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 farmer-participants 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 farmer-participants 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. Owner-

operators 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.

4.

5.6.

7.

8.

9.

10.

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 farmer-

participants’ income losses through indemnity payments. Climatic factors such as floods,

Other Factors

- Credit access to financial institutions

- Damage experienced

Household and Farm Characteristics:

- Household income (farm and non-farm)

- Tenure status- Farm size- Location/distance of

the farm from the lakeshore

Awareness of the PCIC Rice Insurance

Program

Participate/Not Participate in the PCIC Rice Insurance

Program

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:

Floods Typhoons Drought

Other Risk Factors:

Pest and Diseases Earthquakes Fires Nearness to lakeshore

PCIC Rice Insurance Program

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

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:

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:

Zi = = 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 t-

test 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 Non-Participants 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 farmer-

participants 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 non-participants in the Rice Insurance Program, selected lakeshore municipalities in Laguna, wet season, 2012

SOCIO-ECONOMIC CHARACTERISTICS

FARMER- PARTICIPANTS NON- PARTICIPANTS

Average age (years) 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 2Note: 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 off-

farm 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 non-

participants 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 

PARTICIPANTS NON PARTICIPANTSNumber Percent Number Percent

Sex Distribution       Male 31 77.5 29 72.5 Female 9 22.5 11 27.5 Total 40 100.0 40 100.0

Main Occupation Farmer 34 85.0 39 97.5 Government Employee 4 10.0

Private Employment 1 2.5 Agricultural Trader 1 2.5 Carpenter 1 2.5 Total 40 100.0 40 100.0

Engaged in Off Farm Work Yes 10 25.0 6 15.0 No 30 75.0 34 85.0 Total 40 100.0 40 100.0

Engaged in Non Farm Work Yes 21 52.5 17 42.5 No 19 47.5 23 57.5 Total 40 100.0 40 100.0         Tenure Status Owner-operator 9 22.5 5 12.5 Lessee 30 75.0 34 85.0 Owner-tenant 1 2.5 1 2.5 Total 40 100.0 40 100.0

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 non-

farm 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 non-

participants 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 non-

participants.

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

FARM CHARACTERISTICS 

FARMER-

PARTICIPANT NON- PARTICIPANT

Average farm size (ha)a 2.68 2.43

Average number of parcelsb 14.43 12.00

Numbe

r 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

More than 5 km from the lakeshore 7 17.5 21 52.5

Farm Topography

Flat/Low lying 34 85.0 37 92.5

Elevated 6 15.0 3 7.5

Water Sourcec

National Irrigation System 14 35.0 6 15.0

Communal Irrigation System 18 45.0 7 17.5

Pump/Spring 10 25.0 28 70.0a 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 thatInfluence the Farmers’ Decision to Participate or Not to Participate in theRice 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

CCOEFFICIENT t-VALUE MARGINAL

EFFECTS

t-VALUE OF

MARGINAL

EFFECT

Constant 6.30*** 3.06

Independent Variables:

Knowledge Score 10.75*** 3.98 2.66** 4.03

Education 0.24ns 1.41 0.06ns 1.50

Household Income -6.74E-07ns -0.38 -1.67E-07 0.00

Tenure Dummy 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 -0.52** -2.17

Bank availment 0.39ns 1.22 -0.09ns -0.31

X2 80.43***

Pseudo R2 0.72

***, **, and * - mean significant at 1%, 5%, and 10% probability level, respectivelyns – 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 owner-

operator 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 NUMBER PERCENTa Part of the requirement for applying for an agricultural loan 34 85.0

To reduce risk in farming 26 65.0

Recruited by the technician of the cooperative 2 5.0

Convinced by friends 1 2.5aTotal 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.

