WI'S 2461
POLICY RESEARCH WORKING PAPER 2667
Trade Reform and Results from a two-step
simulation that uses a
Household Welfare computable generaJ
equilibrium model and
The Case of Mexico detailed consumption and
income household data
suggest that trade
Elena Ianchovichina liberalization benefits people
Alessandro Nicita in the poorest deciles more
Isidro Soloaga than those in the richer ones.
The World Bank
Development Research Group
Trade
August 2001
POLICY RESEARCH WORKING PAPER 2667
Summary findings
Ianchovichina, Nicita, and Soloaga use a two-step, appropriately: almost zero for North American Free
computationally simple procedure to analyze the effects Trade Agreement (NAFTA) members and higher tariffs
of Mexico's potential unilateral tariff liberalization. First, for nonmembers. Even starting with low tariff
they use a computable general equilibrium model protection, simulation results show that tariff reform will
provided by the Global Trade Analysis Project (GTAP) as have a positive effect on welfare for all expenditure
the new price generator. Second, they apply the price deciles. Under an assumption of nonhomothetic
changes to Mexican household data to assess the effects individual preferences, trade liberalization benefits
of the simulated policy on poverty and income people in the poorer deciles more than those in the
distribution. richer ones.
By choosing GTAP as the price generator, the authors
are able to model Mexico's differential tariff structure
This paper-a product of Trade, Development Research Group-is part of a larger effort in the group to study the effects
of trade policy on poverty. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington,
DC 20433. Please contact Lili Tabada, room MC3-333, telephone 202-473-6896, fax 202-522-1159, email address
Itabada@worldbank.org. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The
authors may be contacted at eianchovichina@worldbank.org, anicita@worldbank.org, or isoloaga@worldbank.org.
August 2001. (49 pages)
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about
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Produced by the Policy Research Dissemination Center
Trade Reform and Household Welfare: The Case of Mexico
Elena lanchovichina, Alessandro Nicita and Isidro Soloaga
World Bank, DECRG-Trade
August 2001
The authors wish to thank Emiko Fukase, Marcelo Giugale, Thomas Hertel, William
Martin, and Dominique Van der Mensbrugghe for their useful comments, although they
are not responsible for any errors remaining. Specific figures and calculations of poverty
and inequality measures are the authors' own and do not necessarily represent or coincide
with the views of the World Bank on the matter.
1. Introduction1
The analysis of the distributional impact of trade reforms plays an important role
in the assessment of who is paying the welfare costs of adjustment, what are the
instruments that could be used to eventually alleviate these burdens, and at what
aggregate economic costs. The analysis is difficult because trade reforms have
macroeconomic linkages, while the effects on income and poverty are inherently
microeconomic issues. Researchers have tackled the analysis in many different ways.
Some have used aggregate indicators such as the levels of wages and
employment, or the value added in different sectors, in order to assess the effects of
different trade regimes on the distribution of income (Beyer et al., 1999; Harrison and
Hansen, 1999; Pissarides, 1997).
As these indicators fail to capture the mix of effects on specific households and
these households' responses to prices, other researchers have tried more elaborate models
that account for the interrelationship between labor markets (rural and urban) and prices
of staple agricultural goods. For instance, Ravallion (1989) used a partial equilibrium
model to examine the rural welfare distributional effects of changes in food prices under
induced wage responses for rural Bangladesh. Levy and van Wijnbergen (1992) also
followed this partial equilibrium approach when analyzing income effects on different
economic groups after changing production and consumption subsidies on agricultural
goods.
Computable general equilibrium (CGE) models offer a more comprehensive way
of modeling the overall impact of policy changes on the economy. These models
incorporate many important economic linkages and are well-suited to explain medium-
to long-term trends and structural responses to changes in development policy. An effort
to adapt CGE models to the analysis of different adjustment programs and to estimate the
costs of other strategies was made in the late 80's by the Organization for Economic
Cooperation and Development (OECD), through the work of Bourguignon, Branson and
l Specific figures and calculations of poverty and inequality measures used in this paper are the authors'
own and do not necessarily represent or coincide with the views of the World Bank on the matter.
2
de Melo (1991).2 Their "macro-micro" model links the short-run impacts of
macroeconomic policies that affect the distribution of income through inflation, interest
rate and other asset price changes with the medium-run impacts of structural adjustment
policies (i.e. incentive reforms) that affect the distribution of income through relative
commodity and factor price changes.
To measure distributive impacts, these extended CGE models map factor income
(land, labor and capital) to different types of households (capitalists, big farmers, small
farmers, landless workers, modem workers, and workers in the informal sector). The
models were applied to analyze different policy changes in several developing countries.3
Comprehensive as they are, these modified CGE models require an important
amount of work and resources. However, sometimes the analysis must be carried out in a
time frame or under budget restrictions that forbid the development of comprehensive
models as those mentioned above, and researchers have to resort to computationally
simple ways to evaluate the distributional impact of trade and price policy reforms.
Research done at the World Bank for Panama (World Bank, 2001a) and, and by
Levinsohn et al. for Indonesia, are examples of such approach.4 The procedure used in
these cases is a straightforward combination of household surveys, which provided the
structure of households' consumption at the moment of the simulation, and of simulated
(World Bank studies) or actual (Levinsohn et al.) price changes. The change in the cost of
living by segments of the population was then used to assess the impact on income
distribution of the various simulations. These indexes, which are Laspeyres cost of living
indexes by household, provide an upper bound measurement of the increase in
expenditure that would be required for each group to purchase the same quantities of
goods as in the base situation.
In the World Bank study of Panama, the re-distributive impact of complete trade
and price liberalization for basic food items was simulated using household data from the
Living Standard Measurement Study (LSMS). The study adopts a "zero elasticity of
2 See Chapter 12 in Dervis, de Melo and Robinson (1982) for a brief description of CGE models that
incorporate income distribution.
3 Results from the application of the so called "maquette" can be found in the special issue of World
Development, 1991, Vol 19, No. 11. See also research done at IFPRI, for instance by Bautista and Thomas
(1997), Minot and Goleti (1998), and Lee-Harris (1999).
4 See also the paper by Agenor et al. (2000).
3
substitution" assumption for producers and consumers of basic agricultural goods, and
applies the change in price to quantities of the base period to get the net impact of the
price change by household. The new prices are obtained by estimating the border prices
of the staple goods in a tariff free scenario.
The World Bank paper on energy price reform in Iran (World Bank, 1998)
combines an input-output table, which shows the input structure in the production of all
final goods, and a consumer expenditure survey, which shows the amount of each final
good purchased by consumers. The overall cost of living effect after a price change on
the different household deciles is then calculated. The new prices are also computed as
the border prices.
The Indonesian study done by Levinshon et al. (1998) adopts a different approach
to get the new prices by using actual price changes, and then predicting how these price
changes would have impacted on households' cost of living, by per-capita income decile.
The common denominator in these last three studies described is their "two-step"
structure: they use first a process that generates the new prices (either simulated or actual
changes), and second a household survey (HJ) to assess the effects on poverty and
income distribution.
This paper follows a similar approach. However, in order to get a computationally
simple way of assessing the re-distributional impact of trade on poverty and inequality,
we propose the use of a particular CGE model, the one coming from the Global Trade
Analysis Project (GTAP), as the price generator. There are a number of reasons for our
choice of methodology for the price generator. First, GTAP is specifically tailored to
simulate trade policy changes, and is well suited to take into account the new wave of
Preferential Trade Agreements (PTA), such as NAFTA and MERCOSUR. Second, the
GTAP database has considerable sectoral and regional detail. It contains input-output
information on 24 countries or regions (13 of them developing countries) and 50 sectors
and captures differences in intermediate input intensities, as well as import intensities, by
use. It is publicly available and regularly updated. Third, if not already in the data set,
some countries could be proxied to those in GTAP. Fourth, there are HH surveys
available for many of the developing countries already included in GTAP. In addition, we
4
assess the impact of trade reform not only on income, but also individual welfare
assuming non-homothetic preferences.
Section 2 outlines the methodology to be used in the measurements of poverty
and inequality. Section 3 provides a brief presentation of the GTAP model, the HH data
available for Mexico, and the corresponding matching of categories between them.
Section 4 provides an assessment of poverty and tariffs structure in Mexico. Section 5
presents and discusses the results and outlines the sensitivity of the results to various
assumptions. Finally, section 6 summarizes the main conclusions.
2. Methodology
The analysis is conducted as follows: first, we compute a series of poverty
measures from the existing household data; second, we measure again the poverty levels
adjusting them for the price effect of the simulation; third, we adopt the price indexes to
analyze the impact that the policy simulation would have on the expenditure side. Finally,
we apply both the expenditure and income sides of the simulation to obtain the change in
welfare.
2.1 Poverty Indicators and Poverty Lines
A credible measure of poverty is a powerful instrument for focusing the attention
of governments and civil society on the living conditions of the poor. Income and
consumption levels are usually the most common indicators for measuring living
standards. An individual is considered poor if his or her consumption falls below some
minimum considered necessary to meet basic needs. The poverty line represents the
minimum income or expenditure necessary to fulfill those basic needs. The poverty line
is bundled with the concepts of utility, welfare and household characteristics. Briefly, the
poverty line can be written as:
pv =e(p,x,u2)
5
In words, the poverty line is the cost efficient consumer's expenditure fiunction e
necessary to attain the minimum level of utility u, compatible with a vector of prices p
and household characteristic x.
The choice of a particular poverty line is always debatable. The literature adopts
various methods for its calculation.5 This study follows the basic needs method.
Consequently, the poverty line is the minimum level of expenditure or income that allows
the consumption of a pre-determined basket of food goods, scaled up to include non-food
needs6. To quantify the minimum intake in terms of products, most of the poverty
assessments on Mexico refer to two studies: the first one was conducted by the
Coordinacion General del Plan Nacional de Zonas Deprimidas y Groupos Marginados
(COPLAMAR) using data from the 1977 household survey; the second one, which uses a
similar methodology, was developed by the Comision Economica para America Latina y
el Caribe (CEPAL) using data collected from the Food and Agriculture Organization
(FAO) and the United Nations (UN) in 198 1.7 In this paper, we use the poverty line
calculated by the CEPAL and we use its basket for updating the poverty line after the
simulation. The poverty line is updated using the price change of the CEPAL basket from
the second through fourth deciles. The CEPAL basket is different for urban and rural
households. Therefore, we have different coefficients for changes in rural and urban
areas.8 The CEPAL study reports two levels of poverty: the poverty line and the
indigence line.9 The indigence line represents the minimum expenditure necessary to
fulfill the basic food budget, and the indigents are defined as persons who reside in a
household with such a low income that even if all of it were used to buy nothing but food,
5 For an extensive discussion on poverty line construction see: Ravallion (1998).
6 The minimum daily calories intake is set at 2165 (FAO/OMS/ONU, 1985)
7 CEPAL calculates the per capita minimum requirement while COPLAMAR calculates the basket at the
household level. The average household of 4.9 members is comprised of 2.7 adults, 1.66 children (ages 3-
14) and 0.47 babies.
8 The coefficients used in this paper are coming from CEPAL and are slightly different to the ones used by
INEGI/CEPAL.
9 The indigence line is also referred to as the extreme poverty line. In almost all developing countries, the
poverty line worked out to be twice the indigence line for urban areas, while in rural areas it was calculated
as being approximately 75% higher than the indigence line.
6
the household would still not be able to satisfy completely the nutritional needs of its
members. We will make use of this distinction in the calculation of the poverty indexes. 10
To assess poverty, we consider three measures based on the Foster-Greer-
Thorbecke (henceforth FGT) class of additively decomposable poverty indexes."1 First,
the headcount ratio (a=O) is simply the share of the population living below the poverty
line. Second, the poverty gap index (c-l) captures the distance separating the poor from
the poverty line as a proportion or that line (the noon poor having zero distance). The
main weakness of this index is that it does not indicate the severity of poverty. The third
measure (a=2) is sensitive to the problem of measuring the severity of poverty.
Therefore, it is referred to as distribution-sensitive FGT. The sensitive FGT gives heavier
weight to the poverty of the very poor than the poverty gap index. The drawback of this
index is that it is less straightforward to interpret. It is essentially composed of two parts:
an amount due to the poverty gap and an amount due to the inequality among the poor.
To analyze inequality issues we compute two more indexes for the income part of the
data: the Gini coefficient and the Theil index.'2
2.2 Price Indexes
To calculate the impact of the policy simulation on the expenditure of the
household, we report the results of the most commonly used indexes: the Laspeyres, the
10 The difference between the poverty lines of rural and urban households derives from the fact that they
have different consumption baskets and face different unit prices. We set different poverty lines according
to rural and urban classifications in the calculation of the FGT indexes, but we do not report separate results
for urban and rural households.
1" These indexes are widely used in the literature for their additive properties and their linkages to the
stochastic dominance theory (Foster, Greer and Thornbecke, 1984). The additive properties makes the
indexes particularly useful in analyzing population subgroups. The FGT class of poverty measures is
formally: Pa = E [(z - y; ) / zra / n where y, is the per capita consumption of the ith individual, n
is the size of the population, z is the poverty line and a is a parameter. The additive property allows us to
decompose the measures across population sub-groups.
Th12 icefcet a ewitn gn 2 .cov(Y, F(Y))
12The Gini coeffcient can be written as: gini ,where Y is the distribution of per
capita income, F(Y) is its cumulative distribution and u is the mean of Y. Theil index can be written as:
theil = I [E-Y'- In-], where Y, is the income of individual i, ,u is the average income, and n is the
size of the population. Note that the Theil index is additive.
7
Paasche, the Fisher and the Tornquist indexes.13 The Laspeyres index does not take into
account substitutability in consumption. Therefore, it underestimates the decrease and
overestimates the increase in the true price index. The Paasche index performs vice-
versa: it underestimates the increase and overestimates the decrease in the true price
index.'4
2.3 The GTAP Household and welfare measures
2.3.1 GTAP Household
The GTAP model (Hertel, 1997) features a regional superhousehold whose
behavior is governed by an aggregate Cobb-Douglas utility function specified over
private household consumption, government spending and savings. Thus, in GTAP, the
regional superhousehold spends a fixed share of its income on private household
consumption, government spending and savings. The model computes the percentage
change in per capita utility from aggregate household expenditure for a given country (or
region) [u(r)] and a money metric equivalent of aggregate utility change, [EV(r)]. The
utility measure, u(r), indicates changes in welfare of the average individual in region r.
The equivalent variation measure, EV(r), summarizes the welfare changes resulting from
a policy shock in dollar values.
13 The Laspeyres price index is formally defined as: PL= q�/p q�p� . The Paasche price index
I i
is given by: qp = / q . The Fisher price index is defined as:
i i
PF = i E O O E I O ,where q stands for quantity and p for price, i denotes the product group and
the superscript represents the state. The Tmrnuquist price index is given
by: in PT = + sh' ) In(P-), where sh is the budget share.
14 The Laspeyres and Paasche indexes represent the worst and the best possible scenarios, respectively.
8
2.3.2 Private demands
Per capita utility from private household expenditures is modeled via a
nonhomothetic Constant Different of Elasticities (CDE) function, which is designed to
capture differential price and income responsiveness across countries (Hanoch, 1975). Its
main virtue is the ease with which it may be calibrated to existing inforrnation on income
and own price elasticities of demand.
