1. Introduction

This paper evaluates the effects of the North American Free Trade Agreement (NAFTA) on the economic growth of Mexico, the United States (US) and Canada. Such a task is carried out by means of an augmented version of the Solow growth model, which is estimated with panel data through two complementary econometric methods: The Arellano-Bond (AB) dynamic panel Generalized Method of Moments (GMM), on the one hand, and Feasible Generalized Least Squares (FGLS) with cross-section weights, on the other. The Arellano-Bond GMM estimator (AB-GMM) is consistent, robust to endogeneity problems and requires no information as to the distribution of the disturbance term, whereas the FGLS estimator accounts for serial correlation and is thus useful in achieving more efficient estimations.

As is well known, NAFTA came into force in January 1994 and was aimed at attaining five major objectives within the region: Liberalize trade in goods and services, improve business practices, protect intellectual property rights, enhance cooperation, and set up a formal system for solving trade controversies (^{Abbott, 2000}). Mexico and Canada were also seeking permanent access to the US market (^{Whalley, 1998}) to raise exports and attract foreign direct investment (FDI). For Mexico, NAFTA was also a means of cementing the structural and macroeconomic reforms undertaken since the early 1980s (^{Moreno-Brid, Rivas, and Ruiz, 2005}). Nonetheless, the long-term outcome pursued by the three countries were economic growth and job creation. Therefore, our study seeks to assess the impact of NAFTA on economic growth.

Our evidence is consistent in indicating that international trade has a positive impact on the Gross Domestic Product (GDP) per capita, controlling for physical capital accumulation, human capital formation, depreciation and even total factor productivity. We also show that human capital formation is the most important source of GDP per capita growth, which highlights Mexico’s need to invest more in long-term formal education, short-term training programs and the whole knowledge transferring process.

The rest of the paper is organized as follows. Section 2 is an overview of the literature concerning the link between trade and economic growth. Section 3 analyses some relevant data on the evolution of trade since the NAFTA implementation. Section 4 describes the theoretical approach, whereas Section 5 specifies and estimates the panel data models. Section 6 briefly identifies Mexico’s winners and losers in the context of NAFTA. Finally, we summarize the findings and policy implications.

2. Brief review of the literature

This section highlights some relevant contributions regarding the impact of trade liberalization on economic growth. We first focus on the orthodox view, namely on those works suggesting that opening up to trade brings about economic growth. Then we deal with the heterodox view, which either finds no relationship between trade and output growth or maintains that such a relationship is potentially negative for some countries.

Based on a sample of 95 developing countries over the 1976-1985 period, ^{Dollar (1992)} provides evidence that a tariff reduction in intermediate goods can encourage economic growth. The linear regressions estimated by this author show that lowering protection to the industry of intermediate inputs played a key role in launching economic growth in the East Asian Tigers.

Using five different criteria of trade restriction, ^{Sachs et al. (1995)} construct an economic openness indicator to determine the impact of trade liberalization on economic growth. The study relies on cross-country growth regressions, which control for other determinants of growth. The empirical analysis comprises 134 countries and pays attention to their trade policy changes over the 1970-1989 period. In this context, these authors conclude that trade openness exerts a positive influence on economic growth.

^{Edwards (1998)} resorts to nine trade openness indicators and assesses their impact on total factor productivity (TFP) and thus on economic growth. Such indicators are combined with a cross-country data set spanning 93 countries during the 1980s. Edwards estimates nine Weighted Least Squares (WLS) regressions, each one including a trade openness indicator and a set of control variables. The evidence shows that TFP rises significantly when developing countries open up to trade.

^{Winters (2004)} reviews the literature concerning the relationship between international trade and growth, concluding that trade liberalization leads to a transitory increase in the growth rate of output. Winters also contends that liberal trade policies cannot raise long-term economic growth unless they are supported by sound policies and institutions. In this context, trade liberalization can have a positive interplay with solid non-discretionary policies as well as with institutional development.

The orthodox view provides plausible explanations as to why international trade raises GDP growth. Some authors argue that international trade gives rise to knowledge spillovers from developed to developing countries (^{Sachs et al., 1995}; ^{Aghion and Howitt, 2009, p. 353}). Other authors contend that an outward-oriented strategy allows countries to better exploit scale economies and capacity utilization (^{Salvatore and Hatcher, 1991}). A related argument is that free trade agreements reduce the technology gap among the countries involved (^{Gonzaga, 2001}).

