Firm Heterogeneity and Costly Trade: A New
Estimation Strategy and Policy Experiments.
Ivan Cherkashiny

Svetlana Demidovaz

The Pennsylvania State University

McMaster University

Hiau Looi Keex
World Bank

Kala Krishna{
The Pennsylvania State University, and NBER
Job Market Paper
November 30, 2009This is a preliminary draft. Please do not cite or circulate without permission of the authors. We arevery grateful to Jonathan Eaton for comments on an earlier draft.
y
Department of Economics, Pennsylvania State University, University Park, PA, 16802.

E-mail:ivt101@psu.edu. Phone: (814) 441 1348.
z
Department of Economics, McMaster University, Hamilton, ON, Canada L8S 4M4. E-mail: demidov@mcmaster.ca. Phone: (905) 525 9140, ext.26095. Fax: (905) 521 8232.
x
Development Research Group –Trade, The World Bank, 1818 H ST NW (MSN MC3-303), Washington,
DC 20433. E-mail: hlkee@worldbank.org. Phone: (202) 473 4155. Fax: (202) 522 1159.
{
Department of Economics, Pennsylvania State University, University Park, PA, 16802.
kmk4@psu.edu. Phone: (814) 865 1106. Fax: (814) 863 4775.

1

E-mail:Abstract
This paper models and estimates the responses of …rms (who di¤er in their productivity
and face …rm and market speci…c demand shocks) to trade policies in di¤erent productand export destinations. The paper does three things. First, it builds a tractable partial
equilibrium model in the spirit of Melitz (2003) which incorporates both of these dimensionsof heterogeneity and is well-suited for empirical work. Second, it shows how to use this model
to estimate the structural parameters of interest using only cross-sectional data. Third, it
uses the model to perform counterfactual experiments regarding the e¤ects of reducing costs,
both …xed and marginal, or of trade preferences (with distortionary Rules of Origin) o¤eredby an importing country. We …nd both have a catalytic e¤ect which greatly increases exports
to all markets. Our counterfactuals make a case for “trade as aid,”as such polices can createa win-win scenario for all parties concerned and is less subject to the usual worries regarding
the e¢ cacy of direct foreign aid.
Keywords: Rules of Origin, Firm Heterogeneity, Demand Shocks, Policy Experiments
F12, F14, F172

1

Introduction

While motivated by a wealth of empirical evidence from longitudinal plant- or …rm-level
data, most of which highlight the productivity di¤erences between domestic …rms and exporters, heterogeneous …rm models based on the ‡agship model of Melitz (2003) have yet
to be estimated structurally in a way suited to trade policy applications. Our work takes aheterogeneous …rm model literally and confronts it with micro data and actual trade policies
to estimate all of its structural parameters, including the various levels of …xed costs. These…xed costs are at the core of the models and serve as hurdles that productive/fortunate …rms
choose to jump, while those that are less so do not. Our paper then uses the estimatedmodel to evaluate the costs of the di¤erent kinds of trade polices used in practice.
In our model, there are two sources of …rm heterogeneity: …rm speci…c productivity as in
the standard Melitz model, and …rm and market speci…c demand shocks. This is motivatedby what we observed in our data set. We use a …rm level data set on Bangladesh garment
producers, exporting mainly to the EU and the US markets. Most …rms follow the strictproductivity hierarchy predicted in Melitz (2003), namely, that …rms export to all markets
that are easier than the toughest market they export to, and more productive …rms export totougher markets. However, there are a number of violators.1 While these violators are smallin terms of their numbers, they are large in terms of their output. This can be rationalized
by introducing …rm and market speci…c demand shocks. Such shocks allow us to explain
why, given its productivity, a …rm may be very successful in one market but not the other.2
In addition to this two dimensional heterogeneity, we also incorporate, albeit simply, various
real world trade policies, such as tari¤s, preferences, rules of origin, and quotas, into ourmodel. We focus only on the partial equilibrium interaction between Bangladeshi …rms andtake the prices and actions of other …rms operating in the EU and US as …xed.
A closely related paper in the literature is the work of Eaton, Kortum and Kramarz(2008) (EKK from here on). EKK uses customs-level data to understand the patterns of1
2

These violations and what might explain them is the subject of Kee and Krishna (2008).
Eaton, Kortum and Kramaz (2008) also observe similar anomalies in their French …rm dataset andpostulate the existence of …rm speci…c demand shocks rather than …rm and market speci…c demand shocks
as we do.

3

french …rms’exports. Their focus is on constructing the simplest model that …ts most of thefacts, and not on trade policy. They also add a reduced form version of Arkolakis’s (2007)
market access cost to explain the presence of many small …rms with a limited attachment
to the market. Since the …rms in our data set are well established, we dispense with thisadditional wrinkle in our model. We see their work as very complementary to ours. Theylook at the “big picture” and try to match the patterns in …rm-level exports by all French…rms, in all industries, to all countries. As a result, their model is unsuited to zooming inon a particular industry and incorporating the relevant trade policy details as our model
is constructed to do. Our paper is also related to Bernard, Redding and Schott (2009),
which also features market demand shocks in order to determine the export behavior of
multi-product …rms.
The model we develop has two quite novel predictions, which are relevant for policy.
First, the model suggests that a small country can increase its exports enormously if granted
preferences that are relatively easy to obtain, and through policies that reduce …xed costs.
These …xed costs need not be monetary. They may simply be due to the red tape or
corruption prevalent in many developing countries. Conversely, factors that raise export
costs, like corruption or bad infrastructure, can really take a toll on exports. Second, themodel suggests that preferences to developing countries can have a catalytic e¤ect. Rather
than diverting trade away from other markets as predicted in settings without …xed industry
entry costs, preferences given by one developed country can signi…cantly raise the exports tothe other market. This occurs because preferences raise the return to entry in the industry.
Once a …rm has entered the industry, it will serve all markets in which it gets an adequate
demand shock. This e¤ect could be large under circumstances relevant for many developing
countries. The e¤ects of such policies are likely to be blunted by the presence of quotas inother markets.
In our estimation, we simulate our model and then match the generated distributions tothose in the data. In this version of the paper, we match …rm productivity and demand shocks
estimated in a related paper, Demidova, Kee and Krishna (2008).3 In ongoing research, we
3

Demidova, Kee and Krishna (2008) take advantage of a natural experiment in trade policy that provides

clean predictions regarding how …rms should sort themselves across markets in this augmented Melitz model.

4adapt the estimation procedure to use only revenue and quantity information which will also
allow us to generate standard errors for our estimates using the standard bootstrap. The
advantage of the latter approach is that such data is commonly available, and this makes ourprocedure much more widely applicable. Our procedure contrasts to the structural dynamic
approach taken in recent work, which is limited to where data is available over a period oftime. Due to our earlier work, Demidova, Kee and Krishna (2008), and Kee and Krishna(2008), we are con…dent the model we estimate is consistent with the data in its essential
aspects and is not just being imposed on it.
The paper proceeds as follows. Section 2 contains a brief discussion of the empiricalapplication and the data. Section 3 lays out the model with the details of the derivationsin the Appendix. Section 4 lays out the estimation outline. The results are presented in
Section 5, while policy counterfactuals are presented in Section 6. Section 7 concludes.

2

The Empirical Application

The application is the apparel sector in Bangladesh. This has two major sub-sectors: garments made from woven cloth, and those made from non-woven material, namely sweaters
and knitwear. We focus on the woven sector in our estimation below since, for reasons madeclear below, we cannot estimate all the parameters in the model in the non-woven sector.
We will describe the setting in some detail as it is the basis for how we incorporate the trade
policy environment in our model.

2.1

The Trade Policy Environment

There are three main components of the trade environment: the trade policy of the US andthe EU, the trade preferences they granted to Bangladesh, and rules of origin upon which
preferences are conditional. Rules of origin or ROOs,specify constraints that must be met inorder to obtain origin and thereby qualify for country speci…c quotas or trade preferences.4
They can take a variety of forms. The important thing to note is that, whatever the form, if
They then show that these predictions are consistent with the data.
4
For a relatively comprehensive and up to date survey see Krishna (2006).

5

ROOs are binding then the choice of inputs used in production di¤ers from the unconstrainedlevel. Thus, from an analytical viewpoint ROOs raise the marginal costs of production when
they are binding. In addition, they can raise the …xed cost of production as compliance with
ROOs must be documented, and a large part of these documentation costs involve learning
the ropes, and thus, can be treated as a …xed cost. We explicitly allow for such costs ofmeeting ROOs in our model.
2.1.1

The US Environment

In 1999-2003, the US had tari¤s of about 20%, applied on a Most Favorite Nations (MFN)
basis, as well as MFA quota restrictions in place in selected apparel categories for mostdeveloping countries, including Bangladesh. Quotas under the MFA were country speci…c, so
exporting was contingent on obtaining origin: that is, unless the good was shown to originate
from Bangladesh, it could not enter under its quota. US Rules of Origin (ROOs) regarding
apparel products are governed by Section 334 of the Uruguay Round Agreements Act.5 For
the purpose of tari¤s and quotas, an apparel product is considered as originating from acountry if it is wholly assembled in the country. No local fabric requirement is necessary.
Thus, the products of a Bangladeshi …rm are not penalized if the …rm chooses to use imported
fabrics. Bangladesh did not have any trade preferences in the US market and had to competewith garment producers from other countries, such as India and China. However, since there
were quotas on other exporters as well, full competition among supplying countries was still
not the case.6
The agreement on Textiles and Clothing (ATC) of the Uruguay Round provided for aphaseout of MFA quotas, but the phaseout was heavily backloaded. Moreover, countries
could and did choose to remove quotas that were less, or even not binding, before moving on

5For details, please, refer to the following website:

http://www.washingtonwatchdog.org/documents/usc/ttl19/ch22/subchIII/ptB/sec3592.html
6Note that less competitive countries are at less of an disadvantage in the US than they would be in theabsence of the quota as the quota in e¤ect guarantees them a niche as long as they are not too ine¢ cient.
Their ine¢ ciency reduces the price of their quota licenses, while the quota licenses of a very competitive
country would be highly priced.

6

to those that were more binding, making this backloading even more pronounced in e¤ective
terms. In the …rst stage which started in January 1995, 16% of the imports in 1990 were to
moved out of quota with another 17% in the second stage which started in 1998. By 2002,
when the third stage started and a further 18% were to be phased out, these were beginningto bite. However, most of the phaseout was to occur in 2005 when the remaining 49% of
the quotas were to be eliminated. Even quotas that were not eliminated had growth factorsthat made them less binding over time. Thus, during 1999-2003, Bangladeshi quotas for the
US were growing. But Bangladesh faced increasing competition in US markets from otherexporters, especially China, whose quota also rose. The presumption was that once quotas
were completely removed, China would dominate the US market.
We assume for modelling purposes in this paper that all of Bangladesh’s exports to the
US are under quota, and as these quotas are bilateral and product speci…c, …rms have no
choice but to meet origin. This is not a bad assumption: despite the ATC, over the period
for which we have data, about 65- 75% of Bangladeshi exports in value terms were underquota. Quota license prices varied over time but the average Bangladeshi price is reported
to be around 7%. See Mlachila and Yang (2004) for more on this topic.
2.1.2

The EU Environment

During the same period, the EU had an MFN tari¤ rate of 12-15% on the various categories ofapparel. Prior to 2001, apparel from Bangladesh entered the EU under the Least Developed
Countries (LDCs) status of the General System of Preferences (GSP) program with a tari¤preference of 100%. Thus, if the MFN tari¤ was 12%, under GSP, Bangladesh would faceno tari¤. There were no o¢ cial quotas, but exports were under surveillance, so a surge
would likely result in quotas. In 2000, the EU formally announced they would implement
the “Everything-But-Arms” (EBA) initiative in 2001, in which Bangladesh, together with48 other LDCs, would have access to the EU, duty and quota free, provided the ROOs weresatis…ed. This e¤ectively removed any inklings of a quota and granted a 100% preference
margin for garment exports of Bangladesh to the EU. It signi…cantly improved the market
environment, in which Bangladesh garment exporters operated.
EU ROOs on apparel products were considerably more restrictive than those in the US.
7

