Discussion Paper No. 944

DEVELOPMENT ACCOUNTING
AND INTERNATIONAL TRADE

Hirokazu Ishise

August 2015

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

Development Accounting and International Trade
Hirokazu Ishise∗
August 21, 2015

Abstract
Development accounting shows that a significant part of cross-country income diﬀerences is attributed to diﬀerences in total factor productivity (TFP), but the sources of
TFP diﬀerences are not well understood. This paper considers the role of international
trade to explain cross-country income diﬀerences in TFP. By using a multi-country
Ricardian trade model, I distinguish trade costs and trade policy factors from a puretechnology factor in TFP. Under the baseline parameterization, my model shows thatconventional TFP measures overestimate fundamental productivity diﬀerences by 30%.
I then show that trade costs significantly influence welfare: small European countries enjoy 10–15% higher welfare through their proximity to larger and more productive neighboring countries, while Oceanian and countries in southern Africa suﬀer from 10–20%
lower welfare due to their remoteness. Trade policy also has impacts: tariﬀs decrease
welfare by 1–10%, while free-trade agreements increase welfare by 1–5%. These gains
from trade are considerably smaller if general equilibrium eﬀects are not considered.
Keywords: Development accounting; Total factor productivity; Cross-country income
diﬀerences; Ricardian trade model; Gains from trade; General equilibrium eﬀects.
JEL Codes: E22, E23, F11, O40, O47.

1

Introduction

Development accounting shows that cross-country income diﬀerences are explained by diﬀerences in the observable factors of capital and labor but also attributed to diﬀerences in total∗

Institute of Social and Economic Research, Osaka University. Address: 6-1 Mihogaoka, Ibaraki, Osaka
567-0047, Japan; Phone: +81-6-6879-8581; Email to: ishise@iser.osaka-u.ac.jp; I would appreciate comments
made by Takumi Naito, Ray Riezman, Katsuya Takii, and seminar/conference/workshop participants at
Hitotsubashi U.; Osaka U.; Kyoto Development Economics Workshop; Spring 2015 JEA meeting; Spring 2015
Midwest International Trade Meeting; Osaka Workshop on Economics of Institutions and Organizations;2015 Summer Workshop of Economic Theory. This work is supported by JSPS KAKENHI Grant Number
26885043. All errors are mine.

1

factor productivity (TFP) (see, e.g., Caselli, 2005; Hsieh and Klenow, 2010). This decomposition leads us to an important question: what can best explain cross-country TFP diﬀerences?
This paper considers the role of international trade to explain cross-country diﬀerences in
TFP. By introducing a multi-country Ricardian international trade structure in the standard development accounting model, I show that conventional TFP measures overestimate
fundamental productivity diﬀerences by 30%. I then show that trade plays an importantrole in determining economic welfare. Small European countries enjoy 10–15% higher welfare
through their proximity to large, productive countries, while Oceanian and South Africancountries suﬀer from 10–20% lower welfare due to their remoteness. Moreover, average tariﬀ rates and free-trade agreements change welfare by as much as 10% for some developing
countries.
My model combines the scheme of development accounting with Alvarez and Lucas’s(2007b) specification of Eaton and Kortum’s (2002) multi-country Ricardian trade model.
I assume that technology diﬀerence in the intermediate goods production sector is the fundamental source of the TFP diﬀerences across countries.1 Countries internationally tradeintermediate goods to produce intermediate composite goods. The intermediate composite
goods are used to produce consumption goods, investment goods, and intermediate goods.
Through this chain of input-output, the aggregate output, and hence aggregate measured
TFP, of each country depends not only on its own technology but also that of the othercountries.
The autarky version of my model implies that standard development accounting is anappropriate method to calculate the underlying technology parameter.2 The reason is thatin a model with intermediate inputs, TFP of the aggregate production function is a weighted
mean of the productivity of sectors (Hulten, 1978). In my model, intermediate goods are
the only source of productivity, and hence the aggregate TFP directly corresponds to thefundamental technology parameter.
This simple correspondence between aggregate TFP and technology parameter does nothold under equilibrium with international trade. Yet, I can calculate productivity parameters
for each country. I calibrate the model using data of 140 countries in 2005. Analogous todevelopment accounting, the model isolates productivity of countries from observed income ofcountries. I then compare the productivity parameters obtained from standard development
accounting to the parameters derived from a model under open-economy. Under the baseline
parameterization, my model shows that the diﬀerences in cross-country productivity required
to explain cross-country income diﬀerence are 30% smaller than the corresponding values fromthe standard development accounting exercise. The input-output chain mechanism amplifies
small diﬀerences in the underlying technology to large diﬀerences in the aggregate measured
1

Including variations in TFP in other sectors is technically possible. I do not include these additionalvariations in order to elucidate the mechanism driven by trade.
2
Waugh (2010) obtains the analogous expression in his model, but does not highlight the result in thismanner. See below for a brief discussion of how my paper diﬀers from his.

2

TFP, which echoes the closed-economy models of Jones (2011, 2013).
Next, I explore the roles of trade costs and trade policies in determining welfare of acountry, and find that these trade barriers play important roles. The calculations are as
follows. First, I solve the equilibrium of the model under alternative (hypothetical) tradebarrier assumptions while fixing baseline productivity parameters. Second, I calculate hypothetical utility values for various scenarios. Finally, I compare the consumption-equivalent
welfare changes of two alternative scenarios, and measure the eﬀects of various trade barrierson the economic welfare of countries. The results show large gains from trade, some eﬀects
of geography, and even smaller impacts from trade policy. The results also show that tradegains quantitatively depend on whether consideration is given to general equilibrium eﬀects(through changes in wages of countries).
Average trade gains are large. Trade gains are especially large for small, developing
countries: for some countries, current welfare is more than four times the welfare underautarky. By comparison, trade gains for large countries are much smaller. In addition, tradegains with general equilibrium eﬀects are generally larger than gains without these eﬀects.
Total trade gains are decomposed into the roles of trade, geography (trade costs), andtrade policy. Trade itself plays the main role in determining the gains from trade, and otherfactors also play some roles. Small European countries enjoy 10-15% higher welfare throughtheir proximity to large productive countries. These large European countries (e.g., Germany,
France, the UK themselves benefit by being near one another and experience higher welfare
by approximately 10%. The negative impacts of geography are prevalent for Oceanian andcountries in southern Africa, which suﬀer from 10–20% lower welfare due to their remoteness.
The largest negative impact, 22%, is (not surprisingly) observed for New Zealand. Trade
policies aﬀect welfare, but less than trade cost does. Average tariﬀ rates and free-trade
agreements change income 5–10% for some developing countries. In general, trade policy has
minor eﬀects on developed countries; tariﬀ rates are already close to zero, so a lower tariﬀ, orremoving tariﬀs through FTAs, does not drastically change welfare. However, the margin of
welfare gains through tariﬀ reduction is not small for some developing countries.
The paper is organized as follows. The rest of the introduction discusses how the paperdraws from and contributes to the literature. The next section presents the model. Section 3
explains data, calibration strategy, and the results. The final section concludes.

1.1

Contributions to the literatureThis paper combines two strands of literature and oﬀers new insights on both of them: development accounting, and application of Ricardian models à la Eaton and Kortum (2002).
Given the results of standard development accounting (Caselli, 2005; Hsieh and Klenow,
2010), researchers examine the sources of TFP diﬀerences. For example, misallocation withina country can be a source of a low aggregate TFP in poor countries (Restuccia and Rogerson,

3

2008; Hsieh and Klenow, 2009). Input-output chain amplifies a small diﬀerence in TFP in
a sector to large aggregate TFP (Jones, 2011, 2013). Other than a few exceptions, thesestudies basically depend on closed-economy analysis. Jones (2013) includes inputs from othercountries, but he does not explicitly construct a model of international trade. Gancia et al.
(2013) consider the role of international technology adoption by including international tradeof goods, but do not highlight the role of trade barriers. My paper complements studies ofdevelopment accounting by examining the role of international trade and trade barriers in
TFP measurement.
Compared with other applications of multi-country Ricardian models (Alvarez et al., 2013;
Alvarez and Lucas, 2007a,b; Arkolakis and Ramanarayanan, 2009; Mutreja et al., 2014a,b;
Waugh, 2010), my contribution is to bring the model into a relatively simple development
accounting framework. As previously noted, my paper builds on Alvarez and Lucas (2007b),
which is a direct extension of Alvarez and Lucas (2007a). Alvarez and Lucas (2007a) respecify the model of Eaton and Kortum (2002) to be a full-fledged general equilibrium modelin which labor is the only fundamental factor of production. They examine the impact oftrade costs and tariﬀ on income across countries, but do not consider the implication of
measured productivity across countries. Alvarez and Lucas (2007b) further include capital as
an additional production factor and analyze the transition dynamics of the model. However,they do not examine steady-state implications of trade costs and welfare.
Many international trade papers analyze the welfare gains from trade and/or gains fromreducing trade costs/tariﬀs (see, e.g., Arkolakis et al., 2012), but not in terms of productivity.
An exception is Arkolakis and Ramanarayanan (2009) who show a two-country version of TFP
decomposition in the spirit of Hulten (1978): in a first order approximation, a change in the
country s aggregate TFP depends on changes in productivities of two-countries. However,
Arkolakis and Ramanarayanan (2009) focus on business cycle properties of the Eaton andKortum (2002) model in a two-country framework. Alvarez et al. (2013) include diﬀusion ofknowledge, but disregard the accounting. Arkolakis et al. (2012) provide a simple formulathat can assess quantitative gains from trade in several trade models, including Eaton andKortum’s (2002).
Waugh (2010) and Mutreja et al. (2014a,b) use versions of Eaton and Kortum (2002) toquantitatively examine cross-country income diﬀerences. Waugh (2010) assumes that capital and labor are exogenously given, consumption and investment goods are produced by asingle final good sector, and tariﬀs are not included. I assume that capital and labor are
endogenously determined, consumption and investment goods productions are distinguished,
and tariﬀs are included. Nevertheless, my model provides an extended decomposition of theaggregate output as seen in Waugh (2010). Waugh (2010) then parameterizes the modelusing observed trade, income and price data to quantitatively assess the impact of trade cost
reduction on the welfare. He focuses on the role of asymmetric trade costs between rich andpoor countries and analyzes the welfare eﬀects of a hypothetical reduction in trade costs from4

current levels. His work serves to explicate implications of reducing trade costs. I instead use
income data to calibrate productivity parameters, which is parallel to standard development
accounting. Mutreja et al. (2014a,b) further extend the model of Waugh (2010) by incorporating capital goods trade, goods-direction-specific trade costs, two types of capital goods,
and international productivity diﬀerences in various sectors. They calibrate the model using
many observable values and successfully replicate various other macro-values.
Contrary to Waugh (2010) and Mutreja et al. (2014a,b), I keep my model simple andcalibrate a minimal number of country specific parameters to highlight the mechanism underlying the cross-country income diﬀerences. Another benefit of using a simple calibration
strategy is that I can include many countries, especially developing ones, in my quantitative
analysis.3 Including many developing countries gives richer insights into the role of trade barriers on welfare. Note also that Waugh (2010) and Mutreja et al. (2014a,b) do not examine
the general equilibrium eﬀects on welfare.
My paper complements Alvarez and Lucas (2007a,b), Waugh (2010) and Mutreja et al.
(2014a,b) by examining the role of technology, international trade and trade barriers for
welfare in a full-fledged general equilibrium model with capital and labor. I also show thequantitative significance of general equilibrium eﬀects in welfare analysis, which is not necessarily highlighted by Alvarez and Lucas (2007a).