According to the heterodox view, by contrast, the effect of trade on economic activity is either statistically insignificant or potentially negative for some countries. ^{Rodríguez and Rodrik (2000)} find that reduced average tariffs are not conducive to economic growth. The authors state further that most econometric models are underspecified because they fail to capture the institutional and structural differences among the countries.

According to ^{Jung and Marshall (1985)}, many studies based on regressions of economic activity over export variables lack reliability to the extent that they do not establish the direction of causality. After performing causality tests between economic growth and exports for 37 developing countries, they find no evidence that exports lead to economic growth. Other papers, also relying on Granger causality tests, reach an analogous conclusion for different countries and regions. For instance, ^{Shan and Sun (1998)} for the case of the little dragons and ^{Shan and Sun (2010)} for the case of Australia.

Using data for 45 developing countries during the 1971-2005 period and panel cointegration tests, ^{Dreger and Herzer (2013)} show that the average long-run effect of exports on non-export GDP is negative. A plausible explanation is that developing countries that open up to trade prematurely, can neither take advantage of the new commercial opportunities abroad nor withstand the sudden arrival of foreign competitors to the domestic market (^{Aghion and Howitt, 2009, pp. 369-371}).

^{Kneller, Morgan, and Kanchanahatakij (2008, p. 701)} analyze the recent skepticism regarding the impact of trade openness on output growth, acknowledging that such an impact is not always positive and that the empirical dispute is not settled. Their study comprises 37 countries and highlights the fact that, although on average there seems to be a positive link between trade and GDP growth, many countries depart significantly from the general result. To the extent that these departures remain unexplained, some investigations consider that focusing on a case-by-case analysis may be more rewarding than taking wide cross-country analyses even further (^{Bhagwati and Srinivasan, 2002}; ^{Kneller, Morgan, and Kanchanahatakij, 2008}). In such a context, this paper focuses on the NAFTA case.

3. Evolution of mexican trade with the US and Canada

This section analyzes the evolution of trade between Mexico and its North American partners, on the one hand, and the rate of economic growth of the three countries since 1994, on the other. Table 1 shows the average annual growth rates of Mexico’s imports and exports (excluding oil) to the US and Canada over different time intervals. Each figure within a parenthesis is the variation coefficient of the corresponding average growth rate. The variation coefficient of a given variable, say Y _t , is calculated as follows:

where EVC stands for the estimated variation coefficient of the growth rate of Y _t ; whereas S is the sample standard deviation of the growth rate of Y _t ; GR¯ is the sample average growth rate of Y _t , and T is the sample size. In this context, (1 + 1/4T) represents the small sample bias-correction factor developed by ^{Rohlf and Sokal (1995)}. Therefore, an EVC is an unbiased and normalized measure of volatility.

The period 1994-2016 was broken down into three subperiods for analytical reasons: The first subperiod (1994-2001) lies between the implementation of NAFTA and the accession of China to the World Trade Organization (WTO) in December of 2001; the second subperiod (2002-2007) covers from the start of China’s export boom in 2002 to the advent of the US economic slowdown in December 2007; and the third subperiod (2008-2016) comprises the years of the crisis and post-crisis of the US economy. Regarding Mexican non-oil exports to the US we can observe in Table 1 that, during the period 1994-2001, this variable experienced the greatest and most stable average annual growth rate (i.e., the variation coefficient is smaller than in the following two periods).

As we move to the following subperiods (i.e., 2002-2007 and 2008-2016), the Mexican non-oil exports to the US exhibited a decreasing average annual growth rate (AAGR) and an increasing volatility, which is conceivably due to the more intense competition from China in the US market. Lastly, throughout the 1994-2016 period, the AAGR of Mexico’s non-oil exports to the US was 9.84% with a variation coefficient of 1.12.

During the whole reference period (1994-2016), the Mexican non-oil imports from the US grew at an AAGR of 6.3% with a variation coefficient of 1.97, so that they were notably less dynamic and more unstable than non-oil exports to the US. Regarding the trade with Canada, we can see that: 1) in the period (2008-2016) exports to Canada grew much faster than imports from that country; and 2) over the whole 1994-2016 period, imports and exports to Canada grew roughly at the same pace. Therefore, the conclusion is that trade flows between Mexico and its two trading partners significantly increased during the 1994-2016 period.

