Global sourcing, firm size and export survival

Bandick, Roger Working Paper Global sourcing, firm size and export survival Economics Discussion Papers, No. 2019-62 Provided in Cooperation with: Kiel Institute for the World Economy – Leibniz Center for Research on Global Economic Challenges Suggested Citation: Bandick, Roger (2019) : Global sourcing, firm size and export survival, Economics Discussion Papers, No. 2019-62, Kiel Institute for the World Economy (IfW), Kiel This Version is available at: https://hdl.handle.net/10419/209122 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Discussion Paper No. 2019-62 | November 14, 2019 | http://www.economics-ejournal.org/economics/discussionpapers/2019-62 Global sourcing, firm size and export survival Roger Bandick Abstract This paper investigates how firm size and global sourcing affect the export surviving probabilities. By using data on export and import transactions disaggregated by destination/origin for the entire Danish manufacturing firms between the periods 1995– 2006, the author is able to classify the firms into different size categories and to observe whether they continue or cease to export. Moreover, he is able to define whether the firms source intermediate inputs from high- or low-wage counties. The results, after controlling for the endogeneity of the international sourcing decision by using IV and matching approach, indicate that firm size is positively correlated with the likelihood of continuing to export. Moreover, for small and medium size firms, global sourcing seems also to increase the probability of staying in the export market but only if they source from highwage countries. However, sourcing inputs from abroad, no matter if it is from high- or low-wage countries, do not seem to significantly affect the export surviving probabilities for larger firms. (Published in Special Issue Recent developments in international economics) JEL F16 F23 J24 L25 Keywords Global sourcing; firm size; export, IV; matching; cloglog Authors Roger Bandick, Department of Management and Engineering, Linköping University, Sweden, and Department of Economics and Business, Aarhus University, Denmark, [email protected] Citation Roger Bandick (2019). Global sourcing, firm size and export survival. Economics Discussion Papers, No 2019-62, Kiel Institute for the World Economy. http://www.economics-ejournal.org/economics/discussionpapers/2019-62 Received October 17, 2019 Accepted as Economics Discussion Paper November 7, 2019 Published November 14, 2019 © Author(s) 2019. Licensed under the Creative Commons License - Attribution 4.0 International (CC BY 4.0) 1 1. Introduction Following the heterogeneous firm trade models (e.g., Melitz, 2003; Bernard et al., 2003; Bernard et al., 2007;), firms are ranked according to their productivity level. Firms with slightly higher productivity than the cut-off level to survive serve the domestic market, and, due to the high start-up costs to become an exporter, only firms that ex-ante are sufficiently productive are able to enter the export market. Indeed, this is a common finding in the empirical literature, summarized by Wagner (2007); firms that are more productive, larger, capital, and skillintensive are more likely to engage in the export market (self-selection into the export market). Previous literature, e.g. Bernard and Jensen (1999); Greenaway and Kneller (2007); Girma et al. (2004): also find evidence for positive ex-post performances once the firms start exporting (learning-by-doing). Implicitly, given these results, we would expect high degree of persistency of the export status, that is, once the firm is able to bear the costs of entering the foreign market it will start to export, and, it will continue to do that for many years due to potential ex-post benefits (Baldwin and Krugman, 1989). However, Besedes and Prusa (2011) found that the median survival rate of the manufacturing exporters of 46 developed and developing countries is only 1-2 years. This short-lived export episode has been found in other studies as well; Eaton et al (2007) for Colombian exporters; Volpe and Carballo (2009) for Peruvian exporters; Ilmakunnas and Nurmi (2010) for Finnish exporters; and, Choquette (2019) for Danish exporters. Nguyen (2012) shows in numerical prediction that more than 30 percent of exporting firms fail. This rise the question why some exporting firms, although they have the required productivity level to enter that market, do not seem to be long-lasting while other equally productive firms survive much longer? To answer this question, this paper go beyond the role of firm´s productivity by introducing two other important characteristics as explanatory variables, namely, the role of global sourcing and firm size. More precisely, the aim is to investigate whether firms’ intra-industry imports of intermediate inputs from different regions (high- or low-wage countries) affect the export survival rates of small, medium and large firms. 