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Journal of International Food & Agribusiness Marketing

ISSN: 0897-4438 (Print) 1528-6983 (Online) Journal homepage: http://www.tandfonline.com/loi/wifa20

Food Quality and Product Export Performance: AnEmpirical Investigation of the EU Situation

Christian Fischer

To cite this article:Christian Fischer (2010) Food Quality and Product Export Performance:

An Empirical Investigation of the EU Situation, Journal of International Food & AgribusinessMarketing, 22:3-4, 210-233, DOI: 10.1080/08974431003641265

To link to this article: http://dx.doi.org/10.1080/08974431003641265

Published online: 25 Jun 2010.

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Food Quality and Product ExportPerformance: An Empirical Investigation

of the EU Situation

CHRISTIAN FISCHERFree University of Bozen-Bolzano, Bolzano, Italy

The relationship between product quality and export performanceis investigated. Five EU countries (Italy, France, Spain, Germany,United Kingdom), 3 product categories (cheese, meat preparations,and wine), 3 export destinations (intra-European Union, extra-

European Union, and world), and 2 periods (19951999 and20002005) are analyzed. The regression results show that the con-nection between product quality and export performance dependson the product category (but not on the period) and differs (butnot in all cases) according to the export destination. An implicationarising from this analysis is that considering international market-

ing of quality food products in teaching and training programscould contribute to the enhancement of EU agribusiness competi-tiveness in increasingly liberalized and quality-conscious markets.

KEYWORDS European Union, export performance, food quality,general linear mixed model, unit values

INTRODUCTION

Food quality has become an increasingly important topic during the lastdecades. In developed countries, driven by aging populations and growingdiet-related health concerns, consumer demand now seems to shift towardhigher quality, more natural, and healthier food (Regmi, 2001). Given todays

Received July 2007; revised November 2007; accepted March 2008.The author gratefully acknowledges financial support from the H. Wilhelm Schaumann

Stiftung, Hamburg, Germany.Address correspondence to Christian Fischer, Faculty of Science and Technology, Free

University of Bozen-Bolzano, Universitatsplatz 5, Piazza Universita 5 I-39100, Bozen,

Bolzano, Italy. E-mail: [email protected]

Journal of International Food & Agribusiness Marketing, 22:210233, 2010

Copyright # Taylor & Francis Group, LLC

ISSN: 0897-4438 print=1528-6983 online

DOI: 10.1080/08974431003641265

210


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globalized markets, it can be assumed thatin line with rising consumptioninternational trade of quality food products (QFPs) is increasing.

Yet, the concept of food quality is elusive. Overall, quality may be seenas an abstract construct, multidimensional in nature (Charters & Pettigrew,

2007). More specifically, in the literature, at least four different quality defini-tions are discussed. It is either referred to as excellence or superiority, as value,asconforming to specifications, or asmeeting or exceeding customer expecta-tions (Reeves & Bednar, 1994; Verdu Jover, Llorens Montes, & del MarFuentes, 2004). The first approach defines quality as best in class, judgedon some to-be-specified criteria. The value approach is an economic one(higher monetary value reflects higher quality), whereas the conforming to

specificationsis a technological one (a quality product is a product that fulfillssome predefined technical standards, however low they may be). The fourthapproach defines quality from the point of view of the final consumer: as long

as she or he is happy with it, it is a quality product. Either way, in order tomeasure quality objectively, a generally accepted reference system is neces-sary, otherwise the general term quality is very subjective and means very lit-tle (Satin, 2002, para. 2). Ninni, Raimondi, and Zuppiroli (2006) state that thequality difference between competing products can be easily analyzed formeasurable characteristics such as: reliability, durability, various indicatorsof performance, and health and safety. However, it becomes more subjective

when it refers to intangible characteristics such as design, taste, and flavor.Here the boundaries of vertical and horizontal differentiation are blurred.Obviously, the intangible characteristics are of particular importance for food

products. Therefore, for these goods, it may perhapsdespite Satinsassertionbe most useful to accept these having subjective qualitiesratherthan one objectivequality, meaning that different people can come to diver-ging quality assessments and, in the end, none of these is superior to another.1

Despite its increasing importance, and probably partly due to thedifficulties of objectively defining quality, not much research has been doneso far on the particularities of the international trade of QFPs. Thus, forexample, it is unclear whether the nature of the international trade of QFPsis similar or potentially structurally different compared with the one of low- or

average-quality food products. Because many QFPs may be more perishable,more sensitive to external influences (e.g., temperature, vibration, light), andoften of higher (monetary) value, more demanding logistics and insuranceissues may affect the international trade of these products, potentially result-ing in different export patterns.

The objectives of the following analysis are threefold: first, to review therecent literature dealing with international trade in QFPs; second, to generateempirical insights into the export patterns (levels and destinations) of foodproducts, depending on their quality level; and third, based on the obtainedresults, to derive conclusions of what this could potentially mean for agribu-

siness management and=or policy making.

Food Quality and Product Export Performance 211


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This articles structure is as follows: After the introduction, Section 2reviews the previous literature related to the topic. Section 3 describes theprocedure of the empirical investigation. Section 4 discusses the obtainedresults. Section 5 concludes by summarizing and pointing out some implica-

tions that arise from the findings. The Appendix provides a technical treat-ment on the generalized least squares estimation method used in this study.

PREVIOUS STUDIES

The literature on the relationships between quality and trade performanceis sparse. The few existing studies have in common that they investigateintercountry quality competition in the sense that they try to find out whethera countrys exports of certain products have higher quality vis-a-vis othercountries producing similar goods.

