safety standards represent less than 3% of total annual exports while costs to maintain compliance represent less than 1% of that total (Cato et al., 2005).

> 10 For example, the EU ban on fish imports from Kenya decreased the country’s export earnings by 37% (Henson et al. 2000). 11 See Cadot and Malouche (2012) for a review of the role of standards and technical regulations for trade. 12 Other examples of trade-impeding effects are Otsuki et al. (2001a, 2001b) who show lower edible groundnuts exports by African countries resulting from tightening an SPS standard and Wilson et al. (2003) who show lower bovine meat imports in countries imposing more stringent food safety standards.

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standards increases the fixed costs of exporting, but once firms enter the market, standards do not impact their level of exports.<sup>13</sup> To our knowledge only three studies examine how standards affect firms’ trade patterns. The first study by Chen et al. (2008) uses cross-sectional data from a World Bank survey of firms covering compliance with technical barriers to trade and firm participation in export markets and show that different types of standards exhibit different links with intensive and extensive margins of exports. Quality standards and labeling requirements are positively correlated with firms’ average export volume across destinations and products but also with their export scope (number of destinations and products) while certification procedures are linked to a decline in export scope. A clear limitation of that study is its use of subjective survey responses by firms of whether their exports have been impacted by different types of standards. The second study by Reyes (2012) shows that the harmonization of EU electronics regulations with international standards (whose compliance is not compulsory) led to the entry of new US exporters of electronics products into the EU. Finally, the third study by Fontagné et al. (2015) examines the impact of SPS concerns raised in the WTO Committee on SPS on the export behavior of French firms using customs data. SPS concerns have a negative impact on firms’ probability of exporting and export value and lead to increases in their export prices. They also find a heterogeneous effect across firms, with the negative effect of SPS concerns being lower for larger firms exporting to multiple destinations. While very informative, the key limitation of their study is that relying on SPS concerns as a measure of importer restrictiveness raises potential endogeneity concerns, as only countries whose exports are depressed by an importer’s SPS are likely to raise a concern at the WTO and thus we expect greater SPS concerns to be linked through this reverse causality channel to a more negative impact on exports. The authors, however, argue that their data are preferable to the use of WTO notifications or traditional sources of information on the existence of a regulation.

# **3. Data**

> 13 All studies mentioned focus on mandatory technical regulations—as our MRLs are—but other studies focus on voluntary technical regulations. Czubala et al. (2009) show an inhibiting effect of voluntary EU standards on African exports of textiles, clothing, and footwear (except for those standards that are internationally harmonized) whereas Shepherd and Wilson (2013) show that voluntary standards in EU food and agriculture markets are trade-inhibiting for all countries and for raw or lightly processed goods, but internationally harmonized EU standards have much weaker trade effects, and in some cases are even tradepromoting.

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# **3.1 Data on Standards**

Our policy variable measures the restrictiveness of mandatory standards imposed on agricultural products based on MRLs of pesticides allowed for agricultural products in several importing countries.<sup>14</sup> The source for our data is the Homologa database obtained from Agrobase-Logigram, a French company that collects information on monthly changes in allowable pesticides for 63 importing countries from each country’s relevant ministry and standardizes the information in terms of language, unit, and format for the period 2006-2012.

The Homologa dataset reports only the importing countries’ list of regulated pesticides. However, many countries use a ‘deferral policy’ for pesticides for which it has not set a specific limit: e,g., in the case of the EU the default MRL is 0.01 parts per million (ppm). Many countries defer to Codex Alimentarius standards, the set of international standards for food safety and consumer protection developed by the Food and Agriculture Organization and the World Health Organization. In fact, many countries, particularly developing countries, do not have a list of regulated pesticides but instead directly defer to Codex standards. For the empirical analysis we will use all the information available for each importing country, including the countries’ deferral policies as well as their default MRLs. Appendix Table A.1 provides the list of importing countries for which we have data on pesticide standards and indicates whether each country has its own set of regulations and what is each country’s deferral policy.

Using Agrobase-Logigram’s Homologa data we matched 243 agricultural products to their corresponding harmonized system (HS) codes at the 6-digit level of disaggregation.<sup>15</sup> The products covered are agricultural products belonging to HS Chapters 06-24 with the exception of HS Chapters 15 and 16 (oils and edible preparations of meat and fish).<sup>16</sup> Table 1 lists for each importing country the number of HS 6-digit products covered by MRLs as well as the number of HS 6-digit products regulated including deferral and default policies. The number of different products regulated in total by all importing countries range from 214 in 2006 to 241 in 2012. The product coverage is very heterogeneous across countries: e.g., in 2012 pesticide limits were set by Brazil on 73 products and by the EU on 136 products. However, the product coverage of each

> 14 Note that these standards apply both to domestic production and to imports of the products.

> 15 While Homologa’s product coverage is greater than 243 products, we were unable to match all products directly to an HS code. 16 We omit HS Chapters 15 and 16 from the analysis because they include animal products for which importing countries also regulate veterinary drug MRLs which are not covered by our standards dataset.

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country is fairly constant across time.<sup>17</sup> The importing countries with MRL regulations with the widest product coverage are the US, Canada, and Australia. However, importing countries such as Japan, Canada, and the EU which have default MRLs are the countries with the widest coverage of products once default MRLs are accounted for. The same is true when we analyze the breadth of MRL regulations regarding the number of pesticides in Appendix Table A.2. In total, the importing countries in our sample regulated 863 pesticides in 2006 whereas by 2012 this number had increased to 992. Japan, the Republic of Korea, Switzerland, and the EU have the most extensive coverage of specific pesticides.

There are three challenges when working with MRL data. First, there are two dimensions of restrictiveness that need to be considered: the number of regulations per product and how strict those regulations are. Second, the heterogeneity of pesticides regulated across products and countries makes it difficult to compare how restrictive one country is relative to another. For example, the 16 pesticides regulated for oranges in the Russian Federation in 2011 might not be included among the 102 pesticides regulated for oranges in Brazil. Thus the question arises: how do we compare Russia’s and Brazil’s restrictiveness for oranges? Averaging MRLs across pesticides for each country-product pair would generate a misleading measure. Pesticides differ on their degree of toxicity and thus highly toxic pesticides always tend to have lower MRLs than less toxic pesticides. The group of pesticides a country chooses to regulate for a specific product will determine if the average MRL is high or low without providing any information on the restrictiveness of those measures relative to other countries. Consequently, it is imperative to normalize MRLs at the product-pesticide level to get a true measure of how restrictive a country’s MRL is, relative to how all other countries are regulating that same pesticide for a given product. Third, it is not clear how to interpret the missing values that originate from a pesticide being regulated in one country but not the other. We cannot replace these missing values with zeros, as is commonly done with missing trade values, because an MRL set to zero is equivalent to banning that pesticide entirely. We choose to fill in these missing values with the maximum MRL (the least restrictive measure) across all importing countries for each productpesticide pair.

> 17 Only 35 products have pesticide limits regulated by all countries in the sample, among which we find: potatoes, tomatoes, peas, beans, apples, oranges, wheat, maize, sorghum, and ground nuts.

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N(a) (1) Rel_restrictiveness = ~~——~~ > ~~MRtoteat MRE aeat~~ - okdt ~~ <u>N(a) MAX at —MINxat</u> n(a)=1 mum mum MRL Kat MRLaxat MAXx at = maxt{MRLa,tag } MINat = min{MRLaxa,t } MRL Kat MRLaxat MRLe at MRLaxat k,a,t
to compare the restrictiveness of MRL standards across countries. More generally, the use of measures of the _relative_ restrictiveness of pesticide standards is important because it informs on the dissimilarity in the stringency of regulatory requirements across the importing country and the exporting country. As such, it allows us to consider whether the presence of relatively stricter pesticide standards in the importing country imposes additional costs on firms and thus limit their market access.<sup>20</sup> That is, our index does capture the presence of a trade barrier given that it accounts for the domestic regulation.

It is important to highlight that MRL standards are updated frequently and our estimating strategy takes advantage of this variability over time. Product registrations are withdrawn, new registrations and MRLs are established, and existing MRLs change on a regular basis. Codex MRLs for example, are updated annually every July. New Zealand typically publishes two MRL amendments per year and local officials report that they seek to update MRLs every four months. However, changes in MRLs are not always towards more restrictive standards, but rather very frequently MRLs are increased thus becoming less restrictive. Table 2 shows that on average only 45 percent of MRLs in the dataset are there for the entire panel available for each country. Of those MRLs available for all years, 9 percent became stricter whereas 10 percent became more lenient.<sup>21</sup> Analyzing the sources of variation in the index of relative restrictiveness of standards suggests that the main source is variation over time is the intensity of standards. This will be an important element to justify the estimating strategy chosen for the analysis.

# **3.2 Data on Exporters**

To measure exporter behavior we use transaction-level customs data for the period 20052012 for 42 developing countries across different regions of the world, obtained from customs agencies (or in a few cases from statistical institutes) collected by the Trade and Integration Unit of the World Bank Research Department, as part of their efforts to expand the Exporter Dynamics Database described in Cebeci et al. (2012). Each country’s raw data-set covers the universe of exporting firms in the agricultural, mining excluding HS Chapter 27 (hydrocarbons

> 20 Winchester et al. (2012) examine how the dissimilarity in sanitary, phyto-sanitary and conformity requirements (including product requirements such as maximum residue limits for pesticides) across the EU and several of its trading partners affect their bilateral trade but do not but consider the stringency of those requirements, as we do in our study. Disdier et al. (2015) study the impact of standards harmonization promoted in North-South trade agreements and show a negative impact on South-South trade as well as on North-South trade when the harmonization is on regional (rather than international) standards but only harmonization is examined, not the stringency of the standards.

> 21 The analysis of the sources of variation in the index is described in Appendix A2.

