Generation and distribution of income in Mexico, 1990-2015

Ayvar-Campos, Francisco Javier; Navarro Chávez, José César Lenin; Giménez, Víctor Article Generation and distribution of income in Mexico, 1990-2015 Journal of Economics, Finance and Administrative Science Provided in Cooperation with: Universidad ESAN, Lima Suggested Citation: Ayvar-Campos, Francisco Javier; Navarro Chávez, José César Lenin; Giménez, Víctor (2020) : Generation and distribution of income in Mexico, 1990-2015, Journal of Economics, Finance and Administrative Science, ISSN 2218-0648, Emerald Publishing Limited, Bingley, Vol. 25, Iss. 49, pp. 163-180, https://doi.org/10.1108/JEFAS-04-2018-0040 This Version is available at: https://hdl.handle.net/10419/253792 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Generation and distribution of income in Mexico, 1990-2015 Francisco Javier Ayvar-Campos and José César Lenin Navarro-Chávez Universidad Michoacana de San Nicolás de Hidalgo, Morelia, Mexico, and Víctor Giménez Department of Business, Universitat Autònoma de Barcelona, Barcelona, Spain Abstract Purpose –This paper aims to review the efficient use of economic and social resources to generate income and, at the same time, reduce the concentration of wealth in the 32 states of the Mexican Republic during the period 1990-2015. Design/methodology/approach –Data envelopment analysis with the inclusion of a bad output was used to diagnose the efficiency of Mexican entities, and the Malmquist–Luenberger index was applied to understand how this efficiency evolves. Findings –The results clearly show that only 3 of the 32 units studied generated and distributed wealth efficiently, while the other 29 must increase their level of income andits distribution. Originality/value –According to the authors’knowledge, this is the first work that performs a temporal analysis of the efficiency in the generation of Human Development Index using bad outputs and the Malmquist–Luenberger index. Keyword Mexico Paper type Research paper 1. Introduction In Mexico, the Human Development Index (HDI) during the period 1990-2015 increased by 17.6 per cent. However, this indicator of welfare is still lower than that of other Latin American economies; one of the main causes is the low level of per capita income in the economy (UNDP, 2018b). At the level of federal entities, Mexico City, Nuevo Le on, Chihuahua, Baja California, Sonora and Aguascalientes stand out as the states with the highest levels of human development, while Hidalgo, Michoacán, Chiapas, Oaxaca and Guerrero have the lowest HDI levels, thus presenting a strong state and regional disparity in social welfare (UNDP, 2011,2016). The dynamics of variables such as public expenditure, level of education and employed personnel, despite the positive trends throughout the study period, reveal the need for higher levels of investment, employment and education as its impact on the income dimension of the national and state HDI has been low (INEGI, 2018a, © Francisco Javier Ayvar-Campos, José César Lenin Navarro-Chávez and Víctor Giménez. Published in Journal of Economics, Finance and Administrative Science. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licenses/by/4.0/ legalcode Income in Mexico 163 Received 22 April 2018 Revised 20 June 2018 Accepted 23 October 2018 Journal of Economics, Finance and Administrative Science Vol. 25 No. 49, 2020 pp. 163-180 Emerald Publishing Limited 2077-1886 DOI 10.1108/JEFAS-04-2018-0040 The current issue and full text archive of this journal is available on Emerald Insight at: https://www.emerald.com/insight/2077-1886.htm 2018b,2018c,2018d,2018e,2018f,2018g, 2018h). In turn, the income concentration data in Mexico indicate that a significant percentage of the states has an asymmetric distribution of wealth, affecting negatively the level of welfare of the society (Tello, 2010;Quiroz and Salgado, 2016;Ortiz et al.,2017). For this reason, it is relevant to establish as a research question how efficient were the 32 entities of the Mexican Republic in the use of their economic and social resources to generate and distribute income during the period 19902015. The results of this study allow to quantify the efficiency in the management of the resources during the analyzed period and, therefore, contribute to the design of strategies and policies that energize the behavior of the income dimension of the HDI. Harttgen and Klasen (2012) conceive human development as the process that expands the opportunities of the persons for lives the life that they value and, therefore, reach a higher level of well-being. Understanding as opportunities the possibility to have a long and healthy life; be literate and possess knowledge; have economic resources that grant a decent standard of living; and be involved in the community life. If we do not own them, many other options and opportunities of life are inaccessible (UNDP, 2018a). Determining the level of human development of an economy is key to establish public policies as it allows to evaluate the evolution of the living conditions of the population; diagnose the problems; and enrich the design of government objectives and strategies (L opez-Calva et al., 2004). In the measurement of human development, the HDI highlights, proposed by the United Nations Development Program (UNDP). This index combines three elements to evaluate the progress of countries in terms of human development: the Gross Domestic Product (GDP) per capita, health and education; each one is included with the same weight in the index (Griffin, 2011;Harttgen and Klasen, 2012). It is due to its simplicity and easy access to the statistical information that is required for its calculation that the HDI has become the most used mechanism to measure human development and social welfare (Le on, 2002;Ord oñez, 2014). Under the vision of human development, and consequently of the HDI, the individual must be the center of the design of public policies and, at the same time, the fundamental instrument of their own development (Griffin, 2011). The distribution of income is the way in which the national product is distributed among those who have contributed to its production, grouping them into homogeneous categories according to the function exercised or according to the nature of the contribution made (Salinas, 1977;Medina, 2001). The concentration of income is caused by multiple factors. The way in which this asymmetry is measured is through inequality indices, which are measures that summarize the distribution of a variable among a set of individuals. Consequently, the inequality in the distribution of wealth is given by the degree of dispersion of income with respect to a reference value (Ruza, 1978;Carrillo and Vázquez, 2005;Ospina and Giraldo, 2005). The indicators of inequality are usually classified as positive and normative measures (Carrillo and Vázquez, 2005;Ospina and Giraldo, 2005; Mazaira et al.,2008). This research uses positive measures as the normative depends on ethical judgments that are reflected in the values chosen for the parameters of the social welfare function (Acevedo, 1986). Of the divers positive inequality measures, this research uses the Gini