2 The underlying assumption, following Roberts and Tybout (1997), is that once a firm has entered the export market, it will continue to export as long as the present and expected future profit is higher as compared to only serving the domestic market. In case of negative profits, the firm will continue to export as long as the losses do not exceed the sunk cost of exiting the export market. These outcomes, however, are conditioned on the information bundle the firm possess prior export entry in which are endogenly driven depending on the characteristics of the firm. Here, international sourcing engagement and firm size play major role in collecting market information, product adaptation and learning abilities, for which directly relates to export profits, sunk cost and probability to survive. Yet theoretically, it is still unclear how global sourcing and firm size affect export survival. On the one hand, firms with international sourcing experience prior export entry may possess valuable market information that may help them to learn how to operate successively and generate profits. In this view, the more knowledge the firms have from their import experience prior export entry the less uncertainty they face and therefore less likely they will exit the export market (Albornoz et al., 2012; Carrére & StraussKahn, 2017; Nguyen, 2012). On the other hand, however, firms with better market information faces lower sunk cost entering and exiting new markets. In this view, the knowledge gained from import experience may reduce the export entry sunk cost in which may induce the firms to experiment with their export entries that ultimately may lead to higher exit probabilities, (Gullstrand and Persson, 2015; Li et al., 2017; Choquette, 2019). Another dimension is that the learning ability from import experience and the level of market uncertainties faced are much dependent on the size of the firm and on the location of the sourcing activities. Small firms are usually more flexible and able to learn and respond rapidly to different shocks, (Beck et al., 2008). However, small firms are more vulnerable to these shocks due to difficulties of obtaining financial funds to either initiate, expand or maintain their export activities. As for different sourcing location, the scope of global sourcing may depend on the location of this activity since different partner countries provide different opportunities. Sourcing from low-wage countries gives the firms access to inputs produced at lower cost as compared to domestic inputs, which entails lower production cost and improving profitability and prolonged export opportunities. However, inputs imported from low-wage countries where institutions are weak may impose high transaction cost for the firms which may lead to lower profitability and ultimately to lower, or at its best no effect on export survival. Sourcing from 3 high-wage countries, on the other hand, gives the firms access to high technological inputs that potentially are not available in the domestic market. Firms may import these advance inputs to complement their core activities, hence the production process is expected to become more efficient, the productivity to increase and the export opportunities to improve (Jabbour, 2010 and Wagner, 2011). However, since inputs from high-wage countries often are associated with high quality they tend to cost more, which eventually entails lower profitability and lower chances to survive. Whether global sourcing and firm size leads to higher or lower survival in the export market is not only of academic interest. From the firms perspective, it is highly important to find out what opportunities and difficulties exist in the market they want to enter in order to be able to counteract and make all the necessary investment. Here, assessing the knowledge and experience in the international market plays essential role in avoiding short export episode, which ultimately means losses of entry sunk costs and potential future profits. From a policy perspective, it is important to identify what type of firms that potentially may face difficulties in the export market in order to implement accurate policies that help these firms to extend their export status. As it is important for the firms to make the necessary investments to prevent loosing future export profits, it is equally important from a macro perspective to investigate the political reforms that are required to prevent losses of future jobs. Only a few papers study the impact of global sourcing and firm size on export exit. Stirbat et al. (2015) show that import experience has strong positive influence on the survival of exporters. Dias-Mora et al. (2015) show that Spanish small manufacturing firms with import experience have lower probability of quitting the export market as compared to firms without import experience. For larger firms, however, sourcing inputs from abroad do not seem to influence the export survival probabilities. Using the same data as Dias-Mora et al. (2015), Córcoles et al. (2019) show that the most internationalized firms have a lower exit rate compared to other exporting firms. Creusen and Lejour (2011) find that the probability of exiting the export market is lower for large Dutch firms. Lo Turco and Maggioni (2013) show that for Italian manufacturing firms, import has enhancing effect on the export probability but only if the import is from low-income countries. Choquette (2019) study the relationship between import-based market experience and market exit decision of 1920 Danish manufacturing exporters between the period 2001 and 2011. In line with sunk cost perspective, she finds that increased market knowledge triggers experimentation-like export behavior, which 4 consequently leads to higher probability of market exit. However, in Choquette (2019), firms exit decision from a specific market do not necessary mean export failure but rather a transition from one market the firms have entered as experiment due to low entry sunk cost to another more sustainable market. In this paper, I use different approach by defining export exit as total withdrawal of all markets, not only exit from one specific market, to better asses the implications of global sourcing and firm size on export market exit. Moreover, to the best of my knowledge, this is one of the first study that focus on the relation between imports of intermediate inputs and export survival by distinguishing the effect from different sourcing location and different size of firms simultaneously. I use