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Occupational Selection in Multilingual Labor
Markets: The Case of Catalonia
N´uria Quellaa,S´ılvio Rendonb
a,bEconomics Department, SUNY-Stony Brook University,
Stony Brook, NY 11794, USA
August 2009
Abstract.- In multilingual labor markets agents with high proficiency in more
than one language may be selected into occupations that require high levels of
skill in communicating with customers or writing reports in more than one lan-
guage. In this paper we measure this effect in Catalonia, where two languages,
Catalan and Spanish, coexist. Using census data for 1991 and 1996, and control-
ling for endogeneity of Catalan knowledge, we find that proficiency in speaking,
reading, and writing Catalan reinforces selection into being employed, being an
entrepreneur, and into white-collar occupations and communication-intensive
jobs. In particular, being able to read and speak Catalan increases the proba-
bility of selection into white collar occupations by betwen 9 and 14 percentage
points, while writing Catalan increases by 6 to 13 percentage points the prob-
ability of engaging in services, and government and educational activities.
JEL Classification: J61, J70, J31, I20.
Keywords: Language; Occupational Selection; Industry; Immigra-
tion; Skill Premium.
Occupational Selection in Multilingual Markets 2
1Introduction
There are activities and types of work that are clearly intensive in communication
skills, namely providing customer service, writing reports, negotiating with trading
partners, closing business agreements, coordinating work activities with co-workers,
preparing presentations for prospective customers or colleagues, teaching, or doing
paperwork. In multilingual labor markets, such as Quebec in Canada or Catalonia in
Spain, language knowledge as a special form of human capital reinforces individuals’
comparative advantage to perform certain jobs. Thus, knowledge of French or Catalan
may be a potentially powerful determinant of workers’ allocation to tasks and, more
generally, of occupational selection.
In this article we quantify this effect by measuring the contribution of language
skills to several patterns of occupational selection. We use Catalonia’s labor mar-
ket, in which the coexistence of Spanish and Catalan provides a good opportunity
to analyze this issue in great detail. We find that knowing Catalan significantly
reinforces occupational selection of individuals born outside Catalonia into being em-
ployed, being an entrepreneur or an independent worker, and permanent employment.
Language proficiency also increases the probability of these individuals selecting into
white collar work, and employment in trade, services, and government and education.
In particular, being able to read and speak Catalan increases selection into white col-
lar occupations by betwen 9 and 14 percentage points, while writing Catalan increases
by6to13percentagepointstheprobabilityofengaginginservices,andgovernment
and educational activities.
Individuals self-select into activities in which they perform relatively better and,
therefore,earnmore. ThisisthebasisforRoy’s(1951)model: tasksindifferent
occupations require different types of skills. This mechanism underlies selection of
individuals into being workers or managers (Lucas 1978), sectorial choice and wage
distribution (Heckman and Sedlacek 1985, Gould 2002), or patterns of schooling,
employment and occupational choice (Keane and Wolpin 1997).
Occupational Selection in Multilingual Markets 3
Because different occupations require different communication skills, language pro-
ficiency is an important determinant of occupational selection. The literature on this
important connection is, however, scarce. Chiswick and Miller (2007) show that
knowledge of English is important in matching immigrants to occupation in the US.
Additionally, some authors show that sharing a common language is important both
in domestic and international trade: according to Hutchinson (2002, 2005) linguistic
distance to English is a crucial determinant of trade between countries; Tadesse and
White (2008) find that greater cultural differences between the US and a trading
partner reduce exports to that country; Sauter (2009) finds that trade in industries
in which direct oral communication with the importer is necessary increases with the
probability that people in another Canadian province speak the same language.
In Catalonia, as in other economies characterized by linguistic diversity, an active
language policy has increased the economic value of Catalan knowledge. Individuals
with more knowledge of Catalan are significantly more likely to be employed (Ren-
don 2007). Our work goes a step further and analyzes the incidence of language
knowledge on the probability that an individual performs certain kinds of jobs over
others. That is, the economic value of Catalan differsacrossactivitiesandoccupa-
tions, so that some are relatively more attractive to those individuals who have the
required level of language knowledge. We use two samples from Census years 1991
and 1996 to estimate the effect of Catalan knowledge on occupational selection.1
First, we estimate a Probit model for the individuals’ level of language proficiency
in order to correct for the possible endogeneity of Catalan knowledge, as it may be
jointly determined with occupational selection. Then,weestimateabivariatePro-
bit model for the probability of choosing a given occupation conditional on a given
Catalan proficiency level. We find that for individuals born outside Catalonia reading
and speaking as well as writing Catalan significantly reinforce occupational selection
into communication-intensive jobs and positions. Specifically, language proficiency
1These are the last census with reliable data on language for Catalonia. The census of 2001 had
several inconsistencies and therefore has not been used in the analysis of language issues.
Occupational Selection in Multilingual Markets 4
increases the probability that individuals select into becoming entrepreneurs or inde-
pendent workers, as well as into permanent employment, white collar work, jobs in
trade and services, and government and educational activities.
The remainder of this paper is organized as follows. The next section presents the
background of multilingualism, language policy, and economic structure in Catalonia.
Section 3 describes the data set and discusses the main descriptive statistics. Sec-
tion 4 explains the estimation procedure and results, and Section 5 details the main
conclusions of this article.
2Background
In this Section we explain the language transition in formal communication from
Spanish to Catalan, and describe the main features of the economic structure of
Catalonia.
2.1 A Multilingual context
Two important language related phenomena occurred on the second half of the twen-
tieth century. On the one hand, the fall of dictatorships in Europe and the process
of decolonization in Africa and Asia. This implied that, in many countries, widely
spoken languages that were only informally used gained official status and were given
priority in education over the former official, usually colonial, languages. On the other
hand, subsequent waves of immigration to Northern Europe and North America gave
rise to the issue of language assimilation of immigrants to their host countries.
Research by most economists has traditionally focused on this second issue, ap-
proaching language as a form of human capital valued by the market, crucial to
convergence between wages of immigrants and natives. A recent stream of the eco-
nomic literature attempts to measure the economic effects of changes in the language
of education. For example the switch from French to Arabic in Morocco; from English
Occupational Selection in Multilingual Markets 5
to Welsh in Wales; from Russian to Estonian in Estonia; or from English to Spanish
in Puerto Rico (see Angrist and Lavy 1977, Grin and Vaillancourt 1998, Sabourin
and Bernier 2003, Angrist et al. 2008). However,evidence on the economic effects of
this kind of language switching is so far inconclusive. While Angrist and Lavy (1977)
find that the language switch in Morocco decreased returns to education, Angrist et
al. (2008) show that once education-specific cohort trends were introduced, English
instruction had no effect on English-speaking ability among Puerto Rican natives.
As other European countries, Spain is characterized by a vast language diversity.
Altogether, around forty percent of the population of Spain live in areas with two
official languages. While Castilian (Spanish) is official in the totality of the terri-
tory, Catalan, Galician, and Basque share co-officiality with Castilian in their own
territories.2A comparison of the importance of these languages in their territories
reveals that Galicia, with little immigration, has the highest proportion of speakers
of their own language. However, Catalonia exhibits the best evolution of indicators
of knowledge, use, and favorable attitudes towards the language. Not only has lan-
guage knowledge increased through the past twenty years, but also the proportion
of individuals who consider Catalan their main language. Language commitment to
one’s own language though is higher in the Basque Land and Navarra: out of those
who speak Basque, the proportion that also write it is higher than its equivalent for
Catalan in Catalonia and Galician in Galicia (Sigu´an 1999)..
