DISCUSSION PAPER SERIESDP13614

THE IMPORTANCE OF CONSUMERTASTE IN TRADE
Hylke Vandenbussche, Bee Yan Aw-Roberts and Yi
Lee

INTERNATIONAL TRADE AND
REGIONAL ECONOMICSISSN 0265-8003

THE IMPORTANCE OF CONSUMER TASTE IN
TRADE
Hylke Vandenbussche, Bee Yan Aw-Roberts and Yi Lee
Discussion Paper DP13614
Published 24 March 2019
Submitted 22 March 2019
Centre for Economic Policy Research
33 Great Sutton Street, London EC1V 0DX, UK
Tel: +44 (0)20 7183 8801
www.cepr.org

This Discussion Paper is issued under the auspices of the Centre’s research programme in
INTERNATIONAL TRADE AND REGIONAL ECONOMICS. Any opinions expressed here arethose of the author(s) and not those of the Centre for Economic Policy Research. Research
disseminated by CEPR may include views on policy, but the Centre itself takes no institutional
policy positions.
The Centre for Economic Policy Research was established in 1983 as an educational charity, topromote independent analysis and public discussion of open economies and the relations among
them. It is pluralist and non-partisan, bringing economic research to bear on the analysis of
medium- and long-run policy questions.
These Discussion Papers often represent preliminary or incomplete work, circulated toencourage discussion and comment. Citation and use of such a paper should take account of its
provisional character.

Copyright: Hylke Vandenbussche, Bee Yan Aw-Roberts and Yi Lee

THE IMPORTANCE OF CONSUMER TASTE IN
TRADE
Abstract
This paper documents the importance of consumer taste in trade flows using Belgian firm-productcustoms data by destination. We identify consumer taste through the use of a control function
approach and estimate it jointly with other demand parameters using a very flexible demand
specification. Consumer taste is identified for every trade flow. The results show that taste
decreases in distance but this relationship is not monotonic. The contribution of consumer taste toactual export revenue ranges between 1-31% depending on the product category in the foodindustry. Overall, the demand shifters, taste and product quality explain twice as much of thevariation in export revenues than cost.
JEL Classification: F14
Keywords: tastes, Quality, productivity, exports, firm-product, consumer heterogeneity
Hylke Vandenbussche - hylke.vandenbussche@kuleuven.be
University of Leuven , KU Leuven and CEPR
Bee Yan Aw-Roberts - byr@psu.edu
Penn State University
Yi Lee - y14lee@gmail.com
National Tsing Hua University

Acknowledgements
We especially thank Jonathan Eaton, Kala Krishna, Mark Roberts, Kim Ruhl, James Tybout and Stephen Yeaple, Lee Branstetter,
Gabriel Felbermayr, Keith Head, Brian Kovak, Giovani Peri, Saso Polanec, Larry Qiu and other seminar participants at Penn State,
Hong Kong University (HKU) and Carnegie-Mellon amongst others. We thank the National Bank of Belgium for customs data.

Powered by TCPDF (www.tcpdf.org)

The Importance of Consumer Taste in Trade ∗Bee Yan Aw †1 , Yi Lee ‡2 , and Hylke Vandenbussche §3
1

The Pennsylvania State University
2

3

National Tsing Hua University

University of Leuven (KULeuven)
March 22, 2019

Abstract
This paper documents the importance of consumer taste in trade flows using Belgian
firm-product customs data by destination. We identify consumer taste through the use of
a control function approach and estimate it jointly with other demand parameters using avery flexible demand specification. Consumer taste is identified for every trade flow. The
results show that taste decreases in distance but this relationship is not monotonic. The
contribution of consumer taste to actual export revenue ranges between 1-31% dependingon the product category in the food industry. Overall, the demand shifters, taste andproduct quality explain twice as much of the variation in export revenues than cost.

JEL Classification: F12, F14
Keywords: tastes, quality, productivity, exports, firm-product∗We especially thank Jonathan Eaton, Kala Krishna, Mark Roberts, Kim Ruhl, James Tybout and Stephen
Yeaple, Lee Branstetter, Gabriel Felbermayr, Keith Head, Brian Kovak, Giovani Peri, Saso Polanec, Larry Qiu
and other seminar participants at Penn State, Hong Kong University (HKU) and Carnegie-Mellon amongst
others. We thank the National Bank of Belgium for customs data.
†
Corresponding author: byr@psu.edu‡
ylee@mx.nthu.edu.tw§
hylke.vandenbussche@kuleuven.be1Introduction
Recent papers in international trade have documented the importance of the demand sidein explaining firm-level variation in revenue and export sales (Feenstra and Romalis (2014),
Foster et al. (2016), Fan et al. (2016), Hottman et al. (2016), Roberts et al. (2018)). In thispaper, we go beyond general firm-level appeal on the demand side by identifying two separate
demand shifters for each product within the firm. The role of consumer taste in demand isanalyzed separately from the role of product quality in demand. For this purpose we use dataon trade flows across countries for the same products. This allows us to identify consumer taste
as a separate source of variation in the data, clearly separating it from quality, market size,
competition effects, distribution costs and markups which may also vary by destination.
There are several reasons for focusing our attention on consumer tastes. First, when firms
export their products abroad, many keys to success are at the firm-product level, such as
the marginal cost of production and the product quality. 1 But some critical determinants for
success seem to go beyond cost and quality issues. For example, the export of horse meat to
the United Kindgom (UK) or the United States (US) is unlikely to be successful, even whenthe quality of the horse meat is very high. Similarly, exporting pork to Muslim countries islikely to have limited export success for religious reasons, regardless of the quality of the pork.
Hitherto this source of consumer heterogeneity has not been quantified. A second reason to
study consumer taste is that over the past decade, firms marketing budgets have risen steeply,
while their expenditures on innovation and cost-saving technology has fallen. 2 A recent survey
by Accenture of around 287 US manufacturing firms, indicated that 61% of responding firms
reported that seeking nirvana through offshoring for costs and productivity reasons was nolonger their priority. Instead, the priority of firms was to get closer to the consumer andtherefore to demand..3 For example, Nike has recently been setting up speed factories to
1Antoniades (2015); Gervais (2015); Fan, Li and Yeaple, (2016, 2018); Verhoogen (2008).

2

The Economist, https://cdn.static-economist.com/sites/default/files/images/print-edition/20150829W BC539.png
http://www.areadevelopment.com/BusinessGlobalization/4-20-2011/backshoring-us-manufacturing-labor-costs1266672.shtml

3

1

produce sneakers in the US again but tailored to local tastes. Adidas has done the same inGermany. 4 Thus, understanding the role of consumer taste can potentially shed new light on
the location strategies that firms pursue which in turn affect the direction and magnitude of
their trade flows.
A third reason is that consumer taste affects sales, revenues and hence firm growth. It
is, therefore important to understand its role relative to other determinants of profits. Thisis an important step forward in the trade literature and potentially also in the industrial
economics literature that studies firm growth and its underlying determinants. Consumer taste
as a micro-economic determinant of exporting will also affect macroeconomic outcomes sinceaggregate exports are an important component of country-level GDP (Gabaix (2016); Giovanniand Levchenko (2012)). For this reason too, understanding the role of taste is important.
The current literature on quantifying taste in trade is limited to specific products. The
importance of taste has been demonstrated for a few specific products using information on
product-specific attributes by Atkin (2013) for rice and Cosar et al. (2018) for cars. Some otherpapers in the literature have used existing and external measures of quality such as Crozet etal. Mayer (2012) for French champagne and Armstrong and Chen (2009) for wine. But only for
relatively few food products, such external measures exist. For most food products, no taste
or quality indicators are available.
Our approach to identifying consumer taste does not use a demand residuals approach since
this would potentially be confounding taste with other unobservable demand and cost shifters.
Instead we want to tease out the part of the demand residual that is likely to best capture
consumer taste. For this purpose, we adopt a control function approach with variables that
capture different dimensions of consumer heterogeneity related to tastes across countries. In thecontrol function for consumer taste, we include variables which have been successfully used in
the international business and gravity literature, such as common spoken languages, religions,4

https://www.forbes.com/sites/retailwire/2017/09/27/nike-can-make-you-custom-sneakers-in-under-an-hour/#29d2a8ce72f62

culture and nationalities which are likely to affect taste.
There is an abundance of anecdotal evidence to suggest that wherever people share thesame language, religion or cultural history, there is strong taste overlap. 5 For example, sea
cucumbers are a delicacy in the Chinese-speaking part of the world but rank low in the tasteof Westerners. Cheese is loved in many English-speaking countries, but many Chinese people
find its taste appalling. Food habits in Australia are closer to the UK and the US than toAsia, clearly illustrating that culture can be more important than physical distance for taste
since Australia is closer to Asia. Immigration and overlap in nationalities seems also associatedwith food habits. For example, pizza which originated from Naples in Italy, came to the US in
the 19th century with the arrival of Italian immigrants. 6 Similarly, it was an influx of German
and Italian immigrants into Australia in the 1950s that helped to expand the wine industry
because they were adept to drinking European varietals. 7 The pizza and wine example illustrate
how local tastes for food can be influenced by foreign immigrants. Common religion between
countries may also generate a common taste. An example of this can be found in the attitudes
towards alcohol in protestant versus Roman catholic countries. Northern European cultures
which tend to be Protestant, perceive many problems related to alcohol which are expressedin numerous public policies to control its consumption and have low alcohol consumption rates
compared to the more Southern European Roman Catholic cultures. 8
In short, there is an overwhelming number examples that justify the use of common language, religion, culture and nationalities indicators in a control function approach for consumer
taste. In this paper we apply it to food products but in principle our approach can be applied
to any product category where taste is important. Combining it with information from ourdatabase, we generate a measure of consumer taste for every firm-product sold in every country. Our empirical identification of consumer taste closely follows the methods adopted in the5

