AICyberLex Nexus

Key Words: Social Security, PMKSBY, PLFS, India, Informal Worker

Introduction:

India has recently overtaken China to take the top spot in the world population rankings with its rapidly growing population. India, the world's third-biggest economy, has huge economic potential, but its per capita income is still quite low. India's per capita income for the fiscal year 2023–24 is ₹2.12 lakh, or around USD 2,500 which is extremely less than the per capita income of other major nations. Approximately 93% of the workforce is working in the unorganised sector, which has become a major contributing reason to this emerging inequality. The unorganised sector, which is marked by low pay, little job security, and not an appropriate social safeguards, has proven to be a constant obstacle in raising per capita income and raising the general standard of living among the people. This study helps to understand and analyse the reason that even though government have implemented the polices still why there is a downfall after numerous hardwork and effort by the government.

The Indian government has implemented several social security schemes which aimed at improving the lives of unorganised sector workers. However, despite these efforts and hardwork, many of these initiatives and objectives have not been fully achieved. One such scheme is the Pradhan Mantri Suraksha Bima Yojana (PMSBY), designed to provide affordable accident insurance to the economically vulnerable. This study seeks to analyse the trends in the unorganised sector in India, focusing on the effectiveness of social security schemes like PMSBY. By utilizing data from the Periodic Labour Force Survey (PLFS) from 2017-18 to 2022-23 and reports on PMSBY, the study aims to understand the reasons behind the limited success of these schemes and provides recommendations for improving their impact on the unorganised workforce.

Through descriptive and statistical analysis, this study explores the challenges faced by unorganised sector workers in accessing social security benefits, such as lack of education, awareness, and the complexity of scheme implementation. To address these issues, first, there must be an effort to raise awareness about the available social security schemes through targeted outreach and education campaigns, particularly in rural and marginalized areas where unorganised sector workers are concentrated so that they are aware about these schemes which the government has implemented . Additionally even expanding the coverage of existing schemes like the Pradhan Mantri Suraksha Bima Yojana (PMSBY) to include broader categories of risk, along with offering enhanced benefits, could significantly improve their effectiveness. Collaborating with local governments, NGOs, and organizations can help bridge the gap between policymakers and workers, ensuring that the schemes are better organised and tailored to the needs of the unorganised sector. Therefore the above mentioned measures can be taken to create the awareness and improve the situation of the unorganised sector of an economy.

Data and Methodology:

This study is based on secondary data. The data includes Annual Periodic Labour Force Survey (PLFS) collected by National Sample Survey Office (NSSO), Ministry of Statistics and Programme Implementation (MOSPI), Government of India. The PLFS data conducted for the six time period such as 2017-18, 2018-19, 2019-20, 2020-21, 2021-22 and 2022-23. To analyse the data, this study use descriptive analysis method. By using the above method, this study tries to examine the trends and patterns of unorganised workers and explains the linkages between the unorganised worker and one of the social security benefit scheme Pradhan Mantri Suraksha Bima Yojana (PMSBY).

Result and Discussion:

Trends of unorganised workers in India by gender

Figure 1: Trends of workforce participation rate (WPR) in unorganised sector in India

Source: Author’s plot using PLFS data

Figure 1 shows the pattern of male and female labour force in the unorganised sector. We observe from 2017-18 to 2020-21 the male and total labour force is higher than the female labour force. But after 2020-21 we see a sudden spike in female labour force participation in the unorganised sector which has crossed both the male and total labour force participation. This spike in women participation continues in 2022-23. In case of male participation there is also an increase in employment but it is not as high as compared to the female labour force participation. This drastic change in employment is caused due to various socio-economic factors explained in Table 1. Some of these factors are gender, education, caste, religion, standard of living, occupation as well as the sector in which a person is employed, i.e., agriculture, manufacturing, non-manufacturing and service sector.

Table 1: Share of Unorganised worker by socio-economic status in India

Source: Author’s estimation using PLFS data

Table 1 outlines the distribution of unorganised workers in India across various socio – economic dimensions between the years 2017-18 and 2022 – 23. The male participation in the unorganised workforce has gradually decreased from 2017-18 (81.4 per cent) to 2022-23 (73.17 per cent). Consequently, according to table 1, the female participation in the unorganised sector has gradually increased from 2017-18 (18.6 per cent) to 2022-23 (26.83 per cent). This could be due to various socio-economic factors like religion, level of education, marital status, place of residence, social groups, economic groups and political stability of the country.

Depending upon the place of residence the involvement of people living in rural areas is more in the unorganised sector compared to the people residing in urban areas. Over time the involvement of rural population in unorganised sector has increased from 58.68 per cent (2017-2018) to 73.19 per cent (2022-23). But the participation of urban population in unorganised sector has decreased from 41.32 per cent (2017-18) to 26.81 per cent (2022-23). According to Mazumder, 1976, migration for better jobs opportunities from rural to urban areas could be a reason why participation of rural population is more than the urban population in the unorganised sector. Also, in rural areas, due to lack of infrastructure, formal education and better employment opportunities there is a rise in rural participation in unorganised sector. On the other hand, better infrastructure, education and skill enhancement opportunities are already available in encouraging people to participate in organised sector compared to the unorganised sector.

