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             \author[1]{Maria Elena Cardero  }

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\date{\small \em Received: 7 April 2015 Accepted: 3 May 2015 Published: 15 May 2015}

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During the NAFTA negotiations, the impact that the execution of this Agreement would imply was comprehensively speculated and investigated. It was guaranteed, among other things, that Mexico would be the country with the greatest impact and a significant number of analysts pointed out that said impact would be positive (Lustig, 1992). While it was additionally mentioned that there would be some problems in sectors such as agriculture â??"where Mexico would be a losing countryâ??", overall it was argued that there would be a greater growth for our country, that also the reforms initiated with the assumption of the Washington Consensus were going to be secured1 Since the early 1980s the country began to experience very important reforms regarding the previous growth model. Along with the accelerated openness that started in the mid-1980s with the entrance to the GATT, public companies began to be privatized (the government productive, financial and services capacity was reduced from 1090 entities, in the beginning of 1984, to 258 in 1994), the structure of public expenditure was modified, and the high public deficit became a surplus, fighting inflation and financial openness were prioritized and deregulation of the economy started., that the country would grow more, that most of the jobs would be created in Mexico and that there would even be a resource mobilization towards Mexico.

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\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
uring the NAFTA negotiations, the impact that the execution of this Agreement would imply was comprehensively speculated and investigated. It was guaranteed, among other things, that Mexico would be the country with the greatest impact and a significant number of analysts pointed out that said impact would be positive (Lustig, 1992). While it was additionally mentioned that there would be some problems in sectors such as agriculture -where Mexico would be a losing country-, overall it was argued that there would be a greater growth for our country, that also the reforms initiated with the assumption of the Washington Consensus were going to be secured \hyperref[b1]{2} However, not all opinions and evaluations were so positive, some authors (Blecker 2006  ; Weintraub 2004) thought it could be , that the country would grow more, that most of the jobs would be created in Mexico and that there would even be a resource mobilization towards Mexico.\par
It was expected (Hufbauer and Schott, 1993) that -in terms of employment, wages, exports and foreign investment-NAFTA had positive effects for Mexico, with a migration reduction from Mexico to the United States.\par
It was also stated that one of the positive aspects of entering into NAFTA would be that many of the jobs created would be for women since, considering that they were "less unionized," they would be mostly hired in the new jobs. \hyperref[b0]{1} This research was supported by PAPIIT funds of the IN306513 project -Employment in Mexico for men and women. A regional and spatial analysis of segregation, segmentation and discrimination (Empleo en México hombres y mujeres. Un análisis regional y espacial de segregación, segmentación y discriminación.) We appreciate the support and financing. \hyperref[b1]{2} Since the early 1980s the country began to experience very important reforms regarding the previous growth model. Along with the accelerated openness that started in the mid-1980s with the entrance to the GATT, public companies began to be privatized (the government productive, financial and services capacity was reduced from 1090 entities, in the beginning of 1984, to 258 in 1994), the structure of public expenditure was modified, and the high public deficit became a surplus, fighting inflation and financial openness were prioritized and deregulation of the economy started.\par
possible to create a deindustrialization process and that no instruments -as in the case of the European Union (such as protectionist agricultural policies, resource transfers to the most underdeveloped areas, free labor mobility across borders, etc.)-that could mitigate the negative effects of an agreement of this nature were being developed.\par
The validity of this Agreement began in January 1994 and, in the same month of that year, an uprising (Zapatista Army of National Liberation or Ejercito Zapatista de Liberación Nacional) occurred in southeastern Mexico, followed by various political and economic events that resulted in a deep crisis that caused a fall in the GDP, in 1995, of over 6 percentage points and an exchange modification above 100 percent. \hyperref[b2]{3} The combined effect of devaluation, fall in production and greater openness caused by NAFTA resulted in a rearrangement of the supply's structure thus increasing imports to 25\% of the GDP in 1995 and exports to 27\% of such GDP without stopping the fall in production.\par
The composition of exports changed dramatically, with a relative decline in oil sales abroad and in agricultural products that, during the 1950s, 1960s and 1970s, had been the main foreign exchange provider \hyperref[b3]{4} , therefore, the manufacturing sector became the determining factor of the composition and evolution of the external sector, not only because of the growing importance manufacturing exports had, but also because of the imports of inputs associated to exports that began to double every two years and that nowadays account for 78\% of exports (Romero, 2009) 5 \hyperref[b2]{3} The openness, started in mid-eighties, accelerated the pace of growth of the trade deficit, which cumulative balance (1988-1993) was 34.5 billion dollars, and the following year such deficit amounted to 18.4 billion dollars, a portion of which was financed by foreign investment and by investment in bonds with currency hedging or denominated in foreign currencies \hyperref[b3]{4} Agricultural exports accounted for 45\% of total exports of goods in 1950; 50.6\% in 1960; and 42.6\% in 1970. Barrón Antonieta (1997) Impacto del tlc en el empleo femenino rural (Impact of NAFTA on rural women's employment). Working paper . \hyperref[b4]{5} In 2005, imports accounted for 30\% of the GDP, but gross fixed investment had fallen from 23.2\% in 1981 of the GDP to 14.4\% in 2005. While between 1970 and 1981 the GDP grew at a real annual rate of 6.9 percent and there was a trade deficit of 2.4\% of the GDP, the international debt crisis and the collapse of oil prices resulted in a drop D II.\par
Industry in Mexico (1994-2014)   The purpose of this research is to evaluate the impact that NAFTA has had on overall growth and on employment of men and women. Particularly, the industrial manufacturing sector, which generates 80\% of exports and probably a higher proportion of imports, is analyzed herein. A review of the manufacturing industry discrimination and occupational segregation nationwide and within manufacturing industry. Over 20 years (1994-2013), Mexico's GDP grew at a very slow rate of only 2.3\% at 2008 prices. Besides the strong fall of the GDP in 1995 (-6.8\%), the GDP fell again (-0.9\%) between 2000 and 2001, and fell again consecutively in 2008 (-1.3\%) and 2009 (-1.2\%). and its participation in the national value added is made and the employment of women and men in the manufacturing industry is studied, with a focus on wage Participation of the Secondary Sector (Figure  {\ref 1}) and particularly the manufacturing industry sector (Figure  {\ref 2}), which had shown an increase as a proportion of the GDP by the end of the 1990s, began to be reduced, thus its contribution became stagnant since 2003 (16.5\%) keeping at that level (16.6\%) until early 2014 (Figure  {\ref 2}). in production that began in 1982 -worsened by trade opennessand in the falling of the average growth rate to only 1.6\% between 1982-1993, with a positive trade balance of 2.1\% of the GDP. In the first 10 years of NAFTA (1994-2005) real GDP growth was barely 2.9\%, with a a trade deficit of 1\% of the GDP. 
