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ARE LABOR MARKETS SEGMENTED IN ARGENTINA?
A SEMIPARAMETRIC APPROACH
Sangeeta Pratap
Erwan Quintin
Research Department
Working Paper 0110
Center for Latin American Economics
Working
Paper 0701
Center for
Latin American
Economics
Working Paper 0201
January 2003

FEDERAL RESERVE BANK OF DALLAS

Are Labor Markets Segmented in Argentina?
A Semiparametric Approach
Sangeeta Pratap
Instituto Tecnológico Autónomo de México
Erwan Quintin
Federal Reserve Bank of Dallas

January 13, 2003

∗

Email: pratap@itam.mx and erwan.quintin@dal.frb.org.
We wish to thank Steve Bronars, Daniel Hammermesh, Hugo Hopenhayn, David Kaplan, Torsten Persson
as well as seminar participants at the University of Texas, Austin, the University of Montréal and Southern
Methodist University for valuable comments. We are also grateful to Fernanda Fenton and Eric Millis for
valuable research assistance. The views expressed in this paper are those of the authors and do not necessarily
reﬂect the position of the Federal Reserve Bank of Dallas or the Federal Reserve System.
Corresponding author: Erwan Quintin, Research Department, Federal Reserve Bank of Dallas, 2200 N. Pearl
Street, Dallas, TX 75201.

Abstract
We use data from Argentina’s household survey to evaluate the hypothesis that informal workers would expect higher wages in the formal sector. Using various deﬁnitions of
informal employment we ﬁnd that, on average, formal wages are higher than informal wages.
Parametric tests suggest that a formal premium remains after controlling for individual
and establishment characteristics. However, this approach suﬀers from several econometric
problems, which we address with semiparametric methods. The resulting formal premium
estimates prove either small and insigniﬁcant, or negative. Neither do we ﬁnd signiﬁcant
diﬀerences in measures of job satisfaction between the two sectors. In other words, the
hypothesis that Argentina’s labor markets are competitive cannot be rejected.

Introduction

Dualistic models of labor markets have pervaded the economic development literature since
the seminal work of Lewis (1954). According to the dualistic view, some workers are unable
to ﬁnd jobs in the formal, regulated sector and must work in ﬁrms where earnings and
working conditions are inferior to what they could expect in the formal sector given their
personal characteristics (see, for instance, Mazumdar, 1975). In this paper, we evaluate the
premise that informal workers would expect higher earnings in the formal sector with data
from Argentina’s permanent household survey for the 1993-1995 time period.
We follow Castells and Portes (1989) and deﬁne informal activities as unregulated activities in a context where similar activities are regulated. As a practical matter, we consider
various deﬁnitions of informal employment based on beneﬁts mandated by Argentina’s labor
laws. For all our beneﬁts-based deﬁnitions, average informal gross wages are signiﬁcantly
lower than their formal counterparts. The question we ask is whether a formal sector premium remains after controlling for observable diﬀerences between workers and jobs. In
particular, formal employees tend to be more educated and experienced than informal employees. Furthermore, the proportion of women is higher in the informal sector. Finally,
informal employees are more likely to work in small establishments than formal employees.
Regression analysis continues to suggest a formal premium for many subgroups, even
after controlling for size and industry eﬀects. Nonetheless, ordinary least square estimates
are biased and inconsistent in this context for at least two reasons, as discussed by Heckman
and Hotz (1986). First, individuals may self-select into a given sector based on observed
and unobserved characteristics that also aﬀect earnings. Moreover, those estimates are
conditional on a given speciﬁcation of earning functions.
We proceed to use semiparametric estimators to control for the potential misspeciﬁcation
of earning functions and the endogeneity of wage and sectoral employment outcomes. Each
formal worker is matched with a set of informal workers with similar personal and job
characteristics in order to obtain an average formality premium. The resulting estimate of
the formal sector premium is not signiﬁcantly positive in any of the three years we consider.
3

We also produce estimates of the formal sector premium for various subgroups, including
women, young workers, and uneducated workers. Formal earnings are not signiﬁcantly higher
than informal earnings for any of those subgroups. In fact, in many subsamples, formal
workers earn less than informal workers with comparable personal and job characteristics. We
then compute a diﬀerence-in-diﬀerence estimate of the formal sector premium that partially
control for selection eﬀects due to unobserved characteristics. The sample size becomes too
small to obtain precise estimates but, again, we ﬁnd no compelling evidence of a positive
formal sector premium.
A key ﬁnding is that controlling for establishment size is important. When we re-estimate
formal sector premia using only employee information, a signiﬁcantly positive formal sector
premium emerges. All else equal, larger establishments or ﬁrms pay higher wages in Argentina as in most economies, including economies where the informal sector, by all accounts,
is small (see Oi and Idson, 1999, for a review.) Since large establishments tend to emphasize
formal employment, the premium many previous studies report as a formal sector premium
could be no more than a standard size-wage premium.
Our data also enables us to compare formal and informal jobs along non-pecuniary dimensions. Earnings are but one element of job satisfaction. It may be the case that informal
workers would prefer formal jobs because they are associated with better beneﬁts or better
working conditions. The survey inquires about whether the respondent is looking for a job
other than the one they currently have, and whether they would like to work more hours.
We ﬁnd no signiﬁcant diﬀerence in the fraction of workers who respond positively to either
question in the two sectors. Taken together therefore, our results cast serious doubt on the
notion that informal workers would typically be better oﬀ in formal occupations.
Our ﬁndings contradict most studies of labor markets in developing nations. Those
studies typically ﬁnd that the relationship between earnings and worker characteristics diﬀers
across sectors (see, for instance, Mazumdar, 1981, Heckman and Hotz, 1986, Roberts, 1989,
Pradhan and van Soest, 1995, Tansel, 1999, and Gong and van Soest, 2001.) Even in the
United States, Dickens and Lang (1985, 1988) ﬁnd “strong” evidence that there are two

distinct labor markets with diﬀerent earning functions. All these papers rely exclusively
on parametric techniques and, therefore, the interpretation of these results is limited by
the potential misspeciﬁcation of earnings functions. Our semiparametric approach partially
circumvents those limitations. Furthermore, our data enable us to account carefully for
establishment size eﬀects, unlike any of the aforementioned studies. Papers which, like ours,
do not reject the competitive labor market assumption include Magnac (1991) and Maloney
(1999).
Our paper also provides a list of facts with which a satisfactory theory of informal economic activities in Latin America should be consistent. Most existing models of the informal
sector predict some wage dualism, or rely on the hypothesis that labor markets are segmented.
For instance, in a direct extension of a model of Harris and Todaro (1970), Fields (1975)
assumes agents can either work in the informal sector or devote their time to searching a
higher paying formal job. Rauch (1991) describes a general equilibrium model where ﬁrms
can choose to violate a minimum wage requirement provided they operate a scale smaller
than a given detection threshold. Some workers ﬁnd jobs in large formal ﬁrms while a fraction of the labor force must accept lower-paying informal jobs. Fortin et al. (1997) extend
Rauch’s framework in several directions and evaluate numerically the quantitative impact
of various public policies on the size and characteristics of the informal sector. Models of
informal activities that, in contrast, do not assume any segmentation between sectors include
Loayza (1996) and Sarte (1999).
Developing nations resort to a vast array of public policies to try and reduce tax evasion
and improve compliance with labor laws. A good understanding of the causes and consequences of informal economic activities is necessary to measure the impact of those policies.
Our results suggest that modeling the informal sector as the disadvantaged end of dualistic
labor markets is likely to lead to misleading inferences, and misguided policy prescriptions.

