metadata: authors: 0: author: Jaud,Melise
1: author: Kukenova,Madina
2: author: Strieborny,Martin
count: World
docna: 0: docna: Export Survival : The Role of Banks and
            Stock Markets
docty: Policy Research Working Paper
abstracts: cdata!: Banks and stock markets play distinct
            roles in helping exporters survive in foreign markets,
            conditional on the specific financial needs of exported
            products. Stock markets rather than banks help exporters who
            lack easily collateralizable tangible assets. Active rather
            than large stock markets promote exports of products
            requiring high levels of working capital. And the trade
            credit can act as a substitute for external financing only
            from banks and only in the presence of well-established
            export links. These results on product-level export survival
            provide new insights into the transmission process from
            finance to the real economy.
display_title: Export Survival : The Role of Banks
            and Stock Markets
listing_relative_url: /research/2023/04/34039394/export-survival-role-banks-stock-markets
topicv3: Financial Sector Development m1327681 1920,Entry and Survival into Exports m1504231 2617,Financial Institutions m1505015 3797
publishtoextweb_dt: 2023-04-11T00:00:00Z
dois: 10.1596/1813-9450-10403
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: 988481
1: author_key: 1091854
2: author_key: 1426676
model_extractions: Policy Research Working Paper 10403 Export Survival The Role of Banks and Stock Markets _Melise Jaud Madina Kukenova Martin Strieborny_ Macroeconomics, Trade and Investment Global Practice April 2023
Policy Research Working Paper 10403

# **Abstract**

Banks and stock markets play distinct roles in helping exporters survive in foreign markets, conditional on the specific financial needs of exported products. Stock markets rather than banks help exporters who lack easily collateralizable tangible assets. Active rather than large stock markets promote exports of products requiring high levels of working capital. And the trade credit can act as a substitute for external financing only from banks and only in the presence of well-established export links. These results on product-level export survival provide new insights into the transmission process from finance to the real economy.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at mjaud@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
# **Export Survival: The Role of Banks and Stock Markets**<sup>_∗_</sup>

Melise Jaud<sup>_†_</sup> Madina Kukenova<sup>_‡_</sup> Martin Strieborny<sup>_§_</sup> JEL classification: G10, G21, F14.

Keywords: banks versus stock markets, transmission from finance to real economy, finance and export survival

> _∗_ The previous version of this paper circulated under the title ”Financial dependence and intensive margin of trade”. We would like to thank Tibor Besedeˇs, Xin Huang, Jean Imbs, Beata Javorcik, Rafael Lalive, Luis Santos-Pinto, Pascal St-Amour and the seminar and conference participants at University of Lausanne, Lund University and the 37th AFFI conference for very useful comments and suggestions. Melise Jaud appreciates the financial support from the Swiss National Science Foundation. Madina Kukenova appreciates the grant received from the Swiss School of Higher Education. Martin Strieborny appreciates the financial support from the Handelsbanken Research Foundation. The most recent version of this paper including an accompanying Online Appendix can be downloaded from www.martinstrieborny.com

> _†_ World Bank; E-Mail: mjaud@worldbank.org

> _‡_ Swiss School of Higher Education; E-Mail: madina.kukenova@sshe.ch

> _§_ University of Glasgow, Adam Smith Business School, Room 469, Main Building, University Avenue, Glasgow, G12 8QQ, United Kingdom. E-Mail: martin.strieborny@glasgow.ac.uk
# **1 Introduction**

Banks and stock markets operate in fundamentally different ways, with stock markets providing a public platform that directly connects investors with firms while banks serve as an intermediary between savers and borrowers. Such structural differences have inspired an extensive literature on the distinct roles of banks and stock markets in the real economy (e.g., Demirg ̈u ̧c–Kunt and Maksimovic 1998, 2002; Levine and Zervos 1998; Beck and Levine 2002; Levine 2002; Beck and Levine 2004; Shen and Lee 2006; Song and Thakor 2010; Demirg ̈u ̧c–Kunt, Feyen, and Levine 2013; Langfield and Pagano 2016). However, the evidence arising from this research is often inconclusive, especially when it comes to the stock markets (see, e.g., Zingales 2015).

This paper examines how domestic banks and stock markets help exporters survive in foreign product markets, moving beyond the traditional focus of existing literature on domestic output. Financial constraints are even more important in international trade than in domestic production due to additional costs like cross-border transport, customs clearance, conforming with foreign product market regulations, etc. The bilateral character of the data in international trade also allows us to exploit the variation in the strength of domestic banks and stock markets across exporting countries while at the same time holding constant the product market structure as well as the legal, regulatory and institutional environment by focusing on a single destination market (the United States). Last but not least, the highly disaggregated product-level data on international trade are available and directly comparable across many different countries at all stages of banking and stock market development.

The focus of our analysis is the different financing needs of exported products that can be addressed by well-developed financial markets and institutions. Certain products rely on extensive investment in physical capital (Rajan and Zingales 1998), high levels of working capital (Raddatz 2006), or a significant use of intangible assets that are less suitable to serve as collateral (Braun 2003; Claessens and Laeven 2003). Two additional transmission channels from finance to the real economy relate to the firm-to-firm trade credit as an alternative source of external finance (Fisman and Love 2003) and to the al1
locative role of the financial sector (Fisman and Love 2007). We compute these different financial needs for narrowly defined industrial sectors and match them with the disaggregated product-level data on international trade. Given that the explored transmission channels might operate differently in the short term and long term, we examine export survival at time horizons ranging from one to ten years. We also allow for the possibility that the size and the activity of domestic stock markets play different roles in shaping export survival at the product level.

Our three main empirical findings are as follows. First, well-developed stock markets rather than strong banks are key in promoting export survival of products from industries with a high share of intangible assets. This result supports the notion that stock markets have an advantage over banks when it comes to promoting innovative sectors of the economy that face increasing returns to scale and a pronounced technological change (see, e.g., Allen 1993; Aghion et al. 2004; Brown, Martinsson, and Petersen 2013, 2017; Hsu, Tian, and Xu 2014). Second, _active_ stock markets play the most important role for the export survival of products with high liquidity needs, possibly reflecting the role active stock markets have in monitoring the efficiency of working capital management (see, e.g., Gill and Biger 2013; Ben-Nasr 2016; Zeidan and Shapir 2017). By contrast, _large_ stock markets are often dominated by banks in promoting these products, especially when it comes to long-term export survival. Third, trade credit operates as a substitute for external finance from banks but not from stock markets. In addition, this substitution works only for _long-term_ export survival, suggesting that the channel can successfully operate only alongside well-established export links where exporters and their foreign customers already enjoy the mutual trust necessary for an extensive use of trade credit.

Overall, our results suggest that banks, large stock markets, and active stock markets all play different roles in shaping product-level export survival, depending on the examined transmission channel and the time horizon under consideration. This relates our work to three distinct strands of the existing literature.

First, the paper is related to the existing literature examining the distinct roles of banks and stock markets. This line of research has investigated the importance of banks 2
and stock markets for output growth (e.g., Demirg ̈u ̧c–Kunt and Maksimovic 1998; Levine and Zervos 1998; Beck and Levine 2004; Shen and Lee 2006), the economic and financial consequences of having a bank-based or a market-based financial system (e.g., Beck and Levine 2002; Demirg ̈u ̧c–Kunt and Maksimovic 2002; Levine 2002; Langfield and Pagano 2016; Bats and Houben 2020), as well as the evolving roles of banks and stock markets during the process of economic development (e.g., Tadesse 2002; Song and Thakor 2010; Demirg ̈u ̧c–Kunt, Feyen, and Levine 2013). We focus on specific transmission channels from banks and stock markets, utilizing disaggregated data on international trade that are available and directly comparable across different countries. One of the channels we examine also relates our work to the recent literature on banks and intangible capital (Dell’Ariccia et al. 2020; Beck et al. 2021) while our findings on large versus active stock markets complement the recent research on different characteristics of stock markets (Bae, Bailey, and Kang 2021).

Second, the paper connects to the strand of literature looking at the role of finance in international trade (e.g., Kletzer and Bardhan 1987; Beck 2002; Greenaway, Guariglia, and Kneller 2007; Manova 2008; Berman and H ́ericourt 2010; Amiti and Weinstein 2011; Ju and Wei 2011; Levchenko, Lewis, and Tesar 2011; Minetti and Zhu 2011; Bricongne _et al._ 2012; Chor and Manova 2012; Becker, Chen, and Greenberg 2013; Muˆuls 2015; Paravisini _et al._ 2015). Several papers in this area look at how the financial sector shapes export entry, export exit or export volume alongside some of the transmission channels that we also explore (e.g., Beck 2003; Svaleryd and Vlachos 2005; Hur, Raj, and Riyanto 2006; Manova 2013; Fan, Lai, and Li 2015; Crino and Ogliari 2017).<sup>1</sup> We focus on the relative importance of banks and stock markets while also allowing for distinct roles for active versus large stock markets. In terms of the outcome variables, we explore both short-term and long-term export dynamics by looking at the export survival across

> 1 Rather than investigating these transmission channels, several other papers look at the role of banks and stock markets in international trade from different angles. Cho et al. (2019) develop a model linking bank and bond financing with firm size distribution, gains from trade, and the real exchange rate. Amissah et al. (2021) explore theoretically and empirically the bi-directional relationship between financial infrastructure and comparative advantage of countries.

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different time horizons.<sup>2</sup> Finally, we examine two additional transmission channels from finance to the real economy that have so far attracted little attention in the finance-trade literature.

Third, the paper relates to the trade survival literature (e.g., Besedeˇs and Prusa 2006a, 2006b; Nitsch 2009; Brenton, Saborowski, and von Uexkull 2010; Besedeˇs and Prusa 2011; B ́ek ́es and Murak ̈ozy 2012; Hess and Persson 2012; Besedeˇs 2013; Cadot _et al._ 2013; Albornoz, Fanelli, and Hallak 2016; Araujo, Mion, and Ornelas 2016). A few papers explore particular aspects of the relationship between finance and export survival. Jaud, Kukenova, and Strieborny (2018) document the disciplining impact of banks on exporting managers, Jaud, Kukenova, and Strieborny (2015, 2019) investigate the importance of financial frictions in the context of agricultural exports, and Besedeˇs, Kim, and Lugovskyy (2014) use the survival analysis to compute a measure of project risk.<sup>3</sup> However, the trade survival literature has so far not systematically looked at the distinct roles of banks and stock markets or the different transmission channels from finance to export survival at different time horizons.

The rest of the paper is organized as follows. Section 2 outlines our empirical approach. Section 3 describes the construction of our dataset. Section 4 discusses the results about the relative importance of banks and stock markets alongside our three main transmission channels. Section 5 explores if and how these results change when we distinguish between large and active stock markets. Section 6 reports the results for two additional transmission channels and other tests. Section 7 concludes and suggests directions for further research.

> 2 The finance-trade literature often focuses on the extensive margin of international trade and measures export success in terms of export entry. However, most exports cease shortly after they start without leaving a significant mark on the long-term export performance of a country (see, e.g., Besedeˇs and Prusa 2006a, 2006b; B ́ek ́es and Murak ̈ozy 2012; Albornoz, Fanelli, and Hallak 2016). Export survival is also an important component of the intensive margin of international trade that often sets apart the successful developing countries from the unsuccessful ones (Besedeˇs and Prusa 2011).

> 3 Bridges and Guariglia (2008) and G ̈org and Spaliara (2014) look at how export status affects the link between financial factors and firm survival but do not examine the link between finance and trade. Jaud, Kukenova, and Strieborny (2021) examine export survival but focus on the role of foreign investors rather than the domestic financial system.

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# **2 Empirical Approach**

We examine the importance of well-developed banks and stock markets for export success at different time horizons through the lenses of five well-established transmission channels from finance to the real economy. Subsection 2.1 describes in more detail the transmission channels, while Subsection 2.2 discusses our estimation strategy.

## **2.1 Transmission Channels**

The transmission channels examined in this paper have been originally developed in papers exploring the impact of finance on economic growth (Rajan and Zingales 1998; Braun 2003; Claessens and Laeven 2003; Fisman and Love 2003, 2007; Raddatz 2006). As outlined below, these channels might be even more important in the context of finance and international trade.