Abbott Nutrition,“Ten Surprising Things that Affect Your Taste,” August, 2017.
https://slice.seriouseats.com/2006/02/a-slice-of-heaven-a-history-of-pizza-in-america.html
7
http://sedimentality.com/wine-history/the-history-of-wine-in-australia/
8
http://www.indiana.edu/ engs/articles/cathprot.htm
63

productivity literature. Similar to productivity, consumer taste is an unobservable in the dataand can thus be proxied in the same way. 9
We account for product quality in the demand specification also by using a control function
approach. We follow the literature in this respect and proxy quality with imported input prices
at the firm level, income levels of the destination countries and firm market shares (Bastos,
Silva and Verhoogen (2018); Khandelwal (2010), De Loecker et al. (2016)). Higher quality
outputs are positively correlated with input prices, income levels and market shares in a given
destination country.
We estimate demand functions on trade data of 1,802 firm-product exports (CN8-level)of Belgian firms in eight different food categories using export quantities and prices by firmproduct-destination. Since CN8 products are defined at a more aggregate level than the scanner
data in Hottman et al. (2016), our product definitions are less refined. However, the advantage
of our trade data is that we have destination-specific information on consumer purchases whichallows us to study very different consumers across countries. The large variation in international
consumer heterogeneity will help us to identify taste differences.
Finally, once we identify consumer taste and product quality as structural parameters, wethen back out marginal cost from prices. Based on the joint demand and supply parameters
we perform a decomposition of export revenues to assess the relative importance of taste in
explaining firms’ export revenues relative to other determinants like quality and cost, market
size and markups. We pursue our analysis at the firm-product level, conditional on firms’ export
market participation. Our findings indicate that consumer taste is an important demand shifter,
separate from quality and about equally important to marginal cost. Consumer taste accounts
between 1-31% of actual export revenue depending on the product category. 109This technique has been widely used in the productivity literature to avoid endogeneity of the inputs in the
production function (Levinsohn and Petrin(2003); Olley and Pakes (1996).
10
We know that the main contribution of the demand side lies at the intensive margin from the work of Roberts
et al. (2018) who assess the role of firm-level demand heterogeneity in export participation. By conditioning
on export participation we ignore fixed entry costs as a source of variation in trade decisions which was studied
earlier by Aw et al. (2011).

4

The aim of our paper is to develop quite general quality and taste measures for a wide
spectrum of traded food products, including those where quality and taste rankings do notexist. We propose a method that is easy to replicate and does not require any functional form
assumptions on the demand or cost side. This will then allow researchers to study demand
related aspects in many more markets in the future.
We structure the rest of the paper as follows. The next section lays out the empirical
identification strategy in general. We discuss all the potential issues that can arise whenestimating demand with and without the use of control functions and how we deal with them.
Section 4 then discusses the construction and performance of the control functions. Next, insection 5 we show how marginal cost is backed out from price data after having estimated thefull set of demand parameters but without using any functional form assumptions. Finally insection 6, we engage in a decomposition of trade flows.

2

General Demand Specification

2.1

Identification

In this section, we lay out the empirical identification of the demand parameters of themodel and the potential endogeneity issues that we face. We leave the detailed discussion of
the control functions to the next section.
Consumers in country d have the following general demand function Qjidt for product iexported by firm j in year t:

qjidt = Qjidt [pjidt , λ(X 0 )jidt , δ(Y 0 )jidt , γidt , jidt ](1)

where qjidt is the quantity of product i sold by firm j that is consumed in country d and yeart, λ(X 0 ) represents the control function for consumer taste and δ(Y 0 ) represents the control

5

functions for quality where X’ and Y’ are vectors of variables that proxy taste and quality
respectively. We apply the insights from Levinsohn and Petrin (2003), Olley and Pakes (1996)
and others by replacing the unobservables λ(.) and δ(.) with observables using polynomial
functions to estimate the coefficients which are then used to predict an index of quality and taste
at jidt level. 11 pjidt is the price (f.o.b.) of product i provided by firm j exclusive of transport costand distribution cost. Firm-level demand in a destination can thus vary due to the export price,
the quality offered, the local taste as well as destination specific characteristics such as market
size and market structure of the destination, captured by γidt , a set of product-country-year
fixed effects. Given that our data are trade flows originating from Belgium γidt also captures
product-country specific distance and distribution costs. Finally jidt is the residual term which
captures additional demand and cost shifters. But there could still be important firm-productvariation of trade cost (transport and distribution costs and exchange rate fluctuations) andmarkups potentially present in the residual (jidt ) that could plague the identification of thedemand parameters.
To address this endogeneity problem, we need to instrument for price. The instrument
for price should be highly correlated with the export price to destination d, but should be
uncorrelated with the part of the transport cost still present in the residual term. An instrumentthat satisfies these conditions is an average price of the same firm-product (ji), but exported
to distant destinations. These destinations should not be in the proximity of country d because
then the firm-product transport cost (τjidt ) will be too similar and correlated between nearby
countries. For this reason our instrument for price must be such that it represents the average
price for the same firm-product that are at least 1000 km apart from destination d. This will
ensure that the average price used as an instrument does not reflect firm-product transport costs
11
By using a control function approach for consumer taste and product quality we avoid the endogeneity bias
arising from correlation of these variables with the residual in the demand function. For instance, if not properly
controlled for, taste will enter the residual of the demand function, rendering E(pjidt , jiddt ) = 0, affecting priceas firms may set a higher price for products with a stronger taste. This would result in a misspecification ofdemand and biased coefficients.

6

to destination d. Or put differently we ensure that our instrument is such that E(pjikt , τjidt ) = 0.
There is however another more subtle issue that can arise and which may still generateendogeneity. Suppose that the pass-through rates of costs (exchange rates, markups or other)
into prices would systematically vary with the size of a firm in a destination (Amiti et al.
(2014), Atkinson and Burstein (2008)). This could potentially undermine our instrumentation
strategy because the price in another market would not necessarily get rid of the size effect.
Whether a firm-product market share is positively correlated across markets is ultimately anempirical question. If being large means that pass-through rates are significantly lower thanfor products with small market shares, than our instrumented price would not correct for thatand the instrumented price could still be correlated with the residual of equation (1).
For this purpose, we verify the bilateral correlations between firm-product ji’s market size
across destination markets. We find it to be very low and not higher than 0.2. Thus whilea firm-product ji can be large in one market, it may end up being small in another market.
This suggests that our instrument is still a good one, because the instrumented price will becharacterized by a different pass-through rate than the price in destination d and therefore theinstrumented price is unlikely to correlate with the residual, jidt .
Estimation of the demand function in equation (1) translates in the following empirical
specification which we estimate with a two-stage least squares (2SLS) estimation to obtainconsistent estimates of the price coefficient and the consumer taste and quality index.

lnqjidt = γidt − σid lnpjidt + lnλ̂jidt + lnδ̂jidt + jidt(2)

where qjidt is the quantity of export sold of product i that firm j sold in country d andyear t. pjidt is the f.o.b. price of the firm’s product i that exports to country d. σid represents
the price elasticities of demand which vary across destination country and (HS4)product markets, 12 γidt represents a set of product(HS4)-country-year fixed effects. lnδ̂jidt is the estimated12

We estimate demand elasticities σ by destination both at the HS4 and HS6 product-level. Results hold7

product quality index evolving from the control function for quality and lnλ̂jidt is the estimatedlocal consumer taste index predicted by the control function for taste. jidt accounts for any
unobserved demand shock correlated with price as well as white noise.
Our instrument for price is defined as:

lnP IVjidt =X
1lnpjikt ,Njit k∈S ,k6=d

(3)

jit

where Sjit is the set of the remote countries that firm-product ji is exported to in year t andNjit is the number of export destinations in addition to country d for the firm-product ji.

2.2

Control Function for Consumer Taste

Control functions have long been used to account for unobservable productivity shocks inthe estimation of production function coefficients (Levinshohn and Petrin (2003); Olley andPakes (1996)). Here we estimate demand functions where we introduce a control function
for consumer taste. For this purpose we define a polynomial function over observables that
capture consumer heterogeneity across countries. Our control function for consumer taste isthen embedded in the demand function and estimated jointly with other demand parameters.
Put differently, to control for the unobservable consumer taste in equation (2) we proxy taste
by using a control function approach, lnλ̂ = λ(X 0 ) where X 0 is a set of proxy variables that
capture the taste of consumers in country d for variety ji. The variables that we include forthis purpose are threefold.
First, we include a country-level dimension of consumer heterogeneity through the use ofbilateral country-level indicators on language, religion, nationality and cultural distance thathave been used in the international business and early gravity literature. We can not just use a
destination level trade share e.g. Belgian trade to China to capture the taste for Belgian goods
independent of the product market definition for σ but we obtain more observations at HS4 level.

8in China, since this would be confounding taste with other factors such as with market size ofthe destination country. A normalization of the trade share would not solve this because wealready include product(HS4)-country fixed effects in the demand function, which would makedestination-level trade shares drop out in the estimation of the demand function. Instead we use
a set of exogenous indicators which each capture a different aspect of consumer heterogeneity.
Second, we include a product-level dimension by accounting for the popularity of a specificBelgian product in the destination compared to other destinations e.g. the share of Belgian
chocolates (all brands) going to China versus the rest of the world.
Third, we include a firm-level dimension e.g. how successful a firm is in selling its product toa particular destination compared to other destinations e.g. how much of a particular Belgian
chocolate brand is shipped to China versus the rest of the world, where we normalize for themarket size differences across destinations. This ultimately results in a firm-product-destination
measure of consumer taste for every trade flow.
The business and in particular the marketing literature, extensively proxies for demand differences between countries by using indicators of cultural distance. One of the most frequently
used indicators in this literature was originally developed by Hofstede (1980) and used extensively ever since. 13 Complementary but separately, the gravity literature has also pointed out anumber of variables that capture important aspects of consumer heterogeneity across countries.
These variables include common spoken languages and common religion between countries, 14
the extent of overlap between countries in nationalities 15 and the cultural distance between
countries. 16
For each of the four indicators e.g. common spoken language, common religion, common
nationality and cultural distance, we construct a distribution for each indicator across destination countries. For the first three indicators, we construct bilateral country pair indices by13

Shenkar (2001), Beugelsdijk et al. (2017), Gollnhofer and Turkina (2015)
Melitz (2008); Egger and Lassman (2012), Melitz and Toubal (2014); Egger and Toubal (2016)15
Felbermayer, Jung and Toubal (2010); Head and Ries (1998); Rauch and Trinidade (2002)
16
Guiso et al. (2009); Felbermayer et al. (2010); Hofstede (1980), Kogut and Singh (1988)
14

9

using a method proposed by Egger and Toubal (2016). The bilateral indices reflect the closeness(or distance) in each dimension between any country pair cd. For example, to construct thecloseness index between countries c and d in terms of common spoken languages (CL), we takethe share of people that speak a language l in country c (slc ) and multiply it by the share ofpeople that speak the language l in country d (sld ). Doing this for every language and summingover languages results in the Egger and Toubal (2016) indicator:

CLcd =

X

slc sld(4)

l

A high value for the indicator CLcd indicates that countries c and d have similar distributionsof spoken languages so that countries c and d are close in common spoken languages. 17 Basedon the same logic, we construct the bilateral closeness indices for common religion (CR) andcommon nationality (CN). In Appendix A we document the construction of this closenessindicators more in detail.
For cultural distance, we turn to the Hofstede (1980) data. In this data there are 6 different
dimensions of culture (i.e. individualism, power distance, masculinity, uncertainty avoidance,long-term orientation and indulgence) for every country. To turn them into one composite
index on cultural distance between any country pair, we follow Kogut and Singh (1988). Thisconsists in calculating the sum of the deviations along each of the six cultural dimensions
between countries c and d (CDcd ) as follows:6

CDcd =

1 X (Ikc − Ikd )2
6 k=1
Vk

(5)

where Ikc represents the index for the k th cultural dimension for country c, Vk is the variance17

Melitz and Toubal (2014) provide a set of variables to capture language difference across countries such ascommon official language, common spoken language, common native language, linguistic proximity. Some ofthese indices are highly correlated but common spoken language was found to have most explanatory power ingravity which is also what we use.

