Relative Prices and Sectoral Productivity†
Margarida Duarte
University of Toronto
Diego Restuccia
University of Toronto
August 2012
Abstract
The relative price of services rises with development. A standard interpretation of this fact
is that cross-country productivity differences must be larger in manufacturing (tradables)
than in services (non-tradables). Using detailed data from the International Comparison
Program we disaggregate the service sector and show that the behaviour of the relative
price is markedly different across two broad classifications of services: traditional services
such as government, health, and education featuring a rising relative price with development
and non-traditional services such as communication, transportation, insurance and financial
services featuring a declining relative price. Moreover, there is a structural transformation
within services whereby the share of non-traditional services rises with development. What
are the productivity implications of disaggregating the service sector? Using a simple development accounting framework and a model of the structural transformation within services,
we find that cross-country productivity differences are large in non-traditional services, at
least as large as those in manufacturing. Development requires also an emphasis on solving
the productivity problem in non-traditional services in poor countries.
JEL classification: O1, O4, E0.
Keywords: Productivity, services, traditional, non-market, structural transformation.
†
Very preliminary and incomplete. Please do not quote. We thank David Lagakos and Xiaodong Zhu
for comments and suggestions. All errors are our own. We gratefully acknowledge the support from the
Connaught Fund at the University of Toronto (Duarte) and the Social Sciences and Humanities Research
Council of Canada (Restuccia). Contact Information: Department of Economics, University of Toronto,
150 St. George Street, Toronto, ON M5S 3G7, Canada. E-mail: margarida.duarte@utoronto.ca and
diego.restuccia@utoronto.ca.
1
1
Introduction
There are large differences in incomes across countries. For instance, measured at international prices, gross domestic product (GDP) per capita in the richest 10 percent of countries
in the world is 40 times that of the poorest 10 percent of countries in 2005. Most studies
agree that labor productivity (and total factor productivity) differences across countries are
the primary factor explaining differences in income across countries. A large literature following Kuznets (1966) has emphasized the importance of the sectoral allocation of factors
and productivity in understanding aggregate outcomes.
The cornerstone limitation of a proper quantitative assessment of the sectoral structure is the
lack of comprehensive sectoral productivity data for a large number of countries. A standard
approach in the literature to circumvent the data limitations is to use sectoral price and
expenditure data to back out sectoral productivity. From this literature, a well-known fact
is that the relative price of services rises with development and the standard interpretation
of this finding is that cross-country differences in productivity are larger in manufacturing
than in services, e.g.: Balassa-Samuelson, Kravis, Heston, and Summers (1983), Hsieh and
Klenow (2007), Herrendorf and Valentinyi (2011). We argue that heterogeneity in the service
sector is important for sectoral productivity implications.
The service sector is very heterogeneous. We disaggregate services between traditional and
non-traditional. We find that the relative price of traditional services rises with income
while that of non-traditional services falls with income. And, as income grows, there is a
structural transformation within the service sector. Keeping the U.S. shares of traditional
and non-traditional services constant across countries implies a relative price of services that
does not vary with income. These facts suggest that a development strategy based solely in
improving productivity primarily in the manufacturing sector may be limited in its effects.
To assess the importance of disaggregating the services sector for productivity implications,
2
we first use a development accounting framework which imposes very little structure by using
all pieces of the data available (expenditures and prices). The analysis reveals that labor
productivity differences in non-traditional services are as large as those in manufacturing and
much larger than those in traditional services. Development is associated with a reallocation
of real expenditures to non-traditional services. To understand more deeply what drives this
process, we then develop a model of the structural transformation that includes traditional
and non-traditional services. The model reinforces the finding that productivity differences
are large in non-traditional services.
The paper is organized as follows. In the next section, we elaborate on a set of facts about
sectoral structure and prices from the ICP data. Section 3 performs a simple development
accounting as a first pass to assess the productivity implications of disaggregating services.
In Section 4, we lay out a standard model of structural transformation to study the reallocation of real expenditures to non-traditional services and study the sectoral and aggregate
implications, we calibrate the model and present the main results. We conclude in Section
5.
2
Facts
We use detailed price and expenditure data from the International Comparisons Program
(ICP) for 2005. The data is the basis for the construction of the widely-used Penn World
Table (PWT) where comparable measures of gross domestic product are available for a large
number of countries and years. The ICP data reports information on 129 detailed expenditure categories (broadly divided among consumption, investment, and government) for 146
countries. We restrict our sample of countries to those with more than 1 million population,
leaving 130 countries. The country coverage represents well the entire world distribution of
income per capita. The data contains information on price indices of individual expendi-
3
ture categories and nominal expenditures (expenditures on individual categories in units of
domestic prices). From these data, nominal expenditures, real expenditures (in units of an
average international price which is common across countries), and prices can be constructed
for arbitrary aggregates such as consumption, investment, tradables, services, among others.1
From the individual categories we construct an aggregate category of services.2 While every
expenditure category may contain a service component, perhaps as an intermediate input,
aggregate expenditure in services is a close approximation to total services in the economy.
We document the behavior of the relative price of services and the expenditure share of
services (nominal and real) across countries. A summary of the data is reported in Table 1.
We emphasize the following facts:
(1) The price of services relative to that of GDP increases with income per capita. That
is, the relative price of services is higher in rich countries compared to poor countries.
