Using Survey Data to Assess the Distributional Effects
of Trade Policy
Guido G. Porto
Development Research Group
The World Bank
I wish to warmly thank Angus Deaton and Gene Grossman for their comments, insights and continuous
support. I thank Alicia Menendez and Marcelo Olarreaga for comments and unconditional help. My special
thanks go to Irene Brambilla. Errors are my sole responsibility.
Correspondence: The World Bank, MailStop MC3-303, 1818 H Street NW, Washington, DC 20433.
email: gporto@worldbank.org
Using Survey Data to Assess the Distributional Effects of Trade Policies
Non-Technical Summary
Guido G. Porto
The purpose of this paper is to develop and apply a methodology to empirically explore
the effects of trade policies on the distribution of income and poverty in developing
countries. The methodology used here is based on two links, one connecting trade
policies to prices and another connecting prices to household welfare. In the first link,
trade reforms in small open economies cause the relative prices of traded goods to
change. In the second link, these price changes affect households as consumers and as
income earners. In fact, there are general equilibrium consumption effects, as households
pay different prices for traded and non-traded goods, and general equilibrium labor
income effects, as factor demand and wages adjust.
The change in the price of traded goods is taken from the exogenous policy shift.
I also develop methodology that allows one to estimate the change in the wages and the
change in the price of non-traded goods based on the exogenous change in the price of
traded goods. I then estimate the change in consumer welfare, measured as compensating
variation, given the changes in prices of traded goods, non-traded goods and wages.
I apply the methodology to the study of the distributional effects of Mercosur on
Argentine families. The main finding of the paper is that Mercosur benefits the average
Argentine household across the entire income distribution. There is evidence of a pro-
poor bias of the regional trade agreement: on average, poor households gain more from
the reform than middle-income households, whereas the impacts on rich families are
positive but not statistically significant. The reason behind these results is that Argentine
trade policy protected the rich over the poor, prior to the reform, and granted some
protection to the poor, after the reform. As a result, the relative pre-Mercosur tariff on
the goods investigated in this paper is higher on relatively skill-intensive goods. This
means that tariff removals would tend to benefit the poor over the rich. These findings
indicate that trade reforms may actually help improve the distribution of income and
reduce poverty in the country.
1 Introduction
The purpose of this paper is to develop a methodology to empirically explore the effects
of trade policies on the distribution of income and poverty in developing countries. As
opposed to most previous work in the trade literature, which looks at aggregate measures
of performance, I use survey data to econometrically assess the general equilibrium effects
of trade reforms. The paper thus substantially extends the existing literature that studies
trade liberalization with household data in partial equilibrium (Deaton, 1989a; Friedman
and Levinsohn, 2003; Edmonds and Pavcnick, 2003).
The methodology used here is based on two links, one connecting trade policies to prices
and another connecting prices to household welfare. In the first link, trade reforms in small
open economies cause the prices of traded goods to change. In the second link, these price
changes affect households as consumers and as income earners. In fact, there are general
equilibrium consumption effects, as households pay different prices for traded and non-traded
goods, and general equilibrium labor income effects, as factor demand and wages adjust. To
measure these welfare impacts, I estimate compensating variations at the household level.
I show that household budget shares approximate the compensating variation originated
in the changes in the prices of consumer goods (Deaton, 1989a, 1989b). In addition, I develop
methods to estimate the responses of the prices of non-traded goods and wages. To measure
the endogenous responses of the prices of non-traded goods, I use time series data to regress
the prices of non-traded goods on the exogenous prices of traded goods. To measure the
general equilibrium reaction of wages, I estimate wage price-elasticities by using an earnings
regression scheme that incorporates the price of traded goods as exogenous regressors. My
method adapts and extends techniques used in demand analysis to the labor market analysis.
The distributional analysis is based on the estimation of the average compensating
variations at different levels of per capita household expenditure. The welfare analysis
1
is thus performed across the entire distribution of income. The individual consumption
effects of traded goods, the individual consumption effects of non-traded goods and the
individual labor income effects are smoothed with non-parametric, locally weighted Fan
(1992) regressions.1
I apply the methodology to the study of the distributional effects of Mercosur on
Argentine families. The experience of Argentina is convenient because it combines enough
available data with a changing trade regime during the 1990s. Argentina therefore provides
a rich environment, in terms of changes in exogenous parameters and of responses of
endogenous variables, to analyze the extent to what individual welfare is affected by trade
reforms and to find magnitudes for some of these effects. In addition, since Argentina
witnessed an increase in income inequality during the 1990s, I can use the methodology to
carry out a comparative distributional analysis of trade policies vis-à-vis other simultaneous
reforms.2
The main finding of the paper is that Mercosur benefits the average Argentine household
across the entire income distribution. There is evidence of a pro-poor bias of the regional
trade agreement: on average, poor households gain more from the reform than middle-income
households, whereas the impacts on rich families are positive but not statistically significant.
These results indicate that trade reforms may actually help improve the distribution of
1The methods used in this paper differ from the Computable General Equilibrium literature in various
ways. CGE models may lead to conclusions that are embedded in assumptions on functional forms rather
than being derived from the data (Deaton, 1987; 1999). At the same time, welfare conclusions can be
sensitive to small changes in those assumptions (Deaton, 1981). In contrast, my work emphasizes the use of
econometric techniques that place few restrictions on estimable parameters, allowing for a much bigger, and
better, role for econometric measurement as opposed to assumptions. It will sometimes be necessary to make
compromises between a non-parametric analysis and a more restrictive strategy: some parameterization will
be needed and even some computation as in the CGE modeling will be unavoidable. My aim is to merge as
much econometric measurement with as few theoretical assumptions as possible, in an attempt to provide
an analysis with a different flavor from most current research on trade policy.
2The Gini coefficient for household per capita income grew from 0.438 in 1992 to 0.480 in 1997. Raising
inequality in Argentina has been very well-documented, in terms of Gini coefficients, Lorenz curves and other
relevant instruments (Gasparini, 1999). Financial reforms, economic deregulation and privatization are some
of the policy reforms adopted by Argentina.
2
income and reduce poverty in the country.
The paper is organized as follows. In section 2, I introduce a simple general equilibrium
trade model of a small open economy to characterize the impacts of trade reforms on prices
and to derive measures of the compensating variations at the household level. Section
3 develops the empirical techniques used to estimate these compensating variations and
implements the methodology on Argentine data. Section 4 concludes.
2 The Model
I begin by deriving the set of theoretical distributional effects caused by trade policies that
are estimated in this paper. I adapt the small open economy models of Dixit and Norman
(1980) and Woodland (1982) to a multi-household context. It is assumed that, for each
family, expenditure on consumption goods is equal to income (factor earnings plus transfers).3
That is,
(1) ej(pT,pNT,uj) = xj0 + wm + kj + j.
j
m
The expenditure function of household j, ej(·), depends on the vector of prices of traded
goods (pT), the vector of prices of non-traded goods (pNT), and required utility uj.
Household income comprises exogenous income, xj0, the sum of the labor income of each
member m, wm, capital income, kj, and government transfers, j.
j
A small open economy faces an exogenous international price pi for traded good i, so
that the domestic price pi is given by
(2) pi = pi(1 + i),
3I do not consider the impacts of trade on savings.
