Regional Development and Rural Unemployment in Low Income States

Regional Development and Rural Unemployment in Low Income States | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Regional Development and Rural Unemployment in Low Income States dinabandhu bag This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3900285/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 This paper aims to determine whether the low-income states have had adequate schemes to contain short or long-run unemployment. It finds that the allocations under rural works programs could impact the short-term unemployment rates. The rural model behaves differently from urban model. It projects that a thousand new applicants to the Live register would raise unemployment by 1.66%, adding one thousand person days to rural works could reduce unemployment by 2%, and adding five new micro, small and medium enterprise (MSME) jobs could reduce unemployment by 1.2%. Since large-scale industrial development is a slower process, the low-income state may emphasize to contain rural employment. The implications of these findings lie in monitoring of the live register and objective targeting of rural works expenditure allocations within the state. JEL cODES : A14, A10, Go, c30 rural works unemployment rate allocation new jobs msme Figures Figure 1 Introduction The generation of gainful employment for both the skilled and unskilled labour in India has been one of the important goals vigorously pursued under the planning process by the central and state governments. However, the dream of planners and administrators to mitigate the problem of unemployment has remained unreachable due to multiple reasons. This paper is a review of employment programs for the low-income state of Odisha. This paper determines if adequate target schemes are available to contain short- or long-run unemployment.It investigates regional variation, existence of disparities in employment within the state. If markets were efficient, there would not exist visible spatial variations in unemployment at regional level. We argue that an understanding of regional unemployment can be obtained from relevant plan measures. For example, the endogenous-growth theory explained that exogenous technological progresswould cause higher output, employment and growth.However, unlike the contemporary European discussions on migration as a significant deterrent of employment observed among the European economies, the lack of adequate Keynesian demand push in vocational economies could be construed as policy inaction that deters employment. Public expenditure and the government's liberal fiscal push are core to employment generation.The unmet fiscal targets would raise the gap between the job seekers and expanding levels of employment. Raj K. N. (1976) found days unemployed as a per cent of days in the Labour force at 20.2% for casual labourers in 1956. R. Sau R (1978) mentioned that Lakdawala (1977) had reported a 7.2% unemployment rate in the 27th round of the NSS. Vaidyanathan (1986) highlighted the importance of non-agricultural employment and its positive impact on reducing unemployment. Lanjouw and Shariff (2004) gave evidence of the distributional effect of employment on agricultural wages. Vyas and George Mathai (1978) had emphasized imparting vocational training, new credit schemes, facilitating marketing services, research and extension assistance to the rural farm workers. Vyas and George Mathai (1978) emphasized creating physical infrastructure for rural industrialization. Lanjouw and Shariff (2004) suggested that the evaluation of employment schemes required exploring beyond mere job creation to improve rural income. Papola and Mishra (1980) found the need for a more adequate focus on pure industrial activity or micro aspects of village industry units. Kurian (1990) mentions that the net addition to the labour force during the 7th Plan was a small decrement in overall unemployment levels (UR). Nayak and Chatterjee (1986) showed by the criteria of disguised unemployment or underemployment that the extent of underemployment varied from 26% to 100% in rural Odisha at the village level. Mahendra Dev (1990) found that person-day unemployment in rural India in 1977-78 existed at 7.47% in Agriculture and 8.70% in the Non-agriculture sector, respectively. Niti Ayog (2022) reports that unemployment's current weekly status (CWS) is higher, remaining at 8.8% from 2017-18 to 2019-20. The recent quarterly bulletins on the UR-CWS showed a rise in unemployment in early 2019 and a marginal decline towards late 2020 (MOSPI, 2021). This could mean the unmet job creation goals are rolling over periods. Similarly, regional disparities in employment generation would exist via large-scale industrial development among the states. The relatively low-income states need to focus on low-scale, low-technology employment generation channels. How much can public expenditure on rural works help reduce unemployment? Could rural micro enterprises (e.g. MSMEs) play a role in reducing short-term or long-term unemployment rates (UR)? Early works of scholars (Birch, 1979; Beck et al., 2003; Ayyagari et al., 2014 ) held that MSMEs were a source of employment and income. Mosk (2010) mentioned that during the inter-war period (1919–1938), the government promoted small and medium-scale enterprises in Japan. Birch (1979) mentioned that large firms lost their way to MSMEs in USA. Kirchhoff and Phillips (1988) found that SMEs with less than 100 employees were job creators in USA. Ayyagari et al. (2014) found that small firms had a larger share of jobs in developing economies than larger firms. Beck et al. (2003) found MSMEs comprise 50–60% of employment globally. This paper aims to evaluate the employment programs in a low-income state, highlight the gaps in their achievements, relate the unmet targets to progress of rural schemes and arrive at logical inferences from secondary data. It also attempts to find differences in district-level rural/urban unemployment. The rest of the paper is divided into sections: employment generation,hypothesis, methodology, results, and conclusions. Employment Generation Programs Table 1 summarizes the prescriptions of employment generation highlighted in policy debates. The schemes are supposed to have been accurately designed to achieve the specific aims of the targeted unemployed. Table 1 highlights the need for dedicated schemes for rural casual workforce. We choose to describe the major national-level programs implemented in the low-income state. The gradual renaming, replacing, merger and rephrasing of various national programs has led to a few major schemes such as the theDeen Dayal Antyodaya Yojana (DDAY) in 2015 and the Prime Minister Employment Generation Programme (PMEGP) in 2008-09. The earlier versions of employment programs include the Rural Landless Employment Guarantee Program (RLEGP) in 1983, the Jawahar Rozgar Yojana (JRY), and the National Rural Employment Program (NREP).Employment Assurance Scheme (EAS) of 1993, the SGSY (Swarna et al.) of 1999, etc. Later, the SGSY was reframed to form the National Rural Livelihood Mission (NRLM) and replaced by the Deen Dayal Antyodaya Yojana (DDAY) in 2015. Khadi and Village Industries Commission (KVIC) introduced the Prime Minister Employment Generation Programme (PMEGP) in 2008-09 by amalgamating the Rural Employment Generation Programme and the Prime Minister Rojgar Yojana (PMRY). The PMEGP scheme had reported employing about 3,487 workers against a target of 5,744 in 2016 in Odisha (MSME Odisha, 2016).The state's coir micro industry development scheme generated about 11,150 workers in 2016 (MSME Odisha, 2016). The NSS report 73 rd round (2015-16) put the number of MSME workers at 33.26 Lakhs.An exploration of the trends in principal status (PS) UR (Unemployment rates) in Odisha is worth the review. Table 2 shows the unemployment rate per 1000 persons/person-days in the labour force reported by the Government of Odisha. NSSO publishes unemployment rate (UR) denoted as the number of persons unemployed per thousand persons in the labour force (both the employed and unemployed). The highest unemployment rate (UR) principal status (PS) was at 217 for urban females. Female UR (CWS) (about 22.9%) have remained more than double the national average (11.2%). Longer-term reduction of unemployment is possible via capital or asset generation programs. Early works have dwelt on testing the efficacy of employment schemes to cause decline in levels of UR. However, there has to be a trade-off between how many jobs are created vis-à-vis the amount of capital expenditure incurred. Similarly, the state has the option to distribute the allocations of capital expenditure across "pure job" schemes against jointly creating rural assets and employment or building productive factories and engaging workers at the same time. However, Moreno-Galbis, E. & Sneessens, H. (2007) suggested that skilled and technical could lead to higher unemployment rates for casual unskilled labourers.The early evaluations also pointed to a rise in the inequality in the distribution of resources. The rise in inequality implies that the State must plan to have an objective basis to re-allocate its gross capital expenditure spread by backward regions within the state. Birch (1979) argued that small firms are important in job creation. Adhikari (2000) suggested concrete public intervention on self-employed enterprises by providing state support to technological upgradation and product development crucial for rural job creation. Mitra and Pandey (2013) found that wage reductions in smaller units raised employment demand.Goldar and Mitra(2013) highlighted the economy-wide role of the unorganized and informal sector. Murthy (2016) suggested that the micro sector played an important role in the development of the economy. Dey (2014) suggested the flexibility, effectiveness and entrepreneurship displayed by micro industries. Contrarian evidence exists that small firms are the engines of job creation (see Roberts & Samuelson, 1989; Leonard, 1986).Small firms had been considered as inefficient parasites who escape from taxes and regulations (Rafael L Porta &Andrei S, 2008). Lewandowska-Gwarda (2018) examined the regional employment pattern and attributed them to socio-demographic and economic structure, including the number of working people, age groups, female labour participation, education, number of enterprises, inward migration, and minimum official wage rates, respectively. While exploring regional imbalance, Lewandowska-Gwarda (2018) found a negative association between capital investments and provincial unemployment rates in 273 districts of Poland.Oded and Murphy (2003) suggested control variables such as the national income and national unemployment rates to investigate the regional patterns in unemployment. Oded and Murphy (2003) proposed that regional unemployment was caused by diversity in industrialization, lower per capita income, higher population density, and population growth, respectively. Kumar et al. (2011) established a negative relation between land ownership size and the likelihood of being engaged in the non-farm sector. Specific works on short-term unemployment in Odisha are few. Mishra B (2010) measured the change in per capita employment by workers per thousand population or employees per thousand people. Mishra B (2010) shows that mining districts in the state had fared better than industrial and non-industrial districts in job creation. Samantaraya et al. (2014) suggest that good irrigation facilities, road connectivity, and proximity to vibrant economic activities facilitate more earning and a better standard of living in Odisha. Khan et al (2021) have used PLFS data to estimate the UR for Odisha using auxiliary variables of Crop area and literacy rates. Khan et al (2021) found the positive explanatory power of district-wise gross cropped area and Female literacy rates to the unemployment rate in Odisha.Srivastava and Srivastava (2010) mention that the lower female employment rates are due to their immobility to seek gainful wage-based jobs away from home. Rural infrastructure and investments in irrigation have dual benefits of boosting farm earnings and engaging rural labour in projects of Mega Lift irrigation, Deep Bore wells, Check Dams, Water Tanks, and Field canals, etc. There are gaps in the achievement of the irrigation potential of Odisha, which remained at 5.6 million hectares in 2020 (1 /3 rd of the net sown area, Department of Water Resources, Odisha). Hypotheses The gaps in empirical findings include efficacy tests of the impact of rural works programs to reduce UR-rural and UR-urban. Similarly, the contribution of plausible employment, such as large industries, MSMEs, cottage and handloom, and micro-enterprises, needs to be tested. The levels of seasonal deployment of rural casual labour could be visible in changes to crop volume production. Given the recent rise in unemployment, we propose conducting relevant tests to assess the past efforts of the state aiming to reduce the UR levels. The efforts are explored in terms of thrust on micro or small scale industry,targets under micro and handloom industry, manpower budget allocations under rural works MGNREGA. The following hypotheses are proposed. PLFS bulletin reports urban short term UR-CWS (current status) where MGNREGA allocations are predominantly rural in nature. Rural labour would migrate to urban areas to look for employment. H10: MGNREGA significantly contributes to reducing short term unemployment. H11: MGNREGA does not contribute to reducing short term unemployment. MGNREGA scheme often undergoes politico-economic transitions and regional outcry to prevent outward migration of rural labour outside the state. H20: The district wise allocations under MGNREGA manpower budget are uniformly distributed in the state. H21: The district-wise allocations under MGNREGA manpower budgets are not uniformly distributed within the state. Traditional thrust on setting up large-scale factories had been limited to power-generating urban regions or mining districts. H30: The large industrial employment significantly contributes to reducing unemployment. H31: The large industrial employment does not contribute to reducing unemployment. MGNREGA has a long implementation history and is the obvious alternative to job seekers. H40: MGNREGA significantly contributes to reducing unemployment in rural and urban Odisha. H41: MGNREGA does not contribute to reducing unemployment in rural and urban Odisha. The MSMEs are evolving and drawing policy attention in past few years and are considered informal growth engines. Lal (1988) mentions that the levels of employment exchange registrants will reflect the demand for formal sector jobs rather than the informal or MSME jobs. H50: MSME employment significantly impact unemployment in Odisha H51: MSME employment does not impact unemployment in Odisha. The tests of hypotheses H10 and H20 are based on Pearson correlation significance tests (see Goldsman, D. (2010)). Goldsman (2010) suggested t-tests for the significance of Pearson product moments correlations between two variables which is compared at a given confidence level (95%) from the T-Table. Further, the test of hypotheses and H30, H40 and H50 are based on regression parameter significance tests State Data We begin with state data in Figure 1, which shows the recent quarterly variation in the weekly status of unemployment reported by the PLFS bulletin. It displays changes to employment during the sample period (2017-2022). All persons' weekly status (CWS) has remained between 10% and 12.7%. The maximum UR (22.9%) is observed for the category of female labour. The quarterly trend in Figure 1 shows that average UR-All person has remained at 11.6% with a deviation of 2.8%. The growth in the UR-CWS during this period is strictly positive over 11%. It shows the average growth in person days was only 2.5% against the average UR-All person increase of 11.6%. The rise in UR-CWS could be due to new job seekers who are added to the base or a shortfall in achieving the previous quarter's target. Table 3 shows the descriptive Pearson correlation (ρ) between Urban UR-CWS and person days generated under MGNREGA (2017-2022) The descriptive correlation tests are presented in Table 3, which shows the significant relationship among the indicators UR-CWS and Person days-All.UR-CWS-Male person days or UR-CWS-Female person days, respectively. Does MGNREGA reduces short-term unemployment? Unfortunately, the Pearson t-test implies that none of the three important correlations, UR-CWS-Male, UR-CWS-Female, UR-CWS-All, are significant at 95% with either of Person days-All. UR-Male person days or UR-female person days, respectively. This suggests that MGNREGA does not contribute to reducing short-term unemployment in Odisha. The first hypothesis, H10 of MGNREGA, which significantly contributes to reducing short-term unemployment, is rejected. MGNREGA does not reduce short-term unemployment in Odisha. Table 4 shows the gross employment under MSMEs in Odisha. The districts of Baudh, Bhadrak, Deogarh,abarangpur and Nayagarh have the lowest shares(1%) compared to the highest share (5%) of the Khurda district in terms of employment. The inequality across the districts is widely visible in terms of the number of units and the average number of employees per unit. Table 5 shows the levels of Gross Employment in large units in Odisha. The Kandhamal District has the most negligible share (rounded off to 0.0%) of larger manufacturing units and large factory employment. However, Angul, Dhenkanal, Jharsuguda and Sambalpur comprised the cumulative share of 70% of gross productive capital deployed, and 34.6% of the employed. Previous findings by Mishra (2010) found that the five major districts of Cuttack, Koraput, Puri, Sambalpur, and Sundargarh contributed to 66% of the total number of factories and 87% of gross employment.The gross employment under large and small-scale units needs to catch up to the employment demand in many districts. We find that the shortfall in gross employment under MSMEs is somehow covered by cottage and handloom sectors in a few districts (e.g., Bargarh, Boudh, Subarnapur, Ganjam, Jajpur, etc.). The concern is the inequality observed in the shares of allocation of productive capital within the state. Kandhamal has the lowest share ( rounded off to 0.0 %) in manufacturing employment. The shares of the employed under large industries widely varies within the state. This confirms the state's unequal presence and distribution of large-scale industries.The unemployment levels could depend on the level of capital investments made in large factories. Lewandowska-Gwarda (2018) could not confirm the presence of a positive association between levels of invested capital and employment for the districts of Poland.The distribution of large industrial employment widely varies within the state. Table 6 shows the Pearson correlation tests among the utilisation indicators under MGNREGA and the descriptive statistics, the shares of Expenditure, Person-Days and Per Capita Expenditure, respectively. Should the district-wise labour demand for rural work programs not be aligned with the population size and unemployment level, such as the manpower budget? One can find wide variation in disbursal and allocation of manpower budgets. The person-days of labour under NREGA do not seem to have progressed each year from 2008 to 2021. Similarly, the average person's employment is also lower. The district of Mayurbhanj is the lead district with a per capita (mean) allocation of INR 6,085, comprising the highest share (16%). This is indeed desirable because Mayurbhanj is one of the predominant tribal districts. However, the districts of Deogarh, Boudh and Bargarh need more attention due to the current low levels of per capita allocation (INR 3,598, INR 1,116, and INR 917), and their current lower per capita expenditure shares (1%) only. Table 6 shows the descriptive Pearson correlation (ρ) test of the three distributions (%) variables of Total expenditure, person days and per capita expenditure for 30 districts. Similarly, a national study (Breitkreuz et al., 2017) reported lower work completion rates under MGNREGA. The MGNREGA budget is better tied to the census data. Are t he district-wise allocations under the MGNREGA manpower budget uniformly distributed in the state? Unfortunately, the Pearson t-test implies that none of the three important correlations, Total expenditure, person days and per capita expenditure, are significant at 95% with either of Person days-All. UR-Male person days or UR-female person days, respectively. Therefore, the second hypothesis, H20, of the presence of non-uniform allocations under MGNREGA manpower budgets cannot be accepted. The district-wise allocations under MGNREGA manpower budget are not uniformly distributed in the state. The unequal allocation is visibly prominent in our sample. In the second part, we continue to fit models for rural and urban split samples to draw inferences on the significance of allocations under rural works and micro industry-related variables at the district level. Empirical Model Durlauf and Quah (1998) mentioned 36 different categories of variables with about 87 usage examples in their unemployment tests. We proceed with Kennedy's (2003) linear model of UR (unemployment rate) as; U 𝑖𝑡 = α + 𝛽 1 Pit + γ 1 Lit + δ 1 INDit + 𝜀𝑖𝑡 (1) Where, U 𝑖𝑡 , is the unemployment rate (NSS) Pit vector refers to activity under MNREGA, e.g., Number of person-days under MNREGA L it vector includes labour force data on District Labour Register, e.g., Outstanding unemployed, New annual labour registrations, etc. The IND it vector incorporates industrial climate, number of reported factories, and the employed across establishments within the district, e.g., MSMES, Micro-loom,cottage, KVIC, large factories, etc. 𝛽s, γs, δs are the associated parameter coefficients, and 𝜀 is the random error, respectively. Sample Data The split sample data includes UR data from PLFS bulletin, DC-MSME Reports, Annual Survey of Industries (ASI), etc. Table 7 reports the statistical summary of the major indicators: gross employment across MGNREGA, small-scale industries, cottage and handloom, micro and handloom and large factories. It also gives the number of new applicants registering at the employment exchange and the outstanding unemployed remaining in the live register of the district. The volume of external placement support provided to the skilled workforce by district employment exchanges needs to be higher (Directorate of Employment, Govt. of Odisha). The NSSO 68 th round reports the rural UR (PS) for few districts are much higher than the urban UR rates (e.g., Cuttack, Sundargarh, Ganjam, Puri, Khordha, etc). The Rural UR is more than 50 (per thousand) in Jagatsinghpur district. The Urban UR is more than 50 (per thousand) in three districts of Sundargarh, Sonepur and Nabarangpur. The higher rural UR (PS) in a few districts could be attributed to differences in allocation and achievement of rural program targets. Khan et al (2021) reported five districts of Ganjam, Cuttack, Gajapati, Khurdha and Kendrapada, which recorded significantly higher URs and lower UR for the districts of Malkangiri, Nabarangpur, Koraput, and Mayurbhanj, respectively. The entry list of variables in the sample included in the models is presented in Table 7, descriptive statistics. Results Table 8 shows the Parameter Estimates of the Unemployment Model (PS) split sample. In order to take into consideration potential issues with endogenity in the dataset, multi-collinearity diagnostics (VIF) are generated. A VIF cut-off value of 1.2 is applied to identify and eliminate collinear variables from the model. The VIF criteria eliminated the variable of Achievement under Crop Loan from the final model. Table 8 shows the parameter estimates of three separate split sample regression models for three categories of unemployment rate: (1) UR-Rural, (2) UR-Urban, and (3) UR-all persons. The (1) UR-Rural model has selected only three significant variables of MSME Employment to Newly registered, person days employment (000s), Outstanding in the District Live Register (000s), respectively. The model also rejected two insignificant variables, No. of Employees in large factories and the Achievement under Crop Loan (INR 10 Millions) during estimation. The (2) UR-Urban model and (3) UR-All persons model parameters appear similar to each other in terms of the choice of significant variables, No. of Employees in large factories and Outstanding in the District Live Register (000s), respectively. We find that the sensitivities of the (1) Rural model are distinct from the (2) Urban model. The common variable of outstanding in the live register (000s) acts a positive and significant control variable. The co-efficient (β) implies that an addition of 1000 applicants to the base of the Live register in the district could add to the UR-All persons by 1.6%. The state could accurately track and plan to contain the live register base from rising year by year. The (2) UR-Rural model correctly selects the MGNREGA variable of the person's days of employment (000s) as negative and significant. However, the person days employment (000s) variable is insignificant in the (1) UR-Urban and UR-All person models. Since the person days employment (000s) is negative and significant, we infer that MGNREGA contributes to the decline in UR- rural, where as it is insignificant to reduce the UR- Urban. The co-efficient (β) of person days implies that an addition of 1000 person days can reduce the UR-Rural by 2%. Does MGNREGA significantly contribute to reducing unemployment in rural and urban Odisha.? The fourth hypothesis, H40 of MGNREGA significantly contributing to reducing unemployment, cannot be rejected. MGNREGA does not reduce unemployment in rural and urban Odisha . The state could contain UR better by monitoring the district live register. The (2) UR-Urban and (3) UR-all persons have chosen another significant variables, No. of Employees in large factories. Urban areas with the availability of power supply have the potential to provide factory jobs to the unemployed. However, factory jobs would need skilled labour and has the potential to attract more skilled labourers with variable wage rates. This means the unskilled and unemployed would become more unemployed due to the skill gap which may be filled in by workforce from outside the district. The co-efficient (β) of the variable of the No. of employees in large factories implies that an addition of 100 new factory jobs would raise the UR-Urban by 2%. Does large industrial employment significantly contribute to reducing unemployment? The third hypothesis, H30, of large industrial employment contributing to reducing unemployment, is rejected. The large industrial employment does not contribute to reducing unemployment. The variable of the ratio of Employment in MSME to No. of newly registered in the Live Register is positive and significant in the(1) UR-Rural model. However, the variable of the ratio of Employment in MSME to No. of newly registered in the Live Register is insignificant in the (2) UR-Urban and (3) UR-All Person models. This implies MSME employment has not been critical in urban locations in the state. The co-efficient (β) of the variable of the ratio of MSME to the number of Newly registered in the Live Register explains the relative absorption of newly registered labour force vis-a-vis those who find employment in MSMEs. The UR rises with the levels of new registration in the live register. The UR falls with the levels of MSME employment. The co-efficient(β) implies an addition of 12 new MSME jobs could reduce UR-Rural by 1%. Does MSME employment significantly impact unemployment in Odisha? The fifth hypothesis, H50, on the contribution of MSME employment to reduce unemployment, cannot be rejected. MSME employment does not impact unemployment in Odessa. The state could leverage the higher sensitivities of MSMEs to reduce rural UR. These results could relate to the findings of Moreno-Galbis, E. & Sneessens, H. ( 2007 ) who suggested that technological change combined with fixed wages in informal sectors can thus lead to higher casual labour unemployment rates. Ode and Murphy (2003) and Srivastava and Srivastava (2010), respectively. Srivastava and Srivastava (2010) mention that the lower female employment rates were due to their immobility to seek gainful wage-based jobs away from their homes. Oded and Murphy (2003) suggested that the lack of industrialization had caused regional variation in unemployment in the USA. Mehrotra et al. ( 2014 ) showed that employment was rising in the unorganized and informal sector. These model sensitivities do not provide ranking criteria to choose the order of important policy action indicators. These results suggest that the UR-rural model behaves differently from the UR-urban model, and the UR-All model is as good as the UR-urban. There are few critical policy implications of these findings. In order of priority, we suggest targeting rural unemployment as a priority using both MGNAREGA and MSME sectors to help maintain the live register levels. At the same time gradual industrialization would help contain the urban unemployment. The state has an advantage in achieving both goals because the skill sets of casual labourers and factory workers differ. The skilled factory workers will not look for menial MGNAREGA or low skilled MSME tasks. Conclusions This paper conducted a review of employment programs in the low-income state in India. It conducted major hypotheses relating to provincial unemployment. It inferred interesting evidence with respect to the gaps in the coverage of employment schemes. It deduced that an addition of 1000 applicants to the base of the live register would add to the UR by 1.6%. It found although MGNREGA contributed to the decline in urban UR, whereas, it is insignificant to reduce the rural UR. It inferred that an addition of 1000 person days can reduce Rural-UR by 2%. The sensitivity of MSME employment ratio implied an addition of 12 new MSME jobs could reduce UR by 1% in the district. It concluded that the UR-rural model behaves a little differently from the UR-urban model. It also highlighted the regional imbalance in small and medium-scale industrialization. The difference between the districts characterizing the highest and lowest unemployment rates indicated spatial diversity in development in the state. The findings agree with Mahendra Dev (1988), who also mentioned the thrust on rural enterprises in Odisha. Parthasarathy (2005) suggested emphasizing township enterprises to raise farmers' incomes. Vyasulu V & A Kumar (1997) also observed that the pattern of industrialization was grossly inadequate. The practical implications of the results are important for policymakers. More emphasis on MGNREGA will reduce rural-urban migration within the state. Given the considerable significance of MSMEs to ensure vocational jobs, more thrust is needed to enlarge the scope of rural MSMEs. This could include micro handloom units, cottage units and similar non-agricultural enterprises. Although the live register of outstanding employed might not accurately reflect the degree of idle labour in the district, it can act as a base to plan for short-term labour deployment targets. Given the lower growth of heavy industrial sector employment in the state, the predominantly rural population of Odisha could be covered only under the single most scheme of MGNREGA. As is evident from recent PLFS data, the continual emphasis on the construction or Infrastructure sector could reduce the urban UR. In any economy, labour mobility is encouraged to permanently contain both urban and rural UR. There are few limitations in our study. The limitations are the inability to display the disparities which would exist at Mandal level within districts in our dataset. As open unemployment is not a condition for being on the employment register, the live registers cannot confirm the level of open unemployed. The sampling frame included only published sources. The design did not conduct the traditional test and control (RCT) method, which is used for between-group comparisons. The employment data is not available from directly relevant sources of rural infrastructure creation or development activities. While this analysis has used data at the district level, there are more opportunities to conduct a review at the village, Mandal, or household level for generating granular analysis. Declarations Conflict of interest : The author has no conflict of interests Funding The author received no funding for this research. Author Contribution conceptualized, data analysis, wrote the main manuscript text, prepared figures,drafted the manuscript, reviewed the manuscript., Acknowledgement: The authors are grateful for the comments by an anonymous reviewer Data The data used in this research is available from public sources. 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(2021) Kumar, A., Kumar, S., Singh, D., Shivjee: Rural Employment Diversification in India: Trends, Determinants and Implications on Poverty. Agricultural Econ. Res. Rev. 24 , 361–372 (2011) Kurian, N.J.: Employment Potential in Rural India: An Analysis, Economic and Political Weekly, Vol. 25, No. 52 (December 29, 1990), A177-A188. (1990) Lakdawala, D.T.: Growth, Unemployment and Poverty, Presidential Address to the All India Labour Economic Conference, December 1977.Tirupati, (1977) LALDEEPAK: The Determinants of Urban Unemployment in India, Indian Economic Review , Vol. 23, No. 1 (January-June 1988), 61–81. (1988) Lanjouw Peter and Shariff Abusaleh: Rural Non-Farm Employment in India, Economic and Political Weekly, Vol. 39, No. 40. 4429–4446. (2004) Lewandowska-Gwarda, K.: Geographically Weighted Regression in the Analysis of Unemployment in Poland. ISPRS Int. J. Geo-Information. 7 (1), 17 (2018) Moreno-Galbis, E., Sneessens, H.: Low-skilled unemployment, capital-skillcomplementarity and embodied technical progress. Recherches économiques de Louvain. 73 , 241–272 (2007). https://doi.org/10.3917/rel.733.0241 MOSPI: Annual Report, Periodic Labour Force Survey (PLFS), May 2022. NSSO, New Delhi. Available at www.mospi.gov.in accessed on 13.06.2022. (2022) Mehrotra, S., Jajati, J.K.: Stalled Structural Change Brings an Employment Crisis in India. Indian J. Labour Econ. 64 , 281–308 (2021) Mahendra Dev, S.: Non-Agricultural Employment in Rural India: Evidence at a Disaggregate Level, Economic and Political Weekly, Vol. 25, No. 28, 1526–1536. (1990) Mishra Banikanta: Agriculture, Industry and Mining in Orissa in the Post-Liberalisation Era: An Inter-District and Inter-State Panel Analysis, Economic and Political Weekly, MAY 15–21, 2010, Vol. 45, No. 20 (MAY 15–21, 2010), 49–68. (2010) Ministry of Rural Development: Annual Development Plan, 2020. (2020) Ministry of Statistics & Program Implementation: Periodic Labour Force Survey (PLFS), Quarterly Bulletin, December, 2020. (2020) Nayak, P., Chatterjee, B.: Disguised Unemployment in Agriculture: A Case Study of Rural Orissa. Indian J. Industrial Relations. 21 (3), 310–334 (1986) NITI Aayog: Government of India, Workforce Changes and Employment: Some Findings from PLFS Data Series, Discussion Paper 1/2022. New Delhi.available at (2022). https://www.nitiayog.in , accessed on 13.07.2022 Oded, I., Kevin, J., Murphy: The effect of industrial diversity on state unemployment rate and per capita income, The Annals of Regional Science, Springer; Western Regional Science Association, vol. 37 (1), 1–14. (2003) Papola, T.S., Misra, V.N.: Some Aspects of Rural Industrialisation, Economic and Political Weekly, Vol. 15, No. 41/43, 1733–1746. (1980) Parthasarathi Ashok: Rural Industrialization Programme: Looking at Khadi and Village Industries Commission, Economic and Political Weekly, Vol. 40, No. 44/45. 4763–4767. (2005) Rafael, L., Porta, Andrei, S.: The Unofficial Economy and Economic Development, NBER Working Papers, 14520, National Bureau of Economic Research, Inc. (2008) Raj, K.N.: Trends in Rural Unemployment in India: An Analysis with Reference to Conceptual and Measurement Problems, Economic and Political Weekly, 1976, Vol. 11, No. 31/33, 1281–1292. (1976) Sau, R.: Growth, Employment and Removal of Poverty. Economic and Political Weekly, 12 (32/33), (1978). Special Number, August. Samantaraya Amaresh, S.A., Kumar, B.B.: Has Odisha become less poor in the last decade? Economic and Political Weekly, Vol. 49, No. 46, 62–67. (2014) Srivastava Nisha and Srivastava Ravi: Women, Work, and Employment Outcomes in Rural India, Economic and Political Weekly, Vol. 45, No. 28. 49–63. (2010) Vaidyanathan, A.: Labour use in rural India: A study of spatial and temporal variation. Economic and Political Weekly. 21 (52), A130–A145 (1986) Vasudevan, G., Singh, S., Gupta, G.: MGNREGA in the Times of COVID-19 and Beyond: Can India Do More with Less? Ind. J. Labour Econ. 63 , 799–814 (2020) Vyasulu Vinod, Kumar, A.V., Arun: Industrialization in Orissa: Trends and Structure. Economic and Political Weekly Vol. 32, No. 22, M46-M53. (1997) Vyas, V.S., Mathai, George: Farm and Non-Farm Employment in Rural Areas: A Perspective for Planning, Economic and Political Weekly, Vol. 13, No. 6/7, Annual Number. (1978) Tables Table 1 to 8 are available in the Supplementary Files section. Additional Declarations No competing interests reported. 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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-3900285","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":270015549,"identity":"81073ff6-4c29-41ce-8c3b-7db58565783c","order_by":0,"name":"dinabandhu bag","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIiWNgGAWjYDCCwwwMzECKcQMDAxuQtgGxGw+QoiUNxG7Ar+UAqpbDMEHcgO8478HPBTV3ZLdLJLA9+PHrvN3a9sNAW2psonFpkTzMlyw949gz450zEtgNe/tuJ287kwjUciwttwGHFoPDPAbSPGyHEzfcSGCT4O25nWx2AKiFseEwPi3Gv3n+QbRI/u05l2x2/iFBLWbSvG0QLdI8Pw7Ymd0gYIskUIv1zL7DxhvOPGw3lm1ITjC7AbQlAY9f+M6fMb5d8O2w7IbjyccevvljZ292Pv3hgw81Nji1IAHGBgbGNoZEsMoEwsph4A+DPfGKR8EoGAWjYKQAABp7a5twEN3qAAAAAElFTkSuQmCC","orcid":"","institution":"National Institute of Technology Rourkela","correspondingAuthor":true,"prefix":"","firstName":"dinabandhu","middleName":"","lastName":"bag","suffix":""}],"badges":[],"createdAt":"2024-01-26 15:14:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3900285/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3900285/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50479074,"identity":"1a123a0d-f592-4868-86e7-6b183b986acc","added_by":"auto","created_at":"2024-02-01 07:06:48","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":69450,"visible":true,"origin":"","legend":"\u003cp\u003eUR-Current Weekly status (%) in Odisha (2017-2022)\u003c/p\u003e\n\u003cp\u003eSource: PLFS Bulletin, MOSPI\u003c/p\u003e","description":"","filename":"fIGUREruralunemp.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3900285/v1/1d2b8e451e40d5584c984950.jpg"},{"id":52252871,"identity":"ede01eeb-3af6-466a-986e-14f156a8745b","added_by":"auto","created_at":"2024-03-08 09:44:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":245666,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3900285/v1/7342b959-3129-42e9-9b21-17bf21fb1426.pdf"},{"id":50479075,"identity":"943f275f-3da9-49d4-9e83-6087f1d552c6","added_by":"auto","created_at":"2024-02-01 07:06:48","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":51410,"visible":true,"origin":"","legend":"","description":"","filename":"TABLESRURALUNEMP.docx","url":"https://assets-eu.researchsquare.com/files/rs-3900285/v1/5bd467d70758d22ff05077ee.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eRegional Development and Rural Unemployment in Low Income States\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe generation of gainful employment for both the skilled and unskilled labour in India has been one of the important goals vigorously pursued under the planning process by the central and state governments. However, the dream of planners and administrators to mitigate the problem of unemployment has remained unreachable due to multiple reasons. This paper is a review of employment programs for the low-income state of Odisha. This paper determines if adequate target schemes are available to contain short- or long-run unemployment.It investigates regional variation, existence of disparities in employment within the state. If markets were efficient, there would not exist visible spatial variations in unemployment at regional level. We argue that an understanding of regional unemployment can be obtained from relevant plan measures. For example, the endogenous-growth theory explained that exogenous technological progresswould cause higher output, employment and growth.However, unlike the contemporary European discussions on migration as a significant deterrent of employment observed among the European economies, the lack of adequate Keynesian demand push in vocational economies could be construed as policy inaction that deters employment. Public expenditure and the government\u0026apos;s liberal fiscal push are core to employment generation.The unmet fiscal targets would raise the gap between the job seekers and expanding levels of employment. Raj K. N. (1976) found days unemployed as a per cent of days in the Labour force at 20.2% for casual labourers in 1956. R. Sau R (1978) mentioned that Lakdawala (1977) had reported a 7.2% unemployment rate in the 27th round of the NSS. Vaidyanathan (1986) highlighted the importance of non-agricultural employment and its positive impact on reducing unemployment. Lanjouw and Shariff (2004) gave evidence of the distributional effect of employment on agricultural wages. Vyas and George Mathai (1978) had emphasized imparting vocational training, new credit schemes, \u0026nbsp;facilitating marketing services, research and extension assistance to the rural farm workers. Vyas and George Mathai (1978) emphasized creating physical infrastructure for rural industrialization. Lanjouw and Shariff (2004) suggested that the evaluation of employment schemes required exploring beyond mere job creation to improve rural income. Papola and Mishra (1980) found the need for a more adequate focus on pure industrial activity or micro aspects of village industry units. Kurian (1990) mentions that the net addition to the labour force during the 7th Plan was a small decrement in overall unemployment levels (UR). Nayak and Chatterjee (1986) showed by the criteria of disguised unemployment or underemployment that the extent of underemployment varied from 26% to 100% in rural Odisha at the village level. Mahendra Dev (1990) found that person-day unemployment in rural India in 1977-78 existed at 7.47% in Agriculture and 8.70% in the Non-agriculture sector, respectively. Niti Ayog (2022) reports that unemployment\u0026apos;s current weekly status (CWS) is higher, remaining at 8.8% from 2017-18 to 2019-20. The recent quarterly bulletins on the UR-CWS \u0026nbsp;showed a rise in unemployment in early 2019 and a marginal decline towards late 2020 (MOSPI, 2021). This could mean the unmet job creation goals are rolling over periods. Similarly, regional disparities in \u0026nbsp;employment generation would exist via large-scale industrial development among the states. The relatively low-income states need to focus on low-scale, low-technology employment generation channels. How much can public expenditure on rural works help reduce unemployment? Could rural micro enterprises (e.g. MSMEs) play a role in reducing short-term or long-term unemployment rates (UR)? Early works of scholars (Birch, 1979; Beck et al., 2003; Ayyagari et al., 2014 ) held that MSMEs were a source of employment and income. Mosk (2010) mentioned that \u0026nbsp;during the inter-war period (1919\u0026ndash;1938), the government promoted small and medium-scale enterprises in Japan. Birch (1979) mentioned that large firms lost their way to \u0026nbsp; MSMEs in USA. Kirchhoff and Phillips (1988) found that SMEs with less than 100 employees were job creators in USA. Ayyagari et al. (2014) found that small firms had a larger share of jobs in developing economies than larger firms. Beck et al. (2003) found \u0026nbsp;MSMEs comprise 50\u0026ndash;60% of employment globally. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis paper aims to evaluate the employment programs in a low-income state, highlight the gaps in their achievements, relate the unmet targets to progress of rural schemes and arrive at logical inferences from secondary data. It also attempts to find differences in district-level rural/urban unemployment. The rest of the paper is divided into sections: employment generation,hypothesis, methodology, results, and conclusions.\u003c/p\u003e\n\u003cp\u003eEmployment Generation Programs\u003c/p\u003e\n\u003cp\u003eTable 1 summarizes the prescriptions of employment generation highlighted in policy debates. The schemes are supposed to have been accurately designed to achieve the specific aims of the targeted unemployed.\u003c/p\u003e\n\u003cp\u003eTable 1 highlights the need for dedicated schemes for rural casual workforce. We choose to describe the major national-level programs implemented in the low-income state. The gradual renaming, replacing, merger and rephrasing of various national programs has led to a few major schemes such as the theDeen Dayal Antyodaya Yojana (DDAY) in 2015 and the Prime Minister Employment Generation Programme (PMEGP) in 2008-09. The earlier versions of employment programs include the Rural Landless Employment Guarantee Program (RLEGP) in 1983, the Jawahar Rozgar Yojana (JRY), and the National Rural Employment Program (NREP).Employment Assurance Scheme (EAS) of 1993, the SGSY (Swarna et al.) of 1999, etc. Later, the SGSY was reframed to form the National Rural Livelihood Mission (NRLM) and replaced by the Deen Dayal Antyodaya Yojana (DDAY) in 2015. Khadi and Village Industries Commission (KVIC) introduced the Prime Minister Employment Generation Programme (PMEGP) in 2008-09 by amalgamating the Rural Employment Generation Programme and the Prime Minister Rojgar Yojana (PMRY). The PMEGP scheme had reported employing about 3,487 workers against a target of 5,744 in 2016 in Odisha (MSME Odisha, 2016).The state\u0026apos;s coir micro industry development scheme generated about 11,150 workers in \u0026nbsp;2016 (MSME Odisha, 2016). The NSS report 73\u003csup\u003erd\u003c/sup\u003e round (2015-16) put the number of MSME workers at 33.26 Lakhs.An exploration of the trends in principal status (PS) UR (Unemployment rates) in Odisha is worth the review. Table 2 shows the unemployment rate per 1000 persons/person-days in the labour force reported by the Government of Odisha. NSSO publishes unemployment rate (UR) denoted as the number of persons unemployed per thousand persons in the labour force (both the employed and unemployed).\u0026nbsp;The highest unemployment rate (UR) principal status (PS) was at 217 for urban females. Female UR (CWS) (about 22.9%) have remained more than double the national average (11.2%).\u003c/p\u003e\n\u003cp\u003eLonger-term reduction of unemployment is possible via capital or asset generation programs. Early works have dwelt on testing the efficacy of employment schemes to cause decline in levels of UR. However, there has to be a trade-off between how many jobs are created vis-\u0026agrave;-vis the amount of capital expenditure incurred. Similarly, the state has the option to distribute the allocations of capital expenditure across \u0026quot;pure job\u0026quot; schemes against jointly creating rural assets and employment or building productive factories and engaging workers at the same time. However, Moreno-Galbis, E. \u0026amp; Sneessens, H. (2007) suggested that skilled and technical could lead to higher unemployment rates for casual unskilled labourers.The early evaluations also pointed to a rise in the inequality in the distribution of resources. The rise in inequality implies that the State must plan to have an objective basis to re-allocate its gross capital expenditure spread by backward regions within the state. Birch (1979) argued that small firms are important in job creation. Adhikari (2000) suggested concrete public intervention on self-employed enterprises by providing state support to technological upgradation and product development crucial for rural job creation. Mitra and Pandey (2013) found that wage reductions in smaller units raised employment demand.Goldar and Mitra(2013) highlighted the economy-wide role of the unorganized and informal sector. Murthy (2016) suggested that the micro sector played an important role in the development of the economy. Dey (2014) suggested the flexibility, effectiveness and entrepreneurship displayed by micro industries. Contrarian evidence exists that small firms are the engines of job creation (see Roberts \u0026amp; Samuelson, 1989; Leonard, 1986).Small firms had been considered as inefficient parasites who escape from taxes and regulations (Rafael L Porta \u0026amp;Andrei S, 2008). Lewandowska-Gwarda (2018) examined the regional employment pattern and attributed them to socio-demographic and economic structure, including the number of working people, age groups, female labour participation, education, number of enterprises, inward migration, and minimum official wage rates, respectively. While exploring regional imbalance, Lewandowska-Gwarda (2018) found a negative association between capital investments and provincial unemployment rates in 273 districts of Poland.Oded and Murphy (2003) suggested control variables such as the national income and national unemployment rates to investigate the regional patterns in unemployment. Oded and Murphy (2003) proposed that regional unemployment was caused by diversity in industrialization, lower per capita income, higher population density, and population growth, respectively. Kumar et al. (2011) established a negative relation between land ownership size and the likelihood of being engaged in the non-farm sector.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSpecific works on short-term unemployment in Odisha are few. Mishra B (2010) measured the change in per capita employment by workers per thousand population or employees per thousand people. Mishra B (2010) shows that mining districts in the state had fared better than industrial and non-industrial districts in job creation. Samantaraya et al. (2014) suggest that good irrigation facilities, road connectivity, and proximity to vibrant economic activities facilitate more earning and a better standard of living in Odisha. Khan et al (2021) have used PLFS data to estimate the UR for Odisha using auxiliary variables of Crop area and literacy rates. Khan et al (2021) found the positive explanatory power of district-wise gross cropped area and Female literacy rates to the unemployment rate in Odisha.Srivastava and Srivastava (2010) mention that the lower female employment rates are due to their immobility to seek gainful wage-based jobs away from home. Rural infrastructure and investments in irrigation have dual benefits of boosting farm earnings and engaging rural labour in projects of Mega Lift irrigation, Deep Bore wells, Check Dams, Water Tanks, and Field canals, etc. There are gaps in the achievement of the irrigation potential of Odisha, which remained at 5.6 million hectares in 2020 (1 /3\u003csup\u003erd\u003c/sup\u003e of the net sown area, Department of Water Resources, Odisha). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHypotheses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe gaps in empirical findings include efficacy tests of the impact of rural works programs to reduce UR-rural and UR-urban. Similarly, the contribution of plausible employment, such as large industries, MSMEs, cottage and handloom, and micro-enterprises, needs to be tested. The levels of seasonal deployment of rural casual labour could be visible in changes to crop volume production. Given the recent rise in unemployment, we propose conducting relevant tests to assess the past efforts of the state aiming to reduce the UR levels. The efforts are explored in terms of thrust on micro or small scale industry,targets under micro and handloom industry, manpower budget allocations under rural works MGNREGA.\u003c/p\u003e\n\u003cp\u003eThe following hypotheses are proposed.\u003c/p\u003e\n\u003cp\u003ePLFS bulletin reports urban short term UR-CWS (current status) where MGNREGA allocations are predominantly rural in nature. Rural labour would migrate to urban areas to look for employment.\u003c/p\u003e\n\u003cp\u003eH10: \u0026nbsp; \u003cem\u003eMGNREGA significantly contributes to reducing short term unemployment.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eH11: \u0026nbsp; \u003cem\u003eMGNREGA does not contribute to reducing short term unemployment.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eMGNREGA scheme often undergoes politico-economic transitions and regional outcry to prevent outward migration of rural labour outside the state.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eH20: \u003cem\u003eThe district wise allocations under MGNREGA manpower budget are uniformly distributed in the state.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eH21: \u003cem\u003eThe district-wise allocations under MGNREGA manpower budgets are not uniformly distributed \u0026nbsp;within the state.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTraditional thrust on setting up large-scale factories had been limited to power-generating urban regions or mining districts.\u003c/p\u003e\n\u003cp\u003eH30: \u003cem\u003eThe large industrial employment significantly contributes to reducing unemployment.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eH31: \u003cem\u003eThe large industrial employment does not contribute to reducing unemployment.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eMGNREGA has a long implementation history and is the obvious alternative to job seekers.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eH40: \u003cem\u003eMGNREGA significantly contributes to reducing unemployment in rural and urban Odisha.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eH41: \u003cem\u003eMGNREGA does not contribute to reducing unemployment in rural and urban Odisha.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe MSMEs are evolving and drawing policy attention in past few years and are considered informal growth engines. Lal (1988) mentions that the levels of employment exchange registrants will reflect the demand for formal sector jobs rather than the informal or MSME jobs.\u003c/p\u003e\n\u003cp\u003eH50: \u003cem\u003eMSME employment significantly impact unemployment in Odisha\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eH51: \u003cem\u003eMSME employment does not impact unemployment in Odisha.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe tests of hypotheses H10 and H20 are based on Pearson correlation significance tests (see Goldsman, D. (2010)). Goldsman (2010) suggested t-tests for the significance of Pearson product moments correlations between two variables which is compared at a given confidence level (95%) from the T-Table. Further, the test of hypotheses and H30, H40 and H50 are based on regression parameter significance tests\u003c/p\u003e"},{"header":"State Data","content":"\u003cp\u003eWe begin with state data in Figure 1, which shows the recent quarterly variation in the weekly status of unemployment reported by the PLFS bulletin. It displays changes to employment during the sample period (2017-2022). All persons\u0026apos; weekly status (CWS) has remained between 10% and 12.7%. The maximum UR (22.9%) is observed for the category of female labour.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe quarterly trend in Figure 1 shows that average UR-All person has remained at 11.6% with a deviation of 2.8%. The growth in the UR-CWS during this period is strictly positive over 11%. It shows the average growth in person days was only 2.5% against the average UR-All person increase of 11.6%. The rise in UR-CWS could be due to new job seekers who are added to the base or a shortfall in achieving the previous quarter\u0026apos;s target.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3 shows the descriptive Pearson correlation (\u0026rho;) between Urban UR-CWS and person days generated under MGNREGA (2017-2022)\u003c/p\u003e\n\u003cp\u003eThe descriptive correlation tests are presented in Table 3, which shows the significant relationship among the indicators UR-CWS and Person days-All.UR-CWS-Male person days or UR-CWS-Female person days, respectively. Does MGNREGA reduces short-term unemployment?\u0026nbsp;Unfortunately, the Pearson t-test implies that none of the three important correlations, UR-CWS-Male, UR-CWS-Female, UR-CWS-All, are significant at 95% with either of Person days-All. UR-Male person days or UR-female person days, respectively. This suggests that MGNREGA does not contribute to reducing short-term unemployment in Odisha. The first hypothesis, H10 of MGNREGA, which significantly contributes to reducing short-term unemployment, is rejected.\u0026nbsp;MGNREGA does not reduce short-term unemployment in Odisha.\u003c/p\u003e\n\u003cp\u003eTable 4 shows the gross employment under MSMEs in Odisha. The districts of Baudh, Bhadrak, Deogarh,abarangpur and Nayagarh have the lowest shares(1%) compared to the highest share (5%) of the Khurda district in terms of employment. The inequality across the districts is widely visible in terms of the number of units and the average number of employees per unit.\u003c/p\u003e\n\u003cp\u003eTable 5 shows the levels of Gross Employment in large units in Odisha. The Kandhamal District has the most negligible share (rounded off to 0.0%) of larger manufacturing units and large factory employment. However, Angul, Dhenkanal, Jharsuguda and Sambalpur comprised the cumulative share of 70% of gross productive capital deployed, and 34.6% of the employed. Previous findings by Mishra (2010) found that the five major districts of Cuttack, Koraput, Puri, Sambalpur, and Sundargarh contributed to 66% of the total number of factories and 87% of gross employment.The gross employment under large and small-scale units needs to catch up to the employment demand in many districts. We find that the shortfall in gross employment under MSMEs is somehow covered by cottage and handloom sectors in a few districts (e.g., Bargarh, Boudh, Subarnapur, Ganjam, Jajpur, etc.). The concern is the inequality observed in the shares of allocation of productive capital within the state. Kandhamal has the lowest share ( rounded off to 0.0 %) in manufacturing employment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe shares of the employed under large industries widely varies within the state. This confirms the state\u0026apos;s unequal presence and distribution of large-scale industries.The unemployment levels could depend on the level of capital investments made in large factories. Lewandowska-Gwarda (2018) could not confirm the presence of a positive association between levels of invested capital and employment for the districts of Poland.The distribution of large industrial employment widely varies within the state. Table 6 shows the Pearson correlation tests among the\u0026nbsp;utilisation indicators under MGNREGA and the descriptive statistics,\u0026nbsp;the shares of Expenditure, Person-Days and Per Capita Expenditure, respectively. Should the district-wise labour demand for rural work programs not be aligned with the population size and unemployment level, such as the manpower budget? One can find wide variation in disbursal and allocation of manpower budgets. The person-days of labour under NREGA do not seem to have progressed each year from 2008 to 2021. Similarly, the average person\u0026apos;s employment is also lower. The district of Mayurbhanj is the lead district with a per capita (mean) allocation of INR 6,085, comprising the \u0026nbsp;highest share (16%). This is indeed desirable because Mayurbhanj is one of the predominant tribal districts. However, the districts of Deogarh, Boudh and Bargarh need more attention due to the current low levels of per capita allocation (INR 3,598, INR 1,116, and INR 917), and their current lower per capita expenditure shares (1%) only.\u003c/p\u003e\n\u003cp\u003eTable 6 shows the descriptive Pearson correlation (\u0026rho;) test of the three distributions (%) variables of Total expenditure, person days and per capita expenditure for 30 districts.\u003c/p\u003e\n\u003cp\u003eSimilarly, a national study (Breitkreuz et al., 2017) reported lower work completion rates under MGNREGA. The MGNREGA budget is better tied to the census data. Are t\u003cem\u003ehe district-wise allocations under the MGNREGA manpower budget uniformly distributed in the state?\u0026nbsp;\u003c/em\u003eUnfortunately, the Pearson t-test implies that none of the three important correlations, Total expenditure, person days and per capita expenditure, are significant at 95% with either of Person days-All. UR-Male person days or UR-female person days, respectively. Therefore, the second hypothesis, H20, of the presence of non-uniform allocations under MGNREGA manpower budgets cannot be accepted. The district-wise allocations under MGNREGA manpower budget are not \u0026nbsp; uniformly distributed in the state.\u003c/p\u003e\n\u003cp\u003eThe unequal allocation is visibly prominent in our sample. In the second part, we continue to fit models for rural and urban split samples to draw inferences on the significance of allocations under rural works and micro industry-related variables at the district level.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEmpirical Model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDurlauf and Quah (1998) mentioned 36 different categories of variables with about 87 usage examples in their unemployment tests. We proceed with Kennedy\u0026apos;s (2003) linear model of UR (unemployment rate) as; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eU\u003csub\u003e𝑖𝑡\u003c/sub\u003e = \u0026alpha; +\u0026nbsp;𝛽\u003csub\u003e1\u0026nbsp;\u003c/sub\u003ePit + \u0026gamma;\u003csub\u003e1\u003c/sub\u003eLit \u0026nbsp;+ \u0026delta;\u003csub\u003e1\u003c/sub\u003eINDit +\u0026nbsp;𝜀𝑖𝑡\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; (1)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhere,\u003c/p\u003e\n\u003cp\u003eU\u003csub\u003e𝑖𝑡\u003c/sub\u003e, is the unemployment rate (NSS)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePit vector refers to activity under MNREGA, e.g., Number of person-days under MNREGA\u003c/p\u003e\n\u003cp\u003eL\u003csub\u003eit\u003c/sub\u003e vector includes labour force data on District Labour Register, e.g., Outstanding unemployed, New annual labour registrations, etc.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe IND\u003csub\u003eit\u003c/sub\u003e vector incorporates industrial climate, number of reported factories, and the employed across establishments within the district, e.g., MSMES, Micro-loom,cottage, KVIC, large factories, etc.\u003c/p\u003e\n\u003cp\u003e𝛽s, \u0026gamma;s, \u0026delta;s are the associated parameter coefficients, and\u0026nbsp;𝜀\u0026nbsp;is the random error, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSample Data \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe split sample data includes UR data from PLFS bulletin, DC-MSME Reports, Annual Survey of Industries (ASI), etc. Table 7 reports the statistical summary of the major indicators: gross employment across MGNREGA, small-scale industries, cottage and handloom, micro and handloom and large factories. It also gives the number of new applicants registering at the employment exchange and the outstanding unemployed remaining in the live register of the district. The volume of external placement support provided to the skilled workforce by district employment exchanges needs to be higher (Directorate of Employment, Govt. of Odisha). The NSSO 68\u003csup\u003eth\u003c/sup\u003e round reports the rural UR (PS) for few districts are much higher than the urban UR rates (e.g., Cuttack, Sundargarh, Ganjam, Puri, Khordha, etc). The Rural UR is more than 50 (per thousand) in Jagatsinghpur district. The Urban UR is more than 50 (per thousand) in three districts of Sundargarh, Sonepur and Nabarangpur. The higher rural UR (PS) in a few districts could be attributed to differences in allocation and achievement of rural program targets. Khan et al (2021) reported five districts of Ganjam, Cuttack, Gajapati, Khurdha and Kendrapada, which recorded significantly higher URs and lower UR for the districts of Malkangiri, Nabarangpur, Koraput, and Mayurbhanj, respectively. The entry list of variables in the sample included in the models is presented in Table 7, descriptive statistics.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e shows the Parameter Estimates of the Unemployment Model (PS) split sample. In order to take into consideration potential issues with endogenity in the dataset, multi-collinearity diagnostics (VIF) are generated. A VIF cut-off value of 1.2 is applied to identify and eliminate collinear variables from the model. The VIF criteria eliminated the variable of Achievement under Crop Loan from the final model. Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e shows the parameter estimates of three separate split sample regression models for three categories of unemployment rate: (1) UR-Rural, (2) UR-Urban, and (3) UR-all persons. The (1) UR-Rural model has selected only three significant variables of MSME Employment to Newly registered, person days employment (000s), Outstanding in the District Live Register (000s), respectively. The model also rejected two insignificant variables, No. of Employees in large factories and the Achievement under Crop Loan (INR 10 Millions) during estimation. The (2) UR-Urban model and (3) UR-All persons model parameters appear similar to each other in terms of the choice of significant variables, No. of Employees in large factories and Outstanding in the District Live Register (000s), respectively. We find that the sensitivities of the (1) Rural model are distinct from the (2) Urban model. The common variable of outstanding in the live register (000s) acts a positive and significant control variable. The co-efficient (β) implies that an addition of 1000 applicants to the base of the Live register in the district could add to the UR-All persons by 1.6%. The state could accurately track and plan to contain the live register base from rising year by year. The (2) UR-Rural model correctly selects the MGNREGA variable of the person's days of employment (000s) as negative and significant. However, the person days employment (000s) variable is insignificant in the (1) UR-Urban and UR-All person models. Since the person days employment (000s) is negative and significant, we infer that MGNREGA contributes to the decline in UR- rural, where as it is insignificant to reduce the UR- Urban. The co-efficient (β) of person days implies that an addition of 1000 person days can reduce the UR-Rural by 2%. Does MGNREGA significantly contribute to reducing unemployment in rural and urban Odisha.? The fourth hypothesis, H40 of MGNREGA significantly contributing to reducing unemployment, cannot be rejected. MGNREGA does not reduce \u003cem\u003eunemployment in rural and urban Odisha\u003c/em\u003e. The state could contain UR better by monitoring the district live register. The (2) UR-Urban and (3) UR-all persons have chosen another significant variables, No. of Employees in large factories. Urban areas with the availability of power supply have the potential to provide factory jobs to the unemployed. However, factory jobs would need skilled labour and has the potential to attract more skilled labourers with variable wage rates. This means the unskilled and unemployed would become more unemployed due to the skill gap which may be filled in by workforce from outside the district. The co-efficient (β) of the variable of the No. of employees in large factories implies that an addition of 100 new factory jobs would raise the UR-Urban by 2%. Does large industrial employment significantly contribute to reducing unemployment? The third hypothesis, H30, of large industrial employment contributing to reducing unemployment, is rejected. The large industrial employment does not contribute to reducing unemployment. The variable of the ratio of Employment in MSME to No. of newly registered in the Live Register is positive and significant in the(1) UR-Rural model. However, the variable of the ratio of Employment in MSME to No. of newly registered in the Live Register is insignificant in the (2) UR-Urban and (3) UR-All Person models. This implies MSME employment has not been critical in urban locations in the state. The co-efficient (β) of the variable of the ratio of MSME to the number of Newly registered in the Live Register explains the relative absorption of newly registered labour force vis-a-vis those who find employment in MSMEs. The UR rises with the levels of new registration in the live register. The UR falls with the levels of MSME employment. The co-efficient(β) implies an addition of 12 new MSME jobs could reduce UR-Rural by 1%. Does MSME employment significantly impact unemployment in Odisha? The fifth hypothesis, H50, on the contribution of MSME employment to reduce unemployment, cannot be rejected. MSME employment does not impact unemployment in Odessa. The state could leverage the higher sensitivities of MSMEs to reduce rural UR. These results could relate to the findings of Moreno-Galbis, E. \u0026amp; Sneessens, H. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) who suggested that technological change combined with fixed wages in informal sectors can thus lead to higher casual labour unemployment rates. Ode and Murphy (2003) and Srivastava and Srivastava (2010), respectively. Srivastava and Srivastava (2010) mention that the lower female employment rates were due to their immobility to seek gainful wage-based jobs away from their homes. Oded and Murphy (2003) suggested that the lack of industrialization had caused regional variation in unemployment in the USA. Mehrotra et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) showed that employment was rising in the unorganized and informal sector. These model sensitivities do not provide ranking criteria to choose the order of important policy action indicators. These results suggest that the UR-rural model behaves differently from the UR-urban model, and the UR-All model is as good as the UR-urban. There are few critical policy implications of these findings. In order of priority, we suggest targeting rural unemployment as a priority using both MGNAREGA and MSME sectors to help maintain the live register levels. At the same time gradual industrialization would help contain the urban unemployment. The state has an advantage in achieving both goals because the skill sets of casual labourers and factory workers differ. The skilled factory workers will not look for menial MGNAREGA or low skilled MSME tasks.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis paper conducted a review of employment programs in the low-income state in India. It conducted major hypotheses relating to provincial unemployment. It inferred interesting evidence with respect to the gaps in the coverage of employment schemes. It deduced that an addition of 1000 applicants to the base of the live register would add to the UR by 1.6%. It found although MGNREGA contributed to the decline in urban UR, whereas, it is insignificant to reduce the rural UR. It inferred that an addition of 1000 person days can reduce Rural-UR by 2%. The sensitivity of MSME employment ratio implied an addition of 12 new MSME jobs could reduce UR by 1% in the district. It concluded that the UR-rural model behaves a little differently from the UR-urban model. It also highlighted the regional imbalance in small and medium-scale industrialization. The difference between the districts characterizing the highest and lowest unemployment rates indicated spatial diversity in development in the state. The findings agree with Mahendra Dev (1988), who also mentioned the thrust on rural enterprises in Odisha. Parthasarathy (2005) suggested emphasizing township enterprises to raise farmers' incomes. Vyasulu V \u0026amp; A Kumar (1997) also observed that the pattern of industrialization was grossly inadequate. The practical implications of the results are important for policymakers. More emphasis on MGNREGA will reduce rural-urban migration within the state. Given the considerable significance of MSMEs to ensure vocational jobs, more thrust is needed to enlarge the scope of rural MSMEs. This could include micro handloom units, cottage units and similar non-agricultural enterprises. Although the live register of outstanding employed might not accurately reflect the degree of idle labour in the district, it can act as a base to plan for short-term labour deployment targets. Given the lower growth of heavy industrial sector employment in the state, the predominantly rural population of Odisha could be covered only under the single most scheme of MGNREGA. As is evident from recent PLFS data, the continual emphasis on the construction or Infrastructure sector could reduce the urban UR. In any economy, labour mobility is encouraged to permanently contain both urban and rural UR. There are few limitations in our study. The limitations are the inability to display the disparities which would exist at Mandal level within districts in our dataset. As open unemployment is not a condition for being on the employment register, the live registers cannot confirm the level of open unemployed. The sampling frame included only published sources. The design did not conduct the traditional test and control (RCT) method, which is used for between-group comparisons. The employment data is not available from directly relevant sources of rural infrastructure creation or development activities. While this analysis has used data at the district level, there are more opportunities to conduct a review at the village, Mandal, or household level for generating granular analysis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003e \u003cem\u003eConflict of interest\u003c/em\u003e :\u003c/h2\u003e \u003cp\u003eThe author has no conflict of interests\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe author received no funding for this research.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003econceptualized, data analysis, wrote the main manuscript text, prepared figures,drafted the manuscript, reviewed the manuscript.,\u003c/p\u003e\u003ch2\u003eAcknowledgement:\u003c/h2\u003e \u003cp\u003eThe authors are grateful for the comments by an anonymous reviewer\u003c/p\u003e \u003ch2\u003eData\u003c/h2\u003e The data used in this research is available from public sources.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdhikari, C.S.: Enterprise Development for Rural Employment: Project Report, Economic and Political Weekly, Vol. 35, No. 21/22 (May 27 - June 2, 2000), 1858\u0026ndash;1864. 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(1978)\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 to 8 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"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 works, unemployment rate, allocation, new jobs, msme","lastPublishedDoi":"10.21203/rs.3.rs-3900285/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3900285/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper aims to determine whether the low-income states have had adequate schemes to contain short or long-run unemployment. It finds that the allocations under rural works programs could impact the short-term unemployment rates. The rural model behaves differently from urban model. It projects that a thousand new applicants to the Live register would raise unemployment by 1.66%, adding one thousand person days to rural works could reduce unemployment by 2%, and adding five new micro, small and medium enterprise (MSME) jobs could reduce unemployment by 1.2%. Since large-scale industrial development is a slower process, the low-income state may emphasize to contain rural employment. The implications of these findings lie in monitoring of the live register and objective targeting of rural works expenditure allocations within the state.\u003c/p\u003e\n\u003cp\u003eJEL cODES : A14, A10, Go, c30\u003c/p\u003e","manuscriptTitle":"Regional Development and Rural Unemployment in Low Income States","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-01 07:06:43","doi":"10.21203/rs.3.rs-3900285/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":"2f23f4f6-d590-45a5-ba4a-e998c995f628","owner":[],"postedDate":"February 1st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-08T09:42:13+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-01 07:06:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3900285","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3900285","identity":"rs-3900285","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}