Aiginger (1997) suggested a method of how to use unit values (UV)2

inorder to discriminate between price and quality competition in internationalmarkets using the case of German exports of industrial goods. Gelhar and Pick(2002) applied Aigingers framework to U.S. food trade flows and found thatalmost 40% of U.S. food exports could be characterized as dominated by qual-ity competition. For imports, the share amounts to 60%. However, the resultsfor bilateral trade flows are much lower, which points to problems involved

when using unit values and net trade figures of economic aggregates. Ninniet al. (2006) explored the role of quality of Italian food products in inter-national markets. They regressed relative market shares of the Italian products

in the import market of several different countries on a quality indicator basedon UVs and other variables. The obtained results suggest that the qualityimage of Italian goods offers protection for some traditional products, but thatthis protection is not strong enough to counteract price competition (p. 2).

No previous studies (to the authors knowledge), however, haveaddressed the issue of intracountry product export performance, that is,

whether a country tends to specialize in exporting QFPs or low- and=oraverage-quality products among the whole range of highly differentiatedproducts it manufactures. In other words, within a nearly defined product

category (e.g., cheese) is a country a high-quality exporter (which indicatesthat the countrys high-quality products are internationally appreciated andsought after) or that the country is rather a low- or average-quality exporter(implying that the countrys QFPs are more domestically preferred but thatinternational demand for them is weak).

PROCEDURE

The general research approach is a deductive, empirical investigation. Thatis, international trade data are analyzed and conclusions are drawn from

the findings.

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The focus of the analysis is to estimate the relationship between foodquality and product export performance (i.e., whether there is a positiveor negative link between the two variables). Although this relationship isestimated within a larger regression model (i.e., one that considers a few

additional control variables), it is not aimed at fully explaining export per-formance. Given that the used statistical models are not completely specified(i.e., other potential export performance affecting variables are left out),omitted variable error may be a problem in the obtained results. However,a serious bias of the estimated regression coefficients would only occur ifthe quality variable is significantly correlated with other, omitted exportperformance determinants.3

Other factors that potentially determine product export performance arenot easy to identify on theoretical grounds. Almost all previous studies haveused companies, economic sectors, or entire countries as units of investi-

gation when analyzing export performance. Here, the research question iswhether within a larger food category, the export performance of similarproducts changes as a function of quality. Many companies produce a rangeof products (typically covering lower quality goods as well as premiumproducts), thus company-specific factors apply to all of them. Other typicalexport performance determinants such as promotion programs, exportenhancement policies (subsidies), or trade-regulating measures (quotas,tariffs, etc.) are usually sector specific (i.e., apply equally to an entire foodcategory). Therefore, these factors do not vary across the products underconsideration. However, there are a few potential export performance-

influencing covariates that would be meaningful to consider in the regres-sions. These are indicators for logistics (transport and=or storage) complexity(e.g., perishability, sensitivity to vibrations), likely to result in higher market-ing costs, and effective final consumer prices relative to incomes in theexport markets. The export performance of a QFP, ceteris paribus, wouldbe higher if logistics complexity is relatively lower and=or relative prices inthe target market are lower.4 Hence, product export performance may beseen as a function ofreal quality (product-specific superiority as perceivedby the final consumer), related logistics complexity (as reflected in marketing

costs), and consumer purchasing power in the target market. In the follow-ing, the indicator used for quality also comprises marketing costs and thetarget market will be roughly controlled for by considering different exportdestinations.

Hence, although omitted variable bias cannot fully ruled out, its magni-tude is uncertain. In any case, here the primary interest lies in the signs of theestimated quality parameters and not in their size.

The operationalization of the variables under consideration is asfollows. First, food quality (within a homogeneous product category) ismeasured by price (i.e., unit value as a price indicator). In other words, it

is assumed that among similar products quality is positively related to price.



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Thus, the aforementioned value approach is adopted for defining quality.Second, trade performance is assessed by using relative export shares.

Measuring Quality

As price proxies, UVs (in 4 per kg) are used, obtained by dividing exportvalues by export quantities. UVs are known to be imperfect price indicators(Holmes, 1973; King, 1993; Shiells, 1991). Their main problem is related tothe fact that an observed change in the unit value may not necessarily be aresult of an underlying price change but may simply reflect a change inthe composition of the goods within the class of exports under consideration.

Another problem relates to invoicing practices. The existence of a lagbetween the time of contract and the delivery of goods can result in differ-ences between the contract value (i.e., the real price) and the UV calculated

from customs declaration when a good is actually delivered in cases wherethe exchange rate changes in between. However, the magnitude of thesemeasurement problems is not clear. For instance, whereas Shiells finds thatfor U.S. trade data import UVs are good import price proxy, Holmes showsfor Canada that domestic UVs (national production values divided by outputquantity) do not well represent industrial selling prices obtained by means ofmanufacturer surveys.

Despite these shortcomings, UVs have been suggested and used as anindicator of quality content (Aiginger, 1997; Gelhar & Pick, 2002). Becausethe UV is output per units of input (e.g., measured in kilograms), for

homogenous and comparable goods the value can indicate differences inquality if unit production costs can be assumed to be equal across the con-sidered countries. However, the UV will also reflect differences in costsand thus high UVs can indicate relative high product quality and=or relativehigh unit costs. In order to distinguish between these two cases, Aigingersuggested looking at the net trade position of the good (aggregate) underconsideration. If within a country a goods UV is high and the goods nettrade position is positive, cross-country UV differences must then be dueto superior quality. However, if a goods UV is high but the corresponding

net trade position is negative, this indicates a cost disadvantage. One prob-lem with this approach is that it requires aggregate data (e.g., cars,cheese, or ice cream) or trade data on commodities (i.e., homogenous,undifferentiated goods) such as meat of sheep, frozen, flat fish, fresh orchilled, and so on in order to be able to calculate net trade positions. Forhighly disaggregated data of certain agricultural products (e.g., Gorgonzolacheese, Bordeaux red wine, etc.), no such trade balance can be calculated(because no other countries produce such goods and consequently noimports can exist). Thus, in general, the higher the level of disaggregation,the more accurate UVs may be as price proxies, but the more difficult it is

to determine whether high UVs reflect high quality or high production costs.

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Yet, if absolute exports of products with high UVs are comparatively high,then this may nonetheless be an indicator of quality (due to an apparentinternational willingness to pay relative high prices for goods with manyclose substitutes).

