Veblen Preferences and Falling Calorie

Consumption in India: Theory and Evidence

By

Amlan Das Gupta

University of British Columbia

Abstract

Despite rapid economic growth India experienced an unexpected decline in average calorie consumption during the time span 1983-2005. This paper is an attempt to demonstrate that preference for conspicuous consumption driven by status competition within one's peer group is a significant explanatory factor for this decline. It begins by adding status seeking preferences to a dual-economy general equilibrium model, demonstrating the theoretical possibility of this idea. Next, the effect of peer group income on calorie consumption is estimated using World Bank data collected from rural India. Using these estimates it is roughly calculated that Veblen competition can account for more than a third of the missing calories. A unique source of variation in peer group income, based on caste-wise domination across villages, is used for identification.

Keywords: Calorie reduction, India, Veblen goods, Caste. JEL codes D11, I 15, O12, O53

Acknowledgments: For his essential guidance and help in this project I would like to thank my supervisor Dr. Mukesh Eswaran and also my supervisory committee members Dr. Pattrick Francois and Dr. Kevin Milligan. I am also grateful for useful comments and suggestions from Dr. Siwan Anderson, Dr. Kaivan Munshi, and the participants of the empirical workshop at the University of British Columbia, especially Dr. Thomas Lemieux and Dr. Nisha Malhotra. I have also benefitted greatly from discussions with my fellow graduate students.

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1. Introduction

There is ample anecdotal evidence to suggest that status, derived from display of wealth, is an important aspect of Indian social life1. This paper will try to convince you that in India status competition is also an important influence on one real economic outcome, namely nutrition. A surprising recent finding is that average calorie consumption in India was declining during the years 1985 to 2005, a period most noted for very high growth rates in the Indian economy

(Deaton and Dreze 2009). This paper is an attempt to explain this phenomenon on the basis of strong preference for status goods coupled with rising incomes. The paper begins with a theoretical framework where such preferences are added to a standard dual-economy general equilibrium model. This framework demonstrates that in order to observe reducing calorie consumption with economic growth, status competition has to coexist with low income elasticity of food. In the subsequent empirical section, consumption choices of a sample of rural Indian households from the states of Uttar Pradesh and Bihar is examined. The evidence suggests that peer group income has a negative impact on calorie consumption. Moreover this effect is much higher than the positive effect of own income. These findings suggest that status competition may explain how calorie consumption may actually fall even though incomes are going up all round.

India today suffers from the dubious reputation for being home to almost a third of the world‟s malnourished children (See Fig 1 attached at the end). So the issue of nutrition generates deep

1 For some of the articles in the popular media regarding conspicuous consumption in India see the following: “India's wealthy flaunt riches as fashion industry booms”, China Daily May 2011; “For India‟s Newly Rich Farmers, Limos Won‟t Do”, New York Times March 2010 and “India‟s Secret Weapon: Its Young Population”, CNBC October 2012.

2 concern. According to the latest report of the National Family Health Survey2 (2005-06) 48% of all children under 5 years of age are stunted3, 20% are wasted and 43% are under weight.

Children suffering from such severe under-nutrition are more susceptible to disease and are much less likely to develop into physically and mentally healthy adults. The adult population also shows signs of this severe malnutrition with extreme thinness or obesity prevalent amongst

43% of Indian men and 48% of Indian women between the ages 15-49. The state of nutrition in

India is critical in the sense that it has ceased to be a humanitarian problem restricted to the poor and threatens the economic/productive capacity of the whole country. According to a World

Bank report published in 2005 (Gragnolati et al. 2005) micronutrient deficiencies alone are costing India US$2.5 billion annually.

Poverty is often cited as the root cause of malnutrition in India4. This is what commonsense would suggest and indeed most studies find a positive income elasticity of food expenditure as well as calorie consumption 5 . So, the spell of high economic growth from the mid-1980s onwards, was expected to make significant inroads into solving malnutrition. Yet, Deaton and

Dreze (2009) demonstrate that in between the years 1983 and 2004 average calorie consumption

2 The NFHS survey is a nationally representative survey conducted under the stewardship of the Ministry of Health and Family Welfare government of India. 3 Nutritional status is typically described in terms of anthropometric indices, such as underweight, stunting and wasting. The terms underweight, stunting and wasting are measures of protein-energy undernutrition and are used to describe children who have a weight-for-age, height (or recumbent length)-for-age and weight-for-height measurement that is less than two standard deviations below the median value of the NCHS/WHO reference group. This is referred to as moderate malnutrition. The terms severe underweight, severe stunting and severe wasting are used when the measurements are less than three standard deviations below the reference median, and mild underweight, stunting and wasting refer to measurements less than one standard deviation below the reference population. Underweight is generally considered a composite measure of long and short-term nutritional status, while stunting reflects long-term nutritional status, and wasting is an indicator of acute short-term undernutrition. 4 Yet the malnutrition statistics we see are far worse than the proportion of people living below the poverty line (27.5% in 2004-05 according to Kotwal et al (2010)). 5 Although there is some debate about the size of this elasticity, most agree that it is greater than zero and higher for low income households (see Subramanian and Deaton (1996) and Behrman and Deolalikar (1987)).

3 in the whole country has fallen6. However during the same period, GDP per capita grew at 4 to 6 percent per annum, real percapita consumption expenditure was rising at 2-4 percent and the relative price of food remained quite stable. So, this fall in calorie consumption cannot be attributed to a negative income elasticity of calories. Also, the authors estimate the calorie Engel curves for different time periods. An interesting feature of these curves is that they are positively sloped but have been shifting downwards over time. So calorie consumption is falling, over time, for each point on the income distribution.

Clearly there must exist other factors, independent of income, which are driving this phenomenon7. One such factor could be the preference for status. The idea, first introduced by

Veblen in his book “Theory of the Leisure Class” (1899), recognises that certain goods are consumed for their value as indicators of higher status or wealth (Veblen goods). A closely related concept is that people care not only about their own income but their relative income vis- a-vis others (Duesenberry 1949). People generally try to catch up with their neighbours8 and distinctly feel worse when they fall behind (Easterlin 1974, 1999, Clark and Oswald 1996,

Luttmer 2005). In an environment where rapid economic growth is pushing up incomes there is a possibility that agents constantly feel pressured to keep pace with the consumption of their neighbours. If this pressure is strong enough we might see a reduction in essential non-Veblen consumption like food. The objective here will be to investigate this possibility.

6 Malnutrition has fallen though, but slowly. According to the National Family Health Survey, the proportion of underweight children remained virtually unchanged between 1998-99 and 2005-06 (from 47% to 46 % for the age group of 0-3 years). 7 This phenomenon of falling calorie/food consumption in a period of economic growth is not a peculiarity of India. There is some evidence that something similar occurred in England during the industrial revolution period of 1770- 1850 (Clark et al. (1995)) and also in China during a phase of rapid growth from 1982-1997 (Du et al. (2002) and Meng et al. (2009)). 8 Hence also referred to as “Keeping up with the Jones”

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The first contribution of this paper is to use preference specifications motivated by Veblen-style status competition in a dual economy model of economic development. There exist other papers which show that status seeking preferences have the potential to generate ex-post utility reducing decisions from agents through a consumption externality (Kelly 2005, Cooper et al 2001,

Hopkins and Korienko 2004 and Eaton and Eswaran 2009). But most of these results are from standard neoclassical general equilibrium models, quality ladder models (as Grossman and

Helpman 1991), strategic interaction settings (game theoretic) or simply partial equilibrium models. On the other hand the dual-economy models, pioneered by Lewis (1954) and developed by Jorgensen (1961), focus on the transition of a less developed economy from being largely agricultural to an industrial one9. Later Eswaran and Kotwal (1993) bring attention to the role of consumer preferences in this transition. In the present paper the Eswaran and Kotwal framework is adapted to incorporate status motivated preferences such as “Keeping up with the Jones”

(KUJ). An immediate implication of assuming "Keeping up with the Jones" (KUJ) is that food consumption falls with a rise in peer group income ceteris paribus, in the partial equilibrium.

Also, comparative statics results from the general equilibrium of this model suggest that under certain conditions we observe the downward shift of Engel curves as documented by Deaton and

Dreze (2009). Consumption of food (the non-Veblen good) would be declining in response to productivity/technology driven growth as long as the Veblen effect is strong enough to overcome the income effect on food generated by rising incomes. Most of the literature agrees that income elasticity of food is quite small and lower for high income households (see Subramanian and

Deaton (1996) and Behrman and Deolalikar 1987), but whether the Veblen effect exists and is

9 According to Lewis (1954) the essence of development was to change from an economy saving 5-6% of GDP to 10-12% of GDP.

