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Does the Presence of Foreign Firms Reduce Domestic Firms’ Financial Constraints in Sub-Saharan Africa?

Does the Presence of Foreign Firms Reduce Domestic Firms’ Financial Constraints in Sub-Saharan... Abstract Firms in the SSAs (sub-Saharan African countries for short) face severe financial constraints. Because financial markets in the SSAs are underdeveloped, policymakers have sought after the establishment of foreign-owned firms in their countries to help, among others, alleviate the financial constraints faced by domestic firms. However, there is no empirical evidence that speaks to the association between foreign firm presence and domestic firms’ financial constraint. Using firm-level data spanning across 36 SSAs from the World Bank Enterprise Survey, we show that the increase in foreign firm presence can ease the financial constraints of domestic firms in the SSAs. One reason is that foreign-owned firms are not only less financially constrained, they are also less likely to apply for bank loans. Therefore, an increase in foreign firm presence may reduce the competition for loans and ease the financial constraints of domestic firms by improving their borrowing success. 1. Introduction The lack of access to finance has been a major concern for businesses in sub-Saharan Africa (Beck et al., 2009; Asiedu et al., 2013; Mlachila et al., 2013; Bah and Fang, 2015). In the SSAs (sub-Saharan African countries for short), the financial sector is usually underdeveloped, dominated by a few big banks,1 and lacks a stock market (Demirgüç-Kunt and Klapper, 2012; Mlachila et al., 2013).2 This has prompted some policymakers in the SSAs to pursue foreign investments, especially the establishment of foreign-owned firms in their countries, as a way of gaining external finance as one of the benefits (Asiedu, 2002; Basu and Srinivasan, 2002; Harrison and McMillan, 2003; Adams, 2009). However, although such policies are in placed, there is no evidence that the presence of foreign-owned firms may ease the financial constraints faced by domestic firms. In this paper, we conduct a cross-country firm-level study to shed light on this issue. It is important to emphasise that at the outset, the association between foreign firm presence and domestic firms’ financial constraint is ambiguous. On the one hand, foreign-owned firms may themselves become a source of finance for local partners in the same industry (Javorcik, 2014; Newman et al., 2015), which helps to raise the productivity and thus creditworthiness of domestic firms (Javorcik and Spatareanu, 2011; Javorcik, 2014). On the other hand, because foreign-owned firms tend to be more profitable and have better reputations, local banks may favour foreign-owned firms over domestic firms in lending. The increase in market competition brought along by foreign-owned firms may also erode the profits of domestic firms and ultimately their capacity to borrow (Harrison and McMillan, 2003).3 As such, how foreign firm presence affects the financial constraints of domestic firms is unclear. Using cross-country firm-level data based the World Bank Enterprise Survey from 2006 to 2010, we show that in the SSAs, a larger foreign firm presence may relieve domestic firms of their financial constraints. To measure how financially constrained a firm is, we use the firms’ responses to World Bank’s survey questions on credit access, as well as objective measures such as whether a firm has had access to overdraft facilities, a credit line or a bank loan, or it had applied for but was denied a loan. We find that in industries where there is a larger foreign firm presence, domestic firms tend to be less financially constrained. One reason is that foreign-owned firms are less financially constrained than domestic firms; as such, they are also less likely to borrow from banks. Given that foreign-owned firms are less likely to seek bank credit, a larger foreign firm presence would benefit domestic firms by reducing the competition for loans and enabling them to borrow more successfully. Our study has policy relevance for the SSAs. Firstly, firms in the SSAs are the most financially constrained compared with firms elsewhere. This has implications on development, as the lack of finance (which is the case for the SSAs) can severely undermine growth (Beck and Demirguc-Kunt, 2006; Bah and Fang, 2015). Therefore, it would be helpful from a policy perspective to know if foreign firm presence may lead to improvements in financial access, which in turn may foster economic growth. Secondly, to gain access to foreign capital, several SSAs have implemented policies to attract foreign investors to establish new firms or assume the ownership of local firms (Adams and Opoku, 2015).4 However, there is no evidence that doing so may relieve the financial constraints experienced in the host country, which this paper speaks to.5 Our paper is related to the literature that focuses on discovering the determinants of firms’ access to finance in developing countries. The literature has identified country-level factors that affect firms’ access to finance, such as legal system and regulatory frameworks (Demirgüç-Kunt and Maksimovic, 1998), stock market development (Demirgüç-Kunt and Levine, 1996), financial market development and liberalisation (Abel, 1980; Laeven, 2003; Love, 2003), as well as firm-level characteristics including age, size, ownership structure, legal status and gender in determining firm’s access to finance (Beck and Demirgüç-Kunt, 2008; Byiers et al., 2010; Asiedu et al., 2013; Aterido et al., 2013; Hansen and Rand, 2014a; Wagner and Weche Gelübcke, 2015). Our work complements these studies by using a cross-country firm-level analysis to explore if foreign firm presence may contribute towards financial access. The rest of the paper is organised as follows. In Section 2, we briefly review the literature. In Section 3, we describe the data sources and the variables used in this paper. In Section 4, we describe our estimating equation and discusses the potential identification issues. In Sections 5 and 6 , we present our baseline results and robustness checks, respectively. In Section 7, we present our concluding remarks. 2. Background Sub-Saharan Africa is one of the fastest developing regions in the world (Young, 2012; McMillan and Harttgen, 2014). However, despite their economic progress, the financial systems in the SSAs remain among the least developed. For example, in the SSAs, the financial sector is typically characterised by a lack of a stock market (Demirgüç-Kunt and Klapper, 2012), a banking industry that is dominated by a few banks,6 interest spreads, margins and overhead costs that are much higher than in other regions, and a very small representation by Non-Bank Financial Institutions (NBFI) in the credit market (Mlachila et al., 2013; Beck and Cull, 2014). Studies have found that the lack of access to finance is the most formidable obstacle to growth, productivity and competitiveness in the SSAs (Nkurunziza, 2010; Bah and Fang, 2015). In fact, firms in the SSAs are the most financially constrained compared with firms elsewhere. For example, 45.6% of firms in the SSAs reported access to finance as the most important constraint in investment while the corresponding number is 14.6% for the OECD (Bah and Fang, 2015). On average, only 23.5% of firms in the SSAs have access to bank loan or line of credit while the corresponding number is 49.1% for OECD (see Appendix A). Because access to finance implicates development, there has been tremendous effort to understand the issue of financial access through cross-country or firm-level analyses. The cross-country analyses typically aim to understand which country-level variables determine access to finance. These studies have found that there is greater access to finance in countries that has legal systems and regulatory frameworks that strongly protect property rights, contract enforcement and credit rights (Demirgüç-Kunt and Maksimovic, 1998), stock market development (Demirgüç-Kunt and Levine, 1996), developed and liberalised financial markets (Gelos and Werner, 2002; Laeven, 2003; Love, 2003), large national markets, income and savings (Demirgüç-Kunt and Klapper, 2012; Mlachila et al., 2013). The firm-level studies typically stress the importance of firm-level characteristics for firms’ access to finance such as age, size, ownership structure, legal status and owners’ gender (Beck et al., 2006; Asiedu et al., 2013; Aterido et al., 2013; Hansen and Rand, 2014a; Wagner and Weche Gelübcke, 2015). For example, using firm-level data from 80 countries, Beck et al. (2006) have found that larger, older and foreign-owned firms are on average less financially constrained. What is striking about the literature is that little is said about how foreign firm presence may affect the financial constraints faced by domestic firms in the host countries. Yet, despite the lack of evidence, policymakers in the SSAs have sought after the establishment of foreign-owned firms to gain some external finance, among other benefits (Te Velde and Morrissey, 2003; Elkins et al., 2006; Foster-McGregor et al., 2015).7 In the literature, closest to our study are works related to the effects of foreign direct investment (FDI) on domestic firms’ financial constraint (Harrison and McMillan, 2003; Harrison et al., 2004). However, these studies do not focus on the effects of foreign firm ownership, nor do they combine cross-country and firm-level information for their analysis as we do.8 To our best knowledge, our paper is the first to employ a cross-country firm-level approach to directly examine how the foreign ownership of firms may affect the financial constraints of domestic firms. As such, it helps to shed light on the relationship between foreign firm presence and the financial constraints of domestic firms, which is ambiguous. For example, through knowledge spillover about new products, technologies and marketing, the presence of foreign-owned firms may improve the productivity of domestic firms in the same industry, and thus, their creditworthiness (Javorcik and Spatareanu, 2011; Javorcik, 2014).9 Banks may also prefer to lend to industries with a large foreign firm presence, which eases the borrowing constraints of other firms in the same industries (Harrison et al., 2004). Besides, foreign-owned firms may themselves bring in capital, and as such, be a source of finance to their local business partners (Harrison et al., 2004). By contrast, the presence of foreign-owned firms may create difficulties for domestic firms to access finance. For example, foreign enterprises in developing countries are likely to be more profitable, have more collateral and better financial ratios. As such, banks may divert credit away from domestic firms to foreign-owned firms. Foreign-owned firms also compete in the products market and potentially erode the market share of domestic firms, and consequently, their ability to borrow (Harrison and McMillan, 2003). Considering these opposing arguments, it is unclear how foreign firm presence may affect domestic firms in the SSAs. 3. Data and descriptive statistics 3.1 Data Our dataset is drawn from the World Bank Enterprise Survey (WBES). In this survey, a total of more than 10,000 non-repeated firms from 36 SSAs are surveyed over the course of 2006 to 2010.10 The WBES questionnaires contain identical questions for all countries, and uses stratified sampling by size, industry and regions to collect the sample of firms for each country. The survey also covers 38 industries at the two ISIC-digit levels and contains information on the access and use of financial services as well as several other relevant firm characteristics that are used here. The definitions on all the variables used in this paper are provided in Table B1 in Appendix B. Following Asiedu et al. (2013) and Hansen and Rand (2014a), we construct several indicators to capture how financially constrained a firm is. These measures are based on managers’ responses to the WBES survey question: ‘to what degree is access to finance an obstacle to the current operation of this establishment?’ Our main measure of financial constraint, which we call it Financial Constraint, is an ordinal variable that takes the value of 0, 1, 2, 3, or 4 if the firm states that finance is either not a problem (i.e., 0), a minor problem (i.e., 1) a moderate problem (i.e., 2), a major problem (i.e., 3), or a severe problem (i.e., 4). In other words, firms that reported themselves to be more financially constrained have higher Financial Constraint scores. To check if our conclusion is robust, we consider three alternative indicators to measure financial constraint. Firstly, people’s perception about the seriousness of their financial situation is not absolute. Thus, the distinction between moderate, major and severe financial constraint could be blurred. For this reason, our first alternative indicator of firm financial constraint is a dummy variable that indicates (i.e., =1) if the firm responds in the survey that access to finance is a moderate, major or severe problem, and 0 if otherwise. We call this indicator Serious Constraint, which indicates if the firm has encountered what it believes to be a moderate to severe problem of financial constraint. Our second and third alternative indicators of firm financial constraint are based on two objective measures. The first, which we call Credit Product Constraint, is a dummy variable that indicates if the firm does not have access to any of the three credit products: overdrafts, lines of credit, or bank loans. The second, which we call Loans Denied, is a dummy variable that indicates if a firm had applied for but was denied a loan. The WBES database provides information on owners’ equity share. Following the literature (see, for example, Javorcik and Spatareanu, 2011; Asiedu et al., 2013), foreign-owned firms are defined as firms in the host country where at least 10% of their equity is foreign held. Domestic firms are defined as firms with less than 10% foreign ownership. We construct measures of foreign firm presence for each industry and country. These measures, described further in Section 4, are associated with the proportion of foreign firms, foreign firms’ share of equity, or employment in the industry and country. Additionally, the WBES database provides a range of relevant firm specific characteristics, such as firm size, ownership type, legal status, technological capacity and financial transparency, which are used here.11 Finally, the World Bank’s World Development Indicators (WDI) database is the source of our country level controls, which include a measure of financial development, legal system and inflation (see Appendix B for the variables’ descriptions). 3.2 Descriptive statistics To appreciate how severe the issue of firm financial constraint is in the SSAs, Figure 1 considers nine major business obstacles encountered by firms, i.e., Access to finance, Access to land, Power outage, Anti-competitive practice, Infeasible tax rate, Crime, Corruption, Political instability, and Licensing & permits. For each obstacle, Figure 1 plots the percentage of domestic and foreign-owned firms that responded that among the nine business obstacles, the obstacle in question was ‘most serious obstacle affecting the operation of the establishment’. Figure 1: Open in new tabDownload slide Nine Major Constraints Faced By Firms in Sub-Saharan Africa Figure 1: Open in new tabDownload slide Nine Major Constraints Faced By Firms in Sub-Saharan Africa Being financially constrained is among the most severe issues facing firms in the SSAs. Figure 1 shows that more firms, both domestic and foreign-owned, have reported the lack of access to finance as the most severe problem they face, than the number of firms reporting other obstacles as their most serious concern. In fact, the lack of finance is more severe than other traditional issues such as the lack of land, access to power, crime and corruption. That being said, foreign-owned and domestic firms are not equally impacted by business restrictions. As Figure 1 shows, foreign-owned firms are less likely than domestic firms to face severe obstacles in running a business. For example, concerning the access to finance, 17.5% of domestic firms report it as the most serious constraint while only 2.5% of foreign-owned firms respond in the same way. Next, Table 1 lists the countries contained in our sample. In turn, for each country, it lists the number of foreign-owned firms, its average Financial Constraint score, and the mean of its Serious Constraint indicator (which reflects the percentage of firms facing serious financial constraint). Table 1: Number of Firms and Financial Constraint Indicators by Country All firms Foreign firms No. of firms % Foreign owned Financial constraint Serious constraint No. of firms Financial constraint Serious constraint Angola 785 22 2.35 0.73 171 2.21 0.68 Botswana 610 46 1.69 0.51 283 1.36 0.41 Burkina Faso 394 14 3.00 0.91 54 2.42 0.77 Burundi 270 17 2.30 0.7 47 2.51 0.72 Cameroon 363 18 2.45 0.83 66 2.18 0.74 Cape Verde 156 15 1.92 0.64 24 2.00 0.69 Chad 150 31 2.22 0.67 47 1.86 0.58 Congo 151 22 2.10 0.66 33 1.96 0.61 D.R. Congo 699 17 2.57 0.77 119 2.21 0.70 Eritrea 179 3 0.43 0.17 6 0.33 0.16 Gabon 179 61 1.58 0.47 110 1.45 0.43 Gambia 174 30 1.79 0.55 52 1.36 0.36 Ghana 494 5 2.65 0.78 25 1.6 0.52 Guinea 223 10 2.55 0.73 25 2.73 0.78 Guinea Bissau 159 9 2.91 0.81 25 2.8 0.73 Ivory Coast 526 17 2.87 0.83 92 2.42 0.70 Kenya 657 12 1.94 0.58 80 1.72 0.55 Lesotho 151 33 1.26 0.36 50 0.89 0.22 Liberia 150 13 1.87 0.56 20 1.25 0.35 Madagascar 445 41 1.88 0.60 185 1.66 0.54 Malawi 150 32 2.08 0.65 49 1.64 0.52 Mali 850 7 2.29 0.66 66 1.87 0.52 Mauritania 237 12 2.12 0.62 28 1.67 0.5 Mauritius 398 11 1.88 0.56 42 1.54 0.52 Mozambique 473 19 2.10 0.62 91 2.06 0.61 Namibia 329 24 1.10 0.31 79 0.77 0.18 Niger 150 23 2.10 0.66 34 1.73 0.52 Rwanda 212 16 1.50 0.47 35 1.31 0.37 Senegal 506 6 2.09 0.61 30 1.7 0.5 Sierra Leone 150 14 1.95 0.62 21 1.52 0.52 South Africa 937 12 0.73 0.22 121 0.61 0.19 Swaziland 307 36 1.56 0.48 111 1.35 0.45 Tanzania 419 12 1.82 0.55 50 1.54 0.48 Togo 155 30 2.28 0.67 47 1.45 0.43 Uganda 563 17 2.31 0.73 94 1.88 0.58 Zambia 484 24 1.32 0.40 117 1.05 0.30 All Countries 13,235 16.8 2.00 0.63 2,534 1.64 0.50 All firms Foreign firms No. of firms % Foreign owned Financial constraint Serious constraint No. of firms Financial constraint Serious constraint Angola 785 22 2.35 0.73 171 2.21 0.68 Botswana 610 46 1.69 0.51 283 1.36 0.41 Burkina Faso 394 14 3.00 0.91 54 2.42 0.77 Burundi 270 17 2.30 0.7 47 2.51 0.72 Cameroon 363 18 2.45 0.83 66 2.18 0.74 Cape Verde 156 15 1.92 0.64 24 2.00 0.69 Chad 150 31 2.22 0.67 47 1.86 0.58 Congo 151 22 2.10 0.66 33 1.96 0.61 D.R. Congo 699 17 2.57 0.77 119 2.21 0.70 Eritrea 179 3 0.43 0.17 6 0.33 0.16 Gabon 179 61 1.58 0.47 110 1.45 0.43 Gambia 174 30 1.79 0.55 52 1.36 0.36 Ghana 494 5 2.65 0.78 25 1.6 0.52 Guinea 223 10 2.55 0.73 25 2.73 0.78 Guinea Bissau 159 9 2.91 0.81 25 2.8 0.73 Ivory Coast 526 17 2.87 0.83 92 2.42 0.70 Kenya 657 12 1.94 0.58 80 1.72 0.55 Lesotho 151 33 1.26 0.36 50 0.89 0.22 Liberia 150 13 1.87 0.56 20 1.25 0.35 Madagascar 445 41 1.88 0.60 185 1.66 0.54 Malawi 150 32 2.08 0.65 49 1.64 0.52 Mali 850 7 2.29 0.66 66 1.87 0.52 Mauritania 237 12 2.12 0.62 28 1.67 0.5 Mauritius 398 11 1.88 0.56 42 1.54 0.52 Mozambique 473 19 2.10 0.62 91 2.06 0.61 Namibia 329 24 1.10 0.31 79 0.77 0.18 Niger 150 23 2.10 0.66 34 1.73 0.52 Rwanda 212 16 1.50 0.47 35 1.31 0.37 Senegal 506 6 2.09 0.61 30 1.7 0.5 Sierra Leone 150 14 1.95 0.62 21 1.52 0.52 South Africa 937 12 0.73 0.22 121 0.61 0.19 Swaziland 307 36 1.56 0.48 111 1.35 0.45 Tanzania 419 12 1.82 0.55 50 1.54 0.48 Togo 155 30 2.28 0.67 47 1.45 0.43 Uganda 563 17 2.31 0.73 94 1.88 0.58 Zambia 484 24 1.32 0.40 117 1.05 0.30 All Countries 13,235 16.8 2.00 0.63 2,534 1.64 0.50 Note: Angola, Botswana, D.R. Congo and Mali were surveyed twice. Open in new tab Table 1: Number of Firms and Financial Constraint Indicators by Country All firms Foreign firms No. of firms % Foreign owned Financial constraint Serious constraint No. of firms Financial constraint Serious constraint Angola 785 22 2.35 0.73 171 2.21 0.68 Botswana 610 46 1.69 0.51 283 1.36 0.41 Burkina Faso 394 14 3.00 0.91 54 2.42 0.77 Burundi 270 17 2.30 0.7 47 2.51 0.72 Cameroon 363 18 2.45 0.83 66 2.18 0.74 Cape Verde 156 15 1.92 0.64 24 2.00 0.69 Chad 150 31 2.22 0.67 47 1.86 0.58 Congo 151 22 2.10 0.66 33 1.96 0.61 D.R. Congo 699 17 2.57 0.77 119 2.21 0.70 Eritrea 179 3 0.43 0.17 6 0.33 0.16 Gabon 179 61 1.58 0.47 110 1.45 0.43 Gambia 174 30 1.79 0.55 52 1.36 0.36 Ghana 494 5 2.65 0.78 25 1.6 0.52 Guinea 223 10 2.55 0.73 25 2.73 0.78 Guinea Bissau 159 9 2.91 0.81 25 2.8 0.73 Ivory Coast 526 17 2.87 0.83 92 2.42 0.70 Kenya 657 12 1.94 0.58 80 1.72 0.55 Lesotho 151 33 1.26 0.36 50 0.89 0.22 Liberia 150 13 1.87 0.56 20 1.25 0.35 Madagascar 445 41 1.88 0.60 185 1.66 0.54 Malawi 150 32 2.08 0.65 49 1.64 0.52 Mali 850 7 2.29 0.66 66 1.87 0.52 Mauritania 237 12 2.12 0.62 28 1.67 0.5 Mauritius 398 11 1.88 0.56 42 1.54 0.52 Mozambique 473 19 2.10 0.62 91 2.06 0.61 Namibia 329 24 1.10 0.31 79 0.77 0.18 Niger 150 23 2.10 0.66 34 1.73 0.52 Rwanda 212 16 1.50 0.47 35 1.31 0.37 Senegal 506 6 2.09 0.61 30 1.7 0.5 Sierra Leone 150 14 1.95 0.62 21 1.52 0.52 South Africa 937 12 0.73 0.22 121 0.61 0.19 Swaziland 307 36 1.56 0.48 111 1.35 0.45 Tanzania 419 12 1.82 0.55 50 1.54 0.48 Togo 155 30 2.28 0.67 47 1.45 0.43 Uganda 563 17 2.31 0.73 94 1.88 0.58 Zambia 484 24 1.32 0.40 117 1.05 0.30 All Countries 13,235 16.8 2.00 0.63 2,534 1.64 0.50 All firms Foreign firms No. of firms % Foreign owned Financial constraint Serious constraint No. of firms Financial constraint Serious constraint Angola 785 22 2.35 0.73 171 2.21 0.68 Botswana 610 46 1.69 0.51 283 1.36 0.41 Burkina Faso 394 14 3.00 0.91 54 2.42 0.77 Burundi 270 17 2.30 0.7 47 2.51 0.72 Cameroon 363 18 2.45 0.83 66 2.18 0.74 Cape Verde 156 15 1.92 0.64 24 2.00 0.69 Chad 150 31 2.22 0.67 47 1.86 0.58 Congo 151 22 2.10 0.66 33 1.96 0.61 D.R. Congo 699 17 2.57 0.77 119 2.21 0.70 Eritrea 179 3 0.43 0.17 6 0.33 0.16 Gabon 179 61 1.58 0.47 110 1.45 0.43 Gambia 174 30 1.79 0.55 52 1.36 0.36 Ghana 494 5 2.65 0.78 25 1.6 0.52 Guinea 223 10 2.55 0.73 25 2.73 0.78 Guinea Bissau 159 9 2.91 0.81 25 2.8 0.73 Ivory Coast 526 17 2.87 0.83 92 2.42 0.70 Kenya 657 12 1.94 0.58 80 1.72 0.55 Lesotho 151 33 1.26 0.36 50 0.89 0.22 Liberia 150 13 1.87 0.56 20 1.25 0.35 Madagascar 445 41 1.88 0.60 185 1.66 0.54 Malawi 150 32 2.08 0.65 49 1.64 0.52 Mali 850 7 2.29 0.66 66 1.87 0.52 Mauritania 237 12 2.12 0.62 28 1.67 0.5 Mauritius 398 11 1.88 0.56 42 1.54 0.52 Mozambique 473 19 2.10 0.62 91 2.06 0.61 Namibia 329 24 1.10 0.31 79 0.77 0.18 Niger 150 23 2.10 0.66 34 1.73 0.52 Rwanda 212 16 1.50 0.47 35 1.31 0.37 Senegal 506 6 2.09 0.61 30 1.7 0.5 Sierra Leone 150 14 1.95 0.62 21 1.52 0.52 South Africa 937 12 0.73 0.22 121 0.61 0.19 Swaziland 307 36 1.56 0.48 111 1.35 0.45 Tanzania 419 12 1.82 0.55 50 1.54 0.48 Togo 155 30 2.28 0.67 47 1.45 0.43 Uganda 563 17 2.31 0.73 94 1.88 0.58 Zambia 484 24 1.32 0.40 117 1.05 0.30 All Countries 13,235 16.8 2.00 0.63 2,534 1.64 0.50 Note: Angola, Botswana, D.R. Congo and Mali were surveyed twice. Open in new tab As Table 1 shows, the number of foreign-owned firms across the SSAs varies substantially, where Botswana has the largest number of foreign-owned firms in the sample (i.e., 283) and Eritrea, which has half of Botswana’s population, has the least (i.e., 6). Additionally, the level of financial constraint faced by firms varies substantially across the SSAs as well. For instance, South Africa has an average Financial Constraint score of 0.73, while Burkina Faso has an average score of 3 (where recall that a score of 0 indicates no financial constraint and a score of 4 indicates severe financial constraint). Based on the average Financial Constraint and Serious Constraint scores for each country, we can see that on average, domestic firms across the SSAs (except for Cape Verde and Burundi) are more financially constrained than foreign-owned firms are. To visualise this result, Figure 2 plots the average Serious Constraint score for domestic firms in the y-axis and foreign-owned firms in the x-axis. The regression line that fits the cross-country data points, which correspond to the ‘Proportion of domestic firms facing moderate to severe financial constraint’ in the y-axis and the ‘Proportion of foreign-owned firms facing moderate to severe financial constraint’ in the x-axis, is flatter than the 45 degree line. This means that across the SSAs, the proportion of domestic firms facing serious financial constraint is larger than the proportion of foreign-owned firms facing the same.12 Figure 2: Open in new tabDownload slide Proportion of Firms Facing Moderate to Severe Financial Constraint By Ownership Type Figure 2: Open in new tabDownload slide Proportion of Firms Facing Moderate to Severe Financial Constraint By Ownership Type In Table 2, we report the share of foreign-owned firms across the SSAs for each industry. The proportion of foreign-owned firms tends to be smaller in the garment, furniture, accounting and computing machinery industries, where less than 15% of firms in these industries are foreign owned. By contrast, foreign-owned firms are more highly represented in the transport equipment, water transport and tobacco industries, and in the case of the tobacco industry, 80% of firms in the SSAs are foreign owned. Table 2: Firm Ownership by Industry Industry Number of firms Domestic Foreign Food 1,629 82.20% 17.80% Tobacco 10 20% 80% Textiles 193 72.54% 27.46% Garments 1,036 88.13% 11.87% Leather 106 83.96% 16.04% Wood 327 86.54% 13.88% Paper 73 65.75% 34.25% Publishing, printing, and recorded media 317 86.12% 13.88% Refined petroleum product 12 83.33% 16.67% Chemicals 374 70.05% 29.95% Plastics & rubber 182 66.48% 33.52% Non metallic mineral products 203 69.46% 30.54% Basic metals 85 72.94% 27.93% Fabricated metal products 585 84.44% 15.56% Machinery and equipment 111 72.94% 27.93% Accounting and computing machinery 2 100% 0.00% Electrical machinery and apparatus 68 75% 25% Radio, television and communication equipment 8 75% 25% Precision instruments 5 80% 20% Motor vehicles, trailers and semi-trailers 33 81.81% 18.18% Other transport equipment 15 33.33% 66.67% Furniture 697 87.52% 12.48% Recycling 6 83.33% 16.67% Electricity, gas, steam and hot water supply 1 0.00% 100% Collection, purification and distribution of water 1 100% 0.00% Services of motor vehicles 486 75.10% 24.90% Wholesale 592 75% 25% Retail 2,825 81.59% 18.41% Hotel and restaurants 1,195 85.36% 14.64% Land transport 172 69.19% 30.81% Water transport 25 44% 56% Air transport 18 55.56% 44.44% Travel agencies 73 71.23% 28.77% Post and telecommunications 38 57.89% 42.11% Activities auxiliary to financial intermediation 1 0.00% 100% Computer and related activities 278 88.49% 11.51% Total 10,060 80% 20% Industry Number of firms Domestic Foreign Food 1,629 82.20% 17.80% Tobacco 10 20% 80% Textiles 193 72.54% 27.46% Garments 1,036 88.13% 11.87% Leather 106 83.96% 16.04% Wood 327 86.54% 13.88% Paper 73 65.75% 34.25% Publishing, printing, and recorded media 317 86.12% 13.88% Refined petroleum product 12 83.33% 16.67% Chemicals 374 70.05% 29.95% Plastics & rubber 182 66.48% 33.52% Non metallic mineral products 203 69.46% 30.54% Basic metals 85 72.94% 27.93% Fabricated metal products 585 84.44% 15.56% Machinery and equipment 111 72.94% 27.93% Accounting and computing machinery 2 100% 0.00% Electrical machinery and apparatus 68 75% 25% Radio, television and communication equipment 8 75% 25% Precision instruments 5 80% 20% Motor vehicles, trailers and semi-trailers 33 81.81% 18.18% Other transport equipment 15 33.33% 66.67% Furniture 697 87.52% 12.48% Recycling 6 83.33% 16.67% Electricity, gas, steam and hot water supply 1 0.00% 100% Collection, purification and distribution of water 1 100% 0.00% Services of motor vehicles 486 75.10% 24.90% Wholesale 592 75% 25% Retail 2,825 81.59% 18.41% Hotel and restaurants 1,195 85.36% 14.64% Land transport 172 69.19% 30.81% Water transport 25 44% 56% Air transport 18 55.56% 44.44% Travel agencies 73 71.23% 28.77% Post and telecommunications 38 57.89% 42.11% Activities auxiliary to financial intermediation 1 0.00% 