COMPETITIVENESS ANALYSIS
Objectives
o
o
o
Analyzing potential for export growth/diversification
Ground the analysis in some sort of economic principles
Provide relevant advice to the government
 Practical leads for action
 Grounded in factual analysis
 Robust
1
THE PRINCIPLES: RICARDO
Postulats
• 2 countries (Portugal, GB)
• Two sectors (wine,drape)
• One production factor (labor),
• CRS
• No transport cost
• No government intervention (tariffs etc.)
• perfect competition (price = unit cost)
Wine
Drape
Endowments
(here, labor
forces)
Portugal
8
4
5
UK
1
2
20
Productivities
2
PORTUGAL’S OPPORTUNITY SET
Wine
40
Indifference
curve
Autarky consumption point
PPF (production. Possibility
frontier)
20
Drape
3
GB’S OPPORTUNITY SET
Wine
PPF
Autarky consumption point
20
Indifference curve
Drape
40
4
INTEGRATED WORLD EQUILIBRIUM UNDER FREE TRADE
Production
Consumption
Wine
Drape
Wine
Drape
Portugal
40
0
20
20
UK
0
40
20
20
Total
40
40
40
40
5
THE RYBCZYNSKI THEOREM
Steel
Effect of FDI
Initial PPF
Effect of immigration
Textile
6
THE HECKSCHER-OHLIN THEOREM
Production point
after structural
adjustment
Steel
Indifference curve
Relative price on word market
(textile cheaper)
Consumption
point after SA
“trade
triangle”
PPF
Textile
Relative autarky price
7
COUNTRY ENDOWMENTS: NEW MEASUREMENT
o
o
o
o
Human capital measured by workforce’s average educational attainment
Physical capital stock measured by investment updated by PIM
Arable land per worker
Subsoil natural resources stock measured in 1994 and 2000
8
HUMAN CAPITAL ENDOWMENT: MOROCCO VS. WORLD
Insufficient investment in education shows up through international comparison
o Morocco has been investing
o But the world has been moving faster
9
PHYSICAL CAPITAL ENDOWMENT: MOROCCO VS. WORLD
Distribution of capital around the world becoming increasingly bi-modal:
o
o
Many countries with very little capital (less than $50’000 per worker)
A few with 4 times that ($200’000 per worker or more)
10
WHY DO INDUSTRIAL POLICY?
Justifying industrial policy requires a market failure
o
o
Synergies
Imperfect information, …
Good 1
Good 1
E1
E1
World price line
World price line
PPF
PPF
E2
Good 2
Zone of increasing
returns/agglomeration in the
production of good 2
Good 2
Targeted by IP
11
«REVEALED» COMPARATIVE ADVANTAGE
Balassa’s revealed-comparative advantage index
X ki / X i
RCA 
Xk / X
i
k
Numerator: share of product (or sector) k in country i’s exports
Denominator: share of that product/sector in world exports
Problem: Doesn’t pick up latent comparative advantage
12
AN ALTERNATIVE MEASURE OF COMPARATIVE ADVANTAGE
Revealed capital intensity of a product:
o
o
o
Take all countries exporting that product
Calculate a pseudo-RCA measure for each
Take a weighted average of capital endowments using
this RCA measure as weight
X ki / X i
 
i  X ki / X i 
i
k
Ki
 k  i  i
L
i
k
Revealed most
intensive in
human capital
13
PUTTING THE MEASURE AT WORK
Revealed least
intensive in
human capital
14
ANALYSING EXPORT PORTFOLIOS: COSTA RICA
Baseline export portfolio: 1991-3
10
8
6
4
2
0
0
2
4
6
8
10
Revealed Human Capital Intensity Index
12
12
Export portfolio 2003-5
0
50000
100000
150000
Revealed Physical Capital Intensity Index
0
200000
50000
100000
150000
Revealed Physical Capital Intensity Index
200000
New products 2003-5
10
8
6
4
2
0
0
2
4
6
8
10
Revealed Human Capital Intensity Index
12
12
Deaths 2003-5
0
50000
100000
150000
Revealed Physical Capital Intensity Index
200000
0
50000
100000
150000
Revealed Physical Capital Intensity Index
200000
15
EXPORT DIVERSIFICATION: A GENERAL LAW
2
4
6
8
10
Theil index shown on vertical axis: measure of export concentration
Income level shown on horizontal axis (GDP per capita)
Countries first diversify, then re-concentrate
0
20000
40000
60000
GDP per capita, PPP (constant 2005 international $)
Theil index
Fitted values
80000
Theil index, Morocco
16
SOURCES OF EXPORT GROWTH
Intensive margin: higher volumes of existing products & destinations
New products
Export
growth
Extensive
margin
New destinations
Sustainability margin: Survival of new products/destinations
Largest contributors to export growth (across countries and time)
•
Intensive margin
•
New-destination margin
17
SOURCES OF EXPORT GROWTH: CROSS-COUNTRY EVIDENCE
Most export growth is at the intensive margin
Next come new destinations
New products almost negligible!