5 strong enough to drive down food consumption despite the income growth, is an empirical question and is addressed next in this paper.

In the empirical section the partial equilibrium result alluded to above is taken to the data using the Living Standard Measurement Survey data of the World Bank . The goal here is to provide evidence for the existence of preferences for keeping up with the Joneses among rural agricultural communities in India. This exercise is similar to other papers which attempt to measure the effect of peer group income on various outcomes e.g. Luttmer (2005) on happiness measures and Charles et al. (2009) on conspicuous consumption. Of these, the approach followed in this paper is closest to Charles et al. (2009) 10. They use the fact that in USA being non-white is a negative signal for wealth and the races known to have lower average incomes have to spend more on conspicuous consumption to make up for this handicap. The authors find that the differences in spending on visible consumption by race can be explained by the average incomes of the particular race. But, while Charles et al. (2009) paper addresses interracial differences in income driving Veblen consumption, here within caste income distributions and its effect on conspicuous consumption are examined. Also rather than highlight the signalling aspect of the

Veblen goods, the setting here is closer to the “keeping up with the Joneses” motivation as regards the consumption of status goods (see Galli 1994).

The main contribution of the paper in the empirical section is the unique instrument used to tackle measurement issues with the peer group income variable. In this sample, low caste individuals living in low caste dominated villages earn higher agricultural income than low castes living in high caste dominated villages (Anderson 2011). This creates an exogenous variation in own caste income among the low castes across the two types of villages. Using this

10 Khamis et al. (2010) use the same methodology as Charles et al.(2009) with Indian data and find mixed results.

6 instrument estimates for peer income effect turn out to be negative and significant. Moreover they are twice in magnitude than the estimated own income effect (which are positive as expected) which is exactly in line with the theory model.

The rest of the paper is arranged as follows: Section II presents the theoretical model; Section III presents the empirical work and Section IV concludes.

2. Dual-economy Model with Veblen Preferences

In this section the basic framework follows Eswaran and Kotwal (1993). We start with a description of the preference structure. Suppose that the agent has a choice between two goods X and Z. Here X is a non-Veblen good for example food, and Z is the Veblen good. Next we make two assumptions which will define the preference structure for the agents.

Firstly it is assumed that the utility function of the agents are quasilinear in food (X) and the

Veblen good (Z). This kind of preference may be expressed as follows:

푉 푥, 푧 = 휑 푥 + 훾푧

Here lower case denotes quantities of these goods. The function 휑 푥 may be a standard increasing and strictly concave function with lim 휑′ 푥 = ∞; the next term is a linear function of 푥→0 the Veblen good Z. This form, although not crucial for the results, helps to highlight the workings of the Veblen preferences11. A feature of this utility function is that at lower levels of consumption of X, marginal utility per dollar spent will be higher than that from the industrial good (which does not change). So until an agent‟s marginal utility per dollar from X has fallen to a constant (which is equal to 훾 divided by the price of the industrial good) there will be no

11 Please see the Appendix 2 for an analysis of the model with the quasilinearity assumption relaxed.

7 consumption of Z. After the marginal utilities per dollar have been equalised, all extra income will go into consumption of Z. This feature is a very apt description of preferences in poorer areas where initially agents devote all their income to subsistence requirements. Luxury goods and other non-necessary goods are consumed only after the basic needs have been fulfilled. The assumption implies that Engel curves for food will not be upward sloping after a certain threshold of income.12

The next assumption is that preferences for the Veblen good Z exhibits "Keeping up with the

Jones" (KUJ) as coined by Gali (1994) and later formalized further by Dupor and Liu (2003).

Unlike the previous assumption this one is crucial and the main driver of the results. According to the formalization of this concept presented in Dupor and Liu (2003), preferences for Z exhibits

KUJ13 if the marginal utility of Z consumption is rising in the average Z consumption of the agent's peer group 푧 . So we have:

푉 푥, 푧 = 휑 푥 + 푣 푧, 푧 ,

′ 휑1, 푣1 > 0, 휑11 < 0, 푣12 > 0, 푣11 = 0 lim 휑 푥 = ∞ 푥→0

In order to maintain quasilinearity the second derivative of the Veblen function with respect to the Veblen good Z is assumed 0.

12 Flat Engel curves can be achieved, as in Eswaran Kotwal (1990), using hierarchical preferences. But quasilinear specification is more tractable as it implies that food demand would respond to price and also to own income in the presence of Veblen preferences. 13 There is another aspect to preferences involving the Veblen consumption or relative consumption. This is the differentiation between jealousy and admiration (Dupor and Liu 2003). For example, if the agent feels jealous that people in the neighbourhood have fancy cars her utility will be falling with a rise in average consumption of fancy cars. This would imply that 풗ퟐ < 0. On the other hand if she admires the higher consumption of fancy cars in the neighbourhood the preference should have 풗ퟐ > 0. The results in the present model will go through with either of these assumptions as long as I have quasi-linearity and KUJ.

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2.1 Partial Equilibrium:

Next I present a simple partial equilibrium result that follows from my assumptions on the preferences. Assume that food X is the numeraire and the price of the Veblen good Z is p.

Proposition 1: Consumption of the non-Veblen good (X) falls with the average income of the group 푦 once the agent has started consuming Veblen good (Z), ceteris paribus.

Proof: See Appendix.

The proposition implies that if there are two similar individuals with the same incomes and facing the same prices, then the one living in a richer neighbourhood will be consuming less food. This is a direct implication of “Keeping up with the Jones” aspect of the preferences. When an agent, who has already started consuming both goods, moves to a richer peer group, she essentially move to a group with higher average Z consumption. This drives up her own marginal utility of Z consumption prompting her to substitute away from food. This is the feature which drives the results in this model. This is also the point which is taken to data in the subsequent empirical section and used as a test for the existence of KUJ for the agents in the sample.

2.2 General Equilibrium:

The partial equilibrium result derived in Proposition 1 above holds own income and prices constant. But during the period when the fall in average calorie consumption in India was observed, average consumption expenditure and incomes were rising rapidly (Deaton and Dreze

2009). Since the objective here is to explain the actual occurrences of that period, it is important to allow for the simultaneous movement of own income and peer group income (relative food

9 prices were roughly constant) and examine whether calorie consumption can still fall. This, of course, can be theoretically modeled only in a general equilibrium framework, which is introduced below.

For the general equilibrium analysis a specific quasi-linear utility function with KUJ is assumed.

It is customary in the literature related to Veblen consumption to model the utility function as

푧 increasing functions of (푧 − 푧 ) or 푧 (see Eaton and Eswaran 2009). In general the Veblen function is assumed strictly concave. But, as discussed above, the particular context of rural/poor households here, makes a quasi-linear specification ideal. With the removal of the quasi-linear assumption the general equilibrium results presented below are valid under a different set of conditions. More details are provided in the Appendix 2.

The specific functional form assumed for an individual i for the sake of tractability is as given below:

1 푈(푥푖, 푧푖) = − + 훾푧푖 + 훼푧 푧푖 − 푧 푥푖

Using these preferences14 it is possible to calculate the demand as a function of price, own income and group income assuming food X to be the numeraire:

푝 푝 푖푓 푦푖 > 푥푖 = 훾 + 훼푧 훾 + 훼푧 … (1)

푦푖 표푡푕푒푟푤푖푠푒

Demand for Z is given by:

14 Note that this specification displays jealousy as long as 푧푖 < 2푧 . Beyond this level of Z consumption we switch to admiration. But this feature is not relevant for the results.

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푝 0 푖푓 푦 < 푖 훾 + 훼푧 푧 = … (2) 푖 푝 푦 − 푖 훾 + 훼푧 표푡푕푒푟푤푖푠푒 푝

Supply Side

This section outlines the production system and resources. The population which is also the labour force is normalized to 1, 푛1 proportion of this population is landless and the rest own an equal share of the total land. All agents supply labour inelastically. The two goods are produced according to the following production functions:

훿 1−훿 푋 = 퐴푁푥 퐻 … (3)

푍 = 퐵푁푧 … (4)

Food production requires both land and labour inputs with decreasing returns to labour. Z is assumed to be an industrial good which only uses labour as input and exhibits constant returns.

Here 퐻 represents land which is a fixed factor, owned equally by the landlords and fully used in the production of X. 푁푥 and 푁푧 are the labour allocations to the agricultural and industrial sector respectively. This completes the description of the supply side.

Demand functions have already been calculated, so the last thing is to define the peer group of the agents. To keep things simple I assume that the peer group g is the same for all agents and comprises of the entire economy. So now 푦푔 ≡ 푛1푦1 + (1 − 푛1)푦2 is the average peer group

11 income used in Veblen comparisons where 푦1 and 푦2 are the incomes of the landless labour group and the landowner group respectively.

