Digitalization and trade participation of SMEs

Recent studies have brought new evidence on the positive role of digitalization, and particularly ICT and the Internet, on exports (Añón Higón & Bonvin, 2022; Fernandes et al., 2019; Kneller & Timmis, 2016). Studies focused specifically on SMEs are scarce. However, SMEs may benefit from digitalization differently from large firms due to their limited resources that impede their ability to compete (Coviello & Martin, 1999). For example, the Internet, being a low-cost means of internationalization, has been shown to reduce trade barriers (Hamill & Gregory, 1997). Therefore, it can help SMEs overcome distance- and entry-related costs in an affordable way (Cassetta et al., 2020; Hagsten & Kotnik, 2017). Further, DTs may provide SMEs with a competitive advantage, which is one of the reasons why they adopt these technologies at first (Dholakia & Ksheti, 2004).

Among the first studies on SMEs,¹ Hamill and Gregory (1997) show that, even at an early stage of development, the Internet helps firms overcome trade-related barriers. Using a sample of SMEs from Ireland, Canada, New Zealand, and Australia, Loane (2005) also finds that the Internet enables small entrepreneurial firms to trade globally. Similarly, Mostafa et al. (2005) show that the Internet helps improve trade, especially when managers have a strong entrepreneurial orientation. Beyond the role of the Internet, Añón Higón and Driffield (2011) observe a positive correlation between the use of ICT by British SMEs and their export performance. According to Hagsten and Kotnik (2017), basic ICT tools such as websites are more effective for accessing foreign markets than advanced ones, such as e-commerce. There is also evidence that digital platforms, such as Alibaba and eBay, are helpful for SMEs trying to enter foreign markets (Jin & Hurd, 2018; Lendle et al., 2016). Finally, regarding Spanish firms, Nieto and Fernández (2005) report that selling online to other businesses increases SMEs' export intensity, while selling to end consumers or having a website has no effect.

However, previous studies have overlooked the importance of digitalization for imports. By reducing communication and coordination costs, DTs can also facilitate imports (Jungmittag & Welfens, 2009). Thanks to digitalization, information can circulate faster, making it easier for buyers and suppliers to connect. Yet, few studies have examined the impact of digitalization on imports. Exceptions include Nath and Liu (2017), who use data for 49 countries to find that ICT enables the import of services, including financial, insurance and telecommunications. Ozcan (2018) shows, for a sample of countries trading with Turkey, that ICT influences both exports and imports, with the effect being more pronounced for imports. Along similar lines, Malgouyres et al. (2021) find that broadband diffusion increases French firms’ imports by about 25%. More recently, a few studies have shifted the focus away from ICTs and examined the impact of automated technologies, primarily robots. For example, Alguacil et al. (2022) show that robot adoption helps Spanish firms to start importing and exporting and leads to an increase in the value and share of imports in total sales. However, the above studies do not consider that export and import decisions are determined simultaneously (Elliott et al., 2019).

The analysis of the indirect impact of digitalization on trade via TFP draws on two strands of literature. First, we contribute to the literature on the role of DTs in shaping productivity. Second, we add to the existing literature on productivity and trade (Melitz, 2003). By bringing these two strands together, this study aims to deepen our understanding of the complex relationships between digitalization, productivity, and trade.

First, the arguments by which DTs enhance productivity are diverse. Digitalization endows firms to source inputs and organize production more efficiently, and facilitates changes in management and organizational practices (Bloom et al., 2014). Yet, the empirical evidence at the firm level is mixed. Early studies focused on ICT found scant support that DTs improve productivity (Cardona et al., 2013). For example, Berndt and Morrison (1995) and Brynjolfsson (1996), both using data from the US before the nineties, find no evidence that IT increases firms’ productivity. As DTs spread and adoption rates increased, the number of studies showing a positive impact on productivity grew. For instance, Brynjolfsson and Hitt (2003) show that computerization increases productivity in US firms in the long term but not in the short term. Hempell (2005) for German firms, and Commander et al. (2011) for firms in Brazil and India, also find a strong positive association between ICT and productivity.

