ArticlePDF Available

Measuring True Income Inequality in Mexico


Abstract and Figures

Recent literature on income distribution in Mexico has found evidence of underestimation ininequality. There is a growing consensus that household su rveys in Mexico do not capturetotal household income, but there is no consensus on a methodological proposal to estimatea more-realistic distribution. This article offers a proposal for adjusting household income,crossing information from household surveys, national accounts, economic censuses, andtax incidence from the Tax Administration System. It shows evidence of a high, persistentincome-distribution inequality, with emphasis on factorial and intracompany inequality.The usual adjustment implies that entire average income increases, but this article identifiesthe main cause of underestimation in distribution, focusing on noncaptured capital incomein the wealthiest households surveyed
Content may be subject to copyright.
Measuring True Income Inequality in
Miguel Reyes, Graciela Teruel, and Miguel L
Recent literature on income distribution in Mexico has found evidence of underestimation in
inequality. There is a growing consensus that household surveys in Mexico do not capture
total household income, but there is no consensus on a methodological proposal to estimate
a more-realistic distribution. This article offers a proposal for adjusting household income,
crossing information from household surveys, national accounts, economic censuses, and
tax incidence from the Tax Administration System. It shows evidence of a high, persistent
income-distribution inequality, with emphasis on factorial and intracompany inequality.
The usual adjustment implies that entire average income increases, but this article identifies
the main cause of underestimation in distribution, focusing on noncaptured capital income
in the wealthiest households surveyed.
La literatura reciente sobre la distribuci
on de ingresos en M
exico ha hallado evidencia de
una subestimaci
on de la desigualdad. Hay un consenso creciente de que las encuestas a
hogares en M
exico no capturan el total de los ingresos del hogar, pero no existe un consenso
respecto a una propuesta metodol
ogica para estimar una distribuci
on m
as realista. Este
ıculo ofrece una propuesta para ajustar los ingresos del hogar, al cruzar la informaci
on de
las encuestas a hogares, las cuentas nacionales, los censos econ
omicos y la incidencia fiscal
del Sistema Tributario. Muestra que hay una alta desigualdad de ingresos, con
enfasis en
una desigualdad factorial e intra-empresarial. El ajuste com
un implica que el promedio de
ingreso total aumenta, pero este art
ıculo identifica la principal causa de la subestimaci
on en
la distribuci
on, y se enfoca en los ingresos capitales no capturados por los hogares m
adinerados en la encuesta.
Key words: inequality, inequality underestimation, household income, Mexico
Latin American Policy—Volume 8, Number 1—Pages 127–148
C2017 Policy Studies Organization. Published by Wiley Periodicals, Inc.
Social and economic inequality has become more relevant, because there is
evidence that it is increasing worldwide. In the academic field, Piketty
(2014), nongovernmental organizations (NGOs) with influence in public opin-
ions, such as OXFAM (2012, 2015), and international organizations like the
Organisation for Economic Co-operation and Development (OECD, 2015),
and the International Monetary Fund (IMF) (Ostry, Loungani, & Furceri,
2016) have emphasized analyzing inequality and its possible effects on well-
being and economic growth.
Globalization has meant deep, widespread, global economic crises, and
associated factors such as unemployment, a drop-in wages and income,
more-precarious labor conditions, subemployment, persistence of an informal,
underground economy, and cuts in social spending that effect redistribution;
these factors have contributed significantly to the global increase in
The Mexican economy—as part of Latin America, historically one of the
most-unequal regions in the world (AUSJAL, 2011, 2012; L
opez-Calva & Lus-
tig, 2010)—has become more unequal, but to a greater degree than what is
documented in most literature on the country (L
opez-Calva & Lustig, 2010;
Lustig, Lopez-Calva, & Ortiz-Juarez, 2011, 2013).
Recent studies on inequality in Mexico show evidence of underestimation
regarding data from household surveys (Campos, Ch
avez, & Esquivel, 2014,
2015; Del Castillo, 2015; Observatorio de Salarios–EQUIDE, 2016; OXFAM,
2015). As Del Castillo Negrete (2015) states, the underestimation of income
has been growing since the 1990s, so the capitation of total household income
has become more difficult. Estimations of inequality have shown the growing
problem of underestimation in relation to income data from National
Accounts (Alarc
on Tosoni, 2014; Del Castillo, 2015). According to 2004 data
from Del Castillo Negrete (2015), there was 2.5 times more underestimation
of income reported in household surveys in 2004, versus national accounts.
For 2014, data from the Observatorio de Salarios–Equide (2016) shows an
underestimation of almost 4 times. These results obtained through the
National Income and Spent in Households Survey (ENIGH) are uncertain,
and probably inaccurate.
Underestimation of income in Mexico obtained with ENIGH has two prob-
lems, underreporting and truncation. Underreporting refers to household
income not reported in the ENIGH surveys. Truncation (Cort
es, 2000; Leyva,
2004) means that households not included in the survey sample are found
mainly in the distribution tails. In particular, truncation—a lack of informa-
tion from households in the high part of distribution curve (Guerrero, L
Calva, & Walton, 2009)—makes it very difficult to infer the real impact of the
most-affluent households on the total income-distribution measurement.
Even though there is a growing consensus about underestimation in house-
hold surveys in Mexico, there is no literature that agrees on a methodological
proposal for estimating a more-realistic distribution or for identifying the size
of the underreporting and truncation in the underestimation. The process of
adjusting (or modifying) the values between the information reported in
128 Latin American Policy
ENIGH and the National Accounts has multiple variables, although all of
them show certain patterns.
1. Distribute part of the distribution among all households, in which case it
is assumed that underestimation is based mostly on underreporting.
2. Distribute the difference among only the richest households, in the high-
est part of distribution curve, which assumes that the main problem of
underestimation lies only in truncation.
3. Distribute the distances between household surveys and national
accounts by income source, which implies that underestimation is due to
underreporting in the entire household distribution by income source and to
truncation in the highest part of the distribution curve.
There are also implications of changes in distribution in the poverty mea-
surement. Assuming that the most–important aspect of underestimation is
underreporting leads researchers to adjust by increasing the average income
of every household, including the poorest. Leaving everything else equal and
maintaining the poverty line by income shows fewer poor households than
there are, which leads to an underestimation in inequality and an overestima-
tion of poverty. On the other extreme, it is assumed that truncation is the
main reason for income underestimation, average household income does not
change, and the new distribution dos not modify poverty by income.
There is a lack of consensus in the literature not only regarding the funda-
mental assumption about what is behind underestimation in inequality but
also regarding the method of estimation. There is no consensus on how to
adjust household-survey information to national accounts (Altimir, 1982;
es, 2000; Hern
andez Laos, 2006; Mart
ınez I., 1960; Mart
ınez H., 1992). Del
Castillo Negrete (2015) did a study on adjustment of household surveys to
national accounts, using Altimir’s (1982) methodology to distribute the differ-
ences between both income sources by type of income, and also taking into
account information that Altimir did not have in his time—administrative
records about occupations (National Occupations Classifications System—
SINCO) for income information on labor, and the National Banking and Val-
ue Commission for information on diverse capital incomes.
The Observatorio de Salarios–EQUIDE Proposal
This article is based on the methodology and results of the 2016 Observa-
torio de Salarios
–EQUIDE Report.
Like in Del Castillo Negrete’s (2015) work,
where the differential between the income reported in the household surveys
and the national accounts was distributed by income source, an adjustment
was done using information from economic censuses published by the
National Institute of Statistics, Geography and Informatics of Mexico (INEGI).
INEGI data, used first by Cort
es (2000) to adjust household income to nation-
al accounts, allows us to decrease the range of arbitrariness and assumptions,
because the information available by income source, capital and labor, is
gathered by kind and size of company, sector, branch, subsector, and kind of
activity, and by federal entity.
