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Trends in Agricultural Wages in India

This paper examines the trends in agricultural wages in India from 1964-65 to 1999-2000, using data from Agricultural Wages in India and Rural Labour Enquiry, after dealing with the limitations of the AWI data. The trends show that there was a slowdown in the rate of growth of real daily wages of male and female agricultural labourers in more than half of the districts in the sample in the 1990s. Earlier, there was a striking rise in the growth of daily real earnings across all states between 1983 and 1987-88. Second, there was a rising trend in the variations in real wages across districts in the 1990s. Third, the differences between the average wages of male and female agricultural labourers have widened over the years. Fourth, the daily wages of male agricultural labourers exceeded the minimum wage levels in most states, while those of women were below the minimum in most states.

Special articles

Trends in Agricultural Wages in India 1964-65 to 1999-2000

This paper examines the trends in agricultural wages in India from 1964-65 to 1999-2000, using data from Agricultural Wages in India and Rural Labour Enquiry, after dealing with the limitations of the AWI data. The trends show that there was a slowdown in the rate of growth of real daily wages of male and female agricultural labourers in more than half of the districts in the sample in the 1990s. Earlier, there was a striking rise in the growth of daily real earnings across all states between 1983 and 1987-88. Second, there was a rising trend in the variations in real wages across districts in the 1990s. Third, the differences between the average wages of male and female agricultural labourers have widened over the years. Fourth, the daily wages of male agricultural labourers exceeded the minimum wage levels in most states, while those of women were below the minimum in most states.

PALLAVI CHAVAN, RAJSHREE BEDAMATTA

T
his paper analyses the trends in agricultural wages in India between 1964-65 and 1999-2000, with focus on the trends in the 1990s. It also critically discusses the methodology used for collecting data on agricultural wages by the major secondary data sources in India. In the context of this discussion, the paper attempts a different approach to construct comparable time series on agricultural wages across states and districts.

In Section I of this paper, we discuss the major sources of secondary data on agricultural wages. We compare these data sources on the basis of the definitions they use and the methodology they employ to collect data on agricultural wages. In Section II, we review the methods of analysis used by some of the earlier studies on agricultural wages. In Section III, we first discuss the trends in nominal and real agricultural wages in India between 1964-65 and 1999-2000 using data from Agricultural Wages in India (AWI), the serial publication of the government of India. These series are worked out at the district level for 46 districts chosen from all 17 states covered by AWI.1 We then analyse the data on earnings of agricultural labourers from the periodic surveys of the National Sample Survey Organisation (NSSO), and published in the Rural Labour Enquiry (RLE). Data for this sub-section are drawn from seven rounds of RLE from 1963-65 to 1999-2000 for the same 17 states considered for the analysis of the AWI data. In Section IV, we provide our concluding comments.

This paper does not attempt to identify and explain the factors determining agricultural wages in India. Our objective is to construct and analyse wage data series that are comparable over the long run, taking into account the problems associated with the existing database on agricultural wages.

I Major Secondary Data Sources on Agricultural Wages in India: Definitions and Methodology

Data on agricultural wages in India were not collected on a regular basis till 1950.2 From 1951-52 onwards, the directorate of economics and statistics (DES) under the ministry of food and agriculture started regular collection of state-level data on agricultural wages, and their publication in two journals, Agricultural Situation in India (monthly) and Agricultural Wages in India (annual).

Apart from AWI, the Rural Labour Enquiry (RLE) is another important source of data on the earnings of agricultural labourers. The RLE surveys were integrated with the employment and unemployment surveys of the NSSO since 1977-78. Also, since 1996-97, the NSSO has initiated a new survey to collect monthly data on agricultural wages. These data are presented in a publication tilted Wage Rates in Rural India.3

Agricultural Wages in India

AWI, as already mentioned, has been the prime source of data since 1951-52 for time series analyses of agricultural wages. The DES collects data on agricultural wages from state governments using a standard proforma. The AWI provides monthly averages of daily wages of unskilled casual labourers. The wage is expected to reflect both cash payment and monetary equivalent of the payment made in kind. This wage is either reported as a consolidated wage rate for “field labour”, or reported separately for different agricultural operations, such as “ploughing”, “sowing”, “weeding”, “reaping and harvesting”, along with wages for “other agricultural labour” and “herdsman”.

Though AWI is the only source of its kind providing extensive data on agricultural wages, it suffers from certain limitations.

The first limitation is regarding the way “wage” is defined. As per the instructions given to states by the DES, “wage” is expected to be the “most commonly current” wage during a given month. It is evident that this definition of wage needs more clarity. This definitional problem was first highlighted by Rao (1972) in a pioneering paper on the credibility of data from AWI.

Second, the error in reporting is likely to increase further, as the reporting agencies for AWI do not include trained investigators, unlike those employed by the NSSO. The reporting agencies often include ‘patwaris’, primary school teachers, ‘mamlatdars/mahalkaries’ and Firka Development Officers, depending upon the structure of administration in each state. Moreover, choice of the data collecting agency is left to the respective state governments.

Third, there is no standardisation of the data collection procedures across states. Every state is instructed to collect wage data from each district. It is also specified that if a state cannot collect data from all districts then, at least one in every five districts may be selected, representing the various agricultural regions. Further, in each district, one “centre” (village), representative of the general agricultural conditions of that district, may be selected. However, the methodology for the selection of centres is left to the discretion of the state authorities. Moreover, despite the directions given above, we find that for several states, AWI covers a very small number of districts. Further, the number of districts covered can vary widely from year to year for a given state.

The fourth problem with the AWI data is the small sample size of villages (often one centre/village per district), which may be inadequate to capture the agricultural employment situation in a given district.4

Fifth, there have been a number of changes in centres/villages used for collecting data in AWI over the years.5 AWI is based on data canvassed from one centre per district. The replacement of one centre by a new one becomes a serious handicap while analysing wage data from AWI, as it can bring about an abrupt shift in the level of wages.

Over time, a change in the centre for data collection may become necessary on account of different factors. However, as found by Baby (1996) for Kerala, if a centre has to be changed, the bureau of economics and statistics in the state has to be informed. The bureau is then expected to conduct a local enquiry and decide whether such a change is necessary. Further, wage data from the new centre have to be collected for a period of six months. Only after ensuring that the wage rate does not differ significantly between the new and the old centres, the bureau is expected to approve the change in centre. However, AWI publications show that no such procedure is followed while changing centres. We found frequent changes in centres for a given district in consecutive years, with a particular centre being replaced by a new one during one year, and the earlier centre reappearing in the subsequent year. Table 1 gives few of the changes in centres noted across states for certain years, and the resulting changes in wage rates for those years. This table clearly shows that continuity in centres is necessary for a meaningful analysis of the AWI data.

Notwithstanding the above-mentioned limitations, Rao (1972) argued that wage data from AWI can be used for long-term trend analysis of agricultural wages. Based on his reliability tests, Rao pointed out that the AWI data can be safely used for crosssectional investigations across regions. The use of AWI data in this paper is primarily to analyse wage trends across districts located in different agro-economic regions within states (see Section II for details).

Rural Labour Enquiry

RLE is an important source of information on the socioeconomic characteristics of rural labour households in general and agricultural labour households in particular. The first two surveys in this series were conducted in 1950-51 and 1956-57, and were called the Agricultural Labour Enquiry (ALE), as they furnished data only on agricultural labour households. It was from 1963-65 that the scope of this survey was extended to all rural labour households.6 From 1977-78, the NSSO took over the task of conducting these surveys from the ministry of labour.

RLE reports statewise earnings from various agricultural operations, namely, “ploughing”, “sowing”, “weeding”, “transplanting”, “harvesting” and “other agricultural operations”, along with a consolidated earnings figure for all operations for casual male and female agricultural labourers. RLE is expected to provide a reliable estimate of agricultural wage earnings, as the data are collected from a sample of agricultural labourers.7

RLE has a clear advantage over the AWI database in terms of standardisation of the sampling procedure and a relatively large sample size of villages. For the RLE of 1993-94, for example, the total number of villages surveyed across the country was 6,951. As against this, the AWI of 1993-94 canvassed only about 291 villages. Notwithstanding these advantages, data from RLE are available only with a gap of five to seven years and not on a continuous yearly basis.