100% Computer and related activities 278 88.49% 11.51% Total 10,060 80% 20% Note: These 38 industries are based on the 2-digit ISIC classification. Open in new tab Table 2: Firm Ownership by Industry Industry Number of firms Domestic Foreign Food 1,629 82.20% 17.80% Tobacco 10 20% 80% Textiles 193 72.54% 27.46% Garments 1,036 88.13% 11.87% Leather 106 83.96% 16.04% Wood 327 86.54% 13.88% Paper 73 65.75% 34.25% Publishing, printing, and recorded media 317 86.12% 13.88% Refined petroleum product 12 83.33% 16.67% Chemicals 374 70.05% 29.95% Plastics & rubber 182 66.48% 33.52% Non metallic mineral products 203 69.46% 30.54% Basic metals 85 72.94% 27.93% Fabricated metal products 585 84.44% 15.56% Machinery and equipment 111 72.94% 27.93% Accounting and computing machinery 2 100% 0.00% Electrical machinery and apparatus 68 75% 25% Radio, television and communication equipment 8 75% 25% Precision instruments 5 80% 20% Motor vehicles, trailers and semi-trailers 33 81.81% 18.18% Other transport equipment 15 33.33% 66.67% Furniture 697 87.52% 12.48% Recycling 6 83.33% 16.67% Electricity, gas, steam and hot water supply 1 0.00% 100% Collection, purification and distribution of water 1 100% 0.00% Services of motor vehicles 486 75.10% 24.90% Wholesale 592 75% 25% Retail 2,825 81.59% 18.41% Hotel and restaurants 1,195 85.36% 14.64% Land transport 172 69.19% 30.81% Water transport 25 44% 56% Air transport 18 55.56% 44.44% Travel agencies 73 71.23% 28.77% Post and telecommunications 38 57.89% 42.11% Activities auxiliary to financial intermediation 1 0.00% 100% Computer and related activities 278 88.49% 11.51% Total 10,060 80% 20% Industry Number of firms Domestic Foreign Food 1,629 82.20% 17.80% Tobacco 10 20% 80% Textiles 193 72.54% 27.46% Garments 1,036 88.13% 11.87% Leather 106 83.96% 16.04% Wood 327 86.54% 13.88% Paper 73 65.75% 34.25% Publishing, printing, and recorded media 317 86.12% 13.88% Refined petroleum product 12 83.33% 16.67% Chemicals 374 70.05% 29.95% Plastics & rubber 182 66.48% 33.52% Non metallic mineral products 203 69.46% 30.54% Basic metals 85 72.94% 27.93% Fabricated metal products 585 84.44% 15.56% Machinery and equipment 111 72.94% 27.93% Accounting and computing machinery 2 100% 0.00% Electrical machinery and apparatus 68 75% 25% Radio, television and communication equipment 8 75% 25% Precision instruments 5 80% 20% Motor vehicles, trailers and semi-trailers 33 81.81% 18.18% Other transport equipment 15 33.33% 66.67% Furniture 697 87.52% 12.48% Recycling 6 83.33% 16.67% Electricity, gas, steam and hot water supply 1 0.00% 100% Collection, purification and distribution of water 1 100% 0.00% Services of motor vehicles 486 75.10% 24.90% Wholesale 592 75% 25% Retail 2,825 81.59% 18.41% Hotel and restaurants 1,195 85.36% 14.64% Land transport 172 69.19% 30.81% Water transport 25 44% 56% Air transport 18 55.56% 44.44% Travel agencies 73 71.23% 28.77% Post and telecommunications 38 57.89% 42.11% Activities auxiliary to financial intermediation 1 0.00% 100% Computer and related activities 278 88.49% 11.51% Total 10,060 80% 20% Note: These 38 industries are based on the 2-digit ISIC classification. Open in new tab Finally, in Table 3, we show the proportion of foreign firms and the proportions of domestic firms in each of the five categories of financial constraints. The table shows that domestic firms are likely to be more severely financially constrained. For example, 23.28% of domestic firms have reported that they face severe financial constraint as opposed to 13.72% of foreign firms that have reported the same. By contrast, about a third of foreign firms have reported that financial constraint is not a problem as opposed to a quarter of domestic firms that have reported the same. For the descriptive statistics of the variables used in this study, please refer to Table C1 in Appendix C. Table 3: Percentage of Financial Constrained Firms Across Ownership Type Financial constraint categories Proportion of foreign firms Proportion of domestic firms Severe 13.72% 23.28% Major 20.53% 23.83% Moderate 16.32% 16.01% Minor 15.67% 12.20% Not a Problem 33.77% 24.68% Financial constraint categories Proportion of foreign firms Proportion of domestic firms Severe 13.72% 23.28% Major 20.53% 23.83% Moderate 16.32% 16.01% Minor 15.67% 12.20% Not a Problem 33.77% 24.68% Open in new tab Table 3: Percentage of Financial Constrained Firms Across Ownership Type Financial constraint categories Proportion of foreign firms Proportion of domestic firms Severe 13.72% 23.28% Major 20.53% 23.83% Moderate 16.32% 16.01% Minor 15.67% 12.20% Not a Problem 33.77% 24.68% Financial constraint categories Proportion of foreign firms Proportion of domestic firms Severe 13.72% 23.28% Major 20.53% 23.83% Moderate 16.32% 16.01% Minor 15.67% 12.20% Not a Problem 33.77% 24.68% Open in new tab 4. Empirical approach Our empirical approach is structured as follows. Preliminary As a preliminary, we first show that foreign firms are less financially constrained by estimating (see Section 5.1 for findings) FCijkt=c1+αForeignijkt+γ′Xijkt+δ′Ckt+μj+μk+μt+εijkt. (1) The dependent variable FCijkt measures the financial constraint experienced by firm i in industry j and in country k, which is reported by this firm in year t. Throughout our paper, our main measure is Financial Constraint, which is an ordinal variable ranging from 0 to 4 where a score of 0 indicates that the firm is not financially constrained and a score of 4 indicates that the firm faces severe financial constraints. The main explanatory variable in this model is Foreign, which is a dummy variable that indicates if the firm is foreign-owned. If domestic firms are more credit constrained than foreign-owned firms are, the coefficient on Foreign will be negative. For controls, Xijkt is a vector of firm-level variables and Ckt is a vector of country-level variables. Because the firms are surveyed once only, Eq. (1) (as are the other regression models estimated here) is a panel at the industry, country and year level, but not at the firm level. The term μj ⁠, generically represents the vector of industry dummies, μk represents the vector of country dummmies, and μt represents the vector of year dummies. Main Estimating Equation We explore if a larger foreign firm presence may help alleviate the financial constraints of domestic firms in the same industry. To do so, we estimate (see Section 5.2 for findings) FCijkt=c2+βPresencejkt+ϕ′Xijkt+ρ′Ckt+μj+μk+μt+εijkt (2) using only the subsample of domestic firms. Our main financial constraint measure, as before, is Financial Constraint. In addition, we consider three alternative measures of financial constraint, namely Serious Constraint, Credit Products Constraint, and Loans Denied discussed in Section 3.1, as a robustness check. Presencejkt, our main explanatory variable, measures the presence of foreign-owned firm in industry j in country k. We measure Presencejkt using the following indicators: (i) the proportion of foreign firms over the total number of firms in industry j, country k and year t, ForeignFirmsProportionjkt=∑i=1nForeignijktTotalnumberoffirmsjkt (3) (ii) the share of foreign firms’ equity of total equity in industry j, country k and year t (Eq. (4)) ForeignEquitySharejkt=∑i=1n(equityijkt*Foreignijkt)∑i=1nequityjkt (4) (iii) the proportion of workers in foreign-owned firms over total number of workers in industry j, country k and year t (Eq. (5)): ForeignWorkersSharejkt=∑i=1n(employeesijkt*Foreignijkt)∑i=1nemployeesijkt (5) The larger these indices are, the larger foreign firm presence is in the industry in terms of the proportion of foreign-owned firms (Eq. (3)), proportion of equity held by foreign-owned firms (Eq. (4)), and the proportion of the workforce force employed by foreign-owned firms (Eq. (5)). Unless stated otherwise, for models with an ordinal dependent variable (specifically Financial Constraint), we estimate them with the Ordered Probit model and report the estimated coefficients in the tables (in Table 10, we consider alternative estimation methods as a robustness check). For models with a binary dependent variable, we estimate them with the Probit model and report the average marginal effects associated with the regressors. Mechanism Finally, we provide some evidence to support an explanation (there could be others) for why foreign firm presence may help to ease the financial constraint of domestic firms (see Section 5.3). Firms compete for finance, which is scarce in the SSAs. We show that foreign-owned firms are not only less financially constrained than domestic firms are, they are also less likely to borrow from banks. Since foreign-owned firms are less likely to seek bank credit, domestic firms would benefit from reduced competition in the credit market when there is greater representation of foreign-owned firms. In Section 5.3, we show that domestic firms in industries with a larger foreign firm presence are indeed more successful in their loan applications. This is one possible explanation for why foreign firm presence may ease the financial constraints of domestic firms. 4.1 Possible empirical issues Our main estimating equation is Eq. (2). We highlight some concerns that could prevent us from imparting a causal interpretation on the association between foreign firm presence and domestic firms’ financial constraint. Reverse Causality The first possible concern is reverse causality. Due to our regression design, we believe that reverse causality is unlikely to be a major confounding problem. Specifically, in our estimating equation (i.e., Eq. (2)), our dependent variable is the financial constraint of the domestic firm and our main explanatory variable is the foreign firm presence of an industry. For reverse causality to occur in the context of Eq. (2), we would need FCijkt (the firm i's financial constraint) to cause Presencejkt (industry j's foreign firm presence); that is, domestic firms would need to drive the composition of foreign firms in the industry. In this regard, we believe it is empirically unlikely for our results to be driven by reverse causality. Firstly, domestic firms in the SSAs are typically smaller. In Table 4, we find that the great majority of domestic firms, about 69% of them, have 19 employees or fewer. Moreover, only 7% of domestic firms employ 100 or more workers, compared with 25% of foreign-owned firms. Therefore, because of their size, it is unlikely for domestic firms to drive foreign firm presence in the industry and for reverse causality to matter empirically. Table 4: The Size of Domestic versus Foreign-Owned Firms Domestic Foreign-owned Small Firm (5–19 employees) 69% 42% Medium Firm (20–99 employees) 24% 33% Large Firm (above 99 employees) 7% 25% Total 100% 100% Domestic Foreign-owned Small Firm (5–19 employees) 69% 42% Medium Firm (20–99 employees) 24% 33% Large Firm (above 99 employees) 7% 25% Total 100% 100% Open in new tab Table 4: The Size of Domestic versus Foreign-Owned Firms Domestic Foreign-owned Small Firm (5–19 employees) 69% 42% Medium Firm (20–99 employees) 24% 33% Large Firm (above 99 employees) 7% 25% Total 100% 100% Domestic Foreign-owned Small Firm (5–19 employees) 69% 42% Medium Firm (20–99 employees) 24% 33% Large Firm (above 99 employees) 7% 25% Total 100% 100% Open in new tab More importantly, it is also unlikely that our conclusion is driven by reverse causal effects that are due to large domestic firms. In Section 6.4, we re-estimate our baseline regression without the top 5% and 10% largest domestic firms in the sample. We show that our conclusion (i.e., that foreign firm presence is statistically significant) still holds when these firms are omitted from the regression. Therefore, there is no evidence that our conclusion is driven by potential reverse causality stemming from these large domestic firms as well. Self-Selection Even if we could rule out reverse causality, it does not imply that the association between foreign firm presence and domestic firms’ financial constraint is causal. For example, foreign firms may still self-select into countries or industries where domestic firms are less financially constrained. Therefore, the association between foreign firm presence and domestic firms’ financial constraint could be jointly determined by certain unobserved country or industry characteristics. To address this concern, we include industry and country fixed effects to purge the possible confounding influence of unobserved industry and country heterogeneity.13 We also control for certain time-varying macroeconomic variables such as financial development (Financial Development), legal system (Legal System) and inflation (Inflation) that may affect foreigners’ decisions to invest in a country. Finally, in place of the country level controls, we use country-year fixed effects to partial out all possible country determinants, whether they are observable or unobservable, time-varying or time-constant. Therefore, the country-year fixed effects will partial out financial development, country-specific policies, and other country-specific characteristics that may jointly determine foreign firm presence and the financial constraints of domestic firms. Measurement of Credit Constraint Another concern stems from an observation by Hansen and Rand (2014a) that research on credit constraints may not be robust to how credit constraints are measured.14 For this reason, we use different indicators to measure financial constraint (i.e., Financial Constraint, Serious Constraint, Credit Products Constraint, Loan Denied) to ensure that our results are not dependent on the way financial constraint is measured. Multicollinearity Lastly, we could be concerned about the multicollinearity, especially when we include a sizable set of firm level and country level controls. To this end, we compute that Variance-Inflation-Factor (VIF) suggested by Belsley et al. (2005), and to save space, we have omitted the results from the paper. We find that all the predictors have an VIF smaller than the rule-of-thumb of 10, above which there is evidence of multicollinearity. Therefore, multicollinearity does not appear to an issue here. 5. Results 5.1 Firm ownership and financial constraint Based on Financial Constraint as the dependent variable, we report our estimates of Eq. (1) in Table 5, which reveal if domestic firms are more financially constrained than foreign-owned firms are. In Column (1), we control for firm characteristics only. In Column (2), we additionally control for inflation, financial development and legal system. In Column (3), we add industry, country and year dummies. In Column (4), we use industry dummies and country times year dummies. We find that all else equal, foreign-owned firms have a Financial Constraint score of 0.1–0.14 points smaller than domestic firms have on average. This difference is statistically significant at the 1% level across all regression specifications, suggesting that domestic firms on average are more financially constrained than foreign firms are. Table 5: Financial Constraint: Foreign-Owned Versus Domestic Firms (1) (2) (3) (4) Dependent variable: Financial constraint Foreign (owned) −0.109*** −0.235*** −0.139*** −0.14*** (0.000) (0.038) (0.000) (0.0373) Female Owned 0.081*** 0.071*** 0.085*** 0.08*** (0.027) (0.029) (0.009) (0.0290) Small (5–19 workers) 0.168*** 0.208*** 0.200*** 0.199*** (0.050) (0.054) (0.055) (0.054) Medium (20–99 workers) 0.082 0.088* 0.085* 0.085* (0.050) (0.052) (0.052) (0.051) Sole Proprietorship 0.008 −0.114*** −0.115*** −0.114*** (0.037) (0.040) (0.040) (0.040) Publicly Traded −0.444*** −0.597*** −0.593*** −0.596*** (0.131) (0.150) (0.150) (0.149) Private or Non-traded −0.158*** −0.154*** −0.156*** −0.156*** (0.038) (0.041) (0.042) (0.041) Financial Transparency −0.342*** −0.241*** −0.231*** −0.23*** (0.029) (0.032) (0.032) (0.037) Technological Capacity −0.311*** −0.185*** −0.181*** −0.180 (0.036) (0.038) (0.038) (0.037) Financial Development −0.003 −0.033** −0.058*** (0.001) (0.015) (0.012) Legal System −0.003 −0.133*** −0.128*** (0.010) (0.028) (0.036) Inflation 0.077*** −0.029 −0.148*** (0.009) (0.028) (0.048) Industry Dummies No No Yes Yes Country Dummies No No Yes No Year Dummies No No Yes No Country-Year Dummies No No No Yes Observations 8,186 7,718 7,518 7,718 (1) (2) (3) (4) Dependent variable: Financial constraint Foreign (owned) −0.109*** −0.235*** −0.139*** −0.14*** (0.000) (0.038) (0.000) (0.0373) Female Owned 0.081*** 0.071*** 0.085*** 0.08*** (0.027) (0.029) (0.009) (0.0290) Small (5–19 workers) 0.168*** 0.208*** 0.200*** 0.199*** (0.050) (0.054) (0.055) (0.054) Medium (20–99 workers) 0.082 0.088* 0.085* 0.085* (0.050) (0.052) (0.052) (0.051) Sole Proprietorship 0.008 −0.114*** −0.115*** −0.114*** (0.037) (0.040) (0.040) (0.040) Publicly Traded −0.444*** −0.597*** −0.593*** −0.596*** (0.131) (0.150) (0.150) (0.149) Private or Non-traded −0.158*** −0.154*** −0.156*** −0.156*** (0.038) (0.041) (0.042) (0.041) Financial Transparency −0.342*** −0.241*** −0.231*** −0.23*** (0.029) (0.032) (0.032) (0.037) Technological Capacity −0.311*** −0.185*** −0.181*** −0.180 (0.036) (0.038) (0.038) (0.037) Financial Development −0.003 −0.033** −0.058*** (0.001) (0.015) (0.012) Legal System −0.003 −0.133*** −0.128*** (0.010) (0.028) (0.036) Inflation 0.077*** −0.029 −0.148*** (0.009) (0.028) (0.048) Industry Dummies No No Yes Yes Country Dummies No No Yes No Year Dummies No No Yes No Country-Year Dummies No No No Yes Observations 8,186 7,718 7,518 7,718 Note: Financial Constraint is an ordinal variable (from 0 to 4) that indicates how financially constrained a firm is. Estimates of the coefficients from the Ordered Probit model are reported here. The constant is suppressed. Robust standard errors are reported in the parentheses. *p < 0.10, **p < 0.05, ***p < 0.01. Open in new tab Table 5: Financial Constraint: Foreign-Owned Versus Domestic Firms (1) (2) (3) (4) Dependent variable: Financial constraint Foreign (owned) −0.109*** −0.235*** −0.139*** −0.14*** (0.000) (0.038) (0.000) (0.0373) Female Owned 0.081*** 0.071*** 0.085*** 0.08*** (0.027) (0.029) (0.009) (0.0290) Small (5–19 workers) 0.168*** 0.208*** 0.200*** 0.199*** (0.050) (0.054) (0.055) (0.054) Medium (20–99 workers) 0.082 0.088* 0.085* 0.085* (0.050) (0.052) (0.052) (0.051) Sole Proprietorship 0.008 −0.114*** −0.115*** −0.114*** (0.037) (0.040) (0.040) (0.040) Publicly Traded −0.444*** −0.597*** −0.593*** −0.596*** (0.131) (0.150) (0.150) (0.149) Private or Non-traded −0.158*** −0.154*** −0.156*** −0.156*** (0.038) (0.041) (0.042) (0.041) Financial Transparency −0.342*** −0.241*** −0.231*** −0.23*** (0.029) (0.032) (0.032) (0.037) Technological Capacity −0.311*** −0.185*** −0.181*** −0.180 (0.036) (0.038) (0.038) (0.037) Financial Development −0.003 −0.033** −0.058*** (0.001) (0.015) (0.012) Legal System −0.003 −0.133*** −0.128*** (0.010) (0.028) (0.036) Inflation 0.077*** −0.029 −0.148*** (0.009) (0.028) (0.048) Industry Dummies No No Yes Yes Country Dummies No No Yes No Year Dummies No No Yes No Country-Year Dummies No No No Yes Observations 8,186 7,718 7,518 7,718 (1) (2) (3) (4) Dependent variable: Financial constraint Foreign (owned) −0.109*** −0.235*** −0.139*** −0.14*** (0.000) (0.038) (0.000) (0.0373) Female Owned 0.081*** 0.071*** 0.085*** 0.08*** (0.027) (0.029) (0.009) (0.0290) Small (5–19 workers) 0.168*** 0.208*** 0.200*** 0.199*** (0.050) (0.054) (0.055) (0.054) Medium (20–99 workers) 0.082 0.088* 0.085* 0.085* (0.050) (0.052) (0.052) (0.051) Sole Proprietorship 0.008 −0.114*** −0.115*** −0.114*** (0.037) (0.040) (0.040) (0.040) Publicly Traded −0.444*** −0.597*** −0.593*** −0.596*** (0.131) (0.150) (0.150) (0.149) Private or Non-traded −0.158*** −0.154*** −0.156*** −0.156*** (0.038) (0.041) (0.042) (0.041) Financial Transparency −0.342*** −0.241*** −0.231*** −0.23*** (0.029) (0.032) (0.032) (0.037) Technological Capacity −0.311*** −0.185*** −0.181*** −0.180 (0.036) (0.038) (0.038) (0.037) Financial Development −0.003 −0.033** −0.058*** (0.001) (0.015) (0.012) Legal System −0.003 −0.133*** −0.128*** (0.010) (0.028) (0.036) Inflation 0.077*** −0.029 −0.148*** (0.009) (0.028) (0.048) Industry Dummies No No Yes Yes Country Dummies No No Yes No Year Dummies No No Yes No Country-Year Dummies No No No Yes Observations 8,186 7,718 7,518 7,718 Note: Financial Constraint is an ordinal variable (from 0 to 4) that indicates how financially constrained a firm is. Estimates of the coefficients from the Ordered Probit model are reported here. The constant is suppressed. Robust standard errors are reported in the parentheses. *p < 0.10, **p < 0.05, ***p < 0.01. Open in new tab Concerning the other control variables, Table 5 shows that firms owned by females tend to be more financially constrained than firms owned by males.15 It also shows that firms that are financially constrained tend to be small, owned by partnerships than corporations or sole proprietors, less financially transparent, and have limited technological capacity (i.e., lacking a website). Firms in countries with better financial development and legal system tend to be less financially constrained as well. 5.2 Foreign firm presence and domestic firms’ financial constraint Previously, our results show that domestic firms tend to be more financially constrained than foreign firms. Here, we show that domestic firms are less financially constrained if there is a larger foreign firm presence in the same industry. To establish this result, we use the sample of domestic firms and regress their Financial Constraint scores on measures of foreign firm presence in their respective industries, along with other firm and country-level control variables and industry, country and year dummies. In Column (1) of Table 6, we estimate Eq. (2) with the proportion of foreign-owned firms, denote by Foreign Firms Proportionjkt, as our measure of foreign firm presence. The negative coefficient on Foreign Firms Proportionjkt suggests that domestic firms are less financially constrained when proportion of foreign-owned firms in the same industry is larger. Table 6: Foreign Firm Presence and Domestic Firms’ Financial Constraint (1) (2) (2) Dependent variable: Financial constraint (domestic firms) Foreign Firms Proportion −0.318** (0.153) Foreign Equity Share −0.320** (0.156) Foreign Workers Share −0.222** (0.089) Female Owned 0.055* 0.055* 0.081** (0.031) (0.031) (0.034) Small (5–19 workers) 0.217*** 0.217*** 0.226*** (0.067) (0.065) (0.068) Medium (20–99 workers) 0.092 0.092 0.095 (0.066) (0.063) (0.066) Sole Proprietorship −0.155*** −0.155*** −0.145*** (0.044) (0.043) (0.046) Publicly Traded −0.481** −0.481** −0.546** (0.200) (0.215) (0.237) Private or Non-traded −0.180*** −0.180*** −0.202*** (0.048) (0.047) (0.050) Financial Transparency −0.241*** −0.241*** −0.243*** (0.035) (0.035) (0.037) Technological Capacity −0.243*** −0.243*** −0.221*** (0.046) (0.045) (0.049) Financial Development −0.063*** −0.063*** −0.056*** (0.015) (0.014) (0.015) Legal System −0.115*** −0.115*** −0.148*** (0.042) (0.041) (0.044) Inflation −0.127** −0.126** −0.177*** (0.055) (0.054) (0.059) Industry, Country & Year Dummies Yes Yes Yes Observations 6,284 6,284 5,409 (1) (2) (2) Dependent variable: Financial constraint (domestic firms) Foreign Firms Proportion −0.318** (0.153) Foreign Equity Share −0.320** (0.156) Foreign Workers Share −0.222** (0.089) Female Owned 0.055* 0.055* 0.081** (0.031) (0.031) (0.034) Small (5–19 workers) 0.217*** 0.217*** 0.226*** (0.067) (0.065) (0.068) Medium (20–99 workers) 0.092 0.092 0.095 (0.066) (0.063) (0.066) Sole Proprietorship −0.155*** −0.155*** −0.145*** (0.044) (0.043) (0.046) Publicly Traded −0.481** −0.481** −0.546** (0.200) (0.215) (0.237) Private or Non-traded −0.180*** −0.180*** −0.202*** (0.048) (0.047) (0.050) Financial Transparency −0.241*** −0.241*** −0.243*** (0.035) (0.035) (0.037) Technological Capacity −0.243*** −0.243*** −0.221*** (0.046) (0.045) (0.049) Financial Development −0.063*** −0.063*** −0.056*** (0.015) (0.014) (0.015) Legal System −0.115*** −0.115*** −0.148*** (0.042) (0.041) (0.044) Inflation −0.127** −0.126** −0.177*** (0.055) (0.054) (0.059) Industry, Country & Year Dummies Yes Yes Yes Observations 6,284 6,284 5,409 Note: Financial Constraint is an ordinal variable (from 0 to 4) that indicates how financially constrained a firm is. Estimates of the coefficients from the Ordered Probit model are reported here. The constant is suppressed. Robust standard errors are reported in the parentheses. *p < 0.10, **p < 0.05, ***p < 0.01. Open in new tab Table 6: Foreign Firm Presence and Domestic Firms’ Financial Constraint (1) (2) (2) Dependent variable: Financial constraint (domestic firms) Foreign Firms Proportion −0.318** (0.153) Foreign Equity Share −0.320** (0.156) Foreign Workers Share −0.222** (0.089) Female Owned 0.055* 0.055* 0.081** (0.031) (0.031) (0.034) Small (5–19 workers) 0.217*** 0.217*** 0.226*** (0.067) (0.065) (0.068) Medium (20–99 workers) 0.092 0.092 0.095 (0.066) (0.063) (0.066) Sole Proprietorship −0.155*** −0.155*** −0.145*** (0.044) (0.043) (0.046) Publicly Traded −0.481** −0.481** −0.546** (0.200) (0.215) (0.237) Private or Non-traded −0.180*** −0.180*** −0.202*** (0.048) (0.047) (0.050) Financial Transparency −0.241*** −0.241*** −0.243*** (0.035) (0.035) (0.037) Technological Capacity −0.243*** −0.243*** −0.221*** (0.046) (0.045) (0.049) Financial Development −0.063*** −0.063*** −0.056*** (0.015) (0.014) (0.015) Legal System −0.115*** −0.115*** −0.148*** (0.042) (0.041) (0.044) Inflation −0.127** −0.126** −0.177*** (0.055) (0.054) (0.059) Industry, Country & Year Dummies Yes Yes Yes Observations 6,284 6,284 5,409 (1) (2) (2) Dependent variable: Financial constraint (domestic firms) Foreign Firms Proportion −0.318** (0.153) Foreign Equity Share −0.320** (0.156) Foreign Workers Share −0.222** (0.089) Female Owned 0.055* 0.055* 0.081** (0.031) (0.031) (0.034) Small (5–19 workers) 0.217*** 0.217*** 0.226*** (0.067) (0.065) (0.068) Medium (20–99 workers) 0.092 0.092 0.095 (0.066) (0.063) (0.066) Sole Proprietorship −0.155*** −0.155*** −0.145*** (0.044) (0.043) (0.046) Publicly Traded −0.481** −0.481** −0.546** (0.200) (0.215) (0.237) Private or Non-traded −0.180*** −0.180*** −0.202*** (0.048) (0.047) (0.050) Financial Transparency −0.241*** −0.241*** −0.243*** (0.035) (0.035) (0.037) Technological Capacity −0.243*** −0.243*** −0.221*** (0.046) (0.045) (0.049) Financial Development −0.063*** −0.063*** −0.056*** (0.015) (0.014) (0.015) Legal System −0.115*** −0.115*** −0.148*** (0.042) (0.041) (0.044) Inflation −0.127** −0.126** −0.177*** (0.055) (0.054) (0.059) Industry, Country & Year Dummies Yes Yes Yes Observations 6,284 6,284 5,409 Note: Financial Constraint is an ordinal variable (from 0 to 4) that indicates how financially constrained a firm is. Estimates of the coefficients from the Ordered Probit model are reported here. The constant is suppressed. Robust standard errors are reported in the parentheses. *p < 0.10, **p < 0.05, ***p < 0.01. Open in new tab Next, we use alternative measures of foreign firm presence. In Column (2), we use the share of employment by foreign-owned firms as a measure of foreign firm presence (i.e., Foreign Workers Sharejkt). In Column (3), we use the share of equity holdings by foreign-owned firms (i.e., Foreign Equity Sharejkt). Regardless of how foreign firm presence is measured, we find that the coefficient on foreign firm presence is negative and statistically significant at the 1% level. In other words, domestic firms tend to be less financially constrained in industries where the proportion of foreign firms is greater (Column (1)), and where foreign firms have a larger share of employment (Column (2)) and equity holdings (Column (3)). Thus, how foreign firm presence is measured does not affect our conclusion that it is positively associated with the easing of domestic firms’ financial constraint. 