Coefficient (Cg), because it allows a simple interpretation of the degree of income concentration and meets the four basic properties of an inequality indicator: is sensitive to the effect of socioeconomic factors of inequality, considers the influence of any social hierarchy on changes of the composition of the population, is consistent with the argument of the Lorenz curve and shows invariance in the face of proportional increases in income (Gradín and del Río, 2001;Medina, 2001;Yáñez, 2010). JEFAS 25,49 164 The income dimension of human development apart of the GDP per capita includes other indicators such as the concentration of income to determine in a more inclusive way the economic well-being of society (Hicks, 1997;Alkire and Foster, 2011). Thus, it reaffirms the fact that there can be no economic well-being if the income generated by a society is not properly distributed among the population that generated it (Mazaira et al.,2008;Yáñez, 2010). Hence, an excessive concentration of income can be considered as negative and, therefore, its decrease is recommended (Quiroz and Salgado, 2016). For it, is possible to point out that the concentration of income has a behavior similar to an unwanted output, while income itself would behave as a desired output. Given that income generation involves the use of resources, it is important, prior to any manipulation of factors, to determine under which combination of socioeconomic inputs an economy is achieving the highest level of income per capita with the lowest concentration of it. In other words, it is relevant to analyze the efficiency in the generation of income. Several studies point the importance of the efficient use of resources to increase the economic well-being of an economy. It is argued that the welfare of society depends on the application of public policies aimed at the efficient use of resources and the promotion of greater equity in the distribution of wealth (Marti c and Savi c, 2001; Cortés, 2003;Stimson et al., 2006;Vargas, 2009;Halkos and Tzeremes, 2010;Tello, 2010; Poveda, 2011;Torres and Rojas, 2015;Quiroz and Salgado, 2016;Ortiz et al., 2017). Thus, the hypothesis of the research is that very few entities of the Mexican Republic were efficient in the usage of their economic and social resources to generate and distribute income, during the period 1990-2015. This has important repercussions on the economic and social well-being of the Mexican population. For the analysis of the efficiency, the literature offers different methodologies. Data envelopment analysis (DEA), developed initially by Charnes et al. (1978),isa methodology widely used as an alternative to parametric methods (Banker et al., 1984; Bemowski, 1991). In essence, DEA compares an observed production unit with a virtual unit, which obtains the same or more product with the same or lesser number of factors. However, unwanted outputs often are produced together with desirable results. In this sense, Pittman (1983) introduces unwanted outputs in the calculation of productivity indexes, adapting the methodology of Caves et al. (1982), and determines the shadow prices of these. The result of this new approach allows to deduce an efficiency measure that, while maximizing the good outputs, minimizes the undesired outputs from a benchmarking process (Serra, 2004). Although the applications of DEA have been mostly in productive units, it is also applied in studies of quality of life, economic well-being, human development and social welfare (Mahlberg and Obersteiner, 2001;Despotis, 2005; Yago et al.,2010;Giménez et al., 2017). Mariano