Quality sorting across export markets:

Alchian-Allen effects and the role of asymmetric tastes

Ina C. Jakel∗

Preliminary and incomplete.

Abstract. The Alchian-Allen hypothesis postulates that due to per unit trade costs the relative

demand for high-quality goods is increasing in distance. We propose a modified version of this

hypothesis for the quality of goods, as perceived by domestic consumers: the relative demand

for high-quality goods is increasing in the proximity in tastes, i.e. depends on how well domestic

and foreign tastes accord. Based on firm-product level data on trade and production in the

Danish Chocolate and confectionery industry, we infer domestic perceived product quality. We

use these quality estimates to explain export market participation and export volumes across

destinations. Domestic quality perceptions have a significantly higher effect on a product’s

export performance if the destination market is similar to the home market in terms of culture

and income.

Keywords: Product quality · Heterogeneous firms · Exports.

JEL classification: L11 · F12 · F14

1 Introduction

The proposition that the quality composition of exports increases with the distance between

trading partners (the Alchian–Allen conjecture, Alchian & Allen 1964) has a firm standing in the

international trade literature; see Hummels & Skiba (2004) for empirical evidence. Intuitively,

I would like to thank Federico Ciliberto, Marc-Andreas Muendler and Frederic Warzynski for numerousvaluable suggestions and Hans-Jorg Schmerer for inspiring discussions. Thanks are also due Christian Gormsen,Svend Greniman Andersen, Christian Søegaard and Valdemar Smith. This work benefited from discussions withseminar and conference participants at ETSG (Leuven), WIFO (Vienna), the Midwestern Trade Meeting (St.Louis), IfW (Kiel) and the University of Southern Denmark. Financial support from the Danish Agency forScience, Technology and Innovation and the Tuborg foundation is gratefully acknowledged.∗Department of Economics and Business, Aarhus University, Denmark. E-mail: InaJ@asb.dk. Tel.: ++45 87165195.

per unit trade costs lower the relative price of high-quality products relative to low-quality

ones, and therefore increase relative demand. More vividly described as “shipping the good

apples out”, the proposition also implies that high quality goods are exported while low quality

goods are retained solely for domestic consumption. Indeed, previous studies have found that

exporters charge higher prices than non-exporters and have interpreted this evidence as being in

accordance with an exporter quality premium; see inter alia Iacovone & Javorcik (2012), Hallak

& Sivadasan (2011) and Kugler & Verhoogen (2012).

This paper proposes a modified Alchian-Allen hypothesis, which relates a product’s perceived

quality (or “appeal”) to its export performance. The literature typically treats quality as some-

thing intrinsic to a product and any variation in demand across countries revealed in the data

is attributed to horizontal product differentiation or demand shocks. However, in international

markets the distinction between vertical and horizontal product differentiation is diffuse: quite

possibly, product pairs exist for which domestic consumers agree on a common ranking of their

desirability (vertical differentiation), but domestic and foreign consumers disagree (horizontal

differentiation).1 In consequence, domestic quality perceptions constitute a good predictor of

export market success only if tastes of domestic and foreign consumers accord well. To the

contrary, if international taste differences are substantial, a product highly valued by domestic

consumers may perform poorly on export markets. The Alchian-Allen hypothesis therefore only

holds for countries with similar tastes, but not on a global scale.

We provide an empirical assessment of the modified Alchian-Allen hypothesis based on Dan-

ish firm-product level data for the Chocolate and confectionery industry. The data are unusually

rich and provide information on domestic as well as on export market sales. A first look at the

data highlights the importance of asymmetric preferences: only about half of the variation in

export sales across markets can be explained by firm-product specific factors such as intrinsic

quality, and destination specific factors such as market size. Following the approach in Berry

(1994), we employ data on domestic market shares and prices to estimate each product’s per-

ceived quality.2 Identification of quality is based on the intuition that, holding price constant,

1For example, the Danish leverpostej (foie gras) is a delicacy for many Danish consumers, but not necessarilyfor non-Danes. To the contrary, the biscuit manufacturer Kelsen was ailing on the domestic market until someyears ago exports to China increased dramatically and thereby saved the company from failing.

2The approach has previously been applied in a trade context by Goldberg (1995) and more recently inKhandelwal (2010). Important advances in disentangling product quality and unit values can also be found inHallak & Schott (2011) and Feenstra & Romalis (2011). Both papers estimate quality at the country level basedon bilateral trade data, where the former relies on the demand side to identify quality, whereas the latter employsthe supply side for identification.

2

high quality products attain higher market shares. Our estimates reflect quality as perceived

from the viewpoint of the domestic consumer.

We link our perceived quality estimates to firm-product level information on sales across ex-

port destinations. Quality, as evaluated by domestic consumers, on average increases both the

destination-specific probability of market entry as well as export volumes. Quantitative predic-

tions however leave considerable room for differences in tastes across countries. In consequence,

domestic product appeal is a significant but imperfect predictor of export market success. Turn-

ing to variation across destinations, we conjecture that the proximity in tastes depends positively

on cultural proximity as well as on the similarity of income levels. The latter idea goes back

to the work of Linder (1983). In line with these predictions, domestic quality perceptions have

a significantly higher impact on export volumes to destinations that are culturally similar to

Denmark. In less developed countries, to the contrary, export sales are entirely unrelated to

domestic quality perceptions.

Our study is most closely related to Crozet et al. (2012). Based on data for French Cham-

pagne producers they show that high-quality firms have a higher likelihood of exporting, export

higher volumes and charge higher prices. They also find that idiosyncratic demand plays an

important role for a firm’s export performance. Their direct measure of product quality draws

on quality ratings by experts. In contrast, our quality measures are linked to preferences of

domestic consumers, implying that they comprise more than objective quality differences. For

our purpose, this is a virtue rather than a vice, because it allows us to study variation in tastes

across countries. Gervais (2012) also infers product quality based on information on domestic

sales and prices, but contrary to our paper his analysis does not consider differences in quality

perceptions across countries.

Our paper fits with a growing number of studies that documents heterogeneity in consumer

preferences across countries. Di Comite et al. (2012) provide evidence of a tight correlation

of export price rankings across markets, but a much weaker correlation of export quantity

rankings. They propose a quadratic preference model with country-specific taste parameters,

where export varieties that match local tastes better succeed in capturing larger market shares.

Eaton et al. (2011) introduce country-specific demand shocks into a model of heterogeneous

firms, which helps to reconcile the theory with the empirical artifact that there is no strict

hierarchy of export destinations. Estimating the model based on French firm-level export data

across destinations, they find enormous idiosyncratic variation in demand across export markets.

3

Their work, and the papers inspired by them, focuses on imperfect correlation of demand across

export markets; see also Nguyen (2012) and Crino & Epifani (2012). To the contrary, we

focus on deviations of foreign from domestic demand. This distinction is important, because

it is domestically perceived quality that matters for the welfare of the domestic consumer.

Therefore, an immediate implication of our analytical framework is that any evaluation of the

welfare impact of trade liberalization needs to be based on domestic consumer preferences. To

the contrary, welfare calculations based on an (internationally) representative consumer can be

misleading.

Our paper is furthermore related to the literature that investigates the role of product quality

in international trade. On the theoretical side, several papers introduce quality as a second source

of firm heterogeneity besides productivity into the workhorse models of international trade; see

inter alia Antoniades (2012), Bacchiega et al. (2010), Baldwin & Harrigan (2011), Hallak &

Sivadasan (2011), Johnson (2012) and Kneller & Yu (2008). On the empirical side, scholars

have studied quality production across countries, arguing that richer countries produce goods

of higher quality (Khandelwal, 2010; Hummels & Klenow, 2005; Hallak & Schott, 2011); as well

as quality consumption across countries, suggesting that high-income countries consume goods

of higher quality (Flach, 2011; Hallak, 2006; Verhoogen, 2008). Finally, our model of consumer

demand is also related to the literature on trade in “cultural goods”; see Francois & van Ypersele

(2002), Janeba (2007) and Rauch & Trindade (2009).

The paper proceeds as follows. The next section introduces the data, explores empirical

patterns of variation in demand across countries and estimates exporter price premia. It thereby

motivates our analysis. Section 3 sets up the theoretical model. Section 4 lays out the empirical

strategy for estimating product quality and presents quality estimates. In section 5, we provide

an empirical test for our modified Alchian-Allen hypothesis. Section 6 concludes the paper.

2 Empirical patterns

This section presents empirical patterns that motivate our emphasis on perceived product quality

and asymmetric tastes in international trade. Specifically, we provide new evidence on export

demand across destinations and on domestic exporter price premia.

4

2.1 Data

Our data for the Chocolate and confectionery industry are derived from the Industrial Com-

modity Statistics provided by Statistics Denmark. The data are based on a survey in which

firms report sales of products measured in both volumes and values. In the following, we refer

to a product produced by a firm as a variety of that product. Product categories are measured

at the the 8-digit level of the combined nomenclature (CN). We complement these data with

information on export sales and quantities by commodity and destination. Combining both

data-sets we are also able to retrieve the volume and value of domestic sales of each variety, as

well as prices (unit values).3

Food products are strongly influenced by customs, culture and national tastes and are there-

fore harder to internationalize (van Mesdag, 2000).4 Looking at Rauch’s classification of ho-

mogeneous vs. differentiated goods (Rauch, 1999), 90% of all products in the Chocolate and

confectionery industry are classified as differentiated. Moreover, the industry is highly inter-

nationalized. It therefore gives an excellent example to study the role of consumer tastes in

shaping demand across destinations.

Our panel covers the period 1995 to 2008 and comprises a total of 80 firms across all years.

Due to firm turnover, the average number of firms in a given year is with 28 much lower; see

table 8 in the appendix. We have information on 41 different product categories, and a total

of 1,098 product-firm-year observations. Table 1 reports summary statistics by export status,

defined at the firm-level or the firm-product level.

55% of firms export at least some of their varieties. Importing is an even more wide-spread

phenomenon, with 75% of non-exporters and as much as 89% of exporters importing some

intermediates. Firms that export on average also have a broader product portfolio than non-

exporters. Turning to the firm-product level, more than 60% of all varieties are exported and the

median number of export destinations is with 7 also high. Moreover, exported varieties have on

average higher domestic market shares than non-exported varieties.5 Unconditionally, exported

varieties have a lower price than varieties that are only sold on the Danish market. Moreover,

export prices are on average much lower than domestic prices. However, since these summary

3The data cleaning is described in more detail in the appendix.

4These differences in cross-cultural tastes are also nicely captured on the website www.hjemve.dk, a servicefor Danish expatriates. Here, you can order Danish products that are hard to obtain abroad. Interestingly, byfar the biggest share of products on the website are food products.

5The construction of market shares is described in detail in section 4.

5

Table 1: Summary statistics Chocolate and confectionerya

Number of CN8 products: 41 – Number of firms: 80 – Number of observations: 1098

Non-Exporterb Exporterb

N (firm-year)=174 N (firm-year)=215

Mean Median Min Max Mean Median Min Max

Product scopeb 1.89 1 1 19 3.58 2 1 19

Importerb 0.75 1 0 1 0.89 1 0 1

Non-Exported varietyc Exported varietyc

N (variety-year)=480 N (variety-year)=618

Mean Median Min Max Mean Median Min Max

Domestic market sharec 0.004 0.001 0.000 0.060 0.006 0.002 0.000 0.056

Domestic pricec 55.68 34.50 1.87 269.28 40.62 29.32 1.76 270.30

Export price (fob)c 27.54 21.68 3.74 167.13

Number of export destinationsc 9.26 7 1 57

a The sample is restricted to firm-product observations with non-missing information on output quantities, and henceprices. b Defined at the firm-level. Exporter coded one if the firm exports at least one product to at least one destination.c Defined at the firm-product level. Exported variety coded one if the variety is exported to at least one destination.

statistics pool observations across all CN8 product categories in the industry, price differenes

may simply be driven by differences in the compositon of products sold to different markets.

Section 2.3 analyzes such price differences between exporters and non-exporters in more detail.

2.2 Sources of variation in export prices and volumes across destinations

We first consider the export market and sources of variation in export prices, quantities and

values across destinations. Table 2 reports the R2 of ordinary least squares (OLS) regressions of

log export prices, quantities and values on different combinations of fixed effects for destinations,

products, firms or varieties. Panel A considers each of these dimensions separately, whereas

Panel B reports the R2 from regressions where the different sets of fixed effects are combined

to explain export prices and volumes. In all regressions, we condition on year effects, which

contribute only negligibly to the explanatory power of the specifications.

