2228581

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Farm Supply Response in India-Pakistan: A Case Study of the Punjab Region Author(s): Raj Krishna Source: The Economic Journal, Vol. 73, No. 291 (Sep., 1963), pp. 477-487Published by: on behalf of the Wiley Royal Economic SocietyStable URL: http://www.jstor.org/stable/2228581Accessed: 01-03-2015 14:37 UTC

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FARM SUPPLY RESPONSE IN INDIA-PAKISTAN: A CASE STUDY OF THE PUNJAB REGION1

INTRODUCTION

IN this paperI present some estimates ofthe " short-run " and " long-run" elasticities of supply (acreage) of agricultural commodities derived from time series data for the Punjab,2 which has always been an important agricultural region of the Indo-Pakistan sub-continent.

The study was intended to put to a test the widely prevalent notion that peasants in poor countries do not respond, or respond very little, or negatively, to price movements.3

The choice of the region has been dictated by the availability of relatively reliable long-period data. Moreover, since agriculture is a location-bound industry, the real alternatives faced by farmers can be formulated and the relevant variables (such as relative price indices) defined more appropriately for a region than for an aggregate of heterogeneous regions.

THE MODEL

The basic model used is the Nerlovian " adjustment " model:4

(1) xt=a + bPt-l + cYt-l + gZt-, + hWt + ut (2) Xt - X-l B(Xt* - Xt-1)

1 The work reported in this paper started in the Ph.D. dissertation " Farm Supply Response in the Punjab: A Case Study of Cotton " submitted by the author to the University of Chicago in September 1961 as a fellow of the Council of Economic and Cultural Affairs. It was continued and completed at the Institute of Economic Growth, Delhi (India), with a grant from the Rockefeller Foundation.

The author is grateful to the Economics Faculty of the University of Chicago, and especially to Professors T. W. Schultz, D. G. Johnson, Zvi Griliches and A, C. Harberger for continuous help in developing the dissertation.

2 " Punjab " means undivided Punjab throughout this paper. 3 See, for example, R. N. Poduval and P. Sen, " Prices, Trade and Marketing of Agricultural

Commodities in India," inJ. P. Bhattacharjee (ed.), Studies in Indian Agricultural Economics (Bombay: The Indian Society of Agricultural Economics, 1958), pp. 88-9. N. S. Joshi and B. R. Dhenkey, Irrigation and Agriculture in the First Five-Year Plan: An Appraisal (Poona: Deccan Book Stall, 1954), pp. 164-5. Mahesh Chand, " Agricultural Terms of Trade and Economic Growth," Indian Journal of Agricultural Economics, 1958, pp. 191-2. B. Misra and S. P. Sinha, " Agriculture and Its Terms of Trade with Special Reference to India," Indian Journal of Agricultural Economics, 1958, pp. 196-7. B. K. Madan, "Presidential Address," Proceedings of the Eighteenth Conference of the Indian Society of Agricultural Economics, 1958, pp. 13-14. Walter C. Neale, " Economic Accounting and Family Farming in India," Economic Development and Cultural Change, Part I (April 1959), pp. 297-8. R. 0. Olson, " Discussion: Impact and Implications of Foreign Surplus Disposal on Underdeveloped Economies," Journal of Farm Economics, December 1960, pp. 1043-4.

The last three references contain very pointed opinions in favour of negligible or negative supply response.

4 Marc Nerlove, Dynamics of Supply (Baltimore: Johns Hopkins Press, 1958); and Distributed Lags and Demand Analysis for Agricultural and Other Commodities (Agriculture Handbook No. 141; Washington: United States Department of Agriculture, 1958). This paper owes a great deal to the pioneer work of Marc Nerlove.


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478 THE ECONOMIC JOURNAL [SEPT.

Xt* is the standard irrigated acreage that farmers would plant in the year t if there were no difficulties of adjustment.' Xt is the standard irrigated acreage actually planted to the crop in the harvest year t. The " standard irrigated " acreage of the crop is the irrigated acreage plus the unirrigated acreage multiplied by a standardisation factor.

P is the relative price of the crop, i.e., the post-harvest price of the crop deflated by an index of the post-harvest prices of the alternative crops.

Y is the relative yield of the crop, i.e., the yield of the crop deflated by an index of the yields of alternative crops.

