KEO Discussion Paper No. 151

The Gravity Model and Trade in Intermediate Inputs
Theresa M. Greaney
University of Hawai‘i
Kozo Kiyota
Keio University, University of Hawai‘i,
and Research Institute of Economy, Trade and Industry

The Gravity Model and Trade in Intermediate Inputs †Theresa M. Greaney
Department of Economics, University of Hawai‘i
Kozo Kiyota††
Keio Economic Observatory, Keio University;
Department of Economics, University of Hawai‘i;
and Research Institute of Economy, Trade and Industry (RIETI)January 21, 2020

Abstract
Is the gravity model as applicable to trade in intermediate inputs as it is to trade in final goods?
One of the contributions of this paper is that we explicitly account for the dual nature of productsthat can be used as either intermediate inputs or final goods. We find that the structural gravity
model performs extremely well for describing bilateral trade in final goods and in intermediate
inputs. Moreover, this continues to hold even when we focus on a subset of countries in whichintermediate inputs trade accounts for a growing share of trade, namely ‘Factory Asia’. However,
the gravity model may perform poorly due to model misspecification (i.e., exclusion of
intranational trade) and/or sample selection, even after the model considers the dual nature of
products. We demonstrate that the poor performance of the gravity model is not attributable tothe large trade flow of intermediate inputs, which supports the continued use of the model asthese trade flows continue to grow in importance worldwide.
Key words: Structural gravity model, Intermediate inputs, Intranational trade, Factory Asia
JEL Classification codes: F14†

The authors have benefited greatly from conversations on this topic with Jota Ishikawa. We also thank seminar
participants at the Japan Society of International Economics (especially Kazunobu Hayakawa), RIETI and the
University of Hawai‘i. The usual disclaimers apply.
††
Kozo Kiyota was a Visiting Colleague at the University of Hawaii when this research was undertaken and would
like to thank Keio University for providing financial support. Kiyota also acknowledges the financial support
received from the JSPS Grant-in-Aid (JP18KK0348, JP19H00598) and a grant-in-aid from Zengin Foundation for
Studies on Economics and Finance.

11. Introduction
The gravity model has been used to analyze the determinants of bilateral trade for over 50
years, since Tinburgen (1962). As Yotov, et al. (2016, p.5) state “Often referred to as theworkhorse in international trade, the gravity model is one of the most popular and successful
frameworks in economics.” Despite this high praise and voluminous literature using the gravity
model to estimate bilateral trade relationships, one may question whether the gravity model isequally applicable to trade in intermediate inputs as it is to trade in final products. Whileconsumers in many countries may share similar demands for apparel and autos, for example, thedemands for textiles and auto parts presumably differ widely across countries depending onwhether a country hosts apparel and auto manufacturing plants or not. Do the same determinants
apply equally well to trade in consumer (i.e., final) goods as to trade in producer (i.e.,intermediate) goods?
This is not a trivial question since international trade in intermediate inputs accounts forthe vast majority of world trade flows in recent years thanks to production fragmentation and thedevelopment of global value chains. The share of intermediate inputs in total imports increased
from 56.9 percent in 1995 to 63.4 percent in 2011 for all industries, and a similar pattern is
confirmed when we focus on the imports of tradable industries and those of manufacturingindustries, as shown in Figure 1. 1 Given the large and increasing role of intermediate inputs inworld trade, we set out to examine whether the gravity model performs as well for describing
trade in intermediate inputs as it does for trade in final consumer products.

=== Figure 1 ===1

Based on the OECD Trade in Value-Added dataset covering 63 economies, which is described further in our data
section.

2

If the pattern of trade in intermediate inputs is largely proportional to trade in final goods,
then our inquiry may be short-lived. However, we find that the correlation between bilateral final
goods trade and bilateral intermediate goods trade has declined noticeably in recent years, as
shown in Figure 1. Although the correlation is strongly positive, it declined over the period from0.959 in 1995 to 0.861 in 2011. This result suggests that the pattern of trade in intermediate
inputs is diverging somewhat from that of trade in final goods. Baldwin and Taglioni (2014),
hereafter referred to as “BT”, find that the gravity model performs well for bilateral trade in all
goods, final goods and intermediate inputs when the analysis covers a wide range of countries
but it performs poorly when applied to a particular subset of countries (i.e., ‘Factory Asia’) in
which intermediate inputs account for a rapidly growing share of trade.
We pursue a similar line of inquiry while making two contributions to the literature by
applying a structural gravity equation inclusive of intranational trade and by explicitly taking
into account the dual nature of goods that can be used as either final consumer goods orintermediate inputs. Our major findings are twofold. We find that the structural gravity equation
performs extremely well for describing bilateral trade in final goods and in intermediate inputs.
In fact, the gravity model performs well even when we focus on a subset of countries in whichintermediate inputs trade accounts for a large and growing share of trade, namely ‘Factory Asia’.
This result is in contrast to the BT finding that the standard gravity model performs poorly when
focused on Factory Asia countries.
Second, we find that the performance of the gravity model declines if the model excludes
intranational trade and/or if it focuses on a very small subset of countries, even after the modelaccounts for the dual nature of products. Our results suggest that the poor performance of the3

gravity model previously attributed to rising shares of intermediate inputs in total trade is more
likely due to misspecification (i.e., the exclusion of intranational trade in estimating the gravity
equation), as was pointed out by Yotov (2012), and/or sample selection, as was similarly argued
by Rose (2017). While this may not sound like a novel finding at first sight, its new important
insight is that the poor performance is not attributable to large trade flows of intermediate inputs.
The remainder of the paper is structured as follows. In the next section we discuss related
strands of literature and how our work fits into these strands. Then we present the methodology
and data used in this study in section 3, followed by our estimation results in section 4. A
summary of our findings and their implications is presented in the final section.

2. Literature Review
This research relates to four strands of literature, one theoretical and three empirical. The
original theoretical backing for the gravity model relied on a demand-side structure with constantelasticity of substitution preferences for final goods that are differentiated by their place of origin(i.e., the Armington (1969) assumption). Since this demand-side framework focused on final
goods trade, growing trade in intermediate goods may present a problem for users of the gravity
model. Demand for intermediate inputs is related to the production locations of final goods
producers rather than to the location of homogeneous consumers worldwide. However, Anderson(2011) summarizes recent research contributions that allow the structural gravity model 2 to be
derived from a supply-side framework so that the gravity model can be applied to trade inintermediate inputs by replacing consumer expenditure shares with producer cost shares. 3

2

A gravity model with solid theoretical foundations is called a ‘structural’ gravity model.
Examples of this literature are Eaton and Kortum (2002), Chaney (2008), and Costinot, Donaldson and Komunjer(2012). Note, however, that the gravity model can be derived from a large class of trade models (Yotov, Piermartini,
Monteiro and Larch, 2016).

3

4

In conjunction with these theoretical developments, a number of empirical studies haveaddressed the issue of intermediate inputs trade in the gravity model framework. This empirical
work can be classified into two strands of research. One strand examines differences in thedeterminants of final goods trade and intermediate inputs trade by first classifying products into
final goods and intermediates, and then estimating the gravity model for each type of good. This
strand includes such studies as Athukorala and Yamashita (2006), Kimura et al. (2007),
Hayakawa (2014) and BT.
The other strand consists of more recent studies that focus on value-added trade rather
than gross trade. This strand of research first estimates the value-added trade, stripping out thevalue of imported inputs from gross trade, and then applies the gravity model to value-added
trade. To estimate the value-added trade, these studies typically utilize international input-output
tables. This strand includes such studies as Johnson and Noguera (2012), Aichele and Heiland(2018) and Kaplan et al. (2018). 4
While these empirical studies made significant contributions to the literature, there is aroom for further investigation. On the one hand, the first strand of studies explicitly focuses onintermediate inputs but does not take into account the dual nature of goods which is taken into
account in the second strand. For example, computers are directly used as final products byconsumers, thereby directly contributing to consumer utility, but they are also used asintermediate inputs by firms to produce other products and services. On the other hand, while thesecond strand of studies takes into account the dual nature of goods, the trade in intermediate
inputs is not analyzed explicitly because these studies focus on net trade flows (gross trade minus

4

An earlier literature focuses on measuring “vertical specialization” in trade, as defined by Hummels, Ishii and Yi(2001) and further examined in Chen, Kondratowicz and Yi (2005).