More recently, the productivity slowdown has sparked new interest in the subject, albeit again with mixed results. Using US firm-level data from 1977 to 2007, Acemoglu et al. (2014) find that the IT intensity does not affect productivity, except in the computer-producing industry. According to DeStefano et al. (2018) broadband has a causal effect on firm size but not on productivity in UK firms in the early 2000s. In contrast, Bartelsman et al. (2019) point to a positive relationship between the share of broadband-connected employees and productivity for European firms. Likewise, Gal et al. (2019) evidence a strong relationship between DT adoption in an industry and productivity gains in a sample of OECD firms.

In contrast to previous studies, we propose that digitalization endogenously affects TFP. By opting for an endogenous process, as proposed by Doraszelski and Jaumandreu (2013) for R&D, we account for uncertainties linked to the success of digitalization, which might explain the heterogeneous results previously obtained.

This study also adds to the literature on productivity and international trade by examining the indirect effect of digitalization on trade through firm productivity. Previous research has established that relatively more productive firms are more likely to export, a phenomenon known as self-selection into exporting (Bernard & Jensen, 1999). This observation forms the basis of Melitz's (2003) theoretical model, which shows that only firms with productivity above a certain threshold level are able to overcome the sunk costs of exporting and enter foreign markets. Similarly, there is evidence of self-selection into importing, with only the most productive firms importing intermediates due to the fixed costs involved (Kasahara & Lapham, 2013). By analyzing the indirect effect of digitalization on trade, this study contributes to a deeper understanding of how these self-selection mechanisms shape the productivity of SMEs engaged in international trade.

To analyze the indirect effect of digitalization, we first need to estimate the TFP. For that, we assume a Cobb–Douglas production function:where y_it, l_it, \({k}_{it}^{NIT}, {k}_{it}^{IT}\), and m_it, stand for the firm’s i logarithm of output, labor, non-ICT capital, ICT capital, and materials. The productivity is denoted by ω_it, and e_it is the error term.

In line with Doraszelski and Jaumandreu (2013), we model the dynamics of productivity as an endogenous Markov process that depends on DIG and a random shock:where g(.) is an unknown function, and \({\xi }_{it}\) is a random shock.

The estimation of Eq. (5) by ordinary least squares (OLS) causes biased and inconsistent estimates because the firm’s choice of (variable) inputs depends on productivity, ω_it (that is only observed by the firm). To address this problem, we apply the GMM-based semi-parametric control function estimator by Wooldridge (2009) for each of the 10 industries. As a result, we obtain industry-specific output elasticity and firm-specific TFP estimates, obtained as residuals. More details on the estimation can be found in the online Appendix, including the elasticity estimates for each industry.

Once TFP is obtained,⁷ it is included as a regressor in Eq. (1). Finally, for digitalization to have an indirect effect through TFP on the export (import) participation equation, two conditions should be met. First, DIG should have a significant impact on TFP; and, second, the coefficient of TFP in the export (import) equation should be significantly positive in support of the self-selection into trade hypothesis. To check the first condition, we consider a linear specification of Eq. (6):where TFP (\({\omega }_{it}\)) is a function of its lag value (\({\omega }_{it-1}\)) and the digitalization index (\({DIG}_{it-1}\)). We also control for other observed firm characteristics⁸ that may influence the evolution of TFP (z_it-1), sector-year dummies (\({\alpha }_{jt}\)), and firm fixed effects (\({\alpha }_{i}\)). We interpret positive and significant estimates of \({\beta }_{2}\) as evidence of enhancing TFP effects from digitalization. Equation (7) is estimated by the two-step system-GMM estimator (Blundell & Bond, 1998).

The data is drawn from the Survey on Business Strategies (ESEE). The ESSE is an annual survey, carried out since 1990, sponsored by the Spanish Ministry of Industry, Tourism and Trade, and administered by the SEPI Foundation. The sample in the survey is representative at the industry-level of the population of Spanish manufacturing firms with more than 10 employees.⁹ The questionnaire provides rich information on the firm's activity, including export and import activities. Yet, some of the questions concerning DTs, specifically online trade and training in ICT, appear as early as 2000 and 2001, respectively, which is why our analysis begins in 2001.

Our initial sample consists of an unbalanced panel of 25,056 observations corresponding to firms observed at least two consecutive periods between 2001 and 2014. From this sample we drop large firms and firms that cannot supply relevant information. After that, we end up with a sample of 12,783 observations corresponding to 1,814 SMEs.

