DISCUSSION PAPM

Report Nc.: ARLT 21

INCOG2 DISTRIBUTION IN INDIA: THE IMPACT OFPOLICIES AND GROWTHj IN THE AGRICULTURAL SECTOR

by

Jaime B. Quizon and Hans P. Binswanger

Research UnitAgriculture and Rural Development Department

Operational Policy, StaffWorld Bank

November 1984

The views presented here are those of. the author(s)1 and they sl.-tould notbe interDreced as :eElectrig th3se oi the World Banik,

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The authors are consultant and staff member of the World Bank.Howev2r. the World Bank does not accept responsibility for the viewsexpressed herein which are those of the authors and snould not beattributed to the World Bank or to its affiliated organizations. Thefindings, interpretations, and conclusions are the results of researchsupported in part by the Bank; they do not necessarily represent officialpolicy of the Bank. The designations employed and the presentation ofmaterial in this document are solely for the convenience of the reader anddo not imply the expression of any opinion whatsoever on the part of theWorld Bank or its affiliates concerning the legal status of any country,territory, area or of its authorities, or concerning the delimitation ofits boundaries, or national affiliation.

Income Distribution in India: The Impact of

Policies and Growth in the Agricultural Sector

Debates about income distribution effects of agricultural trends

or policies have usually centered on "micro effects of the differential

adoption of technology, or the differential access to credit or inputs by

different farm size groups. While these effects are undoubtedly important,

the neglect of "macro" effects - those associated with changes in food

prices, in real, wages and in farm profits - is not warranted. These

macro effects are what we concentrate on in this paper. For example, we

explore how changes in food prices, real rural wage rates and farm profits

associated with the green revolution peritod have affected the distribution

of income betteen the net buyers of food (the rural poor and the urban

groups) and the net sellers of food, i.e., the medium and large farmers.

In an earlier paper, we developed a unified approach to income

distribution questions in agriculture, explored its properties

theoreticaily and sketched how such a model could be empirically

implemented and extended (Quizon and Binswanger 1983). In that theoretical

enquiry, we showed how crucially dependent distributional outcomes of

policies, programs and technical changes are on the final demand conditions

(elastic or inelastic demand), on the factor supply conditions, and on the

factor mobility assumptions.

The theoretical model was very simple and contained only one

agricultu7al commodity and two or three factors. Furthermore, it

concentrated largely on output and factor price effects, ignoring secondary

income feedback loops. Because the present paper is a numerical

implementation of the unified approach, we are now able to overcome many of

these limitations.-

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We present a general equilibrium model of the agricultural sector

of India.1/ The model contains four agricultural outputs with sharply

different demand characteristics. It deals explicitly with the prices and

quantities of agricultural output, labor, fertilizer and draft power and

with residual farm profits. All butput and input prices are endogenously

determined, as are the corresponding quantities (except for land). In the

rural and urban areas, respectively, we distinguish four expenditure

groups. Real incomes of each of these groups are derived by summing up

their agricultural factor incomes with their non-agricultural incomes and

deflating this nominal income by an endogenous income-group-specific price

deflator. Real incomes then determine food demand of each group. We

therefore feed back the real income effect of all endogenous factor prices

and food price changes into the final agricultural demand.

The model is capable of dealing with all income distribution

issues which are transmitted via supply and demand changes of agricultural

commodities. It does not deal, however, with income distribution issues

which are associated with the dynamic "micro" phenomena discussed in the

first paragraph, such as the differential speed in the adoption of

technologies or the differential investment behavior across farm size

groups.

In this paper, we use the model in three different ways. First,

we use it to perform counterfactual analysis, i.e., we use it to predict

certain endogenous quantities and prices for which good macro data are

1/ The model is very similar to the closed economy model of the northernwheat region of India whithin which we performed a number ofexploratory exercises (Quizon, Binswanger and Gupta 1984).

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available (agricultural outputs and inputs and their prices) and compare

the model's predictions with the actual path of those variables. The

purpose of this exercise is to validate the model. In the second mode, we

use selected equations of the model as an accounting device. We fix not

only the exogenous variables to past trends but also those endogenous

variables which we used in the counterfactual mode to verify the model's

performance. This allows us to compute with greater accuracy "predictions"

of variables for which no aggregative data exist, such as agricultural

employment and real wages, real farm profits, etc. This allows us to

compute and evaluate the real incomes of eight expenditure groups in the

society which is consistent with past macro trends and the structure and

parameters of the model. In the accounting mode, we therefore compute the

changes in income distribution over the period 1961 to 1981. Finally, we

use the model in a policy mode where we subject the model to a variety of

policy experiments. In this mode all endogenous variables of the model are

allowed to vary freely in response to changes in individual exogenous

trends or policies affecting the agricultural sector. This section

therefore shows how agricultural trends and income distribution outcomes

could have differed under alternative growth and policy scenarios.

In section 1, we present the structure of the model verbally,

followed in section 2 by a summary of the equations which form the model.

This section can be skipped by those who are not interested in the

mathematics. Section 3 describes the origin of all parameters used for the

model. The large majority of them were econometrically estimated to be

consistent with the model structure. In section 4 we show the results of

the counterfactual analysis. Section 5 uses the model in the accounting

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mode and shows the implied income distribution for the period 1961 to

1981 Finally, section 6 uses the model in the policy mode. The policy

experiments include demographic and urbanization scenarios, technical

changes, agricultural investment programs, and taxation and income

redistribution scenarios.

The paper is an ambitious exercise at trying to understand an

exceedingly complex reality. There are many caveats and limitations which

a reader must appreciate. We will discuss these in detail in the text, but

hope that the paper can further our understanding of the difficult process

despite the limitations.

.5

1. A Brief Overview of the Model

The key feature of the model is that prices and quantities of

outputs and variable factors of production are endogenous. But the model

differs from an economy-wide model in that non-agricultural income and

production are treated as exogenous. The elements of the model are as

follows:

1. Producer core: A system of output supply and factor demand equations

describes producer behavior in each agroclimatic region. It determines

aggregate supplies of:

- rice,- wheat,- coarse cereals,- other agricultural commodities.

It determines aggregate demands for variable factors:

- labor,- draft power,- fertilizer.

A variety of shifter variables may shift each of the supply and demand

curves. They are:

- land (a fixed Eactor),- rainfall,- irrigation,- high yielding varieties (HYV),- roads,- farm capital,- regulated markets,- technological change.

The parameters of this system have been econometrically estimated

for each agroclimatic region and aggregated up to obtain the All-India

parameters. A flexible functional form has been used and all cross price

terms, including those between inputs and outputs, have been estimated. No

separability restrictions have been used. The agroclimatic regions are:

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(a) northern wheat region,(b) eastern rice region,(c) coastal rice regions of south India,(d) semi-arid tropics.

2. Income Groups are defined as Quartiles of the All-India rural and

urban expenditure distributions, respectively, with RI and Ul being the

lowest rural and urban quartiles, respectively, and R4 and U4 the highest.

3. Input Spplies are determined as follows:

land: exogenously given, supplied primarily by each ofthe rural groups; urban groups own only a verysmall portion of agricultural land;

labor: responsive to real rural wage and suppliedprimarily by each of the rural groups and also byurban groups via migration;2!

draft power: responsive to real draft animal rental rates andsupplied by each of the rural groups;

fertilizer: aggregate supply curve, responsive to the price offertilizer relative to non-agricultural goods.

4. Consumer Core: Output Demands. Commodities are demanded by each of

the eight groups according to the prLces of the commodities and according

to their real income. Each group's demand is modeled separately according

to income group specific demand elsticities, i.e., poorer groups have

higher income elasticities than richer groups. The demand systems have

been estimated econometrically. A flexible functional form has been used

so that all Slutzky substitution terms have been directly estimated.

Aggregate demand is the sum of each group's demand.

2/ As non-agricultural output is given, changes in migration will createexcess demand or supply of labor 7in the urban sector, or unemployment.

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5. Nominal income of each goup is computed as their respective supplies

of factors of production times the factor prices, plus an exogenously given

component of non-agricultural income.

6. Real income is defined as nominal income, deflated by an endogenous

consumer price index. The price index reflects all endogenous food price

changes and is specific to the consumption patterns of each income group.

7. Price and quantity determination:

Prices and quantities are determined as those which equate

aggregate demands with aggregate supplies for each of the four agricultural

outputs and the three variable inputs. The quantity of land is exogenous,

but the "land rent" is determined endogenously as the residual farm Drofits

after variable factors have been paid off. Non-agricultural prices are

given exogenously, quantity consumed adjusts.3 /

8. The model solves for these prices and quantity changes simultaneously

and determines for each income group Whe change in:

- nominal income,- price deflator,- real income,- labor and draft power supply,- consumption levels.

The solution is simple because the entire model is written in linear

differential equation form.

9. Base-year initalizing quantities, prices and shares:

The base year is 1973-74 and the initializing values have been

computed largely from the NCAER/ARIS survey [for details see Pal and

Quizon (1984)].

3/ As non-agricultural income is given exogenously, non-agriculturalproduction is also exogenously given, i.e., consumption must adjustvia trade.

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2e The Model in Mathematical Terms

The model is an extension of the unified approach described in Quizon

and Binswanger (1983). Producer behavior is represented by a system of output

supply and factor demand equations called the producer core. Analytically the

producer core is derived from a variable proiit function Ir* - 1T*(V, Z, T), where

11* is maximized variable profits, V = (P, W) is the vector of. prices of outputs

(P) and variable inputs (W), Z is a vector of fixed inputs and T is a technology

index. The output supply and factor demand curves are derived from Il* via

Shepards lemma, i.e., the vector of outputs (Y) and (negative) variable inputs

(-X) is written as Q = [, -X1 ] *° In terms of rates of changes they are

written as

(1.1) i= E. V! + M Z* + E! i :s VIjij J g 2 g g I ~ OV

O is the set of outputs and VI the set of variable inputs. The prime notation

X' of a variable X indicates the total rate if change over time of variable X.

The star notation X* refers to the rate of change of an exogenous variable or

:o the exogenous component of an endogenous variable. aij are the elasticities

supply (or demand) of an output i (or factor i) with respect to a price j. The

Zs are exogenous variables and fixed inputs affecting producer behavior, and the

Bi are supply or demand elasticities with respect to those fixed inputs. Some

iI aTof the Z variables are subject to government policy. E' = -a , atare the

technology shifters of the supply and factor demand equations, holding fixed

inputs constant (for a detailed discussion of these technology concepts used,

see Quizon and Binswanger (19841).

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Output demand is treated in a more disaggregated fashion. Let k 1,

.. .K refer to income groups. Then, total final demand is defined as

(1.2) i k ik i 6

where Yik is the total demand of consumer group k. Rewriting (1.2) in terms of

changes

(1.3) 1 k ik ik jSO

where Xik = Y ik/Yi is the proportion of commodity i consumed by income group k.

The consumption of each income group is described by an income-group-specific

consumer demand system

(1.4) 4 Nk 4(P,mk)

where the underbars denote a column vector of the variable, e.g.,

4 = (Y, Y2, * Yo), 1k is the populacion in income group k and v is the

per capita demand which depends on output prices and per capita income of the

income group. Transforming each equation of (14) into rates of changes leads vo

(1.5) ik Yik + Nk =Z a.f Pj + a mk + Yk + Nk i,j sO

Y* is an exogenous change in per capita demand of income group k, and the a.ikj

and a. are the price and income elasticities of final demand.1m

We assume that the population in each income group grows at an

exogenous rate N*. But the rural population grows vir immigration or viak

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diminished emigration and vice versa for the population. We assume this migra-

tion to be responsive to the real rural wage rate. Differentiating Nk with

respect to time and the real wage and converting to rates of changes leads to

(1.6) Nk + Nk = c (W 7k) + N*

where mk is the migration elasticity into (or for the urban group, out of) the

specific income group with respect to the real rural wage. And Pk is an income

group-specific price deflator defined below. The migration elasticities are

discussed in Quizon and Binswanger (1984). Let the rural income groups be

indexed k 1, e e 4 and the urban group by k - 5 . ., 8. Total labor

supply to agriculture is

8L 3 L , or in rates of changes

k=1 k

8(L17) L = iXkLk

where Ik = L /L is.the proportion ot labor supplied to agricuLture by incomeLk k

group k. Labor supply of income group k is L, = 2N, where X, is total laborK ~

supply per person (the product of the Labor participation rate and the effort

participant). Differenciating with respect to the real wage and time and

converting to rates of changes we find

(1.8) Lk = eVw' + £ + S= sXWL-Pk) + z + Nk

where e is the total labor supply elasticities of income group k and Z* is an

exogenous shifter in the labor supply to agriculture of income group k.

The supply of bullocks is similarly aggregated as

8Xi E > Xik* Rates of change are aggregated as usual

8(1.9) X k, - 1k Xk i = bullocks

The supply of each input is only dependent on its own price, Wi, therefore,

(1.10) Xi (WI- + Xk i = bullocksik ik i k i

While the model contains many Z variables, such as irrigation, rainfall

etc., we treat land (indexed as the first fixed factor ZI) as the only fixed

factor which is a recipient of residual farm profits. The change in residual

farm profits per unit of land (rental rate of land) S' is derived residually from

the profit function, a derivation given in detail in Quizon and Binswanger

(198 4a).

(1.11) S' t+ E 1Qi - ico, VI

where -are variable profit shares, which are positive for outputs andi [*

negative for inputs.

Changes in income-group-specific consumer price levels P' can bek

related to the endogenous changes iU. agricultural output prices as follows

(1.12) Pt =3 p ' Pi + E kP

k ico ik i:o NAk NA

where ik is the share of total consumer expenditures spent on commodity i by

income group k. The subscript NA refers to non-agricultural commodities. The

GDP deflator P is derived in the saame way by dropping the k subscripts.