The CDE implicit expenditure function is given by:
(1) Z B(i,r) * UP(r),l(i.r)r(i.) * [PP(i,r) IE(PP(r),UP(r))]fi(i-r) =,
ie TRAD
where E(.) represents the minimum expenditure required to attain a prespecified level of
private household utility, UP(r), given the vector of private household prices, PP(r) and
traded goods i. Minimum expenditure is used to normalize individual prices, and these
normalized prices are then raised to the powerfi(i,r) and combined in an additive form.
Under this formulation, as the minimum expenditure can not be factored out of the left-
hand side expression, the CDE is an implicitly additive function. Besides capturing
nonhomotheticity, a useful feature of the CDE is that it simplifies into a CES when
pi(i,r) =,8 for all i and into a Cobb-Douglas when,8=O.
2.3.3 The government and savings
GTAP uses an index of current government expenditures to proxy the welfare
derived from the government's provision of public goods and services to private
households in the region. This index is aggregated with private utility in order to make
inferences about regional welfare.
Regarding savings, its inclusion in this static model comes from work done by
Howe (1975), who showed that the intertemporal, extended linear expenditure system
(ELES) could be derived from an equivalent, atemporal maximization problem, in which
savings enters the utility function.
2.3.4 Changes in private income and in private utility
Changes in private utility are calculated in GTAP as:
9
(2) up(r)={yp(r)- Z_[CONSHR(i,r)*pp(i,r)]} / _CONSHR(i,r)*INCPAR(i,r),'
i FTRAD iE TRD
where upfr) is the percentage change in private utility in region r, yp(r) is the percentage
change in private household income in region r, CONSHR(i,r) is the share in total
consumption of good i, pp(ir) is the change in the demand price of commodity i,
INCPAR(i,r) is an income expansion parameter, and i sums over the set of traded
commodities TRAD consumed by the households. The INCPAR(i,r) comes from the CDE
minimum expenditure function that is used to represent private household preferences in
the model and is related to the income elasticity of demand for good i. If preferences are
homothetic, the INCPAR(i,r) equals one for all i. If preferences are not homothetic, the
INCPAR(i,r) are constrained to be strictly positive and are greater than one for superior
goods.
When preferences are homothetic, (2) collapses into the difference between a
Laspeyres price index for income and a Laspeyres index of expenditures:
(3) up(r) = yp(r) - X, [CONSHR(i, r) * pp(i, r)] .16
ieTRAD
We use the Cobb-Douglas form of preferences to check the robustness of our simulation
results.
In turn, household's income is defined as the sum of the household's endowments
(agricultural land, labor, and capital) times the price of these endowments actually faced
by the households:
(4) INCOME = X QO(i, r) * PS(i, r).
ieENDOWMENT
The change in household income yp(r) is then defined as:
(5) yp(r) = X INCOMESHR(i, r) * ps(r) .
ie ENDOWMENT
2.3.5 Our Approach
The key purpose of this paper is to apply formula (2) to the household data in
order to derive information on ihe impact of trade reform on individual welfare. Due to
lack of better infornation, we can not consider variations in pp(i,r) coming from spatial
15 We follow GTAP's notation. Upper case letters denote levels and lower case denotes changes in
percentage.
16 This is the simplest of all commonly used indicators of welfare and real income. See: Sadoulet and de
Janvry (1995).
10
location or from a poor-rich classification of households. Thus, we assume that pp(i, r) is
the same for all households.
Equation (2) takes into account the fact that poor individuals spend a larger
proportion of their income on items with lower income elasticities than rich ones to
determine the effect of a marginal increase in real income on individual welfare. In effect,
formula (2) says that a dollar increase in real income is worth more to the poor individual
than to the rich one.
3 Data
We use GTAP to simulate the effects of trade liberalization on Mexico's
economy. The simulations results include price changes for products and endowments
and changes in domestic demand for products. The model assumes full employment, and
therefore endowment supply is fixed.
The GTAP system counts 50 expenditure groups. These groups can be further
aggregated according to food, manufacturing, services and other primary products. On
the income side GTAP distinguishes between five different sources of income: land,
capital, natural resources, skilled and unskilled labor. A more detailed explanation of the
GTAP model and a description of GTAP sectors can be found in the GTAP appendix.
This study utilizes the 1996 Mexican National Household Income and
Expenditure Survey (ENIGH), which is collected by the Instituto Nacional de Estadistica,
Geografia e Informatica (INEGI). The survey collects a wide range of data. The survey
contains detailed expenditure data on a wide set of consumption goods at the household
level and detailed information on income at the individual level. Moreover, the survey
collects a large array of household characteristics and household members characteristics.
The survey is representative at the national level, and it was drawn using a
stratified, multistage and clustered method. To obtain suitable estimators, we make use of
the survey weights, and adopt the estimating procedures developed specifically for survey
data.'7 In our study, the welfare is measured at the individual level, therefore we make
17 For a review of statistical methods and issues in the analysis of survey data see Deaton (1997).
11
use of equivalence scales to adjust the data accordingly. The data appendix further
discusses the Mexican household survey.
The matching of GTAP and the household survey represents a challenge. In this
type of exercises compromises are the norm more than the exception. In this case, the
extremely detailed information that household surveys incorporate and the condensed
categories of GTAP require a degree of arbitrariness. On the expenditure side, the GTAP
system counts 50 comnmodity categories while the Mexican household data has about 600
different categories. On the income side, GTAP identifies 5 different income sources, and
the household data has 47 categories. In the data appendix, we describe in detail how we
aggregated the household data to fit GTAP aggregations. For the most difficult cases, we
had to use a certain degree of arbitrariness. Nevertheless, the final results give us a
reassuring picture. On the expenditure side, the GTAP domestic consumption shares and
the household expenditure shares look very similar at the aggregate level."8 Figure 1
shows the results of the aggregation. The matching of the service sectors with GTAP
categories had problematic results with large differences across sub-sectors. To solve this
impasse, we decided to aggregate GTAP service sectors into a single category.'9
GTAP and the household survey use different income categorizations. Therefore,
the matching is not as linear as in the expenditure case. The GTAP income composition is
calculated according to the national accounts and distinguishes five income categories:
land, capital, natural resources, skilled and unskilled wages. The household survey
differentiates income according to sources, and in many cases these can be attributed to
more than one GTAP category. 2( Figure 2 shows the results of the income matching.
Differences are large, especially in the share of capital. In GTAP, capital represents more
than 60% of total income, while in the case of household data, this share is less than
18 At a more disaggregate level, the data show some discrepancies. These, however, are restricted to the
manufacturing sector in most cases.
'9 In this particular case, the procedure is justifiable by the fact that the price variations within the service
sectors are extremely small. Because it may not always be the case, in the aggregation tables at the end of
the appendix, we disaggregate across services. For a complete description of the services sector aggregation
of GTAP see Huff, McDougall and Walmsley (1999).
20 For example, income from cooperatives should be correctly subdivided into income from wages, capital
and land.
12
20%.21 The difficulty of income matching is probably only one of the causes of this
discrepancy. Other likely sources of this difference is the income mis-reporting issues
that afflict household surveys.22 This problem necessitates a robustness check. To adjust
for the underreporting issues, this paper follows the practice of equalizing total income to
total expenditure by household. To adjust for the discrepancies between the survey and
the GTAP data, we adopt a procedure with which we use the income composition coming
from GTAP, while maintaining the distribution of each endowment across households
from the household survey. Figure 2 shows the income shares adjusted with this
procedure. The matching process ensures that the income categories in GTAP are closely
aligned with the aggregate income categories of the household survey. The data
aggregation appendix provides a detailed explanation of this procedure.
Table 1 reports the tariff structure for Mexico in 1997 (Estevadeordal, 1999). We
updated the GTAP model with the new tariffs taking into account the different tariff
structure of NAFTA. The tariff structure is quite detailed. For simplicity, tariffs for food
products are set to two levels according to the averages for agriculture products and food
products.
4 Poverty and Trade Policy in Mexico
Despite Mexico's status as a middle-income country and member of the OECD,
poverty is widespread. Poverty issues in Mexico have been the focus of recent studies at
23
the World Bank. In accordance with the results of those studies, we briefly summarize
the basic findings and give a picture of the Mexican society emerging from the 1996
household survey.
The household survey data collected in 1996 shows that poverty is widespread
across both the urban and the rural areas and includes slightly less than half of the total
population. Moreover, one out of seven individuals is considered indigent. Inequality is
21 Even if we attribute all the residual categories- negative savings, transfers and imputed rent, to the capital
share, this share will not reach 50%. Also, wages are very well defined in both GTAP and the household
survey, but while in GTAP they account for about 30% of income, in the household survey they account for
about 50%.
22 For a more detailed discussion see: Rendtel, Langeheine and Berntsen (1998)
23 For example, studies by the World Bank include Wodon (2000), World Bank (1996) and (1 999). Other
studies have been conducted by the Inter-American Development Bank (see Lustig and Szekely (1998)).
13
high, with the poorest 40% of the population collecting about half of the income received
by the richest 10%. For the purpose of the analysis, it is useful to know the income and
expenditure distribution across the various income deciles. The household survey is very
detailed and consumption baskets and income composition can be precisely identified for
each population stratum. As we discussed above, we have aggregated the expenditure and
income categories to fit the GTAP aggregation. Although, this reduces the precision of
the overall picture it makes the data much more tractable. To briefly illustrate the
Mexican situation, we report here some descriptive statistics on income and expenditure
patterns from the household survey. Also, we report the basic poverty and inequality
indicators.
4.1 Consumption
In table 2 we report the consumption shares for the average Mexican household
and for each income decile. The average Mexican household consumes, on per capita
basis, about 1060 pesos per month, of which a quarter goes for food, a quarter goes for
manufactures, and about half is spent on services.24 As expected, the analysis by deciles
shows the sharp decrease in the food consumption share as income increases and a
parallel rise in the consumption of services.25 The share of expenditures in manufacturing
is almost constant across all deciles. At the more disaggregated level, it is possible to
observe the different income elasticity across products. The food basket is quite different
across deciles. According to the household survey, the poor obtain most of their calories
from Cereals and Vegetables. Meanwhile, the richest rely on more expensive foods such
as meat and dairy products. Table 3 displays the composition of the food basket across
deciles.
Figure 3 illustrates graphically the expenditure levels across deciles. It is striking
how most of the wealth is concentrated in the highest deciles. Across deciles, the level of
expenditure on services and manufacturing grows much faster than the one for food.26 In
particular, the expenditure on services, which is almost non-existent in absolute values
24 The total expenditure corresponds to about $14OUS.
25 The category labeled "Residuar' contains expenditures which are attributable mostly to investments or
transfers. Those categories cannot be matched to any GTAP category.
14
for the poorest households, grows quickly across the deciles to reach more than 2000
pesos per month for the wealthier deciles. Total expenditure in manufacturing products
shows a similar pattern on a smaller scale.
4.2 Income
The composition of income reflected in the survey data is different from the
Mexican National Accounts. As explained before, the reason can be attributed partly to
the income mis-reporting issue and partly to the problematic matching of income
categories due to the different classifications in GTAP and the survey. The household
data show that the average Mexican household receives more than half of its income from
wages; income from capital is around 20%; income from residual categories such as
imputed rent, auto-consumption, transferS and negative savings represents more than
30%. Table 4 presents the income decomposition across deciles. The income
composition is very similar across the entire population spectrum, with the only
substantial differences being the wage composition and the composition across the
residual categories. Analyzing the income composition of the poorest deciles we see that
auto-consumption, mostly attributable to production of food for own use, is an important
source of income representing more than 15% of income for the poorest 10% of the
population. Auto-consumption rapidly declines along the income classes. Income from
land represents more than 5% of total income of the poorest deciles. The poor also obtain
a large part of their income through unskilled wages and transfers. Interestingly, imputed
rent, the opportunity cost of the rent of the own house, is slightly more than I0% for all
the classes. This percentage increases slowly across income classes, suggesting that
imputed rent indicates well the level of income.
According to the classification of the household survey, wages are the primary
source of income for all deciles. A significant part of the income of the poorest deciles
comes from unskilled labor, while the richest obtain almost half of their income from
skilled labor. The income of the richest deciles is about 4000 pesos per month,
26 Note that manufacturing products and services include items which are necessary to be able to fulfill the
basic needs- items or services such as basic tools and transportation.
15
meanwhile the income of the poorest deciles is 210 pesos per month, definitely below the
indigence line.27
4.3 Poverty
The poverty line was set according to the CEPAL study at 635.5 and 548.3 pesos
per capita per month for the urban and for the rural population, respectively. The
indigence line was set at 317.8 and 313.3 pesos per capita per month, respectively, for the
urban and the rural residents.28 Table 5 reports the FGT estimates along with their
standard errors. In 1996, about 41% of the Mexican population lived below the poverty
line, meanwhile about 13% lived below the indigence line.
4.4 Inequality
The household survey presents a situation where the poorest 20% of the
population collect less than 5% of total income. Meanwhile, the richest 10% collect about
40% of total income. Table 6 reports the Theil indexes and the Gini coefficient. The Gini
coefficient is 0.465, while the Theil inidex, which gives more weight to the upper and
lower tails, is 0.431. 29 We will analyze the change, if any, of those indexes after the
simulation.
5 Findings
We set all tariffs to zero. Thus the simulation is closer to a theoretical exercise
than a policy study. Nevertheless, setting all tariffs to zero represents a good testing point
for checking the outcomes of the model.
27 In US dollars this is $526 and $28, respectively.
28 In US dollars, those figures correspond to about 83 (urban) and 72 (rural) dollars a month for the poverty
line and to about 41 and 40 dollars a month respectively for the indigence line.
29 It is likely that those numbers are smaller than the actual ones. The fact that we use total expenditure as a
proxy for total income will likely reduce the inequality indexes. Compared with other studies, for example
Wodon (2000), our numbers are effectively smaller. Wodon (2000), using total income, finds that for
Mexico the Gini coefficient is 0.55 and the Theil is 0.52. World Bank poverty assessment 2001 gives an
esimate of the Gini coefficient of 0.4826. Nonetheless, what matters for the purpose of this paper are the
changes in these levels rather than the levels themselves.
16
5.1 Price and Quantities
Given the relatively small rates of protection in Mexico, especially within
NAFTA, we do not expect large effects resulting from the complete abatement of tariffs.
Table 7 reports the price and quantity changes produced by the simulation. As expected,
most of the prices show a decline, the exception being meat and services. Quantities
domestically consumed move accordingly, with larger surges in sectors where prices
dropped more.
The effect of the simulation on the income part results in a decrease of
approximately 3 percentage points in factor returns for land and natural resources.
Returns to capital and labor increase by about one to one and a half percentage points, in
both cases.30
Income parameters are built into GTAP and are related to the income elasticity of
each product group. As expected, they are higher for manufacturing and services than for
food.3'
5.2 Income and Consumption
Table 8 reports the price indexes for consumption and income by deciles. The
overall price indexes show that, as a consequence of the liberalization, the average
expenditure basket slightly decreased, while average income increased by about 1%. On
the income side, endowment returns to skilled labor increased more than returns to
unskilled labor, and land returns declined. Therefore, rich households, which obtain a
large share of income from skilled labor and capital, gain more than the poor ones, in
percentage terms. On the expenditure side, the situation reverses. Because of different
consumption baskets, the poorer households gain, in percentage terms, more than the
richer ones. This effect is due to the overall decrease in the price of food products, which
constitute a large proportion of the consumption basket of the poor. For the rich
households the discount for food and manufacturing products is compensated by the rise
in the price of services, making the price of their consumption basket almost unchanged.
