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The guts of a GUT: Elements of a Grand Unified Theory of Growth

The guts of a GUT: Elements of a Grand Unified Theory of Growth. Lant Pritchett LACEA November 12 th , 2010. Outline of the presentation. What is growth theory a theory of? The four facts a growth theory should explain

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The guts of a GUT: Elements of a Grand Unified Theory of Growth

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  1. The guts of a GUT:Elements of a Grand Unified Theory of Growth Lant Pritchett LACEA November 12th, 2010

  2. Outline of the presentation • What is growth theory a theory of? The four facts a growth theory should explain • Growth phases and phase transitions versus a single linear equation of motion • “Institutions”: general or specific? • Equations of motion for “institutions” a la Hirschman: “unbalanced growth” through “backward linkages” in institutions

  3. Four Facts about Growth • Small group of countries with sustained, non-accelerating, stable growth of 1.8-2.0 ppa producing very high levels of output. • Small group of countries very near subsistence (hence long-run growth near zero) • Small group of countries with very rapid growth over extended periods • Growth rates lack persistence over time—very low correlation of growth from one period to the next—growth is (mostly) an episodic condition (not a characteristic)

  4. First Fact: Long-run stability in growth among now leaders • The growth rate 1870 to 2003 of the 16 leading countries is 1.89 ppa with std dev across countries of only .33 • Predicting US GDP per capita in 2003 using only data from 1870 through 1907 and the simplest possible linear trend in natural logs produces a forecast off by 2 percent ( 29,037 actual versus 28,242 predicted GK 1990 dollars) • Median forecast error for 70 year ahead prediction of all leading countries from pre-depression data is 3.9 percent! • The median acceleration of these 16 countries from 1890-1915 versus 1980-2003 is only .14 ppa • High levels of per capita output produced by moderate, sustained, stable, non-accelerating growth.

  5. Long-run stability: example of US (Cover of Jones’s book on growth)

  6. Same figure, Denmark Predict 2003 levels—≈100 years ahead—almost perfectly Data 1890-1901 to estimate a trend

  7. Fact II: Small number of countries very near subsistence (hence zero long-run growth) • Can infer growth from level if you are willing to assume a minimum level of output—the “Adam and Eve” level • The maximum growth could have been over any period is the growth that takes you from “Adam and Eve” at the beginning to the current observed level. • Countries still near “Adam and Eve” levels implies slow growth—often very slow growth. • Conversely one can ask when the leaders were at the currently observed levels

  8. Poorest countries in Maddison data (GK 1990 $) have cumulatively very slow growth

  9. Mapping current income into modern economic history (post 1820)

  10. Mapping the poorest countries into pre-modern history Peace of Westphalia 1648

  11. Fact III: A small number of countries have grown 27 (or more) years at very rapid rates—2 to 3 times higher than the historical pace of the leaders

  12. Extended rapid growth episodes are concentrated in two regions (East Asia and Europe)

  13. Fact IV: Economic growth is (mostly) a condition of countries, not a characteristic • Characteristics are relatively stable empirical features—being left-handed, being tall (for people), having coast-line, speaking Spanish (for countries). • Conditions are relatively impermanent empirical features—having a cold, being hungry (for people), having just won the World Cup, having recently had an earthquake (for countries). • Characteristics have high persistence and high inter-temporal correlations (the left handed are left handed), conditions have low persistence and low inter-temporal correlations (people with colds are not “the colds”).

  14. Per capita growth is condition-like—R-squared of current growth on past growth is .05 at 5 year horizons and less than .13 even at 25 year horizons–in contrast population growth is a characteristic-like

  15. World distribution of N year growth rates across countries What a “growth as Characteristic” world would look like Proportion Of periods in Growth range Medium growth Country Low growth Country High growth Country World Distribution Growth Rate 0 2 4

  16. Growth as a condition—countries change growth rates and span the possible range of growth experiences Medium growth country which Spends more time in rapid growth and in slow growth than the world Distribution of episodes World Distribution

  17. Ghana—has more episodes of super-rapid growth and of negative growth than the world distribution More time in negative growth More time in rapid growth

  18. How leaders grow—centered in the middle (e.g. Great Britain) No negative No rapid

  19. What stars look like…Singapore with lots of very rapid and no negative (pretty characteristic-like growth)

  20. What a consistently slow grower looks like (Niger)

  21. But countries with the exactly same growth rate have very different distributions—Colombia, overall 1.9 ppa—concentrated

  22. Chile—growth of 2.1 ppa—but with more bust and more boom—roughly the world’s experience

  23. Brazil—overall 2.6 ppa—more boom, not so much bust, more stagnation

  24. Congo—grew “faster” (2.4 ppa) than Colombia or Chile—but most time was either boom or bust

  25. What “the same” growth looks like

  26. Encompassing theory to explain all four facts • Set of leading countries with steady growth around 2 ppa as an “absorbing” state • Set of lagging countries with growth near zero (for a very long time)—(some consistently, others booms followed by busts) • Set of countries with rapid growth (at least twice historical pace of leaders) for extended periods • Lots of countries doing all of the above (negative and zero and moderate and rapid growth) back and forth in episodic fashion (e.g. discrete looking starts and stops)—averaged out to near non-converging growth on the leaders

