Derivation of Nutrient Prices from Household level Consumption Data: Methodology and Application*

Transcription

1 Derivation of Nutrient Prices from Houseold level Consumption Data: Metodology and Application* by Dipankor Coondoo Economic Researc Unit, Indian Statistical Institute, Kolkata, India Amita Majumder Economic Researc Unit, Indian Statistical Institute, Kolkata, India Geoffrey Lancaster Scool of Economics University of Tasmania Private Bag 85 HOBART 7005, Australia Ranjan Ray** Scool of Economics University of Tasmania Private Bag 85 HOBART 7005, Australia February, 006 * Geoffrey Lancaster and Ranjan Ray acknowledge financial support provided by an Australian Researc Council (Discovery Project) Grant. Part of tis researc was carried out during Dipankor Coondoo s visit to te Scool of Economics, University of Tasmania. He acknowledges te ospitality and support of te Scool during is visit. Te usual disclaimer applies. ** Corresponding autor

2 Abstract Te issue of food price comparisons, bot spatially witin a country and between countries, is fundamental to welfare analysis, especially in te context of development. In crosscountry consumer price level comparisons, mismatc of lists of items consumed in different countries is a common problem. Comparison of levels of food prices in two countries becomes difficult if te sets of food items consumed in te two countries are widely different. Te same problem may also arise in regional food price levels comparison for large developing countries like India and Indonesia, were regional eterogeneity in food abit is prevalent. In suc situations, if one ad data on average intake of major nutrients and measure of corresponding nutrient prices by region, a comparison of te level of nutrient prices migt be possible. In tis paper, we propose a regression-based metod of estimation of ouseold level nutrient prices using ouseold level data on food expenditure, intake of various nutrients, total consumer expenditure and ouseold attributes. Tese estimated nutrient prices, being based on ouseolds' actual food consumption information, would be influenced by prices and nutritioncontent of food items consumed, nominal income, ouseold attributes and oter factors caracterizing ouseold preference. Once suc a set of ouseold level nutrient prices and corresponding nutrient intakes are available for different population groups (regions), a set of multilateral nutrient price index numbers may be compiled to compare nutrient price levels across population groups (regions). Suc a set of nutrient price index numbers would be relevant for examining consumer price differentials across population groups (regions). As an illustration, te proposed metod as been applied to te ouseold level data set for 55t round ( ) Consumer Expenditure Survey of te National Sample Survey Organisation (NSSO), Govt. of India. Te data set contains information on food expenditure, total consumer expenditure, ouseold composition and intake of tree major nutrients, viz., carboydrate, protein and fat. Using te estimated ouseold level nutrient prices and te corresponding data on nutrient intake, sets of Elteto-Koves-Szulc (EKS)-type index numbers for nutrient prices and nutrient intake ave been constructed for selected major Indian states, separately for rural and urban sectors. Tese indices are ten compared wit te corresponding EKS-type price and quantity indices for food calculated on te basis of te available data on unit value and quantity of te individual food items. Key Words: Nutrient Prices, Multilateral Price Comparison, EKS-type index number JEL Classification Codes: O, I, C3.

3 . Introduction A well known problem of cross-sectional consumer price level comparison across population groups (viz., countries or regions, communities witin a country) is te problem of omogeneity error arising out of eterogeneity of te baskets of goods consumed by different population groups involved in te comparison. As we know, wen te proportion of goods unique to te population groups in te union set of goods consumed by all population groups togeter is large, te basis of price level comparison becomes weak. Obviously, te problem is less severe if te population groups involved in te price level comparison are fairly similar or omogeneous in respect of culture, institution, tastes and preferences etc.. Te problem of non-comparability of te baskets of representative goods can sow up for different sub-groups of consumer expenditure suc as food, cloting, ousing, communication, recreation etc. However, te problem of eterogeneity of te baskets of food items often dominates as food commands a sizeable part of average consumer s budget, particularly in developing countries and, terefore, difference in levels of prices of food items is a major determinant of te overall price level differential. Needless to mention, non-comparability of te representative food baskets across population groups may upset suc a price level comparison exercise. Wen a comparison of food price levels is jeopardized by te problem of omogeneity error, one may, in principle, tink of an alternative price level comparison done by canging te space of comparison from one of prices of food items to tat of te corresponding nutrient prices. To elaborate, given information on nutrient content (namely, amount of carboydrate, protein, fat etc. per unit) of eac food item and te quantity of eac food item consumed by a ouseold, te total intake of eac nutrient can be worked out. Furter, te aggregate value of all nutrients consumed sould be equal to te total food expenditure of te ouseold. Tat means, corresponding to te nutrient quantities consumed by a ouseold, tere exists a set of implicit nutrient prices suc tat te food expenditure and te aggregate value of all nutrients consumed Tese are generally called macro nutrients, and iron, vitamins, trace minerals, etc. tat we ave not considered in te empirical exercise reported in tis paper are called micro nutrients.

