GONZAGA e GUANZIROLI - 2019

Prévia do material em texto

Full Terms & Conditions of access and use can be found at
https://www.tandfonline.com/action/journalInformation?journalCode=rael20
Applied Economics Letters
ISSN: 1350-4851 (Print) 1466-4291 (Online) Journal homepage: https://www.tandfonline.com/loi/rael20
Returns to experience across tasks: evidence from
Brazil
Gustavo Gonzaga & Tomás Guanziroli
To cite this article: Gustavo Gonzaga & Tomás Guanziroli (2019): Returns to experience across
tasks: evidence from Brazil, Applied Economics Letters, DOI: 10.1080/13504851.2019.1593927
To link to this article: https://doi.org/10.1080/13504851.2019.1593927
Published online: 19 Mar 2019.
Submit your article to this journal 
Article views: 29
View Crossmark data
https://www.tandfonline.com/action/journalInformation?journalCode=rael20
https://www.tandfonline.com/loi/rael20
https://www.tandfonline.com/action/showCitFormats?doi=10.1080/13504851.2019.1593927
https://doi.org/10.1080/13504851.2019.1593927
https://www.tandfonline.com/action/authorSubmission?journalCode=rael20&show=instructions
https://www.tandfonline.com/action/authorSubmission?journalCode=rael20&show=instructions
http://crossmark.crossref.org/dialog/?doi=10.1080/13504851.2019.1593927&domain=pdf&date_stamp=2019-03-19
http://crossmark.crossref.org/dialog/?doi=10.1080/13504851.2019.1593927&domain=pdf&date_stamp=2019-03-19
ARTICLE
Returns to experience across tasks: evidence from Brazil
Gustavo Gonzagaa and Tomás Guanzirolib
aEconomics Department, Pontifical University of Rio de Janeiro, Rio de Janeiro, Brazil; bEconomics Department, University of California Los
Angeles, Los Angeles, USA
ABSTRACT
Using a rich Brazilian panel dataset and an occupation-task mapping, we investigate whether
returns to experience depend on the types of jobs performed by workers. We find that returns to
experience in non-routine tasks, especially returns to analytical tasks, are much larger than
returns to routine tasks. This gap increases with schooling, suggesting that schooling and non-
routine tasks are complementary in the human capital production function. These are important
findings for developing countries similar to Brazil, where approximately 70% of workers’ tasks are
routine.
KEYWORDS
Task-approach; human
capital; returns to
experience; life-cycle wage
profile
JEL CLASSIFICATION
J20; J24; J31
I. Introduction
Recent studies have shown that experience-wage
profiles in rich countries are twice as steep as in
poor countries and that one-third of these differ-
ential returns are explained by educational com-
position (Lagakos et al. 2018). Standard human
capital theory predicts that more-educated work-
ers accumulate more human capital at work,
which would translate into higher productivity
and wages.
In this paper, we extend this notion by investi-
gating whether returns to on-the-job learning
depend on the types of jobs performed by work-
ers, as suggested by Rosen (1972) and Heckman,
Lochner, and Cossa (2002). We test whether jobs
intensive in non-routine tasks lead to more
human capital accumulation than jobs predomi-
nantly characterized by routine tasks.
Autor, Levy, and Murnane (2003) define non-
routine tasks as those involving problem solving
and complex communication activities, while rou-
tine tasks are those accomplished by following
mechanical ‘rules’ and are therefore more easily
replaceable by machines. We extend this idea by
suggesting that routine tasks, given their repetitive
nature, provide less human capital accumulation
than non-routine tasks. We conjecture that non-
routine tasks should foster a more productive
process of on-the-job learning, given the higher
frequency with which workers engage in new
activities. Non-routine tasks with a strong analy-
tical component should lead to more human capi-
tal accumulation than other types of tasks.
Our empirical strategy makes use of
a classification proposed by Spitz-Oener (2006)
that maps each occupation into routine and non-
routine tasks. This allows us to decompose the
total labour market experience of workers into
experience in routine and non-routine tasks
using a rich Brazilian panel dataset. We then esti-
mate the returns to both types of task experience
instead of returns to general job-market experi-
ence, as in traditional log-wage equations.1 We
confirm our conjecture by showing that experi-
ence-wage profiles are steeper for workers who
perform more non-routine tasks in comparison
with workers who perform more routine tasks.
