Introduction

The purpose of this paper is twofold. First, to carry out a comparative analysis of China’s and Mexico’s export dynamism in the US manufacturing market by technology category and subcategory of products during the 1994-2017 period. Second, to estimate a dynamic panel data model through two econometric methods: Feasible Generalized Least Squares (FGLS) with cross-section weights, on the one hand, and the Arellano-Bond Generalized Method of Moments (GMM), on the other. By means of these two techniques, we evaluate the impact of some key variables on Mexico’s export performance across different manufacturing sectors. The selection of explanatory variables relies on three basic elements: economic theory, previous empirical work, and the obtainability of disaggregated data. As shall be seen, several data and econometric issues are addressed to produce a specification that is statistically appropriate as well as theoretically plausible.

To get a proxy for export performance, China’s and Mexico’s import market shares (IMSs) in the US market are estimated over the reference period for four categories and nine sub-categories of manufacturing products. To carry out such a task, ^{Lall’s (2000)} methodology and the Standard International Trade Classification (SITC-Revision 3) system are used. The relative performance of each Chinese and Mexican manufacturing category in the US market is assessed through shifts in IMSs and through changes in the IMS average growth rates across different sample periods. Moreover, for every sample period we calculate the variation coefficient of IMS growth rates, which represent an unbiased and normalized measure of dispersion. The evidence points to the conclusion that China’s IMS growth rates during the period 1994-2017 are in most cases higher and more stable than Mexico’s.

Against this backdrop, we proceed to estimate a parsimonious dynamic panel data model consisting of supply- and demand-side variables, along with a control variable to reduce the omitted variable bias. Such a model draws on ^{Fagerberg (1988)} and is aimed at providing evidence about some major determinants of Mexican IMS dynamics in the US. As we explain in Section 5, due to the lack of fully disaggregated data for some of the variables of the model, the estimation period had to be restricted in this case, which means that it runs from 2007 to 2017. In addition to capturing some degree of heterogeneity among industries of different technology rankings, the evidence across the two econometric methods consistently show that: (i) a real depreciation of the subsector-specific exchange rate (of the peso vis-à-vis the US dollar) reduces, rather than increases, Mexico’s IMSs in the US, which is presumably due to the large import content of Mexican manufacturing exports; (ii) lower unit labor costs (ULCs) in Mexico raise its IMSs in the US; and (iii) reduced ULCs in the US, by contrast, bring down Mexican IMSs given that US manufacturing firms become relatively more competitive. These findings highlight two fundamental problems of the Mexican economy: (i) the overwhelming reliance of the export-oriented manufacturing industries on foreign suppliers of intermediate inputs, capital stock and technology, which would explain the counterintuitive effect of currency depreciation on the country’s export performance, and (ii) the need to increase labor productivity faster than wages so as to reduce ULCs, improve IMSs and cope with Chinese competition in the US market more effectively. In this context, Mexico’s low investment in high-quality formal education and in adequate training programs represents a major obstacle for faster productivity growth.

The remaining of this paper is organized as follows. Section 2 is a disaggregated IMS analysis showing the extent to which different categories and subcategories of Mexican manufacturing products are struggling in the US, especially when their performance is compared with that of the Chinese manufactures. Section 3 is an overview of the recent econometric work on the determinants of manufacturing exports. Section 4 deals with the specification of the panel data model while Section 5 estimates it through two econometric techniques and interprets the findings. The last section examines the policy implications.

Mexico and China in the US Market: A disaggregated analysis

Several papers analyze the competition between Mexico and China in the US manufacturing market (^{Mesquita, 2007}; ^{Gallagher et al., 2007}; ^{Gereffi, 2009}; ^{Lin, 2015}). ^{Mesquita (2007)} argues that China’s international competitiveness, which is based on resource endowments, labor productivity growth, government incentives, and scale factors such as land area and population size, poses a significant threat to the manufacturing industry of Mexico and other Latin American countries. According to Gereffi (2017), another major factor behind China’s success in the US market lies in the implementation of a model of industrial organization, known as supply-chain cities, that has allowed the Chinese manufacturing industry to achieve scale economies and play a more strategic role in the Global Value Chains (GVCs). In this context, shortly after entering the World Trade Organization (WTO) in December 2001, China notably began to perform better than Mexico in the US manufacturing market and this trend continued until 2007 (^{Gallagher et al., 2007}; ^{Gereffi, 2009}), when the global economic crisis emerged. After 2007, China started to lose some steam while Mexico recovered some ground in the US manufacturing market (^{Lin, 2015}). Although these findings are reasonably consistent with the evidence provided here, we also calculate import market shares growth rates and a measure of volatility for this variable, among other differences.

