Tsukuba Economics Working Papers
No. 2021-003

Impact of Trade on Industry-Level Output
byArjad Abbas Khosa, Yoshinori Kurokawa, and Zhengfei Yu
December 2021
This version: May 2022

UNIVERSITY OF TSUKUBA
Faculty of Humanities and Social Sciences
1-1-1 Tennodai
Tsukuba, Ibaraki 305-8571
JAPANImpact of Trade on Industry-Level Output

Arjad Abbas Khosa *Yoshinori Kurokawa †Zhengfei Yu ‡May 12, 2022

Abstract
Does a country’s trade significantly increase industry-level output? Are the impacts larger
for differentiated industries than for homogeneous industries? To empirically answer thesequestions, we extend in two ways the Frankel and Romer (1999) method, which uses thegeographical component of trade as an instrument. First, while Frankel and Romer look at
impacts on the country-level output, we look at those on the industry-level output. Second,
we introduce the index of an industry’s differentiation based on Rauch (1999). Using a twostage least square estimation and data for 20 manufacturing industries and 99 countries in
2015, we find that a country’s trade significantly positively affects industry-level output andoutput per worker and the impacts are larger for the more differentiated industries.
JEL classification: F1, F4, O4
Keywords: Trade, Industry-level output, Differential impacts*

Federal Board of Revenue (FBR) Pakistan. E-mail: arjad.khosa86@gmail.com.
Faculty of Humanities and Social Sciences, University of Tsukuba. 1-1-1 Tennodai, Tsukuba, Ibaraki 3058571, Japan. E-mail: kurokawa.yoshi.fw@u.tsukuba.ac.jp.
‡
Faculty of Humanities and Social Sciences, University of Tsukuba. 1-1-1 Tennodai, Tsukuba, Ibaraki 3058571, Japan. E-mail: yu.zhengfei.gn@u.tsukuba.ac.jp.
†

11. Introduction

1.1

Motivation

Whether trade causes growth has been an important and well-debated question in the trade
literature for several decades. A number of studies have empirically answered this question
by examining the impact of trade on output at the country level. However, establishing a
causal relationship between trade and income has been difficult due to the endogeneity issue
that aggregate trade causes growth in national income or rich countries trade more becauseof a high level of production. Frankel and Romer (1999) investigate the effect of trade on
income at the country level for 63 countries in 1985, accounting for the endogeneity of trade.
They estimate a cross-country regression of income per person on geographic componentsof countries’ trade and conclude that the trade has a significant positive impact on the national
income. Building upon the Frankel-Romer econometric methodology, Irwin and Terviö(2002) test the trade–income relationship using the data for the pre-World War I, the interwar,
and the different post-war periods and confirm that even after accounting for the endogeneityof trade, countries that trade more have higher incomes.

The aforementioned studies examine the impact on the country-level output. However, whenpolicy makers debate trade liberalization, the worry could be not only over the increase inaggregate output but also over the unequal impacts across industries. 1 In fact, recent tradestudies imply that trade can increase output at the industry level and the impacts can differ
across industries. The Melitz (2003) model implies that, through intra-industry reallocationof resources, trade can increase the aggregate productivity of industry and thus increase theoutput at the industry level. Moreover, Chaney (2008) shows, using an extended Melitz
model, that the effect of trade liberalization is larger for differentiated industries than for1

We share the similar spirit with Kehoe et al. (2015). Although their interest is in the impact on exports of tradeliberalization, Kehoe et al. (2015) argue that policy makers’ worry is not over the aggregate increase but overthe unequal impacts across industries.

2homogeneous industries. Impacts of a country’s trade on industry-level output, however,
have been uncharted in the empirical literature of trade and growth.

1.2. Research Question
Thus, motivated by Melitz (2003) and Chaney (2008), this paper now raises the following
questions:i.

Does a country’s trade significantly increase industry-level output?

ii.

Are the impacts larger for differentiated industries than for homogeneous industries?

To empirically answer these questions, this paper estimates the impact of country-level tradeon industry-level output using data for 99 countries and 20 manufacturing industries for theyear 2015. To evaluate the possibly different trade impacts across industries, we construct
and include in our regressions an index that measures the degree of differentiation in eachindustry. This allows us to examine if the trade impacts on industry output are larger forindustries with higher differentiation.

To address the endogeneity in the trade, we follow Frankel and Romer (1999)’s two-stage
least square (2SLS) estimation strategy that uses the geographical component of trade as an
instrument variable. To be specific, the key instrument variable, the predicted trade share in
GDP of a country, is constructed from estimating a gravity equation of bilateral trade withthe geographical characteristics and then aggregating the estimates over its trade partners.
Then we use the predicted trade share and its interaction with the industry differentiationindex to instrument our endogenous trade variable and its interaction with the index. As arobustness check, we also conduct an industry-by-industry estimation by applying the crosscountry regression for each of the 20 manufacturing industries.

1.3. Main Results
Our main results are as follows.

3

i.

All the geographical variables are theoretically consistent and significant, implyingthat results are conventional. The areas of a country and its trading partner negatively
and significantly affect bilateral trade implying high intra-country trade, whereas the
populations of both countries affect the bilateral trade positively and significantly.

ii.

A country’s constructed trade share in GDP from the gravity model is a strong
instrument for the actual overall trade share in GDP. This implies that geographicallocation plays a critical role in shaping the actual trade volume.

iii.

The impact of a country’s trade on industrial output is positive and significant. One
percent point increase in the trade/GDP ratio increases industrial output by 3.008%.

iv.

The impact of a country’s trade on industrial output per worker is also positive andsignificant. One percentage point increase in the trade/GDP ratio increases industrial
output per worker by 1.698%.

v.

The impacts of a country’s trade on industrial output and industrial output per worker
increase with the degree of differentiation in the manufacturing industries.

1.4. Literature Review and Value-added
There are several empirical studies that examine growth and international trade at the country
level. It, however, is difficult to establish a causal relationship between trade and income due
to an endogeneity problem. Frankel and Romer (1999) use a geographic instrument to
identify a positive impact of trade on income. 2 Their cross-sectional estimates for the year
1985 for 63 countries suggest that the impact of geography-based differences in trade isquantitatively large. Moreover, they compare the regression estimates from the OLS with the
instrumental variable least square (IVLS) estimates of trade–income relationships and findthat OLS estimates do not overstate the impact of trade. Irwin and Terviö (2002) confirm thevalidity of the findings of Frankel and Romer (1999) for the pre-World War I, the inter-war,
and the post-war periods and find that countries that trade more as a proportion of their GDP

2

Sachs and Warner (1995), Dollar (1992), and Edwards (1998) are also prominent studies that find a positive
relationship between trade and income, although it is difficult to know the direction of causality.