4 are equal. Tis implies tat given data on food expenditure and te corresponding intake of various nutrients, it may be possible to estimate nutrient prices for individual ouseolds. If data on suc nutrient prices and corresponding nutrient intake are available for different population groups, price levels of nutrients can be compared across population groups in te usual way. Suc a comparison of nutrient price levels would mostly be free of te problem of omogeneity error, even wen te baskets of representative food items of different population groups ave very few common items. Note tat index numbers of nutrient prices may differ from te corresponding index numbers of food item prices. However, index numbers measuring differentials in nutrient prices across population groups sould be useful on teir own for teir possible welfare implications, so far as comparison of living standards is concerned. In tis paper, we propose a procedure for estimating ouseold-specific nutrient prices based on ouseold level data on food expenditure, total consumer expenditure, intake of different nutrients, ouseold attributes etc. Tis procedure is peraps te first of its kind as tere is no reference in te existing literature to any attempt to estimate ouseold-specific nutrient prices from available ouseold level data. Te closest to te present exercise, tat we find in te literature, is te study by Subramanian and Deaton (SD) (996), wo provided Indian evidence on te price of energy (i.e., calorie) consumed. Tey, owever, did not consider te possibility of estimating implicit prices of individual nutrients from a given set of ouseold level data. Let us now turn to te issue of estimation of nutrient prices corresponding to a given set of food item prices. St-Pierre and Glamocic (000) ave proposed a regression-based tecnique of estimation of nutrient prices in wic food item prices are regressed on corresponding nutrient contents of unit food quantities. Nutrient prices estimated tus, owever, will be te same for all ouseolds facing same food items prices, as te procedure does not use any ouseold-specific information. Suc nutrient prices would be unrealistic, because even wen a group of ouseolds faces same food items prices, te implicit nutrient prices will vary from ouseold to ouseold, depending on te nature and composition of food items consumed. Given tastes and preferences and te budget constraint, individual ouseolds may purcase different bundles of food items For a ouseold spending Rs. y on all food consumed and getting c units of calorie from food consumption, te implicit unit price of energy (calorie) is measured to be Rs. (y/c).

5 and tus end up wit different sets of nutrient quantities, even wen tey face te same set of food item prices. Since comparison of consumer price levels as welfare implications, one sould preferably ave ouseold-specific implicit nutrient prices for comparing nutrient price levels across population groups. It may be noted tat any cange in te set of commodity prices (prices of food items, in particular) would lead to a corresponding cange in te set of implicit nutrient prices 3. We propose ere a regression analysis-based procedure for estimation of ouseoldspecific prices/unit values of te major nutrients, viz., carboydrate, protein and fat, using a cross-sectional ouseold level data set on food expenditure, total consumer expenditure, quantities of nutrients consumed and related variables. Te proposed procedure as been applied to te ouseold level data for te year provided by te 55t round Consumer Expenditure Survey of te National Sample Survey Organisation (NSSO), Govt. of India. Te estimated nutrient prices, along wit te corresponding data on nutrient intake, ave been used to make an inter-state comparison of te levels of nutrient prices and nutrient intakes. Briefly, te means (over all-ouseolds) of te estimated ouseold level nutrient prices and te corresponding data on te quantities of nutrient intake ave been used to compile a set of Elteto- Koves-Szulc (EKS) type multilateral price and quantity index numbers for te major Indian States separately for te rural and te urban sector. Te paper is organised as follows: Section explains te proposed procedure of estimation of ouseold-specific nutrient prices. Te nature of data used and te basic results are described in Section 3. In Section 4, te results of application of te estimated nutrient prices (and te corresponding quantity data) to inter-state comparison of te levels of tese variables 3 McFarlane and Tiffin (003) ave considered estimation of sadow prices of nutrients for ouseolds. Broadly, teir procedure is as follows: Consider te following linear programming problem: Minimize p q subject to q Aq η, q 0, were p and q denote te vectors of given prices of food items and te corresponding quantities to be found out, respectively, A denotes te matrix of nutrient composition of food items and η denotes te vector of (given) amounts of nutrients obtained from consumption of all food items. Te solution of te dual of tis problem will give te set of sadow prices of te nutrients. However, altoug suc ouseold-specific estimates of sadow prices of nutrients may be valuable and useful on teir own, te fact tat some of tese may be zero makes tem unattractive for many uses. 3

