1. Economic lessons from crisis Rachel Glennerster (IGC Lead Academic for Sierra Leone and JPAL) Joint work with Tavneet Suri (IGC Agriculture and MIT Sloan)

2. Overview The importance of good data in a crisis Initial sectors of concern and policy focus, not supported by the emerging evidence Economic impacts were not concentrated where the disease was Impact of food aid during the crisis The role of researchers during a crisis Maintaining confidence while calling for help 2

3. Ebola cases end June, Sierra Leone

4. Ebola cases, cordon areas, end Aug

5. Ebola cases, cordon areas, end Sept

6. Ebola cases, cordon areas, end Oct

7. Ebola cases, cordon areas, end Nov

8. Ebola cases, cordon areas, end Dec

9. Ebola cases, cordon areas, end Jan

10. Ebola cases, cordon areas, mid May

11. Incentive is to grab headlines WHO “up to 90%” death rate from Ebola Min of Ag, GDP had already fallen by 30% GDP fell by av of 5% per year during devastating civil war Sept 5, FAO reported 40% of farms in Kailahun abandoned, and lack planting materials In sept rice is maturing in fields: what does abandoned mean? Planting pre Ebola, would not take place again till May 2015 Reports of skyrocketing food prices Restrictions on transport did make this a concern

12. Messaging ignored basic psychology

13. Call reps at 208 markets, 1-2 times month Source: Glennerster and Suri, 2015 www.theigc/country/sierra-leone

14. Food prices similar to 2011/12 Source: Glennerster and Suri, 2015 www.theigc/country/sierra-leone Domestic Rice Imported Rice Imported rice price lower particularly in cordon areas Consistent with results data on rice prices from household survey

15. More high price outliers in 2014 Source: Glennerster and Suri, 2015 www.theigc/country/sierra-leone

16. Household cell phone survey, Jan-Feb ‘15 Source: Fu, Glennerster, Himelein, Rosas, and Suri, 2015 LFS sample = 4199 66% had cell phones 68% of these reached

17. Decline in employment in urban areas Source: Fu, Glennerster, Himelein, Rosas, and Suri, 2015

18. Nonfarm HH enterprises hit badly Source: Fu, Glennerster, Himelein, Rosas, and Suri, 2015 Percent of HH with non-farm business no longer operating rose from 4% to 12% 1/3 cite Ebola as reason their business no longer operates Average business revenues shrunk by 40% >90% urban women worked in non- farm HH enterprises pre-E

19. Little evidence of impact on ag Ebola coincided with the growing and harvest season 93 percent of farming households grow rice In Nov more than half had rice in field, mainly because of rain (72%), not Ebola More than half farming HH hired outside labor Some cite labor constraints for harvest 14% labor constraints in HH vs. 6% labor constraint in community no significant differences across quarantine areas Most farmers never sell rice, prod estimates unreliable No clear signs of probs in cocoa but sample small

20. Economic impacts uncorrelated with disease burden No difference in range of different economic outcomes between cordon vs noncordon areas Only difference is imported rice prices are lower in cordon areas, possibly because of food assistance Main differences between urban and nonurban In Liberia similar declines in employment and revenue in badly hit and less badly hit (xxxxx, Werker, in progress) As in Sierra Leone biggest impacts in capital (Monrovia) Some sectors hit worse than others (e.g. construction) 20

21. Aid: 9% HH received social assistance

22. Aid correlated with Ebola and transport disruption

23. Food prices lower where food aid Matched market price data with HH assistance data Difference-in-difference specification Two way causality possible: Receiving food aid reduces prices High prices in an area trigger food aid Without detailed data on timing cant separate the two Moving from 0-100% assistance reduces rice prices by 10% Suggests food aid on average reduced food prices by 1% Net consumers gain from lower food prices, net producers lose Most poor, even farmers are net consumers rice

24. Conclusion: role of researchers Researchers can play and important role in providing good data in a crisis, to reduce risk of policy effort being diverted to wrong areas But needs much faster turn around time than normal studies Much easier if building on existing research, experience, and in country team Good causal identification can be hard as impacts may not be correlated with direct cause of crisis But good descriptive evidence is very valuable, especially if good pre crisis data to compare with Different methods can be useful: e.g. SMS, cellphone, modeling The press may only remember the high case scenario 24

25. Conclusion: attention vs confidence? Early stages of Ebola crisis showed how difficult it can be to attract the attention and support of the international community to crisis Disease spread from end April-end June with little attention World grown weary of calls for help, only threat of total collapse gets through the noise But risks in overplaying likely impacts Can further reduce domestic confidence with important economic impacts If final results not as big as predicted, risks people concluding it “was not so bad after all” Raises the stakes for the next crisis 25

26. International Growth Centre London School of Economics and Political Science Houghton Street London WC2 2AE www.theigc.org