data on export and import transactions that are disaggregated by destination/origin for the entire Danish manufacturing firms with at least one employee between the periods 1995-2006. To preview the results, controlling for several firm and industry specific characteristics that may affect the export survival rate, it seems that larger and medium size firms have 20 and 23 percent higher export surviving probabilities than smaller firms. The result seems also to indicate that for small and medium size firms, global sourcing increases the probability of staying in the export market, but only if the import is from high-wage countries. However, sourcing inputs from abroad, no matter if it is from high- or low-wage countries, do not seem to significantly affect the export surviving probabilities for larger firms. These results are robust controlling for the endogeneity of the sourcing decision by using IV and matching approach. The rest of the paper is structured as follows. Section 2 discusses the theoretical framework and section 3 outline the estimation strategy used in this paper. Section 4 describes the dataset and present some preliminary indications how firm size and global sourcing affect the export survival probabilities. Section 5 present the empirical results from estimating the hazard model controlling for the possible endogeneity by using the probability of importing as an instrument and by using a matched sample that is generated by propensity score matching approach. Section 6 concludes. 5 2. Theoretical framework To illustrate the strategic decision for a firm to either stay or exit the export market we begin with a minor simplification about revenue and cost functions, as outlined by Ilmakunnas and Nurmi (2010). The revenue from the export market (𝑅𝑖𝑡 𝐸𝑋) and domestic market (𝑅𝑖𝑡 𝐷) can be defined as 𝑅𝑖𝑡 𝐸𝑋(𝑋𝑖𝑡,𝑧𝑡 𝐸𝑋,𝑡) and 𝑅𝑖𝑡 𝐷(𝐷𝑖𝑡,𝑧𝑡 𝐷,𝑡). 𝑋𝑖𝑡 and 𝐷𝑖𝑡 are export and domestic sales, 𝑧𝑡 𝐸𝑋 and 𝑧𝑡 𝐷are vector of exogenous variables that either enhances or reduces the demand such for example business cycle, exchange rate, trade policy etc., and time t account for the accumulated demand learning process the firms acquire while they stay additional year in the export and/or domestic market. The learning process is considered as endogenly driven depending on firms own action such for example collecting market information, product adaptation, engagement in different networks etc. that ultimately affect the speed of the learning process differently among the firms. The cost function can be defined as 𝐶𝑖𝑡(𝑋𝑖𝑡,𝐷𝑖𝑡,𝑧𝑡 𝐶,𝑡 ) for exporting firms and 𝐶𝑖𝑡(𝐷𝑖𝑡 ´,𝑧𝑡 𝐶,𝑡 ) for non-exporting firms, where 𝐷𝑖𝑡 ´ denotes domestic sales when the firm has no export sales and 𝑧𝑡 𝐶 is a vector of variables accounting for different exogenous shocks to input prices. Moreover, as in the revenue function the time t is included to account for the learning process in which firms accumulate knowledge about their production process that help them to produce efficiently and to reduce their production costs over time. Again the learning process is considered to be endogenly driven depending on firms own action in reducing their production costs including better technological implementation, closer cooperation with their supplier, sourcing for cheaper or domestically scarce inputs, improvement of their negotiating power etc. Given the revenue and cost functions, we can derive the profit functions for firms with or without export; 𝜋𝑖𝑡 𝐸𝑋 =𝑅𝑖𝑡 𝐸𝑋(𝑋𝑖𝑡,𝑧𝑡 𝐸𝑋,𝑡)+𝑅𝑖𝑡 𝐷(𝐷𝑖𝑡,𝑧𝑡 𝐷,𝑡)−𝐶𝑖𝑡(𝑋𝑖𝑡,𝐷𝑖𝑡,𝑧𝑡 𝐶,𝑡 ) (1) 𝜋𝑖𝑡 𝐷=𝑅𝑖𝑡 𝐷(𝐷𝑖𝑡,𝑧𝑡 𝐷,𝑡)−𝐶𝑖𝑡(𝐷𝑖𝑡 ´,𝑧𝑡 𝐶,𝑡 ) (2) 6 The difference in profit between being active in both the domestic and export market and only being active in the domestic market is then; 𝜋𝑖𝑡 =𝜋𝑖𝑡 𝐸𝑋 −𝜋𝑖𝑡 𝐷 (3) In addition to equation (3), when deciding to either stay or leave the export market, the firms need to relate their profits to the combined sunk costs associated with entering and exiting this market. For example, a firm that consider to exit at time t need to relate the current and future profits from exporting to the sunk cost they already paid when entering and the sunk cost they need to pay when exiting. Following Roberts and Tybout (1997) notation, firms’ decision to participate on the export market is then given by the following equation: 𝜋𝑖𝑡 +𝛿{𝐸𝑖𝑡[𝑉𝑖𝑡+1(∅𝑖𝑡+1)|𝑌𝑖𝑡 =1]−𝐸𝑡[𝑉𝑖𝑡+1(∅𝑖𝑡+1)|𝑌𝑖𝑡 =0]} ≥𝐹𝑖𝐸𝑁 −[(𝐹𝑖𝐸𝑁 −𝐹𝑖𝐸𝑋)|𝑌𝑖𝑡−1 >0] (4) Where 𝜋𝑖𝑡, as above, measure the profit firm i earns in period t when it serves both the domestic and export market as compared to when it only serves the domestic market. 𝛿 is discount rate, 𝐸𝑖𝑡[𝑉𝑖𝑡+1(∅𝑖𝑡+1)] is expected present value of future profit where expectations are conditioned on the firm-specific information set ∅𝑖, 𝑌𝑖 is a dummy variable equal to one if firm i is exporting and 0 otherwise and (𝐹𝑖𝐸𝑁 +𝐹𝑖𝐸𝑋) is the sum of sunk entry and exit cost of exporting. Following equation (4), the firm, once it has entered the export market, will continue to export as long as the present and expected future value of the difference in profits when it exports as compared to when it does not is positive. It is also apparent from equation (4) that the firm will leave the export market once the losses from staying in the export marked exceed the cost of exiting it. The decision to either stay or exit the export market can then be summarized by the following dynamic discrete equation: 𝑌𝑖𝑡+1 ={1 𝑖𝑓 𝜋𝑖𝑡 +𝛿{𝐸𝑖𝑡[𝑉𝑖𝑡+1(∅𝑖𝑡+1)|𝑌𝑖𝑡 =1]−𝐸𝑡[𝑉𝑖𝑡+1(∅𝑖𝑡+1)|𝑌𝑖𝑡 =0]}>𝐹𝑖𝐸𝑋 0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒 (5) 7 Whether it is profitable for a firm to stay or leave the export market depends not only on the domestic and export sales and sunk cost of entering and exiting but also on the vectors of exogenous variables, (𝑧𝑡 𝐸𝑋, 𝑧𝑡 𝐷 and 𝑧𝑡 𝐶) that account for demand and costs shocks, and