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Nominal per capita income of income group k is M and is defined as the

sum of all net factor incomes accruing to the group plus non-agricultural incomes

MN 4 /

(1.13) Mk 'ikWi + Zlk s Mk

Real per capita income is derived by dividing by the number of people and the

consumer price index, i.e.,

(1.14) Mk MkPkNk

Differentiating (1.13) and (1.14) totally and converting to rates of changes

leads to

(1.6. (WI~ + X! ) + 6 (St + Z* N(1.15) ik i ik Sk 1k) k k MNk k

where the S. are the shares of net income arising from the respective source and

Z* is the exogenous rate of growth of land supplied by group k.1k

Real per capita income of India's rural and urban population is defined

as

(1.16) m k

whero Xik N / /N are the iaitial s.hares of group k in the total population.k k k K

Differentiating and converting into rates of changes leads to

(1.17) m' ' ,E m k Y .

where vk is the proportion of real income accruing to group k.

41 Note that we treat all rural labor supply as an 'agricultural' income here

because.we-assume wage equalization between the agricultural and non-agricultural

labor markets.

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Note that m' is not equal to the conventional definition of a change in

real per capita income which would be

(1.18) m' = M' - N' - P'

where P' is computed from the equivalent of (1.12) but dropping the k subscript,

and M is defined as in (1.13) but again dropping the k subscript. The difference

between (1.17) and (1.18) is that (1.17) utilizes real income weights, where each

group's real income is deflated by a group-specific price deflator. (1.17) thus

closer to a measure of a change in real per capita welfare than (1.18).

The model treats India as a closed economy with respect to agricultural

commodities. Trade by the government is of course allowed, and easily treated as

fixed additions and substitutions from domestic supply. The full model consists of

equations (1.1), (1.3), K1.S (1.6), (1.7), (1.8), (1.9) (1.10), (1.11)(1.12) and

(1.15).e

The equation system can be exhibited in matrix form

(1.19) GU' = K*

where G is a square matrix of elasticities and shares, U' is the column vector of

endogenous variables and K* is a column vector of exogenous shifter variables.

(For simpler examples of such full systems see Quizon and Binswanger [1983 and

19841). The effect of a shift in an exogenous variable on the endogenous variables

in the system can be solved as

(1.20) UP = G K*

which exists so long as the matrix G is non-singular.

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3. Data and Parameter Values

The data used to compile the G matrix come from a variety of sources.

The agricultural commodities, rice, wheat, inferior cereals and other corps, are;

exhaustive in that they account for all crop productioa in the agricultural

sector. Livestock products are aggregated with all other commodities into "other

agricultural commodities".

The commodity-specific output supply and the fertilizer and labor

demand elasticities for the SAT are from Bapna, Binswanger and Quizon (1984), for

North India and the Eastern Rice region from Evenson's (1981) study, and for the

Coastal Rice region of South India from unpublished estimates. Estimation equa-

tions were derived from a normalized quadratic profit function on which all

regularity conditions were imposed, except for the condition of convexity of the

resultant Hessian matrices. The estimates were therefore adjusted in an ex post

manner in order to satisfy this convexity constraint, following trial and error

procedures described in Quizon and Binswangec (1984).

The bullock power demand elasticities have been estimated in Evenson

and Binswanger (1983). Only the own price elasticity and the cross price elas-

ticity with respect to labor are available for bullock power demand.

The output demand elasticities are from Binswanger, Quizon and Swamy

(BQS, 1984) and are averages for all India. Original price coefficient estimates

in the reported demand equations were first adjusted following trial and error

procedures to satisfy convexity restrictions. Then, from the 28th Round of the

National Sample Survey (28th NSS), Tables on Consumer Expenditures, the average

commodity prices, the per capita quantities consumed and the real per capita

expenditures and incomes of each of our defined expenditure quartiles were com-

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puted. These were used with the adjusted convex price coefficient estimates and

the income coefficient estimates from the BQS study to obtain all expenditure-

quartile-specific output price and income elasticities of demand.5 / Total con-

sumption by commodity and by group, computed straightforwardly from: the 28th NSS,

was used to obtain the Xik output consumption weights in (1.3) and the pik

weights in (1.12).

The 1970-71 National Council for Applied Economic Research (NCAER)

Additional Rural Income Survey (ARIS) is a national rural household survey that

contains a wealth of data, including information on household ownership of

different agricultural factors of production, household incomes by income source

and costs of agricultural production by factor of production. The survey does

contain data on hired labor but not, however, on familv labor input. We there-

fore used data on family labor input by farm size group from the Farm Management

studies, matching each household in the NCAER survey with the corresponding farm

size group in the FM study which most closely resembled the agroclimatic features

of the district in which the NCAER household resided. For a number of Semi-Arid

discricts, we used family labor data from the more recent ICRISAT village

scudies. With this addition, the NCXER survey enables computation ot the Xik

input supply weights in (1.7) and (1.9), the p. profit shares in (1.11) and the

5ik income weights in (1.15). Pal and Quizon (1983) describe in detail how all

these shares are computed from the NCAER-ARIS survey.

5/ For the highest income group, urban 4, the estimated elasticities for. coarse cereals had high negative values. These values were reduced toa minimum of -1.

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The only unaccounted parameters in our G matrix thus far are the input

supply elasticities, i.e., eik in (1.6), (1.8) and (1.10). Similar to Quizon and

Binswanger (1984), we assume e to be equal to 0.3 based on Rosenzweig's (1980)

econometric estimates. The migration elasticities, emk, are computed from Dhar

(1980) and are equal to Q.1083 for the rural groups and -0.4356 for the urban

groups.6/ For bullock labor, the own price elasticity is assumed to be equal to

0.4993, i.e., the average of the value weighted sums of the own price elastici-

ties of supply for agricultural outputs in each agroclimatic region. This

follows from the notion that bullocks are reproducible out of agricultural

output. Finally, the fertilizer supply elasticity is set at 4.0, a high value

which reflects opportunities for international trade.

The most important elements of our G matrix are given in Appendix

Tables 2 to 8. All parameters pertaining to cost, income and factor supply

shares are listed in Pal and Quizon (1983)o

The .i matrix (equation (1)) of shifter variables are listed in

Appendix Table 9. These elasticity estimates are from the same estimation equa

tions used to construct the matrix of output supply and variable input demand

elasticities. The complex K* vector of exogenous shifter variables (equation

(1.19)) can be reconstructed from the Appendix tables and other already mentioned

data sources.

6/ Quizon and Binswanger (1984) and Quizon, Binswanger and Gupta (1984)explain how these migration elasticities were computed from Dhar'sstudy.

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4. The Countersactual Analysis

A set of counterfactual expariments are performed to determine

how well the model is able to replicate the performance of key agricultural

variables for which data for the period 1961-1981 are available. For this,

a set of exogenous shocks that correspond to each of the quinquennial years

from 1960-61 to 1980-81 are introduced into the model. The set of

exogenous variables introduced into the system are explained in Appendix

Table 10. The data sources from which these sets of variables are computed

as well as the sources for the actual levels are also given in this table.

The model's predictions of agricultural price and quantity levels are

compared with the actual price and quantity levels for these same years.I/

All data values used in this exercise are three year averages

centered on a particular agricultural year. We use three year averages in

order,to net out output and price fluctuations due to changes in rainfall

conditions. Thus, for example, the average annual rice production for

years 1969-70 to 1971-72 is used as the 1970-71 actual level with which our

predicted levels of rice production are compared. Similarly for the

exogenous variables, the annual average net cropped area, for instance,

over the years 1969-70 to 1971-72 is first computed. Its percentage change

from the same average computed for the base years 1972-73 to 1974-75 is

then used as one of the exogenous shocks for what in the table is referred

to as crop year 1970-71.

7/ Ideally one would want to compare the model's predictions of incomedistribution, results with what actually happened to incomedistribution. Unfortunately data for such a comparison do not exist.

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In .his model validation exercise, we can only introduce those

exogenous variables which are explicitly part of the models. Other

variables, such as the exchange rate, the money supply or the demographic

structure of the population, may affect the agricultural sector but cannot

explicitly be accounted for. The fit of the predicted to actual values can

therefore not be perfect even though care has been taken to include the

most important factors influencing agricultural demands and supplies.

The accuracy of the counterfactual comparisons is also limited by

available data. For example, we need to know the change in prices of

capital goods and other inputs. Such price data are not available directly

and we instead use the rate of change in the weighted wholesale price of

non-food commodities. In other instances, we have to make simplifying

assumptions. For example, we assume that population in all expenditure

classes grows at an equal rate. Also, because data on changes in factor

endowments are not available by expenditure class, we assume identical

rates of change of these variables equal to the national average rates of

change.

In theory our model is capable of determining what will happen

given smaLL or marginal exogenous changes from our base year condicions

(1973-74). However, because our model consists of linear first order

differential equations, introducing larger exogenous shocks are likely to

result in larger prediction errors. Thus, for instance, our model will

more accurately predict what will happen given a 1% increase in

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the population rather than a 10% increase in the population.8 / This fact

is borne out by our model's ability to better replicate agricultural

performance for years closest to the base year, as will be shown later.

In our counterfactual experiments, we use the weighted sum of the

prices of commodities (P) as our numeraire.9/ This means that all the

endogenous and exogenous price and income variables are measured in

inflation adjusted terms. P, the GNP price deflator is *used to convert

these variables to "real" terms.

By so eliminating inflation from our non-agricultural price and

income shocks, we sharply decrease the rate of change in the exogenous

variables we enter into the model. Thus, accuracy of the model's

predictions are enhanced by our use of smaller (real) rather than larger

(nominal) shocks for these variables. 10/

Three of the exogenous shocks listed in Appendix Table 10 deserve

further explanation. The first is item 3, the rate of change in the

capital stock used in agricultural production. This exogenous shock was

constructed by first taking the 1970-71 total value of household owned

livestock, machinery and implements used in agricultural production (as

8/ Note that this error will not arise if the model was written entirelyin level form. However, the solution process would then be morecomplex.

9/ The weights used are the 1973-74 shares of each commodity in totalttoFschold consumption. See equations (1.18) and (1.12).

10/ In a separate exercise, not reported here, we used the rates-of changein nominal non-agricultural prices and incomes as exogenous shocks.This yielded less accurate predictions for the extreme years of oursimulated period when compared with predictions obtained using rates

of change in deflated (by P) non-agricultural prices and incomes.

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estimated by the Reserve Bank of India). The time series for this capital

shock was completed by first adding for a forward year an amount equal to

the year to year rate of change in domestic capital formation in

agriculture at constant prices, and then subtracting from this amount a

depreciation cost equal to 15% of the previous year t s total capital stock

value.

No really appropriate data exists for the rate of change in

non-agricultural household income (MN), item 8 of Appendix Table 10. In

Appendix 1, we show that two independently constructed proxies for this

variable provide widely different estimates. This is unsatisfactory.

Therefore, t-e chose to treat MN implicitly by solving for it residually in

the counterfactual runs. We fed into the model the known growth rate of

real per capita disposable income as an exog.erous shifter variable. This

is available from the National Accounts. Since the model computes real per

capita income from agricultural sources, the difference between aggregate

per capita income and aggregate per capita agricultural income is MN.

Aggregate growth rate in MN is then allocated to the individual expenditure

groups by assuming that they all experienced the same rate of growth in

MN. Obviously, we make this assumption because we have no data in indicate

what really happened to the allocation of MN.1_/

Finally, for technical change (items 13 to 16), we partition the

observed growth in output per hectare of the individual crops or crop

aggregates into a component due to technical change and an endogenous

component. In the absence of data, we assume that 75% of crop-specific

yield growth is due to technical change where technical change includes the

11/ In Appendix I we comparethis implicit growth in non-agricultural incomewith the growth rate of non-agricultural value added and with ourestimate of urban income growth.

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effects of varietal shifts and any other efficiency gains not associated

with increases in inputs or shifter variables which are already explicitly

dealt with in the model.1 2 / Note that our procedure is unable to account

for any factor using bias of tehcnical change (fertilizer-using, irrigation

dependent and perhaps labor saving) which might have been important.

In Table 1, we compare actual and predicted values for three

specific years prior to the base year 1973-74 and for two specific years

thereafter. In all, we trace a total period of 20 years. The actual and

predicted values given in Table 1 are indexed such that the actual 1973-74

level for any given variable is set equal to 100. The ratios of predicted

to actual levels for each variable are also given on each third line of

this table.

As can be seen from the table, the fit between predicted and

actual value is close, in spite of the often very substantial changes which

have occurred in actual values. Of 55 predictions, 23 differ from the

actuals by 10% or more, and only 10 by 20% or more. The poorest

predictions occur for the extreme years 1960-61 and 1980-81. In general

the father away we go from the base year the poorer the predictions.

12/ In India the period 1965-66 to 1974-75 saw the widespread adoption ofnew high yielding varieties (HYVs) of wheat and rice. The rate ofchange in the use of HYVs could have been used as an exogenous shifterin this counterfactual exercise, instead of the crop specific technicalchanges described here. In our early counterfactual runs this was infact done, but the exercise gave extremely poor predictions for years1960-61 and 1980-81. This arose because there were virtually no HYVsin 1960-61 and 1965-66. Also, there were many shifts from one HYV toan even higher yielding variety of a crop in later years, particularlyfrom the base year to 1980-81. HYV statistics cannot capture technicalchanges which occurred during the 1960-61 to 1965-66 period, nor theshifts from one HYV to another during the period 1970-71 to 1980-81..

- 22 -

Table 1

Actual and Predicted Values and Ratiosof Predicted to Actual Values All-India

1960-61 to 1980-81

AGRICULTURAL YEARVARIABLE .

