4.1 Estimated Equation for the Determinants of Outward Investment

BOX 4.1 Estimated Equation for the Determinants of Outward Investment To assess the determinants of firms’ decisions to invest abroad, the analysis considers the following empirical specification:

Yisd = α + β1productivityi + β2typei + β3financei + β4networkid + β5exportid +β6enti + γZsd + λs+ λd + εisd, (B4.1.1)

in which Yisd is an outcome of interest, such as the binary decision of firm i from source country s to invest in goods production, services operations, and trade-supporting services in destination country d. For each investment, productivityi is firm productivity; typei is firm type, including state-owned enterprise, foreign owned, and family owned; financei is firm source of financing, including internal funds, business group, home and host commercial banks, and international banks; networkid is the firm’s network in country d; exportid is the firm’s export experience in country d; enti is a characteristic (i.e., risk appetite) of the entrepreneur; Zsd is a vector of source-destination country characteristics, such as distance, contiguity, and common language; λs and λd are vectors of source and destination country dummies to control for all country-specific factors; and εisd is the error term, which is clustered at the industry level to account for potential correlations in the error term across firms in the same industry.

magnitudes, and they capture factors that are not typically quantified, yet are important for investment entry.

The estimation results are presented first with standard variables, and informationrelated indicators are subsequently included. National policy (which chapter 3 has shown to be important) and other individual country characteristics are captured in the country fixed effects.

INVESTORS ARE NEIGHBORS: SEEKING HIGH-CONNECTIVITY LOCATIONS

The “standard” specification starts with table B.6 in appendix B, which does not include data on being well informed or on knowledge. The results show that trade costs matter, as reflected in the negative and statistically significant impact of distance on investment and the positive impact of contiguity. The impact of distance on services operations and trade-supporting investments, as reflected in the respective coefficients, is more than double that on goods production investments. This outcome is consistent with studies that find that cross-border services delivery requires greater communications with customers and a greater sensitivity to cultural aspects, and involves more complex information (Oldenski 2012). Thus, multinational enterprises from neighboring economies are more likely to be successful in cross-border investments, especially in services operations and trade-supporting investments, than in distant nations.

Theoretically, the impact of trade costs on FDI is ambiguous and depends on the nature of the investment. For example, high tariffs may induce foreign investors to

produce near their consumers to circumvent trade restrictions—a horizontal investment, “tariff-jumping FDI,” or “quid pro quo FDI” (Bhagwati 1987). However, high trade costs may deter firms that engage in vertical investments (in which they produce inputs abroad for assembly activities at home and anticipate frequent border crossings). Similarly, they would reduce trade-supporting FDI and distribution FDI (as reflected in chapter 2).

This result validates the importance of the trade and transportation infrastructure and trade-facilitation initiatives that national governments and partners have supported to reduce trade costs. These investments increase trade but also support FDI.

The case study evidence also validates the importance of proximity for entry strategies. Nepal’s CG Foods first invested in the nearby NER; Bangladesh’s Rahimafrooz first set up a distribution office in nearby Kolkata, West Bengal; and Sri Lankans began by investing in southern India (Timex’s first retail store was in Bengaluru and Brandix’s apparel park is in Visakhapatnam, Andhra Pradesh).

PIONEERING INVESTORS ARE HIGH-PRODUCTIVITY FIRMS WITH INVESTIBLE SURPLUS FUNDS

High-productivity, large firms are the ones that invest abroad (table B.6 in appendix B), consistent with the framework given in chapter 2. Large, productive firms have production volumes that provide them with sufficient funds to incur the sunk costs of entry. This construct applies to all types of investments. The higher the sunk costs, the higher the level of firm productivity needed to cover the sunk costs (thereby restricting the activity to fewer firms). Increases in productivity increase the likelihood of investing for all goods production, services operations, and trade-supporting investments. As expected, a given increase in productivity will have a larger impact on tradesupporting investments because they have the lowest sunk costs. Services operations investments seem to have smaller sunk costs compared with goods production, judging by the larger relative magnitude of the impact of productivity on services investments.

Because the data set includes agriculture, manufacturing, and services firms, there is no standard measurement of productivity that applies to all firms. Firm productivity is measured by the firm’s position in its industry productivity distribution in the home country. Thus, a firm with the highest productivity would be placed in the 99th percentile of the industry productivity distribution. Firm size was similarly measured, that is, relative to the size of other firms in its industry.

Higher-productivity firms participate in all three forms of investment. The median investor is in the 75th percentile of the productivity distribution compared with the median noninvestor, who is at the 50th percentile. Replacing the productivity variable with a turnover measure provides similar results, in that larger firms (those with higher turnover) tend to be the ones that invest. This outcome is to be expected, given the correlation of 0.9 between turnover and productivity (table B.5 in appendix B). Accordingly, the turnover results are not reported.

Financing matters, and the results indicate that internal funds are crucial for all types of investments (table B.6 in appendix B). Internal funds are much more important for trade-supporting and services operations investments. Intraconglomerate financing appears to matter for goods production and trade-supporting investments. Therefore, smaller firms with insufficient internal funds that do not belong to a conglomerate will be constrained. Home commercial banks are important for goods production investments.

NETWORKED FIRMS ARE MORE LIKELY TO INVEST, MAKING INVESTING MORE INCLUSIVE

The regional pioneers have information networks, and these networks make investing abroad more inclusive. Firms with founders or chief executive officers with ethnic or visible social networks abroad tend to have an increased likelihood of investing (table B.7 in appendix B). Firm-level indicators of knowledge connectivity, networks, and bilateral trust could not be used in the regressions, given that they represented knowledge, relationships, and trust today that could be outcomes of investments as opposed to the determinant of investments. Instead, networks were measured by the ethnic or visible social network (for example, marriage) of the founder or chief executive officer. Networks allow for variation in sunk entry costs across firms. The ethnic network variable is statistically significant for all types of investment, but stronger and larger in magnitude for services operations and trade-supporting investments, which is consistent with other studies that find that cultural factors are more important in services industries. The network provides the investor with extra information and an uncertainty-reducing property that drives down the entry costs for the networked investor compared with other investors or potential investors.

For services investments, networks appear to have a larger effect on investment decisions than does productivity. The effect of the ethnic network exceeds the effect of moving up eight rungs in the productivity distribution.9 However, for the same increase in productivity, this same relationship does not hold for goods production and tradesupporting investments. This is an important result, because when entrepreneurs are asked to rank important factors in decision-making, they always tend to rank networks low. As with distance, this effect of ethnic networks may signal the importance of cultural sensitivity in delivering many types of services, such as hospitality, retail, education, and medical services. This result is in line with research that finds that cultural distance is more relevant for mergers and acquisitions in services (Barattieri, Borchert, and Mattoo 2016).

Networks make the investing activity more inclusive—smaller, lower-productivity firms that are networked may also invest abroad, thereby widening the pool of potential investors. The higher the sunk entry costs of an activity, the higher the required level of firm productivity to be a potential investor, implying that fewer firms qualify to be in the pool. Thus, networked firms with medium productivity may also invest in regional markets and, to enter, do not have to be as large and productive as firms without networks.