Chapter - 4 & 6 (Exchange Rate Determination & Goverment Influence on Exchange Rate)
Quality, Trade, and Exchange Rate Pass-Through¤ycepr.org/sites/default/files/events/1846_CHEN -...
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Quality, Trade, and Exchange Rate Pass-Through¤y
Natalie Chen
University of Warwick, CAGE,
CESifo and CEPR
Luciana Juvenal
International Monetary Fund
February 26, 2015
Abstract
We investigate theoretically and empirically the e¤ects of real exchange rate changes on the
behavior of …rms exporting multiple products with heterogeneous levels of quality. Our model
predicts more pricing-to-market and a smaller response of export volumes to a real depreciation for
higher quality products. We …nd strong support for the model predictions using a unique data set
of Argentinean …rm-level wine export values and volumes between 2002-2009 combined with experts
wine ratings to measure quality. Our results have implications for the choice of optimal monetary
and exchange rate policies, and for the understanding of international market segmentation.
JEL Classi…cation: F12, F14, F31
Keywords: Exchange rate pass-through, pricing-to-market, quality, unit values, exports, multi-
product …rms, wine.
¤We thank the Centre for Competitive Advantage in the Global Economy (CAGE) at the University of Warwick, theFederal Reserve Bank of St Louis, and the International Monetary Fund for …nancial support. For helpful comments,we thank Nicolas Berman, Andrew Bernard, Illenin Kondo, Logan Lewis, Philippe Martin, Guillermo Noguera, DennisNovy, Lucio Sarno, Cédric Tille, Olga Timoshenko, Zhihong Yu, our formal discussants Raphael Auer, Julian di Giovanni,Ferdinando Monte, Tim Schmidt-Eisenlohr, and Charles van Marrewijk, and participants at the CAGE Research Meetings2013, CEPR ESSIM 2014, CEPR Exchange Rates and External Adjustment Conference Zurich 2014, EITI Phuket 2014,ETSG Birmingham 2013, Federal Reserve Bank of Atlanta and NYU’s Stern School of Business 2nd Annual Workshopon International Economics, INFER Conference on Regions, Firms, and FDI Ghent 2014, Midwest International TradeMeetings 2013, Tri-Continental International Trade Policy Conference Beijing 2014, DC Trade Study Group, GenevaTrade and Development Workshop, Inter-American Development Bank, International Monetary Fund, London Schoolof Economics, Lund, Nottingham, Utrecht School of Economics, and Warwick. We are grateful to Linda Goldberg forsharing the data on distribution costs. Brett Fawley provided excellent research assistance. The views expressed are thoseof the authors and do not necessarily re‡ect o¢cial positions of the International Monetary Fund.
yNatalie Chen, Department of Economics, University of Warwick, Coventry CV4 7AL, UK. E-mail:[email protected] (corresponding author) and Luciana Juvenal, International Monetary Fund. E-mail:[email protected].
1 Introduction
Exchange rate ‡uctuations have small e¤ects on the prices of internationally traded goods. Indeed,
empirical research typically …nds that the pass-through of exchange rate changes to domestic prices is
incomplete, leading to deviations from the Law of One Price.1 Possible explanations for partial pass-
through include short run nominal rigidities combined with pricing in the currency of the destination
market (Engel, 2003; Gopinath and Itskhoki, 2010; Gopinath, Itskhoki, and Rigobon, 2010; Gopinath
and Rigobon, 2008), pricing-to-market strategies whereby exporting …rms di¤erentially adjust their
markups across destinations depending on exchange rate changes (Atkeson and Burstein, 2008; Knetter,
1989, 1993), or the presence of local distribution costs in the importing economy (Burstein, Neves, and
Rebelo, 2003; Corsetti and Dedola, 2005).2
Thanks to the increasing availability of highly disaggregated …rm- and product-level trade data,
a strand of the literature has started to investigate the heterogeneous pricing response of exporters
to exchange rate changes.3 Amiti, Itskhoki, and Konings (2014) …nd that Belgian exporters with
high import shares and high export market shares have a lower exchange rate pass-through. Berman,
Martin, and Mayer (2012) show that highly productive French exporters change signi…cantly more their
export prices in response to real exchange rate changes, leading to lower pass-through. Chatterjee, Dix-
Carneiro, and Vichyanond (2013) focus on multi-product Brazilian exporters and …nd that within …rms,
pricing-to-market is stronger for the products the …rm is most e¢cient at producing.
Evidence on the role of product-level characteristics in explaining heterogeneous pass-through re-
mains scarce, however. In this paper, we explore how the heterogeneous pricing-to-market behavior
of exporters, which leads to incomplete pass-through, can be explained by the quality of the goods
exported. We model theoretically the e¤ects of real exchange rate changes on the pricing decisions of
multi-product …rms that are heterogeneous in the quality of the goods they export, and empirically
investigate how such heterogeneity impacts exchange rate pass-through for export prices. Assessing the
role of quality in explaining pass-through is a challenge as quality is generally unobserved. To address
this issue we focus on the wine industry, which is an agriculture-based manufacturing sector producing
di¤erentiated products, and combine a unique data set of Argentinean …rm-level destination-speci…c
export values and volumes of highly disaggregated wine products with experts wine ratings as a directly
observable measure of quality.4
The …rst contribution of the paper is to develop a theoretical model to guide our empirical spec-
i…cations. Building on Berman et al. (2012) and Chatterjee et al. (2013), we extend the model of
Corsetti and Dedola (2005) by allowing …rms to export multiple products with heterogeneous levels
of quality. In the presence of additive (per unit) local distribution costs paid in the currency of the
1For a survey of the literature, see Burstein and Gopinath (2014) and Goldberg and Knetter (1997).2Also, Alessandria and Kaboski (2011) stress the role of search frictions as a source of incomplete pass-through.
Nakamura and Steinsson (2012) argue that price rigidity and product replacements lead aggregate import and exportprice indices to appear smoother than they actually are, biasing exchange rate pass-through estimates.
3Many papers examine the response of import prices (which include transportation costs) or consumer prices (whichfurther include distribution and retail costs) to changes in currency values (e.g., Campa and Goldberg, 2005, 2010;Gopinath and Rigobon, 2008). For earlier evidence from the perspective of exporters, see Goldberg and Knetter (1997),Kasa (1992), and Knetter (1989, 1993). For more recent evidence at the …rm-level, see Campos (2010), Fitzgerald andHaller (2014), Fosse (2012), Li, Ma, Xu, and Xiong (2012), and Strasser (2013).
4Other papers that focus on speci…c industries include Auer, Chaney, and Sauré (2014) and Goldberg and Verboven(2001) for cars, Hellerstein (2008) for beer, and Nakamura and Zerom (2010) for co¤ee.
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importing country, which are assumed to be higher for higher quality goods, the model shows that the
demand elasticity perceived by the …rms falls with a real depreciation and with quality. As a result, in
response to a change in the real exchange rate, exporters change their prices (in their own currency)
more, and their export volumes less, for higher quality products. Once we allow higher income coun-
tries to have a stronger preference for higher quality goods, as the evidence from the empirical trade
literature tends to suggest (e.g., Crinò and Epifani, 2012; Hallak, 2006), the heterogeneous response of
prices and quantities to exchange rate changes due to di¤erences in product quality is predicted to be
stronger for exports to higher income destination countries.
The second contribution of the paper is to bring the predictions of the model to the data. The
…rm-level trade data we rely on are from the Argentinean customs which provide, for each export ‡ow
between 2002 and 2009, the name of the exporting …rm, the country of destination, the date of the
shipment, the Free on Board (FOB) value of exports (in US dollars), and the volume (in liters) of
each wine exported. As we do not directly observe prices, we compute FOB export unit values as a
proxy for export prices at the …rm-product-destination level, using the data on the value and volume
exported. In order to assess the quality of wines, we rely on two well-known experts wine ratings, the
Wine Spectator and Robert Parker. Our approach to measuring quality is similar to Crozet, Head, and
Mayer (2012) who match French …rm-level exports data of Champagne with experts quality assessments
to investigate the relationship between quality and trade.
Our data are well-suited for identifying heterogeneous pass-through due to di¤erences in product
quality. First, the level of disaggregation of the customs data is unique because for each wine exported
we have its name, grape (Chardonnay, Malbec, etc.), type (white, red, or rosé), and vintage year
(i.e., the year in which the grapes are harvested). With such detailed information we can de…ne a
“product” in a much more precise way compared to the papers that rely on trade classi…cations such as
the Combined Nomenclature (CN) or the Harmonized System (HS) to identify traded products (e.g.,
Amiti et al., 2014; Berman et al., 2012; Chatterjee et al., 2013). Second, as the quality scores from both
the Wine Spectator and Parker rating systems are assigned to a wine according to its name, grape,
type, and vintage year, the trade and quality data sets can directly be merged with each other. As
a result, when using the Wine Spectator ratings that have the largest coverage of Argentinean wines,
the sample we use for the estimations includes 209 multi-product …rms exporting 6,720 di¤erent wines
with heterogeneous levels of quality (this contrasts with Crozet et al., 2012, who cannot distinguish
between the di¤erent varieties sold by each …rm, and therefore assume that each …rm exports one type
of Champagne only). Aggregating our data at the 12-digit HS level would reduce our sample size by a
factor of six, which would in turn signi…cantly lower the within-…rm variation in the quality of exported
wines as the 209 exporters would only be selling at most …ve di¤erent “products.” Third, the level of
disaggregation of our data ensures that compositional or quality changes do not a¤ect movements in
unit values. For instance, Feenstra and Romalis (2014) demonstrate that the variation in trade unit
values de…ned at the 4-digit SITC level is mostly attributable to di¤erences in product quality. Fourth,
thanks to the granularity of our data, our pass-through estimates are not a¤ected by the “product
replacement bias” put in evidence by Nakamura and Steinsson (2012), which may be a problem when
using aggregate prices data (also, see Gagnon, Mandel, and Vigfusson, 2014). Finally, export values
are FOB and therefore measure the revenue received by exporters at the border, excluding nontradable
components such as transportation costs, tari¤s, or distribution and retailing costs in the importing
country.
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To the best of our knowledge, our paper is the …rst to exploit such highly disaggregated product-level
exports data to investigate empirically the pricing strategies of exporters in response to real exchange
rate ‡uctuations.5 Consistent with other …rm-level studies, we …nd that pass-through for export prices
is large: in our baseline regression, in response to a ten percent change in the real exchange rate,
exporters change their export prices (in Argentinean pesos) by 1.9 percent, therefore pricing-to-market
is low and pass-through is high at 81 percent. Higher quality wines are more expensive and are exported
at a higher price. Most interestingly, we show that the response of export prices to real exchange rate
changes increases with the quality of the wines exported, or in other words pricing-to-market rises with
quality (i.e., pass-through decreases with quality). This e¤ect is economically important as pricing-to-
market is found to be 36 percent higher for the top than for the lowest quality range. This heterogeneity
in the response of export prices to exchange rate changes remains robust to di¤erent measures of quality,
samples, and speci…cations. We also examine the heterogeneous response of export volumes to real
exchange rate ‡uctuations. Export volumes increase in response to a real depreciation, but by less for
higher quality goods. Finally, we …nd that the e¤ect of quality on the responses of export prices and
volumes to changes in the exchange rate is larger for exports to higher income countries. Overall, the
predictions of our model are strongly supported by our empirical results.
One concern with our estimations is the potential endogeneity of quality in explaining unit values
and export volumes. Although both the Wine Spectator and Parker rating systems are based on blind
tastings where the price of each wine is unknown, the tasters are told the region of origin or the
vintage year of each wine. As a result, we cannot rule out that the basic information that the experts
hold about each wine, in combination with their own subjective preferences, a¤ects in a way or the
other their scores, leading to an endogeneity bias. In addition, as wine producers are likely to set
their prices depending on the ratings that are given to their wines, measurement error in the quality
scores is another potential source of endogeneity. In order to overcome this issue, we use appropriate
instruments for quality based on geography and weather-related factors, including the total amount of
rainfall and the average temperatures during the growing season for each province where the grapes
are grown, as well as the altitude of each of the growing regions. Our …ndings remain robust to the
instrumentation of quality.
We also provide some extensions to our benchmark speci…cations. First, when allowing for non-
linearities in the e¤ects of quality, the elasticity of unit values to the exchange rate is shown to be
much larger for the top quality wines, a …nding which can be reconciled with our assumption that
higher quality goods have higher (per unit) distribution costs. Second, as predicted by the model,
pricing-to-market increases with local distribution costs in the importing economy. Third, in addition
to increasing with quality, pricing-to-market also increases with …rm-level productivity, as in Berman
et al. (2012) and Chatterjee et al. (2013). Fourth, once we divide the sample between high and low
quality exporters, high quality …rms price-to-market more than the others. Finally, as in Amiti et al.
(2014), …rms price-to-market more in the countries where they own a larger share of the market.
Although our analysis concentrates on a speci…c sector in a single country, evidence suggests that
cross-country and cross-industry di¤erences in the quality of traded goods are large (Feenstra and Ro-
5Papers using similarly detailed product-level data usually observe retail or wholesale prices rather than trade prices(e.g., Hellerstein, 2008; Nakamura and Zerom, 2010).
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malis, 2014).6 Assuming that our results can be extended beyond the Argentinean wine industry, they
can therefore be expected to have far ranging macroeconomic implications. First, they provide evidence
that quality and per capita income, by increasing pricing-to-market, play a key role in generating devi-
ations from the Law of One Price, and therefore matter to explain the degree of international market
segmentation. Second, as they improve our understanding of how …rms set their prices when exporting
abroad, and in particular on how they react to exchange rate changes across products di¤erentiated
by quality, they have implications for aggregate pass-through, and therefore for the choice of optimal
monetary and exchange rate policies.7 Third, they carry implications for the creation of currency
unions. As the theory of Optimal Currency Areas emphasizes that the synchronization of in‡ation
shocks between two countries is an important condition for a common currency, our results suggest
that higher income countries trading higher quality goods should be less a¤ected by the in‡ationary
impact of exchange rate depreciations, making a common currency between them more desirable.
Our paper belongs to three strands of the literature. The …rst one is the vast literature on incomplete
exchange rate pass-through and pricing-to-market. These papers usually …nd a low degree of exchange
rate pass-through into import prices (e.g., Campa and Goldberg, 2005; Gopinath and Rigobon, 2008)
or consumer prices (e.g., Campa and Goldberg, 2010). Consistent with other recent …rm-level studies
that use exports data for the whole manufacturing sector (e.g., Amiti et al., 2014; Berman et al., 2012;
Chatterjee et al., 2013), we …nd that exchange rate pass-through for export prices is large.
Within this literature, most closely related to our work are Antoniades and Zaniboni (2013), Auer
and Chaney (2009), and Auer, Chaney, and Sauré (2014) who predict, and show, that pass-through
is higher for lower quality goods (only Auer and Chaney, 2009, do not …nd any evidence for such a
relationship).8 While also focusing on quality and pass-through, we di¤er from these papers along
several dimensions. First, we document the pricing-to-market behavior of exporters that leads to
incomplete pass-through. Instead, Auer et al. (2014) estimate the response of markups to exchange
rate changes at the retail-level, while Antoniades and Zaniboni (2013) and Auer and Chaney (2009)
focus exclusively on exchange rate pass-through. Second, our export prices exclude transportation and
local distribution costs, while the import and retail prices used in these papers contain nontradable
components. Third, while we exploit the within-…rm variation in quality of multi-product …rms, these
papers do not use …rm-level data. Finally, our analysis relies on an observable measure of quality, while
Auer et al. (2014) estimate hedonic quality indices and Antoniades and Zaniboni (2013) and Auer and
Chaney (2009) infer product quality from data on prices and unit values, respectively.
Second, as pricing-to-market and incomplete pass-through lead to deviations from the Law of One
Price, our paper relates to studies that examine the behavior of product-level real exchange rates.