Table 2 depicts the behavior of GDP and GDP per capita in the three countries. The first panel relates to the AAGR of GDP and allows for making three points: First, Mexico grew faster during the 1994-2001 subperiod (i.e., before China entered the WTO) than in the two subsequent subperiods. Second, over the whole reference period (1994-2016), the Mexican economic growth was slightly higher than that of the US and Canada. And third, the magnitude of the variation coefficients of the AAGRs indicate that Mexico’s economic growth was the most unstable.

The second panel of Table 2 displays the AAGRs of the GDP per capita of each country, based on current purchasing power parities (PPPs). First, notice that during the 1994-2001 interval, GDP per capita grew much less in Mexico than in its two North American counterparts. This lackluster performance can be explained by the mistake of December 1994 and the ensuing economic crisis. Second, if we focus exclusively on the 2002-2007 and 2008-2016 subperiods, the conclusion is that Mexico’s GDP per capita grew slightly faster than that of the US and Canada. Lastly, Mexico’s GDP per capita, as well as its GDP growth, is highly volatile. The inherent instability of the Mexican economy and the insufficient economic growth must be addressed through a proper combination of economic policies and structural reforms.

4. Theoretical framework

We draw on ^{Mankiw, Romer and Weil (1992)}, as well as ^{Solow (1956)}, to assess the impact of NAFTA on the economic growth of Mexico, the US, and Canada. Here we follow Mankiw, Romer and Weil’s procedure and complement it with trade within the region. So, let us consider first the following production function:

where Y is aggregate output, L is labor, and K and H stand for physical and human capital, respectively. Moreover, A reflects the state of technology and is usually referred to as TFP. The assumptions regarding these variables are:

where s _K and s _H represent the fraction of output invested in physical and human capital, respectively, while δ is the depreciation rate. Thus, Equations [3] and [4] indicate that the evolution of physical and human capital depends positively on the corresponding fraction of output that is invested and negatively on the rate of depreciation.

Equations [5] and [6] show that L and A grow exogenously at rates n and g, respectively. The underlying assumption here is that L = L ₀ e ^nt and A = A ₀ e ^gt , where L ₀ and A ₀ are the initial values of L and A. So, the interaction variable AL denotes the effective units of labor growing at a rate n + g, where n accounts for the change in the size of the labor force and g for the effects of technical progress (i.e., g is the growth rate of TFP). In this perspective, y = Y/AL is the output per effective unit of labor, and k = K/LA and h = H/LA are physical and human capital per effective unit of labor, respectively. The behavior of k (= K/LA) can be obtained in two simple steps. First, we take logs on both sides of k = K/LA, differentiate with respect to time, and then use the fact that K = kLA:

Second, we isolate k˙ to obtain:

Now, to obtain the trajectory of h, we proceed in a similar fashion, that is, we take logs on both sides of k = H/LA, differentiate with respect to time, and use the fact that H = hLA:

Equations [8] and [9] allow Mankiw, Romer and Weil to obtain the following steady-state solutions:

Therefore, the steady-state values of physical and human capital stock per effective unit of labor bear a positive relationship with the fractions of output that are invested and a negative relationship with the growth rates of: Population (n), TFP (g), and depreciation (δ). The last step consists of substituting k* and h* into the production function (i.e., into Equation [2]) and taking logs, so that the income per capita equation can be derived:

Equation [12] represents an augmented Solow growth model and implies that income per capita increases with human capital formation, physical capital accumulation and TFP growth, and falls with population growth. To Equation [12] we will add international trade (TR) as measured by the sum of exports and imports as a share of GDP. Therefore, our empirical version of Equation [12] is given by:

where y _t = Y _t /L _t and B ₁ = lnA ₀, which means that the initial value of A is captured by the intercept term. Moreover,

B3=-α+β1-α-β, B4=α1-α-β, and B5=-β1-α-β

TR _t is international trade, g _t is TFP growth, and v _t is a stochastic disturbance term.

