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Sistema de Información Científica
Red de Revistas Científicas de América Latina y el Caribe, España y Portugal
EconoQuantum / Vol. 14. Núm. 1
n
73
A Quality of Life Index of Mexican cities:
An equalizing-difference approach
R
OBERTO
G
ALLARDO
DEL
Á
NGEL
1
n
Abstract:
The present analysis contains a Quality of Life Index (QLI) for most
medium-large Mexican cities using the equalizing-difference approach. Implicit prices
were constructed using two amenity bundles which include geographical, environ-
mental, social factors such as climate, proximity to coast or metropolitan areas, public
safety, quality of education, access to health care as well as other local public goods.
The ranking includes 92 medium-large cities (municipalities) from a subsample of the
Household Income and Expenditure Survey. The results show that extreme tempera-
tures and criminality are clearly bad and have negative implicit prices. Other variables
such as distance to hospitals and local taxes also have negative implicit prices. The
quality of education, urban metropolitan areas, access to sea coast and federal transfers
have a positive impact on households’ utility. Two different rankings are constructed
using two slightly different amenity bundles to observe for consistency. The estimation
of implicit prices shows that public safety and quality of basic education are the most
valued external factors for Mexican households, followed by the access to tertiary
education.
n
Key words:
Hedonic prices, housing market, quality of life, equalizing-differences,
labour market.
n
JEL
Classifcation:
R3, R32.
n
Resumen:
El presente análisis aplica un Índice de Calidad de Vida para la mayoría
de las ciudades medianas y grandes de México mediante el método de igualación de
diferencia. Se construyeron precios implícitos usando dos canastas de amenidades que
1
Faculty of Economics, Universidad Veracruzana, México. E-mail: rogallardo@uv.mx; and McMaster Uni-
versity. Email: gallarr@univmail.cis.mcmaster.ca. I am very grateful to Michael Veall for his support and
advice during my research visit to McMaster University in 2014. I am also grateful to Jeffrey Racine, Arthur
Sweetman, Byron Spencer, John Leach, Stephen Jones and other professors from the department of econom-
ics at McMaster for their valuable comments and assistance. I must also thank Juan José Jardón, Óscar Javier
Cárdenas, Leobardo Pedro Platas and Jorge Noel Valero for their valuable comments during the 5th Interna-
tional Seminar on Local Public Economics and Finance at the University of Quintana Roo. I also thank two
anonymous referees for their signi
f
cant suggestions and Shirley Sainsbury for the
f
nal English language
check. The author is the only one responsible for any errors in this paper.
74
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Vol. 14. Núm. 1
incluyen factores geográFcos, ambientales y sociales, tales como clima, cercanía a la
costa, áreas metropolitanas, seguridad pública, calidad educativa, acceso a servicios de
salud, así como otro tipo de bienes públicos. El ranking incluye 92 ciudades con datos
de la Encuesta de Ingreso y Gasto de los Hogares (ENIGH). Los resultados muestran
que la temperatura y la criminalidad impactan negativamente en los precios implíci-
tos. Otras variables como distancia a hospitales o impuestos locales también afectan
negativamente dichos precios. En cambio otras variables como la calidad educativa,
áreas metropolitanas, acceso a la costa y transferencias federales impactan de manera
positiva en la utilidad de los hogares. Para efectos de consistencia, se construyeron dos
rankings basados en diferentes canastas de amenidades. La estimación de los precios
implícitos muestran que la seguridad pública y la calidad de la educación básica son los
factores externos más valorados por los hogares mexicanos, seguidos por la educación
media superior.
n
Palabras clave:
Precios hedónicos, mercado de la vivienda, calidad de vida, iguala-
ción-diferencias, mercado de trabajo.
n
Clasifcación
JEL
:
R3, R32.
n
Recepción: 11/02/2014
Aceptación: 18/04/2016
n
Introduction
In the economic literature, there are several indices that intend to capture the well-
being of individuals. Some capture differences in income, health conditions, capital,
productivity, welfare, etc., among human groups with common attributes. Some
others try to measure external variables such as environmental quality. Most indices
offer some information on how individuals or groups compare to each other. From
the Human Development Index to the Environmental Protection Index, all indices are
relatively sound with a theoretical background. These constructions are important for
policy analysis and public decision making in many areas.
In this paper we offer a simple estimation of the Quality of Life Index (QLI) for
Mexican cities, which is an empirical application of the theory of equalizing differences,
formalized by Rosen in 1976 based on previous work on hedonic prices. The important
assumption under this QLI comes from the idea that individuals may be willing to pay
or give up some part of their money income, for amenities they value more. From this
view, quality of life is related to the value of external amenities attached to visible
prices in the market. These amenities may come in the form of clear air, clean water,
safe neighborhoods, access to local public goods, quality of education and health care,
etc. It is difFcult to accept that individuals ignore these amenities when making the
important decision on where to live and work. Although there are many other important
considerations to take into account about locational decisions of households and Frms,
A Quality of Life Index of Mexican cities:.
..
n
75
QLI offers a Frst-hand measure of the relative importance of environmental and social
amenities (or disamenities).
This analysis is perhaps, to the best of the author’s knowledge, the Frst Quality of
Life Index constructed for Mexican cities using hedonic prices approach. Although this
methodology was developed more than three decades ago, there is almost no literature
in the subject for Latin American countries. Another important structural change since
its development is the advance of federalism and devolution of Fscal attributes from
central to local governments. The new relation between levels of governments has
increased the bundle of local public goods available and so the positive (or negative)
externalities derived from them. In this context, the QLI acquires a new relevance as a
useful tool for understanding qualitative differences among regions and cities.
The QLI is just a weighted average valuation of an amenity bundle in each region
or city. The construction of QLI requires Frst the estimation of implicit prices for
every amenity (disamenity), then it uses these implicit prices and the average amenity
provision in every city to obtain the value of the amenity bundle. It offers information
on how these amenities are valued by the average household in every city compared
with other cities. Then the relevant questions are the Fnding of the appropriate micro-
data and the proper estimation of the implicit prices.
The Quality of Life Index using the approach of equalizing differences was Frst
developed by Rosen (1979) and later reFned by Roback (1982). Since then, several
authors constructed on these works and developed different models to estimate QLI
adding new relations with different spatial coverage. Examples are Gyourko
et al.
(1991), which is a QLI construction for US that includes taxes and public goods;
Colombo
et al.
(2012) is a QLI construction for Italy; Albouy
et al.
(2013) is a QLI
construction for Canada which includes cities’ productivity; Berger
et al.
(2007) is a
QLI for Russia and Zheng
et al.
(2009) is a QLI for China. They all use hedonic prices
approach and estimate wage and housing differentials.
±orwardness of the Roback’s model of 1982. The simplicity is justiFed by the reality
of Mexican Municipalities (cities), which are limited to the use of property taxation
and are highly dependent on federal grants as a main source of revenue. The basic
administrative structure in Mexico is the Municipality, which in many cases includes
many cities of different size. We are separating those municipalities where there is a city
with more than one hundred thousand inhabitants. In many cases, these cities make up
the entire municipality, so the concept of city is used in this paper instead of municipality
The paper uses ofFcial data sets from the Mexican National Institute of Statistics,
Geography and Informatics (INEGI). Household characteristics come from the
Mexican National Household Income and Expenditure Survey (ENIGH) of 2010,
while the information about local taxes, grants, and amenities come mainly from the
State and Municipal Data Base System (SIMBAD), both supplied by the INEGI.