on the accumulated demand and production learning processes the firms acquire over time. For example, the exogenous demand or costs shocks, which largely affect the profits, may alter the decision to stay or leave the export market depending if these shocks are more prevalent in the domestic market and whether they are positive or negative. Moreover, the more and faster the firms learn about their export demand and production costs the higher profits they will gain and the more likely they will stay in the export market. Yet, how exogenous shocks and learning process affect the decision to stay or leave the export market depends much on the size of the firm. On the one hand, small firms are more flexible in terms of, for example, adaptation and strategy changes, and, due to their size, they are able to quickly learn about their demand and production process and also respond more rapidly to different shocks (Beck et al., 2008). On the other hand, however, small firms are more vulnerable to these shocks due to difficulties of obtaining financial funds to either initiate, expand or maintain their export activities. Moreover, small firms usually lack of both production capacity and monetary reserves that are necessary during changes in economic environment, and lack of employees that entirely devote their time to learn about the export activities. Another aspect that might influence the sunk entry and exit cost, the effect of exogenous shocks and specially the learning process on the decision to continue to export or not is how integrated the firm is in the international market. As discussed by Sjöholm (2003); Muûls and Pisu (2009) and Stirbat et al. (2015), one of the most important channel for receiving information about the foreign market is to be part of a foreign network and/or being engaged in the international market through global sourcing. From these channels, the firms acquire more information about the foreign market and by using this knowledge, they can reduce the sunk cost associated with exporting and learn more about the export demand. Foreign suppliers may help the firms to get access to important market and customer information that would otherwise be inaccessible (Grant, 1991; Onkelinx and Sleuwaegen, 2010). Firms that source globally may also reduce their production costs and increase their efficiency by utilizing the relatively lower wages in the labor-endowed countries (Agrawal & Farrell, 2003) and/or by getting access to skills that are scarce in the domestic market (Farrell, 2005). Moreover, sourcing from abroad can give the 14 firms that mainly (more than 50 percent of the total import value) source inputs from high-wage countries and 𝑆𝑜𝑟𝑢𝑐𝑖𝑛𝑔_𝑙𝑜𝑤_𝑤𝑎𝑔𝑒𝑖𝑡 that equals to 1 for firms that mainly source inputs from low-wage countries. Table 1 provides summary statistics on the number of firms per year that are engaged in export and/or global sourcing. There are a total of 142,013 observations in the dataset with an average of 11,834 firms over the period 1995-2006. The share of firms with export activity is about 30 percent, half of them are defined as small firms and one third of the exporting firms are also importer. Table 1 here To provide preliminary indications how firm size and global sourcing affect the export survival probabilities, we can use the unconditional Kaplan-Meier estimates given by the following equation: 9 𝑆(𝑡)=∏𝑛𝑗−𝑑𝑗 𝑛𝑗 𝑗|𝑡𝑗<𝑡 (9) where 𝑆(𝑡) denotes the probability of surviving in the export market past time t, 𝑛𝑗 stands for the number of firms that have survived in the export market and 𝑑𝑗 for the number of firms that exited the export market at time t. As Table 2 shows, there are some differences in export survival probabilities among the different types of firms. For instance, after five years, large and medium size importers had around 10 percent higher export surviving ratio than non-importers. At the end of the period, almost 62 and 59 percent of the large and medium size importers survived the export market whereas in comparison only 55 and 46 percent of the large and medium size non-importers survived the export market. As for the small firms, however, global sourcing do not seem to have had any role in determining the export surviving probabilities. Table 2 here 9 The analysis time represents the number of years the firm remained in the export market. 15 There is, however, a major drawback comparing the Kaplan-Meier survival functions since such an analysis does not take into account other factors that may affect the export survival ratio. One such variable is for example productivity where it is, by now, well documented that productive firms are more inclined to export. In Table 3, we observe that productivity and other variables that may affect the export survival probability are unequally distributed across the different types of firms. A Standard t-test shows that all of global sourcing firms, independently on firm size, are older, have higher skill intensity and sales than non-importers. Moreover, labor productivity and capital stocks seem to not differ between large and medium size importer and non-importer whereas it seems that small importing firms have significantly higher productivity and capital stock than small non-importers. Table 3 here Since there are some differences between global and non-global sourcing firms and, also between different size of the firms, the results outlined in the next section will be based on estimating equation (7), that is the semi-parametric complementary log-log model (cloglog) where various firm-, and industry-specific factors are controlled for. Moreover, in order to deal with the potential endogeneity problem, I will use instrumental variable estimation and propensity score matching technique, as discussed above. 