The firm dimension accounts for large part of the variation in prices: with 58% this exceeds

the share of variation attributed to either the destination or the product dimension (28% and

52%, respectively). Not surprisingly, when we replace firm fixed effects with variety fixed effects,

the R2 increases even further. Results from the export volume equations contrast with the

regressions for export prices: The explanatory power of any of the models in table 2 is much

lower for export volumes than for export prices. Moreover, for both export quantities and values,

in Panel A the best fit is achieved in the regressions that account for the destination level.

This highlights the importance of destination-specific factors such as market size in explaining

differences in sales across destinations.

In Panel B, we account for multiple sources of variation simultaneously. The most detailed

6

Table 2: Sources of variation in export prices and volumes across export marketsa

R2 from OLS regressions

Log Price Log Quantity Log Value

Panel A: one dimension of variation

Product, year 0.516 0.137 0.097

Destination, year 0.279 0.336 0.285

Firm, year 0.579 0.157 0.110

Variety, year 0.719 0.239 0.212

Panel B: multiple dimensions of variation

Destination, product, year 0.598 0.439 0.381

Destination, firm, year 0.641 0.428 0.372

Destination, variety, year 0.763 0.548 0.520

a The table reports the R2 from regressions of log export prices, quantities and values on different sets of fixed effects,as specified in the left hand column.

model controls for destination as well as variety-specific factors. With 76%, these two dimensions

explain more than three thirds of the variation in prices, but only about half the variation in

volumes. Hence, after netting out destination-specific factors that affect all firms symemtrically,

demand for a given variety still varies considerably across destinations. Firm-product specific

factors, such as productivity, marginal cost and (intrinsic) product quality are important de-

terminants of prices, but leave a large part of the variation in demand unexplained. In this

paper, we analyze one plausible explanation for this finding: asymmetric tastes across countries

that imply an imperfect correlation of product quality as perceived by consumers in different

countries.

2.3 Price premia for exported varieties

Previous studies have found a positive exporter price premium and interpreted this as evidence

that high-quality firms select into export markets; see inter alia Iacovone & Javorcik (2012),

Hallak & Sivadasan (2011) and Kugler & Verhoogen (2012). In this section, we estimate the price

premium for exported varieties in the Danish Chocolate and confectionery industry by regressing

the log domestic price on an indicator for exported varieties. In addition to simple ordinary least

squares (OLS) estimates, we aim at a richer characterization of the data and report results from

quantile regressions for the five quantiles Q = 10, 25, 50, 75, 90; see Koenker & Hallock (2001).

All regression results have to be interpreted as correlations, and do not give causal effects.

In Panel A of table 3, we include the Exported variety indicator and a set of product-year

fixed effects. The latter controls for differences in average prices across product categories and

differences in the rate of inflation. According to the OLS estimate, on average prices of exported

varieties do not differ significantly from prices of non-exported varieties. However, quantile

regressions reveal that this masks considerable heterogeneity across moments of the conditional

7

Table 3: Exporter price premiaa

Dependent variable: log domestic price

Quantile regressions

OLS Q=10 Q=25 Q=50 Q=75 Q=90

Panel A. without controls

Exported variety -0.1662 0.0176 -0.0742 -0.1799* -0.2839*** -0.3090***(0.121) (0.061) (0.091) (0.094) (0.107) (0.084)

Observations 1,098 1,098 1,098 1,098 1,098 1,098Product-year fixed effects Yes Yes Yes Yes Yes YesRegion fixed effects No No No No No NoR2 0.487

Panel B. with controls

Exported variety 0.2177** 0.2229*** 0.1932*** 0.1532** 0.1594 0.1007*(0.097) (0.057) (0.069) (0.070) (0.128) (0.051)

Log size -0.1828*** -0.1961*** -0.1625* -0.1172** -0.1455 -0.1761***(0.047) (0.072) (0.089) (0.052) (0.113) (0.026)

Log physical labor productivity -0.5282*** -0.5163*** -0.5893*** -0.6263*** -0.6097*** -0.5831***(0.075) (0.123) (0.203) (0.192) (0.097) (0.039)

Importer 0.4091* 0.5507** 0.4874* 0.1641 0.2040 0.1283(0.215) (0.214) (0.281) (0.213) (0.246) (0.108)

Observations 802 802 802 802 802 802Product-year fixed effects Yes Yes Yes Yes Yes YesRegion fixed effects Yes Yes Yes Yes Yes YesR2 0.877

a Standard errors in parentheses. OLS standard errors adjusted for clustering at the firm-product level. Quantile re-gression standard errors based on robust standard errors as suggested in Silva & Machado (2011). *,**,*** denotesignificance at the 10%, 5%, 1% levels, respectively.

distribution. In particular, exported varieties are sold at a price discount of 18% at the median

and up to 30% at the 90th percentile of the conditional distribution. If prices are indeed a signal

for quality, these results are inconsistent with the idea that exported varieties are of higher

quality than non-exported varieties.

In Panel B, we additionally condition on a number of firm- and firm-product level price

determinants: size, productivity, import status and location. Firm size is measured by the

number of employees. We construct an index of physical labor productivity at the firm-product-

level by dividing the total number of employees employed at the firm across products according

to their revenue share and then calculating the number of output units per employee. We also

add a whole battery of region fixed effects, which capture differences in production costs across

regions.

Conditional on firm characteristics, exported varieties are on average sold at a price premium

of around 22%-points. This contrasts sharply with the insignificant coefficient on export status

found in Panel A. However, the positive price premium is not robust to different measures of

productivity: in unreported regressions where we condition on firm productivity measured at

the firm- rather than at the firm-product level, the coefficient on Exported variety turns ins-

ginficant. Moreover, quantile regressions again disclose considerable heterogeneity: the positive

price premium is mainly driven by the lower tail of the conditional distribution, whereas it levels

8

off at higher moments. Turning to our control variables, we find that larger firms sell their prod-

ucts at a discount, possibly due to cost savings from large scale production. High productivity

firms partly pass on their cost advantage to consumers and sell their products at a lower price.

Furthermore, the importer price premium is with 41% points twice as big as the exporter price

premium.

Overall, we find some, albeit limited support for a positive exporter price premium. Table 3

reveals the limitations of using price as an indicator for product quality: prices are determined

by several idiosyncratic factors apart from quality, such as market power, productivity and

marginal costs, all of which are observed only imperfectly. Moreover, the price regressions in

table 2 leave non-negligible room for variety-destination specific factors in explaining variation

in prices across markets. In the remainder of this paper, we construct new indicators of product

quality, which refer to varieties’ perceived quality, as evaluated by domestic consumers.

3 A model of perceived product quality and export performance

In this section, we adapt the discrete choice model of consumer demand to an international

trade context and explore the link between domestic quality perceptions and a variety’s export

market performance.

3.1 Preferences and demand

In the nested logit demand system, the indirect utility of consumer i from consuming one unit

of variety j is given by

ucijt = ξcjt + α(yit − pcjt) + ζigmt + (1− σ)εijt, (1)

where yit is consumer income, pcjt is the price of variety j, t indexes time and c indexes countries.

ξcjt ≡ xjtβc reflects the perceived product quality (or “appeal”) of variety j in country c:

specifically, it can be decomposed into a K×1 vector of product characteristics k, xjkt, multiplied

by a country-specific vector of taste parameters βck. Product characteristics have a time subscript

because they potentially change over time as firms conduct product innovations. In our notation,

the vector xjt does not have a country superscript. Firms possibly adapt there products to some

extent to foreign markets, e.g. to comply with foreign standards and regulations. However, cost

9

minimization considerably limits such product adaptation.6 If firms customize their products to

the tastes of consumers in different countries, this will bias our results towards not finding any

evidence on asymmetric quality perceptions across countries.

The vector βc is allowed to vary across countries, dependent on national tastes.7 The dis-

tinction between vertical and horizontal product differentiation then becomes diffuse: domestic

consumers may agree on the ranking of different varieties, but given the same choice set, domes-

tic and foreign consumers may disagree. Francois & van Ypersele (2002) introduce the concept of

‘cultural goods’ to define goods which face different consumer valuations abroad than at home.

We argue that this concept should be understood broadly and that it encompasses not only

cultural goods in the narrow sense, i.e. arts and literature, but rather any good where national

tastes are influenced by culture, history, customs, climate etc. As a consequence of such cross-

country deviations in consumer tastes, success on the domestic market is an imperfect predictor

of success on export markets.

Products are nested into product groups g = 0, 1 . . . G for which utility is more tightly

correlated than across product groups. Consumer heterogeneity enters through an individual-

specific demand shock, which has two components. The first, εijt, is extreme value distributed

and varies both across individuals as well as across all products. ζigmt captures the part of the

demand shock that for individual i is common across products within product group g, and

hence induces correlation of utility for products within the same nest. Its distribution is such

that ζigmt + (1 − σ)εijt is also an extreme-value distributed random variable (Cardell, 1997).

The parameter σ ∈ [0, 1) reflects the degree of substitutability within a product group: as σ

approaches one, the within-group correlation of utility goes to one. If σ equals zero, the model

reduces to the simple logit model.

Individuals consume one unit of the variety that yields the highest utility. They may also

not consume any of the goods in the industry, which amounts to choosing an outside alternative

(with utility normalized to zero). In the following, we define the mean utility level of variety

j as δcjt ≡ ξcjt − αpcjt. Under the given distributional assumptions, a variety’s market share is

6The marketing literature discusses the trade-off the firm faces when choosing between strategies of adaptationvs. standardization of products across markets; see Schmid & Kotulla (2011) for a review.

7Tastes furthermore may also vary across consumers within a country; see Berry et al. (1995). In that sense,βck reflects the mean utility from a given product attribute for consumers in country c. Indeed, differences acrosscountries in the distribution of consumer characteristics such as age, income etc. may further contribute tocross-country differences in mean consumer tastes.

10

given by (Berry, 1994):

scjt =exp(δcjt/(1− σ))

(Dcgt)

σ[∑

g(Dcgt)

(1−σ)], (2)

where Dcgt ≡

∑j∈Jg exp(δcjt/(1 − σ)). Given (2), demand is given by qcjt ≡ scjtM

ct , where M c

t is

overall market size. For future reference, we denote the denominator in (2) by Dcgt.

3.2 Production

Each firm produces one distinct variety and can therefore also be indexed by j.8 All firms sell

on the domestic market, which we index by d. In addition, firms have the option to export their

variety to market c ∈ C. On each market, firms behave monopolistically competitive.

Production of variety j with characteristics xjkt, k = 1, . . .K, is subject to marginal costs

wjt = h(xj1t, . . . xjKt) + wjt. (3)

Marginal cost has two components: h(·) depends on those product characteristics which affect

both utility and marginal costs. For example, producing a variety with a particular set of

attributes may require the use of costly intermediate inputs. wjt is a vector of marginal cost

determinants that is independent of product characteristics. While (3) allows for a positive

association between perceived quality and marginal cost, we do not impose such a positive

link: a high perceived quality may be the result of a successful marketing strategy, consumer

brand recognition etc. In these cases, perceived quality has an impact on fixed costs but not on

marginal costs.

Being active in an export market is subject to three types of trade costs: fixed costs of

market entry F c, variable trade costs τ c and per unit trade costs T c. The relationship between

consumer (cif) and producer (fob) prices is given by:

pcjt = pc,fobjt τ c + T c.

On market c firm j makes profits

πcjt = (pc,fobjt − wjt)qcjt − F c = [(pcjt − T c)/τ c − wjt]qcjt − F c. (4)

No trade costs have to be incurred when selling on the domestic market, i.e. τd = 1 and T d = 0.

Firms are small in the sense that they do not take into account the impact of their prices on the

8The median firm in our sample produces two different product categories; see table 1. However, undermonopolistic competition firms’ behavior is only influenced by market aggregates, and therefore multi-productfirms choose prices for each of their products independently.

11

denominator in (2). The profit maximizing price is then an (additive) mark-up over marginal

cost pcjt = 1−σα + τ cwjt + T c, which in terms of producer prices reads as

pc,fobjt =1− σατ c

+ wjt. (5)

The mark-up is decreasing in both the degree of substitutability between products of the same

nest, σ, as well as the marginal utility of income, α.9 Furthermore, in fob terms it is decreasing

in the iceberg portion of the transport cost. Given the pricing rule in (5), maximized profits are

πcjt = 1−σατc q

cjt − F c.