4

have higher incomes controlling for the endogeneity of trade. They also conclude that OLS
estimates are not the upward biased indicators of the trade’s impact on the income.

Rodriguez and Rodrik (2000) criticize that the results of Frankel and Romer (1999) are notrobust to controlling for omitted variables, such as latitude (distance from equator). 3 On theother hand, Hall and Jones (1999), Frankel and Rose (2001), and Noguer and Siscart (2005)
find that the geographic-determined portion of trade has a significant impact on income evenafter controlling for latitude. Irwin and Terviö (2002) show mixed results for the inclusion oflatitude: the significance remains for the OLS coefficient but disappears for the 2SLS
coefficient.

Feyrer (2019) finally solves this omitted variable problem by generating a time-varying
geographic instrument. Country pairs with relatively short air routes compared to sea routes
benefit more from improvements in aircraft technology. He thus uses this heterogeneity togenerate a geography-based instrument for trade that varies over time. His results suggestthat Frankel and Romer’s (1999) basic results hold.

In this line of research, by extending Frankel and Romer (1999) we empirically investigate
the impact of country-level trade on industry-level output using the data for 99 countries and
20 manufacturing industries for the year 2015. We contribute to the literature in two ways.
First, while past studies look at impacts on the country-level output, we look at those on theindustry-level output. 4 This allows us to investigate unequal impacts across industries, inparticular, across 20 industries in our case. Second, we introduce the index of an industry’s
differentiation based on Rauch (1999) to an industry-level version of the Frankel and Romer(1999) regression. This makes it possible to investigate whether the impacts are larger for3

See also Rodrik et al. (2004).
Using the data for 50 countries, Mashal (2020) empirically investigates the impact of the industry-level (notcountry-level) trade on the industry-level output, for each of the paper and textile industries and for each of
the years 2008–2017.
45differentiated industries than for homogeneous industries. Thus, our paper would be a goodcomplement to the past country-level studies.

1.5. Organization of the Paper

The remainder of this paper is organized as follows. Section 2 explains our regression
specification and data. Section 3 presents our empirical results. Section 4 concludes this study.

2. Regression Specification and Data

2.1. Regression Specification
In the trade and growth literature, such as Frankel and Romer (1999) and Irwin and Terviö(2002), the cross-country regression equation is written as follows:
𝑙𝑙𝑙𝑙 𝑌𝑌𝑗𝑗 = 𝛽𝛽0 + 𝛽𝛽1 𝑇𝑇𝑗𝑗 + 𝛽𝛽2 𝑙𝑙𝑙𝑙 𝐴𝐴𝑗𝑗 + 𝛽𝛽3 𝑙𝑙𝑙𝑙 𝑃𝑃𝑗𝑗 + 𝜇𝜇𝑗𝑗 , (1)where 𝑌𝑌𝑗𝑗 is the output of country 𝑗𝑗, 𝑇𝑇𝑗𝑗 is the total trade/GDP ratio of country 𝑗𝑗, 𝑃𝑃𝑗𝑗 is the
population of country 𝑗𝑗, and 𝐴𝐴𝑗𝑗 is the area of country 𝑗𝑗.

This paper now evaluates the impact of trade on industry-level output and also uncovers theheterogeneous effects related to the differentiation degree of an industry. To do so, we modify
equation (1) as follows.
𝑙𝑙𝑙𝑙 𝑌𝑌𝑖𝑖𝑖𝑖 = ∅0 + ∅1 𝑇𝑇𝑗𝑗 + ∅2 𝑙𝑙𝑙𝑙 𝐴𝐴𝑗𝑗 + ∅3 𝑙𝑙𝑙𝑙 𝑃𝑃𝑗𝑗 + ∅4 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖 + ∅5 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖 ∗ 𝑇𝑇𝑗𝑗 + 𝜖𝜖𝑖𝑖𝑖𝑖 .

(2)Here, 𝑖𝑖 varies from 1, 2, …, 20 for industries and 𝑗𝑗 varies from 1, 2, …, 99 for countries. The

variable 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖 , measured between zero and one, captures the degree of differentiation ineach industry i. We explain the construction of this index of differentiation in Section 2.2.2.
The variables 𝐴𝐴𝑗𝑗 and 𝑃𝑃𝑗𝑗 denote the area and population of country 𝑗𝑗.
The parameters of interest are the coefficient ∅1 , which measures the effect of country-level

trade on industry-level output, and the coefficient ∅5 , which measures how this effect varies
6with the degree of differentiation of an industry. If estimates of both ∅1 and ∅5 aresignificantly positive, we have affirmative answers to both research questions raised at the
beginning of Section 1.2, that is, a country’s trade significantly increases industry-level
output, and this effect becomes more pronounced for more differentiated industries.

In addition to regression (2), we also estimate the impact of trade on output per worker using
the following specification:𝑙𝑙𝑙𝑙

𝑌𝑌𝑖𝑖𝑖𝑖= 𝜗𝜗0 + 𝜗𝜗1 𝑇𝑇𝑗𝑗 + 𝜗𝜗2 𝑙𝑙𝑙𝑙 𝐴𝐴𝑗𝑗 + 𝜗𝜗3 𝑙𝑙𝑙𝑙 𝑃𝑃𝑗𝑗 + 𝜗𝜗4 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖 + 𝜗𝜗5 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖 ∗ 𝑇𝑇𝑗𝑗 + 𝜌𝜌𝑖𝑖𝑖𝑖 . (3)𝐿𝐿𝑖𝑖𝑖𝑖

The theoretical ground to investigate the heterogeneity of the effect of trade on industry-level
output is based on Chaney (2008), which predicts the impact of trade liberalization to belarger for differentiated industries than for homogeneous industries. His reason is as follows.
Homogeneous goods are identical and therefore have a high elasticity of substitution,
whereas differentiated goods may differ in quality or functional features or design andtherefore have a low elasticity of substitution. Now, suppose that trade barriers are reduced.
Then it causes new and less productive firms to enter the export market. If the elasticity of
substitution is high, low-productivity firms are at a severe disadvantage and these lowproductive firms can capture only a small share in the export market. The impact of these
new entrants on aggregate trade is thus small. On the other hand, if the elasticity of
substitution is low, each firm is sheltered from competition and the new entrants can capturea large share in the export market. The impact of these new entrants on aggregate trade isthus large. Hence, a lower elasticity makes the extensive margin of exports more sensitive totrade liberalization; that is, the impact of trade liberalization is larger for differentiatedindustries having a low elasticity than for homogeneous industries having a high elasticity.