Three Different Equilibria:

The equilibrium of this model is defined by the prices and labour allocation where all markets clear. But due to the unique nature of preferences assumed, demand for grain is kinked in income

(see expression for food demand in equation 1 above). This gives us three different types of equilibria, and which one obtains depends on the magnitudes of the productivity parameters for the two sectors. In the first equilibrium both landlords and landless agents are too poor and cannot afford the Veblen good Z, in the second only the landlords are rich enough to consume Z, and in the third both types of agents have become satiated with food and have started consuming

Z. In the subsequent analysis we will focus on the third sort of equilibrium. Our objective is to characterize the effect of conspicuous consumption on food consumption and it is essential that agents do consume some Z to enable this study.

2.3 Effect of Productivity Improvements

Next we conduct some comparative statics exercises in the model by raising the agricultural and industrial productivity parameters (A and B respectively).

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Proposition 2: In an equilibrium where all agents are satiated with food (X), equilibrium allocation of labour to the agricultural sector (푁푥 ) will fall with any rise in either agricultural or industrial productivity (A or B respectively).

Proof: See Appendix.

Intuitively, when agricultural productivity (A) receives a positive shock, incomes rise. So demand for the industrial Veblen good Z goes up (since agents are assumed to be satiated with food and devote all further income to the consumption of luxury goods). This draws labour out of the agricultural sector in order to cater to the increased demand for Z. Similarly, when industrial productivity (B) goes up it makes the value of the marginal product of labour in the industrial sector higher than that in the agricultural sector (creates an imbalance). This also leads to movement of labour out of the agricultural sector into the industrial sector. In either case labour is drawn out of the agricultural sector raising the land to labour ratio and increasing wage rate. This result is similar in spirit to Eswaran-Kotwal‟s (1993) results but there is an extra dimension introduced by the presence of status seeking behaviour. The rise in income raises average consumption of the Veblen good in the economy. This further pushes up demand for Z

(due to KUJ), leading to magnified effects on wage rate as compared to a model without Veblen competition.

A Possible Resolution to the Calorie Puzzle:

The next proposition investigates the possibility of a calorie reduction as documented by Deaton and Dreze (2009) in Indian data. As mentioned earlier, they have shown that the Engel curves for calorie consumption have been shifting down over time; so calorie reduction has occurred across nearly all income groups. In order to investigate such a possibility in the present model, two

13 things need to be ensured. Firstly, the relative price of the industrial good has to remain constant, and secondly incomes of both groups need to be rising. According to Deaton and Dreze (2009) these were the circumstances prevailing in the Indian economy at the time the calorie reduction was observed (1985 – 2005).

In this model the relative price is given by:

퐴훿 푁 훿−1 푝 = 푥 퐵 퐻

In the equilibrium where everybody is consuming the Veblen good Z, a rise in the productivity parameters B or A will lower labour allocation to agriculture 푁푥 (Proposition 2). So it is possible to change A and B in such a way that price remains constant. We have no way to endogenise the changes in productivity so we will assume here that productivity has changed keeping the relative price constant. The second point about income is also important because, as mentioned before, the Deaton and Dreze (2009) paper actually show that calorie Engel curves are shifting down over time. Although it is not guaranteed in this model that any productivity shock will raise incomes for all, we can however, show the consequences on food consumption in the event that all incomes rise.

Proposition 3: In an equilibrium where all are satiated with food, if agricultural and industrial productivity go up simultaneously keeping relative price of the industrial good constant, and if landlord‟s incomes do not fall, food consumption for both groups fall.

In order to see this, observe the food threshold for both groups after they are satiated with grain, from the demand for food given in equation 1:

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푝 푥푑 = 푖 훾 + 훼푧

Substituting in the value of 푧 using the production function of Z (equation 3) we have:

푝 = 훾 + 훼퐵(1 − 푁푥 )

The shock applied to the system is such that price is constant and B (industrial productivity) is raised. It has already been shown in Proposition 2 that any rise in productivity will cause 푁푥

(labor allocation to agriculture) to go down. Thus we see that in the expression for food demand

(above) the numerator remains constant and denominator increases, resulting in the fall of the overall quantity.

If the relative price of the Veblen good is held constant, the only reason for change in the food demand is the rise in average consumption of Z. Due to the rise in A and B, total income in the economy goes up. This means that total Z consumption (in effect 푧̅ ) will go up as well (since now all extra income goes into Z consumption). This will lead to a fall in the food demand by way of raising the marginal utility of the consumption of Z. So, while the income of both groups go up, food consumption falls. In effect, we will observe the falling Engel curves. (see Figure 2 attached at the end)

Discussion:

A fall in calorie consumption will not be observed always. Assuming the conditions outlined in

Deaton and Dreze (2009) (constant price and rising income) we do see a fall in food consumption. It can be easily shown that the reduction in calories will hold even if the landlord‟s

15 income falls in this model. The stipulation that landlord‟s income does not fall is a (strong) sufficient condition, not a necessary condition, for the result in Proposition 3 to hold.

Amongst the two main assumptions imposed on the preference structure the role of quasi- linearity is not crucial (the other being KUJ). This assumption implies that income effect is totally absent in the demand for food, and is essentially an extreme representation of the fact that food elasticity is found to be quite low for people with sufficient income (see Behrman and

Deolalikar 1987). If on the other hand a positive (but modest) income elasticity of food is introduced in the model it would work against the Veblen effect and push up food consumption when incomes rise. In this case, whether calorie reduction obtains would come down to which of the two effects are stronger. In the appendix a model with positive income effect and KUJ has been used to demonstrate that calorie reduction may still obtain.

Whether KUJ preferences actually exist in the Indian populace and whether or not it can dominate the income effect is an empirical question. This will be the focus of the next section.

3. Empirical Evidence for Veblen Preferences

3.1 The Data

The data used here is from the World Bank series of Living Standards Measurement Survey

(LSMS) conducted in several countries. This particular data is from the Survey of Living

Conditions conducted for the two Indian states of Uttar Pradesh (U.P) and Bihar. Here between

December 1997 and March 1998, 1035 households were surveyed in Bihar from 57 villages in

13 different districts, and another 1215 households were surveyed in U.P from 63 villages in 12

16 districts. The survey is restricted to rural households and most households derive livelihood from agriculture. It provides fairly detailed information about demographics, member‟s characteristics, consumption, access to facilities, means of livelihood and also information about the environment in which the household is situated (village level characteristics). Although similar datasets like Indian Human Development Survey or the Rural Economic Development Survey also contain village level information, LSMS is the only dataset where the variation in village level land-ownership patterns may be used to identify my main coefficient of interest.

The main variables of interest for this exercise are income, caste, price of food and total calorie consumption. The caste of each household is known. The income of the household is generated

(following Anderson 2011) by adding total wage income of all household members, income from enterprise, total proceeds from crop sales, transfers and the total value of home production of in- kind receipts of crops and foods. The calorie consumption variable is constructed by using the household consumption of different food items and multiplying them with estimates of calorie content from Gopalan et al. (1974). Food items used are rice, wheat, barley, maize, bajra (course millet), pulses, sugar, gur (jaggery), eggs, meat/fish, milk, milk-products, potatoes, edible oil and vanaspati (a form of oil). It should be mentioned that certain assumptions had to be made while constructing variables. Firstly, the quantities of rice, pulses etc are multiplied by average amount of calorie content for all types of rice and pulses. This is because the data does not provide information about the exact type of these foods. Also, these calorie estimates are constructed from data about food purchased/produced by the households and it may not coincide with calorie consumption. In particular, household members could have taken meals outside their house or other guests could have had meals in the household, but there is no information about this in the

17 data.15 The price of food is calculated by averaging the prices of all food items used for the calorie consumption, with weights proportional to their share in the food expenditure.

Table 1 attached at the end provides summary statistics for some of the primary variables of interest

The large standard deviation in the average per-capita calorie consumption of the household perhaps reflects the difference in household member‟s characteristics. Patterns of occupations, degree of mechanisation in one‟s job, availability of public goods are other factors that may contribute to differences in calorie consumption (using the arguments of Deaton and Dreze

(2009) and Li and Eli (2010) about calorie requirements). So controls for occupation, crop type choices, factors determining strenuousness of everyday life (like distance to water source) etc will be used in the main regressions.