30 The similar increase of the return of those endowments is probably the cause for which the income effect
on household is not much different when we check for robustness of income composition.
17
In the same table we also report the decomposition across sectors of the Laspayres
index.32 The results are strorngly driven by the consumption shares. Poor households,
which consume half of total income in food products, gain mostly due to the decline in
food prices.. Meanwhile, the rich households obtain most of their gain from reduction in
the prices of manufacturing. Nevertheless, this gain is compensated by the loss of
purchasing power in services. On the income side, as expected, the decomposition shows
that poor households gain mostly from unskilled labor, and simultaneously lose from the
reduced returns to land. The richer households gain mostly from the increased returns to
skilled labor.
5.3 Poverty
Table 9 compares the values of the-FGT and inequality indexes obtained straight
from the survey with the ones obtained after the simulation. The results are in line with
what emerged from the price index analysis. The poverty lines have been updated
according to the new prices of the minimum expenditure baskets, paid by the household
from the second through fourth decile.33 As expected, poverty measures show a slight
reduction in the incidence of poverty. The new level of the headcount index is only half a
percentage point lower than the one computed based on the survey. The Gini coefficient
and the Theil index show, if any, a minimal increase in inequality.
5.4 Utility
The change in utility is positive across all household centiles. Applying the GTAP
output to the household survey produced an average utility increase of about 0.12%. This
31 Future work could aim at estimating this parameter for in Mexico.
32 This is possible due to the additive property of those indexes. The Laspeyres index can be decomposed
I o F p00j pi'
into groups according to: E= E cEi( Pi ( - ) ],where w is the budget share for good
i P, G X ieGL XG Pi
i and x is total expenditure for group G. The effect of each group G in the change is:
pl x�0 U��p
i Pi G XLieG XG Pi
3 Poverty lines were reduced by 0.57% and 0.62% for urban and rural households.
18
is the same value calculated with GTAP. This is indicative that the GTAP data have been
matched sufficiently well with the household survey data.
As it turns out from the data, sorting the observations by expenditure is very
similar to sorting the observations by food expenditure shares. Because GTAP's income
parameters for necessities are smaller than the income parameters for superior goods, the
denominator in equation (2) increases monotonically with the level of expenditures. This
implies that similar increases in real income (Table 8) translate into larger increases in
welfare for the poor individuals than the rich ones. The households that gain the most, in
percentage terms, are the ones at the bottom of the income scale. Meanwhile, the richer
households gain less.
6 Summary
We use a two step computationally simple procedure to analyze the effects of trade
liberalization using household survey data for Mexico. First, we use an already available
CGE model provided by the Global Trade Analysis Project (GTAP) as the price
generator. Second, we apply the changes in prices to the household survey data in order
to assess the effects of the policy simulation on poverty and income distribution. By
choosing GTAP as the price generator, we are able to model the differential tariff
structure quite appropriately (almost zero for NAFTA members and higher tariffs for
non-members). Even starting with a low level of tariff protection, simulation results show
that the impact of tariff reform on welfare will be positive in general for all expenditure
deciles with the poor individuals benefiting proportionately more than the rich ones.
While the proposed methodology offers a simple way to estimate the first-round
effects of trade reform, it has a number of limitations. First, the analysis abstracts from
changes in the individual's occupational choices in response to changes in prices. These
prove to be particularly important in countries where a large number of people make a
choice between self-employment in rural areas and employment for wages in urban areas.
Second, we assume that price changes are uniform across all income groups. Third, the
results reflect price changes that are likely to occur over the medium- to long-run, and
therefore could not be indicative of what would happen in the short-run. Fourth, GTAP
19
does not account explicitly for the adjustment costs in labor markets. Therefore, the
results might underestimate the increase in wages as a result of the trade reform. Fifth,
the methodology employs a static CGE model and therefore ignores any dynamic
considerations. Thus, our result might underestimate economic growth and the boost to
prices in response to trade reform. Sixth, the version of GTAP used in this study does not
have a detailed treatment of the public sector. Therefore, we do not consider alternative
fiscal policies and instead let the model determine the effect of changes in taxes on
income and spending. Finally, in this paper we employ the income elasticity information
from GTAP and we assume that the income elasticities of the average consumer are the
same across countries. Future work should aim to estimate these elasticities for Mexico
and employ them in the analysis of welfare.
20
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23
Figure 1: Average consumption shares in the Mexican
household survey and in GTAP
60.0%
50.0%-
40.0%- _
j30.0% - _ SurveyTA
20.0% -
10.0% -
0.0% -
food manuf oth servic residual
primary
Source: Own calculations based on ENIGH-survey (1996)
F-
Figure 2: Average income composition shares in the
survey, GTAP and adjusted survey
70%-
60%
50% - * Survey
40% - | Z * GTAP
20% 0 Adjusted Survey
10%
0%I
Land Capital Unsk Sk Wage Residual
Wage
Source: Own calculations based on ENIGH survey (1996)
24
Table 1:
Mexican Tariff Structure 1997 (simple averages).
Group Name Code ROW NAFTA
Beverages Tobacco b_t 27.43 22.50
Bovine, equine, ovine meat cmt 14.96 3.47
Fish fsh 18.28 1.46
Cereal grains nec gro 11.29 1.19
Dairy Products mil 14.96 4.12
Animal products nec oap 14.96 4.12
Crops nec cro 11.29 1.19
Other food ofd 14.96 4.12
Meat products nec omt 14.96 4.12
Paddy rice pcr 11.29 1.19
Sugar sgr 14.96 4.12
Vegetables v f 14.96 4.12
Oils and Fats vol 14.96 4.12
Wheat wht 11.29 1.19
Chemical products crp 11.28 2.16
Electronic products ele 14.60 0.56
Metal products fmp 16.01 3.49
Leather products lea 14.18 3.73
Wood products lum 17.16 1.46
Motovehicles mvh 14.98 2.30
Machinery nec ome 13.77 3.92
Manufactures nec omf 13.45 1.29
Transport equipment otn 13.00 1.28
Petroleum, coal products p-c 8.50 2.16
Paper products ppp 9.42 1.68
Textiles tex 15.70 7.06
Wearing apparel wap 19.62 9.01
Other Primary �_p 8.50 2.16
Source: INTAL 1997
25
Table 2:
Consumption shares, overall and b income decile.
Product gro'up sector Overall Income Deciles
1 2 3 4 5 6 7 8 9 10
Beverages Tobacco food 1.81% 1.59% 2.24% 2.31% 2.44% 2.38% 2.40% 2.44% 2.07% 1.94% 1.15%
Bovine, equine, oth meat food 2.37% 1.42% 2.43% 2.43% 3.01% 3.27% 3.20% 3.55% 3.09% 2.79% 1.35%
Fish food 0.37% 0.50% 0.65% 0.47% 0.39% 0.47% 0.40% 0.46% 0.31% 0.42% 0.29%
Cereal nec food 2.32% 13.40% 9.15% 6.91% 5.10% 4.06% 3.19% 2.37% 1.83% 1.16% 0.43%
Dairy Products food 2.97% 1.90% 2.90% 3.61% 4.17% 4.08% 3.73% 4.03% 3.77% 3.35% 1.81%
Animal products nec food 1.13% 2.86% 2.93% 2.61% 2.10% 1.98% 1.63% 1.45% 1.23% 0.88% 0.34%
Crops nec food 0.01% 0.00% 0.00% 0.00% 0.01% 0.00% 0.01% 0.01% 0.01% 0.01% 0.02%
Otherfood food 1.96% 3.30% 3.10% 2.86% 2.69% 2.46% 2.24% 2.40% 2.40% 2.07% 1.18%
Meat products nec food 3.10% 3.83% 4.69% 4.33% 4.88% 4.65% 4.60% 4.18% 3.60% 3.15% 1.59%
Paddy rice food 0.30% 1.14% 0.87% 0.70% 0.62% 0.50% 0.44% 0.35% 0.30% 0.20% 0.09%
Sugar food 0.43% 2.04% 1.46% 1.16% 0.81% 0.78% 0.60% 0.44% 0.42% 0.27% 010%
Vegetables food 4.62% 13.61% 10.77% 9.09% 7.99% 7.14% 6.13% 5.69% 4.76% 3.83% 2.00%
Oils and Fats food 0.71% 2.32% 1.94% 1.81% 1.42% 1.26% 1.04% 0.88% 0.73% 0.49% 0.20%
Wheat food 1.93% 2.55% 3.05% 3.31% 3.01% 3.02% 2.73% 2.47% 2.38% 1.90% 0.92%
Chemical products manuf 5.89% 8.99% 8.58% 8.58% 8.33% 7.77% 7.36% 7.04% 6.60% 6.07% 3.73%
Electronic products manuf 0.54% 0.25% 0.28% 0.25% 0.45% 0.48% 0.45% 0.41% 0.39% 0.55% 0.73%
Metal products manuf 0.07% 0.08% 0.13% 0.11% 0.12% 0.10% 0.07% 0.06% 0.07% 0.04% 0.05%
Leather products manuf 1.03% 0.86% 1.02% 1.35% 1.24% 1.22% 1.24% 1.06% 1.08% 1.14% 0.83%
Wood products manuf 0.55% 0.11% 0.16% 0.24% 0.29% 0.28% 0.34% 0.50% 0.68% 0.58% 0.73%
Motovehicles manuf 1.98% 0.01% 0.02% 0.05% 0.19% 0.15% 0.41% 0.35% 0.64% 1.29% 4.38%
Machinery nec manuf 0.92% 0.15% 0.37% 0.33% 0.66% 0.57% 0.73% 0.75% 0.90% 1.17% 1.14%
Manufacturesnec manuf 0.10% 0.05% 0.07% 0.02% 0.05% 0.09% 0.05% 0.06% 0.06% 0.11% 0.15%
Transport equipment manuf 0.01% 0.00% 0.00% 0.02% 0.01% 0.02% 0.03% 0.02% 0.02% 0.02% 0.01%
Petroleum, coal products manuf 2.75% 0.24% 0.57% 0.63% 1.07% 1.38% 2.08% 2.13% 2.73% 3.66% 3.65%
Paper products manuf 3.06% 2.25% 2.93% 3.31% 3.30% 3.41% 3.35% 3.41% 3.26% 3.36% 2.66%
Textiles manuf 0.26% 0.14% 0.19% 0.20% 0.31% 0.26% 0.20% 0.26% 0.30% 0.27% 0.26%
Wearing apparel manuf 3.59% 3.10% 3.10% 3.65% 3.28% 3.53% 3.73% 3.49% 3.82% 4.20% 3.39%
Other Primary primary 0.53% 6.34% 3.32% 1.85% 1.23% 0.96% 0.46% 0.41% 0.17% 0.07% 0.03%
Services services 51.13% 26.84% 32.88% 37.67% 40.47% 43.35% 46.64% 48.92% 51.82% 53.82% 58.20%
Residual zresid 3.56% 0.14% 0.17% 0.12% 0.37% 0.38% 0.52% 0.43% 0.58% 1.18% 8.57%
Food 24.03% 50.46% 46.20% 41.61% 38.63% 36.03% 32.35% 30.71% 26.89% 22.47% 11.49%
Manufacturing 21.29% 22.57% 20.75% 20.60% 20.52% 20.24% 20.49% 19.94% 20.71% 22.53% 21.74%
Primary 0.53% 6.34% 3.32% 1.85% 1.23% 0.96% 0.46% 0.41% 0.17% 0.07% 0.03%
Services 51.13% 26.84% 32.88% 37.67% 40.47% 43.35% 46.64% 48.92% 51.82% 53.82% 58.20%
Residual 3.56% 0.14% 0.17% 0.12% 0.37% 0.38% 0.52% 0.43% 0.58% 1.18% 8.57%
Montly Expenditure
(Pesos per Month) 1060.4 209.7 334.8 427.8 528.0 640.3 770.3 935.0 1177.7 1643.5 3937.1
(US $ perMonth) 139.5 27.6 44.1 56.3 69.5 84.2 101.4 123.0 155.0 216.2 518.0
Source: Own calulation based on ENIGH survey.
26
Table 3:
Composition of the food basket across deciles.
Product group Income
Deciles
1 2 3 4 5 6 7 8 9 10
Bovine, equine, ovine meat 2.91% 5.54% 6.19% 8.33% 9.71% 10.67% 12.54% 12.45% 13.59% 13.09%
Fish 1.02% 1.47% 1.19% 1.07% 1.40% 1.34% 1.62% 1.24% 2.06% 2.81%
Cereal grains nec 27.43% 20.82% 17.58% 14.09% 12.06% 10.66% 8.38% 7.35% 5.66% 4.18%
Dairy Products 3.88% 6.60% 9.19% 11.51% 12.11% 12.46% 14.25% 15.20% 16.32% 17.50%
Animal products nec 5.86% 6.66% 6.64% 5.81% 5.87% 5.46% 5.14% 4.94% 4.30% 3.31%
Crops nec 0.00% 0.01% 0.01% 0.01% 0.01% 0.02% 0.03% 0.04% 0.04% 0.23%
Other food 6.75% 7.06% 7.28% 7.43% 7.30% 7.48% 8.49% 9.66% 10.08% 11.44%
Meat products nec 7.83% 10.68% 11.03% 13.48% 13.81% 15.36% 14.78% 14.51% 15.33% 15.36%
Paddy rice 2.33% 1.99% 1.79% 1.71% 1.48% 1.48% 1.22% 1.21% 1.00% 0.90%
Sugar 4.17% 3.32% 2.96% 2.24% 2.32% 2.01% 1.56% 1.71% 1.32% 0.93%
Vegetables 27.85% 24.50% 23.14% 22.07% 21.20% 20.48% 20.13% 19.16% 18.64% 19.37%
Oils and Fats 4.75% 4.41% 4.59% 3.93% 3.75% 3.47% 3.10% 2.93% 2.39% 1.98%
Wheat 5.22% 6.95% 8.42% 8.32% 8.98% 9.11% 8.73% 9.60% 9.27% 8.91%
Source: Own calulation based on ENIGH survey.
Figure 3: Monthly consumption
4500.0
4000.0
_ 3500.0- U *Residual
o3000.0
E~ 2500.0-______________________________ 0 Services
20 0.
@ 25000.0 3 Primary
U Manufactures
o0 U Food
(L 105000.0 - r iil X | *Fo
500.0
0.0
1 2 3 4 5 6 7 8 9 10
Income decile
Source: Own calculation based on ENIGH survey (1996)
27
Table 4:
Income distribution, overall and by income docile.