To address endogeneity in the relationship between trade and output in equation (2), we use
geographic variables to construct an instrument for trade in the same fashion as Frankel and7

Romer (1999). To be specific, we first estimate the bilateral trade flow using the gravity
model.
𝑙𝑙𝑙𝑙 �

𝑇𝑇𝑗𝑗𝑗𝑗� = 𝛼𝛼0 + 𝛼𝛼1 𝑙𝑙𝑙𝑙 �𝐷𝐷𝑗𝑗𝑗𝑗 � + 𝛼𝛼2 𝑙𝑙𝑙𝑙 �𝑃𝑃𝑗𝑗 � + 𝛼𝛼3 𝑙𝑙𝑙𝑙 (𝑃𝑃𝑘𝑘 ) + 𝛼𝛼4 𝑙𝑙𝑙𝑙 �𝐴𝐴𝑗𝑗 � + 𝛼𝛼5 𝑙𝑙𝑙𝑙 (𝐴𝐴𝑘𝑘 )𝐺𝐺𝐺𝐺𝐺𝐺𝑗𝑗+ 𝛼𝛼6 �𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑗𝑗𝑗𝑗 � + 𝛼𝛼7 �𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑗𝑗 + 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑘𝑘 � + 𝑒𝑒𝑗𝑗𝑗𝑗 . (4)This equation explains that the bilateral trade of country 𝑗𝑗 with country k relative to country

j’s GDP depends on the distance between the two trading countries �𝐷𝐷𝑗𝑗𝑗𝑗 �, the population (𝑃𝑃)and area (𝐴𝐴) of each country, whether the trading partners have a common border, and ifeither one or both countries are landlocked. The left-hand side variable, bilateral trade relative
to GDP, is measured as the sum of country j’s exports to and imports from country k in US
thousand dollars. To incorporate the argument that trade with the neighboring countries ismore favorable, we include the distance between the countries which is measured as the
distance between the capital cities of the two trading countries. The population of eachcountry is quantified in millions and the area of the country is taken in square kilometers,
whereas the border and landlocked variables are the qualitative variables of geographic
characteristics taken as dummy variables. The above gravity equation is estimated to find thefitted values of bilateral trade. As the above gravity equation takes the trade share and thegeographical factors, such as distance, area, and population, in log form, the coefficients 𝛼𝛼1

to 𝛼𝛼5 express the trade elasticities with respect to distance, area, and population, respectively.
We next aggregate the estimated values of country j’s trade with country k over all partnercountries 𝑘𝑘. We thus obtain 𝑇𝑇�𝑗𝑗 , which is country j’s constructed trade share in GDP calculated
as 𝑇𝑇�𝑗𝑗 = ∑𝑘𝑘≠𝑗𝑗 𝑒𝑒 𝛼𝛼�𝑥𝑥𝑗𝑗𝑗𝑗 .

Now, we estimate our regression model (2) to quantify the impact of trade on industry-level
output in 2SLS. In the first stage, we regress a country’s actual trade share in GDP on theconstructed trade share 𝑇𝑇�𝑗𝑗 from the gravity equation. This gives the estimate of the fact that8

geographical variables account for a major part of the variation in trade share across all thecountries. The first-stage equations can be expressed as follows:
𝑇𝑇𝑗𝑗 = 𝑐𝑐0 + 𝑐𝑐1 𝑇𝑇�𝑗𝑗 + 𝑐𝑐2 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖 ∗ 𝑇𝑇�𝑗𝑗 + 𝑐𝑐3 𝑙𝑙𝑙𝑙 𝐴𝐴𝑗𝑗 + 𝑐𝑐4 𝑙𝑙𝑙𝑙 𝑃𝑃𝑗𝑗 + 𝑐𝑐5 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖 + 𝑢𝑢𝑗𝑗 , (5)𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖 ∗ 𝑇𝑇𝑗𝑗 = 𝑑𝑑0 + 𝑑𝑑1 𝑇𝑇�𝑗𝑗 +𝑑𝑑2 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖 ∗ 𝑇𝑇�𝑗𝑗 + 𝑑𝑑3 𝑙𝑙𝑙𝑙 𝐴𝐴𝑗𝑗 + 𝑑𝑑4 𝑙𝑙𝑙𝑙 𝑃𝑃𝑗𝑗 + 𝑑𝑑5 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖 + 𝑣𝑣𝑗𝑗 . (6)The endogenous variables are country j’s actual trade relative to GDP and its interaction with
the index. The instruments are the estimated geographical component of trade 𝑇𝑇�𝑗𝑗 and its
interaction with the index. Other controls include the population and the area of country j.

In the second stage, the predicted values of regressions (5) and (6) are substituted into themain regression (2) (and (3)) in place of the endogenous terms 𝑇𝑇𝑗𝑗 and 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖 ∗ 𝑇𝑇𝑗𝑗 to estimate

the parameters ∅1 and ∅5 .
2.2. Data

2.2.1 Trade and Output Data
To evaluate our research questions, our key data requirement is data on industrial output andoutput per worker of the manufacturing industries and the bilateral trade data for all thecountries for the year 2015. We extracted the data on industrial output and the number of
employees (labor) working in 20 manufacturing industries across 99 countries from thestatistical database of the United Nations Industrial Development Organization (UNIDO).
The data on bilateral imports has been taken from the IMF’s Direction of Trade Statistics(DOTS). To generate the trade variable for 99 countries, the bilateral import data for eachtrading partner has been added as Trade = Mij + Mji where “M” refers to “Goods, Value ofImports, Cost, Insurance, Freight (CIF)” in US Dollars. Note that the data on “Goods, Valueof Imports, Free on Board (FOB)” is missing for many countries, so we use “Goods, Valueof Imports (CIF).” The data for country-level GDP is taken from the World Bank’s World

9Development Indicators (WDI), and the data on geographical characteristics is obtained fromthe Centre d’Études Prospectives and d’Informations Internationales (CEPII) database. 5

The selected 20 manufacturing industries are given in Appendix A. The classifications are
based on the two-digit International Standard Industrial Classification (ISIC) Rev3.1. Basedon the Competitive Industrial Performance Index (CIP) published by UNIDO, this study
chose 99 countries listed in Appendix A, who are at least in lower-middle rank industry
competitiveness. We started with 101 countries, but the countries for which the industrylevel output was not available were dropped. The final estimation is thus based on 99
countries.

