Disaggregating food consumption parameters

Disaggregating food consumption parameters Designing targeted nutritional interventions

Charles Waterfield

This article is concerned with a new nutritional intervention strategy, which is based on the belief that malnourished people eat different types of food to the well-nourished. The calculation of disaggregated food consumption parameters can identify ‘Inferior’ food commodities; and the subsidization of these commodities will target intervention to those most in need. The effectiveness of this policy depends on the understanding of its theoretical basis. The author introduces the policy maker to this by reviewing the theoretical basis for consumption analysis, and then several key studies. Keywords: Food policy; Nutrition programmes; Food consumption parameters At the time of writing, the author was at the Hubert H. Humphrey Institute of Public Affairs, University of Minnesota. He is now working at USAIDIHAITI, PO BOX 1634, Port-au-Prince, Haiti, West Indies. The author offers his appreciation and gratitude to Professors Simon Fass of the Humphrey Institute of Public Affairs and Vernon Ruttan of the Agriculture and Applied Economics Department, both of the University of Minnesota, for their suggestions and encouragement. ‘Various aspects of this strategy have been covered in C. Peter Timmer, ‘Food prices and food policy analysis in LDCs’, Food Policy, Vol 5, No 3, August 1980, pp 188-99; and in Ftezaul Karim, Manjur Majid and F. James Levinson, ‘The Bangladesh sorghum experiment’, Food Policy, Vol 5, No 1, February 1980, pp 61-3.

0306-9192/85/040337-l

5$3.00 0

A new nutritional intervention strategy has been receiving increasing attention from theorists - though not at this point from policy makers. This strategy is based on the belief that malnourished people consume different kinds of food to well-nourished people, ie the nutritional gap is due to quality differences as well as quantity differences. By calculating food consumption parameters for several disaggregated levels of calorie consumption (or for different income levels, using income level as a proxy for nutritional level), policy analysts can discover ‘inferior’ food commodities, ie commodities consumed in diminishing amounts as income rises, and subsidize these commodities to target interventions to those most in need.’ The effects of subsidizing an inferior commodity are graphically represented in Figure 1. This stylized example shows calorie consumption varying with income. Three calorie sources are considered - rice, dried cassava and ‘other’. Those consumers with income below Y* consume insufficient calories. Subsidizing rice would not be an efficient means of targeting subsidies to the poor since rice consumption rises with increased income. Dried cassava consumption, however, falls with increased income since it is an inferior commodity. Subsidizing dried cassava causes its consumption to rise significantly, improving calorific intake of the consumer. An ‘income effect’ of the subsidy allows the poor to consume more rice as well, because the subsidy means they have more money left over with which they can buy more of the preferred good, rice. The result of this subsidy is that nutrition of the malnourished is improved, while upper-income groups are affected minimally; the subsidy is targeted to those most in need, thus conserving government resources. The food consumption parameters necessary for designing the above targeted intervention include income elasticities for all important food commodities, own-price and cross-price elasticities for these commodities, and commodity budget proportions. All of these parameters must be determined for each disaggregated category, eg if households are

1985 Butterworth

& Co (Publishers)

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337

Disaggregating

food

consumption

parameters Total calories

A---------

Calorie requirement

Caloriesfrom rice

Caloriesfrom other sources

Figure 1. Stylized food consumption patterns as a function of income. Note: Solid lines indicate consumption patterns before a subsidy on dried cassava; dotted lines indicate consumption patterns after a subsidy on dried cassava. The shaded areas indicate increases in calorie intake after the subsidy. Source: Timmer, Falcon and Pearson, Food Policy Analysis, Johns Hopkins University Press, Baltimore, MD, 1983, p 70.

Income

broken down by income level into four categories then four income elasticities for rice must be determined. Once all these disaggregated consumption parameters are known, the effects of changes in household income or commodity prices can be predicted for each group. Various subsidies and income transfers can then be modelled to determine which is most effective in reaching the target group. As a potential policy tool for increasing nutrition levels while making optimum use of government resources, this proposal seems promising. However, the policy’s effectiveness depends largely on the policy maker’s understanding of disaggregated food consumption analysis and its theoretical basis. The purpose of this paper is to introduce the policy maker to this by reviewing first the theoretical basis for consumption analysis, and then several key studies that have been carried out in developing countries.

Economic theory

‘Econometrics is the use of mathematics, statistics, and economic theory to empirically determine economic relationships. Economic theory guides the choice of which parameters are likely to be important in describing systematic behaviour patterns. No relationship is exact because of inaccurate identification of parameters and imprecise measurement. Selecting the proper model - the significant parameters and their relationships - is an art.

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The theory of using inferior commodities to target nutritional interventions to those most in need is developed in a series of articles employing econometric techniques to analyse empirical data from specific countries.2 Previous empirical work has been based on aggregated data, whereby all households were compiled into a single category. However, aggregation masks many relevant behaviour patterns. More recent data bases and econometric methodologies have enabled researchers to disaggregate consumption patterns by level of nutrition. Even though there are still individual distinctions lost in the process of subaggregation, important correlations between nutritional level and dietary composition are brought to light. A number of surrogates are used to determine nutritional standing, the most prevalent being calorie

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Disaggregating food consumption parameters

3The income elasticity of demand for a commodity is a measurement of the sensitivity of quantity consumed (of that commodity) to a change in household income. For convenience, they are divided into three groups. Commodities with negative income elasticities are referred to as inferior commodities (smaller absolute amounts of the commodity are consumed as household income increases). Where income elasticities fall between 0 and 1 .O, they are called necessary commodities since their quantity consumed rises slowlyto-moderately as income rises. Finally, commodities with income elasticities greater than 1.0 are referred to as luxury commodities because they are so desired by the household that the quantity consumed rises even faster than the relative increase in income. 40wn-price elasticity (sometimes called direct price elasticity) is a measure of how sensitive the quantity of a commodity consumed is to a change in the price of that commodity. Cross-price elasticity refers to the change in the quantity consumed of good A as the price of good B changes. Own-price elasticities are expected to be negative, ie as the price of a commodity rises the quantity consumed should fall. Cross-price elasticities are expected to be of smaller absolute magnitude because the price of one good and quantity of a second good have weaker correlation in the mind of the consumer than do the price and quantity of the same good. Cross-price elasticities can be either positive, in the case of substitute goods, or negative, as happens with complementary goods. ‘Price elasticities can be broken down into several components by means of the Slutsky equation: e,, = s,, - /?,a, where e, = overall demand elasticity for good i when the price of good j changes (sometimes referred to as the Cournot elasticity); s, = the pure substitution elasticity for good i when the price of good j changes (this is called the Slutsky elasticity); fi = income elasticity for good i (called the Engel elasticity); aj = budget proportion of good j (amount spent on good j as a percentage of total expenditures). When i = j the own-price components are given; when i # j the cross-price components are given. It is e, which will later be directly estimated from the data; s, must be determined indirectly. Since E, and a, have been observed to vary with income, e,, should also vary with income. %. Peter Timmer, ‘Is there “curvature” in the Slutsky Matrix?‘, Review of Economics and Statistics, Vol 62, No 3, August 1981, pp 395-402.

