FDI determinants and spatial spillovers across Mexico ’ s states ∗

This paper studies the location pattern of Foreign Direct Investment (FDI) in Mexico for the period 1994-2004. An empirical model is specified based on recent FDI theories. This model is estimated using statelevel data and employing spatial econometric techniques. Results suggest that higher education levels and lower delinquency rates are important determinants to attract FDI. Results also suggest a relationship of complementarity between inbound FDI to the host state and inward FDI to its neighboring states.


Introduction
In recent years, many developing countries have tried to attract Foreign Direct Investments (FDI) to compensate for their lack of capital for financing their economic activity. These countries consider FDI to be beneficial as a source of access to markets, technologies and other assets that are not available in the local economy (UNCTAD 2006). Thus, countries compete to propose the most attractive production conditions (i.e., legal environment and economic policies), and their policy-makers take measures to attract foreign capital. The purpose of this paper is to study a broad issue, regarding inward FDI in Mexico, which has not yet received much attention. The paper empirically examines economic theory on multinational enterprises to determine: (1) the motivations for FDI in Mexico, and (2) the determinants of the geographical distribution of FDI at a regional level. Following previous work on spatial econometrics applied to FDI analyses (Baltagi, Egger, and Pfaffermayr 2007;Blonigen et al. 2007), interaction among Mexican states' characteristics, FDI motivations and spatial linkages are studied to explain the location pattern of FDI in Mexico. To the author's knowledge, this is the first study that analyzes spatial linkages for FDI across the Mexican states. This paper is organized as follows. The next section briefly introduces the geographical and industrial configuration of inbound FDI in Mexico. Section three describes the theoretical background and is followed by a discussion on the empirical methodology in section four. Section five presents the results. Finally, conclusions are drawn in section six.

FDI distribution in Mexico
NAFTA and reforms on FDI regulation seem to be successful in attracting FDI, as FDI inflows increased substantially after their implementation. According to official data (available at INEGI), the average yearly FDI inflows/GDP ratio has more than doubled since 1994 (2.61%) compared to [1989][1990][1991][1992][1993]  According to the spatial distribution of FDI inflows in Mexico, FDI is not equally distributed. Figure 1 illustrates the density distribution of FDI inflows during 1994-2004. During this period, more than 60% of the accumulated FDI inflows went to the Federal District (Mexico City), which is Mexico's capital as well as the country's richest entity. FDI is also unequally distributed among the rest of the Mexican states, particularly between the states located in the north and south of Mexico.
[ Figure 1 here] The map analysis suggests that FDI is concentrated in states located mainly near big markets, such as the Federal District and the US. However, to determine why FDI concentrates in these states, as well as to identify the motivations for FDI, a regression analysis based on economic theory is conducted.