2.2.2 The Differentiation Index of Industry
We construct the differentiation index of industry i 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖 as follows. Rauch (1999)

classifies the trading products into two categories. The first category is “homogeneous
products,” which include products traded on an organized exchange (w) and reference priced
products (r), and the second category is “differentiated products” (n). Using the classificationof Rauch (1999), we characterize the chosen 20 manufacturing industries into differentiatedvs. homogeneous industries. 6 Rauch’s classification is at the four-digit Standard International
Trade Classification (SITC) Rev. 2. We match it with our two-digit ISIC Rev. 3.1
classification and then calculate the share of the four-digit differentiated products in each of
our two-digit 20 industries.

To do so, first the four-digit products in each of our two-digit 20 industries are subcategorized
as per the Rauch category of reference price (r), products traded on an organized exchange(w), and differentiated products (n). Table 1 shows the percentage distribution of the products
into homogeneous (r and w) vs. differentiated (n) in each of 20 industries. Rauch gives“Liberal” as well as the “Conservative” classification of products. As shown in Table 1,56

http://www.cepii.fr/CEPII/en/bdd_modele/bdd.asp
The classification is available at https://econweb.ucsd.edu/~jrauch/rauch_classification.html

10however, there are no prominent differences in both ways of classification. For this study,
we rely on the conservative classification to create the differentiation index of an industry.
Table 1 shows that for the Food & Beverages industry, out of all the products produced andtraded only 26.56% matches the definition of differentiated products (n), whereas 40.63% ofproducts are produced and sold at a reference price (r) and 32.81% of products have a properexchange market (w).

However, the 8 industries, namely, Wearing Apparel & Fur,

Publishing & Printing, Machinery & Equipment, Office Accounting & Computing
Machinery, Electrical Machinery & Apparatus, Motor Vehicles, Trailers & Semi-Trailers,
Other Transportation Equipment, and Fabricated Metal Products, are producing fully
differentiated products (n).

Table 1: Differentiated vs. Homogeneous Products in Each Manufacturing Industry based on Rauch (1999)
ISIC Classified
Industry

Conservative Classification
Differentiated

Liberal Classification

Homogeneous

Differentiated

Homogeneous

n (%)

r (%)

w (%)

n (%)

r (%)

w (%)

Food & Beverages

26.56

40.63

32.81

18.75

42.19

39.06

Tobacco Products

11.11

22.22

66.67

11.11

22.22

66.67

Textile

58.3

26.2

15.5

54.8

23.8

21.4

Wearing Apparel &

100

-

-

100

-

-

54.55

45.45

-

54.55

9.09

36.36

20

80

-

20

70

10

Publishing & Printing

100

-

-

100

-

-

Coke, Refined

26.32

42.11

31.58

21.05

47.37

31.58

8.77

89.47

1

8.77

84.21

7.02

Fur
Wood Products
excluding Furniture
Paper & Paper
Products

Petroleum Products &Nuclear Fuel
Chemicals & Chemical
Products

11

Rubber & Plastics

96

4

-

84

16

-

87.88

12.12

-

78.79

15.15

6.06

Basic Metal

18.75

28.13

53.13

12.5

25

62.5

Machinery &

100

-

-

100

-

-

100

-

-

100

-

-

100

-

-

78.57

21.43

-

100

-

-

100

-

-

100

-

-

100

-

-

92.59

-

7.41

92.59

-

7.41

66.67

11.11

22.22

55.56

22.22

22.22

100

-

-

95.65

4.35

-

Products
Non-Metallic Mineral
Products Industries

Equipment
Office Accounting &Computing Machinery
Electrical Machinery& Apparatus
Motor Vehicles,
Trailers & SemiTrailers
Other Transportation
Equipment
Furniture
Manufacturing
Leather, Leather
Products & Footwear
Fabricated Metal
Products
Note: The table shows the shares (%) of differentiated (n) and homogeneous products (r and w) in each of
the 20 manufacturing industries, based on Rauch’s (1999) conservative and liberal classifications.

Next, relying on the conservative classification of Rauch, we use the values of “n” as thedegree of differentiation of an industry as it shows the share of the four-digit differentiatedindustries in each of the two-digit 20 industries. To convert this value into index form, wetransform the values of 0-100 in terms of 0-1 by dividing the values by 100. The variable

12

𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖 depicts the degree of differentiation of industry i from 0 to 1. Closer to 1 means amore differentiated industry and vice versa. The differentiation index of an industry is shownin Table 2.

Table 2: Differentiation Index
ISIC Classified
Industry

Conservative Classification
Differentiation

Differentiated

Index

n (%)

r (%)

w (%)

Food & Beverages

0.266

26.56

40.63

32.81

Tobacco Products

0.111

11.11

22.22

66.67

Textile

0.583

58.3

26.2

15.5

Wearing Apparel &

1.000

100

-

-

0.546

54.55

45.45

-

0.200

20

80

-

1.000

100

-

-

0.263

26.32

42.11

31.58

0.088

8.77

89.47

1

0.960

96

4

-

0.879

87.88

12.12

-

Homogeneous

Fur
Wood Products
excluding Furniture
Paper & Paper
Products
Publishing &Printing
Coke, Refined
Petroleum Products& Nuclear Fuel
Chemicals &Chemical Products
Rubber & Plastics
Products
Non-Metallic
Mineral Products
Industries

13Basic Metal

0.188

18.75

28.13

53.13

Machinery &

1.000

100

-

-

1.000

100

-

-

1.000

100

-

-

1.000

100

-

-

1.000

100

-

-

0.926

92.59

-

7.41

0.667

66.67

11.11

22.22

1.000

100

-

-

Equipment
Office Accounting &Computing
Machinery
Electrical Machinery& Apparatus
Motor Vehicles,
Trailers & SemiTrailers
Other Transportation
Equipment
Furniture
Manufacturing
Leather, Leather
Products & Footwear
Fabricated Metal
Products

3. Empirical Results
3.1. Main Findings
The descriptive statistics of the data that is used to estimate the gravity model of trade inequation (3) is presented in Table 3. Here, bilateral trade (𝑇𝑇𝑗𝑗𝑗𝑗 ) is measured in US dollars, andnominal GDP is also taken in US dollars. The area of each country (A) is measured in square
kilometers, population (P) is measured in millions, and distance (Djk) is calculated as the
distance between populous cities measured in kilometers. The border is a dummy taking
value 1 if a trading partner has a common border, and landlocked is a dummy taking value 1
if either one or both trading countries are landlocked.

