factor markets and resource allocation in colonial punjab

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Factor markets andresource allocation incolonial PunjabAnand V. Swamy aa Department of Economics , University ofMaryland , College Park, MD, 20742, USAPublished online: 23 Nov 2007.

To cite this article: Anand V. Swamy (1998) Factor markets and resourceallocation in colonial Punjab, The Journal of Development Studies, 34:3,97-115, DOI: 10.1080/00220389808422523

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Factor Markets and Resource Allocationin Colonial Punjab

ANAND V. SWAMY

It is often argued that agricultural factor markets in colonial Indiawere underdeveloped, leading to inefficient resource use and slowagricultural development. However, there is little econometricevidence on these issues. This article outlines a model whichincorporates the factor market imperfections discussed in theliterature and tests the model against data from the Punjab. Thereis, in the statistical sense, evidence of resource misallocation;however, these effects are too small to be of much economicsignificance.

INTRODUCTION

The slow growth and low productivity of Indian agriculture during itscolonial period have been the subject of much discussion.1 Variousexplanations, not mutually exclusive, have been advanced: poor technology,excessive taxation, underdeveloped factor markets and so on. However,there is a paucity of econometric evidence on these issues because of thelack of household/farm-level data of the sort which is increasingly availablefor developing countries today. This article exploits a detailed and little-used panel data set from the Punjab, collected over the years 1933-36, toevaluate whether or to what extent factor markets were underdeveloped, byexamining patterns in resource allocation.

The historical literature has emphasised three features of factor marketswhich contributed to the poverty and low productivity of the poorer sections

Anand V. Swamy, Department of Economics, University of Maryland, College Park, MD 20742,USA. The author is very grateful to his thesis adviser, Joel Mokyr, and to Joe Altonji, Chris Udry,and Tara Vishwanath for their help and advice. He also thanks Gaurav Datt, Sumit Guha, JohnWallis, and an anonymous referee for helpful comments. This article was made possible throughsupport provided by the US Agency for International Development under CooperativeAgreement No. DHR-0015-A-00-0031-00 to the Center on Institutional Reform and the InformalSector (IRIS), Center for Economic Growth, Bureau for Global Programs, Field Support andResearch. The author also thanks the Alfred P. Sloan foundation for financial support.

The Journal of Development Studies, Vol.34, No.3, February 1998, pp.97-115PUBLISHED BY FRANK CASS, LONDON

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98 THE JOURNAL OF DEVELOPMENT STUDIES

of the peasantry: high rates of interest on loans, high land rents, andunemployment. It is often argued that the poor peasant, who found it difficultto sell his labour, would lease a small piece of land even at a high rent. Theland would be cultivated very labour-intensively with limited use ofcomplementary inputs, due to the scarcity/high cost of capital. As a resultlabour productivity was very low. Variants of this view recur in the literature,appearing in the work of British officials of the time as well as theirnationalist critics. In his classic work on Punjab agriculture Calvert [1922:206], a British official, wrote: 'There is waste in the miserable system ofrural credit ... The drain of interest ... [if] devoted to improvement wouldbring incalculable benefit.' In a similar vein R.P. Dutt [1943: 85-7], a criticof British rule, talked of the 'mountain of debt' and the 'stagnation anddeterioration of agriculture, the low yields and the waste of labour ... '.However, perhaps the most careful and complete exposition of this view ofthe structure of factor markets and their implications for productivity andresource allocation is in the recent work of Bhardwaj [1985].

Bhardwaj argues that agrarian factor markets in colonial India wereimperfect in a systematic way: richer households received better terms,especially in land and credit markets. She defines four categories ofhouseholds, based on their wealth status, and writes [1985: 315]: 'Thenature of exchange involvement as well as the terms and conditions dependlargely upon the position of the participating household within the resource-status categories given above.' She argues that the poor peasant who paidhigh rents and interest rates also found it difficult to sell his labour anddescribes the consequences for resource allocation as follows [1985: 342]:

The non-availability of assured and continuous employment inducespetty [poor] producers to cling to their tiny holdings and cultivatethem intensively with the help of family labour. This is reflected inhigh intensity of cultivation, relatively high land productivity butlower labour productivity - on small rather than large farms andintense self-exploitation of family labour.

The same ideas are prominent in recent work on the Punjab, which is thefocus of this article. Regarding poor peasants' limited access to creditMukherjee [1980: A50f writes:

... There was wide variation in the amount and terms on which they[farmers] could borrow. A peasant owning some land had better creditthan the landless and could borrow at lower rates of interest withoutactually mortgaging his land ... The tenant's credit was worse thanthat of the landowners; the amount they could borrow was less and theinterest rates higher.

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FACTOR MARKETS AND RESOURCE ALLOCATION: PUNJAB 99

Bhattacharya [1985: 122] echoes these views, emphasising the problem ofunemployment and drawing out the implications for resource allocation andproductivity: "... There was no certainty of stable, secure employment... sothe peasant clung to the land, paying exorbitant rates of interest on loans andhigh rates of rent... [T]he intensity of labour of such a petty cultivator wasnecessarily higher ... .'