10

of the index of the k th cultural dimension. (Ikc − Ikd )2 reflects the distance in the k th culturaldimension between countries c and d and a large (Ikc − Ikd )2 indicates that countries c andd are dissimilar in the k th cultural dimension. Since the range of each cultural dimension isdifferent, we divide (Ikc − Ikd )2 by the variation of the k th cultural dimension. CDcd stands forthe cultural difference between countries c and d and a high CDcd indicates that countries cand d are less similar in culture. As such the CD index is like an average difference across the 6
dimensions of the Hofstede indicator. The squared term corrects for the sign of the differences
between countries but it also gives a larger weight to larger differences which then weigh morein the average.
To obtain firm-product-country variation, we calculate the weighted indices of CL, CR, CN,
CD across all country pairs c and d within a firm-product ji.

mMjidt= sjidt × mBelgium,d , where m = CL, CR, CN, CD(6)where mBelgium,d represents for the mth dimension of the indices on consumer heterogeneity(i.e., closeness in language(CL), closeness in religions(CR), closeness in nationality components(CN), and distance in culture(CD)) between countries d and Belgium. The weight sjidt isthe ratio of firm j’s sales revenue in product(CN8) i that is exported to country d to firm j’s
total export sales in product i, normalized by the market size of the destination country. 18 The
weights, sjidt account for the firm-product’s sales distribution across destinations, normalized
for market size of the destination. To illustrate how the weight sjidt operates consider the
trade in Belgian chocolates. While consumers in China and Japan are similar to each other,
they are very different from Belgium for every indicator of consumer heterogeneity. Based on
the country-level indicators, our control function attributes a low value of consumer taste to18

We normalize by weighting the firm-product sales share with the GDP of the destination relative to the
GDP of a reference country. For example, if country A’s GDP is half of country B’s GDP then we multiply
a firm’s trade flow going to country B by one half. This accounts for the difference in market size between
countries A and B.

11

all Belgian products shipped to both China and Japan. Now suppose that a particular brand
Godiva chocolates sells 5% of its global sales to China and 15% to Japan, this results in a taste
for Godiva chocolates that is stronger in Japan. Suppose that another Belgian chocolate firm,
Neuhaus ships 50% of its chocolates to Japan, than the control function assigns a stronger taste
to Neuhaus in Japan.
And finally, in the control function for taste, we also include a product-level share bydestination, zidt , to capture how much of a product i is shipped to a particular destination d.
So even when China and Japan each get a similar share of Godiva chocolates shipped to them,
if the share of (all) Belgian chocolates shipped to Japan is higher than to China, the taste for all
Belgian chocolates in Japan, including Godiva, will be higher than in China. A normalizationof zidt is not warranted since the estimation of the control function for taste is embedded in
the demand function (equation (2)) which already includes product(HS4)-destination FE(γidt )that controls for product market size effects in the destination. Also zidt does not drop in the
demand estimation since measured at more disaggregate product(HS6)-country level.
The control function for consumer taste λ(X 0 )jidt , then becomes a function of the weighted
bilateral indices between Belgium and the country of destination for spoken language (CL),religion (CR), nationalities (CN) and cultural distance (CD) as follows:lnλ(X 0 )ijdt = lnλ[sijdt ∗ (CL, CR, CN, CD), zidt ](7)

which will be proxied by a polynomial in these variables.19
Table 4 shows that the bilateral correlations of all the four indices are very low. Nevertheless
we experimented with applying a principal component analysis on the four indicators in orderto capture the extent of overlap information between them. This was not the case. In fact, thetaste measure that we obtain by using a control function for taste using the principal-component
19

We use a polynomial of order two. The use of a higher order polynomial of degree three does not affect our
results qualitatively

12

measure is highly correlated with the control function for taste where we just include the fourbilateral indicators (0.83). Therefore we do not gain much by applying a principal-component
method as suggested by the low correlations between the dimensions of consumer heterogeneity
in Table 4. In the estimation of the demand function, each of the (CL, CR, CD, CN) indicators
is significant either by itself or in its interaction terms. 20 Thus, if we can not reduce the numberof consumer heterogeneity dimensions, since this would end up in the residual of the demand
estimation, thereby lowering our estimate of taste.
The resulting taste index then consists of (i) a country-level dimension that captures thebilateral consumer heterogeneity between Belgium and the destination and that affects thetaste index for all products shipped there; (ii) a product-level dimension that indicates how
important a destination d is for a particular exported (CN8) product; (iii) a firm-product-level
dimension normalized for market size, that reflects how much a destination d likes an individual
firm’s product relative to other destinations.
Taste indices can be compared for the same firm-product across destinations or for different
firm-products within destinations. Taste indices can also be aggregated at the level of the
product group or the level of the country and are comparable across products and countries.
In the data, the structural parameter for consumer taste is measured at the most disaggregate level of trade flows in the data e.g. firm-product (CN8)-destination level. But in someproduct categories this is asking a lot from the data as it requires a sufficient number of destinations that each firm-product is shipped to. As a robustness check, we also verify our results
for consumer taste defined at the product-destination level of aggregation but results remain
qualitatively the same.
20

Regression results on the demand estimation are available upon request but will not be shown for brevity

13

2.3

Control Function for Quality

For quality, we follow the literature as to what variables to include in the control function,
δ(Y 0 ). Firms are likely to export high-quality products to high-income countries (Schott (2004)).
Therefore we interpret a higher GDP per capita of the destination country as an indication thata higher quality good is shipped there. (Bils and Klenow (2001) and Hallak (2006)).
Producing high-quality products generally requires high-quality inputs (Kugler and Verhoogen (2011), Bastos, Silva and Verhoogen (2018) and Fan et al. (2018)). To control for input
prices we construct a firm-level import price index by calculating the weighted sum of importprices (unit values) of each imported product within a firm. 21 We normalize import prices of
inputs by their (CN8)product mean to control for absolute price differences across products.
Finally, high quality products may have a higher market share (Khandelwal (2010); De Loecker
et al. (2016)).
Given that in the data, firms may export a product i to several countries other than countryd, we use the weighted sum GDP per capita across all countries(W GDPjit ) that a firm-productpair is exported to. As weights we use the sales share of a firm-product ij to country d in
the total exports of firm-product ij. This measure accounts for the idea that the higher the
average GDP of all the countries that a firm-product is exported to, the higher the quality ofthe product.
In addition, we also include the local GDP per capita of the destination, weighted by thefirm-product market share (LGDPjidt ) in the control function for quality. The weight that we
use is the share of firm-product ij in country d over the total sales of product i in country
d. Additionally, we include the firm-product market share within destination d (fjidt ). This
captures the idea that within a destination, higher quality products have a higher market share.
Our quality measures thus capture the following ideas. The higher the income level (W GDPjit )21

P P
Here P IM Pjt = z o sjzot × IM Pjzot where sjzot is the import share of firm j’s total imports that comefrom good z imported from country o and IM Pjzot is the import price of good z coming from country o.

14

of the destination countries that the firm-product pair ji is exported, the higher the productquality that the firm offers. We further allow firms to quality differentiate across destinations,by including the local GDP per capita of destination d (LGDPjidt ), to capture the notion thatfirms may offer higher quality to countries with higher local GDP per capita. 22 Additionally,
we include the firm-product market share within destination d which attributes higher quality to higher market shares. And finally since producing high-quality products also require
high-quality inputs, we also insert imported input prices in the control function for quality(P IM Pjt ).
The control function for quality then becomes δ(Y 0 )jidt a function of Import prices (P IM Pjt ),the weighted GDP per capita across destinations (W GDPjit ), the weighted local GDP per
capita of the destination (LGDPjidt ) and the firm-product market share within the destination(fjidt ):lnδ(Y 0 )jidt = lnδ[P IM Pjt , W GDPjit , LGDPjidt , fjidt ](8)This control function is introduced in the demand function as a polynomial in these variables,
whose coefficients are simultaneously estimated with other demand parameters.

3

Demand Estimation
We estimate the demand function:
00lnqjidt = γidt − σid lnpjidt + lnλ(X )ijdt + lnδ(Y )jidt + jidt(9)

To ensure that the Corr(lnpjidt , jidt ) = 0, we use the average export prices in other remote
destinations k (ln pji−dt ) as the instrument variable for price as shown in equation (9). We
22

Schott (2004) finds a positive relationship between the unit value of U.S. imported products and the percapita GDP of exporters.