According to Annex II of the GSP (Generalized System of Preferences) guidebook, which
details ROOs of all products, for an apparel product to be considered as having originatedfrom a country, it must start its local manufacturing process from yarn7 , i.e., the use ofimported fabrics in apparel products would result in the product failing to meet ROOs forthe purpose of tari¤ and quota preferences under GSP or EBA for the case of LDCs. It
would, thus, be subject to MFN tari¤s of about 12% to 15%.
Firms making garments from woven material (woven …rms) mostly assemble cut fabrics
into garments. Given the limited domestic supply of woven cloth8 , it commands a premium
price, so woven garment makers can meet ROOs only by paying a roughly 20% higher pricefor cloth, which translates into a signi…cantly higher cost of production, as cloth is the lion’sshare of the input cost. The cost of cloth to FOB price is roughly 70 75% for shirts, dresses,
and trousers9 , which directly translates into a 15% cost disadvantage.10 For this reason, not
all …rms choose to meet ROOs and invoke preferences while exporting woven products to the
EU. This feature allows us to estimate the …xed documentation costs involved in invoking
preferences and meeting ROOs.11
China and other better o¤ developing countries faced EU quotas and did not have duty
free access. See Brambilla et al. (2008) for more on China and the MFA and ATC. In
addition, in 2000, the EU granted Bangladesh SAARC (South Asian Association for Regional
Cooperation) cumulation.12 This meant that as long as 50% of the value added was from7

For the details, please, refer to the following websites:

EBA user guide: http://europa.eu.int/comm/trade/issues/global/gsp/eba/ug.htm; Annex II on GSP:
http://europa.eu.int/comm/taxation_customs/common/publications/info_docs/customs/index_en.htm.
8
Of 1320 million meters of total demand in 2001, only 190 was supplied locally in wovens, while 660 of
940 million meters of knit fabric was supplied locally according to a study by the company, Development
Initiative, in 2005.
9
See Table 33 in Development Initiative (2005).
10
In contrast, India has the ability to meet its woven cloth needs domestically at competitive prices so thatits …rms can avail themselves of GSP preferences in the EU. As a result, Bangladeshi …rms …nd themselves
at a disadvantage in woven garments.
11
We could not estimate documantation costs separately from other …xed costs of exporting if all …rms
chose to meet ROOs as is the case in non-wovens. This is the main reason why we focus on the woven sector
here.
12

See Rahman and Bahattacharya (2000) for more on this.

8

Bangladesh, materials imported from SAARC countries (which included India that had
plenty of textile production) could be used while retaining Bangladeshi origin. Cumulation
relaxed the constraint on using domestic cloth a little, but not fully as cloth could easily
account for more than 50%. It may even have biased exports to the EU towards goods
using cheaper cloth. It is worth noting that even if China and India could export to the EU
quota free, the preferences granted to Bangladesh made the EU a safe haven. This is clearlyre‡ected in the growth of Bangladeshi exports to the EU in this period relative to that to
the US.
Note that an item exported to the US may be considered as a product of Bangladesh
and imported under the quota allocation of Bangladesh. However, the same item may failto meet the ROOs of the EU and would not qualify for the 12-15% tari¤ preference under
the EBA initiative.

2.2

The Data

We use two data sets. We have a survey of …rms in the industry that constitutes roughly
10% of the population of exporters. The …rm level survey was conducted from the period of
November 2004 to April 2005. It covers 350 …rms, which is about 10% of the total populationof the garment …rms currently operating in Bangladesh. It is a retrospective survey thatobtains information from each …rm for the period of 1999-2004. Unfortunately, the sample is
not fully representative.13 As a result, we expect our estimates to change in the next roundwhen we will adjust the data set to be representative and estimate the model using priceand quantity data.
After cleaning the data to exclude outliers and …rms with incomplete information, andusing only the …rms making woven apparel we are left with roughly 765 observations in our
13First it contains almost all the …rms in export processing zones (EPZs), about 45 in number, as well as

another 100 …rms that were inherited from a previous survey. The remainder of the …rms are sampled fromthe non EPZ …rms. This sample is strati…ed by region (Dhaka and Chittagong) and capacity of the …rm(above the mean and below the mean capacity as given in the registry of exporters). Large …rms (de…ned tohave a capacity above the mean) are sampled at a rate of 3 times that of small …rms. In this version of thepaper we use the entire sample without adjusting it to make it fully representative.

9

estimation. To this survey data we matched customs data on all apparel exports that allows
us to see where the …rms exported, how much they exported and what, and whether theyobtained preferences or not.
Our theoretical model builds on the work of Melitz (2003), to which we add anotherdimension of …rm heterogeneity: …rm and market speci…c demand shocks. In Kee andKrishna (2008), we use this model to see how …rms with di¤erent productivities, facing …rm
and market speci…c demand shocks, are predicted to sort themselves and behave as a resultof di¤erences in tari¤s, quotas, and ROOs of the EU and US. The way in which they doso is then shown to be consistent with the model. For example, we …nd that, as predictedby the model, the probability a …rm only exports to the EU decreases with increases in
productivity, with favorable demand shocks in the US, and adverse demand shocks in the
EU. Conversely, the probability a …rm exports to both the EU and the US increases withincreases in productivity and with favorable demand shocks in the US and the EU. We also…nd evidence suggesting those …rms that only export to the US (whose presence is impossible
without demand shocks) are mainly driven by favorable demand shocks in the US together
with adverse demand shocks in the EU, but not by productivity.
We de…ne three kinds of …rms. Firms that sell to the US only (OUS …rms), to the EU
only (OEU …rms), and those that sell to both the EU and the US (AUS …rms). We use
data on the estimated …rm productivity and demand shock distributions we obtained in
Demidova, Kee and Krishna (2008) in our estimation. However, since we are not using raw
data, but constructs based on it, obtaining standard errors for our estimates is much more
complicated. For this reason we do not report standard errors at this stage, but will do soin the future. As will become clear, it is possible to use just information on price, quantity,
and the fractions of …rms of the three types to estimate the parameters of interest in themodel. Such data is usually available from custom records.

3

The Model

We develop a simple partial equilibrium setting based on the setup in Melitz (2003). There
are two main di¤erences between his work and ours. First, to match the facts reported10

above, we allow for an additional dimension of heterogeneity, which we interpret as demand
shocks, though other interpretations are possible. Second, unlike Melitz (2003), whose model
is a general equilibrium one, ours is explicitly a small open economy partial equilibrium one.
We focus exclusively on the exports of Bangladeshi …rms to the US and EU. We have no
information at the …rm level on the exports of other countries. Hence, all we can do is treat
these exports as outside our model. We do so by assuming the price indices capturing thee¤ects of sales from all but Bangladeshi …rms in the US and EU are given, and that the US
and EU are the only markets for Bangladesh. The latter is a good assumption as exports tothese two markets make up about 93% of all exports in wovens. We also do not model thedomestic Bangladeshi market at all. This is not as bad an assumption as it may seem, as
our …rms do not produce much (about 3%) for the domestic market. This is not surprisingas the domestic market demands di¤erent products from those exported.
We …rst set up the demand side where we describe preferences and how we incorporate
demand shocks into the model. Then, we explain the timing of decisions and model how…rms behave in the presence of ROOs: Following this, we outline the equilibrium conditions
in our partial equilibrium model. In the next section we explain how we estimate our modeland provide our estimation results. Finally, we explain the counterfactuals we run and what
they mean.

3.1

Utility

Utility in country j ( j = US, EU) is given by
Uj = (Nj )1(1)(Cj ) ;

where Nj is a competitively produced numeraire good, which is freely traded and takes a unitof e¤ective labor to produce. Cj can be thought of as the services produced by consuming
the exports of apparel from all trading partners. Thus:0

Cj = @

X

i2

1

j

[Xij ]

j

11

j

1
A

j
j

1

;

(2)

wherej is the set of trading partners for country j: Xij denotes the services produced by

the exports of a trading partner i to country j: Each trading partner produces and sells a
continuum of varieties indexed by !. q(!) is the quantity consumed and z(!) is the demand
shock for the variety !: A higher value of z corresponds to a worse demand shock.
Let the sub-utility function also take the CES form so that0

Xij = @
where
j = 1

i;j
1
j

Z

!2

i;j

qij (!)zij (!)

1

1

j
j

j
j

1

d! A

(3)

;

is the set of varieties from country i available to consumers in country j;and> 1 is the elasticity of substitution between the varieties produced by country ifor export to country j.
We can derive the demand function for a variety qij (!) most simply as follows. Minimize
the cost of obtaining a util, i.e., minimizeZ

pij (!)qij (!) d! s.t. Xij = 1:

!2

(4)

i;j

This gives the cost minimizing solution to this problem denoted by aij (!); which is the unit
input requirement of the variety needed to make a util:aij (!) = zij (!)1
= vij (!)
where vij (!)

zij (!)1j(pij (!))
Pij(pij (!))
Pij

(5)

;

and

Pij =

"Z

!2

1

[pij (!)zij (!)]
ij

j

d!

#1 1

j

;

is the cost in country j of obtaining a util from country i’s exports.

12

(6)

As the demand for a variety is aij (!)Xij ; we getqij (!) = vij (!)

jpij (!)
Pij

(7)

Xij :

As z decreases, the demand shock v increases. Thus, our demand function looks just likethe standard one a lá Melitz, except it has a multiplicative demand shock.
Finally,
j

Pij
Pj

Xij =

Rj
Pj

where Pj is the cost in country j of obtaining a util from all sources:0

Pj = @

X

i2[Pij ]

j

1

j

11 1

j

A

and Rj is the total expenditure on the product in country j.

(8)

As we are considering

Bangladeshi …rms exporting to the US and EU, we now drop i as an index and set j 2
fEU; U Sg :3.2

Pricing and Equilibrium

Firms are heterogeneous in their productivity as well as their demand shocks. The production
structure is summarized in Figure 1. Bangladeshi …rms …rst pay fe in order to get theirproductivity draw

from the productivity distribution G ( ) : After observing their ; theyUSEU
decide whether to enter the US and/or EU markets and pay a …xed cost of fm
and fm;respectively. Once this market entry cost is paid, they see the market speci…c demand shock(vU S and vEU ): These demand shocks are drawn from the distributions Hj (v) for j = EU; U S;
where the draws for each …rm are independent across markets. This assumption is convenient
as it allows us to separate the decisions on entry made by a …rm in each market.14 It is also
not inconsistent with the facts: the correlation between the estimates of demand shocks is
close to zero. If …rms decide to sell in market j, they also incur a …xed cost of production,
14

If demand shocks were correlated, then a …rm may choose to enter a market just to get information on

the state of demand.

13

Figure 1: Production Structure for Bangladeshi Exporters.

f: If they further choose to meet ROOs, they pay f + dj rather than f; where dj is the
documentation cost of meeting ROOs.15
A …rm’s decision on whether to sell in a market or not depends on its value ofand v

in the market. As all varieties are symmetric, while productivities and demand shocks di¤er
across …rms, we can drop ! from our notation, keeping onlywith productivity

and v: A …rm in country iand market demand shock vij in market j will earn revenuerij ( ; vij ) = (1tij ) (qij ( )) pij ( ) = (1tij ) vij Pij j

1

pij ( )1

j

Rij ;

(9)

where Rij = Pij Xij is the total sales to market j and tij is the tari¤ on country i by country
j: The ad-valorem tari¤ on Bangladesh by country j; tBD;j is levied on the price so the …rm
receives (1tBD;j )pBD;j per unit sold at the price pBD;j in market j. As the demand shockis multiplicative, it does not a¤ect the price set by a …rm, so that a …rm’s price depends only
on its productivity. Of course, trade policy and transport costs will also a¤ect pro…ts and

15

Note that both the market entry costs and demand shock distributions can di¤er across markets.

