Is urban food demand in the Philippines different from China?

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1 Singapore Management University Institutional Knowledge at Singapore Management University Researc Collection Scool Of Economics Scool of Economics 0-04 Is urban food demand in te Pilippines different from Cina? omoi Fujii Singapore Management University, Follow tis and additional wors at: ttp://in.library.smu.edu.sg/soe_researc Part of te Economics Commons Citation Fujii, omoi. Is urban food demand in te Pilippines different from Cina?. 04. Researc Collection Scool Of Economics. Available at: ttp://in.library.smu.edu.sg/soe_researc/60 is Woring Paper is brougt to you for free and open access by te Scool of Economics at Institutional Knowledge at Singapore Management University. It as been accepted for inclusion in Researc Collection Scool Of Economics by an autorized administrator of Institutional Knowledge at Singapore Management University. For more information, please

2 Is urban food demand in te Pilippines different from Cina? omoi Fujii October 04 Paper No ANY OPINIONS EXPRESSED ARE HOSE OF HE AUHORS AND NO NECESSARILY HOSE OF HE SCHOOL OF ECONOMICS, SMU

3 Is urban food demand in te Pilippines different from Cina? omoi Fujii * Abstract It is essential to understand te consumption pattern of food and ow it canges over time to formulate sound economic policies as well as mareting and pricing strategies. In tis study, we estimate te Quadratic Almost Ideal Demand System wit six rounds of te Family Income Expenditure Survey exploiting te conditional linearity of te demand system. We find tat te Filipino diet as become westernized and tat te canges in urban food demand elsticities are qualitatively similar to tose in urban Cina, especially for meat, fruits, and vegetables. We also offer some policy and business implications. Keywords: demand system; elasticity; generalized least-squares; iterated linear least-squares. Introduction Food is an essential good and tus understanding its demand is important for te formulation of sound agricultural policies and developing sustainable agricultural business. A timely analysis of food demand is important because te structure of food demand can cange over time not only because prices and incomes cange but also because people s taste itself also cange. However, even in countries were food accounts for a sizable sare of expenditure or were agriculture is among te main industries, careful analysis of food demand is often not readily available. In tis study, we analyze te food demand in urban Pilippines and compare it to te one in Cina. is comparison is interesting for two reasons. First, tere are some similarities between Filipino and Cinese food cultures. is is not surprising, because Filipino cuisine * Scool of Economics, Singapore Management University. tfujii@smu.edu.sg

4 as been significantly influenced by Cinese cuisine. e similarities are particularly pronounced in lower and middle-class cuisine because te Cinese first came as traders, settlers and mercants. For example, dises lie noodles, certain sausages, vegetables wrapped in a tin rice wrapper, and meat encased in doug come from te Cinese cuisine and ave been widely absorbed in te Filipino cuisine and cooed in omes and eateries See, Fernandez 986. Second, te economic growt in Cina as been muc faster tan te Pilippines in recent years. For example, according to te World Development Indicators publised by te World Ban, Cina s GDP per capita in constant 0 international dollars is $,554 in 990 and $9,30 in 00. e corresponding figures for te Pilippines are $4,00 in 990 and $5,63 in 00. erefore, we may expect to see more pronounced canges in Cina tan in te Pilippines over te last two decades or so. ere are, owever, two important limitations to tis argument. First, te Filipino food culture as also been eavily influenced by te Spanis food culture, but tis is not applicable to te Cinese food culture. erefore, te westernization of food culture as started muc earlier in te Pilippines. Second, te food culture in Cina is very diverse in itself. For istorical reasons, te Cinese influence did not uniformly come from Cina. Most notable influences come from soutern Cina, particularly around te current Fujian province. Despite tese limitations, te structural canges in food demand Cina as experienced tell us some directions in wic te structural canges in food demand are liely to tae place in te Pilippines. is is particularly true, if te Pilippines were to catc up wit Cina in GDP per capita in te future. We analyze te food demand by estimating te Quadratic Almost Ideal Demand System QUAIDS proposed by Bans et al. 997 wit various rounds of te Family Income Expenditure Survey FIES using a variant of te iterated linear least-squares estimator developed by Blundell and Robin 999. Besides te obvious empirical contributions, we improve on te existing metod by estimating te QUAIDS system for a relatively large number of goods in a reasonably efficient manner by using te conditional linearity of te estimation equations, by taing advantage of te variance-covariance matrix of te unobserved error term, and by directly imposing te restrictions on te parameters required by economic teory. is paper is organized as follows. We first review relevant existing studies on food demand in te Pilippines and Cina in te next section. In Section 3, we present te