During most of its history Catalan has been official in Catalonia. However, from
the forties to the seventies, during Franco’s regime, Spanish was declared the only
official language in Catalonia (as in the rest of Spain), and Catalan was relegated to
informal use. This, combined with massive immigration of Spanish speakers to Cat-
2Catalan is official in Catalonia (6,995,206 inhabitants in 2005), in Valencian Land (4,692,449
inhabitants), and in Balearic Islands (983,131). Galician is official in Galicia (2,762.198) and Basque
is official in the Basque Land (2,124,846) and in the north of Navarra. These regions represent
39.81% of the total Spanish population (44,108,530). Other languages are Asturian or Bable (with
around 600,000 speakers and not official in its territory: Asturias and the north of Castilla-Leon),
and Aran´es or Occitan, official in Valh d’Aran, within Catalonia (9,100 inhabitants).
Occupational Selection in Multilingual Markets 6
alonia in the sixties and seventies, contributes to explain how an important proportion
of native Catalans of a certain age do not master Catalan. During the eighties and
nineties the autonomous Catalan government carried out the ‘Normalization’ policy
with the explicit goal for Catalan to match and replace Castilian (Spanish) as the
official language, particularly, in the fields of education, public administration and
public media. These public policies significantly contributed to the recovery of the
Catalan language in Catalonia, so that in Catalonia there is an economic premium for
knowing Catalan language (Rendon 2007). Before showing how the economic incen-
tive for knowing Catalan varies by occupation and activities, in the next subsection
we will describe briefly the economic structure of Catalonia.
2.2 Economic Structure of Catalonia
Within the European Union Catalonia has approximately the same size and popu-
lation as Denmark, a higher per capita GDP than Portugal or Greece, and inflows
of foreign direct investment above Austria’s or Estonia’s. A densely populated and
heavily urbanized area in the Northeast of Spain, Catalonia has more than 7 million
inhabitants, one quarter of which are concentrated in its capital, Barcelona. It rep-
resents 16% of the Spanish population in a territory that is only 6% of the country’s
total. Catalonia’s GDP is one fifth of Spain’s, and its per capita income level has his-
torically been above the average for Spain. Currently, it is 23% above the average for
the whole European Union (EU-27) in PPP terms, although still below the average
for the EU-15.
Within Spain’s economy Catalonia has traditionally had a preponderant role.
Currently it occupies the first place in terms of overall economic activity, about 20% of
the Spanish total, industry, around 21%, and trade, 18%; occupying the third place in
tourism, 15%, Spain’s main industry (Servei d’Estudis 2008). Its economic structure
is strongly dominated by the manufacturing sector, which represents about 30% of its
GDP, above Spain and EU-15 averages. Its industrial composition is balanced and
Occupational Selection in Multilingual Markets 7
diversified: no single sector’s macromagnitude (production, employment, gross value
added, etc.) represents more than 20% of the total.
In recent years traditional sectors such as textiles have lost ground, both in terms
of gross value added and employment, to more technology intensive industries such as
chemicals, transportation equipment, and optical & precision instruments. Namely,
more than 60% of all Spanish pharmaceutical production and over 50% of all of Spain’s
laboratories are located in Catalonia, which has attracted major biotech companies.
Specifically, Catalonia attracts more than half of all foreign direct investment in Spain
related to R&D activities (INE 2004). Overall, Catalonia’s investment in R&D, at
24.6% of Spain’s total, has only very recently fallen second to Madrid’s 28.4%, still far
ahead of the Basque Country’s 9.5%, ranked third. Also food & beverage processing,
and paper, publishing & printing have expanded.
Within the services sector, tourism, health, financial services, and firm-oriented
services are pivotal. Trade has undergone a two-decade transformation toward larger
retailers and distribution chains. Nevertheless, small and medium enterprises consti-
tute 99% of Catalonia’s business sector. Specifically, firms with 100 employees or less
still absorb about 66% of workers. The overall unemployment rate in Catalonia, al-
though above the EU-15 average, has consistently kept below the Spanish average for
the past two decades: in 1992 it was 13.6%, reached its 21.2% peak in 1994, decreased
to 18.9% in 1996, and stabilized around 8.8% in year 2000.
[Table 1 here]
Table 1 illustrates the sectorial evolution of the employment composition in Cat-
alonia and Spain. Catalonia is clearly more industrial than Spain: in Catalonia
industry represents 30% of employment in 1991 and 26% in 1996 while in Spain these
percentages are 21% and 18%, respectively. Nevertheless, the service sector is clearly
the largest employer in both Catalonia (from 56% of employment to 62%) and Spain
(from 58% to 64%).
Occupational Selection in Multilingual Markets 8
In the next sections we show how in this multilingual growing service economy,
proficiency in reading, speaking and writing Catalan influences agents’ choices for
certain sectors of production.
3Data
We use two samples of 250,000 randomly selected individuals extracted from Census
data for 1991 and 1996,3provided by the Catalan and Spanish National Statistical In-
stitutes (IDESCAT-INE). These datasets contain information on personal attributes
such as gender, age, marital status, schooling, place of residence, place of birth, num-
ber of years in Catalonia, occupational status, and knowledge of Catalan. We combine
the Census data with data on municipalities in order to capture the externality effects
on sample individuals of residing in areas with high employment rates or widespread
Catalan knowledge. We restrict the sample to parents and children from 16 to 60
years of age, born in Spain but not in Catalonia, and participating in the labor force.4
We include individuals living in several types of households: singles, divorced or sepa-
rated individuals living alone, and individuals in multi-personal households, in which
we only consider both parents and their children. The final sample contains 47,053
individuals for year 1991, and 69,043 individuals for 1996. Appendix A.1 details the
sample selection.
Descriptive statistics for all variables by Census year and gender are presented
in Table 2.5Becausewecannotrelateindividuals across Census years or link two
individuals of the same family the data only allow us to analyze the cross-sectional
effect of individual attributes on language selection.6
3Unlike the census of 1991, applied in all of Spain, the census of 1996 was only applied in
Catalonia.
4International immigration rates to Catalonia for the two census years are low. E.g. 4% in 1991.
Moreover, in several estimations we found origin to be non-significant in determining employment
status. However, origin may be more important for explaining wages, for which unfortunately there
is no data in the census.
5Appendix A.2. explains the definition of these variables in further detail.
6In order to study intergenerational language persistence or to restrict the sample to children of
Occupational Selection in Multilingual Markets 9
[Table 2 here]
Occupational variables in Table 2 are ordered by employment status, type of work,
occupation, and activity. Men exhibit higher employment rates, are more likely to
be entrepreneurs and self-employed, and more likely to be permanent workers than
women. Women, on the other hand, are more likely to be found in white collar occu-
pations, mostly in activities within services, and government and education. There
are no important gender differences among individuals working in trade.