  27. “Institutions” are an important, causal, driver of levels of income (AJRobinson, Hall and Jones)—which we can identify because institutions have long persistence “Institutions Rule” in that they are claimed to drive out “policy” (e.g. Rodrik, Subramanian, and Trebbi, Easterly and Levine, AJRobinsonT) “Growth has low persistence” (Easterly et al.) “Growth is episodic” with discrete starts and stops (Ben David and Papell, Pritchett) Accelerations and decelerations of growth are common (Hausmann, et. al.) are common, even among poor countries (Jones and Olken) Big problem with a “unified” theory of growth

  28. Measured rankings of aggregated components of quality of “institutions” tend to be very stable

  29. Damn. Its both.

  30. What a single growth model (esp. single linear equation of motion) cannot do • Most “determinants” of growth are characteristics (e.g. having a coast, good “institutions”) have very high persistence, but growth has low persistence • Related, most growth equations cannot predict the onset of episodes of either growth accelerations or growth decelerations (Hausmann, Pritchett, Rodrik) • The magnitudes of growth dynamics are all out of whack with typical “micro” estimates—much larger total level “impacts” than would be predicted. • Parameter instability is a specification test—and regressions fail • Standard growth models are getting worse as more and more is “TFP”—the residual • Cannot distinguish between covariates that have impact “within state” versus variables associated with higher/lower growth state transitions—e.g. do “institutions” play a role within states or with transitions across states?

  31. The problem We now have a growth empirics (and some accompanying theories) that explains everything except precisely what we wanted a theory and empirics for—to tell us how to accelerate growth rates

  32. In waterfalls, water…well, it falls

  33. …except when it doesn’t: water has had a phase transition to ice

  34. Notation: πRC(..)—probability of transition from Rapid to Collapse, gi(..)—within state growth dynamics

  35. Two needs for a GUT of Growth • A “states and transitions” model that can explain phase transitions across growth states (e.g. from stagnation to boom, from boom to crisis)—and why some countries but not others stay in growth booms. • An equation of motion of “institutions”—how do “institutions” evolve to explain the four big facts of growth?

  36. “Institutions”: General or Specific? • While there are demonstrable general differences between countries in the overall quality of institutions, there are also huge variances of institutions within countries • The “quality” of the institutional environment as it affects specific industries (and/or firms) varies—the “institutions” for tea versus textiles versus pharmaceuticals • Moreover, with weak institutions there is huge variances across firms—the “policy action” that is the results from the application of the policy depends on how it is chosen

  37. Do the rules matter more or less than deals? More variance across firms than across countries

  38. “Favored” versus “Disfavored” firms have massively different experience—even with the same rules • Comparing Doing Business indicators of three different indicators from the Enterprise Surveys (e.g. days to get a construction permit, days to get an operating license, days to clear customs) • Massive differences between the DB estimates and ES estimates in general • Massive differences across firms—up to a year between 10th and 90th percentile in time to get construction permits

  39. Same country: Peru 90th percentile a year DB 210 days 10th percentile 9 days

  40. Three levers to explain the world • A “product space” with products arrayed conceptually according to the extent to which their “capability” or “functionality” inputs are similar • The “receptivity” with which sector/firm performance translates into increased public action to augment capability • The specificity of the “acceptable ask” in receptivity—person/firm specific to economy-wide (an element of politics which is “institutionally” constrained (or not)).

  41. An empirical product space (goods arrayed by how likely they are to be co-exported)

  42. A simplified, conceptual product space arrayed by the similarity of “public action” inputs (laws, regulations, infrastructure, skills, etc.) Cluster of activities with similar Public action inputs

  43. An industry produces when its “intrinsic” profitability (determined by technology, endowments, world prices) plus contribution of public inputs exceeds a threshold—once in production it climbs up towards the potential Cluster of activities with similar Public action inputs

  44. So far, this is “Monkeys and Trees”—The second lever is the receptivity is how the public inputs respond to production--first in height Firms/industries who are producing use some of their revenue/profits to ask for additional public actions---better regulation, protection, specific inputs, tax breaks Cluster of activities with similar Public action inputs

  45. The third element is the “acceptable ask”—what is it that a firm/industry can lobby for? Possibilities?

  46. The third element is the “acceptable ask”—what is it that a firm/industry can lobby for? Only level playing field—government is responsive, in principle, but only for “all actors” actions

  47. Moderate spillovers (to neighbors in this product space)

  48. Firm/industry specific ask—which could include harming (lowering profitability of) close competitors

  49. So, here is the dynamics: industry becomes “capable”, begins to expand output, attempts to pull up a tent after them, the shape of the tent is constrained Positive dynamic feedback loop with (a) receptivity and (b) spillovers

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