**Investment needs.** Firms in certain industries do not generate sufficient cash flows to maintain the necessary level of physical capital from internal financial sources alone. Typical technological reasons for high investment needs include the large scale of a typical investment project or a long gestation period (time span between the start of an investment project and the start of actual production making use of this project) in a given industry. Consequently, the firms in these industries need to rely on providers of external finance like banks and stock markets to fund a significant part of their investment needs (Rajan and Zingales 1998). This would be even more true in the case of exporting that requires substantial additional investments due to adjusting of products to different consumer preferences, satisfying regulatory requirements in foreign countries, establishing and maintaining distribution networks abroad, etc.

**Liquidity needs.** Firms in certain industries require high levels of liquid funds (working capital) to maintain their operations. Typical technological reasons for high liquidity needs are a long production process or the necessity to hold a significant amount of inventories in a given industry. A smooth operating performance in these industries would therefore generally require external funding provided by banks or stock markets (Raddatz 2006). Again, exporting activities magnify liquidity needs, because the time-intensive cross-border shipping, customs clearance, and product distribution in a foreign country

5
further increase the time lag between paying for the purchased inputs and receiving payments for the sold products.

**Asset intangibility.** Firms in certain industries utilize a high share of intangible assets in their production process. The examples of such assets include intellectual property, client lists, blueprints and building designs, brand recognition, human and organizational capital, etc. Intangible assets are less suitable to serve as collateral because they are less durable and can be more easily stolen by competitors or employees. Intangible assets also have a limited liquidation value, both because they are often industry-specific or even firm-specific, and because the management can more easily hide them from the providers of external finance in case of the firm’s default. The lower collateral and liquidation value of intangible assets present an important obstacle for obtaining the necessary external finance (Shleifer and Vishny 1992; Rampini and Viswanathan 2010; Falato et al. 2020). Importantly, it presents a bigger problem in financially underdeveloped countries characterized by less efficient monitoring and screening of borrowers and worse protection of the rights of investors and debtholders. Firms operating in financially underdeveloped countries therefore usually need to possess easily collateralizable tangible assets like plants and machinery in order to obtain the necessary external financing. Consequently, industries relying for technological reasons on a high share of intangible assets would benefit disproportionately more from being located in a financially developed country (Braun 2003; Claessens and Laeven 2003). Given the higher need for external finance in crossborder trade, this would arguably be even more the case when it comes to the exporting activities of such industries.

**Trade credit.** Borrowing from business partners in the form of trade credit can serve as a substitute for external financing by banks and stock markets. Therefore, firms in industries that rely on trade credit financing should benefit comparatively _less_ from the development of formal financial institutions and markets (Fisman and Love 2003). However, this alternative financing channel often relies on well-established trust between buyers and suppliers of given products. When it comes to production growth of industries dependent on trade credit, the effect is therefore more visible in the growth of already established

6
firms rather than in entry of new firms (Fisman and Love 2003). Well-established trust between providers and recipients of trade credit is arguably even more important in international trade where higher risk and longer time spans between production and final delivery put an additional burden on the business partner who provides the trade credit. One would therefore expect trade credit to matter especially for the long-term export survival of products already established in the foreign markets and to matter rather less for the immediate survival of new products that have just entered these markets.

**Global growth opportunities.** Fisman and Love (2007) argue that a well-developed financial system increases an economy’s resource allocation towards firms and sectors that have better growth opportunities. They suggest this allocative transmission channel as an alternative to the channel of investment needs by Rajan and Zingales (1998) and demonstrate that global growth opportunities perform better than investment needs in explaining the impact of the overall financial development on industrial growth. We examine how these results translate into the context of international trade while considering different time horizons of export survival and separate measures for the strength of banks and stock markets.

# **2.2 Estimation Strategy and Empirical Specifications**

An empirical model inspired by Araujo, Mion, and Ornelas (2016) provides us with a unified framework to explore export survival at different time horizons, allowing for the possibility that the examined transmission channels operate differently in the short term and long term. In contrast to the non-linear estimators like logit or probit, this linear probability model also facilitates the inclusion of full sets of diverse fixed effects that are an indispensable part of the identification strategy based on Rajan and Zingales (1998). This difference-in-difference estimation strategy employs proxies of financial vulnerability at the level of industrial sectors that capture the exogenous financing needs driven by technological characteristics of different industrial sectors including the scale of a typical investment project, length of the production process, or typical amount of inventories (see, 7
e.g., Rajan and Zingales 1998, Braun and Larrain 2005; Raddatz 2006).<sup>4</sup> This represents the main advantage of these sectoral measures over firm-level financial variables. The data observed at the firm level are by their very nature a result of both the external financial constraints facing the firm and the endogenous financial decisions of the firm itself, making causal inferences difficult.

Combining the trade survival framework of Araujo, Mion, and Ornelas (2016) with the difference-in-differences identification strategy inspired by Rajan and Zingales (1998) yields the following empirical model: where the dependent variable is the probability of export survival of product _k_ from industrial sector (industry) _i_ exported by country _c_ to the United States. The export survival probability is measured _l_ years ( _l_ = 1 _,_ 5 _,_ 10) after the beginning of export spell ( _t_ 0).<sup>5</sup> Our main variables of interest are the interaction terms of financial vulnerability with banking and stock market development ( _FinancialV ulnerabilityi ∗ Banksc,t_ 0, _Financial Vulnerability i_ * _StockMarkets c,t_ 0). The direct effects of the individual components of these interaction terms ( _FinancialV ulnerabilityi_ , _Banksc,t_ 0, _StockMarketsc,t_ 0) are absorbed by the product and country-time fixed effects ( _δk_ , _δc∗t_ 0).

We use different measures of financial vulnerability to explore different transmission channels from finance to export survival: investment needs, liquidity needs, and asset intangibility. Equation 1 thus comprises nine different specifications that look at the relative importance of banks and stock markets for export survival at three different time

> 4 Other finance papers using the difference-in-differences identification strategy of Rajan and Zingales (1998) include Cetorelli and Gambera (2001), Braun and Larrain (2005), Cetorelli and Strahan (2006), Kroszner, Laeven and Klingebiel (2007), Gupta and Yuan (2009), Strieborny and Kukenova (2016) and many others. Several papers have used similar methodology outside the survival framework when looking at financial or non-financial determinants of international trade. The former include Beck (200), Svaleryd and Vlachos (2005), Hur, Raj, and Riyanto (2006), Manova (2013), Fan, Lai, and Li (2015), Crino and Ogliari (2017), while the latter include Romalis (2004), Levchenko (2007) or Nunn (2007).

> 5 In this survival framework, we thus estimate a linear probability model for various lengths of export spells. See Araujo, Mion, and Ornelas (2016) for a more detailed and more technical discussion.

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horizons alongside three different transmission channels. A positive coefficient _β_ 1 and/or _β_ 2 would suggest that well-developed banks and/or stock markets particularly promote export survival of products from industries that suffer from a given source of financial vulnerability.

As an example, let us pick from the nine variants of Equation 1 the one looking at export survival after 10 years ( _Skic_<sup>_l_=10</sup><sup>_/t_0</sup> ) and focusing on the high share of intangible and thus not easily collateralizable assets in the production process as the source of financial vulnerability.<sup>6</sup> In this specification, an insignificant coefficient _β_ 1 for the interaction term _AssetIntangibilityi ∗ Banksc,t_ 0 and a positive and significant coefficient _β_ 2 for the interaction term _AssetIntangibilityi ∗ StockMarketsc,t_ 0 would have the following economic interpretation: It is only well-developed stock markets but not well-developed banks that promote the long-term export survival of financially vulnerable products from industries that for technological reasons rely on a high share of intangible assets.

In some estimations, we also control for two additional transmission channels from finance to the real economy that are related to the firm-to-firm trade credit as an alternative source of external finance (Fisman and Love 2003) and to the allocative role of the financial sector (Fisman and Love 2007).

All regressions are estimated by OLS with robust standard errors clustered at the country-time level ( _c ∗ t_ 0), where country _c_ refers to the exporting country and time _t_ 0 refers to the beginning of a given export spell.

All estimations contain a full set of product-level fixed effects ( _δk_ ) and fixed effects at the country-time level ( _δc∗t_ 0). The product-level fixed effects control for all product characteristics that could affect the probability of export survival (e.g., large weight or volume of a product increasing the shipping costs). The product fixed effects also absorb the direct effects of all industry characteristics entering the interaction terms included in the regressions (investment needs, liquidity needs, asset intangibility, physical and human capital intensity). The country-time fixed effects (i.e., the interacted fixed effects of the exporting country _c_ and the year _t_ 0 when a given export spell started) control for

> 6 This variant of Equation 1 thus writes: _Skic_<sup>_l_=10</sup><sup>_/t_0</sup> = _β_ 1 _AssetIntangibilityi ∗ Banksc,t_ 0 + _β_ 2 _AssetIntangibilityi ∗ StockMarketsc,t_ 0 + **controls** _kic,t_ 0 _φ_ + _δk_ + _δc∗t_ 0 + _εkic,t_ 0

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all time-varying characteristics of the exporting countries that could affect the chances for the subsequent export survival. Consequently, they absorb the direct effects of those time-varying country characteristics that enter our interaction terms (various dimensions of bank and stock market development, GDP per capita, real exchange rate, endowments of the exporting country with physical and human capital). The country-time effects also control for all time-invariant country characteristics potentially affecting export performance (country size, access to the sea, etc.) and time-varying conditions in the world market (global business cycle, technological progress reducing shipping costs, etc.).<sup>7</sup> Our focus on a single destination market allows for a cleaner identification strategy that relies on a more parsimonious set of fixed effects when controlling for potential sources of the omitted variable bias. In our setting, the included (exporting)country-time fixed effects also control for time-varying origin-destination characteristics like political conflicts or deepening economic interdependence between the exporting and the importing country. This alleviates the need for including complex origin-destination-time fixed effects into our regressions. Similarly, the included product effects also control for various observable and unobservable destination-product characteristics.

Any remaining omitted variable bias affecting the estimates for _β_ 1 and _β_ 2 would need to work simultaneously via time-varying country characteristic correlated with our measures of bank and stock market development and via industry characteristic correlated with our measures of financial vulnerability. To take care of this possibility, we include in all regressions the interactions of financial vulnerability measures with both GDP per capita and real exchange rate of the exporting country, as well as the interactions of physical (human) capital intensity in a given industry with physical (human) capital endowment in a given country. The row vector of control variables ( **controls** _kic,t_ 0) also includes product-level control variables that vary across countries and time: initial export, total export, number of suppliers, and a multiple spell dummy.

> 7 See also the discussion of the fixed effects in the context of the applied difference-in-differences methodology in Raddatz (2006, p. 682-683) or Cetorelli and Strahan (2006, p. 442-443).

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# **3 Data**

Our unit of analysis is an export spell defined as the length (in years) that a country _c_ exports a particular good _k_ to the United States without interruption. There can be multiple spells for a given country-product pair if a country ceases and then re-starts exporting the same product to the US destination market. All time-varying explanatory variables are measured at the beginning of a given export spell.

Table 10 in the Appendix shows summary statistics for the main variables used in our analysis. When controlling for all variables of interest, our final database consists of 252,147 export spells of 3,300 HS 6-digit products exported from 71 countries to the United States over the period 1995-2011. Online Appendix A provides the list of exporting countries in our sample.

## **3.1 Financial Vulnerability**

The standard empirical proxies capturing the different financial vulnerabilities have been developed in a series of papers that follow different industrial classifications at different levels of aggregation. Rajan and Zingales (1998) compute investment needs for 36 different sectors at the level of 3-digit and 4-digit ISIC classification, Claessens and Laeven (2003) compute asset intangibility for 20 sectors following the SIC 2-digit classification, and Raddatz (2006) computes liquidity needs for 70 sectors at the ISIC 4-digit level. Fisman and Love (2003, 2007) follow the definition of 36 ISIC sectors used by Rajan and Zingales (1998) when computing their measures of trade credit dependence and global growth opportunities.

