Estimating the Demand Elasticities - Southeast Asian ... the Demand Elasticities of Rice in the...

Estimating the Demand Elasticities of Rice in the Philippines


Flordeliza A. LanticanMercedita A. SombillaKaren P. Quilloy

Published by SEARCALos Baños, Laguna, Philippines

Printed in the Republic of the PhilippinesFirst Printing, 2013

Philippine Copyright 2013 by SEARCA

Parts of this publication may be quoted without permission by other scholarly writing and in popular writing as long as credit is given to the source. However, it may not be reproduced or transmitted in its entirety in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage or retrieval system, without permission in writing from SEARCA.

The opinions expressed in this publication are those of the authors and do not necessarily reflect SEARCA’s position.

Cover photos by Norbert Marchadesch and Huu Hung Truong

ISBN 978-971-560-159-7


Flordeliza A. LanticanMercedita A. Sombilla

Karen P. Quilloy

Southeast Asian Regional Centerfor Graduate Study and Research in Agriculture (SEARCA)

2013

Table of Contents

List of Tables iiiList of Figures ivList of Appendices vForeword viiAcknowledgment ixAcronyms xExecutive Summary xi

Introduction 1The Almost Ideal Demand System (AIDS) 3Philippine Rice Demand Estimation: An Application of the Linear Approximate

Almost Ideal Demand System (LA/AIDS)5

Consumption of and Expenditure on Rice and Other Food Commodities 10Per Capita Rice Consumption and Expenditure vis-à-vis Socio-demographic Characteristics

12

Per Capita Rice Consumption and Expenditure vis-à-vis Location Variables

14

Expenditure Shares of Rice and Other Food Commodities 16Expenditure Shares of Rice and Other Food Commodities vis-à-vis Socio-demographic Characteristics

18

Expenditure Shares of Rice and Other Food Commodities vis-à-vis Location Characteristics

21

Demand Elasticities of Rice and Other Food Commodities 24Income Elasticities 24Own-Price Elasticities 25Demand Elasticities of Rice vis-à-vis Socio-demographic Characteristics 26Demand Elasticities of Rice vis-à-vis Location Characteristics 29Cross-Price Elasticities 31

Summary 36Conclusion and Policy Implications 38Literature Cited 41Appendices 44The Authors 50

iii

List of Tables

Table No. Page

1 List of shifter variables in LA/AIDS for rice 62 Mean prices, per capita consumption, per capita expenditures

and expenditure shares of selected food commodities 10

3 Price, consumption and expenditure of rice by socio-demographic variables

12

4 Per capita consumption and expenditure of rice by location variables 145 LA/AIDS parameter estimates of price variables 176 LA/AIDS parameter estimates of socio-demographic variables

by type of commodity 19

7 Income and own-price elasticities of selected food commodities 258 Demand elasticities of rice by socio-demographic variables 279 Demand elasticities of rice by location variables 30

10 Marshallian (uncompensated) price elasticities of selected food commodities

33

11 Hicksian (compensated) price elasticities of selected food commodities

35

iv

List of Figures

Figure No. Page

1 Per capita expenditure shares of selected food commodities by income group

18

2 Per capita expenditure shares of selected food commodities by socioeconomic class

20

3 Per capita expenditure shares of selected food commodities by barangay classification

21

4 Per capita expenditure shares of selected food commodities by region

22

v

List of Appendices

Appendix No. Page

1 Heckman’s two-step estimator 442 Socio-demographic variables by barangay classification and

socioeconomic class of households45

3 Location variables by barangay classification and socioeconomic class of households

46

4 Test of significance of socio-demographic and physical variables vis-à-vis expenditure shares of rice and other food commodities using one-way ANOVA

47

5 LA/AIDS parameter estimates of location variables of selected food commodities

48

A List of commodities considered in the LA/AIDS 49

Annex

vii

Foreword

We are pleased to share with you the results of the SEARCA-PhilRice collaborative research titled “Estimating the Demand Elasticities of Rice in the Philippines.” This study seeks to determine the patterns of rice

consumption in the Philippines. It analyzed the responsiveness of rice consumers to changes in their income and price of rice and its substitute commodities. Factors affecting rice consumption with particular focus on the marginalized sector were examined. SEARCA hopes to draw policy implications from the findings to help address the problem of food insecurity and rural poverty in the country. Replication of this study in other Southeast Asian countries is seen as a possibility. We are grateful to PhilRice for providing financial support for this undertaking. We thank and congratulate the project research team from the College of Economics and Management, University of the Philippines Baños, and SEARCA for their outstanding work.

Gil C. Saguiguit, Jr.Director

ix

Acknowledgment

This study was implemented by the Southeast Asian Regional Center for Graduate Study and Research in Agriculture (SEARCA) under the research project entitled “Explaining the Increasing Per Capita Rice Consumption in the Philippines” of the

Philippine Rice Research Institute (PhilRice), under the management and leadership of Dr. Sergio R. Francisco. The preparation of this report was led by the study leader, Dr. Flordeliza A. Lantican from the College of Economics and Management, University of the Philippines Los Baños (CEM-UPLB). At SEARCA, Dr. Mercedita A. Sombilla, former manager of the Research and Development Department, provided overall supervision of the implementation of the study.

The authors would like to express their sincere thanks to Dr. Mahabub Hossain, Executive Director of the Bangladesh Rural Advancement Committee (BRAC), for his extensive comments and suggestions to improve this paper. They are also very grateful to Mr. Eduardo B. Sanguyo, Chief of the Bureau of Agricultural Statistics (BAS) Socio-Economic Statistics Section, for sharing the latest data on agricultural food demand in the Philippines. Lastly, they would like to thank Ms. Elizabeth S. Gatchalian for her valuable contribution to the model programming of the almost ideal demand system (AIDS) used in this study.

x

Acronyms

χ2 Chi-squareAIDS Almost Ideal Demand System

ANOVA Analysis of VarianceARMM Autonomous Region of Muslim Mindanao

BAS Bureau of Agricultural StatisticsCAR Cordillera Administrative Region

DA Department of AgricultureFIES Family Income and Expenditure SurveyIMR Inverse Mills Ratio

LA/AIDS Linear Approximate Almost Ideal Demand SystemNCR National Capital RegionNSO National Statistics Office

PhilRice Philippine Rice Research InstitutePPA Philippine Ports Authority

SEARCA Southeast Asian Regional Center for Graduate Study and Research in Agriculture

SFD Survey of Food Demand for Agricultural Commodities

Note:In this report, PHP, kg, %, kg/year and PHP/kg refer to Philippine Peso, kilogram, percent, kilogram per year, and Philippine Peso per kilogram, respectively.

xi

Executive Summary

This study aimed to analyze the demand for rice in the Philippines and determine the factors affecting it using the linear approximate almost ideal demand system (LA/AIDS). Findings show that rice remains to be a normal and necessity good in

the country as quantity demanded for rice appears to be inelastic to both income and own-price changes. Corn, sweet potato, and cassava are found to be rice substitutes while banana (saba), potato, taro (gabi), milkfish, tilapia, pork, chicken, mango, pineapple, eggplant, and bitter gourd (ampalaya) are considered rice complements.

Rice consumption and expenditure are significantly affected by socio-demographic characteristics, such as, income; socioeconomic class; and sex, age, educational attainment, and type of occupation of the household head. The location of consumers, whether they live in an urban or a rural area or in a relatively rich or poor region, also significantly affects rice consumption and expenditure of consumers.

The major policy implication that can contribute to the country’s strategy of managing rice consumption and rice self-sufficiency goal is the implementation of a highly selective and targeted price subsidy program, particularly focusing on the poor, complemented with strong income-related policies such as livelihood programs and rural development interventions. Encouraging more production and consumption of the major rice substitutes, by disseminating information on its market potential and health benefits, and increasing their market availability is also recommended in the study.

Introduction

Rice remains the basic staple food in the Philippines. For most Filipinos, no meal is complete without rice. It is their main source of carbohydrates and energy and provides a physiological sensation of satiety. It accounts for 20 percent of their

daily household budget. Demand for rice has strengthened over time in the country. While the demand theory states that as income rises, food intake becomes more diversified usually away from the basic staple, e.g., rice and corn in the case of the Philippines, in favor of bread, pasta, or noodles, meat, fish, fruits, and vegetables, the opposite is happening in the Philippines. Demand for rice has increased, despite improvement in per capita incomes, albeit at a slow rate. Annual per capita consumption of rice hovered around 90−92 kilograms (kg) in the 1980s and 1990s. It then gradually rose to an average level of 113 kg per capita per annum at the turn of the century, peaking at 128 kg per capita in 2008 and declining to 119 kg per capita in 2009−2010 (Bureau of Agricultural Statistics [BAS] 2011).

There are concerns, however, on the accuracy of the per capita demand estimates. BAS estimates the total rice demand for food, which accounts for the bulk of domestic requirements, using the disappearance method. It is equated to the total available supply which is total rice supply (i.e., sum of beginning stock, domestic production, and imports minus exports) less all those that go for feeds, seeds, wastes, and processed non-food use. These issues cast doubts on the estimates of per capita rice demand, which is merely the available rice supply for food use divided by population.

In response to the concern on the reliability of rice demand estimates as reported by BAS, the Department of Agriculture (DA) provided funds to conduct the Survey of Food Demand for Agricultural Commodities (SFD) on the consumption of rice and other agricultural commodities in the period 2008−2009. SFD is a nationwide survey which aims to determine the current and emerging consumption patterns of Filipinos with regard to basic food items coming from 11 commodity groups.1 The 2008−2009 SFD was conducted in four quarterly rounds from August 2008 to May 2009, with a total of 13,880 households for each survey round.

Using the SFD data, this study estimated the demand elasticities of rice in the Philippines, which would be used in calculating more accurately the rice demand level based primarily on changes in prices and incomes. Specifically, the study aimed to fit an appropriate demand equation to calculate income and price elasticity parameters

1 Rice; corn; noodles; bread/pandesal; root crops; meat; eggs; fish and marine products; vegetables, legumes, and condiments; fruits; and fresh/pasteurized milk.

2

for rice across income groups and between rural and urban consumers, and identify factors other than prices and income that influence rice demand changes. Using these elasticity parameters, it also sought to validate the level of per capita rice demand and determine the extent of commodity substitution with respect to changes of incomes and relative prices of commodities and assess the vulnerability or resiliency of the low income and rural groups to price and income shocks. Lastly, this study provided policy recommendations to safeguard food security of the more vulnerable segments of the society in case of the occurrence of another food crisis.

A total of 9,122 household-respondents out of the whole SFD sample were considered in the study. These include only those who were able to participate in all the four survey rounds. Such sampling was made to maintain consistency of the characteristics and location of the respondents across the four survey rounds and allow proper estimation of their annual rice consumption. Moreover, only 15 commodities were analyzed. These include mostly the food items which are common to the Filipino diet and belong to top food groups that were eaten at home, according to the Family Income and Expenditure Survey2 of the National Statistics Office (FIES-NSO). Other commodities were excluded due to very few survey responses. Descriptive statistics and complete demand system models were employed in the demand analysis of the SFD data.