et al. (2015) perform an extensive review of the literature that use DEA for the analysis of human development. According to our knowledge, this work is the first that analyzes the efficiency in the generation of income considering bad outputs from a temporal perspective. For it, the Malmquist–Luenberger (ML) index is used to measure changes in the efficiency, technological change and productivity over time, taking into consideration the undesirable outputs of the productive process (Chung et al., 1997). The research is structured in five sections: Section 1 analyzes the socioeconomic aspects of economic well-being. In Section 2, the theoretical elements of human development and income distribution are addressed. In Section 3, the methodological features of the generation and distribution of income DEA model are presented. In Section 4, the main results of the DEA model are exposed, indicating the entities that efficiently used their Income in Mexico 165 resources. Finally, the conclusions are established in Section 5, where the fundamental aspects of the research are highlighted. 2. The income dimension of human development in the entities of Mexico The study of the dynamics of the income dimension of the HDI shows that during the period 1990-2010, the highest income indices were held by the states of Nuevo Le on, Mexico City, Chihuahua, Campeche and Sonora. On the other hand, the entities with the lowest income indices were Chiapas, Oaxaca, Guerrero, Tlaxcala and Hidalgo, which is directly related to the behavior of the GDP per capita (UNDP, 2011,2016). Table I shows that GDP per capita had an increase of 58 per cent during the period 1990-2015 as a result of increase in public spending and investment attraction policies. The states of the country with the highest GDP per capita levels are Campeche, Mexico City, Jalisco, Nuevo Leon, Queretaro, Quintana Roo and Tabasco. The public spending had a major expansion from 33,938m pesos in 1990 to 1,955,597m pesos in 2015. The educational level of the society presented an increase of 45.5 per cent, this is, in 1990, the average level of education was 6.3 years, and in 2015, it was 9.1 years. The employed population grew 116 per cent, excelling Mexico City, State of Mexico, Nuevo Le on, Jalisco, Puebla and Veracruz (Table I). The establishment of companies during this stage was incentivized as they went from 736,860 in 1990 to 5,654,014 in 2015, factor that had a direct impact on the generation of jobs and on the remunerations of the population. An element that also presented development was the Gross Capital Formation, Foreign direct investment being the variable that showed the highest growth during the years studied. Specifically, the states of Baja California, Chihuahua, Guanajuato, Jalisco, State of Mexico, Mexico City, Nuevo Le on, Puebla and Veracruz were the more benefited (INEGI, 2018a,2018b,2018c,2018d,2018e,2018f, 2018g, 2018h). Despite the positive behavior of these indicators, the low impact of the income dimension on the national and state HDI reflects the importance of increasing per capita income levels, as this would lead to higher levels of well-being in the entities of the country. The concentration of income in Mexico decreased during the period 1990-2015, going from 0.519 in 1990 to 0.469 in 2015. When carrying out the analysis by states, it was observed that Baja California Sur, Tlaxcala, Colima, Baja California and State of Mexico presented the highest levels of income distribution, while Oaxaca, Guerrero, Hidalgo, Querétaro and Campeche were the ones that had the highest concentration of income. These results have, as a background, the poor performance of these last entities in terms of generation and distribution of GDP (Table II). 