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such as oil, petroleum, natural gas, coal, etc.), and manufacturing sectors and provides information at the exporter-product-destination-year level for seven variables: country of origin, exporting firm identifier, country of destination, HS 6-digit product, export value, export quantity, and year.<sup>22</sup> Additional details on the data are provided in the Appendix.

Although for each firm we have information on its exports in all sectors, we define the key outcome variables capturing firm export decisions—firm export value, export quantity, export participation, entry and exit variables—at the exporting country-firm-product-destinationyear level _focusing exclusively on agricultural and agro-food products_ , i.e., those belonging to HS Chapters 06-24 (with the exception of HS Chapters 15 and 16), which are the products for which our measures of pesticide standards imposed by importing countries are available.

Regarding the intensive margin of trade at the firm level, the outcome variables that we consider are the value and quantity exported by firm _i_ from country _c_ of product _k_ to importing/destination country _d_ in year _t_ which are given by, respectively, ��,�,�,�,� expressed in current U.S. dollars and ��,�,�,�,� expressed in kilograms. We also calculate unit values as value divided by quantity expressed in U.S. dollars per kilogram.<sup>23</sup> Regarding the extensive margin of trade at the firm level in terms of firms’ decisions to export or to enter or exit a product-destination market, we need to expand (or fill in) the initial dataset described above with only positive exports by adding zeros on some dimensions to be able to define the corresponding outcome variables. If we were to follow the gravity equation literature (particularly studies employing an export participation equation to account for biases in gravity equation estimation) we would expand the initial dataset so as to make it a ‘square’ matrix where every firm in an exporting country would have an observation (a row) for every product-destination-year combination possible.

Given the large number of exporting firms in our sample of 42 developing countries, such an expanded data set would be computationally impossible to handle and would be highly cluttered by zeros as most firms tend to export a single product to a single destination. Moreover, our objective in constructing an expanded dataset is to have observations (rows) that make

> 22 Cebeci et al. (2012) show the quality of the data by comparing the total exports obtained from aggregating the transaction-level customs data at the country level with the total exports obtained at the country level from COMTRADE/WITS (World Integrated Trade Solution). 23 The number of observations in specifications that explain quantities or unit values will be smaller than those in the specifications that explain value given that 8 countries in our sample (Botswana, Chile, the Dominican Republic, Guatemala, Mexico, Macedonia, Pakistan and El Salvador) do not include quantity information in their exporter-level customs datasets.

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economic sense, i.e., that indicate plausible choices for firms without requiring major assumptions. Consider as a first example, an observation from the initial dataset in which firm _i_ starts to export product _k_ to destination _d_ in year t. If in the expanded dataset we add an observation with a 0 export value for firm _i_ product _k_ destination _d_ in year _t-1_ , that implies that in year _t-1_ we are allowing firm _i_ to choose whether to export product _k_ to destination _d_ and the firm chooses not to do so. This seems like a plausible and not overly restrictive assumption.<sup>24</sup> Consider as a second example, firm _i_ exporting products _k_ and _l_ at some point during the sample period in the initial data set. If in the expanded dataset we add observations with 0 export values for firm _i_ for all other possible products (other than _k_ and _l_ ) to any possible destination in any year, this implies that in any year we are allowing firm _i_ to choose whether to export any possible agricultural product to all destinations. This seems like an implausible assumption given that other agricultural products may be completely different from what the firm’s capabilities in terms of its technology, type of land, and other inputs allow her to produce (e.g., if a firm produces and exports tropical fruits it is unlikely that the firm can also produce and export wheat or maize which require completely different environmental conditions to grow).<sup>25</sup> Our choice is therefore to expand the initial dataset along a dimension that retains computational feasibility, does not require implausible assumptions about the firms’ export choice set, and allows us to exploit an interesting type of variability in the data. We expand the initial dataset so that each firm-product-destination has an observation (a row) in all of that exporting country’s sample years, with a 0 export value in a year when exports by the firmproduct-destination are not occurring. Setting up the expanded dataset in this way and including a specific (and stringent) type of fixed effects - discussed in Section 4 - allows us to exploit the panel dimension in the firms’ decisions to export, enter or exit a product-destination market as pesticide standards change over time. Using this expanded dataset, we define:

- a firm export participation dummy ��,�,�,�,� equal to 1 in year _t_ if firm _i_ from country _c_ exports a positive value of product _k_ to destination _d_ , and equal to 0 otherwise;

- a dummy for firm entry into a product-destination market ��,�,�,�,� equal to 1 if firm _i_ exports product _k_ to destination _d_ in year _t_ but did not do so in year _t-1_ , and equal to 0 if the firm did

> 24 The only scenario under which this assumption would be wrong is if the firm only begun to have the capacity (through access to machinery, land, other inputs, etc.) to produce product _k_ in year _t_ not in year _t-1_ or if the firm did not exist in year _t-1_ . 25 Thus, we would be allowing a firm in a country to have the choice to export products that might not be feasible to grow in that country due to climate and soil conditions.

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not export product _k_ to destination _d_ in year _t-1_ and does not start to do so in year _t_ . If the firm continues to export the product-destination market after year _t_ , then the entry dummy becomes missing for years greater than _t_ ;<sup>26</sup>

- a dummy for firm exit from a product-destination market ��,�,�,�,� equal to 1 if firm _i_ does not export product _k_ to destination _d_ in year _t_ but did so in year _t-1_ and equal to 0 if the firm exported product _k_ to destination _d_ in year _t-1_ and continues to do so in year _t_ .

Firms that export to a product-destination market in every year are excluded from the entry analysis (since the entry dummy is missing in all of their years) but are included in the analysis of exit and of the intensive margin of trade.<sup>27</sup> If a firm has positive exports to a productdestination market only in the first year of the sample (and no exports to that product-destination market in later years of the sample), it is included in the exit analysis but not in the entry analysis because we are unable to determine whether the firm entered the product-destination market in that first year or was already exporting there previously. Alternatively, if a firm has positive exports to a product-destination market only in the last year of the sample (and no exports to that product-destination market in previous years of the sample) it is included in the entry analysis but not in the exit analysis.

Table 3 shows for each exporting country the number of firms in our initial dataset. Cambodia and Yemen have the fewest agricultural exporters with less than 50 on average per year, whereas Mexico has the most with an average of 3,313 per year. Table 3 also shows for each exporting country the number of firms in the expanded dataset, which is the same in every year as by construction each firm appearing at least once in the data set has the possibility of exporting in every year in the country’s sample period. Finally, Table 3 shows the total number of observations in the initial dataset that will be used for the intensive margin specifications and the total number of observations in the expanded data set that will be used for the extensive margin specifications. Additional summary statistics on the sample of exporting countries are provided in Appendix C.

> 26 We follow Koenig (2009), Koenig et al. (2010), and Mayneris and Poncet (2015) and allow for multiple export entries over the sample period for a given firm-product-destination. Multiple entries occur in cases where the firm starts exporting to a productdestination market, then stops, and then re-starts exporting to the same product-destination market.

> 27 However, in the case of the exit decision, the firms that export to a product-destination market in every year and thus have a 0 in the dependent variable in every year will be effectively dropped from the estimating sample given the specific fixed effects (exporting country-firm-product-destination) included in our specifications.

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# **4. Empirical Framework**

To examine the effects of pesticide standards imposed by importing countries on the export decisions of firms in developing countries exploiting the panel dimension, we consider the following specification: �2� ��,�,�,�,� ����� ∗���_����������������,�,�,� ��∗�������,�,�,� ��∗��,�,�,�,� ���,�,�,� ��� ���,�,�,�,� where _c_ is an exporting country, _i_ a firm, _k_ a product, _d_ a destination country, _t_ a year, the dependent variable � is either export participation, entry, or exit defined based on the expanded dataset or log export value or log export quantity defined based on the initial dataset. The variable ���_����������������,�,�,� is defined in Eq. (1), tariff�,�,�,� is the log of 1 plus the bilateral tariff imposed by the destination/importing country _d_ on product _k_ from the exporting country _c_ , and � is an independent and identically distributed (i.i.d.) residual.<sup>28</sup> The vector ��,�,�,�,� includes GDP per capita of the destination country and total exports of product _k_ from export country _c_ in year _t_ in all specifications to control for demand and supply effects.<sup>29</sup> Applying the logic of the gravity equation to sectoral trade is not entirely straightforward; when looking at sectoral trade flows and particularly agricultural goods, the idea that trade flows between _c_ and _d_ in a certain product _k_ are increasing in the exporter country’s GDP is not necessarily warranted. In the monopolistic-competition model, larger countries produce more varieties of goods and that contributes to increasing their trade. That is, they do not necessarily export more of each good but they export more goods. Additional firm characteristics and other variables included in the regressions will be described in Section 5.

A key remark on Eq. (2) concerns the stringent exporting country-firm-productdestination country fixed effects ��,�,�,� - for simplicity designated in what follows as firmproduct-destination fixed effects given that each firm belongs to a single exporting country - that are included and account for unobserved heterogeneity at that very finely disaggregated level, in addition to the year fixed effects ��. Accounting for unobserved heterogeneity at such finely

> 28 Simple average applied tariffs are used for importing country-exporting country pairs for each product and year available in the WITS-TRAINS (Trade Analysis and Information System) database. We interpolate observations to fill in missing years. For cases where applied tariff data is not available for a given importing country-exporting country-product-year cell we replace the missing values with Most Favored Nation (MFN) tariffs of the importing country-product-year or with preferential tariffs of the given importing country-exporting country-product-year for importing country-exporting country pairs which have a preferential tariff agreement. In the export participation regression in Section 5.2 we lose approximately 3% of the observations (13,600) due to the inclusion of tariffs in the regression, as tariff data is missing for 21 agricultural products.

> 29 GDP per capita data in constant USD is obtained for all countries from the World Development Indicators (WDI) database.