15

define remote countries based on the following criteria: (1) country k and country d do notshare the same border; (2) country k and country d do not have a colony history; (3) the
distance between countries k and d is at least 1,000 km.
By using 2SLS, the estimation of the demand function in equation (9) allows us to empirically identify three important parameters e.g. the elasticity of demand σ̂id , the consumers’
taste lnλ̂jidt and quality index lnδ̂jidt .
The empirical counterparts to the control functions for quality and taste represented inequations (7) and (8) are constructed at the firm-product-country level quality index (lnδjidt )and taste index (lnλjidt ) as follows:lnδ̂jidt =X
v=P IM,W GDP,LGDP,flnλ̂jidt =

X

X

v
n
β̂vn (Xjidt
Xjidt)

v=P IM,W GDP,LGDP,f n=P IM,W GDP,LGDP,flβ̂l Xjidt+

l=CL,CR,CN,CD,z4Xv
β̂v Xjidt
+

X

X

l
m
β̂lm (Xjidt
Xjidt
)

(10)

l=CL,CR,CN,CD,z m=CL,CR,CN,CD,z

Cost Estimation
We retrieve the demand parameters from estimating equation (9). In particular we obtain

the elasticity of demand (σ) as the regression coefficient on price (∂lnqijdt /∂lnpijdt = σ), theoptimal pricing condition for every destination is then used to back out the marginal cost fromthe prices without using any additional functional forms on the supply side.

pijdt [1 − (1/σid )] = M Cjidt(11)

Since prices are f.o.b. export prices, our estimates of marginal cost are exclusive of transportand distribution cost but inclusive of the marginal cost of production which also includes costs
related to vertical (quality) and horizontal (taste) product differentiation. 23 Our estimates for
23

Firm-product-destination-year (jidt) specific transport and distribution costs are unobservables of the demand function. But our instrumentation strategy, ensures that their presence in the residual does not contami-

16

marginal cost vary at firm-product-destination level since we back out cost from destinationlevel prices.

5

Data Description and Documentation
Our trade data consist of Belgian customs data of manufacturing firms for the period 1998-

2005 with information on firms exports in quantities and values by product and by destinationand firm imports by product and country of origin. The Belgian export data has been handled
at the National Bank of Belgiums (NBB) Trade Database, which covers the entire population ofrecorded trade flows. 24 The trade data are recorded at the firm-product-country-year level, i.e.
they provide annual information on firm-level trade flows by 8-digit Combined Nomenclature(CN8) product and by country. Export prices and import prices are unit values which we
obtain at the level of the trade flow, by dividing export values by quantities. 25
The period 1998-2005 has a congruent reporting threshold for firms to be considered asexporters over time. This threshold at firm-product level was raised in 1998 from 104,115e
to 250,000e but did not change afterwards until 2006. However, during the period of ouranalysis, the HS6 product classification altered three times. To address the changes in productclassifications over time, we concord the product codes along the lines of Bernard et al. (2019). 26
In doing so we lose about 20% of export value in our data, but this ensures that our data arecleaned of product code changes.
In our analysis we focus on the Food industry. Belgium exports a wide range of foodnate the elasticity of demand, estimated as the coefficient on price. Thus, while we cannot separate them, they
do not affect our estimates. See section 2.1.
24
We exclude transactions that do not involve a “transfer of ownership with compensation”. This meansthat we omit transaction flows such as re-exports, the return, replacement and repair of goods and transactions
without compensation, e.g. government support, processing or repair transactions, etc.
25
The CN8-product classification is similar to the HS6 classification for the first 6 digits but offers moreproduct detail in the last two digits.
26
Instructions for concordance of trade classifications over time can be found here:
https://www.sites.google.com/site/ilkevanbeveren/Concordances and is described in Van Beveren et al.
(2012)

17

products. The food industry accounts for 5-6% of total export revenue during 1998-2005. This
results in a sample of 1,802 firm-products in seven different food categories (from HS2, 15-22)
for which we can identify taste in every destination they are exported to.
The raw data on common languages and common religion come from Melitz and Toubal(2014), 27 while data on nationalities as a share of the population come from the World Bank 28
and finally, the cultural distance indicators are obtained from Hofstede (1980)..29 In Table 1
we start by listing the four parameters and the level of aggregation at which they are measured
empirically. This comprises the taste index (λjidt ), the quality index (δjidt ) and the marginal
cost index (cjidt ) which are all estimated at the same level of disaggregation e.g. the firm-(CN8)
product-country-year level. Our preferred estimate of the elasticity of demand (σid ) is at the
product-country level. The reason is that we require a sufficient number of firms selling thesame product to the same country in the data for a well-behaved estimate. Estimating σid
at (HS6) Product-Country level provides a large number of inelastic demand (< 1) estimates.
In our preferred specification, we therefore estimate results using the demand elasticity at the(HS4)product-country level. Results on the importance of the taste parameter remain the same
whether we identify σid at the (HS6) or (HS4)Product level.
Table 2 documents the number of trade flow observations, where each observation is a firmproduct (CN8)-destination export flow. We have over 100,000 trade flows. Observations are
spread relatively evenly over the broad product (HS2)classifications ranging from 15 to 22. 30
For expositional purposes, in Table 2 we aggregate over the destination countries, to show thenumber of observations in regional blocs to give an overview of where exports are being shippedaround the world.
Table 3 documents the variation in the raw data on the bilateral indices between Belgium
27
http://faridtoubal.com/research.htm. This dataset gives bilateral indicators for every pair of countries.
Here we only use the data between Belgium and its trading partners.
28

http://www.worldbank.org/en/topic/migrationremittancesdiasporaissues/brief/migration-remittances-data

29

Hofstede indicator can be found from https://geerthofstede.com/research-and-vsm/dimension-data-matrix/
HS2 range from HS2=15 which is Animal or Vegetable Fats and Oils to HS=22 which is Beverages, Spirits
and Vinegar
30

18and country of destination of common spoken language (CL), common religion (CR), common nationality (CN) and cultural distance (CD), where for expositional purposes we again
aggregate destinations into regional blocs. The details of the construction of the Common Nationality (CN) index is reported in Appendix A. The other variables are from the literature andreflect the raw data. There are 194 countries for which we have bilateral indices on common
language (CL) and common religion (CR), and 208 countries with information on common
nationality (CN). However, there is much less information for the bilateral cultural index (CD)for which we have 62 countries. In the demand function estimation, we only keep countries
with information on these four indices, which implies a loss of 24% of observations.
The correlations between the raw data on the four different bilateral indices, is given in
Table 4. The matrix shows low correlations between all four, which justifies the inclusion of allof them as they seem to capture different aspects of consumer heterogeneity across space.

6

Parameter Estimates

6.1

Elasticity of Demand

From the estimation of the demand specification in equation (9), we obtain the elasticitiesof demand (σid ). Table 5 documents the estimated average elasticities of demand (σid ) which
for expositional purposes we aggregate up to the broad (HS2)product categories. Averagevalues range between 1.9 and 3.15 with standard deviations between 0.5 and 1.4. While thesevalues may appear somewhat low compared to other studies, it has to be kept in mind that they
result from a unique demand specification in which we separately include horizontal and verticaldifferentiation shifters. The inclusion of consumer taste and product quality demand shifters
absorbs some of the variation that otherwise would be attributed to the demand elasticity.
Without the inclusion of control functions for these additional demand shifters, an endogeneity
bias would occur and result in an upward bias on the demand elasticity estimates. Thus it is19

not surprising that our estimated elasticities are lower than those resulting from more standard
demand estimations.

6.2

Average of the Indicators across Regions

The main parameters of interest for this paper can be found in Table 6 where we average
the estimated taste, quality and marginal cost indices by regional bloc. Average marginal
cost and quality of exported products vary much less across destinations than taste. Table
6 indicates that taste of consumers for exported Belgian products varies substantially. The
taste parameter is always positive for any firm-product-destination flow as long as a product ispresent in a market. 31

6.3

Correlations of the Indicators

The correlation matrix between our demand and cost parameters is listed in Table 7. The
low correlation between consumer taste and the marginal cost index confirms that our taste
measure is not picking up the presence of distribution networks. Distribution networks require
cost outlays (Arkolakis (2010)) which can only be financed by highly productive firms with low
marginal cost. The low correlation between marginal cost and taste suggests that our taste
measure captures an inherently different source of variation in the data than the presence ofdistribution networks.

6.4

Variance of the Indicators

One pertinent question that can be raised is whether the variance in the parameter estimates
are mainly determined by the firm, product or destination level. In Table 8 we decompose thevariance of the taste parameter in our data into its three potential sources of variation. In
31

But note that the mean indices are expressed in logs which is why the taste index takes on a negative value
in some regions.

20

columns (1)-(3) we show the decomposition of the variance of the consumer taste index. About
68% of the variation is explained by the country dimension, about 24% by the product dimension
and only 8% by the firm dimension in the data. This suggests that consumer taste is a sourceof variation that is primarily driven by consumer heterogeneity across destination countries.
In columns (4) to (6) of Table 8, we decompose the variance of the quality index. Whether
firms vary the quality of their product across destinations is an open question. In this paper
we do not take an initial stance on how firms set quality. Therefore we empirically identify
quality at firm-product-destination level, which is the most disaggregate level possible in the
trade data. A decomposition of the variance of our quality measures in the data, shows that themain source of quality variation comes from the firm and product-level dimension explaining
respectively 65% and 32% of the variance of the quality index. The country-dimension explains
much less and only accounts for 3% of the quality index variance in our data. Thus, while wecannot exclude quality differentiation by destination, we find it to be small.
And finally in columns (7) to (9), we show the decomposition of the variance of the marginal
cost index. Most of the variation in costs come from the product-level which explains around60%, while the firm-level dimension explains about 27% and the country-dimension about 13%of the overall cost variance. This suggests that for most of the marginal cost differences intrade flows are due to different product-level technology resulting in different marginal costs.

7

The Importance of Consumer Taste

7.1

Consumer Taste and Distance

A first look at the data on the consumer taste index is shown in Figure 1. We aggregate ourmeasure of consumer taste up to the country-level and then plot the average taste for Belgian
food products for each destination in the world. The taste index is normalized between zeroand one as indicated in the legend to the Figure. Darker colors reflect a higher level of the21

destination specific taste index. The world map clearly indicates how consumer taste evolves
in space. Not surprisingly, taste for Belgian exported products is typically strong in nearby
Western European countries. The map also shows that distance is not the only driving force
underlying consumer taste. Taste for Belgian products is also strong in countries very far awayfrom Belgium such as the U.S., Canada and Australia. The correlation between consumer tasteand distance from Belgium in our data, is negative but not stronger than -0.6. This suggeststhat with a doubling of distance, taste falls by half. The scatterplot in Figure 2 between λjidt
and distance (both in logs), shows the negative relationship. Eyeballing Figure 1 shows that
distance from the country of origin is likely to contain information on consumer taste decay.
However consumer taste for Belgian products does not vary monotonically with distance inspace, suggesting that taste is really picking up another source of variation than distance whichis to be expected since in the demand estimation of Equation (9), we included product-country
fixed effects to control for bilateral distance at product market-level.

