IJERD – International Journal of Environmental and Rural Development (2017) 8-1

Research article
erdDetermining Crop Insurance Participation in Laguna,Philippines Using Subsidy and Asymmetric Information
Incentives
DIANE CARMELIZA N. CUARESMA*
University of the Philippines Los Banos, Philippines
Email: dncuaresma@up.edu.ph

JUSTINE ANGELI L. SAQUING
University of the Philippines Los Banos, Philippines

VINCENT PAULO M. TY
University of the Philippines Los Banos, Philippines
Received 15 December 2016

Accepted 27 June 2017(*Corresponding Author)

Abstract The Philippines is susceptible to tropical cyclones which threaten the production ofagricultural crops. To help farmers cover their losses, financial protection through crop
insurance can be employed. This paper studied the effects of the Philippine multi-peril crop
insurance on mean returns per acre. Data on palay (unhusked rice) yields in the province of
Laguna were gathered from 1978 to 2015. Using classical decomposition, the time series data
was decomposed into trend-cycle, seasonality and irregularity components to compute for theActual Production History (APH) yield and to forecast the APH yield for 2016. Using the crop
insurance incentives model of Just et. al., the data were used to explore the subsidy andasymmetric information incentives for insurance participation for both wet and dry seasons.
Three risk classes were considered: low, medium and high, while four insurance guarantee
levels were considered: 100%, 90%, 10% and 0%. It was found that as yield guarantee
increases, the effect of insurance on mean returns also increases. Additionally, as insurance
premium increases, the effect of insurance on mean returns per acre decreases. Results also
indicate that effects of crop insurance on mean returns per acre are highest when risk class is
low and yield guarantee is high.
Keywords crop insurance, subsidy incentive, asymmetric information incentive

INTRODUCTION
The Philippine government has been helping farmers, who are amongst the poorest citizen in thecountry, cover their production losses by offering financial protection through crop insurance. These
insurance products pay the farmer the cost of production input when a partial or total loss isexperienced. A loss is considered partial if the loss is between 10% and 90%; otherwise, if the loss isabove 90%, it is considered a total loss. While some insurance products from the government are free,
farmers are required to pay a premium for these insurance products. The insurance premium varies
according to the season and risk classification. There are two main planting seasons in the country, thewet and the dry season. While there are three risk classes: low-risk, medium-risk or high-risk, which isclassified according to the riskiness of planting rice in that area. The farmer can choose the amount ofcover or guarantee level.
The government heavily subsidizes the premium to help the farmers. This is a great aid to many
low-income farmers, who still finds it difficult to purchase the insurance despite the subsidy. However,Ⓒ ISERD163