14

pricing. Pro…ts areij (; vij ; tij ;

ij )

= (1

tij ) qij ( ) pij ( )= (1tij ) qij ( ) pij ( )w ij

qij ( )

(10)

f

w ij
qij ( )
(1 tij )f:

It is easy to see that …rms set consumer prices as if their marginal costs werewhile receiving only (1(11)= w (1 ijtij ) ,tij ) of their variable pro…ts. As usual, due to the CES framework,

the price paid by consumers is p ( ) =(1 tij ): We set labor units to be such that wages (w)j w ijj; and dj
is equal to a dollar in our partial equilibrium model. Thus, all …xed costs f; fe ; fm

are in terms of labor units and are expressed in dollars.
To sell in a market, a …rm has to pay a …xed production cost f and, if it chooses to meet
ROOs, documentation costs dj as well. However, meeting ROOs could raise direct marginal
costs, and this possibility is allowed for by having direct marginal costs be
are met. Of course,1

when ROOs

1 as ROOs are costly to meet. In addition, there are transportation

costs of the iceberg form

BD;j > 1; j = U S; EU; so that marginal costs are increased bythis factor. As marginal costs remain constant despite these complications, we can look at
the decision-making in each market separately.
3.2.1

Stage 3As usual, the model is solved backwards. In Stage 3 we can de…ne the minimal demand
shock v ( ; PBD;j ) ; j = U S; EU; which allows a …rm with productivity

to earn zero pro…ts

in market j. Due to the fact that pro…ts are increasing with demand shock in each market,
all …rms with a demand shock above v ( ; PBD;j ) will sell in market j: In addition, for the
EU market we de…ne the demand shock v ROO ( ; PBD;EU ) such that additional pro…ts frominvoking the EU ROOs just cover the documentation costs of meeting them.16 From the zeropro…t conditions (see the Appendix for more detail) it can be shown that the relationship

16

Note that there is no such shock for …rms in the US market as all Bangladeshi exporters have to meet

the US ROOs since the US has country speci…c quotas.

15

between v ( ; PBD;EU ) and v ROO ( ; PBD;EU ) isv ROO ( ; PBD;EU ) = C ROO v ( ; PBD;EU ) ;

(12)

dEU
EU 1 (1tBD;EU )

(13)

where
C ROO =

f

EU

1

> 1;

so that only a fraction of …rms in the EU, the most advantaged ones, invoke ROOs. As
expected, C ROO increases, and this fraction decreases, as preferences become less attractive: i.e., as tari¤s are lowered, or the documentation costs or marginal costs of meeting
preferences increase. Equation (12) points out that once we know the demand shock cuto¤,v ( ; PBD;EU ) ; we also know the corresponding one for meeting ROOs.
3.2.2

Stage 2

In Stage 2, we de…ne the productivity level

BD;j of the marginal …rm in market j: For any

productivity level ; the expected pro…t from selling in market j is the integral of pro…ts
over the demand shocks exceeding v ( ; PBD;j ). The …rm with productivity

BDjis, byde…nition, indi¤erent between trying to access market j and not doing so. Hence, givenj; the …xed cost of
its productivity, its expected pro…ts from accessing market j equal fm

doing so.17 As expected, pro…ts are increasing in: only …rms with productivity above

BD;j expect to earn non-negative pro…ts on average once their demand shocks are realized,

and hence, only such …rms choose to try their luck in this market. This gives the cuto¤
productivityjin terms of the model’s parameters. (See equations (17) and (20) in the

Appendix.) Knowing

BD;j and v (; PBD;j ) in each market allows us to depict the trade-o¤

between the demand shocks and productivities of …rms in each market as done in Figures 2
and 3, where a downward sloping locus re‡ects the fact that the demand shock needs to bereally low to force a very e¢ cient …rm to exit the market.

17

Note that the expected pro…ts for the EU market consist of 2 parts: the expected pro…ts from exporting

without ROOs and the expected additional pro…ts from invoking the EU ROOs multiplied by the probabilityof getting high enough demand shock.

16

Figure 2: Demand Shock-Productivity Trade-o¤ for the Exporters to the US.

Figure 3: Demand Shock-Productivity Trade-o¤ for the Exporters to the EU.

17

3.2.3

Stage 1In Stage 1 we use the free entry condition to derive the mass of entrants in the equilibrium.
Our solutions forj; j= U S; EU; depend on the aggregate price indices in the market.

These price indices fall with increases in the mass of entrants. This reduces pro…ts at any
given

and v; which shifts the cuto¤ locus upward and raises the cuto¤ productivity in eachmarket, thereby reducing ex-ante expected pro…ts from entry. The equilibrium entry levelis such that the expected pro…ts from entering the industry, obtaining a productivity draw,
and choosing optimally from there onwards equal the cost of doing so, fe : (See equation (24)in the Appendix.) We will use the model and the available data on Bangladeshi …rms toestimate the model’s parameters. The estimation procedure is outlined next.

4

Estimation Outline

Identi…cation of the parameters is conditional on a number of basic assumptions stated andbrie‡y discussed below. First, the model is structured so that decisions across markets are
made separately. This simpli…es the derivations signi…cantly. However, the assumptions
needed to do so may not hold strictly in the real world. For example, marginal costs may
not be constant. They could decrease, or the …rm could be subject to capacity constraints
so that marginal costs would rise steeply at some point. In addition, incurring some …xed
market entry costs may reduce or raise others, or demand shocks may be correlated acrossmarkets so that entering one market may provide information, which could be valuable inanother. We abstract from all such issues and assume all costs are particular to the market
and that there are no such spillovers across markets. Second, we assume the US and EU
markets make up the entire world market for Bangladesh. This is not such a bad assumption
as in 2004 about 93% of total Bangladeshi exports in apparel went to the sixteen countries inthe EU or to the US. Relaxing this assumption would a¤ect the ex-ante entry condition andwould tend to raise the estimate of fe : Third, we make assumptions about the parametricform taken by the distributions we recover. We assume all entrants draw their productivities(as well as demand shocks) from a distribution that is approximated by a Weibull distribution

18

with density function
f (x) =
where

and

x

1

x

e ( ) ;

(14)

are the shape and scale parameters for the Weibull. The two parameter

Weibull has a very ‡exible form: it can approximate the exponential or normal distribution and, when truncated as required by the model, closely …ts the observed productivity
distributions. We denote the distribution for productivity shocks by G( ). Similarly, thedistribution for demand shocks is denoted by H(v). Demand shock distributions are country
speci…c, while productivity distributions are not.

4.1

Estimation Strategy

We distinguish between what we take as given, the data, and the parameters to be estimated.
4.1.1

Trade Policy Data

We take the values for(the per-unit cost of meeting the ROOs), t (tari¤s), and(transport

costs) to be set at levels roughly in line with the speci…cs of the market. As ROOs involve
using domestic cloth, which is about 20% more expensive than imported cloth in the woven
industry, and as roughly 75% of the cost is the cloth, we assume a 15% cost increase frommeeting ROOs and so set= :85 in wovens. The quotas in the US have a license priceassociated with them. As these quotas are binding, this license price is positive. It has beenroughly estimated to be about 7% of costs,18 which is denoted by

below. As there arequotas in the US, ROOs must be met by all …rms so that for the US market, we cannot
separately estimate documentation and …xed costs. As ROOs are easy to document in the US
since only assembly is required, we set dU S = 0: In the EU as some …rms meet ROOs whileothers do not, we can estimate d and f separately. Transport cost estimates for the apparelindustry range from a low of about 8%19 to a high of roughly 14%20 . We set transport costs
18

In the survey administered by H.L. Kee to a sample of Bangladeshi …rms in the woven sector the average

cost increase from having to buy a license was 7%. This is also in line with estimates in Mlachila and Yang
(2004) for 2003, though their estimates for 2001 and 2002 are higher at about 20%.
19
World Bank (2005), pg. 110.
20
See Gajeewski and Riley (2006), pg 6.

19

of 14% in our estimation. Tari¤s are 12% and 20% in the EU and US, respectively, and t is
set accordingly. This is all summarized in Table 1.
Table 1: Trade Policy Parameters

4.2

t

tROO

+

EU

0.85

0.12

0

1.14

US

1

0.2

0.2

1.14 + 0.07

Estimation Routine

The thirteen parameters we need to estimate consist of
EU ;

EU ;

US;

US;

TFP ;

EU ;

EU
US
U S ; fm =f; fm =f; d=f; fe =f;

T F P ; and f: Typically in such procedures, the strategy involves

guessing the values of the parameters and generating data from the model given these guesses.
The parameters are then chosen to …t certain moments of the data. This is basically what
we do here.
First, we guess values of

EU ;

US
EU
U S ; fm =f; fm =f; d=f;

EU ;

EU ;

US;

US;

T F P ; andT F P : Given these eleven parameters and the ratio of the number of …rms serving the EU

to those serving the US from the data, we can solve numerically for the cuto¤ values fordemand shocks for any given productivity as well as the cuto¤ productivity level in eachmarket. How exactly is made clear below. This tells the program where to truncate andis all the program needs to generate the distributions of TFP and productivity from themodel. In addition, by guessing a value of f; we can generate the distribution of quantities.
We then choose these twelve parameters to make the generated data as close to the actual
data as possible. In our baseline estimation below, we choose to match the distributions of
TFP, quantities and demand shocks for OUS, AUS, and OEU …rms, as well as the shares ofsuch …rms in our data to those generated by the model.
How can knowledge of the eleven parameters and the ratio of the number of …rms serving
the EU to those serving the US from the data yield the required cuto¤s? Take the expression
for ex-ante pro…ts from entering the EU market. This is given by equation (59) in the
Appendix. It is obtained by taking the expected pro…ts in the EU when no ROOs are
invoked, adding the additional pro…ts from meeting ROOs, when doing so is worthwhile, andsetting this sum equal to the costs of entering the EU market. Some additional substitutions
20

are also needed to obtain this expression: in particular, multiplying and dividing the variable
pro…t expressions by the variable pro…ts of the marginal …rm allows variable pro…ts to be
expressed as a ratio of demand shocks times the relevant …xed costs. This is a consequenceof variable pro…ts being multiplicative in the demand shock and the variable pro…ts of themarginal …rm being equal to the relevant …xed costs. Then, dividing both sides by therelevant …xed costs gives the expression below that de…nes the cuto¤ productivity for the
EU market:

dEU
+
f

Z +1

Z +1

v ( BD;EU ;PBD;EU )"C ROO v ( BD;EU ;PBD;EU )C ROO ="

v#v
BD;EU ; PBD;EU

1 dHEU (v)v
C ROO v

BD;EU ; PBD;EUdEU
EU 1 (1tBD;EU )f

EU#

1 dHEU (v) =

1

EU
fm
;
f

(15)

> 1:

Note that the LHS depends only on the cuto¤ demand shock for the marginal …rm, v

BD;EU ; PBD;EU(think of this as a number), while the RHS equals one of the parameters we have set. Similarly, for the US market we have:
Z +1

v ( BD;U S ;PBD;U S )"vv
BD;U S ; PBD;U S#

1 dHU S (v) =

US
fm
:
f

(16)

(See the Appendix for equations (59) and (52) and their derivation.)
Thus, for …xed values of the eleven parameters, equation (15) is a nonlinear equationin only one unknown, (v( BD;EU ; PBD;EU ): Similarly, equation (16) is a nonlinear equationin only one unknown, v( BD;U S ; PBD;U S ): Each has at most one solution as the LHS is adecreasing function in v( BD;j ; PBD;j ).21
Next, we show how to derive the cuto¤ productivities

BD;EU

and

BD;U S : To solve forthe productivity cuto¤s, we de…ne a system of two equations with two unknowns. First,
note that the price index of Bangladeshi exporters to the US (which is simpler to derive as

21

Note that since we have the demand shock cuto¤s for the marginal …rm, we also have the demand sidecuto¤s for all infra-marginal …rms, see equations (53) and (60).