5.3 Credit competition Why does the presence of foreign firms help domestic firms to be less financially constrained? A possible explanation (among possibly others) is that firms compete for credit, but credit is scarce in the SSAs. Because foreign firms tend to be less financially constrained (see Table 5), they would therefore be less likely to borrow. As such, their presence would reduce the competition for finance and increase the success of domestic firms in borrowing from banks (Wang and Wang, 2015). We show that this explanation is empirically plausible. Given that foreign firms are less likely to be financially constrained (see Table 5), we first verify that foreign firms, compared with domestic firms, are also less likely to apply for a loan, controlling for firm and country-level characteristics, as well as industry, country and year dummies (see, also, Hansen and Rand, 2014a).16 To do so, we regress a dummy variable, Loan Application, which indicates if a firm has applied for a bank loan, on the ownership type of the firm (i.e., domestic versus foreign-owned). In Table 7, we estimate this relationship using OLS (Column (1)), Conditional Logit (Column (2)), and Probit (Column (3)).17 All three regressions show that the coefficient on Foreign is negative and statistically significant, and therefore, foreign-owned firms are less likely than domestic firms to apply for a loan. For example, Column (3) shows that compared with domestic firms, foreign-owned firms are 14.2 percentage points less likely to apply for a loan on average. Table 7: Foreign-Owned Firms, Domestic Firms and Loan Application (1) (2) (3) OLS Conditional Logit Probit Dependent variable: Loan Application Foreign −0.034** −0.196** −0.142** (0.014) (0.085) (0.064) Female Owned 0.028*** 0.166*** 0.131*** (0.011) (0.063) (0.041) Small (5–19 workers) −0.131*** −0.679*** −0.444*** (0.020) (0.108) (0.103) Medium (20–99 workers) −0.063*** −0.283*** −0.204* (0.019) (0.103) (0.112) Sole Proprietorship −0.032** −0.202** −0.076 (0.015) (0.090) (0.061) Publicly Traded −0.009 −0.077 −0.044 (0.054) (0.301) (0.174) Private or Non-traded −0.018 −0.119 −0.053 (0.016) (0.092) (0.063) Financial Transparency 0.075*** 0.448*** 0.250*** (0.012) (0.072) (0.078) Technological Capacity 0.041*** 0.223*** 0.117* (0.014) (0.081) (0.064) Inflation −0.011 (0.009) Financial Development −0.002** (0.001) Legal System 0.011 (0.021) Industry, Country & Year Dummies Yes Yes Yes Observations 7,583 7,583 7,374 (1) (2) (3) OLS Conditional Logit Probit Dependent variable: Loan Application Foreign −0.034** −0.196** −0.142** (0.014) (0.085) (0.064) Female Owned 0.028*** 0.166*** 0.131*** (0.011) (0.063) (0.041) Small (5–19 workers) −0.131*** −0.679*** −0.444*** (0.020) (0.108) (0.103) Medium (20–99 workers) −0.063*** −0.283*** −0.204* (0.019) (0.103) (0.112) Sole Proprietorship −0.032** −0.202** −0.076 (0.015) (0.090) (0.061) Publicly Traded −0.009 −0.077 −0.044 (0.054) (0.301) (0.174) Private or Non-traded −0.018 −0.119 −0.053 (0.016) (0.092) (0.063) Financial Transparency 0.075*** 0.448*** 0.250*** (0.012) (0.072) (0.078) Technological Capacity 0.041*** 0.223*** 0.117* (0.014) (0.081) (0.064) Inflation −0.011 (0.009) Financial Development −0.002** (0.001) Legal System 0.011 (0.021) Industry, Country & Year Dummies Yes Yes Yes Observations 7,583 7,583 7,374 Note: In Columns (1) and (2), estimates of the coefficients from the Ordinary Least Squares (OLS) regression and Conditional Logit model are reported. In Column (3), the average marginal effects from the Probit model are reported. The constant is suppressed. Robust standard errors are reported in the parentheses. *p < 0.10, **p < 0.05, ***p < 0.01. Open in new tab Table 7: Foreign-Owned Firms, Domestic Firms and Loan Application (1) (2) (3) OLS Conditional Logit Probit Dependent variable: Loan Application Foreign −0.034** −0.196** −0.142** (0.014) (0.085) (0.064) Female Owned 0.028*** 0.166*** 0.131*** (0.011) (0.063) (0.041) Small (5–19 workers) −0.131*** −0.679*** −0.444*** (0.020) (0.108) (0.103) Medium (20–99 workers) −0.063*** −0.283*** −0.204* (0.019) (0.103) (0.112) Sole Proprietorship −0.032** −0.202** −0.076 (0.015) (0.090) (0.061) Publicly Traded −0.009 −0.077 −0.044 (0.054) (0.301) (0.174) Private or Non-traded −0.018 −0.119 −0.053 (0.016) (0.092) (0.063) Financial Transparency 0.075*** 0.448*** 0.250*** (0.012) (0.072) (0.078) Technological Capacity 0.041*** 0.223*** 0.117* (0.014) (0.081) (0.064) Inflation −0.011 (0.009) Financial Development −0.002** (0.001) Legal System 0.011 (0.021) Industry, Country & Year Dummies Yes Yes Yes Observations 7,583 7,583 7,374 (1) (2) (3) OLS Conditional Logit Probit Dependent variable: Loan Application Foreign −0.034** −0.196** −0.142** (0.014) (0.085) (0.064) Female Owned 0.028*** 0.166*** 0.131*** (0.011) (0.063) (0.041) Small (5–19 workers) −0.131*** −0.679*** −0.444*** (0.020) (0.108) (0.103) Medium (20–99 workers) −0.063*** −0.283*** −0.204* (0.019) (0.103) (0.112) Sole Proprietorship −0.032** −0.202** −0.076 (0.015) (0.090) (0.061) Publicly Traded −0.009 −0.077 −0.044 (0.054) (0.301) (0.174) Private or Non-traded −0.018 −0.119 −0.053 (0.016) (0.092) (0.063) Financial Transparency 0.075*** 0.448*** 0.250*** (0.012) (0.072) (0.078) Technological Capacity 0.041*** 0.223*** 0.117* (0.014) (0.081) (0.064) Inflation −0.011 (0.009) Financial Development −0.002** (0.001) Legal System 0.011 (0.021) Industry, Country & Year Dummies Yes Yes Yes Observations 7,583 7,583 7,374 Note: In Columns (1) and (2), estimates of the coefficients from the Ordinary Least Squares (OLS) regression and Conditional Logit model are reported. In Column (3), the average marginal effects from the Probit model are reported. The constant is suppressed. Robust standard errors are reported in the parentheses. *p < 0.10, **p < 0.05, ***p < 0.01. Open in new tab If foreign-owned firms are less likely to apply for a loan, a larger foreign firm presence may reduce the financial constraint of domestic firms by enabling them to borrow more successfully. For the sample of domestic firms, Table 8 regresses Loan Denied, a dummy that indicates if a domestic firm has had an unsuccessful loan application, on each of the foreign firm presence measures (see Eqs. (3)–(5)). Regardless of how foreign firm presence is measured, Table 8 shows that domestic firms are less likely to be denied a loan when foreign firm presence is larger. For example, Column (1) shows that if the proportion of foreign firms (over total number of firms in an industry) increases by 10 percentage points, domestic firms in the same industry would be 4 percentage points less likely to have had a rejected loan. Therefore, domestic firms have been more successful in securing credit when the proportion of foreign firms in the industry is larger. Table 8: Foreign Firm Presence and Loan Denial to Domestic Firms (1) (2) (3) Dependent variable: Loans denied (domestic firms) Foreign Firms Proportion −0.398*** (0.099) Foreign Equity Share −0.392*** (0.098) Foreign Workers Share −0.156* (0.083) Female Owned −0.004 −0.004 −0.008 (0.070) (0.070) (0.083) Small (5–19 workers) 0.799*** 0.800*** 0.683*** (0.134) (0.134) (0.159) Medium (20–99 workers) 0.383*** 0.384*** 0.316** (0.130) (0.130) (0.154) Publicly Traded 0.225 0.223 0.268 (0.390) (0.390) (0.481) Private or Non-traded −0.043 −0.043 −0.111 (0.107) (0.107) (0.126) Financial Transparency −0.149* −0.338*** −0.0003 (0.077) (0.077) (0.089) Technological Capacity −0.119 −0.159 −0.056 (0.095) (0.095) (0.121) Inflation 0.059*** 0.059*** 0.121*** (0.010) (0.010) (0.013) Financial Development −0.003*** −0.003*** −0.001 (0.001) (0.001) (0.001) Legal System −0.118*** −0.118*** −0.236*** (0.018) (0.018) (0.021) Industry, Country & Year Dummies Yes Yes Yes Observations 2,223 2,223 1,727 (1) (2) (3) Dependent variable: Loans denied (domestic firms) Foreign Firms Proportion −0.398*** (0.099) Foreign Equity Share −0.392*** (0.098) Foreign Workers Share −0.156* (0.083) Female Owned −0.004 −0.004 −0.008 (0.070) (0.070) (0.083) Small (5–19 workers) 0.799*** 0.800*** 0.683*** (0.134) (0.134) (0.159) Medium (20–99 workers) 0.383*** 0.384*** 0.316** (0.130) (0.130) (0.154) Publicly Traded 0.225 0.223 0.268 (0.390) (0.390) (0.481) Private or Non-traded −0.043 −0.043 −0.111 (0.107) (0.107) (0.126) Financial Transparency −0.149* −0.338*** −0.0003 (0.077) (0.077) (0.089) Technological Capacity −0.119 −0.159 −0.056 (0.095) (0.095) (0.121) Inflation 0.059*** 0.059*** 0.121*** (0.010) (0.010) (0.013) Financial Development −0.003*** −0.003*** −0.001 (0.001) (0.001) (0.001) Legal System −0.118*** −0.118*** −0.236*** (0.018) (0.018) (0.021) Industry, Country & Year Dummies Yes Yes Yes Observations 2,223 2,223 1,727 Note: Loan Denied indicates if a (domestic) firm was denied a loan application. The average marginal effects from the Probit model are reported here. The constant is suppressed. Robust standard errors are reported in the parentheses. *p < 0.10, **p < 0.05, ***p < 0.01. Open in new tab Table 8: Foreign Firm Presence and Loan Denial to Domestic Firms (1) (2) (3) Dependent variable: Loans denied (domestic firms) Foreign Firms Proportion −0.398*** (0.099) Foreign Equity Share −0.392*** (0.098) Foreign Workers Share −0.156* (0.083) Female Owned −0.004 −0.004 −0.008 (0.070) (0.070) (0.083) Small (5–19 workers) 0.799*** 0.800*** 0.683*** (0.134) (0.134) (0.159) Medium (20–99 workers) 0.383*** 0.384*** 0.316** (0.130) (0.130) (0.154) Publicly Traded 0.225 0.223 0.268 (0.390) (0.390) (0.481) Private or Non-traded −0.043 −0.043 −0.111 (0.107) (0.107) (0.126) Financial Transparency −0.149* −0.338*** −0.0003 (0.077) (0.077) (0.089) Technological Capacity −0.119 −0.159 −0.056 (0.095) (0.095) (0.121) Inflation 0.059*** 0.059*** 0.121*** (0.010) (0.010) (0.013) Financial Development −0.003*** −0.003*** −0.001 (0.001) (0.001) (0.001) Legal System −0.118*** −0.118*** −0.236*** (0.018) (0.018) (0.021) Industry, Country & Year Dummies Yes Yes Yes Observations 2,223 2,223 1,727 (1) (2) (3) Dependent variable: Loans denied (domestic firms) Foreign Firms Proportion −0.398*** (0.099) Foreign Equity Share −0.392*** (0.098) Foreign Workers Share −0.156* (0.083) Female Owned −0.004 −0.004 −0.008 (0.070) (0.070) (0.083) Small (5–19 workers) 0.799*** 0.800*** 0.683*** (0.134) (0.134) (0.159) Medium (20–99 workers) 0.383*** 0.384*** 0.316** (0.130) (0.130) (0.154) Publicly Traded 0.225 0.223 0.268 (0.390) (0.390) (0.481) Private or Non-traded −0.043 −0.043 −0.111 (0.107) (0.107) (0.126) Financial Transparency −0.149* −0.338*** −0.0003 (0.077) (0.077) (0.089) Technological Capacity −0.119 −0.159 −0.056 (0.095) (0.095) (0.121) Inflation 0.059*** 0.059*** 0.121*** (0.010) (0.010) (0.013) Financial Development −0.003*** −0.003*** −0.001 (0.001) (0.001) (0.001) Legal System −0.118*** −0.118*** −0.236*** (0.018) (0.018) (0.021) Industry, Country & Year Dummies Yes Yes Yes Observations 2,223 2,223 1,727 Note: Loan Denied indicates if a (domestic) firm was denied a loan application. The average marginal effects from the Probit model are reported here. The constant is suppressed. Robust standard errors are reported in the parentheses. *p < 0.10, **p < 0.05, ***p < 0.01. Open in new tab These results suggest that a reduction in loans competition is one reason (among possibly others) for why foreign firm presence is negatively associated with the financial constraint of domestic firms. Foreign-owned firms are not only less financially constrained, they are also less likely to borrow from banks. Thus, the presence of foreign-owned firms reduces the competition for credit and improves the access to finance for domestic firms on average. 6. Robustness checks 6.1 Alternative indicators of financial constraint Our main financial constraint measure (Financial Constraint) is an ordinal variable based on managers’ perceptions on how financially constraint their firms are. However, with respect to measurements of credit constraints, Hansen and Rand (2014a) have shown that different measures may yield different conclusions.18 Therefore, as a robustness check, we consider three alternative measures of financial constraint. Our first measure, Serious Constraint, is a dummy variable that indicates if access to finance is a moderate to severe problem for the firm. Our second measure, Credit Product Constraint, is a dummy variable that indicates if the firm does not have access to at least one of the three credit products, i.e., an overdraft, line of credit, or a bank loan (Aterido et al., 2013; Love and Martnez Pera, 2014). Our third measure, Loan Denied (which we have used in Table 8), is a dummy variable that indicates if a firm has had an unsuccessful loan application. Table 9 shows that all three alternative measures of financial constraint are negatively associated with foreign firm presence (measured by Foreign Firms Proportion), and their associations are statistically significant at the 1% level. Therefore, when the presence of foreign-owned firms is larger, domestic firms within the same industry (as these foreign firms) are less likely to report that they have faced moderate to severe financial constraint, have no access to credit facilities, and were denied loans. Thus, there is no evidence that our results depend on how the financial constraints of domestic firms are measured. Table 9: Robustness Check #1: Alternative Indicators of Financial Constraint (1) (2) (3) Dependent variable: Serious constraint Credit products constraint Loan denied Foreign Firms Proportion −0.249*** −0.168*** −0.398*** (0.054) (0.036) (0.099) Female Owned 0.048*** −0.030*** −0.014 (0.013) (0.009) (0.070) Small (5–19 workers) 0.076*** 0.049** 0.799*** (0.027) (0.021) (0.134) Medium (20–99 workers) 0.036 0.051** 0.383*** (0.027) (0.022) (0.130) Sole Proprietorship −0.061** −0.052*** −0.043 (0.029) (0.018) (0.102) Publicly Traded −0.292*** −0.100** 0.225 (0.044) (0.128) (0.390) Private or Non-traded −0.082*** −0.022* −0.043 (0.013) (0.033) (0.107) Financial Transparency −0.102*** −0.078*** −0.149* (0.014) (0.010) (0.025) Technological Capacity −0.101*** −0.008 −0.119 (0.018) (0.015) (0.095) Financial Development −0.002*** −0.002*** −0.003*** (0.000) (0.000) (0.001) Legal System −0.003 0.030*** −0.118*** (0.003) (0.002) (0.018) Inflation 0.005*** 0.005*** 0.059*** (0.002) (0.001) (0.010) Industry, Country & Year Dummies Yes Yes Yes Observations 6,186 2,223 6,277 (1) (2) (3) Dependent variable: Serious constraint Credit products constraint Loan denied Foreign Firms Proportion −0.249*** −0.168*** −0.398*** (0.054) (0.036) (0.099) Female Owned 0.048*** −0.030*** −0.014 (0.013) (0.009) (0.070) Small (5–19 workers) 0.076*** 0.049** 0.799*** (0.027) (0.021) (0.134) Medium (20–99 workers) 0.036 0.051** 0.383*** (0.027) (0.022) (0.130) Sole Proprietorship −0.061** −0.052*** −0.043 (0.029) (0.018) (0.102) Publicly Traded −0.292*** −0.100** 0.225 (0.044) (0.128) (0.390) Private or Non-traded −0.082*** −0.022* −0.043 (0.013) (0.033) (0.107) Financial Transparency −0.102*** −0.078*** −0.149* (0.014) (0.010) (0.025) Technological Capacity −0.101*** −0.008 −0.119 (0.018) (0.015) (0.095) Financial Development −0.002*** −0.002*** −0.003*** (0.000) (0.000) (0.001) Legal System −0.003 0.030*** −0.118*** (0.003) (0.002) (0.018) Inflation 0.005*** 0.005*** 0.059*** (0.002) (0.001) (0.010) Industry, Country & Year Dummies Yes Yes Yes Observations 6,186 2,223 6,277 Note: Serious Constraint indicates if a firm faces moderate to severe financial constraint. Credit Products Constraint indicates if a firm has no access to credit facilities. Loans Denied indicates if the firm had a rejected loan application. The average marginal effects from the Probit model are reported. Robust standard errors are reported in the parentheses. *p < 0.10, **p < 0.05, ***p < 0.01. Open in new tab Table 9: Robustness Check #1: Alternative Indicators of Financial Constraint (1) (2) (3) Dependent variable: Serious constraint Credit products constraint Loan denied Foreign Firms Proportion −0.249*** −0.168*** −0.398*** (0.054) (0.036) (0.099) Female Owned 0.048*** −0.030*** −0.014 (0.013) (0.009) (0.070) Small (5–19 workers) 0.076*** 0.049** 0.799*** (0.027) (0.021) (0.134) Medium (20–99 workers) 0.036 0.051** 0.383*** (0.027) (0.022) (0.130) Sole Proprietorship −0.061** −0.052*** −0.043 (0.029) (0.018) (0.102) Publicly Traded −0.292*** −0.100** 0.225 (0.044) (0.128) (0.390) Private or Non-traded −0.082*** −0.022* −0.043 (0.013) (0.033) (0.107) Financial Transparency −0.102*** −0.078*** −0.149* (0.014) (0.010) (0.025) Technological Capacity −0.101*** −0.008 −0.119 (0.018) (0.015) (0.095) Financial Development −0.002*** −0.002*** −0.003*** (0.000) (0.000) (0.001) Legal System −0.003 0.030*** −0.118*** (0.003) (0.002) (0.018) Inflation 0.005*** 0.005*** 0.059*** (0.002) (0.001) (0.010) Industry, Country & Year Dummies Yes Yes Yes Observations 6,186 2,223 6,277 (1) (2) (3) Dependent variable: Serious constraint Credit products constraint Loan denied Foreign Firms Proportion −0.249*** −0.168*** −0.398*** (0.054) (0.036) (0.099) Female Owned 0.048*** −0.030*** −0.014 (0.013) (0.009) (0.070) Small (5–19 workers) 0.076*** 0.049** 0.799*** (0.027) (0.021) (0.134) Medium (20–99 workers) 0.036 0.051** 0.383*** (0.027) (0.022) (0.130) Sole Proprietorship −0.061** −0.052*** −0.043 (0.029) (0.018) (0.102) Publicly Traded −0.292*** −0.100** 0.225 (0.044) (0.128) (0.390) Private or Non-traded −0.082*** −0.022* −0.043 (0.013) (0.033) (0.107) Financial Transparency −0.102*** −0.078*** −0.149* (0.014) (0.010) (0.025) Technological Capacity −0.101*** −0.008 −0.119 (0.018) (0.015) (0.095) Financial Development −0.002*** −0.002*** −0.003*** (0.000) (0.000) (0.001) Legal System −0.003 0.030*** −0.118*** (0.003) (0.002) (0.018) Inflation 0.005*** 0.005*** 0.059*** (0.002) (0.001) (0.010) Industry, Country & Year Dummies Yes Yes Yes Observations 6,186 2,223 6,277 Note: Serious Constraint indicates if a firm faces moderate to severe financial constraint. Credit Products Constraint indicates if a firm has no access to credit facilities. Loans Denied indicates if the firm had a rejected loan application. The average marginal effects from the Probit model are reported. Robust standard errors are reported in the parentheses. *p < 0.10, **p < 0.05, ***p < 0.01. Open in new tab 6.2 Alternative estimation methods Our baseline results are obtained by estimating an Ordered Probit model. We check if these results are robust, in terms of sign and statistical significance, to the choice of estimation methods. To do so, we re-estimate our model using Ordinary Least Square (OLS) regression with industry, country, and year fixed effects to control for confounding industry and country unobserved heterogeneity and macroeconomic shocks, and Iteratively Reweighted Least Squares (IRLS) regression to mitigate the influence of outlier observations.19 In Table 10, we report the OLS estimates in Column (1) and the IRLS estimates in Column (2). Both estimates show that domestic firms are less financially constraint (at a 1% level of significance) in industries where the proportion of foreign firms is larger. Therefore, our baseline results are not an artefact of the chosen estimation method. Table 10: Robustness Check #2: Alternative Estimation Methods (1) (2) OLS IRLS Dependent variable: Financial constraint (domestic firms) Foreign Firms Proportion −0.431** −1.021*** (0.196) (0.186) Female Owned 0.060 0.137*** (0.040) (0.045) Small (5–19 workers) 0.215*** 0.238*** (0.083) (0.092) Medium (20–99 workers) 0.079 0.081 (0.082) (0.089) Sole Proprietorship −0.205*** −0.148** (0.057) (0.062) Publicly Traded −0.675*** −0.734*** (0.249) (0.264) Private and Non-traded −0.236*** −0.270*** (0.061) (0.065) Financial Transparency −0.303*** −0.406*** (0.045) (0.049) Technological Capacity −0.307*** −0.348*** (0.057) (0.063) Financial Development −0.006*** (0.000) Legal System −0.034*** (0.010) Inflation 0.021*** (0.005) Industry, Country & Year Dummies Yes Yes Observations 6,451 6,284 (1) (2) OLS IRLS Dependent variable: Financial constraint (domestic firms) Foreign Firms Proportion −0.431** −1.021*** (0.196) (0.186) Female Owned 0.060 0.137*** (0.040) (0.045) Small (5–19 workers) 0.215*** 0.238*** (0.083) (0.092) Medium (20–99 workers) 0.079 0.081 (0.082) (0.089) Sole Proprietorship −0.205*** −0.148** (0.057) (0.062) Publicly Traded −0.675*** −0.734*** (0.249) (0.264) Private and Non-traded −0.236*** −0.270*** (0.061) (0.065) Financial Transparency −0.303*** −0.406*** (0.045) (0.049) Technological Capacity −0.307*** −0.348*** (0.057) (0.063) Financial Development −0.006*** (0.000) Legal System −0.034*** (0.010) Inflation 0.021*** (0.005) Industry, Country & Year Dummies Yes Yes Observations 6,451 6,284 Note: In Columns (1) and (2), estimates of the coefficients from Ordinary Least Squares (OLS) and Iterated Least Squares (IRLS) are reported. Robust standard errors are reported in the parentheses. *p < 0.10, **p < 0.05, ***p < 0.01. Open in new tab Table 10: Robustness Check #2: Alternative Estimation Methods (1) (2) OLS IRLS Dependent variable: Financial constraint (domestic firms) Foreign Firms Proportion −0.431** −1.021*** (0.196) (0.186) Female Owned 0.060 0.137*** (0.040) (0.045) Small (5–19 workers) 0.215*** 0.238*** (0.083) (0.092) Medium (20–99 workers) 0.079 0.081 (0.082) (0.089) Sole Proprietorship −0.205*** −0.148** (0.057) (0.062) Publicly Traded −0.675*** −0.734*** (0.249) (0.264) Private and Non-traded −0.236*** −0.270*** (0.061) (0.065) Financial Transparency −0.303*** −0.406*** (0.045) (0.049) Technological Capacity −0.307*** −0.348*** (0.057) (0.063) Financial Development −0.006*** (0.000) Legal System −0.034*** (0.010) Inflation 0.021*** (0.005) Industry, Country & Year Dummies Yes Yes Observations 6,451 6,284 (1) (2) OLS IRLS Dependent variable: Financial constraint (domestic firms) Foreign Firms Proportion −0.431** −1.021*** (0.196) (0.186) Female Owned 0.060 0.137*** (0.040) (0.045) Small (5–19 workers) 0.215*** 0.238*** (0.083) (0.092) Medium (20–99 workers) 0.079 0.081 (0.082) (0.089) Sole Proprietorship −0.205*** −0.148** (0.057) (0.062) Publicly Traded −0.675*** −0.734*** (0.249) (0.264) Private and Non-traded −0.236*** −0.270*** (0.061) (0.065) Financial Transparency −0.303*** −0.406*** (0.045) (0.049) Technological Capacity −0.307*** −0.348*** (0.057) (0.063) Financial Development −0.006*** (0.000) Legal System −0.034*** (0.010) Inflation 0.021*** (0.005) Industry, Country & Year Dummies Yes Yes Observations 6,451 6,284 Note: In Columns (1) and (2), estimates of the coefficients from Ordinary Least Squares (OLS) and Iterated Least Squares (IRLS) are reported. Robust standard errors are reported in the parentheses. *p < 0.10, **p < 0.05, ***p < 0.01. Open in new tab 6.3 Alternative definition of foreign ownership Up to this point, foreign-owned firms have been defined as firms in the host country where foreigners hold at least 10% percent of their equity.20 We show that our conclusions are robust if we adopt a different definition of foreign ownership based on a different equity share in a local firm that is foreign held. As a robustness check, we deliberately choose a more drastic alternative definition of foreign ownership by considering a firm to be foreign owned if 50% or more of its equity is foreign held. We find that our main conclusions are not affected even with this alternative definition of foreign ownership. This is shown, for example, in Table 11, where the proportion of foreign-owned firms in the industry, and the foreign share of equity and employment are all negatively and statistically significant for domestic firms’ financial constraint. Therefore, our baseline results do not depend on how foreign ownership of firms are defined. Table 11: Robustness Check #3: Alternative Definition of Foreign Ownership (1) (2) (3) Dependent variable: Financial constraint (domestic firms) Foreign Firms Proportion −0.811*** (0.168) Foreign Equity Share −0.220*** (0.067) Foreign Workers Share −0.610*** (0.106) Female Owned 0.109*** 0.125*** 0.163*** (0.034) (0.035) (0.042) Small (5–19 workers) 0.178** 0.302*** 0.305*** (0.069) (0.068) (0.085) Medium (20–99 workers) 0.062 0.145** 0.168** (0.067) (0.066) (0.082) Sole Proprietorship −0.100** −0.086* −0.131** (0.046) (0.047) (0.057) Publicly Traded −0.585*** −0.575** −0.860*** (0.221) (0.241) (0.260) Private or Non-traded −0.168*** −0.198*** −0.235*** (0.049) (0.050) (0.060) Financial Transparency −0.285*** −0.281*** −0.282*** (0.037) (0.038) (0.044) Technological Capacity −0.261*** −0.161*** −0.246*** (0.049) (0.039) (0.060) Financial Development −0.005*** −0.005*** −0.004*** (0.000) (0.001) (0.001) Legal System −0.016** −0.007 0.007 (0.007) (0.008) (0.009) Inflation 0.018*** 0.015*** 0.008 (0.004) (0.005) (0.005) Industry, Country & Year Dummies Yes Yes Yes Observations 5,377 5,168 5,409 (1) (2) (3) Dependent variable: Financial constraint (domestic firms) Foreign Firms Proportion −0.811*** (0.168) Foreign Equity Share −0.220*** (0.067) Foreign Workers Share −0.610*** (0.106) Female Owned 0.109*** 0.125*** 0.163*** (0.034) (0.035) (0.042) Small (5–19 workers) 0.178** 0.302*** 0.305*** (0.069) (0.068) (0.085) Medium (20–99 workers) 0.062 0.145** 0.168** (0.067) (0.066) (0.082) Sole Proprietorship −0.100** −0.086* −0.131** (0.046) (0.047) (0.057) Publicly Traded −0.585*** −0.575** −0.860*** (0.221) (0.241) (0.260) Private or Non-traded −0.168*** −0.198*** −0.235*** (0.049) (0.050) (0.060) Financial Transparency −0.285*** −0.281*** −0.282*** (0.037) (0.038) (0.044) Technological Capacity −0.261*** −0.161*** −0.246*** (0.049) (0.039) (0.060) Financial Development −0.005*** −0.005*** −0.004*** (0.000) (0.001) (0.001) Legal System −0.016** −0.007 0.007 (0.007) (0.008) (0.009) Inflation 0.018*** 0.015*** 0.008 (0.004) (0.005) (0.005) Industry, Country & Year Dummies Yes Yes Yes Observations 5,377 5,168 5,409 Note: A firm is defined as foreign owned here if at least 50% of its equity is foreign held. Financial Constraint is an ordinal variable (from 0 to 4) that indicates how financially constrained a firm is. Estimates of the coefficients from the Ordered Probit model are reported here. The constant is suppressed. Robust standard errors are reported in the parentheses. *p < 0.10, **p < 0.05, ***p < 0.01. Open in new tab Table 11: Robustness Check #3: Alternative Definition of Foreign Ownership (1) (2) (3) Dependent variable: Financial constraint (domestic firms) Foreign Firms Proportion −0.811*** (0.168) Foreign Equity Share −0.220*** (0.067) Foreign Workers Share −0.610*** (0.106) Female Owned 0.109*** 0.125*** 0.163*** (0.034) (0.035) (0.042) Small (5–19 workers) 0.178** 0.302*** 0.305*** (0.069) (0.068) (0.085) Medium (20–99 workers) 0.062 0.145** 0.168** (0.067) (0.066) (0.082) Sole Proprietorship −0.100** −0.086* −0.131** (0.046) (0.047) (0.057) Publicly Traded −0.585*** −0.575** −0.860*** (0.221) (0.241) (0.260) Private or Non-traded −0.168*** −0.198*** −0.235*** (0.049) (0.050) (0.060) Financial Transparency −0.285*** −0.281*** −0.282*** (0.037) (0.038) (0.044) Technological Capacity −0.261*** −0.161*** −0.246*** (0.049) (0.039) (0.060) Financial Development −0.005*** −0.005*** −0.004*** (0.000) (0.001) (0.001) Legal System −0.016** −0.007 0.007 (0.007) (0.008) (0.009) Inflation 0.018*** 0.015*** 0.008 (0.004) (0.005) (0.005) Industry, Country & Year Dummies Yes Yes Yes Observations 5,377 5,168 5,409 (1) (2) (3) Dependent variable: Financial constraint (domestic firms) Foreign Firms Proportion −0.811*** (0.168) Foreign Equity Share −0.220*** (0.067) Foreign Workers Share −0.610*** (0.106) Female Owned 0.109*** 0.125*** 0.163*** (0.034) (0.035) (0.042) Small (5–19 workers) 0.178** 0.302*** 0.305*** (0.069) (0.068) (0.085) Medium (20–99 workers) 0.062 0.145** 0.168** (0.067) (0.066) (0.082) Sole Proprietorship −0.100** −0.086* −0.131** (0.046) (0.047) (0.057) Publicly Traded −0.585*** −0.575** −0.860*** (0.221) (0.241) (0.260) Private or Non-traded −0.168*** −0.198*** −0.235*** (0.049) (0.050) (0.060) Financial Transparency −0.285*** −0.281*** −0.282*** (0.037) (0.038) (0.044) Technological Capacity −0.261*** −0.161*** −0.246*** (0.049) (0.039) (0.060) Financial Development −0.005*** −0.005*** −0.004*** (0.000) (0.001) (0.001) Legal System −0.016** −0.007 0.007 (0.007) (0.008) (0.009) Inflation 0.018*** 0.015*** 0.008 (0.004) (0.005) (0.005) Industry, Country & Year Dummies Yes Yes Yes Observations 5,377 5,168 5,409 Note: A firm is defined as foreign owned here if at least 50% of its equity is foreign held. Financial Constraint is an ordinal variable (from 0 to 4) that indicates how financially constrained a firm is. Estimates of the coefficients from the Ordered Probit model are reported here. The constant is suppressed. Robust standard errors are reported in the parentheses. *p < 0.10, **p < 0.05, ***p < 0.01. Open in new tab 6.4 Omitting the Largest Domestic Firms In Section 4.1, we have discussed why it is unlikely for reverse causality to occur. This is because given that domestic firms are usually small, it will be unlikely for them to have an impact on the