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disaggregated level may account in particular for the fact that certain pathogens and pests are endemic to certain regions, hence the pesticides used are determined by where the crops grow and thus complying with a given MRL standard can be substantially more costly for a firm producing a similar product using a similar production process and at a similar scale simply due to their specific location. The coefficient of interest �� is thus identified based on _within_ firmproduct-destination changes in export participation, export value, export quantity, entry, or exit as pesticide standards change over time in the destination country relative to the exporting country for that product.

The important hypotheses to test in our empirical framework are whether an increase in the stringency of pesticide standards in an importing country relative to an exporting country hampers that exporting country’s firms’ export and entry decisions and encourages their exit decisions. A corollary is that firms in countries with more stringent pesticide standards should have easier market access in destinations with less stringent standards. If relatively more stringent pesticide standards in a destination market increase firms’ variable trade costs to that market, then they would in principle lead to a reduction in firms’ export values (or quantities). But if higher fixed entry costs ensuing from those standards reduced entry into that destination market, incumbent firms’ export values (or quantities) could actually increase. Hence the effect of pesticide standards on the intensive margin of firm exports is theoretically ambiguous.

Table 4 displays summary statistics for all firms’ export decisions that will be used as dependent variables as well as for the measure of relative restrictiveness of pesticide standards.

# **5. Main Results**

# **5.1 Effects of Pesticide Standards on Numbers of Exporters, Entrants, and Exiters**

As a way to motivate and support the results from estimating Eq. (2) for the extensive margin that will be presented from Section 5.2 onwards, we first analyze the effect of relative product standards on firms’ export decisions aggregated to the level of the exporting countryproduct-destination country-year level. Specifically, we estimate the equation: �3� ��,�,�,� ����� ∗���_����������������,�,�,� ��∗�������,�,�,� ��∗��,�,�,� ���,�,� ��� �� �,�,�,� where �������,�,�,� and �� are defined as before, and the dependent variable _Y_ is the number of exporters, entrants, and exiters exporting product _k_ from country _c_ to destination country _d_ in 16
year _t_ . These variables are, respectively obtained as the sums at the exporting country-productdestination country-year level of the firm export, entry, or exit dummies defined based on the expanded dataset in Section 3.2. As an alternative to absolute numbers we also allow _Y_ to be exporter entry rates, exit rates, and entrant survival rates. The entry rate is defined as the number of entrants in year _t_ relative to the total number of exporters in the same year. The exit rate in year _t_ is defined as the number of firms that exported in year _t-1_ but did not export in year _t_ relative to the total number of exporters in year _t-1_ . Similarly, the entrant survival rate in year _t_ is defined as the number of entrants in year t-1 that survive onto year _t_ relative to the number of entrants in year _t-1_ . The vector ��,�,�,� includes GDP per capita of the destination country and total exports by exporting country-product in each year. Given the fixed effects ��,�,� included, the coefficient �� is identified based on _within_ exporting country-product-destination country changes in numbers of exporters, entrants, or exiters as the corresponding pesticide standards change over time in the destination country relative to the exporting country.

The results from estimating Eq. (3) are displayed in Table 5. Poisson regression estimation is used in columns (1)-(3) due to the count nature of the number of exporters, entrants, and exiters while OLS estimation is used in columns (4)-(6) for the rates.<sup>30</sup> Column (1) shows that the restrictiveness of importing country standards relative to exporting country standards have a significant negative effect on the number of firms in country _c_ exporting product _k_ to destination _d_ . The number of entrants, the entry and entrant survival rates of exporters also are significantly lower when importing country standards are more restrictive than exporting country standards, in columns (2), (4), and (6). In contrast, significantly higher numbers of firms exit destination markets with more strict standards relative to their own country’s standards in columns (3) and (5).

These aggregate results suggest that indeed pesticide standards can be a barrier to enter a market. In the next subsection we directly link firms’ export decisions with the relative restrictiveness of standards in the different actual and potential product-destination markets.

# **5.2 Effects of Standards on Firms’ Export Decisions**

> 30 The caveat of using OLS is that the predicted rates may lie outside of the [0,1] interval but that is not a limitation to the qualitative predictions from these regressions which are our focus.

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Tables 6 and 7 present the baseline results from estimating Eq. (2). Inference is based on Huber-White standard errors robust to heteroskedasticity clustered by exporting countryimporting country-product-year since the specifications explain firms’ export decisions with our relative restrictiveness index measured at the more aggregate exporting country-productdestination country-year level (Moulton, 1990).<sup>31</sup> For the export participation, entry and exit regressions a linear probability model is used in columns (1)-(3) of Table 6 since probit models cannot be estimated with the panel-type of fixed effects considered. We also present robustness results estimated using a conditional fixed effects logit model in columns (4)-(6) of Table 6. For the export value and export quantity regressions OLS estimation is used. The use of linear probability models for the export participation, entry, and exit decisions follows the study by Bernard and Jensen (2004) but has the shortcoming that the predicted probabilities may not be meaningful since they can lie outside of the [0,1] interval and thus the magnitude of the effects of the regressors on those export decisions cannot be assessed.

Table 6 shows the estimated effects of pesticide standards on the extensive margin of trade, firms’ export decisions and decisions to enter and/or exit specific product-destination markets. The negative and statistically significant coefficient on _Rel_restrictiveness_ in column (1) suggests firms face a lower probability of exporting when importing country standards are more restrictive than exporting country standards. Similarly, firms’ decisions to start exporting to a new market decrease significantly when the destination’s standards are more stringent than those applied in the domestic market, as seen in column (2). These results are robust when using a conditional logit model in columns (4)-(5).

There are several reasons why more restrictive standards in the importing country decrease the likelihood that a firm from an exporting country with laxer standards enters that market. First, the asymmetry of information that new exporters to a market suffer from, relative to domestic producers or incumbent exporters, constitutes an additional cost to potential exporters that need to collect the information on the regulations imposed across foreign markets. Second, pesticide standards vary greatly from country to country and producers need to adapt production methods to meet the standards imposed by each destination market. Stricter standards are likely to be harder to meet and thus to require a greater investment by producers in order to

> 31 We also considered robust standard errors clustered by exporting firm and our results were robust. Those results are available from the authors upon request.

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comply. As our results show, stricter standards in importing countries relative to exporting countries result in fewer producers attempting to enter those importing countries’ markets.

Columns (3) and (6) of Table 6 show how pesticide standards affect firms’ decisions to stop exporting a product to a destination country. Our results show that stricter standards in the importing country relative to the exporting country are not significantly associated with the likelihood that an exporter will exit that market. Our conjecture was that as the stringency of standards increases firms would have to stop exporting to the market as many would not be able to comply with that change in standards. Some potential rationales for our insignificant findings are that incumbent exporters to a market may receive the necessary information regarding changes in regulations in a timely manner so as to be able to adjust their production process in order to comply with the new regulation without having to stop exporting or being denied entry at the border for not meeting the adequate standards. It is also possible that because more restrictive standards deter entry, incumbent exporters enjoy lower competition and are able to pass-through the cost of regulatory compliance onto consumers and avoid exit. We will, however, show in Section 6 that this insignificant effect of standards on exit is not robust.

Table 7 shows the results for the intensive margin of trade, which should be interpreted as the effect of pesticide standards on exports, conditional on there being positive exports. The estimates show that more restrictive standards imposed by the importing country relative to the exporting country lead to significantly lower firm export values and quantities. The effect on firm export unit values is not significant. More stringent standards imposed by the importing country can result in higher quality products being imported, which would be reflected in higher unit prices, but in the case of our exporting countries it appears that the negative effect of the restrictiveness of standards on export quantities dominates any change in unit prices, thus the overall effect of the restrictiveness of standards on export values is negative.<sup>32</sup>

# **5.3 Further Effects of Standards on Export Entry for Existing Exporters**

In this section we explore the mechanisms that may lie behind the effects of product standards on firms’ decisions to enter a new product-destination market, focusing on firm

> 32 Notice that the sample used for the quantity and the export unit price regressions in columns (2) and (3) of Table 7 is smaller than that used for the export value regression in column (1) due to the lack of quantity information for 8 countries. Unreported results from estimating the export value regression using the same sample as in columns (2) and (3) show a negative and significant coefficient on _Rel_restrictiveness_ . The regression results available from the authors upon request.

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characteristics and network effects. Trade models with heterogeneous firms suggest that trade policies or regulations may affect firms’ export performance differentially depending on firms’ characteristics. The impact of product standards on firms’ export decisions may depend on firm size, which is likely to be associated with firm productivity, and hence with a firm’s ability to overcome additional costs to export. In that setup relatively more restrictive importing country product standards could have a greater detrimental impact on small exporters. Additionally, standards could affect firms entering a new product-destination market differently than firms already exporting product _k_ and beginning to export it to a new destination _d_ , or firms already exporting to destination _d_ and beginning to export product _k_ there.

Network effects may also play a role, i.e., the presence of other firms from the same exporting country in a given destination may alleviate the negative impact of product standards on export entry. A first network effect may occur as firms from a given exporting country receive information - including on product standards - about possible product-destination markets through firms already established in those markets.<sup>33</sup> A second network effect may occur when foreign buyers or distribution networks in an importing country attempt to expand imports to levels that previous exporters alone are unable to meet, and hence new firms are approached to export to that destination. A third possible spillover effect of incumbent exporters on potential entrants is through the availability of inputs in the domestic market, such as replacement pesticides for banned ones or the necessary human capital and/or know-how to implement new production methodologies to meet more stringent foreign standards.

Table 8 shows the results from estimating several variants of Eq. (2) where the measure _Rel_restrictiveness_ enters by itself but also interacted with different measures of firm size and firm size also enters by itself. It is important to highlight that even though we are analyzing the effect of standards on firms’ _entry_ decisions, we focus here on a particular type of entry in year _t_ for existing exporters (i.e., firms which were already exporting in year _t-1_ ): firms’ decisions to enter into new products, new destinations, and/or new product-destinations (depending on the measure of firm size). The reason for doing this is that our proxy for firm size is firm total export value, for which we choose to consider the one-year lag in order to allow past performance to affect current export decisions and to mitigate endogeneity concerns. Thus, the entirely new

> 33 Cadot et al. (2013) show that survival in export markets for African firms rises with the number of firms from the same country exporting the same product to the same destination, with one of the key channels for the effects being the sharing of information among exporters already established in a market.