2

Model

The model is a discrete-time infinite horizon model (t = 0, 1, ...) with no aggregate uncertainty.
The analysis will focus on the steady-state equilibrium, and variables without time subscripts
stand for the steady-state values.

2.1

Setup

The world consists of n countries, and each country is indexed by i, j = 1, ..., n. Each country
has Ni workers, which are exogenously given. In each country, a representative household
supplies labor, and consumes the final consumption good ci . Households cannot move acrosscountries. A household in a country owns capital and lends it to firms in the country. I maketwo assumptions: international financial transactions are not possible, and trade is balanced.
In each country, there are an infinite number of firms that produce consumption goods,
investment goods, or intermediate goods. A consumption (or investment) goods firm produces
its goods using capital, labor and intermediate composite goods. The consumption andinvestment goods are country specific and non-tradeable. An intermediate good producer
produces one of a continuum of intermediate goods using capital, labor and intermediate3

I include 140 countries in the quantitative analysis. Alvarez and Lucas (2007a) include 59 countries andan aggregate representing the rest of the world. Waugh (2010), Mutreja et al. (2014a), and Mutreja et al.
(2014b) include 77, 84, and 88 countries, respectively.

5composite goods. Each intermediate good diﬀers in its cost of production and is distinguishedby the cost parameter υ ∈ Rn . Intermediate goods are internationally tradeable. Intermediate
goods are aggregated into the intermediate composite via a constant elasticity of substitution(CES) function. All the markets are perfectly competitive.
2.1.1

Intermediate composite

Let f (υ) be the density of each variety υ. An intermediate composite good comprises intermediate goods cit (υ),
[∫
qit =

cit (υ)

η−1
η

η
] η−1

f (υ)dυ.

(1)

Let pmit and pit (υ) denote the price of qit and cit (υ), respectively, and then the standard
calculations of CES function implies(∫
1−η

pmit =
2.1.2

pit (υ)1) 1−η

f (υ)dυ.

(2)

Consumption and investment goods

The consumption (or investment) good cit (xit ) is produced from capital, labor and the intermediate composite goods. Let pcit (pxit ) denote the price of the consumption (or investment)
goods. Profit maximization problems are( α 1−α )γc 1−γcmax pcit kcit
lcit
q−rit kcit − wit lcit − pmit qcit ,|{z cit }

(3)

=cit

( α 1−α )γx 1−γx−rit kxit − wit lxit − pmit qxit .
max pxit kxit
lxit
q{z xit }
|

(4)

=xit

2.1.3

Intermediate good

An intermediate good yit (υ) is produced from capital, labor and the intermediate composite
goods. Let p̃it (υ) denote the selling price of the intermediate good yit (υ). A profit maximization problem is)γm(qmit (υ)1−γm −rit kit (υ) − wit lit (υ) − pmit qmit (υ),
p̃it (υ) υit−θ kit (υ)α lit (υ)1−α
|
{z
}

(5)

=yit (υ)

and υit is drawn from an exponential distribution,f (υ) =

( n
∏

)
λi

(
exp −

i=1

n
∑
i=1

6

)
λi υit.

(6)

As Eaton and Kortum (2002) explain, λi determines the average level of productivity incountry i and thereby controls the absolute advantage, while θ determines the variation of
productivity across variety in a country and thus controls the magnitude of comparativeadvantage.
2.1.4

Households

The households maximize the life-time utility∞∑

β t Ni (ψ ln cit + (1 − ψ) ln(1 − lit )) ,

(7)

t=0

subject to budget constraints,
Nit (pcit cit + (1 + τxi )pxit xit ) = Nit (wit lit + rit kit ) + Πit ,(8)

where Πit is lump-sum transfer of tariﬀ revenue, and τxi is a country-specific investment wedge(as used by Hsieh and Klenow (2009)). The role of the investment wedge is explained below.
Capital, Kit = Ni kit , accumulates following the standard formula,
Kit+1 = (1 − δ)Kit + Ni xit .
2.1.5

(9)

Input markets clearing conditions

Capital, labor and intermediate composite are used for various productions,()∫
Ni kcit + kxit + kit (υ)f (υ)dυ = Kit ,()∫
Nit lcit + lxit + lit (υ)f (υ)dυ = Nit lit ,∫qcit + qxit + qmit (υ)f (υ)dυ = qit .
2.1.6

(10)
(11)
(12)

Steady state

In the steady-state, the intertemporal Euler equation implies that
ri
=
pxi

(

)
1
− 1 + δ (1 + τxi ) ≡ Ri ,β

(13)where Ri is a modified version of the user cost of capital.
The model has two fundamental factors of production: capital and labor. Nevertheless,the model is Ricardian, not Heckscher-Ohlin, following an insight of Baxter (1992): in amodel with endogenous capital accumulation, intertemporal optimization, and neoclassical7

production function, the steady-state of a dynamic economy of two-country, two-good, andtwo-factor (capital and labor) international trade model is described as a Ricardian model.
More specifically, in the presence of intertemporal optimization and capital accumulation,
capital-labor ratio is determined by the user costs. Since the user costs depend only on
parameters of the model (β, δ, and τxi ), the model can be reduced to a one factor (labor)model as in a Ricardian model.
It then follows that capital-labor ratio in a country depends only on the parameters β, δ,and τxi . Given large diﬀerence in capital-labor ratio across countries, I include the investment
wedge (τxi ) to vary across countries. Admittedly, this assumption is not an ideal responseto the Lucas puzzle (Lucas, 1990), but it is a simple way to have variation in the capitallabor (and hence capital-output) ratio in the steady-state. The question arises whether thisassumption systematically alters the model s properties, especially diﬀerences in rich andpoor countries. Given the fact that investment-output ratio measured in domestic prices is
not systematically correlated with income per worker (c.f., Hsieh and Klenow, 2007), capitaloutput ratio measured domestically is not systematically correlated with income per worker(see Figure 1 below). Accordingly, the investment wedge does not play a critical role in the
results.
Another potential problem of the investment wedge is its eﬀect on the labor-leisure choice.
As is true in standard RBC models, the steady-state labor depends on the user cost (seeequation (19) below). A variation in τxi can lead to a large variation in labor, which in turnaﬀects other properties of the model. The model’s implications might greatly diﬀer from thoseof an exogenous labor model (as common seen in the development accounting literature). I
include labor-leisure decision to check the eﬀects of τxi on labor and productivity measures.

2.2

Equilibrium expressions

A key parameter in the model is λi > 0. This λi controls the mean of the cost distributionof the intermediate goods. Under autarky, each country produces all the intermediate goods.
Hence, the mean of the distribution, λi , is a key parameter to determine country’s aggregate productivity. Under an open-economy equilibrium, each country produces some of theintermediate goods. The mean productivity determines the range of production. Moreover,
if a country has high average productivity, the country can export more goods in exchange
for additional imported intermediate goods. The country then produces even more because
the imported intermediate goods are used to produce other intermediate goods through the
input-output chain.
2.2.1

Autarky

Under autarky, the available variety is υ ∈ [0, ∞]. All the intermediate inputs are domestically
produced, and the price of intermediate good for a buyer (pi (υ)) equals the seller’s price8

(p̃i (υ)),
yi (υ) = ci (υ),(14)

pi (υ) = p̃i (υ).

(15)

Following calculations (see Appendix B), pmi can be expressed as a function of wi . Other
prices are also linear functions of wi . Once prices are calculated, other variables are easily
calculated.
2.2.2

N country modelInternational trade incurs iceberg trade cost. In particular, one unit of any tradeable good
shipped from j to i results in κij ∈ (0, 1] units arriving in i. Similarly, a country can impose
tariﬀs. If country j ships to country i, ωij ∈ (0, 1] fraction of each dollar arrives as payment
to a seller in j. The tariﬀ revenue is transferred to the household in i as a lump-sum. Intracountry shipment is subject neither to trade costs nor to tariﬀs, κii = ωii = 1.
With the possibility of trade, the price that an intermediate composite good producer ini faces is{}p̃i (υ)pi (υ) = min,(16)jκij ωij
and as shown by Eaton and Kortum (2002), the stochastic formulation of productivity helps
calculate this minimum.4
Let Dij denote the fraction of country i’s spending for intermediate goods from country
j to the total spending for the intermediate goods. This share, Dij , depends on a vector ofwages w = (w1 , .., wn ). The trade balance holds
∑
j̸=i

|

Ni pmi qi Dij ωij =
{z

∑
j̸=i

}

|

total value imported

Nj pmj qj Dji ωji .
{z

(17)

}

total value exported

The equilibrium wage vector w solves a system of equations (Z1 (w), ..., Zn (w)) = 0 and
1 ∑ Nj wj Dji (w)ωji
Ni Fi (w)Zi (w) =−,wi j=1 Ψj − Φj Fj (w)Ψi − Φi Fi (w)n(18)

where Ψi and Φi are terms depending only on parameters (see Appendix B), and Fi = Fi (w) =∑j Dij ωij . This system of equations is completely parallel to an equation (3.18) of Alvarezand Lucas (2007a) and can be numerically solved.
4

In a Ricardian trade model, a country imports goods from the country with the lowest prices. Whenυ follows the exponential distribution, a minimum of multiple random variables follows another exponential
distribution. Using this stochastic formulation, the minimum price has a closed-form expression.

9

Once w is found, other variables are easily calculated. In particular, labor is(li =1 − ψ 1 1 − Fi + γm Fi − (γx (1 − Fi ) + γm Fi ) Ri1+
ψ 1−αγc (1 − Fi ) + γm Fi

αδ

)−1
.

(19)

Labor depends on ωij and prices of intermediate goods from other countries through Fi , andalso depends on τxi through Ri . Similarly, the capital-output ratio in domestic prices is1
pxi ki=
pci ci + pxi xi
δi(

1 − Fi + γm Fi Ri(γc − γx )(1 − Fi )+γc (1 − Fi ) + γm Fi αδ γc (1 − Fi ) + γm Fi

)−1
,

(20)

and GDP divided by price of the investment goods is
Ni (pci ci + pxi xi )= constant × Kiα (li Ni )1−α Ωi
pxi

(

λi
Dii

x)
) θ(1−γ
γ
m

,

(21)

where
Ωi =

αδ
1 − Fi + γm Fi + R((γc − γx )(1 − Fi ))iγc (1 − Fi ) + γm Fi

.

