Earning Disparities in Indo Gangetic Plain Region – Evidence from Periodic Labour Force Survey of India

Indian economy has experienced an enormous change in employment from the independence. Employment totally based on the skills and requirement of the profile gives contributing to major portion to our national income. However, the earning inequality in India has unfavorably obstructed underprivileged in accessing elementary needs like health and education. Periodic labour force survey (PLFS) conducted by National Statistical Office of India generates estimates on earning status at national and state level for both rural and urban sectors separately. This paper demonstrates disaggregate level disparities in earning distribution classified by gender and occupational categories in Indo-Gangetic Plain region of India which includes Bihar, Punjab, Haryana, Uttar Pradesh and West Bengal. This analysis helps in distinguishing the disparities in earning distribution between rural and urban sector in this region classified by gender and occupational categories which often masked at further down the level of disaggregation. This study of earning disparities is directly pertinent for measuring and monitoring the sustainable development goal 8 and 10 and expected to offer extraordinary evidence to policy-makers for recognizing the areas demanding additional consideration. This study is based on rural and urban households, therefore rural and urban occupation categories have been used. There is rural and urban activity


Introduction
The economy of India has developed at verifiably remarkable rates and is presently one of the fastest developing economies in the world. The country has progress appreciably in reform its economy, reduction hardship and fulfilling opportunity for everyday comfort for its widespread population. Significantly agriculture plays an important role in employment and it is the backbone of the Indian economy. Irrespective of this remarkable achievement, the earning distribution in India remains obstinately uneven. The movement of the whole economy decides the behavior of the labour market. The volatility in the economy, both in its inter and intra sectored linkages as well as in the context of economic integration with rest of the world, is reflected in the domestic labour market (MoSPI, 2020). Global economic slowdown creates extreme volatility which can hugely influence the contemporary economic environment. Thus it is immensely important to measure its short term impact on labour market which requires the collection of labour force data at regular interval. In India, labour force participation in unorganized sector is much higher compared to the organized sector. The frequent availability of labour force data is the need of the hour and this leads to the launch of Periodic labour force survey (PLFS) in 2017 by National Statistical Office (NSO), Ministry of Statistics and Program Implementation (MoSPI), Govt. of India. PLFS is conducted with two major objectives. The first objective is to estimate the key employment and unemployment indicators (viz. Worker Population Ratio, Labour Force Participation Rate, Unemployment Rate) in the short time interval of three months for the urban areas only in the Current Weekly Status (CWS), (MoSPI,2020). Secondly to estimate employment and unemployment indicators in both usual status (ps+ss) and CWS in both rural and urban economically important, disparities in average monthly earnings among different working groups are still exist in the IGP region. This paper demonstrated the disparities in earning distribution in IGP region at different level of classification viz. rural, urban by gender and occupation group. In India considerable changed in employment rate, current working rate, earning disparities are basis for adverse effects on Indian economy and this leads to the key motivation for the analysis. The significance for the analysis comes from the stated reasons and outcomes for earning disparities provide analysis of labour force and its economic activities. The rest of the paper is organized as follows. In Section 2, we described the data source along with the methodology implemented while the result and discussions about the analysis is reported in Section 3. Finally some major conclusions are drawn in Section 4.

Material and Methods
In India National Statistical Office (NSO), Ministry of Statistics and Program Implementation (MoSPI), Govt. of India, data are the primary source of official statistics at National and State level. In this analysis we have used PLFS data collected by NSO in the year 2018-19. In urban areas, a rotational panel sampling design has been used with first visit schedule and thrice periodically later with revisit schedule while for rural areas, there was no revisit. Stratified multistage survey design was adopted with the first stage units (FSU) were the Urban Frame Survey (UFS) blocks in urban areas and 2011 Population Census villages in rural areas and the ultimate stage units (USU) were households(MoSPI,2020).This survey includes total of 12800 FSUs in which 7024 villages and 5776 UFS blocks. This survey provides information on earning estimate of every status viz. self-employed worked in household enterprise (includes code 11-own account worker, 12-as an employer and 21-as helper), worked as regular salaried/wage employee and worked as casual wage labour other than public works. To estimate earning from the data earning variable is calculated earnings for regular salaried wage activity and earnings for other activity1. In our analysis all the estimates are averaged as per earning of every employed person.
Our major purpose is to compute the average monthly earning among the rural and urban sector of IGP states viz. Bihar Haryana, Punjab, Uttar Pradesh and West Bengal. Let us first describe the notations for the estimation of average earning of various classifications under consideration. We assume a finite population U of size N and samples of size n is selected from this population. The target variable of interest y is average monthly earning (in Rs.) and we have considered the following information on earnings from employment from PLFS 2018-19 viz. (i) self-employed persons, (ii) regular wage/salaried employees and (iii) casual labour. For regular wage/salaried employees in current weekly status (CWS), information on earnings during the preceding calendar month from the regular wage/salaried work in which the person was employed in the CWS was collected. For self-employed persons in CWS, information on earnings during the last 30 days from the self-employment activity in which the person was working as per current weekly status was collected. It is important to note that that average gross earnings from the self-employment activity have been calculated by excluding those self-employed persons who had reported earning as zero or not reported. For casual labour (other than public works), information on earnings was collected for the casual labour work in which the person was engaged for each day of the reference week i.e. last 7 days preceding the date of the survey. For the sake of the analysis, we have transformed the daily data into monthly data for for the casual labour work. The