As mentioned before, in this article we model the above views and takethe model to the data, using a panel data-set of 144 Punjabi households. Theremainder of this article is organised as follows. The second sectiondiscusses the theoretical literature on modeling poor agrarian economiesand provides a sketch of our model. The third section presents the modeland discusses the comparative static propositions. The fourth sectiondescribes the data. The fifth section takes the model to the data anddiscusses the findings. The final section summarises and concludes.

DISCUSSION OF LITERATURE

It is clear from the recent theoretical work of Carter and Kalfayan [1989],Eswaran and Kotwal [1986], and Feder [1985], that imperfections in thecapital and land lease markets alone would not lead to distortions inresource allocation. Households with ample access to land and capitalwould hire in labour and those with limited access to land and capital wouldhire out their labour, leading to efficient use of resources. For there to bedistortions in resource allocation, labour markets also have to malfunction.Eswaran and Kotwal and Carter and Kalfayan point to two potentialproblems in the labour market: unemployment, on the sellers' side, and theproblem of monitoring hired labour, on the buyers' side. Wealthierhouseholds, for whom land and capital are relatively cheap, but find it costlyto hire labour because of monitoring costs, will cultivate with low labourintensity. On the other hand, poorer households, for whom land and capitalare relatively expensive, which also find it difficult to sell their labour, willcultivate with high labour intensity. Thus, resources will be misallocated,since the marginal product of labour will be higher on the farms cultivatedby wealthier households. In contrast, in a world in which factor marketswork smoothly, resources will be allocated efficiently, and (assuming aconstant returns to scale technology), factor proportions will be equalizedacross farms. Other papers which model the implications for resourceallocation of differential access to land, credit and/or labour marketimperfections include Benjamin [1991], Griffin [1974] and Sen [1981].Empirical evidence of the importance of labour supervision in Indianagriculture is provided by Frisvold [1994]?

The work of Eswaran and Kotwal [1986] and Carter and Kalfayan

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[1989] provides a natural point of departure for us. As is evident from theintroduction there are frequent references in the literature on colonial Indianagriculture in general and the Punjab in particular to the problems ofdifferential access to land and credit, and unemployment. There are fewerreferences to the issue of monitoring. Still it is interesting to note thefindings of a survey in rural Punjab, quoted in Calvert [1922: 89]. Thesurvey which focused on the productivity of tenants-at-will commented thatthough they cultivated poorly, 'even the tenant-at-will cultivates better thanthe paid labourer of the big landlord'. Bhattacharya [1983] also suggeststhat in the Punjab wealthier farmers may have limited their scale ofproduction due to monitoring difficulties.

In light of the above discussion I outline and empirically test a modelwhich has the following key features:

(1) wealthier households borrow capital and lease land on cheaper terms;

(2) households which hire in labour face monitoring costs;

(3) households which hire out their labour face difficulties in findingemployment.

For simplicity the model below has only two inputs, labour and 'non-labour'. 'Non-labour' inputs are a composite of land and other inputs suchas fertiliser, seed, bullock labour, etc.

THE MODEL

In the model outlined below I assume, again for simplicity, that each familyhas only one member (unit of labour). The empirical work is then carriedout in terms of wealth per person, area cultivated per person and so on.

NotationA = Household wealth.Lf = Total amount of family labour used on the farm.H = Hired labour used on the farm.K = Units of the composite non-labour input.P = Price per unit of the composite non-labour input.W = Wage rate.Y = Household income.i = rate of interest.

Assumptions

(1) The production function F(K,L) is CRS. I make the standardassumptions, FK > 0, FL > 0, FKK < 0, FLL < 0, FLK > 0.

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(2) The household takes production decisions and labour allocationdecisions with a view to maximising income.

(3) The rate of interest paid on borrowed capital is decreasing in the wealthof the household, i = i(A), i' < 0.

(4) Hired labour needs to be monitored. This monitoring may be undertakenby family members or by reliable hired workers. The cost of monitoring isthe wage paid to the supervisor or the opportunity cost of family time spenton supervision. Thus, the total cost of an hour of hired labour is the wagepaid for it plus the cost of monitoring. The cost of monitoring depends onthe ratio of hired to family labour. The greater the ratio of hired to familylabour, the higher the cost of monitoring each hired worker.4 Also, as theratio of hired to family labour increases, monitoring costs increase at anincreasing rate. This assumption ensures that the farm is of finite size.5

Thus, the total cost of hiring a unit of labour is W + s(H/Lf), where 's ' is thesupervision cost. I assume that s(0) = 0, s' > 0, s" > 0, s'(0) = 0. Theserestrictions would be satisfied by a supervision cost function of the forms(H/Lf) = a(H/Lf) + b(H/Lf)

2, a > 0, b > 0.