Endowment Factor Income decile
Overall 1 2 3 4 5 6 7 8 9 10
Land 1.63% 5.50% 2.56% 2.11% 1.38% 1.34% 0.92% 0.62% 0.58% 0.41% 0.83%
Capital 11.74% 12.57% 13.47% 11.81% 10.31% 11.70% 11.23% 10.65% 10.71% 11.05% 13.88%
UnskWage 35.78% 42.05% 47.43% 47.60% 47.25% 43.18% 40.13% 36.58% 28.97% 18.65% 5.94%
SkWage 17.99% 1.33% 2.38% 6.37% 10.23% 12.25% 16.59% 21.27% 26.61% 38.01% 44.89%
Negative Savings 4.38% 1.83% 2.76% 2.41% 3.11% 3.59% 3.84% 4.37% 4.58% 6.64% 10.70%
Transfers 11.04% 8.88% 10.65% 12.35% 11.32% 12.24% 12.37% 10.70% 12.79% 10.55% 8.51%
Autoconsumo 4.21% 15.94% 8.15% 4.79% 3.69% 2.91% 1.82% 1.61% 1.53% 1.15% 0.53%
Imputed rent 13.23% 11.89% 12.59% 12.56% 12.70% 12.78% 13.10% 14.19% 14.23% 13.53% 14.72%
Total 1060.4 209.681 334.842 427.773 527.975 640.281 770.316 934.957 1177.73 1643.49 3937.09
Source: Own calulation based on ENIGH survey.
Table 5:
Foster-Greer-Thorbecke indexes (hh survey) _
FGT index Poverty Indigence
Estimate Standard Error Estimate Standard Error
Head Count 0.4123 0.0064 0.1292 0.0047
Poverty Gap 0.1422 0.0030 0.0345 0.0018
Distribution Sensitive 0.0667 0.0020 0.0139 0.0010
Source: Own calulation based on ENIGH survey.
Table:6
Inequality Measures (hh survey)
Inequality Measure Estimate
Theil T 0.4310
Gini coefficient 0.4645
Source: Own calulation based on ENIGH survey.
28
TABLE 7:
Simulation effects on price and quantities consumed (percentage point change)
CATEGORY change in change in value of the
Expenditure price quantity income parameter34
Wheat -4.27 0.15 0.02
Cereal nec -0.22 0.04 0.02
Vegetables, fruit, nuts -0.02 0.06 0.39
Crops nec -1.6 0.34 0.39
Animal products nec -0.03 0.05 0.21
Fishing -0.04 0.06 0.39
Other Primary -0.28 0.2 1.26
Bovine cattle, sheep, horse meat prods 0.14 0.03 0.21
Meat products nec 0.09 0.03 0.21
Vegetable oils and fats -4.57 0.9 0.39
Dairy products -1.42 0.26 0.29
Processed rice -0.75 0.06 0.02
Sugar -0.07 0.06 0.39
Food products nec ,-0.65 0.17 0.39
Beverages and tobacco products -0.46 0.2 0.78
Textiles -0.82 0.3 0.71
Wearing apparel -2.47 0.78 0.71
Leather products -0.66 0.38 1.31
Wood products 0.26 -0.04 1.31
Paper products, publishing -0.65 0.37 1.31
Petroleum, coal products -0.2 0.17 1.31
Chemical, rubber, plastic products -1.17 0.61 1.31
Metal products -2.24 1.11 1.31
Motor vehicles and parts -4.21 1.68 1.24
Transport equipment nec -0.37 0.21 1.24
Electronic equipment -3.21 1.28 1.03
Machinery and equipment nec -5.43 2.15 1.03
Manufactures nec -3.27 1.31 1.03
Services 0.97 -0.3 1.25
Income CDE
Land -3.09
UnSkilled Wages 1.45
Skilled Wages 1.74
Capital 1.51
NatRes -3.35
34 These parameters reflect the structure of the income-consumption path embedded in GTAP's demand
function: higher income elasticities for superior goods.
29
Table 8:
Price indeces for consumption and income.
CDE Income Decile
Consumption Overall 1 2 3 4 5 6 7 8 9 10
Laspeyres 0.9992 0.9970 0.9973 0.9976 0.9978 0.9984 0.9987 0.9992 0.9994 0.9996 1.0001
L_food -0.0020 -0.0031 -0.0031 -0.0032 -0.0030 -0.0027 -0.0026 -0.0024 -0.0022 -0.0017 -0.0009
L_manuf -0.0035 -0.0023 -0.0025 -0.0027 -0.0028 -0.0029 -0.0029 -0.0029 -0.0032 -0.0038 -0.0045
L.prim 0.0000 -0.0002 -0.0001 -0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
L_serv 0.0047 0.0025 0.0030 0.0035 0.0037 0.0040 0.0043 0.0046 0.0048 0.0051 0.0055
Paache 0.9991 0.9969 0.9973 0.9975 0.9978 0.9983 0.9987 0.9992 0.9994 0.9995 1.0000
Fischer 0.9991 0.9970 0.9973 0.9976 0.9978 0.9983 0.9987 0.9992 0.9994 0.9995 1.0000
Tornquist 0.9991 0.9970 0.9973 0.9976 0.9978 0.9983 0.9987 0.9992 0.9994 0.9995 1.0000
Income
Laspeyres 1.0114 1.0081 1.0092 1.0100 1.0104 1.0107 1.0110 1.0113 1.0115 1.0120 1.0123
L_land -0.0004 -0.0015 -0.0011 -0.0008 -0.0006 -0.0005 -0.0004 -0.0003 -0.0002 -0.0002 -0.0001
L_capital 0.0017 0.0017 0.0018 0.0017 0.0017 0.0017 0.0017 0.0016 0.0017 0.0017 0.0018
1_unsk_wages 0.0038 0.0063 0.0067 0.0069 0.0067 0.0064 0.0057 0.0053 0.0044 0.0029 0.0010
L_sk_wages 0.0048 0.0002 0.0004 0.0009 0.0014 0.0018 0.0027 0.0033 0.0043 0.0064 0.0081
L_residual 0.0014 0.0014 0.0013 0.0013 0.0013 0.0013 0.0013 0.0014 0.0013 0.0013 0.0015
LasplNC-LaspCON 0.0122 0.0111 0.0119 0.0124 0.0126 0.0124 0.0123 0.0121 0.0120 0.0125 0.0122
Source: Own calulation based on ENIGH survey.
Table 9:
FGT indexes and inequality measures before and after the simulation.
Poverty Indigence
FGT index Estimate Standard Error Estimate Standard Error
Head Count pre-simulation 0.4117 0.0064 0.129 0.0047
Head Count post-simulation 0.4058 0.0064 0.1239 0.0046
Poverty Gap pre-simulation 0.1419 0.003 0.0345 0.0018
Poverty Gap post-simulation 0.1379 0.003 0.0332 0.0018
Distribution Sensitive pre-simulation 0.0665 0.002 0.0139 0.001
Distribution Sensitive post-simulation 0.0644 0.002 0.0133 0.001
Inequality
Gini coefficient pre-simulation 0.4642
Gini coefficient post-simulation 0.4649
Theil T pre-simulation 0.4302
Theil T post-simulation 0.4316
Source: Own calulation based on ENIGH survey.
30
Figure 4. Utility changes across income percentiles
.017476 - 0
0
0
0
0000 00 0 0
- 0 00 0 00
000 00 0
0 0
0
0 0 0 0 0b
_ 0 00 0 0 0 o
0 0 0 0 0
w 0 00~~~~~~~~~~~~~~~~~~~~~~~
0 000 0 o o o O o co
w 00 0 0
00 0 00� 0o 0000
0 0 0 c o 0 0 0
0 O
_~~~~~~~~~~~~ o0 0o
00
0 0
0
0
0
.009769 O
I I 1Io
1 ~~~~~~~~~~~~~~100
100 quantiles of pctotexp
31
APPENDIX 1: The GTAP Model
The GTAP model (Hertel, 1997) is a standard multi-region applied general equilibrium
model. It has perfectly competitive markets, constant retums to scale technology, and a
supply-side that emphasizes the role of inter-sectoral factor mobility in the determination
of sectoral output. Product differentiation between imports and domestic goods, and
among imports by region of origin, allows for two-way trade in each product category,
depending upon the ease of substitution between products from different regions.
Regional household behavior is governed by an aggregate Cobb-Douglas utility function
specified over composite private consumption, composite government purchases, and
savings. The motivation for including savings in the static utility function derives from
Howe's work which showed that the intertemporal, extended linear expenditure system
(ELES) could be derived from an equivalent, atemporal maximization problem, in which
savings enters the utility function. Private household demands are derived from a
constant difference elasticity (CDE) implicit expenditure function (Hanoch, 1975). The
non-homothetic CDE preferences are easily transformed into CES or Cobb-Douglas
preferences via an appropriate choice of parameters in the preference function.
Land, labor, and capital are fully employed, and all returns to these factors accrue to
households in the region in which they are employed. Global investment is allocated
across regions in order to equate expected rates of return. The sum of regional investment
equals global investment, which in turn must equal the sum of regional savings.
We use the GTAP model in order to simulate the effects of trade liberalization on
Mexico's economy, and specifically on different types of households in the region. The
idea is to use the results from the global trade model jointly with detailed information
from a household survey in Mexico in order to make inferences about the welfare impact
of trade liberalization on various income groups. There are a number of reasons for our
choice of methodology.
32
First, our goal is to propose a methodology that is easy to execute and apply in the
context of any country. Typically, the welfare analysis of trade policies on domestic
consumers is conducted using one-region models that have multiple households,
sophisticated representation of preferences, and a detailed treatment of the domestic
government sector. However, the construction of these single region economy models is
often a complex task that requires modeling expertise and in many cases, country-specific
data. By contrast, with the GTAP model, the implementation of trade policy shocks is a
standard task that is performed with a push of a button.
Second, trade policies typically affect more than one region and the use of detailed single
region models would not capture well changes in the pattern of specialization and trade
flows due to a trade policy shock. In addition, if we were to study the domestic impact of
trade liberalization in the rest of the world, we would need a multi-region applied general
equilibrium model in order to capture endogenously the impact of the trade policy shock
on the economy in question.
Third, the GTAP database has considerable sectoral and regional detail. It contains input
output information on more than 45 sectors and captures differences in intermediate input
intensities, as well as import intensities, by use. It is publicly available and regularly
updated.
There are two features of this treatment that need to be kept in mind when interpreting the
results. GTAP has only one aggregate private household. The government household
preferences differ from those of the private household. The government household
allocates its revenue based on a Cobb-Douglas utility function, and government spending
is a constant share of income. Since the model does not keep track explicitly of
government revenue, changes in tax revenue are treated as changes in regional income,
and affect private household spending, government household spending, and savings.
Thus, a portion of the tax revenue is always transferred to the private household and this
transfer leads to changes in both private spending and savings.
33
The second feature of the model that might affect our results is the treatment of skilled
and unskilled labor. The model assumes full employment and forces wages to adjust
instead. With a change in the standard macro closure, it is possible to reverse this
treatment and adjust the supply of labor while keeping wages fixed in the short run. This
allows us to study the response of labor supply to the trade policy shock over the short
run.
List of commodities in Version 4 of GTAP Database.
No. Sector Code Description
I Food pdr Paddy rice
2 Food wht Wheat
3 Food gro Cereal grains nec
4 Food v_f Vegetables, fruit, nuts
5 Food osd Oil seeds
6 Food c_b Sugar cane, sugar beet
7 Primary pfb Plant-based fibers
8 Food ocr Crops nec
9 Food ctl Bovine cattle, sheep and goats, horses
10 Food oap Animal products nec
11 Food rmk Raw milk
12 Primary wol Wool, silk-worm cocoons
13 Primary for Forestry
14 Food fsh Fishing
1 5 Primary col Coal
16 Primary oil Oil
17 Primary gas Gas
18 Primary omn Minerals nec
19 Food cmt Bovine cattle, sheep and goat, horse meat prods
20 Food omt Meat products nec
21 Food vol Vegetable oils and fats
22 Food mil Dairy products
23 Food pcr Processed rice
24 Food sgr Sugar
25 Food ofd Food products nec
26 Food b_t Beverages and tobacco products
27 Manufacturing tex Textiles
28 Manufacturing wap Wearing apparel
29 Manufacturing lea Leather products
30 Manufacturing lum Wood products
31 Manufacturing ppp Paper products, publishing
34
32 Manufacturing p_c Petroleum, coal products
33 Manufacturing crp Chemical, rubber, plastic products
34 Manufacturing nmm Mineral products nec
35 Manufacturing i_s Ferrous metals
36 Manufacturing nfm Metals nec
37 Manufacturing finp Metal products
38 Manufacturing mvh Motor vehicles and parts
39 Manufacturing otn Transport equipment nec
40 Manufacturing ele Electronic equipment
41 Manufacturing ome Machinery and equipment nec
42 Manufacturing omf Manufactures nec
43 Services ely Electricity
44 Services gdt Gas manufacture, distribution
45 Services wtr Water
46 Services cns Construction
47 Services t_t Trade, transport
48 Services osp Financial, business, recreational services
49 Services osg Public admin and defence, education, health
50 Services dwe Dwellings
35
Appendix 2: Mexican Household Survey
This study utilizes the 1996 Mexican National Household Income and Expenditure
Survey (ENIGH). The survey was collected by the Instituto Nacional de Estadistica,
Geografia e Informatica (INEGI). The survey is stratified, multistage and clustered. The
final sampling unit is the household. The survey was collected from May to October 1996
and reports data for 14,042 households, which are representative of the entire population.
The survey includes income, consumption, household characteristics and individual
characteristics. The income data and especially the consumption data are very
disaggregated. The survey reports 43 income categories subdivided into monetary, non-
monetary and financial income. The consumption data consist of more than 600 different
entries, about half of which are food items. Food and manufacturing products and
services are finely disaggregated. The observations for which there was no information
on expenditure or income for any category were dropped.35
Since household size is not the same across income levels, and because the welfare
measures are concerned with the well-being of individuals, all data were converted to a
per capita basis. This measure of individual welfare still doesn't have a firm theoretical
and empirical basis for the construction of equivalence scales. This paper adopts the
standard practice of dividing household income and expenditure by its residents, with
children of age 14 or less counting as half of adults. Also, to reflect economies of scale
within the household, we scaled this measure to the power of 0.9.36
The measure of total household income is equal to the summation of financial, monetary
and non-monetary income. Non-monetary income includes payment in kind, gifts and
imputed value of rent. Each classification of income was converted on a quarterly basis
and adjusted for inflation. The income expenditure survey provides no information on
35 This resulted in discarding about 1% of the total number of observations.
36
asset ownership. Thus, it is insufficient to make direct connections between income and
expenditure patterns, and between asset ownership and productive activity.37 Total
household consumption is calculated as the sum of monetary and non-monetary
expenditures. By definition and standard practice in household survey analysis, non-
monetary expenditure equals non-monetary income.38 The total amount for each
expenditure category is calculated on a quarterly basis in the same way as income.
In household surveys the data on income is usually underreported.39 This, together with
the lifecycle consumption hypotheses, drove us to adopt the standard procedure of using
total expenditure as a proxy for income.40
36 For a more detailed discussion see Deaton (1997) and Wiggins, Preibish and Proctor (1999). The
substance of the results did not change when total income was divided by the actual number of household
members.
37 The survey does not give enough information to make it possible to match income data to the economic
sectors.Therefore, it is impossible to calculate household specific income effects due to price changes in
particular sectors.
T8 hat is, auto-consumption goods and services must be recorded properly in both income and expenditure.