composition of foreign firms in the industry, which is how foreign firm presence is measured. What about the large domestic firms? Could the statistical significance of foreign firm presence be driven by reverse causality stemming from these firms? In this robustness check, we re-estimate Eq. (2) without the top 5% and 10% largest domestic firms and report the new results in Table 12 and 13 respectively. Even without the large domestic firms, we find that foreign firm presence is still statistically significant at the 1% level. Therefore, there is no evidence that these firms are driving the statistical significance of foreign firm presence in our baseline regressions. Table 12: Robustness Check #4: Excluding the Top 5% Largest Domestic Firms (1) (2) (3) Dependent variable: Financial constraint (domestic firms) Foreign Firms Proportion −0.592*** (0.155) Foreign Equity Share −0.593*** (0.154) Foreign Workers Share −0.551*** (0.109) Female Owned 0.099*** 0.099*** 0.156*** (0.033) (0.033) (0.042) Small (5–19 workers) 0.172** 0.172** 0.265*** (0.079) (0.079) (0.100) Medium (20–99 workers) 0.046 0.046 0.118 (0.077) (0.077) (0.098) Sole Proprietorship −0.121*** −0.121*** −0.123** (0.044) (0.044) (0.057) Publicly Traded −0.376 −0.376 −0.736*** (0.243) (0.243) (0.276) Private or Non-traded −0.196*** −0.196*** −0.224*** (0.048) (0.048) (0.061) Financial Transparency −0.231*** −0.231*** −0.267*** (0.035) (0.035) (0.045) Technological Capacity −0.248*** −0.248*** −0.235*** (0.048) (0.048) (0.062) Financial Development −0.004*** −0.004*** −0.004*** (0.001) (0.001) (0.001) Legal System 0.014* 0.014* 0.034*** (0.008) (0.008) (0.010) Inflation −0.010** −0.010** −0.008 (0.005) (0.005) (0.006) Industry, Country & Year Dummies Yes Yes Yes Observations 5,914 5,914 5,263 (1) (2) (3) Dependent variable: Financial constraint (domestic firms) Foreign Firms Proportion −0.592*** (0.155) Foreign Equity Share −0.593*** (0.154) Foreign Workers Share −0.551*** (0.109) Female Owned 0.099*** 0.099*** 0.156*** (0.033) (0.033) (0.042) Small (5–19 workers) 0.172** 0.172** 0.265*** (0.079) (0.079) (0.100) Medium (20–99 workers) 0.046 0.046 0.118 (0.077) (0.077) (0.098) Sole Proprietorship −0.121*** −0.121*** −0.123** (0.044) (0.044) (0.057) Publicly Traded −0.376 −0.376 −0.736*** (0.243) (0.243) (0.276) Private or Non-traded −0.196*** −0.196*** −0.224*** (0.048) (0.048) (0.061) Financial Transparency −0.231*** −0.231*** −0.267*** (0.035) (0.035) (0.045) Technological Capacity −0.248*** −0.248*** −0.235*** (0.048) (0.048) (0.062) Financial Development −0.004*** −0.004*** −0.004*** (0.001) (0.001) (0.001) Legal System 0.014* 0.014* 0.034*** (0.008) (0.008) (0.010) Inflation −0.010** −0.010** −0.008 (0.005) (0.005) (0.006) Industry, Country & Year Dummies Yes Yes Yes Observations 5,914 5,914 5,263 Note: Financial Constraint is an ordinal variable (from 0 to 4) that indicates how financially constrained a firm is. Estimates of the coefficients from the Ordered Probit model are reported here. The constant is suppressed. Robust standard errors are reported in the parentheses. *p < 0.10, **p < 0.05, ***p < 0.01. Open in new tab Table 12: Robustness Check #4: Excluding the Top 5% Largest Domestic Firms (1) (2) (3) Dependent variable: Financial constraint (domestic firms) Foreign Firms Proportion −0.592*** (0.155) Foreign Equity Share −0.593*** (0.154) Foreign Workers Share −0.551*** (0.109) Female Owned 0.099*** 0.099*** 0.156*** (0.033) (0.033) (0.042) Small (5–19 workers) 0.172** 0.172** 0.265*** (0.079) (0.079) (0.100) Medium (20–99 workers) 0.046 0.046 0.118 (0.077) (0.077) (0.098) Sole Proprietorship −0.121*** −0.121*** −0.123** (0.044) (0.044) (0.057) Publicly Traded −0.376 −0.376 −0.736*** (0.243) (0.243) (0.276) Private or Non-traded −0.196*** −0.196*** −0.224*** (0.048) (0.048) (0.061) Financial Transparency −0.231*** −0.231*** −0.267*** (0.035) (0.035) (0.045) Technological Capacity −0.248*** −0.248*** −0.235*** (0.048) (0.048) (0.062) Financial Development −0.004*** −0.004*** −0.004*** (0.001) (0.001) (0.001) Legal System 0.014* 0.014* 0.034*** (0.008) (0.008) (0.010) Inflation −0.010** −0.010** −0.008 (0.005) (0.005) (0.006) Industry, Country & Year Dummies Yes Yes Yes Observations 5,914 5,914 5,263 (1) (2) (3) Dependent variable: Financial constraint (domestic firms) Foreign Firms Proportion −0.592*** (0.155) Foreign Equity Share −0.593*** (0.154) Foreign Workers Share −0.551*** (0.109) Female Owned 0.099*** 0.099*** 0.156*** (0.033) (0.033) (0.042) Small (5–19 workers) 0.172** 0.172** 0.265*** (0.079) (0.079) (0.100) Medium (20–99 workers) 0.046 0.046 0.118 (0.077) (0.077) (0.098) Sole Proprietorship −0.121*** −0.121*** −0.123** (0.044) (0.044) (0.057) Publicly Traded −0.376 −0.376 −0.736*** (0.243) (0.243) (0.276) Private or Non-traded −0.196*** −0.196*** −0.224*** (0.048) (0.048) (0.061) Financial Transparency −0.231*** −0.231*** −0.267*** (0.035) (0.035) (0.045) Technological Capacity −0.248*** −0.248*** −0.235*** (0.048) (0.048) (0.062) Financial Development −0.004*** −0.004*** −0.004*** (0.001) (0.001) (0.001) Legal System 0.014* 0.014* 0.034*** (0.008) (0.008) (0.010) Inflation −0.010** −0.010** −0.008 (0.005) (0.005) (0.006) Industry, Country & Year Dummies Yes Yes Yes Observations 5,914 5,914 5,263 Note: Financial Constraint is an ordinal variable (from 0 to 4) that indicates how financially constrained a firm is. Estimates of the coefficients from the Ordered Probit model are reported here. The constant is suppressed. Robust standard errors are reported in the parentheses. *p < 0.10, **p < 0.05, ***p < 0.01. Open in new tab Table 13: Robustness Check #5: Excluding the Top 10% Largest Domestic Firms (1) (2) (3) Dependent variable: Financial constraint (domestic firms) Foreign Firms Proportion −0.616*** (0.157) Foreign Equity Share −0.615*** (0.157) Foreign Workers Share −0.601*** (0.111) Female Owned 0.100*** 0.100*** 0.160*** (0.034) (0.034) (0.043) Small (5–19 workers) 0.279 0.279 0.315 (0.175) (0.175) (0.226) Medium (20–99 workers) 0.158 0.158 0.173 (0.175) (0.175) (0.225) Sole Proprietorship −0.124*** −0.124*** −0.120** (0.045) (0.045) (0.058) Publicly Traded −0.422 −0.422 −0.656** (0.257) (0.257) (0.283) Private or Non-traded −0.200*** −0.200*** −0.224*** (0.049) (0.049) (0.062) Financial Transparency −0.232*** −0.232*** −0.266*** (0.036) (0.036) (0.045) Technological Capacity −0.245*** −0.245*** −0.229*** (0.050) (0.050) (0.065) Financial Development −0.004*** −0.004*** −0.004*** (0.001) (0.001) (0.001) Legal System 0.013 0.013 0.033*** (0.008) (0.008) (0.011) Inflation −0.009* −0.009* −0.008 (0.005) (0.005) (0.006) Industry, Country & Year dummies Yes Yes Yes Observations 5,699 5,699 5,060 (1) (2) (3) Dependent variable: Financial constraint (domestic firms) Foreign Firms Proportion −0.616*** (0.157) Foreign Equity Share −0.615*** (0.157) Foreign Workers Share −0.601*** (0.111) Female Owned 0.100*** 0.100*** 0.160*** (0.034) (0.034) (0.043) Small (5–19 workers) 0.279 0.279 0.315 (0.175) (0.175) (0.226) Medium (20–99 workers) 0.158 0.158 0.173 (0.175) (0.175) (0.225) Sole Proprietorship −0.124*** −0.124*** −0.120** (0.045) (0.045) (0.058) Publicly Traded −0.422 −0.422 −0.656** (0.257) (0.257) (0.283) Private or Non-traded −0.200*** −0.200*** −0.224*** (0.049) (0.049) (0.062) Financial Transparency −0.232*** −0.232*** −0.266*** (0.036) (0.036) (0.045) Technological Capacity −0.245*** −0.245*** −0.229*** (0.050) (0.050) (0.065) Financial Development −0.004*** −0.004*** −0.004*** (0.001) (0.001) (0.001) Legal System 0.013 0.013 0.033*** (0.008) (0.008) (0.011) Inflation −0.009* −0.009* −0.008 (0.005) (0.005) (0.006) Industry, Country & Year dummies Yes Yes Yes Observations 5,699 5,699 5,060 Note: Financial Constraint is an ordinal variable (from 0 to 4) that indicates how financially constrained a firm is. Estimates of the coefficients from the Ordered Probit model are reported here. The constant is suppressed. Robust standard errors are reported in the parentheses. *p < 0.10, **p < 0.05, ***p < 0.01. Open in new tab Table 13: Robustness Check #5: Excluding the Top 10% Largest Domestic Firms (1) (2) (3) Dependent variable: Financial constraint (domestic firms) Foreign Firms Proportion −0.616*** (0.157) Foreign Equity Share −0.615*** (0.157) Foreign Workers Share −0.601*** (0.111) Female Owned 0.100*** 0.100*** 0.160*** (0.034) (0.034) (0.043) Small (5–19 workers) 0.279 0.279 0.315 (0.175) (0.175) (0.226) Medium (20–99 workers) 0.158 0.158 0.173 (0.175) (0.175) (0.225) Sole Proprietorship −0.124*** −0.124*** −0.120** (0.045) (0.045) (0.058) Publicly Traded −0.422 −0.422 −0.656** (0.257) (0.257) (0.283) Private or Non-traded −0.200*** −0.200*** −0.224*** (0.049) (0.049) (0.062) Financial Transparency −0.232*** −0.232*** −0.266*** (0.036) (0.036) (0.045) Technological Capacity −0.245*** −0.245*** −0.229*** (0.050) (0.050) (0.065) Financial Development −0.004*** −0.004*** −0.004*** (0.001) (0.001) (0.001) Legal System 0.013 0.013 0.033*** (0.008) (0.008) (0.011) Inflation −0.009* −0.009* −0.008 (0.005) (0.005) (0.006) Industry, Country & Year dummies Yes Yes Yes Observations 5,699 5,699 5,060 (1) (2) (3) Dependent variable: Financial constraint (domestic firms) Foreign Firms Proportion −0.616*** (0.157) Foreign Equity Share −0.615*** (0.157) Foreign Workers Share −0.601*** (0.111) Female Owned 0.100*** 0.100*** 0.160*** (0.034) (0.034) (0.043) Small (5–19 workers) 0.279 0.279 0.315 (0.175) (0.175) (0.226) Medium (20–99 workers) 0.158 0.158 0.173 (0.175) (0.175) (0.225) Sole Proprietorship −0.124*** −0.124*** −0.120** (0.045) (0.045) (0.058) Publicly Traded −0.422 −0.422 −0.656** (0.257) (0.257) (0.283) Private or Non-traded −0.200*** −0.200*** −0.224*** (0.049) (0.049) (0.062) Financial Transparency −0.232*** −0.232*** −0.266*** (0.036) (0.036) (0.045) Technological Capacity −0.245*** −0.245*** −0.229*** (0.050) (0.050) (0.065) Financial Development −0.004*** −0.004*** −0.004*** (0.001) (0.001) (0.001) Legal System 0.013 0.013 0.033*** (0.008) (0.008) (0.011) Inflation −0.009* −0.009* −0.008 (0.005) (0.005) (0.006) Industry, Country & Year dummies Yes Yes Yes Observations 5,699 5,699 5,060 Note: Financial Constraint is an ordinal variable (from 0 to 4) that indicates how financially constrained a firm is. Estimates of the coefficients from the Ordered Probit model are reported here. The constant is suppressed. Robust standard errors are reported in the parentheses. *p < 0.10, **p < 0.05, ***p < 0.01. Open in new tab 7. Conclusion Because their underdeveloped financial sector, policymakers in the SSAs have encouraged the foreign ownership of firms to help, among others, improve the access to finance by domestic firms. However, there is no empirical evidence that a larger foreign presence leads to less financially constrained domestic firms. Moreover, the association between foreign firm presence and domestic firms’ financial constraint is á priori ambiguous. To study this issue, we employ cross-country firm-level data that spans across 36 SSAs. We find that in industries with a larger foreign firm presence, domestic firms tend to be less financially constrained. The direction and the statistical significance of this effect is robust to the way financial constraint of domestic firms and foreign firm presence are measured, to the use of alternative estimation methods and alternative definitions of foreign ownership, among others. Because of the richness of the WBES surveys, we are able to explore further into why the presence of foreign-owned firms may ease the financial constraints of domestic firms. we find that foreign-owned firms are not only less financially constrained, they are also less likely to borrow from banks. Moreover, in industries where foreign firm presence is larger, domestic firms tend to be more successful in securing bank credit. Therefore, there is evidence that foreign firm presence reduces the competition for bank credit, and this helps the domestic firms to improve their access to finance and ease their financial constraints. Our paper has policy relevance, in that it offers new evidence to show that foreign firm presence may address the most commonly voiced concern in operating a business in the SSAs – the lack of finance. Because financial development is an extremely important determinant of growth, it would be useful to follow up on whether the increase in foreign firm presence in the SSAs may also lead to higher levels of growth and socio-economic development. Footnotes 1 For example, the share of two largest banks’ in Burundi account 45% of the total bank asset (Nkurunziza et al., 2012). 2 In the SSAs, the average ratio of private credit to GDP is only 24%. This is dwarfed by the ratio of 77% for all other developing economies, and 172% for high income economies. 3 Some studies looks at the mechanisms through which foreign direct investment affect financial constraints of domestic firms (see, for example, Harrison and McMillan, 2003; Harrison et al., 2004; Héricourt and Poncet, 2009). 4 For example, foreign direct investment to the SSAs has grown from $8 billion in 2000 to $18 billion in 2004, $36 billion in 2006 and $50 billion in 2012. 5 Empirical evidence on credit constraints in the SSAs comes mainly from within-country studies, which are difficult to generalise. See, for example (Harrison and McMillan, 2003) for Cote d’Ivoire, Lashitew (2017) for Ethiopia. 6 For example, the share of two largest banks’ account 45% of the total bank asset in Burundi (Nkurunziza et al., 2012). 7 For example, Mali and Mozambique encourage foreign firm ownership by improving business climate, such privatisation, implementing new laws, and promoting accession to international agreement related to direct foreign investment (Morisset, 2001). Botswana and Mauritius attract foreign firms by improving property rights and reducing restrictive compliance requirements (Basu and Srinivasan, 2002). 8 For example, based on Ivory Coast’s firm-level data, Harrison and McMillan (2003) find that domestic firms could be credit constrained with FDI as foreign enterprises may crowd out domestic firms in the local credit markets. By contrast, Harrison et al. (2004) find that FDI inflow in 34 European countries is associated with reduced firm-level financial constraints. 9 A direct linkage between foreign-owned firms and domestic input suppliers, or foreign firms input providers and domestic firms, can improve the reputation and creditworthiness of domestic firms (Javorcik and Spatareanu, 2009; Newman et al., 2015). 10 Our sample of firms obtained from Angola, Botswana, Burkina Faso, Burundi, Cameroon, Cape Verde, Chad, Congo, DRC, Eritrea, Gabon, Gambia, Ghana, Guinea, Guinea Bissau, Ivory Coast, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritanian, Mauritius, Mozambique, Namibia, Niger, Rwanda, Sierra Leone, Senegal, South Africa, Swaziland, Tanzania, Togo, Uganda and Zambia. 11 Our choice of explanatory variables is based largely on the existing literature related to firms’ access to finance (see, for example, Beck et al., 2006, 2008; Asiedu et al., 2013; Aterido et al., 2013; Hansen and Rand, 2014a, b). 12 If the proportions of domestic and foreign-owned firms facing serious financial constraints are the same, the data points will lie on the 45 degree line. 13 As it turns out, we find that foreign firm presence is statistically significant for domestic firms’ financial constraint whether industry or country fixed effects are controlled for. This suggests that foreign firm presence have an impact on domestic firms’ financial constraint beyond the influence of industry or country fixed effects. 14 Hansen and Rand (2014a) show how three different measures of credit constraints lead to three different estimates of the effects of gender on firms’ credit situation. 15 This result is consistent with Asiedu et al. (2013) for the case of manufacturing firms in the SSAs. 16 Just to emphasise, we consider loan application than the actual amount of loan received. Clearly, the quantum of the loan may depend not only on how needy the firm is, it also depends on the restrictions placed by banks on how much it can borrow. 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Google Scholar Crossref Search ADS WorldCat Appendix Appendix A: Access to Finance of Firms in the SSAs versus the OECD Table A1: Access to Finance of Firms in the SSAs versus the OECD Sub-Saharan Africa OECD Firms with a bank loan/line of credit (%) 23.5 49.1 Firms using banks to finance investment (%) 17.6 31.5 Investment financed by banks (%) 11.2 18.9 Firms using bank to finance working capital (%) 22.1 68.6 Working capital financed by banks (%) 8.6 40.6 Working capital financed by supplier credit (%) 11.2 14.4 Loan requiring collateral (%) 82.26 72.6 Firms identifying finance as a major constraint (%) 42.6 15.9 Sub-Saharan Africa OECD Firms with a bank loan/line of credit (%) 23.5 49.1 Firms using banks to finance investment (%) 17.6 31.5 Investment financed by banks (%) 11.2 18.9 Firms using bank to finance working capital (%) 22.1 68.6 Working capital financed by banks (%) 8.6 40.6 Working capital financed by supplier credit (%) 11.2 14.4 Loan requiring collateral (%) 82.26 72.6 Firms identifying finance as a major constraint (%) 42.6 15.9 Note: The number in the table are averages across firms calculated from WBES (2015) webpage. The number of SSAs and OECD countries used for this computation are 36 and 16 countries respectively. Open in new tab Table A1: Access to Finance of Firms in the SSAs versus the OECD Sub-Saharan Africa OECD Firms with a bank loan/line of credit (%) 23.5 49.1 Firms using banks to finance investment (%) 17.6 31.5 Investment financed by banks (%) 11.2 18.9 Firms using bank to finance working capital (%) 22.1 68.6 Working capital financed by banks (%) 8.6 40.6 Working capital financed by supplier credit (%) 11.2 14.4 Loan requiring collateral (%) 82.26 72.6 Firms identifying finance as a major constraint (%) 42.6 15.9 Sub-Saharan Africa OECD Firms with a bank loan/line of credit (%) 23.5 49.1 Firms using banks to finance investment (%) 17.6 31.5 Investment financed by banks (%) 11.2 18.9 Firms using bank to finance working capital (%) 22.1 68.6 Working capital financed by banks (%) 8.6 40.6 Working capital financed by supplier credit (%) 11.2 14.4 Loan requiring collateral (%) 82.26 72.6 Firms identifying finance as a major constraint (%) 42.6 15.9 Note: The number in the table are averages across firms calculated from WBES (2015) webpage. The number of SSAs and OECD countries used for this computation are 36 and 16 countries respectively. Open in new tab Appendix B: Definition of Variables Table B1: Definition of Variables Variable Definition Dependent variables: measures of financial constraint Financial Constraint Ordinal response by the firm from 0 (no financial constraint) to 4 (severe financial constraint) Serious Constraint =1 if the firm reports having moderate to severe financial constraint Credit Products Constraint =1 if the firm has no access to overdraft, line of credit or bank loan Loans Denied =1 if the firm has applied for but denied formal credit Measures of foreign ownership and foreign firm presence Foreign =1 if the firm has at least 10% of its equity held by foreigners Foreign Firms Proportion Ratio of number of foreign-owned firms to total firms Foreign Equity Share Ratio of equity holdings by foreign-owned firms over total firm equity Foreign Workers Share Ratio of foreign firms’ employee to total employees Firm-level variables Female Owned =1 if at least one principal owner is female Small =1 if the firm has 5–19 employees Medium =1 if the firm has 20–99 employees Sole Proprietorship =1 if the firm is owned by 1 person Publicly Traded =1 for shareholding firm with share trade in the stock market Private or Non-traded =1 for shareholding firm with non-share trade in the stock market Technological Capacity =1 if the firm has a website Financial Transparency =1 if the firm’s annual financial statements were audited Country-level variables Inflation Inflation rate [WDI] Financial Development Ratio of private credit to GDP [WDI] Legal System Protection of property rights index, 0 (weakest) to 10 (strongest) [WDI] Variable Definition Dependent variables: measures of financial constraint Financial Constraint Ordinal response by the firm from 0 (no financial constraint) to 4 (severe financial constraint) Serious Constraint =1 if the firm reports having moderate to severe financial constraint Credit Products Constraint =1 if the firm has no access to overdraft, line of credit or bank loan Loans Denied =1 if the firm has applied for but denied formal credit Measures of foreign ownership and foreign firm presence Foreign =1 if the firm has at least 10% of its equity held by foreigners Foreign Firms Proportion Ratio of number of foreign-owned firms to total firms Foreign Equity Share Ratio of equity holdings by foreign-owned firms over total firm equity Foreign Workers Share Ratio of foreign firms’ employee to total employees Firm-level variables Female Owned =1 if at least one principal owner is female Small =1 if the firm has 5–19 employees Medium =1 if the firm has 20–99 employees Sole Proprietorship =1 if the firm is owned by 1 person Publicly Traded =1 for shareholding firm with share trade in the stock market Private or Non-traded =1 for shareholding firm with non-share trade in the stock market Technological Capacity =1 if the firm has a website Financial Transparency =1 if the firm’s annual financial statements were audited Country-level variables Inflation Inflation rate [WDI] Financial Development Ratio of private credit to GDP [WDI] Legal System Protection of property rights index, 0 (weakest) to 10 (strongest) [WDI] Note: Unless specified in brackets, all the variables are obtained from the World Bank Enterprise Survey. WDI is short for World Development Indicators. AREAER is short for Annual Report on Exchange Arrangements and Exchange Restrictions (from the IMF). Open in new tab Table B1: Definition of Variables Variable Definition Dependent variables: measures of financial constraint Financial Constraint Ordinal response by the firm from 0 (no financial constraint) to 4 (severe financial constraint) Serious Constraint =1 if the firm reports having moderate to severe financial constraint Credit Products Constraint =1 if the firm has no access to overdraft, line of credit or bank loan Loans Denied =1 if the firm has applied for but denied formal credit Measures of foreign ownership and foreign firm presence Foreign =1 if the firm has at least 10% of its equity held by foreigners Foreign Firms Proportion Ratio of number of foreign-owned firms to total firms Foreign Equity Share Ratio of equity holdings by foreign-owned firms over total firm equity Foreign Workers Share Ratio of foreign firms’ employee to total employees Firm-level variables Female Owned =1 if at least one principal owner is female Small =1 if the firm has 5–19 employees Medium =1 if the firm has 20–99 employees Sole Proprietorship =1 if the firm is owned by 1 person Publicly Traded =1 for shareholding firm with share trade in the stock market Private or Non-traded =1 for shareholding firm with non-share trade in the stock market Technological Capacity =1 if the firm has a website Financial Transparency =1 if the firm’s annual financial statements were audited Country-level variables Inflation Inflation rate [WDI] Financial Development Ratio of private credit to GDP [WDI] Legal System Protection of property rights index, 0 (weakest) to 10 (strongest) [WDI] Variable Definition Dependent variables: measures of financial constraint Financial Constraint Ordinal response by the firm from 0 (no financial constraint) to 4 (severe financial constraint) Serious Constraint =1 if the firm reports having moderate to severe financial constraint Credit Products Constraint =1 if the firm has no access to overdraft, line of credit or bank loan Loans Denied =1 if the firm has applied for but denied formal credit Measures of foreign ownership and foreign firm presence Foreign =1 if the firm has at least 10% of its equity held by foreigners Foreign Firms Proportion Ratio of number of foreign-owned firms to total firms Foreign Equity Share Ratio of equity holdings by foreign-owned firms over total firm equity Foreign Workers Share Ratio of foreign firms’ employee to total employees Firm-level variables Female Owned =1 if at least one principal owner is female Small =1 if the firm has 5–19 employees Medium =1 if the firm has 20–99 employees Sole Proprietorship =1 if the firm is owned by 1 person Publicly Traded =1 for shareholding firm with share trade in the stock market Private or Non-traded =1 for shareholding firm with non-share trade in the stock market Technological Capacity =1 if the firm has a website Financial Transparency =1 if the firm’s annual financial statements were audited Country-level variables Inflation Inflation rate [WDI] Financial Development Ratio of private credit to GDP [WDI] Legal System Protection of property rights index, 0 (weakest) to 10 (strongest) [WDI] Note: Unless specified in brackets, all the variables are obtained from the World Bank Enterprise Survey. WDI is short for World Development Indicators. AREAER is short for Annual Report on Exchange Arrangements and Exchange Restrictions (from the IMF). Open in new tab Appendix C: Descriptive Statistics Table C1: Descriptive Statistics Mean Std. dev. Minimum Maximum Financial Constraint 2.00 1.51 0 4 Foreign Ownership 0.19 0.39 0 1 Foreign Firms Proportion 0.18 0.17 0 1 Foreign Equity Share 0.19 0.17 0 1 Foreign Workers Share 0.33 0.24 0 1 Female Ownership 0.29 0.45 0 1 Small (0–19 Workers) 0.63 0.48 0 1 Medium (20–99 Workers) 0.26 0.44 0 1 Large (over 99 Workers) 0.10 0.30 0 1 Sole Proprietorship 0.5 0.5 0 1 Public Traded 0.02 0.15 0 1 Private or Non-traded 0.31 0.46 0 1 Technological Capacity 0.2 0.40 0 1 Financial Transparency 0.47 0.50 0 1 Ination 8.2 4.2 2.05 19.57 Financial Development 38 48.7 5.08 182.62 Legal system 5.25 2.60 2.00 10.00 Mean Std. dev. Minimum Maximum Financial Constraint 2.00 1.51 0 4 Foreign Ownership 0.19 0.39 0 1 Foreign Firms Proportion 0.18 0.17 0 1 Foreign Equity Share 0.19 0.17 0 1 Foreign Workers Share 0.33 0.24 0 1 Female Ownership 0.29 0.45 0 1 Small (0–19 Workers) 0.63 0.48 0 1 Medium (20–99 Workers) 0.26 0.44 0 1 Large (over 99 Workers) 0.10 0.30 0 1 Sole Proprietorship 0.5 0.5 0 1 Public Traded 0.02 0.15 0 1 Private or Non-traded 0.31 0.46 0 1 Technological Capacity 0.2 0.40 0 1 Financial Transparency 0.47 0.50 0 1 Ination 8.2 4.2 2.05 19.57 Financial Development 38 48.7 5.08 182.62 Legal system 5.25 2.60 2.00 10.00 Open in new tab Table C1: Descriptive Statistics Mean Std. dev. Minimum Maximum Financial Constraint 2.00 1.51 0 4 Foreign Ownership 0.19 0.39 0 1 Foreign Firms Proportion 0.18 0.17 0 1 Foreign Equity Share 0.19 0.17 0 1 Foreign Workers Share 0.33 0.24 0 1 Female Ownership 0.29 0.45 0 1 Small (0–19 Workers) 0.63 0.48 0 1 Medium (20–99 Workers) 0.26 0.44 0 1 Large (over 99 Workers) 0.10 0.30 0 1 Sole Proprietorship 0.5 0.5 0 1 Public Traded 0.02 0.15 0 1 Private or Non-traded 0.31 0.46 0 1 Technological Capacity 0.2 0.40 0 1 Financial Transparency 0.47 0.50 0 1 Ination 8.2 4.2 2.05 19.57 Financial Development 38 48.7 5.08 182.62 Legal system 5.25 2.60 2.00 10.00 Mean Std. dev. Minimum Maximum Financial Constraint 2.00 1.51 0 4 Foreign Ownership 0.19 0.39 0 1 Foreign Firms Proportion 0.18 0.17 0 1 Foreign Equity Share 0.19 0.17 0 1 Foreign Workers Share 0.33 0.24 0 1 Female Ownership 0.29 0.45 0 1 Small (0–19 Workers) 0.63 0.48 0 1 Medium (20–99 Workers) 0.26 0.44 0 1 Large (over 99 Workers) 0.10 0.30 0 1 Sole Proprietorship 0.5 0.5 0 1 Public Traded 0.02 0.15 0 1 Private or Non-traded 0.31 0.46 0 1 Technological Capacity 0.2 0.40 0 1 Financial Transparency 0.47 0.50 0 1 Ination 8.2 4.2 2.05 19.57 Financial Development 38 48.7 5.08 182.62 Legal system 5.25 2.60 2.00 10.00 Open in new tab Author notes We are most grateful to the Editor Professor Francis Teal and two anonymous referees for their insightful comments and suggestions. © The Author(s) 2019. Published by Oxford University Press on behalf of the Centre for the Study of African Economies, all rights reserved. For Permissions, please email: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of African Economies Oxford University Press

Does the Presence of Foreign Firms Reduce Domestic Firms’ Financial Constraints in Sub-Saharan Africa?

