Multi-Product Firms and Exchange Rate
Fluctuations∗
Arpita Chatterjee

Rafael Dix-Carneiro

University of New South Wales

University of Maryland

arpita.chatterjee@unsw.edu.au

dix-carneiro@econ.umd.edu

Jade VichyanondInternational Monetary Fundjvichyanond@imf.org
August 11, 2011∗

We would like to thank Penny Goldberg for advice on this project. We are grateful to Saroj Bhattarai, Linda Goldberg, Gene Grossman, Frederic Robert-Nicoud, Esteban Rossi-Hansberg, and trade workshop participants at Princeton University for many
helpful comments and suggestions. Many thanks to João De Negri and IPEA-Brasília for
granting access to Secex and RAIS. Comments and questions are welcome. Please contact
arpita.chatterjee@unsw.edu.au, dix-carneiro@econ.umd.edu or jvichyanond@imf.org.

1

Abstract
This paper studies the effect of exchange rate shocks on export behavior of multi-product firms. We construct a model illustrating how
firms adjust their prices, quantities, product scope, and sales distribution across products in the event of exchange rate fluctuations. In
response to a real exchange rate depreciation, firms increase markups
for all products, but markup increases decline with firm-product specificmarginal costs of production. We find robust evidence for our theoreticalpredictions using Brazilian customs data containing destination-specificand product-specific export sales and quantities. The sample period
covers 1997-2006, during which Brazil experienced a series of drastic
currency fluctuations.
JEL classification: F12, F41
Keywords: Multi-product firms, exchange rate pass-through, productladder, local distribution costs.

2

1

Introduction

The relatively muted response of consumer import prices to exchange rate
fluctuations is a stylized fact that has intrigued economists for many years.1
Understanding this phenomenon is crucial to many issues faced by policymakers, since the degree of exchange rate pass-through has implications for
how currency devaluations affect inflation and hence the conduct of monetary
policy. Furthermore, it may also have important effects on the welfare of exporting firms, importing firms, and consumers. Since there is a symmetry on
how import tariffs and exchange rates affect domestic prices, the study of thedeterminants of exchange rate pass-through may also shed light on how andto what extent domestic prices react to trade liberalization. Finally, understanding exchange rate pass-through is interesting in itself because it helps us
understand how firms set prices and how they react to shocks.
The study of exchange rate pass-through in international macroeconomics
has for a long time focused on aggregate cross-country data. However, due tothe increasing availability of firm- and product-level export and import transaction data, many authors have begun to analyze firm level responses in orderto understand the determinants of incomplete exchange pass-through. This
strand of the literature started with Feenstra, Gagnon and Knetter (1993)
and Goldberg and Verboven (2001) studying price behavior in the international car market, and is experiencing a recent surge with the availability ofofficial customs data. These usually cover all international transactions ofa given country and provide researchers with an unprecedented level of detail.2 This change in focus to firm level data is not surprising given that in
the past decade, the international trade literature established firms as the1

For examples, see Goldberg and Knetter (1997), Burstein, Neves, and Rebelo (2003),
Campa and Goldberg (2005), Campa, Goldberg, and Gonzalez-Minguez (2006), Devereux,
Engel, and Tille 1999, and Devereux and Engel (2002) among others.
2
Examples of this recent literature include, Itskhoki, Gopinath and Rigobon (2008) who
study currency choice as a determinant of pass-through, Itskhoki and Gopinath (2009) who
study the relationship between the frequency of price adjustment and pass-through, andBerman, Mayer and Martin (2011) who study how different exporters react to exchange
rate movements.

3

primary agents of international commerce.3 Firms that participate in international trade are heterogeneous in productivity, produce multiple products
and often exhibit heterogeneous productivity across different products. In thispaper, we explicitly model the effect of exchange rate shocks on the pricing
decisions of heterogeneous multi-product exporters and empirically explore implications of within- and across-firm heterogeneity in explaining exchange ratepass-through using detailed transaction-level customs data from Brazil.
Our model illustrates how heterogeneous firms adjust their prices, quantities and product scope in the event of an exchange rate depreciation, and how
the degree of price and quantity responses varies across products within firms.
The two key features of the model are: 1) Each firm faces a product ladder,i.e. there is a core product that the firm is most efficient at producing (thefirm’s "core competency") and the firm is less efficient at producing products
further away from it; and 2) Each firm pays a local per-unit distribution cost,which implies that markups vary depending on how far the product is fromthe firm’s core competency. Within a given firm, optimal markups are higher
for products closer to the core competency. For these products, the production costs are relatively low, so that distribution costs constitute a significant
fraction of consumer prices, leading to lower perceived demand elasticity andhence higher markups.
Theoretically, we show that in response to an exchange rate depreciation,
producer price increases are more pronounced for products closer to the corecompetency, i.e., those with greater productivity. The reason is that local perunit distribution costs imply different degrees of markups depending on thefirms’ product-specific productivities. Also, firms expand their product scope,and their sales distribution across different products becomes less skewed in
response to a real exchange rate depreciation. These two results imply that
following a devaluation, the importance of non-core (less efficient) products
relative to core products increases in firms’ export baskets, leading to a withinfirm reallocation of resources towards less efficient use.
3

See Melitz (2003), Bernard, Redding and Schott (2011), Nocke and Yeaple (2006), andMayer, Melitz, and Ottaviano (2011) among others.

4

We test the model’s predictions on rich Brazilian customs data. Spanningthe period from 1997 to 2006, during which Brazil experienced a series of major exchange rate fluctuations, the dataset has very detailed information at thefirm, product, and destination levels. That allows us to use exchange rate variation as well as firm-, product-, and destination-specific information in orderto analyze how firms respond to exchange rate movements. We find that the
responses of prices, quantities, firm scope, and sales distributions to exchange
rate fluctuations are consistent with the theoretical predictions. Our key finding is that the relative position of a product within a firm is a statistically andeconomically significant determinant of producer price responsiveness to realexchange rate shocks. This result is robust to different measures of within-firmheterogeneity, and after controlling for a rich set of firm, industry and country
characteristics. Firm productivity - proxied by a set of firm characteristics also plays a key role in determining exchange rate pass-through.
The paper is structured as follows. Section 2 discusses the related literature, section 3 describes the theoretical model and its predictions, section 4
presents the empirical analysis, and section 5 concludes the paper.

2

Related Literature

Our paper is mostly related to Berman, Martin, and Mayer (2011), whostudy optimal price responses to exchange rate movements for French firms.
While their model also features local per-unit distribution costs as the main
driver of heterogeneous price responses, the analysis focuses on single-productfirms and therefore on how high-productivity firms react differently from lowproductivity firms. However, most firms participating in international trade
produce multiple products. By allowing firms to produce more than one product, we are able to obtain additional results, namely on how firms change theirproduct range and how price responses differ from product to product within afirm. Our empirical results also confirm the key conclusion of Berman, Martin,and Mayer (2011) that in response to a real exchange rate depreciation, moreproductive firms increase producer prices further than less productive firms.
5Regarding multi-product firms, our study is similar to Mayer, Melitz andOttaviano (2011), whose primary focus is to understand how export market
conditions, such as market size and degree of competition, affect firms’ relative
sales distribution across products. We adopt their deterministic formulationof product ladders to show how the relative sales distribution across products changes in response to exchange rate movements. Bernard, Redding,and Schott (2011) characterize an alternative formulation to a product ladder, in which firm-product specific preferences are stochastic. While we use
the approach taken by Mayer, Melitz and Ottaviano (2011), our results are
independent of whether we use a deterministic or stochastic formulation for
product ladders.
Mayer, Melitz and Ottaviano (2011) incorporate non-homothetic demandin their framework in order to allow for endogeneity of markups. In our setup,
endogenous markups arise due to the presence of local distribution costs, eventhough the demand structure is derived from CES preferences. However, ourtheoretical and empirical results demonstrate that firms skew their relative
export sales towards less efficient use when facing softer market competition(e.g., after a currency depreciation), a result consistent with Mayer, Melitz
and Ottaviano (2011). All of our theoretical predictions would be unchanged
if we used non-homothetic preferences in our framework. However, CES preferences allow for an analytically tractable framework where we can explicitly
demonstrate how distribution and transportation costs affect producer priceelasticities as well as empirically test these predictions.
There is a significant body of literature which analyzes how non-tradable
distribution costs affect international pricing decisions. Burstein, Eichenbaum,and Rebelo (2005) argue that large declines in real exchange rates associatedwith devaluations stem mainly from the slow price adjustments of nontradable
goods and services. Our paper is closer to Atkeson and Burstein (2008), whoshow that the presence of trade costs and imperfect competition with variablemarkups can explain deviations from relative purchasing power parity, andthese two features of their model are essential in generating firms’ pricing-tomarket behavior.
6