6Compared to the world average, Feenstra and Romalis (2014) estimate that Swiss exports have the highest qualitywhile Chinese exports have the lowest. Across industries, the quality of exports is instead the highest in “Food andbeverages” from France, “Fuels and lubricants” from the US, or “Transport equipment and parts” from Austria.
7As incomplete pass-through determines the extent to which currency changes a¤ect domestic in‡ation in the importingeconomy, it has implications for the implementation of domestic monetary policy. In addition, as incomplete pass-throughdetermines how currency depreciations can stimulate an economy by substituting foreign by domestic goods, it also hasimplications for the evolution of the trade balance and exchange rate policy (Knetter, 1989).
8Basile, de Nardis, and Girardi (2012) develop a model based on Melitz and Ottaviano (2008) and also predict thatpass-through decreases with quality. In contrast, using a translog expenditure function to generate endogenous markupsin a model where …rms are heterogeneous in productivity and product quality, Rodríguez-López (2011) predicts that theresponse of markups to exchange rate shocks decreases with productivity and quality, therefore pass-through increaseswith productivity and quality. In a related study, Yu (2013) shows theoretically that incomplete pass-through resultsfrom …rms adjusting both their markups and the quality of their products in response to a change in the exchange rate.
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Recent contributions that rely on highly disaggregated product-level data include Simonovska (2014)
who studies the positive relationship between the price of tradable goods and per capita income.
Cavallo, Neiman, and Rigobon (2014) …nd that the Law of One Price holds very well within, but not
outside, of currency unions. Burstein and Jaimovich (2012) show that product-level real exchange rates
across countries are four times as volatile as nominal exchange rates.
Third, this paper contributes to the growing literature on quality and trade, which until recently
mostly relied on trade unit values in order to measure quality.9 At the country-level, Hummels and
Klenow (2005) and Schott (2004) focus on the supply-side, and show that export unit values increase
with exporter per capita income. On the demand-side, Hallak (2006) and Hummels and Skiba (2004)
…nd that richer countries have a stronger demand for high unit value exporting countries. Feenstra and
Romalis (2014) decompose the unit values of internationally traded goods into quality and quality-
adjusted prices, and show that developed countries both export and import more of higher quality
goods. Other papers investigate how quality relates to the performance of exporters using …rm-level
data. Manova and Zhang (2012a) focus on Chinese …rm-level export prices, and …nd evidence of quality
sorting in exports. Kugler and Verhoogen (2012), Manova and Zhang (2012b), and Verhoogen (2008)
highlight the correlation between the quality of inputs and of outputs focusing on Mexican, Chinese,
and Colombian …rms, respectively. Bastos, Silva, and Verhoogen (2014) show that exporting to richer
countries leads Portuguese …rms to upgrade the average quality of their outputs, which requires buying
higher quality inputs. Amiti and Khandelwal (2013) and Fan, Li, and Yeaple (2014) show that lower
import tari¤s are associated with an increase in export quality.10
The paper is organized as follows. Section 2 presents the theoretical model, and discusses how the
features of the wine industry can be reconciled with the main assumptions of the model. Section 3
describes the …rm-level exports customs data, the wine experts quality ratings, and the macroeconomic
data we use. Section 4 presents our main empirical results while Section 5 addresses endogeneity.
Section 6 discusses extensions while Section 7 provides robustness checks. Section 8 concludes.
2 A Model of Pricing-to-Market and Quality
Berman et al. (2012) extend the model with distribution costs of Corsetti and Dedola (2005), allowing
for …rm heterogeneity where single-product …rms di¤er in their productivity. They show that more
productive exporters change their prices more than others in response to a change in the real exchange
rate.11 In their appendix, the authors show that a similar result holds if …rms di¤er in the quality
of the (single) good they export: …rms that export higher quality goods change their export prices
more than others in response to real exchange rate changes. Chatterjee et al. (2013) extend the model
of Berman et al. (2012) to multi-product …rms. They show that in response to a change in the real
exchange rate, exporters vary their prices more for the products they are most e¢cient at producing,
and which therefore have smaller marginal costs.
9This approach is criticized by, among others, Khandelwal (2010) who compares exporters’ market shares conditionalon price to infer the quality of exports.
10Also see Baldwin and Harrigan (2011), Bastos and Silva (2010), Hallak and Schott (2011), Hallak and Sidivasan(2011), and Johnson (2012).
11Berman et al. (2012) obtain similar predictions using the models with endogenous and variable markups of Atkesonand Burstein (2008) and Melitz and Ottaviano (2008).
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We build on Berman et al. (2012) and Chatterjee et al. (2013) and extend the model of Corsetti
and Dedola (2005), but allow for heterogeneity in the quality of the goods exported. Given that most
…rms in our data set export multiple products, we model them as multi-product …rms which therefore
di¤erentiates us from Berman et al. (2012). In contrast to the multi-product …rms model of Chatterjee
et al. (2013), we rank the di¤erent goods exported by each …rm in terms of quality rather than
e¢ciency, where higher quality is associated with higher marginal costs.12
2.1 The Basic Framework
The Home country (Argentina in our case) exports to multiple destinations in one sector characterized
by monopolistic competition. The representative agent in destination country has preferences over
the consumption of a continuum of di¤erentiated varieties given by13
() =
·Z
ª[()()]
¡1
¸ ¡1
(1)
where () is the consumption of variety , () the quality of variety , and 1 the elasticity
of substitution between varieties.14 The set of available varieties is ª. Quality captures any intrinsic
characteristic or taste preference that makes a variety more appealing for a consumer given its price.
Therefore, consumers love variety but also quality.
Firms are multi-product and heterogeneous in product quality. The parameter , which denotes
each variety, indicates how e¢cient each …rm is at producing each variety, therefore has both a
…rm- and a product-speci…c component. Each …rm produces one “core” product, but in contrast to
Chatterjee et al. (2013) or Mayer, Melitz, and Ottaviano (2014) who consider that a …rm’s core
competency lies in the product it is most e¢cient at producing – and has lower marginal costs – we
assume that a …rm’s core competency is in its product of superior quality which entails higher marginal
costs (Manova and Zhang, 2012b).
The e¢ciency associated with the core product is given by a random draw ©, therefore each …rm
is indexed by ©. Let us denote by the rank of the products in increasing order of distance from the
…rm’s core, with = 0 referring to the core product with the highest quality. Firms therefore observe
a hierarchy of products based on their quality levels. A …rm with core e¢ciency © produces a product
with an e¢ciency level given by
(© ) = © (2)
where 1. Products with smaller (higher quality) are closer to the core, and have a lower e¢ciency
12Higher quality goods are typically assumed to have higher marginal costs because they require higher quality – andtherefore more expensive – inputs. See Crinò and Epifani (2012), Fan, Li, and Yeaple (2014), Feenstra and Romalis(2014), Hallak and Sivadasan (2011), Johnson (2012), Kugler and Verhoogen (2012), Manova and Zhang (2012a), andVerhoogen (2008).
13For similar preferences, see Baldwin and Harrigan (2011), Berman et al. (2012), Crozet et al. (2012), Fan et al.(2014), Johnson (2012), Kugler and Verhoogen (2012), and Manova and Zhang (2012b).
14As in Kugler and Verhoogen (2012), quality is treated as single-dimensional and is therefore only chosen by …rms.
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(© ). Higher quality goods have a lower e¢ciency because they have higher marginal costs15
( (© )) =
µ
(© )
¶
(3)
where 1 implies that markups increase with quality, and is the wage of the Home country
(Berman et al., 2012).16 The closer a product is from the core with the highest quality (i.e., the
smaller ), the lower is e¢ciency (© ), and the higher are marginal costs and quality ( (© )).
Firms face three types of transaction costs: an iceberg trade cost 1 (between Home and
destination ), a …xed cost of exporting (which is the same for all …rms and products and only
depends on destination ), and an additive (per unit) distribution cost in destination .17 The latter
captures any wholesale and retail costs associated with the selling, storage, marketing, advertising, or
transport of the goods that are paid in the currency of the destination country.18 If distribution requires
units of labor in country per unit sold, and is the wage rate in country , distribution costs are
given by ((© )). As in Berman et al. (2012), we assume that higher quality goods have higher
(per unit) distribution costs. Most importantly, as distribution is outsourced so that distribution costs
are paid in the currency of the importing country, they are una¤ected by changes in the exchange rate.
In units of currency of country , the consumer price in of a variety exported from Home to is
() ´((© ))
+ ((© )) (4)
where () is the export price of the good exported to , expressed in Home currency, and is
the nominal exchange rate between Home and . It is straightforward to see that any change in the
exchange rate leads to a less than proportional change in the consumer price () (i.e., incomplete
pass-through) given that local distribution costs are una¤ected by currency ‡uctuations. The quantity
demanded for this variety in country is
() = ¡1
·((© ) ((© ))
+
¸¡ (5)
where and are country ’s income and aggregate price index, respectively.19 The costs, in the
currency of the Home country, of producing () units of each good (inclusive of transportation
costs) and of selling them to country are
() = ((© ))
(© )+ (6)
15See Crinò and Epifani (2012) and Hallak and Sivadasan (2011) for models where marginal costs decrease with …rmlevel productivity and increase with product quality.
16Auer et al. (2014) observe that producers charge a higher markup when exporting higher quality cars.17We do not model entry and exit in export markets. See Campos (2010) for the e¤ects of entry and exit on pass-through.18Local distribution costs are economically important. Burstein et al. (2003) show that distribution costs represent
between 40 and 60 percent of retail prices across countries. Campa and Goldberg (2010) show that local distributioncosts, which represent between 30 and 50 percent of the total costs of goods exported by 21 OECD countries in 29manufacturing industries, decrease exchange rate pass-through into import prices. Hellerstein (2008) shows that partialpass-through can be explained by markup adjustments and by the presence of local costs in roughly similar proportions.
19The aggregate price index is given by =ª ()
1¡ 1
1¡ .
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Expressed in Home currency, the pro…t maximizing export price for each product the …rm exports to
country is
() =
¡ 1
µ
1 +(© ) ((© ))
¶
(© )= ((© ))
(© ) (7)
where ´ is the real exchange rate between Home and . In contrast to the standard Dixit-
Stiglitz markup (Dixit and Stiglitz, 1977), the presence of local distribution costs leads to variable
markups ((© )) over marginal costs that are larger than ¡1 , increase with quality ((© )),
the real exchange rate (i.e., a real depreciation), and local distribution costs .
The volume of exports () is given by
() =
µ ¡ 1
¶
¡1
·
(© )((© )) +
¸¡(8)
therefore the elasticity (in absolute value) of the exporter’s demand () with respect to the export
price () is
=
¯¯¯¯()
()
()
()
¯¯¯¯ =
+ (© )((© ))
+ (© )((© )) (9)
which is decreasing in quality and with a real depreciation. For a product that is closer to the core,
quality is higher, the elasticity of demand is smaller, and the markup is higher. The model leads to
two testable predictions on the e¤ects of exchange rate changes on export prices and quantities.
Prediction 1 The …rm- and product-speci…c elasticity of the export price () to a change in the
real exchange rate , denoted by and which captures the degree of pricing-to-market, increases with
the quality of the good exported, ((© )):
=
¯¯¯¯()
()
¯¯¯¯ =
(© )((© ))
+ (© )((© ))
Prediction 2 The …rm- and product-speci…c elasticity of the volume of exports () to a change in
the real exchange rate , denoted by , decreases with the quality of the good exported, ((© )):
=
¯¯¯¯()
()
¯¯¯¯ =
+ (© )((© ))
Intuitively, the mechanism is the following. A real depreciation reduces the elasticity of demand
perceived by exporters in the destination country, which allows all …rms to increase their markups. As
higher quality goods have a smaller elasticity of demand, their markups can therefore be increased by
more than for lower quality goods. This leads to heterogeneous pricing-to-market which is stronger
for higher quality goods (i.e., pass-through is lower). In turn, this implies that the response of export
volumes to a real depreciation decreases with quality. This mechanism is similar to Berman et al.
(2012) and Chatterjee et al. (2013), although their focus is on productivity di¤erences in driving
heterogeneous pricing-to-market across exporters, or exporters and products, respectively.
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2.2 Cross-Country Heterogeneity in the Preference for Quality
The evidence in the literature suggests that consumer preferences for quality vary from one country to
the other as preferences are a¤ected by per capita income. In particular, consumers in richer countries
are expected to have a stronger preference for higher quality goods.20 We therefore extend the model of
the previous section and allow for nonhomothetic preferences for quality. The utility function becomes
() =
·Z
ª[( )()]
¡1
¸ ¡1
(10)
where we replace () by ( ) to capture that the preference for quality can increase in per capita
income, . Local distribution costs ( ) are thus higher in higher income countries.21 This
allows us to derive two additional testable predictions.
Prediction 3 The …rm- and product-speci…c elasticity of the export price () to a change in the
real exchange rate , denoted by , increases with the quality of the good exported ((© ) ), and
by more for higher income than for lower income destination countries:
=
¯¯¯¯()
()
¯¯¯¯ =
(© ) ((© ) )
+ (© ) ((© ) )
Prediction 4 The …rm- and product-speci…c elasticity of the volume of exports () to a change in
the real exchange rate , denoted by , decreases with the quality of the good exported ((© ) ),
and by more for higher income than for lower income destination countries:
=
¯¯¯¯()
()
¯¯¯¯ =
+ (© ) ((© ) )
The elasticity of the exporter’s demand with respect to the export price is now decreasing in quality,
per capita income, and with a real depreciation. In response to a real depreciation, …rms can therefore
increase their markups by more for higher quality goods, but also when exporting to higher income
destination countries.22 In turn, this implies that the response of volumes to exchange rate changes
decreases with quality and per capita income. The assumption that consumers in richer countries are
less price-elastic than consumers in poorer countries – which enables …rms to charge higher markups
in richer destinations – is consistent with Alessandria and Kaboski (2011) and Simonovska (2014).
2.3 Relevance for Wine
The predictions of our model, which features monopolistic competition and local distribution costs,
depend crucially on the assumptions that higher quality wines have higher (per unit) distribution costs,
20Crinò and Epifani (2012) …nd that the preference for quality is on average 20 times larger in the richest (the US) thanin the poorest location (Africa) in their sample. Hallak (2006) …nds that rich countries tend to import relatively morefrom countries that produce higher quality goods. Fajgelbaum, Grossman, and Helpman (2011) develop a model whereconsumers have heterogeneous incomes and tastes, and the share of consumers who buy higher quality goods increases inincome. Verhoogen (2008) assumes that Northern consumers are more willing to pay for quality than Southern consumers.Also see Feenstra and Romalis (2014), Jaimovich and Merella (2014), Manova and Zhang (2012a), and Simonovska (2014).
21Dornbusch (1989) shows that the prices of services are lower in poorer than in richer countries, suggesting that localdistribution costs are lower in poorer countries.
22 In Auer et al. (2014), pass-through decreases with quality in rich countries and rises with quality in poor countries.
9
higher marginal costs, and higher markups. This section discusses how the characteristics of the wine
industry can be reconciled with these assumptions.
In terms of market structure, the wine industry is composed of many producers o¤ering a large
number of highly di¤erentiated products for which consumers are willing to pay a wide range of di¤erent
prices. Because of product di¤erentiation, wine producers have market power. Therefore, the sector can
legitimately be assumed to operate in a monopolistically competitive environment (Thornton, 2013).
Our model features the role of local distribution costs in generating incomplete pass-through. In the
case of wine, local distribution costs are indeed important. For instance, in the US, about 90 percent of
wine is distributed through a three-tier distribution system, whereby wine is sold by a producer or by
an importer to a distributor, who then sells it to a retailer, who …nally sells it to consumers. Each tier
involves a markup to compensate for each activity that is carried out. The markup of the distributor is
usually around 30 percent, and covers the costs related to transport, storage, and promotional activities.
In turn, the markup of the retailer varies between 30 percent for grocery stores and wine shops and
200 percent for restaurants, bars, and hotels (Thornton, 2013).