(5) Unemployment is modeled along the lines of Carter and Kalfayan[1989]. If an individual needs off-farm employment for only a few days, shecan choose to look for work in the peak season, when it is easy to find work.On the other hand, as she supplies more days of labour to the market shewill need to look for work in the slack season as well, when it might bedifficult to find work. This kind of seasonal unemployment is modeled byassuming that the number of days of employment found by an individual isH(LS), where Ls is labour supplied to the market and u.'(0) = 1, |x' > 0, u"<0. Thus, the expected return for Ls days of labour supplied to the market isH(LS)W, which is less than LSW for positive values of Ls.

The Household's Decision ProblemThe household decides how much labour to hire in or out and how much ofnon-labour inputs to use, given the costs of various inputs, in order tomaximize profits.

Max F(K, Lf + H) -PK(l+i(A)) - WH - s(H/Lf)H + ]i{\ - Lf)WK, H, Lf

K , H > 0l > L f > 0

The first term in the objective function is the value of the output producedon the farm (assuming that its price is one). The second term is the cost of

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non-labour inputs, including the cost of borrowing money for theirpurchase.6 The third term is the cost of hiring labour, the fourth term is the.cost of supervising hired labour,7 and the fifth term is the expected earningfrom work off the farm.

Given that there are frictions associated with hiring in and hiring outlabour, profit-maximising households will not do both.8 We can thereforeconcentrate on two categories, households which hire in labour ('capitalist')and those which do not hire in labour and may hire out their labour('peasant'). The following predictions follow from our assumptions in astraightforward way. They are summarised in Figure 1. Proofs are availablefrom the author.

Proposition 1. Compared to a household which does not hire in labour('peasant'), a household which hires in labour ('capitalist'):

(a) is wealthier;

(b) uses more non-labour inputs per unit of labour;

(c) produces more output per unit of labour.

This prediction is intuitive. A capitalist household is one which finds itworthwhile to hire in labour after using all of the available family labour.This is because, since it is wealthier than a peasant household, it can borrowat a lower rate of interest and therefore obtains complementary non-labourinputs relatively cheaply. Also, since non-labour inputs are relatively cheapfor the capitalist household, it will use more non-labour inputs per unit oflabour and therefore produce more output per unit of labour.

Since within each category of household the cost of non-labour inputs isdecreasing with wealth we also have a second proposition.

Proposition 2. Within each category of household ('capitalist' and'peasant'): (a) the ratio of non-labour to labour inputs increases with wealth;and (b) output per unit of labour increases with wealth.

DATA AND EMPIRICAL METHODOLOGY

The data are from the Report on the Cost of Production of Crops in thePrincipal Sugarcane and Cotton Producing Tracts in India, published bythe Imperial Council of Agricultural Research (ICAR) in Delhi in 1938. Thereport was the product of a study specially commissioned by the IndianSugarcane Committee and the Indian Cotton Committee, the top decision-making bodies in the sugarcane and cotton industries respectively. Thestudy focused on regions in which cotton and sugarcane were grown, butcollected data for all crops. Data pertaining to farm inputs and outputs were

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FACTOR MARKETS AND RESOURCE ALLOCATION: PUNJAB

FIGURE 1

103

peasant capitalist

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peasant capitalist

WEALTH

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collected for eight households each in six villages each in three districts ofPunjab (Lyallpur, Jullunder, and Gurdaspur), over the years 1933-34,1934-35 and 1935-36. Thus we have data for each of 144 households overa period of three years, for a total of 432 observations.

For the farm as a whole the ICAR collected data on only the followingvariables: gross cropped area, hired and family labour use, bullock labour use,the gross income, the cost of hired labour, the imputed cost of family labour,the imputed opportunity costs of owned capital and owned land used on thefarm, and the total cost of production, including all imputed costs. I compute'non-labour costs' by subtracting hired labour costs and the cost of familylabour from total costs. A detailed breakdown of the components of 'non-labour' costs is provided only for a subset of crops, and hence these variablesare not used in our analysis. However, these are described in the appendix toinform the reader of what is included in the variable 'non-labour costs'.

Sample Selection

The ICAR report [1938: 4] gives us only one sentence about how thesample was selected:

After the Committee had chosen the relevant districts the choice ofvillages and holdings was left to the Provincial Departments ofAgriculture, with the general instruction that they should be asrepresentative as possible, that is, not extreme either in poverty orfertility, but (to use a statistical phrase) more or less modal [italics inoriginal], meaning, taken from that group into which the majority ofvillages or holdings would be naturally classified.

In order to clarify the sample selection issue I have compared the size-distribution of farms in the ICAR data with the figures obtained from alarge-scale inquiry conducted in the 1920s by the Board of EconomicInquiry, Punjab [BEIP, 1928]." The study covered 4,031,000 cultivators inthe Punjab and provided, for each district, a frequency distribution of thehouseholds operating in each farm size category.

The BEIP [1928: 3] used the following procedure: '[Whenever therewas] joint cultivation, the area was divided among the number ofcultivators; e.g. three brothers cultivating jointly twelve acres would appearas three cultivators with four acres each'. Therefore I compare the BEIPdata with the size distribution of area cultivated per family adult maleworker in the ICAR data. In the BEIP data the proportion of farms underfive acres per family adult male is 76 per cent, 67 per cent, and 19 per cent,respectively, in Jullunder, Gurdaspur, and Lyallpur. The correspondingpercentages are in the ICAR data are 62 per cent, 5.8 per cent, and 9 percent, respectively.10 Thus the smaller farms are under-represented.

