1. The Economics of Poverty and Development Alessandro Tarozzi February 13, 2016

2. % of population living on less than $2 per day Based on the 2009 UN Human Development Report 2

3. Literacy % population age 15 and above who can, with understanding, read and write a short, simple statement on their everyday life. Source: 2009-2013, From World Bank Development Indicators For updates and many more indicators see http://data.worldbank.org/indicator/http://data.worldbank.org/indicator/ 3

4. Total Fertility Rate (TFR) # children that would be born to a woman if she were to live to the end of her childbearing years and bear children in accordance with current age-specific fertility rates. Source: 2009-2013, From World Bank Development Indicators 4

5. % Births attended by skilled health staff Source: 2009-2013, From World Bank Development Indicators 5

6. Access to Sanitation % of the population using improved sanitation facilities (flush to piped sewer system, septic tank, pit latrine or ventilated improved pit latrine, or pit latrine with slab, or composting toilet.) Source: 2009-2013, From World Bank Development Indicators 6

7. Malaria in 2014 (# reported cases) Source: World Malaria report 2014World Malaria report 2014 7

8. 8 But progress has been made

9. HIV Prevalence among 15-49 Source: 2009-2013, From World Bank Development Indicators 9

10. Food Insecurity Source: The State of Food Insecurity in the World 2013, FAOThe State of Food Insecurity in the World 2013, FAO “A total of 842 million people in 2011–13, or around one in eight people in the world, were estimated to be suffering from chronic hunger, regularly not getting enough food to conduct an active life.” FAO 2013, p. 2 10

11. Children U5, % Stunting Children U5 with “low height” given age and gender Source: 2009-2013, From World Bank Development Indicators 11

12. Children U5, % Underweight Children U5 with “low weight” given age and gender Source: 2009-2013, From World Bank Development Indicators 12

13. Missing Women Source: Anderson and Ray, Review of Economic Studies (2010) 77, 1262–1300 13

14. Not clear that things will improve with economic development Source: The HinduThe Hindu 14

15. Child Mortality (# per 1,000 live births) Source: 2009-2013, From World Bank Development Indicators 15

16. Life Expectancy at Birth and National Income (The Preston Curve) - 2008 Source: Deaton (2013): The Great Escape 16

17. Is it just money? 17

18. How can we change this? Economic theory –Endogenous growth models Schooling (“human capital”) R&D –Trade –Public Economics (Tax systems) However, it may not be so easy to use this for policy: –How do we improve schooling? More schools? More teachers? Better teachers? More scholarships? Free education? 18

19. How can we test if a policy will work? Theory always a good starting point –E.g. contract theory can tell us what to expect if we change teachers’ incentives But needs to be grounded in reality –E.g. a model that assumes that teachers can be fired and their performance can be observed may not be useful in some situations Data can be used to test theories empirically But this can be very hard 19

20. “Endogenous” Program Placement 20 Do bednets increase malaria risk?

21. Endogenous Program Take-up 21 Are micro-loans good for business?

22. Counterfactual are not... factual Ideally, we would like to observe reality with and without a given policy But in reality, counterfactuals are not observed Massive literature in econometrics to address this problem In recent years, in Development Economics (and in other applied fields as well) an “old” methodological tool has become prominent 22

23. Randomized Controlled Trials In general, correlation between X and Y does not imply causation But what if you can assign X randomly? E.g. suppose you want to see if a program that changes incentives to teachers improves schooling outcomes 23

24. Carry out the new teacher program only in some schools, while others continue with usual business New Program: Treatment Status Quo: Control If assignment is truly random, the program should be the only reason why outcomes different between Treatment and Control schools 24 Randomized Controlled Trials