3.3 Export market entry and export demand

A firm exports its variety to destination c if it can at least break even in the foreign market,

which holds if

1− σατ c

qcjt ≥ F c. (6)

Due to cross-country differences in consumer tastes, domestic and foreign quality perceptions

may differ:

ξcjt = ξdjt − ecjt. (7)

The deviation in quality perceptions, ecjt ≡ xjt(βd − βc), is increasing in the cross-country taste

mismatch βd − βc. If foreign consumers have identical tastes to domestic consumers, ecjt = 0.

ecjt is however different from zero as long as tastes differ across countries at least for some

product characteristic k. Using (7), we can rewrite demand for variety j in country c in terms

of domestically perceived quality:

qcjt = exp[(ξdjt − ecjt − αpcjt)/(1− σ)](Dcgt)−1M c

t . (8)

Defining χcjt as an indicator variable equal to one if variety j is exported to destination c in

year t, the firms’ exporting probability is given by

P (χcjt = 1) = P[lnM c

t − lnDcgt + 1

1−σ [ξdjt − ecjt − αwjtτ c − αT c − (1− σ)] > ln(Fcατc

1−σ )]. (9)

With the firm’s pricing rule in (5) and the demand equation in (8), the optimal quantity of

9More generally, under oligopolistic competition the mark-up is given by (1−σ)/α1−σsc

jt|g−(1−σ)scjt. The mark-up then

also depends on the firm’s within nest and overall market share, scjt|g and scjt.

12

variety j offered in market c is

ln qjt = lnM ct − lnD

cgt + 1

1−σ [ξdjt − ecjt − αwjtτ c − αT c − (1− σ)] + lnχcjt (10)

Similarly, we derive the variety’s fob export value in terms of domestic perceived quality as

ln vc,fobjt = ln(1−σατc +wjt) + lnM c

t − lnDcgt + 1

1−σ [ξdjt− ecjt−αwjtτ c−αT c− (1−σ)] + lnχcjt (11)

We have expressed the export participation and volume equations in (9) to (11) in terms of

a variety’s domestic perceived quality, ξdjt. In the next section, we estimate ξdjt for all varieties

in our sample based on information on domestic market shares and prices. Foreign perceived

quality, ξcjt, to the contrary remains unobserved. The country- and variety-specific deviation of

foreign from domestic tastes, ecjt, is the structural error term in a regression of export quantities

(or values) on domestic quality perceptions and other explanatory variables as specified in (10).

We can therefore employ such regressions to make inferences about the presence of international

differences in tastes.

To highlight our identification strategy, consider first the case of identical quality perceptions

(ecjt = 0). When estimating the export quantity equation, the coefficient on perceived quality

will then give an estimate of 11−σ , which can be compared with external information on σ. But

as soon as domestic and foreign quality perceptions differ (ecjt 6= 0), the estimate of 11−σ will be

biased towards zero (attenuation bias). In essence, the problem of estimating (10) can be seen as

a classical errors-in-variables problem, with ecjt presenting measurement error. The magnitude

of the bias in the coefficient estimate is increasing in the variance of ecjt relative to the variance

in ξcjt, after netting out the other explanatory variables in (10) (Wooldridge, 2001):

plim

[( 1

1− σ

)]=

1

1− σ

(Var(r∗)

Var(r∗) + Var(ec)

), (12)

where r∗ is the residual (or linear projection error) in a regression of ξcjt on all other explanatory

variables. Next, the sample variation in taste deviations can be written as

Var(ec) = N−1∑jt(e

cjt − ec)2 = N−1∑

jt[∑

kxjkt(βdk − βck)−

∑kxk(β

dk − βck)]2

= N−1∑jt[∑

k(xjkt − xk)(βdk − βck)]2, (13)

where bars indicate sample means. Hence, the variance of measurement error is increasing in

international taste differences (βdk−βck), and from (12), so is the attenuation bias in the estimated

coefficient on quality perceptions.

13

3.4 A modified Alchian-Allen hypothesis

Consider two varieties H and L of the same product group g but with different perceived quality

levels such that ξdHt > ξdLt. To frame our analysis of Alchian-Allen effects, we make the following

Assumption 1. ξdHt − ξdLt > α[hHt(·) + wHt + ωHt − hLt(·)− wLt − ωLt]

Assumption 1 states that, on average, domestic consumers obtain a higher indirect utility

from the high-quality variety H (see equation 1). The condition holds if the increase in utility

from the higher perceived quality exceeds possible cost-disadvantages of variety H. Under this

assumption, variety H has a higher market share on the domestic market.10 Similarly, variety

H has a higher potential market share in destination c – and therefore, given the fixed cost of

market entry, a higher probability of being exported – if and only if

ξdHt − ξdLt + ecHt − ecLt > α[hHt(·) + wHt + ωHt − hLt(·)− wLt − ωLt]τ c (14)

First, consider the case of identical tastes for all product attributes (βck = βdk ∀k). The term

on the left hand side of equation (14) then reduces to ξdHt − ξdLt. If additionally transport costs

consist only of the per unit type, condition (14) is satisfied under assumption 1: the variety with

the higher (perceived) quality level is more likely exported and has a higher market share in

destination c.11 If τ c > 1, however, differences in production costs across varieties are amplified

by iceberg transport costs and (14) does not follow immediately from assumption 1.

Indeed, the role of Alchian-Allen effects becomes ambiguous as soon as ∃k ∈ K : βck 6= βdk

and/or τ c > 1. From (8) and (9), the effect on domestically perceived quality on both export

demand and export probabilities is the higher, the more closely tastes across countries are

correlated. To the contrary, the higher the taste mismatch between the destination market and

the domestic market, the lower the predictive power of domestically perceived quality for export

success. In consequence, relative demand for high-quality goods is increasing in the proximity

in tastes, i.e. it depends on the degree of correlation between domestic and foreign quality

perceptions.

10Due to the presence of a fixed cost of market entry, if assumption 1 does not hold, high-quality varietiesare less likely exported even if tastes across countries are the same. Hence, the assumption allows us to focus onconstellations where Alchian-Allen effects may actually arise.

11For the discrete choice model of demand, per unit transport cost decrease the relative price of the highquality good but do not increase relative demand because the latter depends on the difference – not the ratio –of prices, which is independent of T c. This is a specificity of the discrete choice model of demand. See Hummels& Skiba (2004) for a discussion of the opposing effects of per unit and iceberg transport costs on Alchian Alleneffects in international trade.

14

4 Estimation of perceived quality on the domestic market

In this section, we discuss our approach to identification and present demand parameter es-

timates which allow us to infer perceived product quality on the domestic market. We then

present descriptive evidence on exporter quality premia.

4.1 Estimation and identification

If the indirect utility is given as in (1), demand parameters can be estimated from a linear

regression of differences in market shares on prices, product characteristics and the within-nest

market share (Berry, 1994):

ln sdjt − ln sd0t = ξdjt − αpdjt + σ ln sdjt|g. (15)

The dependent variable is the (log of the) domestic market share, sdjt, normalized by the market

share of the outside good, sd0t. sdjt|g is the share of variety j within product group g.

We aim at estimating the demand specification in (15) for our sample of domestic sales in

the Chocolate and confectionery industry. We decompose a variety’s perceived quality ξdjt into

a firm-product fixed effect ξd(CN6)1,j , a time fixed effect ξd2,t, and a deviation from the fixed effect

which is unobserved ξd3,jt. The typical firm’s product portfolio is quite dynamic, with products

being added or dropped from year to year. With firm-product fixed effects specified at the

8-digit level, a non-negligible share of observations does not contribute to identification of the

demand parameters because some varieties are only observed once. We therefore specify firm-

product fixed effects at the 6-digit level of the product classification, denoted by the superscript

CN6. The (structural) error term, ξd3,jt, reflects changes in product characteristics over time,

changes in consumer valuations e.g. due to promotional activities, and for multi-product firms

also variation in the valuation of different CN8 varieties with a common fixed effect ξd(CN6)1,j . The

empirical specification for domestic demand is given by

ln sdjt − ln sd0t = ξd(CN6)1,j + ξd2,t − αpdjt + σ ln sdjt|g + ξd3,jt (16)

The three market shares in (16) are measured as follows: The outside alternative to domestic

production is given by the industry’s imports, measured in quantity terms and denoted qd0t.12

12A valid choice for the outside good needs to satisfy the restriction that its price does not change in responseto the price of the inside goods. Denmark as a small country will have limited impact on the prices at which itimports from world markets. Furthermore, under monopolistic competition the restriction indeed would hold evenfor any of the inside goods. Average market shares in our sample are well below one percent and monopolistic

15

Data on imports and domestic production are then employed to construct the overall market

supply, Mdt =

∑j q

djt + qd0t, as well as the market share of the outside alternative, sd0t = qd0t/M

dt .

Similarly, the market share of variety j is given by sjt = qjt/Mt. We also need to specify the

nesting structure. We allow utility to be more closely correlated for varieties of the same product

category than across categories. Hence, each CN8 code constitutes a separate nest. Accordingly,

nest shares are constructed as sdjt|g = qdjt/(∑

j∈g qdjt).

While the level of disaggregation of products is high, CN8 product categories possibly lump

together several products at a lower level of disaggregation. A high market share might then

simply be a result of a large number of “hidden” varieties that due to our ignorance are aggre-

gated into a single variety j (Khandelwal, 2010). Table 10 in the appendix provides an example

of the degree of disaggregation at the 8-digit level. This example clearly highlight the potential

importance of unobserved product differentiation at a finer level of disaggregation. To accom-

modate this concern, we estimate a second demand specification, where we control for firm size,

measured by the log number of employees. Intuitively, larger firms have a larger portfolio of

(unobserved) varieties.

ln sdjt − ln sd0t = ξd(CN6)1,j + ξd2,t − αpdjt + σ ln sdjt|g + γ ln sizejt + ξd3,jt (16’)

A high realization of the demand shock ξd3,jt may induce producers to raise prices, implying

that the price coefficient is biased towards zero in a simple ordinary least squares regression.

The nest share, sdjt|g, contains part of the dependent variable and is therefore also endogenous.

Accordingly, we need to find at least two exogenous instruments. Following Foster et al. (2008),

the firm’s physical labor productivity constitutes our first instrumental variable. From the

specification of marginal costs in (3) together with firms’ pricing policy in (5) determinants of

marginal costs that are orthogonal to the demand shock constitute valid instruments. Identifi-

cation therefore relies on the assumption that, conditional on ξd(CN6)1,j , labor productivity enters

marginal costs only via wjt. For physical labor productivity this is a much weaker assumption

than for revenue productivity.

Our second instrument is inspired by Nevo (2001). Building on the work of Hausman (1996),

he suggests to instrument the price of some variety j in market c with the price of the same variety

in some other market c′. Similarly, we exploit the two-dimensional panel structure of our data,

which includes not only information for the same variety over time, but for exported products also

competition therefore seems to be a realistic assumption.

16

information on prices on the foreign market. We generalize the identification strategy in Nevo

(2001) to overcome the concern that market-specific valuations may be correlated. Specifically,

we instrument the price and market share of variety j with the weighted average export price

of its competitors in the same nest g:

IVjt =∑

j′∈g,j′ 6=jwj′ · pcj′t, wj′t ≡

sdj′t|g∑j′∈g,j′ 6=j s

dj′t|g

, (17)

where pcj′t is the average export price for variety j across all its export markets. Weights are

given by variety j′’s within-nest market share relative to the combined within-nest market share

of all varieties excluding the one being instrumented. IVjt captures marginal cost shocks to

variety j’s competitors. These affect both the pricing policy as well as the within-nest market

share of variety j, but not its perceived quality, and therefore fulfill the orthogonality condition.

We construct estimates of the quality perceptions of domestic consumers as

ξd

jt = ln sdjt − ln sd0t + αpdjt − σ ln sdjt|g (18)

We also construct a second quality estimate, which adjusts for the presence of hidden varieties

as in (16’):

ξd,adjusted

jt = ln sdjt − ln sd0t + αpdjt − σ ln sdjt|g − γ ln size (18’)

Identification of perceived quality relies on variation in market shares that cannot be explained

by price differences. The estimates are therefore based on the identifying assumption that, for

a given price, products of high appeal to consumers capture a higher market share.

4.2 Demand parameter estimates

Table 4 presents demand parameter estimates based on (16) and (16’). We confront ordinary

least squares (OLS) estimates in columns (1) to (3) with results from the instrumental variable

approach in columns (4) and (5).

The model in column (1) of table 4 includes year effects ξd2,t but excludes the firm-product

fixed effects ξd(CN6)1,j , and thereby completely disregards endogeneity. In accordance with the pre-

dicted bias in the price coefficient, the resulting estimate of α is essentially zero. In column (2),

we additionally condition on ξd(CN6)1,j . This specification controls for time-constant unobserved

heterogeneity, and we expect the endogeneity issue to be mitigated. Indeed, α turns negative

and statistically significant.