5 metodology used in tis study. In Section 4, we describe te data followed by te results in Section 5. Section 6 offers some discussion including some policy and business implications.. Review of existing studies o facilitate te discussion later, we provide a review of some of te important studies on food demand in Cina and te Pilippines in tis section. Cina ere are an increasing number of studies on food demand in Cina, especially in urban Cina in recent years. is is not surprising because te canges in te food demand structure in Cina affect not only te food maret in Cina but also te rest of te world. Here, we discuss a few studies tat are most closely related to ours. e study by Gould and Villarreal 006 is one of te recent studies tat adopt te QUAIDS demand system. ey use it to analyze te structure of food demand in four urban provinces in Cina. According to teir estimates, beef, poultry, and grains oter tan rice are among te food categories wit a relatively ig uncompensated own-price elasticities. For most food items, te differences in expenditure elasticities and uncompensated own price elasticities across different income groups were small. ey also examined te importance of food at ome and food away from ome, and found tat te latter tends to increase wit te ouseold s income level. Zeng and Henneberry 00 estimate food demand only in te urban Jiangsu province. ey find tat tere is no obvious difference in own-price elasticity across different income groups and tat te income elasticity tends to be lower for wealtier ouseolds. Based on tese estimates, tey project te future food demand. ey empasize te importance of income distribution in demand projection as more equal distribution would imply iger food demand even wen te average income remains te same. In a separate study, Zeng and Henneberry 0 argue tat te researcers sould use te demand parameter tat pertain to te relevant income group for te appropriate design of policies and mareting strategies for te population group of interest, because te constant elasticities of food demand among income groups are not supported in te urban Jiangsu province. ese studies igligt te potential importance of addressing te eterogeneous elasticities across different income groups. 3

6 Anoter study tat is closely related to ours is Dong and Fuller 00. Using te Almost Ideal Demand System AIDS developed by Deaton and Muellbauer 980, tey analyze te sift in consumer demand in urban Cina between 98 and 004 wit aggregate data. ey find tat canges in grain consumption can be largely explained by normal price and income effects. On te oter and meat, tey find some evidence for structural cange in te demand of meat, vegetables, fruits, and fis, wic played a less important role in daily food consumption in traditional Cinese diets. Similarly, Hovannisyan and Gould 04 use provincial-level data in urban Cina and test te structural cange in food demand between 00 and 00. ey find evidence tat urban Cinese diet preferences ave canged in teir study period. Namely, tey find tat te magnitudes of uncompensated own-price elasitcities in te seven food categories meat, seafood, vegetables, fruits, grain, eggs, and fats tey used ave decreased wit an exception of eggs. ese decreases are most apparent in te demand of fruits and meats, wic points to teir rising importance in te urban Cinese food diet. Hovannisyan and Gould 0 also analyze te structural cange in demand using ouseold-level expenditure surveys for 995 and 003. Based on an independent test of equality, tey find tat uncompensated own-price elasticity as canged statistically significantly for all goods, except for beef and poultry, and became less elastic for seafood, vegetables, fruits, rice, and dairy products in teir study period. Our approac is similar to Hovannisyan and Gould 0 in te sense tat we use ouseold-level data and a similar test for te presence of structural cange. e empirical evidence from tese studies provides at least tree important implications for our study. First, controlling for demograpic caracteristics of te ouseold is potentially important. Wile tis is not surprising, it is important in practice. Second, bot price and budget elasticities, especially te latter, appear to depend on weter te ouseold is ric or poor to some extent. erefore, we provide disaggregate results by te expenditure quintile. ird, wile te estimated elasticities vary substantially across studies and teir direct comparisons are difficult because of te differences in te geograpic coverage, study periods, and metodologies used, tey tend to find lower budget elasticties over time for most food items. On te oter and, te canges in price elasticties appear to be eterogeneous across food items. We will subsequently verify tat tis is also te case in te Pilippines. 4

7 Pilippines ere ave been several studies on food demand in te Pilippines to date. One of te earliest studies based on ouseold surveys is Quisumbing et al ey use two ouseold surveys conducted by te Food and Nutrition Researc Institute in 978 and 98 to estimate food subsystem and te cross-tabulations taen from four rounds of FIES in 96, 965, 97, and 975 to estimate a translog expenditure system for five groups of goods. Bouis 990 proposes a demand system based upon a utility function tat is additive in bul, variety, and tastes of individual goods and apply to te Pilippines. His estimates sow tat meat tends to ave ig own price elasticity and income elasticity wereas corn is estimated to ave a negative income elasticity bot in urban and rural areas. Similarly, Bouis, Haddad, and Kennedy 99 sow tat bot caloric intae computed from 4-our recall survey and caloric availability computed from food expenditure survey tend to be iger for ricer ouseolds for most food items but tis is not te case for corn. Balisacan 994 reviews earlier studies on food demand in te Pilippines and estimate te AIDS demand system using tree rounds of te FIES data in 985, 988, and 99. He finds tat food items are generally income inelastic. In particular, rice, te major staple, as an income elasticity of On te oter and, corn as a negative income elasticity, a pattern tat is consistent wit above-mentioned studies. A more recent estimate is provided by Mutuc, Pan, and Rejesus 007. ey use FIES data for year 000 to estimate a QUAIDS demand system wit a detailed disaggregation of vegetables. ey find significant difference between te expenditure elasticities of urban and rural ouseolds, wereas tey did not find statistical difference between urban and rural ouseolds in own- and cross-price elasticities. Our study is different from tese earlier studies in several respects. First, many of te studies mentioned above, including tose in Cina, eiter i assume separability between food and non-food items or ii igly aggregate non-food items. However, te separability assumption is not a armless assumption because te total budget for te food may be endogenous. Aggregation of non-food items may appear more innocuous, but te aggregability requires some strong assumptions on te utility function. Wen we lump a variety of non-food goods togeter, te aggregability is less liely to old even as an approximation. We avoid tis issue by directly estimating a demand system wit a relatively large number of goods. 5