5

trade in intermediate inputs). 5 Moreover, as we will discuss below, these studies focus only oninternational trade. None of them takes into account the role of intranational trade.
Building upon these studies, this study attempts to fill in the missing link. We contributeto the literature by explicitly focusing on intermediate inputs trade like the first strand of studies
while accounting for the dual nature of goods like the second strand of studies. This combination
has not been investigated previously in the literature, to the best of our knowledge. Morespecifically, we examine whether the gravity model is as applicable to trade in intermediate
inputs as it is to trade in final goods. In order to distinguish the difference between trade inintermediate inputs and final goods while taking account of the dual nature of goods, we utilize
the OECD Trade in Value-Added (TiVA) database which allows for dual uses of goods throughan input-output table framework. Therefore, trade in final goods is defined as the final demand
from foreign consumers whereas trade in intermediate inputs is defined as the intermediate
inputs used by foreign producers. Our analysis thus provides an alternative decomposition of
trade flows, which is a simple but non-trivial contribution to the literature. 6
The last strand of related empirical work focuses on estimation issues involving thestructural gravity model. Our study builds on the work of Yotov (2012) and Borchert and Yotov(2017) that emphasize the importance of including intranational trade in gravity model
estimations. These studies resolved the “distance puzzle” and “missing globalization puzzle” byapplying the structural gravity model to international and intranational trade flows. We find that

5Instead of estimating the relationship between value-added trade and value-added output (i.e., GDP), Jang andSong (2017) propose estimating the relationship between gross trade and gross output in the presence ofintermediate inputs, but their study also does not explicitly analyze the trade in intermediate inputs.
6
In a different context, Wang, Wei, Yu and Zhu (2018) and Taniguchi (2019) emphasized the importance ofdistinguishing between trade in final goods and trade in intermediate inputs in analyzing the effects of Chinese
imports on local labor markets in the U.S. and Japan, respectively.

6the poor performance of the gravity model in explaining Factory Asia trade identified by BT canbe resolved with the inclusion of intranational trade flows.

3. Methodology and Data
3.1 Methodology
We estimate a gravity model of bilateral trade for both final goods and intermediate
inputs. Let 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑖𝑖𝑖𝑖 be the imports of country 𝑗𝑗 from country 𝑖𝑖. The standard gravity equation is:′
𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑖𝑖𝑖𝑖 = exp�𝐝𝐝′𝑖𝑖 𝛼𝛼 + 𝐝𝐝𝑗𝑗′ 𝛽𝛽 + 𝐰𝐰𝑖𝑖𝑖𝑖
𝛾𝛾� × 𝜀𝜀𝑖𝑖𝑖𝑖 ,(1)

where exp(•) denotes exponential function; 𝐝𝐝𝑖𝑖 and 𝐝𝐝𝑗𝑗 are the vectors of the origin- anddestination-country dummies to capture the origin- and destination-country fixed effects,

respectively; 𝐰𝐰𝑖𝑖𝑖𝑖 is the vector of characteristics of the origin-destination pair (e.g., distance) and𝜀𝜀𝑖𝑖𝑖𝑖 is the disturbance term. The origin- and destination-country fixed effects are the “multilateralresistance terms” in the gravity model of trade (Anderson and van Wincoop, 2003; Feenstra,2016). Although BT utilized a remoteness variable as a proxy for the multilateral resistance term,
Head and Mayer (2014) argued that remoteness variables do not have a rigorous theoreticalfoundation. We thus use fixed effects as multilateral resistance terms while capturing the effectsof country size by origin and destination GDPs, as described below. 7

7

de Mello-Sampayo (2017) offers an alternate approach to handling multilateral resistance by including a“competition factor” that is a distance-weighted sum of all other supplier countries’ characteristics in supplying a
product. In order to compare our gravity model results with prior literature, we adopt the traditional approach ofusing origin- and destination-country fixed effects to handle multilateral resistance.

7

Note also that the disturbance term is assumed to multiply the exponential function. By
specifying the disturbance multiplicatively and assuming Poisson distribution for the
disturbance, we can estimate the gravity model directly by employing Pseudo-Poisson Maximum
Likelihood (PPML) estimation proposed by Santos Silva and Tenreyro (2006). 8 Although theestimation can be done by non-linear least squares, the PPML estimator is more efficient than the
non-linear least squares estimator (Santos Silva and Tenreyro, 2006). 9 In particular the PPML
estimator accounts for heteroskedasticity in the trade data and allows us to include theinformation contained in observations of zero trade flows. 10
The standard gravity equation in equation (1) can be improved by applying it to panel
data and by including intranational trade flows, 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑖𝑖𝑖𝑖𝑖𝑖 , along with international trade flows,

𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑖𝑖𝑖𝑖𝑖𝑖 , as in Yotov (2012) and Borchert and Yotov (2017). 11 These changes produce thefollowing gravity specification:′′′
𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑖𝑖𝑖𝑖𝑖𝑖 = exp�𝐝𝐝′𝑖𝑖 𝛼𝛼 + 𝐝𝐝𝑗𝑗′ 𝛽𝛽 + 𝐰𝐰𝑖𝑖𝑖𝑖
𝛾𝛾 + 𝐱𝐱 𝑖𝑖𝑖𝑖𝑖𝑖
𝜆𝜆 + 𝐲𝐲𝑖𝑖𝑖𝑖′ 𝛿𝛿 + 𝐳𝐳𝑗𝑗𝑗𝑗
𝜁𝜁+𝐝𝐝′𝑖𝑖𝑖𝑖 𝜉𝜉 + 𝐝𝐝′𝑡𝑡 𝜒𝜒� × 𝜀𝜀𝑖𝑖𝑖𝑖𝑖𝑖 ,(2)

where 𝐱𝐱 𝑖𝑖𝑖𝑖𝑖𝑖 is the vector of time-variant country-pair-specific factors; 𝐲𝐲𝑖𝑖𝑖𝑖 and 𝐳𝐳𝑗𝑗𝑗𝑗 are the vectorsof the time-variant origin- and destination-country-specific characteristics, respectively; 𝐝𝐝′𝑖𝑖𝑖𝑖 is thevector of country-specific dummies for intranational trade that capture both internal trade costs

8

Several methods have been proposed to estimate the gravity equation. Based on a Monte Carlo exercise, Head andMayer (2014) argued that structurally-iterated least squares (Anderson and van Wincoop, 2003) are not worth thecomputational effort. Similarly, they argued that Bonus Vetus OLS (Baier and Bergstrand, 2009) is no longer
advisable because its estimates are not robust to missing data and lack preciseness. A more recent study byAnderson, Larch and Yotov (2018) developed a procedure to perform general equilibrium comparative static
analysis of the gravity model with the PPML estimator.
9
Similarly, the use of negative binomial estimates depends on the units of measurement for the dependent variable.
For more detail, see Bosquet and Boulhol (2014).
10
In our dataset, zero trade flows account for less than 1% of observations.
11
See Yotov, Piermartini, Monteiro and Larch (2016) for a discussion of the advantages of estimating the gravity
equation using panel data.