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Summary statistics for the main variables of interest are presented inTable 1. Our measure of wealth is the amount of land owned per familyadult male worker. The ICAR data do not provide any information onhousehold assets other than land. Still, this a fairly good measure of wealthbecause the pre-eminent asset in rural Punjab has been and continues to beland." I divide by the number of family adult male workers (rather thannumber of family adult workers, male and female) because the number offamily adult female workers is typically missing in the case of Lyallpur. Ascan be seen mean land owned per adult family male worker is 9.53 acres.There is substantial variation in the sample, from 1.71 (10th percentile) to20.1 (90th percentile). Mean gross cropped area12 per family adult maleworker is 11.70 acres. Each household has on average 2.58 adult familyworking men, and uses 32 days of labour per acre. The ratio of hired tofamily labour shows huge variation, ranging from 0.09 at the 1 Oth percentileto 1.87 at the 90th percentile. The mean cost of non-labour inputs used perday of labour used on the farm is Rs 1.122. There is substantial variation,from Rs 0.703 at the 10th percentile to Rs 1.577 at the 90th percentile. Thenon-labour costs include all expenses paid out by the households, such asrent, cost of fertiliser, land revenue, and cost of seeds, as well as imputedcosts for inputs owned by the household, such as land and implements. Themean output per day of labour used on the farm is Rs 1.266, varying fromRs 0.799 at the 10th percentile to Rs 1.809 at the 90th percentile.

TABLE 1

SUMMARY STATISTICS

N Mean Std. Dev.

Land owned per adult family male worker (acres) 432 9.53 11.69No. of adult family male workers 432 2.58 1.15Gross cropped area per adult family male worker (acres) 432 11.70 9.98Labour days per acre 432 32.28 10.30Value of output per day of labour (rupees) 432 1.266 0.39Cost of non-labour inputs per day of labour (rupees) 432 1.122 0.33Ratio of hired to family labour 432 0.80 1.51

Empirical testing of the above model would ideally separately test thepredictions for households which hire in labour and those which do not.Unfortunately, the ICAR data merely pertain to what happens on the farm.There is no information on whether or not the household hires out labour.Thus we cannot explicitly identify households which hire out labour. Still itwould appear that we can distinguish the households which hire in labourfrom the rest. However, even this is not possible because of the seasonalpattern of labour demand. In Indian agriculture demand for labour isextremely high during the harvest season. Slight delays in harvesting the

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current crop and planting the new one can be extremely costly. As a result,even households which are net suppliers of labour will hire a few days oflabour in the harvest season.'3 Thus, we do not have a direct way ofdistinguishing the households which are (net) hiring-in labour from others.

Fortunately, our model predicts that across the whole population non-labour inputs per unit of labour and output per unit of labour are increasingin the wealth of the household (see figure 1). In contrast, if factor marketswere perfect, the wealth of the household would have no impact on factorproportions and output per unit of labour. Therefore, we can test for thepresence of market imperfections even without distinguishing householdswhich hire in labour from the rest. The next section of the article reportsresults of empirical testing along these lines.

It should be noted that while the two testable predictions of the modelare tested separately below, they should not be considered independent tests.Under constant returns to scale, if the non-labour inputs per unit of labourare increasing in wealth, output per unit of labour must be increasing inwealth as well.

RESULTS

At the outset it should be noted that the regression results reported belowinclude in all cases dummy variables for the village of the household. Thisis because there are village-specific characteristics such as the type of soil,the main crop, prices and so on, which impact production decisions andoutcomes. The coefficients on the dummies are not reported because thereare too many (18) villages. The reader may be concerned that since apartfrom the village dummies we have only one explanatory variable(household wealth, measured as land owned per family adult male worker)the findings may suffer from omitted variable bias. This issue is addressedat length in the next section of the paper. I also devote a section to addressthe possibility that our results are misleading because the crop mix isdifferent for farmers at different levels of wealth.

The first step is to see whether there is any relationship between wealthand the scale of production, since that is a key premise of the model. Table2 indicates that when a family owns an extra acre per family adult workingmale it typically cultivates an extra 0.52 acres per family adult workingmale. This is an elasticity (at the sample mean) of 0.423. This result isconsistent with the view that even though there are active land lease andcapital markets, these markets do not work well enough to make the scaleof production proportional to family size.

If the scale of production increases with wealth, the extent of reliance onhired labour should also increase. Table 3 indicates that an increase in

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TABLE 2

WEALTH AND THE SCALE OF PRODUCTION (DEPENDENT VARIABLE: GROSSCROPPED AREA IN ACRES PER FAMILY ADULT MALE WORKER)

Explanatory Variable Coefficient

Land owned per family adult male worker

Above coefficient expressed as elasticityR-squaredNF-value, P-valueMean of dep var.

