Crop Insurance Participation Decisions and Their Impact on Net Farm Income Loss of
Rice Farmers in the Lakeshore Municipalities of Laguna, Philippines1
By:
Armand Christopher C. Rola and Corazon T. Aragon2
Abstract
The study was conducted to determine the factors affecting the participation of farmers in the
Philippine Crop Insurance Corporation (PCIC) Rice Insurance Program in selected lakeshore
municipalities of Laguna and to determine the effects of the program in reducing income losses.
Primary data were obtained through personal interviews with 40 sample farmerparticipants and 40 non-participants using pre-tested interview schedules. Descriptive statistics
were used to describe the profile of the participating and non-participating farmer-respondents. A
logit model was estimated to determine the factors that influenced the rice farmer’s decision to
participate in the PCIC Rice Insurance Program while multiple regression analysis was employed
to identify the significant factors affecting percentage yield loss and the amount of indemnity
payments. Cost and returns analysis was conducted to estimate and compare the profit or loss in
2010 (normal year) and in 2012 (calamity year). Mean losses in income of the farmerparticipants before and after receiving their indemnity claims were estimated to determine the
extent to which the farmers’ losses were reduced as a result of their participation in the PCIC
Rice Insurance Program.
Results showed that the major reasons why the farmer-participants joined the program
were: (i) securing rice crop insurance is one of the requirements of the Land Bank of the
Philippines in extending loans to farmers, and (ii) participation in the insurance program is
essential to avoid risk in rice farming; the major reason for non-participation was being unaware
of the existence of the Program. Farmer’s decision to participate in the program was significantly
influenced by their awareness of the program, tenure status, and the distance of their farms from
the lakeshore. This program participation has eased farmers’ financial burden. On average, the
percentage reduction in income losses was 94 percent per farm.
This study recommends among others that PCIC management undertake a more intensive
awareness campaign to promote wider participation among farmers/farmer groups in the PCIC
Rice Insurance Program.
1
To be presented during the Annual Meeting of the Philippine Economic Society, Intercontinental Hotel Manila,
November 15, 2013.This is an abridged version of the undergraduate thesis of the senior author entitled,” Factors
affecting farmers’ participation in the Philippine Crop Insurance Corporation rice insurance program and the effects
of the insurance program in reducing income losses of the rice farmer-participants in selected lakeshore
municipalities in Laguna, wet season 2012”, submitted to the Department of Agricultural Economics, University of
the Philippines Los Baños, College Laguna, April, 2013; under the supervision of Prof. Corazon T. Aragon.
2
Respectively, Research Assistant, UP Center for Integrative and Development Studies (UP-CIDS) and Professor,
University of the Philippines Los Baños.
1. INTRODUCTION
Unexpected events with adverse results such as drought, typhoons, disease infestation, or
earthquake can cause risks in farming activities. The Philippines is very much vulnerable to
these production risks. The Philippines ranks 8th among the top 10 countries that are most
exposed to natural hazards or multiple hazards (Regalado, 2010). Almost annually, heavy crop
damages have been reported as caused by typhoons, droughts, and other natural calamities.
However, risks and uncertainties could be managed so that the impact could be minimized. Risk
management is concerned with reducing the possibility of unfavourable outcomes, or at least
softening their effects. One way of reducing risk is through agricultural insurance. When
disasters happen, farmers and/or poor farming households will have less access to risk
management options needed to cope with the consequences of such events. It has been
repeatedly mentioned that crop insurance through indemnity payments serves as a cushion when
uncertainties occur. Estacio and Mordeno (2001) wrote that crop insurance is a risk
management mechanism designed to even out agricultural risks and blunt the consequence of
natural disasters to make losses, especially to the more marginalized farmers, more bearable.
Several studies have however alleged that the extent by which income loss is reduced through
indemnity is limited because of the small indemnity payment received (Alarkon, 1997; Bacani,
2005; Famorcan, 2006).
Insurance is generally defined as: the form of risk management primarily used to hedge against
the risk of a contingent, uncertain loss (Dickson, 1960). Insurance is likewise defined as the
reasonable shift of the risk of a loss, from one unit to another, in substitute for payment.
Agricultural insurance is not only limited to crops, but also covers livestock, forestry, and even
aquaculture.
Given that they produce the country’s staple which is very much vulnerable to agricultural risks,
rice farmers would benefit from insurance as a strategy to deal with such risks. To address this
problem, the Philippine government has come up with a range of risk management programs for
farmers. One of these measures is the Philippine Crop Insurance Corporation’s (PCIC) Crop
Insurance Program. Agricultural insurance is a government program that provides insurance
protection to agricultural producers against loss of the crops, livestock and agricultural assets on
account of natural calamities, plant pests and diseases and/or other perils. The PCIC, created
through Presidential Decree No. 1467, which was promulgated on 11 June 1989 (PCIC Annual
Report, 2010) is directly responsible for its implementation. Presidential Decree (PD) No. 1467,
as amended by Presidential Decree No. 1733 and Republic Act (RA) 8175, tasks the PCIC to
provide insurance protection to the country’s agricultural producers, particularly the subsistence
farmers, against loss of their crops and non-crop agricultural assets on account of natural
calamities, such as typhoons, floods, drought, earthquake, volcanic eruption, pest and disease
outbreak and/or other perils” (PCIC Annual 2011).
The PCIC Program provides agricultural insurance like rice crop insurance, corn crop insurance
and high-value commercial crop insurance. It has partnerships with cooperatives and financial
institutions like the Land Bank of the Philippines (LBP) for the delivery of indemnity payments.
In Laguna, the National Irrigation Administration Region IV Employees Multipurpose
Cooperative or NEMCO Office in Pila, Laguna and the New Batong Malake Multi-purpose
Cooperative (NBMMPC) office in Los Banos, Laguna are two of the cooperatives that deliver
indemnity payments to the farmers.
Among the types of farmers covered by the crop insurance, rice farmers are included among the
most vulnerable to typhoons, droughts and other extreme or risky events that can lead to
significant losses or damages, subsequently reducing their income. In Laguna, the most
vulnerable to floods are those whose rice farms are situated near the lakeshore. A risk
management option like participating in the Rice Insurance Program of the PCIC may or may not
assure the farmer of a safety net for escaping the poverty trap as it is not generally known to
what extent the Rice Insurance Program of PCIC reduces the income loss of farmers through the
amounts of indemnities that they receive. As argued by Leatham et al (1987) crop insurance
would only be preferred by moderately risk-averse farmers when farm firm failure becomes an
issue. This study hopes to shed light on the role of the PCIC’s rice insurance program on the net
income losses of the participating farmers.
2. OBJECTIVES OF THE STUDY
This study examined the factors affecting the rice farmers’ participation in the PCIC’s Rice
Insurance Program and determined the effects of the program on the reduction in income loss of
the farmer-participants in selected lakeshore municipalities of Laguna. Specifically, the study
aimed to:
1. Describe the socio-economic characteristics of the non-participating and participating
rice farmers in the PCIC’s Rice Insurance Program;
2. Examine the factors that influenced the rice farmers’ decision to participate or not to
participate in the PCIC’s Rice Insurance Program;
3. Determine the effects of selected factors such as farm size, source of risk, farm location,
and variety on the percentage of yield loss;
4. Assess the influence of percentage of yield loss, rice variety, production cost, stage of
the crop when damage occurred, and farm size on the amount of indemnity payments;
and
5. Determine and compare the extent of reduction in the net loss in income of the rice
farmer-participants in the Rice Insurance Program by farm size, farm location, and tenure
status.
