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Does Skill Make a Difference?

Effects of COVID-19 Pandemic on the Rural Non-farm Self-employed in India

This paper examines the importance of skills, especially through vocational training, for the rural non-farm sector in overcoming the disruptions caused by the COVID-19 pandemic. The difference-in-differences technique has been used to assess the differential impact of the COVID-19 pandemic on the earnings of skilled and unskilled self-employed activities. The primary data have been collected from 880 rural non-farm self-employed individuals who hailed from different regions of Karnataka. Although every section of the rural non-farm activities has been adversely affected due to the pandemic, the impact is more severe on unskilled individuals as compared to skilled individuals. Therefore, policymakers need to pay attention to enhancing the provision of formal vocational training for RNFS individuals on a grander scale.

The COVID-19 pandemic, since its outbreak in March 2020, has unleashed massive demand- and supply-side shocks across different economies the world over. In view of measures like travel restrictions and quarantine procedures, the availability and productivity of labour substantially plummeted during the lockdown period and thereafter. This, in turn, led to a widespread increase in unemployment at unprecedented rates. India has been one of the most affected nations going by the scale and period of intense restrictions placed on the population. However, the employment crisis in India started much before the spread of the COVID-19 pande­mic. A substantial slowdown observed in the employment generation during the last one and a half decades has come to dominate the discourse focused on the current Indian economic scenario across polity and academia. Even an employment downturn has been observed, despite the economy witnessing nearly a 10% growth in the gross domestic product in the latter half of the last decade. Since output growth could not ensure employment growth, the economy has moved from a situation of job-less growth to job-loss growth (Kannan and Raveendran 2019).

No doubt, the COVID-19 pandemic has further accentuated the existing employment crisis relentlessly. Not only has it increased the unemployment rate, but it also has reduced work intensity and earnings substantially of those continuing work (ActionAid Association [India] 2020). This has been particularly hurtful considering the significant share of informal workers in the total workforce in India (that is, about 93%, including informal sector workers and informal workers in the formal sector) who continue to remain unprotected, to a large extent, by proper social security measures. Moreover, according to the Periodic Labour Force Survey (PLFS) for 2017–18, about 52% of the workforce are classified as self-employed in India (PLFS 2019). In fact, a majority of them are own-account workers or unpaid family members, the most vulnerable section among the informal workers.

Studies specific to developing countries (Zambia [Mathew et al 2020] and India [Dasgupta and Murali 2020]) and dev­eloped countries (Canada [Lemieux et al 2020], the United States [Kalenkoski and Pabilonia 2020], the United Kingdom [Blundell and Machin 2020], and Japan [Kikuchi et al 2020])have shown that those engaged in self-employment activities are one of the worst-affected sections in the context of the pandemic. They have lost their earnings and working hours and, in many cases, have suffered a permanent loss of employment.

Unemployment and COVID-19

The surveys conducted by various institutions in India, soon after the first wave of the pandemic, have portrayed a similar picture. The Centre for Monitoring Indian Economy data show that in the initial period (April 2020), the casual and self-employed workers made up the majority who lost their employment. Moreover, according to the survey conducted by the Stranded Workers Action Network (SWAN), about 97% of the self-employed had gone without any form of income in the initial months of the lockdown (SWAN 2020). The survey conducted by the Azim Premji University (2020) shows that though self-employed persons were less likely to be unemployed in rural areas, as compared to other sections, their earnings had fallen by 86% during the lockdown period. In Karnataka, there was a decline observed in the average weekly earnings of self-employed persons in the non-farm sector to the extent of about 67%. In India, the non-farm self-employed section—mainly involved in trade and accommodation services, manufacturing and transport, and storage and communication services—has been observed as the most severely affected by the pandemic.

