1 Introduction

In recent years, foreign direct investment (FDI) growth has far outpaced growth in either world production or trade. While merchandise trade grew about 85%, and world production grew 27%, world FDI flows increased by 535% during the 1990s. Remarkably, developing countries benefited disproportionately, raising their share of the world inward stock of FDI from 20.6% in 1990 to over 30% in 1999. While FDI growth slowed down somewhat during the 2000s, it has remained high until very recently, for example rising over 20% into developing countries in 2007 (UNCTAD, various years). Many developing countries are now actively seeking to attract more foreign investment, especially in the form of direct investment, which tends to be far more stable than portfolio investment. Often, this represents a reversal of policies that severely regulated and discouraged FDI. Presumably, the motive for seeking more FDI is its function as a catalyst of development. Positive welfare effects stem from increased employment, forward and backward linkages, and technological spillovers.

The probability of such welfare improvements, and technological spillovers in particular, however, crucially depends on the motive for producing abroad and hence the type of multinational. In the presence of transportation costs, a market access motive gives rise to horizontal multinationals, which roughly duplicate production in multiple locations. If the motive is to exploit comparative advantage from differences in factor endowments, absent international factor prize equalization, the production process becomes vertically fragmented. While there is no consensus in the literature, recent evidence suggests that vertical spillovers are more likely than horizontal ones. Footnote 1

While the early literature has treated horizontal (Markusen 1984) and vertical (Helpman 1984) multinationals separately, recent work has provided a unified general equilibrium framework called the “knowledge-capital” model, in which all types of multinationals arise endogenously (Markusen 2002; see Markusen and Maskus 2001 for a survey). When the theory is taken to the data, the vertical model is strongly rejected in favor of the horizontal one (Carr et al. 2001; Markusen and Maskus 2002; Blonigen et al. 2003). Using different terminology, Brainard (1997) similarly finds support for the proximity-concentration, but not the factor-proportions hypothesis. Only a few studies, e.g. Braconier et al. (2005) and Yeaple (2003), find some support for the vertical model and thus a role for comparative advantage.

The rejection of the vertical model is puzzling not only because of the increased share of developing countries as recipients of FDI, but in light of recent findings that explain much of the growth in world trade with increasing vertical specialization (Hummels et al. 2001; Yi 2003), which suggests that trade and FDI are complements. A possible source of bias in the above cited studies is that they all use data on FDI originating in or targeted at developed countries, usually the United States. Most partner countries are other developed countries, in which case horizontal multinationals would indeed be expected to dominate despite the presence of some developing partners. Using a detailed industry-level data set, this paper in contrast examines the determinants of FDI in a developing country, Mexico. By focusing on a comparatively skilled-labor and capital scarce host country, one would expect considerably more support for a comparative advantage motive. While this would cast some doubt on the result that worldwide FDI is dominated by market access–seeking multinationals, one should regard the results of this paper as complementary to the empirical work cited above, not as evidence against the finding that horizontal multinationals are important.

In addition to providing a different perspective from which to approach the question of which motive for FDI finds more support in the data, this paper contributes to our understanding of the determinants and desirability of FDI in developing countries more generally. Mexico presents an interesting case not only due to its proximity to the United States, but also because it is arguably at an intermediate stage of development. Footnote 2 Hence, its comparative advantage may no longer solely arise from an abundance of unskilled labor, but the availability of relatively more skilled labor than other developing countries as well as positive externalities from decades of foreign investment. Footnote 3

Finally, the availability of host country industry-level information not only on FDI, but factor intensities and other characteristics allows identification not only of country-aggregate, but also industry-specific determinants of FDI and any interactions between them, unlike many of the previous studies (e.g. Carr et al. 2001; Markusen and Maskus 2002; Blonigen et al. 2003).

The results of the empirical analysis lend support to the hypothesis that Mexico’s comparative advantage in (unskilled-)labor intensive production processes is an important, though not the only, determinant of its inward FDI. Skill and capital intensity of production, differences in skill endowments relative to source countries as well as market size and similarity are statistically and economically significant determinants of FDI. When skill differences are very large, FDI flows into sectors that are intensive in total labor, regardless of skill level.