14

Table 3: Descriptive Statistics for the Gravity Model
Variables

Obs.

Mean

SD

Min

Max

Imports

9177

1.61e+09

1.04e+103

4.82e+11

Trade

9177

3.23e+09

1.93e+103

6.27e+11

GDP

9177

7.57e+11

2.22e+126.40e+09

1.80e+13

Trade/GDP

9177

.007

.025

0

.871

Distance

9177

6734.087

4514.887

59.617

19772.34

Population

9177

68.336

196.667.423

1371.22

Area

9177

1150000

2630000316

1.71e+07

Border

9177

3.1%

.173

0

1

Landlocked

9177

32%

.464

0

1

Table 4 presents the result of the gravity model expressed in equation (3). The gravity model
results show that the impact of the distance between the countries on the trade share isnegative and significant and the trade elasticity with respect to distance is greater than 1 inabsolute terms. This means that bilateral trade is more likely to be high with neighboring
countries than with distant countries. The countries’ bilateral trade is positively andsignificantly affected if the trading partner shares the common border. However, in our data,
very few countries have a common border. Bilateral trade share has a negative relationshipwith the areas of both partner countries j and k, whereas the trade share elasticity with respect
to population is greater than zero and the population of both the trading partners significantly
and positively affects the bilateral trade share of countries. Moreover, if any of the countries
are landlocked, the bilateral trade to GDP share of the country decreases.

Table 4: Estimation of Gravity Equation
Variables

𝑙𝑙𝑙𝑙 (𝑇𝑇𝑗𝑗𝑗𝑗 /𝐺𝐺𝐺𝐺𝐺𝐺𝑗𝑗 )𝑙𝑙𝑙𝑙 �𝐴𝐴𝑗𝑗 �-0.123***(0.0164)15

𝑙𝑙𝑙𝑙 (𝐴𝐴𝑘𝑘 )-0.171***𝑙𝑙𝑙𝑙 �𝑃𝑃𝑗𝑗 �

0.133***(0.0165)(0.0221)𝑙𝑙𝑙𝑙 (𝑃𝑃𝑘𝑘 )0.860***

𝑙𝑙𝑙𝑙 (𝐷𝐷𝑗𝑗𝑗𝑗 )-1.170***𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑗𝑗𝑗𝑗

0.972***

�𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑗𝑗 + 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑘𝑘 �-1.088***Constant

3.599***(0.0221)(0.0293)(0.144)(0.0503)(0.306)
Observations

9,177

R-squared

0.347

Note: Robust standard errors in parentheses.
*** p<0.01, ** p<0.05, * p<0.1

The results of our gravity model are as expected and consistent with theoretical implications,signifying that geographical characteristics are the key determinants of the bilateral trade to
GDP share. The R-squared of the gravity model is 0.347, which means that around 35%
variation in bilateral trade is explained by the geographical characteristic included in themodel. Given the fact that the results are from cross-sectional data, the value of R-squared issufficiently good. To avoid the chances of heteroscedasticity, the robust standard errors arecalculated and shown in Table 4.

The above regression coefficients are used to find the constructed bilateral trade to GDP ratioby taking the exponential power of the predicted values and adding all the values countrywise for all the trading partners to construct aggregate trade share 𝑇𝑇�𝑗𝑗 for each country. The16� 𝑗𝑗 are then used in the first-stage equations (5) andconstructed instruments 𝑇𝑇�𝑗𝑗 and 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖 ∗ 𝑇𝑇

(6). We find the minimum eigenvalue statistic in the first stage to be 115.2, which confirms
the relevance of the instruments.

The estimated results of main regressions (2) and (3) are presented in Table 5.
Table 5: Impact of Trade and Differentiation Degree on
the Industry-level Output and Output per Worker
𝑙𝑙𝑙𝑙 𝑌𝑌𝑖𝑖𝑖𝑖

𝑙𝑙𝑙𝑙

𝑌𝑌𝑖𝑖𝑖𝑖
𝐿𝐿𝑖𝑖𝑖𝑖

Variables

OutputOutput per Worker

𝑇𝑇�𝑗𝑗

3.008***

1.698***(0.684)(0.434)

𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖 ∗ 𝑇𝑇�𝑗𝑗

1.309*

0.777*(0.780)(0.435)𝑙𝑙𝑙𝑙 𝑃𝑃𝑗𝑗

1.122***

0.0835**(0.0587)(0.0351)𝑙𝑙𝑙𝑙 𝐴𝐴𝑗𝑗

0.203***

0.177***(0.0675)(0.0445)

𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖-1.560***-1.512***(0.565)(0.312)

Observations

1,561

1,522

R-squared

0.225

0.129

Note: Robust standard errors in parentheses.
*** p<0.01, ** p<0.05, * p<0.1

17

We summarize the main findings as follows: (1) The impact of a country’s trade on industrial
output is positive and significant. One percent point increase in the trade/GDP ratio increases
industrial output by 3.008%. (2) The impact of a country’s trade on industrial output per
worker is also positive and significant. One percentage point increase in the trade/GDP ratio
increases industrial output per worker by 1.698%. (3) The impacts of a country’s trade on
industrial output and industrial output per worker increase with the degree of differentiationin the manufacturing industries.

3.2 An Alternative Approach: Industry-by-industry Estimation
This section employs an alternative strategy to investigate the effect of a country’s trade on
industry-level output. We estimate the following regressions by 2SLS for each industry 𝑖𝑖

separately and obtain the industry-level trade impact 𝛽𝛽1𝑖𝑖 (and 𝛿𝛿1𝑖𝑖 ) for 𝑖𝑖 =1, 2, …, 20.
𝑙𝑙𝑙𝑙 𝑌𝑌𝑖𝑖𝑖𝑖 = 𝛽𝛽0𝑖𝑖 + 𝛽𝛽1𝑖𝑖 𝑇𝑇𝑗𝑗 + 𝛽𝛽2𝑖𝑖 𝑙𝑙𝑙𝑙 𝐴𝐴𝑗𝑗 + 𝛽𝛽3𝑖𝑖 𝑙𝑙𝑙𝑙 𝑃𝑃𝑗𝑗 + 𝜇𝜇𝑖𝑖𝑖𝑖 .