(22)

In this equation, Ni and λi are exogenously given, while Ki , li , Ωi and Dii are endogenously
determined. Still, this equation is useful because the aggregate production is expressed as a
Cobb-Douglas production function.5
If labor-leisure choice is not considered, then li is constant and common across countries.
∑
When the tariﬀ rate is zero for all the pairs (ωij = 1), then Ωi = 1 (because Fi = j Dij ωij∑
and Dij is share, j Dij = 1). Finally, by assuming a perfect substitutability of consumption
and investment goods, pc = px . Under these three assumptions, the measured TFP depends
θ(1−γx )on (λi /Dii ) γm , which is obtained by Waugh (2010).6 Further, under autarky, Dii = 1 andhence the TFP term depends only on λi . This means that development accounting correctly
identifies the productivity parameter if the country is autarkic. Under the possibility of trade,the measured TFP includes additional terms: a term directly related to tariﬀs (Ωi ), and theother summarizing trade dependence (Dii ).
The question is how large λi is. Under autarky, the measured TFP has a one-to-one
relationship to this parameter. However, with the possibility of trade (as in the real world),
λi can be calculated only by solving the model.

5

The expression is normalized by the investment good price rather than the purchasing-power-parity (PPP).
By appropriately defining PPP in the model, I can obtain an alternative expression, but the expression is notas simple as (21).
6Mutreja et al. (2014b) also obtain an analogous expression. They have two types of capital goods, butthey do not have labor-leisure choice and tariﬀs.

10

3

Data, calibration and results

I first explain the data source and calibration strategy, and then present the results.

3.1

Data and calibration

3.1.1

Data

National accounting data comes from Penn World Table 8.0 (Feenstra et al., 2015).7 Throughout the paper, I use GDP per worker (employment) as a measure of the income. The observation year is 2005, but to minimize the eﬀects of business cycles, the values are averages of
2004–2006 values. The number of countries included in my analysis is 140. Availability ofvariables limits the countries included.8
Figure 1 shows capital-output ratio against income per worker across countries. Income
per worker is relative to the US, and the scale of the horizontal axis is the logarithm withbase 2. First, as is well known, income per worker greatly diﬀers across countries. Income inpoor countries is less than 1/64th of the US income level. Second, capital-output ratio is not
systematically correlated with income. Based on this fact, I use a simple method, varying theinvestment wedge τxi , to replicate observed capital-output ratio.
7
SWZ
MNG

K/Y ratio (domestic prices)6

y = 3.15 + 0.03x(0.12) (0.04)

5
NER
MWI

4

3

2

1

LBN

R 2=0.00

COM
LSO

JOR
UKR
CPV BTNTJK
DJI MDABHR
ALB ARG
SGP
JPN
ISL
PER
CYP
RUS SVN ITATHABRA URYECU
MYS
KAZ
MKDPRT FINGABNPLSENSRB
CZEPHLGEO TUN
ESP
KOR
VNM
CAF
LBR
STP
AUT
FRA
AUS
HKG
MRT CHNSYRCOL
DNK
BWA
BEL
LUX
VEN HRV
MAR
GRC
SAU
DEU
IDN
ETH TGOZMB LAO
MUS
CHE
MLT
USA
IRN
SVK
AGOYEM
BGD
LKA
HUN
PRY
NLD
ISR
MNE
SLE BEN
POL
NAM
FJI
MDG
GHA
PAK
OMN
TWN
BOL
CHL
LVA
MEX
BDI
CAN
EST
BRN QATNZL GBR
BFA
TZA
MDV
CMR
ARM ZAFNOR
BGR
MACKWT
KHM
SWE
AZE
IND
GMB
KGZ
LTU
KEN
CIV COG
BIHTUR
MOZ
MLI
GIN
EGY
NGA
IRL
UGA
RWA
TCD
SDN
GNQ

0
1/64

1/16

1/4

1

GDP (PPP) per worker, relative to US

Figure 1: Capital-output ratio (in domestic prices), 2005
Given that capital-output ratio is not strongly correlated with income per worker, diﬀerences in TFP should play a significant role in explaining cross-country income diﬀerences.
7

Data details are in Appendix A.
Typically, employment, capital or tariﬀ rates are missing. I also drop Guinea-Bissau, which shows an
extremely high capital-output ratio. (It is likely explained by a large reduction in output, which is caused bya civil war in 2005.) Contrary to Alvarez and Lucas (2007a), I do not include the “rest of the world” as anadditional country. The sum of the 140 countries I include accounts for more than 99% of the real GDP of
all the countries included in PWT in 2005.
8

11

Prod. param. implied by DA, relative to US

Figure 2 plots conventionally measured TFP using Cobb-Douglas production function withshare parameter α = 0.4 (which is used in my quantitative assessment).9 If TFP perfectly
explains the cross-country income diﬀerences, then the countries are on the 45-degree line
(blue line). Obviously, TFP is strongly correlated with income. The slope (red-dashed line)
is flatter than the 45-degree line, that is, TFP explains part of the income diﬀerences.
QAT

y = -0.01 + 0.59x
2(0.03) (0.01) R =0.97

1

1/4

1/16

BRN
IRL
NOR
KWT
MAC
GNQ
USA
SWE
LUX
GBR
CAN
NLD
OMN
BEL
HKG
CHE
TWN
AUS
AUT
DEU
FRA
DNK
ISR
SAU
FIN
TUR
NZL
ITA
BHR
SGP
ESP
GRC
JPN
KOR
ISL
LTU MLT
CYP
BIH
HRV
SVN
POL
HUN
SVK
CZE
EST
PRT
MEX
MNE
CHL
LVA
MDV
IRN
BGR
MKD
VEN
GAB
ZAF
RUS
MYS
ARG
SRB
URY
EGY
MUS
BWA
SDN
KAZ LBNTUN
ARMCOL
ALB
PER
BRA
ECU
AZE
NAM
SYR
FJI
THA
GEO
JOR
LKA
YEM
PRY
BOL
PAK
COG
UKR
CHN
IND
MAR
TCD
PHL
BTN
IDN
KGZ
NGA
MDA
AGO SWZMNG
TJK
CMRMRT
CIV
DJI
CPV
GHA
GMB
VNM
UGA
KEN
LAO
BGD
STP
MLI
GIN
BEN
KHM
RWA
SEN
ZMB
LSO
TZA
NPL
BFA
COM
SLE
TGO
MDG
MOZ
CAF
NER
ETH
BDI
LBRMWI

1/64

1/16

1/4

1

GDP (PPP) per worker, relative to US

Figure 2: Measured TFP relative to the US, 2005

GDP (Px) per worker, relative to US

In the model, GDP is normalized by the price of the investment good, rather than PPP.
Figure 3 examines whether GDP in PPP diﬀers drastically from GDP in investment goods
price. The countries are approximately on the 45-degree line. In this sense, using GDP in
investment goods price does not greatly change the implication of development accounting.

1

1/4

1/16

1/64

KWT
LUX BRN
BHR
SGP
NOR
USA
y = -0.05 + 1.10x
OMN
SAU
HKG
BELIRL
ISL
ITA
FIN
CAN
(0.04) (0.01) R 2=0.97
AUS
FRA
DNK
MAC
CHE
AUT
GBR
CYP
DEU
ISR
SWE
NLD
JPN
TWN
ESP
GRC
KOR
SVN
MLT
PRT
LBN HRVNZLCZEMYSCHLPOL
TUR
GAB
HUN
SVK
EST
MKD
URY
MEX
ARG
BIH
LTU
BWA
IRN
GNQ
JOR
LVA
ZAF
VEN
SRB
MNE
PER
RUS
TUN
MUS
ECU
BRA
MDV
COL
SYR
BGR
KAZ
THA ALB
NAM
YEM
FJI
EGY
SWZ
MAR
LKA
PHL
BTN
UKR
MNG
CPV
CHN
BOL
ARM
IDN
GEO
PRY
PAK
AZE
AGO
DJI
IND
SDN
MRT
NGA
MDA
VNM
COG
COM
BGD
SEN
GHA
LAO
CMR
STP
BEN
KHM
NPL
KEN
TCD KGZ
TJK
CIV
ZMB
MLI
MWISLEGMB
GINLSOBFA
TZAUGA
NER
TGO
RWA
MDG
CAF
ETH
LBR
MOZ
BDI

1/64

1/16

1/4

QAT

1

GDP (PPP) per worker, relative to US

Figure 3: GDP per worker relative to the US (PPP vs investment goods price), 2005
9

Precisely, TFPi = (Real GDP per worker)1−α /(capital-output ratio)α12

3.1.2

Calibration

Table 1 shows the parameter values common across countries. The parameter basically follows
Alvarez and Lucas (2007a,b).
Table 1: Common parameters
Parameter
α
γc
γx
γm
η
θ
δ
β
ψ
κ

Value
0.4
0.81
0.5
0.5
1.5
0.15
0.07
1.07−1
0.3
0.75

Interpretation
Capital-share in value-added (VA)
VA share in the consumption goods production
VA share in the investment goods production
VA share in the intermediate goods production
Substitution among varieties
Technology variation
Capital depreciation rate
Subjective discounting factor
Consumption share in the utility
Average iceberg trade cost

Alvarez and Lucas (2007a,b) set the share parameters (γc , γx , and γm ) based on the valueadded shares in gross output productions. Mutreja et al. (2014b) use slightly higher valuesfor γc while they use lower values for γx and γm . I conduct sensitivity analysis by changing
parameters.10
The number of workers equals employment in the PWT. In the calibration, this is Ni , not
Ni × li . Hence, my model suggests that development accounting includes the contribution ofendogenous labor choice li , which I analyze later.
Iceberg trade cost relates to distance. The calibration follows the procedure used by
Alvarez and Lucas (2007a) in their additional assessment. First, let distij denote the greatcircle distance between the largest cities of countries i and j, normalized so that the average
distance (excluding distii ) is unity.11 I then set κij = κ0 exp(−κ1 (distij − 1)). The elasticityof trade cost with respect to distance is κ1 = 0.3. The scale parameter κ0 is chosen so that∑ ∑the average trade cost, κ ≡ ni=1 j̸=i κij /n/(n − 1)/2, becomes 0.75. The value equals thebaseline of Alvarez and Lucas (2007a) in which they chose κ and κ1 based on Anderson andvan Wincoop (2004).12
Tariﬀ value is importer specific ωij = ωi for all j ̸= i, and ωi is one minus the most favorednation (MFN) tariﬀ value, excluding a pair of countries with a free trade agreement (FTA).
Tariﬀ rate is zero for a country with an FTA (If i and j have an FTA, ωij = 1. If not,
ωij = ωi = 1 − MFN tariﬀ valuei ).13 I use the MFN values calculated by the World Bank. As
10In this type of model, the elasticity of substitution across industries η does not play any critical role (seeAlvarez and Lucas, 2007a). The only technical restriction regarding η is 1 + θ(1 − η) > 0.
11
The distances are derived from Mayer and Zignago (2011).
12
Using this measure, the minimum trade cost occurs between Austria and Slovakia, while the maximum
trade cost occurs between Paraguay and Taiwan.
13
Appendix A lists the FTAs included in this paper.