The paper proceeds as follows. The next section describes the main features of the FDI data and provides some additional information on FDI in Mexico. Section 3 lays out the conceptual framework guiding the empirical estimation, which is described in more detail in Sect. 4, followed by a discussion of the results. Section 6 offers concluding remarks.

2 FDI in Mexico

Since the data used in this study may not be familiar, they are described in some detail here. FDI data come from the Mexican Ministry of the Economy and consist of FDI flows into Mexico from 1994 to 2000. The data are at the 4-digit industry level, using the Mexican Industrial Classification System (CMAP), which is similar to the 1968 International Standard of Industrial Classification (ISIC). Footnote 4 Table 1 shows summary statistics, broken down by major industry; Table 2 shows the corresponding shares. Just over 60% of FDI has gone into the manufacturing sector during this 7-year period. Wholesale and retail trade as well as financial services also received substantial amounts. Table 3 shows that within the manufacturing sector, the bulk of FDI goes into production of metal products, including automobiles. Thus not surprisingly, many automobile manufacturers, such as General Motors, Ford, and Volkswagen, are among the largest foreign investors.

Table 1 FDI inflows (millions of US$)
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Table 2 FDI inflow shares (in %)
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Table 3 FDI inflow shares, manufacturing subsectors (in %)
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For the entire sample period, FDI inflows fluctuate around 10 billion US$ per year. This is considerably more than Mexico received on an annual basis prior to 1994. Foreign investment flows were low for much of the 1980s. The first substantial increase in FDI in the late 1980s and early 1990s coincided with a major overhaul of Mexico’s investment laws in 1989. Many obstacles to foreign investors, such as licensing requirements and restrictions pertaining to majority ownership, were removed. This change reversed Mexico’s long-standing policy of reserving ownership in many sectors to Mexican nationals or the Mexican state and encouraging foreign investment only in sectors that were deemed crucial to the pursuit of import substitution policies. Footnote 5 At the same time, and earlier than in many other countries in the region, substantial privatizations occurred. By 1994, the number of state-owned enterprises had decreased to only 80, down from 1,155. However, foreign investors participated in this sale only to a small degree. FDI from privatization constituted only 7.9% of total FDI between 1990 and 1995 (Franko 1999: 158–161). Yet, during the first half of the 1990s, Mexico was the major recipient of FDI in Latin America, with a big surge occurring in 1994 after the inception of NAFTA.

Table 4 shows the source country shares of FDI inflows. Most FDI comes from the United States, with European Union countries a distant second. The only other major source countries of FDI are Japan and South Korea. Not surprisingly, investments from developed countries vastly dominate Mexican inward FDI with negligible amounts from developing countries. Footnote 6

Table 4 FDI source country shares (in %)
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3 Conceptual framework

This section discusses the conceptual framework that forms the basis for the empirical estimation. The theoretical determinants of FDI, in particular those that matter for horizontal (market access seeking) versus vertical (comparative advantage) multinationals are drawn from the literature, mainly from the seminal study by Brainard (1997) and the pioneering work of Markusen (1997, 2002), which was put to an empirical test in Carr et al. (2001), Markusen and Maskus (2002) and Blonigen et al. (2003).

Markusen (1997, 2002) refers to his unified approach as the “knowledge-capital model” of the multinational enterprise. This model entails as special cases those of horizontal and vertical multinationals, as well as purely national firms. It draws its name from the assumption that headquarter services (e.g. research and development) can be geographically separated from production activities and supplied simultaneously to several production facilities at low cost. This implies that production is characterized by increasing returns to scale. In the two-country model, horizontal multinationals are then firms with production plants in both countries, but headquarters located in only one. Vertical multinationals have a single plant in the country that does not host the headquarters. Finally, national plants maintain a single plant and headquarters in the same country and may or may not export to the other country. The ordering of skill-intensities in the economy is such that headquarter services are more skilled-labor intensive than production, which in turn is more skilled-labor intensive than the rest of the economy. Finally, it is assumed that national goods markets are segmented. Footnote 7

Market access seeking firms are encouraged by the joint market size of the host and source country, but discouraged by large market size differences since these do not make a subsidiary in the smaller market worthwhile, relative to exports. The bilateral sum of GDP and squared GDP differences are commonly used in the literature to account for market size and country (dis-)similarities (e.g. Carr et al. 2001; Blonigen et al. 2003). Market size should have no independent effect on vertical multinationals. Motives for the latter arise primarily from differences in relative endowments among countries. If countries differ sufficiently with respect to their endowments such that they are in different cones of diversification, the skilled-labor abundant country will be the headquarter site, while the unskilled-labor abundant country will have a comparative advantage in hosting the production facility.

