Polcy Impacts on, Canned, Dred, and Frozen Frut Consumpton: A Censored Demand System Approach Xaonan Lu Graduate Student Washngton State Unversty Hayley H. Chounard Assocate Professor Washngton State Unversty Thomas L. Marsh Professor Washngton State Unversty Jeffrey T. LaFrance Professor Monash Unversty March 2014 Abstract: We nvestgate consumers demand for fresh and processed frut by applyng a two-step estmaton procedure to a Quadratc Expendture System. Usng homescan data, we fnd that household sze, ncome, regon, and prce sgnfcantly affect frut purchases. We estmate own-prce and cross-prce elastctes and expendture elastctes for 18 categores of frut. More processed fruts serve as substtutes for fresh fruts than complements. Often fresh fruts act as normal goods, and processed fruts as nferor goods. Provdng a retal subsdy for fresh frut or mposng a tax on canned and (or) dred frut could promote fresh frut consumpton for a healther det. KEYWORDS: Elastctes, frut, ed frut, Quadratc Expendture System, Two-step censored demand system JEL Classfcaton: D12, C34, L66
1. Introducton frut contans many mportant nutrents ncludng fber, vtamns, and antoxdants. Suffcent daly consumpton could help prevent heart dsease, cancer, dabetes, obesty, and other health concerns (World Health Organzaton 2003). Between 1967 and 2009, fresh frut consumpton grew 25% n the U.S. (Coo 2011). However, only a small proporton of Amercans meet the U.S. Department of Agrculture (USDA) recommendaton of at least two cups of frut daly (Serdula et al. 2004). Many Amercans eat large amounts of processed frut. Nearly 54% of total frut and vegetable consumpton was n processed form (Coo 2011). ed frut enjoys great popularty gven ts avalablty, convenence, and long shelf lfe. However, some argue that nutrent loss durng the freezng process averages nearly 50%, and cannng results n 60% less nutrents on average than fresh frut (Rcman et al. 2007). Addtonally, processed frut mght nclude harmful ngredents and chemcals, such as, hgh levels of sugar, sulfur doxde, and Bsphenol A (BPA). Ths paper nvestgates consumers preferences for fresh and processed frut products and what characterstcs affect purchase decsons. We estmate consumers wllngness to substtute between varous forms of frut ncludng fresh, canned, dred, and frozen products. We then assess the ablty of a retal subsdy for fresh frut to change the types of frut consumed and the quanttes purchased n order to encourage consumpton of fresh fruts for healther dets. We compare the effects of fresh frut subsdes to the other obvous polcy nstrument, a tax on processed frut purchases. To mprove healthy food consumpton patterns and related health outcomes, polcy maers often consder subsdes to ncentvze consumers (Powell et al. 2013). 1
Evdence from several supermaret trals n Europe suggests subsdes for healther food products ncrease ther consumpton (N Mhurchu et al. 2010, Waterlander et al. 2012). The U.S. Farmers Maret Nutrton Program (FMNP) provdes coupons through the Women, Infants, and Chldren Program (WIC) and to senors n the Commodty Supplemental Food Program (CSFP) for the purchase of locally grown fresh fruts. The evaluaton of the programs shows that reduced prces may ncrease consumpton, but mostly asssts those who would have bought these products wthout the subsdy (Anler et al. 1992, Balsam et al. 1994). Herman et al. (2008) fnd that for women wth relatvely low ncomes, a fresh frut subsdy n the WIC program ncreases ther consumpton. Ths prevous wor tests the effectveness of a subsdy for frut expermentally comparng consumpton wth and wthout a subsdy nterventon. We use the estmated elastctes whch accommodate wde applcatons to smulate the demand change of fresh frut assocated wth a prce reducton. A number of studes have examned consumer demand for fresh or processed frut. Durham and Eales (2010) estmate the demand elastctes for fresh frut and obtan more elastc estmates wth respect to own-prce than the average of prevous estmates (Schmtz and Seale 2002, Katchova and Chern 2004). Jones (2006) fnds that lowerncome consumers generally pay lower prces for fresh fruts and have hgher own-prce elastctes. Khanum et al. (2007) and Taba et al. (2011) estmate own-prce and ncome elastctes for processed frut ncludng jam, juce, and pcles n the Peshawar, regon of Pastan. None of these studes consders the demand for processed frut at a dsaggregated level and the substtuton between fresh and processed frut. 2
To accurately estmate the demand functons for fresh and processed frut products, we must recognze that numerous observatons of zero quantty purchased exst. A large proporton of households do not consume any or all fresh or processed fruts. Falng to account for zero consumpton or drectly applyng the ordnary least squares method to censored data generates based and nconsstent estmates. A consumer s choce not to purchase a good mght occur because they do not le the product or they would le the product but do not purchase the good gven ther ncome level and maret prce. We assume consumers purchase behavor could be explaned by two steps. Frst, households decde whether to purchase or not based on the prce and ther demographc nformaton. Second, they choose the amount of frut to purchase. In practce, errors of measurement of expendtures and quanttes demanded, errors of optmzaton by consumers, and other random dsturbances mght nfluence consumers decsons (Wales and Woodland 1983). We use the two-step estmaton procedure to predct partcpaton decsons and explan consumers behavor. Ths strategy has the flexblty to account for many of the reasons we observe zero consumpton. The two-step censored method allows us to dentfy how the probablty of frut purchase vares wth changng demographc characterstcs usng a probt model. Most related studes nclude only demographc varables lnearly and fnd several of them sgnfcantly mpact purchasng decsons (Yen et al. 2002, Yen 2005). We test for a nonlnear specfcaton and ncorporate the prces of goods n the demand system to mprove the power and accuracy of the model. 3