8

and any home biases in consumption; and 𝐝𝐝𝑡𝑡 is the vector of year dummies that capture the year

fixed effects. We follow Yotov (2012) and Borchert and Yotov (2017) in including intranational
trade flows along with international trade flows so that our estimates of bilateral trade costs andpolicies are theory-consistent (i.e., measured relative to internal trade frictions). For observationsof intranational trade flows, zeroes are assigned to the time-invariant and time-variant countrypair-specific factors (i.e., 𝐰𝐰𝑖𝑖𝑖𝑖 and 𝐱𝐱 𝑖𝑖𝑖𝑖𝑖𝑖 ). In this paper, we estimate equation (2) using PPML
estimation.

As an alternative specification, we allow the origin-country-specific effects and thedestination-country-specific effects in equation (2) to vary over time by estimating the following
equation:′′′
𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑖𝑖𝑖𝑖𝑖𝑖 = exp�𝐝𝐝′𝑖𝑖𝑖𝑖 𝛼𝛼 + 𝐝𝐝𝑗𝑗𝑗𝑗
𝛽𝛽 + 𝐰𝐰𝑖𝑖𝑖𝑖
𝛾𝛾 + 𝐱𝐱 𝑖𝑖𝑖𝑖𝑖𝑖
𝜆𝜆+𝐝𝐝′𝑖𝑖𝑖𝑖 𝜉𝜉� × 𝜀𝜀𝑖𝑖𝑖𝑖𝑖𝑖 ,(3)

where 𝐝𝐝𝑖𝑖𝑖𝑖 and 𝐝𝐝𝑗𝑗𝑗𝑗 are the vectors of country-year dummies that capture the country-year fixed
effects and the other right-hand-side variables are the same as in equation (2). Note that thisspecification, unlike equation (2), cannot accommodate other country-year variables such GDP
because the effects in 𝐲𝐲𝑖𝑖𝑖𝑖 and 𝐳𝐳𝑗𝑗𝑗𝑗 are completely subsumed in 𝛼𝛼 and 𝛽𝛽 respectively. 12

12
One may argue that we should employ a three-way fixed effect model: origin-country-year, destination-countryyear, and country-pair fixed effects as in Egger and Pfaffermayr (2003), Baltagi, Egger and Pfaffermayr (2015), andYotov et. al. (2016). While the three-way fixed effects model has strong theoretical backing, some of the results are
difficult to interpret because most of the effects are captured by dummies. This paper includes geographic andcultural distance variables explicitly rather than including country-pair fixed effects because we need to estimate
distance coefficients in order to compare our results with previous literature.

9

3.2 Data
One of the important data issues is to distinguish between final goods trade andintermediate inputs trade. Unfortunately, this distinction is not made in standard national trade
statistics. To overcome this problem, we employ the OECD’s TiVA dataset which distinguishes
final goods and intermediate inputs in bilateral trade. 13 The trade data covers 63 economies,
including 35 OECD members and 28 non-OECD economies, and a rest of world (ROW)
aggregate from 1995 to 2011. 14 The ROW data is used for descriptive statistical analysis only.
Our trade data includes not only international trade but also intranational trade, assuggested by Yotov (2012) and Borchert and Yotov (2017). Intranational trade is defined as thedifference between gross output and total exports. Accordingly, when we focus on intermediate
goods trade, we measure intranational trade as the difference between the gross output ofintermediate inputs and the exports of intermediate inputs. In contrast, when we focus on final
goods trade, we define intranational trade as the difference between the value-added and theexports of final goods so that gross output for all goods is consistent with the sum of output offinal goods and intermediate inputs. The data also come from the OECD’s TiVA database wheregross output and value-added are available at the same country-industry level. 15

13
The TiVA dataset is constructed from an international input-output table, which is based on 34 industrial sectors
including both manufacturing and non-manufacturing sectors. The TiVA database is advantageous in allowing fordual uses of products as inputs and as final consumer goods but it is disadvantageous in that its current framework is
not sufficiently disaggregated to allow us to present product-level statistics on dual use (e.g., extensive and intensive
margins). The 2016 edition of the TiVA dataset is available online at: http://www.oecd.org/sti/ind/measuring-tradein-value-added.htm.
14
We utilize the OECD’s TiVA database rather than other databases such as World Input-Output Database (WIOD)
because of the wide coverage of countries and years. The WIOD Release 2013 covers only 40 countries between
1995 and 2011 (17 years) while the WIOD Release 2016 covers 43 countries from 2000 to 2014 (15 years). See
online Appendix Table A1 for a list of countries included in this study. All Appendix tables are available at:https://sites.google.com/site/greaneyecon/research.
15
In the OECD’s TiVA database, gross output is smaller than value-added in a few country-industries, which leads
to negative intranational trade. These observations are dropped from our analysis.

10

The data for the gravity equation determinants comes from various sources. For timeinvariant country-pair specific variables (𝐰𝐰𝑖𝑖𝑖𝑖 ), we use a standard set of gravity variables such as

distance and dummy variables for common language, colonial relationship and contiguity. Thesevariables are obtained from the CEPII (Centre d’Etudes Prospectives et d’Informations
Internationales) gravity data. 16
The time-variant country-pair variable (𝐱𝐱 𝑖𝑖𝑖𝑖𝑖𝑖 ) is a dummy variable for pair participation in

a Regional Trade Agreement (RTA). We use Mario Larch’s Regional Trade Agreements

Database (Egger and Larch, 2008) to judge if a country pair belongs to a common RTA. 17 The
RTAs in this database include customs unions (e.g., European Union), free trade agreements andeconomic integration agreements (e.g., North America Free Trade Agreement and JapanSingapore economic partnership agreement), and partial scope agreements (e.g., South Asian
Preferential Trade Arrangement).
The origin- and destination-country-specific characteristics (𝐲𝐲𝑖𝑖𝑖𝑖 and 𝐳𝐳𝑗𝑗𝑗𝑗 ) are GDP’s tocontrol for market size. GDP is measured in current thousand US dollars and the data is obtained
from the CEPII gravity data.

4. Estimation Results
4.1 Baseline results
Table 1 presents the estimation results of the gravity model for manufactured goods trade
between all 63 economies in our dataset. We capture the multilateral resistance effects by using
time-invariant origin and destination dummies in columns (1)-(3) and time-variant origin and16

In the CEPII gravity data, distance is measured as the population-weighted distance between major economic
centers across countries.
17
The database includes all multilateral and bilateral RTAs as notified to the World Trade Organization from 1950
to 2017.

11

destination dummies in columns (4)-(6). For each three-column set of results, we show theestimated coefficients for trade in all goods, final goods only and intermediate inputs only. The
results suggest that the determinants of final goods trade are similar to the determinants ofintermediate inputs trade and almost all of the coefficients are significant and of the expected
sign and approximate size. 18 Bilateral trade is decreasing in trade partner distance and increasingin trade partners’ economic size, participation in an RTA, and sharing of a common language
and/or a common border. The only insignificant trade determinant is the colonial relationshipdummy which impacts only 106 directional country-pairs out of approximately 3,905 for eachyear in our dataset which includes fewer developing countries than some past studies. 19=== Table 1 ===Following Santos Silva, Tenreyro, and Windmeijer (2015), we compute R-squared as thesquare of the correlation between the dependent variable and the estimated conditional mean.
Table 1 shows that the fit of the gravity equation is very high for both final goods and forintermediate inputs, with R-squared values greater than 0.995 in all of the specifications. The
strong fit plus reasonable coefficient estimates for both final goods and intermediate inputs tradeconfirm that the gravity model is applicable to both types of trade.