We report the relative price of services against GDP per capita across countries in
Figure 1. On average, the richest 5 percent of countries have a relative price of services
which is a factor of 2 of the price in the poorest 5 percent of countries. This is a well
known fact that has been emphasized in the related literature (see e.g. Kravis, et al.,
Summers and Heston, among many others).
(2) The (nominal) share of services in GDP increases with income per capita, that is, the
share of expenditures in services is larger in rich compared to poor countries. See
Figure 2. While rich countries dedicate about 50 percent of their GDP to service
expenditures, poor countries spend only 30 percent. This fact is also relatively well
know. However, what is less known is whether the expenditure share in services rises
with income because there are more purchases of services in rich countries or because
the prices of those services are higher in rich countries. This observation leads to our
1
2
See the data appendix for more details.
In the appendix we document a detailed list of individual categories included as services.
4
next fact.
(3) The real share of services in GDP, where real refers to being measured at international
prices, does not vary systematically with income per capita. That is, rich and poor
countries tend to spend the same fraction of their real expenditures in services, at an
average of around 50 percent for all countries. See Figure 3.
Table 1: Relative Prices and Expenditure Shares
Deciles
1
2
3
4
5
6
7
8
9
10
Ratio 10/1
Ratio 9/2
RGDPpc Ps /P
0.02
0.56
0.03
0.62
0.05
0.56
0.09
0.60
0.13
0.63
0.19
0.67
0.27
0.65
0.41
0.75
0.66
0.89
0.89
0.96
49.3
20.6
1.70
1.44
sQs sEs
0.48 0.32
0.52 0.38
0.45 0.28
0.49 0.33
0.50 0.37
0.51 0.41
0.51 0.39
0.47 0.41
0.48 0.51
0.41 0.46
0.85 1.45
0.92 1.32
Note: Countries are ranked according to GDP per capita and
divided among deciles. For each decile we report: (1) GDP per
capita relative to that of the United States (RGDPpc), (2) the
price of services relative to the price of GDP (Ps /P ), (3) the
real expenditure share of services to GDP (sQs ), (4) the nominal
expenditure share of services to GDP (sEs ).
The set of categories that comprises services is very heterogeneous. For instance, it comprises categories such as hospital services, household services, insurance, among many others.
The detailed ICP price data also reflects this heterogeneity. In particular, we find that the
behavior of the price of individual service categories with respect to the price of GDP can
differ markedly across categories. To summarize this heterogeneity, we divide services into
two broad categories. The first broad category, which we call traditional services, comprises
5
the government and all service categories in personal consumption expenditures for which
its relative price increases with income across countries. The main components of traditional services are government, actual and imputed rents for housing, health services, and
education.3 The second broad category, which we call non-traditional services, comprises all
service categories in personal consumption expenditures for which its relative price declines
with income across countries. The main components of non-traditional services are transport
services, communication services, and financial and related services.4 Table 2 summarizes
the price and expenditure implications of these two broad categories within services.5
We emphasize the following facts:
(4) The relative price of traditional services increases with income while the relative price
of non-traditional services declines with income. In addition, traditional services are
cheap while non-traditional services are expensive (relative to the price of GDP). See
Figures 4, 5, and 6.
(5) The real share of traditional services (in total services) declines with income while the
real share of non-traditional services increases with income. This reallocation between
the two broad service categories is substantial, with poor countries allocating most of
the real service expenditure to traditional services whereas rich countries allocate 40
percent of the real service expenditure to non-traditional services. See Figure 7.
Richer countries, compared to poorer countries, tend to allocate a higher share of real expenditures to non-traditional services, which are relatively more expensive than traditional
services. This reallocation in development may explain the rising relative price of aggregate
3
These four components represent at least 80 percent of real expenditures in traditional services in all
countries. The cross-country average is 95 percent.
4
These components represent, on average, 55 percent of real expenditures in non-traditional services.
5
We use the terminology traditional and non-traditional to broadly characterize the main distinction
across services categories but the same categories can be broadly characterized as market vs.non-market
services, and broadly speaking our facts are robust to other characterizations of the service sector.
6
Table 2: Relative Prices and Expenditure Shares — Services
Deciles
1
2
3
4
5
6
7
8
9
10
Ratio 10/1
Ratio 9/2
RGDPpc PsT /P
0.02
0.38
0.03
0.44
0.05
0.39
0.09
0.43
0.13
0.49
0.19
0.50
0.27
0.53
0.41
0.63
0.66
0.80
0.89
0.87
49.3
20.6
2.27
1.81
PsN /P
1.91
1.83
1.62
1.26
1.19
1.19
1.10
1.13
1.07
1.11
sQSN
0.06
0.07
0.07
0.10
0.12
0.14
0.15
0.14
0.19
0.16
sESN
0.10
0.11
0.09
0.11
0.12
0.15
0.14
0.14
0.19
0.17
0.58
0.58
2.82
2.69
1.68
1.72
Note: Countries are ranked according to GDP per capita and
divided among deciles. For each decile, we report: (1) GDP per
capita relative to that of the United States (RGDPpc), (2) the
price of traditional services relative to the price of GDP (PsT /P ),
(3) the price of non-traditional services relative to the price of
GDP (PsN /P ), (4) the real expenditure share of non-traditional
services to GDP (sQsN ), (4) the nominal expenditure share of
non-traditional services to GDP (sEsN ).