0.52 (t-stat= 15.59)(p-value = 0.0001)

0.4230.56943230.4, 0.000111.69

Note: The regression equation includes dummy variables for 17 of the 18 villages.

TABLE 3

WEALTH AND THE RATIO OF HIRED TO FAMILY LABOUR(DEPENDENT VARIABLE: RATIO OF HIRED TO FAMILY LABOUR)

Explanatory Variable Coefficient

Land owned per family adult male worker

Above coefficient expressed as elasticityR-squaredNF-value, P-valueMean of dep var.

0.078 (t-stat= 13.68)(p-value = 0.0001)

0.930.45843219.41, 0.00010.7984

Note: The regression equation includes dummy variables for 17 of the 18 vi l lages .

family wealth of one acre per family adult male worker leads to an increaseof 0.078 in the ratio of hired to family labour. This is an elasticity of 0.93 atthe sample mean.

We can now turn to the predictions of our model. To recapitulate, in aworld with perfect factor markets there is no reason to expect a relationshipbetween household wealth and factor proportions or output per unit oflabour.14 On the other hand, our model predicts that the use of non-labourinputs per unit of labour and output per unit of labour increase with wealth.

Column 2 of Table 4 reports the impact of wealth on the number ofrupees of non-labour inputs used per unit of labour. When the householdowns an extra acre of land per family adult male worker it uses an additionalRs 0.002 of non-labour inputs per day of labour. The t-statistic is significantand the associated p-value is 0.03. The mean of the dependent variable is

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TABLE 4

WEALTH AND THE VALUE OF NON-LABOUR INPUTS USED PER UNIT OFLABOUR (DEPENDENT VARIABLE: RUPEES OF NON-LABOUR INPUTS USED PER

DAY OF LABOUR)

Explanatory variable

Land owned per adult male family worker (acres)

Land owned per adult male family worker squared

Elasticity at sample meanNo. of observationsR-squaredF-test for model,P-valueMean dep. variable (rupees per day)F-test comparing specification with village dummiesalone with specification which includes a dummyfor each household

Coefficient

0.00212t-stat = 2.15p-value = 0.03

0.0184320.67748.270.00011.1228F126286 = 0.718

Coefficient

0.0055t-stat = 2.79p-value = 0.005-0.000044t-stat = -1.98p-value = 0.0480.03954320.6846.250.00011.1228F 1 2 6 285 = 0.724

Note: The regression equation includes dummies for 17 of the 18 villages.

1.1228; thus at the sample mean the elasticity is 0.018. Column 3 of thesame table reports the same regression, except for the inclusion of a squaredterm. The linear term is now larger, and the squared term enters negatively,which suggests that the impact of wealth slowly tapers off. Both coefficientsare statistically significant; the p-values associated with these coefficientsare very low, 0.0055 for the linear term and 0.048 for the squared term. Theelasticity at the sample mean now works out to 0.0395. Thus we find thatwhile wealth does impact factor proportions the relevant elasticity is verysmall. Using the distinction made by McCloskey [1985], we have acoefficient which is statistically significant, but not economicallysignificant.

Does output produced per unit of labour increase with householdwealth? Column 2 of Table 5 reports results of a regression of output perunit of labour on the land owned per family adult male worker. It turns outthat a household which owns an extra acre of land per family adult maleworker produces an extra Rs 0.0041 of output per unit of labour. Thecoefficient is strongly statistically significant, with a p-value of 0.0003. Atthe sample mean the elasticity works out to 0.0308. Column 3 of Table 5reports the same regression, with a squared term included. Again, the linearterm is much larger, and the squared term enters negatively, suggesting thatthe impact of wealth is larger at lower levels. At the sample mean the

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TABLE 5

WEALTH AND THE VALUE OF OUTPUT PRODUCED PER UNIT OF LABOUR(DEPENDENT VARIABLE: RUPEES OF OUTPUT PER DAY OF LABOUR)

Explanatory Variable Coefficient Coefficient



Elasticity at sample meanNo. of observationsR-squaredF-test for model,P-valueMean dep. variable (rupees per day)F-test comparing specification with village dummiesalone with specification which includes a dummy foreach household

0.0041t-stat = 3.69p-value = 0.0003

0.03084320.69953.290.00011.266F126,286 = 0.7142

0.0078t-stat = 3.51p-value = 0.0005-0.0000485t-stat = -1.92p-value = 0.05530.05174320.70151.010.00011.266


elasticity works out to 0.0517. Thus, again we have statistical significance,but not economic significance.

It might be questioned whether we are justified in assuming constantreturns to scale. Could it be that wealthier fanners produce more output perunit of labour because of increasing returns to scale? The available evidenceindicates that constant returns to scale is a good assumption for traditionalIndian agriculture. Careful econometric tests, such as those carried out byBardhan [1973], have failed to find evidence of increasing returns.