The ranking of Mexican cities within this new QLI is fairly consistent. Highly
developed modern cities show high QLI. Most of these cities have strong economies,
modern infrastructure and a large service sector, including tourist attractions like
beaches, theatres, good hotels and resorts, etc. They also concentrate better health
76
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EconoQuantum
Vol. 14. Núm. 1
services, education and recreational facilities. These cities are usually connected to
each other within a metropolitan area so they share the spillover of local public goods
and the economies of scale.
On the other hand, low QLI cities have serious urban problems relative to others.
They also have many illegal urban sprawls, a difFcult social network and larger crime
rates relative to others. They also have lower provision of public goods and usually they
beneFt much less from spillovers and from being close to a metropolitan area.
In this work there are two different constructions of QLI using slightly different
amenity bundles. One includes only local taxes and the other also includes federal
grants. Both QLI rankings show some consistency though there are some changes in
the ranking, especially in the top, due to unusually high grants for some cities, but the
bottom of the ranking remains fairly unchanged.
This paper is organized as follow: The Frst section contains the introduction, the
second the theoretical framework, the third contains two subsections to explain the data
and the methodology for estimation, and the last contains our Fnal conclusions.
n
Theoretical background
The idea of using the framework of equalizing differences to develop a QLI came back
from Rosen (1979) and Roback (1982). Consumers (workers) and Frms face a bundle of
amenities in speciFc geographical areas where wages, rents and amenities are in spatial
equilibrium which means that there is no incentive to move. Gourkyo
et al.
, 1991, intro-
duced a model to incorporate taxes and local public goods. This section develops a simple
model following Roback, 1979, and Gourkyo, 1991. The only difference is the addition
of property taxation in the consumption of land services rather than include it only to the
price of land. Local public goods are determined exogenously in the model. The reason
for this comes from the fact that the Mexican Fscal revenue system is highly concentrated
at the federal level and most local government revenue comes from federal grants.
In this world, location and transportation costs are ignored for both consumer and
Frms. Consumers are identical and derive utility from a composite private good
x
, a local
public goods
G
, the consumption of residential land
l
and local amenities
a
. Consumers
are identical in skills and tastes and supply one unit of labour. They also receive a salary
income
w
and pay a property (local) tax
x
. The price of the private good is normalized
to one and the price of land is the rent
r.
They also receive a categorical grant
g
from
the federal government and have a non-labour income of
I.
The consumer problem is to
maximize the following utility function:
(1)
,,
;
UxlG a
^h
The above utility function includes the quality of local public goods in the same
manner as local amenities. The budget constrain for the individual is:
(2)
wg
lr
Ix
lr
x
++
=+
A Quality of Life Index of Mexican cities:.
..
n
77
The problem to the consumer is to maximize 1 respect to 2. From the above problem,
an indirect utility function can be obtained:
(3)
,;
Vw
ra
1
xi
+=
^
^h
h
The frm’s problem is similar as in Roback, 1982, but property taxation is additionally
included. Firms produce an
X
quantity of private goods using constant returns to scale
production function. The relevant factors are land used for production
l
t
and total labor
N
. The amenities bundle
a
enters the production function as follows:
(4)
,;
XflN a
=
t
^h
The problem oF the typical frm is to minimize costs subject to 4. The equilibrium
condition is that unit cost must be equal to product price which is unity:
(5)
,;
Cw
ra
11
x
+=
^
^h
h
The standard conditions are
C
X
N
w
=
and
C
X
l
1
r
x
=
+
t
^h
. If the amenity is
unproductive then
C
0
a
1
and if the amenity is productive then
C
0
a
1
. Industries
may have an incentive to relocate to cities where productive amenities are available.
Finally, a simple local government budget constrain closes the system:
(6)
Gg
r
x
=+
The grants
g
is positive because it is a transfer from federal government to local
residents, then the total amount of public goods consumed are equal to the total
amounts of grants and the local property tax collected. This also implies that local
public goods are not always provided by local governments, which may be the case of
Mexican Municipalities.
2
It is clear from 3 and 5, that wages and rents are determined
in equilibrium in both markets as functions of
a
. Finding the differentials from 3 and 5
and solving for
da
dw
and
da
dr
we fnd the wage and rental diFFerentials as Follow:
(7)
da
d
CV
CV
CV
CV
w
ar
ra
rw
wr
=
(8)
da
d
CV
CV
CV
CV
r
aa
rw
wr
ww
=
+
2
In this simple model, local public goods are exogenously determined by federal government, and are solved
in equilibrium outside this framework. The same is assumed for the input capital in the production function.
78
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EconoQuantum
Vol. 14. Núm. 1
The above equations can be used to solve for
V
a
,
V
w
,
and
C
a
considering the
conditions that
C
X
N
w
=
and
C
X
l
1
r
x
=
+
t
^h
. A relative valuation can be obtained to
measure the total amount of income required to compensate a household for a small
change in
a
, which is called full implicit price IP:
(9)
ln
ln
IP
V
V
l
da
dr
da
dw
da
dr
da
dw
w
1
––
1
w
a
xi
==
+=
^h
The full implicit price of an amenity is the housing price differential
dr
/
da
and
the negative of the wage differential
dw
/
da
. In principle,
/
dw da
0
1
because wages
must be adjusted downwards if there is an amenity. In this case, individuals are willing
to give up some wage income to enjoy an amenity such as fresh air or safe public
parks. We assume that the rent differential is
/
dda
r
0
2
because amenities make land
(housing) expensive for households.
In the last equality, the parameter
1
i
contains information on the total expenditure
on net land consumption by households. The reader may also observe that
ln
da
dr
and
ln
da
dw
can be easily estimated using suitable data and appropriate statistical methods.
Once these differentials are estimated for each amenity (disamenity) a vector of implicit
prices for each amenity can be obtained
IP
a
i
.
Using the vector of implicit prices
IP
a
i
, a QLI can be easily constructed. QLI is the
product of the implicit prices for each amenity by the average value of the trait in each
city
j
:
(10)
,
A
QL
II
P
1
j
i
j
i
A
i
j
=
=
|
where
,,
an
d,
,
iA
jJ
11
ff
==
Thus QLI can be interpreted as the money value that the average household assigns
to the amenity bundle A in the city
j
. This QLI will be high for cities where amenities
are highly valued and a simple ranking may be constructed for comparison.
n
Measuring Quality of Life
The data
Before proceeding to estimate the QLI, we must Fnd suitable data for the experiment.
It is often possible to Fnd labour information and housing data in any household
income-expenditure survey from any country. But it is unusual or extremely rare to Fnd
information about urban and environmental amenities within these types of surveys. So
A Quality of Life Index of Mexican cities:.
..
n
79
we must pool different data sets in order to input information on the amenities side by
side with the labour and housing information.
The labour and housing data used in this work comes from the Mexican National
Household Income and Expenditure Survey (ENIGH) of 2010 and information about
amenities comes from the State and Municipal Data Base System (SIMBAD), both
produced by the Mexican National Institute for Statistics, Geography and Information
(INEGI). The ENIGH contains information from a sample of 27 thousand households
representative for the whole country. The main variables used from this survey includes
household income, characteristics of the head of the household, structural characteristics
of houses, housing expenditure (rents), wage income and other labour market variables.
A subsample was constructed using household heads with a salaried work in the
private sector, when the household is resident in a city with more than 100 thousand
inhabitants. A total subsample of 7,966 households was obtained with enough number
of observations to represent 92 middle sized and large cities. There are two main
reasons behind the construction of this subsample. The Frst has to do with the concept
of the QLI deFned above, where we only include the valuation of households whose
locational decision is decided by wages, rents and, of course, prices of amenities in
every city. We are excluding those households that derive mainly income from capital
and other non labour income as they may also do locational decision considering the
productivity effects of amenities.