services. To provide a first pass at the importance of the reallocation within services in
explaining the fact that the price of services in higher in rich countries, note that the relative
price of services can be decomposed as:
ES /QS
PS
PS
PS
=
= T · sQST + N · (1 − sQST ),
P
P
P
P
where PS /P is the relative price of services (relative to GDP), PSi /P is the relative price of i
services (traditional or non-traditional), and sQST is the real share of traditional services in
total services. We then perform the following counterfactual. We ask what the relative price
of aggregate services would be if there was no reallocation across services categories (we fix
the real share of traditional services to that observed for the United States). We find that
7
without the reallocation across services, the relative price of services would be essentially
the same across rich and poor countries. (See Figure 8). For poorer countries, the relative
price of traditional services is low, the relative price of non-traditional services is high, and
the weight of non-traditional (expensive) services is low. This reallocation in poor countries
compared to rich countries accounts for the low relative price of services.
As alluded to in the introduction, the relative price of aggregate services has often been
viewed as informative of the relative productivity of the service sector in poor countries
relative to rich countries. We argue that the reallocation within services is critical in making
inferences about the productivity of the service sector. In the rest of the paper we work
out the details of how heterogeneity in the service sector is important for the implications
of sectoral productivity across countries.
3
Development Accounting
To start investigating the importance of reallocation across services categories for sectoral
productivity across countries, we follow a large literature in development accounting assessing
the productivity implications of price and expenditure data across sectors and countries.
More specifically, we ask the following question: What are the productivity implications of
disaggregating the service sector?
We closely follow Herrendorf and Valentinyi (2011) in conducting a development accounting
exercise that imposes minimal structure. There are three sectors: manufacturing (m), traditional services (sT ), and non-traditional services (sN ). Production in each sector is governed
by linear technologies requiring labor input:
Yi = Ai Li ,
i ∈ {m, sT , sN },
8
where Yi and Li are output and labor in sector i and Ai is labor productivity in sector i.
Notice that, given our assumption of the production function for each sector, data on labor
productivity across sectors and countries can directly pin down the variables of interest,
i.e., Ai for all countries and sectors. However, such data does not exist, at least for a
comprehensive set of sectors and for a large number of countries. The main difficulty is
that what is available is the value of labor productivity across sectors and countries and
these values can reflect differences in relative prices across sectors within a country as well as
differences in relative prices across countries, potentially confounding true differences in real
productivity. In addition, even if we could make a mapping from the real expenditure data
and output in a sector, generally we will not have the corresponding labor input associated
with that sector specification. Hence, more structure is needed before we can identify Ai
across sectors and countries using data.
We proceed by assuming, in addition to linear technologies in labor, competitive markets
for goods and labor, and perfect factor mobility across sectors. With these assumptions, the
value of labor productivity (the marginal product of labor in this case) is equalized across
sectors. The stand-in firm in each sector maximizes profits by choosing an appropriate
amount of labor, which requires pi Ai = w for all i, where w is the wage rate and pi is the
price of output in sector i. Normalizing the total amount of labor in each country to 1, we
P
P
have that i Li = 1. Then, it follows that the value of aggregate output is i pi Yi = w and
that the labor input is determined by the share of value output,
pi Yi
.
Li = P
i pi Yi
Then, productivity in each sector and country is given by the ratio of output to labor with
the labor inferred from the share value of output,
Ai =
9
Yi
,
sEi
Table 3: Development Accounting Results
Relative
GDPpc
Income elasticity
–
D10
0.89
D5
0.13
D1
0.02
Ratio D10 /D1
49.3
where sEi = Li =
Ai
m
s
sT
sN
1.11 0.87 0.79 1.15
0.86 0.94 1.04 0.80
0.08 0.23 0.31 0.12
0.01 0.03 0.05 0.01
79.2 27.8 21.0 80.5
Ppi Yi .
i p i Yi
To implement this development accounting empirically, we assume that the real expenditure data (Qi ) represents sectoral output in the model (Yi ) and that the share of nominal
expenditure (sEi = Ei /E) represents the share value of output in each sector in the model.
We report the results of the development accounting exercise in Table 3. For each sector
and country, we calculate labor productivity (Ai ) and calculate statistics to illustrate how
sectoral productivity varies with GDP per capita in the cross country data. We compute
the income elasticity by regressing the log of Ai on log GDP per capita, and by calculating
the average Ai for countries in different deciles of the income distribution.
We emphasize the following results from Table 3:
(1) When services are aggregated, we find that the cross-country variation in manufacturing productivity is larger than in services productivity as reflected in the larger
income elasticity in manufacturing than services. A one percent higher income per
capita translates into a 1.12 percent higher productivity in manufacturing productivity whereas only a .85 percent higher productivity in services. For the ratio of the 10
percent richest and poorest countries, differences in manufacturing productivity are
close to 67-fold while for services they are 21-fold. These results are consistent with
the findings in the related literature such as Hsieh and Klenow (2007), Herrendorf and
10
Valentinyi (2011) and the literature emphasizing productivity differences between the
tradable and non-tradable sectors (e.g. Kravis, et al. ).
