metadata: authors: 0: author: Milet,Emmanuel Michel Stephane
1: author: Vargas Da Cruz,Marcio Jose
2: author: Olarreaga,Marcelo
count: World
docna: 0: docna: Online exports and the wage gap
docty: Policy Research Working Paper
subtopic: Skills Development and Labor Force Training,Educational Sciences,International Trade and Trade Rules
teratopic: Information and Communication Technologies,Private Sector Development,Social Protections and Labor,Rural Development
abstracts: cdata!: The development of the Internet is often
            seen as a source of demand for skilled workers and therefore
            a potential driver of the wage gap between skilled and
            unskilled workers. This paper focuses on the impact that
            international trade in online platforms has on the wage gap.
            Because online trade allows smaller firms with relatively
            more unskilled workers to access world markets it can be
            expected a priori that an expansion of online exports
            reduces the wage gap. After correcting for potential
            endogeneity bias in a sample of 22 developing countries for
            which online trade and wage gap data can be matched, the
            study finds that a 1 percent increase in the share of online
            exports over GDP leads to a 0.01 percent decline in the wage gap.
repnme: repnme: Online exports and the wage gap
display_title: Online exports and the wage gap
listing_relative_url: /research/2017/08/27883515/online-exports-wage-gap
topicv3: Trade m1326924 698,Education m1327048 470,Skilled Workers m1504035 2732,Unskilled Workers m1504036 2733,Wage Gap m1582439 4439
publishtoextweb_dt: 2017-08-07T00:00:00Z
historic_topic: Information and Communication Technologies,Private Sector Development,Social Protections and Labor,Rural Development
no_of_pages: 38
author_keys: 0: author_key: 1388223
1: author_key: 1161834
2: author_key: 321058
model_extractions: WPS8160 Policy Research Working Paper 8160 Online Exports and the Wage Gap _Marcio Cruz Emmanuel Milet Marcelo Olarreaga_ Trade and Competitiveness Global Practice Group August 2017
Policy Research Working Paper 8160

# **Abstract**

The development of the Internet is often seen as a source of demand for skilled workers and therefore a potential driver of the wage gap between skilled and unskilled workers. This paper focuses on the impact that international trade in online platforms has on the wage gap. Because online trade allows smaller firms with relatively more unskilled workers to access world markets it can be expected a priori that an expansion of online exports reduces the wage gap. After correcting for potential endogeneity bias in a sample of 22 developing countries for which online trade and wage gap data can be matched, the study finds that a 1 percent increase in the share of online exports over GDP leads to a 0.01 percent decline in the wage gap.

This paper is a product of the Trade and Competitiveness Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at marciocruz@worldbank.org, emmanuel.milet@unige.ch, and marcelo.olarreaga@unige.ch.

_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
# Online Exports and the Wage Gap<sup>_∗_</sup>

Marcio Cruz<sup>_†_</sup> Emmanuel Milet<sup>_‡_</sup> Marcelo Olarreaga<sup>_§_</sup> Keywords: wage gap, online trade. JEL codes: F14, J31, L86.

> _∗_ We are grateful to Rita Almeida, Omar Arias, Irene Brambilla, C ́eline Carr`ere, Alvaro Gonzalez, Daniel Lederman, Andreas Lendle, J ́er ́emy Lucchetti, Monika Mr ́azov ́a, Fr ́ed ́eric Robert-Nicoud, Indhira Santos, Shawn Tan, PierreLouis V ́ezina, and participants at workshops at the World Bank, the Asian Development Bank Institute, the Tunis Business School, and at the Western Economic Association conference in San Diego for very constructive comments and suggestions. We also gratefully acknowledge support from the r4d program on Employment funded by Swiss National Science Foundation and the Swiss Development Cooperation, as well as the Knowledge for Change Program and the World Bank’s Chief Economist Office for Latin America.