We also find that the gap between returns to non-
routine and routine experience increases with
schooling. These are important findings for
a developing country such as Brazil, where
approximately 70% of workers’ tasks are routine.2
Although it has a different focus, our study is
related to the recent literature that emphasizes the
CONTACT Tomás Guanziroli tomguanzi@gmail.com Economics Department, University of California Los Angeles, Los Angeles, United States
1We should note that our measure of task experience is different from the one used in Gathman and Schonberg (2010).
2Our findings should also help explaining cross-country differences in experience-wage profiles, as most workers in less-developed countries perform routine
and manual tasks (World Bank 2012).
APPLIED ECONOMICS LETTERS
https://doi.org/10.1080/13504851.2019.1593927
© 2019 Informa UK Limited, trading as Taylor & Francis Group
http://www.tandfonline.com
http://crossmark.crossref.org/dialog/?doi=10.1080/13504851.2019.1593927&domain=pdf
role of tasks in the accumulation of general and
specific human capital (Poletaev and Robinson,
2008; Gathman and Schonberg, 2010). In particu-
lar, our results have important implications for
wage inequality. We show that returns to non-
routine experience are 5.2 p.p. higher than returns
to routine experience. This implies that after 5
years, workers continuously employed in fully
non-routine jobs would earn, on average, 26%
more than workers continuously employed in
fully routine jobs. Finally, our results could help
improve the design of training programs in devel-
oping countries. These programs should focus on
non-routine skills of young workers, especially on
their analytical skills.
II. Data and task decomposition
We use the Brazilian official registry of workers,
RAIS (from Relação Anual de Informações Sociais),
a very large restricted-access administrative record
collected by the Brazilian Ministry of Labour. It
contains information on the universe of formal
workers, such as wages, hours worked, occupation,
schooling, gender, age, tenure, and dates of hiring
and separation. Data on firms include region, sec-
tor and size. The data are transformed into a panel
by using unique worker and firm identifiers.
Because our data start in 2003, to fully measure
experience, we focus on a sample of all Brazilian
male workers who were 18 years old or less in
2003 and who had a formal job for at least two
years between 2003 and 2010. After applying some
filters to the data,3 we have information on
2,549,775 young workers. We construct detailed
measures of actual experience for all workers by
adding their accumulated tenure in their current
and all previous formal jobs.4
We use the task classification proposed by
Spitz-Oener (2006) to map each Brazilian
occupation in the Brazilian Occupation Code
(CBO, from Classificação Brasileira de Ocupações)
into non-routine tasks (analytical, interactive and
manual) and routine tasks (cognitive and man-
ual). CBO has a full description of the task content
of each 4-digit occupation. This allows us to
decompose the task content of 275 occupations,
which represent 87% of Brazilian formal workers.
The methodology measures the importance of
each task in each occupation such that the sum
of the task-intensity measures within an occupa-
tion equals 100%.5 Each occupation should thus
be a bundle of tasks.
Table 1 illustrates our mapping by showing the
average task composition of all 1-digit occupa-
tions. Each task measure by 1-digit occupation is
the weightedaverage of the task composition at
the 4-digit level, with weights given by the propor-
tion of workers in each 4-digit occupation. The
Table 1. Task composition of one-digit occupations (%).
Non-
Routine
Non-
Routine Routine Routine
Non-
Routine
1-digit
occupation Analytic Interactive Cognitive Manual Manual
Managers 22.3 64.4 13.3 0 0.1
(8.5) (8.5) (9.9) (0.0) (0.9)
Professionals 38.1 40.3 18.6 2.7 0.3
(14.9) (14.6) (10.3) (6.0) (2.0)
Technicians 31.2 14.4 49.7 4.7 0
(17.3) (11.6) (17.6) (10.4) (0.1)
Clerical 1 16.6 55.5 25 1.9
(3.8) (7.8) (12.5) (11.6) (5.3)
Service and Sales 1.1 23.7 49.4 22.1 3.7
(4.7) (12.9) (16.8) (16.4) (5.7)
Machine
Operators
4.3 3.6 18.7 63 10.3
(8.2) (7.2) (19.3) (20) (9.5)
Repair and
Maintenance
12.2 0 49.3 19.7 18.7
(10) (0.0) (21.8) (9.9) (15.5)
Note: The table presents the task decomposition of each 1-giti occupation.
