(i) In stage 1, clusters, each with a population of 200 households, were distributed across seven zones in Delhi. (ii) The proportion of the number of clusters in each zone C (z = 1....7)

zto the total number of clusters across all zones, C was taken as the weight to arrive at the distribution of 27 sample clusters across the seven zones. The number of clusters to be picked up for sampling let us say is X. (iii) The next step concerns

zdrawing of X number of specific sample clusters from C

zz number of clusters located actually in a zone. All the C clusters

zin each zone with their detailed addresses were codified and

put in a box from which X draws were made. Xzi stand

zspecific for the specific cluster in zone Z. The process was repeated for all the seven zones, separately. (iv) Once the specific clusters from each zone were identified, the distribution of 754 sample households was made using the proportion of the number of households in each cluster, (HHXzi), to the total number of households in all 27 clusters (HHX, where X=sigma X), as weights. To this we added 48 households selected from

zthe three clusters, 16 each, where the qualitative survey was carried out. (v) In each of these clusters investigators prepared a listing of households with some identifiable characteristics based on which we drew a lottery to pick up the specific households for the detailed interviews enabling to fill in the quantitative questionnaires.1

More or less the same principle was followed in the survey of 200 slum households in Delhi for 2004-05.

IIIII

Occupational MobilityOccupational MobilityOccupational MobilityOccupational MobilityOccupational Mobility

The ILO report on Kenya (1972) argued that it was not only the high wage formal sector job but also the income opportunity in the informal sector that attracted potential migrants. “Informalsector employment is undesirable when viewed from above by the formal-sector wage or salary earner, but desirable and an independent source of attraction when viewed from below by the rural immigrant” [ILO 1972: 224]. The Todaro (1969) model argued that subsequent to their migration to the urban areas they search for better opportunities and in due course graduate to the formal sector. Breman (1976), however, argued that the idea of moving to better-paid jobs is mostly fictional because those who join the lower ranks of the urban labour system usually remain there. Standing (1977) also feels that it is difficult for them to escape once they join this sector. “The ability to perform regular sustained work tends to evaporate through lack of use, and the poor diet and medical treatment available to those in the irregular sector tend to undermine their physical and mental capacity to do sustained work,” [Standing 1977: 41]. However, within the informal sector the possibility of movement from low productivity jobs to better-paid jobs does exist as several studies based on survey data point out [Banerjee 1986; Mitra 1994; Gupta and Mitra 2002].

The informal sector is not necessarily a manifestation of low productivity jobs only. As the literature on the formal-informal sector relationship points out, the process of interaction between these two sectors is quite complex. Papola (1981) argued that the size of the informal sector is large in situations of both limited industrial spread and rapid industrialisation. In the former case it is mainly a manifestation of residual absorption of labour, whereas in the latter case both are complementary to each other. Also, in this situation the informal sector is described to be no more of low productivity because of its close linkage with the industrial sector and income percolating from this sector to the rest.

As regards the facets of interaction between the informal sector and industrialisation the links may be taken to exist through the following: (a) the labour market, (b) the commodity market, and

(c) the production process. On a priori grounds, the nature of the relationship between the share of informal sector employment and industrialisation can be positive as well as inverse. If informal sector employment is taken to be the outcome of sluggish employment growth in the industry, then it would have a negative relationship with respect to the latter. Tokman (1989) argued that the informal sector shows both an “autonomous element”, i e, activities being associated with those carried out in the rest of the economy, and that is closely integrated with the rest of the economy. Further, he highlights the linkage between poverty and informal sector employment. While arguing that the chances of reducing the size of informal sector workers by absorbing them into modern activities are slim, he postulates a negative relation between high productivity modern sector employment and the low productivity informal sector employment. Also, in the study by Hemmer and Mannel (1989) the possibility of an inverse relation between industrial and informal sector employment exists. With an extension of the formal sector, they argued, demand for labour within this sector at a given minimum wage may increase (or decrease) depending upon the technological conditions, which in turn at a given volume of labour supply including the migrants, reduces (or raises) the volume of labour in the informal sector. Stark (1982) argued that with the expansion of the modern sector, new employment opportunities are created which may eliminate some of the informal sector jobs if the activities are competitive. On the other hand, the relationship can be complementary if expansion of output and employment in the modern sector generate employment in the informal sector through inter-sectoral linkages. When both the forces are at work, the net relationship may turn out to be competitive or complementary depending upon the relative strength of the forces. However, in Stark’s (1982) model, as Datta-Chaudhuri (1989) argued, the expansion or contraction of the informal sector is basically determined by the growth of the formal sector. On the other hand, Datta-Chaudhuri (1989) noted that the informal sector could expand even when the formal sector would stagnate or contract.2