In Table 2 (See attachment at the end), is the breakup of the sample according to occupation

(proportions). Note that this is just the primary means of livelihood as reported by agents. Most of the agents have their own farms. The next biggest chunk are the casual labourers who work for daily wages in other people`s farms. Some are also permanent (having long term contracts with their employers) agricultural workers. Salaried workers and traders (shop owners) are the other sizeable proportions. This information along with information on hours worked will be important as we attempt to control for calorie requirement of the agents. The break down in

Table 2 suggests that the sample is essentially rural and agricultural.

15 Although I can see the monetary value of meals consumed outside from the data, there is no reliable method of calculating the nutritive value of these meals. In the main specification I control for expenditure on outside meals, but they do not appear to have any significant effect on calorie consumption.

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Apart from income generating activity people may also be engaged in strenuous day to day activities. For example, fetching drinking water from far away is one such activity. Also the type of crop being cultivated, fertility of the land owned, amount of irrigation available and degree of mechanization of the different farm processes may also be important determinants of calorie requirement. Table 2 attached at the end gives an idea of the information available about these factors. From the statistics it seems that we should not be too concerned about these factors.

Most people fetch water from relatively close quarters, also average quality of land is the dominant reported soil type. Nevertheless, I will be controlling for them in my regressions.

3.2 Empirical Strategy

The objective here is to investigate the relationship between calorie consumption and the average income of the agent‟s social group after controlling for own income, price of food and other factors affecting calorie requirement. The first step in that exercise is the identification of an appropriate peer group for the agents. Here, the relevant reference group for an agent is assumed to consists of her own caste people living in her own village. This assumption also implies that economic outcomes of other castes are either unobservable or do not matter to agents while they make their own consumption decisions.

In terms of the model presented before we are essentially assuming that the entire population for an agent is her own caste members living in her own village. While choosing the reference group we need to ensure that the agent interacts sufficiently within this group and has the opportunity to observe each other‟s consumption patterns. For our purposes the assumption of own caste people seems to be the best way to proceed. Firstly it is a documented fact that in rural India caste based social interaction is very important. For example, the information passed through

19 these social interactions is good enough to sustain informal credit networks within castes

(Munshi and Rosenzweig (2005, 2006)). The arrangement provides added insurance to rural agents against unforeseen shocks like illness or marriages. As such they are highly valued by the members. Information networks within castes and sub-castes (jatis) are strengthened by strict rules forbidding members to marry outside the caste. These strong caste networks have also been cited as the main reason why spatial migration is extremely low in India (Munshi Rosenzwieg

2005. Anderson 2011) conjectures that inter-village migration is rarely seen in India probably because the caste based networks are not known to extend beyond the village boundaries.

Another piece of evidence on the importance of caste networks is manifested in politics.

Especially in the states of UP and Bihar (on which this analysis is based) caste based political parties are very strong. There is evidence that people will tolerate a lower quality politician in office as long as she belongs to the party that represents one‟s caste (Banerjee and Pande 2007).

Given the strong interaction within the community for people born into the same caste, it seems natural to assume that own caste members will be a good representation of an agent‟s reference group16. Here I will restrict attention to own caste members from the same village because presumably these people will be most visible to the agent and the people they meet and talk to all the time. Also it is often seen that when people compare themselves they tend to do so with people they think are similar to themselves. For example a poor farmer would be less affected with the information that a millionaire has bought a private jet than if told that her fellow farmer in the village has just bought a new television.

16 The assumption is contrary in spirit to the widely documented phenomenon of Sanskritization (Srinivasan 1952) where the lower castes emulate behaviour of the upper castes. But this is an effect which should be particular to castes and hence should be taken into account by including caste fixed effects. Also I show later that most other caste incomes do not affect an agent‟s consumption decision.

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So now we can write out the basic regression as follows:

퐶푖 = 훽0 + 훽1푌푖 + 훽2푌퐶푖 + 훽3푃푖 + 훽4푋푖 + 푒푖 ..... (5)

Here 퐶푖 is log per-capita calorie consumption of the household i in a typical month, 푌푖 is log of household income per-capita, 푌퐶푖 is the log average income per-capita of the people of the same caste as household i living in the same village excluding household i, 푃푖 is log of food price facing the agent, X is a set of individual or village level controls and e is a random error term which will be clustered at the village level.

Measurement Error and Instrument

A possible problem with the specification presented above is that average caste income and calorie consumption may be endogenously determined17. We have very little idea about what determines the incomes of the agents in our dataset. There could be many unobserved characteristics of either the household or the village that systematically affects both calorie consumption and caste income. These things will have to be varying by caste and village. For example, if people belonging to a certain caste enjoy both higher incomes as well as a less labour intensive life due to their being physically better suited to live in a particular area. Or certain caste members who are rich might own facilities (for example water or cattle) in their villages that help them survive on lesser calorie intake. Although I try to control for as many village characteristics as possible, one can never be sure that all possible sources of endogeneity have been accounted for.

17 A Durban Wu Hausmann test of exogeneity of average caste income with the basic specification yields a p value of 0.0002 indicating that OLS is not consistent.

21

Another more important problem is the presence of classical measurement error in the main variable of interest; average income of own caste in the village. This variable is not measured precisely since sometimes there are very few observations of each caste from each village (2-3 or even less). Amongst the three lower caste groups in the sample, the Backward Agricultural castes have 3 or less households sampled in 18 villages, Backward Other Castes has 3 or less sampled in 31 villages and the SC/ST caste group has 3 or less sampled in 22 villages out of a total sample of 72.18 Also in reality caste networks are organised at the sub-caste level (also known as jatis see Munshi Rosenzwieg 2005), however there is no information about sub-castes in this data. So the average caste income that is calculated may be deviating from its true value quite significantly. However, there is no reason to expect any correlation between the measurement error and the observed value.19 As such we may take this to be a case of classical measurement error. This error will likely produce attenuation bias in the coefficients making them lower in magnitude. The best strategy here would be to instrument the average caste income variable. It turns out that there exists a candidate instrumental variable which serves my purpose.

In a recent paper Anderson (2011) uses the same dataset to show that low caste people living in high caste dominated villages have significantly less income than similar low caste people living in low caste dominated villages, where dominance is determined by which caste owns the majority of the land in the village. She goes on to show that this difference is largely attributable to the fact that, in high caste dominated villages, high caste villagers own the water sources and do not sell water to the low caste people. This breakdown in water trading networks reduces the agricultural income of the low castes.

18 The villages, where just 1 household of a particular caste is sampled, have been dropped since the caste income cannot be calculated for such cases. 19 The measurement error is not due to any systematic sampling anomaly, rather it is a result of lower sample size which simply increases the noise in the observed variable.

22

One implication of the finding of Anderson (2011) is that for a low caste agent living in a low caste dominated village her entire peer/social group will have higher income than a similar counterpart living in a high caste dominated village. The strategy in this paper will be to use this variation in the peer group‟s income to identify the Veblen effect on calorie consumption. As an instrument for average caste income in the village a dummy for village dominated by the low castes is used. Using this IV a two stage least squares estimation procedure is carried out, where in the first stage the following equation is run on a sample restricted to low caste people:

푌퐶푖 = 훽0 + 훽1푌푖 + 훽2퐷퐿푖 + 훽3푃푖 + 훽4푋푖 + 푒푖 … (6)

The equation contains 퐷퐿푖 which takes the value one if the responder belongs to a low caste dominated village. In the second stage the estimated values of 푌퐶푖 from this equation is used in the original equation (equation 5) to estimate the parameter of interest. Here also the sample is restricted to low castes only.

In order for 퐷퐿푖 to be a good instrument it is important that this dummy indicating the agent‟s village dominance does not affect calorie consumption on its own except through its effect on peer group income20.

퐸(퐷퐿. 푒) = 0

The first concern is that the breakdown in water trade in the high caste dominated villages (the main reason of the variation according to Anderson 2011), by itself may be affecting calorie consumption. The results could be biased if people with lower access to water (as should be the case in high caste dominated villages) have to expend a lot of energy trying to irrigate their crops by digging or carrying water. To control for this, variables indicating nearness to water sources

20 Of course the dummy will also have an effect on own income but that will be controlled for.

23 like rivers and canals, availability of groundwater and public groundwater projects are included in all the specifications.

A second issue is the presence of unobservables that may vary by caste dominance across villages (For example public goods, resources etc.). As a response to this concern, this paper follows Anderson (2011) in arguing that the variation in caste dominance across villages is largely exogenous to any present economic outcomes. The first step in this is to argue that caste dominance is exogenously determined. The pattern of settlement in this area is determined by migration patterns from over a thousand years ago. Later, just before independence in 1947 land ownership tended to be concentrated in the hands of the upper caste. So there was prevalence of absentee landlordism, that is, the landlord might be living in some other village altogether

(Metcalf 1979). After independence the government undertook a land reform drive to redistribute land to the landless tenants. This resulted in ownership of land passing on to some low caste tenants (Walter C. Neale 1962). From then on land ownership rights have been passed down by older generations by way of inheritance. Formal selling of land is very rare. It has been observed that only 1% of land is sold each year (Dreze et al 1999). So land ownership in a particular village is exogenously determined by the settlement patterns and the land reform drive.