𝑙𝑙𝑙𝑙

𝑌𝑌𝑖𝑖𝑖𝑖
𝐿𝐿𝑖𝑖𝑖𝑖(7)= 𝛿𝛿0𝑖𝑖 + 𝛿𝛿1𝑖𝑖 𝑇𝑇𝑗𝑗 + 𝛿𝛿2𝑖𝑖 𝑙𝑙𝑙𝑙 𝐴𝐴𝑗𝑗 + 𝛿𝛿3𝑖𝑖 𝑙𝑙𝑙𝑙 𝑃𝑃𝑗𝑗 + 𝜂𝜂𝑖𝑖𝑖𝑖 . (8)For each industry i, i = 1, 2, …, 20, we run cross-country regression to estimate the impactsof the trade, area, and population of the country on the industrial output. By comparing theestimated coefficients of all industries, we can analyze whether the impact of trade is thesame for all the industries or it varies across the industries. Moreover, we compare the OLS
estimates with the IVLS estimates of the relationship between the country-level trade andindustrial output.
We rely on the same instrument 𝑇𝑇�𝑗𝑗 constructed in Section 3.1 and run the following first-

stage regression

𝑇𝑇𝑗𝑗 = 𝑐𝑐0 + 𝑐𝑐1 𝑇𝑇�𝑗𝑗 + 𝑐𝑐2 𝑙𝑙𝑙𝑙 𝐴𝐴𝑗𝑗 + 𝑐𝑐3 𝑙𝑙𝑙𝑙 𝑃𝑃𝑗𝑗 + 𝑢𝑢𝑗𝑗 . (9)Then the second stage estimates equations (7) and (8) by replacing 𝑇𝑇𝑗𝑗 with the predicted value

obtained from equation (9). The estimates of coefficients 𝛽𝛽1𝑖𝑖 and 𝛿𝛿1𝑖𝑖 for all 20 industries are18

summarized in Table 6. The detailed estimates of equations (7) and (8) for all industries arepresented in Appendix B.

Table 6: Summary of Impacts of Trade on Industry-level Output and Output per
Worker
ISIC Classified

Industry Output

Industry Output per Worker

Industry𝛽𝛽1𝑖𝑖 Coefficients’𝛿𝛿1𝑖𝑖 Coefficients’Sign

Significance

Magnitude

Sign

Significance

Magnitude

+

***

3.29

+

***

2.10

Tobacco Products

+

*

3.40

+

**

2.93

Textile

+

***

4.59

+

***

3.84

Wearing Apparel

+

-

2.71

+

***

3.67

+

***

6.11

+

***

3.15

+

***

5.31

+

***

2.81

+

***

4.03

+

***

2.40

+

-

3.27

+

-

2.61

+

***

6.02

+

***

3.39

+

***

4.29

+

**

3.06

Food &
Beverages& Fur
Wood Products
excluding
Furniture
Paper & Paper
Products
Publishing &Printing
Coke, Refined
Petroleum
Products &Nuclear Fuel
Chemicals &Chemical
Products
Rubber & Plastics
Products

19Non-Metallic

+

***

3.21

+

***

1.75

Basic Metal

+

***

4.86

+

***

1.50

Machinery &

+

***

7.72

+

***

2.01

+

-

3.44

+

**

2.40

+

***

4.81

+

***

2.39

+

***

6.15

+

-

1.62

+

***

10.32

+

***

3.74

+

***

4.73

+

***

2.24

+

-

1.20

+

**

2.11

+

***

6.06

+

***

2.30

Mineral Products
Industries

Equipment
OfficeAccounting &Computing
Machinery
Electrical
Machinery &Apparatus
Motor Vehicles,
Trailers & SemiTrailers
Other
Transportation
Equipment
Furniture
Manufacturing
Leather, Leather
Products &FootwearFabricated Metal
Products
Note: Blanks in the table indicate that results are insignificant for the industry.

The “industry output” column indicates that the highest impact of a geographical componentof aggregated trade share is on the industry of Other Transportation Equipment. The results
show that one percentage point increase in predicted trade share increases the output of this20

industry by 10.32%. For the Non-Metallic Mineral Product industry, on the other hand, one
percentage point increase in predicted trade share significantly increases the industry-level
output only by 3.21%. On average, the results conclude that the impact of trade on industrial
output varies from 3.21% to 10.32% across different industries. Overall, a country’s trade
positively affects the industrial output of Food & Beverages, Fabricated Metal Products,
Tobacco Products, Machinery & Equipment, Textile, Electric Machinery & Apparatus, NonMetallic Mineral Products, Motor Vehicles, Trailers & Semi-Trailers, Wood Products
excluding Furniture, Other Transportation Equipment, Paper & Paper Products, Furniture,
Manufacturing, Printing & Publishing, Chemicals & Chemical Products, Rubber & Plastics
Products, and Basic Metal. On the other hand, for the industries Office Accounting &Computing Machinery, Wearing Apparel & Fur, Leather, Leather Products & Footwear, andCoke, Refined Petroleum Products & Nuclear Fuel, the impact of trade is positive but notsignificant.

When output per worker is concerned, the impact of trade is generally smaller. Trade sharepositively affects the industrial output per worker of Food & Beverages, Fabricated Metal
Products, Tobacco Products, Machinery & Equipment, Textile, Office Accounting &Computing Machinery, Wearing Apparel & Fur, Electric Machinery & Apparatus, Leather,
Leather Products & Footwear Non-metallic Mineral Products, Wood Products excluding
Furniture, Other Transportation Equipment, Paper & Paper Products, Furniture,
Manufacturing, Printing & Publishing, Chemicals & Chemical Products, Rubber & Plastics
Products, and Basic Metal. However, for the industries Motor Vehicles, Trailers & SemiTrailers and Coke, Refined Petroleum Products & Nuclear Fuel, the impact of trade on output
per worker is positive but not significant. The impact of trade share on the output per worker
is highest for the Textile industry, where one percentage point increase in trade share
increases the output per worker by 3.84%. For Basic Metal, on the other hand, the magnitudeof the impact is significant but the output per worker changes only by 1.5%.