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exporters in year _t_ , i.e., those firms that did not export any product to any destination in year _t-1_ are excluded from the analysis here (though they were included in the estimating sample for entry regressions in Table 6).<sup>34</sup> Column (1) shows results for a regression where size is measured by the (logarithm of) the firm’s total agricultural exports, _Agro_exportsc,i,t-1_ , to all of its destinations (including those for which we do not have MRL data and thus are not part of our sample of importing countries).<sup>35</sup> Hence, this regression estimates the effect of relative standards on entry into new products, new destinations, or new product-destinations. The coefficient on _Rel_restrictiveness_ is negative and significant as in Table 6, indicating that stricter pesticide standards in the importing country relative to the exporting country reduce the likelihood of a firm entering that productdestination market, even after controlling for firm size. Also, all else equal larger firms are more likely to enter into a new product-destination market than smaller ones. Finally, our main coefficient of interest on the interaction between _Rel_restrictiveness_ and firm size ( _Agro_exports_ ) is positive and significant indicating that larger exporters are less negatively impacted by the relative stringency of pesticide standards. This is consistent with the empirical evidence that larger exporters tend to be more productive and thus are able to absorb higher costs related to compliance with foreign product standards. Hence, the size of the firm appears to be a direct mechanism through which the relative restrictiveness of standards affects a firm’s decision to export to a new market.

Column (2) displays results for a regression where size is measured by the (logarithm of the) firm’s exports of agricultural products to destination country _d_ , _Agro_exports_destc,i,d,t-1_ .<sup>36</sup> This regression estimates the effect of relative standards on firms’ decisions to introduce new products into existing destinations. The coefficient on _Rel_restrictiveness_ remains negative and significant, indicating that stricter pesticide standards in the importing country relative to the exporting country reduce the likelihood of a firm introducing a new product into an existing destination, even after controlling for firm size. The coefficient on firm size is positive and significant, indicating a firm is more likely to introduce a new product into a destination country

> 34 Consequently, the number of observations in all entry regressions in Table 8 is substantially smaller than that in the entry regression in column (2) of Table 6. 35 Firm size is obtained as the sum of total agricultural exports in HS chapters 06-24 with the exception of chapters 15 and 16. While the variable entering the regression is the one-year lag of firm size, unreported regressions including the current firm size obtain qualitatively similar results. The results available from the authors upon request.

> 36 All firms which did not already export agricultural products other than k to destination _d_ in year _t-1_ are dropped from the estimating sample for this regression.

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in which it is already a large exporter. The interaction term between _Rel_restrictiveness_ and firm size ( _Agro_exports_dest_ ) has an insignificant coefficient. This lack of significance is actually intuitive. The advantage that larger firms have over smaller firms in meeting restrictive standards can dissipate if the firm is already exporting to the destination. If the advantage of large exporters to meet standards in foreign markets is due to the fact that they can more easily overcome information asymmetries regarding those standards, then already serving a given destination country can help reduce those information asymmetries, even for small firms.

Column (3) shows results where size is measured by the (logarithm of the) firm’s total exports of product _k_ , _Exports_productc,i,k,t-1_ .<sup>37</sup> This regression allows us to analyze the effects of relative standards on firms’ decisions to enter into a new destination with an existing export product. The effect of _Rel_restrictiveness_ remains negative and significant, indicating that stricter pesticide standards in the importing country relative to the exporting country reduce the likelihood of a firm diversifying to new destinations, even after controlling for firm size. Larger firms are also more likely to enter to new destinations. The positive and significant coefficient on the interaction term between _Rel_restrictiveness_ and firm size ( _Exports_product_ ) indicates that the negative effect of relative standards on the likelihood of entering new destinations is not as strong for firms that are already large exporters of a product.

The regression in column (4) of Table 8 is estimated for a specific measure of entry that identifies the existing exporters that enter into a new product-destination market in year _t_ . The results show that stricter pesticide standards in the importing country relative to the exporting country reduce firms’ likelihood of entering into new product-destination markets. The positive coefficient on firm size suggests that larger firms are more likely to enter into new productdestination markets than smaller firms. Moreover, larger firms are less affected in their market entry decisions by the negative effects of more stringent relative restrictiveness of product standards.

The regressions in Table 9 explore further channels through which firms might be able to reduce information asymmetries about foreign markets such as network effects, while also controlling for firm size. Column (1) reproduces the results shown in column (1) of Table 8 while columns (2) and (3) show the role of the network effects proxied either by the (logarithm of the) number of firms from the same country as firm _i_ that export (any) agricultural products to

> 37 All firms which did not already export product _k_ in year _t-1_ are dropped from the estimating sample for this regression.

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each destination in year _t-1_ , _Number_agro_firmsc,d,t-1_ in column (2) or by the (logarithm of the) number of firms from the same country exporting the same product _k_ to the same destination _d_ as firm _i_ in year _t-1_ , _Number_prod_firmsc,k,d,t-1_ in column (3). The estimates show that firms are more likely to enter into a new destination where more firms are already selling agricultural products or where more firms are already selling the exact same product. The coefficient on _Rel_restrictiveness_ is negative and significant in columns (2) and (3). The coefficient on the interaction term between _Rel_restrictiveness_ and _Number_agro_firms_ in column (2) is insignificant suggesting that the number of exporters of agricultural products to a given destination does not mediate the effect of restrictive standards of the importing country on entry into that destination. The interaction term between _Rel_restrictiveness_ and _Number_prod_firms_ in column (3) is positive and significant at the 10% level suggesting that firms can to some extent overcome the negative effect of the relative restrictiveness of destination country standards when more exporters from their home country export the same product to that destination.

These results provide some evidence of positive network effects for firms’ decisions to start exporting to a new destination in helping them overcome the regulatory impediments of restrictive standards in a new destination, but only when firms export the same product. These network effects might be related with information asymmetries considering that information on foreign standards might flow from one firm to another either through labor mobility or through producer associations. Also, the likelihood that a firm can find the necessary inputs and techniques to meet foreign standards will likely increase with the number of home country firms already exporting the same product to a given destination.

# **5.4 Further Effects of Standards on Export Exit**

In this section we explore whether product standards impact firms’ decisions to stop exporting to destinations where they previously sold agricultural products differentially depending on firm size, the product’s importance in firm total exports, or the destination’s importance in firm total exports. The rationale behind the consideration of the product’s importance is that a firm which derives most of its export revenue from one product is more likely to incur in the costs necessary to comply with changes in standards in order to sustain its main source of revenue. In contrast, a firm which sells multiple products is more likely to stop 23
exporting a product that is not important for its overall export portfolio if it is more costly to comply with changes in standards due to the small scale of production. A similar rationale applies to the consideration of the destination country’s importance. The exit regressions focus by definition on the decision to stop exporting to a product-destination market for existing exporters.

Table 10 shows the results from estimating several variants of Eq. (2) where the measure _Rel_restrictiveness_ enters by itself but also interacted with firm size ( _Agro_exportsc,i,t-1_ ) as well as with the share in the firm’s total agricultural exports of exports of the product, _Product_sharec,i,k,t-1_ , or exports of the destination, _Destination_sharec,i,d,t-1_ . Firm size and the shares of the product or destination in firm total exports also enter by themselves. Column (1) explores the role of firm size for the decision to exit a product-destination market. In contrast to what was shown in the baseline estimates in Table 6, once firm size is controlled for, the coefficient on _Rel_restrictiveness_ is positive and significant, indicating that firms are more likely to stop exporting a product to a destination with relatively more restrictive standards than their domestic market. The coefficient on firm size shows that larger firms are less likely to exit their export markets. The interaction term between _Rel_restrictiveness_ and firm size has a negative and significant coefficient indicating that larger firms are less likely to exit a product-destination market due to the relative restrictiveness of that importing country’s pesticide standards.

Column (2) explores the importance of the product while column (3) explores the importance of the destination, in the firm’s total agricultural exports. In both columns the coefficient on _Rel_restrictiveness_ is positive and significant, again in contrast to the finding in Table 6. Also, our estimates show that, all else equal, the more important is a given product or destination to a firm, the less likely is the firm to stop exporting that product or to that destination, respectively. The interaction terms between _Rel_restrictiveness_ and either _Product_share_ or _Destination_share_ have negative coefficients, suggesting a less severe exitinducing effect of more stringent standards for products or destinations that account for a larger share of firms’ export portfolios, but the effects are not significant.

Column (4) presents the results from a specification which controls for firm size, product share, destination share, as well as the interaction between each of these variables and the _Rel_restrictiveness_ measure. The coefficient on _Rel_restrictiveness_ is positive and significant while that on its interaction with firm size is negative and significant as in column (1). The 24
effects of the interaction between _Rel_restrictiveness_ and either the importance of the product or the destination market for firms’ total agricultural exports are negative but insignificant. Therefore, firm size is the channel by which the relative restrictiveness of pesticide standards in the importing country affects the firms’ decision to stop exporting to a product-destination market.

