metadata: authors: 0: author: Rosenow,Samuel Kaspar
count: Colombia
docna: 0: docna: Barrier or Opportunity ? How Trade
            Regulations Shape Colombian Firms’ Export Strategies
docty: Policy Research Working Paper
abstracts: cdata!: Firms increasingly must contend with
            trade regulations to access foreign markets. This paper
            quantifies the relative importance of trade regulations and
            their heterogeneous effects for Colombian firms exporting to
            Latin America between 2007 and 2017, focusing on specific
            types and channels. Using panel evidence from a firm-level
            gravity model with a difference-in-differences
            identification strategy, technical barriers to trade and
            quantity control measures both decrease trade on average.
            Other non-tariff measures and tariffs play a minor role. The
            technical barriers to trade and quantity measures reallocate
            trade from small to big firms. The same mechanism benefits
            firms participating in global value chains. However,
            quantity controls make it more likely that big firms will
            leave export markets to the benefit of smaller ones. The
            results control for the endogeneity of trade regulations and
            are robust to the use of different samples and measures of
            firm size.
display_title: Barrier or Opportunity ? How Trade
            Regulations Shape Colombian Firms’ Export Strategies
listing_relative_url: /research/2024/06/34336029/barrier-or-opportunity-trade-regulations-shape-colombian-firms’-export-strategies
topicv3: Trade Regulation m1326296 1015,Trade Competitiveness and Diversification
            m1326471 765,Non-Tariff Measures m1326926 778,Trade and Firms Entry and Exit m1327644 1193
publishtoextweb_dt: 2024-06-06T00:00:00Z
dois: 10.1596/1813-9450-10795
no_of_pages: 52
author_keys: 0: author_key: 1318546
model_extractions: Policy Research Working Paper 10795

# Barrier or Opportunity?

How Trade Regulations Shape Colombian Firms’ Export Strategies _Samuel Rosenow_ International Finance Corporation June 2024
Policy Research Working Paper 10795

# **Abstract**

Firms increasingly must contend with trade regulations to access foreign markets. This paper quantifies the relative importance of trade regulations and their heterogeneous effects for Colombian firms exporting to Latin America between 2007 and 2017, focusing on specific types and channels. Using panel evidence from a firm-level gravity model with a difference-in-differences identification strategy, technical barriers to trade and quantity control measures both decrease trade on average. Other non-tariff measures and tariffs play a minor role. The technical barriers to trade and quantity measures reallocate trade from small to big firms. The same mechanism benefits firms participating in global value chains. However, quantity controls make it more likely that big firms will leave export markets to the benefit of smaller ones. The results control for the endogeneity of trade regulations and are robust to the use of different samples and measures of firm size.

This paper is a product of the International Finance Corporation. It is part of a larger effort by the World Bank Group to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at srosenow@ifc.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
# Barrier or Opportunity? How Trade Regulations Shape Colombian Firms’ Export Strategies<sup>*</sup>

## Samuel Rosenow

_International Finance Corporation, World Bank Group World Trade Institute, University of Bern_

**JEL Classification** : F13, F14, L11, O19.

**Keywords** : Trade Policy, Colombia, Non-Tariff Measures, Firm Heterogeneity

*I thank Angella Montfaucin, Ana Fernandes, Miriam Manchin, Joseph Francois, Mahdi Ghodsi, Massimiliano Cali, Ralf Peters, Doug Nelson and participants in the Galbino project summer school for their comments. The findings, interpretations, and conclusions expressed in this paper are entirely those of the author. They do not necessarily represent the views of the World Bank or its affiliated organizations, or those of the Executive Directors of the World Bank, their Managements, or the governments they represent. All errors are mine.
# **1 Introduction**

Market access conditions attract policy and academic attention.<sup>1</sup> While tariffs have declined over time, trade regulations matter increasingly for firms wishing to access international markets (UNCTAD and World Bank, 2019). Governments impose these regulations to establish the standards and procedures a product must comply with to be sold in their market. Labeling requirements and safety certification are telling examples, assuring safety and product quality for consumers and encouraging trade. However, trade regulations can also increase exporters’ compliance and thus production costs since additional investment in technology and processes may be required. This implies that trade regulations may both reduce supply and increase demand for tradables.

Firms also face uneven compliance costs of trade regulations, mostly due to market entry costs (Bernard et al., 2012; Melitz and Redding, 2015).2 Fixed market entry costs imply that the most productive companies are most likely to enter and thrive in export markets (Melitz, 2003; Bernard et al., 2011). They are best equipped to deal with diverse regulations, stan dards, testing, and certification procedures across sales markets. Taken together, the overall impact of trade regulations on trade and its distribution across firms is uncertain.

1In the academic literature, market access is commonly associated with export diversification and firm competitiveness, important precursors to economic development. For references regarding the relationship between market access and export diversification, see, for example, Fugazza and Nicita (2013). For effects of market access for firm capabilities, see Falciola et al. (2020). For important reviews of this policy debate, see Nicita and Melo (2018).

2In general, firms face three types of costs. First, firms incur product-specific production costs that reflect their capabilities. Second, firms incur variable trade costs: shipping costs in the form of iceberg trade costs and ad-valorem tariffs. These costs vary with sales, but do not depend on the exporter’s scope. Both production costs and variable trade costs deter exports at all margins. Third, firms incur fixed exporting costs by product and destination market, reflecting the compliance costs of trade regulations. Thus, the market access cost schedule varies by firm-product-destination.

2
This paper quantifies the heterogeneous effects of Latin America’s trade regulations for Colombia’s exporting firms, concentrating on specific types and channels through which they operate: firms’ value of exports and their probability of participating in and exiting from export markets. There is a focus on Latin America and the Caribbean (LAC) as the region has become Colombia’s most important export market. This, coupled with the unique data of trade regulations and other non-tariff measures (NTMs) in LAC, provides an ideal setup.

We use a firm-level gravity model with a difference-in-differences research design that identifies heterogeneity in treatments across NTM types and channels. Our econometric approach accounts for selection and omitted variable bias as well as endogeneity in the timing of NTMs with respect to trade flows into destination markets. Moreover, we test for the robustness of results with stringent fixed effects, different measures of firm size and alternative samples to rule out endogeneity concerns.

The impact of trade regulations varies greatly across types and channels. Of all NTM types considered, only technical barriers to trade (TBTs) — a standard-like measure — and quantity controls explain changes in Colombian firm-level exports. On average, TBTs undermine Colombia’s firmlevel exports behavior across all three channels considered. These results are consistent with a model in which trade regulations increase firms’ compliance costs relative to consumer benefits. The effect is even more pronounced for quantity control measures which undermine Colombian firms’ exports and their likelihood of remaining in markets. Intriguingly, new quantity controls in product-destination markets _lower,_ on average, firms’ exit rates.

Looking at the heterogeneous effects of NTMs, we show that this effect is driven by small firms that benefit from quantity controls. Conversely, big firms are more likely to leave export markets when faced with new quantity controls. We argue that quantity control measures constrain big firms 3
more than small firms and we help to explain this outcome. However, a regressive effect is at play for TBTs for all three channels; big firms benefit from a dampened effect, whereas small firms see their exports and likelihood of remaining in markets significantly reduced. Similarly, firms participating in global value chains (GVC) benefit from a muted trade impact when TBTs or quantity controls are introduced in the sales market. In contrast, non-GVC firms suffer from reduced exports and likelihood of participating in markets.

Our paper improves the understanding of the role of NTMs for firmlevel trade along three dimensions. First, we quantify the relative importance of NTM types for firm-level export decisions. Some NTM types are by definition trade restrictive, such as import prohibitions and quotas. In contrast, the effect of standard-like measures such as sanitary and phytosanitary (SPS) or TBTs on export dynamics depends on compliance costs relative to information benefits and is, in theory, ambiguous.<sup>3</sup> We demonstrate that TBTs and quantity control measures, on average, deter Colombian exports. Our findings are consistent with Adarov and Ghodsi (2023), Disdier et al. (2008), Yousefi and Liu (2013), and Li and Beghin (2012) who find negative TBT effects on trade flows.

Second, we explore the channels through which NTM effects operate. To that end, we distinguish three firm-level export decisions: the value of exports to product-destination markets (intensive margin); the probability of participating in them (extensive margin I) and exit from them (extensive margin II). While we document that TBTs and quantity control measures undermine all three margins, firms mostly react by reducing existing exports. This finding challenges Bao and Qiu (2012) who highlight positive firm-level responses at the intensive margin. At the same time, we discover that firms react to additional types of trade regulations at the extensive margin. In addition to TBTs and quantity controls, increases in tariffs and SPS measures in product-destinations make it less likely that Colombian firms 3See Ronen (2017) for the intuition on trade promoting effects of NTMs. Beghin et al. (2015) find that almost 40% of product lines affected by TBT measures yield negative NTM ad valorem equivalents (AVEs), suggesting a net trade-promoting effect of these measures.

4
participate in these markets. This pattern could reflect exporters’ inability to pay the additional compliance costs and trade diversion to less costly product-destination markets. Our findings are consistent with firm-level evidence from France (Fontagné et al., 2015; Fontagné and Orefice, 2018).

Third, we extend the analysis of heterogeneity in the effects of NTM types across firm characteristics. Our results align with the predominant finding of the empirical literature, that small firms suffer disproportionate impacts of TBTs (e.g., Arkolakis, 2010; Curzi, 2020; Asprilla et al., 2019; Macedoni and Weinberger, 2022). Moreover, we show that GVC firms, defined as those that both import and export, benefit from the same muted trade effects of TBTs as big firms, even though the two firm characteristics differ substantially in definition. However, we also marshal novel evidence that quantity control measures make it less likely that small firms will exit export markets, with the opposite effect occurring for big firms. We argue that the quantity controls are more binding for big firms, increasing their likelihood of leaving markets and, conversely, helping small firms take over their space.

Superior data and methods underpin these three contributions. We combine a panel of Colombian firm-level trade data from 2007 to 2017 with time-varying information on NTMs in regional export markets. Employing a firm-level gravity model with a difference-in-differences research design, we estimate the relative importance of NTM types controlling for unobserved confounders at the firm-product-destination level. In doing so, we address empirical limitations in the literature. This concerns, on the one hand, the use of cross-sectional data to estimate NTM impacts (e.g., Bratt, 2017; Fugazza et al., 2018). On the other hand, many studies evaluate NTMs in isolation or bundled (Disdier and Marette, 2010 or Fontagné et al., 2015), not their joint or relative effects. Moreover, drawing from Kee and Nicita (2022), we also account for the endogeneity of NTMs by predicting NTM selection based on that in neighboring countries. This approach is suitable in our context, given the cultural and legal proximity of Colombia’s regional destination markets and their similarity in introducing trade regulations.

5
The paper is structured as follows. Section 2 explains our data sources and procedures to clean them. Section 3 documents stylized facts about Colombia’s exporting firms and the market access conditions they face. Section 4 explains our empirical identification strategy, defines key variables, and addresses concerns to identify how market access conditions affect Colombian firms trading with the region. The results and robustness tests are presented in Section 5. Sections 5.5 and 5.6 explore heterogeneous NTM effects based on firms’ size and participation in global value chains, respectively. Finally, Section 6 concludes and discusses the implications of our results.

# **2 Data**

The empirical investigation is based on three distinct datasets, all covering the period 2007–2017. The first dataset contains information on export and import transactions collected by the Colombian customs. The second provides data on NTM types applied by Latin American importing countries. The third contains bilateral applied ad-valorem tariffs of Latin American importing countries.