Respondents’ level of Catalan knowledge is self-declared which, given the linguistic
proximity of Catalan and Castilian, may lead respondents to over-report proficiency.7
In order to solve this problem we class individuals who claim to understand and
either only read or only speak Catalan as having a basic level of Catalan knowledge;
individuals who report to read and speak are in the intermediate level; while those
who can also write belong in the advanced level.
We can see a gender gap has opened in 1996. Relative knowledge of Catalan was
similar for both genders in 1991, with the bulk of sample individuals declaring a basic
level of knowledge. In 1996 the proportion of men in the basic level has decreased by
eleven percentage points, whereas for women the decrease is twenty-three percentage
points. A similar proportion of men and women, 27%, claim to have intermediate
knowledge of Catalan, but the proportion of women with and advanced knowledge of
the language, at 23.2%, has more than doubled since 1991, surpassing men’s 13.9%.
Individuals in the sample are on average about 43 years of age, mostly married,
with a growing level of education and Catalan knowledge, especially women.8The
proportion of people affected by the Normalization process is growing over time for
non-Catalan speakers, for whom learning Catalan means more of an investment, we need infomation
on the parents’ language proficiency.
7Self-assessed data can lead to biased inference (Charette and Meng 1994). Corrections for
missclassification, such as the one used by Dustmann and van Soest (2001), require panel data and
are not possible in our current framework.
8The lowest level of schooling is illiteracy, followed by ‘no schooling.’ The percentage of illiterates
in the sample is negligible, while the percentage with no schooling is very small. As for Catalan
knowledge, the improvement persists even after controlling for variation in the samples, as will be
seen below.
Occupational Selection in Multilingual Markets 10
both genders, but more so for women, who were less proficient in Catalan than men
in 1991 but overtake them in 1996. Most people reside in Barcelona, but a growing
percentage of individuals resides in Lleida, Girona and Tarragona, the other three
Catalan provinces. On average, individuals in the sample arrived to Catalonia at the
end of the sixties, 25 years prior to the censuses, when they were no older than 10,
and mainly from Andalusia. There is a slight increase in the number of individuals
born in other Catalan-speaking areas in Spain outside Catalonia (Valencia, Balearics
and La Franja). Most sample individuals live in municipalities where people are
(increasingly) proficient in Catalan, were born in Catalonia, and work in services.
4 Language Effects on Occupation Selection
In this Section we use the Census samples to estimate the effect of language knowledge
on occupational selection. First we correct for the possible endogeneity of language
knowledge, as it may be jointly determined with occupational selection. To this
purpose we estimate a Probit model for the probabilities of an individual exhibiting
either the intermediate or the advanced level of proficiency in Catalan for 1991 and
1996, for males and females separately. Because individuals in the sample are not born
in Catalonia, they are more likely than natives to have faced a language knowledge
decision. In a second stage, we estimate the effect of Catalan knowledge on the
probability of choosing a given occupation. Language knowledge is shown to decisively
influence individuals’ self-selection into occupations, activities and types of work that
are intensive in communication.
4.1 Estimation and Identification of Language Effects
We consider two types of occupational selection: functional9and sectorial. Individuals
are selected into functions or roles in producing income, i.e. into entrepreneurship
9We borrow this term from what is usually called the “functional distribution of income” between
individuals who own different factors of production.
Occupational Selection in Multilingual Markets 11
(and self-employment) or salaried work; into employment or unemployment; and into
permanent or temporary employment. Individuals are also selected into sectors of
production, that is, into different occupations and activities. Our results show that
proficiency in Catalan influences both types of occupational selection.
A Probit estimation of language effects in occupational selection yields unbiased
estimates only if language knowledge is exogenous. As this may not be the case,
we also estimate a bivariate Probit for both types of occupational selection which
does account for language selection. Thus,thecorrectionforselectionisdoneintwo
stages, as in Willis and Rosen (1979).
In the first stage, we estimate a Probit model for level of language knowledge.
We use the following sources of exogenous variation for language proficiency: level
of Catalan knowledge in the individual’s municipality; whether the individual was
affected by the Normalization policy; whether the individual arrived to Catalonia be-
fore age 10; number of years since migration; an interaction term between years since
migration and whether the individual arrived before age 10; and origin variables such
as whether the individual was born in Andalusia or in other Catalan-speaking regions
of Spain.10 These variables represent the externality effect the community of residence
has on the individuals’ level of Catalan knowledge, and the individual’s exposure to
schooling in Catalan and to the Catalan economy. Therefore, they should directly
affect the individual’s proficiency in Catalan, but not the probability of occupational
selection.
In the second stage, we use the estimated parameters of the language selection
equation to estimate the probability of selection into being employed; being an en-
trepreneur or self-employed; into permanent employment; white collar occupations;
and trade, services, and government and educational activities, conditional on a given
Catalan proficiency level. Additional control variables in this equation are: age,
schooling, marital status, province of residence, percentage of individuals employed
10The estimation results of the first stage are shown in Appendix A.3.
Occupational Selection in Multilingual Markets 12
in the municipality of residence and, of these, percentage employed in the service
sector.
4.2 Estimation Results
The language effect on functional selection for the simple Probit and the bivariate
Probit models can be seen in Table 3. We present estimates for effects on employment
status, on being an entrepreneur, an entrepreneur or self-employed, and on having a
permanent job. Clearly, all estimated discrete effects, regardless of gender and census
year, are significantly different from zero. We also report the correlation coefficient
and a chi-square statistic that tests for independence of occupational selection and
Catalan language selection.
[Table 3 here]
We see that knowing Catalan reinforces selection into being employed, being an
entrepreneur, being an entrepreneur or independent worker, and being a permanent
worker. The language effect on being employed is significant for all groups, yielding
systematically lower values once language selection is accounted for,11 except for
women in 1991. The correlation coefficient is positive and low, except for women in
1991, at about 0.155. The contribution is higher for women, for whom it decreases
over time and increases with the level of language proficiency, whereas for men the
evolution is precisely the opposite. These results are similar to those obtained by
Rendon (2007).
Selection into being an entrepreneur is significantly affected by knowledge of Cata-
lan, more so for men than for women. The language contribution is higher when lan-
guagechoiceisaccountedfor,withlowercorrelation between equations for women.
This contribution decreases the higher the level of Catalan knowledge. The effect of
11Between a simple probit model and the baseline bivariate probit model there is an average
difference of up to 0.7 percentage points, that is, 30% in relative terms.
Occupational Selection in Multilingual Markets 13
language on being an entrepreneur or a self-employed worker is higher than on being
an entrepreneur, while it is still higher for men, with the wrong sign for women. It is
also decreasing over Catalan skills and higher once corrected for language selection.
Finally, knowledge of Catalan increases significantly the probability that a worker
has a permanent job. This contribution is generally lower once Catalan selection is
accounted for. It is higher for the intermediate level than for the advance level of
Catalan, and higher for men in 1991, but lower in 1996.
In sum, a simple Probit estimation over-estimates language effects on the proba-
bility that an individual is employed and permanently employed, but under-estimates
an individual’s selection into entrepreneurial and independent work. Language effects
on functional occupational selection are generally stronger for men than for women,
and decreasing over time and language proficiency level.