To compare China’s and Mexico’s export dynamism with greater detail, ^{Lall’s (2000)} methodology and the Standard International Trade Classification (SITC-Revision 3) system are utilized. Therefore, each country’s manufacturing exports are classified according to technology characteristics. Such an analysis is performed at the three-digit SITC level to decompose each country’s manufacturing exports into a significant number of groupings, which are then assigned to four categories and nine sub-categories. Specifically, exports of manufactured goods are broken down into the following categories: resource based, low technology, medium technology, and high technology. As shown in Table 1, each category is further disaggregated into a few sub-categories for analytical purposes. The same scheme is used to decompose US manufacturing imports from the rest of the world, so as to estimate China’s and Mexico’s import market shares (IMSs) in the US by category and subcategory of products. Formally speaking, let X _ijt be the exports of a category of goods i from country j to the US at time t, and Mit be the total US imports of a category of goods i at time t. Therefore, the share in the US import market of a category of goods i exported from country j, denoted IMSijt, is simply calculated as: IMS _ijt = (X _ijt/ M _it )*100. Table 1 depicts the results of this exercise for selected years between 1994 and 2017:

Table 1 allows us to analyze three important stages: (i) Mexico’s manufacturing export expansion following the enactment of the North American Free Trade Agreement (NAFTA) on January 1, 1994; (ii) China’s remarkable manufacturing export growth, mainly after its accession to the WTO in late 2001, which appears to slow down the Mexican export expansion in the following years; and (iii) the somewhat mixed performance of Chinese and Mexican manufacturing industries in the US market after the emergence of the US economic recession in December 2007.

Broadly speaking, China increased its IMS in every category and subcategory of goods considered throughout the period 1994-2017. During this period, the most noticeable progress in terms of IMS gained in the US was in high-technology products, mainly in the electronics and electrical industry (i.e., office machines, automatic data processing equipment, telecommunications equipment, advanced power generating machinery, and the most sophisticated types of electrical machinery and apparatus). Nevertheless, China also achieved considerable market success in: (i) medium-technology manufactures such as parts and accessories of motor vehicles, motorcycles and cycles, synthetic fibers, plastics and chemical products, electrical machinery and equipment, non-electrical machinery, and household type equipment; (ii) low-technology manufactures such as textile goods, footwear, travel goods, furniture and bedding, and toys and sporting goods; and (iii) resource-based manufactures such as processed food and vegetables, snack foods and other edible products, rubber manufactures, and non-metallic mineral commodities (e.g., glass, lime, cement and fabricated construction materials). Breaking down the analysis of Table 1 into different stages, we can see that the most flourishing period for China started with its entrance to the WTO and ended with the advent of the US economic slowdown.

Table 1 also shows the mixed results achieved by Mexico over the 1994-2017 period. On the positive side, Mexican automotive products almost quadrupled their IMS in the US. In stark contrast to China, Mexico performed relatively well not only in the auto parts and accessories industry, but also in standard motor vehicles, special-purpose motor vehicles (primarily for the transport of goods), and even road vehicles (such as buses and tractors for semi-trailers). In some other categories and sub-categories of goods Mexico did not perform as well as China but could still gain a little ground or, at least, hold relatively steady in the US market. Some noticeable examples of this are: (i) high-technology products such as power generating machinery, automatic data process machines, telecommunications equipment, and electrical machinery apparatus; (ii) process products such as dyeing, tanning and coloring materials; perfumery and cosmetics; and plastics in primary and non-primary form; (iii) engineering industries such as electrical machinery and equipment, non-electrical machinery, and house-hold type equipment; (iv) low-technology products outside the textile/fashion cluster, such as iron and steel products, manufactures of metals, and furniture and bedding accessories; and (v) resourced-based products such as processed food and vegetables, alcoholic beverages, snack food and other edible products, and rubber manufactures.

