Is Rural Non-farm Employment Distress Driven in Uttar Pradesh? Evidence from the NSSO rounds and PLF Survey

Is Rural Non-farm Employment Distress Driven in Uttar Pradesh? 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Evidence from the NSSO rounds and PLF Survey Shadab Hashmi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3360499/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Using National Sample Survey Organization (NSSO) data, this study assessed the employment situation in Uttar Pradesh, with a focus on non-farm employment. The primary objective of our study is to investigate the potential causes of employment growth in non-farm activities over time. In Uttar Pradesh, we observed that a higher proportion of the casual workforce and stagnation of rural manufacturing activities, as well as a declining proportion of people who were self-employed and regularly employed, contributed to the casualization of the rural workforce. This suggests that the rural workforce was forced to shift from agricultural to non-farm activities as a result of distress. The key finding is that self-employment and regular employment outside agriculture seemed to be high-return activities, whereas casual employment outside agriculture was linked to low-return activities. Using a multinomial logit model, the study revealed that across all employment status categories, including agricultural labor, cultivators, non-farm regular employment, non-farm self-employment, and non-farm casual employment, the likelihood of working in high-paying non-farm regular and self-employment activities increases with better educational attainment, larger landholdings, and placement among the upper castes in social groups. JEL Code: J22, J24, J46 Development Economics Rural non-farm sector Casualization Rural Manufacturing Figures Figure 1 1. Introduction and Background Despite high economic growth, the structural transformation of the Indian economy has been slow, with a widening labor productivity differential between non-agricultural sectors and agriculture. Labor absorption in the urban economy, especially in the manufacturing sector, has been low; formal sector jobs are few and declining as a share of employment, and labor contracts are becoming informalized. Consequently, combined with rapid population growth, the labor force in rural areas is still growing rapidly. Agricultural growth has not responded to high-income growth and agricultural employment is growing slowly. The rural non-farm sector has emerged as the major source of rural and economy-wide employment growth. According to 2011 census data, Uttar Pradesh is the most populous state in the country, with 16.5 percent of the country's population. Of the total population of the state, 77.7 percent were from rural areas and 22.3 percent were from urban areas. Over the last decade, UP's annual population growth rate has averaged two percent, which is higher than the national average. The state was considered a good case for this study as nearly two-thirds of households depend mainly on agriculture for their livelihoods. This study shows that non-farm employment has grown dramatically over time, moving from 20.1 percent of the rural workforce in 1993–1994 to 40.21 percent in 2019–20, and the size of the rural non-farm economy doubled. Male workers showed a greater increase than their female counterparts, but female employees had a faster and more diverse increase, leading to the conclusion that the drop in agricultural earnings compelled a section of rural women to engage in non-agricultural activities to supplement declining family income, and a considerable proportion of rural females were employed in the service sectors, indicating new opportunities for them due to their educational attainment. The study also shows that following the reform, there was a rise in the construction sector and a decline in rural manufacturing in terms of non-farm employment. This indicates that the primary driver of employment growth is no longer the manufacturing sector, and points to the casualization of rural employment in Uttar Pradesh. As part of our examination of non-farm employment, we categorized workers into three groups: self-employed individuals, regular employees, and casual workers. The proportion of self-employed individuals has steadily decreased over time, whereas the share of casual workers has increased. This trend has contributed to the casualization of the rural workforce, with a declining share of self-employed and regular employees. A study using a multinomial logit model found that higher educational attainment, larger landholdings, and association with upper castes in social groups increase the likelihood of engaging in high-paying non-farm regular and self-employment activities across all employment status categories, including agricultural labour, cultivators, non-farm regular employment, non-farm self-employment, and non-farm casual employment. Furthermore, approximately 40% of monthly household expenditures are attributed to rural non-farm activities, which constitute a significant portion of the total expenditure incurred by households. Additionally, the study revealed that self-employment and regular employment outside agriculture had high returns, whereas casual employment outside agriculture was associated with low returns. During the 1990s, a growing number of agricultural households engaged in small, temporary, part-time, or full-time salaried or professional occupations, businesses, and other activities. Examples of unregistered small enterprises operating in the informal sector include pushcarts, street vendors, roadside kiosks, tea stalls, tempo-taxis, agro service centers, small wayside garages, and workshops. This trend of diversifying away from agriculture is supported by the increasing share of non-farm employment, which is further demonstrated by data from later times. It is also pertinent to mention that since the Mahatma Gandhi National Rural Employment Guarantee Act 2005 (MGNREGA) was implemented, employment patterns have changed significantly throughout rural India. Uttar Pradesh, one of the largest inhabitants of the rural population, is expected to be affected by changes in the rural labour market. To fully comprehend this transformation of the rural workforce and to create a strategic framework for rural development that considers the implications for transition in the rural economy, a thorough, disaggregated study is necessary. This study investigates the patterns and trends of the rural non-farm economy in Uttar Pradesh. This study attempts to assess the employment situation in Uttar Pradesh's non-farm economy in light of the aforementioned facts. Wherever possible, the analysis also makes an effort highlights the regional differences within the state. This study is comprised of nine sections. Section 2 reviews the relevant literature. Section 3 presents the data and methodology of the study. Section 4 describes the size of the non-farm sector in the state. Section 5 discusses sectoral employment distribution. This section also attempts to characterise the nature of employment in the non-farm sector. Section 6 presents the distribution of workers according to their employment status. In Section 7 , we examine monthly household consumption expenditure in population deciles for different employment statuses, including agricultural labor, farming, non-farm self-employment, non-farm regular employment, and casual employment. In Section 8 , we examine the correlates of employment in the non-farm sector among various non-farm employment types, agricultural wage employment, and farming, based on a multinomial logit model. The broad findings are summarised in Section 9 . 2. Literature Review The literature describes two major motivations for the transformation from agricultural to rural non-farm employment (RNFE). The first is the push factor, which examines the behavior of farm households in a risky environment with factors such as landlessness, low capital, drought, missing credit, and insurance markets. Under these circumstances, households try to self-insure themselves through coping behaviors, including turning to non-farm activities as a means of insuring against crop failure (Reardon, 2000 ). For example, individual farm households may migrate seasonally to cities as a safeguard against rural risks (De Haan and Roglay, 2002). The second factor, the pull factor, examines household behavior when farm income is high, productivity is high, and resources are rich. In such cases, farm households tend to diversify into high-return; non-farm activities. For instance, the Green Revolution in India saw an increase in farm income and high rural savings, making capital available for non-farm activities (Hazell and Ramasamy, 1991). On a theoretical level, there are two contrasting views in the literature on the role of rural non-farm employment in structural transformation. One view is that it is a residual sector, and its relative importance in rural areas should decline with agricultural development (Hymer and Resnick, 1969 ). This sector primarily accommodates workers unable to find jobs in the agricultural sector. The growth of rural non-farm employment is largely a manifestation of economic distress caused by the failure of agriculture to absorb the growing rural population (Vaidyanathan 1986 ; Basant and kumar 1989 ; Dev 1990 ; Saith 1991 ; Bhalla 1994 ; Eapen 1995; Fisher et al 1997 ) Another view holds that the rural non-farm sector is a crucial organ for rural development during structural transformation. Growth of the rural non-farm sector can reduce rural poverty, particularly for small and marginal farmers (Ravallion and Datt 1996 ). It plays a significant role in the formation of skills and human capital in rural areas (Hazell and Haggblade 1991 ). There is a strong correlation between agricultural growth and the non-farm sector. The growth of the rural non-farm sector has become vital for agricultural development because of its strong forward and backward linkages (Ranis and Stewart, 1993). RNFE acts as an intermediate during structural transformation, when the share of agriculture decreases and is accompanied by a shift of labor to a corresponding rise in manufacturing and services. It is plausible to assume that many transfers from agriculture to secondary and tertiary sectors occur through RNFE. Understanding the transition through RNFE is crucial for grasping the overall economic growth. The difference between demand-pull and distress-push diversification has implications for evaluating the economic significance of the RNFE sector. Distress-push diversification may require policymakers to develop appropriate social safety nets and interventionist policies to mitigate the short-term negative effects that sometimes accompany this type of diversification. Where demand-pull factors drive the process of diversification, policymakers may seek to provide a suitable enabling environment to support the development of RNFE and sustainable rural livelihoods. However, deciding whether the demand-pull or distress-push factors are at work is not straightforward. The key features of distress-push and demand-pull diversification are outlined below. The table below shows that distress-push diversification occurs in rural areas that have one or more of the following characteristics: geographical isolation, low-quality physical infrastructure, low human capital, underdeveloped markets, resource scarcity, and the incidence of some natural disasters. Demand-pull diversification is possible in the presence of expanding technological innovations (within or outside agriculture), market development, or intensifying links with markets outside the local economy. The Push and Pull factors of RNFE diversification Push Factors • Population growth • Increasing scarcity of arable land and decreasing access to fertile land • Declining farm productivity • Declining returns from farming • Lack of access to farm input markets • Decline in natural resource base • Temporary events and shocks • Absence or lack of access to rural financial markets. Pull Factors • Higher return on labour in RNFE • Higher return on investment in the RNFE • Lower risk of RNFE compared to on-farm activities • Generation of cash in order to meet household objectives • Economic opportunities, often associated with social advantages, offered in urban centers and outside of the region or country. Source: Davis and Pearce(2000) It might be concluded that distress-push diversification characterises households in rural populations that are less endowed or have lower incomes. These households will venture out in non-agricultural activities that return less than demand demand-pull diversification, since higher-return activities typically require higher investment that only richer households can afford. For instance, poorer households obtain a larger share of their non-agricultural income from wage employment, whereas richer households have better opportunities to enter non-agricultural activities in their own independent enterprises. The literature on the determinants and nature of non-farm employment is discussed by probing certain broad relationships. However, a key question to be considered is whether growth in rural non-farm employment is distress-driven or demand-driven, and the role of exogenous factors in this diversification. At the regional level, the dynamics of slow-growing regions have remained largely unexplored. Paired comparisons of slow- and fast-growing regions and of poor and non-poor households within each offer prospects for revealing how aggregate trends in farm and non-farm productivity, changing labor allocation and income composition, and the fluidity of rural-urban interactions drive transformation in a variety of rural settings. 3. Description of data and Methodology The present study is primarily based on the data of four quinquennial rounds and one periodic labour force survey(PLFS) of NSSO on employment and unemployment pertaining to the periods 1993-94, 1999-00, 2004-05, 2009-10 and 2019-20 respectively. Data from these rounds were used to analyse of the distribution of rural workers usually employed (principal status + subsidiary status) in the rural non-farm sector in Uttar Pradesh. The analysis was also restricted to workers in the age-group of 15–59 years. We use multinomial logit estimation to determine the effects of various factors on employment status, which are further divided into the low- and high-skilled activities of the state. The dependent variable is not limited to two categories in this type of regression. A multinomial logistic regression is one in which the predictor variables, which may be continuous or categorical, explain the categorical dependent (outcome) variable by using two or more categories. This is an extension of binary logit model. A reference category is used to compare categories. Similar to the coefficients of the reference group for the dummy variable, the coefficients of the reference group are all zero. Multinomial logistic regression, such as binary logistic regression, estimates the likelihood of categorical membership using maximum the likelihood estimation. Extending the above logit equation into a generalised form, for the ith individual with j choices the utility choice can be assumed as follows: (Greene, 2003) Emp ij = β + β i X ij + U ij (2) The vector of the conditioning variables is denoted as Xi and the vector of the regression parameters is denoted as 𝛽𝑖. It can be assumed that Empij produces the most utility for a specific revealed choice j. So, the statistical model is obtained from the likelihood that option j is chosen over option k, which is: Probability (Emp ij >Emp ij ) for all other k ≠ j (3) A multinomial logit model is used to study the factors that determine low- and high-skilled activities. Given that there are five dependent variables, the following set of coefficients ß are estimated for each occupational category: Let’s assume that y is the dependent choice variable: Probability (y = j/x) \(=\frac{{e}^{{\beta }_{j} {x}_{i}}}{{\sum }_{k=1}^{k=5}{e}^{{\beta }_{k}{x}_{i}}}\) k=1,2,3,4, 5 (4) To normalise the model, we set the parameter vector for agricultural wage Labour to zero (= 0), and the remaining coefficients measure the change in relation to this reference group. Probability (y = j/x) \(=\) \(\frac{{e}^{{\beta }_{j} {x}_{i}}}{1+ {\sum }_{k=2}^{k=5}{e}^{{\beta }_{k}{x}_{i}}}\) (5) For Reference Category Probability (y = 1) = \(\frac{1}{1+ {\sum }_{k=2}^{k=5}{e}^{{\beta }_{k}{x}_{i}}}\) (6) The marginal effect, which considers partial change in probability, is used to interpret the link between a specific variable and the probability result. The marginal effect of a regressor's increase on the likelihood of choosing alternative j is given by δp ij /δx i = p ij (β j − β̅ i ) Marginal effects are interpreted as each unit increase in the independent variable increases/decreases the probability of selecting employment choice j by the marginal effect expressed as a percentage. 