Following Papola (1981), we note that the informal sector grows in both the situations of rapid industrialisation and sluggish industrial growth. But employment in the informal sector, while it is induced by industrial growth, offers better-paid jobs and, hence facilitates rapid upward mobility. Delhi, though it is not an industrial city, has experienced fast growth of several other formal sector activities. Recently the expansion of the IT sector and business process outsourcing services (BPO) has added new dimensions to the economic scenario of the city. Many of the informal sector activities have possibly grown in response to the growth of the activities in the formal sector, though an equally large chunk of the informal sector activities might have come up due to population pressure. It would, therefore, be interesting to examine whether slum dwellers in our sample have been able to locate themselves in better-paid jobs in due course after migrating to the city. A comparison of their first job in the city with their present job can throw light on this aspect. The Todaro model suggests that immediately after migrating to the city, the rural migrants engage themselves in the informal sector, but over time there takes place what he called “eventual attainment of a more permanent modern sector job”. Our hypothesis is that not always are such sectoral movements possible due to the sluggish employment growth in the high productivity sector and/or the high levels of skill requirement in these activities; and hence within the informal sector the job search continues and workers shift from low to relatively high-income jobs. Rather than viewing the informal sector as a stagnant pool of low paid work [Breman 1976], or as a temporary stoppage before moving into the formal sector [Todaro 1969] one may focus on the dynamics of the sector itself.

Table 2: Cross-Classification of Workers by Present and Past OccupationsTable 2: Cross-Classification of Workers by Present and Past OccupationsTable 2: Cross-Classification of Workers by Present and Past OccupationsTable 2: Cross-Classification of Workers by Present and Past OccupationsTable 2: Cross-Classification of Workers by Present and Past Occupations

(Per cent)(Per cent)(Per cent)(Per cent)(Per cent)

Note: (1) The percentage distribution is given relative to the row total.

(2) Occp: occupation, Semi-prof: semi-professionals, Pers: personal services, Manuf: manufacturing, Com: commercial services, trans: transport,

Tail: tailoring, knitting, Const: construction, Sec: security, and Rep: repairing Source: Same as Table 1.

IIIIIIIIII

Change in ContactsChange in ContactsChange in ContactsChange in ContactsChange in Contacts

In accessing the present occupations informal networks or contacts operating through relatives and other caste-kinship bonds seem to have played a major role (Table 3). Around 37 per cent of the currently working individuals stated that they did not take any help from others, and another 8 per cent did not give any specific information on the nature of their contact. The remaining 55 per cent, however, sought help from others in finding jobs in the urban labour market. Though the nature of contact varies across occupations certain broad patterns are quite evident. Among semi-professionals, immediate relatives and present employers turn out to be important sources of supplying job information. Among the sales workers the same contacts again appear to be dominant though general relatives and co-villagers both account for around 15 per cent of the total. Among trade workers, next to the immediate relatives are general relatives, and in personal services inhabitants of the same cluster happen to be the most important source of contact. Among manufacturing workers also, next to the immediate relatives and employers are the inhabitants of the same cluster who helped find jobs. NGOs played an important role in accessing jobs in commercial services. In construction, co-villagers and people from the same clusters are two important sources of contact next to immediate relatives and employers. On the whole, other than self-help, which includes accessing job market information directly or pursuing economic activity independently as own account workers, immediate relatives and the present employers have played an important role in seeking employment opportunities. Secondly co-villagers, people from the same neighbourhood (cluster) and general relatives have also assisted in accessing information on the job market. Private contractors and community leaders do not seem to have offered any significant help in this respect. Even NGOs except in the case of commercial services do not seem to have played any major role.