Table 1: Change in Centres and Daily Wage Rates, State andDistrict-wise, AWI

State – District Daily Wage Rate

Year (Centre) Men Women

Rajasthan – Bundi district

1990-91 (Mendi) – Rs 12 1991-92 (Daulada) Rs 25 (+108 per cent)

Tamil Nadu – Nilgiris district

1989-90 (Gudalur) Rs 16 – 1990-91 (Munanadu) Rs 30 (+87.5 per cent)

Gujarat – Bhavnagar district

1988-89 (Talaja) Rs 12 Rs 10 1989-90 (Kundla) Rs 30 (+150 per cent) Rs 20 (+100 per cent)

Gujarat – Panch Mahal district

1988-89 (Kalaul) 1989-90 (Dohad) Rs 20

Rs 12 (-40 per cent) – Gujarat – Bharuch district 1983-84 (Bharuch) Rs 9 – 1984-85 (Jagadia) Rs 5.5 (-39 per cent) Himachal Pradesh – Mandi district 1983-84 (Sadar) – Rs 10 1984-85 (Sandhaul) Rs. 15 (+50 per cent)

Notes: (1) For each centre, wage in the first year refers to the last monthly wage available for that year, while wage in the second year refers to the first monthly wage available for that year.

(2) Figures in brackets indicate percentage change over the wage

rate noted for the previous centre. Source: AWI, various issues.

Table 2: Studies on Agricultural Wages in India – Details on Methods of Analysis

Study (Year) Area of Study Data Source Operation for Which Calculation of Calculation of Use of Deflator
(Period of Study) Wage Was Calculated Yearly Average Region/State Average
Wage Rate Wage Rate

Rath and Bombay, Madras, Farm Management Simple average of wage na na Cereal prices in Joshi (1966) Punjab, West Bengal, Survey Reports rate for all the operations the nearest market UP (1955-57)

Krishnaji (1971) India – state-wise AWI Field labour – men. In Wage rate for the Weighted average of Consumer price (1956-57 to 1964-65) case wage rates were not peak season month district-level wage rates, index for for men available for field labour was taken as the weights being the agricultural

category, simple average yearly wage rate proportion of population of labourers of the wages for different male agricultural labourers operations was taken in each district as calculated

from the Census of India Bhalla (1979) Punjab (1961-77) Punjab Statistical Ploughing, sowing, n a n a Consumer price

Abstracts weeding, harvesting, other index for agricultural labour, cotton agricultural picking labourers

Jose (1974) India – state-wise AWI and data Field labour – men. In Wage rate for the Weighted average of Consumer price (1956-57 to 1971-72) personally collected case wage rates were peak season month district-level wage rates, index for for men from Krishi Bhavan, not available for field was taken as the weights being the proportion agricultural

New Delhi labour category, simple yearly wage rate of population of male labourers average of the wages for agricultural labourers in different operations each district as calculated was taken from the Census of India

Parthasarathy Andhra Pradesh Season and crop Field labour – men Simple average of Simple average wage Consumer price and Adiseshu (1958-59 to 1978-79) reports, government monthly wage rates rate for all districts index for (1982) for men of Andhra Pradesh agricultural

labourers Jose (1988) India – state-wise AWI Field labour – men and Simple average Weighted average of Consumer price

(1970-71 to 1984-85) women. If wage for field of monthly wage district-level wage rates, index for labour was not given, for rates weights being the agricultural men, wage for ploughing proportion of population labourers was taken. If wage for of male and female ploughing was not available, agricultural labourers in then order of preference each district as calculated was sowing, weeding, from the Census of India harvesting, other agricultural operations. For women, priority was given to sowing operations, order of other preferences being same as men.

Acharya (1989) India – state-wise – AWI and unpublished Field labour – men and Simple average Weighted average of Consumer price NSSO region-wise data from State women. In case wage rates of monthly wage district-level wage rates, index for (1970-71 to 1984-85) Bureaus of for field labour were not rates weights being the mean agricultural

Economics and available, simple average population of male and labourers Statistics of the wages for different female agricultural

operations was taken labourers in each district as calculated from the Census of India

Baby (1996) Kerala (1960-61 to AWI Field labour – men Simple average of – Rice prices and

1989-90) – for men monthly wage rates Consumer price index for agricultural labourers

Parthsarathy India – District AWI Field labour – men. In Simple average of – Consumer price

(1996) and centre-wise case wage rate for field monthly wage rates index for (1985-86 to 1993-94) abour was not available, agricultural for men then sowing was considered. labourers

Harvesting wage too was considered separately Haque (1998) India – state-wise AWI n a n a n a Consumer price

(1970-71 to 1994-95) index for agricultural labourers

Sharma (2001) India – state-wise RLE n a – – Consumer price

(1974-75 to 1993-94) index for agricultural labourers

Sarmah (2002) India – state-wise – AWI and Economic Field labour for Andhra Simple average of Weighted average of Consumer price NSSO region-wise Survey Pradesh, Karnataka and monthly wage rates district-level wage rates, index for (1985-86 to 1994-95) Maharashtra; Simple weights being the agricultural

average of operation-wise proportion of population labourers

wage rates for other states of male and female agricultural labourers in each district as calculated from the Census of India

na - information not cited in the paper. Source: Adapted from Ramakumar (2004).

Economic and Political Weekly September 23, 2006

II Studies on Agricultural Wages in India: Observations on Methods of Analysis

Most of the studies on agricultural wages in India have used data from AWI. Some have made use of the data from RLE, while very few studies in the past have used wage data from the Studies of Economics of Farm Management (SEFM) or cost of cultivation surveys. In this review, we look at only those studies whose primary objective was to analyse long-term trends in real agricultural wages. The studies have been summarised in Table 2.

Most of the studies mentioned in Table 2 have a few methodological problems. The first problem relates to the aggregation of wage rates across districts to arrive at a state or regional wage rate. These studies calculate an average wage rate for a state/region from the districts that are covered from the given state in that particular year. However, the sample of districts covered under AWI has changed frequently for many states from year to year. Further, as already illustrated, the sample of centres covered in every district has also changed frequently, and has been responsible for substantial shifts in the wage rates. Given these constraints, data from AWI cannot be used for calculating aggregate agricultural wages at the state-level, and then comparing these wages across states. This implies that a meaningful analysis of wage data from AWI is possible only at the district level.

Second, the choice of peak season wage rate while working out a yearly wage rate, as was done in Krishnaji (1971) and Jose (1974), may be inappropriate given the intra-year variations in wage rates. Wages can differ considerably within a given year across various agricultural seasons. The selection of the peak season wage rate can therefore result in an over-estimation of wage rate for a given year.

Third, taking an average of all operations for working out a consolidated wage rate can also generate an erroneous data series, as there are missing observations for certain operations.8 Missing observations are commonly encountered in the AWI database. As a result, it is necessary to maintain consistency both in centres and operations, while generating a long-term wage series from AWI.

Methodology Used in Present Study

Given the definitional and methodological limitations with the AWI dataset noted in the earlier section, it is necessary to devise a method of analysis that would take care of these limitations. The present study is an attempt in this direction.

In this paper, we have used data on male and female agricultural wages from AWI from 1964-65 to 1999-2000 – the latest available issue of AWI – for all 17 states reported in AWI. Given the problems with aggregation discussed earlier, we have not aggregated wages across various agricultural occupations. We have used the wage rate reported for “field labour”. States for which such a consolidated wage is not available, we have consistently taken the wage for “ploughing” for men, given that ploughing is primarily performed by men. In the case of women, wherever wage for “field labour” is not available, we have used the wage for “weeding”, as weeding is primarily performed by women. We have worked out simple annual averages of the monthly wage rates to arrive at annual wages.

We have used two price deflators: (a) the retail cereal prices (of rice or wheat depending upon the importance of either of these cereals in the staple diet of a given state) in the market nearest to a given district, as reported in Agricultural Prices in India and (b) the general Consumer Price Index for Agricultural Labourers (CPIAL).9 We believe that cereal price is a useful deflator to calculate real agricultural wage rates given the importance of food in the consumption basket of agricultural labourers. Besides, using state-specific CPIAL implies applying the same price index for every district within a given state.

We have used data from AWI only for a district level analysis of agricultural wages, where the districts chosen are those, which consistently have the same centre throughout the period of analysis.

