revealed comparative advantage and extensive margins of...
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PRELIMINARY DRAFT: Please do not cite.
Revealed Comparative Advantage and Extensive Margins of
Heterogeneous Firms
Steven Husted٭ Isao Kamata
† Shuichiro Nishioka
‡
University of Pittsburgh University of Wisconsin West Virginia University
This version: May 4, 2013
Abstract
Doing business in foreign countries requires the significant amount of fixed and variable costs.
Productive firms that can cover these costs have opportunities in accessing foreign markets.
The number of exporters depends not only on the distribution of firms in that industry but also on
the range of productivity for successful exporters. This paper examines the productivity range of
successful exporters across countries and industries, and finds that the ranges differ
systematically according to revealed comparative advantage. Firms in the comparative advantage
sectors are likely to be successful in foreign markets. This tendency is stronger for a country
pairs involving one developed country and one developing country.
Keywords: Revealed comparative advantages; Product-level gravity model; Extensive margins of
heterogeneous firms
JEL Classification: F14
٭ Department of Economics, 4508 WW Posvar Hall, University of Pittsburgh, Pittsburgh, PA 15216
† Robert M. La Follette School of Public Affairs, 1225 Observatory Drive, University of Wisconsin–Madison,
Madison, WI 53706 ‡ Department of Economics, 1601 University Avenue, West Virginia University, Morgantown, WV 26506
1 Introduction
The idea that only the productive subset of firms are able to export becomes a standard approach
of International Trade (Melitz, 2003). Doing business in foreign countries requires the significant
amount of fixed and variable costs, which distinguish productive exporters from unproductive do-
mestic firms. Thus, international trade plays a crucial role in explaining the average productivity of
countries and industries since trade liberalization and growth of exporters move domestic resources
to productive exporters.
While the literature initiated by Bernard and Jensen (1999) found evidence that exporters
are superior in many productive characteristics than domestic firms in a single nation, there is
no systematic empirical evidence that examines how the ranges of exporters’productivities differ
across countries and industries. The productivity range in this paper is the log difference between
the productivity level of the leading firm and that of the least productive exporter. Theoretically,
Bernard, Redding, and Schott (2006) examined how the ranges of exporters’productivities differ
across a comparative advantage sector and a comparative disadvantage sector. They showed that
firms in the comparative advantage sector have better chances to be exporters. In other words, even
relatively unproductive firms can have better chances to be exporters in the comparative advantage
sector. This paper empirically examines the global variation of productivity ranges across countries
and industries for each product group. We will examine if we can find any systematic associations
between the productivity ranges and the comparative advantages. In particular, we first estimate
the range of productivity for each industry using the binary gravity equation from Helpman, Melitz,
and Rubinstein (2008, hereafter HMR). Then, we obtain the productivity range for each of bilateral
pairs of countries for each product from the estimation results. We are particularly interested in
investigating how the productivity ranges differ across comparative advantage and comparative
disadvantage sectors. For this purpose, we derive the measure of relative revealed comparative
advantage from Balassa (1965).
We find that the measures of relative revealed comparative advantage associate positively with
the corresponding productivity ranges across industries for each pair of an exporter country and
an importer country. The correlation is stronger for the pairs that involve one developed and one
developing countries. The results in this paper indicate a significant role played by comparative
advantage for foreign market entry. In addition, the new concept of productivity range that derived
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based on the firm-heterogeneity model (Melitz, 2003) depends crucially on the traditional sense of
revealed comparative advantage (Balassa, 1965).
2 Productivity Range in Firm-Heterogeneity Model
2.1 Firm-Level Decision to Enter Global Markets
The empirical estimation we will examine in this paper is the product-level estimation of Helpman,
Melitz, and Rubinstein (2008). The gravity equation of HMR is useful for our purpose since it can
study firm-level decision on market entry without using firm-level data.
