OSIPP Discussion Paper: DP-2013-E-002

Prospects for Trade in Intermediates and Trade in Services:
What Does the Gravity Model of Bilateral Trade Tell Us?*
February 22, 2013

Haruka Yane
Graduate student, Osaka School of International Public Policy, Osaka University

Abstract: This study uses product-level international bilateral trade panel data for the period
1995 to 2009, covering 40 countries from the recently released World Input-Output Database(WIOD). Constructing this dataset into a standard gravity model, this paper aims to compare
the magnitude of coefficients across two pairs of equations with different dependent variables:
trade in intermediates in comparison with trade in final products, and total trade in goods as
opposed to total trade in services. So as to investigate these differences further, this study also
analyzes the models by sector. This paper applies the sample selection model in order toaccount for the zero trade flows. Estimation results confirm that trading partners in East Asia
engage much more in both trade in intermediate inputs and assembled final products, especiallythe former, compared to EU and NAFTA blocs. Trade in services presents remarkable resilienceto the current financial crisis. Intermediate services trade, in particular, is affected less by trade
costs and market size. By liberalizing trade and increasing economic freedom, there will be ahighly significant, positive effect on nearly all kinds of trade flows.
Keywords: Trade in intermediates, services trade, gravity model
JEL classification: F14* I would like to thank Professors Hiro Lee and Tsunehiro Otsuki for their helpful comments, and Susan Stone,

Senior Trade Policy Analyst at the Organisation for Economic Co-operation and Development (OECD), for
introducing me to the World Input-Output Database. All remaining errors are my own.

1.

Introduction

This paper’s main goal is to see the differences between what determines theamount of trade in intermediate inputs and trade in final products, and between
determinants of goods trade and services trade. For instance, what factors affect trade inintermediates more as opposed to trade in final products, and do trade costs have asignificantly greater impact on trade in goods or trade in services? It is important todistinguish these trade flows because first, purchasers for intermediate inputs are different
from those for end-use final products. It is usually the firms who buy and use intermediate
inputs for further production, and final products are sold to end-users, often consumers.1
Second, goods and services also have distinctive characteristics2, for instance, services
have an intangible nature, and production and consumption must occur simultaneously(Kimura and Lee, 2006). Therefore, although they are all international trade, theirdeterminants should vary.
Figures 1-4 show how trade in intermediates and trade in services have increased
their prominence in world trade during the past decade. First, Figure 1 shows theincreasing trend in the world’s ratio of foreign to domestic inputs. Firms are increasingly
purchasing and using intermediate inputs from abroad. According to Miroudot, Lanz and
Ragoussis (2009), trade in intermediates account for about 56% of world trade in case ofgoods and 73% in case of services. Figure 2 depicts the increasing share of world’sintermediates trade in total trade, in contrast with the stagnant share of world’s final
products trade in total trade. In addition, you can see how intermediates trade share in total
trade in East Asia is exceptionally high, leading the increasing trend in trade inintermediate products.
Second, trade in services shows higher growth compared to trade in goods, as you
can see from Figure 3. Figure 4 reveals how trade in services has been representing ahigher share of total trade than trade in goods. Moreover, what is striking is that both
figures depict that services trade seems to have been affected much less by the financial
crisis compared to goods trade.

1

While final products highly rely on advertisement and promotion aimed for final consumers,
intermediate inputs trade could be determined more by other factors, such as preferences, customs andhabits. (Miroudot et al., 2009)2

For more on trade in services and the four modes of supply, see the document for General Agreement
on Trade in Services (GATS), available on the World Trade Organization website(http://www.wto.org/english/tratop_e/serv_e/serv_e.htm).

1

Innovations such as the internet and container shipping have revolutionized tradeand value chains. Thanks to these developments, we are seeing more and more of trade inintermediates and trade in total services, even seemingly overpowering the traditional tradein final products and trade in total goods. There has been, however, comparatively muchless attention paid to trade in intermediates and trade in services, mostly due to the lack ofstatistics. Therefore, this present study will try to present new additional evidence on the
different implications for different forms of trade, by using the recently released World
Input-Output Database (WIOD). This is a set of product-level international input-output
tables with bilateral trade flows covering the period of 15 consecutive years.
This study uses the framework of gravity model of bilateral trade to analyze thedifferences in what determines the amount of trade 1) between intermediates and final
products, and 2) and between goods and services. In order to account for the zero tradeflows between countries, estimation of this model follows a sample selection bias model by
the maximum likelihood estimator (see Linders and de Groot, 2006; Cameron and Trivedi,2009).
The results show that trade in intermediate inputs is more sensitive to trade costs
compared to trade in final products. Consistent with trade statistics, trading partners in East
Asia do tend to engage much more in both trade in intermediate inputs and trade inassembled final products, especially the former, compared to EU and NAFTA blocs. Tradein services is much more resilient to the current financial crisis compared to trade in goods.
This may be because services production and trade are affected less by external trade costs
and market size. One important characteristic of services trade to note is that sharing theofficial language is a big factor for two countries to engage in trade.
Further analysis by sector reveals that economic freedom has a highly significant,positive effect on nearly all kinds of trade flows. By liberalizing trade and increasing
economic freedom, agricultural and services exports and manufacturing imports are likely
to rise more than agricultural and services imports. Moreover, when compared toagricultural and manufacturing sectors, services trade, especially intermediate services
trade, showed the same characteristics mentioned above.
In this paper I will assess the differences between intermediate and final products(includes both goods and services products) trade, as well as differences between trade inservices and trade in goods. The rest of the paper is organized as follows. Section II
provides background and reviews the literature. Section III describes the data, gravity
model for international trade, and estimation techniques to account for observations with2

zero trade flows. Section IV discusses the estimation results of sample selection bias modelby the maximum likelihood estimator. Section V investigates the results from Section IV
further by estimating the model disaggregated into three sectors. Section VI concludes.
2.

Literature Review

2.1

Background

Trade in Intermediates‘Global value chains’ have rapidly emerged as production processes have become
more geographically fragmented since the 1990s. International trade and production are
increasingly structured around these ‘global value chains’. A ‘value chain’ can be simply
defined as the full range of activities that firms and workers carry out in order to bring a
product from its conception to its end use and beyond (see Gereffi and Fernandez-Stark,2011). The term ‘global’ comes from the fact that these activities are increasingly spreadover multiple countries. The fragmentation of value chains has been motivated by sourcing
intermediate inputs from more cost-efficient producers, foreign or domestic, in order toenhance efficiency. As a result, domestic production has been increasingly relying on
foreign intermediate inputs, as shown in Figure 1.
With this increasing presence of trade in intermediates, there continues to be arising demand for comprehensive and trustable data on the various dimensions of theinternationalization of production networks. The increasing fragmentation of production
across countries, however, has led current global production networks to be multi-country
and back-and-forth in nature (Koopman, Powers, Wang and Wei, 2011), making it difficult
to capture in statistics.
All official trade statistics are measured in gross terms, which include bothintermediate inputs and final products. They record the value of intermediate inputs tradedalong the value chain, crossing international borders several times, back and forth, forfurther processing. These trade flows are thus counted multiple times. Consequently, thecountry of the final producer appears as creating most of the value of goods and services
traded, giving the misleadingly wrong picture, overlooking the role of countries providing
inputs upstream in the global value chain. For example, an exported good may requireconsiderable intermediate inputs from domestic manufacturers, who, in turn, requireconsiderable intermediate imports, and so, much of the revenue, or value-added, fromselling the exported good may accrue abroad to reflect purchases of intermediate imports3