Table 3: Classification of Districts by Average Real Daily Wage of Male Agricultural Labourers, Deflated by CPIAL, AWI, in Rupees

Wage intervals (Rs) 1964-65 to 1969-70 1970-71 to 1979-80 1980-81 to 1989-90 1990-91 to 1999-2000

5 ≤ wage < 10 Bihar (1, 3), Maharashtra (1,2), Bihar (3), Assam (2,3), Karnataka (1,2,3), Orissa (1,3), Karnataka (2), Gujarat (1), Karnataka (1,3), Gujarat (1,3), Maharashtra (1,2,3), Karnataka (1,3), Tamil Nadu (1,2), Tamil Nadu (1,2) WB (1,2,3), Orissa (1), Gujarat (1,3), West Bengal (2,3), Madhya Pradesh (1,2,3) Tamil Nadu (1,2), Orissa (1,2,3), Tamil Nadu (1,2), Andhra Pradesh (1,2,3), Andhra Pradesh (1,2,3), Rajasthan (2), Uttar Pradesh (1,2), Uttar Pradesh (1,2), Tripura, Tripura, Madhya Pradesh (1,2,3) Madhya Pradesh (1,2,3)

10 ≤ wage < 20 Bihar (2), Himachal Pradesh (1,2,3), Bihar (1,2), Himachal Pradesh (2,3), Bihar (1,2,3), Bihar (1,2,3), Assam (1,2,3), Kerala (1,2,3), Assam (1,2,3), Kerala (2,3), Himachal Pradesh (2,3), Himachal Pradesh (3), Maharashtra (3), Karnataka (2), Maharashtra (2,3), Karnataka (1), Assam (1,2,3), Kerala (2,3), Assam (1,2,3), Gujarat (2), Orissa (2), Gujarat (1,2,3), West Bengal (1,2,3), Maharashtra (2,3), Maharashtra (1,2,3), Punjab (2), Haryana (1,2,3), Orissa (2), Andhra Pradesh (1,2,3), Karnataka (1), Gujarat (1,2,3), Karnataka (1,2,3), Gujarat 1,2,3), Rajasthan (1) Punjab (2), Haryana (2), West Bengal (1,2,3), Orissa (2), ( West Bengal (2,3),

Rajasthan (1,2), Andhra Pradesh (1,2,3), Orissa (1,2,3), Tamil Nadu (1,2),

Uttar Pradesh (1,2), Tripura Punjab (2), Haryana (2), Andhra Pradesh (1,2,3), Rajasthan (1,2), Rajasthan (1,2), Uttar Pradesh (1,2), Tripura Uttar Pradesh (1,2), Tripura,

Madhya Pradesh (1,2,3) 20 ≤ wage < 30 Punjab (1,3) Himachal Pradesh (1), Kerala (1), Kerala (2), Punjab (1,3), Himachal Pradesh (2), Punjab (1,3), Haryana (1,3) Haryana (3) Kerala (2), West Bengal (1),

Punjab (1,2,3), Haryana (2,3) 30 ≤ wage < 40 – – Kerala (3), Haryana (1) Kerala (3), Haryana (1) 40 ≤ wage< 50 – – Kerala (1) Kerala (1) 50 ≤ wage< 60 – – Himachal Pradesh (1) Himachal Pradesh (1)

Note: Real wages have been deflated using CPIAL at the base of 1986-87. Sources: AWI, various issues; Indian Labour Journal, various issues.

Economic and Political Weekly September 23, 2006 We have chosen 46 districts (about 3 districts per state). Of the 46 districts in our sample, 40 districts have the same centres, and in the remaining six districts, centre changes have occurred only once in the reference period.10 For some states, the changes in centres were so frequent across all districts that we had to bring down our original sample size per state from three districts to two districts. Secondly, we have followed the NSSO region classification for selecting our study districts. We have tried to choose districts from different agro-economic regions within a given state. Appendix 1 gives a list of all districts with their centre names.11

We have used a comparable period of analysis for the RLE data on earnings to that of the AWI data, from 1964-65 to the latest RLE of 1999-2000. The RLE data have been collected for the same 17 states used in the case of AWI. We have taken the consolidated figures on agricultural labour earnings for men and women, reported in the RLE.

III Analysis of Agricultural Wages

AWI: A District-Level Analysis of Agricultural Wages

Between 1990-91 and 1999-2000, among all the sample districts, average nominal wage of male agricultural labourers was the highest for a district in Himachal Pradesh, which we have denoted as Himachal Pradesh (1), closely followed by Kerala (1), Kerala (3), Haryana (1) and Punjab (1).12 While the wage rates in districts from Himachal Pradesh, Haryana and Punjab had been relatively high since 1964-65, this was not the case with the districts from Kerala, (3). There was a sharp increase in the wage rates for the Kerala districts during our reference period, which put them in the top bracket by the 1990s. Between 1990-91 and 1999-2000, the district that recorded the lowest level of wage for men was Gujarat (1), closely followed by Madhya Pradesh (3), and all three districts from Orissa.

The ranking of districts based on average female wage during the period of 1990s, was broadly similar to that of male labourers; at the top were Haryana (1), Kerala (3), and Himachal Pradesh (1) and (2). A relatively low wage rate for female labourers was noted for Madhya Pradesh (1), Gujarat (1) and Andhra Pradesh (1).

Levels and Growth of Real Agricultural Wages13

As noted earlier, we have used two deflators to work out the real wage series, retail cereal prices and CPIAL. We found a rising trend in both male and female real wages in the 1980s for majority of the districts (based on both deflators).14 For some districts, real wages peaked in the second half of the 1980s or even in the first few years of the 1990s, and thereafter, showed a declining trend. For some others, the real wages reached a peak in the 1980s, and after undergoing a fall for few years in the initial years of the 1990s, recovered again to a level close to the earlier peak wage rate. For the remaining majority, however, real wages showed a rising trend during the 1980s that continued in the 1990s.

The gap in the real wages of male and female agricultural workers appeared to have widened over years for most districts.

Table 5: Coefficient of Variation in Real Daily Wages of Maleand Female Agricultural Labourers, Deflated by CPIAL, forDistricts Covered in the Sample, AWI

(In per cent)

Category Coefficient of Variation

Wages for Male Wages for Female
Agricultural Labourers Agricultural Labourers
1964-65 39.4 43.0
1970-71 42.4 43.5
1980-81 42.3 44.3
1990-91 26.0 26.7
1991-92 42.0 26.6
1992-93 41.7 30.2
1993-94 39.8 29.0
1994-95 40.7 28.8
1995-96 51.3 34.7
1996-97 49.0 30.2
1997-98 69.8 56.2
1998-99 57.3 35.5
1999-2000 51.8 50.1

Note: Real wages have been deflated using CPIAL at the base of 1986-87. Sources: AWI, various issues; Indian Labour Journal, various issues.

Table 4: Classification of Districts by Average Real Daily Wage of Female Agricultural Labourers, Deflated by CPIAL, AWI, in Rupees

Wage intervals (Rs) 1964-65 to 1969-70 1970-71 to 1979-80 1980-81 to 1989-90 1990-91 to 1999-2000

5 ≤ wage < 10 Bihar (1,2,3), Assam (1,3), Bihar (3), Assam (1,3), Andhra Pradesh (1,2), Andhra Pradesh (1,2), Kerala (1,2,3), Maharashtra (1,2,3), Kerala (1,2,3), Maharashtra (1,2,3), Assam (3), Karnataka (1,2,3), Bihar (1,2), Assam (2), Karnataka (1,2), Gujarat (1,3), Karnataka (1,3), Gujarat (1,3), Maharashtra (1,2,3), Maharashtra (2), Gujarat (1,3), WB (1,2,3), Orissa (1,2), West Bengal (1,2,3), Orissa (1,2,3), Orissa (1,2,3), West Bengal (3), Orissa (1,2), Tamil Nadu (1,2), Tamil Nadu (1,2), West Bengal (1,2,3), Tamil Nadu (1,2), Andhra Pradesh (1,2,3), Andhra Pradesh (1,2,3), Orissa (1,2,3), Tamil Nadu (1,2), Rajasthan (2), Rajasthan (1,2), Haryana (2,3), Haryana (1,2,3), Rajasthan (1,2), Madhya Pradesh (1,2,3) Madhya Pradesh (1) Uttar Pradesh (1,2), Tripura, Uttar Pradesh (1,2), Tripura, Madhya Pradesh (1,2,3) Madhya Pradesh (1,2,3)