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this may also attract opportunistic agricultural producers who use information advantages to obtain
more profits. Though extensive studies are conducted by the insurance providers, farmers are often
more knowledgeable about the risk in planting. Some studies conducted by Just, Calvin and Quiggin(1999), Walters, Shumway, Chouinard and Wandschneider (2014), and Wang, Hanson and Black(2003) suggest the presence of adverse selection and moral hazard in insurance. Profit-maximizing inadverse selection occurs when there is information asymmetry between two parties before theagreement, whereas profit-maximizing in moral hazard occurs when there is a change in behavior after
the agreement (Nickolas, 2016). If premiums paid are not able to cover the indemnities, this may
render the insurance product unsustainable.
Many studies identifying factors affecting farmer participation in crop insurance have beenconducted. According to some literatures, a set of factors that impact farmer participation decisions
include farmer-specific attributes, region-specific attributes and economic factors. Farmer-specificattributes include age, experience, education and income. Region-specific attributes involve farm size,
location and soil quality. Economic factors consist of the insurance premiums and indemnity payments.
Sai, Yulian and Xiaofeng (2010) found that in Chinese household, agricultural land, farmer’seducation, production capacity and transaction costs are the significant factors for families in rural
areas to enroll in crop insurance programs. A study performed in the United States verified the ideathat decreasing transaction cost improves crop insurance participation. According to Ker and Ergun(2003), when transaction cost is reduced through efficient delivery channels, greater insurance
participation can be elicited. A research by Cabas, Leiva and Weersink (2008) found that that farmer
participation in insurance is largely determined by price variables.
In this study, it will be determined if adverse selection is present in the crop insurance program inthe Philippines to aid the insurers in safeguarding the sustainability of their insurance products. This
study can also be useful for farmers who are on a tight budget and are picking only the likely situation
where insurance can help them in safeguarding their income.
OBJECTIVE
The primary aim of this study is to estimate the effects of insurance, particularly the multi-peril rice
insurance, on mean returns (bushels per acre) to the farmer and to look into the influence of subsidy
and asymmetric information in the decision of farmers to participate in crop insurance programs.
METHODOLOGY
In this study, an analysis is made by applying the crop insurance incentives model built by Just, Calvinand Quiggin (1999). Data are collected from two sources: Regional Office of the Department ofAgriculture (DA), and the Philippine Statistics Authority. The data on rice production are collectedfrom the Regional Office of the Department of Agriculture (DA) in Calauan, Laguna. These included
harvest area, average yield (in metric tons per hectare) and yield production (in metric tons) in 2014
and 2015 during wet and dry seasons. There data collected from the Philippine Statistics Authority Bureau of Agricultural Statistics are downloaded from the website. These data are statistics on thequarterly area and volume of palay, and monthly farm-gate prices of palay in Laguna from the years
2006 to 2015.
The analysis uses data on rice production of Filipino farmers in Laguna to identify crop insurance
effects on mean returns per acre. The insurance effects on mean returns are then decomposed into
asymmetric information and subsidy incentives. Subsequently, the crop insurance effects are forecasted
for the following year using forecasted values of the Actual Production History (APH) yield and using
forecasted values of the palay price. Forecasting of APH yield and palay price for 2016 is employed toⒸ ISERD164

IJERD – International Journal of Environmental and Rural Development (2017) 8-1

predict insurance effects on mean returns per acre and to predict the changes in the subsidy and theasymmetric information effects for 2016.
RESULTS AND DISCUSSION
Decomposition of Data Collected
Upon applying classical decomposition, the data collected on production shows that palay productionin the Philippines has an increasing trend (Fig. 1), except for the drop experienced in the first quarter of2010, which can be attributed to the palay damaged due to spread of tungro, rice blasts, black bug, as
well as the onset of El Niño. Another observation from the seasonality component (Fig. 2) of the datais that yields are highest during the wet season and lowest during the dry season. This observation isnecessary in determining the periods when insurance participation are most effective.
Computing the effect of insurance on mean returns per acre shows that as the premium increases,
the effect of insurance to the farmer’s mean return decreases. However, the effect of insurance
increases as the loss increases. This implies that adverse selection can happen for farms in high-riskarea. These results are summarized Table 1, Figs. 3 and 4. Figure 3 shows that as insurance premiumincreases, the effect of insurance on mean return decreases. Figure 4 shows that the effect of insurance
on mean returns increase for higher than 70% of loss.

Fig. 1 Trend-cycle component of Palay yield on a quarterly basis from 2006 to 2015

Fig. 2 Seasonality component of Palay yield on a quarterly basis from 2006 to 2015
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Fig. 3 Relationship between insurance premium and effect of insurance on mean returns
per acre during the wet season when there is total loss
Table 1 Effect of insurance on mean returns per acre
MultiRisk
Cover

Total Loss
PartialLoss
No LossLow
390.02 to790.74-14.25 to
390.02-14.31 to-14.25

Effect of Insurance on Mean Returns per Acre
Wet Season
Dry Season
MediumHighLow
Medium387.43 to384.83 to-10.40 to-12.73 to
788.15785.55-2.55-4.88-16.84 to-19.44 to-12.87 to-15.20 to
387.43384.83-10.40-12.73-16.90 to-19.50 to
12.87-15.20-16.84-19.44High-15.06 to-7.21-17.53 to-15.06-17.53Fig. 4 Relationship between amount of loss and effect of insurance on mean returns
per acre during wet season (original and forecasted price)Ⓒ ISERD

