Socio-economic Conditions & Determinants of Income: A Tell of Informal Workers of Urban Odisha, India

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This column attempts to uncover the determinants of informal income and socioeconomic conditions with respect to migration status. Methods This study was based on primary investigation. Hence the data collection was performed on multi-stage stratified sampling method. The primary data has been collected through a structured questionnaire for the informal sector workers in Cuttack city of Odisha. The data analysis was completed by ANOVA and multiple regression analysis. Results The income of migrant workers is more than the non-migrant workers due to their nature of work and pattern of payment, whereas the non-migrant workers are engaged in household services with lower payment. The important socio economic determinants such as category of land they lived, gender, various age groups of household head, migration status, availability of off-season support, type of employment, social group, religion, principal sectors, number of dependency, and members of the union are responsible for lower income of the informal workers. Conclusion For sustainable and equitable improvements in basic facilities such as housing, electricity, health, vocational education, social security and employment opportunity should be facilitated by the policy makers. In order to achieve these objectives, this study emphasized more on the mobile-education-services, MGNREGS programs and the MSMEs sector by which Sustainable Development Goals 1,2,3,4,6,8,9,12, and 17 can simultaneously be achieved. 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F1000Research 2026, 15 :253 ( https://doi.org/10.12688/f1000research.168208.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Socio-economic Conditions & Determinants of Income: A Tell of Informal Workers of Urban Odisha, India [version 1; peer review: 1 approved with reservations, 1 not approved] Suvendu Barik https://orcid.org/0000-0002-4274-3027 1 , Nishi Kanta Mishra 2 , Monika Saxena 3 Suvendu Barik https://orcid.org/0000-0002-4274-3027 1 , Nishi Kanta Mishra 2 , Monika Saxena 3 PUBLISHED 13 Feb 2026 Author details Author details 1 School of Economics & Commerce, Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha, 751024, India 2 School of Liberal Studies, Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha, 751024, India 3 School of Management, Bennett University, Greater Noida, Uttar Pradesh, 201310, India Suvendu Barik Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Supervision, Writing – Original Draft Preparation Nishi Kanta Mishra Roles: Investigation, Writing – Review & Editing Monika Saxena Roles: Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS Abstract Background Informal employment and its impact on economic growth have been receiving greater attention, but less is known about their socioeconomic conditions and determinants of income in India. This column attempts to uncover the determinants of informal income and socioeconomic conditions with respect to migration status. Methods This study was based on primary investigation. Hence the data collection was performed on multi-stage stratified sampling method. The primary data has been collected through a structured questionnaire for the informal sector workers in Cuttack city of Odisha. The data analysis was completed by ANOVA and multiple regression analysis. Results The income of migrant workers is more than the non-migrant workers due to their nature of work and pattern of payment, whereas the non-migrant workers are engaged in household services with lower payment. The important socio economic determinants such as category of land they lived, gender, various age groups of household head, migration status, availability of off-season support, type of employment, social group, religion, principal sectors, number of dependency, and members of the union are responsible for lower income of the informal workers. Conclusion For sustainable and equitable improvements in basic facilities such as housing, electricity, health, vocational education, social security and employment opportunity should be facilitated by the policy makers. In order to achieve these objectives, this study emphasized more on the mobile-education-services, MGNREGS programs and the MSMEs sector by which Sustainable Development Goals 1,2,3,4,6,8,9,12, and 17 can simultaneously be achieved. READ ALL READ LESS Keywords Regional Migration,Informal Labour Market,Determinants of Income,Public Policy for Sustainable Development Goals Corresponding Author(s) Suvendu Barik ( [email protected] ) Close Corresponding author: Suvendu Barik Competing interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2026 Barik S et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Barik S, Mishra NK and Saxena M. Socio-economic Conditions & Determinants of Income: A Tell of Informal Workers of Urban Odisha, India [version 1; peer review: 1 approved with reservations, 1 not approved] . F1000Research 2026, 15 :253 ( https://doi.org/10.12688/f1000research.168208.1 ) First published: 13 Feb 2026, 15 :253 ( https://doi.org/10.12688/f1000research.168208.1 ) Latest published: 13 Feb 2026, 15 :253 ( https://doi.org/10.12688/f1000research.168208.1 ) Introduction Informal sector employment 1 and its impact on economic growth have received greater attention not only in India but also in all underdeveloped and developing countries in the world ( Banerjee, 1983 ; Breman, 1996 ; Datta, 2016 ; Himanshu, 2019 ; Jha and Lahoti, 2021 ; Barik, 2024 ). In India, it is about 91 percent of the total labor force (475 million) in the informal sector, more than 90 percent in the unorganized sector, and about 91 percent additional laborers were engaged in the unorganized informal sector only (Economic Survey Report 2021-22; Mehrotra, 2022). It is in excess of 92 percent, both in the informal and formal sectors, working as self-employed or casual laborers, and approximately 50 percent of the national income appears in this informal sector. Similarly, the NSSO 73 rd round report (2015-2016) analyzed that major states such as Uttar Pradesh, West Bengal, Tamil Nadu, Maharashtra, and Karnataka occupy all most 50 percent of the workers. The state such as Himachal Pradesh was the lowest contributor (0.58%) to the total workforce, and Uttar Pradesh had the highest (15%) in India. However, the state of Odisha has been occupied with the 13 th position (3% of workers) in engaging in the unincorporated non-agricultural sector in India, except for the construction sector. It accounts for 4.74 percent of workers from the rural areas and 1.57 percent in urban areas, and 98.74 percent and 98.31 percent, respectively, of the total workforce in India. The condition of informal workers in Odisha is diverse in nature, because of the struggle for the survival of the mass poor, lack of suitable job, non-subsistence level of wage rate, and non-sustainability of the work or due to the ‘vicious circle of poverty’. Therefore, in informal sector employment, the income level and determinants of income are of paramount importance. As a result, this study conducted an empirical investigation to understand the different difficulties related to informal sector workers (ISWs) in Odisha, specifically Cuttack. The CTC (Cuttack City) was selected as the sample area, as it is the 1 st capital city and also concentrate the 2 nd highest informal sectors employment (after Bhubaneswar) in Odisha. However, the main purpose of this research is to discover the main determinants of the income of vulnerable ISWs working in the CTC of Odisha. The rest of the study is organized as follows: review of the literatures ( section-2 ), methodology and data ( section-3 ), empirical analysis and discussions ( section-4 ), and finally, conclusions and policy recommendations ( section-5 ). Review of literature In this paper, the literature review is followed by international-level studies (studies made other than India), national-level studies (study at the national level excluding Odisha), and regional-level studies (specifically in Odisha) to understand the causes and consequences of income variation among workers. The literature review begins with the ‘Blinder-Oaxaca Decomposition Theorem, 1973. According to the author, the main causes of wage discrimination among white, black, male, and female workers in the USA are differences in age-wage profiles, unequal income distribution, union membership, occupational status, educational discrimination, and job experience. On the other hand, the author noted that the major causes of male-female wage discrimination in the USA include variations in tradition, culture, and overt discrimination within the same occupation ( Meher and Dash, 2014 ). In this study, the existence of racial income differences (minorities and employment discrimination), which play a crucial role in the income gap between black and white workers, as not considered. Lower income of the black workers is due to very less investment on the human capital, ‘taste for discrimination’ for the black workers and the acquisition of market valued characteristics than the white workers. Similarly, in France, the pay gap among workers is due to gender bias, demand for skilled workers, and non-monetary gains such as job protection in the civil sector ( Odisha Economic Survey, 2020-21 and Tandon, 2022 ). In Europe, disparities in working conditions, occupational exposure, gender, health conditions, immigration status, and employment arrangements are major contributors to the pay disparity between workers (both non-migrants and migrants). According to the IMF (2015) and ILO(2015 and 2021) income inequality sharply worldwide due to the inequality of opportunity for obtaining education, occupational choices reducing skills, weaker social mobility, and workplace inequalities. Income inequality is increasing in emerging economies of G-20 (i.e., Indonesia and China) and decreasing in developing countries (i.e., Brazil and Argentina) due to social and political cohesion, less sustained economic growth, and lower consumption, according to the ILO, IMF, OECD and WBG (2015). Similarly, according to the ILO report (2021), income inequality increases sharply under COVID-19 due to a decrease in relative levels of income, quality of employment opportunities, and inter-sectional division among workforce groups (i.e., on the basis of race and ethnicity, education, age, migration status, formal and informal workers, etc.). On the one hand, Blinder (1973) found that the differences in the age-wage-profile, unequal income distribution, membership in the union, occupational status, educational discrimination and job experience are the main reasons for wage discrimination among white-black and male-female workers in the USA. On the other hand, Oaxaca (1973) observed that differences in tradition, culture, and overt discrimination within the same occupation are important reasons for the male-female wage discrimination in the USA. According to Loury (1977) , racial income differences (minorities and employment discrimination) which play a vital role in the income difference between black and the white workers have not been taken into consideration. Lower income of the black workers is due to very less investment on the human capital, ‘taste for discrimination’ for the black workers and the acquisition of market valued characteristics than the white workers. Similarly, in France, the pay gap among workers is due to gender bias, demand for skilled workers, and non-monetary gains such as job protection in the civil sector. However, in Europe, the important reason for the income variation between workers (both non-migrant and migrant) is the difference in working conditions, occupational exposure, gender, health condition, migration status, and employment arrangements ( Perez et al., 2012 ). In recent years, income inequality has increased worldwide due to the inequality of opportunities for obtaining education, occupational choices reducing skills, weaker social mobility, and workplace inequalities. Income inequality is increasing in emerging economies of G-20 (i.e., Indonesia and China) and decreasing in developing countries (i.e., Brazil and Argentina) due to social and political cohesion, less sustained economic growth, and lower consumption, according to the ILO, IMF, OECD and WBG (2015). Similarly, according to the ILO report (2021), income inequality increases sharply under COVID-19 due to a decrease in relative levels of income, quality of employment opportunities, and inter-sectional division among workforce groups (i.e., on the basis of race and ethnicity, education, age, migration status, formal and informal workers, etc.). Some macroeconomic determinants of income inequality among different income groups have also been identified from panel data by Basumatary et al. (2024) . According to the different income-group countries from 1996 to 2019, the main determinants of low-income countries with a positive impact on income inequality are the HDI, civil liberty, and governance. In lower-middle-income countries, there is a positive association between population growth, globalization, governance, and income inequality. In upper-middle-income countries, economic and population growth, civil liberty, and governance are positively related to income inequality. However, in high-income countries, economic growth, population growth, gender equality, and natural resources have a positive impact on income inequality ( Basumatary et al., 2024 ). A primary work-based study on female informal workers in Hosanna City, Ethiopia, found that socio-economic advancement is also largely influenced byage, work experience, income, savings, and migration ( Lemma and Sharma, 2025 ). In the case of India, Banerjee (1983) explained that a potential migrant has the ability to know his/her real-income differentials at the time of urban informal sector employment or unemployment compared to the formal sector. There is a huge income gap between informal and formal sector employment due to the high wage rate of formal workers, a subsistence level or lower than the subsistent level wage rate of informal workers, lack of human capital, and institutional barriers. Owing to the lack of trade unions, minimum wage laws, unemployment insurance, and welfare benefits in traditional economies, poor workers have a poorer standard of life, which contributes to the income gap. Some of the studies in India observed that the possibility of an informal individual being poor is less in a large city than in any other rural area; in case of the migrant people, it is the higher caste category than the lower caste people. This is due to the existence of different levels of monthly per capita income and consumption in urban and rural areas (due to the impact of urbanization and globalization) and the wage disadvantages of non-migrants in rural areas ( Breman, 1996 ; Dabla-Norris et al., 2015 ). The difference exists among the higher and lower caste categories of people because of the changes in the real wages over time among the male and female workers and the work transformation from agricultural to the non-agricultural work. Due to the overrepresentation of female migrant workers in casual labor, the author noted that migrant workers have lower salary rates than non-migrant workers, although migrants have wage advantages for male workers in regular jobs. According to the study, there is a salary disparity between state and private mining in India (Samanta et. al., 2022). According to them, workers working in the public sector earn higher wages than those in the private sector, and the wage gap is higher at the bottom and lower at the top owing to unequal wage distribution. Similarly, inter-industry wage differentials have also been observed in India due to the absence of convergence and persistence among the manufacturing sector, for which the same skilled laborers receive different wages in different manufacturing industries. According to Padhi et al. (2019) , the main reasons for gender-based wage discrimination in India are mostly non-monetary factors such as defeminisation, casualization, and informalization. Other non-monetary factors such as social problems, education, health, and social protection play a vital role in increasing inequality in India ( Himanshu, 2019 ; Munshi and Singh, 2024 ). Another important factor that also plays a crucial role in increasing the income differences between the low-income households is the COVID-19 pandemic ( Jha and Lahoti, 2021 ). This pandemic not only worsens the financial situation of the nation but also breaks down the backbone of the financial support of many households. However, due to the significant growing technological progress and excess supply of workers, the capital share is increasing significantly at the expense of labor share, but technological progress can increase labor share by mounting suitable employment opportunities ( Ozdemir, 2023 ). In the case of out-migrants in Odisha, the determinants of remittance sending are different due to their social, economic, and demographic characteristics, and economic growth in some sectors ( Odisha Economic Survey, 2020-21 ). According to Meher and Dash, not all out-migrants are able to send the same remittances to their native place of Odisha due to age, sex, place of residence and social group. However, Sahoo and Paltasingh explained that income inequality rises because of the significant growth in some sectors, specifically in the tertiary sectors after the post-reform period. However, inequality in income arises mostly due to the outbreak of COVID-19, and as unemployment increases at a high time, a wage-cut policy was adopted by some of the IT sectors, leading to poor education, reduction in government expenditure except health, and negative economic growth in different sectors differently, etc. ( Odisha Economic Survey, 2020-21 ). Similarly, due to various socio-economic factors, inequality in income and expenditure is also increasing among non-migrant and migrant informal workers in the rural and urban areas of the country ( Barik and Baig, 2022 ; Barik, 2024 ). Thus, it is logical that very few studies have been conducted on the informal sector, informal employment, and informal income at the regional level. It is also important to study the dynamics of informal workers and their determinants of income, specifically among migrant and non-migrant ISWs. Therefore, this paper focuses on the empirical study of the Cuttack (CTC) city of Odisha, as the CTC is the oldest, the 1 st capital city and 2 nd largest ISWs. Methods The present case study is based purely on a household-level primary survey conducted during March-May 2025 in the CTC of Odisha. In this study, the number of household heads (HHds) was 470 (10 percent criteria of the total slum population was applied), which included both non-migrant (NMs) and migrant (Ms) slum-dwellers, specifically informal sector workers (ISWs). HHds are selected based on the strength of the respondent’s occupation (self-employed, wage-employed, and casual laborers), migration status (whether migrated or non-migrated), and keeping in mind the municipality classification (CTC has three Constituencies; Barabati, Chaudwar and Sadar). Thus, the present study was followed by a multi-stage stratified sampling method for collecting relevant data from the ISWs of the CTC, Odisha. However, before collecting the information from the informal respondents, secondary information was also taken into consideration that is from the Bikash Bhavan CMC (Cuttack Municipality Corporation). However, the article is related to the socio-economic problems of informal workers and most of them are illiterate, written consent seems to be impossible. Hence, we preferred to take informed verbal consent. In this regard, we explained the purpose and the content of this research (interview) to the participant, and s/he agreed to participate in the study. Thus, in this study, informed verbal consent was obtained before data collection. Similarly, this research is based on primary survey of the households and specifically to the household head, thus, no minor participants were taken into considered. The methodological steps and the total number of non-migrant and migrant respondents are explained in Figure 1 . Figure 1. Methodological ways to select the sample. Results and discussion Socio-economic summary The social and economic summary of household heads (HHds), specifically informal workers, was classified into five groups. HHds are classified on the basis of their gender (female or male), marital status (married, unmarried, widowed, separated, or divorced), religion (Muslim or Hindu), social group (General, OBC, SC, or ST), general education level (primary, upper-primary, secondary, higher-secondary, graduate, and above or illiterate), employment (wage-employed/self-employed or casual or contract or unpaid laborer), land category they lived (own/private land or government land) and all categories are explained on the basis of migration status (i.e., either non-migrate or migrated) of the informal workers. The socio-economic summary of the HHds is presented in Table 1 , and the values are expressed as percentages. Table 1. Socio-economic summary of the Informal Workers (IWs). Indicator Whether the IWs Migrated? Total (in percent) Yes (in percent) No (in percent) Gender Female 52.9 47.1 22.1 Male 53.0 47.0 77.9 Marital Status Unmarried 40.2 59.8 20.6 Married 55.9 44.1 68.9 Widowed 53.3 46.7 6.4 Separated 100 00 0.2 Divorcee 66.7 33.3 3.8 Religion Muslim 51.6 48.4 13.2 Hindu 53.2 46.8 86.8 Social Group General 64.0 36.0 34.9 OBC 65.3 34.7 20.2 SC 40.6 59.4 30.4 ST 35.3 64.7 14.5 General Education Level Primary 49.2 50.8 25.5 Upper Primary 55.2 44.8 14.3 Secondary 55.4 44.6 25.7 Higher- Secondary 69.2 30.8 2.8 Graduate and Above 50.0 50.0 1.7 Illiterate 51.8 48.2 30.0 Employment Type Regular Wage Employed 43.9 56.1 31.5 Self-Employed 50.4 49.6 29.1 Casual Laborer 62.1 37.9 37.0 Contract laborer 85.7 14.3 1.5 Unpaid laborer 25.0 75.0 0.9 Category of land They Lived Own/Private Land 45.3 54.7 70.0 Government Land 70.9 29.1 30.0 Total 53.0 (249) 47.0 (221) 100 (470) The social and economic profile of the household head (HHds) helps us understand the socio-economic background of informal workers in the city of 470 HHds, 78 percent (366) were male and 22 percent (104) were female. Among the migrant HHds (53%), 53 percent (194) were male migrant and 52.9 percent (55) were female. Conversely, among non-migrant HHds working in the informal sector, 47 percent (172) were male and 47.1 percent (49) were female workers. This means that informal female workers are lower than male workers in both migrant and non-migrant cases due to lack of safety and security, poor housing conditions, and other essential facilities. Based on marital status, married migrant workers were higher (55.9%) than non-migrant workers (44.1%) due to urgency, financial support, and other responsibilities. Similarly, the percentage of total married informal workers (68.9%) is higher than that of unmarried (20.6), widowed (6.4%), separated (0.2%), and divorced (3.8) informal workers in the informal sector due to more freedom, more responsibility, and no more social restrictions on them. Similarly, this study found that in the Cuttack City of Odisha there are mostly two religious groups or categories, (Hindu population sample is 86.8 percent or 408 HHds and Muslims are13.2 percent or 62 HHds, respectively), and who are basically engaged in informal sector work, other than all other religious groups. In the case of both the religious groups (Hindu and Muslims), the migrated samples are higher (i.e., 53.2% and 51.6%) than the non-migrated HHds, but in both cases the Hindu migrated HHds are higher than the Muslim migrated HHds and in case of non-migrated samples, Muslim religious HHds are higher than Hindu religious HHds in the Cuttack City of Odisha. This may mean that Muslim religious groups prefer to migrate less than Hindu religious groups (or it may be other religious groups in India, if the samples are available from other migrated religious groups). Among the various social groups, including migrant and non-migrant HHds, 30 percent (143) were from the SC, 15 percent (68) from the ST, 20 percent (95) from the OBC, and 35 percent (164) from the general category, working in the informal sector. However, in the informal sector general category people are participating in the highest number which is 35 percent then the SC category the 2 nd highest i.e., 30 percent, and the OBC category (20%) and the ST category is the lowest i.e., 15 percent from the total population of samples. This is only because of lack of information, caste-based demand and the existence of untouchability for the SC and ST categories people, but everything becomes reverse for the general and then for the OBC category people. On the other hand, the level of general education explained that illiterates are 35 percent, primary education level is 26 percent, upper primary 14 percent, secondary 26 percent, higher secondary is 3 percent, and graduates and above are about 2 percent only. This mean that the general education level of the people is not able to influence the socio-economic conditions of either migrant or non-migrant workers, as education is not considered an indicator to enter into the informal sector in Cuttack City, Odisha. On the basis of employment, 29 percent (137) are self-employed, 37 percent (174) are casual laborers, 31 percent (148) are regular wage employed, 2 percent (148) are contract laborer and only one percent are HHd unpaid laborers. In between the migrated HHds, 86 percent are contract laborers, 62 percent are casual laborers, 50.4 percent are self-employed, 44 percent are regular wage employed, and only 25 percent are household unpaid laborers. On the other hand, among the non-migrant HHds, 14 percent were contract laborers, 38 percent were casual laborers, 49.6 percent were self-employed, 56 percent were regular wage employed, and 75 percent were unpaid household laborers working in the informal sector of the Cuttack City. However, the study found that among the M informal workers, casual and contract laborers are the highest, and among the non-migrant informal workers, regular wage employed and unpaid laborers are the highest. However, the over all observation of this case study showed that informal M workers are forced to prefer the work as casual labor and informal NM workers are mostly worked as regular wage earners as they are staying there since before and a good network among them, but in both cases, informal workers are generally found to work as casual laborers. Similarly, on the basis of the category of land they lived, 53 percent had migrated and 47 percent had non-migrated HHds, out of which 30 percent (141) lived on the government land, and 70 percent (329) of HHds are living either on private land or self-owned land. However, among migrant HHds, 71 percent live on the government land, and 45 percent live on private or owned land. On the other hand, among non-migrant HHds (329), 29 percent lived on the govt. land, and 55 percent is on private or self-owned land. Thus, the observation explained that the migrant informal workers prefer and are forced to live on the government land and non-migrant informal workers prefer to live on either private or self-owned land, but both of them (migrants or non-migrants) are mostly prefer to live on the government land as they want to avoid rent. Otherwise, informal workers are generally trying to give a very low amount to the government or to local Dalal (local leader), which would be possible, rather than going to a rented house. Migrant and non-migrant informal workers and income variation To understand the income variation among the NM and M informal workers the study has collected and examined the monthly income of the household head and the result of the descriptive statistics is presented in Table 2 . Similar, to the existence of income variation between the groups and among the groups, the study employed the ANOVA analysis which is demonstrated in Table 3 . Table 2. Income of informal non-migrant and migrant workers. Descriptive statistics: (N = 469) Status of migration Income of informal workers Maximum Minimum Mean Std. deviation Income Before Migration 60,000.00 0.00 1272.07 3448.66 Income After Migration 18,000.00 0.00 3278.26 3680.76 Table 3. Group-wise variation in income of the informal workers (ANOVA analysis). Informal workers’ present income Variables name Sum of squares df Mean square F Sig. Category of land They Lived Between Groups 78906766.0 1 78906766.0 5.991 .015 Within Groups 6163751701.8 468 13170409.6 Gender Between Groups 645338763.2 1 645338763.2 53.958 .000 Within Groups 5597319704.6 468 11960084.8 Various Age-groups Household Head Between Groups 202186162.4 7 28883737.5 2.209 .032 Within Groups 6040472305.4 462 13074615.4 Migration Status Between Groups 199024065.3 1 199024065.3 15.412 .000 Within Groups 6043634402.5 468 12913748.7 Availability of Off-Season Supports Between Groups 187771988.8 1 187771988.8 14.513 .000 Within Groups 6054886479.0 468 12937791.6 Type of Employment Between Groups 464824729.4 4 116206182.4 9.352 .000 Within Groups 5777833738.4 465 12425448.9 Social Groups Between Groups 163157663.697 3 54385887.899 4.169 .006 Within Groups 6079500804.1 466 13046139.1 Religions Between Groups 3034797.3 1 3034797.3 .228 .634 Within Groups 6239623670.5 468 13332529.2 Principal Sectors Between Groups 636817391.2 9 70757487.9 5.806 .000 Within Groups 5605841076.6 460 12186611.0 Number of Dependency Between Groups 403436855.9 8 50429607.0 3.981 .000 Within Groups 5839221611.9 461 12666424.3 Marital Status Between Groups 329285635.8 4 82321408.9 6.473 .000 Within Groups 5913372832.0 465 12716930.8 General Education Level Between Groups 206853508.2 6 34475584.7 2.645 .016 Within Groups 6035804959.6 463 13036295.8 Eligible for Paid Leave Between Groups 2595716.1 1 2595716.1 .195 .659 Within Groups 6240062751.7 468 13333467.4 Availability of Social Security Benefits Between Groups 11058710.5 3 3686236.8 .276 .843 Within Groups 6231599757.3 466 13372531.7 Member of the Union Between Groups 302651362.3 2 151325681.2 11.897 .000 Within Groups 5940007105.4 467 12719501.3 Total 6242658467.8 469 The descriptive statistics in Table 2 show that there is a significant difference in income before and after migration. After migration, the average monthly income of the migrant informal workers is higher than that before the migration, that is, the average monthly income is Rs. 3,278.26 and Rs. 1,272.07, respectively. Similarly, it is also the same in the case of the maximum income and the standard deviation of the informal migrant workers, that is, Rs. 60,000.00 and Rs. 3448.66, are higher than the Rs. 18,000.00 and Rs. 3680.76, respectively. However, the pathetic situation is that all the informal workers (NM and M) are facing the ‘situation of zero minimum income’ due to unavailability of work in all the seasons, months, and days in the city. Informal workers and group-wise income variations ANOVA analysis was used to understand whether the variation in income existed (between the groups and among the groups). In this regard, H 0 and H 1 are the null and alternative hypotheses, respectively, considering (where H o implies the existence of variations in mean income, which is equal for all groups, and H 1 implies variations in mean income, which is not equal for all groups). In this study, the null-hypothesis for the category of land they lived (government and private/self-owned land), gender (male and female), various age groups of household head, migration status (migrated or not migrated), availability of off-season support (yes or no), employment types (unpaid laborer, contract laborer, casual laborer, wage employed, and self-employed), social groups (General, OBC, SC or ST), principal sectors, number of dependencies, marital status (married, unmarried, widowed, divorced or separated), general education level (illiterate, primary schooling, upper primary schooling, secondary, higher secondary and graduate and above), and members of the union (yes, no, and not aware) have turned to be significant as the P-values are less than 0.05. This indicates the existence of mean income differences among and within the groups in the informal sector. Therefore, we reject the null hypothesis of these significant variables and conclude that different groups have different mean incomes per month. However, variables such as religion (Hindu or Muslim), eligibility for paid leave (yes or no), and availability of social security benefits (providing only health care, only maternal benefits and only gratuity, only PF/pension, not eligible or not known) were not significant, as the P-values of these variables were greater than 0.05. Thus, rejection of the null hypothesis is not possible for these variables and it can be concluded that there is no variation in the mean income of the different groups of informal-sector workers. Determinants of income of informal workers (Migrant and Non-migrants) To determine the determinants of the income of informal sector workers at the regional level, this study applied regression analysis followed by ANOVA. The analysis was also applied separately for migrant and non-migrant informal workers to identify the differences between these two groups of informal workers. Thus, to determine the important determinants of informal income, the multiple regression equation is formulated (where Y is the monthly informal income, β j is the = coefficient of independent variable, and j = 1, 2, 3, 4, …, 18) as: (1) Y = Constant + Casual Labor ( β 1 ) + Regular Wage Employed ( β 2 ) + SC ( β 3 ) + ST ( β 4 ) + General ( β 5 ) + Dependant ( β 6 ) + General Education Level ( β 7 ) + Vocational Training ( β 8 ) + Off Season Support ( β 9 ) + Gender ( β 10 ) + Union ( β 11 ) + Age of HHHs ( β 12 ) + Age Squared of HHHs ( β 13 ) + Land Owned ( β 14 ) + Migration ( β 15 ) + Services Sector ( β 16 ) + Agriculture Sector ( β 17 ) + Household Sector ( β 18 ) … Similarly, to determine the effective and significant determinants of the migrant and non-migrant informal workers, multiple regression models were formulated separately as follows: (2) Y = Constant + Casual Labor ( β 1 ) + Regular Wage Employed ( β 2 ) + SC ( β 3 ) + ST ( β 4 ) + General ( β 5 ) + Dependant ( β 6 ) + General Education Level ( β 7 ) + Vocational Training ( β 8 ) + Off Season Support ( β 9 ) + Gender ( β 10 ) + Union ( β 11 ) + Age of HHHs ( β 12 ) + Age Square of HHHs ( β 13 ) + Land Owned ( β 14 ) + Services Sector ( β 15 ) + Agriculture Sector ( β 16 ) + Household Sector ( β 17 ) , for i = 1 , 2 … Where, ‘Yi’ is monthly income of migrant informal workers (i = 1) and monthly income of non-migrant informal workers (i = 2), ‘β j ’ = independent variables coefficients, and j = 1, 2, 3, … 17. However, all variables are dummy variables in these models (except age, age squared, and land owned). In this regard, Equations (1) and (2) are estimated using the OLS method, and the results of Equations (1) and (2) are presented in Tables 4 , 5 , and 6 , respectively. Table 4. Determinants of income (Informal workers). Dependant Variable (Y): Monthly Income Variables Coef. (β) Std. Err. t Sig. Level (P) Casual Labor -931.02 367.56 -2.53 0.012 ** Regular Wage Employed -1142.26 393.84 -2.9 0.004 * SC -762.86 445.28 -1.71 0.087 ST -704.64 531.38 -1.33 0.185 General -842.89 413.90 -2.04 0.042 ** Dependent 286.70 97.30 2.95 0.003 * General Education Level 128.16 121.19 1.06 0.291 Vocational Training 798.08 321.22 2.48 0.013 ** Off-season Support 838.28 325.15 2.58 0.01 * Gender 1321.37 419.61 3.15 0.002 * Union -1823.67 484.46 -3.76 0 * Age of HHHs 167.69 59.44 2.82 0.005 * Age Square of HHHs -2.18 0.70 -3.12 0.002 * Land Owned -12.19 4.40 -2.77 0.006 * Migration 1455.30 404.18 3.6 0 * Services Sector -1669.50 511.74 -3.26 0.001 * Agriculture Sector -1939.31 689.35 -2.81 0.005 * Household Sector -1258.44 469.09 -2.68 0.008 * Constant 3930.45 1716.21 2.29 0.022 ** R-squared 0.295 Adj R-squared 0.267 Prob > F 0 Root MSE 3124 F (18, 451) 10.48 Observations (total) 470 * Implies a 1% or Less than 1% significant level; ** Implies a 5% significance level. Table 5. Determinants of income (Only migrant workers). Monthly Income: Dependant Variable (Y) Variables Coef. (β) Std. Err. t Sig. Level (P) Casual Laborer -194.51 404.95 -0.48 0.631 Regular Wage Employed -1269.50 460.24 -2.76 0.006 * SC -134.52 498.84 -0.27 0.788 ST -887.80 634.43 -1.4 0.163 General -138.41 413.12 -0.34 0.738 Dependents 198.27 121.07 1.64 0.103 General Education Level 184.31 125.09 1.47 0.142 Vocational Training 285.70 366.37 0.78 0.436 Off-season Support 1254.29 340.01 3.69 0 * Gender 1641.32 483.29 3.4 0.001 * Union 129.17 540.72 0.24 0.811 Age of the HHHs 146.50 71.49 2.05 0.042 ** Age Square of the HHHs -2.12 0.88 -2.42 0.016 ** Land Owned -18.70 7.55 -2.47 0.014 ** Services Sector -783.73 588.04 -1.33 0.184 Agriculture Sector -47.07 1107.53 -0.04 0.966 Household Sector -1327.80 557.42 -2.38 0.018 ** Constant 1687.80 2053.56 0.82 0.412 R-squared 0.34 Adj R-squared 0.30 Prob > F 0 Root MSE 2482.9 F(17, 231) 7.11 Observations (only migrants) 249 * Implies a 1% or Less than 1% significant level; ** Implies a 5% significance level. Table 6. Determinants of income (Only non-migrant workers). Monthly Income: Dependant Variable (Y) Variables Coef. (β) Std. Err. t Sig. Level (P) Casual Laborer -1579.60 641.00 -2.46 0.015 ** Regular Wage Employed -986.74 636.42 -1.55 0.123 SC -1484.40 801.55 -1.85 0.065 ST -980.59 915.80 -1.07 0.286 General -1869.10 829.67 -2.25 0.025 ** Dependents 272.18 151.31 1.8 0.074 General Education Level 124.82 244.72 0.51 0.611 Vocational Training 1264.04 573.11 2.21 0.029 * Off-season Support 576.49 609.99 0.95 0.346 Gender 1294.17 733.25 1.76 0.079 Union -3555.60 825.98 -4.3 0 * Age of the HHHs 201.06 99.26 2.03 0.044 ** Age Square of the HHHs -2.50 1.11 -2.24 0.026 ** Land Owned -6.97 6.33 -1.1 0.272 Services Sector -2198.30 848.39 -2.59 0.01 * Agriculture Sector -2652.30 957.54 -2.77 0.006 * Household Sector -937.85 762.95 -1.23 0.22 Constant 7374.42 3065.57 2.41 0.017 ** R-squared 0.31 Adj R-squared 0.26 Prob > F 0 Root MSE 3621.3 F (17, 203) 5.44 Observations (only non-migrants) 221 * Implies a 1% or Less than 1% significant level; ** Implies a 5% significance level. In the regression model, as explained by the independent variables, the estimated R-square is 0.2949 which shows that there is only 26 percent of the income variation in ISWs. In the midst of independent variables, the regular wage employed, dependants on the HHds, off-season support to the HHds, gender of the HHds, availability of union for the informal sector workers, age and age square of the HHds, land owned, migration status, service sector, agricultural sector, and household sector are the significant variables influencing at the 1 percent or less than 1 percent significance level. Similarly, casual laborers, general category people, and the availability of vocational training are the momentous variables that influence the income of informal sector workers at the 5% significance level. However, among the independent variables, the number of dependents on the HHds, vocational training of the HHds, off-season support, gender, age of the HHds, and migration status of the ISWs had a positive and significant influence. Overall, socio economic variables played a vital role in determining the monthly income of this informal group. Equation (2) is estimated for migrant workers using the ordinary least squares (OLS) method, and the results are shown in Table 5 . The regression model only for the income of the migrant workers, the estimated R-square of 0.344, explains that there is only 34 percent of the income variation of the ISWs. In the midst of independent variables, the regular wage employed, off-season support to the HHds, gender of the HHds, and the availability of unions for informal sector workers were found to be significant variables at the 1 percent or less than 1 percent significance level. Similarly, the age and age squared of the HHds, land owned and the household sector have been significant variables influencing the income of the informal sector workers at the 5% significance level. However, regular workers, land owned by HHds, and age squared negatively affect the income of M workers. Conversely, off-season support, gender of the HHds and age of the HHds significantly and positively influenced informal migrant workers only. Similarly, Equation (2) is estimated for non-migrant workers using the OLS method, and the results are shown in Table 6 . The regression model only for the income of the non-migrant workers, the estimated R-square of 0.313, explains that there is only 31 percent of the income variation of the informal sector workers. In the midst of independent variables, the availability of vocational training and, labor unions for the informal sector workers, service sector, and agricultural sector seem to have significant variables at the 1% or less than 1% significance level. Similarly, casual laborers, general category people of society, age, and age square of the HHds were found to be significant variables influencing the income of informal sector workers at the 5% significance level. However, the casual laborer, general category people, labor union, age square of the HHds, service sector, and agricultural sector negatively affect informal migrant workers’ incomes. On the other hand, vocational training centers (received either through special training centre or through their hereditary system) and the age of the HHds are positively significant to the income of non-migrant informal workers. Conclusions and policy implications After a ground-level data collection and analysis, the study may be concluded by slating that informal sector workers, both Ms and NMs, have greater attention at both the national and regional levels for their upliftment in modern society. The findings of this study also show that even though the income of non-migrants is lower than that of migrants due to lower paid wage earnings (e.g., security guard jobs, working as a cook or maid in the household sector, and local rickshaw puller), other livelihood facilities such as local unity, social and job security, sanitation, and other facilities are enjoyed by them due to their voting rights. All the informal workers, both M and NM, are facing the ‘situation of zero minimum income’ due to unavailability of work in all the seasons-months-days in the city. Thus, various socio-economic factors such as casual laborers, regular wage employed, general category people, number of dependencies, vocational training, off-season supporters, gender, memberships with the union, age, age squared, land owned by the HHds, migration status, service sector, agriculture sector, and household sector play a crucial role in these lower income groups and variations. However, none of these determinants are constant for non-migrant and migrant informal workers in the city. Over all, informal workers are the ultimate victims in cities. Thus, in this post COVID-19 pandemic situation, there is a need for improvements in the area of basic facilities such as housing, electricity, health, vocational education, and social security through citizen forum (a system of open forum in the Bhubaneswar municipality for discussing the problems is faced by public utility and also used as a platform for providing possible solutions to the problem). The present informal employment study, therefore, needs paramount importance for the country, state and district or from the grass root level (villages) to central government (following the ‘Panchayat Raj System’ in India, i.e. a three-tier development structure for the rural development), 1stly And 2ndly, to develop the poor socio-economic conditions of informal sector workers, both rural and urban governments should facilitate basic amenities such as sanitary facilities, proper drinking water facilities, basic nutritional food facilities at minimal cost, vocational education or training, and regulated shelter facilities with minimal or free costs. In other words, in a traditional sense we can say that we can provide and facilitate the “Roti-Kapda-Makaan” to these vulnerable sections of the state and country. 3rdly, for creating the awareness about health and environment, both the local and central government should facilitate the basic education through “M-E-S” (Mobile-Education-Service, which means, education and awareness services provided by the vehicles to their door step just such as the venders selling their goods and services on everyday basis) to these informal sector workers. However, 4thly, the most important policies need to be re-emphasized by the government—the MGNREGS (Mahatma Gandhi National Rural Employment Guarantee Schemes) and MSMEs (Micro, Small, and Medium Enterprises) — which can play a dual role in reducing unemployment and increasing income both in the village and the urban areas of the country in this post COVID-19, war and inflationary situation. Therefore, by implementing and applying the above policies for the upliftment of the informal sector workers can also be able to achieve many Sustainable Goals, such as 1) No Poverty (by giving the employment opportunities both in the urban and rural areas), 2) Zero Hunger (by making available the basic nutritional food at minimal cost), 3) Good Health and Well-Being (by creating the awareness about the health and environment), 4) Quality Education (by facilitating the basic education and vocational training through new ‘M-E-S’ programme), 6) Clean Water and Sanitation (by facilitating the basic amenities such as sanitary facilities, proper drinking water facilities), 8) Decent Work and Economic Growth (by generating the work in the organized sector), 9) Industry, Innovation and Infrastructure (by re-emphasizing on the MGNREGS and MSME programmes), 12) Responsible Consumption and Production (by providing the basic nutritional food at minimal cost or minimum support price), and 17) Partnerships for the Goals (by the involvement of both the informal sector workers and the state and central government for achieving all these goals). However, this primary research work is also not free from limitations due to its limited sample size (470 only), restricted area (Cuttack District, Odisha only), lack of information before their migration, and the situation after the COVID-19, war, and hyper inflation. This study is also applicable to developing countries such as India, where the number of informal sectors (more than 90%) and workers (more than 91%) are large. Declarations Ethical Clearance: This study meets national and international guidelines for research on humans. In this regard, an ethical approval certificate has been obtained by University level Ethics Committee (ULEC), KIIT Deemed to be University, Bhubaneswar with the ethical clearance certificate no. KIIT/ULEC/012/2025 on dated 15/05/2025. Materials Used: Due to the primary nature of the article, we have prepared a structured questionnaire for the respondents for data analysis (The Figshare DIO/Identifier no 10.6084/m9.figshare.30581063). Data availability Figshare, Data for F1000Research, https://doi.org/10.6084/m9.figshare.30581063.v1 . Barik, S. (2025) . This project contains the following underlying data: • Questionnaire • Field data Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0). Acknowledgements We are grateful to Prof. Mirza Allim Baig (H.O.D.), Department of Economics, Jamia Millia Islamia (A Central University, New Delhi) for his insightful ideas and inspiration for this research. The authors would also such as to thank Prof. Swati Samantaray from the School of Liberal Studies, KIIT Deemed to be University, Bhubaneswar for her valuable feedback and corrections to this research. References Barik S: Do Informal Workers Meet the Consumption Expenditure? A Study from Urban Odisha, India. J. Public Aff. 2024; 24 (1): 1–15. Publisher Full Text Barik S, Baig MA: Income Differentials between Migrant and Non-Migrant Informal Workers: A Study of Urban Odisha. Thailand and The World Economy. 2022; 40 (3): 15–32. Barik S: Data for F1000Research. figshare. 2025. Publisher Full Text Banerjee B: The Role of the Informal Sector in Migration Process: A Test of Probabilistic Migration Models and Labor Market Segmentation for India. Oxf. Econ. Pap. 1983; 35 : 399–422. Publisher Full Text Basumatary IR, Das M, Basumatary S, et al. : Macroeconomic Determinants of Income Inequality among Different Income Group Countries: Evidence from Panel Data. Journal of Social Economic Research. 2024; 11 (1): 111–125. Publisher Full Text Blinder AS: Wage Discrimination: Reduced form and Structural Estimates. The Jounal of Human Resources. 1973; 8 : 436–455. Publisher Full Text Breman J: Footloose Labor: Working in India’s Informal Economy. Cambridge: Cambridge University Press; 1996. Dabla-Norris E, Kochhar K, Suphaphiphat N, et al. : Causes and Consequences of Income Inequality: A Global Perspective. International Monetary Fund: Washington. 2015; 2015 : 1. Publisher Full Text Datta A: Migration, Remittances and Changing Sources of Income in rural Bihar,1999-2011: Some Findings from Longitudinal Study. Econ. Polit. Wkly. 2016; 51 (31): 85–93. Himanshu: Inequality in India: A review of levels and trends. UNU-WIDER Working Paper No. 42/2019.2019. Jha M, Lahoti R: Covid-19: Impact on income inequality in India. Ideas for India. 2021. Reference Source Lemma T, Sharma M: Socioeconomic Advancement of Women in the Informal Sector in Hosanna City, Ethiopia. Cities: The International Journal of Urban Policy and Planning. 2025; 156 : 105510–105580. Publisher Full Text Loury GC: A dynamic theory of racial income differences. Wallace PA, Lamond A, editors. Women, Minorities and Employment Discrimination. Lexington: D.C. Heath and Company; 1977; 153–188. Meher B, Dash PC: Pattern and Determinants of Remittances Sending Behaviour of Out-migrants from Odisha. Jharkhand Journal of Development and Management Studies. 2014; 15 (1): 7227–7243. Munshi K, Singh S: Social status, Economic Development and Female Labor Force (Non) Participation. NBER Working paper Series 32946. 2024. Odisha Economic Survey Report 2020-21: Planning and Convergence Department, Directorate of economics and Statistics: Government of Odisha.2022. Oaxaca RL: Male-Female Wage Differentials in Urban Labor Markets. Int. Econ. Rev. 1973; 14 : 693–709. Publisher Full Text Ozdemir O: The Determinants of Income Distribution: The Role of Progress in Human Capital. Quality and Quality. 2023; 57 : 4193–4227. Publisher Full Text Padhi B, Mishra US, Pattanayak U: Gender-based Wage Discrimination in Indian Urban Labor Market: An Assessment. The Indian Journal of Labor Economics. 2019; 62 : 361–388. Publisher Full Text Perez ER, Benavides FG, Levecque K, et al. : Difference in Working Conditions and Employment Arrangements among Migrants and Non-migrants in Europe. Ethn. Health. 2012; 17 : 563–577. PubMed Abstract | Publisher Full Text Tandon A: Inter-Industry Wage Differentials in Indian Manufacturing: Convergence or Persistence? Econ. Polit. Wkly. 2022; 57 (38): 39–46. Footnotes 1 According to NCEUS 2007, unincorporated private enterprises with less than 10 workers engaged in production of goods and services on a proprietary basis and provide the employment without covering the social security benefits to the workers. Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 13 Feb 2026 ADD YOUR COMMENT Comment Author details Author details 1 School of Economics & Commerce, Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha, 751024, India 2 School of Liberal Studies, Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha, 751024, India 3 School of Management, Bennett University, Greater Noida, Uttar Pradesh, 201310, India Suvendu Barik Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Supervision, Writing – Original Draft Preparation Nishi Kanta Mishra Roles: Investigation, Writing – Review & Editing Monika Saxena Roles: Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information The author(s) declared that no grants were involved in supporting this work. Article Versions (1) version 1 Published: 13 Feb 2026, 15:253 https://doi.org/10.12688/f1000research.168208.1 Copyright © 2026 Barik S et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Barik S, Mishra NK and Saxena M. Socio-economic Conditions & Determinants of Income: A Tell of Informal Workers of Urban Odisha, India [version 1; peer review: 1 approved with reservations, 1 not approved] . F1000Research 2026, 15 :253 ( https://doi.org/10.12688/f1000research.168208.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 13 Feb 2026 Views 0 Cite How to cite this report: Akande RS. Reviewer Report For: Socio-economic Conditions & Determinants of Income: A Tell of Informal Workers of Urban Odisha, India [version 1; peer review: 1 approved with reservations, 1 not approved] . F1000Research 2026, 15 :253 ( https://doi.org/10.5256/f1000research.185374.r461830 ) The direct URL for this report is: https://f1000research.com/articles/15-253/v1#referee-response-461830 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 19 Mar 2026 Rashidat Sumbola Akande , Kwara State University, Malete, Nigeria Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.185374.r461830 The article “Socio-economic Conditions & Determinants of Income: A Tell of Informal Workers of Urban Odisha, India” explores the income dynamics and living conditions of informal workers in Cuttack City, Odisha. The authors collected primary data from 470 household ... Continue reading READ ALL The article “Socio-economic Conditions & Determinants of Income: A Tell of Informal Workers of Urban Odisha, India” explores the income dynamics and living conditions of informal workers in Cuttack City, Odisha. The authors collected primary data from 470 household heads using a multi-stage stratified sampling method and analysed the results with ANOVA and regression techniques. Their findings suggest that migrant workers generally earn more than non-migrant workers, largely because they are engaged in casual or contract labour, while non-migrants are more concentrated in household services with lower pay. Key determinants of income include land tenure, gender, age of household head, migration status, employment type, social group, religion, dependency ratio, and union membership. The authors conclude with policy recommendations that emphasize improving housing, health, education, and employment opportunities. While the study is timely and relevant, several areas require improvement to make the article scientifically sound. The title, though descriptive, is not well phrased. A clearer version such as “Socio-economic Conditions and Determinants of Income among Informal Workers in Urban Odisha, India” would improve readability. The abstract is concise but lacks methodological detail and specific quantitative results, which should be added to give readers a clearer sense of the study’s scope and findings. The literature review is extensive but overly descriptive. It cites many international and national studies without synthesizing them into a coherent narrative or clearly identifying the research gap. The authors should explicitly state how their work addresses an existing gap in the literature. The methodology section needs stronger justification for the sample size and more detail on ethical safeguards, particularly regarding confidentiality. The choice of ANOVA and regression should be explained more thoroughly and justified. The results section provides a rich socio-economic profile but does not adequately interpret the regression analysis. The authors should present coefficients, p-values, and confidence intervals, and discuss the implications of each determinant more explicitly. The tables are informative but require clearer captions and tighter integration into the discussion. The conclusion and policy recommendations are broad and somewhat generic. They should be directly tied to the empirical findings. Finally, the referencing style is inconsistent and should be revised to conform to the journal’s specification. All in-text citations must correspond to entries in the reference list. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? No source data required Are the conclusions drawn adequately supported by the results? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: Sustainable Development, Informal Economy, Migration I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Akande RS. Reviewer Report For: Socio-economic Conditions & Determinants of Income: A Tell of Informal Workers of Urban Odisha, India [version 1; peer review: 1 approved with reservations, 1 not approved] . F1000Research 2026, 15 :253 ( https://doi.org/10.5256/f1000research.185374.r461830 ) The direct URL for this report is: https://f1000research.com/articles/15-253/v1#referee-response-461830 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Bondzie EA. Reviewer Report For: Socio-economic Conditions & Determinants of Income: A Tell of Informal Workers of Urban Odisha, India [version 1; peer review: 1 approved with reservations, 1 not approved] . F1000Research 2026, 15 :253 ( https://doi.org/10.5256/f1000research.185374.r459274 ) The direct URL for this report is: https://f1000research.com/articles/15-253/v1#referee-response-459274 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 18 Mar 2026 Eric Amoo Bondzie , University of Cape Coast, Cape Coast, Ghana Not Approved VIEWS 0 https://doi.org/10.5256/f1000research.185374.r459274 Summary The study uses primary data collected via multi‑stage stratified sampling and a structured questionnaire of informal sector workers in Cuttack, Odisha, analysing determinants of informal income with ANOVA and multiple regression. It finds that migrant workers earn more ... Continue reading READ ALL Summary The study uses primary data collected via multi‑stage stratified sampling and a structured questionnaire of informal sector workers in Cuttack, Odisha, analysing determinants of informal income with ANOVA and multiple regression. It finds that migrant workers earn more than non‑migrants due to job type and payment patterns and identifies key socioeconomic determinants of lower informal incomes: land category, gender, age of household head, migration status, off‑season support, employment type, social group, religion, sector, dependents, and union membership. The authors recommend policy actions to improve housing, utilities, health, vocational education, social security, and employment, highlighting mobile education services, MGNREGS, and MSME support as pathways to advance multiple Sustainable Development Goals. Introduction and objectives: The topic should be "Tale" and not "Tell." Check and correct. A primary household survey with multi‑stage stratified sampling and 470 observations is an appropriate empirical base for descriptive and regression analysis of local informal labour markets. The paper should more explicitly discuss external validity limits (single‑city study), potential seasonal bias (survey March to May), and sample representativeness for different informal subsectors. The paper should add a short paragraph quantifying sampling coverage (share of slum population sampled), response rate, and any non‑response or selection issues; discuss how findings may or may not generalize beyond Cuttack. Literature reviews: The paper would benefit from a tighter theoretical framing that links specific hypotheses to empirical tests. For example, state explicit hypotheses such as: H1: Migrant status increases income because migrants concentrate in higher‑paid casual construction and contract work; H2: Lack of land tenure reduces bargaining power and lowers income. 2. Add recent microeconometric studies on urban informal earnings and migration in India (post‑2018) that use decomposition or quantile regression methods; this will help justify the choice of ANOVA and OLS and suggest alternative estimators. 3. Expand discussion of local institutions (labour market intermediaries, union presence, MGNREGS linkages) that the recommendations later rely on. Methodology: Model specification and dependent variable : Clarify the exact dependent variable (monthly income, log income, per capita household income?) and its distribution. If income is skewed, use log transformation or robust estimators. Regression details missing: Report full regression tables: coefficient estimates, standard errors, R², sample size, and model diagnostics. Indicate whether standard errors are robust to heteroskedasticity. Functional form and heterogeneity : Consider quantile regression to capture heterogeneity across the income distribution (effects on low‑income vs higher‑income informal workers). The paper’s policy claims about the “lower income” group would be strengthened by quantile estimates. Endogeneity and omitted variables : Migration status may be endogenous (self‑selection). Discuss potential bias and, if possible, implement sensitivity checks (e.g., control for pre‑migration characteristics, duration since migration, or use propensity‑score matching as a robustness check). Variable construction and measurement : Provide precise definitions and coding for categorical variables (social group, employment type, off‑season support, union membership). For example, how is “off‑season support” measured? Multiple hypothesis testing : Given many covariates, discuss the risk of false positives and consider reporting adjusted p‑values or focusing on a smaller set of pre‑specified determinants. Results and discussions The manuscript sometimes implies causality (e.g., migration causes higher income). Rephrase to reflect associational evidence unless selection is addressed. The paper reports that migrants earn more but does not present effect sizes in an interpretable way (e.g., percentage difference in mean or median income). Add clear magnitudes and confidence intervals. The discussion would be stronger if the authors linked income differences to mechanisms: show regressions where employment type mediates the migration–income relationship, or present mediation analysis (e.g., include employment type and observe change in migration coefficient). Present robustness checks: alternative income definitions, excluding outliers, separate regressions for men and women, and tests for multicollinearity among covariates. Improve table clarity: include sample sizes per cell, standard deviations, and p‑values for ANOVA. Add a regression appendix with full model outputs. Conclusions and recommendations Distinguish short‑term, medium‑term, and long‑term policy actions. For example, short‑term land tenure regularisation and affordable housing. Specify which agencies should lead (municipal corporation, state labour department, skill development mission) and suggest pilot designs (e.g., a targeted MGNREGS pilot for migrant slum clusters). Even a qualitative discussion of fiscal and administrative feasibility would help policymakers assess trade‑offs. Propose measurable indicators to track progress (changes in average informal income, share of workers with social security, number of trainees placed in MSMEs). Given the low female participation reported, include targeted measures for women’s safety, childcare, and training to increase female labour force participation and earnings. Is the work clearly and accurately presented and does it cite the current literature? No Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? No If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? No Are the conclusions drawn adequately supported by the results? No Competing Interests: No competing interests were disclosed. Reviewer Expertise: Macroeconomics, Monetary Economics, DSGE modelling, Public Economics and Time Series Econometrics. I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Bondzie EA. Reviewer Report For: Socio-economic Conditions & Determinants of Income: A Tell of Informal Workers of Urban Odisha, India [version 1; peer review: 1 approved with reservations, 1 not approved] . F1000Research 2026, 15 :253 ( https://doi.org/10.5256/f1000research.185374.r459274 ) The direct URL for this report is: https://f1000research.com/articles/15-253/v1#referee-response-459274 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 13 Feb 2026 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 Version 1 13 Feb 26 read read Eric Amoo Bondzie , University of Cape Coast, Cape Coast, Ghana Rashidat Sumbola Akande , Kwara State University, Malete, Nigeria Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Akande R. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 19 Mar 2026 | for Version 1 Rashidat Sumbola Akande , Kwara State University, Malete, Nigeria 0 Views copyright © 2026 Akande R. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The article “Socio-economic Conditions & Determinants of Income: A Tell of Informal Workers of Urban Odisha, India” explores the income dynamics and living conditions of informal workers in Cuttack City, Odisha. The authors collected primary data from 470 household heads using a multi-stage stratified sampling method and analysed the results with ANOVA and regression techniques. Their findings suggest that migrant workers generally earn more than non-migrant workers, largely because they are engaged in casual or contract labour, while non-migrants are more concentrated in household services with lower pay. Key determinants of income include land tenure, gender, age of household head, migration status, employment type, social group, religion, dependency ratio, and union membership. The authors conclude with policy recommendations that emphasize improving housing, health, education, and employment opportunities. While the study is timely and relevant, several areas require improvement to make the article scientifically sound. The title, though descriptive, is not well phrased. A clearer version such as “Socio-economic Conditions and Determinants of Income among Informal Workers in Urban Odisha, India” would improve readability. The abstract is concise but lacks methodological detail and specific quantitative results, which should be added to give readers a clearer sense of the study’s scope and findings. The literature review is extensive but overly descriptive. It cites many international and national studies without synthesizing them into a coherent narrative or clearly identifying the research gap. The authors should explicitly state how their work addresses an existing gap in the literature. The methodology section needs stronger justification for the sample size and more detail on ethical safeguards, particularly regarding confidentiality. The choice of ANOVA and regression should be explained more thoroughly and justified. The results section provides a rich socio-economic profile but does not adequately interpret the regression analysis. The authors should present coefficients, p-values, and confidence intervals, and discuss the implications of each determinant more explicitly. The tables are informative but require clearer captions and tighter integration into the discussion. The conclusion and policy recommendations are broad and somewhat generic. They should be directly tied to the empirical findings. Finally, the referencing style is inconsistent and should be revised to conform to the journal’s specification. All in-text citations must correspond to entries in the reference list. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? No source data required Are the conclusions drawn adequately supported by the results? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise Sustainable Development, Informal Economy, Migration I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Akande RS. Peer Review Report For: Socio-economic Conditions & Determinants of Income: A Tell of Informal Workers of Urban Odisha, India [version 1; peer review: 1 approved with reservations, 1 not approved] . F1000Research 2026, 15 :253 ( https://doi.org/10.5256/f1000research.185374.r461830) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/15-253/v1#referee-response-461830 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Bondzie E. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 18 Mar 2026 | for Version 1 Eric Amoo Bondzie , University of Cape Coast, Cape Coast, Ghana 0 Views copyright © 2026 Bondzie E. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Not Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Summary The study uses primary data collected via multi‑stage stratified sampling and a structured questionnaire of informal sector workers in Cuttack, Odisha, analysing determinants of informal income with ANOVA and multiple regression. It finds that migrant workers earn more than non‑migrants due to job type and payment patterns and identifies key socioeconomic determinants of lower informal incomes: land category, gender, age of household head, migration status, off‑season support, employment type, social group, religion, sector, dependents, and union membership. The authors recommend policy actions to improve housing, utilities, health, vocational education, social security, and employment, highlighting mobile education services, MGNREGS, and MSME support as pathways to advance multiple Sustainable Development Goals. Introduction and objectives: The topic should be "Tale" and not "Tell." Check and correct. A primary household survey with multi‑stage stratified sampling and 470 observations is an appropriate empirical base for descriptive and regression analysis of local informal labour markets. The paper should more explicitly discuss external validity limits (single‑city study), potential seasonal bias (survey March to May), and sample representativeness for different informal subsectors. The paper should add a short paragraph quantifying sampling coverage (share of slum population sampled), response rate, and any non‑response or selection issues; discuss how findings may or may not generalize beyond Cuttack. Literature reviews: The paper would benefit from a tighter theoretical framing that links specific hypotheses to empirical tests. For example, state explicit hypotheses such as: H1: Migrant status increases income because migrants concentrate in higher‑paid casual construction and contract work; H2: Lack of land tenure reduces bargaining power and lowers income. 2. Add recent microeconometric studies on urban informal earnings and migration in India (post‑2018) that use decomposition or quantile regression methods; this will help justify the choice of ANOVA and OLS and suggest alternative estimators. 3. Expand discussion of local institutions (labour market intermediaries, union presence, MGNREGS linkages) that the recommendations later rely on. Methodology: Model specification and dependent variable : Clarify the exact dependent variable (monthly income, log income, per capita household income?) and its distribution. If income is skewed, use log transformation or robust estimators. Regression details missing: Report full regression tables: coefficient estimates, standard errors, R², sample size, and model diagnostics. Indicate whether standard errors are robust to heteroskedasticity. Functional form and heterogeneity : Consider quantile regression to capture heterogeneity across the income distribution (effects on low‑income vs higher‑income informal workers). The paper’s policy claims about the “lower income” group would be strengthened by quantile estimates. Endogeneity and omitted variables : Migration status may be endogenous (self‑selection). Discuss potential bias and, if possible, implement sensitivity checks (e.g., control for pre‑migration characteristics, duration since migration, or use propensity‑score matching as a robustness check). Variable construction and measurement : Provide precise definitions and coding for categorical variables (social group, employment type, off‑season support, union membership). For example, how is “off‑season support” measured? Multiple hypothesis testing : Given many covariates, discuss the risk of false positives and consider reporting adjusted p‑values or focusing on a smaller set of pre‑specified determinants. Results and discussions The manuscript sometimes implies causality (e.g., migration causes higher income). Rephrase to reflect associational evidence unless selection is addressed. The paper reports that migrants earn more but does not present effect sizes in an interpretable way (e.g., percentage difference in mean or median income). Add clear magnitudes and confidence intervals. The discussion would be stronger if the authors linked income differences to mechanisms: show regressions where employment type mediates the migration–income relationship, or present mediation analysis (e.g., include employment type and observe change in migration coefficient). Present robustness checks: alternative income definitions, excluding outliers, separate regressions for men and women, and tests for multicollinearity among covariates. Improve table clarity: include sample sizes per cell, standard deviations, and p‑values for ANOVA. Add a regression appendix with full model outputs. Conclusions and recommendations Distinguish short‑term, medium‑term, and long‑term policy actions. For example, short‑term land tenure regularisation and affordable housing. Specify which agencies should lead (municipal corporation, state labour department, skill development mission) and suggest pilot designs (e.g., a targeted MGNREGS pilot for migrant slum clusters). Even a qualitative discussion of fiscal and administrative feasibility would help policymakers assess trade‑offs. Propose measurable indicators to track progress (changes in average informal income, share of workers with social security, number of trainees placed in MSMEs). Given the low female participation reported, include targeted measures for women’s safety, childcare, and training to increase female labour force participation and earnings. Is the work clearly and accurately presented and does it cite the current literature? No Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? No If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? No Are the conclusions drawn adequately supported by the results? No Competing Interests No competing interests were disclosed. Reviewer Expertise Macroeconomics, Monetary Economics, DSGE modelling, Public Economics and Time Series Econometrics. I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. reply Respond to this report Responses (0) Bondzie EA. Peer Review Report For: Socio-economic Conditions & Determinants of Income: A Tell of Informal Workers of Urban Odisha, India [version 1; peer review: 1 approved with reservations, 1 not approved] . F1000Research 2026, 15 :253 ( https://doi.org/10.5256/f1000research.185374.r459274) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. 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