(2) When the service sector is disaggregated between traditional and non-traditional, the
accounting results are markedly different in that the traditional sector features lower
differences in productivity than manufacturing, which are critical in determining the
implications for aggregate services (since traditional services are almost all the services in poor countries and still more than 60 percent in rich countries). In turn, the
non-traditional services look more like manufacturing in terms of the cross country
differences in productivity. In fact, using the income elasticity as a summary indicator
of differences in productivity across countries, non-traditional services feature a larger
elasticity than manufacturing.
The stark implied differences in productivity between traditional and non-traditional services is relevant for development since development involves a reallocation to non-traditional
services. Depending on the factors that drive the reallocation across services, the sectoral
differences in productivity may constitute an obstacle to development or affect negatively
aggregate productivity. We turn next to this issue by considering a model of the structural
transformation in services.
4
Quantitative Analysis
While the development accounting just discussed provides estimates of the sectoral differences in productivity that are consistent with the price and expenditure data across sectors
and countries, development accounting is silent about the forces driving differences in sectoral
quantities in the cross-country data. In this section, we investigate the sectoral productivity
implications in a standard model of reallocation with three sectors: manufacturing, traditional services, and non-traditional services.
11
4.1
Description
The model is a general equilibrium model of the structural transformation closely following
Duarte and Restuccia (2010). There are three sectors: manufacturing, traditional services,
and non-traditional services. We assume homothetic preferences. Reallocation within services is driven by differential productivity growth and non unitary elasticity of substitution
as in Ngai and Pissarides (2007).
Production In each period, three goods are produced: manufacturing (m), traditional
services (sT ), and non-traditional services (sN ) with linear technologies in labor,
Yi = Ai Li ,
i ∈ {m, sT , sN },
where Ai is labor productivity and Li is total labor hours in sector i.
Preferences There is a stand-in representative household with preferences over consumption:
1
u(cm , csT , csN ) = b log(cm ) + (1 − b) log φcρsT + (1 − φ)cρsN ρ ,
with b and φ between 0 and 1 and ρ < 1. Households are endowed with one unit of productive
time each period which can be allocated to work in any sector.
Market Structure We assume that labor and goods markets are competitive and there
are no frictions to labor allocation across sectors. This implies that for the household to
allocate hours in all sectors, the wage in each sector must be equal and we denote it by w.
Given this wage and the price of output, firms in each sector maximize profits by choosing
the labor input:
max pi Ai Li − wLi .
Li
12
Prices are such that markets for output and labor clear.
Equilibrium A competitive equilibrium is a set of prices {pm , psT , psN } and allocations
{cm , csT , csN } for the household and {Lm , LsT , LsN } for firms such that:
1. Given prices, firm’s allocations {Lm , LsT , LsN } maximize profits.
2. Given prices, household’s allocations {cm , csT , csN } maximize utility subject to the
budget and non-negativity constraints.
3. Markets clear:
• Labor market: Lm + LsT + LsN = L.
• Goods markets: cm = Ym ,
csT = YsT ,
csN = YsN .
Characterization From the first order conditions of the firm’s problem, prices of goods
in each sector are given by
pi =
w
.
Ai
From the first order conditions of the consumers, consumption of traditional and nontraditional services satisfy:
A sN
φ
=
AsT
1−φ
cs T
cs N
ρ−1
.
(1)
And consumption of services and manufacturing:
Am
b
cm
=
.
AsT
1 − b cs T + cs N A sT
As
(2)
N
Since consumption in each sector must be produced with the linear technologies and aggregate labor equals one, these two equations and the clearing condition for labor determine
all the endogenous variables in the model. Moreover, as can be seen from equations (1) and
13
Table 4: Calibration
Parameter
Value
Ai
b
φ
ρ
1.0
0.48
0.60
-0.5
Target U.S. Data
Normalization
Share of expenditures in manufacturing
Ratio of expenditures csT /csN
(2), data on real consumption shares are informative about the productivities of the sectors,
a fact we exploit in the quantitative application of the model.
4.2
Calibration
We calibrate a benchmark economy with normalized sectoral productivities to 1 to data for
the United States. The parameters b and φ are calibrated to the share of real expenditures in
manufacturing and the ratio of traditional to non-traditional services. Ideally, the parameter
ρ is calibrated so that the model matches time series for the United States. We are still in the
process of collecting the appropriate data to perform this exercise. Nevertheless, as we show
in the next subsection, the chosen elasticity ρ makes the model match well the reallocation
involved across a set of rich economies that differ from the U.S. data. Calibrated parameters
are reported in Table 4.
4.3
Results
We measure sectoral relative labor productivity across countries (Am , AsT , AsN ) using the
model. We impose three targets:
• shares of real expenditures in manufacturing and traditional services,
14
Table 5: Model Results
Model
D10
D1
m
0.78
0.01
PsT
Pm
m
0.70
0.008
P sN
Pm
P sN
P sT
sN
0.64 0.99 1.21 1.22
0.003 0.21 2.81 12.99
P
Accounting
D10
D1
Ai
sT
0.78
0.05
P
sT
sN
Ai
Pm
Pm
sT
sN
0.85 0.66 0.82 1.05
0.03 0.007 0.22 1.09
P sN
P sT
1.28
4.94
• relative aggregate labor productivity.
The results of the model are summarized in Table 5 together with the results of the development accounting for comparison. Recall that the development accounting matches both
quantities and prices, while the model imposes no restriction on prices. Therefore, the accounting results for prices serve as a comparison of the model with data.