1960-61 1965-66 1970-71 1975-76 1980-81

1. All crops production A 78.46 79.95 101.02 108.23 122.16(TotOut) P 74.83 78.28 98.62 108.49 130.28

R 0.95 0.98 0.98 1.00 1.07

2. Rice production A 82.82 81.49 101.91 106.01 121.37(RiceQ) p 82.65 83.81 100.05 107.68 125,95

R 1.00 1.03 0.98 1.02 1l04

3. Wheat production A 47.32 48.24 99.69 116.10 149.54(WheatQ) P 41.39 52.83 95.59 117.51 162.49

R 0.87 1.10 096 1.01 1.09

4. Coarse cereals production A 89.19 90.96 106.45 108.52 110.81(CerealQ) p 82.12 80.96 99.35 108.52 119.59

R 092 0.89 093 1.00 1.08

5. 'Other crops" production A 83.05 86.86 99.07 107.05 116.10(OcropQ) P 75e57 80.79 97.62 105.26 127,27

R 0.91 0.93 0.99 0.98 1,10

6. Fertilizer consumption A 11.45 32.50 84.30 108.53 205.85(FertQ) P 35.21 58.75 74.44 114.46 182.02

R 3.08 1.81 0.88 1.05 0.88

7, Rice prices A 9;.89 93.68 97.15 101.78 89e97(RiceQ) P 92.98 116.5C 81.19 107.88 120.87

R 1.00 1.24 0.84 e.06 1.34

8, Wheat prices A 100.57 109.06 108.30 106.j6 85.35(WheatQ) p 120.20 134.52 86.33 108.54 103,66

R 1.20 1.23 0.80 1.02 1.21

9. Coarse cereal prices A 93.13 106.49 86.09 90.70 74.70(CerealQ) *P 102.38 116.84 85.77 95.20 101.38

R 1.10 1.10 1.00 1.05 1.36

10. "Other crop" prices A 100.74 99.20 103.56 95.59 101.66(OcropP) P 93.22 114.29 87.44 105.41 126.31

R 0.93 1.15 0.84 1.10 1.24

11. Prices of all commodities A 100.00 100.00 100.00 100l00 100.00P 100.11 113e49 88.80 105e30 119.08R 1.00 1.13 0.89 1.05 1.19

Notes: A refers to actual levelP refers to predicted levelR refers to the ratio of predicted to actual level.

-23-

On balance our quantity predictions are better than our price

predictions. While over the period as a whole we overpredict the growth

rate in agricultural output by roughly .5% per annum, the model accurately

captures the rapid growth of agricultural output from 1965-66 to 1970-71

and from 1975-76 to 1980-81. On the price side, the most disturbing

problem is the overprediction of the rate of growth in agricultural prices

from 1975-76 to 1980-81. Figure 1 shows actual terms of trade moved

rapidly against agriculture during that period. Somehow our model is not

capable of fully capturing this downward trend in agricultural prices. As

seen above, this is not because we fail to accurately predict the output

growth of that period. Rather, discrepancies in our price predictions for

this later period must come from the demand side, i.e., our model results

appear to somewhat exaggerate demand growth. In spite of these

difficulties, our predictions show that our model is able to reasonably

replicate actual Indian agricultural conditions over the simulated period.

Our counterfactual results support the general robustness of our

econometric estimates. We therefore believe that our All-India model can

safely be used in the policy mode to simulate the likely effects of a host

of policies in the intermediate term (5 to 10 years) that pertain to Indian

agriculture.

Among the individual variables, fertilizer consumption in the

pre-green revolution period is the most inaccurately tracked. Our

cour.terfactual experiments overpredict fertilizer consumption in the early

years by a factor of 200%. Note, however, that the level of fertilizer

consumption in 1960-61 is only 11% of the base year value, i.e., this large

prediction error is partly due to the extremely low initial 1960-61 base

- 24 -

Figurel1

Agricultural/Non-Agricultural Terms of Trade, All-IndI.a-Pa/p 1960-61 to 1980-81

p,

na . .

110

I. ..-

80

1960-61 1 66 1970-71 1975-76 1980-81 Year1973-74

P a=price of agricultural commodities

P =price of non-agricultural commoditiesna

-~ * , -

' I ...-....

10 0 I -' -1 .1 ,,

-.............--

-25-

for fertilizer consumption. We also underestimate the very rapid

fertilizer growth in the 1975-76 to 1980-81 period. Both under-predictions

may be partly the consequence of not having a fertilizer-using bias in our

technical change shifters. This was unavoidable because of the lack of

data. Had such a shifter been included in each of the simulated years, we

would have obtained lower fertilizer predictions for the early years and

higher predicted values for the later years.

In addition we have not yet been able to account for change in

the fertilizer subsidy in our counterfactual simulations. This subsidy

grew at a substantial rate since the early 70s but was virtually absent in

the early 60s. Under elastic supply conditions, any increase (decline) in

a fertilizer subsidy would result in substantial increases (decreases) in

available fertilizer, hence also in fertilizer consumption. Thus, our

fertilizer predictions would have again been favorably altered had the

effects of changes in the fertilizer subsidy also been adequately accounted

for in our counterfactual analysis.

In order to account for changes in income distribution, however,

we do not want to rely directly on the counterfactual results.

Over-predicting both total output and the level of prices received by

farmers in 1980-81, for instance, means that residual farm profits (the

difference between farm revenue and variable factor cost) are going to be

over-predicted as well. This implies that the predicted income distri-

bution for 1980-81 would show a bias in favor of large farmers but against

the rural poor and the urban consumers. Therefore, in assessing the

overall trends in the income distribution we should not look at the income

distribution obtained using our model in the counterfactual mode. Rather

26

we will use the accounting mode to compute a reference path of the income

distribution. In this accounting mode, we will use all information which

is available, whether it is on an endogenous or an exogenous variable. We

then use only those parts of the model needed to predict those variables

for which data are not available.

-27-

5. Accounting for Changes in Income Distribution

In this section, we compute a reference path of the real incomes

of each of the groups over the period 1960-61 to 1980-81. We use actual

(as opposed to predicted) estimates of macro-aggregates with the model's

income determination and factor market equations to generate this path.

Thus, unlike in the counterfactual exercise, we no longer solve for

agricultural output quantities and prices or for fertilizer

consumption. 1 Instead we generate the implied income distribution given

actual estimates of these variables as well as all the exogenous variables

that enter the income and factor market equations in the model. The

numbers in Table 2 are indices of predicted levels. They are now

calibrated such that the predicted level for each of the listed variables

is set equal to 100 for the mid-year 1970-71, which we also regard as the

end of the first phase of-the green revolution.

The figures in Table 2 assume that over our simulated 20 year

period both (a) the across quartile shares in the ownership of factor

inputs and (b) the within quartile shares of non-agricultural and of factor

incomes in total income, have remained equal to their respective base year

(1973-74) values. Also, Table 2 income predictions assume that the rates

of growth in the population, in the agricultural capital stock, and in the

non-agricultural income of each quartile have been the same across the

groups. Moreover, there may have been other sources of change in the

13/ We fix fertilizer consumption and prices only for years 1970-71 to1980-81. For 1960-61 and 1965-66 we solve for fertilizer consumptionand prices within the model as we have no data on fertilizer prices(in nutrients/ton) for these years.

- 28 -

Table 2

Simulated Income DistributionsAll-India, 1960-61 to 1980-81

ENDOGENOUS VARIABLES 60-61 65-66 70-71 73-74 75-76 80-81

Real National Per CapitaIncome (actual) (m) 92.69 94.07 100 95.54 96.99 106.0

RI 103.15 99.63 100 95.71 96.80 105.11

Real R2 96.76 93.19 100 94.48 94.74 100.00

per R3 91.81 88.65 100 93.55 93.53 97.85

capita R4 83.88 79.81 100 91.96 91 .31 91,39

income Aggregate Rural* 90e71 87,02 100 93.28 93.17 96.27

by Ul 101.06 111.47 100 100.90 103.17 132.84

quartile tJ2 100.28 115.56 100 101.93 104.60 137.97

U3 99.70 115o65 100 102.29 104.57 135.27

U4 96.94 115.07 100 102.37 104o24 129.35

Aggregate Urban* 98.71 114.85 100 102e09 104.25 132.73

Real agricultural wage rate 114.36 108.41 100 98.13 96.29 81.91

Agricultural employment 86.69 92.91 100 105.83 110.03 117.59

Real agricultural wage bill 102.13 101.93 100 103e73 105.91 98.12

Real residual farm profits 54.78 47.64 100 85.87 87.98 86,73

Non-agricultural income (MN) 72.57 102.37 100 110.25 119.53 179.42

Real per capitadisposable income (m) 92.36 94.48 100 96.69 97.78 113,6

Total agricultural output(actual) (TOTQ) 79.27 81,16 100 q9.,28 107.13 119.63

Price of agricultural commodities/price of non-agricultural commodities 89.79 97.19 100 97.70 91.57 76.28

These estimates of per capita income are computed as in equation (1.17), where it refers to either the rural quartiles(RI to R4) or th urban quartiles (U1 to U4) only.

- 29 -

actual incomes of each quartile over the period which we are unable to

account for as, for instance, income changes due to changes in taxation, in

investment behavior, in people's occupations, in operations of fair price

shops, and so on. Therefore, given the above assumptions, Table 2 shows

what would have happened to the real income of a person in a particular

1973-74 rural or urban expenditure quartile because of changes that have

taken place in agricultural production and technology, agricultural output

and input prices, non-agricultural incomes and prices, and population from

1960-61 to 1980-81. The person is characterised by his or her relative

endowment position compared to the endowment position of persons in the

other groups. While everybody's endowment changes over time, their

relative positions are assumed to be the same.

The last two rows of Table 2 show the actual growth of total

agricultural out-put and of the agricultural to non-agricultural terms of

trade. Agricultural production did not grow by much from 1960-61 to

1965-66, as the latter year was a bad drought-ridden production year.

Output grew eXtremely rapidly, however, during the the early green

revolution period from 1965-66 to 1970-71, and again from 1973-74 onwards.

Agricultural terms of trade rose substantially prior to the green

revolution, stayed fairly constant up to 1973-74 and then dropped

substantially ', 1980-81. These quantity and price movements explain the

movement of farm profits. Farm profits were seriously depressed from

1960-61 to 1965-66 and then nearly doubled during the early green

revolution period. Stagnation in output and prices to 1973-74 meant that

farm profits declined again from their peak in 1971-72 to about 88% of

their former level, where they stayed until 1980-81. The combinati,on of

- 30 -

rapid output growth and price declines seem to have just about offset each

other.

While labor employment in agriculture (predicted from our model)

appears to have grown fairly steady by about 30% over the entire period,

real wages appear to have declined by about the same amount, leaving the

total real wage bill at about the same level as in 1960-61. Real wages

appear to have fallen during the early green revolution and again rapidly

from 1975-76 to 1980-81. Finally, non-agricultural real income appears to

have more than doubled over the entire record, with the most rapid spurts

from just prior to the green revolution, and a massive gain of about 50%

between 1975-76 and 1980-81. This gain is partly because the numeraire by

which non-agricultural income is deflated gives a high weight to

agricultural commodities whose prices have declined. Since all income

groups have non-agricultural incomes, this is the biggest component of

income growth for all groups, especially in the last quinquennium.

The output and factor price trends, and the trends in

agricultural output and non-agricultural income imply the following. Real

aggregate rural per capita income grew by only about 13% during the green

revolution, but subsequently stagnated or even declined. On the aggregate,

rural groups appear to be about equally well-off in 1980-81 as twenty years

earlier. Moreover, despite the drastic shift in the rural distribution of

income from wages to profits, the rural income distribution has been

remarkably stable over the period as a whole. The rural poor did not

suffer as much from the adverse labor income trends (on a per capita basis)

for three reasons. They participated to a small extent in the farm profit

growth as 11.32% of theiz income comes from farm profits. They had

- 31 -

substantial gains in non-agricultural incomes.1+/ And, during the last

period, they benefitted strongly as consumers from the price decline. As

we shall see below in the policy model, if the government had failed to let

agricultural prices fall, e.g, by exporting more of the rapid production

gains, the rural poor could have been substantially worse off in 1980-81

than in 1960-61.

From the point of view of farmers, the green revolution period

was one of substantial gains in profits. But in later periods, these gains

and also-the rapid production gains of the late 70's did not translate into

rapidly rising incomes because prices were dropping. The gains from the

early green revolution were still associated with rising prices, as the

government used the production gains largely to replace imports. But once

self-sufficiency in food grain production was more or less assured, the

extra surplus had to be absorbed domestically. This is therefore a

classic case of the agricultural treadmill where productivity gains are

transmitted to consumers (both rural and urban) via declining prices along

an inelastic demand schedule.

It is therefore not surprising to find that the major gainers

were the urban groups, although their gains were largely a phenomenon

of the last 5-year period. They appear to have gained during the first

quinquennium as well, but those gains arose despite t1 agricultural pricerises not because of them. Slow growtt ,f nn-agricultural incDme and

czonstant prices then lead to an erosion of the urban income gains on a per

capita basis to 1973-74. But then the combination of rapid

non-agricultural growth with declining agricultural terms of trade greatly

benefited the urban groups, with the biggest beneficiaries being the urban

poor because they spend a larger share of their incomes on food.

14/ Note that for the rural groups, only 21% to 26% of this nominal percapita incomes come from non-agricultural sources.

6. Simulations of Alternative Policies and Trends

The accounting framework of the last section does not provide

explanations for why wages, farm profits and the rural income distribution

evolved the way it did. Too many different developments are taking place

simultaneously and transparency is not achieved. A one by one assessment

of how individual changes in policies or trends affect model outcomes is

required to achieve that. Unlike in the counterfactual exercise then,

we are not concerned with modeling the underlying growth path of variables

or the agricultural economy as a whole in these policy exercises. Rather,

we are interested in the changes introduced by a simple departure of an

exogenous variable or a policy from the underlying trend. Figure 2

illustrates this point.

Figure 2

t t+1 Time

- 33 °

Let A describe the normal path of an endogenous variable, such as

per capita income, which would occur in the absence of a specific exogenous

change or policy intervention. Let B be the path which would obtain with

the change or intervention. The value c = b - a is then the difference

between the levels of the variable defined by paths A and B at some period

(t+1), The ratio c/a is the number reported in Tables 3 to 6 for any

endogenous variable. It is the percentage difference in the endogenous

variable caused by the exogenous change or policy intervention. In the

policy mode, we are not concerned with the underlying reference growth path

of the economy or the endogenous variables but only in the changes

introduced by a specific exogenous shock.

In Tables 3 to 6, we provide the growth and distributional

effects from a few simple simulation exercises. In designing the

simulations in Table 3 and 4, we had a time frame of one decade in mind.

For example, in simulation 1.1A population growth is reduced by 10%. This

would correspond to the effects over a decade of a fertility decline

leading to a slowdown of population growth of a little less than 1% per

year. The effects shown are the cumulative changes over a decade which

would result in the growth of the endogenous variables as a result of this

slowdown in population and labor force growth.

