producer demand and welfare benefits of price and weather insurance in rural tanzania alexander...

Producer Demand and Welfare Benefits of Price and Weather Insurance in Rural Tanzania

Alexander Sarris (FAO), Panayiotis Karfakis (Univ. of Athens and FAO) and Luc Christiaensen (World Bank)

FAO, Rome, June 27, 2006

Plan of Presentation

• Background and Motivation• Methodology• Relevant Characteristics of households in

Kilimanjaro and Ruvuma• Price and rainfall risks and household

perceptions of it• Desirability of and variables affecting demand

for price and weather based insurance• The demand and welfare benefit of providing

price and weather insurance• Conclusions and policy implications

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

Methodology

• Use Contingent Valuation (CV) approach (ask directly about willingness to pay given amounts for specific insurance contracts)

• Consider change in the status quo of farmer from q0 to q1

• Let indirect utility of respondent be v(p,q,y,s,), -p vector of prices for market goods

• -y is the respondent’s income• -s is a vector of respondent characteristics• - is the stochastic component of utility.

Methodology (continued)

Let A=“Bid value” = price respondent is asked to pay for insurance contract

1 0Pr{ " "} Pr{ ( , , , , ) ( , , , , )}response is Yes v p q y A s v p q y s (1)

Let C be the maximum WTP for the change from q0 to q1

1 0( , , , , ) ( , , , , )v p q y C s v p q y s (2)

Equation (1) rewritten as

0Pr{ " "} Pr{ ( , , , , ) }response is Yes C p q y s A (3)

As C is a random variable, let GC(.) be the cumulative distribution function (cdf) of C. Then (3) translates into the following.

Pr{ " "} 1 ( )Cresponse is Yes G A (4)

When G=, namely the standard normal cdf, and when C has mean equal to and variance equal to 2 then one has a probit model

Pr{ " "} ( )A

response is Yes

(5)

With probit model of the type

1

Pr{ " "} ( )n

i ii

response is Yes X A

(6)

1

n

i ii

XC

(7)

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 (-)

Household characteristics 1

Kilimanjaro Ruvuma

Unit All Poor Non Poor All Poor Non Poor

Number of households No 190744 63171 128351 173921 96897 77024 Household size Number 5.3 6.5 4.7 5.2 5.7 4.6 Age of Head Years 53.5 50.8 54.8 43.37 43.93 42.67

Annual per capita total expenditure '000 Tsh 214 105 268 162 93 249 Annual per capita total income '000 Tsh 158 80 204 148.6 85.7 227.9

Livelihoods

Share of non-cash income in total income

Percent 43.3 46.5 41.7 58.5 61.0 55.3

Share in total cash income of Coffee Percent 5.4 6.5 4.8 13.5 12.2 15.2 Tobacco Percent 2.4 3.0 1.7 Cashew nuts Percent 9.2 13.0 4.6 Other crops Percent 27.1 22.6 29.3 28.1 26.6 29.9 Non-crop agriculture Percent 14.6 12.4 15.8 3.0 3.3 2.8 Wages Percent 21.8 27.8 18.9 15.5 18.0 12.4 Other non-farm income Percent 31.0 30.7 31.2 28.2 24.1 33.3 Herfindhal Index of cash income diversification

Index 0 to 1 0.556 0.588 0.539 0.619 0.615 0.623 Herfindhal Index of total income diversification

Index 0 to 1 0.438 0.439 0.437 0.361 0.367 0.353

Household characteristics 2

Kilimanjaro Ruvuma

Unit All Poor Non Poor All Poor Non Poor

Asset base

Value of wealth per household 000 Tsh 3375 2334 3888 820 671 1006 Share of wealth from

Agriculture capital Percent 1.6 1.2 1.8 2.9 2.0 4.1

Non agriculture capital Percent 1.4 1.0 1.6 1.0 0.7 1.3

Consumer durables Percent 28.0 23.2 30.4 17.3 17.0 17.8

Agricultural land Percent 18.4 21.3 17.0 23.3 23.9 22.6

Dwellings Percent 58.2 63.3 55.8 45.0 48.1 43.1

Animals Percent 10.2 10.8 10.0 9.5 8.3 11.0

Area of land cultivated Acres 2.66 2.36 2.81 6.1 5.6 5.9

Number of plots cultivated Number 1.96 1.93 1.97 2.6 2.9 3.0

Number of animals in cattle equivalent

Number 2.43 1.97 2.65 1.04 .747 1.42

Education of the head Years 6.3 5.8 6.3 8.1 8.3 8.0

Agricultural productivity

Yield from maize kg/acre 217 160 245 203 167 248

Value added from crop production/acre

'000 Tsh/acre 116 100 125 46 37 57

Value of input for crop production/acre

'000 Tsh/acre 32 25 35 9.76 4.43 11.64

Percentage of households affected by various shocks between 1999 and 2003, by region and status as cash