UVs are calculated in relative terms (RUV) as symmetrized andnormalized deviations from a categorys mean unit value, as suggested byLaursen (1998). That is,

UVkcpt Export valuecpt in 4

Export quantitycpt in kg 1

RUVkcpt UVkcpt

1

nck

Xnckp1

UVkcpt

! 1

, UVkcpt

1

nck

Xnckp1

UVkcpt

! 1

!100;

2

wherekrefers to the category (i.e., cheese, wine, or meat products; see later),cto the country, pto a particular product within k(e.g., Roquefort cheese),tto the period (19951999 or 20002005; see later), and nckis the number ofproducts in a particular category for a certain country. Note that the range ofRUV is [100; 100], where positive (negative) values indicate above (below)average UVs.

Measuring Export Performance

Trade performance is assessed by relative export shares. The measure is amodified version of Balassas index of revealed comparative advantage. Itis defined as the deviation from the expected export share of a product

within a product category, again symmetrized and normalized.The index of revealed comparative advantage (RXA) was defined

by Balassa (1965) as a measure for the export performance of individualindustries in a particular country. . . (p. 105), which can be evaluated by. . . comparing the relative shares of a country in the world exports of indi-

vidual commodities [. . .] where the data have to be made comparablethrough appropriate normalization (p. 105). Thus, the original index wasconstructed in a form such as

RXAct xct

XCc1

xct

! Xct

XCc1

Xct

!; 3

where xct stands for the exports (in 4) of the food processing sector of acountryc in year t, and Xctstands for total country exports of countryc in

year t. Pxct designates total sector exports (considering C countries),



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whereasP

Xctrefers to total aggregate exports in a particular yeart. The RXA,therefore, is an index of the share of a country in the international market of aparticular economic sector corrected (i.e., normalized) for the size of thecountry to which the sector belongs. The correction is necessary because

larger countries can a priori be assumed to have larger market shares simplybecause of their size. Bowen (1983) showed that Balassas RXA may also beinterpreted as the deviation of actual exports, xct, from expected exports,E(xct), where E(xct) can be defined as

Pxct Xct=

PXct, assuming that all

countries engage in all economic activities in equiproportional shares.The (theoretical) range of the RXA index is from [0;1[, with values2[0;

1[, indicating a comparative disadvantage and values2]1;1[ showing a com-parative advantage of an economic sector of a particular country relative tothe other sectors within this country. Thus, the RXA allows determining thestrong and weak sectors within one country by ranking them by descending

RXA scores. Unfortunately, however, the RXA may fail to reliably indicatecomparative (or competitive) advantages within the same sector relative toother countries. The main problem in cross-country comparisons is the dif-ferent frequency distribution of the RXA scores in each country.5 In addition,De Benedictis and Tamberi (2001) show that the effective upper bound ofthe RXA for each country is different. This upper bound is equal to worldtotal trade divided by total trade of countryc(

PXct=Xct) and thus is in gen-

eral relatively small for large and very high for small countries. Consequently,the index scores that are larger than one cannot directly be compared acrossdifferently sized countries (implying the aforementioned normalization for

country size does not work perfectly in the standard notation of the RXA).Thus, because the conventional RXA range is inconvenient for interpretation,a symmetrization has been suggested by Laursen (1998), which yields a rangefor the RXA 2[1; 1] with values below (above) zero indicating below(above) average relative exports:6

SRXAct RXAct1=RXAct1: 4

The main problem with using the RXA in the context of this research is againthat it can only be applied to aggregate data or trade data on commodities(i.e., homogenous, undifferentiated goods) in order to be able to calculateworld sector exports. However, for highly disaggregated data of certainagricultural products (e.g., Gorgonzola cheese, Bordeaux red wine, etc.),no such sums can be calculated because these products are only producedin one single country and thus country exports equal world sector exports.For this reason, a modified version of the RXA is used in the following, whichis related to Bowens (1983) interpretation of the Balassa index. Here therelative export performance (REP) is assessed by the share of the exports

of a particular product p in category (k) exports as deviation from the

216 C. Fischer


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expected export share (1=nck, where nck is the number of p in k for aparticular country c), assuming that preferences for all products p areequal. That is,

REPkcptxkcptPnck

p1xkcpt

1nck

!1 xkcptPnckp1x

kcpt

1nck

!1 ! 100; 5where t refers to the period (19951999 or 20002005; see later). Note thatthe range of the REP is also [100; 100], where positive (negative) valuesindicate above (below) average relative export performance.

It should be stressed that the REP, despite being derived from the RXA,does not compare export performance of the same products across differentcountries. Rather, it measures export performance of different products

relative to other products within a certain product category and for onesingle country.

Data

The raw data were taken from Eurostats COMEXT EU25 Trade Since 1995By CN8 database. These trade data are at the highest available level ofdisaggregation (eight-digit level).

The export data (4 values and kg quantities) are factored in fourdimensions. First, reporters. The five largest EU countries (Germany [DE],

Spain [ES], Great Britain [GB], France [FR], and Italy [IT]) were selected.Second, as destination area three different locations were chosen: withinthe European Union with 15 members (EU-15), outside the EU-15, and

world (i.e., the sum of the former two). Third, three product categories(cheese, meat preparations, and wine) were selected. Each category con-tains a large number of individual products. The maximum number of pro-ducts for each category is as follows: cheese 66, meat preparations 54, and

wine 70.7 However, the actual number of types included in the analysisdepends on the individual country (see next paragraph). The categories

were selected based on their importance to the EU food industry (meat,beverages, and dairy products are the three most important subsectors asmeasured by their shares in total sector value added; see Lienhardt, 2004).The fourth dimension is time. The years 1995 to 2005 were included inthe analysis. Because the used trade data are volatile, the 11 years wereaveraged over two periods (19951999 and 20002005).

