1. Lecture 4: Interpreting Productivity Differences

2. Cross-Country Income Differences

3. Quantitative fit of Solow model 10/23/2015Pasha, Macro Theory II3 The relation between Y/L and s, n, e in steady state

4. Taking the prediction to data 10/23/2015Pasha, Macro Theory II4  is about 1/3,  = ½. g +  ≈ 0.06 Plug in values for saving rates, population growth rates, and e: get predicted Y/L Assume  piecewise linear, declining in e

5. Taking the prediction to data 10/23/2015Pasha, Macro Theory II5 Solves problem that we don’t see E For any country i, Present actual and predicted values relative to U.S.

6. Predicted y doesn’t vary enough 10/23/2015Pasha, Macro Theory II6

7. Problem with the Solow model or  ? 10/23/2015Pasha, Macro Theory II7  determines how differences in s and n map to Y/L If  were higher, differences in s and n would be magnified Reasons why  might be higher Increasing returns to scale Externalities to physical capital

8. III. Productivity Differences

9. Development accounting 10/23/2015Pasha, Macro Theory II9 Assume a Cobb-Douglas production function for every country Then, for any country i:

10. Development Accounting 10/23/2015Pasha, Macro Theory II10 Thus, relative to the U.S., productivity in i is given by Then we can write output in any two countries—say, the U.S. and country i, as:

11. E & Y/L: Hall-Jones (1999) 10/23/2015Pasha, Macro Theory II11

12. What do we conclude? Productivity differences are a big reason for differences in per-capita income – The R 2 is about 0.8, so productivity explains the lion’s share of the variation Explains major puzzles – Why high-human-capital people leave poor countries – Why relatively little physical capital flows to truly poor countries But the Solow model says nothing about why productivity might vary across countries 10/23/2015Pasha, Macro Theory II12

13. Caveats Estimated E is a residual: All errors / mis-specifications end up there Everything is being done under the twin assumptions of CRS and competition These allow us to set  as 1/3 from capital’s share If true  is higher, then much less need (or evidence) for productivity differences 10/23/2015Pasha, Macro Theory II13

14. Reminder: Estimating Productivity Differences

15. Basu, Macro 750Lecture 5 Fall 200515 Development Accounting Thus, relative to the U.S., productivity in i is given by Then we can write output in any two countries—say, the U.S. and country i, as:

16. Basu, Macro 750Lecture 5 Fall 200516 Why Y/L varies: Hall-Jones (1999)

17. Basu, Macro 750Lecture 5 Fall 200517 What is “productivity”? Cross-country productivity differences may mean several different things Might be “real” or measurement error Measurement error in input “efficiency” or “quality” exaggerates measured  E Example: Labor input in poor countries less efficient because of lack of nutrition and medical care in poor countries Taken into account, implies smaller productivity differences

18. Basu, Macro 750Lecture 5 Fall 200518 What might cause “real” productivity differences? Higher prevalence of diseases and pests in tropical countries Technology differences across countries – What are these? How do they come about? Institutional differences more broadly Anything that leads to less efficient allocation of resources would be lower “productivity” – Wrong ‘industrial policy,’ lots of corruption, inefficient credit markets, high level of distortionary taxation, etc.

19. I. Health

20. Basu, Macro 750Lecture 5 Fall 200520 Does health explain  E i s? There are significant differences in the Adult Mortality Rate (AMR) across countries – Latest UN Human Development Report: A citizen of Zambia today has a lower chance of living to age 30 than someone born in Britain in 1840 Among other things, means that workers in poor countries have lower effective labor input than workers in rich countries Weil (2005) looks at micro data on how health affects wages, then uses cross-country data on health to calculate “direct effect” of health on Y/L Direct effect might account for as much as 35 percent of the variation in Y/L across countries

21. Basu, Macro 750Lecture 5 Fall 200521 Indirect effects of health Recall your problem set: Lower expected lifetime leads to optimal choice of fewer years of schooling – What’s the intuition? Other indirect effects? Very hard to guess size of indirect effects Even for high end of plausible magnitudes, direct effect still leaves at least 30-40 percent Y/L differences unexplained

22. Basu, Macro 750Lecture 5 Fall 200522 Proximate vs. causal effects Clearly, accounting for health differences reduces size of cross-country productivity differences But is health an exogenous reason for poverty, or is poor health due to poverty? Is there a parallel with the discussion of growth accounting?