Finally, our paper is related to the literature on incomplete exchange ratepass-through. Auer and Chaney (2009) present a model with goods of different qualities and consumers with heterogeneous preferences for quality. Theyfind that exchange rate shocks are imperfectly passed through to prices andthat the pass-through is greater for low-quality goods than for high-quality
goods. Hellerstein (2008) studies manufacturers’ and retailers’ pass-throughof nominal exchange rate movements in the beer market and estimates a structural econometric model to quantify the extent to which a nominal exchange
rate shock affects domestic and foreign firms’ profits and consumer surplus. In
our model, the elasticity of producer prices with respect to the exchange ratedepends on per-unit local distribution costs. Hence, in our empirical work weallow price responses to vary according to distribution margins, in a manner
similar to Campa and Goldberg (2010). Also, we control for the heterogeneityof producer price responses according to important destination characteristics(such as nominal exchange rate volatility of destination countries, market potential, etc.) to assess the importance of within-firm heterogeneity in explaining price responsiveness, following the empirical international macroeconomics
literature (for example, Campa and Goldberg (2005)).

3

Model

We present a model in which heterogeneous firms in the Home country export
to a variety of destinations. As our empirical section uses data from Brazil, we
use "Home" to refer to Brazilian firms. Firms can export a number of products
to a given destination, with the firm-product specific productivity depending
on how far the product is from the firm’s core expertise. We analyze how
an exchange rate shock affects firms’ optimal price and quantity responses
as well as the number of products exported. An individual firm’s decisions
cannot affect exchange rate movements. Hence, we treat such movements as
exogenous from the point of view of the firm.

7

3.1

Setup

The representative agent in country (destination) c has utility
ZUc =xc (ϕ)

1− σ1

 11
dϕ

1− σ

(1)

X

where xc (ϕ) is the consumption of product ϕ in country c and X denotes theset of traded products. The elasticity of substitution among products is σ > 1.
Each firm has one product corresponding to its core competency; this is the
product which it is most efficient at producing. The productivity associatedwith this "core product" is a random draw θ from a common and known
distribution G(θ) with bounded support on [0, θ]; each firm is therefore indexedby θ. We use m to denote the rank of the product in increasing order of distancefrom the firm’s core competency, with m=0 referring to the core product. The
productivity of a firm with core competency θ in producing product m forcountry c is given by−m
ϕ (m, θ) = θωcθ
, ωcθ > 1

(2)

The above expression defines a firm’s competency ladder, where ωcθ characterizes the length of the ladder.4 Products with higher m are further away fromthe core competency, and the firm is relatively less efficient at producing theseproducts. We denote the total number of products exported by a firm to any
destination c (firm scope) as nc (θ). Firms employ one unit of labor at Home−mto produce θωcθ
units of any variety ϕ. The wage rate at Home is w.
Each firm faces a distribution cost for each unit of any product it exports
to destination c. This cost is meant to capture all expenses associated withdelivering the product to a customer after the product has left Home. Per
unit distribution costs in country c are measured as ηc units of labor hired incountry c.
Because of the presence of local distribution costs, per unit costs depend
4

Our main results are independent of whether the length of the ladder ωcθ depends on
country c characteristics or firm characteristics θ.

8

on both Home and destination wage rates. Let wc be the wage rate in countryc, and εc be the nominal exchange rate between Home and country c expressedin Home currency per country c’s currency. Therefore, an increase in εc is a
depreciation in Home’s currency vis-a-vis country c’s. We call qc ≡ wwc εc thereal exchange rate between Home and country c.
Firms face a fixed cost Fc in exporting to destination c. These fixed costs
are the same for all firms and products and only depend on the country ofdestination c. In addition, there is an iceberg transport cost τc > 1.

3.2

Optimal pricing

In units of country c’s currency, the consumer price of product ϕ(m, θ) is givenbypc (ϕ (m, θ)) τc+ ηc wc(3)
εcwhere pc (ϕ(m, θ)) is the producer price of the good exported to c expressedin Home’s currency. The first term corresponds to the good’s price at country
c’s dock expressed in country c’s currency, and the second term captures the
distribution cost incurred in country c. The quantity demanded in country cof this product is
xc (ϕ) = Yc Pcσ−1

pc (ϕ(m, θ))τc+ ηc wc
εc

−σ
(4)

where Yc is the income of country c and Pc is the price index in country c.
For a firm-product specific productivity ϕ, the cost in the Home currencyof producing xc (ϕ)τc units and selling them in country c is wxcϕ(ϕ)τc + Fc , whichw
implies exporting profits of πc (ϕ) = pc (ϕ) − ϕ xc (ϕ)τc − Fc .
Given the number of products, the first-order condition of optimal pricing
is




w
στc
xc (ϕ)τc 1 − pc (ϕ) −=0(5)
ϕ pc (ϕ)τc + ηc wc εc9leading to the producer price ofσ
pc (ϕ) =σ−1ηc q c ϕ
1+
στc



w
w
= mc (ϕ)ϕϕ(6)

Note that the markup, mc (ϕ), is higher than the usual monopolistic competition markup due to the presence of local distribution costs. Also, the markup
increases with the real exchange rate and with the firm-product specific productivity level ϕ.5 For a more productive firm (high θ), for a product closer tothe firm’s core competency (low m, given θ), or for a depreciated real exchange
rate (high qc ), a large share of the final consumer price does not depend on the
producer price, resulting in a lower perceived elasticity of demand and hence
higher markups.

3.3

Firm Scope

To determine the number of products, note that a firm with productivity
θ earns profits πc (ϕ(nc , θ)) from its marginal product, where πc (ϕ(nc (θ), θ))equals!
Cwqc wc−σ Yc Pcσ−1

1−σ

τc

−n (θ)+1θωcθ cqc+ ηc− Fc ,(7)

where C is a positive constant that only depends on σ. These profits decreasein nc (θ). A product further from the core has a higher variable cost. Thus, afirm earns higher profits on products closer to its core competency.
A firm with productivity θ produces nc (θ) products, where nc (θ) is thelargest integer for which (7) is positive. If (7) is positive only for the top
product, then the firm is a single-product firm producing only its top product.
If the firm-specific productivity θ is so low that it does not earn positive profits
even from its top product, then that firm does not export to destination c.
5Berman, Martin and Mayer (2011), Bergin and Feenstra (2001, 2002, 2009) and Atkeson
and Burstein (2008) have similar predictions on markups.