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consumption level, income level, and food expenditure level (ie the amount spent on food). In addition to ‘static’ consumption behaviour, researchers need to know how different income groups will respond to changes in household income or commodity prices. Policy makers can then implement those changes which most effectively improve the nutrition of those most in need. The most important economic parameters for determining ‘dynamic’ behaviour are income elasticities and price elasticities. The primary reason for calculating income elasticities’ for different income classes is that they are expected to decrease with increasing income, a phenomenon known as Engel’s Law. An important property of income elasticities is that they are not likely to be constant from one price environment to another, because consumer preferences for commodities vary as the relative prices for those commodities vary. This theoretical limitation of income elasticities is often ignored or assumed negligible in practice. The articles under review in this paper are no exception. The second important set of economic parameters needed for designing targeted interventions is the set of price elasticities of demand - both own-price and cross-price - for individual commodities.4 Theorists believe it can be proved that price elasticities vary by income class as do income elasticities.” The expectation is that poor consumers respond more strongly to price changes than the rich. This would stress the importance of the impact that price changes have on the poor. Any change in prices, even normal seasonal price fluctuation, can have a profound impact on those consumers on the nutritional margin.’ Previous empirical work has been frustrated in the attempt to accurately determine price elasticities disaggregated by income class. The immensity of this task can be understood by picturing a threedimensional matrix. Every number in the matrix is assigned a coordinate (i,j,k). For example, the x and y dimensions have ten each, representing ten major food commodities chosen for the study. The z dimension represents the number of income strata, usually about five. The numbers along the five x-y diagonals (ie where i = j) represent own-price elasticities; there are fifty of these, one for each of the ten commodities at each of the five income levels. Numbers away from the diagonal, where i # j, represent cross-price elasticities. Note that there would be one elasticity for the effect of, say, wheat prices on rice consumption, and another for the effect of rice prices on wheat consumption. In all there are 450 cross-price elasticities for a total of 500 (10 x 10 x 5) parameters to be empirically estimated. Not all of these will have a significant impact on the conclusions and many may be assumed to be negligible, obviating the need to calculate them. Nevertheless, the goal of generating disaggregated price elasticities is an ambitious one.

Empirical

data

Although the theoretical base has been laid, successful application of any theory depends on the quality of the data used. Until recently, lack of an extensive data base has been the bottleneck in determining disaggregated consumption parameters. Even now, with several countries having undertaken expensive nutritional surveys, the situation is far from ideal; no surveying technique is entirely objective or accurate,

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‘In a household budget survey, information is collected on a variety of household characteristics such as income, expenditures on various commodities, and number of household members (to calculate per capita figures). Anthropometric data is also frequently collected including statistics such as age, sex, weight, height, and arm circumference (used as an indicator of nutritional well-being). ‘A third source of price variation - product quality differences - can distort data when not taken into consideration. Some surveys separate data by product quality, eg percentage of broken rice kernels. ‘Cheryl Williamson Gray, Food Consumption Parameters for Brazil and their Application to food Policy, IFPRI Research Report No 32, International Food Policy Research Institute, Washington DC, September 1982, p 23. “‘C. Peter Timmer, Walter P. Falcon and Scott R. Pearson, Food Policy Analysis, Johns Hopkins University Press, Baltimore, MD, 1983, p 48.

340

and the ideal world of theory is a gross simplification of the market-place. We will now look more closely at these limitations. The best means of accumulating a database is through household budget surveys.’ Households are picked at random with care being taken to represent all income classes and geographical areas of the population (thus yielding a ‘cross-sectional’ data base). This is particularly difficult to do in Third World settings where the poorest households - those most of interest to the survey - have no permanent addresses and are often unknown to social service offices, where survey lists are compiled. Ideally, each household would be interviewed periodically to see how changes in income and commodity prices affect consumption. Unfortunately, this type of ‘panel’ data (combining cross-sectional and time-series techniques) is expensive because of the need to recontact households. In addition, it is nearly impossible to recontact a significant proportion of low-income households because of their high mobility. If done successfully, panel data can yield relatively accurate values for income and price elasticities; since household tastes and preferences change slowly, changes in behaviour can be attributed to economic behaviour. However, if the household simultaneously experiences changes in both income and the price environment there may be some cross-correlations which prove difficult to separate. Usually, out of necessity or practicality, researchers must work with cross-sectional data only. This poses few problems in estimating income elasticities as long as households are facing roughly the same prices. Income elasticity is determined by a log-log regression of per capita income against per capita quantity within each income stratum. The major drawback with cross-sectional data is in estimating price elasticities. In typical data sets the price variance and the sample size are too small to yield significant results. However, in some of the more recent large-scale database projects, there have been significant price variations. Large geographic sampling areas yield regional price variations due to transportation costs. In addition, large projects take time to complete, thus allowing seasonal price fluctuations to occur.’ When sufficient price variation occurs, commodity quantity can be regressed against commodity prices to determine the price elasticities. Using cross-sectional data for calculating price elasticities has several drawbacks. First, relying on regional price variations to estimate price elasticities may confuse cultural and taste differences, which vary from place to place, with a causal relationship between changing prices and changing consumption patterns. ‘) Second, data is typically aggregated by region, so even if regional price differences exist the degrees of freedom of the regression will be limited by the number of regions surveyed. An additional shortcoming of cross-sectional data is that they yield long-term parameters. “’ Because of tastes and preferences consumers take time to adjust to changing incomes and prices, but cross-sectional data cannot reflect these short-term adjustments. For this, time-series data are needed, where short-term parameters can be measured directly from the adjustment undergone by the household in response to various changes. As consumers change their tastes and preferences in response to income and price changes (typically a five- to ten-year process), their behaviour approaches that predicted by the long-term parameters. Because consumers change relatively slowly, short-term parameters are smaller than long-term parameters. When using consumption para-

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meters to design a nutritional intervention programme it will be important to recognize the difference between short-term and long-term parameters.

Empirical

“Per Pin&up-Anderson, Norha Ruiz de Londona and Edward Hoover, ‘The impact of increasing food supply on human nutrition: implications for commodity priorities in agricultural research and policy’, American Journal of Agricultural Economics, Vol 58, No 2, May 1976, pp 131-42. 12Ragnar Frisch, ‘A complete scheme for comoutina all direct and cross-demand elasiicitiei in a model with many sectors’, Econometrica. Vol 27. 1959. DD 177-96. 13Frisch, op cjf, Ref 12, p 188.’ j4As defined by Frisch, op tit, Ref 12, p 185, ‘good i is want-independent of all other goods if the marginal utility of good i depends only on the quantity of good i and not on any other quantity’. This assumption implies direct additivity of the utility function, ie U(q,, Q2, . . , 4”) = ~49,) + ~(9~) + . + ~49,). The assumption of want independence is relatively safe when one is considering Swiss cheese and electricity, ie the marginal utility of using more electricity in the home is indeoendent of the auantity of Swiss cheese’ consumed. However, want independence is not the equivalent of demand independence; Swiss cheese and electricity are related through the budget equation and are therefore not demand independent. %ut not without first issuing the appropriate caveat (p 133): the assumption of want independence is not likely to be valid for all goods considered; hence, the empirical results of this study should be interpreted with caution.’