crop grower or not Kilimanjaro Ruvuma

Cash crop

no cash crop

cash crop

no cash crop

Total

Health Death 23.1 29.9 16.3 19 21.8 Illness 23.3 22.8 18.5 19.1 21 Climatic Drought 27.8 39.9 2.8 7.1 19.2 Excessive rains 4.3 11.5 4.2 2.2 5.4 Agricultural production Harvest loss 5.2 8.6 6.1 4.4 6 Livestock loss 5.1 8.5 3.1 5.4 5.3 Post harvest cereal loss - - 0.9 2.9 1.7 Economic Cash crop price shock - - 5.8 2.7 4.6 Cereal price shock - - 0.8 5.1 2.5 Unemployment 0.3 1.7 0.2 0 0.5 Property Theft 4.4 6.9 3.7 6.9 5.2 Fire/house destroyed 0.2 1.4 3 3.7 1.9 Land loss 0.2 0.9 0.2 0 0.3

Vulnerability to poverty is high but portion due to covariate shocks varies by region

Number

of hhs Mean vulnerability

Proportion of consumption variance due to covariate factors

Pc expenditures

Kilimanjaro ALL 191,585 0.23 0.15 200.59 Non Poor 128,414 0.15 0.14 251.98 Poor 63,171 0.40 0.15 97.75

Ruvuma ALL 173,932 0.54 0.71 152.24 Non Poor 77,021 0.40 0.67 232.05 Poor 96,911 0.66 0.73 89.04

Source: Sarris and Karfakis (2006)

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

4A. Kilimanjaro

02

46

Pe

rcen

t

0 100 200 300 400Max-Min price rcvd last 10 years over mean price rcvd 20001 2000 2003

Mean:152.9 Var:3489.5

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

0

24

6P

erce

nt

0 100 200 300 400 500Max-Min price rcvd last 10 years over mean price rcvd 2001/2 2000/1 and this yea

Mean:190 Var:12476

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

0

24

68

Per

cent

0 50 100 150 200 250Max-Min price last 10 years over mean price rcvd 2000/01/02/03 & this year for S

Mean:104.2 Var:1605.4

Interest in minimum price coffee insurance among

coffee producing households 4a. Kilimanjaro Round 2

No Yes Total

No 22,454 22,772 45,226 Round 1 Yes 19,976 38,843 58,819 Total 42,430 61,615 104,045

4b. Ruvuma Round 2

No Yes Total

No 3,959 3,198 7,157

Round 1 Yes 12,962 31,183 44,145 Total 16,921 34,381 51,302

Interest in minimum price cashew nut insurance among cashew nut producing households in Ruvuma.

(Number of households)

Round 2

No Yes Total

No 2,779 5,530 8,309 Round 1 Yes 8,916 19,470 28,386 Total 11,695 25,000 36,694

Variables used in the Selection and WTP equations• Household characteristics (e.g education). • Income structure and income level variables (e.g. per capita income,

wealth, shares of cash to total income, share of coffee in total income, whether cash income from coffee is important, a banana production dummy, the share of coffee input costs in total coffee production value, easy access to seasonal credit, and the Herfindhal index of cash income diversification)

• Variables designed to proxy for recent conditions (e.g.the level recent prices received)

• Variables designed to indicate the level of instability faced (e.g. the range of prices received in the last ten years, the number of years in the last 10 when coffee cash income or total income fell below 50% of normal, or whether the household perceives cash crop income as very unreliable)

• Variables designed to capture the importance of different coping mechanisms to shocks affecting livelihoods (used four mechanisms with respective dummies: whether in response to a shock in the past the household used own savings or other own resources, assistance from other non-household family, assistance from non-family (including friends, neighbours, NGOs, government, etc), or whether sought to find new ways to generate income.

• Village level effects

What affects the desirability for minimum price insurance?

• Income instability variables

• Household coping mechanisms

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 (+), while 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 from

round 1.

400 Tsh minimum price contract No of

hh's Average WTP St. Dev.

WTP (Tsh) 63,803 67.93 26.98 WTP (Share of 400Tsh min. price) 63,803 16.98 6.75







Summary statistics of the predicted value of WTP for

coffee minimum price insurance in Ruvuma from round 1.





hh's Average WTP





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

round 1.





hh's Average WTP





Kilimanjaro coffee: Welfare benefit and cost for

minimum price insurance.