6 based on a set of multilateral price and quantity index numbers are discussed. Finally, te paper is concluded in Section 5.. Te Proposed Procedure Suppose we ave a ouseold level data set comprising of per capita food expenditure f ( y ), per capita intake of eac of K nutrients ( η, i i =,,..., K ), per capita income/consumer expenditure or PCE ( y ) and an array of ouseold attributes, for example, ouseold demograpics etc. ( z ) for =,,..., H sample ouseolds. Te food expenditure-nutrient relationsip linking food expenditure to intake of various nutrients may be written as y f K = ν η, =,,..., H, () i= i i were ν i denotes te unobserved price/unit value of te i-t nutrient for te -t ouseold 4,5. Next, let us specify te nutrient price functions to be of te following form: ν i = f i ( y, z, ui ), i =,,..., K, () were (.) is a positive-valued function and u is a random disturbance term. Equation () is a f i generalized form of Prais and Houtakker s (955) quality equation tat relates te price/unit value paid for a commodity to te consumer s income level. Note tat weter i (.) f i 's will be 4 Tese are to be estimated from te given data. Note tat a straigtforward extension of te SD (996) procedure for estimating prices of individual nutrients is not possible as expression () involves K (>) unknown nutrient prices. 5 Note tat if ouseold level data on nutrient quantities obtained from individual food items are available (i.e., η ij : quantity of te it nutrient obtained from te consumption of te jt food item by te t sample ouseold), one may consider estimation of food-item specific nutrient prices (v ij ) using te item expenditure-nutrient relationsip y f j K = ν η, j =,,...,n, =,,..., H, were ij i= ij ouseold, n f te item specific prices of nutrients. f f y j denotes te per capita expenditure on food item j by denotes te number of food items for wic data on nutrient quantities are available and ν ij 's are 4

7 increasing or decreasing functions of income is essentially an empirical issue. Various penomena may give rise to te quality equation. Of tese, consumers quality sensitivity may be one. If several qualities of a commodity are available and te price increases wit quality, a consumer may sift from a lower quality to a iger one if er real income rises. Anoter penomenon tat is likely to affect unit value of a commodity is price concession viz., a ricer consumer buying a larger quantity may get price concession and tus end up paying a lower price per unit. In te present context, it is also possible tat compared to a poor ouseold a ricer ouseold gets nutrients from consumption of more expensive commodities (rater tan from consumption of a iger quality of te same commodity). Te nature of te slope of te quality equation wit respect to income will be determined by te relative strengt of te penomena mentioned above. In order to ensure tat te estimated nutrient prices are positive, we specify () to be of te following form wit an additive random disturbance term 6 : ν = α + β ln y + γ z ) + u, i,,...,k, (3) i ( i i i i = were z is a vector of ouseold attributes 7. Substituting (3) in (), te following estimating equation is obtained 8 : y f = ( α + β ln y + γ z ) η ( α K + β K ln y + γ K z ) η K * + ε, =,,...,H, (4) were * K ε = η u i= i i is te composite equation random disturbance term. Once tis nonlinear food expenditure function is estimated, te ouseold-specific nutrient prices can be derived as 6 It may be pointed out tat tis is one of many possible coices of te functional form of f i (.) in equation (). 7 In te empirical exercise, we ave taken z to be a scalar measuring logaritm of ouseold size. 8 Hencefort, we sall refer to tis and equation () as te food expenditure function. 5

