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Impacts of Climate Shocks on Caloric Intake of Filipinos Department of Science and Technology Food and Nutrition Research Institute Nutritional Assessment and Monitoring Division May 2015

Impacts of Climate Shocks on Caloric Intake of Filipinosstudy. Thirty-one percent of them are engaged in agriculture. Data on per capita caloric intake, information on household heads,

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  • Impacts of Climate Shocks on Caloric Intake of Filipinos

    Department of Science and Technology Food and Nutrition Research Institute

    Nutritional Assessment and Monitoring Division

    May 2015

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    Contents

    List of Tables.............................................................................................................................................................................................. 3 List of Figures............................................................................................................................. ................................................................ 4 Abstract........................................................................................................................................................................................................ 5 Background................................................................................................................................................................................................. 6 Rationale....................................................................................................................................................................................................... 7 Methods

    Data........................................................................................................................................................................................ 7

    Approach.............................................................................................................................................................................. 7 Results and Discussion

    Past and Future Climate Change in the Philippines.......................................................................................... 9

    Main Findings of the Vulnerability Analysis

    Descriptive Statistics............................................................................................................................... 10

    Climate Shocks............................................................................................................................................ 12

    Food Insecurity and Inadequacy of Energy Intake...................................................................... 12

    The Determinants of Food Consumption......................................................................................... 14

    How many and where are vulnerable?............................................................................................. 15

    What are the characteristics of the most vulnerable households?...................................... 18

    Simulating the Impacts of Climate shocks

    Climate Shocks and Vulnerability............................................................................................. ......... 20 Policy Interventions................................................................................................................................. 20

    Conclusion and Recommendations.................................................................................................................................................. 22 Annex........................................................................................................................ ..................................................................................... 24 References................................................................................................................... ................................................................................ 28

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    List of Tables Table 1. Number of sampled households by Region, 2008 NNS............................................................................. ...... 11 Table 2. Socio-Demographic Profile of Household Heads, 2008 NNS........................................................................ 11 Table 3. Household Participation to Government Programs of Households, 2008 NNS................................... 12 Table 4. Estimated Regression Coefficients using Increase in Maximum Temperature.................................... 26 Table 5. Estimated Regression Coefficients using Increase in Percent Changes in Rainfall............................. 27

    Table 6. Estimated Regression Coefficients using Increase in Maximum Temperature (households

    engaged in agriculture only) ................................................................................................ ...................................... 28 Table 7. Estimated Regression Coefficients using Increase in Percent Changes in Rainfall (households

    engaged in agriculture only) ................................................................................................ ...................................... 29 Table 8. Summary of Food Security Status by Vulnerability Model............................................................................ 18 Table 9. Summary of Food Security Status by Vulnerability Model of Households engaged in

    Agriculture(using subpopulation model)............................................................................................................. 18 Table 10. Simulated Impacts on Mean Vulnerability Index............................................................................................... 21

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    List of Figures Figure 1. Past and future climate patterns in Philippines - marked increases in maximum temperature

    but normal rainfall patterns ....................................................................................................................................... 9 Figure 2. Past and future climate patterns in Philippines - marked increases in minimum temperature

    but normal rainfall patterns ....................................................................................................................................... 10 Figure 3. Percent change in Maximum Temperature of 2008 relative to 1979-2006 average......................... 24 Figure 4. Percent change in Precipitation of 2008 relative to 1979-2006 average............................................... 25 Figure 5. Proportion of Food Insecure Households by Region using the Radimer-Cornell Tool..................... 13

    Figure 6. Proportion of Food Insecure Households by Region based on the actual caloric intake............... 14 Figure 7. Proportion of Vulnerable to Food Insecurity Households by Province................................................... 16 Figure 8. Proportion of Vulnerable to Food Insecurity Households by Vulnerability Model............................ 17 Figure 9. Characteristics of Households Across Vulnerability Index............................................................................ 19

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    Impacts of Climate Shocks on Caloric Intake of Filipinos

    Charmaine A. Duante, MSc., Carmina D. Cuarteros, Glen Melvin A. Gironella, Eldridge B. Ferrer, MSc., Cecilia S. Acuin, M.D., Ph.D., Mario Capanzana, Ph.D.,

    Food and Nutrition Research Institute – Department of Science and Technology

    Abstract

    Background. The Philippines is among the top 10 countries worldwide at risk to climate change and disasters.

    Forecasts revealed that there is an increase in the mean annual precipitation of the country, not only in magnitude

    but also in variability. These changes may affect food security and may pose a big challenge for policy makers in

    targeting the most vulnerable population for policy interventions. This paper examined the nature and extent of

    vulnerability of households in the Philippines to the impacts of climate change on food security. Methodology. The

    study utilized the 2008 National Nutrition Survey data on food consumption which was associated with level

    changes in climate shocks from the downscaled climate scenarios provided by Philippine Atmospheric,

    Geophysical and Astronomical Services Administration (PAGASA). A total of 4,854 households were included in the

    study. Thirty-one percent of them are engaged in agriculture. Data on per capita caloric intake, information on

    household heads, assets, and government program participation were analyzed. Vulnerability analyis was used to

    allow the estimation of the probability of falling below the per capita caloric threshold of 2,000 kcal. Results.