The means are weighted averages of 4-digit occupations task composi-
tion. Horizontal sums of the means approximate to 100%. Standard
deviations are in parenthesis. Total sample size is 9,392,906 observations
from 2,49,775 individuals. RAIS, 2003–2010.
3We drop workers employed in the agriculture, military and public sectors. We do not include workers with more than one simultaneous job. To avoid
measurement error, we exclude workers with any variation in schooling across years. For workers with college education, we only include those who were
22 years old or less in 2003.
4Our measure of actual experience does not include experience in the informal sector.
5Formally, we use the task classification to decompose each observation of a worker’s total experience in RAIS data into the five task-experience measures
(and the two more aggregated task-experience measures, routine and non-routine), according to the equation below:
Expki;t ¼
X
o
OccTeno;i�Taskko
� �
where, for each worker i in period t, experience in task k is defined as the cumulative sum (across four-digit occupations) of current and previous
tenure in each occupation o multiplied by the task k content of the occupation. Note that the sum of Expki;t across the k tasks equals total
experience, Expi;t .
2 G. GONZAGA AND T. GUANZIROLI
table shows that technicians, for example, per-
form, on average, a larger proportion of routine
cognitive tasks (49.7%) than analytic (31.7%),
interactive (14.4%) and routine manual (4.7%)
tasks.
Figure 1 describes how average task intensity
varies by years of schooling using data from
a recent Brazilian annual household survey
(PNAD). It shows that the share of routine tasks
is very high (approximately 67%); it is almost
constant in occupations with workers having
between 0 and 10 years of schooling; and it
drops very sharply with schooling, reaching
approximately 37% for workers who have com-
pleted college.
III. Empirical results
We estimate three specifications of a standard
log wage equation. In the first specification
(equation 1), we regress log wages on total
experience, industry θxð Þ, occupation μo
� �
, year
δTð Þ and worker zið Þ fixed effects.6 In the second
specification (equation 2), we replace total
experience Expi;t with ExpRi;t and Exp
N
i;t; which
measure experience in routine and non-routine
tasks, respectively.
ln Wi;t ¼ β0 þ β1Expi;t þ θx þ μo þ zi þ δT
þ ui;t (1)
ln Wi;t ¼ β0 þ βRExpRi;t þ βNExpNi;t þ θi þ μo
þ zi þ δT þ ui;t (2)
In a third specification, we further split the mea-
sures of routine and non-routine task experience
into the 5-task experience classification: routine
cognitive, routine manual, non-routine analytic,
non-routine interactive and non-routine manual.
In all specifications, we use time and worker fixed
effects. This strategy captures the average difference
in returns to experience across tasks under the fol-
lowing identifying assumptions: (a) workers do not
select into occupations based on time-varying unob-
servable variables; and (b) skills are perfectly trans-
ferrable across occupations. We include industry
and occupation fixed effects to account for differen-
tial returns within these dimensions.7
20
40
60
80
)
%(
ksaT
00 2 4 6 8 10 12 14 16 18
Years of schooling
Routine Non-Routine
Figure 1. Task composition by schooling group (%).
Source: PNAD, 2013. Sample of workers from 18 to 65 years old.
6Note that in our sample, an observation unit is constituted of a worker’s log wage (ln Wi;tÞ and her task k experience (Expki;tÞ at any time of the year.
Workers may have more than one observation per year if they changed jobs during that year. Note that observed variables that do not change over time,
such as schooling, gender and race, are picked up by individual fixed effects.
7Note that occupation fixed effects also account for task composition within an occupation.
APPLIED ECONOMICS LETTERS 3
For sake of brevity and due to a straightforward
interpretation of parameters we will only describe
the results of the specification without industry
and occupation fixed effects.8 However, note that
the estimates have little variation across specifica-
tions. The first two columns in Table 2 presents
the estimated coefficients for the traditional log
wage equation (1). We find that increasing total
experience by one year increases the wages of
young workers by 10.3% in Brazil. Note that the
magnitude of this coefficient is larger than those
usually reported in the literature, mainly because
our sample is composed solely of young workers.
Columns 3 to 6 in Table 2 present the task-
approach specification results, which differ by type
of experience, as expected. Column 3 shows that
increasing non-routine experience by one year
leads to a 13.9% increase in wages, a much larger
return when compared with the return to routine
experience (8.7%). This means that working an
additional year in non-routine tasks yields wage
increases 5.2 p.p. higher than working an
additional year in routine tasks.