39 For example, see Lustig and Mitchell (1995).
40 See, for example, Levy (1991) and Sarris (1993).
37
Appendix 3: Data aggregation
The matching of the household survey classification to GTAP categories consists of two
different exercises: consumption matching and income matching. On the expenditure
side, the GTAP system has 50 commodity categories, while the household data includes
about 600 different categories.. The matching of the expenditure side of the two data sets
was facilitated by the use of concordance tables provided by the GTAP website
(www.gtap.org).4' This conversion solves the aggregation problem for most of the food,
manufacturing and other primary sectors. The matching of the service sectors was more
difficult to obtain, due to the various possible interpretations of services acquired by the
households and the GTAP classification. Therefore, we decided to aggregate all the
services in one category. This may seem like a bigger problem than it is. Because in our
simulations the change in price is never very different across the various service
categories of GTAP, this reduces errors due to aggregation.
The matching of the income part of the data with GTAP categories was more
problematic. GTAP uses five different endowment categories, while in the household
survey data there are more than 40. In addition, the two data sets adopt different systems
in classifying income. Therefore, they are more difficult to match and require some
degree of arbitrariness. GTAP income is divided into land, capital, skilled labor, unskilled
labor and natural resources.4it The attained level of education is the variable that allow us
to distinguish between skilled and unskilled labor. An individual is considered skilled if
he had completed secondary school or technical education.43 The household survey
divides income into different categories, some of which are not univocally or clearly
attributable to any single GTAP category. Many of those household income categories
must be attributed to two or more GTAP categories. To calculate the correct sharing
4' In particular, we made use of the HS to GTAP conversion tables available at the GTAP website.
42 We do not match any household survey income category to the GTAP income category - natural
resources. Even if some household income categories could be matched at least in part with income from
natural resources we decided not to do so because the GTAP aggregation of natural resources is mainly
mining sectors and oil which do not have a direct correspondent in the household survey categories.
43 The household survey reports detailed information on the education attained by each individual. It takes
usually 9 years to complete secondary school.
38
coefficients, we use the input output tables of GTAP.44 In the household data, there are
various categories that cannot be matched with those of GTAP. These consist mainly of
transfers and negative savings, whose average income flow we assume do not vary with
the simulation.45 We report the aggregation tables and the sharing coefficients at the end
of this appendix.
Income is usually underreported in the household surveys, and total expenditures usually
exceed total income. This factor, together with consumption smoothing issues prompted
us to use total expenditure as a proxy for total income. Nevertheless, we still maintained
the income structure of the household data. It is likely that different income categories
have different degrees of underreporting. Looking at the income composition of the
survey data, it is very different from the share of GTAP income categories. Because of
the mis-reporting issues mentioned above, as a robustness check we relied on the GTAP
endowment structure, nevertheless still maintaining the distribution of the endowments
across households.46 To do so, we first applied the income shares from GTAP to the total
economy income from the household data to obtain new income levels by endowments.
Then we redistributed the income generated by each endowment across the different
households according to the share of participation of that particular household in that
income source. Finally, to obtain total income for each household, we applied the new
income composition to total expenditure.47
44 For example, the category "income from own business" must be allocated between income from capital
and income from wage. We use the average GTAP coefficient for the service sector to calculate the correct
shares.
45 We relax this assumption for the robustness check, and let these income sources to vary with return to
capital without finding appreciable changes in the results.
46 We maintain the endowment distribution across households by assigning to each household the share of
endowment from the survey data. That is, we control for the fact that the distribution of each endowment is
different across the income percentiles.
47 Ash er,
Formally, we set nsh, = DA -ere where sh is the participation share of household i in the total
e
endowment e, er is the endowment e total return (in levels) according to GTAP shares and nsh is the new
share of endowment e for the household i. Then we applied nsh to total household expenditure to obtain the
household income from each endowrnent.
39
GTAP/HH SURVEY AGGREGATION TABLES
CLASSIFICATION
OF EXPENDITURE
Gtap Sector GTAP Group GTAP Household survey classificatIon
CODE Clave Product name
ALIMENTOS, BEBIDAS Y TABACO
A.- Alimentos
1.- Cereeles
Food Cereal GRO ACOO Makz en grano, pozolero, palomero
Food Cereal GRO A002 Harina de maiz
Food Cereal GRO A003 Masa de maiz
Food Cereal GRO A004 Tortilla de maiz
Food Cereal GRO A005 Fecula de makz (maicena, pdvo pare atole)
Food Cereal GRO A006 Otros productos de maiz: tostadas, hojuelas, pinole, etc.
Food Wheat WHT A007 Harina de tnigo (refinada o integral)
Food Wheat WHT A008 Tortilla de haeina
Food Wheat WHT A009 Galletas saladas
Food Wheat WHT A010 Galletas dulces
Food Wheat WHT A01 1 Pan blanco inctuya pan molido
Food Wheat WHT A012 Pan de dulce
Food Wheat WHT A013 Pan de caja
Food Wheat WHT A014 Pan de marca (panecillos y pastales)
Food Wheat WHT A015 Pasta para sopa
Food Wheat WHT A016 Otros productos de trigo: pasta para tritura, hoiueles, harina preparada, eta
Food Rice PCR A017 Arroz en grano
Food Rice PCR AO 8 Ofos productos dearroz harina, tostado, etc.
Food Cereal GRO A019 Avena
Food Cereal GRO A020 Otros cereales: centeno, cebada, etc.
Food Cereal GRO A021 Frituras prooesadas de trigo o malz
Food 2.- Cames
a) De res y temera
Food Meat: cattle sheep goats horses CMT A022 Bistec y milanesa
Food Meat: cattle sheep goats horses CMT A023 Pulpa (trozo y molida)
Food Meat: cattle sheep goats horses CMT A024 Cocido o retazo con hueso
Food Meat: cattle sheep goats horses CMT A025 Lomo y filete
Food Meat: cattle sheep goats horses CMT A026 Cortes especiales: t-bone, roast beef aguias, etc.
Food Meat: cattle sheep goats horses CMT A027 Chuleta y costilla
Food Meat: cattle sheep goals horses CMT A020 Vlsceras: higado, rifiones, sesos, coraz6n, modula y otras partes de res
b) De puerco
Food Meat product nec OMT A029 Lomo y piema
Food Meat product nec OMT A030 Chuleta y costilla
Food Meat product nec OMT A031 Pulpa, bistec, trozo y molida
Food Meat product nee OMT A032 Visceras: higado, nriones, sesos. ooraz6n, modula y otras partes de puerco
Food Meat product nec OMT c)
Aves
Food Meat product nec OMT A033 Pollo en piezas
Food Meat product nec OMT A034 Pollo entero
Food Meat product nec OMT A035 Gallina entera o en piezas
Food Meat product nec OMT A036 Viscoras: coraz6n. higado, etc., y otras partes del poUlo
Food Meat product nec OMT A037 Otras aves: pavo, pich6n, pato, etc.
Food Meat product nec OMT d) Otras coames
Food Meat product nec OMT A038 Camero y borrego
Food Meat product nec OMT A039 Cabrito
Food Meat product nec OMT A040 Otros: conejo, venado, iguana, etc.
Food Meat product nec OMT e) Cames procesadas
Food Meat product nec OMT A041 Jamon
Food Meat product nec OMT A042 Tocino
Food Meat product nec OMT A043 Salchicha
Food Meat product nec OMT A044 Chorizo y longanize
Food Meat product neoc OMT A045 Cames enchiladas o shumadas
Food Meat product nec OMT A046 Oueso de puerco
Food Meat product nec OMT A047 Came de res seca: coena, machaca, rellena, etc.
Food Meat product nec OMT A048 Otros: pastel de pollo, salami, mortadela, etc.
3.- Pescados y mariscos
a) Poscados y mariscos frescos
Food Fish FSH A049 Huachinango
Food Fish FSH A050 Mojarra
Food Fish FSH A051 Robalo
Food Fish FSH A052 Moro
Food Fish FSH A053 Caz6n, liza y bagre
Food Fish FSH A054 Camar6n
Food Fish FSH A055 Otros pescados y mariscos: trucha, jaiba, osti6n. almeja, etc.
b) Pescados y manisoos procesados
Food Other food nec OFD A056 Sardinas
Food Other food nec OFD A057 AtUjn
Food Other food nec OFD A058 Secos: bacalao, charal, camer6n, etc.
Food Other food nec OFD A059 Otros: abul6n, osti6n, pulpo, etc.
40
4.- Leche y dervados
a)
Leche
Food Dairy Products MIL A060 Pasteurizada
Food Dairy Products MIL A061 No pasteurizada (bronca)
Food Dairy Products MIL A062 Evaporada
Food Dairy Products MIL A063 Condensada
Food Dairy Products MIL A064 En polNo (entera o descremada)
Food Dairy Products MIL A065 Matemizada
Food Dairy Products MIL A066 Otras: cabra, burra, etc
Food Dairy Products MIL b) Quesos
Food Dairy Products MIL A067 Fresco
Food Dairy Products MIL A068 Chihuahua
Food Dairy Products MIL A069 Oaxaca y
asadero
Food Dairy Products MIL A070 Manchego
Food Dairy Products MtL A071 Amarillo
Food Dairy Products MIL A072 Anejo y cotija
Food Dairy Products MIL A073 Reques6n
Food Dairy Products MIL A074 Otros: enchilado, gruyere, parmesano, holandes, crema, etc
Food Dairy Products MIL c) Otros derivados de la leche
Food Dairy Products MIL A075 Crema
Food Dairy Products MIL A076 Mantequilla
Food Dairy Products MIL A077 Otros: yoghurt, jocoque, etc.
S.- Huevos
Food Other animal product OAP A078 Gallina
Food Other animal product OAP A079 Otros: tortuga, pato, pavo, etc
6.- Aceies y grasas
Food Vegetable oil and fats VOL A080 Aceite vegetal
Food Vegetable oil and fats VOL A081 Manteca vegetal
Food Vegetable oil and fats VOL A082 Manteca de puerco
Food Vegetable oil and fats VOL A083 Margarina
Food Vegetable oil and fats VOL A084 Otros: aceite de oliva, enjundia, etc.
7.- Tuberculos
Food Vegetables V_F A085 Papa
Food Vegetables V_F A086 Harina de papa para purn
Food Vegetables V_F A087 Otross camote, yuca, fiame, betabel, etc.
Food Vegetables V_F A088 Papas fiitas en bolsa
Food Vegetables V_F 5.- Verduras, legumbres, legumsinosas y semillas
Food Vegetables V_F a) Verduras y legumbres frescos
Food Vegetables V_F A089 Tomato rojo Uitomate)
Food Vegetables V_F ADGO Tomate verde
Food Vegetables V_F A091 Chile serrano y jalapetlo
Food Vegetables V_F A092 Chile poblano para rellenar
Food Vegetables V_F A093 Otros chiles: habanero, arbol, etc.
Food Vegetables V_F A094 Cebolla
Food Vegetables V_F A095 Ao
Food Vegetables V_F A096 Aguacate
Food Vegetables V_F A097 Repollo 0 cal
Food Vegetables V_F A098 Lechuga
Food Vegetables V_F A099 Zanahora
Food Vegetables V_F AIOO Pepino
Food Vegetables V_F A1IO Ejote
Food Vegetables V_F A102 Chicharo
Food Vegetables V_F A103 Elote
Food Vegetables V_F A104 Chayote
Food Vegetables V_F A105 Calabacitas
Food Vegetables V_F A106 Nopales
Food Vegetabtes V_F A107 Verdolagas, espinacas y acelgas
Food Vegetables V_F A108 Perejit
Food Vegetables VPF A109 Cilantro
Food Vegetables V F A110 Epazote, papalo y apio
Food Vegetables V_F A1Il Verduras mixtas en botsa
Food Vegetables V_F A112 Otros: alcachofa, quelites, romeritos, rabanos, poro, etc.
Food Vegetables V_F b) Verduras y legumbres procesadas
Food Vegetables V_F A113 Chiles envasados
Food Vegetables VPF A114 Chilessecosoenpolvo
Food Vegetables V_F A115 Verduras envasadas (inctuya eeitunas)
Food Vegetables VPF A116 Verduras y legumbres congeladas
Food Vegetables VPF c) Leguminosas
Food Vegetables V_F A117 Fnjol
Food Vegetables V_F A118 Garbanzo
Food Vegetables V_F A119 Otras: lentejas, haba, etc.
Food Vegetables V_F d) Leguminosas procesadas
Food Vegetables V_F Al 20 Fnjol (en caja o leta)
Food Vegetables V_F A121 Otras leguminosas (en lata o secas)
Food Vegetables VP e) Semillas
Food Vegetables V_F A122 Semillas a granel (nuez, pieftn, almendra, cacahuate, etc.)
Food Vegetables V_F A123 Semillas envasadas (nuez, piil6n, almendra, cacahuate, etc.)
Food Vegetables VLF 9.- Frutas
41
Food Vegetab"les V Fa) Fnutas frescas
Food Vegetables V_F A124 Naranja
Food Vegetables VF A125 Lim6n
Food Vegetables VP FA126 Otros citricos: lima, toronja, mandarina, etc.
Food Vegetables V_F A127 Platanotabasco
Food Vegetables V_F A128 Otros patanos: macho, dominico moradoy manzeno
Food Vegetables VPF A129 Manzana o per6n
Food Vegetables V_F A130 Pae
Food Vegetables VF Al 31 Durazno y chabacano
Food Vegetables V_F Al 32 Cirueta
Food Vegetables V_F A133 Fresa
Food Vegetables V_F A134 Guayaba
Food Vegetables V_F A135 Mango
Food Vegetables V_F A136 Mamey
Food Vegetables V_F A137 Papaya
Food Vegetables V_ A138 Mel6n
Food Vegetables V_f A139 Sandia
Food Vegetables V_F A140 Pila
Food Vegetables V_F A141 Jicama
Food Vegetables V\ A142 Uva
Food Vegetables VF A143 Otras: guanabana, granada, tuna, higo, coco, tamarindo, etc.
Food Vegetables ViF b) Frutas procesadas
Food Vegetables V_F A144 Almibaroconserva: durazno mango,pit7a,cereza,etc.
Food Vegetables Vj A145 Cristalizadas y secas: pasitas, dables, chabacano, etc,
Food Vegetables V_F A146 Otras: nAtas endulzadas, enchiladas, etc.
10.- Az0car y mieles
Food Sugar SGR A147 Azucar (blanca y morena)
Food Other food nec OFO A148 Miel de abeja
Food Other food nec OFD A149 Otras: glass, moscabada, piloncillo, miel de maiz, etc.
11 .- Cafe, 1, chocolate
Food Other food nec OFD A150 Catb tostado (en grano a molido)
Food Other food nec OFD A151 Cafe sin tostar (en grano)
Food Other food nec OFD A152 Cafe soluble o instantaneo
Food Other food nec OFD A153 Hojas para te (manzanilla, naranja, etc.)
Food Other food nec OFD A154 Te soluble o instartineo
Food Other food nec OFD A155 Chocolate en tableta o en polvo
Food Other food nec OFD A156 Otros: cocoas etc.