8 Second, unlie te studies mentioned above, we use more recent rounds of FIES data. erefore, our results provide an update on te elasticity estimates. Finally, we estimate elasticities over a long study period using a consistent metodology. is allows us to understand te canges in te structure of food demand. As far as we are aware, no study as investigated te canges in food demand structure in te Pilippines using recent data. 3. Metodology We estimate te demand system using te quadratic almost ideal demand system QUAIDS proposed by Bans et al. 997, wic as become a standard model of te analysis of demand systems. e QUAIDS model nests te AIDS model and retains its attraction of exact aggregability. e QUAIDS model as additional flexibility due to te quadratic logaritmic income term. As a result, some goods may be necessities at some income levels or luxuries at oters in te QUAIDS model. Bot te AIDS and QUAIDS model can be in principle estimated by te standard estimation metods suc as te maximum lieliood estimation MLE. As is well nown, te computational cost of MLE substantially increases as te number of parameters to be estimated goes up. Furtermore, te non-convergence issue is more liely to occur wen te parameter space is ig-dimensional. ese issues can be very serious, wen te number of goods in te demand system is just moderately large, because te number of parameters to be estimated can inflate quicly. For example, witout any additional regressors, te number of parameters to be estimated in te standard QUAIDS model is only, 7, and 47 wen te number of goods in te system is 5, 0, and 0, respectively. erefore, applied researcers interested in te demand system of a particular set of disaggregate goods tended to deal wit tis issue i by focusing on a subset of te goods assuming some form of separability or ii by aggregating te goods tat are not of teir main interest. e first approac is problematic wen separability does not old. Second approac is also problematic wen te goods are not aggregable. Blundell and Robin 999 address tis problem by estimating a large demand system witout numerical maximization in te following manner: Because QUAIDS model is conditionally linear, we can estimate te parameter by an ordinary least-squares OLS regression of te expenditure sares, taing some price indices as given. en, tese price indices are updated wit te estimated coefficients. Using te updated price indices, we run an OLS regression again. is iteration continues until convergence is attained. e iterated 6

9 linear least-squares ILLS estimator tus obtained is straigtforward to implement and runs fast enoug for practically large models as demonstrated by Blundell and Robin 999. In tis study, we use a modified version of tis estimator, wic we refer to as te iterated generalized linear least-squares IGLLS estimation. As te name suggests, we run a feasible generalized least-squares GLS regression instead of an OLS regression in eac iteration. Our metod runs comparably fast and yields more accurate estimates for two reasons. First, we impose te symmetry of te Slutsy matrix in te iterated regressions. is contrasts wit te minimum ci-square estimator developed by Ferguson 958, wic is used to impose constraints after an unconstrained estimator is obtained. Wile te minimum cisquare estimator is asymptotically as efficient as te MLE under some restrictive assumptions Rotenberg, 973, it is not generally so in a finite sample. is issue may be particularly severe wen te variance-covariance matrix of te unrestricted estimator is not reliable. Second, we use te variance-covariance matrix of te residuals in te iterative procedure, so tat te weigts used in te regression are asymptotically optimal. In tis section, we first develop te IGLLS estimator. Because tis is a straigtforward extension of te ILLS estimator and teir derivations are very similar, we sall eep tis discussion sort. We ten discuss ow te IGLSS estimator is used to estimate te QUAIDS demand system. Definition and Asymptotic Properties of te IGLLS estimator Let x and u be a real column M-vector of control variables and a real column K-vector of random error terms, respectively, for ouseold {,,H}. We assume tat te pair x,u is independently and identically distributed and tat E 0 [ u x ] M olds for all, were 0 M is a column M-vector of zeros. e outcome variables of interest are a real column K-vector y, were y satisfies y g x, 0 0 u for some true parameter value 0 contained in te parameter set, wic is an open and convex set on M R. We furter M KD assume tat g : R R is a twice continuously differentiable function wit respect to [,, D ], were we use a superscript to denote eac vector component except tat is used as a transpose operator. For te simplicity of notation, we define a few additional notations. First, we denote te non-singular finite weigting matrix by W E [ y g x, y g x, ] and also define W W E u u 0. Second, we use capital letters to denote staced 0 observations suc tat we ave Y [ y,, y ] and U [ u,, u ]. Finally, wit a sligt H H 7