2 Rice, corn, sweet potato (camote), white (Irish) potato, cassava, taro (gabi), milkfish, tilapia, pork, chicken, banana (saba), mango (ripe), pineapple, eggplant, and bitter gourd (ampalaya). [See Annex for scientific names]

The Almost Ideal Demand System (AIDS)

In the early years, demand studies were characterized by the extensive application of single equation models. These models had been subject to skepticism since they ignored the requirement to satisfy properties such as adding-up, and considered only

a fraction of the total expenditure or budget (Deaton and Muellbauer 1980). Hence, empirical works had been directed towards the estimation of complete demand systems as pioneered by the linear expenditure system (LES), a demand model that imposes the theoretical restrictions of adding-up, homogeneity, and symmetry. The LES presumes that Engel curves are straight lines, goods cannot be inferior, and all goods must be substitutes (Stone 1954). This model was known to be too restrictive, thus analysts were forced to specify it through a priori restrictions in order to generate more degrees of freedom. However, it became evident that accepting all these restrictions without any statistical test did not offer much promise (Bopape 2006).

Due to the limitations of the LES model, alternative models with flexible functional forms, which allow the imposed theoretical restrictions to be statistically tested, have been developed. Among the popular models are the Generalized Leontief, the transcendental logarithmic model, the Rotterdam model, and the Almost ideal demand system (AIDS). Of these functional forms, AIDS has become the most widely adopted econometric approach for demand analysis. Since its introduction, no dramatic advances have yet been made except for some refinements or extensions of the model (Bopape 2006). To date, AIDS has remained the baseline of many recently developed demand models.

The model of AIDS, or the full AIDS model, as other economists refer to it, is a system of non-linear demand equations that gives an arbitrary first-order approximation to any demand system. However, due to the problem of non-linearity in parameters, multicollinearity, statistical bias, and other empirical difficulties encountered when estimating the full AIDS model, Deaton and Muellbauer (1980) developed a linear approximation of the model, which they called the linear approximate AIDS (LA/AIDS). Since LA/AIDS is linearized, the estimation process becomes easier, straightforward, and more plausible, making it more acceptable and popular among agricultural economists.

The popularity of LA/AIDS is attributed to its desirable characteristics as follows: (1) relatively easy to estimate and interpret; (2) satisfies the axioms of choice; (3) flexible as other locally-flexible functional forms but more desirable due to its compatibility with aggregated and disaggregated expenditure data; (4) corresponds with a well-defined preference structure since it is derived from a specific cost function, hence it can be considered as an exact representation of consumers’ preferences, which is crucial in welfare analysis; and (5) adding-up, homogeneity, and symmetry restrictions

4

are easily imposed, and hence it represents a legitimate demand function (Deaton and Muellbauer 1980; Alston and Chalfant 1993; Buse 1994; Eales and Unnevehr 1994; Njonou et al. 2002).

However, a major limitation of LA/AIDS is the zero consumption problem or monotony issue, which is common in household-level micro data (Femenia and Gohin 2007). The zero consumption shares of commodity items can lead to inconsistent and biased estimates when these are ignored. Fortunately, however, empirical analysts made an effort to address this issue by applying censored regressions. Among the popular econometric techniques that deal with the monotony issue are the Tobit estimator, the Heckman’s two-step estimator, and the Amemiya’s two-step estimator.

Further enhancement of AIDS was made in recent years. Some of the recent extensions of the model include the error corrected almost ideal demand system (ECAIDS), the inverse almost ideal demand system (IAIDS), and the quadratic almost ideal demand system (QUAIDS). Of these variants, QUAIDS has gained the most popularity, has been applied to several demand studies, and has often been compared side by side with the popular LA/AIDS.

Philippine Rice Demand Estimation: An Application of the Linear Approximate

Almost Ideal Demand System (LA/AIDS)

Before estimating the demand elasticities of rice in the Philippines, this study provided a descriptive analysis of the characteristics of rice consumers, particularly using frequency counts, means, and percentages, which were

presented in one-way and contingency tables. The consumers’ per capita income, food consumption and expenditures, and price of rice were analyzed using the one-way analysis of variance (ANOVA). Based on F-test,3 ANOVA tests the null hypothesis that there is no difference in the means of dependent variables across different socio-demographic characteristics and locations of the consumers, that is:

Ho: µ1 = µ2 = µ3…= µnH1: at least one of the µs is different from the others

where:Ho = null hypothesisH1 = alternative hypothesisµn = mean of the dependent variables (income, consumption, price and expenditure)

where n is the categories of each socio-demographic or location variable

In addition, socio-demographic characteristics and regional location were cross tabulated with the variables of barangay classification and socioeconomic status and then subjected to Chi-square (χ2) significance test4 using Pearson’s contingency coefficient to test the null hypothesis of no association between the two variables cross tabulated. The value of χ2 test statistic is mathematically defined as:

χ 2 =(Oi −Ei )

2

Eii=1

n

∑

(1)where:

χ2 = Pearson’s cumulative test statistic, which asymptotically approaches a χ2 distribution

Oi = an observed frequencyEi = an expected (theoretical) frequency asserted by the null hypothesisn = sample size

3 Based on F-test, one could determine whether Ho is rejected or not. If the observed F-ratio (Fobs) is greater than the critical value of F-ratio (Fcrit), then Ho is rejected. Alternatively, a probability value (Prob>F) less than .05 indicates a significance level at 5% (or 1% if Prob>F is less than .01) and is interpreted as the justification for rejecting Ho (StatSoft, Inc. 2013).

4 The Chi-square (χ2) test is an appropriate measure of association since the sample size is large enough, which is a main requirement of the test to be able to estimate with sufficient precision. A χ2 with probability of .05 or less is interpreted as the justification for rejecting the null hypothesis (StatSoft, Inc. 2013).

6

LA/AIDS was then employed to analyze the demand for rice and its elasticities. Table 1 presents the list of shifter variables included in the demand model.

Table 1. List of shifter variables in LA/AIDS for rice

Variable Name Variable DefinitionPrice variableslprice_ log of retail price of rice (PHP/kg)lpcorn_ log of retail price of corn (PHP/kg)lpspotato_ log of retail price of sweet potato (PHP/kg)lppotato_ log of retail price of potato (PHP/kg)lptaro_ log of retail price of taro (PHP/kg)lpcassava_ log of retail price of cassava (PHP/kg)lpmilkfish_ log of retail price of milkfish (PHP/kg)lptilapia_ log of retail price of tilapia (PHP/kg)lppork_ log of retail price of pork (PHP/kg)lpchicken_ log of retail price of chicken (PHP/kg)lpbanana_ log of retail price of banana (PHP/kg)lpmango_ log of retail price of mango (PHP/kg)lppineapple_ log of retail price of pineapple (PHP/kg)lpeggplant_ log of retail price of eggplant (PHP/kg)lpbgourd_ log of retail price of bitter gourd (PHP/kg)

Socio-demographic variableslincome_ log of per capita income (PHP/year)Soc 1 for lower socio-economic classes D and E;

0 otherwise (upper classes AB and C)gender 1 if household head is male; 0 otherwiselage_ log of age of household head (in years)educ1 1 if household head finished high school; 0 otherwiseeduc2 1 if household head has university/bachelor’s degree; 0 otherwiseoccu 1 if household head is employed in white-collar job;

0 otherwise (blue-collar job)Location variablesLoc 1 if consumer is located in urban area; 0 otherwise (rural area)REG1 1 for NCR; 0 otherwiseREG2 1 for CAR; 0 otherwiseREG3 1 for Ilocos Region; 0 otherwiseREG4 1 for Cagayan Valley; 0 otherwiseREG5 1 for Central Luzon; 0 otherwiseREG6 1 for CALABARZON; 0 otherwiseREG7 1 for MIMAROPA; 0 otherwiseREG8 1 for Bicol Region; 0 otherwise

7

Variable Name Variable DefinitionREG9 1 for Western Visayas; 0 otherwiseREG10 1 for Central Visayas; 0 otherwiseREG11 1 for Eastern Visayas; 0 otherwiseREG12 1 for Zamboanga Peninsula; 0 otherwiseREG13 1 for Northern Mindanao; 0 otherwiseREG14 1 for Davao Region; 0 otherwiseREG15 1 for SOCCSKSARGEN; 0 otherwiseREG16 1 for Caraga Region; 0 otherwiseREG17 1 for ARMM; 0 otherwise

Originally, AIDS contains only the prices and expenditure as explanatory variables. It is defined in budget form as:

wi =αi + γ ij ln pj +βi ln(xP)

j=1

n

∑ (2)

where:(i,j) = represents the 15 selected commodities (Footnote 2), where i,j= 1,2,…,nwi = per capita expenditure share of ith commodity in the total annual per capita

expenditures on the 15 commodities, pj = retail price (PHP/kg) of jth commodity, x = total per capita expenditures (PHP/year) on the 15 commodities given by

x = piqi

i=1

n

∑

ln P = translog price index defined by

n

i

n

jjiij

n

iii pppP

1 110 lnln

21lnln (3)

αi, γij, and βi are parameters to be estimated.

Using Pollak and Wales' (1978, 1981) linear demographic translating method, AIDS is extended to include more explanatory variables besides price and expenditure. The vector of these variables is expressed as follows:

δkDk = δ1 ln income_+δ2soc+δ3 lnage_+δ4gender +δ5educ1+δ6educ2+δ7occu+δ8loc+δ9REG1+δ10REG2+δ11REG3+δ12REG4+δ13REG5+δ14REG6+δ15REG7+δ16REG8+δ17REG9+δ18REG10++δ19REG11+δ20REG12+δ21REG13+δ22REG14+δ23REG15+δ24REG16+δ25REG17

(4)

Table 1. (continued)

8

where Dk (k=1,2,…,25) is the vector of socio-demographic and location variables defined in Table 1 and is the vector of parameters of corresponding Dk in the model.

Given that the data set is characterized by high proportions of non-consumption of all commodities except rice (monotony issue), the censored regression approach using the Heckman’s two-step estimator (Appendix 1) was applied in LA/AIDS, which is now defined as:

wi =α0 + γ ij ln pj +βi ln(xP*)+δkDk +θiimrj

j=1

n

∑ (5)

where βi is parameter to be estimated and lnP* = wi ln pii=1

n

∑ is the Stone price index, a linear approximation of the translog price index (3) in AIDS; imrj is the inverse Mills ratio for the jth commodity (j = 1,2,…,n) and other variables are as defined previously.

LA/AIDS was estimated using the iterated seemingly unrelated regression (ITSUR), which is usually regarded as an advanced regression method that improves the efficiency of estimates as it considers the simultaneous correlation of the error terms across all commodity demand equations (Heien and Wessells 1990). But before the estimation process, the following parameter restrictions were imposed on the whole demand system to ensure that LA/AIDS conforms to the theory of utility maximization of consumers:

a) adding-up b) homogeneity

αii=1

n

∑ =1 βii=1

n

∑ = 0

γ iji=1

n

∑ = 0 δki=1

n

∑ = 0

γ ijj=1

n

∑ = 0

c) symmetry

γ ij = γ ji

Note that as part of the adding-up restriction, a share equation specified for one commodity in LA/AIDS, in this case, the equation of bitter gourd, was arbitrarily deleted to deal with the singularity problem since the expenditure shares sum to one. Parameter estimates of this equation were recovered based on homogeneity and symmetry restrictions.

With the expenditure shares and parameter estimates derived by ITSUR from LA/AIDS, price and expenditure elasticities can then be estimated.

