In this paper, we apply the methodology developed by Feenstra (1994) and Broda and Weinstein (2006) to estimate the gains from imported variety for the 27 countries of the European Union using Eurostat data from the period of 1999 to 2008. Our results show that newer and smaller member states exhibit high gains from newly imported varieties.Welfare gains from trade; Trade in variety; European Union

Mohler, Lukas

Seitz, Michael

English

Research Papers in Economics

The gains from variety in the European Union
Lukas Mohler,a, Michael Seitzb,1
aFaculty of Business and Economics, University of Basel, Peter Merian-Weg 6, 4002 Basel,
Switzerland
bDepartment of Economics, Ludwig-Maximilians-University of Munich, Ludwigsstrasse 28,
80539 Munich, Germany
Abstract
In this paper, we apply the methodology developed by Feenstra (1994) and
Broda and Weinstein (2006) to estimate the gains from imported variety for the
27 countries of the European Union using Eurostat data from the period of 1999
to 2008. Our results show that \newer" and smaller member states exhibit high
gains from newly imported varieties.
Key words: Welfare gains from trade, Trade in variety, European Union
JEL: F12, F14
1. Introduction
The European Union with its 27 member states constitutes the largest single
market in the world. Over the past decade the economic integration of these
countries into the European Union and into the world economy has led to a surge
in imports in these economies. From 1999 to 2008, the total value of imports
for all member states has doubled where about one third of this increase can be
attributed to the establishment of new trade linkages with new goods or new
trading partners, increasing the number of available products for consumers.
Since the seminal work of Krugman (1979, 1980) and the emergence of the \New
Trade Theory", economists have tried to quantify the welfare gains from newly
imported varieties for consumers. Based on the methodology rst outlined by
Feenstra (1994), Broda and Weinstein (2006) structurally estimate the gains
from trade for the United States and nd positive gains of the magnitude of
2.6 percent of GDP for the period from 1972 to 2001. In this contribution, we
use highly disaggregated trade data at the HS-8 level for the period 1999 to
2008 to estimate the gains from variety for each member state of the European
Union. We nd that for \newer" and smaller member states the availability of
new product varieties is an important source for consumer welfare.
Corresponding author. Tel.: +41 61 267 0770; fax: +41 61 267 1316
Email addresses: lukas.mohler@unibas.ch (Lukas Mohler),
michael.seitz@lrz.uni-muenchen.de (Michael Seitz)
1Tel.: +49 89 2180 6286; fax: +49 89 2180 6227
Preprint submitted to Elsevier June 28, 20092. Empirical strategy
We outline the empirical strategy very concisely. For a more detailed dis-
cussion of the methodology, refer to Feenstra (1994) and Broda and Weinstein
(2006).
We start with a simple CES utility function. A variety is dened as a good
g imported from a country c as in Armington (1969):
Mgt =
 
X
c2C
dgctM
1 g
gct
! 1
1 g
; (1)
where C denotes the set of available countries and hence potentially available
varieties in period t. Mgct is the subutility derived from the imported variety c
of good g in period t and dgct > 0 is the corresponding taste parameter. The
elasticity of substitution among varieties is given by g > 1. The unit-cost
functions derived from this utility function can then be used to obtain an exact
price index as shown in Diewert (1976). Sato (1976) and Vartia (1976) show
that for the CES utility, this exact price index Pg can be written as
Pg =
Y
c2Ig

pgct
pgct 1
wgct
; (2)
where wgct is a log-change ideal weight. So far, the price index in equation
(2) only accounts for a xed set of available varieties Ig, independent of t. To
allow for new and disappearing varieties over time we follow Feenstra (1994):
g = Pg