Columns (4) and (5) report two stage least squares (2SLS) estimates, which employ the two

17

Table 4: Demand parameter estimatesa

Dependent variable: ln sdjt − ln sd0tOLS 2SLS

(1) (2) (3) (4) (5)

Price (α) -0.0026 -0.0144*** -0.0169*** -0.0203*** -0.0224***(0.002) (0.003) (0.004) (0.006) (0.006)

Nest market share (σ) 0.5912*** 0.5384*** 0.4710*** 0.5322** 0.5571***(0.055) (0.075) (0.092) (0.206) (0.209)

Log size (γ) 0.2727*(0.156)

Observations 1,098 1,098 551 551 551R2 0.36 0.80 0.80Year fixed effects Yes Yes Yes Yes YesFirm-CN6 fixed effects No Yes Yes Yes Yes

First stage:– F excluded instruments, Price 19.33*** 30.02***– F excluded instruments, Nest market share 9.86*** 9.61***– R2, Price 0.73 0.74– R2, Nest market share 0.70 0.70

Price elasticity:– Median 0.16 0.81 0.85 1.13 1.30– Q = 25 0.10 0.49 0.53 0.69 0.78– Q = 75 0.27 1.34 1.42 1.87 2.16

a Robust standard errors, adjusted for clustering at the firm-product level, are given in parentheses. *,**,*** denotesignificance at the 10%, 5%, 1% levels, respectively.

instrumental variables described in the previous section to instrument for domestic prices and

within-nest market shares. To ensure comparability, column (3) replicates the OLS specification

of column (2) on the restricted sample for which our instrumental variables are applicable. The

IV approach moves the price coefficient in the intuitive direction: the IV estimate of α in column

(4) is negative and larger in absolute value than the OLS estimate in column (3). Finally, in

column (5) we turn to the 2SLS estimates of the specification in (16’), which adjusts for hidden

varieties. In line with the interpretation that larger firms have a larger (potentially unobserved)

portfolio of varieties, firm size has a positive and significant effect on the normalized market

share. The price coefficient also increases slightly, compared to the estimate in column (4).

Across all specifications the estimated within-nest correlation of utility, σ, lies strictly between 0

and 1, as expected, and varies only marginally across specifications. Notably, the point estimate

of ≈0.56 in column (5) is comparable to the estimate of 0.54 for chocolate reported in Conlon

& Mortimer (2010).

Given the high R2’s, our first stage regressions succeed in explaining a considerable part

of the variation in both prices and within-nest market shares. Furthermore, from the highly

significant F statistics, the excluded instruments have some power in predicting the endogenous

regressors.13 In order to evaluate the plausibility of these parameter estimates, we calculate

13The F statistic is marginally below the critical value of 10 for ln sdjt|g, raising concerns about weak instru-ments. As a robustness check, we estimate the instrumental variable regressions via limited information maximum

18

Table 5: Correlation of ξd

jt with selected firm-product level variablesa

Perceived Quality Price Quantity sold

ξdjt ξ

d,adjustedjt domestic exportb domestic exportb

Perceived Quality ξdjt 1

Perceived Quality ξd,adjustedjt 0.981*** 1

Price, domestic 0.428*** 0.430*** 1

Price, export 0.177*** 0.177*** 0.620***

Quanity, domestic 0.704*** 0.660*** -0.134*** 0.116*** 1

Quanity, export 0.357*** 0.301*** -0.252*** 0.0977* 0.625*** 1

a The table gives pairwise correlation coefficients. *,**,*** denote significance at the 10%, 5%, 1% levels, respectively.b Export prices and quantities are calculated by pooling across all destinations.

own price elasticities of demand, which in the nested logit framework are given by α1−σ [1 −

σsdjt|g − (1 − σ)sdjt]pdjt. The bottom part of table 4 reports median price elasticities, as well as

their first and third quartiles. The median own-price elasticity of demand is below one in all

OLS specifications. The instrumental variable approach to the contrary yields higher demand

elasticities, with a median elasticity that exceeds one in both columns (4) and (5).

4.3 Perceived quality estimates

We employ parameter estimates of column (5) in table 4 to infer each variety’s domestic perceived

quality. Our first quality estimate, ξd

jt is based on equation (18). The second estimate, ξd,adjusted

jt

corrects for hidden varieties as in (18’). As a check on the sensibility of these estimates, we take a

look at correlation coefficients of quality with prices and quantities sold on both the domestic as

well as the export market; see table 5. ξd

jt and ξd,adjusted

jt are highly correlated, with a correlation

coefficient of 0.98.

High-quality varieties sell at a higher price on both domestic as well as on export markets.

This is important for our subsequent analysis, becaue the Alchian-Allen hypothesis presumes

such a positive link between quality and prices. One plausible interpretation of this finding is that

h(·), the part of marginal costs that depends on product characteristics, is an increasing function

of ξdjt. Furthermore, varieties of high appeal to domestic consumers potentially sell at a higher

mark-up, e.g. because the corresponding consumers are less price responsive than consumers

of other varieties.14 The much lower correlation of domestic perceived quality with the average

likelihood (LIML), which is more robust to weak instruments (Stock et al., 2002). Point estimates from the LIMLestimations are almost identical to 2SLS estimates, and the first stage F statistic exceeds the Stock-Yogo weakIV test critical values for both prices and within-nest market shares.

14This latter channel inducing correlation between quality and prices is not captured in our model becauseit disregards consumer heterogeneity, but it is well documented in the industrial organization literature. Forexample, adopting a random coefficients model Berry et al. (1995) find lower demand elasticities for auto modelsin the high quality segment. In consequence, the implied mark-ups are also higher for this type of car.

19

export price compared to its correlation with the domestic price points to the importance of this

latter mechanism.

Turning to domestic and export sales, table 5 confirms that assumption 1 is without loss

of generality: products of high appeal to domestic consumers achieve higher domestic sales,

and therefore higher market shares. Similarly, ξd

jt (ξd,adjusted

jt ) and export volumes are posi-

tively correlated, but the degree of correlation is with 0.36 (0.30) significantly lower than the

corresponding correlation coefficient with domestically sold quantities of 0.70 (0.66).

Figures 1(a) and 1(b) show kernel density estimates for our two measures of perceived quality,

comparing distributions for exported varieties (red lines) and non-exported varieties (blue lines).

The quality interpretation of the Melitz model predicts that high-quality firms sort into export

markets. In line with this interpretation, the average quality of exported varieties exceeds

the one for non-exported varieties. However, quality differences are favorable for exporters

at the lower end of the distribution but weakly favorable for non-exporters at the higher end

of the distribution. Results from a Kolmogorov-Smirnov test show that these differences are

statistically significant in both directions for the adjusted quality estimate ξd,adjusted

jt .

Figures 2(a) and 2(b) are based on quantile regressions of ξd

jt and ξd,adjusted

jt on the indicator

variable Exported variety and a set of product and year fixed effects, which control respectively

for differences in consumer valuations across product groups and aggregate shifts in product

quality over time. The coefficient on export status gives us the exporter quality premium

for each quantile of the conditional distribution. For comparison, the figures also show OLS

estimates and confidence intervals. The OLS regressions reveal positive but insignificant quality

differences between exported and non-exported varieties. To the contrary, quantile regressions

uncover that the exporter quality premium is indeed positive in the lower quantiles, but turns

insignificant or even significantly negative at higher quantiles.15

Are varieties of high quality exported to more markets? Figure 3 plots the share of varieties

that are supplied to at least c = 1, . . . 64 markets against the number of markets served, where

c = 1 for purely domestic varieties. Quality levels are divided into four quartiles.16 Varieties

of the highest quality segment are not consistently exported to more markets. For example, for

15In the Helpman et al. (2004) extension of the Melitz model, high-quality producers might sort into foreigndirect investment. Due to the unavailability of information on foreign affiliates, we cannot rule out that theinsignificant exporter quality premia at the upper end of the distribution reflect that produces of very highquality select into FDI.

16We pool observations across years and calculate quartiles based on all observations. However, we alsoconstruct graphs for each year separately and these paint a very similar picture.

20

0.1

.2.3

−10 −5 0 5 10

Non−exporter Exporter

Average quality non−exporter: 1.41; exporter: 1.99

(a) Perceived Quality ξd

jt

0.1

.2.3

.4

−10 −5 0 5

Non−exporter Exporter

Average quality non−exporter: 0.26; exporter: 0.62

(b) Perceived Quality ξd,adjusted

jt

Figure 1: Densities of perceived quality estimates

−.5

0.5

11.

5

0 .2 .4 .6 .8 1Quantiles

Exporter quality premium (OLS) Exporter quality premium95% confidence interval 95% confidence interval

(a) Perceived Quality ξd

jt

−1

−.5

0.5

11.

5

0 .2 .4 .6 .8 1Quantiles

Exporter quality premium (OLS) Exporter quality premium95% confidence interval 95% confidence interval

(b) Perceived Quality ξd,adjusted

jt

Figure 2: Exporter quality premia (quantile regressions)

.05

.15

.3

.5.75

1

Sha

re o

f var

ietie

s (lo

g sc

ale)

1 2 4 8 16 32 64Number of markets per variety (log scale)

Share of varieties in 1st quartile of qualityShare of varieties in 2nd quartile of qualityShare of varieties in 3rd quartile of qualityShare of varieties in 4th quartile of quality

Number of varieties: 1098

(a) Perceived Quality ξd

jt

.05

.15

.3

.5.75

1

Sha

re o

f var

ietie

s (lo

g sc

ale)

1 2 4 8 16 32 64Number of markets per variety (log scale)

Share of varieties in 1st quartile of qualityShare of varieties in 2nd quartile of qualityShare of varieties in 3rd quartile of qualityShare of varieties in 4th quartile of quality

Number of varieties: 948

(b) Perceived Quality ξd,adjusted

jt

Figure 3: Number of export destinations

21

the unadjusted quality estimate the fraction of varieties sold in c = 2, . . . 8 markets is almost

indistinguishable for the second, third and fourth quality segment, see figure 3(a). Once we

adjust for hidden varieties, low-quality varieties that are between the 25th and the 50th percentile

of the quality distribution have the highest probability of being exported to at least one market,

and the advantage of varieties from the highest quality segment in penetrating more markets

only becomes apparent at more than c = 16 destinations. Compared with the evidence in Crozet

et al. (2012), in our data quality sorting is a much weaker phenomenon. This may reflect both

differences across industries (Champagne vs. Chocolate and confectionery), as well as differences

in the measurement of quality (intrinsic quality vs. appeal to domestic consumers).

5 Perceived quality, asymmetric tastes and export performance

In this section, we use our estimates ξd

jt and ξd,adjusted

jt to take the structural relationships between

domestically perceived quality and central aspects of export performance to the data.

5.1 Empirical approach

We aim at obtaining estimable versions of the destination-specific export probability, export

quantities and fob values in equations (9) to (11). We subsume fixed export costs and per

unit trade costs into a destination fixed effect θc, control for market size M ct by including the

log of GDP17, and add a set of product fixed effect θg to capture the inclusive value of each

nest, lnDcgt. The last point implies that deviations of the inclusive value (or attractiveness) of

each product category g from its cross-country, cross-time average are assumed to be sufficiently

controlled for by including country effects θc and year effects θt. We furthermore add lagged

export status at the firm-destination-level (χcf,t−1) to control for market-specific sunk cost and

the role of export experience in explaining export demand; see Roberts et al. (2012). Adopting

a normal distribution for ecjt implies a probit model for the export participation equation:

P (χcjt = 1) = Θ

[θg + θc + θt + φ ln GDPct + κ ξdjt −

α

1− στ cwjt + ρχcf,t−1

](9’)

Similarly, based on (10) and (11), the estimation equations for the export quantity and the fob

export value are respectively given by

ln qjt = θg + θc + θt + φ ln GDPct + λ ξdjt −α

1− στ cwjt − ecjt + ρχcf,t−1 + lnχcjt, (10’)

17Data on GDP and GDP per capita comes from the World Bank’s World Development Indicators (WDI).

22

ln vc,fobjt = θg + θc + θt + φ ln GDPct + λ ξdjt + f(wjt, τc)− ecjt + ρχcf,t−1 + lnχcjt, (11’)

where f(wjt, τc) is a highly non-linear function of marginal costs and trade costs; see (11).

Estimation of (11’) and (10’) should yield similar parameter estimates for the coefficient on ξdjt.

Export values are less subject to measurement error than export quantities. However, in (11’)

marginal costs enter in a highly non-linear manner, which exacerbates adequately controlling

for wjt, and therefore hampers the identification of λ.