Finally, note that in the policy mode we use non-agricultural

commodities in the numeraire in order to enable us to track the changes in

terms of trades between agriculture and non-agriculture explicitly. The

change in the GNP deflator as shown below is a direct function of the

changes in these terms of trade.

- 34 -

Demographic and Urban Growth Scenarios

In demographic scenario 1.1, population growth in India (rural

and urban) is reduced by 10% (over a decade). Nominal urban income is

reduced by 10% as well, i.e., the exogenous components of nominal urban per

capita income are left constant. In scenario 1.1A the labor force

continues to grow at the same rate as before, i.e., this scenario is a

stylized representation of a reduction in fertility alone which, for the

first decade, would leave the labor force unaffected. Scenario 1.1B then

shows the impact of a combined reduction in population and labor force

growth, i.e., it shows the long-run effects of decline in fertility when it

has had time to translate into reduced labor force growth as well.

In row 1, we see that a fertility decline leads to a substantial

gain in national income of about 5.6% and 5.2% in the short and long-run,

respectively. In row 2, we see that output declines somewhat more sharply

(-1.2%) in the long-run when labor force growth is also reduced than in the

first decade (-0.6%). Aggregate prices reflected in the GNP deflator

decline sharply (-19.4% and -18.1%, respectively).e The main difference

between the first decade and the long-run is in the wage movement. In the

first decade, real wages decline by about 3% because of reduced demand for

agricultural output, whereas in the long-run scenario they increase by

about 10%. This is because in the long-run agricultural labor employment

declines by about 5.5%, less than the initial exogenous decline, as workers

respond to higher real wages with increased supply of effort.

In both scenarios, there is a progressive impact on the rural and

urban income distributions. But rich rural groups, the landowners, lose

only a little in real income in the first decade (-3%) while in the

SAS

Table 3: DEM(JGRAPHIC SCENARIOS

NAME S1.1B PLUS REDUCED URBANILArN SI.IA-PLUS URB-PERCAP St.3 PLUSAG.LAB.UNCH POP GRUWtH SCENARIO S.2 INC#191 S2.1SEPIA SI.1Bt S1.2 Sl.3 S2.1 S2.2

REAL NAT.PER-CAP INC. 5.626 5.210 9.079 14.289 6.4855 20.175TOFAL OUTPUT -0.643 -1.229 0.909 -0.320 0.7365 0.411Q OF RICE PRODUCED -0.392 -1.965 2.318 0.353 1.2488 1.602

WHIEAT PR0OUCEO -4.583 -5.102 10.953 5.251 5.6647 10.916CEREALS PRODUCED -2.430 -3.369 -17.115 -20.484 -5.6129 -26.097OTHiER CRt PRODUCEU) 0.444 0.558 2.034 2.597 0.8355 3.433

GNP DEFLATUR -19.437 -18.124 31.759 13.635 16.6449 30.280PRICES OF RICE -27.400 -25.514 45.020 19.506 23.0392 42.545

WliEAT -34.829 -32.671 58.144 25.461 30.0905 55.557COARSE CEREALS -32.832 -25.811 28,317 2.566 16.5764 19.142OTH4ER CROPS -Zl.412 -20.999 40.118 19.119 21.0811 40.201

REAL WAGE RATE -2.880 10.138 9.422 19.560 -0.3450 19.215LABOR EMPLUYMENT -0.709 -5.462 -5. 918 -11.379 -0.4110 -11.850REAL WAGE BILL -3.590 4.616 3.504 8.181 -0.8160 7.365REAL RESIDUAL PROFITS -36.008 -45.365 58.456 13.091 35.1376 48.229REAL PER CAP.INC.RURAL 1 14.905 15.530 3.029 18.559 -3.8898 14.669

RURAL 2 8.614 r.715 11.617 19.332 1.5063 20.838RURAL 3 4.638 2.462 17.305 19.768 5.1Z92 24.891RURAL 4 -3.337 -r.438 29.864 22.4Z5 12.5948 35.020URBAN 1 14.993 19.612e -20.953 -1.281 4.0965 2.816URBAN 2 16.545 21.01 -26.861 -5.843 2.7331 -3.109URBAN 3 i4.216 19.017 -23.288 -4.2t1 4.4702 0.199URBAN 4 8.442 13.620 -14.667 -1.047 9.1153 8.068

PER CAP.CEREAL CONS.RURAL 1 13.183 12.610 1.244 13.854 -2.9451 10.909RURAL 4 -0.544 -2.243 10.337 8.094 4.6041 12.698URBAN 1 14.642 16.930 -17.040 -0.110 1.9639 1.854URBAN 4 -0.577 0.550 3.154 3.704 6.5442 10.248

AGGREG.PER CAP.CER.CUNS. 8.031 6.801 0.528 r.329 0,9509 8.260

- 36 -

long-run they lose more (-7.4%) as they are faced with higher real

wages. (This and declining demand are reflected in a sharp reduction of

residual farm profits of -45%.) The poor in both the rural and urban

areas gain in both scenarios from the substantial food price declines.

They gain a little more so in the long-run as they also benefit from

increased labor scarcity (+15.5% and +19.7% for the rural and urban poor,

respectively). The urban groups gain most as they not only face lower food

prices but also experience less erosion of their income from rural-urban

migration. Total nominal non-agricultural income is held constant in these

simulations, and as rural/urban migration slows down the same nominal

income is divided by a smaller urban population.

Nutrition is measured here simply as cereal consumption.. It

improves in,all groups except for the rich urban and rural group. For the

poor groups, the improvements are somewhat smaller than the changes in

their real incomes, whereas for the richest groups they are much smaller

than the income changes. This is a reflection of the fzct that richer

groups have lower income elasticities.

Simulation 1.2 is an urbanization scenario. Rural population is

assumed to decline by 10% with respect to the reference path while urban

population increases by 40.2%, enough absorb the rural population. Nominal

urban income is increased by 40.2%, in order to initially hold their

nominal per capita income constant. In the absence of a continued

rural/urban income differential, no migration would occur and the scenario

would be unrealistic.

The main feature of the scenario is the implied reduction in the

number of agricultural producers while the number of consumers are left

- 37-

constant. Therefore agricultural terms of trade have to rise sharply.

They drive the GNP deflator up by 32%.

The reduction in agricultural population leads to a real wage

increase of 9.4%. But the sharp increase in prices also enables residual

farm profits to rise sharply by 58%. These food price and land rent

effects drive- the income distribution effects. Large farmers gain by

nearly 30%. The rural poor benefit from increased wage income. They also

have a limited gain in farm profits as the share of farm profits their real

income is 11.32%. These gains more than offset their losses as consumers

and so their incomes still rise by a modest 3.5%. But the urban poor feel

the impact of the food price rises and their income drops by 21%. The

losses of the second urban quartile are somewhat higher than the losses of

the first quartile. This arises because the poorest urban quartile

supplies some labor to the agricultural sector directly, whereas the second

quartile does not. The urban rich lose less than the poorer groups (-15%)

because their share of expenses on food are smaller.

Simulation 1.3 combines the two scenarios 1.IB and 1.2. Overall

population and labor force growth rates both decline by 10%, but the

decline is accompanied by a rural to urban migration so that the rural

population is initially stabilized while the urban population initially

increases by 30.2%. Evidently the outcomes of these shocks are a

combination of the two previous simulations. A large gain in overall real

per capita income results (+14.3%).. Agricultural prices increase which

leads to a modest increase in residual farm profits (+13%). Therefore, all

agricultural groups experience real income gains of around 20% while the

urban groups lose.

- 38 -

We should note here that this population growth and urbanization

scenario would not be sustainable over time. People would not continue to

move to the urban sector in the face of a substantial decline in real wage

incomes relative to real incomes for a prolonged period of time. To

sustain urbanization, nominal incomes need to rise in the urban sector,

rather than stay constant. We first investigate the effect of such a rise

alone, (scenario 2.1) and then combine it with the demographic cum

urbanization scenarios (scenario 2.2).

Simulation 2.1 lets the exogenous component of urban income

increase by about 19%. Aggregate agricultural output increases only

slightly (+.7%) in response to the increased demand for food because

aggregate supply is quite inelastic. The increased demand instead

translates into a substantial increase in the aggregate price level

(+16.6%), Thus, much of the increased urban consumption must come from

reduced consumption of the lower two rural groups rather than from extra

output. This is reflected in the reduced cereal consumption of rural 1.

Since quantities produced rise little, real wages are largely unaffecced,

but residual farm profits rise by 35% due to the higher prices.

The rural poor lose 3.9% of their real income while large farmers

gain by more than 12%. The urban groups have to share their initial income

gain of 19% with the large farmers. The initial urban gain of 19% is

reduced to a real gain of about 9% for the urban rich and only 3% to 4%.for

the urban poor because of the steep agricultural price increases.

Scenario 2.2 combines the three individual scenarios: slowdown

in population growth and labor force growth (1.IB), faster urbanization

(1.2) and urban income growth (2.1). The effects on the endogenous

- 39 -

variables are largely additive. Real urban incomes stay about constant,

except for the urban rich who gain by 8%. Incomes rise by about 14.7% for

the rural poor, and by 35% for the rural rich. As long as the reduction in

the number of producers in the rural sector and the rise in nominal urban

income cannot be accommodated by more imports, the rural groups are the

main beneficiaries of such changes. The agricultural profit implications

of the price increases, however, mean that the major beneficiaries are the

rural rich although the rural poor benefit substantially as well. It is

important, however, to realize that if the food price rises were prevented

by additional imports, the distributional outcome would be more favorable

to the urban groups and less favorable to the rural rich. The technical

change scenarios below bring this point out quite dramatically.

Technical Change Scenarios

In simulations 4.1 to 4.4 of Table 4, yields of individual crops

or crop groups rise by 20%, a change corresponding to a major varietal

shift such as the Green Revolution. In scenario 405 the yield gain is

smaller, only 10%, but is distributed evenly across all crops.

We present two versions of these scenarios. In the first version

(scenario A) the economy is closed, i.e., the additional production of the

commodity made possible by the technical change has to be consumed in

India. In the second version, indicated by the letter B, the extra

quantity which becomes available via the yield increase is either exported

or, as in the case of wheat used to reduce the imports of the commodity in

question. The exported quantities (or reductions in imports) are simply

computed as the base-year dbmestic production multiplied by 20%. They

therefore represent the quantities which become available from the initial

SAS

Table 4: TECHNICAL CHAN6E AND INCREASED EXPORT SCENARIOS

NAME RICE S4.IA-+ WHEAT S4.ZA-+ CEREAL 54.3A-_ OtHER S4.4A-+ ALL CROP S4.5A-*YLDS+20% EXP+20% YLDS+20% EXP+20% YLDS+202 EXP+20% YLDS+20% EXP420% YLDS*IOX EXP#10XS4.IA S4.IB S4.2A S4.2B S4.3A S4.3Q S4.4A S4.48 S4.5A S4.58

REAL NATePER-CAP INC. 4.1.03 5.6529 1.911 2.0878 1.005 2.0196 7.309 10.517 1.197 10.199TOTAL UUTPUT 5.348 6.1860 2.107 2.1267 2.263 2.4820 10.343 12.176 10.031 11.486Q OF RICE PROUtCED 20.088 27.8993 -1.671 -1.3504 2.824 0.5055 -0.546 0.642 10.341 13.848WHEAT PItODUCED -5.938 1.7839 1.4.330 29.2698 1.45? 1.8239 1.137 9.349 1.493 21.113

LEREALS PRODUCED 4.864 -5.4222 1.492 -3.5135 12.149 22.2094 -4.553 -14.011 1.276 -0.402OTHER CR PRODUCED 0.158 -1.6204 0.814 -0.4886 -0.164 -0.6890 21.191 24.515 11.003 10.858GNP DEFLAFUR -tl.960 [0.1306 -6.594 6.11378 -4.913 5.5918 -12.784 22.235 -18.125 22.048PRICES OF RICE -30.814 9.1019 -8.856 9.5951 -1.805 10.8519 -10.361 35.286 -25.948 32.417WHEAF -21.131 19,6220 -29.255 8.0394 -4.045 13.5452 -8.040 47.115 -31.236 44.*61 1

CUARSE CEREALS -6.138 13.9195 -4u.068 7.2396 -35.499 -6.2422 -17.890 18.840 -33.097 16.879 -.UTHER CROPS -8.119 14.4488 -3.474 7.7902 -3.729 6.8900 -23.7r9 24.046 -19.550 26.581 °REAL WAGE RATL 1.543 2.3909 0.136 0.8243 -2.083 2.1032 0.210 -0.871 -0.091 2.224, 1LABOR EMPLOYMENT 1.137 0.8077 0.231 0.1685 -0.368 0.8290 0.184 -0.880 0.592 0.463REAL WAGE BILL 2.680 3.1987 0.361 0.9921 -2.451 2e9322 0.394 -1.751 0.495 2.686REAL RESIDUAL PROFITS -7.539 36.0744 -4.109 17.6308 -1L081 15.0288 4.211 80.749 -4.309 74.742REAL PER CAP.INC.RURAL 1 1.520 1.3281 2.654 -0.6244 3.978 1.4037 5.738 -2.054 9.945 0.061RURAL 2 5,059 6.0632 1.893 2.0154 1.844 2.6931 5.720 9.635 7.258 10.203RURlAL 3 3.549 9.2226 1.231 3.7091 0.154 3.5387 5.815 11.560 5r674 17.015

RURAL 4 -1.368 14.8451 -0.031 7.2066 -0.122 6.1496 5.199 32.859 1.836 30.530URBAN I 12o021 -6.9022 1.470 -4.4455 3.280 -3.9236 12.786 -18.019 117.19 -16.645URBAN 2 13.607 -7.6933 6.154 -5.4253 1.779 -5.2231 13.469 -20.661 11.505 -19.501URBAN 3 11.088 -7.0681 5.457 -4.6851 1.236 -4.5305 12.599 -18.117 15.190 -17.201URBAN 4 5.742 -4.7746 2. 792 -Ze9093 0.089 -2.6329 11.040 -10.689 9.832 -10.503PER CAP.CEREAL CONS.RURAL 1 11.212 2.0339 2.620 -1.2669 7.826 2.0553 0.628 -3.883 11.143 -0.531

RURAL 4 4.274 6.4587 3.034 2.5921 -0.682 1.1985 -4.274 8.242 1.176 9.246URBAN 1 13.069 -5.0110 9.481 -3.4888 5.626 -2.7839 5.270 -16.955 16.123 -14.120URBAN 4 6.745. 3.5040 0.81Z 0.1971 -1.359 -0.3526 -5.426 -0.806 0.386 1.212AGGREG.PER CAP.CER.CUNS. 10.143 3.69H4 3.513 -0.0045 4.155 1.0150 -0.933 -0.123 8.469 2.293

- 41 -

shock, before farmers have had time to adjust to the altered relative

profitability of producing different crops. (For a detailed discussion of

how technical change is introduced in the model see Appendix III of Quizon

and Binswanger [1984a]).

