Proposal of a water management sustainability index for the 969 sub-basins of Mexico

ABSTRACT

Different components of sustainability have been measured in previous studies at national, regional, and local scales. However, few efforts have aimed to measure water sustainability at the basin level. In this work, a water management sustainability index (WMSI) for quantifying water sustainability at the sub-basin level in Mexico is proposed. Variables related to water management in the environmental, social, economic, and institutional sub-systems of each sub-basin system were selected. The variables were aggregated in four sub-indexes based on a factor analysis, the sub-indexes scores were codified to indicate the level reached of water sustainability. The WMSI classified the water sustainability of the sub-basins of Mexico between poor and deficient, and the code classification indicated the level of compliance of the 969 sub-basins proposed by the National Institute of Statistics and Geography with sustainability criteria.

KEYWORDS:

• Water sustainability
• integrated water management

1. Introduction

Integrated water resources management (IWRM) is one approach to water management that aims to assess the state of socio-ecological systems based on the four sub-systems (economic, social, environmental, and institutional) that influence the sustainability of water management and use in the long term (Jefferies & Duffy, 2011). The sustainability of water resources is the ability to use water in sufficient quantity and quality to meet the needs of human beings and ecosystems, in the present and the future, with the objective of maintaining and protecting the hydrological functioning of basins (Mays, 2007). The watershed management practices are encouraged to be based on institutional factors and water parameters, with strategies to deal with three main challenges: (1) integrating and involving key stakeholders, (2) implementing a territorial management framework and (3) integrating multi-sectorial and territorial specificities (OECD, 2011). In literature the concept of sustainability and water resource management have been extremely combined, but if sustainable fundaments are applied, it is expected that available water resources can be used by the present and future generations (Juwana et al., 2009).

In recent decades, important efforts have been made to measure water sustainability at different scales (da Silva et al., 2020; Fonseca et al., 2017). Despite these efforts, currently, is still necessary to generate a common framework for quantifying sustainability using indicators, especially for the complex context of water management in developing countries (Hák et al., 2016). However, water sustainability assessment requires an integration of its sub-systems into the decision making-process, such an evaluation is usually supported by indicators, indices and systems of indicators to convey the required information (da Silva et al., 2020).

At a global level, several specific case studies have aimed to quantify water sustainability including the West Java Water Sustainability Index (Juwana et al., 2010, 2009), the Water Poverty Index (Sullivan et al., 2003), and the Watershed Sustainability Index (Chaves & Alipaz, 2007). In one notable attempt, the international community proposed a common set of indicators and indexes related with the objectives of sustainable development within the framework of the Agenda 2030 action plan (DESA, 2016). However, these indicators are not well understood or lack a simple or practical interpretation (Hák et al., 2018).

In Mexico, the efforts to evaluate water management sustainability have been focused to evaluate at local level how decision processes are affected by the participation and if the increase of participation results in predictable use of scientific information (Jacobs et al., 2016). Sandoval-Solis et al. (2011) evaluated the performance of scenarios of alternative policies from the perspective of water users and the environment to measure the adaptive capacity of a trans-boundary Basin of Mexico. However, the application of sustainable water management requires important changes in the objectives which decisions are based on and the understanding of articulations within the four sub-systems (Durán-Sánchez et al., 2018).

1.1. Water management in Mexico

In Mexico, IWRM starts following the passage of the National Waters Law in 1992. The National Water Commission (CONAGUA) promoted the decentralization of its functions by the passage of this law in order to encourage citizen participation and the implementation of IWRM considering, among other principles, the hydrological basin as a basic unit for water administration (Cotler, 2004). To regulate the use of surface water in Mexico, the country was divided into 13 hydrological administrative regions (HARs), each with an intervention center under the direction of CONAGUA (CONAGUA, 2012).

Despite the existence of several proposals for decentralized water management in Mexico (Rap & Wester, 2017), criteria for the participation of all stakeholders involved, including both governmental authorities and users, should be stipulated by law (FMA, 2012). However, institutional deficiencies and the absence of consensus among institutions prevent the construction and adequate use of existing knowledge, making it more difficult to effectuate an appropriate management of Mexico’s basins (Cotler, 2004).