used in production.
Trade in Services
Globalization is no longer only about goods; it increasingly involves trade inservices. Many service activities are becoming internationalized. Rapid advances in
information and communications technology (ICT) and infrastructure growth haveincreased the tradability of many service activities, thereby facilitating the sourcing ofservices from abroad. In addition, services trade liberalization has reduced regulatory
barriers in key sectors of the global logistics chain, such as transport, finance andtelecommunications. Business services, for example, are now an integral part of the global
value chain.
In spite of this increasing prominence of trade in services, however, much attention
has been focused on the impact of relevant factors on trade in goods. This is partly due tothe unfortunate fact that official statistical data do not provide much detailed information
on services trade, compared to the goods sector, where they have detailed and timely data
available for a broad range of countries. In contrast, the quality of bilateral services trade
statistics by industry or product is unsurprisingly very low, with many missing
observations.
2.2

Former Empirical Studies

As you can see, there has been a recent rise in attention to both trade inintermediate inputs and trade in services. Yet research has been hindered by the limited
availability of reliable and adequate statistics. Coming up with a methodology todifferentiate between intermediate and final products, as well as measuring trade inservices, has been challenging.
Due to these limitations in the international trade data, there have only been ahandful of empirical studies on trade in intermediate inputs or trade in services. The recent
development (albeit slightly) in data quality and availability, however, have helped boost
the number of studies. This is all thanks to the attempts to devise methods of measuring
value-added trade in the empirical literature. Daudin, Rifflart and Schweisguth (2011) andKoopman et al. (2011) are among the first to explicitly refer to a measurement of trade invalue-added using an empirical framework. These studies rely on an estimatedInter-Country Input-Output (ICIO) table based on the GTAP database to calculate tradeflows in value-added. They proportionally allocate gross trade flows into intermediate and4final goods and distribute across users. Both show that countries and sectors differ widelyin their ratio of value-added to gross trade.
Daudin et al. (2011) identify “who produces what and for whom” by reallocating
the value-added contained in final goods to each country participating in their production.
The authors calculate the share of exports used as inputs to further exports and thedomestic content of imports (that is, domestic value-added that comes back to the country
through intermediates originally exported and re-imported within more processed
products). Koopman et al. (2011) allow two-way trade in intermediates (that is, eachcountry can import intermediate inputs, add value, and then export semi-finished goods toanother country to produce final goods). They successfully provide a full decomposition ofvalue-added exports in a single conceptual framework that encompasses all the previous
measures.
One of the recent prominent empirical works is Miroudot et al. (2009), where they
analyze trade in intermediate goods and services, comparing intermediates trade and final
products trade across goods and services industries, using data at four- to five-year
intervals (mostly years 1995, 2000 and 2005). They use disaggregate trade data at theindustry level. This present paper mainly follows their study because they also make acomparison between goods industry and services industry.
As for research focusing on trade in services, Kimura and Lee (2006) use astandard–type gravity equation to assess the differences between trade in services and tradein goods, utilizing bilateral trade data for 26 OECD member countries for years 1999 and2000. Fukao and Ito (2003) estimate gravity equations to test whether Japan’s market forservices is more closed for establishment transactions, using data on U.S. services exports,
for years 1992 and 1997.
All these existing empirical studies, however, have limitations that this study seeks
to overcome. Former empirical studies either used aggregate trade data, and/or data at four
to five-year intervals. The present study contributes to the intermediates trade and services
trade literature because unlike previous research, this study uses a panel dataset: annual
data from 1995 to 2009 and trade data disaggregated at the product-level. This study also
contributes to the literature by conducting empirical analyses for both trade inintermediates and trade in services.

5

3.

Methodology

3.1

Data

This study uses trade data from the World Input-Output Database (WIOD)3, which
was released for the general public in April 2012. The international supply and use tablecovers annual time-series data from 1995 to 2009 for 40 countries4. In order to maximize
observations for trade in services, trade data broken down by different products were used,instead of those classified by industries5. For trade in services, I strictly followed andincluded all products covered by the General Agreement on Trade in Services6. As a result,
this dataset provides this analysis with a good coverage balance between goods andservices trade.7 All intermediate, final and total goods and services trade data are classified
into 59 products, based on Statistical Classification of Products by Activity (CPA). The
classification of products into goods and services sectors is shown in Appendix Table A.1.
In addition to its disaggregate, product-level data characteristic, the advantage ofthis new input-output database is that it covers a period of 15 consecutive years, whereas
most former studies had to rely on data at five-year intervals more or less (Baier andBergstrand, 2007; Johnson and Noguera, 2012).
In order to approximate non-policy trade costs in a standard gravity model, thisstudy uses proxies discussed in Johnson and Noguera (2012), which are distance, common
language, adjacency (common borders) and colonial links. These data were obtained fromCEPII’s database8. Gross Domestic Product (GDP) of trading partners was used to accountfor their size of markets, or demand for goods and services. This study uses data on GDP,
3

This new World Input-Output Database is available at http://www.wiod.org/database/index.htm.
The core of the database is a set of harmonized national supply and use tables, linked together withbilateral trade data in goods and services. These two sets of data are then integrated into a world
input-output table. See Timmer (2012) for the detailed framework and calculations.
4

Appendix Table A.2 depicts country coverage by continent.

5

The WIOD classifies 59 products (based on CPA) whereas for industries they only have 35 categories(based on NACE rev.1 (ISIC rev.2) classifications).

6

The GATS services sectoral classification list is available at
http://www.wto.org/english/tratop_e/serv_e/serv_e.htm or
http://www.wto.org/english/tratop_e/serv_e/mtn_gns_w_120_e.doc
7

52.5% of the data are on goods trade and 47.5% are on services trade.

8

CEPII databases are available at http://www.cepii.fr/anglaisgraph/bdd/bdd.htm
This study uses contiguity indicator, common colonial origin indicator and common official language
indicator from the CEPII Gravity Dataset. Data on distance, measured as the simple distance between
the capitals in the two countries, is from CEPII GeoDist.