Z is the total irrigated area in all crops of the season. W is rainfall. u is the error term. B is the Nerlovian coefficient of " adjustment." The farmers, it is postu-

lated, are able to increase the acreage of a crop in any year only to the extent of a fraction B of the difference between the acreage they would like to plant and the acreage actually planted in the preceding year.

Equations (1) and (2) yield the estimating equation:

(3) Xt = ao + b2Pt-1 + b3Yt 1 + b4Zt-1 + b5Wt + b6Xt 1 + vt where aO ==aB, b2 = bB, b3 cB, b4 gB, b5 =IhB, b6 = (1 -B) and Vt= But.

Out of the " shifter" variables Y, Z and W, shifting the acreage-price relation, only those are included in the estimating equation of a given crop which were found, on preliminary analysis, to be important factors determining the acreage of that particular crop. Thus, yield has been included in the equations for cotton and rice, because the yield of these crops registered a significant upward trend during the period 1913-14 to 1945-46, to which the data relates, and strongly influenced the acreage of these crops. The trend was due to varietal improvements and the expansion of irrigation brought about by the Government.

Rainfall has been included in the equations for bajra (millet), jowar (sorghum), wheat (unirrigated), gram and barley.2 It is an important factor determining the acreage planted to these crops, for a large part of the total

1 The year is the harvest year, which runs in the Punjab from July to June. 2 The acreage of these crops in a given harvest year is influenced by the rainfall of the same year.

Two-thirds of the annual rain in the Punjab falls in the monsoon season-June to September. The summer crops, bajra and jowar, are sown soon after the onset of the monsoon duringJune andJuly. The farmers make their best guesses about the state of the season from the weather indications in the early monsoon weeks and adjust their crop acreages accordingly. There are also late sowings of some summer crops in August and September. The winter crops, wheat, barley and gram, are planted from the middle of October onwards, and their acreage is influenced in part by the moisture left in the fields by the monsoon. (See H. K. Trevaskis, Punjab of Today, An Economic Survey of the Punjab in Recent Years, 1890-1925, Vol. II (Lahore: Civil and Military Gazette, 1932), p. 161, E. M. Puder, " Agricultural Adjustments to the Natural Environment in the Punjab," unpublished MS. dissertation, University of Chicago, 1925. M. S. Randhawa, Agriculture and Animal Husbandry in India, (New Delhi: Indcian Council of Agricultural Research, 1958), pp, 340-1.



1963] FARM SUPPLY RESPONSE IN INDIA-PAKISTAN 479

acreage (40 % in the case of wheat and 70-80% in the case of other crops) was unirrigated.

In the case of cotton (A)1 and wheat (irrigated), whose acreage recorded a long-term upward trend over the period due to the allocation of a sub- stantial part of the newly irrigated land to them, lagged irrigated acreage in all crops is used as one of the determining variables.

The dependent variable is acreage planted and not output. But the elasticity of planned output with respect to price can be supposed to be at least equal to the elasticity of acreage planted if it is reasonable to assume that inputs other than land are varied at least in proportion to acreage and re- turns to scale are not diminishing.2 This is a reasonable assumption with regard to poor regions, where the rural man-power finds gainful employment only for a part of the year, and, therefore, the labour input can be increased with the land input without much additional cost, and, at the prevailing level of technique, the cost of capital service per unit of land is also small. Hence in the following discussion it will be supposed that the response of the demand for the input of land is a good (minimum) approximation to the response of planned supply.

Changes in input prices have been neglected for want of adequate data. But the little evidence that is available about a few farms in the 'thirties sug- gests that up to the onset of the Second World War the deflated cost of culti- vation did not vary significantly.

No demand relation has been specified on the assumption that the demand curves for individual crops facing Punjab producers were highly elastic. This is not an unreasonable assumption considering that the output of each crop in the Punjab before the Second World War was only a small fraction of the total Indian output.

An " adjustment " model was chosen in preference to an expectational model for several reasons. It may be useful to discuss these reasons briefly.

The choice between different lag models depends, in the first place, on whether the different lags postulated in them are plausible formalisations of the institutional, technological and expectational facts of the sector concerned. Secondly, it depends on the difficulties of estimation presented by them.