Overall, some 26,804 observations were used in the analysis. For theUnited Kingdom, the wine category was not analyzed because the countryis not a significant wine producer. For Spain, only a few wine exportquantities for the second period were available; thus only Period 1 exports

could be included in the analysis. Despite these few missing values, the



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data set covers a whole target population rather than being of sample nature.This needs to be kept in mind when interpreting the statistical significance(i.e., deviations from zero larger than those to be expected due to samplingerror) of the estimation results later in this article.

Data preparation involved the removal of reexport data and of outliers.8

Unfortunately, the raw data also included export flows of products that couldnot have been produced in a certain country (e.g., Navarra red wines inGerman exports or Gorgonzola cheese in French exports). These reexports

were removed from the original data set where possible. However, for manyproducts (e.g., processed cheese, uncooked sausages, etc.), where theproduction is not restricted to a certain geographical area, no potentiallyexisting reexports could be eliminated. Table 1 lists the actual number ofproducts included in the empirical analysis for each category and country(the nck).

Estimation Methods

The relationship between export performance and food quality was esti-mated using regression analysis. First, ordinary least squares (OLS) estimatorsof the slope coefficients were obtained separately for each country andproduct category, controlling for a potential period effect by using a perioddummy variable in all cases (except for wine in Spain). However, in no casedid the dummy coefficient turned out to be significant at least at the 95%confidence level. Second, all data belonging to one country were pooled

and feasible generalized least squares (GLS, which allows for heteroscedas-ticity across and correlation between different panels; see Cameron &Trivedi, 2005) was used to estimate the (nested) fixed effect of food qualityon export performance, controlling for product category and time. A moredetailed description of the GLS estimation procedure is provided in the

Appendix. In the pooled estimations, the slope coefficients were allowedto vary across the product categories for all countries except for Germany.The justification for this is founded in the OLS results: when the slope coeffi-cients for the different product categories displayed the same signs, only oneGLS slope coefficient for the food-quality variable was estimated. Otherwise,

the GLS slopes were allowed to vary.

TABLE 1 Number of Product Types Included in the Empirical Analysis for Each ProductCategory and Country (nck)

DE IT FR ES GB

Cheese 27 25 31 13 15Meat preparations 36 35 41 34 36

Wine 17 25 25 24

Source: Authors calculations from Eurostat data.

Note. DE Germany, IT Italy, FR France, ES Spain, GB Great Britain.

218 C. Fischer


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RESULTS AND DISCUSSION

The estimation results are displayed in Figures 1 to 5 (OLS) and in Tables 2 to6 (GLS). Overall, it emerges that the direction (and the significance) of the

relationship between food product quality and export performance is notsystematic but depends on the country, the product category, and the exportdestination. However, the direction (i.e., the sign of the slope coefficient) isin most cases independent of the used estimation method, yet the signifi-cance levels are not.

In the theoretical better justified yet more complex GLS estimations,additional statistics need to be considered when interpreting the results. Inparticular, these are the estimates for the variance components and themaximum likelihood-related model fit statistics. These measures are allreported in Tables 2 to 6. In general, all variance components were estimated

as statistically highly significant. Often, there seems to be a considerabledifference between the error variance of the first and the second period,

FIGURE 1 Germany: OLS (ordinary least squares) slope estimates of deviations fromexpected exports (y) regressed on deviations from average unit values (x). Note. Boldregression lines indicate that the slope coefficient is statistically significant at the 95% confi-

dence level.

Controlling for period (0 19951999; 1 20002005).



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implying that heteroscedasticity may be an issue in the data. Significantresidual autocorrelation is present in all cases. Thus, on statistical grounds,the use of feasible GLS seems to be justified. The overall fit of the estimatedmodels is not very high as can be seen from the comparatively high valuesof the information criteria Restricted2 log likelihood, Akaike information cri-teria (AIC) and Bayesian information criteria (BIC) (see Appendix for a theor-

etical discussion of these measures). However, it needs to be mentioned, andit can be seen from the scatter plots in Figures 1 to 5, that the here generallyassumed linear relationship between relative export performance and qualitymay not hold in all cases. Often, a nonlinear (e.g., parable-shaped) relation-ship can be observed from the data with many countries exporting relativelyfew low- and high-quality products but many medium-quality ones. Yet,approximating the overall situation through a linear regression line may still

yield a reliable indication of whether relatively more high-quality productsthan low-quality ones are exported (in which case the lines slope wouldbe positive) or vice versa. Nonetheless, the overall fit of this line to the data

can be expected to be poor.

FIGURE 2 Italy: OLS (ordinary least squares) slope estimates of deviations from expectedexports (y) regressed on deviations from average unit values (x). Note. Bold regression lines

indicate that the slope coefficient is statistically significant at the 95% confidence level.Controlling for period (0 19951999; 1 20002005).

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Germany

In the OLS estimations (Figure 1), for all investigated product categories andexport destinations, the relationship between product quality and exportperformance is negative. The slope coefficient is statistically significant (atleast at the 95% confidence level) for total and intra-EU per capita cheese

exports and in addition for wine exports to all three export destinations.Yet, in general, there does not seem to be a major difference between theintra- and extra-EU situations.

The findings differ slightly when using the pooled data estimator (GLS).As the results in Table 2 reveal, the relative export performance situationdiffers significantly between the two considered periods for intra-EU andtotal exports but not for extra-EU exports. As for product category-specificdifferences, the export performance situation does not generally differsignificantly across the three included product categories, at least notbetween wine and cheese. (The mean export performance between the ref-

erence category wine and meat preparations differs, however, significantly

FIGURE 3 France: OLS (ordinary least squares) slope estimates of deviations from expectedexports (y) regressed on deviations from average unit values (x). Note. Bold regression lines

indicate that the slope coefficient is statistically significant at the 95% confidence level.Controlling for period (0 19951999; 1 20002005).



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across all export destinations.) Finally, and most important, food quality, asindicated by the relative unit value, is significantly negatively related torelative export performance across all export destinations.