8 ˆ ˆ ν = ( ˆ α + β ln y + ˆ γ z ),i =,,...,K;,,...,H, (5) i i i i = were ^ denotes estimated value. Estimation of te food expenditure function (4) involves two basic econometric issues. Tese are as follows: First, te food expenditure function () is a matematical identity if all nutrients ave been taken into account. As is well known, consumption of food items yields, in addition to te tree major nutrients (viz., carboydrate, protein and fat tat we ave considered ere), many micronutrients. Wile most of tese micronutrients are obtained as complements of te tree major nutrients, tere are a few residual micronutrients tat are obtained from consumption of specific food items ric in tem. Wereas quantities of te first type of micronutrients would be igly correlated wit quantities of te tree major nutrients consumed, te corresponding correlation for te remaining micronutrients sould be quite weak. If te food expenditure function includes only te tree major nutrients, as we ave done in te present exercise, equation (4) will be a regression function rater tan a matematical identity. In tat case, te equation random disturbance term * ε will include te aggregate of values of all residual micronutrients. Tis, owever, is not likely to give rise to an errors in variables situation since te quantities of te major nutrients, as argued above, are unlikely to be correlated wit te aggregate of te values of all residual micronutrients 9. Next, given te structure of te random disturbance term * ε of te regression function K (4), * var( ε ) = η σ, assuming var( u ) = σ for all. Hence estimation of function (4) i= i i i i 9 It is widely recognized tat intake of food items tat fulfill te requirements of te major nutrients, carboydrate, protein and fat,also provides many of te minor nutrients in a complementary manner. For tis reason, te values of te nutrients explicitly considered in te food expenditure function would include te value of minor nutrients tat are obtained complementarily and equation random disturbance term will include in principle te total value of te * remaining nutrients consumed. Note, owever, tat ε can take bot positive and negative values as u is a purely a random disturbance term. i 6

9 involves problems of bot nonlinearity and eteroscedasticity. In te present exercise, we ave, owever, estimated tis function using te metod of nonlinear least squares ignoring te problem of eteroscedasticity essentially to avoid estimational complications. Te estimates tat we ave got may terefore be inefficient. Note, owever, tat since te estimation is based on large ouseold level data sets, te issue of inefficiency of estimates may not be an important one. Let us briefly enumerate te features of te proposed procedure. An advantage of te present procedure over tat of Subramanian and Deaton (996) mentioned earlier is tat te use of data on intake of nutrients allows us to take into account te inter-ouseold differences in te composition of nutrient intake (tat reflects differences in food abits). Also, tis procedure extends te metodology of Bouis and Haddad (99) and Subramanian and Deaton (996) by recognizing tat te transmission mecanism from food purcases/consumption to calorie generation works via te nutrients. Muc of te existing literature, including te studies mentioned above, overlooks tis intermediate step and, ence, ignores te possibility tat a unit of energy (i.e. calorie) may be generated by vastly different combinations of te tree principal nutrients, viz., carboydrate, fat and protein. Te proposed procedure, being based on regression tecnique, is rater simple and straigtforward. 0 More importantly, unlike te nutritionists approac mentioned earlier, tis procedure does not require information on te prices of food items. Instead, it relies on beavioural and nutritional information available in a set of ouseold level consumer expenditure data. 3. Data and Results Te proposed procedure as been applied to te ouseold level data of te 55 t round Consumer Expenditure Survey of te Indian NSSO, covering te survey period July 999-June 0 Coondoo, Majumder and Ray (004) ave proposed a regression-based metodology tat measures regional price differences, from unit records of ouseold surveys, in te context of large Federal countries suc as India. Te present study extends tat metodology to te calculation of nutrient prices and examines teir regional differences in te context of suc countries. 7