    Seven out of ten households were deemed food insecure as reported by the respondents. Based on the caloric

    intake, the proportion of food insecure among Filipino households and among households engaged in agriculture

    were 16% and 17% respectively. Regression analysis showed that there is no significant linear relationship

    between caloric intake and climate shocks. Though, most of the predictors, specifically characteristics of the

    household head performed as expected. These estimates were robust across different models. Though insignificant

    at 5%, the effect of an inrease in maximum temperature on food consumption is negative. As maximum

    temperature increases, food consumption is expected to decrease. A negative percent change in precipitation of at

    most 20% is expected to increase food consumption, while a positive percent change up to 20% will bring a

    negative effect. At the national level, 15% of the households are vulnerable to food insecurity. High proportions of

    these households were noted in Abra, Ilocos Sur, Mt. Province, Quirino, Aurora, Taralac, Cavite, Marinduque,

    Masbate, Camiguin, Zamboanga Del Sur, Zamboanga Sibugay, Lanao Del Sur and Saranggani. Among the

    households engaged in agriculture, 16.3% are vulnerable to food insecurity. Simulation analysis showed that if the

    maximum temperature is increased by 5% and 10%, the mean vulnerability is expected to increase by 13% and

    19% respectively. Moreover, if the amount of rainfall is increased by 5% and 10%, the mean vulnerability is

    expected to increase by about 10%. Simulating possible interventions showed that increasing the education of

    household heads to at least high school or limiting household size into 4 is expected to decrease the mean

    vulnerability by almost 5 percentage points. Conclusion. Understanding the overall effect of climate change in

    food security is a complex process. This attempt may contribute in designing policy interventions by assessing

    both sensitivity and adaptive capacity at the household level.

    Keywords: vulnerability, food insecurity, inadequate caloric intake, climate shock, NNS

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    1. Background

    Food security is a complex issue that all countries worldwide are dealing with especially with

    climate change as the leading risk factor. As defined in the 1996 World Food Summit, food security

    exists “when all people at all times have access to sufficient, safe, and nutritious food to maintain a

    healthy and active life”. In addition, inclusion of economic access to food that meets one’s dietary

    requirement defines the concept of food security (WHO, 2014).

    According to the Food and Agriculture Organization (FAO) the four pillars of food security are

    availability, access, utilization and stability. Availability relates to supply and agricultural

    production. Moreover, this also plays an important role on food security through distribution and

    exchange of products (Luther, 1999). Since it is crucial to produce quantities of food with sufficient

    basic dietary requirement for daily life, this pillar is the most explored among the four. Access on

    the other hand, refers to the affordability, allocation and food preference of an individual. This is

    also where household income and other sources take their parts. That’s why generating

    reasonable income for farmers, their communities and the overall rural population is very

    important to cope with challenges on food access - since this population are those who are most

    vulnerable to price fluctuations. The third pillar involves food use and metabolism. Food safety is

    the primary key on this pillar and in order to achieve food security, the food utilized by a particular

    household must be of good quality, safe and adequate for each of its members. Lastly, stability

    refers to the capability to obtain food over time (FAO, 1997). Everyone should have access to food

    at the right time, right form and right place. However, these four pillars of food security may be at

    risk due to a single factor - climate change.

    Climate change or erratic weather may lead to a decline in agricultural production (food

    availability) which may cause increase in prices (food access) and eventually lower food

    consumption or a shift in consumer’s preference (food security). Moreover, there could be a

    possible decline in production, individuals depending mainly on agriculture are expected to have

    higher vulnerability to food security (Orewa & Iyanbe, 2010). On the other hand, abrupt level

    change in precipitation, and temperature may affect plant growth and results to a decrease in the

    micronutrients available on the crops. With a shift in the demographic distribution of pests and

    diseases due to climate change, water contamination and food safety (food utilization) may be

    threatened (Lake et al, 2012). Lastly, the pillar which appears to be the most affected by climate

    change is food stability wherein a small effect of climate shock may lead to a great variability in

    prices which repeats the cycle.

    With this information, an analysis of the impacts of climate change on vulnerability to food

    insecurity of Filipino households was carried out. One of the main objectives of this study was to

    quantify the extent to which climate change affect vulnerability to food insecurity with other

    factors as either channels or confounders. To address these issues, a deeper understanding of

    different household settings, their coping mechanisms and strategies that could minimize or cancel

    out these climate shocks were carried out in this study.

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    Other objectives of this study include the identification and mapping of location of vulnerable

    groups, determination of the extent or degree of vulnerability and formulation of policies that

    would help communities or households adapt to climate shocks.

    2. Rationale

    Climate change affects food and nutrition security and further undermines current efforts to reduce hunger and protect nutrition. Furthermore, climate change exacerbates the current unacceptable high levels of food insecurity. Studies that may demonstrate the impacts of climate change particularly on food security not only in the present time but in the future as well are crucial in crafting policies and/or programs or policy directions to address the need on adapting and mitigating impacts of climate change in food security and hunger.

    For example, the International Rice Research Institute (IRRI) conducted direct studies on the effects of observed climate change on crop growth and yield. Through this, more accurate information for assessing the impact of climate change on crop production and eventually, food security may be provided. According to this study, a 10% decline in grain yield for each 1°C increase in the growing-season minimum temperature was observed. Not only temperature, water availability is one of the most critical factors for sustaining crop productivity. Having said that there is an increase in mean temperature, there should also be an increase in water to support crop growth. However, extreme rain events can lead to future crop loss. In fact, NASA in 2002 stressed that global climate models project increasing trends in frequency of extreme precipitation events may continue and even strengthen over the coming decades. Furthermore, Rozenweig emphasized that “the impacts of excess soil moisture due to increased precipitation need to be taken into account because of associated crop losses and potential financial damages”. However, researchers argue that while droughts receive the most attention when it comes to assessing the impacts of climate change on agriculture, excess precipitation should also be a major concern. Rainfall variability from season to season greatly affects soil water availability to crop and thus pose crop production risks. Consequently, these impacts of climate change on crop production negatively affects food availability, conservation, access and utilization and exacerbates socioeconomic risks and vulnerabilities. Also, it is expected to further reduce food productivity and make production even more erratic in regions where agricultural productivity is already low. With local production declining and probable disruptions caused by climate hazards, income generating opportunities and purchasing power will decrease for vulnerable populations. With this, policy directions to address the need to adapt and mitigate impacts of climate change in food security and hunger were needed.