gt
gt 1
 1
g 1
; (3)
where
gr =
P
cIg pgcrxgcr
P
cIgr pgcrxgcr
; r = t;t   1: (4)
The idea of the index g is to correct the conventional price index Pg by multi-
plying it with an additional term which measures the inuence of new and dis-
appearing varieties; this term is called the lambda ratio. The numerator of this
ratio quanties the impact of newly available varieties as gt captures the ratio
of expenditures on varieties available in both periods (i.e. c 2 Ig = (Igt\Igt 1))
relative to the entire set of varieties available in period t (i.e. c 2 Igt). Hence,
gt decreases when new varieties appear. On the other hand, the denominator
of the lambda ratio captures the impact of disappearing varieties. These lower
gt 1 and increase the ratio.
The exact price index also depends on the elasticity of substitution between
varieties: If we observe a high elasticity of substitution, the additional term
( t
t 1)
1
 1 will approach unity and the inuence of the lambda ratio on the price
index is small. This is intuitive since a change in the varieties of homogeneous
goods should not lower the price index.
2To estimate the elasticities of substitution, the stochastic specication of
Feenstra (1994) is used and g is calculated for each good. To obtain the gains
from variety, the price indices of equations (2) and (3) for the goods are then
aggregated into aggregate import price indices. We then take the fraction of
the corrected import price index and the conventional import price index. This
ratio is called the endpoint ratio (EPR):
EPR =
Y
g2G