\section[{6}]{6}\par
Changes made during the 1980s and early 90s that were "tied" (made) with the signing of NAFTA, only generated, since then, a very modest growth of the economy: 3.4\% (1994-2000); 2.2\% (2001-2011); and only 1.1\% in 2013. This profoundly affected productive structure, agriculture, employment of men and women and their salaries . Only some manufacturing sectors experienced sustained growth during 1994 and 2014 as occurred with the food sector (Figure 3); other sectors, such as the chemical subsector also grew, but to a lesser extent. However, in general most sectors had a lower growth or became stagnant. While we analyzed the structure of the processing industry, with the purpose of understanding the fall of its participation in the GDP and employment, we found that a significant part of this fall is due to the low participation of the leading exporting subsectors in the national value added.\par
This decline of industry in the national value added, was largely stimulated by the intense geographical fragmentation of production worldwide, which in the last two decades has been accelerated with the reduction of tariff barriers and transport costs, as well as with the advances in information and telecommunications.\par
In major Mexican export industries -such as the automotive, electronic and aeronautical industriesthere is a growing tendency that final goods will not be produced in a single country anymore. The activities ranging from product's research and development to its recycling, including its production, support services, distribution, marketing, finance and after-sales services, are performed in several countries, through the interaction among subsidiaries of a single multinational company or transactions made between them and their external suppliers. Consequently, the countries participating in these chains, rather than specializing in the full production of final goods or services, specialize in certain tasks or segments of the production process, regionally distributed in the "factory of North America," in the European Union ("factory of Europe") and in the ASEAN + 3 ("factory of Asia")  {\ref 7} The trade in value chains has several features -the first is its close relationship with foreign direct investment; the second is its intense exchange of intermediate goods; the third is the increased import content of exports; and the last is the fundamental role of a wide range of services (financial, legal, logistics, . Within the value chains the main value added lies in the knowledge-intensive activities -such as design and research and development-, which is increasingly linked to intangible aspects -such as quality, timeliness, connectivity, innovation, patentability and registration of trademarks, traceability, safety, environmental conservation, carbon footprint and energy efficiency-, while at the opposite end are the provision of raw materials and assembly activities. The countries which production is classified in the above last section only capture a small part of the value added created in chains. Most of the value is taken by multinational companies usually through transfer pricing, or repatriation of income from their charges related to technology, capacity building and escalation, while countries where value added of exports is very low have to cover social effects, including effects on working conditions, occupational safety and health.  
\section[{Sub. Alimentario}]{Sub. Alimentario}\par
Sub. de bebidas y tabaco Sub. de productos textiles Sub. de productos textiles Sub. de productos textiles Sub. productos derivados del petróleo Sub. Químico Sub. Productos a base de minerales no metálicos Sub. metales básicos Sub. Maquinaria y quipo Sub. otras industrias design and communication, among others), many of which are incorporated as inputs of final traded goods.\par
In the case of Latin America (ECLAC 2013), Brazil and Mexico recorded an increase in the relative content of imported inputs in their exports between 1995 and 2011, but when comparing the weight of imported inputs it was found that in Mexico this is significantly higher than in Brazil (30\% and 12\%, respectively, in 2011), implying that the proportion of the domestically generated value is less in the case of Mexico. This reflects a higher integration of Mexico on links of the production chain involving activities of assembly of final goods that incorporate low value added.\par
At the sectoral level, ECLAC (2014) notes that major Mexican chains of export to the United States are those linked to the automotive industry, especially the chain of parts and accessories of motor vehicles that, in 2011-2012, accounted for 19\% of total exports of intermediate goods of this kind. Second in importance are groups of electricity distribution equipment, electrical devices for splicing and internal combustion engines. Together, these four industries accounted for 43\% of total exports of intermediate goods from Mexico to the United States in 2011-2012. Also, industries producing capital goods as non-electrical machinery, medical equipment, heating and cooling equipment, pumps and compressors, civil engineering machinery and equipment, among others, stand out, which mainly supply parts and components to companies of the "factory of North America," especially located in the United States. Special mention goes to groups of intermediate products corresponding to industries of high-technology capital goods, such as telecommunications equipment, machinery and electrical devices, measuring instruments and devices. \hyperref[b7]{8} Intra-industry trade in Mexico, regarding intermediate goods, made by trading partners, according to the Grubel-Lloyd index is 63\% with the United States, 17\% with Latin America, 15\% with the European Union and 8\% with ASEAN+3. Among the countries of the Latin America region, Mexico is the one which keeps a more vigorous relationship of intraindustry trade with the United States. During 2000-2001 the share of exports associated with this kind of trade was 77\%, which was reduced to 53\% in the 2011-2012 biennium as a result of the competition Mexico is facing in the US market for similar products imported from China. The share of total Mexican exports to the United States has declined from 88\% in 2000 to 79.5\% in 2012, whereas exports to other countries have been increased. ECLAC (2013).\par
According to INEGI \hyperref[b8]{9} However, the Export Value Added of Global Manufacturing (VAEMG in Spanish) in the Mexican case the contribution of Global Manufacturing Production in Exports of goods from Manufacturing Industries was 76\% in 2003 and 69.8\% in 2012, and in manufacturing output was 28\% in both years. \hyperref[b9]{10} as a proportion of manufacturing production did not surpass 11\% between 2003 and 2012. This VAEMG consisted of automotive and truck manufacturing (31.3\%); manufacturing of parts for motor vehicles (18\%); manufacturing of electronic components (8\%); audio and video manufacturing (2.7\%); manufacturing of computers and peripheral equipment (1.4\%) and others (38.5\%). Jobs created by these global manufacturing companies averaged 1,133,000 people during these years, with a slight tendency to stagnation; i.e., the Mexican economy has shifted from labor-intensive goods to intensively imported intermediate goods, despite the low level of wages that prevail in the country 11  \hyperref[b9]{10} The Export Value Added of Global Manufacturing is obtained from companies which inputs come from abroad and its production is aimed at exports; as for companies with foreign majority ownership, and for companies not considered above their exports are intermediate goods. \hyperref[b10]{11} A group that is not listed among the top 20 is the group of products of the yarn and clothing chain (Dussel and Gallagher, 2013). The items that make up this industry are consumer (clothing) and intermediate (textile) goods, segments in which Mexico has traditionally played an important role in the US market. However, competition from similar products of Chinese and Vietnamese origin in that market has determined the loss of competitiveness of the sector in all segments of the chain (yarn, textiles and clothing). Exports of groups corresponding to textiles and clothing fell by half between 2000 and 2012, being reduced from US\$8.3 billion to US\$4.2 billion.\par