The representative consumer in country l determines the demand for each variety ω in prod-
uct i subject to the optimal expenditure (Y lit). The product-level utility function is a standard
CES (Constant Elasticity of Substitution) form: ulit =[∫ω∈Bli
[qli(ω)
]αi dω]1/αi where qlit(ω) is
the consumption of variety ω in product i chosen by the consumer, Bli is the set of varieties in
product i available in country l, and the product-specific parameter αi determines the elasticity
of substitution across varieties so that εi = 1/(1 − αi) > 1. Using the standard utility maxi-
mization problem, we can find the demand function for each variety: qli(ω) =[pli(ω)]
−εiY li
(P li )1−εi where
P li =[∫ω∈Bli
(pli(ω)
)1−εi dω]1/(1−εi).A firm in country k produces one unit of output with a cost minimizing combination of inputs
that costs cki , which is country- and industry-specific cost for unit production. Thus, the unit
cost of production is identical across firms in producer country k’s industry i. 1/aki is firm-specific
productivity measure (i.e., a firm with a lower value of aki is more productive and that with a higher
value of aki is less productive) whose product-specific cumulative distribution function Gi(aki ) has
a country-specific support [aki ,+∞].
Each variety ω is produced by a firm with productivity aki . In addition, if a firm seeks to
sell its variety in foreign country l, it has to pay two types of trade costs: one is a fixed cost of
serving country l (fkli >0) and the other is a variable transport cost (τkli > 1). Since the market is
characterized by monopolistic competition, a firm in country k with a productivity measure of aki
maximizes profits by charging the productivity-adjusted mark-up price: pki = cki aki /αi. If the firm
exports it to country l, the delivery price is pkli = τkli cki aki /αi.
As a result, the associated operating profit from the sales to country l is
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πkli
(aki
)= (1− αi)Y l
i
(τkli c
ki /αiP
li
)1−εi(aki )
1−εi − fkli (1)
where the expected profit is a monotonically increasing function with respect to 1/aki for any pair
of an exporter country k and an importer country l.
Since the profits are positive in the domestic market for surviving firms (Melitz, 2003), all firms
are profitable in home country k. However, sales to an export market such as country l are positive
only when a firm is productive enough to cover both the fixed and variable costs of exporting.
Moreover, the positive observation of country-level exports of product i depends solely on the
most productive firm since the expected profit from equation (1) varies only with the firm-specific
productivity (1/aki ) in each industry. Now, let us pick the most productive firm in country k in
industry i whose productivity level is aki and define the following latent variable Zkli (aki ) as
Zkli (aki ) =(1− αi)Y l
i
(τkli c
ki /αiP
li
)1−εi (aki )1−εi
fklit. (2)
Equation (2) is the ratio of export profits for the most productive firm (see: equation (1)) to the
fixed cost of exporting good i to market l. Positive exports are observed if and only if the expected
profits of the most productive firms in industries are positive: Zkli (aki ) > 1.
Equation (2) provides the foundation for our empirical work. To estimate this equation, we
define fkli = exp(λiϕkli −ekli ) where ϕkli is an observed measure of any country-pair specific fixed trade
costs, and ekli is an error term. Using this specification together with the empirical specification
of variable trade costs: (1 − εi) ln(τkli ) = −γidkl + ukli where dkl is the log of distance between
countries k and l and ukli is an error term, the log of the latent variable zkli (aki ) = ln(Zkli (aki )) can
be expressed as
zkli (aki ) = βi + βki + βli − γidkl − λiϕkli + ηkli (3)
where βki is an exporter fixed effect that captures (1−εi) ln(cki ) and (1−εi) ln(aki ); βli is an importer
fixed effect that captures (εi−1) ln(P li ) and ln(Y li ); ηkli = ukli +ekli is random error; and the remaining
variables are captured in a constant term (βi).
We now define the indicator variable T kli to be 1 when country k exports product i to country
l and to be 0 when it does not. Let ρkli be the probability that country k exports product i to
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country l conditional on the observed variables. Then, we can specify the following logit equation:
ρkli = Pr(T kli = 1|βi, βki , βli, dkl, ϕkli
)(4)
= Λ(βi + βki + βli − γdkl − λitϕkli + ηkli
)
where Λ is the logistic distribution function with time-invariant standard error σηi .1
2.2 Estimation Results
To estimate equation (4) for each product for year 2005, we employ data on bilateral trade across
127 countries2 (16,002 country pairs) for 144 3-digit differentiated products (SITC Rev.3). We
have 2,304,288 potential observations, of which 680,003 observations (29.5%) are non-zero values.
