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This study analyzes the effect of Foreign Direct Investment (FDI) inflows on the employment and wages of low- and high-skilled employees in the manufacture and service sectors in Mexico. The study implements a quarterly panel dataset covering the 32 Mexican states from 2005 to 2018. The econometric model is estimated throughout Fixed- Eec ff ts (FE) and Panel Corrected Standard Errors (PCSE). Employment results indicate that an increase of FDI inflows into the manufacture sector creates a positive effect in low- and high-skilled employment. In the case of service sector, results are inconclusive across models for both categories of employment. In the case of wages, it is found that FDI inflows by the manufacture sector increase marginally in low-skilled wages and no statistical effect is captured in high-skilled wages. Lastly, in service sector, results indicate the effect of FDI inflows are inconclusive in the case of low- skilled and high-skilled wages. Keywords: Foreign Direct Investment, Employment, Mexico, Panel data JEL Classification: F21, F23, F36, J21, J23, R10 American Free Trade Agreement (NAFTA), now called 1 Introduction United States–Mexico–Canada Agreement (USMCA). In its World Investment Reports (UNCTAD 2016, 2017, Waldkirch (2010) states that Mexico has been actively 2018), the United Nations Conference on Trade and trying to attract FDI since the 1980s by relaxing invest Development (UNCTAD) mentioned that Mexico is con- - sidered the primary driver of Foreign Direct Investment ment restrictions on different economic sectors. In (FDI) growth in Central America, where the country has addition, since 1994, NAFTA benefited Mexico as it sub - participated with approximately 70% of the total FDI stantially and permanently increased FDI inflows into inflows to this region in the last years. Nevertheless, the the country. As a result, abundant FDI came into the 2017 and 2018 investment reports for Mexico were not country seeking to exploit Mexico’s comparative advan- very optimistic (UNCTAD 2017, 2018), since FDI inflows tage (Waldkirch 2011). Given the relevance of FDI in the in 2016 and 2017 reached $30 billion dollars each year, economy since the implementation of NAFTA, abundant which is 15% below the amount received in 2015. Such literature has emerged analyzing the effects of FDI on contraction was mainly explained by the uncertainty different economic variables, such as wages, labor pro - that existed at that time about the future of the North ductivity, and employment. Diverse literature has also emerged analyzing the impact of NAFTA on economic sectors, such as manufacturing, maquiladora industry, and the impact on different economic regions in the country. *Correspondence: eduardo.saucedo@tec.mx EGADE Business School, Tecnologico de Monterrey, Ave. Rufino Tamayo, CP. 66269 Garza Garcia, NL, Mexico Full list of author information is available at the end of the article © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creat iveco mmons .org/licen ses/by/4.0/. 9 Page 2 of 15 E. Saucedo et al. Nevertheless, the existing literature about Mexico has Onaran (2012), Lee and Wie (2015) and Bogliaccini and paid limited attention to analyze the effect of FDI on Egan (2017), are examples of studies which analyze the employment and wages in the manufacturing and service effect of FDI on low- and high-skilled local wages. sectors, even though those two economic sectors have Another set of the literature analyzes exclusively the received together around 75% of total FDI inflows that effect of FDI inflows on low- and high-skilled employ - had come into the country since 2005. The scarcity of ment in the host economies. Studies such as Bailey and studies in the field is more noticeable if it is analyzed by Driffield (2007), Blanton and Blanton (2012), Raouf and the effect of FDI inflows on employment and wages when Hafid (2014), Yunus et al. (2015) are examples of this. separating employment in the two categories (low-skilled Lastly, some other literature is just interested in ana- and high-skilled). Such studies could be of relevance and lyzing if FDI inflows have a general positive or negative interest for policy makers, since the design of policies can effect on local employment. Studies such as Radosevic be focused on the groups that are considered a priority et al. (2003), Waldkirch et al. (2009), Villa (2010), Inekwe for a country or a government. (2013) and Bandick and Karpaty (2011) are examples of The study helps fill the gap in the literature by analyzing studies which find that the effect of FDI on employment employment and wages of low- and high-skilled employ- is positive. On the other hand, Girma (2005) and Jenkins ees in the manufacturing and service sectors, which (2006), find that FDI inflows have a negative impact on according to the number of employees and FDI inflows local employment. received in the country, are the two most relevant sectors in the economy. This study is performed using data for 2.1 Literature about the effect of FDI on employment each of the 32 states in Mexico, covering the period from and wages in Mexico 2005 to 2018 at the sub-national level in Mexico. The In a pioneer study for the Mexican economy, Feenstra econometric model implemented in this study is similar and Hanson (1997) study the impact of FDI on the wages to the one used by Hanson (2001), but with a small vari- of skilled labor in Mexico over the period 1975–1988. ation to examine the impact of FDI on low-skilled and They measure FDI using regional data on foreign assem - high-skilled employment and wages. To obtain the coef- bly plants and conclude that FDI growth is positively cor- ficient estimates, a Fixed-Effects model (FE) and a Panel related with the relative demand for skilled labor, which Corrected Standard Errors (PCSE) model are developed at the same time has influenced the increase of wages in and estimated. Results indicate that, in most scenarios, regions where FDI has concentrated. FDI inflows have different effects on low-skilled and Some other studies have been also focused on the effect high-skilled employment in manufacturing and service of FDI on employment according to the skill level. For sectors. example, Nunnenkamp and Bremont (2007) estimate The remainder of this study is organized as follows: dynamic labor demand functions for blue-collar (low- Section 2 provides important background on FDI