21,everyone has to meet US ROOs to obtain origin under MFA quotas) is given by1= MEBDUS(PBD;U S )

Z +1

Z +1

pBD;U S ( )1

US

(17)

hU S ( )g( )d d ;( ;PBD;U S )BD;U S

where MEBD is the mass of Bangladeshi entrants. Similarly, the price index of exporters from
Bangladesh to the EU (here some …rms meet the ROOs and others do not) is given by
1(PBD;EU )

EU

Z +1

MEBD

=

BD;EU

+MEBD

Z +1

Z

ROO (

;PBD;EU )pBD;EU ( )1EU

hEU ( )g( )d d (18)( ;PBD;EU )

Z +1

ROO

BD;EU

( ;PBD;EU )1
pROO
BD;EU ( )EU

hEU ( )g( )d d :Thus, the ratio of price indexes is just:
1(PBD;U S )(PBD;EU )1

US

=
EU

R +1

BD;U S

R +1( ;PBD;U S )pBD;U S ( )1US

hU S ( )g( )d d
(19);

D( BD;EU ; ( ; PBD;EU ))where D( BD;EU ; ( ; PBD;EU )) equals
Z +1

BD;EU

+

Z +1

Z

BD;EU

ROO (

;PBD;EU )pBD;EU ( )1EU

hEU ( )g( )d d

( ;PBD;EU )

Z +1

ROO

( ;PBD;EU )1
pROO
BD;EU ( )

EU

(20)

hEU ( )g( )d d :Note that the RHS of equation (19) depends only on the productivity cuto¤s and the parameter values we have guessed, since the demand shock cuto¤s depend on these. The LHS is
yet to be found.
From the model, we also know that the ratio of the price indices is de…ned by two zeropro…t conditions (see equations (22) and (23)) so that:(1

PBD;UUSS)v( BD;U S ;PBD;U S )(1 tBD;EU )(1 tBD;EU ) EUEU

RBD;EU

1

EU

BD;EU

1

EU

1

US

BD;EU

!

:

(21)

Equating the RHS of equations (19) and (21) gives one equation in two unknowns,

BD;EU

(1

EU
PBD;EU)= v(

BD;EU ;PBD;EU )(1 tBD;U S )US

RBD;U S

(1 tBD;U S ) U S

1US

BD;U S

BD;U S

22

and

BD;U S as we know the demand shock cuto¤s, v( BD;U S ; PBD;U S ) and v( BD;EU ; PBD;EU )from the previous step. RBD;U S and RBD;EU are obtained from data on total exports to the
US and EU in woven apparel using the COMTRADE data base.
To obtain the second equation needed to solve for

BD;EU and

BD;U S ; we use the ratioof the equations for the masses of Bangladeshi …rms that export to the US and EU:
R +1 R +1
h ( )g( )d d
MEBD
( ;PBD;U S ) U S
MBD;U S
BD;U S= BD R +1:
R +1
MBD;EU
h
(
)g(
)d
d
ME
EU
( ;P
)

(22)

BD;EU

BD;EU

From the Bangladeshi data we observe the ratio of the total number of the US exporters
relative to that of the EU exporters, which describes the LHS of equation (22). This gives
us a second equation in

BD;EU

and

BD;U S : Together, they let us solve for the cuto¤ pro-ductivities. Of course, once we know these cuto¤ productivities, we can also calculate the
P

1

US

S
ratio of price indices ( BD;U
).22
1
EU

PBD;EUAs we know the cuto¤s for demand shocks and productivity, once we …x a level of f , wewill know the level of the price index in each market from the zero pro…t condition in stage
3:(1tij ) qij ( ) pij ( )w ij
qij ( ; Pij )(1 tij )f = 0;

whereqij ( ) =pij ( )Pij

= Pij 1 pij ( )

RBD;j
Pij
RBD;j :

While RBD;j is data, only after f is pinned down can Pij be solved for. Once we have
Pij , we have the output of each …rm. In this way, the choice of f a¤ects the the quantity
distribution in the estimation. A larger f results in a larger Pij which moves the generated
22

To obtain deciles of these distributions, we follow a multi step procedure. In contrast to EKK who

use simulation techniques to derive the percentile points of the distributions so that there is a small error
associated with their estimates, we numerically integrate to do so after …nding starting points throughsimulation.

23

quantity distribution to the right.
In this manner, once we choose values for our thirteen key parameters, we know which…rms will actually produce in each market, the price indices in each market, the share of
OUS/OEU/AUS …rms, the fraction of …rms meeting ROOs, and are able to generate the
distributions of TFP, demand shocks and quantities from the model that are the counterpartsof those we choose to match from the data. In other words, we can fully solve the model.
Then we can recover fe from the free entry condition (see equation (69)).
What remains to be speci…ed is the objective function being minimized in the above procedure. We de…ne the share of …rms (in percentages) that are of the OUS, AUS, and OEU
types in the simulated data from the model by OU S m , AU S m , and OEU m ; while OU S e ;AU S e , and OEU e denote their empirical counterparts. Similarly, let ROOm and ROOe de…ne the shares of …rms (in percentages) that meet the EU ROOs in the model and the data,
respectively. Finally, we take the empirical distributions for TFP, demand shocks and quantity (k 2 fT F P; DS; Qg) for each of the three groups of …rms (l 2 fOEU; AU S; OU Sg).23
We de…ne …ve bins for each distribution with the bounds of a bin given by [xi ; xi+20 ] ; with
i = f0; 20; 40; 60; 80g : We de…ne the bounds so that exactly 20% of the mass of each empirical distribution lies in each bin. We calculate the mass that lies in each bin from …rms oftype k of the generated distributions F e;k;l (xi+20 )F e;k;l (xi ) ; which we call Zie : Of course,

Zi = :2 for all i.
The objective function has three components. De…neA=

2

AU S m AU S e
AU S e

2

+

2

B=

ROOm ROOeROOe

OU S m OU S e
OU S e

C=

XXX
k

l

Zie;k;lOEU m OEU e
OEU e

+

Zik;l

2

;

(23)

(24)

;
2

:

(25)

i

Our objective function is then just the sum of these three or A + B + C:24
23

For …rms that sell to both markets (AUS …rms) we distinguish between the distributions in each market

and give each one equal weight.
24
We experiment with di¤erent weights to the di¤erent components and …nd that the results are relatively

24

The choice of which moments to …t comes from the need to identify all our parameters. It
is worth providing some intuition on how all the parameters are being identi…ed. Matching
the shares of the di¤erent types of …rms and the distributions of demand shocks for eachtype of …rm helps identify the parameters of the distributions of demand shocks. Matching
the share of …rms meeting ROOs helps identify documentation costs, while matching the
distributions of TFP for each type of …rm helps identify the parameters of the TFP distributions. Matching the position of the quantity distributions helps pin down …xed costs ofproduction as explained above. The value of the elasticity of substitution a¤ects price and,hence, quantity so that matching quantity distributions for the di¤erent kinds of …rms also
helps pin down these parameters. The shape of the demand shock distribution in a market,
in turn, helps pin down the …xed market entry costs as is evident from equations (15) and(16).
As discussed above, this minimization process will deliver values of the parameters:
EU ;

US;

EU
US
fm
=f; fm=f; d=f;

EU ;

EU ;

US;

US;

TFP ;

TFP ;

and f: Then fromequation (69) we obtain fe :5

Results of the Estimation

In the core estimation, we estimate the following parameters:
fe =f;

EU ;

EU ;

US;

US;

TFP ;

EU ;

EU
US
U S ; fm =f; fm =f; d=f;

T F P ; and f: The results are given below. Table 2 gives theestimated parameters for the TFP distribution, while Table 3 gives the estimated parameters
for the demand shock distributions in the two countries. These distributions are depicted
below.

insensitive to such changes.

25

Table 2. TFP distribution
Shape ( )0.57

Scale ( )2.05

Implied mean shock25

3.31

Implied Std. Dev.

6.21

The distributions of productivity and demand shocks for AUS …rms (in the EU and US)
and of OEU and OUS …rms are given in Figure 4 and 5, respectively. In these …gures, themodel generated data is represented by dashed lines, while the data itself is represented
by solid lines. As is evident, the distributions for OUS …rms …t the worst. In particular,
our simulated data predicts that OUS …rms are predominantly those with low productivities, while the data has fatter tails for such …rms, so there are more OUS …rms with highproductivities in the data than in the simulated data. This very may well be, since ourmodel has no capacity constraints. If capacity constraints are important, then OUS …rms
would tend to be those …rms, which have a better US than EU demand shock, independentof productivity. This would fatten up the high productivity tail of the simulated data andbetter match the actual data. In essence, without capacity constraints, the model has a hardtime explaining why high productivity …rms do not export to both countries. Incorporating
capacity constraints would help …t the data in this dimension.
The distributions of demand shocks are depicted in Figure 5. These distributions are
given for the AUS …rms in the EU and US and then for OEU and OUS types of …rms,
respectively. The means and standard deviations are reported in Table 3. The demand
shocks in the US are larger than those in the EU with greater dispersion. This is consistent
with the di¤erences in the distribution systems in the two countries. Large retailers, like
Walmart, play a much bigger role in the US than in the EU. Firms that are lucky enoughto land an order from such a large buyer will look like they had a higher positive demand
shock. It is also consistent with the fact that the US is the older market for Bangladesh.
This ties in with the work of Foster, Haltiwanger and Syverson (2008, 2008a). They allow
25
2

The mean equals(1 + 2 )(1 + 1 ) where (:)) denotes the standard gamma function and the variance equals(1 + 1 )

2

.

26

100

Cumulative Distribution Function50
Data, AUS firms
Model, AUS firms
0
0

5

10

15

20

25

TFP
100

50
Data, OEU firms
Model, OEU firms
0
2

4

6

8

10

12
TFP

14

16

18

20

22

100

50
Data, OUS firms
Model, OUS firms
0
0

5

10

15
TFP

20

25

30

Figure 4: Productivity distributions.

100
Data, AUS firms
Model, AUS firms

50
0
0

1

2

3

4
5
6
Demand shock in EU

7

8

9

10

Cumulative Distribution Function

100
Data, AUS firms
Model, AUS firms

50
0
0

10

20

30
Demand shock in US

40

50

60

100
Data, OEU firms
Model, OEU firms

50
0
0

2

4

6
Demand shock in EU

8

10

12

100
Data, OUS firms
Model, OUS firms

50
0
0

10

20

30
40
Demand shock in US

50

Figure 5: Demand shock distributions.

27

60

70

35
Productivity / Demand Shocks Trade-off for EU
Productivity / Demand Shocks Trade-off for US
30

Cut-off Demand Shock

25

20

15

10

5

0
1

1.5

2

2.5

3
Productivity

3.5

4

4.5

5

Figure 6: The trade-o¤ between productivity and demand shock.

for both TFP and demand shock di¤erences between …rms and argue that younger …rms
tend to be at least as productive as old ones, but are smaller, i.e., have smaller demand
shocks. They interpret this in terms of …rms having “market capital”(due to advertising or
consumer experience with their goods), which grows slowly over time.
Table 3. Distribution of demand shocks
EU

US

Shape ( )

0.75

0.40

Scale ( )

2.98

5.34

Implied mean shock

3.55

17.7

Implied Std. Dev.

4.80

55.7

The trade-o¤ between the productivity and the cuto¤ demand shock in the US and EU
markets (see Figures 2 and 3 for the theoretical pictures) is depicted in Figures 6 and 7.
The cuto¤ line for the US is actually below that for the EU, but as their demand shock
distributions are di¤erent, this cannot be interpreted as direct evidence that the US is theeasier market. Rather, one should compare the probability of being active in the US market
28

versus the EU market by integrating over the relevant demand and productivity shocks. In
our estimates, these numbers are roughly 0.22 and 0.26 for the US and EU, consistent with
the US being a tougher market.
Table 4 gives the demand elasticities in each market. These are more than unity andclose to the estimates obtained in Demidova, Kee and Krishna (2008). They are a little
higher in the EU and are similar in magnitude to those found in other structural models like
Foster, Haltiwanger and Syverson (2008a).
Table 4. Elasticities of substitution
EU

US

3.92

2.05

The estimates of various …xed costs relative to f are given in Table 5. Market entry
costs into the EU market tend to be roughly half the size of those in the US, suggesting that
the US is the “big boys club” and much harder to enter. Also, …xed costs of entering theindustry (fe ) are much higher than the cost of entering a market (fm ) in both the US andthe EU.
Table 5. Fixed costs relative to fEU

USRelative market entry costs
fm =f

0.82

11.14

Relative documentation costs
d=f

0.60

—

Relative industry entry costs
52.6

fe =f

As described in the estimation outline, at the second step of our estimation we recoverf and, as a result, all market and industry entry costs. These numbers are summarized inTable 6. Note that these numbers, when added up, give a …gure slightly lower than the
sunk cost estimates in Das, Roberts and Tybout (2007) for knit wear, which is a part of
the non-woven apparel industry. Our estimates are unfortunately not directly comparable29

to theirs for two reasons. First, our numbers are for wovens, while theirs are for knit wear.
Second, our numbers should be interpreted as annualized values since our model is static.
It is worth noting that such a rich structure of …xed costs is rarely estimated. Estimates for
documentation costs, for example, are almost impossible to …nd. A strength of our approachis the ability to provide such estimates. Values of some key endogenous variables are given
in Table 7.
Table 6. Fixed costs in $6

f12,600EU
fm

10,300

EU
+ dEU
fm

10,300+7,600

USfm

140,300fe662,300

Policy Experiments

Before turning to the policy experiments, we need to outline how the partial equilibrium
assumption is implemented in the simulations. From the estimation/calibration procedure
US 1
EU 1
above, we can obtain PBD;US and PBD;EU as explained above.