Premium rule Premium

value (Tsh/kg)

Quantity insured (tons)

Number of households

Total premium (million

Tsh)

Premium as share of coffee

sales (percent)

Consumer surplus (million

Tsh)

Consumer surplus as share of

coffee sales (percent)

400 Tsh minimum price Mean WTP 67.9 3367 34,362 228.7 15.5 29.5 2.0 Mean WTP + 1 SD 84.9 2247 12,104 190.8 19.5 2.7 0.3 Mean WTP - 1 SD 51.0 4414 51,878 224.9 11.3 85.4 4.3 Mean WTP - 2 SD 34.0 5352 62,394 181.8 7.5 168.6 7.0



Ruvuma coffee: Welfare benefit and cost for minimum price insurance.


value (Tsh/kg)




Tsh)


sales (percent)


Tsh)





800 Tsh minimum price Mean WTP 74.1 9352 33,044 692.5 19.3 113.6 3.2 Mean WTP + 1 SD 83.3 0 0 0 0 0.0 0.0 Mean WTP - 1 SD 64.8 11200 38,808 725.8 17.0 317.2 7.4 Mean WTP - 2 SD 55.6 12400 42,534 688.8 14.6 549.6 11.7

Ruvuma cashew nuts: Welfare benefit and cost for minimum price insurance.


value (Tsh/kg)




Tsh)


sales (percent)


Tsh)





600 Tsh minimum price Mean WTP 26.5 4789 18,997 126.8 6.2 23.5 1.2 Mean WTP + 1 SD 30.9 0 0 0 0 0.0 0.0 Mean WTP - 1 SD 22.1 6159 22,298 135.9 5.2 70.5 2.7 Mean WTP - 2 SD 17.7 6391 23,965 112.8 4.1 110.8 4.0

Monthly rainfall patterns observed across weather stations in Kilimanjaro are reasonably well correlated

Correlation coefficients of monthly rainfall levels over 1964-2005 among Kilimanjaro stations. (57 out of 210 correlation coefficients are smaller than 0.5)

Station’s name engare nairobi 1.00 west kilimanjaro 0.67 1.00 kibong'oto 0.45 0.52 1.00 misinga farm 0.24 0.27 0.56 1.00 mweka 0.29 0.18 0.43 0.27 1.00 mawingo estate 0.36 0.34 0.66 0.75 0.82 1.00 karanga tpc 0.52 0.52 0.68 0.56 0.37 0.79 1.00 masama estate 0.36 0.33 0.65 0.71 0.49 0.95 0.75 1.00 kibosho mission 0.28 0.23 0.70 0.77 0.47 0.91 0.64 0.92 1.00 lyamungu coffee 0.37 0.37 0.78 0.75 0.40 0.94 0.80 0.91 0.89 1.00 moshi met 0.51 0.53 0.73 0.66 0.37 0.80 0.90 0.79 0.71 0.81 1.00 moshi prison 0.52 0.51 0.75 0.66 0.36 0.85 0.88 0.81 0.76 0.86 0.92 1.00 kilimanjaro airport 0.63 0.59 0.68 0.41 0.27 0.57 0.74 0.59 0.52 0.64 0.78 0.75 1.00 uru west 0.41 0.29 0.65 0.83 0.41 0.95 0.61 0.94 0.87 0.79 0.67 0.78 0.52 1.00 uru estate 0.29 0.33 0.73 0.59 0.45 0.96 0.62 0.87 0.79 0.81 0.65 0.73 0.48 0.85 1.00 old moshi 0.37 0.46 0.70 0.82 0.84 0.89 0.87 0.89 0.76 0.88 0.90 0.91 0.60 0.82 0.84 1.00 maua seminary 0.43 0.53 0.69 0.55 0.40 0.82 0.67 0.76 0.67 0.71 0.70 0.72 0.57 0.66 0.83 0.72 1.00 kilema 0.46 0.45 0.62 0.62 0.20 0.82 0.73 0.81 0.70 0.73 0.79 0.79 0.66 0.70 0.63 0.77 0.72 1.00 kilimanjaro sec school 0.76 0.66 0.48 0.23 0.33 0.30 0.60 0.33 0.35 0.41 0.59 0.56 0.66 0.32 0.27 0.40 0.47 0.55 1.00 rombo mission 0.68 0.69 0.50 0.32 0.31 0.37 0.59 0.38 0.36 0.41 0.61 0.56 0.62 0.32 0.30 0.56 0.51 0.60 0.90 1.00