g
Pg
wgt
; (5)
where wgt is again a log-change ideal weight. This ratio is used to express
the upward (or downward) bias resulting from the change of variety over time:
If the EPR is smaller than one, it means that the variety change has lowered the
import price index. This will be the source of the gains from variety: Weighting
the inverse of this fraction with the import share results in the gains from variety
as a percentage of GDP.2
3. Data and results
We use the database from Eurostat which consists of highly disaggregated
trade data for the EU-27 countries at the HS-8 level. We use quarterly data for
the period of the rst quarter of 1999 to the rst quarter of 2008 to rule out
potential seasonality eects.
Table 1 displays some descriptive statistics for each EU member state regard-
ing the import of varieties.3 The rst four countries are the four largest \old"
member states, the next eleven countries are the smaller \old" member states
and the last ten countries are the \new" member states that joined the union
in 2004 and 2007. From columns 1 and 2 we can see that the total number of
imported goods is relatively constant over time for all countries: While for most
of the \old" member states the number of imported goods decreases slightly,
modest increases can be observed for some of the \new" member states. At the
same time, the average number of worldwide suppliers has increased in most of
the countries, except Hungary and Malta (columns 3 and 4). This translates
into an overall increase of imported varieties for all countries except Cyprus,
Hungary and Malta as shown in columns 5 and 6. Although there is a similar
number of new and disappearing varieties in most countries (columns 7 and 8),
from columns 9 and 10 we can see that the value of new varieties is much larger
than the value of disappearing ones, especially for the \new" member states.
Next, we calculate the lambda ratios in equation (4). They illustrate the
growth or decline in imported variety. Summary statistics of these ratios are
shown in Table 2: For example, the median lambda ratio in Ireland is 0:96 < 1,
2If we assume a Krugman (1980) economy as in Broda and Weinstein (2006).
3In Table 1, product categories are dened at the HS-6 level. At this level, the classication
is harmonized and hence allows for a better comparison in the descriptive statistics. For all
the later results however, we use HS-8 to make use of all available data.
3expressing that the typical product category experienced a positive growth in
variety of about 4%. Note that this measure of variety growth is more sophis-
ticated than others: Instead of just counting new and disappearing varieties as
in Table 1, this measure accounts for the importance of dierent varieties by
using expenditure shares as weights. 4
Finally, we estimate the elasticities of substitution for every imported prod-
uct category of each country. Table 3 shows some descriptive statistics about
the estimated elasticities.5 The median elasticity lies between 3.4 and 4.9.6 The
elasticities are of a similar magnitude as in other contributions, for example in
Broda and Weinstein (2006) or in Broda et al. (2006).
The elasticities and lambda ratios are then used to calculate the corrected
import price indices as in equation (3). Aggregating those indices into an aggre-
gate import price index and taking the fraction of the corrected to the conven-
tional import price index, results in the EPR of (5). It is displayed in column
1 of Table 4. If this ratio is lower than 1, it means the change in variety has
lowered the conventional import price index. The percentage in column 2 of
table 4 expresses the upward (or downward) bias of the conventional import
price index. Column 3 displays the fraction of imports to GDP. Weighting the
inverse of column 1 with the import share gives us the gains from variety as a
fraction of the GDP in column 4.
As an example, Table 4 shows that the EPR in the Netherlands is 0.9921.
This accounts to an upward bias in the conventional price index of 0.79% over the
whole period. Weighting this bias by the import share of 51%, this translates
into a gain from variety of 0.41% of GDP. This gain must be interpreted as
follows: Consumers in the Netherlands are willing to spend 0.41% of GDP in
2008 to have access to the larger set of imported varieties of 2008 instead of the