4. Employments in Rural Non-Farm Sector Table 1 indicates that the rural non-farm sector experienced significant employment growth in the 1990s and again after 2010. The sector's share of employment increased from 20.0% in 1993–1994 to 23.5% in 1999–2000, and then rose further to 33.3% in 2009-10 before leveling off at 40.26% in 2019-20. The data clearly show that rural non-farm employment share more than doubled from the 1990s to 2019-20. Consequently, the sector consistently showed an upward trend during this period. Table 1 Usual Status (Principally + Subsidiary) of Non-farm Workers in Uttar Pradesh Rural Persons Rural Male Rural Female 1993-94 20.0 23.8 10.7 1999-00 23.5 28.2 12.5 2004-05 26.97 33.6 13.3 2009-10 33.3 39.1 14.4 2019-20 40.26 43.27 24.9 Source: Calculated from different rounds of NSSO and PLF survey Table 1 indicates a significant disparity between the number of male and female non-farm workers, with males outnumbering females. Furthermore, male participation in rural non-farm employment increased significantly over time. In 1993-94, 23.8% of rural males were engaged in the non-farm sector, which increased to 28.2% in 1999-00 and further rose to 33.6% in 2004-05. Notably, between 2009-10 and 2019-20, male participation in non-farm employment rose from 39.1–43.27%. During this period, male participation in rural non-farm employment grew, albeit at a moderate rate compared with previous periods. By contrast, female employment in the non-farm sector exhibits surprising trends, displaying sluggish growth in the initial periods and then accelerating later. The proportion of female workers in non-farm activities, which was 10.7% in 1993-94, marginally increased to 12.5% in 1999-00, then rose to 13.3% in 2004-05. It then increased significantly from 14.4–24.9% between 2009-10 and 2019-20. During the initial periods, growth hovered around 1–2%, with a 10% increase in growth during the last two periods. This indicates a break in the stagnation present in female participation in non-farm employment in the 1990s and early 2000s. It appears that female workers' willingness to participate in rural non-farm sectors increased, reflecting the growing importance of rural non-farm activities for rural women in later periods. 5. Industrial Distribution of Workforce in the State The U.P. has a low urbanization rate of 22.7 percent in 2011, with a significant transformation in the rural workforce from the farm to the non-farm sector and its industry-wise composition from 1993-94 to 2019-20. The transformation started gradually in the early nineties, picked up pace from 1993-94 to 1999–2000, further rose from 1999–2000 to 2004-05, and continued to increase from 2009-10 to 2019-20. Table 2 shows the evolution of rural non-farm employment during the survey period from the 1990s to 2019-20. The primary sector saw a decline of 3.7 percent from 1993-94 to 1999-00, followed by a 3.4 percent fall in 2004-05, a 5.9 percent point decrease in 2009-10, and a 7.16 percent fall in 2019-20. The decline in employment in the primary sector is due to the withdrawal of male counterparts, as data suggest. Male employment in the primary sector fell from 76.2 percent to 56.7 percent between 1993-94 and 2019-20, while female employment declined from 89.9 percent to 77.1 percent for the same period. Table 2 Sectoral Distribution of Usual(Principal + Sub) Status Rural Workers in Uttar Pradesh(in percentage) Sectors 1993-94 1999-00 2004-05 2009-10 2019-20 Rural Persons Primary Sector 79.9 76.2 72.8 66.9 59.74 Secondary Sector 8.7 11.4 14.5 20.0 23.32 Mining & Quarrying 0.2 0.1 0.2 0.3 0.09 Manufacturing 6.4 7.7 8.9 7.3 8.3 Electricity,Gas, & Water 0.1 0.1 0.1 0.1 0.21 Construction 2.0 3.5 5.3 12.3 14.72 Tertiary Sector 11.4 12.4 12.8 13.4 16.89 Trade,Hotels & Restaurants 4.3 5.4 6.2 6.7 8.43 Transport Communication 1.5 2.1 2.1 2.5 3.12 Other Services 5.6 4.9 4.5 4.2 5.34 Total Non-Farm 20.1 23.5 27.3 33.4 40.21 Rural Males Primary Sector 76.2 71.8 66.3 60.9 56.73 Secondary Sector 10 13.1 17.3 23.8 26.09 Mining & Quarrying 0.2 0.2 0.2 0.4 0.6 Manufacturing 7 8.3 9.6 7.7 8.03 Electricity,Gas, & Water 0.2 0.2 0.1 0.1 0.25 Construction 2.6 4.4 7.4 15.6 17.21 Tertiary Sector 13.8 15.1 16.3 15.3 17.87 Trade,Hotels & Restaurants 5.1 6.7 8.2 8.0 8.99 Transport Communication 2.1 2.9 3 3.2 3.87 Other Services 6.6 6.9 5.1 4.1 5.01 Total Non-Farm 23.8 28.2 33.6 39.1 43.96 Rural Females Primary Sector 89.9 87.5 86.5 85.4 77.10 Secondary Sector 5 6.9 8.2 8.3 10.77 Mining & Quarrying 0.0 0.0 0.2 0.0 0.00 Manufacturing 4.8 6.4 7.4 6.3 6.7 Electricity,Gas, & Water 0.0 0.0 0.0 0.0 0.03 Construction 0.2 0.5 0.6 2.0 4.04 Tertiary Sector 5.1 5.6 5.1 6.1 12.01 Trade,Hotels & Restaurants 2.1 1.8 1.8 2.6 5.62 Transport Communication 0.0 0.0 0.1 0.1 0.28 Other Services 3 3.7 3.2 3.4 6.11 Total Non-Farm 10.1 12.5 13.3 14.4 22.78 Source: Computed From Different rounds of NSSO Note: Data exclude the state of Uttaranchal Despite the gap narrowing in later periods, the non-farm sector remains more favorable to male workers as a source of employment than female workers. In 2019–20, 43.96 percent of male workers were engaged in the non-farm sector, compared to 22.78 percent of their female counterparts. This trend highlights the importance of the non-farm sector as an alternative source of employment that has emerged as an absorbing labour force, particularly for workers withdrawing from agriculture in search of alternative sources of employment. During the early 1990s, the tertiary sector employed more workers than the secondary sector did. However, the trend reversed in later periods, as the secondary sector absorbed 14.5 percent of workers compared to 12.8 percent in the tertiary sector. The difference widened in 2009-10 when the secondary sector accounted for 20 percent of workers as compared to 13.4 percent employment in the tertiary sector. The trend continued; in 2019–20, the secondary sector contributed 23.32 percent of rural non-farm workers, while the tertiary sector contributed 16.89 percent (Table 2 ). From 1993 to 1994 to 2004–05, the largest share of non-farm employment in the secondary sector was occupied by manufacturing. However, there was a major shift in the later period when the employment proportion in the construction sector rose, leaving manufacturing behind. The structural shift after the post-reform period suggests stagnation in the manufacturing sector in rural non-farm employment and an increasing share of the construction sector, which mainly absorbs casual labor that does not necessarily require specialization. This period also coincided with the enactment of the MGNREGA Act, 2005, under which a significant portion of work was undertaken in the construction sector, supposedly expanding rural connectivity. The emergence of the construction sector as the primary source of rural non-farm employment gives the impression of a distress-induced factor behind the larger share of non-farm employment in rural employment. Male participation was more intense than that of female workers, as data suggest that 17.2 percent of male workers were engaged in the construction sector, while female participation stood at 4.04 percent in the same year. The remaining parts of the secondary sector, such as mining and quarrying, electricity, gas, and water, and employment of both male and female workers showed no sign of change and remained low and stagnated during the entire period (Table 2 ). In the tertiary sector, trade and hotels, and restaurants remained the primary sources of employment throughout the period, and with gradual improvement, the share doubled from 4.3 percent to 8.43 percent from 1993-94 to 2019–20. The other services that employed 5.6 percent of the total non-farm employment in 1993–94 did not show much change and will remain at 5.34 percent in 2019–20. Although male employment, which was 6.6 percent in 1993–94, decreased and settled at 5.01 percent in 2019–20, while female employment, which was 3 percent in 1993–94, improved and reached 6.11 percent in 2019–20, most of the change occurred between 2009 and 10 and 2019–20. Further disaggregation industry-wise reveals that a significant proportion of female employment came from education during the period mentioned above. The transport and communication sector employed 1.5 percent of workers in 1993–94, and its share almost doubled to 3.12 percent in 2019–20. The employment share of both male and female workers was substantially different in the sector, as gauged by the fact that the female proportion of employment remained abysmally low at 0.28 percent, whereas the proportion of males changed from 2.1 percent in 1993–94 to 3.87 percent in 2019–20. Uttar Pradesh (UP) has undergone a structural shift in the composition of rural non-farm employment, as previously mentioned. The construction sector's increasing presence in non-farm employment represents the new reality of the rural non-farm economy, although other sectors, particularly tertiary sectors, have shown modest improvement. The post-reform period saw the emergence of construction and a decrease in the prominence of rural manufacturing in the proportion of non-farm employment, suggesting that the commodity-producing sector is no longer primarily responsible for job growth, which points to the casualization of rural employment in Uttar Pradesh. Additionally, the growing importance of trade, hotels, and restaurants indicates the role of own-account workers in rural non-farm employment. These factors suggest that distress could be the primary factor driving changes in the structure and composition of rural employment in UP. 6. Status Distributions of Rural Non-Farm Workers A notable feature of the rural workforce in Uttar Pradesh is that the self-employed category accounted for the largest portion of reported employment, followed by the casually employed category, with the regularly employed category accounting for a small proportion of total employment. According to Table 3 , 70.74% of the rural workers were self-employed, 21.22% were casually employed, and 8.04% were regularly employed. The table also shows that the proportion of self-employed workers was higher in females compared than in males, and the proportion of casually employed people was higher in males than in women. Regular employment was lower in both categories for males and females (8.63% and 5.79%, respectively). Table 3 Percentage Distribution of Rural Workers by Status and Sectors in Uttar Pradesh, 2019-20 Status of Employment/Sector Males Females Persons Self-Employed 67.30 84.11 70.74 Regularly-Employed 8.63 5.79 8.04 Casually-Employed 24.07 10.1 21.22 Total 100 100 100 Non-Farm Sector Self-Employed 34.87 45.91 35.91 Regularly-Employed 19.71 31.86 20.86 Casually-Employed 45.42 22.23 43.23 Total 100 100 100 Source: Author’s own calculation of unit level data of PLFS, 2019-20 While the employment pattern varied in the non-farm sector in rural UP compared to the overall rural employment in the state, as shown in Table 3 , the proportion of casual workers was higher in the rural non-farm sector, followed by self-employed workers at 43.23 percent and 35.91 percent, respectively. The proportion of regular employees was also higher in the non-farm sector in comparison to its status among rural workers in the state. The proportions of male and female workers in different categories in the non-farm sector also showed a significant deviation from the employment distribution of rural male and female workers. As the table indicates, the highest proportion of female workers was concentrated in the self-employed category, followed by, regular employed at 31.86 percent. The proportions of male workers who were self-employed, regular employees, and casually employed were 34.87 percent, 19.71 percent, and 45.42 percent, respectively. Further analysis of the employment status in the non-farm sector reveals that self-employment, which was nearly 60 percent in the early 1990s, has shown a continuous decline over the years and reached 35 percent in 2019-20 (Fig. 1). The proportion of casually employed individuals, which was around 20 percent in the early 1990s, recorded an upward trend during the period and stood at 43 percent in 2019–20. Regular salaried workers, who comprised 21 percent of the total non-farm employment at the beginning of the 1990s, remained steady throughout the period until 2019–20. In the secondary sector, the largest share of employment was generated under the "casually employed" category, reflecting the temporary nature of jobs in the sector. The "regularly employed" category also had a considerable share of 35.8 percent. The high share of casually employed people was attributable to the construction sector, where 89 percent of employment was casually employed. The lowest share of self-employed people was observed in the secondary sector, which accounted for 22 percent of the total distribution of employment by status. This is seen as a significant shift in the employment distribution in rural non-farm employment, which previously gave the impression that the largest share of employment in the secondary sector was found in the self-employment category, which has been replaced by the casually employed category in recent years. Table 4 Percentage distribution of usually working persons in usual status by broad Status in Employment for each industry of work in rural Uttar Pradesh, 2019-20 Sectors Self-employed Regular wage/salary Casual labour All Primary Sector 89.59 0.22 10.19 100.00 Secondary Sector 22 35.8 42.2 100 Mining and Quarrying 6.32 40.76 52.92 100.00 Manufacturing 48.66 28.9 22.45 100.00 Electricity, gas & water 23.31 72.54 4.14 100.00 Construction 10.05 0.89 89.06 100.00 Tertiary Sector 54.56 38.24 7.2 100 Trade, hotels and Restaurants 84.19 12.95 2.86 100.00 Transport and Communication 47.71 37.22 15.07 100.00 Other services 31.78 64.53 3.69 100.00 Source: Author’s own calculation of the unit-level data of PLFS, 2019-20 Furthermore, the manufacturing sector has the highest proportion of self-employed people (48.66 percent by size) and the lowest proportion of regularly employed people (28.9 percent of total employment distribution in manufacturing activities). Compared to construction activities, the manufacturing sector had the lowest share of casually employed workers. As previously stated, stagnation of rural manufacturing activities, with a decreasing share of self-employment and regular employment, contributed to the casualisation of the rural workforce, indicating a distress-induced shift of the rural workforce in Uttar Pradesh from agriculture to non-farm activities. The proportions of self-employed, regularly employed, and casually employed workers in the electricity, gas, and water industries are 23.31, 72.54, and 4.14 percent, respectively. As previously mentioned, the tertiary sector accounted for 16.89 percent of the total rural non-farm employment in Uttar Pradesh in 2019–20. Table 4 shows that self-employment comprised 54.56 percent of the total employment status in the tertiary sector. The proportion of regularly employed individuals was 38.24 percent, while casual workforce had the lowest share (7.2%) of the total rural employment status in the tertiary sector. In the tertiary sector, the proportion of self-employment was highest in trade, hotels, and restaurants, accounting for 84.19 percent of the total employment distribution. In transportation and communication, the incidence of self-employment and the regularly-employed category, as a percentage of total employment status, were 47.71 and 37.22, respectively. For other services, the highest share of regularly-employed workers (64.53 percent) was reported, followed by self-employment, which covered 31.78 percent of the total employment distribution by status. Table 5 Nature of Job Contract in Rural Uttar Pradesh, 2019-20. Job Contract Regularly-Employed Casually-Employed No Written Contract 72.88 98.79 Written contract for one year or less than a year 4.01 1.18 Written contract for more than one year to three years 1.93 0 Written contract for more than three years 21.17 0.03 Total 100 100 Source: As in Table 4 Table 5 indicates the nature of contracts for both regularly and casually employed individuals in rural Uttar Pradesh during 2019-20. This revelation highlights that a significant portion, 72.88% of regularly employed individuals, didn’t have a written contract detailing their job tenure and benefits. Furthermore, only 21.17% of the regularly employed workers have written contracts for more than three years, which implies that their job is permanent in nature. By contrast, 98.79% of casually employed individuals do not have a written contract, which exposes the vulnerability of these workers without job security and other benefits. 