Measuring True Income Inequality in Mexico 129
In general terms, the adjustment methodology was used in the following
1. Household income is identified by income source, capital, and labor, for
ENIGH–MCS (MSC stands for Socioeconomic Conditions Module), and for
national accounts. The income sources by ENIGH–MCS are reclassified, con-
sidering the role of the production conditions, not their income amount.
“Self-employment” is reclassified as “small-business owner,” and is not con-
sidered as labor, but as capital. The reclassification was done so that only
income from remuneration is considered as obtained by labor.
2. Income is disaggregated by income source (capital or labor) and clus-
tered by income decile for the ENIGH–MCS. Both income amounts are esti-
mated before and after transfers. ENIGH–MCS does not include tax
3. Income reported in the National Account System (SCN) is disaggregated
by income source (capital or labor), before and after taxes. The participation
of productive factors is obtained from the national income at the cost of fac-
tors (without taxes) and in prices of participation–market in the Gross
Domestic Product (GDP), after taxes.
Wage participation in national income5W
Earning participation in national income5p
TP 5Value of gross total production; IC 5Intermediate consumption; GAV-
5Gross Added Value; D5Depreciation; NAV 5Net Added Value; W5wages;
4. Total income is obtained for the ENIGH–MCS and for the national
accounts (NA). ENIGH–MCS income used is before government monetary
transfers. For SCN, the valuation of national income at cost of factors is used
before taxes. Comparable income between ENIGH–MCS and NA is the pri-
mary income.
130 Latin American Policy
5. The clustered factor adjustments are obtained between incomes from
household surveys and SCN (De la Torre, 2005).
6:Adjustment factors by income source5Income in SCN
Income in ENIGH2MCS
7. According to Cort
es (2001), adjustment factors will be disaggregated by
income source (capital and labor), by company size and, depending on avail-
able information, by sector of economic activity
AFW 5Adjustment factor by labor income, that considers: CS 5Company
Size, ES 5Economic sector activity.
AFp5Adjustment factor by capital, that considers: CS 5Company Size,
ES 5Economic sector activity.
8. A new distribution is estimated from primary household income without
taxes and transfers. In the ENIGH–MCS, the adjustment factor for each spe-
cific case is used at a micro level
WAI 5Labor Adjusted Income; oWI 5original Labor Income;
AFW 5Adjustment Factor of Labor Income; pAI 5Capital Adjusted Income;
opI5original Capital Income; AFp5Adjustment Factor of Capital Income.
9. The new secondary income distribution is obtained, and transfers and taxes are
incorporated into primary income distribution. Per Lustig and Higgins (2013), the
net income distribution is built on transfers and income tax. Information on trans-
fers comes from ENIGH–MCS, while the tax incidence of income taxes by income
deciles comes from the Secretary of Treasury and Public Credit (SHCP, 2010).
Measuring True Income Inequality in Mexico 131
ANYW 5Adjusted Net Income by Labor; ANYp5Adjusted Net Income by
Capital; Tr 5Transfers; ITR 5Income Taxes Rate.
10. Estimation of inequality indicators from adjusted ENIGH: Factor distri-
bution, Gini coefficient for income, Gini wages, intercompany Gini, income
distribution by deciles and factors, among others.
Wparticipation in total household income adjusted 5PANYW
pparticipation in total household income adjusted 5PANYp
lN2XYiNA N112iðÞ
Labor Gini 5G5111
lN2XYiNAW N112iðÞ
Intercompany Gini 5G5111
NAY 5Net Adjusted Income; iis indexes households or individuals, Nis
the number of households, individuals or income stratus, lindicates the
average income, and Yi the household, individual or stratus income i. For the
decile calculation, N510 and i51 for the poorest decile.
After the household income estimation, which brings together information
sources on the households (ENIGH), national accounts, economic censuses,
and tax information, was completed, the magnitude of inequality was ana-
lyzed. Per the primary income distribution, the first component to consider is
the inequality between those who participate in the distribution of what is
produced, from different positions. They are gathered into two large catego-
ries, depending on whether they have or not the conditions for producing
and appropriating the surplus, meaning the dichotomy in primary income
distribution between labor and capital. The other component is given by
inequality within each participant in production: wage inequality and inter-
company inequality.
In studies about inequality in Mexico, it was assumed that the most-
important component in distribution was wage inequality. As the Observatorio
de Salarios–EQUIDE Report (2016) states, analyses have been focused on
inequality between salaried people and the effects on general inequality,
132 Latin American Policy
the relationship between general inequality and wage inequality is not linear.
It depends on at least two factors, (1) the weight of salaried working-class
people in the country’s occupational structure, and (2) the labor participation
in the income distribution called primary (before net transfers). Wage
inequality is not the only component of general income inequality.
In other words, in countries with an occupational structure with predomi-
nantly salaried workers and where a large part of GDP corresponds to them,
movements inside the wage distribution have significant effects on general
distribution. Even in that scenario, we leave behind inequality between the
capital and the labor. Additionally, in the case of developing (or emerging)
economies, like Mexico’s, general inequality is related significantly not only
to wage inequality but also to inequality between wages and incomes from
small-business owners (small commerce, small production, independent pro-
ducer), conceptualized as independent work (ILO, 2015).
Inequality in Mexico
General Panorama
Inequality in income distribution in Mexico is one of the persistent charac-
teristics of the country’s contemporary history that literature on inequality
has documented widely since the 1950s (Altimir, 1982; Boltvinik & Laos,
1999; Cort
es, 2000; Cort
es & De Oliveira, 2010; Hern
andez Laos & C
avez, 1979; L
opez Gallardo, 1983; Lustig & L
opez-Calva, 2012; Mart
ınez de
Navarrete, 1970). Recently, Mokomane, Teruel, and Reyes (2017) have esti-
mated the Gini coefficient from 1959 to 2016. Income-distribution inequality
for 2016 is very close to how it was in 1968. See Figure 1.
So far, estimations of inequality face the growing problem of underestima-
tion in income data from national accounts (Alarc
on Tosoni, 2014; Del Cas-
tillo, 2015), which puts in question research results based on the ENIGH’s
inequality data.
There is a good deal of income underestimation in Mexico’s ENIGH. In
2014, income was captured in the household survey 4.3 times compared to
what was obtained in the national accounts numbers, the so-called adjust-
ment factor. The survey reports in Mexican pesos the equivalent of
U.S.$473.165 billion Purchasing Power Parity (PPP) yearly in household
incomes, whereas the national accounts show a total income of U.S.$795.901
billion PPP. Comparing income by source, it can be observed that most
underestimation is found in capital income not captured by the ENIGH. The
adjustment factor for capital income is 14.6 times. The ENIGH reports only
U.S.$98 billion PPP of the U.S.$565 billion PPP that the national accounts
See Table 1.
When the households are gathered by income deciles, the income adjust-
ment from household surveys and national accounts, deciles I–IX lose partici-
pation in distribution, whereas decile X, the most affluent, gains. It means
that before the adjustment of 40% of the wealth produced, the richest 10%
would appropriate an amount very unequal to that of the poorest 10%, but
Measuring True Income Inequality in Mexico 133
after the adjustment, the richest 10% appropriates 67.8% of household income
(see Table 2).
After the adjustment, the deciles that lose the most proportionally based on
their participation are those from I to VII, with a 50% average drop in propor-
tion of average income, while deciles VIII and IX loses approximately 35%,
and the richest gain more of the 60%. In another sense, with data from
ENIGH, while 50% of the poorest population earned 15.5% of the income,
after adjusting the income, that portion of population earned only 6.7%.
It does not mean that average household income has not been adjusted.
Every household has an adjustment that increases its income, but the adjust-
ment in decile X is the greatest by far, the highest presented in the new
household-income distribution (see Table 3). On average, between deciles I
and VII, income doubles, while in deciles VIII and IX, the adjustment income
factor (the times the original ENIGH income data is adjusted to the new
Table 1. Income Underestimation by Income Source (U.S. $Millions PPP),
exico, 2014
Capital income 98,849 1,443,370 14.6 86%
Labor income 374,315 592,824 1.6 14%
Total income 473,165 2,036,194 4.3
Source: Authors’ estimates.