Monthly rainfall patterns observed across weather stations in Ruvuma are quite well correlated

Correlation coefficients of monthly rainfall levels over 1970-2005 among Ruvuma stations (10 out of 105 correlation coefficients are smaller than 0.5)

Station’s name kigonsera 1.00 litembo mission 0.85 1.00 luanda mission 0.85 0.77 1.00 mbambabay 0.66 0.74 0.72 1.00 mbinga mission 0.72 0.83 0.79 0.74 1.00 songea 0.82 0.86 0.78 0.75 0.85 1.00 matimira mission 0.82 0.83 0.79 0.76 0.85 0.90 1.00 peramiho Mission 0.66 0.66 0.75 0.65 0.71 0.77 0.72 1.00 hanga estate 0.73 0.59 0.39 0.39 0.48 0.66 0.57 0.42 1.00 songea agriculture 0.84 0.84 0.79 0.72 0.83 0.94 0.88 0.75 0.60 1.00 matogoro 0.82 0.80 0.83 0.75 0.84 0.89 0.90 0.74 0.60 0.87 1.00 gumbiro Pr. School 0.77 0.74 0.63 0.59 0.68 0.74 0.68 0.54 0.55 0.73 0.68 1.00 mhiga village 0.62 0.65 0.69 0.49 0.71 0.62 0.67 0.42 0.61 0.62 0.60 0.69 1.00 maundi 0.62 0.61 0.49 0.55 0.58 0.69 0.64 0.58 0.31 0.66 0.66 0.42 0.39 1.00 Note: Coefficients are red if the correlation is below 50%

Households’ assessments of yearly rainfall is consistent with objective rainfall measurements

Actual compared to average yearly rainfall in Kilimanjaro (1964-2005) and Ruvuma (1970-2005) and households’ assessments

Kilimanjaro Ruvuma 1964-2005 1970-2005 Long run average yearly rainfall (mm) 1303.47 1106.37 Dec03 to Nov04 Mar04 to Feb05 Survey year actual rainfall (mm) 989.7 1246.04

Households’ rainfall assessment relative to normal rainfall (% of households) Dec03 to Nov04 Mar04 to Feb05 Much above 0.4 4.5 Above 3.5 8.4 Normal 19.2 76.4 Below 41.3 10.6 Much below 35.6 0.0 No of households 182834 162722

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

normal.

Mean StDev Kilimanjaro Much below 2.47 1.42 Somewhat below 2.01 1.23 Normal 3.53 1.77 Somewhat above 1.03 0.88 Much above 0.96 0.47

Ruvuma Much below 0.63 0.94 Somewhat below 1.50 1.24 Normal 5.78 2.21 Somewhat above 1.24 1.15 Much above 0.85 0.74

Similarity between farmers’ perceptions concerning rainfall and their village average is high (index ranging from 0 (no similarity)

to 1 (perfect similarity))

Kilimanjaro

Rombo Mwanga Same Moshi Hai Overall Much below 0.77 0.77 0.75 0.79 0.80 0.78 Somewhat below 0.77 0.74 0.73 0.76 0.78 0.76

Normal 0.82 0.78 0.77 0.82 0.84 0.82 Somewhat above 0.63 0.65 0.65 0.63 0.71 0.65 Much above 0.85 0.80 0.88 0.86 0.89 0.86

Ruvuma

Songea Tunduru Mbinga Namtumbo Overall Much below 0.49 0.48 0.26 0.53 0.39 Somewhat below 0.74 0.73 0.60 0.64 0.66

Normal 0.82 0.84 0.86 0.86 0.85 Somewhat above 0.64 0.61 0.66 0.64 0.64 Much above 0.79 0.65 0.65 0.69 0.68

Perceptions of households concerning rainfall relative to objective rainfall incidence

If rainfall was 1/10, ¼, 1/3 or ½ below normal you would say that it was (% of household responses):

Normal Somewhat below A lot below NA Total Kilimanjaro 1/10 below normal 19.9 52.34 25.85 1.91 100 1/4 below normal 1.69 32.41 63.99 1.91 100 1/3 below normal 2.63 8.49 86.86 2.02 100 1/2 below normal 0.21 1.46 96.42 1.91 100

Number of households 182,775 Ruvuma 1/10 below normal 28.28 53.55 15.71 2.46 100 1/4 below normal 2.55 37.17 57.96 2.32 100 1/3 below normal 0.87 12.22 84.6 2.32 100 1/2 below normal 0.08 1.59 96.01 2.32 100