Our next concern is to examine if the same pattern of contact prevailed in the past as well. Table 4 presents the workers’ past occupations and contacts through which they obtained jobs in the past. Apart from immediate relatives no other source of contact appears to be of any significance in the past. In fact, in some of the occupations like sales and commercial services, immediate relatives helped to a much greater extent than selfhelp. Even in trade and personal services, both immediate relatives and self-help benefited almost an equal percentage of workers. The major difference between the present and past means of job accessibility is that self-help in accessing present jobs turns out to be the most important component in almost all the activities, whereas in the past in a number of activities, immediate relatives have been either more effective or as good as self-help in getting jobs. Keeping in view our definition of self-help, that is, accessing jobs directly or running own economic activity independently or with other family members or friends, this suggests that in the initial stages while entering the urban labour market close relatives matter in a significant way, but subsequently it is the individual entrepreneurship that decides his continuation in the labour market.

It may be interesting to assess if there is any close association between the nature of contact used by individuals in obtaining the present occupation and that in the past. Among those who utilised the help of immediate relatives in the past, only 27 per cent seem to have continued with the same source of contact. The rest developed other contacts; almost one-third of them depended on self-efforts to venture further into the labour market and possibly improve their earnings. On the other hand, those who depended on self efforts in the past continued to do so later as well: about 60 per cent of them accessed present jobs through self efforts and the rest diversified their contacts. It may be inferred from this that initially with respect to certain activities immediate relatives by and large matter in seeking an entry to the labour market but at a later stage self efforts and/or other sources of contact are crucial for the realisation of upward occupational mobility. From this perspective, it may be illustrated that one who accessed the job of a helper in a tea-stall through relatives in the initial stages, has eventually landed up as an owner of a tea-stall in some part of the city. And this would indeed tend to favour upward mobility in a limited sense though. However, in order to draw any such conclusion the occupational mobility needs to be substantiated by income mobility.

In order to compare the past income and the present income of the workers they have been cross-classified by various size classes formed on the basis of present and past income (Table 5). Figures along the leading diagonal give the proportion of workers whose income remained in that size class both in the past and the present. In other words, these are the workers who did not experience any significant change in their income in either direction, and hence did not move out of these size classes.3

The entries above the leading diagonal show the percentage of workers whose incomes in the past were higher than the present. On the other hand, the cells below the leading identify cases which experienced improvement over time. As noted from Table 5 most of the entries above the leading diagonal are either 0 or nominal. On the other hand, elements below the leading diagonal showing upward movement in terms of income size classes are large and account for the bulk of the workers for whom present and past incomes are reported. Some of the entries in particular show significant changes in income of the workers. Around 47 per cent of those who were in the income size classes Rs 0-300, Rs 301-500, Rs 501-1,000 and 1,001-1,500, moved to the present income size class of Rs 1,501-2,500 and another 23 per cent moved to Rs 2,501-4,000.

Though there is evidence in favour of upward income mobility, cases showing stagnancy or downward mobility are also present. In the class of past income of Rs 1,501 to 2,500, around 50 per cent remained in that bracket itself. But it may be noted that more than 35 per cent in that income class in the past have registered an increase. Again, around 83 per cent of those who earned an income of Rs 2,501 to Rs 4,000 per month in the past, are currently in the lower income categories. More importantly, all from past income category of Rs 4,000 and above have slipped to lower income groups. In fact, it appears as if the income mobility is mainly confined to very low levels of past income, while some of those in higher income brackets in the past have experienced a decline. But what is noteworthy is that by and large the percentage of those experiencing downward mobility over time is much less than those who could undergo an increase. Some of the top income classes which reveal largely a decline in the income profile constitute a very small proportion of the total workers. Hence, the number of persons at the aggregate who show improvement in income is much larger than the number of workers whose income declined over time. Therefore, the important point which emerges from the analysis is that the scope for upward mobility exists even among those in the lower income brackets, while among the relatively higher income brackets further mobility is rather sluggish. Public work programmes, and other income support measures including credit assistance, market accessibility and skill upgradation possibly can hasten the process of upward mobility in the relatively lower as well as upper income brackets.

As we piece together information on past and present occupation and employment, contacts and income of the slum workers, there seems to be substantive improvement over time. Though from the detailed listing of occupations, only a nominal number of workers are employed in the formal sector, within the informal sector itself upward mobility has taken place. Improvements are not necessarily to be judged only in terms of change in occupation categories, which we have formed broadly on the basis of the detailed listing; intra-occupational upward mobility in terms of income cannot be ruled out. From this it may be inferred that the decision to migrate is not irrational even when they are seen to be working within the informal sector their entire life, providing little support to the graduation hypothesis of the Todaro model. In the absence of rural diversification, even petty informal sector activities in the urban areas provide a source of livelihood and open up the scope for substantial changes to take place in future as they combine self-initiatives with their other sources of informal networks or “social capital”. Escaping poverty, therefore, is an outcome of various informal mechanisms, operating simultaneously within the informal sector, which engage an overwhelmingly large majority of the workers from slum households. In the next section we examine the association between the duration of migration and the living standard to draw further inferences in the context of upward mobility.