The prediction of our model that pass-through decreases with quality depends crucially on the
assumption that higher quality goods have higher (per unit) distribution costs. In the case of wine,
this assumption is plausible. First, higher quality wines are more costly to transport as they are typi-
cally shipped in temperature-controlled containers, they need heavier bottles and cartons for shipping,
require more careful and therefore more costly handling, and face higher insurance fees (Hummels and
Skiba, 2004). Second, tasting events to promote wines are organized by distributors for higher quality
wines only, which therefore have higher marketing and advertising costs. Third, top quality wines need
to be stored in refrigeration units to maintain their temperature, and are therefore subject to higher
storage costs. Finally, top quality wines are more likely to be sold in specialized wine stores than in
supermarkets, and the former usually charge a higher price because of the professional customer service
they o¤er. In other words, retail costs are higher for top quality wines.
Higher quality wines can also be expected to entail higher marginal costs (Thornton, 2013). First,
the quality of wine depends predominantly on the quality of the grapes. Although grape quality is
mostly a¤ected by geography and weather-related factors, achieving higher quality grapes can be more
costly as it requires, for instance, to restrict the grape yield per acre of vineyard, to grow the grapes
organically, to use labor-intensive treillis systems, and to prune, trim, pick, and sort the grapes more
carefully which in turn necessitates more skilled, manual labor. Higher quality wines also require
higher quality, and therefore more costly inputs such as the additives and yeast types used during the
various stages of fermentation or as preservatives. Second, achieving higher quality wines depends on
the production methods chosen, including the way in which the wine is extracted from the grapes (the
manual labor-intensive “punching down” technique is more costly but results in superior wine quality),
or the type of vessel to be used for ageing and fermentation. Compared to stainless-steel tanks, oak
barrels increase quality by adding ‡avor to the wine, but are much more expensive due to the cost of
the oak (the barrels are between 75 and 90 percent more costly than similar sized stainless-steel tanks,
and add between two to three US dollars to the cost of a bottle of wine), their short lifetime (the oak
‡avors last for three or four vintages only), and the evaporation of the wine due to the porous nature
of the oak (between 10 to 12 percent per year). Quality can be further enhanced by using smaller oak
10
barrels to increase the amount of wood per unit of wine, but these are in turn more expensive. Finally,
higher quality wines need to mature for a longer period of time, which increases storage costs.
As an illustration, Table 1 breaks down into several components the prices of non-EU wines sold
in UK retail outlets (Joseph, 2012). We believe that these …gures should provide us with some useful
insights on the composition of Argentinean wine prices sold in the UK. The table shows that more
expensive, and hence higher quality wines have higher retailing and distribution costs. Also, the
amount that goes to the winemaker, which includes his pro…t as well as the costs of producing the
wine, clearly increases with the price, and therefore with quality. Unfortunately, the table does not
provide any information on the winemaker markup which is the one that is modeled in the theory.
However, anecdotal evidence suggests that the producer markup is also likely to increase with the
price/quality of the wine: for a …ve pound wine sold on the UK market, the producer markup is
estimated to be around 40 pence, and increases to about ten pounds for a 25 pound bottle.23
3 Data and Descriptive Statistics
Our data set gathers information from di¤erent sources: …rm-level customs data, wine experts quality
ratings, and macroeconomic data.
3.1 Firm-Level Customs Data
Our …rm-level exports data are from the Argentinean customs and are provided to us by a private
vendor called Nosis. For each export ‡ow we have the name of the exporting …rm, the country of
destination, the date of declaration, the 12-digit HS classi…cation code, the FOB value of exports (in
US dollars), and the volume (in liters) exported between 2002 and 2009.24 We also have the name of
the wine exported, its type (red, white, or rosé), grape (Malbec, Chardonnay, etc.), and vintage year.
Figure 1 compares the total value of Argentina’s wine exports from the customs data set with the value
reported in the Commodity Trade Statistics Database (Comtrade) of the United Nations (HS code
2204). The data coincide extremely well.
Given that actual export prices are not available, we proxy for them using the unit values of exports
in the currency of the exporter, computed as the ratio of the export value in Argentinean pesos divided
by the corresponding export volume in liters. In order to convert the value of exports (in US dollars)
into pesos, we use the peso to US dollar exchange rate in the month in which the shipment took place.
We then aggregate the data at an annual frequency.25
23See www.thirty…fty.co.uk.24Due to con…dentiality reasons imposed by Argentinean law, the customs data cannot make the name of the exporter
public. However, Nosis uses its own market knowledge to generate a “…rst probable exporter,” a “second probableexporter,” and a “third probable exporter.” To determine the exporter’s identity, we used from the Instituto Nacionalde Vinticultura (INV) the names of all wines and of all the …rms authorized to produce and sell wine and compared,for each wine name, the name of the …rst probable exporter with the authorized exporter reported by the INV. If thisname coincided, we kept the …rst probable exporter. Otherwise, we repeated the procedure with the second probableexporter, and …nally with the third probable exporter. We identi…ed 86 percent of the …rms as “…rst probable exporters,”…ve percent as “second probable exporters,” one percent as “third probable exporters,” while the remaining eight percentcould not be matched. In our sample, these proportions become 96.5, three, one half, and zero percent, respectively.
25Our results still hold when measuring unit values, export volumes, and exchange rates at a quarterly frequency. Thereason why we aggregate the data at an annual frequency is that many of the other variables we use – such as the reale¤ective exchange rates or real GDPs per capita – are not available at a quarterly frequency.
11
We clean up the data in several ways. First, we drop any wine for which either the name, grape,
type, or vintage year is missing, cannot be recognized, or is classi…ed as “Unde…ned.” As sparkling
wines, dessert wines, and other special varieties do not have any vintage year, they are excluded from
the data set. Second, we only keep the export ‡ows recorded as FOB, which therefore exclude shipping
costs, tari¤s, or distribution and retail costs charged in the destination country. Third, as we are
interested in how product quality a¤ects the pricing and export decisions of wine producers, we restrict
our analysis to the manufacturing sector, and drop wholesalers and retailers. Fourth, due to the rise in
in‡ation in 2007 and increasing worries of capital out‡ows, the government introduced some barriers
to obtain import licenses which culminated in an extreme measure whereby …rms were authorized to
import if they exported goods for an equivalent value to those they intended to import.26 We therefore
drop from the sample the …rms unrelated to the wine industry that exported wine over the period.27
Fifth, we drop the wines that are not produced in Argentina.28 Sixth, we drop a number of typos which
we are unable to …x. We exclude the few cases where the vintage year reported is ahead of the year in
which the export took place. We also drop the observations where the value of exports is positive, but
the corresponding volume is zero. Finally, we also exclude some outliers: for each exporter, we drop
the observations where unit values are larger or smaller than 100 times the median export unit value
charged by the …rm.
The recent papers on heterogeneous pass-through typically de…ne a “product” according to trade
classi…cations such as the CN or the HS (e.g., Amiti et al., 2014; Auer and Chaney, 2009; Berman et
al., 2012; Chatterjee et al., 2013). As Table 2 shows, the 6-digit HS classi…es wines into four di¤erent
categories, depending on whether the wines are sparkling or not, and on the capacity of the containers
in which they are shipped (i.e., larger or smaller than two liters). Argentina further disaggregates
the HS classi…cation at the 12-digit level, but this only enlarges the number of di¤erent categories,
or “products,” to eleven. The problem is that changes in unit values de…ned at this level may re‡ect
compositional or quality changes rather than price changes as there may be more than one distinct
product within a single HS code (Feenstra and Romalis, 2014). In contrast, the detail provided by our
data set allows us to de…ne an individual product as a combination between a wine name, type, grape,
vintage year, and the capacity of the container used for shipping (identi…ed using the HS code), so that
compositional or quality changes are unlikely to a¤ect unit values.29 Our cleaned sample includes a
total of 21,647 di¤erent products/wines of which 6,720 can be matched with quality ratings. The 6,720
wines represent 43 percent of the total FOB value of red, white, and rosé wine exported between 2002
and 2009.
26See http://www.ustr.gov/sites/default/…les/2013%20NTE%20Argentina%20Final.pdf. The World Trade Organiza-tion has subsequently ruled that the requirement that imports should be balanced with exports violates global trade rules(The Financial Times, “WTO Rules Against Argentine Import Curbs,” 22 August 2014).
27We discard a total of 157 …rms that include wine wholesalers (20), wine retailers (2), unidenti…ed …rms (70), as wellas …rms that belong to the sectors of agriculture (5), soft drinks or malt liquors (6), textiles (6), chemicals (6), metals(1), paints, glass, and paper (3), machinery (1), motorcycles (1), and services industries (25) such as consultancy or bookpublishing …rms. These …rms represent 12,376 observations in the raw data set (at the transaction-level), or 1.6 percentof the total FOB value of wine exported by Argentina between 2002 and 2009. Of the 157 …rms, only 41 export winesthat can be matched with the quality ratings from the Wine Spectator, and these …rms represent 0.17 percent of the totalFOB value exported over the period (or 1,520 observations). In the …nal data set aggregated at a yearly frequency, the41 …rms represent 345 observations.
28These wines represent 0.7 percent of the total FOB value of wine exported between 2002 and 2009.29 In our sample, each wine is produced, and exported, by one …rm only. In contrast, a product de…ned at the CN or
HS levels can be produced, and therefore exported, by more than one …rm.
12
3.2 Quality Ratings
The editors of the Wine Spectator magazine review more than 15,000 wines each year in blind tastings,
and publish their ratings in several issues throughout the year. The ratings are given on a (50,100)
scale according to the name of the wine, its grape, type, and vintage year, which are characteristics
we all observe in the customs data set. A larger score implies a higher quality.30. Table 3 lists the six
di¤erent categories the wines fall in depending on their scores.
We match the wines from the customs data set with the ones reviewed by the Wine Spectator
by name, type, grape, and vintage year, and each wine is assigned a single quality rating.31 We end
up with 6,720 wines exported by 209 …rms over the 2002-2009 period. Our approach to measuring
quality is similar to Crozet et al. (2012) for Champagne. However, they are unable to distinguish
between the di¤erent varieties sold by each …rm, and therefore assume that each …rm exports one type
of Champagne only. In addition, their ratings are only measured on a (1,5) scale, with a larger value
indicating a higher quality.
We rely on the Wine Spectator for our baseline regressions because it has the largest coverage of
Argentinean wines. However, in the robustness section, we check the sensitivity of our results using an
alternative rating produced by Robert Parker. Parker is a leading US wine critic who assesses wines
based on blind tastings, and publishes his consumer advice and ratings in a bimonthly publication,
the Wine Advocate.32 His rating system also employs a (50,100) point scale, with wines being ranked
according to their name, type, grape, and vintage year, and with a larger value indicating a higher
quality.33 Table 3 lists the di¤erent categories considered by Parker. Compared to the Wine Spectator,
the scores are slightly more generous (for instance, a wine ranked 74 is “Not recommended” by the
Wine Spectator, but is “Average” according to Parker). The customs data and the Parker ratings can
be matched for 3,969 wines exported by 181 …rms.
Figure 2 plots the Wine Spectator against the Parker ratings for the 2,433 wines (exported by 135
…rms) that have scores from both sources. For the sake of clarity, for both ratings quality is measured
between one and six, where each value corresponds to one of the di¤erent bins de…ned in Table 3. A
value of one indicates that the wine is either “Not recommended” or “Unacceptable,” while a value of
six that the wine is “Great” or “Extraordinary.” For Parker, notice that only the three highest quality
bins are depicted in the graph.34 Each circle is sized proportionately to the number of wines at that
30Quality depends on the amounts of characteristics embodied in a wine, and is assessed by evaluating those char-acteristics. Subjective quality refers to the evaluation of consumers based on personal tastes and preferences, whereasobjective quality assesses the characteristics that are independent of personal preferences such as balance, complexity,…nish, concentration, or ‡avor intensity. The aim of wine critics is to provide an objective assessment of wine quality,and they may therefore conclude that a wine is of high quality even though they personally dislike it (Thornton, 2013).However, as we discuss later, some argue that experts ratings can, to some extent, be subjective, and therefore re‡ect thepersonal preferences of the tasters.
31The quality scores are time-invariant. Although some evidence suggests that the quality of Argentinean wines (andtheir price) is not much a¤ected by ageing (Thornton, 2013), in some of our regressions we include vintage year …xede¤ects to control for product characteristics. When explaining unit values, the coe¢cients on the vintage year …xed e¤ectsare larger for older wines, indicating that older wines tend to be more expensive. A higher price for older wines couldcapture that quality improves with ageing, but may also re‡ect the costs incurred by producers for storing the wines ininventories for several years.
32Both the Wine Spectator and Parker are US-based ratings. Other ratings have a poor coverage of Argentinean wines.33Starting with a base score of 50, Parker assigns additional points for appearance (5 points), smell (15 points), taste
and …nish (20 points), and overall quality and ageing potential (10 points). Instead, the Wine Spectator does not makepublic how the points are awarded.
34When using Parker in our regressions, four out of the six bins listed in Table 3 are however included in our sample.
13
point in the …gure. Most wines are rated as “Above average/very good” (a value of four) by Parker,
and are either “Good” or “Very good” (a value of three or four) according to the Wine Spectator,
again suggesting that Parker is, on average, more generous in its ratings. A relatively large number
of wines are rated as “Outstanding” (a value of …ve) by both tasters, and some as either “Great” or
“Extraordinary” (a value of six). Notice that a few wines are ranked as “Not recommended” (a value
of one) by the Wine Spectator, but “Outstanding” (a value of 5) according to Parker. The two ratings
are, however, positively correlated. The Pearson correlation is equal to 0.51, while Kendall’s correlation
index of concordance between the two ratings is 0.47.
3.3 Macroeconomic Data
The data on real GDPs per capita (in US dollars) and labor compensation as a share of GDP are from
the Penn World Tables, and the consumer price indices (CPI) and nominal exchange rates from the
International Financial Statistics (IFS) of the International Monetary Fund (IMF). The real exchange
rate is de…ned as the ratio of consumer price indices times the average yearly nominal exchange rate,
and an increase indicates a real depreciation of the Argentinean peso. The real e¤ective exchange
rates are sourced from the IFS and the Bank of International Settlements, and an increase indicates
a real depreciation. Bilateral distances are from the Centre d’Etudes Prospectives et d’Informations
Internationales (CEPII). Real wages are obtained by multiplying the labor compensation share by the
destination country’s GDP, and de‡ating using the corresponding CPI.
During the 2002-2009 period, Argentina witnessed major nominal and real exchange rate ‡uctua-
tions. Figure 3 illustrates the evolution of the monthly nominal and real exchange rates between the
Argentinean peso and the US dollar. After the …nancial crisis of 2001, the …xed exchange rate system
was abandoned and as a result, in 2002, the peso depreciated both in nominal and real terms by up to
75 percent. The export boom that followed lead to a massive in‡ow of US dollars into the economy,
which helped to appreciate the peso against the dollar. The peso then remained stable – but slightly
appreciated in real terms – until 2008 when it depreciated again with the advent of the global …nancial
crisis and the increase in domestic in‡ation.
3.4 Descriptive Statistics
Table 4 summarizes our trade data by year, and shows that the exports included in our sample increased
threefold between 2002 and 2009. A total of 794 wines were exported by 59 di¤erent …rms in 2002,
while in 2009 this increased to 151 …rms exporting 1,833 di¤erent wines. Over the whole period, our
sample includes 6,720 wines exported by 209 di¤erent wine producers.35 As shown in Table 5, these
…rms exported an average of 139 di¤erent wines, from a minimum of one to a maximum of 510 (in the
sample, 15 …rms appear as having exported one wine only; in reality, they exported more than one
wine, but only one could be matched with the quality ratings). Exporters charged between two cents
and 381 US dollars per liter of wine exported, with an average of …ve US dollars per liter.36 They
exported to an average of 24 di¤erent destinations, from a minimum of one to a maximum of 65. The
35We observe 882 di¤erent wine names, 23 grapes, three types, and 22 vintage years (between 1977 and 2009).36The Trade Unit Values Database developed by the CEPII uses the bilateral trade values and quantities collected by
the United Nations to compute “reliable and comparable unit values across countries” (Berthou and Emlinger, 2011, p.3).At the 6-digit level of the HS classi…cation, the FOB unit values of Argentinean wine exports between 2002 and 2009 varybetween 7 cents and 16 US dollars per liter exported. Given the much …ner disaggregation level of our data, unit valuesbetween 2 cents and 381 US dollars per liter sound reasonable.