7. Monthly Consumption Expenditure in Different Status of Employment in Rural Uttar Pradesh (UP) To obtain an idea of the share of expenditure for different employment sources in the PLFS survey of the NSSO for agricultural and non-agricultural activities, we took the monthly consumption expenditure of different employment groups from farm to non-farm activities. In Table 6 , it is revealed that almost 39 percent of household monthly expenditure in rural UP has arisen from non-farm activities, compared to 55 percent from farming and 5.8 percent from agricultural labour. Therefore, it is evident that non-farm income constitutes an important part of earnings, which translates into consumption expenditure. Overall, non-farm regular, self-employment, and casual employment contribute 8.24, 13.77, and 16.96 percent of total monthly consumption expenditure, respectively. Further examination of non-farm sources of monthly consumption expenditure across different per capita expenditure deciles reveals that consumption expenditure from regular and self-employment activities of non-farm sources steadily increases as we move from the low per capita consumption decile to the high per capita consumption decile. For the lowest decile, non-farm regular and self-employment contributed 4.38 and 10.26 percent of the total monthly consumption expenditure, respectively, which increased to 21.2 and 19.02 percent of the total consumption expenditure for the highest decile. Table 6 also suggests that, for the lowest decile in rural UP, non-farm casual and agricultural wage labour contribute 29.2 and 11.12 percent of the total monthly consumption expenditure, respectively, and that the share drops to 9.3 and 0.92 percent for the highest decile. The share of cultivation in the lowest and highest consumption deciles shows improvement as one moves from the lowest to the highest decile. Table 6 Monthly Consumption Expenditure in Rural Uttar Pradesh by Per Capita Consumption Decile, 2019-20 Decile Cultivation Agriculture Wage Labour Non-farm Regular Employment Non-farm Self Employment Non-farm Casual Employment Total non-farm Employment Per Capita Monthly Consumption expenditure(in Rupees) Lowest 45.04 11.12 4.38 10.26 29.2 43.84 3396 D2 59.34 5.21 5.16 10.21 20.08 35.45 4762 D3 58.94 6.05 4.5 10.15 20.37 35 5478 D4 58.79 6.53 7.1 12.53 15.06 34.69 6052 D5 58.61 6.49 6.33 11.62 16.94 34.89 6790 D6 58.31 3.63 8.8 15.43 13.83 38 7755 D7 60.61 2.8 8.99 16.48 11.12 36.59 8402 D8 55.53 7.62 9.72 17.38 9.75 36.85 9530 D9 52.54 2.84 9.19 19.47 15.97 44.63 11427 Highest 49.49 0.92 21.2 19.09 9.3 49.59 17176 Total 55.22 5.82 8.24 13.77 16.96 38.96 8076.8 Source: Calculated from unit level data of Periodic Labour Force Survey of NSSO, 2019-20 The pattern of per capita monthly consumption expenditure among the different deciles is consistent with the idea that the non-farm sector is a collection of a range of activities, including both productive and low-productivity ones. The former is associated with upward mobility, a high standard of living, and a successful transition from agriculture to the non-farm sector, whereas the latter is more likely to be residual activities into which people are pushed due to a lack of work in agricultural activities or insufficient work to meet their needs. Table 6 shows that high per capita consumption expenditure is associated with regular non-farm employment, which is highly productive, whereas low per capita consumption expenditure is associated with low-productive casual employment. Notably, a larger percentage of consumption expenditure is in the highest deciles for non-farm self-employment, indicating the high productivity of activities associated with non-farm self-employment. This suggests that a section of people involved in non-farm self-employment exhibit high returns, which also reflects high consumption expenditure. The important implication of these findings is that they demonstrate that the share of consumption expenditure is high among non-farm regular and self-employed workers, and low in casual employment. 8. Multinomial Logit Estimates of Farm and Non-Farm Employment Probabilities A multinomial logit model was used to examine the individual, household, and regional factors that influence the likelihood of non-farm employment in rural Uttar Pradesh. The model considers five broad employment categories: farming/cultivation, agricultural wage labor, non-farm self-employment, non-farm regular employment, and non-farm casual employment. The study also included individual-level factors such as sex, age, educational status, and caste. At the household level, information was gathered on the size of the household, land owned, and land available for cultivation. As Uttar Pradesh is characterized by vast landscapes and regional variation, the model was designed to capture the regional dimension in explaining employment patterns in agriculture and the non-farm sector. A multinomial logit model requires the selection of a particular category as the numeraire for comparison with other categories. In our study, we chose agricultural labor wages as the numeraire for the comparison group. This allowed us to examine how other employment categories differed from those in the comparison group. Therefore, the parameter estimates for the categories in the model should not be interpreted as correlates of employment in a specific occupational category, but rather as indicators of the strength of association between a particular explanatory variable and the respective occupational category, relative to the same explanatory variable as agricultural wage labor. 8.1 Results of Multinomial Logistic Regression for NSSO round of 2009-10 Table 7 provides parameter estimates for the multinomial logit for rural Uttar Pradesh (marginal probabilities of the variables are shown in appendix A1). The results show that women are more likely to be involved in farming and agricultural wage labour than in any other non-farm employment category. The parameter estimates for all three non-farm activities were significantly negative and appeared positive only for farming. Therefore, it can be concluded that women are confined to agriculture-related activities, whereas men are more likely to work in non-farm activities. The parameter estimate based on age reveals that the young have a relatively higher probability of working in agricultural wage labour. As age increases, the probability of employment in other occupations also increases compared with agricultural wage labour. This result is consistent with the notion that farming, regular employment, and self-employment in the non-farm sector are more suited for older people because of the capital and necessary skills required to be employed in these occupations. The results suggest that completing primary or middle education increases the likelihood of working in farming or non-farm activities and decreases the likelihood of working as agricultural wage labour. Additionally, Table 8 shows that as the level of education improves, the likelihood of working in non-farm regular employment increases significantly relative to agricultural wage labour. The table also reveals that individuals with secondary education are more likely to work in non-farm casual employment compared to agricultural wage labour, but the association is weaker. Overall, it is clear that education has a positive impact on the probability of employment in the non-farm sector for different occupations, but the odds of being a casual non-farm worker are lower than those of self-employment and regular employment relative to agricultural wage labour. Household size was positively and significantly related to all four occupational categories, but a strong association was more prominent in non-farm self-employment and casual employment relative to agricultural wage labour. This finding demonstrates that individuals from large households are more likely to engage in non-farm self-employment and casual employment. As noted earlier, non-farm self-employment, and more particularly regular non-farm employment, corresponds to productive activities, and the results indicate that large households in rural Uttar Pradesh are heterogeneous in character, comprising both poor and rich. Table 7 Parameter Estimate for Multinomial Logit Model (2009-10) (Agriculture Wage Labour as Comparison Group) Number of obs: 24,836 Wald chi2(48): 10938.66 Prob > chi2:0 Pseudo R2 = 0.2898 Farming/Cultivation Non-Farm Self-Employed Non-Farm Regularly Employed Non-Farm Casually Employed Variables Coefficients Prob-Value Coefficients Prob-Value Coefficients Prob-Value Coefficients Prob-Value Sex 0.72 0 -1.14 0 -0.47 0 -2.43 0 Age 0.04 0 0.044 0 0.058 0 0.035 0 Literate below Primary 0.35 0.001 0.55 0 0.53 0.01 0.075 0.46 Primary/Middle 0.53 0 0.54 0 1.95 0 0.026 0.69 Secondary 2.35 0 2.29 0 4.02 0 1.07 0 Higher Education 2.22 0 2.32 0 5.42 0 -0.73 0.07 Household-Size 0.06 0 0.11 0 0.062 0 0.081 0 Land-Owned 0.55 0 0.57 0 0.78 0 0.22 0.006 Land-Cultivated 1.59 0 -0.45 0 0.014 0.82 -0.79 0 ST -1.099 0.002 -0.19 0.58 -0.84 0.07 0.11 0.75 SC -1.55 0 -1.59 0 -1.73 0 -0.10 0.34 OBC -0.51 0 -0.68 0 -1.11 0 -0.25 0.032 Western 0.42 0 -0.044 0.63 0.69 0 -1.03 0 Central 0.93 0 0.62 0 0.52 0.003 -0.19 0.085 Eastern 1.065 0 0.66 0 1.05 0 -0.37 0 Intercept -6.096 0 -0.095 0.702 -4.99 0 3.49 0 Parameter estimates of landowning variables suggest that individuals exhibiting land are more likely to take up either cultivation or any of the non-farm activities relative to engaging in agricultural wage labour. The variable coefficient shows that the strength of the association is stronger with non-farm regular and self-employment, followed by cultivation and casual employment. This finding is consistent with a study by Lanjouw and Shariff ( 2004 ), which suggests that landowning households have opportunities for both cultivation and non-farm activities through the wealth effect, and agricultural wage labour is the least chosen occupation, even relative to casual non-farm casual employment. The positive and highly significant coefficient of land cultivated indicates that farming is the preferred option for households involved in cultivation; relative to agricultural wage employment. The negative coefficient for non-farm casual employment suggests that this option is the least preferred for households that own land for cultivation, compared to agricultural wage labor. This indicates that non-farm casual work is generally seen as a last resort and is mostly taken up in the absence of agricultural activity. The coefficients for other employment categories were statistically insignificant. According to Table 7 , individuals belonging to ST, SC, and OBC have a lower probability of involvement in cultivation, self-employment, and regular employment in the non-farm sector compared to agricultural wage labourers. This suggests that the majority of productive employment in cultivation and non-farm activities is occupied by 'Others', while individuals from disadvantaged social groups such as SC, ST, and OBC are more likely to be employed in agricultural wage labor and non-farm casual work. The southern region had the lowest level of development and served as the reference group. The parameter estimates indicate that individuals in the western, central, and eastern regions of the state are more likely to engage in cultivation, self-employment, and regular employment in the non-farm sector compared to agricultural wage labor. The negative coefficient for non-farm casual employment in all three regions suggests that non-farm casual employment is more prevalent in the southern region than in the other regions. These findings confirm that individuals in the western, eastern, and central parts of the state are more engaged in remunerative farm and non-farm activities, whereas non-farm casual employment is more common in the southern region. 8.2 Results of Multinomial Logistic Regression for NSSO round of 2019-20 Multinomial logit regression is also performed on the PLFS rounds of 2019–20 to understand the recent changes in the occupational structure of rural employment shaped by individual, household, and regional characteristics. As evident from Table 8 , the coefficient of gender in farming indicates that relative to agricultural labour, men have fewer chances to work in agriculture and have a higher probability of working in non-farm casual and self-employment. With an increase in age, there are more opportunities to work in cultivation, non-farm self-employment, and regular employment relative to agricultural labour, while casual wage labour in the non-farm sector is not a preferred choice with an increase in age. Therefore, young people are more likely to work in agricultural wage labour and casual non-farm work (the marginal probabilities of the variables are shown in Appendix A2). Similar to earlier results, this time as well, education is strongly and significantly associated with employment outside the agricultural wage labour. The parameter estimate at all levels of education, from primary to higher education, is consistently positive for all four occupational categories, with coefficient values increasing significantly for self-employment and regular noon-farm employment with increases in level of education. The study found that the highest probability of being employed as a regular non-farm worker is associated with a higher level of education, followed by non-farm self-employment and cultivation. Household size, was positively related to all four occupational categories, corroborating the results of the previous round. This indicates that as household size increases, the probability of being involved in cultivation and all non-farm activities increases in comparison to being engaged in agricultural wage labour. It can be observed that individuals belonging to SC and OBC have relatively fewer chances to engage in any of the four occupational categories compared to agricultural wage labour, as shown in Table 8 . This reaffirms the previous outcome that 'Others' from social groups have better chances to access cultivation and non-firm activities. The coefficients for ST are statistically insignificant owing to their high p-value. For region-wise comparison, the southern region was chosen as the reference category, similar to the previous model. The parameter estimates for the western region are negative for all four occupational categories, while it was