Overall, then, it appears that Germany exports relatively more lowerand medium-quality food products than high-quality ones in all threeconsidered product categories.

ItalyLooking at the scatter plots (Figure 2), the Italian situation is almostcompletely different compared with the one of Germany. The estimatedslope coefficients are positive for cheese, close to zero for meat preparations,and negative for wine to all export destinations. They are statistically signifi-cant (at least at the 95% confidence level) for extra-EU cheese exports and for

wine to all export destinations.The pooled data estimates reveal that, similar to Germany, in particular the

wine and meat preparations, export situation differ structurally. However, in

contrast to the German situation, no period effect can be observed. As for the

FIGURE 4 Spain: OLS (ordinary least squares) slope estimates of deviations from expectedexports (y) regressed on deviations from average unit values (x).Note. Controlling for period

(0 19951999; 1 20002005).

222 C. Fischer


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quality indicator, given that its sign differs across the three analyzed productcategories, the coefficients have been estimated separately for each category.However, none of the estimates differs significantly from zero (see Table 3).

Overall, the Italian export situation is less clear than the German one.Due to the diversities across the three products, the pooled data analysisdoes not yield significant estimates for the qualityexport performancerelationship. Nevertheless, when looking at the data, it appears that Italy isa significant exporter of high-quality cheese and meat preparations but notof high-quality wines.

France

Figure 3 shows that the situation in France is the exact mirror image to theone in Italy: a negative qualityexport performance relationship for cheeseand meat preparations and a positive one for wine. For intra-EU exports ofmeat preparations there seems to exist no clear relationship because it canbe seen that mostly medium-quality products are exported and about equallyfew low- and high-quality ones. Only the slope coefficients for extra-EUexports of meat preparations and wine exports to all destinations are statisti-cally significant (95%confidence level).

The GLS estimates show that no significant differences across the con-

sidered product categories and periods appear to exist. Similar to the Italian

FIGURE 5 UK: OLS (ordinary least squares) slope estimates of deviations from expectedexports (y) regressed on deviations from average unit values (x).Note. Controlling for period(0 19951999; 1 20002005).



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situation, the slope coefficients of the quality variable were estimated categoryspecific. The results indicate that there is a significant controlled qualityexport performance association for total and extra-EU exports of meat pre-parations (negative) and extra-EU exports of wine (positive; see Table 4).

Overall, France seems therefore to be a quality exporter of wine only. Inthe other product categories, export performance is highest for low- andaverage-quality products.

Spain

The Spanish situation (Figure 4) is the most heterogeneous among thecountries considered. There appears to be a difference in the direction ofthe export performancequality relationship between the intra- and extra-EUexport situations in particular for cheese but also to a smaller extent for meat

preparations and wine. For cheese, the relationship is negative for the total

TABLE 2 Germany: Pooled Regression Results (Feasible GLS Estimates) From a Linear MixedModel With One Repeated Measurement

Dependent variable: Relative (deviation from expected) exports

Regressors Total Intra-EU Extra-EU

Tests of fixed effects (F values)Intercept 18.3 22.0 22.5

CATEGORY 2.32 1.54 2.16PERIOD 7.94 7.72 2.24Relative unit value 22.3 10.1 17.0

Fixed-effect parameter estimatesIntercept 7.69 13.6 11.4CATEGORY cheese 17.1 13.3 22.4CATEGORY meat prep 32.4 26.5 35.1

CATEGORYwine 0a 0a 0a

PERIOD 1 (19951999) 5.68 6.12 4.19

PERIOD 2 (20002005) 0a

0

a

0

a

Relative unit value .771 .556 .872

Variance=Covariance-matrix componentsy

r21 3,013.2 3,043.2 3,452.0

r22 2,568.7 2,611.7 3,090.8

r12 2,632.8 2,638.4 2,965.2

Model statistics#of obs. 160 160 160Restr.2 log lik. 1,516.5 1,532.1 1,582.2

AIC 1,522.5 1,538.1 1,588.2BIC 1,531.6 1,547.2 1,597.3

Source: Authors estimations from Eurostat data.Note. ( , ) statistically significantly different from zero at least at 99% (95%, 90%) confidence level;

significant levels are based on panel-robust standard errors.aReference category.yRestricted maximum likelihood estimates.

224 C. Fischer


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and intra-EU situation but positive for extra-EU exports. For meat prepara-

tions, the situation is almost vice versa: a positive relationship for the totaland intra-EU situation and a neutral (or only slightly positive one) for extra-EUexports. For wine, the slope coefficients are positive except for the intra-EUsituation. However, none of the OLS quality coefficients has been estimatedas being statistically significant at the 95% confidence level.

The pooled data estimates show that there is a category effect forintra-EU exports and a period effect for extra-EU exports. Within theEuropean Union, the exports of meat preparations differ significantly fromthe ones of wine. Outside the European Union, for some reasons, relativeexports have been higher in the 19951999 period than in the 20002005

one. Similar to Italy and France, the quality coefficients were estimated

TABLE 3 Italy: Pooled Regression Results (Feasible GLS Estimates) From a Linear MixedModel With One Repeated Measurement




CATEGORY 3.51 3.70 4.61

PERIOD .000 .018 .133Relative unit value

(CATEGORY).062 .058 .234

Fixed-effect parameter estimatesIntercept 24.0 22.3 27.4

CATEGORY cheese 10.8 13.8 16.8CATEGORY meat prep 35.6 37.5 40.8

CATEGORYwine 0a 0a 0a

PERIOD 1 (199599) .036 .275 .649PERIOD 2 (200005) 0a 0a 0a

Relative unit value (cheese) .089 .159 .191Relative unit value

(meat preparations).036 .011 .042

Relative unit value (wine) .074 .074 .160


r21 2,922.7 2,965.3 2,690.2

r22 2,796.6 2,826.3 2,693.0

r12 2,724.1 2,721.0 2,563.1

Model statistics# of obs. 170 170 170

Restr.2 log lik. 1,605.9 1,627.2 1,597.0AIC 1,611.9 1,633.2 1,603.0BIC 1,621.2 1,642.5 1,612.3

Source: Authors estimations from Eurostat data.