Total value of imports from these non-zero observations is 6,394 millions US dollars. We prepare
the following bilateral indexes for the estimation of equation (4): distance between two countries
(distancekl), dummy variables for common border (boerderkl), common language (languagekl), the
sizes of trade costs (trade_costkl), business start-up costs (startupkl), and legal origin (legalkl) are
developed from Head et al. (2010) and the World Development Indicators. We use the log of the
sum of each index from the two (exporter and importer) countries to create the business-related
cost variables (i.e., trade_cost, startup, and legal).
We report the estimation results of equation (4) for each of the 144 differentiated products in
Table 1. For each product, we have at most 16,002 observations. The median value of observations is
15,624, of which 30.3% are non-zero observations on average. Although we have 16,002 observations
for each product, we have to drop the observations if a country exports that product to all 1261See Baldwin and Harrigan (2011) for further discussions on the estimation method and strategy.2We mark * for 38 developed countries. We divide developed and developing countries according to the nominal
GDP per capita above and below $20,000. The 127 countries are Albania, Algeria, Argentina, Australia*, Austria*,Bahamas*, Bahrain*, Bangladesh, Belarus, Belgium*, Belize, Bhutan, Bolivia, Botswana, Brazil, Bulgaria, BurkinaFaso, Cambodia, Cameroon, Canada*, Central African Rep., Chile, China, Hong Kong*, Colombia, Comoros, CostaRica, Côte d’Ivoire, Croatia, Cyprus*, Czech Republic*, Denmark*, Dominica, Dominican Republic, Ecuador, Egypt,El Salvador, Estonia, Ethiopia, Fiji, Finland*, France*, Gabon, Gambia, Germany*, Ghana, Greece*, Guatemala,Guinea, Guyana, Honduras, Hungary, Iceland*, India, Indonesia, Ireland*, Israel*, Italy*, Jamaica, Japan*, Jordan,Kazakhstan, Kenya, Kuwait*, Latvia, Lebanon, Lithuania, Luxembourg*, Madagascar, Malawi, Malaysia, Maldives,Mali, Mauritania, Mauritius, Mexico, Mongolia, Morocco, Mozambique, Namibia, Netherlands*, New Zealand*,Nicaragua, Niger, Nigeria, Norway*, Oman, Pakistan, Panama, Paraguay, Peru, the Philippines, Poland, Portu-gal*, Qatar*, South Korea*, Romania, Russian Federation, Rwanda, Saudi Arabia*, Senegal, Singapore*, Slovakia,Slovenia*, South Africa, Spain*, Sri Lanka, Sudan, Suriname, Sweden*, Switzerland*, Syria, Thailand, Trinidad andTobago*, Tunisia, Turkey, Ukraine, United Arab Emirates*, United Kingdom*, Tanzania, Uruguay, USA*, Venezuela,Viet Nam, Yemen, Zambia, and Zimbabwe.
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trading partners, imports that product from all 126 countries, or does not export or import the
product at all. For example, Japan exported “passenger vehicles”(SITC 781) to all 126 countries.
In this case, we cannot estimate the probability of exports for Japanese auto industry since the
observed probability is 100 percent. Given the large number of estimates we have for each product,
we do not report all the results. Instead, in the table we provide summary statistics for the estimated
coeffi cients, the proportion of coeffi cients that have the expected sign, and the proportion of those
that are statistically significant at the 5% level.
The probability of successful exports from country k to l (ρkli ) is negatively related to the log
of distance between two countries. 100 percent of product-level estimates carry negative signs and
are statistically significant for all years. Many of the coeffi cients on the business- and trade-related
variables are statistically significant with the predicted signs, supporting the important roles played
by trade costs in influencing the market entry.