In (10’) and (11’), λ gives the semi-elasticity of export volumes with respect to quality, as

perceived by domestic consumers. In a first step, we estimate λ ignoring variation in tastes

across export destinations. This approach is informative about the average relationship between

domestic and foreign perceived quality in our data. Specifically, (10) and (11) suggest that

λ = 11−σ if domestic and foreign tastes are symmetric. However, as soon as tastes differ, λ

is biased towards zero; see equation (12). We can therefore combine estimates of λ with the

estimate of σ obtained from the estimation of the domestic demand equation in section 4.2, to

calculate λ ≡ λ(1− σ). We expect λ ≈ 1 if domestic and foreign consumers agree on the appeal

of all varieties, whereas λ < 1 if foreign tastes differ from domestic tastes. In section 5.2, we

allow λ to depend on the characteristics of the trading relationship.

Estimation of (9’) to (11’) depends on the availability of data on variety-specific marginal

costs as well as variable trade costs τ c. Marginal costs are potentially correlated with ξdjt, im-

plying that λ will be downward biased if wjt is omitted from the regression. We employ two

measures of marginal costs to accommodate this concern. The first is physical labor produc-

tivity, which is inversely related to marginal costs. Our second measure of wjt builds on the

intuition that import prices reflect the quality of intermediate inputs. They are therefore cor-

related with the part of marginal costs that depends on the characteristics of the final product,

h(xj1t, . . . xjKt). We use the average (standardized) input price for each firm over all its imports

as a proxy for h(·).18

We derive two alternative measures of variable trade costs. The first is given by the weighted

distance from Denmark to country c.19 The second modifies the approach to measuring geo-

graphic barriers in Eaton & Kortum (2002) to our set-up. Specifically, export prices are in free

18For each intermediate input, we standardize import prices to have a variance of one and mean of zero. Thestandardization of import prices corrects for the fact that average unit values vary across intermediate products andnot all firms import all intermediates. Since importing is an even more widespread phenomenon than exporting,import prices are applicable for around 90 percent of the sample.

19Geographic distance data are taken from the CEPII database, http://www.cepii.fr/welcome.asp.

23

on board terms, and therefore do not include trade costs. However, from the pricing rule in

(5), the mark-up charged in any destination c depends negatively on τ c. The difference between

domestic and export prices, pdjt − pcjt = 1−σα − 1−σ

ατc , is thus increasing in trade costs. More

broadly, it captures deviations between domestic and export mark-ups and therefore reflects the

relative degree of competition in destination c compared to the home market. We denote pd−pc

as the destination-specific average of this difference, calculated over all varieties exported to

destination c. To control for the term τ cwjt, we interact physical labor productivity with these

two measures of trade barriers.20

In section 5.2 we turn to variation in perceived quality across destinations. Tastes depend

on customs, culture etc. The more similar Denmark and the export destination are in these

dimensions, the higher the expected effect of ξdjt on export volumes. We employ three measures

for the proximity in tastes: (i) linguistic proximity, (ii) (the inverse of) genetic distance and (iii)

GDP per capita. The first two destination characteristics are measures of cultural proximity.

Linder (1983) introduced the idea that countries at the same stage of development have similar

preferences, which motivates the inclusion of GDP per capita among our list of export market

characteristics.

Melitz & Toubal (2012) provide a measure of linguistic proximity which is based on lexical

similarity between forty words; see Melitz & Toubal (2012) for details. Compared to simple

dummy variables for common language, this measure offers considerably more variation, and

thereby paints a very distinct picture of the degree to which people from different nations

are able to communicate and interact. While linguistic proximity is correlated with cultural

proximity, it also captures other factors such as the ease of communication. We therefore

proceed by employing another measure for the relatedness of two cultures, (the log of) genetic

distance. Genetic distance is based on differences in the distribution of gene variants across

populations and measures “the time since two populations have shared common ancestors”

(Spolaore & Wacziarg, 2009, p. 470). It therefore captures “(...) divergence in the whole set of

implicit beliefs, customs, habits, biases, conventions etc. that are transmitted across generations

– biologically and/or culturally (...)” (Spolaore & Wacziarg, 2009, p. 471).

We restrict the sample to the 69 destinations for which we have at least ten observations with

positive export volumes. After this restriction, the destination-specific export probability in our

20Interactions of import prices with trade costs are insignificant across specifications and are therefore droppedfrom the final estimations.

24

sample still constitutes a mere nine percent. If we disregard variety-destination combinations

with zero exports, estimates of the impact of perceived quality on export volumes are downward

biased: A variety of poor quality from the viewpoint of the domestic consumer with positive

export sales to destination c needs to have a high destination-specific deviation of foreign from

domestic perceived quality (i.e. high ecjt). Hence, ecjt and ξdjt in the selected sample with positive

export sales are negatively correlated, leading to a downward bias in λ. Following the approach

in Eaton & Kortum (2001), Crozet et al. (2012) propose a Tobit model treating the market-

specific minimum observed value of qcjt (vc,fobjt ) as the censoring point.21 Their Monte Carlo

simulations confirm that the estimator corrects for the selection bias.

5.2 The extensive and intensive margin of exporting

Table 6 shows results from the estimation of equations (9’), (10’) and (11’) for the two alternative

quality estimates ξd

jt and ξd,adjusted

jt . We first disregard all observations with zero export sales

and estimate (10’) and (11’) by ordinary least squares. Consecutively, we add these observations

back in and estimate the Tobit model, which corrects for selection into exporting.22

Perceived product quality has a positive and significant impact on the probability of export-

ing: a unitary increase in ξd

jt (ξd,adjusted

jt ) raises the probability of being active in a specific market

c by 0.9 to 1.3 percentage points. Furthermore, we find a positive effect of domestic perceived

quality on export volumes, which is roughly equal in magnitude for export quantities and fob

values. In line with the predicted direction of selection bias, the Tobit model yields coefficients

on perceived quality that are around twice the magnitude of those from the OLS regressions.

Differences in estimated coefficients for our two quality measures are also instructive. High val-

ues of ξd

jt may partly be driven by a large number of unobserved varieties that are aggregated

into a single CN8 code. Such unobserved varieties have a positive impact on export demand

that goes beyond the pure effect of perceived quality. Accordingly, regressions which employ

21More specifically, we treat qc = minj∈Ωd(qcjt)−0.0001 and vc,fob = minj∈Ωd(vc,fobjt )−0.0001 as the censoringpoints such as to not treat the marginal exported variety as censored. The export volume equations are thenestimated via interval regression, where for each non-exported variety ln qcjt (ln vc,fobjt ) is treated to lie anywhere

in the interval (∞; ln vc,fob]. Given the lack of suitable exclusion restrictions, the Tobit estimator is preferred tothe Heckman selection model proposed in Helpman et al. (2008).

22All standard errors are adjusted for clustering at the variety level. Our perceived quality estimates aregenerated regressors taking from the estimation of the domestic demand equation in section 4. Standard errorsshould therefore be bootstrapped to correct for sampling variation in the domestic demand parameters α and σ.In practice, the presence of the large set of dummy variables for destinations, products and years together witha low number of uncensored observations leaves some parameters unidentified in each of the re-sampling steps.Standard errors reported in tables 6 and 7 could therefore not be based on the bootstrap.

25

Tab

le6:

Exte

nsi

ve

and

inte

nsi

ve

mar

gin

ofex

por

tin

ga

Per

ceiv

edqu

ality

esti

mate

:ξd jt

Per

ceiv

edqu

ality

esti

mate

:ξd,adjusted

jt

Pro

bit

OL

ST

ob

itO

LS

Tob

itP

rob

itO

LS

Tob

itO

LS

Tob

itχc jt

lnvc,fob

jt

lnvc,fob

jt

lnqc jt

lnqc jt

χc jt

lnvc,fob

jt

lnvc,fob

jt

lnqc jt

lnqc jt

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

Perceived

Quality

0.0

127***

0.7

029***

1.9

581***

0.6

863***

1.8

566***

0.0

091***

0.6

813***

1.3

496***

0.6

873***

1.2

949***

(0.0

02)

(0.1

07)

(0.3

56)

(0.1

11)

(0.3

37)

(0.0

02)

(0.1

15)

(0.3

99)

(0.1

20)

(0.3

78)

Norm

alizedim

port

price

0.0

041

-0.2

101

0.2

655

-0.7

837*

0.1

492

0.0

074

-0.0

961

0.7

108

-0.6

802

0.5

617

(0.0

08)

(0.4

19)

(1.2

53)

(0.4

40)

(1.1

77)

(0.0

09)

(0.4

14)

(1.2

95)

(0.4

25)

(1.2

13)

Log

laborproductivity

0.0

061

0.8

039

0.8

866

0.9

816

0.9

596

0.0

052

0.7

644

0.6

548

0.9

524

0.7

457

(0.0

05)

(0.6

01)

(0.8

15)

(0.6

27)

(0.7

78)

(0.0

06)

(0.6

11)

(0.8

22)

(0.6

34)

(0.7

85)

Log

laborproductivity

-0.0

010

-0.1

396*

-0.1

528

-0.1

126

-0.1

573

-0.0

012

-0.1

531**

-0.1

593

-0.1

257

-0.1

639*

×Log

distance

(0.0

01)

(0.0

74)

(0.1

02)

(0.0

79)

(0.0

97)

(0.0

01)

(0.0

75)

(0.1

04)

(0.0

80)

(0.0

98)

Log

laborproductivity

0.0

002***

0.0

032

0.0

230***

0.0

005

0.0

220***

0.0

002***

0.0

021

0.0

231***

-0.0

005

0.0

221***

×(pd−pc)

(0.0

00)

(0.0

06)

(0.0

06)

(0.0

06)

(0.0

06)

(0.0

00)

(0.0

06)

(0.0

06)

(0.0

06)

(0.0

06)

Lagged

expo

rtstatusχc f,t−

10.1

707***

0.7

465***

10.4

184***

0.7

299***

9.9

430***

0.1

866***

0.7

733***

10.8

223***

0.7

534***

10.3

212***

(0.0

28)

(0.1

80)

(0.7

16)

(0.1

85)

(0.7

02)

(0.0

29)

(0.1

77)

(0.7

01)

(0.1

83)

(0.6

88)

Log

GDP

-0.0

021

-0.6

375**

-0.5

500

-0.6

632**

-0.4

894

-0.0

027

-0.6

830**

-0.6

225

-0.7

069**

-0.5

583

(0.0

06)

(0.2

69)

(0.8

77)

(0.2

66)

(0.8

59)

(0.0

07)

(0.2

73)

(0.8

67)

(0.2

71)

(0.8

48)

Ob

serv

ati

on

s43,3

42

4,0

28

46,3

25

4,0

28

46,3

25

43,3

42

4,0

28

46,3

25

4,0

28

46,3

25

R2

–0.4

22

–0.4

65

––

0.4

17

–0.4

63

–Y

ear

fixed

effec

tsY

esY

esY

esY

esY

esY

esY

esY

esY

esY

esD

esti

nati

on

fixed

effec

tsY

esY

esY

esY

esY

esY

esY

esY

esY

esY

esP

rod

uct

fixed

effec

tsY

esY

esY

esY

esY

esY

esY

esY

esY

esY

esλ≡λ

(1−σ

)–

0.3

10.8

70.3

00.8

2–

0.3

00.6

00.3

00.5

7H

0:λ

=1

(p-v

alu

e)

–0.0

00

0.4

00

0.0

00

0.2

34

–0.0

00

0.0

23

0.0

00

0.0

11

Ha

:λ<

1(p

-valu

e)

–1.0

00

0.8

00

1.0

00

0.8

83

–1.0

00

0.9

89

1.0

00

0.9

95

Sta

nd

ard

dev

iati

on

of

lnec jt

––

1.9

89

–1.9

36

––

2.0

06

–1.9

52

aT

he

tab

lere

port

sm

arg

inal

effec

tsev

alu

ate

dat

the

sam

ple

mea

nfo

rth

ep

rob

itm

od

elan

dco

effici

ent

esti

mate

sfo

rO

LS

an

dT

ob

itm

od

els.

Rob

ust

stan

dard

erro

rs,

ad

just

edfo

rcl

ust

erin

gat

the

firm

-pro

du

ctle

vel

,are

giv

enin

pare

nth

eses

.*,*

*,*

**

den

ote

sign

ifica

nce

at

the

10%

,5%

,1%

level

s,re

spec

tivel

y.

26

ξd,adjusted

jt rather than ξd

jt as explanatory variable imply a lower impact of perceived quality on

export values and quantities; see columns (6) to (10).

