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 development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. 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 References Agenor, P, et al. (2000) "Macroeconomic framework for poverty reduction strategy papers". Mimeo. World Bank. Atkinson, A. B. (1987) "On the Measurament of Poverty", Econometrica, Volume 55, Issue 4, 749-764. Bautista, R.M. and Marcelle Thomas (1997) "Income effects of alternative trade policy adjustments on Phillippine rural households: a general equilibrium analysis" IFPRJ TDM Discussion Paper # 22. Beyer, H., Patricio Rojas and Rodrigo Vergara (1999) "Trade Liberalization and wage inequality". 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World Bank, (2001b), "Mexico's Poverty Assessment ". 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