    3. Methods

    3.1 Data The study utilized the data of 7th National Nutrition Survey of the Philippines conducted in 2008,

    a nationally representative survey by the Food and Nutrition Research Institute, Department of

    Science and Technology. A total of 3,377 enumeration areas with 4,854 sample households were

    included in the study. Among the variables included in this study were socio-demographic profile

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    of the household head (e.g. sex, age, education), household size, information, communication and

    transportation infrastructures, access to electricity and government programs participated. Per

    capita caloric intake based on consumption unit was used as the measure of food consumption.

    The FNRI data was matched with the constructed shocks from the downscaled climate data

    (rainfall, maximum and minimum temperature for each province) provided by the Philippine

    Atmospheric Geophysical and Astronomical Services Administration (PAGASA) . An increase in

    maximum temperature and decrease in minimum temperature as well as level changes in

    precipitation, temperature in 2008 relative to its respective past 20 year average (1979-2006)

    were expressed as shocks.

    3.2 Approach

    This study aims to assess the impacts of climate change on food consumption, measured through

    per capita caloric intake (kcal). Per capita caloric intake was computed by dividing the household’s

    total intake to the household’s aggregated consumption unit (cu). Caloric threshold was set to

    2000 kcal. Caloric intake falling below this threshold means the household did not meet the

    minimum energy requirement (under light activity).

    Two climate variables were observed in the study: temperature (maximum and minimum) and

    precipitation. The study considered to look at the effect of the changes in level/percent changes in

    these variables. The formulas used in calculating the shocks were as follows:

    ∆ 𝐌𝐚𝐱𝐢𝐦𝐮𝐦 𝐓𝐞𝐦𝐩𝐞𝐫𝐚𝐭𝐮𝐫𝐞:

    Maximum Temperature2008 − Mean Maximum Temperature1979−2006

    ∆ 𝐌𝐢𝐧𝐢𝐦𝐮𝐦 𝐓𝐞𝐦𝐩𝐞𝐫𝐚𝐭𝐮𝐫𝐞:

    Minimum Temperature2008 − Mean Minimum Temperature1979−2006

    % 𝐜𝐡𝐚𝐧𝐠𝐞 𝐢𝐧 𝐩𝐫𝐞𝐜𝐢𝐩𝐢𝐭𝐚𝐭𝐢𝐨𝐧:

    Precipitation2008 − Mean Precipitation1979−2006Mean Precipitation1979−2006

    x 100%

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    Linear Regression Analysis was used to determine which variables are significantly associated and

    what is the direction of these associations with per capita caloric intake. Generalized Least Squares

    was used to account for heteroskedasticity. Through this, estimates of the model of calorie

    consumption were computed. Then, the vulnerability of each household to food security was

    estimated.

    Vulnerability Analysis was used to provide a quantitative measure of probability that a particular

    household will be food insecure under different circumstances. Not only for its capability to

    predict, this model identifies the risks that households are exposed to, while also estimating their

    impacts to food security. Nevertheless, this analysis is also useful for preparedness planning since

    it allows policymakers to prioritize and design interventions.

    Vulnerability to food security was defined as:

    𝐕𝐭,𝐚 = F(z) ∫(z − xt+1)a

    z

    a

    f(Xt+1)

    F(z)dxt+1

    In simple terms, it is the normal probability that the household’s expected dietary energy

    consumption 𝑥𝑡+1, measured in Kilocalories, will fall below a threshold z (Christiaensen &

    Boisvert, 2000).

    To compare the present state of food security and their vulnerability, profiling of the households

    based on their present state of food security was done. The movement of the vulnerability index

    along with the variables that has great impact on caloric intake were observed and assessed for

    possible policies in the future.

    4 Results and Discussion

    4.1 Past and Future Climate Changes in the Philippines

    The past and future climate patterns in the Philippines are shown in Figure 1. The climate data

    from 1979 up to 2010 were hindcast while the data from 2011 up to 2050 were bias corrected

    GCM scenario (MPEH5 A2).

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    As observed on the graph, there is an increasing trend in maximum temperature all throughout the

    years. However, precipitation seemed to have normal fluctuations though, very slight increasing

    trend can be observed. On the other hand, there is also an increase in the minimum temperature

    through time (Figure 2).