The distinction between expectation lags and adjustment lags is important in theory; for the former are supposed to reflect the manner in which past experience determines the expected values of the variables such as prices and yields, which in turn determine the levels of output and inputs intended by producers, while the latter reflect technological and/or institutional con- straints which permit only a fraction of the intended levels to be realised during a given short period. Assuming that both types of lag are important, and neither can be supposed a priori to be non-existent, which means that

1 Cotton (A) stands for the so-called " American " varieties and cotton (D) for the local varieties grown in the Punjab.

2 Marc Nerlove, Dynamics of Supply, op. cit., pp. 67-8.




none of the lag coefficients is unity, ideally a model should specify a separate lag coefficient for each expectational variable and a different adjustment lag coefficient. But such a model presents serious estimation problems.'

We have to choose, therefore, between a model which provides for an adjustment lag only and a model which neglects the adjustment lag in favour of expectation lags.

In a pure expectation lags model a simplification may be made by assuming that the expectation lag coefficients of different expectational variables are identical. Thus, if all our variables were expectational we could write:

(4) Xt-a + bPt* + cYt* + gZt* + hWt* + ut (5) Pt*-Pt- 1B(Pt - P* 1) (6) Yt* -Yt~_ l B(Yt- l- Yt*1l) (7) Zt * -Zt I- B (Zt - 1 Zt*lt ) (8) -* Wtll B(Wt- - W*1)

This model yields the estimating equation:

(9) Xt = ao + b2Pt l + b3Yt-1 + b4Zt-1 + b5Wt_1 + b6Xt_1 + wt

where a = aB, b2 bB, b3 = cB, b4 = gB, br - hB, b6 = (1 B) and Wt Utt - ( B)ut-

The estimating equation (9) derived from the pure expectation model is the same as equation (3) derived from a pure adjustment model except for the lag in W and the error term, which has serial correlation in equation (9) but not in equation (3).

The difficulties due to serial correlation peculiar to distributed-lag models have been discussed by Koyck, Klein, Nerlove and Griliches.2 The essential difficulty is illustrated by our second model. If the ut in this model is supposed to be serially uncorrelated, then the wt in the estimating equation (9) is automatically serially correlated, since wt - (1 - B)ut 1. If, on the other hand, Wt is taken to be serially independent, ut is serially correlated.3

Various ways have been suggested to meet this problem. Koyck and

1 Models of this type and the difficulties involved are discussed in Marc Nerlove, Distributed Lags and Demand Analysis (Washington, 1958), pp. 68-9, and Dynamics of Supply (Baltimore, 1958), pp. 236-40. A model specifying separate price expectation and yield expectation equations and a separate acreage adjustment equation was also tried in connection with the present analysis. But the results only confirmed the difficulty, mentioned by Mr. Nerlove, of estimating unique and distinct values of the expectation lags and the adjustment lag, apart from the general serial correlation problems common to distributed lag models, which are briefly discussed below.

2 L. M. Koyck, Distributed Lags and Investment Analysis (Amsterdam: North Holland Publishing Company, 1954); L. R. Klein, " The Estimation of Distributed Lags," Econometrica, 1958, pp. 553- 65; Nerlove, Distributed Lags and Demand Analysis (Washington, 1958) and Dynamics of Supply (Baltimore, 1958); and Zvi Griliches, "A Note on Serial Correlation Bias in Distributed Lag Models," Econometrica, January 1961, and" Distributed Lags, Disaggregation and Regional Demand Functions for Fertilizer," Journal of Farm Economics, February 1959.

3 Klein, op. cit., p. 560; Nerlove, Distributed Lags, op. cit., p. 76.



19631 FARM SUPPLY RESPONSE IN INDIA-PAKISTAN 481

Klein have suggested a method for estimating coefficients on the assumption that ut -rut- et, where et is serially independent.

But neither this method nor the model yielding equation (9) has been used in this paper, for, in the first place, the assumption made in this model of identical expectation lags for different expectational variables may be questionable. Secondly, if different coefficients of expectation are specified for the two or more expectational variables the number of variables in the resulting estimating equation becomes very large. Even for a model with only two expectational variables the estimating equation has six determining variables;1- and with only twenty to thirty observations available many degrees of freedom are lost. Moreover, the estimates are not unique.2 When many expectational variables are involved, and equations for many commodities have to be estimated, iterative procedures too become very cumbersome.