7.2

Taste for Chocolates

Both our measures of quality and consumer taste generate results that are intuitive and that
correspond with case based evidence of quality and taste in some product categories. A good
example is chocolates, a well-known Belgian export product. This is a product category forwhich the CN8-product classification makes a distinction between high-end dark chocolates withhigh cocao content (18062010) and low-end milk chocolates with low cocao content (18062030).
Since cocao is the main and most expensive ingredient in chocolates, dark chocolate is generally
regarded as higher quality than milk chocolate. When we compare this independent productquality ranking embedded in the product classification with the quality and taste indices obtained from our empirical strategy above, our priors are confirmed. In every destination where
chocolates are sold, we find that high-end dark chocolates with a cocao content exceeding 30%,
has a higher quality index than the low-end milk chocolate with a much lower cocao content.
22

Marginal cost for dark chocolates is significantly higher than for milk chocolates. In terms oftaste, we consistently find stronger taste for the sweeter low-end milk chocolate than for more
bitter high-end dark chocolate. These quality and taste rankings of chocolates are independentof the specification that we run or the level of aggregation of our consumer taste variable. The
values generated by our taste and quality indices correspond quite well with industry reportson the chocolate market and chocolate consumption where milk chocolate typically holds thelargest market share but dark chocolate is known to be more expensive and to have healthbenefits. 32

7.3

The Decomposition of Export Revenues

How important is consumer taste in explaining export performance at the firm-productcountry-year level? A decomposition of export revenues, which are a function of both demandand cost estimates, will allow us to assess the relative importance of consumer taste compared
to quality and marginal cost.
Our decomposition is in the spirit of Hottman et al. (2016), but whereas they pursued it
at firm-level, our decomposition is at firm-product-country level. The coefficients arising fromthe decomposition can be interpreted as the percentage variation in export revenues that is
explained by each particular indicator including consumer taste.
Based on the estimated demand function (Eq. (2)) and the firm’s optimal price (Eq. (11)),firm j’s export revenue of product i in country d can be expressed as:lnrjidt = lnpjidt + lnqjidt
bjidt + lnδbjidt + jidt= γidt + (1 − σid )lnpjidt + lnλ
σid bjidt + lnδbjidt + jidt+(1 − σid )lncjidt + lnλ= γidt + (1 − σid )ln
σid − 1
|
{z
}
Midt

32

https://www.grandviewresearch.com/industry-analysis/chocolate-market

23

(12)

Equation (12) shows how export sales revenue at firm-product-country level can be decomposed into its separate components: the variation of market size and market competition, including markup variations (Midt ), firm-product-destination costs (lncjidt ), firm-productdestination quality (lnδ̂jidt ) and firm-product-destination consumer taste (lnλ̂jidt ).
Following Hottman et al. (2016), we regress each component of the right-hand side ofequation (12) on lnrjidt to get the contribution of each component of firm-product-destination
export revenue on total export revenues. This is given in equation (13).

Midt = βM lnrjidt + εMjidt
ˆ = βλ lnrjidt + ελlnλjidtjidt
ˆ = βδ lnrjidt + εδlnδjidt
jidt(1 − σid )lncjidt = βc lnrjidt + εcjidt
jidt = βR lnrjidt + εR
jidt

(13)

Each of the β coefficients in equation (13) can now be interpreted as the “percentage variation of the revenue explained by the indicator”. As such the β coefficients can directly becompared with each other.
Empirical findings of the decomposition are reported in Table 9. The decomposition resultson consumer taste, given by βλ , vary between 11-17%, depending on whether we define consumer
taste at its most disaggregate level e.g. the firm-product-country level (col. (1)-(2)) or at the
product-country level (col. (3)-(4)) and depending on whether we normalize the weight sijdt inthe consumer taste control function by the market size of the destinations (columns (4)). In
general we can say that defining consumer taste at its most disaggregated level in the data as
in column (1), results in higher βλ , while normalizing for market size lowers βλ as in column(4). Decomposition results however appear stable and the importance of consumer taste in
explaining export revenue is not too much affected by the level of aggregation and of the24weighting scheme that we used in the control function for consumer taste. 33
The corresponding estimated coefficients for the quality index ranges between 20-23% andfor the marginal cost index between 13-14%. Market size and other effects account for about
7-8% of the variation in firm-product-country export revenues. The coefficient on the residualcomponent ranges between 42-45%, depending on the specification. 34 Despite the fact thatthe residual component is substantial, the relative importance of consumer taste is important.
Relative to the other structural parameters quality and cost, we find consumer taste in foodproducts to be about equally important to marginal cost in the decomposition of trade flows.
Table 9, shows that demand shifters (taste and quality) together explain twice as much of thevariation in export revenues than costs, independent of the specification in the decomposition.
However, decomposition results vary a lot by product category. In Appendix Table A-3
we show decomposition results for every HS4 product category based on specification (2) inTable 9 which features a taste estimate of 13% which is in the middle of the range of estimates.
Table A-3 decomposition results show that the importance of consumer taste index varies a lot
depending on the product category and ranges between 1-31%. For example in product group
Belgian Ice cream (2105), consumer taste explains 27% of the export revenues, compared to 24%
explained by quality and 7% explained by marginal costs. Appendix Table A-3 decomposition
results show that in some product categories, consumer taste can become more important thanquality or marginal cost at firm-product-country level to explain variation in export revenues.
33

In Aw et al. (2017), consumer taste was proxied by product-country dummies and picked up more of thevariation in the decomposition. Here we tease out the part of the residual that is related to taste and findsmaller results.
34
Goodness-of-fit measures in firm-level panel data are typically very low, especially at the level of disaggregation that we consider in the data.

25

7.4

Robustness Checks

7.4.1

Balanced Panel Results

Thus far in the analysis, we have used all observations in our data set. But the question canbe raised whether there are selection effects at work when not every product is exported to every
destination. For this purpose we take a look at the minimum cutoff level of the indices and how
they vary with distance from Belgium. If there are selection effects at work then we should findthat the composition of Belgian export products differs across destination countries. Table 10shows a positive correlation between distance to destination and the minimum product quality
present in a destination. This suggests that the further away the destination, the higher theminimum quality offered in that country. 35 We also find a positive correlation between distanceand the minimum cost at firm-product level. To understand this result, we have to keep inmind that the cost index used here also include the cost of producing higher quality. Therefore
our cost measure is not simply the inverse of productivity as it is in other models. Similar
to the minimum quality, the results in Table 10 suggest that the minimum cost of productspresent in a destination goes up in distance. 36 Finally, in column (2) of Table 10, we find anegative correlation between the distance to destination and the minimum tastes index present
in the market. To understand this result we refer to Figure 1 clearly showing how taste decaysin space. For every product shipped to a further destination, taste will be lower. hence theminimum cutoff in taste will also be lower in more distant destinations which is what is reflected
in Table 10.
Results in Table 10 on the unbalanced panel data show that product composition varies
across destinations. Fewer products are shipped to more distant destinations. Only productsof higher quality, higher productivity and higher taste make it to more distant destinations. In
35

Baldwin and Harrigan (2011) find similar results.
The positive correlations between distance to destination and the quality (cost) threshold also hold if the
1 percentile of quality (cost) index is used instead of the minimum level of quality (cost) indices across firms
within one destination.
36

26

the empirics, we condition on firms being present in a market and we do not explicitly study
entry into export markets. The results in Table 10 suggest that a decomposition of export
revenues into cost, quality and taste determinants, as we did in the previous section, may beaffected by a different product composition being present in each destination.
To verify whether results change in a balanced panel we present Table 11 where we now
only include trade flows that are present in every destination. Results for the balanced panel
are similar to the ones in Table 9 even though the number of observations drops substantially.
Taste continues to feature as an important determinant (10-16%) in the decomposition ofexport revenues. Compared to marginal cost, consumer taste together with quality still explain
the large fraction of data variation in export revenue. The low values on the marginal costcoefficients in the balanced panel can be ascribed to the fact that the only products shippedto every country in our data belong to the food category chocolates (1806) for which costdifferences between firm-products appear to be very low.

7.4.2

Age of the Firm

We next examine whether our results on consumer taste are picking up the age of the firme.g. how long a firm-product has been present in a destination market. In order to define a
firm-product age, we first drop the firm-(CN8)-product-destination combinations that appear
in the first year of our panel since we have no information on how long they have been in thedestination market e.g. we do not know their age.
Next, we run an OLS regression of our taste measure on ln(age). This results in a very
low correlation of 0.03. When we then insert ln(age) as a separate regressor in the demand
equation (9), the age variable is not significantly different than zero. The correlation of ourtaste variable in the models without and with age (whenever we have information on age), isaround 0.98 implying that the ranking of our earlier taste index does not change much whencontrolling for the firm-product age in the demand function estimation as illustrated in taste

27

with and without age included in the demand are plotted in Figure 3 which clearly showing
the strong correlation between the two measures.