## **2.1 Firm-Level Customs Data**

We use transaction-level export and import data from Colombia’s customs agency DIAN. The data is part of the expansion to the Exporter Dynamics Database, as described in Fernandes et al. (2016). The data cover the universe of exporting firms in all sectors at the exporter-HS 6-digit productdestination-year level and includes seven variables: country of origin, exporting firm identifier, country of destination, HS 6-digit product, export value, export quantity, and year. Information on export and import values is expressed in US dollars and is FOB (free on board). We processed the raw dataset to a series of cleaning procedures, as detailed in Cebeci and Fernandes (2015).

To merge with the tariff variable described below, we convert the HS 6
product nomenclatures of 2007, 2012, and 2017 to the HS combined version. Moreover, we exclude all firm observations from the mining sector (HS chapter 25–27). That helps to avoid potential bias from Latin America’s commodity price cycles during 2007–2017. Thus, our sample frame contains all Colombian exporting firms between 2007 and 2017, except those from the mining sector.

# **2.2 NTM Data**

We use NTM data from UNCTAD and the Latin American Integration Association (LAIA) for its 18 core members in LAC between 2007 and 2017.<sup>4</sup> The raw data is recorded at the reporter-national tariff line leveldestination-NTM, 4-digit, and year level. We apply two steps to process the data, resulting in a novel and unexploited dataset comprising an exhaustive and updated set of trade regulations in Latin America between 2007 and 2017.

First, given a change in the classification of NTMs in 2012 we reconcile two NTM classifications pertaining to the periods 2007–2011 and 2012–2017. To that end, we derive a correspondence table between the pre-2012 and post-2012 classification at the 1-digit NTM chapter level using LAIA’s data collection in both classifications in 2011 and 2012. Using the new classification as the reference, we reclassified NTMs between 2007 and 2011 at the chapter level, consistent with UNCTAD’s (2019) Mast Classification M5. As a result, we observe the number of NTMs of each type (e.g. SPS, TBT, PSI, quantity control) for all Latin American reporter-destinationproducts continuously in each year between 2007 and 2017.

Second, we aggregate NTM data from the national tariff line level to the HS 6-digit product level. The background is that NTM data are collected at the national tariff line at 10 digits. However, since we take into account exports to various destination countries that do not harmonize national tariff line classifications, our two datasets cannot be satisfactorily merged 4Argentina, Bolivia, Brazil, Chile, Costa Rica, Colombia, Cuba, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, Panama, Peru, Paraguay, Uruguay and the República Bolivariana de Venezuela.

7
at that level. Aggregation results in little attrition. Moving from 10- to 6-digit product classification implies a reduction of about 6% in the number of observations included in our reference sample.

## **2.3 Tariff Data**

Ad-valorem tariffs for Colombia’s 17 regional trading partners come from UNCTAD TRAINS. Measured at the importer-HS 6-digit product level between 2007 and 2017, tariffs reflect the effectively applied rate, which is defined as the lowest available tariff. If a preferential tariff exists, it is used as the effectively applied tariff. Otherwise, the Most-Favored-Nation (MFN) applied tariff is used.

Thus, while we measure tariffs in terms of ad valorem price effects, NTMs are measured in count terms. The reason is that bilateral, product-specific ad valorem equivalents (AVEs) of NTMs remain scarce, despite recent progress for standard-like NTMs (Adarov and Ghodsi, 2023).

# **3 Stylized Facts**

Before diving into our empirical exercise, we present a series of stylized facts on Colombian exporting firms as well as the tariffs and NTMs they face in their regional sales markets.

## **3.1 Colombian Firm-Level Exports**

Table 1 shows the evolution of Colombian exports between 2007 and 2017 and their geographical composition in eight major regions, following the World Bank classification. Three results stand out. First, Colombian exports reached a peak in 2012 (first row) and declined from then until 2016. This decline was driven by the collapse of oil prices, which matter for 35% of Colombian exports related to minerals. Second, Latin American and Caribbean (LAC) countries have become the most important destination market for Colombian exports, surpassing North America, and, in particular, the United States, since 2012. Given the importance of regional exports 8
for Colombian firms, this provides us with sufficient external validity to explore their trade response to market access conditions in LAC. Third, the importance of East Asian and Pacific destination markets for Colombian exporters has doubled between 2007 and 2017 to 10% of total exports now.

Table 1: Colombian exports (2007 base) and selected destinations

|Destination|**2007**|**2008**|**2009**|**2010**|**2011**|**2012**|**2013**|**2014**|**2015**|**2016**|**2017**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|World|1.00|1.25|1.10|1.36|1.97|2.08|2.00|1.85|1.22|1.04|1.27|
|East Asia & Pacific|0.05|0.04|0.05|0.08|0.06|0.09|0.11|0.13|0.11|0.08|0.10|
|EasternEurope|0.01|0.01|0.01|0.01|0.00|0.00|0.00|0.00|0.01|0.01|0.01|
|Latin America & Caribbean|0.37|0.39|0.31|0.29|0.33|0.32|0.32|0.31|0.36|0.34|0.37|
|Middle East & N. Africa|0.01|0.01|0.01|0.01|0.01|0.01|0.01|0.01|0.01|0.01|0.01|
|North America|0.37|0.40|0.41|0.44|0.40|0.37|0.33|0.28|0.29|0.35|0.30|
|SouthAsia|0.00|0.00|0.02|0.02|0.01|0.02|0.05|0.05|0.02|0.01|0.01|
|Sub-Saharan Africa|0.00|0.00|0.00|0.01|0.01|0.01|0.00|0.01|0.01|0.01|0.00|
|Western Europe|0.19|0.16|0.19|0.15|0.18|0.17|0.18|0.20|0.20|0.20|0.19|

Source: Author’s calculation based on the original Colombian customs data. Note: This table displays Colombian exports as a share of total exports and is normalized in 2007.

Since NTM data are only available for LAC countries, the focus of the paper is on Colombian exports to LAC countries. Thus, the following tables explore how export margins have evolved, taking LAC countries as the destination. Table 2 reveals that the number of firms exporting to LAC countries followed a U-shaped pattern over the period under investigation. The number of firms decreased by about 10% during the global financial crisis of 2007–2010 and then steadily increased to reach a new peak in 2017. In relative terms, however, the importance of LAC firms remained stable at around 68% of all exporting firms over the whole period. Moreover, the importance of firms only exporting to LAC destination markets remained stable between 2007– 17. Indeed, both the number and the corresponding share of firms exporting to LAC countries exclusively wavered at around 3,500 firms or 44% of all exporting firms in Colombia during the whole period, respectively.

Looking at the product diversification of Colombian firms yields three insights. First, multi-product firms dominate Colombian exporters. They make up 91% of all exporting firms, a share which remained remarkably stable between 2007–17. Second, Table 3 reveals that Colombian exporters increased the diversification of their product portfolio from an average of 9
Table 2: Number of exporting firms and destination markets

|Destination|**2007**|**2008**|**2009**|**2010**|**2011**|**2012**|**2013**|**2014**|**2015**|**2016**|**2017**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Number of exporting firms to LAC|5,615|5,815|5,696|5,098|5,255|5,520|5,508|5,450|5,604|5,612|5,738|
|Number of exporting firms to LAC only|3,383 3|,713|3,669|3,114|3,286|3,526|3,535|3,462|3,518|3,523|3,621|
|Share of LAC firms in total firms|0.68|0.69|0.70|0.69|0.71|0.73|0.71|0.70|0.69|0.68|0.68|
|Share of LAC onlyfirms in total firms|0.41|0.44|0.45|0.42|0.44|0.46|0.46|0.45|0.43|0.43|0.43|

Source: Author’s calculation based on Colombian customs data.

Note: This table displays Colombian firm-level export dynamics to Latin America and the Caribbean (LAC), measured in absolute (number of firms) and relative terms (share in total number of exporting firms). The third and fourth rows report the number and the share of firms among those exporting to some LAC country that exports exclusively to the region.

19 HS 6-digit products in 2007 to 26 products in 2017. Third, the increase in product diversification is driven by the expansion of regional export relationships, rather than extraregional ones. Put in perspective, Colombian exporting firms show a relatively high level of product diversification; Peruvian firms, for example, only export, on average, 8.5 HS 6-digit products (Fugazza et al., 2018).

Table 3: Number of exported products per firm

||Me|an<br>|Med|ian<br>|Maxi|mum<br>|
|---|---|---|---|---|---|---|
||LAC|All|LAC|All|LAC|All|
|2007|19.5|17.4|10.0|9.0|128|131|
|2008|20.5|18.3|10.0|9.0|133|133|
|2009|22.8|20.1|11.0|10.0|180|181|
|2010|23.3|20.8|11.0|10.0|175|175|
|2011|26.0|22.9|12.0|11.0|193|195|
|2012|27.0|23.7|13.0|11.0|202|204|
|2013|29.0|25.7|13.0|11.0|207|209|
|2014|29.4|26.1|13.0|12.0|202|204|
|2015|29.6|26.0|13.0|11.0|207|208|
|2016|28.6|25.4|13.0|11.0|182|186|
|2017|26.6|23.6|12.0|11.0|153|155|

Source: Author’s calculation based on Colombian customs data. Note: This table reports the number of exported HS 6-digits products by Colombian firms over time and by LAC and world destination markets.

Colombian exporting firms are increasingly diversified in their sales markets. Table 4 shows that the average share of the sales market grew from 6.4 in 2007 to 7 in 2017. What is more, Colombian firms export to an additional five destination markets beyond the region, resulting in a total of 12 destination markets globally (columns 2 and 3).

10
Table 4: Number of destinations per firm

||Me|an|Med|ian|Maxi|mum|
|---|---|---|---|---|---|---|
||LAC|All|LAC|All|LAC|All|
|2007|6.4|10.1|6.0|8.0|16|45|
|2008|6.5|10.3|6.0|8.0|16|48|
|2009|6.6|10.7|6.0|9.0|16|48|
|2010|6.7|11.2|7.0|9.0|17|51|
|2011|6.8|11.5|7.0|9.0|17|51|
|2012|6.9|11.6|7.0|9.0|17|49|
|2013|7.0|12.0|7.0|9.0|17|51|
|2014|7.0|12.3|7.0|9.0|17|61|
|2015|7.0|11.9|7.0|9.0|17|61|
|2016|7.0|11.9|7.0|9.0|17|58|
|2017|6.9|11.5|7.0|9.0|16|60|

Source: Author’s calculation based on Colombian customs data. Note: This table reports the number of destination markets by Colombian exporters over time.

# **3.2 Tariffs in LAC**

Colombian exporters saw declining tariffs in their regional destination markets between 2007 and 2017. The applied tariff decreased steadily from 6.7% in 2007 to only 4.1% in 2017, as seen in Table 5, as Colombia ratified numerous preferential trade agreements with regional partners over the period under investigation.<sup>5</sup> This is most evident for 2017 when tariff reductions of the Pacific Alliance between Colombia and Mexico, Peru and Chile, came into effect. At the same time, Most-Favored-Nation (MFN) tariffs negotiated at the World Trade Organization (WTO) declined only marginally from 9.1% in 2007 to 8.7% in 2017, reflecting the persistent deadlock in multilateral trade negotiations.