We recompute these five measures at a significantly more disaggregated level of 192 industrial sectors (120 4-digit and 72 3-digit sectors according to the SIC classification). After merging these measures with our trade data and other control variables, the final sample comprises 174 industrial sectors (108 4-digit and 66 3-digit SIC sectors). Defining financial vulnerability across narrowly defined sectors not only allows for a more precise measurement, but it also represents a better fit with the highly disaggregated data on 11
international trade described in Subsection 3.3.<sup>8</sup> To ensure the comparability of different measures capturing different aspects of financial vulnerability, we compute the measures in a unified way and normalize them to be between zero and one.<sup>9</sup> Following Rajan and Zingales (1998), we compute _Investment needs_ as the difference between capital expenditures (Compustat item 128) and cash flow (Compustat item 110), divided by capital expenditures.<sup>10</sup> This proxy captures the share of the necessary investment into physical capital that cannot be financed from internally generated funds and therefore requires external financing. The reliance on large US firms covered in Compustat in the computation assures that the resulting share reflects the investment that a representative firm in the industry truly requires but cannot finance itself. For smaller US firms and most other firms around the world, the observed use of external financing represents an equilibrium outcome between the firm’s demand and the available supply from local banks and stock markets. By contrast, large publicly traded firms in the US face only minimal frictions in accessing external finance so that the observed amount of their external funding represents a good measure of their actual financing needs (Rajan and Zingales 1998).

Following Raddatz (2006), we compute _Liquidity needs_ as the median ratio of total inventories to sales (i.e. dividing Compustat item 3 by Compustat item 12). A higher

> 8 The use of more broadly defined sectors in the original papers fits with the prevailing aggregate focus of the finance-growth literature at that time. Existing trade literature has mostly followed these broader industries when using the difference-in-difference identification strategy adopted in this paper. More recently, both finance and trade papers using disaggregated data have started to recompute some of these measures for narrowly defined industrial sectors. Crin`o and Ogliari (2017) recompute investment needs and asset tangibility for 273 SIC industries in their study on financial frictions and product quality in international trade. Tong and Wei (2011) recompute investment needs and liquidity needs for around 250 SIC industries when investigating the impact of financial crisis on the firm-level stock market returns.

> 9 In the first step, we compute median value of a given measure for each US firm in Compustat over the period 1989-2006. In the second step, we look at the computed measures for all firms in a given industry and take the value of the median firm to represent the industry, following the approach of Raddatz (2006). Other approaches used in the original papers include taking mean rather than median in the first step or eliminating the two-steps procedure altogether and take an average value across all firm-year observations in a given industry in just one step. More fundamentally, none of the measures in the original papers is normalized in order to allow for better comparability with other measures. This is only natural given that each of these papers focuses primarily on one particular transmission channel and the measure of financial vulnerability associated with it.

> 10 Compustat reports data for different firms and years in different formats. For formats 1/2/3, we compute the cash flow as the sum of following variables: funds from operations (operating income + depreciation), decreases in inventories, decreases in receivables, and increases in payables. For format 7, we use the variable cash flow directly provided in Compustat.

12
value for this ratio means a lower share of investment in inventories that can be financed by ongoing revenue, suggesting a higher level of liquidity needs that have to be financed externally. The reason for focusing on inventories rather than the overall investment in working capital is to capture the technologically driven exogenous part of liquidity needs. The completion of final goods take longer in certain industries, requiring higher inventory values during the production process. This technological link is arguably weaker for other parts of liquid assets like cash (Raddatz 2006, p. 685).

Following Claessens and Laeven (2003), we measure _Asset intangibility_ as the ratio of the net value of intangible assets (Compustat item 33) to the net fixed assets (Compustat item 8).<sup>11</sup> In Compustat, the intangibles include the value of blueprints or building designs, patents, copyrights, trademarks, franchises, organization costs, client lists, computer software patent costs, licenses, and goodwill. The ratio thus measures for a representative firm in a given industry the relative importance of assets that are less suitable to serve as collateral due to their lack of physical manifestation as opposed to the easily collateralizable fixed assets.

The proxies for our two additional transmission channels also rely on Compustat data on large US firms. Following Fisman and Love (2003), we compute _Trade credit dependency_ as the ratio of accounts payable (Compustat item 70) to total assets (Compustat item 6). The ratio represents the share of total assets that is financed by trade credit, capturing the ability of a representative firm in a given industry to rely on informal credit from its business partners rather than on formal financing from financial markets and intermediaries. We measure _Global growth opportunities_ by the actual growth in real sales for the representative firm in a given industry in the US. This proxy is based on the argument outlined in Fisman and Love (2007) that large publicly traded US firms respond optimally to worldwide industry-specific shocks to growth opportunities, making the actual growth rate of these firms a good proxy for these growth opportunities.

Table 1 shows that all five measures are only weakly and often negatively correlated with each other. The measures thus seem to represent genuinely distinct transmission

> 11 The net fixed assets equal gross property, plan and equipment (Compustat item 7) minus accumulated depreciation, depletion and amortization (Compustat item 196).

13
Table 1: Correlations among the measures of financial vulnerability

||Investment needs|Liquidity needs|Asset intangibility|Growth opp.|Trade credit|
|---|---|---|---|---|---|
|Investment needs|1|||||
|Liquidity needs|-0.0497|1||||
|Asset intangibility|-0.1822|-0.1346|1|||
|Growth opportunities|0.2399|0.0512|-0.1042|1||
|Trade credit|-0.0391|-0.0693|-0.1704|0.0256|1|

The table reports correlations among five measures of financial vulnerability described in Subsection 3.1. All measures are computed at the level of the 4-digit and 3-digit sectors according to the SIC classification.

channels from finance to the real economy. A closer look at the exposure of individual industries to the three main sources of financial vulnerability further reinforces this point. For example, the production process in the cigarettes industry (SIC code 2111) is highly dependent on intangible assets. At the same time, the cigarettes industry generates high levels of cash flow, which makes it less dependent on external funding for its investment needs. Another example involves the two industries representing publishing and printing of newspapers (SIC code 2711) and periodicals (SIC code 2721) that face low liquidity needs but use high levels of intangible assets in their production process. Online Appendix B provides more details by listing 15 industries with both the lowest and the highest levels of investment needs, liquidity needs and asset intangibility.<sup>12</sup> Given that all five variables are measured at the industry level, their direct effects are captured in our regressions by the included product fixed effects ( _δk_ ). Consequently, the variables described in this subsection enter the regressions explicitly only as part of interaction terms with the measures capturing the strength of financial institutions and markets that are described in the next subsection.

# **3.2 Financial Institutions and Markets**

Our main measure capturing the strength of financial institutions in the exporting country is called _Banks_ . This standard measure of a well-developed banking sector represents the ratio between credit provided by deposit-taking banks to the private sector and GDP of a given country. In additional tests, we also use two alternative measures looking at the broader role played by financial institutions in the real economy. _Bank assets_ represent the value of the claims held by deposit-taking banks vis-a-vis the domestic real

> 12 As we normalize all measures to be between zero and one, the value for the least (most) exposed industry for every measure is zero (one).

14
non-financial sector, again normalized by the GDP level in a given country. _Total credit_ represents the ratio of all credit to the private sector over GDP. The variable thus includes credit provided both by deposit-taking banks and by other financial institutions.

Our main measure of the depth of financial markets in the exporting country is called _Stock markets_ . It represents the ratio between stock market capitalization (the market value of all listed stocks) and GDP of a given country, and it is a standard measure of well-developed stock markets. _Stock market turnover_ (the ratio between the value of stock market transactions and the stock market capitalization) serves as a proxy for an active and liquid (rather than large) stock market. In additional tests, we also use _Stock market value traded_ (the value of stock market transactions divided by GDP).

The data are from the Global Financial Development Database that builds on the earlier work by Beck et al. (2000) and has been substantially extended by Cih ́ak<sup>ˇ</sup> et al. (2012). The database contains various indicators of financial development across countries and over time and is regularly updated.

All measures representing financial institutions and markets vary over time. We measure them at the beginning of a given export spell in order to capture the impact of the predetermined financial environment on the subsequent export performance. The direct effects of these time-varying country-level financial variables are thus captured in our regressions by the included country-time fixed effects ( _δc∗t_ 0), with time referring to the beginning of a given export spell ( _t_ 0). Consequently, the variables described in this subsection enter the regressions explicitly only as part of interaction terms with the industry-level measures of financial vulnerability.

# **3.3 Product-Related Trade Variables**

The export survival rate in the US market and all trade-related control variables at the product level (initial export, total export, number of suppliers, a multiple spell dummy) are computed using the BACI dataset developed by the CEPII and described in Gaulier and Zignago (2010). The dataset provides harmonized bilateral trade flows for more than 5,000 HS 6-digit products and 143 countries. Export flows are reported annually 15
in values (thousands of US dollars) and quantities.<sup>13</sup> Trade data at the HS 6-digit level allows us to account for export failures at a detailed product level which would otherwise be undetected when using more aggregated industry data. In our estimations, we focus on the manufacturing exports to the United States during the 1995-2011 period.<sup>14</sup> The three time-varying variables (initial export, total export, number of suppliers) are measured at the beginning of the export spell. The terms ‘given year’ or ‘time’ thus refer to the initial year of a given export spell. _Initial export_ is measured at the productcountry-time level and represents the export value of a given product exported by a given country to the US market in the initial year of exporting. The variable captures the levels of mutual trust and expectations about the potential future exports that exist among the trade partners at the beginning of a new trade relationship. _Total export_ is measured at the product-country-time level and represents the overall value of the country’s exports of a given product to the world market in a given year. It serves as a proxy for the size and overall export performance of a given exporting country in that given product. _Number of suppliers_ is measured at the product-time level and counts the number of countries that export a given product in a given year to the United States. The variable can represent both the level of competition and the potential market size for a given product in the US destination market.

A dummy variable called _Multiple spell_ is measured at the product-country level. It equals one if a given country exports a given product to the United States during two or more separate export spells, identifying instances when the country at some point stops exporting a product but later re-enters the US market with the same product again. The inclusion of this variable accounts for the possibility that repeated exits and re-entries into exporting of the same product to the same destination might affect this product’s chances for export survival at various time horizons.

> 13 We do not need export values for measuring the export survival rate but we do use them for constructing some of the trade-related control variables.

> 14 We start our sample period in 1995 due to the high number of missing values before 1994. In particular, we are using BACI in HS classification from 1992 that covers the period 1994-2011. As the survival analysis relies on the length of export spells, we cannot use the data from the initial year. This leaves us with the data for 1995-2011. We finish our sample period in 2011 to match the time period for which we have data for all our industry-level variables.

16
## **3.4 Other Control Variables**

We include in our regressions also several country-level and industry-level variables from the real economy. The real _GDP per capita_ is reported in constant 2005 US dollars. _Real exchange rate_ is computed as the nominal effective exchange rate (a measure of the value of a currency against a weighted average of several foreign currencies) divided by a price deflator or index of costs. The source for both variables is the World Development Indicators of the World Bank. We also control for countries’ endowments with physical and human capital, relying on data from the Penn World Table (version 8.1). The stock of _physical capital_ per worker in a given country is constructed according to the perpetual inventory method. _Human capital_ per worker is calculated from the average years of schooling in a given country using attainment data. These time-varying economic characteristics of exporting countries are measured at the beginning of a given export spell. We also include two time-invariant industry-level characteristics - _physical capital intensity_ and _human capital intensity_ . Both variables are from Braun (2003) and are measured at the ISIC 3-digit level.

The direct effects of these real-economy variables are absorbed by the country-time and product fixed effects included in our empirical specifications. However, these control variables do explicitly enter the regressions as parts of various interaction terms.

# **4 Banks vs Stock Markets**

This section relies on standard measures of well-developed banks and stock markets when examining their relative importance for export survival at different time horizons. Subsection 4.1 provides preliminary graphical evidence, both by capturing the overall impact of banks and stock markets on export survival, and by further exploring the transmission channels related to high investment needs, high liquidity needs, and asset intangibility. Subsection 4.2 reports full results from additional regressions focusing on these three channels from finance to export survival (Equation 1).

17
# **4.1 Graphical Evidence**

Figure 1a and Figure 1b report the Kaplan-Meier survival functions that capture the probability of export spells (continuous exporting of a given product from a given country to the US market) to survive after year 1,2, etc. The survival probability in the first year is one by default given the annual frequency of the data. Figure 1a compares the average survival in the US market for products exported from countries with a welldeveloped versus underdeveloped domestic banking sector. The solid line captures the export survival for products from countries at the 25th percentile of banking development while the dashed line captures the export survival for products from countries at the 75th percentile of banking development. Analogously, Figure 1b compares average export survival for products from countries at the 25th percentile versus the 75th percentile of the stock market development. In both figures, the dashed line is located above the solid line, suggesting that products have better survival chances in the US market if they are exported from countries with a strong domestic banking sector or a well-developed stock market.