The numbers in bold are an emphasis of the values in the tables, according to the explanation in
the text that references the tables.
Figure 1. Income Inequality in Mexico (Gini Coefficient), Nonadjusted to
National Accounts, 1950–2016
Source: Mokomane, Teruel, and Reyes (2017).
134 Latin American Policy
distribution) is at 2.7 and 2.8, respectively. The contribution of the wealthiest
decile to underestimation is 76% of the total. Empiric evidence allows us to
infer that there is underreporting of income along the whole distribution
curve, and a significant truncation in the highest part.
Income inequality adjusted with national accounts shows a Gini of 0.74.
The official Gini, made based on data from original surveys, is 0.52. With offi-
cial data, Mexico is ranked 121 regarding equality in income distribution,
from a sample of 138 countries. With income adjusted to national accounts,
Table 3. Income Underestimation by Household Deciles (U.S.$Millions
exico, 2014
ENIGH total
ENIGH total
Adjustment @
Contribution to
I 853 1,651 1.94 0.10%
II 7,146 13,573 1.9 0.40%
III 14,291 28,860 2.02 0.90%
IV 21,828 39,068 1.79 1.10%
V 29,163 52,805 1.81 1.50%
VI 35,502 70,449 1.98 2.20%
VII 44,059 92,378 2.1 3.10%
VIII 55,477 149,720 2.7 6.00%
IX 73,411 207,692 2.83 8.60%
X191,434 1,379,992 7.21 76.00%
Total 473,163 2,036,187 4.3 100.00%
Source: Authors’ elaboration, based on INEGI (2015a, 2015b, 2015c).
Note. Values are presented in dollars PPA for 2014.
The numbers in bold are an emphasis of the values in the tables, according to the explanation in
the text that references the tables.
Table 2. Income Distribution Clustered in Deciles, M
exico, 2014
Income adjusted
to CN
I 0.20% 0.10%
II 1.50% 0.70%
III 3.00% 1.40%
IV 4.60% 1.90%
V 6.20% 2.60%
VI 7.50% 3.50%
VII 9.30% 4.50%
VIII 11.70% 7.40%
IX 15.50% 10.20%
X 40.50% 67.80%
Total 100.00% 100.00%
Source: Authors’ estimates.
Measuring True Income Inequality in Mexico 135
Mexico would be in last place globally, as the most-unequal country (Moko-
mane et al., 2017). Likewise, with household income adjusted, the 1% richest
hoards 32.6% of the income, the same amount as 90% of the population,
meaning that 1.3 Mexican individuals earn the same amount as 115 million
other Mexicans.
The adjustment must also consider how the absolute distances or gaps
between the poorest decile (poorest 10%) and the richest decile (richest 10%)
have grown. In general household income, the distance grew 122%. On labor
income, the growth is marginal when compared to that of capital income; the
distance between owners in the poorest income decile vs. corporate people or
capital owners in the high part of distribution increases from 249 to 1,322
times (see Figure 2).
Factorial Inequality
The distribution with original data from ENIGH at the individual level by
income deciles and occupational status shows that the origin of income is, on
average, 79.1% for labor, and 20.9% for capital (see Table 4). The accuracy of
these results that show the decrease or increase in inequality based on what
occurs with wage incomes, representing almost 80%, is low, because the data
obtained for income from household surveys is underestimated. They do not
consider the real weight of the capital. If it were considered, the distribution
of primary household income between capital and labor would be 71% and
29%, respectively. See Table 4.
With household income adjusted by national accounts, besides the fact that
the participations of labor and capital revert, and the 10% richest appropriate
67.8% rather than 40% of income, the composition in the richest decile
reverses. Where two-thirds of the total income of decile X were in labor
Figure 2. Decile X vs. Decile I
Source: Authors’ elaboration, based on INEGI (2015a, 2015b, 2015c).
136 Latin American Policy
income (remunerations), with the adjustment, more than 80% of this decile’s
income is appropriated by the capital that now corresponds to it, going from
67.8% of income to 55.7% (see Table 5).
To revise the accuracy of the household-income estimation adjusted with
information from the National Accounts and the Economic Census, the labor
income was calculated with different methods. The first one considers the
participation of the amount received by employees (remunerated individuals)
in the GDP. The GDP considers the amount of the net added tax but not its
incidence at household level. Wage participation in the GDP for 2014 was
32%. The second method for obtaining the wage participation in the GDP is
by calculating the Net Added Value at Factor Cost (without indirect taxes or
subsides), which is 26%. Labor participation shows an intermediate level
Table 4. Income Distribution (Mexico, 2014): Nonadjusted ENIGH
Decile Labor Capital Total income
I 0.1% 0.1% 0.2%
II 0.9% 0.6% 1.5%
III 2.1% 0.9% 3.0%
IV 3.7% 0.9% 4.6%
V 5.2% 1.0% 6.2%
VI 6.5% 1.0% 7.5%
VII 8.0% 1.3% 9.3%
VIII 10.1% 1.6% 11.7%
IX 13.2% 2.3% 15.5%
X 29.3% 11.2% 40.5%
Total 79.1% 20.9% 100.0%
Source: Authors’ elaboration, based on INEGI (2015b).
Table 5. Income Distribution (Mexico, 2014): ENIGH Adjusted with NA
Decile Labor Capital Total Income
I 0.0% 0.1% 0.1%
II 0.3% 0.4% 0.7%
III 0.6% 0.8% 1.4%
IV 1.1% 0.8% 1.9%
V 1.7% 0.9% 2.6%
VI 2.1% 1.3% 3.5%
VII 2.7% 1.8% 4.5%
VIII 3.6% 3.8% 7.4%
IX 4.9% 5.3% 10.2%
X 12.1% 55.7% 67.8%
Total 29.1% 70.9% 100.0%
Source: Authors’ elaboration, based on INEGI (2015a, 2015b, 2015c).
Measuring True Income Inequality in Mexico 137
among obtained estimations at factor cost (26%) and market prices from the
GDP (32%). See Figure 3.
Piketty’s work (2014) shows evidence of income distribution between labor
and capital from a historical perspective, from 1770 to 2010, using data from
several authors and literature from 20 different countries. According to recent
evidence, for France and England, capital would concentrate 30% of national
income, while labor corresponds to 70%. In Mexico, available information
shows the opposite situation. Labor makes up approximately 30% of national
income, and capital makes up 70%.
According to data from economic censuses, of the inequality components
(interfactorial and intrafactorial), the one that shows the most inequality in
terms of gaps is that presented between average wages and average profits,
between average factorial income of capital and labor; average highest profits
are 2,420 times the average lowest wages. It would take a worker earning
minimum wage 6.63 years to earn what the highest average profit earner
makes in one day (See Figure 4).
Intrafactorial Inequality
Inequality analyses must also consider the primary distribution between
labor and capital and comprehend the inequality between them. Literature on
inequality identifies the weight of wage inequality as an important compo-
nent in general inequality. The weight of intracompany inequality is barely
Beyond the size and weight of the occupied population, capital represents
two-thirds of wealth generated in Mexico. Intracompany inequality is much
larger than labor inequality and general-income inequality.
As an indicator of intracompany inequality, the Gini coefficient shows evi-
dence of something emblematic in the literature—perfect inequality. The
intracompany inequality before the ENIGH-adjusted distribution data is at
0.90. When it is adjusted to national accounts, it becomes more unequal,
resulting in a Gini coefficient of 0.97, technically a perfect inequality.
Figure 3. Labor Participation in National Income, M
exico, 2014—Different Methods
Source: Authors’ elaboration, based on INEGI (2015a, 2015b, 2015c).
138 Latin American Policy
The intrafactorial inequality (inside each factor) is less severe when intra
wage inequality is measured. With original ENIGH data, a coefficient of 0.56
becomes 0.62 with adjusted distribution (see Figure 5).