The second step in the argument is to show that the two kinds of villages are very similar on most observable characteristics. This is an extremely important observation in this context since village fixed effects are not being used; it is thus quite comforting to know that the villages are similar at least on the caste dominance line (see Table attached at the end). 21 Table 3 compares

21 Table 3 and 4 are replications of Tables 1 and 2 in Anderson (2011), but they are equally relevant here. I have of course added and subtracted some variables according to the requirements in the present context.

24 some village level variables and Table 4 looks at household characteristics across the two types of villages. As far as village characteristics are concerned the equivalence of means cannot be rejected for any of the variables. This suggests that the villages of the two types are similar in most ways. Regarding the household level characteristics there are some that are significantly different like literacy, but these variables can be controlled for. Also the comparison of calorie consumption and mean caste income gives us some indication about the result we might expect later.

The last concern regarding the IV is the possibility of caste based migration across villages or inter-caste mobility. But the latter concern can be dismissed all together because of the strict rules of the Hindu caste system in India. Though sociologists have documented a phenomenon

“Sanskritization” where lower castes try to graduate into a higher caste by trying to emulate the ways and practices of the upper caste, this process is not the same thing as caste mobility. See

Srinivas (1952). Also to be noted is that if Sanskritization has been going on it will only make my results weaker. Since there is a complete absence of high caste people from the low caste dominated villages the residents of low caste dominated villages have less opportunity to emulate high castes and so should be consuming more calories.

Permanent inter-village migration is also very minimal in India (Munshi and Rosenzweig 2005,

2006). The main reason for this is believed to be the caste based consumption smoothing/insurance networks that are not known to extend beyond the village.

25

Also to test the validity of the instrument intuitively I follow Murray's (2006) suggestion and include the low caste dominated dummy in a reduced form regression of the second stage. The coefficient of the dummy is negative and significant in all the specifications.

3.3 Results

This section begins with the basic OLS results of equation 5 as presented in Table 5 and Table 6.

The coefficient on the average income of the household's caste from that village is very small, mostly insignificant and always negative (except for one specification). The basic regression includes controls for state, caste fixed effects, total number of hours worked by the members of the household and average age in the household. Technically it is also possible to include village fixed effects in this regression but since there are about 120 villages and about 2000 households there is not enough data to precisely estimate all these parameters. Subsequent specifications introduce dummies for each of the 25 districts22 (in column 2), occupation controls (column 3), and dummies to indicate whether the household is engaged in the cultivation of cereals, cash crops, oilseeds or bulbous roots (column 4). The rationale for inclusion of these controls is to either take care of any spatial characteristics of the location of the agent (for example people in hilly areas may have less calories than others living in plains) and remove the effects of some special occupation or crop cultivation that requires more effort than others (for example paddy cultivation is supposed to be a back breaking work). The fifth and sixth specifications (column 5 and 6) includes controls for distance to nearest water source, quality of land owned, percentage of land owned irrigated, and also distance to basic facilities like public distribution shops,

22 Each state in India is divided into a number of districts. 13 districts from the state of Bihar and 12 from Uttar Pradesh are included in this survey. Public good distribution is largely administered by the district level bureaucracy in India.

26 hospitals/medical centers and schools. Also, some parts of calorie consumption may also be motivated by Veblen competition. For e.g. more expensive foods like meat and milk products may carry a status value in itself. Introducing controls for demographics and occupations of households helps control for different perceptions among households about what constitutes

Veblen goods and Veblen bads.

Amongst the controls added own income has a positive and significant coefficient, as expected.

Some other controls not reported are also interesting. For example, average age in the household and average age squared are both significant. Coefficient of average age is positive and age- squared is negative indicating a concave relationship between calorie consumption and average age in the household. Caste dummies for the two lowest castes are also significant and both have a positive sign. Amongst the controls for occupation and crop cultivation the only significant ones are interest income (negative effect) and pulses cultivation (negative effect).

Next in Table 6 the same regressions using log calorie consumption and log incomes are reported using a sample of low castes only (castes included are backward agricultural, schedule castes and schedule tribes). This is done in order that we may be able to compare the OLS and the IV results which is also run on a sample of just the low-castes. Here the coefficient of interest (average own caste income) is negative and the base specification (one without any controls except age and hours worked) is also significant. But all coefficients are small in magnitude and much lower than those of own income which are always significant.

Next, the main IV results are presented , the first stage (Table 7) followed by the second stage

(Table 8). The basic specification (column 1) has own income, literacy, state and caste controls, and also controls for availability of water and average age of household members. In the next 5

27 regressions I add the controls analogous to those added in the OLS specifications earlier. The last one is a regression on a restricted sample of people working on their own farms and uses controls for degrees of mechanization like use of tractors etc. The purpose of this is again to address the calorie requirement issue. This is a two stage least square estimation where the first stage equation is as follows:

푌퐶푖 = 훽0 + 훽1푌푖 + 훽2퐷퐿푖 + 훽3푃푖 + 훽4푋푖 + 푒푖

푌퐶푖 is again log average caste income of the village where agent i lives. Significance of the coefficient of the dominance dummy (퐷퐿푖 ) indicates the validity of the instrument in terms of being a determinant of the variable average caste income. As we can see that the F statistic for most of the columns are very high. The F values are way higher than the rule of thumb value of

10 indicating the joint significance of the first stage equation.

Next in the second stage the estimated values, 푌 퐶푖, of log average caste income according to the above specification are used to instrument for it in our main equation. Here the dependent variable is per-capita calorie consumption.

퐶푖 = 훽0 + 훽1푌푖 + 훽2푌 퐶푖 + 훽3푃푖 + 훽4푋푖 + 푒푖

The calorie measurements are per-capita calorie intake per month measured in kilocalories. As can be seen from Table 8, the main coefficient of interest is negative in all specification and significant for all except the basic one (without any controls). This is somewhat expected as the uncontrolled regression cannot account for idiosyncratic variations in calorie requirement, tastes and preferences or availability of helpful public resources.

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So the results (refer to Table 8 Column 7, specification with all controls) indicate that a 1 % change in average income of the agent‟s caste results in a 0.22 % fall in the per-capita monthly calorie consumption. In order to get some idea about meaning/significance of this magnitude we first note that mean caste income per-capita in high caste dominated villages is Rs. 665.53 on average. The same statistic for low caste dominated villages is Rs. 1141.17. So we can say that living in a low caste dominated village implies that the person has a comparison group which on an average is richer by Rs. 475 per-capita annually. This sort of a differential in income of the comparison group leads to a fall in daily calorie consumption per-capita of about 15.7%. These magnitudes are actually much higher than the magnitudes we obtain through the OLS estimation.

This disparity may be due to the attenuation bias from the measurement error in peer group income.

The next question is how effective are these numbers in explaining the calorie reduction documented in Deaton and Dreze (2009). In order to get an idea about this a couple of rough calculations will be useful. Now according to Deaton et al (2009) rural per-capita household expenditure went up from 251.3 in 1983 (38th round of NSS) to 318.3 in 2004-5 (61st round of

NSS) in real terms. Assume that the average income of the comparison group goes up by exactly the same amount as per-capita household expenditure. Then, using the estimates in this paper, the fall in mean per-capita calorie consumption due to rise in peer income alone is about 5.8 %.

Actual findings from the Deaton et al (2009) paper puts the fall in calorie consumption during this period to about 9%. Next if we take into account the effect of own income (which increases calorie consumption) the figure above has to be revised down to 3.19%. But, even this is more than a third of the actual fall as calculated by Deaton and Dreze (2009). So the estimates explain a significant portion of the calorie puzzle (more than a third of the missing calories).

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More importantly, in the context of the theoretical model presented above, these results demonstrate that own income has about half the influence that peer group income has on calorie consumption. Although we cannot conclude from this evidence that the observed calorie reduction is necessarily through the Veblen channel, it seems very probable.

Robustness:

1.Influence of High Castes: Sanskritization?

The first robustness checks are designed to test the assumption made earlier about the irrelevance of other group incomes to the calorie consumption decision. The most relevant test for this would be to include the other castes‟ incomes in the basic regression. The main argument against the assumption made earlier is the concept of "Sankritization" as first pointed out in Srinivas (1952).