9

The uncompensated or Marshallian own-price and cross-price elasticities are defined as:

eii =γ ii −βiwi

wi

− 1

(6)

eij =γ ij −βiwj

wi

(7)

If income effect5 has to be ignored, then the compensated or Hicksian own-price and cross-price elasticities must be estimated instead. In the Hicksian demand function, prices of other goods and utilities are the ones held constant, not the income. Hicksian own-price and cross-price elasticities, which are derived from the income elasticities and Marshallian price elasticities using the Slutsky equation, are respectively expressed as follows:

eii* = eii +wiηi

eij* = eij +wjηi

(8) and (9)

To compute for income elasticity, expenditure elasticity (ηi ), which is defined as:

ηi =

βiwi

⎛

⎝⎜

⎞

⎠⎟+1

was estimated first because LA/AIDS cannot provide direct estimation of income elasticity. Note that expenditure elasticity could only be used as a proxy for income elasticity if the complete or total food and non-food expenditure data are considered. Since only a portion of the total expenditures was used in this analysis and thus, cannot represent the actual responsiveness of the demand for good on the changes in income, estimation of the following Engel function was done instead to derive income elasticity from expenditure elasticity:

where Y is the total per capita income (PHP/year); α0 , α1, and θ are parameters to be estimated; and other variables are as defined previously. Note that income is used as a proxy for total expenditures since data on all food and non-food expenditures are not available.

From equation (11), income elasticity (ηiY ) of the ith commodity can be computed as:

ηiY =α1+

βiwi

⎛

⎝⎜

⎞

⎠⎟+1

⎡

⎣⎢

⎤

⎦⎥

where the first term is the responsiveness of food expenditure with respect to income change and the second term is the expenditure elasticity of the ith commodity.

5 Income effect is the change in consumption of a good due to change in real income or purchasing power. For instance, a decrease in price of a good increases a consumer’s purchasing power, inducing him to eschew lower-priced or inferior goods for more expensive alternatives. Substitution effect is an effect caused by a relative price change, controlling the change in real income, which induces a consumer to substitute away from relatively more expensive goods, to choosing cheaper alternatives when prices of goods increase.

(10)

(11)

(12)

ln x =α0 +α1 lnY +θ lnP*

Consumption of and Expenditure on Rice and Other Food Commodities

Based on the 2008−2009 SFD, a Filipino spends an average of PHP 6,676/year on the 15 selected food commodities, which is about 22 percent of his annual per capita income (Table 2). Among the commodity groups, cereals, which is largely made

up of rice, posted the largest consumption (112 kg/year) and expenditure (PHP 3,376/year) and comprised the largest share in total food expenditure6 (51%) and income (11%), all in per capita basis. This is expected since rice is a common staple food in the Philippines and for the longest time, accounts for the bulk of the total food intake of Filipinos. Meat, which is the most expensive among the commodity groups, followed in terms of expenditures (PHP 1,885/year), comprising 28 percent of the total food expenditure and 6 percent of the annual income. Each of the rest of the commodities made up less than 7 percent of the total food expenditure.

Table 2. Mean prices, per capita consumption, per capita expenditures, and expenditureshares of selected food commodities

Commodity

Average Buying Price

(PHP/kg)

Volume of Consumption

(kg/year)

Total Expenditure (PHP/year)

Percent Share of Expenditure to

Total Food Expenditure*

Average Annual Income

Cereals 28.63 112.16 3,376.33 50.58 11.13Rice 30.10 110.58 3,335.76 49.97 11.00Corn 27.16 1.58 40.57 0.61 0.13

Root crops 24.62 11.07 190.75 2.86 0.63Sweet potato 18.55 4.71 85.73 1.28 0.28Potato 46.65 0.59 21.40 0.32 0.07Taro 19.75 1.41 23.94 0.36 0.08Cassava 13.51 4.36 59.69 0.89 0.20

Fish 93.46 5.92 554.07 8.30 1.83Milkfish 106.56 2.88 306.92 4.60 1.01Tilapia 80.36 3.04 247.15 3.70 0.81

Meat 139.20 13.37 1,884.96 28.24 6.21Pork 152.37 7.30 1,124.46 16.84 3.71Chicken 126.04 6.07 760.51 11.39 2.51

Fruits 35.59 19.04 431.16 6.46 1.42Banana 19.82 15.11 266.67 3.99 0.88Mango 56.22 2.66 127.67 1.91 0.42Pineapple 30.73 1.27 36.83 0.55 0.12

6 Throughout the paper, “total food expenditure” would refer to the total expenditures for the 15 commodities (see Footnote 2) included in this study; not to the total expenditures for all foods per se.

11

Commodity


(PHP/kg)

Volume of Consumption

(kg/year)


Percent Share of Expenditure to

Total Food Expenditure*

Average Annual Income

Vegetables 34.38 6.10 196.46 2.94 0.65Eggplant 28.40 3.78 105.27 1.58 0.35Bitter gourd 40.36 2.32 91.18 1.37 0.30

Total Food Expenditure (PHP/year/person) 6,675.88 100.00 -Average Annual Income (PHP/year/person) 30,336.84 - 100.00

Source: Basic data was taken from BAS (2009)

Note: Total food expenditure here refers to the total spending on the 15 commodities covered in the study (see Footnote 2).

In many Asian countries, the overall importance of rice as a source of calories has been declining over the decades. For most of the high-income and middle-income Asian countries like Japan (Chern et al. 2003), South Korea (D Gordon Development 2010), Taiwan (Yang 2006), Indonesia (Sudaryanto et al. 2002), and Thailand (Isvilanonda and Kongrithi 2008), rice consumption and expenditure exhibit a declining trend which could be attributed to the countries’ economic growth and urbanization. Particularly, the continuously changing tastes and preferences of Asian consumers towards a more diversified and convenient diet, especially at this time when the various modern retail markets have already boomed in many countries. In addition, the changing economic condition, socio-demographic, and geographic characteristics of consumers may also have contributed to the decline in rice demand in the said countries. Surprisingly, in the Philippines, which is also a middle-income country, an increasing rice demand has been observed since the last two decades. In fact while China, Indonesia, India, Bangladesh, Vietnam, and the Republic of Korea have all been showing a decline in share of rice in the total food consumption and total calorie intake since 1970 or 1980, the Philippines has exhibited an opposite trend (Timmer 2010).

At the national level, a Filipino consumed an average of 111 kg/year of rice which was equivalent to an expenditure of PHP 3,336/year, given its price of PHP 30/kg, in 2008−2009 (Table 2). At the household level, rice consumption and expenditure reached 568 kg/year and PHP 17,123/year, respectively, during the same period. Although the per capita rice consumption in 2008−2009 became lower than the national average during the recent years of 2004−2007 (ranging from 116 kg/year to 126 kg/year), such level of consumption had remained relatively higher compared to the volume of rice consumption in the 1990s (92 kg/year) and early 2000s (106 kg/year) (BAS 2011). The recent unusual decline in per capita rice consumption could have only been led by the sharp increase in world prices of rice in the first quarter of 2008 due to the global rice crisis that occurred in 2007−2008.


12

Per Capita Rice Consumption and Expenditure vis-à-vis Socio-demographic Characteristics

Rice consumption and expenditure differ significantly across socio-demographic characteristics of consumers. The higher income groups and upper classes of the society had less rice consumption compared to the lower income groups and socioeconomic classes (Table 3). In terms of expenditure, the lower to middle income groups paid more for rice than the higher income groups, although this trend is not perfectly smooth due to varying rice prices that consumers paid, which affected their total rice spending.

Table 3. Price, consumption and expenditure of rice by socio-demographic variables

Socio-demographic Variables


(PHP/kg)

Volume of Consumption (kg/year)


Household Per Capita Household Per CapitaPer capita income group (70.15)*** (150.29)*** (2.62)** (43.83)*** (29.02)***

PHP 16,000 and below

28.98 633.34 112.02 18,482.95 3,258.83

PHP 16,001 to PHP 26,000

30.10 548.53 112.85 16,536.29 3,402.03

PHP 26,001 to PHP 96,000

31.59 497.50 109.56 15,886.14 3,457.21

PHP 96,001 to PHP 196,000

32.94 463.41 101.80 16,497.18 3,354.52

More than PHP 196,000

31.47 281.93 80.85 13,145.56 2,503.55

Socioeconomic class (54.20)*** (12.65)*** (173.21)*** (91.08)*** (4.54)**Lower classes 29.87 563.56 112.71 16,703.92 3,281.19Upper classes 30.72 591.09 101.34 19,084.62 3,347.42

Sex (8.49)*** (190.92)*** (4.09)** (136.74)*** (0.01)ns

Male 30.04 584.89 110.98 17,575.67 3,337.70Female 30.48 469.65 109.06 14,409.46 3,324.12

Age group (2.01)ns (112.56)*** (20.58)*** (93.97)*** (16.32)***17 to 21 years old 29.04 356.77 111.26 10,285.95 3,196.8122 to 35 years old 29.98 472.66 104.55 14,209.87 3,151.4936 to 46 years old 29.95 620.88 110.18 18,566.23 3,301.5647 to 55 years old 30.23 632.97 113.52 19,142.40 3,431.2856 to 65 years old 30.18 567.48 113.98 17,158.78 3,439.2266 years old and above 30.38 479.65 112.25 14,625.98 3,403.63

13



(PHP/kg)



Household Per Capita Household Per Capita

Educational attainment1(190.91)*** (81.58)*** (52.10)*** (21.88)*** (19.53)***(263.76)*** (33.52)*** (21.68)*** (1.00)ns (0.54)ns

Did not finish high school

29.49 591.34 114.20 17,508.64 3,380.92

At least high school graduate

30.93 537.73 106.03 16,607.02 3,275.31

No vocational/bachelor’s degree

29.78 575.16 111.65 17,160.78 3,332.31

Has vocational/bachelor’s degree

32.20 525.38 104.67 16,882.61 3,357.73

Type of occupation (210.47)*** (6.87)** (35.33)*** (3.36)* (2.32)***White-collar job 31.80 558.90 106.59 17,768.07 3,386.86Blue-collar job 29.61 581.54 112.33 17,254.78 3,334.06

Average 30.11 568.41 110.71 17,123.06 3,335.76


Note: Values in parentheses are F-ratios. ***, **, and * indicate significance of the variable at 1%, 5%, and 10% probability level, respectively; ns means not significant.

There are two sets of educational attainment variable: (1) high school vs. non-high school graduate and (2) college/vocational vs. non-college/vocational graduate

These results follow the negative trend in global rice consumption with respect to income which has been observed since the early 1960s (Timmer 2010). Such tendency of consumers to consume less rice as they consume more of relatively high-cost quality food items, such as meat and fish, as their income increases, is just a reflection of Engel’s Law as well as of Bennet’s Law, which states that consumption of starchy staples declines in favor of consuming more of other food items as household income increases (Timmer et al. 1983). Corresponding to the relationship between rice consumption and income and socioeconomic class, consumers whose household head is more educated and works in a white-collar job, also consumed less rice than those whose head is less educated and is engaged in a blue-collar job (Table 3). This is expected since such characteristics likely entail a relatively more affluent standard of living for the whole family. Other socio-demographic characteristics that were found significant include gender and age group of household head.


14

Per Capita Rice Consumption and Expenditure vis-à-vis Location Variables

Consumers’ barangay classification and regional location strongly affect the per capita rice consumption in the Philippines. Since the 1970s, rural consumers have been consuming more rice than the urban consumers. In 2008−2009, per capita rice consumption (114 kg/year) and spending (PHP 3,412/year) in the rural areas still remained to be larger than their counterparts in the urban areas (103 kg/year or PHP 3,137/year) (Table 4). This could be due to the availability of a wider range of alternative foods to rice in the urban markets, such as breads, biscuits, noodles, as well as high-cost quality foods which are rich in protein, iron, and vitamins (e.g., fish, meat, fruits, and vegetables).