    0.00

    1000.00

    2000.00

    3000.00

    4000.00

    5000.00

    6000.00

    21.00

    21.20

    21.40

    21.60

    21.80

    22.00

    22.20

    22.40

    22.60

    22.80

    1979 1984 1989 1994 1999 2004 2009 2014 2019 2024 2029 2034 2039 2044 2049

    Figure 2: Past and future climate patterns in Philippines - marked increases in minimum temperature but normal rainfall patterns

    Mean Minimum Temperature PrecipitationLinear (Mean Minimum Temperature) Linear (Precipitation)

    0.00

    1000.00

    2000.00

    3000.00

    4000.00

    5000.00

    6000.00

    28.5028.7028.9029.1029.3029.5029.7029.9030.1030.3030.5030.7030.9031.1031.3031.50

    1979 1984 1989 1994 1999 2004 2009 2014 2019 2024 2029 2034 2039 2044 2049

    Figure 1: Past and future climate patterns in Philippines - marked increases in maximum temperature but normal rainfall patterns

    Mean Maximum Temperature PrecipitationLinear (Mean Maximum Temperature) Linear (Precipitation)

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    4.2 Vulnerability Analysis

    Vulnerability analysis examines the probability of a particular household to fall below the caloric

    threshold using variables that may serve as either measure of shock or coping mechanism. Some of

    the variables used were information on household head and characteristics of household, climate

    shocks and government programs. If there is a high risk of disaster, man-made or natural among a

    community, it’s vulnerability tends to go up. Usually, the population groups that are highly

    vulnerable to food insecurity include children, pregnant women, lactating mothers, female-headed

    households, the elderly and disabled, households with limited ownership and those under threat of

    drought and other disasters or hazards (WFP, n.d.). Basically, vulnerability analysis on food security

    aims to determine the households who are chronically food insecure and temporarily food secure,

    and on the other hand, households who are temporarily insecure and permanently food secure.

    4.2.1 Descriptive Statistics

    Table 1 shows the distribution of households by region. A total of 4,854 households were included in

    the study. The total number of households per region ranges from 154 – 455 with an average of 256.

    Among the households engaged in agriculture, more than 100 households were located in Cagayan

    Valley, Visayas and SOCCSKARGEN. As expected, the least proportion was found in NCR (National

    Capital Region).

    Region All

    households %

    Households engaged in agriculture

    %

    Ilocos Region 291 6.00 74 4.94 Cagayan Valley 256 5.27 123 8.21 Central Luzon 385 7.93 88 5.87 Bicol 280 5.77 86 5.74 Western Visayas 346 7.13 105 7.01 Central Visayas 368 7.58 112 7.48 Eastern Visayas 268 5.52 115 7.68 Zamboanga Peninsula 224 4.61 90 6.01 Northern Mindanao 241 4.96 69 4.61 Davao Region 254 5.23 84 5.61 SOCCSKARGEN 259 5.34 123 8.21 NCR 454 9.35 5 0.33 CAR 193 3.98 89 5.94 ARMM 154 3.17 78 5.21 CARAGA 207 4.26 77 5.14 CALABARZON 455 9.37 88 5.87 MIMAROPA 219 4.51 92 6.14

    Total 4854 100 1,498 100

    Table 1. Number of sampled households by Region, 2008 NNS

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    The mean age of the household heads was 50 years, while the mean years of schooling was 8 or

    approximately 2nd year high school. In addition, 17% of the households was female-headed.

    Majority owns a television set and a telephone/cellphone. Eighty percent of the households had

    access to electricity. The percentage of households which owned radio, motorcycle or bicycle and car

    were 47%, 17% and 6% respectively. The mean years of schooling of household heads engaged in

    agriculture is 2 years less (6-elementary) than that of the general population. Only 4% of these

    households were female-headed and little proportion of these has communication/transportation

    means.

    Characteristics All Households

    Households engaged in agriculture

    Mean/Prevalence SD Mean/Prevalence SD

    Age of Household Head 50 13.9 49 12.8

    Years of Schooling of household Head 8 3.8 6 3.3

    Household Size 5 0.4 5 2.5

    Sex of Household head (female) 0.17 2.6 0.04 0.2

    Ownership of radio 0.47 0.5 0.41 0.5

    Ownership of television 0.65 0.5 0.46 0.5

    Presence of electricity 0.80 0.4 0.64 0.5

    Ownership of telephone/cellphone 0.54 0.5 .0.37 0.5

    Ownership of car 0.06 0.2 0.02 0.1

    Ownership of motor or bicycle 0.17 0.4 0.11 0.3

    The proportions of household participating to specific government programs are shown in Table 3.

    Less than 10% of the sampled households availed nutrition education, credit grant and fish

    production. Meanwhile, more than 70% participated in food production and fruit trees/vegetables

    production. Among the households engaged in agriculture, at least 76% participated in food

    production, fruit trees/vegetables production and livestock production programs of the government.

    Government Programs All Households

    Households engaged in agriculture

    % SD % SD Nutrition Education 6.5 25.7 7.5 27.6 Credit grant 4.7 31.1 7.4 32.7

    Tinadahan Natin 18.1 38.5 16.5 37.1 Botika ng Baranggay 25.9 43.8 28.6 45.2 Free Medical and Dental 24.4 44.9 24.8 44.9

    Food Production 77.7 41.6 92.3 26.6 Produce Fruit trees/Vegetables 70.9 45.4 85.9 34.8 Produce Livestock 54.6 49.8 76.8 42.2

    Produce Fish 2.7 16.2 4.3 20.2

    Table 2. Socio-Demographic Profile of Households, 2008 NNS

    Table 3. Household Participation to Government Programs, 2008 NNS

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    4.2.2 Climate Shocks

    About 8 out of 10 households nationwide or 68 out of 81 provinces experienced a decrease in maximum temperature relative to its 1979-2006 average (Annex: Figures 3 and 4). On the other hand, no household experienced a negative percent decrease in minimum temperature. More than half of the households experienced a positive percent change in rainfall, clustering up to 20%.

    4.2.3 Food Insecurity and Inadequate Energy Intake

    Figure 5 shows the 2008 regional estimates of food insecurity based on the Radimer-Cornell tool.