10

3.4

Key Predictions

Here we present the key predictions from our theoretical mechanism.
Producer price and quantity response: Producer prices increase following a real depreciation. From (6) it is clear that the markup increases withreal exchange rate through the impact of the real exchange rate on the local
distribution cost component. The producer price elasticity is given byηc q c ϕστc + ηc qc ϕ

(8)The producer price elasticity with respect to real exchange rates is less than
1; thus real exchange rate depreciation reduces the price faced by consumers,
despite the producer price increase. Hence the quantity response to a realexchange rate depreciation is positive.
Note that the producer price elasticity is specific to each firm and to eachproduct. In fact, (8) increases in both firm-specific and product-specific productivity. Hence, in response to a real exchange rate devaluation, more productive firms increase prices more than less productive firms. Moreover, multiproduct firms increase producer prices more for products closer to the corecompetency than for those further away. Due to firms’ higher efficiency at
producing core products and to local distribution costs, production costs account for a relatively small fraction of the consumer price. Consequently, theperceived demand elasticity is lower, leading to higher markups. This translates into higher price increases for these products as a result of a depreciation.
For single-product firms, since the price response is stronger for more productive firms, the quantity response is weaker for those firms. Similarly, formulti-product firms, the quantity response is weaker for products closer to thecore competency than for those further away.
Moreover, the producer price elasticity increases in per-unit distribution
costs and decreases with transportation costs. This follows from the markup,
mc (ϕ), which is increasing in distribution costs and falling with transportation
costs.
In addition to price responses, the theoretical model yields the following
11

implications regarding firm scope adjustment, and changes in relative sales.
Firm scope: A firm (weakly) increases its number of products exported
to destination c in response to a depreciation. The intuition for this result isthe following. Before the depreciation, profits for all exported products are
positive and are decreasing with the distance of the product from the firm’s
core competency. The product furthest away from the core competency, or the"marginal product," is the last product that yields positive profits (the next
product yields negative profits, reducing total profits). When the depreciation
occurs, profits increase for all products, including the pre-depreciation nextto-marginal product, which may now make positive profits. As a result, thefirm has an incentive to expand the range of products further down the ladder.
Changes in relative sales: Consider two products of a firm, where product 1 is higher up in the product ladder than product 2, i.e. ϕ1 > ϕ2 . Then theratio of the sales of product 1 to the sales of product 2 decreases in responseto an exchange rate depreciation.6 The shift in the relative sales distribution
following a depreciation is due to the fact that price increases are not homogeneous across products within firms. Since price increases are more pronounced
for products closer to the core competency, quantity responses for these products are relatively muted, leading to an increase in sales that is proportionately
smaller than an increase in sales of products further away from the core competency. Thus, in the presence of endogenous markups, a real exchange rate
depreciation implies a within-firm reallocation of resources towards less efficient use. Relative sales also become less skewed in response to a decrease intransportation costs. Similarly, an increase in transportation costs and/or areal exchange appreciation imply tougher competition in export markets which
induces a firm to skew its export sales towards its best performing products,
in a manner similar to Mayer, Melitz and Ottaviano (2011).7
6

See appendix for derivation.
Mayer, Melitz and Ottaviano (2011) allow for endogenous markups through nonhomothetic demand. In our set up, local distribution costs give rise to endogenous markups.
All of our results go through with the specification of markup endogeneity à la Mayer, Melitz
and Ottaviano (2011).
7

12

4

Empirical Analysis

4.1

Data

Here we describe the primary sources of data that we use. In this paper weonly use data from manufactured products (those attached to NAICS 2-digit
industries 15 to 37).
4.1.1

Secex - Customs Data

These records describe every legally registered export transaction from Brazilian firms. For each transaction, the available information includes exporting
firm (establishment level), identified by its unique 14-digit identifier CN P J(Cadastro Nacional de Pessoa Jurídica); the exported good at the 8-digit level
NCM (Nomenclatura Comum do Mercosul); country of destination; value ofthe transaction in US$; number of units and/or weight (in kg) of the shipment;
and year and month of the transaction. The same type of data is also available
for import transactions.
The data present both weight and quantity columns. For some transactions only weight or quantity is reported, and for others both are reported. Inorder to choose in what unit unit-values are computed, we construct for every product-destination pair a most frequently reported unit throughout the
sample period. We compute unit values dividing total sales of product i, fromfirm j, to destination c at time t by the total quantity of product i, from firmj, to destination c at time t. While Secex is available since 1990, there is no
information on quantities until 1996, which makes it impossible to computeunit-values. Therefore, we only use data from 1997 to 2006, which is a period
when Brazil suffered several shocks in its exchange rate.
4.1.2

RAIS

These records consist of all legally registered Brazilian firms. Every year,
firms are required by law to report data on each of their establishments andemployees as well as several firm-specific variables. In particular, we construct13

measures of skill composition, number of employees, and distribution of wages
for each firm. Firms are identified by their unique registry number (CNPJ),
therefore we merge Secex and RAIS in order to obtain firm-level informationfor exporters. Unfortunately, RAIS does not provide information on domestic
sales, revenue, capital, or other balance-sheet variables. Consequently, we
cannot construct standard variables to proxy for TFP, such as revenue per
worker. For that reason, we proxy for TFP using firm size, skill composition,
average hourly wages, and importance of imported inputs.
4.1.3

Aggregate (economy-wide and sector-level) data sources

We obtain data on exchange rates, population, price indices and GDP for different destinations from the Penn World Table (PWT). Information on several
aggregate variables is available in PWT from 1950 to 2007 for 190 countries.
We use foreign supply potential variables from Head and Mayer (2004) for
years 1997 to 2006 as a measure of destination-specific market size. The construction of the measures of supply potential is discussed in greater details in
Redding and Venables (2004).8 Finally, we use data on average distribution
margins from Campa and Goldberg (2010) as a measure of the importance ofdistribution costs at the sector level.
4.1.4

Descriptive Statistics

The firm-level data that we use is very comprehensive and well-suited to empirically explore the hypotheses of the theoretical mechanism we present in thispaper. In addition, Brazil underwent major real exchange rate fluctuations
over the period of our study, which makes this dataset particularly attractive to study the questions at hand. Figure 1 illustrates the time path of themonthly nominal exchange rate between the Brazilian real and the US dollar.
The currency was pegged to the dollar until early 1999 when it faced a sharp
8

The supply potential measures take into account the distances of a country from othermajor markets as well as its GDP. For example, even if Netherlands and New Zealand have
similar GDP, Netherlands has a higher supply potential because of its central location in
Europe.

14

depreciation. After that, it faced another period of sharp depreciations in 2002
due to uncertainty in Argentina as well as increasing uncertainty vis-a-vis presidential candidate Lula’s economic policies prior to taking office. Soon after
president Lula became president and brought continuity to his predecessor’s
sound economic policies, the currency started to appreciate gradually. Figure
2 shows the evolution of the annual real exchange rate with respect to the dollar - which is the frequency we will be working with in this paper - and Figure
3 shows the annual variation in the real exchange rate, which depreciated 45%
between 1998 and 1999.
Next, we highlight a few important characteristics of the firm-level dataset.
An important contribution of our paper is to highlight how within-firm heterogeneity shapes firms’ responses to real exchange rate shocks. We model
within-firm heterogeneity as a deterministic product ladder within a firm. The
empirical counterpart of a theoretical product in our dataset is an 8-digit level
NCM code. In Table 1 we illustrate a few examples of NCM codes. For example, in the eyeglass industry the different products in the data are plastic eyeglass frames, metal eyeglass frames, safety eyeglasses, sunglasses, telescopes,
binoculars, etc. These product categories in the data are sufficiently different
for them to correspond to the theoretical notion of distinct products and allow
for the possibility of different firms having core expertise in different products.
Ours is the first paper to study the multi-product aspect of firms in determining exchange rate pass-through. Most firms export more than one productin the dataset. Guided by our model, we define a multi-product firm as a
firm-destination-year triplet with strictly more than one product sold. A single product firm in a given year is defined as a firm-destination-year triplet
with only one product sold. We compare multi-product and single-productfirms in Table 2. Multi-product firms account for nearly half of the firms inthe data. However, they account for approximately two thirds of total employment and almost three quarters of total export value. The last column"fraction of unit-value observations" looks at the share of unit-values in ourdataset that come from multi-product firms and single-product firms. Approximately 90% of unit value observations are associated with multi-product15

firms. Overall, the average number of products exported by a firm to a given
country is 53.8, while the median number of products is 11, as seen in Table
3 which lists the top 10 export industries in Brazil.
Comparing different industries, we can see that there is a significant degreeof heterogeneity across industries.9 In the "Food and Beverages" industry(NAICS 15) the median number of products exported by a given firm to agiven destination is 3, whereas in the "Assembly of Automotive Vehicles"industry (NAICS 34) the median number of products exported by a given firm
to a given destination is 39.
Consistent with our key modeling assumption, we observe great heterogeneity across products within firms, as Table 4 shows. On average, for agiven firm, the revenue generated by the product with the highest revenue isapproximately three times greater than the revenue generated by the product with the second highest revenue. In fact, the revenue generated by the
top product is at least twice as large as the revenue generated by the sum of
all the other products. This evidence is consistent with the product-ladderassumption in the theoretical model.
Finally, it is informative to display some descriptive statistics related toBrazilian firms’ destinations. The median number of destinations is equal to
10 in a given year, the 25th percentile is equal to 4 destinations and the 75th
percentile is equal to 22 destinations. Table 5 shows the top 10 export destinations over the 1997-2006 period. They account for 60% of all manufactured
goods exports from Brazil, with the United States and Argentina accounting
for more than one third of total exports.