We examine the association between per capita total expenditure (PCE) per month for the sample households and the duration of migration in terms of the number of years spent in the city (DMIG) after controlling for other variables like household size (HHSZ), gender of the household head (GEND, 0 for males and 1 for females), proportion of the number of children in the age group 0-14 to the total number of household members in the household (PCHILD), literacy (dummy with a value of

Table 4: Past Occupation and ContactsTable 4: Past Occupation and ContactsTable 4: Past Occupation and ContactsTable 4: Past Occupation and ContactsTable 4: Past Occupation and Contacts

Past Occupation Percentage Who Received Self-Help Help from Immediate Relatives

Semi-professional 11.1 66.7 Sales 25.9 18.5 Trade 30.4 30.4 Personal Service 30.8 30.8 Manufacturing 19.4 41.9 Commercial Service 40.9 13.6 Transport 22.7 54.5 Tailoring, Knitting 16.7 50.0 Construction 25.9 42.0 Security 14.3 28.6 Repairing 6.3 18.8 Total 24.5 35.8

Source: Same as Table 1.

Table 5: Cross-Classification of Workers by PastTable 5: Cross-Classification of Workers by PastTable 5: Cross-Classification of Workers by PastTable 5: Cross-Classification of Workers by PastTable 5: Cross-Classification of Workers by Past

and Present Incomeand Present Incomeand Present Incomeand Present Incomeand Present Income

(In per cent in each size class)

(Past Income in Rs) Present 0-300 301-501-1001-1501-2501-4001-5000 Total Income 500 1000 1500 2500 4000 5000 and

0-300 3.7 0.0 1.2 0.0 0.0 0.0 0.0 0.0 1.2 301-500 3.7 0 .0 0.0 0.0 0.0 0.0 0.0 0.0 0.8 501-1000 5.6 2.0 4.7 2.9 4.5 33.3 0.0 0.0 4.7 1001-1500 11.1 11.8 17.6 14.7 9.1 33.3 100 0.0 14.5 1501-2500 35.2 56.9 44.7 58.8 50.0 16.6 0.0 100 47.1 2501-4000 31.5 19.6 23.5 14.7 22.7 0 0.0 0.0 22.4 4001-5000 9.3 2.0 3.5 5.9 9.1 0 0.0 0.0 5.1 5000 and

above 0.0 7.8 4.7 2.9 4.5 16.6 0.0 0.0 4.3 Total 100 100 100 100 100 100 100 100 100

Notes: (1) Each column adds up to 100 per cent.

(3) Incomes are given in 1999-2000 prices. Source: Same as Table 1.

1 for those who are literate and 0 for illiterates, EDUC), dummy differentiating between short and long distance migrants from the natives (SDMIG and LDMIG respectively), CAST dummy representing 1 for scheduled caste and scheduled tribe population and 0 otherwise, TOK dummy taking a value of 1 for having access to token or voter’s identity or ration card and 0 otherwise, and NGO representing the accessibility of the household to the services of non-government organisations (1 and 0 for positive and negative responses respectively). Besides, six zone specific dummies are considered as the city of Delhi has been divided into seven zones in total. The income of the household head (HHINC) is also taken as a determinant of the standard of living. In an alternate specification we have considered the occupation dummies, instead of income of the principal earner. As there are three types of employment included in the survey, two dummies for self-employment (SELF = 1, 0 otherwise) and regular wage or salaried employment (RSAL = 1, 0 otherwise) have been included, casual employment being the reference category. Similarly, 10 occupation specific dummies (OCCPi, i= 0 to 9) are considered, repair services (OCCP10) being the reference group.

Among the female headed households – where the principal earner is a woman – the standard of living is expected to be lower than that of the male-headed household as labour market discrimination yields lower earnings for women compared to male workers. With a rise in the percentage of children in the household, per capita consumption may actually rise as many of them join the labour market directly or indirectly and augment the family income. Since their volume of consumption is less than that of an adult, their participation in the labour market even with meagre earnings tends to raise the standard of living in per capita terms rather than deteriorate. Among the scheduled castes/tribes the levels of living are usually believed to be lower than others as they may not qualify for high-income jobs due to the lack of skill.