5. Empirical analysis

Two complementary econometric techniques are used to estimate our model: Feasible Generalized Least Squares, FGLS, with cross-section weights, on the one hand, and the Arellano-Bond GMM, AB-GMM, on the other. The FGLS estimator accounts for serial correlation and is thus useful in improving efficiency. This methodology will be applied to a fixed effects panel data model. To specify such a model, we first rewrite Equation [13] so as to consider that we are dealing with three countries and 21 periods (i.e., N = 3 and T = 21):

where i and t denote the cross-section and time subscripts, respectively, and β _1i allows the intercept term to change from one country to another. The second step is to replace β _1i by an empirical expression that allows for capturing the heterogeneity among countries:

where D _2i and D _3i are the dummy variables of the 0, 1 form associated with Canada and Mexico, respectively. This means that α₁ is the reference point, that is, the intercept term associated with the US, whereas α₂ and α₃ are intercept differentials. It is worth mentioning that the random effects model was ruled out, because to estimate it the number of cross-section units must be greater that the number of explanatory variables and such a condition is not fulfilled here.

The AB estimator (^{Arellano and Bond, 1991}) draws heavily on the GMM developed by ^{Hansen (1982)}. The GMM estimator is robust to endogeneity problems and does not require knowledge about the exact distribution of the disturbance term. These features are useful to ensure consistency and to make conventional significant tests more reliable when the distribution of the error term is unknown. Finally, the AB-GMM is applied to a dynamic panel data model (i.e., a model that includes a lagged dependent variable, lny _t-1 , on the right-hand side), so that the effects of the other explanatory variables on y _t are now conditioned on the past behavior of this variable. See Equation [16]:

where u _it = μ _i + v _it , so that in this case μ _i captures the heterogeneity among countries. The standard assumptions here are that μi~IID(0,σμ2) and vi~IID(0,σv2). For simplicity, Equation [16] can be rewritten as:

where γ is a scalar, xit' is 1xK, β is Kx1 and K = 5, since we have five explanatory variables in xit'. Now, if ln y _it depends on μ _i so does lny _it-1 . Of course, the lagged dependent variable, lny _it-1 , can also be correlated with the second component of the error term (v _it ). Moreover, one or more variables in xit' may involve endogeneity problems and thus be correlated with u _it , in which case the Ordinary Least Squares (OLS) estimator would be biased and inconsistent.

The AB estimator gets rid of the endogeneity problems by creating a set of instrumental variables that exploits the orthogonality conditions that exist between the lags of the explanatory variables in levels (i.e., the lags of y _it-1 and xit') and the second component of the disturbance term, v _it . To make it easier for the instrumental variables to satisfy the orthogonality conditions, we must suppress the individual effect, μ _i , by first differencing. After eliminating μ _i , the instrumental variables must only be orthogonal to v _it . See Equation [16˝]:

The instrumental variables are created in a sequential fashion from t = 3 to the last observation in the series. For t = 3, Equation [16˝] becomes:

The instruments created for Equation [17] are lny _i1 , x _i1 and x _i2 . All of them are uncorrelated with (v _i3 - v _i2 ), whereas lny _i1 is strongly correlated with (lny _i2 - lny _i1 ) and x _i1 and x _i2 are both strongly correlated with (xi3'-xi2'). Similarly, for t = 4 we have that:

The instruments created in this case are lny _i1 and lny _i2 for (lny _i3 - lny _i2 ), on the one hand, and x _i1 , x _i2 and x _i3 for (xi4'-xi3'), on the other. The procedure continuous in this way to the last observation (T). The matrix of instruments for each county is given by:

As we are dealing with three countries in this case, Z=[Z1',Z2',Z3']' and the following orthogonality conditions are satisfied E(Zi'Δvi)=0 for every i (^{Hansen, 1982}; ^{Ahn and Schmidt, 1995}). As ^{Baltagi (2008, pp. 147-155)} shows, Equation [16˝] can be pre-multiplied by Z in vector form to produce Equation [20]:

where ΔX stands for the stacked matrix of data regarding Δx _it , with dimension N(T - 2)(K. The AB estimator is built in two steps: The first is to estimate the parameters γ and β in [20] by Generalized Least Squares (GLS), thereby giving rise to the first-stage consistent estimators γ^1 and β^1:

where VN=∑i=1NZi'(Δvi)(Δvi)'Zi. Let us denote the residuals emerging from the first-step consistent estimator as Δv _GLS . ^{Arellano and Bond (1991)} derive the second-step GMM estimator for γ^2 and β^2 by: 1) replacing Δv in Equation [20] by Δv _GLS , and 2) making use of formulation [21] once more, but this time with Δv _GLS rather than (v. Put differently, the second-step estimator makes use of the first-step residuals to construct a consistent covariance matrix that allows one to relax the assumption that Δvit~IID(0,σv2).