It was therefore decided to use only the adjustment model yielding equation (3). The advantage of this model is that if the estimated residuals vt of equation (3) are found to be serially uncorrelated, then, since vt = But, the ut are also serially uncorrelated. And, therefore, the estimated coefficients are not likely to be affected by serial correlation. It is true that the adjustment model oversimplifies expectation behaviour. But in view of the difficulties involved, it was regarded as the best feasible choice.

ESTIMATES

Multiple regression coefficients of equation (3) with suitable shifter variables for each crop computed with 1914-15 to 1945-46 data for 1 1 crops are shown in Table I. The facts relevant to the interpretation of the results and the inferences suggested by them are discussed below.

RESULTS FOR INDIVIDUAL CROPS

Cotton (A). The acreage of cotton (A) registered a steep increase from about 400,000 acres in 1922 to more than 1,800,000 acres in 1944. A part of this increase came from the substitution of American varieties for the local varieties-the former yielding about 8% more fibre than the latter, and a price premium of 31 % on an average during the period. The yield of cotton (A) itself was almost doubled between the early 'twenties and the early 'forties. The main explanation of the increase in acreage lies, however, in the increase in the total canal-irrigated area of the province from 4i5 million acres in 1901 to 12i5 million acres in 1943. In the ten major cotton (A) districts 975,000 acres were added to cotton (A) out of 1,535,000 acres of

Nerlove, Distributed Lags, op. cit., p. 60. 2 Nerlove, Distributed Lags, op. cit., p. 62.

No. 29I.-VOL. LXXIII. KK




TABLE I (a)

Acreage Response Functions for Some Punjab Crops

Regression coefficients.

symbol. ,Crop period. - tt - _t- *

_ t,

- R. ~~~~~~~~tI- ,tt ...-1 .** W. I - 1- -

CA Cotton (A) * 623 - 034 - 056 0 96 1922-23 to 1941-42 (1.08) (0.09) (0-13)

CD Cotton (D) * 6 83 3 24 - 0*45 0-85 1922-23 to 1943-44 (1.36) (1.14) (0.13)

M Maize * 2-12 - - 0*60 0-79 1914-15 to 1943-44 (0.51) (0.13)

Pt-2 Pt-, S Sugar-cane * 145 0 72 - - 044 0-66

1915-16 to 1943-44 (0.38) (0.49) (0.20)

R Rice * 3 07 8-01 - 0-48 0 79 1914-15 to 1945-46 (0.99) (3.16) (0.15)

BJ Bajra t 0 03 0417 -0 12 0-76 0 92 1914-15 to 1945-46 (0*01) (0.06) (0.05) (0.09)

J Jowar + -4 70 -0 30 8-80 0 59 1914-15 to 1943-44 (2.75) (0.12) (3.85)

WW Wheat ? 4.74 2 61 - 0,41 0-92 1914-15 to 1943-44 (2.08) (1.18) (0.24)

WD Wheat + 8-44 5 36 59 16 0 71 1914-15 to 1945-46 (4.96) (4.19) (13-20)

G Gram + -11-48 111.11 0-66 1914-15 to 1945-46 (7.96) (26.77)

B Barley + 3 02 - 20 50 0 23 0 54 1914-15 to 1945-46 (3.59) (6.46) (0.17)

* The dependent variable is standard irrigated acreage. t The dependent variable is the ratio (%) of the unirrigated area under bajra to the unirrigated

area in all kharif (summer) crops. + The dependent variable is unirrigated area. ? The dependent variable is irrigated area. ? In the case of jowar the relative price variable is an index of the own price of jowar deflated

by an index of the price of bajra only. In the case of wheat (unirrigated) the own price index of wheat is deflated by the price index of gram only; and in the case of wheat (irrigated) the deflator is an index of ten alternative crops. In the barley and gram equations the deflator is an index of the price of wheat only. In the case of all other crops the deflator is an index of the prices of six alternative kharif crops.

The appropriate deflators for the price of each trop were selected on the basis of an analysis of acreage shifts.

tt The yield of wheat (unirrigated) is relative to tlle yield of gram; and that of cotton (D) and rice to an index of the yields of six alternative summer crops.

** In the wheat (irrigated) equation Z is the irrigated area in all rabi (winter) crops; in the case of jowar and cotton (A) it is the irrigated area in all kharif (summer) crops; and in the case of bajra it is the ratio (%) of the irrigated area in kharif crops to the total kharif area.

11 In the equations for gram, jowar and wheat (unirrigated) the lagged acreage variable has been omitted after preliminary experiments showed its coefficient to be small and non-significant.




