Firm Heterogeneity and Ricardian Trade:
The Impact of Domestic Competition on Export Performance
Matilde Bombardini∗, Christopher J. Kurz†and Peter M. Morrow ‡June 2009
Preliminary Draft

Abstract
High productivity promotes exports at both the sector and firm level. This is the primaryprediction of models of Ricardian comparative advantage and firm heterogeneity, respectively.
We integrate these two models to investigate how sector-level productivity affects firms’ export
performance. Using data for Chilean and Colombian plants, we find that, conditioning on ownplant productivity, sector-level productivity reduces the propensity to export and the volume ofexports. This contradicts the canonical model of firm heterogeneity. We rationalize the evidence
through a model with sector-specific factors of production. Productive sectors bid up the returnto their specific factor making firms less likely to cover the fixed costs of exporting. We findevidence of this mechanism looking at sector-level relative wages and relative productivity.

∗

The University of British Columbia, CIFAR, NBER, RCEA.
Federal Reserve Board of Governors.
‡
University of Toronto.
†

1

1

Introduction

The link between productivity and output is among the most robust of all economic relationships.
Extremely productive individuals, firms, and industries thrive in nearly every market structure.
With regard to firms, productivity differences can come from a variety of sources. When discussing
the origins of these productivity differences, it is natural to ask if a firm’s performance is due to its
own productivity or due to the productivity of the industry in which it resides.
Two common models of international trade provide strikingly different answers to this question.
First, a venerable literature on Ricardian comparative advantage emphasizes differences in productivity at the industry level across countries and examines its implications for the structure of anation’s exports, e.g. Dornbusch, Fischer, and Samuelson (1977) (henceforth DFS). In this model,
all firms in an industry possess the same productivity level and all that matters for export performance is comparative advantage across industries. Alternately, a more recent literature examines
firms that possess heterogeneous productivity levels within an industry and models how this affects
a firm’s ability to export relative to other firms in the industry. In this class of models, productivity
is an attribute of the firm itself and industry productivity is merely an aggregate construct. Clearly,
reality lies someplace between, with a firm’s productivity level determined both by the industry
and country in which it resides and as well as at the firm-level. This paper constructs a bridge
between the two literatures by asking how the productivity of peer firms affects both domestic andexternal outcomes, such as export probability, exports, sales, and value added, through the lens of
the firm heterogeneity literature (e.g. Melitz, 2003). Because we study this question in the context
of the canonical firm heterogeneity literature, we do not consider technology “spillovers” nor any
technology transfer.1
Specifically, we construct a model with three countries in which there are two small open
economies that export to a common world market in two different industries. Firms are heterogeneous within each industry as in Melitz (2003). We summarize the question and the identification
strategy with a simple thought experiment. Assume that comparative advantage is defined by average productivity across sectors. More precisely, suppose that when comparing productivity acrosscountries and industries, one country possesses a larger advantage in average productivity in one
sector relative to another sector. This is the comparative advantage industry and the other is thecomparative disadvantage industry. If we consider two firms with the same level of productivity, one
1e.g. Keller (2002).

2

residing in each sector, will the firm residing in the comparative advantage industry have superior,equal, or inferior outcomes to the firm residing in the comparative disadvantage sector?
Using plant level data for Chile and Colombia for 1990 and 1991, we find that, conditional on
own productivity, plants with relatively more productive domestic peer firms sell less at home andabroad and have a lower propensity to export. This result can come from a variety of reasons
including, but not restricted to, product market competition, and factor market competition. We
focus on factor market competition as a channel explaining our results. We argue that inclusionof sector-specific inputs is an intuitively appealing way of amending the canonical model to beconsistent with the data. As average productivity in the industry rises, the wage of the specificfactor associated with that industry increases leading to a higher average wage bill for a firm of agiven productivity level in that industry.
In theory, sector-specific inputs can be thought of as factors of production that cannot easily
be moved from industry to industry. These can be industry-specific knowledge of workers or
physical capital that diminishes in capacity if moved from one industry to another. Ramey andShapiro (2001) and Neal (1995) explore the specificity of capital and labor, respectively, and findsuch specificity to be important. In addition, Heckman and Pages (2000) look at labor market
regulations in Latin America. They find that labor market regulations in Chile and Colombia
make labor quite immobile due to extensive hiring and firing costs based on seniority. While ourmodel does not assume which factor of production is specific and to what degree, we examine thespecificity of different types of labor as a channel for our results.
To assess the role of factor market competition, we use a difference-in-difference strategy to
examine the relationship between industry wages and average productivity across countries andindustries. Our results indicate that industries with higher relative average productivity tend
to pay higher relative average wages, and higher relative wages for both skilled and unskilledworkers. Looking at the relationship between industry-level productivity and firm-level outcomesalong with the relationship between industry-level productivity and wages, we estimate that if therelative average productivity of firms in an industry doubles, relative factor prices will increase byapproximately 21.9%. For a firm of a given productivity level, this will lead to the probability ofexporting falling by 6.3% and the level of exports falling by 17.7%.
Examining and comparing productivity and export performance in these two countries is appropriate for three reasons. First, because of the myriad of labor market restrictions in both countries3

discussed in Heckman and Pages (2000), the existence of specific factors is more reasonable thanin countries where labor markets are less restricted. Second, because we are working with detailed
plant level data, we can verify that definitions of output, employment, and capital stock are reasonably comparable across countries. This will assist in the development of measures of productivity
that are comparable across countries. Third, these two countries export in similar industries tosimilar markets and are likely to face similar competitive conditions in world markets based on
their geographic location and level of development.
Given our data, our model of small open economies exporting to the rest of the world is appropriate as trade between Chile and Colombia is negligible. Colombian exports to Chile comprise lessthan 1% of its total exports and Chilean exports to Colombia comprise less than 3% of its totalexports. In contrast, exports to G7 countries, Brazil, and Argentina combined comprise 71.3% ofChilean exports and 63.4% of Colombian exports for 1990-1991.2
Figure 1 plots Chilean and Colombian exports at the SITC one-digit level to their ten largest
destination markets, normalized by world exports to that destination in that industry.3 An upward
sloping relationship suggests that these two countries compete in similar countries and industries.
In addition, while both countries experienced substantial macro-economic turbulence associatedwith the Latin American debt crisis, much of this turbulence had subsided by 1990.
Section 2 briefly reviews the literature that we draw upon and derives aspects of the canonicalmodel against which we contrast our framework. Section 3 presents the model. Section 4 describes
the data and offers empirical evidence. Section 5 concludes.

2

Relation to the Literature

Early research on comparative advantage developed models of comparative advantage based on
industry-level differences in productivity. Research initially prospered as empirical work validated
many of its primary predictions (MacDougall, 1951,1952, and Stern, 1962) and theoretical developments smoothed some of the model’s rough edges (DFS). However, work stalled as the model’s
basic predictions were viewed as no longer worth testing and theory ran up against intractable
generalizations (Wilson, 1980). More recently, the Ricardian model has experienced a renaissancewith work exploring how to integrate it with the Heckscher-Ohlin tradition (Harrigan, 1997 and2
3

IMF Direction of Trade Statistics Database (2008).
A similar pattern holds without normalization.

4

Morrow, 2008) and how to extend it to the case of many countries (Eaton and Kortum, 2002, andCostinot and Komunjer, 2007). However, this literature maintains the representative firm assumption in which all operating firms in an industry possess the same level of productivity or in which
there is only a single firm possessing a given level of productivity in an industry. Consequently, it
is unable to make any predictions regarding firm variation within an industry.
In contrast, recent work on firm-level heterogeneity has instead emphasized differences in productivity across operating firms within an industry (Bernard, Eaton, Jensen, and Kortum, 2003,
Melitz, 2003, and Melitz and Ottaviano, 2008). This literature and its progeny have developed atractable framework in which firms with higher productivity levels possess higher levels of output
and employment and are more likely to undertake costly, but productive, activities than their lessproductive peers. However, this literature has been unable to extend the model beyond two asymmetric sectors where one of the sectors is homogenous (Demidova, 2008, and Falvey, Greenaway,
and Yu, 2004).4 Consequently, while this literature makes very crisp predictions regarding firm
heterogeneity within an industry, it runs into some difficulty in comparing firms across industries.
We now briefly sketch out the elements of the baseline firm heterogeneity model to illustrate
its empirical predictions for small open economies exporting to a common market. Suppose that
one of multiple small open economies exports to a large “world” market. Assume that any small
open economy examined is small enough that it does not affect the world equilibrium. Market
structure is Dixit-Stiglitz with an elasticity of substitution across varieties of σ > 1 where each firm
produces a unique variety. φf ic represents productivity for firm f in industry i in country c. wc
is a country-specific wage. A firm’s revenue from exports to the World can be described using arevenue function where Ai is a demand shifter in the world market that each small open economy
takes as given:5r(φf ic ) = Ai
ρφf ic σ−1.
τ wc

Consequently, we can express relative export revenue from the World market for two firms in
different countries but the same industry as follows where we assume that countries c and c0 both4
An exception is Bernard, Redding, and Schott (2007) in which there are two differentiated sectors but producticity
draws come from completely symmetric distributions. Okubo (2007) extends the results of Demidova (2008) to acontinuum of sectors.
5iAi = E1−σ where Ei is world expenditure in industry i and Pi is the CES price index, τ represents iceberg
Pitransportation costs, and ρ = σ−1. Similar notation is used by Helpman, Melitz, and Yeaple (2004).
σ

