A study on Analyzing the Aftermath of PMKVY on the Public of Madhya Pradesh Region

A study on Analyzing the Aftermath of PMKVY on the Public of Madhya Pradesh Region

Dr. Gaura Chauhan, Ms. Shorya Gupta, Ms. Vartika Singh

Assistant Professor, ITM University, Gwalior

DOI: https://doi.org/10.51244/IJRSI.2024.1105015

Received: 18 April 2024; Accepted: 01 May 2024; Published: 31 May 2024

ABSTRACT

Indian economy is currently one of the world’s largest economies. India continues to hold immense potential for a prosperous future, largely due to its advantageous demographic distribution strategy. This strategy aims to bolster the economy by capitalizing on most of the population falling within the 15 to 59 age brackets. By focusing on employment and development, India can effectively support domestic production while simultaneously catering to the global requirement for skilled labor. This research study focused on the reach of Pradhan Mantri Kaushal Vikas Yojana (PMKVY) in the Madhya Pradesh region. This focus is motivated by the findings of the India Skill Report 2023, compiled by Wheebox, which indicates that Madhya Pradesh does not rank among the top three states in terms of the supply of employable talent. Pradhan Mantri Kaushal Vikas Yojana (PMKVY) during World Youth Skills Day. These initiatives were introduced under the Ministry of Skill Development and Entrepreneurship (MSDE) with the objective of bridging the gap between the demand and supply of employability skills. The extent of awareness of PMKVY among the public of Madhya Pradesh, the effectiveness of PMKVY in MP region, the loopholes of PMKVY in MP region, and the impact of demographic variables (Gender, Marital Status, Age) on PMKVY is being evaluated.

Keywords: Pradhan Mantri Kaushal Vikas Yojana (PMKVY), Ministry of Skill Development and Entrepreneurship (MSDE), Economy, Demographic Variables

INTRODUCTION

The Indian economy is currently experiencing significant transformations and demonstrating consistent growth, positioning it as most significant economies globally. Nirmal Singh, the CEO and founder of Wheebox, highlighted in India’s Skill Report 2023 that India is presently situated in an era where “while the rest of the world is aging, India remains youthful.”

India continues to hold immense potential for a prosperous future, largely due to its advantageous demographic distribution strategy. The demographic dividend, which entails altering the age composition of the population, plays a pivotal role. This strategy aims to bolster the economy by capitalizing on the majority of the population falling within the 15 to 59 age brackets. By focusing on employment and development, India can effectively support domestic production while simultaneously catering to the global requirement for skilled labor.

Efforts aimed at enhancing employable skills have the potential to expedite economic growth. Employability skills encompass essential abilities and personal attributes necessary for any job. These skills encompass effective interpersonal communication, collaboration, problem-solving capabilities, and adaptability to fit into various work environments. Furthermore, they encompass the expertise that empowers individuals to thrive and succeed in their careers.

Several employability skills have been identified as crucial. These skills include:

1. Communication skills
2. Leadership
3. Problem-solving
4. Teamwork
5. Reliability
6. Self-management
7. Planning and organization
8. Technology proficiency
9. Initiative
10. Continuous learning

These skills are recognized as vital for individuals seeking employment and for achieving success in the professional arena.

On July 15, 2015, Prime Minister Modi unveiled the Pradhan Mantri Kaushal Vikas Yojana (PMKVY), marking World Skills Youth Day. This initiative, spearheaded by the Ministry of Skill Development and Entrepreneurship (MSDE), aims to bridge the gap between skill supply and demand in India. Central to PMKVY is The goal of Prime Minister Modi is to establish India as a worldwide center. The Skill Mission India under PMKVY is committed to empowering youth with employable skills, prioritizing certification, excellence, and job placement. Its primary objective is to furnish young individuals with the necessary skills to explore various employment opportunities.