    Seven in ten households were deemed food insecure, as reported by respondents to have

    experienced anxiety (that food may run out before they can get money to buy more and/or food

    bought did not last and they did not have enough money to get more) at least once in the past 3

    months before the interview. High proportions of these households were noted in Bicol, Central

    Visayas, SOCCSKSARGEN, ARMM and Nothern Mindanao (82.1, 80.7, 84.9, 84.5, 80.7 respectively).

    Expectedly, prosperous regions like Central Luzon, NCR, CALABARZON and Davao recorded the least

    number of food insecure households.

    Figure 5. Proportion of Food Insecure Households by Region using the Radimer-Cornell Tool

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    Proportions of households with inadequate energy intake by region is presented in Figure 6. At the

    national level, 16% or 4 in 25 households have inadequate energy intake.

    Using the two measures of food security, Bicol Region, Central Visayas, ARMM and SOCCSKARGEN

    can be considered as provinces with high prevalence of food insecurity.

    Per capita caloric intake < 2,000 kCal

    Figure 6. Proportion of Food Insecure Households by Region based on the actual caloric intake

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    4.2.4 The Determinants of Food Consumption

    Two models of food consumption were constructed (Annex: Table 4 and 5). Each model included

    the same variables but unique climate shock. Results showed that most of the predictors particularly

    the characteristics of the household head performed as expected. These estimates were in fact

    robust in both models. All of the socio-demographic characteristics of the household head were

    highly associated with a higher level of food consumption. The age and years of schooling of the

    household head has a monotonic relationship with the per capita caloric intake of the household. As

    expected, as household size increases, the per capita intake decreases. Ownership of television set,

    telephone/cellphone and good source of drinking water shows a highly significant association with

    food consumption.

    It is also shown in the models that all climate shocks remain insignificant. Though insignificant at

    5%, the effect of an increase in maximum temperature on food consumption is negative. As

    maximum temperature increases, food consumption is expected to decrease. Moreover, a negative

    percent change in precipitation at most 20% is expected to increase food consumption. While a

    positive percent change up to 20% will bring a negative effect, the sign reverses for percent change

    greater than 20%.

    To arrive at the potential impact, adaptive capacities should also be assessed together with the

    sensitivity and hazards discussed above. Among the government programs participated in by the

    households before or during the survey year included the free medical and dental check up and

    livestock production. These factors showed a highly significant negative association with food

    consumption. This result may have been due to the target population of these government

    programs, where expected participants are from the poor sectors of the society.

    Subpopulation analysis on households engaged in agriculture was carried out (Annex: Table 6 and

    7). Results revealed that the effect of an increase in maximum temperature on food consumption

    became negative, but still insignificant. Surprisingly, a percent decrease in rainfall between 10% and

    20% showed a significant negative impact on food consumption. This means that if there is a percent

    decrease in rainfall within the said range, there is an expected decrease in the food consumption of

    those households engaged in agriculture. In both models, educational attainment and sex of the

    household, ownership of television set and some sources of drinking water and participation in free

    medical and dental check-up did not switch signs but became insignificant. These models also

    showed less fit on the data but did not affect the over-all vulnerability of the population.

    4.2.5 How many and where are the vulnerable?

    Following the regression analysis, the vulnerability index for each household was computed using

    the predicted caloric intake and its variance. The normal probability that the household’s percapita

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    caloric intake will fall below the threshold (2,000 kcal) was calculated. Figure 7 shows the

    proportion of vulnerable households by province. Results showed that in 2008, about 16% of the

    households have inadequate caloric intake. In the future period, we can expect 15% or 3 out of 20

    Filipino households falling below the caloric threshold. On the provincial level, high proportions

    were noted in Abra, Ilocos Sur, Mountain Province, Quirino, Aurora, Tarlac, Cavite, Marinduque,

    Masbate, Capiz, Camiguin, Zamboanga del Sur, Zamboanga Sibugay, Lanao del Sur and Saranggani.

    Figure 7. Proportion of Vulnerable to Food Insecurity Households by Province

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    Using the 2 climate shock models, temperature and rainfall, at the national level, the proportion of

    vulnerable households across models were almost the same. However, across regions, the estimates

    generated by the two models have discrepancies. Using the temperature model, some provinces like

    Northern Mindanao, NCR and CARAGA have relatively high estimates while some provinces have

    relatively low estimates.

    The over-all profile of the households remained consistent in both models at the national level. On

    the average, 80% of the households are permanently food secure. These households have an

    average probability of being food insecure that is far below the vulnerability cut-off (0.5) which

    implies that these households’ food security status is stable without any interventions. Three

    percent are temporarily food secure. These are the households that are currently food secure but

    likely to be food insecure in the future. Almost five percent are temporarily food insecure. Although

    very close, the average vulnerability of the households belonging to this classification is still below

    the cut-off point.

    These households are more sensitive to exposures and sudden changes that make them shift food

    security status. Nearly, twelve percent are chronically food insecure. These are the households that h

    are currently food insecure and are likely to be food insecure in the future. This group is the most

    important target on choosing policy instruments/interventions.

    0

    5

    10

    15

    20

    25

    Figure 8. Proportion of Vulnerable to Food Insecurity Households by Vulnerability Model

    Temperature Rainfall

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    Food Security Status

    Future

    Non-Vulnerable Vulnerable

    Temperature Rainfall Temperature Rainfall

    Current Food Secure 80.3 (3898) 80.4 (3903) 3.5 (170) 3.4 (165)

    Food Insecure 4.7 (228) 4.6 (223) 11.5 (558) 11.6 (563)

    *general population Meanwhile, using the temperature model specific for the households engaged in agriculture, there are more temporarily food insecure households and less chronically food insecure. On the other hand, using the rainfall model, there are less temporarily food secure households and more permanently food secure households. Though, the differences in proportion between the 2 climate shock models were found insignificant.