# **6. Robustness Results**

In this section we describe a number of robustness checks that we performed to our results. We re-estimated the export participation, entry, and exit regressions including interactions with firm size for three variants of the sample. The first set of regressions whose results are shown in columns (1)-(3) of Table 11 restricts the sample of importing countries and exporting countries to be only those that draft specific MRL regulations, that is, the sample excludes the countries that use solely Codex standards as their own regulations. The effect of the importing country’s relative restrictiveness of standards on firms’ export participation and entry into new destinations remains negative and significant while the effect on firms’ exit is positive and significant. The coefficient on the interaction between _Rel_restrictiveness_ and firm size is significant in both the entry and exit specifications suggesting that the effect of pesticide standards on firms’ export decisions depends on firm size. Smaller firms are more negatively affected by the relative stringency of importing country standards than larger firms. The second set of regressions whose results are shown in columns (4)-(6) of Table 11 restricts the sample in terms of the years covered, keeping only years 2008-2012 and thus addressing the fact that in 2006 and 2007 there is missing standards data for a few destinations including the EU, as seen in Table 2. Again, the results show that the relative restrictiveness of standards of the importing country negatively impacts a firm’s decision to export and firm size determines how much the standards affect the firm’s decision. The third set of regressions whose results are shown in columns (7)-(9) of Table 11 restricts the sample of products to those belonging to HS chapters 7 and 8 (edible vegetable and edible fruits). The match on MRL standards obtained from Homologa database was better for these two HS chapters. Again all results are qualitatively maintained.

We conduct one final robustness check in which we include additional sets of stringent fixed effects in order to control for unobservables that might not be fully controlled for in our 25
baseline estimation.<sup>38</sup> In addition to including in all specifications the panel-type firm-productdestination fixed effects, we include the set of importing country-year, exporting country-year, and product-year fixed effects in the first variation in columns (1), (4), (7), and (10) of Table 12, importing country-product-year fixed effects in columns (2), (5), (8), and (11) of Table 12, and exporting country-product-year fixed effects in columns (3), (6), (9), and (12) of Table 12. These various types of fixed effects are more flexible than the specific control variables included in our previous specifications (destination country GDP per capita or exporting country-product total exports). For example exporting country-year and destination country-year fixed effects may account for the differential effects of macroeconomic shocks in the exporting and the destination country and are particularly relevant for us to account for given that our sample period encompasses the recent Global Financial Crisis (GFC). Also, exporting country-productyear fixed effects account for supply shocks such as weather shocks affecting a product’s crop whereas destination country-product-year fixed effects account for demand shocks such as an increased preference for certain types of fruits. The estimates in Table 12 show that the conclusions remain the same as in the baseline specification: the relative restrictiveness of importing country standards affects negatively firms’ export participation and entry into new markets and affects positively firms’ exit from a given market. The results also confirm that larger firms can more easily overcome the negative effects of standards.

# **7. Conclusion**

This paper examines the importance of standards’ regulations in influencing the ability of firms in developing countries to exploit export opportunities for agricultural and food products. Our evidence shows that the relative restrictiveness of importing country pesticide standards restricts market access for those firms. Importantly, our findings suggest a significant effect of restrictive importing country standards in depressing both the firm extensive and intensive margins of exports. Our results are consistent with recent trade models which predict that only the most productive firms are able to overcome the fixed costs of exporting. Obtaining information on foreign standards and adjusting production processes to comply with those

> 38 In unreported results we also re-estimated the export participation, entry, and exit decisions including instead of GDP per capita of the destination country and total exports by export country-product as in Eq. (2) the more traditional gravity regressors of GDP of the destination country and GDP of the export country as an alternative way to control for demand and supply effects. The effects of the importing country’s relative restrictiveness of standards were qualitatively maintained.

26
standards increase the fixed costs to reach foreign markets. This helps explain the stronger negative effect of the relative restrictiveness of an importing country’s standards on entry and exit decisions for smaller firms.

The international community has attempted to overcome the trade-distortive effects of standards through the WTO SPS Agreement and other trade tools. This has included decades of discussion about the benefits of harmonization of standards, use of international consensus standards through international bodies such as Codex Alimentarius, and potential cost of domestic standards that deviate from international norms. Limited progress has been made, however, in these various steps to mitigate the negative affect of discriminatory or duplicative national standards. Moreover, a number of developing countries lack access to compliance resources, including scientific and technical expertise, information and finance to exploit the opportunities offered in various trade agreements.

Looking ahead, it is important that new trade talks consider and address the channels through which NTMs affect trade. SPS standards, including MRLs, continue to be developed by national governments and this heterogeneity is likely to remain in place since full global harmonization of such standards is impossible. Furthermore, the development of new and deeper trade agreements in which NTMs are included—which is likely to be the case for the TTIP—will greatly impact third-country firms in developing countries that may be unable to meet the new agreed-upon standards. For a developing country seeking to support its agricultural exporters entering and surviving in markets with more stringent SPS standards, the best strategy is for the government to help exporters meet foreign standards by creating the necessary testing facilities and customs clearance procedures, and by facilitating the imports of the necessary inputs to meet foreign standards.

Future research on the effects of NTMs on trade should expand the analysis to the manufacturing sector using measures that account not only for the number of standards but also for their stringency, as we have done in this paper with standards for agricultural products. In addition, while our study focuses on the impact of “de jure” regulations, the implementation of these regulations at ports and border crossing points may or may not be fully enforced by the importing country’s authorities. Investigating those effects is left for future work.

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29
**<u>Table 1. Number of Products Regulated by Pesticide Standards by Importing Country</u>**

|||Nu|mber of Sp|ecific Prod|ucts Regul|ated|||Number o|f Products|Regulated|or With D|efault MRL||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||2006|2007|2008|2009|2010|2011|2012|2006|2007|2008|2009|2010|2011|2012|
|Argentina|102|110|110|110|115|109|110|214|219|232|240|242|241|241|
|ASEAN|‐|‐|102|102|102|102|102|‐|‐|147|148|149|149|148|
|Australia|139|154|155|152|152|152|152|139|154|155|152|152|152|152|
|Brazil|59|66|66|72|73|73|73|147|152|141|145|149|150|149|
|Canada|118|142|157|160|161|164|165|214|219|232|240|242|241|241|
|Chile|146|149|136|136|138|85|85|146|149|136|137|142|143|142|
|China|87|87|87|43|43|44|44|87|87|87|43|43|44|44|
|CODEX|146|149|136|137|140|141|140|146|149|136|137|140|141|140|
|Colombia|‐|136|136|136|136|136|137|‐|149|136|137|140|141|141|
|Dom. Republic|146|149|136|137|140|141|140|183|192|191|191|192|193|192|
|Egypt|‐|‐|‐|136|136|136|137|‐|‐|‐|240|242|241|241|
|European Union|‐|‐|125|135|136|136|136|‐|‐|232|240|242|241|241|
|Honduras|146|149|136|137|140|141|140|183|192|191|191|192|193|192|
|India|‐|134|149|105|105|105|105|‐|149|150|151|151|152|151|
|Israel|93|87|87|87|87|87|87|149|152|139|140|143|144|143|
|Japan|129|117|115|119|116|116|106|214|219|232|240|242|241|241|
|Republic of Korea|92|92|100|98|100|100|100|92|92|100|98|100|100|100|
|Malaysia|42|86|86|84|84|84|88|214|219|232|240|242|241|241|
|Mexico|66|66|72|67|67|67|67|167|184|182|180|181|182|182|
|Morocco|146|149|136|137|140|141|140|146|149|171|175|177|177|176|
|New Zealand|142|109|99|86|86|86|86|214|219|232|240|242|241|241|
|Panama|146|149|136|137|140|141|140|183|192|203|205|206|207|206|
|Russian Federation|32|95|113|112|113|111|111|32|95|113|112|113|111|111|
|Singapore|‐|‐|125|125|125|125|125|‐|‐|150|151|151|152|151|
|South Africa|89|99|99|99|99|99|99|214|219|232|240|242|241|241|
|Switzerland|125|127|133|133|145|141|141|125|127|147|150|146|142|142|
|Taiwan, China|68|68|75|77|77|97|97|68|68|75|77|77|97|97|
|Thailand|102|102|102|102|102|102|124|154|157|149|150|153|154|157|
|Turkey|103|103|103|98|137|134|134|103|103|134|137|138|136|136|
|Ukraine|‐|‐|‐|114|114|114|114|‐|‐|‐|114|114|114|114|
|United States|166|183|180|180|181|182|182|166|183|180|180|181|182|182|
|Total|214|219|232|240|242|241|241|214|219|232|240|242|241|241|

Note: ‘-‘ indicates that the country does not have data for that particular year. ASEAN is the Association of Southeast Asian Nations.

30
# **Table 2. Changes in Pesticide Standards over Time by Importing Country**

||Number of<br>Observations<br>(at country‐HS|Share of<br>Observations<br>with Complete|Of Observat<br>(MRLs a|ions with Com<br>vailable in ev|Share with<br>plete Panel<br>ery year)|
|---|---|---|---|---|---|
||product‐<br>pesticide‐year<br>level)|Panel (MRLs<br>available in<br>every year)|Share with<br>No Change<br>in MRLs|Share with<br>Stricter<br>MRLs|<br>More<br>Lenient<br>MRLs|
|Argentina|90,084|49%|68%|27%|5%|
|ASEAN|5,193|75%|85%|11%|4%|
|Australia|11,508|46%|90%|4%|6%|
|Brazil|20,865|23%|63%|8%|29%|
|Canada|90,084|49%|97%|1%|2%|
|Chile|21,540|16%|42%|11%|46%|
|China|2,714|32%|88%|3%|8%|
|CODEX|19,674|16%|42%|12%|45%|
|Colombia|19,668|20%|82%|3%|23%|
|Dom. Republic|25,875|28%|47%|14%|39%|
|Egypt|87,314|93%|96%|3%|2%|
|European Union|88,333|87%|92%|4%|4%|
|Honduras|25,875|28%|47%|14%|39%|
|India|20,970|23%|54%|11%|40%|
|Israel|21,804|26%|71%|7%|22%|
|Japan|90,084|49%|86%|9%|5%|
|Republic of Korea|13,240|40%|87%|9%|4%|
|Malaysia|90,084|49%|67%|27%|6%|
|Mexico|10,585|49%|79%|9%|11%|
|Morocco|64,918|27%|71%|10%|19%|
|New Zealand|90,084|49%|96%|3%|1%|
|Panama|67,566|30%|65%|17%|18%|
|Russian Federation|10,490|1%|74%|18%|8%|
|Singapore|6,945|82%|93%|5%|2%|
|South Africa|90,084|49%|63%|4%|32%|
|Switzerland|67,619|20%|66%|18%|16%|
|Taiwan, China|5,862|67%|93%|6%|1%|
|Thailand|19,924|20%|58%|9%|33%|
|Turkey|65,225|18%|87%|7%|6%|
|Ukraine|3,385|100%|99%|0%|0%|
|United States|10,180|46%|75%|12%|13%|
|Total|1,257,776|45%|81%|9%|10%|

Note: ASEAN is the Association of Southeast Asian Nations.