4.2

Econometric Analysis

4.2.1

Response of Prices and Quantities to Real Exchange Rates

In this section, we first test our model’s key predictions concerning producer
prices. The two key predictions are that (1) producer prices increase following
9

Products are attached to industries using a correspondence table from NCM codes to
2-digit NAICS industries at http://www.ibge.gov.br/concla/cl_corresp.php?sl=3

16

a real depreciation, and (2) producer prices increases are more pronounced
for more productive firms, and within-firm for products closer to the corecompetency. We estimate the following reduced-form regression to test thefirst prediction:ln pijct = µijc + ϕ(t) + β ln (RERct ) + Xjt−1 γ + Zct δ + εijct(9)where pijct is the producer price in 2006 R$ charged by firm j for product i indestination c in year t, µijc is a product-firm-destination fixed effect, ϕ(t) is a
5th degree time polynomial, RERct is the real exchange rate of country c inyear t with respect to Brazil, Zct is a vector of characteristics of destination cin year t, Xjt−1 is a vector of firm j’s characteristics in year t − 1, and εijct isan error term.
The coefficient β captures the long-run response (in the co-integration
sense) of the producer price to real exchange rate fluctuations and is the key
parameter to be estimated. We estimate (9) using the fixed effects estimator.
For each triplet ijc, β is identified by the correlation between the deviationsof prices to the mean log-price of ijc across time and deviations of ln (RERct )to the mean of each country c across time. The real exchange rates RERct are
assumed to follow an exogenous process, as this is a partial equilibrium model.
We control for a flexible time trend with the time polynomial term in the
regression. The firm characteristics that we control for include log of firm
size measured by number of employees (ln(Empjt−1 )), fraction of skilled (highschool completed or higher) workers in the firm (Skilljt−1 ), log of the average
wage paid in the firm (ln(w̄jt−1 )) and a measure of importance of imported
inputs relative to total wage bill at the firm level (ln(Impjt−1 ), where Impjt−1
Imports of F irm j in year t−1). All of these characteristics are
is given by 1 + T otal
W age Bill of F irm j in year t−1
empirically established indicators of a firm’s latent productivity.10 All thesevariables are lagged in order to avoid correlation between contemporaneousshocks to prices and contemporaneous innovations to firm-level characteristics.
The quantity counterpart of producer price responsiveness follows natu10

See Verhoogen (2008), Kugler and Verhoogen (2009), and Kugler and Verhoogen (2011).

17

rally. From (8), the elasticity of producer prices with respect to real exchange
rates is less than one, hence the consumer price falls and the quantity exported increases following a real exchange rate depreciation. We estimate thefollowing reduced form regression to test this prediction:ln Qijct = µijc + ϕ(t) + β ln (RERct ) + Xjt−1 γ + Zct δ + εijct(10)Table 6 reports the results concerning the responsiveness of producer priceand quantity to real exchange rates. The coefficient estimate for log realexchange rate is positive and significant in both cases. Increases in the realexchange rate (real depreciations) are associated with increases in producer
prices and quantity exported. The producer price elasticity is estimated to
be of approximately 0.24, which implies an exchange rate pass-through toimport prices abroad (in the destination’s currency) of around 0.76, before local
distribution costs (which further attenuate the pass-through to consumers).
The estimated import price elasticity obtained using similar firm-level
French data is of 0.83 (see Berman, Martin, and Mayer (2011)). Similarly
to our study, this elasticity is also before local distribution costs and is remarkably close to our estimate. Using country- and industry-level data forOECD countries, Campa and Goldberg (2005) obtain an elasticity of 0.64
(Campa and Goldberg (2005)). They also show that the United States have apass-through of 0.4, which is significantly lower than in other countries in theOECD.
The positive responsiveness of producer prices to exchange rate movements
is robust when we estimate equation (9) for each industry. Interestingly, Figure
4 shows a high degree of heterogeneity of producer price responsiveness fordifferent industries. Such heterogeneity is further investigated below.
Next we present more detailed results regarding price adjustments for products within a given firm. The model predicts that the response of producer
prices to a real depreciation is greater for products closer to the firm’s corecompetency than for those further away. To test this prediction we estimate
the following equation:18

ln pijct =µijc + ϕ(t) + β1 ln (RERct ) + β2 ln (RERct ) × Ladderijct +(11)

ln (RERct ) × Xjt−1 β3 + Xjt−1 γ + Zct δ + εijct
In order to test how different firms adjust prices for different products
following exchange rate movements, we interact ln(RERct ) with variables indicating the relative position of the product within the firm and with firm-levelvariables that proxy for productivity.
Ladderijct is a variable that indicates the relative position of good i among
those sold by firm j to destination c in year t. The relative position is basedon sales of each product of a given firm, to a given destination at a given year.
Given a firm-destination-year triplet, the product with highest volume of sales
is the core product (m = 0), the product with second highest volume of sales
is the next to core product (m = 1), and so on and so forth. It is easy toshow that ranking products according to sales is consistent with the model
outlined in the previous section: given a firm-destination pair, products withhigher sales are those with higher productivity and hence closer to the corecompetency.
The following "ladder" variables are used: Bottomijct is an indicator forwhether product i is below the median ranking for sales of firm j to country
c in year t; N otCoreijct is an indicator for whether product i is NOT the
product with highest sales of firm j to country c in year t - i.e., it is not thecore product for triplet jct; Secondijct is an indicator for whether product i is
the second product with highest sales of firm j to country c in year t11 ; andRankingijct is the sales ranking of product i among the products sold by firm
j to country c in year t (with lower rank associated with products with higher
export sales).
We also allow the responsiveness of producer price to real exchange rate
movements to depend on firm productivity, since our theoretical model predicts
that producer price elasticity is also higher for firms with higher productivity.
11

For specifications using this variable, only products ranked first or second are kept.

19

We use log of firm size, fraction of skilled workers in the firm, log of theaverage wage paid in the firm, and a measure of importance of imported inputs
relative to the total wage bill as proxies for productivity. These variables
are appropriately lagged in order to avoid correlation with the error term.
For instance, current prices depend on current productivity, but productivity
is unobserved. Current levels of employment and wages may immediately
react to shocks in productivity, leading to current errors being correlated withcurrent firm-level variables.
Table 7 presents the results from the estimation of (11). The prediction on
the product ladder is strongly confirmed and very robust to the specificationof the ladder variable. For all four specifications of the ladder, we observe thatfirms’ producer price response is significantly lower for products further awayfrom their core expertise. The magnitude of the product ladder is also economically important. For example, we observe that, all else equal, for products
below median sale (of firm j to country c in year t) producer price responsiveness is 9 percentage points lower than for products with above mediansales; this is an economically important magnitude in view of the overall priceelasticity of 0.24 (obtained from the estimated β parameter in Table 6).
We also confirm the prediction that following a depreciation, more productive firms - measured by bigger size, higher fraction of skilled workers, orpaying higher wages - increase markups to a greater extent than less productive firms. This set of results lend support to a similar result found in Berman,
Martin and Mayer (2011) concerning heterogeneous responses of firms to realexchange rate shocks.12
We also find that the higher the ratio between imports of inputs and thewage bill of the firm, the higher the response of prices to a depreciation will be.
This may reflect the fact that importers are more productive and hence increase
markups further, but it also reflects the fact that following a depreciation, costs
12

Berman, Martin and Mayer (2011) restrict their sample to only single product firmdestination-year triplets. To compare our results more directly with theirs we estimateda similar regression restricting our sample to only single-product firms. Our results withregard to firm productivity being a strong determinant of producer price responsiveness are
not as robust in this restricted sample. Results are available upon request.