3. CONCEPTUAL FRAMEWORK
The decision of the rice farming households to participate in the Rice Insurance
Program is expected to be influenced by selected household and farm characteristics
(household income, tenure status, farm size, farm location relative to the lakeshore),
access to credit from banking institutions, membership in a cooperative, and their extent
of awareness about the PCIC Rice Insurance Program (Figure 1). Rice farmers who have
higher household income are less likely to participate in the Rice Insurance Program than
the farmers with lower household income because even if they incur a loss, they would
still have other sources of income to be used for the next cropping season. Owneroperators are more likely to participate in the Rice Insurance Program than leaseholders.
Meanwhile, farmers whose rice farms are near the lakeshore are more likely to participate
in the Rice Insurance Program than the farmers whose farms are located farther from the
lakeshore since they expect to incur more crop losses due to flooding. Meanwhile, rice
farmers who avail of a loan from banks have a higher probability of participating in the
Rice Insurance Program than non-bank borrowers because it is a bank requirement for
accessing loans from a bank (e.g., Land Bank). Rice farmers who lack or have limited
knowledge about the Rice Insurance Program are less likely to participate in this crop
insurance program of the PCIC than those who have more knowledge about the features
of the program. Rice farmers who have experienced damages due to flood, drought,
earthquake, and pest and disease incidence are more likely to participate in the Rice
Insurance Program of PCIC.
Household and Farm
4.
Characteristics:
Other Factors
- 5.Household income
6.(farm and non-farm)
- Tenure status
- 7.Farm size
- Location/distance of
8.
the farm from the
9.lakeshore
Awareness of the
PCIC Rice
Insurance Program
- Credit access to
financial
institutions
- Damage
experienced
10.
Participate/Not Participate in the
PCIC Rice Insurance Program
Figure 1. Conceptual framework showing the factors affecting the rice farmers’
decision to participate/not participate in the PCIC Rice Insurance Program
Figure 2 shows the effects of the PCIC Rice Insurance Program on the reduction of rice farmerparticipants’ income losses through indemnity payments. Climatic factors such as floods,
typhoons and droughts cause severe crop damages and result in yield and income losses of the
farmers.
Other factors like pest and disease incidence, fire, and earthquake also contribute to crop
damages which would result in income losses. With the participation of the farmers in the
PCIC’s Rice Insurance Program, these losses are expected to be reduced through indemnity
payments. The extent of income loss reduction, however, may vary among the rice farmers due
to variation in the amount of indemnity payments received by the farmer-participants. The
amount of indemnity payments are expected to be influenced by the percentage of yield loss,
total production cost incurred at the time of damage, stage of the crop at the time of loss. PCIC
categorized the percentage of yield loss as the total loss (90 percent and above), partial loss (10
percent and below 90 percent), and no loss (10 percent or less). It is, therefore, expected that the
amount of indemnity received by the rice farmer-participants would increase with the increase in
the percentage of yield loss.
Climatic Factors:



Other Risk Factors:
Floods
Typhoons
Drought
Crop Damage/
Income Loss
Amount of Insurance Indemnity
- Percentage of yield loss
- Production cost
- Stage of cultivation at the
time of loss
- Variety
- Farm size
- Reduced Income
-
Loss




Pest and
Diseases
Earthquakes
Fires
Nearness to
lakeshore
PCIC Rice
Insurance
Program
Figure 2. Conceptual framework showing the factors affecting the amount of indemnity
payments and the effects of the PCIC Rice Insurance Program on income loss
reduction of rice farmers in selected municipalities in Laguna
The amount of production inputs that can be covered by the program is from PhP 39,000
to PhP 52,000 per hectare depending on the rice variety. It is also expected that farmers who
incurred higher total production cost at the time of loss would receive a higher amount of
indemnity payment compared to those who incurred lower total production cost. Under the Rice
Insurance Program, the stages of cultivation covered are planting (direct seeding or
transplanting) to harvesting (flowering or reproductive stage). It is, therefore, expected that the
amount of indemnity payment would be higher if the time of loss is during the flowering or
reproductive stage compared to early stages of the rice crop. The Rice Insurance Program covers
ceilings for inbred and hybrid varieties. A maximum of 20 percent to cover a portion of the value
of the expected yield can be received at the option of the farmer entailing additional cost. It is
expected that the amount of indemnity received will be higher for rice farmers using hybrid rice
variety because of its higher expected yield
and production cost as compared to the rice farmers who planted inbred rice variety. It is also
expected that large farms would receive higher indemnity payments per farm than those with
smaller farms since they pay more insurance premiums.
Since the percentage of yield loss is a determinant of the amount of indemnity payment,
the factors that affect the percentage of yield loss will be assessed in this study. The percentage
of yield loss may vary depending on the farm’s location relative to the lakeshore, source of
risk/damage, variety, and farm size. It is expected that rice farmers whose farms are near the
lakeshore will have higher percentage of yield losses than those whose farms are far from the
lakeshore. It is further expected that rice farmers whose wet season crops were damaged by
typhoons and floods incurred higher percentage of yield loss than those whose crops were
damaged due to pests and diseases. Rice farmers who planted hybrid rice varieties are also
expected to have higher percentage of yield loss than those who used inbred varieties. Although
the hybrid rice variety has a higher yield than inbred varieties, it is more susceptible to pests and
diseases and is vulnerable to floods (IRRI, 2005). Rice farmers who have large farms are
expected to have higher percentage of yield loss than those with smaller farms.
Hypotheses of the Study
1. Household income, tenure status of the farmer, farm size, location/distance of the farm from
the lakeshore, loan borrower in a bank, and extent of awareness of the rice crop insurance
program significantly influence the rice farmers’ decision to participate in the PCIC Rice
Insurance Program.
2. The percentage of yield loss is significantly affected by the location/distance of the farm
from the lakeshore, source of risk/damage, rice variety, and farm income.
3. The percentage of yield loss, total production cost incurred at the time of damage, stage of
the crop at the time of loss, rice variety, stage of cultivation at the time of loss, and farm size
are the significant determinants of the amount of indemnity payments.
4. The rice farmers’ mean loss in farm income is significantly reduced as a result of their
participation in the PCIC Rice Insurance Program.
5. The mean loss in farm income after receiving indemnity payments is significantly higher for
farmer-participants with large farms compared to those with small farms.
4. METHODOLOGY
Types of Data and Methods of Data Collection
Both primary and secondary data were used in this study. Primary data were collected through
personal interviews with 40 rice farmers participating and 40 non-participating farmers in the
PCIC Rice Insurance Program in selected towns of Laguna around the coast of Laguna de Bay
using pre-tested interview schedule. The data collected only covered the most recent calamity
period that hit the province of Laguna, which was identified during the 2012 wet season cropping
period. At that time, the typhoon Habagat destroyed the rice crop of most of the farmers along
the lakeshore.
Secondary data including the names of rice farmer-participants who received indemnity
payments, the percentage of estimated yield loss by source of damage/risk, and the amount of
indemnity payments that they received were obtained. Sources of secondary data included the
National Irrigation Administration Region IV Employees’ Multipurpose Cooperative (NEMCO)
Office in Pila, Laguna and the New Batong Malake Multi-purpose Cooperative (NBMMPC)
office in Los Baños, Laguna.