A Quadratc Expendture System of frut demand s estmated usng a two-step censored estmaton approach. The results suggest that most demographcs and ownprces affect frut purchasng decsons-especally household sze, annual ncome, and regon. Educaton and age also have postve mpacts on determnng frut consumpton, although the effect of age s relatvely small. As expected the prce of the frut negatvely affects the lelhood of purchasng that frut. We fnd that all ordnary own-prce elastctes are negatve, and that an ncrease n fresh (processed) frut prce generally results n an ncreased demand for processed (fresh) frut. Expendture elastctes suggest that fresh fruts are normal goods, whle processed fruts are nferor goods n general. The results also suggest that provdng a 20% subsdy for fresh frut generates an ncrease of 0.85 pounds fresh frut consumpton, whle mposng a 20% tax on canned or dred frut results n approxmately 0.23 pounds more fresh frut demand on average. Ths study contrbutes to the lterature by provdng a thorough analyss of fresh and processed frut demand system at a relatvely dsaggregated level usng censored data. Dfferent from prevous studes usng a two-step censored method, our frst step probt model ncludes square and nteracton terms as well as other frut prces to mprove the accuracy of frut purchase probablty predcton. In addton, we address the heteroscedastcty and endogenety ssue of expendture n the second step demand system usng a Generalzed Method of Moments (GMM) method. We are among the frst to use estmated elastctes to consder subsdes and taxes on fruts to encourage more fresh frut consumpton. 4
The remander of ths paper s organzed as follows. In Secton 2, we specfy the econometrcs model and estmaton strategy. The two-step censored demand system ncorporates a multvarate probt model n the frst step and a Quadratc Expendture System representng consumers preferences n the second step. Secton 3 provdes the data descrpton. Emprcal results are gven n Secton 4. Secton 5 dscusses the effect of subsdy for fresh frut consumpton and an alternatve polcy nterventon, a tax on processed frut to encourage the fresh frut consumpton. We conclude the paper n Secton 6. 2. Model and Emprcal Approach The econometrc model s a two-step double hurdle model of multvarate dscrete contnuous choce. For the contnuous choce component, we assume the Quadratc Expendture System (QES) of Polla and Wales (1978) and Howe, Polla, and Wales (1979). The ndrect utlty functon for ths stage of the model s 1 (1) py b p p, y a p p b c, where p denotes the prce of product and y s the expendture on all products of nterest. We estmate the parameters a,b, c, and. Applyng Roy s dentty, we wrte the mean demand functon for good, condtonal on postve quantty as, b c b c E q q a y a p p y a p, p p (2) 0 2 1 Note that one must have restrctons on a for ndrect preferences to be well-defned at zero consumpton levels for the q. 5
where q represents the quantty of product. The model satsfes Slutsy symmetry by constructon. For addng-up and homogenety condtons, we restrct b 1 and c 1. Before we can estmate the condtonal demand functons n Equaton (2), we must address the fact there are numerous observaton of zero quanttes purchase for each good. Tobn (1958) was the frst to show that drectly applyng the ordnary least squares method to a censored regresson model generates based and nconsstent estmates. He develops a method, now nown as the standard Tobt model, to obtan consstent estmates when the random error term added to Equaton (2) s the sole reason for observatons that are censored at zero consumpton. Amemya (1974) extend ths model to apply for a set of demand equatons. Alternatvely, Hecman (1979) consders zero observatons to be the result of a non-partcpaton decson by the ndvdual. He derves a probt model for the partcpaton decson usng all sample observatons, followed by a second stage estmator usng the subsample wth postve quanttes. Cragg (1971) formulates the double-hurdle model wheren t s assumed that household two separate decsons for partcpaton and quantty purchased. Frst, the household decdes whether or not to purchase the good. Second, condtonal on beng a maret partcpant, the household decdes on how much to purchase. Some consumers mght not purchase a product purely due to economc factors, whch generates a corner soluton n ther constraned optmzaton problem. If the prce decreases suffcently, these consumers wll become maret partcpants and purchase a postve amount of the product. For other consumers, ther preferences may lead them to 6
never purchase the product no matter what happens to prce or other economc factors. They smply do not le the product. If zero consumpton can result from ether a corner soluton or dfferences n preferences, a double-hurdle model can be used to capture demand behavor more flexbly. Snce we have no way to now for sure whether economc factors or consumers preferences may lead to zero frut consumpton, we apply the double-hurdle model, also nown as a two-step censored model, to estmate the system of demand equatons. The two-step censored method consders non-purchases n the frst step usng a probt estmaton and explans the zero observatons n demand equatons due to economc factors n the second step. The strategy has the flexblty to ncorporate other measurement errors n the data besdes consumers random preferences and could solve for a demand system wth hgh dmensonalty. We use the two-step estmaton procedure proposed by Shonwler and Yen (1999) for a system of seemngly unrelated equatons. Equaton (3) shows the household partcpaton decson, * * (3) 1 f d d 0,, d * z 0 f d 0 where d equals 1 f the household purchases the product and zero otherwse. Here, denotes the correspondng latent varable whch depends on a vector of demographc characterstcs and prces of all goods of nterest, conformable vector of parameters, and as the random error. * d z, and whch represents the In the frst step, we estmate multvarate probt models usng a maxmum lelhood method to predct the purchase lelhood of each type of frut. We assume the probablty of frut purchase depends on household sze, ncome, educaton, age, the 7