5

face the same iceberg transportation costs in servicing the world export market:r(φf ic )φf ic wc0 σ−1.
=r(φf 0 ic0 )φf 0 ic0 wc
In this case, industry productivity should have zero effect on relative export performance as demandis determined by firm- and country- but not industry-level characteristics. We refer to this as theprediction of the “baseline model” in the small open economy case.
Alternately, Demidova (2008) presents a rich two-country model that predicts that industry
productivity should have a positive effect on exporting probability conditional on own firm productivity. Hers is a model in which there are only two countries trading with each other. The
intuition of her model can be presented via a framework with a productive North and an unproductive South. If the Northern market is filled with relatively productive firms, this will dampen
the incentive for Southern firms to enter the export market. This will lead to a lower rate of entry
in the South and less competitive conditions for the reasons exposited in Melitz (2003). This willthen provide greater incentive for Northern firms to export to the South. Hence firms in the comparative advantage industry will have a lower productivity threshold for exporting than firms in
the comparative disadvantage industry. This will result in industry productivity having a positiveeffect on the probability of exporting.
The fact that we do not find this result empirically does not imply that the model of Demidova(2008) is incorrect or misleading. Rather, we believe that it is simply inappropriate in this context
of two small open economies exporting to a large common market. Her model would be the correct
one to reference in the case of two large countries that trade in large volumes, because the generalequilibrium mechanisms in her model are more likely to be present than in the case we examine.
Bernard, Redding, and Schott (2007) present a model in which asymmetries come from differences in factor abundance and the interaction of these differences with the firm heterogeneity
model. However, a small open economy version of their model would predict a positive coefficient
on industry productivity. This is because average productivity is higher in the sector in which thecountry possesses a Heckscher-Ohlin comparative advantage. However, as long as fixed costs arein the same factor proportions as variable costs and factor price equalization fails, the comparativeadvantage sector will have a relatively lower export cutoff. This comes from the fact that therelative factor price of the relatively abundant factor rises with liberalization but is still less thanin the country where it is a relatively scarce factor.
6

We now present our framework. This model is consistent with the empirical evidence presented
in Section 4 that industry productivity has a negative effect on plant level outcomes both at home
and abroad.

3

Model

Consider a small open economy, that we refer to as country c. The preferences of the representative
consumer are defined over the consumption of three aggregates of differentiated goods according toa Cobb-Douglas utility function:
U=

2
Y

Qαic

i=0

where Qic is a CES aggregator over set Iic of varieties of good i consumed in country c:
Z
Qic =

qic (ω)

σ−1
σ

 σ

σ−1

dω

with σ > 1.

ω∈Iic

Demand in c for variety ω in sector i is:
qic (ω) =where Pic =

hR

1−σ
dω
ω∈Ii pic (ω)

i 1

1−σ

αEc pic (ω)−σ
Pic1−σ, pic (ω) is the price faced by consumers in c for variety ω ofgood i and Ec is aggregate expenditure in country c.
Home produces, consumes and exports goods 1 and 2. Good 0 is imported from the rest of
the world to balance trade.6 Varieties in industry i are produced with labor, freely mobile acrosssectors, and a factor specific to sector i, that we denote by Kic and earns return sic . We use
the terms “sector” and “industry” interchangeably. This specific factor can be physical or humancapital or any factor of production that is immobile over the time span considered. The laborendowment of the economy is equal to the population size Lc . Each worker earns a wage wc .
The setup within each sector i is similar to Melitz (2003): there is continuum of firms, eachproducing a different variety, and characterized by a productivity level denoted by φ. A firm with
6

This assumption aims at minimizing general equilibrium effects across sectors. Note that good 0 is not a homogenous good introduced to pin down the wage level.

7

productivity φ produces quantity q and faces the following homothetic total cost function:q
wc1−η sηic
T Cic (q, φ) = f +φwhere f is a fixed cost and η is the share of costs spent on the specific factor Kic . The parameterη is restricted to be the same across sectors. We normalize wc to one. The higher φ is, the lower
total costs of producing quantity q. As shown by Melitz (2003) a firm with productivity φ charges
a lower price pic (φ), has higher revenues ric (φ) and earns higher profits πic (φ) compared to otherfirms in the same sector:
pic (φ) =

sηic
ρφ

1−σ
sηic
ric (φ) = αEc
Pic ρφ η 1−σsicαEc
πic (φ) =− f sηicσ
Pic ρφ

where ρ = σ−1
σ . We depart from Melitz in adopting, for analytical tractability, a specific distributionof the productivity parameter φ. The distribution can potentially vary across sectors and countries
and will be the main source of asymmetry, apart from population and specific factor size. We
follow a large number of papers (e.g. Chaney (2008) and Helpman, Melitz, Yeaple (2004)) inassuming that, within each sector i, the productivity parameter φ follows a Pareto distribution
with parameters k and φm,ic . The cumulative density function of parameter φ is therefore:Gic (φ) = 1 −

φm,ic
φ

k
.

(1)

The parameter φm,ic , which we allow to vary across sectors, represents the lower bound of the
support of the distribution.7 Therefore in a sector with higher φm,ic , firms draw from a distribution
with a higher average productivity. We refer to distributions of productivity as being “superior” ifthey possess higher minimum draws. This is the source of Ricardian differences in the model.8
We allow for free entry into each sector i. We follow Melitz (2003) in assuming that firms must
pay a fixed cost fe sηic to draw a level of productivity in sector i. Once firms observe their level of7

We restrict k > σ − 1 to ensure that all integrals converge.
A higher φm,ic also implies a higher standard deviation of productivity, but the coefficient of variation, a normalized measure of dispersion, does not depend on φm,ic .
88

productivity they can produce or, if the productivity parameter is too low, exit without producing.
If they decide to produce, then each year they face an exogenous probability of exiting the market δ.
We consider the steady-state equilibrium where entry is equal to exit in each sector and aggregate
sector-wide variables are constant. Therefore in the steady state a prospective entrant can expect
a constant profit each year, conditional on surviving.
Upon drawing a productivity level φ, a firm makes two decisions. First, it decides whether
to produce or not for the domestic market. We indicate by φd,ic the productivity threshold fordomestic production. The threshold φd,ic is such that profits in the domestic market of a firm withthat level productivity, πd,ic (φd,ic ) are zero. Firms with productivity below φd,ic exit immediately.
Firms with productivity above φd,ic continue to operate. Second, the firm decides whether to export
or not. Firms that export have to bear an additional fixed cost fx sηic and a per-unit transport costof the iceberg type. For each unit sold abroad, a firm must ship τ units, with τ > 1. We indicate
by φx,ic the threshold for exporting. The threshold φx,ic is such that profits in the foreign market
for a firm with that level of productivity, πx,ic (φx,ic ), are zero. Firms with productivity below φx,ic
do not export. Since marginal costs are constant in the quantity produced, the two decisions are
independent.
Since the focus of the empirical section is on a small open economy, we depart from Melitz(2003) in that we simplify the interaction of the country with the rest of the world. In this paper,
we consider a partial equilibrium setting, where the world represents an export market for firms
in the country, but the country is too small to affect aggregate variables in the world market. In
Melitz (2003) the analysis considers many symmetric countries, with each country exporting toand importing from every other country. Demidova (2008) and Falvey, Greenaway and Yu (2006)
consider a world consisting of two countries trading with one another. Their setup is appropriate
to analyze the interaction among large countries. We also disregard imports into the country since
we are not interested in differences across sectors coming from differential import penetration.
The focus of the paper is on understanding exporting behavior of firms in a small open economy
characterized by productivity differences across sectors and we aim to simplify the other elementsof the model as much as possible. In particular we assume that the world market expenditure leveland price index in a given sector i are summarized by a constant Ai . Therefore revenues in the9world market for an exporting firm of productivity φ in sector i are:rx,ic (φ) = Aiτ sηic
ρφ

1−σ
.

(2)

Market conditions in the world market, Ai , are not affected by firms’ export decisions in a small
country c, because we assume the country is small.
The free entry condition for sector i is summarized by the following equation, which states that,conditional on producing (drawing a productivity parameter higher than φd,ic ), the discounted
expected stream of profits is equal to the entry cost:[1 − G (φd,ic )]π ic= fe sηic ,δ

(3)where πδic is the discounted constant expected profit. The expected profit is comprised of sales
in the domestic market, where expected profits are π d,ic , and sales in the foreign market, whereexpected profits are π x,ic , weighted by the probability of exporting:
π ic = π d,ic +1 − G (φix )π x,ic .
1 − G (φid )(4)

As shown in Melitz (2003) expected profits in each market coincide with the profits of a firm
characterized by an average productivity level, where the average for firms producing in the domestic
market, φd,ic , is defined as follows:
σ−1

φd,ic =

1
1 − G (φd,ic )Z ∞φσ−1 g (φ) dφ

φd,ic

and the average productivity of exporting firms, φx,ic , is defined as:σ−1φx,ic =

1
1 − G (φx,ic )Z ∞φσ−1 g (φ) dφ.