31
**<u>Table 3. Number of Firms and Observations by Exporting Country</u>**

|||Numbe|r of Expor|ting Firms|by Expor|ting Coun|try‐Year||Number of Obs|ervations by Ex<br>|porting Country<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|
|||||Initial|Dataset||||Initial Dataset|Tim<br>Expand|e‐Series<br>ed Dataset|
||2005|2006|2007|2008|2009|2010|2011|2012|Across All Years|Per Year<br>|Across All Years<br>|
|Albania|43|81|77|74|77|90|99|85|1,164|644|5,152|
|Burkina Faso|‐|‐|129|136|109|128|131|160|1,629|1,210|7,260|
|Bangladesh|243|383|321|408|343|355|429|‐|6,584|3,680|25,760|
|Bulgaria|516|779|‐|‐|‐|‐|‐|‐|9,049|6,565|13,130|
|Bolivia|‐|241|252|245|256|244|230|213|5,515|2,352|16,464|
|Botswana|70|90|99|109|118|113|‐|‐|1,881|1,149|6,894|
|Chile|790|963|981|1,018|1,037|1,092|1,155|1,029|54,127|20,024|160,192|
|Cote d'Ivoire|‐|‐|‐|‐|310|300|332|374|5,315|3,351|13,404|
|Cameroon|‐|‐|111|138|137|‐|‐|‐|973|696|2,088|
|Colombia|‐|‐|817|756|708|‐|‐|‐|7,713|4,648|13,944|
|Costa Rica|540|690|698|707|705|747|729|627|20,838|8,671|69,368|
|Dom. Republic|‐|‐|610|469|580|480|547|475|21,891|12,633|75,798|
|Ecuador|‐|426|458|488|542|‐|‐|‐|6,932|3,831|15,324|
|Ethiopia|‐|‐|‐|542|692|799|706|652|9,437|5,964|29,820|
|Georgia|95|176|117|132|130|150|151|163|2,581|1,716|13,728|
|Guatemala|617|738|797|782|767|809|‐|‐|20,342|8,780|52,680|
|Croatia|‐|‐|243|236|235|238|254|273|4,241|1,954|11,724|
|Iran|‐|1,525|1,521|1,472|1,233|1,277|‐|‐|18,726|13,021|65,105|
|Jordan|79|110|122|125|117|133|‐|‐|1,580|1,341|8,046|
|Kenya|‐|781|600|649|683|‐|‐|‐|6,974|4,545|18,180|
|Kyrgyzstan|‐|85|93|96|134|131|117|99|2,057|1,190|8,330|
|Cambodia|‐|‐|44|35|45|‐|‐|‐|199|157|471|
|Lebanon|‐|‐|‐|402|381|388|398|401|16,001|9,658|48,290|
|Morocco|452|637|642|651|665|696|700|614|17,773|8,095|64,760|
|Madagascar|‐|‐|187|215|224|246|357|225|4,396|2,769|16,614|
|Mexico|1,522|4,236|3,508|3,604|3,693|‐|‐|‐|48,279|28,681|143,405|
|Macedonia|204|328|349|319|322|355|‐|‐|6,388|3,509|21,054|
|Mauritius|59|85|96|96|97|96|92|87|3,634|1,773|14,184|
|Malawi|‐|81|87|73|86|82|108|111|1,616|628|3,943|
|Nicaragua|150|218|217|248|245|253|240|‐|5,419|2,676|18,732|
|Pakistan|805|1,110|1,133|1,239|1,502|1,541|‐|‐|21,914|12,559|75,354|
|Peru|643|980|1,009|1,053|1,122|‐|‐|‐|18,275|9,927|49,635|
|Paraguay|‐|‐|181|193|204|196|193|186|3,349|1,549|9,294|
|Romania|346|500|312|394|464|659|775||10,998|6,443|45,101|
|Senegal|‐|‐|‐|117|121|132|142|153|1,512|1,028|5,140|
|El Salvador|157|197|193|198|220|‐|‐|‐|3,136|1,420|7,100|
|Tanzania|172|205|206|232|263|275|292||4,681|2,742|19,194|
|Uganda|‐|‐|184|164|172|232|‐|‐|2,287|1,433|5,732|
|Uruguay|146|174|182|169|190|201|218|187|5,268|2,392|19,136|
|Yemen|‐|‐|‐|49|35|28|19|19|240|167|835|
|South Africa|1,019|1,416|1,411|1,457|1,453|1,414|1,390|1,839|107,562|49,039|392,312|
|Zambia|56|83|129|104|135|180|212|‐|1,706|1,126|7,882|
|Total|8,724|17,318|18,116|19,594|20,552|14,060|10,016|7,972|494,182|249,171|1,600,559|

Note: - indicates that the country does not have data for that particular year.

32
**<u>Table 4. Summary Statistics on Key Dependent and Independent Variables</u>**

||Number of<br>Observations|Mean|Std. Dev.|Minimum|Maximum|
|---|---|---|---|---|---|
|Export Participation Dummyc,i,k,d,t|796,636|0.32|0.47|0|1|
|Entry Dummyc,i,k,d,t|535,056|0.22|0.41|0|1|
|Exit Dummyc,i,k,d,t|240,074|0.45|0.50|0|1|
|Log Export Valuec,i,k,d,t|256,882|9.25|3.40|‐9.06|20.91|
|Log Export Quantityc,i,k,d,t|159,914|8.57|3.79|‐6.91|20.24|
|Log Export Unit Pricec,i,k,d,t|159,914|0.51|1.25|‐16.12|13.17|
|Rel_restrictivenessc,k,d,t|796,636|‐0.06|0.52|‐1|1|

Note: log export value or quantity are defined based on the initial data set whereas export participation, entry, or exit are defined based on the expanded data-set. Export value is measured in USD, export quantity in kilograms, and export unit value in USD per kilogram.

**<u>Table 5. Effects of Standards on Numbers of Exporters, Entrants, and Exiters</u>**

||Number of<br>exportersc,k,d,t<br>(1)|Number of<br>entrantsc,k,d,t<br>(2)|Number of<br>exitersc,k,d,t<br>(3)<br>Depende|Exporter entry<br>ratec,k,d,t<br>(4)<br>nt Variable:|Exporter exit<br>ratec,k,d,t<br>(5)|Entrant suvival<br>ratec,k,d,t<br>(6)|
|---|---|---|---|---|---|---|
|Rel_restrictivenessc,k,d,t|‐0.247<br>[0.040]***|‐0.445<br>[0.072]***|0.486<br>[0.090]***|‐0.044<br>[0.019]**|0.050<br>[0.020]**|‐0.060<br>[0.035]*|
|Log 1+Tariffc,k,d,t|‐0.112<br>[0.014]***|‐0.161<br>[0.024]***|‐0.093<br>[0.024]***|‐0.013<br>[0.006]**|‐0.019<br>[0.006]***|0.005<br>[0.010]|
|Log GDP per capitad,t|0.976|2.174|1.417|0.001|0.359|0.148|
||[0.171]***|[0.244]***|[0.263]***|[0.046]|[0.052]***|[0.075]**|
|Log Total Exportsc,k,t|0.199<br>[0.008]***|0.224<br>[0.011]***|‐0.007<br>[0.009]|‐0.007<br>[0.003]**|‐0.028<br>[0.003]***|0.033<br>[0.004]***|
|Exporting Country‐|||||||
|Product‐Destination|Yes|Yes|Yes|Yes|Yes|Yes|
|Country Fixed Effects|||||||
|Year Fixed Effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Estimation Method|Poisson|Poisson|Poisson|OLS|OLS|OLS|
|Observations|82,793|78,871|69,214|48,365|46,302|31,484|
|R‐squared||||0.573|0.589|0.566|

Notes: Robust standard errors in brackets. OLS standard errors are clustered at the exporting country-importing country-product level. ***, **, and * indicate significance at 1%, 5%, and 10% confidence levels, respectively.

33
**<u>Table 6. Effects of Standards on Firm Export Participation, Entry, and Exit</u>**

||||<br>Dependen|<br> <br>t Variable:|||
|---|---|---|---|---|---|---|
||Export<br>Participation|Export<br>Entry|Export<br>Exit|<br> <br>Export<br>Participation|Export<br>Entry|Export<br>Exit|
||Dummyc,i,k,d,t<br>(1)|Dummyc,i,k,d,t<br>(2)|Dummyc,i,k,d,t<br>(3)|<br>Dummyc,i,k,d,t<br>(4)|Dummyc,i,k,d,t<br>(5)|Dummyc,i,k,d,t<br>(6)|
|Rel_restrictivenessc,k,d,t|‐0.074<br>[0.011]***|‐0.032<br>[0.016]**|0.009<br>[0.031]|‐0.425<br>[0.036]***|‐0.237<br>[0.049]***|‐0.098<br>[0.182]|
|Log 1+Tariffc,k,d,t|‐0.030<br>[0.005]***|‐0.025<br>[0.007]***|0.015<br>[0.008]*|‐0.192<br>[0.011]***|‐0.195<br>[0.013]***|0.053<br>[0.046]|
|Log GDP per capitad,t|0.260<br>[0.036]***|0.008<br>[0.047]|‐0.549<br>[0.095]***|1.621<br>[0.091]***|0.494<br>[0.105]***|‐5.863<br>[0.465]***|
|Log Total Exportsc,k,t|0.047<br>[0.002]***|0.045<br>[0.002]***|‐0.071<br>[0.005]***|0.276<br>[0.005]***|0.256<br>[0.006]***|‐0.352<br>[0.021]***|
|Exporting Firm‐Importing<br>Country‐Product Fixed Effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Year Fixed Effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Estimation method|Linear<br>Probability<br>Model|Linear<br>Probability<br>Model|Linear<br>Probability<br>Model|Conditional<br>Logit|Conditional<br>Logit|Conditional<br>Logit|
|Observations|796,636|535,056|240,074|634,930|396,153|93,606|
|R‐squared|0.351|0.242|0.703||||
|Pseudo R‐squared||||0.013|0.031|0.302|

Notes: Robust standard errors in brackets, clustered at the exporting country-importing country-product-year level. ***, **, and * indicate significance at 1%, 5%, and 10% confidence levels, respectively. Dependent variables are defined as follows: export participation equals one if there is positive trade between countries and zero otherwise; entry equals one if firm _i_ exports product _k_ to destination _d_ in year _t_ but did not do so in year _t-1_ and zero if the firm did not export product _k_ to destination _d_ in year _t-1_ and does not start to do so in year _t_ . Similarly, exit equals one if firm _i_ does not export product _k_ to destination _d_ in year _t_ but did so in year _t-1_ and zero if the firm exported product _k_ to destination _d_ in year _t-1_ and continues to do so in year _t_ .