Furthermore, although underestimation of capital income is 14.6 times in
ENIGH, vs. 1.6 for labor income and 4.3 for general income, the underestima-
tion adjustment factors reported by ENIGH and by the economic census
adjusted in national accounts are differentiated by company size. The larger
the enterprise, the greater the adjustment factor, for both labor and capital
Figure 4. Average Wages/Average Profits
Source: Authors’ elaboration, based on INEGI (2015a, 2015b, 2015c).
Figure 5. Wages and Company Inequality—Gin Coefficient,| M
exico, 2014
Source: Authors’ elaboration, based on INEGI (2015a, 2015b, 2015c).
Measuring True Income Inequality in Mexico 139
income, where the highest value for both cases is found in the larger compa-
nies, those with more than 500 workers. The adjustment factor of capital
income there is on average 842 times, whereas the labor income is only 3.42
times than before the adjustment. This situation greatly increases intracompa-
ny adjusted inequality underestimation. On the one hand, regardless of the
company size, adjustment factors of capital income are generally larger than
those for labor. On the other hand, persons that do not appear in the high
part of the ENIGH distribution are mainly in companies with 251–500
employees or in those with more than 500 workers (see Table 6)
The gap between average profits of big corporations (companies that have at
least 1,000 workers) is 1,265 times the lowest average income represented by
small companies (those that have up to two employees). The owner of a compa-
ny with more than 500 workers has an average monthly income of approximate-
ly U.S.$934,000 dollars PPP, whereas a microentrepreneur with two or fewer
employees has an average monthly income of U.S.$738 dollars PPP, even under
the average adjusted wage income (U.S.$937 monthly PPP) (see Figure 6).
The highest average profits per sector are concentrated in the corporative sec-
tor, followed by the mining and financial sectors. Monthly, an entrepreneur in
the corporative sector (where the holdings and companies that cluster the big-
gest economic groups in Mexico are) earns, on average, approximately U.S.$17.5
million dollars PPP per month. On the opposite side, in the lowest part, a small
owner in the agricultural sector earns almost U.S.$693 dollars PPP, 25,457 less
than corporative sector owners (Observatorio de Salarios–EQUIDE, 2016).
The clustering of data into averages hides the depth of inequality expressed
by the Gini coefficient of “perfect inequality.” For example, in the agricultural
sector the average profit is U.S.$693 dollars PPP; when it is analyzed by com-
pany size, businesses with three to five employees have average earnings of
U.S.$189 dollars PPP, with profit rates of 5% on capital invested, versus cor-
porations with 1,000 workers or more, with an average profit of approximate-
ly U.S.15.9 million dollars PPP and an average profit rate of 41%.
Table 6. Adjustment Factors by Company Size: Labor and Capital Income
Company size Labor income Capital income
Up to 2 people 1.01 3.04
From 3 to 5 people 1.19 1.45
From 6 to 10 people 1.31 8.18
From 11 to 15 people 1.28 4.56
From 16 to 20 people 1.34 8.56
From 21 to 30 people 1.41 27.09
From 31 to 50 people 1.56 26.88
From 51 to 100 people 1.63 19.45
From 101 to 250 people 1.63 94.9
From 250 to 500 people 2.19 192.28
More than 500 people 3.42 872.03
Source: Authors’ elaboration, based on INEGI (2015a, 2015b, 2015c).
140 Latin American Policy
The sectors with the largest inequality in average profits are mining,
manufacturing industries, housing services, construction, retail commerce,
agriculture, wholesale commerce, and mass-media-information services. Sec-
tors with less inequality by average income are corporative, transport, real-
state, energy, financial services and insurance, health, waste-management,
professional, and cultural and educative services.
Meanwhile, wage inequality is still high and persistent, although less deep
than the intracompany inequality. Without adjustment and due to the income
underestimation with bias toward the highest level of distribution and capital
income that exists in the household surveys, the weight of inequality in
wages in inequality studies is very relevant (Lustig & L
opez Calva, 2010). It
is showed by comparing the Gini in the current total income, without adjust-
ment of 0.52, with the wage Gini of 0.58.
Reducing inequality at this stage has important implications for the reduc-
tion of general inequality. Although the ENIGH is far from presenting the
elements of inequality in Mexico that affect the perpetuation and deepening
of inequality such as intracompany inequality and inequality between labor
and capital, it does not mean that wage inequality is not high or that it
should not be considered.
The Gini on current total income estimated with the adjustment to informa-
tion from the national accounts—0.74 vs. 0.52 without adjustment—is far
above the Gini estimated for inequality among wages, at 0.62.
Some of the inequity factors that continue to contribute to wage inequality
are not linked necessarily to productivity, such as gender gaps, marital status,
kind of occupation (formal and informal), and factors that are associated
more directly with productivity like education, age, work hours, and others.
Figure 6. Monthly Average Income by Company Size (Dollars PPP), M
exico, 2014
Source: Authors’ elaboration, based on INEGI (2015a, 2015b, 2015c).
Measuring True Income Inequality in Mexico 141
For example, based on information from the National Occupation and
Employment Survey (ENOE)
adjusted also to economic censuses and national
indicates the persistent gender discrimination in the labor market. On average,
informal workers (who do not have social-security services) earn 10% lower
than formal-sector workers. Marital status also counts when establishing average
remuneration, with an approximate difference of 6% higher income. In Mexico
in 2014, the average wage was U.S.$928, but because of gender discrimination, a
woman earns only U.S.$779 dollars, and an informal worker, U.S.$835 dollars
PPP, but being married raises the salary to U.S.$983 dollars PPP. Likewise, one
year more of education improves the salary by 6% on average, and years senior-
ity by 3.6%. Living in less-urbanized areas also means lower wages; workers in
rural areas learn 15% less than workers in the urban ones (see Table7).
The bigger the company, the higher the average wages. Companies with 2–5
people, representing 12% of the total labor force, show average wages between
approximately U.S.$385 and U.S.$459 monthly PPP. At the other extreme, cor-
porations, making up 29% of labor force, offer average wages between approxi-
mately U.S.$1,167 and U.S.$1,676 PPP monthly. Average distance between the
extremes is 4.3 times the salary, although it must be considered that, as the
base is taken from averages, the highest and lowest wages do not appear in
this type of absolute gap measures of distance or inequality (see Figure 7).
Comparative Results with Other Studies
Lorenz curves show that the farther away from an equidistant distribution
(vertical line of 458), the more unequal something is. When comparing recent
Figure 7. Monthly Average Wage by Company Size (Dollars PPP), M
exico, 2014
Source: Authors’ elaboration, with data from INEGI (2015b, 2016).
142 Latin American Policy
Figure 8. Lorenz Curves: Different Estimations of Inequality in Mexico
Source: Authors’ estimation, based on data from Campos et al. (2014); Del Castillo Negrete (2015);
INEGI (2015a); Observatorio de Salarios–EQUIDE (2016).
Figure 9. Distribution by Income Deciles: ENIGH vs. Alternative Estimates in Mexico
Source: Campos et al. (2014); Del Castillo Negrete (2015); INEGI (2015b); Observatorio de Sal-
arios–EQUIDE (2016).
Measuring True Income Inequality in Mexico 143
literature on inequality in income distribution by using Lorenz curves, the
distribution made with the ENIGH information is the less unequal, because
the average income of households is underestimated, particularly those in the
highest part of distribution. The most unequal are results by Del Castillo
Negrete (2015) and those by the Observatorio de Salarios–EQUIDE (2016).