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research findings

An initial attempt at disaggregating food consumption parameters by income class was made by Pinstrup-Anderson et al.” The intent of their article was to develop and test a model for estimating the distribution of food supply increases among consumer groups and the way this distribution would affect total food consumption as well as calorie and protein nutrition. They found that commodities have varying distributional impacts due to the ways different income groups respond to the price changes brought on by the supply increases. The authors then considered the implications of these results for setting commodity priorities in agricultural research and policy. Testing the model consisted of a series of recursive general equilibrium equations for predicting the distribution of food supply increases among different income groups. However, these equations required knowledge of the complete set of income and price elasticities for each income group. Since no such extensive determination of price elasticities for different food commodities at different income levels had ever been attempted, the authors had to develop a procedure for doing this as well. The data used were from a household survey taken in Cali, Colombia. Data was collected from 230 families on the quantities consumed, and prices paid, for 22 different foods. Household size and income were also recorded, and all of the information was compiled into five income categories. The families were reinterviewed eighteen months later, but as 30% of the families could not be contacted the cross-sectional data were small in number and the time-series data were severely flawed. Since accepted methodologies for determining price elasticities required accurate and extensive time-series data, the authors chose to apply a technique developed by Frisch12 for estimating a complete set of own- and cross-price elasticities when income elasticities, budget proportions, and money flexibility are known. The first two can be easily calculated from cross-sectional data; money flexibility can be indirectly determined when the own-price elasticities of a few goods are known.13 The entire methodology, however, rests on the critical assumption of want independence among the goods under consideration.14 This assumption is often ambiguous when the goods in question are oranges and eggs, and even more so in the case of beef and pork. In any given meal, an individual’s desire for pork is almost certainly related to the amount of beef consumed in that meal. Nevertheless, in the absence of any better alternative the authors chose to apply the Frisch methodology.is Income elasticities and budget proportions were estimated directly from the data by the means described earlier. The number of data points in each cell ranged from 30 to 80, enough to give an adequate level of significance to the results. Income elasticity estimates were consistent with expectations, being higher for luxury products than staples and decreasing with higher incomes. Budget proportions were all below 120/

Toh’e Frisch methodology

was then used first to calculate

money

341

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parameters

16Jon A. Brandt and Joseph B. Goodwin, ‘The impact of increasing food supply on human nutrition - implications for commodity priorities in agricultural research and policy: comment’, American Journal of Agricultural Economics, August 1980, pp 588-91. 17Per Pinstrup-Anderson, ‘The impact of increasing food supply on human nutrition: implications for commodity prices in agricultural research and policy: reply’, American Journal of Agriculfural Economics, August 1980, pp 592-3. ‘8Pinstrup-Anderson, op tit, Ref 17, p 592. ‘%. Peter Timmer and Harold Alderman, ‘Estimating consumption parameters for food policy analysis’; Amehcan Journal of Agricultural Economics, Vol 61, No 5, December 1979, pp 982-7.

flexibility and then the complete price elasticity matrix (the own-price elasticity matrix is reproduced in Table 1). As expected, the poor were found to respond much more quickly to price changes than the rich for nearly all commodities. When the impacts of various commodity supply increases were estimated, the results were divided into direct and indirect effects. Large, negative, indirect effects on the malnourished strata resulted from increases in the supply of some luxury commodities because of the high own-price elasticities combined with severe budget constraints and poor relative nutrient contents - people substitute the luxury good for a more nutritional commodity. In fact, increasing the supply of some foods may actually exacerbate malnourishment due to strongly negative indirect effects which overcome the positive direct effects. The authors conclude that nutritional distribution should be considered when establishing commodity priorities in agricultural research and policy. The assumption of want independence, however, did not go unchallenged. Brandt and Goodwin published a critique in which they claimed that usage of the Frisch methodology was inappropriate.lh They showed that if the goods are actually ‘want dependent’ the own-price elasticities will be in error by the sum of the now non-zero crosscommodity ‘want elasticities’. The authors then calculated price elasticities by both the Frisch method and a time-series method, using an extensive Canadian database. Based on the assumption that the timeseries data yielded accurate results the authors found the Frisch value to be consistently over-estimated, typically by a factor of two. Pinstrup-Anderson responded to the critique” by re-stating that the primary purpose of the study was not to estimate price elasticities but to develop a methodology for estimating the nutritional impact of supply expansions. The urgency of the problem demanded that he and his co-authors ‘settle for directions and orders of magnitude if greater precision could not be obtained’.” Thus, the results of the PinstrupAnderson et al analysis remained ambiguous, yet interest was kindled in calculating consumption parameters disaggregated by income class. Timmer and Alderman,‘” recognizing the limitations of present Table 1. Estimated direct price elasticities Commodity Beef Pork

Source: Per Pinstrup-Anderson, Norha Flub de London0 and Edward Hoover, ‘The impact of increasing food supply on human nutrition: implications for commodity priorities in agricultural research and policy’, American Journal of Agricultural Economics,Vol 58, No 2. May 1976, p 137. a Weighted average using total quantity sumed by strata as weights.

342

con-

Eggs Milk Rice Maize Beans Lentils Peas Other grains Potatoes Cassava Vegetables Tomatoes Plantain Oranges Other fruits Bread and pastry Butter and margarine Sugar Cooking oils and fats Processed food

I -1.457 -1.887 - 1.343 -1.788 -0.426 -0.630 -0.812 -0.897 -1.132 -0.869 -0.410 -0.226 -1.117 -1.169 -0.530 - 1.389 -1.293 -0.651 -2.792 -0.320 -0.838 -1.850

by stratafor Cali,Colombia.

II - 1.305 -1.608 ~ 1.227 ~1.621 -0.399 -0.548 -0.778 PO.903 -1.128 -0.496 -0.417 -0.279 -0.986 -1.247 -0.486 -0.962 -1.203 -0.558 -2.225 -0.296 -0.814 -1.416

Strata Ill

IV

V

Average”

-0.993 -1.119 ~1.262 -1.121 -0.397 -0.441 -0.649 PO.734 -0.757 -0.389 -0.312 ~0.246 -0.877 -0.997 -0.395 -0.789 -0.847 -0.327 -1.499 -0.295 ~0.581 -1.295

-0.692 -0.823 PO.754 -0.642 -0.262 ~0.000 PO.453 -0.620 -0.585 -0.291 ~0.000 -0.000 -0.379 ~0.463 -0.000 -0.644 -0.670 -0.243 -0.693 ~0.203 -0.298 ~0.673

-0.499 -0.698 -0.349 -0.201 -0.187 ~0.000 ~0.251 -0.428 -0.517 -0.253 ~0.000 -0.000 PO.199 -0.278 -0.000 PO.293 -0.500 -0.000 PO.395 -0.091 -0.141 -0.430

-0.839 -1 .Oll PO.925 -0.771 -0:354 PO.445 ~0.600 -0.641 -0.698 -0.478 ~0.255 -0.187 -0.685 -0.824 ~0.376 -0.694 -0.749 -0.310 - 1.082 -0.245 -0.507 -0.904

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Disaggregating

‘This method assumes that all consumers in a region face the same price regardless of their income level. “The model used to derive these estimates was developed as follows: Neoclassical household consumption functions treat the quantity consumed of each commodity as a function of various parameters, eg 0, = r(Y, P,, P2,