Number of households 161,619

Reasons for which households indicated they were not interested

in rainfall (or drought) insurance Why not interested in drought insurance? (% out of total households in the region)

Kilimanjaro I cannot pay any amount for rainfall 29.28 I am short of funds in the period before planting 1.98 I have other pressing cash needs in the period before planting 1.15 Declines in rainfall do not hurt me too much 4.70 I have other means of covering losses due to bad rainfall 0.82 Major declines in rainfall do not occur too often 0.94 Other 14.32 % of households not interested 53.19 Total number of households 182,775

Ruvuma I cannot afford to pay any amount 20.71 I am short of funds in the period before planting 0.78 I have other pressing cash needs in the period before planting 0.46 Declines in rainfall do not hurt me too much 17.32 I have other means of recovering losses due to bad rainfall 0.21 Major droughts do not occur too often 20.20 Other 3.48 NA 2.44 % of households not interested 65.60

Total number of households 161,619

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 (-) • Degree of vulnerability (-)

Kilimanjaro. Summary statistics for WTP for rainfall insurance

Drought WTP Kilimanjaro –10% rainfall decline below normal

22000 Tsh contract

No of hh's Average WTP St. Dev.

WTP (Tsh) 182539 4997.7 5491.4 WTP (Share on 22000Tsh) 182539 22.7 25.0

38000Tsh contract


WTP (Tsh) 182539 5082.3 7747.4 WTP (Share of 38000Tsh) 182539 13.4 20.4

61000 Tsh contract



Drought WTP Kilimanjaro –1/3 rainfall decline below normal

24000 Tsh contract



41000Tsh contract



66000 Tsh contract



Ruvuma. Summary statistics for WTP for rainfall insurance

Drought WTP Ruvuma –10% rainfall declien below normal 12000 Tsh contract



21000Tsh contract


WTP (Tsh) 18738 5285.3 5040.6

WTP (Share on 21000Tsh) 18738 25.2 24.0 35000 Tsh contract



Drought WTP Ruvuma –1/3 rainfall decline below normal

20000 Tsh contract



35000Tsh contract



58000 Tsh contract



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

Premium value (000Tsh/acre)

Acres insured


Total premium (million sh)

Premium as shareof crop sales

Consumer surplus (million sh)

Consumer surplus as share of crop sales

Acres cultivated

22000tsh contact At mean WTP 5.0 118,434.6 77,061.4 591.9 3.3 829.8 4.7 241,611 At +1 Sdev WTP 10.5 66,715.2 32,504.1 699.8 7.6 320.5 3.5 117,800

38000sh contract At mean WTP 5.1 86,208.6 61,570.6 438.1 2.8 1,017.8 6.5 204,385 At +1 Sdev WTP 12.8 45,581.9 27,589.5 584.8 6.8 481.2 5.6 108,665


Total number of households/acres 182,834 504,152

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


Acres insured



Premium as share of crop sales



Acres cultivated




Total number of households 182,834 504,152

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


Acres insured






Acres cultivated


21000sh contract At mean WTP 0.6 38,848.3 16,219.2 24.1 0.3 271.4 3.6 164,927 At +1 Sdev WTP 3.0 32,408.6 11,608.0 98.6 1.7 186.9 3.3 115,648


Total number of households 162,722 1,216,465

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


Acres insured






Acres cultivated




Total number of households 162,722 1,216,465

Conclusions and policy implications (1)

• 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 and weather

based income 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)


• 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 30-55 percent of actuarially fair premium. In Ruvuma average WTP only 5-18 percent of actuarially fair premium.


• In Kilimanjaro for 10 percent rainfall shortfall, about 30-40 percent of households would purchase the insurance at the average WTP, insuring 40-45 percent of their total acres cultivated. The insured land would constitute 15-20 percent of total cultivated land.

• In Kilimanjaro, for insurance against a 1/3 rainfall shortfall, participation at average WTP would be around 25-35 percent of households, and they would insure 40-45 percent of their cultivated acres. Total area insured would be around 15-20 percent of total cultivated land.

• For Ruvuma and for the 10 percent rainfall shortfall, the participation at average WTP would be of only 10-15 percent of households, insuring about 20-30 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.

• Above numbers decline significantly when computed at the actuarially fair values of the contracts.

• Market based weather insurance not easily viable. • Provision of subsidised weather insurance could reduce

considerably the vulnerability of poor households

producer demand and welfare benefits of price and weather insurance in rural tanzania alexander...

Documents

total income

income structure

minimum price coffee

income level variables

respondents incomes

additional price

supported price

welfare benefit of price