φx,ic

It is easy to verify that, analogously to Melitz (2003), the conditions determining the domestic
and foreign cutoffs, πd,ic (φd,ic ) = 0 and πx,ic (φx,ic ) = 0, yield the following relationships between

10the cutoffs and the average profits in the domestic and foreign markets:

!σ−1
φ
d,ic
= sηic f 
− 1
φd,ic


!σ−1
φ
x,ic
− 1 .
= sηic fx 
φx,icπ d,icπ x,ic(5)(6)

In Melitz (2003) φx,ic is a simple function of the domestic cutoff φd,ic , which depends only on
exogenous parameters.9 Therefore the three equations (3), (5) and (6) uniquely determine theequilibrium cutoffs and the mass of firms is determined residually in a two-step procedure.10 In
our case, because of the lack of symmetry between the domestic market and the world market, thecutoffs are determined simultaneously with the mass of firms. This is because both the mass offirms and the cutoffs are partially determined by the return to the specific factor.
To solve for the equilibrium we also employ a sector-specific factor market clearing condition. In
the steady state the free-entry condition guarantees that there are no pure profits (positive profits
of operating firms are equal to the total cost of entry into the market), so that firms’ revenues are
split between the mobile factor and the specific factor, with a share η earned by the specific factor.
Aggregating up to the entire sector i, the following equation states that a share η of total revenues
in sector i are paid to Kic :ηMic ric = sic Kic(7)

where ric are the expected revenues of a firm operating in sector i.
Consider now a second small open economy c0 exporting to a large world market. We assume
that the two countries do not export (or export a negligible amount) to each other and that they
only export to a common third market, facing the same transport costs. We allow the two countries
to differ in size, both in terms of population and specific factor endowments, and most importantly,
we assume that the countries productivity distributions are such that country c0 has a comparativeadvantage in sector 1 while country c has a comparative advantage in sector 2. Specifically wemake the following assumption:
9

See equation (19) on page 1711 of Melitz (2003). This condition holds only under symmetry, otherwise total
expenditure and price indices are not the same across countries and the relationship between exporting and domestic
cutoff depends on endogenous variables.
10
The mass of firms is determined employing the condition that, in the absence of profits, total revenues are equal
to total labor income.

11

Assumption 1 The distribution of productivity in sectors 1 and 2 in countries c and c0 is such
φ

φ

0

m,1c
that φm,2c< φm,1c0 .
m,2cThe question we address is whether, given individual productivity, firms in sector 1 in country c
are more or less likely to export than firms in sector 2 in the Home country, relative to firms in theφφ0

R φx,1c0 .
Foreign country. In this model this question is equivalent to investigating whether φx,1c
x,2c
x,2c

Since the revenues of the least productive exporting firm in c can be expressed as11 rx,ic (φx,ic ) =σfx sηic and a similar expression holds for country c0 , then the ratio between the revenues of twosηic
ηsic0 wc1−η
0marginal exporting firms in c and c0 is given by. Using the expression for export revenuesin (2), the relationship between the export cutoffs in the two countries in sector i is given by thefollowing equation:
φx,ic0
=
φx,ic



sic0
sic

η

1−η

 σ

σ−1

(8)

(wc0 )

Notice that, everything else equal, the larger the return to the specific factor in sector i, the higher
the exporting cutoff. Because we are interested in the effect of being in the comparative advantage
sector on relative export performance across industries and countries, we take the ratio of (8) acrossthe two sectors 1 and 2 and rearrange to find the following relationship between relative export
cutoffs and relative specific factor returns across:
φx,2c /φx,2c0=
φx,1c /φx,1c0s2c /s2c0
s1c /s1c0

 ησ

σ−1

(9)

Equation (9) illustrates how export cutoffs are related across countries and sectors in the same
fashion as the returns to the specific factors in those countries and sectors. The higher is the relative
return to the specific factor in sector i the higher is the relative export cutoff in sector i, the lower
the probability of exporting for a firm of a given productivity in sector i. Therefore, if comparativeadvantage uniquely determines the relative ranking of returns to the specific factor across countries
then we have a link between aggregate sector productivity and export performance by individual
firms. We establish this in the following proposition, where we make use of a restriction on sector
size that we impose for the remainder of the paper.
Assumption 2 Within each country c each sector i is endowed with the same amount of specificfactor: Kic = Kc ∀i, c.
11This relationship derives from the fact that πx,ic (φx,ic ) =12rx,ic (φx,ic )− fx sηic .
σProposition 1 If firms in c draw their productivity parameter from a relatively superior distribution in sector 2 than in sector 1, compared to c0 , i.e.
φm,1c
φm,2c
<
φm,1c0φm,2c0then the relative return to the specific factor in c is higher in sector 2 than in sector 1 , comparedto c0 , i.e.
s1c
s2c
<
0
s1c
s2c0
and, given its productivity level φ, a firm in c is more likely to export in sector 1 than in sector 2,
compared to c0 , i.e.
φx,1c
φx,2c
<
φx,1c0φx,2c0
Proof. In Appendix.
The intuition for the result is simple. As firms in a sector draw from a productivity distribution
with a higher average, firms in the sector are on average more productive, produce more and havea high demand for the specific factor, which is available in fixed amount. Tougher competition forthis input’s services drives up the return to the specific factor. Everything else equal, a firm in a
relatively more productive sector faces higher costs and therefore is less likely to be able to cover
the fixed cost of exporting, and reducing its probability of exporting.
The same mechanism explains why firms in comparative advantage sectors export less, fora given level of productivity. The following proposition establishes the result that, conditionalon exporting, export revenues are relatively higher for Home firms in sector 1 than in sector 2,
compared to Foreign, conditional on own firm productivity.
Proposition 2 If firms in c draw their productivity parameter from a relatively better distribution
in sector 2 than in sector 1, compared to c0 , i.e.
φm,2c
φm,1c
<
φm,1c0φm,2c0then, given its productivity level φ, a firm in c has higher export revenues in sector 1 than in sector
2, compared to c0 , i.e.
rx,1c (φ)
rx,2c (φ)>
rx,1c0 (φ)
rx,2c0 (φ)
13Proof. We employ the definition of export revenues in (2) to find the following relative export
performance measure across sectors and countries:
rx,1c (φ)
rx,2c (φ)>=
0rx,1c (φ)
rx,2c0 (φ)

s1c s2c/s1c0 s2c0η(1−σ).

Since we showed in proposition (1) that ss1c0 / ss2c0 and we assumed that σ > 1, then the result
1c

2c

follows.
Proposition 2 can be used to motivate a reduced form expression for export revenues. Notethat (log) plant level (export) revenue in foreign markets can be written as follows:ln r(φf ic ) = ln Ai + (σ − 1) ln φf ic + (1 − σ) [η ln sic + (1 − η) ln wc )] + νf ic ,(10)

where sic is the wage of the specific factor, wc is the wage of the mobile factor, νf ic represents a
random disturbance, Ai is an industry-specific demand shifting term that plants in both countries
take as given in a given industry and is controlled for by industry fixed effects.12 Assume that Ψ
represents the elasticity of the return to the specific factor to industry productivity. Therefore theelasticity of average wages with respect to industry productivity is equal to ηΨ > 0. We can thenrewrite equation (10) as:ln r(φf ic ) = ln Ai + (σ − 1) ln φf ic + (1 − σ)ηΨ ln φic + (1 − σ)(1 − η) ln wc + νf ic .

(11)

With these theoretical results in hand, we now gauge their validity using plant level data that
we now describe.

4

Empirical Results

This section explores the empirical predictions of Section 3 that, controlling for own productivity,a plant in a comparative advantage industry produces and exports less. Section 4.1 describes thedata employed and our measures of productivity. Section 4.2 describes the baseline results that areinconsistent with the baseline model of firm heterogeneity. Section 4.4 explores the factor market
competition channel between industry productivity and plant-level outcomes: A 100% increase inpeer firm productivity increases average industry wages by 21.9%. For a firm of a given productivity
12

We also assume that this term contains industry specific mark-ups and iceberg transportation costs.

14

level, this leads to the probability of exporting falling by 6.3% and the level of exports falling by
17.7%.13 In section 4.4 we also briefly discuss the role that product market competition might playin generating the results.

4.1

Data

Plant-level data come from the statistical agencies Instituto Nacional de Estadistica and Departmento Administrartivo Nacional de Estadistica for Chile and Colombia, respectively. These data
have been used extensively in the trade literature.14 Industry affiliation is at the ISIC (Rev. 2)
3-digit level. Because plant-level exports are only available for Chile starting in 1990 and theColombian export data is available until 1991, we only use 1990 and 1991 in our analysis. All of
our results are cross-sectional. Table 1 presents summary statistics for the data including the total
number of observations in each year and the country composition of each industry. 15 Due to therespective sizes of the countries, approximately 70% of the observations are for Colombian plants
and the remainder are Chilean.
The focus of this study is on plant and industry productivity. Because of difficulties in comparing
intermediates and capital stocks across countries, we employ value added per worker as a measureof productivity.16 Production and non-production workers are weighted by their shares in the total
wage bill by country and industry. We examine value added measures of total factor productivity
in the robustness section to take into account differences in capital intensities across plants andindustries. In order to compare productivity differences across countries, we ensure that the data
are comparable.17 As a starting point, we must ensure that output and inputs are measured in thesame units.
We also want to remove non-productivity related relative price differences in output and investment. We use 3-digit output deflators from the central bank of each country to put all output
data in 1980 constant country-specific pesos for each country. We then use the average December
13