Next, we will gather annual data over the 1994-2014 period¹ for Mexico, the US and Canada for the following variables: GDP per capita (as a proxy for GDP per effective unit of labor) at current PPPs, TFP at current PPPs, physical capital stock per person (as a proxy for physical capital per effective unit of labor) at current PPPs, human capital index based on years of schooling and returns to education, international trade as measured by the sum of non-oil exports and non-oil imports as a share of GDP, average depreciation rate of the capital stock, and population growth rate.²

6. Some winners and losers in the context of NAFTA

Conventional economic theory contends that international trade produces a net gain for each of the countries involved. Nonetheless, within each country one can always identify winners and losers. The winners are supposed to gain more than the losers lose, so that the country overall can obtain a net benefit. In this context, this section identifies some winners and losers in the case of Mexico.

In their analysis of the Mexican economic transformation under NAFTA, ^{Gereffi and Martínez (2005, p. 126)} recommend focusing on export performance given that the country’s development strategy is predominantly export-oriented. Therefore, our identification of winners and losers under NAFTA is based on the export performance of different industries in the US market. The export performance of each industry is assessed in terms of import market shares (IMSs) in the US. To calculate Mexico’s shares in the US import market, we resort to ^{Lall’s (2000)} methodology and to the Standard International Trade Classification (SITC) Revision 3.

In this perspective, the following steps were taken: First, Mexico’s exports are classified into several groupings, which are then assigned to five technology categories: 1) primary products, 2) resource based, 3) low technology, 4) medium technology, and 5) high technology. Second, these categories are further decomposed into subcategories, each one with different technology characteristics (see Table 7). Third, by means of the same procedure, US imports from the rest of the world are classified. Finally, Mexico’s shares in the US import market are calculated for each category and subcategory of products. These shares are referred to as import market shares (IMSs). Table 7 shows the outcome of these calculations for categories of goods (1) through (5) and their corresponding subcategories. This is done for selected years over the 1994-2017 period.

Table 7 shows the evolution of Mexico’s shares in the US import market since 1994. From this standpoint, the main winners were the automotive industry (MT1), the agroforest-based manufactures (RB1), and the electronic and electrical products (HT1). Of course, the most remarkable progress in terms of IMS gained in the US was in automotive products, given that this industry almost quadrupled its IMS in the US during the 1994-2017 period. The second place is for the Mexican agroforest-based industry, whose IMS rose from 4.28% in 1994 to 12.28% in 2017. Finally, the third place goes for electrical and electronic products, whose IMS increased from 8.23% in 1994 to 15.18% in 2017.

The auto industry is undoubtedly the jewel of the crown, but its success history began years before the advent of NAFTA as this industry was part of the country’s industrialization strategy during the import-substitution model (^{Moreno-Brid, 1996}). Under this regime, specific government policies and decrees were implemented to support this industry in developing the productive capacity needed to face foreign competition. Then, in the mid-1980s and more decisively after the implementation of NAFTA, trade protectionism gave way to a gradual liberalization process, paving the way for the automotive industry to become an export-oriented industry (^{Moreno-Brid, 1996}).

The enactment of NAFTA meant easy access to the US market for firms located in Mexico, which in combination with low-cost row materials and a cheap workforce with different levels of qualification, gave rise to huge flows of FDI into the auto industry (^{Barragán and Usher, 2009}). The continuous attraction of FDI was also sustained by a growing industrial and transportation infrastructure and by a manufacturing export boom (^{Middlebrook and Zepeda, 2003}). Also, the Mexican auto industry became a promising recipient of FDI because the multinational enterprises were facing difficult times in the US and other mature markets due to high production costs, heavy regulations and increasing competition from Asian producers (^{Dávalos, 2016}). In fact, the proliferation and consolidation of Global Value Chains (GVCs) in the auto industry and the key role given to Mexico in the North American production network were useful for multinational enterprises to lower production costs, restore profit margins and increase sales across the world (^{Dávalos, 2016}).