13

shown in Figure 4, the tariﬀ value is negatively correlated with income. The red-dashed line
shows a regression line. The slope is negative and significantly diﬀerent from zero.
35
30

COM

y = 3.07 - 2.33x(0.52) (0.17)

DJI

R 2=0.46

Tariff rate (%)

25
20
15
10
5
0

BTN TUN
MDV
EGY
IRN
MAR
CMR COGGAB
GNQ
SDN
TCD IND

CAF
ETH
BDI

BGD
RWA GMBNGACPV
PAK
STP
KHM
SYR
TGO
ZMB
SLE
BEN
NPL
MWI
SEN
NER
CIV
KEN
TZA MLI
GHA
VNM
BFA
BRA
JOR VENMOZ
MRTLBR
COL ARG
THA
MEX
GIN
ECU
AZE
LKA
URY
SWZ
UGA
LSO
CHN
PRY
RUS
PER
MDG
KOR
FJI
YEM
ZAF
BWA
SRB
BGR
KAZ
BIH
MKD
LAO AGOBOL
MLT
ALBMYSLBN
UKR
IDN NAMGEO MUS MNE
TWN
PHL
SAU
BHR
TJK
MNG
AUS
MDA
CHL NZLOMNCAN KWT QAT
ARM
KGZ
ISR
ISL
JPN USA BRN
SVN
SVK
HRV
TUR
POL
CZE
CYP
EST
DNK
AUT
BEL
HUN
GBR
LVA
LUX
ITA
IRL
ESP
SWE
FRA
FIN
GRC
DEU
NLD
PRT
LTU
MACNOR
CHE
SGP
HKG

1/64

1/16

1/4

1

GDP (PPP) per worker, relative to US

Figure 4: Tariﬀ values, 2005
Two sets of parameters remain: the investment wedge τxi and the productivity parameterλi . They are calibrated to match capital-output ratio (20) and GDP divided by investment
goods price (21). That is, given all the parameters and hypothetical values of {λi , τxi }ni=1 , theexcess demand function (18) is solved. Next, I calculate variables in the equilibrium. The
search ends when the model and data values of capital-output ratio and GDP/px match.

3.2

Implied productivity

Figure 5 compares the productivity parameter obtained from the development accounting anda comparable parameter obtained from the model under the open-economy equilibrium.
θ(1−γx )/γm
Productivity parameters are λi, not λi . and relative to the US (that is, the US is
normalized to unity). As shown by equation (21), this transformation makes this productivity
parameter exactly comparable to the measured TFP from development accounting.
If international trade does not aﬀect outcomes, then the countries are on the 45-degreeline. Model values are positively correlated with the conventional measure of TFPs, butcountries are not on the 45-degree line. The productivity parameters are smaller under the
open-economy equilibrium.
A simple regression implies that the slope coeﬃcient is 0.66, and the intercept is almost
zero. This means that productivity parameters required for replicating observed cross-country
income diﬀerences is about 70% of what is required in the simple development accounting. In
other words, conventional TFP measures overestimate fundamental productivity diﬀerencesby 30%.
14

Prod. param. with trade, relative to US

1.8
1.6
1.4

y = -0.05 + 0.66x 2(0.01) (0.03) R =0.84

1.2
QAT

KWT
USA

1
0.8

AUS
SAU GBR
CAN
TWN
SGP
OMN
JPN
DEU
HKG
FRA
0.6
ITA
SWE
NZL
KOR ISRBELNLDESPCHELUX MACCHL GRC
AUT
TUR
BHR
DNK
FIN
ZAF
GNQ
0.4
MYS MEX
PRT
IRN
POL
ISL
CYP
BRAARGSVN
HUN
HRV
VEN CZE
GAB
BWA
MLT
EGY
RUS
PER
URY
COL
SVK
LTU
BIH
LBN
0.2
EST
THA
ECU
MUS
CHN
IND
TUN
BGR
SYR
SDN
LVA
YEM
SRB
NAM
IDN
MDV
KAZ
FJI
NGA
MKD
LKA
PAK
PHL
JOR
MAR
MNE
BOL
AGO
AZE
PRY
ARM
BGD
ALB
VNM
UKR
COG
CMR
SWZ
GHA
TCD
KEN
GEO
KHM
BTN
CIV
MRT
UGA
BEN
DJI
SEN
CPV
MLI
LAO
MNG
ZMB
TZA
GIN
NPL
KGZ
RWA
COM
BFA
MDA
MWI
GMB
SLE
MDG
MOZ
STP
ETH
LSO
TJK
TGO
BDI
NER
CAF
LBR

NOR
IRL
BRN

0

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

Prod. param. implied by DA, relative to US

Figure 5: Productivity parameters by development accounting vs model with trade
A regression imposing no interception implies that the slope is 0.59 (“Regression 2” ofrow (1) of Table 2). In this case, the implied variation in the productivity is even smaller
than 30%. An alternative measure of the productivity comparison is the ratio of the standard
deviations: the standard deviation of the productivity measure (relative to the US) calculated
from the model divided by that from the development accounting (“SD ratio”). This measurecaptures the diﬀerence in variation between two productivities. The ratio is 0.72. Again, theproductivity parameter in the model with trade has smaller variation than what development
accounting indicates. The ratio is also a measure of overestimation, and this measure implies
28% overestimation.
In summary, although the exact magnitude depends on the measurement ranging 28%–
41%, TFP measures of development accounting overestimate fundamental productivity differences.
Table 2: Trade barrier variations
Case

Regression 1

Regression 2 SD ratio

y = −0.05 + 0.66 x

y = 0.59 x

y = −0.03 + 1.00 x

y = 0.96 x

y = −0.05 + 0.61 x

y = 0.54 x(4) High trade cost, κ = 0.675 y = −0.06 + 0.72 x

y = 0.64 x

y = −0.06 + 0.65 x

y = 0.57 x(1) Baseline(0.01)(2) Autarky(0.01)(3) Low trade cost, κ = 0.825(0.01)(0.01)(5) No tariﬀ(0.01)

15(0.03)(0.02)(0.03)(0.03)(0.03)

0.72(0.02)

1.02(0.01)

0.67(0.02)

0.78(0.02)(0.02)

0.71

3.2.1

Trade barriers and implied productivity

Table 2 shows the role of trade barriers for the productivity calculations. Row (1) is thebaseline case.
Row (2) shows the case under autarky. There are slight diﬀerences in productivity parameters obtained by development accounting and by a model under autarky because eachmeasures labor slightly diﬀerently. For development accounting, labor is measured as thetotal number of employed workers (Ni ); for the model under autarky, labor is measured as
the total number of employed workers times average hours worked (Ni li ). As shown by Row(2), the productivity parameter diﬀerence is small. The contribution of leisure is small, andthe variation in τxi does not have a systematic consequence in the accounting.
Rows (3) and (4) examine robustness of the implication with respect to the value of κij .
A 10% lower trade cost (which means high κij by setting κ = 0.75 × 1.1) implies a slightly
larger impact of trade, and a 10% higher trade cost (κ = 0.75 × 0.9) implies a slightly smaller
impact. Nevertheless, the changes in the slope coeﬃcients and SD ratio are not large.
For (5), the tariﬀ is removed. This assumption is frequently used in the literature, inparticular by Waugh (2010) and Mutreja et al. (2014a,b). The exclusion of tariﬀ does notchange the implications. The slope coeﬃcients and SD ratio are close to the baseline values.
3.2.2

Parameter variations

Table 3 examines the sensitivity of the quantitative results to parameter changes. Again, Row(1) is the baseline case.
Rows (2) and (3) change a key parameter of the model, θ. The parameter θ translates λi tothe country’s productivity, and amplifies the eﬀects of comparative advantage. Large θ implies
a small diﬀerence in λi , which leads to a large diﬀerence in the aggregate productivity. In thiscase, even without large diﬀerences in the productivity, the model implies a large diﬀerence
in income across countries. The direct eﬀect of this transmission process is adjusted byθ(1−γx )/γm
comparing productivity with λi. This adjustment, however, does not adjust any
indirect eﬀects that arise through input-output and international trade chain.
The literature does not perfectly agree on the value of θ. Eaton and Kortum (2002),
Alvarez and Lucas (2007a), Waugh (2010), and Mutreja et al. (2014b), use 0.12, 0.15, 0.18,
and 0.25, respectively. Row (2), θ = 0.1, is a lower bound of these values, while (3), θ = 0.3,
is an upper bound. Changes in θ are quantitatively important. Under small θ, the impliedslope coeﬃcients and SD ratio are larger than the baseline. The international trade does
little to explain cross-country TFP variations. On contrary, under large θ, the contributionof trade becomes large. The magnitude of overestimation is in a range of 10%–70%. Amongthe values, θ = 0.15 gives fairly conservative numbers.
The remaining rows change the values of production share parameters (γc , γx , γm and α).
As shown by Rows (4) and (5), a change in γc does not aﬀect the implications. Under low
16

Table 3: Parameter variations
Case

Regression 1

Regression 2 SD ratio

y = −0.05 + 0.66 x

y = 0.59 x

y = −0.04 + 0.78 x

y = 0.72 x

y = −0.05 + 0.40 x

y = 0.32 x

γc = 0.70 (BL: 0.81) y = −0.05 + 0.66 x

y = 0.59 x

γc = 0.90 (BL: 0.81) y = −0.05 + 0.66 x

y = 0.59 x

γx = 0.45 (BL: 0.5)

y = −0.05 + 0.63 x

y = 0.56 x

γx = 0.55 (BL: 0.5)

y = −0.06 + 0.70 x

y = 0.62 x

γm = 0.45 (BL: 0.5)

y = −0.05 + 0.63 x

y = 0.56 x

γm = 0.55 (BL: 0.5)

y = −0.05 + 0.69 x

y = 0.61 x(10) α = 0.35 (BL: 0.4)

y = −0.07 + 0.67 x

y = 0.57 x

y = −0.04 + 0.66 x

y = 0.60 x(1)

Baseline (BL)(0.01)(2)

θ = 0.10 (BL: 0.15)(0.01)(3)

θ = 0.30 (BL: 0.15)(0.01)(4)(0.01)(5)(0.01)

(6)
(7)
(8)
(9)

(0.01)(0.01)(0.01)(0.01)(0.01)(11) α = 0.45 (BL: 0.4)

(0.01)(0.03)

0.72(0.02)(0.03)

0.82(0.02)(0.04)

0.50(0.02)(0.03)

0.72(0.02)(0.03)

0.72(0.02)(0.03)

0.69(0.02)(0.03)

0.75(0.02)(0.03)

0.70(0.02)(0.03)

0.74(0.02)(0.03)

0.73(0.02)(0.03)

0.71(0.02)(γc = 0.7) or high (γc = 0.9) share parameter, the regression lines are almost identical tobaseline , and the ratio of the standard deviations are also very close to baseline.
The choice of γx (Rows (6) and (7)) mildly aﬀects the implications. When γx is lower(higher), the slope is flatter (steeper) and the SD ratio is smaller (larger). The eﬀects are,however, small in absolute value. Rows (8) and (9) show the eﬀects of a change in γm . A
higher γm implies a flatter slope, but the change is small. The last rows, (10) and (11), show
the eﬀects of α, and the eﬀect of this parameter is also limited.
Overall, except for θ, variations in parameter values have limited impacts on the implications. The quantitative implication depends on θ. Even for small θ, the trade shows asignificant role for productivity calculations. A large θ amplifies the role of trade. Comparedto the literature, my baseline value yields a conservative result.