Along with the sector of employment, the educational level of persons also plays an important role when it comes to the effects of COVID-19. It is seen that most of the casual and self-employed workers are uneducated or less educated. This inc­reases their chances of being more vulnerable to such crisis conditions (Kapoor 2020). While there are only limited studies dealing with the impact of skills in overcoming crisis situations with a specific reference to India, studies from other countries point to a disparity in outcomes from economic shocks between skilled and unskilled labourers. In general, the less skilled labourers are observed to be more vulnerable to crisis situations and, therefore, are often unable to move to a pre-shock state compared to skilled workers (Artuç and McLaren 2015). The COVID-19 pandemic has had a negative ­impact on nearly all types of occupation, but low-skilled labo­urers have had to face the extreme effects of the pandemic, as compared to other groups, as (i) they are less likely to be paid proper wages; and (ii) less likely to have skills to continue working from home or their occupation demands physical presence at the workplace (Berube and Bateman 2020).

Other existing studies also solidify the hypothesis that workers with a lower educational level are less likely to retain employment during the pandemic. Especially when it comes to working remotely from home, it is found that only people with higher education and information and communications techno­logy (ICT) skills could maintain working (Malkov 2020; Saltiel 2020; Dingel and Neiman 2020). Bartik et al (2020) have found that people with lower educational levels were more likely to lose their job. Their estimates show that persons without a high school degree have an 11% higher chance of not working in the early phases of the virus spread. In fact, the ­affected sectors are more likely to have employed people with lower education (Mongey et al 2020). The labourers having higher education are found with a better holding in the crisis period (Jain et al 2020; Dang and Giang 2020). Another ­expected trend is the surge in the number of low- and semi-skilled workers being either pushed into unemployment or other menial jobs with a greater emphasis given to the expo­sure of digital transformation in the post-pandemic period (Park and Inocencio 2020; Scarpetta et al 2020).

Moreover, in rural areas, low educational levels are a major hindrance to the expansion of productive and innovative non-farm sector employment. The lack of skilled humanpower can be expl­ained by lower educational attainments, affecting their job prospects negatively (Saha and Verick 2016). Not only does general education influence rural non-farm self-employment, but it is also influenced by vocational training and entrepreneurship education significantly. For instance, a study related to Nigeria by Afolabi et al (2017) shows that entrepreneurship education leads to a positive impact on self-employment initiatives. Further, Lazear (2004, 2005) argues that wage employees can be specialised in certain skills, whereas those self-­employed need to possess multiple skills to perform a variety of tasks. Singh (2008), using data for the Indian economy, shows that possessing both general and vocational education have a significant impact on accessing better-paying non-farm activities in rural India. In this context, investment in skill deve­lopment is an important policy intervention aimed at creating better employment opportunities, increasing producti­vity and achieving overall growth of the economy and is considered hel­pful, particularly to the non-farm sector (Sanghi and Srija 2015).

Over the years, even with the implementation of skill development programmes at various levels, there has been no expe­cted increase in persons who have gained vocational training and thereby entering the category of skilled labour (NSSO 66th and 68th survey rounds). Several initiatives have been undertaken by the government towards promoting the skill levels among those engaged as self-employed in the rural non-farm economy. For instance, the Entrepreneurship Skill Development Programme, the Pradhan Mantri Vikas Kaushal Yojana, and the Management Development Programme implemented by the Ministry of Micro, Small and Medium Enterprises; Swarnjayanti Gram Swarozgar Yojana by the Ministry of Rural Development; 150 short courses conducted by the National Institute of Rural Development and Panchayati Raj; 51 training centres run by the Khadi and Village Industries Commission; vocational training centres in tribal areas run by the Ministry of Tribal ­Affairs; 15 Food Craft Institutes run under the state governments by the Ministry of Tourism; and the Support to Training and Emp­loyment Programme for Women by the Ministry of ­Women and Child Development.

While a number of existing studies have investigated the ­issue of the importance of skills in overcoming crisis situations with a specific reference to wage employment, it often neglects to provide a clearer picture regarding the importance of skills, especially vocational training, in overcoming crisis situations with a specific reference to rural non-farm self-employed (RNFS) individuals. Therefore, this paper focuses on assessing the capabilities of skilled workers as compared to unskilled workers in the non-farm sector in overcoming the disruptions caused by the COVID-19 pandemic.