inflows skilled) and white-collar (high-skilled) workers in Mexico and their effects on employment and wages. Section 3 for the period 1994–2006. Their findings do not support describes the econometric model implemented in this the widely-held view that FDI inflows add to white-collar study. Section 4 introduces the dataset and explains the employment. However, the positive effect on blue-collar distribution of FDI and employment in manufacturing employment diminishes when increasing skill intensity and service sectors around the country. Section 5 dis- in manufacturing industries. Waldkirch et al. (2009) in a cusses the results of the estimated regression, and the last study on employment of non-maquiladora manufactur- section presents the conclusion of this study. ing activities, find that FDI has a statistically significant and positive effect on both blue and white-collar manu - 2 Literature review facturing employment. Later, Waldkirch (2010) suggests Most studies available in the literature which analyze the that wages may be negatively affected by FDI, particularly effects of the FDI inflows on the local economy, com - in maquiladoras, because large FDI tend to reduce skilled monly mention that FDI inflows bring new and more wages. Some other studies, such as Turner and Martínez sophisticated technology into the host economy, expand (2003) and Loría and Brito (2005) associate FDI received firms’ production capabilities, and create some effects on by multinationals that operate in Mexico with an increase employment, wages and labor productivity. In addition, in employment in the country especially in the manufac- an important portion of the literature has focused on ture sector. Lastly, Vergara et al. (2015) analyze the effect categorizing employees according to skill levels and ana- of FDI on five industrial sectors in the northern region of lyzing the effect of FDI inflows on the wages of those dif - Mexico during the period from 2004 to 2013, and they ferent categories of employees. Girma et al. (2002), Owen find a positive correlation between FDI and employment and Yu (2008), Pandya (2010), Hanousek et al. (2011), only in the electricity sector. The effect of FDI on low and high‑skilled employment and wages in Mexico: a study for the manufacture… Page 3 of 15 9 results of standard errors, with zero or little loss of 3 Econometric model efficiency, compared to FGLS. Thus, in this study, the The generic labor market supply and demand equations problems previously mentioned are addressed using proposed by Hanson (2001) are used in this study to PCSE. Additionally, for comparison purposes, fixed investigate the effects of FDI on low-skilled and high- effects regression results are shown. skilled employment. The reduced-form equations from such model have the following form: 4 Data A,k LnL = βlnW + A α + B α + C α + ϑ jt 1 t 2 t 3 jt (1) jt jt The dataset covers the 32 Mexican states on a quar - terly basis from 2005 to 2018. Data are drawn from the k A,k Mexican Institute of Statistics, Geography, and Infor- LnW = lnW + A η + B η + C η + μ . jt 1 t 2 t 3 jt (2) jt jt mation (INEGI), which collects the dataset for employ- ment and wages via the National Survey of Occupation Equations (1) and (2) can be obtained by assuming and Employment (ENOE). Similarly, INEGI includes that both labor supply and labor demand are in equi- the dataset for population and the Ministry of Economy librium. The terms A , B, and C represent vectors related keeps track of FDI inflows. to state, country, and international conditions, respec- As aforementioned, this study focuses on analyzing the tively. The superscript k refers to low- or high-skilled effect of FDI inflows on different low- and high-skilled categories, while the subscript j and t refer to each of categories in employment and wages in the manufac- the 32 Mexican states and the quarterly periods during turing and service sectors. Table 6 in Appendix shows 2005–2018. Additionally, W represents wages, and W the average employment distribution for low- and high- stands for alternative wages. Briefly, an alternative wage skilled employment for the manufacture and service sec- for state j is a weighted average of wages in every state tors for each of the Mexican states during the period from except state j. The terms α –α , and η –η are vectors of 1 3 1 3 2005 to 2018, which is the period analyzed in this study. parameters; β and are scalar values; and ϑ and μ are jt jt It can be observed that Estado de Mexico and Mexico error terms assumed to be independent and identically City are the places that most capture employment, either distributed with zero mean and constant variance. low- or high-skilled employment either in the manufac- Equations (1) and (2) are estimated in this study ture or service sectors. Those two states together cap - throughout different econometric models. Breusch and ture around 25% of the total high-skilled employment in Pagan Lagrangian multiplier test indicates that the use manufacture and service sectors in the country. Similarly, of panel data techniques is more appropriate than OLS Table 6 shows that the top five states, as a group, capture and, similarly, the Hausman test allows to indicate that around 50% of high-skilled employment in the country, the use of fixed effects is preferable to random effects in while the bottom five states in the table capture around the estimation of both equations. Therefore, tables only 5% of the high-skilled employment in the country, either show the estimates for fixed effects. Additionally, the in the manufacture or service sectors. In the case of the possibility of the existence of the classic problems of low-skilled employment; shares are less concentrated on data panels is considered. The results of the Wooldridge the top states and are slightly better distributed across all tests to prove the existence of serial correlation in the Mexican states. estimates, as well as a modified Wald test to check for Table 7 in Appendix section shows the average monthly the presence of heteroskedasticity, and a Breusch– salaries in real terms (2013 = 100) for the low- and high- Pagan test to identify contemporary correlation prob- skilled employees working in the manufacture and service lems in the residuals, suggest that it is necessary to sectors in each of the Mexican states during 2005–2018. correct all of the problems. Moreover, endogeneity The manufacture sector shows an important