These estimates use the average parameter values for the year 1990 and 1991.
E.g. Tybout and Roberts (1996), Levinsohn (1993), Hsieh and Parker (2007), Levinsohn and Petrin (2003, 2008),
Hallak and Sivadasan (2009).
15
We drop industries related to tobacco and petroleum refining. (ISIC 314, 353, and 354).
16
Value added is defined as the value of output minus the value of intermediates such as materials and contractwork.
17
In addition to the measures mentioned here, we have ensured that the plant level measures of output, value
added, employment, and investment aggregate to virtually the same numbers as the UNIDO 3-digit data set which
has been used widely to conduct cross country studies e.g. Antweiller and Trefler (2002), Hanson and Xiang (2004),
and Morrow (2008).
1415

exchange rate for 1980 in each country to transform output in each sector into non-PPP adjusted
1980 U.S. dollars. Finally, we use constructed disaggregated 1980 PPP price indexes from the Penn
World Tables to transform these values into PPP adjusted 1980 U.S. dollars. We construct these
PPP price indexes are at the 3-digit ISIC level. Because of our difference-in-difference strategy,
all (multiplicative) country-specific and industry-specific terms in productivity (and all outcome
variables) will be differenced out. See the Data Appendix for more details.
For each country, skilled and unskilled workers are proxied by similarly defined non-production
and production workers. We use the procedure outlined in Bils and Klenow (2000) and Caselli(2005) to calculate effective labor input at the country level. While this will not matter for measuresof value added per worker, it will affect our calculations of value added TFP in the robustness
section. We estimate one physical Chilean worker to be 2.04 effective workers and one Colombian
worker to be approximately 1.65 effective workers.18 When calculating the value added measureof productivity, capital stock is calculated using the perpetual inventory method. We deflate
investment using country specific investment deflators, nominal exchange rates, and the Penn World
Tables country-specific PPP investment deflator in a manner similar to how we deflate output.
Because plant-level value added per worker measures contain some measurement error, we
instrument for plant level value added per worker using its one year lagged value for the sameplant. Industry value added per worker is measured as the weighted arithmetic average of plantlevel value added per worker within that ISIC 3-digit industry-country-year panel where the weights
correspond to value added.19 Because collinearity between plant and industry productivity might
be a concern in industries dominated by a few large firms, we drop industries with less than 25
plants in either country.20 An analysis of variance reveals that 16% of the overall variation in value
added per worker across plants and industries is explained by differences across industries. For
value added measure of TFP this share is 45%.21
18

See Caselli (2005) for details. We augment skilled and unskilled labor equivalently.
Results are unchanged when we take a geometric instead of an arithmetic mean.
20
This leads to us droping ISICs 361,362, 371, and 372. Eslava et al. (2009) make an identical restriction on
industry size.
21
This suggests that there are greater differences in capital intensity across firms within an industry than acrossindustries.
19

16

4.2

Results

We now present the empirical results that test our model and discuss how they contrast with thebaseline model. In the following specifications observations are indexed by plant (f ), industry (i),country (c). Equation (12) estimates production (yf ic ) or value added (vaf ic ) as a function of plantand industry productivity:0
qf ic = βplant φf ic + βind φic + βchile chilec + βind
IN Di + f ic

qf ic ∈ {yf ic , vaf ic },(12)

where φf ic and φic are plant and industry level productivity, chilec is a binary variable taking avalue of 1 for Chilean plants and 0 for Colombian plants, and IN Di is a vector of industry-specificfixed effects that control for factors including but not restricted to world demand and scale at theindustry level.
Equation (13) estimates a similar relationship for the probability of exporting:0
P r (EXPf ic > 0) = F βplant φf ic + βind φic + βchile chilec + βind
IN Di + νf ic ,(13)

where F (•) is the logit operator for export participation. We also estimate linear probability ofexporting models for ease of interpretation.
Under the baseline model of firm heterogeneity with small open economies, the coefficient on
industry productivity for exporting probability and exports should be zero as wages will be countryand not country-industry specific and all CES price indexes on world markets will be controlled forby industry-specific fixed effects. Table 2 presents results for equation (12). Table 3 results fromequation (13) combined with results for a linear probability model.22
As expected from numerous firm/plant level studies, plant productivity is a very strong determinant of the value of production and value added at the plant level. However, conditional onplant productivity, industry productivity is estimated to have a negative effect on plant production where this estimate is significantly different from zero at least at the 10% level of confidence.
Similar results hold for the probability of exporting.
The results for domestic performance would be expected in any closed economy model in whichmore productive peer firms shift in a given firm’s residual demand curve. However, because small
22

For all regressions, we have experimented with weighted least squares estimation with weights corresponding tofirm and/or industry size. The point estimates and standard errors change negligibly.

17

open economies are likely to have little, if any, effect on the World residual demand curve, theresults pertaining to exporting are less obvious.
For most specifications we can reject the null that the coefficients for φf ic and φic are equaland opposite in sign (with the exceptions of columns (4) and (8) in Table 3). If we abandon the
restriction on the number of plants per industry we can reject that null at the 5% confidence level
for all specifications. This indicates that if all plants in a country-industry panel share the same
relative productivity level the country will have higher sales in a given industry. Consequently,
familiar results based on a Ricardian model of comparative advantage continue to hold in the point
estimates. A caveat is that this particular aspect of the results is less robust at the 4-digit level
which we discuss below.
Figures 2, 3, and 4 present this information graphically. In each graph, we purge the left-handside variable from Tables 2 and 3 of plant productivity and the fixed effects listed. We then purge
industry-level productivity of the same variables. Finally, we collapse the left hand side variables
down to their industry-year-country means and transform them into Chilean relative to Colombian
values. Finally, we plot them against Chilean relative to Colombian industry productivity. Datain the figures are pooled for the years 1990 and 1991.23
Tables 4 and 5 decompose the value of production into domestic sales and exports where domestic sales are defined as the value of production minus exports. This allows us to see if and how
superior distributions of productivity affect domestic and external performance differentially. The
qualitative results from Tables 2 and 3 continue to hold for both domestic and foreign sales.24 Own
plant productivity increases sales while the productivity of other plants in the industry diminishes
sales both at home and abroad. Again, this is a shortcoming of the baseline model in which industry
productivity should not have any effect on export performance. For domestic sales we can reject atconventional confidence levels the null hypothesis that the coefficients on own plant productivity
and industry productivity are of equal magnitude and opposite sign. For exports we reject thesame null at the 3% level for 1990, and 15% for 1991. These results should be interpreted in lightof the fact that a minority of firms export leading the sample size to fall by 83% for the export
revenue equations.
As illustrated in the theory section, the results for export performance are inconsistent with the23

As a note, the outlier ISIC 312 contains miscellaneous food products which is less likely to be comparable acrosscountries due to its “bag” nature..
24
There are slightly fewer observations for domestic revenue than total revenue because of a small number of firmsthat export but do not sell domestically.

18

small open economy version of the firm heterogeneity model presented as well as the asymmetric
two country version of firm heterogeneity model of Demidova (2008). It is consistent with our
specific factors version of the firm heterogeneity model.

4.3

Robustness

This section explores the robustness of our results in three directions. First, we analyze the results
at the 4-digit level. Second, we examine the possibility that industry productivity is merely picking
up higher order effects in plant productivity. Third, we employ value added measures of total factor
productivity instead of value added per worker.
Tables 6a and 6b present results at the 4-digit level of aggregation. A priori we believe thatthese results should be taken with caution because we lose 66 of 80 possible 4-digit ISIC industries
by only examining industries in which both countries produce (or export in the case of export
related variables) and which have at least 25 plants. Therefore the 3-digit analysis is our preferred
specification.25 Consequently, these results suggest that the general patterns hold at a finer level ofdisaggregation, even though the point estimates are less precise. While the general pattern of thecoefficients does not change, the relative magnitudes of plant and industry productivity narrow (orreverse for export values) so that the null hypothesis that the coefficient on industry productivity
measure is equal in absolute magnitude to the coefficient on plant level productivity cannot berejected.
Table 7 presents results based on value added TFP instead of value added per worker. For
brevity we present results pooled across years 1990 and 1991. The advantage of using value added
TFP is that we purge differences in capital intensity across firms and industries from value added
per worker. The cost is that comparing capital inputs across countries can be problematic due tomeasurement error (e.g. difficulties in reporting the value of capital stock). To estimate value added
total factor productivity we use the estimation procedure outlined in Levinsohn and Petrin (2003)(LP).26 Productivity estimation occurs at the 3-digit ISIC level with regressions run within and not
across countries. Since there are 20 3-digit ISIC sectors and two countries, we estimate productivity
separately for 40 different panels. This allows us to estimate output elasticities that are countryindustry specific. Point estimates for the input elasticities are obtained from estimating these25

This is consistent with the preference of 3-digit specifications of Levinsohn (1993) and Fernandez (2007)
Because of identification issues with the Levinsohn-Petrin procedure (e.g. Ackerberg, Caves, and Frazer, 2009.
The presence of pervasive missing observations for investment precludes using the Olley-Pakes (1996) method.) wehave also estimated TFP using OLS estimation. The results are virtually identical.
26

19

regressions over 1982-1996 for Chile and 1982-1991 for Colombia. We assume that the production
function is as given in equation (14) where lower case letters are natural logarithms; value added(va) is a function of capital stock (k), non-production workers (npw) and production workers (pw):vaf t = β0 + βnpw npwf t + βpw pwf t + βk kf t + f t .

(14)

We use materials as the omitted proxy variable. Because the regression is run at the industrycountry level, industry and country subscripts (i and c) in equation (14) are suppressed. The
results are even stronger than the results for value added per worker.
Table 8 presents specification including a quadratic term for own plant productivity to control
for non-linear effects that industry productivity may be proxying for. The results show a convexity
in the relationship between own plant productivity and plant-level production, value added, andexports. There is a slight concavity in the propensity to export. However the coefficient on industry
productivity changes little when higher order terms for plant value added per worker are added.

4.4

Transmission and Magnitudes

The results above suggest that plants of a given productivity level attain superior economic outcomes both at home and abroad when they reside in less economically competitive industries. Whileboth factor and product market competition are likely contributors to this result, we focus on factor market competition in this paper. This section examines the empirical link between industry
productivity and industry wages to examine whether the factor market competition channel can
explain the entirety of the results in section 4.2. As specified in the theory section, if factors aresector-specific, a superior distribution of productivity in an industry bids up the wages of the specific factor. Conditional on a given level of plant productivity, this leads to a lower probability ofexporting and a lower level of exports conditional on exporting. This subsection presents regression
and scatterplot evidence consistent with the idea of factor market competition drives a portion of
the results in Tables 2 and 3.
To identify this mechanism, we estimate the regressions in equation (15) which use the same
notation as equation (12).27 These estimate plant level average wages, average wages for skilled
27

Note testing whether the coefficient on industry TFP is positive in these regressions is equivalent to regressing
the left hand side variables on plant and industry productivity and testing the linear restriction that the sum of thecoefficients on φf ic and φic is positive.