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Analysis of the Subsidy and Asymmetric Information Incentives
This effect is decomposed into subsidy and asymmetric information incentives. Subsidy incentiverefers to the financial aid given by the government to increase farmer participation in crop insurance
programs, whereas the asymmetric information incentive is defined to be the incentive associated with
the inaccuracies in insurance underwriting because of incomplete information for the side of theinsurer.
Table 2 Subsidy effect of insurance
Yield
Guarantee> 90%
10% to 90%< 10%

Subsidy Effect of InsuranceLow-13.55 to585.63-14.33 to-13.55-14.33

Wet Season
Medium-16.14 to583.04-16.92 to-16.14-16.92High-18.74 to
580.44-19.52 to-18.74-19.52Low-12.09 to587.09-12.87 to-12.09-12.87

Dry Season
Medium-14.42 to584.76-15.20 to-14.42-15.20High-16.75 to582.43-17.53 to-16.75-17.53The subsidy effect of insurance on mean net income of farmers is presented in Table 2. Results
show that for both cropping seasons, the subsidy effect on mean net income is very high in low-riskarea and at > 90% loss, while the least effect appears in high-risk area and at < 10% loss. This suggeststhat the largest increase of mean net income due to subsidy occurs when a farmer from a low-risk area
experiences a total loss. This implies that a farmer will benefit from the insurance if he is from a lowrisk area and he sees that for that cropping season losses are likely to occur. However, it is also seenthat adverse selection is greater during the dry season than during the wet season. As for the insurer,
this means that he should be more careful in underwriting the insurance product during the dry season.
By comparing subsidy effects across yield guarantees for both wet and dry seasons, high positive
mean subsidy effects of crop insurance are perceived for some yield guarantees between 90% to 100%.
This means that for these yield guarantees, the subsidy greatly increases mean net income whenagricultural loss is at least 90%. It can also be perceived that low negative mean subsidy effects are atyield guarantees from 0% to 90%. This means that insurance subsidy causes a slight decrease on mean
net income for yield guarantees 0% to 90%.
By comparing the values obtained by cropping season, it can be identified that the values for thedry season are greater than that of the wet season. Higher mean net income effects are expected during
the dry season because there are less storms and typhoons that hinder the growth of palay. It is seenthat adverse selection is greater during the dry season than during the wet season.
Another significant observation is that when the yield guarantee is low, the subsidy effect ofinsurance on mean returns per acre is also low. This implies that there is a positive association between
these two. It is determined that as the premium increases, the effect of insurance on mean returns peracre decreases linearly.
As for asymmetric information effects (Table 3), calculations show that for dry season the meaneffect on income is negative when farmer, in any area, chooses the highest guarantee level. This meansthat farmers cannot exploit the insurer for insufficient information, or that adverse selection is greater
during the wet season than during the dry season. This implies that farmers are less likely to getinsurance during dry season, and are less likely to choose maximum coverage during this season.
By comparing asymmetric information effects across yield guarantees for both cropping seasons,it can be observed that the mean net income effect is very high when yield guarantee is 90%, while it isvery low when guarantee is 10%. When yield guarantee is 100%, negative asymmetric information
effects are also very high. However, for other yield guarantees 10% and 90%, positive asymmetricⒸ ISERD

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information effects are very low. It can be deduced here that for dry season, farmer cannot fully exploit
the insurers for insufficient information.
Table 3 Asymmetric information effect of insurance
Yield
Guarantee
100%90%10%Low
205. 11403.57
0.07

Asymmetric Information Effect of Insurance
Wet Season
Dry Season
MediumHighLow
Medium205.11205.11-589.64-589.64403.57403.57
1.70
1.70
0.07
0.07
0.00
0.00

High-589.64
1.70
0.00

By comparing the asymmetric information across cropping seasons, it can be identified that thevalues for the dry season are less than that of the wet season. This means that higher mean net income
effects due to asymmetric information are expected during the wet season. It is seen that adverse
selection due to asymmetry of information is greater during the wet season than during the dry season.
CONCLUSION
In conclusion, this study finds that adverse selection due to subsidy and asymmetric information
incentives is existent in the multi-risk crop insurance product. The results support that the adverse
selection is highest when yield guarantee is high. In most cases, adverse selection is highest when risk
is low and the yield guarantee is high. For the farmer, he will benefit if he is from a low-risk area andinsures when he expects that there will be losses for a certain cropping season.
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