23. II. Geography

24. Basu, Macro 750Lecture 5 Fall 200524 Strong correlation Source: McArthur-Sachs (2001, NBER wp 8114)

25. Basu, Macro 750Lecture 5 Fall 200525 (Mis-)Fortunes of location Strong negative correlation between low Y/L and living closer to the equator One way to interpret this: Geography is critical determinant of development View of Landes (1998) and Sachs and co-authors (2000, 2001, 2002, 2003, etc) Diamond (1997) has a different argument

26. Basu, Macro 750Lecture 5 Fall 200526 Why? Diamond (1997) claims gains from living in continents that are long East-West instead of North- South: transmission of useful ideas Landes/Sachs et al. stress diseases/pests In tropics, no hard winter to kill pests that destroy crops Several tropical diseases highly virulent, and need warm ambient temperature (e.g., malaria) Sachs argues this is the major reason why malaria was easy to destroy in the U.S. South, but still persists in the tropics

27. Basu, Macro 750Lecture 5 Fall 200527 Should we be sceptical? Possible that malaria was destroyed in U.S. because U.S. is rich, not because its latitude is higher Again, argument of reverse causation: Is disease prevalence due to poverty? Sachs (2005) argues this direction too But to explain correlation in data, need some other reason why tropics are poor

28. III. Institutions

29. Basu, Macro 750Lecture 5 Fall 200529 Institutional quality Indexes of corruption, effective legal systems, vote-buying, etc. show a strong tendency for poor countries to have bad institutions These worsen resource allocation, for example via cronyism or outright theft Suggests that good institutions may be part of higher productivity of rich countries Note: Some “bad” institutions may be good for Y/L—e.g., “liberal dictatorship,” as in Chile – Democracy uncorrelated with growth (Barro)

30. Basu, Macro 750Lecture 5 Fall 200530 How to investigate? Correlation does not establish causation Being poor due to low productivity may make institutional quality worse, not the other way around So we need to find cases where institutions vary for reasons unrelated to (current) income In econometrics terminology, we need to find an instrument

31. Basu, Macro 750Lecture 5 Fall 200531 The IV strategy Thus, we look for some variable Z that is correlated with Inst but uncorrelated with , and use Z as an instrument: We can express per-capita income as a regression: where Inst is institutional quality, X is all other variables that affect income, and  is an error term Our concern is that  might be correlated with Inst

32. Basu, Macro 750Lecture 5 Fall 200532 New proposed instrument Mortality rate of early colonialists (Acemoglu- Johnson-Robinson in a series of papers) Historical argument: Institutions “better” where settlers meant to live; more “extractive” where they just wanted to plunder and leave Settler mortality found to be a good predictor of institutional quality today (Z is relevant) Settler mortality not influenced by today’s wealth/poverty (Z is exogenous)

33. Basu, Macro 750Lecture 5 Fall 200533 Correlation between 19 th century settler mortality and current Y/L Source: McArthur-Sachs (2001, NBER wp 8114)

34. Basu, Macro 750Lecture 5 Fall 200534 Results, attacks, rebuttals AJR: Exogenous variation in institutions explains a lot of productivity and Y/L differences across countries Sachs and others: Settler mortality just proxy for diseases today AJR: Include current disease rate as a control (in X ). Disease insignificant; Inst significant Sachs (2003, NBER wp 9490): Regresses Y/L on Inst, instrumented with LMORT, but put in malaria as X variable (also instrumented) Finds malaria is significant (as is Inst)

35. Basu, Macro 750Lecture 5 Fall 200535 What do we conclude? Institutions seem important Debate is whether institutional quality accounts for all differences in income Not clear that “institutions rule” (Rodrik et al., 2004, NBER wp) This is the research frontier Note: Debate is driven by ideas and insights, not technique