5. Result Before turning to the main results in this paper, I need first to discuss and outline the relevant firm-specific characteristics in year t-1 that may affect firms’ probability to source intraindustry intermediate inputs from abroad in year t, i.e. the variables to be included in the covariate 𝑋𝑖𝑡−1 of equation (8). As discussed above and in line with Abraham and Taylor (1996) and Bandick (2016), the reasons for a firm to contract out activities are often influenced by three general motives; to save labor costs, to reduce workload volatility and to gain from economies of scale. For this reason, the probit model will include the following firm-level variables; log average skilled and 16 unskilled wage costs to account for labor costs, growth (in terms of sales) as compared to the industry to account for workload volatility, and as a proxy for economies of scale, I will use log level of sales, log capital stock and skill intensity. The result from the probit model is shown in Table 4. The result in column (1) are in line with the predictions outlined by Abraham and Taylor (1996), labor cost, growth relative to the industry as proxy for workload volatility, log level of sales and skill intensity as proxy for economies of scale, are all positively related to firms decision to source from abroad. In column (2), I estimate an alternative model of equation (8) including productivity level of the firm. 10 The result seems to indicate that ex-ante productivity is also a significant determinant for the global sourcing decision. Hence, we draw the conclusion that global sourcing firms, at some extent, do have better ex-ante characteristics than non-global sourcing firms. It is therefore highly important to control for this endogeneity in the empirical analysis, otherwise the estimate of the causal effect of global sourcing could potentially be biased as is discussed above. Table 4 here One way to deal with this endogeneity problem is, as discussed above, to construct an instrumental variable by using the two different models of Table 4 to calculate the predicted probability for a firm to source inputs from abroad. The two alternative IV:s are then separately included in equation (7) to, along with other firm-specific characteristics, determine the role of global sourcing on firms export survival probabilities. By using alternative models of equation (8), I will be able to check whether the results of the hazard models below depend on the process by which the instrument was generated. As an alternative approach, we can create a valid counterfactual of firms that do not source inputs from abroad but have similar characteristics as those firms that do source inputs from abroad. This can be created by using the same set of variables as presented in Table 4, model (1) and (2) to estimate the propensity scores and select the nearest control firms as a match for these global sourcing firms. After establishing that the propensity score matching procedure is reliable and robust by using a number of balancing tests (more details of these tests are found in Appendix) the matched sample can then be used to estimate the hazard model given by equation (7). 10 The productivity is measured by value added per employee. Using TFP instead does not significantly change the result obtained in Table 4, column (2). 17 However, in order to establish a benchmark how the surviving probability in the export market is affected by firm size and global sourcing, I first estimate equation (7) without controlling for the possible endogeneity of firms decision to source from abroad. This result is presented in Table 5. All estimations are stratified by industry and year and the table report the hazard ratios (exponentiated coefficients). This means that a coefficient less than one implies that the respective independent variable increases the probability of survival while a coefficient greater than one implies negative effect on survival, ceteris paribus. Furthermore, the estimations in the first four columns of Table 5 are based on the entire sample where the omitted group are small firms and in column (3) and (4) the omitted group also include non-global sourcing firms. The result in column (1) seems to indicate that larger and medium size firms have better export surviving probabilities than smaller firms. One explanation could be that the former type of firms, as shown in Table 3, are more productive and have higher capital stock and sales that can help them to overcome various obstacles in the export market. However, controlling for several firm and industry specific characteristics that potentially influence export survival rate positively, the result in column (2) still suggests that larger and medium size firms have 20 and 23 percent higher export surviving probabilities as compared to smaller firms. Beside firm specific characteristics, differences in export survival rates can also be explained by how integrated the firms are in the international market through global sourcing, as outlined in section 2. In column (3), I therefor include the dummy variable 𝐺𝑙𝑜𝑏𝑎𝑙_𝑆𝑜𝑟𝑢𝑐𝑖𝑛𝑔𝑖𝑡 that equals to 1 if firm i source inputs from abroad. In line with the prediction given by Stirbat et al. (2015) and the findings in Dias-Mora et al. (2015), firms that source inputs from abroad have higher probability of surviving the export market as compared to those firms that do not source inputs from abroad. In column (4), I divide the global sourcing dummy into two dummies; 𝑆𝑜𝑟𝑢𝑐𝑖𝑛𝑔_ℎ𝑖𝑔ℎ_𝑤𝑎𝑔𝑒𝑖𝑡 that equals to 1 for firms that mainly (more than 50 percent of the total import value) source inputs from high-wage countries and 𝑆𝑜𝑟𝑢𝑐𝑖𝑛𝑔_𝑙𝑜𝑤_𝑤𝑎𝑔𝑒𝑖𝑡 that equals to 1 for firms that mainly source inputs from low-wage countries. The result in column (4) suggests that among the global sourcing firms only those that source from high-wage countries that experience better export survival probabilities. Those that source from low-wage countries, on the other hand, do not seem to have different survival rate than firms that do not source inputs from abroad. 