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Preliminarypleasedonotcite.

CausesoftherapidgrowthofobesityanddiabetesinMexico:socio-demographicfactors.

April2016

Alfonso Miranda*, Jody Sindelar**, and Susan W. Parker* *Centro de Investigación y Docencia Económicas **Yale School of Public Health

March 16, 2016 Corresponding author. Susan Parker, CIDE Carretera Mexico Toluca 3655 Col. Lomas de Santa Fe 01219 Mexico DF susan.parker@cide.edu

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Abstract Obesity surged in the 1990s in Mexico and Mexico has become the world’s most obese country. Obesity is now an important social, economic, and public and personal health problem in Mexico; and it will likely continue as such for generations. Despite its importance and cost to the county, obesity in Mexico has only recently received significant attention. Much is still unknown. To address this knowledge gap we document the prevalence, trends, and differences by socio- demographic characteristics in population rates of overweight, obesity and morbid obesity from 1988 until 2012. We add to the literature by analyzing data from several sources. Specifically, we use five nationally-representative nutritional surveys conducted at the household level: the National Survey of Nutrition ENN (1988 and 1999), the National Health Survey ENSA (2000) and the National Survey of Health and Nutrition ENSANUT (2006 and 2012). We provide graphical and statistical analyses of the data. Our analyses of these data provide insights into this important social, economic, and health problem, despite limitations to the data. One limitation for example is that obesity is measured only for women and only for women aged 20 to 44 in the first two surveys covering the 1990s; yet they are the best, and perhaps only, publically available national data over this time period. We find that the prevalence of being overweight doubled for women 20-44 during the 1990s and then continued to grow during the next decade but at a slower rate. And the proportion of obese women more than tripled over this period. Shockingly the percentage of women in this age group that was morbidly obese increased by more than 7 times over the same period. From the regression results, we find that education at higher levels is quite significant and protective for females, but not for men. Specifically, having some high school or college education has negative impacts on the three measures of BMI for women. And in the case of the probability of being overweight and obese, college has a more protective effect than does high school. For men education has a positive and significant effect on the probability of being overweight for all education levels. That is, men with higher education are more likely to have a higher BMI. Age was found to have a positive and significant impact on BMI for both men and women. Indigenous language has a positive impact on all three measures of weight for women, but is insignificant for men at all levels of weight. Our study contributes to the understanding of how the Mexican population has grown heavier over time. The greatest growth, at least for women, was in the 1990s. This raises the puzzle as to why the tremendous growth occurred in the 1990s for Mexico but it occurred in the 1980s in the US. Another puzzling question emphasized by our findings is why higher levels of education appear to be protective for women and not for men. For men, those with no formal education are least likely to be overweight, presumably due to low income and physical jobs. These are important questions that need to be addressed to gain a full understanding and to address the epidemic of obesity. The epidemic of obesity is costly to Mexico in terms of lost productivity, compromised public health, suffering, and worse health including the growth of diabetes. Our study represents an advance in the knowledge base necessary to pose puzzles to solve, hypothesize underlying reasons for relationships, and propose policies and interventions to address obesity in Mexico.

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Introduction. Obesity surged in the 1990s in Mexico and now Mexico is the most overweight and obese

country in the world1. This paper documents the tremendous growth in obesity in Mexico

by bringing together data on weight from a number of different national surveys of Mexico.

A better understanding of the prevalence and growth of obesity by over time and by

gender, age, income and socio-economic status will serve as a knowledge base for further

inquiry into this important problem.

Obesity in Mexico is an important social, economic and public health problem. The

consequences of this huge growth in obesity are likely to be costly to Mexico in terms of

economic, health and personal costs and these costs will likely continue for generations

(9-12). Obesity also results in suffering due to diabetes, hypertension and other obesity

related diseases; in turn these generate the need for costly medical care. A recent report

by McKinsey Global Institute indicated that while the economic and social impacts of

obesity are severe worldwide, for Mexico they are particular costly; the report rank obesity

as the largest social burden for Mexico (9).

Despite the importance and spread of obesity in Mexico, the prevalence, trends, and

differences by gender, income and education have not been fully documented. This paper

presents data on overweight, obesity and morbid obesity from 1988 to 2012 in Mexico. We

use five nationally-representative, household level, nutrition surveys: the National Survey

of Nutrition ENN (1988 and 1999), the National Survey of Health ENSA 2000, and National

Survey of Health and Nutrition ENSANUT (2006 and 2012). Unfortunately, in 1988 and

1999, data are available only for women 20- 45; but there are no other nationally

1Mexicohasthehighestrateofoverweightandobesitynot-withstandingsomeverysmall,islandcountriessuchasSamoa.