In order to interpret the coefficient estimate on domestic quality perceptions in terms of the

match between domestic and foreign tastes, we calculate λ ≡ λ(1− σ) based on σ retained from

column (5) of table 4. Notably, the resulting value of λ, which is reported at the bottom part of

the table, is below the benchmark of one for all specifications. However, it varies significantly

across estimators, between 0.3 in the OLS estimations and 0.57-0.87 in the Tobit model. We

test the hypothesis that λ is smaller than one against the null hypothesis H0 : λ = 1. The null

hypothesis cannot be rejected in the Tobit estimations of columns (3) and (5) of table 6, but is

largely rejected once we use the hidden-variety adjusted quality measure; see columns (8) and

(10). Furthermore, the alternative hypothesis Ha : λ < 1 can never be rejected. These findings

are broadly in line with asymmetric quality perceptions across countries, implying that domestic

perceived quality constitutes an imperfect predictor of export market success.

The estimated effect of other determinants of export market performance is largely as ex-

pected. Lagged export status at the firm-destination level is positively related to current export

status at the variety-destination level and the effect is economically significant, implying consid-

erable market-specific sunk costs. The substantial effect of export experience on export volumes

also highlights the role of market-specific knowledge in international trade. Recall that we are

not in a position to discern whether firms sell a product with the same characteristics on the

domestic and the export market. As firms gain experience in a market they learn about foreign

demand, adjust their product characteristics to foreign tastes and therefore succeed in increasing

export sales. In that sense, controlling for χcf,t−1 is essential for using λ to draw conclusions

about the presence and magnitude of asymmetric tastes across countries.

The predicted impact of the normalized import price is in general ambiguous: on the one

hand, import prices are a measure of marginal costs and as such tend to decrease both export

probabilities and volumes. On the other hand, higher intermediate input prices signal higher

output quality and may therefore increase exports in addition to the effect of perceived qual-

ity. Table 6 shows that, when we condition on past export experience, import prices do not

significantly affect a variety’s export performance. However, in unreported regressions where

χcf,t−1 is omitted, coefficient estimates turn positive and significant in both the Probit and the

Tobit model. Physical labor productivity is more important for export market participation and

export volumes in highly competitive markets (high value of pd−pc). To the contrary, the effect

27

of productivity is independent on the distance of the destination market, confirming results in

Bastos & Silva (2010). Finally, conditional on the country fixed effect θc, which captures the

part of market size that is time-invariant, log GDP does not contribute further to explaining

export market performance.

5.3 The intensive margin of exporting and proximity in tastes

How does the effect of perceived quality on export volumes vary across export markets? To

validate our modified Alchian-Allen hypothesis, we include interaction terms of domestic quality

perceptions with various country characteristics Ψc. Table 7 reports results from Tobit models

with ln qcjt as dependent variable, based on the following estimation equation:

ln qjt = θg + θc+ θt+φ ln GDPct+λ0 ξdjt+λ1 ξ

djt ·Ψc− α

1− στ cwjt− ecjt+ρχcf,t−1 + lnχcjt. (19)

Intuitively, tastes of foreign consumers are likely to accord well with domestic tastes in

countries that are culturally similar, but less so in countries that are culturally distant. The

respectively positive and negative coefficient estimates on the interaction terms with linguistic

proximity and genetic distance are indicative of cross-country variation in the effect of quality

that is consistent with our priors; see columns (1),(7) and (2),(8). Figures 4 and 5 visualize the

taste (mis-)match across trade partners as a function of country characteristics Ψc,

λc

= (λ0 + λ1 ·Ψc)(1− σ).

When linguistic proximity is high, foreign tastes are similar to domestic tastes and λc

is

not significantly different from one; see figure 4. To the contrary, in countries where the native

language is very different, foreign tastes deviate from the tastes of the Danish consumer, and λc

is significantly below the benchmark of one. Likewise, tastes of consumers in countries that share

a lot of traits with the Danish population are very similar to domestic tastes, but in destinations

where genetic distance is high we again find λc< 1; see figure 5. Estimated deviations from

domestic tastes turn out to be more pronounced in the case of our hidden-varieties adjusted

quality measure, but the qualitative pattern is visible for both ξd

jt and ξd,adjusted

jt .

The proximity in tastes between two countries depends not only on their cultural proximity,

but may also depend on whether the countries are at a similar stage of development; see Linder

(1983). More in general, preferences may be non-homothetic, implying that high-income coun-

tries consume goods of higher quality. The coefficient on the interaction term between domestic

perceived quality and GDP per capita is indeed positive and highly significant; see columns (3)

28

Tab

le7:

Des

tin

atio

nch

arac

teri

stic

san

dp

roxim

ity

inta

stes

a

Tob

itm

od

elfo

rlnqc jt

Per

ceiv

edqu

ality

esti

mate

:ξd jt

Per

ceiv

edqu

ality

esti

mate

:ξd,adjusted

jt

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

Perceived

Quality

1.4

952***

2.7

291***

-0.6

548

2.8

691***

-0.2

078

0.2

686

0.9

635***

2.1

602***

-1.3

431

2.4

658***

-0.6

606

-0.2

517

(0.3

41)

(0.4

52)

(1.0

49)

(0.9

06)

(1.3

05)

(1.2

92)

(0.3

72)

(0.5

02)

(1.0

60)

(0.9

55)

(1.3

38)

(1.3

07)

Perceived

Quality

0.2

452***

0.1

922***

0.2

286***

0.1

515**

×Lingu

isticproximity

(0.0

85)

(0.0

74)

(0.0

87)

(0.0

74)

Perceived

Quality

-0.1

847***

-0.1

085*

-0.1

839***

-0.0

949

×Log

genetic

distance

(0.0

64)

(0.0

59)

(0.0

66)

(0.0

58)

Perceived

Quality

0.2

561**

0.1

671*

0.2

318**

0.2

688**

0.1

913**

0.2

383**

×Log

GDP

percapita

(0.1

08)

(0.0

91)

(0.1

10)

(0.1

10)

(0.0

92)

(0.1

12)

Perceived

Quality

-0.1

379

0.0

194

-0.0

230

-0.1

590

-0.0

191

-0.0

470

×Log

distance

(0.1

15)

(0.1

19)

(0.1

23)

(0.1

17)

(0.1

20)

(0.1

26)

Norm

alizedim

port

price

0.2

086

0.1

137

0.1

940

0.1

678

0.2

236

0.1

419

0.6

080

0.5

385

0.5

805

0.6

043

0.6

273

0.5

655

(1.1

83)

(1.1

76)

(1.1

83)

(1.1

80)

(1.1

85)

(1.1

80)

(1.2

20)

(1.2

17)

(1.2

16)

(1.2

20)

(1.2

23)

(1.2

22)

Log

laborproductivity

1.3

995*

1.3

437*

0.9

316

1.4

734*

1.2

029

1.1

736

1.0

837

1.0

068

1.2

509

0.7

222

0.9

983

0.9

136

(0.7

62)

(0.8

06)

(0.7

78)

(0.8

63)

(0.8

57)

(0.8

51)

(0.7

71)

(0.8

19)

(0.8

74)

(0.7

84)

(0.8

61)

(0.8

58)

Log

laborproductivity

-0.2

172**

-0.2

063**

-0.1

581

-0.2

270**

-0.1

932*

-0.1

872*

-0.2

101**

-0.1

974*

-0.2

326**

-0.1

651*

-0.2

013*

-0.1

882*

×Log

distance

(0.0

95)

(0.1

03)

(0.0

97)

(0.1

12)

(0.1

11)

(0.1

12)

(0.0

97)

(0.1

06)

(0.1

14)

(0.0

98)

(0.1

12)

(0.1

14)

Log

laborproductivity

0.0

229***

0.0

216***

0.0

262***

0.0

220***

0.0

255***

0.0

248***

0.0

229***

0.0

219***

0.0

221***

0.0

261***

0.0

255***

0.0

247***

×(pd−pc)

(0.0

06)

(0.0

06)

(0.0

06)

(0.0

06)

(0.0

06)

(0.0

06)

(0.0

06)

(0.0

06)

(0.0

06)

(0.0

06)

(0.0

06)

(0.0

06)

Lagged

expo

rtstatusχc f,t−

19.9

309***

9.8

861***

9.9

475***

9.9

383***

9.9

336***

9.9

016***

10.3

177***

10.2

595***

10.3

132***

10.3

221***

10.3

195***

10.2

720***

(0.7

04)

(0.6

96)

(0.7

00)

(0.7

01)

(0.7

01)

(0.6

94)

(0.6

89)

(0.6

88)

(0.6

87)

(0.6

86)

(0.6

86)

(0.6

86)

Log

GDP

-0.5

403

-0.5

279

0.9

547

-0.5

102

-2.0

875

1.9

417

-0.6

001

-0.6

106

-0.5

966

1.1

077

-1.8

423

2.0

814

(0.8

45)

(0.8

26)

(2.5

47)

(0.8

55)

(3.1

97)

(2.4

33)

(0.8

37)

(0.8

18)

(0.8

46)

(2.5

32)

(3.1

67)

(2.4

04)

Ob

serv

ati

on

s45,5

76

38,2

44

46,3

25

46,3

25

45,5

76

38,2

44

45,5

76

38,2

44

46,3

25

46,3

25

45,5

76

38,2

44

Yea

rfi

xed

effec

tsY

esY

esY

esY

esY

esY

esY

esY

esY

esY

esY

esY

esD

esti

nati

on

fixed

effec

tsY

esY

esY

esY

esY

esY

esY

esY

esY

esY

esY

esY

esP

rod

uct

fixed

effec

tsY

esY

esY

esY

esY

esY

esY

esY

esY

esY

esY

esY

esS

tan

dard

dev

iati

on

of

lne jt

1.9

31

1.9

37

1.9

33

1.9

34

1.9

31.9

35

1.9

48

1.9

55

1.9

49

1.9

51

1.9

47

1.9

53

aT

he

tab

lere

port

sco

effici

ent

esti

mate

s.C

olu

mn

s(3

),(5

),(6

),(9

),(1

1)

an

d(1

2)

ad

dit

ion

ally

contr

ol

for

GD

Pp

erca

pit

a(c

oeffi

cien

tn

ot

rep

ort

ed).

Rob

ust

stan

dard

erro

rs,

ad

just

edfo

rcl

ust

erin

gat

the

firm

-pro

du

ctle

vel

,are

giv

enin

pare

nth

eses

.*,*

*,*

**

den

ote

sign

ifica

nce

at

the

10%

,5%

,1%

level

s,re

spec

tivel

y.

29

.4.6

.81

1.2

1.4

0 1 2 3 4Linguistic Proximity

Effect of perceived quality95% confidence intervalPredicted country effects

(a) Perceived Quality ξd

jt

0.5

11.

5

0 1 2 3 4Linguistic Proximity

Effect of perceived quality95% confidence intervalPredicted country effects

(b) Perceived Quality ξd,adjusted

jt

Figure 4: Linguistic Proximity

.2.4

.6.8

11.

2

3 4 5 6 7 8Log Genetic Distance

Effect of perceived quality95% confidence intervalPredicted country effects

(a) Perceived Quality ξd

jt

0.2

.4.6

.81

3 4 5 6 7 8Log Genetic Distance

Effect of perceived quality95% confidence intervalPredicted country effects

(b) Perceived Quality ξd,adjusted

jt

Figure 5: Genetic distance

0.5

11.

5

6 7 8 9 10 11Log GDP per capita

Effect of perceived quality95% confidence intervalPredicted country effects

(a) Perceived Quality ξd

jt

−.5

0.5

1

6 7 8 9 10 11Log GDP per capita

Effect of perceived quality95% confidence intervalPredicted country effects

(b) Perceived Quality ξd,adjusted

jt

Figure 6: GDP per capita

30

and (9) of table 7. The implied variation in the impact of perceived quality on exported quanti-

ties is substantial, as visualized in figure 6: perceived quality has virtually no impact on export

quantities to destinations with a low income per capita. To the contrary, export quantities are

strongly increasing in quality in countries with a high GDP per capita.

We contrast these findings with the traditional Alchian-Allen hypothesis that high quality

goods are shipped to more distant markets. When applied to perceived quality, the hypothesis

is largely rejected by the data: an interaction term between ξdjt and log distance enters insignif-

icantly, and we cannot reject the hypothesis that the impact of ξdjt is constant across markets of

varying distance from Denmark.

