Competitiveness analysis

1. 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

2. 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)

3. Portugal’s opportunity set Wine Indifference curve 40 Autarky consumption point PPF (production. Possibility frontier) Drape 20

4. GB’s opportunity set Wine PPF Autarky consumption point 20 Indifference curve Drape 40

5. Integrated world equilibrium under free trade

6. The rybczynski theorem Steel Effect of FDI Initial PPF Effect of immigration Textile

7. Production point after structural adjustment PPF Relativeautarkyprice “tradetriangle” The heckscher-Ohlin theorem Indifferencecurve Steel Relativepriceonwordmarket (textilecheaper) Consumption point after SA Textile

8. 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

9. 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

10. 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)

11. Why do industrial policy? • Justifying industrial policy requires a market failure • Synergies • Imperfect information, …

12. «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

13. 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

14. Putting the measure at work Revealed least intensive in human capital

15. Analysing export portfolios: Costa Rica Baseline export portfolio: 1991-3 Export portfolio 2003-5 New products 2003-5 Deaths 2003-5

16. 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

17. 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

18. Sources of export growth: Cross-country evidence Most export growth is at the intensive margin Next come new destinations New products almost negligible!

19. An alternative decomposition of intensive and extensive margins

20. Your market share in your export portfolio Big fish in a small pond Small fish in a big pond Weight of your export portfolio in world trade

21. An extension to geographical markets

22. Your market share in your destination portfolio Big fish in a small pond Small fish in a big pond Weight of your destination portfolio in world trade

23. Roland Berger’s analysis for the Moroccan gov (i)

24. An example: textile industry analysis bla

25. Market analysis bla

26. Recommendations in terms of export promotion (i) bla

27. Recommendations in terms of export promotion (iI) bla

28. Tunisia’s export promotion • 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.

29. Estimating « treatment effects » (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

30. Estimating « treatment effects » (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.

31. Exporters spreading 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

32. Results vs. expectations Initial expectations… …vs. what happened

33. 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 Dummies, add up to treatment

34. Impact effect, by activity 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

35. 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?

36. A rough cost-benefit analysis 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?

37. Analysis and understanding «Big hits» bla

38. Summing up … Your job