Column 5 reports results for the specification
with the 5 task experience measures. The results
are consistent with the notion that tasks involving
more intellectual work provide more on-the-job
learning opportunities, which should lead to more
human capital accumulation. Estimated returns to
analytical experience, for example, are very large
(18.7%), well above returns to interactive experi-
ence (10.2%) or any other task experience. Within
routine tasks, we find that cognitive experience
presents larger returns than manual experience
(9.9% and 8.2%, respectively). Routine cognitive
tasks have even higher returns to experience than
non-routine manual tasks (9.2%).
Figure 2 presents estimates of experience
returns for the same specification as in column 3
of Table 2 run separately by schooling groups. We
find larger returns to non-routine experience
when compared with returns to routine experi-
ence for all groups of workers. The figure also
shows that the gap between returns to non-
routine and routine experience significantly
increases with schooling. This suggests that
Table 2. Log-wage equation. traditional and the task approach.
Traditional Task-Approach
(1) (2) (3) (4) (5) (6)
Experience 0.103*** 0.095***
(0.001) (0.001)
Task Experience:
Routine 0.087*** 0.080***
(0.001) (0.001)
Cognitive 0.099*** 0.098***
(0.001) (0.001)
Manual 0.082*** 0.070***
(0.001) (0.001)
Non-Routine 0.139*** 0.128***
(0.001) (0.001)
Analytic 0.187*** 0.176***
(0.001) (0.001)
Interactive 0.102*** 0.085***
(0.001) (0.001)
Manual 0.092*** 0.091***
(0.001) (0.001)
Occupation FE X X X
Industry FE X X X
Year FE X X X X X X
Worker FE X X X X X X
Observations 9,392,906 9,392,906 9,392,906 9,392,906 9,392,906 9,392,906
R-squared 0.878 0.884 0.879 0.884 0.879 0.884
Number of workers 2,549,775 2,549,775 2,549,775 2,549,775 2,549,775 2,549,775
Note: Each column reports results from a regression of log real wages on some type of experience. Regressions include year and worker fixed effects
and may include 4-digit industry and 4-digit occupation fixed effects. Robust standard errors are in parenthesis (*** p <01). RAIS, 2003–2010.
8In our main specification, we interpret coefficients associated with task experience as the speed at which wage grows for workers employed in each task. In
the specification with industry and occupation fixed effects, we interpret coefficients associated to task experience as the average wage growth conditional
on being in a certain industry and occupation weighted by a function of industry and occupational composition.
4 G. GONZAGA AND T. GUANZIROLI
schooling and more-complex tasks are comple-
ments in the human capital production function.
In sum, our findings confirm our conjecture
that a job’s attributes have an impact on human
capital accumulation and, consequently, on esti-
mated returns to experience. Non-routine tasks,
especially analytic tasks, have much larger returns
than routine tasks. This differential in returns to
experience is larger for more-educated workers.
These are important findings for improving the
design of training programs in a developing coun-
try such as Brazil, where approximately 70% of
worker tasks are classified as routine tasks.
Acknowledgments
We thank Bruno Funchal for sharing the initial task-
occupation mapping. We thank Juliano Assunção,
Leonardo Rezende, Rodrigo Soares, Gabriel Ulyssea,
Eduardo Zilberman, and participants in seminars at IPEA
and the 4th LACEA Labor Network Conference for valuable
comments and suggestions.
Disclosure statement
No potential conflict of interest was reported by the authors.
References
Autor, D., F. Levy, and R. J. Murnane. 2003. “The Skill
Content of Recent Technological Change: An Empirical
Exploration.” The Quarterly Journal of Economics 118 (4):
1279–1333. doi:10.1162/003355303322552801.
Funchal, B., and J. Soares. 2013. "Understanding Demand for
Skills after Technological Trade Liberalization". Fucape
Working Papers 40. Fucape Business School.
Gathmann, C., and U. Schonberg. 2010. “How General Is
Human Capital? A Task-Based Approach.” Journal of
Labor Economics 28 (1): 1–49. doi:10.1086/649786.
Heckman, J. J., L. Lochner, and R. Cossa. 2002. “Learning-By
-Doing Vs. On-The-Job Training: Using Variation
Induced by the EITC to Distinguish between Models of
Skill Formation”. NBER Working Papers 9083.
Lagakos, D., B. Moll, T. Porzio, N. Qian, and T. Schoellman.