18 In column (5) to (7), I separately analyze how global sourcing from different regions affect the export survival probabilities for the three different type of firms. The results in column (5) and (6) indicate that, for small and medium size firms, sourcing form high-wage countries increases the export survival probabilities by about 20-25 percent as compared to firms that do not source inputs from abroad. Sourcing form low-wage countries, however, seem to have no significant effect on the export survival. Lastly, the result in column (7), seems to indicate that global sourcing, no matter if it is from high- or low-wage countries, do not significantly affect the export surviving probabilities for larger firms. Table 5 here The results in Table 5 are, however, based on the assumption that firm’s global sourcing decision is exogenously determined. As discussed by Görg et al. (2008) and shown in Table 3 and 4, this is unlikely to be the case since there are strong reasons to believe that only “better” firms, in terms of productivity, skill intensity etc., are engaged in global sourcing. Although this is, to some extent, accounted for by the inclusion of the large number of relevant firm characteristics, I explicitly correct for the possible endogeneity by using the probability for a firm to source inputs from abroad (as in Table 4) as an instrument and the matched sample (generated by propensity score-matching approach) when estimating the hazard model. The instruments for the two global sourcing dummies are the predicted values obtained by estimating equation (8) with 𝑆𝑜𝑟𝑢𝑐𝑖𝑛𝑔_ℎ𝑖𝑔ℎ_𝑤𝑎𝑔𝑒𝑖𝑡 or 𝑆𝑜𝑟𝑢𝑐𝑖𝑛𝑔_𝑙𝑜𝑤_𝑤𝑎𝑔𝑒𝑖𝑡 as dependent variable, respectively. The result based on the first IV model (similar to model 1 in Table 4) is reported in Table 6; columns (1) to (3). Table A.2 in the Appendix present the result based on the second IV model (similar to model 2 in Table 4). Since, to my knowledge, there is no formal method of testing the exogeneity assumption in the context of a hazard model, we may use a standard Hausman test to get a rough indicator whether this assumption holds. These tests, reported at the bottom of Table 6 and A.2, reject the assumption of exogeneity of the global sourcing dummy. The two sets of the IV:s in Table 6 and A.2, column (1-3) provide similar result as found in Table 5. Again the results point out that smaller and medium size firms that source from highwage countries face higher export survival ratio as compared to those that do not source from abroad while similar type of firms sourcing from low-wage countries do not have these positive 19 effect on the export survival (as compared to firms that do not source from abroad). For larger firms, however, global sourcing no matter whether it is from high- or low-wage countries seems, as obtained in Table 5, to not affect the export survival probabilities. While the relevance is to some extent shown in the IV-generating probit in Table 4, there is, to the best of my knowledge, no test of instrument validity in the context of this non-linear hazard estimation. Hence, the results above are reliable under the assumption of instrument validity, which cannot be tested. I therefore use an approach which does not depend on such an assumption. Similar to Greenaway and Kneller (2007) and Bandick and Görg (2010), I estimate equation (7) on a matched sample generated by propensity score-matching procedure including firms that are similar to each other in many aspects but differ in terms of sourcing inputs from abroad or not. 11 These results are presented in Table 6 and A.2; column (4-6). As in the previous columns the point estimate suggests that export surviving probabilities are higher in smaller and medium size firms that source from high-wage countries. For larger firms and firms sourcing from low-wage countries, however, export survival probabilities are not affected by global sourcing activities. Table 6 here 6. Conclusions and remarks The general conclusion from the heterogeneous firm trade literature is that, given the high startup costs, only sufficiently productive firms are able to enter the export market and, once these firms become exporters, their performances will improve ex-post. Implicitly, we should then expect export hysteresis, that is, once the firm start to export it will remain as an exporter. However, the empirical literature on trade duration often find that not all firms survive the export market and many of these are very short-lived as exporter. To answer the question why some exporting firms do not seem to be long lasting while other survive much longer, I examine in this paper the role of global sourcing and firm size on export 11 In order to find out whether the propensity score matching procedure is reliable and robust, I perform a number of balancing tests suggested in the recent literature (e.g., Smith and Todd, 2005). More details of these tests are found in Appendix A. 20 surviving probabilities. More precisely, I investigate whether firms’ intra-industry imports of intermediate inputs from different regions (high- or low-wage countries) affect the export survival rates of small, medium and large firms. I use data on export and import transactions that are disaggregated by destination/origin for the entire Danish manufacturing firms with at least one employee between the periods 1995-2006. Moreover, since the interest of this paper is to evaluate the implications of global