10 000. Te required information including tat on ouseold level nutrient intake is provided by te NSSO. As already mentioned, we ave considered tree nutrients, viz., carboydrate, protein and fat, in te analysis. Using te proposed procedure, ouseold-specific prices of tese tree nutrients ave been estimated first, separately for rural and urban sector of sixteen major Indian States. Tese States are Andra Prades (AP), Assam (AS), Biar (BH), Gujarat (GU), Haryana (HA), Himacal Prades (HP), Karnataka (KA), Kerala (KE), Madya Prades (MP), Maarastra (MH), Orissa (OR), Punjab (PU), Rajastan (RA), Tamil Nadu (TN), Uttar Prades (UP) and West Bengal (WB). In te second stage of te analysis, te state-specific estimated allouseold mean nutrient prices and te corresponding mean nutrient quantities ave been used to construct state-specific EKS index numbers for nutrient price and intake levels, separately for te rural and urban sectors. Let us first summarise te results of estimation of te food expenditure function (4). Tis function as five explanatory variables (viz., te tree nutrient quantities, logaritm of PCE and logaritm of ouseold size) and nine parameters and is nonlinear in parameters. It as been estimated by nonlinear least squares metod. In every case, te iterative estimation process as converged quite rapidly and te converged parameter estimates are found to be robust in respect of coice of initial parameter values. Te sample size (i.e., te number of sample ouseolds) and te squared correlation coefficient ( R ) between observed and estimated ouseold level per capita food expenditure (obtained from te fitted food expenditure function) are reported in Tables and by State for te rural and urban sectors. Te value of R ranges between (KA) and (HA) for te rural sector and between (MP) and (WB) for te urban sector. In 8 out of 3 cases te R value turned out to be less tan In tis round, data on consumption of food were recorded using bot a '7-day' and a '30-day' recall period. In tis study we ave used te latter data. For consideration of space, te estimated parameters and te detailed regression results are not presented ere. Tese will be available to interested readers, if requested. 3 Considering te fact tat tis estimation as been done on ouseold level data set, a sould be regarded as satisfactory. R value greater tan 0.6 8

11 Tables and also give te state-specific aritmetic mean of estimated ouseold level nutrient prices along wit teir estimated standard errors for te tree nutrients for rural and urban sectors. Note tat, given te procedure used for estimating te ouseold level nutrient prices, analytical derivation of te standard errors of mean estimated nutrient prices is difficult, if not impossible. Here, bootstrapping metod (wit 000 resamples) as been used to arrive at tese estimated standard errors. As is evident, te estimated mean prices are all statistically significant. It may be noted tat te extent of variation of mean nutrient prices across state is indeed quite large for all te tree nutrients. For carboydrate, te mean estimated price varies between Rs. 7. per kg. (KE) and Rs. 7.6 per kg. ( HP) for rural sector and between Rs per kg. (WB) and Rs (AS) for urban sector. For protein, te corresponding ranges are Rs..47 (OR) Rs (KE) for rural sector and Rs. 6. (PU) Rs (WB) for urban sector. Finally, for fat, te price range is Rs (UP) Rs (HA) for rural sector and Rs..88 (AS) Rs (OR) for urban sector. Te coefficient of variation of te state-specific estimated mean price of carboydrate, protein and fat work out to be 33, 80 and 83 per cent, respectively, for rural sector and 6, 97, and 9 per cent, respectively, for urban sector. Tese values are found to be muc larger tan te coefficients of variation of te state-specific mean per capita intake of carboydrate, protein and fat, wic work out to be 6, 6 and 4 per cent, respectively, for rural sector and 4, 7 and 5 per cent, respectively, for urban sector. Te statespecific mean and standard deviation of ouseold level per capita intake of carboydrate, protein and fat for te rural and urban sectors calculated from te given data set are reported in Tables 3 and Multilateral Indices of Nutrient Prices and Nutrient Intake As already mentioned, we ave made a multilateral comparison of levels of nutrient price and intake across states separately for te rural and urban sectors based on te estimated 9