With education and with accessibility to a “token for ownership of property” or voter’s identity or ration card and services provided by the NGOs, the standard of living is expected to improve. Among the migrants the long duration ones are supposed to be better off because of their familiarity with the urban job market. With a rise in income – or alternately employment/occupation categories which are expected to provide stable jobs and income – per capita expenditure is likely to increase. It is an empirical question whether short-distance migrants are better off as compared to the long-distance ones or otherwise. Though there is no strong reason to expect any specific result on a priori grounds, the short-distance migrants may be better off due to the property that they have at the place of origin. In other words, better initial conditions possibly allow them to access relatively higher income jobs as they are at an advantage from social and cultural point of view as well.4 The long distance migrants are on the other hand at the mercy of the labour-contractors, and their job-search is also highly fragmented and limited by their contacts in a culturally more alien atmosphere.

Access to certain type of clusters endowed with basic amenities is possible only through contacts. In this respect certain communities or groups with specific regional/cultural background operate more prominently than the rest. Some of these factors are expected to get captured by the zone specific dummies. In other words, the significance of the zone specific dummies can bring out the differences in the standard of living that can be attributed to location specific advantages/disadvantages and cultural factors of the groups residing in these areas.

The empirical results reported in Table 6 can be summarised as follows. In the equation for per capita consumption expenditure, one of the variables which is highly significant is household size that reduces the standard of living. Literates appear to be better off as compared to those who are illiterate. With a rise in the percentage of children it is confirmed that consumption expenditure per capita increases, which indirectly tends to confirm the participation of children in the labour market. Income of the household head has a positive effect on consumption per capita. Interestingly, access to a token etc, does not seem to raise the level of living; rather it dampens the per capita expenditure. This is possibly because households with access to a token or voter’s identity or ration card invest in housing, which could at times be at the cost of consumption expenditure. Instead of income if we consider the employment categories in terms of dummies, those who are in regular wage or salaried jobs appear to have lower consumption expenditure per capita while that of self-employed is not different from the casual workers. This is possibly because if the household head is engaged in a stable employment the probability of other members joining the labour

Table 6: Regression Results for Per Capita Total MonthlyTable 6: Regression Results for Per Capita Total MonthlyTable 6: Regression Results for Per Capita Total MonthlyTable 6: Regression Results for Per Capita Total MonthlyTable 6: Regression Results for Per Capita Total Monthly

Expenditure (Model: OLS)Expenditure (Model: OLS)Expenditure (Model: OLS)Expenditure (Model: OLS)Expenditure (Model: OLS)

Exp Var Equation 1 Equation 2 Equation 3

HHSZ -117.34 (-12.45)* -95.19 (-10.69)* -95.06 (-10.70)* DMIG 44.93 (0.82) 46.83 (0.84) 41.85 (0.75) EDUC 90.59 (2.97)* 80.67 (2.58)* 73.40 (2.33)* GEND 16.39 (0.26) -3.33 (-0.05) 20.03 (0.31) PCHILD 1.98 (2.75)* 0.68 (0.98) 0.51 (0.74) HHINC 0.04 (6.25)* CAST -27.48 (-0.92) -23.01 (-0.74) -15.27 (-0.49) ZONE1 -22.22 (-0.36) -23.34 (-0.37) -18.50 (-0.29) ZONE2 -137.53 (-1.88)** -135.53 (-1.81)** -129.61 (-1.73)** ZONE3 -43.62 (-0.68) -36.30 (-0.56) -30.33 (-0.47) ZONE4 -146.97 (-2.32)* -148.06 (-2.29)* -144.17 (-2.23)* ZONE5 -62.87 (-0.95) -71.66 (-1.07) -81.59 (-1.20) ZONE6 -68.80 (-0.81) -69.98 (-0.81) -80.12 (-0.93) NGO 63.96 (1.17) 64.55 (1.16) 59.25 (1.07) TOKEN -265.77 (-4.31) 297.78 (-4.73)* -288.44 (-4.56)* SDMIG -16.06 (-0.20) -38.58 (-0.47) -42.80 (-0.52) LDMIG 59.23 (0.71) 70.44 (0.83) 65.60 (0.77) SELF 29.48 (0.79) RSAL -113.67 (-3.06)* OCCP0 58.99 (0.58) OCCP1 -151.70 (-1.65)** OCCP2 -69.22 (-0.78) OCCP3 -280.85 (-2.80)* OCCP4 -117.65 (-1.24) OCCP5 -80.11 (-0.87) OCCP6 -9.27 (-0.08) OCCP7 -11.49 (-0.09) OCCP8 -187.47 (-2.18)* OCCP9 -103.15 (-0.58) INTERCEPT 1496.59 (11.79)* 1648.31 (12.52)* 1719.04 (11.49) R2 0.26 0.24 0.24 N 800 800 800