10 abuse of notation, we also define te following relationsip: G [ g x,,, g xh, ]. By definition, we ave Y 0 0 G U. Notice tat eq. is a standard linear equation once G is taen as given. e basic idea of te ILLS estimator is essentially built on tis idea. at is, if we ave an ˆ p estimate of in te pt iteration, ten we can update te estimator by running te ordinary least-squares OLS estimation. However, tis estimation is bound to be inefficient wen u is correlated across. is is indeed liely in te estimation of demand system because te error terms across goods are liely. Hence, instead of running OLS, we run a feasible generalized least-squares FGLS regression in eac iteration to obtain a more efficient estimate. equation: o do so, we first estimate te weigting matrix ˆ ˆ p W given by te following Wˆ ˆ p H y g x, ˆ p ˆ p y g x, ˆ p 0 ˆ p ˆ p. en, we run a FGLS regression conditional on ˆ p G in eq. using ˆ ˆ p W as a weigting matrix to obtain a new updated estimator in te following manner: ˆ p G ˆ p I Wˆ ˆ p p p p G ˆ G ˆ I Wˆ ˆ Y H were I H is an H H -identity matrix and is te Kronecer-product operator. H, 3 erefore, once we ave an initial estimate ˆ0, we obtain a sequence of estimates ˆ 0 ˆ,, ˆ, by continuing te iteration. We obtain our iterated generalized linear least squares IGLLS as a limit of tis sequence. Notice tat te only difference between te IGLLS and ILLS is te presence of weigting. erefore, if we use I K instead of ˆ ˆ p W in eq. 3 above, we obtain te ILLS estimator. Because IGLLS is taen as a limit of te sequence, te IGLLS estimator ˆ satisfies te following equation by construction: ˆ G ˆ I Wˆ ˆ G ˆ G ˆ I Wˆ ˆ Y H H. 4 It can be sown tat ˆ is a consistent estimator of 0 and asymptotically normally distributed under suitable regularity conditions as sown in te following teorem: 8

11 eorem Let e d be a row D-vector wose dt component is one and all te oter g x, o components are zero and define te following quantity: x, o e d 0 d, wose dt column vector is te partial derivative of g wit respect to te dt component of multiplied by 0. Furter define: M Eg x, W g x, and Q M Eg x, W x, en, under suitable regularity conditions, we ave: ˆ.. s o. a and ˆ d H N0, Q M Q, d e asymptotic variance can be estimated by replacing 0 wit its estimate ˆ in Q 0 and M 0 above. Application of te IGLLS estimator to te QUAIDS demand system We now apply te IGLLS estimator to te QUAIDS demand system. Suppose tat tere are N goods in te economy, and denote te column N-vector of te logaritmic prices by N p [ p,, p ]. We let te logaritmic expenditure be m. e QUAIDS demand system proposed by Bans et al. 997 follows from te following indirect utility function: ln, m a p v m p c p, 5 b p wic is an extension of te Price-Independent Generalized Logaritmic PIGLOG indirect utility function used by Muellbauer 976 and satisfies exact aggregability. e price indices ap, bp and cp are defined in te following manner: a p a0 p p p, b p exp p, and c p p, were,, N,,, N,,, N n, n, and n N. We set a 0 to be te observed minimum value of m following Deaton and Muellbauer 980 and Bans et al Applying tese definitions and te Roy s identity in eq. 5, we ave te following column N-vector of expenditure sare functions w N w,, w : w m a p m a p p. 6 b p, n 9

12 Because te expenditure sares adds up to one wen summed across all te goods, w as to satisfy w for all p, were N is a column N-vectors of ones. erefore, tis adding-up N constraint requires te following restrictions on,,, and : N, N N 0, and N 0N. Since all of tese constraints are linear in te parameters, we can impose te constraints simply by eliminating te redundant parameters from te equations. at is, we can rewrite te adding-up constraints as follows: N n N N N n N, n N, n N, n, and m, n for n {,, N}. n n m Wit tese constraints, te Nt equation in eq. 6 is trivially satisfied. us, we can simply drop te N-t equation to arrive at a system of K=N estimation equations. Furtermore, note tat symmetry of te Slutsy matrix requires: Γ= Γ. erefore, togeter wit te addingup constraint, we must ave: N n, N n, m for n {,, N} m 0. Using tis, we can rewrite te system of equations in eq. 6 wit te Nt component dropped. o tis end, We denote w,,,, and wit teir Nt component dropped by w ~, ~, ~, ~, and ~. Similarly, we denote Γ wit its last row and column dropped by ~. We furter define ~ N p to be a K-vector of normalized prices, wose t element is p p, N and also define a ~ a p. en, we can rewrite te system of estimation equations as follows: 0 0 N ~ w ~ ~ ~ m a~ ~ ~ p m a~ ~ p ~ ~ p b ~, p were ~ ~ ~ a p and b ~ p are defined as follows: a~ ~ ~ p a~ ~ ~ p ~ p ~ ~ ~ 0 p and ~ b p exp ~ p. e symmetry constraint for te estimation of QUAIDS models are often not imposed wen running regressions, as is te case wit Blundell and Robin 999, but by te minimum ci-square distance estimator. As Blundell 988 and Browning and Megir 99 argue, tis approac as an advantage tat te resulting ci-squared statistic can be used to test te symmetry. However, te minimum ci-square distance estimator requires accurate estimation of te variance-covariance matrix of te unrestricted estimator. is can be problematic in a finite sample wen te number of goods in te economy is large. is is an important issue