. From such \hyperref[b11]{12} Informality refers to the type or nature of the Economic Unit: when it is engaged in the production of goods and/or services for the market and operates from home resources and without keeping basic accounting records; and from the labor perspective, refers to any work being performed without the protection of the legal or institutional framework, regardless of whether the economic unit that uses their services are unregistered companies or businesses of formal homes or companies. Therefore, the integrated concept of informality includes both employment in the informal sector -and other traditional phenomena related to informality (self-employment in subsistence agriculture and unpaid work)-, and informality or employment streams with no protection of social security which services are used by economic informal employed people, 10\% are unpaid workers; 34.1\% are self-employed; 3.1\% are employers; and 52.8\% are subordinate and paid workers. As for the minimum daily wage that in early 1994, measured at 2010 prices, was \$81.26 pesos, in 2014 it had fallen to 58 pesos per day \hyperref[b12]{13} units different to those of the informal sector. Informality, understood in its broadest sense, is the set of economic activities carried out by individuals who, due to the context in which they work, they cannot invoke in their favor the legal and institutional framework. \hyperref[b12]{13} The minimum wage in the last 34 years was reduced from 166 pesos in 1980 to only 58 pesos a day in 2014.    From the 7.9 million of employed people in the manufacturing industry in 2014 (5 million men and 2.8 million women) slightly less than half work informally, with a higher proportion of women (47\%) who work informally compared to men (36\%). The food industry generates the greatest number of jobs (1.8 million, 22\% of the total) from which 1.0 million are informal jobs and only 706,000 are formal; most of informal jobs (588,000) are done by women. The manufacturing of transportation equipment is the second largest generator of jobs, but in this area the number of informal employed people is low (26,000). By contrast, branches of manufacturing of textiles and clothing are jointly generate 898,000 jobs, from which 67\% of workers are informal and from the total of informal workers (603,000) 71\% are women. 72\% of female jobs are concentrated in only a few branches: food industry, textile and clothing industry, manufacturing of computer equipment, transportation equipment and their parts, and other industries. a) Formal and Informal Employment Among men, informal jobs in some branches have a very high participation such as in furniture manufacturing (70\%), in the manufacture of textiles and clothing (57\% and 58\% respectively), in the manufacturing of products made of non-metallic 3 minerals (54\%) and of metal products (53\%). Although the percentage in the food industry is less than half (48\%), in absolute numbers is where we find the largest number of male informal workers (456,000 people). As for women, 73\% of informal jobs are in the food industry (589,000 women), 88\% in textile manufacturing, 66\% in clothing and 49\% in other manufacturing industries. b) Educational Levels in the Processing Industry (IT, in Spanish)\par
Education levels in manufacturing industry are, in general, low and disparate. 42\% of people who in 2014 are working in manufacturing industry (MI) have completed secondary school, 20\% primary school, 15\% high school, 12\% are professionals and the rest did not of women, particularly in high school education and among professionals. Both among men and among women the highest number of professionals are located in the food and chemical industry, in the manufacturing of transportation equipment and in the manufacturing of computers and communication. complete primary or not specified. On average, educational levels of men are slightly higher than those c) Hourly income The average hourly income in the industry is \$30 pesos; the highest level of hourly income is paid in the oil industry: \$78.80; and the lowest is paid in the textile industry, except for clothing, \$12.4 pesos. On average, there is no other sector that pays income as high as the oil industry, because the sector that follows is the chemical industry, which is nearly half \$39.1, followed by the primary metals industry \$37.4. By type of employment those who obtain higher revenues are employers, particularly manufacturers and generators of electrical devices and accessories (\$145.3 pesos) followed by manufacturers of mineral coal and oil derivatives (\$93.8 pesos), of textile products, excluding clothing (\$76.9) and of machinery and equipment (\$75.2). Wage earners, on average, earn \$29 pesos, however, wages range between \$20 and \$40 pesos except in the oil industry, which they amount to \$78 pesos. 
\section[{IV. Employment with Wage Discrimination and Occupational Segregation}]{IV. Employment with Wage Discrimination and Occupational Segregation}\par
In order to make the wage discrimination analysis, the Blinder-Oaxaca (1973) model is used, which calculates the pay gap between men and women based on the linear theory of wage determination proposed by J. Mincer. The Blinder-Oaxaca method is based on two assumptions:\par
1. All individuals have the same characteristics and skills. 2. It is understood that, as they are facing the same labor market, thus they are facing the same employment opportunities. From a theoretical point of view, an equal increase in any of the characteristics studied between two workers should provide the same, and in the same magnitude, for both.\par
Based on Mincer's function, the natural logarithm of income ( ?????? ) depends positively on education ( ?? ), on work experience ( ?? ) and on work experience squared (?? 2 ). The effects of human capital stock on the level and distribution of income coming from labor earnings are given by the coefficients that go together with these variables, being specifically ?? 1 the rate of return on education and ?? 2 the rate of return on work experience:?????? ?? =? +?? 1 ?? 1 + ?? 2 ?? ?? + ?? 3 ?? 1 2 + ?? ?? ?? 1 ?? ?? 2 > 0 ?? 3 < 0\par
The independent variables can be grouped into a single matrix (???), and ?? will be the column vector of coefficients corresponding to such matrix, which must be estimated. If we build this equation for men (??) and another for women (??) the result is:?? ?? = ?? ????? ?? ? + ?? ?? ?? ?? = ?? ????? ?? ? + ?? ??\par
Where ?? ?? and ?? ?? are the error terms. With the estimation of these coefficients, the quantifying of the capital stock effect over labor income is obtained. We thus obtain the double breakdown of the pay gap, where we can distinguish its two components: Q: Difference explained U: Discrimination and effect of unobserved variables \hyperref[b13]{14} Work experience is measured by the years that the individual has been working in the same company. Employed men have more years of almost constant experience during all the years observed and nearing 8 years of experience. The average work experience of The age variable was measured with the age that the individual was when the survey was conducted. Age squared means that the more the age increases the more income will also increase; however, there comes a moment when the increase starts to decrease.\par
The years of schooling are measured as the accumulated years of education per school year (primary, secondary, high school, college and postgraduate studies). While both men and women have increased their years of schooling, employed women have shown, throughout the analysis period, a nationwide average education level higher than men.\par
The source of information for the construction of the variables is the National Survey on Occupation and Employment (ENOE) of the National Institute of Statistics and Geography (INEGI) for the period 2005-2014. Differentials of hourly wage income between men and women were built considering only the national working population between 14 and 65 years of age, for the first quarter of each year. The level of unionization of Mexican workers is generally very low, and a decrease in the number of unionized men, from 2006 to 2014, is registered from 10.8\% to 8\% and from 14\% to 10\% among women. 
