Rainfall Insurance and Informal Groups

Transcription

1 Rainfall Insurance and Informal Groups Evidence from a Field Experiment in Ethiopia Stefan Dercon 1, Ruth Vargas Hill 2, Ingo Outes-Leon 1, Alebel Bayrou 3, Daniel Clarke 1, Alemayehu Seyoum Taffesse 2 1 University of Oxford 2 International Food Policy Research Institute 3 Ethiopian Development Research Institute Clermont-Ferrand, June 2011

2 Basis risk and risk-sharing groups Risk-sharing within groups is commonly practiced in rural Africa. Groups find it hard to manage risks that affect all group members simultaneously, such as catastrophic weather events. Can index insurance be used as a tool to transfer large covariate shocks (extreme shortfalls in rain) away from groups, whilst encouraging group members to share smaller agricultural risks among themselves?

3 Research program Long-run research project in Ethiopia to look at this question. In particular, focusing on funeral insurance mutuals. First year (2009): experimental games played with individuals and with groups observed how information and decisions were made on building block index insurance contracts by groups. Second year (2010): sales of index insurance to groups in a small number of villages all contracts, policies and marketing was the same, but training was randomized to test what happens when sharing basis risk is suggested. Third year (2011): (i) sales of index insurance to groups or individuals in randomly selected villages, (ii) randomization of commitment to ex-ante rules in group contract villages. Next year (we hope): pilot a number of ex-ante rules perhaps in combination with savings or credit product to mitigate group level basis risk.

4 Research program Long-run research project in Ethiopia to look at this question. In particular, focusing on funeral insurance mutuals. First year (2009): experimental games played with individuals and with groups observed how information and decisions were made on building block index insurance contracts by groups. Second year (2010): sales of index insurance to groups in a small number of villages all contracts, policies and marketing was the same, but training was randomized to test what happens when sharing basis risk is suggested. Third year (2011): (i) sales of index insurance to groups or individuals in randomly selected villages, (ii) randomization of commitment to ex-ante rules in group contract villages. Next year (we hope): pilot a number of ex-ante rules perhaps in combination with savings or credit product to mitigate group level basis risk.

5 Groups and demand Groups can increase demand for insurance for a number of reasons: Share basis risk Groups might make better decisions: Group leaders are more financially educated Might be best placed for understanding insurance products and explaining them to member farmers Reduce transaction costs in purchasing insurance and making claims. When groups are used as intermediaries they can increase levels of trust in insurance products. All marketing and retailing in this study was through groups, so our focus is on the group potential for mitigating basis risk.

6 This study Rainfall deficit index policies are marketed through pre-existing risk-sharing groups: funeral societies called iddirs. All products were an individual contract retailed through the iddir. Iddir leaders managed the payment of premiums and signed contracts on behalf of individual members that had signed up. All marketing and training about these products was through the iddir. Iddir leaders were trained on the product, and were asked to select additional members of the iddir to also receive training. We randomize the content of the training provided to iddir leaders: Training A: Focused on the individual benefits of insurance, and illustrated how to choose the right policies for an individual farmer. Training B: Focused on the group benefits of insurance, and illustrated how to choose the right policies for a group, and how groups could enable risk sharing.

7 Why might training increase demand Let the basis risk associated with an index product for farmer i in community j be denoted by b ij. The idiosyncratic component of the basis risk that is unique to that farmer is ε ij, and the component farmer i shares with others in community j is µ j. Why would the training increase demand? If mutual insurance groups are not already sharing all ε ij encouraging a mutual insurance network to internalize the idiosyncratic part of basis risk (essentially crowding in" more informal insurance) will reduce the basis risk from b ij to µ j. The relative magnitude of ε ij to b ij will determine how large an effect such mutualization will have on demand. At the extreme, if ε ij is negligible, there will be no impact from offering insurance to groups instead of individuals.