8

Conclusion
This paper is a generalization of the approach used in Aw et al. (2017) where consumer

taste was estimated from a structural trade model with explicit demand and cost functional
forms. We innovate our earlier approach by now proposing an identification strategy that isvery general and independent of functional forms on the demand and cost side. Despite usinga very different approach, we reach the same conclusion e.g. that consumer taste for many
products and destinations is an important source of sales variation in the data.
In contrast to other literature, we do not apply a residuals approach to approximate consumer taste. Instead we tease out that part of the demand residual that is most likely capturingconsumer taste heterogeneity. Our approach for identifying consumer taste consists in applying
a control function approach similar to that used in the productivity literature. The variables
that we include in the control function are coming from the international business and marketing literature as well as the early gravity literature and all reflect different aspects of consumer
heterogeneity across countries. The approach is easy to replicate for anyone with access tofirm-level customs data.
We find consumer taste to be important in explaining exporters’ success. Taste is a fundamentally different source of heterogeneity in the data than quality even though the twodimensions are often lumped together to make up firm-appeal. In the data we find quality ofa product to vary mostly by firm, while consumer taste for a product varies more by country
than by firm.
This has important implications for firm growth. When firms enter export markets, theyneed to recognize the important role played by destination specific consumer tastes. The liter-

28

ature thus far indicate that export entry patterns are solely ascribed to entry costs (Albornoz
et al. (2012), Morales et al. (2019)). But the findings in this paper put forward an interesting hypothesis - that similarity of consumer tastes between the countries can also affect entry
patterns.
Another line of future research for which consumer taste can be important is the gravity
literature (Redding and Weinstein (2019)). Without disentangling consumer taste from distancein a gravity model, the role attributed to distance will be overestimated. Distance is typicallyconsidered as a proxy for transport cost, but in this paper we show that distance in part also
reflects the decay of consumer taste in space. Without explicitly controlling for consumer taste,
the distance coefficient in gravity models will be biased.
Our finding that the demand side is important for export revenue may also offer a potential
explanation for the productivity puzzle. In recent years, firms invest less in cost reductions andproductivity improvements, but more in product innovation and marketing (Peters, Roberts,
Vuong and Fryges (2017); Van Beveren and Vandenbussche (2010). This comes as no surprise
to us given that in the decomposition of export revenues, we find demand factors to explain
most of firms’ success.
The importance of consumer tastes also underlines that firm learning about the demandside of the market is crucial. This is consistent with the very recent research on firm dynamics
and the growth of new firms through demand accumulation’ emphasized by Foster, Haltiwanger
and Syverson (2016) and customer accumulation by Eaton, Eslava, Jinkins, Krizan and Tybout(2015). In particular, Foster et al. (2016) show that the size gap between new businesses andestablished ones does not reflect productivity gaps but rather show differences in demand fundamentals. The findings in this paper confirm the importance of demand at a very disaggregate
level of the trade data.

29

References[1] Albornoz, Facundo, Héctor Calvo Pardo, Gregory Corcos, and Emanuel Ornelas (2012)“Sequential exporting” Journal of International Economics, 88(1), pp. 17-31.
[2] Amiti, Mary, Oleg Itskhoki and Jozef Konings (2014) “Importers, Exporters, adn Exchnage
Rate Disconnect,” American Economic Review, 104(7), pp. 1942-1978.
[3] Armstrong, Mark and Yongmin Chen (2009) “Inattentive Consumers and Product Quality,” Journal of the European Economic Association, 7(2-3), pp. 411-422.
[4] Antoniades, Alexis (2015) “Heterogeneous Firms, Quality, and Trade”, Journal of International Economics, 95, pp. 263-273.
[5] Arkolakis, Costas, (2010) “Market penetration costs and the new consumers’ margin ininternational trade” Journal of Political Economy, 118, pp. 1151-1199.
[6] Atkeson, Andrew and Ariel Tomás Burstein (2008) “Pricing-to-Market, Trade Costs, andInternational Relative Prices,” American Economic Review, 98(5), pp. 1998-2031.
[7] Atkin, David (2013), “Trade, Tastes and Nutrition in India”, American Economic Review,103(5): pp. 1629-1663.
[8] Aw, Bee-Yan, Mark Roberts and Daniel Yi Xu (2011) “R&D investment, exporting, andproductivity dynamics”, American Economic Reveview 101 (4), pp. 1312-1344.
[9] Aw, Bee-Yan and Yi Lee (2017) “Demand, Costs and Product Scope in the Export Market”, European Economic Review 100, pp. 28-49.
[10] Baldwin, Richard and James Harrigan (2011) “Zeros, quality and space: trade theory andtrade evidence”, American Economic Journal: Microeconomics 3 (2), pp. 60-88.
[11] Bastos, Paulo, Joana Silva and Eric Verhoogen (2018) “Export Destinations and Input
Prices,” American Economic Review, 108(2), pp. 353-392.
[12] Bernard, Andrew, Emily Blanchard, Ilke Van Beveren, and Hylke Vandenbussche (2019)“Carry-along Trade”, Review of Economic Studies, February, 86(2), pp.526-563.
[13] Beugelsdijk, Sjoerd, Tatiana Kostova and Kendall Roth (2017) “An overview of Hofstedeinspired country-level culture research in international business since 2006” Journal ofInternational Business Studies, 48(1), pp. 30-47.
30

[14] Bils, Mark and Peter Klenow (2001), “Quantifying Quality Growth”, American Economic
Review, Vol. 91(4), pp. 1006-1030.
[15] Cosar, Kerem, Paul Grieco, Shengyu Li and Felix Tintelnot (2018), “What Drives Home
Market Advantage?”, Journal of International Economics, 110(1), pp. 135-150.
[16] Crozet, Matthieu, Keith Head and Thierry Mayer (2012) “Quality Sorting and Trade:Firm-level Evidence for French Wine,” Review of Economic Studies, 79(2), pp. 609-644.
[17] De Loecker, Jan, Pinelopi Goldberg, Amit Khandelwal and Nina Pavcnik (2016) “Prices,
Markups and Trade Reforms”, Econometrica, Vol. 84, Issue 2, pp. 445-510.
[18] Eaton, Jonathan, Marcela Eslava, David Jinkins, C.J. Krizan, and James Tybout (2015)“A Search and Learning Model of Export Dynamics”, Meeting Papers 1535, Society for
Economic Dynamics.
[19] Egger, Peter and Andrea Lassmann (2012) “The Language Effect in International Trade:a Meta-analysis,” CEconomic Letter, 116(2), pp.221-224.
[20] Egger, Peter and Farid Toubal (2016) “Common Spoken Languages and International
Trade,” Chapter 9 in The Palgrave Handbook of Economics and Language, Edited by
Ginsburgh and Shlomo Weber, pp.263-289[21] Fan, Haichao, Yao Amber Li and Stephen Yeaple (2016) “Trade Liberalization, Quality
and Export Prices”, Review of Economics and Statistics, March, pp. 328-358.
[22] Fan, Haichao, Yao Amber Li and Stephen Yeaple (2018) “On the Relationship Between
Quality and Productivity: Evidence from Chinas Accession to the WTO,” Journal ofInternational Economics, 110(1), pp. 28-49.
[23] Feenstra Robert and John Romalis, (2014) “International Prices and Endogenous Quality”,
The Quarterly Journal of Economics, Oxford University Press, vol. 129(2), pp. 477-527.
[24] Felbermayr, Gabriel J., Benjamin Jung and Farid Toubal (2010) “Ethnic Networks, Information, and International Trade: Revisitng the Evidence,” Annals of Economics andStatistics, 97/98, pp.41-70[25] Foster, Lucia, John Haltiwanger and Chad Syverson (2016) “The Slow Growth of New
Plants: Learning about Demand?”, Economica, 83(329), pp. 91-129.

31

[26] Gabaix, Xavier (2016) “Power Laws in Economics: An Introduction”, Journal of Economic
Perspectives, Vol. 30, No. 1, pp. 185-206.
[27] Gervais, Antoine (2015) “Product Quality and Firm Heterogeneity in International Trade”,
Canadian Journal of Economics, 48(3), pp. 1152-1174.
[28] Giovanni, Julian and Andrei Levchenko (2012) “Country Size, International Trade, andAggregate Fluctuations in Granular Economies”, Journal of Political Economy 120(6), pp.
1083-1132.
[29] Gollnhofer, Johanna Franziska and Ekaterina Turkina (2015) “Cultural Distance and Entry
Modes - Implications for Global Strategy,” Cross Cultural Management: An International
Journal, 22(5), pp. 21-41[30] Guiso, Luigi, Paola Sapienza and Luigi Zingales (2009) “Cultural Biases in Economic
Exchange,” Quarterly Journal of Economics, 124, pp. 1095-1131.
[31] Hallak, Juan Carlos (2006) “Product Quality and the Direction of Trade”, Journal ofinternational Economics 68 (1), pp. 238-265.
[32] Hausman, Jerry A. (1996) “Valuation of New Goods under Perfect and Imperfect Competition,” The Economics of New Goods, edited by Timothy F. Bresnahan and Robert J.
Gordon, pp. p. 207 - 248.
[33] Head, Keith and John Ries (1998) “Immigration and Trade Creation: Econometric evidence from Canada,” Canadian Journal of Economics, 31, pp. 46-62.
[34] Hottman, Colin, Stephen Redding and David Weinstein (2016) “Quantifying the sourcesof Firm Heterogeneity”, The Quarterly Journal of Economics, Vol. 131, Issue 3, pp. 12911364.
[35] Hofstede, Geert (1980) Culture’s consequences: International differences in work-related
values. Beverly Hills: Sage Publications.
[36] Khandelwal, Amit (2010) “The long and short (of) quality ladders”, Review of Econmic
Studies, 77(4), pp. 1450-1476.
[37] Kogut, Bruce and Harbir Singh (1988) “The Effect of National Culture on the Choice ofEntry Mode,” Journal of International Business Studies, 19(3), pp. 411-432.

32

[38] Kugler Maurice and Eric Verhoogen (2011) “Prices, Plant Size, and Product Quality”,
Review of Economic Studies, 79, pp. 307-339.
[39] Levinsohn James and Amil Petrin (2003) “Estimating Production Functions Using Inputs
to Control for Unobservable”, Review of Economic Studies, 70, pp. 317-341.
[40] Melitz, Jacques (2008) “Language and Foreign Trade,” European Economics Review, 52,
pp. 667-699.
[41] Melitz, Jacques and Farid Toubal (2014) “Native Language, Spoken Language, Translation
and Trade,” Journal of International Economics, 93, pp. 351-363.
[42] Morales, Eduardo, Gloria Sheu and Andrés Zahler (2019) “Extended Gravity”, Forthcoming in Review of Econimc Studies.
[43] Olley, Steven and Ariel Pakes (1996) “The Dynamics of Productivity in the Telecommunications Equipment Industry” Econometrica, 64(6), pp. 1263-1297.
[44] Peters, Bettina, Mark Roberts, Van Anh Vuong, Helmut Fryges (2017) “Estimating Dynamic R&D Demand: An Analysis of Costs and Long-run Benefits” The Rand Journal of
Economics, 48(2), pp. 409-437.
[45] Rauch, James and Vitor Trindade (2002) “Ethnic Chinese Networks in International
Trade,” Review of Economics and Statisitcs, 84(1), pp. 116-130.
[46] Redding, Stephen and David Weinstein (2019) “Aggregation and Gravity Equation,”
NBER Working Paper, No.25464.
[47] Roberts, Mark, Daniel Yi Xu, Xiaoyan Fan and Shengxing Zhang (2018) “The Role of Firm
Factors in Demand, Cost, and Export Market Selection for Chinese Footwear Producers”,
Review of Economic Studies, 85, pp. 2429-2461.
[48] Schott, Peter (2004) “Across-Product versus Within-Product Specialization in International Trade”, Quarterly Journal of Economics, 119(2), pp. 647-678.
[49] Shenkar, Oded (2001) “Cultural Distance Revisited: Towards a More Rigorous Conceptualization and Measurement of Cultural Differences,” Journal of International BusinessStudies, 43, pp.12-17.
[50] Van Beveren, Ilke and Hylke Vandenbussche, (2010) “Product and process innovation andfirms’ decision to export,” Journal of Economic Policy Reform, vol. 13(1), pp. 3-24.
33

[51] Van Beveren, Ilke, Andrew Bernard and Hylke Vandenbussche (2012) “Concording Tradeand Production data over time”, NBER Working Paper 18604.
[52] Verhoogen, Eric (2008) “Trade, Quality Upgrading, and Wage Inequality in the Mexican
Manufacturing Sector”, Quarterly Journal of Economics, 123(2), pp. 489-530.