\section[{3}]{3}\par
Nationally, the professional variable -and in line with the education variable-shows a higher percentage of employed women with bachelor's or engineering degrees, where 1\% is the average of this percentage during the observed period, and 0.9\% for men.\par
Also, binary variables are added -where the value of 1 is given if the characteristic is presented and 0 if it is not presented-in order to identify whether the individual is married or not, whether he/she is head of household or not, whether he/she belongs to a union and whether he/she is a professional employee or not, considering that said person has higher education studies.\par
On average 70\% of men participating in the labor market are married, this percentage being reduced to 51\% for women. The number of married women increased from 49\% in 2006 to 53\% in 2014.\par
Within the period 2005-2014, 66\% of employed men are heads of household, while women have an smaller percentage although it has increased in recent years -ranging from 19\% in 2006 to 23\% in 2014. \hyperref[b14]{15} In the case of Mexico, at national level, the gender pay gap is evident, and has been present for the past nine years remaining always in favor of men even when it has been reduced in 4.49 percentage points from 2005 to 2014. P>z 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Source: Compiled from INEGI's data. \hyperref[b14]{15} It is noteworthy that the calculation of this variable is made by asking directly to the individual if he/she is the head of household, so it should not be interpreted that the head of household is the person who earns more within the household.\par
The portion of the gap to be related to the observable characteristics tells us that, if discrimination did not exist in Mexico, there would be a pay gap in favor of women. From 2005 to 2011, employed women have a schooling level higher than that of men, and employed men have higher levels of work experience throughout the period, thus favoring the increase in the pay gap. However, this variable has diminishing returns so, as the number of years of work experience increases, salary will increase but to a lesser extent.  
\section[{A}]{A}\par
With the unionization variable we can deduce that most people who work and are affiliated with a union are women, although this coefficient is very small and, moreover, the number of unionized men is close to that of women.\par
Among the features where the male gender is dominant, there are the married and head of household variables.\par
The coefficient of the age variable changes its sign in 2009, i.e., from 2005-2008 the age of men working in Mexico is above the age of women, but from 2009 the situation changed women being older.\par
The second part offered by the Blinder-Oaxaca methodology is the part of wages that is not explained by the observable characteristics, which is considered gender discrimination. Table \hyperref[tab_9]{8} shows the portion of the pay gap that is considered as discrimination.  1. There are variables that have not been taken into account in the analysis, with a strong discrimination in favor of men and the constant is absorbing such weight. 2. That women have access to jobs belonging to branches or sectors with very low wages, and men to branches or sectors with higher wages. 3. It is also possible that this constant is absorbing the effect of the difference in the hours worked, as women spend fewer hours in paid activities than men.\par
When making the Blinder-Oaxaca breakdown into the three major sectors of activity in Figure 8, the total pay gap by sectors and years is presented; most of the pay gap occurs in the secondary sector, also presenting a high discrimination level, which makes up more than 50\% of said gap. When analyzing the industry by branches significant differences were found. Not in all branches unexplained discrimination is found or statistical differences are not significant. The branches in which unexplained differences (discrimination) are high are the textile inputs industry, the textile industry -except clothing-and the clothing industry.\par
For example, in the textile industry the wage differential ranges around 100\% between men and women, in which the explained portion (allocations) is on average 30\%, while the unexplained portion (discrimination) is 70\%.\par
In the manufacturing of leather products the differential tended to decrease in the intermediate years, being increased again in 2013 and 2014; the explained portion of this differential averaged 7.6\% for the allocations of each individual, while discrimination was 15\%.\par
In the branch of manufacturing of computer equipment and communications the differential on average is quite high (26.8\%), the explained portion ranged between 8 and 24\% and the unexplained between 7 and 17\% in some years, in order that the statistical difference is reduced and not significant at the end of the period studied.\par
In the transportation equipment industry an average wage differential of 25.8\% is observed and such differential is explained by 14\% for each individual's allocations and by 12\% for wage discrimination between men and women. It is highlighted that the wage differential leads to a decreasing trend throughout the period while discrimination is stabilized between 9 and 14\%.    