With this backdrop, the paper is organised as follows. In the following section, the methodology has been explained along with data sources used for analysis. Then, it moves on to exp­lain the overall impact of COVID-19 on the RNFS businesses. This is followed by a descriptive and empi­rical analysis of the difference between skilled and unskilled self-employed workers in managing the economic shock caused by the COVID-19 pandemic. The paper concludes with suggesting policy implications.

Methodology and Data Sources

The difference-in-differences (DID) technique has been used to assess the differential impact of the COVID-19 pandemic on the earnings of skilled and unskilled self-employed individuals. The primary data were collected from 880 self-employed individuals who hailed from different regions of Karnataka. A multistage stratified random sampling technique was used, and the survey was conducted across four administrative divisions of Karnataka. Two districts (one developed and one developing) were chosen from each of the four administrative divisions. Further, from each of these eight districts, two villages were selected belonging to two different talukas. Finally, 55 self-employed individuals were surveyed, based on a detailed structured questionnaire, from each of the villages. Therefore, a total of 880 samples were covered from 16 villages for the present study. The above data were collected during September–November 2020. The study portrays a picture of the recovery from business losses during the lockdown period of the first wave of the pandemic in 2020.

Samples were divided into the control and treatment groups based on their possession of formal vocational training. The control group included self-employed individuals who had not undergone formal vocational training and the treatment group consisted of the self-employed individuals who had been formally trained through vocational training under the “Skill India” mission. The sample was divided into 355 self-employed individuals in the treatment group and 525 in the control group.

The “before” and “after” criteria were used for understanding the impact of the COVID-19 pandemic on self-employed indi­viduals’ earnings. In this context, it is important to mention that details about the present monthly earnings (considered as post-COVID-19 earnings) and also the monthly earnings just ­before the outbreak of the pandemic based on the ­recall method (considered as pre-COVID-19 earnings) were asked to the participants. Moreover, it is essential to mention that only those self-employed workers who had maintained their past income and expenditure details somewhat in the written form were considered in order to minimise recall bias and come up with precise estimates.

The model can be specified as follows:

yit = ao + a1 FVTi + a2 Postt+a3 FVTi * Postt + b’Xit+ εit

where yit represents the earnings of the ith self-employed individual at the time t. FVTi indicates the ith self-employed individual with formal vocational training. Post is a binary variable with a value of 1 for post-COVID-19 and 0 for the pre-COVID-19 situation. Therefore, the coefficient α1 represents the marginal effect of formal vocational training and α2 represents the marginal effect of COVID-19. Most importantly, the coefficient of the interactive term between FVT and post (α3) indicates the selective impact of formal vocational training on the earnings of self-employed individuals during the post-lockdown period. X is the vector of other control variables.

As a large number of existing studies have already shown that earnings of self-employed workers are very much dependent on several socio-economic and business-related factors, this paper has used a number of such factors as control variables in the regression analysis (Appendix Table A1). In fact, having a number of control variables in the regression equation enhances the precision of DID estimates. It even helps us check the robustness of DID estimates in terms of their sign and statistical significance level with the inclusion of different control variables.

The Impact of COVID-19 on the RNFS Activities

Before going into the DID estimates, this section tries to draw certain inferences pertaining to the overall impact of COVID-19 on the rural non-farm self-employment activities. In this regard, Table 1 provides information on the percentages of self-­employment activities adversely affected by the COVID-19 pandemic. By the term “self-employed activities,” self-employed own-account enterprises that do not employ any hired workers in their business are referred to; they are managed mainly by family workers. From Table 1, it is observed that a whopping 98.75% had experienc­ed their business being negatively affected due to the containment me­asures enforced by the government during the first wave of the Covid-19 pandemic. More­­over, the constraints had ensued a direct income loss for 94.2% of self-employed individuals.

In addition to this, questions related to the reasons for such an adverse impact on self-employment activities during the COVID-19 pandemic were asked. The responses pointed out several demand- and supply-side reasons and the same have been presented in Figure 1.