presence of chld, race, urban or rural area, regon, and prces of all goods n the demand system. Usng stepwse model selecton and consderng the magntudes of the effects, we ncorporate household sze and ts square term, ncome and squared ncome, educaton, age, the presence of chld, race, urban or rural area, regon, own prce and squared own prce, prces of other goods n the demand system, and all possble two-way nteractons between sze, ncome, and own prce n the fnal probt model. 2 Next, those consumers that decde to purchase the product determne how much to purchase. Equaton (4) represents the demand functon, 2 * b c b c * p p (4) q a y a p p y a p, q d q, where q represents the observed quantty demanded, * q s the correspondng latent demand determned by the purchase decson and economc factors ncludng prces and expendture. To ncorporate demographc nformaton to the demand functon, we let a a a h, where a 0 s a constant and h l represent the demographc varables 0 l l l ncludng household sze, ncome, educaton level, age, presence of chld, race, and regon. The parameters for estmaton nclude a,b, c, and, and s the addtve error term. For household t, assume the vector of dsturbances u, 1, 2,...,, 1, 2,..., t t t t t nt t t nt s multvarate normal wth (5) E ut, us f t s In 0 otherwse 2 The square of age and the nteracton between age and sze are ncorporated based on the stepwse model selecton. But the effects of them are too small to be ncluded n the fnal probt model. 8
where,,,..., E E dag. t t j t t 1 2 n In the second step, we nclude the predcted probabltes of purchases found n the frst step to generate consstent estmates. The mean of q becomes, 2 b c b c E( q,, ) ( ) ( p y z z a y a p p y a p z ), p p (6) where () and () are a unvarate standard normal cumulatve dstrbuton functon and a normal probablty densty functon, respectvely. We rewrte Equaton (6) as, 2 b c b c q ( ) z a y a p p y a p ( z ) p p, (7) where s a heteroscedastc error term wth 0 E, and t ncludes random preference, measurement error n quanttes and expendtures, optmzaton error by the consumer, measurement error on purchase nformaton due to reportng, and other possble errors leadng to zero consumpton. The uncompensated own-prce elastcty for product, the uncompensated prce elastcty for product wth respect to product j ( j), and the expendture elastcty for product wth censorng are derved as, 3 (8) E q p, y, z p e p E q p, y, z z q z p q z p p p E q p, y, z z e z p z q z p p q p E q p, yz,, 3 See the dervaton of Equatons (8)-(10) n Appendx 1. 9
c y 2 1 a p b a 2a c where e 1 c b p y a p q pq q. (9) E q,, p y z p j ej p E q p, y, z j z q z p j q z p p p E q p, y, z j j j z ej z p j p p q p E q p, y, z z q z j j j, c j y a p 2 b a p 2a p c ej c b p y a p pq pq pq. j j j j where 2 z y c (10) 2 ey b c b p y a p. E q p, y, z p Equaton (7) s estmated usng GMM regresson to account for heteroscedastcty and endogenety of expendture. We suspect reverse causalty exsts snce the expendture contans the nformaton of quantty demanded. LaFrance (1991) shows expendture seldom s strctly exogenous. So smply ncludng expendture could result n based estmates. The expendture for next year, the year 2005, s used as an nstrument varable to address the endogenety ssue. We fnd a strong correlaton between the expendture ths year and the nstrument varable and assume that the nstrument varable s 10
exogenous. 4 From the Wald statstc and correspondng p-value, we fal to reject the null hypothess of moment-equaton valdty at 0.05 type I error. 3. Data We use Nelsen Homescan Data for 2004, whch ncludes expendtures and quanttes of food consumpton as well as demographc nformaton for 125,000 households n 52 marets n the 48 contnental states. We aggregate expendtures and quanttes of frut products from weely to annual levels to reduce the observatons of zero consumpton due to nventory and other reasons except for economc factors and consumers dsle of the good. For fresh frut, we nclude apples, aprcots, cherres, nectarnes and peaches, pears, plums, and other frut from fresh frut taen from the UPC codes and random weghts secton of the Nelsen dataset. 5 The random weghts department records purchases of frut sold by weght. We aggregate processed frut nto (1) canned apples and apple sauce; (2) canned aprcots; (3) canned nectarnes and peaches; (4) canned pears; (5) all other canned frut; (6) dred apples; (7) dred aprcots; (8) dred plums; (9) other dred frut; (10) frozen nectarnes and peaches; and (11) other frozen frut. 6 We 4 The correlaton between the endogenous expendture and the nstrument varable s 0.86 and most of the correlaton between quantty and the nstrument varable s lower than 0.30. Gven the avalablty of data, we test the valdty of other possble nstrument varables ncludng prces and ncome n 2005 and 2006 and expendture n 2006. However, t turns out they are not as strong as the expendture n 2005. We estmate ths demand system usng seemngly unrelated regresson wth Whte s covarance for results wthout control of expendture endogenety. The results of GMM regresson wth nstrument varable are more desrable snce there are a larger number of sgnfcant parameters and estmated elastctes ft better wth economc ntuton. 5 Other fresh fruts nclude berres, grapefrut, ws, pneapples, oranges, cranberres, bananas, lemons, lmes, papayas, avocados, guavas, tangernes, cantaloupes, honeydew, mangos, tangelos, watermelons, frut salad-platter-baset, and other remanng fresh frut. 6 Other canned fruts nclude canned berres, canned cherres, canned grapefrut, canned oranges, canned pneapples, canned plums, canned prunes, canned mxed frut, and remanng canned frut. Other dred frut 11