Note that version B corresponds to an assumption of state

trading; it is not an open economy model with trade in many commodities

according to international prices. Further note that the government.is

unlikely to move to such full compensation, but would probably alter

imports or exports by a magnitude somewhere in between zero adjustment in

scenario A and full adjustment in scenario B. The reader can find any

desired intermediate point by computing the appropriate linear combination

of the impacts of scenarios A and B.

When an increase of rice yields of 20% has to be absorbed

domestically, it results first in sharp declines of the rice price (-31%)

and in the price of its closest substitute wheat (-21%). Rice production

increases by about 20% while wheat production declines by about 6%. Prices

of the other agricultural commodities also decline by around 6-8% and

therefore the GNP deflator declines by about 12%. Total agricultural

output increases by about 5%. The price decline and increased agricultural

output implies a real national income gain of about 4%.

The increased agricultural output requires only modestly larger

labor inputs (+1.1%). The increase is modest because less labor is now

required per unit of rice output (although more labor may be required per

hectare of area under rice). The increased demand results in modestly

higher real agricultural wages (+1.5%).

- 42 -

The agricultural price declines combined with the rise in wages

lead to a reduction in residual farm profits despite the increase in

agricultural productivity. The price effects, the farm profit effects, and

the wage effects largely explain the distributional outcome. The net

buyers of food gain and the more so the larger their expenditures share on

food. The urban poor (groups 1 and 2) are the biggest gainers (+12% to

+13.6%). 'The rural poor also spend most of their income on food. Moreover

they benefit from the rise in the real wage bill. The reduction in farm

profits affects them only slightly and they end up with a net real income

gain of 7.5%. The rural rich, on the other hand, are net sellers of food.

They derive much income from farm profits and their gain as consumers is

not quite sufficient to offset that loss. Their real income falls by-

1o4%e

A decision to export all the initial gain in rice production

sharply alters the distributional outcome. As national income rises by

5.7%, domestic demand increases, leading to substantial food price

increases even for rice, the crop which experiences the technical change.

The domestic price level now rises by 10% rather than the fall in the

previous scenario where the extra production had to be absorbed

domestically. Aggregate agricultural output rises by more than in the

closed economy scenario because of the opportunity to export. Rice

production increases by 28%, i.e., 8% more than the technical change

shock. Increased profitability leads to extra resources being allocated to

rice.

Employment and real wages increase modestly. But the price

increases, combined with the increased efficiency in production leads to a

- 43 -

rise in residual farm profits of 36%. Price, wage and profit changes

combine to produce a regressive distributional'impact. All urban consumer

groups lose with poor being hardest hit. Their cereal consumption now

declines.iL5 The losses of the rural poor on the consumption side reduce

their income gain from wages and farm-profits to a mere 1.3%. The rural

rich experience a major gain in income of 14.8%, since the profit effect

dominates their losses as consumers.

The sharp contrast in distributional impact according to trade

policy arises in all other technical change scenarios, although magnitudes

and other details differ significantly by commodity. Except for coarse

cereals, the gains of the urban groups are larger than those of any rural

group when domestic absorption of the extra output from the technical

change is forced. (In coarse cereals, the gains of the rural poor exceed

those of the urban group only because urban consumers use very little

coarse cereals in their diets.) On the other hand, exporting the initial

gain from the technical change always leads to losses for urban consumers

and is associated with a sharply regressive distribution of the benefits in

the rural areas.

Apart from these points, there are some differences in the

magnitudes of effects associated with crop-specific technical changes.

These are partly a reflection of the shares of each commodity in

agricult.ural output. Rice and other commodities have the largest shares,

26.7% and 51.3%, respectively, in the total value of crop output.

Therefore, technical change in these commodities contributes-most to

15/ urbanl loses slightly less than urban2 because of the greater direct

participation of urbanl population in the agricultural labor market.

- 44 -

national income. The shares of coarse cereals and wheat are roughly.10.7%

and 11.3%, respectively, so their national income contributions are more

modest. However, final demand elasticities matter as well. Other

commodities have the highest income elasticity. Therefore, in the no-trade

scenario the declinie in the own price of other crops (=23.8%) is smaller

than the declines in the own price of any other crop following an equal

technical change. Coarse cereals are at the other extreme; a 20% yield

increase leads to a 35.5% decline in coarse cereals prices. The income

distribution impacts of these two scenarios differ accordingly. Technical

change in other crops benefits urban groups fairly evenly and disparities

among rural groups are also modest. Technical change in coarse cereals

benefits the poor urban and poor rural groups most, while neither the urban

nor the rural rich gain.

As the all-crops scenarios illustrate again, trade policy is the

major determinant of the distributional outcome of technical change. The

gains for the urban poor can vary from a high of 17.8% to a low of -16.6%,

depending on how much of the gain in yields is used to export or reduce

imports. For the rural rich, gains can varv from 108% to 30.5%. (Note

however, that consumer de-mand for food is sufficiently price and income

elastic to prevent a decline in real income for the rural rich even when no

trade occurs, a distinct possibility if food demand is price and

income-elastic.) The impact on the urban rich can vary from a gain of 9.8%

to a loss of 10.5%.

When technical change occurs in all crops without trade, the

poorest rural group gains 9% from the price declines but virtually nothing

from wage rises. With export of the full gains from technical change, they

- 45 -

lose as consumers but gain as wage earners and, to a small extent, from the

massive rise in farm profits. On balance however, they would still be

much better off without trade. For the second quartile the situation is

already reversed. The positive farm profit effects outweigh the negative

food price effects on the consumption side. With exports 6f.the total

yield increase from technical change, the gains of the second rural

quartile are 10.2%, without exports they drop to 7.3%.

A remarkable feature of technical change scenarios is the modest

impact it has on real wages and the wage bill, regardless of the trade

scenario. The largest absolute change in the real wage bill is +3.2% in

scenario 4.1B. In the model, it does not arise because labor supply is

elastic. The total supply elasticity of rural labor, including the

migration response is less than 0.5. Neither is labor demand very elastic,

it is only -.48. And indeed, when labor is withdrawn from the rural areas

either by reduced fertility (scenario 1.1) or by exogenous rural-urban

migration (scenario 1.2), real wages increase sharply.

The stability 'of real wages in the case of technical change

arises because technical change has relatively contradictory effects on

labor demand. As the same output can be .,roducad with less labor (and

other inputs) per unit output, labor demand declines as yields increase.

But increased real incomes shift output demand curves and low prices often

contribute to commodity demand. These forces result in a corresponding

outward shift in labor demand. It is the balance between the offsetting

forces which determines the final labor demand impact. Moreover, labor

supply is a function of the real w,,ge rather than a nominal wage, which may

add to stability, compared to a situation where supply responds to nominal

wages.

- 46 -

The findings of Table 4 should dispel the notion that technical

change is responsible for the wage decline we observed in the accounting

mode of section 5. Such decline must instead be the results of unfavorable

labor force growth trends or inadequate employment growth in the

non-agricultural sector.

Investment Scenarios and Fertilizer Subsidies

In this section we present only closed economy scenarios. Of

course one could combine the scenarios here with state trade as in the

technical change cases. In scenario 3.1 of Table 5, irrigation investment

is accelerated such that the percentage of area irrigated increases by

10%. This leads to an increase in aggregate output of 2.7% and a drop in

the aggregate price level of 5.8%.- Because irrigation is labor using,

labor employment and real wages rise slightly. Residual farm profits

decline by 4.8% as a consequence of the slightly higher labor costs and

lower output prices. The distributional outcomes follow from these price

and profit changes. The landless gain modestly (+2.9%) and large farmers

lose (-0.7%). All urban households gain substantially, with the poorest

showing the largest gains (+6%).

In the aggregate, real per capita income rises modestly (+1.7%).

Quantity and price changes of individual commodities thus reflect shifts in

income distribution rather than aggregate income growth. Wheat shows the

biggest production increase and price drop.

Scenario 3.2 focuses.on expanding capital inputs such as

machines, tractors and livestock - via expanded rural credit for example

-- and on improving marketing infrastructure. Both of these are

accelerated by 10%. Real per capita income decreases slightly (0.2%) as a

SAS

Table 5: INVESTMENT SCENARIOS

NAME APEA IRRIGATED CAPITAL INPUTS 53.1 AND FERTILIZERINCREASES BY 10o MARKET INFRA +101 .50l OF S3.2 SUBSIUY53.1 S3.2 53,3 53.4

REAL NAT.PER_CAP INCOHE 1.712 -0.1984 l.613 1.3010TOTAL OUTPUT 2.720 0.8071 3.124 1.2567Q Uf RICE PRODUCED 0.636 -1.2353 0.018 0.3442

WHEAT PRODUCED 5.140 1.3389 5.810 1.2905CEREALS PRODUCED 1.878 2.1207 2.938 -2.1322OTHER CR PRODUCED 3.482 1.4681 4.216 2.4684

GNP DEFLATOR -5.756 -2.9504 -1.231 -1.1340PRICES OF RICE -h.926 -2.1094 -7.981 -1.7581

WHtEAT -12.771 -5.3449 -15.443 -1.7814COARSE CEREALS -9.387 -7.1.892 -12.982 1.2509OTHER CROPS -6.376 -3.9325 -8.343 -1.9381

REAL WAGE RATE 0.705 0.4210 0.916 -1.8998LABOR EMPLOYMLNT 0.438 0.2463 0.561 -0.7691REAL WAGE BILL lel43 0.6674 1.477 -2.6688REAL RESIDUAL PROFITS -4.792 -8.2004 -0.893 5.5813REAL PER CAP.1I1C.RURAL 1 2.917 1.6419 3.738 -0.3536

RURAL 2 1.714 0.0250 l.727 0.7526RURAL 3 0.897 -lO1.64 0.389 1.5446RURAL 4 -0.674 -2.3912 -1.869 2.5395URBAN 1 6.040 2.6642 7.372 0.5977URBAN 2 5.728 2.5649 7.010 0.7436URBAN 3 5.154 2.3810 6.345 0.6025URBAN 4 3.503 1.7347 4.370 0.399!

PER CAP.CEREAL CONS.RURAL t 2.570 1.2111 3.175 -0.7351RURAL 4 -0.071 -1.3015 -0.721 0.5953URBAN 1 5.591 2.2545 6.719 0.0649URBAN 4 -0.382 -0.9498 -0.857 -0.3330

AGGREG.PER CAP.CER.CONS. 1.844 0.0403 1.864 0.0713

- 48 -

consequence of producer losses. Aggregate agricultural output increases by

about 0.8% and the price index drops by 3%. The nature of the income

distribution impacts is similar to those of increased irrigation but

because the output effect is smaller, the disparities induced among income

groups are substantially less than from increased irrigation.

Scenario 3.3 combines the two previous scenarios, but with a

concentration on irrigation which is accelerated twice as much (+10%) as

capital and marketing (+5%). Since distributional effects of scenarios 3.1

and 3.2 are so similar, their combined effects are largely a matter of

increased magnitudes.

Finally, scenario 3.4 is a simple fertilizer subsidy scheme in

which farm level prices of fertilizers are subsidized by 20%.16/ As we

are assuming a high supply elasticity (+4) of fertilizers (either from

expanded domestic capacity or additional imports), this results in.a

considerable shift of the supply curve. Moreover, we assume fertilizer is

not rationed, i.e., that the subsidy is actually transmitted to farmers and

not simply reflected in higher black market prices.

Aggregate agricultural output increases by about 1.3% with the

output gains concentrated in wheat and other crops. Coarse cereals, which

are not fertilizer responsive, decline by 2.1% as resources are shifted

towards crops with higher fertilizer responsiveness. The GNP deflator

declines by 1.1%, leading to gains for urban consumers. Fertilizer is

substituted in part for labor and the real wage bill declines by 2.7%.

Thus the rural poor lose as their wage losses outweigh their consumer

16/ Indian fertilizer subsidy policy is complex and a more detailedanalysis is required to assess its exact impact.

- 49 -

gains. The rural rich, on the other hand, gain from higher farm profits.

The fertilizer subsidy and lower wages more than offset the negative effect

of output prices on farm profits.

The rural poor's cereal consumption declines by more than their

real income loss, as coarse cereal prices rise. They spend a relatively

larger share of expenditures on these crops (18%) which are not highly

fertilizer responsive.

Taxation and Income Distribution Scenarios

Simulations 6.1 to 6.3 in Table 6 introduce various forms of

taxation purely as revenue measures. Rs. 12 billion in income is taken

from the rural sector and used for unspecified purposes which do not affect

agricultural demand or supply. In scenario 6.1, a land tax is levied at a

rate of 10% of the residual farm profits here used as a proxy for land

rents (but not for rents received by landlords).

In scenario 6e2, progressive rural income tax is levied, a scheme

which does not exist at present. In order to raise Rs. 12 billion in

1973-74 prices, a rate of 3.1% and 6.2% is required on nominal incomes of

the third and fourth rural quartiles, respectively. The two poorer

quartiles are untaxed.

Scenario 6.3 imposes an excise tax on non-agricultural goods. In

the model, this is achieved by an exogenous increase of the price index of

non-agricultural goods of 9.7%. This would obviously translate into higher

rates of excise taxation for taxed commodities, as the list of goods and

services would not cover all non-agricultural commodities. Unlike the land

and income tax, the excise tax falls also on the urban group.