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representative data sets over this period. Thus while we document the rapid rise in

obesity rates for women in the 1990s we cannot do so for men. Fortunately we can

examine the full age range for adults from 18 and older for both genders in 2000, 2006 and

2012.

We begin by presenting descriptive trends of the increase in the proportion of the

population which is overweight, obese and morbidly obese over this period. We then turn

to regression analysis to document the socio-demographic characteristics of the

population which are associated with increases in obesity over this period. We focus

particular attention on the role of education and in particular whether education has a

protective effect on obesity and how these effects may have changed over the period of

growing obesity.

A better understanding of the prevalence and growth of obesity over time and by gender,

age, income and socio-economic status will serve as the knowledge base for further

inquiry into this critical issue. By analyzing the data, we provide the knowledge base

necessary to pose puzzles to solve, hypothesize underlying reasons for relationships, and

propose policies and interventions to address obesity in Mexico.

Methods and Data

We document the level and trends of obesity in Mexico over the last two decades

distinguishing among those overweight, obese, and morbidly obese. Specifically, we first

provide graphs and cross tabulations of the proportion of overweight, obese and morbidly

obese for men and women.

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We have relatively large groups of the overweight and obese but the percentage that is

morbidly obese is relatively small. Thus, these results may be more fragile, but are

interesting as it is the fastest growing group and the group most likely to suffer severe health

problems related to obesity.

Next we estimate a number of logit regressions of the three measures of obesity (all binary

indicators) for women and men separately regressions. We estimate these measures of

overweight and obesity as a function of key exogenous variables: age, education, urban

location, indigenous language, and income per household member. We explore a number

of models for the regression analysis and present both municipal fixed effects (545) and

state (32) fixed effect logit regressions. We also present municipal random effects models.

The random and fixed effects specifications exploit the fact that we have pooled cross-

sectional data with many Mexican municipalities being sampled more than twice in the

multiple different surveys. We also analyse results by year to study whether the effects of

the socio-demographic variables change over time. The logit regression tables present

average semi-elasticities, which help to compare across all specifications.

When we estimate the outcome of overweight, we use the full sample that is available to us.

Those overweight are compared to the non overweight (the rest of the sample). However,

when we examine obesity, the relevant sample declines because we are selecting those

who are obese (i.e. BMI>30) and comparing them to the same reference group as we did

for the overweight, that is, those who with a BMI less than 25. Thus we are always comparing

the fatter group to the same reference group but the number in our ‘fat’ group dwindles with

the stricter measures of weight.

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The main tables are reported in the text whereas additional specifications that serve as

sensitivity analyses are provided in the appendices.

Data sets and samples.

We use data from five different surveys: (i) the Encuesta Nacional de Nutrición 1988

(ENN88); (ii) the Encuesta Nacional de Nutrución 1999 (ENN99); (iii) the Encuesta Nacional

de Salud 2000 (ENSA00) (iv) the Encuesta Nacional de Salud y Nutrición 2006

(ENSANUT06); and (v) the Encuesta Nacional de Salud y Nutricion 2012 (ENSANUT12).

All surveys are nationally-representative, cross-sectional data collected at the household

level and give information as far back in time as it is possible in Mexico for calculating

obesity intensity rates based on micro data. Because each survey was designed

independently, it is not always possible to measure the same set of variables across all five

data points and important work of harmonization was needed to build a comparable time

series. However, for the variables here described, we believe a good approximation has

been achieved.

A major limitation of the data is that the target population of ENN88 and ENN99 was only

women in reproductive ages and children. Therefore weight and height were collected only

for women ages 20-45 in these surveys2. The target population of the other surveys we use

(ENSA00, ENSANUT06, and ENSANUT12) include a random sample of all members of the

interviewed households, including adults and children of all ages.3 As a consequence, we

2Weightandheightwerealsocollectedforchildren,weleavethetopicofchildobesityforaseparatepaper.3Inparticular,inallENSA2000,ENSANUT06andENSANUT12informationaboutallmembersofthehouseholdwas initially collected in a household rooster and a household questionnaire. However, after the initialhousehold interview, only one adult, one child, and one adolescent were selected for further individualinterview. In particular, anthropometrics are collected only for household members selected for furtherindividualinterview.