statistically insignificant for non-farm regular employment. Similar to previous results, the negative and statistically significant coefficient of the non-farm casual category for the western and central regions showed that the incidence of casual non-farm employment was more pronounced in the southern region. Table 8 Parameter Estimate for Multinomial Logit Model (2019-20) (Agriculture Wage Labour as Comparison Group) Number of obs: 8,141 Wald chi2(48): 1914.77 Prob > chi2:0 Pseudo R2 = 0.1580 Farming/Cultivation Non-Farm Self-Employed Non-Farm Regularly Employed Non-Farm Casually Employed Variables Coefficients Prob-Value Coefficients Prob-Value Coefficients Prob-Value Coefficients Prob-Value Sex -0.51 0 0.311 0.057 -0.016 0.928 2.01 0 Age 0.023 0 0.010 0.059 0.018 0.059 -0.014 0.01 Literate below Primary 0.799 0.022 1.044 0.007 1.705 0 0.40 0.277 Primary/Middle 0.594 0 0.946 0 1.544 0 0.006 0.969 Secondary 1.29 0 1.61 0 2.49 0 0.34 0.075 Higher Education 2.296 0 2.95 0 4.56 0 0.15 0.746 Household-Size 0.098 0.001 0.12 0 0.1 0.002 0.068 0.029 ST -0.11 0.914 0.096 0.93 0.44 0.691 1.6 0.129 SC -2.045 0 -1.76 0 -1.66 0 -0.29 0.262 OBC -0.504 0.036 -0.37 0.142 -0.53 0.04 0.12 0.658 Western -1.05 0 -1.2 0 -0.48 0.123 -1.196 0 Central -0.94 0.001 -1.17 0 -0.68 0.04 -1.74 0 Eastern 0.305 0.268 0.18 0.506 0.23 0.478 0.21 0.463 Intercept 3.06 0 1.103 0.042 -0.94 0.11 -0.25 0.646 9. Conclusions The employment situation in Uttar Pradesh as a whole was assessed in this study with a focus on non-farm employment. State-specific data show an increase in non-farm employment. Although female workers have also experienced a recent uptick in such activities, male workers prefer non-farm activities. However, a large portion of this work was part-time in nature. The primary objective of our study was to investigate the potential causes of employment growth in non-farm activities over time, specifically (i) whether it was due to agricultural growth's demand-pull factors, (ii) distress-push factors that drove people to engage in non-farm activities because they were unable to find employment in the farm sector, and (iii) factors exogenous to the farm sector. The first scenario is disregarded because agricultural growth,—characterized by a steadily declining agricultural contribution to the net state domestic product and a low income share,—could not have resulted in an increase in non-farm employment. On the other hand, the type and extent of non-farm employment choices made by individuals and households are partially determined by external factors, including land endowments, educational attainment, and regional developments. The distress-push factor, which was mostly responsible for employment growth in the non-farm sector, seems to be the dominant force. The industrial composition of the workforce highlights the importance of employment growth in the secondary sector. Another noteworthy fact is that the post-reform era saw the rise of construction and the substantial reduction of rural manufacturing in the proportion of non-farm employment, suggesting that the sector producing commodities was no longer primarily responsible for the increase in employment and pointing to the casualization of rural employment in Uttar Pradesh. Additionally, the growing significance of retail, hotels, and restaurants indicates that account workers play a significant role in rural non-farm employment. All of these considerations suggest that distress may be the primary driver of changes in the organisation and composition of rural employment in the UP. We conducted a survey of employment patterns in terms of the job status of workers who had an impact on employment in search of additional evidence before drawing our conclusions. In Uttar Pradesh, we observed that a higher proportion of the casual workforce and stagnation of rural manufacturing activities, as well as a declining proportion of people who were self-employed and regularly employed, contributed to the casualization of the rural workforce. This suggests that the rural workforce was forced to shift from agricultural to non-farm activities as a result of the distress. Further evidence supporting our theory that non-farm workers were primarily motivated to their predicament by distress factors was provided by the large presence of landless and marginal landowners engaged in non-farm activities, many of whom had little education, and the majority belonged to socially downtrodden classes with limited assets. In addition, it was demonstrated that in rural UP, non-farm activities accounted for approximately 39% of monthly household expenditures in 2019–20. Additionally, the distribution of per capita consumer spending across deciles demonstrates that non-farm activities include both productive and subsistence occupations. The key finding is that self-employment and regular employment outside agriculture seemed to be high-return activities, whereas casual employment outside agriculture was linked to low-return activities. The study also revealed that across all employment status categories, including agricultural labour, cultivators, non-farm regular employment, non-farm self-employment, and non-farm casual employment, the likelihood of working in high-paying non-farm regular and self-employment activities increases with better educational attainment, larger landholdings, and placement among the upper castes in social groups. Declarations Availability of Data and Materials : Data and materials can be made available on demand. Conflict of Interest Statement : The corresponding author states that there are no conflicts of interest. Competing Interests: The paper is part of my PhD thesis. Ethical approval : The study was approved by the institutional review board of JNU for research. Funding : Not applicable Authors’ contributions : The author formulated the theory, came up with the idea, and carried out the calculations. Additionally, he gathered secondary data and checked the analytical methods. Acknowledgements : Not Applicable Disclaimer: No funding was received to assist with the preparation of this manuscript. References Abdulai, A., and Delgado, C.L. 1999. Determinants of non-farm earnings of farm-based husbands and wives in northern Ghana. American Journal of Agricultural Economics 81(1): 117-130. Abraham, Vinoj. 2011. Agrarian distress and rural non-farm sector employment in India , Centre for development studies, Kerala, India. Basant, Rakesh. 1993. Diversification of Economic Activities in Rural Gujarat: Key Results of a Primary Survey, The Indian Journal of Labour Economics, 36(3), pp 361-86. 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Diversification of Economic Activities and Non-Agricultural Employment in Rural India: An Exploratory Analysis, Economic and Political Weekly, 31(33), pp 2243-51. Visaria, Pravin and Rakesh basant 1995. Non-Agricultural Employment in India: Trends and Prospects, Sage Publications, New Delhi Supplementary Files Appendix.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3360499","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":238138596,"identity":"5b3008c7-f3c5-4d01-b252-903a798e822b","order_by":0,"name":"Shadab Hashmi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIiWNgGAWjYDACCSBibDgAZhxIqACKMDM3kKDlwRmQFkYStDA+bAMJEdAiP7v54Y2fO+7Iy8/uTjyQOK82mr8dqOVHxTacWgzuHDO27D3zzHDDnbMbDiRuO5474zBjA2PPmdu4tUgkmEnwth1m3CCRC9JyLLcBqIWZsQ23FvkZ6d8k/7Ydtp8/A6RlzrHc+YS0MNzIMZMG2pLYcAOkpaEmdwMhLQY3coqtZc8cTt4A0pJw7EDuRqCWg/j8AnTYxptvdxy2BTps88cfNXW5884fPvjgRwUeh6GBw2DyANHqgaCOFMWjYBSMglEwQgAAQdRsq2i3li0AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-1436-2435","institution":"Rajendra College","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Shadab","middleName":"","lastName":"Hashmi","suffix":""}],"badges":[],"createdAt":"2023-09-16 06:30:06","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false,"coiExplicitlySet":false},"doi":"10.21203/rs.3.rs-3360499/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3360499/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":44199098,"identity":"fe5677b1-7344-4daa-bf94-87d432005d3d","added_by":"auto","created_at":"2023-10-06 16:41:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":30650,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePercentage distribution of Rural Workers by Usual Status in Non-Farm Sector in Uttar Pradesh since 1993\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSource: Computed From Different rounds of NSSO\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3360499/v1/9945f4cb3ffda49c0843cc74.png"},{"id":44199561,"identity":"5bf51d56-327c-47b0-b441-a63bf32b7e43","added_by":"auto","created_at":"2023-10-06 16:49:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":579960,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3360499/v1/5f1a7389-b4b1-4ca0-8d2b-b8959a2a7b5e.pdf"},{"id":44199099,"identity":"331cd278-2788-41c0-bfb8-b31a61e1d8fa","added_by":"auto","created_at":"2023-10-06 16:41:04","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15847,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-3360499/v1/6697d2870bcafea4625dd08b.docx"}],"financialInterests":"","formattedTitle":"\u003cp\u003e\u003cstrong\u003eIs Rural Non-farm Employment Distress Driven in Uttar Pradesh? Evidence from the NSSO rounds and PLF Survey\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction and Background","content":"\u003cp\u003eDespite high economic growth, the structural transformation of the Indian economy has been slow, with a widening labor productivity differential between non-agricultural sectors and agriculture. Labor absorption in the urban economy, especially in the manufacturing sector, has been low; formal sector jobs are few and declining as a share of employment, and labor contracts are becoming informalized. Consequently, combined with rapid population growth, the labor force in rural areas is still growing rapidly. Agricultural growth has not responded to high-income growth and agricultural employment is growing slowly. The rural non-farm sector has emerged as the major source of rural and economy-wide employment growth.\u003c/p\u003e \u003cp\u003eAccording to 2011 census data, Uttar Pradesh is the most populous state in the country, with 16.5 percent of the country's population. Of the total population of the state, 77.7 percent were from rural areas and 22.3 percent were from urban areas. Over the last decade, UP's annual population growth rate has averaged two percent, which is higher than the national average. The state was considered a good case for this study as nearly two-thirds of households depend mainly on agriculture for their livelihoods. This study shows that non-farm employment has grown dramatically over time, moving from 20.1 percent of the rural workforce in 1993\u0026ndash;1994 to 40.21 percent in 2019\u0026ndash;20, and the size of the rural non-farm economy doubled. Male workers showed a greater increase than their female counterparts, but female employees had a faster and more diverse increase, leading to the conclusion that the drop in agricultural earnings compelled a section of rural women to engage in non-agricultural activities to supplement declining family income, and a considerable proportion of rural females were employed in the service sectors, indicating new opportunities for them due to their educational attainment.\u003c/p\u003e \u003cp\u003eThe study also shows that following the reform, there was a rise in the construction sector and a decline in rural manufacturing in terms of non-farm employment. This indicates that the primary driver of employment growth is no longer the manufacturing sector, and points to the casualization of rural employment in Uttar Pradesh. As part of our examination of non-farm employment, we categorized workers into three groups: self-employed individuals, regular employees, and casual workers. The proportion of self-employed individuals has steadily decreased over time, whereas the share of casual workers has increased. This trend has contributed to the casualization of the rural workforce, with a declining share of self-employed and regular employees.\u003c/p\u003e \u003cp\u003eA study using a multinomial logit model found that higher educational attainment, larger landholdings, and association with upper castes in social groups increase the likelihood of engaging in high-paying non-farm regular and self-employment activities across all employment status categories, including agricultural labour, cultivators, non-farm regular employment, non-farm self-employment, and non-farm casual employment. Furthermore, approximately 40% of monthly household expenditures are attributed to rural non-farm activities, which constitute a significant portion of the total expenditure incurred by households. Additionally, the study revealed that self-employment and regular employment outside agriculture had high returns, whereas casual employment outside agriculture was associated with low returns.\u003c/p\u003e \u003cp\u003eDuring the 1990s, a growing number of agricultural households engaged in small, temporary, part-time, or full-time salaried or professional occupations, businesses, and other activities. Examples of unregistered small enterprises operating in the informal sector include pushcarts, street vendors, roadside kiosks, tea stalls, tempo-taxis, agro service centers, small wayside garages, and workshops. This trend of diversifying away from agriculture is supported by the increasing share of non-farm employment, which is further demonstrated by data from later times.\u003c/p\u003e \u003cp\u003eIt is also pertinent to mention that since the Mahatma Gandhi National Rural Employment Guarantee Act 2005 (MGNREGA) was implemented, employment patterns have changed significantly throughout rural India. Uttar Pradesh, one of the largest inhabitants of the rural population, is expected to be affected by changes in the rural labour market. To fully comprehend this transformation of the rural workforce and to create a strategic framework for rural development that considers the implications for transition in the rural economy, a thorough, disaggregated study is necessary. This study investigates the patterns and trends of the rural non-farm economy in Uttar Pradesh.\u003c/p\u003e \u003cp\u003eThis study attempts to assess the employment situation in Uttar Pradesh's non-farm economy in light of the aforementioned facts. Wherever possible, the analysis also makes an effort highlights the regional differences within the state. This study is comprised of nine sections. Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reviews the relevant literature. Section \u003cspan refid=\"Sec3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the data and methodology of the study. Section \u003cspan refid=\"Sec4\" class=\"InternalRef\"\u003e4\u003c/span\u003e describes the size of the non-farm sector in the state. Section \u003cspan refid=\"Sec5\" class=\"InternalRef\"\u003e5\u003c/span\u003e discusses sectoral employment distribution. This section also attempts to characterise the nature of employment in the non-farm sector. Section \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents the distribution of workers according to their employment status. In Section \u003cspan refid=\"Sec7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, we examine monthly household consumption expenditure in population deciles for different employment statuses, including agricultural labor, farming, non-farm self-employment, non-farm regular employment, and casual employment. In Section \u003cspan refid=\"Sec8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, we examine the correlates of employment in the non-farm sector among various non-farm employment types, agricultural wage employment, and farming, based on a multinomial logit model. The broad findings are summarised in Section \u003cspan refid=\"Sec11\" class=\"InternalRef\"\u003e9\u003c/span\u003e.