# **3.3 Non-Tariff Measures in Latin America**

The NTM dataset contains 6,211 regulations for Colombia’s 17 destination markets in LAC between 2007 and 2017. To study the variation of regu-

> 5The Pacific Alliance, signed in 2013 between Colombia and Mexico, Peru and Chile, reduced 92% of tariffs between members in 2016. Moreover, the free trade agreement (FTA) between Colombia and Costa Rica entered into force on August 1, 2016. As a result, 74% of industrial products became duty-free.

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Table 5: Simple average tariff (applied and MFN) to Colombian exports in Latin American countries, 2007–2017

|**Applying country**|**Tariff**|**2007**|**2008**|**2009**|**2010**|**2011**|**2012**|**2013**|**2014**|**2015**|**2016**|**2017**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|ARG|Applied|4.5|3.6|3.4|2.5|2.0|1.4|0.8|1.2|1.1|1.1|1.0|
||MFN|11.1|11.0|12.3|12.3|12.3|12.3|13.2|13.4|13.4|13.4|13.5|
|BOL|Applied|7.9|7.9|9.6|10.3|10.3|10.3|10.6|10.6|10.6|10.6|10.5|
||MFN|8.7|8.7|10.4|11.1|11.1|11.1|11.5|11.4|11.4|11.5|11.7|
|BRA|Applied|4.0|3.0|2.3|1.5|0.7|2.5|13.0|2.0|2.0|2.1|2.1|
||MFN|12.1|13.0|13.3|13.4|13.4|13.4|13.4|13.5|13.5|13.5|13.5|
|CHL|Applied|5.5|5.4|5.5|5.4|5.3|5.3|5.9|5.9|0.6|5.9|0.7|
||MFN|6.0|6.0|6.0|6.0|6.0|6.0|6.0|6.0|6.0|6.0|6.0|
|CRI|Applied|6.1|6.6|5.7|5.7|5.6|5.7|5.7|5.7|5.7|5.7|5.7|
||MFN|6.1|6.6|5.7|5.7|5.6|5.7|5.7|5.7|5.7|5.7|5.7|
|CUB|Applied|10.6|10.6|10.6|10.6|10.6|10.6|10.2|10.2|10.2|10.2|10.2|
||MFN|10.6|10.6|10.6|10.6|10.6|10.6|10.2|10.2|10.2|10.2|10.2|
|ECU|Applied|11.4|11.2|11.2|11.7|10.2|10.2|10.2|10.5|10.7|11.0|11.0|
||MFN|12.2|11.9|11.8|11.8|10.8|10.8|10.8|11.1|11.4|11.8|11.8|
|GTM|Applied|5.5|5.5|5.5|4.5|4.2|4.0|3.7|3.4|3.3|3.3|3.3|
||MFN|5.8|5.8|5.8|5.8|5.8|5.8|5.7|5.7|5.7|5.7|5.7|
|HND|Applied|5.9|5.8|5.9|5.9|5.9|5.8|5.8|5.8|2.1|2.1|2.1|
||MFN|5.9|5.9|5.9|5.9|5.9|5.8|5.8|5.8|5.8|5.8|5.8|
|MEX|Applied|2.6|2.4|2.3|8.9|8.2|7.8|7.5|1.6|6.9|6.8|0.8|
||MFN|12.9|12.8|11.8|9.8|9.1|8.7|8.3|8.1|7.8|7.7|7.6|
|NIC|Applied|5.9|5.8|5.8|5.9|5.8|5.8|5.8|5.8|5.8|5.8|5.8|
||MFN|5.9|5.8|5.8|5.9|5.8|5.8|5.8|5.8|5.8|5.8|5.8|
|PAN|Applied|7.5|7.5|7.4|7.2|7.1|7.1|7.0|7.0|7.0|7.0|7.0|
||MFN|7.5|7.5|7.4|7.2|7.1|7.1|7.0|7.0|7.0|7.0|7.0|
|PER|Applied|9.3|6.1|5.5|5.5|4.0|4.0|3.8|3.8|3.0|3.0|0.9|
||MFN|9.4|6.1|5.5|5.5|4.1|4.1|3.9|3.9|3.0|3.0|3.0|
|PRY|Applied|6.3|9.5|4.8|4.2|3.6|3.0|2.2|1.7|1.1|1.0|0.9|
||MFN|10.3|10.3|10.3|10.3|10.3|10.3|10.2|10.1|10.1|10.1|10.0|
|SLV|Applied|5.7|5.8|5.7|5.8|5.8|4.5|4.2|3.9|3.8|3.8|3.4|
||MFN|6.1|6.2|6.1|6.1|6.1|6.1|6.1|6.1|6.1|6.1|6.1|
|URY|Applied|4.8|4.2|3.6|3.0|2.5|2.0|1.5|1.1|0.8|0.7|0.7|
||MFN|10.5|10.5|10.5|10.5|10.5|10.5|10.6|10.6|10.6|10.5|10.5|
|VEN|Applied|10.8|10.9|10.9|10.7|10.9|10.9|10.9|7.5|4.1|3.9|4.0|
||MFN|13.3|13.4|13.4|13.2|13.4|13.4|13.4|13.0|13.1|12.8|13.9|
|LAC average|Applied|6.7|6.6|6.2|6.4|6.0|5.9|6.4|5.2|4.6|4.9|4.1|
||MFN|9.1|8.9|9.0|8.9|8.7|8.7|8.7|8.7|8.6|8.6|8.7|

Source: Author’s calculation based on UNCTAD TRAINS tariff data. Note: This table displays Colombia’s most-favored nation (MFN) and applied tariffs over time in its LAC destinations markets. MFN tariffs measure the normal non-discriminatory tariffs charged on imports from Colombia. Applied refers to the effectively applied tariff, which is defined as the lowest available tariff. If a preferential tariff exists, it will be used as the effectively applied tariff. Otherwise, the MFN applied tariff will be used. The LAC average reports Colombia’s simple average of MFN and applied import tariff rates across the 17 LAC destination markets, respectively.

lations over time, we document the share of all regulations entered before vs. after 2007, the first year of data collection, and the share remained active vs. abolished in 2017, the last year of data collection. This leaves us with four margins of variation, of which all but one – regulations entered prior to 2007 that remained active in 2017 – can be exploited in our empirical strategy. Of the 6,211 regulations, 17 (0.3%) were implemented prior 12
to 2007 and abolished before 2017; 2,367 (38.1%) regulations were implemented before 2007 and we still in place in 2017; 84 (1.4%) regulations were introduced and then abolished between 2007 and 2017; and 3,743 (60.3%) regulations were introduced between 2007 and 2017 and remained active in 2017. Table 6 reports the corresponding figures for each LAC member. Ecuador and Panama stand out in the region as they introduced more than 83% of regulations between 2007 and 2017, far above the region’s average. In sum, this means that the majority of applicable regulations in 2017 were implemented after 2007. This provides us with sufficient variation to estimate the impact of NTMs on Colombian exports using an identification strategy based on the introduction and withdrawal of NTMs.

<u>Table 6: NTM turnover</u>

||Entry before<br>|2007<br>|Entry after<br>|2007<br>|
|---|---|---|---|---|
||Abolished before 2017|Active in 2017|Abolished before 2017|Active in 2017|
|ARG|1.3|65.5|3.4|29.8|
|BOL|0.0|74.4|0.0|25.6|
|BRA|0.3|39.2|3.0|57.6|
|CHL|0.2|52.9|0.6|46.2|
|CRI|0.0|27.3|0.3|72.4|
|CUB|0.0|66.7|0.0|33.3|
|ECU|0.1|10.9|2.0|87.0|
|GTM|0.0|62.9|0.0|37.1|
|HND|0.0|49.3|0.0|50.7|
|MEX|1.7|40.5|2.7|55.1|
|NIC|0.0|39.9|0.0|60.1|
|PAN|0.0|15.1|1.0|83.9|
|PER|0.0|38.6|0.0|61.4|
|PRY|0.0|56.3|0.0|43.7|
|SLV|0.0|61.6|0.0|38.4|
|URY|0.0|43.2|0.3|56.5|
|VEN|0.0|59.7|1.1|39.2|
|Total|0.3|38.1|1.4|60.3|

Source: Author’s calculation based on UNCTAD’s NTM data. Note: This table reports the turnover of NTMs for each of the 17 LAC countries. The row “Total” reports the average of the total NTM turnover in all 17 LAC countries.

Next, we study the margin with the greatest importance: regulations introduced between 2007 and 2017 and active in 2017. There the composition of NTMs is heavily skewed toward technical measures: SPS and TBTs make up 40% and 52%, respectively, of all new regulations introduced be13
tween 2007 and 2017. Table 7 shows the number of active regulations in 2017 by country and NTM-type and its share implemented after 2007, the first year of NTM data. The majority of NTMs reported in 2017 are implemented after 2007. For the whole sample, this is the case for 53% of SPS measures, 48% of TBTs and 59% of quantity control measures. Costa Rica and Panama stand out in particular as they have implemented more than 86% of their SPS measures and TBTs since 2007. Given the prominence of technical measures, the empirical section will focus on the effect of SPS, TBTs, quantity control and PSIs measures.

Table 7: Regulations implemented since 2007 and effective in 2017

||SPS|measures||TBTs|Pre-shi|pment Inspections|Quantit|y control measures||Other|
|---|---|---|---|---|---|---|---|---|---|---|
||2017|Since 2007|2017|Since 2007|2017|Since 2007|2017|Since 2007|2017|Since 2007|
|ARG|143|41%|265|26%|9|22%|10|40%|18|28%|
|BOL|31|23%|37|19%|1|0%|5|60%|8|50%|
|BRA|199|54%|491|63%|3|67%|36|50%|7|29%|
|CHL|260|45%|194|49%|5|40%|4|50%|13|31%|
|CRI|163|86%|115|56%|0|0%|8|75%|6|33%|
|CUB|24|50%|40|25%|1|0%|6|33%|1|0%|
|ECU|150|73%|802|92%|6|67%|24|83%|6|67%|
|GTM|75|20%|71|56%|0|0%|8|50%|5|0%|
|HND|84|50%|52|50%|0|0%|0|0%|4|75%|
|MEX|134|45%|236|64%|3|33%|18|78%|3|0%|
|NIC|146|61%|258|57%|0|0%|22|91%|5|80%|
|PAN|539|95%|128|47%|11|100%|8|75%|8|0%|
|PER|231|72%|92|39%|3|33%|1|0%|10|30%|
|PRY|27|22%|74|49%|3|33%|15|73%|7|14%|
|SLV|18|78%|221|33%|0|0%|5|100%|6|50%|
|URY|114|60%|174|55%|6|33%|12|58%|8|62%|
|VEN|58|33%|91|40%|3|33%|10|80%|12|42%|
|Total|2,396|53%|3,341|48%|54|27%|192|59%|127|35%|

Source: Author’s calculation based on UNCTAD’s NTM data. Note: This table reports the number of active regulations in 2017 by country and NTM type.

In addition to the number of active regulations and the relative importance of each NTM-type, another way to measure the incidence of NTMs is through their product coverage. Table 8 reports the number and share of products affected by at least one active NTM in 2017. Cuba and Argentina exhibit the highest product coverage: 98% of products that could be produced and imported at the HS 6-digit level are affected by at least one technical measure (SPS, TBT or PSI) while non-technical measures, such as price and quantity restrictions, affect at least 96% of imported products. At the other extreme, Paraguay has the lowest product coverage with 9% of products affected by at least one technical measure and less than 14
## 6% by non-technical measures.