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Oxford University Press
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© The Author(s) 2019. Published by Oxford University Press on behalf of the Centre for the Study of African Economies, all rights reserved. For Permissions, please email: journals.permissions@oup.com
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0963-8024
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1464-3723
DOI
10.1093/jae/ejz001
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Abstract

Abstract Firms in the SSAs (sub-Saharan African countries for short) face severe financial constraints. Because financial markets in the SSAs are underdeveloped, policymakers have sought after the establishment of foreign-owned firms in their countries to help, among others, alleviate the financial constraints faced by domestic firms. However, there is no empirical evidence that speaks to the association between foreign firm presence and domestic firms’ financial constraint. Using firm-level data spanning across 36 SSAs from the World Bank Enterprise Survey, we show that the increase in foreign firm presence can ease the financial constraints of domestic firms in the SSAs. One reason is that foreign-owned firms are not only less financially constrained, they are also less likely to apply for bank loans. Therefore, an increase in foreign firm presence may reduce the competition for loans and ease the financial constraints of domestic firms by improving their borrowing success. 1. Introduction The lack of access to finance has been a major concern for businesses in sub-Saharan Africa (Beck et al., 2009; Asiedu et al., 2013; Mlachila et al., 2013; Bah and Fang, 2015). In the SSAs (sub-Saharan African countries for short), the financial sector is usually underdeveloped, dominated by a few big banks,1 and lacks a stock market (Demirgüç-Kunt and Klapper, 2012; Mlachila et al., 2013).2 This has prompted some policymakers in the SSAs to pursue foreign investments, especially the establishment of foreign-owned firms in their countries, as a way of gaining external finance as one of the benefits (Asiedu, 2002; Basu and Srinivasan, 2002; Harrison and McMillan, 2003; Adams, 2009). However, although such policies are in placed, there is no evidence that the presence of foreign-owned firms may ease the financial constraints faced by domestic firms. In this paper, we conduct a cross-country firm-level study to shed light on this issue. It is important to emphasise that at the outset, the association between foreign firm presence and domestic firms’ financial constraint is ambiguous. On the one hand, foreign-owned firms may themselves become a source of finance for local partners in the same industry (Javorcik, 2014; Newman et al., 2015), which helps to raise the productivity and thus creditworthiness of domestic firms (Javorcik and Spatareanu, 2011; Javorcik, 2014). On the other hand, because foreign-owned firms tend to be more profitable and have better reputations, local banks may favour foreign-owned firms over domestic firms in lending. The increase in market competition brought along by foreign-owned firms may also erode the profits of domestic firms and ultimately their capacity to borrow (Harrison and McMillan, 2003).3 As such, how foreign firm presence affects the financial constraints of domestic firms is unclear. Using cross-country firm-level data based the World Bank Enterprise Survey from 2006 to 2010, we show that in the SSAs, a larger foreign firm presence may relieve domestic firms of their financial constraints. To measure how financially constrained a firm is, we use the firms’ responses to World Bank’s survey questions on credit access, as well as objective measures such as whether a firm has had access to overdraft facilities, a credit line or a bank loan, or it had applied for but was denied a loan. We find that in industries where there is a larger foreign firm presence, domestic firms tend to be less financially constrained. One reason is that foreign-owned firms are less financially constrained than domestic firms; as such, they are also less likely to borrow from banks. Given that foreign-owned firms are less likely to seek bank credit, a larger foreign firm presence would benefit domestic firms by reducing the competition for loans and enabling them to borrow more successfully. Our study has policy relevance for the SSAs. Firstly, firms in the SSAs are the most financially constrained compared with firms elsewhere. This has implications on development, as the lack of finance (which is the case for the SSAs) can severely undermine growth (Beck and Demirguc-Kunt, 2006; Bah and Fang, 2015). Therefore, it would be helpful from a policy perspective to know if foreign firm presence may lead to improvements in financial access, which in turn may foster economic growth. Secondly, to gain access to foreign capital, several SSAs have implemented policies to attract foreign investors to establish new firms or assume the ownership of local firms (Adams and Opoku, 2015).4 However, there is no evidence that doing so may relieve the financial constraints experienced in the host country, which this paper speaks to.5 Our paper is related to the literature that focuses on discovering the determinants of firms’ access to finance in developing countries. The literature has identified country-level factors that affect firms’ access to finance, such as legal system and regulatory frameworks (Demirgüç-Kunt and Maksimovic, 1998), stock market development (Demirgüç-Kunt and Levine, 1996), financial market development and liberalisation (Abel, 1980; Laeven, 2003; Love, 2003), as well as firm-level characteristics including age, size, ownership structure, legal status and gender in determining firm’s access to finance (Beck and Demirgüç-Kunt, 2008; Byiers et al., 2010; Asiedu et al., 2013; Aterido et al., 2013; Hansen and Rand, 2014a; Wagner and Weche Gelübcke, 2015). Our work complements these studies by using a cross-country firm-level analysis to explore if foreign firm presence may contribute towards financial access. The rest of the paper is organised as follows. In Section 2, we briefly review the literature. In Section 3, we describe the data sources and the variables used in this paper. In Section 4, we describe our estimating equation and discusses the potential identification issues. In Sections 5 and 6 , we present our baseline results and robustness checks, respectively. In Section 7, we present our concluding remarks. 2. Background Sub-Saharan Africa is one of the fastest developing regions in the world (Young, 2012; McMillan and Harttgen, 2014). However, despite their economic progress, the financial systems in the SSAs remain among the least developed. For example, in the SSAs, the financial sector is typically characterised by a lack of a stock market (Demirgüç-Kunt and Klapper, 2012), a banking industry that is dominated by a few banks,6 interest spreads, margins and overhead costs that are much higher than in other regions, and a very small representation by Non-Bank Financial Institutions (NBFI) in the credit market (Mlachila et al., 2013; Beck and Cull, 2014). Studies have found that the lack of access to finance is the most formidable obstacle to growth, productivity and competitiveness in the SSAs (Nkurunziza, 2010; Bah and Fang, 2015). In fact, firms in the SSAs are the most financially constrained compared with firms elsewhere. For example, 45.6% of firms in the SSAs reported access to finance as the most important constraint in investment while the corresponding number is 14.6% for the OECD (Bah and Fang, 2015). On average, only 23.5% of firms in the SSAs have access to bank loan or line of credit while the corresponding number is 49.1% for OECD (see Appendix A). Because access to finance implicates development, there has been tremendous effort to understand the issue of financial access through cross-country or firm-level analyses. The cross-country analyses typically aim to understand which country-level variables determine access to finance. These studies have found that there is greater access to finance in countries that has legal systems and regulatory frameworks that strongly protect property rights, contract enforcement and credit rights (Demirgüç-Kunt and Maksimovic, 1998), stock market development (Demirgüç-Kunt and Levine, 1996), developed and liberalised financial markets (Gelos and Werner, 2002; Laeven, 2003; Love, 2003), large national markets, income and savings (Demirgüç-Kunt and Klapper, 2012; Mlachila et al., 2013). The firm-level studies typically stress the importance of firm-level characteristics for firms’ access to finance such as age, size, ownership structure, legal status and owners’ gender (Beck et al., 2006; Asiedu et al., 2013; Aterido et al., 2013; Hansen and Rand, 2014a; Wagner and Weche Gelübcke, 2015). For example, using firm-level data from 80 countries, Beck et al. (2006) have found that larger, older and foreign-owned firms are on average less financially constrained. What is striking about the literature is that little is said about how foreign firm presence may affect the financial constraints faced by domestic firms in the host countries. Yet, despite the lack of evidence, policymakers in the SSAs have sought after the establishment of foreign-owned firms to gain some external finance, among other benefits (Te Velde and Morrissey, 2003; Elkins et al., 2006; Foster-McGregor et al., 2015).7 In the literature, closest to our study are works related to the effects of foreign direct investment (FDI) on domestic firms’ financial constraint (Harrison and McMillan, 2003; Harrison et al., 2004). However, these studies do not focus on the effects of foreign firm ownership, nor do they combine cross-country and firm-level information for their analysis as we do.8 To our best knowledge, our paper is the first to employ a cross-country firm-level approach to directly examine how the foreign ownership of firms may affect the financial constraints of domestic firms. As such, it helps to shed light on the relationship between foreign firm presence and the financial constraints of domestic firms, which is ambiguous. For example, through knowledge spillover about new products, technologies and marketing, the presence of foreign-owned firms may improve the productivity of domestic firms in the same industry, and thus, their creditworthiness (Javorcik and Spatareanu, 2011; Javorcik, 2014).9 Banks may also prefer to lend to industries with a large foreign firm presence, which eases the borrowing constraints of other firms in the same industries (Harrison et al., 2004). Besides, foreign-owned firms may themselves bring in capital, and as such, be a source of finance to their local business partners (Harrison et al., 2004). By contrast, the presence of foreign-owned firms may create difficulties for domestic firms to access finance. For example, foreign enterprises in developing countries are likely to be more profitable, have more collateral and better financial ratios. As such, banks may divert credit away from domestic firms to foreign-owned firms. Foreign-owned firms also compete in the products market and potentially erode the market share of domestic firms, and consequently, their ability to borrow (Harrison and McMillan, 2003). Considering these opposing arguments, it is unclear how foreign firm presence may affect domestic firms in the SSAs. 3. Data and descriptive statistics 3.1 Data Our dataset is drawn from the World Bank Enterprise Survey (WBES). In this survey, a total of more than 10,000 non-repeated firms from 36 SSAs are surveyed over the course of 2006 to 2010.10 The WBES questionnaires contain identical questions for all countries, and uses stratified sampling by size, industry and regions to collect the sample of firms for each country. The survey also covers 38 industries at the two ISIC-digit levels and contains information on the access and use of financial services as well as several other relevant firm characteristics that are used here. The definitions on all the variables used in this paper are provided in Table B1 in Appendix B. Following Asiedu et al. (2013) and Hansen and Rand (2014a), we construct several indicators to capture how financially constrained a firm is. These measures are based on managers’ responses to the WBES survey question: ‘to what degree is access to finance an obstacle to the current operation of this establishment?’ Our main measure of financial constraint, which we call it Financial Constraint, is an ordinal variable that takes the value of 0, 1, 2, 3, or 4 if the firm states that finance is either not a problem (i.e., 0), a minor problem (i.e., 1) a moderate problem (i.e., 2), a major problem (i.e., 3), or a severe problem (i.e., 4). In other words, firms that reported themselves to be more financially constrained have higher Financial Constraint scores. To check if our conclusion is robust, we consider three alternative indicators to measure financial constraint. Firstly, people’s perception about the seriousness of their financial situation is not absolute. Thus, the distinction between moderate, major and severe financial constraint could be blurred. For this reason, our first alternative indicator of firm financial constraint is a dummy variable that indicates (i.e., =1) if the firm responds in the survey that access to finance is a moderate, major or severe problem, and 0 if otherwise. We call this indicator Serious Constraint, which indicates if the firm has encountered what it believes to be a moderate to severe problem of financial constraint. Our second and third alternative indicators of firm financial constraint are based on two objective measures. The first, which we call Credit Product Constraint, is a dummy variable that indicates if the firm does not have access to any of the three credit products: overdrafts, lines of credit, or bank loans. The second, which we call Loans Denied, is a dummy variable that indicates if a firm had applied for but was denied a loan. The WBES database provides information on owners’ equity share. Following the literature (see, for example, Javorcik and Spatareanu, 2011; Asiedu et al., 2013), foreign-owned firms are defined as firms in the host country where at least 10% of their equity is foreign held. Domestic firms are defined as firms with less than 10% foreign ownership. We construct measures of foreign firm presence for each industry and country. These measures, described further in Section 4, are associated with the proportion of foreign firms, foreign firms’ share of equity, or employment in the industry and country. Additionally, the WBES database provides a range of relevant firm specific characteristics, such as firm size, ownership type, legal status, technological capacity and financial transparency, which are used here.11 Finally, the World Bank’s World Development Indicators (WDI) database is the source of our country level controls, which include a measure of financial development, legal system and inflation (see Appendix B for the variables’ descriptions). 3.2 Descriptive statistics To appreciate how severe the issue of firm financial constraint is in the SSAs, Figure 1 considers nine major business obstacles encountered by firms, i.e., Access to finance, Access to land, Power outage, Anti-competitive practice, Infeasible tax rate, Crime, Corruption, Political instability, and Licensing & permits. For each obstacle, Figure 1 plots the percentage of domestic and foreign-owned firms that responded that among the nine business obstacles, the obstacle in question was ‘most serious obstacle affecting the operation of the establishment’. Figure 1: Open in new tabDownload slide Nine Major Constraints Faced By Firms in Sub-Saharan Africa Figure 1: Open in new tabDownload slide Nine Major Constraints Faced By Firms in Sub-Saharan Africa Being financially constrained is among the most severe issues facing firms in the SSAs. Figure 1 shows that more firms, both domestic and foreign-owned, have reported the lack of access to finance as the most severe problem they face, than the number of firms reporting other obstacles as their most serious concern. In fact, the lack of finance is more severe than other traditional issues such as the lack of land, access to power, crime and corruption. That being said, foreign-owned and domestic firms are not equally impacted by business restrictions. As Figure 1 shows, foreign-owned firms are less likely than domestic firms to face severe obstacles in running a business. For example, concerning the access to finance, 17.5% of domestic firms report it as the most serious constraint while only 2.5% of foreign-owned firms respond in the same way. Next, Table 1 lists the countries contained in our sample. In turn, for each country, it lists the number of foreign-owned firms, its average Financial Constraint score, and the mean of its Serious Constraint indicator (which reflects the percentage of firms facing serious financial constraint). Table 1: Number of Firms and Financial Constraint Indicators by Country All firms Foreign firms No. of firms % Foreign owned Financial constraint Serious constraint No. of firms Financial constraint Serious constraint Angola 785 22 2.35 0.73 171 2.21 0.68 Botswana 610 46 1.69 0.51 283 1.36 0.41 Burkina Faso 394 14 3.00 0.91 54 2.42 0.77 Burundi 270 17 2.30 0.7 47 2.51 0.72 Cameroon 363 18 2.45 0.83 66 2.18 0.74 Cape Verde 156 15 1.92 0.64 24 2.00 0.69 Chad 150 31 2.22 0.67 47 1.86 0.58 Congo 151 22 2.10 0.66 33 1.96 0.61 D.R. Congo 699 17 2.57 0.77 119 2.21 0.70 Eritrea 179 3 0.43 0.17 6 0.33 0.16 Gabon 179 61 1.58 0.47 110 1.45 0.43 Gambia 174 30 1.79 0.55 52 1.36 0.36 Ghana 494 5 2.65 0.78 25 1.6 0.52 Guinea 223 10 2.55 0.73 25 2.73 0.78 Guinea Bissau 159 9 2.91 0.81 25 2.8 0.73 Ivory Coast 526 17 2.87 0.83 92 2.42 0.70 Kenya 657 12 1.94 0.58 80 1.72 0.55 Lesotho 151 33 1.26 0.36 50 0.89 0.22 Liberia 150 13 1.87 0.56 20 1.25 0.35 Madagascar 445 41 1.88 0.60 185 1.66 0.54 Malawi 150 32 2.08 0.65 49 1.64 0.52 Mali 850 7 2.29 0.66 66 1.87 0.52 Mauritania 237 12 2.12 0.62 28 1.67 0.5 Mauritius 398 11 1.88 0.56 42 1.54 0.52 Mozambique 473 19 2.10 0.62 91 2.06 0.61 Namibia 329 24 1.10 0.31 79 0.77 0.18 Niger 150 23 2.10 0.66 34 1.73 0.52 Rwanda 212 16 1.50 0.47 35 1.31 0.37 Senegal 506 6 2.09 0.61 30 1.7 0.5 Sierra Leone 150 14 1.95 0.62 21 1.52 0.52 South Africa 937 12 0.73 0.22 121 0.61 0.19 Swaziland 307 36 1.56 0.48 111 1.35 0.45 Tanzania 419 12 1.82 0.55 50 1.54 0.48 Togo 155 30 2.28 0.67 47 1.45 0.43 Uganda 563 17 2.31 0.73 94 1.88 0.58 Zambia 484 24 1.32 0.40 117 1.05 0.30 All Countries 13,235 16.8 2.00 0.63 2,534 1.64 0.50 All firms Foreign firms No. of firms % Foreign owned Financial constraint Serious constraint No. of firms Financial constraint Serious constraint Angola 785 22 2.35 0.73 171 2.21 0.68 Botswana 610 46 1.69 0.51 283 1.36 0.41 Burkina Faso 394 14 3.00 0.91 54 2.42 0.77 Burundi 270 17 2.30 0.7 47 2.51 0.72 Cameroon 363 18 2.45 0.83 66 2.18 0.74 Cape Verde 156 15 1.92 0.64 24 2.00 0.69 Chad 150 31 2.22 0.67 47 1.86 0.58 Congo 151 22 2.10 0.66 33 1.96 0.61 D.R. Congo 699 17 2.57 0.77 119 2.21 0.70 Eritrea 179 3 0.43 0.17 6 0.33 0.16 Gabon 179 61 1.58 0.47 110 1.45 0.43 Gambia 174 30 1.79 0.55 52 1.36 0.36 Ghana 494 5 2.65 0.78 25 1.6 0.52 Guinea 223 10 2.55 0.73 25 2.73 0.78 Guinea Bissau 159 9 2.91 0.81 25 2.8 0.73 Ivory Coast 526 17 2.87 0.83 92 2.42 0.70 Kenya 657 12 1.94 0.58 80 1.72 0.55 Lesotho 151 33 1.26 0.36 50 0.89 0.22 Liberia 150 13 1.87 0.56 20 1.25 0.35 Madagascar 445 41 1.88 0.60 185 1.66 0.54 Malawi 150 32 2.08 0.65 49 1.64 0.52 Mali 850 7 2.29 0.66 66 1.87 0.52 Mauritania 237 12 2.12 0.62 28 1.67 0.5 Mauritius 398 11 1.88 0.56 42 1.54 0.52 Mozambique 473 19 2.10 0.62 91 2.06 0.61 Namibia 329 24 1.10 0.31 79 0.77 0.18 Niger 150 23 2.10 0.66 34 1.73 0.52 Rwanda 212 16 1.50 0.47 35 1.31 0.37 Senegal 506 6 2.09 0.61 30 1.7 0.5 Sierra Leone 150 14 1.95 0.62 21 1.52 0.52 South Africa 937 12 0.73 0.22 121 0.61 0.19 Swaziland 307 36 1.56 0.48 111 1.35 0.45 Tanzania 419 12 1.82 0.55 50 1.54 0.48 Togo 155 30 2.28 0.67 47 1.45 0.43 Uganda 563 17 2.31 0.73 94 1.88 0.58 Zambia 484 24 1.32 0.40 117 1.05 0.30 All Countries 13,235 16.8 2.00 0.63 2,534 1.64 0.50 Note: Angola, Botswana, D.R. Congo and Mali were surveyed twice. Open in new tab Table 1: Number of Firms and Financial Constraint Indicators by Country All firms Foreign firms No. of firms % Foreign owned Financial constraint Serious constraint No. of firms Financial constraint Serious constraint Angola 785 22 2.35 0.73 171 2.21 0.68 Botswana 610 46 1.69 0.51 283 1.36 0.41 Burkina Faso 394 14 3.00 0.91 54 2.42 0.77 Burundi 270 17 2.30 0.7 47 2.51 0.72 Cameroon 363 18 2.45 0.83 66 2.18 0.74 Cape Verde 156 15 1.92 0.64 24 2.00 0.69 Chad 150 31 2.22 0.67 47 1.86 0.58 Congo 151 22 2.10 0.66 33 1.96 0.61 D.R. Congo 699 17 2.57 0.77 119 2.21 0.70 Eritrea 179 3 0.43 0.17 6 0.33 0.16 Gabon 179 61 1.58 0.47 110 1.45 0.43 Gambia 174 30 1.79 0.55 52 1.36 0.36 Ghana 494 5 2.65 0.78 25 1.6 0.52 Guinea 223 10 2.55 0.73 25 2.73 0.78 Guinea Bissau 159 9 2.91 0.81 25 2.8 0.73 Ivory Coast 526 17 2.87 0.83 92 2.42 0.70 Kenya 657 12 1.94 0.58 80 1.72 0.55 Lesotho 151 33 1.26 0.36 50 0.89 0.22 Liberia 150 13 1.87 0.56 20 1.25 0.35 Madagascar 445 41 1.88 0.60 185 1.66 0.54 Malawi 150 32 2.08 0.65 49 1.64 0.52 Mali 850 7 2.29 0.66 66 1.87 0.52 Mauritania 237 12 2.12 0.62 28 1.67 0.5 Mauritius 398 11 1.88 0.56 42 1.54 0.52 Mozambique 473 19 2.10 0.62 91 2.06 0.61 Namibia 329 24 1.10 0.31 79 0.77 0.18 Niger 150 23 2.10 0.66 34 1.73 0.52 Rwanda 212 16 1.50 0.47 35 1.31 0.37 Senegal 506 6 2.09 0.61 30 1.7 0.5 Sierra Leone 150 14 1.95 0.62 21 1.52 0.52 South Africa 937 12 0.73 0.22 121 0.61 0.19 Swaziland 307 36 1.56 0.48 111 1.35 0.45 Tanzania 419 12 1.82 0.55 50 1.54 0.48 Togo 155 30 2.28 0.67 47 1.45 0.43 Uganda 563 17 2.31 0.73 94 1.88 0.58 Zambia 484 24 1.32 0.40 117 1.05 0.30 All Countries 13,235 16.8 2.00 0.63 2,534 1.64 0.50 All firms Foreign firms No. of firms % Foreign owned Financial constraint Serious constraint No. of firms Financial constraint Serious constraint Angola 785 22 2.35 0.73 171 2.21 0.68 Botswana 610 46 1.69 0.51 283 1.36 0.41 Burkina Faso 394 14 3.00 0.91 54 2.42 0.77 Burundi 270 17 2.30 0.7 47 2.51 0.72 Cameroon 363 18 2.45 0.83 66 2.18 0.74 Cape Verde 156 15 1.92 0.64 24 2.00 0.69 Chad 150 31 2.22 0.67 47 1.86 0.58 Congo 151 22 2.10 0.66 33 1.96 0.61 D.R. Congo 699 17 2.57 0.77 119 2.21 0.70 Eritrea 179 3 0.43 0.17 6 0.33 0.16 Gabon 179 61 1.58 0.47 110 1.45 0.43 Gambia 174 30 1.79 0.55 52 1.36 0.36 Ghana 494 5 2.65 0.78 25 1.6 0.52 Guinea 223 10 2.55 0.73 25 2.73 0.78 Guinea Bissau 159 9 2.91 0.81 25 2.8 0.73 Ivory Coast 526 17 2.87 0.83 92 2.42 0.70 Kenya 657 12 1.94 0.58 80 1.72 0.55 Lesotho 151 33 1.26 0.36 50 0.89 0.22 Liberia 150 13 1.87 0.56 20 1.25 0.35 Madagascar 445 41 1.88 0.60 185 1.66 0.54 Malawi 150 32 2.08 0.65 49 1.64 0.52 Mali 850 7 2.29 0.66 66 1.87 0.52 Mauritania 237 12 2.12 0.62 28 1.67 0.5 Mauritius 398 11 1.88 0.56 42 1.54 0.52 Mozambique 473 19 2.10 0.62 91 2.06 0.61 Namibia 329 24 1.10 0.31 79 0.77 0.18 Niger 150 23 2.10 0.66 34 1.73 0.52 Rwanda 212 16 1.50 0.47 35 1.31 0.37 Senegal 506 6 2.09 0.61 30 1.7 0.5 Sierra Leone 150 14 1.95 0.62 21 1.52 0.52 South Africa 937 12 0.73 0.22 121 0.61 0.19 Swaziland 307 36 1.56 0.48 111 1.35 0.45 Tanzania 419 12 1.82 0.55 50 1.54 0.48 Togo 155 30 2.28 0.67 47 1.45 0.43 Uganda 563 17 2.31 0.73 94 1.88 0.58 Zambia 484 24 1.32 0.40 117 1.05 0.30 All Countries 13,235 16.8 2.00 0.63 2,534 1.64 0.50 Note: Angola, Botswana, D.R. Congo and Mali were surveyed twice. Open in new tab As Table 1 shows, the number of foreign-owned firms across the SSAs varies substantially, where Botswana has the largest number of foreign-owned firms in the sample (i.e., 283) and Eritrea, which has half of Botswana’s population, has the least (i.e., 6). Additionally, the level of financial constraint faced by firms varies substantially across the SSAs as well. For instance, South Africa has an average Financial Constraint score of 0.73, while Burkina Faso has an average score of 3 (where recall that a score of 0 indicates no financial constraint and a score of 4 indicates severe financial constraint). Based on the average Financial Constraint and Serious Constraint scores for each country, we can see that on average, domestic firms across the SSAs (except for Cape Verde and Burundi) are more financially constrained than foreign-owned firms are. To visualise this result, Figure 2 plots the average Serious Constraint score for domestic firms in the y-axis and foreign-owned firms in the x-axis. The regression line that fits the cross-country data points, which correspond to the ‘Proportion of domestic firms facing moderate to severe financial constraint’ in the y-axis and the ‘Proportion of foreign-owned firms facing moderate to severe financial constraint’ in the x-axis, is flatter than the 45 degree line. This means that across the SSAs, the proportion of domestic firms facing serious financial constraint is larger than the proportion of foreign-owned firms facing the same.12 Figure 2: Open in new tabDownload slide Proportion of Firms Facing Moderate to Severe Financial Constraint By Ownership Type Figure 2: Open in new tabDownload slide Proportion of Firms Facing Moderate to Severe Financial Constraint By Ownership Type In Table 2, we report the share of foreign-owned firms across the SSAs for each industry. The proportion of foreign-owned firms tends to be smaller in the garment, furniture, accounting and computing machinery industries, where less than 15% of firms in these industries are foreign owned. By contrast, foreign-owned firms are more highly represented in the transport equipment, water transport and tobacco industries, and in the case of the tobacco industry, 80% of firms in the SSAs are foreign owned. Table 2: Firm Ownership by Industry Industry Number of firms Domestic Foreign Food 1,629 82.20% 17.80% Tobacco 10 20% 80% Textiles 193 72.54% 27.46% Garments 1,036 88.13% 11.87% Leather 106 83.96% 16.04% Wood 327 86.54% 13.88% Paper 73 65.75% 34.25% Publishing, printing, and recorded media 317 86.12% 13.88% Refined petroleum product 12 83.33% 16.67% Chemicals 374 70.05% 29.95% Plastics & rubber 182 66.48% 33.52% Non metallic mineral products 203 69.46% 30.54% Basic metals 85 72.94% 27.93% Fabricated metal products 585 84.44% 15.56% Machinery and equipment 111 72.94% 27.93% Accounting and computing machinery 2 100% 0.00% Electrical machinery and apparatus 68 75% 25% Radio, television and communication equipment 8 75% 25% Precision instruments 5 80% 20% Motor vehicles, trailers and semi-trailers 33 81.81% 18.18% Other transport equipment 15 33.33% 66.67% Furniture 697 87.52% 12.48% Recycling 6 83.33% 16.67% Electricity, gas, steam and hot water supply 1 0.00% 100% Collection, purification and distribution of water 1 100% 0.00% Services of motor vehicles 486 75.10% 24.90% Wholesale 592 75% 25% Retail 2,825 81.59% 18.41% Hotel and restaurants 1,195 85.36% 14.64% Land transport 172 69.19% 30.81% Water transport 25 44% 56% Air transport 18 55.56% 44.44% Travel agencies 73 71.23% 28.77% Post and telecommunications 38 57.89% 42.11% Activities auxiliary to financial intermediation 1 0.00% 100% Computer and related activities 278 88.49% 11.51% Total 10,060 80% 20% Industry Number of firms Domestic Foreign Food 1,629 82.20% 17.80% Tobacco 10 20% 80% Textiles 193 72.54% 27.46% Garments 1,036 88.13% 11.87% Leather 106 83.96% 16.04% Wood 327 86.54% 13.88% Paper 73 65.75% 34.25% Publishing, printing, and recorded media 317 86.12% 13.88% Refined petroleum product 12 83.33% 16.67% Chemicals 374 70.05% 29.95% Plastics & rubber 182 66.48% 33.52% Non metallic mineral products 203 69.46% 30.54% Basic metals 85 72.94% 27.93% Fabricated metal products 585 84.44% 15.56% Machinery and equipment 111 72.94% 27.93% Accounting and computing machinery 2 100% 0.00% Electrical machinery and apparatus 68 75% 25% Radio, television and communication equipment 8 75% 25% Precision instruments 5 80% 20% Motor vehicles, trailers and semi-trailers 33 81.81% 18.18% Other transport equipment 15 33.33% 66.67% Furniture 697 87.52% 12.48% Recycling 6 83.33% 16.67% Electricity, gas, steam and hot water supply 1 0.00% 100% Collection, purification and distribution of water 1 100% 0.00% Services of motor vehicles 486 75.10% 24.90% Wholesale 592 75% 25% Retail 2,825 81.59% 18.41% Hotel and restaurants 1,195 85.36% 