    Food Security Status

    Future

    Non-Vulnerable Vulnerable

    Temperature Rainfall Temperature Rainfall

    Current Food Secure 79.4 (1190) 79.6 (1193) 3.7 (55) 3.5 (52)

    Food Insecure 4.5 (67) 4.3 (64) 12.4 (186) 12.6 (189)

    *subpopulation

    4.2.6 What are the characteristics of the vulnerable households? To examine the movement of variables across different levels of vulnerability, the likelihood of being

    food insecure in the future were categorized. The levels of vulnerability highlights the different food

    security status - permanently food secure, transitorily food secure/insecure and chronically food

    insecure. Each variable in the model was associated with the vulnerability across different

    households.

    Figure 9 shows the different household characteristics graphed with the vulnerability index

    obtained using the rainfall model. The vulnerability index ranges from 0 to 1 and that as it

    approaches 1, the household becomes vulnerable to food insecurity. Apparently, when the trend line

    Table 8. Summary of Food Security Status by Vulnerability Model*

    Table 9. Summary of Food Security Status of households engaged in

    agriculture by Vulnerability Model*

  • 19 | P a g e

    tends to go down, households that are more likely to be vulnerable have less assets like television

    set telephone/cellphone, radio, motorcycle/bicycle and car. However, for female headed

    households, the trend line tends to go up. This means that higher proportion of female-headed

    households are found to be vulnerable to food insecurity.

    4.3 Simulating Impacts of Climate Change

    The simulation conducted was not related to the dynamics of vulnerability rather comparative

    statistics. Vulnerability under several assumptions were calculated using the estimates obtained in

    the previous section. In this simulation, the process of computing the vulnerability was not repeated,

    instead, the vulnerability indices obtained through the first analysis were compared to the

    vulnerability indices under the different assumptions.

    4.3.1 Climate Shocks and Vulnerability

    Considering the individual impacts of maximum temperature and rainfall in food consumption, the

    following scenarios were examined:

    1. Increase maximum temperature by 5%

    2. Increase maximum temperature by 10%

    0.00

    0.10

    0.20

    0.30

    0.40

    0.50

    0.60

    0.70

    0.80

    0.90

    < 0.30 0.31 - 0.50 0.51 - 0.60 0.61 - 0.70 0.71 - 0.80 0.81 - 0.90 0.91 - 1.0

    Female headed household Presence of Radio

    Presence of TV Presence of Electricity

    Presence of Telephone/Cellphone Presence of Car

    Presence of Motorcyce/Bicycle

    Figure 9. Characteristics of Households Across Vulnerability Index

  • 20 | P a g e

    3. Increase precipitation by 5%

    4. Increase precipitation by 10%

    Controlling for the effects of other factors, an additional 5% and 10% in the maximum temperature

    increases the average vulnerability at the national level by 14% and 19% respectively. While,

    increasing the amount of precipitation by 5% and 10% increases the average vulnerability both by

    10%.

    4.3.2 Policy Interventions

    Considering the effects of the policy variables in food consumption, several alternative hypotheses

    were formulated and tested:

    1. Increase education of household head minimum to 6 years (elementary level)

    2. Increase education of household head minimum to 10 years (high school level)

    3. Limiting Household Size to 4 members

    4. Limiting Household Size to 6 members

    5. Access to credit

    6. Produce fruit/trees or vegetables in garden

    7. Produce Livestock

    8. Produce Fish

    Holding other factors constant, education has the largest impact on vulnerability. The education of

    the household head contributes as asset and thus assumed to increase the adaptive capacity of

    household, decreasing the potential impact of climate shocks. Simulation analysis showed that

    increasing the education of household heads to at least high school or limiting household size into 4

    is expected to decrease the mean vulnerability by almost 5 percentage points. This may imply that a

    higher level of education than elementary education will decrease vulnerability by almost 4%

    Furthermore, household’s initiative to produce fruit trees/vegetables, poultry/livestock or fish

    production decreases vulnerability but very minimal.

  • 21 | P a g e

    Variable Condition

    Simulated Actual

    v1 temperature

    v1 rainfall

    v1

    Climate Variable

    adds 5% maximumtemperature 0.3837

    0.2477

    adds 10% maximum temperature 0.4416

    less 5% rainfall

    0.3491

    less 10% rainfall 0.3491

    Household Information

    minimum education of 6 years 0.2463 0.2465

    minimum education of 10 years 0.2055 0.2059

    minimum education of 11 years 0.1871 0.1875

    maximum of 4 members 0.2065 0.2068

    maximum of 6 members 0.2387 0.2389

    Government Program Participation

    Nutrition Education 0.2511 0.2514

    Credit Grant 0.2404 0.2410

    Food Production 0.2500 0.2502

    Tindahan Natin 0.2507 0.2511

    Botika ng Baranggay 0.2487 0.2490

    Free Medical/Dental Check up 0.2576 0.2576

    Produce Fruit Trees/Vegetables 0.2455 0.2456

    Produce Livestock 0.2437 0.2438

    Produce Fish 0.2536 0.2533

    *v1 – mean vulnerability As a matter of fact, the said factors may as well serve as channels for climate shocks and instead of

    helping decrease the effect of climate change through its adaptive capacity, it may have increased the

    sensitivity of a household thus canceling its negative effect on the the potential impact of climate

    change. A household that chooses to raise fruit-bearing trees or vegetables mainly for household

    consumption or for income generation, will surely be affected whenever climate shocks exists.