Pradhan Mantri Kaushal Vikas Yojana

The Pradhan Mantri Kaushal Vikas Yojana (PMKVY) prioritizes youth skill development and employment training as its central objective. This initiative entails placing young individuals into training programs nationwide, with a focus on specific industries and job roles. The overarching aim is to equip them with the necessary tools for self-sufficiency and dignity. PMKVY emphasizes the issuance of certifications, particularly targeting individuals who have discontinued formal education but aspire to enter the workforce. Through tailored training aimed at enhancing foundational skills, PMKVY endeavors to address the growing demand for certified workers and bridge the skills gap. Ultimately, the primary thrust of the policy is to empower young individuals with the requisite skills to thrive in their chosen fields. The nation grapples with two significant challenges: a high rate of youth underemployment and a scarcity of appropriate resources and training avenues. PMKVY aims to tackle these issues by equipping young individuals with practical work skills, thereby preparing them for gainful employment and fostering sustainable livelihoods.

This research study focused the reach of Pradhan Mantri Kaushal Vikas Yojana (PMKVY) in the Madhya Pradesh region. This research study focus is motivated by the findings of the India Skill Report 2023, compiled by Wheebox, which indicates that Madhya Pradesh does not rank among the top three states in terms of the supply of employable talent. Furthermore, Madhya Pradesh does not feature in the top 10 states with the highest employability rates. These observations have generated interest in evaluating the effectiveness of PMKVY specifically in the context of Madhya Pradesh.

RESEARCH METHODOLOGY

The Study: It is a study of causal. The information was gathered via a survey method.

The Sample Design

Population: In this study, the population consisted of M.P. Region in India.

Sampling Technique: In this investigation, a deliberate sampling strategy was employed.

Sampling Size: The sample size of the study was 800 participants residing in M.P. Region of India.

Sampling Element: The study’s sampling consisted of specific individual in MP region.

Tools used for Data Collection: Standardized questionnaires was adopted to collect data on (SD), (VT), (I), (CO), (EL), (SL).

Data collection utilized a Likert-style scale, where respondents rated from 1 indicating strong disagreement to 5 indicating strong agreement.

Tools used for Data Analysis:

• The Cronbach’s Alpha Reliability Test was used to assess the validity of the surveys on PMKVY, (SD), (VT), (I), (CO), (EL), and (SL).
• EFA was used to determine the underlying variables of the PMKVY, (SD), (VT), (I), (CO), (EL), and (SL).
• Partial least squares (PLS), a structural equation modelling (SEM) method, was used to test the model.

Objectives

1. To find out the extent of awareness of PMKVY among the public of Madhya Pradesh.
2. To understand the effectiveness of PMKVY in MP region.
3. To analyze the loopholes of PMKVY in MP region.

HYPOTHESIS

• H1- significant impact of skill development on employability level
• h2- significant impact of vocational training on employability level
• h3- significant impact of employability on standard of living
• h4- significant impact of career opportunities on employability level
• h5- significant collaborative impact of employability level and career opprtunity on standard of living

REVIEW OF LITERATURE

According to a study conducted by (Bapat S. and Pandey A. 2019), the perception of PMKVY beneficiaries towards the programme was examined in the Mysore District of Karnataka. The researchers selected Mysore and Chamarajanagar as the two centers for their investigation within the state of Karnataka. The findings of the survey revealed that the beneficiaries’ overall perception of the PMKVY scheme was highly positive. However, the study also shed light on several significant flaws in PMKVY, which include a lack of awareness among beneficiaries, the absence of a grievance or feedback portal, insufficient entrepreneurship opportunities provided by the program, the absence of trades that cater to local needs at the centers, inconvenient locations of the centers that necessitate long travel distances for students, and a weak mechanism for job placements.

SIGNIFICANT IMPACT OF SKILL DEVELOPMENT ON EMPLOYABILITY LEVEL-

In a study conducted by (Behera B. and Gaur M. in 2022), the impact of skill development training on employability in India was examined. The Skills India Report 2021, cited by researchers, revealed a rise in India’s employment rate from 37.22% in 2015 to 45.9% in 2021.The report suggested that improving the secondary school system would enhance the educational standards of workers and young individuals. It also emphasized the need to enhance skill-training opportunities and job prospects for young women to create more career possibilities. Additionally, the report stressed the importance of expanding access to high-quality training and strengthening the skill delivery structure. Furthermore, effective coordination among various stakeholders was deemed necessary to balance the supply and demand of skills.