Table 4 exhibits the effects of Catalan knowledge on sectorial selection by gender,
year, and level of language knowledge. The contribution of language to the probability
of an individual selecting a white collar occupation is high, around 10 percent on
average, for both genders and census years within either level of language proficiency.
This contribution is always higher for women, increasing over skills for men and
decreasing for women. It is also decreasing over time, except for men that can write
Catalan. This contribution is lower for Biprobit than for Probit estimations for all
groups, while correlation between language and occupational choice is strong, between
0.15 and 0.24.
[Table 4 here]
We can also see a differentiated gender pattern in the contribution of language to
individuals selecting an activity. Regardless of language proficiency level and census
year, the contribution of language to men selecting trade is higher than for women.
Correction for language selection reinforces this difference for the intermediate level
of Catalan, increasing the language effect for men and decreasing it for women. How-
ever, correlation between sectorial occupational choice and language is low. Overall,
Occupational Selection in Multilingual Markets 14
the contribution of speaking Catalan to trade is positive and significant, whereas
writing has a negative or zero contribution; it is strongly negative for women and
writing. Thus, reading and speaking Catalan reinforces selection into trade, but
writing Catalan reinforces selection away from trade.
Selection into services by language knowledge is positive and increasing in language
skills for both genders: for reading and speaking language effects are between 2% and
4%, while for writing Catalan they are between 7% and 13%. They are higher for
womenthanformen,andincreasingovertime for men, but decreasing for women.
Correction for endogeneity of language reveals higher language coefficients than a
simple Probit estimation. Even though correlation between sectorial occupational
choice and language is low, indepedence between language and occupational selection
it statistically rejected.
Knowing Catalan undisputedly increases the probability of selection into both
services and government and education activities, more so for writing, around 8 per-
centage points, than for reading and speaking, 2 percentage points , and notoriously
more for women than for men. The correction for language selection yields higher
values of the language contribution for both skill levels. Correlation between sectorial
selection and language skill level is relatively higher for government and education,
especially for women.
In sum, a simple Probit model over-estimates the effects of intermediate language
proficiency for selection into white collar occupations, as well as the effects of advanced
knowledge for selection into trade, but under-estimates selection of individuals with
advanced knowledge of Catalan into white collar occupations, and services, govern-
ment and education activities. Language effects in sectorial occupational selection are
generally stronger for women than for men, and increasing over time and language
proficiency level.
Summarizing, in all of these estimations language proficiency does make a signif-
icant difference in occupational selection, increasing the probability that individuals
Occupational Selection in Multilingual Markets 15
engage in jobs in which communicative skills are needed, such as entrepreneur, self-
employed worker, permanent employment, white collar occupations, and in activities
such as trade, services, and government and education.
4.3 Quality of Instruments
The instruments we use in the benchmark estimation are municipality variables cap-
turing the externality effect of the community of residence on Catalan knowledge;
origin variables; assimilation variables such as years passed since migration, and age
of arrival; and whether the individual was affected by Normalization. These instru-
ments are related to knowledge of Catalan, but not to occupational selection. In
order to assess the quality of these instruments, we follow ?and proceed to exclude
subsets of them and compare the resulting estimated language effects on occupational
selection.12
Table 5 presents the language contribution to functional occupational selection
for all subsamples by instruments. That is, for the benchmark Bivariate Probit es-
timation, which includes all instruments; and for the estimations excluding, first,
municipal variables (percentage of people born in Catalonia and percentage of peo-
ple who write Catalan in the municipality), then origin variables (born in Andalusia,
Valencia-Balearics, Franja), and finally assimilation variables (arrived before 10, years
since migration, arrived before 10×years since migration). We can see the premium
is sensitive to exclusion restrictions, which shows that it is the instruments that affect
the contribution language skills have on occupational selection, not the shape of the
nonlinear functional form.
12Unlike linear models, limited dependent variable models do not have estimated sample errors
nor established tests of instruments. Identification of a recursive bivariate probit model requires
exogeneity of the sources of variation (Maddala 1983). Specifically, there might be unaccounted in-
dividual characteristics that determine Catalan knowledge that are also correlated with occupational
choice, which may produce biased results. To reinforce identification of this effect in the occupation
equation we have to exclude the variables used in the language equation, that is, the instruments.
Occupational Selection in Multilingual Markets 16
[Table 5 here]
Clearly, assimilation variables have the largest effect on the estimated coefficient,
followed by origin and municipal variables. Overall, omission of these variables brings
coefficients closer to those in the simple Probit model. For employment status, ex-
cluding municipality variables has a larger effect than omitting origin variables. For
selection into entrepreneurial activity, leaving out municipality variables is more im-
portant in 1991, while in 1996 excluding origin has a larger effect, for both skill levels.
These results also apply for selection into entrepreneurial and independent work. As
for selection into permanent employment excluding municipality variables is more im-
portant for women than excluding origin variables in both years, whereas the opposite
holds for men.
In Table 6 we report the results for sectorial occupational selection. Again, as-
similation variables are crucial in identifying the premium effect. For selection into
white collar occupations, municipality matters more than origin for all groups, except
for women in 1991. For trade, however the effect of origin is stronger than the effect
of municipality variables, whereas for services it is municipality variables, and for
government and education activities is municipality, except for men in 1991.
[Table 6 here]
Summarizing, identification of unbiased language effects on occupational selection
relies crucially on assimilation variables, such as years since migration, whether the
individual arrived before being 10 years old, and an interaction term between these
variables. Municipality variables generally rank second in importance for most se-
lections of occupations, with the notorious exceptions of permanent employment for
men and trade activities, for which origin variables matter most.
Occupational Selection in Multilingual Markets 17
5 Conclusions
When making occupational choices, workers consider their relative skill levels, i.e.
their comparative advantages in different occupations. In this article, we have shown
that language skills play a significant role in selecting individuals into communication-
intensive jobs and positions.
For a sample of Spanish individuals who were not born in Catalonia, we show
that language knowledge significantly reinforces occupational selection into being em-
ployed, working as an entrepreneur or independent (self-employed) worker, and hav-
ing permanent employment. Language knowledge also contributes to selection into
white collar occupations and trade, services, and government and education activities.
These effects are relatively large: reading and speaking Catalan increases selection
into white collar occupations by around 10 percentage points for all subsamples, while
writing Catalan increases by around 8 percentage points the probability of engaging in
services, and government and educational activities. Interestingly, reading and speak-
ing Catalan reinforces selection into trade, but writing Catalan reinforces selection
away from trade.
In entrepreneurial jobs, self-employment, and permanent employment the lan-
guage contribution is stronger for men than for women, and decreasing over time
and over language levels. The opposite holds for white collar occupations and trade,
services, and government and educational activities, in which language effects are
stronger for women than for men and increasing over time and over language levels.
We als o find that it is necessary to correct for language selection in order to avoid
biased measurement of language effects in occupational selection; there is significant
correlation between occupational selection and language selection. To this purpose,
we used several types of instruments, namely assimilation, origin, and municipal-
ity variables. Of these, assimilation variables are shown to be most important for
identification of language effects on occupational selection.
In sum, our results indicate that language knowledge is an important component
Occupational Selection in Multilingual Markets 18
in the set of skills that constitute an individual’s comparative advantage. As such, it
contributes to selection into relatively more communication-intensive jobs, activities,
and occupations. Entrepreneurial and independent work require more communication
than salaried work, for instance in relations with customers, providers, and associates.