So far, we have considered each destination characteristic in isolation. Columns (5), (6) and

(11), (12) of table 7 report results from Tobit models where we estimate cross-country variation

in demand dependent on cultural proximity, while simultaneously controlling for Alchian-Allen

effects and the effect of GDP per capita. Results confirm that the effect of domestic perceived

quality on export volumes co-varies positively both with cultural proximity as well as with the

income level in the destination market. Importantly, the positive (negative) interaction effect of

perceived quality with linguistic proximity (genetic distance) is robust to adding an interaction

term of perceived quality with income per capita. In consequence, our results on variation

across countries cannot be attributed entirely to non-homothetic demand. Overall, the evidence

supports our modified Alchian-Allen hypothesis: domestic perceived quality has a significantly

higher impact on export market success in markets where tastes are expected to be similar to

domestic tastes.

Our identification of taste heterogeneity across export markets relies on the exogeneity of

quality, as perceived by domestic consumers. Two potential concerns are reverse causality

and omitted variables bias. Reverse causality arises if the valuation of domestic consumers

depends on the variety’s export success, e.g. because this is interpreted as a sign of quality.

Domestic consumers to the contrary might also attach high valuations to varieties that are

only sold domestically, e.g. because they represent something genuinely local. The direction of

the resulting bias is therefore unclear. Furthermore, we doubt that consumers have sufficient

information regarding varieties’ export performance, rendering reverse causality implausible.

Omitted variable bias may be due to unobserved marginal cost determinants which are

positively correlated with quality and therefore imply a downward bias in λc. As a first ro-

bustness check, we include firm-product fixed effects, which control for the part of marginal

31

costs that is constant across time and markets. Second, we adjust the strategy in Roberts

et al. (2012) to our framework in order to construct a firm-specific cost index from infor-

mation on prices across domestic and export markets. In our model, prices are given by

pc,fobjt = 1−σατc + h(xj1t, . . . xjKt) + wjt + ωjt. Subsuming the mark-up into a market-specific

fixed effect, and controlling for observed cost determinants wjt,

pc,fobjt = θc + θt + θg + θf + wjt + ωcjt. (20)

The firm fixed effect θf serves as an estimate for firm-specific marginal costs, though it also

captures heterogeneity in mark-ups. Hence, if high-quality producers charge higher mark-ups,

the estimated cost component overstates the true correlation between marginal costs and quality.

Regressions where we control for θf will therefore tend to overstate the role of perceived quality.

Results are reported in tables 11 and 12 in the appendix. Notably, all results are qualitatively

robust to controlling for unobserved firm-product heterogeneity and firm-specific marginal costs

components, though quantitative implications differ. Regressions with firm-product fixed effects

yield much higher variation in tastes across countries, compared to our benchmark estimates

in table 7. When we replace observed marginal cost determinants with the estimated firm-cost

component, compared to our benchmark regressions we find a higher effect of perceived quality

on export volumes across all countries. However, the main finding that its impact co-varies

positively with both cultural proximity and per capita income is sustained.

6 Conclusion

This paper provides novel evidence on how peceived product quality is related to success on

export markets. We set up a simple utility framework and discuss the conditions under which

varieties of high appeal from the viewpoint of the domestic consumer are more successful also

on export markets. In particular, we show that the existence and strength of this link between

domestic quality perceptions and export market success depends on how well domestic and

foreign tastes accord. We infer domestic quality perceptions from information on domestic

market shares and prices. We then relate a variety’s domestic perceived quality to measures of

export market performance across destinations. Varieties with a higher perceived quality are

more likely exported and have higher export values. However, the impact of quality evaluations

of domestic consumers on export market sales reveals a mismatch between tastes of domestic

and foreign consumers that varies in a consistent way across countries. In particular, this taste

32

mismatch is increasing in the cultural distance of the destination market.

References

Alchian, A. A., & Allen, W. R. (1964). University Economics. Belmont, Calif.: Wadsworth.

Antoniades, A. (2012). Heterogeneous firms, quality, and trade. Mimeo, Georgetown University.

Bacchiega, E., Minniti, A., & Palestini, A. (2010). Quality, distance and trade: a strategic

approach. Working Papers 717, Dipartimento Scienze Economiche, Universita’ di Bologna.

Baldwin, R., & Harrigan, J. (2011). Zeros, quality, and space: Trade theory and trade evidence.

American Economic Journal: Microeconomics, 3 (2), 60–88.

Bastos, P., & Silva, J. (2010). The quality of a firm’s exports: Where you export to matters.

Journal of International Economics, 82 (2), 99–111.

Bernard, A. B., Blanchard, E. J., Beveren, I. V., & Vandenbussche, H. Y. (2012). Carry-along

trade. NBER Working Papers 18246.

Berry, S., Levinsohn, J., & Pakes, A. (1995). Automobile prices in market equilibrium. Econo-

metrica, 63 (4), 841–90.

Berry, S. T. (1994). Estimating discrete-choice models of product differentiation. RAND Journal

of Economics, 25 (2), 242–262.

Cardell, N. S. (1997). Variance components structures for the extreme-value and logistic distri-

butions with application to models of heterogeneity. Econometric Theory , 13 (2), pp. 185–213.

Conlon, C. T., & Mortimer, J. H. (2010). Demand estimation under incomplete product avail-

ability. Boston College Working Papers in Economics 798.

Crino, R., & Epifani, P. (2012). Productivity, quality and export behaviour. The Economic

Journal , 122 (565), 1206–1243.

Crozet, M., Head, K., & Mayer, T. (2012). Quality sorting and trade: Firm-level evidence for

French wine. Review of Economic Studies, 79 (2), 609–644.

Di Comite, F., Thisse, J.-F., & Vandenbussche, H. (2012). Verti-zontal differentiation in mo-

nopolistic competition. CEPR Discussion Papers 8752.

Eaton, J., & Kortum, S. (2001). Trade in capital goods. European Economic Review , 45 (7),

1195–1235.

Eaton, J., & Kortum, S. (2002). Technology, geography, and trade. Econometrica, 70 (5),

1741–1779.

Eaton, J., Kortum, S., & Kramarz, F. (2011). An anatomy of international trade: Evidence

33

from French firms. Econometrica, 79 (5), 1453–1498.

Feenstra, R. C., & Romalis, J. (2011). International prices and endogenous quality. Mimeo.

Flach, L. (2011). Quality upgrading and price heterogeneity: Evidence from Brazilian exporters.

Mimeo.

Foster, L., Haltiwanger, J., & Syverson, C. (2008). Reallocation, firm turnover, and efficiency:

Selection on productivity or profitability? American Economic Review , 98 (1), 394–425.

Francois, P., & van Ypersele, T. (2002). On the protection of cultural goods. Journal of

International Economics, 56 (2), 359–369.

Gervais, A. (2012). Product quality and firm heterogeneity in international trade. Mimeo,

University of Notre Dame.

Goldberg, P. K. (1995). Product differentiation and oligopoly in international markets: The

case of the U.S. automobile industry. Econometrica, 63 (4), 891–951.

Hallak, J. C. (2006). Product quality and the direction of trade. Journal of International

Economics, 68 (1), 238–265.

Hallak, J. C., & Schott, P. K. (2011). Estimating cross-country differences in product quality.

The Quarterly Journal of Economics, 126 (1), 417–474.

Hallak, J. C., & Sivadasan, J. (2011). Firms’ exporting behavior under quality constraints.

Working Papers 628, Research Seminar in International Economics, University of Michigan.

Hausman, J. A. (1996). Valuation of new goods under perfect and imperfect competition. In

The Economics of New Goods, NBER Chapters, (pp. 207–248).

Helpman, E., Melitz, M., & Rubinstein, Y. (2008). Estimating trade flows: Trading partners

and trading volumes. The Quarterly Journal of Economics, 123 (2), 441–487.

Helpman, E., Melitz, M. J., & Yeaple, S. R. (2004). Export versus FDI with heterogeneous

firms. American Economic Review , 94 (1), 300–316.

Hummels, D., & Klenow, P. J. (2005). The variety and quality of a nation’s exports. American

Economic Review , 95 (3), 704–723.

Hummels, D., & Skiba, A. (2004). Shipping the good apples out? An empirical confirmation of

the Alchian-Allen conjecture. Journal of Political Economy , 112 (6), 1384–1402.

Iacovone, L., & Javorcik, B. (2012). Getting ready: Preparation for exporting. CEPR Discussion

Papers 8926.

Janeba, E. (2007). International trade and consumption network externalities. European Eco-

nomic Review , 51 (4), 781–803.

34

Johnson, R. C. (2012). Trade and prices with heterogeneous firms. Journal of International

Economics, 86 (1), 43.

Khandelwal, A. (2010). The long and short (of) quality ladders. Review of Economic Studies,

77 (4), 1450–1476.

Kneller, R., & Yu, Z. (2008). Quality selection, Chinese exports and theories of heterogeneous

firm trade. Discussion Papers 08/44, University of Nottingham, GEP.

Koenker, R., & Hallock, K. F. (2001). Quantile regression. Journal of Economic Perspectives,

15 (4), 143–156.

Kugler, M., & Verhoogen, E. (2012). Prices, plant size, and product quality. Review of Economic

Studies, 79 (1), 307–339.

Linder, S. B. (1983). An essay on trade and transformation. Stockholm: Almqvist & Wiksell.

Melitz, J., & Toubal, F. (2012). Native language, spoken language, translation and trade.

Working Papers 2012-17, CEPII research center.

Nevo, A. (2001). Measuring market power in the ready-to-eat cereal industry. Econometrica,

69 (2), 307–42.

Nguyen, D. X. (2012). Demand uncertainty: Exporting delays and exporting failures. Journal

of International Economics, 86 (2), 336–344.

Rauch, J. E. (1999). Networks versus markets in international trade. Journal of International

Economics, 48 (1), 7–35.

Rauch, J. E., & Trindade, V. (2009). Neckties in the tropics: a model of international trade and

cultural diversity. Canadian Journal of Economics, 42 (3), 809–843.

Roberts, M. J., Xu, D. Y., Fan, X., & Zhang, S. (2012). A structural model of demand, cost,

and export market selection for chinese footwear producers. NBER Working Papers 17725.

Schmid, S., & Kotulla, T. (2011). 50 years of research on international standardization and

adaptation – from a systematic literature analysis to a theoretical framework. International

Business Review , 20 (5), 491–507.

Silva, J. S., & Machado, J. (2011). Qreg2: Stata module to perform quantile regression with

robust standard errors. Statistical Software Components, Boston College Department of Eco-

nomics.

Spolaore, E., & Wacziarg, R. (2009). The diffusion of development. The Quarterly Journal of

Economics, 124 (2), 469–529.

Stock, J. H., Wright, J. H., & Yogo, M. (2002). A survey of weak instruments and weak

35

identification in generalized method of moments. Journal of Business & Economic Statistics,

20 (4), 518–29.

van Mesdag, M. (2000). Culture-sensitive adaptation or global standardization – the duration-

of-usage hypothesis. International Marketing Review , 17 , 74–84.

Verhoogen, E. A. (2008). Trade, quality upgrading, and wage inequality in the mexican manu-

facturing sector. The Quarterly Journal of Economics, 123 (2), 489–530.

Wooldridge, J. M. (2001). Econometric Analysis of Cross Section and Panel Data. The MIT

Press.

A Data appendix

Commodity and trade statistics. Our sample for the Danish Chocolate and confectionery industry

(“Manufacturing of cocoa, chocolate and sugar confectionery”) is obtained by combining different

data sources. In the commodity statistics, the reporting unit is the Kind of Activity Unit (KAU),

which is the sum of a firm’s workplaces engaged in the same economic activity (industry). The

survey comprises all Kind of Activity Units with at least 10 employees. In the survey, firms are

asked to report their sales volume in terms of quantities and values, and for each product they

produce. Sales are reported independent on in which market the product is sold and therefore

include both domestic as well as export sales. Products are coded according to the 8-digit level

of the combined nomenclature (CN8).

The trade statistics are derived from two different sources: Intrastat and Extrastat. Intrastat

is based on data reported by Danish enterprises with total annual imports of goods and/or

exports of goods over respectively, DKK 3.7 mio. and DKK 5.0 mio. in 2012. Extrastat is based

on data reports concerning customs and supplies collected from the Danish tax authorities in

connection with imports and exports of goods to/from Denmark and from/to non-EU member

countries. If the value of a transaction is not over DKK 7,500 and the weight is not over 1,000

kg, these goods can be recorded under a special commodity item (other goods). Values in both

the domestic as well as the export market are measured in DKK and deflated with the consumer

price index to 2000 values. Information on quantities is declared as net weight (in kg), including

the packaging normally used when the commodity is sold in retail trade, but excluding transport

packaging.

Domestic quantities and prices. Based on the commodity and trade statistics, we construct

domestic sales in both values and quantities. The value and quantity of exports comprises both

36

sales of goods manufactured by the firm itself as well as re-sale of traded goods23, whereas

the commodity statistics only include the former type of sales. Traded goods are instead re-

ported under a single code which does not allow to distinguish resale of different commodities.