3
where i is the trade tariff. There are constant returns to scale in the production of these
goods and firms participate in competitive markets. Thus, for each good i, price is equal to
unit production cost
(3) pi = ci(w),
where ci(·) is the unit cost function and w is the vector of factor prices. The system
of equations in (3) characterizes the general equilibrium relationship between the prices
of traded goods and the prices of production factors. As product prices change, some
sectors expand while some others contract. This causes changes in relative factor demands,
depending upon the relative factor intensities used in different sectors. As a result,
wages adjust. In a two-good, two-factor model, this relationship is described by the
Stolper-Samuelson theorem: an increase in the price of a good causes an increase in the
return to the factor intensively used in its production (and a decline in the return to the
other factor). However, the association between prices and factor returns is more general
and multidimensional models only deliver correlations among prices of goods and prices of
factors (Dixit and Norman, 1980; Helpman, 1984).4
If there are at least as many traded goods as factors, then the system of equations in
(3) fully determines the prices of the factors of production, w, as a function of the vector of
prices of traded goods, pT. This assumption is adopted in this paper.
The equilibrium prices of non-traded goods are derived from the demand-supply equality
in domestic markets. That is,
(4) ej(pT,pNT,uj) = r(pT,pNT, v),
pk pk
j
4This means that, with many goods and factors, Stolper-Samuelson effects are not necessarily expected.
4
where k denotes a non-traded good, r(·) stands for the GDP function of the economy and
v for a vector of factor endowments (Dixit and Norman, 1980). In (4), the demand for a
non-traded good k is given by the derivative of the expenditure function with respect to the
price of that good (Shepard's Lemma), whereas its supply is given by the own-price derivative
of the GDP function (Hotelling's Lemma). Given the factor endowments and the prices of
traded goods, the equilibrium prices of the non-traded goods are endogenously determined
by the functions5
(5) pk = pk(pT, v).
If there are at least as many traded goods as factors, the exogenous prices of all the traded
goods uniquely determine the vector of factor prices and these, in turn, uniquely determine
the prices of the non-traded goods through the zero-profit conditions in these sectors. In
this case, neither factor endowments nor demand conditions affect equilibrium prices of
non-traded goods. This means that, in general equilibrium, there will be an aggregate
relationship between the prices of non-traded goods and the prices of traded goods, as follows
(6) pk = pk(pT).
The welfare evaluation of trade policies comprises two steps: the effects of trade reforms on
prices, and the effects of price changes on household welfare. In the first step, the parameters
used to identify trade policy reforms are given by the vector of import tariffs . These tariff
reforms cause movements in the domestic prices of the traded goods, according to equation
(2). In the second step, there are a couple of general equilibrium responses triggered by
5Notice that, more generally, I can write pk = pk(pT,v,), where stands for preference shifters, income
distribution or other variables.
5
these price changes: the changes in factor prices given by equations (3), particularly wages
for workers with varying skills, and the induced impacts on the prices of non-traded goods
given by equation (6). The welfare impacts caused by the changes in the consumer prices of
traded and non-traded goods are called consumption effects. The welfare impacts caused by
the changes in wages are called labor income effects.
The changes in household welfare are computed with measures of compensating
variations, the income needed to compensate households for a change in a tariff i. These
compensations can be measured with the change in exogenous income, xj0, that would leave
the family indifferent to the pre-policy situation. Differentiating equation (1) and assuming
public transfers and capital income away, the compensating variation for a change in the
tariffs on good i is given by6
dxj0 lnpk
(7) = sji + sjk jmjwmpi lnpidln i,
ej lnpi - lni
kNT m
where sji is the budget share spent on traded good i by household j, sjk is the budget
share spent on non-traded good k, jm is the share of the labor income of member m on
total family income. The wage price-elasticities jwmpi capture the proportional change in
the wage earned by member m caused by a change in the price of good i. Note that
equation (7) shows the negative of the compensating variation, as a fraction of total initial
household expenditure. Thus, the compensating variation is measured, as in Hicks (1939)
and Mas-Colell, Whinston and Green (1995), as the revenue of a hypothetical planner that
has to compensate a household for a policy change. Accordingly, a positive compensating
variation means a positive revenue for the planner, a negative transfer for the household and
a welfare gain.
6In this paper, I do not consider the impacts of trade reforms on government transfers and on non-wage
income (profits, returns to specific factors). See section 3 below.
6
Equation (7) captures three welfare impacts: the consumption effects of traded goods,
the consumption effects of non-traded goods and the labor income effects. The consumption
effects of traded goods can be approximated to first order by the budget share sji. The
consumption effects of non-traded goods can be approximated by the product of the
elasticity of the prices of non-traded goods with respect to the prices of the traded goods
(pkpi = ln pk/ ln pi) and the respective budget shares (sjk). The labor income effects can be
approximated by the weighted sum across household members of the wage price-elasticities
(jwmpi).
The aim of this paper is to get an estimate of the compensating variation for each
household. Some of the terms in (7) are data, others are estimable parameters, and others
are observed policy parameters. The changes in tariffs dln i represent the policy parameters
that can be recovered from the legislation that implemets Mercosur. Budget shares, sji and
sjk, are data; they can be obtained from expenditure surveys, which are readily available in
many developing countries. The induced changes in the prices of traded goods, ln pi/ ln i,
and the endogenous responses of the prices of non-traded goods, lnpk/ ln pi, and wages
jwmpi need to be estimated.
The compensating variation in (7) captures first order effects only. Extensions dealing
with higher order changes require the estimation of own-price and cross-price demand
elasticities, a difficult task given the available data in most developing countries. Since
substitution effects are ignored, the welfare effects will be mismeasured. However, as Deaton
(1989a) points out, the price elasticities will be irrelevant for the distributional effects, unless
demand elasticities are a function of household expenditure. If this is not the case and all
households face the same substitution terms, then the absolute welfare effect at the household
level will be distorted but the measurement of the relative distributional effects will not be
affected.
7
Another remark is that (7) is obtained under the assumption of exogenous international
prices in a small country. If this were not the case, the first order effects should include
additional terms involving the budget shares of other traded goods and the endogenous
responses of their prices. Measuring these effects requires, as before, an estimation of the
equilibrium response of prices (including both elasticities of demand and of supply) for all
traded goods. For most developing countries, the small country assumption can be reasonable
so that these issues will not arise.
A different concern with the approximation to the change in labor income arises when
factor endowments are endogenous (with fixed factor supply, the compensating variation
in (7) is properly measured). Here, there are two different problems. As in the case of the
substitution terms in consumption, there can be mismeasurement in the distributional effects
if the supply of labor (or the consumption of leisure) responds to product price changes.7
The total welfare effect will be mismeasured to an extent given by the elasticity of hours
worked, but the relative impact on household well-being will not be affected if this elasticity
does not vary across levels of income. A different problem is that factor prices (wages) may
react to the endogenous response in hours worked, something that will not happen under
the assumption that the number of traded goods is greater than the number of factors.