, f’,,

H, q where

0, = quantity consumed of commodity i by the household; Y = household income; P, = prices of various commodities; H = household size (sometimes including age and sex distribution); J = household tastes (approximated by ethnic, religious, educational, and regional variables). Theorists then experiment with different forms of this function until they hit on one that yields the best results. For the parameters which concern us (income and price elasticities) the form chosen is usually:

a = income elasticity of good i; b,, = own-price elasticity of good i; b,, = crossprice elasticity of good i; c = quantity consumed which is independent of the parameters listed. Taking the log of both sides, LnQ,=Lnc+aLnY+b,,LnP,+$b,P, A linear regression can be run on this equation after plugging in Q, Y, and P from the data. The form chosen by Timmer and Alderman is identical to this with two exceptions. They replace ‘a Ln Y’ with ‘a, Ln Y + a, Ln Y2 ‘. The income elasticity is continued

on page

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food

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parumerers

methodologies, set out to develop a new approach for calculating the full matrix of price elasticities for disaggregated income classes. They recognized that by surveying a large cross-sectional sample over large spatial and temporal diversity, significant variance in commodity prices could be captured. After data are compiled into income class and regional ‘cells’, the average values for the parameters in each cell are used as the data points for the regressions. For example, if there are 12 income classes and 24 regions there will be 288 (ie 12 times 24) points for estimating income elasticities and 24 points for estimating price elasticities.*O The data used by the authors were from the 1976 Indonesian Socio-Economic Survey. A total of 54 000 households were surveyed, 18 000 in each trimester of 1976. Data were collected on over 100 food commodities yet the authors chose to concentrate only on rice, fresh cassava, and corn - the three major foodstuffs accounting for more than two-thirds of average calorie intake. The authors assumed that any nutritional policy would have to deal with one of these three commodities. Data were reported for 12 income classes and 24 provinces, separately for urban and rural consumers, and for three different time periods - a total of 1800 data points in all. Table 2 indicates the elasticity estimates produced by the Indonesian analysis. *’ Income elasticities for the poor are extremely high. Rice is almost a luxury good for the poor, and the rich continue to increase their consumption with increasing income. Fresh cassava also has a large income elasticity, not becoming an inferior good until the fourth quartile. Such substantial income elasticities for food indicate that income transfers may be an effective way of dealing with malnutrition.22 The price elasticities are unique in that they are the first income-classspecific price elasticities with a high level of statistical significance ever reported. 23 Highly releva n t t o the analysis is the finding that the poor are much more sensitive to price changes than are the rich. The Slutsky equation” shows that this declining price elasticity could be due to the Table 2. Income and price elasticities of demand for food in Indonesia. 1

2

3

4

Low

Low-Mid

High-Mid

High

Average

1548 <2000

2513 2000-3000

3876 3000-5000

9085 >5000

5412 6151

Per capita total Expenditure (f?p/month)

Value (TX) Range Propotiion of Indonesian population Susenas sample

Population weight

0.106 0.154

0.185 0.237

0.321 0.324

0.388 0.285

0.997 1.168 0.839 0.994 0.740 0.776

0.759 0.924 0.522 0.679 0.584 0.615

0.533 0.704 0.230 0.394 0.435 0.470

0.070 0.364 -0.369 -0.046 0.130 0.246

0.265 0.581 -0.047 0.410 0.261 0.471

-1.921 - 1.284 -0.561 -0.329

- 1.475 -0.818 -1.081 -0.849

-0.743 -0.780 -0.811 -0.579

-1.105 -0.804

Income elasticities

Rice: Urban Rural Fresh cassava: Urban Rural Calories? Urban Rural Price efasticifies

Source: C. Peter Timmer and H. Alderman, ‘Estimating consumption parameters for food policy analysis’, American Journal of Agriculfural Economics, Vol61, No 5, December 1979, p 986. a Calories from rice, shelled maize and fresh cassava only.

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Rice Fresh cassava Calories? Urban Rural

-1.156 -0.943 -0.943 -0.711

-0.514

Cross price elasticities

Rice with fresh cassava Fresh cassava with rice

cF87

cz85

0.765

Disaggregating food consumption paramerers influence of the budget proportion and income elasticity, both of which have been shown to decline with increased income. However, as Timmer shows in a later paper, even the pure substitution elasticity (ie the Slutsky elasticity) can be shown to vary by income class. The significance of this finding is in the need to understand how price changes affect the nutritional level of the poor: ‘If, as hundreds of millions are, the poor are already at the nutritional margin of survival, even modestly higher food prices may have profound welfare effects to the nutritional status . . . Price effects may be much more important of the poor than income effects.‘24 Only one set of cross-price elasticities - those representing the effect of cassava prices on rice consumption - was calculated with any level of significance. At all incomes rice is readily substituted for cassava when cassava prices rise. The effect of rice prices on quantity of cassava consumed could not be determined confidently due to the very small budget shares devoted to cassava.25 All of the elasticities in Table 2 are significantly higher in absolute magnitude than values typically reported in the literature. The main reason for this is that these elasticities, being estimated from crosssectional data, represent long-term responses to changes in incomes and prices. As explained earlier, long-term parameters are always larger than short-term parameters. Nevertheless, these elasticities are still surprisingly large. Timmer and Alderman explain their size as follows:

[In] the multi-staple food economy of Indonesia with prevailing low levels of average calorie intake, such parameters would be quite consistent with an economically calculating population . The income elasticities for rice show that Indonesians do have strongly held food preferences and will exercise them as income permits. But the price elasticities reflect an ability to adjust consumption parameters in economically rational directions despite those preferences.26

continued

from page 343

then ‘a, + 2 a, Y. The a, coefficient should prove to be negative; thus, the income elasticity declines smoothly with increasing income, as expected due to Engel’s Law. Then, because of the data available, they use household expenditure rather than income. Similar results are expected, however, because low income households save very little money (see Timmer, op tit, Ref 6, p 399). ‘%mmer and Alderman, op dt, Ref 19, 986. =/bid, p 986. 24Timmer, op tit, Fief 6, p 396. 251bid, p 399. 26Timmer and Alderman, op tit, Ref 19, p 987. “Gray, op tit, Ref 9. 28Households were surveyed on their food purchases, consumption, and total expenditures during a seven-day period. Anthropomorphic and socioeconomic data were also collected. Data were aggregated into 9 income groups and 22. regions allowing 198 points for estimation of income elasticities and 22 points for estimation of price elasticities.