\section[{Concluding Remarks}]{Concluding Remarks}\par
The results show that, despite the optimistic forecasts made during the NAFTA negotiations, results have been fairly poor. Indiscriminate and quick openness transformed the model of import substitution into an importer-exporter model and deeply integrated to the factory of "North America" in value chains where the added value of these chains in Mexico is, in recent years, barely 11\%. NAFTA did not resulted in more jobs neither for women nor for men, nor led to the creation of better wages. The processing industry is characterized by: ? A very small growth in the last ten years\par
? Its employment levels fell by more than 2 million jobs from 2005 to 2009, recovering 1 million in 2014.\par
? There is a high informality in the industry and particularly in the branches where women work (food, clothing and apparel, etc).\par
? Educational levels are quite low, especially compared with other sectors.\par
? And income per hour worked, especially among employees, is also very low -particularly in the more traditional branches. The results, in terms of segregation and wage discrimination, point out that in this subsector is where higher levels of discrimination exist throughout the Mexican economy. There is also a significant employment segregation since the bulk of female employment is concentrated in only 7 branches.\par
There are significant differences by branches, since unexplained discrimination was not found in all branches, or statistical differences are not significant. The branches in which unexplained differences (discrimination) are very high are the textile inputs industry, the textile industry -except clothing-and the clothing industry.\par
Almost 60\% of employment in Mexico is informal. The absence of unemployment insurance and low employment growth are two main causes of this phenomenon and its persistence; and probably one of the causes of this discrimination is the high level of informality that prevails in these manufacturing sectors. However, there is also wage discrimination in areas with low levels of informality -for example in the automotive industry-, so that this phenomenon of informality cannot be generalized as the cause of discrimination.\par
Informality is a widespread phenomenon in the Latin American region. In fact, Mexico is one of the countries with the "lowest" levels of informality especially when compared to countries such as Bolivia. The solution of informality depends on many factors and specific policies to address it, some of which have been already applied in Mexico, but the results are still unknown.\par
In this essay we have tackled employment and its low growth, together with the low economic growth of economy in recent years. Both phenomena are closely related and, in order that one can be increased, there must be a higher economic growth and higher wages.\par
Undoubtedly for the above reasons Mexico must establish a consistent and long-term policy for job creation -well-paid jobs that expand national and local demand of goods produced domestically and that not only encourage imports of final and intermediate goods. Therefore, one of the many things that must be done is to establish policies that promote re-industrialization of Mexico, favoring the restoration of national value chains with horizontal and vertical industrial policies 16 \hyperref[b15]{16} Please refer to the IDB report.  A growth and national research and development. It is not just about being part of the value chains, the challenge is to increase the share of value added generated locally and to elevate the chain hierarchy, moving from simple activities to other more complex. This process is not simple or spontaneous. Depends crucially on effective public policies and on having a commitment to this objective. For example, it is necessary to have a critical mass of skilled human resources, a quality infrastructure in terms of logistics and telecommunications and an appropriate business environment, including a proper protection of intellectual property. The challenge is to build differentiating components, beyond the endowment of natural resources or low labor costs.\par
Wage discrimination against women and their employment segregation must be fought in all industries where higher levels exist (textiles and clothing industry, among others). Probably it will be necessary to also increase the educational level of many of those women because the analysis showed that, unlike what exists nationally in several industrial branches, lower educational levels are recorded. 
\section[{Bibliography}]{Bibliography}\begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-2.png}
\caption{\label{fig_0}}\end{figure}
           \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{}
\end{longtable} \par
 
\caption{\label{tab_0}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{P{0.38747016706443915\textwidth}P{0.1805489260143198\textwidth}P{0.05984486873508353\textwidth}P{0.07505966587112171\textwidth}P{0.07100238663484487\textwidth}P{0.07607398568019093\textwidth}}
Year\tabcellsep 1994\tabcellsep 2000\tabcellsep 2005\tabcellsep 2010\tabcellsep 2014e\\
National GDP\tabcellsep \multicolumn{5}{l}{83018.7 101584.28 108423.31 118498.59 133048.23}\\
GDP Manufacturing Industry\tabcellsep \multicolumn{5}{l}{13964.96 18997.23 18732.15 19325.29 22435.17}\\
Food Subsector\tabcellsep \multicolumn{2}{l}{3202.34 3921.04}\tabcellsep 4296.12\tabcellsep 4625.82\tabcellsep 4916.7\\
Beverages and Tobacco Subsector\tabcellsep 576.86\tabcellsep 754.05\tabcellsep 849.19\tabcellsep 909.4\tabcellsep 1030.36\\
Textile Products Subsector\tabcellsep 81.78\tabcellsep 140.35\tabcellsep 130.09\tabcellsep 113.36\tabcellsep 119.83\\
Wood Subsector\tabcellsep 238.96\tabcellsep 297.86\tabcellsep 206.49\tabcellsep 180.97\tabcellsep 212.52\\
Paper Subsector\tabcellsep 234.38\tabcellsep 327.89\tabcellsep 355.13\tabcellsep 412.83\tabcellsep 453.06\\