REASONS NUMBER PERCENTa

Unaware of PCIC Rice Insurance Program 22 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 5.3a 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 PER HECTARE

ITEM Coefficient t-Value Coefficient t-Value

Constant 48.42*** 3.07 48.42*** 3.07

Coefficients of Independent Variables:

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***, **, 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 YieldLoss, 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% 2 6,00026-50% 13 7,06551-75% 7 10,84876-100% 16 17,895F-Value 15.07***

Variety Planted:

Hybrid 35 11,838Local 5 12,360t-Value 46.20***

Range of Production CostLess than Php 25000 11 9,527Php 25000-49999 15 10,818Php 50000 and above 14 14,932F-Value 51.95***

Stage of Crop ProductionVegetative to Tillering 12 10,013Flowering 27 12,406Harvesting 1 21,000F- Value 18.79***

Farm Size Range:1.5 ha and below 19 11,318Above 1.5 ha 21 12,432

t -Value 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 F-

statistics. 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 farmer-participants, selected lakeshore municipalities in Laguna, wet season, 2012

 PER FARM PER HECTARE

ITEM Coefficient t-Value Coefficient t-Value

Constant -411.53 -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 ofRice Crop

1539.01 0.55 2316.72 0.79

Variety Dummy -1370.79 -0.37 -1573.30 -0.40

Farm Size 817.51* 1.69

F-Statistic 4.80*** 4.20***

R2 0.41 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 2012Wet 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 owner-

participants; while a meager gross margin of PhP 3,649.67 was received by the tenant-

participants. 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 farmer-

participants 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 farmer-

participants 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     PER HECTARE

ITEM  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) 122,945.00 133,410.57 44,782.25 54,113.12 58,719.44 19,710.50

Production Cost (PhP)

56,272.78 61,062.94 57,558.35 24,767.95 26,876.29 25,333.78

Profit/Loss Before Receiving Indemnity (PhP)

66,672.23 72,347.63 -12,776.10 26,345.17 31,843.15 -5,623.28

Amount of Indemnity Received (PhP)

0 0 11,952.85 0 0 5,260.94

Profit/LossAfter Receiving Indemnity (PhP)

66,672.23 72,347.63 -823.25 29,345.17 31,843.15 -362.35

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

  

0-5 KILOMETERS   >5 KILOMETERS

ITEM  Normal Year (2010)

Normal Year in 2012 Prices

2012 Actual with Calamity   Normal

Year (2010)Normal Year in

2012 Prices2012 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

Profit/Loss Before Receiving Indemnity (PhP) 30,790.72 33,411.75 -9,483.68 28,348.87 30,762.03 -2,962.62

Amount of Indemnity Received (PhP) 0 0 6,776.94 0 0 4,216.08

Profit/Loss After Receiving Indemnity (PhP) 30,790.72 33,411.75 -2,706.75 28,348.87 30,762.03 1,253.46

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

ITEM  Normal Year (2010)

Normal Year in 2012 Prices

2012 Actual with Calamity   Normal

Year (2010)Normal Year in

2012 Prices2012 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

Amount of Indemnity Received (PhP) 0 0 5,746.34 0 0 4,300.00

profit/Loss After Receiving Indemnity (PhP) 28,994.82 31,462.97 -4,561.03 30,038.75 32,595.77 7,949.67

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 >2 HECTARES

ITEM  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) 56,488.76 61,297.31 18,795.29 53,028.85

57,542. 20,128.21

Production Cost (PhP) 25,179.04 27,322.38 25,566.50 24,580.32 26,672.69 25,227.56

Profit/Loss BeforeReceiving Indemnity (PhP) 31,309.72 33,974.93 -6,771.21 28,448.53 30,870.18 -5,099.35

Amount of Indemnity Received (PhP) 0 0 8,147.82 0 0 3,943.33

Profit/Loss AfterReceiving Indemnity (PhP) 31,309.72 33,974.93 1,376.62   28,448.53 30,870.18 -1,156.03

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    >2 HECTARES

ITEM  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) 69,947.83 75,902.06 23,273.48 194,647.06 211,216.19 73,882.35

Production Cost (PhP) 31,178.22 33,832.23 31,658.00 90,224.24 97,904.48 92,600.00

Profit/Loss Before Receiving Indemnity (PhP) 38,769.61 42,069.83 -8,384.52 104,422.82 113,311.71 -18,717.65

Amount of Indemnity Received (PhP) 0 0 10,089.13 0 0 14,474.34

Profit/Loss After Receiving Indemnity (PhP) 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 theReduction 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)

t- VALUE

All Farms, Per Farm 11,952.85 -8.22

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)

Small Farms (Per Ha)

4,300.00

8,147.82

-3.96

-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 -5.79aThis 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 bio-

physical 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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