The segmentation hypothesis

It is useful to begin by formalizing the wage segmentation hypothesis. To do this, consider an
economy populated by agents who diﬀer in terms of a ﬁnite list X of personal characteristics.
They are employed either in the formal (F) sector or the informal (I) sector. Both sectors
oﬀer a menu of jobs described by a vector Y of characteristics that include industry and
establishment size.
Let w F (X, Y, ) and w I (X, Y, ) denote integrable random variables that give the agent’s
log earnings in, respectively, the formal and the informal sector, as a function of their personal and job characteristics, and exogenous sources of uncertainty denoted by . The wage
segmentation hypothesis can be stated as:
S:

E(w F (X, Y, ) − w I (X, Y, )|X, Y ∈ A) > 0
for a non-negligible subset A of characteristics.

In this paper, we ask whether such a subset of personal and job characteristics can be found
in the set of workers sampled by Argentina’s household survey between 1993 and 1995.

The data

Argentina’s biannual household survey collects socio-economic information from a rotating
panel of urban households, in May and October of each year. Households remain in the
sampled for four periods. The information is collected via individual visits. A household
questionnaire is used to record the basic demographic and dwelling characteristics of the
household. Individual questionnaires are used to collect each household member’s basic
demographic data, employment status, the revenues and beneﬁts they derive from their
primary and secondary occupation, as well as the size of the establishment and the industry
in which they work. Hours worked are reported for a recent week, income is reported by
source for a recent month.
6

Between 1993 and 1995, the survey covered over 30,000 households in 25 urban centers.
We concentrate on the “Gran Buenos Aires” area, i.e., Buenos Aires and its suburbs. City size
and location are important determinants of wages that would complicate the interpretation
of our results. Approximately 4,500 households are surveyed in the Buenos Aires area in
each wave.
The results we report pertain to real wages, using Argentina’s consumer price index
as a deﬂator. We only consider earnings from primary occupations. While the survey
includes some information on secondary occupations, it provides no information on secondary
employers. We discard employees who report that they work more than 80 hours a week.
Our ﬁnal sample consists of 15,693 observations.
We classify workers as formally or informally employed according to whether they receive
various beneﬁts mandated by Argentina’s labor laws. The basis of our earnings comparison between sectors is wages before taxes. In reality, most informal workers are able to
evade income taxation. Comparing before-tax wages thus strongly favors the segmentation hypothesis. Accounting for income taxation should only strengthen our results.1 By
comparing wages directly, we also implicitly ignore non-pecuniary dimensions of jobs. In
section 7, we will use questions on job satisfaction to gauge the potential importance of
those dimensions.

Characteristics of formal and informal workers

In this section, we compare the average characteristics and earnings of formally and informally employed workers. Table 1 in the appendix shows that average hourly earnings are
signiﬁcantly higher in the formal sector than in the informal sector for all possible beneﬁtsbased deﬁnitions of informal employment. The ﬁrst row of each section of the table gives the
average hourly wage of workers who receive a given beneﬁt, the second row gives the same
1

Doing this may be diﬃcult however because the appropriate tax rate depends on the household’s overall
income. Although the survey inquires about income from various sources, that information is often missing
and is unreliable when available.

statistic for workers who do not receive the beneﬁt. The last row of each section provides
a t-statistic based on the diﬀerences in means for the two subgroups. In all cases, mean
wages are signiﬁcantly higher for those individuals who receive mandated beneﬁts than for
individuals who do not receive them. These ﬁndings appear broadly consistent with the
segmented view. The question we ask is the extent to which diﬀerences in individual and
establishment characteristics can account for this pattern.
Henceforth, to shorten the exposition, an employee is considered informal if they do not
receive pension or unemployment insurance beneﬁts. Average earnings in the two sectors for
this deﬁnition are shown in the bottom panel of table 1. Table 2 shows that according to this
deﬁnition, informal employment accounts for roughly a third of our sample. It also shows
several marked diﬀerences between sectors. Formal employees tend to be more experienced
and educated than informal employees. In addition, the proportion of women is higher
among informal employees. Finally, formal employees tend to work in larger establishments
that informal employees.
The panel structure of our data also enables us to compare the characteristics of individuals who change occupations and sectors to those whose employment status remains the
same from one sampling period to the next. Table 3 in the appendix shows that, on average,
roughly 10% of formal employees transit to informal employment from one sampling period
to the next in our sample, while over 25% of informal employees become formally employed.
Table 4 shows that employees who switch from the formal to the informal sector tend to
be younger and less educated than employees who remain in the formal sector. Conversely,
employees who remain in the informal sector tend to be younger and less educated than
employees who enter the formal sector. In addition, workers who enter the formal sector see
the highest rise in their gross wages.
It is important to note, however, that the mobility patterns shown in tables 3 and 4
cannot be interpreted as direct evidence or counter-evidence of labor market segmentation
(Maloney, 1999, also makes this point.) The fact that individuals who enter the formal sector
tend to be older and more educated than their counterparts who remain in the informal sector

could be the result of barriers to entry for certain subgroups, but it could simply reﬂect the
fact that the two sectors emphasize diﬀerent skills for other reasons. For instance, formal
activities tend to be more capital intensive than informal activities (see e.g. Thomas, 1992,
pp76-77.) If unskilled labor is a better substitute for capital than skilled labor, the informal
sector will emphasize unskilled work whether or not labor markets are segmented. Rejecting
the hypothesis that labor markets are competitive requires evidence that similar earning
relevant characteristics are compensated diﬀerently in the two sectors. We now set about
ﬁnding such evidence.

Parametric tests of the segmentation hypothesis

Table 5 in the appendix shows the outcome of regressing log real hourly wages on year
dummies, individual, establishment and industry characteristics, as well as a dummy variable called Sector which takes value 1 if the individual is formally employed, 0 otherwise.
Variables are deﬁned in more details in appendix A. The table shows that in a speciﬁcation
without any interaction terms, the impact of the sector variable is positive and signiﬁcant
even after controlling for establishment, industry and educational characteristics. Education, size and industry eﬀects are large and signiﬁcant.2 The second speciﬁcation shown
in table 5 includes as regressors individual and establishment variables interacted with the
Sector variable. The Sector dummy is now only marginally signiﬁcant, but several of the
interacted terms have a signiﬁcant impact on wages, notably age and some industry dummies. Simple calculations based on those coeﬃcients continue to show a signiﬁcantly positive
formal premium for many subgroups, and this remains true for all basic variations of the
baseline speciﬁcation shown in table 5.3 In other words, the results shown in table 5 support
2

