Risk Sharing and Transaction Costs: Evidence from Kenya s Mobile Money Revolution. William Jack and Tavneet Suri

Transcription

1 Risk Sharing and Transaction Costs: Evidence from Kenya s Mobile Money Revolution William Jack and Tavneet Suri

2 Research Questions What is the role of the financial sector in development? How important is access to financial intermediation? Cell phone innovations are leap-frogging : allow financial transactions across areas where banks are thin Here we think about dramatically reducing transactions costs in an economy where networks are important

3 Research Questions In developing economies, govt. safety nets (health, u/e insurance) do not exist; neither does formal insurance A large fraction of Sub-Saharan Africa is agricultural (low and extremely variable incomes) Households often resort to informal mechanisms to smooth risk between them (e.g. Suri, 2011)

4 Research Questions Lots of evidence that HHs help each other smooth risk But this insurance is not perfect or efficient Why not? Reasons in the literature: Moral hazard Asymmetric information Commitment problems A more boring reason: transaction costs

5 Summary of Findings There are gains to smoothing consumption from lowering transaction costs The consumption of households who use mobile money is about 7% - 10% less sensitive to income shocks Transactions costs pose a significant barrier to optimal risksharing Simple technologies like mobile money can alleviate such inefficiencies

6 Outline of Presentation Simple theoretical implications Background on mobile money in Kenya Testing the theory Survey data Results on consumption smoothing Results on remittances Falsification test and other robustness checks Conclusions

7 Theoretical Implications Set up a simple three person risk sharing environment Use a simplex to understand the role of transaction costs As transaction costs are lowered, households are able to smooth risks more completely Consumption of individuals with access to the technology will be less responsive to shocks Households will share risk with more members of their network

8 Outline of Presentation Simple theoretical implications Background on mobile money in Kenya Testing the theory Survey data Results on consumption smoothing Results on remittances Falsification test and other robustness checks Conclusions

9 Background on Mobile Money Called M-PESA Remote account storage accessed by simple SMS Cash-in/out services provided by M-PESA agents Limits on transaction sizes ($500) and on money stored on account ($750) Fees charged on all transactions except deposits

10 Growth of Mobile Phones in Kenya Millions of subscribers Fixed lines Mobile lines

11 Adoption of M-PESA Number of Registrations Apr-07 Oct-07 Apr-08 Oct-08 Apr-09 Oct-09 Apr-10 Oct-10

12 What do People Use M-PESA For? 100% 80% 60% 40% 20% 0%

13 Frequency of M-PESA Use 40% 30% 20% 10% 0% Daily Weekly Every 2 Weeks Monthly Every 3 Months Every 6 Months Less Often

14 M-PESA Agents Individuals trade e-float for cash with M-PESA agents Stand-alone agents, shop-keepers, supermarkets, gas stations, etc. Agents must have a bank account and internet access Face a non-trivial inventory management problem, predicting customer demand for both e-float and cash

15 Growth of Agent Network Number of Agents Apr-07 Oct-07 Apr-08 Oct-08 Apr-09 Oct-09 Apr-10 Oct-10

16 Agent Network: June 2007

17 Agent Network: Dec 2007

18 Agent Network: June 2008

19 Agent Network: Dec 2008

20 Agent Network: June 2009

21 Agent Network: Dec 2009

22 Agent Network: April 2010

23 4 Distance to the closest agent (km) 3.5 Improving Agent Access % Change 40% Change 28% Change 14% Change 33% Change Round 1 Round Mean Distance (km) 5th Percentile 25th Percentile 50th Percentile 75th Percentile Percentiles of Distance to Closest Agent

24 Financial Intermediation in Kenya

25 Transaction Costs I 1,400 1,200 1,000 Tariff ,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 Amount deposited and sent Postapay M-PESA: Reg to reg Western Union

26 Transaction Costs II Frequency Round 1 Cost (KShs) Hand Delivery by Self 13.5% 1.6 Bus Delivery Through Driver 3% Western Union 0.4% 108 Postal Bank 2.9% Direct Deposit 6.7% 85 M-PESA 60.8% 49.8 Other 3.3% 78.0 Average distance travelled is 200km which is a 400 KShs ride