Sampling Procedure
Rice growing, lakeshore municipalities in Laguna composed of Bay, Calauan, Pila, Sta. Cruz and
Victoria which are very prone to flooding caused by typhoons were purposively selected as the
study areas. These municipalities have also the highest number of rice farmers who received
indemnity payments.
Selection of the Sample Farmer-Respondents
From the list provided by NEMCO and NBMMPC, a total of 40 farmer-respondents were
randomly chosen. All the 40 rice farmer-participant respondents filed indemnity claims at the
PCIC Office in Region 4. The same number of farmers whose rice farms are situated near the
participants’ calamity-affected rice farms were purposively chosen in each municipality to serve
as the respondents under the non-participant category.
Analytical Procedure
Logit Analysis
Econometric methods that can be used in studying farmers’ participation in a crop
insurance program are binary models such as logit and probit analyses. Logit and probit models
are certain types of regression models in which the dependent or response variable is
dichotomous in nature, taking a 1 or 0 value (Vashist, 2011).The logit technique allows the
examination of the effects of a number of variables on the underlying probability of a
dichotomous dependent variable. The logit model uses a cumulative logistic probability function
while the probit model emerges from the normal distribution function. The chief difference
between logit and probit models is that the logistic curve has slightly flatter tails while the
normal or probit function approaches the axes more quickly than the logistic curve. The sigmoid
or S-shaped curve of the cumulative logistic function very much resembles the cumulative
distribution function of a random variable (Gujarati, 1988). Qualitatively, logit and probit models
give similar results, but the estimates of the two parameters are not directly comparable. The
logit model is generally used in preference to the probit model for the following reasons (Green,
1990; Vasisht, 2011; and Fernando, 2012): (1) logit analysis produces statistically sound results.
By allowing the transformation of a dichotomous dependent variable to a continuous variable
ranging from –α to infinity, the problem of out of range estimates is avoided; (2) logit analysis
provides results which can be easily interpreted and the method is simple to analyze; and (3) it
gives parameters which are asymptotically consistent, efficient and normal so that the analogue
of the regression t-test can be applied.
Considering the advantages of logit analysis over probit analysis, logit analysis was
employed to determine the factors that significantly influence the decision of the rice farmers to
participate in PCIC Rice Insurance Program. The logit regression model was estimated using
STATA 10 software program.
The general form of logit regression model is specified as:
P  f (   X ) 
e(   X )
1

(   X )
 (   X )
1 e
1 e
Where: P is the vector of probabilities of a choice,
E is the base of natural logarithms,
X is the vector of independent variables,
α is the constant, and
β is the vector of other estimated coefficients corresponding to X in the model.
In order to apply a linear form, the above function can be written as follows:
Ln[Pi/(1-Pi)] = α + βiXi + εi
where: i presents the individual farmer i,
ε is error term.
In this study, the empirical model of the simple logit functional form to determine the
farmer’s choice of whether to participate or not participate in the Rice Insurance Program is
shown below:
 p
Zi = Ln  i
 1  pi

 = α0 + α1.educ + α2.hincome + α3.fsize + α4.distancdummye + α5.loandummy+

α6.awareness + ui
where:
pi
= the probability of choice of farmer i with regard to participation in
the Rice Insurance Program. The value of the dependent variable is 1
if a farmer chooses to participate in the program and it takes a value
of 0 if a farmer decides not to participate in the program.
α0
= intercept
educ
= level of education of the rice farmer in years
hincome
= household income in pesos per year
fsize
= actual farm area planted to rice in hectares
distancedummy = dummy variable for distance of the rice farm from the lakeshore of
Laguna de Bay. This variable was used to capture the effect of
location of the farm from the lakeshore. A value of 0 was assigned
for farms with distance of less than of equal to five kilometers (km)
from the lake and 1for farms with distance of more than 5 km from
the lake
Loandummy
= dummy for loan availment from a bank, where a farmer who availed
of a loan was assigned a value of 1 and zero otherwise
knowledge
= extent of knowledge of the Rice Insurance Program measured in
terms of knowledge score. The knowledge score was determined using
screening questions to test each farmer’s knowledge or extent of
awareness of the objectives, insurance application requirements,
insured risks and indemnity, and claims process under the PCIC Rice
Insurance Program (Appendix 1). The knowledge score was computed
as the number of correct answers to 20 questions asked of the farmers
about the rice crop insurance program and its processes. This was part
of the interview schedule. A point for a correct answer and no point for
a wrong answer will be given. The total number of correct answers was
divided by the total number of questions and multiplied by 100 to get
the percentage knowledge score. The highest percentage score was 100
percent while zero was the lowest.
αi (i = 1 to 6)
= coefficients of independent variables in the logit model
e
= the base of natural logarithms and approximately equal to 2.718
ui
= error term
The parameters were estimated by using maximum likelihood estimation (MLE)
technique. The marginal effects of the probability of choice of the farmers were also estimated.
To determine the partial effect of factor Xi on Pi, the marginal effect of Xi on Pi was calculated
by taking the partial derivative of Pi with respect to Xi. In the logit model, the marginal effect
represents the change in probability caused by a unit change in Xi, ceteris paribus.
To test the significance of the coefficients of the explanatory variables in the model, the ttest was used as follows: tC =  /Se(  )
H0: β = 0 (the independent variable has no effect on the decision to participate in the Rice
Insurance Program
H1: β ≠ 0 (the independent variable has an effect on the decision to participate in the Rice
Insurance Program
where:  is the estimated coefficient of the independent variable in the model; and
Se(  ) is estimated standard error of coefficient of the independent variable
Reject H0 if tC> t critical value at an appropriate level of significance
Cost and Returns Analysis
Cost and returns analysis on per farm and per hectare bases was undertaken to determine the
extent of total income loss incurred by the rice farmer-participants before receiving indemnity
payments. Gross margin was used as measure of profit in rice production. Gross margin was
computed as follows:
Gross Margin = Gross Return – Total Variable Cost
A positive gross margin means that rice production is profitable. Conversely, a negative value of
gross margin indicates a loss in rice production. This will be referred to in this study as income
loss before receiving indemnity claims.
Estimation of Loss in Farm Income after Receiving Indemnity Claims
Loss in farm income after receiving indemnity claims was estimated as follows:
Income Loss in Farm Income before Receiving Indemnity Claims – Amount of
Indemnity Received. The t-test of means was employed to find out if the mean loss in income
after receiving the indemnity claims by the farmer-participants was significantly reduced as a
result of their participation in the Rice Insurance Program. Per hectare comparison was also
done. In addition, net loss in farm income was compared between small (less than or equal to 1.5
ha) and large farms (above 1.5 ha). The t-test of means was conducted to determine if there was a
significant difference in the mean loss in farm income per hectare of per farm after receiving
indemnity claims between these two farm size groups.
Multiple Regression Analysis for factors affecting yield loss
Multiple regression analysis was conducted to determine the factors affecting yield loss.
