Export Survival and Comparative Advantage

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Export Survival and Comparative Advantage

Regional Seminar on Export Diversification, October 27-28, 2010

Bolormaa Tumurchudur, UNCTAD Miho Shirotori, UNCTAD

Alessandro Nicita, UNCTAD

(Work in progress)

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Overview

Export growth can be boosted

By exporting more of existing products (at the intensive margin)

By exporting more new products (at the extensive margin)

By fewer failures of exports (at the sustainability margin)

Extensive margin growth is more important than intensive margin growth

Hummels & Klenow (2005), Pham & Martin (2007), Brenton & Newfarmer (2007, 2009)

Durations of export relations are very low: median export spell length is one to two years

Besedes & Prusa (2006), Nitsch (2007)

Therefore the key element to achieving higher export growth are longer bilateral trade-relationships and higher survival rates of those.

However product churning is necessary to select core products

It is important for the design of export-promotion policies to search for

robust and policy related determinants of export survival.

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Research question

What is the role of comparative

advantage in Export Duration?

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Outline

Overview

Literature on Export Survival Determinants

How to define the product with Comparative Advantage?

Export duration: Prima-facie evidence

Survival Analysis

Results: Stratified Cox PH Model estimation

Conclusion

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Empirical Studies on Export Survival

Seminal work of Besedes & Prusa (2006a)

Nitsch (2009)

- using 10 year panel of German Imports he found that gravity variables influenced the duration of trade flows

Fugazza & Molina (2009)

extended the panel of bilateral flows between 96 countries using, as regressors, gravity variables and time required for export procedures as proxies for fixed costs.

Obashi (2009)

found that vertical relationships have lower hazard rates than the exports of final goods and that they are less sensitive to trade costs

Growing number of papers have used firm-level datasets :

Gorg et al. (2008) have found that longer survival for products located close to the firm’s core competencies and to the country’s comparative advantage by using Hungarian firm level data,

Carballo & Volpe (2008) studied how diversification strategies affect survival of firm

level activity by using Peruvian firm level data.

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Role of Comparative advantage in Export Survival:

How to define the product with comparative advantage?

Identify comparative advantage of a product is one way of providing guidance of supporting a particular sector (pick winners)

However as the traditional measure Balassa’s RCA index defines country’s current trade pattern, it cannot be used

UNCTAD developed an alternative approach by building a recent database on Revealed Factor Intensity Indices at the Product

Level. It extended the PRODY index developed by Hausman,

Hwank and Rodrik (2005) to Revealed Factor Intensities.

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Revealed Factor Intensity Indices

Methodology inspired by Hausmann, Hwang &

Rodrik (2007)

RFII for each traded good is calculated as a weighted average of the factor

abundance of the countries exporting that good.

Weights are a variant of Balassa’s RCA indices

∑

= _k ⁱ ⁱ

k t e

RFII ( ) ω

{ ⁱ ⁱ ⁱ }

i k h l

e ⇒ , ,

( ^/ ^/ )

i i

i k

k i i

i k

X X X X ω =

∑

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Distance to Comparative Advantage

Eucleadean distance between the vector of the country’s endowments and the vector of the product revealed factor intensity

We standardized the absolute differences between the product factor intensities and the country’s factor endowments to have zero mean and unit variance.

2 2

2 ( ˆ ) ( ˆ )

ˆ )

( _c _k _c _k _c _k

ck std k k std h h std l l

D = − + − + −

c c

c h l

k , , - country’s human capital, capital and land endowments

- revealed factor intensities of product k

k k

k h l

k ˆ , ˆ , ˆ

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Export duration: Prima-facie evidence

01020304050Percent

0 1 2 3 4 5 6 7 8 9 10

Export duration, in years

Whole sample 23'417'160 17% 7'819'052 2

Northern countries 15'323'754 16% 4'785'047 2 Developing Countries 7'906'207 19% 2'932'031 2

LDCs 187'199 35% 101'974 1

DCs and LDCs by regions

Africa 1'003'121 25% 434'223 1

Americas 1'955'757 23% 766'385 1

Asia 5'134'528 18% 1'833'397 2

Asian by region

Eastern Asia 1'741'382 13% 543'764 2

Pacific Asia 22'379 32% 11'199 1

South Asia 1'008'432 21% 394'696 1

Southeast Asia 1'737'659 20% 649'231 2

Western Asia 624'676 19% 234'507 2

Median duration time at risk Incidence

rate

No. Of subjects

Distribution of Export duration, uncensored observations

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Export duration and Comparative advantage: Prima-facie evidence

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1 1.2 1.4 1.6 1.8

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(mean) spell_lengh Fitted values