2018. “Life-cycle Wage Growth across Countries.” Journal
of Political Economy 126 (2): 797–849. doi:10.1086/696225.
Maxim, P., and C. M. Robinson. 2008. “Human Capital
Specificity: Evidence from the Dictionary of
Occupational Titles and Displaced Worker Surveys,
1984–2000.” Journal of Labor Economics 26 (3): 387–420.
doi:10.1086/588180.
Rosen, S. 1972. “Learning and Experience in the Labor
Market.” Journal of Human Resources 7 (3): 326–342.
doi:10.2307/145087.
Spitz-Oener, A. 2006. “Technical Change, Job Tasks, and
Rising Educational Demands: Looking outside the Wage
Structure.” Journal of Labor Economics 24 (2): 235–270.
doi:10.1086/499972.
World Bank. 2012. World Development Report 2013: Jobs.
Washington, DC: World Bank. doi: 10.1596/978-0-8213-
9575-2.
4%
6%
8%
10%
12%
14%
16%
Elementary or less Middle School High School or more
Routine Non-Routine
n
r
u
t
e
r
e
c
n
ei
r
e
p
x
e
o
t
Figure 2. Returns to routine and non-routine experience by schooling group.
Note: The figure summarizes the results of four regressions run separately for each schooling group. The specification is the same as in column 2 of
Table 2. RAIS, 2003–2010.
APPLIED ECONOMICS LETTERS 5
https://doi.org/10.1162/003355303322552801
https://doi.org/10.1086/649786
https://doi.org/10.1086/696225
https://doi.org/10.1086/588180
https://doi.org/10.2307/145087
https://doi.org/10.1086/499972
https://doi.org/10.1596/978-0-8213-9575-2
https://doi.org/10.1596/978-0-8213-9575-2
Online Appendix
The task measure was created using the occupational
descriptions of the Brazilian classification of occupations
(Classificação Brasileira de Ocupações – CBO), also available
from the Brazilian Ministry of Employment and Labor. This
database describes occupations by their task content. We
have the task content of 275 four-digit occupations, which
represents 87% of workers’ observations.9 Differently from
the task measures available in the US (O-Net and DOT), or
in Germany (BIBB), our task data characterizes the intensity
of use of each task in an occupation such that the sum of the
task measures within an occupation equals 100%.
The construction of the task measure proceeded as fol-
low. First, the occupations descriptions provide a few num-
ber of activities for each occupation. Then, using Appendix
Table A1, we classify activities into five types of tasks (non-
routine analytical, non-routine interactive, routine cognitive,
routine manual and non-routine manual). For example, the
activity ‘analyze the economic environment’ in the econo-
mist occupation is classified as an analytical task, since it
concerns the ‘Analyzing’ activity. Next, each task measure
in an occupation is calculated as the ratio between the
number of that task’s activities and the total number of
activities in that occupation. For example, the economist
occupation has seven analytical activities out of ten activ-
ities. Hence, we stipulate that 70% of an economist time is
spent performing analytical tasks. In this study, we also use
the more aggregated task definitions: routine and non-
routine. Their use is simple: routine tasks include the rou-
tine cognitive and manual tasks, and non-routine tasks
include non-routine manual, analytical and interactive tasks.
Table A1. Correspondence between tasks and descriptors.
As proposed by
Spitz-Oener (2006) Correspondence in CBO*
Non-Routine Analytic Researching, Investigating, Analyzing, Examining, Studying, Evaluating, Planning, Budgeting, Making diagnosis, Judging.
Non-Routine
Interactive
Negotiating, Practicing Law, Coordinating, Leading people, Teaching, Training, Spreading knowledge, Instructing, Selling,
Marketing.
Routine Cognitive Calculating, Programming, Transforming, Bookkeeping, Recording, Measuring, Verifying.
Routine Manual Operating, Distributing, Transporting, Equipping, Assembling.
Non-Routine Manual Repairing, Renovating, Serving, Accommodating, Cleaning.
Source: Funchal and Soares (2013). Note: The right column provides the activities that should be included in each task. *CBO is the Brazilian classification of
occupations. It embraces four-digit occupational codes and their descriptions.
9The CBO structure is coded into 614 four-digit occupations. Task data comes from Funchal and Soares (2013).
6 G. GONZAGA AND T. GUANZIROLI
	Abstract
	I. Introduction
	II. Data and task decomposition
	III. Empirical results
	Acknowledgments
	Disclosure statement
	References
	Online Appendix