The success for a firm from a country’s certain industry in exporting to a destination market
depends only on its firm-specific productivity. Using this property, we estimate the ranges of
productivities that enable firms to export from country k to l for each product i. We define the
lowest productivity level or the cut-off productivity level (1/akli ) as that point where a firm from
country k has zero profit in a foreign market l: πkli (akli ) = 0. Note that a firm with a higher value
of 1/aki is more productive. Thus, we should have 1/akli < 1/aki if firms in country k’s industry i
succeed in exporting to country l.
Now, by using equation (2), we can show that zkli (aki ) is a function of the log of the relative
productivity: zkli (aki ) = (εi − 1) ln(akli /a
ki
). Remember that ρkli is the predicted probability of
market access, which is estimated from equation (4). Let zkli (aki )/σηit = Λ−1(ρkli ) be the predicted
value of the log of the latent variable. Then, we can show the relationship between our estimates of
the log of the latent variable and the log difference between the highest and the cut-offproductivities:
zkli (aki )/σηi ≈ (εi − 1)[ln(
1/aki
)− ln
(1/akli
)]. (5)
According to the Melitz model, more firms will choose to enter an export market over time if
they are increasingly able to achieve positive profits in foreign markets. As we discussed above,
this could be due to any of a number of factors including rising standards of living throughout
the world, technological advances in transportation technology, or country-specific advances in
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production technology at the industry level. While it is intuitive to understand that trade costs are
fundamental determinants of exporters’productivity range, it is not well-known what determines
the productivity range within a country across 144 products. To get some insights about the
cross-product characteristics, we examine the case of Thailand. Figure 1-1 shows the scatter plot
across the number of each industry’s trading partners against the average value of zkli (aki )/σηi for
k=Thailand across 126 trading partners. Since we estimate equation (4) from the zero-one data of
trade partners and zkli (aki )/σηi is its fitted value, it is not surprising to find the tight correlation
between these two numbers. Thus, whether or not an industry from an exporter country has larger
values of productivity range depends critically on the success of market access. We also present
the case for the United States in Figure 1-2 as an example of large developed countries. The figure
suggests that as countries develop, the number of trading-partner countries increase across all
products. Although the United States does not have a comparative advantage in apparel product
(i.e., "cotton fabrics, women" SITC 652), the United States exports this product to 112 markets.
This tendency that developed countries have more markets even for comparative disadvantage
sectors may involve the issue of product-differentiation in quality (Schott, 2004).
3 Relative Revealed Comparative Advantages
The traditional idea of the revealed comparative advantage by Balassa (1965) is a useful measure
to understand the structure of the commodity trade. Since the data on commodity trade is by far
the best available source of global data, this measure has been used to measure the competitiveness
of industries worldwide by development-related agencies and policy makers.
In particular, we prepare the well-known index for revealed comparative advantage proposed
by Balassa (1965):
RCAki =
∑lm
kli /∑
i
∑lm
kli∑
k
∑lm
kli /∑
k
∑i
∑lm
kli
(6)
where mkli is the value of country l’s imports of product i from country k.
If RCAki is greater than unity, the commodity i in country k is revealed to have a comparative
advantage in producing product i. Notice that the RCA index represents the structure of commodity
trade relative to the world average. For example, the RCA index for k=U.S. and i=Aircraft (SITC
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792) is around 4. This simply means that the share of Aircraft in U.S. total exports is four times
greater than that of world trade. This simply means that the share of Aircraft in U.S. total exports
is four times greater than that of world trade.3
Table 2 reports the highest RCA index value for each of selected products across 127 exporter
countries. For example, Ivory Coast had the highest RCA value of “Chocolate and Cocoa” and
the Switzerland had the highest RCA value of “Watches & Clocks”. Table 3 reports the list
of the products and the number of their trading partners for the top and bottom ten products
for some selected developed and developing countries. According to Table 3, regardless of the
comparative advantage or disadvantage products, the United States export products to almost all
trading partners. This tendency holds not only for productive small-size country such as Austria
but also large-size newly developed country such as China. For the least developing countries such
as Bangladesh, they do not have any export markets for their comparative disadvantage products.