14
mean Wine Spectator rating is 85, the lowest rated wine receives a score of 55, and the highest receives
a score of 97. The distribution across wines is symmetric as the mean and the median are equal. The
Parker scores vary between 72 and 98, with an average of 87 (only four out of the six bins listed in Table
3 are therefore included in our sample). Table 6 shows that, with the exception of Brazil, Argentinean
wine producers mostly exported to developed economies, the United States being the top destination
market.
Table 7 provides a snapshot of our data. For con…dentiality reasons, we can neither report the
exporter nor the wine names, therefore these are replaced by numbers and letters instead. The table
shows that, whether we use the Wine Spectator or the Parker ratings, individual …rms exported wines
with heterogeneous levels of quality (between 73 and 92 for Firm 1, and between 85 and 98 for Firm 2).
In addition, higher quality wines are, on average, sold at a higher price. Besides, the table illustrates
that the Law of One Price fails: in 2006, Firm 1 exported the same wine to two di¤erent destinations,
but charged 13.38 US dollars per liter to the United States, versus 11.09 US dollars per liter to China.
Similarly, in 2008, Firm 2 charged 43.27 US dollars to the United Kingdom, versus 24.65 US dollars
to Thailand for the same liter of wine exported. The di¤erences in the prices of these wines re‡ect
di¤erences in markups as identical wines have the same marginal cost. Interestingly, the prices, and
therefore the markups, are higher for exports to higher income countries, which is consistent with
Simonovska (2014) who argues that exporters extract higher markups for identical products when
selling to richer countries with lower demand elasticities.
Table 8 reports descriptive statistics by quality bin of the Wine Spectator. Wines that are either
“Good” or “Very good” represent the largest share of the sample (in terms of the total number of
observations, the number of …rms exporting these wines, and the share of exports in the sample).
At the other two extremes, “Great” wines, followed by “Not recommended” wines, have the smallest
coverages. Compared to other wines, “Great” wines are more expensive, and are exported to a smaller
number of destinations (per wine exported) which are, on average, richer (Hallak, 2006; Verhoogen,
2008), and more distant (Baldwin and Harrigan, 2011; Bastos and Silva, 2010; Feenstra and Romalis,
2014; Hummels and Skiba, 2004; Johnson, 2012).
Finally, Table 9 describes the data across …rms. Each …rm is allocated to one of the Wine Spectator
quality bins, based on the highest quality wine the …rm exported over the period. Firms exporting
higher quality wines tend to export a higher quality, on average.37 Interestingly, these …rms also tend
to export more variety. Taken together, the 68 …rms which exported “Great” and “Outstanding” wines
exported “Not recommended” wines as well. They exported a larger number of di¤erent wines, which
are shipped to a larger number of destinations (per wine exported), and represent the largest share of
exports in the sample. At the other extreme, only one …rm exported exclusively “Not recommended”
wines, while 12 …rms exported “Mediocre” wines as their highest quality.
4 Empirical Analysis
In order to check if Prediction 1 holds in the data, we estimate the following reduced-form regression
ln = 1 ln + 2 ln £ + + + (11)
37A …rm can share its expertise in winemaking across all of its products, raising their average quality (Thornton, 2013).
15
where is the export unit value of wine exported by …rm to destination country in year ,
expressed in pesos per liter of wine exported, and is our proxy for export prices. is the average
real exchange rate between Argentina and country in year (an increase in indicates a real
depreciation). The quality of wine is denoted by , and the Wine Spectator ratings are used
for our benchmark speci…cations.
The export price in the exporter’s currency is a markup over marginal costs. In order to identify a
pricing-to-market behavior, which requires markups to respond to exchange rate changes, the regression
needs to control for product-speci…c marginal costs (Knetter, 1989, 1993).38 As we assume in the model
of Section 2, higher quality wines can be expected to have higher marginal costs. We therefore perform
within estimations, and control for time-varying product-speci…c marginal costs (which are, as a matter
of fact, also …rm-speci…c) by including product-year …xed e¤ects, .39 As product-year …xed e¤ects
are collinear with quality, the direct e¤ect of quality drops out from the regression. Firm-destination
…xed e¤ects, , are also included. The coe¢cients to be estimated are 1 and 2 and is an error
term. Prediction 1 requires 2, the coe¢cient on the interaction between the real exchange rate and
quality, to be positive.40
As all variables are in levels (rather than …rst di¤erences), the estimated coe¢cients can be thought
of as capturing the long term response of unit values to changes in each of the explanatory variables.
In addition, given the level of disaggregation of the data, changes in real exchange rates are assumed to
be exogenous to the pricing decisions of individual …rms.41 Finally, as quality takes on a single value
for each product, robust standard errors are adjusted for clustering at the product-level.
To test Prediction 2 we estimate
ln = 1 ln + 2 ln £ + 3 + + + (12)
where is the FOB export volume (in liters) of wine exported by …rm to destination country
in year . To be consistent with gravity models, we include destination-year-speci…c variables ,
including the importing country’s real GDP per capita and real e¤ective exchange rate as a proxy for
country ’s price index (Berman et al., 2012). Prediction 2 requires the coe¢cient on the interaction
term, 2, to be negative.
4.1 Baseline Results
Panel A of Table 10 reports the results for export unit values. In order to get a sense of how quality
a¤ects unit values, and therefore allow the coe¢cient on quality to be estimated in the regression, we
…rst estimate equation (11) by replacing the product-year …xed e¤ects by …rm-year dummy variables
38Distinguishing between changes in markups and changes in marginal costs is also important from a policy point ofview as changes in costs are e¢cient, while changes in markups are not (Burstein and Jaimovich, 2012).
39As each wine is only produced in a single vintage year, product-speci…c marginal costs are time-invariant. Still, weinclude product-year …xed e¤ects to control for any other time-varying costs that may di¤er across products, and thereforeacross di¤erent qualities, such as storage costs.
40When the interaction between the exchange rate and quality is excluded from (11), 1 captures the degree of pricing-to-market. Exchange rate pass-through for export prices is given by (1¡ 1)£ 100.
41Berman et al. (2012) estimate a similar regression in levels. Other papers focus on …rst di¤erences in order to addressnon-stationarity (e.g., Gopinath et al., 2010; Gopinath and Rigobon, 2008). Given the level of disaggregation of our data,computing …rst di¤erences in unit values reduces our sample size by four, and our estimates become insigni…cant.
16
(to control, among other factors, for the time-varying marginal costs or reputation of each exporter),
and simultaneously control for product characteristics by including grape, type, vintage year, HS, and
province of origin of the grapes …xed e¤ects.42 Fixed e¤ects for the wine names are not included as
they are collinear with the …rm …xed e¤ects (as each wine is exported by one producer only). Column
(1) only includes the exchange rate and quality as regressors, and shows that higher quality wines are
exported at a higher price, which is consistent with equation (7) and with the …ndings of Crozet et al.
(2012) for Champagne. Also, as expected, exporters increase their export prices in response to a real
depreciation.
Column (2) includes product-year …xed e¤ects to control for the time-varying marginal costs of
producing each wine, therefore quality drops out from the regression. When the real exchange rate
‡uctuates, exporters change their export prices: in response to a ten percent real depreciation, they
raise their prices (in pesos) by 1.9 percent, therefore pricing-to-market is low and pass-through for
export prices is high at 81 percent. This contrasts with the …ndings of papers that use import rather
than export prices, e.g., Gopinath and Rigobon (2008) …nd that the pass-through for import prices in
the US is only 22 percent.43 The large degree of pass-through we …nd for the wine industry is, however,
consistent with the …ndings of other papers that use …rm-level exports data for the whole manufacturing
sector. For instance, pass-through for export prices is estimated at 79 percent for Belgian exporters
(Amiti et al., 2014), 92 percent for French exporters (Berman et al., 2012), 77 percent for Brazilian
exporters (Chatterjee et al., 2013), 86 percent for Danish exporters (Fosse, 2012), and at 94 percent
for Chinese exporters (Li, Ma, Xu, and Xiong, 2012).44
The estimated coe¢cient on the exchange rate in column (2) however hides a signi…cant amount
of heterogeneity in the degree of pass-through across products. To see this, column (3) interacts the
exchange rate with quality. Its estimated coe¢cient is positive and signi…cant, providing support to
Prediction 1 that the elasticity of export prices to a change in the real exchange rate increases with
quality – in other words, pass-through decreases with quality. The latter …nding is consistent with the
theoretical predictions of Auer and Chaney (2009) and Basile, de Nardis, and Girardi (2012), and with
the empirical results of Antoniades and Zaniboni (2013) and Auer et al. (2014).45
Both quality and pass-through may vary independently from each other over time. We therefore
need to ensure that the negative relationship between quality and pass-through that we …nd is not
42“Old World” wines produced in Europe are labelled by the region where they are made and where the grapes aregrown. In contrast, wines in Argentina follow the tradition of “New World” wines and are classi…ed by the grape varietyused for producing the wine, and the grapes can be grown in di¤erent provinces. This is the reason why, in our regressions,we control for the province of origin of the grapes. According to Thornton (2013), the location where the grapes are grownmay also a¤ect wine prices through its reputation.
43One possible reason for low pricing-to-market, and therefore for high pass-through, is transfer pricing between multi-national …rms exporting to foreign subsidiaries. If markups are not adjusted by exporters but by their subsidiaries inthe destination country, pass-through can be high for export prices but low for consumer prices (Knetter, 1992). Thisscenario is unlikely in our case as our sample includes wine producers only and no multinational …rms.
44We checked if exporters adopt di¤erent pricing strategies depending on whether the Argentinean peso appreciates ordepreciates in real terms. We found that unit values respond signi…cantly more to appreciations than to depreciations.This asymmetric pattern is consistent with …rms trying to maintain export market shares by reducing the domesticcurrency prices of their exports which become less competitive when the peso appreciates, and with Marston (1990) whoshows that pricing-to-market by Japanese …rms is stronger when the Japanese yen appreciates.
45 In response to a change in the exchange rate, multi-product …rms could also modify the composition of their exportsand therefore change the number of products they sell abroad (see Berman et al., 2012, for a discussion, and Chatterjeeet al., 2013, for some evidence). One way to address this possibility is to restrict the sample to …rms exporting only onewine to each destination. This reduces our sample to 341 observations, and our estimates become insigni…cant.
17
driven by pass-through being low when quality is high for unrelated reasons. To address this possibility,
column (4) interacts the real exchange rate with year dummies. Reassuringly, the coe¢cient on the
interaction between the exchange rate and quality remains positive and signi…cant.
Column (5) replaces the …rm-destination …xed e¤ects by …rm-destination-year dummies to control
for excluded characteristics that vary by …rm-destination-year (such as the time-varying demand of a
country for a …rm’s exports, or the presence of long term contracts between exporters and importers in
each destination country). In column (6), we estimate a di¤erence-in-di¤erence speci…cation by includ-
ing destination-year, …rm-destination, and product-year …xed e¤ects. In both cases the exchange rate
drops out, but the interactions between the exchange rate and quality remain positive and signi…cant.46
As shown in Figure 3, Argentina has experienced two major exchange rate regime shifts during the
sample period. First, after the …xed exchange rate between the Argentinean peso and the US dollar
was abandoned in 2001, the peso depreciated greatly with respect to the US dollar throughout 2002.
Second, with the …nancial crisis that started in 2008, the peso depreciated again with respect to the
US dollar. In addition, the crisis might have prompted consumers to substitute towards lower quality
goods (a “‡ight from quality,” see Burstein, Eichenbaum, and Rebelo, 2005; Chen and Juvenal, 2015),
and impacted the …nancial constraints of exporters (Strasser, 2013). Columns (7) and (8) therefore
restrict the sample to the post-2002 and pre-2008 periods, respectively, while column (9) focuses on
the 2003-2007 period only. Our results continue to hold.
Panel B reports the results of estimating equation (12) for export volumes. In column (1), where
the product-year …xed e¤ects are replaced by …rm-year, grape, type, vintage year, HS, and province
of origin dummy variables, the coe¢cient on quality is negative and signi…cant, which at …rst glance
contrasts with the positive relationship between trade and quality that is usually estimated in the
literature (e.g., Crozet et al., 2012). One crucial di¤erence between our regression and, for instance,
Crozet et al. (2012), is that we estimate the within-…rm e¤ect of quality on export volumes. The
negative coe¢cient on quality indicates that when a …rm exports di¤erent wines with heterogeneous
levels of quality to a given destination in a given year, the higher quality wines are exported in smaller
quantities. This is consistent with San Martín, Troncoso, and Brümmer (2008) who observe that
higher quality wines are produced in smaller quantities, and with Thornton (2013) who notes that
higher quality wines are produced from grapes grown in low-yield vineyards. Besides, exports increase
with a real depreciation. The elasticity is large and equal to 1.378, which is consistent with evidence
that the trade elasticities for emerging economies are generally larger than for developed countries.47
Once we control for product-year …xed e¤ects in column (2), the exchange rate elasticity of export
volumes becomes insigni…cant. However, once we allow this elasticity to vary with the level of quality,
the results in column (3) show that, consistent with Prediction 2, the interaction between the exchange
rate and quality is negative and signi…cant, therefore the response of export volumes to changes in
exchange rates decreases with quality. This …nding remains robust to interacting the exchange rate with
year dummies (column 4), to including …rm-destination-year …xed e¤ects (column 5), to a di¤erence-
in-di¤erence speci…cation (column 6), and (less so) to di¤erent sample periods (columns 7 to 9).
46Given that markups may vary with destination market size (Melitz and Ottaviano, 2008), we checked, and con…rm,that our results remain robust to controlling for real GDP or real GDP per capita in our regressions.
47Haltmaier (2011) estimates an elasticity of total Argentinean exports to the real trade-weighted exchange rate of six.
18
In order to provide a quantitative evaluation of the economic e¤ects of quality, we discuss the
exchange rate elasticities evaluated at the mean value of quality in the sample (which is equal to 85),
as well as the elasticities calculated at the mid score of each quality bin de…ned in Table 3.48 In column
(3) of Panel A, the mean elasticity is equal to 0.193, therefore average pass-through is 80.7 percent.49
The elasticity is equal to 0.157 for “Not recommended” wines (at a score of 62), and increases to 0.180
for “Mediocre” wines (at a score of 77), which represents a 15 percent increase in pricing-to-market.
Moving from one quality bin to the other further increases pricing-to-market by an additional four
percent for “Good” (at a score of 82 and an elasticity of 0.188), “Very good” (at a score of 87 and an
elasticity of 0.196), “Outstanding” (at a score of 92 and an elasticity of 0.204), and “Great” wines (at a
score of 97.5 and an elasticity of 0.213). Pricing-to-market is thus about 36 percent higher for “Great”
than for “Not recommended” wines. The e¤ect of quality on pass-through is therefore economically
important, but smaller than in Auer et al. (2014) who …nd that the pass-through rate for a low quality
car is about four times larger than for a top quality car. In column (3) of Panel B, the exchange rate
elasticity of export volumes is equal to 0.858 at the mean value of quality. It is equal to 0.794 for
“Great” wines, and increases to 0.974 for “Not recommended” wines.