12 state-specific mean nutrient prices and te corresponding mean nutrient quantity data. Te multilateral index number procedure of Elteto, Koves and Szulc (EKS) (see Elteto and Koves, 964 and Szulc, 996) as been used for tis purpose. Te EKS procedure is briefly explained below. Let i i ( p,q,i =,,...,S ) denote te vectors of te mean nutrient prices and intakes for te states and let F P ij denote te Fiser binary price index number for state j wit state i taken as base 4. Te corresponding EKS multilateral price index number for state j wit state i taken as base is given by P S EKS F F S ij = ( Pil.Plj ) l=. It may be mentioned ere tat te EKS index is circularity-consistent - i.e., te resulting index numbers guarantee transitivity of price level comparison, by construction. Using te state-specific estimates of mean nutrient prices and te corresponding data on mean nutrient quantities, we ave computed te set of state-specific EKS nutrient price and quantity index numbers, separately for rural and urban sector. Tese estimated nutrient price and quantity index numbers are reported, respectively, in Tables 5, 6 along wit te corresponding EKS food price and quantity indices (compiled by using te state-specific mean unit value and per capita quantity consumed of individual food items computed from te given ouseold level data set). Let us first consider te ordering of states in respect of te value of nutrient price index. For te rural sector, te ordering 5 is as follows: HA f RA f PU f HP f UP f BI f OR f MP~KA f AP f GU~KE~MH~WB f AS f TN. Te corresponding ordering of states for te urban sector is as follows: HP f BI f AS f PU~RA~UP f HA f MP~WB f KE f AP~KA~MH~OR~TN f GU. p 4 kjqki pkjqkj F k k Te formula for tis index number is as follows: P ij = { }, were p kj is te mean price of te p q p q k t nutrient in te j t state and is te corresponding mean quantity. q kj 5 Here "state f state " means tat state as a iger level of price/consumption of nutrients tan state and state~state means tat te levels of te two states are same. k ki ki k ki kj 0

13 As regards te level of nutrient intake in te rural sector, te index numbers suggest te following ordering of states: KE f GU~TN f PU f HP f WB~AS f HA f KA f AP f RA~MH f BI f OR f MP f UP. Te corresponding ordering of states for te urban sector is obtained to be KE f GU~HI f MH f KA f AS~WB f TN~OR f PU~HA f AP f RA f BI f MP f UP. As te above orderings would suggest, te nutrient price levels are iger in HA, RA, PU, HI, UP and BI and lower in KR, AP, GU, KE, MH, WB and TN for bot sectors. Te position of te remaining states (viz., AS, MP and OR) vary in te rural and urban sector orderings. From te orderings based on nutrient intake index, it appears tat for bot sectors KE, GU, HP, WB, AS and TN ave iger average nutrition intake levels, wereas UP, MP, BI, RA, AP and OR ave lower average nutrition intake levels. Te position of MH, PU, KA and HA are somewat different in te rural and urban orderings. A comparison of te food and nutrient price indices given in Table 5 suggests an overall picture of disagreement of te two sets of rankings. For example, for te rural sector, KE and UP ave te igest and lowest food price index value, respectively, wereas te ranking of tese states are and 5 in terms of te nutrient price index value. Similar cases of rank reversals are also tere for te urban sector. Similar patterns of disagreement of rankings may be noted in te case of food quantity and nutrient intake indices as well. Finally, to summarise te extent of inter-state differentials in te levels of food and nutrient prices and te corresponding food quantities and nutrient intake, we ave calculated te coefficient of variation of te state-specific indices for food prices, nutrient prices, food quantity and nutrient intake for rural and urban sector. Also, to summarise te extent of agreement between state-pecific nutrient price indices and food price indices and between state-specific nutrient intake indices and food quantity indices, we ave calculated Spearman s rank correlation between tese pairs of indices for rural and urban sector. Te estimated coefficients of variation and rank correlation coefficients are presented in Table 7. Te following observations can be made about inter-state variation on te basis of te results given in Table 7: (i) for all te four indices, variation is larger for te rural sector; (ii) for

14 bot sectors, variation in food prices and food quantities are smaller tan te corresponding variations in nutrient price and nutrient intake; (iii) for bot food and nutrient and bot sectors, variation in prices is smaller tan tat in quantities, and finally, (iv) te Spearman s rank correlation coefficient is positive and significant only for food and nutrient quantity indices for te rural sector. 5. Conclusions Tis is basically a paper wit some metodological content. Here we ave proposed a procedure for estimating ouseold-specific nutrient prices from ouseold level data on consumer expenditure. Altoug tis new procedure as resemblance to a procedure suggested by nutritionists, it is far more versatile and provides ouseold-specific estimate of nutrient prices. Te estimated nutrient prices ave been used to calculate sets of multilateral EKS index numbers tat sow te extent of inter-state variation in te levels of nutrition prices and nutrition intake in rural and urban sectors of India. Te empirical results obtained from tis illustrative exercise, being te first of its kind, are informative, interesting and useful. To mention a few, it is found tat te inter-state variations in te mean prices of nutrients are greater tan te corresponding variations in te mean food prices, particularly for te rural sector and inter-state variations in food quantity and nutrient intake are muc greater tan te corresponding variations in food and nutrient prices for bot te sectors. Te metodology of calculation of ouseold-specific nutrient prices proposed ere and te results of te illustrative empirical application of tis metodology presented ere sould evoke wider interest tan te immediate context of India on wose data set te illustrative exercise as been done. Te importance of tis topic stems from te fact tat availability of ouseold/region-specific nutrient prices may ease calculation of regional cost of living indices, modify teir connotations by adding nutritional considerations into suc indices and tus bring qualitative canges, so far as indexing of wages and/or calibration of poverty lines for te measurement of poverty incidences at regional level (tat may govern regional distribution of public resources etc.) is concerned. Nutrient prices can also be important in te discussion on efficiency wages formulation. In te teory of efficiency wages, nutrient consumption plays a