select products that at least 2 percent of households purchase n the sample. We do not nclude frut juce n the dataset as we want to consder only food products and not drns. Based on Moschn et al. (1994) and Henneberry et al. (1999), we assume fresh and processed frut satsfy wea separablty from other food. Annual average prce, whch s nown as unt value (Deaton 1988), equals the rato of aggregated expendture to aggregated quantty. Snce store prce data s not avalable, we mpute predcted prces for censored observatons. For each product, we specfy the observed prce as a lnear functon of household sze, ncome, regonal dummy varables, as well as urban dummy varables, and estmate and predct the prces usng ordnary least squares. The dataset ncludes 38,433 purchase records of fresh and processed fruts. Table 1 shows demographc nformaton for households n the dataset. The average annual household ncome s $52,300, whch s hgher than the medan of U.S. annual ncome $44,389 n 2004 (U.S. Census Bureau). Nearly 22% of household heads fnshed hgh school as a maxmum educaton level, 63% of household heads graduated from college, and 16% ganed a post college degree. The educatonal attanment here s hgher than the U.S. average level where 27.7% of populaton obtans a Bachelors or hgher degree. We use educaton level to capture household exposure to health nformaton whch cannot be reflected n annual ncome. Household heads average the age of 44. Approxmately, 25% of households have a chld under the age of 18. Whte households account for nearly 80% of all households, whch matches the U.S. populaton (U.S. Census Bureau). Nearly 85% nclude dred bananas, dred pneapples, rasns, dates, and other dred frut.other frozen frut ncludes frozen apples, frozen aprcots, frozen berres, and other frozen frut. 12
of partcpatng famles resde n urban regons, whch s about 5% above the natonal level (The World Ban). We present frut expendture statstcs and percentages of consumng households n Table 2. Of the whole sample, 53% and 15% consumers purchase fresh apples and peaches, whle the numbers are 37% and 41% for processed apples and peaches, respectvely. Only 5% of households consume processed cherres, and the fewest households consume fresh aprcots, at 2%. Consumers spend more on fresh frut n total for both the whole sample and the consumng households. Expendtures on fresh apples, peaches, and cherres are relatvely hgh. Among the whole sample, famles spend more on processed peaches and apples, whle the expendtures on processed plums and cherres are the hghest for consumng households. Table 3 dsplays summary statstcs for annual quanttes and average annual prces for fresh and processed fruts. Households purchase more fresh frut than processed frut whle payng less for fresh frut on average. Apples and peaches enjoy the greatest popularty among all the fresh and processed frut. and processed aprcots and fresh cherres have the hghest prces and the lowest consumpton for consumng households. We observe a large quantty dfference between the whole sample and the consumng households, whch ndcates the necessty to account for the non-purchasng decsons. 4. Emprcal Results We report the rates of correct predcton and the frst-step margnal effects from the probt models for fresh and processed frut n Tables 4-5. The rates of correct 13
predcton for the lelhood of frut purchase range from 63% to 98%. We correctly predct more than 81% of the sample for all the frut consumpton except fresh apples. Most of the demographc characterstcs are sgnfcant at the 10% level. We fnd that the mpacts of household sze vary by type of fresh fruts, but playng a sgnfcant postve role for processed fruts. Among fresh fruts, household annual ncome postvely affects the consumpton of aprcots, cherres, pears, and plums, however, the mpacts are relatvely small. Increasng ncome leads to a lower probablty of processed frut consumpton ncludng apples, peaches, and pears gven ther negatve margnal effects. We observe a postve effect of educaton on frut consumpton by comparng the margnal effects for hgh school and college graduates. The more educated household mght have more exposure to the health nformaton so they may purchase more healthy products. The probabltes of purchasng both fresh and processed frut sgnfcantly ncrease wth age, whch mght be due to the fact that older people mght care more about ther health so they may consume more of all types of frut. However, the effect of age on frut purchase s small. Households wth chldren under 18 are more lely to purchase fresh frut except for cherres and peaches. In partcular, fresh apple has the largest margnal effect of presence of chld and fresh apples may fnd ther way nto bagged lunches or chldren s snacs more than other frut. Race sgnfcantly affects the purchase of fresh and processed frut. Generally, whte famles have a hgher probablty of consumng processed frut, whle blac, Asan, and Hspanc households are less lely to buy processed frut and have a hgher probablty of purchasng fresh frut. Regon has a sgnfcant mpact on frut purchase but t dffers by frut. Compared wth fresh frut, the households resdng n an urban area are more lely to consume processed frut. 14
Own prce plays a negatve role n determnng the consumpton for all nds of frut and t generally has a larger effect on processed frut consumpton than fresh frut, whch ndcates that prce change would result n a greater fluctuaton n the probablty of processed frut purchase. (processed) frut prces affect the probablty of fresh (processed) frut consumpton more sgnfcantly than processed (fresh) frut prces, whle the sgns of the effects vary by category of fruts. The results of parameters n the second-step estmaton of the demand system are shown n Appendx 2. We use these results to generate prce and expendture elastctes usng Equatons (8)-(10). For statstcal nferences, we use the delta method to calculate the approxmate standard errors of elastctes. We report the compensated prce elastctes consderng aggregated and dsaggregated processed products n Tables 7-8. We fnd the roles of prces consstent wth economc theory: (1) an ncrease n frut prce would lead to decreased quantty demanded, (2) an ncrease n processed frut prce would generally result n ncreased consumpton of fresh frut, and vce versa. Table 6 provdes the compensated prce elastctes and expendture elastctes wth aggregated processed frut. We fnd sgnfcantly negatve compensated own prce elastcty estmates. pears, fresh plums, and processed peaches have own-prce elastctes greater than unty, whch reflect elastc demand. fruts generally have larger own-prce elastctes than processed fruts. It suggests that demand of fresh frut has the greater change gven the same prce varaton. We fnd the compensated cross prce elastctes less than one, wth about half sgnfcant at the 10% level. Ths mples that fruts tend to have nelastc cross prce elastctes. Among fresh fruts, nether substtutes nor complements domnate, whle 15