Sustainable water management in basins is a challenge that requires the identification of priority areas where economic resources should be invested in order to provide solid support for decision making. In a previous work hydrological regions that share similar characteristics were identified as a starting point for constructing water management strategies according to the specific characteristics and necessities of socio-ecosystems in Mexico (Cervantes-Jiménez et al., 2017).

Indexes are one tool of great value that enable the local conditions of socio-ecosystems to be evaluated based on reliable indicators. Furthermore, indexes can help to reduce complexity, to compare and rank results and to communicate the scientific outputs (Reckien, 2018). In addition, indexes can facilitate comparative analyses and represent one mechanism for informing decision making based on IWRM (Esty & Porter, 2005; Myllyviita et al., 2011). For this reason, in this work, a water management sustainability index (WMSI) is proposed for the sub-basins of Mexico to aid in the identification of specific priority problems and the formation of strategic and tactical development plans at the local level.

2. Materials and methods

In Mexico, the National Hydrological Program (CONAGUA, 2014) monitors the fulfillment of the objectives and goals in water management through indicators. Many of these are not found in public databases and do not have spatial information. For that reason, information on key economic, social, environmental, and institutional variables related with water management was collected (Appendix 1). Variables were used if they were according with the selection criterion: (1) The indicator is available in public National databases, (2) The indicator has availability temporal information and (3) The indicator belongs to one of four sustainability dimensions. Database was represented for the reference year of 2010 or, in absence of variables for this year, in 2005 were selected.

2.1. Databases in Mexico

In Mexico, the National Institute of Statistics and Geography (INEGI) is responsible for generating statistical and geographical information. Its statistical databases contain information generated from the following censuses: (1) the Population and Housing Census (every 10 years), (2) the Economics Census (every 5 years), and (3) the Agricultural, Livestock, and Forestry Census (every 10 years). Additionally, INEGI generates geographic products on characteristics such as relief, vegetation, climate, soil and water. Accordingly, the variables related with the social sub-system and water sustainability were taken into account. In the social sub-system, the variable of average disability-adjusted life years (DALYs) generated by Gómez-Albores (2012) was also included. This latter variable combines the time lived with disability and the time lost due to premature death because of water-related intestinal disease.

The National System of Municipal Information (SNIM) provides statistics for each municipality based on census data. The censuses cover period of five years since 2005 or 2010. Variables related with the economic and social sub-systems were included from these databases.

The variables of the environmental sub-system were obtained from the databases of the National System of Environmental Indicators (SNIA) of the Secretariat of the Environment and Natural Resources (SEMARNAT), which provides a brief overview of the current state of Mexico’s environment and natural resources as well as the changes to the state of the environment (SEMARNAT, 2010). The water indicators in this system provide information related with surface water extraction, water availability, the degree of pressure on water resources, the reutilization of wastewater, etc. Although these data span more than 10 years, the level of spatial detail is poor (only available at the HAR or state level). Meanwhile, the bioclimatic variables were obtained from the databases of WorldClim-Global Climate Data (Hijmans et al., 2005). These latter variables are available in raster format and represent the averages of the 1970–2000 period at a spatial resolution of 1 km².

In the institutional sub-system, three variables related with water management were included. The databases of CONAGUA were considered to obtain information on the number of water management organizations in each HAR of Mexico. The number of businesses at the municipal level was obtained from the Mexican Business Information System (SIEM). Finally, the number of higher educational institutions at the municipal level was obtained from the databases of the National Association of Universities and Higher Education Institutions in Mexico (ANUIES).

2.2. Pre-treatment of variables

Given that the spatial scale (state, municipal, or HAR) and the data format (database, vector or raster) of the variables differed, the variables were pre-treated before analysis. The variables at the municipal level were transformed to densities per km² and were distributed at the sub-basin level using a disaggregation model based on the distance between sub-basins and the centroids of urban localities. Accordingly, the average values of climate variables were extracted for the 969 sub-basins of the country. The treatment, management, and spatial representation of the variables was carried out with the geographical information systems Idrisi Selva (Eastman, 2012) and ArcMap 10.2 (Esri, 2011).