20

workers, and average wages for unskilled workers as a function of where the firm lies in the distribution of the industry and as a function of the average productivity in the industry itself.

0
wk,f ci = βdeviation devf ic + βind φci + βchile chilec + βIN
D IN Di + νf ci

k ∈ {avg, skill, unskill} (15)where devf ic = φf ci − φci . We show that plant level wages are increasing in industry level productivity conditioning on where the plant lies in the distribution of that industry.28 While there
is a large literature showing that more productive plants pay higher wages, we are agnostic as towhy more productive plants pay higher wages and are more interested in whether plants in moreproductive industries pay higher wages conditional on their own characteristics relative to industry
averages.29 Table 9 presents regression results at the 3-digit level.
The results suggest that both plant and industry productivity have a positive effect on wages
with the elasticity for own plant productivity (relative to industry average) much larger than thatof industry productivity. Averaging the coefficients in columns (1) and (4) of Table 9, we findthat a doubling of industry productivity increases average wages at the plant level by 21.9%. The
regression results suggest that the marginal effect of industry productivity is stronger for skilledlabor than for unskilled labor. We find this result to be reassuring based on work on Latin Americanand United States labor markets by Heckman and Pages (2000) and Diebold, Neumark, and Polsky(1997).
In examining Latin American labor market frictions, Heckman and Pages (2000) argue that costsof labor adjustment are based partially on tenure with labor markets being subject to a myriad ofrestrictions in general. They find that while Colombian labor market restrictions underwent major
liberalization in 1990, labor market restrictions post-liberalization were still much higher than theindustrialized country average. They also find that similar results hold for Chile. Studying labormarket tenure in the United States, Diebold, Neumark, and Polsky (1997) find that skilled workers
tend to have longer tenure spells than unskilled workers. If we assume that these results hold acrossborders, it is a reasonable assumption that skilled labor is more of a fixed factor then unskilled28

For original work on the link between exporting and wages see Bernard and Jensen (1995). See Brown and Medoff(1989) for a complementary study on the link between size and wages. Different models posit different reasons forthe relationship between exporting and wages (e.g. Verhoogen (2008) and Amiti and Davis (2008)).
29
Note that because we estimate the input elasticities separately across countries and industries with production
and non-production workers, we can partially control for differing but time invariant skill composition within acountry-industry panel as these should show up as differences in the input elasticities.

21

labor. Consequently, the results above are consistent with those findings. Figure 5 presents thesame information via scatterplot. Based on regression results and scatterplot, there is preliminary
evidence that industry productivity bids up the industry wage that plants face. We now performa back of the envelope calculation to assess the strength of this mechanism.
4.4.1

Quantifying own and peer-firm productivity effects

The negative effect of industry productivity on plant level outcomes conditional on plant productivity is at least partially due to some combination of factor and product market competition. We canuse the results from Tables 5 and 9a to provide bounds on how much of the negative coefficient on
industry productivity can be due to the factor market competition. Considering equation (11) anddividing the coefficient on industry productivity by the (negative) coefficient on plant productivity
yields an estimate of ηΨ. This is what the elasticity of average wages with respect to industry wages
would need to be for the factor market competition channel to explain the entire magnitude of thecoefficient on industry productivity. Based on our estimates in Table 5 the average value of σ − 1 is
0.826, which implies ηΨ = 0.389.30 Because our estimates imply an elasticity of average wages toindustry productivity of approximately 0.219, factor market competition is 56% of the magnitude
it would need to be to be consistent with factor market competition explaining the entirety of ourresults.
Given our assessment of the importance of factor market competition, we now ask how own
and peer-firm productivity affects external (international) outcomes through this specific channel.
Specifically we ask three questions:
1. If a plant moves from the 25th percentile of the within industry productivity distribution to
the 75th percentile. How will its export performance change?
2. If we move a plant of a given productivity level from an industry in the 25th percentile of theindustry-by-industry distribution of productivity to an industry in the 75th percentile, howwill this affect its export performance?
3. If a firm is a representative firm in an industry in the 75th percentile of industry-level productivity differences, how will its performance differ from a representative firm in the 25th
30

This is calculated by dividing the average coefficient on industry TFP by the average coefficient on plant TFP
in the export revenue equation.

22

percentile? e.g. what is the net effect on export performance of a firm being a representative
firm in a (Ricardian) comparative advantage industry compared to a (Ricardian) comparative
disadvantage industry?
Table 10 presents summary statistics regarding the distribution of firm and industry productivity. Column 1 reports the difference in productivity between Chile and Columbia within an industry.
For example in ISIC 311 Columbia is 60% more productive than Chile. Column 2 presents thatdifference relative the median difference, removing absolute advantage. This column also showsthat the industry at the 75th percentile is 118.5% relatively more productive than the industry at
the 25th percentile.31 For Column 3 we rank all plants within an industry according to productivity regardless of the country in which they reside. We then compute the difference in productivity
between the plant at the 75th percentile and the plant at the 25th percentile. For example, in ISIC
industry 311 a firm moving from the 25th percentile to the 75th percentile becomes 129.5% moreproductive.
Given that the average point estimate on own plant productivity is 0.826 and the median change
in productivity moving through distribution of a given industry is 0.782, the answer to question 1
is that a within-sector improvement in productivity leads to exports increasing by approximately
64.6%. Answering question 2, a firm moving from a comparative advantage industry to a comparative disadvantage industry will see its sales fall by 21.4% due to factor market competition.32
Answering question 3, if a firm moves from a comparative disadvantage industry to a comparativeadvantage industry as a mean firm in each industry, its productivity will increase by 118.5%. The
direct effect of own firm productivity will be to increase export revenue by 97.9%. However theindirect of factor market competition will be to diminish export revenues by 21.4%. This meansthat the net effect of such a movement on export sales will be an increase of 76.5%.
Theory maps into export probability equation less clearly when examining the propensity toexport. However, we feel that it is illustrative to perform a similar exercise as with export levels
using the linear probability models. We assume that a similar proportion (56%) of the coefficient on industry productivity for the linear probability of exporting is explained by factor market
competition as in the regressions explaining export levels.
The average of estimates from Table 3, Column 6 and 8 imply that if a plant moves from31
32

This is calculated as 0.631-(-0.554).
This figure is derived as −0.826 ×
1−σ

1.185

25−75 prod gain× 0.219ηΨ

23

the 25th to the 75th percentiles of the within industry distribution, it will be 13.8% more likely
to export. If a plant moves from a relatively unproductive industry in the 25th percentile to a
relatively productive industry in the 75th percentile (holding its own productivity constant), it will
be approximately 7.4% less likely to export. Finally if a firm moves from being a representativeplant in a comparative disadvantage industry to a representative firm in a comparative advantage
industry, the direct effect of own firm productivity will be to increase its propensity to export by
20.9%. However the indirect of factor market competition will be to diminish the propensity toexport by 7.4%. This means that the net effect of such a movement on the probability of exporting
will be 13.5%.
Clearly there is a large residual of the negative coefficient that remains unexplained. We havesought to err on the side of caution by focusing on a specific mechanism in both the theoreticaland empirical work. Another very likely explanation for this is due to product market competition
abroad from peer firms in the same exporting country. A common assumption of the imperfectcompetition and international trade literature is that the elasticity of substitution between domestic
varieties is the same as between a domestic and a foreign variety. In this canonical framework, thiswill lead to product market competition at home but not abroad as all countries face the same CES
price aggregator in foreign markets.
However, if the elasticity of substitution is different for within- and across-country varieties,
this can lead to product market competition abroad as a function of country-industry productivity.
We are unaware of any work calculating different elasticities of substitution across domestic andforeign varieties. It would be interesting to calculate how much of a difference must exist in theseelasticities to generate the above results.

5Conclusion

This paper provides an theoretical and empirical framework to assess how plant- and industrylevel productivity differences interact in determining plant-level outcomes. Specifically, we ask
how the productivity of peer firms affects outcomes related to exporting in the context of small
open economies exporting to a large World market. We do this in the context of a model where
productivity varies both across industries and firms within the industries as in DFS (1977) and
Melitz (2003), respectively. Using plant-level data for Chile and Colombia for 1990 and 1991, we
find that more productive domestic peer plants diminish domestic sales, exports, and the propensity24

to export conditional on own-plant productivity.
This result can come from a variety of reasons including, but not restricted to, product market
competition and factor market competition. To discipline and identify a channel for our results, we
focus on factor market competition. We argue that inclusion of sector-specific inputs is an intuitively
appealing way of amending the model to be consistent with the data. As average productivity inthe industry increases, the wage of the specific factor increases, raising the average wage bill for afirm of a given productivity level.
To assess the role that factor market competition may play, we examine relative wages in anindustry across countries and how they relate to relative average productivity in the industry
controlling for country-specific and industry-specific effects. We find that industries with higher
average productivity tend to pay higher wages controlling for the proportion of skilled workers
in the industry and differing labor-input elasticities across industries and countries. We estimatethat if the average productivity of plants in an industry doubles, factor prices will increase byapproximately 21.9%. For a firm of a given productivity level, this will lead to the probability ofexporting falling by 6.3% and the level of exports falling by 17.7%.
Avenues for future research are plentiful. First, we can ask how non-conventional productmarket competition contributes to these results. Specifically, an elasticity of substitution between
domestic varieties that is greater than between domestic and foreign varieties can contribute to
generating this result. Second, we can ask how the short run specificity of factors at the sectorlevel can diminish the gains from trade liberalization given firm heterogeneity within those sectors.