10 ≤ wage < 20 Madhya Pradesh (1,2,3), Bihar (1,2), Bihar (1,2,3), Bihar (2), Assam (2), Karnataka (2), Himachal Pradesh (1,2,3), Madhya Pradesh (1,2,3), Himachal Pradesh (2,3), Gujarat (2), Punjab (1,3), Kerala (2), Gujarat (2), Assam (1,2), Kerala (1,2,3), Assam (3), Kerala (1,3), Haryana (1) Haryana (1) Gujarat (1,2,3), West Bengal (1), Maharashtra (1,3),

Andhra Pradesh (3), Karnataka (1,2,3), Gujarat (2), Punjab (1,2,3), Haryana (1,2,3), West Bengal (1,2), Orissa (3), Rajasthan (1,2), Andhra Pradesh (3), Uttar Pradesh (1,2), Tripura Haryana (2), Rajasthan (1),

Uttar Pradesh (1,2), Tripura, Madhya Pradesh (2,3) 20 ≤ wage < 30 – Punjab (1,3) – Himachal Pradesh (1), Kerala (2), Haryana (1,3)

Note: Real wages have been deflated using CPIAL at the base of 1986-87. Sources: AWI, various issues; Indian Labour Journal, various issues.

Economic and Political Weekly September 23, 2006 Tables 3 and 4 present the classification of districts based on the levels of CPIAL-deflated wage rates for male and female labourers respectively. They show that across all districts, the real wage received by an average female agricultural worker was lower than the real wage received by a male agricultural worker. There was definitely a rise in the level of real wages across districts for male workers, as shown by the shift of districts to higher wage intervals between 1964-70 and 1990-2000. This shift, however, was far less pronounced in the case of real wages of female agricultural workers. Even in the 1990s, most districts continued to be in the lower wage intervals in the case of female workers.

There was a rising trend in the variation in real wages across districts for both male and female agricultural workers. This rising trend in the variation of real wages was particularly notable in the 1990s (Table 5).

For more than half of the districts in our sample, the decade of 1980s was the period of highest growth in real wages for both male and female labourers. However, there was a distinct slowdown in the growth rates of both male and female agricultural wages in majority of the districts in our sample in the 1990s.

We divided the entire time period into four phases based on four decades for each district and worked out the trend growth rates for each period.15 For the grain price-deflated real wage series, there was slower growth in the 1990s as compared to the 1980s across majority of the districts in our sample (Table 7). In the 1990s, there was a slowdown in the growth of male agricultural wages in about 58 per cent of the total districts for

Table 6: Rate of Growth in Real Daily Wages of Male and Female Agricultural Labourers, Deflated by CPIAL, AWI

(Per cent per month)

District Male Wage Female Wage
July 1964 to July 1970 to July 1980 to July 1990 to July 1964 to July 1970 to July 1980 to July 1990 to
June 1970 June 1980 June 1990 June 2000 June 1970 June 1980 June 1990 June 2000
Andhra Pradesh 1 -0.06* -0.23* 0.50* 0.04 -0.06* -0.001 0.36* 0.34*
Andhra Pradesh 2 0.05* 0.04* 0.39* 0.24* 0.22* -0.11* 0.45* 0.27*
Andhra Pradesh 3 0.02 0.36* 0.38* -0.43* 0.06* 0.62* 0.14* -0.13
Assam 1 -0.01* 0.17* 0.21* -0.33* nc nc nc nc
Assam 2 - -0.22* 0.59* -0.16* nc nc nc nc
Assam 3 0.01* -0.05* 0.28* -0.07* -0.02* -0.10* 0.13* nc
Bihar 1 0.06* 0.14* 0.12* 0.01 0.04* 0.04* 0.12* 0.01*
Bihar 2 0.05* -0.04* 0.17* 1.60* nc nc nc 1.6*
Bihar 3 -0.04 0.23* 0.33* 0.50* c nc nc 0.50*
Gujarat 1 0.03* 0.48* 0.27* -0.91* 0.16* 0.54* 0.11* -0.29*
Gujarat 2 -0.23* 0.67* -0.36* 0.39* -0.19* 0.52* -0.49* 0.30
Gujarat 3 0.09* 0.12* 0.40* 0.41* 0.11* 0.15* 0.32* 0.27*
Haryana 1 0.02 0.10* 0.16* 0.55* nc nc nc 0.03
Haryana 2 0.04* -0.20* 0.33* 0.13* -0.01 0.20* -0.22* 0.2*
Haryana 3 0.05 0.02* 0.28* 0.31* nc nc nc 1.35*
Himachal Pradesh 1 0.05* -0.29* 0.54* 0.82* 0.04* -0.17* 0.44* 0.38*
Himachal Pradesh 2 -0.08* -0.31* 0.74* 0.19 -0.06* -0.22* 0.34* -0.36*
Himachal Pradesh 3 0.06* -0.25* -0.21* 2.67* 0.22* -0.12* -0.39* 1.01*
Karnataka 1 -0.10* 0.16* 0.41* 1.17 nc nc nc 1.17
Karnataka 2 -0.05* -0.12* -0.18* -0.29 -0.26* -0.14* -0.13* -0.29
Karnataka 3 0.01* 0.11* 0.26* 2.89* 0.02* 0.15* 0.26* 2.89*
Kerala 1 0.09* 0.39* 0.02 1.17* 0.06* 0.05* 0.01 0.23*
Kerala 2 0.03* 0.10* 0.10* 0.64* 0.06* 0.10* 0.01 0.4*
Kerala 3 0.01 0.20* 0.05* 0.78* 0.05* 0.49* 0.31* 0.32*
Maharashtra 1 -0.01 -0.03* - 2.07* nc nc nc 3.13
Maharashtra 2 0.04* - - -0.95* nc nc nc 0.15
Maharashtra 3 0.16* -0.12* 0.31* 0.07 0.22* -0.06* 0.45* 0.3*
Madhya Pradesh 1 0.14* 0.53* 0.52* 1.36* -0.02 0.20* 0.02* 1.86
Madhya Pradesh 2 0.17* 0.28* 1.06* 0.70* -0.01 0.23* 0.48* -0.11
Madhya Pradesh 3 0.01 0.06* 0.22* -0.28* -0.01 0.10* 0.13* -0.04
Orissa 1 -0.01 0.09* 0.43* -0.11* -0.02* 0.31* 0.16* 0.09*
Orissa 2 -0.08* 0.14* 0.51* -0.20* 0.03* 0.22* 0.27* 0.38*
Orissa 3 - 0.11* 0.43* 0.07 - 0.07* 0.34* 0.12*
Punjab 1 0.07* 0.02* -0.01* -0.09 nc nc nc nc
Punjab 2 0.14* -0.27* 0.59* -0.14 nc nc nc nc
Punjab 3 0.05* -0.03 0.07* - nc nc nc nc
Rajasthan 1 -0.002 0.36* -0.17* 1.71* -0.76 0.03* 0.02 2.70*
Rajasthan 2 0.01 -0.13* 0.28* - nc nc nc nc
Tamil Nadu 1 -0.01* -0.06* 0.01 1.67* -0.01 0.06* -0.09* 0.67
Tamil Nadu 2 -0.02* 0.33* 0.15* 1.25* -0.03* 0.30* 0.24* 0.76*
Tripura 0.02* -0.03 0.43* -0.04 0.21* 0.15* 0.40* -0.05
Uttar Pradesh 1 0.06* 0.03* 0.30* 0.51* - 0.02* 0.13* nc
Uttar Pradesh 2 0.23* 0.11* 0.45* 1.06 nc nc nc 2.14*
West Bengal 1 -0.02 0.15* 0.43* -0.16* 0.01* 0.24* 0.30* 0.79*
West Bengal 2 -0.02 0.01 0.39* 0.48* -0.02 -0.07* 0.24* 0.41*
West Bengal 3 -0.06* 0.46* 0.18* 1.51* -0.04* 0.72* -0.03* 0.77*

Notes: (1) Compound growth rates are worked out on real wages deflated by CPIAL at the base 1986-87.

-indicates growth rate not calculated due to non-availability of enough observations.