Percentage yield loss (PYL) in 2012 (i.e., with calamity) was estimated as follows:
= Expected yield in a normal year – Actual harvested yield in 2012 x 100
Expected yield in a normal year
The percentage yield loss regression model is expressed as:
PYL = a + b1X1 + b2X2+ b3X3 + b4X4+ b5X5
Where:
Y
= percentage of yield loss in percent
X1
= Dummy for distance of the farm from the lakeshore
X2
= Flood risk/damage dummy (0= no floods; 1= flood/typhoon)
X3
= Pest risk/damage dummy (0 = no pest and disease incidence; 1= with
pest and disease attack)
X4
= Rice variety dummy (1= hybrid variety; 0 = inbred variety)
X5
= farm size in hectares
For the per hectare analysis, X5 was omitted in the percentage yield loss regression
model.
To determine the significant factors affecting the amount of indemnity payments, multiple
regression analysis was also employed. The multiple regression model with the amount of
indemnity payments as the dependent variable is shown below:
AIP = a + b1X1 + b2X2 + b3X3+b4X4+b5X5
Where: AIP = amount of indemnity payment in pesos
X1= percentage yield loss
X2 = total production cost at the time of loss, in pesos
X3= Dummy for the stage of the cultivation when damage occurred (0 = before flowering
or reproductive stage and 1 = during the flowering or reproductive stage)
X4 = Rice variety dummy (0 = inbred variety and 1= hybrid variety)
X5 = farm size in hectares
X5 was omitted in the AIP per hectare regression model.
In both multiple regression models, the t-test was used to determine the significant independent
variables that affect the amount of indemnity payment (the dependent variable). The F-test was
utilized to determine the overall significance of the estimated regression model. The coefficient
of multiple determination (R2) was estimated to examine the goodness of fit of the data.
5. RESULTS AND DISCUSSION
The Study Area
The province of Laguna belongs to the CALABARZON Region in Luzon. It is located southeast
of Metro Manila, south of the province of Rizal, west of Quezon province, north
of Batangas, and east of Cavite. The province also envelops the southern shorelines of Laguna de
Bay, which is the largest lake in the country. Its capital, Santa Cruz, is one of the selected
municipalities and is also near Laguna de Bay. The other lakeshore municipalities included in the
study are Bay, Pila, Victoria and Calauan.
Socioeconomic Characteristics of the Sample Farmer Respondents: Participants and NonParticipants in the Rice Insurance Program
Age, Household Size, Household Income and Education
Results of the t-test of means did not show significant differences in the mean age,
household size, household income, number of children below 18 years old, the and number of
employed children between the farmer-participants and the non- participants at 10 percent
probability level, except for the mean educational attainment of the household head, which is
significantly different between the two farmer groups at 1 percent probability level (Table 1).
The rice farmer-participant respondents have an average age of 52.65 ranging from 31 to
99 years old. The non-participant respondents meanwhile have an average age of 53.55 years
ranging from 31-79 years and most of them are from their 50’s to 60’s. The average household
size of the participant-respondents and the non-participant respondents is four (Table 1).
The non-participants have an average annual household income amounting to PhP
198,100 while the farmer-participants’ average household income is PhP 190,741. The farmerparticipants have significantly higher average formal education than the non-participants with
10.18 and 7.38 years, respectively. On average, both farmer-respondent categories mentioned
that more or less one of their children is below 18 years of age and that least one of them is
employed.
Table 1. Average age, household size, household income, educational attainment of the
household head, number of children below 18 years old, and number of
employed children, 40 sample farmer-participants and 40 sample nonparticipants in the Rice Insurance Program, selected lakeshore municipalities in
Laguna, wet season, 2012
SOCIO-ECONOMIC
CHARACTERISTICS
Average age (years)
FARMERPARTICIPANTS
NON- PARTICIPANTS
52.65
53.55
Household size (number)
4
4
Household income (PhP)
190741
198100
Formal education (years)
10.18
7.38***
Number of children below 18 years old
1
1
Number of employed children
2
2
Note: The results of the t-test of means did not show significant differences in the mean age, household size,
household income, number of children below 18 years of age, and the number of employed children
between the farmer-participants and the non- participants at 10% probability level, except for
education which is significantly different between the two farmer groups at 1% probability level
Gender Distribution, Main Occupation, and Engagement in
Off and Non-Farm Activities
The number and percent reporting by gender distribution, main occupation and in offfarm and non-farm activities of the farmer respondents are shown in Table 2. Majority of the
farmer-participants and non-participants are mostly male representing 77.5 and 72.5 percent of
the total, respectively. The main occupation of most of the farmer-participants and the nonparticipants is farming as cited by 85 and 97.5percent of the respondents, respectively.
Table 2. Number and percent reporting by sex distribution, main occupation,
engagement in off- and non-farm activities, and tenure status, 40
sample farmer-participants and 40 non-participants in the Rice
Insurance Program in selected lakeshore municipalities in Laguna,
wet season, 2012
ITEM
Sex Distribution
Male
Female
Total
Main Occupation
Farmer
Government Employee
Private Employment
Agricultural Trader
Carpenter
Total
PARTICIPANTS
Number
Percent
NON PARTICIPANTS
Number
Percent
31
9
40
77.5
22.5
100.0
29
11
40
72.5
27.5
100.0
34
4
1
1
85.0
10.0
2.5
2.5
39
97.5
2.5
100.0
40
100.0
1
40
Engaged in Off Farm Work
Yes
No
Total
10
30
40
25.0
75.0
100.0
6
34
40
15.0
85.0
100.0
Engaged in Non Farm Work
Yes
No
Total
21
19
40
52.5
47.5
100.0
17
23
40
42.5
57.5
100.0
Tenure Status
Owner-operator
Lessee
Owner-tenant
Total
9
30
1
40
22.5
75.0
2.5
100.0
5
34
1
40
12.5
85.0
2.5
100.0
Table 2 also shows that a relatively higher percentage of the farmer-participants (25%)
are engaged in off-farm jobs than the non-participant respondents (15%). Both farmer groups
reported that they worked as hired labor in other farms during peak labor demands (e.g., planting
and harvesting) to augment their household incomes. In addition, both farmer categories claimed
that they have made themselves available even in non- farm work particularly during the lean
labor demand for their respective farms. A higher percentage (52.5%) of the farmer-participants
compared to non-participant respondents (42.5%) mentioned that they likewise engage in nonfarm jobs.
Majority of the farmer-participants are leaseholders (75 percent). Only 22.5 percent were
owner-operators. A lone respondent (2.5%) is both an owner as well as a tenant. Most (85%) of
the non-participants are also leaseholders. About 12.5 percent tilled their own farms and only one
non-participant is both an owner and tenant of the farms that he tills.
Farm Characteristics
Results of the t-test of means showed that the mean farm size and the mean number of
parcels were not significantly different between the sample farmer-participants and the nonparticipants at 10 percent probability level. The average farm size of a participant-respondent is
2.68 hectares while that of the non-participant is 2.43 hectares (Table 3). The farmer-participants
also reported to have an average number of parcels of 14 compared with 12 for the nonparticipants.
Most (82.5%) of the sample farmer-participants reported that their rice farms are situated
less than five kilometers away from the lakeshore. Conversely, majority of the non-participants
(52.5%) mentioned that their rice farms are located more than five kilometers away from the
lakeshore. Most of the farms of both farmer groups (85% of the farmer-participants and 92.5% of
the non-participants) are situated in low lying/flat areas.
The major source of irrigation water of the sample farmer-participants is communal
irrigation system (45%), followed by the National Irrigation System (35%). Only 25 percent of
the farmer-participants rely on pumps and spring water for irrigation. In contrast, majority (70%)
of the non-participants use pumps or spring water to irrigate their farms. Only 17.50 percent and
15 percent of the non-participants source their irrigation water from the communal irrigation
system and the National Irrigation System, respectively.