Results and Discussion
The PLFS surveys undertaken by the NSO is primary source of labour force and population participation employment in the structure. This section has taken into account the earnings estimates profile of IGP states of India and its variations among the rural and urban sector, subsequently male and female classification and occupation category.
[ Table 1 about here] [ Table 2A about here] [ Table 2B about here] The sectoral disparity in average monthly earning is further classified by gender in Table -2B [ Table 2C about here] The sectoral disparity in average monthly earning is classified further by occupational group in In addition, Bengal when compared to the national average.
[ Table 2D, Table 2E and Table 2F about  In what follows, we described the sectoral disparity in average monthly earning between rural and urban sector in Table 2D -2F via two way classification (by gender and occupational group). Sectoral disparity by gender for the casual labour is reported in Table   2D. For rural sector, the average monthly earning of male casual labour varies from Rs. Moreover, the earning gap between male and female is the highest in Punjab (nearly 270%) and lowest in Bihar (nearly 54%). Besides Punjab and Bihar, the remaining states have the earning gap of nearly 123% or more. This is also described in Fig. 4. In the same way, For rural sector, the average monthly earning gap between male and female regular wage/ salaried group, is highest in West Bengal (almost 119%) and lowest in Haryana (nearly 27%) and in urban sector, this gap is highest in West Bengal (almost 65%) and lowest in Bihar (nearly -1%). It is evident from Fig. 4 that self-employed group in both rural and urban sector has significant amount of earning gap between male and female irrespective of the states while this gap is reduced to a great extent for the remaining occupational groups.
Furthermore, for most of the states, the earning gap between male and female in both rural and urban sector is nearly below 50% for the casual labour and regular wage/ salaried group while it is more than 100% for the self-employed group in most of the states except Bihar.
The variation in earning gap between different occupation groups is higher for Punjab and West Bengal while this variation is much lower in Bihar. In addition, the spatial mapping of distribution of inequality in average monthly earning between rural and urban sector classified by gender and occupational group is given in appendix. This will certainly help the readers and policy-makers to visualize the distribution pattern of inequality in the IGP region and identifying the areas required further attention.

Intra-year disparity in average monthly earning
In this subsection, we discuss about the intra-year disparity in average monthly earning by considering the quarter-wise estimate of average monthly earning(in Rs.). Table 3 Table 3 and Fig. 5, it is also observed that between quarters, Bihar has the lowest variation in earning gap ranging from 46% to 68% whereas it is highest in Haryana ranging from 26% to 85%.
[ Table 3 and Figure 5 about here]  Table 5 and Fig. 6, it is also observed in urban sector of IGP that between quarters, West Bengal has the lowest variation in earning gap ranging from 41% to 55% whereas it is highest in Bihar ranging from -33% to 67%. It is also important to note that, the average monthly earning of urban female in Bihar is more than its counterpart for every quarter except Q1.
[ Table 4 and Table 5 about here] [ Figure 6 about here]  Table 7 and Fig. 7, it is also observed in regular wage/salaried population of IGP that between quarters, Uttar Pradesh has the lowest sectoral variation in earning gap fluctuating from 28% to 60% whereas it is highest in Haryana fluctuating from 1% to 95%.
[  Table 8 and Fig. 7, it is also observed in casual labour population of IGP that between quarters, Bihar has the lowest sectoral variation in earning gap fluctuating from 9% to 19% whereas it is highest in Uttar Pradesh fluctuating from -9% to 26%. It is also important to note that, states like Punjab, Haryana and Uttar Pradesh have higher average monthly earning for casual labour in rural sector than its counterpart.

Conclusion
In spite of being significantly important for both economy and agriculture of the country, sectoral disproportions in average monthly earning between male and female as well as among different working groups are still exist in the IGP region in India. With most of the major agrarian states, the IGP region provide noteworthy contribution towards India's GDP via significant amount of agricultural and economical activities. Therefore it is noteworthy to consider this region for this analysis in which we made an attempt to get a picture of sectoral disparity in average monthly earning among the states of this region. We considered a whole year and an intra-year analysis to obtain a clearer picture down the level of further disaggregation. This analysis demonstrates the average monthly earning from employment among the rural and urban sector in the IGP states as well as the diversity and disparity in earning. It is to conclude that for both rural and urban sector, major agrarian states like Haryana and Punjab has higher average monthly earning while for the remaining three states viz. Bihar, Uttar Pradesh and West Bengal, it is lower than the national average.              Figure A3. Spatial distribution of estimate of average monthly earning by occupation in rural (A = Self-employed; B = Regular wage/salaried; C = casual labour) and urban (D = Self-employed; E = Regular wage/salaried; F = casual labour) sector of IGP region.