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Determining Optimal Subsidy Levels for Agricultural Insurance Take-up in China
Initial registration date
November 28, 2016
November 28, 2016 10:42 PM EST
University of Michigan
Other Primary Investigator(s)
University of California, Berkeley
University of California, Berkeley
Additional Trial Information
Many new products presumed to be privately beneficial to the poor have a high price elasticity of demand and ultimately zero take-up rate at market price. This has led governments and donors to provide subsidies to increase take-up, with the concern of trying to limit their cost. In this study, we use data from a two-year field experiment in rural China to define the optimum subsidy scheme that can insure a given take-up for a new weather insurance for rice producers. We build a model that includes the forces that are known to be determinants of insurance demand, provide reduced form confirmation of their importance, validate the dynamic model with out-of sample predictions, and use it to conduct policy simulations. Results show that the optimum current subsidy necessary to achieve a desired take-up rate depends on both past subsidy levels and past payout rates, implying that subsidy levels should vary locally year-to-year.
Cai, Jing, Alain Janvry and Elisabeth Sadoulet. 2016. "Determining Optimal Subsidy Levels for Agricultural Insurance Take-up in China." AEA RCT Registry. November 28.
In the first year, we randomized subsidy policies at the village level by offering either a partial subsidy of 70% of the actuarially fair price or a full subsidy. The product was first offered with as 70% subsidy, and two days later 62 randomly selected villages were surprised with an announcement that the insurance will be offered for free to all, regardless of whether they had agreed to buy it or not at the initial price. These villages are called the "free sample," while the remaining 70% subsidy villages are called the "non-free sample."
In the second year, we randomly assigned eight prices to the product at the household level, with subsidies ranging from 40% to 90%. This creates eight different price treatment subgroups. Except for the price, everything else remained the same in the insurance contract as in the first year. Only two or three different prices were assigned within each village. In both years, we offered information sessions about the insurance policy to farmers, in which we explain the insurance premium, the amount of government subsidy, the responsibility of the insurance company, the maximum payout, the period of coverage, the rules for loss verification, and the procedures for making payouts. Households made their insurance purchase decision immediately after the information session. In
the second-year information session, we also informed farmers of the list of people in the village who were insured and of the payouts made during the first year at both the household and village level.
Intervention Start Date
Intervention End Date
Primary Outcomes (end points)
Primary Outcomes (explanation)
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
We construct a model of response to stochastic experiences in which individuals update their valuation of a weather insurance product with their recent experience. In our model, we specify three recognized channels through which recent experience can affect demand: (1) the effect of experiencing payout, with an expected positive effect on take-up if there has been an insured shock and a payout has been received, and a
negative erosion effect if a premium has been paid and either no shock occurred or a shock occurred without a corresponding payout, (2) the effect of observing network payout experiences, which follows the same process of positive and negative effects in relation to stochastic payouts, and (3) a habit forming effect, with past use of the product influencing current demand. We model how these channels would be impacted by subsidies through three separate effects: (1) a scope effect where subsidies enhance take-up and hence the opportunity of witnessing payouts, (2) an attention effect where a lower insurance cost for the individual leads to lower attention given to information generated by payout experiences, and (3) a price anchoring effect, where low past prices reduce current willingness to pay.
Experimental Design Details
Village and Individual level
Was the treatment clustered?
Sample size: planned number of clusters
Sample size: planned number of observations
Sample size (or number of clusters) by treatment arms
Non-free: 72 villages
Free: 64 villages
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
INSTITUTIONAL REVIEW BOARDS (IRBs)
Post Trial Information
Is the intervention completed?
Intervention Completion Date
December 31, 2011, 12:00 AM +00:00
Is data collection complete?
Data Collection Completion Date
December 31, 2014, 12:00 AM +00:00
Final Sample Size: Number of Clusters (Unit of Randomization)
Was attrition correlated with treatment status?
Final Sample Size: Total Number of Observations
Final Sample Size (or Number of Clusters) by Treatment Arms
Reports, Papers & Other Materials
Subsidy Policy and Insurance Demand - NBER Working Paper
Cai, Jing, Alain de Janvry, and Elisabeth Sadoulet. "Subsidy Policies and Insurance Demand." Working Paper, September 2016.
REPORTS & OTHER MATERIALS