Table 8: Number (share) of products affected by at least one active NTM in 2017

||Technical|Non-technical|
|---|---|---|
|ARG|4,479 (81)|5,205 (95)|
|BOL|1,930 (35)|945 (17)|
|BRA|3,960 (72)|1,221 (22)|
|CHL|3,309 (60)|107 (2)|
|COL|2,963 (54)|3,254 (59)|
|CRI|1,777 (32)|322 (6)|
|CUB|5,363 (98)|5,362 (97)|
|ECU|2,937 (53)|2,198 (40)|
|GTM|1,233 (22)|54 (1)|
|HND|1,633 (30)|588 (11)|
|MEX|1,848 (34)|837 (15)|
|NIC|1,898 (35)|752 (14)|
|PAN|1,717 (31)|111 (2)|
|PER|2,045 (37)|84 (2)|
|PRY|1,527 (28)|393 (7)|
|SLV|1,937 (35)|88 (2)|
|URY|2,555 (46)|367 (7)|
|VEN|4,891 (89)|4,803 (87)|

Source: Author’s calculation based on UNCTAD’s NTM data. Note: The total number of products at the HS 6-digit is 5,400 (HS Combined).

# **3.4 Colombian Firms and Non-Tariff Measures in LAC**

We now study the extent to which Colombian exporters face NTMs in their regional product-destination sales markets. The focus is on technical trade regulations since they are the most frequently used type of NTM, as discussed above. To make sense of the high-dimensional data, we define three groups of exporting firms. Fully exposed firms (green bar chart) are defined as those facing at least one NTM in each product-destination market. Partially exposed firms (blue bar chart) enjoy at least one productdestination market without trade regulation. The last group is made up of firms whose exports do not face any trade regulations in productdestination markets.

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# **4 Methodology**

## **4.1 Empirical Framework**

We use a firm-level structural gravity model to explain firm behavior at the intensive and extensive margins of exports as a function of _changes_ in NTMs and tariffs. Specifically, where the subscripts _f_ , _p_ , _j_ and _t_ denote, respectively, Colombian firm, HS 6-digit product, LAC destination country, and year. _i_ refers to Colombia, the only exporting country.

We consider three dependent variables _Yf pjt_ to capture firm-level export decisions. This requires expanding the initial dataset so that each firmproduct-destination has an observation in all sample years, with a 0 export value in a year when exports by the firm-product-destination are not occurring.<sup>7</sup> Setting up the dataset in this way for all three margins of exports 7The objective in constructing the expanded dataset is to have observations that are computationally feasible and make economic sense, i.e., that indicate plausible firm choices with the fewest assumptions possible. To build intuition on our fill-in procedure, consider an observation from the initial dataset in which firm f starts to export product _p_ to destination _j_ in year _t_ . If in the expanded dataset we add an observation with a 0 export value for firm _f_ product _p_ destination _j_ , in year _t_ − 1, this implies that in year _t_ − 1 we are allowing firm f to choose whether to export product _p_ to destination _j_ and the firm chooses not to do so. This seems like a plausible and not overly restrictive assumption.

In contrast, expanding the initial dataset into a square matrix where every firm has an observation for every product-destination-year combination possible would retain many 17
and using stringent fixed effects allows us to exploit the panel dimension in the firms’ decision to export or exit a product-destination market as NTMs change over time. Using this expanded dataset, we define our dependent variables _Yf pjt_ as follows:

1. Intensive margin: a continuous outcome variable _Expf pjt_ capturing exports by Colombian firm _f_ of product _p_ to LAC destination country _j_ in year _t_ and equal to 0 otherwise;

2. Extensive margin I: a firm export participation dummy Participation _f pjt_<sup>equal to 1 in year</sup><sup>_t_if Colombian firm</sup><sup>_f_exports a positive value of</sup> products to destination _j_ , and equal to 0 otherwise. Participation reflects both the creation and the continuation of a trade relationship; 3. Extensive margin II: a dummy for firm exit from a product-destination market Exit _f pjt_ equal to 1 if Colombian firm _f_ does not export product _p_ to destination _j_ in year _t_ but did so in year _t_ − 1 and equal to 0 if the firm exported product _p_ to destination _j_ in year _t_ − 1 and continues to do so in year _t_ .<sup>8</sup> Our variable of interest is _NTM_<sup>_l_</sup> , a count variable that indicates the num- _ijpt_ ber of NTMs of type l applied on imports from Colombia _i_ of product _p_ by country _j_ at time _t_ . The set _L_ includes the most frequent NTM 1-digit zeros – as most firms tend to export a single product to a single destination – and would impose computational challenges. Similarly, less data-intensive fill-in procedures lack economic sense. Consider a second example to that end: firm f exports products p1 and p2 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 _f_ for all other possible products in any year, this implies that in any year we are allowing firm f to choose whether to export any possible product. This is an implausible assumption because other products may be completely different from what the firm’s capabilities in terms of its technology and other inputs allow it to produce.

8Firms 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 (firm-product-destination) included in our specifications. Moreover, 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 not included in the exit analysis.

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chapters reported in the data: SPS measures, TBTs, PSIs, quantity control measures, and price control measures.

We include a variety of control variables in equation 1. First, we isolate the impact of tariffs from NTMs. _ln_ ( 1 + _τijpt_ ) controls for the applied ad valorem tariff rate imposed by destination markets _j_ on imports from Colombia _i_ in HS 6-digit product _p_ in year _t_ . To include zero tariffs, we add 1 before taking the natural log.

Second, we control for product-level demand conditions in destination markets, such as time-varying business cycles or import-demand shocks. These are proxied by the natural log of Imports _f pjt_ , which represents imports by destination market _j_ of product _p_ in year _t_ . Note that we subtract firm _f_ ’s exports from these imports to avoid correlation by construction for large firms, hence the subscript _f_ in Imports _f pjt_ .

Moreover, we exploit the granularity of the data to control for three types of interacted fixed effects. This strongly reduces concerns about alternative explanations. First, firm-destination-HS 6-digit product fixed effects _ωf jp_ account for unobserved heterogeneity at the panel level and allow us to identify our coefficients of interest based on within firm-destination-product changes in exports as NTMs enter into force over the sample period. Second, firm-product-year fixed effects _ω f pt_ capture product-specific productivity differences of Colombian firms and supply shocks. Third, destination-year fixed effects _ωjt_ account for time-variant demand and macroeconomic shocks in destination markets.<sup>9</sup> Taken together, these fixed effects control for multilateral resistance terms in a structural gravity equation (Baier and Bergstrand, 2007; Felbermayr et al., 2020). Our final control variables are the Inverse Mills Ratios of each NTM type l _I M_<sup>ˆ</sup> _Rljpt_ , discussed in detail in the next section.

Our coefficients of interest _β_ 2, _l_ measure the percentage change in export value, probability of participation, and exit following the introduction of an NTM of type l for Colombian exporters to a destination-product market relative to product-destination markets without NTMs.

> 9Note that we cannot control for HS 6-digit product-destination-year fixed effects as they would absorb all variation in our variable of interest _NTM_<sup>_l_</sup> . _pjt_

19
We estimate all three margins of exports in equation 1 with the Pseudo Poisson Maximum Likelihood (PPML) estimator. It represents the standard in the trade literature for outcome variables with a high number of zeros and the presence of nonnegative data (Santos Silva and Tenreyro, 2006; Correia et al., 2020). Inference is based on Huber-White robust standard errors, clustered at the HS6 product-destination level to control for autocorrelation and heteroscedasticity.

# **4.2 Identification Issues**

Estimating equation 1 faces two econometric challenges. First, our sample may suffer from selection bias since not all Colombian firms export products to destination markets in every year of our sample. Indeed, the imposition of NTMs in destination markets may prevent Colombian firms from exporting products over time, meaning that these observations would not appear in our dataset. Thus, using a logarithmic dependent variable excludes cases with zero exports, generating selection bias in favor of bigger sales markets. To address selection bias at the intensive margin, we use an exponential formula in equation 1, include zeros to the export data along the time dimension and we estimate it with PPML.

Second, omitted variable bias may occur since NTMs may correlate with the unobserved component of a Colombian firm’s exports _ε f pjt_ . This concern pertains especially to protectionist trade policy measures that might be imposed to control Colombia’s exports. Specifically, if larger Colombian exports induce authorities in, say, Argentina, to impose more protectionist measures, then our NTM coefficients could be biased because the error term correlates with the NTM regressors. To account for omitted variable bias, we follow Kee and Nicita (2022) and estimate a probit control function for each type l of NTM: 20
where _NTMljpt_ indicates a dummy outcome variable equal to 1 if destination _j_ has an NTM of type l on product _p_ in year _t_ and 0 otherwise; _Φ_ the cumulative distribution function of the standard distribution; _γl_ ′ evaluates to which extent a country is more likely to implement an NTM of type l if its three closest countries implement an NTM of type l’; and _~~NTM~~ l_ ′ _jpt_ the average NTM of type l’ of the three closest countries of destination _j_ .<sup>10</sup> While the introduction of NTMs in Argentina could be correlated with that of Argentina’s neighbors due to regional and cultural proximity, Colombia’s exports to Argentina of a particular product should not influence the trade policies of Argentina’s closes countries for the same product. Put differently, we argue that Argentina’s political economy motives against Colombian imports will not directly shape trade policies among Argentina’s neighbors for those Colombian products. As a result, these instruments meet the criteria for the exclusion restriction. Table A1 shows positive estimates of the probit control function, suggesting that the neighbors’ imposition of NTMs predicts the country’s own implementation of NTMs.

ˆ Next, we obtain _γ l_ ′ to compute the Inverse Mills Ratio (IMR) for each type of NTM-type l: where _φ_ represents the standard normal density function. IMR captures the hazard of non-selection: if the IMR for an NTM is higher, the importing country is less likely to have implemented the NTM, considering the NTMs imposed by its neighbors. We include the IMR vector as a control variable in equation 1 to account for the correlation that the importing country enforces an NTM despite a high IMR, our endogeneity concern. This ensures that we compare treated and untreated units that have similar chances of being treated, based on the actions of neighboring countries. Our prefer-

> 10To define closeness, we rely on bilateral distance between countries, weighted by population of main cities, provided by Mayer and Zignago (2011).

21
ence over an instrumental variable approach stems from the binary nature of the endogenous NTM variable and the utilization of a PPML estimator with high-dimensional fixed effects.

In addition, we conduct a host of robustness tests to rule out omitted variable bias. First, we lag both tariffs and NTM variables by one year, following Fugazza et al. (2018). Our expectation is that the use of an NTM in a previous year is exogenous to firms’ exports in the current year. Second, following Fernandes et al. (2021), we exclude from the sample the largest exporting firms in Colombia which may influence the imposition of NTMs in destination markets. The largest exporting firms are identified for each destination market and year as those in the top 1% of the distribution of total firm exports. Third, and in the same vein, we exclude from the sample the HS 2-digit sectors with the highest degree of export concentration across firms. Concentrated sectors are defined as those for which the largest 1% of firms are responsible for more than 50% of sector exports in at least one sample year. Fourth, we run placebo tests to show that future NTMs do not have an effect on current firm-level export values, participation or entry. To that end, we evaluate the placebo treatment of leading NTM types and tariffs by one year.

In the Appendix B, we conduct additional robustness tests to show that NTM overlap and collinearity are not a concern for our identification strategy.

# **5 Results**

## **5.1 Summary Statistics**

Table 9 displays the summary statistics of our variables. Panel A displays the sample for the intensive margin of trade, i.e., for all observations in a firm, product, destination, and year combination. Conversely, Panel B reports the statistics for both extensive margins I and II: probability of export participation and export exits.