Table 4. Per capita consumption and expenditure of rice by location variables

Location VariablesAverage

Buying Price (PHP/kg)



Household Per Capita Household Per CapitaBarangay classification (54.20)* (52.95)*** (243.48)*** (27.47)*** (109.62)***

Urban 30.72 533.93 102.47 16,318.04 3,137.30Rural 29.87 581.70 113.88 17,433.21 3,412.22

Region (102.10)*** (30.55)*** (54.86)*** (47.34)*** (69.07)***NCR 30.47 457.51 87.18 13,854.10 2,612.21CAR 29.99 635.08 114.59 18,978.40 3,355.54Ilocos Region 31.48 537.60 107.67 16,891.33 3,382.85Cagayan Valley 29.62 541.83 109.32 15,951.54 3,217.79Central Luzon 29.92 513.90 101.61 15,329.24 3,037.26CALABARZON 31.87 471.77 96.88 14,890.90 3,071.17MIMAROPA 27.89 527.12 110.57 14,602.61 3,083.86Bicol Region 26.52 565.76 105.20 15,120.91 2,796.33Western Visayas 32.97 612.17 117.78 20,134.80 3,884.84Central Visayas 29.41 502.40 106.76 14,793.28 3,162.32Eastern Visayas 29.86 631.24 121.57 18,798.35 3,637.88Zamboanga Peninsula 31.27 580.29 114.57 16,173.79 3,588.08Northern Mindanao 28.37 590.74 120.70 16,856.67 3,428.64Davao Region 30.27 544.99 112.51 16,545.50 3,408.54SOCCSKSARGEN 29.46 580.85 117.59 17,172.82 3,476.50ARMM 33.85 695.66 119.02 23,597.10 4,039.79Caraga 28.31 628.05 119.84 17,722.78 3,406.88

Average 30.11 568.41 110.71 17,123.06 3,335.76



15

Of the 17 regions, the smallest rice consumption and expenditure were noted in NCR (87 kg/year worth PHP 2,645), which is clearly a non-rice producing region (Table 4). On the other hand, the largest rice consumption and expenditure were reported in Eastern Visayas (121 kg/year) and ARMM (PHP 4,040), respectively, where more than 80 percent are rural consumers (Appendix 3).

It should be stressed that in a highly rich and urbanized region like NCR, rice substitutes tend to be more available due to the presence of several modern retail markets, which induce more diversity in the consumers’ food baskets. In fact, Metro Manila recorded the highest per capita consumption of rice substitutes which include bread, noodles, and other cereal products way back in the 1980s and 1990s (Aguilar 2005). Moreover, since Eastern Visayas and ARMM are two of the most impoverished regions in the Philippines, the smaller proportion of rice spending of its residents could also have resulted from consumers shifting away from rice as food staple in favor of the cheaper food alternatives like root crops.

Expenditure Shares of Rice and Other Food Commodities

The breakdown of expenditures of consumers on the 15 food commodities is strongly affected by prices, socio-demographic variables including income, and location variables (see Appendix 4 for the results of ANOVA using F-test). On the

other hand, based on LA/AIDS estimation, all variables, except educational attainment (college graduates vs. non-graduates) significantly affect expenditure shares of rice and most of the other commodities.

Except for the prices of milkfish, pork, and bitter gourd, own-prices of all commodities significantly affect their respective expenditures and expenditure shares (Table 5). For the majority of the commodities with significant coefficients, a positive relationship is observed between expenditure share and own-price. This is consistent with the expectation since expenditure is a function of price and volume of consumption. Hence, when commodity price rises, its expenditure also increases and so, too, its expenditure share, provided that other factors are held constant.

For prices of other commodities, it is notable that the price of rice is the most influential factor affecting expenditure shares of all commodities except potato, milkfish, tilapia, and mango. Correspondingly, expenditure share of rice is the most affected by the prices of other commodities excluding the four commodities mentioned above. Varied associations between expenditure shares and cross-prices are exhibited. What is apparent, however, is that except for potato and milkfish, expenditure shares of all commodities decrease as price of rice increases and conversely, expenditure share of rice increases as prices of the other commodities except potato and milkfish, fall (Table 5).

Tabl

e 5.

LA

/AID

S p

aram

eter

est

imat

es o

f pric

e va

riabl

es, P

hilip

pine

s, 2

008-

2009

Expe

nditu

re

Shar

e (w

i)In

terc

ept

Pric

e Va

riabl

es (l

n P j)

Rice

Corn

Sweet potato

Potato

Taro

Cassava

Milkfish

Tilapia

Pork

Chicken

Banana

Mango

Pineapple

Eggplant

Bitter gourd

Ric

e2.

6166

0.18

58−0

.007

4−0

.011

70.

0013

−0.0

086

−0.0

097

0.00

57−0

.001

7−0

.052

5−0

.042

3−0

.030

5−0

.008

1−0

.004

4−0

.010

0−0

.006

0

Cor

n0.

0504

−0.0

074

0.03

77−0

.004

30.

0019

−0.0

064

−0.0

090

−0.0

010

0.00

64−0

.021

50.

0128

−0.0

029

−0.0

072

−0.0

040

0.00

010.

0047

Sw

eet p

otat

o0.

0480

−0.0

117

−0.0

043

0.01

45−0

.000

9−0

.001

3−0

.007

00.

0060

0.00

55−0

.003

20.

0001

0.00

20−0

.002

2−0

.001

30.

0013

0.00

24

Pot

ato

−0.0

293

0.00

130.

0019

−0.0

009

−0.0

136

0.00

50−0

.001

30.

0083

0.00

35−0

.005

0−0

.003

90.

0015

−0.0

002

−0.0

008

−0.0

017

0.00

58

Taro

−0.0

485

−0.0

086

−0.0

064

−0.0

013

0.00

50−0

.002

4−0

.005

10.

0174

0.01

14−0

.005

3−0

.001

0−0

.006

00.

0064

−0.0

017

−0.0

025

0.00

02

Cas

sava

0.07

95−0

.009

7−0

.009

0−0

.007

0−0

.001

3−0

.005

10.

0320

0.00

35−0

.003

3−0

.007

40.

0134

−0.0

013

−0.0

029

−0.0

017

−0.0

021

0.00

19

Milk

fish

−0.2

271

0.00

57−0

.001

00.

0060

0.00

830.

0174

0.00

35−0

.006

40.

0014

−0.0

052

−0.0

331

0.01

19−0

.010

00.

0057

0.00

62−0

.010

2

Tila

pia

−0.1

361

−0.0

017

0.00

640.

0055

0.00

350.

0114

−0.0

033

0.00

14−0

.049

90.

0442

−0.0

203

0.00

86−0

.006

40.

0059

0.00

07−0

.006

2

Por

k−0

.731

4−0

.052

5−0

.021

5−0

.003

2−0

.005

0−0

.005

3−0

.007

4−0

.005

20.

0442

0.02

380.

0493

0.00

440.

0046

−0.0

052

−0.0

087

−0.0

123

Chi

cken

−0.6

435

−0.0

423

0.01

280.

0001

−0.0

039

−0.0

010

0.01

34−0

.033

1−0

.020

30.

0493

−0.0

314

0.00

880.

0061

0.00

410.

0067

0.03

07

Ban

ana

0.06

05−0

.030

5−0

.002

90.

0020

0.00

15−0

.006

0−0

.001

30.

0119

0.00

860.

0044

0.00

880.

0034

0.00

36−0

.003

1−0

.002

70.

0024

Man

go−0

.005

2−0

.008

1−0

.007

2−0

.002

2−0

.000

20.

0064

−0.0

029

−0.0

100

−0.0

064

0.00

460.

0061

0.00

360.

0231

−0.0

041

−0.0

009

−0.0

019

Pin

eapp

le−0

.010

5−0

.004

4−0

.004

0−0

.001

3−0

.000

8−0

.001

7−0

.001

70.

0057

0.00

59−0

.005

20.

0041

−0.0

031

−0.0

041

0.00

95−0

.000

40.

0015

Egg

plan

t0.

0308

−0.0

100

0.00

010.

0013

−0.0

017

−0.0

025

−0.0

021

0.00

620.

0007

−0.0

087

0.00

67−0

.002

7−0

.000

9−0

.000

40.

0154

−0.0

014

Bitt

er g

ourd

−0.0

315

−0.0

060

0.00

470.

0024

0.00

580.

0002

0.00

19−0

.010

2−0

.006

2−0

.012

30.

0307

0.00

24−0

.001

90.

0015

−0.0

014

−0.0

008

Sou

rce:

Bas

ic d

ata

was

take

n fro

m B

AS

(200

9)N

ote:

The

sha

ded

estim

ates

indi

cate

sig

nific

ance

at 1

%, 5

%, a

nd 1

0% p

roba

bilit

y le

vels

, as

show

n be

low

.

sign

ifica

nt a

t 1%

pro

babi

lity

leve

lsi

gnifi

cant

at 5

% p

roba

bilit

y le

vel

sign

ifica

nt a

t 10%

pro

babi

lity

leve

lno

t sig

nific

ant

18

Expenditure Shares of Rice and Other Food Commodities vis-à-vis Socio-demographic Characteristics

Besides commodity price, income is a very important factor affecting a person’s food spending. It is evident that expenditure shares of rice as well as root crops significantly decrease as the income group of consumers becomes higher (Figure 1). In contrast, shares of meat and fish, which are the relatively expensive calorie sources, increase with income.

Figure 1. Per capita expenditure shares of selected food commodities by income group

In accordance with these results, LA/AIDS parameters reveal that annual per capita income significantly affects the expenditure shares of the majority of the commodities. As the consumer’s income increases, expenditure shares of rice and other basic energy foods such as corn and root crops (excluding potato) decrease, possibly because consumers tend to allocate their extra income to buying additional “luxury” foods like meat and fish (Table 6). This trend, which is consistent with the food expenditure pattern that has been observed since the 1980s (Aguilar 2005), follows Bennet’s Law. It should be stressed that cereals and root crops are relatively inexpensive sources of calories, and hence, play a more important role in the diet of low-income consumers. For the relatively richer consumers, these commodities tend to become more inferior.