13 especially because te strengt of te ILLS estimator is in its ability to estimate large demand systems. erefore, we directly impose te symmetry constraint by suitably transforming te problem. We note tat te symmetry and adding-up constraints imply tat tere are L KK+/ free parameters in Γ. Wit a sligt abuse of notation, we write te tese free l, parameters by, for l / wit K. It is also convenient to define te mapping from l to te corresponding pair of indices. at is, we ave l i l, i l for all l by defining: i i i l max in i l and i i l i l l l. o apply te IGLLS estimator in te estimation of a QUAIDS demand system, it is useful to define a few matrices. Let us define: A m a~ ~ m a~ ~ p p I K and A ~ I K b ~, p were I K is a K K-identity matrix and te subscript denotes a ouseold. Furtermore, let us 3 define a K L-matrix A, wose,l element is ~ i l p if i l, ~ i l p if i l, and zero oterwise. Using tese notations, we can write te system of estimation equations as follows: w~ g x, u 6 K K L were te set of parameters to estimate is [,,,,,,,, ], te observable caracteristics are ~ 3 x m, p, and g x, [ I, A, A, A ]. Note tat is unconstrained, because bot te adding-up and symmetry constraints ave already been internalized. So far, we ave ignored te potential eterogeneity in demand across different ouseolds wit different demograpic groups. o address tis issue, we also include a few demograpic variables suc as te ouseold size, te gender of te ouseold ead, and te educational attainment of te ouseold ead using te metod adopted by Abdulai 00, wic adjusts te intercept term a 0 by te demograpic caracteristics of te ouseold. o estimate te variance-covariance matrix for te IGLLS estimator, it is necessary to find, defined in eorem. o tis end, we define to be an L-vector of quadratic x logaritmic prices wose l-t element is i l i l p p if l i l x, K i, and i l i l p p if i l i. Using tis, it can be sown tat can be written as l

14 ],,, [, 3 K B O B B x, were O K is a K K-matrix of zeros and B, B, and 3 B are defined as follows: p b a m B ~ ~ ~, p b a m B ~ ~, and b a m B ~ ~ 3. It is convenient to present te results in terms of te elasticity. e budget elasticities and uncompensated price elasticities, for ouseold are given by te equations:,,, n n n p p b p a m w p b p a m w, 7 were is te Kronecer delta. We aggregate te elasticities found in tis way by taing te weigted average wit te weigts being equal to te ouseold s sare of te total sample expenditure for te good of interest. 4. Data For our empirical application, we combine FIES data wit te annual Consumer Price Index CPI data, bot of wic are collected by te National Statistical Office NSO of te Pilippines. e FIES contains detailed questions on consumption and expenditure as well as some oter caracteristics of te ouseold. We focus on urban single-family ouseolds eaded by a married woring-age person wit at least one cild and no more tan seven cildren to ave reasonably omogeneous ouseold composition. We use six rounds of te FIES data in 998, 99, 994, 000, 003, and 006 for tis study, wic contain 4584, 7577, 76, 070, 865, and 789 ouseolds, respectively. e CPI data are based year 000 and available at te provincial level or lower for an overwelming majority of te FIES ouseolds. For a small fraction of FIES ouseolds were te CPI data are not available at te provincial level, we use te regional CPI data for te survey year. o use te differences in te price canges across provinces over time, we divide te data into pre-997 period i.e., 988, 99, and 994 and post-997 period i.e., 000, 003, ad 006. ere are about eigty provinces in te Pilippines during te study period, toug te definitions of provinces cange sligtly over time. We use te finest geograpic disaggregation tat is possible in te data.

15 In tis study, we only tae te urban sample in te FIES data set. is coice is driven by two considerations. First, te CPI data are mainly collected in urban areas. erefore, CPI may not capture very well te actual price system tat rural ouseolds face. Second, most studies on food demand in Cina we are aware of are focused on urban areas. erefore, to facilitate te cross-country comparisons between Cina and te Pilippines, it is sensible to use only te urban data. Because te definition of goods between te FIES and CPI are not te same, we ave aggregated bot data across goods so tat te definitions of goods in te two data sets matc. As a result of tis aggregation, we ave te price and expenditure sare for eac ouseold and for eac of te 9 items of goods expenditure categories, wic include seven food items and non-food items. able sows te definition of te 9 expenditure categories as well as teir expenditure sare in 988 and 006 disaggregated by te per-capita expenditure quintile of te ouseold, were Q represents te top ricest quintile and Q5 te bottom poorest quintile. e reported figure in eac cell is calculated as te average sare for eac item and quintile weigted by te product of te ouseold s total expenditure and te ouseold s sample weigt. As can be seen from able, tere are some consistent patterns tat are observed over te study period. For example, te sare of cereal item # expenditure is lower for ricer quintiles, a finding tat is expected from previous studies. able also sows tat tere is some eterogeneity in te relationsip between expenditure sare for non-food items and total expenditure quintile. For example, ricer ouseolds tend to allocate a iger sare of expenditure on te rental of dwelling unit item #, transportation and recreation item #6, communication item #7, and ouseold furnising and equipment item #8. However, tere is no suc relationsip for fuel, ligt and water item # and only a wea relationsip is observed for medical care item #4 and personal care and ouseold operation item #5. able is also consistent wit te westernization of Filipino diet during te study period. Wile te expenditure sares for major food items ave declined, te relative declines are different across food items. erefore, te relative importance of dairy and eggs item # and meat item #5 witin te food budget as increased over time. On te oter and, cereals Apparently, Mutuc, Pan, and Rejesus 007 ave used FIES data for year 000, wic contain te expenditure and quantity for eac food item. However, te data we purcased from te NSO only contain te expenditure data and tus we cannot derive te implicit prices ouseolds face from te FIES data. Furtermore, it would not be possible to obtain relevant quantities for non-food items. erefore, we cose to aggregate goods instead in tis study. 3