This is the notion that lower castes are continuously trying to emulate the behaviour of the upper castes with the view to climbing up the social status ladder. Since higher castes are richer and they consume fewer calories on an average, the existence of Sanskritzation would imply that agents living in high caste dominated areas will be consuming lower calories, thus reducing their calorie consumption differential with their companions in low caste dominated regions. (Note that there are no high castes in the low caste dominated villages so no such mechanism could be working there.) In other words if this mechanism is working then the previous analysis underestimates the Veblen effect.

In order to test this, high caste average income is included as a regressor into the basic regressions presented in Table 6. Of course the specification suffers from the whole gamut of problems that were initially present (mainly the measurement error issue). Another problem with this is the very few numbers of observations due to the complete absence of the high caste agents

30 from low caste dominated villages. Also, for the very same reason, it is not possible to include high caste average income in the IV regressions since all the low caste dominated agents will have a missing value totally eliminating the variation.

The results are reported in Table 9. Inclusion of the high caste‟s average income does not change the result much. In fact the coefficient on the high caste income variable is insignificant lending further credence to the assumption made earlier.

2. Non-food Expenditure

Although we have seen that food consumption falls with rise in peer group income we still have no idea where the saving from this lower consumption goes. The next set of regressions is designed to investigate where the money generated from lowering calorie consumption is being used. Data is available for some durables and some non-food items (although many of the non- food items may be classified as essentials). The IV estimation done in tables 7 and 8 is repeated here with a series of expenditures on non-food items as the dependent variable. In each regression all the controls used in table 8 are also included.

The results are reported in Table 10. All coefficients except the one on children‟s clothing turn out to be insignificant. Thus not much can be deduced about where the money is going from the lower food consumption. Yet the coefficient on children‟s clothing is perfectly consistent with our story.

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3. Calories from Veblen Foods

Agents derive calories from all kinds of food items. Some of these might be Veblen goods themselves. For example for many societies meat or dairy products are considered to be elite foods associated with a higher status or wealth. This sub-section focuses on certain food items which may be viewed by agents as high status items. Here, the IV regression is run with the same specification as before but calories derived from more elite/fashionable foods such as milk, meat, eggs, sugar etc as dependent variable.

The coefficients for own caste income (reported in Table 11) are all insignificant and positive except in column 7 where all controls are included. This suggests that for foods which may be perceived as status goods by the agents the effect goes the other way round. One of course, would have expected all the coefficients here to be positive and significant, but there is some confounding in this data since our choice of food items as Veblen or non-Veblen is quite arbitrary. A proper analysis of what is considered richer food can only be done with more fine data like for example what Charles et al (2009) have done with a survey specially designed for the purpose of identifying Veblen goods from non-Veblen ones. Another interesting feature here is that the coefficient for Veblen food prices is positive and significant in all specifications whereas the coefficient for own income is negative (although insignificant in some specifications). Since positive price effect is usually associated with conspicuous consumption goods, this evidence suggests that the choice of food items is perhaps not grossly incorrect.

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4. Conclusion and Discussion of Alternate Explanations

The objective of the paper was to demonstrate that the decrease in calorie consumption in India during the period 1985-2005 may have been due to (to some extent at least) the manifestations of status competition. The paper contributes towards this in two parts. First in the theoretical part it is shown that in a general equilibrium of a standard dual-economy model with Veblen preferences (as represented by "Keeping up with the Jones" specification), it is possible to generate the decrease in food consumption with economic growth under certain conditions. It turns out that the elasticity of food demand with respect to own income is required to be modest in comparison to the Veblen effect in order for us to see calorie reduction with income growth.

In the empirical part, the main contribution of this paper is to demonstrate a robust negative relationship between peer group‟s income and own calorie consumption. What is more, the magnitude of the coefficients of peer group income are much larger than those of own income.

The result is of course limited in its external validity and is not a general equilibrium result, but estimating peer income effect for the whole country in the general equilibrium pose insurmountable complications.

The take away message is that peer group‟s behaviour does have some effect on preferences of an individual. This is consistent with the conditions generated in the theory part, although it is not totally conclusive. An alternate interpretation of this result is that people derive information about how to spend their money by observing the behaviour of their peer group. Although this explanation cannot be ruled out, it is possible to claim that this is also consistent with the Veblen theory as long as agents only look at richer neighbours while deciding what to consume. The strong relationship with higher income is perhaps indicative of this.

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Here it is also worth mentioning that people can be getting status signals from various overlapping peer groups. For example people may be moving in two different groups and he may be rising in the income hierarchy in one and falling in the other at the same time. This makes measurement and quantification of the Veblen effect very complicated. Here the focus has been to isolate and identify the Veblen effect from one particular peer group while controlling as best as we can the influence of other groups. The objective is to establish the existence of the effect and also to have some idea about its magnitude.

As far as the calorie puzzle in India is concerned, findings of this paper can make a significant contribution to explaining it. Incomes have been rising across almost all levels of the income distribution in the last couple of decades. This fact, when coupled with my finding that a rise in income of the peer group leads to a fall in the calorie consumption of agents, leads to predictions very similar to the falling Engel curves as described in Deaton and Dreze (2009). Using the estimated rise in consumption over 1984 to 2004 by Deaton et al (2009) we see that these estimates can explain a significant part of the missing calories (roughly one third).

This finding has some serious policy implications as well. Firstly, it suggests that policy makers should account for the fact that just aiming to increase incomes (like paying out cash transfers) for all may not help to achieve goals like improving nutritional intake. In my estimates the coefficient on own income is positive but almost half of that of the peer group income. So calorie intake would not rise as long as the agent does not perceive own income to be going up much more than her peer group‟s income. On the other hand information dissemination drives that try to sell food consumption as an important household goal (and may be fashionable goal) might help clear the perception that nutrition comes second to taste or status.

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Alternative Explanations

Deaton and Dreze (2009) explore many explanations for this puzzle. They show, for example, that it is not driven by either food prices or fall in incomes. They also rule out the possibility that it is driven by a budget squeeze on food due to the introduction of new non-food essentials to the choice set (like education and sanitation). In this latter case we would expect people to shift towards cheaper calorie sources. Instead people are shifting to costlier foods. Two possible explanations are that either people‟s tastes and preferences have changed, or their calorie requirements have fallen due to the introduction of machines in all walks of life. The latter explanation was tested in Li and Eli (2010), where the authors constructed measures of calorie requirements based on activity levels reported by survey respondents in India. But although this explained a lot of the changes in food composition it could not explain the fall in calorie consumption over time. The question that arises is what other explanation could be plausible?

Another explanation is suggested by Banerjee and Duflo in their book “The Poor Economics”

(2011). Here it is pointed out that the poor are often misinformed about the nutrient values of different foods. So they may be spending more on fats and less on cereals without the knowledge that the total nutrition intake is falling due to this switch. It is very plausible that when people get richer they would switch to tastier foods, but what is strange is that people are also reducing their overall calorie intake while doing this. Preference for tasty food is a universal human characteristic but if this is the cause of the calorie reduction it should occur everywhere and at all times. It does not explain why we observe this only in certain limited cases.

One interesting observation is that whenever there has been a known case of reducing average calorie consumption, it has always been accompanied by rapid economic growth. Some

35 examples are England during the industrial revolution (1770-1850) and also in China more recently (Clark et al 1995, Du et al 2002 and Meng et al 2009). In all these situations, the economy at the time was experiencing a structural change and incomes were going up fast. This leads one to suspect that rapidly rising incomes might have something to do with lowering demand for food over the entire income distribution.

Subramanium and Deaton (1996) gives us some more interesting insights. This paper looks to verify empirically the notion, that poverty leads to low nutrition, which in turn causes people to be less productive and receive lower wages. They find (using National Sample Survey data from the Indian state of Maharashtra) that the required calories for a poor worker to lead a healthy life can be bought with about 5% of their daily wages. So they conclude that income is not a constraint on nutrition rather it is the other way round. The main reason why elasticity of food remains very low as income increases is the substitution of cheaper calories to more expensive ones. One possible way this could happen is through higher conspicuous consumption by agents with a status-seeking motive. So this story is also consistent with the observation that calorie reduction occurs often in the presence of rapid economic growth.