Among the 98.75% of those affected, from Figure 1, it could be seen that the major reason cited for the negative impact from the demand side was the decline in sales during the lockdown. A reduction in sales was reported by about 58.73% of the respondents. The major issue from the supply side was the decline in income as a means to sustain the increasing costs in maintaining businesses (37.63% reported the same). Along with these issues, other supply-side constraints included the inability to procure inputs, difficulty to continue production, mobility issues, etc. Thus, the breakdown of supply chain activities was also a cause of concern for the businesses.

However, a small section mentioned that it had not been ­affected by the lockdown, as it was involved in essential services, while a few of them cited the inflow of returned mig­rants. This was seen mainly in the retail sector, which was able to sustain itself during this period.

Although almost every section of the RNFS activities was adversely affected by the outbreak of the COVID-19 pandemic, the impact might not be the same across them. Since the objective of the paper was to examine the importance of skills in the rural non-farm sector in ­overcoming the disruptions caused by the COVID-19 pandemic and the subsequent lockdown measures, the sample was segregated into skilled and unskilled self-employed individuals before carrying out the analysis, which is presented in the ­subsequent sections.

The Differential Impact on Skilled and Unskilled RNFS

It is essential to mention here that individuals possessing formal vocational training under the Skill India programme are referred to as skilled and those not possessing formal vocational training as unskilled. To see whether skills make any difference to the impact, this paper’s analysis begins by presenting the monthly earnings of the skilled and unskilled rural non-farm self-employed individuals in the pre- and post-lockdown situations (Figure 2).

Figure 2 depicts that the mean earnings of skilled indivi­duals are much higher as compared to unskilled individuals both in the pre- and post-lockdown situations. This implies that possessing formal vocational training helps RNFS individuals earn a higher income. The t-value of the mean earnings difference between the skilled and unskilled individuals is also found to be statistically significant for both the pre- and post-lockdown situations. When it comes to the loss of earnings due to the lockdown, it is observed that though every section of the rural non-farm self-employment activities had been adversely affected by the outbreak of COVID-19 pandemic and the subsequent lockdown measures; the difference in the earnings between pre- and post-lockdown situations of the unskilled individuals is found to be higher than that of skilled individuals. This implies that the impact of lockdown has been more severe for unskilled individuals than skilled individuals.

In addition to a descriptive analysis, the impact of lockdown measures during the COVID-19 pandemic on the earnings of skilled and unskilled RNFS individuals based on the DID technique is also examined, as discussed in the methodology section. One should also note that the differences between the mean earnings of the RNFS individuals might depend on their socio-economic conditions and also on several business-related factors. Within these, the impact of education on the self-employed is perhaps the most studied factor. Besides considering the formal vocational training programmes as a proxy for skilled and unskilled individuals, individual educational levels in the regression model have also been controlled in this analysis. In fact, while considering aspects that affect the business outcome, it is seen that educated individuals have the edge over the less educated. This edge is based on the grounds of access to more information and opportunities as well as access to credit sources, better managerial skills, and networking ability. The studies by Meager et al (2011) and Cressy (1996) show that an increase in ­human capital contributes to better self-employed business outcomes. It has been determined that higher education has also increased the chances of business success. Studies by Cressy (1996), Van Praag and Cramer (2001), and Meager et al (2011) show that a higher educational level also leads to better entrepreneurial outcomes. While Cressy (1996) points out that higher qualification has a positively significant impact on survival rates, Van Praag and Cramer (2001) and Meager et al (2011) support the claim showing that highly educated individuals have a better knowledge about opportunities that suit their qualification, thereby increasing the chances of success. It is also important to note that education improves one’s efficacy and self-esteem, increases the ability of individuals to find better opportunities, and even boosts their chances of success (Robinson and Sexton 1994).

There is a debate regarding the ranking that is given to education and experience in the growth of business. Studies show that it is a combined effect of both experience and education that is important. Millán et al (2012) came to the conclusion that a person with formal education and previous experience with the market is more likely to survive. But Robinson and Sexton (1994) and Henley (2005) point out that though the experience is important, it is considered secondary to education. Lazear (2004) used the concept of “jack of all trades” as a determining factor of entrepreneurial outcome. His study concludes that a person with skills acquired via formal education is more likely to outperform a person with just a certain level of education. 