Table 3.Farm characteristics of 40 sample farmer-participants and 40 non-participants
in the Rice Insurance Program, in selected lakeshore municipalities in
Laguna, wet season, 2012
FARMERFARM CHARACTERISTICS
PARTICIPANT
NON- PARTICIPANT
Average farm size (ha)a
2.68
2.43
Average number of parcelsb
14.43
12.00
Number
Percent
Number
Percent
Geographical Location
Less than 1 km from the lakeshore
16
40.0
11
27.5
1-5 km from the lakeshore
17
42.5
8
20.0
7
17.5
21
52.5
34
85.0
37
92.5
6
15.0
3
7.5
National Irrigation System
14
35.0
6
15.0
Communal Irrigation System
18
45.0
7
17.5
Pump/Spring
10
25.0
28
70.0
More than 5 km from the lakeshore
Farm Topography
Flat/Low lying
Elevated
Water Sourcec
a Results
of the t-test of means showed that there is no significant difference in the mean farm size between the
two farmer groups (t-value is 0.429) at 10% probability level
b
Results of the t-test of means showed that there is no significant difference in the mean number of parcels
between the two farmer groups (t-value is 0.705) at 10% probability level
c The total percentage exceeds 100% since two participant and one non-participant reported two sources of
irrigation water
Results of Logit Analysis Showing the Factors the Significant Factors that
Influence the Farmers’ Decision to Participate or Not to Participate in the
Rice Insurance Program of PCIC
Results of the logit analysis reveals that knowledge score or awareness of the farmer
about the Rice Crop Insurance Program, tenure status, and the distance of the farm from the lake
were the significant factors that influenced the farmer’s decision on whether to participate or not
participate in the PCIC Rice Insurance Program (Table 4).
Table 4.Results of logit analysis showing the factors that influence the farmer’s decision to
participate or not participate in PCIC Rice Insurance Program, 80 sample
farmer-respondents, selected lakeshore municipalities in Laguna, wet season,
2012
VARIABLE
Constant
CCOEFFICIENT
t-VALUE
6.30***
3.06
10.75***
3.98
MARGINAL
EFFECTS
t-VALUE OF
MARGINAL
EFFECT
Independent Variables:
Knowledge Score
Education
2.66**
0.24ns
1.41
-6.74E-07ns
-0.38
1.72*
1.59
0.40*
1.82
Area Planted
0.16ns
0.59
0.04ns
0.57
Distance from the lake
-2.34*
-1.76
Household Income
Tenure Dummy
Bank availment
0.39ns
X2
80.43***
Pseudo R2
1.22
0.06ns
4.03
-1.67E-07
-0.52**
-0.09ns
1.50
0.00
-2.17
-0.31
0.72
***, **, and * - mean significant at 1%, 5%, and 10% probability level, respectively
ns – not significant at 10% probability level
As expected, the coefficient of knowledge score is highly significant at one percent
probability level and is positive, indicating that the higher the awareness of the farmer or
knowledge about the Rice Insurance Program, the higher is the probability that he/she will
participate in the program.
The coefficient of the tenure dummy variable is positive and significant at 10 percent
probability level. This means that an owner-operator is more likely to participate in the program
than a lessee. In other words, the more secure the tenure status, the higher is the probability that
the farmer will participate in the insurance program.
Meanwhile, the coefficient of dummy for the distance of the farm from the lake is
negative and significant at 10 percent probability level. The negative coefficient indicates that
the farther the distance of the farm from the lakeshore, the lower the probability that a farmer
will participate in the Rice Insurance Program.
Household income, educational attainment of the farmer, area planted, and credit access from
a bank were found to have no significant influence on the farmer’s decision to participate in the
Rice Insurance Program at 10 percent probability level. The possible reason why credit access
from a bank has insignificant effect on farmers’ participation in the Rice Insurance Program is
that both the participant and the non-participant-respondents might have availed of loans from
banking institutions.
The regression line is quite robust with a Х2 highly significant and Pseudo R2 equal to
0.72. The Pseudo R2 implies that the variation in the independent variables collectively explain
72 percent of the variation in the probability of farmers’ participation in the Rice Insurance
Program.
Marginal effects refer to the changes in probability given a unit change in the independent
variable and are a more useful basis for interpreting the results of the logit model. The estimated
marginal effects as shown in Table 4 suggest that the impact of the independent variables such as
knowledge about the program, tenure, and distance of the farm from the lakeshore on the
farmer’s decision to participate in the PCIC Rice Insurance Program is statistically significant. In
particular, knowledge about the program as measured by the knowledge score appeared to have a
higher impact on the probability of participating in the PCIC Program. For example, a 1 percent
increase in the knowledge score will increase the probability of participation in the PCIC Rice
Insurance Program by approximately 3 percent, holding other factors constant. An owneroperator has 0.4 percent more probability to be a participant in the PCIC Rice Insurance Program
than the tenant/lessee while an increase in farm distance by more than five kilometers from the
lakeshore lowers the probability of participation by 0.52 percent.
Reasons for Participating/Non-participating in the Rice Insurance Program
The sample farmer-participants cited that the major reason for their participation in
the program is that getting rice insurance is part of the lending requirement of the LBP
when they borrowed from the bank (Table 5).
Table 5. Reasons for participating in the Rice Insurance Program, 40 sample
farmer-participants, selected lakeshore municipalities in Laguna, wet
season, 2012.
REASONS FOR PARTICIPATING
Part of the requirement for applying for an agricultural loan
a
NUMBER
34
PERCENTa
85.0
To reduce risk in farming
26
65.0
Recruited by the technician of the cooperative
2
5.0
Convinced by friends
1
2.5
Total exceeded 100 percent due to multiple responses of some respondents
Majority (95%) of the farmer non-participant respondents for the 2012 wet season crop
have not previously participated in the program, the major reason for non-participation in
the program was their being unaware of the existence of the rice crop insurance program
of the PCIC as cited by 57.9 percent of the farmer respondents who have not previously
participated in the program (Table 6). The other major reason for non-participation, as
reported by 39.5 percent of farmer non-participant respondents, was their “being busy” to
attend to the documentation requirements of the program. It was also found in this study,
that technical assistance of the technicians of NEMCO and NBMMC has been ably
provided.
Table 6. Reasons for non-participation in the PCIC Rice Insurance Program, 38
sample non-participants, selected lakeshore municipalities of Laguna,
2012 wet season.
NUMBER
22
PERCENTa
57.9
Too busy to participate
15
39.5
Benefit of insurance not proven
6
15.8
Can financially sustain farming business
5
13.2
Too many requirements
2
5.3
REASONS
Unaware of PCIC Rice Insurance Program
a
Total percentage exceeds 100% due to multiple responses of some respondents
Results of Regression Analysis to Assess the Factors Affecting Percentage Yield Loss
Results of the multiple regression analysis to determine the factors affecting percent yield loss
showed that the flood risk variable had a positive and significant coefficient. The coefficient
implies that 25 percent of the yield loss in the sample farms is due to the occurrence of floods
(Table 7). The pest risks coefficient is negative and not significantly different from zero in both
equations. The same is true with farm location, rice variety, and farm size which are not
statistically significant at 10 percent probability level.