Fundamental differences in distribution are found in the adjustment meth-
ods. While Campos et al. (2014) adjust income in the highest part of distribu-
tion, leaving intact the income for deciles I–IX,
Del Castillo Negrete’s (2015)
and our estimation are made by distributing the adjustment by income
source. Analyzing distribution in detail, by income deciles, the most-similar
Table 7. Average Indicators of Labor Force in Mexico (Dollars PPP), 2014
Average Monthly Wage $928.27
Average Monthly Wage (Women) $779.74
Average Monthly Wage (Nonformal) $835.44
Average Monthly Wage (Married) $983.96
Average Wage for one more year
of schooling
Average Wage for one more year
of experience
Differential for living in urban areas
with 15,000 to 99,999 habitants
Differential for living in urban areas
with 2,500 to 15,000 habitants
Differential for living in rural areas
with fewer than 2,500 habitants
Source: Authors’ elaboration, with data from INEGI (2016), adjusted with values from INEGI
Note. ENOE (INEGI, 2016) adjustment factors are higher than those of the ENIGH (2015b), so a
greater level of underestimation is assumed. Adjustment factors of average remunerations in
ENIGH (2015b) compared to national accounts is on average 1.6. For the ENOE (INEGI, 2016),
the average adjustment factor is 3.4.
Table 8. Inequality Indicators
et al. (2014)
Del Castillo
Negrete (2015)
de Salarios–
EQUIDE (2016)
Gini (Total
0.69 0.70 0.68 0.62
Gini (Primary
- 0.74 0.74 -
Source: Campos et al., 2014; Del Castillo Negrete, 2015; Bustos, 2015; Observatorio de Salarios–
EQUIDE, 2016.
144 Latin American Policy
data regarding distribution (Lorenz curves—Figure 8—and income distribu-
tion by deciles—Figure 9) come from Del Castillo Negrete (2015) and the
Observatorio de Salrios–EQUIDE (2016).
The poorest distribution deciles (I–V)
have 15.5%, using official sources (ENIGH), 11.7%, in Bustos (2015), 8.8%, in
Campos et al. (2014), and 6.7–6.6%, in Del Castillo Negrete (2015) and the
Observatorio de Salarios–EQUIDE (2016).
According to different estimations, the richest 10% appropriate 53.8%–
67.8% of national income, versus 40% reported by household surveys. The
estimation of Gini shows evidence of lower variability, because—except in
Bustos (2015), who obtains a lower Gini (0.62)—studies coincide with a Gini
between 0.68 and 0.70 for current total income and 0.74 for primary adjusted
income (see Tables 7 and 8).
Recent literature on inequality in Mexico finds evidence of an increasing
underestimation of income reported in household surveys, which results in
underreporting among those who do not declare the exact amount of their
income, whether out of ignorance, insecurity, fear of losing some prerogatives
of social programs, fear of being charged taxes, because part or all of their
income is obtained from nonlegal sources, or simply because they do not
want to. Underestimation is also a result of truncation, individuals that are
not in the household surveys or that were not surveyed and for whom there
is no mechanism for capturing general information.
Some methodologies have made it possible to determine the size of under-
estimation in the ENIGH, but the difficult point is how to identify how much
underreporting and truncation affect the estimations. This article seeks to con-
tribute to the discussion on the subject of underestimation of inequality in
Mexico; where the differential between reported income on the household
surveys and from national accounts was distributed by income source, an
adjustment was made using economic census data published by INEGI.
How can we measure true inequality in Mexico? With our method and
with other similar studies on the magnitude of inequality, we identify the
main source of underestimation of distribution in the highest part of the
curve, resulting from capital income not captured by the household surveys.
Income inequality adjusted with national accounts shows a Gini of 0.74. The
official Gini elaborated with data from original surveys is 0.52. According to
evidence presented in this article, the richest 1% of the population receives
the same amount of income as the other 90%. There are 1.3 million Mexicans
who earn the same as 115 million. The richest 10% earn 67% of total national
income, and primary income distribution between labor and capital is 74%
and 26%, respectively.
The adjustment also shows that the absolute gaps between the poorest dec-
ile (poorest 10%) and the richest have widened. The gap widened 122% for
general household income. Labor income growth is marginal when compared
with that of capital, where income of owners in the poorest income decile is
249 to 1,322 times less than the capital of the highest income distribution.
Measuring True Income Inequality in Mexico 145
Wage inequality is an important factor that influences general inequality,
but is less important than intracompany inequality. Together, intracompany
inequality and primary income inequality distribution between capital and
labor are much deeper and significant. The lowest average wages are approx-
imately 2,420 times lower than the highest. Intracompany inequality shows
evidence of perfect inequality, very close to Gini of 1 (0.97).
Deep, persistent inequality in Mexico, and its link to a growth in poverty,
lead us to propose an agenda for the country that brings together economic
initiatives rather than carrying them out in an isolated manner. Public-policy
efforts are required for redesigning social policy toward a more-redistributive
system, toward an economic policy that strengthens inner market and redis-
tribution not only between labor and capital but also within the capital class.
An in-depth revision and a comprehensive reorientation of economic, social,
wage, industrial, and commercial public policy are needed.
About the Authors
Miguel Reyes is the director of the Observatorio de Salarios at Universidad
Iberoamericana, Puebla campus.
Graciela Teruel is the director of the Instituto para el Desarrollo con Equi-
dad (EQUIDE) at Universidad Iberoamericana, Mexico City campus.
Miguel L
opez is the director of the undergraduate Economics Program at
the Universidad Iberoamericana, Puebla campus.
The authors would like to thank Eduardo Bermejo L
opez and Jorge Abascal
enez for their help with the research.
The Observatorio de Salarios is an interdisciplinary research center at the Universidad Iberoamer-
icana Puebla, in Mexico. Research guidelines are based on the analysis of contemporary and historical
wages and living standards; wage inequality and poverty; and wages and migration. At is also a place
where associated researches from other international institutions participate, such as Fordham Uni-
versity in New York, CUNY, the Universidad Aut
onoma de Bercelona, and Appalachian State
The EQUIDE is the Universidad Iberoamericana Ciudad de M
exico’s Sustainable Development
with Equity Research Institute. It is an interdisciplinary research institute with guidelines on labor,
health research, environment, sustainable development, poverty, and inequality.
For a complete description of the reclassification, see the 2016 Inform of the Observatorio de Sal-
For a deeper literature revision about the subject, see Observatorio de Salarios–EQUIDE (2016, p. 11).
To obtain data in PPP dollars, the conversion factors published by the World Bank (2017) are
The average used for the estimation of inequality within each sector in average profits was the
standard deviation in logarithms.
This part of the study was based on an estimation made by Mincer (1974), augmented. There are
countless studies linking remuneration in labor markets to characteristics and attributes such as edu-
cation, gender, hours worked, ethnic origin, and geographic localization, among others. The most-
recent studies for Mexico can be consulted at Harberger and Guillermo-Pe
on (2012).
ENOE data has been adjusted based on the information from economic censuses and national
Campos et al. (2014) analyze various scenarios, where income distribution of national accounts is
compared to that of the ENIGH in the highest part of distribution, deciles IX and X. In this article,
annual average income of deciles from I to IX does not move. Only decile X moves, but although the
average income does not move, distribution is modified, because the participation of each decile in
national income has been adjusted due to the change in participation of decile X.
146 Latin American Policy
One of the differences with Del Castillo Negrete’s (2015) estimation is in the sources of informa-
tion for the adjustment with national accounts. Whereas he uses administrative data on wages and
data on property income and capital, our proposal is made based on information from economic
on Tosoni, G. (2014). Participaci
on salarial y crecimiento econ
omico en Am
erica Latina,
1950–2011. Revista Cepal.,113(2014-08), 43–60.
Altimir, O. (1982). La distribuci
on del ingreso en M
exico: 1950–1977. In C. Bazdresch, J. Reyes
Heroles, & G. Vera (Eds.), Distribuci
on del ingreso en M
exico. Ensayos, cuaderno 2, tomo II (pp.
16–95). M
exico: Banco de M
AUSJAL. (2011). Informe del Observatorio Latinoamericano de Pobreza 2010, An
alisis de la arqui-
tectura de las heterogeneidades sociales, los riesgos sociales y las pol
ıticas p
ublicas aplicadas
en 9 pa
ıses de Am
erica Latina. Puebla: Konrad Adenauer Stiftung, Universidad Iberoameri-
cana Puebla.