SAS

Table 6: TAX ANI) INCOME REDISTRIBUTION SCENARIOS

NAML LANJ PROGRESSIVE EXCISl RUR1 INC.+30 RURI 114C.+30% RURI INC.+30: LAND IRANSFERTAX INCOME TAX rAX TlRDU LAND TAX PROG.INC.TAX EXCISE TAX PUR4-TOJ{URI

S6.1 S6.2 6.3 S7.1 S7.2 S7.3 S8.1

REAL NAT.PER-CAP INCOIME -3.140 -2.912 -2.6710 0.3212 0.5493 0.790 0.5148TOTAL uUTPUT -0.286 -0.245 -0.0232 0.1234 0.1652 0.387 0.1816U OF RICE PRtULJCLD -1.001 -0.1101 -O.2658 0.6761 0.8706 i.411 1.0007

WHEAT PRODUCEI) -3.114 -2.135 -2So0596 0.8562 1.1952 1.910 1.3450CEREALS PRODUCED 3.589 3.210 2.2392 -0.1165 -0.4359 -1.466 -0.6000OTHER CR PRODUCED -0.1.63 -U.1I34 0.0314 -0.2641 -0.2844 -0.069 -0.3152

GNP DEFLAJOR -7.108 -6.050 2.1919 3.6805 4.7385 12.980 5.1852PRICES 01- RICE -10.467 -8.804 -0.3922 6.0101 7.6725 16.085 8.4471

WHEAT -14.069 -12.150 -3.5069 6.4638 8.3828 17.026 9.2070COARSE CEREALS -5.610 -4.440 3.Z461 5.6081 6.7712 14.464 1.2515UtHER CROPS -8.603 -7.406 -0.3214 3.7514 4.9485 12.033 5.4119 1

REAL WAGE RATE 0.359 0.400 -O.5431 0.5670 0.6090 -0.335 0.6345 nLABOR EMPLOYMENT 0.281 0.2 ro -0.0917 0.0966 0.0850 -0.216 0.0817 CREAL WAGE BILL 0.640 O.670 -0.6348 0.6637 0.b940 -0.611 0.7162REAL RESIDUAL PROFITS -25.099 -1.2.35 -5.0277 -3.3741 8.8895 16.697 9.7617REAL PER CAP.INC.RURAL I 0.578 1.456 -1.0035 21.0657 28.7433 26.Z84 28.6210

RURAL 2 -2.986 -0.451 -1.5646 -2.2461 0.2836 -0.284 0.3062RURAL 3 -5.412 -4.883 -2.1214 -2.3861 -1.8576 0.904 -1.3731RURAL 4 -10.101 -10.759 -3.5141 -2.2367 -2.8945 4.350 -4.3051URBAN 1 5.821 5.592 -1.1921 -3.6191 -3.8483 -10.632 -4.2275URBAN 2 6.7Z1 6.086 -1.8118 -3.5681 -4.2031 -12.101 -4.6251URBAN 3 5.984 5.440 -2.5675 -3.0284 -3.5726 -11.580 -3.9332URBAN 4 3.819 3.662 -4.5412 -1.8766 -2.0334 -10.237 -2.2434

PER CAP.CEREAL CUNS.RURAL t 0.587 0.916 -0.3684 16.5210 16.9098 15.565 16.7291RURAL 4 -3.095 -3.355 0.6239 -1.0579 -1.3114 2.661 -1.8168URBAN 1 4.860 4.542 -0.5174 -3.4659 -3.7840 -8.843 -4.1465URBAN 4 -0.289 -0.211 -0.0376 -0.3834 -0.3058 -0.132 -0.3563

AGGREG.PER CAP.CER.CONS. -0.591 -0.463 -0.2015 0.5796 0.7069 0.969 0.7826

- 51 -

Simulations 7.1 to 7.3 are income redistribution schemes in which

the nominal per capita income of the poorest rural quartile is gi.ven a

boost of 30% which is financed out of different sources of tax revenues.

In scenario 7.1 the revenue source is a land tax discussed above which is

just sufficient to finance the welfare payments, i.e., no leakages are

assumed. In scenario 7.2 the progressive income tax is used to finance a

welfare payment that increases the nominal incomes of the landless by 30%.

In scenario 7.3 the excise tax at the rate of 9.7% is used to finance the

welfare payment of 30% to the poorest rural quartile. I

The land tax alone translates into substantial price drops (GNP

deflator is -7%) but a minimal decline in aggregate agricultural output

(-0.3%). The decline in residual farm profits is 25%. Urban groups gain

from the price decline while rural groups except the poorest face a real

income loss, and the more so the richer they are. This income

redistribution leads td a reallocation of production towards coarse cereals

(+3.6%) and away from rice, and especially wheat (-1% and -3.2%,

respectively).

Qualitatively, the effects of the progressive rural income tax

are very similar to those of the land tax. The only exception is the much

smaller farm profit effect of the income tax which s only the result of

reduced final demand rather than a direct tax effect. Because the rural

poor escape taxation directly and the rural rich carry the entire tax

burden, the income distribution effect is more progressive under the income

tax than the land tax.

The excise taxes have a more even incidence than either the land

or the real income taxes as they fall also directly on the urban groups.

- 52

Urban and rural rich are most heavily penalized-, -3.5% and -4.5%,

respectively.

In the income distribution scenarios 7.1 to 7.3, a sufficient tax

is levied to finance an initial boost of 30% of the nominal income of the

poorest rural quartile. Depending on how the tax is levied, this initial

gain is somewhat eroded, to 27.3% in the case of the excise tax, to 27.8%

in the case of the land tax and to 28.7% in the case of the income tax.

Because the rural poor have a higher propensity to conserve food than the

rich, food prices have to rise. In the land and income tax scenarios, the

richer rural groups lose very little because rising food prices increase

farm profits. The rich rural group loses about as much as the untaxed rich

urban groups. The urban groups end up financing part of the income

transfer to the rural poor via higher food prices. In the excise tax

scenario, the price level rises particularly fast (+13%) because both the

food price rises and the excise tax affect the price level. Therefore,

almost the entire burden is carried by the urban groups; large farmers even

show a net gain as a consequence of the increased food demand of the rural

poor.

Finally, in scenario 8.1, sufficient land is transferred from the

fourth rural quartile to the first quartile to give them an initial income

boost of 30%. The effects are very similar to the ruraL land tax and

income tax scenario, although the rural rich lose a bit more as they are

the only taxed group.

Thus a combination of taxing the rich to support the rural poor

ends up in small losses, or in some cases even a net gain for the rural

rich, rather than in a net loss, an important and initially

counterintuitive result.

- 53 -

We note again that this outcome is critically dependent on

letting food price levels rise. If, for example, the government decided to

accommodate the increased food demand via increased imports, the large

farmers would inevitably end up as losers as their farm profit rates would

not increase. Note that the government can also alter the income

distribution by direct food distribution at subsidized prices to rural or

urbarn groups. These issues are discussed in a separate paper (Binswanger

and Quizon, 1984).

- 54 -

7. Concluding Remarks

The analysis just completed leaves us, the authors, with several

sobering thoughts. First of all, the counterfactual exercise shows that

even with the best of efforts it is extraordinarily difficult to capture in

a model the full set of major factors which affect the agricultural sector

of India. While we are capable of tracing the major trends in Indian

agriculture over the past twenty years, there are difficulties in the

prediction of prices.

In order to compute a reference path for the income distribution

over the two past decades, we therefore chose to use the model in an

accounting mode. In the accounting mode, we fix whatever we can to the

level of reliable statistical series and use selected equations of the

model to predict those variables for which existing data are defficient.

Thus, we anchor the computations on as many known quantities as we can.

This seems to provide a reasonably good fix of the impact of agrictltural

variables on the evolution of the income distribucion.

There is surprisingly little change over time in the distribution

of real income across the rural groups. This arises despite what the data

imply to be a dramatic shift of real incomes from wage income to farm

profits. The gain in farm profits appears to be closely associated with

the productivity gains and price trends of the early green revolution

period (1965-66 to 1970-71). While real wages also dropped somewhat during

that period, the technical change simulations in the policy mode of the

model show convincingly that the adverse wage trends cannot be accounted

for by the technical changes but must come from adverse labor supply trends

- 55 -

and inadequate non-agricultural employment growth. That the rural poor did

not lose more substantially from this adverse movement in factor prices,

arises because they share somewhat in farm profit growth, non-agricultural

income growth, and the consumer benefits from declining terms of trade

between agricultural and non-agricultural commodities. Of course, there

are probably subgroups of the rural poor, such as the landless or pure

agricultural workers, who suffered more severely than indicated.

Another sobering result is the fact that despite the good

agricultural performance of India over the past 20 years, real rural per

capita incomes cannot have risen more than a few percent. Even the rural

rich appear to have lost the considerable irncome gains of the early green

revolution to higher numbers and 'falling prices. Where there are

undoubtedly regions with a better income performance, the aggregate

analysis balances the gains of some regions with losses in others. We will

present more disaggregated regional effects at a later stage.

A further sobering point is that any simple-minded notions of how

agricultural development- trends or policies affect income distribution are

likely to be wrong. Much of the debate, for example, of the distributional

impact of technical change seems to have excessively concentrated on the

nature of technical change itself. In our analysis, trade policy seems to

be a far more important determinant of distributional outcomes than the

nature of agricultural growth. All growth oriented policies or technical

changes tend to benefit net buyers of food if the extra agricultural output

must be absorbed domestically, rather than being used to reduce imports or

increase exports. Forcing domestic absorption has been the most general

policy pursued by India, except that the early gains of the green

- 56 -

revolution were largely used to reduce imports. But the gains of the 1970s

were not used to expand exports and the net buyers in rural and urban areas

have benefitted.

There are, of course, differences in the impact of different

technical changes or investment policies. For example, in accordance with

intuition, technical changes in coarse cereals benefits poor rural

consumers more than technical change in other commodities. An irrigation

investment has greater labor demand effects than a fertilizer subsidy,

which tends to encourage the substi.tution of fertilizers for labor.

But none of the agricultural development measures can

substantially affect rural labor demand and wages compared to changes in

demographic labor -supply growth or growth in non-agricultural labor

demand. Of course this does not mean that we should not pay attention to

agricultural choice of techniques or other employment determinants.

Rather, it suggests that major changes must eventually come from

demographic changes and non-agricultural employment growth.

The importance of trade decisions on direct income distribution

policies has not been explicitly shown in this paper but is in another

paper (Binswanger and Quizon, 1984). Nevertheless, it is implicit in the

analysis of income distribution presented here. The scenarios show that

options exist to increase the incomes of the poorest group in society, the

rural poor. These opt-ions need not substantially affect the rural rich or

farm production incentives. The food price effects of the extra final

demand from distributing income to the poor are such that they raise farm

profits nearly enough to offset the impact of direct taxes or land

transfers used to pay for the income transfers to the poor. In the case of

- 57 -

the excise tax, the burden is not borne by the rural rich at all but by the

urban groups.

But again we should note the crucial dependence of these findings

on the implied trade assumption. If the government would let the extra

demand for food generated by distributional policies be accommodated via

food imports, prices would not rise, the rural rich would lose and the

urban poor would be largely unaffected.

Finally, the analysis shows that advocacy of simpleminded free

trade in food commodities ignores the massive income effect that food

prices have on all the poor. Unlike in countries with little landless

labor, the greatest number of poor, the rural landless, are net buyers of

food. While their real income is not quite as exposed to food price

changes as that of the urban poor, the dependence is still very large.

Letting food prices fluctuate freely with the vagaries of the very limited

international market would be quite irresponsible in a country where a

large proportion of the population faces real nutritional risks. Trade

decisions in food have distributional implications which are simply

unimportant for trade decisions in industrial commodities or for food trade

decisions in middle or high income countries.

- 58 -

APPENDIX I

On Constructing an Index forNon-Agricultural Income (MN)

This appendix shows how two different proxies for the rate of change

in non-agricultural household income, or item 8 of Appendix 10, are computed from

available data. The first is the rate of change in non-agricultural value added

at current prices. This is directly available in the National Accounts. The

second is the rate of change in total urban non-agricultural income computed

from Na,.ional Accounts aggregates as follows. First, for each year over the

simulated period, total rural household consumption is assumed to be 75% of total

household consumption (at current prices). Total annual rural household saving

over the same period is then obtained as a percent of household savings, given

the ratios of total rural household savings to total household savings.-/ Rural

household income is the sum of rural consumption and rural savings. Urban house-

hold income is the difference of private disposable income (as given in the

National Accounts) and rural household income. The rates of change in urban

household income are computed from this completed annual series. These estimates

and the computed rates of change in non-agricultural value added are then

reconverted into levels with the 1973-74 levels given a value of 100. These

estimates are subsequently deflated by P (also indexed such that its 1973-74

level is equal to 100). Table 1 shows the percentage rates of change of non-

agricultural value added and of urban household income from the base year 1973-74,

after deflation by P. Except for 1960-61 these estimates are widely different.

1/ Our method for computing rural household consumption and saving follows thesame procedures given in Krishna and Raychaudhuri (1980), except in our case,the ratio of rural to total household savings is allowed to vary over time.Whereas Krishna and Raychandhuri assume a 25% ratio for all years, our ruralto total household savings ratio increases from 25% for 1960-61 to 59.73%for 1980-81. Our ratios are obtained from National Sample Survey data forintermittent years over the simulated period.

- 59 -

Table 1

Percentage Rates of Change of Non-AgriculturalValue Added and Urban Household Income from Base

Year 1973-74

Non-agricultural Urban household Non-agriculturalvalue added income household income

used in simulation(1) (2) (3)

1960-61 -27.41 -29.49 -26.621965-66 -13.46 -23.55 0.411970-71 1.23 -4.64 -9.231975-76 21.58 -3.26 9.461980-81 68.76 29.12 52.90

- 60 -

For the counterfactual simulation, we therefore chose to treat non-agricultural

income as an endogenous variable by fixing the rate of growth in real per-capita

dispersable income in our simulation runs. The last column of Table 1 shows the

implied percentage rate of change in MN as obtained from our counterfactual

experiments.