RiceCornSweet potatoPotatoTaroCassavaMilkfishTilapiaPorkChickenBananaMangoPineappleEggplantBitter gourd

PHP 16,000and below

PHP 16,001to PHP 26,000

PHP 26,001to PHP 96,000

PHP 96,001to PHP 196,000

more thanPHP 196,000

Income Group (PHP/year)

Exp

endi

ture

Sha

re

100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

19

Table 6. LA/AIDS parameter estimates of socio-demographic variables, by commodity

Expenditure Share (wi)

Socio-demographic Variables (Dk)

income_ soc gender age_ educ1 educ2 occu

Rice −0.0206 0.0371 0.0487 0.0280 −0.0280 −0.0046 −0.0110

Corn −0.0018 −0.0028 0.0019 0.0006 −0.0035 0.0008 0.0003

Sweet potato −0.0008 0.0003 0.0045 0.0023 −0.0032 −0.0003 −0.0004

Potato 0.0002 −0.0014 0.0001 −0.0002 0.0004 0.0011 0.0003

Taro −0.0008 −0.0005 0.0012 0.0002 −0.0013 −0.0001 −0.0006

Cassava −0.0007 0.0003 0.0055 0.0035 −0.0029 0.0010 −0.0013

Milkfish 0.0035 −0.0067 −0.0077 −0.0045 0.0044 −0.0012 0.0026

Tilapia −0.0016 −0.0025 −0.0070 −0.0046 −0.0004 −0.0051 0.0007

Pork 0.0091 −0.0169 −0.0272 −0.0118 0.0241 0.0015 0.0030

Chicken 0.0070 −0.0100 −0.0207 −0.0097 0.0062 0.0030 0.0065

Banana 0.0000 −0.0015 0.0045 −0.0014 0.0006 0.0012 −0.0013

Mango 0.0031 0.0012 −0.0029 −0.0022 0.0039 0.0025 0.0030

Pineapple 0.0008 0.0005 −0.0007 −0.0001 −0.0004 0.0002 0.0002

Eggplant 0.0010 0.0021 −0.0013 −0.0007 −0.0004 −0.0001 −0.0016

Bitter gourd 0.0016 −0.0003 0.0003 0.0007 0.0006 −0.0000 −0.0004

Socioeconomic class is another significant variable affecting food expenditure shares of all commodities. Akin to the trend of food expenditure shares by income group, the shares of rice, corn, banana, and root crops were larger for the lower classes of the society compared to the upper classes, while the opposite was observed for the expenditure shares of meat, fish, and some fruits (Figure 2).

Source: Basic data was taken from BAS (2009)Note: The shaded estimates indicate significance at 1%, 5%, and 10% probability levels, as shown below.

significant at 1% probability level significant at 5% probability level

significant at 10% probability level not significant

20

Figure 2. Per capita expenditure shares of selected food commodities by socioeconomic class

This is also verified by the results from LA/AIDS with socio-demographic translating method, which indicates that as consumers become more affluent, the expenditure share of rice in total food spending significantly decreases, while the shares of meat and fish increase (Table 6). However, socioeconomic class does not appear to be as highly significant as the income variable, as reflected in the fewer parameters that are found significant.

The breakdown of food expenditures vis-à-vis socioeconomic class could be largely explained in relation to income. However, some psychological factors may have also influenced the consumers’ expenditure decisions, especially those of the upper class. According to Veblen (1899), some of the upper classes in the society measure the utility of a commodity solely by price. The greater the price of the commodity, the more they spend on that commodity, mainly for the purpose of displaying their wealth in order to gain pride and social distinction. This type of good, which is referred to as “prestigious good” is a luxury commodity which has a prestige value for the richer section of the society. The consumption of prestigious goods is known as “conspicuous consumption.”

Other factors that significantly affect the per capita expenditure shares of consumers for the selected commodities, particularly rice, include sex, age group, educational attainment, and type of occupation of the household head.


21

Expenditure Shares of Rice and Other Food Commodities vis-à-vis Location Characteristics

Per capita food expenditure shares by barangay classification

The breakdown of food expenditures varies significantly between rural and urban consumers. Rice comprises a larger portion of a rural consumer’s total food expenditure than that of an urban consumer (Figure 3 and Appendix 5). Likewise, rural consumers spend a slightly greater proportion of their budget on corn, root crops (excluding potato), banana, pineapple, and eggplant than those of the urban consumers. Conversely, expenditure shares of meat and fish of rural consumers are smaller compared to those of their urban counterparts. Based on the estimates of ANOVA and LA/AIDS, the difference in expenditure shares of all commodities (except potato, tilapia, and bitter gourd based on LA/AIDS) appears to be highly significant between urban and rural areas (Appendix 4 and Appendix 5).

Figure 3. Per capita expenditure shares of selected food commodities by barangay classification

Part of the reason for the contrasting food expenditure shares between urban and rural consumers could be the varying tastes and lifestyles of the consumers due to the differences in their income levels, the society that they live in, as well as the existing market environment in the urban and rural areas. Since modern retail markets such as hypermarkets, supermarkets, hotels and restaurants, and fast food chains are more prominent in the urban areas, a wider range of food choices becomes more available to the consumers. This alters consumers’ food habits by inducing them to shift away from rice in favor of other food commodities. Moreover, the higher commodity prices


22

in the urban markets compared to rural markets may also have partly augmented the expenditures on luxury items, such as meat and fish, and their corresponding shares in total food expenditure.

Per capita food expenditure shares by region

Across different regional locations of the consumers, spending on food commodities also varies widely. Consumers in the regions from Luzon (or from the northern part of the Philippines), except MIMAROPA, have lower expenditure shares than those in the Visayas and Mindanao regions (southern part of the country) (Figure 4).

Figure 4. Per capita expenditure shares of selected food commodities by region

This is expected since, as revealed in the study, rice consumption and expenditure in the regions of Luzon are smaller than in the Visayas and Mindanao regions, as they allocate their budget to a wider range of foods. Moreover, since many of the regions in Luzon, especially NCR and CALABARZON are highly urbanized and rich, more food choices are readily available due to the presence of many modern retail markets.


Exp

endi

ture

Sha

re

100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

NC

R

CA

R

Iloco

s R

egio

n

Cag

ayan

Val

ley

Cen

tral L

uzon

CA

LAB

AR

ZON

MIM

AR

OPA

Bic

ol R

egio

n

Wes

tern

Vis

ayas

Cen

tral V

isay

as

Eas

tern

Vis

ayas

Zam

boan

ga P

enin

sula

Nor

ther

n M

inda

nao

Dav

ao R

egio

n

SO

CC

KS

AR

GE

N

AR

MM

Car

aga

Region

23

Among all the regions, the country’s second poorest region—ARMM, where most of the respondents are rural consumers (Appendix 3)—posts the largest share of rice and the smallest combined share of meat and fish in their total food expenditure. Conversely, NCR, which is the country’s richest region and where all consumers are from urban areas, registers the smallest rice expenditure share and the largest meat and fish expenditure shares (Figure 4).

Using LA/AIDS, the food expenditure shares were also examined to find out whether or not there is a significant difference in the breakdown of spending if a consumer lives in one region or not. Generally, all region dummies were found to be highly significant in the expenditure share equations of the commodities, except the region dummies for CAR, Cagayan Valley, and SOCCSKSARGEN (Appendix 5). This means that consumers’ food expenditure breakdown in the three regions mentioned does not vary much from that in other regions

Demand Elasticities of Rice and Other Food Commodities

Prices and income are the most important factors affecting consumers’ demand for rice. To get a better understanding of the magnitude of their effects, income elasticities, own-price elasticities, and cross-price elasticities were estimated

at sample means and based on the estimated parameters of the corresponding commodities from LA/AIDS. It should be stressed that the uncompensated (Marshallian) and the compensated (Hicksian) demand functions provide different own-price elasticity estimates for rice.

Income Elasticities

In high-income Asian countries like Japan, China, Taiwan, and South Korea, rice has already become an inferior good after reaching the income threshold at which consumers start to substitute rice with higher quality and more varied foods (Hossain 1997). But in most of the developing and/or newly-industrialized Asian countries like Thailand, India, and the Philippines, rice remains to be a necessity good which thus, has an inelastic demand. This suggests that a percentage rise in the total food expenditure and income would effect a less than 1 percent change in rice demand (BAS 2009).

In the Philippines, rice remains to be a normal and a necessity good for consumers, which only indicates the continuing importance of rice in the diet of Filipinos. It has an inelastic demand with respect to changes in income, which suggests that a percentage increase in a consumer’s income will effect less than a percentage rise in his quantity demanded for rice. The income elasticity of rice is estimated at 0.47 (Table 7), which means that a percentage increase in a consumer’s income will lead to 0.47 percent increase in his quantity demanded for rice. This is close to the recent estimate (0.51) of Abad Santos et al. (2010) using the 2003 FIES data as well as to the estimates in most of the past studies since the 1960s (Duff 1991; Canlas 1983; Goldman and Ranade 1976; San Juan 1978; Huang and David 1993; Barker et al. 1985; Ingco 1991; Llanto 1996; Chen et al. 2009), which range from 0.25 to 0.66, indicating an inelastic demand for rice with respect to income changes. Corn, sweet potato, cassava, banana, and eggplant are also found to be necessity goods (Table 7), while the rest of the commodities have income elasticities greater than one, implying that they are luxury goods with highly responsive demand to income changes.

25

Table 7. Income and own-price elasticities of selected food commodities

CommodityMean Budget

Share (%)Income

ElasticityOwn-Price Elasticity

Marshallian HicksianRice 49.97 0.4722 −0.5046 −0.2650Corn 0.61 0.4579 −0.9615 −0.9583Sweet potato 1.28 0.5742 −0.9812 −0.9734Potato 0.32 2.1433 −1.0123 −1.0069Taro 0.36 1.7972 −1.0049 −0.9981Cassava 0.89 0.0772 −0.9539 −0.9534Milkfish 4.60 2.1445 −1.0261 −0.9462Tilapia 3.70 1.7593 −1.0557 −1.0010Pork 16.84 1.9055 −1.0359 −0.7554Chicken 11.39 2.1098 −1.1385 −0.9302Banana 3.99 0.9654 −0.9939 −0.9550Mango 1.91 1.4772 −0.9940 −0.9650Pineapple 0.55 1.9283 −1.0024 −0.9923Eggplant 1.58 0.9844 −0.9870 −0.9716Bitter gourd 1.37 1.5261 −1.0230 −1.0030


Own-Price Elasticities

All estimates of the own-price elasticities of rice carry the expected negative sign. On the other hand, all the absolute values of the elasticities of the rest of the commodities are close to unity, suggesting high responsiveness of its quantity demanded to their own price changes. Rice has an inelastic demand with respect to own-price changes as indicated by its Marshallian own-price elasticity of −0.50 (Table 7). This suggests that a percentage increase in the price of rice will lead to 0.50 percent decline in quantity demanded for rice as a result of both substitution effect and income effect (see Footnote 5).

Consumers’ quantity demanded for rice tends to be even more inelastic if after a price increase, their real income does not fall. Such a trend is true for all normal goods. In breaking down the substitution and income effects of a rice price change, the results show that if consumers are compensated to have the same purchasing power after a 1 percent price increase, their rice consumption will only decline by 0.26 percent as indicated by the Hicksian own-price elasticity (Table 7) as compared to the higher Marshallian elasticity, which involves quite a strong income effect.7

7 Marshallian own-price elasticity (−0.50) is the sum of substitution effect (−0.26) and income effect (−0.24), while Hicksian own-price elasticity (−0.26) is just the substitution effect.

26

Unlike for rice and pork, the discrepancies between the Marshallian and Hicksian estimates for the rest of the commodities are not very significant. This implies that the weight of the income effect of the own-price change on the quantity demanded for these commodities is not very strong. It is the substitution effect, which largely explains the increase in its quantity demanded due to the fall in commodity prices.

The less responsiveness of quantity demanded for rice to its own-price is expected since rice mainly comprises the food baskets of the Filipinos, especially of the poor; hence, it cannot be easily dispensed with from their daily meals. The estimated own-price elasticity of rice is within the range of most of the estimates reported in the earlier studies, which is between −0.20 and −0.63 (Canlas 1983; Ferrer-Guldager 1977; Kunkel et al. 1978; Bouis 1982; San Juan 1978; Bouis 1989; Barker et al. 1985; Ingco 1991; Abad Santos et al. 2010). However, there is no clear indication of whether rice demand has already become more inelastic than before because previous demand studies differ in terms of the type of data and the methodology used in the analysis. Thus estimates cannot be simply compared based on their values.