The Problem of Bias due to Omitted Variables

We have only two explanatory variables in the regressions reported above:village dummies (which reflects variables which are common to the village,such as input prices, wages and so on) and household wealth, as measuredby land owned. The reader may be concerned that our results are subject tobias because of omitted variables. For example, suppose that wealthierfarmers are also better farmers. Then they would produce more output perunit of labour used; however, this would not indicate market imperfectionsof any kind. Unfortunately, we cannot include measures which might reflectfarming ability, such as age or education, as explanatory variables, becausewe do not have these data.

There is a standard way of addressing this problem [Hsiao, 1986] whenwe have multiple observations for each household (panel data). We include

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a dummy for each household in the regression equation (the fixed effectsapproach). The coefficient on the dummy variable then reflects the impactof the omitted time-invariant household-specific variables. The question forus is, which is the right specification, one with only village dummies(Tables 4 and 5), or one with household dummies (fixed effects)? Clearly,the fixed effects specification is more general. The specification with onlyvillage dummies is equivalent to running a fixed effects regression, with therestriction that the coefficient on the household dummy is the same for allhouseholds in a village. This restriction can be tested by using an F-test. Thenull hypothesis is that the coefficient on the household dummy is the samefor all households in a village. If the null is rejected, we will have to go witha fixed effects specification. If the null is not rejected our currentspecification is appropriate. The bottom rows of Tables 4 and 5 report thevalues of the F-statistic. These are not statistically significant. Therefore, Iconclude that our current specification is the correct one, and that theestimates are not biased due to the omission of household-specificcharacteristics, such as (say) farming ability.

Are our Results Misleading because Crop-Mix Varies with Wealth?

Thus far our analysis of factor proportions has been for the farm as a whole.The reader may be concerned that the above results are misleadmg becausefarmers at different levels of wealth grow different kinds of crops. Forexample, it could be that for a given crop richer farmers use much morenon-labour inputs per unit of labour than poorer farmers, but for the farm asa whole this ratio is not very different, due to differences in the crop-mix.In order to address this issue I compare factor proportions across wealthlevels for the main crop, that is, wheat. The results of this analysis arereported in Table 6. The results indicate that farmers at different wealthlevels use the same factor proportions in wheat cultivation (the coefficienton wealth is not significantly different from zero), reinforcing ourconclusion that there is no significant resource misallocation.

DISCUSSION AND CONCLUSION

The literature on the agrarian economy of colonial India often portrays land,labour, and credit markets as being very underdeveloped, leading todistortions in resource allocation. There has been little econometricexamination of this issue. In this article I first sketch a model which showshow the distortions in factor markets which are mentioned in the literaturecan lead to distortions in resource allocation. The model predicts thatwealthier households will use more non-labour inputs per unit of labour andwill produce more output per unit of labour. I examine a data-set from the

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FACTOR MARKETS AND RESOURCE ALLOCATION: PUNJAB

TABLE 6

111

WEALTH AND THE VALUE OF NON-LABOUR INPUTS USED PER UNIT OFLABOUR IN WHEAT CULTIVATION (DEPENDENT VARIABLE: RUPEES OF NON-

LABOUR INPUTS USED PER DAY OF LABOUR)

Explanatory Variable



Elasticity at sample meanNo. of observationsR-squaredF-test for model,P-valueMean dep. variable (rupees per day)F-test comparing specification with village dummiesalone with specification which includes a dummy foreach household

Coefficient

0.000631t-stat = 0.14p-value = 0.88

0.0044140.1854.980.00011.41F125 270 = 0.91

Coefficient

0.0058t-stat = 0.64p-value = 0.52-0.000068t-stat = -0.66p-value = 0.51-0.00894140.1864.740.00011.41F125,269 = 0.9118


Punjab province which provides information on resource-use at the farmlevel and find that, consistent with the predictions of the model, wealthierhouseholds use more non-labour inputs per unit of labour, and produce moreoutput per unit of labour. The effects are, however, very small and not ofmuch economic significance.

What are the implications of our results for the larger literature onresource allocation in poor agrarian economies? How do we account for thefact that while the theoretical model is supported by the data, the effectsidentified are so small? This could be because technology in Punjabiagriculture in the 1930s was relatively simple. One might expect that thedifferences in labour productivity between rich and poor farmers would behigher in, say, the Green Revolution areas where there is heavy reliance onpurchased inputs such as chemical fertiliser, pesticides, etc. and agricultureis more mechanized. In such an environment poorer farmers who haveinferior access to capital and hence use less modern inputs and techniquesmight be much less productive than richer farmers.

In fairness it should be mentioned, however, that the sample selectionprocedure which was used to collect the ICAR data could have affected ourresults. The smallest farms, which were cultivated by the poorest

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households, are not in the sample; it could be that these poorest farmerscultivated with much higher labour intensity than the larger farmers. Subjectto this caveat, I conclude, in contrast with much of the existing literature,that factor markets in colonial Punjab worked fairly well, allocatingresources fairly efficiently.

final version received July 1997

NOTES

1. Perhaps the most comprehensive source is the Cambridge Economic History of India, Vol.1,edited by Dharma Kumar [1982].