6

GDP per capita and population mainly from the International Monetary Fund (IMF)’s
World Economic Outlook (WEO) database9. Descriptive statistics for continuous variables
are shown in Table 1.
Furthermore, Kimura and Lee (2006) add Economic Freedom of the World (EFW)
index, published by the Fraser Institute of Canada since 1996, to their gravity equation.
This index measures the degree of economic freedom in five major areas. Due to thisindex’s limitation regarding year coverage, this study uses Index of Economic Freedom10
from the Heritage Foundation instead.
For exclusion restrictions in the Heckman sample selection models, this study
follows Helpman, Melitz and Rubinstein (2008), using country-level data on regulation
costs of firm entry, collected and analyzed by Djankov, La Porta, Lopez-de-Silanes, andShleifer (2002). These entry costs are measured by their effects on the number of days,
number of legal procedures and the relative cost (as a percentage of GDP per capita)
needed for an entrepreneur to legally start operating a business.
3.2

Model

This study estimates bilateral trade using a standard gravity model, initially
proposed by Tinbergen (1962). Gravity equations have been favored in trade literature
mainly because of their high explanatory power, empirically succeeding at predicting
bilateral trade flows. However, the model has been criticized for lacking respectable
theoretical foundations, causing it to suffer from incorrect specifications. Anderson andvan Wincoop (2003) developed a solution by augmenting the traditional equation withmultilateral resistance terms (for example, calculating remoteness indices, or adding
exporter and importer fixed effects), adding strong theoretical foundations to the model.
Feenstra (2004) thus explains that gravity equation has finally succeeded empirically in thefield of international trade.
9

IMF’s World Economic Outlook Database October 2012 is available at
http://www.imf.org/external/pubs/ft/weo/2012/02/weodata/index.aspx
Due to missing values, I used data from the World Bank’s World Development Indicators(http://databank.worldbank.org/ddp/home.do?Step=12&id=4&CNO=2)
for Lithuania during the period 1995-1998. The values for GDP, GDP per capita and population were all
exactly the same for Lithuania in 1999 in both databases.
10

Index of Economic Freedom is available at http://www.heritage.org/index/
Aiming to measure the degree of economic freedom in the world's nations, it is a series of 10 economic
measurements created by the Heritage Foundation and the Wall Street Journal in 1995. See Beach andKane (2008) and Miller and Kim (2012).

7

Inspired by Sir Isaac Newton’s work on Law of Universal Gravitation11, for which
he published a paper in 1687, Tinbergen (1962) linked the functional form to international
trade flows, predicting a gravity relationship for trade flows analogous to Newton’s law. It
holds that the bilateral trade flows between two countries are directly proportional to the
trading countries’ respective economic sizes, and inversely related to trade costs. Tradecosts can be proxied by the geographical distance12 between the two trading countries,
while their size of the market can be proxied using their respective GDPs. Therefore, inother words, the underlying assumption of the gravity model of bilateral trade is that tradeflows should increase as a trading partner’s GDP rises, and should decrease if distance
between a trading partner is farther.
Following Miroudot et al. (2009)’s gravity regression model, the standard gravity
equation to be estimated here is:
/
/

where
is the (natural log of) bilateral imports of total, intermediate or final goods
and/or services product p, between country i and country j in year t;
is the natural log of importer country’s GDP;is the natural log ofexporter country’s GDP;is the natural log of importer country’s GDP per capita;
is the natural log of exporter country’s GDP per capita;
is natural log of the geographic distance between the capitals of country iand country j;refers to a dummy variable indicating whether the country pairs share acommon land border;
11

A calculator based on this law can be found here: http://www.pythia.com.ar/?id=gravlaw

12

Miroudot and Ragoussis (2009) add that in addition to transport costs, distance, used as a proxy for
trade costs, also captures regulatory differences as well as cultural differences between countries.

8refers to a dummy variable indicating whether the country pairs share acommon official language;refers to a dummy variable indicating whether the country pairs ever hadcolonial ties;
is natural log of the overall score of the Index of Economic
Freedom for country i in year t;
is natural log ofthe overall score of the Index of Economic Freedom for country j in year t;
takes the value 1 if both countries are members of the European Union (EU);takes the value 1 if both countries are members of the Association of Southeast Asian
Nations (ASEAN) plus China, Japan, Korea and Taiwan;
takes the value 1 if both countries are members of North American Free Trade
Agreement (NAFTA);= year fixed effects;= product fixed effects; i = importer (reporter/destination)

country subscript; j = exporter (partner /origin) country subscript; p = product subscript;
and t = year subscript.
Based on this standard gravity equation, this study will pool across products13. The
dependent variable is bilateral trade in (1) intermediate goods and services, (2) final goods
and services, (3) total goods, and (4) total services, respectively, with the same regressors.
This study follows Miroudot et al. (2009) and Kimura and Lee (2006) with regard to using
the same gravity equation framework as goods trade for services trade.
This paper estimates import equations because the dataset consists of the same setof home and partner countries: exports are defined as mirror flows from imports (i.e.,values of exported products from country j to country i are assumed to be equal to thevalues of imported products of country i from country j) in the WIOD trade data this study
uses (Timmer, 2012).
In addition, this study adds Index of Economic Freedom, following Kimura andLee (2006), to measure restrictiveness and examine the effect of economic freedom oninternational trade. This index scores countries on 10 factors: business freedom, trade
freedom, monetary freedom, government size/spending, fiscal freedom, property rights,
investment freedom, financial freedom, freedom from corruption, and labor freedom.
13

For each importer-exporter country pair in a given year, 59 products for both trade in intermediates
and final products; 31 products for trade in goods, and 28 products for trade in services.

9Following Frankel (1997), I have also included regional binary variables for each year totest the effects of membership in a common trading bloc. With the increase in free trade
agreements (FTAs) throughout the whole international economy, this study chooses EU,
East Asia (ASEAN plus China, Japan, Korea and Taiwan) and NAFTA as trading bloc
dummy variables. The three regional variables represent a country’s formal membershipstatus of a trading bloc with the other country in the corresponding year.
Finally, I acknowledge the fact that importer country effects and exporter country
effects should be included in the estimation in order to control for the multilateral priceterms. However, inclusion of these variables causes multicollinearity diagnostics. First,
examination of the variance inflation factor, which shows how much the variance of thecoefficient estimate is being inflated by near-collinearity, revealed to be 20.86 (tolerance is0.048) for GDP.14 In addition, when checking the correlations of the estimated coefficients,
approximately 95% of the coefficients for the importer fixed effect dummies and exporter
fixed effect dummies showed high correlations of over 0.90 (the highest being 0.996),
between importing country’s GDPi, and exporting country’s GDPj, respectively. Therefore,
due to these indications of possible collinearity problems, both importer and exporter fixed
effects are not accounted for in the current analysis.15
3.3

Estimation Techniques

Standard procedure to estimate multiplicative gravity models for trade is to take thelogarithms so as to be able to estimate it in linear form. This study, however, uses a dataset
for which around 40% of the bilateral trade data between country i and country j is in factzero.16 Zero trade flows are actually quite common in the bilateral international trade
matrix (see, for example, Haveman and Hummels, 2004; Silva and Tenreyro, 2006;
Helpman et al., 2008). The proportion of zeros increases in line with country diversity(40*39 bilateral country pairs for this study) as well as sectoral disaggregation (59classified products in this analysis). On the one hand, presence of zeros tends to bias
estimates (especially OLS gravity model estimates), but on the other hand, disregarding thezeros means throwing away some potentially interesting information. Although the
14

A commonly given rule of thumb is that VIFs of 10 or higher (or equivalently, tolerances of 0.10 orless) may be reason for concern. (Chatterjee and Hadi, 2006)
15

Kimura and Lee (2006) also excluded exporter and importer country fixed effects from their gravity
model because it “would incur a multicollinearity problem” (p. 113).