In particular, this conﬁrms that the positive relationship between size and wages documented for many
countries is also present in Argentina. For instance, in our 1993 sample, the average wage of employees in
establishments with more than 500 workers is 1.6 times greater than the average wage of employees with 25
workers or fewer.
3
This includes speciﬁcations where all individual variables are interacted with the Gender variable. Findings for each year taken separately were similar, although speciﬁc coeﬃcients can diﬀer markedly from year
to year. To be concise, we only report results for the pooled sample. Other results are available from the

hypothesis S.
So far the analysis has ignored the endogeneity of the selection decision into the formal or
informal sector. To control parametrically for self-selection we implement a test suggested by
Heckman and Hotz (1986). We split our sample into two subsamples along formal/informal
lines and then estimate wage regressions with a two-step correction for selection separately
for each subsample. Under the hypothesis that labor markets are competitive, estimated
coeﬃcients should not diﬀer signiﬁcantly in the two subsamples.
We assume that the selection decision of individuals depends on age, gender, education
and whether or not they have a relative in the formal sector. The last variable does not appear
to aﬀect wages but has a signiﬁcant impact on sector assignments. Results are shown in table
6. Several coeﬃcients in the estimated earning functions turn out to be very diﬀerent in the
two samples. Consider for instance the impact of age, a variable which is highly signiﬁcant in
both regressions. The absolute value of the coeﬃcient of the age squared term is much higher
in the informal sector than in the formal sector, suggesting that age-earning proﬁles tend
to be more concave in the informal sector. Once again, simple calculations based on these
results show a signiﬁcant formal sector premium for many subgroups. Thus strong evidence
of segmentation remains even after controlling for potential selection bias. Note, however,
that this approach is based on strong parametric assumptions, both about the form of the
selection bias and the form of wage functions. We now turn to semiparametric methods to
address those shortcomings.

Semiparametric estimators

To relax parametric assumptions about the wage function and the form of the selection bias,
we now implement a semiparametric matching estimator. We view employment in the formal
sector as the treatment variable. Informal sector employees therefore, constitute the control
group. As in section 2, let w F and w I denote the log wages of formal and informal sector
authors upon request.

employees respectively, and let X and Y be the sets of individual and job characteristics.
Using the terminology of the program evaluation literature (LaLonde 1986, Heckman,
LaLonde and Smith 1999), we deﬁne the formal sector premium as the following average
treatment eﬀect:

α = E w F |X, Y, Sector = 1 − E w I |X, Y, Sector = 1 .
In order to estimate the last term, we make the following conditional independence assumption (also known as the ignorability of assignment condition) of Rosenbaum and Rubin (1983,
1984):
w F , w I ⊥ Sector|X, Y.
This assumption requires that selection only take place on observables, i.e. on the basis
of characteristics spanned by X and Y. The average treatment eﬀect estimator can then be
written as:

α = E w F |X, Y, Sector = 1 − E w I |X, Y, Sector = 0

In non experimental studies like ours, where assignment to treatment is non random, the
covariates may vary systematically between groups. In such cases, Dehejia and Wahba
(forthcoming) suggest that propensity score based matching estimators may perform better.4
After indexing workers in the sample of interest, write i ∈ F if the worker is formally
employed, i ∈ I otherwise. Also denote by pi the propensity score P (Sector = 1|Xi , Yi)
of individual i given their vector (Xi , Yi) of personal and job characteristic. The matching
estimator of the formal sector premium becomes
αM =

wiF −

i∈F

ηij wjI

(1)

j∈I

Relying on propensity scores also enables one to get around the practical diﬃculty of matching individuals
directly along several dimensions with a ﬁnite sample. Rosenbaum and Rubin (1983, 1984) establish that if
the conditional independence condition holds, and propensity scores are almost surely interior, the matching
estimator remains valid if we condition on the propensity score, rather than on the covariates themselves.

where ηij ∈ [0, 1] denotes the weight assigned to informal worker j in building a comparison
wage for formal worker i, and decreases with |pi − pj |. In other words, the comparison observations in the informal sector are weighted on the basis of the proximity of their propensity
score to the corresponding formal observation.
This use of propensity scores, while standard, is not uncontroversial. Smith and Todd
(2001) show that the results obtained by Dehejia and Wahba are not robust to changes in
sample composition and changes in the variables included in the estimation of the propensity
score. Heckman et. al. (1997, 1998) argue that the reliability of matching estimators
depends not so much on the matching technique chosen but on the quality of the data. In
an experimental context they ﬁnd that their results are most reliable when (i) the data are
comparable across control and treatment groups, i.e. it comes from the same or a similar
source (ii) the treatment and control group operate in the same labor market and (iii) the
data contains a rich set of variables for estimating the propensity score.
The non-experimental nature of our sample makes it impossible to directly estimate the
bias associated with our estimates, but the conditions listed above are largely met by our
data. The data for both types of workers come from a single survey, and the restriction of
the sample to the Gran Buenos Aires Area implies that all individuals are working under
similar macroeconomic conditions. Furthermore, we make use of a large number of ﬁrm level
and individual level variables to estimate propensity scores.
More generally, the validity of the matching estimator we use depends on the ability of
propensity scores to account for cross-sector diﬀerences. Propensity scores turn out to be
an eﬀective proxy for individual and establishment characteristics in our application, as we
argue in the next section. There we stratify our sample on the basis of propensity scores
and ﬁnd that the treatment and control group are very similar in each propensity strata.
The diﬀerences that remain are mainly in terms of age and gender. These are addressed by
computing matching estimators for each gender and for diﬀerent ages separately. We also
ﬁnd that our results are robust to diﬀerent matching techniques and sample compositions,
which conﬁrms the reliability of our estimations.

Another concern is the possibility that the conditional independence assumption may
be violated. Recall that this occurs if selection into the formal sector depends on unobserved heterogeneity which aﬀects wages but cannot be included as a conditioning variable
in estimating the propensity score. This potential problem can be partially addressed by
combining the matching estimator with a diﬀerence-in-diﬀerence estimator (see e.g Blundell
and Costa Dias 2000.) Denote by I → F the set of workers who move from the informal
sector to the formal sector from one period to the next, and denote by I → I the set of
workers who remain in the informal sector. The diﬀerence-in-diﬀerence estimator of the
average treatment eﬀect is given by
αM D =

F,t+1
I,t
I,t+1
I,t
− wi −
ηij wj
− wj
wi

i∈I→F

j∈I→I

where t and t + 1 denote two consecutive periods. Diﬀerencing removes the components of
wages which is attributable to unobserved but ﬁxed heterogeneity. This estimator is based
on the assumption that wages in the control group sector evolve in the same way as wages
in the treatment would have, had they not been treated. Correspondingly, the conditional
independence assumption becomes
F,t+1

w
− w I,t , w I,t+1 − w I,t ⊥Sector t+1 |P (Sector t+1 = 1|X, Y ).
The changes in wages for both movers and stayers must be independent of whether a change
in sector occured, conditioning on the probability of the individual being in the formal sector
at time t + 1. We now turn to implementing the estimators constructed in this section.