In our sample, Colombian firms export, on average, around US $151,000 22
Table 9: Summary Statistics

|**Variable**|**N**|**Mean**|**sd**|**Min**|**Max**|
|---|---|---|---|---|---|
|**Panel A - Intensive Margin of Exports**||||||
|Exports (in USD)_f_,_p_,_j_,_t_|253,212|151,748|1,936,183|0.0|298,207,937|
|Log (Tariff_i_,_j_,_p_,_t_)<br>|253,212|1.224|1.27|0.0|4.4|
|SPS_i_,_j_,_p_,_t_|253,212|1.910|3.02|0.0|69.0|
|TBT_i_,_j_,_p_,_t_|253,212|3.577|3.60|0.0|31.0|
|Pre-Shipment_i_,_j_,_p_,_t_|253,212|0.117|0.33|0.0|3.0|
|Quantity Control_i_,_j_,_p_,_t_|253,212|0.493|0.79|0.0|5.0|
|PriceControl_i_,_j_,_p_,_t_|253,212|0.305|0.49|0.0|5.0|
|Log (Import Demand_f_,_p_,_j_,_t_)|253,212|10.593|5.55|-2.7|20.8|
|<br>Firm size 1: Exports per product (in USD)_f _,_t_|39,365|679,199|6,211,960|0.0|327,057,215|
|Firm size 2: Exports per sector (in USD)_f _,_t_|28,786|3,421,507|19,717,287|0.0|399,821,436|
|Firm size 3: Total exports (in USD)_f _,_t_|24,247|6,850,923|25,315,153|0.1|399,821,877|
|GVC dummy: firm exports and imports_f _,_t_|253,212|0.244|0.43|0.0|1.0|
|**Panel B - Extensive Margin of Exports**||||||
|Prob (Export Participation_f_,_p_,_j_,_t_)|253,212|0.570|0.50|0.0|1.0|
|Prob (Export Exit_f_,_p_,_j_,_t_)|17,221|0.551|0.50|0.0|1.0|
|<br>Log (Tariff_i_,_j_,_p_,_t_)<br>|17,221|1.167|1.26|0.0|4.0|
|SPS_i_,_j_,_p_,_t_<br>|17,221|1.998|2.95|0.0|29.0|
|TBT_i_,_j_,_p_,_t_|17,221|3.951|3.65|0.0|28.0|
|Pre-Shipment_i_,_j_,_p_,_t_|17,221|0.151|0.37|0.0|3.0|
|Quantity Control_i_,_j_,_p_,_t_|17,221|0.528|0.79|0.0|5.0|
|PriceControl_i_,_j_,_p_,_t_|17,221|0.328|0.50|0.0|5.0|
|Log (Import Demand_f_,_p_,_j_,_t_)|17,221|11.171|5.19|-0.4|19.8|
|<br>Firm size 1: Exports per product (in USD)_f _,_t_|5,239|659,912|2,991,801|0.1|42,844,647|
|Firm size 2: Exports per sector (in USD)_f _,_t_|3,546|1,080,343|4,747,268|0.1|48,113,669|
|Firm size 3: Total exports (in USD)_f _,_t_|2,762|1,496,305|5,610,819|0.1|48,113,669|
|GVC dummy: firm exports and imports_f _,_t_|17,221|0.264|0.44|0.0|1.0|

This table presents descriptive statistics for the three dependent variables between 2007 and 2017. The upper panel presents the sample used in the estimations of the intensive margin: export value. The lower panel presents results used in the estimations of the extensive margin I and II: probability of export participation and export exits. The table also includes statistics on the incidence of NTM types and tariffs, control variables and firm size measures.

per product and destination market. However, this average masks a great dispersion of firm-level exports as seen by the massive standard deviation.<sup>11</sup> Panel B shows that export participations happen in 57% of possible cases. That is, Colombian firms export, on average, into 57% of all productdestination-year combinations in our sample for the extensive margin I. This means export participation is a likely event, on average. Similarly, the 11While the underlying export values are measured in nominal US dollars, note that our regressions have destination-year fixed effects among others, so our dependent variables can be interpreted in real, not nominal, terms.

23
probability of firms leaving product-destination markets occurs in 55.1% of possible cases.

The incidence of NTMs varies by type. In our sample, TBTs are ubiquitous and occur, on average, 3.5 times in firm’s export relationships (productdestination-year cells) at the intensive margin and 3.9 times at the extensive margin. SPS and quantity control measures are present, on aver- age, 1.9 and 0.5 times of firms’ export relationships. On average, Colombian firms face 1.2% of applied ad valorem tariffs in regional destination markets in our sample.

Our three proxies for firm size – explained in Section 5.5 – indicate that firms that already export (intensive margin) are bigger than those with intermittent exports (extensive margin II). Colombian firms participating in global value chains (GVC), defined as those that both import and export goods, make up 24% in our sample.<sup>12</sup>

# **5.2 Intensive Margin of Trade**

Column (1) of Table 10 evaluates the principal component of all five NTM types. Column (2) provides our baseline results with all NTM types and control variables. The remaining columns provide robustness tests. Specifically, Column (3) lags all NTM types and tariff variables by one year. Column (4) excludes large Colombian exporting firms that may influence the imposition of NTMs in destination markets. To the same effect, Column (5) excludes HS 2-digit sectors with the highest degree of export concentration across Colombian firms. Finally, in Column (6) we run placebo tests to show that future NTMs do not have an effects on current firm-level export values. All columns use the battery of stringent fixed effects _α f jp_ , _δ f pt_ and _ηjt_ that allow us to interpret coefficients akin to a difference-in-differences setup. For expositional clarity, the coefficients of the IMR are not reported. For comparison purposes, we report standardized coefficients in all tables by normalizing the regressors to have mean zero and unit standard deviation.

12This GVC definition follows World Bank (2020).

24
The impact of market access conditions on the intensive margin of exports is, on average, negative. Column (1) suggests that an increase of one standard deviation of the principal component of NTMs is associated, on average, with a decrease of 19% in firm exports.<sup>13</sup> The impact of market access conditions varies greatly across NTM types, however. Column (2) shows that quantity controls undermine firm-level exports the most, followed by TBTs. Specifically, an increase of one standard deviation in quantity control in Colombia’s destination markets associated with, on average, a 43% decrease of firm-level exports. In contrast, the introduction of new TBT measures in Colombia’s destination markets translates into a 9% reduction of its firm-level exports. Tariffs, SPS, quantity and price control do not have, on average, an effect on Colombian firms’ exports. As for control variables, their estimated coefficients yield the expected significance and sign; increases in import demand at destination increases Colombian firm-level exports.

Our robustness tests confirm the relative importance of quantity controls over TBTs at the intensive margin of exports. Columns (3) show that the results obtained with lagged NTMs and tariffs are consistent with our baseline estimates. Moreover, excluding the largest firms and concentrated sectors in Columns (4) and (5) maintains the consistency of our findings, providing additional proof that reverse causality is not driving our results. Consistent with our baseline results, all three robustness tests highlight the importance of quantity controls, followed by TBTs, to discourage Colombian firm-level exports. Finally, Column (6) provides evidence that future introductions of NTMs are not correlated with existing firm-level exports. Overall, we note the extremely high explanatory power of our model, as evidenced by the adjusted _R_<sup>2</sup> of 0.95.

# **5.3 Extensive Margin of Trade I**

Models with heterogeneous firms predict a negative effect of market access conditions on the extensive margin of trade, as measured by firms’ export 13exp(-0.2179) -1 25
participation.

The empirical results shown in Table 11 are in line with theoretical predictions. Column (1) suggests that an increase of one standard deviation of the principal component of NTMs is associated, on average, with a decrease of 13% of firms’ probability of participating in exports. Considering the 57% unconditional probability of export participation, the estimated economic impact of NTMs is thus sizable.

Various NTM types influence firms’ probability of export participation. While coefficients of tariffs, quantity controls, TBTs, and SPS are consistently negative and statistically coefficients across all specifications, quantity controls and tariffs undermine firms’ export participation probabilities the most. However, the magnitude of coefficients is low compared to those at the intensive margin. Moreover, our robustness tests in Columns (3)–(5) highlight the relative importance of tariffs and quantity controls in lowering firms’ export participation. In demonstrating consistency with our baseline results, they marshal evidence that reserve causality is not driving our results. Finally, demand conditions at destination positively affect the extensive margin in all specifications. This implies that the likelihood of exporting is larger in bigger destination markets.

# **5.4 Extensive Margin of Trade II**

NTMs may also force Colombian firms to stop exporting to a productdestination market. To explore this hypothesis, we evaluate the roles that tariffs and specific NTM types play for firms’ probability of export exits, the second extensive margin of interest.

We find that, on average, NTMs increase firms’ probability to stop exporting. Column (1) in Table 12 suggests that a one standard deviation increase of the principal component of NTMs is associated, on average, with a increase of 22% in firms’ exit probability. This implies a big economic impact, considering the 55% unconditional probability of stopping exporting.

Our results show that new TBTs in product-destination markets lead to higher exit rates from those markets. Column (2) suggests that the intro26
duction of TBT measures in Colombia’s product-destination markets translates into a 22% increase in firms’ likelihood of leaving export markets. Other NTM types and tariffs sustain no effect. Control variables have the expected sign and statistical significance.

Intriguingly, however, we find that that new quantity controls in destination-product markets, on average, lower the exit rates of Colombian firms from those markets. The effect is consistently negative and statistically significant across all specifications in Columns (2)–(5). A potential rationale for this finding is that we are picking up average firm-level responses across the entire firm-size distribution. Indeed, we will show in the next section 5.5 that small firms benefit from quantity controls to the detriment of bigger firms, which are more constrained by the imposition of quantity controls in product-destination markets.

Our robustness tests in Columns (3)–(5) confirm the importance of TBTs in increasing firms’ exit rates, maintaining similar coefficients as in our baseline specification. Similarly, the robustness tests confirm that new quantity controls reduce exit rates for Colombian firms. This provides additional evidence against reverse causality, i.e., showing that the effect of quantity controls for firms’ exit rates is not the result of pressure by influential domestic firms or sectors to design NTMs to their advantage. Finally, our placebo test in Column (6) also shows that no future NTM-type correlates with firms’ present exit rate.

We also find that increases in tariffs and SPS measures in productdestination markets make it less likely that Colombian firms will continue exporting to these markets. This effect is significant and robust across all specifications, revealing that firms are responsive to additional market access conditions at this margin.

# **5.5 Results by Firm Size**

Larger firms may be able to more easily overcome the fixed costs needed to comply with a new market access conditions in the importing country. That is why we expect, on average, a smaller export-restricting effect of 27
NTMs for larger firms. Indeed, the largest exporters could gain from new market access conditions as demand is redirected toward them when small exporters are priced out of the market through new market access conditions.

The literature, however, provides inconclusive evidence on the heterogeneous impact of NTM types and firm size. Fugazza et al. (2018) shows that new tariffs, TBT and PSI measures in destination markets benefit very large Peruvian exporters, while hurting smaller ones. On the other hand, Fernandes et al. (2021) finds that provisions that harmonize SPS and TBT regulations in PTAs are more beneficial for exports of smaller firms.

To explore this hypothesis, we consider the following specification: Our coefficients of interest are _β_ 5, _l_ and _β_ 6 in equation 4. They measure how the size of a firm creates a varied impact of NTMs and tariffs on the three margins of firm-level exports.