In a study conducted by (Suhagin et al. in 2018), the significance of information and communication technology (ICT) and the development of worker skills was highlighted. The researchers specifically focused on the impact of the Pradhan Mantri Kaushal Vikas Yojana (PMKVY) by analyzing secondary data. The findings indicated that PMKVY had a substantial influence on unemployed individuals, contributing to the enhancement of their workforce capabilities. The study concluded that due to the rapidly evolving corporate landscape, it is crucial to maintain adaptability within the new framework. Furthermore, skill development is an ongoing process, necessitating continuous training and retraining of workers to equip them with the skills needed to tackle emerging business challenges.

SIGNIFICANT IMPACT OF VOCATIONAL TRAINING ON EMPLOYABILITY LEVEL-

Vocational training programs have been found to have significant positive impacts on incomes and employment prospects, as highlighted by a study conducted by (Lee, et al.  2019). This research demonstrates that training programs have an overall favorable effect on employment, as supported by the findings of (Hirshleifer, et al. in 2014). In a study by (Ullah, et al. in 2021) the training provided by the Federally Administered Tribal Areas Development Authority (FATA-DA) to over 52,000 individuals in 70 different technologies and trades were examined. The research findings revealed that FATA-DA training significantly contributed to the socioeconomic advancement of young people, leading to increased youth employment rates upon completion of the training.

SIGNIFICANT IMPACT OF EMPLOYABILITY ON STANDARD OF LIVING-

According to a study conducted by (Tkemaladze,2017), effective employment is a crucial element in driving economic advancement, resulting in a significant decrease in poverty levels and an enhancement in the overall standard of living.

Bilevicience et al. (2016) conducted a theoretical analysis that examined the connection between employment and quality of life. The authors selected specific factors that demonstrated a positive correlation between employment and the overall quality of life.

SIGNIFICANT IMPACT OF CAREER OPPORTUNITIES ON EMPLOYABILITY LEVEL-

According to a recent study conducted by (Pshembayeva et al.2022), the level of economic growth in a country is influenced by the effectiveness and development of career development programs.

As highlighted in a study conducted by (Robert C. Merchant Jr.2010), it is imperative for organizations to prioritize professional development as we approach the twenty-first century. By providing opportunities for employees to meet their career aspirations, organizations can retain valuable staff members, whereas those neglecting professional development risk losing them.

RESEARCH METHODOLOGY

This research investigates the utilization of a survey to assess the primary impacts of skill development on employability level, vocational training on employability level, employability level on standard of living, career opportunities on employability level. The Likert scale ranged from 1 (Strongly Disagree) to 5 (Strongly Agree). A total of 800 individuals were selected using purposive sampling from both urban and rural areas, from whom data were collected. Gender, marital status, and education were employed as demographic factors to further categorize the participants. Cronbach’s Alpha reliability was employed to assess the reliability of each variable. A case study approach was utilized to explore the potential of each variable. The conceptual model was tested using Smart PLS software via structural equation modeling (SEM).

FINDINGS AND DISCUSSION

Table 1: Showing Cronbach’s α Reliability values

S. No.	Variable	Cronbach’s Alpha	No. of Item
V1	Skill Development	.920	18
V2	Employability Level	.862	20
V3	Vocational Training	.868	21
V4	Standard of Living	.913	19
V5	Career Opportunities	.885	22

The reliability of each scale, encompassing Skill Development, Employability Level, Vocational Training, Standard of Living, and career opportunity, was evaluated using the Cronbach alpha reliability test. The resulting Cronbach’s α values ranged from 0.862 to 0.920 for each scale. According to Nunnally (1975), a Cronbach’s alpha of 0.70 indicates sufficient reliability for further testing, and thus the questionnaire was deemed reliable. The reliability values for each variable fall within high confidence bounds, indicating the trustworthiness of these adjustments.