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In summary, the authors show that the poor respond differently than the rich, that income-class-specific consumption parameters should be calculated, and that they have developed a sound methodology for calculating these parameters when an adequate cross-sectional data set is available. Gray’s work,*’ building upon the foundation established by Timmer and Alderman, is a detailed report of her elaborate attempts to analyse Brazilian nutritional data. She provides an abundance of analytical information - 49 tables in a 65 page book. Her main objective is to determine the responses of malnourished Brazilians to changes in incomes and relative prices, in order to evaluate the effect of government policies on nutrition. The cross-sectional data used were collected from 55 000 families living in all regions of Brazi1.28 Gray found that in the Brazilian setting income was a poor measure for calorific consumption. To avoid distortions based on income figures, she decided to compute parameters for various levels of calorie consumption as well. Protein consumption levels were ignored because Brazilians have a relatively high level of protein intake. Table 3 shows four significant characteristics of the Brazilian diet. First, most people eat a varied diet. For example, the most malnourished segment of the urban population has five different commodities or commodity groups from which they receive at least 10% of their calories. Second, there is great dissimilarity in dietary composition between urban and rural areas. The rural population depends more on

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Table 3. Shares of average per capita calorie intake in Brazil by calorie consumption group (%). Lowest 15% Urban

Total

Rural

Urban

Total

Rural

Urban

17.1 8.8 6.0 1.6 0.6 36.0 34.5 1.6 10.8 24.0 0.4 1.0 6.5 1.5 4.9 1.5 1.1 0.2 0.1 2.5 0.4

33.9 17.2 3.2 10.2 3.3 17.3 15.6 1.7 13.5 12.5 0.6 1.5 8.6 3.7 4.8 3.2 1.4 0.6 1.1 8.3 0.6

32.2 17.6 3.9 7.8 2.9 18.3 16.5 1.8 13.0 14.2 0.7 1.4 8.0 3.2 4.8 3.4 1.8 0.6

36.7 19.2 4.5 6.4 6.6 0.7 6.1 2.6 13.3 10.7

35.9 19.1 7.6 2.4 6.9 13.5 10.4 3.1 12.7 13.9 0.8 1.2 7.2 1.9 5.2 5.0 3.5 0.7

37.4 19.4 1.6 10.2 6.2 4.4 2.1 2.2 13.7 7.7 1.3 2.5 10.3 5.0 5.3 7.2 4.1 1.2

a.2 0.6

20.9 11.2 7.2 1.8 0.7 33.2 31.5 1.7 10.7 21.7 0.4 1.0 6.7 1.8 4.9 2.2 1.7 0.3 0.2 2.7 0.4

36.3 19.9 2.7 9.9 3.8 12.9

Beverages

31.2 15.9 3.6 8.8 2.9 20.3 18.6 1.7 13.1 14.3 0.6 1.4 8.2 3.4 4.9 3.0 1.4 0.6 1.o 7.3 0.6

Percent of total population in group

15.7

2.6

13.1

29.9

7.8

22.1

Eggs

2aThese figures are in percentages - even though relative distribution is consistent, quantities vary considerably depending on income. Differences in quantity thus account for differences in nutritional levels. aOGray, op cit. Ref 9, p 20. =/bid, p 31.

FOOD POLICY

November

1985

Highest 70%

Rural

Cereals Rice Maize Wheat bread Other Roots Cassava flour Other Sugar Legumes Vegetables Fruits Meat and fish Beef Other Dairy products Milk

Source: Gray, see text, op cit. Ref 9, p 18 (calculations based on data from Fundagao lnstituto Brasileiro de Geografia e Estatistica, Estudo National da Despesa Familiar: Consumo Alimentar, Antropometrica: Dados Prelimina(es, 4 vols, Rio de Janeiro: IBGE, 1977, 1978).

Lowest 30%

Total

Other Oils and fats

1.o

11.2 1.8 13.8 11.5 0.8 1.5 8.5 3.8 4.7 3.8 1.8 0.8 1.2 10.1 0.6

1.o 1.s 0.8 3.5 5.2 6.2 3.0 1.o

1.4

0.9

1.8

11.8 0.9

9.2 0.6

14.2

70.1

32.4

37.7

1.3

cassava flour and legumes, while the urban population consumes much more rice and wheat bread. This implies that no one policy will reach all of the malnourished. A third characteristic is that dietary composition is remarkably consistent independent of income when considering purely urban areas or purely rural areas; for example, rice provides 19.9% of the calories for the ‘lowest 30%-urban’ group and 19.4% for the ‘highest 70%-urban’ group, and the figures for sugar are 13.8% and 13.7% respectively.29 The final characteristic is that malnutrition is heavily concentrated in the urban areas - about three-quarters of the malnourished live in urban areas even though poverty is more extensive in the rural areas.30 It is this factor which causes income to be a poor measure for nutritional well-being. Gray’s model is essentially the same as that of Timmer and Alderman. From this model, estimates were determined for income and price elasticities for various calorific consumption groups. Rural income elasticities seem to be significantly different than urban elasticities for staple foods only (see Table 4). Most elasticities display the expected pattern decline as income increases. The high negative elasticities for cassava flour are particularly noteworthy. For all consumption classes, in both urban and rural areas, cassava flour is considered an inferior commodity; and would be a good candidate for a targeted nutritional subsidy except for the fact that most cassava flour is consumed in the rural areas while most malnutrition is in the urban areas. Own-price elasticities are given in Table 5. As expected, they are negative or statistically zero for all commodities. Their large absolute size shows that consumers readjust their long-term consumption patterns in response to price changes. The large elasticities for cereals are attributed to the availability of substitutes.31 The elasticities approach zero with increasing income largely because of the effect of falling budget shares and income elasticities in the Slutsky equation. As in the previous studies, an important conclusion is that the poor respond differently to the rich to changes in prices and income. Gray calculated cross-price elasticities only for those commodities

345

Disaggreguting

food consumption

parameterr Table 4. Income elasttcities consumption group.

for calorie

intake from

Lowest 15% Urban Rural Cereals Rice Maize Wheat bread

Roots Cassava flour

0.838 0.915 0.314b 0.850 -0.807 -2.78 0.646

Legumes

“This is not significantly different at the 0.05 level from the urban estimate. bThis is not significantly different at the 0.05 level from zero. ’ This is the increase in daily per capita calorie intake from an increase of Cr $1 .OOin annual per capita income. Source: Gray, see text, op cif, Ref 9. p 25 (calculations based on data from Fundacao lnstituto Brasileiro de Geografia e Estatistica, Esiudo National da Despesa Familiar: Consume Alimentar, Antropometrica: Dados Preliminares. 4 vols, Rio de Janeiro: IBGE, 1977, 1978).

321bid, p 31. 33/hid, p 35. 34/hid, p 21.

346

-0.074b

0.702a 1.12a 1.18 2.28 -0.542= -2.24a

various

foods

Lowest 30% Rural Urban 0.540 0.799 0.16b 0.739 -0.684 -2.57

1.27 1.98 1.72 0.388a ~0.872~ -3.21

0.861

0.601

0.987

0.058a

~0.067~

0.049a

in Brazil

by calorie

Highest 70% Urban Rural -0.032 -0.151 -0.203 0.208

0.109 0.106 -0.600a 0.213a

-0.017 -0.289

-0.100a -0.740a

0.034 -0.188

0.08ga -0.169a

Vegetables

1.52

1.29a

1.21

1.13a

0.322

0.252a

Fruits

0.400b

0.81ga

1.50

1.92”

0.537

0.331

Meat and fish Beef

0.271 0.957

0.564a 1.70a

0.355 0.841

0.117a 0.5743

0.329 0.408

0.274a 0.472a

Dairy products Milk Eggs

0.560 1.58 0.865

1.11a 2.16a 0.263a

0.653 1.47 1 .Ol

1.43 2.59= 0.622a

0.554 0.509 0.340

0.335 0.297a 0.419a

Oils and fats

1.80

1.87

1.50

0.173

0.360a

Total calories

0.221

0.422

0.229

0.447

0.078

0.085=

Mean per capita daily calorie intake (calories)

1693

1657

1784

1795

2199

2347

Mean per capita annual expenditure (Cr $)