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Palmer-jones, R., & K. Sen, (2001). "On Indian overty Puzzles and Statistics of Poverty" . Economic and Political Weekly , 201-17. Patnaik, U. (2004). "The Republic of Hunger". Social Scientist , 9-35.- (2007) “Neoliberalism and Rural Poverty in India”. Economic and Political Weekly 3132-50

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Radhakrishna, R. (2005). “Food and Nutrition Security of the Poor: Emerging Perspectives and Policy Issues”. Economic and Political Weekly 1817-21 Ray, R and G. Lancaster, (2005) “On Setting the Poverty Line Based on Estimated Nutrient Prices: Condition of Socially Disadvantaged Groups during the Reform Period”,Economic & Political Weekly, 40(1), 1 January, 46-56. Smith, A. (1759) “The Theory of Moral Sentiments” London, A Milar Srinivas, M.N. (1952). “ Religion and Society Among the Coorgs of South India” (Oxford, 1952), p. 30: Subramanian, S. and Angus Deaton: “ The Demand for Food and Calories”, Journal of Political Economy, Vol. 104, No. 1 (Feb., 1996), pp. 133-162 Veblen, T. (1899). “The Theory of the Leisure Class: An Economic Study of Institutions”, New York: Macmillan Company.

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Figures and Tables

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Appendix 1: Proofs

Proposition 1: Consumption of the non-Veblen good falls with the average income of the group once the agent has started consuming Veblen good.

Proof: This can be shown for any general utility function of the form assumed above. The agent‟s problem is as follows:

푴풂풙 푉 푥푖 , 푧푖 = 휑 푥푖 + 푣 푧푖, 푧

푆푡: 푥푖 + 푝푧푖 ≤ 푦푖

Taking the Lagrange multiplier to be λ we have the first order conditions as follows:

푓. 표. 푐 ∶ 휑1 푥푖 = 휆,

푣1 푧푖, 푧 = 휆푝

푠표 푣1 푧푖, 푧 = 푝휑1 푥푖

This of course assumes that the poor have enough money to consume some Z. Then totally differentiating with respect to 푦 we have:

휕푧 휕푧 푣 푖 + 푣 휕푥 11 휕푦 12 휕푦 푖 = 휕푦 푝휑11

The RHS is less than 0 since 휑11 < 0, 푣11 = 0, 푣12 > 0 and average Z is sure to go up with average income as long as any single person has been satiated with food in the group.

Solving the General Equilibrium :

Using the production functions and resources specified in the main body of the paper we may calculate incomes of the two types of agents (Landlords and Landless labourers). Agents receive wages for their

54 labour which is equal to the marginal product of labour and the landlords get rent from land which is also the marginal product of land. So if w and v are the wage and rental rates respectively we can write the incomes of the two groups as follows

훿−1 푦1 = 푤 = 훿퐴(푁푥 /퐻 ) … (퐴. 1)

훿 퐻 푁푥 훿−1 푁푥 퐻 푦2 = 푤 + 푣 = 훿퐴( ) + 퐴 1 − 훿 … (A. 2) 1−푛1 퐻 퐻 1−푛1

Also profit maximization for the industrial good implies that marginal cost of producing one more unit of industrial good is equal to the wage.

So p = w/B ... (A.3)

Also assume that

푁푥 + 푁푧 = 1 ... (A.4) and

H = 퐻 ... (A.5), which are the market clearing conditions for land and labour. Next we use Walras‟ law to drop the market clearing condition for the industrial good, and hence we can concentrate on the food market clearing condition using the demand functions given in equation 1 in the main body of the paper.

The food market clearing equation can be of three kinds depending on which equilibrium we are in. The first is the case where neither group is rich enough to consume Z. So all income is spent on food. The supply of grain is given by the production function. So in equilibrium we must have:

N 푛 푦 (푁 ) + (1 − 푛 )푦 (푁 ) = A H ( x )δ … (A. 6.1) 1 1 푥 1 2 푥 H

Next if the landlords have been satiated with food but the workers have not we have:

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푝 N δ 푛 푦 푁 + 1 − n = A H x … (A. 6.2) 1 1 푥 1 훾+훼푧 H

Here I have used equations A.1 and A.3 to substitute the values of p and w in the food threshold expression. Lastly we come to the last equilibrium where both income groups are satiated with food and have started consuming Z:

푝 푁 훿 = 퐴 퐻 푥 … (퐴. 6.3) 훾+훼푧 퐻

Since the relative price of the industrial good and the incomes can be expressed in terms of 푁푥 we can write all three above equations in terms of the labour allocation to agriculture. From here we calculate 푁푥 which solves the general equilibrium because the incomes and the relative price of the Veblen good p, can be easily calculated using equations A.1, A.2 and A.3.

Proof of Proposition 2

Proposition 2: In an equilibrium where all agents are satiated with the necessary good X, allocation of labour to the agricultural sector will fall with any rise in either industrial or agricultural productivity.

Proof: The market clearing condition for X determines the allocation of labour to the agricultural sector 푁푥 . This 푁푥 is the key variable in this model which can be used to then calculate all the remaining incomes prices and consumptions. This key equation is given by equation A.6.3 as:

푝 푁 훿 = 퐴 퐻 푥 … (퐴. 6.3) 훾+훼푧 퐻

푤 퐴훿 푁 훿−1 Here 푝 = = 푥 as given by equation (A.3) and 푍 = 푛 푧 + 1 − 푛 푛 = 퐵(1 − 푁 ). 퐵 퐵 퐻 1 1 1 2 푥

Substituting in the values of 푍 푎푛푑 푝 we can write equation 6c as follows:

훿−1 훿 퐴훿 푁 2 1 푁 푥 = 퐴 퐻 푥

퐵 퐻 훾 + 훼퐵 1 − 푁푥 퐻

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훿+1 퐴 푁 2 1 ⟹ 푥 퐻 − = 0 … (퐴. 7) 훿 2 퐻 훾퐵 + 훼퐵 1 − 푁푥

It can be easily verified that increases in both A and B would cause the LHS to increase leaving the system in disequilibrium. In order to re-equalise the two sides 푁푥 will have to adjust. So it remains to be seen how the LHS responds to a rise in 푁푥 . If LHS rises with 푁푥 then a rise in productivity parameters A and B would cause a fall in the equilibrium value for 푁푥 in this model. Differentiating the LHS with respect to 푁푥 we have the following:

훿−1 훿 + 1 퐴 푁 2 훼퐵2 푥 − … 퐴. 8 2 훿 3 퐻 2 2 2 훾퐵 + 훼퐵 1 − 푁푥

If the expression (A.8) above turns out to be positive then we may conclude that allocation of labour to agriculture falls with a rise in productivity. The expression A1 has two parts. The first of these, which is always positive, goes to infinity as 푁푥 → 0 (as long as 훿 ≠ 1). The second part is negative and its magnitude is highest at 푁푥 = 1. This is a finite quantity given by

훼퐵1/2 3 2 훾 2

So since the positive part can be very large and the negative part is finite for any parameter value we may conclude that there always exists some 푁푥 for which (A.8) is positive and 푁푥 falls with a rise in productivity. It remains to be seen whether expression (A.8) is positive or not at the equilibrium value of 푁푥 . In order to check this I substitute into (A.8) the equation (A.7) which gives us the equilibrium value of 푁푥 .

훿−1 훿 + 1 퐴 푁 2 훼퐵2 퐴. 8 == 푥 − 2 훿 3 퐻 2 2 2 훾퐵 + 훼퐵 1 − 푁푥

훿+1 훿 + 1 퐴 푁 2 훼퐵2 = 퐻 푥 − … (퐴. 8.1) 2푁 훿 3 푥 퐻 2 2 2 훾퐵 + 훼퐵 1 − 푁푥

Equation (A.7) may be rewritten as follows:

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훿+1 퐴 푁 2 1 푥 퐻 = 훿 2 퐻 훾퐵 + 훼퐵 1 − 푁푥

Substituting in the value of the LHS from above into expression (A.8.1) we have:

훿 + 1 1 훼퐵2 − 2푁 2 3 푥 훾퐵 + 훼퐵 1 − 푁푥 2 2 2 훾퐵 + 훼퐵 1 − 푁푥

2 2 훾퐵 + 훼퐵 1 − 푁푥 훿 + 1 − 훼퐵 푁푥 ⟹ 3 2 2 2푁푥 훾퐵 + 훼퐵 1 − 푁푥

2 훿 + 1 훾퐵 + 훼퐵 훿 + 1 − 푁푥 ⟹ 3 > 0 2 2 2푁푥 훾퐵 + 훼퐵 1 − 푁푥

This above expression is always positive as 푁푥 lies in between 0 and 1. Thus (A.8.1) is always positive at the equilibrium 푁푥 . Hence allocation of labour to the agricultural sector always falls when either of the productivity parameters is raised.