3 The Distributional Effects
This section describes the methods used to assess the distributional effects of trade policies
in Argentina. Specifically, I investigate the welfare effects of adopting of Mercosur, a
7Notice that the first order distributional effect of leisure consumption is zero. To see this, write the
expenditure function with p, Lj (hours worked) and uj as arguments (as in Dixit and Norman, 1980). The
the property that ej/Lj = w delivers the result.
income-expenditure equality requires ej(p,Lj,uj) = w(·)Lj +xj0. Differentiating with respect to pi and using
8
regional trade agreement among Argentina, Brazil, Paraguay and Uruguay.8 I introduce the
econometric techniques that are needed to estimate each of the different components of the
compensating variations at the household level, equation (7). I describe how to recover the
changes in the prices of the traded goods (section 3.1) and how to analyze the consumption
effects of traded goods (section 3.2) and non-traded goods (section 3.3). Section 3.4 explains
how to estimate the changes in wages and the labor income effects. The household total
compensating variations including all these effects are studied in section 3.5.
3.1 Price Changes of Traded Goods
The main characteristic of this paper is the measurement of the welfare effects of trade policies
in general equilibrium, a task that requires comparable consumption and labor income effects
for a given family. To study the consumption effects, I need data on budget shares, which are
available in the National Household Expenditure Survey (ENGH). This is a comprehensive
expenditure survey in Argentina that adopts a specific classification of consumption goods
which includes four traded goods and three non-traded goods. The traded goods are Food &
Beverages, Clothing, House Equipment and Maintenance Goods, and Other Traded Goods.
The non-traded goods are Housing, Transport and Communication, Health and Education,
and Leisure related Goods.9 This classification of goods is adopted in this paper.
To estimate the impacts of Mercosur on prices, I look at the effects of the elimination of
tariff barriers among country members and the implementation of common external tariffs on
the rest of the world, the main reforms introduced by the regional trade agreement. For each
traded good i, these impacts correspond to the term ( ln pi/ ln i)dln i in equation (7).
8The methodology could also be applied to other actual or hypothetical trade reforms, such as unilateral
trade liberalization.
9The ENGH survey collects data on over ninety goods. The aggregation used here exploits the availability
of price indices for traded and non-traded goods published by the National Institute of Statistics and Censuses
in Argentina. See Appendix A1 for more details.
9
I assume that Argentina is a small country that faces exogenous prices of traded goods. In
order to accommodate the discriminatory tariffs applied by Mercosur and the cross-hauling
prevalent in trade data, I introduce regional differentiation across goods, in the tradition of
the Armington (1969) model. Specifically, I follow Whalley (1985) and Shoven and Whalley
(1992) and assume that products are differentiated by large trading blocks (North America,
Western Europe, Eastern Europe, Asia, Latin America, and Other).
The price index for traded good i, pi, is defined as an import-share weighted average of
the price of the varieties coming from each of these regions. Let im be the intrazone tariff
on Mercosur members, and let irw be the common external tariff on all other varieties. The
change in the (logarithmic) price of good i is given by
(8) d ln pi = imd ln(1 + im) + irwd ln(1 + irw),
where the weights are the fraction of imports of a good i coming from Mercosur members
(im), and the fraction of imports coming from the rest of the world (irw). Using data on
import shares, intrazone tariffs and common external tariffs, I use (8) to compute the price
changes originated in Mercosur.10
The data on the tariffs levied on each of the four traded goods before and after Mercosur
are listed in Table 1. In 1992 (before Mercosur), there is some fair degree of protection
(column 1). The average tariff on imports of Clothing is 20.4 percent, the highest. House
Equipment gets a 16.1 percent tariff and Other Goods, 12.7 percent. The lowest average rate
is on Food & Beverages, 6.9 percent. The intrazone tariff (column 2) decreases to zero for
10Given the data available in Argentina, it was impossible to produce econometrically better estimates of
the price changes for the goods for which there are consumption data (food and beverages, clothing, etc.).
Using other sources of data, such as import and export prices for goods in the Harmonized System, Chang
and Winters (2002) are able to use econometric methods. The assumptions adopted in this section are closer
to the CGE literature than the rest of my study; this should be interpreted as a necessary compromise
between prior restrictions and measurement.
10
all goods but Clothing, which gets a 3.3 percent tax. There is also some protection granted
by the common external tariff (column 3). The rate on Food and Beverages is 13.7 percent,
twice the pre-Mercosur rate. The tariffs on the remaining three goods are slightly lower than
in 1992, 17.9 percent (on Clothing), 14.2 percent (on House Equipment) and 11.7 percent
(on Other Goods).
Column (4) of Table 1 shows my estimates of the total changes in the prices of traded
goods caused by Mercosur (equation 8). The price of Food & Beverages increases by 1.4
percent; instead, the prices of Clothing, House Equipment and Other Goods decline by 6.4
percent, 5.4 percent and 3.2 percent, respectively. These are the price changes for the first
link of my methodology.
3.2 The Consumption Effects of Traded Goods
I investigate next the consumption effects of traded goods, which are given by the term
sji ( lnpi/ ln i) d ln i in equation (7). To measure these terms, I need data on the
budget share spent on these goods by each Argentine household. The National Household
Expenditure Survey (ENGH) provides this information.11
The households interviewed in the ENGH survey comprise the baseline sample used
in my distributional analysis. For each of these households, I measure the consumption
effects of traded goods by multiplying the budget share sji and the price changes induced by
Mercosur ( ln pi/ ln i)dlni (section 3.1). I use these household welfare effects to study
the distributional effects of trade policies across the entire distribution of income. Since there
are 21,127 households in the expenditure survey (ENGH), I need a procedure to summarize
all this information in a useful way. To do this, I estimate average welfare effects at different
points along the per capita expenditure spectrum. These averages are the right measures
11See Appendix A1 for details on the ENGH survey.
11
for my distributional purpose since they reflect the marginal effect of a price change on a
hypothetical social welfare function.12
One way to compute these conditional averages is to estimate a parametric linear
regression of the welfare effects - the compensating variations cvj defined by (7) - on the
logarithm of per capita household expenditure (xj). This would define the conditional
expectation as E[cvj|xj] = b1 + b2xj, where b1 and b2 are estimable parameters. The
distributional analysis can be performed by plotting a line, E[cv|x] = b1 + b2x , using the
OLS coefficients b1 and b2 to predict the regression function.
One problem with this approach is that the fit can be poor if the relationship between
local changes in household welfare and per capita household expenditure is nonlinear. In
these cases, the details of the statistical association between the household welfare effects and
the logarithm of per capita expenditure can be more suitably explored with a non-parametric
procedure. Non-parametric regressions allow the data to choose the best local shape of the
regression function m(x) = E[Y |X = x], thereby reducing the biases inherent in the global
fit.
In what follows, I use locally weighted regressions (Fan, 1992; Fan and Gijbels, 1996;
Pagan and Ullah, 1999). Conceptually, I run different linear (or polynomial) regressions of
the welfare effects on the logarithm of per capita expenditure using only local data points.