In activities such as trade, services, and government and education, individuals need
to relate to others, not only through reading and speaking, but also by producing
written documents (reports, instructions, etc.), which require higher levels of pro-
ficiency in Catalan. Accordingly, individuals with higher language skills engage in
these occupations.
This analysis is indicative of how occupational selection occurs in a multilingual
economy. Monolingual agents who migrate to a multilingual region in the same
country may have the option not to learn the local language. However, they do
learn it, because, among other possible reasons, this knowledge improves their job
matching, especially in communication-intensive activities and occupations.
Occupational Selection in Multilingual Markets 19
Appendix
A.1. Sample Selection
The following table illustrates the importance of the selection criteria in constructing
the sample.
1991 1996
Total sample 250 000 250 000
Only main household members: parents and children -17 654 -17 903
Only individuals between 16 and 60 years old -82 297 -81 770
Only Spaniards -5 740 -4 745
Only if year of arrival in Catalonia available -3 788
Only individuals in the labor force -47 421 -44 809
Only if Catalan language variable available -25
Only if born outside Catalonia -49 810 -27 942
Selected sample 47 053 69 043
A.2. Definition of the Variables
Theconstructionofeachvariableispresentedbelow.
Employment.- The census reports a variable called “relationship with the
activity.” An individual is employed if he or she reports to be working; un-
employed if he or she reports to be not working and looking for his/her first
job or having worked before. Individuals who report other options (military
service, retired, student, working at home) are excluded from the sample.
Type of work.- This variable is literally called “professional situation;” it
includes entrepreneurs who hire workers, which we call “Entrepreneur;” self-
employed professionals or independent workers who do not hire other workers,
which we call “Self-Employed;” and Temporary Workers. There are other
categories, such as cooperatives and salaried workers.
Occupation.- This variable is literally called “occupation, profession or craft”
and is specific to the interviewed individual. It is coded according to the
Catalan Code of Occupations (CCO-94). We group occupations into “White
collar,” mostly professionals in trade and services; and the rest, mostly “Blue
collar,” involving more manual labor.
Activity.- This variable provides information on the industrial sector of the
firm where the worker performs his or her work. It is coded according to the
Catalan Classification of Economic Activities (CCAE-93). We group them
into ‘Trade,’ ‘Services,’ and ‘Government and Education.’13
Schooling.- The census reports the maximum level of studies attained by the
individual. To each level, we assign the number of years of schooling.
13Our exact grouping for the classification of occupation and activities is available from the authors
upon request.
Occupational Selection in Multilingual Markets 20
Age.- It is the census year, 1991 or 1996, minus the year of birth.
Normalization.- If the individual was younger than 12 years old in 1984 this
dummy variable takes the value of one, it is zero otherwise.
Married.- This variable takes the value of one if the respondent reports to
be currently married; it is zero if the respondent reports to be a widow(er),
separated, or divorced.
Residence variables.- The census reports the municipality and province of
residence for each individual. With this information we construct dummies
for Lleida, Girona and Tarragona.
Origin variables.-The census reports the municipality and the province of
birth for each individual. With this information we construct dummies for
individuals who are not born in Catalonia, Andalusia, Valencia, Balearics or
La Franja.
YSM (Years since Migration).- The census reports the year of arrival to
Catalonia. YSM is the census year minus this number. We also construct a
dummy indicating if somebody arrived when s/he was no older than 9.
Municipal variables.- We use the residence variable to assign each individual
the corresponding information of the municipality.
A.3. Catalan Knowledge
Table A1 reports the coefficients of the selection equation into reading and speaking
CatalanontheleftsideandintowritingCatalanontherightside. Thecovariates
used in the estimations are the variables described before, including squared and
interaction terms. There are some common features in the estimated coefficients for
the four subsamples. Individuals with more years of schooling are more likely to
know Catalan. Older individuals are more likely to read and speak Catalan, but less
likely to write it, possibly a result of being schooled during the years in which the
use of Catalan was exclusively informal. The probability of knowing Catalan is thus
increasing both in schooling and age, but at a decreasing rate. Older individuals
exhibit lower returns to schooling, especially in the probability of writing Catalan.
[Table A1 here]
Occupational Selection in Multilingual Markets 21
Individuals who are not married or live outside Barcelona are more likely to know
Catalan, especially to read and speak it. In areas with a higher percentage of employ-
ment in services, arguably the more urban areas, it is less likely that an individual
knows Catalan. On the contrary, in areas with a higher percentage of individuals who
know Catalan, it is more likely that an individuals knows Catalan, which is indicative
of the importance of social interactions at the local level. In areas where the percent-
ages of people being employed or being born in Catalonia are higher an individual is
more likely to read and speak Catalan, but less likely to write it. As with age, this
difference across Catalan skills may stem from growing up in the times when the use
of Catalan was purely informal.
Individuals affected by Normalization are more likely to read and speak Catalan.
This effect is weaker in 1996 than in 1991, greater for men than for women, and
greater for reading and speaking than for writing skills.
Individuals who arrived to Catalonia when they were younger than 10 are more
likely to know Catalan. More exposure to the local culture, captured by the variable
years since migration, makes language assimilation more likely. However, the effect
of local exposure on knowing Catalan is stronger for individuals who arrived at a
mature age. Individuals who were born in Andalusia are less likely to know Catalan,
especially women. On the contrary, individuals born outside Catalonia in areas were
Catalan is used, such as Valencia, Balearics and La Franja, are more likely to know
Catalan.
We also report indicators of overall fit, pseudo R2statistics. They are around
0.30, a relatively high value for a discrete variable, which reveals the fairly good
explanatory power of the covariates in this estimation.
Occupational Selection in Multilingual Markets 22
References
Angrist, J., Chin, A. and Godoy, R. (2008), ‘Is Spanish-only schooling responsible for the
Puerto Rican language gap?’, Journal of Development Economics 85(1-2), 105—128.
Angrist, J. D. and Lavy, V. (1997), ‘The Effect of a Change in Language of Instruction on
the Returns to Schooling in Morocco’, Journal of Labor Economics 15, 48—76.
Charette, M. and Meng, R. (1994), ‘Explaining language proficiency. Objective versus self-
assessed measures of literacy’, Economic Letters 44, 313—321.
Chiswick, B. R. and Miller, P. W. (2002), ‘Immigrant Earnings: Language Skills, Linguistic
Concentrations and the Business Cycle.’, Journal of Population Economics 15, 31—57.
Chiswick, B. R. and Miller, P. W. (2007), Matching Language Proficiency to Occupation:
The Effect on Immigrants’ Earnings. The University of Western Australia, Department
of Economics. Economics Discussion Working Papers. 07-07.
Dustmann, C. and van Soest, A. (2001), ‘Language Fluency and Earnings: Estimation with
Misclassified Language Indicators’, Review of Economics and Statistics 83, 663—674.
Gould, E. D. (2002), ‘Rising Wage Inequality, Comparative Advantage, and the Grow-
ing Importance of General Skills in the United States’, Journal of Labor Economics
20(1), 105—147.
Grin, F. and Vaillancourt, F. (1998), Language Revitalisation Policy: An Analytical Survey.