21

The results of industry-by-industry estimation are in line with our main findings in theprevious section with respect to the first research question. It also illustrates the heterogeneity
in the effect of a country’s trade on industry-level output.

4. Conclusion
By extending Frankel-Romer’s (1999) estimation strategy, this paper has investigated theeffect of country-level trade on industry-level output for 20 leading manufacturing industries,
both for developed and developing countries for the year 2015. Given the argument that there
exists an endogeneity in the trade and industrial output relationship, the trade constructed by
the geographical variables is used as an instrument for the total trade relative to GDP ofcountries. The results have indicated that the population and area of the country and the tradevolume significantly and positively affect the industrial output and output per worker. Basedon Rauch (1999), this paper has characterized the industries into two broader categories ashomogeneous and differentiated industries. Our findings also show the heterogeneous trade
impacts across industries, which is related to the degree of industry differentiation. It has
confirmed that in the differentiated industries, the average impacts of predicted trade shareon the industry-level output and output per worker are moderately high as compared to thehomogeneous industries.

Appendix.
A. The List of Industries and Countries
Table A.1: List of 20 Manufacturing Industries (ISIC-3)
Food & Beverages

Fabricated Metal Products

Tobacco Products

Machinery & Equipment

TextileOffice Accounting & Computing

Wearing Apparel & Fur

Electrical Machinery & Apparatus

Leather, Leather Products & Footwear

Motor Vehicles, Trailers & Semi-Trailers

Wood Products excluding Furniture

Other Transportation Equipment

22

Paper & Paper Products

Furniture, Manufacturing

Printing & Publishing

Coke, Refined Petroleum Products & Nuclear Fuel

Chemicals & Chemical Products

Non-Metallic Mineral Products Industries

Rubber & Plastics Products

Basic Metal

Table A.2: List of 99 Countries
Algeria

Argentina

Armenia

Australia

Austria

Azerbaijan

Bahrain

Bangladesh

BelarusBelgium

Bolivia

BrazilVietnam

Bulgaria

CanadaChile

BruneiChina

Colombia

USA

Cuba

Czech

Côte d'Ivoire

Darussalam
Costa Rica

Croatia

Rep
Denmark

Ecuador

Egypt

Estonia

Ethiopia

FinlandFranceGeorgia

Germany

GreeceHungary

India

Indonesia

Iran

Iraq

IrelandIsrael

Italy

Japan

JordanKazakhstan

Kenya

South Korea

KuwaitKyrgyz Rep

LatviaLebanonLithuania

Luxembourg

MalawiMalaysia

Malta

MexicoMoldova

Mongolia

MoroccoMyanmar

Netherlands

New ZealandNigeria

NorwayOman

Pakistan

PanamaPeru

Philippines

PolandPortugal

Qatar

Romania

RussiaSaudi Arabia

SerbiaSingapore

Slovak Rep

Slovenia

South Africa

Spain

Sri Lanka

Sudan

SwedenSwitzerland

Tajikistan

Tanzania

ThailandTunisia

TurkeyTurkmenistan

UgandaUkraine

UAE

UnitedUruguay

Uzbekistan

Kingdom23

B. Detailed Results of Industry-by-industry Estimation
The estimated IVLS results for the impact on industry-level output (equation (7)) are shown in Table B.1.a and Table B.1.b.

Food &

Table B.1.a: 2SLS Regression Results: Manufacturing Industrial Output (𝐋𝐋𝐋𝐋𝐋𝐋 𝐘𝐘𝒋𝒋 )FabricatedTobaccoMachinery

Beverages

Metal

Products

Industries

Products

TextileOfficeWearing

Electrical

Leather,

Non-Metallic&

Accounting &ApparelMachinery

Leather

MineralEquipment

Computing&&

Products

Products

Machinery

Fur

Apparatus&

Industries

Footwear𝑇𝑇�𝑗𝑗

3.292***

6.07***

3.403*

7.720***

4.599**

3.441

2.71

4.815***

1.207

3.219***(0.866)(1.584)(1.721)(2.145)*(2.140)(1.573)(1.680)(1.266)(0.914)𝑙𝑙𝑙𝑙 𝐴𝐴𝑗𝑗

0.939***

1.124***

1.471***

1.148***

1.193**

1.770***

1.117***

1.275***

1.281***

0.921***(0.133)(0.186)(0.265)(0.227)*(0.294)(0.141)(0.180)(0.158)(0.125)(1.146)(0.144)𝑙𝑙𝑙𝑙 𝑃𝑃𝑗𝑗

0.284**

0.246-0.08410.485*

0.455**-0.4200.07580.168-0.0587

0.207(0.124)(0.206)(0.289)(0.281)*(0.361)(0.176)(0.199)(0.165)(0.124)(0.168)Constant

15.09***

11.95***

14.05***

7.435

8.636**

18.86***

14.61***

12.53***

15.60***

14.66***(1.875)(3.254)(4.384)(4.489)*(5.378)(3.040)(3.371)(2.693)(1.952)

68

79

81

79

83

(2.728)Observations

83

81

53

83

82

24

R-squared

0.611

0.388

0.444

0.326

0.621

0.390

0.505

0.471

0.532

0.606

Note: Robust standard errors in parentheses.
*** p<0.01, ** p<0.05, * p<0.1

Table B.1.b:2SLS Regression Results: Manufacturing Industrial Output (𝐋𝐋𝐋𝐋𝐋𝐋 𝐘𝐘𝐣𝐣 )

Motor

Wood

Other

Paper &

Furniture,

Printing &Chemicals &Coke,

Rubber

Basic

Vehicles,Products

Transportation

Paper

Manufacturing

Publishing

ChemicalRefined&

Metal

Trailers

excludingEquipment

Products

Products

Petroleum

Plastics& Semi-

Furniture

Products

Products

Trailers& Nuclear
Fuel

𝑇𝑇�𝑗𝑗

6.152**

6.110***

10.32***

5.316***

4.732***

4.034***

6.027***

3.271

4.298**

4.860****(1.621)(3.424)(1.471)(1.486)(1.152)(1.314)(2.072)*(1.401)𝑙𝑙𝑙𝑙 𝐴𝐴𝑗𝑗

1.704**

0.677***

1.865***

1.052***

1.192***

0.956***

1.300***

1.226***

1.209**

1.087****(0.216)(0.368)(0.202)(0.165)(0.156)(0.184)(0.225)*(0.169)(1.936)(1.391)(0.261)𝑙𝑙𝑙𝑙 𝑃𝑃𝑗𝑗
Constant(0.175)0.386