Table 7. Results of multiple regression analysis showing factors affecting yield loss on a per
farm and per hectare basis of 40 participant respondents in selected lakeshore
municipalities in Laguna, wet season, 2012
PER FARM
ITEM
PER HECTARE
Coefficient
t-Value
Coefficient
t-Value
48.42***
3.07
48.42***
3.07
Farm Location
-3.36ns
-0.35
-s3.90ns
0.41
Flood Risk
25.55*
1.80
25.80*
1.84
Pest Risk
-4.12ns
-0.31
-4.61ns
-0.36
Rice Variety
10.39ns
0.72
11.07ns
0.79
Farm Size
-0.59ns
-0.39
F-Statistic
1.85
2.33**
R2
0.21
0.21
Constant
Coefficients of Independent
Variables:
***, **, and * mean significant at 1% , 5%, and 10%, respectively
ns – not significant at 10% probability level
Only the F-statistic of theper hectare equation is significant at 5 percent level. This
implies that the independent variables collectively affect the percent yield loss for the per hectare
analysis. The R2 in both forms of equation is 0.21, which indicates that only about 21 percent of
the variation in percent yield loss is explained by the independent variables included in the
equation (Table 7). Only the hypothesis that flood risk affects percent yield loss was proven.
Comparison of Mean Amount Indemnity Received by Percent Yield
Loss, Variety Planted, Production Cost, and Farm Size
Table 8 compares the average amount of indemnity received by the farmer-participants
by percent yield loss, variety planted, production cost, stage of production when calamity
occurred, and farm size.
Results indicate that the average amounts of indemnities received by the farmer-participants
increased as the percent yield loss increases. The average amount on indemnity for rice farms
that incur yield losses ranging from 0-25 percent was estimated at PhP 6,000 while rice farms
that recorded losses that ranged from 26-50 percent were given average indemnities amounting
to PhP 10,848. The most affected farms (76-100 percent yield loss) were indemnified with an
average of PhP17,895. These findings suggest that the severity of damage to rice crops increases
as manifested in percentage losses were fairly treated in terms of higher average amounts of
indemnities. The average amount of indemnity is also positively influenced by the total cost of
production incurred by the farmers. For a farmer that recorded a total production less than
PhP25,000, an average indemnity of PhP9,527 has been received. For production costs that
belong to the ranges PhP25,000-49,999 and PhP50,000 and above, average indemnities of PhP
10,818 and PhP 14,932 were respectively provided by the PCIC. Similarly, this study found out
that average amount of indemnity increases when the time of disaster occurred during the later
stage of crop production.
Table 8. Average amount of indemnity received by 40 sample farmer-participants in
selected lakeshore municipalities in Laguna, wet season, 2012
ITEM
NUMBER
AVERAGE AMOUNT OF
INDEMNITY (PhP)
Range of Percent Yield Loss:
0-25%
26-50%
51-75%
76-100%
F-Value
2
13
7
16
6,000
7,065
10,848
17,895
15.07***
Variety Planted:
Hybrid
Local
t-Value
35
5
11,838
12,360
46.20***
Range of Production Cost
Less than Php 25000
Php 25000-49999
Php 50000 and above
F-Value
11
15
14
9,527
10,818
14,932
51.95***
Stage of Crop Production
Vegetative to Tillering
Flowering
Harvesting
F- Value
12
27
1
10,013
12,406
21,000
18.79***
Farm Size Range:
1.5 ha and below
Above 1.5 ha
t -Value
19
21
11,318
12,432
21.28***
***Significant at 1% probability level
When natural calamities happened during vegetative, flowering and harvesting stages of
crop production, average indemnity amounts of PhP10,003, PhP12,406 and PhP21,000 were
received by the farmer-participants, respectively. Results also show that the average amounts of
indemnities similarly increase with farm size but on a smaller proportion. Moreover, affected
farms that utilized local rice varieties received an average amount of which is slightly higher
than rice farms planted to hybrid varieties of rice. Results of the Analysis of Variance (ANOVA)
in Table 8 suggest that the variations among groups for percent yield loss, production cost, and
the stage of the crop categories were highly significant at one percent probability based on the Fstatistics. However, ANOVA does not indicate which pairs of groups are significantly different
from each other. The t-test of means was therefore conducted to assess which pairs of groups for
the afore-mentioned categories are significantly different. Further analysis showed that all the
pairs of groups compared for the afore-mentioned categories are significantly different at one
percent probability level.
Results of Regression Analysis to Assess the Factors Affecting
the Amount of Indemnity Payment
In both the farm and per hectare equations, percent yield loss exhibits highly significant
regression coefficients, indicating that it influences the amount of indemnity payments
significantly (Table 9). At the farm level, an increase of one percent in the percent yield loss
increases the amount of indemnity paid by PhP137. On a per hectare basis, the amount would be
PhP145. Results also indicate that at the farm level, the regression coefficient of production cost
is positive and highly significant at one percent probability level. This is also true for the per
hectare basis.
Table 9. Results of multiple regression analysis showing the factors affecting the amount
of indemnity payment on per farm and per hectare basis, 40 sample farmerparticipants, selected lakeshore municipalities in Laguna, wet season, 2012
PER FARM
ITEM
Constant
Coefficient
-411.53
PER HECTARE
t-Value
Coefficient
t-Value
-0.12
-3156.36
-0.75
Regression Coefficients of Independent Variables:
Percent Yield Loss
137.91***
3.38
145.33***
3.42
Production Cost
0.088***
2.67
0.19
1.46
Stage of Cultivation of
Rice Crop
1539.01
0.55
2316.72
0.79
Variety Dummy
-1370.79
-0.37
-1573.30
-0.40
Farm Size
F-Statistic
R2
817.51*
4.80***
0.41
1.69
4.20***
0.32
***, * - Significant at 1% and 10% probability level, respectively
The stage of cultivation of the rice crop represented by the before flowering stage and
during and after the flowering stage did not yield significant results. The coefficient is positive,
but not significant at 10 percent probability level. Variety also does not have a significant
influence on the amount of indemnity payment.
At the farm level equation, farm size had a positive and statistically significant
coefficient. The result implies that as farm size increases by one hectare, the amount of
indemnity payment increases by PhP817.
The estimated F-statistics shows the overall significance of the estimated equation. As
shown in Table 9, the F-statistics for both farm and per hectare equations are significant, thus
implying that the variables taken together, affect the dependent variable (amount of indemnity).
R2 at the farm equation is 0.41, while R2 in the per hectare equation is 0.32. For the per farm
equation, the R2 value indicates that 41 percent of the variation in the amount of indemnity is
explained all the independent variables included in the estimated regression model.
Comparison of the Costs and Returns between the 2010 and 2012
Wet Season Rice Crops
Results of the cost and returns analysis for a normal year represented by 2010 wet season
and for a calamity year represented by the 2012 wet season are summarized in Table 10. The
average per farm gross margin or profit in the last normal year 2010 was PhP 66,672.23.
Adjusting this to 2012 prices using the CPI as deflator, the 2010 wet season deflated gross
margin (expressed in 2012 prices) is estimated to be PhP 72,347.63. On the other hand, the
average loss per farm during the wet season 2012 was PhP 12,776.10. Farmers reported the cause
of loss to Habagat.