We consider two mutually exclusive firm-size categories. As a baseline, we define firm size by the export market share a firm has in a 6-digit product market in its first defined year in the sample.<sup>14</sup> Our definition of firm size is guided by how specific NTMs are; we assume that most are applicable to very narrowly defined HS 6-digit products. The underlying reasoning is that the introduction or withdrawal of NTMs could differentially benefit firms that are small from the point of view of that product’s market.

> 14We are limited to rely on export-based measures of firm characteristics because we do not have information on headcount, turnover or capital of Colombian exporting firms. We define firm size categories separately for each of the three margins of exports. This helps to account for the fact that firms have, on average, greater exports – and are thus bigger – at the intensive margin, as opposed to the extensive margins.

28
We then binarize the continuous firm-size variable based on its median and evaluate the differential impact of NTMs on _BigFirmf_ in equation 4.

We conduct two robustness checks on the definition of firm-size categories. First, we define firm size based on the export market share a firm has in an HS 2-digit sector in its first defined year in the sample. Second, we define firm size based on the export market share a firm has in its first defined year in the sample, considering all products and sectors. Thus, our additional firm-size definitions grow broader in scope.<sup>15</sup> Our results confirm the heterogeneous nature of market access conditions on firm size. Table 13 attests to that end for the intensive margin of Colombian firm-level exports. The exports of small firms decline significantly when TBT and in particular quantity control measures are introduced in their export markets. In contrast, the effect is muted for big firms. That is why the interaction effects between TBT and Quantity controls and _BigFirmf_ are statistically significant and positive. Moreover, this effect is consistent across our three definitions of firm size in Columns (2)–(4). It confirms previous evidence of the export-promoting effect of TBTs for big Peruvian exporters in Fugazza et al. (2018).

Similarly, TBT and quantity control measures exert a regressive effect on the extensive margin of firm-level exports. Table 14 shows that small firms are significantly less likely to export when new TBT and quantity control measures are introduced in sales markets. Conversely, the impact is muted for big firms, irrespective of how we define firm sizes in columns (2)–(4).

Firm size also shapes how TBTs and quantity control measures affect the likelihood of firms leaving markets. Table 15 reveals that small firms are more likely to leave sales market when TBT measures are introduced. That is why the baseline TBT coefficient – pertaining to small firms – is statistically significant and positive across all three definitions of firm size in Columns (2)–(4). In contrast, the likelihood of leaving these markets is not elevated for big firms, even though the interaction effect is not statistically

> 15As in equation 1, we use the same battery of three sets of high-dimensional fixed effects, include zeros along the time dimension and rely on the PPML estimator for all three margins of exports.

29
significant.

The only progressive NTM effect comes from new quantity controls. They decrease the likelihood of exits from product-destination markets for small firms, yet increase it for big firms. This finding is consistent across all three definitions of firm size, as shown in the coefficients of Quantity Control _i_ , _j_ , _p_ , _t_ × Big Firm _f_ in Columns (2)–(4). An explanation for this finding is a composition effect: quantity controls are more binding for big firms, increasing their likelihood of leaving markets. Quantity controls, in turn, impose fewer constraints for smaller exporters, making them less likely to leave markets.

# **5.6 Results for GVC Firms**

Firms participating in global value chains (GVCs) may face muted effects from NTMs. Since they boast extensive contractual relationships, including from importing, these firms enjoy enhanced access to information about regulatory changes, market conditions, and compliance requirements. As a result, GVC firms may find it easier to meet evolving regulatory requirements emanating from NTMs.

To explore this hypothesis, we re-estimate specification 4 by interacting GVC Firm _f t_ with tariffs and NTM types. We define GVC Firm _f t_ as an indicator variable equal to 1 if a firm both imports and exports goods in a given year and 0 otherwise. GVC firms make up around 24% of all observations, as seen in Table 9. Intriguingly, only 20% of big firms are also GVC firms, while 55% of GVC firms are also big firms.

Table 16 demonstrates a heterogeneous impact of market access conditions for GVC firms. Column (2) shows results for the intensive margin; Column (4) for the extensive margin I and Column (6) for the extensive margin II. To put these results into perspective, Columns (1), (3) and (5) provide the respective baseline results presented above.

Looking at the intensive margin, GVC firms benefit from a dampened effect from new trade regulations in product destination markets. While nonGVC firms see a significant decline of their exports associated with 30
new quantity control, SPS and TBT measures, the positive interaction coefficients for GVC firms in Columns (2) suggest these effects are muted for them. Both effects are statistically significant and of economic relevance.

GVC firms are also more likely to export as market access conditions affect them less than non-GVC firms. Column (4) shows that tariffs, pre-shipment and quantity control measures undermine the likelihood of non-GVC firms participating in exports. In contrast, these measures have a muted impact on GVC firms’ likelihood to participate in exports as their positive and significant interaction effect attests to.

Intriguingly, GVC firms are more likely to leave markets when new quantity control measures are introduced. This is reflected in the positive and statistically significant interaction effect Quantity Control _i_ , _j_ , _p_ , _t_ × GVC Firm _f t_ in in Column (6), indicating a higher exit probability for GVC firms. Conversely, non-GVC firms are less likely to exit markets when quantity controls are introduced. A potential explanation is that new quantity measures are more binding for GVC firms than non-GVC firms due to their elevated exports in the first place.

# **6 Conclusion**

This paper studies how NTMs, the dominant instrument of today’s trade policy, challenge firm-level export decisions to access foreign markets. Our panel analysis of Colombian firms exporting to Latin America reveals that both TBT and quantity control measures decrease their exports on average. We rationalize this trade-deterring effect through an increase in firms’ compliance costs relative to consumer benefits. Other non-tariff measures and tariffs play a minor role.

These average effects of trade regulations mask significant heterogeneities. TBT and quantity measures reallocate trade from small to big firms and those participating in global value chains. However, quantity control measures exert progressive effects under specific conditions, making it more likely that big firms will leave export markets, with the opposite effect occurring for small firms. We argue that the quantity controls are 31
more binding for big firms, increasing their likelihood of leaving markets and conversely helping small firms to take over their space.

Our results are important for policy. First, they support evidence that trade regulations today are more trade restrictive than tariffs (Nicita and Melo 2018). This highlights the importance of mechanisms to harmonize trade regulations across countries to reduce their costs. Chief among them are international trade agreements, including through the World Trade Organization (Fernandes et al., 2021). That will effectively help firms access international markets, which is critical for lifting demand-side constraints on national development (Goldberg and Reed 2023).

Second, our finding that even standard-like measures like TBTs undermine Colombian firm-level export decisions aligns with firms choosing not to embrace a new signal due to its associated costs and benefits. It challenges the trade-promoting effects of TBTs and that consumers receive useful information about product quality and increase trade, relative to the adverse impact on trade caused by any increase in cost (Zavala et al. 2023; Beghin et al. 2015). Moreover, it underscores the need for assistance to help firms comply with these trade regulations. This is especially warranted when market failures prevail, notably consumers take time to adjust their demand after receiving new quality signals (Bai, 2021).

Third, our finding that trade regulations tend to favor large firms at the expense of small ones implies a concentration of world markets. It suggests that big firms benefit from protectionism, not globalization. This contrasts with liberalizing trade, which would result in a more equal distribution of export market shares among firms. It would also likely reduce wage inequality within the exporting country since small firms tend to be more less skilled relative to large firms (Cruz et al., 2017). However, with today’s new economic interventionism fueling trade protectionism, this is all but happening. Technological change and the backlash against globalization are making export-oriented industrialization, as seen in East Asia, much more difficult to achieve.

32
Table 10: Intensive margin: Existing exports

|**Dependent Variable:**Exports_f pjt _|||||||
|---|---|---|---|---|---|---|
||PCA|Baseline||Robus|tnesstests||
||||Lags|w/o large firms|w/o concentrated sectors|Placebo|
||(1)|(2)|(3)|(4)|(5)|(6)|
|NTM Principal Component_i_,_j_,_p_,_t_|-0.2179<br>(0.044)***||||||
|Log (Tariff_i_,_j_,_p_,_t_)||-0.0351|-0.0696|-0.0438|-0.0982|-0.0333|
|||(0.036)|(0.054)|(0.047)|(0.051)*|(0.051)|
|SPS_i_,_j_,_p_,_t_||0.0021|-0.0146|-0.0914|-0.0161|-0.0328|
|||(0.048)|(0.042)|(0.050)*|(0.048)|(0.048)|
|TBT_i_,_j_,_p_,_t_||-0.0971|-0.0811|-0.0968|-0.1387|-0.0650|
|||(0.044)**|(0.028)***|(0.042)**|(0.039)***|(0.044)|
|Pre-Shipment_i_,_j_,_p_,_t_||0.0388|-0.0154|0.0094|0.0323|0.0350|
|||(0.053)|(0.046)|(0.049)|(0.047)|(0.056)|
|QuantityControl_i_,_j_,_p_,_t_||-0.5739|-0.4905|-0.2355|-0.3295|-0.2107|
|||(0.158)***|(0.155)***|(0.100)**|(0.115)***|(0.159)|
|PriceControl_i_,_j_,_p_,_t_||-0.0483|-0.0410|-0.1115|-0.0793|0.0420|
|||(0.040)|(0.036)|(0.028)***|(0.035)**|(0.046)|
|OtherNTMs_i_,_j_,_p_,_t_||0.1252|0.1040|0.2088|0.0491|0.0289|
|||(0.101)|(0.097)|(0.100)**|(0.097)|(0.075)|
|Log (Import Demand_f _,_p_,_j_,_t_)|0.0448|0.0324|0.0251|0.0291|0.0285|0.0340|
||(0.009)***|(0.006)***|(0.006)***|(0.005)***|(0.007)***|(0.007)***|
|Observations<br>|253212|253212|227964|200738|250213|219733|
|Adjusted_R_<sup>2</sup>|0.93|0.96|0.96|0.94|0.96|0.96|
|Fixed Effects<br>−|_p _−_j_,_f _−_p _−_t_,_j _−_t_|_f _−_p _−_j_,_f _−_p _−_t_,_j _−_t_|_f _−_p _−_j_,_f _−_p _−_t_,_j _−|_t_<br>_f _−_p _−_j_,_f _−_p _−_t_,_j _−|_t_<br>_f _−_p _−_j_,_f _−_p _−_t_,_j _−_t_<br>_f _|−_p _−_j_,_f _−_p _−_t_,_j _−_t_|

