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Competitiveness analysis. Objectives 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 . The principles: ricardo. Postulats

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Competitiveness analysis

  • Objectives

  • 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


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)


Portugal’s opportunity set

Wine

Indifference curve

40

Autarky consumption point

PPF (production. Possibility frontier)

Drape

20


GB’s opportunity set

Wine

PPF

Autarky consumption point

20

Indifference curve

Drape

40



The rybczynski theorem

Steel

Effect of FDI

Initial PPF

Effect of immigration

Textile


Production point after structural adjustment

PPF

Relativeautarkyprice

“tradetriangle”

The heckscher-Ohlin theorem

Indifferencecurve

Steel

Relativepriceonwordmarket

(textilecheaper)

Consumption point after SA

Textile


Country endowments: New measurement

  • 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


Human capital endowment: Morocco vs. World

  • Insufficient investment in education shows up through international comparison

  • Morocco has been investing

  • But the world has been moving faster


Physical capital endowment: Morocco vs. World

  • Distribution of capital around the world becoming increasingly bi-modal:

  • Many countries with very little capital (less than $50’000 per worker)

  • A few with 4 times that ($200’000 per worker or more)


Why do industrial policy?

  • Justifying industrial policy requires a market failure

  • Synergies

  • Imperfect information, …


«Revealed» comparative advantage

Balassa’s revealed-comparative advantage index

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


An alternative measure of comparative advantage

  • Revealed capital intensity of a product:

  • 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

Revealed most intensive in human capital


Putting the measure at work

Revealed least intensive in human capital


Analysing export portfolios: Costa Rica

Baseline export portfolio: 1991-3

Export portfolio 2003-5

New products 2003-5

Deaths 2003-5


Export diversification: A general law

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


Sources of export growth

Intensive margin: higher volumes of existingproducts & destinations

New products

Extensive margin

Export growth

New destinations

Sustainabilitymargin: Survival of new products/destinations

  • Largestcontributors to export growth (across countries and time)

  • Intensive margin

  • New-destination margin


Sources of export growth: Cross-country evidence

Most export growth is at the intensive margin

Next come new destinations

New products almost negligible!



Your market share in your export portfolio margins

Big fish in a small pond

Small fish in a big pond

Weight of your export portfolio in world trade



Your market share in your marginsdestination portfolio

Big fish in a small pond

Small fish in a big pond

Weight of your destination portfolio in world trade




Market analysis margins

bla




Tunisia’s export promotion margins

  • Program covers 2005-2009

  • 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

  • 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.


Estimating « treatment effects » margins(i)

  • Matching-DID estimator (see Heckman et al. 1998, Blundell & Costa Dias 2009):

  • τ is treatment year and wij are the weights used in the matching (kernel, NN).

  • Compares the change in outcomes for FAMEX firms relative to the change in outcomes for matched control firms before and after FAMEX

  • 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


Estimating « treatment effects » margins(ii)

One possible fix: restrict matching for treatment firm treated in τ(i) to controls observed in τ(i):

Alternative: revert to a regression framework using propensity score as weights (Hirano, Imbens and Ridder 2003).

That is, estimate a simple DID equation

with unit weights for treated firms and for controls.


Exporters s marginspreading themselves too thin?

  • Cumulative effects:

  • Disappear after 2 years for export value

  • Remain significant up to 5 years for # of products and destinations

Note: All regs include firm controls and year effects

Baseline impact effect


Results vs. expectations margins

Initial expectations…

…vs. what happened


Impact effect, by objective margins

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

Dummies, add up to treatment


Impact effect, by activity margins

Effects of prospection (getting to know) and promotion (getting known) seem most significant (suggesting informational market failure?)

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


Externalities margins

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?


A rough cost-benefit analysis margins

Estimated treatment benefit

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

On grant: TND 128.5k additional exports for 21.7k TND (6 for 1)

On total investment: TND 128.5k for 43.4k TND (3 for 1)

Is exports the right metric? Value added? Profits?



Summing up margins

… Your job


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