> _†_ World Bank and UFPR, email: marciocruz@worldbank.org

> _‡_ University of Geneva, email: emmanuel.milet@unige.ch

> _§_ University of Geneva, CEPII, and CEPR, email: marcelo.olarreaga@unige.ch
# **1 Introduction**

We explore the impact of online international trade on the wage gap between skilled and unskilled workers. In a sample of 22 developing countries for which we have data on cross border online trade, and labor surveys to estimate the wage gap, we find that a one percent increase in the share of online exports over GDP reduces the wage gap by 0.01 percent. This can be explained by the information providing mechanisms in online platforms that reduce the fixed cost of exporting, allowing smaller firms with a more unskilled workforce to export. We provide evidence supporting this mechanism as the reduction in the wage gap associated with online exports is larger in countries with a larger share of small This result is important for at least three reasons. First, while world trade increased by 92 percent (8 percent annual growth) between 2004 and 2012, cross-border online trade grew more than 7 times faster (see Figure 1 where the left axis shows the growth in world trade, and the right axis the growth in online trade). Even if cross-border e-commerce only represents around 20 percent of online sales, it has been rapidly growing and its share is expected to double over the next 5 years (UNCTAD, 2015). For policy makers, it is important to assess early on the consequences of such a rapidly growing phenomenon. Second, the wage gap has been declining in the developing world over the last decade (Cruz and Milet, 2017). Our sample is no exception, with an average annual decline of 1 percent as can be seen from Figure 2. Understanding the forces behind the decline in the wage gap, and the role played by employment in small firms, can provide useful tools in the fight against income inequality. Third, it contributes to the debate on how technological progress affects skilled and unskilled workers, and ultimately income inequality. Does technological progress promote or reduce income inequality? While the development of the Internet has often been seen as skilled biased and contributing to income inequality (Akerman et al., 2015), this paper suggests otherwise. Online platforms that allow small firms to access world markets help unskilled workers and contribute to a reduction in income inequality.<sup>1</sup> We face several challenges. First, data on online cross border trade flows are not yet collected by any national or international agency, although some proposals have been put forward (UNCTAD, 2015). As a proxy we use cross-border flows on the eBay platform made available by Lendle et al.

> 1As noted by Varian (2005): “ _big enterprises were the first to reap the benefits of this technological progress. But the impact of information technology on small and medium-size enterprises may yet turn out to have the most impact on the economy_ ”. He also remarked more recently that small businesses are taking advantage of technologies which used to be the sole privilege of large companies: “ _[. . . ] even the smallest company can now afford a communications and computational infrastructure that would have been the envy of a large corporation 15 years ago_ ”(Varian, 2011).

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(2016). One problem with using eBay flows is that the platform originally started as a peer-to-peer online market where second-hand products were auctioned, even if it rapidly became a platform mainly used by professional sellers on fixed price transactions. Because we want our proxy to reflect the access of firms to foreign consumers via an online platform rather than the general expansion of e-commerce, we decided to disregard transactions that were undertaken under the auction system. We are still left with measurement error due to the fact that cross border eBay flows are only a fraction of total online transactions between firms and foreign consumers. We will address this measurement error using an instrumental variable strategy described below.

The second challenge we face is endogeneity. Indeed, other unobserved factors such as technological progress may simultaneously determine online trade and the wage gap, thus introducing a bias in our estimates. To correct for this (and measurement error) we use an instrumental variable estimator. We use three instruments: i) the time varying geography determinants of bilateral online trade flows following Feyrer (2009); ii) the value of domestic online sales in each of the partner countries; and iii) the average import tariff in the importing countries. These three variables are likely to be valid instruments. Indeed, geography related variables and variables capturing demand shocks in importing countries are unlikely to be correlated with _other_ determinants of the wage gap in the _exporting_ country.

The third challenge is to provide estimates of the wage gap between skilled and unskilled workers that are consistent across countries. We do this using the methodology and data provided by Cruz and Milet (2017). The raw data are based on labor and household surveys in different countries and at different points in time harmonized by the World Bank in the International Income Distribution Database (I2D2). This makes feasible the use of the same methodology and variables to estimate the wage gap across countries and time.

We are not the first to look at the determinants of the wage gap between skilled and unskilled workers. Labor economists have emphasized the role played by changes in supply and demand to explain changes in the wage gap and ultimately in wage inequality. The workhorse framework linking supply and demand to factor prices is developed in Katz and Murphy (1992), later refined by several authors (see Card and Lemieux, 2001, for instance). The most recent version of this model features skill-biased technologies, i.e. technologies that shift outward the relative demand for skilled workers. This skill-biased technology change (SBTC hereafter) induces a permanent shift in the demand for the labor service of skilled workers.<sup>2</sup> However, technological change need not be

> 2The fact that wage inequalities increased at the same time when many industries were investing in computers

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biased in favor of skilled workers. Luo (2017) shows that as Europe was experiencing significant technological progress during the 500 years before the First World War, the wage gap between skilled and unskilled workers declined by more than 50%. In our empirical model we will control for both demand and supply side determinants of the wage gap. Our contribution is to add as potential demand driver the growth in online international trade.