complements domnate n processed fruts. More fruts serve as net substtutes than net complements between fresh and processed fruts gven a majorty of postve cross-prce elastctes. Specfcally, we fnd that about half of the cross-prce elastctes of fresh frut wth respect to processed frut are sgnfcant, wth only a few sgnfcant cross-prce elastctes of processed frut wth respect to fresh frut. Ths mples a prce change of processed frut has a more sgnfcant mpact on demand of fresh frut than the reversed effect. We note that the cross-prce elastctes of processed apples wth respect to all other nds of frut approach zero. Thus, the quantty demanded of processed apples seems nsenstve to the prce changes of other fruts. Consumers mght treat processed apples as necesstes. The expendture elastctes are lsted n the last row n Table 6. fruts have postve and sgnfcant expendture elastctes wth the excepton of fresh cherres. fruts generally act as normal goods. The expendture elastctes for fresh apples, peaches, and pears exceed one. We obtan negatve expendture elastctes for all processed fruts wth the excepton of apples and cherres, whch ndcate that processed fruts more act as nferor goods. Among all the fruts, fresh peaches have the largest expendture elastcty and processed apples the smallest n absolute value. Table 7 dsplays the compensated elastctes and expendture elastctes wth dsaggregated processed frut. Compared wth the results of aggregated processed frut, we fnd that most of the own-prce elastctes of fresh frut become more elastc wth dsaggregated processed frut, whch mght be the case that consumers would be more responsve to prce change f more varety of frut offered. We fnd elastc and sgnfcant 16
own-prce elastctes of canned and dred apples. So ther demand changes are larger compared wth other nds of processed frut. Among the cross-prce elastctes, canned and dred fruts generally serve as net substtutes for fresh fruts, whle nether net substtutes nor net complements domnate between fresh and frozen fruts. Dfferent from the case wth aggregated processed frut, we notce elastc cross-prce elastctes wth many nvolvng frozen fruts. Ths suggests that the demand of frozen fruts responds to prce varatons of other fruts and frozen frut prce changes generate relatvely large effects on other frut consumpton. We estmate all of the cross-prce elastctes of canned apples less than 0.03 n absolute values, whle they are relatvely large for dred apples, suggestng that canned apples contrbute to the nsenstve demand of aggregated processed apples. We fnd nsgnfcant cross-prce elastctes of canned peaches and other frozen frut, whch mples that the prce change of other frut does not generate sgnfcant effect on the demand of these fruts. We fnd the expendture elastctes of fresh frut slghtly smaller than the expendture elastctes n the aggregate processed frut. Except for other dred fruts, the expendture elastctes of processed fruts reman negatve, but only canned peaches have a sgnfcant expendture elastcty. Dred apples, dred plums, and frozen peaches have large expendture elastctes above unty, ndcatng that households would consume relatvely less of them f expendture ncreases. The uncompensated prce elastctes are presented n Appendx 3. We fnd the uncompensated own-prce elastctes smlar to the compensated own-prce elastctes. Some varatons between uncompensated and compensated cross-prce elastctes exst 17
stemmng from expendture effects. For example, we observe a negatve uncompensated cross-prce elastcty of fresh pears wth respect to fresh apples and a correspondng postve compensated cross-prce elastcty. Ths suggests that fresh pears and fresh apples are gross complements and net substtutes. 5. Impacts of Polcy Interventons on and ed Frut We consder how polcy maers mght use varous nstruments to encourage the consumpton of fresh frut. Table 8 provdes the results of the quantty changes for each category of frut after provdng a 20% retal subsdy for fresh frut. We use the compensated elastctes wth dsaggregated processed frut to generate these results. Wth the 20% subsdy for fresh frut, the consumpton of fresh frut ncreases 0.05%. It ndcates that average household would consume 0.85 pounds more fresh frut annually gven a 20% prce reducton. Addtonally, we observe for a small ncrease of 0.28 pounds of processed frut gven the fresh frut subsdy. Ths annual fresh frut subsdy would cost $0.21 per capta when calculated at the sample means of prce and quantty. As an alternatve to provdng a subsdy, polcy maers mght mpose a tax on processed frut. Snce we consder more than one type of frut, the effects of taxes and subsdes whch are determned by own- and cross-prce elastctes are not necessarly equvalent. Some suggest taxes on specfc foods or nutrents may curb unhealthy eatng habts. Taxes on soft drns and snac foods result n small changes n consumpton (Fletcher et al. 2010, Kuchler et al. 2005). Most often taxes on fat drectly show lmted health effects (Chounard et al. 2007, Clar and Dttrch 2010). We show the demand changes gven a 20% tax on processed frut n Table 8. Surprsngly, an ncrease n the 18
prce of processed frut due to a tax results n less consumpton of fresh frut. The decrease of fresh frut consumpton occurs due to the relatvely large negatve cross-prce elastctes. The consumpton of most fresh frut would ncrease and decrease for most processed frut f polcy maers mpose a tax on canned and (or) dred frut. We obtan consstent results n whch substtutes domnate between canned, dred, and fresh fruts. Levyng a tax on frozen frut would not encourage the consumpton of fresh frut or lower the amount of processed frut eaten n general. Each consumer pays $0.08, $0.05, $0.04, and $0.03 for the annual tax on all processed frut, canned, dred, and frozen frut, respectvely. However, a tax on processed frut mght ncrease the consumpton of less healthy products such as chps or baed goods and ultmately reduce the health of consumers. 6. Conclusons We nvestgate consumer demand for 18 categores of fresh and processed fruts usng Nelsen Homescan data n 2004. We examne a Quadratc Expendture System usng a two-step estmaton procedure to provde accurate estmates gven numerous observatons of zero consumpton. The models estmated n ths research provde emprcal evdence to help us understand the mportance of consumers demographcs and prce n mang purchase decsons. The results suggest that household sze, annual ncome, educaton level, race, resdence locaton, and prce play mportant roles n determnng frut consumpton. Based on the results of compensated prce elastctes and expendture elastctes, we fnd negatve and sgnfcant own-prce elastctes for fresh and processed fruts. 19