AUSJAL. (2012). Carta AUSJAL 32: Reyes, miguel, heterogeneidades sociales y pol
ıtica social en Am
Latina (Vol. 1). Caracas, Venezuela: AUSJAL.
Boltvinik, J., & Laos, E. H. (1999). Pobreza y distribuci
on del ingreso en M
exico: Siglo Vein-
tiuno Editores.
Bustos, A. (2015). Estimation of the distribution of income from survey data, adjusting for com-
patibility with other sources. Statistical Journal of the IAOS,31(4), 565–577.
Campos, R., Ch
avez, E., & Esquivel, G. (2014). Los ingresos altos, la tributaci
optima y la
on posible. Mexico: PNFP. mimeo.
es, F. (2000). La distribuci
on del ingreso en M
exico en
epocas de estabilizaci
on y reforma econ
A. M
exico: Porr
es, F. (2001). El c
alculo de la pobreza en M
exico a partir de la encuesta de ingresos y gastos.
Comercio Exterior,51(10), 879–884.
es, F., & De Oliveira, O. (2010). Los grandes problemas de M
exico. Desigualdad social. Mexico: El
Colegio de M
De la Torre, R. (2005). Ingreso y gasto en la medici
on de la pobreza.M
exico: Secretar
ıa de Desarrollo
Del Castillo Negrete, M. (2015). La magnitud de la desigualdad en el ingreso y la riqueza en M
Una propuesta de c
alculo (no. 39531). Mexico: Naciones Unidas Comisi
on Econ
omica para
erica Latina y el Caribe (CEPAL).
Guerrero, I. L., L
opez-Calva, F., & Walton, M. (2009). The inequality trap and its links to low
growth in Mexico. In S. Levy & M. Walton (Eds.), No growth without equity? (pp. 111–156).
Washington, DC: Palgrave Macmillan y Banco Mundial.
Harberger, A. C., & Guillermo-Pe
on, S. (2012). Estimating private returns to education in Mexico.
Latin American Journal of Economics,49(1), 1–35.
andez Laos, E. (2006). Pobreza y distribuci
on del ingreso en M
exico. Pobreza y distribuci
del ingreso en M
exico. Econom
ıaUNAM,6(16), 101–106.
andez Laos, E., & C
ordova Ch
avez, J. (1979). Estructura de la distribuci
on del ingreso en
exico. Comercio Exterior,29(5), 561–568.
ILO. (2015). Informe mundial sobre salarios 2014/2015. Salarios y desigualdad de ingresos. Ginebra: OIT.
Retrieved from—dgreports/—dcomm/—publ/
INEGI. (2015a). Encuesta Nacional de Ingresos y Gastos de los Hogares 2014. M
odulo de Condiciones Soci-
omicas [Microdatos de la muestra]. Instituto Nacional de Estad
ıstica y Geograf
ıa. Retrieved
INEGI. (2015b). Censo Econ
omicos 2014. Instituto Nacional de Estad
ıstica y Geograf
ıa. Retrieved
INEGI. (2015c). Cuentas Nacionales, Banco de Informaci
on Econ
omica. Instituto Nacional de Estad
tica y Geograf
ıa. Retrieved from
INEGI. (2016). Encuesta Nacional de Ocupaci
on y Empleo (ENOE), poblaci
on de 15 a~
nos y m
as de edad.
Instituto Nacional de Estad
ıstica y Geograf
ıa. Retrieved from
Leyva, G. (2004). El ajuste del ingreso de la ENIGH con la contabilidad nacional y la medici
on de la
pobreza en M
exico, serie: Documentos de investigaci
on, 19. M
exico: SEDESOL.
opez-Calva, L. F., & Lustig, N. (Eds.) (2010). Declining inequality in Latin America: A decade of pro-
gress? Washington, DC: The Brookings Institution Press and UNDP.
opez Gallardo, J. (1983). La distribuci
on del ingreso en M
exico: Estructura y evoluci
on. El Tri-
mestre Econ
omico,50(4), 2227–2256.
Measuring True Income Inequality in Mexico 147
Lustig, N., & Higgins, S. (2013). Commitment to equity assessment (CEQ): Estimating the incidence of
social spending, subsidies, and taxes-handbook. New Orleans: Tulane University.
Lustig, N., & L
opez-Calva, L. F. (2012). El mercado laboral, el Estado y la din
amica de la desi-
gualdad en Am
erica Latina: Brasil, M
exico y Uruguay. Pensamiento Iberoamericano,10, 3–28.
Lustig, N., Lopez-Calva, L. F., & Ortiz-Juarez, E. (2013). Deconstructing the Decline in Inequality in
Latin America. Working Papers 1314. New Orleans: Tulane University, Department of
Lustig, N., Luis, F., Lopez-Calva, L. F., & Ortiz-Juarez, E. (2011). The decline in inequality in Latin
America: How much, since when and why. Working Papers 1118. New Orleans: Tulane Universi-
ty, Department of Economics.
ınez, H. I. (1992). Algunos efectos de la crisis en la distribuci
on del ingreso en M
exico. Mexico:
ınez, I. (1960). La distribuci
on del ingreso y el desarrollo econ
omico de M
exico: Instituto
de Investigaciones Econ
omicas, Escuela Nacional de Econom
ıa, UNAM.
ınez de Navarrete, I. (1970). La distribuci
on del ingreso en M
exico. Tendencias y perspecti-
vas. In El perfil de M
exico en 1980 (Vol. 1). Mexico: Siglo XXI editores.
Mincer, J. (1974). Education, experience, and the distribution of earnings and employment: An
overview. In F. T. Juster (Ed.), Schooling, experience, and earnings (Human Behavior & Social
Institutions) (pp. 1–94). Cambridge, MA: NBER.
Mokomane, Z., Teruel, G., & Reyes, M. (2017). Global south powers in transition: A comparative
analysis of Mexico and South Africa. In D. Geldenhuys & H. Gonz
alez (Eds.), Social and terri-
torial inequality (pp. 235–279). M
exico: Universidad Iberoamericana; Guadalajara: Universidad
de Guadalajara; South Africa: Pretoria University.
Observatorio de Salarios–EQUIDE. (2016). Los salarios y la desigualdad en M
exico. Puebla: Universi-
dad Iberoamericana Puebla. Retrieved from
OECD. (2015). In it together: Why less inequality benefits all. Paris: Organization for Economic Co-
operation and Development.
Ostry, J. D., Loungani, P., & Furceri, D. (2016). Neoliberalism: Oversold? Finance & Development,
53(2), 38–41.
OXFAM. (2012). Left Behind by the G20? How inequality and environmental degradation threaten to
exclude poor people. Oxfam International. Retrieved from
OXFAM. (2015). Desigualdad extrema en M
exico: Concentraci
on del poder econ
omico y pol
ıtico. Oxfam,
23, elaborado por Gerardo Esquivel. M
exico: OXFAM.
Piketty, T. (2014). Capital in the twenty-first century. Cambridge, MA: Harvard University Press.
SHCP. (2010). Distribuci
on del pago de impuestos y recepci
on del gasto p
ublico por deciles de
hogares y personas. Resultados para el a~
no de 2010. M
exico: Secretar
ıa de Hacienda y
edito P
ublico. Retrieved from
World Bank. (2017). Conversion factors. World Bank. Retrieved from http://datos.bancomundial.
148 Latin American Policy
... Bustos et al. (2017), por ejemplo, sugieren que las cifras oficiales de distribución del ingreso en deciles subestiman su concentración. En sus cálculos, el 10% más rico de la hogares concentra alrededor de 53% del ingreso (contra 35% en las estadísticas oficiales basadas en encuestas) y, más aún, que el 1% más rico de los hogares concentra alrededor de la quinta parte del total, o más que el 60% más pobre (ver Reyes et al. (2017) o Campos-Vazquez et al. (2018) para estimaciones similares). Este trabajo busca contribuir a la agenda de investigación sobre desigualdad por doble vía: por un lado vinculando la distribución del ingreso observada con el funcionamiento estructural del sistema económico y, por otro, distinguiendo las particularidades regionales. ...