16

38.9%, 44% and 37% of the observations are zero, for trade in intermediates, final products, and total
goods and services, respectively.

10

literature is still undecided as to the best way to deal with the zero trade flows, thefollowing are among the few of the approaches commonly taken in empirical studies.
One of the approaches is to simply drop the pairs with zero bilateral trade from thedataset by estimating the log-linear form (for example, see Johnson and Noguera, 2012).17
This means, however, that an important observation is left out of the model: the zero trade
flow. This is undesirable because the omitted observations contain information as to why
low levels of trade flow are observed, or why some countries trade in some products whileothers do not.
In order to avoid throwing away observations with zero-values, Eichengreen and
Irwin (1994; 1998) estimate their model using the logarithm of (1 +) as thedependent variable. However, Linders and de Groot (2006), among others, showed that thisapproach is prone to yield upward biased estimates. Santos Silva and Tenreyro (2006)
indicate that log linear models cannot be expected to provide unbiased estimates underheteroskedasticity. They propose an econometric solution for the zero-value problem incount data: a Poisson pseudo-maximum-likelihood model (PPML). This model, commonlyused for count data, is known for its ability to deal with occurrence of zeros and discrete
nonnegative nature of the dependent variable (see Greene, 2008).
The standard Poisson pseudo-maximum-likelihood model, however, is vulnerable
to problems of over-dispersion18 and excess zero flows19. Therefore, in order to overcome
these problems, it is highly necessary to re-estimate using zero-inflated negative binomial
regression or zero-inflated Poisson regression models, following Burger, van Oort and
Linders (2009). Yet, these estimations failed to converge by using the algorithm
implemented in STATA.
Consequently, the alternative procedure this study pursues is the sample selection
model, correcting for the possible selection bias. Martin and Pham (2008) indicate that
Heckman maximum likelihood estimators can outperform PPML estimators as long as thetrue identifying restrictions are available. Heckman’s sample selection model comprises a
17

Taking logarithms effectively drops such observations from the sample because log(0) is undefined.

18

The Poisson specification has been criticized because it has the property that its mean is equal to its
variance, known as the equidispersion property. Empirical work most often rejects this specification infavor of overdispersion (describes the feature Var(y|x)>E(y|x) in a regression model). To model thisoverdispersion, the negative binomial specification is usually employed.
19

Another problem with the Poisson specification is that it cannot explain the occurrence of excess
zeros. The zero-inflated Poisson in its simplest form gives a constant zero-inflation probability q tononusers and (1-q) to users. This is estimated using maximum likelihood methods.

11

probit selection equation (or participation equation) to determine whether a particularbilateral trade flow will be zero or positive (i.e., whether two countries decide to engage intrade), with the dependent variable:
10
and an error term,00~N(0,1).

The resultant outcome equation to estimate the relationship between trade values
and explanatory variables using only the truncated sample of observations with positive
trade flows is:

where~N(0,σ).

There are two ways of estimating the model: Heckman’s two stage method andmaximum likelihood estimation. The maximum likelihood estimation should provide themost efficient estimates because the model is derived by assuming that the error terms inboth equations have normal distributions (see Cameron and Trivedi, 2009).20
Estimation by maximum likelihood uses assumptions that correlated errors arejointly normally distributed with mean 0 and homoskedastic, with:(ε, ~N 0,0, , ,) whereindicates the correlation coefficient corr(ε, ; andthat normalization is used in the probit regression model: Var(u)= =1.
Furthermore, in applied work, it is common to impose exclusion restrictions inorder to deal with identification issues concerning the specification of the selection
equation. Therefore, the selection equation should have exogenous variables that are
excluded from the second stage trade-flow equation. These excluded variables need to beable to generate substantial (nontrivial) variation in the selection variable (i.e., theprobability of selection), while not directly affecting the outcome variable (Cameron andTrivedi, 2009).
Helpman et al. (2008) explain that this excluded variable requires to be tradebarriers that have impact on fixed trade costs but do not affect variable trade costs. They
construct a bilateral variable from data on entry costs of forming new firms as a measure of
the fixed costs of trade. These variables reflect regulation costs that do not affect a firm’s
20

As stated before, this is consistent with the findings of Martin and Pham (2008), who showed that the
maximum likelihood estimator outperformed the two-step estimator in their Monte Carlo simulations.

12

volume of exports to a particular country, hence satisfying the requisite exclusion
restrictions. This study constructs and uses the same regulation costs of firm entry as
Helpman et al. (2008).21
In sum, for this study, I have decided to use maximum likelihood estimation of2000 Nobel Laureate James Heckman’s sample selection model (see Heckman, 1979). For
the excluded variables, I follow Helpman et al. (2008) and use the number of days andlegal procedures along with relative entry costs (as a percentage of GDP per capita).
4.

Main Results

In Tables 2 and 3, I present the results of the estimation of sample selection bias
model by the maximum likelihood estimator. The first two columns from the left are
results from regressions with Entry Dummy as the dependent variable. Entry Dummy is anindicator for whether or not the importing and exporting country pairs actually trade (i.e.,
have more than zero trade flows). The third and fourth columns from the left show theresults from the second stage outcome equation, with Trade Volume as the dependent
variable. Trade Volume is the natural logarithm of bilateral trade flows, if positive.
For the analysis of the results, I will mainly focus on the outcome equation results(columns highlighted with gray shade in the tables). For all four estimations, based on theWald test of independent equations, the estimated correlation between the error terms ishighly significantly different from zero and positive, rejecting the null hypothesis that thetwo parts are independent at the 1% level, showing strong evidence of a sample selection
problem. Moreover, the excluded variables, procedures&days and relative cost are
statistically significant in all four first-stage probit estimations at the 1% level.
I also conducted truncated OLS regressions discussed in the previous section, using
the natural logarithm of imports as the dependent variable, thus leaving out pairs ofcountries with zero bilateral trade flows. Comparing the results with maximum likelihood
estimates in Tables 2 and 3, in general, the OLS regression coefficients are smaller inabsolute value.22 Omitting zero trade flow observations and taking into account only the21

Strictly following Helpman et al. (2008), I constructed an indicator for high fixed-cost trading
country pairs, consisting of pairs in which both importing and exporting countries have entry regulation
measures above the cross-country median. One variable is based on the sum of number of days andprocedures, while the other uses relative entry costs.
22

What is worth noting, however, is that the truncated OLS coefficients for the trading bloc binary
variables are larger compared to the sample selection model outcome regression results.