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can calculate obesity rates from 1988-2012 only for women in reproductive age but for

women of other ages only from 2000 onward and for all men only from 2000 onward. With

respect to our control variables, questions permit the construction of all variables for all years

that data are available with the exception of household income which is only available in

2000- 2012.

Definitions and descriptive stastistics.

Overweight, obese and morbidly obese. Data on height and weight were measured and

collected on adult respondents selected for further intervew, with the exceptions noted

above. Following international definitions, we define for an adult: overweight at BMI over

25, obesity at BMI over 30, and morbid obesity at BMI over 40. BMI is calculated as the

body mass measured in kilograms divided by the square of the body height measured in

meters (i.e. kg/m2).

Income per household member. Data were collected on total household income and

household size. Household income includes all types of individual income sources which

are summed to attain a total income per family. We divided this income measure by

household size to obtain household income per person. Respondents were not asked

about income in 1988, 1999, only in 2000, 2006 and 2012.

Age. We measure age as a continuous variable from age 20 to above.

Education. Using information on the education level and grades completed by each

individual, we construct the following categories of education: primary (1-6), secondary (7-

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9), high school (10-12) and college education (12+) with no education as the omitted

category.

Indigenous language. A binary variable indicates those whose primary language is an

indigenous language.

Geographical indicators. Several geographical indicators are provided. We use a binary

indicator on living in urban areas as a control variable. We also control for geography

through our models of municipal random and fixed effects models. Data on state and

region were also available but we prefer the specifications using municipalities because

the latter provides more specific and detailed control for geographically relevant factors

such as food prices, food culture, access to different types of food, etc. The appendices

provide results for alternative specifications using fixed effects for region and state.

Labor force participation. Labor force participation refers to participation in the labor market

during the previous week.

Population weights. All descriptive statistics and regression analyses are population

weighted according to the corresponding survey weights and thus provide national level

representative estimates.

Table 1 provides some descriptive statistics on the data used. In the Table we aggregate

and provide overall statistics in the case of women, for the five rounds of data used and for

men for the three rounds of data used. Thus the descriptive statistics for women reflect the

period from 1988 through 2012 whereas those for men reflect the period 2000 to 2012. Table

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1 reports descriptive statistics only for the sample of individuals age 20 to 45 in order to have

a comparable set of individuals over time in these descriptive statistics.

Table 1 shows that over this period that the majority of the population, both male and female,

have education levels which do not exceed secondary school. About 40 percent of women

and 33 percent of men have primary schooling or less over the period. About 8 percent of

individuals report speaking an indigenous language. The large majority of males, nearly 90

percent, report working during the previous week whereas only about 30 percent of females

report working during the previous week.

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Table1.DescriptiveStatisticsforWomanandMen 1a.DescriptivestatisticsforWomen

Variable Description Obs MeanStd.Dev. Min Max

overweight =1ifBMI>25 61983 0.61 0.49 0 1obese =1ifBMI>30 61983 0.27 0.44 0 1morbidobese =1ifBMI>40 61983 0.02 0.15 0 1urbanstatus =1ifurbanarea 62945 1.51 0.50 1 2indigenouslanguage =1ifspeaksanindigenouslanguage 62942 0.08 0.27 0 1primary =1ifhighestqualificationisprimaryschool 62839 0.42 0.49 0 1secondary =1ifhighestqualificationissecondaryschool 62839 0.27 0.44 0 1highschool =1ifhighestqualificationispreparatoryschool 62839 0.13 0.34 0 1college =1ifhighestqualificationisuniversity 62839 0.13 0.34 0 1workstatus =1ifworks 62945 0.30 0.46 0 1

age age 62945 32.00 7.27 20 45Note:BodyMassIndex(BMI)isdefinedasweight(inkg)dividedbysquaredheight(inmeters). 1b.DescriptivestatisticsforMen

Variable Description Obs MeanStd.Dev. Min Max

overweight =1ifBMI>25 23408 0.65 0.48 0 1obese =1ifBMI>30 23408 0.24 0.42 0 1morbidobese =1ifBMI>40 23408 0.01 0.11 0 1urbanstatus =1ifurbanarea 23408 1.61 0.49 1 2indigenouslanguage =1ifspeaksanindigenouslanguage 23407 0.08 0.28 0 1primary =1ifhighestqualificationisprimaryschool 23408 0.33 0.47 0 1secondary =1ifhighestqualificationissecondaryschool 23408 0.33 0.47 0 1highschool =1ifhighestqualificationispreparatoryschool 23408 0.16 0.37 0 1college =1ifhighestqualificationisuniversity 23408 0.16 0.37 0 1workstatus =1ifworks 23408 0.88 0.33 0.00 1.00

age Age 23408 32.3314 7.464481 20 45Note:BodyMassIndex(BMI)isdefinedasweight(inkg)dividedbysquaredheight(inmeters).