Full-text available
Este trabajo busca contribuir a la agenda de investigación sobre desigualdad por doble vía: por un lado vinculando la distribución del ingreso observada con el funcionamiento estructural del sistema económico y, por otro, distinguiendo las particularidades regionales. Para realizar el análisis estructural a nivel regional, este trabajo se beneficia de la construcción de ocho matrices de contabilidad social (MCS) para la economía mexicana de 2013 (siete a nivel regional y una nacional) que son resultado de un proyecto de largo aliento sobre economía regional en México (ver los análisis colectivos publicados en Dávila-Flores (2015) y Dávila-Flores (2019a)). Las MCS son herramientas analíticas que cumplen un papel doble. Por un lado son un instrumento contable de la macroeconomía de una región con resolución a escala intermedia, es decir, son identificables tanto los sectores de actividad económica como las instituciones, incluyendo a los hogares organizados en deciles. Por otro, son modelos de determinación del ingreso basados en la teoría de la interdependencia económica, representada en este caso por medio de las propensiones medias de gasto, para el caso de las instituciones, y de las relaciones tecnológico-productivas de las transacciones inter-sectoriales de insumo-producto, para el caso de las actividades económicas. Las preguntas que motivan este trabajo buscan i) dilucidar las interacciones entre las estructuras productivas regionales con la generación y distribución del ingreso entre los deciles de hogares, ii) identificar aquellas regiones en las que se genera una distribución del ingreso más desigual, y iii) identificar las categorías de hogares más y menos favorecidas por el funcionamiento observado de las economías regionales. Para responder a estas preguntas, el trabajo reporta un experimento basado en una transferencia equitativa a todos los hogares de las regiones del país. Las propensiones de gasto capturados en los patrones de consumo estimulan las respuestas productivas regionales y la generación de valor agregado, mediada por las demandas regionales de los factores de la producción, y su posterior distribución entre los deciles de hogares. El experimento permite analizar la manera en la que una transferencia equitativa se transforma en una distribución desigual del ingreso, al tiempo de distinguir los patrones de concentración regional de la actividad económica, ambas características de la economía mexicana contemporánea.
... Así, la evidencia sugiere que las recesiones económicas experimentadas y el bajo crecimiento del producto interno bruto (pib) per cápita en México han incrementado la pobreza y la desigualdad en el país. Además de la elevada desigualdad en la economía mexicana, trabajos recientes han mostrado evidencia de la existencia de una subestimación de la desigualdad en México reportada en las encuestas de hogares (Reyes, Teruel y López, 2017). De esta manera, existen indicios de una relación positiva entre la desigualdad del ingreso, la pobreza y el crecimiento económico para la economía mexicana. ...
Full-text available
La economía mexicana se ha caracterizado por un crecimiento reducido acompañado de una elevada desigualdad del ingreso y pobreza. Con el objetivo de estimar la relación y la causalidad de la desigualdad y la pobreza en el crecimiento económico, se estableció un modelo de datos de panel a escala estatal, con el fin de relacionar el coeficiente de Gini y el índice de pobreza con la tasa de crecimiento del producto interno bruto (PIB). El coeficiente de la variable de la educación media superior resultó positivo, lo que sugiere que la formación educativa es relevante para promover el crecimiento. Los resultados muestran que el coeficiente de Gini y la proporción de la pobreza sobre el total de la población tienen una correlación inversa con el crecimiento económico. La prueba de Granger indica un efecto de causalidad de la desigualdad y la pobreza en el crecimiento
... However, it is a country with many poor people, since 41.9% of its population, as of 2018, lived below the national poverty line [Worldbank, 2020]. Mexico is a country with a very unequal distribution of wealth [Reyes et al., 2017;Bustos et al., 2017]. According to INEGI [2020], as of the first quarter of 2020, 27.7 million Mexican workers were employed in informal jobs; this is equivalent to 54.2% of the economically active population. ...
Full-text available
Cholula (Puebla, Mexico), an ancient pre-Hispanic city, since 1971 is home to UDLAP, Universidad de las Américas Puebla. Over the years, the presence of UDLAP has deeply affected the fabric of the city, transforming a once rural center into a university town. Students are very important for the economy of Cholula: one example is avenida 14 Oriente, a street entirely devoted to nightlife and student fun. Since the pandemic, most students returned to their hometowns. This had huge consequences on the microeconomy of the city, especially for informal traders. In this paper, we discuss the changes brought about by the pandemic in the socio-economic dynamics of Cholula. To do so, we rely on fieldwork, complementing it with information found in magazines, newspapers and local media.
... All this evidence suggests that skin color is an important circumstance in determining an individual's access to advantages in life in societies where skin color is among the dimensions of social stratification. In this paper, we provide the first estimations of inequality of opportunity in a measure of wealth accounting for skin color in Mexico, a country with high levels of inequality (Bustos & Leyva, 2017;Castillo Negrete Rovira, 2017;Reyes et al., 2017), low social mobility rates for those located at the extremes of the wealth distribution 5 and for which increasing evidence points to skin color as an important factor of stratification. Relying on the Intergenerational Social Mobility Module (MMSI 2016) of the National Household Survey, we provide estimations of inequality of opportunity, which are nationally representative for the Mexican population between 25 and 64 years old. ...
We document the contribution of skin color toward quantifying inequality of opportunity over a proxy indicator of wealth. Our Ferreira–Gignoux estimates of inequality of opportunity as a share of total wealth inequality show that once parental wealth is included as a circumstance variable, the share of inequality of opportunity rises above 40%, overall and for every age cohort. By contrast, the contribution of skin tone to total inequality of opportunity remains minor throughout.
... As a result of the correction, the Gini rose from 0.45 to 0.63, but the measure of poverty fell. Reyes et al. (2017) proposed a method for adjusting incomes to deal with income truncation in the top tail and underreporting of various income components in the rest of the distribution. This adjustment increased the Mexican Gini from 0.52 to 0.74, or even as high as 0.97, apparently making Mexico the most unequal country globally. ...
Full-text available
This article assesses the redistributive effects of fiscal instruments in Mexico in 2010-2014, correcting for top-income measurement problems. Two correction methods are applied-survey-sample reweighting for households' nonresponse probability and replacing of top incomes using smooth Pareto distributions-to reestimate the effects of pensions, transfers, taxes, and subsidies. These corrections yield higher inequality measures, consistent between the reweighting and replacing methods. Taxable income shows the highest inequality and undergoes the highest upward correction for top-income problems, whereas nontaxable income is strongly equalizing. Contributory pensions are inequality-neutral, while transfers, taxes, and subsidies are equalizing. In-kind transfers, cash-like transfers, and direct taxes have the strongest equalizing effects. Top-income measurement challenges retain their magnitude across years 2010, 2012, and 2014, but household nonresponse becomes more positively selected, causing greater biases in later years.
... The present research examines these relations in Mexico. The Mexican economy is one of the most inequitable among Latin American countries (Reyes et al., 2017), with 44% of the population living in poverty (Varela Llamas and Ocegueda Hernández, 2020). Mexico is ranked as number 74 in the Human Development Index (HDI) ranking, which is a measure of the development of a country that considers three dimensionslife expectancy, education (i.e., expected years of schooling and mean years of schooling), and gross national income per capita (United Nations Development Programme, 2020). ...