More processed fruts serve as substtutes for fresh fruts than complements. Often fresh fruts act as normal goods, and processed fruts as nferor goods. Provdng subsdes for fresh fruts could encourage a small ncrease n ther consumpton. Gven a 20% prce reducton, average household would consume 0.85 pounds more fresh fruts n a year. The annual natonal fresh frut subsdy would cost $6.2 mllon. As an alternatve to a subsdy, we fnd that mposng a tax on all processed frut would lead to a decrease of fresh frut consumpton. A prce ncrease of canned and (or) dred frut would decrease the consumpton of processed frut and ncrease the amount of fresh frut purchased, but wth a smaller effect compared wth the subsdy. Polcy maers should carefully consder a tax on processed frut as t mght ncrease the consumpton of less healthy products such as chps or baed goods and ultmately reduce the health of consumers. 20
References Amemya, T. 1974. Multvarate Regresson and Smultaneous Equaton Models When the Dependent Varables are Truncated Normal. Econometrca 42, 999-1012. Anler, J.A., Wnne, M., and Drae, L.T. 1992. An Evaluaton of the Connectcut Farmers Maret Coupon Program. Journal of Nutrton Educaton 24, 185-191. Balsam, A., Webber, D., and Oehle, B. 1994. The Farmers Maret Coupon Program for Low-Income Elders. Journal of Nutrton for the Elderly 13, 35-42. Chounard, H.H., Davs, E.D., LaFrance, T.J., and Perloff, M.J. 2007. Fat Taxes: Bg Money for Small Change. Forum for Health Economcs and Polcy 10 (2), Artcle 2. Clar, J. and Dttrch, O. 2010. Alternatve Fat Taxes to Control Obesty. Internatonal Advances n Economc Research 16(4), 388-394. Cragg, J. 1971. Some Statstcal Models for Lmted Dependent Varables wth Applcaton to the Demand for Durable Goods. Econometrca 39(5), 829-844. Coo, R. 2011. Tracng Demographcs and U.S. Frut and Vegetable Consumpton Patterns. Department of Agrcultural and Resource Economcs, Unversty of Calforna, Davs. Deaton, A.S. 1988. Qualty, Quantty, and Spatal Varaton of Prce. Amercan Economc Revew 78, 418-430. Durham, C. and Eales, J. 2010. Demand Elastctes for Frut at the Retal Level. Appled Economcs 42, 1345-1354. Fletcher, M.J., Frsvold, E.D., and Tefft, N. 2010. Can Soft Drn Taxes Reduce Populaton Weght? Contemporary Economc Polcy 28 (1), 23-35. Hecman, J. 1979. Sample Selecton Bas as a Specfcaton Error. Econometrca 47(1), 153-161. Henneberry, S.P., Pewthongngam, K., and Qang, H. 1999. Consumer Food Safety Concerns and Produce Consumpton. Journal of Agrcultural and Resource Economcs 24, 98-113. Herman, D.R., Harrson, G.G., Aff, A.A., and Jens, E. 2008. Effect of a Targeted Subsdy on Intae of Fruts and Vegetables among Low-ncome Women n the Specal Supplemental Nutrton Program for Women, Infants, and Chldren. Amercan Journal of Publc Health 98, 98-105. Howe, H., Polla, R.A., and Wales, T.J. 1979. Theory and Tme Seres Estmaton of the Quadratc Expendture System, Econometrca 47, 1231-1248. Jones, E. 2006. The Economcs of Eatng Fruts and Vegetables: Recognzng Dscernble Patterns for Obesty Dfferences among Lower- and Hgher-Income Consumers. Selected Paper prepared for presentaton at the Southern Agrcultural Economcs Assocaton Annual Meetngs. Katchova, A.L. and Chern, W.S. 2004. Comparson of Quadratc Expendture System and Almost Ideal Demand System based on Emprcal Data. Internatonal Journal of Appled Economcs 1, 55-64. Khanum, S., Chsht, F.A., Khan, D., and Kran, B. 2007. Estmaton of Demand for ed Frut and Vegetable Products n Hayatabad, Peshawar. Sarhad Journal of Agrculture 23(4), 1273-1278. 21
Kuchler, F., Tegene, A., and Harrs, J.M. 2005. Taxng Snac Foods: Manpulatng Det Qualty or Fnancng Informaton Programs? Revew of Agrcultural Economcs 27(1), 4-20. LaFrance, J.T. 1991. When s Expendture Exogenous n Separable Demand Models? Western Journal of Agrcultural Economcs 16(1), 49-62. McFadden, D. 1974. The Measurement of Urban Travel Demand. Journal of Publc Economcs 3, 303-328. Moschn, G., Moro, D. and Green, R.D. 1994. Mantanng and Testng Separablty n Demand Systems. Amercan Journal of Agrcultural Economcs 76, 61-73. N Mhurchu, C., Blaely, T., Jang Y., Eyles, H.C., and Rodgers, A. 2010. Effects of Prce Dscounts and Talored Nutrton Educaton on Supermaret Purchases: a Randomzed Controlled Tral. Amercan Journal of Clncal Nutrton 91(3), 736-747. Polla, A.R. and Terence, J.W. 1978. Estmaton of Complete Demand Systems from Household Budget Data: The Lnear and Quadratc Expendture Systems. Amercan Economc Revew 68, 348-359. Powell, L.M., Chrqu, J.F., Khan, T., Wada, R., and Chaloupa, F.J. 2013 Assessng the Potental Effectveness of Food and Beverage Taxes and Subsdes for Improvng Publc Health: a Systematc Revew of Prces, Demand and Body Weght Outcomes. Obesty Revews 14(2), 110-128. Rcman, J.C., Barrett, D.M., and Bruhn, C.M. 2007. Nutrtonal Comparson of, Frozen and Canned Fruts and Vegetables. Part 1. Vtamns C and B and Phenolc Compounds. Journal of the Scence of Food and Agrculture 87, 930-944. Schmtz, T.G. and Seale, Jr. J.L. 2002. Import Demand for Dsaggregated Fruts n Japan. Journal of Agrcultural and Appled Economcs 34, 585-602. Serdula, M.K., Gllespe, C., Kettel-Khan L., Farrs, R., Seymour, J., and Denny, C. 2004. Trends n Frut and Vegetable Consumpton among Adults n the Unted States: Behavoral Rs Factor Survellance System, 1994-2000. Amercan Journal of Publc Health 94, 1014-1018. Shonwler, J.S. and Yen, T.S. 1999. Two Step Estmaton of a Censored System of Equatons. Amercan Journal of Agrcultural Economcs 81, 972 982. Taba, B., Khan, M., Nazr, M., Sajjad, M., Jan, D., and Jan, A.U. 2011. Estmaton of Demand for ed Frut and Vegetables Products n Professor Colony (Peshawar). Interdscplnary Journal of Contemporary Research n Busness 3(8), 688-704. Tobn, J. 1958. Estmaton of Relatonshps for Lmted Dependent Varables. Econometrca 26(1), 24-36. Waterlander, W.E., Steenhus, I.H., de Boer M.R., Schut, A.J., and Sedell, J.C. 2012. Introducng Taxes, Subsdes or Both: The Effects of Varous Food Prcng Strateges n a Web-Based Supermaret Randomzed Tral. Preventve Medcne 54, 323-330. Yen, S.T. 2005. A Multvarate Sample-Selecton Model: Estmatng Cgarette and Alcohol Demands wth Zero Observatons. Amercan Journal of Agrcultural Economcs 87(2), 453-66. Yen, S.T., Kan, K., and Su, S. 2002. Household Demand for Fats and Ols: Two-Step Estmaton of a Censored Demand System. Appled Economcs 34(14), 1799-1806. 22