34

Table 1: Level of Estimated Parameters
Parameters
σidt
λjidt
δjidt
cjidt

Variables

Level of Analysis

Demand Elasticities(HS4)Product-Country
Taste Index
Firm-(CN8)Product-Country-Year
Quality Index
Firm-(CN8)Product-Country-Year
Marginal costs Index Firm-(CN8)Product-Country-Year

Note: We also identified demand elasticities (σ) at (HS6)Product-Country level and the decomposition results did not have asignificant change. Estimating σ at (HS6)Product-Country level provides a large number of (HS6)Product-Country markets
with inelastic demand (σ < 1). Therefore, we estimate the demand elasticity at the (HS4)Product-Country level.

Table 2: Number of Observations by (HS2)Industries and Regions
15

16

17

18

19

20

21

22

Total

AU
EA
EE
ME
NA
SA
SAM
SSA
WE

65
1
360
78
1,283
662
522
139
41
36
49
8
311
74
321
64
4,990 11,273

174
706
1,137
787
406
81
327
443
7,294

507
1,945
2,482
1,816
1,018
181
1,002
376
11,046

75
337
1,193
579
208
57
282
337
7,719

84
605
1,367
1,156
443
52
506
395
10,073

85
558
1,562
664
205
89
319
471
8,972

108
675
1,203
524
377
120
395
448
6,234

1,099
5,264
10,889
6,187
2,734
637
3,216
2,855
67,601

Total

7,942

11,355

20,373

10,787

14,681

12,925

10,084100,482

12,335

Note: Regions: AU: Australia and New Zealand, EA: East Asia, EE: East Europe, ME: Middle East, NA: North America, SA:
South Asia, SAM: South America, SSA: Sub-Saharan Africa , WE: West Europe.
(HS2)Industries: 15: Animal or Vegetable Fats and Oils, 16: Meat, Fish or Crustaceans, 17: Sugars and Sugar Confectionery,
18: Cocoa and Cocoa Preparations, 19: Preparations of Cereals, Flour, Starch or Milk, 20: Preparations of Vegetables, Fruit,
Nuts, 21: Miscellaneous Edible Preparations, 22: Beverages, Spirits and Vinegar.

35

Table 3: Average Bilateral Indices on Consumer Heterogeneity between Belgium and Destinations

AU
EA
EE
ME
NA
SA
SAM
SSA
WE

CL

CR

CN

CD

0.57810.16980.19540.1232
0.61880.05890.2946
0.23490.58660.14800.08890.2006
0.03760.25320.08650.37050.18900.29870.00060.00010.00030.00090.00120.00010.00010.00020.00552.22432.26961.41091.8414
1.88322.27692.36890.97181.7329Note: CL: Common Spoken Language Index, CR: Common Religion Index; CN: Common Nationality index; CD: Culture
Difference Index. Regions: AU: Australia and New Zealand, EA: East Asia, EE: East Europe, ME: Middle East, NA: North
America, SA: South Asia, SAM: South America, SSA: Sub-Saharan Africa ,WE: West Europe.
The details of the construction of Common Nationality (CN) variable is reported in Appendix A. The other variables reflect
the raw data.
There are 194 countries with the bilateral indices on CL and CR, and 208 countries with information on CN. However, thereis less information for the bilateral cultural index CD for which we have 62 countries. In the demand function estimation, weonly keep countries with information on these four indices and lose 24% of observations.

Table 4: Correlation Matrix of Consumer Heterogeneity Indices

CL
CR
CN
CD

CL

CR

CN

CD

1
0.09090.351-0.059910.1689-0.20311-0.34951

Note: CL: Common Spoken Language Index, CR: Common Religion Index; CN: Common Nationality index; CD: Cultural
Difference Index.

36

Table 5: Average Demand Elasticities by (HS2)Sectors
HS2
Industries

Mean(σ)S.D.(σ)Number of(HS4)Product-Country Pairs

15
16
17
18
19
20
21
22

3.15452.25492.46901.7656
2.2111
3.06312.1231
1.91231.2242
1.01541.05470.54130.99071.48560.8161
0.6634

41
33
69
39
46
81
60
34

Note: The demand elasticities are estimated at (HS4)Product-Destination level. Here we show the average over productcategories and regional blocs. Similar elasticities are obtained at HS6-destination levels.
There are around 27% of HS4-Destination markets with estimated demand elasticities less than one. Only HS4-Destination
markets with demand elasticities greater than one are reported in the Table.
(HS2)Industries: 15: Animal or Vegetable Fats and Oils, 16: Meat, Fish or Crustaceans, 17: Sugars and Sugar Confectionery,
18: Cocoa and Cocoa Preparations, 19: Preparations of Cereals, Flour, Starch or Milk, 20: Preparations of Vegetables, Fruit,
Nuts, 21: Miscellaneous Edible Preparations, 22: Beverages, Spirits and Vinegar.

Table 6: Summary Statistics on Demand and Cost Indices
RegionQuality Index (lnδ̂)

Taste Index (lnλ̂)

MC Index (lnĉ)

AU
EA
EE
ME
NA
SA
SAM
SSA
WE

4.16174.01374.03894.01053.66624.44794.15103.98643.71860.07210.14040.12740.30900.37540.02030.06390.28021.1351-0.4427-0.1306-0.01630.9242-0.2548-0.18620.1941
0.05220.3508

S.D.

1.36601.03100.9501

Note: Regions: AU: Australia and New Zealand, EA: East Asia, EE: East Europe, ME: Middle East, NA: North America, SA:
South Asia, SAM: South America , SSA: Sub-Saharan Africa, WE: West Europe.

Table 7: Correlation Matrix of Quality, Tastes and MC indicesQuality Index (lnδ̂)
Taste Index (lnλ̂)
MC Index (lnĉ)Quality Index (lnδ̂)

Taste Index (lnλ̂)

MC Index (lnĉ)1-0.0623-0.18421-0.0600

1

37

Table 8: Variance Decomposition of Indices
Variation in:
Firm
Product
Country

Taste Index

Quality Index

MC Index

7%
23%
70%

65%
32%
3%

27%
60%
13%

100%

100%

100%

We decompose the variations of the taste index into three components: (1) Variations across firms within the same (HS6)ProductCountry market; (2) Variations across (HS6)Products within the same country; (3) Variations across countries.

Table 9: Decomposition of Firm-Product Export Revenuesln(TR)
(1)

(2)

(3)

(4)

0.17
(.002)∗∗∗βδ (Quality)
0.20(.002)∗∗∗βc (MC)
0.15(.003)∗∗∗βM (Market Competition)
0.07(.003)∗∗∗βR (Demand Residuals)
0.42(.003)∗∗∗

0.13(.002)∗∗∗
0.22(.002)∗∗∗
0.14(.003)∗∗∗
0.08(.003)∗∗∗
0.43(.003)∗∗∗

0.13(.002)∗∗∗
0.23(.002)∗∗∗
0.13(.003)∗∗∗
0.07(.003)∗∗∗
0.44(.003)∗∗∗

0.11(.002)∗∗∗
0.22(.002)∗∗∗
0.14(.003)∗∗∗
0.08(.003)∗∗∗
0.45(.003)∗∗∗

Observations

37,92837,48037,617βλ (Tastes)

38,949Specification: (1) Firm-(CN8)Product-Country level Taste Index, (2) Firm-(CN8)Product-Country level Tastes Index, normalized by local GDP, (3) (CN8)Product-Country level Tastes Index, (4) (CN8)Product-Country level Tastes Index, normalized by
local GDP.
Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1
When estimating the demand function, we loss 52% of total number of observations due to the following criteria: (1) dropping
outliers in lnp (loss 6% of total number of observations); (2) dropping observations without information on the proxy variables oftastes index and IV for prices (loss 38% of total number of observations); (3) dropping (HS4)Product-country markets with thenumber of Belgium firms less than twenty. (loss 7% of total number of observations) Even we loss 51% number of observations,the sample in the demand function estimation captures 70% of total export value in the Belgium Food Industry.
Please refer Equations (13) and (??) for the regression equations.

38

Table 10: Relationship between Minimum Indices and Distance from Belgiumln(Distance)
Constant
Year dummy(HS2)Industry Dummy
Observations
R-squared

Minimum
Qualityindex

Minimum
Tastesindex

Minimum
Cost
index

0.1690(0.014)∗∗∗
1.9553(0.124)∗∗∗
yes
yes
1,935
0.113

-0.0509(0.007)∗∗∗0.4891(0.050)∗∗∗
yes
yes
1,935
0.060

0.1396(0.015)∗∗∗-1.9616(0.148)∗∗∗
yes
yes
1,935
0.197

Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1
We also tried to use the quality (tastes, cost) index at 1% as the minimum cutoff value and get the similar results.
After controlling the market size (ln(GDP)) of the destination country, the relationship between distance and the minimum
cutoff value did not change.