This table estimates equation 1 for the intensive margin of Colombian firm-level exports. Columns (2-6) show coefficients standardized with zero mean and unit standard deviation. All columns use firm-HS6 product-destination, firm-HS6 product-year and destination-year fixed effects. All columns also control for the Inverse Mills Ratios for each NTM type following estimation of probit model of NTM selection based on NTM intensity in neighboring countries. Clustered standard errors at the HS6 product-year level are presented in parenthesis. ∈ _p <_ 0.10,∈∈ _p <_ 0.05,∈∈∈ _p <_ 0.01
Table 11: Extensive margin I: Probability of export participation

|**Dependent Variable:**Probability|of Export Participation_f pjt_<br>||||||
|---|---|---|---|---|---|---|
||PCA|Baseline||Robus|tnesstests||
||||Lags|w/o large firms|w/o concentrated sectors|Placebo|
||(1)|(2)|(3)|(4)|(5)|(6)|
|NTM Principal Component_i_,_j_,_p_,_t_|-0.1448||||||
||(0.008)***||||||
|Log (Tariff_i_,_j_,_p_,_t_)||-0.0483|-0.0571|-0.0512|-0.0488|-0.0242|
|||(0.011)***|(0.011)***|(0.013)***|(0.011)***|(0.022)|
|SPS_i_,_j_,_p_,_t_||-0.0280|-0.0283|-0.0363|-0.0307|-0.0219|
|||(0.013)**|(0.013)**|(0.016)**|(0.013)**|(0.024)|
|TBT_i_,_j_,_p_,_t_||-0.0343|-0.0424|-0.0496|-0.0348|-0.0098|
|||(0.012)***|(0.012)***|(0.014)***|(0.012)***|(0.013)|
|Pre-Shipment_i_,_j_,_p_,_t_||0.0099|-0.0142|0.0169|0.0089|-0.0030|
|3||(0.009)|(0.009)|(0.012)|(0.009)|(0.013)|
|QuantityControl_i_,_j_,_p_,_t_||-0.0537|-0.0454|-0.0391|-0.0501|-0.0193|
|||(0.021)***|(0.022)**|(0.022)*|(0.021)**|(0.022)|
|PriceControl_i_,_j_,_p_,_t_||-0.0043|0.0018|-0.0073|-0.0044|0.0008|
|||(0.014)|(0.014)|(0.016)|(0.014)|(0.014)|
|OtherNTMs_i_,_j_,_p_,_t_||-0.0091|0.0248|-0.0053|-0.0172|-0.0254|
|||(0.022)|(0.020)|(0.030)|(0.022)|(0.019)|
|Log (Import Demand_f _,_p_,_j_,_t_)|0.0184|0.0110|0.0105|0.0138|0.0114|0.0107|
||(0.001)***|(0.001)***|(0.001)***|(0.002)***|(0.001)***|(0.002)***|
|Observations|253212|253212|227964|200738|250213|219733|
|Adjusted_R_<sup>2</sup>|0.13|0.15|0.15|0.15|0.15|0.15|
|Fixed Effects|_f _−_p _−_j_,_f _−_p _−_t_,_j _−_t_|_f _−_p _−_j_,_f _−_p _−_t_,_j _−_t_|_f _−_p _−_j_,_f _−_p _−_t_,_j _−|_t_<br>_f _−_p _−_j_,_f _−_p _−_t_,_j _−_t_|<br>_f _−_p _−_j_,_f _−_p _−_t_,_j _−_t_<br>_f _|−_p _−_j_,_f _−_p _−_t_,_j _−_t_|

This table estimates equation 1 for the extensive margin I of Colombian firm-level exports. Columns (2-6) show coefficients standardized with zero mean and unit standard deviation. All columns use firm-HS6 product-destination, firm-HS6 product-year and destination-year fixed effects. All columns also control for the Inverse Mills Ratios for each NTM type following estimation of probit model of NTM selection based on NTM intensity in neighboring countries. Clustered standard errors at the HS6 product-year level are presented in parenthesis. ∈ _p <_ 0.10,∈∈ _p <_ 0.05,∈∈∈ _p <_ 0.01
Table 12: Extensive margin II: Probability of export exits

|**Dependent Variable:**Probability|of Export Exit_f pjt_||||||
|---|---|---|---|---|---|---|
||PCA|Baseline||Robus|tnesstests||
||||Lags|w/o large firms|w/o concentrated sectors|Placebo|
||(1)|(2)|(3)|(4)|(5)|(6)|
|NTM Principal Component_i_,_j_,_p_,_t_|0.1995<br>(0.031)***||||||
|Log (Tariff_i_,_j_,_p_,_t_)||-0.0545|0.0382|-0.0614|-0.0493|-0.0390|
|||(0.057)|(0.055)|(0.059)|(0.058)|(0.070)|
|SPS_i_,_j_,_p_,_t_||0.1098|0.0390|0.0860|0.1316|0.0898|
|||(0.087)|(0.084)|(0.094)|(0.088)|(0.093)|
|TBT_i_,_j_,_p_,_t_||0.1939|0.2936|0.2148|0.2012|0.0538|
|||(0.072)***|(0.077)***|(0.083)***|(0.074)***|(0.080)|
|Pre-Shipment_i_,_j_,_p_,_t_||0.0516|0.1018|-0.0357|0.0777|-0.1040|
|||(0.096)|(0.079)|(0.101)|(0.093)|(0.099)|
|QuantityControl_i_,_j_,_p_,_t_||-0.2712|-0.1944|-0.3128|-0.3292|-0.1175|
|||(0.114)**|(0.101)*|(0.125)**|(0.114)***|(0.132)|
|PriceControl_i_,_j_,_p_,_t_||-0.0495|-0.1269|0.1028|-0.0229|-0.0480|
|||(0.087)|(0.118)|(0.108)|(0.090)|(0.092)|
|OtherNTMs_i_,_j_,_p_,_t_||-0.1053|-0.1477|-0.1053|-0.0639|-0.0706|
|||(0.074)|(0.060)**|(0.077)|(0.076)|(0.078)|
|Log (Import Demand_f _,_p_,_j_,_t_)|-0.0282|-0.0211|-0.0210|-0.0310|-0.0218|-0.0322|
||(0.006)***|(0.008)**|(0.008)**|(0.010)***|(0.008)***|(0.010)***|
|Observations|17221|17221|17221|13455|16961|13354|
|Adjusted_R_<sup>2</sup>|0.13|0.18|0.18|0.18|0.18|0.18|
|Fixed Effects|_f _−_p _−_j_,_f _−_p _−_t_,_j _−_t_|_f _−_p _−_j_,_f _−_p _−_t_,_j _−_t_|_f _−_p _−_j_,_f _−_p _−_t_,_j _−|_t_<br>_f _−_p _−_j_,_f _−_p _−_t_,_j _−|_t_<br>_f _−_p _−_j_,_f _−_p _−_t_,_j _−_t_<br>_f _|−_p _−_j_,_f _−_p _−_t_,_j _−_t_|

This table estimates equation 1 for the extensive margin II of Colombian firm-level exports. Columns (2-6) show coefficients standardized with zero mean and unit standard deviation. All columns use firm-HS6 product-destination, firm-HS6 product-year and destination-year fixed effects. All columns also control for the Inverse Mills Ratios for each NTM type following estimation of probit model of NTM selection based on NTM intensity in neighboring countries. Clustered standard errors at the HS6 product-year level are presented in parenthesis. ∈ _p <_ 0.10,∈∈ _p <_ 0.05,∈∈∈ _p <_ 0.01
Table 13: Intensive margin: Existing exports

|**Dependent Variable:**Exports_f pjt_|||||
|---|---|---|---|---|
|<br>Big Firm: exports > median in||HS6 product-Year|HS2 sector-Year|Year|
||(1)|(2)|(3)|(4)|
|Log (Tariff_i_,_j_,_p_,_t_)|-0.0351|-0.0024|-0.0036|-0.2244|
||(0.036)|(0.093)|(0.085)|(0.107)**|
|Log (Tariff_i_,_j_,_p_,_t_) x Big Firm_f _,_t_||-0.0332|-0.0314|0.1962|
|||(0.090)|(0.090)|(0.108)*|
|SPS_i_,_j_,_p_,_t_|0.0021|-0.0840|-0.1102|0.0374|
||(0.048)|(0.097)|(0.067)|(0.114)|
|SPS_i_,_j_,_p_,_t _x Big Firm_f _,_t_||0.1022|0.1256|-0.0300|
|||(0.115)|(0.079)|(0.126)|
|TBT_i_,_j_,_p_,_t_|-0.0971|-0.2802|-0.2091|-0.0552|
||(0.044)**|(0.078)***|(0.099)**|(0.160)|
|TBT_i_,_j_,_p_,_t _x Big Firm_f _,_t_||0.1452|0.1136|0.1566|
|||(0.071)**|(0.060)***|(0.073)**|
|Pre-Shipment_i_,_j_,_p_,_t_|0.0388|-0.0929|-0.0327|0.1404|
||(0.053)|(0.060)|(0.077)|(0.062)**|
|Pre-Ship._i_,_j_,_p_,_t _x Big Firm_f _,_t_<br>||0.1676|0.0797|-0.1017|
|3||(0.064)***|(0.087)|(0.073)|
|QuantityControl_i_,_j_,_p_,_t_|-0.5739|-0.5769|-0.7281|-0.7879|
||(0.158)***|(0.134)***|(0.151)***|(0.150)***|
|Quantity Control_i_,_j_,_p_,_t _x Big Firm_f _,_t_||0.267|0.1658|0.2286|
|||(0.103)***|(0.082)**|(0.100)**|
|PriceControl_i_,_j_,_p_,_t_|-0.0483|-0.1686|-0.0706|-0.0782|
||(0.040)|(0.059)***|(0.053)|(0.100)|
|Price Control_i_,_j_,_p_,_t _x Big Firm_f _,_t_||0.1442|0.0173|0.0242|
|||(0.072)**|(0.055)|(0.097)|
|Log (Import Demand_f _,_p_,_j_,_t_)|0.0324|0.0319|0.0322|0.0323|
||(0.006)***|(0.006)***|(0.006)***|(0.006)***|
|Observations|253212|253212|253212|253212|
|Adjusted_R_<sup>2</sup>|0.96|0.96|0.96|0.96|
|Fixed Effects|_f _−_p _−_j_, _f _−_p _−_t_, _j _−|_t_<br>_f _−_p _−_j_, _f _−_p _−_t_, _j _−_t_|_f _−_p _−_j_, _f _−_p _−_t_, _j _−_t_|_f _−_p _−_j_, _f _−_p _−_t_, _j _−_t_|

This table estimates equation 4 for the intensive margin of Colombian firm-level exports. Columns (1-4) show coefficients standardized with zero mean and unit standard deviation. All columns use firm-HS6 product-destination, firm-HS6 product-year and destination-year fixed effects. All columns also control for the Inverse Mills Ratios for each NTM type following estimation of probit model of NTM selection based on NTM intensity in neighboring countries. Clustered standard errors at the HS6 product-year level are presented in parenthesis.