Determinants of FDI location: theoretical background
There has been considerable progress in recent years in terms of theory regarding Multinational Enterprises (MNEs) and FDI location behavior. Dunning (1973) proposed that Ownership, Location and Internalization (OLI) advantages encouraged firms to undertake foreign investment. In this framework, location is perceived as an advantage that firms can obtain by locating production abroad (i.e., scale economies). However, OLI framework was not built on a formal setting. Since Markusen (1984) and Helpman (1984 Vertical foreign investments refer to a fragmentation of the production process into stages of production that are each produced in different locations. Vertical FDI is motivated by the differences in factor prices, especially by differences in labor costs. "Complex FDI" versions of the KC model have been developed more recently. The literature mainly distinguishes between two types of these models: (1) complex horizontal or export platform FDI and (2) complex vertical or vertical specialization FDI. In an export platform FDI model (Ekholm, Forslid, and Markusen 2007; Baltagi, Egger, and Pfaffermayr 2007) a home country firm sets up a production plant in a region that benefits from better access and lower production costs than the home country, and the region serves as a production platform for exports to a group of "neighboring" regions. Finally, in complex vertical models, MNEs separate their production process into multiple vertical activities and put them in locations offering the lowest costs (trade and/or production). Baltagi, Egger, and Pfaffermayr (2007) suggest that, with these complex modes of MNEs organization, host country characteristics are not the only determinants attracting FDI; the host neighbors' characteristics could also play an important role. For each Mexican state i = 1, ..., N and for each time period t = 1, ..., T , the empirical specification takes the following form: where According to economic theory, the FDI location decision depends on MNEs' strategies and on the host region's location advantages (Dunning 1973;Markusen 2002). Because FDI strategies can be summarized into market-oriented and resource-seeking FDI, a region's market size, resource endowments and geographical position will determine its location advantages. Independent variables included in K are then variables concerning location advantage differences among Mexico's states.
As a measure of a state's market advantages, two variables are employed: (1) GDP per capita and (2) population. GDP per capita is related to purchase power, while population is related to market size. According to KC models, hor-izontal FDI will be high if countries' economies are similar in development, while differences among countries' development lead to vertical FDI (Markusen 2002).
Because the main sources of FDI are developed countries, GDP per capita's coefficient sign is expected to reflect differences between vertical and horizontal FDI.
However, GDP per capita can also be an indicator of an abundance of factor endowments that facilitate all types of FDI. Concerning population and market size, NEG models suggest that size matters not only for the potential market demand but also for the agglomeration incentives. Population's coefficient sign is then expected to be positive for both FDI motivations.
As endowment-related variables, skilled-labor, wages, agglomeration forces and available infrastructure in the host state are included. The average years of schooling for individuals over 15 is employed as a proxy of skilled-labor endowments. KC models also suggest that multinational enterprises require a certain level of skilled workers for production; skilled-labor endowments are then expected to have a positive influence on attracting FDI, as they did in Jordaan where W is an N × N spatial weight matrix of known constants, and ρ is the scalar autoregressive parameters of spatial lag.
W is constructed as follows. For any year t, a weight matrix W is defined as: where n i,j is the functional form of the neighboring relationship between Mexican states i and j. Matrix W is standardized so that the sum of every row of the to state-i will be at the expense of state-j. Location choice will be influenced by both the size of the state's market and its proximity to other states' markets.
Proximity to foreign markets is not an important factor because production is supposed to be sold only in the domestic market.
Pure vertical FDI. In this case, the parent firm outsources part of its production process to a lower cost country. With two states as potential FDI hosts, the parent firm has the choice of location for its operations. Hence, states i and j are rivals when attracting this kind of FDI. The spatial lag coefficient should be negative, and the location decision, in this case, will not be influenced by the proximity to the Mexican market or foreign markets.
Export platform FDI. In this case, the parent firm decides to open a filial to supply a third country's market. The investment decision between states i and j will be influenced principally by the proximity to foreign markets. Once again, states i and j are rivals to attract FDI.
Vertical specialization FDI. In this case, the parent firm divides its production process into the production of multiple intermediate goods produced in different regions. If the parent firm already produces an intermediate good in state-i, it would be profitable to open a production plant in state-j to take advantage of the proximity to its customer or supplier plant located in state-i. This means that FDI to state-i would be a complement of FDI to state-j, and one can expect a regional agglomeration of FDI for proximity to supplier/customer reasons. If agglomeration forces extended to local or national firms, then proximity to other (Mexican or foreign) markets would have a positive influence on attracting FDI. Table 1 summarizes the theoretical hypothesis explained in this section. The motivation for FDI can then be deduced from these variable estimates.
[ Table 1 here] Finally, Table 2 shows the descriptive statistics, and Appendix A presents a detailed description of variables and the sources of the dataset. [