4 Productivity Range and Revealed Comparative Advantages
To examine the revealed comparative advantages bilaterally, we use equation (6) from exporter
country k and importer country l and develop the following relative revealed comparative advan-
tages:
ln(RRCAkli ) = ln(RCAki )− ln(RCAli) (7)
Figure 2 plots the log of productivity range to that of the relative revealed comparative advan-
tage for Thailand as an exporter and USA an importer. Here, we have 137 of 144 commodities. In
this figure, the positive number in the log of the relative revealed comparative advantage implies
that industry i in Thailand has relatively larger share in exports relative to that in the United
States. Interestingly, there is a clear positive and statistically significant relationship between these
two variables. It is worth emphasizing that while we estimate productivity ranges from product-
level data we develop the RRCA measures from export shares in each country relative to world
exports. This tendency is not limited to South countries’access to North markets. For example,
3The index has criticized because there is no theoretical foundation. For this aspect, Constinot et al. (2011)develops the RCA index from the Eaton and Kortum (2002) model by using the non-zero observations in trade. We,on the other hand, study the role of comparative advantage for the range of productivities for exporters.
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even the access of Japan to Brazilian market, we find the similar positive relationship between these
two variables.
Finally, we estimate the following equation to investigate the association between the produc-
tivity ranges and the revealed comparative advantage for each exporter-importer pairs.
ln(RRCAkli ) = akl + bklzkli (8)
The results of estimating equation (8) are summarized in Table 4. In the table, we report
the average values of bkl for each subset of the trading partners according to the development
level. For example, we report the country-pairs with an exporter and an importer both from
developing countries in the intersection of the first column and the first raw. The average value of
the coeffi cients (bkl) estimated from the equation above is 0.117. The highest average belongs to
the subsets with an importer from developed countries and an exporter from developing countries.
The results indicate that the productivity ranges are strongly correlated with relative revealed
comparative advantages for this subset. Finally, the second panel in Table 4 reports the portion of
the statistically significant coeffi cients. Regardless of the development stages of the countries, most
of the estimated values are positive and statistically significant at the 5% confidence level.
5 Conclusions
This paper examined the productivity range of successful exporters across countries and industries
from data consist of 127 countries and 144 products. We found that the productivity ranges differ
systematically according to revealed comparative advantage. In particular, firms in the comparative
advantage sectors are likely to be successful in foreign markets. This tendency is stronger for a
country pairs involving one developed and one developing countries.
References
[1] Baldwin, Richard, and James Harrigan, 2011, "Zeros, Quality, and Space: Trade Theory and
Trade Evidence," American Economic Journal: Microeconomics, pp. 60-88.
8
[2] Costinot, Arnaud, Dave Donaldson, and Ivana Komunjer, 2011, "What Goods Do Countries
Trade? A Quantitative Exploration of Ricardo’s Ideas," Mimeo.
[3] Balassa, Bela, 1965, "Trade Liberalization and Revealed Comparative Advantage,"Manchester
School of Economic and Social Studies 33, pp. 99-123.
[4] Bernard, Andrew B., and J. Bradford Jensen, 1999, "Exceptional Exporter Performance:
Cause, Effect, or Both?" Journal of International Economics 47, pp. 1-25
[5] Bernard, Andrew B., Stephen Redding, and Peter K. Schott, 2007, "Comparative Advantage
and Heterogeneous Firms," Review of Economic Studies 74(1), pp. 31-66.
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metrica, pp. 1741-1779.
[7] Head, Keith, Thierry Mayer, and John Ries, 2010, "The Erosion of Colonial Trade Linkages
after Independence," Journal of International Economics, pp. 1-14.