13

sample with non-zero bilateral trade seem to cause an overall underestimation of estimates.
Lastly, thetests of equality of coefficients across the two pairs of outcome equations inTables 2 and 3 reveal that coefficients for most regressors can be compared with astatistically significant difference (results are available upon request).23
Trade in Intermediates and Trade in Final Products
Table 2 presents the results for trade in intermediates and trade in final products
from the estimation of sample selection bias model by the maximum likelihood estimator.
The results show that they are consistent with the findings of Miroudot et al. (2009).
The impact of trade costs on intermediate inputs trade is relatively higher,
compared to that on final products trade. Two adjacent countries tend to engage in trade inintermediate inputs approximately 93% more than two otherwise similar countries. In
contrast, two adjacent countries trade final products about 80% more.24 In addition, trade
in intermediates is negatively affected by distance slightly more than trade in final products.
These differences in trade costs may be because intermediate inputs tend to be less subject
to consumers’ preferences; hence the price-elasticity of their demand tends to be higher andfirms switch easily from one supplier to another.
On the other hand, trade in intermediate inputs is affected less by the recent
financial crisis (98% decrease) than trade in final products (117% decrease). This may also
be because of the differences in purchasers – consumers’ demand for final use products
decreased much more compared to parts and components demand for production.
Moreover, two East Asian countries engage in trade in intermediates 111% morethan two otherwise similar countries (which is in line with the high intermediates tradeflows within East Asia, as seen in Figure 2), and in trade in final products by 62% more. In
contrast, common EU membership will on average increase two member countries’ tradeby about 20% and 25%, for intermediate inputs and final products, respectively. The results
regarding the East Asian region depict the thriving trade within the region of not onlyintermediate inputs, but also of assembled final products.

23

The purpose of this test is to assess whether or not the coefficients in the multiple equations are
significantly equal.
24

The formula to compute this effect is
1 100%, whereis the estimated coefficient. So
here, for example, the computation will be [exp(0.656)-1.0]*100=92.7%] and[exp(0.59)-1.0]*100=80.4%], for intermediates trade and final products trade, respectively.

14

Trade in Goods and Trade in Services
Table 3 shows the regression results with goods imports and services imports as
dependent variables, respectively, from the estimation of sample selection bias model by
the maximum likelihood estimator.
The estimated coefficients for distance between importing country and exporting
country tell us that when the distance between two trading partners increase by 1%, tradein goods decreases by 1.29% whereas trade in services drops by 0.78%. Trade in services isaffected less by distance and adjacency compared to trade in goods. A similar point can bemade about market size: size of the market has a much smaller impact on services trade
than in the case of goods trade.
In addition, services trade is much more sensitive to common language than tradein goods. When two countries share the same official language, they tend to engage inservices trade approximately 148% more, compared to a 26% increase for goods trade.25
This is also consistent with Miroudot et al. (2009)’s results.
Trade in services shows much more resistance to the current financial crisis
compared to trade in goods. This is consistent with the evident resilience of services trade
to the financial crisis in Figures 3 and 4. The respective estimated coefficients of the 2009
year variable indicate that goods trade has been affected negatively by 139%, whereas
services trade merely fell by 54%. This is consistent with Borchert and Mattoo (2009).
They explain that services trade is weathering the current crisis much better than goods
trade for two reasons: demand for a range of traded services is less cyclical, and services
trade and production are less dependent on external finance.
As for trading blocs, two members of the EU trade goods about 21% more andservices about 17% more than otherwise similar countries. The coefficient for NAFTA bloc
for services trade is a significant -1.0, indicating that any of the three pairs of countries
trade much less than expected, given their market size, distance and such.
Furthermore, the positive impact of economic freedom on services trade is slightly
greater compared to its effect on goods trade. A 1% increase in economic freedom of theimporting (exporting) country will increase trade between the two countries by 1.9%
(2.5%) for goods and 2.3% (2.9%) for services. This implies that as countries move toward
25

The formula to compute this effect is
1 100%, whereis the estimated coefficient. So
here, for example, the computation will be [exp(0.228)-1.0]*100=25.6%] and[exp(0.908)-1.0]*100=147.9%], for goods trade and services trade, respectively.

15

economic liberalization, services trade will grow faster than goods trade (Kimura and Lee,2006).
5.

Further Investigation by Sector

In order to find out how and why these differences emerge, this study explores
estimations of trade in intermediates and trade in final products by disaggregating thedataset into three broad sectors: primary, secondary, and tertiary.26
Here, I also use the sample selection model, with the same regressors as theprevious section. The only differences are the dependent variables and that the model isestimated using Heckman’s two step method27. The results are presented in Table 4.
Columns (1)-(3) show the results for intermediates trade by agricultural, manufacturing
and services sectors, respectively; Columns (4)-(6) show those for final products trade. The
inverse Mills ratio, or lambda, is highly significant in all six equations. It should be notedthat the inverse Mills ratio in the manufactures estimation, for both intermediates and final
products trade, is negative and significant. This result indicates that the error terms in thefirst step and second step equations have a negative correlation. Thus, the unobserved
factors that make engagement in bilateral trade more likely tend to be associated withlower amounts of manufacturing trade in general.
In the previous section, results strongly revealed intermediate inputs trade’s higher
sensitivity to trade costs compared to final products trade. The source of this may be
explained by characteristics unique to the agricultural sector. That is, adjacency andcommon language have the strongest positive impact (200% and 150%, respectively) on
agricultural intermediates trade.
The high trade flows in East Asia is contributed the most by agricultural andmanufacturing intermediate inputs: two East Asian countries tend to trade agriculturalintermediate inputs 82% more, manufacturing inputs 101% more, than otherwise similarcountries. In addition, two East Asian countries engage in final manufacturing products
trade 58% more. This is consistent with our findings in the previous section that there are
relatively high flows of trade in both intermediate inputs and final assembled products.
26

The primary sector (or agriculture, mining and quarrying sector) consists of CPA codes 1,2,5,10-14.
The secondary sector (or manufacturing sector) comprises CPA codes 15-37. The tertiary sector (orservices sector) is as shown in Appendix Table A.1.
27

The two step method is usually used more often instead of maximum likelihood because it isconsistent and more straightforward to estimate, especially with large datasets (see Greene, 2008).

16

There is also a significantly strong and positive relationship between EU trade bloc andfinal agricultural products trade.
Even when compared to agricultural and manufacturing sectors, services sector isstill affected the least by market size and trade costs, especially more so for intermediates
trade. Moreover, both intermediate and final services trade revealed to be the most resilientto the financial crisis. That is, intermediate services trade decreased merely by 131%, andfinal services trade dropped by 125%.
Furthermore, economic freedom has a highly significant, positive effect on nearly
all kinds of trade flows. Among them, trade flows of agricultural products have thestrongest relationship with economic freedom, followed by services products trade. For
these two sectors, the coefficients of economic freedom are greater for exporting countries
than for importing countries. This implies that economic freedom has greater impact on
agricultural and services exports than on agricultural and services imports. In contrast,
manufacturing products trade is impacted most by the importing country’s economic
freedom. Hence, manufacturing imports are affected by economic freedom more thanagricultural and services imports. Economic freedom also has a balanced effect between
manufacturing imports and exports.
6.

