L’Italia fatta in casa
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Alberto Alesina Professor of Economics Department of
Economics Harvard University e-mail: aalesina@harvard.edu with
the research assistance of Dorian Carloni Master of Science student in
Economics Bocconi
University
e-mail:
dorian.carloni@studbocconi.it
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Andrea
Ichino
Professor
of Economics e-mail:
andrea.ichino@unibo.it
Giampaolo Lecce
Master of Science student in
Economics
Bocconi University
e-mail: glecce@studbocconi.it
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In this website you
can download the data used in this book and some documentation
(feel free to contact us for
questions related to the book or to this website)
Summary
Italians
feel that the "family'' plays a crucial economic role in their society
because it produces a large amount of goods and services that contribute to the
welfare of Italians but are not considered by official statistics. This book
studies to what extent this feeling corresponds to reality.
We
use time use and wage data for Italy, US, Norway and Spain, and measure, with
different methods, the average value of individual market and home work in each
of these countries. Undoubtedly the evidence shows that Italy's position in
terms of GDP with respect to comparable countries would improve considerably if official
statistics considered home production together with production for the
market.
The
book then proceeds to highlight the other face of the substantial benefits
produced by the Italian family, represented by its costs in terms of gender
differences, social capital, educational system, labor market and the welfare
state. We show that the economic effects of the family, positive or negative,
can travel very far from the living room of a household.
In
this web site you can download the micro-data used in the book and some
documentation. We plan to extend the analysis to more countries with the goal
of producing a scientific publication on the value of home production.
Comments
and suggestions are very welcome, in particular if any mistake remains in what
we did.
Research approach
In order to estimate the value of
market and home production in the four countries that we have considered, we
proceeded in two steps.
·
First we extracted time use data from the Multinational Time Use Study (MTUS), and reclassified individuals’
reported daily activities into three main aggregates: paid work, home
production and leisure, as described below.
·
Second we assigned an economic value to those activities using wage data
obtained from representative surveys of the population in each country. Time dedicated to home production was evaluated with two methods:
·
The Opportunity Cost method, which evaluates
each hour of home production at the potential alternative wage that each
individual can receive in the market.
·
The Market Cost method, which evaluates each
hour of home production at the cost at which home services can be bought in the
market, measured as the unskilled wage.
For further
details see our book and this reference: Katharine G. Abraham and Christopher
D. MacKie (ed), Beyond the Market: Designing Nonmarket Accounts for the United
States, Washington, DC, The National Academies Press, 2005.
The final dataset
(book_4.dta) in Stata format, and the Stata code that we have used to create and
analyze this file from the original data sources can be downloaded here.
The original data
must be requested to the provider. In what follows you will find more
information on these sources..
Time
use data
The Multinational Time Use Study (MTUS)
contains cross-country comparable data on time spent by a representative sample
of the population in 41 activities during a random working day of a week. We
focused on the most recent time use data available for Italy (2002), the US
(2003), Spain (2002) and Norway (2000), and considered exclusively individuals
20 to 74 years old. We used the original data files named MTUSW552.dta and
MTUSW_53.dta (accessible to registered users at www.timeuse.org) and classified the 41 activities
in three groups, as described below.