Just as demand for a variety is the product of the variety’s share of demand (which
depends inversely on own price relative to the aggregate one) times total demand, own
revenue is the product of the variety’s share of revenue (which depends inversely on own
price relative to the aggregate one) times total revenue as given below.

RBD;j =

(PBD;j )1(PBD;j )1 j
Pj
+ i2 ( BD) [Pi;j ]1

j

Rj :

(26)

For example, RBD;U S and RU S are approximated by the total Bangladeshi sales of woven
apparel to the US and the total exports of woven apparel to the US. Thus, we can invertPequation (26) to obtain i2 ( BD) [Pi;U S ]1 U S ; which is what we call P BD;U S : We canobtain P BD;EU in an analogous manner. In our simulations, we keep P BD;EU and P BD;U S…xed in accordance with our partial equilibrium assumptions.

30

We are now ready to look at some policy questions. Our …rst experiment deals witha question of considerable policy importance, namely, the costs of preferences. Developed
countries typically give preferences to developing ones, but require that exporters meet origin
requirements as done by the EU in the EBA. As a result, obtaining these preferences can bequite costly. Consequently, such preferences can be much less generous than they seem. We
use our model to quantify the impact of making such preferences easier/harder to obtain.26
We show, for example, that removing the home yarn requirement results in a surge of entry
and exports.
The second experiment looks at the e¤ects of subsidies to …xed costs. Here, the issue
is that of policy e¤ectiveness. Which subsidies are most e¤ective in terms of promoting
exports? This is relevant for developing countries for a number of reasons. Foreign exchange
may be valuable in itself due to the existence of a “foreign exchange gap”. Moreover, exports
may provide needed tax revenues, or more generally, may be a source of externalities. To
examine this question, we …rst look at the e¤ectiveness of a given dollar value of a subsidy
to di¤erent kinds of …xed costs, as in Das, Roberts and Tybout (2007). Here, we consider
both the short and long run e¤ects and …nd they can go in opposite directions. We also lookat welfare and revenue e¤ects.27 Our work suggests that in the absence of any response fromother countries, as might be expected for a small country (recall the price index of competingproducts is given in the model), a reduction in the …xed costs …rms face can greatly increase
their exports. We discuss the reason for this and provide a decomposition of the relevant
margins.
A interesting and novel …nding is that liberalization in one country can raise exports
to the other rather than lowering them as would be expected a priori. These cross markete¤ects are very large.

26

Mattoo, Roy and Subramaniam (2002) look at the AGOA (the Africal Growth and Opportunity Act)and (on the basis of back of the envelope calculations based on a simple competitive model) argue that
preferences are undone to a large extent by restrictive ROOs.
27
We also decompose the e¤ects into those on the mass of entry (intensive margin on number of …rms),
the cuto¤s (extensive margin on …rms), and the output per …rm (intensive margin on output).

31

6.1

Documentation Costs, Preferences, and ROOs

The preferences given to Bangladeshi exporters by the EU in the woven industry are restrictive for two reasons. First, there is the requirement of using more expensive domestic fabric.
Second, there are documentation costs involved (see Table 6).
6.1.1

Long Run E¤ects

We begin by considering the e¤ects of a series of policies in the long run, i.e., when entry
has time to occur. Table 7 looks at three policy changes and their e¤ects in the long run(i.e., when entry adjusts). Column 1 has the status quo, namely, preferences in the EU thatare costly to meet in wovens. Column 2 looks at the e¤ect of removing these preferencescompletely. This means making the tari¤ in the EU 12% for all Bangladeshi …rms. Column
3 shows the e¤ects of raising documentation costs by a factor of ten relative to the statusquo. Column 4 shows the e¤ect of keeping preferences as in the status quo, but removing
the cost of meeting them in terms of higher priced cloth. To approximate this, we make the
ROOs costless to meet in terms of marginal production costs in wovens. This is a simple wayof capturing policies, like regional cumulation,28 which makes ROOs less costly to meet.29
All reductions in costs make Bangladeshi …rms more optimistic about their expected
pro…ts, and hence, the mass of entrants rises. In addition, more relaxed EU ROOs allow agreater share of Bangladeshi exporters to meet them. This e¤ect also expands the market
share of Bangladeshi exporters in the EU market. There are also strong cross-market e¤ects.
A more liberal policy in the EU results in a greater mass of entrants into the industry, whichraises Bangladeshi exporters’ share in the US market, and reduces the price index there,
though not by as much as that in the EU. Of course, quotas in the US (which we have notyet fully incorporated) will blunt such e¤ects reducing the impact of unilateral liberalizationon the part of the EU.
An important thing to note in Table 7 is that despite ROOs being costly to meet, the28For example, if cheap Indian cloth could be used in production without compromising Bangladeshi

origin, costs of meeting ROOs would fall.
29
Bombarda and Gamberoni (2009) focus on such issues in the context of the Pan European system of
cumulation that EU FTA partners have to respect to gain preferential access to European market.

32

woven apparel industry relies greatly on the presence of the EU preferences. Our model
suggests that in the absence of these preferences, as shown in Column 2, entry would fallconsiderably. Consequently, EU imports from Bangladesh would fall from $2,351 million
to $1,341 million and US imports from $435 million to $222 million, highlighting the crossmarket e¤ects of EU policies.
Raising documentation costs by a factor of ten, as in Column 3, also reduces EU (to$2,067 million) and US imports (to $381 million) from the status quo. Note this is a far
smaller e¤ect than the removal of preferences. Finally, when the home yarn requirement isremoved so that preferences are not costly to obtain, both EU and US imports explode. The
model suggests this would result in exports to the EU of $17,760 million and to the US of$2,839 million. Of course, this is not seen in the data as US exports are still under quota inthis period, and while SAARC cumulation might reduce the cost of meeting EU ROOs, itdoes not eliminate them.
The reason why raising documentation costs by a factor of ten has a relatively small e¤ect
is that marginal …rms (who are smaller, and have lower productivity and so lower sales) are
the ones more a¤ected by this change. This suggests that policies that a¤ect marginal costs(like removal of preferences, as in Column 2, or reducing the cost of meeting preferences, as
in Column 4) also a¤ect entry, and hence, …rms of all types, and tend to have more bangthan ones that a¤ect only marginal …rms.
It is worth pointing out that giving preferences results in enormous e¤ects (both in
the US and EU), even when there are restrictive ROOs, compared to back of the envelope
calculations that ignore the role of entry like Mattoo et. al. (2003). When exporting becomesless promising, as when documentation costs rise or preferences are removed, the direct e¤ect
on pro…ts is negative, which raises the productivity cuto¤. As a result, there is a fall in entryof Bangladeshi …rms. This fall in entry raises the price index in both the US and EU (as isevident in Table 7), making pro…ts swing upwards, which, in turn, acts to reduce the cuto¤
productivity. The latter e¤ect on the cuto¤ dominates empirically so that when exporting
becomes less promising, productivity cuto¤s of Bangladeshi …rms fall in both the US and
EU.30
30

Note this is not a a full GE setup where …rms choose which country to enter, Bangladesh, the US or33

6.1.2

Long Run Welfare Consequences

What about the welfare e¤ects of these policies? There are two main channels through which
policy regarding Bangladeshi …rms a¤ects the welfare of the EU households: via consumer
surplus and tari¤ revenue. Changing policies impacts the value of tari¤ revenues, T REU ,earned by the EU both via the number of Bangladeshi exporters who pay a tari¤ and via
the volume of their sales.
In addition, policy changes a¤ect the EU price index. In particular,
2

where

P

i2

PEU = 4(PBD;EU )( BD)[Pi;EU ]1

1

EU

EU

+
i2

X

( BD)

31 1

EU

[Pi;EU ]

1

EU

5

(27)

;

= P BD;U S . Recall that P BD;U S was calculated earlier and isheld …xed at this level in our counterfactual experiments. However, (PBD;EU )1

EUchanges

as we change the EU policies. The change in welfare is approximately the change in tari¤
revenue plus the change in consumer surplus. The latter is roughly equal to the consumptionof the aggregate good (services) times the change in the aggregate price. The e¤ect on tari¤
revenue and the percentage change in the price index are also reported in Table 7.
As is evident, removing preferences given to Bangladesh by the EU increases EU welfare
by roughly 135 million dollars. Thus, it seems like giving preferences is not in the EU’s own
narrow self interest. The removal of preferences reduces ex-ante pro…ts and, hence, entry.
This reduction in the mass of entering …rms results in a very large fall in exports to both the
EU and US, with a consequent fall in consumer surplus and tari¤ revenue in the US and afall in consumer surplus, but an increase in tari¤ revenue in the EU. While US welfare falls,
EU welfare rises as the revenue e¤ect dominates. Removing EU preferences reduces welfare
in the US by about 45 million dollars, while raising it by about 135 million in the EU.
When documentation costs are raised, ex-ante pro…ts fall as does the mass of entry. This
raises prices, which acts to reduce welfare, but as fewer …rms invoke ROOs, tari¤ revenues
increase, which acts to raise welfare. The latter e¤ect dominates so that welfare in the EU
rises by about 33 million dollars, while welfare in the US falls by about 12 million dollars.
the EU. In such settings, protection in a country raises the productivity cuto¤.

34

Finally, removing the home yarn requirement raises ex-ante pro…ts, and hence entry, withconsequent increases in welfare and tari¤ revenue. Note that welfare rises in both the US
and EU by 309 million and 513 million dollars, respectively.31
6.1.3

Short Run Results

Table 8 looks at the same policy changes, but limits the analysis to the short run. In
calculating these impact e¤ect estimates, we turn o¤ the entry channel and look at the e¤ect
on …rms that have already decided to be in an industry and market. Hence, we keep the massof …rms that enter the industry and the productivity cuto¤ of …rms that enter a particularmarket …xed at their initial estimated levels and allow the experiment only to a¤ect theposition of the productivity-demand shock trade-o¤s, and via this, all other variables.32
Preferences and Documentation Costs In the short run, removing preferences makes
the market directly less attractive. As a result, the demand shock cuto¤ for any given
productivity rises. Since each active …rm has lower marginal costs (recall ROOs raised
marginal cost when met), the price it charges falls. However, …rms now pay tari¤s, whichraises the price consumers pay. Since tari¤s are 12%, while the cost disadvantage is 15%,
the price charged to consumers falls, which reduces the price index. This fall in the priceindex raises the sales of Bangladeshi …rms, and their share in EU imports. But what theyreceive post tari¤ falls, and this is what results in exit in the long run. Tari¤ revenues risequite considerably in the short run, but from comparing them to those in Table 7, we see
this will only be temporary. Bangladeshi export revenues fall when preferences are removed,since each …rm sells less and some do not sell at all. However, these e¤ects are muted as wehave turned o¤ the main channel, namely, entry/exit. It is worth emphasizing the di¤erencein the long run export e¤ects (both in the US and EU) of preferences, even when there are
31

Giving preferences has less of a bang than removing the home yarn requirement as ROOs are quite strict

and the tari¤ revenue losses from giving preferences are not outweighted by the consumer surplus gains.
32
Another way to say this is that we have bygones be bygones: …rms that have entered the industry andmarket only choose whether they can cover their variable and …xed costs of production. Those that have too
low a demand shock to do so exit. Thus, the productivity cuto¤ is not a¤ected unless there is no demand
shock, at which a …rm with the cuto¤ productivity level wants to export.