3.3

Trade barriers, productivity and income

The eﬀect of trade barriers on income is a subject of long-standing research, and Table
4 presents results through the lens of my model. This table includes only a few selected
countries to highlight the results. Complete tables are provided at the end of the paper(Table C1).
The calculations are as follows. First, I solve the equilibrium of the model under alternative (hypothetical) trade barrier assumptions while fixing baseline productivity parameters{λi , τxi }ni=1 . Second, I calculate hypothetical utility values for various scenarios. Finally, I
17compare the consumption-equivalent welfare changes of two alternative scenarios, and measure the eﬀects of various trade barriers on the economic welfare of countries.
Table 4: Eﬀects of trade barriers
Total Sym. Dist- Avg.
High Low No
Country
Code eﬀect TC ance tariﬀ FTA ACR TC TC tariﬀ
1 Sao Tome & Principe STP 5.48 6.25 0.93 0.95 1.00 2.20 0.88 1.12 1.06
2 Liberia
LBR 5.06 5.55 0.95 0.96 1.00 2.17 0.88 1.12 1.05
3 Gambia, The
GMB 4.34 4.78 0.98 0.93 1.00 2.10 0.88 1.12 1.08..
.
57 Luxembourg
LUX 2.51 2.17 1.17 0.98 1.01 1.79 0.89 1.11 1.01
58 Paraguay
PRY 2.49 2.83 0.90 0.94 1.04 1.77 0.88 1.12 1.02..
.
127 Mexico
MEX 1.36 1.40 0.98 0.96 1.04 1.29 0.90 1.12 1.01..
.
130 New Zealand
NZL 1.34 1.74 0.78 0.97 1.02 1.27 0.90 1.11 1.01..
.
138 Japan
JPN 1.12 1.17 0.97 0.99 1.00 1.10 0.96 1.05 1.01
139 Australia
AUS 1.09 1.31 0.84 0.99 1.00 1.07 0.97 1.04 1.01
140 United States
USA 1.05 1.07 0.98 1.00 1.00 1.04 0.98 1.02 1.00
Mean
2.40 2.51 1.01 0.96 1.00 1.66 0.89 1.11 1.04
Std.
0.89 1.01 0.10 0.03 0.01 0.27 0.02 0.01 0.03
Max
5.48 6.25 1.17 1.03 1.04 2.20 0.98 1.13 1.12
Min
1.05 1.07 0.78 0.89 0.99 1.04 0.87 1.02 0.97
The countries are listed in descending order of the “Total eﬀect.” The total eﬀect compares
the consumption-equivalent welfare of the observed income and the consumption-equivalent
welfare under autarky. The value means that for small countries (such as Gambia), the current
consumption is more than four times of the (hypothetical) consumption under autarky. The
value is smallest for the US, but even the US shows gains of 5%.
Arkolakis et al. (2012) provide an alternative formula to calculate gains from trade. Theyshow that for many trade models, gains from trade can be computed using the import penetration rate and the trade elasticity parameter. In particular, for a model of Eaton andKortum (2002), the import penetration rate is 1 − Dii , while the trade elasticity is θ.14 Using
the equilibrium values, the column “ACR” shows the total gains from trade calculated by theformula of Arkolakis et al. (2012). There are three important diﬀerences “Total eﬀect” and“ACR.” First, “ACR” does not include income change through tariﬀ revenue. However, theexperiment below shows that the quantitative eﬀect of tariﬀ revenue is small.
Second, and more important, the formula of Arkolakis et al. (2012) is based on the modelof Eaton and Kortum (2002), not Alvarez and Lucas (2007a). The practical diﬀerence isthat Eaton and Kortum (2002) include a nonmanufacturing sector to easily determine the14

The parameter θ in Alvarez and Lucas (2007a) is 1/θ of Eaton and Kortum (2002) and Arkolakis et al.
(2012). I follow the notation of Alvarez and Lucas (2007a).

18

wage, while Alvarez and Lucas (2007a) (and hence my model) solve the general equilibrium
to determine the wage.
Third, “ACR” does not include leisure, and this assumption also leads to a simple determination of wage. The second and the third diﬀerences reveal that the central diﬀerence
between “Total eﬀect” and “ACR” is whether consideration is given to general equilibriumeﬀects through wage change (for all countries).
A comparison between “Total eﬀect” and “ACR” shows that the discrepancy is large,
particularly for small countries; changes in the rest of the world dramatically aﬀect wages
in small countries. In contrast, changes in the rest of the world have a relatively smaller
impact on wages in large countries. In any case, both “Total eﬀect” and “ACR” show huge
welfare gains from trade, especially for small developing countries. At the same time, generalequilibrium eﬀects have significant quantitative impacts on the gains from trade.
The remaining columns, “Sym. TC,” “Distance,” “Avg. tariﬀ,” and “FTA,” decompose
the total eﬀects into the contribution of trade, geography, tariﬀs, and FTAs, respectively.
Column “Sym TC” considers the following situation: tariﬀ rates are zero for all the pairs,
and trade costs are set to κij = 0.75 for all i ̸= j. I compare the (consumption-equivalent)
welfare under this hypothetically “symmetric” world to the welfare under autarky. Basically,
the values in this column focus on the eﬀects of opening trade after eliminating all the heterogeneity in geography and trade policy. The values in “Sym TC” are descending, as seen in“Total eﬀect.” In this sense, simply opening trade is the main instigator of the welfare gains
from trade. However, the discrepancy is large for some countries.
The next column in Table 4, “Distance,” examines the eﬀects of introducing distance-based
trade costs. The column compares welfare under the symmetric world and welfare under acase with distance-based trade costs (as in the baseline) but without tariﬀs. The numeratorof the ratio does not consider any geographical features (or tariﬀs), but the denominator
explicitly include the eﬀects of distance. Note that, in this model, a country located near
many large, productive countries faces lower trade costs than countries more distant fromthese large producers. It follows that the proximate country can produce more than a countryof equal productivity that lacks easy access to productive trade partners. An interpretationof “Distance” column is the contribution of geographical proximity to productive countries
to income.
The values are large for small European countries such as Luxembourg. Other small
European countries (e.g., Belgium, Czech Republic) enjoy similar benefits (see Table C1).
These countries achieve high income partly from their proximity to large and productive
other European countries (as Germany and France). Even for large European countries (e.g.,
Germany, France, the UK), being close to one another increases income by approximately
10%. The value for Canada (1.13) is positive, indicating benefit from proximity to the U.S.
In contrast, geographically isolated countries show low values. Among developed countries,
New Zealand (0.78) and Australia (0.84) face the largest limitations to trade with other19

productive countries. Other Oceanian and countries in south Africa (e.g., Fiji, South Africa,and Mozambique) suﬀer from location disadvantage (see Table C1).
The next column in Table 4, “Avg. tariﬀ,” shows the ratio of the welfare under the
distance-based trade cost with no tariﬀ assumption to the welfare under the distance-based
trade cost with a country-specific uniform tariﬀ assumption.15 This column measures theeﬀects of average tariﬀ rate of a country on welfare. The values for developed countries areclose to unity since average tariﬀ rates are very low for developed countries. The eﬀects are notlarge for developing countries, as well. Presumably, imposing a high tariﬀ, makes diﬃcult fora country to enjoy production gains by importing productive intermediate goods. However,many of the trade partners of developing countries are developed countries in which tariﬀ rates
are low. Even for a developing country that imposes high tariﬀs so that the import price isdisturbed, exporting prices are not greatly disturbed. Moreover, tariﬀs directly contribute toincome through tariﬀ revenue. Consequently, a high tariﬀ rate does not necessarily imply low
welfare. In fact, the Comoros and Djibouti have two of the highest tariﬀ rates in the model(see Figure 4), but the eﬀect of tariﬀ is 0.96 for the Comoros and 0.92 Djibouti(see Table
C1). While these eﬀects are larger than average, the largest eﬀect is for 0.89 for Equatorial
Guinea (see Table C1). In addition, imposing a tariﬀ sometimes improves the welfare (1.03
for Swaziland). Nevertheless, the eﬀect of tariﬀ is, in general, negative (on average −4%),
and the negative eﬀects are prevalent for developing countries that impose high tariﬀ rates.
The last column, “FTA,” shows the ratio of the welfare under the distance-based trade
cost with country-specific uniform tariﬀ assumption (without FTAs) to the welfare under the
distance-based trade cost with FTA-adjusted tariﬀ assumption. A country with multiple FTA
partners has easy access to the partners’ inputs, resulting in high income.16 This eﬀect turns
out to be small for most of the countries for the several reasons. First, developed countries
already have a tariﬀ rate close to zero, so that additional eﬀects from FTAs are minimal.
Second, developing countries usually lack FTA partners, thereby minimizing eﬀects. Notable
exceptions are Mexico and Paraguay. While the average tariﬀ is relatively high (11.4) inMexico, which is included among high-income countries, it also has many FTA partners. The
negative eﬀect of tariﬀ for Mexico (0.96) is almost oﬀset by the positive eﬀect of FTAs (1.04).
In Paraguay, where the tariﬀ rate is 9.2, The FTAs (based on Mercosur) provide cheap accessto imports from Brazil and Argentina and increase the welfare.
The last three columns in Table 4 provide additional examinations: increasing the trade
costs by 10% (“High TC”), decreasing trade costs by 10% (“Low TC”), and removing all the
tariﬀs (“No tariﬀ”). For these columns, the denominator of the comparison is the baseline
welfare. Changes in trade costs almost uniformly translate into changes in the welfare. The
cross-country diﬀerences in the welfare gain are small. However, the US generally experiences
15

Note, however, that this model treats tariﬀ rates as exogenous parameters and does not include any
endogenous determination of tariﬀs. The values in this column do not necessarily capture causal impacts of
tariﬀs on income.
16
Again, the causal impact may diﬀer.

20

the smallest change.
The eﬀects of tariﬀ removal are not large in general. This small eﬀect reflects the small
impacts of trade policy. For some developing countries, however, the eﬀects are more that
5%.