* indicates coefficient in the growth equation to be significant at 10 per cent or less.

nc indicates districts not covered in the sample for wages of female labourers due to large data gaps. Sources: AWI, various issues; Indian Labour Statistics, various issues; API, various issues.

which data were available. In the case of female agricultural wages, there was a slowdown in the growth rate in the 1990s in about 67 per cent of the total districts for which data were available.

Similarly, for the male wage series deflated by CPIAL, the rate of growth slowed-down in the 1990s compared to the 1980s in 50 per cent of our sample districts for which data were available (Table 6). In the case of female real wages also, a slowdown in the 1990s as compared to the 1980s was noted in 50 per cent of our sample districts for which data were available.

Two further conclusions are worth highlighting here. First, the percentage of districts with a slowdown in real wage growth in the 1990s was higher when the cereal price deflator was used than when the CPIAL was used. Secondly, the percentage of districts that experienced a slower growth in real wages of female workers in the 1990s was higher than the percentage of districts that experienced a slower growth in real wages of male workers when cereal prices were used as deflators.

RLE: A State-wise Analysis of Agricultural Wage Earnings

As per the latest RLE of 1999-2000, Kerala and Himachal Pradesh had the highest level of nominal and real earnings for male agricultural labourers – a finding similar to the one arrived at based on the district-level data from AWI. As regards female labourers, the levels of nominal and real earnings were highest for Punjab, closely followed by Kerala.

There was a rise in the nominal earnings of male and female labourers across the seven rounds of RLE in all the states. Among all the RLE rounds, the first major rise in nominal earnings was registered between 1983 and 1987-88. Male and female nominal

Table 7: Rate of Growth in Real Daily Wages of Male and Female Agricultural Labourers, Deflated by Cereal Prices, AWI

(Per cent per month)

District Male Wage Female Wage
July 1964 to July 1970 to July 1980 to July 1990 to July 1964 to July 1970 to July 1980 to July 1990 to
June 1970 June 1980 June 1990 June 2000 June 1970 June 1980 June 1990 June 2000
Andhra Pradesh 1 -0.93* -0.10* 0.29* -0.70* -1.22* 0.15* 0.15* 0.15
Andhra Pradesh 2 -0.14* 0.07* 0.11* 0.74* 0.33* -0.11* 0.24* 0.17
Andhra Pradesh 3 -0.45* 0.56* 0.16* -0.68* -0.28* 0.85* -0.09* -0.31*
Assam 1 -0.58* 0.07 0.53* -0.63* nc nc nc nc
Assam 2 - -0.31* 1.17* 0.46* nc nc nc nc
Assam 3 -0.17 -0.29* 0.68* 0.13 -0.45* -0.49* 0.76* -
Bihar 1 0.03 0.41* 0.21* 0.33* -0.24* 0.61* 0.29* -0.86*
Bihar 2 0.35* 0.10 0.45* 1.96* nc nc nc nc
Bihar 3 -0.78* 0.51* 0.48* -0.69* nc nc nc 1.28
Gujarat 1 0.03 0.55* 0.40* -0.61* 0.59* 0.58* 0.15* 0.01
Gujarat 2 -0.29* 0.26* -0.25* 0.63* -0.17* 0.07 -0.44* 0.90*
Gujarat 3 0.02 0.24* 0.47* 0.48* 0.04 0.37* 0.35* 0.56
Haryana 1 -0.25 0.28* 0.33* 0.63* nc nc nc -0.44*
Haryana 2 -0.04 -0.08* 0.48* 0.20* -0.01 0.27* -0.08* 0.40*
Haryana 3 -0.09 0.18* 0.54* 0.38* nc nc nc 0.70
Himachal Pradesh 1 0.06 -0.17* 2.17* - 0.06 -0.08* 1.69* -
Himachal Pradesh 2 -0.62* -0.15* 1.71* - -0.52* -0.10* 1.28* -
Himachal Pradesh 3 0.01 -0.15* -1.04* - 0.17* 0.04 -0.81* -
Karnataka 1 -0.26* 0.37* 0.43* -0.11 nc nc nc 0.82
Karnataka 2 -0.30* -0.03 -0.15* -0.42* -0.21* -0.04 -0.10 -0.59*
Karnataka 3 0.14* 1.04* 0.55* 0.33 0.27* 1.32* 0.54* 0.45
Kerala 1 1.05* 1.06* 0.33* 1.16* 0.87* -0.11 0.43* 0.05
Kerala 2 0.58* 0.01 0.65* 0.52* 0.66* -0.17* 0.49* 0.10*
Kerala 3 0.27* -0.11 0.60* 0.33* 0.11* 0.74* 0.82* 0.08
Maharashtra 1 -0.91* -0.13* - 1.18* nc nc nc -0.45
Maharashtra 2 -0.47* - - 1.34 nc nc nc 2.47*
Maharashtra 3 -0.02 -0.09* -0.14* 1.74* 0.03 -0.03 -0.06 -0.69
Madhya Pradesh 1 -0.11 0.26* -0.05* 0.17* -0.65 0.95 0.10 -0.41
Madhya Pradesh 2 0.04 -0.16* 0.41* 0.95* -0.21* 0.48* 0.40* 0.56
Madhya Pradesh 3 -0.10 0.14* 0.20* -0.99* -0.45* 0.76* 0.33* -1.23*
Orissa 1 -0.53* 0.40* 0.44* -0.53* -0.67* 0.75* 0.31* -0.15*
Orissa 2 -1.77* 0.61* 0.64* -0.78* -0.29* 1.03* 0.22* -0.77*
Orissa 3 - 0.84* 0.67* 0.16 - 0.88* 0.91* -0.14
Punjab 1 0.19* 0.20* 0.10* -0.06 nc nc nc nc
Punjab 2 0.43* -0.14* 0.81* -0.14 nc nc nc nc
Punjab 3 -0.02 0.13* 0.43* -3.81 nc nc nc nc
Rajasthan 1 -1.12 1.20* -0.67* 0.61 -0.94 0.34* 0.25* 1.28
Rajasthan 2 3.02* -0.08 0.53* 1.13 nc nc nc nc
Tamil Nadu 1 0.56* -0.93* -0.04 2.38 1.09* -0.19* 0.14* 0.16
Tamil Nadu 2 0.17* 0.17* 0.17* 1.91* -0.38* 0.17* 0.35* 0.63*
Uttar Pradesh 1 0.72* 0.17* 0.66* 0.89* - 0.04 0.44* 0.89*
Uttar Pradesh 2 2.43* 0.25* 1.00* 1.57* nc nc nc nc
West Bengal 1 -0.42* 0.34* 0.61* -0.19* -0.30* 0.44* 0.47* 0.41*
West Bengal 2 -0.43* 0.21* 0.55* 0.44* -0.50* 0.11* 0.38* 0.32*
West Bengal 3 -0.61* 0.75* 0.53* 0.90* -0.52* 1.20* -0.06 -0.22

Notes: - indicates growth rate not calculated due to non-availability of enough observations.

* indicates coefficient in the growth equation to be significant at 10 per cent or less.

nc indicates districts not covered in the sample for wages of female labourers due to large data gaps. Sources: AWI, various issues; API, various issues.

earnings rose by about 50 per cent for majority of the states. For some states, the magnitude of change in earnings between the two RLEs was as high as 100 to 200 per cent. Across states, this increase was sustained during the subsequent rounds of 1987-88, 1993-94, and 1999-2000.

Levels and Growth in Real Agricultural Wage Earnings

There was a significant growth in the real earnings of both male and female agricultural labourers between 1983 and 1987-88 (Table 8). The increase in real earnings was striking, particularly against the backdrop of negative growth rates recorded between 1977-78 and 1983 for nearly all states. The period between 1983 and 1987-88 could be delineated as the period of highest growth in agricultural wage earnings for most states in our sample. This period of high growth was followed by a significant slump in the growth of real earnings between 1987 and 1993-94 for a majority of the states. The downtrend in the growth of real earnings for both male and female labourers continued between 1993-94 and 1999-2000 across majority of the states.

