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Structural Transformation

Structural Transformation. Ricardo Hausmann Kennedy School of Government and Center for International Development Harvard University. Development seems to be more than producing more of the same. Increasing diversity Changing what you produce Self-discovery externalities

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Structural Transformation

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  1. Structural Transformation Ricardo Hausmann Kennedy School of Government and Center for International Development Harvard University

  2. Development seems to be more than producing more of the same • Increasing diversity • Changing what you produce • Self-discovery externalities • Coordination failures • Progress when progress is easy: quality improvements • Growth collapses

  3. Development entails diversification, not specialization Source: Imbs and Wacziarg (2003)

  4. Rich countries produce rich-country goods…

  5. Background: Hausmann, Hwang & Rodrik • Measuring the revealed ‘sophistication’ of exports • How sophisticated is a particular product? • Using this measure, how sophisticated is a country’s export basket?

  6. You become what you export: initial level of sophistication and subsequent growth

  7. What you produce is determined by a lot more than “fundamentals” (I) Partial associations between EXPY and human capital (left panel) and institutional quality (right panel)

  8. Problems with structural transformation • Information Externalities: • Self-discovery spillovers • Coordination Externalities • Public inputs and training externalities

  9. Coordination externalities and the evolution of comparative advantage

  10. Hausmann and Klinger (2007) • Every product requires a number of factors of production that are relatively specific • E.g. producing asparagus requires a certain type of soil, mechanized farming equipment, agribusinesses firms that know the market, • but also such “public goods” such as port infrastructure, road system, cold-storage facilities, phytosanitary regulations, market access agreements, etc.

  11. Implication • The distance from the products in which a country has accumulated its specific human capital to alternative products may affect the speed of its structural transformation • But what do we mean by “distance” and how would we measure it empirically?

  12. Monkeys & the Product Space • Our metaphor: • Products are like trees • Firms are like monkeys • Growth can happen by: • Having more monkeys in the same trees: more of the same • Improved quality in the same trees: move up the tree • Hwang 2006 finds rapid and unconditional convergence within trees • Or structural transformation: jumping to more valuable trees • HHR (2006) show that this last step drives growth in a significant fashion

  13. Empirical implementation • Monkeys tend to jump short distances • Control for any time-varying national characteristic • Human capital, rule of law, financial conditions • Control for any time-varying product characteristic • Price, PRODY

  14. Implementing the model • The ‘proximity’ (φ ) of two products captures how easily the capabilities to produce one can be used to produce the other: measure of the cost of jumping. • φAB = min {P(RCA A | RCA B),P(RCA B | RCA A)} • Proximity of Cotton Undergarments to: • Synthetic undergarments: 0.78 • Overcoats: 0.51 • Centrifuges 0.02 • Proximity of CPUs to: • Digital central storage units: 0.56 • Epoxide resins: 0.50 • Unmilled rye: 0

  15. Visual Representation of the Product Space

  16. New Work • “The Product Space and its Consequences for Economic Growth” with Hidalgo, Klinger & Lazlo-Barabasi • How can we map this product space visually? • Could the topography of the export space help explain bimodal income distribution and the lack of convergence?

  17. Step 1: Maximum Spanning Tree

  18. Step 2: Overlay Strong Links 0.4 > 0.4 – 0.55 0.55 – 0.65 0.65 <

  19. Step 3: Add Products Nodes sized according to World Exports, darker links are stronger (red is strongest)

  20. Nodes sized according to World Exports, darker links are stronger (red is strongest)

  21. Step 3: Add Products

  22. Regions Produce in Different Areas of the Space

  23. Malaysia: 1975-2000

  24. Malaysia 1975

  25. Malaysia 1980

  26. Malaysia 1985

  27. Malaysia 1990

  28. Malaysia 1995

  29. Malaysia 2000

  30. Monkeys jump to nearby trees

  31. 0 0 .5 .5 .6 .4 .5 .3 For all the surrounding trees you occupy, add their “proximity” (conditional probability) to the new tree, divided by the total number of ‘roads leading to Rome ’ Measuring density around a tree • We use these pairwise distances to measure how close a country’s entire export basket is to an unoccupied tree: Density This is a measure of the ‘density’ around a particular good

  32. Density for jumps (green) versus non-jumps (brown)

  33. Does the product space matter? • More formally, we estimate: where X is a vector of country+year and product+year dummies, controlling for all time-varying country and product-level characteristics. • Standard errors clustered at the country level, density normalized into units of standard deviation

  34. 1 standard deviation increase in density associated with 6.2 percentage point increase in the probability of having RCA in that good in the next period • The unconditional probability is 1.27%: almost 5-fold increase • This effect dominates the influence of having RCA in the Leamer or Lall category

  35. The model at the country level How green is your valley?

  36. Proposition • It is easier for a country to move to a higher EXPY if the unoccupied trees are near and fruity • We need an equivalent measure of “density” at the country level • We call it “open forest”

  37. 2,000 x 0 1,000 x .6 1,600 x .3 Take the scaled distance from the tree you occupy to trees you don’t Multiplied by the ‘fruitiness’ of the potential tree Open_forest • Open forest measures the value of the option to move to a higher EXPY • It calculates the value of the unoccupied trees, by weighing their proximity and their PRODY And add that together for the whole export basket

  38. open_forest vs. GDP p.c. (logs), 2000

  39. Open Forest & EXPY Growth 1-standard deviation in open forest is associated with higher EXPY growth of 1.6 percentage points per year.

  40. Quality improvements and convergence What happens when countries can upgrade within the same products? Based on Hwang (2007)

  41. There is no unconditional convergence of GDP per capita

  42. But there is unconditional convergence given the within-product quality distance to the frontier (Hwang 2006)

  43. The evolution of within-product quality (Hwang 2006) • Quality in any particular product converges to the frontier at a rate of 5-6% per year • This happens unconditionally • Countries that are further away from the quality frontier grow faster • When a country develops a new product, it tends to enter at a lower quality • Therefore, the development of new products creates more room for within-product quality upgrading, and subsequently faster growth

  44. Africa and LAC have the lowest gaps in the products they are in

  45. Recent work by Kugler, Stein and Wagner Does quality matter for jumping to new trees?

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