3.4

Other variables

In this section, I examine other implied values of the model.
3.4.1

Labor

In the standard development accounting, labor is just Ni , number of employed workers in acountry. In my model, eﬀective labor is Ni li , which augments the endogenous labor-leisurechoice. As shown in (19), this li depends on τxi through Ri and other parameter values.
A question is whether a high τxi increases (or decreases) li so as to change the productivity. Figure 6 examines whether endogenous labor is systematically correlated with income.
Clearly, this is not the case. The endogenous part of labor forms a flat line, indicating thatthe productivity measure is not systematically contaminated with the endogenous componentof labor.
0.32
MNG
SWZ

Labor per worker

0.3

y = 0.25 + 0.00x(0.00) (0.00)

LBN

R 2=0.06
JOR
UKR

0.28

0.26

0.24

0.22

0.2

TJK
CPV
MDA
BTN

SGP
BHR
JPN
ITA
ALB ARG
CYP
ISL
SVN
FIN
BRA
MKDPRT
PERURYCOMCZE ESP
RUS
MYS
THA
HKG
AUT
FRA
AUS
ECUKAZDNK
PHLGEOBEL
SRB HRV
LUX
DEU
GRC
KOR
CHE
SEN
USA
SVK
CHN
SAU
NPL
MLT
COL
IDN
DJI
HUN
BWA
LBR
NLD
VNM
MUS
GAB
POL
ISR
STPMRT
LAO
PRY
VEN
MNE
LVA
SYR
MEX
AGO
CHL
CAN
CAF
EST
NAM
OMN
TWN
YEM
LKA TUNTGOZMB
BRN
NZL
GBR
BOL
MAR
FJI
MACNORQATETHMDGIRN
SWE
BGD
SLE BENLTU
KWT
GHAKGZ PAK ARM ZAF
BGR
BFA
TUR
TZA
BDI
AZE
IRL
BIH
IND
KHM
KEN
CMR
MDV
GMB
MOZ
CIV
GIN
MLI
UGA
NGACOG
EGY
RWA
TCD
SDN
GNQ
LSO

NER
MWI

1/64

1/16

1/4

1

GDP (PPP) per worker, relative to US

Figure 6: Equilibrium labor

3.4.2

Trade-GDP ratio

Figure 7 compares data and model values of imports to GDP ratio. When the model perfectly
replicates the ratio, the countries are on the 45-degree line.
Admittedly, the model does not work perfectly. Since my calibration does not aim to
mimic this import and GDP dimension at all, a perfect match is not expected. Moreover,21

0.9
y = -0.32 + 1.61x 2(0.11) (0.30) R =0.23

0.8

SWZ
LUX
NLD
EST

GNQ

0.7

IM/Y, data

0.6
0.5
0.4
0.3
0.2
USA

0.1

MYS
IRL

HUN
BHR
SVN
LSO
MLT
DNK
CHE
SVK
ISL
AUT
SWE
DEU
CZE
FIN
FJI
AGO
TWN
JOR
NOR
LTU
SAUQAT
NAM
OMN
BRN
DJI
ISR
CAN
CIV KWT MUSLVACYPGABBIHLBR FRA PRT
THAMRT
CPVITA
KORNZL
HRV
GBR
TUN
BGR
ZMB
MKD
SEN
BWA
MOZCHL
ESP
LBN
NGA
MAC
VNM KAZPOL
MDVMARVEN
MEX PHL MDA
COM
KHM
TGO
MNE
SYR
AUS ZAFMNG
STPECUSRB GRC
MLIAZEYEMPRYURY UKR
TUR
GIN
JPN
BTN
KEN
LKA
GHA
ALB
MWI
CHN
RUS
IDN
BOL
ARM
SLE
MDG
CMR
PER
IRN
GEO
ARG
NER
KGZ
BRATCD
COL
LAO
BGD
TZA
TJK
GMB
BFA
BEN
ETH
CAF
PAK
BDI
UGA
EGY
SDN
IND RWA
NPL
COG

0
0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

IM/Y, model

Figure 7: Import to GDP ratio, data and model(The figure excludes three outliers: Belgium, Hong Kong, and Singapore. The data-based importto-GDP ratios in these countries are approximately 1.2, 1.6, and 2.0, respectively, but the model
ratios are approximately 0.4. However, these three countries are included in calculations for the
regression line.)

in this simple framework, it is almost impossible to replicate trade-hub countries like Hong
Kong and Singapore (which, at noted, are outliers and do not appear in Figure 7). However,
the regression line, which is dotted line, has positive slope, so that the direction is correct.
3.4.3

Relative price

Hsieh and Klenow (2007) emphasize that cross-country diﬀerences in relative price between
consumption and investment goods in domestic prices help explain cross-country diﬀerences ininvestment rate, and my model provides a simple explanation of the cross-country diﬀerencesin the relative price. Researchers have long recognized the cross-country diﬀerence in theinvestment rate, but Hsieh and Klenow (2007) provide new perspectives: (1) investment rate
is positively correlated with income when investment rate is measured in PPP prices but is
not correlated when the rate is measured in national prices; (2) a high domestic relative priceof investment (to GDP price) in poor countries is a key driver to explain this gap; and (3) low
domestic consumption prices in poor countries explain the high price of investment goods inthose same countries. Hsieh and Klenow (2007) then argue that the relative diﬀerence acrosssectors in total factor productivity of investment and consumption goods can account for the
relative price diﬀerences.
In my model, the relative price is positively correlated with income driven by (1) the
diﬀerence in the total factor productivity of intermediate goods producing sector, and (2)the diﬀerence in the share of the use of the intermediate goods in producing investment and22

consumption goods. In particular, under autarky
θ(γc −γx )pci= Constant × λi γm .
pxi

(23)

Remember that in the baseline calibration, the production of consumption goods requires
fewer intermediate goods than the production of investment goods does, meaning γc > γx .
Since productivity λi is higher in richer countries, and the power term is positive, pci /pxi is
positively correlated with productivity, and hence income. Under open-economy equilibrium,
this simple formula does not hold, but the analogous eﬀect prevails.
KWT
BHR
SAU
OMN
SGP
ISLHKGp c/p x (domestic prices), data

LBN
MWI
YEM

JOR

QAT

COM

1

BWA
MYS
FIN
GAB KORITALUXNOR
DNK
CHE
PRT
CYP
CPV SYR
USA
ISR
TWN
BEL
CAN
AGO
CHL SVN
FJI
JPN
ECU
VNM
CHN
MAC
AUS
URY
DEU
GRC
MNG
THA
NPL BGDIRL BRN
MDVKHM
NAMBRA
FRA
GBR
BTN
AUT
PHL
MLT
ZAF
PER
DJI MAR
SWE
ESP
NLD
TUN
HRV
LAO
NZL
BEN
COL
SRB TURSLEIDNLKAPOLSENNGAIND
MKD
MNE
MEX
GHA MRTPAK EGY MUSARG
BDI
CZE
BIH
HUN
EST
BFA
TZAZMB
RWA
GNQ
NER
IRN
SWZ
ALB
LVA SVK
BGR
GIN
VEN
MLI TCDBOL
ETH TGO
KAZ
CMR
STP
PRY
RUSLTUKEN
LBR
UGASDN GEO
CAF
MDG
GMB
ARM
UKR
LSO
MDA
MOZ
AZE
COG
CIV KGZ
TJK

y = -0.02 + 0.12x
2(0.05) (0.02) R =0.28

1/4
1/64

1/16

1/4

1

GDP (PPP) per worker, relative to US

Figure 8: Relative prices of consumption and investment (data)p c/p x (domestic prices), data

LBN
MWI

1

JOR
YEM
COM
BWA
MYSKOR
FIN
GAB
ITA
NOR
DNK
CHE
LUX
PRT
CYP
CPV
USA
ISR
SYR
TWN
BEL
CAN
AGO
CHL
SVN
FJI
JPN
ECU
VNM
CHN
MAC
AUS
URY
DEU
GRC
MNG
BRN
THA
NPLKHM
IRL
MDV
NAM
FRA
BRA
GBR
BTN
AUT
BGD
PHL
MLT
ZAF ESP
DJI MARPERSWE
NLD
TUN HRVLAO
NZLBEN
SRB POL
SLE
TUR
LKACOLNGA
INDSEN IDN
MKD
MNE
ARG
GHA
MRTPAK EGY
MUSMEX
BDI
CZE
BIH
HUN
EST
BFA
RWA
NER TZA
IRN
SWZ
ZMB
ALB
LVA
BGR
TCD
SVK GNQ
GIN
VEN
MLI
BOL
ETH
TGO STPKAZ
CMRLTU
KEN PRY RUS
LBR
UGA
CAF
MDG GMB GEO SDN
ARM
UKR
LSO MDA
MOZ
AZE
COG
KGZ
CIV

y = 0.01 + 0.32x(0.05) (0.04)

TJK

1/4
1/16

KWT
BHR
SAU
OMN QAT
SGP
HKG
ISL

1/4

2

R =0.34

1

p c/p x (domestic prices), model

Figure 9: Relative prices of consumption and investment (model vs data)
Figure 8 shows the relative price of consumption and investment goods in PWT8.0 data.
23

As summarized by Hsieh and Klenow (2007), who used older data, the relative price is positively associated with income. Figure 9 shows the model values positively correlate with data
values, thereby explain a part of the variations in data. In the model, the main driver ofthe relative price diﬀerence is λi , and hence the variations are small. More country-specificparameters might explain the variations around the upward sloping line. Actually, Mutreja
et al. (2014b) almost perfectly replicate the relationship between the relative price and income,likely thanks to their more complex model structure and additional parameters. However,
the main point illustrated by these figures is that TFP of intermediate production is an important determinant of the relative price of investment to consumption goods if the share ofintermediate use diﬀers across sectors.

4

Conclusion

This paper considers the role of international trade to explain cross-country diﬀerences in
TFP. By introducing a multi-country Ricardian international trade structure in the standard development accounting model, I show that conventional TFP measures overestimate
fundamental productivity diﬀerences by 30%.
I then show that trade plays important role for determining economic welfare. Small
European countries enjoy 10–15% higher welfare through their proximity to large productive
countries, while Oceanian and countries in southern Africa suﬀer from 10–20% lower welfare
due to their remoteness. Moreover, average tariﬀ rates and free-trade agreements change
welfare up to 10% for some developing countries. By comparing trade gains with or without
general equilibrium eﬀects, the gains are in general larger for cases with general equilibriumeﬀects.
My model is silent about the fundamental source of productivity diﬀerence in intermediate
good producing sector. Yet, my result implies that adding this refinement to development
accounting can help make the challenging task of explaining TFP diﬀerences less diﬃcult.
However, depending on the trade frictions and production structures, a source of TFP diﬀerences may or may not be amplified. I leave other trade-incorporated development accountings
as a future task.