On a per hectare basis, the average gross margin was PhP 29,345.17 in 2010 and PhP
31,843.15 as expressed in 2012 prices. The loss per hectare during the wet season of 2012 was
PhP 5,623.28. In effect, the total income loss per farm and per hectare as a result of the calamity
in 2012 relative to the 2010 normal year was PhP 59,571.53 and PhP 26,219.87, respectively.
This can also be construed as the cost of damage during the wet season 2012 due to the calamity.
Meanwhile, Table 11 shows the costs and returns comparison between farms which are
near the lake (0-5 km from the lakeshore) and those far from the lake (>5 km from the lakeshore)
on a per hectare basis. The average per hectare gross margin of a farm near the lake during the
wet season 2010 was PhP 30,790.72, which is equivalent to PhP 33,411.75 in 2012 prices. Those
that are far from the lake had an average gross margin of PhP 28,348.87 in wet season 2010.
Adjusted to 2012 prices, this is estimated at PhP 30,762.03. The average loss in wet season 2012
experienced by the participants with farms near the lake was estimated to be PhP 9,483.68 as a
result of the Habagat while participants whose farms are far from the lake has a lower estimated
loss of PhP 2,962.62. These results show the higher vulnerability of the farms near the lake than
those farther from the lake to the vagaries of the extreme weather events.
The comparison of per hectare costs and returns during the normal year and the calamity
year between the farmer-participants by tenure status is summarized in Table 12. The average
per hectare gross margin in 2010 of the owner-participants was PhP 28,994.82 which is
equivalent to PhP31,462.97 in 2012 prices. The tenant farmer- participants had a 2010 wet
season net income average of PhP30,038.75, equivalent to PhP 32,595.77 in 2012 prices. During
the wet season 2012, the average loss of PhP 10,307.37 was experienced by the ownerparticipants; while a meager gross margin of PhP 3,649.67 was received by the tenantparticipants. The positive gross margin of the tenants was attributed to the lower production cost
they incurred compared to the owners, which may be a function of the low levels of input use of
the tenants.
Tables 13 and 14 illustrate the comparison between the costs and returns between farmerparticipants who operate small farms (0.5-1.9 ha) and those who have larger farms (>2 ha), on
per hectare and per farm basis, respectively. The average gross margin per hectare of the farmerparticipants who have small farms in a normal year (2010) was PhP 31,309.72, which is
equivalent to PhP 33,974.93 in 2012 prices (Table 25). During the calamity year of 2012, the
average loss per hectare incurred by the participants who have small farms was estimated at PhP
6,771.21. Those who have large farms had an average gross margin per hectare in 2010 of
PhP28,448.53, equivalent to PhP 30,870.18 in 2012 prices. An average net loss, however, for the
participants who have large farms was estimated to be PhP 5,099.35 per hectare in 2012 wet
season.
On the other hand, the average gross margin per farm of small farms during the normal
year (2010) was PhP 38,769.61, and equivalent to PhP 42,069.83 in 2012 prices (Table 14). For
the large farms, these figures were PhP 104,422.82 in 2010 actual prices and PhP 113,311.71 in
2012 prices. During the calamity year (2012), the estimated average loss per farm was PhP
8,384.52 for small farms. For large farms, the estimated loss of the participants was estimated to
be
PhP
18,717.65,
on
the
average.
Table 10. Average gross income, production cost, amount of indemnity received, and profit/loss per farm and per hectare of
40 sample farmer-participants in the Rice Insurance Program, selected lakeshore municipalities in Laguna, wet season,
2010 and 2012
PER FARM
ITEM
Gross Income
(PhP)
Production Cost
(PhP)
Profit/Loss
Before Receiving
Indemnity (PhP)
Amount of
Indemnity
Received (PhP)
Profit/Loss
After Receiving
Indemnity (PhP)
PER HECTARE
Normal
2012 Actual
Year in 2012
with Calamity
Prices
Normal Year
(2010)
Normal Year in
2012 Prices
2012 Actual
with Calamity
Normal Year (2010)
122,945.00
133,410.57
44,782.25
54,113.12
58,719.44
19,710.50
56,272.78
61,062.94
57,558.35
24,767.95
26,876.29
25,333.78
66,672.23
72,347.63
-12,776.10
26,345.17
31,843.15
-5,623.28
0
0
11,952.85
0
0
5,260.94
66,672.23
72,347.63
29,345.17
31,843.15
-362.35
-823.25
Note: Gross margin analysis was used in estimating profit/loss in rice production. Production costs only include variable costs.
Table 11. Average gross income, production cost, amount of indemnity received, and profit/loss per hectare by farm location
of 40 sample farmer-participants, in the Rice Insurance Program, selected lakeshore municipalities in Laguna, wet
season, 2010 and 2012
>5 KILOMETERS
0-5 KILOMETERS
Normal
Year (2010)
Normal Year in
2012 Prices
2012 Actual
with Calamity
Normal
Year (2010)
Normal Year in
2012 Prices
2012 Actual
with Calamity
Gross Income(PhP)
54,813.92
59,479.89
15,121.36
53,630.11
58,195.32
22,873.42
Production Cost(PhP)
24,023.19
26,068.14
24,605.04
25,281.25
27,433.29
25,836.04
30,790.72
33,411.75
-9,483.68
28,348.87
30,762.03
-2,962.62
0
0
6,776.94
0
0
4,216.08
30,790.72
33,411.75
-2,706.75
28,348.87
30,762.03
1,253.46
ITEM
Profit/Loss Before
Receiving Indemnity (PhP)
Amount of Indemnity
Received (PhP)
Profit/Loss After Receiving
Indemnity (PhP)
Note: Gross margin analysis was used in estimating profit/loss in rice production. Production costs only include variable costs.
Table 12. Average gross income, production cost, amount of indemnity received, and profit/loss per hectare by tenure status of
40 sample farmer-participants in the Rice Insurance Program, selected lakeshore municipalities in Laguna, wet
season, 2010 and 2012
OWNER
TENANT
Normal
Year (2010)
Normal Year in
2012 Prices
2012 Actual
with Calamity
Normal
Year (2010)
Normal Year in
2012 Prices
2012 Actual
with Calamity
Gross Income (PhP)
56,737.33
61,567.04
18,038.92
48,918.03
53,082.13
23,019.67
Production Cost (PhP)
27,742.51
30,104.07
28,346.29
18,879.28
20,486.36
19,370.00
Profit/Loss Before
Receiving Indemnity (PhP)
28,994.82
31,462.97
-10,307.37
30,038.75
32,595.77
3,649.67
0
0
5,746.34
0
0
4,300.00
28,994.82
31,462.97
-4,561.03
30,038.75
32,595.77
7,949.67
ITEM
Amount of Indemnity
Received (PhP)
profit/Loss After Receiving
Indemnity (PhP)
Note: Gross margin analysis was used in estimating profit/loss in rice production. Production costs only include variable costs.
Table 13. Average gross income, production cost, amount of indemnity received, and profit/loss per hectare by farm size,
40 sample farmer-participants in the Rice Insurance Program, selected lakeshore municipalities in Laguna, wet
season, 2010 and 2012
0.5-1.9 HECTARES
ITEM
Normal Year
(2010)
Normal Year
in 2012 Prices
>2 HECTARES
2012 Actual
with Calamity
Normal Year
(2010)
Normal Year
in 2012 Prices
2012 Actual
with Calamity
Gross Income (PhP)
56,488.76
61,297.31
18,795.29
53,028.85
57,542.