dispersion problems were tested with the C statistic, yet no evi- in salaries between low- and high-skilled employees. For dence was found to state that the estimations should be example, the highest salaries in the high-skilled manufac- corrected for endogeneity. ture sector are in Coahuila, registering $7549 real Mexi- In Cameron and Trivedi (2010), the use of Feasible can pesos, a value 76% higher than the highest low-skilled Generalized Least Squares (FGLS) and Panel Corrected salaries in the manufacture sector in Baja California, with Standard Error (PCSE) is recommended for data pan- $4301 real Mexican pesos per month. It is worth identify- els where T is greater than N (in this study T = 56 and ing that some states have high-skilled salaries which are N = 32). However, Beck and Katz (1995) demonstrate above the country’s average, but at the same time those that FGLS produces results with strongly underesti- same states report low-skilled salaries below the country mated standard errors and, through Monte Carlo sim- average; this is observed in Guanajuato, Colima, Jalisco, ulations, they find that PCSE produces more accurate 9 Page 4 of 15 E. Saucedo et al. Table 1 Total country FDI 2005–2018; Percentage total FDI. Source: Own estimations using data from the Ministry of Economy 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 Total FDI 26,013 21,124 32,477 29,468 18,066 27,260 25,601 21,191 48,399 29,879 35,775 30,534 32,915 32,694 (Millions of dollars) Manufacture 51.5% 52.9% 42.5% 31.3% 39.8% 52.8% 44.8% 44.4% 64.8% 61.8% 49.3% 57.3% 45.5% 48.3% (% Total FDI) Services 27.9% 38.9% 36.5% 37.9% 38.7% 24.7% 32.5% 8.4% 12.1% 10.0% 20.6% 19.6% 15.4% 14.2% (% Total FDI) Other 20.6% 8.2% 21.0% 30.8% 21.5% 22.5% 22.7% 47.2% 23.1% 28.2% 30.2% 23.1% 39.1% 37.5% (% Total FDI) Durango, to mention a few. In the case of the service sec- In the case of “light grey” states, they captured more tor, the state with the highest high-skilled salaries in this than 1%, but less than 4% of the total FDI received in sector is Baja California Sur (touristic place) with $7696 the country in each respective sector. In the case of the per month, while its neighbor, the state of Baja Califor- manufacturing sector, there are 8 states out of 32 in “light nia, shows the highest salaries for low-skilled workers grey” which, as a group captured around 20% of the total with $4538. FDI received in the country in the analyzed period. In Table 1 shows the total FDI received in the country in the case of the service sectors, there are 8 states, which every year analyzed in this study and the percentage that accounted for 12% of the total FDI received in the coun- manufacture and service sectors represent in the total try in the aforementioned sector. Lastly, the 10 “dark FDI. On the one hand, the lowest value received in FDI grey” states accounted for 70% of the total FDI received in the country is observed in 2009, during the economic in the manufacturing sector and 80% of the FDI received crisis when the country received 18,000 million dollars. in the service sector. Figures 1b and 2b show the FDI On the other hand, the highest value is observed in 2013, received by the top five states in each sector in the entire when the country received more than 48,000 million dol- period. These five states captured 49% in the manufactur - lars. It is worth mentioning that the manufacture sector ing sector and 65% in the service sector. These numbers has been historically the economic sector with the high- confirm that FDI in both sectors is highly concentrated est volumes of FDI inflows into the country. The lowest in a few states. Lastly, Fig. 2 in Appendix section captures values appear also during the 2008 economic crisis, when how FDI evolved in each of the states in the country dur- Mexico captured 31% of the total FDI, and the highest ing the entire period analyzed in the study. value is observed in 2013, when almost 65% of the total Table 2 presents descriptive statistics for all variables FDI came into the country. Regarding the service sector, used in this paper. The effect of FDI upon employment the lowest value appears in 2012, when it captured 8.4%, and wages, both disaggregated in low-skilled and high- and the highest appeared from 2006 to 2009, when the skilled, are the main variables of interest in this study; value ranged around 38% of the total FDI received in the therefore, FDI is broken down into two different FDI country. categories. As previously discussed, this paper explores Figure 1 illustrates how FDI in the manufacturing and the effect of FDI on different kinds of employment and service sectors has been distributed across the country wages. For such reason, the dependent variables in the during the period 2005–2018. As it can be seen above, regression model are low-skilled employment and wages in maps 1a and 2a, the “white” states are those which in the manufacturing and service sectors, as well as high- received the least amount of FDI either in the manufac- skilled employment and wages in the manufacturing and turing or service sectors during the entire period. In fact, service sectors. all the “white” states captured less than 1% of the total As mentioned earlier, the main independent variable is FDI received by each sector in the whole period. Out FDI inflows into the country. With regards to the Ministry of the country’s 32 states, there are 14 “white” states in of Economy’s FDI classification, FDI database is divided Fig. 1a, all of which, as a group, captured 5.5% of the total in the following categories: new FDI, FDI originated from FDI in the manufacturing sector received in the country the reinvestment of profits, and FDI between compa - during the 2005–2018 period. In the case of Fig. 2a, the nies related to transactions made between domestic and 14 states in “white” captured, as a group, 7.8% of the total foreign companies. New FDI refers to investment that FDI received in the country in the service sector in the comes exclusively into the country to expand production entire period. and is expected to have a direct impact on employment. The effect of FDI on low and high‑skilled employment and wages in Mexico: a study for the manufacture… Page 5 of 15 9 Fig. 1 Geographic distribution of accumulated FDI by sectors, 2005–2018 (Source: Own estimates using data from the Ministry of Economy) Vergara et al. (2015) and Cabral et al. (2016); both papers in the country. We identify this issue