6.1

The matching estimator

We begin by estimating propensity scores with a probit speciﬁcation. The dependent variable is Sector, our dummy variable for formal employment. The independent variables are
age, gender, an indicator variable which takes the value 1 if any other family member was

employed in the formal sector in that year, and dummies for establishment size and education. Not surprisingly, table 7 shows that propensity scores rise with establishment size, age
and education and that men are more likely to be formally employed than women. Table
8 gives the relative frequency of the propensity score for individuals in the formal and in
the informal sector for each year. Naturally, the proportion of formal (treated) workers rises
with the propensity score. What is important for our estimation technique is that there be
enough overlap in all strata, which is the case here.5
As we mentioned, the average characteristics of formal and informal workers are very
diﬀerent. However, conditioning on propensity scores signiﬁcantly reduces those diﬀerences.
Tables 9 to 13 compares employees in the two sectors for 5 subsamples corresponding to 5
diﬀerent propensity scores intervals. These subsamples show that individual and job characteristics become markedly closer than in table 2. Consider, for instance, table 10 which
describes the sample of workers whose propensity score falls between 0.20 and 0.40. All these
employees, be they formal and informal, work in establishments with fewer than 6 workers.
The distribution of educational characteristics also becomes very similar across sectors. As
for high propensity scores, table 13 shows that most individuals whose propensity score falls
between 0.8 and 1 tend to work in large establishments, and a large fraction of those individuals have some tertiary education, in both sectors. One characteristic for which large
diﬀerences remain in those tables is gender, particularly for low propensity scores. Below we
present separate estimates for males and females to address this concern.
We compute our matching estimator in two ways. First, in the calliper matching estimation, each formal sector is matched with the set of informal sector workers whose propensity
scores are within δ = 10−4 of the propensity score of the formal worker under consideration.6
The propensity score and the matching estimator are computed separately for each year.
5

The fact that treated observations are over-represented at high propensity scores raises our estimated
standard errors. As discussed in footnote 7, in matching with replacement, standard errors increase when
certain controls are repeatedly used. We also veriﬁed that all propensity scores are interior.
6
Results for δ = 10−3 were similar.

The resulting version of expression (1) is
α

1
NM F

wiF

−

i∈F

nij wjI

j∈I

where NM F is the number of observations in the formal sector that could be matched, and,
for all (i, j) ∈ F × I,

nij =



 0



if |pi − pj | > δ
1
|pi −pj |

1
{i,j:|pi −pj |≤δ} |pi −pj |

otherwise

The weights, therefore, vary in inverse proportion with the distance between propensity
scores. Second, we also report a “nearest neighbor” estimate of the formal sector premium,
where each formal sector worker is matched with the informal worker who has the closest
propensity score.
Table 14 presents the results for both techniques. In contrast with the parametric results,
the wage premium is negative for 1994 and 1995 and is positive and not signiﬁcantly diﬀerent
from zero for 1993 for the calliper estimator. The nearest neighbor estimator yields a small
estimate for the wage premium in the formal sector which does not signiﬁcantly diﬀer from
zero in any year.7 Thus no systematic formal sector premium can be found in our sample.
Naturally, these numbers could hide signiﬁcant variations in wages for speciﬁc types of
individuals in the sample. Table 15 splits the sample according to various criteria. Inter7

A consistent estimate of the variance of the calliper matching estimator is

2
F
I
1
{i,j:|pi −pj |≤δ} nij
V ar w +
.V ar w
NMF
NMF

Notice that it is inversely related to the number of observations which can be matched. For the nearest
neighbor estimator the corresponding expression is

2
I
F
1
i∈I ni
.V ar w
V ar w +
.
NF
NF
There is a high penalty for using certain controls often. Indeed, i∈{I} n2i is small when informal workers
are all used a comparable number of times, which occurs when the composition of the treated (formal) and
the control (informal) group is similar.

estingly, workers with low propensity scores show a (signiﬁcantly) negative premium. These
subcategories comprise low skill individuals working in poorly paid occupations. This suggests that the formal sector does not oﬀer higher wage expectations to low income workers.
As the propensity score rises, the wage premium usually goes up. It becomes (marginally)
signiﬁcant only in one year in the 0.8-1.0 range. Table 15 also shows that the formal sector
premium for women and low education workers is negative and statistically signiﬁcant in
1994. For males, the premium is negative in all years, and signiﬁcant in 1994. There is,
therefore, no evidence that returns to age, education and gender are higher in the formal
sector than in the informal sector.

6.2

The importance of controlling for employer size

Large ﬁrms and establishments pay more in most countries, regardless of whether the informal economy is large or small. Since establishments tend to be larger in the formal sector,
formal wages will appear signiﬁcantly higher in any study where size variables are not available, or not used as a controls. This, naturally, occurs with our data as well. Table 16
presents the results of computing calliper matching estimators without taking account of
establishment size in the probit. A signiﬁcant formal sector premium emerges in all subsamples. But our results above indicate that this apparent formal sector premium is in fact a
size-wage premium of the sort one ﬁnds in most economies.

6.3

The diﬀerence-in-diﬀerence matching estimator

To try and control for ﬁxed but unobserved earning determinants, we divide our sample
into 5 subperiods and, in each period, compare the change in wages for individuals who
moved from the informal to the formal sector with the corresponding change for comparable
individuals who have stayed in the informal sector. Workers are matched on the basis of
their propensity scores at the end of the period.8 The details of our sample splits are shown
8

Using the beginning of period propensity score would bias our results since individuals who transit to
the formal sector tend to move to bigger establishments. The change in wages would include a size premium.

in table 17. The second column shows the number of transitions from the informal to the
formal sector in each subperiod. The third column shows the number of individuals who
stayed in the informal sector.
As table 18 shows, the resulting estimate of the formal sector premium is negative for most
years. The formal sector premium is still negative in most cases and statistically signiﬁcant
at the 10% level in at least two transition periods. For completeness we also compute this
estimator for various sub-groups, even though the small size of the corresponding samples
bars us from obtaining precise estimates. Results are then mixed, but they appear to conﬁrm
our previous ﬁnding that formal sector premia are often signiﬁcantly negative for groups that
are more likely to operate informally, such as women and low-education workers.

Other measures of segmentation

While we ﬁnd no signiﬁcant diﬀerence in gross wages across sectors, formal employment
may still dominate informal employment when one takes into account other aspects of jobs
that are valued by employees. Most obviously, informal workers do not receive pension or
unemployment insurance beneﬁts, and taking the value of those beneﬁts into account could
aﬀect our results. Since we compare before-tax wages, the value of those beneﬁts would ﬁrst
have to oﬀset the fact that informal workers become subject to income taxation when they
enter the formal sector. This is unlikely since, as discussed by Pessino (1997), it is a common
view that in Argentina “workers regard most [social security] contributions as taxes” given
the level of uncertainty in the administration of retirement pensions. Nevertheless, directly
testing whether accounting for beneﬁts would alter our ﬁndings requires some independent
evidence on the perceived value of beneﬁts, which we do not have.
But Argentina’s household survey contains several questions that attempt to gauge the
respondent’s satisfaction with their current job. For instance, the survey asks all employees
whether they are currently looking for another job. If informal workers tend to be more
dissatisﬁed with their job, the fraction of workers with a given set of job and personal

characteristics who answer the question positively should be higher in the informal sector.
Table 2 shows that on average, for all years, more workers are looking for another job in the
informal sector than in the formal sector. But much like for wages, these average diﬀerences
could stem solely from diﬀerences in the distribution of job and personal characteristics
across sector. In fact, table 20 shows that no signiﬁcant diﬀerences between sectors remain
after controlling for those characteristics via calliper matching techniques. This is true as
well for all our basic sample splits.
The survey also asks whether workers would like to work more hours. Here too, as shown
in table 2, a larger fraction of informal workers answer that question positively. But once
again, these average diﬀerences disappear after controlling for personal and job characteristics, as table 20 shows. In fact, it is not even the case that informal workers work signiﬁcantly
fewer hours than formal workers with similar personal and job characteristics (see bottom
panel of table 20.) In summary, the proxies for job satisfaction which our data contains
provide no evidence that formal jobs are considered by employees to be superior to informal
jobs.