Sundaram (2001) observed using NSSO data that at the all-India level, for most of the categories of rural labour, the growth rate of average earnings in the 1990s (based on 1993-94 and 1999-2000 rounds) was “about the same or higher” than that between 1983 and 1993-94 (ibid:3046). Our findings do not support Sundaram’s observations. Sundaram used data for adult rural casual wage labourers from the unit-level dataset of the NSSO. On the other hand, our data are drawn from the RLE. The data on earnings in RLE are for usually occupied workers belonging to agricultural labour households. Secondly, Sundaram left out the 1987-88 round of the NSSO – the round between 1983 and 1993-94 – in his analysis; the reason appears to have been that 1987-88 was a drought year. Nevertheless, when we attempted a comparison of growth in wage earnings from RLE between 1983 and 1993-94 (leaving out the 1987-88 round as was done by Sundaram and between 1993-94 and 1999-2000, our earlier conclusion of a decline in growth rate in the latter period continued to hold. The decline was in fact even more prominent; it was observed for wage earnings of both male and female agricultural labourers in every state.

Between the RLE of 1983 and 1987-88, there was not only a remarkable growth in the real earnings, but the interstate gap in real earnings also narrowed considerably, as brought out by the sharp fall in the coefficient of variation in real earnings (Table 9). However, during the successive RLEs of 1993-94 and 1999-2000, a slowdown in the growth of both male and female real earnings was accompanied by a rise in the inter-state variations. Data from the AWI had also given a similar result for the 1990s.

Changes in Male-Female Earnings Ratio

Between the RLEs of 1977-78 and 1983, there was a distinct decline in the male-female earnings ratio across majority of the states in our sample (Table 10). This trend, however, could not be maintained over the following period. The period between 1983 and 1987-88 – when there was a remarkable increase in earnings for both male and female labourers – was marked by a rise in the disparity between male and female earnings for majority of the states. Between 1983 and 1987-88, in 14 out of 17 states (82 per cent), real earnings of male labourers were growing at a rate higher than earnings of female labourers. There was a further widening of the male-female earnings gap between 1987-88 and 1993-94 across states, though in varying degrees.

Table 9: Coefficient of Variation in Real Daily Earnings of Maleand Female Agricultural Labourers, Across States, RLE

(Per cent)

Category 1964-65 1974-75 1977-78 1983 1987-88 1993-94 19992000

Male wage

earnings 28.9 34.3 30.9 40.7 25.8 26.1 29.8 Female wage

earnings 28.2 26.4 30.5 40.4 26.1 28.7 36.2

Note: Earnings have been deflated using CPIAL at the base 1986-87. Sources: GoI (1968), NSSO (1987, 1994, 1997, 2003).

Table 8: Rate of Growth in Real Daily Earnings of Male and Female Agricultural Labourers, RLE

(Per cent per annum)

State Male Earnings Female Earnings
Period 1 Period 2 Period 3 Period 4 Period 5 Period 6 Period 1 Period 2 Period 3 Period 4 Period 5 Period 6
Andhra Pradesh -1.46 8.76 -1.56 9.52 2.75 3.75 -1.00 7.00 0.38 7.00 3.26 2.67
Assam -3.19 8.20 1.64 0.50 2.38 1.44 -3.30 11.75 3.91 -0.87 1.03 1.76
Bihar -1.09 5.88 -3.89 9.86 1.01 4.31 -1.22 7.39 -4.77 10.52 0.95 4.48
Gujarat -0.77 8.96 -3.39 5.95 3.02 3.00 -1.13 12.68 -5.31 10.75 2.81 1.15
Haryana* - 4.78 -6.06 4.70 5.76 4.62 - 9.96 -4.97 10.66 1.54 1.71
Himachal Pradesh - 3.54 -12.98 21.53 3.14 3.88 - 10.26 -15.29 27.92 1.47 5.07
Karnataka 0.00 4.99 -5.85 14.03 3.51 3.34 -0.25 6.57 -2.40 10.78 3.89 1.89
Kerala -0.15 8.16 -3.66 8.54 3.45 5.12 1.67 7.21 -0.86 6.31 2.61 4.19
Madhya Pradesh -2.67 7.29 -2.87 12.08 3.12 1.23 0.56 -0.89 -1.93 12.72 2.37 1.48
Maharashtra -2.67 7.95 -3.31 12.47 3.37 2.12 -1.74 10.01 -1.86 11.46 2.61 2.67
Orissa -3.00 7.18 -2.36 7.72 3.67 0.76 -2.69 9.66 0.35 4.40 3.13 1.44
Punjab 2.00 3.61 -3.51 5.75 4.61 -0.10 -0.28 8.00 -7.30 9.33 10.46 5.41
Rajasthan -2.70 8.88 -12.09 17.76 6.80 2.05 -1.50 8.34 -7.06 17.32 4.83 1.10
Tamil Nadu - 8.91 -12.48 22.14 6.22 3.62 - 7.49 -3.65 9.73 5.60 3.21
Tripura -3.59 4.85 3.10 9.30 -0.84 2.66 -2.66 4.84 -0.69 13.81 -3.60 5.25
Uttar Pradesh 2.11 5.93 -5.55 11.29 3.46 1.92 1.30 4.32 -5.35 12.42 3.25 1.99
West Bengal -2.28 6.35 -6.78 16.05 2.27 2.25 -1.60 8.25 -4.70 13.82 1.70 2.12

Notes: (1) Period 1= 1964-65 to 1974-75; Period 2= 1974-75 to 1977-78; Period 3= 1977-78 to 1983; Period 4= 1983 to 1987-88; Period 5= 1987-88 to 1993-94 and Period 6= 1993-94 to 1999-2000.

(2) Exponential growth rates are worked out for earnings deflated by CPIAL at the base 1986-87.

* For Period 1, growth in earnings has been reported for Punjab and Haryana together.

-Not available. Sources: GoI (1968), NSSO (1987, 1994, 1997, 2003).

Comparison of Actual Agricultural Wages with male agricultural labourers in 2004 was above the minimum wage Minimum Wages fixed by the government (Table 11).16 In the case of wages of female agricultural labourers, the situation was more dismal. In

Minimum wage rates have been specified by the government every state except four, daily wages of female labourers were in order to ensure that wages do not fall below a subsistence below the minimum wages in 2004. The four states where female minimum for the working class. For all states except Madhya wages were higher than minimum wages were Kerala, Himachal Pradesh and West Bengal, the level of nominal daily wage of Pradesh, Assam and Gujarat.

Table 10: Male-Female Agricultural Wage Earnings Ratio,IV State-wise, RLE

Conclusions

State 1964-65 1974-75 1977-78 1983 1987-88 1993-94 1999

2000 The objective of this paper was to examine the trends in Andhra agricultural wages in India from 1964-65 to 1999-2000, using Pradesh 1.42 1.35 1.44 1.28 1.44 1.39 1.50 data from AWI and RLE. In the course of this exercise, we noted

Assam 1.30 1.32 1.16 1.01 1.08 1.19 1.16

certain limitations of the AWI dataset. These limitations pertained

Bihar 1.16 1.18 1.11 1.17 1.14 1.14 1.13

mainly to the method of collection and presentation of data by

Gujarat 1.24 1.29 1.12 1.27 1.02 1.03 1.17 Haryana* -1.23 1.20 1.25 1.36 1.17 1.08 AWI, which made it difficult to construct a comparable time series Himachal

Pradesh -1.54 1.20 1.41 1.09 1.22 1.13 Appendix: List of Districts and Centres Chosen for Study,Karnataka 1.53 1.57 1.48 1.19 1.38 1.35 1.48 State-wise Kerala 1.72 1.41 1.46 1.23 1.36 1.44 1.53

State Referred as District Centre (Village)

Madhya Pradesh 1.29 0.90 1.24 1.17 1.14 1.20 1.18

Andhra Pradesh Andhra Pradesh (1) Visakhapatanam Vadadi Maharashtra 1.91 1.72 1.59 1.46 1.52 1.61 1.55