Treasury Working Paper 98/6. Mimeo.
Heckman, J. J. and Sedlacek, G. (1985), ‘Heterogeneity, Aggregation, and Market Wage
Functions: An Empirical Model of Self-Selection in the Labor Market’, Journal of
Political Economy 93, 1077—1125.
Hutchinson, W. K. (2002), ‘Does Ease of Communication Increase Trade? Commonality of
Language and Bilateral Trade’, Scottish Journal of Political Economy 49, 544—556.
Hutchinson, W. K. (2005), ‘”linguistic Distance” as a Determinant of Bilateral Trade’,
72, 1—15.
INE (2004), Encuesta de Innovaci´on Tecnol´ogica en las Empresas. Instituto Nacional de
Estad´ıstica.
URL: http://www.ine.es/inebase/cgi/axi
Keane, M. and K.Wolpin (1997), ‘The Career Decisions of Young Men’, Journal of Political
Economy 10(3), 473—522.
Lucas Jr., R. E. (1978), ‘On the Size Distribution of Business Firms’, Bell Journal of
Economics 9(2), 508—523.
Maddala, G. S. (1983), Limited-dependent and qualitative variables in econometrics,Cam-
bridge University Press, Cambridge.
Rendon, S. (2007), ‘The Catalan Premium: Work and Language in Catalonia’, Journal of
Population Economics 20(3), 669—686.
Occupational Selection in Multilingual Markets 23
Roy, A. D. (1951), ‘Some Thoughts on the Distribution of Earnings’, Oxford Economic
Papers 3, 135—146.
Sabourin, C. and Bernier, J. (2001), Government responses to language issues. international
examples. Office of the Languages Commissioner of Nunavut. Mimeo.
Sauter, N. (2009), Talking Trade: Language Barriers in Intra-Canadian Commerce. FIW
Working Paper series, 023.
Servei d’Estudis (2008), Anuario Econmico de Espaa 2008, Caja de Ahorros y Pensiones de
Barcelona, ”La Caixa”, Barcelona.
Sigu´an, M. (1999), Conocimiento y uso de las lenguas en Espa˜na. (investigaci´on sobre el
conocimiento y uso de las lenguas cooficiales en las Comunidades Aut´onomas Biling¨ues.
Centro de Investigaciones Sociol´ogicas (CIS), 22, Madrid.
Tadesse, B. and White, R. (2008), Cultural Distance as a Determinant of Bilateral Trade
Flows: Do Immigrants Counter the Effect of Cultural Distance? Forthcoming in
Applied Economic Letter.
Willis, R. and Rosen, S. (1979), ‘Education and Self-Selection’, Journal of Political Economy
87(5), S7—S36.
Occupational Selection in Multilingual Markets 24
Table 1: Sectorial Employment
Region Catalonia Spain
Censur Year 1991 1996 1991 1996
Total Employed (in %)
Agriculture, fishing and forestry 3.48 3.54 9.86 8.15
Mining, quarrying and extracting 0.82 0.75 1.06 1.00
Manufacturing 30.43 25.75 21.03 18.02
Construction 9.45 8.22 9.73 8.97
Services 55.81 61.75 58. 32 63.86
TOTAL (in thousands) 2,247 2,463 13,203 13,931
Source: INE, Regional Accounts
Table 2: Summary Statistics
Census Year 1991 1996
Gender Men Women Men Women
Occupational Variables
Employed 91.0 77.0 86.3 74.2
Type of work: % Entrepreneurs 6.0 3.6 7.3 4.8
Type of work: % Self-Employed 11.7 11.4 13.0 10.4
Type of work: % Temporary Workers 22.9 34.7 24.8 32.1
Occupation: % in White Collar 30.1 49.8 38.0 60.7
Activity: % in Trade 27.0 27.0 28.0 28.2
Activity: % in Services 15.0 43.6 17.0 44.4
Activity: % in Government and Education 5.7 11.2 8.3 14.6
Catalan Knowledge
Basic: Reading or Speaking 69.8 72.7 59.3 49.8
Intermediate: Reading and Speaking 19.9 17.5 26.8 27.0
Advanced: Writing 10.3 9.8 13.9 23.2
Socio-Demographic Variables
Age 42.4 42.5 44.3 41.5
Years of Education 6.4 5.7 7.3 7.7
% Married 82.3 85.6 81.2 71.8
% Normalized 0.8 0.6 2.2 3.6
% Residence in Lleida 3.1 3.2 3.3 3.2
% Residence in Girona 6.5 5.9 6.9 7.2
% Residence in Tarragona 7.8 7.3 8.7 8.4
Years Since Migration 25.0 24.8 28.4 26.8
% arrived age <10 28.6 35.4 32.0 38.0
% Born in Andalusia 64.0 62.4 63.0 57.9
% Born in Valencia-Balearics 3.4 3.8 3.5 4.3
%BorninFranja 1.2 1.4 1.2 1.5
Municipality Variables:
% Advanced Catalan Knowledge 37.4 37.5 43.9 44.5
% Born in Catalonia 63.9 64.0 65.4 65.8
% Work in Services 51.8 52.1 58.1 59.1
Occupational Selection in Multilingual Markets 25
Table 3: Language Effects on Functional Occupational Selection
Standard errors in small fonts
Language Skills Intermediate: Reading and Speaking Advanced: Writing
Census Year 1991 1996 1991 1996
Gender Men Wom. Men Wom. Men Wom. Men Wom.
Employed
Probit 3.06 8.17 5.08 6.80 2.03 7.94 2.45 6.05
1.58 1.34 2.20 1.39 1.12 1.27 1.15 1.33
Biprobit 1.88 9.67 2.15 1.70 1.15 12.03 1.79 3.54
0.91 1.79 0.82 0.37 0.57 2.74 0.79 1.13
ρ0.068 0.155 0.058 0.031 0.034 0.154 0.040 0.057
0.018 0.013 0.017 0.019 0.024 0.017 0.022 0.023
χ215.06 150.12 12.20 2.64 2.11 83.43 3.36 6.29
Entrepreneur
Probit 1.63 0.60 1.50 0.48 0.76 0.47 0.95 -0.19
1.03 0.36 0.84 0.33 0.48 0.28 0.52 0.13
Biprobit 2.48 0.47 2.18 0.74 0.72 0.38 1.25 -0.35
0.90 0.21 0.70 0.26 0.28 0.16 0.44 0.14
ρ0.121 0.077 0.102 0.054 0.029 0.050 0.047 -0.023
0.019 0.031 0.020 0.034 0.026 0.040 0.026 0.039
χ240.65 5.95 24.78 2.55 1.30 1.43 3.25 0.32
Entrepreneur and Self-Employed
Probit 2.93 2.09 4.23 0.87 -0.02 1.16 1.27 -0.73
1.23 0.93 1.55 0.43 0.01 0.51 0.45 0.35
Biprobit 5.13 2.05 3.36 -0.34 1.45 1.25 2.46 -2.80
1.33 0.59 0.79 0.11 0.44 0.38 0.79 1.20
ρ0.114 0.098 0.075 -0.010 0.027 0.048 0.044 -0.073
0.014 0.019 0.015 0.024 0.019 0.026 0.020 0.028
χ264.96 24.96 23.60 0.17 1.92 3.38 4.81 6.83
Permanent Worker
Probit 5.92 4.59 6.55 5.49 4.11 2.83 3.24 4.70
2.16 1.05 1.63 1.07 1.65 0.70 0.90 0.97
Biprobit 1.98 1.78 3.13 0.17 0.48 -1.04 0.35 1.20
0.72 0.46 0.89 0.04 0.19 0.33 0.12 0.41
ρ0.042 0.030 0.062 0.003 0.008 -0.015 0.006 0.018
0.016 0.020 0.018 0.023 0.022 0.024 0.023 0.027
χ22.11 11.51 9.89 0.23 1.79 16.31 2.71 0.04
Occupational Selection in Multilingual Markets 26
Table 4: Language Effects on Sectorial Occupational Selection
Standard errors in small fonts
Language Skills Intermediate: Reading and Speaking Advanced: Writing
Census Year 1991 1996 1991 1996
Gend e r Me n Wom. M e n Wo m. Men Wom. Me n Wom.