PAID WORK
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HOUSEHOLD WORK
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LEISURE
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AV1 PAID WORK
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AV6 COOKING/WASHING UP
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AV4 SCHOOL/CLASSES
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AV2 PAID WORK AT HOME
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AV7 HOUSEWORK
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AV14 RECEIVING PERSONAL SERVICES
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AV3 SECOND JOB
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AV8 ODD JOBS
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AV15 MEALS/SNACKS
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AV5 TRAVEL TO/FROM JOB
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AV9 GARDENING
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AV16 SLEEP/NAPS
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AV10 SHOPPING
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AV17 LEISURE TRAVEL
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AV11 CHILD CARE
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AV18 EXCURSION, TRIPS
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AV12 DOMESTIC TRAVEL
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AV19 PLAYING SPORTS
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AV13 DRESS/PERSONAL CARE
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AV20 WATCHING SPORTS
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AV21 WALKS
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AV22 AT CHURCH
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AV23 CIVIC ORGANIZATIONS
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AV24 CINEMA/THEATRE
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AV25 DANCE/PARTY
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AV26 SOCIAL CLUBS
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AV27 PUBS
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AV28 RESTAURANTS
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AV29 VISITING FRIENDS
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AV30 LISTENING TO RADIO
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AV31 WATCHING TV
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AV32 LISTENING TO MUSIC
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AV34 READING BOOKS
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AV35 READING PAPERS/MAGAZINES
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AV36 RELAXING
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AV37 CONVERSATION
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AV38 ENTERTAINING FRIENDS
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AV39 KNITTING/SEWING
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AV40 PASTIMES/HOBBIES
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AV41 UNKNOWN ACTIVITIES
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A more detailed description of each
activity and of the weights (the variable: PROPWT) needed to go from the sample
to the original population, can be found at http://www.timeuse.org/mtus/documentation/
Time use
data do not contain information on individual wages. Therefore, to attribute
values to the time spent in the different activities we had to get wage
information from different sources as described below.
Wage
data
Wage data were obtained separately
for each country, as described below. For each country we created cells of
individuals defined by sex, age and level of education. For each of these cells
we computed the average net and gross hourly wages. We assigned to each
individual in the time use dataset the average net and gross hourly wage of the
corresponding cell.
To convert monetary values for the US and
Norway into Euros, we used exchange rate information provided by the OECD in
the publication «OECD
Factbook 2008: Economic, Environmental and Social Statistics»,
Italy
We used data for 2002 and 2004 extracted from
the “Survey on
Household Income and Wealth (SHIW)” collected by the Bank of Italy. The
data are available for download at http://www.bancaditalia.it/statistiche/indcamp/bilfait. The survey contains yearly net
earnings and hours worked for a sample of the Italian population. With this
information we computed net hourly wages for Italian non-self-employed workers
aged 20 to74, by sex age and education. Maria Rosaria Marino of the Bank of
Italy kindly provided us with data on workers’ gross wages. All computations
were performed using the weight PESOFL, to obtain representative statistics for
the Italian population.
USA
We used data from the 2003 “Current Population Survey (CPS)”,
conducted by the Bureau of the Census for the Bureau of Labor Statistics. The
data are freely available on the NBER website at http://www.nber.org/data/current-population-survey-data.html . Particularly useful for our
analysis was the 2003 CPS March Supplement, which contains extensive
information on annual gross wages, taxes, social contributions (Federal Income
Contributions Act (FICA) tax) and hours worked. We considered only employed
workers 20 to74 years old and created a dataset with hourly net and gross wages
by sex, age and level of education. All computations were performed using the
weight MARSUPWT, included in CPS March Supplement 2003, to obtain
representative statistics for the US population.
Spain
We used data from the 2002 “Encuesta
de estructura salarial (EES)” conducted by the Spanish “Instituto Nacional de Estatistica”, which were
kindly made available to us by Prof. Samuel Bentolila
of CEMFI. The dataset contains extensive
information on monthly gross wages, social contributions, taxes and hours
worked in Spain. We calculated net and gross hourly wages by sex, age and level
of education and considered only individuals 20 to 74 years old.
Norway
We used Census data collected by
“Statistics Norway” in 2001. In the case of this country we could not access
individual data, for privacy reasons. We therefore asked and received average
gross yearly earnings by sex, age and level of education, computed by
“Statistics Norway”. We are grateful to Prof. Oddbjorn Raaum of the Ragnar Frisch Center for Economic
Research who put us in touch with “Statistics Norway”. We then estimated
annual net earnings using the information on tax rates and social security
contributions published on the “OECD
Tax Database”. Finally hourly wages were computed using information on the
average annual hours worked in Norway as reported by the OECD.
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