set of 1999.
4. Interpretation of the results and concluding remarks
For most countries the biases and hence the gains from variety are positive.
However, results dier across countries and three dierent groups can be identi-
ed. First, for the largest four countries in the EU in terms of GDP, the impact
of traded variety is very small or even slightly negative for our period. This
may be explained by the smaller import shares and by the fact that these coun-
tries have already been strongly integrated into the EU and the world economy
4Note that there are fewer lambda ratios than product groups: Some lambda ratios cannot
be dened at the HS-8 level since there is no common variety at the beginning end the end
of the chosen time period. Then, we dene the lambda ratio at the SITC-5 level. This is
handled exactly as in Broda and Weinstein (2006).
52,039 elasticities may seem like too few considering that Malta imported 2,093 goods in
1999 even at the HS-6 level. However, some product categories in small countries are imported
from very few partner countries and for only a very short period. For these goods it is not
possible to estimate an elasticity of substitution. See Feenstra (1994) for more information
about this estimation technique.
6Note that the means are heavily inuenced by some outlier elasticities.
4before this period. Secondly, for all the smaller \old" member states we nd
modestly positive gains from imported variety, with the exception of Finland.
Finally, for the \newer" member states of the European Union, with the excep-
tion of Malta, the gains are strongly positive, mostly larger than 1% of GDP.
This result reects the eects of the ongoing integration of these countries into
the European single market and into the world economy as well as their higher
growth rates and higher import shares. For example, the gains from variety for
Estonia sum up to 2.74% of GDP, which is of the same magnitude as Broda and
Weinstein (2006) nd for the United States for their much longer period from
1972 to 2001. Our results show that especially for fast growing, less developed
and smaller countries, the establishment of new trade linkages and the import
of new varieties are an important source of welfare gains via trade.
5References
Armington, P. S., 1969. A theory of demand for products distinguished by place
of production. International Monetary Fund Sta Papers 16, 159{178.
Broda, C., Greeneld, J., Weinstein, D. E., 2006. From groundnuts to global-
ization: A structural estimate of trade and growth. NBER Working Paper
12512.
Broda, C., Weinstein, D. E., 2006. Globalization and the gains from trade.
Quarterly Journal of Economics 121 (2), 541{585.
Diewert, W. E., 1976. Exact and superlative index numbers. Journal of Econo-
metrics 4 (2), 115{145.
Feenstra, R. C., 1994. New product varieties and the measurement of interna-
tional prices. American Economic Review 84 (1), 157{177.
Krugman, P. R., 1979. Increasing returns, monopolistic competition, and inter-
national trade. Journal of International Economics 9 (4), 469{479.
Krugman, P. R., 1980. Scale economies, product dierentiation, and the pattern
of trade. American Economic Review 70 (5), 950{959.
Sato, K., 1976. The ideal log-change index number. Review of Economics and
Statistics 58 (2), 223{228.
Vartia, Y. O., 1976. Ideal log-change index numbers. Scandinavian Journal of
Economics 3 (3), 121{126.
6A. Tables
Table 1: Descriptive Statistics
Total no. Mean no. Total no. Varieties Value (mil. Eur)
of goods of countries of varieties disapp. new. disapp. new.
1999 2008 1999 2008 1999 2008 1999 2008 1999 2008
France 5,079 4,995 15.25 17.08 77,448 85,327 7,778 7,922 8,002 15,181
Germany 5,010 4,888 18.68 20.31 93,574 99,251 9,449 9,082 12,121 22,934
Great Britain 4,979 4,914 15.53 17.47 77,325 85,831 8,124 7,950 8,839 15,713
Italy 5,032 4,934 14.19 16.28 71,387 80,346 7,363 7,528 5,574 9,394
Austria 4,909 4,809 10,16 13,01 49,871 62,577 5,138 5,606 1,750 3,776
Belgium 5,006 4,920 11,16 14,44 55,870 71,031 5,706 6,359 3,747 8,880
Denmark 4,624 4,721 8.54 11.41 39,496 53,852 3,983 5,092 1,352 2,809
Finland 4,661 4,557 8,54 10,29 39,791 46,874 4,146 4,197 945 2,569
Greece 4,685 4,628 7,54 9,26 35,306 42,863 3,720 4,003 796 1,344
Ireland 4,664 4,525 5.71 7.33 26,619 33,162 3,082 3,096 1,607 2,161
Luxemburg 4,279 4,339 4,47 5,00 19,147 21,694 2,154 2,381 502 797