35

restrictive ROOs, and the short run ones in Table 8. Back of the envelope calculations that
ignore the role of entry like Mattoo et. al. (2003) could easily underestimate these long rune¤ects, or even get the e¤ect on welfare reversed. Documentation cost increases have similare¤ects, except that they do not a¤ect the demand shock cuto¤, only the margin where …rms
make use of ROOs.
Allowing the Use of Imported Cloth Removing the no home yarn requirement has asomewhat surprising e¤ect. Here, the demand shock cuto¤ rises, while the price index falls.
The reason is that allowing foreign cloth to be used reduces the marginal cost of …rms thatmeet ROOs. This lower cost of production for …rms that used to meet ROOs and continue todo so reduces their price. In addition, more …rms choose to meet ROOs, and these …rms also
reduce their price as they do not pay tari¤s and do not have to use costly domestic cloth.
Thus, the aggregate price index falls. This raises the demand shock cuto¤ the marginalexporter (not the marginal …rm meeting ROOs) requires to export. Bangladeshi export
revenue rises, but again, the e¤ects are muted in the short run.

6.2

Subsidizing Fixed Costs

Which …xed costs should be subsidized? Is there a di¤erence? Table 9 looks at this question interms of promoting exports. It compares the e¤ectiveness of a given dollar value ($1,000,000)
of a subsidy to di¤erent kinds of …xed costs. In this, it follows Das, Roberts and Tybout(2007). The results suggest the export e¤ects are very large but of roughly the same size
irrespective of the …xed costs being subsidized. A policy maker wanting to increase exports
would get about a thirty dollar increase in export revenue for every dollar spent reducing…xed costs. We also …nd that cross market e¤ects are large: if the EU market entry is
subsidized, the US market entry (and tari¤ revenue) rises by almost as much as the EU
market entry (and tari¤ revenue). Thus, policies have large spillover e¤ects.

36

Table 7. Long-run equilibrium implications of policy changes (value in dollars)Baseline

No preferences

Higher doc. costs

No home yarn req.

Tari¤ in EU (tBD;EU )

12%

12%

12%

12%

Tari¤ in EU, ROO (tROO
BD;EU )

0%

12%

0%

0%

Tari¤ in US (tBD;U S )

20%

20%

20%

20%

Cost disadvantage ( )

0.85

1.00

0.85

1.00

Relative Documentation costs (d=f )

0.60

0.00

6.0

0.60

EU imports from Bangladesh

2,351m

1,341m

2,067m

17,760m

US imports from Bangladesh

435m

222m

381m

2,839mBangladeshi Exports

Change in Bangladesh …rms’ market share, %Share of EU imports

5%

-43%

Share of US imports

1.4%

-49%

-12.1%+655%-12.4%+553%

Change in number of …rms, %Implied # of entrants

2,583-49.5%-14.4%+619%

Implied # of …rms trying EU

859-49.3%-14.1%+600%

Implied # of …rms trying US

844-49.3%-14.3%+584%# of successful exporters to EU

682-49.3%-14.2%+567%# of successful exporters to US

563-48.9%-12.1%+582%

Productivity cuto¤ in EU

2.4347-0.85%-0.22%+4.53%

Productivity cuto¤ in US

2.5025-0.66%-0.17%+8.06%

Demand shock cuto¤ in EU

2.2403+0.25%-0.00%+34.1%

Demand shock cuto¤ in US

1.1186

0.00

0.00

0.00

Share of ROO …rms (model)

43%

0%

Share of AUS …rms

61.8%

61.8%

61.8%

59.8%

Share of OEU …rms

26.8%

26.9%

26.9%

27.0%

Share of OUS …rms

11.4%

11.3%

11.3%

13.2%

Change in Productivity cuto¤s for exporters, %Change in Demand Shock cuto¤s, %Share of …rms invoking ROO
13.4%

100%

Share of AUS, OEU and OUS …rms

Change in EU and US price indices, %Price Index in EU

100%+0.77%+0.22%-13.48%

Price Index in US

100%+0.66%+0.17%-7.45%

Change in tari¤ revenues, %Tari¤ Revenue in EU

7,506k+2044%

+505%

-100%

Tari¤ Revenue in US

87,000k

-49%

-12%

+553%

Approximated change in welfare
Change in welfare in EU ($)—

135,325k+32,692k+309,401k

Change in welfare in US ($)—-45,558k-11,580k+513,198k

37

6.3

The Responsiveness of Trade Flows to Trade Barriers

It is worth explaining how we get such a large e¤ect on exports given there is free entry.
First, subsidies raise the mass of entrants considerably. Second, spillover e¤ects of policies
across markets magnify the export increase due to any given increase in entry. When subsidies attract …rms into the Apparel industry, these entrants export not just to the EU, butwherever they have a good demand shock.33 Third, due to the presence of demand shocks,
the marginal and average …rms are large. With or without demand shocks, the marginal…rm producing has variable pro…ts that just cover its …xed costs of production. Without
demand shocks, this means that the variable pro…ts of the …rm with the cuto¤ productivity
just equal its …xed costs of producing. With demand shocks, this is true only at the cuto¤
demand shock for each …rm, including the …rm with the cuto¤ productivity. At all better
demand shocks greater than the cuto¤ demand shock, the …rm with the cuto¤ productivity
has higher pro…ts and sales than it needs to produce. As a result, the marginal and average…rm tends to be larger in the presence of demand shocks. This also helps explain why exports
rise greatly when the mass of …rms rises.
6.3.1

The Relevant Margins

We want to decompose export changes due to policy in our counterfactuals into their component parts. The basic idea is quite simple. We ask how much of the exports change is due
to changes in the exports of existing …rms (the intensive margin), how much is due to cuto¤schanging (one part of the extensive margin), and how much is due to the entry of …rms (theentry margin, which is the other part of the extensive margin).
Let total exports be X. Let x denote the exports of the individual …rm. Total exports
di¤er in the two periods, 0 and 1; as the mass, productivity, and demand shock cuto¤schange, which results in the changing of exports per …rm. Thus, X(M1e ;1 ; v1 ; x1 ) denotes

total exports when M1e mass of …rms enter, the productivity and demand shock cuto¤s
33

It is worth noting that in contrast to heterogeneous …rm models without demand shocks, where thisextensive margin cannot raise imports by very much as marginal …rms are high cost and, hence, high priceand low sales …rms, here entrants can contribute signi…cantly to exports. Even if their productivity is low
or costs are high, a good demand shock can result in large exports!

38

are those in period 1; and the output per …rm corresponds to that in period 1: Similarly,
X(M0e ;e
0 ; v1 ; x1 ) denotes total exports when M0 mass of …rms enter, the productivity cuto¤

for entry and for each market are that in period 0; while the demand shock cuto¤s in eachmarket for each productivity corresponds to those in period 1: The change in total exportscan (by adding and subtracting the relevant terms) be decomposed as follows:X(M1e ;

1 ; v1 ; x 1 )X(M0e ;

0 ; v 0 ; x0 )= [X(M0e ;0 ; v 0 ; x1 )X(M0e ;

0 ; v0 ; x0 )](Intensive Margin)+ [X(M0e ;0 ; v1 ; x 1 )X(M0e ;

0 ; v 0 ; x1 )(Extensive Margin:+ X(M0e ;1 ; v 1 ; x1 )X(M0e ;

0 ; v1 ; x1 )]Via Cuto¤s;+ [X(M1e ;1 ; v1 ; x 1 )X(M0e ;

1 ; v1 ; x1 )]Via Entry ).

(28)

What would we expect to happen through these margins? Any policy will have an impact
on exports via the exports of existing …rms, i.e., the intensive margin. In addition to thedirect e¤ect on exports of the policy, changes in the price index in response to the policy
will also a¤ect the exports of existing …rms. If, for example, the price index of Bangladeshi
Apparel falls, each Bangladeshi …rm faces more competition from other Bangladeshi …rms
as the price index of Bangladeshi apparel is lower and this lowers the aggregate price index.
This force works to reduce an existing …rm’s exports at any given price. This is captured
in the intensive margin in our decomposition. In our simulations, independent of what they
are, the exports of existing …rms do not change very much in response to policy, so that thisintensive margin counts for little.
In addition, the changes in entry a¤ect the price index via the extensive margin in termsof the demand shock and productivity cuto¤s. Again, these e¤ects are small. It is the exportsof new entrants that drives over 90% of the increase in exports, i.e., the entry margin, in thedecomposition.
The question still remains how such a small subsidy could result in such a large increasein entry. The answer is that the relationship between pro…ts ex-ante and the mass of …rms
is very ‡at in the estimated model. As the mass of …rms that enter rises, pro…ts fall o¤ very
39

slowly. A subsidy, for example, shifts these ex-ante pro…ts upwards. As the above mentioned
curve is ‡at, even a small shift up results in a large change in the intersection of the curvewith the x axis (which is the zero pro…t condition pinning down entry).
This curve is likely to be ‡at when Bangladeshi …rms are a small part of the world’s
exports (so that there are a lot of such other exporters to steal consumers away from).
Simulations revealed that this is indeed the case: as the share of Bangladesh in exports
rises, the increase in exports in this kind of a simulation falls very fast. This suggests thatdeveloping countries, especially small ones whose exports are not large enough to disrupt
markets might be able to raise exports a lot by focusing on policies that reduce entry costs ofvarious kinds. These polices need not even be subsidies. Nor do they need to be very costly
to implement. For example, promoting export fairs that allow buyers and sellers to meet
more easily could reduce …xed costs of exporting, as could workshops on how to institutethe quality requirements needed by foreign buyers. Putting the needed documentation for
obtaining preferences on the web to reduce documentation costs is another example of apotentially low cost, high return policy.
6.3.2

The Role of

How much of our results are due to the fact that our estimated elasticities of substitution
are quite low? Krugman (1980) predicts that in a homogeneous …rm setup, a low elasticity
of substitution makes demand inelastic, reducing the impact of trade barriers on trade ‡ows.
In other words, the e¤ect of trade barriers via the intensive margin is weak when substitution
is limited.
Chaney (2008) argues that a low elasticity of substitution between goods magni…es thee¤ect of trade barriers on trade ‡ows when …rm heterogeneity is added to the model. Note
this is exactly the opposite of what Krugman predicts. In the presence of …rm heterogeneity,
there are additional e¤ects via the productivity cuto¤s. Trade barriers raise prices and thisin turn raises the price index. The increase in the price index allows less productive …rms tosurvive. When elasticity of substitution is low, such …rms are not at a severe disadvantage,as their products di¤er considerably from those of other …rms. As a result, these …rms cansell a good deal so that trade ‡ows are very responsive to trade barriers via the extensive
40

margin with a low elasticity of substitution. In other words, the extensive margin e¤ects on
trade ‡ows are strong when

is low, and these dominate in the comparison.

However, Chaney (2008) assumes the mass of entry is …xed and so ignores the entry
margin completely. Whenis low, the ex-ante pro…t condition is quite ‡at as new entrant’s

products do not compete directly with those of existing …rms: their goods make room forthemselves in the product space. Thus, trade barriers which shift ex-ante pro…ts will have alarge e¤ect on entry and trade ‡ows. Thus, both cuto¤ and entry margins are more powerfulwhen

is low.

But most of the action on trade ‡ows, at least empirically, comes from the entry margin,
not the cuto¤ or intensive one. Thus, while it is fair to say that the low value of

esti-

mated makes trade ‡ows more responsive to trade barriers, which, in turn, translates intolarge leverage for policy in our counterfactual experiments, the channel by which it does so
empirically is not the margin emphasized in Chaney (2008).

41

Table 8. Short-run equilibrium implications of policy changes.
Baseline

No preferences

Higher doc. costs

No home yarn req.