27 Outline of Presentation Simple theoretical implications Background on mobile money in Kenya Testing the theory Survey data Results on consumption smoothing Results on remittances Falsification test and other robustness checks Conclusions

28 Household Survey 3,000 households Rural and a lot of urban and semi urban 3 rounds (so far) 2008, 2009, 2010 Non-negligible attrition, but not bad for a survey that has urban and semi urban areas included

29 Households Surveyed Uganda Somalia Tanzania Indian Ocean

30 Network Coverage Nairobi

31 Economic Activity POPULATION DENSITY (number of people per sq. km) > <= 20 No data OTHER FEATURES District boundaries Selected national parks and reserves Water bodies

32 Summary Statistics I Round 1 Round 2 Mean SD Mean SD M-PESA User Own Cell Phone Per Capita Consumption Per Capita Food Consumption Total Wealth HH Size Education of Head (Years)

33 Summary Statistics: Economic Round 1 Round 2 Mean SD Mean SD Financial Access Dummies Bank account Mattress Savings and Credit Cooperative Merry Go Round/ ROSCA Household Head Occupation Dummies Farmer Professional Occupation Househelp Run a Business Unemployed

34 Summary Statistics: By Adoption Early Adopters Late Adopters Non- Adopters Own Cell Phone Per Capita Consumption Education of Head (Years) Negative Shock Agricultural Shock Illness Shock Send Remittances Receive Remittances Bank account Mattress

35 Summary Statistics: Remittances I Round 1 Round 2 Sent Received Sent Received Overall Remittances No of Remittances per Month Total Value Total Value (% of Consumption) Average Distance Net Value Received Demand for overall remittances fell from round 1 to round 2

36 Summary Statistics: Remittances II Round 1 Round 2 Sent Received Sent Received M-PESA Remittances Number of Remittances Total Value 7,965 9,924 7,879 4,790 Average Distance Non M-PESA Remittances Number of Remittances Total Value 9,709 13,674 4,615 5,058 Average Distance Shift into M-PESA; and M-PESA sent over longer distances

37 Outline of Presentation Simple theoretical implications Background on mobile money in Kenya Testing the theory Survey data Results on consumption smoothing Results on remittances Falsification test and other robustness checks Conclusions

38 Empirical Strategy I Use an extension of Gertler and Gruber (2002) specification: where c ijt is consumption of HH i in location j at time t Shock is a measure of the income shock β is the coefficient of interest X ijt are covariates (demographics, economic)

39 Empirical Strategy II Consumption c = Shock + User + User * Shock + controls Shocks don t hurt users so much ( ) User Users are richer ( ) Non-user ( ) Shocks hurt ( ) Shock No shock Shock status

40 Basic Results OLS A Panel A Panel Without Nairobi M-PESA User 0.553*** ** [0.037] [0.036] [0.047] [0.049] Negative Shock *** 0.241** [0.038] [0.116] [0.169] [0.141] User*Negative Shock 0.101** 0.176*** 0.156** 0.150** [0.050] [0.050] [0.062] [0.065] Shock, Users *** 0.052* [0.033] [0.028] [0.035] [0.037] Shock, Non-Users *** ** [0.038] [0.032] [0.043] [0.045]

41 Different Shock Measures Total Consumption Non-Health Consumption Weather Shock Illness Shock Illness Shock M-PESA User [0.0358] [0.0420] [0.0407] Negative Shock [0.3352] [0.1640] [0.1686] User*Shock ** ** ** [0.1511] [0.0738] [0.0692] Shock, Users [0.0903] [0.0418] [0.0404] Shock, Non-Users *** ** [0.0959] [0.0500] [0.0483]

42 Using Agent Roll Out Agents w/in 1km Agents w/in 2km Agents w/in 5km Agents w/in 20km Distance to Agent Negative Shock *** [0.152] [0.153] [0.160] [0.140] [0.203] Agents [0.039] [0.031] [0.024] [0.006] [0.054] Agents*Shock 0.055*** 0.050*** 0.021** *** [0.019] [0.015] [0.010] [0.005] [0.019]