In the following section, a regression was run in order to ­understand the impact of formal vocational training on the earnings of the RNFS businesses. Therefore, to come up with precise estimates, the DID analysis was performed even with controlling for the socio-economic and business-related factors. In fact, in order to show the robustness of the DID results, a comparative analysis of the DID results was also conducted with and without covariates (Table 2). 

In Table 2, the regression results without covariates are presented, considering only variables such as post-COVID-19 dummy, FVT dummy, and the interactive term between FVT dummy and post-COVID-19 dummy. The coefficient of the int­eraction term is the coefficient for DID, which shows the actual effect of FVT during the post-lockdown period. Table 2 shows that though the coefficient for post-COVID-19 is negative and statistically significant, the interaction term in this model is positive and statistically significant, implying that in the post-lockdown period, while there is a decline in the earnings of both the groups, the treated group or, in other words, the section with formal training has experienced a lower decline.

Moreover, in order to ensure the robustness of DID estimates, the regression model with different control variables was estimated. The method of estimation using covariates is used to account for the unaccounted/unexplained variations within the simple DID model. Here, the paper considered the effect of being part of a minority or backward social group, marital status, age, level of education, form of ownership of business, and location of enterprise. The sign and statistical significance level of the interaction term is consistent even after controlling for several socio-economic and business-related variables.

Moreover, regarding the other control variables, the coefficient for “age” is found to be positive and statistically significant, but the coefficient for “age-square” is negative and statistically significant, implying that the earnings from self-­employment grow with an increase in the experience level, but after a certain age, it starts decreasing. As expected, the impact on earnings rises with an increase in self-employed individuals’ educational level. As far as individuals’ ­social identity is concerned, RNFS individuals belonging to a weaker socio-economic status show lower earnings. For instance, females account for lower earnings than males from RNFS businesses, and the earnings of individuals belonging to the minority religious groups are lower than those of non-­minority religious groups. Further, as for the coefficient associated with business-related variables, though it is found that the coefficient insignificant for proprietary enterprises, the coefficient for RNFS businesses within household premises is negative and significantly related to the earnings.

In fact, the importance of skill during the COVID-19 pandemic has been clearly revealed based on the decomposition of DID coefficient in Table 3.

From Table 3, it is observed that though both the skilled and unskilled RNFS individuals account for a statistically significant income loss during the COVID-19 pandemic, the difference in the earnings for pre- and post-lockdown situations of unskilled individuals is higher than that of skilled workers based on the DID analysis of both with and without covariates. For instance, for the model with covariates, the diff­erence in the coefficients associated with the control and treated groups is 0.225 for the pre-lockdown period, whereas, for the post-lockdown period, the difference has increased to 0.483. Similarly, for the model without covariates, the difference in the coefficients of the control and treated groups is 0.303 for the pre-lockdown period, whereas the difference has increased to 0.561 for the post-lockdown period. The difference in differences coefficient between the periods is also positive (0.258) and statistically significant for both the models. 

However, one may be interested in knowing whether skill plays a critical role in overcoming the disruptions only for a specific income group among the RNFS or it is vital to every inc­ome group. Therefore, the DID coefficients (with covariates) across 0.1 quantile, 0.25 quantile, 0.5 quantile, 0.75 quantile, and 0.9 quantile are estimated and presented in ­Table 4. Interestingly, it is seen that the DID coefficients are positive and statistically significant across all the five quantiles, implying that imparting skill through formal vocational training is critical to every section of the RNFS.

Why Does Skill Make a Difference?

In the preceding section, it has been established that skill can make a difference even in the time of an economic crisis. However, one may be interested in knowing why such a difference exists or how skill plays an important role. Therefore, this section provides a descriptive analysis to understand why and how skill can be effective in creating a positive impact on RNFS businesses. The prospects of different types of skill acquired through the formal vocational training programme that helps business growth in Figure 3.