Demand Elasticities of Rice vis-à-vis Socio-demographic Characteristics

From LA/AIDS with the socio-demographic translating method, the socio-demographic characteristics, except household head’s tertiary educational attainment dummies, are found to affect strongly rice expenditure shares. Thus, it is also important to analyze the demand elasticities of rice vis-à-vis these significant socio-demographic variables.

The demand elasticities of rice by socio-demographic variables are all found to be less than one. This indicates that, regardless of the characteristics of the consumers, quantity demanded for rice is inelastic to the changes in both consumer’s incomes and price of rice. However, the magnitude of the consumers’ demand responsiveness differs across the categories of the socio-demographic variables.

By income group, quantity demanded for rice tends to be less responsive to income changes as consumers become richer (Table 8). This is consistent with the previous rice demand studies (Quisumbing 1986; Bouis 1989) in the Philippines. The same is true for income elasticity by socioeconomic class. The upper classes, which often have relatively high income and low rice consumption and expenditure share, have lower income elasticity than the lower classes. An obvious reason for this trend could be the fact that, since richer consumers, unlike the poor, have more purchasing power to buy foods because of their greater income, a change in their income is less likely to alter their food consumption patterns, especially for rice, which they do not consume as much as the poorer sector of the economy does.

27

Table 8. Demand elasticities of rice by socio-demographic variables

Socio-demographic Variables Income ElasticityOwn-Price Elasticity

Marshallian HicksianPer capita income group

PHP 16,000 and below 0.5785 −0.5019 −0.1655

PHP 16,001 to PHP 26,000 0.5087 −0.4883 −0.2434

PHP 26,001 to PHP 96,000 0.4830 −0.4301 −0.3570

PHP 96,001 to PHP 196,000 0.4468 −0.5554 −0.4143

More than PHP 196,000 0.4448 −0.7497 −0.5681

Socioeconomic classLower classes 0.4877 −0.4814 −0.2235Upper classes 0.4520 −0.5159 −0.3126

SexMale 0.4775 −0.5040 −0.2592Female 0.4502 −0.5154 −0.3008

Age group17 to 21 years old 0.5889 −0.5451 −0.224322 to 35 years old 0.4831 −0.4278 −0.179036 to 46 years old 0.4705 −0.5087 −0.265847 to 55 years old 0.4916 −0.5390 −0.290956 to 65 years old 0.4538 −0.5177 −0.292666 years old and above 0.4168 −0.5139 −0.3100

Educational attainment

Did not finish high school 0.5418 −0.4987 −0.1939At least high school graduate 0.4438 −0.5466 −0.3494

Type of occupationWhite-collar jobs 0.4996 −0.4894 −0.2226Blue-collar jobs 0.4817 −0.5496 −0.3348

Average 0.4722 -0.2637 -0.2650

Source: Basic data was taken from BAS (2009)Note: The demand elasticities vis-à-vis educational attainment (vocational/college graduates vs. non-graduates) are not presented anymore since this is not a significant factor of per capita rice expenditure share.

Own-price elasticities of rice by income group generally show an opposite trend. The quantity demanded for rice of the lower income groups is less responsive to a rice price change compared to that of the higher income groups, regardless of whether the own-price elasticity is uncompensated or not (Table 8). The increasing trend in own-price elasticities as income increases is more distinct when the effect of price change on the purchasing power of consumers is isolated, as shown in the Hicksian estimates. The income effect of a price change is expected to be greater for the low income groups than for the high income groups.

28

The same trend is observed for own-price elasticities in terms of socioeconomic class. The quantity demanded for rice by the lower classes is less responsive to own-price changes compared to the quantity demanded by the upper classes.

Overall, the results suggest that poor consumers, who are largely dependent on rice as a source of calories, would be less affected by the changes in price of rice because rice is considered a very basic food constituting their diet. Hence, they may not be willing to replace it with other commodities, even if the rise in price is compensated (i.e., through price subsidies). Besides, the poor may not have the capability to buy and shift to other food commodities, unlike the rich who have more purchasing power and whose food baskets are more diversified.

The Hicksian estimates also confirm the tendency of the richer consumers to be more willing to substitute away from rice in favor of other food alternatives when the price of rice increases while their purchasing power is kept constant. As the diet of richer consumers is often more diversified and as rice tends to be a relatively less important item in their food baskets, it becomes easier for them to replace rice with other commodities. The tendency of the upper classes to have “conspicuous consumption” of rice, which makes them very concerned with the prestige that they can gain from the value or price of the type of rice that they will buy (i.e., preferring brown rice to government-subsidized rice or the “NFA rice”), could also be another reason why they are more responsive to rice price changes.

Income and own-price elasticities of rice also vary across different characteristics of household heads. By sex and age group of household head, consumers whose household is headed by a female and an older person tend to have less responsive demand for rice with respect to income changes (Table 8). This is plausible since rice appears to be a relatively less important food for them and their food baskets are more diverse compared to consumers with male and younger household heads, based on the food expenditure shares (see Table 6). Hence, a change in the consumer’s income will induce allocation of their additional budget to more types of foods other than rice compared to consumers with male and older household heads, who are more “rice-dependent.”

While the trend of own-price elasticities by age group is not clear, the own-price elasticity estimates by sex suggest that consumers with female household heads have more responsive quantity demanded for rice with respect to rice price changes compared to those with male heads. The greater willingness of the consumers under the female category to adjust their rice consumption when the price of rice changes could be attributed to the fact that female-headed households have more diverse diets and therefore have more types of substitutable foods to select from. Moreover, unlike consumers with female heads, consumers under the male category may have already been consuming their required volume of rice, so that there is no more room or it is not rational anymore to increase it further even if it becomes cheaper.

29

By educational attainment and occupation type, consumers with a household head who did not graduate from high school and has a blue-collar job have a more responsive demand for rice with respect to income changes than those whose heads graduated from high school and have a white-collar job. However, they have less responsive quantity demand for rice with respect to rice price changes.

It is apparent that the demand elasticity trend vis-à-vis educational attainment and occupation type is similar to the trend in terms of income group and socioeconomic class, primarily because educational attainment and occupation often reflect economic status. A more educated person with a white-collar job heading his household is likely to have a more affluent economic status than someone who is less educated and works in a blue-collar occupation. Thus, the reason behind the demand elasticity trend observed here could be largely income-related.

Demand Elasticities of Rice vis-à-vis Location Characteristics

Like socio-demographic variables, the location of consumers in terms of barangay classification and geographic region significantly affects the responsiveness of the demand for rice to changes in income and price of rice. Since the 1970s, the rice demand of rural consumers has been known to be more sensitive to income changes but less sensitive to rice price changes (Kunkel et al. 1972; Goldman and Ranade 1976; Bouis 1989; Llanto 1996). The same demand pattern was observed in the Philippines from 2008 to 2009. Rural consumers have higher income elasticities and lower own-price elasticities than the urban consumers (Table 9). Note that this corresponds to the pattern observed in demand elasticities by income group, socioeconomic class, educational attainment, and occupation type. Given that incomes of rural consumers were relatively smaller than their urban counterparts (Appendix 2), rural consumers, whose purchasing power is often limited, tend to be more responsive to any change in their income. For instance, an increase in income of a rural consumer may induce more consumption of rice, which they consider the most important part of their diet, as they become capable of purchasing an additional amount of this staple food.

In terms of own-price elasticities, since the volume of rice consumption and its expenditure share were both larger for rural consumers compared to their urban counterparts, the greater dependence on rice as a source of energy of the rural consumers may be the main reason why their quantity demanded for rice is less sensitive to any adjustment in the price of rice. Besides, because urban consumers tend to have greater variety in their food consumption and more purchasing power than the rural consumers, they tend to be more versatile in shifting to other food commodities whenever there is high rice price inflation, and hence are more responsive to a price change compared to rural consumers.

30

Table 9. Demand elasticities of rice by location variables

Location Variables Income ElasticityOwn-price Elasticity

Marshallian HicksianBarangay classification

Urban 0.4182 −0.5774 −0.3919Rural 0.5076 −0.4983 −0.2263

RegionNCR 0.3823 −0.6487 −0.5060CAR 0.3823 −0.5757 −0.4092Ilocos Region 0.3960 −0.6527 −0.4696Cagayan Valley 0.4780 −0.7561 −0.5503Central Luzon 0.4696 −0.7261 −0.5482CALABARZON 0.4736 −0.8897 −0.6883MIMAROPA 0.5196 −0.5614 −0.2662Bicol Region 0.4824 −0.5131 −0.2600Western Visayas 0.4620 −0.5040 −0.2357Central Visayas 0.5632 −0.5323 −0.2459Eastern Visayas 0.5019 −0.4926 −0.1848Zamboanga Peninsula 0.6817 −0.7517 −0.3436Northern Mindanao 0.6056 −0.5875 −0.2790

Davao Region 0.5349 −0.5821 −0.3008SOCCSKSARGEN 0.5230 −0.4813 −0.1922ARMM 0.8808 −0.5261 −0.0904Caraga 0.4885 −0.5053 −0.2227

Average 0.4722 -0.2637 -0.2650


By region, the pattern of demand elasticities matches the trend of elasticities by income group, socioeconomic class, and other income-related socio-demographic characteristics. Rice is considered a necessity good in the whole country, wherein ARMM posts the highest income elasticity (most responsive to income changes) while NCR and CAR post the lowest (least responsive to income changes) (Table 9). This could be explained by the fact that ARMM is a very poor region, where people have very limited purchasing power and are highly dependent on rice as a source of calories, while NCR and CAR are among the country’s richest regions, which had the least rice consumption and spending. Therefore, demand for rice of consumers from ARMM is expected to be more sensitive to their income changes than those from NCR.

With respect to changes in price of rice, the rich region of CALABARZON has the most responsive quantity demanded for rice while Eastern Visayas and ARMM, which are

31

among the country’s poorest regions, have the least responsive quantity demanded for rice, based on the Marshallian and the Hicksian own-price elasticity, respectively.

It is remarkable that except for the regions of MIMAROPA and Bicol, Luzon regions post relatively higher Hicksian own-price elasticities compared to the regions from the Visayas and Mindanao, primarily because most of the regions with the lowest poverty incidence in the country come from Luzon while those with the highest poverty incidence are mostly from the Visayas and Mindanao.

Cross-Price Elasticities

The Marshallian and Hicksian cross-price elasticities are summarized in Tables 10 and 11. Each elasticity matrix is presented with own-price elasticities as diagonal elements and cross-price elasticities as off-diagonal elements. Cross-price elasticities of rice with respect to prices of other commodities are read vertically or by column.

Marshallian cross-price elasticities

The positive Marshallian cross-price elasticities indicate that corn, sweet potato, and cassava are gross substitutes for rice. Consequently, the rice demand is expected to rise as its substitutes become more expensive relative to rice. Particularly, rice shows the strongest substitution response for cassava as it has the highest cross-price elasticities of 0.51 (Table 10). This suggests that a percentage increase in the price of cassava will induce a 0.51 percent rise in the quantity demanded for rice. Corn is the second most substitutable crop to rice, with a cross-price elasticity of 0.31.

Consistent with the expectation, all the substitutes identified are similar to rice, as these are also a good source of carbohydrates and a common staple food of the poor since they are relatively cheap. However, it should be noted that elasticities of rice with respect to prices of its substitutes are quite inelastic—meaning, the percentage increase in quantity demanded for rice is not greater than the percentage change in the price of the other commodity. Moreover, while quantity demanded for rice responds positively to price changes of its substitutes, all of the commodities are not very responsive to price changes of rice since their cross-price elasticities with respect to rice are all less than 0.10.