43 Agent Roll Out: Correlates I Agents w/in 2km Agents w/in 5km Dist to Agent Coefficient Coefficient Coefficient Log Wealth * Cellphone Ownership HH Head Can Read * HH Head Can Write HH Head Education HH Has a Bank account HH has a SACCO HH has a ROSCA Negative Shock Rainfall Shock Illness Shock

44 Agent Roll Out: Correlates II Distance to Nairobi Agents w/in 1km Agents w/in 2km Agents w/in 5km Dist to Agent Period 1 Changes Period 1 Changes Period 1 Changes Period 1 Changes * [0.0028] [0.0013] [0.0055] [0.0028] [0.0091] [0.0045] [0.0056] [0.0012]

45 Remittances Overall Shock Illness Shock Prob [Receive] Number Received Total Received (Root) Prob [Receive] Total Received (Root) M-PESA User 0.160*** 0.253** 10.77*** 0.182*** 12.48*** [0.047] [0.127] [3.71] [0.041] [3.079] Shock [0.143] [0.427] [11.70] [0.149] [11.13] User*Shock 0.135** 0.343* 8.067* 0.144** [0.063] [0.177] [4.668] [0.070] [5.312] Shock, Users 0.066* * 6.470** [0.037] [0.112] [3.283] [0.042] [3.289] Shock, Non-Users [0.041] [0.120] [2.652] [0.044] [3.061]

46 Network Size? Distance Travelled Network Size Fraction of Network Overall Illness Overall Illness Overall Illness M-PESA User *** 0.194*** 0.102*** 0.116*** [63.50] [53.52] [0.065] [0.053] [0.036] [0.031] Shock ** [130.6] [149.5] [0.211] [0.223] [0.131] [0.126] User*Shock ** ** 0.253*** 0.101** 0.110* [81.0] [90.86] [0.087] [0.097] [0.048] [0.060] Shock, Users * ** 0.121** 0.046* 0.045* [31.31] [40.46] [0.056] [0.057] [0.024] [0.026] Shock, Non-Users [63.49] [71.99] [0.058] [0.062] [0.038] [0.044]

47 Outline of Presentation Simple theoretical implications Background on mobile money in Kenya Testing the theory Survey data Results on consumption smoothing Results on remittances Falsification test and other robustness checks Conclusions

48 Falsification Test I Agents w/in 2km Maize Consumption Crop Consumption OLS Panel OLS Panel Shock*Agents [0.083] [0.068] [0.085] [0.065] Shock Measure (Positive) 0.418*** 0.412*** 0.400*** 0.377*** [0.074] [0.068] [0.069] [0.062] Agents [16.855] [16.796]

49 Falsification Test II Total Food Food Expenditure Dist to Agent Agents w/in 2km M-PESA User [0.0740] [0.0763] Shock ** [0.1915] [0.1995] [0.2990] [0.1987] User*Shock ** ** [0.0866] [0.0872] Agent Variable [0.1068] [0.1023] Agent*Shock ** *** [0.0318] [0.0350]

50 Shocks Coefficient SE M-PESA User Cellphone Ownership Agents within 1km Log Distance to Agent HH Head Education HH Has a Bank account HH has a SACCO Occupation - Business ** Occupation Farmer Occupation - Professional Occupation - Sales Household size

51 Conclusions I Life in developing countries can be precarious Crop failure, health shocks, job loss, etc. Given M-PESA reduces transaction costs so much and seems to be used primary for P2P transfers, it may help reduce vulnerability When facing an income shock, households can easily receive (and spend) money from friends and family

52 Conclusions II Mobile banking expands access to financial services It improves the ability to weather serious shocks Negative shocks cause non-users of M-PESA to reduce their consumption by about 7% M-PESA users can smooth these shocks perfectly Some of this from users being wealthier, educated, etc. But, M-PESA itself a significant source of risk sharing

53 Thank You

54 How do People Send Money? Direct deposit 7% Other 6% Hand 32% M-PESA 46% Bus 9%

55 Without Transaction Costs

56 With Transaction Costs