Concluding Remarks

This study assesses how the same factors, such as economic size and trade costs,
can significantly affect intermediate, final and total goods and services trade flows
differently. With the technological development in ICT and transportation, trade inintermediates and trade in services have been increasing their prominence in international
trade. However, constrained by data limitations, there have not been that many empiricalstudies. Therefore, this paper contributes to the literature by conducting empirical analyses
for both trade in intermediate inputs and trade in services. This study uses the recently
released World Input-Output Database (WIOD), a product-level international bilateral
trade panel data for the period 1995 to 2009, covering 40 countries.
Using the gravity model of bilateral trade, I estimated and compared determinantsfor different types of trade. In order to take zero trade flows into account, this study adopts
the sample selection bias model. The main findings of this paper can be summarized asfollows:

17

1. Trade in intermediate inputs
Trade in intermediate inputs is more sensitive, albeit slightly, to trade costs
compared to trade in final products. This may be because intermediate inputs tend to beless subject to consumers’ preferences, and thus the price-elasticity of their demand tends
to be higher and firms switch easily from one supplier to another.
2. Trade in East Asian regional bloc
There is much higher trade flow when two East Asian countries engage in trade,
especially intermediate inputs trade, compared to EU and NAFTA countries. The
prosperous trade within the East Asian region consists of both trade in intermediate inputs
and trade in assembled final products. Further analysis by sector shows that the main
products traded are manufacturing intermediate inputs and final products, along with
agricultural intermediates.
3. Trade in services
Compared to trade in goods, trade in services is much more resilient to the financial
crisis. This may be explained by the findings that trade costs and market size have a muchsmaller impact on services trade. In other words, services production and trade are affectedless by external trade costs and market size. The results are the same even when comparedto agricultural and manufacturing sectors, especially more so for intermediate services
trade. One important characteristic of services trade to note is that sharing the official
language is a big factor for two countries to engage in trade.
4. Economic freedom: Implications
The positive impact of economic freedom on services trade is slightly greatercompared to its effect on goods trade, implying the importance of economic liberalization
for further growth in services trade. Analysis by sector reveals that economic freedom hasa highly significant, positive effect on nearly all kinds of trade flows. By liberalizing tradeand increasing economic freedom, agricultural and services exports and manufacturing
imports are likely to rise more than agricultural and services imports.
Through empirical analysis, this present study showed that trade in intermediate
inputs and trade in services will continue to play an important role in international trade. It
is necessary to distinguish trade in different products and whether they are for intermediateuse or final consumption, when enforcing policies.
Finally, there is scope for further and deeper analysis. First of all, it is necessary to18

figure out a method to incorporate importer and exporter country fixed effects without
having to drop other regressors. There is also room for improvement for analyzing theeffects on trade values by disaggregating the products further, and focusing on detailed
product characteristics and their variety. Conducting a structural analysis to see thechanges across time may give us some more insights as well.

References
Anderson, J. E. and E. van Wincoop (2003), Gravity with gravitas: A solution to the border
puzzle. American Economic Review, 93(1), 170-192.
Baier, S. L. and J. H. Bergstrand (2007), Do free trade agreements actually increase
members’ international trade? Journal of International Economics, 71, 72-95.
Beach, W. W. and T. Kane (2008), Chapter 4: Methodology: Measuring the 10 Economic
Freedoms. In: 2008 Index of Economic Freedom.
Borchert, I. and A. Mattoo (2009), The crisis-resilience of services trade. World Bank
Policy Research Working Paper Series 4917, The World Bank.
Burger, M. J., F. G. van Oort and G. –J. M. Linders (2009), On the specification of thegravity model of trade: Zeros, excess zeros and zero-inflated estimation. Spatial
Economic Analysis, 4(2), 167-190.
Cameron, A. C. and P. K. Trivedi (2009), Microeconometrics: Methods and Applications.
New York: Cambridge University Press.
Chatterjee, S. and A. S. Hadi (2006), Regression Analysis by Example. New York: Wiley.
Daudin, G., C. Rifflart and D. Schweisguth (2011), Who produces for whom in the world
economy? Canadian Journal of Economics, 44(4), 1403-1437.
Djankov, S., R. La Porta, F. Lopez-de-Silanes and A. Shleifer (2002), The regulation ofentry. Quarterly Journal of Economics, 117(1), 1-37.
Eichengreen, B. and D. A. Irwin (1998), The role of history in bilateral trade flows. In:
Jeffrey A. Frankel, ed., The Regionalization of the World Economy (pp.33-62). Chicago,
IL and London: University of Chicago Press.

19Eichengreen, B. and D. A. Irwin (1994), Trade blocs, currency blocs and the reorientationof world trade in the 1930s. Journal of International Economics, 38, 1-24, July 1993,
revised July 1994.
Feenstra, R. C. (2004), Chapter 5: Increasing returns and the gravity equation. In: His
Advanced International Trade: Theory and Evidence, Princeton University Press.
Frankel, J. A. (1997), Regional Trading Blocs in the World Economic System. Washington
D.C.: Institute for International Economics.
Fukao, K. and K. Ito (2003), Foreign direct investment and services trade: the case ofJapan, In: T. Ito and A. O. Krueger, eds., Trade in Services in the Asia Pacific Region,
NBER East Asia Seminar on Economics (EASE), Volume 11 (pp.429-480). Chicago, IL
and London: University of Chicago Press.
Gereffi, G. and K. Fernandez-Stark (2011), Global Value Chain Analysis: A Primer. Centeron Globalization, Governance & Competitiveness (CGGC), Duke University, North
Carolina, USA.
Greene, W. H. (2008), Econometric Analysis. New Jersey: Pearson Education, Inc.
Haveman, J. and D. Hummels (2004), Alternative hypotheses and the volume of trade: The
gravity equation and the extent of specialization. Canadian Journal of Economics,
37(1), 199-218.
Heckman, J. (1979), Sample selection bias as a specification error. Econometrica, 47,
153-61.
Helpman, E., M. Melitz and Y. Rubinstein (2008), Estimating trade flows: Trading partners
and trading volumes. Quarterly Journal of Economics, 123, 441-487.
Johnson, R. C. and G. Noguera (2012), Fragmentation and trade in value added over four
decades. NBER Working Paper Series 18186, June 2012.
Kimura, F. and H.-H. Lee (2006), The gravity equation in international trade in services.
Review of World Economics, 142(1), 92-121.
Koopman, R., W. Powers, Z. Wang and S.-J. Wei (2011), Give credit where credit is due:
Tracing value added in global value chains. NBER Working Papers Series 16426,
September 2010, revised September 2011.
Linders, G. J. M. and H. L. F. de Groot (2006), Estimation of the gravity equation in thepresence of zero flows. Tinbergen Institute Discussion Paper, No. 06-072/3.
20

Martin, W. and C. S. Pham (2008), Estimating the gravity model when zero trade flows are
frequent. World Bank manuscript, available at:
http://www.deakin.edu.au/buslaw/aef/workingpapers/papers/2008_03eco.pdf.
Miller, T. and A. B. Kim (2012), Chapter 1: Defining Economic Freedom. In 2012 Index of
Economic Freedom.
Miroudot, S. and A. Ragoussis (2009), Vertical trade, trade costs and FDI. OECD Trade
Policy Working Paper, No. 89, OECD.
Miroudot, S., R. Lanz and A. Ragoussis (2009), Trade in intermediate goods and services.
OECD Trade Policy Working Paper, No. 93, OECD.
Santos Silva, J. M. C. and S. Tenreyro (2006), The Log of Gravity. Review of Economics
and Statistics, 88(4), 641-658.
Timmer, M. P. (Ed.) (2012), The World Input-Output Database (WIOD): Contents, sources
and methods. WIOD Working Paper No. 10.
Tinbergen, J. (1962), Shaping the World Economy. New York: Twentieth Century Fund.