in our data, but this focus on the analysis of new FDI inflows among all FDI is the only data available about FDI state inflows, hence categories, which is more related with employment. The there is no way to eliminate such potential bias. Bel- main independent variable is available in US dollars, so lak (1998), Jordaan (2008) and Mollick et al. (2006) are FDI is transformed to real pesos (2013 = 100) by consid- papers about FDI inflows which also mention the same ering the nominal exchange rate for the period, and then bias problem in their analysis. As Chiquiar (2005) sug- a deflator is built based on the 2013 consumer price index gests, NAFTA (now called USMCA) has given Mexico’s from INEGI. regions more economic independence and thus reduced Once FDI inflows are analyzed at state level, it is found the importance of Mexico City as the country’s mar- that Mexico City has received the highest amount of ket leader. Nevertheless, FDI inflows have been strongly FDI inflows in the entire nation, reaching up to 19.1% of concentrated in few states. For example, the top five FDI the total FDI inflows received in the country during the inflow attractors in the country, namely Mexico City, 2005–2018 period. It is important to mention that such Estado de Mexico, Nuevo Leon, Chihuahua, and Jalisco, numbers can produce a bias in the analysis because, in together received around 50% of the total FDI inflows many cases, headquarters of companies are in Mexico into the country during the period analyzed in this study. City, which is the capital of the country, and those head- The regression model estimation also includes alterna - quarters receive the money to be invested, but such tive wage as a control variable. In terms of the former, the money (FDI flows) could be invested in a different place approach of Cabral et al. (2016) is applied in considering 9 Page 6 of 15 E. Saucedo et al. Table 2 Statistics period summary: 2005–2018. Source: Author’s calculations using data from the INEGI and the Ministry of Economy Variables Observations Mean Std. dev. Min Max Dependent variables Employment Secondary school or lower degree, manufacturing sector 1792 189,974 320,008 9231 5,968,144 At least high-school, manufacturing sector 1792 68,294 144,871 2292 2,830,844 Secondary school or lower degree, services sector 1792 377,279 623,435 66,047 11,558,841 At least high-school, services sector 1792 292,862 572,942 43,823 11,004,266 Monthly wage (average in MXN) Secondary school or lower degree, manufacturing sector 1792 4194 965 1711 11,470 At least high-school, manufacturing sector 1792 6974 2351 1712 17,764 Secondary school or lower degree, services sector 1792 4419 956 2024 9026 At least high-school, services sector 1792 7773 2335 1894 15,236 Regressors Population 1792 3629,924 3056,025 540,154 17,694,804 Alternative monthly wage (average in MXN) Secondary school or lower degree, manufacturing sector 1792 4245 765 2689 6569 At least high-school, manufacturing sector 1792 6928 1192 4583 10,138 Secondary school or lower degree, services sector 1792 4454 761 3011 6662 At least high-school, services sector 1792 7724 1314 5333 11,129 Foreign direct investment Manufacture (Millions of current dollars) 1792 115.2 225.8 − 326.8 3185.8 Services (Millions of current dollars) 1792 51.9 196.3 − 2450.0 2693.6 state-level real wages of employees in the formal sec-5 Empirical results tor (2013 = 100). Regarding the latter, it is possible to 5.1 The effect of FDI on employment estimate a spatial weighted average of wages except for The aim of this study is to analyze the impact of FDI the state i based on the distance that exists between the on different types of employment. Table 3 presents government statehouse i and government statehouse in FE and PCSE regression results for the four different the remaining states. Moreover, control variables also employment-dependent variables covered in this study: include population levels of each state. All these variables low-skilled and high-skilled employment in the manu- in the dataset are available on a quarterly basis. Carlino facturing and service sectors. The estimates are obtained and Mills (1987), Clark and Murphy (1996) and Lastly, throughout a panel that includes the entire country (32 Boarnet (2005) provide arguments to justify why popu- states) and for a panel dataset that includes just 10 states lation size is an important factor to explain employment which are the most important receivers of FDI in the growth. country either in manufacture or service sectors. In this It is relevant to mention that lagged values for some of sense, Table 3 shows regressions coefficient estimates the independent variables are included in the economet- for the entire country as well as for the top 10 FDI states ric models, since it is reasonable to expect that employ- receivers. The exercise for the top 10 FDI states receiv - ment levels recorded in a specific quarter of the year are ers is included, since FDI inflows received in the coun - not the result of the explanatory variables of that same try are highly concentrated in few states. According to quarter, instead it is the result from the past fluctuations the Akaike criterion, test results indicate that just one lag of those variables. The optimal lag length for each inde - period should be included in the regression for the FDI pendent variable was selected through the application of the Akaike information criterion. Moreover, to test the robustness of the results, estimates were also obtained for the top five FDI states receivers in the country, but they are not reported in the table because the lack of space and because results are similar to those obtained for the entire country and for the top 10 states reported in Table 3. Results for the top five FDI states receivers are available upon request. The effect of FDI on low and high‑skilled employment and wages in Mexico: a study for the manufacture… Page 7 of 15 9 Table 3 Employment estimation models according to skill levels; Period: 2005: Q1–2018: Q4 ManufactureServices Variables Low-SkilledHigh-Skilled Low-SkilledHigh-Skilled FE PCSE FE PCSE FE PCSE FE PCSE 32 states 10 states 32 states 10 states 32 states 10 states 32 states 10 states 32 states 10 states 32 states 10 states 32 states 10 states 32 states 10 states 11.9*** 7.6*** 3.9** 2.2* 12.2*** 7.5*** 4.4*** 2.2** -0.3* -0.4**-0.001-0.1 -0.2 -0.2 0.1 0.1 Log FDI in the Sector (2.2) (1.5) (1.6) (1.2) (2.1) (1.7) (1.7) (1.1) (0.2) (0.2) (1.1) (0.3) (0.2) (0.2) (1.3) (0.8) 10.8*** 7.8*** 7.2*** 5.5*** 11.2*** 7.7*** 7.7*** 5.1*** -0.3* -0.2 -0.1 0.001 -0.8*** -0.7*** -0.4 -0.3 L1. Log FDI in the Sector (2.2) (1.7) (1.6) (1.2) (2.1) (1.8) (1.7) (1.1) (0.2) (0.2) (1.1) (0.3) (0.2) (0.2) (1.3) (0.8) 28.0 8.5 22.4*** 17.7** 12.6 1.1 2.2 7.9-2.4 -49.7 -9.1* -52.2*** -33.1 -42.9 -38.7*** -55.9*** Log Wages in the Sector (25.4)(45.4)(5.7) (7.6) (21.3)(35.6)(4.1) (5.5) (40.8)(34.2)(5.4) (8.8) (26.3)(31.0)(4.2) (6.7) -79.6 127.3 40.5** 143.1 123.7 -51.4 11.5 -107.1* -204.1 -162.4 -61.2 61.3 -331.4*** -238.0* -85.2 -90.8 Log Alt. Wage in the Sector (186.2) (199.9) (75.5)(95.3)(116.6) (165.6) (57.9)(64.5)(187.3) (190.3) (68.0)(100.1) (117.6) (130.5) (62.1)(82.3) 251.7*** 137.1* 222.7** 119.3** 307.0*** 234.9*** 254.0* 222.2*** 174.5** 247.3*** 158.0*** 216.6** 194.8*** 259.6*** 188.9*** 242.5*** Log Total Population (93.0)(70.0)(107.6) (60.2)(83.3)(83.4)(133.7) (54.8)(80.7)(78.5)(60.1)(104.6) (75.1)(55.3)(52.1)(93.1) -2433.3 -2417.1 -2738.0* -2094.7 -5033.8** 0-2917.3 -1479.5 536.7 0.001 -413.8 -2032.7 1819.0 -330.0 -345.0 -1032.6 Constant (2074.3) (1561.1) (1433.5) (1283.4) (1769.7) (0)(2126.8) (1050.5) (2167.5) (0.1) (868.7) (1471.3) (1587.7) (1207.8) (705.1) (1454.1) Sample Size 1760 550 1760 550 1760 550 1760 550 1760 550 1760550 1760 550 1760 550 R2 Within 0.109 0.456 0.354 0.576 0.052 0.363 0.326 0.493 R2 Overall 0.855 0.840 0.882 0.948 0.860 0.829 0.826 0.930 0.820 0.919 0.913 0.948 0.831 0.925 0.906 0.949 Standard errors are in parentheses *p < 0.10; **p < 0.5; ***p < 0.01 Remarks: Regressions include dummy variables to control for fixed and time effects of each state and quarter The estimated coefficients in the table have been multiplied by 100 to avoid dealing with too many decimals Heteroskedasticity robust standard errors are shown in parentheses in the case of fixed effect estimation 9 Page 8 of 15 E. Saucedo et al. independent variable, no matter if the model is estimated wage differences among regions encourages migration, for the entire country or the top 10 FDI states receivers. and implies that when wages increase in the rest of the Results in Table 3 indicate that manufacturing FDI country, except in state ‘i’ (alternative wage), then work- has a positive and statistically significant effect on high- ers in state ‘i’ have an incentive to leave the state, where skilled manufacture employment, such estimates are con- wages should subsequently increase to keep more work- sistent either for fixed effects or PCSE models either for ers from migrating. Lastly, similar to Boarnet (2005), the the entire country or for the top 10 FDI states receivers. population variable is positive and significant across all Such results are aligned with those found in most of the estimates, no matter the econometric model or the num- previous research studies mentioned in Sect. 2, which ber of states included in the analysis. argue that FDI inflows in this sector increase the demand for low- and high-skilled jobs. Lagged values presented 5.2 The effect of FDI on wages a higher coefficient than the contemporary FDI in the Table 4 regression results show how FDI has an impact case of the PCSE estimates. Similar results for Mexico on the different wage categories in the manufacturing in which FDI inflows in the manufacture sector have a and service sectors. As in Table 3, each econometric positive effect on employment in the same sector can be model is estimated either for the entire country or for the found in Waldkirch et al. (2009), who analyze the non- top 10 FDI states receivers in the country. Regarding the maquiladora manufacturing activities in Mexico during number of lags, Akaike criterion indicates similar results the 1994–2006 period. In a different study about Mexico, as the one obtained for Table 3, no matter if the model is Cota (2011) also finds that FDI inflows had a positive estimated for the entire country or the top 10 FDI states. effect on high-skilled employment in the manufacture In the case of estimates for the entire country, results industry. International literature for different countries for the manufacturing sector indicate that FDI inflows with similar results can be found in Bailey and Driffield either contemporaneous or lagged values both have sta- (2007), Raouf and Hafid (2014), Hoxhaj et al. (2016) and tistically significant effects on low-skilled wages and such Souare and Zhou (2016). effect are consistent for FE and PCSE models. In the case The results for the service sector either for low- or of top 10 FDI states estimates, results show only a posi- high-skilled employment, shown in the right-hand col- tive and statistically significant effect just for the lagged umn in Table 3, are not very conclusive, although, in value of FDI in the PCSE estimates. terms of fixed effects the FDI inflow effects on the differ - Results for the entire country could be an indicator ent employment categories are negative and statistically that FDI inflows are replacing low-skilled jobs in the significant. Nevertheless, once the correction of econo - manufacturing sector. Such effect could indicate that metric problems is considered implementing the PCSE, new FDI inflows in the manufacturing sector could be it is not possible to make inferences about the effects of creating new jobs that are not well paid. Literature about FDI inflows on the low- or high-skilled employment in other countries, such as Bailey and Driffield (2007) and the service sector since coefficients are not statistically Onaran (2012) found similar results. Estimates obtained significant. Results for the top 10 FDI states receivers are in Table 4 also indicate that FDI inflows in the manufac - also consistent with results obtained for the entire coun- turing sector have no impact on high-skilled wages. Such try, either for the fixed effects or PCSE models. results are consistent throughout FE and PCSE models, The control variables included in the econometric no matter if estimates are for the entire country or for the model highlight the fact that wages present a positive and top 10 FDI states receivers. statistically significant effect only for the PCSE for the Regarding the service sector, wages estimates are not low-skilled manufacture sector jobs. In this case, results very conclusive for low-skilled and high-skilled wages, are also consistent for the entire country and for the top regardless of the data used for the