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eThe literature describes two major motivations for the transformation from agricultural to rural non-farm employment (RNFE). The first is the push factor, which examines the behavior of farm households in a risky environment with factors such as landlessness, low capital, drought, missing credit, and insurance markets. Under these circumstances, households try to self-insure themselves through coping behaviors, including turning to non-farm activities as a means of insuring against crop failure (Reardon, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). For example, individual farm households may migrate seasonally to cities as a safeguard against rural risks (De Haan and Roglay, 2002). The second factor, the pull factor, examines household behavior when farm income is high, productivity is high, and resources are rich. In such cases, farm households tend to diversify into high-return; non-farm activities. For instance, the Green Revolution in India saw an increase in farm income and high rural savings, making capital available for non-farm activities (Hazell and Ramasamy, 1991).\u003c/p\u003e \u003cp\u003eOn a theoretical level, there are two contrasting views in the literature on the role of rural non-farm employment in structural transformation. One view is that it is a residual sector, and its relative importance in rural areas should decline with agricultural development (Hymer and Resnick, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1969\u003c/span\u003e). This sector primarily accommodates workers unable to find jobs in the agricultural sector. The growth of rural non-farm employment is largely a manifestation of economic distress caused by the failure of agriculture to absorb the growing rural population (Vaidyanathan \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1986\u003c/span\u003e; Basant and kumar \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Dev \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Saith \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Bhalla \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Eapen 1995; Fisher et al \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1997\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eAnother view holds that the rural non-farm sector is a crucial organ for rural development during structural transformation. Growth of the rural non-farm sector can reduce rural poverty, particularly for small and marginal farmers (Ravallion and Datt \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). It plays a significant role in the formation of skills and human capital in rural areas (Hazell and Haggblade \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). There is a strong correlation between agricultural growth and the non-farm sector. The growth of the rural non-farm sector has become vital for agricultural development because of its strong forward and backward linkages (Ranis and Stewart, 1993). RNFE acts as an intermediate during structural transformation, when the share of agriculture decreases and is accompanied by a shift of labor to a corresponding rise in manufacturing and services. It is plausible to assume that many transfers from agriculture to secondary and tertiary sectors occur through RNFE. Understanding the transition through RNFE is crucial for grasping the overall economic growth.\u003c/p\u003e \u003cp\u003eThe difference between demand-pull and distress-push diversification has implications for evaluating the economic significance of the RNFE sector. Distress-push diversification may require policymakers to develop appropriate social safety nets and interventionist policies to mitigate the short-term negative effects that sometimes accompany this type of diversification. Where demand-pull factors drive the process of diversification, policymakers may seek to provide a suitable enabling environment to support the development of RNFE and sustainable rural livelihoods. However, deciding whether the demand-pull or distress-push factors are at work is not straightforward. The key features of distress-push and demand-pull diversification are outlined below.\u003c/p\u003e \u003cp\u003eThe table below shows that distress-push diversification occurs in rural areas that have one or more of the following characteristics: geographical isolation, low-quality physical infrastructure, low human capital, underdeveloped markets, resource scarcity, and the incidence of some natural disasters. Demand-pull diversification is possible in the presence of expanding technological innovations (within or outside agriculture), market development, or intensifying links with markets outside the local economy.\u003c/p\u003e \u003cp\u003eThe Push and Pull factors of RNFE diversification\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePush Factors\u003c/p\u003e \u003cp\u003e\u0026bull; Population growth\u003c/p\u003e \u003cp\u003e\u0026bull; Increasing scarcity of arable land and decreasing access to fertile land\u003c/p\u003e \u003cp\u003e\u0026bull; Declining farm productivity\u003c/p\u003e \u003cp\u003e\u0026bull; Declining returns from farming\u003c/p\u003e \u003cp\u003e\u0026bull; Lack of access to farm input markets\u003c/p\u003e \u003cp\u003e\u0026bull; Decline in natural resource base\u003c/p\u003e \u003cp\u003e\u0026bull; Temporary events and shocks\u003c/p\u003e \u003cp\u003e\u0026bull; Absence or lack of access to rural financial markets.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePull Factors\u003c/p\u003e \u003cp\u003e\u0026bull; Higher return on labour in RNFE\u003c/p\u003e \u003cp\u003e\u0026bull; Higher return on investment in the RNFE\u003c/p\u003e \u003cp\u003e\u0026bull; Lower risk of RNFE compared to on-farm activities\u003c/p\u003e \u003cp\u003e\u0026bull; Generation of cash in order to meet household objectives\u003c/p\u003e \u003cp\u003e\u0026bull; Economic opportunities, often associated with social advantages, offered in urban centers and outside of the region or country.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eSource: Davis and Pearce(2000)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIt might be concluded that distress-push diversification characterises households in rural populations that are less endowed or have lower incomes. These households will venture out in non-agricultural activities that return less than demand demand-pull diversification, since higher-return activities typically require higher investment that only richer households can afford. For instance, poorer households obtain a larger share of their non-agricultural income from wage employment, whereas richer households have better opportunities to enter non-agricultural activities in their own independent enterprises.\u003c/p\u003e \u003cp\u003eThe literature on the determinants and nature of non-farm employment is discussed by probing certain broad relationships. However, a key question to be considered is whether growth in rural non-farm employment is distress-driven or demand-driven, and the role of exogenous factors in this diversification. At the regional level, the dynamics of slow-growing regions have remained largely unexplored. Paired comparisons of slow- and fast-growing regions and of poor and non-poor households within each offer prospects for revealing how aggregate trends in farm and non-farm productivity, changing labor allocation and income composition, and the fluidity of rural-urban interactions drive transformation in a variety of rural settings.\u003c/p\u003e"},{"header":"3. Description of data and Methodology","content":"\u003cp\u003eThe present study is primarily based on the data of four quinquennial rounds and one periodic labour force survey(PLFS) of NSSO on employment and unemployment pertaining to the periods 1993-94, 1999-00, 2004-05, 2009-10 and 2019-20 respectively. Data from these rounds were used to analyse of the distribution of rural workers usually employed (principal status\u0026thinsp;+\u0026thinsp;subsidiary status) in the rural non-farm sector in Uttar Pradesh. The analysis was also restricted to workers in the age-group of 15\u0026ndash;59 years.\u003c/p\u003e \u003cp\u003eWe use multinomial logit estimation to determine the effects of various factors on employment status, which are further divided into the low- and high-skilled activities of the state. The dependent variable is not limited to two categories in this type of regression. A multinomial logistic regression is one in which the predictor variables, which may be continuous or categorical, explain the categorical dependent (outcome) variable by using two or more categories. This is an extension of binary logit model. A reference category is used to compare categories. Similar to the coefficients of the reference group for the dummy variable, the coefficients of the reference group are all zero. Multinomial logistic regression, such as binary logistic regression, estimates the likelihood of categorical membership using maximum the likelihood estimation.\u003c/p\u003e \u003cp\u003eExtending the above logit equation into a generalised form, for the ith individual with j choices the utility choice can be assumed as follows: (Greene, 2003)\u003c/p\u003e \u003cp\u003eEmp\u003csub\u003eij\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;β\u0026thinsp;+\u0026thinsp;β\u003csub\u003ei\u003c/sub\u003eX\u003csub\u003eij\u003c/sub\u003e+ U\u003csub\u003eij\u003c/sub\u003e (2)\u003c/p\u003e \u003cp\u003eThe vector of the conditioning variables is denoted as Xi and the vector of the regression parameters is denoted as \u0026#120573;\u0026#119894;. It can be assumed that Empij produces the most utility for a specific revealed choice j. So, the statistical model is obtained from the likelihood that option j is chosen over option k, which is:\u003c/p\u003e \u003cp\u003eProbability (Emp\u003csub\u003eij\u003c/sub\u003e \u0026gt;Emp\u003csub\u003eij\u003c/sub\u003e ) for all other k\u0026thinsp;\u0026ne;\u0026thinsp;j (3)\u003c/p\u003e \u003cp\u003eA multinomial logit model is used to study the factors that determine low- and high-skilled activities. Given that there are five dependent variables, the following set of coefficients \u0026szlig; are estimated for each occupational category: Let\u0026rsquo;s assume that y is the dependent choice variable:\u003c/p\u003e \u003cp\u003eProbability (y\u0026thinsp;=\u0026thinsp;j/x) \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(=\\frac{{e}^{{\\beta }_{j} {x}_{i}}}{{\\sum }_{k=1}^{k=5}{e}^{{\\beta }_{k}{x}_{i}}}\\)\u003c/span\u003e\u003c/span\u003e k=1,2,3,4, 5 (4)\u003c/p\u003e \u003cp\u003eTo normalise the model, we set the parameter vector for agricultural wage Labour to zero (=\u0026thinsp;0), and the remaining coefficients measure the change in relation to this reference group.\u003c/p\u003e \u003cp\u003eProbability (y\u0026thinsp;=\u0026thinsp;j/x) \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(=\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{{e}^{{\\beta }_{j} {x}_{i}}}{1+ {\\sum }_{k=2}^{k=5}{e}^{{\\beta }_{k}{x}_{i}}}\\)\u003c/span\u003e\u003c/span\u003e (5)\u003c/p\u003e \u003cp\u003eFor Reference Category\u003c/p\u003e \u003cp\u003eProbability (y\u0026thinsp;=\u0026thinsp;1) = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{1}{1+ {\\sum }_{k=2}^{k=5}{e}^{{\\beta }_{k}{x}_{i}}}\\)\u003c/span\u003e\u003c/span\u003e (6)\u003c/p\u003e \u003cp\u003eThe marginal effect, which considers partial change in probability, is used to interpret the link between a specific variable and the probability result. The marginal effect of a regressor's increase on the likelihood of choosing alternative j is given by\u003c/p\u003e \u003cp\u003eδp\u003csub\u003eij\u003c/sub\u003e/δx\u003csub\u003ei\u003c/sub\u003e= p\u003csub\u003eij\u003c/sub\u003e (β\u003csub\u003ej \u0026minus;\u003c/sub\u003e β̅\u003csub\u003ei\u003c/sub\u003e)\u003c/p\u003e \u003cp\u003eMarginal effects are interpreted as each unit increase in the independent variable increases/decreases the probability of selecting employment choice j by the marginal effect expressed as a percentage.\u003c/p\u003e"},{"header":"4. Employments in Rural Non-Farm Sector","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e indicates that the rural non-farm sector experienced significant employment growth in the 1990s and again after 2010. The sector's share of employment increased from 20.0% in 1993\u0026ndash;1994 to 23.5% in 1999\u0026ndash;2000, and then rose further to 33.3% in 2009-10 before leveling off at 40.26% in 2019-20. The data clearly show that rural non-farm employment share more than doubled from the 1990s to 2019-20. Consequently, the sector consistently showed an upward trend during this period.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUsual Status (Principally\u0026thinsp;+\u0026thinsp;Subsidiary) of Non-farm Workers in Uttar Pradesh\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRural Persons\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRural Male\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRural Female\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1993-94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1999-00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2004-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2009-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019-20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSource: Calculated from different rounds of NSSO and PLF survey\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e indicates a significant disparity between the number of male and female non-farm workers, with males outnumbering females. Furthermore, male participation in rural non-farm employment increased significantly over time. In 1993-94, 23.8% of rural males were engaged in the non-farm sector, which increased to 28.2% in 1999-00 and further rose to 33.6% in 2004-05. Notably, between 2009-10 and 2019-20, male participation in non-farm employment rose from 39.1\u0026ndash;43.27%. During this period, male participation in rural non-farm employment grew, albeit at a moderate rate compared with previous periods.\u003c/p\u003e \u003cp\u003eBy contrast, female employment in the non-farm sector exhibits surprising trends, displaying sluggish growth in the initial periods and then accelerating later. The proportion of female workers in non-farm activities, which was 10.7% in 1993-94, marginally increased to 12.5% in 1999-00, then rose to 13.3% in 2004-05. It then increased significantly from 14.4\u0026ndash;24.9% between 2009-10 and 2019-20. During the initial periods, growth hovered around 1\u0026ndash;2%, with a 10% increase in growth during the last two periods. This indicates a break in the stagnation present in female participation in non-farm employment in the 1990s and early 2000s. It appears that female workers' willingness to participate in rural non-farm sectors increased, reflecting the growing importance of rural non-farm activities for rural women in later periods.\u003c/p\u003e"},{"header":"5. Industrial Distribution of Workforce in the State","content":"\u003cp\u003eThe U.P. has a low urbanization rate of 22.7 percent in 2011, with a significant transformation in the rural workforce from the farm to the non-farm sector and its industry-wise composition from 1993-94 to 2019-20. The transformation started gradually in the early nineties, picked up pace from 1993-94 to 1999\u0026ndash;2000, further rose from 1999\u0026ndash;2000 to 2004-05, and continued to increase from 2009-10 to 2019-20. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the evolution of rural non-farm employment during the survey period from the 1990s to 2019-20. The primary sector saw a decline of 3.7 percent from 1993-94 to 1999-00, followed by a 3.4 percent fall in 2004-05, a 5.9 percent point decrease in 2009-10, and a 7.16 percent fall in 2019-20. The decline in employment in the primary sector is due to the withdrawal of male counterparts, as data suggest. Male employment in the primary sector fell from 76.2 percent to 56.7 percent between 1993-94 and 2019-20, while female employment declined from 89.9 percent to 77.1 percent for the same period.