87
Production Cost (PhP)
25,179.04
27,322.38
25,566.50
24,580.32
26,672.69
25,227.56
Profit/Loss Before
Receiving Indemnity (PhP)
31,309.72
33,974.93
-6,771.21
28,448.53
30,870.18
-5,099.35
0
0
8,147.82
0
0
3,943.33
1,376.62
28,448.53
30,870.18
-1,156.03
Amount of Indemnity
Received (PhP)
Profit/Loss After
Receiving Indemnity (PhP)
31,309.72
33,974.93
20,128.21
Note: Gross margin analysis was used in estimating profit/loss in rice production. Production costs only include variable costs.
Table 14. Average gross income, production cost, amount of indemnity received, and profit/loss per farm by farm size, 40
sample farmer-participants in the Rice Insurance Program, selected lakeshore municipalities in Laguna, wet season,
2010 and 2012
0.5-1.9 HECTARES
ITEM
Gross Income (PhP)
Production Cost (PhP)
Profit/Loss Before
Receiving Indemnity (PhP)
Amount of Indemnity
Received (PhP)
Profit/Loss After Receiving
Indemnity (PhP)
>2 HECTARES
Normal
Year (2010)
Normal Year in
2012 Prices
2012 Actual
with Calamity
Normal
Year (2010)
Normal Year in
2012 Prices
2012 Actual
with Calamity
69,947.83
75,902.06
23,273.48
194,647.06
211,216.19
73,882.35
31,178.22
33,832.23
31,658.00
90,224.24
97,904.48
92,600.00
38,769.61
42,069.83
-8,384.52
104,422.82
113,311.71
-18,717.65
0
0
10,089.13
0
0
14,474.34
38,769.61
42,069.83
1,704.61
104,422.82
113,311.71
-4,243.30
Note: Gross margin analysis was used in estimating profit/loss in rice production. Production costs only include variable costs.
Effects of Participation in the Rice Insurance Program on the
Reduction of Farm Income Losses
As shown in Tables 10 to 14, the average amounts of losses before the farmers received
their indemnity claims were lower than the average amounts of losses after receiving their
indemnity claims. The reduction in farmers’ losses was due to the payment of their indemnity
claims by PCIC.
Table 15 summarizes the results of the t-test of means to determine whether the reduction
in the farmers’ income losses was significant. It can be noted that the amounts of losses were
significantly reduced as a result of the sample farmers’ participation in the Rice Insurance
Program at one percent probability level. Considering all the farmer-participants, the average
amount of loss reduced was PhP 11,952.85 per farm and PhP 5,260.94 per hectare.
On average, the amount of loss reduced was higher for farmers with farms located near
the lakeshore (PhP 6,774.94/ha) as compared to those whose farms are situated far from the
lakeshore (PhP 4,216.08) since the former received higher indemnity payments considering that
they incurred substantial losses compared to the latter. On a per farm basis, the average amount
of loss reduced for bigger farms (PhP 14,474.34) was higher that that of the small farms
(PhP10,089.13). Big farms received a larger amount of indemnity compared to small farms.
Table 15. Results of the t-test of means to determine the significance of the reduction in
income losses as a result of the farmer-participants participation in the PCIC
Rice Insurance Program, 40 sample farmer-participants, selected lakeshore
municipalities in Laguna, wet season, 2012
ITEM
AVERAGE AMOUNT
OF INCOME LOSS
REDUCEDa (PhP)
11,952.85
t- VALUE
All Farms, Per Hectare
5,260.94
-6.10
Farms Near the Lakeshore (Per Ha)
6,776.94
-5.42
Farms Far from the Lakeshore (Per Ha)
4,216.08
-6.39
Owner (Per Ha)
5,746.34
-7.36
Tenant (Per Ha)
4,300.00
-3.96
Small Farms (Per Ha)
8,147.82
-4.85
Large Farms (Per Ha)
3,943.33
-5.05
Small Farms (Per Farm)
10,089.13
-6.04
Large Farms (Per Farm)
14,474.34
-5.79
All Farms, Per Farm
-8.22
a
This is the difference between the average amount of loss before receiving the indemnity payment and the average
amount of loss after receiving the indemnity payment. The resulting difference is the amount of indemnity payment.
6.
CONCLUSIONS AND RECOMMENDATIONS
Findings from the logit analysis revealed that the farmer’s decision to participate in the
program was significantly influenced by their awareness of the program, tenure status, and the
distance of their farms from the lakeshore. The estimated marginal effects suggest that the
impact of the independent variables such as the farmer’s knowledge about the program, tenure
status, and the distance of the farm from the lakeshore on the farmer’s decision to participate in
the PCIC Rice Insurance Program is statistically significant. In particular, knowledge about the
program as measured by the knowledge score appeared to have a higher impact on the
probability of participating in the PCIC Program.
Multiple regression results showed that percent yield loss is highly influenced by flood
risks. Regression results likewise revealed that the significant factors that affected the amount of
indemnity payment were percentage yield loss, production cost, and farm size. The amount of
indemnity received by a farmer-participant was found to increase with the increase in percentage
yield loss. The amount of indemnity was also positively influenced by the total cost of
production and farm size.
Results of the study showed that the sample farmers’ participation in the Rice Insurance
Program of the PCIC has eased their financial burden as a direct result of the indemnities they
received. The reduction in mean income losses as a result of the sample farmers’ participation in
the PCIC Rice Insurance Program was highly significant at one percent probability level. On
average, the percentage reduction in income losses was 94 percent per farm.
Based on the results of the study, the following recommendations are suggested:
1. The PCIC management should undertake a more intensive awareness campaigns among
participating farmers/farmer groups and other stakeholders to include lending
institutions and NGOs. The awareness campaign should focus on the details of the
mechanics of the program such as the loss cap provisions by type of pest attack and stage of
crop production. Lack of awareness among the farmer-participants has been the root cause of
the problems, such as delays in processing of documents and failed expectations of the
farmers. Non-participation in the program was also attributed to lack of knowledge or
awareness of the program;
2. A more accurate estimation procedure in assessing crop yield loss must be developed by
the PCIC. This recommendation is an offshoot of the major problem on the standing crop
basis that was mentioned by the farmers as a major basis in assessing both the prevalence and
severity of the damage on the rice farms caused by natural calamities. Perhaps a more
technically-based approach may be adopted by the team of adjusters and can possibly be
facilitated by updating their capabilities through attendance in actual damage-estimation
training. A training module of this type maybe requested from the Department of Agriculture
or from academic institutions such as the College of Agriculture (CA) of UPLB;
3. The risk classification of the insured farms should be updated. Given the changing biophysical environment as a result of the climate change phenomenon, it is time for the PCIC to
update the risk classification of rice farms. Since premium payments vary by risk
classification, a single risk classification category for Laguna (e.g., high risk) should be
revised depending on the particular location of the participating farmer’s farm.
ACKNOWLEDGEMENT
The authors would like to express their gratitude to Mr Virgilio Lawas of the New Batong
Malake Multipurpose Cooperative (NBMMC) and Mr Robert Acuno of the National Irrigation
Administration Region IV Employees Multipurpose Cooperative (NEMCO) for providing us the
secondary information in the study. We are also thankful to Mr Pablo Rocela of the PCIC
Region IV Office for sharing his insights in the conduct of the study. Lastly, we wish to express
our sincere appreciation to all the rice farmer respondents who shared their precious time to
provide us the field data needed in the conduct of this study.
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