Conclusion

We ﬁnd no evidence of a formal sector wage premium in Buenos Aires and its suburbs with
data from the Permanent Household Survey for the 1993-1995 time period. While wages are
higher on average in the formal sector, this apparent premium disappears after controlling
semiparametrically for individual and establishment characteristics. In fact, we ﬁnd that
groups often thought to be queuing for formal sector jobs such as young and uneducated
workers would expect lower wages in the formal sector. These ﬁndings are all the more
striking that we do not take into account the fact that informal employees usually become
subject to income taxation when they enter the formal sector. Furthermore, measures of job
satisfaction available in our data do not suggest that informal workers are more dissatisﬁed
with their jobs.

The analysis yields several ancillary results of interest. We ﬁnd that controlling for establishment characteristics, particularly size, is important. In both sectors, large establishments
pay more in Argentina, as they do in most countries. We interpret this ﬁnding as suggesting
that much of the formal sector premium previous studies report is in fact a standard wage
premium.
Our data also conﬁrm that the distribution of age, gender and education characteristics
diﬀers markedly across sectors. There remains to explain how these diﬀerences can arise
in a context where labor markets appear to be competitive. There are many potential
explanations. To cite but one, ﬁrms that operate informally tend to operate at a lower
capital ratio than formal ﬁrms, in part because they have limited access to outside ﬁnancing
(See Thomas, 1992, for a discussion.) To the extent that unskilled labor is a better substitute
for physical capital than skilled labor, the informal sector will tend to emphasize unskilled
labor, regardless of whether labor markets are segmented. Formalizing and testing this
and other potential explanation are natural avenues for future work. But it is clear that
segmentation arguments are not necessary to account for salient features of labor markets in
developing nations. Since those arguments do not appear to be founded on strong empirical
evidence, their prevalence in the development literature is surprising.

Deﬁnition of the variables

Real hourly wages
Hourly wages are calculated by dividing monthly income derived from primary occupations by

52
12

times weekly hours. Argentina’s Consumer Price Index is used to obtain real

wages. The earnings of individuals who receive an “aguinaldo” are multiplied by

13
.
12

The

aguinaldo or “Christmas bonus” refers to two payments of half a month worth of earnings
that employers are required by law to make to their employees.
Sector assignments
The Sector variable takes value 1 if the individual receives both pension and unemployment insurance beneﬁts, 0 otherwise.
Establishment size
Establishment size is measured in terms of employment. We created dummy variables
for the following categories: 0 to 5 employees, 6 to 25 employees, 26 to 50, 51 to 100, 101 to
500, and more than 500 employees.
Industry
Establishments are also classiﬁed according to the three-digit International Standard
Industrial Classiﬁcation. We created a dummy variable for each two-digit category.
Education levels
The survey reports the highest educational level achieved by individuals in eight mutually
exclusive categories. A dummy called High-school takes value 1 if the individual’s education
level is in one of the ﬁve following categories: Nacional, Comercial, Normal, Técnica, Otra
enseñanza media. Dummies were also created for Primary, Superior (senior high-school) and
University educational levels.
Household members in the formal sector
The dummy variable Fhousehold takes value 1 if a member of the individual’s household
(other than the individual him or herself) is formally employed, 0 otherwise.

Tables

Table 1: Diﬀerences in average real wages, Buenos Aires and its suburbs

1993
Obs. Mean
Severance pay
3344 4.2665
No severance pay
1922 3.2501
T-statistic
9.13
Paid vacations
3732 4.1983
No paid vacations
1534 3.1590
T-statistic
8.80
Retirement beneﬁts
3528 4.2431
No retirement beneﬁts
1738 3.1900
T-statistic
9.24
Unemployment insurance
3283 4.2832
No unemployment insurance
1983 3.2536
T-statistic
9.31
At least one beneﬁt
3784 4.1858
No beneﬁt
1482 3.1543
T-statistic
8.65
F = 1 (Unemployment and retirement beneﬁts) 3261 4.2870
F =0
2005 3.2588
T-statistic
9.32
Notes: Wages in 1995 pesos, and corrected for bonuses (aguinaldo).

1994
Obs. Mean
3416 4.6221
1845 3.4864
9.80
3743 4.5514
1518 3.4162
9.29
3601 4.5916
1660 3.4260
9.80
3420 4.6076
1841 3.5108
9.46
3798 4.5418
1463 3.3985
9.26
3406 4.6129
1855 3.5094
9.53

1995
Obs. Mean
3340 4.4074
1826 3.1652
10.39
3614 4.3385
1552 3.1063
9.87
3469 4.3688
1697 3.1496
10.01
3364 4.3967
1802 3.1685
10.24
3677 4.3265
1489 3.0837
9.84
3344 4.3940
1822 3.1870
10.08

Table 2: Individual and job characteristics of formal and informal sector employees
1993
Formal

Informal

Education
None
0.004
0.006
Primary
0.311
0.476
High-school
0.414
0.377
Superior
0.069
0.037
University
0.202
0.104
Establishment size (employees)
5 or fewer
0.126
0.592
6 to 25
0.273
0.244
26 to 50
0.159
0.055
51 to 100
0.120
0.045
101 to 500
0.181
0.041
More than 501
0.142
0.023
Gender
Male
0.652
0.544
Female
0.348
0.456
Another family member in the formal sector
Yes
0.445
0.346
No
0.555
0.654
Average age
37.43
33.62
Hours worked
45.27
40.92
Would you like to work more hours?
Yes
0.243
0.300
No
0.752
0.699
Are you looking for another job?
Yes
0.136
0.231
No
0.861
0.760
Observations
3261
2005

1994
Formal Informal

1995
Formal Informal

0.003
0.307
0.413
0.086
0.192

0.009
0.476
0.390
0.026
0.099

0.003
0.344
0.387
0.076
0.190

0.008
0.465
0.364
0.034
0.045

0.145
0.275
0.148
0.133
0.168
0.131

0.587
0.262
0.055
0.040
0.033
0.024

0.141
0.271
0.144
0.133
0.190
0.121

0.623
0.246
0.036
0.030
0.045
0.020

0.644
0.356

0.573
0.427

0.627
0.373

0.532
0.468

0.456
0.544
37.19
45.12

0.361
0.639
33.43
39.82

0.421
0.579
37.33
44.51

0.325
0.675
33.26
38.32

0.252
0.748

0.343
0.657

0.329
0.670

0.430
0.570

0.138
0.860
3406

0.295
0.705
1855

0.197
0.802
3343

0.400
0.600
1822

Notes: Entries give the fraction of employees in each category. Age is measured in years.