Netherlands 4,910 4,849 13.38 15.61 65,714 75,681 6,857 7,607 4,624 16,092
Portugal 4,763 4,726 7,96 8,98 37,092 42,453 3,849 4,091 1,033 1,850
Spain 4,982 4,882 10,43 13,01 51,978 63,535 5,442 5,821 2,827 9,701
Sweden 4,812 4,758 11,13 12,74 53,548 60,625 5,286 5,530 1,875 4,155
Bulgaria 4,077 4,439 6,60 9,14 26,945 40,593 2,852 5,021 109 719
Cyprus 3,702 3,624 6,52 6,58 24,142 23,831 3,034 2,768 109 474
Czech Republic 4,834 4,806 11,12 12,02 53,766 57,785 5,344 5,393 627 2,907
Estonia 3,979 4,160 6,43 8,07 25,576 33,561 2,887 3,817 114 581
Hungary 4,140 4,013 11,63 11,46 48,168 46,003 6,163 5,498 763 3,312
Latvia 3,831 4,230 5,93 8,49 22,720 35,930 2,425 4,336 100 526
Lithuania 3,994 4,349 7,12 9,35 28,451 40,682 2,837 4,620 92 631
Malta 3,515 3,257 4,95 4,55 17,384 14,827 2,709 1,827 244 283
Poland 4,867 4,848 12,57 13,35 61,181 64,712 5,889 5,994 1292 3,904
Romania 4,288 4,672 8,46 11,84 36,281 55,317 3,524 6,056 256 1,593
Slovakia 4,445 4,570 7,69 8,85 34,184 40,447 3,484 4,276 234 1,231
Slovenia 4,489 4,595 8,59 8,89 38,578 40,836 3,896 4,240 252 884
7Table 2: Lambda Ratios
Nobs Mean Median 5% perc. 95% perc.
France 1,797 1.82 0.99 0.43 1.83
Germany 1,574 1.47 0.99 0.38 1.93
Great Britain 1,306 2.07 0.98 0.31 1.80
Italy 1,630 1.63 0.98 0.33 1.90
Austria 1,438 2.47 0.99 0.36 2.18
Belgium 1,622 4.33 0.98 0.30 2.06
Denmark 1,099 1.74 0.97 0.27 2.11
Finland 1,208 2.02 0.97 0.18 2.83
Greece 1,173 1.36 0.95 0.19 2.28
Ireland 1,321 2.27 0.96 0.23 3.14
Luxemburg 1,269 1.94 1.00 0.28 1.93
Netherlands 1,457 4.70 0.98 0.23 2.31
Portugal 1,243 2.90 0.99 0.27 2.45
Spain 1,416 1.69 0.96 0.21 2.06
Sweden 1,227 3.12 0.97 0.28 2.05
Bulgaria 682 1.31 0.81 0.11 2.30
Cyprus 506 1.77 0.97 0.17 3.75
Czech Republic 1,247 2.08 0.98 0.22 2.84
Estonia 720 7.01 0.90 0.13 3.12
Hungary 768 10.27 1.00 0.21 3.55
Latvia 656 1.59 0.79 0.09 3.11
Lithuania 814 1.35 0.83 0.08 2.29
Malta 542 2.31 1.00 0.17 6.27
Poland 1,232 11.59 0.99 0.21 3.34
Romania 874 7.86 0.86 0.13 3.05
Slovakia 853 3.83 0.96 0.17 2.73
Slovenia 993 1.96 0.97 0.24 2.92
8Table 3: Estimated Elasticities of Substitution
Nobs Mean StE Median Maximum Minimum
France 10,491 11.29 0.83 4.22 5,504.26 1.02
Germany 10,193 14.19 2.63 4.68 21,469.27 1.04
Great Britain 9,930 10.46 1.47 3.84 11,977.77 1.00
Italy 9,797 14.28 1.42 4.60 8,177.53 1.01
Austria 8,317 9.60 1.41 3.70 11,062.26 1.02
Belgium 9,338 8.53 0.40 4.10 2,633.42 1.00
Denmark 7,631 7.94 0.55 3.42 3,033.52 1.03
Finland 6,765 14.23 2.00 4.04 12,064.90 1.01
Greece 6,728 9.04 0.72 3.41 2,928.15 1.00
Ireland 6,210 10.80 1.29 3.47 5,561.19 1.00
Luxemburg 5,173 13.70 1.40 3.51 4,055.22 1.00
Netherlands 8,698 16.90 3.81 4.38 29,244.75 1.03
Portugal 7,182 12.03 1.47 3.65 6,326.31 1.00
Spain 9,179 9.05 0.53 3.89 2,811.72 1.00
Sweden 7,723 289.95 280.12 4.32 2,163,410.00 1.00
Bulgaria 5,314 12.63 1.30 4.57 5,875.99 1.01
Cyprus 2,815 27.81 8.74 4.56 21,620.79 1.02
Czech Republic 7,526 13.54 1.84 4.33 10,539.79 1.00
Estonia 4,697 97.25 80.31 4.18 376,959.60 1.00
Hungary 6,914 13.61 0.94 4.52 3,224.69 1.01
Latvia 4,542 13.16 1.49 4.33 4,371.04 1.01
Lithuania 5,138 13.46 2.32 4.50 11,296.21 1.01
Malta 2,093 8.89 0.69 3.59 794.74 1.00
Poland 8,129 13.02 1.19 4.31 5,560.56 1.01
Romania 6,437 15.85 2.49 4.89 15,217.73 1.01
Slovakia 6,189 10.60 0.77 3.98 3,034.62 1.00
Slovenia 6,329 11.71 1.83 4.07 10,740.02 1.00
9Table 4: Import Price Index Bias and the Gains from Variety
EPR Bias Import Share GFV
France 1.0167 -1.67% 0.24 -0.41%
Germany 1.0013 -0.13% 0.26 -0.03%
Great Britain 0.9959 0.41% 0.21 0.09%
Italy 0.9924 0.76% 0.21 0.17%
Austria 0.9917 0.83% 0.41 0.33%
Belgium 0.9940 0.60% 0.84 0.51%
Denmark 0.9753 2.47% 0.30 0.75%
Finland 1.0023 -0.23% 0.29 -0.06%
Greece 0.9846 1.54% 0.25 0.38%
Ireland 0.9957 0.43% 0.38 0.15%
Luxemburg 0.9980 0.20% 0.56 0.11%
Netherlands 0.9921 0.79% 0.51 0.41%
Portugal 0.9917 0.83% 0.34 0.27%
Spain 0.9781 2.19% 0.26 0.59%
Sweden 0.9925 0.75% 0.30 0.21%
Bulgaria 0.9706 2.94% 0.53 1.57%
Cyprus 0.9847 1.53% 0.38 0.57%
Czech Republic 0.9856 1.44% 0.65 0.92%
Estonia 0.9646 3.54% 0.76 2.74%
Hungary 0.9817 1.83% 0.62 1.13%
Latvia 0.9694 3.06% 0.52 1.66%
Lithuania 0.9772 2.28% 0.60 1.41%
Malta 1.0232 -2.32% 0.69 -1.55%
Poland 0.9903 0.97% 0.35 0.35%
Romania 0.9726 2.74% 0.43 1.19%
Slovakia 0.9707 2.93% 0.79 2.34%
Slovenia 0.9800 2.00% 0.60 1.21%
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