Notes: 1 Figures in parentheses give t ratios. * and **represent significance at 5, 10 and 20 per cent levels respectively. 2 The occupation categories are as follows:OCCP0 = Semiprofessionals, OCCP1= Sales Workers, OCCP2 = Trade Workers, OCCP3 = Personal Services, OCCP4 = Manufacturing, OCCP5 = Commercial Services, OCCP6 = Transport, OCCP7 = Tailoring etc, OCCP8 = Construction, OCCP9 = Security Workers.

Source: Based on slum survey, 1999-2000.

market is low, and hence the consumption expenditure tends to get affected adversely. Occupation wise the regression results show that on an average consumption per capita is lower in the household if the head is engaged in personal services. The dummy for construction also takes a negative sign. Across space, zones 2 and 4 are found to have lower levels of living compared to the rest.

It is surprising that the duration of migration does not turn out to be significant in the equation for per capita expenditure. This could be due to the inclusion of the income variable in the equation

– the effect of migration is possibly captured by the income variable. However, as we estimate a binomial logit model for those who can save and cannot save, irrespective of the nature or the amount of saving, the following results are obtained. With a rise in the duration of migration the probability of not saving declines, implying that the long duration migrants are more likely to save. Similarly, with literacy and income, the likelihood of saving rises. Households, which have access to services provided by NGOs are also more likely to save.

The empirical results also indicate that the long-distance migrants are less likely to save compared to the short-distance migrants and the local residents, though in terms of consumption expenditure the differences were not statistically significant. As income is replaced by the nature of employment dummies, we note that household heads in regular wage employment show a lower probability to save than the casual labour dependent households. Similar results were also observed in the case of consumption expenditure, which go against the common belief. But it could be due to the fact, as mentioned above, that jobs of the householdheads, which are relatively less uncertain, possibly keep other members of the households outside the labour market. Also, it is natural that a more risk prone household would show a higher

Table 7: Binomial Logit Model for Income MobilityTable 7: Binomial Logit Model for Income MobilityTable 7: Binomial Logit Model for Income MobilityTable 7: Binomial Logit Model for Income MobilityTable 7: Binomial Logit Model for Income Mobility

(Maximum Likelihood Estimate)

Notes:(1) For income mobility those who had a change: 1, and those who did not: 0

Source: Based on Slum Survey (2004-05).

probability to save to face contingency than a household which expects to receive a steady flow of income. All this would suggest that casual labour dependent households may end up having a larger magnitude of total household income and a higher probability to save than others.5 Relating to occupation dummies the empirical results show that commercial services and transport hold the possibility of a higher saving. Across space, those in zone 1 are more likely to save compared to their counterparts in other zones.

Based on the survey of 200 slum households in 2004-05, we have tried to identify certain determinants of income mobility. This is tried in a binomial logit framework assigning 1 to those whose income changed and 0 to those whose income did not change. Since the bulk of the respondents reported an increase in income, modest though, between the entry level job and the present job (after adjusting for price changes), these results may be interpreted to understand upward mobility.

Some of the social capital networks are perceived in terms of connections with relatives (NETW1), co-villagers and neighbours (NETW2), members of the same caste group (NETW3), friends (NETW4), colleagues at or employer of the present or previous job (NETW5), religious organisations, NGOs, private contractors and government employment exchange (NETW6), slum or community leader (NETW7), who helped them migrate or access information on the job market. The effect of these networks is examined in the income mobility function keeping self-help as the comparison category.