Figure 1: Export survival and financial development <!-- Start of picture text --> (a) Banking development (b) Stock market development<br><!-- End of picture text --> <!-- Start of picture text --> 0 5 10 15 20 0 5 10 15 20<br>Years elapsed Years elapsed<br>Low High Low High<br>1.00 1.00<br>0.75 0.75<br>0.50 0.50<br>Survival probability Survival probability<br>0.25 0.25<br>0.00 0.00<br><!-- End of picture text --> Note: The solid line (denoted ‘Low’) captures the export survival for products from countries at the 25th percentile of banking (stock market) development. The dashed line (denoted ‘High’) captures the export survival for products from countries at the 75th percentile of banking (stock market) development.

Next, we explore the specific transmission channels that could explain the reducedform relationship between finance and export survival from Figure 1a and Figure 1b. Figure 2 examines these transmission channels in the context of strong banks, and Fig18
<!-- Start of picture text --> Re Investment needs a Liquidity needs & Asset intangibility<br>wo pels wo<br>woio} wo° wofo}<br>Oo lo} Oo<br>wo wo wo<br>° ° io}<br>MaG2) Si Ml s5es6y “77 & PHO] "a Bone! 4S ewe Si 9 HO "Al eresrdy S i6eyess: 91 IO<br>Years elapsed Years elapsed Years elapsed<br>— Coef. estimate 90th percentile<br><!-- End of picture text -->
<!-- Start of picture text --> Investment needs Liquidity needs Asset intangibility<br>N TM N<br>Oo Oo oO<br>"47234567890 '1234567890'12345 67 8 9 10<br>Years elapsed Years elapsed Years elapsed<br>— Coef. estimate 90th percentile<br><!-- End of picture text -->
# **4.2 Main Channels**

This subsection provides a further analysis of the three transmission channels from welldeveloped financial markets and institutions to the export survival at the product level. Additionally to the fixed effects and trade-related control variables included in regressions summarized by Figure 2 and Figure 3, the regressions reported both in this subsection and in the remainder of the paper control also for non-financial channels related to the Heckscher-Ohlin forces of comparative advantage in international trade, the economic development, the business cycle fluctuations, and changes in the real exchange rate. As we now report extensive regression tables rather than summarizing graphs, we do not provide all results for the export survival after 1,2,...,10 years but focus on the export survival at the one, five, and ten year time horizon.

Table 2 looks at the transmission channel related to products from industries with high investment needs. In columns (1) to (3) of Table 2, we look whether a well-developed banking system particularly promotes such products, taking as a dependent variable the export survival after one, five, and ten years. In columns (4) to (6) of Table 2, we focus on the importance of stock markets, again looking at the product-level export survival after one, five, and ten years. A positive and significant coefficient for the main interaction term (investment needs interacted with banks in the first three columns, investment needs interacted with stock markets in the last three columns) would confirm that providing external finance for investment into physical capital is an important transmission channel from finance to export survival. We also control for the non-financial transmission channels related to economic development and currency value, by interacting our measure of investment needs with both GDP per capita and real exchange rate. Furthermore, we include other control variables that could affect the probability of export survival at the product level - initial export, total export, number of suppliers, multiple spell dummy, and interactions of physical (human) capital intensity at the industry level with physical (human) capital endowment at the country level. The last two interaction terms are based on the standard Heckscher-Ohlin theory of international trade and control for the importance of countries’ factor endowments in shaping the international trade flows.

21
Table 2: Banks, stock markets, and investment needs

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Dep. var.: Survival|1y|5y|10y|1y|5y|10y|
|investment needs _×_ banks|-0.011|0.034|0.056<sup>_b_</sup>||||
||(0.047)|(0.033)|(0.027)||||
|investment needs _×_ stock markets||||0.034|0.062<sup>_b_</sup>|0.057<sup>_a_</sup>|
|||||(0.040)|(0.026)|(0.020)|
|investment needs _×_ GDPpc|-0.004|-0.005|-0.010|0.006|-0.002|0.002|
||(0.018)|(0.012)|(0.010)|(0.017)|(0.011)|(0.009)|
|investment needs _×_ real exchange rate|-0.642|-0.396|-0.407<sup>_c_</sup>|-1.933<sup>_b_</sup>|-0.703|-0.420|
||(0.464)|(0.258)|(0.215)|(0.874)|(0.638)|(0.528)|
|initial export|0.020<sup>_a_</sup>|0.016<sup>_a_</sup>|0.012<sup>_a_</sup>|0.020<sup>_a_</sup>|0.016<sup>_a_</sup>|0.012<sup>_a_</sup>|
||(0.002)|(0.002)|(0.001)|(0.002)|(0.002)|(0.001)|
|total export|0.041<sup>_a_</sup>|0.032<sup>_a_</sup>|0.024<sup>_a_</sup>|0.041<sup>_a_</sup>|0.032<sup>_a_</sup>|0.024<sup>_a_</sup>|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|number of suppliers|0.003<sup>_a_</sup>|-0.001<sup>_a_</sup>|-0.004<sup>_a_</sup>|0.003<sup>_a_</sup>|-0.001<sup>_b_</sup>|-0.004<sup>_a_</sup>|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.001)|(0.000)|
|multiple spell|-0.042<sup>_a_</sup>|-0.268<sup>_a_</sup>|-0.351<sup>_a_</sup>|-0.044<sup>_a_</sup>|-0.274<sup>_a_</sup>|-0.360<sup>_a_</sup>|
||(0.008)|(0.015)|(0.021)|(0.008)|(0.016)|(0.022)|
|phys. cap. intensity _×_ physical capital|-0.139<sup>_b_</sup>|-0.166<sup>_a_</sup>|-0.100<sup>_b_</sup>|-0.047|-0.080|-0.023|
||(0.067)|(0.047)|(0.040)|(0.079)|(0.053)|(0.043)|
|hum. cap. intensity _×_ human capital|0.274<sup>_a_</sup>|0.265<sup>_a_</sup>|0.221<sup>_a_</sup>|0.279<sup>_a_</sup>|0.289<sup>_a_</sup>|0.244<sup>_a_</sup>|
||(0.027)|(0.021)|(0.019)|(0.031)|(0.024)|(0.021)|
|Observations|252,147|252,147|252,147|243,509|243,509|243,509|
|R-squared|0.263|0.546|0.692|0.254|0.547|0.695|
|Country-Time FE|yes|yes|yes|yes|yes|yes|
|Product FE|yes|yes|yes|yes|yes|yes|

Dependent variable is the probability of export survival of product _k_ from industrial sector (industry) _i_ exported by country _c_ to the USA. Export survival probability is measured _l_ years after the beginning of export spell, with _l_ = 1 in columns (1) and (4), _l_ = 5 in columns (2) and (5), and _l_ = 10 in columns (3) and (6). The regressions are estimated by OLS and contain a full set of fixed effects at the product level and the (exporting country)*time level, with time referring to the beginning of a given export spell. Investment needs is defined at the industry level _i_ and represents the difference between capital expenditures and cash flow, divided by capital expenditures. Banks represents the ratio between credit from deposit-taking banks to the private sector and GDP in a given exporting country _c_ . Stock markets is the ratio between stock market capitalization and GDP of a given exporting country _c_ . Other variables entering regressions directly or as a part of interaction terms include GDPpc (GDP per capita in country _c_ reported in constant 2005 US dollars), real exchange rate (nominal effective exchange rate divided by price deflator or index of costs of country _c_ ), initial export (export value of a product _k_ exported by country _c_ to the US in the initial year of exporting), total export (value of all exports from country _c_ to the world market), number of suppliers (number of countries exporting product _k_ to the US), multiple spell (dummy variable that equals one if country _c_ exports product _k_ to the US during more than one export spell), and interaction terms between physical and human capital endowments of country _c_ and the corresponding capital intensities at the industry level. All time-varying explanatory variables are measured at the beginning of the export spell. Robust standard errors are clustered at the (exporting country)*time level, with time referring to the beginning of a given export spell. _a_ , _b_ , _c_ denote statistical significance at the 1%, 5%, and 10% level, respectively.

The results reported in Table 2 suggest that stock markets play a more robust role than banks when it comes to facilitating exports of products with high investment needs. While banks have a significant role for the export survival only at the longest horizon of ten years (third column), stock markets promote export survival of these products at horizons of both five and ten years (fifth and sixth column). As for the non-financial channels, economic development seems to play no particular role in promoting exports of products with high investment needs as documented by the insignificant results for the interaction 22
term of investment needs and GDP per capita. The interaction term of investment needs and real exchange rate is also insignificant in four out of six specifications.<sup>16</sup> Table 3 explores the transmission channel related to high liquidity needs of products from industries that require large amount of working capital. Again, we explore separately the role of banks (first three columns) and stock markets (last three columns), looking at export survival over the horizons of one, five, and ten years as our dependent variable. The results suggest that strong banks play a somewhat more important role than deep stock markets in promoting export survival of products with high liquidity needs, especially in the longer term. In the case of the immediate survival over a one-year horizon, both banks and stock markets promote exports of products with high liquidity needs as attested by positive and significant main interaction terms both in column (1) and in column (4) of Table 3. At the time horizon of five years, the significance level for the main interaction term is higher for banks in column (2) than for stock markets in column (5) of Table 3. And when it comes to the very long-term survival of ten years reported in columns (3) and (6) of Table 3, only banks seem to play a significant role. As for the non-financial channels, the interaction term including real exchange rate is insignificant in four out of six specifications, similarly to the previous table. However, the interaction term of liquidity needs and GDP per capita is positive and significant in all six columns of Table 3, suggesting that a higher level of economic development over-proportionately benefits the exports of products that require external finance to fund their working capital.

Table 4 examines the transmission channel linked to products from industries with a high share of intangible assets. When it comes to promoting exports of such products, it is only deep stock markets and not strong banks that matter. The interaction term between asset intangibility and banks is insignificant in the first three columns, while the interaction term of asset intangibility with stock markets is positive and highly significant in the last three columns of Table 4. As for the non-financial channels, economic development does not seem to play a particular role in promoting exports from industries

> 16 These insignificant results do not imply that economic development and the real exchange rate do not have any impact on export performance in general. The results for the two interaction terms in our difference-in-difference framework merely suggest that this impact does not vary between products with high versus low investment needs.

23
Table 3: Banks, stock markets, and liquidity needs

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Dep. var.: Survival|1y|5y|10y|1y|5y|10y|
|liquidity needs _×_ banks|0.103<sup>_a_</sup>|0.082<sup>_a_</sup>|0.050<sup>_b_</sup>||||
||(0.039)|(0.029)|(0.022)||||
|liquidity needs _×_ stock markets||||0.127<sup>_a_</sup>|0.052<sup>_b_</sup>|-0.009|
|||||(0.026)|(0.024)|(0.020)|
|liquidity needs _×_ GDPpc|0.059<sup>_a_</sup>|0.035<sup>_a_</sup>|0.028<sup>_a_</sup>|0.061<sup>_a_</sup>|0.043<sup>_a_</sup>|0.038<sup>_a_</sup>|
||(0.013)|(0.012)|(0.010)|(0.013)|(0.011)|(0.009)|
|liquidity needs _×_ real exchange rate|-1.246<sup>_b_</sup>|-0.362|-0.638|-1.392<sup>_c_</sup>|-0.317|-0.415|
||(0.596)|(0.504)|(0.394)|(0.722)|(0.586)|(0.470)|
|initial export|0.020<sup>_a_</sup>|0.016<sup>_a_</sup>|0.012<sup>_a_</sup>|0.020<sup>_a_</sup>|0.016<sup>_a_</sup>|0.012<sup>_a_</sup>|
||(0.002)|(0.002)|(0.001)|(0.002)|(0.002)|(0.001)|
|total export|0.041<sup>_a_</sup>|0.032<sup>_a_</sup>|0.024<sup>_a_</sup>|0.041<sup>_a_</sup>|0.032<sup>_a_</sup>|0.024<sup>_a_</sup>|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|number of suppliers|0.003<sup>_a_</sup>|-0.001<sup>_a_</sup>|-0.004<sup>_a_</sup>|0.003<sup>_a_</sup>|-0.001<sup>_b_</sup>|-0.004<sup>_a_</sup>|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.001)|(0.000)|
|multiple spell|-0.042<sup>_a_</sup>|-0.268<sup>_a_</sup>|-0.352<sup>_a_</sup>|-0.044<sup>_a_</sup>|-0.274<sup>_a_</sup>|-0.360<sup>_a_</sup>|
||(0.008)|(0.015)|(0.021)|(0.008)|(0.016)|(0.022)|
|phys. cap. intensity _×_ physical capital|-0.056|-0.110<sup>_b_</sup>|-0.060|0.027|-0.028|0.016|
||(0.069)|(0.050)|(0.042)|(0.081)|(0.059)|(0.046)|
|hum. cap. intensity _×_ human capital|0.265<sup>_a_</sup>|0.259<sup>_a_</sup>|0.218<sup>_a_</sup>|0.273<sup>_a_</sup>|0.285<sup>_a_</sup>|0.241<sup>_a_</sup>|
||(0.027)|(0.021)|(0.019)|(0.031)|(0.024)|(0.022)|
|Observations|252,147|252,147|252,147|243,509|243,509|243,509|
|R-squared|0.264|0.546|0.692|0.254|0.547|0.695|
|Country-Time FE|yes|yes|yes|yes|yes|yes|
|Product FE|yes|yes|yes|yes|yes|yes|