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Results

Descriptive graphs.

We now turn to a descriptive graphs and tables of the trends in obesity in Mexico. First we

summarize the primary stylised facts on obesity in Mexico over the last two decades in

graphical and tabular form. We document the level and trends of obesity in Mexico

distinguishing between those overweight, obese, and morbidly obese for men and women

separately. We examine the genders separately to allow for differential trends over time and

differences by socio-economic and demographic factors. Specifically we examine

differences by age, gender, education, ethnicity, and income.

Trends in the proportion of overweight adults by gender

Measures of obesity over time. Figure 1 and Table 2 present the proportion of overweight

women and men age 20 to 45 over the study period. While in 1988 only 34% of women

were classified as being overweight, by 2012 nearly 71% of women fell in that category. This

is a staggering growth with more than a doubling in the proportion of women with a BMI of

25 and over. Most of the rise seems to have occurred during the decade of the1990s; from

1988 to 2000 the percentage of women who are overweight grew from 34% to 60%. For

men, data are only available for years 2000, 2006 and 2012. However, Table 2 and Figure

1 show that by 2012 slightly over 68% of Mexican men were classified as being overweight,

a rate that is consistent with that for women. In 2000 60% of women and 57% of men were

obese, but while the growth rate of obesity flattened out post 2000, the rate grew somewhat

more rapidly for men than women; although in 2012 men were slightly less likely to be

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overweight and obese. Thus while we cannot document men’s obesity in the period from

1988 to 2000, we suggest that it might have been rising at a similar rate to that of women.

TABLE 2: RATES OF OVERWEIGHT, OBESITY AND MORBID OBESITY BY GENDER, YEAR AND PERCENTAGE CHANGE: MEN AND WOMEN 20 TO 45

WOMEN MEN

YEAR

Overweight

Obese

Morbid Obese

Overweight

Obese

Morbid Obese

1988 0.343 0.097 0.005 1999 0.601 0.243 0.02 2000 0.596 0.245 0.021 0.57 0.164 0.009 2006 0.674 0.306 0.029 0.645 0.227 0.01 2012 0.71 0.341 0.036 0.681 0.271 0.018

% CHANGE 88-12

2.07

3.52

7.2

00-12 1.19 1.40 1.71 1.19 1.65 2 Figure1

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Figure 2 graphs obesity rates by age. The slightly concave relationship between age and

obesity rates is seen for both men and women. This concave relationship is consistent with

data from the US and other countries. After peaking in the age range of 40-50 years old, the

obesity rate declines slightly with age. This slight decline can be explained by several factors

including: older people are more likely to be sick and lose weight, heavier individuals are

likely to die earlier due to diabetes and other diseases, and heavier muscle tissue is lost with

age. The profiles by gender are slightly different in shape and timing, but are generally in

sync. For women, the large jump in obesity rates from 1988 to 1990 is seen clearly in Figure

2 by the upward shift in the curves.

Figure 2.

Obesity by education. Figure 3 presents the evolution of overweight rates by education and

gender.4 For women, there is a clear, but modest, negative education gradient in 1988;

better educated women had a lower probability of being overweight than less educated

women. The differences by education are reduced by 1999 as women of all levels of

education increase their probability of being overweight. Interestingly, the odds that women

4Notethatthedataweusearecrosssectionalandsothataseducationlevelsincreaseoverthisperiod,thedistributionofthepopulationamongeducationgroupschanges,inparticularfewerindividualswillbeinthelowereducationgroupsrelativetohigherlevelsofeducation.

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with more education become overweight increase faster than the odds of women with less

education: who started at a lower rate but soon catch up. Specifically, the percentage

overweight for women with no education increased from 36.9% in 1988 to 56.7% in 1999;

whereas for university-educated women the percentages overweight increased from 19.4%

to 53.9%. These translate to a growth of about 54 percent for women with no education and

180 percent for women with college. Thus this higher growth in overweight by the better

education implies that after 1999 differences in overweight rates by education for women

are relatively small. For women there is still an advantage to higher education in 2012.