Expanding export relationships
New destinations, existing products
New products, existing destinations
New products to new destinations
Death of export relationships
Shrinking export relationships
-40
-20
0
20
40
60
80
100
120
18
AN ALTERNATIVE DECOMPOSITION OF INTENSIVE AND EXTENSIVE
MARGINS
Intensive and extensive margins
Hummels-Klenow’s original formulation (product-wise)
Let Ki be the set of products exported by country i, 𝑋𝑘𝑖 the dollar value of i’s exports of product k to the
world, and 𝑋𝑘𝑊 the dollar value of world exports of product k.
The (static) intensive margin is defined by HK as
𝐼𝑀𝑖 =
𝑋𝑘𝑖
𝑊
𝐾 𝑖 𝑋𝑘
𝐾𝑖
In words, the numerator is i’s exports and the denominator is world exports of products that are in i’s
export portfolio.
The extensive margin (also static) is
𝑋𝑀𝑖 =
𝑋𝑘𝑊
𝑊
𝐾 𝑊 𝑋𝑘
𝐾𝑖
19
Your market
share in your
export portfolio
1.2
Intensive and Extensive Margin in Products, 1998-08
1
Big fish in a
small pond
India
Indonesia
.8
.6
India
.4
Vietnam
Pakistan
.2
Intensive Margin
Indonesia
85
Vietnam
90
Pakistan
95
Extensive Margin
1998
2008
Small fish in
a big pond
100
Weight of your
export portfolio
in world trade
20
AN EXTENSION TO GEOGRAPHICAL MARKETS
Extension to geographical markets
Let Di be the set of destination markets where i exports (anything from one to 5’000 products—it
doesn’t matter), XiD the dollar value of i’s total exports to destination d, and XWd the dollar value of world
exports to destination d (i.e. d’s total imports). All these dollar values are aggregated over all goods.
Intensive margin
𝐼𝑀𝑖 =
𝐷𝑖
𝑋 𝑖𝑑
𝐷𝑖
𝑋 𝑊𝑑
𝐷𝑖
𝑋 𝑊𝑑
𝐷𝑊
𝑋 𝑊𝑑
Extensive margin
𝑋𝑀𝑖 =
21
Your market share in
your destination
portfolio
1
1.2
Intensive and Extensive Margin in Markets, 1998-08
Big fish in India
a
small pond
Indonesia
.8
.6
India
.4
Vietnam
.2
Intensive Margin
Indonesia
Small
fish in a
big pond
Vietnam
Pakistan
97.5
98
98.5
99
Extensive Margin
1998
2008
Pakistan
99.5
100
Weight of your
destination
portfolio in world
trade
22
ROLAND BERGER’S ANALYSIS FOR THE MOROCCAN GOV (I)
23
AN EXAMPLE: TEXTILE INDUSTRY ANALYSIS
bla
24
MARKET ANALYSIS
bla
25
RECOMMENDATIONS IN TERMS OF EXPORT PROMOTION (I)
bla
26
RECOMMENDATIONS IN TERMS OF EXPORT PROMOTION (II)
bla
27
TUNISIA’S EXPORT PROMOTION
Program covers 2005-2009
o
o
Mixture of matching grants and technical assistance to
 Develop an export activity/grow out of single-buyer relationships
 Get into new markets
 Export new products
455 firms had completed Famex programs at end-2009
Activities co-financed by FAMEX
o
o
o
o
o
Prospection: acquisition of information on foreign markets, purchase of data,
or missions abroad to visit foreign exhibitions
Promotion: production of marketing information (design, production and
publication of ads in various media), firm representation in fairs and
exhibitions, and mailings
Product development: production of samples, package design
Firm development: organizational issues like setting up a marketing watch,
an export cell, or an export-oriented business plan
Foreign subsidiary creation: legal, consulting, rental and salary costs for the
first year of establishment.