On the negative side, it is fair to say that the textile-fashion cluster turned out to be a big loser. As Table 7 shows, this industry performed relatively well during the first seven-to-eight years of NAFTA. Such a trend, however, was ultimately reversed owing to the increasing competition from Chinese textile products in the US market, mainly after the accession of this country to the WTO in December 2001. In this manner, the share of Mexico’s textile-fashion cluster in the US went down from 9.64% in 2002 to 4.21% in 2017.

This disappointing performance can be explained by several factors: First, apart from a deficient productive capacity, the domestic textile-fashion industry has been suffering from an underdeveloped public infrastructure and an obsolete technology (^{Sánchez, Vázquez, and Richardt, 2012}). Second, there have been extended episodes of increasing or at least non-decreasing unit labor costs in the industry, given that labor productivity has failed to increase faster than wages over extended periods of time (^{Gazol, 2004}). Third, there has been strong competition not only from China but from other Asian countries (^{Minian, Martínez, and Ibáñez, 2017}).

Another industry that was certainly not a winner under NAFTA, but broadly speaking could manage to hold relatively steady in the US market was the primary products industry. As Table 7 indicates, this industry maintained an IMS in the US inside the interval of 14-to-16 percentage points over the 2002-2017 period. Within this industry, however, the Mexican agriculture has been a major cause for concern. Based on a comparative analysis, ^{Ayvar, Navarro, and Armas (2018)} show that the Mexican agriculture does not exhibit a revealed comparative advantage, as opposed to its North American counterparts. Under trade liberalization, the role of the Mexican government in fostering the national agriculture was significantly limited, given the suppression of producer price supports, the retrenchment of funds devoted to development programs, and the land reform (^{Calva, 2007}). Secondly, not only did the trade opening process was exceedingly fast but it was also combined with an overvalued currency, which made things even harder for this sector (^{Calva, 2007}).

In contrast, the US and Canadian agricultural sectors remained highly protected through government subsidies, which allowed them to develop a high-tech agriculture, thereby raising production and setting competitive prices (^{Cabrera, 2015}).

7. Conclusions

Rather than undertaking a wide cross-country study as to the impact of trade on economic growth, we follow a case-by-case approach as suggested by some authors (^{Bhagwati and Srinivasan, 2002}; ^{Kneller, Morgan, and Kanchanahatakij, 2008}). In this context, this paper assesses the impact of NAFTA on economic growth within the region. To carry out this task, we use an augmented version of the Solow growth model and estimate it by two complementary econometric methodologies. The first is FGLS with cross-section weights, which allows for a more efficient estimation of the regression parameters. The second is the AB dynamic panel GMM, which is consistent, robust to endogeneity problems, and requires no knowledge about the behavior of the error term.

Regardless of the econometric method employed, the empirical evidence leads to the following conclusions. First, GDP per capita responds positively to international trade, controlling for a whole set of variables: Total factor productivity, physical capital stock per capita, human capital formation, and the depreciation rate. Therefore, to a certain extent, NAFTA seems to have enhanced the output per capita of the countries involved, which makes our findings consistent with the orthodox view.

Although international trade seems to have produced a net gain for Mexico, the US and Canada, within each country one can always identify winners and losers and a brief exercise in this regard was conducted for the Mexican case. Of course, the new provisions stemming from the US-Mexico-Canada Agreement (USMCA) signed on November 30, 2018, represent a game changer for the three nations. In particular, the tougher rules of origin incorporated in this new trade deal are expected to render negative effects on the auto industry and the textile-fashion cluster (^{Burfisher, Lambert, and Matheson, 2019}).

The second conclusion of this paper is that human capital formation is the most prominent source of GDP per capita growth. This evidence underlines the importance of devoting more resources to long-term formal education, short-term training programs, and the entire knowledge transferring network. This is especially true in the case of Mexico given that: 1) its labor force is less qualified than that of the US and Canada, and 2) the Mexican government has been prone to reduce educational expenditure to improve public finances.

Finally, our evidence shows that GDP per capita is also a direct function of physical capital per person and TFP. While physical capital accumulation stems from the acquisition of machinery, equipment and infrastructure, TFP basically relates to technological change. In this context, the parameter estimates provided are in line with the notion that human capital formation, physical capital accumulation, TFP, and international trade interact dynamically to raise economic growth. The salient role played by human capital accumulation in generating economic growth brings attention to the fact that education and training become more critical as industries develop and move toward the global technology frontier.