TABLE I (b)

Acreage Response Functions for Some Punjab Crops

Adjustment coefficients and elasticities. Serial correlation

Equation Crop, eriod. according to symbol. Exp EB. Durbin-Watson

_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ T e s t.

SR LR. SR. LR.

CA Cotton (A) 0 44 0-72 1-62 (Z) 1 28 2-87 No S.C. 1922-23 to 1941-42

CD Cotton (D) 0.55 0 59 1 08 (Y) 0*39 0-72 No S.C. 1922-23 to 1943-44

M Maize 0 40 0-23 0 56 - Test 1914-15 to 1943-44 inconclusive

Pt-2 S Sugar-cane 0-56 0 34 0-60 - No S.C.

1915-16 to 1943-44 Pt- 017 030

R Rice 0-52 0-31 0 59 (Y) 0 90 172 No S.C. 1914-15 to 1945-46

BJ Bajra 0-24 0 09 0-36 (Z) 0-20 0-83 No S.C. 1914-15 to 1945-46 (W) -0 08 -0 34

J Jowar - - -0-58 (W) - 0 30 No S.C. 1914-15 to 1943-44 (Z) - -0 60

WW Wheat 0 59 0-08 0-14 (Z) 0 45 0 77 No S.C. 1914-15 to 1943-44

WD Wheat - 0-22 (Y) - 0-15 No S.C. 1914-15 to 1945-46 (W) 0-36

G Gram - -0 33 (W) - 0-87 No S.C. 1914-15 to 1945-46

B Barley 0 77 0 39 0 50 (W) 0-91 1.19 No S.C. 1914-15 to 1945-46

Exp is elasticity of acreage with respect to relative price. Exo is elasticity of acreage with respect to other variables. SR is short-run; LR is long-run.

See also footnotes to Table I (a).

additional irrigated land made available for all summer crops during 1922- 41. Thus nearly two-thirds of the additional irrigated kharif area was devoted to cotton (A).

The estimated elasticities show that the acreage of cotton (A) has been highly responsive both to its relative price and the expansion of total irrigation capacity. These variables, along with lagged acreage, explain as much as 92 % of the variance of acreage.

Cotton (D). Relative yield is an important explanatory variable besides




relative price in the equation for cotton (D). The acreage of cotton (D) also increased due to the expansion of irrigation and the upward trend in yield in the middle of the period under study, but declined steeply during the early 'forties. The price elasticity of the acreage of cotton (D) is also significantly high.

Maize. No shifter variable turned out to be important in the case of maize. Therefore, price elasticity has been calculated from a regression of acreage on relative price and lagged acreage only.

Sugar-cane. Sugar-cane occupies the field for ten to twelve months. Since planting begins in March, the acreage planted in the crop year t is influenced more by the post-harvest price of the year t - 2 than the post- harvest price of the year t - 1, for the preparations for plantings of the year t begin even before the sugar season (December-March) of the immediately preceding harvest is over. Hence the larger and more significant effect of

Pt-2 than of Pt-1 in the estimated equation. Rice. As in the case of cotton, yield was specified as one of the explana-

tory variables in the rice equation. And the response of the rice acreage with respect to yield is quite significantly high: 0 9 in the short-run and 1b7 in the long-run.

Bajra. In the bajra equation the dependent variable is not the absolute bajra acreage, but the ratio of the unirrigated acreage in bajra to the unirrigated acreage in all kharif (summer) crops. Bajra is a dry crop with nearly 80% of the acreage unirrigated. It is, in fact, one of the best dry- area crops.1 That is why, interestingly, the coefficient of rainfall is significantly negative: the lower the rainfall, the larger the proportion of the kharif area allocated to bajra. Although bajra is one of the inferior food grains, the acreage of bajra recorded a sharp upward trend during the inter- war period. The hypothesis which seems to explain this phenomenon is that as the proportion of the irrigated area in all kharif crops to the total area in all kharif crops, Z, rose (from about a third to about a half) the proportion of the unirrigated kharif area devoted to bajra increased pari passu. In other words, as more irrigated area became available for the kharif crops which did well only under irrigation the peasants devoted an increasing proportion to the unirrigated area to the best dry kharif crop, viz., bajra. Hence the significant coefficient of Z in the bajra equation.

