1. 1 Case Studies on Credit, Savings & Insurance

2. CMF Study- Credit CMF researchers conducted the study in the urban neighborhoods of Hyderabad, AP in partnership with MFI Spandana By randomizing the selection, two groups of 52 slums were created which were virtually equivalent along all demographic, social and economic characteristics. Spandana then offered micro-credit into one group of 52 slums, called the treatment group, and for the duration of the study refrained from introducing micro-credit into the other group of 52 slums, the control group.

3. Key Findings Business Investment: Microcredit can lead to new business creation. 32% more new businesses started in the areas where Spandana operated compared to areas where HHs were not offered any microcredit services. Social Impact: No significant impact of microcredit provision on health, education or women empowerment Entrepreneurship: Those HHs that already owned businesses or were likely to start one spent more durable goods as a result of microcredit access compared to those who were least likely to start a business. Those who started a new business cut back on temptation goods (tobacco, alcohol, tea) and invested more. This switch from temptation goods to investment and durable consumption in the groups with businesses is an encouraging finding.

4. Does Microfinance Repayment Flexibility Affect Entrepreneurial Behavior and Loan Default? Study conducted in partnership with the Village Welfare Society (VWS) in West Bengal 169 groups (5-member borrowing groups) considered, offering 84 groups an extra grace period of 2 months before repayment. The rest paid on standard schedule.

5. Key Findings Borrowers in the grace period group spent 8% more on business investments, and a significant percentage more on raw materials. Borrowers who were given a grace period were twice as likely to start a new business compared to the control group. However, grace period borrowers were significantly more likely to default on their loans than the control group- they had 6-8% greater chance of defaulting.

6. Savings (Summary of several findings) Kenya Study (Dupas and Robinson 2011): To study the effects of savings constraints on the poor, small business owners were randomly provided with access to savings accounts. Asked to keep daily logbooks with detailed information on business investments, expenditures, and health shocks. 4-6 months after account opening, women in the treatment group had 45 percent higher daily investment in their businesses than women in the comparison group. Savings accounts also seemed to make women somewhat less vulnerable to health shocks. The logbooks showed that women without savings accounts were forced to draw down their working capital in response to illness.

7. Yet- why dont poor people save? Ananth, Karlan, and Mullainathan (2007) –Conducted a survey that showed it would not take much for vegetable market sellers in India who usually finance the purchase of daily inventory with loans from the moneylenderto save a very small amount from the business every day. –Tried a number of different techniques to make these vendors use savings for their businesses. Yet nothing seemed to change the typical practice of frequent borrowing from the moneylender. Psychological theories on why people do not save- –Value the present more, and therefore prefer spending available funds immediately rather than saving them. –People want to save, but self-control issues make it difficult for them to resist the temptation to use extra cash today rather than save it for tomorrow.

8. Commitment Savings? Malawi Study: Farmers offered savings account. Treatment groups: 1) ordinary accounts, or 2) both ordinary and commitment accounts. A control group was not offered any account but was tracked alongside the treatment groups. Findings: –Commitment treatment had a large positive effect on the amounts of deposits and withdrawals. –Farmers in this treatment group had a 26 percent increase in agricultural input use, 22 percent increase in value of crop output in subsequent harvest, 17 percent increase in household total expenditure reported in the past 30 days. –Farmers who had access to only the ordinary account showed lower or non significant impacts in terms of those same outcomes, suggesting the commitment device played an important role for these results.

9. Savings- Self Control Issue or Social Pressure? Those who were identified as having self-control problems experienced no different effects from the commitment savings than their peers. Commitment savings accounts had a higher impact for wealthier households, a subgroup that may be under more pressure to share. The existence of the commitment device may have allowed farmers to credibly claim that money was inaccessible. It could be that the introduction of commitment savings devices work well for those who take them up but could also harm members of their social network.

10. Other Savings Techniques Savings reminders: Savers were sent letters (Peru) or SMS text messages (Bolivia and Philippines) reminding them to save. Some savers received generic messages; others received messages that referenced a specific purchase that the saver said she wanted to make with her savings. The studies found that reminders increased average savings balances overall by 6 percent, but this impact increased substantially, to 16 percent, for the Peruvian savers when the reminder referred to a purchase goal. Labeled Savings: Ghana clients were given the opportunity to open separate, parallel savings accounts labeled education, business, housing,. Savers eligible to open parallel accounts saved 31 percent more on average than those in the comparison group, with the greatest effect seen for the education label.

11. Insurance A major effort is currently underway to expand access to insurance products to help poor households deal with weather and health shocks. Recent findings suggest that micro- insurance has positive impacts on poor households, yet- take up rate is very low.

12. CMF Study- Weather insurance take-up Marketing experiments: Assigned a sample of potential clients to receive various offers and information about the product- each designed to isolate one possible cause of low take-up Randomized along the following dimensions- –The price of the policy –The availability of cash in the household to purchase the policy –Understanding of rainfall measurements by the potential client –Level of trust of the potential client towards the insurance scheme or the insurance marketing agent –Framing of the information describing the insurance.

13. Preliminary Findings: The household demand for weather insurance was highly price-elastic, ranging from -0.66 to -0.88. This means that 100% increase in the price of the product is expected to decrease demand by 66-88%. Liquidity constraint was another significant determinant of insurance purchase. The trustworthiness of the insurance provider was significant. An endorsement by a trusted organization increases take up by 10.4% if HHS are familiar with the organization. Framing effects were not significant as a determinant of insurance purchase. A household visit increases the purchase rate by 11.9-17.2%