The literature has also looked at the importance of trade as a determinant of the wage gap. Some trade economists have stressed the fact that the timing of rising wage inequalities in developed and developing countries coincided with a rapid increase in international trade flows (see Feenstra and Hanson, 2001; Harrison and McMillan, 2011, for surveys). Feenstra and Hanson (1999) find that imports of intermediate inputs account for about 15 percent of the increase in the relative wage of skilled workers in the United States over the period 1979-1990. SBTC is twice as important and accounts for about a third of the increase. Evidence from developing countries is more scarce, but also suggests that wage inequalities have been driven by episodes of trade liberalization in the 1980s and 1990s (Goldberg and Pavcnik, 2007). Lower tariffs imply that many developed economies outsourced part of their production process to lower income countries, which as emphasized by the “trading tasks” models (Feenstra and Hanson, 1996; Grossman and Rossi-Hansberg, 2008) can lead to increased wage inequalities in both developed and developing countries as long as the tasks being outsourced to developing countries are considered as unskilled in the rich country and skilled in the low-income country. Autor et al. (2013) find that trade with China hurts more unskilled workers in the United States, and Autor (2014) and Autor et al. (2015) argue that the negative impact of imports from China on wage and employment of unskilled workers in the United States is much larger than the impact of technological change.

More recently, income inequality has been declining in many developing countries, especially in Latin America (De la Torre et al., 2015). Lustig et al. (2013) review some of this literature and conclude that the decline in overall inequalities in Latin American countries is to a large extent the consequence of a decline in the wage gap, income redistribution associated with government transfers, and the boom in commodity prices (Gasparini et al., 2011).

To sum up, the existing literature has concluded that both trade and skill biased technological progress play a role in explaining changes in the wage gap between skilled and unskilled workers. Importantly, these changes substantially contribute to the evolution in income inequality. However, gave strong support to the skill-biased technology change hypothesis (Krueger, 1993; Berman et al., 1994; Autor et al., 1998; Acemoglu, 2002).

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to our knowledge the impact of online cross border trade on the wage gap between skilled and unskilled workers is yet to be investigated.

The rest of the paper is organized as follows. Section 2 presents the empirical strategy and section 3 the data. Section 4 discusses the empirical results of the impact of online exports on the wage gap. Section 5 explores theoretically and empirically the role played by small firms in explaining the impact of online exports on the wage gap. Section 6 concludes.

# **2 Empirical strategy**

We explain variations in the wage skill gap within countries and over time with the following empirical model: where _β_ s are parameters to be estimated, ( _skilled/unskilled_ ) _c,t_ is the relative supply of skilled workers, _δc_ and _ηt_ are country and year fixed effects, and _εc,t_ is the error term. We expect _β_ 1, which captures the impact of online exports on the wage gap to be negative. Indeed, the reduction in the fixed cost of entering foreign markets brought by online platforms helps smaller and less productive firms which tend to hire more unskilled workers, to enter international markets.<sup>3</sup> This again increases the relative labor demand for unskilled workers. Total exports over GDP controls for the overall impact of trade openness on the wage gap. The expected sign is ambiguous as some of the early studies have shown that trade increased the wage gap, whereas some of the more recent studies have shown that the commodity boom has contributed to a reduction of the wage gap. The relative supply of skilled workers should reduce the wage gap and therefore _β_ 3 is expected to be negative. The share of Internet users in the population is a proxy for skilled biased technological change (Akerman et al., 2015) and we therefore expect _β_ 4 to be positive.

An important concern with the estimation of equation (1) is the endogeneity of online and total

> 3For evidence that small firms have a more unskilled workforce, see the classic work by Brown and Medoff (1989) or Hamermesh (1980). For evidence that online markets help small firms access otherwise unreachable international markets see Lendle et al. (2013). They show that 90 percent of US firms on the eBay platform export and that their size is much smaller than the size of traditional US exporting firms. Lendle and Olarreaga (2017) provide similar evidence for firms in developing countries.