[8] Helpman, Elhanan, Marc J. Melitz, and Yona Rubinstein, 2008, "Estimating Trade Flows:
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[9] Melitz, Marc J., 2003, "The Impact of Trade on Intra-industry Reallocations and Aggregate
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[10] Schott, Peter K., 2004, "Across-Product Versus Within-Product Specialization in International
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Figures and Tables
Table 1. Logit Estimates for 144 Differentiate Products
Expected Sign match Sign match
signs (%) & 5% level
ln (distance ) - 100.0 100.0 -1.689 -0.950 -2.284 0.252
Border + 99.3 94.4 0.917 1.965 -0.074 0.330
Language + 100.0 100.0 0.879 1.765 0.390 0.176
Trade cost - 95.1 69.4 -1.404 1.155 -3.108 0.776
Startup - 95.1 67.4 -1.453 0.564 -3.269 0.809
Legal + 66.7 20.1 0.067 0.380 -0.387 0.138
Observations 15,624 16,002 8,274 1,194
% of non-zero observations 0.303 0.498 0.124 0.086
st errors of regression 0.597 0.644 0.496 0.026
McFadden r-squared 0.274 0.305 0.221 0.018
Median Min St.DevMax
Table 2. RCA Index for Selected Products (SITC. Rev 3. 3-digit)
2005
RCA Country
FISH,DRIED,SALTED,SMOKED 741.4 Maldives
CHOCOLATE,OTH.COCOA PREP 72.1 Côte d'Ivoire
MEDICAMENTS 9.3 Ireland
EXPLOSIVES & PYROTECHNICS 41.5 Zambia
LEATHER 217.0 Nigeria
PEARLS & PRECIOUS STONES 72.6 Botswana
SILVER & PLATINUM 48.0 South Africa
TELEVISION RECEIVERS 15.2 Slovakia
SOUND RECORDER 5.1 Indonesia
TELECOMM EQUIPMENT 4.9 Finland
TRANSISTORS 11.0 Philippines
PASS.MOTOR VEHCLS. 2.7 Slovakia
TRUNK & SUIT-CASES 5.0 Viet Nam
CLOTHNG (MENS) 35.8 Bangladesh
FOOTWEAR 20.1 Viet Nam
OPTICAL INSTRUMENTS 11.6 Rep. of Korea
WATCHES & CLOCKS 27.1 Switzerland
BABY CARRIAGE,TOYS, & GAMES 4.2 China
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Table 3. Top 10 and Bottom 10 Products in RCA (Selected Countries, 2005)
3.1. Developed countries
Exporter: USA Exporter: Austria
Top 10 RCA Markets Products RCA Markets Products
1 4.23 111 ENGINES,MOTORS NON-ELECT 13.23 98 NON-ALCOHOL.BEVERAGE,NES
2 3.81 122 AIRCRAFT,ASSOCTD.EQUIPNT 11.27 53 RAILWAY TRACK IRON,STEEL
3 2.54 121 PREPRD ADDITIVES,LIQUIDS 6.74 100 GLASSWARE
4 2.35 124 MEASURE,CONTROL INSTRMNT 6.36 60 RAILWAY VEHICLES.EQUIPNT
5 2.34 124 MEDICAL INSTRUMENTS NES 3.80 75 WOOD, SIMPLY WORKED
6 2.25 124 ELECTRO-MEDCL,XRAY EQUIP 3.35 70 PAPER,PULP MILL MACHINES
7 2.17 92 EXPLOSIVES,PYROTECHNICS 3.23 85 WOOD MANUFACTURES, NES
8 2.14 106 WORKS OF ART,ANTIQUE ETC 3.20 74 MONOFILAMENT OF PLASTICS
9 2.01 125 MEDICINES,ETC.EXC.GRP542 3.11 80 METALWORKING MACHNRY NES
10 1.89 98 TOBACCO, MANUFACTURED 3.09 100 MUSICAL INSTRUMENTS,ETC.
Exporter: USA Exporter: Austria
Bottom 10 RCA Markets Products RCA Markets Products
1 0.07 122 FOOTWEAR 0.01 14 FISH,DRIED,SALTED,SMOKED
2 0.08 119 MENS,BOYS CLOTHNG,X-KNIT 0.04 51 PEARLS,PRECIOUS STONES
3 0.08 113 POTTERY 0.05 14 CINE.FILM EXPOSD.DEVELPD
4 0.09 117 WOMEN,GIRL CLOTHNG,XKNIT 0.06 38 SHIP,BOAT,FLOAT.STRUCTRS
5 0.12 119 TRUNK,SUIT-CASES,BAG,ETC 0.06 59 RADIO-BROADCAST RECEIVER
6 0.12 116 WATCHES AND CLOCKS 0.10 20 FURSKINS,TANNED,DRESSED
7 0.13 123 OTHR.TEXTILE APPAREL,NES 0.12 87 TRUNK,SUIT-CASES,BAG,ETC
8 0.13 110 WOMEN,GIRLS CLOTHNG.KNIT 0.12 39 SILVER,PLATINUM,ETC.