6

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7

Appendix

7.1

Proof of Proposition 1

In this section we derive a number of intermediate results that will be subsequently employed in
the proof of the Proposition (1).
From the definition of φd,ic and φx,ic it is useful to derive the following expressions:
φd,ic
φd,ic

!σ−1
=

φx,ic
φx,ic

28

!σ−1
=

k
k+1−σ

(16)

We substitute (4) in (3) and replace π d,ic and π x,ic with their expressions in (5) and (6). Substituting(16) and the Pareto cumulative density function (1) in the resulting expression, yields the following
condition:
k+1−σ
δfe
=f
σ−1



φm,ic
φd,ic

k


+ fx

φm,ic
φx,ic

k
.

(17)

Average firm revenues ric can be rewritten, similarly to Melitz (2003), as ric = π ic + f +1−G(φx,ic )f . Using the free-entry condition (3) to substitute π ic and the Pareto cdf, average firm
1−G(φd,ic ) x

revenues can be expressed as:
ric = δfe

φd,ic
φm,ic

k


+f +

φd,ic
φx,ic

k

k

!

fx

(18)

By replacing (18) in (7) we obtain the following equation:
ηMic σsηic


δfe

φd,ic
φm,ic

k


+f +

φd,ic
φx,ic

fx

= sic Kic .

(19)

We report the equivalent condition for the mobile factor, which earns a share 1−η of total revenues:(1 − η) Mic σsηic


δfe

φd,ic
φm,ic

k


+f +

φd,ic
φx,ic

!

k
fx

= Lic .

(20)

The next expression we derive can be seen as a sort of goods market equilibrium condition.
Since there are no imports in sector i, the entire share α of domestic expenditure spent on varieties
produced in sector i accrues to domestic firms as revenues in the domestic market. Let us denoteby rd,ic (φ) the domestic revenues of a firm with productivity φ in sector i. If Yc is aggregate income(which is equal to aggregate expenditure Ec ) then αYc = Mi rd,ic , where rd,ic are average revenues

σ−1
φd,ic
in the domestic market. Since rd,ic /rd,ic (φd,ic ) = φd,icand rd,ic (φd,ic ) = σf sηic 33 then we canestablish, using (16), the following condition:
αYc = Mickσf sηic
k+1−σ

(21)

For the remainder of the proof we are going to employ conditions (20), (19), (21), (17) and thecorrespondent conditions for the Foreign country, along with (8).
A result that will prove useful later is obtained by dividing equation (20) by (19) for sector 1
33

id )This relationship derives from the fact that πid (φid ) = rid (φ− f sηi .
σ29

to find the following expression:(1 − η)s1c K1c
= L1c
η

(22)

We obtain a similar expression for sector 1 in the country c0 :(1 − η)s1c0 K1c0
= wc0 L1c0
η

(23)

Analogous expressions can be derived for sector 2 in both countries.
We start by deriving M1c and M1c0 from (21) and the analogous condition for c0 . We thensubstitute them into (20) and its equivalent for c0 and, after some manipulation isolate φkd,1c andφkd,1c0 , assuming that equation (20) can be inverted:[L1c k − αYc (k + 1 − σ) (1 − η)] φkm,1c φkx,1ci
φkd,1c = f h
δfe φkx,1c + fx φkm,1c αY (k + 1 − σ) (1 − η)
φkd,1c0[L1c0 kwc0 − αYc0 (1 − η)(k + 1 − σ)] φkm,1c0 φkx,1c0i= f hδfe φkx,1c0 + φkm,1c0 fx αYc0 (k + 1 − σ)(1 − η)

Now we can substitute φkd,1c and φkd,1c0 into (17) and its Foreign equivalent to find φkx,1c andφ∗kx,1c : :φkx,1c =φkx,1c0=

L1c fx φkm,1c (σ − 1)δfe (k − σ + 1) [L1c − (1 − η) Yc α]
wc0 L1c0 fx φkm,1c0 (σ − 1)

(24)
(25)

δfe (k + 1 − σ) [wc0 L1c0 − (1 − η)Yc0 α]

Substituting (24) and (25) into (8) delivers the expression:
wc0 L1c0 [L1 − (1 − η) Yc α]
L1c [wc0 L1c0 − (1 − η)Yc0 α]

φm,1c0
φm,1c

k


=

s1c0
s1c

 kση

σ−1

(wc0 )kσ(1−η)
σ−1

.

(26)

First note that the expressions within each of the brackets are positive. This is because the first term
represents total income to labor in that sector. The second term is the share of domestic revenues
accruing to labor in each sector. As long as revenues from exports are positive the difference
between the two terms is positive. An analogous expression for sector 2 is:30



wc0 L2c0 [L2c − (1 − η)Yc α]
L2c [wc0 L2c0 − (1 − η)Yc0 α]

φm,2c0
φm,2c

k


=

s2c0
s2c

 kση

σ−1

(wc0 )kσ(1−η)σ−1(27)

Dividing equation (26) by (27) gives the following expression:



 ηkσ
s1c0 s2c σ−1
L1c0 L2c [L1c − α(1 − η)Yc ] [wc0 L2c0 − α(1 − η)Yc0 ] φm,1c0 φm,2c k=
L1c L2c0 [wc0 L1c0 − α(1 − η)Yc0 ] [L2c − α(1 − η)Yc ] φm,2c0 φm,1cs2c0 s1c
P
Since Yc = 2i=1 Lic + sic Kic , we can now substitute the expressions for L1c and L1c0 from (22)and (23) and the analogous expressions for L2c and L2c0 . After some simplification we find thefollowing:s1c0 K1c0 s2c K2c [s1c K1c − α (s1c K1c + s2c K2c )] [s2c0 K2c0 − α (s1c0 K1c0 + s2c0 K2c0 )]s1c K1c s2c0 K2c0 [s1c K1c0 − α (s1c0 K1c0 + s2c0 K2c0 )] [s2c K2c − α (s1c K1c + s2c K2c )]φm,1c0 φm,2c
φm,2c0 φm,1c

k


=

(28)
Under the assumption that Kic = Kc ∀i, c after some simplification we can rewrite (28) as:2c
1 − α − α ss1c

s1 − α − α s2c00
1c

{z

|

}

Γ

φ

s

×

1 − α − α s1c00  φm,1c0 φm,2c k
1−α−α
|
{z

2c
s1c
s2c

=

s1c0 s2c
s2c0 s1c

 ηkσ

σ−1

}

∆

0 φm,2cφm,2c0 φm,1c



s

s

We prove that φm,1c0 φm,1c > 1 implies s1c00 s2c> 1 by contradiction. We have to consider two
1c
m,2c

2c

s

0 φm,2cφs< 1. This implies that, since φm,1c0 φm,1c > 1,
cases. First, assume to the contrary that s1c00 s2c
1c
2c

m,2c

s

Γ∆ < 1. Because ss2c
< s2c00 then Γ > 1 and ∆ > 1 which contradicts the condition Γ∆ < 1.
1c
1c

φ

s

s

0 φm,2c

2c= 1. This implies that, since φm,1c0 φm,1c > 1, it must
Second, assume to the contrary that s1c00 s1c
2c

m,2c

be the case that Γ∆ < 1, but our assumption implies that Γ = 1 and ∆ = 1, which is not possible
given out assumption. Since both cases lead to a contradiction we have proved the result.

8

Data Appendix

The key to plotting the above figures involves making the Chilean and Colombian firms as comparable as possible. This involves making output, labor input, capital input, and materials input
comparable. I explain these in turn. The December average exchange rate is used to translate all
variables into nominal U.S. dollars.

31

s1c0 s2c
s2c0 s1c

 ηkσ

σ−1

8.1

Labor Input

Labor is broken down into skilled and unskilled in each data set. Units of labor are measured in thenumber of workers in each data set. In addition, we can allow for the effectiveness of labor to varyacross countries as in Caselli (2005) and as used by Morrow (2008). Using this procedure, labor istransformed into effective labor using data on educational attainment and assumptions about thereturns to schooling. Labor input is assumed to be EL where L is the physical quantity of labor
employed and E is the effectiveness of labor. The effectiveness of labor without any schooling isnormalized to E = 1. Labor is assumed to become 13% more productive per year of schoolingfor years one through four of educational attainment, 10% more productive per year for years four
through eight, and 7% per year for subsequent years. Based on these measures one unit of physicallabor is assumed to be 2.04 units of effective labor for Chile and 1.65 for Colombia.