12.- Espedas y Aderezos
Food Other food nec OFD A157 Sal
Food Other food nec OFD A158 Pimienta, clavo y comino
Food Other food nec OFD A159 Canela
Food Otherfood nec OFD A160 Mayonesa
Food Other food nec OFD A161 Mostaza
Food Other food nec OFD A162 Salsa catsup
Food Other food nec OFD A163 Salsas picantes
Food Other food nec OFD A164 Mole
Food Other food nec OFD A165 Concentrados de polio y tomate
Food Other food nec OFD A166 Vinagre
Food Other food nec OFD A167 Otros condimientos: aderezos, ablandadores, polvo para hornear
13.- Otros alimentos
a) Alrmentos preparados para bebe
Food Other food nec OFD A168 Alimentos colados y picados de cualquier combinaci6n
Food Other food nec OFD Al 69 Cereales, sopas y galletas pars bebe
Food Other food nec OFO A170 Jugos de frutas y verduras de cualquier combinaci6n
b) AMimentos preparados (pars consumir en cesa)
Food Other food nec OFO A171 Camitas y chicharr6n
Food Other food nec OFD A172 Pollos rostizados
Food Other food nec OFD A173 Barbacoa
Food Otherfoodnec OFD A174 Bisia
Food Other food nec OFD Al75 Pizzas
Food Other food nec OFD Al 76 Otros: sopa, guisados, ensaltdas, tortas, encrtidos, etc.
c) Alimentos diversos
Food Other food nec OFD A177 Chapulines, gusano de maguey, etc.
d) Dulces y postres
Food Other food nec OFD At 78 Gelatines, nanes y pudines en polvo
Food Other food nec OFD A179 Gelatinas, fnanes y pudines
Food Other food nec OFD AlSO Paletas, caramelos y otras golosinas
Food Other food nec OFD AISI Cajetas, jamoncilos y dulcas de leche
Food Other food nec OFD Al 82 Mermeladas, ates, jateas y crema de cacahuats
Food Other food nec OFD A183 Helados y nieves
Food Otherfood nec OFD A184 Otros: chilacayote, cocada, visnaga, alegrtas, etc.
14.- Servicao de miolino
Food Other food nec OFD A185 Nixtamalyotros
Food Other food nec OFD A186 Gastos conexos pars preparar alimentos
15.- Alimentos para animnales domesticos
Food Other food nec OFD A187 Animalesdoespearnimiento
Food Other food nec OFD Al88 Animales pare trabao y de producci6n
16.- Bebidas
48
42
1. - Bebidas no alcoholicas
Food Beverages and tobaoco B_T A189 Refrescos o bebidas con o sin gas y jugos naturales
Food Beverages and tobacco BT A190 Agua nineral (con o sin sabor)
Food Beverages and tobacco BT Al1l Jugos y n6ctares enlatados
Food Beverages and tobacco B_T A192 Agua puriicada
Food Beverages and tobacco B_T A193 Concentrado y polvo para preparar agua
Food Beverages and tobacco B_T A194 Otros: hielo, granadina, jarabe natural, etc.
Food Beverages and tobacco B_T 2.- Bebidas alcoh6licas
Food Beverages and tobacco BT Al 95 Cerveza
Food Beverages and tobacco CT Al 96 Brandy
Food Beverages and tobacco BT Al 97 Pulque
Food Beverages and tobacco CT A198 Tequila
Food Beverages and tobacco BT A199 Whisky
Food Beverages and tobacco BT A200 Ron
Food Beverages and tobacco B_T A201 Aguardiente, mezcal, s5tol
Food Beverages and tobacco B_T A202 Vinos de mesa
Food Beverages and tobacco B_T A203 Otros: sidra, rompope, jerez cremas, vodka, etc.
Food Beverages and tobacco B_T A204 Bebidas preparadas
B - Alimentos y bebidas consumidas fuera del hogar
Services Recreation and other services ROS A205 1) Desayuno
Services Recreaton and other services ROS A206 2) Comida
Services Recreabon and other services ROS A207 3) Cana
Services Recreation and other services ROS A208 4) Entrecomidas
C.- Tabamo
Food Beverages and tobacco B_T A209 Cigarros
Food Beverages and tobacco B_T A210 Puros
Food Beverages and tobacco B_T A21 1 Tabaco (en hoia y picado)
TRANSPORTE PUBLICO
Services Transport nec OTP B001 Metro
Services Transport nec OTP B002 Autobus
Services Transport nec OTP B003 Trolebuis, tranvia
Services Transport nec OTP B004 Colectivo (pesero)
Services Transport nec OTP B005 Taxi, radio taxi (sitb)
Services Transport nec OTP BOOS Autobus foraneo
Services Transport nec OTP B007 Otros (bono de transporte, carretas: etc.)
LtMPIEZA Y CUIDADO DE LA CASA
A. Articulos de limpieza y cuidado de la casa
Manufacturing Chemical rubber plastc prods CRP COO Detergentes
Manufacturing Chemical rubber plastic prods CRP C002 Jabon de barra
Manufacturing Chemical rubber plastc prods CRP C003 Blanqueadores
Manufaduring Chemical rubber plastic prods CRP C004 Limpiadores (en polvo o liqtado)
Manufacturing Chemical rubber plastic prods CRP C005 Papel sanitano
Manufaduring Chemical rubber plastic prods CRP C00S Servilletas y papel absorbante
Manufacturing Chemical rubber plastic prods CRP C007 Platos y vasos desechables, papel aluminio y encerado
Manufacturing Chemical rubber plastic prods CRP COOB Escobas y trapeadores
Manufacturing Chemical rubber plastic prods CRP C009 Fbras, estropajos y escobetas
Manufacturing Chemical rubber plastic prods CRP C010 Jergas y trapos de cocina
Manufacturing Chemical rubber plastc prods CRP C01I Cerillos
Manufacturing Chemical rubber plastic prods CRP C012 Pilas
Manufacturing Chemical rubber plastic prods CRP C0t3 Focos
Manufacturing Chemical rubber plastic prods CRP C014 Cera y limpia muebles
Manufacturing Chemical rubber plastic prods CRP C015 Insecticidas
Manufacturng Chemical rubber plastic prods CRP C016 Desodorante ambiental y sanitario
Manufacturing Metal Products FMP C017 Recipientes de lamine (oubetas, tinas, etc.)
Manufacturing Chemical rubber plastic prods CRP C01 8 Redpientes de plastico (cubetas. tinas, mangueras, etc.)
Manufacturing Chemical rubber plastic prods CRP C019 Otros articulos: suavizantes de telas, etc.
B. Servicios para el hogar
Sevices Recreation and other services ROS C020 Servicio domAstico
Services Recreation and other services ROS C021 Lavanderia
Services Recreation and other services ROS C022 rintoreria
Services Recreation and other sewices ROS C023 Jardineria
Services Recreation and other services ROS C024 Otros servicios: fumigacion, etc.
CUIDADOS PERSONALES
A. Articulos para el cuidado personal
Manufacturing Chemical rubber plastc prods CRP D001 Jab6n de tocador
Manufacturing Chemical rubber plastic prods CRP D002 Lociones y prfumes
Manufacturing Chemical rubber plastic prods CRP D003 Pasta dental y enjuague bucal
Manufacturing Chemical rubber plastic prods CRP D004 Champus, tintes y enjuagues
Manufacturing Chemical rubber plastic prods CRP 0005 Desodorante
Manufacturing Chemical rubber plastic prods CRP D006 Crema, bfillantina y crema para afeitar
Manufacturing Chemical rubber plastic prods CRP D007 Navajas y reastrilos pare afeitar
Manufacturing Chemical rubber plastic prods CRP D000 Polvo y maquiltd,e facial
Manufacturing Chemical rubber plastic prods CRP D009 Sombra, tapiz labial y de cas, delineador, etc,
Manufacturing Chemical rubber plasUc prods CRP D010 Articulos de tocador para babe
Manufacturing Chemical rubber plastic prods CRP D011 Pafluelos desechables
Manufacturing Chemicat rubber Plastic prods CRP 0012 Patales dasechables
Manufacturing Chemical rubber plastic prods CRP D013 Toellas sanitarias
Manufacturing Chemical nubber plastc prods CRP D014 Cepillo, peine y cepillo dentrlfico
Manufacturing Machinerwand Equipment OME 0015 Articulos ectdricos (rasuradora, scadora. etc.)
Services Recreation and other senrices ROS D016 Reparacion ylo mantenimiento de articuAos antenores
Manufacturing Chemical rubber plastic prods CRP W017 Otros: esmaltes y limas para uftas. pasadores, etc,
B. Serviros para el cuidado personal
43
Services Recreation and other services ROS D018 Corte de cabello y peinado
Services Recreaton and other services ROS D019 BaAos y masajes
Services Recreation and other services ROS D020 Permanentes y tintes
Services Recreation and other services ROS D021 Manicure
Services Recreation and other services ROS D022 Otros servicios: rasurar, depitar, etc.
EDUCACION, CULTURA Y RECREACION
A. Servidos de educad6n
Services Pub admin defence health education OSG E001 Preprimaria
Services Pub admin defence health education OSG E002 Primaria
Services Pub admin defence health education OSG E003 Secundaria
Services Pub admin defence health educaboin OSG E004 Preparatoria, vocacional 0 normal
Services Pub admin defence health education OSG E005 Superior (Licendaturas, Medicos, etc.)
Services Pub admin defence health education OSG E006 Posgrado (Maestrias, dodorados, especislidades
Services Pub admin defence health education OSG E007 Carrera tecnica o comercial
B. Servidos de educad6n
Services Pub admin defence health education OSG E008 Estancias infantles (excepto preprimaria)
Services Pub admin defence health education OSG E009 Ensetanza adiaonal
Services Pub admin defence health education OSG E010 Educaci6n especial para discapacitados
Services Pub admin defence health education OSG E011 Intemados
Services Pub admnin defence health education OSG E012 Cuidado de nielos (Persona particular)
Services Transport ne OTP E013 Transporte esolar
C. Articulos educalivos
Manufacturing Paper Products Publishing PPP E014 Libros para la escuela
Manufacturing Paper Products Publishing PPP E015 Material escolar cuademos, carpetas, etc.
Manufacturing Electronic eequip ELE E016 Equipo escolar: miquinas de escribir, calculadoras, etc~
Manufacturing Paper Products Publishing PPP E017 Material pare actividades tecnol6giceas (educacidn formal)
Manufacturing Paper Products Publishing PPP E018 Material pars Educaca6n Tcnicea
Manufacturing Paper Products Publishing PPP E019 Material pare Educaci6n Adieional
Services Recreation and other services ROS E020 Reparad6n ylo mantenimiento de equipo escolar
D. Articuloe de cultura y recreaci6n
Manufacturing Paper Products Publishing PPP E021 Encidopedias y libros (excluya los de la escuela)
Manufacturing Paper Products Publishing PPP E022 Peridioos
Manufacturing Paper Produets Publishing PPP E023 Revistas
Manufacturing Machinery and Equipment OME E024 Audiocassete, discos y discos compactos
Manufacturing Machinery and Equipment OME E025 Otros
E. Servicios de recreaci6n
Services Recreation and other services ROS E026 Cines
Services Recreation and other services ROS E027 Teatros y concertos
Services Recreabon and other services ROS E028 Bares y Centros noctumos ( tnduye alimenlos, babides tabaco, cover, propinas, etc.)
Services Recreation and other services ROS E029 Espectaculos deportivos
Services Recreation and other services ROS E030 Loteria y juegos de azar
Services Recreation and other services ROS E031 Cuotas a: centros sociales, asodadones, dubes, etc.
Services Recreation and other services ROS E032 Servido de television por cable, satilite, pago por evento y paquetes.
Services Recreation and other services ROS E033 Renta de: cassetes para video juego, discos cortpactos y video cassete.
Services Recreation and other services ROS E034 Otros gastos de recreacidn: circos, museos, terias, juegos mecinicos, balnearios. etc.
COMUNICACIONES Y SERVtCIOS PARA VEHICULOS
A. Comunicaciones
Services Transport nec OTP F001 Telefono particular
Services Transport nec OTP F002 Telefono pCiblico
Services Transport nec OTP F003 Correo: estampillas, paqueteria, etc.
Services Transport nec OTP F004 Telegrafo
Services Transport nec OTP F005 Otros: Telex, gimos, fax pGblioo, etc.
B. Combustible, Mantenimiento y Servicios para veh(cutos
Manufacturing Petroleum coal products P C F006 Gasolina, diesel o gas
Manufacturing Petroleum coal prodcts P_C F007 Aceites y hluricantes
Services Recreation and other services ROS F008 Pensi6n y Estacionamiento
Services Recreation and other services ROS F009 Lavedo y engrasado
Services Recreation and other services ROS F010 Otros servidos: encerado, reparad6n de llantas, etc.
VMENDA Y SERVICIOS DE CONSERVACION
A. Vivienda
1. Propia
Services Dwellings DWE G001 Valrw estimado del alquiler Only in autoconsumo
Services Dwellings OWE G002 Cuota pagada
Services Water WTR G003 Agua
Services Dwellings DWE G004 Impuesto predial
2. Rentada o alquilada
Services Dwellings OWE G005 Alquiler
Services Water WTR G006 Agua
3. Recibida como prestad6n
Services Dwellings OWE G007 Valor estimado del alquiler Only in autoconsumo
Services Water WTR G008 Agua
Services Dwellings DWE G009 Cuota o pago por la vivienda
4. Prestada
Services Dwellings OWE G010 Valor estimado del alquiler Only in autooonsumo
Servies Water WTR G011 Agua
Services Dwellings OWE G012 Impuesto predial
S. Alquiler de terrenos para uso exdusivo dle la vivienda
Services Dwellings DWE G013 Alquiler
Services Water WTR G014 Agua
6. Otra situacion de Ia vivienda
Services Dwellings DWE G015 Valor estimado del alquiler
Services Dwellings OWE G016 Cuota, renta o pago porla vivienda
44
Services Water WTR G017 Agua
Services Dwellings DWE GO1 Impuesto predial
7. Sfdo para hogares adcionales
Services Dwellings DWE G019 Cuota.rentsopagowoelovivienda
Services Water WTR G020 Agua
Services Dwellings DWE G021 Impuesto predial
B. Servicids por ronservacron
1. Cuota por servicios de ronservacion
Services Dwellings DWE G022 Recolecci6n de baura
Services Dwellings OWE G023 Cuotas de vigilancia
Services Dwellings DWE G024 Cuotas de administracidn
Services Dwellings DWE G025 Otros serviros
2. Etectriridad y combustible
Services Electricity ELY G026 Energia el6rtrica
Services Gas distribution GOT G027 Gas
Primary Oil OIL G028 Petr6leo
Primary Coal COL G029 Carbon
Prmary Forestry FOR G030 Lefta
Manufacturing Petroleum coal products P_C G031 Combustible pare calentar
Manufacturing Chemieal rubber plasbc prods CRP 0032 Velas y veladoras
Manufacturing Chemical rubber plastic prods CRP G033 Otros combustibles: carton, papel, etc.