15 central role as a determinant of productivity 6. Wile it is true tat muc of tis teory as concentrated on intake of nutrients rater tan teir prices, from a policy viewpoint, an analysis of bot nutrient intake and nutrient prices, sould be important, especially if autorities wis to ensure tat ouseolds get sufficient resources to be able to consume a balanced diet and meet minimum calorie requirement. 6 Following Leibenstein (957), Mirlees (975) and Stiglitz (976), te teory of efficiency wages predicts a nonlinear functional dependence of productivity on nutrient intake see Strauss and Tomas (998) for a review of empirical evidence on tis dependence. Conspicuous, by its absence, is te lack of similar evidence on te impact of nutrient prices on ealt and productivity. 3

16 References Bouis, H.E and L.J. Haddad (99), Are estimates of calorie-income elasticities too ig? A recalibration of te plausible range, Journal of Development Economics, 39, Coondoo, D., Majumder, A. and R. Ray (004), A Metod of Calculating Regional Consumer Price Differentials wit Illustrative Evidence from India, Review of Income and Wealt, 50(), Elteto, O, and P. Koves (964): On an Index Number Computation Problem in International Comparison (in Hungarian), Statisztikai Szemle, 4, Leibenstein, H. (957), Economic Backwardness and Economic Growt: Studies in te Teory of Economic Development, New York, Wiley McFarlane, I. and R. Tiffin (003): Te Minimum Cost of Adequate Nutrition using Locally Available Food Items, presented at 5t International Conference on Dietary Assessment Metods, Ciang Rai, Tailand, 6-9 January 003. Mirlees, J.A. (975), A Pure Teory of Underdeveloped Economies in Agriculture in Development Teory, edited by LG Reynolds, New Haven, Conn. Yale University Press. Prais, S. J. and H. S. Houtakker (955): Te Analysis of Family Budgets, Cambridge University Press, Cambridge. Stiglitz, J. (976), Te Efficiency Wage Hypotesis, Surplus Labour and te Distribution of Income in LDCs, Oxford Economic Papers, 8, St-Pierre and D. Glamocic (000): Estimating Unit Costs of Nutrients from Market Prices of Foodstuffs, Journal of Dairy Science, 83, 6, Strauss, J. and D. Tomas (998), Healt, Nutrition and Economic Development, Journal of Economic Literature, 36(), Subramanian, S. and A.S. Deaton (996), Te Demand for Food and Calories, Journal of Political Economy, 04(), Szulc, B. (964): Indices for Multiregional Comparisons, Przeglad Statystyczny, 3, pp

17 Table : Summary Statistics for different Nutrients by State: NSS 55 t Round, Rural State Sample Size R Mean* Estimated price (Rs./kg.) of Carboydrate Protein Fat Standard Deviation Mean Standard Deviation Mean Standard Deviation () () (3) (4) (5) (6) (7) (8) (9) Andra Prades (0.05) Assam (0.05) Biar (0.037) Gujarat (0.077) Haryana (0.085) Himacal Prades (0.45) Karnataka (0.063) Kerala (0.65) Madya Prades (0.08) Maarastra (0.088) Orissa (0.05) Punjab (0.08) Rajastan (0.065) Tamil Nadu (0.069) Uttar Prades (0.0) West Bengal (0.07) (0.8) (0.9) (0.068) (0.498) (0.068) (0.0) (0.086) (0.34) (0.93) (0.045) (0.04) (0.697) (0.563) (0.73).9.58 (0.78) (0.558) (0.39) (0.86) (0.3) (0.76) (0.6) (0.4) (0.55) (.07) (0.055) (0.78) (0.440) (0.407) (0.89) (0.74) (0.08) (0.308) * Figures in brackets in columns (4), (6) and (8) are te standard errors of te mean estimated price obtained by bootstrapping