Full-text available
We explored the home learning environments of 173 Mexican preschool children (aged 3–6 years) in relation to their numeracy performance. Parents indicated the frequency of their formal home numeracy and literacy activities, and their academic expectations for children’s numeracy and literacy performance. Children completed measures of early numeracy skills. Mexican parent–child dyads from families with either high- or low-socioeconomic status (SES) participated. Low-SES parents ( n = 99) reported higher numeracy expectations than high-SES parents ( n = 74), but similar frequency of home numeracy activities. In contrast, high-SES parents reported higher frequency of literacy activities. Path analyses showed that operational (i.e., advanced) numeracy activities were positively related to children’s numeracy skills in the high- but not in the low-SES group. These findings improve the understanding of the role of the home environment in different contexts and provide some insights into the sources of the variable patterns of relations between home learning activities and children’s numeracy outcomes. They also suggest that SES is a critical factor to consider in research on children’s home numeracy experiences.
Full-text available
Motivated by the empirical evidence of the effects of unanticipated nominal interest rate increases on the evolution of household inequality in Mexico, which highlight the importance of insurance mechanisms to deal with idiosyncratic risks, the paper uses a Heterogeneous Agent New Keynesian model to analyze the relationship between monetary policy and household inequality. The model is able to capture the main features that characterise both the business cycle dynamics, as well as the distribution of income and wealth. Results indicate that heterogeneity affects the transmission of monetary policy, and that the design of monetary policy has important distributive effects.
Full-text available
El propósito de este artículo es mostrar un panorama económico y sociodemográfico del percentil más rico de México. El artículo se divide en dos partes: 1) haciendo uso de las encuestas de ingreso y gasto de los hogares en México a través del periodo 1984-2018 se hace una correlación de los ingresos absolutos y relativos entre diferentes periodos para observar la oscilación de su ingreso absoluto, su participación en el total del ingreso, la acumulación periódica y el tipo de procedencia del ingreso; 2) asumiendo que existe una desestimación sobre los ingresos en la parte más alta de las encuestas, se realiza una modificación en los ingresos absolutos basada en porcentajes similares realizados por otros autores.
Full-text available
This paper argues that explaining both the level and the changes in the inequality of the distribution of economic resources in society requires complementing explanations based on human capital theory with insights from social stratification theory. The integration of both allows explaining horizontal inequalities and explaining the aggregate levels of economic inequality in a society. We exemplify the potential of this integration through a reinterpretation of the literature on economic inequalities in Mexico during the XXIst century. This reinterpretation focuses on how institutions stratify the access to the different components of human capital and how said components are valued in the labour market. We argue that a complete understanding of distributional dynamics in societies with persistent inequalities can be achieved through this interdisciplinary exercise.
Using the inter-regional economic inequality index and the Gross State Product per capita for the Mexican States over the period 1940-2015, we apply regime dynamics and hierarchical cluster analysis for segmenting the sample into regimes of Mexican states with similar performance. Robust econometric models are studied showing the direction of causality between economic inequality and income per capita, and the existence of a U-shaped curve for the interdependence between economic growth vs economic inequality, and threshold levels. We additionally demonstrate the existence of inequality traps. The education literacy rate as a control variable indicates an inverted U-shaped curve.
Full-text available
High inequality is a characteristic feature of Latin America. After rising in the 1990s, however, income inequality in the region has declined while it has increased in other parts of the world. For the region as a whole, the Gini co efficient declined from an average of 0.550 in the early 2000s to 0.496 circa 2012. Of the 18 countries with available data, 16 experienced a decline in their Gini coefficient during this period. What explains this remarkable shift in inequality trends in Latin America?
Full-text available
Desde el año 2000, la desigualdad ha disminuido en la mayoría de los países de América Latina, aunque también ha aumento en algunos. Este artículo analiza las causes que sybyacen a la dinámica de la desigualdad, enfocándose en dos casos en los que la desigualdad se redujo (Brasil y México= y uno en el que ésta se elevó Uruguay). El análisis sugiere que tanto las fuerzas del mercado como la acción del Estado jugaron un papel importante en la explicación de la dinámica d ella desigualdad. En particular, el descenso de la desigualdad en Brasil y México está asociado con la caída de los diferenciales salariales según nivel educativo y, en menor medida, las transferencias. Por el contrario, la principal fuerza que explica el aumento de la desigualdad en Uruguay y durante 1994-2007 es el incremento en los retornos a la educación.
Full-text available
En este articulo se construyen las series de la participacion salarial en el producto interno bruto (pib) de 15 economias de America Latina y la correspondiente al conjunto de estas entre 1950-2010. Se determina, con diferentes metodologias, la tendencia no lineal de esta variable, corroborando la presencia de dos grandes ciclos. Se incluye una discusion de diversos autores, especialmente clasicos y postkeynesianos, que exploran la vinculacion de esta variable con el nivel de actividad economica. Asimismo, se demuestra la pertinencia del enfoque postkeynesiano para explicar que las variaciones del pib real estan determinadas por la participacion salarial, la formacion bruta de capital y las exportaciones de bienes y servicios, entre las principales variables. Sin embargo, la contribucion de la variacion de la participacion salarial al crecimiento del producto real es menos importante a partir de los anos ochenta.
ste documento es el segundo del proyecto sobre la distribución del ingreso en México, iniciado hace algunos años por el Banco de México, S.A. El propósito de dicho proyecto es analizar la distribución del ingreso en nuestro país, especialmente a partir de los resultados de la encuesta de Ingreso-Gasto realizada en 1977 por la Secretaría de Programación y Presupuesto y en la que participó el Banco de México, S.A.
This study explores the relationship between education and wages in Mexico. It contributes to our understanding of the structure of wages, helping explain individuals' choices concerning education level. First, we estimate the age-earnings functions for each level of education. Then, taking into account some important costs of added years of study, we estimate the net present value of investment in human capital in each of four steps up the educational ladder. We estimate the internal rate of return associated with investment in each successive step considering different scenarios, two of which take into account prospective economic growth and mortality.
atin America is often singled out for its high and persistent income inequality. Toward the end of the 1990s, however, income concentration began to fall across the region. Of the seventeen countries for which comparable data are available, twelve have experienced a decline, particularly since 2000. This book is among the first efforts to understand what happened in these countries and why. Led by editors Felipe López-Calva and Nora Lustig, a panel of distinguished economists undertakes in-depth analyses of Argentina, Brazil, Mexico, and Peru. In addition, they provide essential background in the form of overviews of the relationship between markets and inequality, the political economy of redistribution, and the evolution of income inequality in the advanced industrialized economies. Two factors account for much of the decline in inequality: a decrease in the wage gap between skilled and low-skilled labor, and an increase in government transfers targeted to the poor. Thanks to the timeliness and sophistication of these essays, Declining Inequality in Latin America is likely to become a standard reference in its field.
In this paper, we present an approach for the estimation of income distributions, which deals with survey data shortcomings through simultaneous consideration of other statistical sources and through adjustment for compatibility with all of them. We show how our proposal deals both with survey income under-reporting, and with under representation of households with very large incomes, which are known to affect the results of the survey. Our proposal has the purpose of selecting the distributional model that best fits the data from the survey, using a Constrained Pseudo Log-likelihood criterion, and is based on well-established statistical criteria and methods and thus reduces the need for subjective or arbitrary choices. The proposed procedure is applied to Mexican data from the National Survey on Household Income and Expenditure for the year 2012 and from Mexico's System of National Accounts, two sources that produce widely differing results regarding total national household current income. We show that, among all fitted models, a satisfactory explanation is given by a 4-parameter Generalized Beta Type 2 distribution. The chosen distribution has little impact on the official poverty measurement. The Gini coefficient, however, reaches a value as high as 0.803.
This handbook presents a step-by-step guide to applying the incidence analysis used in the multi-country Commitment to Equity Project (CEQ). We define the pre- and post-net transfer income concepts, discuss the methodological assumptions used to construct them, explain how taxes, subsidies and transfers should be allocated at the household level, and suggest what to do when the information of taxes and transfers is not included in the household survey. We also describe the indicator that are used to assess the distributive impact, progressivity and effectiveness of social spending, subsidies and taxes. In addition, we present a sample Stata code for producing some of the indicators.