Andhra Pradesh (2) Karimnagar Cheppial Orissa 1.49 1.44 1.32 1.12 1.31 1.36 1.29

Andhra Pradesh (3) Nellore Talamanchi Punjab 1.47 1.88 1.60 2.03 1.72 1.17 0.81

Assam Assam (1) Sibsagar Dhemaji Rajasthan 1.61 1.41 1.44 1.03 1.05 1.20 1.28

Assam (2) Barpeta Karertal Tamil Nadu -1.57 1.65 0.93 1.59 1.65 1.70

Assam (3) Nowgong Topakuchi Tripura 1.44 1.29 1.29 1.62 1.32 1.61 1.35

Bihar Bihar (1) Hazaribag Choparan

Uttar Pradesh 1.18 1.29 1.37 1.36 1.29 1.31 1.30 Bihar (2) Dhanbad Devli

West Bengal 1.33 1.23 1.15 1.01 1.11 1.15 1.16 Bihar (3) Saran Parsa Pozhi

– Not available. Gujarat Gujarat (1) Baroda Sankheda/

* For the RLE 1964-65, earnings ratio has been reported for Punjab and Baroda Haryana together. Gujarat (2) Rajkot Gondal/Rajkot Sources: GoI (1968), NSSO (1987, 1994, 1997, 2003). Gujarat (3) Mehsana Chanasma/Kadi Haryana Haryana (1) Hissar Mangali

Table 11: Daily Minimum Wages and Actual Wages of Male and Haryana (2) Ambala Shahpur Female Agricultural Labourers, State-wise, 2004 Haryana (3) Mahendargarh Shehrapur

Himachal Himachal Pradesh (1) Kinnaur Chini Pradesh Himachal Pradesh (2) Bilaspur Lakhanpur State Minimum Actual Difference Actual Difference Himachal Pradesh (3) Sirmaur Nahan Wage Wage (Men) Wage (Women) Karnataka Karnataka (1) Kollar Doddasivara (Men) (Women) Karnataka (2) Chikmangalur Jayapura/

(In rupees)

(1) (2) (3) = (2-1) (4) (5) = (4-1) Annegere Karnataka (3) Shimoga Aralapura/

Andhra Pradesh 52.00 59.88 7.88 32.71 -19.29

SomalapuraAssam 50.00* 69.70 19.70 55.06 5.06

Kerala Kerala (1) Alappuzha KaruvattaBihar 50.00 58.27 8.27 44.47 -5.53

Kerala (2) Palakkad Elapully Gujarat 50.00 69.15 19.15 51.41 1.41

Kerala (3) Kannur Panur Haryana 84.29** 84.73 0.44 75.87 -8.42

Madhya Madhya Pradesh (1) Shahjapur Mamon BadodiaHimachal Pradesh 65.00 123.00 58.00 84.60 19.60

Pradesh Madhya Pradesh (2) Hoshangabad Sangakherkalan Jammu and Madhya Pradesh (3) Satna Kotar Kashmir 45.00 121.71 76.71 --Maharashtra Maharashtra (1) Thane Palghar Karnataka 56.30 59.29 2.99 36.23 -20.07

Maharashtra (2) Osmanabad Bembli Kerala 100.00^ 238.71 138.71 100.13 0.13

Maharashtra (3) Nagpur NarkhedMadhya Pradesh 56.96 50.95 -6.01 36.58 -20.38 Maharashtra 48.00 63.00 15.00 34.09 -13.91

Orissa Orissa (1) Cuttack Kendrapada Orissa 52.50 54.11 1.61 39.33 -13.18

Orissa (2) Ganjam Aska Punjab 87.59 ---Orissa (3) Sambalpur Kaisiria Rajasthan 67.30 82.94 15.64 48.58 -18.72 Punjab Punjab (1) Sangrur Fatehgarh Tamil Nadu 54.00 117.21 63.21 39.61 -14.40 Punjab (2) Gurdaspur Narotmehra

Tripura 50.00 74.33 24.33 --Punjab (3) Kapurthala Bulla Rai Uttar Pradesh 58.00 60.56 2.56 50.58 -7.42 Rajasthan Rajasthan (1) Bharatpur Khanewa West Bengal 107.99** 84.48 -23.51 49.63 -58.36 Rajasthan (2) Banswara Anjana

Tamil Nadu Tamil Nadu (1) Thanjavur Alangudi/ Notes: (1) Minimum wage refers to the daily wage stipulated for unskilled

Pulavarantham agricultural worker as on December 31, 2004.

Tamil Nadu (2) Tirunelveli Malayankulam (2) Actual wages for men and women refer to wages from ploughing

Tripura Tripura Lembucherra Lembucherra and weeding respectively.

Uttar Pradesh Uttar Pradesh (1) Lucknow Mau -Not reported.

Uttar Pradesh (2) Varanasi Kathodi * Wage with food, shelter and clothing.

West Bengal West Bengal (1) Burdwan Burdwan ** Wage with meal.

West Bengal (2) Coochbehar Coochbehar ^ Wage for light work.

West Bengal (3) Purulia Purulia Source: NSSO (2004), www.indiastat.com.

of agricultural wages. The time series of agricultural wages in this paper was constructed taking care of these limitations of the AWI data. Our major findings based on the AWI data supplemented with data from RLE are as follows:

First, there was a slowdown in the rate of growth of real daily wages of male and female agricultural labourers in more than half of the districts included in our sample in the 1990s. The slowdown was noted for wage series deflated by both the retail prices of cereals as well as the CPIAL. The RLE data also showed a declining rate of growth of real daily earnings of agricultural labourers in the 1990s. There was a striking rise in the growth of daily real earnings across all states between 1983 and 1987

88. Between 1987-88 and 1993-94, and further between 199394 and 1999-2000, there was a distinct slowdown in the rate of growth of real earnings for both male and female agricultural labourers across a majority of the states.

Second, there was a rising trend in the variations in real wages across districts in the 1990s. There was also a rise in the interstate variations in male and female real earnings between 1987-88 and 1993-94 and then, between 1993-94 and 1999-2000 in contrast to the decline in variation that occurred between 1983 and 1987-88.

Third, both AWI and RLE data showed that the differences between the average wages of male and female agricultural labourers have widened over the years. The RLE data showed that the level of and growth in earnings of female labourers tended to be lower than those in the earnings of male labourers across most states. This trend was particularly visible after 1987-88.

Fourth, we compared the daily wages of both male and female agricultural labourers with the statutory minimum wages. The daily wages of male agricultural labourers exceeded the minimum wage levels in most states. However, the daily wages of female agricultural labourers were below the minimum wage levels in most states. This fact, combined with the rising male-female earnings ratio indicates that gender disparities in wages in the Indian countryside are widening.

EPW

Email: pallavichavan@gmail.com

Notes

[A draft of this paper was presented at the All India Conference on ‘Agriculture and Rural Society in Contemporary India’, Barddhaman, December 17-20, 2003. The authors thank V K Ramachandran, Madhura Swaminathan, R Ramakumar and the other participants of this conference for their comments.]

1 With the formation of three new Indian states in 2000, districts Hazaribag, Dhanbad and Shahjapur do not belong to Bihar and Madhya Pradesh anymore. However, to maintain comparability with the earlier data points, we have considered these three districts as parts of Bihar and Madhya Pradesh in our paper.

2 Till 1950, the main sources of secondary data were the quinquennial wage census carried out by different states, and the annual administration reports of the government [Bansil 1984; Rath and Joshi 1966].

PAPERS INVITED ON

SOCIO ECONOMIC APPRAISAL OF BT COTTON

Empirical papers are invited on impact of BT Cotton farming in 7 states of India. The objective is to bring out various aspects of BT Cotton production on the social or economic conditions of the agricultural community. Papers on some states will be preferred: (1) Punjab/Haryana, (2) Madhya Pradesh, (3) Andhra Pradesh, (4) Gujarat, (5) Tamil Nadu,

(6) Maharashtra, (7) Karnataka.

The selected papers will be a part of a report on “Socio Economic Appraisal of BT Cotton Producing Areas” as case studies in a summarized format with due acknowledgement and referencing.

The empirical papers should be between 5 to 10,000 words. Variants of previously written/published papers may be submitted as well, provided they are accompanied with appropriate references and approvals from the publishers.

Eminent scholars, researchers from Agricultural Universities or a social scientist who have been involved in these areas of research are requested to submit their papers at the earliest possible instance. The papers will be selected state-wise on a “first come first served” basis before November 1, 2006.

Authors of selected papers will be provided with Rs 10,000 (Rupees Ten Thousand Only) as an honorarium per paper.

Contact Details

Saurabh Thakur

Indicus Analytics

Nehru House, 2nd Floor 4, Bahadur Shah Zafar Marg, New Delhi – 110002 Ph: 011-42512400/01/02 Email: saurabh@indicus.net Web: www.indicus.net

3 Besides these two sources, crop-wise monthly data on agricultural wages at the state-level are provided in the publication Cost of Cultivation of Principal Crops in India.