On the negative side, from 2002 to 2017 the Mexican textile/fashion cluster experienced a significant market share reduction, which basically explains the lackluster performance of Mexico’s low-technology products in the US import market. During the same period (2002-2017), Mexico was surpassed by China in two categories: resource-based and high-tech products. Particularly, China’s high-technology industry got way ahead of Mexico´s. In 2017, for instance, China held an IMS in the US that was almost three times as large as that of Mexico. Another reason for concern is that, although Mexico remains a dominant player in automotive products, China has been challenging Mexico in the other two subcategories of the medium-technology sector: process products and engineering industries. On balance, we can see that the best years for Mexico were between 1994 and 2002, whereas its most complicated years concurred with China’s export upsurge between 2002 and 2008.

Previous econometric work

As stated at the outset, the second objective of this paper is to evaluate the impact of some key variables on Mexico’s export performance across different manufacturing industries. As explained above, we resort to IMSs in the US as a measure of export performance for each manufacturing subsector. To increase the IMS of, say, high-tech products, it is necessary that Mexican exports of high-tech products grow faster than the corresponding US imports. Along these lines, this section mostly relies on the econometric evidence concerning the determinants of manufacturing exports.

Export functions are commonly specified under one of the following four approaches: (i) gravity models of trade, (ii) augmented gravity models of trade, (iii) standard demand models, and (iv) amplified export models comprising demand- and supply-side variables. The traditional gravity trade model postulates that exports volumes from country A to country B are a positive function of the countries’ economic sizes and a negative function of the transportation costs involved (^{Tinbergen, 1962}). The augmented gravity trade equations take account of other important factors, such as shared borders of the trading partners, common language, and infrastructure (^{Bayoumi and Eichengreen, 1997}; ^{Bougheas, Demetriades, and Morgenroth, 1999}). To provide the gravity trade equations with greater explanatory power, some authors introduce variables that are no longer related to space and geography, such as exchange rates and prices (^{Soloaga and Winters, 2001}; ^{Martínez-Zarzoso and Nowak-Lehmann, 2002}).

The third strand of the literature is the one embodied by standard demand models, which posit export volumes as a function of the real exchange rate and external demand. External demand is usually measured by the level of economic activity abroad or by the total amount of imports from the nation’s major trading partners. In the case of the developing economies, the evidence provided by ^{Reinhart (1995)}, ^{Senhadji and Montenegro (1999)} and ^{Garcés (2008)} suggests that: (i) exports are more responsive to external demand than to changes in relative prices, and (ii) exchange rate depreciation raises exports by lowering their foreign-currency price. One caveat to this conclusion is the evidence that the greater importance of GVCs has significantly altered the relationship between real exchange rate movements and manufacturing exports (^{Swarnali, Appendino, and Ruta, 2017}; ^{Cheng et al., 2016}).

Hybrid models consisting of supply- as well as demand-side variables represent the fourth strand of the literature. The export function formulated by ^{Fagerberg (1988)} combines supply- and demand-side variables. Under ^{Fagerberg (1988)} primal specification, the ratio of exports to foreign income plays the role of dependent variable (rather than export volumes). Put differently, X/Y* would be the dependent variable of the export function, where X and Y* denote export volumes and foreign income, respectively. The explanatory variables are relative prices, technological competitiveness and a measure of productive capacity.

The relevance of incorporating supply-side variables in export functions is pointed out by Riedel (1998), who shows that working with only demand-side variables gives rise to miss-specification problems and thus biased estimates of relative price and external demand elasticities. ^{Riedel’s (1988)} contribution further encouraged the use of amplified export functions comprising demand- and supply-side variables, such as labor productivity and wages. In the case of developing economies some examples stand out. Based on data from the Argentinian economy, ^{Catão and Falsetti (2002)} show that higher productive capacity fosters manufacturing exports and vice versa. Moreover, ^{Mbaye and Golub (2002)} find that increasing unit labor costs (ULCs) lower manufacturing exports in the case of Senegal. The empirical evidence provided by ^{Aysan and Hacihasanoglu (2007)} points to the same conclusion in the case of Turkey.