1860

742

2322

898

Mean family annual expenditure (Cr $)

8751

2473

11 322

3794

50 782

16 950

Marginal calorie intake (calories)

0.201

0.942

0.176

0.894

0.016

0.073

10383

2719

hypothesized to have a significant effect on other commodities. This was done primarily because of data limitations and a need to avoid multicollinearity resulting from including too many variables in a regression. She concluded that ‘Many questions remain concerning the cross-price effects between foods important in the diets of the poor. Although these estimates provide a starting point for this type of analysis, reliable results require more detailed and exact data, probably in time series form.‘“2 When own- and cross-price effects are combined (similar to the way in which Pinstrup-Anderson et al combined direct and indirect effects) the overall effects of relative price changes on total calorific consumption can be seen (Table 6). The most crucial commodity price is that for rice. When rice prices rise calorific consumption falls considerably for the malnourished. Note that price elasticity for bread is positive, which means that a lowering of the price of bread would actually reduce the calorific intake of malnourished individuals. Another significant figure in Table 6 is the per capita income elasticity of calorie consumption. The ‘lowest 15%’ have an elasticity of only 0.221 even though they are more than 400 calories below requirements. Theoretically, it would seem that malnourished people would have a much higher income elasticity. Gray claims that ‘the need to improve the quality of the diet is as strong as the need to increase food quantity and that quality is measured not only by the desirability of each food component in itself but also by the degree of variety found in the diet.‘33 Public policy in Brazil is to subsidize commodities such as wheat, milk, beef and vegetable oils. 34 As Table 3 indicated, however, these commodities are consumed primarily by the well-nourished. Present

FOOD POLICY

November

1985

Disaggregating

Table 5. Own-price elasticities for calories Consumption

a This is not significantly different from zero at the 0.05 level, using a one-tailed test. Source: Gray, see text, op tit, Ref 9, p 25 Icalculations based on data from Fundaceo institute Brasileiro de Geografia e Estatistl’ca, Esfudo National da Despesa Familiar: Consume Alimenfar, Antropometrica; Dados Prehinares, 4 vols, Rio de Janeiro: IBGE, 1977, 1978; and Funda@o lnstituto Brasileiro de Geografia e Estatistica, Estudo National da Despesa familiar: Despesa das Familias; Dados Preliminares, 6 vols, Rio de Janeiro: IBGE, 1977, 1978).

=/bid, p 58. 36Mark Pitt, ‘Food preferences and nutrition in rural Bangladesh’, Review of Economics and Statistics, Vol65, No 1, February 1983, pp 105-l 4. %tandard methodologies for estimating consumption parameters are not applicable on these household data because of bias problems that arise when the consumption of a commodity is zero. Thus, Pitt used the limited dependent variable model (‘tobit’) developed by James Tobin in ‘Estimation of relationships for limited dependent variables’, Econometrica, Vol 26, 1958, pp 24-36, which uses probability to allow for the case of non-consumption.

FOOD POLICY

November

1985

in Brazil. Income group

group

Lowest 30%

Highest 70%

Lowest 30%

Middle 50%

Highest 20%

Cereals

-1.17 (0.239)

-0.803 (0.225)

0.1 93a (0.150)

-0.804 (0.254)

-0.173 (0.142)

0.1 63a (0.205)

Rice

-5.52 (0.477)

-4.59 (0.379)

-0.982 (0.217)

-4.31 (0.482)

-2.95 (0.352)

-1.15 (0.243)

Maize

-1.91 (0.193)

-1.59 (0.161)

-0.800 (0.107)

-1.77 (0.198)

-1.09 (0.130)

-0.584 (0.141)

Wheat bread

-2.17 (0.173)

-1.80 (0.151)

-0.821 (0.130)

-1.96 (0.197)

-0.845 (0.171)

-0.731 (0.181)

-1.05 (0.152)

-1 .Ol (0.129)

-0.703 (0.180)

-1.36 (0.187)

-0.758 (0.197)

-0.231a (0.179)

-1.09 (0.542)

-1.34 (0.470)

-0.002= (0.540)

-1.26 (0.580)

-1.05a (0.606)

-0.319= (0.772)

Sugar

-0.899 (0.265)

-1.21 (0.183)

-0.520 (0.179)

-1.39 (0.257)

-0.962 (0.202)

-0.588 (0.167)

Legumes

-0.561 (0.265)

-0.328a (0.215)

-0.613 (0.150)

-0.600 (0.236)

-0.457 (0.176)

-0.628 (0.250)

Vegetables

-0.926 (0.305)

-0.770 (0.214)

-0.393 (0.205)

-0.410= (0.339)

-0.234a (0.183)

-0.267a (0.173)

Fruits

-1.34 (0.338)

-1.06 (0.256)

-0.635 (0.189)

-0.895 (0.409)

-0.566 (0.210)

-0.378 (0.168)

Meat and fish

-0.718 (0.286)

-0.401 (0.215)

-0.007a (0.101)

-0.553 (0.228)

-0.140a (0.130)

-0.108a (0.160)

-2.04 (0.211)

-2.15 (0.180)

-1.24 (0.188)

-2.35 (0.195)

-1.29 (0.215)

-0.819 (0.209)

-0.813 (0.445)

-0.610 (0.324)

-0.801 (0.241)

-0.270a (0.398)

-0.636 (0.269)

-0.845 (0.295)

Milk

0.398’ (1.21)

1.46a (0.935)

0.514a (0.447)

2.87 (1 .OO)

-0.095a (0.651)

-0.468= (0.518)

Eggs

-0.790 (0.290)

-0.624 (0.211)

-0.332 (0.142)

-0.770 (0.322)

-0.451 (0.163)

-0.124= (0.178)

-0.60ga (0.524)

-0.176a (0.460)

0.029a (0.062)

-0.337= (0.604)

0.375a (0.388)

0.356= (0.494)

Cassava flour

are standard

foods

parameters

Lowest 15%

Roots

Note: The numbers in parentheses errors.

from various

food consumption

Beef Dairy products

Oils and fats

subsidies are therefore not helping the malnourished; in fact, subsidizing wheat bread may harm them. Rice, especially low-quality rice, and cassava flour show the best promise for targeting nutritional aid.j5 A Brazilian government concerned with helping the poor and conserving money would change its subsidy policy. Pitt’s research on Bangladesh36 is substantially different from the preceding work in a number of ways. The main difference is that he determines not only the commodity price elasticity matrix but a complete nutrient price elasticity matrix as well. In addition, he has access to a large set of panel data - 5750 rural households interviewed in four successive quarters - which are still accessible at the household level. He is thus able to perform his estimations at the household level rather than the regional level, giving him more degrees of freedom for estimating parameters.37 Pitt’s model allowed for the smooth estimation of expenditure-level-specific parameters avoiding the discontinuities that result from performing separate regressions for each expenditure class. For reporting purposes, Pitt chose a representative household at ‘percentile 25’ representing the median of the top 50% of food consumers, and a representative household at ‘percentile 90’ representing the median level for the lowest 20% of households. He found that expenditure elasticities vary significantly among foods and expenditure levels. Wheat stands out as an inferior commodity for 347

Disaggregating

food consumption

parameters Table 6. Price and income elasticities of total calorie intake in Brazil. Income distribution