4 Rao (1972) argued that the sample size chosen under AWI was not just small but was likely to be atypical in nature. He found that the centres/ villages chosen under AWI were big villages of a semi-urban nature and not representative of the “general run of villages” in that district (p 49). As a result, Rao further hypothesised that data from AWI may not capture the intra-seasonal or “finer” variations within an agricultural season.

5 See Krishnaji (1971), Acharya (1989) and Baby (1996).

6 During the RLE of 1963-65, data on earnings were collected between October 1964 and September 1965. Hence, it has been referred to as the RLE of 1964-65 in this paper.

7 By definition, however, RLE does not collect data on agricultural wage per se. It collects data on payments received in cash or kind or both cash as well as kind or those that are receivable for the work done during the reference week [NSSO 1978:10]. The total weekly earnings are then averaged to estimate the average daily earnings.

8 For example, if Rs 10, Rs 20 and Rs 25 are wage rates of weeding, sowing and ploughing respectively. In the next year, Rs 10 and Rs 20 are reported as wage rates for weeding and sowing respectively. Then, an exercise of working out the average of all operations would yield Rs 18.34 as the wage in the first and Rs. 15 in the second case. It is not that the wage has fallen between these two years but simply because the wage on ploughing has not been reported, we get a lower average in the second year.

9 The idea of using retail cereal prices as deflators for calculating real wages, taking three/two districts per state came from a set of class assignments that were done at Indira Gandhi Institute of Development Research, Mumbai guided by V K Ramachandran. See Chavan (1999), Misra (1999), Ramakumar (1999), Sinha (1999) and Thomas (1999).

10 These districts belong to states where no district with a consistent centre was available during our entire reference period.

11 It can be argued that some of the chosen districts may well be more industrialised/urbanised and possibly, not the best choices for studying the agricultural wage situation. However, this sample has been formed with the objective of circumventing the methodological problems with the AWI dataset, and the selection of districts has been guided by the availability of data from this source.

12 See Appendix 1.

13 Gaps in data were one major problem we encountered in working out a long-term series of real wages for female labourers. Any gap in data is simply described as “data not reported or labour not employed” in the AWI publications. On account of large number of missing observations, we dropped certain districts from our sample while working out the real wage series for female labourers.

14 We could not work out the cereal price-deflated wage series for Tripura, as the data on cereal prices were not reported for this state in API. Further, data on cereal prices for Himachal Pradesh were available only up to 1985-86 and, hence, the wage series has been worked out only up to 1985-86.

15 On account of data gaps, we retained the monthly frequency of wages to work out the growth rates.

16 For this analysis, we have used the latest data on state-level earnings published by the NSSO in Wage Rates in Rural India.

References

Acharya, S (1989): ‘Agricultural Wages in India: A Disaggregated Analysis’, Indian Journal of Agricultural Economics, Vol 44, No 2, April-June, pp 121-39.

Baby, A (1996): Trends in Agricultural Wages in Kerala, Occasional Paper Series, Centre for Development Studies, Thiruvananthapuram.

Bansil, P C (1984), Agricultural Statistics in India, Oxford and IBH Publishing Company, Third Edition, New Delhi.

Bhalla, Sheila (1979): ‘Real Wages Rates of Agricultural Labourers in Punjab, 1961-77: A Preliminary Analysis’, Economic and Political Weekly, Vol 14, No 26, June 30, pp A-57 to A-68.

Chavan, Pallavi (1999): ‘A Study of Real Agricultural Wages in Gujarat, Maharashtra and Karnataka’, IGIDR Class Assignment, November, Mumbai.

Government of India (1968), Agricultural Labour in India: A Compendium of Basic Facts, Labour Bureau, Department of Labour and Employment, Shimla.

–: Agricultural Prices in India, Directorate of Economics and Statistics, Ministry of Agriculture, New Delhi.

–: Agricultural Wages in India, Directorate of Economics and Statistics, Ministry of Agriculture, New Delhi.

–: Cost of Cultivation of Principal Crops in India, Directorate of Economics and Statistics, Ministry of Agriculture, New Delhi.

–: Indian Labour Statistics, Labour Bureau, Ministry of Labour, Shimla.

Haque, T (1998): ‘Regional Trends, Patterns and Determinants of Agricultural Wages in India’, Indian Journal of Labour Economics, Vol 41, No 4, pp 845-60.

Jose, A V (1974): ‘Trends in Real Wage Rates of Agricultural Labourers’, Economic and Political Weekly, Review of Agriculture, Vol 9, No 13, March 30, pp A-25 to A-30.

Krishnaji, N (1971): ‘Wages of Agricultural Labour’, Economic and Political Weekly, Vol 6, No 39, September 25, pp A-148 to A-151.

Misra, Neeta (1999): ‘A Study of Real Agricultural Wages in Haryana, Rajasthan and Punjab’, IGIDR Class Assignment, November, Mumbai.

National Sample Survey Organisation (1978): Glossary of Technical Terms Used in National Sample Survey, New Delhi.

  • (1987): Rural Labour Enquiry, 1977-78 – Final Report on Indebtedness among Rural Labour Households, 32nd Round of NSS, Labour Bureau, Shimla.
  • (1994): Rural Labour Enquiry, 1983 – Report on Wages and Earnings of Rural Labour Households, 43rd Round of NSS, Labour Bureau, Chandigarh/Shimla.
  • (1997): Rural Labour Enquiry, 1993-94 – Report on Wages and Earnings of Rural Labour Households: 50th Round of NSS, Labour Bureau, Shimla.
  • (2003): Rural Labour Enquiry, 1999-2000 - Report on Wages and Earnings of Rural Labour Households: 55th Round of NSS, Labour Bureau, Shimla.
  • (2004): Wage Rates in Rural India – 2003-04, New Delhi.
  • Parthasarathy, G (1996): ‘Recent Trends in Wages and Employment of Agricultural Labour’, Indian Journal of Agricultural Economics, Vol 51, Nos 1-2, pp 145-67.

    – (1998): ‘Minimum Wages in Agriculture: A Review of Indian Experience’, in R Radhakrishna and Alakh N Sharma (eds), Empowering Rural Labour in India: Market, State and Mobilisation, Institute of Human Development, New Delhi, pp 203-19.

    Parthasarathy, G and K Adiseshu (1982): ‘Real Wages of Agricultural labour in Andhra Pradesh’, Economic and Political Weekly, Vol 27, No 31, July 31, pp 1245-48.

    Ramakumar, R (1999): ‘Agricultural Wages in Kerala, Tamil Nadu and Andhra Pradesh’, IGIDR Class Assignment, November, Mumbai.

    – (2004): ‘Socio-economic Characteristics of Agricultural Workers: A Case Study in the Malabar Region of Kerala’, PhD thesis submitted to the Indian Statistical Institute, Kolkata.

    Rao, V M (1972): ‘Agricultural Wages in India – A Reliability Analysis’, Indian Journal of Agricultural Economics, Vol 27, No 3, July-September, pp 39-61.

    Rath, Nilakantha and R V Joshi (1966): ‘Relative Movements of Agricultural Wage Rates and Cereal Prices – Some Indian Evidence’, Artha Vijnana, Vol 8, No 2, June, pp 115-26.

    Sarmah, Sasank (2002): ‘Agricultural Wages in India: An Analysis of Regions and States’, Indian Journal of Labour Economics, Vol 45, No 1, pp 89-116.

    Sharma, H R (2001): ‘Employment and Wage Earnings of Agricultural Labourers: A State-Wise Analysis’, Indian Journal of Labour Economics, Vol 44, No 1, pp 27-38.

    Sinha, Sucharita (1999): ‘A Study of Real Agricultural Wages in Assam, West Bengal and Orissa’, IGIDR Class Assignment, November, Mumbai.

    Sundaram, K (2001): ‘Employment and Poverty in the 1990s: Further Results from the NSS 55th Round Employment Unemployment Survey, 1999-2000’, Economic and Political Weekly, Vol XXXVI, No 32, August 11-17, pp 3039-49.

    Thomas, Jayan Jose (1999): ‘Real Wages in Agriculture: A Study of the States of Bihar, Madhya Pradesh and Uttar Pradesh’, IGIDR Class Assignment, November, Mumbai.

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