Consequently, foreign investment that is based on a comparative advantage motive should flow into industries that are relatively low-skilled labor intensive. Moreover, relative country skill abundance and industry skill intensity should interact if FDI reflects what Yeaple (2003) refers to as a “chain of comparative advantage”. From a skilled-labor scarce host country perspective this means that moderately skilled-labor abundant source countries would invest disproportionately in unskilled-labor intensive industries, whereas highly skilled-labor abundant source countries would invest in what are, from their perspective, relatively unskilled-labor intensive industries, but from the host country perspective could be relatively skilled-labor intensive industries. This is because, as pointed out by Feenstra and Hanson (1997), the range of goods produced in a skilled-labor abundant country encompasses more skilled-labor intensive industries whereas the range of goods produced in a skilled-labor scarce country encompasses more unskilled-labor intensive industries. Hence, what passes as skilled-labor intensive in one country appears relatively unskilled-labor intensive in another.

Other industry characteristics influencing inward foreign investment are capital intensity, plant and corporate scale economies and the degree of concentration. In a world with, say, three factors, skilled labor, unskilled labor, and capital, an unskilled-labor abundant country that is capital scarce has a comparative advantage not only in industries characterized by a low share of skilled workers, but also low capital intensity. The knowledge-capital model as well as Brainard (1997) and others emphasize the positive effect of corporate scale economies on FDI as headquarter services can be spread across many plants, but the negative effect of plant scale economies since these encourage concentration in just one place of production.

The final elements that impact FDI in this framework are transport and more generally trade and investment costs. If transport costs are low, a firm might substitute exports for foreign production. On the one hand, parent country trade costs should have a negative effect on multinational activity of all types since exporting back to the home country from a foreign country would be more costly relative to home production. Host country trade costs, on the other hand, should encourage multinational activity, the well-known tariff-jumping argument. Note, however, that their effect is potentially ambiguous as they make imports of intermediates more costly. A model of assembly of intermediates for re-export would predict that host country tariffs would deter such operations. Host country investment costs should have a negative effect.

The next section describes the empirical implementation of the theory and discusses some econometric issues.

4 The empirical model

Formally, the basic estimating equation is as follows:

$$ \begin{aligned} FDI_{ijt} &=\beta _{1}SumGDP_{jt}+\beta _{2}\left( DiffGDP \right) _{jt}^{2} \, + \, \beta_{3}diffskill_{jt}+\beta _{4}skillintensity_{it}\\ &\quad+\beta _{5}\left( diffskill\times skillintensity\right) _{ijt} \, + \, \beta _{6}klratio_{it}+\beta _{7}numfirms_{it}+\beta _{8}firmsize_{it}\\ &\quad+\beta _{9}invcost_{t}+\beta _{10}transport_{ij}+\beta _{11} sourcetariff_{ijt}+\beta _{12}hosttariff_{ijt}\\ &\quad +\beta _{13}distance_{j}+\varepsilon _{ijt} \end{aligned} $$
(1)

The subscripts indicate each regressor’s variation over time (t), across industries (i) and source countries (j). Country aggregate variables (the sum of GDP, squared GDP differences, skill differences with Mexico, and distance) do not vary across industries. Industry variables include measures of factor intensity (skilled labor and capital intensity), proxies for corporate and plant scale economies, and industry-level transportation and trade costs (tariffs, distance). The latter vary by source country and host tariffs are included as well.

Skill abundance is measured as the difference in the endowment share of skilled labor in the source country and Mexico. Skill intensity is measured as the share of skilled workers in an industry. This is consistent with the definition used by Feenstra and Hanson (1997). Skill intensities exhibit substantial heterogeneity across sectors, ranging from about 5 to over 55% skilled labor (see the summary statistics in Table 5). By including an interaction term between the skilled labor abundance of a source country and the skill intensity of an industry, the effect of skill differences is allowed to vary across industries and likewise, the effect of skill intensity of an industry may vary across source countries of FDI.