18

The seemingly small size coefficients compared with gravity estimates in earlier literature can be explained bynoting that the inclusion of intranational trade changes the interpretation of these coefficients. They now represent
the effect of market size on sales inclusive of home market size, not the effect of trade partner size on sales relative
to the size of other trade partners. As we will confirm in Table 3, once we exclude intranational trade, we get size
coefficients that are comparable to the earlier literature.
19
Fifty-three colonial relationships become 106 directional country-pairs because trade is measured by imports, soeach colonial relationship is counted twice for each partner’s imports from the other.

124.2 Results for Factory Asia
Next we consider whether the gravity model performs equally well when we focus on asubset of countries that are closely linked through global value chains. Indeed, BT argue that thegravity model performs poorly when it is applied to a subset of countries in which intermediate
inputs trade accounts for a growing share of total trade, namely ‘Factory Asia’ countries. Theyfind that the size coefficient is somewhat small and declining over time and the distancecoefficient is much lower (in absolute value) than estimates found in previous studies. To
examine the cause of these disparities, we apply our structural gravity model to the seveneconomies included in BT’s Factory Asia (i.e., Indonesia, Japan, Malaysia, the Philippines,
South Korea, Taiwan and Thailand). 20
Table 2 shows gravity results for manufactured goods’ trade between Factory Asia
economies only. These results indicate that the gravity model works equally well for final goods
and intermediate inputs even when we restrict trade flows to the Factory Asia subset of
economies. The R-squared values are extremely high for each specification and the size anddistance coefficients are significant and of the expected signs for both types of goods. The size
coefficient on origin country may seem somewhat small at 0.409 for all goods Factory Asia tradecompared with 0.582 found previously for all goods all countries trade, but we should not expect
very similar coefficients when we restrict the variation in trade observations to a small subset ofcountries. 21 The origin-country size estimate is consistent with applying the gravity model to an

20

Our data also confirm the trend identified in BT of the increasing role of intermediate inputs in total Factory Asia
trade. Intermediates accounted for 59.6 percent of total Factory Asia trade in 1995 and 73.9 percent in 2011. Notealso that the definition of ‘Factory Asia’ is different across studies. For example, ‘Factory Asia’ in Ito and Vézina
(2016) consist of nine countries (i.e., the seven countries included in BT plus China and Singapore). As we will
discuss, our main results continue to hold even when the analysis includes China while excluding Japan.
21
In fact, for these seven economies, intra-Factory-Asia trade was only 5.1 percent of total manufactured goods
imports in 1995, and 3.9 percent in 2011.

13

alternative subset of countries such as the European Union (0.445) or to an expanded subset such
as East and Southeast Asia (0.518). 22=== Table 2 ===The distance coefficient for Factory Asia trade, at -0.615 for all goods, is only slightly
smaller (in absolute value) than the -0.737 found for all countries’ trade. With the small subset of
economies, the RTA and common language dummies are no longer significant trade
determinants, while colonial ties and contiguity now have negative impacts on trade. The RTA
result is unsurprising since five of the seven economies have RTAs in place with each other prior
to the start of our trade dataset in 1995 (i.e., Indonesia, Malaysia, the Philippines, South Korea,
and Thailand) and one has no RTAs with other Factory Asia economies (i.e., Taiwan). This
leaves the RTA dummy changing value only for Japan’s RTAs with Indonesia, Malaysia, thePhilippines and Thailand during our dataset time period. The common language result reflects
only one Factory Asia country pair, Malaysia and Taiwan, with a common official language (i.e.,
Chinese), while colonial ties are limited to only two country pairs (i.e., Japan and South Korea,
Japan and Taiwan) and contiguity is limited to only one country pair (i.e., Malaysia andThailand).
We also test the robustness of our results by estimating the structural gravity model for
manufactured goods trade for two alternative subsets of economies, the EU and an expandedgroup of economies in East and Southeast Asia. 23 We confirm the strong fit of the gravity model

22

The online Appendix Tables A2 and A3 present the estimation results for the EU and East and Southeast Asia,
respectively. The EU includes the 27 member countries that joined prior to 2012 and East and Southeast Asia
includes 12 countries, including all seven Factory Asia countries. See online Appendix Table A1 for a complete list.
23
These results are shown in online Appendix Tables A2 and A3.

14

for both final goods and intermediate inputs trade for these alternate subsets of economies. 24 We
also test the robustness of our results to our selection of industry coverage which has been
limited to manufactured goods thus far. 25 Again, we find the gravity model works well for
describing trade in both final goods and intermediate goods for Factory Asia even when we
expand the industries included.

4.3 When does gravity fail?
We found that the poor performance of the gravity model is not caused by intermediate
inputs trade, as was proposed by the BT study. We now seek alternate explanations. Tables 3 and4 show the gravity model results for manufactured goods trade for all countries and for Factory
Asia only after we drop intranational trade observations. Table 3 shows results that are very
comparable to standard gravity model results in terms of the size coefficients (0.74 to 0.84) andthe distance coefficients (-0.73 to -0.79) for final goods and intermediate inputs. However,reducing the country coverage to Factory Asia only while excluding intranational trade, as in
Table 4, we see the problematic result of an insignificant and small (in absolute value) distancecoefficient in most of the specifications. We also see much wider gaps between the size
coefficients for final goods relative to intermediate goods. A comparison of Tables 2 and 4
illustrates that the gravity model performs well for Factory Asia trade provided the estimation
includes intranational trade.

24
The EU results in the online Appendix Table A2 show particularly strong distance and RTA estimates. The former
is likely caused by greater reliance on expensive land-based transportation methods relative to ocean-based shipping
when we focus only on intra-EU trade and the latter is likely caused by ten countries joining the EU in 2004 and two
more joining in 2007.
25
The online Appendix Tables A4 and A5 show gravity model estimates for Factory Asia trade in tradable sectors(i.e., agriculture, mining and manufactured goods industries) and all sectors, respectively.

15

=== Tables 3 & 4 ===We also can find problematic gravity results if we consider alternate subsets of countries
even when intranational trade is included. When we estimate the gravity model for Factory Asia
minus Japan the origin-country size estimate becomes negative or close to zero and insignificant
for all goods, final goods and intermediate inputs. 26 If Japan is dropped from Factory Asia while
China is added, we can recover significance in the origin-country size coefficient and the gravity
model appears to “work” again. 27 Table 5 presents a summary description of the gravity
estimations done in BT and in our study, with table references, along with a bottom-line
assessment of whether the gravity model has worked or not in each case. A negative assessment
reflects insignificant and/or unusual coefficients for size and/or distance.

=== Table 5 ===In summary, the poor performance of the gravity model in a previous study does notseem attributable to intermediate inputs trade but rather to other factors such as model
specification (i.e., inclusion of intranational trade) and/or sample selection. 28 The gravity model
should be applied to international and intranational trade flows for large sets of countries to getbetter estimates of trade determinants based on wider variation in observations. Rose (2017) alsoemphasizes the importance of using larger datasets (i.e., many countries and years of observed

26
The online Appendix Table A6 shows the estimation results for Factory Asia minus Japan, but including
intranational trade.
27
The online Appendix Table A7 presents the estimation results for Factory Asia, dropping Japan and adding China.
28
BT differs from our study in several other ways (e.g., longer time period, log linear OLS instead of PPML, anddifferent division of goods into consumer goods versus intermediates by industry coding rather than by usage) but
we focus here on what we find to be an important difference—their exclusion of intranational trade.