Oil-Derivative Products Subsector\tabcellsep 693.78\tabcellsep 851.42\tabcellsep 902.21\tabcellsep 838.2\tabcellsep 754.43\\
Chemical Subsector\tabcellsep \multicolumn{2}{l}{1946.84 2551.88}\tabcellsep 2615.76\tabcellsep 2572.19\tabcellsep 2585.87\\
Products Made of Non-metallic Minerals\tabcellsep 735.86\tabcellsep 880.25\tabcellsep 1036.35\tabcellsep 980.64\tabcellsep 1069.19\\
Subsector\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Basic Metals Subsector\tabcellsep 972.87\tabcellsep 1504.66\tabcellsep 1494.07\tabcellsep 1343.77\tabcellsep 1627.94\\
Machinery and Equipment Subsector\tabcellsep 396.35\tabcellsep 498.73\tabcellsep 611.11\tabcellsep 674.76\tabcellsep 889.01\\
Other Industries Subsector\tabcellsep 349.48\tabcellsep 418.76\tabcellsep 397.75\tabcellsep 440.6\tabcellsep 478.33\\
\multicolumn{2}{l}{Estimated value. Source: Own calculations based on INEGI.}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
BIE.\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_1}Table 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{}
\end{longtable} \par
  {\small\itshape [Note: 9 INEGI: (April 2014) Export Value Added of Global Manufacturing 2003-2012,]} 
\caption{\label{tab_2}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2} \par 
\begin{longtable}{P{0.6036553524804177\textwidth}P{0.0850739773716275\textwidth}P{0.0821148825065274\textwidth}P{0.07915578764142733\textwidth}}
Indicator\tabcellsep Total\tabcellsep Men\tabcellsep Women\\
Total population\tabcellsep 119 224 847\tabcellsep 57 734 965\tabcellsep 61 489 882\\
14 years and over population\tabcellsep 88 595 829\tabcellsep 42 109 633\tabcellsep 46 486 196\\
Economically Active Population\tabcellsep \tabcellsep \tabcellsep \\
(EAP)\tabcellsep 51 790 637\tabcellsep 32 171 182\tabcellsep 19 619 455\\
Employed\tabcellsep 49 305 839\tabcellsep 30 645 359\tabcellsep 18 660 480\\
Unemployed\tabcellsep 2 484 798\tabcellsep 1 525 823\tabcellsep 958 975\\
Employed population by sector of\tabcellsep \tabcellsep \tabcellsep \\
economic activity\tabcellsep 49 305 839\tabcellsep 30 645 359\tabcellsep 18 660 480\\
Primary\tabcellsep 6 660 593\tabcellsep 5 966 908\tabcellsep 693 685\\
Secondary\tabcellsep 11 957 708\tabcellsep 8 908 656\tabcellsep 3 049 052\\
Tertiary\tabcellsep 30 420 552\tabcellsep 15 578 104\tabcellsep 14 842 448\\
Unspecified\tabcellsep 266 986\tabcellsep 191 691\tabcellsep 75 295\\
Average schooling of the\tabcellsep \tabcellsep \tabcellsep \\
economically active population\tabcellsep 9.6\tabcellsep 9.3\tabcellsep 10\\
Average income per hour worked\tabcellsep \tabcellsep \tabcellsep \\
of the employed population\tabcellsep \tabcellsep \tabcellsep \\
(Pesos)\tabcellsep 31.3\tabcellsep 31.6\tabcellsep 30.9\\
Unemployment rate\tabcellsep 4.8\tabcellsep 4.7\tabcellsep 4.9\\
Labor informality rate. Rates\tabcellsep \tabcellsep \tabcellsep \\
calculated against employed\tabcellsep \tabcellsep \tabcellsep \\
population.\tabcellsep 58.2\tabcellsep 57.8\tabcellsep 58.8\\
\multicolumn{2}{l}{Source: Own calculations based on ENOE, INEGI.}\tabcellsep \tabcellsep \\
\multicolumn{4}{l}{Note: Data contain the expansion factors adjusted to population estimates}\\
\multicolumn{4}{l}{showed by 2010-2050 demographic projections of CONAPO, updated in April}\\
2013.\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{2}{l}{From the signing of NAFTA employment in the}\tabcellsep \tabcellsep \\
\multicolumn{2}{l}{manufacturing industry began to grow, reaching a peak}\tabcellsep \tabcellsep \\
\multicolumn{2}{l}{of 7.3 million people in 2000, but was reduced by more}\tabcellsep \tabcellsep \\
\multicolumn{2}{l}{than 2.3 million in 2009 and recovered to 7.9 million of}\tabcellsep \tabcellsep \\
employed people in 2014.\tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_3}Table 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3} \par 
\begin{longtable}{P{0.3137382075471698\textwidth}P{0.14634433962264148\textwidth}P{0.15737028301886793\textwidth}P{0.14834905660377357\textwidth}P{0.08419811320754718\textwidth}}
\tabcellsep \tabcellsep 1995-2014\tabcellsep \tabcellsep \\
Year\tabcellsep Men\tabcellsep Women\tabcellsep Total\tabcellsep \\
1995\tabcellsep 3515074\tabcellsep 1507217\tabcellsep 5022291\tabcellsep \\
1996\tabcellsep 3707631\tabcellsep 1866873\tabcellsep 5574504\tabcellsep \\
1997\tabcellsep 3888434\tabcellsep 2110710\tabcellsep 5999144\tabcellsep \\
1998\tabcellsep 4308905\tabcellsep 2389063\tabcellsep 6697968\tabcellsep \\
1999\tabcellsep 4504136\tabcellsep 2561960\tabcellsep 7066096\tabcellsep \\
2000\tabcellsep 4631865\tabcellsep 2752611\tabcellsep 7384476\tabcellsep \\
2001 2002 2003\tabcellsep 4500583 4330112 4306454\tabcellsep 2757097 2566906 2536945\tabcellsep 7257680 6897018 6843399\tabcellsep Year 2015\\
2004\tabcellsep 4357285\tabcellsep 2563964\tabcellsep 6921249\tabcellsep 59\\
2005 2006 2007 2008 2009 2010 2011 2012 2013\tabcellsep 4282004 4277370 3453881 3511392 3365010 4510304 4511215 4705685 4819461\tabcellsep 2616949 2664814 1786708 1790342 1738501 2591031 2566042 2672669 2701721\tabcellsep 6898953 6942184 5240589 5301734 5103511 7101335 7077257 7378354 7521182\tabcellsep Volume XV Issue II Version I\\
\multicolumn{4}{l}{2014 Source: Own calculations based on ENE y ENOE, INEGI. 5040197 2854762 7894959 Between 2005 and 2014 total employment in manufacturing industry increased by 1 million people and most of these jobs (757,000) were for men.}\tabcellsep ( ) -A Global Journal of Human Social Science\end{longtable} \par
 
\caption{\label{tab_4}Table 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{P{0.28371161548731644\textwidth}P{0.18951935914552737\textwidth}P{0.07489986648865153\textwidth}P{0.03858477970627504\textwidth}P{0.02837116154873164\textwidth}P{0.07489986648865153\textwidth}P{0.03858477970627504\textwidth}P{0.027236315086782377\textwidth}P{0.07489986648865153\textwidth}P{0.01929238985313752\textwidth}}