The socio-ecological variables were normalized to determine whether positive values had a beneficial or detrimental effect on water sustainability. Normalization is used because it is a linear transformation that preserves the classification and correlation structure of the variables, and allows the coupling use of variables with different scales (Tran et al., 2010). This process was performed using the Delphi technique with area experts from the Inter-American Institute of Water Science and Technology (Instituto Interamericano de Tecnología y Ciencias del Agua [IITCA]) (Cervantes-Jiménez et al., 2017). This technique allows for structured and interactive communication between groups of experts, enabling solutions to complex problems to be found (Landeta, 2006). Based on the beneficial direction of the effect generated by each variable, the scale of values of each variable was modified to a range of 0–1 using the Min–Max rescaling transformation method (Yoon, 2012), where 0 was the worst status and 1 was the best status. The database of these variables and the spatialization and modulation procedures can be consulted in greater detail in the work of Cervantes-Jiménez et al. (2017).

2.3. Factor analysis

Factor analyses (FA) is a technique to reduce the number of variables, creating a series of synthetic variables that explain most of the variance of the system based on a correlation matrix. This technique analyzes covariance (communality) and the goal is to reproduce the correlation matrix with a few orthogonal components (Tabachnick et al., 2007). The FA were used to group the input variables of each sub-system. The eigenvalues of synthetic variables constructed (Principal factors) indicate the contribution of each factor to the total variance of the sub-system, and the loading of the variables represents the importance of each variable for the factor (Satyanarayanan et al., 2017).

Therefore, a FA was carried out using a rotation matrix (varimax) for each sub-system to obtain a simpler interpretation of the factors (Tabachnick et al., 2007). The procedure was carried out in the Systat version 12 software (Systat Software Inc) for the environmental, social, and economic sub-systems. This technique was not applied to the institutional sub-system because its variables were not correlated. The relevance of the analyses was verified using the Kaiser-Meyer-Olkin (KMO) criterion using the Psych package in the R statistical software (R Development Core Team, 2009). Factors with an eigenvalue equal to or greater than 1 were selected (Kaiser, 1960).

The variables were normalized so that the highest value would coincide with the highest level of water sustainability with de Min–Max procedure. Thus, if the sum of the weights is positive, the Pf shows a positive condition and, in contrast, if the sum is negative, the best condition will correspond with the most negative factor (Figure 1).

Figure 1. Method for the construction of the Water Management Sustainability Index (WMSI).

2.4. Weighting and aggregation of the sub-indexes

The weighting of the variables in each sub-index was performed using the proportion of explained variance for each Pf in each sub-system as a reference. However, the weights of the sub-systems of the WMSI was calculated from the equal contribution of the four sub-systems (Equation (1)). The range of values of the WMSI varied between 0 and 100, where 0 is the worst condition and 100 is the best condition. Therefore, each sub-system had a maximum value of 25.(1) $\text{WMSI}=\left(E_s+S_s+E{n}_s+I_s\right)$(1) where $\text{SWMI}$ is the value of the Sustainable Water Management Index per pixel, $E_s$ is the score of economic sub-index, $S_s$ is the score of social sub-index, $E{n}_s$ is the score of environmental sub-index, and $I_s$ is the score of institutional sub-index.

The performance scale of the sustainability indexes is established subjectively in the water sustainability indexes (Guidolini et al., 2018), thus in this work is proposed a technique based on the coding of the sub-indexes ranking to improve the interpretation of the WMSI. Since the WMSI does not reflect the specific conditions of the sub-systems of the sub-basins, a combination matrix with the ESEI codes (Economic, Social, Environmental, and Institutional) was designed in which the scores of the sub-indexes were represented by codes. For example, in the institutional sub-system, the code 1 corresponded with a sub-index value of 0–5.0, whereas the code 2 was assigned to a value of 5.0–10.00, and so on to the maximum ESEI code score of 5. The resulting ESEI codes were composed of four digits corresponding respectively with each sub-system (Figure 1). Under this codification system, 1,024 distinct combinations were possible. The ideal code in a water sustainable sub-basin is 5555, which would indicate that the conditions of the four sub-indexes are excellent (ESEI total value of 20). Meanwhile, the most unfavorable conditions are represented by the code 1111 (ESEI total value of 4).

3. Results

A database of 69 variables was constructed for the 969 sub-basins of Mexico, including 26 variables in the environmental sub-system, 24 in the social sub-system, 16 in the economic sub-system, and 3 in the institutional sub-system.