Table 1: Descrptve Statstcs for Household Demographcs Varable Defnton Mn Max Mean Standard Devaton Household Sze Number of household members 1.00 9.00 2.40 1.32 Household Income Annual ncome n $10,000 0.25 12.50 5.23 3.21 Hgh School Max educaton of household head some/graduated hgh school 0.00 1.00 0.22 0.41 College Max educaton of household head some/graduated college 0.00 1.00 0.63 0.48 Post College Max educaton of household head post college 0.00 1.00 0.17 0.36 Age Mean age of female and male heads 10.00 70.00 43.50 15.78 Presence of Chld Presence of chld under 18 0.00 1.00 0.25 0.43 Whte Household race s whte 0.00 1.00 0.80 0.40 Blac Household race s blac 0.00 1.00 0.09 0.29 Asan Household race s Asan 0.00 1.00 0.02 0.14 Hspanc Household race s Hspanc 0.00 1.00 0.07 0.25 Other Race Household race s other race 0.00 1.00 0.02 0.13 Urban Resdes n urban area 0.00 1.00 0.85 0.36 East Resdes n east regon 0.00 1.00 0.16 0.37 Central Resdes n central regon 0.00 1.00 0.23 0.42 South Resdes n south regon 0.00 1.00 0.38 0.49 West Resdes n west regon 0.00 1.00 0.24 0.42 23
Table 2: Annual Expendtures for and ed Frut and the Percentage of Consumng Households Frut Whole Sample Expendture ($) Consumng Households Expendture ($) Percentage of Consumng Households n Whole Sample Mean Standard Devaton Mean Standard Devaton Apple 7.84 17.98 14.89 22.56 52.66% Aprcot 0.08 1.01 3.83 5.67 2.21% Cherry 1.09 5.20 11.06 12.8 9.86% Peach 1.34 5.65 9.02 12.04 14.89% Pear 0.64 3.40 6.54 8.95 9.73% Plum 0.46 2.38 4.88 6.20 9.42% Other Frut 34.26 53.54 40.19 55.89 85.25% ed Apple 2.11 5.87 5.77 8.54 36.66% ed Aprcot 0.71 3.54 5.67 8.49 12.53% ed Cherry 0.40 3.05 7.23 10.99 5.46% ed Peach 2.76 8.04 6.77 11.45 40.81% ed Pear 1.02 4.04 4.38 7.44 23.30% ed Plum 1.12 5.76 8.59 13.77 13.06% Other ed Frut 12.06 19.29 13.97 20.11 86.32% Notes: Consumng households purchase frut at least once a year. The sample sze of the whole sample s 38,433. Peach ncludes nectarnes and peaches. 24
Table 3: Annual and ed Frut Quanttes Purchased and Prces for Whole Sample and Consumng Households Frut Whole Sample Quantty (pounds) Consumng Households Quantty (pounds) Prce ($/pound) Mean Standard Devaton Mean Standard Devaton Mean Standard Devaton Apple 8.47 17.60 16.08 21.58 1.02 1.94 Aprcot 0.05 0.53 2.17 2.87 1.97 1.47 Cherry 0.58 3.30 5.89 8.90 2.52 1.89 Peach 1.17 4.92 7.82 10.50 1.32 1.29 Pear 0.69 3.74 7.13 9.91 1.08 0.93 Plum 0.36 1.78 3.80 4.56 1.57 2.77 Other Frut 24.84 48.10 29.14 50.88 2.34 1.86 ed Apple 2.11 5.35 5.74 7.57 1.49 3.00 ed Aprcot 0.24 1.17 1.93 2.75 3.54 3.16 ed Cherry 0.08 0.58 1.49 2.03 6.02 6.03 ed Peach 1.82 4.53 4.46 6.20 1.74 1.67 ed Pear 0.70 2.38 2.99 4.18 1.59 1.39 ed Plum 0.47 2.31 3.62 5.43 3.11 5.38 Other ed Frut 6.26 8.41 7.25 8.65 2.03 1.51 Note: Peach ncludes nectarnes and peaches. 25
Table 4: Margnal Effects from Probt Estmaton for Frut Consumpton Varable Demographc Varables Apple Aprcot Cherry Peach Pear Plum Other Household Sze -0.052*** -0.006*** 0.009*** -0.029*** -0.030*** 0.008*** 0.023*** Household Income -0.063*** 0.004*** 0.020*** -0.010*** 0.004*** 0.005*** 0.018*** Hgh School -0.029*** -0.015*** -0.031*** -0.026*** -0.039*** -0.028*** -0.057*** College -0.028*** -0.008** -0.015** -0.012-0.017-0.016-0.031** Age 0.003*** 0.000*** 0.001*** 0.000*** 0.001*** 0.000*** 0.002*** Presence of Chld 0.040*** 0.002-0.009*** -0.003** 0.003*** 0.004*** 0.015*** Whte -0.003 0.007 0.009 0.010-0.013* -0.005 0.013 Blac -0.012-0.008 0.016** 0.025*** 0.005* 0.016*** -0.021* Asan -0.026 0.004** 0.037*** 0.010** 0.005-0.008-0.005 Hspanc 0.006 0.013** 0.019** 0.038*** 0.013 0.025*** 0.018 Urban 0.035*** 0.031 0.116*** 0.073*** 0.090*** 0.137*** -0.045*** East 0.121*** -0.010*** 0.014*** -0.005 0.023*** 0.026*** -0.009 Central 0.126*** -0.038*** -0.024*** -0.050*** -0.028*** -0.050*** 0.057** South 0.092*** -0.013*** -0.012*** -0.032*** -0.021*** 0.002-0.017*** Prces Apple -0.107*** 0.001*** 0.002*** 0.005*** 0.003*** 0.003*** 0.029*** Aprcot 0.018-0.088*** 0.013*** 0.030*** 0.015*** 0.013*** 0.024 Cherry 0.009** 0.002*** -0.240*** 0.014*** 0.004*** 0.017*** 0.026*** Peach 0.011* 0.001 0.005*** -0.266*** 0.002 0.006*** 0.075*** Pear 0.073*** 0.003*** 0.013*** 0.061*** -0.096*** 0.020*** 0.038*** Plum 0.017*** 0.001*** 0.001 0.002 0.005*** -0.249*** 0.007 Other Frut -0.050*** -0.015*** -0.041*** -0.110*** -0.068*** -0.065*** -0.351*** ed Apple 0.000 0.000 0.002** 0.001 0.000 0.000 0.002 ed Aprcot 0.001 0.000 0.000 0.000 0.000 0.000-0.002 ed Cherry -0.001 0.000 0.000 0.000 0.000 0.000-0.001 ed Peach 0.000 0.001** 0.004*** 0.005*** 0.003** 0.006*** 0.001 ed Pear 0.002 0.000 0.002 0.000 0.000 0.000-0.003 ed Plum 0.001 0.000 0.001*** 0.001 0.000 0.000 0.000 Other ed Frut 0.000 0.001** 0.003*** 0.002** 0.004*** 0.001-0.002* Log Lelhood -22653-2892 -8574-10880 -9512-9369 -12620 Pseudo R-squared 0.148 0.291 0.307 0.327 0.224 0.219 0.215 Rate of Correct Predcton 0.627 0.981 0.927 0.884 0.911 0.913 0.811 Notes: Peach ncludes nectarnes and peaches. The reference levels of educaton, race, and regon are post college, other race, and west, respectvely. Pseudo R-squared s a lelhood rato ndex that s analogous to the R-squared n the lnear regresson model suggested by McFadden (1974). The margnal effects are calculated at the sample means. Sgnfcance levels: ***0.01; **0.05; *0.10. 26