21

Figure 1: Trade in Intermediates – Ratio of Foreign to Domestic Intermediates
25%

20%

15%

10%

5%

0%
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
World

Source: Author’s calculation based on WIOD.

Figure 2: Trade in Intermediates and Final Products as a Share of Total Imports
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
World imports in intermediates
East Asia imports in intermediates
World imports in final products

Source: Author’s calculation based on WIOD.
22

Figure 3: Imports of Goods and Services (year 2000=100)
250

200

150

100

50

0
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
Goods

Services

Source: Author’s calculation based on WIOD.

Figure 4: Trade in Goods and Services as a Share of Total Imports (year 2000=100)
120
100
80
60
40
20
0
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
Goods

Services

Source: Author’s calculation based on WIOD.
23

Table 1: Descriptive Statistics for Continuous Variables

VariableIntermediates
imports
Final imports
Total imports
GDP
GDP per capita
Distance
Total goods
imports
Total services
imports

Unit of measurein million
US dollars
in million
US dollars
in million
US dollars
in million
US dollars
in million
US dollars
km
in million
US dollars
in million
US dollars

Obs.

Mean

Std. Dev.

Min

Max

1,380,600

39.7

365

0

59,7001,380,600

25.8

327

0

55,1001,380,600

65.6

591

0

72,3001,380,600

883,000

1,890,000

3,660

14,300,000

1,380,600

0.01950.01660.00040.11891,380,600

5,133

4,433

59.62

18,523.81

725,400

104

778

0

72,300655,200

23.2

249

0

44,70024

Table 2: Trade in Intermediates and Trade in Final Products:
Estimation of Sample Selection Bias Model
Entry Dummy
Dependent
Variable:

Trade Volume

Intermediates
Imports

Final Products
Imports

Intermediates
Imports

Final Products
Imports

0.100***

0.094***

0.947***

0.917***(0.004)(0.004)(0.007)(0.007)0.189***

0.196***

1.014***

1.032***(0.004)(0.004)(0.007)(0.007)ln(GDP/pop)i

0.139***

0.169***-0.214***-0.075***(0.007)(0.007)(0.014)(0.014)ln(GDP/pop)j0.040***

0.048***-0.218***-0.239***(0.007)(0.007)(0.013)(0.014)lnDistance-0.359***-0.357***-1.107***-1.094***(0.007)(0.007)(0.013)(0.013)-0.014-0.0140.656***

0.590***(0.026)(0.026)(0.042)(0.043)-0.096***-0.095***0.466***

0.442***(0.028)(0.029)(0.047)(0.048)

0.026

0.029

-0.0310.010(0.031)(0.031)(0.052)(0.052)2.079***

lnGDPilnGDPjAdjacency
Common language
Colonial ties
Index of Economic Freedomi

0.074

0.124**

2.221***(0.050)(0.051)(0.097)(0.100)0.487***

0.382***

2.643***

2.807***(0.050)(0.052)(0.099)(0.102)-0.095***-0.090***0.180***

0.227***(0.012)(0.012)(0.022)(0.023)

East Asia bloc

-0.473***-0.507***0.748***

0.481***(0.051)(0.051)(0.090)(0.084)NAFTA bloc

-0.958***-0.934***-0.022-0.002(0.093)(0.097)(0.139)(0.137)

Year 2009-0.135***-0.147***-0.683***-0.774***(0.010)(0.010)(0.018)(0.019)procedures&days

0.122***

0.144***(0.013)(0.013)-0.082***-0.067***

Index of Economic Freedomj
EU bloc

relative cost
Constant(0.012)(0.012)-6.838***-7.977***-44.806***-49.727***(0.298)(0.305)(0.555)(0.571)

0.097***

0.109***

ρ
Number-of-obs.

1,212,686

1,212,686(0.003)(0.004)760,610

695,741

Notes: Entry Dummy is an indicator for whether or not the importing and exporting country pairs actuallytrade (i.e., have more than zero trade flows). Trade Volume is the natural logarithm of bilateral trade flows if
positive. The standard errors for rho are based on Wald test of independent equations.
Estimates are made with time and product fixed effects. Clustered standard errors are shown in parentheses.
* p<0.1, ** p<0.05, *** p<0.01

25

Table 3: Trade in Goods and Trade in Services: Estimation of Sample
Selection Bias Model
Entry Dummy

Trade Volume

Dependent
Variable:

Goods
Imports

Services
Imports

Goods
Imports

Services
ImportslnGDPi

0.292***

0.018***

1.013***

0.820***(0.006)(0.005)(0.008)(0.012)0.494***

0.042***

1.115***

0.857***(0.013)lnGDPj(0.006)(0.005)(0.007)-0.0020.224***-0.222***-0.023(0.010)(0.009)(0.015)(0.026)-0.095***0.116***-0.278***-0.128***(0.010)(0.009)(0.014)(0.026)-0.586***-0.267***-1.287***-0.783***(0.011)(0.009)(0.014)(0.023)

Adjacency

0.294***-0.080**0.680***

0.624***(0.056)(0.032)(0.047)(0.075)

Common language

0.219***-0.171***0.228***

0.908***(0.055)(0.037)(0.051)(0.090)ln(GDP/pop)iln(GDP/pop)jlnDistanceColonial ties

-0.030

0.050

0.016

-0.042(0.063)(0.039)(0.056)(0.097)0.379***-0.076

1.949***

2.311***(0.072)(0.068)(0.105)(0.181)0.987***

0.379***

2.473***

2.897***(0.071)(0.068)(0.114)(0.176)-0.099***-0.107***0.193***

0.160***(0.020)(0.016)(0.025)(0.040)

East Asia bloc

-0.222*-0.564***0.725***

0.720***(0.118)(0.071)(0.091)(0.196)NAFTA bloc

-0.862***-0.964***0.252*-1.035***(0.268)(0.126)(0.136)(0.262)-0.122***-0.162***-0.870***-0.432***(0.019)(0.012)(0.020)(0.035)procedures&days

0.071***

0.183***(0.018)(0.018)relative cost

-0.138***-0.071***(0.017)(0.016)-18.610***-3.490***-45.018***-45.055***(0.456)(0.396)(0.619)(1.018)0.066***

0.131***

Index of Economic Freedomi
Index of Economic Freedomj
EU bloc

Year 2009

Constantρ
Number-of-obs.

637,174

575,512(0.005)(0.006)503,181279,165

Notes: Entry Dummy is an indicator for whether or not the importing and exporting country pairs actuallytrade (i.e., have more than zero trade flows). Trade Volume is the natural logarithm of bilateral trade flows if
positive. The standard errors for rho are based on Wald test of independent equations.
Estimates are made with time and product fixed effects. Clustered standard errors are shown in parentheses.
* p<0.1, ** p<0.05, *** p<0.01

26

Table 4: Trade in Intermediates and Trade in Final Products: Estimation Resultsby Sector
Intermediates Imports
SectorDependent
Variable:lnGDP ilnGDP jln(GDP/pop)iln(GDP/pop)jlnDistance
Adjacency
Common language
Colonial ties
Index of Economic Freedomi
Index of Economic Freedomj
EU bloc
East Asia bloc
NAFTA bloc
Year 2009
Excluded variables:
dprocdays
dcost

lambdaR-squared
Adj-R-squared
Number-of-obs.