At each point in the support of the log per capita household expenditure (x), I carry out the
following program (Fan, 1992)
2
(9) min xj - x ,
1,2 j cvj - 1 - 2xj K h
j
12 The use of averages at different income levels was first introduced by Deaton (1989a, 1989b) to the study
of export taxes on rice in Thailand, and has been successfully applied to the study of coffee and cocoa prices
in Côte d'Ivoire (Benjamin and Deaton, 1993), food prices in Côte d'Ivoire (Budd, 1993), and the economic
crisis in Indonesia (Friedman and Levinsohn, 2003).
12
where cvj is the compensating variation accruing to household j, K(·) is a Gaussian Kernel
function, and h is the bandwidth that defines the local data to be used in the non-parametric
regression.13 In (9), I have introduced sampling weights for household j, j, that are used
to correct biases (due to sample design or differential response, for instance) so that the
sample is representative of the population. The average compensating variation at each level
of income can be estimated by m(x) = 1 + 2x.
The consumption effects of traded goods caused by Mercosur are plotted in Figure 1.
The solid line plots the average welfare effect conditional on the level of per capita household
expenditure, computed with the Fan regressions (9). The broken lines plot the confidence
bands, computed with a bootstrap procedure. In the case of the consumption effects of traded
goods, the source of randomness is the sampling variability of households (and therefore of
budget shares) in the expenditure survey. To get the confidence bands, I take random samples
of households from the ENGH survey and I recompute the Fan regressions. The confidence
bands are built using the standard error (approximated with the interquantile range) of the
estimated regression functions, after repeating the procedure two hundred times. In all the
following figures, I measure household per capita expenditure in monthly dollars.
Figure 1 shows the total consumption effects caused by the joint change in the price of the
four traded goods. The estimated compensating variation curve is upward sloping, indicating
a pro-rich bias. For middle-income and rich households, the average consumption effect is
found to be positive, and significantly different from zero. In contrast, I find significant
welfare losses for the poorest households in Argentina. The gains extend to 0.75 percent
of initial household expenditure; the losses amount to less than 0.5 percent of expenditure.
There is simple intuition for these results. The Mercosur-induced increase in the price of
Food & Beverages produces negative average compensating variations that are larger for the
13 The non-parametric regression techniques used here are explained in detail in Appendix A3.
13
poorest households (due to Engel Law). For the poor, in fact, these losses dominate the
gains generated by the declines in the prices of the remaining traded goods, the net effect
being negative as a result. For middle-income and rich households, the opposite is true.
3.3 Consumption Effects on Non-Traded Goods
The consumption effects of non-traded goods are given by the terms
sjk ( ln pk/ ln pi) ( ln pi/ ln i) d ln i in equation (7). Their estimation requires
k
multiplying the household budget shares in the ENGH expenditure survey (sjk), the change
in the prices of the traded goods (section 3.1) and the endogenous responses of the prices of
non-traded goods ( ln pk/ ln pi), which need to be estimated.
3.3.1 Price Changes of Non-Traded Goods
In section 2, I showed that the prices of the non-traded goods k, (Housing, Transport &
Communication, Health & Education and Leisure Goods), are a function of the exogenously
given prices of the traded goods. That is
(10) pk = pk(pFB, pC, pEQ, pOG).
where pFB, pC, pEQ and pOG are the prices of Food & Beverages, Clothing, House Equipment
and Other Goods, respectively. Equation (10) can be estimated using time series data on
monthly prices from 1992 to 1999.14 To introduce some dynamics in price adjustment, I
define
(11) pk = pk1(pFBt, pCt, pEQt,pOGt) + pk2(pFBt-1, pCt-1,pEQt-1, pOGt-1).
14Appendix A1 describes the price data.
14
To estimate a flexible function form, I approximate the functions pk1(·) and pk2(·) with a
Taylor expansion so that the model is
1
log pkt = a00 + a0i log pit + aii log pit log pi +
t
2 b0i log pit-1
iT iT i T iT
1
(12) + + ctc + µt.
2 bii log pit-1 log pit-1
iT i T
In (12), I regress the monthly prices of each non-traded good pk on the monthly prices
(current and lagged) of the four traded goods (plus interactions at t and t - 1 separately);
µt is an error term. To account for the role of technical progress, I include year dummies
and time trends in the vector of controls ct.
To avoid the problem of spurious regression, I follow the usual practice of estimating
the model in first differences.15 Prior to estimation, I introduce the restrictions imposed by
theory: homogeneity of degree one in prices and symmetry (aii = ai ; bii = bi ) require
i i
that iTa0i + iTb0i = 1, iTaik = 0, iTbik = 0, i T ii
a = 0, and i T i i
b = 0. I
consistently estimate the variance of the coefficients using the Newey-West correction for
autocorrelation in the residuals, using twelve lags.
Table 2 reports the estimated elasticities for two regression models per price of non-traded
good, one with year dummies and a trend and another with year dummies only. There
are only minor differences in these two specifications. The price of Housing, Transport &
Communication is positively related to the prices of Food & Beverages, Clothing and Other
Goods, and negatively related to the price of House Equipment. The price of Health &
Education reacts negatively to changes in the prices of Food & Beverages and Clothing, and
positively to changes in the price of House Equipment and Other Goods. Finally, there is
a negative relationship between the prices of Leisure Goods and Clothing, and a positive
15I cannot reject the hypothesis that the series involved in the regressions are I(1) according to the Dickey
and Fuller (1979) tests (Phillips, 1986; 1987).
15
association with the prices of Food and Beverages, House Equipment and Other Goods.
Since I am estimating complex, general equilibrium relationships, there are no theoretical
predictions. Correlations are not transitive and thus the elasticities in Table 2 can show any
sign (Dixit and Norman, 1980). Nevertheless, I argue that the coefficients pass the following
intuitive test. Suppose, as it is the case, that Food & Beverages and Clothing are intensive in
unskilled labor relative to House Equipment. Similarly, assume that Health & Education is
intensive in skilled labor relative to Housing, Transport & Communication.16 Then, increases
in the relative prices of Food & Beverages and Clothing would generate an increase in the
relative wage of unskilled labor and, hence, a decrease in the price of Health & Education
and an increase in the price of Housing, Transport & Communication. Similarly, increases
in the price of House Equipment would generate an increase in the relative wage of skilled
workers and an increase in the price of Health & Education and a decrease in the price of
Housing, Transport & Communication. These are, in principle, the essence of my findings
in Table 2.
The last row in Table 2 shows the total change in the index price of each of the non-traded
goods that can be attributed to Mercosur. For each category of non-traded goods k, I
compute ipkpidln pi, using the predicted change in the prices of traded goods (column 4 of
Table 1) and the estimated relationship between the prices of traded and non-traded goods
(Table 2). I estimate that Mercosur effects a decrease in the price of all three categories
of goods: that of Housing, Transport & Communication, by 0.9 percent, that of Health &
Education, by 4.4 percent and that of Leisure Goods, by 4 percent (the latter two being
significant).
16See Table 4 below for evidence on these factor intensities.