\tabcellsep \tabcellsep 2005\tabcellsep \tabcellsep \tabcellsep 2010\tabcellsep \tabcellsep \tabcellsep 2014\\
\tabcellsep \multicolumn{2}{l}{Women Men}\tabcellsep \multicolumn{3}{l}{Total Women Men}\tabcellsep \multicolumn{3}{l}{Total Women Men}\tabcellsep Total\\
Food industry\tabcellsep \multicolumn{9}{l}{704174 741731 1445905 789744 952832 1742576 807239 944399 1751638}\\
Beverage and tobacco\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
industry\tabcellsep 30653\tabcellsep \multicolumn{2}{l}{155750 186403}\tabcellsep 41372\tabcellsep \multicolumn{2}{l}{195549 236921}\tabcellsep 48952\tabcellsep \multicolumn{2}{l}{214071 263023}\\
Manufacturing of textile\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
inputs\tabcellsep 99339\tabcellsep \multicolumn{2}{l}{100029 199368}\tabcellsep 40999\tabcellsep 94831\tabcellsep 135830\tabcellsep 64501\tabcellsep 75440\tabcellsep 139941\\
Manufacturing of textile\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
products, excluding\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
clothing\tabcellsep 162205\tabcellsep 74493\tabcellsep \multicolumn{2}{l}{236698 158369}\tabcellsep 42767\tabcellsep \multicolumn{2}{l}{201136 168026}\tabcellsep 41913\tabcellsep 209939\\
Manufacturing of garments\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
and clothing accessories\tabcellsep \multicolumn{9}{l}{562428 294937 857365 538626 265809 804435 424103 263938 688041}\\
Manufacturing of leather\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
products\tabcellsep 82893\tabcellsep \multicolumn{2}{l}{175275 258168}\tabcellsep 99992\tabcellsep \multicolumn{2}{l}{191446 291438}\tabcellsep 80327\tabcellsep \multicolumn{2}{l}{163136 243463}\\
Wood industry\tabcellsep 23818\tabcellsep \multicolumn{2}{l}{137713 161531}\tabcellsep 12011\tabcellsep \multicolumn{2}{l}{106758 118769}\tabcellsep 9514\tabcellsep \multicolumn{2}{l}{123004 132518}\\
Paper industry\tabcellsep 38674\tabcellsep 87191\tabcellsep 125865\tabcellsep 35628\tabcellsep \multicolumn{2}{l}{104728 140356}\tabcellsep 45855\tabcellsep \multicolumn{2}{l}{105896 151751}\\
Printing and related\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
  {\small\itshape [Note: © 2015 Global Journals Inc. (US) A Source: Own calculations based on ENOE, INEGI.]} 
\caption{\label{tab_5}Table 4 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{5} \par 
\begin{longtable}{P{0.85\textwidth}}
Gender and formal or informal\end{longtable} \par
  {\small\itshape [Note: © 2015 Global Journals Inc. (US)]} 
\caption{\label{tab_6}Table 5 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{6} \par 
\begin{longtable}{P{0.4899135446685879\textwidth}P{0.08328530259365993\textwidth}P{0.04899135446685879\textwidth}P{0.0685878962536023\textwidth}P{0.08573487031700289\textwidth}P{0.07348703170028818\textwidth}}
\multicolumn{3}{l}{in 2014 employment}\tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep Total\tabcellsep \\
Sector and subsector of economic activity\tabcellsep \tabcellsep \tabcellsep Own\tabcellsep Wage\tabcellsep \\
\tabcellsep \multicolumn{2}{l}{Total Employer}\tabcellsep account\tabcellsep earner\tabcellsep Pieceworker\\
Secondary sector\tabcellsep 30.4\tabcellsep 50.0\tabcellsep 31.4\tabcellsep 29.3\tabcellsep 26.2\\
Food industry\tabcellsep 25.8\tabcellsep 41.6\tabcellsep 28.4\tabcellsep 24.1\tabcellsep 21.3\\
Beverage and tobacco industry\tabcellsep 27.3\tabcellsep 37.1\tabcellsep 51.6\tabcellsep 25.7\tabcellsep 28.8\\
Manufacturing of textile inputs\tabcellsep 18.8\tabcellsep 41.7\tabcellsep 5.6\tabcellsep 25.1\tabcellsep 0.0\\
Manufacturing of textile products, excluding\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
clothing\tabcellsep 12.4\tabcellsep 76.9\tabcellsep 9.1\tabcellsep 21.9\tabcellsep 18.6\end{longtable} \par
 
\caption{\label{tab_7}Table 6 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{7} \par 
\begin{longtable}{P{0.17222222222222222\textwidth}P{0.6777777777777778\textwidth}}
\tabcellsep 2005-2014 Gender Pay Gap\\
\tabcellsep 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014\\
Wage (\%Men/Women) differential\tabcellsep 7.41 3.33 4.37 5.97 4.66 4.05 2.63 2.76 3.53 2.92\end{longtable} \par
 
\caption{\label{tab_8}Table 7 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{8} \par 
\begin{longtable}{P{0.20275229357798166\textwidth}P{0.26123853211009174\textwidth}P{0.1676605504587156\textwidth}P{0.031192660550458717\textwidth}P{0.031192660550458717\textwidth}P{0.031192660550458717\textwidth}P{0.031192660550458717\textwidth}P{0.031192660550458717\textwidth}P{0.031192660550458717\textwidth}P{0.031192660550458717\textwidth}}
\tabcellsep \tabcellsep \multicolumn{5}{l}{2005-2014 Gender Pay Discrimination}\tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep \multicolumn{9}{l}{2005 2006 2007 2008 2009 2010 2011 2012 2013 2014}\\
Discrimination\tabcellsep 9.05 6.56\tabcellsep 5.04\tabcellsep 7.32\tabcellsep 7.44\tabcellsep 6.65\tabcellsep 5.24\tabcellsep 5.90\tabcellsep 7.19\tabcellsep 5.83\\
P>z\tabcellsep 0.00 0.00\tabcellsep 0.00\tabcellsep 0.00\tabcellsep 0.00\tabcellsep 0.00\tabcellsep 0.00\tabcellsep 0.00\tabcellsep 0.00\tabcellsep 0.00\\
\multicolumn{2}{l}{Source: Compiled from INEGI's data.}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_9}Table 8 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{9} \par 
\begin{longtable}{P{0.09577464788732394\textwidth}P{0.06965428937259922\textwidth}P{0.08380281690140844\textwidth}P{0.1469270166453265\textwidth}P{0.02612035851472471\textwidth}P{0.06312419974391806\textwidth}P{0.08924455825864276\textwidth}P{0.04788732394366197\textwidth}P{0.1523687580025608\textwidth}P{0.030473751600512165\textwidth}P{0.04462227912932138\textwidth}}
Variable\tabcellsep 2005\tabcellsep 2006\tabcellsep \multicolumn{3}{l}{2007 2008 2009}\tabcellsep \multicolumn{2}{l}{2010 2011}\tabcellsep \multicolumn{2}{l}{2012 2013}\tabcellsep 2014\\