8 Other explanations? If the training sessions which suggested sharing ε ij resulted in better understanding of the product, increased demand may result. Training sessions which suggested sharing may have made the collective nature of drought more salient and thereby increasing demand for formal insurance products that were reliable in times of such covariate shocks. Suggestions to share policies may have encouraged individuals in the group to divide policies amongst themselves, and the increased divisibility that resulted could increase demand.

9 Context Highly heterogenenous farming practices and outcomes some evidence that this is partly due to risk-coping but suggests the ratio of ε ij to b ij could be quite high. May also mean that the most appropriate index for group j = 1 will be quite different from the most appropriate index for group j = 2. Flexibility in the index contract taken by the group may be desirable. It may also be the case that µ j is non-negligible. Deficit rainfall is by far the highest source of risk in the areas we are working, and the most covariate. Too much rain is also a problem: 2009 survey 44% of farmers reported serious losses in wealth and consumption due to drought in last 4 years, and 22% report losses due to too much rain and floods. Major non-rainfall yield risks pests, disease, livestock or birds destroying the crop are quite observable and do not appear as highly covariate as rainfall risk. Take reports of risks to crop production over five years for households. Run fixed effects regression with time dummies. R-squared relative to rainfall shocks: disease=0.27, insect=0.38, livestock and birds=0.04.

10 Context Funeral insurance mutuals are widespread throughout rural Ethiopia, in the areas where we are working almost all households are members of at least one (often more than one). Characteristics of these societies: Although starting as informal associations asking for contributions at the time of a funeral, many now require up-front payments (perhaps supplemented at the time of death) looks like insurance. Payments are equal regardless of age, wealth or family size strong preference for equality and simple rules. Wealthier farmers who want more funeral coverage join more iddirs. Membership is largely defined by geographical location. Regular meetings and mandatory funeral attendance put a physical constraint on how far away your iddir can be. Members are neighbours. Iddirs have evolved to performing other functions: health insurance, livestock death, fire, loans in the event of harvest losses, loans for inputs. Not all members will pay for other benefits (for example only livestock owners would pay the additional fees for livestock death coverage). Coverage that implies an unobservable event: multi-purpose health insurance, harvest losses etc is often given as a loan rather than a grant.

11 Policies offered Nyala Insurance S.C. introduced an individual index-based rainfall insurance in rural Ethiopia. The policies took the form of monthly coupons whereby a fixed payout would be due if the monthly rainfall fell short of a particular precipitation target (Hill and Robles 2011) Policies were calibrated using the historic data from the local rainfall station. Six policies were introduced: Two policies for each of the rainy season months: July, August and September. Severe Shortfall : For a premium of 100 Birr, the farmer could receive a payment of 500 Birr with a chance of 1/5. Very Severe Shortfall : For a premium of 50 Birr, the farmer could receive a payment of 500 Birr with a chance of 1/10.

12 Timeline of activities January: listing and survey of iddirs in selected kebeles. early May: iddirs randomly allocated to training A (individual) or B (group sharing). mid May: Leaders of iddirs attended training A or B. Nominated members to receive individual training. beginning June: demand forms collected and policies issued. mid-end June: household survey sampling frame of the household survey was constituted by the full memberships of iddirs that took part in the training exercises. beginning July to end September: insurance coverage.

13 Federal structure of iddirs Some iddirs have developed a federated structure whereby a large iddir has several smaller sub-iddirs" underneath it. We did not always know the full federated structure but restricted the list of iddirs to include only iddirs of 100 members or more this excluded most sub-iddirs. All iddir leaders of the large iddir (typically a committee of 3-5), and leaders of any of the sub-iddirs within it were eligible to attend the training session.