4.2 Heterogeneity across Destination Countries
We estimate equation (11) for unit values, and include interactions between the real exchange rate,
quality, and real GDP per capita. The results are reported in column (1) of Panel A in Table 11. At the
mean value of per capita income and of quality, the response of unit values to a change in the exchange
rate is equal to 0.150. Consistent with Prediction 1, this elasticity further increases with quality, and
is equal to 0.085 for “Not recommended” wines, and to 0.186 for “Great” wines. Most importantly,
the triple interaction between the exchange rate, quality, and real GDP per capita is positive and
signi…cant, which indicates that the exchange rate response of unit values to quality increases with per
capita income, providing direct support to Prediction 3. This …nding is consistent with the assumption
of Alessandria and Kaboski (2011) and Simonovska (2014) that consumers in richer countries have
smaller demand elasticities than consumers in lower income countries, allowing …rms to extract higher
markups from richer destinations.50 It is also consistent with the results of Alessandria and Kaboski
(2011) who show that US …rms price-to-market more when exporting to richer countries.
According to our model in Section 2, the exchange rate elasticity of unit values decreases with the
trade costs of exporting to each destination. We therefore check that our …ndings on the e¤ects of per
capita income are not a¤ected by di¤erences in trade costs between high and low income countries.
48The elasticities are evaluated at a value of 62 for the wines that are “Not recommended,” 77 for “Mediocre,” 82 for“Good,” 87 for “Very good,” 92 for “Outstanding,” and 97.5 for “Great” wines. The number of quality scores betweenbins is therefore not homogeneous, but comparing exchange rate elasticities across bins is intuitively convenient. Lookingat the e¤ect of a one standard deviation increase in quality from its mean level is not illustrative as this corresponds toless than four points on the quality scale, which is not large enough to move from one bin to the other.
49Strasser (2013) shows that …nancially constrained …rms price-to-market less than unconstrained …rms. In column (8),when restricting the sample to the period before the start of the …nancial crisis, the exchange rate elasticity evaluatedat the mean value of quality is equal to 0.332. Pricing-to-market is therefore stronger and pass-through is lower at 66.8percent, which is consistent with Argentinean exporters becoming more …nancially constrained during the crisis period.
50 It however contrasts with Crozet et al. (2012) who …nd that French exporters of Champagne charge higher prices tolower income countries. Their interpretation is that lower income countries do not produce much sparkling wine, thereforeFrench exporters charge higher prices when they enter the markets of these countries. We checked, and con…rm, that ourresults remain robust to controlling for the competition that may arise from wine production in destination countries.We estimated equations (11) and (12), and included total wine output (in tons) of each destination country in each year(Food and Agriculture Organization of the United Nations), and its interaction with the exchange rate.
19
Column (2) further interacts the exchange rate with the bilateral distance between Argentina and each
destination country.51 Consistent with the model, this interaction is negative and signi…cant. Most
importantly, controlling for bilateral distance does not alter the evidence in favor of Prediction 3 as the
triple interaction remains positive and signi…cant. Besides, at the mean value of quality, the exchange
rate elasticity of unit values is equal to 0.127. It is insigni…cant for “Not recommended” wines, but
increases to 0.163 for “Great” wines, again providing evidence in support of Prediction 1.
Also predicted by our model is that pricing-to-market increases with local distribution costs in
the importing country. As the latter can be correlated with per capita income, we verify that our
results are not driven by cross-country di¤erences in distribution costs. One way to proxy for local
distribution costs is to follow Goldberg and Verboven (2001) and to rely on the real wage in each
destination market as a proxy for local costs, under the assumption that labor costs represent a large
share of local distribution costs (also see Section 6.2). The data are not available for all countries so our
sample size is slightly reduced. We interact the exchange rate with the log of real wages, and the results
in column (3) show that, consistent with expectations, the response of unit values to a change in the
exchange rate increases with wages, and thus with distribution costs. This exercise does not, however,
qualitatively a¤ect the evidence in favor of Prediction 3 (only the magnitude of pricing-to-market is
increased as the exchange rate elasticity evaluated at the mean value of quality is equal to 0.362, and
rises from 0.296 for “Not recommended” wines to 0.398 for “Great” wines).
As the Wine Spectator is a US-based rating, a …nal concern is that it may not be representative
of taste preferences for quality in other countries.52 In column (4), we therefore drop the US from the
sample. Finally, in column (5) we simultaneously drop the US and control for the e¤ects of distance and
of wages on the responsiveness of unit values to exchange rate changes. On average, the exclusion of the
US from the sample decreases exchange rate pass-through (at the mean value of quality, the exchange
rate elasticities are equal to 0.196 and 0.364 in columns 4 and 5, respectively), but the evidence in
favor of Prediction 3 largely holds as the triple interactions between the exchange rate, quality, and
per capita income remain positive and signi…cant.53
The results for export volumes are reported in Panel B. The triple interactions between the ex-
change rate, quality, and per capita income are all negative, which is consistent with Prediction 4, but
insigni…cant. Still, consistent with Prediction 3, the exchange rate elasticities decrease with quality.
For instance, in column (5), the mean elasticity of export volumes is equal to 2.040, and it increases
from 1.992 for “Great” wines to 2.129 for “Not recommended” wines.
51The model does not predict that the exchange rate elasticity of prices to trade costs varies with per capita income.As a result, we do not interact this elasticity with per capita income.
52For instance, US consumers have a preference for fruiter wines with less alcoholic content while Europeans prefer lessfruity wines with higher alcohol content (Artopoulos, Friel, and Hallak, 2011).
53Some recent papers argue that the demand for quality is not only determined by income, but also by the distribution ofincome within a country, and that richer households typically spend a larger share of their income on higher quality goods.Fajgelbaum et al. (2011) derive conditions under which richer, as well as more unequal countries, have a larger demandfor higher quality goods. Mitra and Trindade (2005) show that more unequal countries import more of higher qualitygoods. If the demand for quality is indeed stronger for more unequal countries, the response of unit values to changes inthe exchange rate should increase with quality by more for more unequal countries, while simultaneously controlling forincome per capita. We estimated equation (11) for unit values, and interacted both the exchange rate and quality withthe Gini coe¢cient of each destination country (World Bank World Development Indicators), while controlling for percapita income. We found that the exchange rate response of unit values to quality increases by more for more equal thanfor more unequal countries, where the distribution of income tends to be more equal in higher income countries.
20
Another way to investigate our predictions is to split the countries included in our sample between
high and low income, using the World Bank’s classi…cation based on GNI per capita in 2011.54 We
estimate equations (11) and (12) and interact the exchange rate and its interaction with quality with a
dummy for higher and a dummy for lower income countries. The results for unit values are reported in
column (1) of Panel A in Table 12. Interestingly, the coe¢cient on the interaction between the exchange
rate and quality is positive and signi…cant for higher income destinations only. This …nding is consistent
with Prediction 3, and with Auer et al. (2014) who predict that exchange rate pass-through decreases
with quality for higher income countries only. Qualitatively, the results remain robust to controlling
for bilateral distance (column 2), wages (column 3), excluding the US (column 4), and to all of them
simultaneously (column 5). Consistent with Alessandria and Kaboski (2011), pricing-to-market is also
stronger for exports to richer destinations. For instance, in column (5), at the mean value of quality the
exchange rate elasticity is insigni…cant for lower income countries. It is instead signi…cant and equal
to 0.302 for higher income countries, and increases from 0.252 for “Not recommended” wines to 0.329
for “Great” wines. In Panel B, the results for export volumes provide direct support for Prediction
4 as in all cases, the interactions between the exchange rate and quality are signi…cantly negative for
higher income countries only.
5 Endogeneity
One concern with our estimations is the potential endogeneity of quality in explaining unit values and
export volumes. First, although the Wine Spectator ratings are produced from blind tastings where the
“price is not taken into account in scoring,” the “tasters are told [...] the general type of wine (varietal
and/or region) and the vintage” year.55 Similarly for Parker, “neither the price nor the reputation
of the producer/grower a¤ect the rating in any manner,” but the “tastings are done in peer-group,
single-blind conditions (meaning that the same types of wines are tasted against each other, and the
producers names are not known).”56 As a result, we cannot rule out that the basic information that the
experts hold about each wine, in combination with their own subjective preferences, a¤ects in a way or
the other their scores, leading to an endogeneity bias which direction is, however, unclear.57 Second,
some papers question the reliability and usefulness of wine ratings as a measure of quality. Ashenfelter
and Quandt (1999) and Quandt (2007) argue that wine ratings fail to convey any useful information
about quality, and observe that the disagreements between experts are substantial. Hodgson (2008)
…nds that experts are also inconsistent as they do not assign the same scores to the same wines at
di¤erent tastings. Measurement error in the quality scores can thus generate a positive endogeneity
bias as wine producers are likely to set a higher price for a wine that is awarded a higher score because
they believe consumers are willing to pay more for higher ranked wines. To tackle the endogeneity of
quality, we use appropriate instruments.
The set of instruments we rely on to explain the variation in wine quality includes geographic and
weather-related factors. Indeed, the literature devoted to explaining the quality of wine highlights that
the amount of rainfall, and the average temperatures during the growing season, are strong determinants
54Low income countries have a GNI per capita of less than 4,035 US dollars.55See http://www.winespectator.com/display/show?id=about-our-tastings.56See https://www.erobertparker.com/info/legend.asp.57Psychologists argue that experts ratings tend to be subjective and may depend on the personal preferences of the
tasters. For instance, Parker is known to have a strong preference for fruit-‡avored wines and rewards them with a higherscore (Thornton, 2013).
21
of quality (Ashenfelter, 2008; Ramirez, 2008; Thornton, 2013). In the Southern hemisphere, the growing
period spans the period from September (in the year before the vintage year) to March. In order to
allow for the e¤ects of temperature and rainfall to be nonlinear throughout the growing season, we
consider as instruments the average temperature, and the total amount of rainfall, for each growing
province in each month between September and March (Ramirez, 2008).58 Besides, one particularity
of Argentina’s wine industry is the high altitude at which some of the growing regions are located, and
there are strong reasons to believe that altitude contributes to variations in quality because it reduces
the problems related to insects or grape diseases that a¤ect quality at a low altitude. We therefore use
the altitude of each province as an additional instrument for quality.59 The data on monthly average
temperatures (in degrees Celsius), total rainfall (in millimeters), and altitude (in meters) are from the
National Climatic Data Center of the US Department of Commerce.60 Gaps in the data are …lled
using online information, although missing information for some provinces and vintage years results in
a slightly reduced sample.61
Endogeneity could also arise due to measurement error in the real exchange rate, and in particular
in the Argentinean CPI. Since 2007, the credibility of the o¢cial CPI data has indeed been widely
questioned by the public. In fact, the IMF has issued a declaration of censure, and called on Argentina
to adopt remedial measures to address the quality of the o¢cial CPI data.62 Alternative data sources
have shown considerably higher in‡ation rates than the o¢cial data since 2007.63 Using online data from
supermarket chains, Cavallo (2013) constructs a CPI for Argentina, and shows that the corresponding
in‡ation rate is three times larger than the o¢cial estimate. To address measurement error, we therefore
use the CPI from Cavallo (2013) for 2008 and 2009 to update the o¢cial CPI series, and construct a
new real exchange rate that we use in our estimations.
In order to establish how the instrumentation a¤ects the way quality determines unit values and
export volumes, we …rst focus on speci…cations where the product-year …xed e¤ects are replaced by
…rm-year dummy variables, and product characteristics are controlled for by including grape, type,
vintage year, HS, and province of origin …xed e¤ects. As the instruments are only available over a
reduced sample, we replicate our benchmark OLS estimations reported in column (1) of Panels A and
B of Table 10. The results, reported in column (1) of Panels A and B of Table 13 for prices and
quantities, respectively, show that our main …ndings go through over the smaller sample.
Column (2) of Panel A regresses by Instrumental Variables (IV) unit values on the real exchange
rate and quality. The coe¢cient on quality is positive and signi…cant, but is smaller compared to the
OLS estimate in column (1). This positive bias suggests that wine tasters tend to assign higher scores
to more expensive wines, or that producers charge a higher price when their wines receive a higher
score. Column (3) further shows that our IV estimates are not much a¤ected by using the CPI data
from Cavallo (2013), and are thus robust to measurement error in the real exchange rate. Panel B looks
58The temperatures, rainfall, and altitude are measured at the following weather stations (province): CatamarcaAero (Catamarca), Paraná Aero (Entre Ríos), Santa Rosa Aero (La Pampa), La Rioja Aero (La Rioja), Mendoza Aero(Mendoza), Neuquén Aero (Neuquen), Bariloche Aero (Río Negro), Salta Aero (Salta), and San Juan Aero (San Juan).
59One concern is that our instruments could be correlated with unobserved productivity shocks. The inclusion of…rm-year or product-year …xed e¤ects controls for these shocks so the exclusion restriction of our instruments is satis…ed.
60This is available at http://www.ncdc.noaa.gov/IPS/mcdw/mcdw.html.61Online information is taken from www.tutiempo.net.62Further information can be found at http://www.imf.org/external/np/sec/pr/2013/pr1333.htm.63See http://www.economist.com/node/21548242.
22
at export volumes. In column (2), the instrumented e¤ect of quality on export volumes is negative and
signi…cant, and is in turn larger in magnitude compared to the OLS estimate in column (1). Again,
the results remain similar once we use the adjusted CPI data in column (3). For all regressions in
columns (2) and (3), the Kleibergen-Paap F statistic (equal to 93, with a critical value equal to 21,
Stock and Yogo, 2005) largely rejects the null of weak correlation between the excluded instruments
and the endogenous regressors.
Next, we regress unit values on the exchange rate, quality, and its interaction with the exchange
rate. Columns (4), (5) and (6) report the OLS and IV results, using either the o¢cial or the adjusted
CPI data, respectively. The set of instruments for quality, and for the interaction term, includes
the monthly temperatures, rainfall, and altitude variables, as well as each of the variables interacted
with the exchange rate.64 Interestingly, in columns (5) and (6) the coe¢cient on the interaction term
increases to 0.014 from a value of 0.002 in column (4). The e¤ect of quality on pass-through is therefore
much larger in the instrumented regressions.
Columns (7) to (9) report the OLS and two IV speci…cations once we include the product-year …xed
e¤ects. As before, the instrumentation in columns (8) and (9) in‡ates the coe¢cient on the interaction
term at a value of 0.015 compared to an OLS estimate of 0.001 in column (7). As a result, the OLS
and IV exchange rate elasticities are respectively equal to 0.201 and -0.089 (but insigni…cant) for wines
that are “Not recommended,” 0.223 and 0.143 for “Mediocre,” 0.231 and 0.220 for “Good,” 0.238 and
0.298 for “Very good,” 0.245 and 0.375 for “Outstanding,” and to 0.253 and 0.460 for “Great” wines.
In other words, the instrumentation decreases exchange rate pass-through for the higher quality wines.
In addition, notice that the di¤erence in pricing-to-market between “Great” and “Outstanding” wines
is equal to 23 percent once we instrument, and to three percent otherwise. For export volumes in Panel
B, the IV coe¢cients on the interaction terms are not statistically signi…cant.
The …rst-stage estimates of the IV regressions in column (5) of Panels A and B (which use the
o¢cial CPI data) are reported in Table A1 in the appendix (the …rst-stages for the other regressions
are qualitatively similar), and show that climate variation a¤ects wine quality. The positive coe¢cients
on the January and February temperatures are consistent with the …nding in the literature that warmer
temperatures during the harvest period are typically associated with higher quality. Also, the positive
coe¢cient on the October rainfall, and the negative coe¢cients on the January and February precip-
itations, are consistent with the expectation that precipitation during the earlier part of the growing
season is good for quality, while a dry climate during the harvest period is more favorable for crops
(Ramirez, 2008).
6 Extensions
This section discusses …ve extensions to our benchmark speci…cations. First, we allow for nonlinearities
in the e¤ects of quality. Second, we show that pricing-to-market increases with local distribution costs in
the importing economy. Third, we provide evidence that in addition to increasing with quality, pricing-
to-market also increases with …rm-level productivity. Fourth, once we divide the sample between high
64 In columns (2) and (3), which exclude the interaction between the exchange rate and quality, altitude is dropped as aninstrument as it is collinear with the province …xed e¤ects. In columns (5), (6), (8) and (9) which include the interactionterm, the interaction between the exchange rate and altitude is used as an instrument.