2. In the context of rural Punjab, the same broad perspective also informs the work of Hamid[1982].

3. The findings of Bardhan [1973], Benjamin [1991] and Deolalikar and Vijverberg [1987],however, do not support the view that hired labor is less productive than family labour.

4. If most of the labour is provided by the family, the hired labour will need little explicitmonitoring, since family members will watch the hired labourer who is working alongside.On the other hand, if most of the labour is hired, supervision may be necessary.

5. Eswaran and Kotwal [1986] make a similar assumption with the same justification.6. Of course, not every household will be borrowing money. For households which use their

own funds 'i' can be interpreted as the opportunity cost of funds.7. I assume that hired labor can be paid at the end of the season (when the final output is

available), so that money does not have to be borrowed for this purpose. This is only asimplification and the predictions of the model do not hinge on this.

8. In reality because of the seasonal nature of labour demand even households which are netsuppliers of labour to the market may hire in labour in the harvest season, when there aretasks to be accomplished with great urgency and family members are working 16 hour days.This does not alter the analytics of the model, but it does complicate the empirical testing. Idiscuss this later below.

9. I am grateful to Sumit Guha for suggesting this.10. The ICAR data provide information only on the gross cropped area, not the physical area of

the farm. Thus, if a piece of land was cropped twice during the year it would be countedtwice. Bhattacharya [1986: Ch.2] suggests that cropping intensity in Jullunder variedbetween 1.2 and 1.7. In the exercise discussed above, where I compare the BEIP and ICARdata, I have divided gross cropped area for Jullunder (for each household) by 1.4, in order toobtain an approximation of the net sown area or physical area of the farm. In Gurdaspur andLyallpur double cropping was less frequent, and I simply use the gross cropped area as anapproximation for the physical size of the farm. These approximations are not used in the restof this article; they are used merely to help us understand the sample selection issue.

11. According to the Report of the All-India Rural Credit Survey, conducted in 1951-52, 65.6per cent of household wealth in the Punjab region was land.

12. Gross cropped area is different from the physical area of the farm because if, say, a piece ofland was cropped twice during the year, it would be counted twice.

13. In her well-known work, Production Conditions in Indian Agriculture, Bhardwaj [1974: 26]writes: 'In harvesting periods, in order to ensure that the yield is not adversely affected,operations may have to be conducted within a very short period. This urgency to complete acertain volume of work may necessitate the hiring in of labor even on small farms ... '.

14. Unless wealthier households are (say) better farmers. I discuss and rule out this possibility ata later point.

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REFERENCES

Amin, S., 1984, 'Small Peasant Commodity Production and Rural Indebtedness: The Culture ofSugarcane in Eastern U.P., c. 1880-1920', in R. Guha (ed.), Subaltern Studies 1: Writings onSouth Asian History and Society, New Delhi: Oxford University Press.

Bardhan, P.K., 1973, 'Size, Productivity, and Returns to Scale: An Analysis of Farm-level Datain Indian Agriculture', Journal of Political Economy, Vol.81, No.16, pp. 1370-86.

Benjamin, D., 1991, 'Household Composition, Labour Markets, and Labour Demand: Testing forSeparation in Agricultural Household Models', Econometrica, Vol.60, No.2, pp.287-322.

Bhardwaj, K., 1974, Production Conditions in Indian Agriculture, Cambridge: CambridgeUniversity Press.

Bhardwaj, K. 1985, 'A Note on Commercialization', in Raj et al. [1985].Bhattacharya, N., 1983, 'The Logic of Tenancy Cultivation: Central and South-east Punjab,

1870-1935', Indian Economic and Social History Review, Vol.20, No. 2, pp.121-69.Bhattacharya, N., 1985, 'Agricultural Labour and Production: Central and South-east Punjab,

1870-1940', in Raj et al. [1985].Bhattacharya, N., 1986, 'Agrarian Change in Punjab, 1880-1940', unpublished Ph.D. dissertation,

Jawaharlal Nehru University, New Delhi.Board of Economic Inquiry, Punjab, 1928, Size and Distribution of Cultivators ' Holdings in the

Punjab, Rural publication No.4.Calvert, H., 1922, The Wealth and Welfare of the Punjab: Being Some Studies in Punjab Rural

Economics, Lahore: Civil and Military Gazette Press.Carter, M. and J. Kalfayan, 1989, 'A General Equilibrium Exploration of the Agrarian Question',

Mimeo, Department of Agricultural Economics, University of Wisconsin-Madison.Darling, M., 1978 [1945], The Punjab Peasant in Prosperity and Debt, New Delhi: South Asia

Books.Deolalikar, A. and W. Vivjerberg, 1987, 'A Test of Heterogeneity of Family and Hired Labour in

Asian Agriculture', Oxford Bulletin of Economics and Statistics, Vol.49, No.3, pp.291-305.Eswaran, M. and A. Kotwal, 1985, 'A Theory of Contractual Structure in Agriculture', American

Economic Review, Vol.75, No. 3, pp.162-77.Eswaran, M. and A. Kotwal, 1986, 'Access to Capital and Agrarian Production Organization',

Economic Journal, Vol.96, No.382, pp.482-98.Feder, G., 1985, 'The Relationship Between Farm Size and Farm Productivity: The Role of

Family Labour, Supervision, and Credit Constraints', Journal of Development Economics,Vol.18, No.2, pp.297-313.