On the other hand, potato, taro, fish, meat, fruits, and vegetables are considered as rice complements as denoted by the negative Marshallian cross-price elasticities. All the cross-price elasticities of rice, with respect to prices of other commodities, are less than 1.00 in absolute terms. Thus, a percentage increase in the prices of most of the rice complements would decrease the quantity demanded for rice by less than 1 percent. Potato, chicken, and milkfish are among the most complementary commodities to rice, having an estimated cross-price elasticity of nearly 0.60.

32

Note that the rice complements are the commodities which are usually prepared in different viands or as main ingredients in many Filipino dishes and are often served with rice, with the exception of mangoes and pineapple, which are served as desserts or drinks. Most of these rice complements also supplement other commodities as they are usually used together as ingredients in a single cuisine or eaten side-by-side during meals.

Tabl

e 10

. Mar

shal

lian

(unc

ompe

nsat

ed) p

rice

elas

ticiti

es o

f sel

ecte

d fo

od c

omm

oditi

es

With

Res

pect

to

Pric

e of

(j)

Dem

and

Elas

ticity

of (

i)

Rice

Corn

Sweet potato

Potato

Taro

Cassava

Milkfish

Tilapia

Pork

Chicken

Banana

Mango

Pineapple

Eggplant

Bitter gourd

Ric

e−0

.504

60.

0032

−0.0

047

0.00

34−0

.008

40.

0023

0.03

440.

0092

−0.0

094

0.04

52−0

.016

80.

0096

−0.0

016

−0.0

004

−0.0

016

Cor

n0.

3115

−0.9

615

0.01

70−0

.001

1−0

.000

9−0

.002

30.

0387

−0.0

088

0.07

710.

0576

0.01

570.

0016

0.01

280.

0027

0.01

41

Sw

eet p

otat

o0.

2365

0.01

22−0

.981

20.

0010

0.00

090.

0088

0.01

790.

0219

0.05

210.

0473

0.01

330.

0086

0.00

010.

0094

0.00

91

Pot

ato

−0.6

027

−0.0

111

−0.0

162

−1.0

123

−0.0

035

−0.0

085

−0.0

405

−0.0

350

−0.1

663

−0.1

094

−0.0

448

−0.0

244

−0.0

039

−0.0

201

−0.0

125

Taro

−0.4

270

−0.0

089

−0.0

119

−0.0

012

−1.0

049

−0.0

084

−0.0

131

−0.0

143

−0.1

203

−0.0

784

−0.0

369

−0.0

128

−0.0

042

−0.0

136

−0.0

091

Cas

sava

0.51

57−0

.001

50.

0175

0.00

650.

0038

−0.9

539

0.04

210.

0136

0.10

790.

1215

0.03

760.

0175

0.00

640.

0124

0.00

79

Milk

fish

−0.5

922

0.00

85−0

.015

5−0

.001

30.

0116

−0.0

065

−1.0

261

−0.0

282

−0.2

070

−0.1

634

−0.0

165

−0.0

404

−0.0

133

−0.0

021

−0.0

199

Tila

pia

−0.4

043

−0.0

325

−0.0

031

−0.0

017

0.00

71−0

.024

9−0

.020

5−1

.055

7−0

.032

6−0

.126

9−0

.007

50.

0124

−0.0

016

−0.0

181

−0.0

174

Por

k−0

.568

6−0

.015

5−0

.029

1−0

.002

1−0

.009

1−0

.044

9−0

.073

40.

0486

−1.0

359

−0.0

232

−0.0

274

−0.0

366

−0.0

223

−0.0

170

−0.0

167

Chi

cken

−0.5

975

−0.0

082

−0.0

142

−0.0

007

−0.0

058

0.01

36−0

.092

6−0

.088

1−0

.097

0−1

.138

5−0

.042

4−0

.018

80.

0077

−0.0

110

0.01

58

Ban

ana

−0.0

038

−0.0

077

−0.0

048

0.00

16−0

.004

8−0

.001

40.

0323

0.02

500.

0190

0.00

92−0

.993

9−0

.003

7−0

.003

2−0

.000

60.

0035

Man

go−0

.243

9−0

.013

3−0

.007

0−0

.003

30.

0009

−0.0

066

−0.0

350

0.01

29−0

.085

7−0

.041

9−0

.023

6−0

.994

00.

0040

−0.0

109

0.00

23

Pin

eapp

le−0

.492

30.

0029

−0.0

153

−0.0

002

−0.0

036

−0.0

088

−0.0

413

−0.0

261

−0.1

519

−0.0

765

−0.0

409

−0.0

116

−1.0

024

−0.0

123

−0.0

160

Egg

plan

t−0

.016

5−0

.006

20.

0027

−0.0

020

−0.0

010

−0.0

026

0.01

77−0

.004

80.

0043

0.01

120.

0003

−0.0

027

0.00

24−0

.987

0−0

.001

3

Bitt

er g

ourd

−0.2

779

0.00

27−0

.004

10.

0011

−0.0

008

−0.0

104

−0.0

237

−0.0

234

−0.0

791

−0.0

194

−0.0

180

−0.0

016

−0.0

066

−0.0

099

−1.0

230

Sou

rce:

Bas

ic d

ata

was

take

n fro

m B

AS

(200

9)

34

Hicksian cross-price elasticities

While only three Marshallian cross-price elasticities of rice carry positive signs, all the Hicksian cross-price elasticities are positive, indicating that the income effect outweighs the substitution effect in 11 out of 14 commodity equations (Table 11). Based on the Hicksian cross-price elasticities, which only considered the substitution effect of a price change, all commodities are potential substitutes for rice. Among the commodities, milkfish is noted as the most substitutable to rice (0.56) while pork is the least substitutable (0.45). This only shows that if the limited income is not an issue to the consumers, they could be more willing to substitute more types of commodities to rice, besides the usual cheap substitutes like corn and root crops. Similar to the Marshallian cross-price elasticities for the rice substitutes, the cross-price effects between rice and other commodities still remain to be inelastic as reflected in the absolute values of its cross-price elasticities, which are less than 1.00.

Most of the elasticities of rice with respect to prices of other commodities are consistent with the estimates of Abad Santos et al. (2010) which also identified corn, cassava, and other root crops as main rice substitutes, and vegetables, pork, and fish as its complements based on the Marshallian cross-price elasticities. Similarly, all the commodities become rice substitutes when the Hicksian cross-price elasticities are estimated. The cross-price elasticity estimates in this study are generally higher compared to the estimates of Abad Santos et al. (2010). But note that this study used the consumption data for 2008−2009 while the other study used an expenditure data for 2003. Thus, the degree of responsiveness of rice demand with respect to change in prices of other commodities may have already changed over time.

Tabl

e 11

. Hic

ksia

n (c

ompe

nsat

ed) p

rice

elas

ticiti

es o

f sel

ecte

d fo

od c

omm

oditi

es, P

hilip

pine

s, 2

008-

2009

With

Res

pect

to

Pric

e of

(j)

Dem

and

Elas

ticity

of (

i)

Rice

Corn

Sweet potato

Potato

Taro

Cassava

Milkfish

Tilapia

Pork

Chicken

Banana

Mango

Pineapple

Eggplant

Bitter gourd

Ric

e−0

.265

00.

0066

0.00

170.

0045

−0.0

068

0.00

720.

0510

0.02

320.

0565

0.08

930.

0016

0.01

840.

0007

0.00

680.

0043

Cor

n0.

5432

−0.9

583

0.02

310.

0000

0.00

070.

0024

0.05

480.

0047

0.14

090.

1003

0.03

350.

0101

0.01

510.

0096

0.01

98

Sw

eet p

otat

o0.

5315

0.01

63−0

.973

40.

0024

0.00

300.

0149

0.03

840.

0391

0.13

330.

1016

0.03

590.

0195

0.00

300.

0182

0.01

64

Pot

ato

0.54

620.

0050

0.01

43−1

.006

90.

0046

0.01

530.

0393

0.03

190.

1498

0.10

230.

0431

0.01

780.

0073

0.01

410.

0157

Taro

0.53

360.

0045

0.01

360.

0033

−0.9

981

0.01

140.

0537

0.04

160.

1440

0.09

860.

0366

0.02

260.

0052

0.01

500.

0144

Cas

sava

0.54

02−0

.001

20.

0181

0.00

660.

0040

−0.9

534

0.04

380.

0150

0.11

460.

1260

0.03

950.

0184

0.00

660.

0131

0.00

85

Milk

fish

0.55

740.

0246

0.01

500.

0041

0.01

970.

0172

−0.9

462

0.03

870.

1093

0.04

840.

0715

0.00

19−0

.002

10.

0321

0.00

83

Tila

pia

0.53

57−0

.019

30.

0218

0.00

270.

0137

−0.0

055

0.04

48−1

.001

00.

2260

0.04

630.

0645

0.04

700.

0076

0.00

990.

0056

Por

k0.

4510

−0.0

012

−0.0

020

0.00

26−0

.001

9−0

.023

9−0

.002

60.

1080

−0.7

554

0.16

460.

0507

0.00

10−0

.012

40.

0133

0.00

83

Chi

cken

0.53

320.

0077

0.01

580.

0046

0.00

220.

0370

−0.0

140

−0.0

223

0.21

41−0

.930

20.

0442

0.02

280.

0187

0.02

260.

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Summary

Rice remains the most important food item in the daily meals of Filipinos. It comprises the bulk of their total volume of consumption and amount of expenditures for the 15 most commonly consumed agricultural food commodities

in the Philippines. However, at the individual level, rice consumption and expenditure levels vary significantly across socio-demographic characteristics and locations of the consumers.

In general, consumers with lower income and socioeconomic class and reside in rural areas and poor regions are found to have higher rice consumption and share in total food expenditure than their counterparts. The same buying and consumption pattern is also observed for consumers whose households are headed by male individuals who are relatively old, less educated, and have blue-collar jobs.

Based on the estimated demand elasticities derived from LA/AIDS, quantity demanded for rice appears to be inelastic to its own-price and to consumer’s income. This means that a higher income and/or a lower price of rice will induce a rise in quantity demanded for rice but not by greater than the percentage change in income and price of rice. Therefore, rice remains to be a normal and a necessity good.

However, the sensitivity of quantity demanded for rice to the change in price of rice is not only explained solely by the change in its price itself (substitution effect) but also by the change in the real income (income effect) of consumers. About a quarter of the change in quantity demanded for rice is due to the change in their purchasing power or real income caused by a price change. Hence, if the purchasing power of the consumers is adjusted to remain at the same level after a price increase, a smaller rate of change in quantity demanded for rice for every percentage change in the price of rice could be expected.

It is also remarkable that quantity demanded for rice tends to be less responsive to changes in income and more responsive to changes in price of rice for consumers who have higher income and socioeconomic status, live in urban areas and relatively rich regions, and whose household head is female, relatively old, more educated, and has a white-collar job. Moreover, consumers with these characteristics are found to be less responsive to income changes than to rice price changes.

37

Among the other selected commodities, the estimated Marshallian cross-price elasticities indicate that corn, sweet potato, and cassava are substitutes for rice, of which cassava and corn are the most substitutable. On the other hand, potato, taro, milkfish, tilapia, pork, chicken, banana, mango, pineapple, eggplant, and bitter gourd are all considered rice complements. However, if the effect of the changes in commodity prices on real income is ignored, all these commodities mentioned above are found to be potential substitutes for rice, wherein milkfish is found to be the most substitutable. Nonetheless, the degree of responsiveness of quantity demanded for rice to the changes in prices of these related commodities remains quite inelastic.