8.2

Capital Input

Capital was constructed using the perpetual inventory method in which capital stock is available
for a single year and then subsequent capital stock is calculated by adding observed investment
and subtracting assumed depreciation. Capital and investment are measured in thousands of thenominal domestic currency in each data set. For both countries, the depreciation rate is imposed
to be 5% per year for buildings, 10% for machinery, and 20% for vehicles.
The following describes the construction of investment for Colombia:• Building Investment=Purchases of New Fixed Assets: Structures+Purchases of Old Fixed
Assets: Structures+Production of Assets: Structures+Reappraisal of Fixed Assets: StructuresSales of Fixed Assets: Structures.
– Note: Despite the fact that we observe data on the purchases and sales of land in the
Colombian data set, we choose not to include it in our measures of the capital stock.
This is because we do not observe separate investment in land in the Chilean data set
until 1989 and we are making the two data sets as comparable as possible.
• Machinery Investment=Purchases of New Fixed Assets: Machinery+Purchases of Old Fixed
Assets: Machinery+Production of Assets: Machinery)+Reappraisal of Fixed Assets: MachinerySales of Fixed Assets: Machinery .
• Transportation Investment=Purchases of New Fixed Assets: Transportation+Purchases of
Old Fixed Assets: Transportation+Production of Assets: Transportation+Reappraisal of
Fixed Assets: TransportationSales of Fixed Assets: Transportation.
• Office Investment=Purchases of New Fixed Assets: Office+Purchases of Old Fixed Assets:
Office+Production of Assets: Office+Reappraisal of Fixed Assets: OfficeSales of Fixed Assets:
Office.
Each of these is deflated by the economy-wide investment deflator that is described next. Thereis no distinction between different types of investment with regards to the deflator used. Two
dimensions need to be taken into consideration when constructing the deflator. First, the domestic
investment price deflator must be used to make the investment numbers comparable over time.
This is common to nearly all firm level studies. However, because we are trying to make two firm32level data sets comparable, we also need to translate these variables into a common real currency.
We choose to do this using the Penn World Tables PPP investment deflator.
Because the PWT deflators are measured by the country’s PPP price level relative to the
U.S., the first two terms in the denominator convert nominal 1980 Colombian values to PPP
adjusted 1980 U.S. dollar values. The second and third terms in the denominator control for
nominal and PPP price changes over time within Colombia and convert current Colombian pesos into PPP adjusted 1980 Colombian pesos. is taken to be 50.92 which is the December average Peso-Dollar exchange rate as in IMF international Financial statistics. PI is taken from
Penn World Tables Edition 6.1. The Producer Price Index for capital is taken from : http ://www.banrep.gov.co/statistics/stap rices.htm. The domestic capital formation deflator is takenfrom the PPI summary link which opens up to the file Òis rea0 15.xls.Ó All annual values use theJune value of the Capital Formation variable where values are indexed so that June, 1980 is thebase period.
The following describes investment for Chile:• Building Investment=Investment in New Buildings+Investment in Used Buildings Sales of
Buildings Improvements Done by Third Parties: Buildings+ Buildings Produced for Own
Use.
Note: Investment in land is not available for years prior to 1989 and, therefore, capital stock
does not include investment in land.
• Machinery Investment=Investment in New Machinery+Investment in Used Machinery Salesof Used Machinery Improvements Done by Third Parties: Machinery+ Machinery Produced
for Own Use.
• Vehicles Investment=Investment in New Vehicles +Investment in Used Vehicles Sales of Used
Vehicles Improvements Done by Third Parties: Vehicles + Vehicles Produced for Own Use
Buildings are deflated using the implicit price deflator for construction. Machinery and Vehicles
are deflated using the implicit price deflator for machinery. Nominal Pesos are transformed into
real PPP adjusted U.S. dollars using a similar expression to the one for Colombia except that KPPI
is now replaced by one of the two deflators described above.

8.3

OutputThere are two measures of output for the Chilean Data. There is income which includes salesof goods produced, sales shipped to other establishments, resales, work done for third parties
and repairs done for third parties. Then there is gross output which includes income, electricity
sold, buildings produced for own use, machinery produced for own use, vehicles produced for own
use and final inventory of goods in process. The specific calculation for gross output is (see Liu
documentation)
There is a measure of the value of (gross) production in the Colombian data set. According to the
documentation, this includes the value of all goods and byproducts produced by the establishment
during the year at sales prices plus the value of work done for others, plus the value of electricity
sold, work done within house, and addition to inventories. It is fairly clear that Gross Productionin the Colombian data set included: the value of all goods and by-products sold, revenue from work
done for third parties, value of electricity sold, value of operational income (value of installation,
repair, and maintenance), change in Business inventories, and tax certificate revenue.
33

Revenue is reported in thousands of nominal Colombian Pesos. They are transformed into
thousands of 1980 Colombian Pesos using the 3-digit ISIC producer price index which is available
at: http : //www.banrep.gov.co/statistics/sta prices.htm. The specific spreadsheet is provided at
the link Producer Price Index (PPI) produced and consumed goods which links to the spreadsheet
i srea 015.xls.All variables are the June values with all observations indexed so that the value
for 1980=1.00. Similar data was used for Chile that is available from the Instituto Nacional de
Estadisticas and was graciously provided by David Greenstreet.
PPP Price deflators were constructed using the disaggregated PPP benchmark data that isavailable from the Penn World Tables. Unfortunately, the benchmark data are only available atfive year intervals. In addition, the level of disaggregation changes from year to year. We choose
to use the values from 1980 because Chile and Colombia are not covered in the 1985 survey. One
fortuitous aspect of the 1980 benchmark is that it is available at the greatest level of disaggregation.
The 1980 benchmark covers 155 industrial groupings, the 1985 benchmark covers 135 industrial
groupings, and the 1996 benchmark only covers 31 industrial groupings. Consequently, we choose
to use the 1980 data. This means that we are making the implicit assumption that all changes in
the PPP deflator after 1980 can sufficiently be accounted for by a country fixed effect in which allindustry level PPP deflators grow at the same rate. The mean (across industries) relative PPP
deflator for Chile relative to Colombia is 1.747 and the median is 1.440. These can be compared
to relative values of the PPP GDP deflator of 1.409, and 1.061 for investment goods.

34

Table 1
Data Summary
1990

1991

ISIC

#Chile# Colombia

Total# Chile# Colombia

Total

311
312
321
322
323
324
331
332
341
342
351
352
355
356
369
381
382
383
384
390
Total

657
34
210
153
31
66
131
52
33
83
27
107
32
92
60
211
85
28
54
35
2,181

761
154
380
717
86
201
136
171
137
296
125
271
61
293
246
462
287
188
201
139
5,312

1,418
188
590
870
117
267
267
223
170
379
152
378
93
385
306
673
372
216
255
174
7,493

642
34
210
142
30
66
124
52
35
88
27
107
32
90
60
204
81
29
52
33
2,138

710
148
361
654
83
195
118
159
127
282
120
262
59
288
231
431
272
170
185
122
4,977

1,352
182
571
796
113
261
242
211
162
370
147
369
91
378
291
635
353
199
237
155
7,115

Table 2
Value of Production

RHS Variable
VA per workerf ict

VA per workerict

1990

1991

1990

1991

Production(1)(2)

Production(3)(4)

Value Added(5)(6)

Value Added(7)(8)

1.37∗∗∗(0.054)

1.426∗∗∗(0.054)−0.516∗(0.295)1.426∗∗∗(0.06)

1.515∗∗∗(0.053)−0.892∗∗∗(0.291)1.423∗∗∗(0.058)1.479∗∗∗(0.06)−0.517∗(0.293)

1.469∗∗∗(0.053)1.555∗∗∗(0.04)−0.87∗∗∗(0.282)

Observations
7493
7493
7115
7115
7493
7493
7115
7115
Industries
20
20
20
20
20
20
20
20
Industry FE
3 digit
3 digit
3 digit
3 digit
3 digit
3 digit
3 digit
3 digitCountry FE
yes
yes
yes
yes
yes
yes
yes
yes
Restriction p-val
0.002
0.027
0.000
0.012
Standard errors in parentheses. Standard errors clustered by country-industry level (e.g. Chile ISIC 311).
***=1% level, **=5% level,*=10% level,.

35

Table 3
Propensity to Export
1990

1991

Logit

Logit

RHS Variable

(1)

(2)

(3)

(4)

VA per workerf ict

1.204∗∗∗(0.08)

1.288∗∗∗(0.089)1.287∗∗∗(0.105)1.415∗∗∗(0.107)

VA per workerict−0.651∗∗(0.332)

1990

1991

Linear Prob.
(5)(6)

Linear Prob.
(7)(8)

0.149∗∗∗(0.012)−1.018∗∗∗(0.264)0.158∗∗∗(0.014)−0.085∗(0.047)

Obs.
7493
7493
7115
7115
7493
7493
Industries
20
20
20
20
20
20
Industry FE
yes
yes
yes
yes
yes
yes
Country FE
yes
yes
yes
yes
yes
yes
Restriction p-val
0.027
0.107
0.099
The dependent variable=1 if there are positive levels of exports, zero otherwise.
Standard errors in parentheses clustered by country-industry level (e.g. Chile ISIC 311).
***=1% level, **=5% level, *=10% level

Table 4
Domestic Revenue
1990

1991

RHS Variable

(1)

(2)

(3)

(4)

VA per workerf ict

1.338∗∗∗(0.054)

1.392∗∗∗(0.053)1.372∗∗∗(0.053)1.456∗∗∗(0.047)

VA per workerict−0.485∗(0.282)−0.85∗∗∗(0.28)

Obs.
7472
7472
7102
7102
Industries
20
20
20
20
Industry FE
yes
yes
yes
yes
Country FE
yes
yes
yes
yes
Restriction p-val
0.000
0.027
“Standard errors in parentheses.
***=1% level, **=5% level, *=10% level. Standard errors clustered bycountry-industry level (e.g. Chile ISIC 311).

36

0.18∗∗∗(0.016)

0.195∗∗∗(0.018)−0.143∗∗∗(0.042)

7115
20
yes
yes

7115
20
yes
yes
0.195

Table 5
Export Revenue
1990

1991

RHS Variable

(1)

(2)

(3)

(4)

VA per workerf ict

0.755∗∗∗(0.108)0.773∗∗∗(0.109)0.809∗∗∗(0.097)0.879∗∗∗(0.112)

VA per workerict−0.51∗(0.283)-0.132(0.269)

Obs.
1251
1251
1491
1491
Industries
20
20
20
20
Industry FE
yes
yes
yes
yes
Country FE
yes
yes
yes
yes
Restriction p-val
0.02180.1463“Domestic Revenue” is the value of production - value of exports.
“Standard errors in parentheses.
***=1% level, **=5% level. Standard errors clustered bycountry-industry level (e.g. Chile ISIC 311).