Occupation: White Collar
Probit 10.92 14.74 10.63 14.72 8.25 10.34 7.54 8.95
3.76 5.43 3.53 6.07 2.80 4.20 2.55 4.13
Biprobit 10.08 14.03 8.93 13.35 9.08 18.26 11.25 12.66
2.96 3.76 2.50 4.59 2.81 5.80 3.65 5.63
ρ0.200 0.228 0.169 0.237 0.145 0.236 0.169 0.195
0.014 0.020 0.015 0.022 0.019 0.025 0.020 0.027
χ2203.59 89.14 122.73 114.23 59.41 56.51 72.15 51.04
Activity: Trade
Probit 2.80 4.42 2.35 4.64 1.88 -1.01 0.10 -1.58
0.44 0.70 0.33 0.78 0.29 0.15 0.01 0.24
Biprobit 3.75 1.53 3.88 0.23 0.10 -2.65 -0.35 -6.39
0.70 0.32 0.61 0.04 0.02 0.68 0.08 1.59
ρ0.065 0.025 0.069 0.004 0.001 -0.036 -0.005 -0.104
0.014 0.021 0.014 0.020 0.018 0.025 0.019 0.024
χ222.37 34.14 22.64 0.07 0.22 25.00 0.06 18.60
Activity: Services
Probit 0.50 -2.34 2.69 -3.12 2.77 4.58 5.47 5.09
0.18 0.28 0.97 0.36 0.97 0.55 1.89 0.59
Biprobit 2.12 3.98 2.80 1.93 7.11 13.44 9.02 11.55
0.71 0.80 0.95 0.33 1.94 3.29 2.24 3.30
ρ0.058 0.057 0.074 0.031 0.147 0.155 0.180 0.158
0.016 0.019 0.017 0.020 0.020 0.023 0.020 0.023
χ212.79 27.15 18.74 2.32 51.71 16.50 77.47 47.98
Activity: Government and Education
Probit 1.00 2.97 1.88 3.55 2.63 5.43 3.54 7.58
0.72 2.81 1.25 2.23 1.76 4.75 2.20 4.66
Biprobit 1.73 2.84 1.83 4.54 5.84 8.87 7.31 12.04
1.17 2.64 1.16 3.09 2.88 5.05 3.00 5.43
ρ0.102 0.221 0.086 0.167 0.235 0.403 0.243 0.333
0.020 0.022 0.020 0.026 0.023 0.022 0.023 0.025
χ225.06 92.48 17.68 40.42 96.07 268.77 108.07 159.99
Occupational Selection in Multilingual Markets 27
Table 5: Language Effects on Functional Occupational Selection
.by Exclusion of Instruments Standard errors in small fonts
Language Skills Interm: Reading and Speaking Advanced: Writing
Census Year 1991 1996 1991 1996
Gender Men Wom. Men Wom. Men Wom. Men Wom.
Employed
Benchmark 1.88 9.67 2.15 1.70 1.15 12.03 1.79 3.54
0.91 1.79 0.82 0.37 0.57 2.74 0.79 1.13
No muni 1.64 9.76 2.18 1.82 0.84 11.97 1.97 3.71
0.79 1.60 0.82 0.38 0.41 2.39 0.84 1.11
No origin 1.91 9.31 2.30 1.72 1.22 11.81 1.95 3.65
0.92 1.60 0.87 0.37 0.60 2.47 0.84 1.12
No assimilation 1.52 9.99 1.47 2.12 1.00 12.66 1.37 4.16
0.73 1.51 0.55 0.43 0.49 2.50 0.58 1.23
Entrepreneur
Benchmark 2.48 0.47 2.18 0.74 0.72 0.38 1.25 -0.35
0.90 0.21 0.70 0.26 0.28 0.16 0.44 0.14
No muni 2.54 0.48 2.10 0.65 0.81 0.46 1.14 -0.38
0.89 0.21 0.66 0.22 0.30 0.19 0.37 0.15
No origin 2.50 0.49 2.28 0.83 0.74 0.38 1.35 -0.32
0.88 0.21 0.71 0.28 0.28 0.15 0.45 0.12
No assimilation 2.79 0.54 2.45 0.91 1.08 0.44 1.60 -0.18
0.95 0.23 0.74 0.30 0.40 0.18 0.51 0.07
Entrepreneur and Self-Employed
Benchmark 5.13 2.05 3.36 -0.34 1.45 1.25 2.46 -2.80
1.33 0.59 0.79 0.11 0.44 0.38 0.79 1.20
No muni 5.38 2.13 3.44 -0.17 1.82 1.44 2.52 -2.41
1.32 0.57 0.77 0.06 0.52 0.41 0.74 1.00
No origin 5.24 2.01 3.55 -0.05 1.62 1.25 2.65 -2.60
1.30 0.55 0.80 0.02 0.47 0.36 0.80 1.08
No assimilation 5.97 2.29 4.20 0.11 2.37 1.60 3.21 -2.45
1.40 0.59 0.91 0.03 0.68 0.45 0.94 1.01
Permanent Worker
Benchmark 1.98 1.78 3.13 0.17 0.48 -1.04 0.35 1.20
0.72 0.46 0.89 0.04 0.19 0.33 0.12 0.41
No muni 1.97 2.95 3.77 1.17 0.52 -0.25 0.77 2.08
0.70 0.74 1.04 0.27 0.20 0.07 0.25 0.64
No origin 2.06 1.80 3.25 0.29 0.60 -1.08 0.43 1.30
0.75 0.47 0.91 0.07 0.23 0.32 0.15 0.41
No assimilation 2.34 3.26 2.60 1.65 0.75 0.36 -0.06 2.61
0.83 0.79 0.71 0.38 0.28 0.10 0.02 0.79
Occupational Selection in Multilingual Markets 28
Table 6: Language Effects on Sectorial Occupational Selection
by Exclusion of Instruments. Standard errors in small fonts
Language Skills Interm: Reading and Speaking Advanced: Writing
Census Year 1991 1996 1991 1996
Gender Men Wom. Men Wom. Men Wom. Men Wom.