14.64% Land transport 172 69.19% 30.81% Water transport 25 44% 56% Air transport 18 55.56% 44.44% Travel agencies 73 71.23% 28.77% Post and telecommunications 38 57.89% 42.11% Activities auxiliary to financial intermediation 1 0.00% 100% Computer and related activities 278 88.49% 11.51% Total 10,060 80% 20% Note: These 38 industries are based on the 2-digit ISIC classification. Open in new tab Table 2: Firm Ownership by Industry Industry Number of firms Domestic Foreign Food 1,629 82.20% 17.80% Tobacco 10 20% 80% Textiles 193 72.54% 27.46% Garments 1,036 88.13% 11.87% Leather 106 83.96% 16.04% Wood 327 86.54% 13.88% Paper 73 65.75% 34.25% Publishing, printing, and recorded media 317 86.12% 13.88% Refined petroleum product 12 83.33% 16.67% Chemicals 374 70.05% 29.95% Plastics & rubber 182 66.48% 33.52% Non metallic mineral products 203 69.46% 30.54% Basic metals 85 72.94% 27.93% Fabricated metal products 585 84.44% 15.56% Machinery and equipment 111 72.94% 27.93% Accounting and computing machinery 2 100% 0.00% Electrical machinery and apparatus 68 75% 25% Radio, television and communication equipment 8 75% 25% Precision instruments 5 80% 20% Motor vehicles, trailers and semi-trailers 33 81.81% 18.18% Other transport equipment 15 33.33% 66.67% Furniture 697 87.52% 12.48% Recycling 6 83.33% 16.67% Electricity, gas, steam and hot water supply 1 0.00% 100% Collection, purification and distribution of water 1 100% 0.00% Services of motor vehicles 486 75.10% 24.90% Wholesale 592 75% 25% Retail 2,825 81.59% 18.41% Hotel and restaurants 1,195 85.36% 14.64% Land transport 172 69.19% 30.81% Water transport 25 44% 56% Air transport 18 55.56% 44.44% Travel agencies 73 71.23% 28.77% Post and telecommunications 38 57.89% 42.11% Activities auxiliary to financial intermediation 1 0.00% 100% Computer and related activities 278 88.49% 11.51% Total 10,060 80% 20% Industry Number of firms Domestic Foreign Food 1,629 82.20% 17.80% Tobacco 10 20% 80% Textiles 193 72.54% 27.46% Garments 1,036 88.13% 11.87% Leather 106 83.96% 16.04% Wood 327 86.54% 13.88% Paper 73 65.75% 34.25% Publishing, printing, and recorded media 317 86.12% 13.88% Refined petroleum product 12 83.33% 16.67% Chemicals 374 70.05% 29.95% Plastics & rubber 182 66.48% 33.52% Non metallic mineral products 203 69.46% 30.54% Basic metals 85 72.94% 27.93% Fabricated metal products 585 84.44% 15.56% Machinery and equipment 111 72.94% 27.93% Accounting and computing machinery 2 100% 0.00% Electrical machinery and apparatus 68 75% 25% Radio, television and communication equipment 8 75% 25% Precision instruments 5 80% 20% Motor vehicles, trailers and semi-trailers 33 81.81% 18.18% Other transport equipment 15 33.33% 66.67% Furniture 697 87.52% 12.48% Recycling 6 83.33% 16.67% Electricity, gas, steam and hot water supply 1 0.00% 100% Collection, purification and distribution of water 1 100% 0.00% Services of motor vehicles 486 75.10% 24.90% Wholesale 592 75% 25% Retail 2,825 81.59% 18.41% Hotel and restaurants 1,195 85.36% 14.64% Land transport 172 69.19% 30.81% Water transport 25 44% 56% Air transport 18 55.56% 44.44% Travel agencies 73 71.23% 28.77% Post and telecommunications 38 57.89% 42.11% Activities auxiliary to financial intermediation 1 0.00% 100% Computer and related activities 278 88.49% 11.51% Total 10,060 80% 20% Note: These 38 industries are based on the 2-digit ISIC classification. Open in new tab Finally, in Table 3, we show the proportion of foreign firms and the proportions of domestic firms in each of the five categories of financial constraints. The table shows that domestic firms are likely to be more severely financially constrained. For example, 23.28% of domestic firms have reported that they face severe financial constraint as opposed to 13.72% of foreign firms that have reported the same. By contrast, about a third of foreign firms have reported that financial constraint is not a problem as opposed to a quarter of domestic firms that have reported the same. For the descriptive statistics of the variables used in this study, please refer to Table C1 in Appendix C. Table 3: Percentage of Financial Constrained Firms Across Ownership Type Financial constraint categories Proportion of foreign firms Proportion of domestic firms Severe 13.72% 23.28% Major 20.53% 23.83% Moderate 16.32% 16.01% Minor 15.67% 12.20% Not a Problem 33.77% 24.68% Financial constraint categories Proportion of foreign firms Proportion of domestic firms Severe 13.72% 23.28% Major 20.53% 23.83% Moderate 16.32% 16.01% Minor 15.67% 12.20% Not a Problem 33.77% 24.68% Open in new tab Table 3: Percentage of Financial Constrained Firms Across Ownership Type Financial constraint categories Proportion of foreign firms Proportion of domestic firms Severe 13.72% 23.28% Major 20.53% 23.83% Moderate 16.32% 16.01% Minor 15.67% 12.20% Not a Problem 33.77% 24.68% Financial constraint categories Proportion of foreign firms Proportion of domestic firms Severe 13.72% 23.28% Major 20.53% 23.83% Moderate 16.32% 16.01% Minor 15.67% 12.20% Not a Problem 33.77% 24.68% Open in new tab 4. Empirical approach Our empirical approach is structured as follows. Preliminary As a preliminary, we first show that foreign firms are less financially constrained by estimating (see Section 5.1 for findings) FCijkt=c1+αForeignijkt+γ′Xijkt+δ′Ckt+μj+μk+μt+εijkt. (1) The dependent variable FCijkt measures the financial constraint experienced by firm i in industry j and in country k, which is reported by this firm in year t. Throughout our paper, our main measure is Financial Constraint, which is an ordinal variable ranging from 0 to 4 where a score of 0 indicates that the firm is not financially constrained and a score of 4 indicates that the firm faces severe financial constraints. The main explanatory variable in this model is Foreign, which is a dummy variable that indicates if the firm is foreign-owned. If domestic firms are more credit constrained than foreign-owned firms are, the coefficient on Foreign will be negative. For controls, Xijkt is a vector of firm-level variables and Ckt is a vector of country-level variables. Because the firms are surveyed once only, Eq. (1) (as are the other regression models estimated here) is a panel at the industry, country and year level, but not at the firm level. The term μj ⁠, generically represents the vector of industry dummies, μk represents the vector of country dummmies, and μt represents the vector of year dummies. Main Estimating Equation We explore if a larger foreign firm presence may help alleviate the financial constraints of domestic firms in the same industry. To do so, we estimate (see Section 5.2 for findings) FCijkt=c2+βPresencejkt+ϕ′Xijkt+ρ′Ckt+μj+μk+μt+εijkt (2) using only the subsample of domestic firms. Our main financial constraint measure, as before, is Financial Constraint. In addition, we consider three alternative measures of financial constraint, namely Serious Constraint, Credit Products Constraint, and Loans Denied discussed in Section 3.1, as a robustness check. Presencejkt, our main explanatory variable, measures the presence of foreign-owned firm in industry j in country k. We measure Presencejkt using the following indicators: (i) the proportion of foreign firms over the total number of firms in industry j, country k and year t, ForeignFirmsProportionjkt=∑i=1nForeignijktTotalnumberoffirmsjkt (3) (ii) the share of foreign firms’ equity of total equity in industry j, country k and year t (Eq. (4)) ForeignEquitySharejkt=∑i=1n(equityijkt*Foreignijkt)∑i=1nequityjkt (4) (iii) the proportion of workers in foreign-owned firms over total number of workers in industry j, country k and year t (Eq. (5)): ForeignWorkersSharejkt=∑i=1n(employeesijkt*Foreignijkt)∑i=1nemployeesijkt (5) The larger these indices are, the larger foreign firm presence is in the industry in terms of the proportion of foreign-owned firms (Eq. (3)), proportion of equity held by foreign-owned firms (Eq. (4)), and the proportion of the workforce force employed by foreign-owned firms (Eq. (5)). Unless stated otherwise, for models with an ordinal dependent variable (specifically Financial Constraint), we estimate them with the Ordered Probit model and report the estimated coefficients in the tables (in Table 10, we consider alternative estimation methods as a robustness check). For models with a binary dependent variable, we estimate them with the Probit model and report the average marginal effects associated with the regressors. Mechanism Finally, we provide some evidence to support an explanation (there could be others) for why foreign firm presence may help to ease the financial constraint of domestic firms (see Section 5.3). Firms compete for finance, which is scarce in the SSAs. We show that foreign-owned firms are not only less financially constrained than domestic firms are, they are also less likely to borrow from banks. Since foreign-owned firms are less likely to seek bank credit, domestic firms would benefit from reduced competition in the credit market when there is greater representation of foreign-owned firms. In Section 5.3, we show that domestic firms in industries with a larger foreign firm presence are indeed more successful in their loan applications. This is one possible explanation for why foreign firm presence may ease the financial constraints of domestic firms. 4.1 Possible empirical issues Our main estimating equation is Eq. (2). We highlight some concerns that could prevent us from imparting a causal interpretation on the association between foreign firm presence and domestic firms’ financial constraint. Reverse Causality The first possible concern is reverse causality. Due to our regression design, we believe that reverse causality is unlikely to be a major confounding problem. Specifically, in our estimating equation (i.e., Eq. (2)), our dependent variable is the financial constraint of the domestic firm and our main explanatory variable is the foreign firm presence of an industry. For reverse causality to occur in the context of Eq. (2), we would need FCijkt (the firm i's financial constraint) to cause Presencejkt (industry j's foreign firm presence); that is, domestic firms would need to drive the composition of foreign firms in the industry. In this regard, we believe it is empirically unlikely for our results to be driven by reverse causality. Firstly, domestic firms in the SSAs are typically smaller. In Table 4, we find that the great majority of domestic firms, about 69% of them, have 19 employees or fewer. Moreover, only 7% of domestic firms employ 100 or more workers, compared with 25% of foreign-owned firms. Therefore, because of their size, it is unlikely for domestic firms to drive foreign firm presence in the industry and for reverse causality to matter empirically. Table 4: The Size of Domestic versus Foreign-Owned Firms Domestic Foreign-owned Small Firm (5–19 employees) 69% 42% Medium Firm (20–99 employees) 24% 33% Large Firm (above 99 employees) 7% 25% Total 100% 100% Domestic Foreign-owned Small Firm (5–19 employees) 69% 42% Medium Firm (20–99 employees) 24% 33% Large Firm (above 99 employees) 7% 25% Total 100% 100% Open in new tab Table 4: The Size of Domestic versus Foreign-Owned Firms Domestic Foreign-owned Small Firm (5–19 employees) 69% 42% Medium Firm (20–99 employees) 24% 33% Large Firm (above 99 employees) 7% 25% Total 100% 100% Domestic Foreign-owned Small Firm (5–19 employees) 69% 42% Medium Firm (20–99 employees) 24% 33% Large Firm (above 99 employees) 7% 25% Total 100% 100% Open in new tab More importantly, it is also unlikely that our conclusion is driven by reverse causal effects that are due to large domestic firms. In Section 6.4, we re-estimate our baseline regression without the top 5% and 10% largest domestic firms in the sample. We show that our conclusion (i.e., that foreign firm presence is statistically significant) still holds when these firms are omitted from the regression. Therefore, there is no evidence that our conclusion is driven by potential reverse causality stemming from these large domestic firms as well. Self-Selection Even if we could rule out reverse causality, it does not imply that the association between foreign firm presence and domestic firms’ financial constraint is causal. For example, foreign firms may still self-select into countries or industries where domestic firms are less financially constrained. Therefore, the association between foreign firm presence and domestic firms’ financial constraint could be jointly determined by certain unobserved country or industry characteristics. To address this concern, we include industry and country fixed effects to purge the possible confounding influence of unobserved industry and country heterogeneity.13 We also control for certain time-varying macroeconomic variables such as financial development (Financial Development), legal system (Legal System) and inflation (Inflation) that may affect foreigners’ decisions to invest in a country. Finally, in place of the country level controls, we use country-year fixed effects to partial out all possible country determinants, whether they are observable or unobservable, time-varying or time-constant. Therefore, the country-year fixed effects will partial out financial development, country-specific policies, and other country-specific characteristics that may jointly determine foreign firm presence and the financial constraints of domestic firms. Measurement of Credit Constraint Another concern stems from an observation by Hansen and Rand (2014a) that research on credit constraints may not be robust to how credit constraints are measured.14 For this reason, we use different indicators to measure financial constraint (i.e., Financial Constraint, Serious Constraint, Credit Products Constraint, Loan Denied) to ensure that our results are not dependent on the way financial constraint is measured. Multicollinearity Lastly, we could be concerned about the multicollinearity, especially when we include a sizable set of firm level and country level controls. To this end, we compute that Variance-Inflation-Factor (VIF) suggested by Belsley et al. (2005), and to save space, we have omitted the results from the paper. We find that all the predictors have an VIF smaller than the rule-of-thumb of 10, above which there is evidence of multicollinearity. Therefore, multicollinearity does not appear to an issue here. 5. Results 5.1 Firm ownership and financial constraint Based on Financial Constraint as the dependent variable, we report our estimates of Eq. (1) in Table 5, which reveal if domestic firms are more financially constrained than foreign-owned firms are. In Column (1), we control for firm characteristics only. In Column (2), we additionally control for inflation, financial development and legal system. In Column (3), we add industry, country and year dummies. In Column (4), we use industry dummies and country times year dummies. We find that all else equal, foreign-owned firms have a Financial Constraint score of 0.1–0.14 points smaller than domestic firms have on average. This difference is statistically significant at the 1% level across all regression specifications, suggesting that domestic firms on average are more financially constrained than foreign firms are. Table 5: Financial Constraint: Foreign-Owned Versus Domestic Firms (1) (2) (3) (4) Dependent variable: Financial constraint Foreign (owned) −0.109*** −0.235*** −0.139*** −0.14*** (0.000) (0.038) (0.000) (0.0373) Female Owned 0.081*** 0.071*** 0.085*** 0.08*** (0.027) (0.029) (0.009) (0.0290) Small (5–19 workers) 0.168*** 0.208*** 0.200*** 0.199*** (0.050) (0.054) (0.055) (0.054) Medium (20–99 workers) 0.082 0.088* 0.085* 0.085* (0.050) (0.052) (0.052) (0.051) Sole Proprietorship 0.008 −0.114*** −0.115*** −0.114*** (0.037) (0.040) (0.040) (0.040) Publicly Traded −0.444*** −0.597*** −0.593*** −0.596*** (0.131) (0.150) (0.150) (0.149) Private or Non-traded −0.158*** −0.154*** −0.156*** −0.156*** (0.038) (0.041) (0.042) (0.041) Financial Transparency −0.342*** −0.241*** −0.231*** −0.23*** (0.029) (0.032) (0.032) (0.037) Technological Capacity −0.311*** −0.185*** −0.181*** −0.180 (0.036) (0.038) (0.038) (0.037) Financial Development −0.003 −0.033** −0.058*** (0.001) (0.015) (0.012) Legal System −0.003 −0.133*** −0.128*** (0.010) (0.028) (0.036) Inflation 0.077*** −0.029 −0.148*** (0.009) (0.028) (0.048) Industry Dummies No No Yes Yes Country Dummies No No Yes No Year Dummies No No Yes No Country-Year Dummies No No No Yes Observations 8,186 7,718 7,518 7,718 (1) (2) (3) (4) Dependent variable: Financial constraint Foreign (owned) −0.109*** −0.235*** −0.139*** −0.14*** (0.000) (0.038) (0.000) (0.0373) Female Owned 0.081*** 0.071*** 0.085*** 0.08*** (0.027) (0.029) (0.009) (0.0290) Small (5–19 workers) 0.168*** 0.208*** 0.200*** 0.199*** (0.050) (0.054) (0.055) (0.054) Medium (20–99 workers) 0.082 0.088* 0.085* 0.085* (0.050) (0.052) (0.052) (0.051) Sole Proprietorship 0.008 −0.114*** −0.115*** −0.114*** (0.037) (0.040) (0.040) (0.040) Publicly Traded −0.444*** −0.597*** −0.593*** −0.596*** (0.131) (0.150) (0.150) (0.149) Private or Non-traded −0.158*** −0.154*** −0.156*** −0.156*** (0.038) (0.041) (0.042) (0.041) Financial Transparency −0.342*** −0.241*** −0.231*** −0.23*** (0.029) (0.032) (0.032) (0.037) Technological Capacity −0.311*** −0.185*** −0.181*** −0.180 (0.036) (0.038) (0.038) (0.037) Financial Development −0.003 −0.033** −0.058*** (0.001) (0.015) (0.012) Legal System −0.003 −0.133*** −0.128*** (0.010) (0.028) (0.036) Inflation 0.077*** −0.029 −0.148*** (0.009) (0.028) (0.048) Industry Dummies No No Yes Yes Country Dummies No No Yes No Year Dummies No No Yes No Country-Year Dummies No No No Yes Observations 8,186 7,718 7,518 7,718 Note: Financial Constraint is an ordinal variable (from 0 to 4) that indicates how financially constrained a firm is. Estimates of the coefficients from the Ordered Probit model are reported here. The constant is suppressed. Robust standard errors are reported in the parentheses. *p < 0.10, **p < 0.05, ***p < 0.01. Open in new tab Table 5: Financial Constraint: Foreign-Owned Versus Domestic Firms (1) (2) (3) (4) Dependent variable: Financial constraint Foreign (owned) −0.109*** −0.235*** −0.139*** −0.14*** (0.000) (0.038) (0.000) (0.0373) Female Owned 0.081*** 0.071*** 0.085*** 0.08*** (0.027) (0.029) (0.009) (0.0290) Small (5–19 workers) 0.168*** 0.208*** 0.200*** 0.199*** (0.050) (0.054) (0.055) (0.054) Medium (20–99 workers) 0.082 0.088* 0.085* 0.085* (0.050) (0.052) (0.052) (0.051) Sole Proprietorship 0.008 −0.114*** −0.115*** −0.114*** (0.037) (0.040) (0.040) (0.040) Publicly Traded −0.444*** −0.597*** −0.593*** −0.596*** (0.131) (0.150) (0.150) (0.149) Private or Non-traded −0.158*** −0.154*** −0.156*** −0.156*** (0.038) (0.041) (0.042) (0.041) Financial Transparency −0.342*** −0.241*** −0.231*** −0.23*** (0.029) (0.032) (0.032) (0.037) Technological Capacity −0.311*** −0.185*** −0.181*** −0.180 (0.036) (0.038) (0.038) (0.037) Financial Development −0.003 −0.033** −0.058*** (0.001) (0.015) (0.012) Legal System −0.003 −0.133*** −0.128*** (0.010) (0.028) (0.036) Inflation 0.077*** −0.029 −0.148*** (0.009) (0.028) (0.048) Industry Dummies No No Yes Yes Country Dummies No No Yes No Year Dummies No No Yes No Country-Year Dummies No No No Yes Observations 8,186 7,718 7,518 7,718 (1) (2) (3) (4) Dependent variable: Financial constraint Foreign (owned) −0.109*** −0.235*** −0.139*** −0.14*** (0.000) (0.038) (0.000) (0.0373) Female Owned 0.081*** 0.071*** 0.085*** 0.08*** (0.027) (0.029) (0.009) (0.0290) Small (5–19 workers) 0.168*** 0.208*** 0.200*** 0.199*** (0.050) (0.054) (0.055) (0.054) Medium (20–99 workers) 0.082 0.088* 0.085* 0.085* (0.050) (0.052) (0.052) (0.051) Sole Proprietorship 0.008 −0.114*** −0.115*** −0.114*** (0.037) (0.040) (0.040) (0.040) Publicly Traded −0.444*** −0.597*** −0.593*** −0.596*** (0.131) (0.150) (0.150) (0.149) Private or Non-traded −0.158*** −0.154*** −0.156*** −0.156*** (0.038) (0.041) (0.042) (0.041) Financial Transparency −0.342*** −0.241*** −0.231*** −0.23*** (0.029) (0.032) (0.032) (0.037) Technological Capacity −0.311*** −0.185*** −0.181*** −0.180 (0.036) (0.038) (0.038) (0.037) Financial Development −0.003 −0.033** −0.058*** (0.001) (0.015) (0.012) Legal System −0.003 −0.133*** −0.128*** (0.010) (0.028) (0.036) Inflation 0.077*** −0.029 −0.148*** (0.009) (0.028) (0.048) Industry Dummies No No Yes Yes Country Dummies No No Yes No Year Dummies No No Yes No Country-Year Dummies No No No Yes Observations 8,186 7,718 7,518 7,718 Note: Financial Constraint is an ordinal variable (from 0 to 4) that indicates how financially constrained a firm is. Estimates of the coefficients from the Ordered Probit model are reported here. The constant is suppressed. Robust standard errors are reported in the parentheses. *p < 0.10, **p < 0.05, ***p < 0.01. Open in new tab Concerning the other control variables, Table 5 shows that firms owned by females tend to be more financially constrained than firms owned by males.15 It also shows that firms that are financially constrained tend to be small, owned by partnerships than corporations or sole proprietors, less financially transparent, and have limited technological capacity (i.e., lacking a website). Firms in countries with better financial development and legal system tend to be less financially constrained as well. 5.2 Foreign firm presence and domestic firms’ financial constraint Previously, our results show that domestic firms tend to be more financially constrained than foreign firms. Here, we show that domestic firms are less financially constrained if there is a larger foreign firm presence in the same industry. To establish this result, we use the sample of domestic firms and regress their Financial Constraint scores on measures of foreign firm presence in their respective industries, along with other firm and country-level control variables and industry, country and year dummies. In Column (1) of Table 6, we estimate Eq. (2) with the proportion of foreign-owned firms, denote by Foreign Firms Proportionjkt, as our measure of foreign firm presence. The negative coefficient on Foreign Firms Proportionjkt suggests that domestic firms are less financially constrained when proportion of foreign-owned firms in the same industry is larger. Table 6: Foreign Firm Presence and Domestic Firms’ Financial Constraint (1) (2) (2) Dependent variable: Financial constraint (domestic firms) Foreign Firms Proportion −0.318** (0.153) Foreign Equity Share −0.320** (0.156) Foreign Workers Share −0.222** (0.089) Female Owned 0.055* 0.055* 0.081** (0.031) (0.031) (0.034) Small (5–19 workers) 0.217*** 0.217*** 0.226*** (0.067) (0.065) (0.068) Medium (20–99 workers) 0.092 0.092 0.095 (0.066) (0.063) (0.066) Sole Proprietorship −0.155*** −0.155*** −0.145*** (0.044) (0.043) (0.046) Publicly Traded −0.481** −0.481** −0.546** (0.200) (0.215) (0.237) Private or Non-traded −0.180*** −0.180*** −0.202*** (0.048) (0.047) (0.050) Financial Transparency −0.241*** −0.241*** −0.243*** (0.035) (0.035) (0.037) Technological Capacity −0.243*** −0.243*** −0.221*** (0.046) (0.045) (0.049) Financial Development −0.063*** −0.063*** −0.056*** (0.015) (0.014) (0.015) Legal System −0.115*** −0.115*** −0.148*** (0.042) (0.041) (0.044) Inflation −0.127** −0.126** −0.177*** (0.055) (0.054) (0.059) Industry, Country & Year Dummies Yes Yes Yes Observations 6,284 6,284 5,409 (1) (2) (2) Dependent variable: Financial constraint (domestic firms) Foreign Firms Proportion −0.318** (0.153) Foreign Equity Share −0.320** (0.156) Foreign Workers Share −0.222** (0.089) Female Owned 0.055* 0.055* 0.081** (0.031) (0.031) (0.034) Small (5–19 workers) 0.217*** 0.217*** 0.226*** (0.067) (0.065) (0.068) Medium (20–99 workers) 0.092 0.092 0.095 (0.066) (0.063) (0.066) Sole Proprietorship −0.155*** −0.155*** −0.145*** (0.044) (0.043) (0.046) Publicly Traded −0.481** −0.481** −0.546** (0.200) (0.215) (0.237) Private or Non-traded −0.180*** −0.180*** −0.202*** (0.048) (0.047) (0.050) Financial Transparency −0.241*** −0.241*** −0.243*** (0.035) (0.035) (0.037) Technological Capacity −0.243*** −0.243*** −0.221*** (0.046) (0.045) (0.049) Financial Development −0.063*** −0.063*** −0.056*** (0.015) (0.014) (0.015) Legal System −0.115*** −0.115*** −0.148*** (0.042) (0.041) (0.044) Inflation −0.127** −0.126** −0.177*** (0.055) (0.054) (0.059) Industry, Country & Year Dummies Yes Yes Yes Observations 6,284 6,284 5,409 Note: Financial Constraint is an ordinal variable (from 0 to 4) that indicates how financially constrained a firm is. Estimates of the coefficients from the Ordered Probit model are reported here. The constant is suppressed. Robust standard errors are reported in the parentheses. *p < 0.10, **p < 0.05, ***p < 0.01. Open in new tab Table 6: Foreign Firm Presence and Domestic Firms’ Financial Constraint (1) (2) (2) Dependent variable: Financial constraint (domestic firms) Foreign Firms Proportion −0.318** (0.153) Foreign Equity Share −0.320** (0.156) Foreign Workers Share −0.222** (0.089) Female Owned 0.055* 0.055* 0.081** (0.031) (0.031) (0.034) Small (5–19 workers) 0.217*** 0.217*** 0.226*** (0.067) (0.065) (0.068) Medium (20–99 workers) 0.092 0.092 0.095 (0.066) (0.063) (0.066) Sole Proprietorship −0.155*** −0.155*** −0.145*** (0.044) (0.043) (0.046) Publicly Traded −0.481** −0.481** −0.546** (0.200) (0.215) (0.237) Private or Non-traded −0.180*** −0.180*** −0.202*** (0.048) (0.047) (0.050) Financial Transparency −0.241*** −0.241*** −0.243*** (0.035) (0.035) (0.037) Technological Capacity −0.243*** −0.243*** −0.221*** (0.046) (0.045) (0.049) Financial Development −0.063*** −0.063*** −0.056*** (0.015) (0.014) (0.015) Legal System −0.115*** −0.115*** −0.148*** (0.042) (0.041) (0.044) Inflation −0.127** −0.126** −0.177*** (0.055) (0.054) (0.059) Industry, Country & Year Dummies Yes Yes Yes Observations 6,284 6,284 5,409 (1) (2) (2) Dependent variable: Financial constraint (domestic firms) Foreign Firms Proportion −0.318** (0.153) Foreign Equity Share −0.320** (0.156) Foreign Workers Share −0.222** (0.089) Female Owned 0.055* 0.055* 0.081** (0.031) (0.031) (0.034) Small (5–19 workers) 0.217*** 0.217*** 0.226*** (0.067) (0.065) (0.068) Medium (20–99 workers) 0.092 0.092 0.095 (0.066) (0.063) (0.066) Sole Proprietorship −0.155*** −0.155*** −0.145*** (0.044) (0.043) (0.046) Publicly Traded −0.481** −0.481** −0.546** (0.200) (0.215) (0.237) Private or Non-traded −0.180*** −0.180*** −0.202*** (0.048) (0.047) (0.050) Financial Transparency −0.241*** −0.241*** −0.243*** (0.035) (0.035) (0.037) Technological Capacity −0.243*** −0.243*** −0.221*** (0.046) (0.045) (0.049) Financial Development −0.063*** −0.063*** −0.056*** (0.015) (0.014) (0.015) Legal System −0.115*** −0.115*** −0.148*** (0.042) (0.041) (0.044) Inflation −0.127** −0.126** −0.177*** (0.055) (0.054) (0.059) Industry, Country & Year Dummies Yes Yes Yes Observations 6,284 6,284 5,409 Note: Financial Constraint is an ordinal variable (from 0 to 4) that indicates how financially constrained a firm is. Estimates of the coefficients from the Ordered Probit model are reported here. The constant is suppressed. Robust standard errors are reported in the parentheses. *p < 0.10, **p < 0.05, ***p < 0.01. Open in new tab Next, we use alternative measures of foreign firm presence. In Column (2), we use the share of employment by foreign-owned firms as a measure of foreign firm presence (i.e., Foreign Workers Sharejkt). In Column (3), we use the share of equity holdings by foreign-owned firms (i.e., Foreign Equity Sharejkt). Regardless of how foreign firm presence is measured, we find that the coefficient on foreign firm presence is negative and statistically significant at the 1% level. In other words, domestic firms tend to be less financially constrained in industries where the proportion of foreign firms is greater (Column (1)), and where foreign firms have a larger share of employment (Column (2)) and equity holdings (Column (3)). Thus, how foreign firm presence is measured does not affect our conclusion that it is positively associated with the easing of domestic firms’ financial constraint. 