16
3.3.2 The Distributional Effects
I turn next to the estimation of the average distributional effects at different levels of income
using the locally weighted non-parametric regressions. In Figure 2, the continuous solid line
plots the average welfare effect and the broken lines, the confidence bands. In the case of the
consumption effects on non-traded goods, there are two sources of randomness: the sampling
variability of budget shares and the estimation of the responses of the prices of non-traded
goods. To jointly account for these sources of variability, I use bootstrap methods as follows.
In each replication of the bootstrap loop, I resample from the ENGH household
expenditure survey. This step accounts for the random variation in budget shares. To deal
with the variance of the estimated elasticities, I resample from their empirical asymptotic
distribution. The model in (12) delivers a vector of coefficients 1 and a covariance matrix
1, such that 1 d N(1,1), where 1 is the true vector of parameters and 1 is its
asymptotic variance.17. In each loop, I obtain a new set of elasticities for non-traded goods
that are assigned to the resampled ENGH households. The Fan regression is run on the
bootstrapped sample to deliver a new estimate of the average welfare effects. After 200
replications, I compute the standard error of the estimated regression functions to build the
confidence bands.
In Figure 2, the aggregate average compensating variations are positive, and significantly
different from zero, at (almost) all income levels.18 The gains are also increasing in the
logarithm of per capita expenditure, suggesting a pro-rich bias. The compensations range
from over 0.3 percent to over 1 percent of initial household expenditure, being therefore
similar in magnitude to those found for the case of traded goods. This result highlights the
17To do the resampling in practice, I define 01 as random vector of draws from N(0,I) and R1 as the
Cholesky decomposition of 1. I compute R101 + 1 N(1,1).
18Notice that the confidence bands are wider than those found in Figure 1 because of the additional error
introduced by the estimated non-traded price elasticities.
17
importance of non-traded goods in the aggregate consumption effects, a topic that has never
been addressed before in the related literature.
The following explanation accounts for these pro-rich consumption effects of non-traded
goods. The average compensating variation for Housing, Transport & Communication
is not significantly different from zero at any income level because its Engel curve (for
budget shares) is relatively flat and the Mercosur-induced decline in its price is statistically
insignificant. In contrast, the budget-share Engel curves for Health & Education and
Leisure Goods slope strongly upwards (from nearly 0 percent at the bottom of the income
distribution to nearly 10 percent at the upper end) and the Mercosur-induced changes
in prices are statistically significant (table 2). These facts produce upward sloping and
significant distributional effects.
3.4 Labor Income Effects
This section assesses the labor income effects of Mercosur, which are given by the term
jwmpi ( lnpi/ lni)dlni in equation (7). The key component of these labor income
impacts is the wage price-elasticities, which are estimated next.
3.4.1 Estimation of the Wage Elasticities
In the model of section 2, wages wj are a function of the prices of the traded goods (pjT)
and other determinants zj.19 That is,
(13) wj = wj(pjT, zj).
19I do not study the impact of trade on unemployment. Therefore, the analysis of this paper provides
results that are conditional on being employed. This omission does not suggest that the issue is not relevant.
Rather, it is an assumption that allows me to make progress in the estimation of the general equilibrium
effects of trade policies.
18
In what follows, I develop an econometric method to estimate equation (13) based on the use
of household survey data. In Argentina, these data are available in the Permanent Household
Survey (Encuesta Permanente de Hogares, EPH), a labor market survey with information
on wages, employment, hours worked, and individual and household characteristics.20
The main problem of using survey data on wages in a regression that includes product
prices of traded goods as exogenous regressors is the lack of price variability at the household
level. To deal with this problem, I propose to identify the wage price-elasticities by exploiting
the time variability in prices and the time variability in surveys. In this sense, my method
is related to techniques used in demand analysis, such as Deaton (1997), who exploits the
regional variability in unit values to estimate systems of demand parameters, and Wolak
(1996), who uses the time variation in CPS surveys and prices to recover those parameters.
Similarly, Goldberg and Tracy (2003) estimates wage responses to changes in exchange rates
by using the time variability of CPS surveys in the United States. The procedure proposed
here extends these techniques to the estimation of the wage price-elasticities needed to assess
trade policies.
In order to exploit the time variability of prices, I need repeated cross sections of
household surveys. In Argentina, the Encuesta Permanente de Hogares (EPH) is gathered
biannually in May and October. Thus, I can use a time series of sixteen surveys from 1992
to 1999 (two per year) to identify the elasticities.21
The responses of factor prices to changes in product prices vary by factor due to differences
in relative factor intensities across sectors. I capture heterogeneous wage responses by
considering that workers supply different types of labor services depending upon their
educational attainment. It is assumed that there exist three different types of labor in
20The data are described in detail in Appendix A2.
21I use the 1992-1999 data because the period before 1992 was characterized by inflation and
macroeconomic imbalances and the period after 1999 by a severe recession and financial crisis.
19
the economy: unskilled labor (comprising workers with only primary education), semiskilled
labor (including those with completed secondary school), and skilled labor (comprising those
workers with a college degree). The estimating equation is22
(14) log wj = log pji ej i + ej d + zj + j,
iT
where ej stands for a vector representing the jth row of the matrix of dummy variables for
educational attainments (primary, secondary, college), and i is the vector of coefficients
that captures the differential impact of price pi on wages. In (14), the logarithm of the wage
earned by individual j is regressed on a set of exogenous variables zj (age, age squared, gender
and marital status), education dummies, and the log prices of the traded goods interacted
with the education dummies. The regression also includes a time trend to control for the
role of technical change. The last term j represents a disturbance error.
The model can be consistently estimated with ordinary least squares since prices
for traded goods are exogenously set in world markets.23 Before estimation, I impose
homogeneity of degree one in prices for the different labor demands. The estimation of
the standard errors has to take into account the clustering effects that may arise when
aggregate variables (like prices) are used to explain a microvariable (Kloek, 1981). Clustering
effects are corrected by using a robust procedure that modifies the Huber-White formulas
for heteroskedasticity. I assume that the clustering is specific to the time period, region and
education in which the individual is observed.
Results are listed in Table 3, which includes twelve wage price-elasticities, three responses
for each of the prices of the four traded goods. The price of Food & Beverages shows
22This is a varying coefficient model, as in Raj and Ullah (1981), Hsiao (1986) and Swamy (1971).
23To account for the endogeneity of the trade reform, I run separate regressions using domestic prices and
international prices, which are exogenous, as regressors. The coefficients estimated with these two models
were statistically similar, indicating that (14) delivers consistent wage price-elasticities.
20
a positive association with the wages of low-education workers (with elasticities of 2.06 for
unskilled workers and 0.12 for semiskilled workers) and a negative association with the wages
of high-education workers (with an elasticity of -1.42). These goods appear to be produced
with relative low-skill intensive techniques of production.24 The elasticities are statistically
significant for unskilled and skilled workers.
Changes in the price of Clothing impact negatively and significantly on the wages of
high-skilled individuals (with an elasticity of -5.05) and semiskilled workers (with an elasticity
of -1.8). In contrast, the response of the wage of low-skilled workers is positive (1.19) and
significant. These findings support the idea that textiles are intensive in low-skilled labor.