Calorie distribution Lowest 15%

Lowest 30%

Highest 70%

Lowest 30%

Middle 50%

Highest 20%

-0.477 (0.060)

-0.416 (0.056)

0.039a (0.049)

-0.453 (0.091)

-0.211 (0.060)

0.44a (0.073)

Maize

-0.056’ (0.039)

-0.090 (0.022)

-0.044 (0.016)

-0.053= (0.033)

-0.063 (0.021)

-0.031a (0.029)

Bread

0.115 (0.044)

0.103 (0.026)

0.093 (0.033)

0.076= (0.047)

0.060= (0.035)

0.067a (0.050)

Cassava flour

0.104 (0.043)

0.131 (0.027)

-0.036a (0.026)

0.045= (0.043)

0.019= (0.031)

0.002” (0.045)

Sugar

-0.042a (0.104)

0.061a (0.050)

0.164 (0.036)

0.096” (0.056)

0.146 (0.042)

0.074a (0.063)

Legumes

0.027a (0.063)

0.047a (0.044)

0.273a (0.644)

0.021 a (0.063)

0.161 (0.056)

0.226 (0.071)

Note: The numbers reported are coefficients of double log consumption functions and thus represent price and income elasticities. The numbers in parentheses are standard errors.

Vegetables

-0.040a (0.076)

-0.074a (0.046)

-0.205 (0.041)

-0.072a (0.076)

-0.097a (0.056)

-0.206 (0.070)

Fruits

0.086 (0.036)

-0.104 (0.025)

0.033a (0.024)

-0.042a (0.046)

-0.041a (0.029)

0.043a (0.034)

“This is not significantly different from zero at the 0.05 level using a two-tailed test. b This is derived from a log-log quadratic term with coefficients: [n(income)]* n (income) -0.026 0.578 (0.076) (0.004)

Beef

-0.012a (0.061)

-0.026a (0.043)

-0.006a (0.041)

-0.015a (0.069)

0.016a (0.049)

0.064a (0.063)

Milk

-0.205” (0.103)

-0.136 (0.059)

-0.103 (0.044)

-0.167 (0.081)

-0.162 (0.057)

-0.161 (0.060)

Eggs

-0.012= (0.061)

-0.001” (0.037)

-0.162 (0.032)

-0.033a (0.069)

-0.156 (0.039)

-0.127 (0.054)

Oils and fats

0.037a (0.069)

-0.062a (0.063)

0.029a (0.062)

-0.053a (0.069)

-0.009” (0.071)

0.066a (0.103)

Per capita income

0.221 (0.033)

0.229 (0.020)

0.076b

0.260 (0.037)

0.176 (0.016)

0.039 (0.011)

Rural income (dummy)

0.202 (0.093)

0.216 (0.043)

0.007a (0.011)

0.165 (0.036)

0.024= (0.029)

0.016a (0.027)

Rural interest (dummy)

-1.34 (0.612)

-1.45 (0.293)

0.075a (0.097)

-1.22 (0.266)

-0.0263 (0.227)

-0.059a (0.243)

Family size

0.020= (0.038)

0.001= (0.026)

0.107 (0.030)

-0.040a (0.035)

0.004a (0.062)

0.150a (0.097)

0.676

0.926

0.927

0.952

0.959

0.905

Variables Prices Rice

For all other classes not significant.

the quadratic

term was

Source: Gray, see text, op cif, Ref 9, p 34 (calculations based on data from Funda@o lnstituto Brasileiro de Geografia e Estatistica. Estudo National da Despesa Familiar: Consumo Alimentar, Antropometrica; Dados Preliminares, 4 vols, Rio de Janeiro: IBGE, 1977, 1976; and Funda@o lnstituto Brasileiro de Geografia e Estatistica, Estudo National da Despesa Familiar: Despesa das Familias; Pados Preliminares, 6 vols, Rio de Janeiro: IBGE, 1977, 1976).

38Pitt, op tit,

348

Ref 36, p 112.

R2

both representative households, meaning that wheat is consumed in larger absolute quantities by lower-expenditure households. This characteristic makes it an ideal candidate for a targeted subsidy programme. The entire matrix of own- and cross-price elasticities is reproduced in Table 7. A surprising number of cross-price elasticities for percentile 90 are found to be significant, with the absolute values of 24 of the 72 elasticities exceeding 0.250. The largest substitution cross-price elasticities are for wheat demand with respect to the price of rice - if rice prices go up, consumers will heavily substitute wheat for rice. To describe the overall effect of commodity price changes on nutrition, Pitt calculated the complete nutrient price elasticity matrix (Table 8). Wheat has a uniformly negative row of nutrient elasticities for the low-expenditure household, thus implying that subsidizing wheat will increase the intake of all nutrients. Pulses, on the other hand, have a mostly positive row of nutrient elasticities. Pulses, an important and inexpensive source of protein, have been considered for price subsidies by the Bangladeshi government.38 However, these nutrient price elasticities show that a subsidy will actually reduce the nutrient intake of the already malnourished population.

FOOD POLICY

November

1985

Disaggregating Table 7. Own- and cross-price

Percentile 25 expenditure Price of: Rice Wheat Pulses Fish Mustard oil Onions Spices Milk Potatoes Percentile 90 exoenditure P&e of: Rice Wheat Pulses Fish Mustard oil Onions Spices Milk Potatoes

Source: M. Pitt, ‘Food preferences

and nutrition in rural Bangladesh’, Review of Economics and. Stafisfics, Vol 65, No 1, February 1983, p 110.

3glbid,

Table 8. Nutrient price elasticities

Rice Wheat Pulses Fish Mustard oil Onions Spices Milk Potatoes Percentile 90 expenditure Rice Wheat Pulses Fish Mustard oil Onions Spices Milk Potatoes

Quantity of: Mustard oil Onions

Rice

Wheat

Pulses

Fish

-0.832 0.003 -0.175 0.002 0.131 -0.032 -0.003 -0.088 0.046

1.079 -0.063 0.348 0.044 -0.624 0.099 -0.133 -0.151 -0.061

-0.157 -0.084 -0.512 -0.114 -0.183 0.005 0.116 -0.030 -0.174

-0.914 -0.083 0.511 -0.967 -0.381 0.310 0.047 0.507 -0.265

-0.586 -0.218 -0.094 0.072 -0.716 -0.082 0.091 0.069 -0.016

-0.088 -0.181 0.297 -0.261 0.009 -0.599 0.088 -0.070 0.002

-1.301 0.011 -0.121 0.019 0.026 -0.067 -0.005 0.050 0.162

1.061 -0.719 0.330 0.095 0.064 0.189 -0.134 -0.332 -0.334

0.364 -0.169 -0.679 -0.071 -0.493 0.002 0.248 0.256 -0.283

-0.351 -0.016 0.310 -0.660 -0.220 0.348 0.109 0.478 -0.410

-0.894 0.021 -0.078 -0.060 -0.094 -0.104 -0.080 -0.106 0.122

0.117 -0.117 0.104 -0.026 0.148 -0.489 -0.040 -0.105 0.078

Milk

Potatoes

0.107 -0.155 0.065 -0.068 -0.264 0.144 -0.648 0.131 0.146

-0.868 -0.192 0.344 0.057 -0.045 -0.280 0.019 -0.246 -0.020

-0.295 -0.516 0.093 -0.150 -0.095 -0.336 0.144 -0.028 -0.963

0.076 -0.024 0.024 -0.019 0.018 0.022 -0.759 0.065 0.195

-1.326 0.495 0.118 0.058 -0.299 -0.440 0.080 -1.084 -0.154

-0.266 -0.552 0.172 -0.390 0.027 -0.543 0.388 0.439 -1.684

in Bangladesh.