20

of imported inputs increase, leading to an increase in prices.
Theoretically, we attribute the heterogeneity in price responses to productivity dispersion across firms and within-firm productivity dispersion acrossproducts. Table 7 confirms our predictions in the data. However, there are
additional reasons for heterogeneity in producer price responses across firms- firms may operate in very different industries, or export to very different
destinations. These different industries may have very different local distribution costs, and different destinations may have different transportation costs.
From our theoretical model, (8) illustrates that the producer price elasticity
with respect to real exchange rates increases with distribution costs and decreases with transportation costs. Also, in our model we make the simplifying
assumption that all firms price their products in the Home currency. However,the pass-through can also vary according to destination characteristics, suchas market size or exchange rate volatility. These factors potentially affect exporters’ currency invoicing decisions, and in the presence of price stickiness,
may affect the producer price elasticity.13 In order to allow for the possibility of producer price responsiveness to vary according to several industry anddestination characteristics, we estimate the following equation:ln pijct = µijc + ϕ(t) + Xjt−1 γ + Zct δ+(12)

β1 ln (RERct ) + β2 ln (RERct ) × Ladderijct + ln (RERct ) × Xjt−1 β3 +ln (RERct ) × Destct β4 + β5 ln (RERct ) × ln(DIST M Gind(i) ) + εijct
We continue to allow price responses to vary depending on the relative
position of a good in the product ladder of a given firm (captured by the term
Ladderijct ) and on the productivity of firms (captured by the term Xjt−1 ). We
include two other lagged measures of firm performance as additional measuresof firm productivity: number of products exported by firm j to country c inyear t − 1 (denoted by N U M P RODjct−1 ) and number of export destinationsof firm j in year t (denoted by N U M DESTjt−1 ). These are proxies for pro13

See Bhattarai (2009) for empirical evidence and literature survey.

21

ductivity since our model implies that the number of products sold by a firm
to a given destination increases with productivity. Likewise, the number ofdestinations also increases with the productivity of the firm.
In addition, we allow destination characteristics (denoted by Destct ) toaffect producer price responsiveness. In the destination characteristics, we
include the distance between the largest city in Brazil and the largest city
in country c (denoted by Distc ) as our measure of transport cost, market
potential of country c in year t (denoted by M KT P OTct ) as our measureof market size, and log variance of the annual real exchange rate between
country c’s currency and the US dollar (denoted by XRAT V OLc ) as ourmeasure of exchange rate volatility. Market potential is constructed in Headand Mayer (2004). This measure is essentially a country’s market size weighted
by its bilateral distances from other countries, where the weighting attempts
to control for the country’s accessibility from other markets. Market potential
is used instead of GDP because market potential should better approximate
the degree of competition at the destination’s market. Mayer, Melitz andOttaviano (2011) use this measure for a similar purpose.
We also allow the price responsiveness to vary according to distribution
margins at the two-digit industry level (denoted by DIST M Gind(i) ). Each8-digit product i is assigned to a two-digit industry according to a correspondence table that maps NCM codes to NAICS industries. Distribution margins
are meant to capture the components of the consumer price that are not included in the producer price. We use the measure constructed in Campa andGoldberg (2010), which is calculated from input-output tables of various countries and consists of transportation and storage costs as well as wholesaler andretailer charges.
Table 8 confirms our key prediction that product ranking is a major determinant of variability in producer price responsiveness, and hence exchange
rate pass-through. Even after controlling for heterogeneity contributed by ahost of firm, industry and country characteristics, all four measures of theladder continue to be statistically and economically significant determinantsof price responses to real exchange rate shocks. At the firm level, in addi22

tion to firm size and average wages, the number of products exported also
emerge as an important determinant of producer price response to exchange
rate movements.
An increase in bilateral distance and hence associated transportation costs
reduces producer price responsiveness, consistent with our theoretical model.
An increase in distribution costs, as predicted by (8), increases producer priceresponsiveness via its impact on the local component of per unit costs; however,
the result is not statistically significant. This last finding is not surprising,
given the difficulty in measuring distribution costs.
Our empirical results with regard to exchange rate volatility and market potential confirm the empirical evidence and economic intuition from theendogenous currency invoicing literature. Higher exchange rate volatility andsmaller market potential are associated with a smaller chance of local currency
pricing, and hence a smaller response of producer prices to real exchange rates.
The empirical results in this section firmly establish that product ranking
is a key component of producer price responsiveness to real exchange rate fluctuations. We now proceed to empirically test the remaining set of predictionsof our theoretical model.
4.2.2

Response of Product Scope to Real Exchange Rates

Our theoretical mechanism predicts an increase in product scope following areal exchange rate depreciation. We test this prediction by estimating thefollowing equation:ln (1 + N U M P RODjct ) = µjc +ϕ(t)+β ln (RERct )+Xjt−1 γ+Zct δ+εijct (13)where N U M P RODjct measures the number of products exported by firm j
to country c in year t, and µjc is a firm-destination fixed effect. We use twodifferent samples to test the robustness of our predictions. In the first sample
we only include firm-destination pairs that sell at least one product in every
single period of the dataset (this only accounts for the intensive margin of23

product scope). In the second sample, for each firm-destination pair we record
the first and the last year of positive export sales. These are the periods during which each firm-destination pairs are "active" in our sample. We only
keep observations for periods during which the firm-destination pair is active,
including periods of zero sales. As opposed to the first sample, the second sample includes both the intensive margin of product scope (firms increasing anddecreasing the number of products exported to a given destination), and theextensive margin of firms exiting a given destination in a given year, possibly
due to exchange rate movements.
We report the results in Table 9. In both samples, product scope does
increase in response to depreciation, confirming the prediction of the model.14
4.2.3

Response of Skewness of Sales Distribution to Real Exchange
Rates

In this section, we test our model’s predictions concerning the response of
relative sales to real exchange rate fluctuations. From our theoretical model,a real exchange rate depreciation leads to weaker market competition and, in
response, firms focus their economic activities on products further away fromtheir core expertise. Thus, skewness of export sales falls in response to realexchange rate depreciation. We test this prediction by estimating equation:

ln Skewnessjct = µjc + ϕ(t) + β ln (RERct ) + Xjt−1 γ + Zct δ + εijct(14)

where Skewnessjct is measured by either sales of the core product relative tosales of the second-mostimportant product of firm j in country c in year t R1 
jct(denoted by R2 ) or sales of the core product relative to sales of the rest ofjct

14

In order to be fully rigorous, we would need to estimate a tobit model for equation (13).
However, the maximum likelihood estimation of tobit models with fixed effects introduces anincidental parameters problem (see Greene (2004)). Since the OLS estimator of β is biased
towards zero, and we find statistically significant positive estimates for this parameter, weonly report results using OLS.

24

the products of firm j in country c in year t (denoted by

R1
P jctkRjct!
), and µjc

k6=1

is a firm-destination fixed effect.
Table 10 reports the results. In both cases skewness decreases in response
to a depreciation. The results are statistically significant when the second
measure of skewness is used.
The results obtained estimating 13 14 empirically confirm our theoreticalresult that following an exchange rate depreciation, firms reallocate resources
towards less efficient use. This is consistent with the key finding of Mayer,
Melitz and Ottaviano (2011).