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——— (2007b): “Capital Accumulation and International Trade,” mimeo.
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70, 1741–1779.
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Gancia, G., A. Müller, and F. Zilibotti (2013): “Structural Development Accounting,” in Advances in Economics and Econometrics, Tenth World Congress of the Econometric Society, ed. by D. Acemoglu, M. Arellano, and E. Dekel, New York: Cambridge
University Press, vol. II: Applied Economics.
Hsieh, C.-T. and P. J. Klenow (2007): “Relative Prices and Relative Prosperity,” American Economic Review, 97, 562–585.
——— (2009): “Misallocation and Manufacturing TFP in China and India,” Quarterly Journal of Economics, 124, 1403–1448.
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2, 207–223.
Hulten, C. R. (1978): “Growth Accounting with Intermediate Inputs,” Review of Economic
Studies, 45, 511–518.
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Lucas, Jr., R. E. (1990): “Why Doesn’t Capital Flow from Rich to Poor Countries?”
American Economic Review, 80, 92–96.
Mayer, T. and S. Zignago (2011): “Notes on CEPII’s Distances Measures : the GeoDist
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Trade Flows, and Barriers to Trade,” European Economic Review, 70, 383–398.
Mutreja, P., B. Ravikumar, and M. Sposi (2014b): “Capital Goods Trade and Economic Development,” mimeo.
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A

Appendix A: Data

A.1

Data

A.1.1

Data sources

GDP, capital, employment and other basic variables are obtained from Penn World Table 8.0(Feenstra et al., 2015).
The tariﬀ data is obtained from the World Bank.17 The data has many missing observations, and for maximizing the availability of data, the MFN values are averaged over theperiod 2003–2007. If the value is missing for some of the years, I use the average of the valuesof the remaining (observable) years.
FTA data is taken from World Trade Organization.18 Among the FTAs eﬀective by
2005, the FTAs covering both goods and services are chosen. The list includes EFTA (3countries), EU (25 countries), Mercosur (4 countries), NAFTA (3 countries), and following bilateral (or one-to-an alliance) FTAs: Jordan–Singapore, India–Singapore, Thailand–
New Zealand, Japan–Mexico, Thailand–Australia, US–Australia, Mexico–Uruguay, Republicof Korea–Chile, US–Chile, US–Singapore, China–Macao, Singapore–Australia, China–Hong
Kong, Japan–Singapore, US–Jordan, New Zealand–Singapore, Chile–Mexico, Canada–Chile,
Colombia–Mexico, Australia–New Zealand, EFTA–Chile, EFTA–Mexico, EFTA–Singapore,
EU–Mexico, EU–Macedonia, EU–Chile17

http://econ.worldbank.org/WBSITE/EXTERNAL/EXTDEC/EXTRESEARCH/0,,contentMDK:
21051044~pagePK:64214825~piPK:64214943~theSitePK:469382,00.html
18
http://rtais.wto.org/UI/PublicAllRTAList.aspx

26

The distance data is basically taken from Mayer and Zignago (2011). I treat Serbia andMontenegro as diﬀerent countries, but the dataset does not. For calculating the distancefrom Montenegro, I first obtain the GPS code of Podgorica and then calculate the great-circle
distances to other countries using Havesine equation.
A.1.2

List of countries

Albania (ALB), Angola (AGO), Argentina (ARG), Armenia (ARM), Australia (AUS), Austria (AUT), Azerbaijan (AZE), Bahrain (BHR), Bangladesh (BGD), Belgium (BEL), Benin(BEN), Bhutan (BTN), Bolivia (BOL), Bosnia and Herzegovina (BIH), Botswana (BWA),
Brazil (BRA), Brunei (BRN), Bulgaria (BGR), Burkina Faso (BFA), Burundi (BDI), Cambodia (KHM), Cameroon (CMR), Canada (CAN), Cape Verde (CPV), Central African Republic(CAF), Chad (TCD), Chile (CHL), China (CHN), Colombia (COL), Comoros (COM), Congo,
Republic of (COG), Cote d’Ivoire (CIV), Croatia (HRV), Cyprus (CYP), Czech Republic(CZE), Denmark (DNK), Djibouti (DJI), Ecuador (ECU), Egypt (EGY), Equatorial Guinea(GNQ), Estonia (EST), Ethiopia (ETH), Fiji (FJI), Finland (FIN), France (FRA), Gabon(GAB), Gambia (GMB), Georgia (GEO), Germany (DEU), Ghana (GHA), Greece (GRC),
Guinea (GIN), Hong Kong (HKG), Hungary (HUN), Iceland (ISL), India (IND), Indonesia(IDN), Iran (IRN), Ireland (IRL), Israel (ISR), Italy (ITA), Japan (JPN), Jordan (JOR),
Kazakhstan (KAZ), Kenya (KEN), Korea, Republic of (KOR), Kuwait (KWT), Kyrgyzstan(KGZ), Laos (LAO), Latvia (LVA), Lebanon (LBN), Lesotho (LSO), Liberia (LBR), Lithuania (LTU), Luxembourg (LUX), Macao (MAC), Macedonia (MKD), Madagascar (MDG),
Malawi (MWI), Malaysia (MYS), Maldives (MDV), Mali (MLI), Malta (MLT), Mauritania(MRT), Mauritius (MUS), Mexico (MEX), Moldova (MDA), Mongolia (MNG), Montenegro(MNE), Morocco (MAR), Mozambique (MOZ), Namibia (NAM), Nepal (NPL), Netherlands(NLD), New Zealand (NZL), Niger (NER), Nigeria (NGA), Norway (NOR), Oman (OMN),
Pakistan (PAK), Paraguay (PRY), Peru (PER), Philippines (PHL), Poland (POL), Portugal(PRT), Qatar (QAT), Russia (RUS), Rwanda (RWA), Sao Tome and Principe (STP), Saudi
Arabia (SAU), Senegal (SEN), Serbia (SRB), Sierra Leone (SLE), Singapore (SGP), Slovak
Republic (SVK), Slovenia (SVN), South Africa (ZAF), Spain (ESP), Sri Lanka (LKA), Sudan(SDN), Swaziland (SWZ), Sweden (SWE), Switzerland (CHE), Syria (SYR), Taiwan (TWN),
Tajikistan (TJK), Tanzania (TZA), Thailand (THA), Togo (TGO), Tunisia (TUN), Turkey(TUR), Uganda (UGA), Ukraine (UKR), United Kingdom (GBR), United States (USA),
Uruguay (URY), Venezuela (VEN), Vietnam (VNM), Yemen (YEM), Zambia (ZMB).

B

Appendix B: Computation

See separate appendix.

27

C

Appendix C: Eﬀects of trade barriers (full table)
Table C1: Eﬀects of trade barriers (full table)

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42

Total Sym. Dist- Avg.
High Low No
Country
Code eﬀect TC ance tariﬀ FTA ACR TC TC tariﬀ
Sao Tome & Principe STP 5.48 6.25 0.93 0.95 1.00 2.20 0.88 1.12 1.06
Liberia
LBR 5.06 5.55 0.95 0.96 1.00 2.17 0.88 1.12 1.05
Gambia, The
GMB 4.34 4.78 0.98 0.93 1.00 2.10 0.88 1.12 1.08
Moldova
MDA 4.33 3.93 1.12 0.98 1.00 2.11 0.89 1.11 1.02
Central African Rep. CAF 4.26 4.81 0.95 0.94 1.00 2.07 0.88 1.11 1.07
Tajikistan
TJK 4.18 4.13 1.04 0.97 1.00 2.09 0.88 1.11 1.03
Cape VerdeCPV 4.14 4.22 0.99 1.00 1.00 2.03 0.88 1.12 1.01
Niger
NER 3.96 4.07 0.99 0.99 1.00 2.02 0.88 1.12 1.02
Kyrgyzstan
KGZ 3.90 3.92 1.04 0.96 1.00 2.06 0.88 1.11 1.04
Togo
TGO 3.89 4.32 0.95 0.95 1.00 2.03 0.88 1.12 1.06
Montenegro
MNE 3.87 3.52 1.13 0.97 1.00 2.05 0.89 1.11 1.03
BhutanBTN 3.84 3.86 1.01 0.98 1.00 1.96 0.88 1.12 1.02
Comoros
COM 3.79 4.54 0.87 0.96 1.00 1.93 0.88 1.11 1.04
BurundiBDI 3.76 4.51 0.91 0.92 1.00 2.01 0.89 1.11 1.09
Djibouti
DJI 3.75 4.15 0.98 0.92 1.00 1.94 0.89 1.11 1.08
Sierra Leone
SLE 3.68 4.08 0.96 0.94 1.00 2.00 0.88 1.12 1.06
Lesotho
LSO 3.63 4.50 0.83 0.98 1.00 1.99 0.88 1.12 1.03
Mongolia
MNG 3.60 3.46 1.04 1.00 1.00 1.99 0.88 1.12 1.00
Mauritania
MRT 3.53 3.68 1.00 0.96 1.00 1.98 0.88 1.12 1.04
Armenia
ARM 3.46 3.32 1.08 0.97 1.00 1.99 0.89 1.11 1.04
Albania
ALB 3.45 3.11 1.13 0.99 1.00 1.97 0.89 1.11 1.02
Georgia
GEO 3.40 3.24 1.08 0.97 1.00 1.97 0.89 1.11 1.03
Laos
LAO 3.31 3.45 1.00 0.96 1.00 1.96 0.88 1.12 1.05
GuineaGIN 3.29 3.64 0.97 0.94 1.00 1.95 0.88 1.12 1.07
Burkina Faso
BFA 3.25 3.52 0.98 0.94 1.00 1.93 0.88 1.12 1.06
Macedonia
MKD 3.23 2.91 1.13 0.98 1.01 1.95 0.89 1.11 1.02
Mali
MLI 3.20 3.50 0.98 0.93 1.00 1.93 0.88 1.12 1.07
Malta
MLT 3.16 2.90 1.11 0.97 1.01 1.94 0.89 1.11 1.02
RwandaRWA 3.11 3.74 0.92 0.91 1.00 1.91 0.89 1.11 1.10
Benin
BEN 3.01 3.36 0.96 0.94 1.00 1.88 0.88 1.12 1.06
LatviaLVA 2.99 2.68 1.13 0.98 1.01 1.91 0.89 1.11 1.01
Estonia
EST 2.99 2.70 1.12 0.98 1.01 1.91 0.89 1.11 1.01
SenegalSEN 2.96 3.12 0.99 0.96 1.00 1.85 0.88 1.12 1.04
Maldives
MDV 2.94 3.52 0.93 0.90 1.00 1.85 0.88 1.11 1.12
Chad
TCD 2.92 3.30 0.98 0.90 1.00 1.87 0.89 1.11 1.11
Bosnia & Herzegovina BIH 2.90 2.66 1.14 0.96 1.00 1.87 0.89 1.11 1.04
Swaziland
SWZ 2.88 3.36 0.83 1.03 1.00 1.80 0.88 1.12 0.97
Nepal
NPL 2.87 2.99 1.01 0.95 1.00 1.83 0.88 1.11 1.06
Malawi
MWI 2.82 3.35 0.87 0.97 1.00 1.81 0.88 1.11 1.03
Madagascar
MDG 2.80 3.51 0.84 0.95 1.00 1.84 0.88 1.11 1.06
Iceland
ISL 2.80 2.58 1.11 0.98 1.00 1.86 0.88 1.12 1.02
Azerbaijan
AZE 2.79 2.77 1.07 0.94 1.00 1.84 0.89 1.11 1.06
28

Table C1: Eﬀects of trade barriers (full table)

43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87

Country
Mozambique
Cote d’Ivoire
Congo, Rep. of
Ethiopia
Lithuania
Cambodia
UgandaJordanZambiaCyprusSerbiaSlovenia
Bulgaria
Slovak Rep.
Luxembourg
Paraguay
Croatia
Ghana
Tanzania
Ukraine
Macao
Cameroon
Fiji
LebanonKenya
Tunisia
Namibia
Hungary
Equatorial Guinea
MoroccoBolivia
Syria
Czech RepublicBahrain
Mauritius
Sudan
Gabon
Yemen
Kazakhstan
BruneiAngolaVietnam
Sri Lanka
Bangladesh
Denmark