16 item #, te most important food category in te traditional Filipino diet, as witnessed te largest absolute decline in te expenditure sare during our study period. able describes te ouseold caracteristic variables used in tis study. e reported figures are te mean witin eac quintile weigted by te sample weigt. e first row HHSIZE sows tat poor quintiles tend to ave a larger ouseold and tat te ouseold size as declined for all te quintiles over te study period. e second, tird, and fourt rows sow tat te ouseold eads in ricer quintiles tend to be better educated tan tose in poorer quintiles. Note tat tose wo ave at least some secondary [college] education are automatically deemed to ave at least some primary [secondary] education. erefore, HHSEC [HHCOL] is by definition no larger tan HHPRI [HHSEC] for all quintiles. e fourt row sows tat te top quintile is disproportionately represented by female eaded ouseolds. We find no obvious difference in ead s age across different quintiles. 5. Results We estimate te QUAID demand system for te 9 items using te IGLLS estimator presented in Section 3 for and periods separately. In all te regressions, we control for te region and year. able 3 reports te estimated coefficients on demograpic sifters and teir statistical significance. For example, it sows tat te expenditure sare for cereals tends to increase by.7 and.53 percentage points, respectively, for te periods of and wen te ouseold as one additional member, after controlling for a variety of oter factors. able 3 also sows tat better educated ouseolds tended to spend a iger sare of expenditure on major protein sources including dairy and eggs item #, fis and seafood item #3, and meat item #5 for te period, even after controlling for a variety of oter factors including te total budget. Wile tis pattern still exists for t period, but te gap between educated and non-educated ouseolds appear to ave narrowed sligtly. ables 4 and 5 respectively present te total budget elasticities and uncompensated own-price elasticitties for food items based on eq. 7 as well as teir canges over time and te statistical significance of te canges due to te independent test of equality. o obtain tese estimates taing account of bot te model and sampling errors, we randomly draw te parameters from a normal distribution wit te estimated asymptotic mean and variance for,000 rounds of simulation and impute te elastisities for eac ouseold for a bootstrapped 4

17 sample in eac round. We ten aggregate over eac quintile and obtain an estimate for eac round. aing te mean and standard deviation of tese estimates over all te rounds, we ave te estimated point estimates and teir standard errors. able 4 sows tat te budget elasticity is smaller for ricer quintiles for all food items. e table also sows tat te budget elasticity as generally declined over time. is is not surprising given te economic growt tat as taen place over te study period. e only exception is cereals item # for te top quintile, wic is also not so surprising because ouseolds in te top quintile is already able to fully satisfy teir basic needs and cereals are, terefore, budget inelastic. is pattern did not cange over time. able 5 sows tat te uncompensated own-price elasticities are striingly similar across quintiles for all te food items except for cereals item #. Cereals are clearly inferior goods for te top quintile but it is a normal good for poorer quintiles. able 5 also sows tat tere as been a statistically and economically significant decline in te magnitude of elasticity for fruits and vegetables item #4 and beverages item #7, wereas tere as been a significant increase for meat item #5. Bot ables 4 and 5 strongly indicate te presence of structural cange between 988 and 006 as as been found in studies in Cina. Wile our results cannot be directly compared wit te studies on food demand in Cina because of te difference in te definition of food items, coverage of time periods, and te metodology used to derive elasticities, tere are some common patterns observed in te canges in food demand between te two countries. First, increases in te magnitude of uncompensated own-price elasticity for meat ave been observed in several studies in Cina. For example, Hovannisyan and Gould 04 report tat te meat price elasticity as canged from to in teir study period between 00 and 00. For earlier periods, Hovannisyan and Gould 0 estimate uncompensated own-price elasticities for beef, por, and poultry for 995 and 003. e elasticities for por and poultry as increased substantially wereas tat for beef sligtly declined. Second, as wit our study, Hovannisyan and Gould 0 also find tat te uncompensated own-price elasticities for vegetables and fruits ave declined in teir magnitudes between 995 and to for vegetables and to for fruits. For te period between 00 and 00, Hovannisyan and Gould 04 indicate tat vegetables ave become less elastic wereas fruits ave become only sligtly more elastic. Finally, Hovannisyan and Gould 0 find tat te budget elasticity of demand as declined for a majority of food items tey studied, wic is similar to wat we find. 5