16

trade) to obtain unbiased estimates of currency union trade effects. Smaller datasets can produce
biased multilateral resistance estimates through the use of time-varying country fixed effects,
which in turn biases estimates of time-varying country-pair trade determinants. The preferred
specification in Rose (2017) cannot be used to resolve the BT problem with the gravity model
because size and distance effects are subsumed in fixed effects in Rose (2017), but the small
sample critique is applicable. 29

5. Concluding Remarks
This paper estimates the gravity model separately for intermediate inputs trade and final
goods trade by utilizing the OECD TiVA database that covers 1995 through 2011. Our major
findings are twofold. First, the structural gravity equation performs extremely well for describing
bilateral trade in final goods and in intermediate inputs. Moreover, this continues to hold evenwhen we focus on a subset of countries in which intermediate inputs trade accounts for a large
and growing share of trade, namely ‘Factory Asia’.
Second, the gravity model may perform poorly due to model misspecification (i.e.,
exclusion of intranational trade) and/or sample selection issues even after the model takes into
account the dual nature of products. We demonstrate that the poor performance of the gravity
model is not attributable to the large trade flow of intermediate inputs, which supports thecontinued use of the model as these trade flows continue to grow in importance worldwide.

29

Heid et al. (2017) present a new strategy for using the structural gravity model to estimate effects of nondiscriminatory trade policies while also controlling for multilateral resistance.

17

References
Aichele, Rahel and Inga Heiland (2018) “Where Is the Value Added? Trade Liberalization and
Production Networks”, Journal of International Economics, 115: 130-144.
Anderson, James E., Mario Larch and Yoto V. Yotov (2018) “General Equilibrium Analysis
with PPML”, The World Economy, 41(10): 2750-2782.
Anderson, James E. (2011) “The Gravity Model”, Annual Review of Economics, 3: 133-160.
Anderson, James E. and Eric van Wincoop (2003) “Gravity with Gravitas: A Solution to theBorder Puzzle”, American Economic Review, 93(1): 170-192.
Armington, Paul S. (1969) “A Theory of Demand for Products Distinguished by Place ofProduction”, International Monetary Fund Staff Papers, 16(1): 159-178.
Athukorala, Prema-chandre and Nobuaki Yamashita (2006) “Production Fragmentation andTrade Integration”, North American Journal of Economics and Finance, 17(3): 233-256.
Baldwin, Richard E. and Daria Taglioni (2014) “Gravity Chains: Estimating Bilateral Trade
Flows When Parts and Components Trade Is Important”, Journal of Banking and
Financial Economics, 2(2): 61-82.
Baltagi, Badi H., Peter Egger and Michael Pfaffermayr (2015) “Panel Data Gravity Models ofInternational Trade”, in Badi H. Baltagi (ed.), The Oxford Handbook of Panel Data,
Oxford University Press.
Baier, Scott L. and Jeffrey H. Bergstrand (2009) “Bonus vetus OLS: A Simple Method forApproximating International Trade-Cost Effects Using the Gravity Equation”, Journal ofInternational Economics, 77(1): 77-85.
Borchert, Ingo and Yoto V. Yotov (2017) “Distance, Globalization, and International Trade”,
Economics Letters, 153: 32-38.
Bosquet, Clément and Hervé Boulhol (2014) “Applying the GLM Variance Assumption to
Overcome the Scale-Dependence of the Negative Binomial QGPML Estimator”,
Econometric Review, 33(7): 772-784.
Chaney, Thomas (2008) “Distorted Gravity: The Intensive and Extensive Margins ofInternational Trade”, American Economic Review, 98(4): 1707-1721.
Chen, Hogan, Matthew Kondratowicz and Kei-Mu Yi (2005) “Vertical Specialization and Three
Facts about U.S. International Trade”, North American Journal of Economics andFinance, 16(1): 35-39.
Costinot, Arnaud, Dave Donaldson and Ivana Komunjer (2012) “What Goods Do Countries
Trade? A Quantitative Exploration of Ricardo’s Ideas”, Review of Economic Studies,
79(2): 581-608.

18

de Mello-Sampayo, Felipa (2017) “Competing-destinations Gravity Model Applied to Trade inIntermediate Goods”, Applied Economics Letters, 24(19): 1378-1384.
Eaton, Jonathan and Samuel Kortum (2002) “Technology, Geography, and Trade”,
Econometrica, 70(5): 1741-1779.
Egger, Peter H. and Mario Larch (2008) “Interdependent Preferential Trade Agreement
Memberships: An Empirical Analysis”, Journal of International Economics, 76(2): 384399.
Egger, Peter and Michael Pfaffermayr (2003) “The Proper Panel Econometric Specification of
the Gravity Equation: A Three-way Model with Bilateral Interaction Effects”, Empirical
Economics, 28(3): 571-580.
Feenstra, Robert C. (2016) Advanced International Trade: Theory and Evidence, 2nd edition,
Princeton University Press.
Hayakawa, Kazunobu (2014) “Bilateral Tariff Rates and International Trade: Finished Goods
versus Intermediate Goods”, International Economics and Economic Policy, 11(3): 353370.
Head, Keith and Thierry Mayer (2014) “Gravity Equations: Workhorse, Toolkit, andCookbook”, Gopinath, Gita, Elhanan Helpman and Kenneth Rogoff (eds.), Handbook ofInternational Economics, North-Holland, Oxford, UK, Vol. 4: 131-195.
Heid, Benedikt, Mario Larch and Yoto V. Yotov (2017). “Estimating the Effects of Nondiscriminatory Trade Policies within Structural Gravity Models”, CESifo Working Paper,
No. 6735, Center for Economic Studies and Ifo Institute (CESifo), Munich.
Hummels, David, Jun Ishii and Kei-Mu Yi (2001) “The Nature and Growth of Vertical
Specialization in World Trade",” Journal of International Economics, 54(1): 75-96.
Ito, Tadashi and Pierre-Louis Vézina (2016) “Production Fragmentation, Upstreamness, andValue Added: Evidence from Factory Asia 1990-2005”, Journal of the Japanese andInternational Economies, 42: 1-9.
Jang, Sujin and E. Young Song (2017) “Gravity with Intermediate Goods Trade”, East Asian
Economic Review 21(4): 295-315.
Johnson, Robert C. and Guillermo Noguera (2012) “Accounting for Intermediates: Production
Sharing and Trade in Value Added”, Journal of International Economics, 86(2): 224-236.
Kaplan, Lennart C., Tristan Kohl and Inmaculada Martinez-Zarzoso (2018). “Supply-Chain
Trade and Labor Market Outcomes: The Case of the 2004 European Union
Enlargement”, Review of International Economics, 26: 481-506.

19

Kimura, Fukunari, Yuya Takahashi, and Kazunobu Hayakawa (2007) “Fragmentation and Parts
and Components Trade: Comparison between East Asia and Europe”, North American
Journal of Economics and Finance, 18(1): 23-40.
Rose, Andrew K. (2017). “Why Do Estimates of the EMU Effect on Trade Vary So Much?”,Open Economies Review, 28: 1-18.
Santos Silva, J. M. C. and Silvana Tenreyro (2006) “The Log of Gravity”, Review of Economics
and Statistics, 88(4): 641-658.
Santos Silva, J. M. C., Silvana Tenreyro and Frank Windmeijer (2015) “Testing Models for NonNegative Data with Many Zeros”, Journal of Econometric Methods, 4(1): 29-46.
Taniguchi, Mina (2019) “The Effect of An Increase in Imports from China on Local Labor
Markets in Japan,” Journal of the Japanese and International Economies, 51: 1-18.
Tinbergen, Jan (1962) Shaping the World Economy: Suggestions for an International Economic Policy,
Twentieth Century Fund, New-York.