Table 6aFour Digit ISIC Results (1990)

RHS Variable
va per workerf ic

Revenue(1)1.388∗∗∗(0.105)

Value Added(2)
1.518∗∗∗(0.068)Domestic Rev.
(3)
1.346∗∗∗(0.111)

Exports(4)0.805∗∗∗(0.168)Pr(exp>0)(5)1.604∗∗∗(0.134)

VA per workeric−0.997∗∗∗(0.233)−1.141∗∗∗(0.18)−0.967∗∗∗(0.223)−0.866∗∗(0.361)−1.442∗∗∗(0.263)

Obs.
Industries
Industry FE
Country FE
Restriction p-val

5000
14
yes
yes
0.053

4994
14
yes
yes
0.031

4893
14
yes
yes
0.047

723
14
yes
yes
0.849

5000
14
yes
yes
0.483

Table 6b
Four Digit ISIC Results (1991)

RHS Variable
VA per workerf ict

Revenue(1)1.408∗∗∗(0.111)

Value Added(2)
1.529∗∗∗(0.081)

Domestic Rev.
(3)
1.355∗∗∗(0.108)

Exports(4)
0.936∗∗∗(0.127)Pr(exp>0)(5)1.618∗∗∗(0.180)VA per workerict−0.952∗∗∗(0.216)−1.133∗∗∗(0.154)−0.952∗∗∗(0.208)−0.82∗∗∗(0.239)−1.605∗∗∗(0.308)

Obs.
4799
4793
4789
887
4799
Industries
14
14
14
14
14
Industry FE
yes
yes
yes
yes
yes
Country FE
yes
yes
yes
yes
yes
Restriction p-val
0.0435
0.024
0.056
0.578
0.964
These notes apply to both Table 6a and 6b. The dependent variable in the logit =1
if there are positive levels of exports, zero otherwise. Standard errors presented in.
parentheses. ***=1% level of certainty. Standard errors clustered by country3-digit industry level (e.g. Chile ISIC 311).

37

Table 7
Robustness: Value Added productivity (pooled 1990-1991)

Value
RHS Variable

(1)

(2)

tfpf ict

1.022∗∗∗(0.133)1.507∗∗∗(0.079)

Value Added(3)(4)
1.072∗∗∗(0.137)−1.043∗∗∗(0.138)

tfpict

1.571∗∗∗(0.08)

Exports

Pr(exp> 0)
(7)
(8)

(5)

(6)

0.635∗∗∗(0.1)

1.03∗∗∗(0.126)−1.073∗∗∗(0.14)

0.922∗∗∗(0.177)−0.697∗∗∗(0.144)1.456∗∗∗(0.121)−1.048∗∗∗(0.164)

Obs.
14608
14608
14608
14608
2763
2763
14608
14608
Industries
20
20
20
20
20
20
20
20
Industry-Time FE
yes
yes
yes
yes
yes
yes
yes
yes
Country-Time FE
yes
yes
yes
yes
yes
yes
yes
yes
Restriction p-val
0000The dependent variable=1 if there are positive levels of exports, zero otherwise. Standard errors in parentheses.
***=1% level. Standard errors clustered by country-industry level (e.g. Chile ISIC 311).

Table 8Robustness: Non-Linear Firm Productivity (pooled 1990-1991)
Value
RHS Variable

(1)

(2)

VA per workerf ic

1.466∗∗∗(0.051)1.09∗∗∗(0.123)1.513∗∗∗(0.053)0.072∗∗∗(0.025)

VA per worker2f ic

VA per workeric

Value Added(3)(4)−0.663∗∗(0.302)−0.698∗∗(0.309)1.255∗∗∗(0.114)

Exports(5)(6)
0.837∗∗∗(0.088)0.049∗∗(0.020)−0.656∗∗(0.295)−0.68∗∗(0.3)

0.509∗∗(0.293)Pr(exp> 0)(7)(8)
1.348∗∗∗(0.092)0.053(0.048)-0.302(0.272)-0.35(0.296)-0.05(0.066)−0.802∗∗∗(0.306)

Obs.
14608
14608
14608
14608
2763
2763
14608
Industries
20
20
20
20
20
20
20
Industry-Time FE
yes
yes
yes
yes
yes
yes
yes
Country-Time FE
yes
yes
yes
yes
yes
yes
yes
Standard errors in parentheses.
***=1% level, **=5% level. Standard errors clustered by country-industry level (e.g. Chile ISIC 311).

38

1.649∗∗∗(0.399)−0.787∗∗∗(0.304)
14608
20
yes
yes

Table 9
Wages, Firm productivity, and Industry productivity (3-digit)
1990
Dependent Variable

1991
Dependent Variable

RHS Variable

Average Wage
(1)

Skilled Wage
(2)

Unskilled Wage
(3)

Average Wage
(4)

Skilled Wage
(5)

Unskilled Wage
(6)

VA per workerf ic
VA per workeric

0.373∗∗∗(0.029)0.473∗∗∗(0.059)0.286∗∗∗(0.019)0.396∗∗∗(0.023)0.492∗∗∗(0.049)0.305∗∗∗(0.016)

VA per workeric

0.267∗∗∗(0.078)0.35∗∗∗(0.101)0.216∗∗∗(0.054)0.17∗∗∗(0.08)

0.239∗∗(0.104)0.145∗∗∗(0.057)

Observations
Industries
Industry FE
Country FE

7493
20
yes
yes

7493
20
yes
yes

7493
20
yes
yes

7115
20
yes
yes

7115
20
yes
yes

7115
20
yes
yes

Table 1025th and 75th Percentiles of TFP Differences
ISIC]

Rel. VA/L(1)-0.5970.789-1.235-0.980-0.040-0.326-1.1280.200
0.384-0.207-1.994-1.204-0.110
0.066
0.379
0.157
-0.2950.274-0.264-1.207-0.236-0.326diff(Rel. VA/L)(2)-0.1341.252-0.771-0.5170.423
0.137-0.665
0.663
0.847
0.256
-1.531-0.740
0.353
0.529
0.842
0.620
0.168
0.737
0.199
-0.744

V A/L75 − V A/L25
(3)
1.295
1.469
0.783
0.506
0.717
0.511
0.874
0.329
1.394
0.600
1.178
1.057
0.834
0.781
0.868
0.756
0.613
1.079
0.748
0.588
0.782
0.868

Obs.
(4)
1352
182
571
796
113
261
242
211
162
370
147
369
91
378
291
635
353
199
237
155

311
312
321
322
323
324
331
332
341
342
351
352
355
356
369
381
382
383
384
390
Median(Weighted) Mean
25th percentile-0.55475th percentile
0.631
Column (1) presents the raw difference in (log) Chilean relative to Colombian
industry TFP. Column (2) presents that difference relative to the mediandifference. Column (3) presents the (log) difference between a firm in the 75th
percentile and one in the 25th when industry data is pooled across countries

39Figure 1:

Brazil Bolivia

Bolivia

BrazilCos
USA Venezuela
Spain
Uruguay
SwedenJapan
Korea
Rep.Rep.
Brazil Japan
CanadaKorea
Ecuador
PeruNetherlands
Spain Turkey
Peru Urugu
Italy
FmArgentina
German India
FRJapanFrance,Monac
Argentina
Argentina Sweden Peru
France,Monac
Fm GermanFR
FRBrazil
UK
USA
France,MonacFm German
USA
USA
Peru
Fm German
FR
France,Monac
FRUSA
PanamaCanadaUKFm German
UK
Italy
Switz.Liecht
USA Austria
Japan
Belgium−Lux
Venezuela
China HK SAR
Spain
Ecuador Panama
UK
Mexico Panama
Netherlands
PanamaIsraelUSA
PanamaNeth.Ant.Aru
FmBelgium−Lux
German Panama
FR
Costa Rica
Spain
MexicoFrance,Monac
Italy
FinlandFm German FR
UK Canada
Venezuela
CanadaFrance,Monac
USA
BrazilIndonesia
USA
Belgium−Lux
France,Monac
German FR
MexicoFrance,Monac Fm
Fm German
FR
UK
Italy
USA

−12

−10

(log) Share Chile
−8
−6

−4

−2

Brazil

−12

−10

−8
−6
(log) Share Colombia−4−2

Figure 2:2(log) Output and Industry Value Added per Worker
331
331
352 352
322

1

351
351

(log) Output
−1
0

371

322

390
371

321

311

390

321

−2

385

332
324
332
342 324342
311
323 323
341341
369
362
369
362
381381
382
384
355 382
384
355
356
356
385
383
383

−3

312
312

−1.5

−1

−.5
0
.5
(log) Industry Value Added per Worker

401Figure 3:2(log) Value Added and Industry Value Added per Worker

1

331352331
322

352

351
351

322

371

(log) Value Added
−1
0

390
371

321

311

390

321

385

342
311

385

332
332
341
341
362
323
369
369
323
382
362
381381
355 382384
384 355 356324
324 342

−2

383
383

−3

312
312

−1.5

−1

−.5
0
.5
(log) Industry Value Added per Worker

1

Figure 4:

2

Exporting Probability and Industry TFP

Exporting Probability
0
1

351
351
352
331
331

352
371

371
321
390

321

322

311
322
385

332 341369 332
362
369
342
341
324
362
381
342
312
324355
381
312
385
356
382
382
384
384 355 356
383
383

−1

390

311

323

−2

323

−1.5

−1

−.5
0
.5
(log) Industry Value Added per Worker

41

1Figure 5:(log) Wage and Industry Value Added per Worker(log) Avg. Wage
0
.5

1

372

352
341
381
332
342
369
362
382 323
383
356
322
324
361
385
311
384
331
390
355

351

312

−.5

371

321

−2

−1
0
1
(log) Industry Value Added per Worker

422