Table 2: KMO and Bartlett’s test of adequacy and sphericity

S. No.	Variable	KMO Value	Bartlett’s test (chi square value)	Sig. value
V1	Skill Development	.862	1323.850	.000
V2	Employability Level	.821	2631.820	.000
V3	Vocational Training	.931	2651.740	.000
V4	Standard of Living	.846	1083.013	.000
V5	Career Opportunities	.930	2410.875	.000

Bartlett’s sphericity test is a statistical procedure used to determine the significance of all associations in a correlation matrix (Kline, 1994). The test compares the computed item-to item correlation matrix with identity matrix. A significant value in the number of items-item correlation matrix suggests a notable deviation from the identity matrix. The outcomes of Bartlett’s test of sphericity were found to be significant, suggesting that the data was appropriate for exploratory factor analysis (EFA). The value of Bartlett’s Test of Sphericity must be below than 0.05 for EFA to be appropriate (Hung Van Truong, Cuong Hung Pham & Nhan Hoang Vo, 2016). It was established that a relationship existed as the chi-square values for each measurement surpassed the significance threshold. Consequently, this dataset is imperative for conducting significant research. Norusis, (1992) KMO test result of 0.50 or above shows that the quantity of samples used for factor analysis is acceptable.

The KMO results presented in Table 2 indicate values of 0.862, 0.821, 0.931, 0.846, and 0.930, respectively, all exceeding 0.5. This suggests that the data obtained from the survey is appropriate for conducting factor analysis.

The collected data from all three measurements underwent statistical analysis. Factors were transformed using the varimax orthogonal transformation method, while principal component analysis (PCA) was applied to convert factors into components. Subsequently, all variables identified in the search results were consolidated into a unified variable, with the event name serving as the variable’s identifier.

Indicator loading

Initiating the statistical analysis process involves assessing factor loadings. The model’s temporal reliability is considered satisfactory when the agreement among the loadings exceeds 0.708, indicating that the model elucidates over 50% of the scale variance (Sarstedt, Hair, & Risher, 2019).

Internal consistent reliability/ Composite reliability

To assess the reliability of scores derived from PLS, alongside Cronbach’s alpha and composite reliability, the “rho A” coefficient introduced by Henseler et al. (2014) was utilized. A “rho A” value of 0.7 or higher indicates satisfactory reliability (Henseler et al., 2014). However, “rho A” values exceeding 1 are uncommon and should be excluded from the model. Specifically, the cumulative “rho A” values for the components of professional growth, professional level, vocational training, living, and working stand at (0.929, 0.856, 0.926, 0.869, and 0.838) respectively, surpassing 0.7 yet below 1. These values signify a high degree of reliability and a robust structure.

Convergent validity

In evaluating the validity of the measurement scale through convergent validity, the average variance extracted (AVE) was scrutinized. A latent variable with a convergent validity score of 0.50 or higher indicates that it explains more than half of the variance in its indicators (Ringle & Sarstedt, 2011; Hair). With AVE values for each construct—0.807, 0.723, 0.727, 0.826, and 0.623—all surpassing 0.50, they demonstrate excellent convergent validity.

Discriminate validity

The Fomell-Larcker Criterion and HTMT are two methods for determining discriminant validity. Fornell and Larcker (1981) proposed a conventional approach where the square root of AVE for every latent variable can be utilized to create discriminant validity if such value is greater than other correlation coefficients among the latent variables.

Table 3: Fornell-Larcker Criterion

	Skill Development	Employability Level	Vocational Training	Standard of Living	Career Opportunities
Skill Development	.962
Employability Level	.825	931
Vocational Training	.721	.845	.946
Standard of Living	.665	.821	.741	.930
Career Opportunities	.616	.652	.621	.854	.846

The assessment conducted through the Fornell and Larcker technique indicates that the square root of the average variance extracted (AVE) should exceed the correlation between constructs. As illustrated in Table 7, both the square root of AVE and the correlation between the components are presented. It was concluded that the findings satisfactorily adhere to the criteria outlined by Fornell and Larcker in 1981.