India being a diverse country is home to many religions majorly Hindu, Muslim, and Christianity. In our study the minority religions are also considered under ‘others’. Evidence suggests that participation rate among the Hindus is more compared to the other religions but it is also observed that this participation is decreasing from 80.79 per cent (2017-18) to 79.32 (2022-23). Amongst Muslims there is a rise in participation in the unorganised sector from 13.33 per cent (2017-18) to 15.29 per cent (2022-23). In case of Christians the participation in the organised sector has increased from 2.36 per cent (2017-18) to 2.79 per cent (2021-22) but in 2022-23 the organised sector has decreased to 2.21 per cent. For minority religions the participation in unorganised sector is decreasing from 24.2 per cent (2017-18) to 21.05 per cent (2022-23).

For more than 3000 years, the institution of caste still plays a significant role in the Indian economic society. The major caste used in this analysis are ST, SC, OBC and others. ST’s involvement in unorganised sector shows a rising trend from 9.53 per cent (2017-18) to 12.4 per cent (2021-22), which later falls to 11.11 per cent (2022-23). In case of SC we observed an increase in employment from 25.82 per cent (2017-18) to 29.04 per cent (2021-22), which falls down in 2022-23 (27.37 per cent). For OBC we observe a stable employment in the unorganised sector ranging within 41.23 per cent to 41.83 per cent. After 2020-21 (41.47 per cent) there is a fall in employment to 40.46 per cent (2022-23). In case of Others, there is a continuous fall in employment over time from 24.2 per cent (2017-18) to 17.29 per cent (2021-22), later increasing to 21.05 per cent in 2022-23.

For our study we have divided the standard of living into 5 MPCE Quintile groups, where MPCE Quintile 1 is the poorest and MPCE Quintile 5 is the richest. There is a range of increase and decrease in employment in the unorganised sector within the scope of 20.61 per cent to 25.72 per cent in MPCE Quintile 1. Within MPCE Quintile 2 we observe an employment participation range of 20.15 per cent to 22.98 per cent. Similarly, in MPCE Quintile 3 participation rate in the unorganised sector range from 19.4 per cent to 26.53 per cent. For MPCE Quintile 4 the participation rate varies from 18.69 per cent to 22.17 per cent. In case of MPCE Quintile 5 the employment rate increase from 2017-18 (11.74 per cent) to 2020-21 (17.59 per cent), later decreasing to 11.86 per cent (2022-23).

The sectoral employment taken for this study are agriculture, manufacturing, non-manufacturing and service sectors. In case of agriculture, we observe a rapid increase in employment for unorganised sector from 4.18 per cent (2017-18) to 26.35 per cent (2022-23). In manufacturing sector there is a gradual fall in unorganised workforce from 18.4 per cent (2017-18) to 11.56 per cent (2022-23). For the non-manufacturing sector, there is an increase in unorganised workforce from 38.51 per cent (2017-18) to 42.29 per cent (2020-21), which later falls down to 32 per cent in 2021-22 and again increase to 34.68 per cent in 2022-23. Lastly, in the service sector, there is a rise and fall in unorganised workforce and remains within the scope of 27.41 per cent to 40.24 per cent.

The unorganised workforce revolves within varies types of occupations available in the economy. For this study we have taken some occupations available in the PLFS dataset. For administrative level jobs there is a downward trend in unorganised workforce from 1.09 per cent (2017-18) to 0.49 per cent (2022-23). This fall could be due to increase in educated individuals in our economy but there is lack of increase in job roles forcing educated individuals to participate in lower job positions. For professionals too we observe a downward trend in unorganised workforce from 3.48 per cent (2017-18) to 2.29 per cent (2022-23). People in the technical job market initially observed a rise in unorganised workers from 4.33 per cent (2017-18) to 4.48 per cent (2019-20) but later this fell down to 1.99 per cent (2022-23). For clerical jobs initially there was a rise in employment in 2018-19 (2.88 per cent) but later started to drop in the consequent years to 1.88 per cent (2022-23). For sales and service workers too the employment of unorganised workers fell after 2018-19 (13.09 per cent) to 9.65 per cent (2022-23). In case for skilled agricultural labourers, we observe and increase in unorganised workforce from 1.14 per cent (2017-18) to 1.89 per cent (2021-22) but later fell down in 2022-23 (1.34 per cent). In case of craftmanship there was a rise in unorganised workforce until 2019-20 (23.7 per cent) but later fell down in the consequent years to 12.56 per cent (2022-23). Amongst plant and machine operators there has been a gradual fall in unorganised labourers from 2017-18 (10.54 per cent) to 2022-23 (6.66 per cent). In elementary occupations there is a gradual rise in unorganised workforce sector from 2017-18 (41.13 per cent) to 2022-23 (62.15 per cent).

Conclusion and Policy Suggestions:

The objective of this study was to identify and address various complexities faced by the people working in unorganised sector. Some of these complexities are lack of social security and health benefits, level of education, types of jobs available, gender, religion, caste, economic groups within the country. We also look into the schemes available for this sector and its impact. After the analysis we came to a conclusion that only the supply side data is disclosed to the public but the demand side data which can only be provide by MNC giants like swiggy, Zomato and OLA is not disclosed to the public. So, the lack of these data creates a major gap and hinders application of any type of budget allocation and policy suggestions by the government. Disclosing the data by MNCs will help reduce a major gap in our economy. It will help the government in allocating necessary budget and schemes to provide health and social security to the workers in the unorganised sector.

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