Wang, Zhi, Shang-Jin Wei, Xinding Yu and Kunfu Zhu (2018) “Re-examining the Effects of
Trading with China on Local Labor Markets: A Supply Chain Perspective”, NBER
Working Paper No. 24886.
Yotov, Yoto V. (2012) “A Simple Solution to the Distance Puzzle in International Trade”,
Economics Letters, 117(3): 794-798.
Yotov, Yoto V., Roberta Piermartini, José-Antonio Monteiro and Mario Larch (2016) An
Advanced Guide to Trade Policy Analysis: The Structural Gravity Model, UNCTAD and
WTO, Geneva.

20

.86

.55

.88

.9
.92
Correlation

Share of Intermediate Inputs
.6
.65

.94

.7

.96

Figure 1. Share of Intermediate Inputs to Total Imports
and Correlation between the Imports of Intermediate Inputs and Final Goods, 1995-2011

1995

1997

1999

2001

2003

2005

2007

2009

All industry (left)

Tradables (left)

Manufacturing (left)

Correlation (right)

2011

Notes: Figure indicates the import share of intermediate inputs to total imports and the correlation between the imports ofintermediate inputs and those of final goods. For sources, see main text.

Table 1. Regression Results: Manufacturing and All Countries

Origin country's GDP (log value)
Destination country's GDP (log value)
Distance (log value)
RTA dummyCommon official language dummyColonial relationship dummy
Contiguity dummy
Number of observations
R-squared
Intranational trade
Fixed effects
Year
Origin and destination
Origin-year and destination-year

(1)

(2)

0.604***[0.076]
0.546***[0.071]-0.680***[0.024]0.473***[0.051]
0.242***[0.069]-0.039[0.089]0.389***[0.061]
67,3600.99639
Yes(3)
Intermediate
inputs
0.566***[0.071]0.646***[0.072]-0.773***[0.022]0.360***[0.050]0.225***[0.072]-0.006[0.087]0.343***[0.055]67,3600.99392
Yes

All goods

Final goods

0.582***[0.067]0.627***[0.068]-0.737***[0.022]
0.393***[0.049]0.233***[0.071]-0.019[0.087]0.366***[0.057]67,3600.99511
Yes
Yes
Yes
No

Yes
Yes
No

Yes
Yes
No

(4)

(5)

(6)
Intermediate
inputsAll goods

Final goods-0.740***[0.022]0.385***[0.049]0.233***[0.070]-0.020[0.086]
0.366***[0.057]67,3600.99943
Yes-0.686***[0.024]0.472***[0.052]0.252***[0.068]-0.041[0.088]0.382***[0.062]
67,3600.99836
Yes-0.776***[0.022]0.351***[0.049]0.222***[0.071]-0.006[0.086]
0.344***[0.055]67,3600.99964
Yes

No
No
Yes

No
No
Yes

No
No
Yes

Notes: We employ PPML to estimate the gravity model. ***, **, and * indicate statistically significant at 1%, 5% and 10% levels,
respectively. Figures in brackets are standard errors, clustered by country pairs. R-squared is computed as the correlation between
actual and fitted values.

Table 2. Regression Results: Manufacturing and Factory Asia

Origin country's GDP (log value)
Destination country's GDP (log value)
Distance (log value)
RTA dummyCommon official language dummyColonial relationship dummy
Contiguity dummy
Number of observations
R-squared
Intranational trade
Fixed effects
Year
Origin and destination
Origin-year and destination-year

(1)

(2)

0.509***[0.085]0.584***[0.087]-0.636***[0.028]0.094[0.107]-0.027[0.156]-0.187[0.120]-0.410***[0.128]831
0.99976
Yes(3)
Intermediate
inputs
0.364***[0.130]0.614***[0.129]-0.607***[0.038]0.143[0.119]0.126[0.179]-0.267**[0.134]-0.205[0.134]831
0.99745
Yes

All goods

Final goods

0.409***[0.110]0.631***[0.109]-0.615***[0.033]0.119[0.111]
0.079[0.173]-0.252**[0.126]-0.268**[0.126]831
0.99854
Yes
Yes
Yes
No

Yes
Yes
No

Yes
Yes
No

(4)

(5)

(6)
Intermediate
inputsAll goods

Final goods-0.622***[0.031]
0.143[0.101]0.087[0.172]-0.256**[0.122]-0.275**[0.125]831
0.99997
Yes-0.645***[0.024]0.147[0.091]-0.006[0.157]-0.191*[0.114]-0.390***[0.127]831
0.99996Yes-0.615***[0.034]0.180*[0.104]0.144[0.177]-0.276**[0.131]-0.210[0.131]831
0.99996
Yes

No
No
Yes

No
No
Yes

No
No
Yes

Notes: We employ PPML to estimate the gravity model. ***, **, and * indicate statistically significant at 1%, 5% and 10% levels,
respectively. Figures in brackets are standard errors, clustered by country pairs. R-squared is computed as the correlation between
actual and fitted values. Common currency dummy is not included because none of the country is applicable.

Table 3. Regression Results: Manufacturing and All Countries without Intranational Trade

Origin country's GDP (log value)
Destination country's GDP (log value)
Distance (log value)
RTA dummyCommon official language dummyColonial relationship dummy
Contiguity dummy
Number of observations
R-squared
Intranational trade
Fixed effects
Year
Origin and destination
Origin-year and destination-year

(1)

(2)

0.843***[0.048]0.880***[0.045]-0.729***[0.033]0.371***[0.056]
0.249***[0.069]-0.074[0.090]0.365***[0.063]
66,3710.91767No(3)Intermediate
inputs
0.744***[0.055]0.814***[0.051]-0.779***[0.031]0.323***[0.057]0.231***[0.072]-0.021[0.087]0.347***[0.058]66,3710.91450
No

All goods

Final goods

0.784***[0.048]0.846***[0.047]-0.758***[0.031]0.335***[0.055]0.238***[0.071]-0.041[0.087]0.359***[0.059]66,3710.91688
No
Yes
Yes
No

Yes
Yes
No

Yes
Yes
No

(4)

(5)

(6)
Intermediate
inputsAll goods

Final goods-0.764***[0.031]0.329***[0.058]0.247***[0.069]-0.047[0.087]0.348***[0.059]66,3710.93515No-0.731***[0.033]0.389***[0.059]0.266***[0.067]-0.077[0.089]0.347***[0.063]
66,371
0.94306No-0.787***[0.031]0.305***[0.059]0.236***[0.070]-0.029[0.086]
0.339***[0.058]66,3710.93202
No

No
No
Yes

No
No
Yes

No
No
Yes

Notes: We employ PPML to estimate the gravity model. ***, **, and * indicate statistically significant at 1%, 5% and 10% levels,
respectively. Figures in brackets are standard errors, clustered by country pairs. R-squared is computed as the correlation between
actual and fitted values.