Table 4: Heterotrait-Monotrait Ratio (HTMT)

	Skill Development	Employability Level	Vocational Training	Standard of Living	Career Opportunities
Skill Development
Employability Level	.812
Vocational Training	.871	.895
Standard of Living	.821	.813	.989	.930
Career Opportunities	.814	.856	.898	.888	.876

Heterotrait-Monotrait Ratio (HTMT)

The HTMT ratio, which assesses the similarity of latent variables, was also investigated.

Values of this ratio more than 0.85 indicate that the two variables are similar (Hair et al., 2017); consequently, all the HTMT ratio values fulfilled the condition.

Multicollinearity Assessment

When independent variables exhibit multicollinearity, regression coefficients tend to increase. Thus, it is imperative to maintain multicollinearity within a specified range to ensure the accuracy of multiple regression coefficients. A widely used measure for assessing multicollinearity is the variance inflation factor (VIF). A VIF value of five or less indicates acceptable levels of collinearity that do not significantly affect regression coefficients. As indicated by the Smart PLS results (1.496, 1.000, 1.262, 1.002, 1.236), all VIF values are below 5. Therefore, in line with Haider, Jabeen, and Ahmad (2018), it can be concluded that the estimated variance is not affected by collinearity.

Predictive Relevance: The Stone- Geisser’s Q2 (values)

The prediction accuracy (Q2) of the relevant endogenous variable model was determined through the blinded PLS method. Employing the blindfold technique, individual PLS prediction models were constructed for each variable incorporated within the designated framework. This was accomplished through the computation of Q2 prediction, with a value higher than zero representing that the exogenous variables have predictive relevance (significance) for the endogenous variable under consideration (Hair, Ringle and Sarstedt, 2011).

Inner Model Path Coefficient Sizes and Significance

The coefficient of determination (R2) for the endogenous latent variable “skill development” is 0.926, suggesting that two endogenous latent variables—standard of living and career opportunities—jointly explain 92.6% of the variance in performance. Within the internal model, lifestyle demonstrates the highest influence on employability level (0.865), followed by the mediation effect of standard of living and career opportunities on employability level (0.926) and career opportunities (0.186) The correlation coefficient of 0.185 highlights the significance of the theory concerning the relationship between skill development and livelihood. The hypothesized path mediating the association between vocational training and career opportunities is statistically significant with a value of (0.750).

Checking Structural Path Significance in Bootstrapping

Bootstrapping is a non – parametric method for assessing the statistical validity of PLS-SEM (Davison and Hinkley 1997; Efron and Tibshirani 1993), which includes repeated random sampling with a substitution from the actual sample to produce a sample of bootstrap in order to generate standard errors for hypothesis testing, the desired population distribution is an accurate representation of the sample distribution presumed by the procedure. The bootstrap sample allows the estimated PLS-SEM coefficients to be tested for significance. (Henseler, Ringle, and Sinkovics 2009).

CONCLUSION

A large portion of the youth population in the country has low employability, and there is a dearth of skilled and trained resources. Through the teaching of young industry-relevant skills that will prepare them for work and a sustainable way of life, PMKVY seeks to address these issues.

The scope of the Pradhan Mantri Kaushal Vikas Yojana (PMKVY) in the state of Madhya Pradesh will be the main topic of this study proposal. The results of the India Skill Report 2023, compiled by Wheebox, which show that Madhya Pradesh does not rank among the top three states in terms of the supply of marketable talent, served as the impetus for this focus. Additionally, Madhya Pradesh is not among the top 10 states with the greatest rates of employability. These findings have sparked interest in determining how effective PMKVY is specifically in the context of Madhya Pradesh.

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