Table 11: Decomposition of Firm-Product Revenues (Balanced Panel)ln(TR)
(1)

(2)

(3)

(4)

0.16
(.004)∗∗∗βδ (Quality)
0.20(.005)∗∗∗βc (MC)
0.03(.004)∗∗∗βM (Market Competition)
0.07(.004)∗∗∗βR (Demand Residuals)
0.53(.007)∗∗∗

0.11(.004)∗∗∗
0.22(.006)∗∗∗
0.03(.004)∗∗∗
0.09(.004)∗∗∗
0.55(.007)∗∗∗

0.11(.004)∗∗∗
0.22(.006)∗∗∗
0.03(.004)∗∗∗
0.07(.004)∗∗∗
0.57(.007)∗∗∗

0.10(.004)∗∗∗
0.23(.006)∗∗∗
0.03(.005)∗∗∗
0.08(.004)∗∗∗
0.57(.007)∗∗∗

Observations

5,170

5,153

5,107

βλ (Tastes)

5,358

Note: Balanced panel includes products that are sold to all countries and there are only four products are included: 18062010,
18063290, 18069019, 18069031.
Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1
Specification: (1) Firm-(CN8)Product-Country level Taste Index, (2) Firm-(CN8)Product-Country level Tastes Index, normalized by local GDP, (3) (CN8)Product-Country level Tastes Index, (4) (CN8)Product-Country level Tastes Index, normalized by
local GDP39

Figure 1: Taste for Belgian Food Exports

0.5Tastes Index
1
1.5

2

2.5

Note: Dark color indicates stronger taste for Belgian export products.

5

6

7

8

9

10

ln(Distance)
Tastes Index

Fitted values

Figure 2: Relationship between Average Taste and Distance to Belgium, by Destination

40

Taste Index, controlled for Firm−Product Age
0
5
10
15
−5
−5

0
5
10
Taste Index, without controlling for Firm−Product Age

15

Figure 3: Taste with and without Controlling Firm-Product Age

41

Appendix A
Here we illustrate how we construct a bilateral indicator of closeness between any two
countries. Consider the following hypothetical example where we have information on theshare of migrants by country of origin as given in Table A-1.
Table A-1: Percent of Population, by Nationality
Country

Share of the migrants over population (%)
China Mexico
Korea

U.S.
France
Japan

1
0.5
1

2
1
0

0.1
0
1

Table A-1 indicates that the distribution of nationality components is similar between the
U.S. and France compared to the nationality components between Japan and the other two
countries. We construct and indicator of bilateral closeness in nationality between any countrypair based on the Toubal and Egger (2016) index as follow:

CNU.S.,F rance = 1 × 0.5 + 2 × 1 + 0.1 × 0 = 2.5
CNU.S.,Japan = 1 × 1 + 2 × 0 + 0.1 × 1 = 1.1
CNF rance,Japan = 0.5 × 1 + 1 × 0 + 0 × 1 = 0.5

It’s clear that the CN index between the U.S. and France (2.5) is higher than the CN index
between the U.S. and Japan (1.1). This reflects the fact that the U.S. and France have similar nationality distribution comparing with the nationality distribution between the U.S. andJapan. Based on the same logics, we construct the closeness indices for common language (CL)and common religion distribution (CR) for any country-pair.
The top part of Table A-2 presents the top ten countries with the highest closeness indicesbetween the destination countries and Belgium for common language CL, common nationality
42

(CN ), common religious (CR), and culture distance (CD). The bottom part of Table A-2
reports the ten countries that are most dissimilar to Belgium in terms of these four indices.
For example, Netherlands and France have the highest similarity in the distribution of spoken
language with Belgium. Indonesia, Korea and Taiwan are less closer to Belgium in terms of
spoken language. European countries have similar nationality distributions and the nationality
components in Asian countries are dissimilar to Belgium.
Table A-2: Indices of Consumer Heterogeneity by Countries
1. Top ten countries relative to BelgiumCountry
Netherlands
FranceU.K.
Ireland
CanadaDenmark
Germany
SwedenAustriaMaltaCL
0.99980.89520.68250.66280.65840.65480.63860.62010.62000.6124

Country

CN

CountryItaly
1.8700 Malta
France1.0630 Venezuela
Netherlands 1.0600 Spain
Morocco1.0500 Argentina
Turkey0.5570 Italy
Spain
0.4890 Colombia
Germany
0.3540 Poland
Portugal
0.2800 Croatia
U.K.
0.2670 Ireland
Greece0.1810 France

CR

CountryCD

0.74560.73050.71550.69990.68470.68470.6832
0.66840.66490.6489France
Malta
Czech
Spain
Italy
Germany
TurkeyPolandBrazilGreece0.21930.53820.55440.69610.69680.80820.92050.97740.9842
1.0428

2. Bottom ten countries relative to BelgiumCountry

CL

Peru
RussianBrazilBangladesh
Vietnam
China
IranIndonesia
Korea
Taiwan0.04910.03270.01570.01180.00870.00590.0026000Country

CN

Country

CR

CountryCD

Bangladesh
Korea
LatviaEstonia
Lithuania
MexicoTrinidad
Malaysia
El Salvador
Taiwan0.00590.00410.00390.00370.00350.00290.00230.00160.00120.0008Thailand
SwedenU.K.
GreeceLatviaFinland
TaiwanEstonia
Hong KongJapan

0.00140.00120.00070.0004000000Hong Kong
Ireland
Malaysia
Colombia
Slovakia
SwedenTrinidadSingapore
Venezuela
Denmark

2.80352.82292.94612.97172.98223.25543.33213.4231
3.53133.8605

Note: CL: Common Spoken Language Index, CR: Common Religious Index; CN: Common Nationality index; DC: Differencein Culture Index.
Countries with the higher value of CL, CR and CN are more closer to Belgium in terms of CL, CR and CN. In contrast, countries
are closer to Belgium in term of culture if the countries have lower value of CD.
There are 62 countries with information of all indices. Trinidad: Trinidad and Tobago.

43

Table A-3: Decomposition of Export Revenue, by (HS4)Products(HS4)Sector

βλ

βδ

βc

βM

1501
1507
1511
1513
1515
1516
1517
1518
1601
1602
1604
1605
1701
1702
1704
1799
1806
1901
1902
1904
1905
2004
2005
2007
2008
2009
2102
2103
2104
2105
2106
2201
2202
2203
2208

0.22
0.13
0.04*
0.02*
0.13
0.20
0.20
0.08*
0.11
0.13
0.31
0.19
0.14
0.12
0.11
0.06
0.11
0.20
0.13
0.11
0.12
0.16
0.18
0.20
0.17
0.20
0.29
0.17
0.20
0.27
0.11
0.19
0.01
0.07
0.16

0.48
0.12
0.03* 0.15*
0.31
0.33
0.02* 0.21
0.57 -0.07* 0.03* 0.42
0.52 -0.001* 0.39 0.07*
0.35
0.20
0.17
0.15
0.34
0.06
-0.01* 0.41
0.30
0.08
0.16
0.27
0.37
0.20
0.24 0.11*
0.26
0.06
0.26
0.31
0.25
0.06
0.08
0.47
0.28
0.08
0.06
0.27
0.11 -0.06*
0.10
0.65
0.23
0.08
0.09
0.46
0.28
0.07
0.04
0.49
0.20
0.22
0.06
0.41
0.30
0.21
0.10
0.33
0.18
0.03
0.07
0.61
0.23 0.01*
0.15
0.40
0.27
0.09
0.09
0.41
0.42 -0.01*
0.09
0.40
0.21
0.07
0.05
0.55
0.23
0.30
-0.05
0.36
0.30
0.15
0.08
0.29
0.18
0.18
0.11
0.34
0.28
0.03
0.10
0.41
0.37 -0.01*
0.09
0.35
0.27 0.004*
0.23
0.21
0.27
0.05
0.003* 0.51
0.22
0.10
0.19
0.30
0.24
0.07
0.11
0.30
0.25
0.05
0.07
0.52
0.36 -0.05*
0.15
0.35
0.35
0.03
0.46
0.14
0.38
0.04
0.19
0.31
0.16
0.05
0.10
0.53

Note:* Insignificant at 10%.

44

βR

no.(observations)
17
64
22
13
333
183
1,361
18
442
3,472
145
59
584
752
3,999
210
11,198
421
627
131
1,970
2,027
763
1,549
1,196
246
190
489
66
990
3,091
48
18
746
488

Table A-4: (HS4)Product Definition
HS4

Definition

1501
1507
1511
1513
1515
1516
1517
1518
1601
1602
1604
1605
1701
1702
1704
1806
1901
1902
1904
1905
2004
2005
2007
2008
2009
2102
2103
2104
2105
2106
2201

Pig fat (including lard) and poultry fat
Soya-bean oil and its fractions
Palm oil and its fractions
Coconut (copra), palm kernel or babassu oil and their fractions
Fixed vegetable fats and oils (including jojoba oil) and their fractions
Animal or vegetable fats and oils and their fractions
Margarine; edible mixtures or preparations of animal or vegetable fats or oils
Animal or vegetable fats, oils, fractions, modified in any way
Sausages and similar products of meat, meat offal or blood
Prepared or preserved meat, meat offal or blood
Prepared or preserved fish; caviar and caviar substitutes prepared from fish eggs
Crustaceans, molluscs and other aquatic invertebrates, prepared or preserved
Cane or beet sugar and chemically pure sucrose, in solid form
Sugars, sugar syrups, artificial honey, caramel
Sugar confectionery (including white chocolate), not containing cocoa
Chocolate and other food preparations containing cocoa
Malt extract; flour/starch/malt extract products, no cocoa (or less than 40% by weight)
Pasta
Prepared foods obtained by swelling or roasting cereals or cereal productBread, pastry, cakes, biscuits, other bakers’ wares
Vegetables preparations (frozen)
Vegetables preparations(not frozen)
Jams, fruit jellies, marmalade
Fruit, nuts and other edible parts of plants
Fruit juices (including grape must) and vegetable juices
Yeasts (active or inactive); prepared baking powders
Sauces and preparations therefor
Soups and broths and preparations therefor; homogenised composite food preparations
Ice cream and other edible ice; whether or not containing cocoa
Food preparations not elsewhere specified or included
Waters, including natural or artificial mineral waters and aerated waters, not containing added sugar
Waters, including mineral and aerated waters, containing added sugar or sweetening
matter, flavoured
Beer made from malt
Ethyl alcohol, undenatured; of an alcoholic strength by volume of less than 80%
volume; spirits, liqueurs and other spirituous beverages

2202
2203
2208

45