Surprisingly however, for men, in 2102, better educated men now have higher rates of being

overweight.

Figure3

Income and obesity. Figure 4 displays the overweight rates by income decile and gender.

Because income is asked only in the last three surveys, there are only three years of data:

2000, 2006 and 2012. The income measure used to contract the income deciles is total per

capita household income. Interesting the income profiles of obesity are very different for

men and women. For women, all three years of data show increasing and then decreasing

rates of overweight with income level with women in the middle income deciles having the

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highest probability of being overweight in each of the three years. For men however the

trends are quite different and furthermore are changing rapidly overtime. In the years 2000

and 2006 the probability of being overweight increases continuously with income.

Nevertheless by 2012 the relationship is flat, that is, there are few differences by income

levels in the probability of men being overweight. The graphs for women show a slight

protective effect for higher income women, but for men more income may result in greater

rates of overweight at least prior to 2012; in 2012 there is less of a relationship of income

with being overweight. It is possible that for men, greater income may mean greater

consumption of food and beverages and a more sedentary job.

Figure 4

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3 Trends in the proportion of obese adults We now summarize the stylised facts for the evolution of obesity over time by key covariates.

Figure 5 presents all the graphs together and the discussion here will emphasise new

insights and differences that emerge when looking at obesity rates rather that overweight

rates. Recall that obese is a subcategory of overweight and morbid obesity is a subcategory

of obesity.

For obesity there is a clear upwards trend in the proportion of the population who are obese.

For women, in 1988 9.7% of Mexican women age 20 to 45 were classified as obese,

whereas by 2012 the rate was 34.2%, implying that the level of obesity more than tripled

over this period. In comparison, the growth in the proportion overweight doubled over the

same period (Table 2). As before, most of the growth seems to have occurred during the

1990s. For men we only have data for 2000 through 2012, and thus see a flatter profile

overtime with the proportion of men age 20-45 who are obese increasing from about 17

percent to about 28 percent during this period.

Figure5

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Looking at obesity rates by age and gender we see, very similarly to the case of overweight,

that women are more likely to be obese as they age up until about the age of 50 where the

proportion of women obese declines steadily with age. And as with overweight, the obesity-

age profile has shifted upwards over time, implying that new generations of Mexicans are

more likely to becoming obese at younger ages. For men during the period 2000 to 2012,

the overall pattern of obesity increases with age until middle age and then declines

qualitatively similar to that of women. However, there is a much less steep profile of

increasing obesity with age for men than for women and a less steep profile of decreasing

obesity after about age 45.

Turning now to obesity and its relationship with education, Figure 5 shows that women with

lower levels of education continue over the period to have the highest rates of obesity. The

lowest rates of obesity are consistently women with high school and college with similar

proportional increases by education. Over the 2000 to 2012, the proportion of obese men

continues to increase at all levels of education. As with the proportion overweight, higher

educated men are more likely to be obese over the time period studied.

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The last two panels of Figure 5 show the relationship of income to obesity by gender, and

as with overweight, shows significant differences by gender. For women, the probability of

being obese initially increases with income and then decreases with higher income deciles

for all three years of analysis (2000-2012). For men however the relationship is relatively

flat, unlike the overweight proportion for men, and shows little evidence of differences in

obesity by income level for men over the three years.

Fig.5(Cont.)

3 Proportion of morbid obese adults

The proportion of morbidly obese adults in Mexico does not yet exceed five percent of the

population but our data show that the rate of increase over the period is extremely large. In

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particular, the proportion of women classified as morbidly obese (e.g.. BMI>40) went from

0.05% of the population in 1988 to 3.6% in 2012; thus the rate increased by over 7 times.

Note that the rate of growth that we have observed is higher as the category of overweight

increases. Specifically, over the period 1988 to 2012, the proportion of overweight women

doubled, the proportion of obese women more than tripled and the proportion of morbidly

obese women increased by more than 7 times.