- 61 -

Appendix Table 1

Agroclimatological Regions and the States andUnion Territories of India that Comprise Them

Agroclimatological State/UnionRegion Territory Districts

Semi-A-rid Tropics Andhra Pradesh Adilabad, Nizamabad, Karimnagar(SAT) Medak, Warangal, Mahbubnagar,

Hyderabad, Nalgonda, Khammam,Kurnool, Guntur, VishakapatnamAnantapur, Cuddapah, Ongole,Nellore, Chitoor

Gujarat AllKarnataka Bidar, Gulbarga, Bijapur,

Belgaum, Dharwar, Raichur,Shimoga, Bellary, Chikmagalur,Chitradturga, Hassan, Tumkur,Mandya, Mysore, Bangalore, Kolar

Madhya Pradesh AllMaharashtra AllRajasthan AllTamil Nadu Dharmapuri, The Nilgiris,

Coimbetore, Salem, TiruchirapalliPudukkottai, Madurai, Ramanatha-puram, Tirunelveli

Dadra & NagarHaveli

Eastern Rice (ER) Arunachal Pradesh AllAssam AllBihar AllManipur AllMeghalaya AllMizoram AllNagaland AllOrissa AllTripura AllUttar Pradesh Jalaun, Jhansi, Hamirpur, Banda,

Fatehpur, Rae Bareli, Sultanpur,Faizabad, Basti, Allahabad,Pratapgarh, Jaunpur, Azamgarh,Gorakhpur, Mirzapur, Varanasi,Ghazipur, Ballia, Deoria

- 62 -

(Cont'd.) Appendix

Agroclimatological State/UnionRegion Territory Districts

West Bengal AllCoastal Rice Andhra Pradesh Srikakulam, East Godavari, West

Godavari, KrishnaGoa -Karnataka North Kanara, South Kanara, CoorgKerala AllPondicherryTamil Nadu Chingliput, North Arcot, South

Arcot, Thanjavur, KanyakumariNorthern Wheat (NW) Chandigard --

Delhi ---Haryana AllHimachal Pradesh AllJammu & Kashmir AllPunjab AllUttar Pradesh Dehradun, Saharanpur, Bijnor,

Nainital, Muzaffarnagar, Meerut,Moradabad, Rampur, Bulandshahr,Budaun, Bareilly, Pilibhit,Mathura, Aligarh, Agra, Etah,Mainpuri, Farukhabad, ShahjahanpuKheri, Etawah, Hardoi, Sitapur,Kanpur, Unnao, Lucknow, BarabankiBahraich, Gonda

1/ For those states that fall into two agroclimatological regions, districts areallocated and identified individually.

- 63 -

Appendix Table 2

Price Elasticities of the Producer Core for All India 1/

PricesA;

Coarse OtherQuantities Rice Wheat Cereals Crops Fertilizer Labor Bullocks

Rice 0.5531 -0.1280 -0.1271 -0.1834 -0,0206 -0.0940 0.0000

Wheat -0.0900 04454 -0.1583 -0.0879 -0.0614 -0.0479 0.0000

Coarse Cereals -0.2280 -0.1088 0.7554 -0.2039 0.1791 -0.3986 0.0000

Other Crops -0.1632 -0.0320 -0.0652 0.2955 -0.1011 0.0663 0.0000

Fertilizer 0.00.26 0.1203. -0.4635 0.7525 -0.8355 0.4278 0.0000

Labor 0.1019 0.0228 0.2045 -0.0489 0.0753 -0.4782 0.1225

Bullocks 0.0000 0.0000 0.0000 0.0000 0.0000 0.1335 -0.4041

1/ Elasticities are computed at base year 1973-74 prices and quantities. Estimates areaggregated from Evenson (1981), Evenson and Binswanger (1983), Bapna, Binswanger andQuizon (1984).

- 64 -

Appendix Table 3

Own Price and Expenditure Elasticities of Demand,By Commodity and By Expenditure Quartile, All-India 1/

A. Own price elasticities of demand for:

Expenditure Coarse OtherQuartile Rice Wheat Cereals Food Non-Food

Rural 1 -0.7752 -0.7172 -0.6153 -0.8857 -0.5431

Rural 2 -0.8311 -0.7247 -0.5507 -0.8077 -0.5529

Rural 3 -0.8735 -0.7217 -0.4544 -0.7878 -0.5530

Rural 4 -1.0363 -007218 -000000 -0.7255 -0.5258

Urban 1 -0.8088 -0.7233 -0.5883 -0.8426 -0.5484

Urban 2 -0,8425 -0.7241 -0.5255 -0.8009 -0.5532

Urban 3 -0e9420 -0.7195 -0.2758 -0.7604 -0.5463

Urban 4 -1,1286 -0.6786 -0.0000 -0.7123 -0.4943

B. Income elasticities of demand for:

Expenditure Coarse OtherQuartile Rice Wheat Cereals Food Non-Food

Rural 1 0.8196 1.1325 0.0653 1.2734 1e4408

Rural 2 0.7436 1.039 -0.3328 11540 1.5724

Rural 3 0.6825 1.0011 -0.5889 1.1158 1.5760

Rural 4 03768 0.8966 -1.0000 1.02154 1.5504

Urban 1 0.7771 1.0760 -0.1302 1.2040 1.5211

Urban 2 0.7271 1.0279 -0.4055 1.1420 1.5761

Urban 3 0.5672 0.9505 -0.9449 1.0682 1.5698

Urban 4 0.0844 0.7698 -1.0000 0.9827 15350

1/ Elasticities are computed at base year 1973-74.Drices andquantities. Estimates are from Binswanger, yui on and wamy (1984).

- 65 -

Appendix Table 4

Shares of Commodities in Consumption of Each Expenditure-Quartile

Commodity

Expenditure Coarse OtherQuartile Rice Wheat Cereals Food Non-Food Total

Rural 1 0.3152 0.0847 0.1792 0.2643 0.1565 1.0000

Rural 2 0.2789 0.1021 0.1215 0.3128 0.1848 1.0000

Rural 3 0.2611 0.1029 0.0870 0.3364 0.2126 1.0000

Rural 4 0.1389 0.1254 0.0613 0.3661 0.3082 1.0000

Urban 1 0.2004 0.1525 0.0744 0.3875 0.1851 1.0000

Urban 2 0.2503 0.0935 0.0346 0.4004 0.2212 1.0000

Urban 3 0.1923 0.0955 0.0265 0.4073 0.2784 1.0000

Urban 4 0.0926 0.0634 0.0101 0.4261 0.4077 1.0000

1/ Data is from the 28th NSS, Tables on Consumer Expenditures, 1973-74.

- 66 -

Appendix Table 5

Shares of Each Expenditure Quartile in All-IndiaTotal Consumption, By Commodity

Commodity

Expenditure Coarse OtherQuartile Rice Wheat Cereals Food Non-Food

Rural 1 0.1405 0.0672 0.2125 0.0530 0.0505

Rural 2 0.2027 0.1293 0.2432 0.0998 0.0935

Rural 3 0.1938 0.1606 0.1877 0.1146 0.1152

Rural 4 0.1857 0.3131 0.2262 0.2186 0.2946

Urban 1 0.0476 0.0812 0.0507 0.0766 0.0394

Urban 2 0.0806 0.0616 0.0310 0.0910 0.0530

Urban 3 0.0837 0.0885 0.0308 0.1361 0.1097

Urban 4 0.0655 0.0985 0.0179 0.2103 0.2441

Total 1.0000 1.0000 1.0000 1.0000 1.0000

1/ Data is from the 28th NSS, Tables on Consumer Expenditures,1973-74.

- 67 -

Appendix Table 6

Shares of Each Expenditure Quartile in theTotal Supply of Agricultural Inputs

All-India

Agricultural Input

Expenditure Agricultural AgriculturalOuartile Labor Bullocks Land Owned

Rural 1 0.2380 0.0973 0.1137

Rural 2 0.2651 0.1514 0.1625

Rural 3 0.2466 0.2327 0.2553

Rural 4 0.2315 0.4402 0.4685

Urban 1 0.0118 0.0063 0.0669

Urban 2 0.0014 0.0097 0.0359

Urban 3 0.0051 0.0260 0.0320

Urban 4 0.0005 0.0358 0.0377

Total 1.0000 1.0000 1.0000

1/ Data is from the 1970-71 NCAER-ARIS Survey and theNational Sample Survey (26NSS), Tables on Landholdings, All India

- 68 -

Appendix Table 7

Shares in Total Income from AgriculturalInputs, by Expenditure Quartile,

All-India

Agricultural Input

Agricultural Total Agri-Expenditure Agricultural Bullocks Residual Implements & culturalQuartile Labor Farm Profits Machinery Income

Rural 1 0.5283 0.0256 0.1132 0.0737 0.7408

Rural 2 0.4278 0.0254 0.2422 0.0567 0.7521

Rural 3 0.3383 0.0258 0.3298 0.0498 0.7437

Rural.4 0.2215 0.0241 0.4726 0.0646 0.7828

Urban 1 0.0713 00201 0.0069 00000 0.0983

Urban 2 0.0028 0.0099 0.0359 00000 0.0486

Urban 3 0.0061 0.0169 00320 0.0000 0.0550

Urban 4 0.0002 0.0093 0.0377 0.0000 0.0472

All Groups 0.2042 0.0157 0.2213 0.0380 0.4792

1/ Data is from the 19770-71 NCAER-ARIS Survey.

- 69 -

Appendix Table 8

Shares in Total Real Income byExpenditure Quartile, All India

Rural 1 0.0898 Urban 1 0.0345

Rural 2 0.1425 Urban 2 0.0456

Rural 3 0.1752 Urban 3 0.0696

Rural 4 0.3185 Urban 4 0.1244

Total Rural 0.7260 Total Urban 0.2740

Total National = 1.0000

Shares of Agricultural Commodities inthe Value of Total Agricultural Output

All-India

Coarse OthersRict ~ Wheat Cereals Crops Total

2666 .1073 .1128 .5133 1.0000

Note: Data is from the National Accounts.

Shares of Agricultural Inputs in theTotal Cost of Agricultural Production

All-India

AgriculturalAgricultural Bullocks Residual Fertilizer Implements &

Labor Farm Profits Machinery

0.3258 0.1086 0.3088 0.0331 0.0979

Note: Data is from the 1970-71 NCAER-ARIS Survey.

- 70 -

Appendix Table 8 (Cont'd.)

Shares in the Population byExpenditure Quartile, All India

Rural 1 0.2002 Urban 1 0.0498

Rural 2 0.2002 Urban 2 0.0498

Rural 3 0.2002 Urban 3 0.0498

Rural 4 0.2002 Urban 4 0.0498

Total Rural 0.8009 Total Urban 0.1991

Total National = 1.0000

Note: Data is from the 1970-71 NCAER-ARIS Survey.

- 71 -

Appendix Table 9

Output Supply Elasticities WithRespect to Exogenous Shifter Variables I/

COMMODITY RAIN HYV IRK ROADS LAND CAPITAL

Rice 0.3563 0.2755 0.0011 -0.2116 0.4801 -0.0458

Wheat 0.2178 0.3764 0.7965 -0.0488 0.2871 0.2566

Coarse Cereals -0.0575 -0.1931 0.2547 0.3207 0.2000 0.1025

Other Crops 0.0750 0.0340 0.3629 0.0911 0.3056 0.1155

Fertilizer 0.1558 0.5606 0.6370 0.5422 0.0000 0.0000

Labor 0.0557 0.0526 0.0917 -0.0027 0.61291 0.0761

Bullocks 0.0578 0.0441 0.1022 0.0018 0.8882 -0.0183

1/ Elasticities are computed at base year 1973-74 quantities. Estimates areaggregated from Evenson (1981), Evenson and Binswanger (1983) and Bapna,Binswanger and Quizon (1984).

- 72 -

Appendix Table 10

Data Description and Data Sources for Counterfactual AnalysisA. For Actual Price and Quantity Levels

Variable Additional Description1 / Data Source 2 /

1. Rice production Production index for rice Index Numbers of Area, Production and Yieldof Principal Crops in india - CropwiseDirectorate of Economics and Statistics,Ministry of Agriculture

2. Wheat production Production index for wheat Same as I above

3. Coarse cereals production Production index for coarse Same as 1 abovecereals

4. "Other crops" production Production index for pulses Same as 1 aboveand non-food grains

5. All crops production Production index for all crops Same as 1 above

6. Fertilizer consumption Computed as the sum of Production, Imports, Distribution andfertilizer production, net Consumption of Fertilizers, The Fertilizerfertilizer imports, and net Association of Indiawithdrawals from fertilizerstock

7. Rice prices3 / Wholesale price index for rice Index Numbers of Wholesale Prices in India,Economic and Scientific Research Foundation

8. Wheat prices 3 / Wholesale price index for wheat Same as 6 above.

9. Coarse cereal prices3 / Wholesale price index for Same as 6 above.coarse cereals

10. "Other crop" prices3 / Production weighted wholesale Same as 6 above.price index for "other crops"

11. Prices of all commodities3 / Consumption weighted price index Same as 6 abovefor all commodities (food andnon-food articles)

12. Real per capita income Personal disposal be income at Macroeconomic Aggregate and Population,constant prices divided by Central Statistical Office, Department ofthe population. In the counter- Statistics, Ministry of Planningfactual mode, this was eventuallyused as an exogenous shock.

- 73 -

B. For the Exogenous Shifters

Exogenous Shock Additional Description Data Sources

1. Rate of change in net Area under Principal Crops in India,cropped area Directorate of Economics and Statistics,

Ministry of Agriculture

2. Rate of change in Assumed to be equal across Population by Sex, Sex Ratio, Percentagepopulation expenditure groups over Decadal Variation of Population and Urban

given period of time. Population as a Percentage of TotalPopulation, 1901-1981, Census of India

3. Rate of change in capital Assumed to be equal across Gross Domestic Capital Formation byused in agricultural expenditure groups over given Industry of Use, Central Statisticalproduction period of time. Capital refers Office, Department of Statistics, Ministry

to the value of household owned of Planning.livestock and machinery and Proportion of Households Reporting andimplements used in agricultural Average Value per Household ofproduction. How this variable Individual Items of Assets and Liabilitieswas constructed is explained in as of 30th June 1071 according to Assetthe text. Groups, Reserve Bank of India.