The price of House Equipment is positively and significantly associated with the wages
of skilled workers (with an elasticity of 6.04) and semiskilled workers (with an elasticity of
1.92). For unskilled workers, I find a negative and significant association between wages and
prices. These findings suggest that House Equipment & Maintenance Goods is a relatively
skill intensive product category.
The price of Other Goods impacts positively and significantly on the wages of semiskilled
and skilled workers, and negatively but insignificantly on the wages of unskilled workers.
This suggests a relative skilled labor intensity in the production of these goods.25
Interestingly, my results are consistent with the correlations that are implied by the
multidimensional version of the Stolper-Samuelson theorem. According to my estimates,
Food & Beverages and Clothing are revealed to be relatively low-skill intensive goods, while
24Evidence that supports this statement is given in Table 4. See below.
25I have extensively examined the robustness of the wage price-elasticities. The benchmark regression in
Table 3 includes some basic controls such as age, age squared, marital status, gender and education dummies.
The results for the controls are as I might expect; a male is likely to earn a higher wage than a female (39.7
percent higher, actually) and a married person earns a 13.9 percent higher wage than a single individual.
Experience, as measured by a polynomial in age, and education also have the usual positive effect on wages.
I have tried a variety of variable included to control for additional determinants of wages (technical progress
and factor endowments): time trends, no controls, yearly dummy variables, and dummies identifying whether
observations come from May or October. The general picture that emerges from all these regressions is very
similar to the picture described in Table 3. There are, of course, some minor differences in magnitudes but
none in the signs. These results suggest that the findings are robust.
21
House Equipment (appliances, chemicals) is revealed to be relatively skill-intensive goods.
Looking at employment data on the EPH (Permanent Household Survey), Table 4 reports
evidence that supports this conclusion. It shows that 76.4 percent of the workforce employed
in Food and Beverages is of low skills, while 4.4 percent is of high skills; for Clothing, the
shares are 73.6 percent and 2.2 percent respectively. In contrast, 67.9 percent of the workforce
employed in House Equipment comprises unskilled labor and 5.4 percent, skilled labor. This
evidence indicates higher proportions of skilled labor over unskilled labor in House Equipment
than in Food & Beverages and Clothing.
Finally, there is evidence supporting a multidimensional version of the magnification
effects, according to which factor prices would react more than proportionately to price
changes. These magnification effects explain why some of the wage price-elasticities are
relatively large. Leamer (1996), Baldwin and Cain (1997) and Grossman and Levinsohn
(1989) have found similar magnitudes.
3.4.2 Distributional Effects
The non-parametric regression that describes the average distributional effects at different
levels of income is displayed in Figure 3. The estimation of the confidence bands also
requires bootstrap methods. As I explained before, I want to make the consumption
effects comparable to the labor income effects. This means that the households used
in the distributional analysis are those interviewed in the expenditure survey (ENGH).
Thus, I resample from the ENGH survey. To deal with the variance of the estimated
wage price-elasticities, I take samples of these elasticities from the asymptotic distribution
2 d N(2,2), where 2 is the vector of parameters and 2 is the variance. I assign these
bootstrapped elasticities to the resampled ENGH households and I rerun the Fan regressions
on these samples. After 200 loops, I estimate the interquantile range of the distributional
22
effects (at each income level) to build the confidence bands.
In what follows, I focus on the labor income effects of the household head.26 Figure
3 plots the average total welfare effects caused by the reaction of wages to the changes in
the four prices of traded goods (together with the confidence bands). I find that Mercosur
has a positive effect on the average labor income of the poor and a negative effect on the
average labor income of the rich: the distributional curve slopes downward and the average
compensating variations are significantly different from zero, and significantly different across
income levels. The magnitudes are important: while the gains, for middle-income to poor
households, can reach 7 percent of their initial expenditure, the losses, for rich households,
can reach 5 percent of initial expenditure.
There is a simple explanation for these results, which is based on the revealed factor
intensity of the traded goods. Mercosur brings about changes in tariffs on traded goods
that generate an increase in the relative price of unskilled intensive goods (mainly due to
the increase in the price of Food & Beverages and the larger decline in the price of House
Equipment). These induced price movements impact negatively on the wages of more skilled
workers and positively on the wages of less educated individuals, producing a pro-poor bias.
3.5 The Total Distributional Effects of Mercosur
The fundamental result of my investigation is reported in Figure 4, which displays the total
distributional impacts of Mercosur, the sum of consumption effects (traded and non-traded)
and labor income effects, across the entire distribution of income. I find that Mercosur
produces welfare gains for the average Argentine household almost across the entire income
26For some households in the sample, there is more than one working member. To account for their role
in the welfare effects, I need information on the share of income earned by each household member, which
is not available in the ENGH Survey. I therefore focus here on the household head, the major source of
reported income by Argentine households. A more complete analysis, including the responses of the labor
income of the head and non-head members together with capital income can be found in Porto (2002). The
results remain mainly unchanged.
23
distribution. Whereas the gains are statistically significant for poor and middle-income
households, the welfare effects are negative, but not significant, at the upper tail of the
income distribution. In addition, the gains are significantly higher for the average poor
household in the country, indicating that Mercosur has had pro-poor distributional effects.
In fact, the average compensating variation for the poor amounts to up to 6 percent of initial
expenditure, whereas for middle-income households the gains amount to around 3 percent.
Given the trends of increasing poverty and inequality observed in Argentina during the
1990s, these findings have to be put into context. They indicate that the changes in tariffs
on consumption goods adopted in Mercosur cause the relative price of unskilled intensive
goods to increase. The Stolper-Samuelson correlations then dictate an increase in the wages
of unskilled workers and a decline in the wages of skilled workers. Consumption effects are
also important and favor rich families, but the magnitudes are not large enough to outweigh
the pro-poor labor income effects (except for the very rich families).
Naturally, Mercosur has introduced other changes that I have not considered here. The
impacts on non-labor income (profits, returns to capital and specific factors, rents) and
unemployment, and the effects of the new structure of protection on intermediate inputs
have not been examined. The Brazilian devaluation in 1999, which significantly hurt
unskilled Argentine workers, has not been addressed either. Notice also that domestic prices
can change due to changes in tariffs (the effects explored here) and exogenous changes in
international prices (due to shocks and policies in the rest of the world). These are indeed
important determinants of the increase in inequality and poverty observed in Argentina
during the 1990s, but their role is not the topic of investigation of this paper.27
The impacts estimated in the paper are relatively small, with gains of, at most, 6 percent
27Notice that Argentina simultaneously adopted, during this period, a large number of economic reforms,
including macroeconomic stabilization, financial reforms, privatization and economic deregulation. This
paper has not addresses any of these topics.
24
of initial expenditure. It is certainly plausible that all the other reforms introduced in
the country, together with some unmeasured effects of Mercosur, outweigh the progressive
impacts of the changes in tariffs on consumption goods. My conclusion is that without the
tariff changes originating in the regional trade agreement, poverty in Argentina would have
been still higher.