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exports flows, as well as measurement error associated with the use of eBay cross border flows as a proxy for online exports. We address this by instrumenting online and total trade flows using three exogenous sources of variation. First, we use the time-varying distance determinant of online and total exports as in Feyrer (2009). We expect the impact of distance to vary over time due to innovations in the transport sector that has shifted shipments towards air transport, as well as technological progress in the information and telecommunication sector. We expect the impact of distance to be different for online and total trade because technological improvements, which reduce information barriers and that we proxy using geographic distance, are likely to be stronger for online transactions given the information providing mechanisms in online platforms such as the top rated seller or buyer information in online markets (Lendle et al., 2016). Thus the variation in the use of these instruments comes from the differential impact of distance across time and across online and The second set of instruments capture foreign demand shocks for online and total exports in importing countries. In the case of online trade, domestic demand is computed as the sum of domestic online transactions and online imports. For total trade, we simply use the GDP of the importing country. We expect demand in the importing country to be positively correlated with online and total export flows of exporting countries, and uncorrelated with other determinants of the wage gap in the exporting country.

Finally, we use the average tariff imposed by the importing country. To differentiate the tariff imposed on online and total trade, we use the average import tariff on manufacturing products for online trade and the average tariff on all products (including raw materials and agricultural products) for total trade. There is no reason to believe that the average tariff imposed by the importing country is correlated with the wage gap in the exporting country other than through its impact on bilateral trade, which we expect to be negative.

We estimate the following six equations: 5
where ln( _online exports_ ) _x,m,t_ and ln( _exports_ ) _x,m,t_ are respectively the online and total exports from country _x_ to country _m_ in year _t_ . _βx,m_ and _αx,m_ control for time-invariant country-pair specific determinants of trade flows such as distance, colonial linkages, common language, etc. _βx,t_ and _αx,t_ are year and country specific shocks in the exporting country. We use the estimate in equations (2)(7) to predict the log of bilateral online and total exports. To estimate the impact of online trade on the wage gap, we need to aggregate the bilateral exports flows at the exporter and year level. We first take the exponential of the predicted bilateral trade flows estimated by equations (2)-(7). We then aggregate these trade flows at the exporter and year level by summing the trade flows over all importers each year. After taking logs we use these six variables to instrument the log share of online and total exports in GDP.

# **3 Data**

The variable of interest is the wage gap between skilled and unskilled workers. We borrow the data and methodology from Cruz and Milet (2017). The data source is the International Income Distribution Database (I2D2) put together by the World Bank which compiles more than 1,000 household and labor force surveys from 164 countries over the period 1960-2014 covering more than 120 million individuals. The data have been harmonized to the extent possible ensuring that conceptual variables have similar meaning, allowing for cross-country analysis.<sup>4</sup> For the purpose of this study, we selected a restricted sample of 22 countries for which sufficient data are available.<sup>5</sup>

> 4See Montenegro and Hirn (2009) and Montenegro and Patrinos (2014) for more information about the database.

> 5See table 1 for a list of countries and surveys.

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We kept surveys for which individual information on gender, age, education achievement, and labor income (wage) was available and consistent over the period 2004-2012 for which we have cross-border trade data. We kept the working age population, and dropped individuals below 15 and above 64 years of age.<sup>6</sup> Finally, we kept workers who reported being paid employees rather than self-employed. Wages are reported in various units depending on the country being surveyed. We converted them into monthly full-time equivalent.<sup>7</sup> We exclude the top and bottom 1% of the wage distribution as non-response and under-reporting are typically quite severe among highincome households, and the bottom end of the distribution is likely to suffer from measurement error, misreporting, and omissions to consider financial transfers within the household (such as remittances for instance). There are five categories of educational achievement: no education or incomplete primary, complete primary, incomplete secondary, complete secondary, and postsecondary.

We define skilled workers as workers with at least a complete secondary education.<sup>8</sup> The group of unskilled workers is made of individuals without education (below primary), incomplete primary and complete primary education.<sup>9</sup> Following Katz and Murphy (1992) and Acemoglu and Autor (2011), wages and labor supply are defined in terms of _efficiency units_ by adjusting for the composition of the workforce. We follow Acemoglu and Autor (2011) and define 50 demographic groups of individuals based on the five education categories (no education or incomplete primary education, complete primary education, incomplete secondary education, complete secondary education, and post-secondary education), five job experience groups (0-9 years, 10-19 years, 20-29 years, 3039 years, and more than 40 years), and gender (50=5 education groups _×_ 5 experience groups _×_ 2 genders). We regress the individual log wage on the five education dummies, an experience quartic, the full interaction between the education dummies and the experience quartic, a dummy for

> 6This restriction does not affect our results. Individuals outside the [15-64] years old interval account for on average 4.7% of the population, and 4.6% of the total wage bill.