4. Rate of change in the Assumed equal to the rate of Total Buffalos and Cattle Used for Work,supply of draft animals growth in the total number Directorate of Economics and Statistics,in agricultural production of buffalos and cattle used for Ministry of Agriculture.

work only. Also assumed to beequal across expendituregroups over given period oftime.

5. Rate of change in the "Other inputs" is taken to be Value of Output from Agriculture,supply of "other inputs' all inputs other than land, Central Statistical Office, Department ofin agricultural production labor, draft power, capital Statistics, Ministry of Planning.

(as defined above), andfertilizer. The rate ofchange in the supply of thisinput is assumed to be equalto the rate of growth in thevalue of total agriculturalproduction (at constant prices)

6. Rate of change in Non-agricultural prices are Index of Wholesale Prices, Economic andnon-agricultural prices3 / weighted averages of wholesale Scientific Research Foundation.

prices of non-food articles.

7. Rate of change in the Assumed equal to 6 above Same as 6 above.price of capital servicesand "other inputs" usedin agriculturalproduction3!

8. Rate of change in non- Assumed to be equal across Net Domestic Production at Factor Cost byagricultural income3 / expenditure groups over given period Industry of Origin, Central Statistical

of time. Initial estimate of this Office, Department of Statistics, Ministryexogenous shock were obtained from of Planning.two independently computed indices Macroeconomic Aggregates and Population,of non-agricultural incomes. How- Central Statistical Office, Departmentever, this variable was treated as of Statistics, Ministry of Planning.endogenous in the counterfactualexercise (see text and Appendix I).

9. Rate of change in the Defined as the rate of change Availability of Food Grains in India,domestic availability in the net available rice Directorate of Economics and Statistics,of rice due to trade supply, i.e., net imports of Ministry of Agriculture.and buffer shock rice plus net releases fromoperations government-held stocks of rice

- 74

10. Rate of change in the Same as 9 above, but for wheat Same as 9 abovedomestic availability ofwheat due to trade andbuffer stock operations

11. Rate of change in the Same as 9 above, but for Same as 9 abovedomestic availability of coarse cerealscoarse cereals due totrade and buffer stockoperations

12. Rate of change in the Same as 9 above, but for Production, Imports, Distribution anddomestic availability of fertilizer Consumption of Fertilizers, The Fertilizerfertilizer due to trade Association of India.and buffer stock operations

130 Technical change in For 1960-61 to 1980-81 assumed to Index Numbers of Area, Production and Yieldrice production be equal to 75% of the rate of of Crops in India - Cropwise, Directorate

change in rice yield per hectare of Economics and Statistics, Ministry ofAgriculture.

14. Technical change in Same as 13 above, but for wheat Same as 13 abovewheat production

15. Technical change in Same as 13 above, but for Same as 13 abovecoarse cereals coarse cerealsproduction

16. Technical change in Same as 13 above, but for Same as 13 above"other crops" other crops"production

17. Rate of change in the Percentage irrigated area in Gross Area Under Irrigation by Crops,percentage of irrigated the ratio of gross area under Directorate of Economics and Statistics,area irrigation to gross cropped Ministry of Agriculture.

area Area Under Principal Crops in India,Directorate of Economics and Statistics,Ministry of Agriculture.

18. Rate of change in road Road density is defined as Extra-Municipal Roads (Classified Accordingdensity the ratio of road length to Surface) Including National Highways

(in km) to total geographic Maintained by P.W.D. and Local Bodies andarea (in km2). Roads refers Roads Constructed in C.D. and N.E.S. Blocksto all surfaced and motorable Ministry of Shipping and Transport.unsurfaced roads

1/ Data values are three year averages. They are indexed such that the average for the three years 1972-73 to 1974-75- is equal to 100.

2/ Only table headings and the agency which reports them are listed here.

3/ These variables are deflated by e. See text for further explanation.

- 75 -

List of Symbols Used

B = bullock services

IQ.i aT Q at = technology shifters; i.e., shifts in output supplies and

i factor demands for given fixed input levels. These areprofit function definitions.

G = square matrix of elasticities and shares.

K* = column vector of exogenous shifter variables.

L = labor services.

M = total nominal income.

m = real per capital income.

MN = non-agricultural income.

N = population.

NA = non-agricultural commodities.

P = output prices.

P = output price indices.

Q = [Y, -XI = vector of outputs and (negative) variable inputs.

S = rent to fixed factor.

si = share of output i in total revenue or share of factor i in input prices.

t = time.

- 76 -

Ut = column vector of endogenous variables.

V = [P, W] = vector of output and variable input prices.

W = wage rate or variable input prices.

w = real wage rate.

X = variable inputs.

Y = outputs.

y = per capita output.

Z = fixed factors; Zi refers to land.

T = technology index.

Modifiers of variables unless already defined above:

X = level.

TX, X = a column and a row vector of the X variables, respectively.

dX 1Xt = total rate of change (n growth rate) of variable X with respect

dt X to time.

X*= exogenous component of the rate of change of a variable (except thatff* stands for maximized variable profits).

Indices and sets of inputs and outputs:

g = shifter variables.

i = commodities (outputs, inputs).

j = commodities (outputs, inputs).

k = income groups.

K = set of income groups or their total number.

0 = set of outputs or total number of outputs.

I = set of inputs or total number of inputs.

VI = set of variable inputs or their total number.

- 77 -

Definitions of parameters:

a = commodity demand elasticities.

= output supply and factor demand elasticities from the profit function.

£ - factor supply elasticities.

Shares and proportions:

6ik = share of factor i in the income of income group k.ik

x.k = proportion of factor i supplied by income group k, but also refers tothe proportion of commodity i consumed by income group k.

iN = share of income group k in the population.

Pik = share of commodity i in the total expenditures of income group k.

"' = proportion of real income accruing to income group k.

- 78 -

REFERENCES

Bapna, Shanti, Hans P. Binswanger and Jaime B. Quizon (1984). "Systemsof Output Supply and Factor Demand Equations for Semi-Arid TropicalIndia," Indian Journal of Agricultural Economics, forthcoming.

Binswanger, Hans P. and Jaime B. Quizon (1984). "Distributional Consequencesof Alternative Food Policies in India," World Bank, ARU Discussion PaperNo. 20, August 1984.

Binswanger, Hans P., Jaime B. Quizon and Gurushri Swamy (1984). "The Demandfor Food and Foodgrain Quality in India," Indian Economic Journal,forthcoming.

Phar, Sanjay (1980). An Analysis of internal Migration in India. UnpublishedPh.D. dissertation. Yale University.

Evenson, Robert E. (1983). "Green Revolution in North Indian Agriculture:An Ex-Post Assessment of Economic Effects," Economic Growth Center,Yale University, mimeo.

Evenson, Robert E. and Hans P. Binswanger (1981). "Estimating Labor DemandFunction for Indian Agriculture," in Contractual Arrangements, Employmentand Wages in Rural Labor Markets in Asia, Hans P. Binswanger andMark R. Rosenzweig (eds.), Niew Haven: Yale University Press.

Krishna, Raj and G.S. Raychandhuri (1980). Treaids in Rural Savings andPrivate Capital Formation in India. World Bank Staff Working PaperNo. 382. Washington, D.C.: The World Bank.

Quizon, Jaime B. and Hans P. Binswanger (1983a). "Income Distribution inAgriculture: A Unified Approach," American Journal of AgriculturalEconomics, August 1983.

Quizon, Jaime B. and Hans P. Binswanger (1984). "Factor Gains and Lossesin the Indian Semi-Arid Tropics: A Didactic Approach to Modellingthe Agricultural Sector," World Bank, ARU Discussion Paper No. 9,May 1984 (revised).

Quizon, Jaime B., Hans P. Binswanger and Devendra Gupta (1984). "TheDistribution of Income in IndLa's Northern Wheat Region," World Bank,ARU Discussion Paper No. 10, June 1984 (revised).

Pal, Ranjan and Jaime B. Quizon (1983). "Factor Cost, Income and SupplyShares in Indian Agriculture," World Bank, ARU Discussion Paper No. 15,December 1983.

Rosenzweig, Mark R. (1980). "Neoclassical Theory and the Optimizing Peasant:An Econometric Analysis of Market Family Labor Supply in a DevelopingCountry," The Quarterly Journal of Economics, February 1980.

DISCUSSION PAPERSAGR/Research Unit

Report No.: ARU 1'Agricultural Mechanization: A Comparative Historical Perspective

by Hans P. Binswanger, October 30, 1982.

Report No.: ARU 2The Acquisition of Information and the Adoption of New Technology

by Gershon Feder and Roger Slade, September 1982.

Report No.: ARU 3Selecting Contact Farmers for Agricultural Extension: The Training and.

Visit System in Haryana, Indiaby Gershon Feder and Roger Slade, August 1982.

Report No.: ARU 4The Impact of Attitudes Toward Risk on Agricultural Decisions in Rural

India.by Hans P. Binswanger, Dayanatha Jha, T. Balaramaiah and Donald A. SiliersMay 1982.

Report No.: ARU 5Behavioral and Material Determinants of Production Relations in Agricultureby Hans P. Binswanger and Mark R. Rosenzweig, June 1982, Revised 10/5/83.

Report No.: ARU 6The Demand for Food and Foodgrain Quality 4^n India

by Hans P. Binswanger, Jaime B. Quizon and Gurushri Swamy, November 1982.

Report No.: ARU 7Policy Implications of Research on Energy Intake and Activity Levels with

Reference to the Debate of the Energy Adequacy of Existing Diets inDevelopment Countriesby Shlomo Reutlinger, May 1983.

ReDort No.: ARU 8More Effective Aid to the World's Poor and Hungry: A Fresh Look at

United States Public Law 480, Title II Food Aidby Shlomo Reutlinger, June 1983.

Report No.: ARU 9Factor Gains and Losses in the Indian Semi-Arid Tropics:A Didactic Approach to Modeling the Agricultural Sectorby Jaime B. Quizon and Hans P. Binswanger, September L983, Revised May 1984.

Revort No.: ARU 10The Distribution of Income in India's Northern Wheat Regionby Jaime B. Quizon, Hans P. Binswanger and Devendra Gupta, August 1983.Revised June 1984.

Report No.: ARU 11Population Density, Farming Intensity, Patterns of Labor-Use and Mechanization

by Prabhu L. Pingali and Hans P. Binswanger, September 1983.

Revort No.: ARU 12The Nutritional Impact of Food Aid: Criteria for the Selection of

Cost-Effective Foodsby Shlomo Reutlinger and Judit Katona-Apte, September 1983.

Discussion Papers (Cont'd.)

Repgrt No.: ARU 13Project Food Aid and Equitable Growth: Income-Transfer Efficiency First!

by Shlomo Reutlinger, August 1983.

Report No. : ARU 14Nutritional Impact of Agricultural Projects: A Conceptual Framework for

Modifying the Design and Implementation of Projectsby Shlomo ReutIlinger, August 2, 1983.

Report No.: ARU 15Patterns of Agricultural Protection by Hans P. Binswanger and Pasquale L.

Scandizzo, November 15, 1983.

Report No.: ARU 16Factor Costs, Income and Supply Shares in Indian Agriculture

by Ranjan Pal and Jaime Quizon, December 1983.

Report No.: ARU 17Behavioral and Material Determinants of Production Relations in Land AbundantTropical Agricultureby Hans P. Binswanger and John McIntire, January 1984.

Report No.: ARU 18The Relation Between Farm Size and Far-m Productivity: The Role of Family

Labor, Supervision and Credit Constraints*by Gershon Feder, December [983.

Report No.: ARU 19A Comparative Analysis of Some Aspects of the Training and Visit System of

Agricultural Excension in Indiaby Gershon Feder and Roger Slade, February 1984.

Report No. : ARU 20Distributional Consequences of Alternative Food Policies in Indiaby Hans Po. Binswanger and Jaime B. Quizon, August 31, 1984.

Report No.: ARU 21Income Distribution in India: The Impact of Policies and Growth in the Agricultural

Sector, by Jaime B: Quizon and Hans P. Binswanger, November 1984.

Report No.: ARU 22'Population-Density and Agricultural Intensification:' A Study of the Evolution ofTechnologies in Tropical Agriculture, by Prabhu L. Pingali and Hans P. Binswangei,

October 17, 1984.

Report No.: ARU 23The Evolution of Farmining Systems and Agricultural Technology in Sub-Saharan Africa,by Hans P. Binswanger and Prabhu L. Pingali, October 1984.

Report No.: ARU 24Population Density and Farming Systems - The Changing Locus of Innovations and

Technical Change, by Prabhu L. Pingali and Hans P. Binswanger, October 1984.Report No.: ARUJ 25

The Training and Visit Extension System: An Analysis of Operations andEffects, by G. Feder, R.H. Slade and A.K. Sundaram, November 1984.

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Report No.: ARU 26The Role of Public Policy in the Diffusion of New Agricultural Technology,

by Gershon Feder and Roger Slade, October 1984.

Report No.: ARU 27Fertilizer Subsidies: A Review of Policy Issues with Special Emphasis

on Western Africa, by Haim Shalit and Hans P. Binswanger, November 1984.

Report No.: ARU 28From Land-Abindance to Land-Scarcity: The Effects of Population Growth

on Production Relations in Agrarian Economies, by Mark R. Rosenzweig,Hans P. Binswanger, and John McIntire, November 1984.

Report No.: ARU 29The Impact of Rural Electrification and Infrastructure on Agricultural

Changes in India, 1966-1980, by Douglas F. Barnes and Hans P. Binswanger,December 1984.

Report No.: ARU 30Public Tractor Hire and Equipment Hire Schemes in Developing Countries

(with Special Emphasis on Africa). A study prepared by the OverseasDivision, National Institute of Agricultural Engineering (OD/NIAE), byP.J. Seager and R.S. Fieldson, November-1984.