Table 3: Transitions among occupations and sectors

From \ To
Unemployed
Formal
employee
Informal
employee
Employer
Own-account
worker
Unpaid
worker

Out of
labor force
51
(9.5)
161
(2.7)
77
(2.7)
13
(1.7)
64
(2.4)
2
(1.4)

Unemployed
208
(38.7)
58
(1.0)
122
(4.3)
9
(1.2)
133
(5.0)
5
(3.4)

Formal
employee
63
(11.7)
4876
(82.2)
737
(26.2)
57
(7.6)
153
(5.7)
16
(11.0)

Informal
employee
94
(17.5)
638
(10.8)
1469
(52.1)
46
(6.1)
382
(14.4)
25
(17.2)

Employer
5
(0.9)
38
(0.6)
39
(1.4)
402
(53.5)
182
(6.8)
12
(8.3)

Own-account
worker
114
(21.2)
156
(2.6)
347
(12.3)
212
(28.2)
1722
(64.8)
43
(29.7)

Unpaid
worker
3
(0.6)
5
(0.1)
26
(0.9)
12
(1.6)
23
(0.9)
42
(29.0)

Notes: Sample consists of the 5 inter-survey periods between 1993 and 1995. The table records the number
of transitions to and from each possible employment status between sampling periods. The corresponding
percentages are in parenthesis.

Table 4: Characteristics of workers who switch sectors

Initial/Terminal Occupation
Formal employee/Formal employee
Formal employee/Informal employee
Informal employee/Formal employee
Informal employee/Informal employee

Age
37.88
(0.19)
34.59
(0.42)
38.10
(0.34)
33.00
(0.39)

Tertiary % change in
education
gross wage
20.43
8.79
(0.63)
(1.07)
14.04
8.91
(1.06)
(2.04)
14.85
13.85
(1.01)
(2.29)
10.33
8.63
(0.87)
(1.56)

Notes: Sample consists of the 5 inter-survey periods between 1993 and 1995. Standard errors are in parenthesis.

Table 5: OLS regressions
Dependent variable is log
Baseline
speciﬁcation
0.0459 (10.57)
-0.0005 (-9.69)
0.0734 (2.95)
0.2535 (9.73)
-0.0162 (-22.12)

Age
Age2
Gender†
Sector††
Hours
Marital
Status ∗
Establishment Size
6 to 25
26 to 50
51 to 100
101 to 500
≥ 501
Education Levels
Primary
High-school
Superior
University
Industry
Mining
Manufacturing
Electricity, Gas, Water
Construction
Retail
Transport
Finance
Services
Year 1994 dummy
Year 1995 dummy
R2

real hourly wages
Speciﬁcation 2: all variables
interacted with Sector
0.0539 (8.22) -0.0215 (-2.33)
-0.0006 (-7.85)
0.0003 (2.54)
0.0719 (1.58)
0.0294 (0.54)
0.3738 (1.78)
-0.0168 (-15.85)
0.0022 (1.49)

0.1845 (7.22)

0.2263 (5.18)

-0.0692 (-1.28)

0.1003
0.1738
0.1771
0.2254
0.3177

(3.38)
(4.54)
(4.27)
(5.72)
(7.16)

0.1192
0.0722
0.1745
0.2441
0.4276

(2.64)
(0.75)
(1.69)
(2.53)
(3.53)

-0.0382 (-0.63)
0.1087 (1.02)
-0.0155 (-0.14)
-0.0410 (-0.38)
-0.1389 (-0.98)

0.1166
0.2698
0.4529
0.5312

(1.55)
(3.64)
(5.40)
(6.73)

0.0417
0.1073
0.2458
0.4180

(0.37)
(0.96)
(1.63)
(3.05)

0.0895 (2.24)
0.1649 (3.54)
0.1079 (1.95)
0.0073 (0.19)
0.1504 (3.58)
-0.0075 (-0.17)
-0.1405 (-3.21)
0.1689 (4.08)
0.1078 (4.51)
0.0022 (0.09)
0.4180

0.0499 (0.61)
0.2013 (2.00)
0.0037 (0.04)
-0.0106 (-0.17)
0.0273 (0.33)
0.0453 (0.47)
0.1054 (1.18)
0.1994 (3.06)
0.1095 (4.57)
0.0036 (0.15)
0.4205

0.0961
0.2441
0.3107
0.1629

(0.47)
(1.19)
(1.33)
(0.93)

0.0503 (0.54)
-0.0524 (-0.48)
0.2124 (1.92)
0.0253 (0.32)
0.1703 (1.78)
-0.0664 (-0.60)
-0.2782 (-2.71)
-0.1522 (-1.72)

Notes: T-statistics based on heteroscedasticity consistent standard errors are in parenthesis. In the second
speciﬁcation, the right-hand panel shows coeﬃcients and t-statistics for variables interacted with the sector
variable. † 1=Male, 0=Female, †† 1=Formal Sector, 0=Informal Sector, ∗ 1=Married, 0=Single. Omitted
education dummy is no education, omitted establishment size is 5 or fewer employees, omitted industry
dummy is agriculture.

Table 6: OLS regressions with two-step correction for selection bias
Dependent variable is log real hourly wages

Age
Age2
Gender
Hours
Marital
Status
Establishment Size
6 to 25 emp.
26 to 50
51 to 100
101 to 500
≥ 501
Education Levels
Primary
High-school
Superior
University
Industry
Mining
Manufacturing
Electricity, Gas, Water
Construction
Retail
Transport
Finance
Services
Year 1994 dummy
Year 1995 dummy
ρ

Formal sector
0.0348 (6.05)
-0.0004 (-5.06)
0.1240 (4.18)
-0.0145 (-14.27)

Informal sector
0.0553 (8.32)
-0.0007 (-7.98)
0.0795 (1.55)
-0.0170 (-16.01)

0.1715 (5.47)

0.2251 (4.84)

0.0860
0.1872
0.1630
0.2064
0.2950

(2.15)
(4.24)
(3.36)
(4.40)
(5.73)

0.1198
0.0641
0.1672
0.2461
0.4244

(2.66)
(0.66)
(1.62)
(2.54)
(3.62)

0.2895
0.5341
0.7730
0.7829

(2.83)
(5.29)
(7.05)
(7.30)

0.0761
0.1483
0.2970
0.4691

(0.65)
(1.19)
(1.59)
(3.16)

0.1031 (2.25)
0.1489 (2.83)
0.2147 (3.15)
0.0172 (0.37)
0.1961 (4.07)
-0.0217 (-0.42)
-0.1695 (-3.34)
0.0499 (0.84)
0.1397 (4.78)
0.0669 (2.22)
0.1020 (5.25)

0.0560 (0.69)
0.1975 (2.08)
0.0101 (0.12)
-0.0058 (-0.19)
0.0368 (0.45)
0.0487 (0.50)
0.1092 (1.22)
0.2078 (3.19)
0.0623 (1.52)
-0.1048 (-2.46)
0.0047 (0.03)

Notes: T-statistics are in parenthesis. The selection equation is: P rob(Sector = 1) = −1.3809 + .0163Age +
.3576Gender + .2448M status + .3433P rimary + .7656Highschool + 1.4240Superior + 1.1179U niversity +
.3115F household, where F household = 1 if the worker has a formally employed family member, 0 otherwise.
All variables in the selection equation are signiﬁcant at the 1% level. The last row of the table gives the
estimated correlation between the error term of the selection equation and the error term of the wage
equation. Omitted variables are the same as in table 5.