> 7Wages reported in the various surveys are either daily, weekly, by-weekly, monthly, bi-monthly, quarterly, annually or hourly wages. In the case of hourly wages, we multiplied by the number of hours worked during the day, and by the number of weeks during the month (52 weeks divided by 12 months). In the case where wages are reported on a hourly basis and information on the number of hours is missing, we made the assumption that workers worked 8 hours per day.

> 8In unreported descriptive statistics, we show that the real wages of workers with complete secondary and postsecondary education follow similar trend over time, and so do the wages of workers with below complete secondary education. This similarity in the trends suggest that there is a higher degree of homogeneity within the skilled and unskilled workers rather than between them.

> 9In unreported descriptive statistics, we looked at the evolution of the real wage for each of these education categories. Real wages of workers with complete secondary or post-secondary education followed a similar pattern, quite different from the one for workers with at most incomplete secondary education.

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whether the individual lives in an urban area, and an industry dummy<sup>10</sup> . We then predict wages at the corresponding education levels and five experience levels (at 5, 15, 25, 35, 45 years of experience, corresponding to the average experience in each experience group defined above), in each industry. We perform separate estimations for men and women. We then take the weighted average of the predicted log wage for skilled and unskilled workers. Weights are defined as the average of the labor share of each of the 50 demographic groups in each industry over time.<sup>11</sup> Figure 3 describes the evolution of the wage gap and online exports in some selected countries.<sup>12</sup> The countries were selected so that the variables have similar dimension for illustration purposes. Nevertheless, figure 3 suggests that if the wage gap declined in most countries in our sample, this is not the case in the Philippines, for instance.

The online trade data are borrowed from Lendle et al. (2016), where we only consider fixed price transactions, as the mechanism we have in mind does not go through the auctioning of second-hand goods.<sup>13</sup> It contains bilateral cross-border flows on the eBay platform over the period 2004-2012. Cross border flows represented on average 20 percent of the total value of transactions on the eBay platform during this period. As shown in figure 1 online trade was growing faster than overall trade over the period 2004-2012.

Table 2 provides summary statistics for all the variables used in the estimations of equations (1)-(7). The data source for GDP, average tariffs, and the share of Internet users is the World Bank’s World Development Indicators. Information on distance comes from the CEPII distance database (Mayer and Zignago, 2011).

# **4 Online exports and the wage gap**

Table 3 presents the ordinary least square estimates. All regressions include country and year fixed effects, and standard errors are clustered at the country-level to account for intertemporal correlation in the error term for each country. In all columns online exports has a negative sign and is statistically significant at the 1 percent level. Using the estimates of column (4) which corresponds to equation (1) we find that a 1 percent increase in the share of online exports over GDP decreases

> 10There are ten broad industries in the I2D2: agriculture, mining, manufacturing, public utilities, construction, retail and wholesale, transport, finance, public administration, and others.

> 11See Cruz and Milet (2017) for further details on the construction of the wage gap.

> 12We selected countries based on the comparable magnitudes in terms of changes in the wage gap and online trade. Graphs for other countries show similar patterns.

> 13We also used as a robustness check all online trade flows on eBay which include auction transactions. Results are similar and often larger in magnitude.

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the wage gap by 0.01 percent. The log of total exports over GDP is never statistically significant. The coefficient on the relative supply of skilled workers is as expected negative and statistically significant.<sup>14</sup> The share of Internet users, which we use as a proxy for technological change, has a negative and statistically significant sign, suggesting that as the number of Internet users increases the wage gap between skill and unskilled workers declines.