Total Sym. Dist- Avg.
High Low No
Code eﬀect TC ance tariﬀ FTA ACR TC TC tariﬀ
MOZ 2.78 3.61 0.83 0.93 1.00 1.83 0.88 1.12 1.08
CIV 2.76 3.13 0.95 0.93 1.00 1.83 0.88 1.12 1.08
COG 2.76 3.36 0.91 0.90 1.00 1.81 0.88 1.11 1.11
ETH 2.75 3.07 0.97 0.93 1.00 1.80 0.89 1.11 1.08
LTU 2.73 2.45 1.13 0.98 1.01 1.85 0.89 1.11 1.01
KHM 2.73 3.00 0.99 0.92 1.00 1.81 0.88 1.12 1.08
UGA 2.70 3.11 0.93 0.94 1.00 1.82 0.89 1.11 1.07
JOR 2.67 2.49 1.07 1.01 0.99 1.77 0.89 1.11 1.00
ZMB 2.63 3.24 0.86 0.94 1.00 1.78 0.88 1.11 1.06
CYP 2.61 2.44 1.09 0.98 1.00 1.82 0.89 1.11 1.02
SRB 2.58 2.33 1.14 0.98 1.00 1.78 0.89 1.11 1.03
SVN 2.53 2.23 1.15 0.98 1.01 1.80 0.89 1.11 1.01
BGR 2.53 2.35 1.13 0.96 1.00 1.78 0.89 1.11 1.04
SVK 2.53 2.23 1.15 0.98 1.01 1.80 0.89 1.11 1.01
LUX 2.51 2.17 1.17 0.98 1.01 1.79 0.89 1.11 1.01
PRY 2.49 2.83 0.90 0.94 1.04 1.77 0.88 1.12 1.02
HRV 2.44 2.18 1.15 0.98 1.00 1.77 0.89 1.11 1.02
GHA 2.42 2.70 0.95 0.94 1.00 1.73 0.88 1.12 1.06
TZA 2.41 2.89 0.89 0.93 1.00 1.73 0.89 1.11 1.07
UKR 2.40 2.16 1.12 1.00 1.00 1.72 0.89 1.11 1.00
MAC 2.39 2.37 1.03 0.97 1.01 1.76 0.88 1.12 1.02
CMR 2.38 2.77 0.94 0.91 1.00 1.70 0.89 1.11 1.10
FJI 2.37 3.14 0.79 0.95 1.00 1.72 0.87 1.13 1.05
LBN 2.34 2.16 1.08 1.01 1.00 1.69 0.89 1.11 0.99
KEN 2.33 2.74 0.92 0.93 1.00 1.70 0.89 1.11 1.08
TUN 2.28 2.19 1.12 0.94 1.00 1.64 0.89 1.11 1.07
NAM 2.24 2.77 0.85 0.96 1.00 1.69 0.88 1.12 1.05
HUN 2.23 1.98 1.14 0.98 1.01 1.71 0.89 1.11 1.01
GNQ 2.21 2.63 0.94 0.89 1.00 1.66 0.89 1.11 1.12
MAR 2.20 2.16 1.09 0.94 1.00 1.63 0.88 1.12 1.07
BOL 2.20 2.58 0.90 0.95 1.00 1.67 0.88 1.13 1.06
SYR 2.19 2.15 1.08 0.95 1.00 1.64 0.89 1.11 1.05
CZE 2.16 1.89 1.15 0.98 1.01 1.68 0.89 1.11 1.01
BHR 2.15 2.12 1.04 0.98 1.00 1.65 0.89 1.11 1.03
MUS 2.15 2.68 0.83 0.96 1.00 1.66 0.89 1.11 1.04
SDN 2.13 2.39 1.00 0.90 1.00 1.64 0.89 1.11 1.12
GAB 2.11 2.41 0.93 0.94 1.00 1.58 0.89 1.11 1.06
YEM 2.10 2.21 1.00 0.96 1.00 1.63 0.89 1.11 1.05
KAZ 2.09 2.09 1.03 0.96 1.00 1.62 0.89 1.11 1.04
BRN 2.05 2.21 0.96 0.97 1.00 1.63 0.88 1.12 1.04
AGO 2.05 2.41 0.89 0.96 1.00 1.61 0.89 1.11 1.05
VNM 2.02 2.10 1.01 0.95 1.00 1.58 0.88 1.11 1.05
LKA 2.01 2.25 0.94 0.95 1.00 1.59 0.89 1.11 1.06
BGD 1.97 2.12 1.00 0.93 1.00 1.55 0.89 1.11 1.08
DNK 1.96 1.73 1.14 0.98 1.01 1.60 0.89 1.11 1.01
29

Table C1: Eﬀects of trade barriers (full table)
Country
88 Uruguay
89 Ireland
90 Finland
91 Portugal
92 Austria
93 Botswana
94 Switzerland
95 Poland
96 Ecuador
97 Philippines
98 Belgium
99 Greece
100 Oman
101 Sweden
102 Pakistan
103 Israel
104 Norway
105 Netherlands
106 Egypt
107 Venezuela
108 Nigeria
109 Qatar
110 Colombia
111 Turkey
112 Kuwait
113 Peru
114 Hong Kong
115 Russia
116 Thailand
117 Canada
118 Iran
119 Spain
120 Malaysia
121 Singapore
122 Indonesia
123 France
124 United Kingdom
125 Taiwan
126 Italy
127 Mexico
128 Germany
129 Argentina
130 New Zealand
131 Saudi Arabia
132 Chile

Total Sym. Dist- Avg.
High Low No
Code eﬀect TC ance tariﬀ FTA ACR TC TC tariﬀ
URY 1.96 2.27 0.87 0.95 1.04 1.58 0.88 1.12 1.01
IRL 1.94 1.72 1.14 0.98 1.01 1.60 0.89 1.11 1.01
FIN 1.91 1.74 1.12 0.98 1.01 1.58 0.89 1.11 1.01
PRT 1.89 1.73 1.10 0.98 1.01 1.58 0.89 1.11 1.01
AUT 1.87 1.65 1.14 0.98 1.01 1.57 0.89 1.11 1.01
BWA 1.86 2.32 0.84 0.96 1.00 1.53 0.89 1.11 1.05
CHE 1.84 1.63 1.15 0.99 1.00 1.55 0.89 1.11 1.02
POL 1.84 1.64 1.13 0.98 1.01 1.55 0.89 1.11 1.01
ECU 1.83 2.02 0.95 0.96 1.00 1.50 0.88 1.13 1.04
PHL 1.81 1.88 0.99 0.97 1.00 1.52 0.89 1.12 1.03
BEL 1.80 1.57 1.16 0.98 1.01 1.53 0.89 1.11 1.01
GRC 1.80 1.65 1.10 0.98 1.01 1.53 0.89 1.11 1.01
OMN 1.80 1.84 1.01 0.96 1.00 1.52 0.89 1.11 1.04
SWE 1.79 1.61 1.12 0.98 1.01 1.53 0.89 1.11 1.01
PAK 1.77 1.86 1.03 0.93 1.00 1.47 0.89 1.11 1.08
ISR 1.77 1.70 1.07 0.97 1.00 1.51 0.89 1.11 1.03
NOR 1.77 1.60 1.13 0.98 1.00 1.52 0.89 1.11 1.02
NLD 1.72 1.51 1.15 0.98 1.01 1.50 0.89 1.11 1.01
EGY 1.71 1.77 1.06 0.91 1.00 1.44 0.89 1.11 1.10
VEN 1.70 1.79 1.00 0.95 1.00 1.44 0.88 1.13 1.05
NGA 1.69 1.93 0.95 0.92 1.00 1.44 0.89 1.11 1.09
QAT 1.68 1.69 1.03 0.97 1.00 1.46 0.89 1.11 1.04
COL 1.60 1.73 0.97 0.96 1.00 1.39 0.88 1.12 1.04
TUR 1.60 1.51 1.10 0.98 1.00 1.43 0.90 1.11 1.03
KWT 1.56 1.55 1.04 0.97 1.00 1.40 0.90 1.10 1.04
PER 1.55 1.79 0.90 0.97 1.00 1.36 0.89 1.12 1.04
HKG 1.55 1.56 1.01 0.97 1.01 1.41 0.89 1.11 1.02
RUS 1.54 1.45 1.08 0.98 1.00 1.35 0.90 1.10 1.02
THA 1.53 1.62 0.98 0.97 1.00 1.35 0.90 1.11 1.04
CAN 1.49 1.33 1.13 0.97 1.02 1.37 0.88 1.13 1.00
IRN 1.49 1.52 1.05 0.93 1.00 1.30 0.90 1.10 1.07
ESP 1.48 1.37 1.10 0.98 1.01 1.37 0.90 1.10 1.01
MYS 1.45 1.57 0.95 0.97 1.00 1.32 0.90 1.10 1.03
SGP 1.45 1.55 0.94 0.97 1.02 1.35 0.90 1.10 1.01
IDN 1.43 1.61 0.92 0.97 1.00 1.31 0.91 1.10 1.03
FRA 1.41 1.28 1.11 0.99 1.01 1.32 0.91 1.10 1.01
GBR 1.38 1.26 1.11 0.98 1.01 1.30 0.91 1.10 1.01
TWN 1.37 1.40 1.02 0.97 1.00 1.28 0.91 1.10 1.03
ITA 1.37 1.27 1.09 0.99 1.00 1.29 0.91 1.10 1.01
MEX 1.36 1.40 0.98 0.96 1.04 1.29 0.90 1.12 1.01
DEU 1.35 1.24 1.10 0.99 1.01 1.28 0.92 1.09 1.01
ARG 1.35 1.57 0.86 0.97 1.02 1.25 0.91 1.10 1.01
NZL 1.34 1.74 0.78 0.97 1.02 1.27 0.90 1.11 1.01
SAU 1.34 1.36 1.01 0.97 1.00 1.25 0.92 1.09 1.03
CHL 1.33 1.61 0.85 0.96 1.01 1.27 0.91 1.10 1.03
30

Table C1: Eﬀects of trade barriers (full table)
Country
133 Korea, Republic of
134 India
135 China
136 South Africa
137 Brazil
138 Japan
139 Australia
140 United States
MeanMedian
Std.
Max
Min

Total Sym. Dist- Avg.
High Low No
Code eﬀect TC ance tariﬀ FTA ACR TC TC tariﬀ
KOR 1.32 1.32 1.03 0.97 1.00 1.23 0.91 1.10 1.03
IND 1.29 1.39 1.00 0.93 1.00 1.20 0.92 1.09 1.07
CHN 1.25 1.26 1.01 0.97 1.00 1.18 0.93 1.09 1.03
ZAF 1.20 1.52 0.82 0.97 1.00 1.15 0.94 1.07 1.04
BRA 1.17 1.34 0.89 0.98 1.00 1.12 0.94 1.07 1.02
JPN 1.12 1.17 0.97 0.99 1.00 1.10 0.96 1.05 1.01
AUS 1.09 1.31 0.84 0.99 1.00 1.07 0.97 1.04 1.01
USA 1.05 1.07 0.98 1.00 1.00 1.04 0.98 1.02 1.00
2.40 2.51 1.01 0.96 1.00 1.66 0.89 1.11 1.04
2.22 2.26 1.00 0.97 1.00 1.68 0.89 1.11 1.03
0.89 1.01 0.10 0.03 0.01 0.27 0.02 0.01 0.03
5.48 6.25 1.17 1.03 1.04 2.20 0.98 1.13 1.12
1.05 1.07 0.78 0.89 0.99 1.04 0.87 1.02 0.97

31