23
and low quality exporters, higher quality …rms price-to-market more than others. Finally, …rms price-
to-market more in the countries where they own a larger share of the market.
6.1 Nonlinearities
The prediction of our model that pass-through decreases with quality depends crucially on the assump-
tion that higher quality goods have higher (per unit) distribution costs, which include any wholesale
or retail costs associated with the selling, storage, marketing, advertising, or transport of each good in
the destination country.65 If (per unit) distribution costs are indeed higher for top quality wines, our
model predicts that the elasticity of prices to the exchange rate should be larger for top quality wines.
To check this assumption, we estimate equation (11) for unit values, allowing for nonlinearities in the
e¤ects of quality by using six variables for quality, i.e., one for each of the six quality bins de…ned in
Table 3, each interacted with the exchange rate.
The results are reported in Table 14. Column (1) shows that the interaction between the exchange
rate and quality is about three times larger for “Great” than for lower ranked wines. As we cannot
reject that the coe¢cients on the interactions for the lower quality wines are equal, in column (2) we
classify wines into two di¤erent bins only, i.e., with a score above or below 94. The coe¢cient on the
interaction is still much larger for “Great” wines, a …nding which can be reconciled with our assumption
that higher quality goods have higher (per unit) distribution costs.
In column (2), when calculating the exchange rate elasticities evaluated at the mid score of each
quality bin, we …nd that in response to a ten percent real depreciation, exporters raise their prices (in
pesos) by 1.8 percent for the lowest quality wines (at a mid score of 72), and by 6.5 percent for the
top quality wines (at a mid score of 97.5). This di¤erence is thus much larger than in our benchmark
speci…cation, reported in column 3 of Table 10, where pricing-to-market is 36 percent higher for “Great”
than for “Not recommended” wines. The results for export volumes are reported in columns (3) and (4).
In column (4), the coe¢cient on the interaction between the exchange rate and quality is signi…cantly
negative for the lower ranked wines only.
6.2 Distribution Costs
Our model predicts that pricing-to-market increases with local distribution costs in the importing
economy. One way to explore this prediction is to use the data on distribution costs collected by
Campa and Goldberg (2010) for 21 countries and 29 industries between 1995 and 2002, and to compute
a destination-speci…c measure of distribution costs for the “Food products and beverages” industry
averaged over time. However, due to limited country coverage, this approach reduces our sample size
by more than half. As in Section 4.2, we therefore instead follow Goldberg and Verboven (2001) and
rely on the real wage in each destination market as a proxy for local costs.66
We divide our sample into two groups, namely, high wage and low wage destination countries,
according to whether their wage is above or below the median wage in the sample. Columns (1) and
(2) of Table 15 report the results for unit values and export volumes, separately for countries with high
65Using models that do not feature this assumption, Antoniades and Zaniboni (2013), Auer and Chaney (2009), Aueret al. (2014), and Basile et al. (2012) also predict a negative relationship between pass-through and quality.
66Although higher quality wines can be expected to have higher distribution costs, in this section we focus on di¤erencesin local distribution costs across export destinations.
24
and low wages. Consistent with expectations, Panel A shows that …rms price-to-market more, and by
more for higher quality wines, in the countries with higher wages, and therefore higher distribution
costs. At the mean value of quality, the exchange rate elasticity is signi…cant at 0.206 in higher wage
countries, but insigni…cant in lower wage countries. This …nding is consistent with Berman et al.
(2012) who show that the response of unit values to changes in the exchange rate increases with local
costs, especially for higher productivity …rms. Symmetrically, Panel B shows that in response to a
real depreciation, exporters increase export volumes only when exporting to lower distribution costs
countries (at the mean value of quality, the elasticity is insigni…cant for higher wage countries, but
signi…cant at a value of 2.843 for lower wage countries).
6.3 Productivity as a Source of Heterogeneity
As the literature has focused on productivity in explaining incomplete and heterogeneous pass-through
(Berman et al., 2012; Chatterjee et al., 2013), we explore whether, in our data, higher productivity
…rms also price-to-market more than the others.
Without any information on the …rm-level characteristics that are required to measure productivity
(such as value-added or employment), we construct a variable that proxies for …rm size as the latter
has been shown to correlate strongly with productivity (see, for instance, Bernard, Eaton, Jensen, and
Kortum, 2003).67 We use the total volume (in liters) of FOB exports of each …rm in each year, and
divide our sample into two groups of larger and smaller …rms, according to whether their total yearly
exports are above or below the median total exports in the sample. Columns (3) and (4) of Table 15
report the results for unit values and export volumes, separately for larger and smaller …rms. Panel
A shows that only the larger, and therefore the more productive …rms, signi…cantly price-to-market in
response to a change in the exchange rate (at the mean value of quality, the elasticity is signi…cant at
0.164 in column 3, but insigni…cant in column 4), and they do so by more for higher quality wines.
Symmetrically, Panel B reveals that smaller …rms increase their export volumes by more than the others
(at the mean value of quality, their exchange rate elasticity is signi…cant at 2.762, but the elasticity is
insigni…cant for larger …rms). However, the response of export volumes to a real depreciation decreases
with quality for larger …rms only.
6.4 The Quality of Exporters
As illustrated by Table 7, individual …rms tend to export wines with heterogeneous levels of quality.
Table 9 shows that there is also some heterogeneity in the average quality exported by …rms. Firms
can therefore be divided into two groups of higher and lower quality exporters by splitting the sample
at the median of average …rm-level quality, which varies between 72 and 92. This sample split is not
perfect as average …rm-level quality does not include unrated wines, but it still captures that …rms
export wines with heterogeneous levels of quality: quality varies between 55 and 97 for higher quality
exporters (average quality is equal to 87), and between 55 and 94 for lower quality exporters (average
quality is equal to 83).
Columns (1) and (2) of Table 16 report the results for unit values and export volumes, separately
for higher and lower quality exporters. At the mean value of quality, pricing-to-market is stronger
67 In the case of wine, larger …rms can adopt productivity enhancing equipment and technologies that cannot be usedby smaller …rms (Thornton, 2013).
25
for higher than for lower quality exporters (the elasticities are equal to 0.258 and 0.127, respectively).
Symmetrically, lower quality exporters increase export volumes by more than higher quality …rms do
(with an elasticity of 0.957 versus 0.848 for higher quality …rms). Pricing-to-market increases with
quality for higher quality exporters only, while the response of export volumes to a change in the
exchange rate decreases with quality for both higher and lower quality exporters.
6.5 Export Market Shares
According to Amiti et al. (2014), exporters have smaller markups in the markets where they have a
lower market share, making it di¢cult to adjust their markups in response to changes in the exchange
rate. As a result, …rms price-to-market less, and have a higher pass-through, in the countries where
they own a smaller share of the market (see, also, Atkeson and Burstein, 2008). To check if this holds
in our data, we compute the market share of each …rm by export destination and by year as the share
of its total exports in the total value exported by all …rms. Based on the median market share in each
destination and in each year, we then divide …rms into two groups, namely, high and low market share
…rms. The distribution of quality is not much a¤ected by this split as quality varies between 55 and
97 for higher market share …rms, and between 55 and 96 for lower market share …rms.
Columns (3) and (4) of Table 16 report the results for unit values and export volumes, separately
for exporters with higher and lower market shares. Consistent with Amiti et al. (2014), Panel A
shows that, at the mean value of quality, pass-through is incomplete at 81 percent for exporters with
higher market shares (the elasticity is equal to 0.190), and is complete for lower market share exporters
(the elasticity is insigni…cant). Pass-through decreases with quality only for the exporters with higher
market shares.
The results for volumes are reported in Panel B. Columns (3) and (4) show that a real depreciation
increases the export volumes of the higher market share exporters by less for higher quality goods, but
has no e¤ect on the export volumes of the lower market share exporters, at any level of quality. In other
words, a real depreciation only bene…ts the higher market share exporters. One possible interpretation
is that market shares signal quality. In response to a real depreciation, the higher market share
exporters expand at the expense of the others because consumers in the destination country believe
that these …rms produce higher quality goods. Caminal and Vives (1996) develop a theory of the
informational value of market shares, and show that consumers with private but imperfect information
on quality trust that a higher market share signals a higher quality. Another related interpretation
is that in contrast to the lower market share …rms, the higher market share exporters bene…t from a
real depreciation because they have acquired a good reputation in terms of quality and reliability in
the destination market. Macchiavello (2010) provides evidence that reputation is indeed an important
determinant of a …rm’s success in export markets.68
68To summarize, our results show that larger, and therefore more productive …rms, exporting a higher quality andowning higher shares of export markets price-to-market more than the others, and by more for higher quality goods.Larger …rms tend to have higher market shares (the correlation is equal to 0.53). In contrast, the higher quality …rms arenot larger, and do not own higher export market shares (the correlations between …rm-level quality, on the one hand, andmarket shares and …rm size, on the other hand, are equal to -0.06 and -0.04, respectively).
26
7 Robustness
This section discusses alternative speci…cations we implement to ensure the robustness of our …ndings.
Despite some variation across speci…cations in the magnitude of the e¤ect of quality on pass-through,
the broad similarity of the results supports the paper’s main conclusions.
The Measurement of Quality In order to minimize noise in the measurement of quality when
de…ned on a (50,100) scale, in column (1) of Table 17 we use a variable which takes on values between
one and six, where each value corresponds to one of the di¤erent bins de…ned by the Wine Spectator
(see Table 3). A value of one indicates that the wine is “Not recommended,” while a value of six that
the wine is “Great.” In column (2), quality is measured using the Parker ratings. In column (3), in
order to reduce the subjectiveness of the quality scores, and the fact that di¤erent tasters disagree on
wine quality, we only include the wines which Wine Spectator scores fall in the same quality bin as
the Parker ratings (as shown in Figure 2, these wines belong to categories four, …ve, and six only).
Finally, given that both the Wine Spectator and Parker are US-based ratings, and therefore may not
be representative of perceived wine quality in other countries, column (4) excludes the US from the
sample. Qualitatively, our results largely hold up.
Most of the empirical literature on quality and trade relies on trade unit values as a proxy for quality.
Indeed, our regressions indicate that on average, unit values increase with quality. We therefore check
if our results remain robust to measuring quality using the information contained in unit values. One
advantage of this approach is that we can estimate our regressions on a much larger sample, including
all wines for which the Wine Spectator or the Parker ratings are missing.
First, in each year, we calculate the mean unit value (in pesos) of each wine across all destinations,
and for each …rm we rank its wines by unit values in decreasing order (Berman et al., 2012; Chatterjee
et al., 2013; Mayer et al., 2014). For each …rm, the wine with the highest average unit value has a rank
equal to one, the second a rank equal to two, etc., and we use these ranks as an inverted measure for
quality. Second, we repeat the procedure, but for each wine exported to all destinations in all years,
using the mean unit value of each wine (in pesos) de‡ated by the Argentinean CPI. The interactions
between the exchange rate and the product ranks are expected to be negative for unit values, and
positive for export volumes.
The results, reported in columns (5) and (6), are consistent with expectations, and therefore contrast
with Auer and Chaney (2009) who do not …nd any evidence that pass-through is a¤ected by quality
when quality is inferred from trade unit values. Columns (5) and (6) use the product ranks computed
by …rm-product-year, and by …rm-product, respectively.
Unrated Wines Recall that due to missing observations on the Wine Spectator ratings, our sample
covers 43 percent of the total value of red, white, and rosé wine exported by Argentina between 2002 and
2009. In order to include some unrated wines in the sample, we calculate an average Wine Spectator
rating by wine name and type, and assign this rating to all wines with the same name and type. This
increases our sample coverage to 63 percent of total exports over the period. We apply this procedure
to compute average quality both on a (50,100) and on a (1,6) scale. The results are reported in columns
(7) and (8) of Table 17.
27
Another way to include unrated wines in the sample is as follows. First, we identify the wines which
vintage year is missing, and assign to each of them the scores of the wines with the same name, brand,
and type on a (1,6) scale. In general, quality does not vary much across vintage years, therefore this
assumption sounds reasonable.69 Second, we assign a value of one to the wines exported by …rms that
only export unrated wines (by either the Wine Spectator or Parker). We do this under the assumption
that these …rms produce wines which are of a too low quality to be considered by experts (this approach
is similar to Crozet et al., 2012, who assume that unrated …rms are the lowest quality exporters). This
exercise increases our sample coverage to 60 percent of total exports between 2002 and 2009. The
results are reported in column (9) of Table 17 (the interaction between the exchange rate and quality
is insigni…cant for export volumes).
The Nature of Wine Our results might be a¤ected by two characteristics that are speci…c to the
nature of wine. First, wine is an exhaustible resource: once a wine with a speci…c vintage year runs
out, the producer can no longer produce, and therefore export, that variety. Second, the production
of higher quality wines is subject to capacity constraints as the availability of high quality grapes is
limited (San Martín et al., 2008; Thornton, 2013).
In order to control for the exhaustible nature of wine, we construct a new sample and de…ne a
product according to the name of the wine, its grape, and type, but ignore the vintage year.70 The
209 …rms in the sample export a smaller number of products (2,790 versus 6,720 wines in the original
sample) to a larger number of countries (78 versus 24 destinations, on average). Quality is computed
as the unweighted average of the scores assigned to all wines with the same name, type, and grape, and
varies between 55 and 96.2.71 The correlation between average quality (across vintage years) and the
original quality measure (which varies across vintage years) is equal to 93.7 percent, con…rming that
quality does not vary much across vintage years. The results for unit values and volumes are reported
in columns (1) and (3) of Table 18.
To address the issue of capacity constraints, we would need to control for total output per wine
which is unobserved. As a proxy, we rely on the total volume (in liters) of exports of each wine to
all destinations between 2002 and 2009. Total exports are however endogenous to the denominator
of unit values in equation (11), and to the dependent variable in equation (12). We therefore classify
wines into three categories (based on the 33 and 66 percentiles of total exports), and create an
indicator variable which varies between one and three, with a larger value indicating a greater volume
of exports.72 We then include this indicator, on its own and interacted with the exchange rate, in
columns (2) and (4) of Table 18 for prices and quantities, respectively. Our results remain unaltered.
Exchange Rate After the large devaluation of the peso in 2002, the peso was allowed to ‡uctuate
with respect to the US dollar within a “crawling band that is narrower than or equal to +/-2 percent”
(Reinhart and Rogo¤, 2004). This means that variations in the real exchange rate between the peso
69 If wine producers conclude that the grapes grown during a particular year do not satisfy their quality standards, theymay decide not to use them in order to preserve the quality and reputation of a wine (Thornton, 2013).
70This exercise allows us to get closer to the CES utility function assumption of a …xed set of varieties as there is noreason for wines to run out once we exclude the vintage year.
71The results remain similar if quality is computed as a weighted average of the original quality scores, with weightsgiven by the export shares of each wine to each destination country in each year.
72The results remain similar if we use the total volume of exports instead of the indicator variable.
28
and the US dollar may have essentially come from movements in domestic prices (Li et al., 2012). We
verify in column (1) of Table 19 that our results still hold after excluding from the sample the US and
all the other countries which currencies are pegged to the US dollar.
In order to address the quality of the o¢cial CPI data, in column (2) of Table 19 we use the real
exchange rate computed using the CPI from Cavallo (2013).73 Finally, although the model in Section
2 derives predictions for the e¤ects of the real exchange rate, many papers estimate pass-through
regressions using the nominal rate (e.g., Gopinath et al., 2010; Gopinath and Rigobon, 2008). Column
(3) of Table 19 includes the nominal exchange rate, its interaction with quality, and separately controls
for the destination country’s CPI.
Extensive Margin Campos (2010) argues that the intensive and extensive margins of adjustment
have opposite e¤ects on pass-through. On the one hand, a depreciation reduces the average price
charged by existing exporters. On the other hand, a depreciation makes exporting a more pro…table
activity, therefore more …rms enter the export market. Given that entrants are less productive and
therefore charge higher prices, the extensive margin pushes the average export price up, reducing pass-
through. In column (1) of Table 20, we estimate equations (11) and (12) on a sample that captures
the intensive margin, and only includes the …rms exporting in all years to any destination.