estimates, be the entire 10 FDI states. country or the top 10 FDI states receivers. Such results Alternative wages are not statistically significant in are inconclusive because in the FE model the FDI variable most cases. In the case of manufacturing, results are has a positive and significant effect, but when the PCSE positive and statistically significant for low-skilled jobs, model is estimated, the coefficients are no longer signifi - but negative for high-skilled jobs in the case of the top cant. As in the case of service sector employment, results 10 states. Only in the high-skilled service sector the effect do not allow for a strong inference to be made in terms of the alternative wages is negative and significant either of the effect of FDI inflows on low-skilled wages. FDI for the entire country or for the top 10 states when they inflows to the service sector are found to increase wages are estimated with FE. As expected, a negative impact To be consistent with Table 3, estimates were also obtained for the top five on the level of employment by the variable alternative FDI states receivers in the country, but they are not reported here. Results are wages is observed. This is consistent with the idea that available upon request. The effect of FDI on low and high‑skilled employment and wages in Mexico: a study for the manufacture… Page 9 of 15 9 Table 4 Wages estimation models according to skill levels; Period: 2005: Q1–2018: Q4 Manufacture Services Variables Low-Skilled High-SkilledLow-Skilled High-Skilled FE PCSE FE PCSE FE PCSE FE PCSE 32 states 10 states 32 states 10 states 32 states 10 states 32 states 10 states 32 states 10 states 32 states 10 states 32 states 10 states 32 states 10 states 0.7* 0.2 0.6** 0.2 0.4 0.4 0.30.1 0.10.001 -0.1 -0.1 0.6*** 0.4** 0.2 0.3 Log FDI in the Sector (0.4) (0.6) (0.3) (0.3) (0.7) (0.8) (0.7) (0.6) (0.1) (0.1) (0.4) (0.5) (0.2) (0.2) (0.5) (0.4) 0.9** 0.6 1.0*** 0.8*** 0.6 0.3 0.2-0.1 0.3*** 0.3*** -0.001 0.0010.001 -0.1 -0.4 -0.3 L1. Log FDI in the Sector (0.4) (0.5) (0.3) (0.3) (0.5) (0.7) (0.6) (0.6) (0.1) (0.1) (0.4) (0.5) (0.1) (0.2) (0.5) (0.4) 2.6 1.5 2.9*** 3.5** 2.3 0.4 1.12.7 -0.2 -6.6 -0.7 -7.1***-6.7 -14.2-8.0*** -17.7*** Log Employment in the Sector (2.5) (8.2) (0.6) (1.6) (3.6) (12.0)(1.0) (2.6) (3.5) (4.9) (0.5) (1.1) (6.9) (9.2) (0.8) (1.8) -64.2**-56.0 -46.0** -21.8 -66.0 -164.7** -54.5** -89.1*-68.1 -162.4 -13.0-55.2 -106.9 -133.6 6.2 -32.8 Log Alt. Wage in the Sector (29.9) (48.4)(20.8) (43.2) (53.8)(77.6)(26.2) (46.5) (52.1) (108.0) (23.9) (51.5) (68.3) (111.0) (25.4) (52.1) -24.0 -19.1 -21.5 -22.0 -7.7 -82.3 2.2-86.3 -12.61.4 -5.2 7.8-9.8 22.5 7.6 50.2 Log Total Population (23.5) (38.2)(21.4) (45.8) (85.9)(103.1) (29.4) (67.5) (27.9) (35.6) (22.7) (20.1) (59.6) (82.4) (35.0) (41.7) 1651.4***0.001 1460.3***1324.4 1568.2 3720.2 1347.6***3020.7***1585.9** 0.0011033.6**1334.7**2087.3* 1927.4 835.4 716.4 Constant (446.9) (0.1) (354.5) (833.7) (1392.7) (2223.7) (497.0) (1052.5)(709.3) (0.1) (408.4) (620.9) (1106.9)(1377.6)(603.4) (887.5) Sample Size 1760 550 1760 550 1760550 1760550 1760 5501760550 1760 5501760550 R2 Within 0.371 0.534 0.571 0.750 0.5850.689 0.688 0.781 R2 Overall 0.759 0.758 0.956 0.973 0.721 0.833 0.921 0.9460.803 0.8430.980 0.9820.810 0.857 0.966 0.971 Standard errors are in parentheses *p < 0.10; **p < 0.5; ***p < 0.01 Remarks: Regressions include dummy variables to control for fixed and time effects of each state and quarter The estimated coefficients in the table have been multiplied by 100 to avoid dealing with too many decimals Heteroskedasticity robust standard errors are shown in parentheses in the case of fixed effect estimation 9 Page 10 of 15 E. Saucedo et al. Table 5 The effect of FDI on the labor market; A control and treatment effect comparison Manufacture Services Employment Wages EmploymentWages Low-skilledHigh-skilled Low-skilledHigh-skilled Low-skilled High-skilled Low-skilled High-skilled Fixed Fixed Fixed Fixed Fixed Fixed Fixed Fixed PCSE PCSE PCSE PCSE PCSE PCSE PCSE PCSE Effects Effects Effects Effects Effects Effects Effects Effects Control 15.70 15.64 15.87 15.71 13.51 13.48 15.3515.47 12.5512.53 Treatment 21.26 20.54 21.29 20.63 18.62 18.59 19.4419.90 19.0519.44 32 states Difference 5.55 4.91 5.42 4.92 5.11 5.11 4.09 4.42 6.51 6.91 Signif. at the YesYes YesYes YesYes YesYes YesYes 1% level Control 14.90 15.07 15.54 15.54 13.42 15.7115.76 10.3613.17 Treatment 20.87 20.17 20.86 20.13 18.15 19.3419.79 18.8118.72 10 states Difference 5.98 5.10 5.32 4.58 4.72 3.63 4.03 8.45 5.55 Signif. at the YesYes YesYes YesYes YesYes Yes 1% level Columns in grey indicate that FDI in Tables 3 and 4 was not statistically significant, in such scenario the “control effect” model and the “treatment effect” models were equal in both the high-skilled and low-skilled employment in including the FDI independent variable, and when such the case of FE models. variable is excluded from the models. The difference Control variable results indicate that low-skilled jobs in obtained in the estimated dependent variable once both the manufacturing sector have a positive and statistically types of models are estimated allows us to identify the significant effect on wages. Such results come exclusively effect that FDI is having either on employment or wages. from the PCSE model. Estimates for high-skilled jobs are When the econometric models do not include the FDI not significant in either the FE or PCSE models. Results variable they are called “control effect” models, and when are aligned for the entire country panel dataset or the top the FDI is included, they are referred to as “treatment 10 FDI states dataset. In the case of the service sector, effect” models. results show that employment level generates a negative Once the estimated betas are obtained for the “con- and statistically significant effect on wages. As expected, trol effect” model, then an expected employment mean, such results are obtained just in the PCSE models in the and an expected wage mean is obtained for each of the case of high-skilled jobs. states in the entire country. Using the estimated means Regarding alternative wages, results indicate that the in employment and wages obtained for each state, a gen- alternative wages variable has a negative and statistically eral mean is obtained for both dependent variables. The significant effect on wages in the manufacturing sector, same process is repeated for the “treatment effect” model either for