Table 5 Summary statistics for the base sample (14,628 observations)
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Specifically, the marginal effect of skill intensity of an industry on FDI is given by

$$ \frac{\partial FDI}{\partial skillintensity}=\beta _{4}+\beta_{5} diffskill $$
(2)

The theory predicts that this correlation will be positive when skill differences are large, but negative when they are small. Recall that FDI from countries that are very skill abundant with respect to Mexico would flow in what are, from Mexico’s perspective, relatively skill intensive industries, generating a positive correlation. FDI from countries that are moderately skill abundant would flow into unskilled-labor intensive industries, generating a negative relationship. The preceding implies that β4 < 0 and β5 > 0. Then, a threshold skill difference can be computed at which the correlation switches signs. Note that such a result would not only support Yeaple’s (2003) finding of a “chain of comparative advantage”, but would refine it as he did not distinguish between moderate and large differences in human capital.

Similarly, the marginal effect of skill differences on FDI is given by

$$ \frac{\partial FDI}{\partial diffskill}=\beta _{3}+\beta_{5}\; skillintensity $$
(3)

If β3 < 0 and β5 > 0, then greater differences in skill endowments will tend to lower FDI, but less so in skill-intensive industries. Again, depending on the magnitude of the coefficients, there may be a threshold level of skill intensity at which the total effect switches signs. However, theory suggests that this derivative is expected to be positive, regardless of the skill intensity of an industry, since more skill-abundant countries outsource a greater range of inputs.

Since endowment differences are defined as source country minus Mexican relative endowments, differences are positive when the source is skilled-labor abundant, but negative when the source is skilled-labor scarce. In the estimation below, this issue is dealt with in two ways. First, I interact a dummy indicating whether the parent country is skilled-labor abundant or skilled-labor scarce with the difference measure (sabdiffskill and habdiffskill, respectively). There are also two interaction terms with skill intensity in that case. This avoids confounding positive and negative values, but does not impose a symmetric effect. Second, I take the ratio of skilled to unskilled labor of the source country relative to Mexico, in which case a value below one indicates that the source country is skilled-labor scarce, a value above one indicates that it is skilled-labor abundant. Footnote 8

During the sample period, Mexico’s share of skilled labor in the total labor force is about 15%, while the corresponding (unweighted) share in the FDI source countries is 24%. Thus, Mexico is skilled-labor scarce with respect to most sources. It is skilled-labor abundant only with respect to some smaller developing countries such as El Salvador, Columbia, Costa Rica, Malaysia or Turkey and even in those cases sometimes only for some years of the sample period.

This can be seen in Fig. 1, which shows FDI observations inserted into an Edgeworth box. Mexico, whose origin is located in the North-East corner, is large relative to most source countries as relative country size roughly changes along the North-East/South-West diagonal and most observations of relative skill endowment are concentrated near the South-West corner. Observations above this diagonal indicate skilled-labor abundant source countries, whereas observations below indicate skilled-labor scarce source countries. Studies such as Carr et al. (2001) that use only US data tend to suffer from the problem that there are few observations in the regions where substantial vertical multinational activity would be expected. This is where endowment differences exist, but country size differences are not too large. Figure 1 shows that this is of no concern here since observations cover a sufficiently large part of the Edgeworth box that the knowledge-capital model would predict the presence of both vertical and horizontal multinationals.

Fig. 1
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FDI observations in an Edgeworth box. Notes: Each circle represents one Mexico-source country-year FDI observation. Source country skilled-labor share is on the vertical axis, source country unskilled-labor share is on the horizontal axis. The source country is relatively skilled-labor abundant when observations are located above the diagonal

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Since capital may also matter in practice, as discussed above, a measure of industries’ capital intensity, klratio, defined as net fixed assets per worker, is included in the empirical specification. The proxies for plant- and firm-level scale economies are average firm size, defined as the value of total gross production divided by the total number of firms, and the total number of firms in an industry. While data availability precludes the use of the same measures as in Brainard (1997), the advantage of this measure is that we use information on scale economies in industries that FDI is actually flowing into. Both Brainard (1997) and Yeaple (2003) use US firm information for outward FDI even though it is well known that industry characteristics may vary across countries within the same industry. Using host country information avoids this problem.