34
**Table 7. Effects of Standards on Firm Export Value, Quantity, and Unit Price**

||<br>D|<br>ependent Variabl|<br>e:|
|---|---|---|---|
||Log Export<br>Valuec,i,k,d,t|Log Export<br>Quantityc,i,k,d,t|Log Unit<br>Pricec,i,k,d,t|
||(1)|(2)|(3)|
|Rel_restrictivenessc,k,d,t|‐0.173|‐0.179|0.020|
||[0.057]***|[0.065]***|[0.032]|
|Log 1+Tariffc,k,d,t|‐0.045|‐0.071|‐0.003|
||[0.016]***|[0.023]***|[0.011]|
|Log GDP per capitad,t|1.340|0.852|0.182|
||[0.164]***|[0.189]***|[0.078]**|
|Log Total Exportsc,k,t|0.321|0.232|0.045|
||[0.015]***|[0.016]***|[0.012]***|
|Exporting Firm‐Importing<br>Country‐Product Fixed Effects|Yes|Yes|Yes|
|Year Fixed Effects|Yes|Yes|Yes|
|Estimation method|OLS|OLS|OLS|
|Observations|256,882|159,914|159,914|
|R‐squared|0.948|0.959|0.922|

Notes: Robust standard errors in brackets, clustered at the exporting country-importing country-product-year level. ***, **, and * indicate significance at 1%, 5%, and 10% confidence levels, respectively.

35
# **<u>Table 8. Effects of Standards on Firm Export Entry:  Role of Firm Size</u>**

|||Dependen|t Variable:||
|---|---|---|---|---|
||Export Entry<br>Dummyc,i,k,d,t<br>(1)|Export Entry<br>Dummyc,i,k,d,t<br>(2)|Export Entry<br>Dummyc,i,k,d,t<br>(3)|Export Entry<br>Dummyc,i,k,d,t<br>(4)|
|Rel_restrictivenessc,k,d,t|‐0.190<br>[0.025]***|‐0.109<br>[0.034]***|‐0.253<br>[0.034]***|‐0.180<br>[0.072]**|
|Log Agro_exportsc,i,t‐1|0.051<br>[0.001]***|||0.030<br>[0.003]***|
|Rel_restrictivenessc,k,d,tX Log Agro_exportsc,i,t‐1|0.010<br>[0.002]***|||0.011<br>[0.005]**|
|Log Agro_exports_destc,i,d,t‐1||0.040<br>[0.002]***|||
|Rel_restrictivenessc,k,d,tX Log Agro_exports_destc,i,d,t‐1||0.000<br>[0.002]|||
|Log Exports_productc,i,k,t‐1|||0.044<br>[0.002]***||
|Rel_restrictivenessc,k,d,tX Log Exports_productc,i,k,t‐1|||0.021<br>[0.003]***||
|Log 1+Tariffc,k,d,t|‐0.020<br>[0.004]***|‐0.012<br>[0.006]**|‐0.017<br>[0.009]*|‐0.050<br>[0.012]***|
|Log GDP per capitad,t|0.030|‐0.336|0.296|0.087|
||[0.042]|[0.051]***|[0.061]***|[0.114]|
|Log Total Exportsc,k,t|0.041<br>[0.002]***|0.038<br>[0.002]***|0.047<br>[0.004]***|0.043<br>[0.007]***|
|Exporting Firm‐Importing Country‐Product Fixed Effects|Yes|Yes|Yes|Yes|
|Year Fixed Effects|Yes<br>Linear|Yes<br>Linear|Yes<br>Linear|Yes<br>Linear|
|Estimation method|Probability<br>Model|Probability<br>Model|Probability<br>Model|Probability<br>Model|
|Observations|297,409|168,151|132,626|40,436|
|R‐squared|0.379|0.465|0.406|0.635|

Notes: Robust standard errors in brackets, clustered at the exporting country-importing country-product-year level. ***, **, and * indicate significance at 1%, 5%, and 10% confidence levels, respectively. The dependent variable is defined as follows: entry equals one if firm _i_ exports product _k_ to destination _d_ in year _t_ but did not do so in year _t-1_ and zero if the firm did not export product _k_ to destination _d_ in year _t-1_ and does not start to do so in year _t_ .

36
# **Table 9. Effects of Standards on Firm Export Entry: Role of Firm Size and Network** **<u>Effects</u>**

||<br>De|<br>pendent Varia|<br>ble:|
|---|---|---|---|
||Export Entry<br>Dummyc,i,k,d,t<br>(1)|Export Entry<br>Dummyc,i,k,d,t<br>(2)|Export Entry<br>Dummyc,i,k,d,t<br>(3)|
|Rel_restrictivenessc,k,d,t|‐0.190<br>[0.025]***|‐0.152<br>[0.050]***|‐0.186<br>[0.026]***|
|Log Agro_exportsc,i,t‐1|0.051<br>[0.001]***|0.050<br>[0.001]***|0.049<br>[0.001]***|
|Rel_restrictivenessc,k,d,tX Log Agro_exportsc,i,t‐1|0.010<br>[0.002]***|0.009<br>[0.002]***|0.009<br>[0.002]***|
|Number_agro_firmsc,d,t‐1||0.110<br>[0.010]***||
|Rel_restrictivenessc,k,d,tX Number_agro_firmsc,d,t‐1||‐0.007<br>[0.010]||
|Number_product_firmsc,d, k, t‐1|||0.079<br>[0.005]***|
|Rel_restrictivenessc,k,d,tX Number_product_firmsc,d,k,t‐1|||0.007*<br>[0.006]|
|Log 1+Tariffc,k,d,t|‐0.020<br>[0.004]***|‐0.016<br>[0.004]***|‐0.015<br>[0.004]***|
|Log GDP per capitad,t|0.030|‐0.100|‐0.009|
||[0.042]|[0.038]***|[0.038]|
|Log Total Exportsc,k,t|0.041<br>[0.002]***|0.041<br>[0.002]***|0.038<br>[0.002]***|
|Exporting Firm‐Importing Country‐Product Fixed Effects|Yes|Yes|Yes|
|Year Fixed Effects|Yes|Yes|Yes|
||Linear|Linear|Linear|
|Estimation method|Probability<br>Model|Probability<br>Model|Probability<br>Model|
|Observations|297,409|297,409|297,409|
|R‐squared|0.379|0.381|0.382|

Notes: Robust standard errors in brackets, clustered at the exporting country-importing country-product-year level. ***, **, and * indicate significance at 1%, 5%, and 10% confidence levels, respectively. The dependent variable is defined as follows: entry equals one if firm _i_ exports product _k_ to destination _d_ in year _t_ but did not do so in year _t-1_ and zero if the firm did not export product _k_ to destination _d_ in year _t-1_ and does not start to do so in year _t_ .

37
# **Table 10. Effects of Standards on Firm Export Exit: Role of Firm Size and Network Effects**

|||Dependen|t Variable:||
|---|---|---|---|---|
||Export Exit<br>Dummyc,i,k,d,t<br>(1)|<br>Export Exit<br>Dummyc,i,k,d,t<br>(2)|Export Exit<br>Dummyc,i,k,d,t<br>(3)|<br>Export Exit<br>Dummyc,i,k,d,t<br>(4)|
|Rel_restrictivenessc,k,d,t|0.413<br>[0.128]***|0.450<br>[0.131]***|0.470<br>[0.137]***|0.505<br>[0.140]***|
|Log Agro_exportsc,i,t‐1|‐0.093<br>[0.003]***|‐0.095<br>[0.003]***|‐0.095<br>[0.003]***|‐0.096<br>[0.003]***|
|Rel_restrictivenessc,k,d,tX Log Agro_exportsc,i,t‐1|‐0.033<br>[0.009]***|‐0.033<br>[0.009]***|‐0.036<br>[0.009]***|‐0.036<br>[0.009]***|
|Product_sharec,i,k,t‐1||‐0.084<br>[0.037]**||‐0.076<br>[0.037]**|
|Rel_restrictivenessc,k,d,tX Product_sharec,i,k,t‐1||‐0.070<br>[0.046]||‐0.069<br>[0.046]|
|Destination_sharec,i,d,t‐1|||‐0.084<br>[0.017]***|‐0.076<br>[0.017]***|
|Rel_restrictivenessc,k,d,tX Destination_sharec,i,d,t‐1|||‐0.032|‐0.031|
||||[0.046]|[0.046]|
|Log 1+Tariffc,k,d,t|0.008|0.008|0.007|0.007|
||[0.010]|[0.010]|[0.010]|[0.010]|
|Log GDP per capitad,t|‐0.603|‐0.600|‐0.593|‐0.592|
||[0.112]***|[0.114]***|[0.112]***|[0.113]***|
|Log Total Exportsc,k,t|‐0.073<br>[0.006]***|‐0.072<br>[0.006]***|‐0.073<br>[0.006]***|‐0.073<br>[0.006]***|
|Exporting Firm‐Importing Country‐Product Fixed<br>Effects|Yes|Yes|Yes|Yes|
|Year Fixed Effects|Yes<br>Linear|Yes<br>Linear|Yes<br>Linear|Yes<br>Linear|
|Estimation method|Probability<br>Model|Probability<br>Model|Probability<br>Model|Probability<br>Model|
|Observations|240,074|240,074|240,074|240,074|
|R‐squared|0.709|0.709|0.709|0.709|

Notes: Robust standard errors in brackets, clustered at the exporting country-importing country-product-year level. ***, **, and * indicate significance at 1%, 5%, and 10% confidence levels, respectively. The dependent variable is defined as follows: exit equals one if firm _i_ does not export product _k_ to destination _d_ in year _t_ but did so in year _t-1_ and zero if the firm exported product _k_ to destination _d_ in year _t-1_ and continues to do so in year _t_ .