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Survival Analysis: Introduction

Allows us to focus on the long-term sustainability of trade relationships

Export duration represents the number of years during which country c exports k products to its partners

Allows us to deal with non negative survival times and their skewed distributions

Allows us to deal with censored observations

Start of study, 1993 End of study, 2003

censored

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Survival Analysis

Non parametric: Kaplan Meier Survival Estimation

Semi-parametric: Cox Proportional Hazard Model

Database

on bilateral trade flows from 1993-2003

93 exporters (26 North countries, 49 DCs and 18 LDCs)

Distance to comparative advantage (UNCTAD data on Revealed Factor Intensity Indices at the HS 6 digit level)

Examine the relationship between survival times distribution and some

covariates of interest.

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Non-parametric Analysis: Kaplan Meier

The probability that a trade relationship will survive longer than time t.

∏ ≤ ⎟ ⎟

⎠

⎞

⎜ ⎜

⎝

⎛ −

=

t

t j

j j

j n

d t n

S ) ˆ (

0.000.250.500.751.00

0 2 4 6 8 10

analysis time

High distance Low distance

Kaplan-Meier survival estimates

0.000.250.500.751.00

0 2 4 6 8 10

analysis time

DCs LDCs

North

Kaplan-Meier survival estimates

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Semi-parametric Cox Proportional Hazard Model

The Cox Proportional Hazard regression model is given by )

...

exp(

)

( t X ₁ ₁ X _p _p

h X t

h = β + + β

• The predictors, X ,... ₁ X _p are assumed to act additively on log h t x .

• log h t x changes linearly with the β s.

• The effect of the predictors is the same at all times t.

• Makes no assumption about the form of h (t )

The Stratified Cox Model allows the form of the underlying hazard function to vary across levels of stratification variables.

C j

X t

h j Z X t

h , = = _j ( ) exp( β ), = 1 ,... (number of levels in Z)

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Estimation results: Comparative Advantage

Stratified Cox Proportional Hazard estimation

All Northern

Countries DCs LDCs Asia

Initial export (log) 0.964* 0.972* 0.948* 0.952* 0.945***

(-9.14) (-7.53) (-9.11) (-4.45) (-6.37)

Distance (log) 1.093* 1.091* 1.087* 1.175* 1.062***

(14.49) (12.19) (6.61) (4.57) (3.69) Market Diversification (log) 0.271* 0.230* 0.318* 0.526* 0.260***

(-14.94) (-10.52) (-11.17) (-5.13) (-10.73) Product Diversification (log) 0.293* 0.388* 0.199* 0.568* 0.182***

(-9.11) (-5.09) (-10.47) (-3.29) (-9.22)

Observations 7,819,052 4,785,047 2,932,031 101,974 1,833,397

Robust z statistics in parentheses

* significant at 10%; significant at 5%; * significant at 1%

Fixed effects are controlled by stratifying at exporter, importer and product Clustered by reporter

227 . 0 1 227 . 1 1 093

. 1

1 ⁽ ¹⁰ ⁾

) 10

ln( − = ^Ln − = − =

β

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Estimation results: Export Diversification

Model 1 Model 2 Model 3 Model 4 Model 5

Initial export (log) 0.962* 0.964* 0.964* 0.966* 0.964***

(-8.48) (-9.14) (-9.18) (-8.49) (-9.18)

Distance (log) 1.161* 1.093* 1.046 1.067 1.046

(23.62) (14.49) (0.9) (1.35) (0.9)

Market Diversification (log) 0.271* 0.269* 0.266* 0.269*

(-14.94) (-14.97) (-14.81) (-14.97)

Product Diversification (log) 0.293* 0.294* 0.291* 0.294*

(-9.11) (-9.11) (-8.99) (-9.11)

Distance (log)Market Diversification(log) 1.067 1.069* 1.067***

(10.53) (10.71) (10.53)

Distance(log)Product Diversification(log) 0.979 0.976* 0.979***

(-3.41) (-4.25) (-3.41) Stratified by

Exporter yes yes yes yes yes

Importer yes yes yes yes yes

Product (HS 6 digit) yes yes yes yes yes

Clustered

Exporter yes yes yes yes yes

Importer

Observations 7'819'052 7'819'052 7'819'052 7'045'032 2'527'787

Robust z statistics in parentheses

* significant at 10%; significant at 5%; * significant at 1%

Stratified Cox Proportional Hazard estimation

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Conclusions

Median export duration is 2 years for the whole sample (7’819’052 obs)

Initially large exports will have longer duration

Robust negative relationship between the distance to comparative advantage and the export survival.

Further robustness check using Parametric Survival

(Duration) Models

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Export Survival and Comparative Advantage