We emphasize the following results from the model:
1. For countries in the richest decile, the implications of the model are nearly identical
to the ones from the development accounting both in terms of quantities and relative
prices. For these countries, productivity differences with the United States are as large
in non-traditional services as in manufacturing.
2. For countries in the poorest decile, the productivity implications in the model are similar to the accounting in manufacturing and traditional services. But the productivity
differences in non-traditional services are much larger. To put it differently, for the
model to reconcile the shares of real expenditures in the data for the poorest countries,
productivity in non-traditional services must be very low (an order of magnitude lower
than in the accounting).
15
Table 6: Experiments
10% ↑ Am
10% ↑ AsN
%↑
D10
2.1
2.6
GDPpc
∆(p.p.)sLsN
D5 D1 D10
D5
D1
0.8 1.4
0
0
0
2.5 2.2 -0.56 -0.58 -0.56
3. A modification of the model is needed to account for the relative price behaviour of
non-traditional services in poor countries.
These results reinforce our emphasis that heterogeneity in the service sector is important for
productivity implications.
4.4
Experiments
To illustrate the implications of disaggregating the service sector for aggregate productivity
growth, we conduct two experiments whereby we increase productivity in the manufacturing sector or the non-traditional service sector by 10% in each case and we conduct these
experiments for countries in the richest, poorest and middle deciles of the income distribution. Table 6 reports the results. The results show that an increase in productivity in
non-traditional services has a larger impact on aggregate productivity than the increase in
manufacturing and this is true for rich and poor countries.
5
Conclusion
A non-trivial and growing subset of services categories feature a falling relative price with
income (non-traditional services). A standard development accounting exercise uncovers
the importance of disaggregating services for productivity implications. We find that labor
16
productivity differences in non-traditional services are at least as large as those in manufacturing (the model predicts much larger differences than in manufacturing). The process of
development involves a reallocation to non-traditional services which in turn hinges on productivity in that sector. Facilitating development requires solving the productivity problem
in non-traditional services in poor countries. Doing so may require policy reforms that are
more elaborate than the typical “openness-to-trade” recipe.
17
References
Baily, Martin, Diana Farrell, and Jaana Remes, “Domestic Services: The Hidden Key to
Growth,” McKinsey Global Institute, 2005.
Baily, Martin, and Robert Solow, “International Productivity Comparisons Built from the
Firm Level,” Journal of Economic Perspectives, 15 (2001), 151-172.
Baumol, William, “Macroeconomics of Unbalanced Growth: The Anatomy of Urban Crisis,”
American Economic Review, 57 (1967), 415-426.
Duarte, Margarida, and Diego Restuccia, “The Role of the Structural Transformation in
Aggregate Productivity,” Quarterly Journal of Economics, (2010).
Echevarria, Cristina, “Changes in Sectoral Composition Associated with Growth,” International Economic Review, 38 (1997), 431-452.
Gollin, Douglas, Stephen Parente, and Richard Rogerson, “The Role of Agriculture in Development,” American Economic Review Papers and Proceedings, 92 (2002), 160-164.
`
Herrendorf, Berthold, and Akos
Valentinyi, “Which Sectors Make the Poor Countries so
Unproductive?,” Journal of the European Economic Association, forthcoming.
Heston, Alan, Robert Summers, and Bettina Aten, Penn World Table Version 6.2, Center for International Comparisons of Production, Income and Prices at the University of
Pennsylvania, 2006. <http://pwt.econ.upenn.edu>.
International Labour Office,
LABORSTA Database,
Bureau of Statistics,
2008.
<http://laborsta.ilo.org/>.
Kongsamut, Piyabha, S´ergio Rebelo, and Danyang Xie, “Beyond Balanced Growth,” Review
of Economic Studies, 68 (2001), 869-882.
Kuznets, Simon, Modern Economic Growth. (New Haven, CT: Yale University Press, 1966).
18
Maddison, Angus, “Economic Growth and Structural Change in the Advanced Countries,”
in Western Economies in Transition, Irving Leveson and Jimmy Wheeler, eds. (London:
Croom Helm, 1980).
Ngai, Rachel, and Christopher Pissarides, “Structural Change in a Multisector Model of
Growth,” American Economic Review, 97 (2007), 429-443.
O’Mahony, Mary, and Marcel P. Timmer, “Output, Input and Productivity Measures at the
Industry Level: The EU KLEMS Database,” The Economic Journal, 119 (2009), F374F403. Data set downloadable at www.euklems.net.
Pilat, Dirk, “Labour Productivity Levels in OECD Countries: Estimates for Manufacturing
and Selected Service Sectors,” OECD Working Paper No. 169, 1996.
Rogerson, Richard, “Structural Transformation and the Deterioriation of European Labor
Market Outcomes,” Journal of Political Economy, 116 (2008), 235-259.
Summers, Robert, and Alan Heston, “The Penn World Table: an expanded set of international comparisons, 1950-1988,” Quarterly Journal of Economics, 106 (1991), 327-368.
Timmer, Marcel P., and Gaaitzen J. de Vries, “Structural Change and Growth Accelerations
in Asia and Latin America: A New Sectoral Data Set,” Cliometrica, 3 (2009), 165-190.
Data set downloadable at www.ggdc.net.