18 6. Discussion In tis paper, we ave estimated QUAIDS demand systems over a long period of time using a consistent metodology. Wile we ave focused on te food demand, we cose to estimate te wole demand system to avoid assuming separability and excessively aggregating nonfood items. However, tis necessitates te estimation of a large demand system, wic involves a large number of parameters. is issue becomes even more serious wen some ey demograpic variables are included in te regression as tey inflate te number of parameters to be estimated. o address tese issues, we exploit te conditional linearity of te QUAIDS demand system and developed and applied te IGLLS estimator. Using six rounds of te FIES data, we ave estimated te QUAIDS demand system wit 9 goods. We find tat te urban Filipino diet is getting more westernized and tat te food demand in te Pilippines as structurally canged during our study period between 988 and 006. In particular, te canges in demand for meat, vegetables, and fruits in te urban Pilippines ave been qualitatively similar to tose observed in Cina. e estimation results presented in tis study ave some policy implications. For example, as we ave seen in te case of recent food inflation, te prices also affect poverty eterogeneously across ouseolds Fujii, 03. erefore, ow food demand canges according to te canges in prices and incomes and ow it varies across ouseolds are crucial for te assessment and formulation of economic policies, including agricultural subsidies, taxes, infrastructure investment, and social protection. Our results ave also some business implications. In general, if marets are segmented and people in different budget quintiles respond differently to price or total budget canges, ten separate mareting and pricing strategies may be needed for different per-capita expenditure quintiles. As we can see from ables 4 and 5, tere is a mared difference across quintiles for te demand of cereals item #. However, te price elasticities for oter major food items including dairy and eggs item #, fis and seafood item #3, fruits and vegetables item #4, and meat item #5 are rater similar across quintiles. erefore, we do not ave evidence to suggest tat separate pricing strategies are needed for tese items in te Pilippines. 6

19 References Abdulai, A. 00 Houseold demand for food in Switzerland: a quadratic almost ideal demand system. Swiss Journal of Economics and Statistics. 38: -8. Balisacan, A.M. 994 Demand for food in te Pilippines: responses to price and income canges. Pilippine Review of Economics and Business. 3: Bans, J., Blundell, R. and Lewbel, A. 997 Quadratic Engel curves and consumer demand. Review of Economics and Statistics. 794: Blundell, R. 988 Consumer beavior: eory and empirical evidence: a survey. Economic Journal : Blundell, R. and J.M. Robin 999 Estimation in large and disaggregated demand systems: An estimator for conditionally linear systems. Journal of Applied Econometrics. 43, Bouis, H.E. 990 Evaluating demand for calories for urban and rural populations in te Pilippines: implications for nutrition policy under economic recovery. World Development. 8: Bouis, H., Haddad, L. and Kennedy, E. 99 Does it matter ow we survey demand for food?: Evidence from Kenya and te Pilippines. Food Policy. 75: Browning, M. and Megir, C. 99 e effect of male and female labor supply on commodity demands. Econometrica. 594, Deaton, A. and Muellbauer, J. 980 An almost ideal demand system. American Economic Review. 703: Dong, F. and Fuller, F. 00 Dietary structural cange in Cina s cities: empirical fact or urban legend. Canadian Journal of Agricultural Economics. 58: Ferguson,.S. 958 A metod of generating best asymptotically normal estimates wit application to estimation of bacterial densities. Annals of Matematical Statistics 94, Fernandez, D.G. 986 Food and te Filipino. In Enriquez, V.G. ed Pilippine World- View. Institute of Souteast Asian Studies, Singapore, Singapore. Fujii,. 03 Impact of food inflation on poverty in te Pilippines. Food Policy. 39:

20 Gould, B.W. and Villarreal, H.J. 006 An assessment of te current structure of food demand in urban Cina. Agricultural Economics. 34: -6. Hovannisyan, V. and Gould, B.W. 0 Quantifying te structure of food demand in Cina: An econometric approac. Agricultural Economics 4: s-s7. Hovannisyan, V. and Gould, B.W. 04 Structural cange in urban Cinese food preferences. Agricultural Economics 45: Muellbauer, J. 976 Commodity preferences and te representative consumer. Econometrica. 44: Mutuc, M.E.M., Pan, S. and Rejesus, R.M. 007 Houseold vegetable demand in te Pilippines: Is tere an urban-rural divide? Agribusiness. 34: Quisumbing, M.A.,Fu.R., Valerio,.E., Red, E.R., and Villavieja, G.M. 988 Flexible functional form estimates of Pilippine demand elasticities for nutrition policy simulation. Pilippine Institute for Development Studies Woring Paper No Pilippine Institute for Development Studies, Manila, te Pilippines. Rotenberg,. 973 Efficient Estimation wit A Priori Information Yale University Press Zeng, Z. and Henneberry, S.R. 00 e impact of canges in income distribution on current and future food demand in urban Cina. Journal of Agricultural and Resource Economics. 35: 5-7. Zeng, Z. and Henneberry, S.R. 0 Houseold food demand by income category: Evidence from ouseold survey data in an urban Cinese province. Agribusiness. 7:

21 9 able. Expenditure sares in percentage by expenditure items and per-capita expenditure quintiles for 988 and 006. Expenditure item Q Q Q3 Q4 Q5 otal Q Q Q3 Q4 Q5 otal Cereals Dairy and eggs Fis and seafood Fruits and vegetables Meat Miscellaneous food Beverages incl alcool obaco Clote Housing maint. & repair Rental of occupying dwelling Fuel, ligt & water Education Medical care Personal care and HH operation Recreation ransportation and communication HH furnising and equipments Oter non-food items otal Number of observations Source: Autor's calculation based on te FIES sample used in tis study.

22 0 able. Houseold caracteristics by per-capita expenditure quintiles for 988 and 006. Variable Description Q Q Q3 Q4 Q5 otal Q Q Q3 Q4 Q5 otal HHSIZE Houseold Size HHPRI Head as at least some primary educ % HHSEC Head as at least some secondary educ % HHCOL Head as at least some college educ % HHFEM Female eaded ouseold % HHAGE Head's age Source: Autor's calculation based on FIES data.

23 able 3. e estimated QUAIDS coefficients on demograpic variables. # HHSIZE HHPRI HHSEC HHCOL HHFEM HHAGE ***.53 *** -.03 *** -.09 *** -.03 *** -0.6 *** *** -0.8 *** 0.6 *** 0.3 *** 0.07 *** 0.05 ***.68 *** 0.86 *** 0.8 *** 0.48 ** 0.86 *** 0. * 0.65 *** 0.46 ***.97 ***.93 *** 3.36 *** 3.6 *** ***.3 *** 4.07 *** 4.07 *** 3.67 ***.73 *** 4.9 ***.85 ***.30 ***.56 ***.3 *** 3.5 *** 4.8 ***.8 *** 3.4 *** 4.63 *** 3.73 *** 3.48 *** 3.46 *** 3.08 *** *** 0.87 *** -.38 *** *** *** 0.39 *** 0.8 *** 0.5 *** 0.7 *** 0.30 *** 0. *** -0.3 ** *** *** *** * *** *** -0.9 * *** *** *** *** ** *** -0.7 *** *** *** *** 0.3 *** *** *** *** *** -0.7 *** *** -0.6 *** *** -0.0 ** 0.0 *** *** -0.5 *** -.06 *** 0.9 *** *** 0.95 *** ***.80 ***.68 *** 0.63 ***.89 ***.4 *** 3.8 ***.5 ***.33 ***.6 *** 0.90 *** 0.63 ***.59 ***.4 *** 0.46 ** *** -.66 *** *** 0.3 ** 3.44 ***.07 *** 0.99 *** -0.4 * *** *** *** *** -0.5 ** ** -0.4 *** *** 0.40 ***.50 *** 0.8 *** -0.9 ** -0.9 *** -0.7 *** -.00 *** *** *** -. *** *** -.6 *** *** -.0 *** * *** *** -.7 *** *** *** 6. *** 0.84 *** *** -0.3 ** *** *** -0.3 *** 0. ** 0.4 ** -0.9 *** *** *** -0.3 *** 0.4 *** 0.3 *** 0.34 *** 0.3 *** *** *** *** *** 0.9 *** *** 0.4 *** 0.53 *** *** *** -.5 *** *** -.74 *** *** -.37 *** -.84 *** -.0 *** *** -.9 *** -.39 *** -.77 *** -.95 *** -.5 *** -.67 *** -3.0 *** Note: Autor's calculation based on FIES and CPI data. Statistical significance at 0, 5, and percent levels are indicated by *, **, and ***, respectively. All coefficients are expressed in percentage.

24 able 4. Budget elasticities of demand for food items. # Yr Q Q Q3 Q4 Q5 otal Diff *** *** *** * Diff *** -0.3 *** -0.3 *** *** -0.5 *** *** Diff -0.6 *** -0.7 *** *** -0.6 *** -0.9 *** *** Diff *** -0. *** *** *** *** -0.9 *** Diff *** *** *** *** *** *** Diff *** -0. *** -0. *** -0.3 *** *** -0.6 *** Diff *** -0.8 *** *** -0.9 *** -0.9 *** *** Note: Autor's calculation based on FIES data. Standard errors in paranteses. Statistical significance at 0, 5, and percent levels are indicated by *, **, and ***, respectively.

25 3 able 5. Uncompensated own-price elastiticties of demand for food items. # Yr Q Q Q3 Q4 Q5 otal Diff Diff Diff Diff 0.33 *** 0.50 *** 0.4 *** 0.04 *** 0.84 *** 0.6 *** Diff *** *** *** *** *** *** Diff Diff *** *** *** *** 0.65 *** 0.60 *** Note: Autor's calculation based on FIES data. Standard errors in paranteses. Statistical significance at 0, 5, and percent levels are indicated by *, **, and ***, respectively.

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