5

Robustness Exercises

In this section, we investigate the robustness of our results regarding the heterogeneity of producer price responsiveness to exchange rate movements.
Our first exercise restricts our sample to firm-product-destination tripletsthat are always active, getting rid of the extensive margin of adjustment. The
extensive margin can be a source of incomplete exchange rate pass-through tothe extent that a depreciation moves the Melitz export productivity threshold up, inducing less efficient firm-product pairs to start exporting. Thatphenomenon alone could potentially explain aggregate producer price responsiveness to exchange rates, even if producers did not adjust markups following
exchange rate movements (see Campos (2010)). However, Table 11 showsthat even after restricting the sample to firm-product-destination triplets thatare always active throughout 1997 to 2006, more productive firms tend toincrease producer prices more than less productive firms following a devaluation. Within firms, products closer to the core face larger increases in producer
prices following a devaluation.
Our second exercise investigates whether the product ladder result is robust
across industries. Equation (11) is separately estimated for each NAICS 2-digit
industry. Results are reported in Table 12 and are shown to be remarkably
robust across industries.
25

Next, we investigate alternative specifications for the Ladderijct variables
in regression (11). In our previous specifications, we allowed the ranking of agiven product within a given firm and destination to vary over time. In thenext two exercises we use Ladderijct variables that are constant over time. The
specifications in Table 13 keep only multi-product firms and use only productsthat were ALWAYS core and that were NEVER core for a given firm ina given destination. Therefore, we have a fixed ranking, avoiding products
changing positions in the ladder in the regressions. The coefficient on the
N everCoreijc variable is still statistically and economically significant.
Another specification for the ladder variable computes rankings at the firmdestination level. For each firm-destination pair, we compute total sales of eachproduct from 1997 to 2006. Rankings are based on these total sales and do
not vary over time. Results using this new definition for rankings are shownin Table 14 - note that the ladder variables no longer display time subscripts.
Results remain robust, except for when we compare the core product with thesecond product.
As a last robustness test, we estimate regression (11) with 2 lags in realexchange rates:

ln pijct = µijc + ϕ(t) + Xjt−1 γ + Zct δ+(15)

β10 ln (RERct ) + β11 ln (RERct−1 ) + β12 ln (RERct−2 ) +(β20 ln (RERct ) + β21 ln (RERct−1 ) + β22 ln (RERct−2 )) × Rankingijct +000(ln (RERct ) β30+ ln (RERct−1 ) β31+ ln (RERct−2 ) β32) × Xjt−1 + εijct

The long-run response of producer prices to real exchange rates is obtained
summing the coefficients β0 + β1 + β2 .15 Table 15 displays these long-run
responses. Our results are still robust to this specification and notion of longrun.
We also estimate a similar version of equations (13) and (14) in order
15

This notion of long-run is distinct from the notion of long-run relationship in the cointegration sense and are not necessarily related.

26

to obtain the long-run responses of product scope and skewness of sales toexchange rate fluctuations:ln (1 + N U M P RODjct ) = µjc + ϕ(t) + β0 ln (RERct ) +(16)

β1 ln (RERct−1 ) + β2 ln (RERct−2 ) + Xjt−1 γ + Zct δ + εijct


ln

1
Rjct
2
Rjct


=µjc + ϕ(t) + β0 ln (RERct ) + β1 ln (RERct−1 ) +(17)

β2 ln (RERct−2 ) + Xjt−1 γ + Zct δ + εijct
Tables 16 and 17 show the long-run estimates of the last two equations.
In conclusion, our results regarding the across- and within-firm heterogeneity of producer price responses to fluctuations in real exchange rates are robust
to the specification of the ladder variables used. The results are also present
even when analyzing each industry in isolation and are robust to the notionof long-run used.

6

Conclusion

We build a theoretical model to explain how multi-product firms adjust prices
in response to exchange rate fluctuations. When there is an exchange rate
depreciation, firms increase their product range and raise producer prices.
The increase in producer prices is greater for products with high sales thanfor those with low sales, a consequence of local distribution costs. As a result,firms’ sales distributions become less concentrated in products with the highest
sales. We empirically test the model’s implications on Brazilian customs data
and find that firms’ responses to exchange rate movements are consistent withthe theoretical predictions.
The theoretical mechanism of this paper has implications for the lack ofsensitivity of aggregate exports to real exchange rate movements. In our model,27

in response to a real exchange rate depreciation there is a within-firm reallocation of resources towards less efficient use and, hence, sales of products further
from the core product increase more.16 While multi-product firms dominate
international trade, these firms export their top products relatively more in
the presence of fixed costs of exporting. Thus, the theoretical mechanism fromour simple model would imply a muted response of aggregate exports to realexchange rate shocks. We leave assessing the quantitative implications of ourmechanism in understanding the exchange rate disconnect puzzle in a morequantitatively relevant model for future work.
16

Similarly, across firms, less efficient firms increase their sales relatively more in response
to a real exchange rate depreciation.

28

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implications. Quaterly Journal of Economics, forthcoming.
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32

Tables

Table 1: Examples of NCM codes
NCM

Description

64011000
64019100
64019200
64019900
64021200
64021900
64029100
64031200
64031900

Waterproof shoes made of rubber or plastic with metal toe protector
Waterproof shoes made of rubber or plastic covering the knees
Waterproof shoes made of rubber or plastic covering the ankles
Other seamless waterproof shoes made of rubber or plastic
Shoes for ski and snowboard made of rubber or plastic
Shoes for other sports made of rubber or plastic
Other shoes made of rubber or plastic covering the ankles
Shoes for ski and snowboard made of leather
Shoes for other sports made of leather

90031100
90031910
90031990
90039010
90039090
90041000
90049010
90049020
90049090
90051000
90058000
90059010
90059090

Plastic eyeglasses frames
Metal eyeglasses frames
Eyeglasses frames, other materials
Eyeglass hinges
Other parts for eyeglasses frames
Sunglasses
Eyeglasses for correction
Safety eyeglasses
Other eyeglasses for protection or similar articles
Binoculars
Telescopes
Parts and accessories of binoculars
Parts and accessories of telescopes

Table 2: Single- versus Multi-Product Firms

Single-Product Firms
Multi-Product Firms

Fraction ofExporters

Fraction of
Employment

Fraction ofExport Value

Fraction of
Unit-Value Obs.

0.48
0.52

0.35
0.65

0.24
0.76

0.11
0.89

33

Table 3: Top 10 Export Industries
2-digit NAICS
15
27
34
24
29
35
32
21
19
31

Industry Description
Food and Beverages
Metallurgy
Assembly of Automotive Vehicles
Chemicals
Machinery and Equipment
Other Transportation Equipment
Electronic Components
Pulp, Paper and Paper Products
Leather Products and Shoes
Electrical MachineryAll

Fraction of Total
Export ValueMedian #of Products

0.23
0.15
0.14
0.08
0.08
0.06
0.04
0.03
0.03
0.02
1.00

3
9
39
7
19
7
19
8
3
29
11

Table 4: Relative Importance of Products In Firm Export SalesMedianExport Value 1st / Export Value 2nd
Export Value 1st / Total Export Value of the Rest

3.36
2.42

Table 5: Top 10 Destinations for Manufactured Products
Destination

Percentage of Exports

United States
Argentina
Netherlands
MexicoGermanyJapan
Chile
RussiaGreat Britain
Belgium

24.5
10.1
4.7
4.2
3.5
3.0
2.9
2.5
2.4
2.4

34

Table 6: Response of Producer Prices and Quantities to Exchange Ratesln(RERct )ln(Empjt−1 )Skilljt−1ln(w̄jt−1 )ln(Impjt−1 )ln(P CGDPct )Observations
R-squared(1)
Prices(2)
Quantities

0.2353***[0.021]0.0118[0.008]0.0112[0.029]0.0740***[0.023]0.0561***[0.020]0.0504[0.032]0.2354***[0.054]0.1137***[0.031]0.1273**[0.064]0.0176[0.048]0.0220[0.039]0.9617***[0.111]2239401
0.943

22410780.935

Robust standard errors clustered at the firm level in brackets*** p < 0.01, ** p < 0.05, * p < 0.1