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Appendix 2: Model with Positive Income Effect

The fact that we observe this falling calorie consumption is due to the two important assumptions. First is the assumption of “Keeping up with the Joneses” (KUJ) which pushes up the marginal utility of Z with rising average income. The second important assumption is that of quasi-linearity which means that there is no income effect to the consumption of X. For a necessary good like X having no income effect at high levels of income is resonable. But one may want to see what happens in the model if there is some income effect. In order to do that I need to remove the quasi-linearity from the preferences. Here I apply the simplest modification conserving KUWJ and jealousy but replacing the linear term with Z by a concave one:

1 푧 훽 푈 푥푖, 푧푖 = − − , 푤푖푡푕 훽 > 0 푥푖 푧푖

Here 훽 measures the strength of KUJ feature in the utility function. Higher is 훽 greater is the rise in marginal product of Z with peer group income/ Z consumption. With this utility we can calculate the implicit demand functions as follows:

푦푖 푦푔 − 푥 푥푖 = 푤푕푒푟푒 푧 = = 퐵(1 − 푁푥 ) 푝(푧 )훽/2 + 1 푝

As expected this demand also goes down with group income, but in this case food demand is also positively related to own income. So whether food demand falls or not depends on how own incomes and the peer group income change in the general equilibrium.

Proposition 4: A sufficient condition for the equilibrium labour allocation to agriculture to fall with a rise in industrial or agricultural productivity is: 1 < 훽 < 2

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Proof: The equilibrium condition for the market for X is as follows:

푛 푦 (1 − 푛 )푦 푛 훿 1 1 + 1 2 = 퐴 퐻 푥 푝푧 훽/2 + 1 푝푧 훽/2 + 1 퐻

Substituting in the values in terms of 푛푥 , and with some simplification this can be reduced to the following:

훽 −1 1−훿 훿+1 훽 1− 2 2 2 2 퐴퐵 퐻 푁푥 − 훿(1 − 푁푥 ) = 0 … (퐴. 9)

Clearly, the LHS goes up with any increase of A. But a rise in B causes LHS to rise only if 1 < 훽 . Let us suppose for the time being that 1 < 훽. Now, in order to prove the proposition we need to see if the LHS rises with 푁푥 . Differentiating we get:

훽 −1 1−훿 훿−1 −훽 훿+1 훽 퐴퐵 2 퐻 2 푁 2 + 훿 1 − (1 − 푁 ) 2 … (퐴. 10) 2 푥 2 푥

The expression (A.10) above is clearly positive for all 훽 < 2. But this is not necessary. Even for higher values of 훽 the expression (A.10) may turn out to be positive for some values of 푁푥 . To see this I look at the value of expression (A.10) in equilibrium by substituting equation (A.9) into expression (A.10):

훽 훿 + 1 1− 훽 −훽 2 훿 1 − 푁푥 + 훿 1 − 1 − 푁푥 2 2푁푥 2

훽 − 훿 + 1 1 − 푁 + 2 − 훽 푁 2 푥 푥 = 훿 1 − 푁푥 2푁푥

훽 − 훿 + 1 + 1 − 훿 − 훽 푁 2 푥 = 훿 1 − 푁푥 2푁푥

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Again this expression cannot be negative if 훽 < 2. But even for higher 훽 this may be positive for low values of 푁푥 .

The intuition for this result is slightly involved. First note that if 훽 < 1 food demand will increase with a rise in B. To see this, note that a rise in B impacts food demand through two different channels. Firstly, through “Keeping up with the Jones”, via a rise in average/aggregate production of Z at the same labour allocation. This effect reduces food demand. It is in turn countered by the fall in price of Z (which increases demand for food). If 훽 < 1 the impact of a falling price dominates and demand for food goes up. This would result in an inflow of labour to the agricultural sector and not out.23

But very high 훽 is also a problem in this model. The reason is the dependence of food demand on the aggregate Z production. After the initial productivity shock the system tries to restore equilibrium by adjusting 푁푥 (proportion of labour working in agriculture). When 푁푥 falls aggregate Z production goes up. If the impact of this on food demand is too big it may never reach equilibrium (food demand falls faster than food supply), or it may reach an equilibrium by increasing 푁푥 .

When 훽 ∈ 1,2 , 훽 is not so low as to increase food demand with B and neither so high as to make food demand fall faster than supply. Hence we would observe falling 푁푥 with rising A or

B. But it should also be pointed out that 훽 ∈ 1,2 is a sufficient condition. As above, even for some 훽 > 2 food demand may be falling slower than supply with decreasing 푁푥 . The speed of

23 Also note, due to the way the industrial sector is set up, any rise in B leaves the expenditure on Z (푝 × 푧) unchanged. Hence any change in equilibrium labour allocation will have to be due to changes in the food market induced by changes in price or average Z consumption

61 adjustment of these quantities also depends on the initial equilibrium 푁푥 , before the productivity shock occurs. So if the initial 푁푥 was low enough we may have 푁푥 falling with productivity even for some 훽 > 2.

Calorie Reduction

Note that no calorie reduction will be observed if 푁푥 does not fall with rise in productivity parameters in this model. If 푁푥 were to rise, aggregate food production would rise even with no rise in agricultural productivity, implying that some if not all would have to be consuming more food. So we expect calorie reduction only when 푁푥 falls. But, this is not enough to ensure calorie reduction as it was in the earlier model. Here we have the income effect to contend with. While a falling 푁푥 and rising B reduces food demand a rise in income pulls it in the opposite direction.

Of course higher is 훽, greater is the negative impact of the falling 푁푥 . In the end it boils down to whether the income effect would dominate or the KUJ effect.

Any further conclusions about calorie reduction is difficult to make as I do not have a closed form solution for 푁푥 . But simulations may give us some further insight into the possibility. In particular I can display the influence of the KUJ parameter 훽 by simulating the food demand through the model for different values of 훽. In the following table I start with the following initial parameter values: A=1, B=1, 훽 = 1.7 , 훿 = 0.5 and 퐻 = 2 . For this combination of parameters, equilibrium values of the key variables of the model are given below for comparison:

A B 푁푥 Price ( Z) 푦1 푦2 푥1 푥2

1 1 0.44 1.0612 1.06 2.95 0.65 1.81

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Here 푥1, 푥2 represent food demands and 푦1, 푦2 represents incomes of the landless and landowners respectively

Next I raise A to 2, increase B keeping price constant about 1.0612 and not the changes in incomes and food demand for the two groups. Then I repeated the process for subsequently higher values of 훽.

Table A.1: Simulation Showing the effect of Rising Productivity on Food Demand for Increasing Values of 훽

훽 A B 푁푥 Price(Z) 푦1 푦2 푥1 푥2

1.7 1 1 0.44 1.06 1.06 2.95 0.65 1.81

2 3.18 0.18 1.06 3.37 5.74 1.01 1.72

1.9 1 1 0.47 1.02 1.02 2.97 0.66 1.97

2 3.66 0.14 1.02 3.73 5.87 0.93 1.47

2 1 1 0.49 1 1 2.99 0.66 1.99

2 4 .125 1 4 6 0.88 1.33

2.2 1 1 0.99 0.7 0.71 3.53 0.71 3.53

2 8.3 0.06 0.7 5.87 7.23 0.64 0.77

Notes: There are four sets of simulations reported for increasing values of 훽 as indicated in Column 1. For each simulation there are two sets of productivity parameters (second set having higher values) across whom relative price of the industrial good is held constant (fifth column). The values of other parameters are held constant for these simulations are given on page 100.

In each case, (characterized by different values of 훽), I find that incomes of both groups rise after the rise in productivity parameters. The relative price of Z has been kept constant at the original level. Food demand for landowners always falls after the process of productivity increase. For lower 훽 food demand for the landless agent are rising, but note that as we go to higher levels of

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훽 (stronger KUWJ) this increase gets smaller and smaller till when 훽 = 2.2 we see a fall in food demand for both groups. The simulations give us an indication that even without quasilinear specification it is possible to observe calorie reduction if the KUWJ aspect in the preferences are strong enough. The graphs below simulate the model for an increase in A from 1 to 3, with B increasing correspondingly to hold relative price constant. This exercise is carried out for 4 different values of 훽, 1.9, 2.0, 2.1 and 2.15.

The Figure A.1 above, displays how total food consumption evolves as we increase productivity parameters keeping relative price of Z constant. As expected this is increasing for lower 훽s but eventually turns negatively sloped for higher 훽. Figures A.2 and A.3 are similar to the first one, except that they show the food consumption of the landless and the landowners

64 separately. Food consumption of the landless is a concave function which becomes more and more concave with higher 훽. Eventually for 훽 = 2.1, 2.15 we observe falling food consumption at higher values of productivity. On the other hand the landowner‟s consumption of food is always falling. The persistent fall in the landlord‟s food consumption may be attributed to the fact that with falling 푁푥 land rental rates are falling and hence landlord‟s total income does not rise as fast as the landless labourer‟s income.

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