28
ESTIMATING « TREATMENT EFFECTS » (I)
Matching-DID estimator (see Heckman et al. 1998, Blundell & Costa Dias 2009):
ˆ PSM DID  iT S  yi   jCS wij y j 


yi  ln yi  ln yi , 1
τ is treatment year and wij are the weights used in the matching (kernel, NN).
o Compares the change in outcomes for FAMEX firms relative to the change in
outcomes for matched control firms before and after FAMEX
o Controls for differences in pre-treatment attributes through matching
Problem: Tunisian firms received FAMEX assistance in different years, so τ = τ(i),
and calendar time matters for performance
29
ESTIMATING « TREATMENT EFFECTS » (II)
One possible fix: restrict matching for treatment firm treated in τ(i) to
controls observed in τ(i):
ˆ PSM DID '  iT S  ln yi ,t (i )  ln yi ,t (i )1    jCS wij  ln y j ,t (i )  ln y j ,t (i )1 


Alternative: revert to a regression framework using propensity score as
weights (Hirano, Imbens and Ridder 2003).
That is, estimate a simple DID equation
 ln yit     I it  Xi γ   t  uit
with unit weights for treated firms and
𝑟𝑖 = 𝑝𝑖
1 − 𝑝𝑖
for controls.
30
EXPORTERS SPREADING THEMSELVES TOO THIN?
Cumulative effects:
o
o
Disappear after 2 years for export value
Remain significant up to 5 years for # of products and destinations
Difference
TY-(TY-1)
TY-(TY-1)
(TY+1)-(TY-1) (TY+2)-(TY-1) (TY+3)-(TY-1) (TY+4)-(TY-1) (TY+5)-(TY-1)
Estimator
PSM-DID
(0)
WLS reg.
(1)
WLS reg.
(2)
WLS reg.
(3)
WLS reg.
(4)
WLS reg.
(5)
WLS reg.
(6)
0.562
(2.66)**
R-squared
0.511
(3.08)***
0.17
0.707
(3.53)***
0.22
0.499
(2.27)**
0.22
0.120
(0.45)
0.22
-0.113
(-0.34)
0.23
0.008
(0.02)
0.25
Nb. destinations 0.113
(4.33)**
R-squared
0.150
(6.10)***
0.15
0.187
(6.81)***
0.19
0.178
(5.60)***
0.20
0.122
(3.43)***
0.22
0.113
(2.45)**
0.27
0.141
(2.65)***
0.28
Nb. products
0.147
(4.68)***
0.15
0.171
(4.62)***
0.19
0.163
(4.09)***
0.23
0.081
(1.77)*
0.25
0.119
(2.06)**
0.25
0.170
(2.67)***
0.28
12,263
12,263
9,915
7,526
5,087
2,656
Outcome
Total exports
R-squared
Observations
0.11
(5.59)***
BASELINE IMPACT EFFECT
Note: All regs include firm controls and year effects
31
RESULTS VS. EXPECTATIONS
FAMEX
5000
Initial expectations…
4000
3000
2000
1000
0
2004
2005
2006
2007
2008
2009
2010
…vs. what
happened
No FAMEX
Average Export value per firm (KTD)
Average Export value per firm (KTD)
No FAMEX
6000
FAMEX
6000
5000
4000
3000
2000
1000
0
2004
2005
2006
2007
2008
2009
2010
32
IMPACT EFFECT, BY OBJECTIVE
Effects on primary stated objective (along diagonal) do not appear either larger or
more precisely estimated; most precisely estimated effects are always on # of
destinations
Difference
TY-(TY-1)
TY-(TY-1)
TY-(TY-1)
Estimator
WLS reg.
WLS reg.
WLS reg.