Note. ( ) statistically significantly different from zero at least at 99% (95%) confidence level; significant

levels are based on panel-robust standard errors.aReference category.yRestricted maximum likelihood estimates.



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category specific. The results show that the only significant effect (negative)

is for extra-EU exports of meat preparations. The reason this is not reflectedin Figure 4 is that by pooling the two periods into one data set, the regressionline running through the combined data (almost) has a zero slope. If aregression line is produced separately for the two periods, the one for thesecond period has a positive slope whereas the one for the first period hasa negative one. This also explains why there is a highly significant periodeffect for extra-EU exports (see Table 5).

Overall, the Spanish situation seems to be rather balanced. When look-ing at the data, it becomes obvious that high export performance is fairlyevenly distributed across the entire quality spectrum. Hence, Spain exports

cheeses, meat preparations, and wines of all quality categories to about an

TABLE 4 France: Pooled Regression Results (Feasible GLS Estimates) From a Linear MixedModel With One Repeated Measurement




CATEGORY .259 .471 .318PERIOD .524 .094 .029Relative unit value

(CATEGORY)2.04 .229 10.7


CATEGORY cheese .931 1.35 10.2CATEGORY meat prep 8.30 11.2 9.28CATEGORYwine 0a 0a 0a

PERIOD 1 (19951999) 1.23 .599 .341PERIOD 2 (20002005) 0a 0a 0a





r21 2,816.9 2,703.4 2,616.3

r22 2,758.2 2,834.6 2,781.2

r12 2,655.9 2,592.6 2,514.8

Model statistics#of obs. 194 194 194





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equal extent. However, it also appears that high-quality cheeses and winestend to be exported rather to extra-EU destinations.

United Kingdom

The UK situation (Figure 5) is similar to the Spanish one. For cheese, the esti-mated slope coefficients are negative for intra-EU exports and positive forextra-EU ones, resulting in a (almost) zero slope coefficient for total exports.For meat preparation, the situation is vice versa: a positive relationship forintra-EU exports and a negative one for extra-EU exports, with a resultingslightly positive slope coefficient for total exports. However, none of the coeffi-

cients turned out to be statistically significant (95% confidence level; Figure 5).

TABLE 5 Spain: Pooled Regression Results (Feasible GLS Estimates) From a Linear MixedModel With One Repeated Measurement




CATEGORY 1.97 4.17 .644PERIOD 1.69 .888 5.19

Relative unit value(CATEGORY)

1.70 .347 2.82


CATEGORY cheese 17.5 5.08 2.41CATEGORY meat prep 15.3 30.2 15.6CATEGORYwine 0a 0a 0a

PERIOD 1 (19951999) 5.36 4.13 8.84

PERIOD 2 (20002005) 0a 0a 0a





r21 2,785.1 2,247.7 3,014.2

r22 2,965.0 2,366.9 3,161.5

r12 2,502.5 1,885.5 2,758.7

Model statistics# of obs. 118 118 118







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The GLS results show that, given the underlying heterogeneous exportsituation, no parameter estimate is statistically significant (see Table 6).

Overall, and similar to Spain, when looking at the scatter plots, it

appears that the United Kingdom exports products across the entire qualityspectrum. However, the UK emphasis seems to rest on medium-qualityproducts irrespective of the export destination.

CONCLUSIONS

In summary, this article has investigated the relationship between productquality (as indicated by UV) and export performance, both measured inrelative terms. The estimation results show that the connection between pro-

duct quality and export performance depends on the product category (but

TABLE 6 UK: Pooled Regression Results (Feasible GLS Estimates) From a Linear Mixed ModelWith One Repeated Measurement



Tests of fixed effects (F-values)Intercept 16.4 19.8 25.1

CATEGORY .001 .055 .312PERIOD .773 .128 .012Relative unit value

(CATEGORY).404 .472 .584


CATEGORY cheese .529 3.89 8.11CATEGORY meat prep 0a 0a 0a

PERIOD 1 (19951999) 3.39 1.40 .597

PERIOD 2 (20002005) 0a

0

a

0

a




r21 2,856.4 3,135.0 2,557.4

r22 2,951.1 3,049.1 2,640.3

r12 2,526.7 2,703.5 1,855.7

Model statistics#of obs. 102 102 102Restr. 2 log lik. 1,008.7 1,013.4 1,032.0

AIC 1,014.7 1,019.4 1,038.0

BIC 1,022.4 1,027.1 1,045.7


Note. statistically significantly different from zero at least at 99% confidence level; significant levels are

based on panel-robust standard errors.aReference category.yRestricted maximum likelihood estimates.

228 C. Fischer


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not on the period) and differs (but not in all cases) according to the exportdestination. Although the signs of the estimated slope coefficients are stable,the obtained statistical significance levels for these parameters depend on theestimation method (OLS or GLS).

The estimation results reveal that Italy displays strong export perform-ance in high-quality cheese and meat preparations but not in high-qualitywines. In France, the situation is exactly vice versa: higher relative export per-formance for lower and medium-quality cheeses and meat preparations butstrongest export performance for high-quality wines. In both Spain and theUnited Kingdom, relative export performance appears to be equally distribu-ted across the entire quality spectrum. Finally, in Germany, there is a negativequality export performance connection for all three product categories, imply-ing that high-quality products play only a minor role in the countrys exports.