4 Concluding Comments
In this paper, I have developed a methodology to estimate the distributional effects of trade
policies (and other pricing policies in general) in developing countries. The methodology is
characterized by a very intensive use of household survey data, an approach seldom followed
in the assessment of trade policies in the related literature. In addition, the paper has looked
at the general equilibrium effects of trade reforms using econometric techniques, rather than
Computable General Equilibrium modeling. This has allowed for a more important role for
measurement, as opposed to assumptions, in the estimation of the distributional impacts of
trade policies. Importantly, the use of household survey data has allowed me to explore the
distributional analysis of trade reforms across the entire income distribution.
My general equilibrium welfare analysis builds on the estimation of the household
compensating variations that would leave each Argentine family indifferent to the situation
before the policy change. I have measured consumption effects, as consumers pay different
prices for traded and non-traded goods, and labor income effects, as factor prices react to
trade policies. The methodology has been applied to the study of the distributional impacts
of the adoption of a regional trade agreement like Mercosur. The procedure can, in principle,
be also applied to the experiences of other developing countries.
I have found that Mercosur has had pro-poor distributional effects. The reason is that
25
Argentine trade policy protected the rich over the poor, prior to the reform, and granted
some protection to the poor, after the reform. As a result, the relative pre-Mercosur tariff is
higher on relatively skilled intensive goods. This means that tariff removals would tend to
benefit the poor over the rich. These findings indicate that trade has not been responsible
for the increase in poverty and income inequality observed in Argentina during the 1990s, at
least in terms of the protection granted to final goods. The role of other factors and policies,
such as deregulation, privatization, social security reforms, labor market reforms, monetary
stabilization and industrial policy is yet to be explored.
Appendix A1. The Household Expenditure (ENGH) Survey
The National Household Expenditure Survey (ENGH) contains data on consumption at
the household level. In Argentina, the consumption classification involves nine groups of
goods, with many subcategories in each group. These nine groups are Food and Beverages,
Clothing, Housing, House Equipment, Entertainment, Education, Health, Transport and
Communication, Other Goods and Servicies. The National Institute of Statistics and
Censuses constructs price indices for these consumption goods.
The ENGH Survey, conducted from March 1996 to March 1997, provides information on
household monthly expenditure on over ninety goods. For simplicity, I designate each of the
minor categories of goods as a traded or a non-traded good and include them in one of the
seven consumption aggregates in the text. Traded goods include Food & Beverages (including
Tobacco), Clothing (Textiles, Apparel and Footwear), House Equipment & Maintenance
Goods (Chemicals, Plastic, Appliances), and Other Traded Goods. Nontraded goods include
Housing, Transport & Communication, Health & Education, and Leisure Goods. I construct
the price indexes for these seven aggregates as a geometric weighted average of the index
prices of all subcategories, using budget shares in 1996 as weights.
The ENGH is a comprehensive survey that covers over 21,127 households (once outliers
are eliminated) across urban areas in Argentina. Some basic features of the data are as
follows. The mean household per capita expenditure in Argentina during 1996/1997 was
251.2 dollars per month, with a standard error of 246 dollars. Argentine households spent,
on average, 47% of their budget on Food & Beverages. Housing, Transport & Communication
accounted for 20.9% of the budget while Other Traded Goods accounted for another 8.5%.
7.8% of the average budget went to Clothing, 6.3% was spent on Health & Education and
5.7% was spent on Leisure Goods. Finally, 3.7% of total household expenditure was allocated
to House Equipment & Maintenance Goods.
26
Appendix A2. The Permanent Household Survey
In this Appendix, I describe the data used to estimate the wage price-elasticities. My method
identifies these elasticities using a time series of household surveys and prices. In Argentina,
the main source of labor market information is the Permanent Household Survey, Encuesta
Permanent the Hogares, or EPH. These surveys are collected in May and October in each
year.
The key insight of the empirical methodology is the use of the wage data in the EPHs
with the price data for consumption goods. Identification comes from the time variation in
prices and surveys. Specifically, I use data from 1992 to 1999, sixteen surveys in total.
Table A.1 reports the average log wages, in each time period, for each of the three
educational categories. In each time period, I observe that College graduates earned higher
(average) wages than Secondary school graduates who, at the same time, earned higher wages
than Primary school workers. The wages of primary and secondary school graduates grew
until 1994, when they started to decline. The trend in the wages of skilled individuals is
similar albeit with two distinct characteristics: the initial increase in wages at the beginning
of the decades continued beyond 1994 and well into 1995, and the decrease after 1995 was
somewhat attenuated.
Table A.1 also reports the time series of prices of the four traded goods, from 1992 to 1999.
Biannual observations, corresponding to the months of May and October (when the EPHs
are collected), are shown. The price of Food & Beverages increased substantially during
1992 and 1993 and stabilized afterwards, albeit with a somewhat upward trend (except in
1999). There was a substantial decline in the price of Clothing during the 1990s. In contrast,
the price of House Equipment remained relatively stable through time, whereas the price of
other goods showed an upward trend.
Appendix A3. Non-Parametric Regressions
Fan (1992, 1993) regressions approximate the regression function for the welfare effects m(·)
with a Taylor expansion in a neighborhood of a previously selected point in the support of
the log per capita household expenditure (x). This gives
d
(A1) m(xj) = + (xj - x) + a(xj - x)a,
a=2
where d stands for the order of the local polynomial used in the Taylor expansion and
a = (1/a!)((am(·)/xa). An estimate of , , provides a good estimate for the conditional
expectation for the welfare effects, m(x), at different points along the per capita household
expenditure spectrum, x. The local coefficients at these different levels of x can be estimated
with a locally weighted least squares fit. That is, the estimators, at a pre-selected point x,
27
would solve
2
d
(A2) min xj - x ,
,,2,...,d cvj - - (xj - x) - a(xj - x)a K
h
j a=2
where cvj stands for the compensating variation accruing to household j.
The bandwidth h defines the local data used in the non-parametric regression; it
determines the complexity of the model; that is, the trade-off between the local details, or
bias, and the variance (Fan and Gijbels, 1996). When h 0, the model becomes increasingly
complex since it just interpolates the data28; when h , the model becomes increasingly
simple for it converges to the standard, global polynomial fit. In this sense, it is said that h
controls the complexity of the model. The trade-off between bias and variance suggests that
the optimal h should be chosen to minimize the asymptotic mean integrated square error
of the regression. In most applications, however, visual inspection (subjectively choosing
among various regression functions estimated for different values of h, will work well.
The function K(·) in (A2) represents the kernel function that attaches weights to different
observations. The idea is that, for each datum along the income distribution, observations
closer to x should receive greater weight than observations farther away. Notice that the
combination of local regressions with kernel smoothing allows the non-parametric regression
to be locally design adaptive. This means that the regression adapts to the design of the
random sample and therefore its bias does not depend on the density (or the derivative of
the density function) of the pre-selected point x. Since the different available kernels do not
play a decisive role in the properties of the estimators, my applications in this study use a
Gaussian Kernel.