3. Methodology The idea of Farrell (1957), who explains that to measure the efficiency of a set of productive units, it s necessary to know the function of production and the frontier of efficiency, has been applied empirically through two methodologies: stochastic frontiers estimation and DEA measurements. The first involves the use of econometrics, and the second involves linear programming algorithms and benchmarking. DEA is a technique used to measure the comparative efficiency of homogeneous units. Starting from the inputs and outputs, this method provides a classification of the Decision Making Unit (DMU), giving them a relative efficiency score. A DMU is efficient when there is no other (or combination of them) that produces more output, without generating less of the rest and without consuming more inputs. In this case, we speak of an output-oriented model, while in the opposite case, it is called an input-oriented model. DEA models take JEFAS 25,49 166 State 1990 1995 2000 2005 2010 2015 GDP per capita (Pesos) Aguascalientes 7,272 9,145 11,724 14,270 17,368 14,332 Baja California 8,612 10,825 13,053 15,492 17,445 14,972 Baja California Sur 10,744 10,256 11,412 14,864 14,823 17,201 Campeche 10,571 15,308 15,477 20,276 20,819 41,776 Chiapas 3,641 3,566 3,717 4,760 4,585 5,043 Chihuahua 9,253 10,677 13,437 17,149 21,009 14,125 Colima 8,990 7,683 9,013 11,668 12,433 12,355 Ciudad de México 19,999 19,291 23,400 30,911 34,413 28,689 Coahuila 7,319 11,004 12,159 16,377 17,306 18,356 Durango 6,211 6,528 7,425 10,833 10,907 10,419 Estado de México 7,209 6,150 6,895 8,557 9,453 8,289 Guanajuato 5,453 5,473 6,577 8,671 9,311 10,727 Guerrero 4,990 4,386 4,994 6,574 5,942 6,090 Hidalgo 6,285 4,519 5,216 6,929 6,611 8,562 Jalisco 8,633 7,497 9,120 11,581 11,612 13,338 Michoacán 4,248 4,358 4,996 6,649 7,121 7,823 Morelos 9,411 6,706 7,676 10,811 9,074 9,015 Nayarit 6,457 4,495 5,146 7,014 8,578 9,028 Nuevo Le on 12,677 13,449 16,522 22,185 23,730 22,112 Oaxaca 3,756 3,593 3,856 5,420 5,614 6,260 Puebla 4,933 5,177 6,626 8,459 9,387 8,133 Querétaro 6,743 9,208 11,035 13,878 15,690 16,872 Quintana Roo 18,111 12,516 14,313 17,913 15,093 15,231 San Luis Potosí 5,583 5,883 6,696 9,532 11,641 11,598 Sinaloa 7,988 6,116 6,833 9,184 9,040 11,211 Sonora 7,728 10,004 10,789 14,237 17,607 17,580 Tabasco 5,461 5,311 5,718 7,938 8,244 16,639 Tamaulipas 7,161 8,491 10,060 13,840 12,181 13,540 Tlaxcala 4,310 4,116 4,943 6,223 6,034 7,086 Veracruz 4,390 5,093 5,150 7,366 8,343 8,982 Yucatán 6,662 5,740 7,494 9,854 9,644 10,334 Zacatecas 4,120 4,559 4,755 6,610 7,799 9,172 Public spending (millions of Pesos) Aguascalientes 268 1,126 4,634 8,403 13,441 22,524 Baja California 1,907 5,106 21,843 20,764 30,537 42,143 Baja California Sur 161 776 3,161 5,868 9,556 16,305 Campeche 276 1,727 6,082 10,186 15,138 23,169 Chiapas 944 4,927 18,554 34,424 57,418 87,811 Chihuahua 791 4,223 14,518 26,563 44,555 66,599 Colima 204 840 3,326 5,746 8,827 16,665 Ciudad de México 7,707 17,991 56,676 79,624 130,541 210,845 Coahuila 552 3,252 10,867 19,859 38,234 44,812 Durango 328 942 7,327 11,706 25,024 33,969 Estado de México 2,316 13,185 41,977 88,876 171,651 246,145 Guanajuato 718 3,676 15,484 28,192 48,465 81,367 Guerrero 602 1,691 14,382 23,673 39,798 55,580 Hidalgo 320 2,309 9,324 17,806 27,397 46,139 Jalisco 2,976 11,452 25,587 44,201 73,161 96,809 Michoacán 558 3,525 15,443 27,409 48,321 62,741 Morelos 378 1,389 6,793 11,724 19,544 28,242 (continued) Table I. Data of the income factor in Mexico, 1990-2015 Income in Mexico 167 State 1990 1995 2000 2005 2010 2015 Nayarit 272 1,309 5,596 8,920 16,517 21,198 Nuevo Le on 3,325 9,149 21,315 34,393 59,417 86,631 Oaxaca 1,495 7,631 14,733 25,974 51,711 70,202 Puebla 671 4,298 19,301 31,532 54,491 84,600 Querétaro 299 2,221 6,823 12,398 20,841 30,789 Quintana Roo 212 1,021 5,105 10,176 23,018 31,485 San Luis Potosí 367 2,356 9,761 18,318 27,761 42,795 Sinaloa 679 3,128 10,654 18,249 35,340 47,721 Sonora 981 3,464 11,631 21,530 44,105 57,500 Tabasco 1,256 3,423 14,023 28,068 35,013 47,262 Tamaulipas 766 3,302 13,517 22,976 43,696 52,599 Tlaxcala 292 681 4,820 7,689 16,458 21,523 Veracruz 1,664 6,368 28,088 47,807 98,322 114,417 Yucatán 337 1,080 3,617 12,846 21,768 34,548 Zacatecas 314 1,459 6,310 11,241 24,748 30,462 Degree of schooling (years) Aguascalientes 6.7 7.3 7.9 8.7 9.46 9.7 Baja California 7.5 7.9 8.2 8.9 9.54 9.8 Baja California Sur 7.4 7.9 8.4 8.9 9.69 9.9 Campeche 5.8 6.5 7.2 7.9 8.53 9.1 Chiapas 4.2 4.8 5.6 6.1 6.73 7.3 Chihuahua 6.8 7.3 7.8 8.3 9.01 9.5 Colima 6.6 7.1 7.7 8.4 9.12 9.5 Ciudad de México 8.8 9.2 9.7 10.2 10.81 11.1 Coahuila 7.3 7.8 8.5 9 9.79 9.9 Durango 6.2 6.8 7.4 8 8.74 9.1 Estado de México 7.1 7.6 8.2 8.7 9.48 9.5 Guanajuato 5.2 5.8 6.4 7.2 7.9 8.4 Guerrero 5 5.6 6.3 6.8 7.55 7.8 Hidalgo 5.5 6 6.7 7.4 8.21 8.7 Jalisco 6.5 7 7.6 8.2 8.98 9.2 Michoacán 5.2 5.8 6.4 6.9 7.62 7.9 Morelos 6.8 7.3 7.8 8.4 9.17 9.3 Nayarit 6.1 6.7 7.3 8 8.72 9.2 Nuevo Le on 8 8.4 8.9 9.5 10.17 10.3 Oaxaca 4.5 5.1 5.8 6.4 7.08 7.5 Puebla 5.6 6.2 6.9 7.4 8.14 8.5 Querétaro 6.1 6.8 7.7 8.3 9.26 9.6 Quintana Roo 6.3 7.1 7.9 8.5 9.3 9.6 San Luis Potosí 5.8 6.4 7 7.7 8.51 8.8 Sinaloa 6.7 7.1 7.6 8.5 9.28 9.6 Sonora 7.3 7.8 8.2 8.9 9.6 10 Tabasco 5.9 6.5 7.2 8 8.78 9.3 Tamaulipas 7 7.5 8.1 8.7 9.48 9.5 Tlaxcala 6.5 7.1 7.7 8.3 9.13 9.3 Veracruz 5.5 6 6.6 7.2 7.84 8.2 Yucatán 5.7 6.3 6.9 7.6 8.26 8.8 Zacatecas 5.4 5.9 6.5 7.2 7.89 8.6 (continued) Table I. JEFAS 25,49 168 advantage of the know-how of the DMUs and once determined who is efficient and who is not, set improvement goals for the inefficient, and based on the achievements of the efficient (Bemowski, 1991;Navarro and Torres, 2003;Serra, 2004). In our case, the model was oriented to the output because the ultimate goal of economic well-being is to maximize income and minimize the concentration of it. Due to the existence of undesirable outputs, for the calculation of the annual efficiency levels, a model based on a directional distance function (DDF) was used (Färe et al.,1994), precisely with the objective to maximize income while minimizing the concentration of it, given the amount of available resources. The DDF models has been widely used in efficiency studies (Sueyoshi and Goto, 2010;Färe et al., 2005;Watanabe and Tanaka, 2007). The mathematical expression of it is as follows: State 1990 1995 2000 2005 2010 2015 Employed personnel (persons) Aguascalientes 212,365 292,184 331,083 406,782 460,428 518,514 Baja California 565,471 785,060 906,369 1,181,866 1,318,160 1,512,261 Baja California Sur 102,763 142,847 169,014 225,302 258,651 357,412 Campeche 149,983 214,141 243,323 326,946 345,981 394,634 Chiapas 854,159 1,101,341 1,206,621 1,552,418 1,722,617 1,898,952 Chihuahua 773,100 1,041,766 1,117,747 1,328,974 1,276,383 1,539,769 Colima 133,474 178,907 199,692 256,986 289,025 340,008 Ciudad de México 2,884,807 3,449,206 3,582,781 3,957,832 3,985,184 4,147,971 Coahuila 586,165 724,729 822,686 965,240 1,040,436 1,247,782 Durango 347,275 402,351 443,611 556,402 576,977 724,360 Estado de México 2,860,976 