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSectoral Distribution of Usual(Principal\u0026thinsp;+\u0026thinsp;Sub) Status Rural Workers in Uttar Pradesh(in percentage)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSectors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1993-94\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1999-00\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2004-05\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2009-10\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2019-20\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRural Persons\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary Sector\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary Sector\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMining \u0026amp; Quarrying\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eManufacturing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElectricity,Gas, \u0026amp; Water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstruction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertiary Sector\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrade,Hotels \u0026amp; Restaurants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransport Communication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Services\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Non-Farm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e40.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRural Males\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary Sector\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e56.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary Sector\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMining \u0026amp; Quarrying\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eManufacturing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElectricity,Gas, \u0026amp; Water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstruction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertiary Sector\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrade,Hotels \u0026amp; Restaurants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransport Communication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Services\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Non-Farm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRural Females\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary Sector\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e77.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary Sector\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMining \u0026amp; Quarrying\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eManufacturing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElectricity,Gas, \u0026amp; Water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstruction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertiary Sector\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrade,Hotels \u0026amp; Restaurants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransport Communication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Services\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Non-Farm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eSource: Computed From Different rounds of NSSO\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: Data exclude the state of Uttaranchal\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDespite the gap narrowing in later periods, the non-farm sector remains more favorable to male workers as a source of employment than female workers. In 2019\u0026ndash;20, 43.96 percent of male workers were engaged in the non-farm sector, compared to 22.78 percent of their female counterparts. This trend highlights the importance of the non-farm sector as an alternative source of employment that has emerged as an absorbing labour force, particularly for workers withdrawing from agriculture in search of alternative sources of employment.\u003c/p\u003e \u003cp\u003eDuring the early 1990s, the tertiary sector employed more workers than the secondary sector did. However, the trend reversed in later periods, as the secondary sector absorbed 14.5 percent of workers compared to 12.8 percent in the tertiary sector. The difference widened in 2009-10 when the secondary sector accounted for 20 percent of workers as compared to 13.4 percent employment in the tertiary sector. The trend continued; in 2019\u0026ndash;20, the secondary sector contributed 23.32 percent of rural non-farm workers, while the tertiary sector contributed 16.89 percent (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFrom 1993 to 1994 to 2004\u0026ndash;05, the largest share of non-farm employment in the secondary sector was occupied by manufacturing. However, there was a major shift in the later period when the employment proportion in the construction sector rose, leaving manufacturing behind. The structural shift after the post-reform period suggests stagnation in the manufacturing sector in rural non-farm employment and an increasing share of the construction sector, which mainly absorbs casual labor that does not necessarily require specialization. This period also coincided with the enactment of the MGNREGA Act, 2005, under which a significant portion of work was undertaken in the construction sector, supposedly expanding rural connectivity. The emergence of the construction sector as the primary source of rural non-farm employment gives the impression of a distress-induced factor behind the larger share of non-farm employment in rural employment. Male participation was more intense than that of female workers, as data suggest that 17.2 percent of male workers were engaged in the construction sector, while female participation stood at 4.04 percent in the same year. The remaining parts of the secondary sector, such as mining and quarrying, electricity, gas, and water, and employment of both male and female workers showed no sign of change and remained low and stagnated during the entire period (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the tertiary sector, trade and hotels, and restaurants remained the primary sources of employment throughout the period, and with gradual improvement, the share doubled from 4.3 percent to 8.43 percent from 1993-94 to 2019\u0026ndash;20. The other services that employed 5.6 percent of the total non-farm employment in 1993\u0026ndash;94 did not show much change and will remain at 5.34 percent in 2019\u0026ndash;20. Although male employment, which was 6.6 percent in 1993\u0026ndash;94, decreased and settled at 5.01 percent in 2019\u0026ndash;20, while female employment, which was 3 percent in 1993\u0026ndash;94, improved and reached 6.11 percent in 2019\u0026ndash;20, most of the change occurred between 2009 and 10 and 2019\u0026ndash;20. Further disaggregation industry-wise reveals that a significant proportion of female employment came from education during the period mentioned above. The transport and communication sector employed 1.5 percent of workers in 1993\u0026ndash;94, and its share almost doubled to 3.12 percent in 2019\u0026ndash;20. The employment share of both male and female workers was substantially different in the sector, as gauged by the fact that the female proportion of employment remained abysmally low at 0.28 percent, whereas the proportion of males changed from 2.1 percent in 1993\u0026ndash;94 to 3.87 percent in 2019\u0026ndash;20.\u003c/p\u003e \u003cp\u003eUttar Pradesh (UP) has undergone a structural shift in the composition of rural non-farm employment, as previously mentioned. The construction sector's increasing presence in non-farm employment represents the new reality of the rural non-farm economy, although other sectors, particularly tertiary sectors, have shown modest improvement. The post-reform period saw the emergence of construction and a decrease in the prominence of rural manufacturing in the proportion of non-farm employment, suggesting that the commodity-producing sector is no longer primarily responsible for job growth, which points to the casualization of rural employment in Uttar Pradesh. Additionally, the growing importance of trade, hotels, and restaurants indicates the role of own-account workers in rural non-farm employment. These factors suggest that distress could be the primary factor driving changes in the structure and composition of rural employment in UP.\u003c/p\u003e"},{"header":"6. Status Distributions of Rural Non-Farm Workers","content":"\u003cp\u003eA notable feature of the rural workforce in Uttar Pradesh is that the self-employed category accounted for the largest portion of reported employment, followed by the casually employed category, with the regularly employed category accounting for a small proportion of total employment. According to Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, 70.74% of the rural workers were self-employed, 21.22% were casually employed, and 8.04% were regularly employed. The table also shows that the proportion of self-employed workers was higher in females compared than in males, and the proportion of casually employed people was higher in males than in women. Regular employment was lower in both categories for males and females (8.63% and 5.79%, respectively).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003ePercentage Distribution of Rural Workers by Status and Sectors in Uttar Pradesh, 2019-20\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eStatus of Employment/Sector\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMales\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFemales\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePersons\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSelf-Employed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e67.30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e84.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e70.74\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRegularly-Employed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.04\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCasually-Employed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21.22\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eNon-Farm Sector\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSelf-Employed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34.87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e45.91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35.91\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRegularly-Employed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19.71\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31.86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20.86\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCasually-Employed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e45.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e43.23\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\"\u003eSource: Author\u0026rsquo;s own calculation of unit level data of PLFS, 2019-20\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eWhile the employment pattern varied in the non-farm sector in rural UP compared to the overall rural employment in the state, as shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, the proportion of casual workers was higher in the rural non-farm sector, followed by self-employed workers at 43.23 percent and 35.91 percent, respectively. The proportion of regular employees was also higher in the non-farm sector in comparison to its status among rural workers in the state. The proportions of male and female workers in different categories in the non-farm sector also showed a significant deviation from the employment distribution of rural male and female workers. As the table indicates, the highest proportion of female workers was concentrated in the self-employed category, followed by, regular employed at 31.86 percent. The proportions of male workers who were self-employed, regular employees, and casually employed were 34.87 percent, 19.71 percent, and 45.42 percent, respectively.\u003c/p\u003e\n\u003cp\u003eFurther analysis of the employment status in the non-farm sector reveals that self-employment, which was nearly 60 percent in the early 1990s, has shown a continuous decline over the years and reached 35 percent in 2019-20 (Fig.\u0026nbsp;1). The proportion of casually employed individuals, which was around 20 percent in the early 1990s, recorded an upward trend during the period and stood at 43 percent in 2019\u0026ndash;20. Regular salaried workers, who comprised 21 percent of the total non-farm employment at the beginning of the 1990s, remained steady throughout the period until 2019\u0026ndash;20.\u003c/p\u003e\n\u003cp\u003eIn the secondary sector, the largest share of employment was generated under the \"casually employed\" category, reflecting the temporary nature of jobs in the sector. The \"regularly employed\" category also had a considerable share of 35.8 percent. The high share of casually employed people was attributable to the construction sector, where 89 percent of employment was casually employed. The lowest share of self-employed people was observed in the secondary sector, which accounted for 22 percent of the total distribution of employment by status. This is seen as a significant shift in the employment distribution in rural non-farm employment, which previously gave the impression that the largest share of employment in the secondary sector was found in the self-employment category, which has been replaced by the casually employed category in recent years.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003ePercentage distribution of usually working persons in usual status by broad Status in Employment for each industry of work in rural Uttar Pradesh, 2019-20\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSectors\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSelf-employed\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eRegular wage/salary\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCasual labour\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAll\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePrimary Sector\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e89.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSecondary Sector\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e35.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e42.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMining and Quarrying\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e40.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e52.92\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eManufacturing\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48.66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e28.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e22.45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eElectricity, gas \u0026amp; water\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23.31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e72.54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConstruction\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e89.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTertiary Sector\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54.56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e38.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTrade, hotels and Restaurants\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e84.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12.95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTransport and Communication\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e47.71\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e37.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e15.