sourcing and firm size on export survival, I define export exit as when the firms withdrawal of all export markets, not only exiting from one specific market. Controlling for several firm and industry specific characteristics that may affect the export survival rate, the result suggest that larger and medium size firms have 20 and 23 percent higher export surviving probabilities than smaller firms. The result seems also to indicate that for small and medium size firms, global sourcing increases the probability of staying in the export market, but only if the import is from high-wage countries. However, sourcing inputs from abroad, no matter if it is from high- or low-wage countries, do not seem to significantly affect the export surviving probabilities for larger firms. These results are robust controlling for the endogeneity of the sourcing decision by using IV and matching approach. The findings of this paper have important implications for academic researchers, managers and policymakers. In order to evaluate the role of global sourcing on export survival accurately it is important for the academic researchers to consider the following issues. Firstly, since there is reason to believe that firms sourcing from abroad are inherently better in many aspects it is highly important to control for this self-selection to not falsely attribute the higher export survival rates entirely to global sourcing. Secondly, since the scope of global sourcing may differ where this activity is located it is important to separate between different sourcing locations as these may affect export survival differently. Thirdly, to avoid the problem that export exit may involve transition from one market to another, it is better to define the export exit decision as when the firms withdrawal totally from the export market. As for the managers, global sourcing seems to play an essential role for export survival, at least for small and medium size firms. However, although it may be tempting to source inputs from low-wage countries to lowering the production costs, it should be recognized that such purchases might involve hidden costs that possibly overshadow the potential positive influence on export performances. The result in this paper clearly point at this direction. Firms that source 21 inputs from high-wage countries, on the other hand, seem to have better export survival rate as compared to, in many other dimension, similar firms that do not source from abroad. Finally, as for the policymakers, the result suggests that, comparing to other firms, small firms do potentially face difficulties in the export market. To avoid short export episode, small firms seems to need accurate policies that help them to engage in global sourcing, especially from high-wage countries. 22 References Abraham, K.G. and Taylor, S.K., (1996), “Firms’ Use of Outside Contractors: Theory and Evidence.” Journal of Labour Economics, 14(3), 394-424. Agrawal, V. and Farrell, D., (2003), “Who wins in offshoring?”. The McKinsey Quartely, 4; 36-41 Albornoz, F., Pardo, H. F. C., Corcos, G., and Ornelas, E., (2012) “Sequential exporting”. Journal of International Economics, 88(1): 17–31. Baldwin, R. and Krugman, P., (1989), “Persistent Trade Effects of Large Exchange Rate Shocks”. 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Journal of Management Studies, 42(3); 695-704. 30 Table 4 Firms probability to engage in global sourcing Variables Model (1) Model (2) Sales 0.198 (15.08)a 0.141 (5.65)a Skill intensity 0.188 (3.23)a 0.183 (3.13)a Capital stock -0.010 (0.95) 0.001 (0.02) Growth relative to industry 0.028 (3.21)a 0.028 (3.25)a Average skilled wage 0.002 (2.29)b 0.002 (2.51)b Average unskilled wage 0.001 (4.21)a 0.001 (4.33)a Labor productivity 0.079 (2.70)a Industry dummies Yes Yes Year dummies Yes Yes Pseudo R2 0.106 0.106 LR chi2 4,266 4,273 Observations 30,919 30,919 Notes: The dependent variable 𝐺𝑙𝑜𝑏𝑎𝑙_𝑠𝑜𝑢𝑟𝑐𝑖𝑛𝑔𝑖𝑡= 1 if firm i is engaged in global sourcing (according to the narrow definition). Z-statistics are within parentheses. All the explanatory variables are lagged one year. Labor productivity is value added per employee and skill intensity is the share of employees with post-secondary education at the firm level. Industries are defined at the two-digit level (21 industries). a, b and c indicate significance at 1, 5 and 10 percent levels, respectively. 31 Table 5 Global sourcing, size and export survival. Complementary log-log model; Global sourcing as exogenous Notes: Estimations are stratified by industry and year. Industries are defined at the two-digit level (21 industries). Z-statistics in parentheses. a, b, c indicate significance at the 1, 5, and 10 percent levels, respectively. Variables All firms Small firms Medium Firms Large firms (1) (2) (3) (4) (5) (6) (7) Firm size _large 0.582 (7.76)a 0.804 (2.38)b 0.816 (2.22)b 0.819 (2.17)b _medium 0.684 (7.43)a 0.770 (4.47)a 0.773 (4.42)a 0.774 (4.40)a Global sourcing 0.775 (4.85)a _high-wage 0.756 (5.15)a 0.749 (4.46)a 0.804 (2.16)b 0.803 (1.45) _low-wage 1.030 (0.20) 1.049 (0.28) 0.800 (0.65) 1.253 (0.45) Industry control Empl.Growth 1.014 (0.54) 1.014 (0.54) 1.015 (0.56) 1.018 (0.56) 1.087 (1.58) 1.005 (0.07) Other firms ceasing export 1.382 (12.13)a 1.367 (11.72)a 1.368 (11.75)a 1.358 (9.89)a 1.357 (5.78)a 1.572 (5.70)a Firm controls Labor productivity 0.663 (6.64)a 0.670 (6.37)a 0.671 (6.33)a 0.756 (3.81)a 0.606 (3.62)a 0.589 (4.41)a Capital intensity 0.896 (4.26)a 0.900 (4.07)a 0.900 (4.03)a 0.825 (7.01)a 0.811 (4.20)a 0.962 (0.65) Skill empl. 1.013 (0.55) 1.016 (0.66) 1.016 (0.66) 1.012 (0.47) 0.861 (3.17)a 0.933 (0.81) Observations 25,502 25,502 25,502 25,502 12,573 8,321 4,608 Wald Chi Square 347a 641a 666a 667a 420 216a 143a 32 Table 6 Global sourcing, size and