The findings from this analysis suggest that although there does not seem to

be a systematic relationship between food quality and export performance,high-quality products clearly play a considerable role in the food exports of atleast some countries (in this study, Italy, France). This underlines the point thatit is justified, and even necessary, to give special attention to this sort of trade.One possibility to do this, for instance, could be the introduction of specializeduniversity courses in international marketing of quality food products. Today, afew other highly specialized postgraduate management courses already existthat may serve as a model (e.g., Essec Business Schools Masters of Business

Administration in Luxury Brand Management or the University of SouthAustralias Masters of Wine Marketing). In addition, specialized short courses

(or other training programs) could be designed for the target group of alreadypracticing agribusiness professionals. Given that the issue seems to be ofrelevance for several EU countries, an integrated, pan-European teaching=train-ing program and thus the pooling of the expertises from different countries mayperhaps be most useful. In any case, such specialized capacity-building initiativesfor current and=or future agribusiness leaders can clearly be expected to contrib-ute positively to the enhancement of the competitiveness of EU food companies,

which operate in increasingly liberalized and quality-conscious markets.Future research may investigate why some countries in some product

categories perform better in exporting high-quality products than others.One possible reason could be that exports simply reflect productionproficiency, that is, that countries which produce relative more high-qualityfood products also export more of these. However, this hypothesis wouldneed empirical confirmation.

NOTES

1. In other words,de gustibus non est disputandum.

2. A unit value is the quotient of nominal sales to a physical unit of measure (Gelhar & Pick, 2002).



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3. A multiple regression should operate on a complete list of causes. If omitted variables are corre-

lated with included variables, then the estimated parameters will be biased. On the other hand, if omittedvariables are orthogonal to all included variables, then parameter estimates will be unbiased, though the

sampling error attached to the parameter estimates will be higher than it need be (Deegan, 1976).

4. Consider the example of high-quality cheese. A matured Parmesan hard cheese is easier to

transport and store than an ultrafresh goat pyramid even though objective quality may be the same.

Given the higher logistics ease, ceteris paribus, export performance of the former may be higher. Exportperformance of Parmigiano may also be higher because its main export markets may be more affluent

countries than the ones to which the goat cheese is exported, thus making the former relatively lessexpensive and resulting in comparatively stronger demand.

5. See Hinloopen and Van Marrewijks (2001) study on the empirical distribution of the Balassa index

and the problem of the incomparability of RXA scores across countries.

6. However, it should be noted that despite symmetrization the problem of unequal distribution and

different country-specific upper bounds remains.

7. The covered product codes are-cheese: 40610104069099, meat preparations: 1601001016029099,

and wine: 2204101122042199. The exact description of the individual products can be found on the

Internet through the Eurostat Comext database or can be obtained on request from Christian Fischer.

8. Only two product types (16022011 and 16022019: preparations of goose or duck liver) were

removed in the meat preparations category. In almost all countries, these products displayed extremelyhigh unit values and very low export performance, thus making them very influential outliers.

REFERENCES

Aiginger, K. (1997). The use of unit values to discriminate between price and qualitycompetition. Cambridge Journal of Economics, 21, 571592.

Balassa, B. (1965). Trade liberalization and revealed comparative advantage. TheManchester School of Economic and Social Studies, 33, 99123.

Baltagi, B., Song, S., & Jung, B. (2001). The unbalanced nested error component

regression model. Journal of Econometrics, 101, 357381. doi:10.1016=S0304-4076(00)00089-0

Bowen, H. (1983). On the theoretical interpretation of indices of trade intensity andrevealed comparative advantage. Weltwirtschatliches Archiv, 119, 464472.

Cameron, C., & Trivedi, P. (2005). Microeconometrics: Methods and applications.New York, NY: Cambridge University Press.

Charters, S., & Pettigrew, S. (2007). The dimensions of wine quality. Food Qualityand Preference, 18, 9971007. doi:10.1016=j.foodqual.2007.04.003

De Benedictis, L., & Tamberi, M. (2001). A note on the Balassa index of revealedcomparative advantage. Retrieved from http://papers.ssrn.com/sol3/papers.cfm?abstract_id=289602

Deegan, J. (1976). The consequences of model misspecification in regressionanalysis. Multivariate Behavioral Research, 11, 237248.

Gelhar, M., & Pick, D. (2002). Food trade balances and unit values: What can theyreveal about price competition? Agribusiness: An International Journal,18(1), 6179. doi:10.1002=agr.10007

Hill, T., & Lewicki, P. (2006). Statistics: Methods and applications. Tulsa, OK:StatSoft.

Hinloopen, J., & Van Marrewijk, C. (2001). On the empirical distribution of theBalassa index.Weltwirtschaftliches Archiv, 137, 135.

Holmes, R. (1973). The inadequacy of unit value indexes as proxies for Canadian

industrial selling price indexes. Review of Income and Wealth, 19, 271277.

230 C. Fischer


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King, A. (1993). A note on export unit values indices in competitiveness variables.Bulletin of Economic Research, 45(1), 6977.

Laursen, K. (1998). Revealed comparative advantage and the alternatives asmeasures of international specialisation (Druid Working Paper 98 (30)).Retrieved from http://www.druid.dk/wp/pdf_files/9830.pdf

Lienhardt, J. (2004). Statistics in focus: The food industry in Europe. Retrievedfrom http://epp.eurostat.cec.eu.int/portal/page?_pageid=1073,1135281,1073_1135295&_dadportal&_schemaPORTAL&p_product_codeKS-NP-04-039

Ninni, A., Raimondi, M., & Zuppiroli, M. (2006). On the success of made in Italy:An appraisal of quality-based competitiveness in food markets (University ofParma Working Paper WP10=2006, Serie: Economia e Politica Economia).Retrieved from http://ideas.repec.org/p/par/dipeco/2006-ep10.html

Reeves, C., & Bednar, D. (1994). Defining quality: Alternatives and implications.Academy of Management Review, 19, 419445.

Regmi, A. (Ed.). (2001). Changing structure of global food consumption and trade.

Economic Research Service. Washington, DC: U.S. Department of Agriculture.Retrieved from http://www.ers.usda.gov/Publications/wrs011/

Satin, M. (2002).Food quality and international trade. Food and Agriculture Organi-zation of the United Nations (FAO) Rome, Italy. Retrieved from http://

www.fao.org/ag/AGS/Agsi/gtz_html.htmShiells, C. (1991). Errors in import-demand estimates based on unit-value indexes.