35

Table 7: Responsiveness of Producer Prices to Real Exchange Rates along the
Product Ladderln(RERct )ln(RERct ) × Bottomijct(1)
Bottom/Top(2)
Core/Not Core

(3)First/Second(4)
Log Ranking-0.0137[0.064]-0.0934***[0.019]-0.0120[0.065]0.1213***[0.037]-0.0587[0.071]ln(RERct ) × N otCoreijct-0.0669***[0.010]ln(RERct ) × Secondijct-0.0360***[0.008]ln(RERct ) × ln(Rankingijct )ln(RERct ) × ln(Empjt−1 )ln(RERct ) × Skilljt−1
ln(RERct ) × ln(w̄jt−1 )ln(RERct ) × ln(Impjt−1 )Observations
R-squared

0.0204***[0.008]0.0801*[0.045]0.0386**[0.019]0.0488***[0.016]0.0216***[0.008]0.0843*[0.045]0.0410**[0.019]0.0492***[0.016]0.0121***[0.005]0.0883***[0.026]0.0072[0.013]0.0264**[0.011]-0.0445***[0.008]0.0307***[0.008]0.0755*[0.041]
0.0581***[0.021]0.0496***[0.016]2239401
0.943

2239401
0.943

8203120.977

2239401
0.943

Robust standard errors clustered at the firm level in brackets*** p < 0.01, ** p < 0.05, * p < 0.1

36

Table 8: Decomposition of Producer Price Responsivenessln(RERct )ln(RERct ) × Bottomijct(1)
Top/Bottom(2)
Core/NotCore(3)
First/Second(4)
LogRanking

0.8661***[0.307]-0.1010***[0.025]0.9571***[0.306]0.7495***[0.203]0.9291***[0.293]ln(RERct ) × N otCoreijct-0.0945***[0.012]ln(RERct ) × Secondijct-0.0494***[0.012]ln(RERct ) × ln(Rankingijct )ln(RERct ) × ln(Empjt−1 )ln(RERct ) × Skilljt−1
ln(RERct ) × ln(w̄jt−1 )ln(RERct )×ln(1 + N U M P RODjct−1 )ln(RERct )×ln(1 + N U M DESTjt−1 )ln(RERct ) × ln(Impjt−1 )ln(RERct ) × ln(M KT P OTct )ln(RERct ) × ln(DISTc )ln(RERct ) × ln(DIST M Gind(i) )ln(RERct ) × XRAT V OLc
Observations
R-squared

0.0231**[0.009]-0.0728[0.069]0.0418*[0.025]0.0160***[0.006]-0.0012[0.006]
0.0340[0.022]0.0595***[0.010]-0.1710***[0.024]0.0296[0.057]-2.5830***[0.325]0.0250***[0.009]-0.0684[0.069]0.0448*[0.025]0.0185***[0.006]-0.0022[0.006]
0.0342[0.022]0.0582***[0.010]-0.1753***[0.024]0.0216[0.057]-2.6048***[0.321]0.0113*[0.006]-0.0801*[0.042]0.0394**[0.018]0.0298***[0.008]-0.0037[0.004]0.0139[0.013]0.0577***[0.007]-0.1385***[0.017]0.0197[0.045]-2.3586***[0.177]-0.0480***[0.014]0.0334***[0.011]-0.0691[0.070]0.0529*[0.028]0.0259***[0.007]-0.0092[0.007]0.0375*[0.022]0.0512***[0.010]-0.1622***[0.029]0.0087[0.053]-2.4651***[0.378]1275305
0.948

1275305
0.948

4704900.977

1275305
0.948

Robust standard errors clustered at the firm level in brackets*** p < 0.01, ** p < 0.05, * p < 0.1

37

Table 9: Response of Number of Products to Exchange Ratesln(RERct )ln(Empjt−1 )Skilljt−1ln(w̄jt−1 )ln(Impjt−1 )ln(P CGDPct )Observations
R-squared(1)
Sample of firm-destination
pairs that were always
selling at least one product(2)
Sample of firm-destination
pairs from the periodof first to last export sale

0.0962***[0.016]0.1041***[0.012]0.1181***[0.040]0.0499*[0.027]0.0279*[0.017]0.1929***[0.034]0.1624***[0.010]0.0535***[0.006]
0.0680***[0.020]-0.0152[0.019]0.0091[0.009]0.2352***[0.020]

82402
0.853

6005030.728

Robust standard errors clustered at the firm level in brackets*** p < 0.01, ** p < 0.05, * p < 0.1

38

Table 10: Response of Skewness of Sales to Exchange Rates(1)
Dependent Variable 1 
Rln Rjctln
2
jctln(RERct )ln(Empjt−1 )ln(w̄jt−1 )Skilljt−1ln(Impjt−1 )ln(P CGDPct )Observations
R-squared(2)
R1
P jctk
Rjct

!

k6=1

-0.0449[0.030]0.0016[0.011]-0.0201[0.033]-0.0383[0.049]0.0181[0.019]-0.0803[0.064]-0.0744**[0.033]-0.0281**[0.013]-0.0453[0.037]-0.0585[0.055]0.0064[0.021]-0.1373**[0.068]2688830.652

2688830.687

Robust standard errors clustered at the firm level in brackets*** p < 0.01, ** p < 0.05, * p < 0.1

39

Table 11: Firm-Product-Destination Triplets Always Activeln(RERct )ln(RERct ) × Bottomijct(1)
Top/Bottom(2)
Core/NotCore(3)
Top/Bottom(4)
Top/Bottom-0.1908**[0.097]-0.0809***[0.022]-0.1994**[0.098]-0.0265[0.070]-0.2440**[0.114]ln(RERct ) × N otCoreijct-0.0415**[0.020]ln(RERct ) × Secondijct-0.0212[0.017]ln(RERct ) × ln(Rankingijct )ln(RERct ) × ln(Empjt−1 )ln(RERct ) × Skilljt−1
ln(RERct ) × ln(w̄jt−1 )ln(RERct ) × ln(Impjt−1 )ln(Empjt−1 )Skilljt−1ln(w̄jt−1 )ln(Impjt−1 )ln(P CGDPct )Observations
R-squared

0.0371***[0.011]0.1520**[0.060]0.0340[0.029]0.0549*[0.029]0.0605***[0.014]0.0140[0.047]0.1309***[0.037]0.1039***[0.035]0.0742[0.056]

0.0377***[0.010]0.1515**[0.060]0.0348[0.029]0.0552*[0.028]0.0605***[0.014]0.0140[0.047]0.1313***[0.037]0.1039***[0.035]0.0739[0.057]0.0257***[0.008]0.0937**[0.044]0.0108[0.021]
0.0527**[0.021]0.0363***[0.010]-0.0349[0.042]0.1162***[0.022]
0.0912***[0.017]0.0267[0.033]-0.0267**[0.011]0.0454***[0.013]0.1345**[0.057]0.0441[0.031]0.0554**[0.027]0.0601***[0.014]0.0106[0.047]0.1386***[0.037]0.1042***[0.035]0.0734[0.056]2074030.901

207403
0.901

93998
0.941

2074030.901

Robust standard errors clustered at the firm level in brackets*** p < 0.01, ** p < 0.05, * p < 0.1

40

Table 12: Product Ladder By Industry
NAICS

Bottomijct

N otCoreijctln(Rankingijct )

Obs.