3,908,623 4,462,361 5,553,048 6,195,622 7,065,112 Guanajuato 1,030,160 1,304,041 1,460,194 1,887,033 1,961,002 2,381,939 Guerrero 611,755 776,577 888,078 1,164,045 1,301,453 1,390,303 Hidalgo 493,315 690,874 728,726 926,353 932,139 1,208,638 Jalisco 1,553,202 2,180,447 2,362,396 2,870,720 3,073,650 3,424,781 Michoacán 891,873 1,105,816 1,226,606 1,595,979 1,602,495 1,903,548 Morelos 348,357 504,109 550,831 663,781 719,727 778,745 Nayarit 233,000 286,693 318,837 408,313 430,055 544,513 Nuevo Le on 1,009,584 1,317,418 1,477,687 1,832,395 1,975,245 2,225,108 Oaxaca 754,305 955,626 1,066,558 1,408,055 1,450,587 1,621,204 Puebla 1,084,316 1,446,039 1,665,521 2,161,852 2,358,045 2,564,998 Querétaro 288,994 428,651 479,980 651,557 683,693 766,182 Quintana Roo 163,190 259,071 348,750 518,040 655,226 738,156 San Luis Potosí 529,016 616,679 715,731 935,462 979,539 1,116,158 Sinaloa 660,905 818,932 880,295 1,139,861 1,110,501 1,290,410 Sonora 562,386 751,405 810,424 957,211 972,978 1,309,197 Tabasco 393,434 546,794 600,310 731,237 762,850 907,599 Tamaulipas 684,550 903,894 1,013,220 1,271,428 1,308,505 1,491,450 Tlaxcala 196,609 290,914 328,585 430,958 439,084 531,163 Veracruz 1,742,129 2,145,521 2,350,117 2,701,735 2,852,644 3,092,678 Yucatán 407,337 531,197 618,448 788,841 899,766 977,644 Zacatecas 294,458 267,925 353,628 524,128 541,914 600,148 Source: Own elaboration based on the INEGI (2018a,2018b,2018c,2018d,2018e,2018f,2018g, 2018h), Banco de México (Banxico) (2018),Banco Mundial (2018) and Secretaría de Educaci on Pública (SEP) (2018) Table I. Income in Mexico 169 Max b s:t X K k¼1 l kytkm yot m1þ b ðÞ m¼1...M X K k¼1 l kbtkh #bot h1 b ðÞ h¼1...H X K k¼1 l kxtkn #xot nn¼1...N b 0; l k0k¼1...K (1) Table II. The coefficient of Gini in Mexico, 1990-2015 Sate 1990 1995 2000 2005 2010 2015 National 0.519 0.518 0.516 0.499 0.482 0.469 Aguascalientes 0.488 0.471 0.454 0.481 0.507 0.451 Baja California 0.476 0.461 0.446 0.476 0.506 0.432 Baja California Sur 0.458 0.475 0.493 0.489 0.485 0.447 Campeche 0.504 0.512 0.520 0.517 0.514 0.484 Chiapas 0.543 0.542 0.542 0.541 0.541 0.512 Chihuahua 0.509 0.508 0.507 0.490 0.473 0.465 Colima 0.536 0.520 0.505 0.511 0.517 0.507 Ciudad de México 0.510 0.487 0.465 0.470 0.476 0.460 Coahuila 0.500 0.506 0.511 0.465 0.420 0.440 Durango 0.486 0.482 0.478 0.474 0.470 0.431 Estado de México 0.520 0.509 0.498 0.483 0.468 0.438 Guanajuato 0.519 0.522 0.525 0.479 0.433 0.513 Guerrero 0.542 0.545 0.549 0.532 0.516 0.480 Hidalgo 0.528 0.530 0.531 0.498 0.465 0.467 Jalisco 0.560 0.542 0.523 0.492 0.461 0.445 Michoacán 0.543 0.523 0.502 0.496 0.489 0.438 Morelos 0.532 0.547 0.561 0.491 0.420 0.452 Nayarit 0.501 0.497 0.493 0.490 0.488 0.471 Nuevo Le on 0.499 0.484 0.469 0.483 0.498 0.515 Oaxaca 0.517 0.541 0.565 0.537 0.509 0.503 Puebla 0.563 0.559 0.554 0.518 0.481 0.505 Querétaro 0.583 0.556 0.529 0.508 0.487 0.484 Quintana Roo 0.538 0.554 0.571 0.524 0.477 0.464 San Luis Potosí 0.551 0.548 0.545 0.526 0.507 0.463 Sinaloa 0.515 0.498 0.481 0.474 0.466 0.457 Sonora 0.497 0.496 0.495 0.487 0.479 0.487 Tabasco 0.540 0.530 0.520 0.499 0.478 0.457 Tamaulipas 0.522 0.511 0.500 0.474 0.449 0.476 Tlaxcala 0.485 0.501 0.518 0.471 0.425 0.395 Veracruz 0.538 0.548 0.558 0.546 0.533 0.489 Yucatán 0.526 0.558 0.590 0.526 0.462 0.481 Zacatecas 0.492 0.508 0.523 0.522 0.521 0.499 Source: Own elaboration based on data published by the CONEVAL (2018a,2018b) JEFAS 25,49 170 case reflected that all the states presented a negative evolution in their efficiency and productivity over the period studied. 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