9 0.14 122 SOUND RECORDER,PHONOGRPH 0.13 64 TELEVISION RECEIVERS ETC
10 0.15 122 CLOTHNG,NONTXTL;HEADGEAR 0.13 95 PARTS,FOR OFFICE MACHINS
3.2. Developing countries
Exporter: Bangladesh Exporter: Mexico
Top 10 RCA Markets Products RCA Markets Products
1 31.48 87 MENS,BOYS CLOTHNG,X-KNIT 8.18 61 TELEVISION RECEIVERS ETC
2 25.50 93 OTHR.TEXTILE APPAREL,NES 5.46 67 METERS,COUNTERS,NES
3 23.66 77 MENS,BOYS CLOTHING,KNIT 4.92 95 ELECTR DISTRIBT.EQPT NES
4 14.67 81 WOMEN,GIRL CLOTHNG,XKNIT 3.54 58 RADIO-BROADCAST RECEIVER
5 11.44 72 WOMEN,GIRLS CLOTHNG.KNIT 3.53 49 GOODS,SPCL TRANSPORT VEH
6 8.99 44 LEATHER 2.95 90 MEDICAL INSTRUMENTS NES
7 8.36 88 TEXTILE ARTICLES NES 2.17 95 ALCOHOLIC BEVERAGES
8 4.87 79 CLOTHNG,NONTXTL;HEADGEAR 2.01 92 FURNITURE,CUSHIONS,ETC.
9 3.34 53 POTTERY 2.00 87 ROTATING ELECTRIC PLANT
10 2.98 66 TEXTILE YARN 2.00 72 LIGHTNG FIXTURES ETC.NES
Exporter: Bangladesh Exporter: Mexico
Bottom 10 RCA Markets Products RCA Markets Products
1 0.00 1 SILVER,PLATINUM,ETC. 0.01 43 METAL REMOVAL WORK TOOLS
2 0.00 1 EXPLOSIVES,PYROTECHNICS 0.01 4 FURSKINS,TANNED,DRESSED
3 0.00 9 PIGMENTS, PAINTS, ETC. 0.02 57 PRINTNG,BOOKBINDNG MACHS
4 0.00 4 ELECTRO-MEDCL,XRAY EQUIP 0.02 37 PAPER,PULP MILL MACHINES
5 0.00 3 METERS,COUNTERS,NES 0.02 43 AIRCRAFT,ASSOCTD.EQUIPNT
6 0.00 9 SOUND RECORDER,PHONOGRPH 0.02 36 TOBACCO, MANUFACTURED
7 0.00 4 OTH.POWR.GENRTNG.MACHNRY 0.03 11 RAILWAY TRACK IRON,STEEL
8 0.00 3 RADIO-BROADCAST RECEIVER 0.03 32 MACH-TOOLS,METAL-WORKING
9 0.00 12 DOM.ELEC,NON-ELEC.EQUIPT 0.03 27 MONOFILAMENT OF PLASTICS
10 0.00 21 PARTS,TRACTORS,MOTOR VEH 0.04 25 PEARLS,PRECIOUS STONES
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Table 4. RRCA versus Productivity Range
Average of coefficients and standard deviations
South as an importer North as an exporter
coef (st. dev) coef (st. dev)
South as an exporter 0.117 (0.122) 0.203 (0.164)
North as an exporter 0.081 (0.069) 0.083 (0.076)
% of significance at the 5% level
South as an importer North as an exporter
% share obs % share obs
South as an exporter 53.8 2924 73.0 2113
North as an exporter 48.9 2577 47.1 1280
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