PRENDAS DE VESTIR, CALZADO Y ACCESORIOS
A. Para personas de 3 aftos y rods
Manufacturing Wearing apparel WAP H001 Pantalones par horrrbre de fibras sint6ticas
Manufacturing Wearing apparel WAP H002 Pantalones pare hombre de mezdilla
Manufacturing Wearing apparel WAP H003 Otros pantalones para hombre
Manufacturng Wearing apparel WAP H004 Partalones para muier de fibras sinteticas
Manufacturing Wearing apparel WAP H005 Pantadones para rmujor de mezdilla
Manufactunng Wearing apparel WAP HOOB Otros pantalones para mi4er
Manufacturing Wearing apparel WAP H007 Gemisas para hornbre
Manufacturing Wearing apparel WAP H008 Playeras para hombre
Manufacturing Wearing apparel WAP H009 Blusas y playeras para muier
Manufacturing Wearing apparel WAP HOIt Traes
Manufacturng Wearing apparel WAP HOII Sacos pare hombre
Manufactunng Wearing apparel WAP H012 Vestidos
Manufacturing Wearing apparel WAP H013 Conjuntos
Manufactunng Wearing apparel WAP H014 Faldas
Manufacturing Wearing apparel WAP H015 Sudteres
Manufacturing Wearing apparel WAP HOt6 Abrigos
Manufacturing Wearing apparel WAP H017 Chamarras y chaquetas
Manufacturng Wearing apparel WAP HOIS Calzorrillos y twuzas
Manufacturing Wearing apparel WAP H019 Camisstas
Manufacturing Wearing apparel WAP H020 Calcetinas, calcetas y mrtas
Manufactunng Wearing apparel WAP H021 Partaletas
Manufacturing Wearing apparel WAP H022 Brnsieres y fajas
Manufacturng Wearing appaet WAP H023 Foridos y corpiAos
Manufacturing Wearing apparel WAP H024 Medias, pantmedias y tobimedias
Manufactunng Wearing apparel WAP H025 Pijamas y camisones
Manufacturing Wearing apparel WAP H026 Batas
Manufacturing Wearing apparel WAP H027 Gabardinas
M.anufacuring Wearing appare WAP H028 lmparnables y tnrngas
Manufacturng Wearing apparel WAP H029 Unifornes y prendas de vestir para actividades educativas, artisticas y deportivas
Manufaduring Wearing apparel WAp H3O0 VestiTnenrta para eventos especiales derivados de la e0ucacidn
Manufacturing Wearing apparel WAP H031 Telas, confecciones y reparaciones
Manufacturing Wearing apparel WAP H032 Otras prendas para hombre (corbatas, rtc.)
Manufacturng Wearing apparel WAP H033 Otras prendas para mrier (rebozo, etc.)
B. Para menores de 3 atos
Manufacturing Wearing apparel WAP H034 PaAlales de tola
Manufacturing Wearing apparel WAP H035 Calzones de hule
Manufacturing Wearing apparel WAP H036 Pantalones
Manufacturing Wearing apparel WAP H037 Vestidos, trajes y mamelucos
Manufacturing Wearing apparel WAp H038 Blusas y playeras
Manufacturing Wearing apparel WAP H039 Sueteres y rhambritas
Manufacturing Wearing apparel WAP H040 Camisetas
Manufacturng Wearing apparel WAP H041 Calzones de tola
Manufacturing Wearing apparel WAP H042 CGlcetines y calcetas
Manufacturing Wearing apparel WAP H043 Pijames y batas
Manufaduring Wearing apparel WAp H044 Telas, confecciones y reparari6n
Manufacturing Wearing apparel WAP H045 Otras prendas para beb6: baberos, delantales, fajillas, etc.
C. Calzado y su reparaci6n
Manufacturing Leather products LEA H046 Zapatos de piel pare horbre
Manufacturing Leather products LEA H047 Zapatos de piel pare rujer
Manufacturng Leather products LEA H048 Zapatos de piel pare manes de 3 ahtos
Manufacturing Wering apparel WAP H049 Zapatos de plstisco pare hombre
Manufactunng Wearing apparel WAP H-0iO Zapatoas de plistico Para mujor
Manufacturing Weoing apprel WArP OSIt Zapetos de pstico p menores de 3 atos
Manuifacturing Wearng apparel WAP H052 Tenis
Manufacturing Wearing apparel WAP H053 Otros tipos de catzado: huaraches, etc
Services Recreation eVW othwe servicss ROS H054 Servictos de limpieza y reparacion de calzado
Manufacturng Wearing apparel WAP H0i5 Otros: agutetas, cromnas, cepillos, etc.
0. Accesorros y efecdos personatas
Manufacturing Leather products LEA H056 Sombreros, gorros y cachuchas
45
Manufacturng Leather products LEA H057 Bolsas
Manufacturing Leather products LEA H058 Portafolios
Manufactunng Leather products LEA H059 Cinturones, carteras, monederos
Manufacturing Wearing apparel WAP H060 Joyeria de fantasia
Manufacturing Wearing apparel WAP H061 Relojes de pulso
Manufacturing Wearing apparel WAP H062 Encendedores, cigarreras y polveras
Manufacturng Wearing apparel WAP H063 Otros accesorios: diademas, lentes oscuros, etc.
Manufactunng Wearing apparel WAP H064 Articulos y accesorios para et bebb.
Services Recreation and other services ROS H065 Reparaci6n ySo mantenirniento de los articus anteriaores(espcitftque)
CRISTALERIA, BLANCOS Y UTENSILIOS DOMESTICOS
A. Cristaleria, vajillas y utensilios domesticos
Manufacturing Chemical rubber plastic prods CRP tO(1 Vajilla complete de cristal, barr, plestico, etc,
Manufacturing Chemical rubber plastic prods CRP 1002 Piezas sueltas de vajilla de cristal, banro, plistico, etc.
Manufacturng Chemical rubber plastic prods CRP 1003 Recipientes o cajas de plistico para la cocins
Manufacturing Chemical rubber plastic prods CRP 1004 Vasos, copas y jarras de cristal, plastico, ceramica, etc.
Manufacturng Chemical rubber plastic prods CRP 1005 Cubiartos
Manufacturng Chemical rubber plastic prods CRP 1006 Objetos omamentales
Manufacturing Chemical rubber plastic prods CRP 1007 Accesorios de hule y pltstico: jabotiera, tapetes, etc.
Manufacturing Chemica) rubber plastic prods CRP 1008 Reloj de pared o mesa
Manufacturing Metal Products FMP 1009 Bateria de cocina y piezas sueltas
Manufacturing Metal Products FMP 1010 Otla express
Manufacturing Metal Products FMP tO11 Otros utensilios: tijeras, abrelatas, pinzas para hielo, etc.
Manufacturng Metal Products FMP 1012 Herramientas: pinzas, martilio, taladro, etc
Services Recreabon and other services ROS 1013 Reparaci6n y/o Mantenimiento de los articulos anteriores
S. Blancos, manteleria y articulos de merceria
Manufacturing Textiles TEX 1014 Colchones
Manufacturing Textiles TEX 1015 Colchonetas
Manufacturing Textiles TEX 1016 Cobertores y cobijas
Manufacturing Textiles TEX 1017 Sabanas
Manufacturing Textiles TEX 1018 Fuedas
Manufacturing Textiles TEX 1019 Colchas
Manufacturng Textiles TEX 1020 Manteles y servilletas
Manufacturing Textiles TEX 1021 Toallas
Manufacturing Textiles TEX 1022 Cortmas
Manufacturing Textiles TEX 1023 Telas, confecciones y reparadones de articulos para el hogar
Manufacturing Chemical rubber plastic prods CRP 1024 Hilos, hilazas y estambres
Manufacturing Chemical rubber plastic prods CRP 1025 Aguyas, cierres, botones y broches
Manufacturing Manufactures nec OMF 1026 Otros articulos: hamaces, almohadas, cojines, secadores, etc.
CUIDADOS DE LA SALUD
A. Atenci6n primaria o ambulatoria (no hospitalaria ni embarazo)
Services Pub admin defence health education OSG J001 Consultas mndicas
Services Pub admm defence health education OSG J002 Consultas dentales
Services Pub admin defence health education OSG J003 Consultas con el ocutiste, optometrista u oftalmologo
Services Pub adrnin defence health education OSG J004 Medicamentos recetados y vacunas
Services Pub admin defence health educaton OSG J005 Analisis cdinicos
Services Pub admin defence health education OSG J006 Rayos X, Ultrasonidos, Tomogrnfias,Electroencefalogramas etc.
Services Pub admin defence health education OSG J007 Hierbas medicinales, amuletos y remedios ceaseaos
Services Pub admin defence health education OSG J008 Servicios no profesionales (curancdero, huesero, etc.)
Services Pub admin defence health education OSG J009 Otros: ambulancias, aplicaciones de inyacciones, etc.
S. Atenci6n hospitafaria (no induye parto)
Services Pub admin defence health education OSG J010 Honorarios por servidos profesionates
Services Pub admin defence health educabon OSG Jut1 Medicamentos recetados
Services Pub admin defence health educaton OSG J012 Analisis clinicos
Services Pub admin defence health education OSG J013 Estudios Medicos: Rayos X, Ultrasonidos, Tomograflas, Electrocardiogramas
Services Pub admin defence health education OSG J014 Hospitalizacidn
Services Pub admin defence health educaton OSG J015 Otros: ambulancias, etc.
C. Servicos medicos y medicamentos durante el embarazo
Services Pub admin defence health educatiorn OSG .1016 Consultas medicas
Services Pub admin defence health education OSG J017 Servicdos de partere
Services Pub admin defence health educaborn OSG J018 Medicamentos recetados
Services Pub admin defence health education OSG J019 Analisis dinicos
Services Pub admin defence health education OSG J020 Estudios medicos, rayos X, ultrasonido, etc.
Services Pub admin defence health education OSG J021 Servicios no profesionates (comadrona, bruja, etc.)
Services Pub admin defence health educabon OSG J022 Hierbas medicinales, remedios caseros y otros
Services Pub admin defence health education OSG J023 Hospitalizaci6n durante el embarazo no parto
Services Pub admin defence health education OSG J024 Otros: Aplicad6n, inyecciones, ambulancdas
D. Servicdos medicos durante el parto
Services Pub admin defence health education OSG J025 Honorarios por servidtos profesionales
Services Pub admin defence health education OSG J026 Servicios de partera
Services Pub admin defence health education OSG J027 Medicamentos recetados
Services Pub admin defence health educaton OSG J028 Hospitalizacid6n, sanatonos, dinicas, etc.
Services Pub admin defence health education OSG J029 Analisis dinicos
Services Pub admin defence health educatiorn OSG J030 Estudios medicos, rayos X, ultrasonido, etc.
Services Pub admin defence health education OSG J031 Servicdos no profesioneles (comadrona, curandero, etc.)
Services Pub admin defence health education OSG J032 Otros: ambulancias, etc.
E. Medicamentos sin receta
Manufacturing Chemical rubber ptastic prods CRP J033 Material para primaros auxitios (algod6n, gasa, jeringas, etc.)
Manufacturing Chemical rubber plastic prods CRP J034 Anticonceptivos
Manufacturing Chemical rubber plastic prods CRP J035 Vitaminas
Manufacturing Chemical rubber plastic prods CRP J036 Analgesicos, Antidiarrbicos
Antibi6ticos,
Manufacturing Chemical rubber plastic prods CRP J037 Jarabes, t6nicos y brebajes
46
Manufacturng Chemical rubber plastic prods CRP J038 Otros medicamentos sin receta
F. Aparstos ortopbdirco y teraplutco
Manufacturing Machinery and Equipment OME J039 Anteooos y lentes de contacto
Manuracturing Machinery and Equipment OME J040 Placas y puertes dentales
Manufadurig Machinery and Equipment OME J041 Aparatos para sordera
Manufacturing Machinery and Equiprihent OME J042 Otros aparatos: ortopedicos (mulatas, sillas de ruedas, etcl
Services Recreation and other services ROS J043 Reparacion yho Mantenimiento do los aparatos anteriores(especiflque)
G. Seguro medico
Services Insurances ISR 3044 Cuotas a hospitales o dinicas
Services Insurances ISR J045 Cuotas a compafias aseguradoras
ENSERES DOMESTICOS Y MANTENIMIENTO DE LAVrVIENDA
A. Enseres dombsticos
Manufacturing Machinery and Equipment OME K001 Ventilador
Manufacturing Machinery and Equipment OME K002 Aparatos telteonicos
Manufacturing Machinery and Equiprnent OME K003 Aparatos de aire acondidonado
Manufacturing Machinery and Equipment OME K004 Maquina de
coser
Manufacturing Machinery and Equipment OME K005 Cocina integral
Manufadturing Machinery and Equipment OME K006 Estufa de gas
Manufacturing Machinery and Equipment OME K007 Estufas de otros combustibles (petr6leo, carbon, etc.)
Manufacturing Machinery and Equiprent OME K008 Refrigerador
Manufacturing Machinery and Equipment OME K009 Licuadora
Manufacturing Machinery and Equipment OME K010 Batidora
Manufacturing Machinery and Equipment OME K1011 Plancha
Manufacturing Machinery and Equiprent OME KD12 Extractor de)ugos
Manufacturing Machinery and Equipment OME K013 Lavadora
Manufacturing Machinery and Equipment OME K014 Aspiradora
Manufacturing Machinery and Equipment OME K015 Calentador de gas
Manufacturig Machiery and Equipment OME K016 Calentador de otros combustibles
Manufacturing Machinery and Equipment OME K017 t,amparas elctricas
Manufacturing Machinery and Equipment OME K101 Lamparas de otros combustibles
Manufacturing Machinery and Equipment OME K019 Otros aparatos: tostador, calefactor, omno de microondas, etc.
Services Recreation and other services ROS K020 Reparacidn ylo mantenimiento de los articulos anteriores (especifique)
B. Muebles
Manufacturing Wood Products LUM K021 Juego de recamara
Manufacturing Wood Products LUM K022 Piezas sueltas de recamara (camas, tocadores, fiteras, cunas, c6modas. buros, roperos, etc.)
Manufacturing Wood Products LUM K023 Juego de comedor o antecomedor
Manufaduring Wood Products, WUM K024 Piezas sueltas para comedor o antecomedor (mesa, silla, etc)
Manufacturing Services LUM K025 Juego de sala
Manufacturing Wood Producta LUM K026 Piezas sueltas pare sala (mesa de centro, etc.)
Manufactunng Wood Products LUM K027 Muebles para cocina (gabinete, mesa, etc.)
Manufiacturing Wood Producta LUM K028 Alfombras y tapetes
Manufacturing Wood Producta LUM K029 Otros muebles: ibrero, escritorio, mesa para tv., etc.
Recreation and other services ROS K030 Reparaci6n y/o mantenimiento de los articulos anteriores(espeaftque)
C. Mantenimiento, reparaci6n y ampliaci6n de la vivienda que habita el hogar.
Services Dwellings DWE K031 Materiales para: reparaci6n, mantenimiento y ampliaci6n
Services Dwellings DWE K032 Servicos de: reparacidn, manteniniento y ampliaci6n, etc.
D. Mantenimiento, roparaci6n, ampliad6n y construoci6n de la vivenda quo no habita el hogar.
Services Dwellings UWE K033 Materiales pare: reparaci6n, mantenimiento, ampliacd6n y construccion
Services Dwellings DWE K034 Serviciaos para: reparaci6n, mantenimiento, amplisci6n y construcci6n
ARTICULOS DE ESPARCIMIENTO
A Articulos y equipo audiovisual
Manufacturng Electronic equipment ELE L001 Radio y radio despertador sin tocacntas
Manufacturing Electronic equipment ELE L002 Estirso a modular
Manufadurng Electronic equipment ELE L003 Grabadora con o sin despertador excepto con disco compacto
Manufacturing Electronic equipment ELE L004 T. V. blanco y negro
Manufacturng Elecronic equipment ELE L005 T. V. color
Manufacturing Electronic equipment ELE L006 Videocasseter
Manufacturing Electronic equipment ELE L007 Computadora
Manufadcturing Elecronic equipment ELE L008 Antena parabolica
Manufacturing Eletronic equpment ELE L009 Accesorios: bocinas, audifonos, antena aerea, etc.