14 Federal structure and mixing of training types Excluding iddirs of 100 members or less did not perfectly exclude all sub-iddirs and some of the iddirs on our list were sub-iddirs. For these sub-iddirs there was a possibility that they were allocated to one training type whilst their main iddir to another. At the main iddir level these iddirs were mixed some leaders had type A training and some type B training. All analysis is conducted using the type of treatment leaders in the main iddir as a whole attended. There are thus three treatment groups: All leaders received training A All leaders received training B Some leaders received training A and some training B The probabilities of allocation to treatment varied depending on whether or not an iddir had one of its sub-iddirs on the list. No sub-iddirs on the list, probability of allocation to treatment A or B was 0.5 Iddirs with one sub-iddir on the list, probability of allocation to treatment A or B was 0.25 and mixed was 0.5.

15 Tests of balance Data from baseline survey of iddirs and characteristics of members collected in the household survey that are unlikely to have changed as a result of the training (characteristics of the household head, land ownership etc.) to test balance across training type: of 39 variables tested 3 are significantly different at 5% or less.

16 Assessing compliance We compare training allocation and training attendance to assess compliance. We find there was one training team that did not stick to the protocol half of the non-compliance we observe comes from this team alone. We test robustness of our results to including and excluding this data (excluding 2 kebeles) Aside from this we observe 92% compliance with training allocation at the iddir levels. 6 out of 71 iddirs went to the wrong training session, not clear why: 1 iddir switched from A to B 1 switched from mixed to B 1 switched from A to mixed 3 switched from B to mixed We test the robustness of our results to endogenous switching by estimating the ITT and LATE also.

17 Description of take-up Training A Training B Mixed Leader Trained member Untrained member Total

18 ATE estimates Farmer trained (t ij ) 0.317*** 0.299*** 0.295*** (0.059) (0.055) (0.055) Training B (g ij ) 0.140* (0.077) (0.030) (0.038) (0.043) Mixed training (0.078) (0.020) (0.040) (0.046) g ij t ij 0.190* 0.242** 0.242** (0.108) (0.098) (0.098) Mixed t ij (0.090) (0.088) (0.087) Constant 0.233*** *** 0.222*** (0.045) (0.020) (0.081) (0.077) Basic characteristics No No Yes Yes District fixed effects No No No Yes Observations R Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

19 ATE estimates, reduced sample Farmer trained (t ij ) 0.317*** 0.292*** 0.285*** (0.059) (0.056) (0.056) Training B (g ij ) 0.140* (0.079) (0.030) (0.039) (0.045) Mixed training (0.092) (0.020) (0.047) (0.049) g ij t ij 0.190* 0.246** 0.250** (0.108) (0.098) (0.098) Mixed t ij (0.103) (0.103) (0.101) Constant 0.233*** ** 0.209** (0.047) (0.020) (0.093) (0.089) Basic characteristics No No Yes Yes District fixed effects No No No Yes Observations R Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

20 ITT and LATE ITT LATE Farmer trained (t ij ) 0.315*** 0.296*** 0.293*** 0.292*** 0.268*** 0.264*** (0.056) (0.054) (0.053) (0.066) (0.062) (0.061) Training B (g ij ) (0.027) (0.037) (0.040) (0.033) (0.048) (0.055) Mixed training (0.018) (0.050) (0.058) (0.020) (0.052) (0.061) g ij t ij ** 0.193* * 0.209* (0.104) (0.098) (0.098) (0.129) (0.125) (0.126) Mixed t ij 0.185* 0.219** 0.218** ** 0.249** (0.111) (0.109) (0.108) (0.127) (0.121) (0.119) Constant ** 0.198** * 0.196** (0.018) (0.094) (0.091) (0.020) (0.096) (0.093) Basic characteristics No Yes Yes No Yes Yes District fixed effects No No Yes No No Yes Observations R Robust standard errors in parentheses

21 Number and value of policies bought Number of policies Value (Birr) ATE ITT ATE ITT Farmer was trained (t ij ) 0.342*** 0.345*** *** *** (0.083) (0.075) (5.694) (5.205) Training B (g ij ) (0.055) (0.049) (3.852) (3.547) Mixed training (0.069) (0.084) (4.699) (5.781) g ij t ij 0.308** 0.231* * (0.133) (0.128) (8.386) (7.789) Mixed training t ij (0.144) (0.173) (9.899) (11.362) Constant (0.147) (0.147) (10.292) (10.258) Basic characteristics Yes Yes Yes Yes District fixed effects Yes Yes Yes Yes Observations R Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