In consequence, domestic sales of exporting firms may be underestimated when calculated by

subtracting exports from overall sales.24 Furthermore, the export statistics are collected at the

firm-level whereas the commodity statistics are firm-level aggregates of sales by kind-of-activity

units (KAU). Only KAUs within manufacturing report their sales, implying that sales of KAUs

in e.g. the service sector will be included in the trade but not the commodity statistics. Again,

this implies that domestic sales of exporters may be downward biased when combining both data

sources. On the other hand, thresholds for trade data imply an upward bias in the domestic

sales of exporters.

To partly address these issues, we construct a measure t of the share of traded goods in

overall sales of a firm, as well as a measure p of the number of KAUs that are in manufacturing

relative to the total number of KAUs associated with a given firm. We then calculate domestic

sales by subtracting (1− t) · p· export quantity (value) from the overall quantity (value). Prices

(unit values) are constructed as sales value over quantity and give the average price of a variety

in a market. Measurement error in quantities and hence prices is pervasive. To mitigate the

resulting influence of outliers, we trim prices and quantities by 1 percent at the upper and lower

tail of the distribution. Table 8 reports for each year the number of firms, products and varieties

in the sample.

Industry classification and import data. A correspondence table links each CN8 product

code to a 4-digit NACE Rev. 2 industry code. The correspondence table allows us to infer

product codes belonging to Chocolate and confectionery (Nace Rev.2, 1082). Table 9 gives a

complete list of the 6-digit product codes included in the analysis. Furthermore, table 10 shows

the 8-digit product categories forming part of one particular CN6 code, in order to give an idea

of the level of disaggregation at the 8-digit level. Data on imports by CN8 codes comes from

Eurostat’s Comext database and is aggregated up to the industry level in order to construct

total imports.

B Additional results

23Traded goods are goods that are bought and resold without further processing.

24Recently, Bernard et al. (2012) document the importance of Carry-along trade, and we similarly find thatthis is an important phenomenon also in many Danish industries, though less so in the food sector.

37

Table 8: Sample coverage by yeara

Number of ...Year Varieties Firms Products1995 69 23 261996 111 39 261997 68 26 251998 60 24 231999 62 25 232000 74 27 262001 78 29 282002 115 32 322003 70 23 262004 75 25 282005 78 29 272006 81 30 292007 81 28 302008 76 29 29a The sample is restricted to firm-product observations with non-missinginformation on output quantities, and hence prices.

Table 9: List of CN6 codes in Chocolate and confectionery

CN6 code Description

170410 Chewing gum, whether or not sugar coated

170490 Sugar confectionery not containing cocoa, incl. white chocolate (excl. chewing gum)

180310 Cocoa paste (excl. defatted)

180400 Cocoa butter, fat and oil

180500 Cocoa powder, not containing added sugar or other sweetening matter

180610 Cocoa powder, sweetened

180620 Chocolate and other food preparations containing cocoa, in blocks, slabs or bars weighing > 2 kg or in liquid,paste, powder, granular or other bulk form, in containers or immediate packings of a content > 2 kg (excl. cocoapowder)

180631 Chocolate and other preparations containing cocoa, in blocks, slabs or bars of <= 2 kg, filled

180632 Chocolate and other preparations containing cocoa, in blocks, slabs or bars of <= 2 kg (excl. filled)

180690 Chocolate and other preparations containing cocoa, in containers or immediate packings of <= 2 kg (excl. inblocks, slabs or bars and cocoa powder)

200600 Vegetables, fruit, nuts, fruit-peel and other edible parts of plants, preserved by sugar “drained, glace or crystal-lized”

Table 10: Example of the level of disaggregation: List of CN8 codes for CN6=180690

CN8 code Description

18069011 Chocolates and chocolate products in the form of pralines, whether or not filled, containing alcohol

18069019 Chocolates and chocolate products in the form of pralines, whether or not filled, not containing alcohol

18069031 Chocolates and chocolate products, filled (excl. in blocks, slabs or bars and pralines)

18069039 Chocolates and chocolate products (excl. in blocks, slabs or bars, pralines and filled)

18069050 Sugar confectionery and substitutes thereof made from sugar substitution products, containing cocoa

18069070 Preparations containing cocoa, for making beverages

18069090 Preparations containing cocoa, in containers or immediate packings of <= 2 kg (excl. chocolate, pralines andother chocolate products, sugar confectionery and substitutes therefor made from sugar substitution products,spreads and preparations containing cocoa for making beverages, and cocoa powder)

38

Tab

le11

:R

obu

stn

ess

anal

ysi

s:fi

rm-p

rod

uct

fixed

effec

tsa

Tob

itm

od

elfo

rlnqc jt

Per

ceiv

edqu

ality

esti

mate

:ξd jt

Per

ceiv

edqu

ality

esti

mate

:ξd,adjusted

jt

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Perceived

Quality

-0.0

377

1.7

329***

-3.6

931***

2.1

043***

-0.0

679

1.8

173***

-4.1

696***

2.3

912***

(0.1

59)

(0.2

21)

(0.4

48)

(0.3

62)

(0.1

61)

(0.2

28)

(0.4

67)

(0.3

73)

Perceived

Quality

0.3

834***

0.3

981***

×Lingu

isticproximity

(0.0

41)

(0.0

43)

Perceived

Quality

-0.2

658***

-0.2

852***

×Log

genetic

distance

(0.0

33)

(0.0

35)

Perceived

Quality

0.4

288***

0.4

768***

×Log

GDP

percapita

(0.0

44)

(0.0

46)

Perceived

Quality

-0.2

165***

-0.2

570***

×Log

distance

(0.0

45)

(0.0

47)

Norm

alizedim

port

price

-0.8

008*

-0.6

764

-0.7

339

-0.7

846

-0.8

082*

-0.6

846

-0.7

511

-0.7

943*

(0.4

72)

(0.5

10)

(0.4

73)

(0.4

78)

(0.4

75)

(0.5

12)

(0.4

75)

(0.4

79)

Log

laborproductivity

2.6

159***

2.3

387**

1.3

889

2.5

928***

2.3

766***

2.0

120**

1.4

107

2.3

945***

(0.8

77)

(0.9

61)

(0.8

86)

(0.8

96)

(0.8

76)

(0.9

58)

(0.8

85)

(0.8

85)

Log

laborproductivity

-0.2

683***

-0.2

013*

-0.1

284

-0.2

687**

-0.2

382**

-0.1

591

-0.1

310

-0.2

443**

×Log

distance

(0.1

04)

(0.1

15)

(0.1

05)

(0.1

06)

(0.1

04)

(0.1

15)

(0.1

05)

(0.1

05)

Log

laborproductivity

0.0

359***

0.0

342***

0.0

425***

0.0

356***

0.0

357***

0.0

342***

0.0

409***

0.0

355***

×(pd−pc)

(0.0

07)

(0.0

07)

(0.0

07)

(0.0

07)

(0.0

07)

(0.0

08)

(0.0

07)

(0.0

07)

Lagged

expo

rtstatusχc f,t−

16.5

690***

6.5

211***

6.5

979***

6.6

393***

6.5

659***

6.5

123***

6.5

791***

6.6

293***

(0.1

83)

(0.1

99)

(0.1

82)

(0.1

84)

(0.1

83)

(0.1

99)

(0.1

82)

(0.1

84)

Log

GDP

-0.2

392

-0.2

187

-0.1

408

-0.2

149

-0.2

131

-0.1

541

(0.5

23)

(0.5

46)

(0.5

24)

(0.5

24)

(0.5

47)

(0.5

24)

Ob

serv

ati

on

s45,5

76

38,2

44

46,3

25

46,3

25

45,5

76

38,2

44

46,3

25

46,3

25

Yea

rfi

xed

effec

tsY

esY

esY

esY

esY

esY

esY

esY

esD

esti

nati

on

fixed

effec

tsY

esY

esY

esY

esY

esY

esY

esY

esF

irm

-pro

du

ctfi

xed

effec

tsY

esY

esY

esY

esY

esY

esY

esY

eslne jt

1.6

91

1.7

00

1.6

94

1.7

00

1.6

92

1.7

00

1.6

95

1.7

00

aT

he

tab

lere

port

sco

effici

ent

esti

mate

s.C

olu

mn

s(3

)an

d(7

)ad

dit

ion

ally

contr

ol

for

GD

Pp

erca

pit

a(c

oeffi

cien

tn

ot

rep

ort

ed).

Rob

ust

stan

dard

erro

rsare

giv

enin

pare

nth

eses

.*,*

*,*

**

den

ote

sign

ifica

nce

at

the

10%

,5%

,1%

level

s,re

spec

tivel

y.

39

Tab

le12

:R

obu

stn

ess

anal

ysi

s:fi

rmco

st-c

omp

onen

ta

Tob

itm

od

elfo

rlnqc jt

Per

ceiv

edqu

ality

esti

mate

:ξd jt

Per

ceiv

edqu

ality

esti

mate

:ξd,adjusted

jt

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Perceived

Quality

2.3

077***

3.3

866***

0.5

042

3.1

710

1.7

324***

2.9

158***

-0.3

628

3.1

157***

(0.4

01)

(0.4

37)

(1.3

00)

(0.0

00)

(0.3

77)

(0.4

97)

(1.2

98)

(1.0

90)

Perceived

Quality

0.2

277**

0.2

204**

×Lingu

isticproximity

(0.0

96)

(0.1

00)

Perceived

Quality

-0.1

725**

-0.1

925**

×Log

genetic

distance

(0.0

74)

(0.0

77)

Perceived

Quality

0.2

168*

0.2

471*

×Log

GDP

percapita

(0.1

23)

(0.1

28)

Perceived

Quality

-0.0

738

-0.1

434

×Log

distance

(0.0

00)

(0.1

40)

Firm

cost

compo

nen

t-0

.2120***

-0.2

312***

-0.1

910***

-0.2

062

-0.2

028***

-0.2

280***

-0.1

830***

-0.2

107***

(0.0

43)

(0.0

47)

(0.0

40)

(0.0

00)

(0.0

46)

(0.0

50)

(0.0

42)

(0.0

57)

Firm

cost

compo

nen

t0.0

175***

0.0

204***

0.0

147***

0.0

166

0.0

175***

0.0

212***

0.0

149***

0.0

185***

×Log

distance

(0.0

05)

(0.0

05)

(0.0

04)

(0.0

00)

(0.0

05)

(0.0

05)

(0.0

04)

(0.0

06)

Firm

cost

compo

nen

t-0

.0004*

-0.0

003

-0.0

005**

-0.0

003

-0.0

004*

-0.0

003

-0.0

005**

-0.0

003

×(pd−pc)

(0.0

00)

(0.0

00)

(0.0

00)

(0.0

00)

(0.0

00)

(0.0

00)

(0.0

00)

(0.0

00)

Lagged

expo

rtstatusχc f,t−

19.7

741***

10.1

936***

10.2

276***

10.2

257***

10.3

607***

10.2

763***

10.3

591***

10.3

509***

(0.7

21)

(0.6

93)

(0.6

94)

(3.3

85)

(0.7

30)

(0.7

32)

(0.7

23)

(0.7

27)

Log

GDP

-0.5

889

-0.5

608

-0.5

098

-0.7

007

-0.7

497

-0.6

881

(0.8

38)

(0.8

34)

(0.0

00)

(0.8

21)

(0.8

15)

(0.8

27)

Ob

serv

ati

on

s48,8

10

44,5

14

53,9

53

53,9

53

48,8

10

40,9

31

49,6

11

49,6

11

Yea

rfi

xed

effec

tsY

esY

esY

esY

esY

esY

esY

esY

esD

esti

nati

on

fixed

effec

tsY

esY

esY

esY

esY

esY

esY

esY

esP

rod

uct

fixed

effec

tsY

esY

esY

esY

esY

esY

esY

esY

eslne jt

1.9

20

1.9

38

1.9

33

1.9

35

1.9

45

1.9

50

1.9

45

1.9

47

aT

he

tab

lere

port

sco

effici

ent

esti

mate

s.C

olu

mn

s(3

)an

d(7

)ad

dit

ion

ally

contr

ol

for

GD

Pp

erca

pit

a(c

oeffi

cien

tn

ot

rep

ort

ed).

Rob

ust

stan

-d

ard

erro

rs,

ad

just

edfo

rcl

ust

erin

gat

the

firm

-pro

du

ctle

vel

,are

giv

enin

pare

nth

eses

.*,*

*,*

**

den

ote

sign

ifica

nce

at

the

10%

,5%

,1%

level

s,re

spec

tivel

y.

40