As discussed earlier an important problem with the estimates in table 3 is measurement error as online exports are only captured by cross-border transactions that occurred over the eBay platform. Other platforms like Amazon, or Alibaba are excluded. Moreover, many electronic transactions do not occur over the platforms but directly on firms’ web pages.<sup>15</sup> There may also be omitted variable bias if the role of the Internet, which is imperfectly captured by the share of Internet users, is correlated with both online exports and the wage gap. In order to correct for these two problems we instrument online and total exports using equations (2)-(7). Results of the estimation of equations (2)-(7) are shown in table 4. The log of online exports over GDP is the dependent variable in columns (1)–(3), and the log of total exports over GDP in columns (4)–(6). Timevarying distance and trade costs have the expected negative signs but with very different point estimates. The marginal impact of distance on total export flows tends to declining over time for both online and total trade. As can be seen from column (4) in 2004 the impact of distance on total trade is 12 percent smaller than in 2012 (the reference year). For online trade the impact is more than 30 percent smaller in 2005 (we do not use 2004 as it is imprecisely estimated). Columns (2) and (5) report the estimates of equations (3) and (6) where we use the domestic demand for online products (column 2) and GDP (column 5) in partner countries as instruments for online exports and total exports respectively. In both columns the coefficients on the demand variables are positive and statistically significant, as expected. Finally, columns (3) and (6) report the estimates of equations (4) and (7) where the instruments are respectively the average tariff on manufacturing products and the average tariff on all products in the importing country. As expected, import tariffs are negatively correlated with export flows. The correlation matrix of total and online trade flows with their predicted values is given in table 5. All trade flows are highly correlated, but it is important to note that the correlation between the predicted trade flows and the actual flows

> 14Its magnitude, -0.10 can be interpreted in terms of the elasticity of substitution between skilled and unskilled workers. Following a simple supply and demand framework _`a la_ Katz and Murphy (1992), assuming that technological change is exogenous to the supply of skilled workers, the coefficient we estimate is (minus) the inverse of the elasticity of substitution between skilled and unskilled workers. Our estimate in column (4) therefore implies an elasticity around 10.

> 15Note that the latter may be less likely for exporters from developing countries which need independent feedback mechanisms provided by these platforms to create a reputation as a reliable business partner.

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is always above 0.84, suggesting that our instrumental variables are doing a reasonable job in explaining observed trade flows. This is not surprising given that we use the gravity framework.

Table 6 provides the instrumental variable estimates of equation (1). In the first three columns we report the estimates using one of the three types of instruments for each export flow (online and total). In columns (4) to (6) we use two instruments for each trade flow, which allows us to test for the validity of our instruments. IV (2) + (3) corresponds to the first two instruments for each trade flow (i.e. obtained form equations (2) and (3) for online exports, and from equations (5) and (6) for total exports. The same logic applies to IV (2)+(4) and IV (3)+(4). In column (7) we use all instruments.

All the estimated coefficients on the share of online exports are negative as expected and statistically significant at the 1 percent level. The estimates in column (4) suggest that a 1 percent increase in online exports would lead to a 0.01 percent decline in the wage gap, which is very close to the OLS estimate. The estimated coefficient on total exports is also always negative and statistically significant with a point estimate around -0.05. The coefficient on the relative supply of skilled workers remains negative across the four specifications, but is always statistically insignificant. The share of Internet users is negative and statistically significant at the 10 percent level. At the bottom of the table we report various statistics attesting the overall validity of our instruments. The p-value of the F-test for the first stage is strongly below the admitted threshold of 5% in all specifications, and the Hansen J test shows that the overidentification restriction are valid (except perhaps in column (6) when we only use demand and tariff shocks as instruments). The Cragg-Donald F-test also attests of the strength of our instruments.

# **5 The role of small**

As discussed earlier a potential explanation for the negative impact of online exports on the wage gap is that the reduction in the fixed cost of exporting benefits small firms which tend to be less skill-intensive. While small firms are under-represented in the population of regular exporters, anecdotal evidence suggest that they account for a significant share of the firms engaged in crossborder e-commerce. The company “Ecommerce Europe” which describes itself as “the voice of the e-commerce sector in Europe” represents over 25,000 online shops across Europe. Its 2016 e-commerce report surveyed 585 European firms engaged in e-commerce and shows that 334 of the 10
surveyed firms are what they call “pure-players” (i.e. not retailers or wholesalers).<sup>16</sup> Most of these independent firms (72%) have fewer than 10 employees and 84% have fewer than 20 employees, and two-thirds engage in cross-border e-commerce. In developing countries, anecdotal evidence taken form UNCTAD (2015) shows that when facing substantial challenges while engaging in e- commerce, many micro and small enterprises rely on existing online platforms. Such platforms propose a variety of service that micro and small firms would not be able to provide on their own such as advertising, product management, shipping, and most importantly payments via credit cards, PayPal, or bank accounts.<sup>17</sup> We first develop a simple analytical model to analyze the role of small firms in explaining the impact of online exports on the wage gap. We then empirically assess whether the impact of online exports on the wage gap is stronger in countries with a larger share of employment in small firms.