low or high-skilled wages in most econometric and a general mean is obtained again for both depend- models, regardless of the panel dataset included in the ent variables. Such general mean values are presented in regression. Such negative coefficient between alternative the “control effect” and “treatment effect” cells in Table 5 wages and wages indicates that an increase in manufac- for each of the econometric models implemented in this turing wages in state “i” creates a negative effect on wages study. The row labelled “difference” is just the differ - in the same sector in the remaining states, which is con- ence between the general mean obtained for the “control trary to expectations. When the exercise is replicated for effect” model and the general mean in the “treatment the 10 states that received the higher FDI inflows from effect” model. Lastly, a t-test is implemented to estimate if each of the sectors, the results remain consistent with there is a statistical difference between both means. If so, those that have been discussed. In the case of the service then it is indicated as “yes” and then it is concluded that sector, the alternative wage variable is not statistically FDI has a statistically significant effect either on employ - significant for any model or panel dataset. Regarding the ment or wage levels. The same exercise is repeated for population variable, results indicate that such variable is each econometric model estimated in the study, and for not statistically significant across models, regardless of the econometric panel when 32 or 10 states are included the econometric model or dataset used, and such results in the study. Masso et al. (2008), Becker and Muendler are consistent for the manufacturing and service sectors. (2008), Lu et al. (2017) and Bannò et al. (2014) are some The dependent variables in the econometric models in this study are employment and wages. Table 5 com- pares those econometric models when they are estimated An α = 0.01 is used in the t-test. The effect of FDI on low and high‑skilled employment and wages in Mexico: a study for the manufacture… Page 11 of 15 9 studies that analyze the applications of treatment effects, in specific sectors and geographic regions, in order to those studies include FDI as a relevant variable. develop better policies for the country in these major areas. Acknowledgements 6 Conclusion An earlier version of this study was presented at the 93rd Annual Conference It has been always important to analyze the effect of of the Western Economic Association International ( WEAI) in Vancouver, FDI inflows on labor markets in the host economy. Few Canada. The authors wish to thank the discussant and are grateful for com- ments received from anonymous referees in previous versions of this study. studies in the literature have been focused on separat- The usual disclaimer applies. ing the Mexican labor market according to employee skill categories (education level) and analyzing how FDI Copyright and intellectual property We declare this manuscript does not infringe the copyright, moral rights, or inflows influence them. The objective of this study is other intellectual property rights of any other person. to contribute to filling this gap by separating employ - In addition, we want to testify that this paper is original and has not ment and wages from the manufacture and service sec- incurred in any type of plagiarism. Furthermore, we would like to declare that this manuscript has been sub- tors into low- and high-skilled categories and analyzing mitted only to this journal and has not been published or partially published how FDI inflows into these sectors impact employment elsewhere. and wages of those categories. The study is a panel data analysis made up of the 32 Mexican states, spanning Authors’ contributions from 2005 to 2018. Manufacturing and service sectors ES—Has used in a different research paper the econometric model used in are analyzed, as those are the country’s largest receivers this paper. In addition, he has worked previously in topics related with Foreign Direct Investment. So, he provided the idea to analyze the effect of FDI on of FDI. During the analyzed period in this study, those Employment. In addition, he was involved in the literature review, introduc- sectors captured between 60 and 90% of the coun- tion, and conclusion. TO—Provided some help with the literature review sec- try’s total FDI and created the highest number of jobs tion, interpretation results and with style correction of the text, as English is his native language. HZ—Collected the data and was in charge of the economet- among all economic sectors in the country. rics in this paper. All authors read and approved the final manuscript. When the panel dataset is estimated for the entire country, employment results indicate that an increase Availability of data and materials The data used in this paper is open to public in different Mexican websites. of FDI inflows into the manufacture sector creates an Information about how to get such data is mentioned in the paper in the data increase in both low-skilled and high-skilled employ- section. In addition, it is mentioned in the paper the following statement: “The ment. The effect persists even when the sample is datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.” reduced to the 10 states that received the most FDI in the period under study. Low- and high-skilled employment Ethics approval and consent to participate results in the service sector are inconclusive across the The authors of this study declare this manuscript does not infringe the copy- right, moral rights, or other intellectual property rights of any other person. econometric models, regardless of sample reduction. A The authors of this study testify that this paper is original and has not incurred positive effect in wages is found for FDI inflows received in any type of plagiarism. The authors of this study declare that the article by the manufacture sector when estimated for the entire is original, has not already been published in a journal, and is not currently under consideration by another journal The authors of this study agree to the country, and no statistical effect is captured in high- terms of the SpringerOpen Copyright and License Agreement. skilled wages. Lastly, it is found that low-skilled and high- skilled wages results are inconclusive across econometric Consent for publication Not