The order d of the local polynomial must also be chosen. As with h, the order of the
polynomial is chosen to control the trade-off between bias and variance in the estimation
of the conditional expectation m(·). Intuitively, a higher order local polynomial achieves a
lower bias since the fit has more degrees of freedom to adapt to the data (Fan, 1992; Ruppert
and Wand, 1994). In general, a higher order d for the local polynomials reduces the bias but
increases the variance of the estimates (since there are more parameters to estimate). Given
that the complexity of the model is driven by the bandwidth h, the literature recommends
restricting the local polynomial to be of relatively low order. Furthermore, a recent finding
by Ruppert and Wand (1994) and Wand and Jones (1995) shows that the local polynomial
used to fit the data should always be odd. This is because, starting from an even order,
increasing the order of the local polynomial by one unit does not affect the variance. In the
application in the text, a local linear regression is used.
28This is true unless there are multiple observations at the same x, in which case the interpolation involves
the average of these observations at x.
28
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Wooland, A. D. (1982). International Trade and Resource Allocation, Advanced Textbooks
in Economics, vol. 19, North Holland, Amsterdam.
32
Figure 1. Consumption Effects of Traded Goods
1
.75
.5
ontia ture
ariv xpendie .25
gn d
0
compensati householfo
%
-.25
-.5
-.75
3 4 5 6 7 8
log per capita expenditure
Figure 2. Consumption Effects of Non-Traded Goods
2
1.5
ontia ture
ariv xpendie
gn d 1
compensati householfo
% .5
0
3 4 5 6 7 8
log per capita expenditure
Figure 3. Labor Income Effects of Mercosur
10
7.5
5
ontia ture
ariv xpendie 2.5
gn d
0
compensati householfo
% -2.5
-5
-7.5
3 4 5 6 7 8
log per capita expenditure
Figure 4. Total Welfare Effect
8
6
eru
oni
4
iat
arv penditxe
ing
at
ehold 2
pens
moc housfo
%
0
-2
3 4 5 6 7 8
log per capita expenditure
Table 1
Tariff Structure and Price Changes
Mercosur
Tariff Intrazone Common External Price Change
1992 Tariff Tariff Mercosur
1996 1996
(1) (2) (3) (4)
Food & Beverages 6.9 0.05 13.7 1.4
Clothing 20.4 3.3 17.9 -6.4
House Equipment 16.1 0.05 14.2 -5.4
& Maintenance Goods
Other Traded Goods 12.7 0.05 11.7 -3.2
Notes: computations based on tariffs rates taken from the Argentine legislation (Decrees 2657/91 and 998/95).
There is an average import tax characterizing the baseline situation in 1992 (column 1). Mercosur implemented an Intrazone Tariff
(column 2) and a Common External Tariff on imports coming from the rest of the world (column 3).
The price changes in columns 4 are computed according to equation (8) in the text.
Table 2
The Responses of the Prices of Non-Traded Goods
Argentina
Housing, Transport Health Leisure
& Communications & Education Goods
(1) (2) (1) (2) (1) (2)
Food & Beverages 0.469 0.484 -0.049 -0.020 0.064 0.129
5.056 5.582 -0.752 -0.349 1.024 2.143
Clothing 0.237 0.250 -0.225 -0.201 -0.270 -0.216
1.192 1.088 -2.021 -1.809 -3.934 -2.501
House Equipment -0.413 -0.427 0.775 0.748 0.890 0.831
-1.522 -1.476 3.722 3.486 2.865 2.647
Other Goods 0.707 0.693 0.500 0.473 0.316 0.257
2.535 2.408 2.006 1.988 0.956 0.785
Wald Test 13.79 13.86 65.79 55.08 24.66 34.43
Price Change
due to Mercosur -0.9 -4.4 -4.0
(t-test) -0.2 -2.1 -1.9
Notes: The prices of the three non-traded goods are regressed on the prices of the four traded goods using a time series
of monthly prices from 1992 to 1999. A translog specification is used to estimate the elasticities shown in the Table.
The regressions were run using first differenced price series to avoid spurious correlation. For each price of non-traded,
column 1 includes year dummies and a trend and column 2 includes year dummies only.
Coefficients in bold. t-statistics in italics.
Table 3
Wage Responses to Changes in Prices of Traded Goods
Educational Categories of Labor
Argentina
Food & Beverages Clothing House Equipment Other Goods
Primary Education 2.06 1.19 -2.02 -0.22
(unskilled labor) 7.22 2.08 -2.99 -0.98
Secondary Education 0.12 -1.80 1.92 0.76
(semiskilled labor) 0.44 -3.26 2.91 3.43
College Education -1.42 -5.05 6.04 1.42
(skilled labor) -3.77 -6.65 6.99 4.42
R2 of the Regression 0.34
Notes: for each of the three different educational attainments, the table shows the response of wages to changes in the prices
of four traded goods. Using data from the Permanent Household Survey, the log of monthly wages is regressed on the log of
prices interacted with educational dummies. The regression includes the following controls: age, age squared, gender and marital
status, education dummies and time trends.
The cluster-corrected t-statistics are reported in italics below the coefficients.
Table 4
Factor Intensities by Skills
Argentina 1996
Food & Beverages Clothing House Equipment
Primary Education 76.40 73.60 67.90
(unskilled labor)
Secondary Education 19.20 24.20 26.70
(semiskilled labor)
College Education 4.40 2.20 5.40
(skilled labor)
Note: the numbers are the proportion of each type of labor (unskilled, semiskilled, skilled) on total labor
employment in the different sectors.
Source: Permanent Household Survey (EPH).
Table A.1
Average Wages and Prices in Argentina: 1992 - 1999
Wages Prices
Unskilled Semiskilled Skilled Food and House Other Sample
Beverages Clothing Equipment Goods Size
May 1992 5.716 6.026 6.368 76.2 83.9 88.0 88.1 19695
Oct 1992 5.809 6.130 6.485 83.8 83.5 91.7 93.0 20830
May 1993 5.883 6.193 6.575 88.1 82.8 93.6 97.6 22934
Oct 1993 5.924 6.253 6.628 90.7 80.3 93.7 101.4 22777
May 1994 5.871 6.233 6.650 87.8 79.6 93.2 105.2 28636
Oct 1994 5.890 6.258 6.678 91.0 78.7 93.3 107.8 28645
May 1995 5.887 6.269 6.716 91.2 78.1 95.2 109.8 27377
Oct 1995 5.825 6.231 6.681 93.0 75.3 96.1 110.2 31575
May 1996 5.802 6.225 6.647 91.1 73.2 93.7 110.0 33459
Oct 1996 5.772 6.190 6.644 93.0 70.7 93.3 109.5 30304
May 1997 5.749 6.201 6.664 90.4 69.1 92.8 109.4 35134
Oct 1997 5.754 6.189 6.687 92.7 67.1 92.6 109.1 35051
May 1998 5.783 6.201 6.718 93.6 66.4 93.0 108.9 32840
Oct 1998 5.765 6.215 6.744 93.8 63.6 93.0 108.4 31027
May 1999 5.697 6.158 6.694 89.6 62.4 92.6 108.1 28070
Oct 1999 5.690 6.138 6.657 88.2 59.1 92.1 107.9 27718
Source: Wage data are from the Permanent Household Survey (EPH). Prices are reported by the National Institute of Statistic and
Census (INDEC). The price data refer to the price index of the main categories of consumption goods in the National Household
Expenditure Survey (ENGH) in Argentina.