Manufacturing Electronic equipment ELE L80O Videocassetes
Manufacturing Eledronic equipment ELE L011 Reproductor de discos compactos para vehiculo y auto esterea
Manufacuring Electronic equipment ELE L012 Reproductor do disco compacto
Manufacturing Electronic equipment ELE L013 Alquiler do t.v. y equipo
Manufacturing Electronic equipment ELE L014 Otros aparatos: regresadora de video, reproductor de eassets personal (walkman), etc.
ServIces Recreafion and other services ROS LO1 5 Reparaci6n y mantenimiento de los articulos anteriores
B. Equipo fotografloo y de video
Manufacturing Electronic equipment ELE L016 Proyectores
Manufacturing Electronic equipment ELE L017 Cbmaras fotogrfikcs y de video
Manufacturing Electronic equipment ELE L018 Material fotogrbfico, pellculas, lentes, etc.
Manufacturing Elecronic equipment ELE L019 Otros articulos y servicios: tripie, alquiler de equipo: proyectores, etc.
Services Recreation and other services ROS L020 Repanadon y mantenimiento do bos articuiOs anteriores
C. Otros articulos do esparcimiento
Manufacturing Manufactures nec OMF L021 Juguetes
Manufacuring Manufactures nec OMF L022 Juegos electrnicos. vidsojuegos
Manufacuring Manufacures nee OMF L023 Instrumentos musicales
Manufacuring Manufadures nec OMF L024 Articulos de deports y ceaceria
Food Crops nec OCR L025 Art7culos de jardiner7: plantas, flors, maoetas. ierra, abono, etc.
Servies Recreation and ofer services ROS L026 Reparaci6n y mantenimienlo de los wrticuts ntantrios (especifique)
Manufactunng Manufadures nec OMF L027 Compra y cuidado de animales domiesticos (excluya alimeintaci6n)
TRANSPORTE
47
A. Servidos de transports
Services Transport nec OTP MOOI Transporte foraneo
Services Transport nec OTP M002 Transporte ferroviaro
Services Transport nec OTP M003 Transporte aereo
Services Transport nec OTP M004 Servicios de carga y mudanza
Services Transport nec OTP MOOS Cuotas de autopista
Services Transport nec OTP M006 Otros: lanchs, barco, carreta, alquiler de vehiculos, etc
8 Adquisici6n de vehicutos de uso particular
Manufacturng Motor Vehides MVH M007 Autom6vil yho Guayin
Manufacturing Motor Vehicles MVH MO8 Camioneta (Pick Up)
Manufacturing Motor Vehides MVH M009 Motoneta y motocicleta
Manufacturing Transport Equipment OTN MOID Bicicleta
Manufacturing Transport Equipment OTN MOI 1 Otros: remolque, landha, etc
C. Refacciones, partes, accesorios y mantenimiento de vehirulos
Manufacturing MotorVehicles MVH M012 Llantes
Manufacturing Motor Vehicles MVH M01 3 Acumulador
Manufacturing Motor Vehides MVH M014 Refaociones: bujies, bandas, filtros, etc.
Manufacturing Motor Vehicles MVH M01 5 Partes de vehiculos: vidrios, salpicadera, etc.
Manufacturing Motor Vehicles MVH M016 Accesonos: espejos, manijas, antenas, etc.
Services Recreation and other services ROS M017 Servicdo de afinaci6n, alineacidn y balanoso
Services Recreation and other services ROS M018 Otros servicdos: ajuste de motor. de irenos, hqolateria, pintura, etc.
OTROS GASTOS
A. Gastos diversos
Services Business services OBS N001 Servicios profesionales: abogados, notarios, arquitectas, etc. (no incluya m6dicos)
Services Business services OBS N002 Funerales, cementerios
Services Recreabion and other services ROS N003 Paquetes para fiesta (sal6n, comida, orquesta)
Services Recreation and other services ROS N004 Gastos turisticos: paquetes, hospedaje, alirmentos, tours, etc.
Services Recreaton and other services ROS N005 Hospedaje o alojamiento (oon o sin alimento)
Services Pub admin defence health educabon OSG N006 Gastos en cargos comunales para festividades locales
Services Pub admin defence health education 05G N007 Cqntribucrones para obras de servicio publico local
Services Insurances ISR N008 Seguros de automovil
Services Insurances ISR N009 Seguros contra incendio, daeios, riasgos, educaci6n y seguro de vida
Services Business services OBS N010 Otros gastos diversos no comprendidos en las categorias anteriores (especifique)
B. Transferencias
Residual Savings SAV N011 Indemnizauones pagadas a terceros
Residual Savings SAV N012 Perdidas y robos en dinero (excluya negocios)
Residual Savings SAV N013 Ayuda a parientes y personas no miembros del hogar (en dinero)
Residual Savings SAV N014 Contribuciones a instituciones benificas, iglesias, cruz roja (en dinero), incduye los servicios
eclesuistcos
Services Pub admin defence health educabon OSG N015 Servicios del sector publico: expedici6n de pasaportes, actas, titulos, etc.
Services Pub admin defence health educalion OSG N016 Tramites para vehiculos: licencias, tenencias, placas, verificao6n vehicular, etc
EROGACIONES FINANCIERAS Y DE CAPITAL
Residual Savings SAV 0001 Dep6sitos en cuentas de ahorros, tandas, cajas de ahorro, etc.
Residual Savings SAV Q002 Prestamos a terceros
Residual Savings SAV Q003 Pagos a Tarjeta de Credito Bancaria o Casa Comercial
Residual Savings SAV 0004 Pago de deudas a la empresa donde trabajan ylo a otras personas o insttuciones (exctuya
Cr6ditos Hipotecarios)
Residual Savings SAV Q005 Compra de monedas nacionales o extranjeras, metales preciosos, athajas, obras de arte, etc.
Residual Savings SAV 0006 Seguro de Vida
Residual Savings SAV 0007 Herencias, dotes y legados
Residual Savings SAV 0008 Compra de casas, condominios, locales o torrenos que no habite of hogar
Residual Savings SAV Q009 Compra de terrenos, cases o condominios que habits el hogar
Residual Savings SAV 0010 Pago de hipotecas de bienes inmuebles: casas, tenrenos, edhficios, etc.
Residual Savings SAV 0011 Otras erogaecones no consideradas en las preguntes anteriores, especifique
Residual Savin,gs SAV 0012 Compra de maquinaria, equipo, animales destinados a fa produccidn, etc utilizados en
negocios propiedad del hogar
Residual Savings SAV 0013 Balance negativo en negocios proPiedad del hogar no agropecuario y agropecuario
Residual Savings SAV 0014 Compra de valores: cedulas, acdones y bonos
Residual Savings SAV 0015 Compra de marcas, patentee y derechos de autor
Residual Savings SAV T Other transfers
CLASSIFICATION
OF INCOME
GTAP Sector Household Sector
INGRESOS NETOS DEL HOGAR
A Ingresos netos por remuneraciones al trabajo
Wages P001 Sueldos, salarios, jomal y horas extras
Wages P002 Comisiones, propinas y destajo
Wages P003 Aguinaldo, gratificaciones, promios y recompensas adicionales
Wages P004 Primas vacacionales y otras presteacones en efedcivo
Wages P005 Reparto de uilicdades endowment shares (from ilo
teables)
B. Ingresos netos de negodos propios Land Wages Capital
Wages and Capital P006 Negocios industriales 28% 72%
Wages and Capital P007 Negocios comerciales 35% 65%
Wages and Capital P008 Prestaci6n de servidos 35% 65%
48
Wages Land and Capital P009 Producci6n agricola 17% 36% 47%
Wages Land and Capital PO10 Produccion pecuana y derivados 17% 36% 47%
Wages Land and Capital P011 Produccion forestal 17% 36% 47%
Wages Land and Capital P012 Recolaccidn de flora. productos forestales y 17% 36% 47%
caza
Wages and Capital P013 Acuacultura y pesca 28% 72%
C. Ingresos netos por cooperativas
Wages Land and Capital P014 Sueldos o salanos 5% 36% 59%
Wages Land and Capital P015 Ganandas o utilidades 5% 36% 59%
D. Ingresos netos por renta de la propiedad
Capital P016 Alquiler de berras y terrenos
Capital P017 Alquiler de casa, edifidos, locales y otros inmuebles
Capital P018 Intereses provenientes de inversiones a plazo fpjo
Capital P019 Intereses provenientes de cuentas de ahorro
Capital P020 Intereses provenientes de prestamos a tercenrs
Capital P021 Intereses provenientes de acciones, bonos y c6dulas
Capital P022 Alquier de marcas, patentes y derechos de autor
E.
Transferencias
Wages P023 Jubilaciones yfo pensiones
Transfers P024 Indemnizaciones recbidas de seguros contra riesgos y terceros
Transfers P025 Indemnizaciones por despido y accidentes de trabajo
Transfers P026 Becas y donativos provenientes de instituciones
Transfers P027 Regalos y donativos originados dentro del pals
Transfers P028 tngresos provenientes de otros paises
Land P029 Benefido de PROCAMPO
F. Otros Ingresos corrientes
Negative Savingvs P030 Venta de vehiculos, aparatos electricos de segunda mano, etc.
Negative Savings P031 Otros ingresos corrientes no considerados en los anteorires
PERCEPCIONES FINANCIERAS Y DE CAPITAL
Negative Savings P032 Retiro de inversiones, ahorros, tandas, cajas de ahorros, etc.
Negative Savings P033 Ingresos por prestamos a terceros que hizo a otras personas no miembros del hogar
Negative Savings P034 Prestamos de personas no miembros del hogar o institucones (exduya pr6stamos hipatecanos)
Negative Savings P035 Venta de monedas, metales precosos, joyas y obras de arte
Negative Savings P036 Venta de valores, acdones, cedulas y bonos
Negative Savings P037 Venta de derechos de autor, patentes y marcas
Negative Savings P038 Herencias, dotes, loterias y legados
Negative Savings P039 Venta de casas. terrenos, condominios, etc.
NegaUve Savings P040 Venta de maquinaria, equipas, animales destinados a la producci6n, vehiculos, etc. utileiados en el
negocio propiedad del hogar
Negative Savings P041 Prestamos hipotecarios por bienes inmuebles: casas, terrenos, edificios y locales
Negative Savings P042 Seguros de vida
Negative Savings P043 Otras percepciones de capital no consideradas en las anteriores
49
Policy Research Working Paper Series
Contact
Title Author Date for paper
WPS2641 Is Russia Restructuring? New Harry G. Broadman July 2001 S. Craig
Evidence on Job Creation and Francesca Recanatini 33160
Destruction
WPS2642 Does the Exchange Rate Regime Ilker Doma, July 2001 A. Carcani
Affect Macroeconomic Periormance? Kyles Peters 30241
Evidence from Transition Economies Yevgeny Yuzefovich
WPS2643 Dollarization and Semi-Dollarization in Paul Beckerman July 2001 P. Holt
Ecuador 37707
WPS2644 Local Institutions, Poverty, and Christiaan Grootaert July 2001 G. Ochieng
Household Welfare in Bolivia Deepa Narayan 31123
WPS2645 Inequality Convergence Martin Ravallion July 2001 P. Sader
33902
WPS2646 Foreign Direct Investment and Bartlomiej Kaminski July 2001 L. Tabada
Integration into Global Prodluciion Beata K. Smarzynska 36896
and Distribution Networks: The Case
of Poland
WPS2647 The Politics of Monetary Sector Chibuike U. Uche July 2001 A. Al-Mashat
Cooperation among the Economic 36414
Community of West African States
WPS2648 Methodologies to Measure the Gender Elizabeth Sharader July 2001 M. Correia
Dimensions of Crime and Violence 39394
WPS2649 The Impact of the AIDS Epidemic on Martha Ainsworth July 2001 H. Sladovich
the Health of the Elderly in Tanzania Julia Dayton 37698
WPS2650 Sources of China's Economic Growth, Yan Wang July 2001 A. Datoloum
1952-99: Incorporating Humarn Capital Yudong Yao 36334
Accumulation
WPS2651 China's Growth and Poverty Shaohua Chen July 2001 A. Datoloum
Reduction: Trends between 1990 Yan Wang 36334
and 1999
WPS2652 Demand for World Bank Lendilig Dilip Ratha July 2001 S. Crow
30763
WPS2653 The Impact of Farm Credit in Pakistan Shahidur R. Khandker August 2001 P. Kokila
Rashidur R. Faruqee 33716
WPS2654 Thirst for Refor? Private Sector Luke Haggarty August 2001 P. Sintim-Aboagye
Participation in Providing Mexico Penelope Brook 37644
City's Water Supply Ana Maria Zuluaga
Policy Research Working Paper Series
Contact
Title Author Date for paper
WPS2655 Measuring Services Trade Aaditya Mattoo August 2001 L. Tabada
Liberalization and its Impact on Randeep Rathindran 36896
Economic Growth: An Illustration Arvind Subramanian
WPS2656 The Ability of Banks to Lend to Allen N. Berger August 2001 A. Yaptenco
Informationally Opaque Small Leora F. Klapper 31823
Businesses Gregory F. Udell
WPS2657 Middle-Income Countries: Peter Fallon August 2001 D. Fischer
Development Challenges and Vivian Hon 38656
Growing Global Role Zia Oureshi
Dilip Ratha
WPS2658 How Comparable are Labor Demand Pablo Fainzylber August 2001 A. Pillay
Elasticities across Countries? William F. Maloney 88046
WPS2659 Firm Entry and Exit. Labor Demand, Pablo Fajnzylber August 2001 A. Pillay
and Trade Reform: Evidence from William F. Maloney 88046
Chile and Colombia Eduardo Ribeiro
WPS2660 Short and Long-Run Integration: Graciela Kaminsky August 2001 E. Khine
Do Capital Controls Matter? Sergio Schmukler 37471
WPS2661 The Regulation of Entry Simeon Djankov August 2001 R. Vo
Rafael La Porta 33722
Florencio Lopez de Silanes
Andrei Shleifer
WPS2662 Markups, Entry Regulation, and Bernard Hoekman August 2001 L. Tabada
Trade: Does Country Size Matter? Hiau Looi Kee 36896
Marcelo Olarreaga
WPS2663 Agglomeration Economies and Somik Lall August 2001 R. Yazigi
Productivity in Indian Industry Zmarak Shalizi 37176
Uwe Deichmann
WPS2664 Does Piped Water Reduce Diarrhea Jyotsna Jalan August 2001 C. Cunanan
for Children in Rural India? Martin Ravallion 32301
WPS2665 Measuring Aggregate Welfare in Martin Ravallion August 2001 C. Cunanan
Developing Countries: How Well Do 32301
National Accounts and Surveys Agree?
WPS2666 Measuring Pro-Poor Growth Martin Ravallion August 2001 C. Cunanan
32301