Triplett, J. and B. Bosworth. 2004. Productivity in the U.S. Services Sector: New Sources
of Economic Growth. Brookings Institution Press, Washington DC.
U.S. Census Bureau, Department of Commerce, Historical Statistics of the United States:
Colonial Times to 1970 (Part I). (Washington, DC: U.S. Government Printing Office,
1975).
World Bank, World Development Indicators, 2008. Available on-line:
<http://devdata.worldbank.org/dataonline/>.
19
A
A.1
Data Sources and Definitions
ICP Data
The International Comparison Program (ICP) provides parity and expenditure data for 129
categories for 146 countries for the year 2005. The parity for each category (basic heading)
is generated by the ICP based on detailed price data collected in each country. The parity
ppij for each basic heading i, i = 1, ..., m, in country j, j = 1, ..., n, is expressed in units of
currency of country j to the numeraire currency (the U.S. dollar). The ICP also provides
expenditure data, in national currency units, for each basic heading in each country, Eij .
The expenditure data are obtained from national account systems. Expenditure over all
basic headings aggregates to GDP. At the basic heading level, parities allow expenditure
data to be converted into a common currency, making it comparable across countries. We
convert each country’s expenditure for a basic heading to U.S. dollars by computing notional
quantities, defined as qij = Eij /ppij .
The ICP aggregates basic heading parities and expenditures into higher levels of aggregation
`
(such as GDP) using the Eltet¨
o, K¨oves, and Szulc (EKS) method. Although the EKS is
considered the most appropriate method to compare the different aggregates of the GDP
across economies, the expenditures by aggregate are not additive to higher levels of aggregation. We aggregate the detailed ICP data using the Geary and Khamis (GK) method, which
produces additive results. For the purpose of our paper, additive consistency is an important
property because it enables the calculation of shares (e.g., the share of real services in real
GDP) and their comparison across countries.6
The GK method delivers a set of international prices, πi for each basic heading i. The
P
valuation of country j’s output in international prices is then RGDPj = m
i=1 πi qij . The
6
Note that computing GDP in country j in a common currency by simply adding up notional quantities
for all basic headings would use the relative prices between basic headings that prevailed in the United
States, the numeraire country. Hence, the result would not be invariant to the base country.
20
international price for heading i is defined as
ppij
j=1 P P Pj qij
Pn
,
j=1 qij
Pn
πi =
(3)
where P P Pj is the purchasing power parity over GDP for country j, given by
m
P P Pj =
X
GDPj
, where GDPj =
Eij .
RGDPj
i=1
(4)
The international prices are defined so that they imply a purchasing power parity over GDP
for each country that is consistent with the prices. We obtain the international prices πi by
iterating on equations (3) and (4), given an initial guess for P P Pj . At each iteration we
scale the πi ’s so that the P P P for the United States is 1 and we assume that the parity for
net exports is 1.
After computing international prices, we restrict the data set to countries with more than
one million inhabitants in 2005. Our restricted data set covers 130 countries.
TBC
21
Figure 1: Relative Price of Services across Countries
1
Relative Price of Services
CHE
NOR
KWT
USA
DNK
FIN
BEL
FRA
IRL
HKG
DEU
LBN SAUITA
SWE
CAN
GBR
AUS
NLD
PRT JPN
ISR
ESPAUT
GRC
OMN
KOR
SVN
CHL
MAR
NZL
MEX
NPL
URY
BRA
SGP
ECU
BGD
BIH
IRQ
PERZAFARG
IDNSYR
TWN
PHL
MWI
CHN COL
ZMB
HUN
EST
POL
LVA
CIV
HRV
SEN IND
UGA
KEN
SRB
NAM MKD
MUS
MYS
YEM
TGO
CZE
THA
ROM
TUR
PRY TUN
BGR
LBR
TZA
GMB
NER
SVK
BEN
VEN LTU
BFA
ETH
ALB
IRN
AGO LKA
BOL
LSO
MDA
MRT
MDG CMR
GNBSLE
SDN
GEO
RUS
KAZ
SWZ
EGY
VNM
RWA
PAK
BWA
UKR BLR
MLI KHM MNG
BDI MOZ
LAO
GHA
GAB
KGZ
CAF GIN
COG
NGA
ARM
ZWE
GNQ
TJKAZE
TCD
JOR
0.8
0.6
ZAR
0.4
0.2
0
5
6
7
8
9
10
GDP per capita (PPP adjusted, log)
11
12
Note: Data for 2005 from ICP. The relative price of service refers to the PPP price of total
services relative to the PPP price of GDP.
22
Figure 2: Share of Services across Countries
GMB
Share of Services (domestic prices)
0.6
USA
ISR
GRCGBR
ZMB
MWI
JOR
FRA
PRTESP
SWE
BIH
LBN
AUS
BRABGR
CHE
KEN
DNK
NZL
NLD
AUT
ZAF
JPN
FIN
BEL
IRQ
ITA
MEX
CAN
TWN
MDA
DEU
URY HRV
NAMCOLMKD
BOL
ROM POL
LSO
HUNSVN HKG
LVA
KOR
CZE
MUS
MAR
TUN SRB
UGA
SVK
EST
IRL
LBR
TGO
PER
ALB
CHL
GNBSLE
ARG
TUR
NOR
GEO
ECU
KAZ
BFA
THA MYS LTU
RWABEN
KGZ EGY
PHL
PRY
IRN
OMN
MLI
SGP
BDINER
BLR
SWZ UKR
CIV
MDG
SYR
VEN
ETH
SEN
IND
VNM
KHM YEM LKA
SAU
RUS
BWA
CHN
KWT
MRT IDN
CMR
TJK
NPL
0.5
0.4
0.3
ZAR
0.2
5
6
7
ARM
GAB
AZE
GNQ
TZA
0.1
0
MNG
PAK
GIN BGD LAO
ZWE
MOZ GHA
SDN
AGO
TCD
CAF
COG
NGA
8
9
10
GDP per capita (PPP adjusted, log)
11
12
Note: Data for 2005 from ICP. The share of service refers to nominal expenditures in total
services relative to nominal GDP.