First, it is interesting to find from Figure 3 that as per 53.04% of the respondents, they received better ideas for business during the training and it was stated as the most common factor behind their business growth. Second, as per 33.13% of the respondents, they are applying new technical knowledge to their businesses that they had acquired through training. It could also be useful in policymaking for understanding how sharing new or updated ideas about business and its working can lead to a positive impact. Often soft skills and management and marketing skills are expected to facilitate business growth. In fact, managerial skills are considered one of the most basic and important skills that determine the survival of businesses. Existing studies also argue that an increase in ­human capital or education can lead to better managerial skills with higher chances of business prospering (Bates 1990; Kim 2008). Likewise, in this study, it could also be observed that besides sharing new ideas and imparting technical knowledge, formal vocational training programmes have also been successful in enhancing soft skills and business skills. More specifically, for 15.05% of the respon­dents, there has been an improvement in their interaction/communication skills after the train­ing. Moreover, for 3.04% and 7.14% of the respondents, there has been an improvement in managerial skills and marketing skills, respectively.

In the initial stages of self-employment businesses, it is seen that the role of social capital, such as networking and support mechanisms, can create a positive environment for their exp­ansion. While most businesses rely on friends and families as their primary contact source for support, it is seen that their educational institutions and mentors also have an important role for educated individuals. Their guidance and contacts give a push in the direction of success, especially in the initial period. The study by Greene and Saridakis (2008) shows a positive relationship between the support mechanisms and skills acquired through training. Especially during crisis periods, these informal support systems form one of the main means of revival in terms of informal credit, career advice, and access to information. Figure 4 provides an insight into the differences existing in the business strategies used by skilled and unskilled self-employed individuals. Interestingly, the difference emerges when the percentage of inv­olvement in the business strategies is looked at. The most common strategy utilised both by the skilled (53.24%) and unskilled (61.52%) RNFS businesses relates to expanding production or increasing the number of services provided. Here, it is seen that unskilled self-employed have invested more in increasing the production of their products but with a much lower emphasis provided to the diversification of their business (21.90%) as compared to the skilled self-employed (35.49%). This could be bec­ause of better access to ideas received through vocational training and skill development programmes that helped them diversify their production. Both the skilled and unskilled self-employed workers could see a potential market in urban areas and accordingly plan to expand their business there. The train­ed businesspersons have given more importance to delivering their services to bigger businesses that act as customers for their products and services to move to other rural localities if their market was saturated in the present location.

Besides following different strategies for the promotion of businesses, skill may have a strong association with the use of technology. This is important as the relationship between technology and businesses can be considered as a proxy for the level of capital investment made in one’s business. It plays an essential role in continuing with businesses when physical movement is restricted at the time of the pandemic. In this context, one may be interested in knowing whether there exists any difference in the penetration of ICT between skilled and unskilled self-employed individuals. More specifically, whether skilled self-employed individuals are in a better position to reap the benefits of ICT in their businesses as compared to unskilled self-employed individuals. Thus, Figure 5 presents a comparative analysis of access to different technologies among skilled and unskilled self-employed individuals.

Access to ICT facilities has been assessed based on smartphone, computer, laptop, tablet, broadband connection, WiFi facility, printer, e-billing facility, online debit/credit card transaction, and mobile banking. From Figure 3, it could be seen that skilled self-employed individuals have better access in terms of all indicators of ICT facilities when compared to the unskilled self-employed individuals. Thus, skilled self-employed individuals are able to reap the following benefits of ICT and expand their businesses. These benefits can be largely confined to five categories: (i) better productivity and efficiency of the firm—using technology to increase productivity has been the cornerstone to achieve economic growth; (ii) better marketing strategy—using websites and social media presence for marketing products even outside of the local space; (iii) incre­asing sales—using various platforms of e-commerce as a means to reaching out to wider customer base and increasing one’s market share; (iv) better financial inclusion—using the method of digital transactions and banking services that create better access to formal credit; and (v) as a medium of networking between enterprises that can be an effective way of increasing one’s capability (Duncombe and Heeks 2005; Lee et al 2010; Mbuyisa and Leonard 2017). Moreover, better integration of new technology and skills has also been proved to be helpful in adapting to changing scenarios (vander Sluis et al 2008).