Jowar. The R2 of the equation for jowar, another inferior grain crop, is very low indeed. But the coefficient of rainfall is significant. And the coefficient of relative price is negative and significant at the 10 % level. Jowar thus turns out to be the only important Punjab kharif crop whose acreage is not responsive, or possibly negatively responsive, to relative price movements.

Wheat. Of the total area in wheat about half used to be irrigated and half unirrigated. Separate regressions have been run for the irrigated and

1 See M. S. Randhawa, Agriculture and Animal Husbandry in India (New Delhi: Indian Council of Agricultural Research, 1958), p. 119.




the unirrigated area, for the patterns of crop substitution and other factors determining wheat acreage in the irrigated and unirrigated tracts are different. The unirrigated area is largely determined by rainfall, the price coefficient being only marginally significant. The irrigated area had a marked trend due to the expansion of irrigation and was significantly responsive to relative price movements.

Barley and Gram. In the rabi crop complex gram and barley like wheat (unirrigated) turn out to be crops whose acreage is more or less insensitive to relative price movements, but depends significantly on rainfall.

GENERAL CONCLUSIONS

The estimated coefficients of adjustment and elasticities of acreage with respect to price, are presented in Table II, along with elasticities of acreage estimated by other workers for other regions for comparison.

TABLE II

Estimated Coefficients of Adjustment (B) and Price Elasticities of Acreage

Elasticity. Commodity and period. B. _-

Short run. Long run.

Punjab (Our Estimates) Cotton (A) (1922-41) 0 44 0-72 1-62 Cotton (D) . . . (1922-43) 0 55 0 59 1-08 Maize . . (1914-43) 0 40 0-23 0-56 Sugar-cane . . . (1915-43) 0 56 0 34 0 60 Rice . . . . . (1914-45) 0 52 0-31 0 59 Bajra . . . . (1914-45) 0-24 0 09 0 36 Jowar . . . . (1914-43) - -058 * Wheat (irrigated) . . (1914-43) 0 59 0-08 0 14 Wheat (unirrigated) . (1914-45) _ 0-22 * Barley . . (1914-45) 0 77 0 39 t 0.50 t Gram . . . . (1914-45) -0 33 t

India (Mr. Venkataraman's Estimnate) t Jute . . . . (1911-38) 0-64 1 0-46 1 0-73

U.S.A. (Mr. Nerlove's Estimates) ? Cotton . . . (1909-32) 0-51 0 34 0-67 Wheat . . . . (1909-32) 0-52 0-48 0 93 Corn . . . . (1909-32) 0 54 0-10 0-18

* Coefficient significant only at the 10% level. t Coefficient not significant. + L. S. Venkataraman, "A Statistical Study of Indian Jute Production and Marketing with

Special Reference to Foreign Demand," Unpublished Ph.D. Dissertation (Department of Economics, University of Chicago, June 1958).

? Marc Nerlove, " Estimates of the Elasticities of Supply of Selected Agricultural Commodities," Journal of Farm Economics, 1956, pp. 496-509.




The estimated elasticities presented will, it is hoped, be a useful addition to the existing repertory of the elasticities of supply (acreage) of different agricultural commodities in different parts of the world.

The estimates can be used in a number of ways. They are useful for cross-checking the acreage forecasts for individual crops. At present the forecasts in India are based almost entirely on an assessment of climatic factors. But the acreage predicted by a model of the type used in this paper is a resultant of the effect of climatic as well as economic factors.

The estimates can also be used to gauge the effects of given policies- taxes, subsidies, price supports, etc.-on the relative acreages of different crops and facilitate the choice of maximum net-benefit policies.

The following conclusions may be drawn from our analysis and results. It will be seen that of the major Punjab crops, jowar seems to be the only

crop which might possibly have a negative response to relative price movements. Barley and gram seem to be rainfall crops. All other crops have positive short-run price elasticities varying from as low a figure as 0-1 in the case of wheat and bajra to the medium magnitudes 0-2-04 in the case of maize, sugar-cane and rice up to 06 and 07 in the case of cotton. The corresponding long-run elasticities range from 0415 to 1 6. It is significant that the long-run elasticity of cotton acreage exceeds unity.

The models of farm supply behaviour which have been found to work with the data for Western countries not only do not break down when applied to Indian data but yield plausible, interesting and internationally comparable results.

