Facilitating Agricultural Commodity Price and Weather Risk Management: Policy Options and Practical Instruments Alexander Sarris Director, Trade and Markets Division, FAO Presentation at the International Conference on Rural Finance Research, FAO Headquarters, Rome, March 2007

Outline of presentation Background and motivation Agricultural productivity and credit Constraints to expanding intermediate input use in agriculture Risks faced by rural households Vulnerability and consumption smoothing The demand for commodity price insurance The demand for weather insurance Operationalizing the use of price and weather insurance

Background and motivation. Some major questions relevant to agricultural land productivity Is agricultural land productivity a factor in growth and poverty reduction? What are the factors affecting land productivity? Are there inefficiencies in factor use among smallholders? If yes in which markets? Determinants of intermediate input demand and access to seasonal credit

Tanzania. Rural farm household characteristics 1

Tanzania. Rural farm household characteristics 2

Variables that affect significantly household consumption expenditures Agricultural land productivity (+) Size of cultivated land (+) Age of household head (-) Size of household (-) Education (+) Whether household has electricity (+) Whether household receives remittances (+) Easy access to seasonal credit (+)

What affects total factor productivity in farm production? Estimate production function via IV regressions All standard factors of production (land, labor, capital, intermediate inputs) are significant Significant TFP determining variables include education, irrigation, role of formal credit, weather shocks, and cultivation of specific types of cash crops

Tanzania. Marginal products of production factors compared to market prices of the factors (means across the reported groups).

What affects the demand for intermediate inputs? Share of non-wage income in total income (+) Household vulnerability to income shocks (probability of consumption falling below poverty level) (-) Land cultivated (-) Education (+) Household size (+) Age (-) Easy access to seasonal credit (+) Household assets (value of dwellings, values of durables, number of animals) (+) Easy physical access to inputs (village connectivity variables such as roads, bus service) (+) Existence of input shop in village (+) Regular interaction with extension services (+)

What determines access to seasonal credit? Size of land (+) Having a bank account (+) Belonging to rural financial group (SACCO) (+) Whether linked to buyer through contracts (+)

Agricultural household risks: Background and motivation Small agricultural commodity producers face many income and non-income risks Individual risk management and risk coping strategies maybe detrimental to income growth Considerable residual income risk and vulnerability Is there a demand for additional price and weather related income insurance in light of individual existing risk management strategies? What is the welfare benefit of price and weather based insurance? Is there a rationale for market based or publicly supported price and weather based safety nets

Tanzania: Percentage of households affected by various shocks between 1999 and 2003, by region and status as cash crop grower or not

Tanzania: Agricultural household vulnerability to price and weather shocks is high but portion due to covariate shocks varies by region

Variables likely to affect WTP Degree of risk aversion (+) Degree of consumption smoothing (-) Household vulnerability to poverty (+/-) Degree of unpredictability (variability) of future prices or incomes (+) Variance of returns of insurance contract (-) Correlation between returns to insurance and future income (-)

Variability of nominal prices received for coffee in Kilimanjaro over the previous 10 years.

Variability of nominal prices received for coffee in Ruvuma over the previous 10 years.

Variability of nominal prices received for cashew nuts in Ruvuma over the previous 10 years.

Interest in minimum price coffee insurance among coffee producing households

Interest in minimum price cashew nut insurance among cashew nut producing households in Ruvuma. (Number of households)

What affects the WTP for minimum price insurance? Kilimanjaro: bid value (-), income (-), the number of coffee trees (-), total value of wealth (+), whether cash income from coffee is important (+), Herfindhal index (+), coping mechanism variables (-), easy access to credit (-). Predictive value is quite high, more than 70 percent correct predictions. Ruvuma coffee. Bid value (-), Importance of coffee in income (+), easy access to seasonal credit (+), share of cash to total income (-), number of coffee trees (-), past price variability (-), coping mechanism involving the use of new ways to earn income (-). Share of correct predicted values is more that 80 percent. Ruvuma cashew nuts. Bid value (-), Income (+), number of cashew trees (+), importance of cashew income (+), whether cashew income declined in the recent past (+), ease of access to seasonal credit (-), coping mechanism relating to use of new ways to earn income (-) Percent correct predictions larger than 74 percent.