The firm level index of digitalization is based on the work of Calvino et al. (2018) at sector level. This index is conceived under the consideration that digitalization is a complex phenomenon that can hardly be captured by a single indicator. Moreover, DTs are interrelated, with the impact of one technology being enhanced by the use of another. Hence, the effectiveness of DTs should be assessed considering them as a whole and not individually.

To create this index, we use several dimensions that aim to represent the extent of digitalization of Spanish firms in the period of analysis. These dimensions are: i) the technological components (proxied by ICT capital, computer programming services, and the implementation of software programs either hired or developed by the firm); ii) the digital-related human capital (proxied by personnel training in software and information technology); iii) the extent of automation (measured by the use of robots, computer-aided design, flexible systems, and LAN); iv) the way digitalization changes how firms interact with their stakeholders (measured by the ownership of an internet domain and webpage, and the use of different modalities of e-commerce: b2b, b2c, and e-buying). In total, the synthetic index collapses information on 13 components that, measured in different ways, contain relevant information of the digital transformation. In Table 9 of the Appendix, we compare the variables we use to those of Calvino et al. (2018). We also analyze distinctively the role of automation from other DTs, referred here as ICTs. Hence, we construct an automation index that captures the extent of automation, measured by dimension iv) of the general index. The rest of dimensions will be part of the ICT index.

The procedure for building the overall index can be summarized as follows. First, variables in monetary units (ICT investment and training costs) are capitalized and their relative value to the industry-year mean is classified according to the decile of the distribution to which they belong. The result is then rescaled in the [0–1] range. Categorical variables available only every 4 years (use of robots, CAD, flexible systems, and LAN) are first extrapolated and then normalized in the [0–1] interval. The rest of the categorical variables are not transformed. As a result, we end up with 13 variables ranging from 0 to 1. Finally, to obtain a synthetic index, we combine the information of these variables as an unweighted sum. The result is subsequently normalized in the [0–1] interval. Values close to 0 imply that the firm in that period is little digitalized, while values close to 1 suggest a high degree of digitalization.

In Fig. 1, we show the digitalization of manufacturing firms in Spain from 2001 to 2014 using the digitalization index. According to the left panel of Fig. 1, firms have undergone a process of digitalization, which was much faster at the beginning of the 21 century and that slowdown later on because of the 2008 financial crisis. The degree of digitalization varies according to firm size, with SMEs being less digitalized than large firms.

Figure 2 plots the digital transformation by industry from 2001 to 2014. All sectors have endured a process of digitalization, which for some industries, such as agricultural and industrial machinery, and transport equipment, has been remarkable. By 2014, the most digitalized industries are transport equipment, agricultural and industrial machinery, and the electrical goods sectors. Textiles, timber and furniture, and food, beverages, and tobacco are the least digitalized. This is in line with the taxonomy presented by Calvino et al. (2018).

Table 1 shows the percentage of observations contained in each category according to the export and import status. The percentage of observations corresponding to SMEs that export is approximately 60%, while those that do not export equals 40%. Similar percentages are obtained for importers and non-importers.

Descriptive statistics are presented in Table 2. We first compare SMEs that export with non-exporters. Exporters are on average larger, more productive, more innovative, have more human capital, and a larger stake of foreign ownership. More interestingly, exporters are also more digitalized than non-exporters. Moreover, SMEs that export have a lower relative markup than those that do not. This may be because exporters may face a tougher competitive environment in foreign markets than their peers serving only the domestic market, requiring them to bear lower markups to remain competitive relative to the more efficient foreign competitors. Similar to exporters, importers are, on average, more digitalized, larger, more productive, more innovative, with more human capital, a higher stake of foreign ownership, and lower mark-ups.