Occupation: White Collar
Benchmark 10.08 14.03 8.93 13.35 9.08 18.26 11.25 12.66
2.96 3.76 2.50 4.59 2.81 5.80 3.65 5.63
No muni 9.75 13.06 8.75 12.64 8.58 17.25 10.81 12.22
2.80 3.32 2.41 4.30 2.53 5.15 3.26 5.21
No origin 10.18 13.94 9.22 13.51 9.14 18.30 11.48 12.93
2.90 3.57 2.53 4.61 2.70 5.56 3.50 5.58
No assimilation 9.60 14.37 7.96 13.60 8.99 18.57 10.25 13.02
2.67 3.50 2.15 4.58 2.60 5.51 3.01 5.52
Activity: Trade
Benchmark 3.75 1.53 3.88 0.23 0.10 -2.65 -0.35 -6.39
0.70 0.32 0.61 0.04 0.02 0.68 0.08 1.59
No muni 3.33 1.10 3.18 -0.53 -0.24 -2.40 -0.94 -6.73
0.59 0.22 0.47 0.10 0.05 0.58 0.20 1.57
No origin 4.02 1.76 3.99 0.31 0.34 -2.42 -0.28 -6.31
0.71 0.35 0.59 0.06 0.08 0.59 0.06 1.48
No assimilation 4.08 1.72 3.63 0.27 0.65 -2.00 -0.15 -5.97
0.66 0.31 0.49 0.05 0.14 0.48 0.03 1.36
Activity: Services
Benchmark 2.12 3.98 2.80 1.93 7.11 13.44 9.02 11.55
0.71 0.80 0.95 0.33 1.94 3.29 2.24 3.30
No muni 2.08 3.37 2.82 1.61 6.89 12.43 8.76 10.97
0.70 0.62 0.96 0.27 1.79 2.76 1.88 2.85
No origin 2.05 3.73 2.82 1.94 7.02 13.26 9.02 11.53
0.68 0.69 0.94 0.32 1.79 3.03 1.93 3.06
No assimilation 1.25 2.48 1.65 0.33 6.18 11.92 7.31 9.66
0.42 0.41 0.55 0.05 1.55 2.63 1.50 2.46
Activity: Government and Education
Benchmark 1.73 2.84 1.83 4.54 5.84 8.87 7.31 12.04
1.17 2.64 1.16 3.09 2.88 5.05 3.00 5.43
No muni 1.83 2.77 1.83 4.50 5.86 8.48 7.14 11.82
1.24 2.60 1.16 3.07 2.82 4.71 2.75 4.96
No origin 1.70 2.79 1.82 4.55 5.79 8.76 7.24 12.03
1.14 2.59 1.15 3.06 2.75 4.73 2.76 5.00
No assimilation 1.07 2.38 0.86 3.69 4.98 7.97 5.54 10.82
0.74 2.27 0.55 2.48 2.43 4.42 2.21 4.50
Occupational Selection in Multilingual Markets 29
Table A1: Language Equation (First Stage Estimation)
Standard errors in small fonts
Language Skills Intermediate: Reading and Speaking Advanced: Writing
Census Year 1991 1996 1991 1996
Gender Men Wom Men Wom Men Wom Men Wom
Constant -3.10 -4.19 -3.54 -3.82 -1.20 -2.68 -0.94 -2.20
0.32 0.34 0.40 0.53 0.39 0.42 0.48 0.58
Schooling 27.01 35.07 32.94 37.29 32.85 36.98 34.64 38.05
×10−21.75 2.16 2.50 3.48 2.15 2.55 3.05 3.96
Schooling2-67.92 -86.33 -74.23 -91.27 -63.67 -72.15 -50.44 -73.00
×10−45.22 6.37 8.07 11.23 6.45 7.62 10.88 14.18
Age -0.17 0.28 -0.29 -0.21 -0.88 -0.35 -1.15 -1.10
×10−10.10 0.10 0.13 0.17 0.12 0.13 0.15 0.19
Age20.00 -0.04 0.01 0.00 0.11 0.04 0.12 0.10
×10−30.01 0.01 0.01 0.02 0.01 0.01 0.02 0.02
Age ×Schooling -6.39 -14.28 -12.95 -10.78 -18.70 -18.62 -22.06 -11.73
×10−43.12 3.75 3.88 5.37 3.67 4.33 4.32 5.62
Married -0.54 -1.75 0.24 -0.68 -2.31 -2.39 -1.58 -1.99
×10−10.28 0.28 0.29 0.33 0.35 0.34 0.35 0.36
Lleida resident 2.33 1.64 0.48 0.62 2.01 1.08 0.91 0.06
×10−10.57 0.57 0.62 0.88 0.72 0.72 0.74 0.92
Girona resident 2.30 1.71 1.88 1.38 1.53 1.26 2.43 0.54
×10−10.40 0.41 0.45 0.62 0.50 0.53 0.54 0.69
Tarragona resident 1.32 0.66 2.20 1.49 0.91 0.16 1.78 1.24
×10−10.36 0.38 0.37 0.53 0.46 0.49 0.44 0.58
%Mun
aEmployed -0.21 -1.15 -0.85 0.15 -1.60 -1.24 -2.12 0.68
0.59 0.62 0.54 0.72 0.76 0.79 0.68 0.82
%Mun
aServices -0.82 -0.65 -0.21 -0.25 -1.10 -1.10 -0.77 -0.62
0.09 0.09 0.10 0.14 0.12 0.12 0.13 0.16
%Mun
aBorn 2.40 2.08 2.21 1.71 3.03 2.71 3.07 1.91
in Catalonia 0.20 0.21 0.26 0.36 0.25 0.27 0.32 0.40
%Mun
aWrite 0.97 1.55 1.50 1.68 -0.32 0.04 -0.28 0.80
Catalan 0.21 0.22 0.25 0.35 0.27 0.29 0.31 0.39
Normalizedb0.56 0.49 0.35 0.27 0.52 0.59 0.23 0.19
0.11 0.12 0.09 0.11 0.12 0.12 0.09 0.11
Arrived younger -4.27 -4.79 -4.09 -3.92 -1.78 -1.83 -0.92 -1.26
than 10 0.22 0.23 0.28 0.38 0.18 0.20 0.20 0.27
YSM c0.84 0.17 0.97 0.52 0.84 0.17 0.97 0.52
0.71 0.13 0.71 0.98 0.71 0.13 0.71 0.98
Arrived younger 0.21 0.29 0.78 0.63 0.21 0.29 0.78 0.63
than 10×YSM c0.73 0.81 0.77 0.36 0.73 0.81 0.77 0.36
Born in Andalusia 0.56 0.63 0.64 0.60 0.56 0.63 0.64 0.60
0.57 0.36 0.02 0.89 0.57 0.36 0.02 0.89
Born in Valencia 0.26 0.90 0.38 0.57 0.26 0.90 0.38 0.57
-Balearics 0.90 0.42 0.95 0.40 0.90 0.42 0.95 0.40
Born in Franja 0.95 0.49 0.01 0.43 0.95 0.49 0.01 0.43
0.20 0.17 0.27 0.30 0.20 0.17 0.27 0.30
Pseudo R20.18 0.22 0.19 0.22 0.21 0.25 0.24 0.28
aPercentage in municipi;bAffected by Normalization policy; cYSM=Years Since Migration.