In the economic sub-system, the resulting Pf explained 73.5% of the variance and was related to a good force work condition. The presence of a large overall economically active population and large economically active population with a job (Ec 8 and Ec 11), each one explained 22% of the variance of the factor (Figure 2(A)). In the social sub-system, the variables were aggregated in three Pf that explained 84.5% of the total variance (Figure 2(B)). In the environmental sub-system, 26 variables were included, and five Pf explaining 83.8% of the total variance (Figure 2(C)).

Figure 2. Weights of the main factors in the sub-systems: (A) economic, (B) social, and (C) environmental. Ev: Explained variance (%).

3.1. Integration of the WMSI

On the economic sub-index (Es), most of the sub-basins in the country received a poor score (0.0–5.0). However, in the urban sub-basins, economic conditions were slightly more favorable and, in particular, the sub-basin of the Valle de México presented excellent economic conditions (Figure 3).

Figure 3. Economic sub-index of water management sustainability.

On the social sub-index (Ss), most of the country received a poor (0.0–5.0) or acceptable score (10.0–15.0). The social conditions in the most populated urban sub-basins of the country were scored as good (Figure 4).

Figure 4. Social sub-index of water management sustainability.

On the environmental sub-index (Ens), conditions varied between deficient (5.0–10.0) and good (15.0–20.0) across the country (Figure 5). On the institutional sub-index (Is), most sub-basins varied between the categories of deficient and good (Figure 6).

Figure 5. Environmental sub-index of water management sustainability.

Figure 6. Institutional sub-index of water management sustainability.

After summing the sub-indexes of the WMSI, most sub-basins of the country obtained an overall score within the categories of deficient (20.0–40.0) or acceptable (40.0–60.0). This classification is a simple means of assessing conditions in the sub-basins but is not descriptive of these conditions (Figure 7). For this reason, the scores of the sub-indexes were codified (ESEI codes) to obtain a classification that would provide more precise information about the conditions of each sub-basin.

Figure 7. Water Management Sustainability Index for the 969 sub-basins of Mexico.

Under this classification, 97% of sub-basins belong to 14 distinct ESEI codes (Map, Table 1). The most recurrent code is 1232 (276 sub-basins), which is indicative of poor economic conditions (1), deficient social conditions (2), acceptable environmental conditions (3), deficient institutional conditions (2).

Table 1. Number of sub-basins per ESEI code.

Code	Number of sub-basins
	E	S	E	I
1	954	218	0	0
2	8	740	101	450
3	3	10	577	291
4	3	1	288	228
5	1	0	3	0

Meanwhile, 3% of the national territory (29 sub-basins) can be grouped in the category of uncommon sub-basins (Map). The WMSI value of this category is higher than most of the other sub-basins but remains within the category of acceptable (Figure 7). Furthermore, the ESEI codes reveal a high level of socio-economic inequality across Mexico (Figure 8): 4 sub-basins have values of 4 or 5 on the economic sub-index and, of these sub-basins, only one achieved the highest value (4) on the social sub-index (Puebla), and the three remaining sub-basins correspond with Mexico City and Toluca. In the category of uncommon, there are sub-basins classified with the best environmental conditions and sub-basins with the poorest conditions according to the ESEI codes (Table 2).

Figure 8. Characterization of water management sustainability of the uncommon sub-basins of Mexico using ESEI codes.

Table 2. ESEI codes for uncommon category of sub-basins of Mexico.