The measure of Mexican investment cost accounts for both formal investment barriers as well as the overall economic climate that affects the decision where to invest. In addition to industry-level source and host country tariffs, which take into account the selective reductions resulting from NAFTA, I include transport costs. These are defined as the c.i.f./f.o.b. factor and obtained from the World Bank’s bilateral trade database. Finally, distance is measured as the distance between country capitals. Its sign is theoretically ambiguous since it can proxy for both trade and investment costs. It is included since it usually performs well in gravity-type models (e.g. Carr et al. 2001; Blonigen et al. 2003). A detailed description of the data and variables can be found in the "Data Appendix". Table 6 lists all source countries.

Table 6 FDI source countries included in the base sample
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Only FDI flows are available at the industry-level distinguished by source country, which means that there are many zeros in the data. Hence, a Tobit model is estimated. Footnote 9 Since no FDI stocks are observed, observations are treated as censored if an industry-year-source country observation is zero and there has not been a positive flow in that industry in a previous time period. If anything, this treats too many observations as censored and results are biased against finding significant effects. As a robustness check, FDI stocks are constructed from aggregate stocks and the disaggregated flows and used as the dependent variable instead. See the "Data Appendix" for details.

Industry size varies substantially, implying potential heteroscedasticity in the error structure. I deal with this issue by using industry size as weights and estimating robust standard errors. Since not all regressors vary along all dimensions, disturbances may be correlated within groups. While the coefficients would still be unbiased, they are inefficient and variances and hence standard errors could be biased (Kloek 1981). Thus, clustering is taken into account and all estimates are adjusted accordingly. A complicating factor is that there are two clusters: industries as well as source countries. Below, I focus on results that are adjusted for clustering on industry. Footnote 10

Serial correlation is another potential problem in the data. Besides ignoring the time dimension and estimating a cross-section, this issue is dealt with by conducting Wooldridge’s (2002) test for serial correlation in panel data. Other robustness checks include inserting time and country fixed effects.

5 Results

In this section, I report results from estimating (1) in the manner outlined above, including a number of robustness checks. The discussion focuses on both the statistical and the economic significance of market access and comparative advantage motives of multinational activity in Mexico.

Column 1 of Table 7 shows results from estimating the full sample with FDI flows as the dependent variable. The coefficient on the bilateral sum of GDPs is positive, the one on squared GDP differences is negative, both significant at the 1% level. This indicates the importance of market size and similarity and is consistent with a market access motive for FDI which has been well established in the literature (Brainard 1997; Carr et al. 2001; Blonigen et al. 2003) for the United States and OECD countries.

Table 7 Weighted tobit estimates
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The coefficients on both sector-level skill intensity and the capital-labor ratio are negative and significant at the 1% level, which is consistent with a comparative advantage motive for FDI. Since the interaction term between skill differences and skill intensity is significantly positive, however, the marginal effect of sector-level skill intensity on FDI is given by \(\frac{\partial FDI}{\partial skillintensity}=-3,\!694+21,\!540 \times sabdiffskill, \) which is negative when sabdiffskill is less than 0.172. The mean skill difference being about 0.1, this is true for 24 out of 39 countries that are skilled-labor abundant relative to Mexico. For these countries, FDI and skill intensity are negatively related. However, for highly skilled-labor abundant countries, skill intensity and FDI are positively correlated. This result represents a refinement of Yeaple’s (2003) “chain of comparative advantage”, where skill differences led to outsourcing of unskilled-labor intensive production. To explore it further, I split the sample below into moderately and highly skill abundant countries.