Table 4. Regression Results: Manufacturing and Factory Asia without Intranational Trade

Origin country's GDP (log value)
Destination country's GDP (log value)
Distance (log value)
RTA dummyCommon official language dummyColonial relationship dummy
Contiguity dummy
Number of observations
R-squared
Intranational trade
Fixed effects
Year
Origin and destination
Origin-year and destination-year

(1)

(2)

All goods

Final goods

0.462***[0.096]0.689***[0.106]-0.212[0.154]-0.041[0.104]0.147[0.171]
0.072[0.167]0.299[0.276]714
0.94776
No0.703***[0.123]0.780***[0.127]-0.200[0.156]0.015[0.104]0.086[0.153]0.178[0.192]0.164[0.271]714
0.92735
No(3)Intermediate
inputs
0.373***[0.099]0.590***[0.112]-0.225[0.154]-0.047[0.114]0.172[0.177]0.035[0.167]0.348[0.275]714
0.94871
No

Yes
Yes
No

Yes
Yes
No

Yes
Yes
No

(4)

(5)

(6)
Intermediate
inputsAll goods

Final goods-0.274*[0.154]0.054[0.115]0.177[0.173]0.011[0.162]0.256[0.262]714
0.96365No-0.214[0.168]0.061[0.139]0.097[0.166]0.164[0.204]0.194[0.259]714
0.96832
No-0.278*[0.151]
0.033[0.109]0.199[0.176]-0.020[0.156]0.309[0.263]714
0.96430
No

No
No
Yes

No
No
Yes

No
No
Yes

Notes: We employ PPML to estimate the gravity model. ***, **, and * indicate statistically significant at 1%, 5% and 10% levels,
respectively. Figures in brackets are standard errors, clustered by country pairs. R-squared is computed as the correlation between
actual and fitted values.

Table 5: Summary of Gravity Model Tests
AuthorCountries

Industries

Baldwin-Taglioni(2014)All
Factory Asia

Manufacturing
Manufacturing

Include Intra-national
Trade?
No
No

All
Factory Asia
All
Factory Asia
EUSE Asia
Factory Asia
Factory Asia
Factory Asia w/o Japan
Factory Asia w/o Japan, w/China

Manufacturing
Manufacturing
Manufacturing
Manufacturing
Manufacturing
Manufacturing
TradableAll
Manufacturing
Manufacturing

Yes
Yes
No
No
Yes
Yes
Yes
Yes
Yes
Yes

Greaney-Kiyota (2019)

Gravity Model Works?

Table

Yes
No

BT 1
BT 2

Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
No
Yes

1
2
3
4
A2
A3
A4
A5
A6
A7

Notes: We use Baldwin and Taglioni's (2014) definition of Factory Asia (i.e., Indonesia, Japan, Malaysia, the Philippines, South Korea, Taiwan and
Thailand). The gravity model is assessed to "not" work when it produces insignificant and/or unusual size and/or distance coefficients.

Table A1. List of Countries
ISO
Code

Country name

OECD

ARG

Argentina

AUS

Australia*

AUT

Austria*

BEL

Belgium

*

BRA
BRN
BGR

Bulgaria

KHM

Cambodia

CAN

Canada

*

CHL

Chile

*

CHN

China

COL
CRI

EU

E&SE

Factory

ISO

Asia

Asia

Code

Country name

OECD

EU

LTU

Lithuania

LUX

Luxembourg*

MYS

Malaysia

*

MLT

Malta

BrazilMEX

MexicoBrunei Darussalam

MAR

Morocco

NLD

Netherlands*

NZL

New Zealand*

NOR

Norway

*

PER

Peru

PHL

Philippines

Colombia

POL

Poland*Costa Rica

PRT

Portugal*

HRV

Croatia

ROU

Romania

CYP

Cyprus*

RUS

Russian Federation

CZE

Czech Republic

*

*

SAU

Saudi Arabia

DNK

Denmark**

SGP

Singapore

EST

Estonia**

SVK

Slovak Republic

*

*

FIN

Finland**

SVN

Slovenia

*

*

FRA

France**ZAF

South Africa

*
*

*

*
*

*

*

KOR

South Korea

*

*

ESP

Spain

*

*

HKG

Hong Kong, China

SWE

Sweden**

HUN

Hungary**

CHE

Switzerland*

ISL

Iceland

*

TWN

IND

India

THA

IDN

Indonesia

TUN

Tunisia

IRL

Ireland*TUR

Turkey*ISR

Israel*GBR

United Kingdom

*

ITA

Italy

*

USA

United States

*

JPN

Japan

*

VNM

Viet Nam

LVA

Latvia

*

*

*

*

*

*

*

*

*

*

*

*

Germany***

Greece

*

Asia

*

GRC

*

Factory

Asia

*
*

DEU

*

E&SE

*

*

Taiwan**Thailand

*

*

*
*

*

Notes: EU includes 27 EU member countries before 2012. E&SE Asia includes 12 East and Southeast Asian countries. Factory
Asia includes seven countries that are focused by Baldwin and Taglioni (2014).

Table A2. Regression Results: Manufacturing and European Union
(1)

(2)

(3)

(4)

(5)

InterAll goods

Final goods

mediateAll goods

Final goods

inputsOrigin country's GDP (log value)
Destination country's GDP (log value)
Distance (log value)
RTA dummyCommon official language dummyColonial relationship dummy
Contiguity dummy(6)Intermediate
inputs

0.445***

0.529***

0.429***[0.109][0.128][0.118]0.512***

0.686***

0.413***[0.103][0.113][0.116]-1.159***-1.111***-1.189***-1.155***-1.108***-1.185***[0.059][0.064][0.060][0.060][0.065][0.060]3.232***

3.231***

3.252***

3.203***

3.209***

3.222***[0.421][0.454][0.422][0.422][0.457][0.424]0.354***

0.380***

0.334***

0.357***

0.383***

0.337***[0.117][0.122][0.118][0.117][0.123][0.118]

0.078

0.114

0.053

0.081

0.118

0.056

[0.094][0.102][0.092][0.095][0.105][0.093]0.095*

0.074

0.112**

0.097*

0.076

0.112**[0.052][0.055][0.053][0.052][0.055][0.053]

Number of observations

12,35812,35812,35812,35812,35812,358R-squared

0.99453

0.98926

0.99489

0.99915

0.995750.99946

Yes

Yes

Yes

Yes

Yes

Yes

Intranational trade
Fixed effects
Year

Yes

Yes

Yes

No

No

No

Origin and destination

Yes

Yes

Yes

No

No

No

Origin-year and destination-year

No

No

No

Yes

Yes

Yes

Notes: We employ PPML to estimate the gravity model. ***, **, and * indicate statistically significant at 1%, 5% and 10%
levels, respectively. Figures in brackets are standard errors, clustered by country pairs. R-squared is computed as thecorrelation between actual and fitted values.

Table A3. Regression Results: Manufacturing and East and Southeast Asia
(1)

(2)

(3)

(4)

(5)

InterAll goods

Final goods

mediateAll goods

Final goods

inputsOrigin country's GDP (log value)
Destination country's GDP (log value)
Distance (log value)
RTA dummyCommon official language dummyColonial relationship dummy
Contiguity dummy(6)Intermediate
inputs

0.518***

0.551***

0.506***[0.101][0.132][0.097]0.642***

0.508***

0.655***[0.101][0.130][0.097]-0.550***-0.507***-0.567***-0.553***-0.509***-0.571***[0.021][0.023][0.020][0.021][0.023][0.020]0.378***

0.378***

0.391***

0.408***

0.404***

0.436***[0.113][0.115][0.112][0.106][0.114][0.101]0.337*

0.349

0.329*

0.344*

0.366*

0.338*[0.201][0.217][0.191][0.192][0.209][0.182]-0.366-0.394-0.315-0.360-0.383-0.310[0.232][0.240][0.220][0.223][0.234][0.211]0.258**

0.430***

0.155

0.255**

0.409***

0.155[0.112][0.136][0.096][0.110][0.134][0.095]

Number of observations

2,431

2,431

2,431

2,431

2,430

2,431

R-squared

0.99885

0.99893

0.99860

0.99988

0.999700.99992

Yes

Yes

Yes

Yes

Yes

Yes

Intranational trade
Fixed effects
Year

Yes

Yes

Yes

No

No

No

Origin and destination

Yes

Yes

Yes

No

No

No

Origin-year and destination-year

No

No

No

Yes

Yes

Yes

Notes: We employ PPML to estimate the gravity model. ***, **, and * indicate statistically significant at 1%, 5% and 10%
levels, respectively. Figures in brackets are standard errors, clustered by country pairs. R-squared is computed as thecorrelation between actual and fitted values.