23
Figure 3: Real Share of Services across Countries
1
0.9
0.8
GMB
TCD
0.7
Real Share of Services
TJK
0.6
0.5
ZAR
0.4
0.3
KGZ
MDA
BGR
ZMB
BOL
ZWE
MWI
KEN LSO
BDI
GNBSLE
GEO
KAZ
ROM
MLI
NAM UKR
BIH
HRVSVK ISR
MKD
BRA
RWA
ZAFBLR
TWN
IRQ ALB
POL CZE
COL
GRC
ARM
EGY
TUN
SRB
LBR
MUS
KHM
HUN NZL GBRUSA
LVA
GIN
UGA
SWZ
TGO
BFA
URYMEX
TUR
ESP
EST
LTUPRT FRA
VNM
AZE
BWA
AUT
MDG
IRN
AUS
LAO
RUS
SWE
NLD
JPN
BEN
PER
LBN
COG
ARG
THA
NGA
JOR
CAN
ITADNK
PRY
SVN
ETH
NERMOZ GHA
VEN
MYS
MAR
BEL
FIN
LKA ECU
DEU
KOR
MRT MNG
CMR
GAB
CAF
CHL
CHE
PAK
PHL
CIV IND
HKG
SEN YEM SYR
IRL
SGP
SDN
CHN
OMN
GNQ
IDN
NOR
AGO
NPL
SAU
BGD
KWT
0.2
TZA
0.1
0
5
6
7
8
9
10
GDP per capita (PPP adjusted, log)
11
12
Note: Data for 2005 from ICP. The real share of service refers to real expenditures in total
services relative to real GDP. Real refers to expenditures or GDP at international prices.
24
Figure 4: Relative Price of Traditional Services across Countries
1
Relative Price of Traditional Services
JOR
0.8
USA
CHE
KWT
DNK
BEL
FIN
FRA
IRL NOR
ITA
PRT
CAN
SWE
DEU
NLD
HKG
SAU
AUS
ESPGBR
KOR
LBN
ISR
GRCJPN
AUT
SVN
MEX OMN
NZL
IRN
CHL
0.6
0.4
ZAR
0.2
SGP
IRQ
NPL
MAR
BRA
SYR
TWN
URY
ZAF
ARG
ECU
BGD
IDN
HUN
PER
BIH
EST
COL SRB
CZE
POL
ROMHRV
ZMB
PHL CHN
MYSLVA
MKD
GMB
NAM
YEM
IND
TUR
THA
SVK
CIV
TUN
TZA UGA
VENMUSLTU
LBR
SEN
MDG
BGR
AGO PRY
ETH
NER
BFA
KEN LSO
ALB
SWZ
MRT
BWA
MDA
SLE
RWA
BOL
RUS
BLR
KAZ
LKA
MNG
GNBTGO BENCMRPAK
MWI
EGY
GEO
VNM
MOZ
SDN
UKR
MLI
KHMLAO
BDI
GAB
GHA
KGZ
CAF GIN
COG
ARM
ZWE
TJKAZE
NGA
GNQ
TCD
0
5
6
7
8
9
10
GDP per capita (PPP adjusted, log)
11
12
Note: Data for 2005 from ICP. Price of traditional services relative to the price of GDP.
Traditional services include ...
25
Figure 5: Relative Price of Non-traditional Services across Countries
4
Relative Price of Non−Traditional Services
3.5
3
GMB
GNBSLE
MRT
2.5
ZWE
ZAR
UGA
BWA
LSO
TCD
RWA
MWI
BDI
GHA
GAB
TGO
NAM
LAO
BFA
SWZ
MLI
BEN
CAF MDG
SEN
KHM VNM
BGD
GIN KEN
MAR
MUS
TZA NPL
GNQ
COG
AGO
NERMOZ
CIV CMRYEM
ETH
PRY
BIH
MNG
NOR
SDN PHL
IRQ
ZMB NGA
LBN
MKD
EGY
ZAF
ECU
CHN
T
UR
COL
TUN
URY
ARM
BGR
BRA
GEO
CHE
BOL
CHLLVA
JPN
HKG
DNK
DEU
IND IDN
ARG
GBR
SWE
PER
FIN
KAZ
HRV
AUT
BEL
AUS
IRLKWT
CAN
ISR
FRA
NLD
GRC
ITA
THAVEN
SGP
EST
JOR
PAKKGZ
SAU
ESP
LKA
SYRALB
MYS
SRB
TWN
HUN
POLPRT
SVKSVN
USA
RUS
OMN
KOR
LTU
MDA UKRROM
MEX
CZE
NZL
BLR
LBR
2
1.5
1
TJKAZE
0.5
0
IRN
5
6
7
8
9
10
GDP per capita (PPP adjusted, log)
11
12
Note: Data for 2005 from ICP. Price of non-traditional services relative to the price of GDP.