    Though, the main reason of production is to support household consumption, in cases where the

    supply is not enough in the market or if the goods are available but not affordable, small-scale and

    large-scale production of fruit and vegetables may also be affected by the same climate shocks. When

    this happens, the household will not have any other option but to become more vulnerable. This is

    why households that rely only on agriculture are expected to be more vulnerable.

    Table 10. Simulated Impacts on Vulnerability Index

  • 22 | P a g e

    5 Conclusion and Recommendations

    Food security may change from time to time. For policy makers, this poses a big challenge in

    targeting the population for formulating interventions that will aid households or individuals to

    adapt or adjust to actual or expected climate shocks. This study considered three food security

    status: chronically food insecure, permanently food secure and temporarily food secure/insecure.

    The crucial part of the study is to correctly classify each household by their current and future food

    security status. It is very important specifically for policy makers to correctly classify the population

    in order to develop a well-targeted policy intervention in the future.

    For further research and development, other factors that link household to agriculture should be

    studied to better capture each household’s sensitivity. Moreover, the adaptive capacity of each

    household plays a very important role to accurately assess the possible potential impact of erratic

    weather on food security. It will also be beneficial for the simulation of policies and formulation of

    interventions with significant effect on the target population.

    Further studies on the possible food substitution using panel data, e.g. FIES and its relation to

    climate shocks could also be done. In order to explore the channels through which climate change

    impacts to consumption of Filipino households pass through, it is recommended to obtain data from

    production or from market to establish a strong significant relationship. Other policy interventions

    may be considered like fertilizer, irrigation, sprays and other factors related to production. Other

    outcomes like proportion of households that meet 100% adequacy of energy requirement may also

    be explored.

  • 23 | P a g e

    Annex

    Figure 3. Change in Maximum Temperature of 2008 relative to 1979-2006 average.

  • 24 | P a g e

    Figure 4. Change in Precipitation of 2008 relative to 1979-2006 average.

  • 25 | P a g e

    Table 4. Estimated Regression Coefficients using Increase in Maximum Temperature

    log (food consumption) Coef. Std. Err. t P>t [95% Conf. Interval]

    Ho

    use

    ho

    ld In

    form

    atio

    n

    log (household size)*** -1.035 0.014 -74.790 0.000 -1.062 -1.008

    log (age of hh head)*** 0.103 0.027 3.850 0.000 0.051 0.156

    log (educational attainment of hh head)*** 0.034 0.011 3.020 0.003 0.012 0.056

    sex of hh head(female)*** -0.080 0.020 -3.990 0.000 -0.119 -0.041

    ownership of radio -0.012 0.015 -0.790 0.432 -0.041 0.018

    ownership of television*** 0.061 0.020 3.060 0.002 0.022 0.100

    presence of electricity 0.000 0.023 0.000 0.999 -0.045 0.045

    ownership of telephone/cellphone**** 0.105 0.017 6.230 0.000 0.072 0.138

    ownership of car 0.062 0.031 1.980 0.048 0.001 0.123

    ownership of motor or bicycle 0.032 0.020 1.600 0.111 -0.007 0.071

    Source of drinking water

    waterworks-public tapped/peddler/delivered 0.041 0.026 1.550 0.121 -0.011 0.093

    deep well - tubed/piped within residential area*** 0.102 0.025 4.060 0.000 0.053 0.151

    deep well - public use 0.014 0.023 0.620 0.538 -0.031 0.060

    private well with faucet 0.015 0.047 0.330 0.744 -0.076 0.107

    open dug well - owned -0.005 0.054 -0.100 0.922 -0.111 0.100

    open dug well - public 0.060 0.041 1.460 0.145 -0.021 0.141

    spring water piped into residential area*** 0.083 0.030 2.790 0.005 0.025 0.142

    rainwater 0.147 0.095 1.550 0.122 -0.039 0.333

    spring water -0.013 0.032 -0.390 0.699 -0.076 0.051

    mineral/bottled water 0.048 0.025 1.930 0.054 -0.001 0.096

    others 0.280 0.141 1.990 0.047 0.004 0.557

    Cimate Shocks increase in maximum temperature -0.033 0.095 -0.350 0.723 -0.219 0.152

    decrease in minimum temperature 0.000 (omitted)

    Go

    vern

    me

    nt

    Pro

    gram

    Par

    tici

    pat

    ion

    Nutrition Education -0.016 0.028 -0.580 0.559 -0.071 0.039

    Credit grant 0.033 0.034 0.980 0.326 -0.033 0.100

    Food Production -0.046 0.035 -1.340 0.181 -0.114 0.022

    Tinadahan Natin -0.016 0.019 -0.860 0.390 -0.054 0.021

    Botika ng Baranggay -0.006 0.017 -0.390 0.699 -0.039 0.026

    Free Medical and Dental*** -0.057 0.016 -3.520 0.000 -0.089 -0.025

    Produce Fruit trees/Vegetables 0.031 0.029 1.090 0.276 -0.025 0.087

    Produce Livestock* 0.036 0.018 2.010 0.045 0.001 0.072

    Produce Fish -0.027 0.043 -0.620 0.534 -0.112 0.058

    Constant 6.917 0.175 39.520 0.000 6.574 7.260

    R-Squared 56.17

    Adjusted R-Squared 55.97

  • 26 | P a g e

    Table 5. Estimated Regression Coefficients using Increase in Percent Changes in Rainfall

    log (food consumption)