The Review of Economics and Statistics, 73, 378382.Verbeke, G., & Molenberghs, G. (Eds.). (1997).Linear mixed models in practice: An

SAS-oriented approach. New York, NY: Springer-Verlag.Verdu Jover, A., Llorens Montes, F., & del Mar Fuentes, M. (2004). Measuring per-

ceptions of quality in food products: The case of red wine. Food Quality and

Preference, 15, 453469. doi:10.1016=j.foodqual.2003.08.002

CONTRIBUTOR

Christian Fischer is Associate Professor of agricultural economics andmanagement. He holds a doctorate in agricultural economics, Giessen Univer-sity, Germany; specialized masters degree in agribusiness management(MSMAI), Lyons Graduate School of Management (EM Lyon) & Ecole NationaleSuperieure Agronomique Montpellier (ENSAM); a graduate certificate in inter-

national economics, The University of Adelaide, Australia; and a master ofscience degree in food economics, Giessen University, Germany.

APPENDIX

Given the special nature of the data (i.e., a short unbalanced panel, or,more accurately, a nested cross section with one repeated measurement), ageneral linear model (GLM) was fitted for estimating the slope coefficient

of product quality with regard to product export performance. Formally,



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the GLM (i.e., a fixed-effects specification that is assumed to be linear inparameters; Verbeke & Molenberghs, 1997) can be specified as

yktl/k/t Xl clk;tx

lkt ekt: A:1

lis the overall (grand) mean (a fixed parameter equivalent to the usual inter-cept), /:: the fixed main-effect parameter for the two categorical predictors(i.e., factors) category (k 1, . . . , K) and time period (t 1, . . . , Tk), c

lk;t

the fixed parameters of l included (nonstochastic) covariates xlkt (l 1, . . . ,L), and ektthe random error (disturbance) ofykt. (For the research problemdescribed in this paper, K 3 and Tck2 [1, 2], depending on the analyzedcountry and category, andL is in general larger than 1 because nested effects

were considered).

Equation (A.1) can be simplified to yktx0ktbk;tekt, where x0ktdenotes a (row) vector of m inputs (factors, covariates, and interactions)and bk;t the (column) vector of corresponding parameters (including the/::). Rewritten in full matrix notation, Equation (A.1) becomes

y X bk;te; A:2

where y is aPK

k1IkTk1 response vector, X is aPK

k1IkTk m inputmatrix, b is a m 1 parameter vector, and e is a

PKk1IkTk1 disturbance

vector.

Given the panel structure of the data, where the different product typesare treated as subjects, Ey X bk;t, and Var(y) Var(e) is assumed to bei.i.d. N(0, r2t), thus disturbances in the two panels may be contempora-neously correlated and potentially heteroscedastic (i.e., displaying noncon-stant variance). As a consequence, bk;t in Equation (A.2) cannot efficientlybe estimated by pooled ordinary least squares (OLS) regression. Instead, amethod that takes into account autocorrelated and nonconstant residualerrors needs to be used. One such method is feasible generalized leastsquares (FGLS), where

bbFGLS X0bVV1Xh i1X0bVV1y A:3

(see Cameron & Trivedi, 2005) and where implementation requires theconsistent estimation ofV, the variancecovariance matrix of disturbancese. In order to consistently estimate V, one usually needs to make explicitassumptions about the underlying structure of its variance components,but it is possible (and was done in this case) to treat Vas being completely

unstructured, that is, r21 r12r21 r

2

2 .

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The estimation of variance components can be done in different ways.Under the i.i.d. multivariate normal assumption for e, maximum likelihoodestimation methods are usually employed, with two possible options:maximum likelihood (ML) or restricted maximum likelihood (REML). A

weakness of the ML method is that the estimates are biased in small samples(Cameron & Trivedi, 2005). Moreover, because REML does explicitly takeinto account the loss of the degrees of freedom involved in estimating thefixed effects, it is the recommended option in models containing manyfixed-effect parameters (Verbeke & Molenberghs, 1997). The 2 timeslog-likelihood of REML is (Cameron & Trivedi, 2005)

2REMLV ln jVj NTp ln r0V1r

NTp 1ln

2p

NTp

ln X0V1X ; A:4

wherer yX X0V1X 1

X0V1y,jVjdenotes the determinant ofV,Nthenumber of subjects, and p is the rank ofX. The variance components ofVcan be computed by maximizing Equation (A.4); however, in general, thereare no closed-form solutions. Therefore, Newton and scoring algorithms areusually used to find the solution numerically, starting with some initial valuefor residual error variance r2. Assuming i.i.d. N(0, r2), residual sum ofsquares from OLS regression usually yields rr2 to be used as starting value.

OnceVhas been estimated, original data Xand yare then accordingly

transformed and OLS regressions are run on the adjusted data, yielding auto-correlation and heteroscedasticity-adjusted bbk;t and respective panel-robuststandard errors. For the significance tests of the included factors, Analysis of

Variance (ANOVA) (i.e., method of moments-type) estimators, whichequate quadratic sums of squares to their expectations and solve the resultingequations for the unknowns, are used. Baltagi, Song, and Jung (2001)showed that ANOVA methods perform well in estimating the regression coef-ficients in unbalanced nested error-component regression models. Given thatthe data set in this paper is unbalanced and that the main interest is in thesignificance of the remaining differences in the factor category (or marginal)

means, Type III sums of squares are used (Hill & Lewicki, 2006).In maximum likelihood-based FGLS regressions, conventionally only

the final value of the (restricted) 2 times log-likelihood function andderived information criteria (such as the Akaike information criteria [AIC]and the Bayesian information criteria [BIC]) are calculated (see Cameron &Trivedi, 2005, for a discussion), on the basis of which appropriate (nested)models are selected. However, for assessing the overall fit of a model tothe underlying data, theses statistics are less useful because they cannot becompared across different nonnested models.


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