5.3 Credit competition Why does the presence of foreign firms help domestic firms to be less financially constrained? A possible explanation (among possibly others) is that firms compete for credit, but credit is scarce in the SSAs. Because foreign firms tend to be less financially constrained (see Table 5), they would therefore be less likely to borrow. As such, their presence would reduce the competition for finance and increase the success of domestic firms in borrowing from banks (Wang and Wang, 2015). We show that this explanation is empirically plausible. Given that foreign firms are less likely to be financially constrained (see Table 5), we first verify that foreign firms, compared with domestic firms, are also less likely to apply for a loan, controlling for firm and country-level characteristics, as well as industry, country and year dummies (see, also, Hansen and Rand, 2014a).16 To do so, we regress a dummy variable, Loan Application, which indicates if a firm has applied for a bank loan, on the ownership type of the firm (i.e., domestic versus foreign-owned). In Table 7, we estimate this relationship using OLS (Column (1)), Conditional Logit (Column (2)), and Probit (Column (3)).17 All three regressions show that the coefficient on Foreign is negative and statistically significant, and therefore, foreign-owned firms are less likely than domestic firms to apply for a loan. For example, Column (3) shows that compared with domestic firms, foreign-owned firms are 14.2 percentage points less likely to apply for a loan on average. Table 7: Foreign-Owned Firms, Domestic Firms and Loan Application (1) (2) (3) OLS Conditional Logit Probit Dependent variable: Loan Application Foreign −0.034** −0.196** −0.142** (0.014) (0.085) (0.064) Female Owned 0.028*** 0.166*** 0.131*** (0.011) (0.063) (0.041) Small (5–19 workers) −0.131*** −0.679*** −0.444*** (0.020) (0.108) (0.103) Medium (20–99 workers) −0.063*** −0.283*** −0.204* (0.019) (0.103) (0.112) Sole Proprietorship −0.032** −0.202** −0.076 (0.015) (0.090) (0.061) Publicly Traded −0.009 −0.077 −0.044 (0.054) (0.301) (0.174) Private or Non-traded −0.018 −0.119 −0.053 (0.016) (0.092) (0.063) Financial Transparency 0.075*** 0.448*** 0.250*** (0.012) (0.072) (0.078) Technological Capacity 0.041*** 0.223*** 0.117* (0.014) (0.081) (0.064) Inflation −0.011 (0.009) Financial Development −0.002** (0.001) Legal System 0.011 (0.021) Industry, Country & Year Dummies Yes Yes Yes Observations 7,583 7,583 7,374 (1) (2) (3) OLS Conditional Logit Probit Dependent variable: Loan Application Foreign −0.034** −0.196** −0.142** (0.014) (0.085) (0.064) Female Owned 0.028*** 0.166*** 0.131*** (0.011) (0.063) (0.041) Small (5–19 workers) −0.131*** −0.679*** −0.444*** (0.020) (0.108) (0.103) Medium (20–99 workers) −0.063*** −0.283*** −0.204* (0.019) (0.103) (0.112) Sole Proprietorship −0.032** −0.202** −0.076 (0.015) (0.090) (0.061) Publicly Traded −0.009 −0.077 −0.044 (0.054) (0.301) (0.174) Private or Non-traded −0.018 −0.119 −0.053 (0.016) (0.092) (0.063) Financial Transparency 0.075*** 0.448*** 0.250*** (0.012) (0.072) (0.078) Technological Capacity 0.041*** 0.223*** 0.117* (0.014) (0.081) (0.064) Inflation −0.011 (0.009) Financial Development −0.002** (0.001) Legal System 0.011 (0.021) Industry, Country & Year Dummies Yes Yes Yes Observations 7,583 7,583 7,374 Note: In Columns (1) and (2), estimates of the coefficients from the Ordinary Least Squares (OLS) regression and Conditional Logit model are reported. In Column (3), the average marginal effects from the Probit model are reported. The constant is suppressed. Robust standard errors are reported in the parentheses. *p < 0.10, **p < 0.05, ***p < 0.01. Open in new tab Table 7: Foreign-Owned Firms, Domestic Firms and Loan Application (1) (2) (3) OLS Conditional Logit Probit Dependent variable: Loan Application Foreign −0.034** −0.196** −0.142** (0.014) (0.085) (0.064) Female Owned 0.028*** 0.166*** 0.131*** (0.011) (0.063) (0.041) Small (5–19 workers) −0.131*** −0.679*** −0.444*** (0.020) (0.108) (0.103) Medium (20–99 workers) −0.063*** −0.283*** −0.204* (0.019) (0.103) (0.112) Sole Proprietorship −0.032** −0.202** −0.076 (0.015) (0.090) (0.061) Publicly Traded −0.009 −0.077 −0.044 (0.054) (0.301) (0.174) Private or Non-traded −0.018 −0.119 −0.053 (0.016) (0.092) (0.063) Financial Transparency 0.075*** 0.448*** 0.250*** (0.012) (0.072) (0.078) Technological Capacity 0.041*** 0.223*** 0.117* (0.014) (0.081) (0.064) Inflation −0.011 (0.009) Financial Development −0.002** (0.001) Legal System 0.011 (0.021) Industry, Country & Year Dummies Yes Yes Yes Observations 7,583 7,583 7,374 (1) (2) (3) OLS Conditional Logit Probit Dependent variable: Loan Application Foreign −0.034** −0.196** −0.142** (0.014) (0.085) (0.064) Female Owned 0.028*** 0.166*** 0.131*** (0.011) (0.063) (0.041) Small (5–19 workers) −0.131*** −0.679*** −0.444*** (0.020) (0.108) (0.103) Medium (20–99 workers) −0.063*** −0.283*** −0.204* (0.019) (0.103) (0.112) Sole Proprietorship −0.032** −0.202** −0.076 (0.015) (0.090) (0.061) Publicly Traded −0.009 −0.077 −0.044 (0.054) (0.301) (0.174) Private or Non-traded −0.018 −0.119 −0.053 (0.016) (0.092) (0.063) Financial Transparency 0.075*** 0.448*** 0.250*** (0.012) (0.072) (0.078) Technological Capacity 0.041*** 0.223*** 0.117* (0.014) (0.081) (0.064) Inflation −0.011 (0.009) Financial Development −0.002** (0.001) Legal System 0.011 (0.021) Industry, Country & Year Dummies Yes Yes Yes Observations 7,583 7,583 7,374 Note: In Columns (1) and (2), estimates of the coefficients from the Ordinary Least Squares (OLS) regression and Conditional Logit model are reported. In Column (3), the average marginal effects from the Probit model are reported. The constant is suppressed. Robust standard errors are reported in the parentheses. *p < 0.10, **p < 0.05, ***p < 0.01. Open in new tab If foreign-owned firms are less likely to apply for a loan, a larger foreign firm presence may reduce the financial constraint of domestic firms by enabling them to borrow more successfully. For the sample of domestic firms, Table 8 regresses Loan Denied, a dummy that indicates if a domestic firm has had an unsuccessful loan application, on each of the foreign firm presence measures (see Eqs. (3)–(5)). Regardless of how foreign firm presence is measured, Table 8 shows that domestic firms are less likely to be denied a loan when foreign firm presence is larger. For example, Column (1) shows that if the proportion of foreign firms (over total number of firms in an industry) increases by 10 percentage points, domestic firms in the same industry would be 4 percentage points less likely to have had a rejected loan. Therefore, domestic firms have been more successful in securing credit when the proportion of foreign firms in the industry is larger. Table 8: Foreign Firm Presence and Loan Denial to Domestic Firms (1) (2) (3) Dependent variable: Loans denied (domestic firms) Foreign Firms Proportion −0.398*** (0.099) Foreign Equity Share −0.392*** (0.098) Foreign Workers Share −0.156* (0.083) Female Owned −0.004 −0.004 −0.008 (0.070) (0.070) (0.083) Small (5–19 workers) 0.799*** 0.800*** 0.683*** (0.134) (0.134) (0.159) Medium (20–99 workers) 0.383*** 0.384*** 0.316** (0.130) (0.130) (0.154) Publicly Traded 0.225 0.223 0.268 (0.390) (0.390) (0.481) Private or Non-traded −0.043 −0.043 −0.111 (0.107) (0.107) (0.126) Financial Transparency −0.149* −0.338*** −0.0003 (0.077) (0.077) (0.089) Technological Capacity −0.119 −0.159 −0.056 (0.095) (0.095) (0.121) Inflation 0.059*** 0.059*** 0.121*** (0.010) (0.010) (0.013) Financial Development −0.003*** −0.003*** −0.001 (0.001) (0.001) (0.001) Legal System −0.118*** −0.118*** −0.236*** (0.018) (0.018) (0.021) Industry, Country & Year Dummies Yes Yes Yes Observations 2,223 2,223 1,727 (1) (2) (3) Dependent variable: Loans denied (domestic firms) Foreign Firms Proportion −0.398*** (0.099) Foreign Equity Share −0.392*** (0.098) Foreign Workers Share −0.156* (0.083) Female Owned −0.004 −0.004 −0.008 (0.070) (0.070) (0.083) Small (5–19 workers) 0.799*** 0.800*** 0.683*** (0.134) (0.134) (0.159) Medium (20–99 workers) 0.383*** 0.384*** 0.316** (0.130) (0.130) (0.154) Publicly Traded 0.225 0.223 0.268 (0.390) (0.390) (0.481) Private or Non-traded −0.043 −0.043 −0.111 (0.107) (0.107) (0.126) Financial Transparency −0.149* −0.338*** −0.0003 (0.077) (0.077) (0.089) Technological Capacity −0.119 −0.159 −0.056 (0.095) (0.095) (0.121) Inflation 0.059*** 0.059*** 0.121*** (0.010) (0.010) (0.013) Financial Development −0.003*** −0.003*** −0.001 (0.001) (0.001) (0.001) Legal System −0.118*** −0.118*** −0.236*** (0.018) (0.018) (0.021) Industry, Country & Year Dummies Yes Yes Yes Observations 2,223 2,223 1,727 Note: Loan Denied indicates if a (domestic) firm was denied a loan application. The average marginal effects from the Probit model are reported here. The constant is suppressed. Robust standard errors are reported in the parentheses. *p < 0.10, **p < 0.05, ***p < 0.01. Open in new tab Table 8: Foreign Firm Presence and Loan Denial to Domestic Firms (1) (2) (3) Dependent variable: Loans denied (domestic firms) Foreign Firms Proportion −0.398*** (0.099) Foreign Equity Share −0.392*** (0.098) Foreign Workers Share −0.156* (0.083) Female Owned −0.004 −0.004 −0.008 (0.070) (0.070) (0.083) Small (5–19 workers) 0.799*** 0.800*** 0.683*** (0.134) (0.134) (0.159) Medium (20–99 workers) 0.383*** 0.384*** 0.316** (0.130) (0.130) (0.154) Publicly Traded 0.225 0.223 0.268 (0.390) (0.390) (0.481) Private or Non-traded −0.043 −0.043 −0.111 (0.107) (0.107) (0.126) Financial Transparency −0.149* −0.338*** −0.0003 (0.077) (0.077) (0.089) Technological Capacity −0.119 −0.159 −0.056 (0.095) (0.095) (0.121) Inflation 0.059*** 0.059*** 0.121*** (0.010) (0.010) (0.013) Financial Development −0.003*** −0.003*** −0.001 (0.001) (0.001) (0.001) Legal System −0.118*** −0.118*** −0.236*** (0.018) (0.018) (0.021) Industry, Country & Year Dummies Yes Yes Yes Observations 2,223 2,223 1,727 (1) (2) (3) Dependent variable: Loans denied (domestic firms) Foreign Firms Proportion −0.398*** (0.099) Foreign Equity Share −0.392*** (0.098) Foreign Workers Share −0.156* (0.083) Female Owned −0.004 −0.004 −0.008 (0.070) (0.070) (0.083) Small (5–19 workers) 0.799*** 0.800*** 0.683*** (0.134) (0.134) (0.159) Medium (20–99 workers) 0.383*** 0.384*** 0.316** (0.130) (0.130) (0.154) Publicly Traded 0.225 0.223 0.268 (0.390) (0.390) (0.481) Private or Non-traded −0.043 −0.043 −0.111 (0.107) (0.107) (0.126) Financial Transparency −0.149* −0.338*** −0.0003 (0.077) (0.077) (0.089) Technological Capacity −0.119 −0.159 −0.056 (0.095) (0.095) (0.121) Inflation 0.059*** 0.059*** 0.121*** (0.010) (0.010) (0.013) Financial Development −0.003*** −0.003*** −0.001 (0.001) (0.001) (0.001) Legal System −0.118*** −0.118*** −0.236*** (0.018) (0.018) (0.021) Industry, Country & Year Dummies Yes Yes Yes Observations 2,223 2,223 1,727 Note: Loan Denied indicates if a (domestic) firm was denied a loan application. The average marginal effects from the Probit model are reported here. The constant is suppressed. Robust standard errors are reported in the parentheses. *p < 0.10, **p < 0.05, ***p < 0.01. Open in new tab These results suggest that a reduction in loans competition is one reason (among possibly others) for why foreign firm presence is negatively associated with the financial constraint of domestic firms. Foreign-owned firms are not only less financially constrained, they are also less likely to borrow from banks. Thus, the presence of foreign-owned firms reduces the competition for credit and improves the access to finance for domestic firms on average. 6. Robustness checks 6.1 Alternative indicators of financial constraint Our main financial constraint measure (Financial Constraint) is an ordinal variable based on managers’ perceptions on how financially constraint their firms are. However, with respect to measurements of credit constraints, Hansen and Rand (2014a) have shown that different measures may yield different conclusions.18 Therefore, as a robustness check, we consider three alternative measures of financial constraint. Our first measure, Serious Constraint, is a dummy variable that indicates if access to finance is a moderate to severe problem for the firm. Our second measure, Credit Product Constraint, is a dummy variable that indicates if the firm does not have access to at least one of the three credit products, i.e., an overdraft, line of credit, or a bank loan (Aterido et al., 2013; Love and Martnez Pera, 2014). Our third measure, Loan Denied (which we have used in Table 8), is a dummy variable that indicates if a firm has had an unsuccessful loan application. Table 9 shows that all three alternative measures of financial constraint are negatively associated with foreign firm presence (measured by Foreign Firms Proportion), and their associations are statistically significant at the 1% level. Therefore, when the presence of foreign-owned firms is larger, domestic firms within the same industry (as these foreign firms) are less likely to report that they have faced moderate to severe financial constraint, have no access to credit facilities, and were denied loans. Thus, there is no evidence that our results depend on how the financial constraints of domestic firms are measured. Table 9: Robustness Check #1: Alternative Indicators of Financial Constraint (1) (2) (3) Dependent variable: Serious constraint Credit products constraint Loan denied Foreign Firms Proportion −0.249*** −0.168*** −0.398*** (0.054) (0.036) (0.099) Female Owned 0.048*** −0.030*** −0.014 (0.013) (0.009) (0.070) Small (5–19 workers) 0.076*** 0.049** 0.799*** (0.027) (0.021) (0.134) Medium (20–99 workers) 0.036 0.051** 0.383*** (0.027) (0.022) (0.130) Sole Proprietorship −0.061** −0.052*** −0.043 (0.029) (0.018) (0.102) Publicly Traded −0.292*** −0.100** 0.225 (0.044) (0.128) (0.390) Private or Non-traded −0.082*** −0.022* −0.043 (0.013) (0.033) (0.107) Financial Transparency −0.102*** −0.078*** −0.149* (0.014) (0.010) (0.025) Technological Capacity −0.101*** −0.008 −0.119 (0.018) (0.015) (0.095) Financial Development −0.002*** −0.002*** −0.003*** (0.000) (0.000) (0.001) Legal System −0.003 0.030*** −0.118*** (0.003) (0.002) (0.018) Inflation 0.005*** 0.005*** 0.059*** (0.002) (0.001) (0.010) Industry, Country & Year Dummies Yes Yes Yes Observations 6,186 2,223 6,277 (1) (2) (3) Dependent variable: Serious constraint Credit products constraint Loan denied Foreign Firms Proportion −0.249*** −0.168*** −0.398*** (0.054) (0.036) (0.099) Female Owned 0.048*** −0.030*** −0.014 (0.013) (0.009) (0.070) Small (5–19 workers) 0.076*** 0.049** 0.799*** (0.027) (0.021) (0.134) Medium (20–99 workers) 0.036 0.051** 0.383*** (0.027) (0.022) (0.130) Sole Proprietorship −0.061** −0.052*** −0.043 (0.029) (0.018) (0.102) Publicly Traded −0.292*** −0.100** 0.225 (0.044) (0.128) (0.390) Private or Non-traded −0.082*** −0.022* −0.043 (0.013) (0.033) (0.107) Financial Transparency −0.102*** −0.078*** −0.149* (0.014) (0.010) (0.025) Technological Capacity −0.101*** −0.008 −0.119 (0.018) (0.015) (0.095) Financial Development −0.002*** −0.002*** −0.003*** (0.000) (0.000) (0.001) Legal System −0.003 0.030*** −0.118*** (0.003) (0.002) (0.018) Inflation 0.005*** 0.005*** 0.059*** (0.002) (0.001) (0.010) Industry, Country & Year Dummies Yes Yes Yes Observations 6,186 2,223 6,277 Note: Serious Constraint indicates if a firm faces moderate to severe financial constraint. Credit Products Constraint indicates if a firm has no access to credit facilities. Loans Denied indicates if the firm had a rejected loan application. The average marginal effects from the Probit model are reported. Robust standard errors are reported in the parentheses. *p < 0.10, **p < 0.05, ***p < 0.01. Open in new tab Table 9: Robustness Check #1: Alternative Indicators of Financial Constraint (1) (2) (3) Dependent variable: Serious constraint Credit products constraint Loan denied Foreign Firms Proportion −0.249*** −0.168*** −0.398*** (0.054) (0.036) (0.099) Female Owned 0.048*** −0.030*** −0.014 (0.013) (0.009) (0.070) Small (5–19 workers) 0.076*** 0.049** 0.799*** (0.027) (0.021) (0.134) Medium (20–99 workers) 0.036 0.051** 0.383*** (0.027) (0.022) (0.130) Sole Proprietorship −0.061** −0.052*** −0.043 (0.029) (0.018) (0.102) Publicly Traded −0.292*** −0.100** 0.225 (0.044) (0.128) (0.390) Private or Non-traded −0.082*** −0.022* −0.043 (0.013) (0.033) (0.107) Financial Transparency −0.102*** −0.078*** −0.149* (0.014) (0.010) (0.025) Technological Capacity −0.101*** −0.008 −0.119 (0.018) (0.015) (0.095) Financial Development −0.002*** −0.002*** −0.003*** (0.000) (0.000) (0.001) Legal System −0.003 0.030*** −0.118*** (0.003) (0.002) (0.018) Inflation 0.005*** 0.005*** 0.059*** (0.002) (0.001) (0.010) Industry, Country & Year Dummies Yes Yes Yes Observations 6,186 2,223 6,277 (1) (2) (3) Dependent variable: Serious constraint Credit products constraint Loan denied Foreign Firms Proportion −0.249*** −0.168*** −0.398*** (0.054) (0.036) (0.099) Female Owned 0.048*** −0.030*** −0.014 (0.013) (0.009) (0.070) Small (5–19 workers) 0.076*** 0.049** 0.799*** (0.027) (0.021) (0.134) Medium (20–99 workers) 0.036 0.051** 0.383*** (0.027) (0.022) (0.130) Sole Proprietorship −0.061** −0.052*** −0.043 (0.029) (0.018) (0.102) Publicly Traded −0.292*** −0.100** 0.225 (0.044) (0.128) (0.390) Private or Non-traded −0.082*** −0.022* −0.043 (0.013) (0.033) (0.107) Financial Transparency −0.102*** −0.078*** −0.149* (0.014) (0.010) (0.025) Technological Capacity −0.101*** −0.008 −0.119 (0.018) (0.015) (0.095) Financial Development −0.002*** −0.002*** −0.003*** (0.000) (0.000) (0.001) Legal System −0.003 0.030*** −0.118*** (0.003) (0.002) (0.018) Inflation 0.005*** 0.005*** 0.059*** (0.002) (0.001) (0.010) Industry, Country & Year Dummies Yes Yes Yes Observations 6,186 2,223 6,277 Note: Serious Constraint indicates if a firm faces moderate to severe financial constraint. Credit Products Constraint indicates if a firm has no access to credit facilities. Loans Denied indicates if the firm had a rejected loan application. The average marginal effects from the Probit model are reported. Robust standard errors are reported in the parentheses. *p < 0.10, **p < 0.05, ***p < 0.01. Open in new tab 6.2 Alternative estimation methods Our baseline results are obtained by estimating an Ordered Probit model. We check if these results are robust, in terms of sign and statistical significance, to the choice of estimation methods. To do so, we re-estimate our model using Ordinary Least Square (OLS) regression with industry, country, and year fixed effects to control for confounding industry and country unobserved heterogeneity and macroeconomic shocks, and Iteratively Reweighted Least Squares (IRLS) regression to mitigate the influence of outlier observations.19 In Table 10, we report the OLS estimates in Column (1) and the IRLS estimates in Column (2). Both estimates show that domestic firms are less financially constraint (at a 1% level of significance) in industries where the proportion of foreign firms is larger. Therefore, our baseline results are not an artefact of the chosen estimation method. Table 10: Robustness Check #2: Alternative Estimation Methods (1) (2) OLS IRLS Dependent variable: Financial constraint (domestic firms) Foreign Firms Proportion −0.431** −1.021*** (0.196) (0.186) Female Owned 0.060 0.137*** (0.040) (0.045) Small (5–19 workers) 0.215*** 0.238*** (0.083) (0.092) Medium (20–99 workers) 0.079 0.081 (0.082) (0.089) Sole Proprietorship −0.205*** −0.148** (0.057) (0.062) Publicly Traded −0.675*** −0.734*** (0.249) (0.264) Private and Non-traded −0.236*** −0.270*** (0.061) (0.065) Financial Transparency −0.303*** −0.406*** (0.045) (0.049) Technological Capacity −0.307*** −0.348*** (0.057) (0.063) Financial Development −0.006*** (0.000) Legal System −0.034*** (0.010) Inflation 0.021*** (0.005) Industry, Country & Year Dummies Yes Yes Observations 6,451 6,284 (1) (2) OLS IRLS Dependent variable: Financial constraint (domestic firms) Foreign Firms Proportion −0.431** −1.021*** (0.196) (0.186) Female Owned 0.060 0.137*** (0.040) (0.045) Small (5–19 workers) 0.215*** 0.238*** (0.083) (0.092) Medium (20–99 workers) 0.079 0.081 (0.082) (0.089) Sole Proprietorship −0.205*** −0.148** (0.057) (0.062) Publicly Traded −0.675*** −0.734*** (0.249) (0.264) Private and Non-traded −0.236*** −0.270*** (0.061) (0.065) Financial Transparency −0.303*** −0.406*** (0.045) (0.049) Technological Capacity −0.307*** −0.348*** (0.057) (0.063) Financial Development −0.006*** (0.000) Legal System −0.034*** (0.010) Inflation 0.021*** (0.005) Industry, Country & Year Dummies Yes Yes Observations 6,451 6,284 Note: In Columns (1) and (2), estimates of the coefficients from Ordinary Least Squares (OLS) and Iterated Least Squares (IRLS) are reported. Robust standard errors are reported in the parentheses. *p < 0.10, **p < 0.05, ***p < 0.01. Open in new tab Table 10: Robustness Check #2: Alternative Estimation Methods (1) (2) OLS IRLS Dependent variable: Financial constraint (domestic firms) Foreign Firms Proportion −0.431** −1.021*** (0.196) (0.186) Female Owned 0.060 0.137*** (0.040) (0.045) Small (5–19 workers) 0.215*** 0.238*** (0.083) (0.092) Medium (20–99 workers) 0.079 0.081 (0.082) (0.089) Sole Proprietorship −0.205*** −0.148** (0.057) (0.062) Publicly Traded −0.675*** −0.734*** (0.249) (0.264) Private and Non-traded −0.236*** −0.270*** (0.061) (0.065) Financial Transparency −0.303*** −0.406*** (0.045) (0.049) Technological Capacity −0.307*** −0.348*** (0.057) (0.063) Financial Development −0.006*** (0.000) Legal System −0.034*** (0.010) Inflation 0.021*** (0.005) Industry, Country & Year Dummies Yes Yes Observations 6,451 6,284 (1) (2) OLS IRLS Dependent variable: Financial constraint (domestic firms) Foreign Firms Proportion −0.431** −1.021*** (0.196) (0.186) Female Owned 0.060 0.137*** (0.040) (0.045) Small (5–19 workers) 0.215*** 0.238*** (0.083) (0.092) Medium (20–99 workers) 0.079 0.081 (0.082) (0.089) Sole Proprietorship −0.205*** −0.148** (0.057) (0.062) Publicly Traded −0.675*** −0.734*** (0.249) (0.264) Private and Non-traded −0.236*** −0.270*** (0.061) (0.065) Financial Transparency −0.303*** −0.406*** (0.045) (0.049) Technological Capacity −0.307*** −0.348*** (0.057) (0.063) Financial Development −0.006*** (0.000) Legal System −0.034*** (0.010) Inflation 0.021*** (0.005) Industry, Country & Year Dummies Yes Yes Observations 6,451 6,284 Note: In Columns (1) and (2), estimates of the coefficients from Ordinary Least Squares (OLS) and Iterated Least Squares (IRLS) are reported. Robust standard errors are reported in the parentheses. *p < 0.10, **p < 0.05, ***p < 0.01. Open in new tab 6.3 Alternative definition of foreign ownership Up to this point, foreign-owned firms have been defined as firms in the host country where foreigners hold at least 10% percent of their equity.20 We show that our conclusions are robust if we adopt a different definition of foreign ownership based on a different equity share in a local firm that is foreign held. As a robustness check, we deliberately choose a more drastic alternative definition of foreign ownership by considering a firm to be foreign owned if 50% or more of its equity is foreign held. We find that our main conclusions are not affected even with this alternative definition of foreign ownership. This is shown, for example, in Table 11, where the proportion of foreign-owned firms in the industry, and the foreign share of equity and employment are all negatively and statistically significant for domestic firms’ financial constraint. Therefore, our baseline results do not depend on how foreign ownership of firms are defined. Table 11: Robustness Check #3: Alternative Definition of Foreign Ownership (1) (2) (3) Dependent variable: Financial constraint (domestic firms) Foreign Firms Proportion −0.811*** (0.168) Foreign Equity Share −0.220*** (0.067) Foreign Workers Share −0.610*** (0.106) Female Owned 0.109*** 0.125*** 0.163*** (0.034) (0.035) (0.042) Small (5–19 workers) 0.178** 0.302*** 0.305*** (0.069) (0.068) (0.085) Medium (20–99 workers) 0.062 0.145** 0.168** (0.067) (0.066) (0.082) Sole Proprietorship −0.100** −0.086* −0.131** (0.046) (0.047) (0.057) Publicly Traded −0.585*** −0.575** −0.860*** (0.221) (0.241) (0.260) Private or Non-traded −0.168*** −0.198*** −0.235*** (0.049) (0.050) (0.060) Financial Transparency −0.285*** −0.281*** −0.282*** (0.037) (0.038) (0.044) Technological Capacity −0.261*** −0.161*** −0.246*** (0.049) (0.039) (0.060) Financial Development −0.005*** −0.005*** −0.004*** (0.000) (0.001) (0.001) Legal System −0.016** −0.007 0.007 (0.007) (0.008) (0.009) Inflation 0.018*** 0.015*** 0.008 (0.004) (0.005) (0.005) Industry, Country & Year Dummies Yes Yes Yes Observations 5,377 5,168 5,409 (1) (2) (3) Dependent variable: Financial constraint (domestic firms) Foreign Firms Proportion −0.811*** (0.168) Foreign Equity Share −0.220*** (0.067) Foreign Workers Share −0.610*** (0.106) Female Owned 0.109*** 0.125*** 0.163*** (0.034) (0.035) (0.042) Small (5–19 workers) 0.178** 0.302*** 0.305*** (0.069) (0.068) (0.085) Medium (20–99 workers) 0.062 0.145** 0.168** (0.067) (0.066) (0.082) Sole Proprietorship −0.100** −0.086* −0.131** (0.046) (0.047) (0.057) Publicly Traded −0.585*** −0.575** −0.860*** (0.221) (0.241) (0.260) Private or Non-traded −0.168*** −0.198*** −0.235*** (0.049) (0.050) (0.060) Financial Transparency −0.285*** −0.281*** −0.282*** (0.037) (0.038) (0.044) Technological Capacity −0.261*** −0.161*** −0.246*** (0.049) (0.039) (0.060) Financial Development −0.005*** −0.005*** −0.004*** (0.000) (0.001) (0.001) Legal System −0.016** −0.007 0.007 (0.007) (0.008) (0.009) Inflation 0.018*** 0.015*** 0.008 (0.004) (0.005) (0.005) Industry, Country & Year Dummies Yes Yes Yes Observations 5,377 5,168 5,409 Note: A firm is defined as foreign owned here if at least 50% of its equity is foreign held. Financial Constraint is an ordinal variable (from 0 to 4) that indicates how financially constrained a firm is. Estimates of the coefficients from the Ordered Probit model are reported here. The constant is suppressed. Robust standard errors are reported in the parentheses. *p < 0.10, **p < 0.05, ***p < 0.01. Open in new tab Table 11: Robustness Check #3: Alternative Definition of Foreign Ownership (1) (2) (3) Dependent variable: Financial constraint (domestic firms) Foreign Firms Proportion −0.811*** (0.168) Foreign Equity Share −0.220*** (0.067) Foreign Workers Share −0.610*** (0.106) Female Owned 0.109*** 0.125*** 0.163*** (0.034) (0.035) (0.042) Small (5–19 workers) 0.178** 0.302*** 0.305*** (0.069) (0.068) (0.085) Medium (20–99 workers) 0.062 0.145** 0.168** (0.067) (0.066) (0.082) Sole Proprietorship −0.100** −0.086* −0.131** (0.046) (0.047) (0.057) Publicly Traded −0.585*** −0.575** −0.860*** (0.221) (0.241) (0.260) Private or Non-traded −0.168*** −0.198*** −0.235*** (0.049) (0.050) (0.060) Financial Transparency −0.285*** −0.281*** −0.282*** (0.037) (0.038) (0.044) Technological Capacity −0.261*** −0.161*** −0.246*** (0.049) (0.039) (0.060) Financial Development −0.005*** −0.005*** −0.004*** (0.000) (0.001) (0.001) Legal System −0.016** −0.007 0.007 (0.007) (0.008) (0.009) Inflation 0.018*** 0.015*** 0.008 (0.004) (0.005) (0.005) Industry, Country & Year Dummies Yes Yes Yes Observations 5,377 5,168 5,409 (1) (2) (3) Dependent variable: Financial constraint (domestic firms) Foreign Firms Proportion −0.811*** (0.168) Foreign Equity Share −0.220*** (0.067) Foreign Workers Share −0.610*** (0.106) Female Owned 0.109*** 0.125*** 0.163*** (0.034) (0.035) (0.042) Small (5–19 workers) 0.178** 0.302*** 0.305*** (0.069) (0.068) (0.085) Medium (20–99 workers) 0.062 0.145** 0.168** (0.067) (0.066) (0.082) Sole Proprietorship −0.100** −0.086* −0.131** (0.046) (0.047) (0.057) Publicly Traded −0.585*** −0.575** −0.860*** (0.221) (0.241) (0.260) Private or Non-traded −0.168*** −0.198*** −0.235*** (0.049) (0.050) (0.060) Financial Transparency −0.285*** −0.281*** −0.282*** (0.037) (0.038) (0.044) Technological Capacity −0.261*** −0.161*** −0.246*** (0.049) (0.039) (0.060) Financial Development −0.005*** −0.005*** −0.004*** (0.000) (0.001) (0.001) Legal System −0.016** −0.007 0.007 (0.007) (0.008) (0.009) Inflation 0.018*** 0.015*** 0.008 (0.004) (0.005) (0.005) Industry, Country & Year Dummies Yes Yes Yes Observations 5,377 5,168 5,409 Note: A firm is defined as foreign owned here if at least 50% of its equity is foreign held. Financial Constraint is an ordinal variable (from 0 to 4) that indicates how financially constrained a firm is. Estimates of the coefficients from the Ordered Probit model are reported here. The constant is suppressed. Robust standard errors are reported in the parentheses. *p < 0.10, **p < 0.05, ***p < 0.01. Open in new tab 6.4 Omitting the Largest Domestic Firms In Section 4.1, we have discussed why it is unlikely for reverse causality to occur. This