Frisvold, G.B., 1994, 'Does Supervision Matter? Some Hypothesis Tests Using Indian Farm-level Data', Journal of Development Economics, Vol.43, No.2, pp.217-38.

Griffin, Keith B., 1974, The Political Economy of Agrarian Change: An Essay on the GreenRevolution, Cambridge, MA: Harvard University Press.

Hamid, N., 1982, 'Dispossession and Differentiation of the Peasantry in the Punjab DuringColonial Rule', Journal of Peasant Studies, Vol.10, No.1, pp.52-72.

Hsiao, C., 1986, Analysis of Panel Data, Cambridge: Cambridge University Press.Imperial Council of Agricultural Research (ICAR), 1938, Report on the Cost of Production of

Crops in the Principal Sugarcane and Cotton-Producing Tracts in India, Vol.1 andSupplement, Simla: Government of India Press.

Kumar, D. (ed.), 1982, The Cambridge Economic History of India, Volume II: C.1757-C.1970,Cambridge: Cambridge University Press.

McCIoskey, D., 1985, 'The Loss Function Has Been Mislaid: The Rhetoric of SignificanceTests', American Economic Review, Vol.75, pp.201-5.

Mukherjee, M., 1980, 'Some Aspects of Agrarian Structure of the Punjab, 1925-47', Economicand Political Weekly, Vol.25, pp.A46-A58.

Raj, K.N. et al. (eds.), 1985, Essays on the Commercialization of Indian Agriculture, New Delhi:Oxford University Press.

Sen, A., 1981, 'Market Failure and Control of Labour Power: Towards an Explanation ofStructure and Change in Indian Agriculture: Part I', Cambridge Journal of Economics, Vol.5,No.3, pp.300-50.

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APPENDIX

For the farm as a whole the ICAR provides the figure for 'Total cost' and figures for the cost ofhired labour and the imputed cost of family labour. I compute the cost of non-labour inputs bysubtracting hired labour costs and the cost of family labour from the total costs.

The ICAR report provides the detailed breakdown of the components of total costs for the'principal crops'. From this one can find out all the components that go into the cost of non-labour inputs. They are the following:

Cost of Bullock Labour: This was computed by adding up the following components, which werecalculated separately:

(a) cost of feed for the bullocks;

(b) depreciation, charged at 12 per cent on the estimated value of the bullocks in the first year ofthe enquiry;

(c) interest on capital embodied in the bullocks, charged at 8 per cent;

(d) cost of housing;

(e) miscellaneous;

(f) cost of bullock labor and human labour used for the maintenance of working bullocks, forexample, cleaning sheds, bringing feed from the city or from the fields;

(g) loss due to death, if any;

(h) from these items were deducted the value of manure obtained from bullocks and any receiptsobtained from hiring out the bullocks, to arrive at the net cost.

Cost of Marketing: This includes costs of preparation for the market and transportation to themarket.

Cost of Seeds: If purchased the actual cost was taken down. If grown on the farms, a price wasimputed.

Cost of Fertiliser: Farmyard manures were charged at values on the spot if possible, else at theirvalue at the nearest point where they could be sold, less cost of transport to that point. Greenmanures were valued at cost of production. Artificial fertilisers were assigned costs at the actualamount paid. For farmyard and green manures, different provinces were allowed to use theirdiscretion to allot a certain amount of the value to succeeding crops, since these organic manureshave effects on the soil beyond the current season.

Implement Charges: The cost of each implement or group of implements was calculated from allexpenses incurred during the year, including repairing, oiling, labor of upkeep, depreciation, andinterest. The interest was the amount that could have been earned had the capital embodied in theimplements on the farm been loaned out. If an implement was used on more than one crop, thecost was distributed between the crops on the basis of the bullock working days for each crop.

Irrigation rates: Payment per acre for canal water used.

Cost of Lifting Water: These are imputed interest and depreciation charges on the well.

Interest on working capital: No explicit definition is provided. Since we know that interest onequipment, value of bullocks, and the well are already included in 'cost of bullock', 'implementcharges', and 'cost of lifting water', respectively, this figure must include only the imputedinterest on expenditure on working capital in the sense of variable capital.

Cesses: Usually the headman of the village had to be given five per cent of the land revenue.

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Twelve per cent of the land revenue was also given to the District Board, which performed thefunctions of rural administration in the district.

Land Revenue: The major land tax. The tax was based on an assessment of the productivity of theland.

Rent: Rent of land leased in.

Rental value of owned land: An imputed figure for the amount that may have been earned, hadthe cultivator leased out owned land under self-cultivation.

General charges: Miscellaneous expenses.

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factor markets and resource allocation in colonial punjab

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