Along with network dummies, education dummies (EDUi=1,2.3)6 with illiterates as the comparison group, household size (HHSZ), caste groups differentiating among scheduled caste/tribe (SCST), backward caste (OBC) and the rest as the comparison group, gender dummy (GEND with 0 for males and 1 for females), access to political contact (POLC, 1 for having contact and 0 for its absence) and availability of property at the place of origin (PROP, 1 for having property and 0 otherwise) and age (AGE) as a proxy for experience in the job market are included as explanatory variables. Age-square (AGSQ) is also considered to verify if the possibility of mobility declines after a certain age.

The estimates of the binomial logit model attempted show that age (not linearly though)7 and household size raise the probability of experiencing income mobility. Workers from larger households are possibly compelled to strive hard to enhance their income. Female workers are less likely to experience income mobility compared to the male workers. Networks which operate through relatives and neighbours/co-villagers tend to reduce the scope for upward mobility though they are indispensable at the time of entry to the labour market. Caste-kinship bonds are important in the initial stages to access jobs but for further mobility – whether intra- or inter-occupational – self-initiatives bring in more information and generates skill and other prerequisites. Education does not turn out to be an important determinant of mobility, suggesting that skill and work experiences play a crucial role in explaining rise in income in the informal sector. Further, there is no evidence in support of caste discrimination in the context of mobility. Political contact too does not help in augmenting income though there is ample evidence showing slums being used as major “vote banks” [Edelman and Mitra 2006].

VIVIVIVIVI

ConclusionConclusionConclusionConclusionConclusion

In this paper we made an attempt to examine the upward income mobility hypothesis. Though most of the workers seem to be employed in the informal sector both in the past and present, a large majority of them have experienced improvements in terms of earnings. Both inter- and intra-occupational changes within the informal sector itself have been beneficial to the workers. At the entry point, networks through relatives, etc, are important but at a later stage self-efforts help them improve their nature of job and climb up the income ladder. On the whole, though the graduation hypothesis, i e, transfer of labour from the informal to the formal sector, does not seem to receive much empirical support, movements within the informal sector are substantive and prove to be beneficial. However, the cases of downward income mobility, particularly among those located in the higher income brackets in the past, have also been noted. The only major support in favour of the hypothesis is that the cases of downward mobility are much fewer in number than upward mobility. The duration of migration does not seem to have any significant effect on the expenditure per capita, but it shows a positive influence on the probability to save. With experience the workers are likely to gain in terms of income mobility. To conclude, while the evidence in favour of upward mobility is not overwhelming, it would be equally erroneous to reject the supporting facts howsoever few they may be. Income support measures including the scope to up-grade skill can fasten the process of upward mobility in the relatively lower income brackets and stop the process of downward mobility in the relatively higher income brackets. What is noteworthy is that even in these low income households, with no major income support policy the workers are able to earn a source of livelihood based on self-initiatives and “social capital”. These informal security mechanisms and the self-efforts need to be taken into consideration in framing the policy directives so that they remain cost efficient as well as compatible with the needs and requirements of the residents. Such complementary relationship is essential in making policies popular and effective.

mr:

Email: mitra@ide.go.jp arup@iegindia.org

NotesNotesNotesNotesNotes

[The author would like to thank an anonymous referee for constructive comments. An earlier version of the paper was presented at a 2005 WIDERconference, and at the International Economic Association conference, 2005. Thanks to the participants for their suggestions. With the financial support from the World Bank (V Rao and M Woolcock, Development Economics Research Group, Washington DC) the 1999-2000 survey was carried out in Delhi slums under the IEG-World Bank joint project on urban poverty, social capital and risk management. The survey for the year, 2004-05 was, however, carried out by the author himself.]

1 Based on the detailed quantitative survey carried out in 1999-2000 we

later went back to the households to clarify certain responses in relation

to the income mobility aspect, as the original survey did not include some

of the responses clearly. 2 Mitra (1994) noted that in relative terms industry and informal sectors are

inversely related though the elasticity of employment in the informal sector

with respect to the employment in the industrial sector is positive.

3 However, within the groups, movements might have taken place.

4 With a rise in the distance between the places of origin and destination in India, the social differences increase and so also the linguistic variations.

5 Since total household income of these households (in an attempt to reduce uncertainty) is greater than others, their consumption per capita may also be higher than others.

6 With illiterates as the reference category, EDU1 represents those who are literate and those who have studied up to class 9, EDU2 encompasses those who have studied above class 9 and completed secondary education but not graduation, and EDU3 represents graduates or those who have acquired a higher level of education including technical and non-technical, professional, and vocational courses.

7 This means that after a certain age the probability of mobility declines.

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