Dependent variable is the probability of export survival of product _k_ from industrial sector (industry) _i_ exported by country _c_ to the USA. Export survival probability is measured _l_ years after the beginning of export spell, with _l_ = 1 in columns (1) and (4), _l_ = 5 in columns (2) and (5), and _l_ = 10 in columns (3) and (6). The regressions are estimated by OLS and contain a full set of fixed effects at the product level and the (exporting country)*time level, with time referring to the beginning of a given export spell. Liquidity needs represents the median ratio of total inventories to sales in industry _i_ . Other variables are described in Table 2. All time-varying explanatory variables are measured at the beginning of the export spell. Robust standard errors are clustered at the (exporting country)*time level, with time referring to the beginning of a given export spell.<sup>_a_</sup> ,<sup>_b_</sup> ,<sup>_c_</sup> denote statistical significance at the 1%, 5%, and 10% level, respectively.

characterized by a high share of intangible assets, as the corresponding interaction term is marginally significant only in two out of six specifications. By contrast, the interaction term of asset intangibility and real exchange rate is negative and highly significant in all six columns of Table 4. This suggest that a strong domestic currency disproportionately hurts exports of those products whose manufacturing process heavily relies on intangible assets.

A comparison of the results from Table 4 with Figure 2 and Figure 3 demonstrates the importance of controlling for alternative determinants of export survival like economic development, exchange rate or factor endowments. According to Figure 2 and Figure 3, both strong banks and deep stock markets promote exports of products from industries with a high share of intangible assets. Stock markets maintain this beneficial impact also 24
after controlling for the differential impact of GDP per capita, real exchange rate, and countries’ endowments with physical and human capital in columns (4) to (6) of Table 4. By contrast, the positive impact of banks on export survival of products from industries with a high share of intangible assets completely disappears when we control for the same additional variables in columns (1) to (3) of Table 4.

Table 5 runs a direct horse race between banks and stock markets for each of the three main transmission channels, allowing for a further scrutiny of the results reported in the previous tables. Columns (1) to (3) of Table 5 correspond to Table 2, looking at products requiring external finance for investment into physical capital. Columns (4) to (6) of Table 5 correspond to Table 3, focusing on products requiring external finance for working capital. Columns (7) to (9) of Table 5 correspond to Table 4, looking at products whose manufacturing process relies on intangible assets. Like in the previous three tables, the dependent variable is probability of export survival after one, five, and ten years, but the main interaction terms including banks and stock markets enter the regression simultaneously rather than separately. We also control for the same non-financial transmission channels and additional variables as in Table 2, Table 3, and Table 4.

The results in Table 5 are in accordance with results from the previous three tables. Stock markets seem to play a more important role in promoting exports with high investment needs (first to third columns of Table 5) while banks seem to be more important for export survival of products with high liquidity needs (fourth to sixth columns of Table 5). These two sets of results apply especially when it comes to export survival at longer horizons. And it is only deep stock markets and not strong banks that promote export survival of products from industries with a high share of intangible assets, independently on the examined time horizon (seventh to ninth columns of Table 5).

The results for non-financial channels are also in accordance with the previous tables. Economic development disproportionately promotes export survival for products with high liquidity needs (fourth to sixth columns of Table 5), and a strong domestic currency disproportionately hurts export performance of products from industries with a high share of intangible assets (seventh to ninth columns of Table 5).

25
Table 4: Banks, stock markets, and asset intangibility

|Dep. var.: Survival|(1)<br>1y|(2)<br>5y|(3)<br>10y|(4)<br>1y|(5)<br>5y|(6)<br>10y|
|---|---|---|---|---|---|---|
|asset intangibility _×_ banks|0.067<br>(0.056)|0.065<br>(0.043)|0.036<br>(0.032)||||
|asset intangibility _×_ stock markets||||0.213<sup>_a_</sup><br>(0.039)|0.181<sup>_a_</sup><br>(0.030)|0.087<sup>_a_</sup><br>(0.028)|
|asset intangibility _×_ GDPpc|0.017|0.025<sup>_c_</sup>|0.015|0.014|0.022<sup>_c_</sup>|0.011|
||(0.018)|(0.015)|(0.013)|(0.017)|(0.013)|(0.011)|
|asset intangibility _×_ real exchange rate|-2.114<sup>_a_</sup>|-2.055<sup>_a_</sup>|-1.534<sup>_a_</sup>|-2.829<sup>_a_</sup>|-3.340<sup>_a_</sup>|-2.298<sup>_a_</sup>|
||(0.759)|(0.728)|(0.558)|(1.049)|(0.775)|(0.614)|
|initial export|0.020<sup>_a_</sup>|0.016<sup>_a_</sup>|0.012<sup>_a_</sup>|0.020<sup>_a_</sup>|0.016<sup>_a_</sup>|0.012<sup>_a_</sup>|
||(0.002)|(0.002)|(0.001)|(0.002)|(0.002)|(0.001)|
|total export|0.041<sup>_a_</sup>|0.032<sup>_a_</sup>|0.024<sup>_a_</sup>|0.041<sup>_a_</sup>|0.032<sup>_a_</sup>|0.024<sup>_a_</sup>|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|number of suppliers|0.003<sup>_a_</sup>|-0.001<sup>_a_</sup>|-0.004<sup>_a_</sup>|0.003<sup>_a_</sup>|-0.001<sup>_b_</sup>|-0.004<sup>_a_</sup>|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.001)|(0.000)|
|multiple spell|-0.042<sup>_a_</sup>|-0.268<sup>_a_</sup>|-0.351<sup>_a_</sup>|-0.044<sup>_a_</sup>|-0.274<sup>_a_</sup>|-0.360<sup>_a_</sup>|
||(0.008)|(0.015)|(0.021)|(0.008)|(0.016)|(0.022)|
|phys. cap. intensity _×_ physical capital|-0.123<sup>_c_</sup>|-0.146<sup>_a_</sup>|-0.089<sup>_b_</sup>|-0.033|-0.063|-0.016|
||(0.067)|(0.046)|(0.039)|(0.079)|(0.054)|(0.043)|
|hum. cap. intensity _×_ human capital|0.268<sup>_a_</sup>|0.258<sup>_a_</sup>|0.217<sup>_a_</sup>|0.274<sup>_a_</sup>|0.282<sup>_a_</sup>|0.241<sup>_a_</sup>|
||(0.027)|(0.021)|(0.019)|(0.031)|(0.024)|(0.021)|
|Observations|252,147|252,147|252,147|243,509|243,509|243,509|
|R-squared|0.263|0.546|0.692|0.254|0.547|0.695|
|Country-Time FE|yes|yes|yes|yes|yes|yes|
|Product FE|yes|yes|yes|yes|yes|yes|

Dependent variable is the probability of export survival of product _k_ from industrial sector (industry) _i_ exported by country _c_ to the USA. Export survival probability is measured _l_ years after the beginning of export spell, with _l_ = 1 in columns (1) and (4), _l_ = 5 in columns (2) and (5), and _l_ = 10 in columns (3) and (6). The regressions are estimated by OLS and contain a full set of fixed effects at the product level and the (exporting country)*time level, with time referring to the beginning of a given export spell. Asset intangibility is the ratio of the net value of intangible assets to the net fixed assets in industry _i_ . Other variables are described in Table 2. All time-varying explanatory variables are measured at the beginning of the export spell. Robust standard errors are clustered at the (exporting country)*time level, with time referring to the beginning of a given export spell.<sup>_a_</sup> ,<sup>_b_</sup> ,<sup>_c_</sup> denote statistical significance at the 1%, 5%, and 10% level, respectively.

For space reasons, we do not report in the remaining sections the coefficients for following control variables: initial export, total export, number of suppliers, multiple spell dummy, and interaction terms between physical and human capital endowments of country _c_ and the corresponding capital intensities at the industry level.

# **5 Size vs Activity of Stock Markets**

The results reported in the previous section revealed several patterns regarding the importance of banks and stock markets for export survival at the product level. This section explores if and how these patterns change when we distinguish between _large_ and _active_ stock markets.

In Table 6, we replace the traditional measure of deep stock markets - the normalized stock market capitalization (the market value of all listed shares divided by GDP) with 26
Table 5: The three main channels: Banks versus stock markets

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
|Dep. var.: Survival|1y|5y|10y|1y|5y|10y|1y|5y|10y|
|investment needs _×_ banks|-0.003|0.006|0.046<sup>_c_</sup>|||||||
||(0.052)|(0.035)<br>|(0.028)<br>|||||||
|investment needs _×_ stock markets|0.032|0.064<sup>_b_</sup>|0.044<sup>_b_</sup>|||||||
||(0.044)|(0.027)|(0.020)|||||||
|investment needs _×_ GDPpc|0.006|-0.003|-0.008|||||||
||(0.019)|(0.012)|(0.010)|||||||
|investment needs _×_ real exchange rate|-1.944<sup>_b_</sup>|-0.744|-0.646|||||||
||(0.920)|(0.657)|(0.551)|||||||
|liquidity needs _×_ banks||||0.029|0.068<sup>_b_</sup>|0.063<sup>_a_</sup>||||
|||||(0.041)|(0.032)|(0.023)||||
|liquidity needs _×_ stock markets||||0.112<sup>_a_</sup>|0.029|-0.026||||
|||||(0.028)|(0.026)|(0.020)||||
|liquidity needs _×_ GDPpc||||0.058<sup>_a_</sup>|0.036<sup>_a_</sup>|0.030<sup>_a_</sup>||||
|||||(0.014)|(0.013)|(0.010)||||
|liquidity needs _×_ real exchange rate||||-1.456<sup>_c_</sup>|-0.630|-0.668||||
|||||(0.758)|(0.637)|(0.485)||||
|asset intangibility _×_ banks|||||||-0.031|-0.026|-0.009|
||||||||(0.062)|(0.045)|(0.035)|
|asset intangibility _×_ stock markets|||||||0.224<sup>_a_</sup>|0.187<sup>_a_</sup>|0.091<sup>_a_</sup>|
||||||||(0.043)|(0.032)|(0.031)|
|asset intangibility _×_ GDPpc|||||||0.015|0.025|0.012|
||||||||(0.019)|(0.016)|(0.014)|
|asset intangibility _×_ real exchange rate|||||||-2.631<sup>_b_</sup>|-3.129<sup>_a_</sup>|-2.184<sup>_a_</sup>|
||||||||(1.115)|(0.829)|(0.662)|
|initial export|0.019<sup>_a_</sup>|0.016<sup>_a_</sup>|0.012<sup>_a_</sup>|0.019<sup>_a_</sup>|0.016<sup>_a_</sup>|0.012<sup>_a_</sup>|0.019<sup>_a_</sup>|0.016<sup>_a_</sup>|0.012<sup>_a_</sup>|
||(0.002)|(0.002)|(0.001)|(0.002)|(0.002)|(0.001)|(0.002)|(0.002)|(0.001)|
|total export|0.041<sup>_a_</sup>|0.032<sup>_a_</sup>|0.024<sup>_a_</sup>|0.041<sup>_a_</sup>|0.033<sup>_a_</sup>|0.024<sup>_a_</sup>|0.041<sup>_a_</sup>|0.032<sup>_a_</sup>|0.024<sup>_a_</sup>|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|number of suppliers|0.003<sup>_a_</sup>|-0.001<sup>_b_</sup>|-0.004<sup>_a_</sup>|0.003<sup>_a_</sup>|-0.001<sup>_b_</sup>|-0.004<sup>_a_</sup>|0.003<sup>_a_</sup>|-0.001<sup>_b_</sup>|-0.004<sup>_a_</sup>|
||(0.000)|(0.001)|(0.000)|(0.000)|(0.001)|(0.000)|(0.000)|(0.001)|(0.000)|
|multiple spell|-0.045<sup>_a_</sup>|-0.276<sup>_a_</sup>|-0.363<sup>_a_</sup>|-0.046<sup>_a_</sup>|-0.276<sup>_a_</sup>|-0.363<sup>_a_</sup>|-0.045<sup>_a_</sup>|-0.276<sup>_a_</sup>|-0.363<sup>_a_</sup>|
||(0.008)|(0.016)|(0.022)|(0.008)|(0.016)|(0.022)|(0.008)|(0.016)|(0.022)|
|phys. cap. intensity _×_ physical capital|-0.056|-0.099<sup>_c_</sup>|-0.039|0.020|-0.044|0.003|-0.044|-0.082|-0.031|
||(0.080)|(0.055)|(0.044)|(0.082)|(0.060)|(0.048)|(0.079)|(0.055)|(0.044)|
|hum. cap. intensity _×_ human capital|0.284<sup>_a_</sup>|0.292<sup>_a_</sup>|0.248<sup>_a_</sup>|0.278<sup>_a_</sup>|0.287<sup>_a_</sup>|0.244<sup>_a_</sup>|0.279<sup>_a_</sup>|0.285<sup>_a_</sup>|0.244<sup>_a_</sup>|
||(0.032)|(0.025)|(0.022)|(0.032)|(0.025)|(0.022)|(0.032)|(0.024)|(0.022)|
|Observations|235,294|235,294|235,294|235,294|235,294|235,294|235,294|235,294|235,294|
|R-squared|0.259|0.548|0.698|0.259|0.548|0.699|0.259|0.548|0.699|
|Country-Time FE|yes|yes|yes|yes|yes|yes|yes|yes|yes|
|Product FE|yes|yes|yes|yes|yes|yes|yes|yes|yes|