# **5.1 A conceptual framework**

Building on Ekholm and Midelfart (2005) and Yeaple (2005) it can be shown that a reduction in the fixed cost of exporting faced by small firms leads to a reduction in the wage gap between skilled and unskilled workers, and that this reduction is larger in the presence of a relatively larger share of small<sup>18</sup> On the demand side we have a representative consumer with CES preferences over two imperfectly substitutable composite goods _Xh_ and _Xl_ who maximizes her utility with respect to her budget constraint _E_ . Each composite good is also a CES aggregate of individual varieties produced by monopolistically competitive firms. The aggregate production and corresponding price index in each sector is given by:

> 16 Reports are available at `https://www.ecommerce-europe.eu/` .

> 17In developing countries it is also important to recognize the role played by mobile phones in facilitating the access by micro and small business to e-commerce. According to WTO (2013), “ _The most popular ICT in developing countries and one which is progressing very rapidly in Africa and Asia, in particular India, is the mobile phone. Mobile phones are increasingly playing a larger role in the expansion of e-commerce in developing countries, especially among users without terminal connections. [..] In many developing countries, [...] they are increasingly being used for data application such as m-commerce and m-banking. In a number of African countries, notably Kenya, South Africa, Tanzania and Zambia, mobile telephones are being used to do personal banking services_ ”.

> 18 We develop a simple closed economy model, but it can be easily reinterpreted as an open economy model by either having consumers and producers being located in different countries, or by adding a second set of consumers in a different country in which a fixed cost needs to be paid in order to reach them.

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We assume that _σ > ε >_ 1 meaning there is a greater substitutability between varieties within each composite good than in final consumption.

On the supply side, firms in each sector behave monopolistically. Firms in sector _h_ incur a fixed cost paid in skilled labor, and a unit skilled labor requirement to produce one unit of output. Firms in sector _l_ use a different technology. They incur a lower fixed cost paid in skilled labor, and a unit unskilled labor requirement to produce one unit of output.<sup>19</sup> The cost functions of a typical firm in each sector are given by: where _δ <_ 1 is the discount on the fixed cost faced by firms in sector _l_ . Our objective is to see what is the impact of a reduction in _δ_ (the fraction of fixed cost paid by firms in sector _l_ ) on the wage gap, and how this changes with the share of firms in sector _l_ . Firms maximize their profit, and we get the standard pricing rule, where firms charge a constant markup over their marginal cost.

Free-entry in each sector drives profits to zero and gives us the equilibrium quantities produced by a typical firm in each sector:

> 19In the appendix we develop the more general case where both sectors use unskilled and skilled labor, but with different intensity.

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where _ω_ is the wage gap ( _ω_ = _wh/wl_ ). The revenues of a typical _h_ -type firms are given by: _Rh_ = _phqh_ = _Fσwh_ and the revenues of a typical _L_ -type firms are: _Rl_ = _plql_ = _δFσwh_ . With _δ_ smaller than one, it follows that _Rh/Rl_ = 1 _/δ_ is greater than one, meaning that _h_ -type firms have greater revenues than _l_ -type firms.

Within each sector we can express the aggregate quantities consumed _Xh_ and _Xl_ , as well as the corresponding price indices _Ph_ and _Pl_ , as a function of the number of firms in each sector, _Nh_ and _Nl_ : Relative demand for the two composite goods is given by: Substituting equations (15) and (16) into equation (17), and then using equations (11) to (14), we can express the wage gap as a function of _δ_ , and the relative number of firms: It is straightforward to see that the derivative of _ω_ with respect to _δ_ is positive and that the second derivative with respect to _δ_ and _Nl/Nh_ is also positive. Thus, a lower fixed cost faced by smaller and less skill-intensive firms (due to online platforms) leads to a reduction in the wage-gap and the effect is stronger the larger is the share of smaller firms.<sup>20</sup>

> 20Note that the number of firms is endogenous in this monopolistic competitive model. In the appendix we

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# **5.2 Empirical evidence**

To empirically investigate the role of small firms in cross-border e-commerce, we go back to the harmonized I2D2 and extract information on firm size. We compute, for each country and year the share of workers employed in firms with fewer than 10 employees.<sup>21</sup> It is on average 45 percent, and it oscillates between 15 percent in Russia and 74 percent in Costa Rica. This suggests that for many countries in our sample the share of employment in small firms is sufficiently large as to have an economically significant impact on the wage gap.