    Coef. Std. Err. t P>t [95% Conf. Interval]

    Ho

    use

    ho

    ld In

    form

    atio

    n

    log (household size)*** -1.034 0.014 -74.640 0.000 -1.062 -1.007 log (age of hh head)*** 0.102 0.027 3.810 0.000 0.050 0.155 log (educational attainment of hh head)*** 0.034 0.011 3.020 0.003 0.012 0.056 sex of hh head(female)*** -0.080 0.020 -3.970 0.000 -0.119 -0.040 ownership of radio -0.011 0.015 -0.770 0.444 -0.041 0.018 ownership of television*** 0.061 0.020 3.060 0.002 0.022 0.100 presence of electricity -0.001 0.023 -0.050 0.958 -0.046 0.044 ownership of telephone/cellphone*** 0.104 0.017 6.170 0.000 0.071 0.137 ownership of car 0.061 0.031 1.950 0.051 0.000 0.122 ownership of motor or bicycle 0.032 0.020 1.590 0.113 -0.008 0.071 Source of drinking water

    waterworks-public tapped/peddler/delivered 0.038 0.026 1.430 0.154 -0.014 0.090 deep well - tubed/piped within residential area*** 0.101 0.025 3.990 0.000 0.051 0.150 deep well - public use 0.012 0.023 0.510 0.607 -0.034 0.058 private well with faucet 0.013 0.047 0.270 0.785 -0.079 0.105 open dug well - owned -0.008 0.054 -0.160 0.876 -0.115 0.098 open dug well - public 0.060 0.041 1.440 0.150 -0.022 0.141 spring water piped into residential area*** 0.080 0.030 2.680 0.007 0.022 0.139 rainwater 0.153 0.095 1.610 0.108 -0.034 0.340 spring water -0.014 0.033 -0.430 0.668 -0.078 0.050 mineral/bottled water 0.048 0.025 1.950 0.052 0.000 0.097 others 0.279 0.141 1.980 0.048 0.002 0.556

    Cimate Shocks

    Percent Change in Rainfall < -20% 0.000 (omitted) -20% < Percent Change in Rainfall < -10% 0.001 0.002 0.290 0.772 -0.003 0.004

    -10% < Percent Change in Rainfall < 0% 0.001 0.003 0.440 0.663 -0.005 0.008 0 < Percent Change in Rainfall < 10%" -0.003 0.004 -0.610 0.545 -0.011 0.006 10% < Percent Change in Rainfall < 20% -0.001 0.002 -0.910 0.363 -0.005 0.002 Percent Change in Rainfall > 20% 0.000 0.001 0.390 0.694 -0.002 0.003

    Go

    vern

    men

    t P

    rogr

    am

    Par

    tici

    pat

    ion

    Nutrition Education -0.017 0.028 -0.590 0.554 -0.072 0.039 Credit grant 0.031 0.034 0.910 0.363 -0.036 0.098 Food Production -0.047 0.035 -1.350 0.178 -0.116 0.022 Tinadahan Natin -0.017 0.020 -0.880 0.381 -0.056 0.021 Botika ng Baranggay -0.007 0.017 -0.440 0.662 -0.040 0.025 Free Medical and Dental*** -0.056 0.016 -3.470 0.001 -0.088 -0.024 Produce Fruit trees/Vegetables 0.033 0.029 1.160 0.247 -0.023 0.089 Produce Livestock* 0.037 0.018 2.050 0.041 0.002 0.073 Produce Fish -0.025 0.043 -0.580 0.561 -0.110 0.060

    Constant 6.930 0.176 39.450 0.000 6.586 7.275

    R-Squared 56.45 Adjusted R-Squared 56.14

  • 27 | P a g e

    Table 6. Estimated Regression Coefficients using Increase in Maximum Temperature*

    log (food consumption) Coef. Std. Err. t P>t 95% Conf Interval

    Ho

    use

    ho

    ld In

    form

    ati

    on

    log (household size)*** -1.017 0.026 -39.600

  • 28 | P a g e

    Table 7. Estimated Regression Coefficients using Increase in Percent Changes in Rainfall*

    log (food consumption) Coef. Std. Err. t P>t 95% Conf Interval

    Ho

    use

    ho

    ld In

    form

    ati

    on

    log (household size)*** -1.014 0.026 -39.470 20% 0.003 0.002 1.890 0.060 0.000 0.007

    Go

    vern

    me

    nt

    Pro

    gram

    Par

    tici

    pa

    tio

    n

    Nutrition Education -0.045 0.049 -0.930 0.355 -0.141 0.051 Credit grant 0.080 0.059 1.350 0.177 -0.036 0.195 Food Production -0.098 0.078 -1.260 0.208 -0.251 0.055 Tinadahan Natin -0.007 0.038 -0.170 0.862 -0.081 0.068 Botika ng Baranggay 0.002 0.029 0.060 0.953 -0.056 0.059 Free Medical and Dental -0.042 0.030 -1.390 0.163 -0.101 0.017 Produce Fruit trees/Vegetables 0.127 0.054 2.370 0.018 0.022 0.232 Produce Livestock 0.101 0.037 2.760 0.006 0.029 0.173 Produce Fish -0.003 0.065 -0.050 0.961 -0.130 0.123

    Constant 6.865 0.229 29.980

  • 29 | P a g e

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