Table A4. Regression Results: Tradable Sectors and Factory Asia
(1)

(2)

(3)

(4)

(5)

InterAll goods

Final goods

mediateAll goods

Final goods

inputsOrigin country's GDP (log value)
Destination country's GDP (log value)
Distance (log value)
RTA dummyCommon official language dummyColonial relationship dummy
Contiguity dummy
Number of observations
R-squared
Intranational trade(6)
Intermediate
inputs0.440***

0.536***

0.400***[0.093][0.084][0.106]0.594***

0.587***

0.585***[0.099][0.085][0.117]-0.583***-0.715***-0.516***-0.587***-0.717***-0.522***[0.033][0.027][0.037][0.031][0.026][0.035]

0.165

0.131

0.162

0.181*

0.121

0.196**[0.102][0.109][0.107][0.093][0.103][0.092]0.076-0.012

0.118

0.082

-0.0250.136[0.200][0.151][0.228][0.199][0.155][0.225]-0.356**-0.277**-0.379**-0.358**-0.268**-0.385**[0.142][0.118][0.159][0.140][0.116][0.157]-0.269-0.475***-0.153-0.272-0.466***-0.154[0.179][0.130][0.214][0.177][0.132][0.211]

831

831

831

831

831

831

0.998710.99972

0.99765

0.999960.99997

0.99995

Yes

Yes

Yes

Yes

Yes

Yes

Fixed effects
Year

Yes

Yes

Yes

No

No

No

Origin and destination

Yes

Yes

Yes

No

No

No

Origin-year and destination-year

No

No

No

Yes

Yes

Yes

Notes: Tradable sectors include agriculture, mining and manufacturing industries. We employ PPML to estimate thegravity model. ***, **, and * indicate statistically significant at 1%, 5% and 10% levels, respectively. Figures in brackets
are standard errors, clustered by country pairs. R-squared is computed as the correlation between actual and fitted
values. Common currency dummy is not included because none of the country is applicable.

Table A5. Regression Results: All Industry and Factory Asia
(1)

(2)

(3)

(4)

(5)

InterAll goods

Final goods

mediateAll goods

Final goods

inputsOrigin country's GDP (log value)
Destination country's GDP (log value)
Distance (log value)
RTA dummyCommon official language dummyColonial relationship dummy
Contiguity dummy
Number of observations
R-squared
Intranational trade(6)
Intermediate
inputs

0.432***

0.426***

0.435***[0.081][0.094][0.096]

0.586***

0.552***

0.599***[0.082][0.094][0.100]-0.630***-0.726***-0.576***-0.632***-0.729***-0.580***[0.030][0.025][0.034][0.029][0.023][0.033]

0.134

0.037

0.147

0.145

0.052

0.168*[0.100][0.106][0.105][0.095][0.101][0.096]

0.063

0.053

0.075

0.070

0.058

0.088

[0.190][0.132][0.224][0.188][0.134][0.222]-0.322**-0.201*-0.363**-0.325**-0.200*-0.369**[0.136][0.107][0.154][0.134][0.105][0.153]-0.306*-0.312***-0.268-0.307*-0.303***-0.268[0.164][0.111][0.205][0.163][0.112][0.204]

831

831

831

831

831

831

0.99989

0.99993

0.99934

0.99999

1.00000

0.99998

Yes

Yes

Yes

Yes

Yes

Yes

Fixed effects
Year

Yes

Yes

Yes

No

No

No

Origin and destination

Yes

Yes

Yes

No

No

No

Origin-year and destination-year

No

No

No

Yes

Yes

Yes

All industry includes agriculture, mining, manufacturing and services industries. Notes: We employ PPML to estimate thegravity model. ***, **, and * indicate statistically significant at 1%, 5% and 10% levels, respectively. Figures in brackets
are standard errors, clustered by country pairs. R-squared is computed as the correlation between actual and fitted
values. Common currency dummy is not included because none of the country is applicable.

Table A6. Regression Results: Manufacturing and Factory Asia, Dropping Japan
(1)

(2)

(3)

(4)

(5)

InterAll goods

Final goods

mediateAll goods

Final goods

inputsOrigin country's GDP (log value)
Destination country's GDP (log value)
Distance (log value)
RTA dummyCommon official language dummy
Contiguity dummy
Number of observations
R-squared
Intranational trade(6)
Intermediate
inputs-0.0300.120-0.069[0.144][0.145][0.154]0.681***

0.899***

0.626***[0.144][0.146][0.154]-0.790***-0.928***-0.703***-0.792***-0.925***-0.707***[0.125][0.114][0.131][0.122][0.111][0.127]1.512

2.476***

0.851

1.513

2.428***

0.872[1.042][0.933][1.079][1.006][0.905][1.047]0.309***

0.364***

0.283**

0.313***

0.358***

0.290**[0.114][0.133][0.124][0.113][0.133][0.122]-0.388*-0.651***-0.224-0.392**-0.637***-0.231[0.203][0.187][0.213][0.198][0.179][0.208]

610

610

610

610

610

610

0.99605

0.99597

0.995030.99993

0.999750.99994

Yes

Yes

Yes

Yes

Yes

Yes

Fixed effects
Year

Yes

Yes

Yes

No

No

No

Origin and destination

Yes

Yes

Yes

No

No

No

Origin-year and destination-year

No

No

No

Yes

Yes

Yes

Notes: We employ PPML to estimate the gravity model. ***, **, and * indicate statistically significant at 1%, 5% and 10%
levels, respectively. Figures in brackets are standard errors, clustered by country pairs. R-squared is computed as thecorrelation between actual and fitted values. Common currency dummy is not included because none of the country isapplicable.

Table A7. Regression Results: Manufacturing and Factory Asia, Dropping Japan and Adding China
(1)

(2)

(3)

(4)

(5)

InterAll goods

Final goods

mediateAll goods

Final goods

inputsOrigin country's GDP (log value)
Destination country's GDP (log value)
Distance (log value)
RTA dummyCommon official language dummy
Contiguity dummy
Number of observations
R-squared
Intranational trade(6)
Intermediate
inputs

0.472***

0.432***

0.485***[0.091][0.111][0.091]0.581***

0.557***

0.586***[0.095][0.116][0.096]-0.592***-0.582***-0.592***-0.595***-0.590***-0.595***[0.018][0.022][0.017][0.019][0.023][0.018]0.674***

0.802***

0.648***

0.727***

0.868***

0.710***[0.080][0.076][0.088][0.064][0.042][0.073]0.435***

0.469***

0.411**

0.414***

0.484***

0.382**[0.158][0.179][0.161][0.147][0.166][0.148]0.138-0.002

0.184

0.123

-0.0320.163[0.135][0.174][0.125][0.128][0.164][0.117]

831

831

831

831

831

831

0.99955

0.999220.999510.99998

0.99994

0.99998

Yes

Yes

Yes

Yes

Yes

Yes

Fixed effects
Year

Yes

Yes

Yes

No

No

No

Origin and destination

Yes

Yes

Yes

No

No

No

Origin-year and destination-year

No

No

No

Yes

Yes

Yes

Notes: We employ PPML to estimate the gravity model. ***, **, and * indicate statistically significant at 1%, 5% and 10%
levels, respectively. Figures in brackets are standard errors, clustered by country pairs. R-squared is computed as thecorrelation between actual and fitted values. Common currency dummy is not included because none of the country isapplicable.