3
We also decided to exclude household heads working in a public sector job as the
public service in Mexico has some important institutional arrangements that may also
affect locational decisions. Some individuals in public jobs may not be able to choose
location like those in the military. Furthermore, almost all public workers are unionised
and then willing to bargain wage hikes or other fringe beneFts (e.g. support for rent
payments) in places where there are highly-valued amenities for both households and
Frms. The effect of unionisation may be important especially in large cities. Due to
possible rigidities in the labour market, we decided to exclude public workers in this
analysis and leave these groups of workers for further research.
4
The construction of the above subsample of private-sector salary workers is
representative for the whole country. The objective is to make simple the empirical
analysis as well as to Ft properly the theoretical model. The main scientiFc objective is
to obtain a vector of households’ valuations that may be used as weights to understand
how these workers value local amenities. The vector
IP
a
i
. contains the mean valuation
of every amenity (disamenity) for the entire sample of private-sector and salaried
workers.
3
The productivity effect on
F
rms is decided by the cost-saving effects of amenities and are not included in
this analysis. Although some amenities with positive implicit prices for households may also have positive
productivity effects on
F
rms, but this may not be the case for all
F
rms.
4
There is no reason to assume public workers will not behave as any other worker in any other sector. The rea-
son for this exclusion only obeys to the lack of information on institutional variables, which may be important
for a proper analysis. We believe that the theory of equalizing differences in the labour market is general and
applies for all kind of workers and sectors. We also believe that all individuals have their own valuation of non
market goods, which may be approached by implicit-price analysis and estimation.
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EconoQuantum
Vol. 14. Núm. 1
These weights then can be used along mean values of the amenities (disamenties) to
construct the QLI.
As mentioned before, there is no information at city level, so we used information
at municipality level. In most cities, the total population is the same as the entire
municipal population. Table 1 at the end of the paper shows the 92 main cities used
for this analysis, with the total city population and the percentage from total municipal
population. On average, city-level population represents 85% of the entire municipal
population in this analysis.
Information on wages and rents were also obtained from the ENIGH. Wage income
can be easily estimated for every member of the household and information on rents
paid by the household is also included in the data sets.
5
In the survey, households were
asked to provide an imputed value of rents for their estates (land and house), later we
used this imputed rents as a proxy for market rents.
Information about amenities was obtained from the SIMBAD such as climate,
precipitation, crime, education, health and fscal attributes. Several data sets were
constructed and later pooled to construct a unique data set with labour, housing and
amenities information. Standard statistics of this data set are shown in Table 2 at the
end of this paper.
Climate and precipitation data was used to capture the weather conditions in every
city. A crime rate for every city was constructed dividing the total number of crimes
by total population, in order to obtain a relative measure of public safety. Dummies
variables were constructed to capture the advantages of being located next to the coast
as well as the advantages of being located in a metropolitan area. These two variables
also capture important aspects of urban agglomeration such as low transport cost,
positive externalities of developed markets, among others.
In order to capture the quality effects of some local public goods provided by
federal and state governments, a tertiary education ratio and a teacher-student ratio
were constructed. These ratios provide also a good incentives for relocation and
many households might also value the provision of tertiary education and the positive
externalities of living close to well educated neighbours. The teacher-student ratio
captures the intensity and also quality of primary education in every city.
The time-to-hospital variable accounts for the number of hours a family must travel
to the nearest hospital in case of medical emergency. This variable was introduced in
the regression as the inverse of the travel time to the nearest hospital which can be
interpreted as a convenience or accessibility ratio. The average time of travel is about
half an hour to the nearest hospital, but there are 9 households that declare more than 20
hours oF travel, and fve oF these are located in Mexico city and From those, two declare
taking up to 45 hours of travel even though these households are located inside the city.
A possible explanation could be the segmentation in the social security in Mexico where
some households might take a long travel time to arrive to their assigned hospitals.
5
An important assumption is that households are identical in their labour effort and labour supply. Labour
productivity differences are neither included in the theoretical model nor in the statistical estimation and left
for further research.
A Quality of Life Index of Mexican cities:.
..
n
81
The last two variables inside the amenity vector are Municipal taxes and local
public goods provided by the city in the form of local public infrastructure. In Mexico,
municipalities have few taxes at their disposal, and perhaps the most important is the
property tax. This tax is a good instrument to observe the fscal eFFort oF every city as
well as the provision of local public goods. One problem with local taxes in Mexico is
that they only represent about 10% of the total municipal revenue. In order to properly
include the quality-effect of local public goods provided by the city, federal grants must
also be included in the analysis. One problem is that categorical grants were almost
perfect collinear with local taxes as they are linked through a design formula. On the
other hand, non-matching grants cannot be combined with categorical grants as they
are not entirely committed to provide local public goods. So, a third variable was used
to capture the effect of grants, particularly those categorical grants that are used to build
local public inFrastructure. IF city fscal revenue From taxes is small compared to grants,
then it is possible to capture the effect of local public goods provided using the amount
of investment in municipal infrastructure per household.
6
Although the theory assumes that all households are identical, in practice we must
control for workers’ heterogeneity. For that purpose, information about the head of
household was used to capture individual-labour market characteristics such as gender,
years of formal education, job experience, ethnicity and possible physical disabilities.
Some dummy variables were used to capture information about industry level and
labour market characteristics. These dummies captured information about types of
jobs such as managers, machinery operators or professional jobs as well as jobs in
agriculture.
Finally, a vector of structural housing characteristics contains information about the
number of rooms in the house, and the availability of a sewage system and hot water
inside the house.
n
The econometrics
The General Equilibrium Model implies that all markets (market goods, labour and
land) are in equilibrium. But the market prices of interest that make for this equilibrium
are, of course, wages and rents. Then we proceeded to estimate a reduced-form of
wage and housing expenditures equations in order to estimate implicit prices as in 9.
The functional forms follows standard Mincerian-type wage equations and housing
equations which are common in the economic literature:
(11)
,
ln
wX
MZ
0123
bbbb
e
=+++
+
where
,
N
0
2
+
ev
e
^h
(12)
ln
rQ
Z
01
2
mm mn
=+ ++
where
,
N
0
2
+
nv
n
^h
6
Total federal grants were also used in the statistical analysis with similar results.
82
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EconoQuantum
Vol. 14. Núm. 1
Where
X
is a vector of individual characteristics for the households’ heads,
M
is a vector of industry-level and labour market variables,
Q
is a vector of structural
characteristics of housing, and
Z
is a vector of amenities. The vector of amenities
Z
was included to capture implicit valuation of non market goods and
3
b
and
2
m
give
an estimate of the wage and housing differentials in 9. If the amenities are statistically
signifcant, then it is possible to oFFer an implicit price. Both 11 and 12 are explicit
semi-log functional forms that follows the standard Mincerian and housing regressions.
Another feature of these functional forms is to allow for a straightforward estimation
of the differentials in 9.
7
The frst approach was to perForm traditional cross-section OLS regressions on 11
and 12 using the sample of 7,966 households. Several regressions were performed with
different explanatory variables. We used information criterion (Akaike and Schwarz) in
order to observe for the quality of the regression models. For the wage equation 11, we
used 20 explanatory variables and for the housing equation 12 we used 14, from which
10 variables were included as amenities in the vector
Z
. As for this vector of amenities,
we decided to include information on weather (temperature and precipitation), incidence
of crimes as proxy of public safety, access to sea coast (seascape), metropolitan area
(urban spillovers), tertiary and primary education index (university and teacher/student
ratios), time to the nearest hospital in case of emergency, local taxes (property tax) and
investment on local infrastructure (federal and state transfers).