We also compute the share of skilled workers in small and large firms to check whether small firms are less skilled-intensive. Columns (2) and (3) of table 7 show that on average only 25 percent of workers in small firms (firms with fewer than 10 employees) are considered skilled. In large firms (firms with more than 50 employees) 49 percent of workers are skilled. Comparing each row in columns (2) and (3) of table 7 we observe in all countries a larger share of skilled workers in large The variation across countries in the share of employment in small firms suggests that if the mechanism at work is the one described above, then the impact of online exports on the wage gap is likely to be heterogeneous across countries. In order to test this heterogeneity and provide evidence for our mechanism, we re-estimate equation (1) with an interaction between online exports and two dummies indicating whether the country is above or below the median share of employment in small firms in our sample. The median is at 44 percent in our sample. We use a dummy for two reasons. First, the size of the firm is not systematically reported by all individuals in the I2D2 surveys and the resulting information may be quite noisy. In addition, the I2D2 does not provide the exact number of employees, but an interval with a lower and upper bound for firm size. Second, the relationship between the impact of online exports on the wage gap and the share of workers in small firms may not be linear. The solution to these two problems is to use a dummy variable instead of the actual continuous variable. We expect the coefficient on the interaction of online exports with the dummy indicating a share of employment in small firms above the median to be negative and larger than the coefficient on the interaction of online exports with the dummy indicating a share of employment in small firms below the median. Because we are ultimately endogenize the number of firms and solve for a full model with more general assumptions on the production side to show that the wage gap increases with the fixed costs faced by firms in the less skill-intensive sector, and that this effect is larger the larger is the endowment of unskilled to skilled workers.

> 21Information on firm size is unfortunately not available for Colombia, the Dominican Republic, Sri Lanka, and the Philippines.

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interested in the interaction between the role of small firms and online exports, we also interact the total exports with the same dummies to ensure that the mechanism we describe is not also present in the case of total exports. If the channel through which exports affect the wage gap works through the ability of small firms to export via online platforms, the interaction terms between total exports and the dummies should not be statistically different from one another.

The instrumental variable estimates are reported in table 8. The interaction between online exports and the dummy indicating a share of employment in small firms above the median is negative and statistically significant in all specifications. It is almost identical to the baseline results. Note that the coefficient is very stable across specifications and precisely estimated (p-value below 1%). On the other hand, the interaction with the dummy indicating a share of employment in small firms below the median is not statistically different from zero. Thus, the negative impact of online exports on the wage gap is observed in countries with a larger share of small firms. The impact of total exports on the wage gap remains negative and statistically significant.<sup>22</sup>

# **6 Concluding remarks**

We examine the impact of online exports in reducing the wage gap between skilled and unskilled workers. Our econometric results suggest that as the share of online exports increases the wage gap declines. The economic significance of the impact is small as the elasticity of the wage gap with respect to the share of online exports is 0.01, reflecting the fact that online cross-border trade is still a relatively small phenomenon. However, with online cross border trade growing at a pace seven times faster than world trade, its contribution to reductions in wage inequality is bound to increase.

We also find that the impact is stronger in countries where the share of the labor force in small firms is larger. Small firms are more likely to benefit from the reduction in the fixed cost of exporting provided by online markets. They also tend to hire relatively more unskilled workers. Thus, as the share of online exports increases, this leads to a relative increase in the demand faced by small firms, which will in turn increase the relative demand for unskilled workers, and reduces the wage gap. In fact we find that the negative impact is only observed in countries with a large

> 22We also interact total exports with the same dummies as for online exports. The coefficients on the two interactions are not statistically different from each other. Thus, the impact of total exports on the wage gap does not depend on the relative importance of small firms in the economy.

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is below the median, an increase in online exports has no impact on the wage gap.

These results suggest that reducing barriers to online trade may be desirable for reasons that go beyond economic efficiency, as it may help reduce labor income inequality. Anyone who has tried to purchase goods online across borders knows that there are still important administrative barriers to international trade online. These include unclear taxationm customs requirements, and long customs delays. Solutions to these problems need to be considered. Examples include the introduction of higher de minimis thresholds under which no customs duties or other taxes (such as VAT) apply, the introduction of streamlined procedures for payment of customs duties and other taxes, such as e-Customs or e-taxation, or a coordination effort by exporting and importing countries to allow for the payment of customs duties online.

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