Quarterly Frequency We check in column (2) of Table 20 that our results remain robust to higher
frequency sampling, and estimate equations (11) and (12) using unit values, export volumes, and
the real exchange rate de…ned at a quarterly frequency, and replace the product-year …xed e¤ects by
product-quarter dummy variables. Due to data limitations, the real GDPs per capita and real e¤ective
exchange rates are measured annually.
Dynamics In order to introduce dynamics in equations (11) and (12), we include one lag, and then
two lags, on the exchange rate and its interaction with quality. In the volumes regressions, lags on
the real e¤ective exchange rate and real GDP per capita are also included. The results are reported
in columns (3) and (4) of Table 20 for the speci…cations with one and two lags, respectively. In both
columns, the estimated coe¢cients are the sum of the coe¢cients on the contemporaneous values and
the lags of each variable (Gopinath and Itskhoki, 2010; Gopinath et al., 2010).
Wholesalers and Retailers We have restricted our analysis to wine producers only, and therefore
dropped wholesalers and retailers from the sample. As can be seen from column (5) of Table 20, our
results still hold when including these …rms in the sample.
Currency of Invoicing A large body of the recent literature is devoted to understanding how the
currency of invoicing used for trade a¤ects exchange rate pass-through (e.g., Gopinath et al., 2010).
In our data set, we do not observe the currency in which Argentinean …rms price their exports. The
Datamyne, a private vendor of international trade data, provides us with the invoicing currency of
…rm-level exports between 2005 and 2008. Over the period, Argentinean …rms priced wine exports
mostly in US dollars (88 percent), followed by euros (7.6 percent), Canadian dollars (3 percent), pound
sterling (1.2 percent), and in some cases in Japanese yen, Swiss francs, Uruguayan pesos, Australian
73Given that we adjust the CPI data for two years only, the results in column (2) remain very similar to the ones incolumn (3) of Table 10. We checked, and con…rm, that all our other results are robust to using the Cavallo (2013) index.
29
dollars, or Danish krones. Due to the predominance of the US dollar as an invoicing currency for
exports, the regression in column (6) of Table 20 expresses unit values in US dollars per liter.
8 Concluding Remarks
This paper analyzes the heterogeneous behavior of exporters to changes in real exchange rates due to
di¤erences in product quality. In order to understand the mechanisms through which quality a¤ects
the pricing and quantity decisions of …rms, the …rst contribution of the paper is to present a model
where …rms export multiple products with heterogeneous levels of quality. The second contribution is
to bring the testable predictions of the model to the data. We combine a unique data set of Argentinean
…rm-level destination-speci…c export values and volumes of highly disaggregated wine products between
2002 and 2009 with a data set on experts wine ratings to measure quality. Given the very rich nature of
our data set, our main sample includes 6,720 di¤erent wines exported by 209 wine producers over the
period. Overall, our results …nd strong support for the predictions of the model, and quantitatively,
the e¤ect of quality on heterogeneous pass-through is economically important.
To conclude, our …ndings help to explain incomplete pass-through by highlighting the role played
by product quality. As we are only focusing on a single industry, we do not know whether the empirical
regularities documented in this paper hold more generally, although the results of Auer et al. (2014) for
cars, and of Antoniades and Zaniboni (2013) for consumer goods, are consistent with ours. Provided
better data to measure quality for other industries become available, future research should test whether
our results extend beyond the Argentinean wine industry. Another promising avenue for future research
is to use our data to identify the pricing-to-market behavior of exporters by assuming that di¤erences
in the prices of the same wines sold to multiple destinations re‡ect di¤erences in markups as these
wines are subject to the same marginal cost. This is the approach adopted by Auer et al. (2014),
Burstein and Jaimovich (2012), Cavallo et al. (2014), Fitzgerald and Haller (2014), and Simonovska
(2014).
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34
Table 1: Price Breakdown for Non-EU Wine Sold in Retail Outlets in the UK
Retail price £5.76 £7.19 £8.83 £10.09
VAT (20%) £0.96 £1.20 £1.47 £1.68
Retail margin £1.92 £2.40 £2.94 £3.36
Duty £1.90 £1.90 £1.90 £1.90
Distributor margin £0.11 £0.21 £0.40 £0.51
Common Customs Tari¤ £0.11 £0.11 £0.11 £0.11
Transport £0.13 £0.13 £0.13 £0.13
Winemaker £0.63 £1.25 £1.88 £2.40
Source: Joseph (2012)
Table 2: Harmonized System Classi…cation
6-digit 12-digit Description
22.04.10 22.04.10.10.000.D Sparkling wine – Champagne variety
22.04.10.90.000.G Sparkling wine – Not Champagne variety
22.04.10.90.100.M Sparkling wine – Gassi…ed wine (i.e., aerated using CO2)
22.04.10.90.900.F Sparkling wine – Other
22.04.21 22.04.21.00.100.A Sweet wine; 2 liters
22.04.21.00.200.F Fine wine; 2 liters
22.04.21.00.900.U Other wine; 2 liters
22.04.29 22.04.29.00.100.W Sweet wine; 2 liters
22.04.29.00.200.B Fine wine; 2 liters
22.04.29.00.900.P Other wine; 2 liters
22.04.30 22.04.30.00.000.X Other grape must
Notes: The table reports the 6-digit HS codes for wine products, the corresponding 12-digit codes as used by the
Argentinean customs, and the types of wines classi…ed under each code.
Table 3: Experts Ratings
Wine Spectator (50,100) Robert Parker (50,100)
95-100 “Great” 96-100 “Extraordinary”
90-94 “Outstanding” 90-95 “Outstanding”
85-89 “Very good” 80-89 “Above average/very good”
80-84 “Good” 70-79 “Average”
75-79 “Mediocre” 60-69 “Below average”
50-74 “Not recommended” 50-59 “Unacceptable”
Notes: For both the Wine Spectator and Robert Parker rating systems, the table reports the quality bins corresponding
to the scores of the di¤erent wines.
35
Table 4: Summary Statistics on Exports Data by Year
Year Firms Wines Exports (US dollars) Observations
2002 59 794 36,504,644 2,067
2003 73 933 50,664,899 3,056
2004 107 1,171 69,640,144 3,923
2005 120 1,517 91,261,787 5,330
2006 150 1,731 112,540,681 6,793
2007 148 1,860 131,147,970 7,407
2008 148 1,804 125,851,505 6,865
2009 151 1,833 108,177,602 6,135
Total 209 6,720 725,789,235 41,576
Notes: For each year in the sample, the table reports the number of exporters, the number of wines exported, the value
of FOB exports (in US dollars), and the number of observations. The row “Total” reports the total number of …rms, the
total number of wines, the total value of FOB exports, and the total number of observations between 2002 and 2009.
Table 5: Summary Statistics
Observations Min Max Mean Median Std. dev.
Unit values (US dollars/liter) 41,576 0.02 381 5.3 3.6 6.7
Number of wines exported 41,576 1 510 139 120 130
Number of destinations 41,576 1 65 24 21 16
Wine Spectator 41,576 55 97 85 85 3.8
Parker 26,705 72 98 87 87 2.3
Notes: For each variable listed in the …rst column, the table reports its minimum, maximum, mean, median value, and
standard deviation in the sample as well as the number of observations available.
Table 6: Top Export Destinations 2002-2009
Destinations % of FOB exports
United States 30.8
Netherlands 10.3
United Kingdom 9.3
Brazil 7.2
Canada 6.4
Denmark 6.0
Finland 3.2
Sweden 3.2
Switzerland 2.8
Germany 2.3
France 1.8
Notes: The table reports total wine exports to each destination country as a
share of total wine exports over the period 2002-2009.
36
Table 7: Snapshot of the Data
Firm Year Destination Name Type Grape Vintage Quality Unit value
Wine Spectator
1 2006 United States A Red Malbec 2005 73 3.27
1 2006 United States B Red Mezcla 2003 92 13.38
1 2006 China B Red Mezcla 2003 92 11.09
Parker
2 2008 United Kingdom C Red Cabernet Sauvignon 2007 85 4.00
2 2008 United Kingdom D Red Mezcla 2004 98 43.27
2 2008 Thailand D Red Mezcla 2004 98 24.65
Notes: The …rm and wine names are con…dential and are replaced by numbers and letters instead. The table reports, for
individual wines – de…ned according to their name, grape, type, and vintage year – exported to speci…c destinations in
speci…c years, their quality rating from either the Wine Spectator or Parker and their unit value of exports (in US dollars
per liter).
Table 8: Descriptive Statistics by Quality Bin
Wine Spectator Obs. Firms Export Unit Destinations Distance GDP per
Quality Bin share value per wine capita
“Great” (95-100) 80 6 0.05 27.4 3 10,137 30,386
“Outstanding” (90-94) 5,667 68 14.81 11.9 11 9,570 26,668
“Very good” (85-89) 16,382 128 43.73 4.4 14 9,823 26,278
“Good” (80-84) 16,961 155 36.10 4.0 11 9,880 26,414
“Mediocre” (75-79) 2,282 80 4.32 3.8 6 9,655 28,012
“Not recommended” (50-74) 204 24 1.00 3.9 11 9,908 25,672
Notes: For each quality bin of the Wine Spectator, the table reports the number of observations, the number of exporters,
exports as a share of total exports (%), the average unit value (in US dollars per liter), the number of destinations per wine
exported, the average distance shipped (in kilometers), and the average income per capita of the destination countries (in
US dollars), in the sample.
Table 9: Descriptive Statistics for Exporters by Quality Bin
Firms exporting wines Obs. Firms Export Min Max Mean Wines Destinations
that are at most: share quality quality quality per wine
“Great” (95-100) 3,719 6 16.53 77 97 89 180 16
“Outstanding” (90-94) 27,919 62 67.71 60 94 85 182 13
“Very good” (85-89) 8,614 80 13.72 55 89 83 74 8
“Good” (80-84) 1,232 48 2.00 65 84 81 30 5
“Mediocre” (75-79) 91 12 0.04 72 79 77 6 3
“Not recommended” (50-74) 1 1 0.00 72 72 72 1 1
Notes: The table reports, for …rms classi…ed by Wine Spectator quality bin based on the highest quality wine the …rm
exports, the number of observations, the number of exporters, exports as a share of total exports (%), the minimum,
maximum, and mean quality exported, the number of wines, and the number of destinations per wine exported, in the
sample.
37
Table 10: Baseline Results
(1) (2) (3) (4) (5) (6) (7) (8) (9)
Panel A: Dependent variable is unit value
ln 0140(0038)
0190(0041)
0059(0062)
– – – 0036(0064)
0190(0076)
0162(0079)
0032(0003)
– – – – – – – –
ln£ – – 0002(0000)
0002(0000)
0002(0000)
0002(0000)
0002(0000)
0002(0000)
0002(0000)
Panel B: Dependent variable is export volume
ln 1378(0516)
0868(0611)
1289(0616)
– – – 1845(0781)
0555(0695)
1780(1032)
¡0050(0006)
– – – – – – – –
ln£ – – ¡0005(0002)
¡0005(0002)
¡0004(0002)
¡0005(0002)
¡0005(0002)
¡0003(0002)
¡0002(0002)
ln 0724(0536)
0425(0633)
0420(0632)
0437(0642)
– – 1031(0793)
¡0193(0720)
1202(1057)
ln 2167(0265)
2315(0335)
2294(0334)
1943(0332)
– – 2178(0356)
1442(0499)
1328(0567)
Fixed E¤ects
Firm-dest Yes Yes Yes Yes – Yes Yes Yes Yes
Firm-year Yes – – – – – – – –
Product-year – Yes Yes Yes Yes Yes Yes Yes Yes
Firm-dest-year – – – – Yes – – – –
Dest-year – – – – – Yes – – –
Grape Yes – – – – – – – –
Type Yes – – – – – – – –
Vintage year Yes – – – – – – – –
Province Yes – – – – – – – –
HS Yes – – – – – – – –
Sample Full Full Full Full Full Full 2003/09 2002/07 2003/07
Observations 41,576 41,576 41,576 41,576 41,576 41,576 39,509 28,576 26,509
Notes: In (4), the real exchange rate is interacted with year dummies (not reported). Robust standard errors are adjusted
for clustering at the product-level in parentheses. , , and indicate signi…cance at the 1, 5, and 10 percent levels,
respectively. Unit values are in pesos per liter and export volumes are in liters. The quality ratings are from the Wine
Spectator.
38
Table 11: Income per Capita
(1) (2) (3) (4) (5)
Panel A: Dependent variable is unit value
ln 1631(0548)
2139(0594)
1155(0606)
1815(0557)
1975(0671)
ln 0126(0152)
0140(0152)
¡0071(0180)
0073(0156)
¡0103(0186)
ln£ ¡0011(0006)
¡0011(0006)
¡0010(0006)
¡0013(0006)
¡0012(0006)
ln£ ln ¡0173(0060)
¡0156(0061)
¡0144(0062)
¡0188(0062)
¡0145(0064)
ln £ ¡0001(0002)
¡0001(0001)
¡0001(0002)
0000(0002)
¡0001(0002)
ln£ £ ln 0001(0000)
0001(0000)
0001(0001)
0002(0001)
0002(0001)
ln£ ln – ¡0077(0039)
– – ¡0084(0039)
ln£ ln – – 0028(0011)
– 0025(0011)
ln – – 0286(0105)
– 0274(0107)
Panel B: Dependent variable is export volume
ln ¡0047(2092)
¡2155(2418)
2419(2335)
¡1207(2104)
¡1223(2685)
ln 3843(0647)
3791(0648)
2681(0775)
4530(0663)
3491(0794)
ln£ 0019(0021)
0018(0021)
0019(0022)
0031(0022)
0031(0022)
ln£ ln 0111(0216)
0044(0218)
0082(0218)
0231(0218)
0135(0220)
ln £ ¡0020(0007)
¡0020(0007)
¡0021(0007)
¡0028(0007)
¡0030(0008)
ln£ £ ln ¡0002(0002)
¡0002(0002)
¡0002(0002)
¡0004(0002)
¡0004(0002)
ln 0400(0631)
0275(0643)
0710(0639)
0238(0645)
0372(0666)
ln£ ln – 0304(0161)
– – 0328(0159)
ln£ ln – – ¡0050(0042)
– ¡0049(0043)
ln – – 1366(0451)
– 1233(0457)
Sample Full Full Full Excl. US Excl. US
Observations 41,576 41,576 41,180 36,714 36,318
Notes: Firm-destination and product-year …xed e¤ects are included. Robust standard errors are adjusted for clustering
at the product-level in parentheses. , , and indicate signi…cance at the 1, 5, and 10 percent levels, respectively. Unit
values are in pesos per liter and export volumes are in liters. The quality ratings are from the Wine Spectator.
39
Table 12: High versus Low Income Countries
(1) (2) (3) (4) (5)
Panel A: Dependent variable is unit value
ln£ 0303(0124)
1083(0330)
¡0219(0254)
0317(0124)
0667(0405)
ln£ 0010(0068)
0891(0342)
¡0357(0189)
0052(0073)
0646(0392)
ln£ £ ¡0000(0001)
¡0000(0001)
0000(0001)
0000(0001)
0000(0001)
ln£ £ 0002(0001)
0002(0001)
0002(0001)
0002(0001)
0002(0001)
ln£ ln – ¡0102(0038)
– – ¡0105(0039)
ln£ ln – – 0033(0011)
– 0030(0011)
ln – – 0188(0069)
– 0197(0071)
Panel B: Dependent variable is export volume
ln£ 1003(0722)
¡0971(1571)
3468(1226)
0988(0704)
1151(1892)
ln£ 1384(0628)
¡0849(1660)
3573(1016)
1438(0650)
1009(1899)
ln£ £ 0000(0003)
0000(0003)
0000(0003)
¡0001(0003)
¡0001(0003)
ln£ £ ¡0006(0002)
¡0006(0002)
¡0006(0002)
¡0008(0002)
¡0008(0002)
ln 0416(0635)
0283(0654)
0703(0647)
0259(0650)
0353(0683)
ln 2283(0337)
2305(0337)
0979(0532)
2297(0345)
1130(0540)
ln£ ln – 0243(0162)
– – 0272(0160)
ln£ ln – – ¡0048(0045)
– ¡0049(0045)
ln – – 1433(0457)
– 1323(0461)
Sample Full Full Full Excl. US Excl. US
Observations 41,576 41,576 41,180 36,714 36,318
Notes: Firm-destination and product-year …xed e¤ects are included. Robust standard errors are adjusted for clustering
at the product-level in parentheses. , , and indicate signi…cance at the 1, 5, and 10 percent levels, respectively. Unit
values are in pesos per liter and export volumes are in liters. The quality ratings are from the Wine Spectator.