In this section, we run some robustness checks.¹⁴ The results are presented in Table 5, where, for clarity, we show only the AMEs of DIG and TFP.¹⁵ As a first robustness check (columns 1 and 2), we follow Wooldridge (2005) and model the unobserved heterogeneity terms, α_i’s, including the time means of all variables contained in the x vector.¹⁶ Second (columns 2 and 3), we follow Mañez et al. (2020), and model the distribution of α_i, conditional on the pre-sample mean of the dependent variable, instead of using the within means. Here, the pre-sample means are calculated as the within-firm mean of export and import propensity for pre-sample years, which in our case correspond to the period 1998–1999. The third robustness check deals with the fact that TFP is an estimated regressor, which could render the standard errors inaccurate and affect inference. To address this problem, we report bootstrapped standard errors (see columns 5 and 6). The fourth check uses instead of the leave-one-out mean instrument in the first-step of the CF approach, the second lag of the dependent variable together with the regulatory index in communications.¹⁷ The results of the second-stage are presented in columns 7 and 8. In this case too, the first-stage residual is not significant in the trade equations, corroborating that DIG does not suffer from endogeneity. For the final check we estimate in columns 9 and 10 two static linear probability FE models to control for unobserved firm characteristics not fully captured by the Wooldridge (2005) approach and that can simultaneously affect the probability of using DTs and accessing foreign markets. However, linear probability models have the disadvantage that the estimated probabilities are not restricted to the interval [0–1].¹⁸ Overall, the results of the above checks were broadly consistent with the baseline estimates, except for the FE model where the direct impact of digitalization on imports became insignificant.

At this point, we have shown that digitalization has a direct and indirect impact on the export and import participation of SMEs. Now, our goal is to assess which firms and industries benefit most from digitalization. Previous studies have shown that the relationship between DTs and firm performance is heterogeneous, with some firms or industries being more successful in exploiting DTs than others (DeStefano et al., 2018).

Thus, considering that the take-up of DTs varies widely across industries, we first perform the analysis distinguishing between firms in high- and low-digitalized industries following the classification by Calvino et al. (2018) (see Table 10). In principle, it is unclear whether the trade effect of digitalization is greater for firms in low-digitized industries or vice versa. While firms in low-digitalized industries have more to gain from DTs, the digital transformation may be more effective when many firms in an industry use DTs intensively because of the potential for knowledge spillovers (Laursen & Meliciani, 2010).

The trade impact of DIG and TFP in low-digitalized industries (columns 1 and 2) and high-digitalized industries (columns 3 and 4) is displayed in Table 6. Digitalization in low-digitalized industries directly facilitates exports and has an indirect effect on both exports and imports via productivity. However, in high-digitalized industries, digitalization only affects exports directly but not via TFP. In contrast, the decision to import is only indirectly affected by digitalization through TFP. While firms in highly digitalized industries still appear to benefit from the use of DTs, it is precisely in more digitally disadvantaged sectors where SMEs can gain more from the use of DTs, both directly and indirectly through TFP gains.

Second, DTs have been linked to the fragmentation of the GVC and the decision to offshore and outsource as they reduce the transaction and adjustment costs of moving some activities outside the firm (Rasel, 2017). At the same time, SMEs are under-represented in GVCs, and DTs may open up new avenues for them to play a more active role (Gopalan et al., 2022). Given that the integration in GVCs varies across industries, we perform the analysis distinguishing between firms in sectors that are low- and highly integrated into GVCs (see Table 10). Here, the classification on GVC participation is based on the OECD “GVC forward linkage” indicator at the industry level for Spain for the year 2000, which is expressed as the share of domestically produced inputs used in third countries' exports.

The trade impact of DIG and TFP in industries with low-participation (columns 5 and 6) and with high-participation in GVCs (columns 7 and 8) is displayed in Table 6. The results show that in low-GVC integrated sectors, digitalization exerts a direct and indirect impact on exports, while digitalization increases the probability of importing just through the productivity channel. In industries with high participation in GVCs, digitalization directly increases the probability of exporting, but there is no indirect effect through TFP. In contrast, digitalization has a direct and indirect impact on import participation.

Finally, while both automation and ICTs may bring productivity gains to the firm, it seems plausible that the effect of these technologies on trade may be different. They potentially have different implications for the international division of labor and trade activities. Automation technologies -including robots- are more likely to reduce the number of tasks and may accelerate the substitution of humans for machines, and thus, they are likely to induce the reshoring of some tasks previously outsourced. In contrast, ICTs, particularly communication technologies, help to overcome physical distance, reduce matching and coordination costs, and thus, are likely to encourage fragmentation of the production processes (Baldwin, 2016), leading to more trade. To assess this, we estimate model (1) distinguishing two dimensions of the digitalization index: the automation index, and the ICT index. The results presented in Table 7 are in line with the above arguments. We show that, while ICT influences both export and import participation decisions, the automation index has no direct impact. Nevertheless, the productivity effect of both ICT and automation leads to a higher probability of importing and exporting.