Dependent variable is the probability of export survival of product _k_ from industrial sector (industry) _i_ exported by country _c_ to the USA. Export survival probability is measured _l_ years after the beginning of export spell, with _l_ = 1 in columns (1), (4) and (7), _l_ = 5 in columns (2), (5) and (8), and _l_ = 10 in columns (3), (6) and (9). The regressions are estimated by OLS and contain a full set of fixed effects at the product level and the (exporting country)*time level, with time referring to the beginning of a given export spell. All variables are described in Table 2, Table 3, and Table 4. All time-varying explanatory variables are measured at the beginning of the export spell. Robust standard errors are clustered at the (exporting country)*time level, with time referring to the beginning of a given export spell.<sup>_a_</sup> ,<sup>_b_</sup> ,<sup>_c_</sup> denote statistical significance at the 1%, 5%, and 10% level, respectively.

the stock market turnover (the value of stock market transactions relative to the market value of all listed shares). While stock market capitalization measures the size of the stock market relative to the size of the economy, the stock market turnover captures liquidity or activity of a stock market in a given country. Otherwise, the specifications correspond to Table 5.

Columns (1) to (3) of Table 6 explore the impact of active stock markets on the export survival of products with high investment needs. The interaction term including 27
Table 6: The three main channels: The role of active stock markets

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
|Dep. var.: Survival|1y|5y|10y|1y|5y|10y|1y|5y|10y|
|investment needs _×_ banks|0.006|0.036|0.065<sup>_b_</sup>|||||||
||(0.050)|(0.035)|(0.028)|||||||
|investment needs _×_ stock market turnover|0.043<br>(0.037)|0.015<br>(0.028)|0.025<br>(0.021)|||||||
|investment needs _×_ GDPpc|0.009|-0.004|-0.008|||||||
||(0.019)|(0.013)|(0.011)|||||||
|investment needs _×_ real exchange rate|-2.004<sup>_b_</sup><br>(0.925)|-0.684<br>(0.669)|-0.592<br>(0.562)|||||||
|liquidity needs _×_ banks||||0.065|0.061<sup>_b_</sup>|0.031||||
|||||(0.040)|(0.029)|(0.023)||||
|liquidity needs _×_ stock market turnover||||0.090<sup>_a_</sup>|0.066<sup>_a_</sup>|0.043<sup>_a_</sup>||||
|||||(0.026)|(0.021)|(0.015)||||
|liquidity needs _×_ GDPpc||||0.056<sup>_a_</sup>|0.035<sup>_a_</sup>|0.030<sup>_a_</sup>||||
|||||(0.014)|(0.013)|(0.010)||||
|liquidity needs _×_ real exchange rate||||-1.161|-0.384|-0.498||||
|||||(0.763)|(0.620)|(0.479)||||
|asset intangibility _×_ banks|||||||0.076|0.060|0.030|
||||||||(0.061)|(0.043)|(0.031)|
|asset intangibility _×_ stock market turnover|||||||0.099<sup>_a_</sup>|0.079<sup>_a_</sup>|0.045<sup>_b_</sup>|
||||||||(0.036)|(0.028)|(0.018)|
|asset intangibility _×_ GDPpc|||||||0.008|0.021|0.010|
||||||||(0.019)|(0.016)|(0.013)|
|asset intangibility _×_ real exchange rate|||||||-2.126<sup>_c_</sup>|-2.716<sup>_a_</sup>|-1.967<sup>_a_</sup>|
||||||||(1.160)|(0.859)|(0.660)|
|Observations|232,766|232,766|232,766|232,766|232,766|232,766|232,766|232,766|232,766|
|R-squared|0.259|0.549|0.700|0.260|0.549|0.701|0.259|0.549|0.701|
|Country-Time FE|yes|yes|yes|yes|yes|yes|yes|yes|yes|
|Product FE|yes|yes|yes|yes|yes|yes|yes|yes|yes|
|Full set of controls included|yes|yes|yes|yes|yes|yes|yes|yes|yes|

Dependent variable is the probability of export survival of product _k_ from industrial sector (industry) _i_ exported by country _c_ to the USA. Export survival probability is measured _l_ years after the beginning of export spell, with _l_ = 1 in columns (1), (4) and (7), _l_ = 5 in columns (2), (5) and (8), and _l_ = 10 in columns (3), (6) and (9). The regressions are estimated by OLS and contain a full set of fixed effects at the product level and the (exporting country)*time level, with time referring to the beginning of a given export spell. Stock market turnover is measured as the ratio between the value of stock market transactions and the stock market capitalization. Other variables are described in Table 2, Table 3, and Table 4. The full set of controls also includes initial export, total export, number of suppliers, multiple spell, and interaction terms between physical and human capital endowments of country _c_ and the corresponding capital intensities at the industry level. All time-varying explanatory variables are measured at the beginning of the export spell. Robust standard errors are clustered at the (exporting country)*time level, with time referring to the beginning of a given export spell. _a_ , _b_ , _c_ denote statistical significance at the 1%, 5%, and 10% level, respectively.

stock market turnover is insignificant across all time dimensions. This contrasts with the previous results for the size of stock markets that did promote export survival of these products at the longer term horizons of five and ten years (second and third columns of Table 5). While active stock markets do affect the real economy in other contexts (e.g., Levine and Zervos 1998, Manova 2008), our results suggest that they do not play a significant role in promoting the export survival of products with high investment needs.

Columns (4) to (6) of Table 6 focus on the role played by active stock markets in the export survival of products with high liquidity needs. In the previous specifications looking at large stock markets, banks dominated stock market capitalization in promoting the longer term export survival for these products (fifth and sixth columns of Table 5). The results are very different when looking at the activity of the stock market instead.

28
The interaction term of liquidity needs and stock market turnover is positive and highly significant across all time horizons (fourth to sixth columns of Table 6), while strong banks seem to have a significant effect only at the time horizon of five years (fifth column of Table 6). It is thus active rather than large stock markets that play the crucial role in promoting export survival of products with high liquidity needs.

Finally, the last three columns of Table 6 examine the importance of active stock markets for the export survival of products whose manufacturing process requires a high share of intangible assets. Here the results are qualitatively the same as in the last three columns of Table 5. Whether we look at the size or the activity dimension, it is welldeveloped stock markets rather than banks that improve the export performance of these products across all time horizons.

Comparing the results in Table 6 with Table 5 suggest that the relative impact of banks and stock markets often depends on the distinction between large and active stock markets. In the remainder of this section, we further explore this issue by running an alternative horse race. Instead of looking at the relative importance of banks and large/active stock markets within individual transmission channels, we examine the relative importance of the three main transmission channels for the overall impact of banks, large stock markets, and active stock markets. Besides the main interaction terms, each specification in Table 7 also controls for interaction terms of the three measures of financial vulnerability with GDP per capita and with real exchange rate (coefficients for these six additional interaction terms not reported due to space reasons).

In columns (1) to (3) of Table 7, we interact each of the main proxies of financial vulnerability with our measure of banking development and allow all three interaction terms to enter the regressions simultaneously. The results suggest that strong banks shape the export survival especially through the transmission channel of liquidity needs. When it comes to the long-term export survival at the time horizon of ten years (third column), well-developed banks also help products with high investment needs. Independent of the time horizon of export survival, banks do not seem to play any role in promoting products whose manufacturing process requires a high share of intangible assets.

29
Table 7: An alternative horse race across transmission channels

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
|Dep. var.: Survival|1y|5y|10y|1y|5y|10y|1y|5y|10y|
|investment needs _×_ banks|0.002|0.044|0.064<sup>_b_</sup>|||||||
||(0.047)|(0.033)|(0.027)|||||||
|liquidity needs _×_ banks|0.103<sup>_a_</sup>|0.083<sup>_a_</sup>|0.052<sup>_b_</sup>|||||||
||(0.039)|(0.029)|(0.022)|||||||
|asset intangibility _×_ banks|0.063|0.065|0.038|||||||
||(0.056)|(0.043)|(0.032)|||||||
|investment needs _×_ stock markets||||0.054|0.076<sup>_a_</sup>|0.062<sup>_a_</sup>||||
|||||(0.040)|(0.026)|(0.019)||||
|liquidity needs _×_ stock markets||||0.128<sup>_a_</sup>|0.053<sup>_b_</sup>|-0.007||||
|||||(0.026)|(0.024)|(0.020)||||
|asset intangibility _×_ stock markets||||0.215<sup>_a_</sup>|0.184<sup>_a_</sup>|0.090<sup>_a_</sup>||||
|||||(0.039)|(0.030)|(0.028)||||
|investment needs _×_ stock market turnover|||||||0.055|0.021|0.035<sup>_c_</sup>|
||||||||(0.036)|(0.027)|(0.019)|
|liquidity needs _×_ stock market turnover|||||||0.097<sup>_a_</sup>|0.070<sup>_a_</sup>|0.046<sup>_a_</sup>|
||||||||(0.025)|(0.021)|(0.015)|
|asset intangibility _×_ stock market turnover|||||||0.111<sup>_a_</sup>|0.083<sup>_a_</sup>|0.047<sup>_a_</sup>|
||||||||(0.034)|(0.027)|(0.017)|
|Observations|252,147|252,147|252,147|243,509|243,509|243,509|240,981|240,981|240,981|
|R-squared|0.264|0.546|0.692|0.255|0.547|0.695|0.255|0.549|0.697|
|Country-Time FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Product FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Full set of controls included|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

Dependent variable is the probability of export survival of product _k_ from industrial sector (industry) _i_ exported by country _c_ to the USA. Export survival probability is measured _l_ years after the beginning of export spell, with _l_ = 1 in columns (1), (4) and (7), _l_ = 5 in columns (2), (5) and (8), and _l_ = 10 in columns (3), (6) and (9). The regressions are estimated by OLS and contain a full set of fixed effects at the product level and the (exporting country)*time level, with time referring to the beginning of a given export spell. Each specification controls for interaction terms of the three measures of financial vulnerability with GDP per capita and with real exchange rate. All variables are described in Table 2, Table 3, and Table 4. The full set of controls also includes initial export, total export, number of suppliers, multiple spell, and interaction terms between physical and human capital endowments of country _c_ and the corresponding capital intensities at the industry level. All time-varying explanatory variables are measured at the beginning of the export spell. Robust standard errors are clustered at the (exporting country)*time level, with time referring to the beginning of a given export spell. _a_ , _b_ , _c_ denote statistical significance at the 1%, 5%, and 10% level, respectively.