Even though skill plays an essential role in promoting RNFS businesses, a large number of individuals do not seem to be participating in formal vocational training. Thus, different reasons for not participating in the formal vocational training have been identified, as presented in Figure 6.

Figure 6 depicts that the reasons why people do not show an inclination towards attending formal vocational training and they include unawareness regarding training programmes (36.52%) and a lack of sufficient knowledge regarding the usefulness of enrolling in formal training centres (17.98%). This calls for reassessing the building awareness strategy used for the schemes, as even with all the spending on the advertisement, it has been unable to reach one-third of the potential self-employed individuals who could have opted for it. The other two main reasons were the investments they were ready to make on self-learning (28.09%) and banking on hereditary skills (10.67%). This shows that a third of the population is inclined towards informal learning, which could be due to the lack of educational qualifications required for attending formal training. The lack of time to invest in education is also a major factor that impacts individuals’ selection. This is seen more among informally trained individuals, as they are learning on the job and are unable to divert time to this form of education.

Conclusions and Policy Implications

The COVID-19 pandemic has created an economic shock across the world in an unprecedented manner. The different sections of the society have been affected in specific ways, and there is a disparity in terms of the impact on the high- and low-income groups, physical space, social groups, and the nature of emp­loyment. Therefore, it is important to look into each section and their issues to get a clearer picture. In this study, the focus was on the self-employed in the rural non-farm sector in Karnataka in an attempt to understand the role of skill development policies in overcoming the employment crisis.

Based on both descriptive as well as DID analyses, it is obser­ved that though every section of the RNFS individuals has been adversely affected by the outbreak of the COVID-19 pandemic and the subsequent lockdown measures, the decline in the earnings of unskilled individuals is higher than that of skilled individuals. When considering the income differential analysis done based on the quantile DID analysis, it is seen that imparting skill through formal ­vocational training is critical among every section of the RNFS. This provides a conclusive proof for the state and the individuals to invest in skill development not only for economic growth, but also as a support mechanism for households to overcome crisis situations.

Now, if one connects the business strategies used by skilled and unskilled individuals to their ability of overcoming the crisis, it can be seen that they have had a better chance of diversifying their products/services, not solely investing in increasing production. This could have come about because of the ideas they had received while attending formal training or their interactions with other business owners. Besides adopting different strategies for the promotion of businesses, skill has a strong association with the use of ICT facilities in the businesses that might have played an important role in continuing with businesses even during the pandemic. Studies point to the need for expanding the scope of the skill development programmes from providing just technical skill-based education to updating the existing skills that could help households move away from unproductive enterprises to more advanced technology-based businesses. This could help inc­rease one’s productivity and earning. But in order to expand it, it is important to ensure ready access to better public infrastructure and public training programmes in close proximity (NCEUS 2009).

Even though skill plays an important role in promoting RNFS businesses, a large number of individuals do not show an inclination to participate in formal vocational training progra­mmes. Therefore, policymakers need to pay attention to enh­ancing formal vocational training for RNFS individuals on a grander scale. The lack of awareness and clarity regarding formal vocational training programmes and their usefulness has been found as the major reason for not participating in the formal training programmes. Therefore, there is a need for rethinking the model used for advertising vocational education and training and skill development programmes, as it is not reaching every section of the rural population as expected. Therefore, it is important to focus on better strategies that could help expand the reach of such programmes. These strategies could include the involvement of locals who have benefited from training programmes testimony to make people aware of the potential benefits of formal vocational training. 

Another reason for people being unable to enrol for formal training is the lack of time required for participation. This could be because they have to work for longer hours to earn a decent income for their family or their occupation req­uires them to work for long hours, which goes past school timing. First, if they were provided with a scholarship (or income protection) for the course of the training period, it could attract more people to such institutions. Second, there could be indi­viduals willing to learn, provided there is an option for learning in terms of an evening college format with the timing issue solved to an extent.  


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Updated On : 11th Jul, 2022
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