40
Table 13: Endogeneity
(1) (2) (3) (4) (5) (6) (7) (8) (9)
Panel A: Dependent variable is unit value
ln 0157(0040)
0158(0040)
0159(0040)
0026(0062)
¡0998(0561)
¡0985(0562)
0111(0066)
¡1048(0491)
¡1048(0491)
0033(0003)
0022(0010)
0022(0010)
0033(0003)
0021(0010)
0022(0010)
– – –
ln£ – – – 0002(0000)
0014(0007)
0014(0007)
0001(0000)
0015(0006)
0015(0006)
Panel B: Dependent variable is export volume
ln 1131(0528)
1180(0553)
1180(0553)
1456(0535)
¡0915(1848)
¡0907(1847)
0982(0632)
¡1085(2046)
¡1086(2046)
¡0049(0006)
¡0100(0040)
¡0100(0040)
¡0050(0007)
¡0074(0039)
¡0072(0039)
– – –
ln£ – – – ¡0004(0002)
0025(0021)
0025(0021)
¡0004(0002)
0022(0024)
0021(0024)
ln 0516(0549)
0562(0578)
0561(0578)
0511(0548)
0579(0582)
0578(0582)
0287(0643)
0333(0611)
0333(0611)
ln 2599(0275)
2609(0263)
2609(0263)
2573(0274)
2772(0304)
2770(0303)
2629(0344)
2795(0333)
2795(0333)
Estimator OLS IV IV OLS IV IV OLS IV IV
CPI O¢cial O¢cial Cavallo O¢cial O¢cial Cavallo O¢cial O¢cial Cavallo
Observations 37,723 37,723 37,723 37,723 37,723 37,723 37,723 37,723 37,723
Notes: Firm-destination, …rm-year, grape, type, vintage year, province, and HS …xed e¤ects are included in (1)-(6). Firm-
destination and product-year …xed e¤ects are included in (7)-(9). Robust standard errors are adjusted for clustering at the
product-level in parentheses. , , and indicate signi…cance at the 1, 5, and 10 percent levels, respectively. Unit values
are in pesos per liter and export volumes are in liters. The quality ratings are from the Wine Spectator. Instruments
include the monthly average temperatures and total rainfall per province over the growing period (September to March)
and the altitude of each province.
Table 14: Nonlinearities
(1) (2) (3) (4)
Dependent variable Unit value Unit value Export volume Export volume
ln 0042(0101)
0067(0062)
1230(0694)
1290(0616)
ln£ (50¡ 74) 0002(0001)
– ¡0004(0005)
–
ln£ (75¡ 79) 0002(0001)
– ¡0004(0005)
–
ln£ (80¡ 84) 0002(0001)
– ¡0004(0004)
–
ln£ (85¡ 89) 0002(0001)
– ¡0004(0004)
–
ln£ (90¡ 94) 0002(0001)
– ¡0005(0004)
–
ln£ (50¡ 94) – 0001(0000)
– ¡0005(0002)
ln£ (95¡ 100) 0006(0003)
0006(0003)
¡0002(0004)
¡0003(0003)
ln – – 0424(0631)
0417(0632)
ln – – 2308(0334)
2293(0334)
Observations 41,576 41,576 41,576 41,576
Notes: Firm-destination and product-year …xed e¤ects are included. Robust standard errors are adjusted for clustering
at the product-level in parentheses. , , and indicate signi…cance at the 1, 5, and 10 percent levels, respectively. Unit
values are in pesos per liter and export volumes are in liters. The quality ratings are from the Wine Spectator.
41
Table 15: Distribution Costs and Productivity
(1) (2) (3) (4)
Panel A: Dependent variable is unit value
ln 0102(0081)
¡0101(0168)
¡0013(0072)
0063(0116)
ln£ 0001(0000)
0003(0001)
0002(0001)
0001(0001)
Panel B: Dependent variable is export volume
ln ¡0821(1187)
3111(1247)
0208(0718)
2921(1282)
ln£ ¡0003(0003)
¡0003(0006)
¡0007(0002)
¡0002(0003)
ln ¡0930(1201)
1270(1299)
¡0830(0769)
2181(1262)
ln 4184(0658)
1003(0499)
2674(0414)
1494(0559)
Importers’ wage High Low All All
Exporters’ size All All Large Small
Observations 20,708 20,472 20,819 20,757
Notes: Firm-destination and product-year …xed e¤ects are included. Robust standard errors are adjusted for clustering
at the product-level in parentheses. , , and indicate signi…cance at the 1, 5, and 10 percent levels, respectively. Unit
values are in pesos per liter and export volumes are in liters. The quality ratings are from the Wine Spectator. Columns
(1) and (2) report the results for the high and low wage destination countries, while columns (3) and (4) report the results
for the large and small exporters, respectively.
Table 16: The Quality of Exporters and Export Market Shares
(1) (2) (3) (4)
Panel A: Dependent variable is unit value
ln ¡0002(0102)
0132(0075)
0047(0065)
¡0064(0155)
ln£ 0003(0001)
¡0000(0001)
0002(0000)
0002(0001)
Panel B: Dependent variable is export volume
ln 1267(0898)
1432(0851)
1257(0799)
0569(1694)
ln£ ¡0005(0003)
¡0006(0003)
¡0005(0002)
¡0001(0003)
ln 0160(0916)
0755(0878)
0571(0823)
¡0349(1766)
ln 2498(0481)
2079(0465)
2263(0454)
2763(0799)
Exporters’ quality High Low All All
Exporters’ market shares All All High Low
Observations 20,721 20,855 22,898 18,678
Notes: Firm-destination and product-year …xed e¤ects are included. Robust standard errors are adjusted for clustering
at the product-level in parentheses. , , and indicate signi…cance at the 1, 5, and 10 percent levels, respectively. Unit
values are in pesos per liter and export volumes are in liters. The quality ratings are from the Wine Spectator. Columns
(1) and (2) report the results for the high and low quality exporters, while columns (3) and (4) report the results for the
high and low market share exporters, respectively.
42
Table 17: The Measurement of Quality and Unrated Wines
(1) (2) (3) (4) (5) (6) (7) (8) (9)
Panel A: Dependent variable is unit value
ln 0168(0043)
¡0000(0115)
¡0253(0189)
0101(0067)
0153(0033)
0148(0033)
0046(0057)
0166(0035)
0167(0035)
ln£ 0006(0003)
0002(0001)
0005(0002)
0002(0000)
– – 0002(0000)
0008(0002)
0006(0002)
– – – – ¡0009(0001)
¡0012(0002)
– – –
ln£ – – – – ¡0000(0000)
¡0000(0000)
– – –
Panel B: Dependent variable is export volume
ln 0941(0608)
3421(0813)
2547(1508)
1325(0631)
2116(0546)
2223(0545)
2948(0555)
2377(0535)
2131(0546)
ln£ ¡0022(0009)
¡0017(0005)
¡0020(0007)
¡0006(0002)
– – ¡0008(0002)
¡0029(0009)
¡0005(0008)
– – – – 0069(0013)
0095(0017)
– – –
ln£ – – – – 0000(0000)
0000(0000)
– – –
ln 0422(0631)
1350(0765)
0337(1361)
0256(0646)
1690(0559)
1769(0559)
1728(0548)
1731(0548)
1583(0562)
ln 2301(0334)
2114(0420)
2580(0686)
2310(0343)
1923(0276)
2017(0275)
1891(0271)
1893(0271)
1976(0280)
Sample Full Full Same bin Excl. US Full Full Mean Mean Unrated
Quality WS [1,6] Parker WS WS Rank Rank WS WS [1,6] WS [1,6]
Observations 41,576 26,705 8,982 36,714 86,882 86,882 67,589 67,589 64,302
Notes: Firm-destination and product-year …xed e¤ects are included. Robust standard errors are adjusted for clustering at the
product-level in parentheses. , , and indicate signi…cance at the 1, 5, and 10 percent levels, respectively. Unit values are in
pesos per liter and export volumes are in liters.
Table 18: The Nature of Wine
(1) (2) (3) (4)
Dependent variable Unit value Unit value Export volume Export volume
ln 0025(0067)
0077(0062)
1957(0657)
1041(0616)
ln£ 0002(0001)
0001(0000)
¡0005(0003)
¡0004(0002)
ln£ – ¡0004(0003)
– 0061(0010)
ln – – 1167(0636)
0454(0631)
ln – – 1802(0319)
2312(0332)
Sample No vintage Full No vintage Full
Observations 44,147 41,576 44,147 41,576
Notes: Firm-destination and product-year …xed e¤ects are included. Robust standard errors are adjusted for clustering
at the product-level in parentheses. , , and indicate signi…cance at the 1, 5, and 10 percent levels, respectively. Unit
values are in pesos per liter and export volumes are in liters. The quality ratings are from the Wine Spectator.
43
Table 19: Exchange Rate
(1) (2) (3)
Panel A: Dependent variable is unit value
ln 0117(0070)
0059(0062)
0026(0065)
ln£ 0001(0000)
0002(0000)
0002(0000)
ln – – 0277(0062)
Panel B: Dependent variable is export volume
ln 1570(0685)
1289(0616)
0827(0613)
ln£ ¡0007(0002)
¡0005(0002)
¡0005(0002)
ln 0525(0693)
0420(0632)
0349(0630)
ln 2717(0375)
2294(0334)
1798(0341)
ln – – 2031(0655)
Sample Excl. US dollar peg Full Full
Exchange Rate Real Real Nominal
CPI O¢cial Cavallo O¢cial
Observations 34,372 41,576 41,576
Notes: Firm-destination and product-year …xed e¤ects are included. Robust standard errors are adjusted for clustering
at the product-level in parentheses. , , and indicate signi…cance at the 1, 5, and 10 percent levels, respectively. Unit
values are in pesos per liter and export volumes are in liters. The quality ratings are from the Wine Spectator.
Table 20: Robustness
(1) (2) (3) (4) (5) (6)
Panel A: Dependent variable is unit value
ln 0076(0061)
0014(0054)
0058(0062)
0019(0066)
0059(0062)
0041(0061)
ln£ 0002(0000)
0001(0000)
0001(0000)
0001(0000)
0001(0000)
0002(0000)
Panel B: Dependent variable is export volume
ln 0953(0603)
0918(0328)
1382(0632)
1203(0660)
1296(0616)
–
ln£ ¡0005(0002)
¡0003(0002)
¡0005(0002)
¡0005(0002)
¡0005(0002)
–
ln 0037(0621)
0334(0344)
¡0922(0893)
¡0940(0886)
0426(0632)
–
ln 2316(0332)
2150(0363)
2702(0353)
2669(0371)
2291(0335)
–
Sample Intensive Quarterly Full Full All …rms Full
Lags None None One Two None None
Unit values Pesos Pesos Pesos Pesos Pesos US dollars
Observations 35,594 58,016 41,576 41,576 41,632 41,576
Notes: Firm-destination and product-year …xed e¤ects are included. In (2), the product-year …xed e¤ects are replaced by
product-quarter dummy variables. Robust standard errors are adjusted for clustering at the product-level in parentheses., , and indicate signi…cance at the 1, 5, and 10 percent levels, respectively. The quality ratings are from the Wine
Spectator.
44
Figure 1: Argentina’s Total Wine Exports (million US dollars)
Sources: Nosis and United Nations Comtrade
Figure 2: Comparison between the Wine Spectator and Parker ratings
45
Figure 3: Argentinean peso per US dollar, January 2002 to December 2009
Source: International Financial Statistics
46
Appendix Table A1: First-Stage Instrumental Variables Regressions
(1) (2) (3) (4)
Regression in Table 13 Col. (4) Panel A Col. (4) Panel A Col. (4) Panel B Col. (4) Panel B
Dependent Variable ln£ ln£
Temperature September ¡0111(0095)
¡0687(0207)
¡0110(0096)
¡0658(0207)
Temperature October ¡0177(0050)
0038(0119)
¡0177(0050)
0020(0119)
Temperature November 0358(0146)
¡0217(0323)
0358(0146)
¡0198(0323)
Temperature December ¡0189(0089)
¡0326(0215)
¡0190(0090)
¡0314(0215)
Temperature January 0287(0087)
¡0222(0180)
0287(0087)
¡0216(0180)
Temperature February 0183(0065)
0038(0172)
0183(0065)
0042(0172)
Temperature March ¡0579(0077)
¡0334(0166)
¡0578(0077)
¡0336(0166)
Rainfall September ¡0022(0004)
¡0008(0010)
¡0022(0004)
¡0008(0010)
Rainfall October 0014(0002)
¡0008(0006)
0014(0002)
¡0008(0006)
Rainfall November 0003(0002)
¡0002(0006)
0003(0002)
¡0002(0006)
Rainfall December 0004(0003)
¡0017(0006)
0004(0003)
¡0017(0006)
Rainfall January ¡0004(0001)
¡0003(0001)
¡0004(0001)
¡0003(0001)
Rainfall February ¡0004(0001)
0001(0003)
¡0004(0001)
0001(0003)
Rainfall March ¡0002(0001)
0006(0004)
¡0002(0001)
0005(0004)
Temperature September£ ln ¡0001(0017)
0202(0142)
¡0001(0017)
0206(0142)
Temperature October£ ln ¡0017(0010)
¡0135(0055)
¡0017(0010)
¡0133(0056)
Temperature November£ ln 0005(0024)
0275(0179)
0005(0024)
0280(0179)
Temperature December£ ln ¡0007(0017)
0071(0101)
¡0007(0017)
0055(0102)
Temperature January£ ln ¡0003(0020)
0042(0121)
¡0003(0020)
0032(0122)
Temperature February£ ln 0009(0019)
0083(0136)
0008(0019)
0092(0136)
Temperature March£ ln 0009(0021)
¡0160(0129)
0008(0021)
¡0151(0129)
Rainfall September£ ln ¡0002(0001)
¡0008(0010)
¡0002(0001)
¡0008(0010)
Rainfall October£ ln 0000(0000)
0003(0004)
0000(0000)
0004(0004)
Rainfall November£ ln 0001(0001)
0003(0004)
0001(0001)
0003(0004)
Rainfall December£ ln ¡0002(0000)
0003(0003)
¡0002(0000)
0003(0003)
Rainfall January£ ln ¡0001(0000)
¡0003(0001)
¡0000(0000)
¡0003(0001)
Rainfall February£ ln 0000(0000)
¡0006(0003)
0000(0001)
¡0006(0003)
Rainfall March£ ln ¡0000(0000)
¡0003(0003)
¡0000(0000)
¡0003(0003)
Altitude£ ln 0000(0000)
0003(0001)
0000(0000)
0003(0001)
ln 0016(1155)
72351(7657)
0775(1424)
71017(8170)
ln – – 0800(0832)
¡2604(2259)
ln – – 0131(0445)
¡7303(1030)
Notes: Firm-destination, …rm-year, grape, type, vintage year, province, and HS …xed e¤ects are included. Robust standard
errors are adjusted for clustering at the product-level in parentheses. , , and indicate signi…cance at the 1, 5, and 10
percent levels, respectively.
47