Final Products Imports
SectorAgriculture Manufacture

Services

Agriculture Manufacture

Services(1)1.135***(0.010)1.288***(0.014)-0.152***(0.017)-0.954***(0.017)-1.293***(0.019)1.098***(0.046)
0.918***(0.050)0.060(0.055)1.230***(0.131)
4.065***(0.139)0.280***(0.033)0.601***(0.083)0.093(0.130)-0.982***(0.069)(2)
1.028***(0.003)
1.117***(0.003)-0.367***(0.006)-0.160***(0.005)-1.333***(0.005)0.625***(0.014)0.085***(0.015)-0.018(0.017)2.439***(0.041)2.403***(0.045)0.149***(0.011)
0.698***(0.027)0.084**(0.040)-0.867***(0.021)(3)0.858***(0.004)0.962***(0.004)0.642***(0.012)0.180***(0.010)-1.501***(0.011)0.457***(0.024)0.347***(0.028)0.026(0.029)1.881***(0.064)3.815***(0.064)-0.220***(0.017)-0.886***(0.061)-3.883***(0.094)-0.836***(0.035)(4)
1.008***(0.012)
1.398***(0.017)0.163***(0.022)-0.967***(0.021)-1.571***(0.023)0.644***(0.050)0.884***(0.057)0.137**(0.062)-0.104(0.159)4.021***(0.169)0.867***(0.041)-0.222**(0.095)-0.037(0.153)-1.318***(0.084)(5)
0.979***(0.003)1.144***(0.003)-0.120***(0.006)-0.254***(0.006)-1.314***(0.006)
0.582***(0.015)0.121***(0.016)-0.005(0.017)2.207***(0.043)2.714***(0.048)0.218***(0.011)0.455***(0.026)
0.197***(0.042)-0.936***(0.023)(6)
0.908***(0.004)0.987***(0.004)0.547***(0.012)0.162***(0.010)-1.501***(0.011)0.446***(0.024)0.370***(0.028)0.129***(0.030)1.747***(0.065)3.509***(0.064)-0.251***(0.017)-1.026***(0.061)-3.748***(0.089)-0.810***(0.035)-0.058***(0.010)-0.102***(0.010)0.095***(0.009)-0.200***(0.008)0.166***(0.005)-0.074***(0.005)-0.051***(0.013)0.045***(0.012)0.110***(0.008)-0.149***(0.008)0.187***(0.005)-0.083***(0.005)2.236***(0.072)0.330
0.329
79,134-0.301***(0.043)0.589
0.589
403,822

6.213***(0.082)0.446
0.446
277,6542.057***(0.082)0.496
0.496
43,453-0.082**(0.037)0.585
0.585
388,595

6.050***(0.079)0.421
0.421
263,693

Notes: Uses Heckman's two-step estimation procedure to estimate the sample selection model.
Estimates are made with time and product fixed effects. Robust standard errors are shown in parentheses.
* p<0.1, ** p<0.05, *** p<0.0127Appendix Tables
Appendix Table A.1: Products Description with CPA Classification
Goods Sector
CPA
1
2
5

Services Sector

Product

CPA

Products of agriculture, hunting and40

related services
Products of forestry, logging and41

related services
Fish and other fishing products;

45

services incidental of fishing

Product
Electrical energy, gas, steam and hot
waterCollected and purified water,
distribution services of water
Construction work
Trade, maintenance and repair services

10

Coal and lignite; peat

50

of motor vehicles and motorcycles;
retail sale of a

Crude petroleum and natural gas;
11

Wholesale trade and commission tradeservices incidental to oil and gas

51

extraction

services, except of motor vehicles andmotorcycle
Retail trade services, except of motor

12

Uranium and thorium ores

52

vehicles and motorcycles; repair
services of person

13

Metal ores

55

Hotel and restaurant services

14

Other mining and quarrying products

60

15

Food products and beverages

61

Water transport services

16

Tobacco products

62

Air transport services

17

Textiles63

18

Wearing apparel; furs

64Land transport; transport via pipeline
services

Supporting and auxiliary transport
services; travel agency services
Post and telecommunication services
Financial intermediation services,19

Leather and leather products

65

except insurance and pension funding
services

Wood and products of wood and cork
20

Insurance and pension funding(except furniture); articles of straw and66

plaiting mateservices, except compulsory social
security services

28

Appendix Table A.1 (continued)
Goods Sector

Services Sector

CPA

Product

CPA Product21

Pulp, paper and paper products

67

22

Printed matter and recorded media

70

23

24

Coke, refined petroleum products andnuclear fuelsServices auxiliary to financial
intermediation
Real estate services
Renting services of machinery and71

equipment without operator and of
personal and house

Chemicals, chemical products andman-made fibers

72

Computer and related services

25

Rubber and plastic products

73

Research and development services

26

Other non-metallic mineral products

74

Other business services
Public administration and defense

27

Basic metals

75

services; compulsory social security
services

28

Fabricated metal products, except
machinery and equipment80

Education services
Health and social work services

29

Machinery and equipment n.e.c.

8530

Office machinery and computers

90

31
32
33
34
35
36
37

Electrical machinery and apparatus
n.e.c.
Radio, television and communicationequipment and apparatus
Medical, precision and optical
instruments, watches and clocks
Motor vehicles, trailers andsemi-trailers

91
92

Sewage and refuse disposal services,sanitation and similar services
Membership organization services n.e.c.
Recreational, cultural and sporting
services

93

Other services

95

Private households with employed persons

Other transport equipment
Furniture; other manufactured goods
n.e.c.
Secondary raw materials
52.5%

47.5%

29

Appendix Table A.2: Country Coverage by ContinentContinent
EuropeAmericas

Country

ISO

CountryISO

Austria
Belgium
Bulgaria
Cyprus

AUT
BEL
BGR
CYP

BrazilCanadaMexicoUnited States

BRA
CAN
MEX
USA

Czech Republic
Germany

CZE
DEU

Obs.
Percentage

Denmark

DNK

Spain
Estonia
FinlandFranceUnited Kingdom
GreeceHungary
Ireland

ESP
EST
FIN
FRA
GBR
GRC
HUN
IRL

Australia
China
Indonesia
India
Japan
South Korea
RussiaTaiwanItaly
Lithuania
Luxembourg
LatviaMalta
Netherlands
PolandPortugal
Romania
Slovak Republic
Slovenia
SwedenTurkey

ITA
LTU
LUX
LVA
MLT
NLD
POL
PRT
ROU
SVK
SVN
SWE
TUR

Obs.
Percentage

Obs.
Percentage

138,060
10%Asia Pacific966,420
70%

30

AUS
CHN
IDN
IND
JPN
KOR
RUS
TWN
276,12020%