applicable. models, regardless of the sample reduction. The study of the effect of FDI inflows on the Mexican Competing interests and funding labor market is a topic that deserves more attention than We declare that we have not received any type of funding or grant for the elaboration of this research paper. For that reason, we do not have any conflict in other economies, as the proximity of Mexico to the of interest, including any type of financial interest related with the paper. US, the most important economy in the world, makes it an attractive host economy for their investment com- Author details EGADE Business School, Tecnologico de Monterrey, Ave. Rufino Tamayo, CP. pared to other countries. Additionally, lower wages and 66269 Garza Garcia, NL, Mexico. Deloitte Mexico, Ave. Juarez, CP. 64000 Mon- the trade agreement with the US and Canada, have made terrey, NL, Mexico. Mexico an important place to attract foreign compa- nies. Therefore, given the importance of Mexico in the Appendix international arena to attract FDI inflows, it is relevant See Tables 6, 7 and Fig. 2. for Mexican policy makers to analyze in more detail the effects that FDI inflows have on employment and wages 9 Page 12 of 15 E. Saucedo et al. Table 6 Employment distribution across Mexican States; Average values, 2005–2018. Source: Author´s elaboration with ENOE data State Manufacture employment Services employment Low skilled % High skilled % Low skilled % High skilled % Estado de Mexico 820,626 13.5 1,692,239 14.0 311,789 14.3 1,143,951 12.2 Mexico City 303,848 5.0 1,113,072 9.2 179,402 8.2 1,210,375 12.9 Jalisco 426,909 7.0 731,174 6.1 149,888 6.9 539,360 5.8 Veracruz 204,052 3.4 622,907 5.2 80,992 3.7 437,722 4.7 Puebla 307,481 5.1 432,779 3.6 90,090 4.1 319,540 3.4 Nuevo Leon 344,895 5.7 598,906 5.0 106,720 4.9 324,198 3.5 Guanajuato 403,055 6.6 460,579 3.8 102,897 4.7 265,470 2.8 Tamaulipas 166,195 2.7 335,442 2.8 91,132 4.2 254,266 2.7 Baja California 209,704 3.4 288,887 2.4 82,998 3.8 238,976 2.6 Chihuahua 247,651 4.1 295,606 2.4 90,648 4.1 227,961 2.4 Sinaloa 91,119 1.5 253,425 2.1 38,882 1.8 232,437 2.5 Sonora 139,969 2.3 251,248 2.1 60,714 2.8 222,294 2.4 Chiapas 104,600 1.7 283,913 2.4 21,361 1.0 233,473 2.5 Michoacan 245,059 4.0 502,765 4.2 62,376 2.9 332,723 3.6 Coahuila 214,302 3.5 340,929 2.8 89,849 4.1 275,060 2.9 Guerrero 137,781 2.3 282,567 2.3 25,853 1.2 236,446 2.5 Oaxaca 185,636 3.1 311,022 2.6 34,972 1.6 228,359 2.4 Tabasco 53,490 0.9 212,541 1.8 20,833 1.0 172,345 1.8 Hidalgo 124,788 2.1 223,342 1.8 35,036 1.6 169,701 1.8 San Luis Potosi 127,974 2.1 228,552 1.9 41,564 1.9 159,978 1.7 Yucatan 143,443 2.4 276,569 2.3 30,559 1.4 175,605 1.9 Quintana Roo 36,305 0.6 221,175 1.8 11,396 0.5 171,463 1.8 Morelos 68,072 1.1 216,530 1.8 28,209 1.3 153,139 1.6 Queretaro 127,682 2.1 188,952 1.6 54,585 2.5 151,358 1.6 Durango 76,363 1.3 143,698 1.2 20,251 0.9 103,373 1.1 Aguascalientes 491,166 8.1 917,986 7.6 224,607 10.3 858,809 9.2 Nayarit 33,764 0.6 122,654 1.0 11,427 0.5 103,612 1.1 Tlaxcala 94,202 1.5 105,608 0.9 36,114 1.7 94,871 1.0 Zacatecas 53,106 0.9 125,800 1.0 12,821 0.6 89,891 1.0 Baja California Sur 31,268 0.5 101,367 0.8 15,652 0.7 94,966 1.0 Campeche 26,722 0.4 86,395 0.7 6,909 0.3 72,536 0.8 Colima 37,943 0.6 104,294 0.9 14,885 0.7 77,324 0.8 Total 6,079,169 100 12,072,919 100 2,185,413 100 9,371,581 100 Figure 2 shows the FDI flows received by each state City shows permanent swings along the entire ana- along all the period analyzed in this study. As can be lyzed period. All those constant sharp changes shown seen, FDI inflows show sharp changes in most of the in most states can be helpful to justify the inclusion of states during the 2008–2009 economic crisis as well difference-in-difference model into the analysis. as during the 2013–2014 period when some economic sectors deregulated. Some other states such as Mexico The effect of FDI on low and high‑skilled employment and wages in Mexico: a study for the manufacture… Page 13 of 15 9 Table 7 Rank average monthly real wage across Mexican States, 2005–2018; Economic sectors and labor skills. Source: Author´s elaboration with ENOE Data Manufacture sector Services employment Entities Low skilled Entities High skilled Entities Low skilled Entities High skilled Baja California 4301 Coahuila 7549 Baja California 4538 Baja California Sur 7696 Baja California Sur 4254 Tamaulipas 6698 Baja California Sur 4480 Sinaloa 7085 Nuevo Leon 4101 Hidalgo 6550 Sinaloa 4146 Coahuila 7057 Coahuila 3892 Nuevo Leon 6471 Sonora 3980 Sonora 7036 Sonora 3841 Baja California 6285 Quintana Roo 3947 Campeche 6978 Chihuahua 3772 Chihuahua 6265 Chihuahua 3912 Quintana Roo 6942 Guanajuato 3760 Sonora 6130 Nuevo Leon 3829 Michoacán 6919 Sinaloa 3747 San Luis Potosi 5987 Michoacán 3722 Hidalgo 6807 Tabasco 3627 Tabasco 5986 Colima 3683 Chihuahua 6740 Colima 3613 Michoacán 5887 Tabasco 3675 Chiapas 6671 Tamaulipas 3575 Sinaloa 5877 Coahuila 3664 Baja California 6656 Quintana Roo 3405 Quintana Roo 5849 Campeche 3592 Colima 6564 Jalisco 3392 Baja California Sur 5702 Nayarit 3557 Tabasco 6476 Durango 3388 Veracruz 5598 Hidalgo 3420 Nayarit 6351 Michoacán 3379 Yucatan 5353 Durango 3389 Yucatan 6303 Average 3225 Average 5259 Average 3383 Nuevo Leon 6069 Hidalgo 3207 Colima 5159 Tamaulipas 3337 Durango 5912 Aguascalientes 3111 Jalisco 5132 Jalisco 3315 Average 5862 Nayarit 3061 Mexico City 5119 Guanajuato 3173 Tamaulipas 5854 Mexico City 3059 Nayarit 4916 Chiapas 3141 San Luis Potosi 5736 San Luis Potosi 3042 Durango 4783 Mexico City 3141 Zacatecas 5719 Veracruz 3015 Guanajuato 4712 Guerrero 3131 Mexico City 5446 Querétaro 2995 Oaxaca 4635 San Luis Potosi 3114 Jalisco 5277 Zacatecas 2773 Querétaro 4629 Veracruz 3040 Oaxaca 5145 Campeche 2765 Puebla 4612 Yucatan 3035 Veracruz 4954 Puebla 2737 Campeche 4497 Oaxaca 3028 Puebla 4819 Estado de Mexico 2692 Chiapas 4496 Querétaro 2992 Guanajuato 4814 Tlaxcala 2612 Aguascalientes 4413 Zacatecas 2860 Tlaxcala 4771 Chiapas 2610 Zacatecas 4400 Aguascalientes 2805 Aguascalientes 4661 Yucatan 2447 Guerrero 3910 Estado de Mexico 2801 Guerrero 4656 Guerrero 2435 Tlaxcala 3809 Puebla 2794 Querétaro 4311 Oaxaca 2386 Estado de Mexico 3684 Tlaxcala 2697 Estado de Mexico 3802 Morelos 2193 Morelos 3197 Morelos 2315 Morelos 3372 Real values, (2013 = 100) 9 Page 14 of 15 E. 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Journal for Labour Market Research – Springer Journals
Published: Jul 13, 2020
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