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOther services\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e64.53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003eSource: Author\u0026rsquo;s own calculation of the unit-level data of PLFS, 2019-20\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eFurthermore, the manufacturing sector has the highest proportion of self-employed people (48.66 percent by size) and the lowest proportion of regularly employed people (28.9 percent of total employment distribution in manufacturing activities). Compared to construction activities, the manufacturing sector had the lowest share of casually employed workers. As previously stated, stagnation of rural manufacturing activities, with a decreasing share of self-employment and regular employment, contributed to the casualisation of the rural workforce, indicating a distress-induced shift of the rural workforce in Uttar Pradesh from agriculture to non-farm activities. The proportions of self-employed, regularly employed, and casually employed workers in the electricity, gas, and water industries are 23.31, 72.54, and 4.14 percent, respectively.\u003c/p\u003e\n\u003cp\u003eAs previously mentioned, the tertiary sector accounted for 16.89 percent of the total rural non-farm employment in Uttar Pradesh in 2019\u0026ndash;20. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e shows that self-employment comprised 54.56 percent of the total employment status in the tertiary sector. The proportion of regularly employed individuals was 38.24 percent, while casual workforce had the lowest share (7.2%) of the total rural employment status in the tertiary sector. In the tertiary sector, the proportion of self-employment was highest in trade, hotels, and restaurants, accounting for 84.19 percent of the total employment distribution. In transportation and communication, the incidence of self-employment and the regularly-employed category, as a percentage of total employment status, were 47.71 and 37.22, respectively. For other services, the highest share of regularly-employed workers (64.53 percent) was reported, followed by self-employment, which covered 31.78 percent of the total employment distribution by status.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eNature of Job Contract in Rural Uttar Pradesh, 2019-20.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eJob Contract\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eRegularly-Employed\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCasually-Employed\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo Written Contract\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e72.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e98.79\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWritten contract for one year or less than a year\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.18\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWritten contract for more than one year to three years\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWritten contract for more than three years\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\"\u003eSource: As in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e indicates the nature of contracts for both regularly and casually employed individuals in rural Uttar Pradesh during 2019-20. This revelation highlights that a significant portion, 72.88% of regularly employed individuals, didn\u0026rsquo;t have a written contract detailing their job tenure and benefits. Furthermore, only 21.17% of the regularly employed workers have written contracts for more than three years, which implies that their job is permanent in nature. By contrast, 98.79% of casually employed individuals do not have a written contract, which exposes the vulnerability of these workers without job security and other benefits.\u003c/p\u003e"},{"header":"7. Monthly Consumption Expenditure in Different Status of Employment in Rural Uttar Pradesh (UP)","content":"\u003cp\u003eTo obtain an idea of the share of expenditure for different employment sources in the PLFS survey of the NSSO for agricultural and non-agricultural activities, we took the monthly consumption expenditure of different employment groups from farm to non-farm activities. In Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, it is revealed that almost 39 percent of household monthly expenditure in rural UP has arisen from non-farm activities, compared to 55 percent from farming and 5.8 percent from agricultural labour. Therefore, it is evident that non-farm income constitutes an important part of earnings, which translates into consumption expenditure. Overall, non-farm regular, self-employment, and casual employment contribute 8.24, 13.77, and 16.96 percent of total monthly consumption expenditure, respectively.\u003c/p\u003e \u003cp\u003eFurther examination of non-farm sources of monthly consumption expenditure across different per capita expenditure deciles reveals that consumption expenditure from regular and self-employment activities of non-farm sources steadily increases as we move from the low per capita consumption decile to the high per capita consumption decile. For the lowest decile, non-farm regular and self-employment contributed 4.38 and 10.26 percent of the total monthly consumption expenditure, respectively, which increased to 21.2 and 19.02 percent of the total consumption expenditure for the highest decile. Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e also suggests that, for the lowest decile in rural UP, non-farm casual and agricultural wage labour contribute 29.2 and 11.12 percent of the total monthly consumption expenditure, respectively, and that the share drops to 9.3 and 0.92 percent for the highest decile. The share of cultivation in the lowest and highest consumption deciles shows improvement as one moves from the lowest to the highest decile.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMonthly Consumption Expenditure in Rural Uttar Pradesh by Per Capita Consumption Decile, 2019-20\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecile\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCultivation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAgriculture Wage Labour\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon-farm Regular Employment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon-farm Self Employment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNon-farm Casual Employment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTotal non-farm Employment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePer Capita Monthly Consumption expenditure(in Rupees)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLowest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e43.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3396\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4762\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5478\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e34.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e34.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6790\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7755\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e60.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e36.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8402\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e36.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9530\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e44.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11427\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHighest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e49.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e17176\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e38.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8076.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eSource: Calculated from unit level data of Periodic Labour Force Survey of NSSO, 2019-20\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe pattern of per capita monthly consumption expenditure among the different deciles is consistent with the idea that the non-farm sector is a collection of a range of activities, including both productive and low-productivity ones. The former is associated with upward mobility, a high standard of living, and a successful transition from agriculture to the non-farm sector, whereas the latter is more likely to be residual activities into which people are pushed due to a lack of work in agricultural activities or insufficient work to meet their needs. Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows that high per capita consumption expenditure is associated with regular non-farm employment, which is highly productive, whereas low per capita consumption expenditure is associated with low-productive casual employment. Notably, a larger percentage of consumption expenditure is in the highest deciles for non-farm self-employment, indicating the high productivity of activities associated with non-farm self-employment. This suggests that a section of people involved in non-farm self-employment exhibit high returns, which also reflects high consumption expenditure. The important implication of these findings is that they demonstrate that the share of consumption expenditure is high among non-farm regular and self-employed workers, and low in casual employment.\u003c/p\u003e"},{"header":"8. Multinomial Logit Estimates of Farm and Non-Farm Employment Probabilities","content":"\u003cp\u003eA multinomial logit model was used to examine the individual, household, and regional factors that influence the likelihood of non-farm employment in rural Uttar Pradesh. The model considers five broad employment categories: farming/cultivation, agricultural wage labor, non-farm self-employment, non-farm regular employment, and non-farm casual employment. The study also included individual-level factors such as sex, age, educational status, and caste. At the household level, information was gathered on the size of the household, land owned, and land available for cultivation. As Uttar Pradesh is characterized by vast landscapes and regional variation, the model was designed to capture the regional dimension in explaining employment patterns in agriculture and the non-farm sector.\u003c/p\u003e\n\u003cp\u003eA multinomial logit model requires the selection of a particular category as the numeraire for comparison with other categories. In our study, we chose agricultural labor wages as the numeraire for the comparison group. This allowed us to examine how other employment categories differed from those in the comparison group. Therefore, the parameter estimates for the categories in the model should not be interpreted as correlates of employment in a specific occupational category, but rather as indicators of the strength of association between a particular explanatory variable and the respective occupational category, relative to the same explanatory variable as agricultural wage labor.\u003c/p\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e8.1 Results of Multinomial Logistic Regression for NSSO round of 2009-10\u003c/h2\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e provides parameter estimates for the multinomial logit for rural Uttar Pradesh (marginal probabilities of the variables are shown in \u003cspan class=\"InternalRef\"\u003eappendix\u003c/span\u003e A1). The results show that women are more likely to be involved in farming and agricultural wage labour than in any other non-farm employment category. The parameter estimates for all three non-farm activities were significantly negative and appeared positive only for farming. Therefore, it can be concluded that women are confined to agriculture-related activities, whereas men are more likely to work in non-farm activities. The parameter estimate based on age reveals that the young have a relatively higher probability of working in agricultural wage labour. As age increases, the probability of employment in other occupations also increases compared with agricultural wage labour. This result is consistent with the notion that farming, regular employment, and self-employment in the non-farm sector are more suited for older people because of the capital and necessary skills required to be employed in these occupations.\u003c/p\u003e\n \u003cp\u003eThe results suggest that completing primary or middle education increases the likelihood of working in farming or non-farm activities and decreases the likelihood of working as agricultural wage labour. Additionally, Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e shows that as the level of education improves, the likelihood of working in non-farm regular employment increases significantly relative to agricultural wage labour. The table also reveals that individuals with secondary education are more likely to work in non-farm casual employment compared to agricultural wage labour, but the association is weaker. Overall, it is clear that education has a positive impact on the probability of employment in the non-farm sector for different occupations, but the odds of being a casual non-farm worker are lower than those of self-employment and regular employment relative to agricultural wage labour.\u003c/p\u003e\n \u003cp\u003eHousehold size was positively and significantly related to all four occupational categories, but a strong association was more prominent in non-farm self-employment and casual employment relative to agricultural wage labour. This finding demonstrates that individuals from large households are more likely to engage in non-farm self-employment and casual employment. As noted earlier, non-farm self-employment, and more particularly regular non-farm employment, corresponds to productive activities, and the results indicate that large households in rural Uttar Pradesh are heterogeneous in character, comprising both poor and rich.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eParameter Estimate for Multinomial Logit Model (2009-10) (Agriculture Wage Labour as Comparison Group)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth colspan=\"9\" align=\"left\"\u003e\n \u003cp\u003eNumber of obs: 24,836\u003c/p\u003e\n \u003cp\u003eWald chi2(48): 10938.66\u003c/p\u003e\n \u003cp\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;chi2:0\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Pseudo R2 = 0.2898\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFarming/Cultivation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-Farm\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSelf-Employed\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-Farm Regularly Employed\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-Farm Casually Employed\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoefficients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProb-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoefficients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProb-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoefficients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProb-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoefficients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProb-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLiterate below Primary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrimary/Middle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigher Education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHousehold-Size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLand-Owned\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLand-Cultivated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWestern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCentral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEastern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.702\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eParameter estimates of landowning variables suggest that individuals exhibiting land are more likely to take up either cultivation or any of the non-farm activities relative to engaging in agricultural wage labour. The variable coefficient shows that the strength of the association is stronger with non-farm regular and self-employment, followed by cultivation and casual employment. This finding is consistent with a study by Lanjouw and Shariff (\u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e), which suggests that landowning households have opportunities for both cultivation and non-farm activities through the wealth effect, and agricultural wage labour is the least chosen occupation, even relative to casual non-farm casual employment. The positive and highly significant coefficient of land cultivated indicates that farming is the preferred option for households involved in cultivation; relative to agricultural wage employment. The negative coefficient for non-farm casual employment suggests that this option is the least preferred for households that own land for cultivation, compared to agricultural wage labor. This indicates that non-farm casual work is generally seen as a last resort and is mostly taken up in the absence of agricultural activity. The coefficients for other employment categories were statistically insignificant.