Schooling\tabcellsep -8.39\tabcellsep -7.49\tabcellsep \multicolumn{3}{l}{-6.52 -5.75 -6.47}\tabcellsep \multicolumn{2}{l}{-6.09 -3.75}\tabcellsep \multicolumn{3}{l}{-7.49 -6.44 -3.19}\\
P>z\tabcellsep 0.00\tabcellsep 0.00\tabcellsep 0.00\tabcellsep 0.00\tabcellsep 0.00\tabcellsep 0.00\tabcellsep 0.03\tabcellsep 0.00\tabcellsep 0.00\tabcellsep 0.05\\
Experience\tabcellsep -8.12\tabcellsep -6.06\tabcellsep \multicolumn{3}{l}{-3.92 -4.58 -5.00}\tabcellsep \multicolumn{2}{l}{-6.15 -4.51}\tabcellsep \multicolumn{3}{l}{-2.39 -6.02 -5.93}\\
P>z\tabcellsep 0.00\tabcellsep 0.00\tabcellsep 0.01\tabcellsep 0.00\tabcellsep 0.00\tabcellsep 0.00\tabcellsep 0.01\tabcellsep 0.16\tabcellsep 0.00\tabcellsep 0.00\\
Experience 2\tabcellsep 0.92\tabcellsep 0.18\tabcellsep \multicolumn{2}{l}{-0.65 0.53}\tabcellsep -0.91\tabcellsep 1.87\tabcellsep -0.10\tabcellsep \multicolumn{2}{l}{-0.49 0.80}\tabcellsep 1.78\\
P>z\tabcellsep 0.33\tabcellsep 0.87\tabcellsep 0.40\tabcellsep 0.57\tabcellsep 0.33\tabcellsep 0.03\tabcellsep 0.92\tabcellsep 0.59\tabcellsep 0.34\tabcellsep 0.04\\
Training\tabcellsep 0.08\tabcellsep -0.26\tabcellsep \multicolumn{3}{l}{-0.24 -0.45 -0.35}\tabcellsep 0.03\tabcellsep -0.16\tabcellsep 0.03\tabcellsep 0.17\tabcellsep -0.08\\
Variable\tabcellsep 2005\tabcellsep 2006\tabcellsep \multicolumn{3}{l}{2007 2008 2009}\tabcellsep \multicolumn{2}{l}{2010 2011}\tabcellsep \multicolumn{2}{l}{2012 2013}\tabcellsep 2014\\
P>z\tabcellsep 0.56\tabcellsep 0.13\tabcellsep 0.16\tabcellsep 0.01\tabcellsep 0.00\tabcellsep 0.85\tabcellsep 0.27\tabcellsep 0.84\tabcellsep 0.17\tabcellsep 0.48\\
Age\tabcellsep -2.38\tabcellsep \multicolumn{3}{l}{-3.81 -17.13 2.47}\tabcellsep \multicolumn{3}{l}{-13.60 12.93 -16.87}\tabcellsep \multicolumn{3}{l}{2.99 34.71 17.04}\\
P>z\tabcellsep 0.86\tabcellsep 0.33\tabcellsep 0.23\tabcellsep 0.86\tabcellsep 0.30\tabcellsep 0.32\tabcellsep 0.22\tabcellsep 0.82\tabcellsep 0.01\tabcellsep 0.19\\
Age 2\tabcellsep 6.82\tabcellsep 7.55\tabcellsep \multicolumn{2}{l}{10.87 -1.17}\tabcellsep 11.25\tabcellsep -6.24\tabcellsep 12.44\tabcellsep \multicolumn{3}{l}{0.39 -13.32 -8.32}\\
P>z\tabcellsep 0.34\tabcellsep 0.31\tabcellsep 0.14\tabcellsep 0.88\tabcellsep 0.10\tabcellsep 0.36\tabcellsep 0.09\tabcellsep 0.96\tabcellsep 0.05\tabcellsep 0.23\\
Married\tabcellsep -3.41\tabcellsep -3.04\tabcellsep \multicolumn{2}{l}{-4.23 -4.38}\tabcellsep -2.94\tabcellsep -3.92\tabcellsep -4.16\tabcellsep \multicolumn{3}{l}{-3.44 -5.23 -3.95}\end{longtable} \par
  {\small\itshape [Note: 65( )]} 
\caption{\label{tab_10}Table 9 :}\end{figure}
 			\footnote{The ratio of wages, with regard to the GDP, in real terms fell from 36.2 (1970-1981)  to 30.4 (1982-1993) and to 30.3 (1983-2004).} 			\footnote{© 2015 Global Journals Inc. (US)} 			\footnote{The Employment of Women in the Manufacturing Industry after NAFTA. Discrimination and Segregation} 			\footnote{The Mexican aviation industry has experienced a strong growth in recent years, so that the country ranks first in aeronautical manufacturing investment worldwide, with about US\$36 billion from 1990 to 2012, and an annual average growth of goods exports of 14\% in the last decade. Likewise, the United States is the main destination for Mexican exports of aeronautical products (74\% of the total), a significant portion goes to Canada (8\%), which is also a member of the "factory of North America;" other destinations of the industry are France, the UK and Japan.} 			\footnote{The Employment of Women in the Manufacturing Industry after NAFTA. Discrimination and Segregation © 2015 Global Journals Inc. (US)} 		 		\backmatter  			 \par
The schooling and experience variables show a wage discrimination in favor of women. The married variable shows that the fact women are married helps to improve their wage, since there is a favorable discrimination against them in this variable -married women are better paid than married men. The last variable showing a discrimination in favor of women is the degree of unionization, i.e., affiliated women are better paid than affiliated men.\par
Again, it bears mentioning the situation of the age variable, which is no longer significant, i.e., we can say that there is no discrimination in terms of age.\par
The age and head of household variables, which are significant in the observable characteristics are not on discrimination.\par
Having analyzed the two components of the gender pay gap we see that all variables we have used indicate a pay gap in favor of women, which is not real. As can be seen most of the weight of this gap is related to the model constant, which leads us to believe some possible reasons: A Source: Prepared in accordance with the Blinder-Oaxaca methodology and data of ENOE, INEGI.\par
In most years the primary sector has a pay gap in favor of women but with reduced discrimination and with almost 100\% of the gap in connection with the characteristics observable during the years of study.\par
The tertiary sector has a situation similar to that of the secondary sector's situation since the pay gap in this sector is presented in favor of males, but it is different because it is made up primarily by the effect of characteristics observable on wage.\par
In view of these sectoral data, it can be said that the secondary sector is the one that makes the difference and creates a high pay gap formed mostly by issues of discrimination (or factors affecting this sector specifically, which are not being considered), given that the tertiary and primary sector move in opposite directions and are made almost in the same way, thus the secondary sector remaining as the main cause of the pay gap in Mexico.\par
In general, in the processing industry there is high discrimination as well as an average wage differential of 32\% with decreasing trend, since in 2005 it has a value of 38\% while in 2014 its value is reduced to 29\%.\par
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