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Figure6

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In terms of the age profile, we confirm the trend observed for the overweight and obese,

that new generations of women, and most likely men, tend to become morbid obese earlier

in life than previous generations. However, men and women seem to have responded

differently with respect to age. Women seem to have a constant or only slightly increasing

probability of becoming morbidly obese over their lifecycle, which hints at the hypothesis

that this condition develops early in life while women are children as opposed to being the

result of a long-term weight cumulative process that happens during the life-cycle --- which

seem to be the case for becoming overweight and obese. That is, acute cases of obesity

appear early in life. For men, counter intuitively, the odds of becoming morbidly obese seem

to decrease with age. There are at least two potential explanations. One is that men are not

different from women and morbid obesity appears early in life and stays with the individual

for the whole life, but that earlier generations of men are more likely to be morbid obese. In

other words, that the observed downward trend is due to a change in the composition of the

population over time and younger generations of men are more likely to be morbidly obese

than older generations. If this is true, then the age profile for men is likely to look similar to

the age profile of women in the future. However, an alternative explanation is that the

probability of men becoming morbidly obese genuinely decreases with age even after we

control for the cohort effect (i.e. the population composition effect). With pooled cross-

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sectional data we cannot really clarify this question, as without longitudinal data, the time

effect is confounded with the cohort effect.

Figure 6 shows a clear gradient of education for women but not for men. In the case of

women, we find a positive relationship between education and obesity, meaning that more

educated women have higher odds of becoming morbidly obese. The evidence is counter-

intuitive, and challenges what one would expect from the perspective of the Human Capital

model of Becker.

Finally, the lower panel of Figure 6 shows the relationship of being morbidly obese to income

levels. For women, the probability of being morbidly obese increases slightly with higher

income levels throughout the entire income distribution and in all three years of analysis

(2000 to 2012). The increase in the probability of being morbidly obese with income has a

higher gradient in 2012 implying larger increases in the probability of becoming morbidly

obese in the higher income groups than in the lower income groups. For men however, the

probability of being morbidly obese is flat with income in 2000 and 2006, but becomes

somewhat positive in 2012, particularly in the middle income deciles, which show large

increases in morbid obesity over the period.

4 Regression analyses

Table 3 contains estimates from the logit municipality fixed effects models of the three

types of obesity for men and women. The sample size for all estimates is large, ranging

from the smallest of 11,209 to the largest at 52,959. The sample size is smaller for men

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because we only have three survey years while for women we have five. Also, for the

surveys conducted from 2000 onwards, we found that men are more likely to refuse

providing data on weight and height. So, we loose proportionally more men than women

when building the analytical sample due to item non-response on the key variables to

calculate BMI.

Another important issue on data construction and regressions is that to fit logistic models

with a fixed effect at municipio level, our selected analysis tool, we need the response

variable to have variation over all cells defined by the values that take the control

variables. As we use stricter definitions of obesity, going from overweight to morbid obese

the “treatment” gets fatter. And with it the cell size of the cases for which the response

variable, y, takes 1 shrinks. Because we fit a logit fixed effects at municipio level, to keep

one municipality in the sample we need to have variation (i.e. enough cases of y=0 and

y=1) for all combinations of values that the explanatory variables take. Further, because

age is treated as a continuous variable, it really gets difficult to have enough (at least two)

women (or men) of the same age (and all other combinations of control variables) with y=1

and one with y=0 when the response variable indicates a fatter and fatter outcome. This is

why we loose sample as we consider a fatter outcome as the response variable.

We now turn to discuss empirical results. Notice that in table 3 we report average semi-

elasticities (see Kitasawa 2012) to allow comparisons of effects across random and fixed

effects specifications. One disadvantage of semi-elasticities, however, is that it cannot be

interpreted as marginal effects or as the effect of changing a control on the probability of

observing a 1 response.

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Education. As can be seen in Table 3, higher levels of education are negatively related to

the probability of being overweight, obese and morbidly obese for women. Specifically, high

school and college relative to lower levels of educational attainment have negative impacts

on the three measures of BMI for women. In the case of overweight and obese, college has

a more protective effect than does high school. This is a ‘dose response’ to education. For

morbid obesity, this relationship does not hold, but both educational levels are still highly

significant and protective. Primary education has a positive and significant impact only for

the category of overweight. Recall that no formal education is the omitted category.

For men, the relationship of obesity and education is different. Education does not protect

against obesity, the sign is positive and significant for all education levels for overweight and

is positive and significant for high school and college for the obese. It is insignificant for

morbidly obese, but the sample of morbidly obese men is small in the three years of data

and is less than half the rate of women.