38
**<u>Table 11. Results from Robustness Checks – Restricted Samples</u>**

|||||De|pendent Varia|ble:||||
|---|---|---|---|---|---|---|---|---|---|
||Export|Export|Export|<br>Export|Export|Export|<br>Export|Export|<br>Export|
||Participation|Entry|Exit|<br>Participation|Entry|Exit|<br>Participation|Entry|Exit|
||Dummyc,i,k,d,t|Dummyc,i,k,d,t|Dummyc,i,k,d,t|<br>Dummyc,i,k,d,t|Dummyc,i,k,d,t|Dummyc,i,k,d,t|<br>Dummyc,i,k,d,t|Dummyc,i,k,d,t|<br>Dummyc,i,k,d,t|
||Destinat|ion‐Restricted|Sample|Year|‐Restricted Sa|mple|Produ|ct‐Restricted|Sample|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|Rel_restrictivenessc,k,d,t|‐0.116<br>[0.015]***|‐0.247<br>[0.039]***|0.302<br>[0.160]*|‐0.069<br>[0.031]**|‐0.104<br>[0.044]**|‐0.148<br>[0.203]|‐0.117<br>[0.018]***|‐0.109<br>[0.040]***|0.603<br>[0.179]***|
|Log Agro_exportsc,i, t‐1||0.048|‐0.089||0.047|‐0.098||0.053|‐0.096|
|||[0.002]***|[0.003]***||[0.001]***|[0.004]***||[0.001]***|[0.003]***|
|Rel_restrictivenessc,k,d,tX||||||||||
|Log Agro_exportsc,i,t‐1||0.007|‐0.029||0.005|0.016||0.003|‐0.044|
|||[0.002]***|[0.011]**||[0.002]**|[0.013]||[0.002]|[0.013]***|
|Log 1+Tariffc,k,d,t|‐0.027|‐0.048|‐0.023|‐0.029|‐0.001|0.004|‐0.029|‐0.018|‐0.005|
||[0.008]***|[0.014]***|[0.021]|[0.007]***|[0.007]|[0.015]|[0.008]***|[0.007]***|[0.012]|
|Log GDP per capitad,t|0.225|0.192|‐0.361|0.407|0.199|‐0.052|0.387|0.185|‐0.762|
||[0.067]***|[0.077]**|[0.185]*|[0.054]***|[0.056]***|[0.140]|[0.056]***|[0.062]***|[0.141]***|
|Log Total Exportsc,k,t|0.042<br>[0.002]***|0.038<br>[0.003]***|‐0.077<br>[0.009]***|0.046<br>[0.002]***|0.040<br>[0.002]***|‐0.068<br>[0.008]***|0.054<br>[0.003]***|0.048<br>[0.003]***|‐0.082<br>[0.009]***|
|Exporting Firm‐Importing<br>Country‐Product Fixed Effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year Fixed Effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
||Linear|Linear|Linear|Linear|Linear|Linear|Linear|Linear|Linear|
|Estimation method|Probability|Probability|Probability|Probability|Probability|Probability|Probability|Probability|Probability|
||Model|Model|Model|Model|Model|Model|Model|Model|Model|
|Observations|441,796|157,212|118,455|608,241|236,042|143,881|478,185|167,137|128,989|
|R‐squared|0.359|0.395|0.697|0.439|0.471|0.745|0.347|0.386|0.705|
|Notes: Robust standard errors in brack<br>confidence levels, respectively. Depend<br>equals one if firm_i_exports product_k_to<br>to do so in year_t_. Similarly, exit equals<br>in year_t-1_and continues to do so in yea|ets, clustered a<br>ent variables ar<br>destination_d_in<br>one if firm_i_doe<br>r_t_.|t the exportin<br>e defined as f<br>year_t_but did<br>s not export p|g country-imp<br>ollows: export<br>not do so in ye<br>roduct_k_to des|orting country-<br>participation e<br>ar_t-1_and zero<br>tination_d_in ye|product-year l<br>quals one if th<br>if the firm did<br>ar_t_but did so i|evel. ***, **,<br>ere is positive<br>not export pro<br>n year_t-1_and|and * indicate<br>trade between<br>duct_k_to destin<br>zero if the firm|significance<br>countries and<br>ation_d_in year<br>exported pro|at 1%, 5%, and 10%<br>zero otherwise; entry<br> _t-1_and does not start<br>duct_k_to destination_d_|

39
# **<u>Table 12. Results from Robustness Checks – Alternative Fixed Effects</u>**

|||||||Dependen|t Variable:||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Log Export|Log Export|Log Export|Export|Export|Export|Export|<br>Export|<br>Export|Export|<br>Export|<br>Export|
||Valuec,i,k,d,t<br>|Valuec,i,k,d,t<br>|Valuec,i,k,d,t<br>|Participation<br>Dummyc,i,k,d,t<br>|<br> <br>Participation<br>Dummyc,i,k,d,t<br>|<br>Participation<br>Dummyc,i,k,d,t<br>|<br> <br>Entry<br>Dummyc,i,k,d,t<br>|<br>Entry<br>Dummyc,i,k,d,t<br>|<br>Entry<br>Dummyc,i,k,d,t<br>|Exit<br>Dummyc,i,k,d,t<br>|<br> <br>Exit<br>Dummyc,i,k,d,<br>|<br>t<br>Exit<br>Dummyc,i,k,d,t<br>|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Rel_restrictivenessc,k,d,t|‐0.155<br>[0.071]**|‐0.158<br>[0.125]|‐0.382<br>[0.080]***|‐0.030<br>[0.008]***|‐0.068<br>[0.013]***|‐0.178<br>[0.009]***|‐0.042<br>[0.029]|‐0.272<br>[0.043]***|‐0.177<br>[0.031]***|0.255<br>[0.098]***|0.200<br>[0.115]*|0.328<br>[0.105]***|
|Log Agro_exportsc,i, t‐1|||||||0.039<br>[0.001]***|0.041<br>[0.001]***|0.039<br>[0.001]***|‐0.041<br>[0.003]***|‐0.037<br>[0.003]***|‐0.045<br>[0.003]***|
|Rel_restrictivenessc,k,d,tX|||||||||||||
|Log Agro_exportsc,i,t‐1|||||||0.003|0.008|0.001|‐0.021|‐0.017|‐0.023|
||||||||[0.002]*|[0.002]***|[0.002]|[0.007]***|[0.008]**|[0.007]***|
|Log 1+Tariffc,k,d,t|‐0.027|‐0.003|‐0.089|‐0.009|0.003|‐0.054|‐0.017|‐0.002|‐0.038|‐0.004|‐0.004|0.027|
||[0.019]|[0.039]|[0.017]***|[0.002]***|[0.005]|[0.002]***|[0.005]***|[0.013]|[0.004]***|[0.008]|[0.020]|[0.007]***|
|Exporting Firm‐Importing|||||||||||||
|Country‐Product Fixed|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Effects|||||||||||||
|Exporting Country‐Year,|||||||||||||
|Importing Country Year,<br>and Product‐Year Fixed|Yes|No|No|Yes|No|No|Yes|No|No|Yes|No|No|
|Importing Country‐|||||||||||||
|Product‐Year Fixed|No|Yes|No|No|Yes|No|No|Yes|No|No|Yes|No|
|Effects|||||||||||||
|Exporting Country‐|||||||||||||
|Product‐Year Fixed|No|No|Yes|No|No|Yes|No|No|Yes|No|No|Yes|
|Effects|||||||||||||
|||||Linear|Linear|Linear|Linear|Linear|Linear|Linear|Linear|Linear|
|Estimation method|OLS|OLS|OLS|Probability|Probability|Probability|Probability|Probability|Probability|Probability|Probability|<br>Probability|
|||||Model|Model|Model|Model|Model|Model|Model|Model|Model|
|Observations|261,661|261,661|261,661|827,878|827,878|827,878|282,957|282,957|282,957|216,938|216,938|216,938|
|R‐squared|0.949|0.956|0.951|0.365|0.408|0.375|0.417|0.514|0.445|0.759|0.805|0.763|

Notes: Robust standard errors in brackets, clustered at the exporting country-importing country-product-year level. ***, **, and * indicate significance at 1%, 5%, and 10% confidence levels, respectively. Dependent variables are defined as follows: export participation equals one if there is positive trade between countries and zero otherwise; entry equals one if firm _i_ exports product _k_ to destination _d_ in year _t_ but did not do so in year _t-1_ and zero if the firm did not export product _k_ to destination _d_ in year _t-1_ and does not start to do so in year _t_ . Similarly, exit equals one if firm _i_ does not export product _k_ to destination _d_ in year _t_ but did so in year _t-1_ and zero if the firm exported product _k_ to destination _d_ in year _t-1_ and continues to do so in year _t_ .

40