Empirical results
To identify if there is spatial dependence of FDI among Mexican states, Moran's I and Geary's C statistics are computed for the dependent variable. Table 3 shows these statistics' values as well as their Z-scores and their p-values under the null hypothesis of spatial independence. Results suggest the presence of significant positive spatial autocorrelation for the four weighting matrices. Indeed, Moran's I is always positive, Geary's C is always smaller than 1, and p-values for both statistics are always lower than 0.05.
[ would experience better improvements in infrastructure and GDP per capita.
[ Table 4 here] We can suspect that a correlation between a state's GDP per capita and the other independent variables could lead to biased results. GDP per capita is then dropped from the specification, and the model is estimated. Results, drawn from columns 3 and 4 of Table 4 Table 4. Without controlling for fixed effects (columns 5 and 7), the coefficient's sign and significance matches the coefficients obtained in previous regressions.
Only distance to Mexico City lost its importance. Concerning the spatial lag relationship, results suggest a positive relationship or complementarity between FDI invested in neighboring states and FDI invested in the average state.
When controlling for fixed effects, the coefficient's value and the significance of the spatially lagged FDI increase considerably. This result can also be interpreted as an agglomeration tendency of multinational firms over time. When controlling for fixed effects and spatial dependency, infrastructure and wages become non-significant. Only years of schooling and delinquency continue to be significantly correlated to inbound FDI.
The spatial lag relationship, together with the negative correlation between FDI and the distance to both the US and Mexico City, supports the idea that most of the inbound FDI in Mexican states is of a complex vertical FDI type or vertical specialization. This result also suggests that these agglomeration externalities extend between MNEs, local firms and international firms. However, for attracting FDI, the stronger importance of the distance to the US compared to the distance to Mexico City suggests that MNEs' main motivation is to serve the US market rather than the national market. Blonigen et al. (2007) find that the spatial interrelationship is sensitive to the weighting matrix. This idea is applied to this paper's case. To carry out sensitivity tests, the alternative weighting matrices are employed. Neighboring relationships are supposed to go beyond sharing a common border. Hence, alternative specifications assign a weight that decreases with distance. These alternative weighting matrices, then, capture a higher spatial correlation. Table 5 shows the results of using alternative weighting matrices. As shown in columns 1 and 2, when the spatial relationship is limited to a distance of less than 883.412 km, the spatially lagged FDI coefficient increases. When the neighboring relationship is extended to all states (columns 3 and 4), the coefficient's value increases considerably. Hence, attracting FDI into a state would lead to an increase in the attractivity of the whole country.

Sensitivity tests
[ Table 5 here] To check for the sensibility of the distance measures coefficient, an alternative weighting matrix is constructed using road distance between state pairs.
Without controlling for fixed effects (column 5), the spatial lagged FDI coefficient is similar to those found using weighting matrix W 1 and W 2 , but this variable is not significant at the 5% level. When controlling for fixed effects, the coefficient's value and significance are very similar to those found with other weighting matrices. Finally, a common result to those found previously is the importance of years of schooling and low delinquency rates in attracting FDI.

Conclusions
Mexico is a country that is characterized by strong regional differences for at- Indeed, without controlling for fixed effects, estimators tend to give long-run estimates, whereas models using fixed effects tend to give short-run estimates (Baltagi 2005 which could lead to a bigger concentration of FDI in these states in the following years. Figure 1: Spatial distribution of accumulated FDI inflows (1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004). Source: Elaborated by the author with data from the Mexican Ministry of Economy     Notes. * significant at 5%; ** significant at 1%. Robust standard errors to both heteroskedasticity and autocorrelation are in parentheses under coefficient estimates. Each regression also includes a time trend variable and a constant. Columns (5) to (8) are estimated by two-step GMM using the first spatial lags of exogenous variables (W · X i ) as excluded instruments for the spatially lagged dependent variable W · F DI.
(3) Notes. * significant at 5%; ** significant at 1%. Robust standard errors to both heteroskedasticity and autocorrelation are in parentheses under coefficient estimates. Each regression also includes a time trend variable and a constant. Results are estimated by two-step GMM using the first spatial lags of exogenous variables (W · X i ) as excluded instruments for the spatially lagged dependent variable W · F DI. The weighting matrix W 2 in columns (1) and (2) is constructed using 883.42 kilometers as a threshold for neighboring relationships. In columns (3) to (6), the weighting matrix is defined as an inverse distance weight matrix, but the distance measure differs between the weighting matrix W 3 and the weighting matrix W

A Variables definition and data sources
Variable name Description Source FDI The FDI stock (F DIt) for each year t is computed by applying the perpetual inventory method to annual FDI inflows (f dit). The applied formula is: and l are all counties with more than one hundred thousand inhabitants in 1990 that belong to state-i and state-j, respectively; distance between counties is measured as the great circle distance from the biggest city in the county. A value of θ = −1 is set because, as pointed out by Head and Mayer (2002), this value is frequently found in gravity equations literature.
Counties population data was obtained from INEGI -1990 Censo General de Población y Vivienda; and counties'coordinates were obtained from world-gazetteer data set.
Road distance Road distance is the weighted road distance between states' most important urban agglomerations (those over 150,000 inhabitants).
Population data from INEGI -1990 Censo General de Población y Vivienda; and road distance data from the Secretaría de Comunicaciones y Transportes (SCT) 'Rutas punto a punto' application.
Notes. *Data transformed to constant USD employing the real exchange rate Mexico Peso / US Dollar reported by the Centro de Estudios de las Finanzas Públicas de la H. Cámara de Diputados.