22 Who purchased insurance (ATE) Farmer was trained (t ij ) 0.279*** (0.057) Iddir leader (0.082) Training B (g ij ) (0.045) g ij t ij 0.261** (0.118) g ij t ij iddir leader (0.150) Mixed training (0.047) Mixed t ij (0.102) Mixed t ij iddir leader (0.140) Constant 0.208** (0.082) R

23 Discussing insurance (1) (2) (3) (4) (5) (6) Talk Talk Talk Number above 50 1 to 15 Training B (g ij ) 0.103* 0.105* * -0.05* 0.129** (0.057) (0.059) (0.065) (4.889) (0.03) (0.061) Mixed training * -0.05* 0.120* (0.066) (0.072) (0.074) (4.922) (0.03) (0.066) Constant 0.797*** 0.925*** 0.935*** *** 0.05* 0.797*** (0.040) (0.119) (0.122) (4.788) (0.03) (0.055) Basic char. No Yes Yes No No No District f.e.s No No Yes No No No Observations R Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

24 Better training Demand Millimeters Basis risk Probability Maths Farmer was trained (t ij ) 0.317*** 0.256*** 0.111* (0.059) (0.083) (0.066) (0.152) (0.206) Training B (g ij ) (0.030) (0.095) (0.096) (0.181) (0.209) Mixed training (0.020) (0.108) (0.089) (0.194) (0.235) g ij t ij 0.190* (0.108) (0.110) (0.104) (0.220) (0.277) Mixed training t ij * (0.090) (0.134) (0.097) (0.210) (0.306) Constant ** 0.633*** 1.469*** 2.020*** (0.020) (0.069) (0.062) (0.143) (0.162) Observations R Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

25 Salience of covariate nature of basis risk Is the rainfall measured at the weather station a good measure of the rainfall on your field? Farmer was trained (t ij ) 0.242*** 0.243*** (0.068) (0.077) Training B (g ij ) 0.281** 0.335*** (0.117) (0.118) Mixed training (0.084) (0.101) g ij t ij *** *** (0.107) (0.110) Mixed training t ij (0.110) (0.117) Constant 0.245*** 0.423*** (0.065) (0.127) Basic characteristics No Yes District fixed effects No Yes Observations R Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

26 Training and purchases (1) (2) (3) (4) Discussed Decided Shared Bought because with others jointly policies of others Farmer was trained (t ij ) (0.513) (0.348) (0.292) (0.463) Training B (g ij ) (0.711) (0.482) (0.404) (0.643) Mixed training (0.144) (0.097) (0.082) (0.129) g ij t ij (0.724) (0.491) (0.412) (0.654) Constant 1.000* *** (0.503) (0.341) (0.286) (0.454) Observations R Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Mixed training t ij dropped because of collinearity

27 Conclusions Demand was substantially increased when groups were exposed to training that encouraged sharing of payouts within a group. One mechanism for this higher level of take-up may come from the ability of groups to mitigate some of the basis risk inherent in these products. Data we have is consistent with this view and suggest that if farmers are increasing informal risk sharing it is being done in small groups of selected farmers. Future work: What would be the magnitude of the effects in less cohesive groups, or groups that are not as familiar with formalized risk-sharing? Do sharing rules have to be formalized at the time of insurance purchase? What kind of rules can members credibly commit to? How much of ε ij can be shared? Observable events, binary events. State contingent loans for cases where moral hazard is likely? What about µ j? Can savings or contingent credit help groups insure this across time? There are potentially other advantages to group contracts (reduced per unit transaction costs, increased trust in insurer), what is the combined effect of offering insurance through groups?