25. Impact Evaluation with RCT Difference-in-Differences (3.5-2.1) – (2.5-2) = 1.4 – 0.5 = 0.9 25

26. In a nutshell... 1.Come up with a bright idea 2.Arm yourself with patience and determination... 3.Find an implementation partner 4.Seek (possibly a lot of) funding 5.Obtain ethical approvals! 6.Make sure that Treatments and Controls are truly comparable before your program starts (baseline data) 7.Implement the program (interim data) 8.Evaluate the program (end-line data) 9.Publish the results (often years after 1.) 10.Policy outreach if results are promising 26

27. Advantages of RCTs –If implemented well, rigorous and simple –Allows to interpret the results causally –Well-tailored to answering specific questions Drawbacks –Not always possible e.g. Macro policies Ethical issues –Will not tell you why the results where what they were Results often not so easy to interpret –Not so simple to implement Non-compliance Attrition –External validity Are results useful in other places/times? General equilibrium effects? Long-term effects? “Partner selection bias” 27

28. Examples (1) malaria & demand for bednets Caused by Plasmodium spp, (Pf the deadliest), transmitted by Anopheles mosquitoes Huge global burden, in 2005 300- 660m Pf cases/year, 80m in India, huge health/econ costs, no vaccine Many RCTs showed intensive distribution/use of ITNs can substantively lower malaria 28

29. Examples (1) malaria & demand for bednets Adoption of preventive health products in LDCs often low and demand highly elastic, even at very low prices –Liquidity/credit constraints often cited among key factors –Free provision/subsidies often advocated, e.g. WHO Externalities often key However, cost-recovery often important for proponents of sustainability of welfare programs May help targeting & usage 1st RCT evaluating demand of health-protecting products offered on credit vs. free or control conditions 29

30. Examples (1) malaria & demand for bednets 141 villages randomly selected from list of 900 served by micro- lender Bharat Integrated Social Welfare Agency (BISWA) ~2,000 households, 10,000 individuals. 30

31. What did we find? More than 50% of households purchased one bednet or more However, mixed evidence of improvements in malaria indices Interpretation: –Loans may help increasing demand, however: Not everyone will buy, reducing benefits of “externalities” Do not address low usage rates Policy implications –Loans can help sustainability, but at the expense of effectiveness 31

32. Example 2: Microcredit The poor often lack access to formal credit –Interest rates from moneylenders often very high, 40-200% year common “Moral hazard” and “Adverse selection” make it very hard for formal credit sector to operate in poor village economies Perhaps the poor could start new businesses and get out of poverty if only they could borrow now, and repay once their business is successful –Self-sustainable in best-case scenario –But, especially in the past, formal credit sector did not lend to the poor Subsidized, public programs of lending often perform poorly –High default rates –Elite capture –Political motivation 32

33. In a nutshell, use local networks to reducing monitoring costs, leading to lower interest rates, high repayment rates, more access to credit Potential to provide an escape from poverty First proponent: M Yunus (Nobel Peace Prize 2006) # of very poor families with a microloan –About 8 million in 1997, and ~140 million in 2010. Does it really work? Hard to evaluate –Selective program placement (where do MFI operate?) –Selective borrowing (who borrows?) 33 Example 2: Microcredit

34. Recent collection of papers evaluate MC in a wide set of contexts Common methodology: randomly determine who is offered micro-loans American Economic Journal: Applied Economics 2015, 7(1): 1–21 –Rural and urban Sonora state, Mexico –Rural Mongolia –Bosnia –Hyderabad, India –Rural Morocco –Amhara and Oromiya, rural Ethiopia 34 Example 2: Microcredit

35. Findings Uptake significantly less than universal –Take-up hard to predict Overall, no evidence of generalized ‘transformative power’ of MF, but systematic evidence of some improvements for some groups Wide range of responses in ‘social outcomes’, perhaps due to varying emphasis chosen by different MFIs Also, no evidence of perverse effects due to vicious cycles due to over-borrowing or wasteful spending Statistical power often limited: hard to reject even large impacts (both positive and negative) for many outcomes

36. Looking ahead in Development Economics Very hot field in Economics Great data now available to answer lots of very important questions......and opportunities to make you own data when needed Lots of progress, but still more to do... 36