In columns (4) to (6) of Table 7, we interact the three financial vulnerability proxies with the size dimension of stock market development. At all the time horizons, the positive impact of large stock markets on export survival manifests itself especially through alleviating the financial vulnerability arising from the presence of intangible assets. Large stock markets also promote products with high investment needs, especially when it comes to export survival at longer time horizons. When it comes to products with high liquidity needs, the size of stock markets matters in particular for the short-term export survival.

In columns (7) to (10) of Table 7, we interact our proxies of financial vulnerability with the activity of a given stock market. Similarly to large stock markets explored in the previous three columns, the active stock markets play an important role in promoting the export survival of products whose manufacturing process requires a high share of intangible assets. However, the other two transmission channels operate differently in the case 30
of active stock markets. Across all time horizons, alleviating the financial vulnerability of products with high liquidity needs represents an important transmission channel from active stock markets to export survival. At the same time, stock market activity does not play a substantial role in promoting exports of products with high investment needs.

Overall, the results of the horse race across transmission channels in Table 7 are consistent with the previous findings from horse races between banks and large stock markets in Table 5 and between banks and active stock markets in Table 6.

# **6 Further Tests**

## **6.1 Additional Channels**

This subsection extends the baseline empirical model to include the transmission channels of trade credit and global growth opportunities. Besides examining the robustness of our previous results, the exploration of these two channels in the context of export survival is worthwhile in its own right.

Table 8 takes as a point of departure Table 5 in Subsection 4.2, adding the interaction terms of trade credit dependency with both banks and stock markets. These interaction terms allow us to examine if trade credit can act as a substitute for external finance provided by financial intermediaries and markets. According to this hypothesis, firms that have access to trade credit from their business partners suffer relatively less in countries with underdeveloped financial systems. Such firms would therefore also benefit somewhat less than other firms from the process of financial development. Consequently, we would expect _negative_ coefficients for these additional interaction terms as well-developed banks and stock markets would be comparatively _less_ useful in promoting export survival of products from industries that can rely on trade credit. Note that in our difference-indifferences framework, the negative coefficients do not imply that well-developed financial markets and institutions actually hurt the export survival of these products. The products from industries that can rely on trade credit simply do not benefit from a well-developed financial system quite as much as products from industries that lack access to trade credit as an alternative form of financing.

The results in Table 8 show that the distinction between banks and stock markets as 31
well as the time horizon of export survival both matter for this additional transmission channel. The interaction term of trade credit dependency with banks has the expected negative sign and is highly significant for the long-term export survival at the horizons of both five years (second, fifth, and eighth columns of Table 8) and ten years (third, sixth, and ninth columns of Table 8). By contrast, the interaction term of trade credit dependency with stock markets is never significant in these specifications. When it comes to the short-term survival at the one-year horizon (first, fourth, and seventh columns of Table 8), the interaction terms of trade credit with both banks and stock markets are insignificant.

Table 8: Additional channel from finance to export survival: Trade credit

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
|Dep. var.: Survival|1y|5y|10y|1y|5y|10y|1y|5y|10y|
|investment needs _×_ banks|-0.004|0.002|0.042|||||||
||(0.052)|(0.035)<br>|(0.028)<br>|||||||
|investment needs _×_ stock markets|0.032|0.065<sup>_b_</sup>|0.045<sup>_b_</sup>|||||||
||(0.044)|(0.027)|(0.020)|||||||
|investment needs _×_ GDPpc|0.007|-0.003|-0.007|||||||
||(0.019)|(0.012)|(0.010)|||||||
|investment needs _×_ real exchange rate|-1.949<sup>_b_</sup>|-0.755|-0.657|||||||
||(0.920)|(0.658)|(0.552)|||||||
|liquidity needs _×_ banks||||0.029|0.064<sup>_b_</sup>|0.059<sup>_b_</sup>||||
|||||(0.041)|(0.032)|(0.023)||||
|liquidity needs _×_ stock markets||||0.112<sup>_a_</sup>|0.030|-0.025||||
|||||(0.028)|(0.026)|(0.019)||||
|liquidity needs _×_ GDPpc||||0.058<sup>_a_</sup>|0.035<sup>_a_</sup>|0.030<sup>_a_</sup>||||
|||||(0.014)|(0.013)|(0.010)||||
|liquidity needs _×_ real exchange rate||||-1.458<sup>_c_</sup>|-0.635|-0.673||||
|||||(0.758)|(0.637)|(0.486)||||
|asset intangibility _×_ banks|||||||-0.029|-0.019|-0.002|
||||||||(0.063)|(0.045)|(0.036)|
|asset intangibility _×_ stock markets|||||||0.226<sup>_a_</sup>|0.185<sup>_a_</sup>|0.089<sup>_a_</sup>|
||||||||(0.042)|(0.032)|(0.031)|
|asset intangibility _×_ GDPpc|||||||0.015|0.025|0.012|
||||||||(0.019)|(0.016)|(0.014)|
|asset intangibility _×_ real exchange rate|||||||-2.647<sup>_b_</sup>|-3.148<sup>_a_</sup>|-2.201<sup>_a_</sup>|
||||||||(1.115)|(0.828)|(0.662)|
|trade credit dependency _×_ banks|-0.014|-0.050<sup>_a_</sup>|-0.051<sup>_a_</sup>|-0.007|-0.044<sup>_a_</sup>|-0.046<sup>_a_</sup>|-0.011|-0.048<sup>_a_</sup>|-0.050<sup>_a_</sup>|
||(0.023)|(0.016)|(0.012)|(0.023)|(0.016)|(0.012)|(0.023)|(0.016)|(0.013)|
|trade credit dependency _×_ stock markets|-0.007|0.015|0.015|-0.003|0.015|0.014|-0.015|0.008|0.012|
||(0.020)|(0.015)|(0.011)|(0.020)|(0.015)|(0.011)|(0.019)|(0.015)|(0.011)|
|Observations|235,294|235,294|235,294|235,294|235,294|235,294|235,294|235,294|235,294|
|R-squared|0.259|0.548|0.699|0.259|0.548|0.699|0.259|0.548|0.699|
|Country-Time FE|yes|yes|yes|yes|yes|yes|yes|yes|yes|
|Product FE|yes|yes|yes|yes|yes|yes|yes|yes|yes|
|Full set of controls included|yes|yes|yes|yes|yes|yes|yes|yes|yes|

Dependent variable is the probability of export survival of product _k_ from industrial sector (industry) _i_ exported by country _c_ to the USA. Export survival probability is measured _l_ years after the beginning of export spell, with _l_ = 1 in columns (1), (4) and (7), _l_ = 5 in columns (2), (5) and (8), and _l_ = 10 in columns (3), (6) and (9). The regressions are estimated by OLS and contain a full set of fixed effects at the product level and the (exporting country)*time level, with time referring to the beginning of a given export spell. Trade credit dependency is the ratio of accounts payable to total assets in industry _i_ . Other variables are described in Table 2, Table 3, and Table 4. The full set of controls also includes initial export, total export, number of suppliers, multiple spell, and interaction terms between physical and human capital endowments of country _c_ and the corresponding capital intensities at the industry level. All time-varying explanatory variables are measured at the beginning of the export spell. Robust standard errors are clustered at the (exporting country)*time level, with time referring to the beginning of a given export spell.<sup>_a_</sup> ,<sup>_b_</sup> ,<sup>_c_</sup> denote statistical significance at the 1%, 5%, and 10% level, respectively.

32
Trade credit can thus act as a substitute only for external financing by banks and not by stock markets. Furthermore, this transmission channel matters only for longterm export survival. In other words, trade credit only promotes exports of products that are already well-established in the destination market, while not being particularly beneficial for products whose exports started only recently. These results are consistent with the argument by Fisman and Love (2003) that firms first need to establish a positive reputation before being able to rely on trade credit from business partners as a substitute for a formal bank credit.

Regarding our three main transmission channels from finance to export survival, Table 8 confirms the findings from Table 5. Deep stock markets promote long-term export survival of products with high investment needs (columns (1) to (3) of Table 8). Strong banks play a more important role in promoting export survival of products with high liquidity needs over longer time horizons, while deep stock markets are decisive for the one-year survival of these products (columns (4) to (6) of Table 8). And stock markets dominate banks across all time horizons when it comes to improving the export performance of products from industries with a high share of intangible assets (columns (7) to (9) of Table 8).

Table 9 adds to the nine specifications from Table 5 the interaction terms of growth opportunities with banks and with stock markets. Fisman and Love (2007) find that in the context of promoting industrial growth, the interaction term of financial development with growth opportunities dominates the interaction term of financial development with investment needs. In the context of export performance, it turns out to be the export survival at longer time horizons where the inclusion of the channel related to growth opportunities affects the results for the channel related to investment needs. When looking at the short-term survival of one year in column (1) of Table 9, neither banks nor stock markets seem to particularly promote products with high investment needs, in accordance with the previous findings in Table 5. However, when it comes to the long-term export survival at the horizon of five and ten years in columns (2) and (3) of Table 9, it is banks rather stock markets that promote export performance of products with high investment 33
needs. This is the opposite result compared to the findings in Table 5. Part of the explanation might lie in a possible collinearity problem between various interaction terms. The fact that the interaction terms of growth opportunities with banks and stock markets are both highly significant but their regression coefficients have opposite signs throughout Table 9 would also support this interpretation.<sup>17</sup> Table 9: Additional channel from finance to export survival: Growth opportunities

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
|Dep. var.: Survival|1y|5y|10y|1y|5y|10y|1y|5y|10y|
|investment needs _×_ banks|0.045|0.064<sup>_c_</sup>|0.090<sup>_a_</sup>|||||||
||(0.053)|(0.034)|(0.028)|||||||
|investment needs _×_ stock markets|-0.003|0.036|0.024|||||||
||(0.047)|(0.027)|(0.021)|||||||
|investment needs _×_ GDPpc|0.005|-0.005|-0.009|||||||
||(0.019)|(0.012)|(0.011)|||||||
|investment needs _×_ real exchange rate|-1.945<sup>_b_</sup>|-0.743|-0.645|||||||
||(0.920)|(0.656)|(0.550)|||||||
|liquidity needs _×_ banks||||0.022|0.059<sup>_c_</sup>|0.056<sup>_b_</sup>||||
|||||(0.042)|(0.032)|(0.023)||||
|liquidity needs _×_ stock markets||||0.120<sup>_a_</sup>|0.035|-0.022||||
|||||(0.029)|(0.027)|(0.019)||||
|liquidity needs _×_ GDPpc||||0.058<sup>_a_</sup>|0.035<sup>_a_</sup>|0.030<sup>_a_</sup>||||
|||||(0.014)|(0.013)|(0.010)||||
|liquidity needs _×_ real exchange rate||||-1.452<sup>_c_</sup>|-0.629|-0.667||||
|||||(0.759)|(0.637)|(0.484)||||
|asset intangibility _×_ banks|||||||-0.020|-0.013|0.000|
||||||||(0.062)|(0.045)|(0.035)|
|asset intangibility _×_ stock markets|||||||0.217<sup>_a_</sup>|0.181<sup>_a_</sup>|0.086<sup>_a_</sup>|
||||||||(0.042)|(0.032)|(0.031)|
|asset intangibility _×_ GDPpc|||||||0.013|0.023|0.011|
||||||||(0.019)|(0.016)|(0.014)|
|asset intangibility _×_ real exchange rate|||||||-2.625<sup>_b_</sup>|-3.149<sup>_a_</sup>|-2.199<sup>_a_</sup>|
||||||||(1.114)|(0.828)|(0.661)|
|growth opportunities _×_ banks|-0.065<sup>_a_</sup>|-0.079<sup>_a_</sup>|-0.060<sup>_a_</sup>|-0.060<sup>_a_</sup>|-0.072<sup>_a_</sup>|-0.051<sup>_a_</sup>|-0.062<sup>_a_</sup>|-0.075<sup>_a_</sup>|-0.054<sup>_a_</sup>|
||(0.021)|(0.016)|(0.012)|(0.021)|(0.016)|(0.012)|(0.020)|(0.015)|(0.012)|
|growth opportunities _×_ stock markets|0.049<sup>_a_</sup>|0.038<sup>_a_</sup>|0.027<sup>_a_</sup>|0.052<sup>_a_</sup>|0.041<sup>_a_</sup>|0.027<sup>_a_</sup>|0.044<sup>_a_</sup>|0.036<sup>_a_</sup>|0.026<sup>_a_</sup>|
||(0.017)|(0.012)|(0.010)|(0.016)|(0.012)|(0.009)|(0.015)|(0.012)|(0.010)|
|Observations|235,294|235,294|235,294|235,294|235,294|235,294|235,294|235,294|235,294|
|R-squared|0.259|0.548|0.699|0.259|0.548|0.699|0.259|0.548|0.699|
|Country-Time FE|yes|yes|yes|yes|yes|yes|yes|yes|yes|
|Product FE|yes|yes|yes|yes|yes|yes|yes|yes|yes|
|Full set of controls included|yes|yes|yes|yes|yes|yes|yes|yes|yes|

Dependent variable is the probability of export survival of product _k_ from industrial sector (industry) _i_ exported by country _c_ to the USA. Export survival probability is measured _l_ years after the beginning of export spell, with _l_ = 1 in columns (1), (4) and (7), _l_ = 5 in columns (2), (5) and (8), and _l_ = 10 in columns (3), (6) and (9). The regressions are estimated by OLS and contain a full set of fixed effects at the product level and the (exporting country)*time level, with time referring to the beginning of a given export spell. Growth opportunities is the growth in real sales for the representative firm in industry _i_ in the US. Other variables are described in Table 2, Table 3, and Table 4. The full set of controls also includes initial export, total export, number of suppliers, multiple spell, and interaction terms between physical and human capital endowments of country _c_ and the corresponding capital intensities at the industry level. All time-varying explanatory variables are measured at the beginning of the export spell. Robust standard errors are clustered at the (exporting country)*time level, with time referring to the beginning of a given export spell.<sup>_a_</sup> ,<sup>_b_</sup> ,<sup>_c_</sup> denote statistical significance at the 1%, 5%, and 10% level, respectively.