Sub-basin	City	ID	HAR^a	ESEI code
Río Apatlaco	Cuernavaca	279	Balsas	2233
Río Corona -Verde	Guadalajara	407	Lerma-Santiago	3233
Río Verde – Santa Rosa	Guadalajara	430	Lerma-Santiago	2233
Río Colorado	Mexicali	974	Río Colorado	1122
Río Monterrey	Monterrey	658	Bravo – Conchos	2232
Alvarado	Puebla	265	Papaloapan	4443
Río Atoyac – San Martín Texmelucan	Puebla	298	Balsas	2243
Río San Francisco – Puerto de Veracruz	Puerto de Veracruz	285	Papaloapan	2232
Río Verdiguel	Toluca	286	Lerma-Santiago	4344
Río Otzolotepec	Toluca	304	Lerma-Santiago	1344
Río Jaltepec	Toluca	326	Lerma-Santiago
Río Otzolotepec – Atlacomulco	Toluca	336	Lerma-Santiago
Río Tejalpa	Toluca	290	Lerma-Santiago	2244
Río Almoloya – Otzolotepec	Toluca	302	Lerma-Santiago
Río Cuautitlán	Valle de México	319	Pánuco	4342
Lago Texcoco and Zumpango	Valle de México	333	Pánuco	5332
Tepotzotlán	Valle de México	338	Pánuco	3332
Río Zayula		127	Grijalva – Usumacinta	1254
Río Platanar		141	Grijalva – Usumacinta
Río La Sierra		170	Grijalva – Usumacinta
Río Nautla		278	Tuxpan – Nautla	3343
Río Chacté		108	Grijalva – Usumacinta	1344
Río Axtla		455	Pánuco	1342
Río Guadalupe		962	Baja California	2232
Río Cocoraque		772	Sonora Sur	1123
Río Cedro		780	Sonora Sur
Río Bacanora		836	Sonora Sur
Río Bravo – Ventanas		849	Bravo – Conchos	1122
Río Conchos – Cuchillo Parado		879	Bravo – Conchos

^aHAR: Hydrological administrative region, ID: Identifier.

4. Discussion

The extreme conditions of socio-economic inequality in Mexico are undoubtedly reflected in the results of this study. According to the WMSI, Mexico is distinguished by the poor or deficient social and economic conditions in most of its sub-basins. The conditions of these latter two sub-systems cause the WMSI to generate a similar value across most of the country, even though the scores for the environmental and institutional sub-systems are more heterogeneous. Therefore, based on the global WMSI score, it is not possible to identify the sub-systems that require greater attention. For this reason, it is important to adopt a regional approach and to take into account the scores of the different sub-systems in order to guide development plans and propose rehabilitation or management actions (Cervantes-Jiménez et al., 2017).

In this respect, the ESEI codes reveal the overall panorama of water sustainability in Mexico as well as the specific conditions of the different sub-basins. The group of uncommon sub-basins partially coincides with the urban sub-basins with high population density that showed high values on the social and economic sub-indexes (Cervantes-Jiménez et al., 2017). Some of these sub-basins were classified as acceptable by the WMSI, but not all of them had good environmental conditions. On the other hand, some of the uncommon sub-basins presented high values on the environmental or institutional sub-indexes but not on the social or economic sub-indexes. In this context, according to Kong et al. (2016), these types of analyses are important because of their capacity to improve the integration of information related to the four main axes of hydrologic sustainability (economic, social, environmental, and institutional) in addition to promoting the social and interdisciplinary participation of stakeholders.

Furthermore, it is important to ensure that the databases of sub-systems are continually updated to ensure that the results of different tools, such as the one developed in this work, can accurately quantify the water sustainability. It is also necessary to strengthen the set of water sustainability indicators and the Mexican databases related with water management because much of the available information is not current and certain indicators, such as those relating to the sustainability of the water supply, are not reported on (Richter et al., 2018). It has been reported that the institutional dimension is one weakness aspect for water management, the reason in part is the way that institutions uses new research related to water management to take decisions (Jacobs et al., 2016). In this sense, the lack of information in the institutional sub-system is especially notable and would help to strengthen the WMSI. Additionally, institutional indicators related to the professional status of decision-makers and of the success of collaborations or projects, among other indicators, would be helpful for reflecting the condition of the different institutional sub-systems in Mexico.

5. Conclusion

The Water Management Sustainability Index (WMSI) proposed was capable of modeling the conditions of the economic, social, environmental, and institutional sub-systems of hydrological sustainability in Mexico within a spatiotemporal framework. According to the WMSI of the sub-basins of the country, the most vulnerable sub-systems in terms of water sustainability are the economic, social, and institutional sub-systems. However, for this latter sub-system, the information available at the national level is limited.

In general, the environmental sub-system did not receive low scores in the ESEI codification of the sub-basins. This may be related to the abundance of water resources in Mexico, which may be viewed as an opportunity during decision making to improve water management. Some environmental aspects could not be included in the environmental sub-index because information was lacking. The WMSI values provide an overall panorama of the economic, social, environmental, and institutional conditions in Mexico, but the generation of the ESEI codes allowed a more precise diagnosis of the sustainability of the different sub-systems of the sub-basins.

Undoubtedly, one core issue in this work was related to the sources of information, especially the information for quantifying connectivity in the institutional sub-index. However, the present study is maybe the first in which the institutional sub-system was included as a measure of water sustainability in Mexico.

Software

The processing of the databases was performed in Microsoft Excel 2016. In the Idrisi Selva software, point data were transformed to spatial data. The graphs were made in Sigma Plot (version 11.0). The references were managed with Zotero (version 5.0.47).

Open Scholarship

This article has earned the Center for Open Science badge for Open Data. The data are openly accessible at 10.5281/zenodo.3783872.

This article has earned the Center for Open Science badge for Open Materials. The materials are openly accessible at 10.5281/zenodo.3783872

Acknowledgements

The first author wishes to thank the Autonomous University of the State of Mexico for the scholarship to further her studies in the Water Science Program.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data supporting the findings of this study are freely accessible in the ‘Datos Abiertos México’ section of the following webpage: https://datos.gob.mx/.

The data for average disability-adjusted life years (DALYs) due to intestinal disease and mean natural water availability used in this study are available in the supplementary materials of the following thesis: Gómez-Albores, M.A. Modelación Geomática de Medidas de Frecuencia y de Asociación, Aplicada a Enfermedades Vinculadas con el Agua. Ph.D. Thesis, Universidad Autónoma del Estado de México, Facultad de Ingeniería, Centro Interamericano de Recursos del Agua (CIRA), Toluca de Lerdo, México, 2012.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This work was supported by Universidad Autónoma del Estado de México [grant number PhD Scholarship].

Appendix

Appendix 1. Variables used for the Water Management Sustainability Index. Beneficial (+) or harmful (−) effect.

Table

Sub-system	Geo-database	ID	Variable	Units/ km^2 of sub-basin	Effect fo Hydrologic sustainability	Description
Economic	SNIM	Ec1	Overall economic participation rate	%	+	Ratio of working population to overall population with capacity to work (≥15 years)
		Ec2	Male economic participation rate	%	+	Ratio of male working population to overall population with capacity to work (≥15 years)
		Ec3	Female economic participation rate	%	+	Ratio of female working population to overall population with capacity to work (≥15 years)
		Ec6	Economically inactive male population	Inhabitant	−	Economically inactive male population
		Ec7	Economically inactive female population	Inhabitant	−	Economically inactive female population
		Ec8	Overall economically active population	Inhabitant	+	Population with a job, or looked for a job in the reference week (≥12 years)
		Ec9	Economically active male population	Inhabitant	+	Economically active male population
		Ec10	Economically active female population	Inhabitant	+	Economically active female population
		Ec11	Overall economically active and with a job	Inhabitant	+	Population that had a job in the reference week (≥12 years)
		Ec12	Economically active male population with a job	Inhabitant	+	Economically active male population with a job
		Ec13	Economically active female population with a job	Inhabitant	+	Economically active female population with a job
		Ec14	Overall economically active population without a job	Inhabitant	−	Number of inhabitants who did not work or hold a job but looked for work in the previous week (≥12 years)
		Ec15	Economically active male population without a job	Inhabitant	−	Male population economically active without a job
		Ec16	Economically active female population without a job	Inhabitant	−	Female population economically active without a job
Social	INEGI	S1	Proportion of urban land use per sub-basin	%	−	Percentage of sub-basin occupied by urban areas (more than 2,500 inhabitants)
	SNIM	S2	Population attending school	Inhabitant	+	Population older than 3 years attending school
Social	SNIM	S3	Education level of female population	Year	−	Average years of schooling of female population
		S4	Education level of male population	Year	+	Average years of schooling of male population
		S5	Overall education level	Year	+	Average years of schooling of population
		S6	Population density	Inhabitant	−	Number of inhabitants per km^2
		S7	Human Development Index (HDI)	Range 0–1	+	Human Development Index in 2005
		S8	Marginalization index	Range 0–100	−	Marginalization index
		S9	Population that speaks an indigenous language	Inhabitant	+	Number of inhabitants (≥3 years) that speak an indigenous language
		S10	Male population that speaks an indigenous language	Inhabitant	+	Number of male inhabitants (≥3 years) that speak an indigenous language
		S11	Female population that speaks an indigenous language	Inhabitant	+	Number of female inhabitants (≥3 years) that speak an indigenous language
		S12	Density of inhabited houses	House	−	Total number of inhabited houses
		S13	Houses with running water	House	+	Number of inhabited houses with running water
		S14	Houses without running water	House	−	Number of inhabited houses without running water
		S15	Houses with drainage service	House	+	Total number of inhabited houses connected to the drainage network or a septic tank
		S16	Houses without drainage service	House	−	Total number of inhabited houses without drainage
		S17	Houses with water, drainage, and electricity	House	+	Number of inhabited houses with running water, drainage, and electricity
		S18	Houses with basic goods	House	+	Number of inhabited houses with television, radio, refrigerator, washer, automobile, phone, cell phone, computer, and internet
		S19	Houses without basic goods	House	−	Number of inhabited houses without television, radio, refrigerator, washer, automobile, phone, cell phone, computer, and internet
		S20	Houses with electricity	House	+	Number of inhabited houses with electricity
		S21	Houses without electricity	House	−	Number of inhabited houses without electricity
Social	SNIM	S22	Houses with sanitary facilities	House	+	Number of inhabited houses with toilet or sanitary facilities
	Gómez-Albores (2012)	S23	Average disability-adjusted life years (DALYs) due to intestinal disease	Rate * 10,000 inhab	−	Life years lost due to intestinal diseases
		S24	Average natural water availability	m^3/year	+	Mean natural water availability per capita
Environmental	SNIA-SEMARNAT	E1	Soil degradation	%	−	Soil degradation caused by humans
		E2	Wastewater treatment plant efficiency	%	+	Ratio between treatment capacity of treatment plant and actual volume of treated wastewater
		E3	Disinfected water supply	L/inhab/day	+	Volume of disinfected water supply
		E4	Biochemical Oxygen Demand (indicator of contamination)	%	−	Average annual BOD values range from 30 to 120 mg/L
		E5	Biochemical Oxygen Demand (indicator of high levels of contamination)	%	−	Average annual BOD values higher than 120 mg/L
		E6	Intensity of groundwater use	%	−	Ratio between extraction volume and natural recharge of aquifer
	WORLDCLIM	E7	Average annual minimum temperature (Tmin)	°C	+	Period average, 1950–2000
		E8	Average annual maximum temperature (Tmax)	°C	−	Period average, 1950–2000
		E9	Temperature seasonality	%	−	Standard deviation of temperature
		E10	Isothermality	%	+	Ratio of mean diurnal range to annual range
		E11	Mean temperature of the coldest month	°C	+	Period average, 1950–2000
		E12	Mean temperature of the warmest month	°C	−	Period average, 1950–2000
		E13	Mean diurnal range in temperature	°C	−	Monthly average multiplied by the difference between monthly Tmax and Tmin
		E14	Annual range in temperature	°C	−	Difference between Tmax of warmest month and Tmin of coldest month
		E15	Tmin of the coldest month	°C	+	Period average, 1950–2000
		E16	Tmax of the warmest month	°C	−	Period average, 1950–2000
Environmental	WORLDCLIM	E17	Mean temperature of the wettest quarter	°C	−	Average of three wettest months of the year
		E18	Mean temperature of the driest quarter	°C	−	Average of three driest months of the year
		E19	Average annual precipitation	mm	+	Period average, 1950–2000
		E20	Precipitation seasonality	%	+	Precipitation coefficient of variation
		E21	Precipitation level of the wettest quarter	mm	+	Average of three wettest months of the year
		E22	Precipitation level of the driest quarter	mm	+	Average of three driest months of the year
		E23	Precipitation level of the wettest month	mm	+	Period average, 1950–2000
		E24	Precipitation level of the driest month	mm	+	Period average, 1950–2000
		E25	Precipitation level of the warmest quarter	mm	+	Average of three warmest months of the year
		E26	Precipitation level of the coldest quarter	mm	+	Average of three coldest months of the year
Institutional	ANUIES	I1	Higher education institutions	Institution	+	Number of higher education institutions registered in ANUIES
	SIEM	I2	Business density	Businesses	−	Number of businesses registered in the Mexican Business Information System
	CONAGUA	I3	Total water management organizations	Committees	+	Number of councils, commissions, basin committees, and groundwater committees per HAR