Table 7: Results of Probit estimation of propensity scores

Age
Gender
FHousehold
Establishment Size
6 to 25
26 to 50
51 to 100
101 to 500
≥ 501
Education
Primary
High-school
Superior
University

1993
0.0135 (0.0016)
0.2161 (0.0438)
0.2520 (0.0425)

1994
0.0134 (0.0016)
0.1249 (0.0442)
0.2200 (0.0423)

1995
0.0151 (0.0016)
0.2013 (0.0440)
0.2296 (0.0441)

0.9601
1.4489
1.4243
1.6716
1.8223

(0.0513)
(0.0718)
(0.0790)
(0.0758)
(0.0911)

0.7920
1.3663
1.3771
1.6826
1.7141

(0.0502)
(0.0728)
(0.0803)
(0.0812)
(0.0951)

0.9323
1.6582
1.6925
1.6865
1.7771

(0.0510)
(0.0843)
(0.0878)
(0.0753)
(0.0998)

-1.5025
-1.2181
-1.0624
-1.0896

(0.0819)
(0.0757)
(0.1144)
(0.0884)

-1.2957
-0.9549
-0.4620
-0.8732

(0.0810)
(0.0731)
(0.1165)
(0.0879)

-1.3796
-1.1825
-0.7888
-1.1351

(0.0823)
(0.0761)
(0.1184)
(0.0872)

Notes: The dependent variable is 1 if the individual is in the formal sector. The High-school dummy includes
normal, technical and commercial high school education. Omitted education dummy is no education, omitted
establishment size is 5 or fewer employees. Asymptotic standard errors are in parentheses.

Table 8: Frequency distribution of propensity scores

P (Sector = 1|X, Y )
0.00 to 0.20
0.20 to 0.40
0.40 to 0.60
0.60 to 0.80
0.80 to 1.00

1993
1994
Formal Informal Formal Informal
0.016
0.166
0.003
0.071
0.097
0.401
0.108
0.437
0.082
0.127
0.095
0.166
0.293
0.192
0.246
0.199
0.513
0.115
0.549
0.126

1995
Formal Informal
0.007
0.088
0.113
0.476
0.060
0.127
0.234
0.171
0.586
0.137

Table 9: Individual and establishment characteristics, 0.0 < P (Sector = 1|X, Y ) ≤ 0.2
1993
Formal
Informal
Education
None
0.00
0.00
Primary
0.80
0.85
High-school
0.20
0.15
Superior
0.00
0.00
University
0.00
0.00
Establishment size (employees)
5 or fewer
1.00
1.00
6 to 25
0.00
0.00
26 to 50
0.00
0.00
51 to 100
0.00
0.00
101 to 500
0.00
0.00
More than 501
0.00
0.00
Gender
Male
0.47
0.33
Female
0.53
0.67
Another family member in the formal sector
Yes
0.12
0.12
No
0.88
0.88
Average age
27.04
26.37
Observations
51
332

1994
Formal Informal

1995
Formal Informal

0.00
1.00
0.00
0.00
0.00

0.00
0.64
0.23
0.00
0.14

0.00
0.76
0.11
0.00
0.13

1.00
0.00
0.00
0.00
0.00
0.00

0.67
0.33

0.37
0.63

0.05
0.95

0.29
0.71

0.00
1.00
20.22
9

0.16
0.84
21.42
132

0.00
1.00
22.86
22

0.05
0.95
20.66
160

Table 10: Individual and establishment characteristics, 0.2 < P (Sector = 1|X, Y ) ≤ 0.4
1993
Formal
Informal
Education
None
0.00
0.00
Primary
0.40
0.41
High-school
0.45
0.46
Superior
0.03
0.03
University
0.12
0.11
Establishment size (employees)
5 or fewer
1.00
0.99
6 to 25
0.00
0.01
26 to 50
0.00
0.00
51 to 100
0.00
0.00
101 to 500
0.00
0.00
More than 501
0.00
0.00
Gender
Male
0.68
0.49
Female
0.32
0.51
Another family member in the formal sector
Yes
0.42
0.43
No
0.58
0.57
Average age
35.99
35.68
Observations
315
805

1994
Formal Informal

1995
Formal Informal

0.00
0.46
0.46
0.00
0.07

0.00
0.52
0.42
0.00
0.06

0.00
0.45
0.42
0.01
0.12

0.00
0.52
0.39
0.02
0.08

1.00
0.00
0.00
0.00
0.00
0.00

0.63
0.37

0.48
0.52

0.59
0.41

0.48
0.52

0.40
0.60
34.35
367

0.39
0.61
33.12
811

0.39
0.61
35.65
378

0.37
0.63
33.21
868

Table 11: Individual and establishment characteristics, 0.4 < P (Sector = 1|X, Y ) ≤ 0.6
1993
Formal
Informal
Education
None
0.00
0.00
Primary
0.60
0.63
High-school
0.28
0.28
Superior
0.04
0.03
University
0.09
0.05
Establishment size (employees)
5 or fewer
0.16
0.18
6 to 25
0.83
0.82
26 to 50
0.00
0.00
51 to 100
0.01
0.00
101 to 500
0.00
0.00
More than 501
0.00
0.00
Gender
Male
0.61
0.65
Female
0.39
0.35
Another family member in the formal sector
Yes
0.25
0.30
No
0.75
0.70
Average age
33.43
31.08
Observations
268
254

1994
Formal Informal

1995
Formal Informal

0.00
0.60
0.28
0.05
0.08

0.00
0.49
0.36
0.07
0.08

0.00
0.46
0.39
0.05
0.11

0.00
0.46
0.41
0.05
0.08

0.35
0.65
0.00
0.00
0.00
0.00

0.41
0.59
0.00
0.00
0.00
0.00

0.34
0.66
0.00
0.00
0.00
0.00

0.40
0.60
0.00
0.00
0.00
0.00

0.71
0.29

0.60
0.40

0.51
0.49

0.57
0.43

0.33
0.67
36.53
322

0.36
0.64
34.65
308

0.30
0.70
35.00
199

0.29
0.71
35.88
232

Table 12: Individual and establishment characteristics, 0.6 < P (Sector = 1|X, Y ) ≤ 0.8
1993
Formal
Informal
Education
None
0.00
0.02
Primary
0.37
0.35
High-school
0.42
0.45
Superior
0.07
0.06
University
0.15
0.12
Establishment size (employees)
5 or fewer
0.00
0.02
6 to 25
0.00
0.02
26 to 50
0.15
0.13
51 to 100
0.64
0.69
101 to 500
0.05
0.05
More than 501
0.01
0.01
Gender
Male
0.61
0.65
Female
0.39
0.35
Another family member in the formal sector
Yes
0.44
0.37
No
0.56
0.63
Average age
34.79
34.32
Observations
954
384