Tari¤ in EU (tBD;EU )

12%

12%

12%

12%

Tari¤ in EU, ROO (tROO
BD;EU )

0%

12%

0%

0%

Cost disadvantage ( )

0.85

1.00

0.85

1.00

Relative Documentation costs (d=f )

0.60

0.00

6.0

0.60

Change in Bangladesh …rms’ market share, %Share of EU imports

5%+9.77%+1.81%+55.6%

Change in mass of …rms, %Implied # of entrants

2,583

…xed

…xed

Implied # of …rms trying EU# of successful exporters to EU

…xed

859

…xed

…xed

…xed

682

-0.15%

0.00-0.44%

Change in Productivity cuto¤s for exporters, %Cuto¤ in EU

2.4347

0.00

0.00

0.00

Change in Demand Shock cuto¤, %Demand shock cuto¤ in EU

2.2403+0.52%

0.00%+3.01%

Change in price index in EU
Aggregate price index in EU

100%-0.17%

Tari¤ revenues in EU

7,506k+4026%-0.03%-1.01%

Change in tari¤ revenues collected
+605%

-100%

Change in Bangladesh revenues before tari¤
Value of Bangladeshi Exports: RBD;EU

2,351k

+9.77%+1.81%+55.6%

Change in Bangladesh revenues after tari¤
Revenue of BD …rms: (1tBD;EU )RBD;EU2,344k-3.09%—+306,192k-0.12%+56.1%

Approximated change in welfare
Change in welfare in EU ($)

42+46,007k+16,137k

Table 9
Government spends $1 million ( 1%) on compensation on total
Woven Sector

Baseline

Industry

EU market

US market

Documentation

Fixed

case

entry costs

entry costs

entry costs

costs

Costs

Original (estimated)—

662,645

10,328140,242

7,609

12,592After Compensation—

662,262

9,198

139,082

4,809

11,812$ Compensation per …rm / entrant

—

383

1130

1160

2,800

780

Approx. amount to be spent

—

1000k

997k

996k

998k

996k

Change in Bangladesh market share
EU market

5%+1.18%+1.17%+1.16%+1.06%+1.16%

US market

1.4%+1.59%+1.57%+1.71%+1.44%+1.62%

Change in number of …rms
Implied # of Entrants into Industry

2,583+1.12%+1.12%+1.12%+1.12%+1.12%

Implied # of …rms trying EU market

859+1.05%+2.74%+1.05%+1.05%+1.62%

Implied # of …rms trying US market

844+1.09%+1.09%+1.66%+1.09%+1.38%# of successful EU exporters

682+1.06%+1.77%+1.06%+1.06%+2.13%# of successful US exporters

563+1.73%+1.73%+2.16%+1.73%+3.03%

Productivity cuto¤ for EU

2.4347+0.02%-2.49%+0.02%+0.01%-0.74%

Productivity cuto¤ for US

2.5025+0.02%+0.02%-0.73%+0.01%-0.28%

Cuto¤ productivities

Cuto¤ demand shocks
Demand shock cuto¤ in EU

2.24030.00%+7.70%

0.00%+0.02%-4.08%

Demand shock cuto¤ in US

1.1186

0.00%

0.00%

+0.80%

0.00%-5.90%

Share of …rms using ROO
Share of EU …rms invoking ROO

43.41%

43.41%

43.0%

Tari¤ revenue in EU

7,506k

+4.09%+4.74%

Tari¤ revenue in US

87,000k+1.59%+1.57%

43.41%

51.7%

42.9%

+4.07%-34.46%+4.47%+1.71%+1.44%+1.15%

Tari¤ revenue change

Change in Revenue of Bangladesh …rms after tari¤s
Revenue from EU market after tari¤

2,343k

+1.16%+1.16%+1.15%+1.17%+1.15%

Revenue from US market after tari¤

348k+1.57%+1.57%+1.71%+1.44%+1.62%

Change of price index in the EU and in the US
Price index in EU

100%-0.02%-0.02%-0.01%-0.01%-0.01%

Price index in US

100%-0.18%-0.18%-0.18%-0.18%-0.18%

Approximated change in welfare
Change in welfare in EU ($)

—

+666k

+710k

+657k

-2,278k+685k

Change in welfare in US ($)—+2,172k+2,154k+2,288k+2,035k+2,207k

Policy E¢ ciency Measure
Market size gain (after tari¤) per

—

32.9

dollar given to the …rms / entrants

43

32.6

33.1

32.6

32.6

7

Conclusion

We provide a simple way of estimating the structural parameters of a heterogeneous …rm
model. One of the advantages of our approach is that it uses only cross sectional data torecover all the structural parameters of the model, including …xed costs at di¤erent levels.
These include entry costs at the industry and market levels as well as …xed costs of production
and documentation costs needed to obtain preferences. Moreover, all our estimates seemreasonable and roughly in line with previous work.
The policy implications inherent in our counterfactual simulations are quite provocative.
We think of these as making a case for “trade as aid”. Recently, there have been serious
doubts cast on the e¢ cacy of direct aid. It may be diverted to the pockets of those in poweror used ine¤ectively. Giving aid and having it be e¤ective in terms of growth or a reductionin poverty are two very di¤erent things. For example, governments may cut back their own
support for the poor as aid grows.
In contrast, “trade aid” works through market forces. For example, in our application, preferences given by the EU are responsible for a huge increase in export ‡ows from
Bangladesh to the EU and to the US, rather than diverting trade away from the US market
to the EU market. In this manner, trade preferences or other forms of trade facilitation by
one country can have a powerful e¤ect on exports to all markets, and on output, exports,
and employment in the recipient developing country.
It is worth emphasizing that trade aid is a form of aid that can easily create a win-win
scenario which is much easier to sell to all parties concerned. The developed country giving
preferences wins as its consumers face lower prices and it still obtains some tari¤ revenues
from those …rms that choose not to invoke preferences. Other developed countries also standto gain as entry reduces the price of the goods they import so they would not have any
reason to complain. In addition, the developing country gets to increase its exports, earning
foreign exchange and employing its labor force.
Our results have some lessons for developing and transition countries. Corruption andbureaucracy raise …xed and marginal costs facing by …rms. Our work suggests that evensmall increases in such costs can result in huge reductions in entry, production, and exports

44

of a country. Conversely, reining in such costs can do much good. Our work can also beseen as highlighting the importance of other initiatives that reduce search costs or inherent
uncertainties in the market that raise costs. Thus, export fairs, tribunals for dealing withcomplaints about product quality, and other policies that reduce the costs of doing businessin developing countries may have unexpectedly large e¤ects.

References[1] Brambilla, Irene, Khandelwal, Amit, and Peter K. Schott (2008). “China’s Experience
Under the Multi…bre Arrangement (MFA) and the Agreement on Textiles and Clothing(ATC)”. Mimeo, Yale University.
[2] Das, Sanghamitra, Mark Roberts, and James Tybout (2007). “Market Entry Costs,
Producer Heterogeneity, and Export Dynamics”, Econometrica 75(3), 837-873.
[3] Bombarda, Pamela and Elisa Gamberoni (2009). “Firm Heterogeneity, Rules of Origin
and Rules of Cumulation” Working Paper. Graduate Institute of International Studies
and University of Bologna.
[4] Demidova, Svetlana, Hiau Looi Kee, and Kala Krishna (2008). “Do Trade Policy Differences Induce Sorting? Theory and Evidence From Bangladeshi Apparel Exporters,”
mimeo.
[5] Eaton, Jonathan, Samuel Kortum, and Francis Kramarz (2008). “An Anatomy of International Trade: Evidence from French Firms,”mimeo.
[6] Foster, Lucia, John Haltiwanger, and Chad Syverson (2008). “Reallocation, Firm
Turnover, and E¢ ciency: Selection on Productivity or Pro…tability?”, American Economic Reivew 98(1), 394-425.
[7] Foster, Lucia, John Haltiwanger, and Chad Syverson (2008a). “The Slow Growth of
New Plants: Learning About Demand?”Mimeo.

45

[8] Gajewski, Greg and Alex Riley (2006). “Bangladesh Export Trade Practices and theirE¤ect on the Competitiveness of the Garment Industry”. USAID case study.
[9] Kee, Hiau Looi, and Kala Krishna (2008). “Firm Level Heterogeneous Productivity and
Demand Shocks: Evidence from Bangladesh,”American Economic Review 98(2).
[10] Krishna, Kala (2006). “Understanding Rules of Origin,” Estevadeordal et al. (eds.),
Rules of Origins, Oxford University Press.
[11] Mattoo, Aaditya, Devesh Roy and Arvind Subramanian (2003). “The Africa Growthand Opportunity Act and its Rules of Origin: Generosity Undermined?” The World
Economy, Blackwell Publishing, vol. 26(6), pages 829-851.
[12] Melitz, Marc J. (2003). “The Impact of Trade on Intra-Industry Reallocations andAggregate Industry Productivity,”Econometrica 71(6), 1695-1725.
[13] Montfort, Mlachila and Yongzheng Yang (2004). “The End of Textile Quotas: A case
Study of the Impact on Bangladesh”. IMF Working Paper 108.
[14] Rahman, Musta…zur and Debapriya Bhattacharya (2008). “Regional Cumulation Facility Under EC-GSP: Strategic Response From Short and Medium Term Perspectives.”
Center for Policy Dialogue Occasional Paper 9, Dhaka, Bangladesh.
[15] Tybout, James (2003) “Plant- and Firm-Level Evidence on the ‘New’Trade Theories”(in Handbook of International Trade, Oxford: Basil-Blackwell, 2003).
[16] World Bank (2005). Bangladesh Growth and Export Competitiveness. Poverty Reduction and Economic Management Sector Unit, South Asia Region, Report No. 31394-BD.

46

8

Appendix

8.1

Model Derivations

This Appendix contains the detailed derivations of the equilibrium conditions for the model
described in the paper. As usual, the model is solved backwards. We begin with Stage 3:
8.1.1

Stage 3: Production Decisions

Exporting to the US Consider a Bangladeshi exporter with productivity

and demand

shock v. The US does not give tari¤ preferences to Bangladeshi garments, and the presenceof country-speci…c quotas in most categories makes meeting ROOs mandatory for exports.
This means that Bangladeshi …rms exporting to the US have no choice but to meet ROOs.34
They have to pay the tari¤ of 20%. As a result, the …rm maximizes
max

pBD;U S

BD;U S (; v) = (1BD;U StBD;U S ) pBD;U S qBD;U S (pBD;U S ; v)

qBD;U S (pBD;U S ; v)

f;
(29)

where
US

pBD;U S
qBD;U S (pBD;U S ; v) = v
PBD;U S

RBD;U S;PBD;U S

(30)

;

(31)

f

(32)

so that the …rm charges
pBD;U S =(1

1
tBD;U S )BD;U SUSand earns the following pro…ts:
BD;U S (; v) =rBD;U S ( ; v)
US

where
rBD;U S ( ; v) = (134tBD;U S ) vRBD;U S (PBD;U S )

US

1

(1

1
tBD;U S )1
BD;U S

US

:

(33)

US

We assume documentation is easy so that documentation costs are zero in the US and that as only

assembly is required for origin, meeting ROOs is costless so

47

= 1:

Note that the price set by a …rm does not depend on its market speci…c shock v. However,v a¤ects the level of the …rm’s pro…ts. In particular, for any productivity level ; there exists
a minimal demand shock v ( ; PBD;U S ) ; such that
BD;U S ((34); v ( ; PBD;U S )) = 0;

and by using the expression for revenues, we get an equation for v ( ; PBD;U S ) :v ( ; PBD;U S ) =

USf

(1

tBD;U S ) RBD;U S (PBD;U S ) U S 1 1

1tBD;U S

BD;U S

US

1

:

(35)

US

The mass of Bangladeshi exporters selling in the US is thus:
MBD;U S = MEBDZ +1

BD;U SZ +1(36)

hU S ( )g( )d d ;( ;PBD;U S )where MEBD denotes the mass of entrants in Bangladesh.
Exporting to the EU Now, we consider Bangladeshi exporters to the EU. This is slightly
more complicated than the US case since when Bangladeshi …rms export to the EU they havean additional choice. They can export invoking the ROOs and pay zero tari¤s, or ignore
the preferences and pay the tari¤ tBD;EU without meeting ROOs. If …rms meet ROOs, they
incur an additional documentation cost of d as well as an increase in marginal costs due tonot using the least cost input mix. As a result, only …rms with very favorable demand shocks
will choose to pay d and meet the EU ROOs.
The …rm maximizes
max 0;

BD;EU (; v) ;ROO
BD;EU ((37); v) ;

where
BD;EU (; v) =ROO
BD;EU (; v) =

max

(1

max

ROO
pROO
BD;EU qBD;EU pBD;EU ; v

pBD;EUpROO
BD;EUtBD;EU ) pBD;EU qBD;EU (pBD;EU ; v)