As predicted by theory, almost all explanatory variables selected were signifcant,
but a BreuschPagan and a White test reveal a serious problem of heteroskedasticity
in the simple OLS regression.
8
A second OLS regression with robust standard errors
resulted again with almost all explanatory variables being highly signifcant.
Correcting for heteroskedasticity does not solve all problems in our data. In
our experiment, we are dealing with household information grouped by cities
(municipalities) which brings into the picture the problem of intraclass correlation.
The origin of this problem is very common when data is grouped (clustered), in this
case by cities or states. The OLS assumes that the standard errors of estimates are
computed from data sets where observations are independent from each other and,
in our experiment, we expect that preferences and responses are somehow similar in
each city, municipality or State. This problem is completely natural as we know that
individuals influence each other within a group. This intraclass correlation aFFects the
standard deviations oF our estimates, making it diFfcult to perForm signifcance tests.
7
Instead of elasticities, the vectors of estimates
3
b
and
2
m
express the relative change on wages and rents due
to absolute changes in the amenities. In other words,
ln
da
dw
da
dw
w
1
3
ii
b
==
and
ln
da
dr
da
dr
r
1
2
ii
m
==
, which
is the main reason for using a semi-log functional in this experiment.
8
For the wage equation with all variables, the Breusch-Pagan test reports a
2
|
= 40.23 and the White test
reports a
2
|
= 589.59. Then we must reject the null hypotheses of constant variance and homoskedasticity
respectively. For the housing equation with all variables, the Breusch-Pagan test reports a
2
|
= 329.17 and the
White test reports an
2
|
= 733.99, which are also evidence of heteroskedasticity.
A Quality of Life Index of Mexican cities:.
..
n
83
Interclass correlation is not a problem to worry about when groups are small (e.g.
households), but it becomes problematic when membership within a group increases
(e.g. school, zone, city, etc.).
The most common answer to this problem is to use clustered standard errors,
assuming that there is no correlation among groups. Two OLS regressions corrected by
clustering in the 92 cities were performed, one with infrastructure expenditure and one
without it.
9
The results from the regressions are in Table 3, showing only the coefFcients
and standard errors of the amenities (vector
Z
). The coefFcients by themselves are a
little difFcult to interpret at Frst hand. But we know that a positive and signiFcant
coefFcient in the wage equation means a disamenity while the same is an amenity for
the housing equation. A negative and signiFcant coefFcient is an amenity for workers
while a disamenity for landowners.
The advantage of the wage and housing regressions in 11 and 12 is that they
allow us to estimate implicit prices of amenities directly. These implicit prices
IP
a
i
are calculated using mean monthly wages and rents. These prices express the implicit
valuation of the average household for non-market goods as weather, public safety or
education spillovers. Some of them are negative, which means that these non-market
goods are indeed bads, or goods that reduce utility. Negative implicit prices for climate
and crime shows that extreme temperatures decrease rents and high crime rates must
compensate households with higher wages. Access to hospitals in terms of time (or
distance) and local taxes are also bads, as wage differentials outweigh rents differentials.
This is understandable as the higher the distance from a hospital and more local taxes
decreases household utility. All other amenities have positive implicit prices which
means that they increase households’ utility and influence positively the valuation of
the entire bundle of amenities.
A close look to the estimates of the regression in Table 3 shows that amenities such
as precipitation, coastal location, metropolitan areas, teacher-student ratio and tertiary
education ratio are positive, which mean that prices of housing (land) will increase with
them. On the side of wage differential, only criminality, student-teacher ratio, local
taxes, the inverse of time to hospital and the local public expenditure in infrastructure
are statistically signiFcant. The variable (inverse) time to hospital expresses the number
of hours to arrive to the nearest hospital in case of emergency. This explanatory variable
is an inverse term and both coefFcients (wages and rents) are positive. This is puzzling
because it means that quality of health care is better when the hospital is relatively far
from our location. This is perhaps the result of the under provision and segmentation of
health care system in Mexico.
With the estimation of wage and housing expenditure differentials and the full
implicit prices for every amenity, the Fnal step was to calculate the QLI using the
implicit price from Table 3. The price in every trait (amenity) is multiplied by the
average trait in every city. We constructed two QLI using two amenity bundles, with
and without transfers, and then proceeded to rank every city. The QLI Fnal rankings
9
A similar regression was performed clustering by state rendering similar levels of signi
F
cance.
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as shown in Table 5. This QLI contains the valuation of each amenities bundle by the
average household in every city.
The advantage (or disadvantage) of the implicit prices methodology is that it may be
used with different amenity bundles. Two different QLI were constructed to observe the
consistency of the QLI itself when the amenities bundle changes.The Frst QLI1 includes
only local taxes and the second QLI2 includes additionally local public investment in
infrastructure. There are substantial differences in tax collection and grants allocation
among cities in Mexico, which may affect how households may value external factors.
For example, Mexico City collects an average of more than ten thousand pesos per
household in taxes, but only receive little more than seven hundred pesos in local
public infrastructure from federal grants. On the other hand, Nuevo Laredo collects
almost nine hundred pesos in taxes per household but invests more than 14 thousand
pesos in infrastructure using federal grants. The new valuation is, of course, product
of the redistributive effect of grants. This Fscal allocation affects the valuation of the
amenities bundle and the perception of quality of life. Something similar happened for
other cities such as Cuernavaca and San Juan Del Río, who sharply improved in the
ranking in similar manner. A scatter plot between QLI1 and QLI2, in Fgure 1, shows
that for most cities the estimation of a QLI is fairly consistent as both QLIs are highly
correlated.
10
The three cities that increase abruptly in the ranking due to unusually high
level of federal grants are Nuevo Laredo, Cuernavaca and San Juan del Río, marked
with stars in Figure 1.
Figure 1
Correlation between two amenity bundles
Source: Own elaboration.
10
A Correlation Coe
F
cient of 0.7125 increases to 0.8607 when the outliers Nuevo Laredo, Cuernavaca and San
Juan Del Río are dropped from the sample.
4000
3000
2000
1000
0
-1000
-1000
0
1000
QLI1
QLI2
2000
A Quality of Life Index of Mexican cities:.
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Another important consideration is the statistical confdence on the QLI ranking.
We must be able to construct confdence intervals For each QLI in order to assess how
much the position of a city may vary within the ranking. As we know, the amount of
amenities in each city is fxed, at least in the period oF analysis. Then, the only source
of variability are the implicit prices. But in our theoretical setting, implicit prices are
just weights obtained from a regression analysis on the overall sample. Therefore, we
may use the standard deviation of each estimate to simulate implicit price variability.
We performed 1000 simulations on the implicit prices and recalculated the valuation
of amenities for each city.
11
Then, we obtained the standard deviations for each QLI
as shown in columns SD-1 and SD-2 in Table 5. With this information at hand, we are
able to obtain confdence intervals to evaluate each city ranking. In the frst ranking, we
observe that Campeche is still better than Acapulco at 95% confdence. But it is diFfcult
to assess whether Veracruz is better than Villa de Álvarez as both are statistically similar
and their confdence intervals overlap. There are similar cases where the QLI’s are very
close to each other and diFFerences in the ranking are not signifcant, some clear examples
are Toluca and Monterrey or Chalco and Navojoa. The case of Oaxaca is noteworthy
because is a city in the bottom of both rankings with an extremely low valuation.
n
Concluding remarks
Although the theoretical model is rather basic for our estimation, it offers powerful
insights about the determinants of the spatial (non arbitrage) equilibrium among
households and frms. The method oF implicit prices oFFers a straightForward valuation
of non-market goods and it is intrinsically linked to households’ welfare. In this sense,
it is an objective method for estimation of non-market prices using information from
visible market prices such as wages and rents. Implicit prices from Table 3 are weights
(average) of such valuations for the whole group, in this case the Mexican households
working in the private sector of the economy. They can be used for reference and also
used for public policy design. Implicit prices in Table 3 tell us that public safety and
access to basic education are highly valued within the Mexican Households’ utility. The
third most valued amenity is the access to tertiary education (college and university).
Then, any public policy designed to decrease crime rates and increase access and quality
of basic and college education may certainly increase households’ welfare in Mexico.
Coincidentally, in the present time, both public safety and basic education reform are
the two top issues in the political agenda in Mexico.
The QLI is a construction that contains information of non-market prices but also
about the mean provision oF amenities (disamenities) in a specifc location. It can also be
tailored to match real-life preferences for certain amenities in any location, community,
society or country. It offers the possibility to rank groups according to their valuation of
these external attributes which allow us to design and target social and environmental
11
We generated new implicit prices simulating the estimates in the form
e
ii
i
bb
=+
u
t
, where
i
b
t
is the estimate
for the amenity
i
and
,
eN
0
2
i
+
v
b
^h
. The same procedure was done for the
i
m
coeFfcients in the housing
regression.
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policies. The QLI is not an all-purpose index, and it is only one of several analytical
tools we may use to judge individuals’ well-being. The Bohemian Index, for example,
is a different ranking of cities according to their urban infrastructure that foster a
creative or bohemian class (high quality-highly developed human capital individuals).
This index explains how cities enhance development according to their ability to attract
creative individuals and subsequently, frms.
Our QLI ranking offers some interesting information on the valuation of amenities
in different Mexican cities. With the present amenity bundles, it may be said that cities
such as Campeche, Acapulco or Xalapa Enríquez have a high QLI and cities such
as Oaxaca, Ciudad Cuauhtémoc and Ciudad Acuña have a low QLI. One important
observation is that this ranking may be aFFected by the confdence interval oF the
explanatory variables. If the standard deviations of the estimates are large enough,
it might be diFfcult to assert whether Campeche is absolutely better than Acapulco
or if Oaxaca is absolutely worse than Ciudad Acuña, but it would be plausible that
Campeche has a QLI higher than Oaxaca. This problem is particularly troublesome in
the middle oF the ranking. The confdence interval oF the estimates might be aFFected
by the statistical method used,
12
but a straightforward use for a QLI might be just to
compare cities in the very top of the ranking with those in the very bottom.
The two rankings of Table 5 give us important information on which Mexican
cities the amenity bundles are more valued. The QLI cannot tell us whether an average
household in Campeche is better off than an average household in Oaxaca. It rather tells
us that the amenity bundle is more valued in Campeche than in Oaxaca by an average
household. It would be diFfcult to aFfrm that changes in the ranking are exclusively
due to changes in preferences alone. The QLI may be affected by the amenity package
in some regions that might be determined by nature over time. Then, the QLI may
change not only by the components in the bundle but also by changes on nature.
Another important consideration is the demographic changes (household structure).
For example, young workers may prefer some cities while senior workers and retirees
may prefer others, affecting indirectly implicit prices in such places. Furthermore,
land supply and availability may be also restricted by institutional arrangements and
geographical factors. Despite all these shortcomings, the QLI is still a valuable source
oF inFormation to observe how some amenities (disamenities) influence household’s
locational decision across Mexican cities.
Changes in the top of the ranking of Table 5 are more visible when federal transfers
(grants) are included in the amenity bundle as a proxy of local public infrastructure. But
some cities still remain in the top 20 and might be considered places with high quality
of life, such as Acapulco or Campeche. But city ranking in the bottom remains almost
unchanged even after the inclusion of transfers. The city of Oaxaca is of particular
interest because it is in the bottom of both rankings with the highest crime rate, very
little taxes and small investment in infrastructure.
12
Gyourko
et al.
(1991) simulate the standard errors for the QLI from a random effect non lineal model, which
shows higher standard errors than the traditional OLS. In such situations, comparison becomes more di
Ff
cult
and the best possible solution was to compare the top 20 vs the 20 bottom QLI in the ranking.
A Quality of Life Index of Mexican cities:.
..
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87
Although there is no spatial analysis in this work, it might be noted that most cities
close to the US border usually have a low QLI such as Ciudad Juarez, Mexicali and
Tijuana though cities such as Heroica Matamoros are better ranked. The city of Nuevo
Laredo became the frst place in the second ranking when local public inFrastructure is
included. One possible interpretation for the case of Nuevo Laredo might be the federal
and state grants for improvements in public safety, because border cities are relatively
more exposed to criminal activity.
Cities within states along the Gulf of Mexico usually have high QLI. These cities
have the advantage of low transport cost and access to better communication routes,
though there are cities along the pacifc coast that also have high QLI such as Acapulco,
Tepic and Colima. Mexico City is a place where QLI is relatively low even though
criminality is not a decisive issue compared with other cities with higher crime rate
per cápita. The main disadvantage For Mexico City comes From the fscal arrangements
in place, where Mexico City residents are compelled to pay high taxes but receive
relatively little transfers per cápita.
The QLI is a fairly good measure of the households’ valuation of amenities using
information from households’ wage income and housing expenditure. In Mexico, it
shows clearly that criminality is a bad and households are willing to pay for suppressing
this disamenity. The QLI in this work may also be used as an instrument for public policy
and can help to understand how Mexican households value their environment and are
willing to pay for additional quantities of some amenities such as quality education.
The information from Table 5 offers important insights and can also be used for
policy design. For example, investing in public safety and education in the bottom
10 cities in the ranking may not change signifcantly the ranking, but may reduce the
relative distance between the low and high QLI cities. It is assumed that any change
in the amenity bundle may affect the locational equilibrium, but we know that market
prices may also adjust and, in this case, wages and housing prices will move to account
for that change. So, there is no reason to expect many households relocating as many
other conditions are fxed by nature (weather, coastal location, metropolitan areas, etc.).
But as some other amenities such as the quality of education and public safety can be
influenced directly or indirectly by policy, then the inFormation in this work is certainly
relevant for policy planners.
This work does not include the valuation oF frms, and an extended model is needed
to capture productivity differences among cities. This paper only offers information on
the households’ side, and we must account for other complex factors that affect wages
such as work effort or unionisation. Further research must be done to improve the
theoretical framework and estimation methods on implicit prices that suit the Mexican
spatial, demographic, social and economic reality.
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No.
City
Population
%
1
Acapulco
789,971
85.3
2
Aguascalientes
797,010
90.6
3
Altamira
212,001
55.9
4
Apocada
523,370
89.3
5
Atizapán de
Zaragoza
489,937
99.8
6
Campeche
259,005
85.1
7
Cancún
661,176
95.0
8
Celaya
468,469
72.7
9
Chalco
310,130
54.4
10
Chetumal
244,553
61.8
11
Chihuahua
819,543
98.7
12
Chilpancingo de
los Bravo
241,717
77.5
13
Chimalhuacán
614,453
99.7
14
Ciudad Acuna
136,755
98.2
15
Ciudad
Cuauhtémoc
154,639
73.7
16
Ciudad Juárez
1,332,131
99.2
17
Ciudad Madero
197,216
100.0
18
Ciudad Obregón
409,310
73.0
19
Ciudad Valles
167,713
74.3
20
Ciudad Victoria
321,953
94.8
21
Ciudad de México
8,851,080
98.0
22
Ciudad del
Carmen
221,094
76.6
23
Coatzacoalcos
305,260
77.3
24
Colima
146,904
93.5
25
Córdoba
196,541
71.7
26
Cuautitlán Izcalli
511,675
94.7
27
Cuautla
175,207
88.1
28
Cuernavaca
365,168
92.7
29
Culiacán
858,638
78.7
30
Ensenada
466,814
59.9
31
Fresnillo de Glez.
Ech.
213,139
56.7
32
Gómez Palacio
327,985
78.5
33
Guadalajara
1,495,189
100.0
34
Guadalupe
678,006
99.4
Table 1
Relative size of main city population from the total municipality population
No.
City
Population
%
35
Hermosillo
784,342
91.2
36
Heroica Guaymas
149,299
75.7
37
Heroica
Matamoros
489,193
92.0
38
Iguala de la
Independencia
140,363
84.4
39
Irapuato
529,440
72.0
40
Ixtapaluca
467,361
69.0
41
Jiutepec
196,953
82.5
42
La Paz
251,871
85.4
43
León
1436,480
86.2
44
Los Mochis
416,299
61.6
45
Manzanillo
161,420
80.6
46
Mazatlán
438,434
87.0
47
Mérida
830,732
93.6
48
Mexicali
936,826
73.6
49
Monclova
216,206
99.6
50
Monterrey
1,135,550
100.0
51
Morelia
729,279
81.9
52
Naucalpan de
Juárez
833,779
95.0
53
Navojoa
157,729
72.2
54
Nezahualcóyotl
1110,565
99.5
55
Nogales
220,292
96.5
56
Nuevo Laredo
384,033
97.3
57
Oaxaca de Juárez
263,357
96.8
58
Pachuca de Soto
267,862
95.8
59
Piedras Negras
152,806
98.3
60
Poza Rica de
Hidalgo
193,311
95.8
61
Puebla de
Zaragoza
1539,819
93.1
62
Querétaro
801,940
78.1
63
Reynosa
608,891
96.8
64
Salamanca
260,732
61.4
65
Saltillo
725,123
97.9
66
San Cristóbal
Ecatepec
1,656,107
99.9
A Quality of Life Index of Mexican cities:.
..
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No.
City
Population
%
67
San Cristóbal de
las Casas
185,917
85.0
68
San Francisco
Coacalco
278,064
100.0
69
San Juan del Río
241,699
57.5
70
San Luis Potosí
772,604
93.6
71
San Nicolás de los
Garza
443,273
100.0
72
Soledad de
Graciano Sánchez
267,839
95.2
73
Tampico
297,554
99.9
74
Tepic
380,249
87.5
75
Tijuana
1,559,683
83.4
76
Tlalnepantla de
Baz
664,225
98.4
77
Tlaquepaque
608,114
94.7
78
Toluca
819,561
59.7
79
Torreón
639,629
95.2
No.
City
Population
%
80
Tulancingo de
Bravo
151,584
67.6
81
Tultitlán de
Mariano Escobedo
486,998
81.2
82
Tuxtla Gutiérrez
553,374
97.1
83
Uruapan
315,350
83.9
84
Veracruz
552,156
77.6
85
Victoria de
Durango
582,267
89.1
86
Villa de Álvarez
119,956
98.0
87
Villahermosa
640,359
55.2
88
Xalapa de
Enríquez
457,928
92.8
89
Xico
357,645
99.6
90
Zacatecas
138,176
93.4
91
Zamora de
Hidalgo
186,102
76.1
92
Zapopan
1,243,756
91.9
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Variable
Mean
Std. Dev.
Min
Max
Ln salary income
8.807
0.960
1201
12.440
Ln rentas
7.364
0.767
1609
11.849
Amenities (disamenities)
Climate (Max-Min)
7.780
3.573
2
16
Precipitation (Max-Min)
475.034
256.070
100
1700
Crime rate (per 100,000 inhab)
0.024
0.014
0.003
0.124
Coast
0.155
0.362
0
1
Metropolitan area
0.815
0.388
0
1
Tertiary education ratio
0.207
0.051
0.055
0.315
Teacher/student ratio
0.052
0.007
0.038
0.074
Time to hospital (1/hours of travel)
3.362
3.379
0.022
60
Local taxes (per household)
3067.297
3441.085
312.832
10149.28
Local infrastructure (per household)
2189.075
1532.531
388.161
14651.97
Individual and labour market characteristics
Gender
0.808
0.394
0
1
Education (years)
10.358
4.375
0
21
Experience
37.836
11.769
11
79
Experience2
1570.036
947.040
121
6241
Indian
0.212
0.409
0
1
Handicap
0.034
0.180
0
1
Managers
0.068
0.251
0
1
Professionals
0.200
0.400
0
1
Farming
0.023
0.150
0
1
Operator
0.139
0.346
0
1
Housing-structural characteristics
Number of rooms
4.085
1.756
1
21
Sewer
0.974
0.161
0
1
Air conditioning
0.156
0.363
0
1
Hot water
0.554
0.497
0
1
Table 2
Standard statistics
A Quality of Life Index of Mexican cities:.
..
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With transfers
Without transfers
ln wage
ln rent
Implicit
price
ln wage
ln rent
Implicit
price
Amenities (disamenities)
Coef.
Se.
Coef.
Se.
Coef.
Se.
Coef.
Se.
Climate (Max-Min)
0.0001
0.005
-0.012*
0.007
-27.48
0.002
0.006
-0.012*
0.007
-27.95
Precipitation (Max-Min)
0.00001
0.00005
0.00019***
0.00007
0.42
0.000011
0.000055
0.00019***
0.00007
0.42
Crime rate
(per 100,000 inhab.)
2.126**
0.993
1.585
1.591
-21,216.63
2.413**
1.034
1.541
1.593
-24,081.12
Coast
0.045
0.052
0.114*
0.058
254.23
0.048
0.054
0.113*
0.058
253.13
Metropolitan area
0.073
0.053
0.126**
0.060
281.96
0.089*
0.051
0.124**
0.059
276.77
Tertiary education ratio
-0.204
0.416
1.335**
0.520
2,988.80
-0.266
0.404
1.345**
0.521
3,010.42
Teacher/student ratio
-4.336*
2.207
-10.388**
3.953
20,025.30
-4.905**
2.283
-10.308**
3.972
25,883.43
Time to hospital
(1/hours of travel)
0.026***
0.004
0.026***
0.003
-203.59
0.026***
0.004
0.026***
0.003
-203.33
Local taxes
(per household)
0.000018***0.0000060.00006***0.000006
-0.05
0.000022***
0.000005
0.000055***
0.000006
-0.10
Local infrastructure
(per household)
-0.000024**0.000011
0.000004
0.000011
0.24
Note: The ***, ** and * symbols represent coefficients that are statistically significant different than zero al 1%, 5% and 10% respectively. The total number of
observations is 7,966. Clustered standard errors (se) by city are next to the coefficient (Coef.) column. For the wage equations the
R
2
= 0.2682 before transfers and
R
2
= 0.2693 after transfers. For the housing equations the
R
2
= 0.4697 before transfers and
R
2
= 0.4698 after transfers. The AIC and BIC for the wage equation before
transfers were 19,500.74 and 19,640.4 respectively, while after transfers were 19,490.63 and 19,637.28. The AIC and BIC for the housing equation before transfers
were 13,359.69 and 13,457.45 respectively, while after transfers were 13,361.11 and 13,361.11 and 13,465.85. A mean monthly wage income of $9,979.60 MEX
and a mean monthly rent of $2,238.10 MEX were used for the estimation of implicit prices.
Table 3
OLS clustered regression with full implicit price of amenities
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Table 4
Households’ monthly mean wage income and rent
No.
Municipality
City
Wage Income
Rent
Sample
1
Acapulco de Juárez
Acapulco
6,786.36
1,170.98
102
2
Aguascalientes
Aguascalientes
10,291.11
1,730.97
113
3
Altamira
Altamira
7,902.68
1,961.48
27
4
Apodaca
Apodaca
12,565.12
1,992.59
27
5
Atizapán de Zaragoza
Atizapán de Zaragoza
15,236.76
4,581.91
47
6
Campeche
Campeche
11,238.67
1,871.43
49
7
Benito Juárez
Cancún
10,736.10
2,344.30
79
8
Celaya
Celaya
7,326.44
1,206.67
90
9
Chalco
Chalco
6,577.08
827.27
22
10
Othon P. Blanco
Chetumal
7,069.87
1,107.63
59
11
Chihuahua
Chihuahua
11,065.80
2,222.57
113
12
Chilpancingo de los Bravo
Chilpancingo de los Bravo
13,005.61
2,542.86
21
13
Chimalhuacán
Chimalhuacán
6,159.61
1,250.00
54
14
Acuna
Ciudad Acuna
8,080.45
1,775.86
29
15
Cuauhtémoc
Ciudad Cuauhtémoc
10,420.51
1,324.07
27
16
Juárez
Ciudad Juárez
7,202.94
1,283.84
99
17
Ciudad Madero
Ciudad Madero
10,886.18
2,145.83
24
18
Cajeme
Ciudad Obregón
9,402.54
2,050.00
32
19
Ciudad Valles
Ciudad Valles
6,883.03
2,272.73
22
20
Victoria
Ciudad Victoria
16,908.76
2,037.14
35
21
Ciudad de México
Ciudad de México
12,552.79
3,486.52
1,479
22
Carmen
Ciudad del Carmen
12,830.01
4,190.20
51
23
Coatzacoalcos
Coatzacoalcos
9,001.70
2,402.78
36
24
Colima
Colima
8,865.94
1,473.08
65
25
Córdoba
Córdoba
6,905.25
1,632.61
23
26
Cuautitlán Izcalli
Cuautitlán Izcalli
14,619.99
3,360.98
41
27
Cuautla
Cuautla
8,892.78
1,648.21
28
28
Cuernavaca
Cuernavaca
9,855.50
2,093.88
49
29
Culiacán
Culiacán
8,996.46
1,748.65
74
30
Ensenada
Ensenada
8,745.59
1,693.51
77
31
Fresnillo
Fresnillo de Glez. Ech.
5,248.09
1,235.11
47
32
Gómez Palacio
Gómez Palacio
7,181.77
1,102.38
84
33
Guadalajara
Guadalajara
11,016.00
2,768.03
61
34
Guadalupe
Guadalupe
13,921.19
3,270.37
27
35
Hermosillo
Hermosillo
9,599.71
1,668.28
93
36
Guaymas
Heroica Guaymas
18,903.98
5,176.19
21
37
Matamoros
Heroica Matamoros
6,666.90
1,415.43
47
38
Iguala de la Independencia
Iguala de la Independencia
6,903.78
1415.63
32
39
Irapuato
Irapuato
6,945.84
1,309.28
97
A Quality of Life Index of Mexican cities:.
..
n
93
No.
Municipality
City
Wage Income
Rent
Sample
40
Ixtapaluca
Ixtapaluca
7,961.81
1,585.19
27
41
Jiutepec
Jiutepec
8,462.12
1,665.52
29
42
La Paz
La Paz
11,476.07
2,329.38
80
43
León
León
8,892.06
1,701.22
245
44
Ahome
Los Mochis
8,617.05
1,770.83
24
45
Manzanillo
Manzanillo
7,581.95
1,333.33
33
46
Mazatlán
Mazatlán
6,954.92
1,314.06
64
47
Mérida
Mérida
10,111.40
2,000.51
801
48
Mexicali
Mexicali
11,120.53
2,253.05
82
49
Monclova
Monclova
10,164.31
1,716.28
43
50
Monterrey
Monterrey
18148.34
4,307.69
26
51
Morelia
Morelia
9,193.83
2,425.00
56
52
Naucalpan de Juárez
Naucalpan de Juárez
11610.14
2,676.00
75
53
Navojoa
Navojoa
5668.96
1,015.79
38
54
Nezahualcóyotl
Nezahualcóyotl
7,785.94
1,688.13
107
55
Nogales
Nogales
8,442.32
1,687.50
48
56
Nuevo Laredo
Nuevo Laredo
7,026.51
1,795.65
23
57
Oaxaca de Juárez
Oaxaca de Juárez
10,655.41
2,919.67
61
58
Pachuca de Soto
Pachuca de Soto
10,301.16
2,420.95
74
59
Piedras Negras
Piedras Negras
10,797.07
1,983.33
30
60
Poza Rica de Hidalgo
Poza Rica de Hidalgo
15342.20
3,266.67
27
61
Puebla
Puebla de Zaragoza
7,733.07
1,940.42
71
62
Querétaro
Querétaro
11,219.59
2,502.48
101
63
Reynosa
Reynosa
8,153.67
2,234.38
32
64
Salamanca
Salamanca
6,352.97
1,495.88
85
65
Saltillo
Saltillo
8,134.09
3,101.81
95
66
Ecatepec de Morelos
San Cristóbal de Ecatepec
8,108.21
1,898.68
151
67
San Cristóbal de las Casas
San Cristóbal de las Casas
7,519.97
1,479.44
107
68
Coacalco de Berriozaba
San Francisco Coacalco
11,878.60
2,311.11
18
69
San Juan del Río
San Juan del Río
8,789.41
1,440.00
40
70
San Luis Potosí
San Luis Potosí
7,899.72
1,779.67
91
71
San Nicolás de los Garza
San Nicolás de los Garza
1,2711.42
2,247.06
17
72
Soledad de Graciano Sánchez Soledad de Graciano Sánchez
9,188.44
1,200.00
27
73
Tampico
Tampico
7,080.57
1,683.33
36
74
Tepic
Tepic
12,314.12
1,844.00
75
75
Tijuana
Tijuana
13,137.56
2,864.65
113
76
Tlalnepantla de Baz
Tlalnepantla de Baz
8,859.44
3,031.82
66
77
Tlaquepaque
Tlaquepaque
5,926.90
1,270.59
17
78
Toluca
Toluca
10,349.51
2,094.81
310
94
n
EconoQuantum
Vol. 14. Núm. 1
No.
Municipality
City
Wage Income
Rent
Sample
79
Torreón
Torreón
9,258.06
1,287.50
64
80
Tulancingo de Bravo
Tulancingo de Bravo
8,948.00
1,227.27
44
81
Tultitlán
Tultitlán de Mariano Escobedo
7647.69
1,464.29
49
82
Tuxtla Gutiérrez
Tuxtla Gutiérrez
8,805.87
1,828.34
397
83
Uruapan
Uruapan
7,821.25
1,307.14
28
84
Veracruz
Veracruz
6858.85
1,600.00
34
85
Durango
Victoria de Durango
8,262.68
1,419.78
91
86
Villa de Álvarez
Villa de Álvarez
9,725.05
1,564.42
52
87
Centro
Villahermosa
8,814.22
2,149.04
104
88
Xalapa
Xalapa de Enríquez
7,406.59
2,476.92
26
89
Valle de Chalco Solidar
Xico
6,885.91
1,090.91
33
90
Zacatecas
Zacatecas
9,564.23
2,480.00
35
91
Zamora
Zamora de Hidalgo
7,460.42
1,286.36
22
92
Zapopan
Zapopan
12,702.37
2,594.87
39
Means & total
9,979.60
2,238.10
7,966
A Quality of Life Index of Mexican cities:.