Outcome
Total exports
(1)
Nb. destinations Nb. products
(2)
(3)
0.467
(1.15)
0.563
(2.48)**
0.184
(0.78)
0.144
(2.66)***
0.171
(4.95)***
0.085
(2.42)**
0.130
(1.89)*
0.156
(3.43)***
0.082
(1.72)*
0.17
12,263
0.16
12,263
0.15
12,263
Objective
Start exporting
New destinations
New products
R-squared
Observations
Dummies, add up to treatment
33
IMPACT EFFECT, BY ACTIVITY
Effects of prospection (getting to know) and promotion (getting known) seem most
significant (suggesting informational market failure?)
Difference
TY-(TY-1)
TY-(TY-1)
TY-(TY-1)
Estimator
WLS reg.
WLS reg.
WLS reg.
Outcome
Total exports
(1)
Nb. destinations
(2)
Nb. products
(3)
0.039
(2.03)**
0.028
(3.06)***
-0.014
(-0.96)
-0.022
(1.12)
-0.003
(-0.15)
0.007
(2.07)**
0.006
(3.20)***
0.000
(0.06)
0.001
(0.27)
-0.000
(-0.00)
0.009
(2.05)**
0.004
(1.11)
-0.003
(-1.39)
-0.002
(-0.41)
0.002
(0.49)
0.205
12,187
0.168
12,187
0.156
12,187
Activity (amounts in TND)
Market prospection
Promotion
Product development
Firm development
Foreign subsidiary creation
R-squared
Observations
Amounts
Total (program) amounts
disbursed by activity: See last
slide
Selection correction based on
PSM, i.e. corrects only for
selection into the program; not
into particular activity amounts
34
EXTERNALITIES
Impressive absence of results—like Bernard & Jensen 2004. Bad proxy? Or no
externalities? If anything, negative (also like B&J): poaching of managers/workers
using taxpayer’s money?
Estimator
Outcome
Exposure to FAMEX benef. t-1
Exposure to FAMEX benef. t-2
Exposure to FAMEX benef. t-3
Firm Fixed Effects (Within)
First diff. of total
First diff. of nb.
First diff. of nb.
exports
Products
destinations
Sample of control firms only
(3)
(4)
(7)
(8)
(11)
(12)
-0.016
(0.39)
0.037
(0.85)
0.012
(0.31)
-0.122
(1.39)
-0.019
(0.18)
-0.028
(0.28)
-0.060
(0.76)
0.004
(0.87)
0.005
(1.25)
0.005
(1.39)
0.000
(0.03)
-0.005
(0.47)
-0.004
(0.43)
-0.008
(1.11)
0.000
(0.03)
0.005
(0.83)
0.006
(1.12)
-0.022
(1.95)
-0.020
(1.44)
-0.015
(1.14)
-0.022*
(2.05)
2,618
Yes
Yes
Yes
0.02
10,316
2,618
Yes
Yes
Yes
0.02
7,802
2,618
Yes
Yes
Yes
0.01
10,316
2,618
Yes
Yes
Yes
0.01
7,802
2,618
Yes
Yes
Yes
0.02
10,316
2,618
Yes
Yes
Yes
0.02
7,802
Exposure to FAMEX benef. t-4
Number of firms
Firm fixed effects
Sector-year fixed effects
Location-year fixed effects
R-squared
Observations
35
A ROUGH COST-BENEFIT ANALYSIS
Estimated treatment benefit
o
o
o
Average annual export growth for control firms, 2004-2008: 8.35%
Estimated annual export growth for treated firms in treatment year:
8.35%*1.667= 13.9%
Average exports per firm in 2004: TND 2’308K
Estimated treated-firm exports in treatment year: TND 2’308K *1.139 = TND 2’629K
Estimated treated-firm exports in treatment year: TND 2’308K *1.083 = TND 2’501K
Difference attributable to treatment: TND 128.5K
Treatment cost
Average grant amount disbursed per Famex beneficiary: TND 21.7K
Total cost (matching grant + firm own expense) : TND 2*21.7K = TND 43.4K
Return based on impact effect
o On grant: TND 128.5k additional exports for 21.7k TND (6 for 1)
o On total investment: TND 128.5k for 43.4k TND (3 for 1)
Is exports the right metric? Value added? Profits?
36
ANALYSIS AND UNDERSTANDING «BIG HITS»
bla
37
SUMMING UP
… Your job
38