Other authors establish the positive interaction between technology-related variables (i.e., number of patents granted and research and development (R&D) expenditure) and exports (^{Montobbio and Rampa, 2005}; ^{Menji, 2010}). Lastly, developing country’s manufacturing exports are closely related to Foreign Direct Investment (FDI), given that it generates production capacity, technological transfers and better business practices, which in turn allow these countries to play a more influential role in the global production chains (^{Pacheco-López, 2005}; ^{Montobbio and Rampa, 2005}).

The model

This section deals with identifying some major determinants of Mexico’s IMSs in the US. This exercise is carried out for 21 manufacturing subsectors over the 2007-2017 period. Moreover, three elements are considered in specifying the econometric model and the reference period (2007-2017): economic theory, prior empirical work, and the obtainability of disaggregated data. Although we clarify this point in the empirical analysis section, here it is necessary to underline that no disaggregated data are available for key technology-related variables, such as research and development (R&D) expenditure. Nonetheless, drawing on ^{Fagerberg (1988)} and Riedel (1998), it was possible to formulate a parsimonious model consisting of supply- and demand-side variables, coupled with a control variable to address the omitted variable bias. First, let us highlight that ^{Fagerberg (1988)} specifies the following export function based on ^{Schumpeter’s (1943)} approach:

where Xt is the export volume at time t, Yt* is the foreign income level, Qt is the real exchange rate, CUt is capacity utilization, and Tt and Tt* denote the domestic and foreign technological competitiveness, respectively. Moreover, β1 is a proportionality parameter while β2, β3 and β4 represent the elasticities of exports with respect to the real Exchange rate, technological competitiveness and capacity utilization, respectively. As a dependent variable, many papers drawing on the Schumpeterian tradition replace the ratio of exports to foreign income level, Xt/Yt*, by the share of exports in world trade (^{Romero and Mc-Combie, 2016, p. 7}). We follow this approach, so that Xt/Yt* becomes Xt/Mt*, where Mt* is total US imports from the rest of the world. From the Mexican standpoint, US income level (Yt*) and total US imports (Mt*) are both proxies for external demand, but the latter variable allows for the calculation of Mexico’s IMSs in the US. In addition, there was no choice but to replace ^T t and Yt* by ULCs in Mexico and the US, respectively. The problem was that Tt and Tt* are usually proxied by patents granted and R&D expenditures and no disaggregated data are available for these two variables in the Mexican case, which prevents the estimation of a panel data model. In contrast, not only are there disaggregated data available for ULCs, but this variable can be regarded as a comprehensive measure of international cost competitiveness of industries as well as firms (^{OECD, 2017, p. 72}). Under these circumstances, Equation (2) becomes:

where, as stated before, Mt* denotes total US imports from the rest of the world while ULCt and ULCt* are unit labor costs in Mexico and the US, respectively. Given that Mt* allows for the estimation of Mexico´s IMSs in the US, Equation (3) becomes:

where IMSt=Xt/Mt*. Moreover, percentage capacity utilization (CUt) reflects changes in domestic demand for exportable manufactures, so that its inclusion is helpful in distinguishing between increases in IMSs resulting from lower-than-expected internal demand for tradable manufactures, on the one hand, and rises in IMSs coming from lower ULCs at home or higher ULCs abroad, on the other. This is the reason why some papers use capacity utilization as a control variable (^{Paiva, 2003}; ^{Berrettoni and Castresana, 2007}). Moreover, percentage capacity utilization can also capture changes in the volume of manufacturing goods produced, in which case this variable might bear a positive relationship with IMSs. Next, it was deemed convenient for empirical purposes to separate the effect of domestic ULCs from the effect of foreign ULCs on Mexico’s IMS in the US. Hence, Equation (4) becomes:

To rewrite Equation (5) in terms of a standard panel regression model, we simply: (i) take logs on both sides of the equation, (ii) include a second subscript to acknowledge that we are dealing with several cross-section units, and (iii) incorporate a disturbance term. This exercise yields:

where β1´=1nβ1, the lower-case variables are simply the natural logs of the upper-case variables, and uit is an error term. Moreover, subscripts i and t denote the cross-section unit (i.e., the industry) and the year, respectively, and the regression parameters can be interpreted as elasticities. In such a context, the data concerning IMSs in the US and the real exchange rate had to be calculated for each manufacturing sub-sector. The next equation allows for the possibility that industries are heterogeneous:

where β1i´ is an intercept term that can vary across industries. Equation (7) represents a balanced panel consisting of 231 observations, given that we are dealing with 21 industries (N=21) of different technological ranking and 11 years (T=11), ranging from 2007 to 2017. The industries analyzed here are: (i) food; (ii) beverages and tobacco; (iii) textile mills; (iv) textile product mills; (v) apparel; (vi) leather and allied products; (vii) wood products; (viii) paper; (ix) printing and related support activities; (x) petroleum and coal products; (xi) chemical manufacturing; (xii) plastics and rubber products; (xiii) nonmetallic mineral products; primary metal manufacturing; (xv) fabricated metal product manufacturing; (xvi) machinery; (xvii) computer and electronic products; (xviii) electrical equipment and appliances; (xix) transportation equipment; (xx) furniture and related products; and (xxi) miscellaneous manufacturing. This classification differs to a certain extent from the one used to carry out the IMS analysis, but it allows us to increase substantially the number of cross-sections units and thus the number of observations to estimate the model.

Empirical analysis

Conclusions

Broadly speaking, the data show that: (i) China’s IMSs in the US grew faster than Mexico’s over different time horizons, and (ii) China’s IMS growth rates were noticeably more stable than Mexico’s. In this regard, one important exception is the Mexican medium technology sector during the 2008-2017 period, which displayed a higher and more stable IMS growth rate than its Chinese counterpart. Furthermore, our econometric evidence suggests that an undervalued peso may not be an effective policy to regain international competitiveness, which is to some extent consistent with previous empirical research cited in this paper. In other words, the estimated model shows that a real depreciation of the peso-dollar exchange rate reduces rather than increases Mexico’s IMSs in the US. Conventional economic theory contends that real currency depreciation makes exports cheaper in terms of foreign currency, thus strengthening international competitiveness and raising IMSs in the US. This is the demand-side effect of currency depreciation, which seems to interact with a supply-side effect of opposite sign stemming from the high import content of Mexican manufacturing exports. Put briefly, the implication is that the supply-side effect of currency depreciation tends to dominate the demand-side effect, mainly because of the heavy reliance of the Mexican manufacturing sector on imported intermediate inputs, not to mention capital stock and technology. In Table 4, the first difference of the subsector-specific real exchange rate is lagged one period (i.e., q _it ) because the influence of import prices on export prices seems to be stronger after one year, which is consistent with the notion that the ability of firms to endure higher costs erodes as times progresses.

The net negative impact of real currency depreciation on IMSs in the US reflects the overwhelming reliance of the export-oriented manufacturing industries on global suppliers of intermediate inputs, capital stock and technology. In this context, a well-known policy prescription that continues to receive little attention is to establish efficient production chains between small- and medium-sized businesses producing for the domestic market, on the one hand, and large manufacturing exporters, on the other. A structural change in this direction could lessen to a certain degree the volatility of IMS growth rates by restraining the disruptive effect of currency depreciation.

An increase in domestic ULCs brings down IMSs in the US. This is consistent with the finding of ^{Mbaye and Golub (2002)} and ^{Aysan and Hacihasanoglu (2007)} in terms that higher ULCs discourage manufacturing exports. As is well known, the abatement of ULCs requires labor productivity to grow faster than wages, whereas the key to improve labor productivity is to invest more in human capital formation (^{Padilla and Juárez, 2007}). In this perspective, education spending could contribute to reducing ULCs, strengthening manufacturing competitiveness, and increasing IMSs in the US. The responsiveness of IMSs to ULCs brings attention to the insufficient investment in formal education and job-related training. To provide the workforce with the appropriate knowledge, skills and abilities, the Mexican government should spend more rather than less on human capital formation. What is more, the Mexican government should spend better by addressing, in association with other relevant parties, the specific training requirements of each subsector of the manufacturing industry. Lastly, the estimation results show that a fall in US ULCs will lead to a reduction in Mexico’s IMSs. The rationale here is that lower ULCs in the US allow US-made products to be more competitive vis-á-vis Mexican products, which in turn slows down Mexican sales in the US.