> ∈ _p <_ 0.10,∈∈ _p <_ 0.05,∈∈∈ _p <_ 0.01
Table 14: Extensive margin I: Probability of export participation

|**Dependent Variable:**Probability o|f Export Participation_f pjt_||||
|---|---|---|---|---|
|Big Firm: exports > median in||HS6 product-Year|HS2 sector-Year|Year|
||(1)|(2)|(3)|(4)|
|Log (Tariff_i_,_j_,_p_,_t_)|-0.0483|-0.0487|-0.0659|-0.0302|
||(0.011)***|(0.015)***|(0.020)***|(0.020)|
|Log (Tariff_i_,_j_,_p_,_t_) x Big Firm_f _,_t_||0.0007|0.0228|-0.0223|
|||(0.017)|(0.022)|(0.022)|
|SPS_i_,_j_,_p_,_t_|-0.0280|-0.0096|-0.0240|-0.0145|
||(0.013)**|(0.022)|(0.023)|(0.025)|
|SPS_i_,_j_,_p_,_t _x Big Firm_f _,_t_||-0.0313|-0.0077|-0.0198|
|||(0.025)|(0.026)|(0.028)|
|TBT_i_,_j_,_p_,_t_|-0.0343|-0.0568|-0.0874|-0.0874|
||(0.012)***|(0.017)***|(0.022)***|(0.023)***|
|TBT_i_,_j_,_p_,_t _x Big Firm_f _,_t_||0.0351|0.0700|0.0671|
|||(0.019)*|(0.025)***|(0.026)**|
|Pre-Shipment_i_,_j_,_p_,_t_|0.0099|-0.0179|-0.0071|0.0155|
||(0.009)|(0.013)|(0.016)|(0.019)|
|Pre-Ship._i_,_j_,_p_,_t _x Big Firm_f _,_t_<br>||0.0469|0.0226|-0.0062|
|3||(0.012)***|(0.017)|(0.020)|
|QuantityControl_i_,_j_,_p_,_t_|-0.0537|-0.1089|-0.1008|-0.0686|
||(0.021)***|(0.022)***|(0.027)***|(0.028)**|
|Quantity Control_i_,_j_,_p_,_t _x Big Firm_f _,_t_||0.0925|0.0574|0.0553|
|||(0.017)***|(0.023)**|(0.025)**|
|PriceControl_i_,_j_,_p_,_t_|-0.0043|-0.0053|0.0004|-0.0427|
||(0.014)|(0.019)|(0.024)|(0.025)*|
|Price Control_i_,_j_,_p_,_t _x Big Firm_f _,_t_||0.0016|-0.0074|0.0460|
|||(0.021)|(0.027)|(0.027)*|
|Log (Import Demand_f _,_p_,_j_,_t_)|0.0110|0.0109|0.0110|0.0110|
||(0.001)***|(0.001)***|(0.001)***|(0.001)***|
|Observations|253212|253212|253212|253212|
|Adjusted_R_<sup>2</sup>|0.15|0.15|0.15|0.15|
|Fixed Effects|_f _−_p _−_j_, _f _−_p _−_t_, _j _−_t_|_f _−_p _−_j_, _f _−_p _−_t_, _j _−_t_|<br>_f _−_p _−_j_, _f _−_p _−_t_, _j _−_t_<br>|_f _−_p _−_j_, _f _−_p _−_t_, _j _−_t_|

This table estimates equation 4 for the extensive margin I of Colombian firm-level exports. Columns (1-4) show coefficients standardized with zero mean and unit standard deviation. All columns use firm-HS6 product-destination, firm-HS6 product-year and destination-year fixed effects. All columns also control for the Inverse Mills Ratios for each NTM type following estimation of probit model of NTM selection based on NTM intensity in neighboring countries. Clustered standard errors at the HS6 product-year level are presented in parenthesis. ∈ _p <_ 0.10,∈∈ _p <_ 0.05,∈∈∈ _p <_ 0.01
Table 15: Extensive margin II: Probability of export exit

|**Dependent Variable:**Probabilityof Ex|port Exit_f pjt_||||
|---|---|---|---|---|
|Big Firm: exports > median in|(1)|HS6 product-Year<br>(2)|HS2 sector-Year<br>(3)|Year<br>(4)|
|Log (Tariff_i_,_j_,_p_,_t_)|-0.0545|-0.0671|-0.0596|-0.0010|
||(0.057)|(0.074)|(0.081)|(0.080)|
|Log (Tariff_i_,_j_,_p_,_t_) x Big Firm_f_,_t_||0.0723|0.0272|-0.0659|
|||(0.102)|(0.105)|(0.100)|
|SPS_i_,_j_,_p_,_t_|0.1098|-0.0437|0.0408|-0.1746|
||(0.087)|(0.124)|(0.128)|(0.142)|
|SPS_i_,_j_,_p_,_t _x Big Firm_f_,_t_||0.3218|0.1290|0.4476|
|||(0.168)*|(0.168)|(0.177)**|
|TBT_i_,_j_,_p_,_t_|0.1939|0.2654|0.3064|0.2809|
||(0.072)***|(0.106)**|(0.113)***|(0.116)**|
|TBT_i_,_j_,_p_,_t _x Big Firm_f _,_t_||-0.0746|-0.1811|-0.1256|
|||(0.150)|(0.149)|(0.143)|
|Pre-Shipment_i_,_j_,_p_,_t_|0.0516|0.0292|0.0221|0.0468|
||(0.096)|(0.139)|(0.139)|(0.143)|
|Pre-Ship._i_,_j_,_p_,_t_x Big Firm_f_,_t_<br>||0.0108|0.0461|-0.0358|
|<br>3||(0.178)|(0.164)|(0.181)|
|Quantity Control_i_,_j_,_p_,_t_|-0.2712|-0.2904|-0.3151|-0.2507|
||(0.114)**|(0.133)**|(0.118)***|(0.120)**|
|Quantity Control_i_,_j_,_p_,_t_x Big Firm_f_,_t_||0.0857|0.0129|0.0921|
|||(0.095)|(0.090)|(0.088)|
|PriceControl_i_,_j_,_p_,_t_|-0.0495|-0.1460|-0.1276|-0.0254|
||(0.087)|(0.110)|(0.106)|(0.160)|
|Price Control_i_,_j_,_p_,_t _x Big Firm_f_,_t_||0.1414|0.1469|-0.0319|
|||(0.118)|(0.117)|(0.155)|
|Log (Import Demand_f_,_p_,_j_,_t_)|-0.0211|-0.0208|-0.0217|-0.0215|
||(0.008)**|(0.009)**|(0.009)**|(0.009)**|
|Observations<br>|17221|16672|17077|17141|
|Adjusted_R_<sup>2</sup>|0.18|0.18|0.18|0.18|
|~~Fixed Effects~~<br>_~~f~~_~~−~~|_~~p~~_~~− ~~_~~j~~_~~,~~_~~f~~_~~− ~~_~~p~~_~~− ~~_~~t~~_~~,~~_~~j~~_~~− ~~|_~~t~~_<br>_~~f~~_~~− ~~_~~p~~_~~− ~~_~~j~~_~~,~~_~~f~~_~~− ~~_~~p~~_~~− ~~_~~t~~_~~,~~_~~j~~_~~− ~~_~~t~~_|_~~f~~_~~− ~~_~~p~~_~~− ~~_~~j~~_~~,~~_~~f~~_~~− ~~_~~p~~_~~− ~~_~~t~~_~~,~~_~~j~~_~~− ~~_~~t~~_|_~~f~~_~~− ~~_~~p~~_~~− ~~_~~j~~_~~,~~_~~f~~_~~− ~~_~~p~~_~~− ~~_~~t~~_~~,~~_~~j~~_~~− ~~_~~t~~_|

This table estimates equation 4 for the extensive margin II of Colombian firm-level exports. Columns (1-4) show coefficients standardized with zero mean and unit standard deviation. All columns use firm-HS6 product-destination, firm-HS6 product-year and destination-year fixed effects. All columns also control for the Inverse Mills Ratios for each NTM type following estimation of probit model of NTM selection based on NTM intensity in neighboring countries. Clustered standard errors at the HS6 product-year level are presented in parenthesis. ∈ _p <_ 0.10,∈∈ _p <_ 0.05,∈∈∈ _p <_ 0.01
Table 16: Heterogeneous effects of market access conditions for GVC firms

|**Dependent Variable:**|||||||
|---|---|---|---|---|---|---|
||Expor<br>|ts_f _,_j_,_p_,_t_<br>|Prob (Export Pa<br>|rticipation_f _,_j_,_p_,_t_)<br>|Prob (Expo<br>|rt Exits_f _,_j_,_p_,_t_)<br>|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Log (Tariff_i_,_j_,_p_,_t_)|-0.0351|0.1146|-0.0483|-0.0713|-0.0545|-0.0781|
||(0.036)|(0.130)|(0.011)***|(0.018)***|(0.057)|(0.095)|
|Log (Tariff_i_,_j_,_p_,_t_) x GVC Firm_f _,_t_||-0.1509||0.0258||0.0516|
|||(0.118)||(0.014)*||(0.080)|
|SPS_i_,_j_,_p_,_t_|0.0021|-0.1688|-0.0280|0.0126|0.1098|-0.1405|
||(0.048)|(0.055)***|(0.013)**|(0.022)|(0.087)|(0.134)|
|SPS_i_,_j_,_p_,_t _x GVC Firm_f _,_t_||0.1720||-0.0470||0.3144|
|||(0.062)***||(0.020)**||(0.129)**|
|TBT_i_,_j_,_p_,_t_|-0.0971|-0.2451|-0.0343|-0.0293|0.1939|0.0967|
||(0.044)**|(0.101)**|(0.012)***|(0.027)|(0.072)***|(0.182)|
|TBT_i_,_j_,_p_,_t _x GVC Firm_f _,_t_||0.1463||-0.0046||0.0932|
|||(0.106)||(0.024)||(0.168)|
|Pre-Shipment_i_,_j_,_p_,_t_|0.0388|-0.0033|0.0099|-0.0245|0.0516|0.1074|
|3|(0.053)|(0.103)|(0.009)|(0.013)*|(0.096)|(0.119)|
|Pre-Ship._i_,_j_,_p_,_t _x GVC Firm_f _,_t_<br>||0.0506||0.0391||-0.0740|
|||(0.079)||(0.010)***||(0.074)|
|QuantityControl_i_,_j_,_p_,_t_|-0.5739|-0.7828|-0.0537|-0.0904|-0.2712|-0.5528|
||(0.158)***|(0.185)***|(0.021)***|(0.026)***|(0.114)**|(0.132)***|
|Quantity Control_i_,_j_,_p_,_t _x GVC Firm_f _,_t_||0.2191||0.0364||0.2449|
|||(0.119)*||(0.020)*||(0.081)***|
|PriceControl_i_,_j_,_p_,_t_|-0.0483|-0.1134|-0.0043|-0.0138|-0.0495|-0.0685|
||(0.040)|(0.064)*|(0.014)|(0.021)|(0.087)|(0.126)|
|Price Control_i_,_j_,_p_,_t _x GVC Firm_f _,_t_||0.0655||0.0102||0.0216|
|||(0.061)||(0.016)||(0.094)|
|Log (Import Demand_f _,_p_,_j_,_t_)|0.0324|0.0321|0.0110|0.0110|-0.0211|-0.0212|
||(0.006)***|(0.006)***|(0.001)***|(0.001)***|(0.008)**|(0.008)**|
|Observations<br>|253212|253212|253212|253212|17221|17221|
|Adjusted_R_<sup>2</sup>|0.96|0.96|0.15|0.15|0.18|0.18|
|Fixed Effects|_f _−_p _−_j_,_f _−_p _−_t_,_j _−_t_|_f _−_p _−_j_,_f _−_p _−_t_,_j _−_t_|_f _−_p _−_j_,_f _−_p _−_t_,_j _−_t_|_f _−_p _−_j_,_f _−_p _−_t_,_j _−_t_|_f _−_p _−_j_,_f _−_p _−_t_,_j _−_t_|_f _−_p _−_j_,_f _−_p _−_t_,_j _−_t_|

This table estimates equation 4 for the intensive margin in columns (1) and (2); extensive margin I in column (3) and (4) and extensive margin II in column (5) and (6). GVC firm is an indicator variable equal to 1 if the firm both imports and exports goods in a given year and 0 otherwise. All columns show coefficients standardized with zero mean and unit standard deviation. All columns use firm-HS6 product-destination, firm-HS6 product-year and destination-year fixed effects. All columns also control for the Inverse Mills Ratios for each NTM type following estimation of probit model of NTM selection based on NTM intensity in neighboring countries. Clustered standard errors at the HS6 product-year level are presented in parenthesis. ∈ _p <_ 0.10,∈∈ _p <_ 0.05,∈∈∈ _p <_ 0.01