Conclusions and Policy Implications

The continuously increasing rice demand in the Philippines amid its declining importance in the consumers’ food baskets in most Asian countries today has attracted interest to conduct a study in the country to validate the demand

estimates and analyze the factors affecting it. The result of the study clearly indicates that the rise of per capita consumption of rice from 113 kg in 2000 to 128 kg during the food crisis in 2008−2009 could not seem to be explained by the theoretical underpinning of consumer behavior. The sizes of the estimated demand elasticity are not that huge to instigate the sudden change. In fact, demand for rice during the food crisis should have declined. The current method of estimating per capita consumption, which is based on disappearance method has to be rectified.

The results of the study point to several policy implications which could be useful in developing strategic actions towards the country’s rice self-sufficiency goal by 2013, of which one of the key approaches is to reduce domestic rice demand in the following years.

The primary consumers of rice are the poorer sector of the economy, particularly the rural poor. Their diet is predominantly based on rice, which they consider a very basic food item in their food baskets. While this is also true for the rich consumers, unlike the poor, however, the more affluent consumers have more diverse food baskets as they consume more of other luxury foods like fish, meat, fruits, and vegetables. Due to the poor’s greater dependence on rice as a source of calories, they are more adversely affected by the rice supply shortages and are more vulnerable to food price inflation. Their limited purchasing power to buy enough rice as well as other supplementary foods for their family worsens, resulting in malnutrition, food insecurity, and poverty.

Theoretically, the easiest way for the government to decrease the consumption level of rice in the Philippines is to increase substantially the prices of rice, so that it would become less affordable to the consumers, especially to the poor consumers, who are the major consumers of rice. However, this measure is not feasible in reality because such a strategy will certainly worsen food insecurity, hunger, and malnutrition in the country. A more plausible strategy is to implement differential policies that can effectively reduce rice consumption but, at the same time, will not hurt the consumers, particularly the poor. Any policy that will improve the purchasing power of the targeted consumers, primarily through price-related or income-related policies, is expected to lead to a decline in rice consumption of consumers and diversification of their diet in favor of other commodities.

It is clear that when rice price subsidies are provided to the consumers, they become less responsive to price changes. But it is apparent that the rich and the urban consumers are relatively more responsive to price changes than the poor and rural

39

consumers. Hence, subsidies will only induce more rice consumption by the non-poor and urban consumers. Besides, they are expected to benefit more from a price subsidy program since they have more purchasing power than the poor and rural consumers even prior to the provision of subsidies.

Given this scenario, it is strongly recommended that a selective and targeted price intervention be implemented as a short-run strategy. Rice at subsidized prices must only be availed of primarily by the poorest of the poor and perhaps should be more accessible in the rural areas and poor regions like Eastern Visayas, Caraga, ARMM and the Bicol Region, where poverty incidence is very high. Whether or not specific price policies must also be implemented for the rural poor and the urban poor still needs to be deeply explored as this study did not have disaggregated analysis of the rice demand of the poor.

Despite the expected positive impact of selective and targeted price policies, especially in terms of potentially improving food security and reducing hunger of the poor, implementing income-generating interventions such as livelihoods and rural development programs should be given priority by the government. As poor consumers are found to be more responsive to income changes than price changes, income-oriented policies can potentially have greater effect on promoting less rice consumption in favor of other commodities, especially for those with more income-elastic demand for rice. These appear to have more sustainable and long-term impact on rice demand compared to price-related policies, such as the direct sale of rice at subsidized prices, which could only induce more rice consumption especially by the non-poor.

The lower rice consumption level associated with the richer and upper classes of the society and with the urban consumers implies that if per capita income and standard of living of the poor Filipinos improve, less increase in rice demand will be expected from them as the population of the country continues to expand. Hence, more intensive poverty reduction programs must be continued, especially in the poor regions of the country as these will still be the most effective ways to manage rice consumption. Since most of the poor consumers come from the rural areas, rural development is an urgent action that needs to be done to reduce poverty incidence in these areas through provision of better job and income opportunities to its residents and consequently to reduce potentially the rice consumption of rural consumers.

Encouraging establishment of more modern retail markets and food establishments, which can induce more diversification and balance in the diet of consumers, in both rural and urban areas, will make the consumers less dependent on rice and therefore lessen the impact of future changes in income and prices on the demand for rice. Consequently, with better economic status and more variety in the diet of the rice consumers, they can be more willing and able to substitute different types of commodities for rice, besides the usual rice substitutes of the poor.

40

Boosting the production and consumption of major rice substitutes which include corn, sweet potato, and especially cassava, by disseminating information on their market potential, nutrition and health benefits, and increasing their market availability will help fast-track the achievement of the country’s goal of managing rice consumption, specifically of diversifying its food staples. This will also relieve the pressure on rice producers in lessening rice supply deficits through increased production and on the government in procuring rice imports whose supply is now starting to get scarce in the global markets.

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Appendices

Appendix 1. Heckman’s two-step estimator

First stage

Dichotomous choice problem: (i)

If the household consumes the ith food item (i.e., wi > 0), then the latent variable ψi = 1, and zero otherwise. The variables pi and x are the price of ith commodity and total food expenditure, respectively.

The model given by (i) is estimated using the Probit regression to generate the inverse Mills ratio (IMR):

for zero consumption, (ii)

for non-zero consumption, (iii)

where imri is the inverse Mills ratio for the ith food item, Ф is the standard normal cumulative probability function and ϕ is the standard normal density probability function. Other variables are as defined previously.

Second stage

The estimates from (ii) and (iii) are then used as instruments in the second-stage regression:

wi =α0 + γ ij ln pj +βi ln(xP*)+ δikDk

k=1

n

∑ +θiimrij=1

n

∑ (iv)

where θi is the estimated coefficient of the IMR of the ith commodity and other variables are as defined previously. However, since IMRs can take on any value, restricting θiimri∑ = 0 cannot be imposed. To preserve the adding-up property,

IMR estimates for one commodity (j) must be deleted and this nth relation will be specified as,

wi =α0 + γ ij ln pj +βi ln(xP*)+ δikDk

k=1

n

∑ − θ jimrjj=1

n−1

∑j=1

n

∑ (v)

Appendix 2. Socio-demographic variables by barangay classification and socioeconomic class of households


Barangay Classification Socioeconomic Class

Urban RuralUpper

Classes (AB &C)

Lower Classes (D&E)

Per capita income group (666.12)*** (4.4e+03)***PHP 16,000 and below 705 3,653 0 4,358PHP 16,001 to PHP 26,000 540 1,256 62 1,734PHP 26,001 to PHP 96,000 1,069 1,406 1,073 1,402PHP 96,001 to PHP 196,000 168 215 361 22More than PHP 196,000 55 55 110 0

Socioeconomic class (413.25)***Lower classes 778 828 - -Upper classes 1,759 5,757 - -

Sex (38.82)*** (3.38)ns

Male 2,081 5,737 1,353 6,465Female 456 848 253 1,051

Age group (35.87)*** (75.12)***17 to 21 years old 2 31 2 3122 to 35 years old 419 1,348 229 1,53836 to 46 years old 742 1,967 437 2,27247 to 55 years old 574 1,456 432 1,59856 to 65 years old 466 1,040 333 1,17366 years old and above 334 743 173 904

Educational attainmenta (455.88 & 261.58)*** (451.01 & 652.85)***Did not finish high school 1,000 4,221 537 4,684At least high school graduate 1,537 2,364 1,069 2,832No vocational/bachelor’s degreeb 1,956 5,929 1,070 6,815Has vocational/bachelor’s degreec 581 656 536 701

Type of occupation (294.64)*** (390.94)***White-collar jobs 571 689 449 811Blue-collar jobs 1,457 5,110 853 5,714


Note: Values in parentheses are F-ratios. ***, **, and * indicate significance of the variable at 1%, 5%, and 10% probability level, respectively; ns means not significant. a There are two sets of educational attainment variable: (1) high school vs. non-high school graduate and (2) college/vocational vs. non-college/vocational graduateb Includes all educational attainment levels other than college graduate and postgraduatec Includes vocational graduates, college graduates and postgraduates

Appendix 3. Location variables by barangay classification and socioeconomic class

Location VariablesBarangay Classification Socioeconomic Class

Urban Rural Upper Classes (AB &C)

Lower Classes (D&E)

Barangay classification (413.25)***Urban - - 1,759 778Rural - - 828 5,757

Region (2.0e+03)*** (968.00)***NCR 475 0 258 217CAR 106 223 124 205Ilocos Region 95 293 61 327Cagayan Valley 73 280 29 324Central Luzon 354 431 184 601CALABARZON 366 272 143 495MIMAROPA 96 438 54 480Bicol Region 118 579 45 652Western Visayas 126 529 61 594Central Visayas 99 278 41 336Eastern Visayas 117 584 45 656Zamboanga Peninsula 71 286 52 305Northern Mindanao 109 437 52 494Davao Region 100 468 134 434SOCCSKSARGEN 40 366 46 360ARMM 104 584 226 462Caraga 88 537 51 574


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0004

Sou

rce:

Bas

ic d

ata

was

take

n fro

m B

AS

(200

9)N

ote:

The

sha

ded

estim

ates

indi

cate

sig

nific

ance

at 1

%, 5

%, a

nd 1

0% p

roba

bilit

y le

vels

, as

show

n be

low

.

sign

ifica

nt a

t 1%

pro

babi

lity

leve

lsi

gnifi

cant

at 5

% p

roba

bilit

y le

vel

sign

ifica

nt a

t 10%

pro

babi

lity

leve

lno

t sig

nific

ant

Annex A. List of commodities considered in the LA/AIDS

Common Name Scientific NameRice Oryza sativaCorn Zea maysSweet potato (camote) Ipomoea batatasPotato Solanum tuberosumTaro (gabi) Colocasia esculentaCassava Manihot esculentaMilkfish (bangus) Chanos chanosPork meat Sus domesticusChicken meat Gallus gallus domesticusTilapia Tilapia (genus)Banana (saba only) Musa acuminata × balbisianaMango (ripe only) Mangifera indicaPineapple Ananas comosusEggplant Solanum melongenaBitter gourd (ampalaya) Momordica charantia

Note: Names in parentheses are the local names commonly used in the Philippines.

The Authors

Flordeliza A. Lantican is a professor in agricultural economics and former Dean of CEM-UPLB. Mercedita A. Sombilla is the Agriculture Staff Director of the National Economic and Development Authority (NEDA) and former Manager of the Research and Development Department of SEARCA.

Karen P. Quilloy is an assistant professor at the Institute of Cooperatives and Bio-Enterprise Development, CEM-UPLB and former research assistant at the Research and Development Department of SEARCA.

EDITORIAL STAFF

AdvisersGil C. Saguiguit, Jr.

Bessie M. BurgosMa. Celeste H. Cadiz

Managing Editor Mariliza V. Ticsay

Production Coordinators Avril Adrianne D. Madrid

Angela Mae S. MiñasArlene A. Nadres

Language and Style EditorPaz Eulalia L. Saplala

Layout and Cover DesignJuan Teodoro V. Katigbak

Estimating the Demand Elasticities - Southeast Asian ... the Demand Elasticities of Rice in the...

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