0.710***

0.414

0.436*

0.170

0.183

0.334

0.04640.175

0.522**(0.284)(0.218)(0.450)(0.220)(0.195)(0.161)(0.205)(0.272)(0.217)(0.254)7.706*

6.007*

3.090

9.089***

12.60***

12.97***

10.71***

16.11***

13.23**

9.234**(4.440)(3.428)(7.194)(3.336)(3.109)(2.445)(3.076)(4.320)*(3.754)(3.298)25

Observations

78

84

74

80

84

80

81

65

80

83

R-squared

0.480

0.319

0.383

0.345

0.461

0.450

0.501

0.352

0.476

0.499

Note: Robust standard errors in parentheses.
*** p<0.01, ** p<0.05, * p<0.1

26

Next, the IVLS results for the impact of trade on output per worker (equation (8)) are presented in Table B.2.a and Table
B.2.b.

Industries

𝐘𝐘
Table B.2.a: 2SLS Regression Results: Manufacturing Industrial Output Per Worker �𝐋𝐋𝐋𝐋𝐋𝐋 � 𝒋𝒋�𝑳𝑳 ��

Food &

FabricatedTobaccoMachinery

Beverages

Metal

Products

Products

TextileOfficeWearing

Electrical&Accounting &ApparelEquipment

Computing& Fur

Machinery

𝑇𝑇�𝑗𝑗

𝒋𝒋

Leather,

Non-

Machinery

Leather

Metallic&

Products

MineralApparatus&

Products

FootwearIndustries

2.105***

2.304***

2.936**

2.01***

3.848***

2.401**

3.671***

2.387***

2.112**

1.749***(0.618)(0.564)(1.264)(0.733)(0.844)(1.189)(0.840)(0.771)(0.927)(0.626)0.114

0.193*

0.117

0.199*

0.04010.167

0.04510.08070.0441

0.0992(0.0893)(0.114)(0.239)(0.103)(0.110)(0.112)(0.110)(0.0911)(0.113)(0.100)0.155-0.01150.337*

0.02860.459***

0.08140.325**

0.176

0.122

0.0396(0.101)(0.135)(0.171)(0.117)(0.170)(0.177)(0.129)(0.115)(0.151)(0.0964)8.219***

9.462***

5.788*

9.121***

2.732

8.681***

4.037**

7.656***

7.803***

9.692***(1.443)(1.677)(2.906)(1.716)(2.465)(2.786)(1.960)(1.801)(2.251)(1.399)Observations

82

80

51

81

81

67

76

79

75

82

R-squared

0.097

0.125

0.054

0.095

0.144

0.099

0.134

0.072

0.062

0.113

𝑙𝑙𝑙𝑙 𝐴𝐴𝑗𝑗
𝑙𝑙𝑙𝑙 𝑃𝑃𝑗𝑗
Constant

Note: Robust standard errors in parentheses.
*** p<0.01, ** p<0.05, * p<0.1

27

𝐘𝐘

Industries

Table B.2.b: S2SLS Regression Results: Manufacturing Industrial Output Per Worker �𝐋𝐋𝐋𝐋𝐋𝐋 � 𝒋𝒋�𝑳𝑳 ��

Motor

Wood

Other

Paper &

Vehicles,Products

Transportation

Paper

Furniture,

Trailers &excludingEquipment

Products

Manufacturing

Semi-

Furniture𝒋𝒋

ChemicalCoke,

Rubber &BasicPrinting &&RefinedPlastics

Metal

Publishing

ChemicalPetroleum

Products

Products

Products &Trailers

Nuclear
Fuel

𝑇𝑇�𝑗𝑗

1.625

3.149***

3.744***

2.817**

2.243***

2.397***

3.395***

2.610

3.060**

1.505**(0.987)(0.929)(1.334)*(0.702)(0.549)(0.850)(1.657)(1.406)(0.741)(0.723)𝑙𝑙𝑙𝑙 𝐴𝐴𝑗𝑗
𝑙𝑙𝑙𝑙 𝑃𝑃𝑗𝑗
Constant

0.171*

0.00418

0.273**

0.01400.182**

0.0640

0.221

0.255

0.09520.0887(0.0999)(0.118)(0.136)(0.123)(0.0905)(0.0954)(0.134)(0.170)(0.204)(0.0990)0.163

0.352**

0.284

0.315**

0.04200.164*

0.220

0.153

0.01230.130(0.141)(0.162)(0.184)(0.134)(0.118)(0.0958)(0.158)(0.254)(0.256)(0.124)8.310***

4.747**

4.765

5.995**

8.517***

7.393***

6.561***

9.674**

9.617***

9.462***(2.246)(2.378)(2.987)*(1.696)(1.318)(2.243)(3.913)(3.568)(1.772)(1.849)Observations

76

83

70

79

82

78

80

60

79

81

R-squared

0.070

0.088

0.116

0.086

0.128

0.127

0.111

0.042

0.112

0.034

Note: Robust standard errors in parentheses.
*** p<0.01, ** p<0.05, * p<0.1

28

The first stage regression results estimated by using equation (9) are shown in Table B.3.

Table B.3: First Stage Regression Results
𝑻𝑻𝒋𝒋

Variables� 𝒋𝒋
𝑻𝑻

0.717***

𝒍𝒍𝒍𝒍 𝑷𝑷 𝒋𝒋-0.0487(0.222)(0.0325)𝒍𝒍𝒍𝒍 𝑨𝑨 𝒋𝒋-0.0514*

Constant

1.228***(0.0308)(0.350)Observations

99

R-squared

0.356

Note: Robust standard errors in parentheses.
*** p<0.01, ** p<0.05, * p<0.1

The coefficient of constructed trade share 𝑇𝑇�𝑗𝑗 is 0.717, which confirms that the constructed share contains enough information

about the actual trade share of the countries. A one-unit change in the constructed trade share changes the actual trade share by

0.71 units, and it is significant with the t-value of 3.25, which corresponds to F stats of 10. The results are consistent with thoseof Frankel and Romer indicating that the geographical component of the trade is a good instrument for actual trade.

29

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