15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36

-0.0552***
0.0425-0.0818***-0.0859***-0.0439**-0.0490-0.1311*-0.2073*
0.0371-0.0575***-0.1369***-0.0570**-0.0434*-0.1061***-0.0444-0.7278***-0.1431***-0.3403***-0.1894**-0.0423-0.1954-0.1047***-0.0530***-0.1107-0.0746***-0.1164***-0.0723***-0.0669*-0.0525
0.03130.0073-0.0271-0.0835**-0.0374-0.0184-0.0831***-0.0512**-0.3414***-0.1078**-0.1446**-0.1131*-0.0794**-0.1773*-0.0889***-0.0307***-0.0064-0.0569***-0.0905***-0.0464**-0.0657**-0.0495-0.0687
0.0128-0.0505***-0.0663***-0.0289**-0.0012-0.0371**-0.0231*-0.3816***-0.0603**-0.1605***-0.1107**-0.0344*-0.1217**-0.0632***138619668
85244
114700
86307
40521
17827
26386
2763
197587118014
90681
74092
20389643088714697
18346846166
97283
1579369146
102513

Robust standard errors clustered at the firm level in brackets*** p < 0.01, ** p < 0.05, * p < 0.1

41

Table 13: Always Core Products vs. Never Core Products(1)
AlwaysCore/NeverCoreln(RERct )0.0072[0.096]-0.0938***[0.036]0.0295***[0.011]0.0785[0.072]0.0343[0.036]0.0491[0.030]0.0065[0.012]-0.0083[0.049]0.0740**[0.038]0.0207[0.016]0.0305[0.058]ln(RERct ) × N everCoreijcln(RERct ) × ln(Empjt−1 )ln(RERct ) × Skilljt−1
ln(RERct ) × ln(w̄jt−1 )ln(RERct ) × ln(Impjt−1 )ln(Empjt−1 )Skilljt−1ln(w̄jt−1 )ln(Impjt−1 )ln(P CGDPct )Observations
R-squared

801376
0.963

Robust standard errors clustered at the firm level in brackets*** p < 0.01, ** p < 0.05, * p < 0.1

42Table 14: Ranking Using Total Sales Over The Whole Periodln(RERct )ln(RERct ) × Bottomijc(1)
Top/Bottom(2)
Core/NotCore(3)
First/Second(4)
LogRanking-0.0249[0.064]-0.1108***[0.019]-0.0146[0.065]0.1145***[0.035]-0.0457[0.066]ln(RERct ) × N otCoreijc-0.0539***[0.020]ln(RERct ) × Secondijc

0.0126[0.016]ln(RERct ) × ln(Rankingijc )ln(RERct ) × ln(Empjt−1 )ln(RERct ) × Skilljt−1
ln(RERct ) × ln(w̄jt−1 )ln(RERct ) × ln(Impjt−1 )ln(Empjt−1 )Skilljt−1ln(w̄jt−1 )ln(Impjt−1 )ln(P CGDPct )Observations
R-squared

0.0200***[0.008]0.0798*[0.045]0.0370*[0.019]0.0489***[0.016]0.0115*[0.007]-0.0003[0.027]0.0696***[0.022]0.0495***[0.018]0.0517[0.032]0.0210***[0.007]0.0826*[0.045]0.0403**[0.019]0.0491***[0.016]0.0115*[0.007]0.0002[0.027]0.0696***[0.022]0.0495***[0.018]0.0512[0.032]0.0074*[0.004]0.0859***[0.025]0.0117[0.013]0.0229**[0.010]0.0104**[0.005]-0.0533**[0.022]0.0738***[0.014]0.0329***[0.008]-0.0004[0.021]-0.0231***[0.008]0.0252***[0.007]0.0763*[0.042]0.0492**[0.020]0.0494***[0.016]0.0111[0.007]-0.0000[0.027]0.0726***[0.020]0.0497***[0.018]0.0519*[0.031]22085560.942

22085560.942

7074930.972

2208556
0.942

Robust standard errors clustered at the firm level in brackets*** p < 0.01, ** p < 0.05, * p < 0.1

43Table 15: Product Ladder - Specification with Lagsln(RERct )ln(RERct × Ladderijct )ln(RERct ) × ln(Empjt−1 )ln(RERct ) × Skilljt−1
ln(RERct ) × ln(w̄jt−1 )ln(RERct ) × ln(Impjt−1 )Top/Bottom

Core/NotCore

LogRanking-0.0200[0.0710]-0.0907***[0.0213]0.0243***[0.0084]0.0745[0.0459]
0.0260[0.0201]
0.0332*[0.0177]-0.0163[0.0727]-0.0720***[0.0101]
0.0258***[0.0081]0.0774*[0.0456]
0.0293[0.0202]
0.0337*[0.0178]-0.0662[0.0781]-0.0485***[0.0124]
0.0364***[0.0083]
0.0619[0.0422]0.0491***[0.0213]0.0333*[0.0185]

Robust standard errors clustered at the firm level in brackets*** p < 0.01, ** p < 0.05, * p < 0.1

Table 16: Number of Products - With Lags

β0 + β1 + β2

(1)
Sample of firm-destination
pairs that were always
selling at least one product(2)
Sample of firm-destination
pairs from the periodof first to last export sale

0.0655***[0.0191]

0.0830***[0.0132]

Robust standard errors clustered at the firm level in brackets*** p < 0.01, ** p < 0.05, * p < 0.1

44

Table 17: Skewness of Sales - With Lags

β0 + β1 + β2

Value1/Value2

Value1/ValueRest-0.0382[0.0370]-0.0538[0.0403]

Robust standard errors clustered at the firm level in brackets*** p < 0.01, ** p < 0.05, * p < 0.1

45Figures

4

3.5

R$/US$

3

2.5

2

1.5

1
Apr97

Sep98

Jan00

May01

Oct02

Feb04

Jul05

Nov06

Figure 1: Evolution of the Monthly Nominal Exchange Rate R$/US$,
Jan1997-Dec2006

3.8

3.6

3.4

2006 R$/US$

3.2

3

2.8

2.6

2.4

2.2

2

1.8
1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

Figure 2: Evolution of the Annual Real Exchange Rate 2006 R$/US$, 1997-2006

46

0.5

0.4

0.3

0.2

0.1

0

−0.1

−0.2

−0.3
1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

Figure 3: Annual Variation in the Real Exchange Rate 2006 R$/US$, 1997-2006

Office Machinery and Data Processing Equipment
Coke, Oil Refining, Nuclear Fuel, Alcohol
Food and Beverages
Metal Products
Rubber and Plastics
Chemicals
Pulp, Paper and Paper Products
Wood Products
Leather Products and Shoes
Furniture
Machinery and Equipment
Non-Metallic Minerals
Electrical Machinery
TextilesMetallurgy
TobaccoMedical Equipment, Industrial Automation Equipment,…
Assembly of automotive vehicles, trailers and carts
Other Transportation Equipment
Apparel and Accessories
Electronic Components and Communication Devices
Publishing and Printing
0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

Figure 4: Producer Price Responsiveness to Exchange Rates by Industry

47

Appendix - Skewness of Sales
The import price is given by
σwc
pc (ϕ)τc
=
c
σ−1



τc
ηc
+
ϕqc
σ


(18)

and the consumper price in destination c is given by
pc (ϕ)τc
σwc+ ηc wc =
c
σ−1



τc
+ ηc
ϕqc


(19)

Sales for a product ϕ measured in producer currency is therefore


σ
σ−1

1−σ

−σ


τc
w −σ
η c qc ϕ
σ−1w Y c Pc
+ ηc
1+
ϕ c
στc
ϕqc

(20)

Relative sales of product ϕ1 vis-a-vis product ϕ2 , ϕ1 > ϕ2 , is given by

qc ϕ1
1 + ηcστ
c



τc
+ ηc
ϕ1 qc



qc ϕ2
1 + ηcστ
c



τc
+ ηc
ϕ2 qc

−σ 
−σ 

1
ϕ1



1
ϕ2



(21)

We show that the derivative of (21) w.r.t the real exchange rate, qc , is(σ − 1) (ϕ2 − ϕ1 ) ηc τc2 σηc + ϕτ1cqc

−σ

ϕ2 (τc (1 + σ) + ηc qc )<0
ϕ1 (τc + ϕ1 qc ηc ) (τc + ϕ2 qc ηc ) (τc σ + ϕ2 qc ηc )2(22)
since σ > 1 and ϕ2 < ϕ1 . Thus, real exchange rate depreciation leads to a
reduction in the skewness of sales within a firm.
η + ϕτ2cqc

−σ48