..
n
95
Table 5
Quality of Life Index for México 2010
City
Rank-1
QLI-1
SD-1
Rank-2
QLI-2
SD-2
Campeche
1
1829.9
139.1
2
2774.4
117.5
Acapulco
2
1725.7
113.0
4
2518.4
97.5
Xalapa de Enríquez
3
1675.1
139.1
12
1943.8
119.1
Veracruz
4
1634.6
109.4
17
1783.0
91.4
Villa de Álvarez
5
1625.9
125.7
26
1554.3
104.8
Tampico
6
1498.9
92.6
25
1588.8
74.1
Poza Rica de Hidalgo
7
1473.3
115.7
15
1791.4
97.5
Tepic
8
1411.3
90.8
13
1854.5
71.6
San Nicolás de los Garza
9
1401.0
98.8
9
2028.9
84.2
Coatzacoalcos
10
1394.6
99.7
3
2583.5
83.2
Altamira
11
1343.6
108.8
28
1517.3
93.6
Ciudad Madero
12
1324.4
68.4
18
1716.1
52.3
Ciudad del Carmen
13
1274.5
86.9
14
1836.6
68.5
Morelia
14
1269.2
93.4
7
2092.1
76.5
Guadalupe
15
1241.0
101.2
37
1334.2
85.4
Colima
16
1217.1
111.3
33
1381.2
89.7
Jiutepec
17
1190.7
123.6
10
2003.4
106.6
Villahermosa
18
1161.0
80.1
27
1523.5
66.4
Torreón
19
1156.0
102.2
48
1148.8
85.3
Tuxtla Gutiérrez
20
1145.9
72.6
22
1607.1
59.6
Puebla de Zaragoza
21
1117.1
88.7
16
1791.0
73.6
Apocada
22
1107.1
83.5
21
1613.7
71.3
Iguala de la Independencia
23
1097.7
132.5
6
2297.8
110.9
Los Mochis
24
1062.5
71.5
24
1595.0
58.4
Manzanillo
25
1046.9
97.4
40
1293.9
81.3
Soledad de Graciano Sánchez
26
1031.5
70.5
67
908.7
56.2
Chilpancingo de los Bravo
27
1019.3
74.8
11
1989.3
58.1
Ciudad Valles
28
1017.5
110.3
45
1250.7
91.2
Heroica Matamoros
29
1008.7
73.9
23
1604.8
59.5
Nezahualcóyotl
30
1005.3
102.7
53
1070.3
87.0
Heroica Guaymas
31
958.5
47.8
47
1156.8
38.4
Saltillo
32
654.6
93.8
42
1278.0
77.9
Mazatlán
33
945.7
69.0
44
1253.2
56.8
Tlaquepaque
34
921.4
73.5
61
969.4
63.2
Ixtapaluca
35
920.2
100.6
66
926.6
86.4
Toluca
36
861.5
86.9
62
957.9
73.8
Monterrey
37
859.2
69.9
30
1455.4
56.9
Culiacán
38
851.9
46.9
19
1709.8
34.8
Atizapan de Zaragoza
39
844.5
75.5
58
982.8
65.4
96
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Vol. 14. Núm. 1
City
Rank-1
QLI-1
SD-1
Rank-2
QLI-2
SD-2
Córdoba
40
842.7
94.3
60
976.2
74.7
Zamora de Hidalgo
41
838.1
83.6
49
1129.5
67.0
Xico
42
838.1
93.2
54
1064.1
79.9
Tulancingo de Bravo
43
835.1
106.8
75
803.3
90.5
Tultitlán de Mariano Escobedo
44
828.5
70.4
56
1011.3
59.5
Hermosillo
45
781.5
34.1
36
1343.8
25.2
San Luis Potosí
46
773.4
92.9
31
1454.7
77.0
Cuernavaca
47
754.1
129.7
5
2513.3
111.5
Navojoa
48
748.4
72.4
63
955.5
59.0
Chalco
49
746.7
89.1
71
875.5
77.1
Cuautitlán Izcalli
50
743.2
64.7
74
816.5
54.6
Ciudad Victoria
51
724.7
90.0
50
1125.9
72.6
Ciudad Obregón
52
701.4
50.8
43
1261.1
39.3
Uruapan
53
698.5
89.1
73
831.2
72.0
Fresnillo de Glez. Ech.
54
686.4
100.3
20
1690.6
82.7
San Francisco Coacalco
55
682.6
82.7
68
906.3
69.3
Cancún
56
681.7
19.4
70
885.4
12.2
Naucalpan de Juárez
57
677.1
64.9
64
946.2
55.6
Gómez Palacio
58
672.2
90.8
65
933.5
74.3
Chimalhuacán
59
654.2
82.6
59
977.1
69.6
Pachuca de Soto
60
653.1
87.0
41
1290.7
71.6
Aguascalientes
61
648.1
66.6
35
1347.1
50.6
Chetumal
62
646.7
90.9
76
769.7
74.9
Celaya
63
605.6
114.1
39
1297.9
97.2
Tlalnepantla de Baz
64
597.4
61.1
69
890.0
50.5
San Cristóbal de las Casas
65
595.8
68.1
46
1187.2
53.7
La Paz
66
579.1
86.8
79
711.5
70.8
Monclova
67
576.8
62.2
78
718.4
45.7
Salamanca
68
561.6
93.8
57
989.4
78.1
León
69
557.8
63.2
38
1324.0
50.5
Chihuahua
70
548.8
62.1
51
1079.5
50.6
Nuevo Laredo
71
544.3
64.6
1
3946.9
52.0
Mérida
72
533.8
98.7
55
1013.9
82.1
San Cristóbal Ecatepec
73
502.5
77.6
84
573.5
64.6
Querétaro
74
498.9
27.8
29
1462.2
22.8
Cuautla
75
458.9
80.6
32
1414.0
61.4
Guadalajara
76
444.6
25.5
82
624.2
14.7
Piedras Negras
77
406.3
54.5
85
496.4
38.8
San Juan del Río
78
401.6
55.5
8
2056.3
46.3
Reynosa
79
395.7
41.9
52
1072.0
30.7
Irapuato
80
377.1
75.2
34
1354.3
62.7
Victoria de Durango
81
369.9
105.1
80
688.1
86.0
A Quality of Life Index of Mexican cities:.
..
n
97
City
Rank-1
QLI-1
SD-1
Rank-2
QLI-2
SD-2
Zapopan
82
345.6
3.1
86
471.9
10.3
Mexicali
83
327.6
82.6
83
620.0
68.0
Ciudad de México
84
276.9
11.9
81
662.2
3.4
Ciudad Juárez
85
270.3
47.1
87
471.4
38.9
Ciudad Cuauhtémoc
86
175.2
63.7
89
144.9
49.8
Zacatecas
87
150.7
64.8
72
841.3
49.1
Ensenada
88
137.1
81.8
88
196.6
67.1
Tijuana
89
48.1
26.4
77
729.4
16.7
Nogales
90
-104.2
27.0
90
142.9
16.7
Ciudad Acuna
91
-205.4
47.7
91
-248.0
32.1
Oaxaca de Juárez
92
-1194.2
109.3
92
-880.4
92.0
98
n
EconoQuantum
Vol. 14. Núm. 1
n
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