Summary statistics of the predicted value of WTP for coffee minimum price insurance in Kilimanjaro.

Summary statistics of the predicted value of WTP for coffee minimum price insurance in Ruvuma.

Summary statistics of the predicted value of WTP for cashew nut minimum price insurance in Ruvuma.

Conclusions and policy implications. Demand for price insurance Producer households are affected by a variety of shocks, and prominent among them are health and death related ones, as well as weather induced ones. Shocks induce considerable variability of incomes Most prevalent coping mechanism through own savings and asset depletion. There seems to be considerable variability in prices received for the main cash crops and incomes. This induces considerable interest in minimum price insurance. Instability variables contribute positively to the demand for price insurance, while the existence of coping mechanisms contributes negatively, as expected. Large estimated values of individual WTP for coffee and cashew nut price insurance. Higher in Kilimanjaro than Ruvuma Considerable welfare benefits (net of costs) of minimum price insurance. Market based price insurance viable (premiums comparable to option prices in organized exchanges)

Average number of years in past 10 that households report rainfall as being in different ranges relative to normal.

Reasons for which households indicated they were not interested in rainfall (or drought) insurance

Empirical results. What variables affect WTP for Weather insurance? Bid values (-) Size of household (+) Per capita income (+) Education (+) Share of cash in total income (+) Use of self insurance to cope with shocks (+) Rely on family assistance to cope with shocks (-) Access to short term credit (-) Degree of vulnerability (-) Proportion of correct predictions (>70%)

Summary statistics of the WTP for rainfall insurance in Kilimanjaro (AFP denotes the approximate Actuarially Fair Price of the contract in Tsh/acre).

Summary statistics of the WTP for rainfall insurance in Ruvuma (AFP denotes the approximate Actuarially Fair Price of the contract in Tsh/acre).

Kilimanjaro. Welfare benefits and cost of rainfall insurance (10% rainfall reduction)

Kilimanjaro. Welfare benefits and cost of rainfall insurance (1/3 rainfall reduction)

Ruvuma. Welfare benefits and cost of rainfall insurance (10% rainfall reduction)

Ruvuma. Welfare benefits and cost of rainfall insurance (1/3 rainfall reduction)

Conclusions and policy implications; Weather insurance Interest in rainfall insurance higher in Kilimanjaro, a richer and more exposed to rainfall shocks region Vulnerability contributes negatively to the demand for insurance, while the existence of self insurance coping mechanisms contribute positively or negatively, depending on the type of mechanism. Considerable demand for weather insurance in Kilimanjaro and higher for contracts paying out when decline in rainfall is 10% below normal. Weak demand in Ruvuma. In Kilimanjaro average WTP is about percent of actuarially fair premium. In Ruvuma average WTP only 5-18 percent of actuarially fair premium.

Conclusions and policy implications; Weather insurance In Kilimanjaro for 10 percent rainfall shortfall, about percent of households would purchase the insurance at the average WTP, insuring percent of their total acres cultivated. The insured land would constitute percent of total cultivated land. In Kilimanjaro, for insurance against a 1/3 rainfall shortfall, participation at average WTP would be around percent of households, and they would insure percent of their cultivated acres. Total area insured would be around percent of total cultivated land. For Ruvuma and for the 10 percent rainfall shortfall, the participation at average WTP would be of only percent of households, insuring about percent of their total area cultivated. At actuarially fair prices, however, participation would fall to less than 10 percent of households, insuring about 30 percent of their cultivated land. At the actuarially fair value, about percent of all rural households in Kilimanjaro would insure about acres (about 6-17 percent of total land cultivated) resulting in a consumer surplus or benefit to society of more than 300 million Tsh or 300 thousand US dollars. Market based weather insurance not easily viable. Provision of subsidised weather insurance could reduce considerably the vulnerability of poor households

Practical instruments Smallholders are willing to pay for insurance, but how? Could be implicitly included in the cost of formal loans from banks Banks could provide the price insurance, so as to recover the loans, and reinsure the risk with market based instruments Index based weather insurance could also be provided through banks, as part of their lending programs.