The results for the other two main transmission channels from finance to export survival are not substantially affected by the inclusion of the channel related to global growth opportunities.

> 17 Fisman and Love (2007) do not encounter this problem as they interact growth opportunities only with a general measure of financial development, which is the sum of the two proxies capturing the strength of the banks and the depth of stock markets in a given country.

34
## **6.2 Stock Market Value Traded and Broader Measures of Bank Development**

This subsection looks at three alternative measures of well-developed financial markets and institutions. First, we use the normalized stock market value traded (the value of stock market transactions divided by GDP) that lies somewhere in-between our measures for size and activity of stock markets.<sup>18</sup> In another set of additional tests, we replace the traditional measure of a well-developed banking sector - credit provided by deposit-taking banks to the private sector divided by GDP - by two broader proxies for importance of financial institutions in the real economy. One proxy looks at a broader measure of claims banks have vis-a-vis the rest of the economy: the ratio of bank assets to GDP. The other proxy looks beyond traditional banks at a broader group of financial institutions: the total credit by both banks and other financial institutions divided by GDP.

The results for these alternative measures broadly confirm the results from Section 4. The only qualitative difference is the impact of stock market value traded on products with high investment needs. Compared to stock market capitalization, the stock market value traded seems to matter for export survival of these products at shorter time horizons. For space reasons, we report the detailed results for the stock market value traded and the two alternative bank measures in Online Appendix C.

# **7 Conclusions**

We examine the transmission process from financial markets and institutions in exporting countries to short-term and long-term export survival at the product level. Our results suggest that banks, active stock markets, and large stock markets all play distinct roles in helping exported products to survive in the highly competitive US destination market.

Large and active stock markets promote both short-term and long-term export survival of products from industries with a high share of intangible assets. Strong banks do not seem to play any significant role in this regard. Given the association of intangible assets with innovative activities and a pronounced technological change, our findings pro-

> 18 Note that multiplying the stock market capitalization (the market value of all listed shares divided by GDP) with the stock market turnover (the value of stock market transactions relative to the market value of all listed shares) yields the stock market value traded. For a further discussion of various dimensions and measures of banking and stock market development see, e.g., Levine and Zervos (1998), Beck, Demirg ̈u ̧c–Kunt, and Levine (2000, 2010), Manova (2008), Strieborny and Kukenova (2016).

35
vide further support for the notion that stock markets are more important than banks in promoting R&D investment (Aghion et al. 2004; Brown, Martinssson, and Petersen 2013), encouraging innovation (Hsu, Tian, and Xu 2014), and helping industries that face increasing returns to scale and rapid technological change (Allen 1993; Brown, Martinsson, and Petersen 2017). Our product-level evidence on export survival also complements the recent research on banks and intangible capital. Dell’Ariccia et al. (2020) show that banks decrease their commercial lending if local firms increasingly use intangible capital. Beck et al. (2021) find that liquidity created by banks promotes only tangible investment and consequently does not benefit those countries that specialize in industries using intangible assets.

When it comes to promoting export survival of products with high liquidity needs, it is specifically the active but not the large stock markets that matter. This result might be related to the roles played by corporate governance and shareholders’ monitoring in improving the efficiency of working capital management (see, e.g., Gill and Biger 2013, Ben-Nasr 2016, Zeidan and Shapir 2017). Arguably, it is the activity rather than size of the stock market that strengthens the ability of shareholders to monitor the firms’ management. In particular, active and liquid stock markets make it easier for shareholders to put the management under pressure by selling or threatening to sell the shares of the firm (see, e.g., Edmans and Manso 2011 or Edmans and Holderness 2017). In accordance with the traditional role of the banking system in covering the working capital needs of firms, well-developed banks also promote export survival of products with high liquidity needs, especially at longer time horizons.<sup>19</sup> Our empirical analysis also reveals the necessity of long-term export links if trade credit among business partners is to serve as a viable source of export financing. Based on our results, informal credit between upstream and downstream firms cannot substitute for well-developed financial markets and institutions in promoting immediate survival of 19 Besides the traditional route of bank loans, banks can support firms’ working capital management also by specialized instruments like letters of credit. These instruments are particularly important in international trade and at the same time do not directly enter our proxies for banking development. The results in this paper might therefore represent only a lower bound for the importance of a welldeveloped banking system for the export survival of products with high liquidity needs.

36
newly exported products. The ability to rely on trade credit matters only for long-term export survival, helping products that are already well-established in the destination market. Trade credit also seems to serve as a substitute for external financing from banks rather than from stock markets, in accordance with the previous research focusing on industrial output (e.g., Fisman and Love 2003).

No clear pattern emerges in the export survival of products with high investment needs. This is especially the case when we control for the alternative channel of growth opportunities. These results complement previous research on production and stock returns. Fisman and Love (2007) provide evidence that growth opportunities dominate investment needs in the transmission process from financial development to industrial growth. Tong and Wei (2011) find less robust results for the investment channel in the context of the transmission from the 2007-2009 financial crisis to stock market returns at the firm level, especially when compared to the channel of liquidity needs.

Our results on product-level export survival suggest three possible directions for further research on the distinct roles played by financial markets and institutions in international trade. First, it seems that the specific source of products’ financial needs affects the relative importance of banks versus stock markets in promoting export success. It matters whether given products need to fund their investment needs related to physical capital, to fund their liquidity needs related to working capital, or to overcome the focus of more rudimentary financial systems on collateralizable tangible assets. Second, the relative importance of banks versus stock markets can vary even for the same group of products, depending on the specific time horizon used for measuring export success. Third, the distinction between the size and the activity of stock markets matters substantially. Further research on these three topics could improve our understanding of the transmission process from finance to the real economy.

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# **APPENDIX**

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Table 10: Summary statistics

|Variable<br>|Obs.<br>|Mean<br>|Std. Dev.<br>|Q1<br>|Median<br>|Q3<br>|
|---|---|---|---|---|---|---|
|investment needs|252147|0.39|0.05|0.37|0.38|0.40|
|liquidity needs|252147|0.25|0.08|0.21|0.26|0.30|
|asset intangibility|252147|0.05|0.05|0.02|0.04|0.07|
|trade credit dependency|252147|0.40|0.11|0.34|0.39|0.46|
|global growth opportunities|252147|0.41|0.16|0.29|0.40|0.52|
|investment needs x banks|252147|0.28|0.19|0.12|0.25|0.38|
|investment needs x stock markets|235294|0.22|0.21|0.07|0.15|0.30|
|investment needs x GDPpc|252147|3.60|0.69|3.18|3.65|3.99|
|investment needs x real exchange rate|252147|-0.87|0.12|-0.90|-0.86|-0.82|
|investment needs x bank assets|252082|0.31|0.20|0.15|0.27|0.42|
|investment needs x total credit|251588|0.28|0.20|0.12|0.26|0.39|
|investment needs x stock market value traded|233886|0.13|0.18|0.02|0.06|0.16|
|investment needs x stock market turnover|232766|0.20|0.20|0.07|0.16|0.28|
|liquidity needs x banks|252147|0.18|0.14|0.07|0.15|0.25|
|liquidity needs x stock markets|235294|0.14|0.14|0.04|0.09|0.19|
|liquidity needs x GDPpc|252147|2.31|0.79|1.82|2.28|2.75|
|liquidity needs x real exchange rate|252147|-0.56|0.17|-0.67|-0.57|-0.46|
|liquidity needs x bank assets|252082|0.20|0.15|0.09|0.16|0.27|
|liquidity needs x total credit|251588|0.18|0.14|0.07|0.15|0.25|
|liquidity needs x stock market value traded|233886|0.08|0.12|0.01|0.04|0.10|
|liquidity needs x stock market turnover|232766|0.13|0.14|0.04|0.10|0.17|
|asset intangibility x banks|252147|0.03|0.05|0.01|0.02|0.05|
|asset intangibility x stock markets|235294|0.03|0.05|0.00|0.01|0.03|
|asset intangibility x GDPpc|252147|0.47|0.49|0.14|0.32|0.63|
|asset intangibility x real exchange rate|252147|-0.11|0.12|-0.16|-0.08|-0.04|
|asset intangibility x bank assets|252082|0.04|0.05|0.01|0.02|0.05|
|asset intangibility x total credit|251588|0.04|0.05|0.01|0.02|0.05|
|asset intangibility x stock market value traded|233886|0.02|0.03|0.00|0.00|0.02|
|asset intangibility x stock market turnover|232766|0.03|0.05|0.00|0.01|0.03|
|trade credit dependency x banks|252147|0.28|0.21|0.12|0.24|0.39|
|trade credit dependency x stock markets|235294|0.22|0.22|0.07|0.15|0.30|
|global growth opportunities x banks|252147|0.29|0.23|0.11|0.23|0.40|
|global growth opportunities x stock markets|235294|0.23|0.24|0.06|0.14|0.31|
|banks|252147|0.71|0.47|0.31|0.65|0.98|
|stock markets|235294|0.56|0.52|0.17|0.37|0.76|
|GDPpc (log)|252147|9.24|1.28|8.38|9.52|10.34|
|real exchange rate|252147|-2.23|0.02|-2.24|-2.23|-2.22|
|bank assets|252082|0.78|0.51|0.39|0.69|1.08|
|total credit|251588|0.73|0.50|0.30|0.68|1.00|
|stock market value traded|233886|0.33|0.45|0.04|0.15|0.40|
|stock market turnover|232766|0.52|0.51|0.17|0.41|0.73|
|initial export|252147|2.95|2.42|1.18|2.48|4.23|
|total export|252147|6.27|2.69|4.40|6.31|8.13|
|number of suppliers|252147|37.99|19.36|24.00|35.00|48.00|
|multiple spell|252147|0.73|0.44|0.00|1.00|1.00|
|physical capital intensity|252147|0.06|0.02|0.05|0.06|0.07|
|human capital intensity|252147|0.98|0.23|0.81|1.06|1.13|
|physical capital intensity x physical capital|252147|0.74|0.27|0.62|0.71|0.86|
|human capital intensity x human capital|252147|2.17|0.57|1.73|2.24|2.62|
|physical capital|252147|11.46|0.91|10.94|11.68|12.17|
|human capital|252147|2.20|0.25|2.08|2.27|2.38|
|We measure all time-varying explanatory variables at the beginn<br>therefore reduced to the initial ear of a iven exort sell -_t_Th|ing of a given expor<br>variables_GDPc_|t spell. <br>_initial_|The time di<br>_~~e~~xort t_|mension in<br>_otal_<br>_exo_|our datas<br>_t_are t|et is<br>aken|
|y g p p 0. <br>in log terms.|_pc,t_0|,|_pck,t_0,|<br>_p_|_ck,t_0||

42