Protein

Fat

Carbohydrate

Calories

Thiamine

Riboflavin

Niacin

-0.418 -0.037 -0.048 -0.102 -0.097 0.019 -0.006 -0.035 -0.037

-0.588 -0.129 0.071 -0.153 -0.372 -0.005 0.037 0.064 -0.062

-0.549 -0.014 -0.115 -0.002 0.016 -0.018 -0.015 -0.092 0.017

-0.529 -0.026 -0.091 -0.024 -0.029 -0.012 -0.011 -0.074 0.004

-0.536 -0.099 0.196 -0.301 -0.206 0.021 0.012 0.060 -0.110

-0.165 -0.034 -0.027 -0.028 -0.153 0.017 -0.035 -0.081 -0.022

-0.214 -0.036 -0.049 -0.003 -0.121 -0.005 -0.027 -0.100 -0.018

-0.281 -0.050 -0.047 -0.003 -0.114 -0.023 -0.023 -0.106 -0.019

-0.474 -0.020 -0.089 -0.007 -0.018 -0.010 -0.020 -0.090 0.004

-0.191 -0.261 0.016 -0.028 -0.044 0.062 -0.015 -0.028 -0.118

-0.441 -0.112 0.056 -0.147 -0.099 0.048 -0.015 -0.012 -0.106

-0.553 -0.203 -0.011 0.034 0.017 0.005 -0.034 -0.050 -0.001

-0.484 -0.210 -0.000 0.016 0.002 0.016 -0.032 -0.048 -0.026

-0.087 -0.177 0.105 -0.198 -0.135 0.105 0.024 0.021 -0.249

0.129 -0.391 0.088 0.036 0.001 0.080 -0.060 -0.127 -0.151

0.055 -0.373 0.067 0.048 0.002 0.060 -0.053 -0.131 -0.134

0.009 -0.329 0.028 0.043 -0.030 0.046 -0.045 -0.134 -0.129

-0.394 -0.254 0.025 0.035 0.019 0.026 -0.042 -0.072 -0.043

Source: M. Pitt, ‘Food preferences

FOOD POLICY

in Bangladesh.

Finally, Pitt calculated the nutrient expenditure elasticities, finding them to range from 0.45 to 0.78 for the low-expenditure household. Pitt interprets this to mean that even poorly nourished households can improve their nutrition simply by altering their diet.‘” Taste must evidently play an important role, as it does in Brazil. Pitt gleaned three lessons from his research: the poor respond differently to changes in prices and total expenditure than do the rich. Thus, consumption parameters need to be disaggregated by income class; substitution effects are strong and cannot be ignored, as was shown in the case of subsidizing pulses; and income transfers may not be as effective at increasing nutrition as are programmes which encourage the consumption of more nutritious foods.

p 113.

Percentile 25 expenditure

elasticities

food consumption parameters

and nutrition

November

1985

in rural Bangladesh’,

Review

of Economics

and Statistics, Vol 65, No 1, February

1983,

p 111.

349

Disaggregating food consumption parameters

Conclusions

4”Timmer and Alderman, 986. “‘Karim, op tit, Ref 1.

350

op cif, Ref 19, p

Three main areas of consensus stand out in the articles reviewed. First, the determination of disaggregated consumption parameters has certainly been shown to be desirable. All the empirical work indicates that poor people respond much more sensitively to changes in household income and relative food prices than rich people. These differences in behaviour, if known to the policy maker, can be used to design nutrition programmes which efficiently target government resources to the poor. The articles reviewed were also consistent in pointing out that lowering the price of certain commodities could result in increased malnutrition, despite the best intentions of the policy maker. The identities of commodities with this quality were not intuitively obvious; indeed, some of these commodities are presently being subsidized. A final area of consensus among the authors is that nutrition could be improved simply by redistributing present food expenditures. Obviously, tastes and preferences have significance even for the malnourished; quality and variety are as important as quantity. Yet tastes are not immutable; lower prices can cause long-term shifts in consumer behaviour. A significant number of differences exist among the articles, some of which may be attributable to differences in the societies evaluated and some to weaknesses in the methodologies applied. The first and most significant difference is that income and price elasticities vary tremendously from society to society. Some of this variance is certainly due to differing cultural preferences and relative price environments. Some is also due to the confusion of long- and short-term parameters necessitated by data limitations. Another methodological cause of variance is the intermixing of income, calorie, and expenditure elasticities, due partly to data limitations and partly to theoretical limitations. In sum, little can be learned in the area of cross-country comparisons until methodologies have become more standardized. A second, more minor discrepancy occurs in determining the effectiveness of income transfers in improving nutrition. Most researchers generally agree that income transfers are a poor means of improving nutrition due to low income elasticities for calorie consumption. Timmer and Alderman are the exception, claiming that ‘directing income to the poor will be a quite efficient way to improve their calorie however, that their analysis dealt intake’.40 It must be remembered, only with three food commodities and that the estimated parameters were long-term parameters. Something must be done for the malnourished in the short-term. As in any fledgling methodology, numerous weak areas demand improvement. First and foremost is the improvement and standardization of survey techniques. Good data are expensive, but the old adage, ‘garbage in, garbage out’, still rings true. Methodologies and models cannot be standardized until survey techniques are standardized. Another weak area is the shortage of case studies. An experiment with subsidizing sorghum in Bangladesh showed promising results.41 Additional countries, possibly with outside financial assistance, need to experiment with subsidizing an inferior commodity. Only then will we know whether further research and refinement of this methodology is warranted. Many key figures have a weak level of significance, and so the theoretical results cannot be completely relied on. Better results await FOOD POLICY

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the improvement of databases and methodologies. Finally, further attempts should be made to isolate income effects from price effects, keeping in mind that income elasticities are not constant from one price environment to another. Policy makers need to recognize that subsidizing an inferior commodity is not a nutritional panacea. Even if given away, no one commodity would reach all of the malnourished population because of administrative and availability limitations as well as the tastes and preferences of the poor. An additional warning is justified regarding an inherent limitation of this approach. After all the expense and effort of determining disaggregated consumption parameters, it is possible that no ideal commodity may be found. For example, in Brazil no commodity was found that is eaten by the poor and not by the rich. Although some commodities, such as low-quality rice, were found to make better vehicles than others, targeting nutritional subsidies in Brazil is far from ideal. As with any empirical study in the social sciences, these results look less impressive and less secure after carefully studying the methodology and data than they do upon first impression. The strengths and weaknesses need to be carefully evaluated, but when the alternatives are considered the proposal of using disaggregated consumption parameters to target nutritional programmes holds much promise:

@TimmeT and Alderman,

op cit. Ref 19,

987.

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The strategy calls for high political commitment to increasing the access of the poor to adequate food supplies, but it may also be the only financially feasible way of coping with protein-calorie malnutrition over the next several decades.42

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