Non-traditional services include ...
26
Figure 6: Relative Price of Non-traditional to Traditional Services across Countries
Rel. Price of Non−traditional to Traditional Services
15
ZWE
10
GNQ
ZAR
5
0
5
6
GNB
SLEGHA
NGA
GAB
BDI CAF
MRTCOG
GIN
GMB
BWA
MWIRWA
LAO
LSO
ARM
UGA
MLI KHM
TGO
LBR
VNM
BEN
SWZ
MOZ BFA
KGZ NAM
SDN MNG
KENCMR
MDG
SEN AGO
EGY
GEO
NER TZA
MUS
ETH
PRY
BOL
CIV
TJK
UKR BGR
KAZ
YEM
BGD
AZE
PAK
LKA
TUN
ALB
MARCHN
T
UR
RUS
MKD
ZMB IND PHL
NPL
BIH
MDA ECU
COL
THA
VEN
HRV
LVA
SVK
ZAF
IRQ
BLR
LTU
URY
IDN
MYS
PERBRA
ARG
EST
SRB
POL
ROM
HUN
CHL
CZE TWN
SYR
LBN
SGP NOR
JPN
AUT
GBR
GRC
HKG
ISR
DEU
AUS
SWE
SVN
OMN
CAN
ESP
NLD
SAU
DNK
MEX PRT
FIN
IRL
NZL
KOR
BEL
ITA
FRA
CHE
KWT
JOR
USA
IRN
7
8
9
10
GDP per capita (PPP adjusted, log)
Note: Data for 2005 from ICP.
27
11
12
Real Share of Non−Traditional Services (in Total Services)
Figure 7: Real Share of Non-traditional Services across Countries
0.5
MEX
PRT
GBR
KOR
IRL
GRCAUT
NLD
HKG
USA
DNK
ITA
FRA
LKA
CHE
BEL
DEU
SVN FIN
THA
SWE
MDA
NZL CAN
MYS
SGP
PER
NOR
AUS
CHL
IND
SVK
JPN
HUN
TWN
JOR
HRVEST
ECU
ALB
ARG LTU
BGR
URY
VEN
CZE
ISR
LBN
CHN
LVA
SRB
ROM
PHL
BOL
PRY TUN
BRA RUS
IDN
BIH
OMN
TJK
CIV
KEN
KWT
GEO
COL
MKD
UKR
MWITGOZMB CMRPAK
TUR
ZAFBLR
MAR
BEN SDN
KGZ
KAZ
NER
SEN
EGY
MUS
YEM
NPL
AZE
BGD
ETH
SAU
TZA
VNM SYR
KHM
NGA
AGO
BFA
CAF UGA
MLI
GNQ
COG
MNG NAM
ARM
GIN
BDI
LBR
GHA TCD
GNB
GAB
MOZ
LSO
LAO
SWZ
MRT
MDG
RWA
IRQ
SLE
BWA
ZWE GMB
IRN
POL
0.4
0.3
0.2
0.1
ZAR
0
ESP
5
6
7
8
9
10
GDP per capita (PPP adjusted, log)
11
12
Note: Data for 2005 from ICP. The real share of non-traditional services refers to real
expenditures in non-traditional services relative to real expenditures in total services. Real
refers to expenditures at international prices.
28
Figure 8: Counterfactual Relative Price of Services
2
1.8
Counterfactual Relative Price of Services
1.6
GMB
SLE
GNB
1.4
MRT
UGA
1.2
ZAR
LBR
ZWERWA
MWI
LSO
BWA
CHENOR
NAM
DNK
BFA
BGD TCD MARSWZ
JOR
USA
LBN
TGO
KWT
FIN
GHA
GAB
BEL
NPL
HKG
IRL
FRA
DEU
MDG SEN LAO
SWE
ITA
GBR
CAN
JPN
NLD
MUS
AUS
KEN
BEN
TZA MLI
PRT
IRQ
SAU
VNM
AUT
ESP
ISR
BIH
CHL
GRC
AGO
CIV KHM YEM
KOR
ZAF
NER
URY
SVN
PHL
OMN
BRA
ECU
CAF GINZMB
ETH
COLMKD
MEX
CHN PER
IDNPRY
NZL
ARG
MOZ
SGP
TUR
CMR MNGSYR TUN
LVA
HRV
BGR
INDCOG
SDN
EST
HUN TWN
SRB
EGY
POL
MYS
GNQ
BOLGEO
THA ROM
CZE
SVK
KAZ LTU
NGA PAK LKA ALB VEN
RUS
MDA ARM
UKR
KGZ
IRN
BLR
BDI
1
0.8
0.6
0.4
TJKAZE
0.2
0
5
6
7
8
9
GDP per capita (PPP adjusted, log)
10
11
Note: The relative price of service is a weighted average of the relative prices of traditional
and non-traditional services with the weight being the real share of each time of services.
In the counterfactual, we compute the relative price of services for each country using the
country relative prices but assuming the real share of traditional and non-traditional services
to be the one of the United States.
29
12