export survival. Complementary log-log model; IV and Matching approach, Model (1) Notes: see Table 5 . IV Matched sample Variables Small firms Medium firms Large firms Small firms Medium Firms Large firms (1) (2) (3) (4) (5) (6) Global sourcing _high-wage 0.758 (2.08)b 0.749 (4.46)a 0.804 (1.56) 0.756 (5.15)a 0.894 (3.46)a 0.804 (1.16) _low-wage 0.936 (0.20) 1.049 (0.28) 0.900 (0.65) 1.030 (0.20) 1.011(0.72) 1.080 (0.55) Industry control Empl.Growth 1.012 (0.36) 1.018 (0.56) 1.087 (1.58) 1.015 (0.56) 1.032 (1.06) 1.036(0.79) Other firms ceasing export 1.374 (9.91)a 1.358 (9.89)a 1.357 (5.78)a 1.368 (11.75)a 1.212 (4.51)a 1.357 (5.78)a Firm controls Labor productivity 0.703 (4.82)a 0.756 (3.81)a 0.606 (3.62)a 0.671 (2.33)a 0.679 (2.81)a 0.832 (1.62) Capital intensity 0.844 (5.31)a 0.825 (7.01)a 0.811 (4.20)a 0.900 (4.03)a 0.915 (3.10)a 0.944 (0.71) Skill empl. 0.990 (0.37) 1.012 (0.47) 0.861 (3.17)a 1.016 (0.66) 0.922 (2.26)a 0.917 (1.89)c Observations 12,573 8,321 4,608 9,061 5,483 2,085 Wald Chi Square 478a 420a 216a 367a 120a 97a Hausman test (p-value) 0.019 0.014 0.021 Linktest (hatsq) 0.008 (1.31) 0.005 (1.18) 0.027 (1.16) 33 Appendix A Balancing tests for the matching procedure I perform a number of balancing tests suggested in the literature (e.g., Smith and Todd, 2005) to check the reliability and robustness of the propensity score matching procedure. The first test is to examine the standardized difference (or bias), that is, mean difference between global sourcing and control firm scaled by the average variance, for all the variables in the vector X in equation (8). This test is reported in Table A.1 and A.2 for the two set of propensity score models. We should note that the lower the standardized bias the more balanced or similar the global sourcing and control firms are in terms of the variables included in the vector X of equation (8). Although there is no formal criterion, but a value of 20 of the standardized bias is considered to be serious. As seen in Table A.1 and A.2 the standardized bias between the firms included in the matching sample is heavily reduced as compared to the unmatched sample and are all less than 10 %. As a second test I report, in the last column of A.1 and A.2, a formal paired t-test for the differences in the variables between global sourcing and control firms. While these differences seem all to be significant in the unmatched sample (not growth relative to industry), they are all insignificant in the matching sample which means that the matching procedure has created a sample of firms with no significant difference in terms of the variables under consideration. 34 Table A.1a Balancing test for the matching sample, Model (1) Variable Sample Mean Standardized bias Bias reduction t-test Treated Control t p>|t| Sales Unmatched 13.437 12.972 18.1 10.66 0.000 Matched 13.437 12.959 9.2 49.2 1.34 0.113 Skill intensity Unmatched 0.178 0.151 20.3 13.70 0.000 Matched 0.178 0.170 6.2 69.6 1.54 0.124 Capital stock Unmatched 16.758 16.461 20.0 13.30 0.000 Matched 16.758 16.638 8.1 59.7 1.14 0.255 Growth relative to industry Unmatched Matched -1.865 -1.865 -1.631 -1.947 -2.0 0.7 65.1 -1.42 0.41 0.155 0.681 Average skilled wage Unmatched Matched 189.35 189.35 186.27 188.38 6.5 2.0 68.5 4.34 1.24 0.000 0.217 Average unskilled wage Unmatched Matched 132.89 132.89 129.9 131.26 8.1 4.4 45.4 5.39 1.61 0.000 0.108 35 Table A.1b Balancing test for the matching sample, Model (2) Variable Sample Mean Standardized bias Bias reduction t-test Treated Control t p>|t| Sales Unmatched Matched 13.437 13.437 12.972 12.959 18.1 9.2 49.2 10.66 1.34 0.000 0.113 Skill intensity Unmatched 0.178 0.151 20.3 13.70 0.000 Matched 0.178 0.170 5.9 70.8 1.53 0.126 Capital stock Unmatched 16.758 16.461 20.0 13.30 0.000 Matched 16.758 16.629 8.7 56.7 1.18 0.238 Growth relative to industry Unmatched Matched -1.865 -1.865 -1.631 -1.981 -2.0 1.0 50.8 -1.42 0.57 0.155 0.566 Average skilled wage Unmatched Matched 189.35 189.35 186.27 188.36 6.5 2.1 67.8 4.34 1.27 0.000 0.204 Average unskilled wage Unmatched Matched 132.89 132.89 129.9 131.25 8.1 4.5 45.0 5.39 1.64 0.000 0.108 Labor Unmatched 13.000 12.972 17.6 11.84 0.000 productivity Matched 13.000 12.959 9.8 44.6 1.58 0.113 36 Table A.2 Global sourcing, size and export survival. Complementary log-log model; IV and Matching approach, Model (2) Notes: See Table 5 IV Matched sample Variables Small firms Medium Firms Large firms Small firms Medium firms Large firms (1) (2) (3) (4) (5) (6) Global sourcing _high-wage 0.849 (1.70)c 0.756 (2.61)a 0.930 (0.47) 0.755 (3.73)a 0.817 (2.31)a 0.881 (1.02) _low-wage 0.911 (1.12) 0.927 (1.14) 1.380 (0.94) 1.118 (0.64) 0.851 (0.44) 1.287 (1.16) Industry control Empl.Growth 1.016 (0.49) 1.089 (1.62) 1.004 (0.06) 1.025 (0.70) 1.041 (0.67) 1.168 (1.50) Other firms ceasing export 1.373 (9.48)a 1.372 (5.97)a 1.587 (5.80)a 1.349 (9.10)a 1.330 (4.78)a 1.454 (3.83)a Firm controls Labor productivity 0.731 (4.40)a 0.589 (3.86)a 0.558 (4.05)a 0.794 (2.88)a 0.720 (2.18)b 0.746 (1.30) Capital intensity 0.816 (6.91)a 0.812 (4.36)a 0.920 (1.42) 0.936 (2.26)a 0.924 (3.73)a 0.900 (0.87) Skill empl. 1.003 (0.11) 0.849 (3.17)a 0.921 (0.99) 1.016 (0.66) 0.872 (2.60)a 0.808 (1.93)c Observations 12,573 8,321 4,608 9,061 5,483 2,085 Wald Chi Square 425a 275 134a 268a 139a 74a Hausman test 0.023 0.014 0.021 Linktest (hatsq) 0.006 (1.28) 0.004 (1.15) 0.023 (1.11) Please note: You are most sincerely encouraged to participate in the open assessment of this discussion paper. You can do so by either recommending the paper or by posting your comments. Please go to: http://www.economics-ejournal.org/economics/discussionpapers/2019-62 The Editor © Author(s) 2019. Licensed under the Creative Commons License - Attribution 4.0 International (CC BY 4.0).