\u003c/p\u003e\n \u003cp\u003eAccording to Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e, individuals belonging to ST, SC, and OBC have a lower probability of involvement in cultivation, self-employment, and regular employment in the non-farm sector compared to agricultural wage labourers. This suggests that the majority of productive employment in cultivation and non-farm activities is occupied by \u0026apos;Others\u0026apos;, while individuals from disadvantaged social groups such as SC, ST, and OBC are more likely to be employed in agricultural wage labor and non-farm casual work. The southern region had the lowest level of development and served as the reference group. The parameter estimates indicate that individuals in the western, central, and eastern regions of the state are more likely to engage in cultivation, self-employment, and regular employment in the non-farm sector compared to agricultural wage labor. The negative coefficient for non-farm casual employment in all three regions suggests that non-farm casual employment is more prevalent in the southern region than in the other regions. These findings confirm that individuals in the western, eastern, and central parts of the state are more engaged in remunerative farm and non-farm activities, whereas non-farm casual employment is more common in the southern region.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e8.2 Results of Multinomial Logistic Regression for NSSO round of 2019-20\u003c/h2\u003e\n \u003cp\u003eMultinomial logit regression is also performed on the PLFS rounds of 2019\u0026ndash;20 to understand the recent changes in the occupational structure of rural employment shaped by individual, household, and regional characteristics. As evident from Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e, the coefficient of gender in farming indicates that relative to agricultural labour, men have fewer chances to work in agriculture and have a higher probability of working in non-farm casual and self-employment. With an increase in age, there are more opportunities to work in cultivation, non-farm self-employment, and regular employment relative to agricultural labour, while casual wage labour in the non-farm sector is not a preferred choice with an increase in age. Therefore, young people are more likely to work in agricultural wage labour and casual non-farm work (the marginal probabilities of the variables are shown in \u003cspan class=\"InternalRef\"\u003eAppendix\u003c/span\u003e A2).\u003c/p\u003e\n \u003cp\u003eSimilar to earlier results, this time as well, education is strongly and significantly associated with employment outside the agricultural wage labour. The parameter estimate at all levels of education, from primary to higher education, is consistently positive for all four occupational categories, with coefficient values increasing significantly for self-employment and regular noon-farm employment with increases in level of education. The study found that the highest probability of being employed as a regular non-farm worker is associated with a higher level of education, followed by non-farm self-employment and cultivation. Household size, was positively related to all four occupational categories, corroborating the results of the previous round. This indicates that as household size increases, the probability of being involved in cultivation and all non-farm activities increases in comparison to being engaged in agricultural wage labour.\u003c/p\u003e\n \u003cp\u003eIt can be observed that individuals belonging to SC and OBC have relatively fewer chances to engage in any of the four occupational categories compared to agricultural wage labour, as shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e. This reaffirms the previous outcome that \u0026apos;Others\u0026apos; from social groups have better chances to access cultivation and non-firm activities. The coefficients for ST are statistically insignificant owing to their high p-value. For region-wise comparison, the southern region was chosen as the reference category, similar to the previous model. The parameter estimates for the western region are negative\u0026nbsp;for all four occupational categories, while it was statistically insignificant for non-farm regular employment. Similar to previous results, the negative and statistically significant coefficient of the non-farm casual category for the western and central regions showed that the incidence of casual non-farm employment was more pronounced in the southern region.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab8\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eParameter Estimate for Multinomial Logit Model (2019-20) (Agriculture Wage Labour as Comparison Group)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth colspan=\"9\" align=\"left\"\u003e\n \u003cp\u003eNumber of obs: 8,141\u003c/p\u003e\n \u003cp\u003eWald chi2(48): 1914.77 \u0026nbsp; \u0026nbsp; Prob\u0026thinsp;\u0026gt;\u0026thinsp;chi2:0 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Pseudo R2 = 0.1580\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eFarming/Cultivation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eNon-Farm Self-Employed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eNon-Farm Regularly Employed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eNon-Farm Casually Employed\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoefficients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProb-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoefficients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProb-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoefficients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProb-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoefficients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProb-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.928\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLiterate below Primary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.705\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.277\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrimary/Middle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.946\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.969\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigher Education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.746\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHousehold-Size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.691\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.262\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.658\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWestern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCentral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEastern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.463\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.646\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"9. Conclusions","content":"\u003cp\u003eThe employment situation in Uttar Pradesh as a whole was assessed in this study with a focus on non-farm employment. State-specific data show an increase in non-farm employment. Although female workers have also experienced a recent uptick in such activities, male workers prefer non-farm activities. However, a large portion of this work was part-time in nature.\u003c/p\u003e \u003cp\u003eThe primary objective of our study was to investigate the potential causes of employment growth in non-farm activities over time, specifically (i) whether it was due to agricultural growth's demand-pull factors, (ii) distress-push factors that drove people to engage in non-farm activities because they were unable to find employment in the farm sector, and (iii) factors exogenous to the farm sector. The first scenario is disregarded because agricultural growth,\u0026mdash;characterized by a steadily declining agricultural contribution to the net state domestic product and a low income share,\u0026mdash;could not have resulted in an increase in non-farm employment. On the other hand, the type and extent of non-farm employment choices made by individuals and households are partially determined by external factors, including land endowments, educational attainment, and regional developments. The distress-push factor, which was mostly responsible for employment growth in the non-farm sector, seems to be the dominant force.\u003c/p\u003e \u003cp\u003eThe industrial composition of the workforce highlights the importance of employment growth in the secondary sector. Another noteworthy fact is that the post-reform era saw the rise of construction and the substantial reduction of rural manufacturing in the proportion of non-farm employment, suggesting that the sector producing commodities was no longer primarily responsible for the increase in employment and pointing to the casualization of rural employment in Uttar Pradesh. Additionally, the growing significance of retail, hotels, and restaurants indicates that account workers play a significant role in rural non-farm employment. All of these considerations suggest that distress may be the primary driver of changes in the organisation and composition of rural employment in the UP.\u003c/p\u003e \u003cp\u003eWe conducted a survey of employment patterns in terms of the job status of workers who had an impact on employment in search of additional evidence before drawing our conclusions. In Uttar Pradesh, we observed that a higher proportion of the casual workforce and stagnation of rural manufacturing activities, as well as a declining proportion of people who were self-employed and regularly employed, contributed to the casualization of the rural workforce. This suggests that the rural workforce was forced to shift from agricultural to non-farm activities as a result of the distress. Further evidence supporting our theory that non-farm workers were primarily motivated to their predicament by distress factors was provided by the large presence of landless and marginal landowners engaged in non-farm activities, many of whom had little education, and the majority belonged to socially downtrodden classes with limited assets.\u003c/p\u003e \u003cp\u003eIn addition, it was demonstrated that in rural UP, non-farm activities accounted for approximately 39% of monthly household expenditures in 2019\u0026ndash;20. Additionally, the distribution of per capita consumer spending across deciles demonstrates that non-farm activities include both productive and subsistence occupations. The key finding is that self-employment and regular employment outside agriculture seemed to be high-return activities, whereas casual employment outside agriculture was linked to low-return activities. The study also revealed that across all employment status categories, including agricultural labour, cultivators, non-farm regular employment, non-farm self-employment, and non-farm casual employment, the likelihood of working in high-paying non-farm regular and self-employment activities increases with better educational attainment, larger landholdings, and placement among the upper castes in social groups.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e: Data and materials can be made available on demand.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest Statement\u003c/strong\u003e: The corresponding author states that there are no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u003c/strong\u003e The paper is part of my PhD thesis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e: The study was approved by the institutional review board of JNU for research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e: The author formulated the theory, came up with the idea, and carried out the calculations. Additionally, he gathered secondary data and checked the analytical methods.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e: Not Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclaimer:\u003c/strong\u003e No funding was received to assist with the preparation of this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdulai, A., and Delgado, C.L. 1999. Determinants of non-farm earnings of farm-based husbands and wives in northern Ghana. \u003cem\u003eAmerican Journal of Agricultural Economics\u003c/em\u003e 81(1): 117-130.\u003c/li\u003e\n\u003cli\u003eAbraham, Vinoj. 2011. Agrarian distress and rural non-farm sector employment in India , Centre for development studies, Kerala, India.\u003c/li\u003e\n\u003cli\u003eBasant, Rakesh. 1993. 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Non-Agricultural Employment in India: Trends and Prospects, Sage Publications, New Delhi\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Rural non-farm sector, Casualization, Rural Manufacturing","lastPublishedDoi":"10.21203/rs.3.rs-3360499/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3360499/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUsing National Sample Survey Organization (NSSO) data, this study assessed the employment situation in Uttar Pradesh, with a focus on non-farm employment. The primary objective of our study is to investigate the potential causes of employment growth in non-farm activities over time. In Uttar Pradesh, we observed that a higher proportion of the casual workforce and stagnation of rural manufacturing activities, as well as a declining proportion of people who were self-employed and regularly employed, contributed to the casualization of the rural workforce. This suggests that the rural workforce was forced to shift from agricultural to non-farm activities as a result of distress. The key finding is that self-employment and regular employment outside agriculture seemed to be high-return activities, whereas casual employment outside agriculture was linked to low-return activities. Using a multinomial logit model, the study revealed that across all employment status categories, including agricultural labor, cultivators, non-farm regular employment, non-farm self-employment, and non-farm casual employment, the likelihood of working in high-paying non-farm regular and self-employment activities increases with better educational attainment, larger landholdings, and placement among the upper castes in social groups.\u003c/p\u003e\n\u003cp\u003eJEL Code: J22, J24, J46\u003c/p\u003e","manuscriptTitle":"Is Rural Non-farm Employment Distress Driven in Uttar Pradesh? Evidence from the NSSO rounds and PLF Survey","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-10-06 16:40:59","doi":"10.21203/rs.3.rs-3360499/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dc2f104c-d0db-4bde-9276-d41ae9ae00d7","owner":[],"postedDate":"October 6th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":24788015,"name":"Development Economics"}],"tags":[],"updatedAt":"2024-07-08T09:19:24+00:00","versionOfRecord":[],"versionCreatedAt":"2023-10-06 16:40:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3360499","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3360499","identity":"rs-3360499","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}