If we compare 1 our estimates for the Punjab with Mr. Nerlove's estimates for the United States we find that while the elasticity of the acreage of wheat in the Punjab was much lower than that in the United States, the elasticities of cotton and maize acreage in the Punjab were significantly higher. This is a remarkable result for a poor agricultural economy like that of the Punjab in the inter-war period. The elasticity of cotton in the Punjab was also higher than that for jute-another fibre crop-for India as a whole.

Our analysis shows that the more correctly we are able to specify the relevant non-price variables, the more significant are the net regression coefficients and elasticities of the price variable that we get. The arguments between the protagonists of the price variable and the non-price variables thus appear to be barren and superfluous. In the context of econometric work on supply response the net effect of price variables can be properly measured only if the non-price variables determining supply are well- specified, and vice versa.

1 Mr. Nerlove and Mr. Venkataraman used only relative price and lagged acreage as determining variables, while we have used other relevant variables as well. But the elasticities may still be compared, as they are computed from "net " regression coefficients-net of the effects of other relevant variables. The " relative price" is relative to an index of the relevant " substitute " crop or other prices in each case.




The price-factor versus non-price-factors debate also turns from an either- or debate into a how-much-this-and-how-much-that debate. Our results show, for example, that in the determination of acreage, price alone was the important factor identified in the case of maize and sugar-cane; price was a more important factor than yield (in terms of elasticities) in the case of cotton (D); irrigation capacity was more important than price in the case of cotton (A), bajra and wheat (irrigated); yield was more important than price in the case of rice; and rainfall was almost all-important in the case of unirrigated wheat, barley and gram.

Our analysis also reveals that a priori beliefs about the responsiveness of the output of individual crops to price movements and other factors cannot be accepted at their face value. No general presumption in favour of the irresponsiveness of crop output to prices in poor economies can be upheld. The responsiveness, however, varies as between different crops and regions. When more studies of the responsiveness of crop output in other poor regions are carried out inter-regional comparisons of responsiveness can be made.

The coefficients of adjustment (B) estimated from our equations indicate that the rapidity of adjustment of the acreages of crops by the peasants in response to changing circumstances are not very different from those estimated for the United States. The Punjab peasants were evidently not unusually tardy in adjusting fairly " rationally " to changes in their economic environment.

RAJ KRISHNA

Institute of Economic Growth, Delhi, India.



Article Contentsp. [477]p. 478p. 479p. 480p. 481p. 482p. 483p. 484p. 485p. 486p. 487

Issue Table of ContentsThe Economic Journal, Vol. 73, No. 291, Sep., 1963Front MatterThemes in Dynamic Theory [pp. 401 - 421]Two Early Articles by Alfred Marshall [pp. 422 - 430]Sidgwick's Theory of International Values [pp. 431 - 441]On the Disequilibrium Behaviour of a Multi-Sectoral Growth Model [pp. 442 - 457]The Ohlin-Heckscher Theory of the Basis of Commodity Trade [pp. 458 - 476]Farm Supply Response in India-Pakistan: A Case Study of the Punjab Region [pp. 477 - 487]Reviewsuntitled [pp. 488 - 492]untitled [pp. 492 - 495]untitled [pp. 495 - 497]untitled [pp. 497 - 498]untitled [pp. 499 - 500]untitled [pp. 501 - 502]untitled [pp. 502 - 504]untitled [pp. 504 - 505]untitled [pp. 506 - 508]untitled [pp. 508 - 509]untitled [pp. 509 - 510]untitled [pp. 511 - 512]untitled [pp. 512 - 515]untitled [pp. 515 - 517]untitled [pp. 517 - 520]untitled [pp. 520 - 521]untitled [pp. 522 - 523]untitled [pp. 523 - 525]untitled [pp. 525 - 527]untitled [pp. 527 - 528]untitled [pp. 529 - 530]untitled [pp. 530 - 532]

Notes and MemorandaTax Incidence and Growth: A Comment [pp. 533 - 535]Observations on Dynamic Incidence [pp. 535 - 546]Incidence and Growth Further Considered [pp. 547 - 553]Hire-Purchase Controls and the Demand for Cars: A Comment [pp. 553 - 556]Hire-Purchase Controls and the Demand for Cars: A Reply [pp. 556 - 558]Current Topics [pp. 558 - 561]

Recent Periodicals and New Books [pp. 562 - 602]Back Matter [pp. i - xi]

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