48

BD;EU

BD;EU

qBD;EU (pBD;EU ; v)

qBD;EU pROO
BD;EU ; v

f + dEU

f

;

(38)
:

The pricing rule for each type of exporter is:
pBD;EU =1

1
tBD;EU

BD;EU

and pROO
BD;EU =

EU

BD;EU

(39)

:

EU

Exporters that do not invoke preferences earn the following revenues and pro…ts:
rBD;EU ( ; v) = (1BD;EU (tBD;EU ) vRBD;EU (PBD;EU ) EU 1

rBD;EU ( ; v); v) =

1

1
tBD;EU

1

EU

BD;EU

(40)

EU

(41)

f;

EU

while exporters that invoke the EU ROOs have
1
ROO
rBD;EU( ; v) = vRBD;EU (PBD;EU ) EU 1

EU

BD;EU

;

EU
ROO
BD;EU (; v) =ROO
rBD;EU( ; v)

f + dEU :

(42)

EU

Consider a …rm with productivity : We can de…ne 2 demand shock cuto¤s, v ( ; PBD;EU )and v ROO ( ; PBD;EU ) : Firms with v 2v ( ; PBD;EU ) ; v ROO ( ; PBD;EU ) do not invoke

ROOs as their demand shock and hence their market is small so that it is not worth their
while to incur dEU . Firms with v 2 v ROO ( ; PBD;EU ) ; 1 …nd it worthwhile to meet the
EU ROOs. These shocks are implicitly de…ned by
BD;EU (ROO
BD;EU; v ROO ( ; PBD;EU )BD;EU; v ( ; PBD;EU )) = 0;

; v ROO ( ; PBD;EU )

(43)

= 0;

where the second equation comes from setting the additional pro…ts from invoking the EU
ROOs to zero. Thus we have:v ( ; PBD;EU ) =

EU f

(1

tBD;EU ) RBD;EU (PBD;EU ) EU 1 1

49

1tBD;EU

BD;EU
EU

EU

1

;

(44)

and:
EU
EU d

v ROO ( ; PBD;EU ) =(EU(1 tBD;EU ) EU ) RBD;EU (PBD;EU ) EU 1
tBD;EU ) EU
dEUv ( ; PBD;EU ) ;(1 tBD;EU ) EU ) f

1

(1

=

(

EU

BD;EU

1

EU

1

EU

(45)

or
v ROO ( ; PBD;EU ) = C ROO v ( ; PBD;EU ) ;

(46)

dEU
EU 1 (1tBD;EU )

(47)

where
C ROO =

f

EU

1

> 1:

Note that from the expressions above, v ROO ( ; PBD;EU ) and v ( ; PBD;EU ) are decreasing in:In the case of the US exporters, only …rms with a productivity above

BD;U S will try toaccess the US market, while those with a productivity above the cuto¤ level

BD;EU will try

to access the EU market. These cuto¤s are de…ned below in the stage 2 problem.
The masses of exporters that sell in the EU, but do not or do meet the ROOs are,respectively:

MBD;EU =

MEBD

Z +1

BD;EU

ROO
MBD;EU

=

MEBD

Z +1

BD;EU

8.1.2

Z

ROO (

;PBD;EU )hEU ( )g( )d d ;( ;PBD;EU )

Z +1

ROO (

hEU ( )g( )d d :(48);PBD;EU )Stage 2: Market Entry Decision

Consider a …rm who has drawn a productivity level and has to decide whether to enter amarket. Firms who expect non-negative pro…ts from trying to enter market j will do so.
Again, we …rst look at the US market …rst.

50

The US Market For a Bangladeshi …rm with productivity ; the expected pro…t of entering the US market isUS
fm=

Ev [ BD;U S ( ; v)]

Z +1

v ( ;PBD;U S )BD;U S (; v) dHU S (v)

US
fm
:

h

i1

(49)

Note that(1
BD;U S (tBD;U S ) vRBD;U S (PBD;U S ) U S 1; v) =US=rBD;U S ( ; v)

1
1 tBD;U S

BD;U S
US

US

f

f

US

rBD;U S ( ; v)frBD;U S ( ; v ( ; PBD;U S ))
v
= f
1 :
v ( ; PBD;U S )
=

f
(50)

Thus
Ev [ BD;U S ( ; v)]

US
fm
=f

Z +1

v
v ( ;PBD;U S ) v ( ; PBD;U S )1 dHU S (v)The expected pro…ts of accessing the US market are increasing in

US
fm
:

(51)

since v ( ; PBD;U S ) is

decreasing in : Let the productivity of the marginal Bangladeshi …rm, which is indi¤erent
between accessing the US market or not, be denoted byabove
market.

BD;U S : All …rms with productivity

BD;U S expect to earn non-negative pro…ts and, therefore, will try to access the US
BD;U S is de…ned by

Ev
Z +1

v ( BD;U S ;PBD;U S )BD;U S"v

BD;U S ; v

v
BD;U S ; PBD;U S

US
= fm
; ()
#

1 dHU S (v) =

US
fm
:
f

(52)

Equation (52) is important for several reasons. First, by solving it, we obtain the minimal
demand shock for the marginal …rm from Bangladesh, v

51

BD;U S ; PBD;U S

which is a key stepin the estimation prcedure: Second, it shows this demand shock does not depend on the perUS

unit costs of selling there, only on fmf , and the demand shock distribution HU S (v) : Finally,knowing v

BD;U S ; PBD;U S; we can express the expected pro…ts at Stage 2 for any …rm as

a function of its own productivity

BD;U S : To see this, noteand the cuto¤ productivity

that from the de…nition of v ( ; PBD;U S ) in equation (35):USv ( ; PBD;U S )=v BD;U S ; PBD;U S

BD;U S1(53);

so thatEv [ BD;U S ( ; v)] = f

=f

Z +1

BD;U S

US

Z +1

1

v ( BD;U S ;PBD;U S )vv ( ;PBD;U S ) v ( ; PBD;U S )2

1 dHU S (v)
3

v

6
4

US

BD;U S7
15 dHU S (v) :1

v

BD;U S ; PBD;U S(54)

Thus, the expected pro…ts of a …rm depends on its own productivity , the cuto¤ productivity level,

BD;U S ; the demand shock for that level, v

BD;U S ; PBD;U S; and of course, thedistribution of demand shocks. Now let us look at the exporters to the EU.
The EU Market For a …rm with productivity ; the expected pro…t of entering the EU
market is==

Ev max BD;EU ( ; v) ;Z vROO ( ;PBD;EU )v ( ;PBD;EU )Z +1

v ( ;PBD;EU )

+

Z +1

BD;EU (

BD;EU (

v ROO ( ;PBD;EU )ROO
BD;EU (; v); v) dHEU (v) +

EU
fm
Z +1

v ROO ( ;PBD;EU )ROO
BD;EU (; v) dHEU (v)EUfm; v) dHEU (v)ROO
BD;EU (

; v)

BD;EU (52; v) dHEU (v)

EU
fm
:

(55)

As in the US case,
BD;EU (; v) = fvv ( ; PBD;EU )

(56)

1 ;

while
ROO
BD;EU (

; v)

BD;EU (; v) = dEU

v ROO (v
; PBD;EU )

(57)

1 :

Hence
EU
fm
Ev max BD;EU ( ; v) ; ROO
BD;EU ( ; v)
Z +1
v
= f
1 dHEU (v)v ( ;PBD;EU ) v ( ; PBD;EU )
Z +1
v
EU
+d
1 dHEU (v)ROO( ; PBD;EU )v ROO ( ;PBD;EU ) v

EU
fm
:

(58)

The careful reader may wonder what ensures that the above maximum of two
functions can be written in this simple way. This follows from the fact thatv ROO

= C ROO v

BD;EU ; PBD;EU

BD;EU ; PBD;EUso that, as long as C ROO > 1; at all values of ; the demand shock cuto¤ line in Figure 3
lies below the demand shock cuto¤ line to invoke ROOs.
From the expression above, the expected pro…ts of accessing the EU market are increasingin ; so if we denote the productivity of a marginal Bangladeshi …rm exporting to the EU
by

BD;EU ; then all …rms with productivity above

The equation for

BD;EU is given by

Ev max BD;EU
Z +1

dEU
+
f

Z +1

BD;EU will try to access the EU market.

BD;EU ; vv ( BD;EU ;PBD;EU )v ROO ( BD;EU ;PBD;EU )

"

"

v

;

ROO
BD;EU

BD;EU ; vv
BD;EU ; PBD;EU
v

v ROO

BD;EU ; PBD;EU

Again, solving the equation above for v

BD;EU ; PBD;EU

53

#

EU
= fm
;

or

1 dHEU (v)#1 dHEU (v) =

EU
fm
:
f

(59)

; we can express the expectedpro…ts at Stage 2 for any …rm as a function of its own productivity

and the cuto¤ produc-

BD;EU : To see this, note thattivityEUBD;EUv ( ; PBD;EU ) =1
BD;EU ; PBD;EU

v

(60)

;

so thatEv [ BD;EU ( ; v)]
= f

Z +1

BD;EU

EU

1

v ( BD;EU ;PBD;EU )

2
6
4

v
BD;EU

EU

1

v

BD;EU ; PBD;EU

3

(61)

7
15 dHEU (v) :Moreover, the expected additional pro…ts coming from the possibility of getting a favorable enough demand shock to invoke ROOs can be expressed as
Ev
EU

= d

ROO
BD;EU (

Z +1

BD;EU

; v)

EU

BD;EU (

; v)

1

v ROO

( BD;EU ;PBD;EU )

2
6
4

v
BD;EU

EU

1

v ROO

BD;EU ; PBD;EU

(62)

3

7
15 dHEU (v) ;where, from the analysis above,
v ROO

ROO
v
BD;EU ; PBD;EU = C

ROO=
BD;EU ; PBD;EU ; C

fdEU
EU 1 (1tBD;EU )EU(63)
For our estimation exercise, it is useful to show here how the analysis of Stage 2 can beused together with the data available to calculate the productivity cuto¤s in both the US
and EU markets. Recall that for a marginal US exporter, we have

BD;U S

BD;U S ; v

BD;U S ; PBD;U S

54

= 0;

(64)

1

> 1:

or
(1

tBD;U S ) v

US 1
BD;U S ; PBD;U S RBD;U S (PBD;U S )

US

h

1

BD;U S

1 tBD;U S

US

BD;U S

i1

US

= f:
(65)

Thus,

BD;U S ="v

BD;U S ; PBD;U S(1tBD;U S ) RBD;U S (PBD;U S )
f US

US

1

#1 1

US

(1

1
tBD;U S )BD;U S:

US(66)
Similarly, the productivity of a marginal EU exporter is given by"

BD;EU = v

8.1.3

BD;EU ; PBD;EU

1tBD;EU RBD;EU (PBD;EU ) EU 1
EU f

#1 1

EU

(1

1
BD;EU
:
tBD;EU ) EU
(67)

Stage 1: Entering the Industry

Entry occurs until the expected pro…ts that could be earned by a Bangladeshi …rms in all
their potential markets equals entry costs:
E+E

max Ev max

max Ev
BD;EU

BD;U S

BD;EU ; v;

55

( ; v)
ROO
BD;EU

US
fm
;0
BD;EU ; v

(68)
EU
fm
;0

= fe :

Using the analysis of Stage 2, we can rewrite this expression as
Z +1

BD;U S

2Z +1
6
4

BD;U SUS

1v ( BD;U S ;PBD;U S )

2
6
4

v
BD;U SUS1
BD;U S ; PBD;U Sv

3

7
15 dHU S (v)USfmdG ( )
f"#
Z +1 (Z +1v+
1 dHEU (v)v ( BD;EU ;PBD;EU ) v
BD;EU ; PBD;EU
BD;EU
#
)
"
Z
EU
fm
fe
dEU +1v
1 dHEU (v)dG ( ) = ;+ROO
f vROO ( BD;EU ;PBD;EU ) v
ff
BD;EU ; PBD;EU

(69)
where
EU

BD;EU

1

v

BD;EU ; PBD;EU=v

BD;EU ; PBD;EUv ROO

BD;EU ; PBD;EU= v ROO

and
EU

BD;EU

1

56

BD;EU ; PBD;EU: