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.

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.

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.

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.

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.

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: 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 ) … (1)

Similarly, to determine the effective and significant determinants of the migrant and non-migrant informal workers, multiple regression models were formulated separately as follows: 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 … (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.

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.