1994
Formal Informal

1995
Formal Informal

0.00
0.37
0.44
0.04
0.16

0.03
0.31
0.49
0.02
0.15

0.00
0.39
0.35
0.09
0.17

0.04
0.32
0.44
0.04
0.16

0.01
0.01
0.15
0.74
0.01
0.01

0.04
0.04
0.12
0.77
0.01
0.01

0.00
0.00
0.03
0.92
0.03
0.00

0.04
0.04
0.02
0.91
0.02
0.00

0.64
0.36

0.74
0.26

0.64
0.36

0.72
0.28

0.42
0.58
35.39
837

0.40
0.60
33.23
370

0.41
0.59
36.18
784

0.36
0.64
35.24
312

Table 13: Individual and establishment characteristics, 0.8 < P (Sector = 1|X, Y ) ≤ 1.0
1993
Formal
Informal
Education
None
0.01
0.02
Primary
0.20
0.20
High-school
0.43
0.40
Superior
0.08
0.10
University
0.27
0.28
Establishment size (employees)
5 or fewer
0.00
0.01
6 to 25
0.03
0.05
26 to 50
0.22
0.27
51 to 100
0.15
0.20
101 to 500
0.33
0.28
More than 501
0.27
0.19
Gender
Male
0.69
0.75
Female
0.31
0.25
Another family member in the formal sector
Yes
0.55
0.49
No
0.45
0.51
Average age
40.16
38.58
Observations
1673
230

1994
Formal Informal

1995
Formal Informal

0.01
0.20
0.42
0.13
0.25

0.04
0.27
0.38
0.08
0.24

0.00
0.26
0.38
0.11
0.25

0.02
0.28
0.31
0.10
0.30

0.00
0.06
0.20
0.21
0.30
0.24

0.02
0.08
0.24
0.22
0.25
0.18

0.00
0.03
0.23
0.22
0.31
0.21

0.01
0.10
0.23
0.22
0.30
0.14

0.64
0.36

0.71
0.29

0.65
0.35

0.60
0.40

0.52
0.48
38.76
1871

0.47
0.53
40.01
234

0.49
0.51
38.52
1961

0.46
0.54
36.64
250

Table 14: Matching estimators
Period
1993
1994
1995

calliper
Nearest neighbor
-0.084 (0.075)
0.052 (0.081)
-0.183 (0.072)
0.110 (0.075)
-0.168 (0.079)
0.022 (0.088)

Notes: In Calliper matching, δ = 10−4 . Standard errors are in parenthesis.

Table 15: Calliper matching estimator for various subgroups

α
-0.523
-0.291
-0.338
-0.222
0.369
-0.064
-0.116
-0.055
-0.228
0.444

1993
Std. error
0.345
0.149
0.149
0.136
0.174
0.094
0.098
0.126
0.115
0.214

α
-0.389
-0.452
-0.254
-0.045
-0.092
-0.181
-0.137
-0.282
-0.298
0.005

1994
Std. error
0.370
0.135
0.198
0.131
0.145
0.089
0.091
0.129
0.102
0.221

α
-1.415
-0.443
0.045
-0.156
-0.045
-0.150
-0.043
-0.360
-0.077
-0.087

1995
Std. error
0.505
0.136
0.246
0.146
0.144
0.095
0.108
0.112
0.110
0.167

Notes: Low education individuals have some primary education or less.

Table 16: Calliper matching estimator without controlling for establishment size

α
Full sample
0.240
Age ≤ 40
0.312
Females
0.172
Males
0.275
Low education 0.083

1993
Std. error
0.049
0.048
0.068
0.049
0.049

α
0.228
0.228
0.111
0.259
0.107

1994
Std. error
0.044
0.040
0.060
0.044
0.042

α
0.212
0.226
0.115
0.262
0.099

1995
Std. error
0.044
0.042
0.062
0.044
0.042

Table 17: Sample transitions
Period
Movers Stayers
5-1993 to 10-1993
116
205
10-1993 to 5-1994
103
206
5-1994 to 10-1994
104
221
10-1994 to 5-1995
63
170
5-1995 to 10-1995
73
230

Table 18: Diﬀerence-in-diﬀerence Calliper matching estimator, δ = 10−3
Period
αM DD
5-1993 to 10-1993 -0.506
10-1993 to 5-1994 -0.708
5-1994 to 10-1994 -0.639
10-1994 to 5-1995 -0.221
5-1995 to 10-1995 0.436

Std. error
0.452
0.361
0.295
0.302
0.526

Notes: We use a lower value of δ because of the reduced number of observations.

Table 19: Diﬀerence-in-diﬀerence Calliper matching estimator for subgroups
Period
Males
5-1993 to 10-1993 0.253 (0.543)
10-1993 to 5-1994 0.165 (0.536)
5-1994 to 10-1994 -0.437 (0.335)
10-1994 to 5-1995 0.589 (0.515)
5-1995 to 10-1995 0.250 (0.719)

Females
Low education
-1.305 (0.732) -1.036 (0.907)
-0.165 (0.536) -1.664 (0.437)
-0.666 (0.781) -0.234 (0.318)
-1.253 (0.318) -0.491 (0.227)
1.234 (1.333) 1.010 (0.745)

Age ≤ 40
-0.1419 (0.518)
-1.348 (0.429)
-0.725 (0.328)
0.158 (0.367)
0.496 (0.719)

Notes: Standard errors in parenthesis. Age ≤ 40 refers to individuals below 40 years of age at the end of the
period.

Table 20: Matching estimators for measures of job satisfaction
1993
Are you looking for another job?
Full sample
0.012 (0.030)
Men
-0.012 (0.038)
Women
0.063 (0.051)
Age ≤ 40
0.010 (0.039)
Primary or
less education
0.011 (0.053)
Large establishments -0.036 (0.051)
Would you like to work more hours?
Full sample
-0.016 (0.021)
Men
-0.023 (0.025)
Women
-0.036 (0.038)
Age ≤ 40
-0.023 (0.025)
Primary or
less education
-0.048 (0.034)
Large establishments 0.187 (0.061)
How many hours do you work a week in
Full sample
-0.061 (0.027)
Men
-0.021 (0.027)
Women
-0.108 (0.059)
Age ≤ 40
-0.088 (0.035)
Primary or
less education
0.005 (0.051)
Large establishments -0.048 (0.053)

1994
-0.041
-0.048
-0.084
-0.065

(0.031)
(0.038)
(0.057)
(0.041)

1995
-0.069
-0.042
-0.147
-0.066

(0.034)
(0.044)
(0.049)
(0.042)

-0.021 (0.050)
-0.036 (0.060)

-0.074 (0.053)
-0.138 (0.070)

-0.059
-0.044
-0.136
-0.047

-0.097
-0.033
-0.237
-0.081

(0.023)
(0.030)
(0.038)
(0.028)

-0.011 (0.035)
-0.052
0.083 (0.063)
-0.078
your primary occupation?
-0.014 (0.031)
-0.033
-0.045 (0.031)
-0.082
0.093 (0.070)
0.109
-0.004 (0.042)
-0.079

(0.038)
(0.063)

(0.022)
(0.026)
(0.042)
(0.028)

-0.015 (0.056)
0.013 (0.052)

(0.035)
(0.035)
(0.062)
(0.042)

0.013 (0.061)
0.114 (0.075)

Notes: Entries are calliper matching estimators for answers to the questions in italics. In bottom panel, we
compare log(hours worked) in the two sectors. Standard errors are in parenthesis.

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Full text of Working Papers (Federal Reserve Bank of Dallas) : Are Labor Markets Segmented in Argentina? A Semiparametric Approach, Center for Latin American Economics Working Paper 0701

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