The marginal effect of skill differences is given by \(\frac{\partial FDI}{\partial sabdiffskill}=-4,\!123+21,\!540\times skillintensity,\) which is positive when a sector’s skill intensity is greater than 0.19. The mean skill intensity in the sample is 0.29 and only about 16% of sectors have a skill intensity below 0.19, accounting for just over 5% of FDI. Moreover, the coefficient on skill differences alone is only significant at the 10% level and, unlike the other factor variables, not consistent across specifications. For the few countries that are skilled-labor scarce relative to Mexico, neither skill differences nor skill intensity are statistically significant. Hence, greater skill differences raise FDI. This is an important result as it demonstrates that comparative advantage considerations are important for multinational activity. It is in contrast to findings from studies that use only US data, such as Carr et al. (2001), who document an inverse relationship between skill differences and multinational activity.

The coefficient on host tariffs is significantly negative in most specifications, the opposite of the expected sign if FDI were tariff-jumping, providing additional evidence consistent with vertical FDI, where higher tariffs make importing intermediates more costly. The coefficient on source country tariffs, in contrast, is generally positive, although usually not significantly so.

In order to evaluate the economic significance of these results, Table 8 calculates the marginal effects of a change in skill differences, skilled labor and capital intensities, as well as market similarity. At the means of the data, a one standard deviation increase in skill differences is predicted to raise FDI by close to 200,000 US$, which is just under 20% of the mean non-zero flow. A one standard deviation increase in the skill intensity of a sector reduces FDI by about 165,000 US$. This effect is somewhat smaller than that reported in Blonigen et al. (2003) using either US or OECD data, yet still substantial. The negative effects of higher capital intensity or greater market dissimilarity are about three times as large. All results are statistically significant. Thus, factor endowment differences, the factor intensity of production and market similarity are all statistically as well as economically important determinants of inward FDI. This can be taken as evidence that both market access and comparative advantage considerations propel firms to locate in Mexico.

Table 8 Marginal effects of skill differences, skill intensity, capital intensity and GDP differences, evaluated at the means of the data
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To get an even better sense of these magnitudes, we can perform various thought experiments. Suppose Mexican GDP doubled (holding other countries’ GDP constant), which would increase the Mexican market and reduce market dissimilarity. This would increase FDI by about 26%. Note that if other countries’ GDP were to rise also, this number would increase since the positive effect from greater joint market size outweighs the negative effect from greater market dissimilarity, at least as long as they do not grow faster than Mexico. Suppose Mexican’s skilled-labor share would increase such that the skill gap with the the United States were cut in half. This is estimated to reduce FDI by about 35%. Among sectors receiving substantial amounts of FDI, the most affected sectors would be food processing, basic chemicals and printing, whereas the automotive, appliances and artificial and synthetic fiber sectors would hardly be affected. If both GDP and skill differences were to change simultaneously, FDI would be reduced by about 9%, owing to the fact that the negative effect from lower FDI due to reduced comparative advantage outweighs the positive effect from greater market access.

The results are qualitatively and quantitatively very robust to different specifications, which will therefore be discussed only briefly. Columns 2 and 3 of Table 7 use a measure of FDI stocks, constructed from aggregate stocks and industry-level flow data, as the dependent variable, both in a panel and a cross-section context. The Wooldridge test indicates, however, that serial correlation is a concern in the panel specification, though not in any specification with FDI flows as the dependent variable. Column 4 includes time fixed effects, with almost identical results, indicating that most of the variation is across industries and source countries. Column 5 replaces some source country variables with US data as one might expect non-US investors to use Mexico primarily as an export platform. Besides the robustness of the factor variable coefficients, it is noteworthy that only US transport costs have a significant effect.

Across all specifications, the results regarding the effect of plant and corporate scale economies are mixed. The number of firms in an industry has a significantly negative effect on FDI. A large number of firms may indicate a lack of scale economies or a relatively competitive industry. Transport costs have the expected negative sign, although they are statistically significant only in the export platform specification. Source country tariffs are positive, but generally not significant. Host country (Mexican) tariffs appear with a negative sign and are significant in some specifications. This is not surprising given the importance of imported intermediate products for assembly in Mexico and probably reflects the deterring effect of high tariffs on these intermediates.

The bottom of the table reports results from a generalized Hausman test to compare the model alternatives to the base specification. While it rejects equality of all common coefficients for all but one of the other models, the null of equality is never rejected for the main variables of interest, those involving factor endowments and intensities and market size and similarity, when flows are the dependent variable (not reported).