Age and other control variables. As expected, all measures of BMI increase significantly with

age for both female and male. And the impact is greater for obese and morbidly obese as

compared to overweight for women. For men, the relationships are similar, but age is not

significant for the morbidly obese. As suggested in the descriptive analysis, age is positively

related to the probability of being overweight and the probability of being obese but is not

significantly related to the probability of being morbidly obese. While our analysis is cross-

sectional, this analysis is suggestive that morbid obesity may begin early in the life-cycle

and persist with age. More research is needed.

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Indigenous language has a positive impact on all three measures of weight for women, but

is insignificant for men at all levels of weight. Urban is the only significant variable for the

male morbidity obese; but again there is a relatively small sample of morbidly obese men.

Conclusion.

We analyzed the tremendous increase in obesity in Mexico between 1988 and 2012. We

found large increases in the proportion of overweight, obesity and morbidly obesity over the

period. Another finding is that the growth is faster for the more stringent measures of BMI..

In particular, we find that the prevalence of overweight doubles for women aged 20-44 in the

1990s and then continued to grow but at a slower rate. While the rate of growth for obese

women more than tripled over this period; and shockingly the percentage of women who are

morbidly obese increased by more than 7 times. Because we only have data for men from

2000-2012, we cannot document the tremendous growth by BMI categories that we

document for women. But by comparing the growth rate from 2001-2012 we can see that

the growth rate in obesity measures for men was faster as the BMI measure became more

stringent.

Perhaps the most worrying of our results is the tremendous increase in morbid obesity over

this period. From the regression results, we find that education at higher levels is quite

significant and protective for females, but not for men. Specifically, high school and college

have negative impacts on the three measures of BMI for women. And in the case of

overweight and obese, college has a more protective effect than does high school. For men,

however the findings are reversed; education has a positive and significant for all education

levels for overweight. Age was found to have a positive and significant impact. Indigenous

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language has a positive impact on all three measures of weight for women, but is

insignificant for men at all levels of weight.

In summary, we have furthered the understanding of how the Mexican population has grown

heavier over time. The greatest growth, at least for women, occured in the 1990s. And

education is not universally protective as we might have thougt. Our findings raise a number

of questions that we will continue to pursue in future research. First, why the tremendous

growth in obesity occurred in Mexico during the 1990s but occurred earlier for the US. A

second puzzling question is why higher levels of education appear to be protective for

women and not for men. For men, those with no formal education are least likely to be

overweight, presumably due to low income and physical jobs. The epidemic of obesity is

costly to Mexico in terms of lost productivity, compromised public health, more suffering, and

worse health including the growth of diabetes. A better understanding of the causes and

socio-economic and demographic differences is warranted given the scope and importance

of the problem. The ultimate goal would be to reverse the astonishing growth.

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Table3:Average(semi)elasticitiesofPr(y=1|x,u) Women Men

Variable overweight obese morbidobese overweight obese morbidobeseurban 0.003 -0.0076 0.0809 -0.0165 0.0409 0.3562*

(0.010) (0.019) (0.070) (0.014) (0.035) (0.176)age 0.0292*** 0.0447*** 0.0466*** 0.0241*** 0.0349*** 0.0074

(0.001) (0.001) (0.005) (0.001) (0.002) (0.009)indigenouslanguage -0.0631** -0.1478*** -0.5239** 0.0009 -0.0772 0.3592

(0.022) (0.043) (0.183) (0.032) (0.076) (0.326)primary 0.0899*** -0.0116 -0.0659 0.0820* 0.0858 0.0958

(0.020) (0.040) (0.144) (0.041) (0.105) (0.472)secondary 0.0249 -0.1629*** -0.2754 0.1534*** 0.1669 -0.0299

(0.021) (0.042) (0.149) (0.041) (0.105) (0.472)highschool -0.0918*** -0.3595*** -0.6394*** 0.1954*** 0.2280* -0.0444

(0.023) (0.046) (0.171) (0.043) (0.107) (0.482)College -0.1609*** -0.4661*** -0.4686** 0.1989*** 0.2132* -0.1315

(0.022) (0.045) (0.161) (0.043) (0.108) (0.487)MunicipioFE yes yes yes yes yes yesYearFE yes yes yes yes yes yesNo.municipios 545 531 338 327 323 118Years 1988-2012 1988-2012 1988-2012 2000-2012 2000-2012 2000-2012No.Obs 52959 52577 44188 18008 17942 11209Note.Standarderrorsinbrackets.FortheLinearprobability(LPM)modelsRobuststandarderrorsclusteredatmunicipiolevelareshown.*p<.05;**p<.01;***p<.001.