18 Table : Summary Statistics for different Nutrients by State: NSS 55 t Round, Urban State R of Estimated price (Rs./kg.) of Sample te fitted Carboydrate Protein Fat Size food exp. Mean* Std. Mean Std. Mean Std. equation Dev. Dev. Dev. () () (3) (4) (5) (6) (7) (8) (9) Andra Prades (0.) (0.56) (0.6) Assam (0.376) Biar (0.35) Gujarat (0.56) Haryana (0.370) Himacal Prades (0.556) Karnataka (0.40) Kerala (0.9) Madya Prades (0.073) Maarastra (0.9) Orissa (0.67) Punjab (0.66) Rajastan (0.300) Tamil Nadu (0.044) Uttar Prades (0.3) West Bengal (0.0) (0.38) (0.675) (0.46) (0.59) (0.49) (0.45) (0.) (0.89) (0.678) (0.7) (0.094) (0.56) (0.986) (0.77) (.030) (0.09) (0.567) (0.380) (0.694) (.709) (0.569) (0.346) (0.337) (0.034) (.87) (.033) (0.078) (0.43) (0.58) (0.7) *Figures in brackets in columns (4), (6) and (8) are te standard errors of te mean estimated price obtained by bootstrapping

19 Table 3: Summary Statistics of Nutrient Intake (kg. per person per 30 days) by State, NSS 55 t Round, Rural State Sample Size Carboydrate Protein Fat Mean Intake Standard Mean Deviation Intake Mean Intake Standard Deviation Standard Deviation () () (3) (4) (5) (6) (7) (8) Andra Prades Assam Biar Gujarat Haryana Himacal Prades Karnataka Kerala Madya Prades Maarastra Orissa Punjab Rajastan Tamil Nadu Uttar Prades West Bengal Table 4: Summary Statistics of Nutrient Intake (per 30 days per ouseold) by State NSS 55 t Round, Urban State Sample Size Carboydrate Protein Fat Standard Mean Standard Mean Deviation Intake Deviation Intake Mean Intake (Kg) Standard Deviation (Kg) (Kg) () () (3) (4) (5) (6) (7) (8) Andra Prades Assam Biar Gujarat Haryana Himacal Prades Karnataka Kerala Madya Prades Maarastra Orissa Punjab Rajastan Tamil Nadu Uttar Prades West Bengal

20 Table 5: State-specific EKS Index Numbers for Food and Nutrient Prices by sector wit Andra Prades taken as te base State, NSS 55 t Round. State EKS Index Number for Rural EKS Index Number for Urban Food Prices (Unit Nutrient Prices Food Prices Nutrient Prices Values) (Unit Values) () () (3) (4) (5) Andra Prades Assam Biar Gujarat Haryana Himacal Prades Karnataka Kerala Madya Prades Maarastra Orissa Punjab Rajastan Tamil Nadu Uttar Prades West Bengal Table 6: State-specific EKS Index Numbers for Food and Nutrient Quantities by sector wit Andra Prades taken as te base State, NSS 55 t Round. State EKS Index Number for Rural EKS Index Number for Urban Food Quantities Nutrient Quantities Food Quantities Nutrient Quantities () () (3) (4) (5) Andra Prades Assam Biar Gujarat Haryana Himacal Prades Karnataka Kerala Madya Prades Maarastra Orissa Punjab Rajastan Tamil Nadu Uttar Prades West Bengal

21 Table 7: Coefficient of variation of state-specific EKS Index Numbers (measured wit Andra Prades taken as te base state) and Spearman s rank: NSS 55 t Round Measure for Rural Urban () () (3) (4) Coefficient of variation of state-specific EKS indices for Spearman s rank correlation between Coefficient of variation of state-specific EKS indices for Spearman s rank correlation between ** significant at % level. Food prices Nutrient prices EKS food and nutrient price indices Food quantities Nutrient intakes EKS food and nutrient quantity indices **