To further investigate the result that the magnitude of skill differences between source countries and Mexico matters for their effects on FDI, I split the sample into three parts: moderately skilled-labor abundant source countries (those with skill differences below the threshold level of 0.172 identified above), highly skilled-labor abundant source countries (those above the threshold level) and countries that are skilled-labor scarce relative to Mexico. The results are reported in columns 1–3 of Table 9. Focusing on the skill variables, when skill differences are relatively small, only the coefficient on skill intensity is significant and negative. Since the interaction term is insignificant, FDI from all of these countries flows into relatively unskilled-labor intensive industries. The marginal effect of an increase in industry skill intensity on FDI is estimated to be larger than in the full sample, both in absolute and in relative terms given the smaller average size of FDI flows. For highly skilled-labor abundant countries, the statistical significance of the skill variables is similar to that found in the full sample, but a look at the estimated marginal effects reveals that only skill differences and the sector-level capital ratio significantly affect FDI from these countries. Sector-level skill intensity does not have a significant effect. Thus, the ambivalent result with respect to the correlation between FDI and skill intensity appears to be driven by those countries that are highly skilled-labor abundant. They invest in labor-intensive industries in general, regardless of skill level. Conversely, skill differences actually have a negative and quite large effect on FDI from countries that are unskilled-labor abundant relative to Mexico. This is intuitive as no comparative-advantage FDI from these countries would be expected in Mexico.

Table 9 Weighted tobit estimates
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Columns 4 and 5 of Table 9 report some final robustness checks. Column 4 shows results where skill differences are now defined as the ratio of the endowment of skilled to unskilled workers in the source country divided by the ratio of skilled to unskilled workers in Mexico. Now, a value greater than one indicates a skilled-labor abundant source country, a value of less than one indicates that Mexico is relatively skilled-labor abundant. Sector-level skill intensity is now defined as the ratio of skilled to unskilled workers in each industry as well. The only notable difference to the base case is that the estimated marginal effect of a one standard deviation increase in skill differences is about 50% higher. Column 5 reports results from including country fixed effects. The results with respect to the negative effects of higher skill or capital intensity are similar to the base case, but skill differences no longer have a significant effect on FDI at the margin. It is thus apparent that the correlation between skill differences and FDI arises primarily from their cross-country rather than their time series variation. This is intuitive as factor endowments change only slowly over time and the panel is quite short. Similarly, Carr et al. (2001) find their results weakened when they include country fixed effects.

To sum up, the results provide evidence that multinational activity in Mexico is determined by comparative advantage, but that a market access motive is also present. They support Yeaple’s (2003) result that skilled-labor scarce countries attract FDI primarily in unskilled labor intensive sectors, but extends it to show that the scope of skill differences matters as well. They complement studies such as Brainard (1997), Carr et al. (2001) and Blonigen et al. (2003) which use only US or other developed country data and therefore find little evidence of comparative advantage FDI.

6 Conclusion

Attracting more foreign investment, especially in the form of FDI, has been a policy objective for many developing countries recently, reversing decades of severely regulated or even hostile treatment of foreign investors. The benefits from foreign investment, which accrue in the form of increased employment, forward and backward linkages and technological spillovers, crucially depend on the type of multinational activity that is attracted. While there is no consensus in the literature, recent evidence indicates that vertical spillovers are more likely than horizontal ones.

The existing empirical literature on multinationals thus far has overwhelmingly favored the market access (horizontal) over the comparative advantage (vertical) motive. Given that most of the data used comes from US or other developed country sources, this finding is not overly surprising. By using industry-level data from a developing host country, this paper has shed some light on the determinants and types of multinational activity from a different perspective.

The results confirm that comparative advantage in (unskilled) labor intensive industries is a statistically and economically important determinant of inward FDI in Mexico. Skill endowment differences are positively correlated with FDI. As Mexico grows relatively more skilled labor (and capital) abundant, FDI based on this motivation will decrease. However, there is also evidence that an increase in market size and a decrease in dissimilarity to developed country markets will increase FDI, where the net effect will depend on the relative magnitude of the changes. Future work ought to investigate this question for other developing countries with similarly detailed data which will provide important insights into the potential welfare effects of recent policies that seek to attract more foreign direct investment.