Table 5: Margnal Effects from Probt Estmaton for ed Frut Consumpton Varable Demographc Varables ed Apple ed Aprcot ed Cherry ed Peach ed Pear ed Plum ed Other Household Sze 0.023*** 0.032*** 0.038*** 0.041*** 0.021*** 0.036*** 0.028*** Household Income -0.003*** 0.028*** 0.027*** -0.004*** -0.003*** 0.022*** 0.005*** Hgh School -0.014** -0.032*** -0.015*** 0.027*** 0.007-0.019*** -0.013*** College -0.003-0.021*** -0.007*** 0.003 0.008-0.018*** -0.008 Age 0.002*** 0.002*** 0.000*** 0.003*** 0.002*** 0.003*** 0.003*** Presence of Chld 0.008-0.001-0.006-0.003 0.009-0.020*** -0.005 Whte 0.049*** 0.004 0.006 0.000 0.026* -0.013 0.028*** Blac 0.008-0.032*** -0.019** 0.025-0.030* -0.004 0.014 Asan -0.073*** -0.006-0.007-0.058*** -0.067*** 0.002-0.013 Hspanc -0.018-0.002-0.008-0.040** -0.025 0.002 0.001 Urban 0.226*** 0.118*** 0.086*** 0.197*** 0.204*** 0.187*** 0.042*** East -0.036*** -0.004-0.010*** -0.012-0.030*** -0.006* 0.010** Central -0.166*** -0.058*** 0.005** -0.080*** -0.090*** -0.078*** 0.010** South -0.043*** 0.006 0.020*** 0.022*** -0.017* -0.010** 0.023*** Prces Apple 0.006* -0.005 0.000 0.003* 0.002-0.002 0.001 Aprcot 0.005 0.009** 0.005 0.003-0.001 0.002 0.012 Cherry 0.002 0.002 0.003*** -0.002-0.001 0.003-0.002 Peach -0.006-0.009** 0.000 0.001-0.001 0.002-0.003 Pear 0.004* 0.003 0.001-0.003-0.001 0.006-0.011** Plum -0.001 0.001 0.001 0.003 0.000-0.005* -0.001 Other Frut 0.004*** 0.002*** 0.001** 0.002* 0.003* 0.001 0.003*** ed Apple -0.852*** 0.001 0.001* 0.000 0.000 0.001-0.001 ed Aprcot -0.001-0.316*** 0.001*** -0.012*** -0.012*** 0.006*** 0.000 ed Cherry 0.001 0.002** -0.125*** -0.005*** -0.004** 0.000-0.002 ed Peach 0.005*** 0.005*** 0.003*** -0.720*** -0.005*** 0.002-0.002 ed Pear 0.010*** 0.002 0.000 0.002-0.627*** -0.004* -0.001 ed Plum 0.000 0.000 0.000 0.001 0.000-0.355*** 0.000 Other ed Frut 0.001 0.002** 0.002*** -0.011*** -0.011*** 0.002** -0.294*** Log Lelhood -15742-8855 -4838-18834 -15459-10053 -12885 Pseudo R-squared 0.377 0.390 0.406 0.275 0.259 0.325 0.160 Rate of Correct Predcton 0.812 0.926 0.966 0.753 0.838 0.911 0.844 Notes: Peach ncludes nectarnes and peaches. The reference levels of educaton, race, and regon are post college, other race, and west, respectvely. The margnal effects are calculated at the sample means. Sgnfcance levels: ***0.01; **0.05; *0.10. 27
Frut and Expendture Apple Aprcot Table 6: Compensated Prce and Expendture Elastctes wth Aggregated ed Frut Cherry Peach Pear Plum Apple -0.88*** 0.18 0.01 0.24*** 0.20*** 0.14*** 0.07*** 0.02 0.01-0.23** -0.01-0.03-0.02-0.02** Aprcot -0.14-0.69** 0.12-0.50-0.40-0.27-0.06* 0.01 0.07** 0.30 0.04 0.09 0.08** 0.04** Cherry 0.18*** 0.75* -0.96*** 0.60*** 0.49*** 0.33*** 0.09*** -0.01* -0.07-0.39** -0.04-0.10** -0.09* -0.05** Peach -0.03-0.47** 0.12-0.84*** -0.18-0.12 0.01 0.01 0.08 0.06 0.03** 0.06 0.06 0.02 Pear -0.01** -0.24 0.06** -0.10-1.19*** -0.05 0.01* 0.01 0.04 0.01 0.02 0.03 0.03 0.01 Plum 0.01-0.09 0.03-0.03-0.02-1.08*** 0.01* 0.02 0.02-0.02 0.01 0.01 0.01 0.00 Other 0.54*** 1.55** -0.16 1.51*** 1.25*** 0.86*** -0.70*** 0.02-0.07** -1.20-0.07** -0.22*** -0.18* -0.13** Apple 0.01** 0.05-0.01 0.04 0.04 0.02 0.01* -0.55*** 0.00-0.03 0.00-0.01-0.01 0.00 Aprcot -0.01*** 0.04* -0.02 0.03*** 0.03*** 0.02*** 0.01*** 0.00-0.72*** -0.02 0.00-0.01** -0.01* -0.00* Cherry -0.14* -0.30 0.01-0.36-0.30-0.21-0.09** 0.00 0.00-0.39* 0.01 0.05 0.03 0.03 Peach 0.03*** 0.11-0.02 0.09*** 0.07*** 0.05*** 0.01*** 0.00-0.01-0.06-1.81*** -0.02** -0.01* -0.01* Pear 0.02*** 0.10-0.02 0.07** 0.06*** 0.04*** 0.01*** 0.00-0.01-0.04-0.01-0.40** -0.01* -0.01* Plum 0.06*** 0.28-0.05** 0.21*** 0.17*** 0.11*** 0.03*** 0.00-0.03** -0.13-0.02-0.04** -0.85*** -0.02* Other 0.07** 0.57-0.13 0.34 0.27 0.17 0.01 0.01** -0.08*** -0.13-0.04-0.08-0.07-0.57*** Expendture 1.53*** 0.67** -0.80* 2.06*** 1.42 0.60*** 0.41*** 0.12-0.27 0.15-0.37** -0.35*** -0.15-0.13*** Notes: The table shows the prce elastcty gven that the prce of the good shown n the column changes. Peach ncludes nectarnes and peaches. Sgnfcance levels: ***0.01; **0.05; *0.10. Other Apple Aprcot Cherry Peach Pear Plum Other 28
Frut and Expendture Apple Aprcot Table 7: Compensated Prce and Expendture Elastctes wth Dsaggregated ed Frut Cherry Peach Pear Plum Other Can Apple Apple -0.97*** 0.07 0.10 0.06 0.07*** 0.08 0.08*** 0.01** 0.02 0.01 0.02 0.01-0.26*** 0.04** 0.00 0.06** -0.63 0.04 Can Aprcot Aprcot 0.26** -1.97*** 0.92*** 2.03 0.35 0.59* 0.09*** -0.03-0.32** -0.06-0.23*** -0.19-1.00-0.14-0.96 0.30** 4.21** -0.59 Cherry 0.02 0.33** -0.62*** -0.41-0.03-0.08 0.03*** 0.02 0.08 0.02 0.06** 0.05 0.05 0.06 0.21-0.03-1.33*** 0.15 Peach -0.06** 0.48** -0.28-1.10** -0.09-0.17 0.00 0.02 0.12** 0.03 0.09 0.07 0.24** 0.07 0.33-0.08-1.72 0.22 Pear 0.02 0.07 0.00-0.05-1.61*** 0.00 0.02*** 0.01 0.02 0.01 0.01 0.01-0.05 0.02 0.03 0.01-0.33 0.03 Plum 0.01 0.26-0.09-0.30-0.01-1.85*** 0.03*** 0.01 0.07 0.02 0.05 0.04 0.00 0.05 0.16-0.01-1.09 0.12 Other 0.43*** -0.40** 0.79*** 1.24* 0.43* 0.56*** -0.56*** 0.03-0.09 0.02-0.04-0.06-1.42** 0.08-0.48** 0.34** -0.06-0.16 Can Apple 0.01-0.02 0.02 0.04 0.01 0.02 0.01** -1.25*** -0.01 0.00 0.00 0.00-0.03 0.00-0.02 0.01 0.06-0.01 Can Aprcot 0.02* -0.12 0.08* 0.18 0.03 0.05 0.01*** 0.00-0.30* -0.01-0.02-0.02-0.09-0.01-0.09 0.03 0.37-0.05 Can Peach 0.02* -0.05 0.04 0.09 0.02 0.03 0.01*** 0.00-0.01-0.62-0.01-0.01-0.05 0.00-0.04 0.02 0.14-0.02 Can Pear 0.03** -0.14*** 0.11** 0.23 0.04 0.07* 0.01*** 0.00-0.03-0.01-0.74*** -0.02-0.12-0.01-0.11** 0.04 0.44** -0.06 Can Other 0.01-0.05 0.04 0.08 0.01 0.02 0.00 0.00-0.01 0.00-0.01-1.04*** -0.04-0.01-0.04 0.01 0.17-0.02 Dry Apple -0.08** -0.14-0.06** 0.04-0.06-0.05-0.08*** -0.02-0.04-0.02-0.04-0.02-1.35*** -0.05-0.05-0.05 0.89-0.07 Dry Aprcot 0.05** -0.14 0.13 0.25 0.06 0.08 0.03*** 0.00-0.03 0.00-0.02-0.02-0.17-0.47*** -0.11 0.05 0.38-0.06 Dry Plum 0.07-0.48 0.30** 0.71 0.11 0.19 0.01-0.01-0.12-0.02-0.09-0.07-0.29-0.06-0.71*** 0.09** 1.64-0.22 Dry Other 0.03 0.13-0.02-0.12 0.01 0.00 0.03*** 0.01 0.04 0.01 0.03 0.02-0.07 0.03 0.07-0.62*** -0.64 0.06 Frozen Peach -0.42** 1.57*** -1.26*** -2.64* -0.52** -0.82* -0.19*** 0.03 0.38** 0.06 0.27** 0.23** 0.53 0.13 0.21-0.43-2.11 0.71 Frozen Other 0.11* -0.52 0.37 0.82 0.14 0.24 0.04*** -0.01-0.13-0.02-0.09-0.08-0.41** -0.05-0.39** 0.12 1.67-0.67*** Expendture 1.36*** 0.30* -0.36** 0.82** 0.88*** 0.54 0.40*** -0.55-0.27-0.34* -0.97-0.64-1.22-0.61-1.10 0.10* -1.05-0.84 Notes: The table shows the prce elastcty gven that the prce of the good shown n the column changes. Peach ncludes nectarnes and peaches. Sgnfcance levels: ***0.01; **0.05; *0.10. Can Peach Can Pear Can Other Dry Apple Dry Aprcot Dry Plum Dry Other Frozen Peach Frozen Other 29