is because given that domestic firms are usually small, it will be unlikely for them to have an impact on the composition of foreign firms in the industry, which is how foreign firm presence is measured. What about the large domestic firms? Could the statistical significance of foreign firm presence be driven by reverse causality stemming from these firms? In this robustness check, we re-estimate Eq. (2) without the top 5% and 10% largest domestic firms and report the new results in Table 12 and 13 respectively. Even without the large domestic firms, we find that foreign firm presence is still statistically significant at the 1% level. Therefore, there is no evidence that these firms are driving the statistical significance of foreign firm presence in our baseline regressions. Table 12: Robustness Check #4: Excluding the Top 5% Largest Domestic Firms (1) (2) (3) Dependent variable: Financial constraint (domestic firms) Foreign Firms Proportion −0.592*** (0.155) Foreign Equity Share −0.593*** (0.154) Foreign Workers Share −0.551*** (0.109) Female Owned 0.099*** 0.099*** 0.156*** (0.033) (0.033) (0.042) Small (5–19 workers) 0.172** 0.172** 0.265*** (0.079) (0.079) (0.100) Medium (20–99 workers) 0.046 0.046 0.118 (0.077) (0.077) (0.098) Sole Proprietorship −0.121*** −0.121*** −0.123** (0.044) (0.044) (0.057) Publicly Traded −0.376 −0.376 −0.736*** (0.243) (0.243) (0.276) Private or Non-traded −0.196*** −0.196*** −0.224*** (0.048) (0.048) (0.061) Financial Transparency −0.231*** −0.231*** −0.267*** (0.035) (0.035) (0.045) Technological Capacity −0.248*** −0.248*** −0.235*** (0.048) (0.048) (0.062) Financial Development −0.004*** −0.004*** −0.004*** (0.001) (0.001) (0.001) Legal System 0.014* 0.014* 0.034*** (0.008) (0.008) (0.010) Inflation −0.010** −0.010** −0.008 (0.005) (0.005) (0.006) Industry, Country & Year Dummies Yes Yes Yes Observations 5,914 5,914 5,263 (1) (2) (3) Dependent variable: Financial constraint (domestic firms) Foreign Firms Proportion −0.592*** (0.155) Foreign Equity Share −0.593*** (0.154) Foreign Workers Share −0.551*** (0.109) Female Owned 0.099*** 0.099*** 0.156*** (0.033) (0.033) (0.042) Small (5–19 workers) 0.172** 0.172** 0.265*** (0.079) (0.079) (0.100) Medium (20–99 workers) 0.046 0.046 0.118 (0.077) (0.077) (0.098) Sole Proprietorship −0.121*** −0.121*** −0.123** (0.044) (0.044) (0.057) Publicly Traded −0.376 −0.376 −0.736*** (0.243) (0.243) (0.276) Private or Non-traded −0.196*** −0.196*** −0.224*** (0.048) (0.048) (0.061) Financial Transparency −0.231*** −0.231*** −0.267*** (0.035) (0.035) (0.045) Technological Capacity −0.248*** −0.248*** −0.235*** (0.048) (0.048) (0.062) Financial Development −0.004*** −0.004*** −0.004*** (0.001) (0.001) (0.001) Legal System 0.014* 0.014* 0.034*** (0.008) (0.008) (0.010) Inflation −0.010** −0.010** −0.008 (0.005) (0.005) (0.006) Industry, Country & Year Dummies Yes Yes Yes Observations 5,914 5,914 5,263 Note: Financial Constraint is an ordinal variable (from 0 to 4) that indicates how financially constrained a firm is. Estimates of the coefficients from the Ordered Probit model are reported here. The constant is suppressed. Robust standard errors are reported in the parentheses. *p < 0.10, **p < 0.05, ***p < 0.01. Open in new tab Table 12: Robustness Check #4: Excluding the Top 5% Largest Domestic Firms (1) (2) (3) Dependent variable: Financial constraint (domestic firms) Foreign Firms Proportion −0.592*** (0.155) Foreign Equity Share −0.593*** (0.154) Foreign Workers Share −0.551*** (0.109) Female Owned 0.099*** 0.099*** 0.156*** (0.033) (0.033) (0.042) Small (5–19 workers) 0.172** 0.172** 0.265*** (0.079) (0.079) (0.100) Medium (20–99 workers) 0.046 0.046 0.118 (0.077) (0.077) (0.098) Sole Proprietorship −0.121*** −0.121*** −0.123** (0.044) (0.044) (0.057) Publicly Traded −0.376 −0.376 −0.736*** (0.243) (0.243) (0.276) Private or Non-traded −0.196*** −0.196*** −0.224*** (0.048) (0.048) (0.061) Financial Transparency −0.231*** −0.231*** −0.267*** (0.035) (0.035) (0.045) Technological Capacity −0.248*** −0.248*** −0.235*** (0.048) (0.048) (0.062) Financial Development −0.004*** −0.004*** −0.004*** (0.001) (0.001) (0.001) Legal System 0.014* 0.014* 0.034*** (0.008) (0.008) (0.010) Inflation −0.010** −0.010** −0.008 (0.005) (0.005) (0.006) Industry, Country & Year Dummies Yes Yes Yes Observations 5,914 5,914 5,263 (1) (2) (3) Dependent variable: Financial constraint (domestic firms) Foreign Firms Proportion −0.592*** (0.155) Foreign Equity Share −0.593*** (0.154) Foreign Workers Share −0.551*** (0.109) Female Owned 0.099*** 0.099*** 0.156*** (0.033) (0.033) (0.042) Small (5–19 workers) 0.172** 0.172** 0.265*** (0.079) (0.079) (0.100) Medium (20–99 workers) 0.046 0.046 0.118 (0.077) (0.077) (0.098) Sole Proprietorship −0.121*** −0.121*** −0.123** (0.044) (0.044) (0.057) Publicly Traded −0.376 −0.376 −0.736*** (0.243) (0.243) (0.276) Private or Non-traded −0.196*** −0.196*** −0.224*** (0.048) (0.048) (0.061) Financial Transparency −0.231*** −0.231*** −0.267*** (0.035) (0.035) (0.045) Technological Capacity −0.248*** −0.248*** −0.235*** (0.048) (0.048) (0.062) Financial Development −0.004*** −0.004*** −0.004*** (0.001) (0.001) (0.001) Legal System 0.014* 0.014* 0.034*** (0.008) (0.008) (0.010) Inflation −0.010** −0.010** −0.008 (0.005) (0.005) (0.006) Industry, Country & Year Dummies Yes Yes Yes Observations 5,914 5,914 5,263 Note: Financial Constraint is an ordinal variable (from 0 to 4) that indicates how financially constrained a firm is. Estimates of the coefficients from the Ordered Probit model are reported here. The constant is suppressed. Robust standard errors are reported in the parentheses. *p < 0.10, **p < 0.05, ***p < 0.01. Open in new tab Table 13: Robustness Check #5: Excluding the Top 10% Largest Domestic Firms (1) (2) (3) Dependent variable: Financial constraint (domestic firms) Foreign Firms Proportion −0.616*** (0.157) Foreign Equity Share −0.615*** (0.157) Foreign Workers Share −0.601*** (0.111) Female Owned 0.100*** 0.100*** 0.160*** (0.034) (0.034) (0.043) Small (5–19 workers) 0.279 0.279 0.315 (0.175) (0.175) (0.226) Medium (20–99 workers) 0.158 0.158 0.173 (0.175) (0.175) (0.225) Sole Proprietorship −0.124*** −0.124*** −0.120** (0.045) (0.045) (0.058) Publicly Traded −0.422 −0.422 −0.656** (0.257) (0.257) (0.283) Private or Non-traded −0.200*** −0.200*** −0.224*** (0.049) (0.049) (0.062) Financial Transparency −0.232*** −0.232*** −0.266*** (0.036) (0.036) (0.045) Technological Capacity −0.245*** −0.245*** −0.229*** (0.050) (0.050) (0.065) Financial Development −0.004*** −0.004*** −0.004*** (0.001) (0.001) (0.001) Legal System 0.013 0.013 0.033*** (0.008) (0.008) (0.011) Inflation −0.009* −0.009* −0.008 (0.005) (0.005) (0.006) Industry, Country & Year dummies Yes Yes Yes Observations 5,699 5,699 5,060 (1) (2) (3) Dependent variable: Financial constraint (domestic firms) Foreign Firms Proportion −0.616*** (0.157) Foreign Equity Share −0.615*** (0.157) Foreign Workers Share −0.601*** (0.111) Female Owned 0.100*** 0.100*** 0.160*** (0.034) (0.034) (0.043) Small (5–19 workers) 0.279 0.279 0.315 (0.175) (0.175) (0.226) Medium (20–99 workers) 0.158 0.158 0.173 (0.175) (0.175) (0.225) Sole Proprietorship −0.124*** −0.124*** −0.120** (0.045) (0.045) (0.058) Publicly Traded −0.422 −0.422 −0.656** (0.257) (0.257) (0.283) Private or Non-traded −0.200*** −0.200*** −0.224*** (0.049) (0.049) (0.062) Financial Transparency −0.232*** −0.232*** −0.266*** (0.036) (0.036) (0.045) Technological Capacity −0.245*** −0.245*** −0.229*** (0.050) (0.050) (0.065) Financial Development −0.004*** −0.004*** −0.004*** (0.001) (0.001) (0.001) Legal System 0.013 0.013 0.033*** (0.008) (0.008) (0.011) Inflation −0.009* −0.009* −0.008 (0.005) (0.005) (0.006) Industry, Country & Year dummies Yes Yes Yes Observations 5,699 5,699 5,060 Note: Financial Constraint is an ordinal variable (from 0 to 4) that indicates how financially constrained a firm is. Estimates of the coefficients from the Ordered Probit model are reported here. The constant is suppressed. Robust standard errors are reported in the parentheses. *p < 0.10, **p < 0.05, ***p < 0.01. Open in new tab Table 13: Robustness Check #5: Excluding the Top 10% Largest Domestic Firms (1) (2) (3) Dependent variable: Financial constraint (domestic firms) Foreign Firms Proportion −0.616*** (0.157) Foreign Equity Share −0.615*** (0.157) Foreign Workers Share −0.601*** (0.111) Female Owned 0.100*** 0.100*** 0.160*** (0.034) (0.034) (0.043) Small (5–19 workers) 0.279 0.279 0.315 (0.175) (0.175) (0.226) Medium (20–99 workers) 0.158 0.158 0.173 (0.175) (0.175) (0.225) Sole Proprietorship −0.124*** −0.124*** −0.120** (0.045) (0.045) (0.058) Publicly Traded −0.422 −0.422 −0.656** (0.257) (0.257) (0.283) Private or Non-traded −0.200*** −0.200*** −0.224*** (0.049) (0.049) (0.062) Financial Transparency −0.232*** −0.232*** −0.266*** (0.036) (0.036) (0.045) Technological Capacity −0.245*** −0.245*** −0.229*** (0.050) (0.050) (0.065) Financial Development −0.004*** −0.004*** −0.004*** (0.001) (0.001) (0.001) Legal System 0.013 0.013 0.033*** (0.008) (0.008) (0.011) Inflation −0.009* −0.009* −0.008 (0.005) (0.005) (0.006) Industry, Country & Year dummies Yes Yes Yes Observations 5,699 5,699 5,060 (1) (2) (3) Dependent variable: Financial constraint (domestic firms) Foreign Firms Proportion −0.616*** (0.157) Foreign Equity Share −0.615*** (0.157) Foreign Workers Share −0.601*** (0.111) Female Owned 0.100*** 0.100*** 0.160*** (0.034) (0.034) (0.043) Small (5–19 workers) 0.279 0.279 0.315 (0.175) (0.175) (0.226) Medium (20–99 workers) 0.158 0.158 0.173 (0.175) (0.175) (0.225) Sole Proprietorship −0.124*** −0.124*** −0.120** (0.045) (0.045) (0.058) Publicly Traded −0.422 −0.422 −0.656** (0.257) (0.257) (0.283) Private or Non-traded −0.200*** −0.200*** −0.224*** (0.049) (0.049) (0.062) Financial Transparency −0.232*** −0.232*** −0.266*** (0.036) (0.036) (0.045) Technological Capacity −0.245*** −0.245*** −0.229*** (0.050) (0.050) (0.065) Financial Development −0.004*** −0.004*** −0.004*** (0.001) (0.001) (0.001) Legal System 0.013 0.013 0.033*** (0.008) (0.008) (0.011) Inflation −0.009* −0.009* −0.008 (0.005) (0.005) (0.006) Industry, Country & Year dummies Yes Yes Yes Observations 5,699 5,699 5,060 Note: Financial Constraint is an ordinal variable (from 0 to 4) that indicates how financially constrained a firm is. Estimates of the coefficients from the Ordered Probit model are reported here. The constant is suppressed. Robust standard errors are reported in the parentheses. *p < 0.10, **p < 0.05, ***p < 0.01. Open in new tab 7. Conclusion Because their underdeveloped financial sector, policymakers in the SSAs have encouraged the foreign ownership of firms to help, among others, improve the access to finance by domestic firms. However, there is no empirical evidence that a larger foreign presence leads to less financially constrained domestic firms. Moreover, the association between foreign firm presence and domestic firms’ financial constraint is á priori ambiguous. To study this issue, we employ cross-country firm-level data that spans across 36 SSAs. We find that in industries with a larger foreign firm presence, domestic firms tend to be less financially constrained. The direction and the statistical significance of this effect is robust to the way financial constraint of domestic firms and foreign firm presence are measured, to the use of alternative estimation methods and alternative definitions of foreign ownership, among others. Because of the richness of the WBES surveys, we are able to explore further into why the presence of foreign-owned firms may ease the financial constraints of domestic firms. we find that foreign-owned firms are not only less financially constrained, they are also less likely to borrow from banks. Moreover, in industries where foreign firm presence is larger, domestic firms tend to be more successful in securing bank credit. Therefore, there is evidence that foreign firm presence reduces the competition for bank credit, and this helps the domestic firms to improve their access to finance and ease their financial constraints. Our paper has policy relevance, in that it offers new evidence to show that foreign firm presence may address the most commonly voiced concern in operating a business in the SSAs – the lack of finance. Because financial development is an extremely important determinant of growth, it would be useful to follow up on whether the increase in foreign firm presence in the SSAs may also lead to higher levels of growth and socio-economic development. Footnotes 1 For example, the share of two largest banks’ in Burundi account 45% of the total bank asset (Nkurunziza et al., 2012). 2 In the SSAs, the average ratio of private credit to GDP is only 24%. This is dwarfed by the ratio of 77% for all other developing economies, and 172% for high income economies. 3 Some studies looks at the mechanisms through which foreign direct investment affect financial constraints of domestic firms (see, for example, Harrison and McMillan, 2003; Harrison et al., 2004; Héricourt and Poncet, 2009). 4 For example, foreign direct investment to the SSAs has grown from $8 billion in 2000 to $18 billion in 2004, $36 billion in 2006 and $50 billion in 2012. 5 Empirical evidence on credit constraints in the SSAs comes mainly from within-country studies, which are difficult to generalise. See, for example (Harrison and McMillan, 2003) for Cote d’Ivoire, Lashitew (2017) for Ethiopia. 6 For example, the share of two largest banks’ account 45% of the total bank asset in Burundi (Nkurunziza et al., 2012). 7 For example, Mali and Mozambique encourage foreign firm ownership by improving business climate, such privatisation, implementing new laws, and promoting accession to international agreement related to direct foreign investment (Morisset, 2001). Botswana and Mauritius attract foreign firms by improving property rights and reducing restrictive compliance requirements (Basu and Srinivasan, 2002). 8 For example, based on Ivory Coast’s firm-level data, Harrison and McMillan (2003) find that domestic firms could be credit constrained with FDI as foreign enterprises may crowd out domestic firms in the local credit markets. By contrast, Harrison et al. (2004) find that FDI inflow in 34 European countries is associated with reduced firm-level financial constraints. 9 A direct linkage between foreign-owned firms and domestic input suppliers, or foreign firms input providers and domestic firms, can improve the reputation and creditworthiness of domestic firms (Javorcik and Spatareanu, 2009; Newman et al., 2015). 10 Our sample of firms obtained from Angola, Botswana, Burkina Faso, Burundi, Cameroon, Cape Verde, Chad, Congo, DRC, Eritrea, Gabon, Gambia, Ghana, Guinea, Guinea Bissau, Ivory Coast, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritanian, Mauritius, Mozambique, Namibia, Niger, Rwanda, Sierra Leone, Senegal, South Africa, Swaziland, Tanzania, Togo, Uganda and Zambia. 11 Our choice of explanatory variables is based largely on the existing literature related to firms’ access to finance (see, for example, Beck et al., 2006, 2008; Asiedu et al., 2013; Aterido et al., 2013; Hansen and Rand, 2014a, b). 12 If the proportions of domestic and foreign-owned firms facing serious financial constraints are the same, the data points will lie on the 45 degree line. 13 As it turns out, we find that foreign firm presence is statistically significant for domestic firms’ financial constraint whether industry or country fixed effects are controlled for. This suggests that foreign firm presence have an impact on domestic firms’ financial constraint beyond the influence of industry or country fixed effects. 14 Hansen and Rand (2014a) show how three different measures of credit constraints lead to three different estimates of the effects of gender on firms’ credit situation. 15 This result is consistent with Asiedu et al. (2013) for the case of manufacturing firms in the SSAs. 16 Just to emphasise, we consider loan application than the actual amount of loan received. Clearly, the quantum of the loan may depend not only on how needy the firm is, it also depends on the restrictions placed by banks on how much it can borrow. 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Google Scholar Crossref Search ADS WorldCat Appendix Appendix A: Access to Finance of Firms in the SSAs versus the OECD Table A1: Access to Finance of Firms in the SSAs versus the OECD Sub-Saharan Africa OECD Firms with a bank loan/line of credit (%) 23.5 49.1 Firms using banks to finance investment (%) 17.6 31.5 Investment financed by banks (%) 11.2 18.9 Firms using bank to finance working capital (%) 22.1 68.6 Working capital financed by banks (%) 8.6 40.6 Working capital financed by supplier credit (%) 11.2 14.4 Loan requiring collateral (%) 82.26 72.6 Firms identifying finance as a major constraint (%) 42.6 15.9 Sub-Saharan Africa OECD Firms with a bank loan/line of credit (%) 23.5 49.1 Firms using banks to finance investment (%) 17.6 31.5 Investment financed by banks (%) 11.2 18.9 Firms using bank to finance working capital (%) 22.1 68.6 Working capital financed by banks (%) 8.6 40.6 Working capital financed by supplier credit (%) 11.2 14.4 Loan requiring collateral (%) 82.26 72.6 Firms identifying finance as a major constraint (%) 42.6 15.9 Note: The number in the table are averages across firms calculated from WBES (2015) webpage. The number of SSAs and OECD countries used for this computation are 36 and 16 countries respectively. Open in new tab Table A1: Access to Finance of Firms in the SSAs versus the OECD Sub-Saharan Africa OECD Firms with a bank loan/line of credit (%) 23.5 49.1 Firms using banks to finance investment (%) 17.6 31.5 Investment financed by banks (%) 11.2 18.9 Firms using bank to finance working capital (%) 22.1 68.6 Working capital financed by banks (%) 8.6 40.6 Working capital financed by supplier credit (%) 11.2 14.4 Loan requiring collateral (%) 82.26 72.6 Firms identifying finance as a major constraint (%) 42.6 15.9 Sub-Saharan Africa OECD Firms with a bank loan/line of credit (%) 23.5 49.1 Firms using banks to finance investment (%) 17.6 31.5 Investment financed by banks (%) 11.2 18.9 Firms using bank to finance working capital (%) 22.1 68.6 Working capital financed by banks (%) 8.6 40.6 Working capital financed by supplier credit (%) 11.2 14.4 Loan requiring collateral (%) 82.26 72.6 Firms identifying finance as a major constraint (%) 42.6 15.9 Note: The number in the table are averages across firms calculated from WBES (2015) webpage. The number of SSAs and OECD countries used for this computation are 36 and 16 countries respectively. Open in new tab Appendix B: Definition of Variables Table B1: Definition of Variables Variable Definition Dependent variables: measures of financial constraint Financial Constraint Ordinal response by the firm from 0 (no financial constraint) to 4 (severe financial constraint) Serious Constraint =1 if the firm reports having moderate to severe financial constraint Credit Products Constraint =1 if the firm has no access to overdraft, line of credit or bank loan Loans Denied =1 if the firm has applied for but denied formal credit Measures of foreign ownership and foreign firm presence Foreign =1 if the firm has at least 10% of its equity held by foreigners Foreign Firms Proportion Ratio of number of foreign-owned firms to total firms Foreign Equity Share Ratio of equity holdings by foreign-owned firms over total firm equity Foreign Workers Share Ratio of foreign firms’ employee to total employees Firm-level variables Female Owned =1 if at least one principal owner is female Small =1 if the firm has 5–19 employees Medium =1 if the firm has 20–99 employees Sole Proprietorship =1 if the firm is owned by 1 person Publicly Traded =1 for shareholding firm with share trade in the stock market Private or Non-traded =1 for shareholding firm with non-share trade in the stock market Technological Capacity =1 if the firm has a website Financial Transparency =1 if the firm’s annual financial statements were audited Country-level variables Inflation Inflation rate [WDI] Financial Development Ratio of private credit to GDP [WDI] Legal System Protection of property rights index, 0 (weakest) to 10 (strongest) [WDI] Variable Definition Dependent variables: measures of financial constraint Financial Constraint Ordinal response by the firm from 0 (no financial constraint) to 4 (severe financial constraint) Serious Constraint =1 if the firm reports having moderate to severe financial constraint Credit Products Constraint =1 if the firm has no access to overdraft, line of credit or bank loan Loans Denied =1 if the firm has applied for but denied formal credit Measures of foreign ownership and foreign firm presence Foreign =1 if the firm has at least 10% of its equity held by foreigners Foreign Firms Proportion Ratio of number of foreign-owned firms to total firms Foreign Equity Share Ratio of equity holdings by foreign-owned firms over total firm equity Foreign Workers Share Ratio of foreign firms’ employee to total employees Firm-level variables Female Owned =1 if at least one principal owner is female Small =1 if the firm has 5–19 employees Medium =1 if the firm has 20–99 employees Sole Proprietorship =1 if the firm is owned by 1 person Publicly Traded =1 for shareholding firm with share trade in the stock market Private or Non-traded =1 for shareholding firm with non-share trade in the stock market Technological Capacity =1 if the firm has a website Financial Transparency =1 if the firm’s annual financial statements were audited Country-level variables Inflation Inflation rate [WDI] Financial Development Ratio of private credit to GDP [WDI] Legal System Protection of property rights index, 0 (weakest) to 10 (strongest) [WDI] Note: Unless specified in brackets, all the variables are obtained from the World Bank Enterprise Survey. WDI is short for World Development Indicators. AREAER is short for Annual Report on Exchange Arrangements and Exchange Restrictions (from the IMF). Open in new tab Table B1: Definition of Variables Variable Definition Dependent variables: measures of financial constraint Financial Constraint Ordinal response by the firm from 0 (no financial constraint) to 4 (severe financial constraint) Serious Constraint =1 if the firm reports having moderate to severe financial constraint Credit Products Constraint =1 if the firm has no access to overdraft, line of credit or bank loan Loans Denied =1 if the firm has applied for but denied formal credit Measures of foreign ownership and foreign firm presence Foreign =1 if the firm has at least 10% of its equity held by foreigners Foreign Firms Proportion Ratio of number of foreign-owned firms to total firms Foreign Equity Share Ratio of equity holdings by foreign-owned firms over total firm equity Foreign Workers Share Ratio of foreign firms’ employee to total employees Firm-level variables Female Owned =1 if at least one principal owner is female Small =1 if the firm has 5–19 employees Medium =1 if the firm has 20–99 employees Sole Proprietorship =1 if the firm is owned by 1 person Publicly Traded =1 for shareholding firm with share trade in the stock market Private or Non-traded =1 for shareholding firm with non-share trade in the stock market Technological Capacity =1 if the firm has a website Financial Transparency =1 if the firm’s annual financial statements were audited Country-level variables Inflation Inflation rate [WDI] Financial Development Ratio of private credit to GDP [WDI] Legal System Protection of property rights index, 0 (weakest) to 10 (strongest) [WDI] Variable Definition Dependent variables: measures of financial constraint Financial Constraint Ordinal response by the firm from 0 (no financial constraint) to 4 (severe financial constraint) Serious Constraint =1 if the firm reports having moderate to severe financial constraint Credit Products Constraint =1 if the firm has no access to overdraft, line of credit or bank loan Loans Denied =1 if the firm has applied for but denied formal credit Measures of foreign ownership and foreign firm presence Foreign =1 if the firm has at least 10% of its equity held by foreigners Foreign Firms Proportion Ratio of number of foreign-owned firms to total firms Foreign Equity Share Ratio of equity holdings by foreign-owned firms over total firm equity Foreign Workers Share Ratio of foreign firms’ employee to total employees Firm-level variables Female Owned =1 if at least one principal owner is female Small =1 if the firm has 5–19 employees Medium =1 if the firm has 20–99 employees Sole Proprietorship =1 if the firm is owned by 1 person Publicly Traded =1 for shareholding firm with share trade in the stock market Private or Non-traded =1 for shareholding firm with non-share trade in the stock market Technological Capacity =1 if the firm has a website Financial Transparency =1 if the firm’s annual financial statements were audited Country-level variables Inflation Inflation rate [WDI] Financial Development Ratio of private credit to GDP [WDI] Legal System Protection of property rights index, 0 (weakest) to 10 (strongest) [WDI] Note: Unless specified in brackets, all the variables are obtained from the World Bank Enterprise Survey. WDI is short for World Development Indicators. AREAER is short for Annual Report on Exchange Arrangements and Exchange Restrictions (from the IMF). Open in new tab Appendix C: Descriptive Statistics Table C1: Descriptive Statistics Mean Std. dev. Minimum Maximum Financial Constraint 2.00 1.51 0 4 Foreign Ownership 0.19 0.39 0 1 Foreign Firms Proportion 0.18 0.17 0 1 Foreign Equity Share 0.19 0.17 0 1 Foreign Workers Share 0.33 0.24 0 1 Female Ownership 0.29 0.45 0 1 Small (0–19 Workers) 0.63 0.48 0 1 Medium (20–99 Workers) 0.26 0.44 0 1 Large (over 99 Workers) 0.10 0.30 0 1 Sole Proprietorship 0.5 0.5 0 1 Public Traded 0.02 0.15 0 1 Private or Non-traded 0.31 0.46 0 1 Technological Capacity 0.2 0.40 0 1 Financial Transparency 0.47 0.50 0 1 Ination 8.2 4.2 2.05 19.57 Financial Development 38 48.7 5.08 182.62 Legal system 5.25 2.60 2.00 10.00 Mean Std. dev. Minimum Maximum Financial Constraint 2.00 1.51 0 4 Foreign Ownership 0.19 0.39 0 1 Foreign Firms Proportion 0.18 0.17 0 1 Foreign Equity Share 0.19 0.17 0 1 Foreign Workers Share 0.33 0.24 0 1 Female Ownership 0.29 0.45 0 1 Small (0–19 Workers) 0.63 0.48 0 1 Medium (20–99 Workers) 0.26 0.44 0 1 Large (over 99 Workers) 0.10 0.30 0 1 Sole Proprietorship 0.5 0.5 0 1 Public Traded 0.02 0.15 0 1 Private or Non-traded 0.31 0.46 0 1 Technological Capacity 0.2 0.40 0 1 Financial Transparency 0.47 0.50 0 1 Ination 8.2 4.2 2.05 19.57 Financial Development 38 48.7 5.08 182.62 Legal system 5.25 2.60 2.00 10.00 Open in new tab Table C1: Descriptive Statistics Mean Std. dev. Minimum Maximum Financial Constraint 2.00 1.51 0 4 Foreign Ownership 0.19 0.39 0 1 Foreign Firms Proportion 0.18 0.17 0 1 Foreign Equity Share 0.19 0.17 0 1 Foreign Workers Share 0.33 0.24 0 1 Female Ownership 0.29 0.45 0 1 Small (0–19 Workers) 0.63 0.48 0 1 Medium (20–99 Workers) 0.26 0.44 0 1 Large (over 99 Workers) 0.10 0.30 0 1 Sole Proprietorship 0.5 0.5 0 1 Public Traded 0.02 0.15 0 1 Private or Non-traded 0.31 0.46 0 1 Technological Capacity 0.2 0.40 0 1 Financial Transparency 0.47 0.50 0 1 Ination 8.2 4.2 2.05 19.57 Financial Development 38 48.7 5.08 182.62 Legal system 5.25 2.60 2.00 10.00 Mean Std. dev. Minimum Maximum Financial Constraint 2.00 1.51 0 4 Foreign Ownership 0.19 0.39 0 1 Foreign Firms Proportion 0.18 0.17 0 1 Foreign Equity Share 0.19 0.17 0 1 Foreign Workers Share 0.33 0.24 0 1 Female Ownership 0.29 0.45 0 1 Small (0–19 Workers) 0.63 0.48 0 1 Medium (20–99 Workers) 0.26 0.44 0 1 Large (over 99 Workers) 0.10 0.30 0 1 Sole Proprietorship 0.5 0.5 0 1 Public Traded 0.02 0.15 0 1 Private or Non-traded 0.31 0.46 0 1 Technological Capacity 0.2 0.40 0 1 Financial Transparency 0.47 0.50 0 1 Ination 8.2 4.2 2.05 19.57 Financial Development 38 48.7 5.08 182.62 Legal system 5.25 2.60 2.00 10.00 Open in new tab Author notes We are most grateful to the Editor Professor Francis Teal and two anonymous referees for their insightful comments and suggestions. © The Author(s) 2019. Published by Oxford University Press on behalf of the Centre for the Study of African Economies, all rights reserved. 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Journal of African EconomiesOxford University Press

Published: Aug 1, 2019

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