By and large, the literature s broad consensus is that rural-urban migration results from forward-looking behaviour that aims to maximize an individual or household s expected well-being over some time horizon by means of relocation. It is in all cases a cost-benefit calculation, comparing a stream of expected future benefits with costs, both appropriately discounted to the present. On the whole, the issue of upward mobility (Basu, 2000; Dubey, 2006; Kuznets, 1966; Roy et al., 1992; Zhang, 2003) and the inter-play of various factors that qualify a migrant to experience upward mobility is complex and rich quantitative information is required to lend support to theoretical underpinnings (Mitra and Tsujita, 2006). With an ideal thinking, from point of view migrants a good job in formal sector with social security can produce a considerable stream of monetary benefits and also there would be a certain system to protect them against calamities such as retirements, disability, death, sickness and maternity and etc. Hence an inquiry about one of the main causes of migration (employment or better employment), which is a very important feature of households livelihoods, play a considerable role to achieve better understanding of above issues.
In a study of migration, the inquiry into the discussion of the present occupation of the migrant as well as the non-migrants helps in appreciating the divers fields of economic activity in which they are engaged and also in understanding the state they might have reached in the course of their occupational career (Santhapparaj, 1996). Occupation classification does not completely reveal the nature of employment of the migrants. It was, therefore, considered useful to divide the employments of the migrants in the area of destination into analytically more appropriate categories of formal sector and informal employments.
Many migrants, who move to cities would like to obtain a non-manual job in the formal sector, but find it difficult to do so. They need to have a certain level of education to be able to compete with the urban residents for these jobs. So, the majority of them are entering the informal sector who are unable to find jobs in the formal sector. Their main reason for taking part in the informal sector is to use what little skills they have to earn enough income to sustain their lives. As mentioned before, urban informal sector activities can be labor intensive, the labor coming from every member of the household that is able to work.
The urban informal sector, unlike its formal counterpart, includes all activities that are unregulated and small scale in nature. Not much attention was given to this unregulated, unorganized and mostly illegal sector till the 1970s. Observations were made in several developing countries that certain labor market activities failed to show up in statistics relevant in the formal modern sector.
Although many numbers of studies have recognized the existence of formal and informal sectors in the urban areas of the developing countries, there is, as yet, no agreement on definition leading to demarcation of these two sectors. However, a number of distinguishing characteristic have been suggested by Ferrer-i-Carbonell and Gërxhani (2004), Paul (1989) and Reddy et al. (2003).
Migrants interested in getting employment in the formal sector, find the informal sector as a safety net to fall back on if things do not work out for them. The informal sector is therefore seen as a cause of rural-urban migration, because it lowers the risk of the individual being unemployed once they move to the cities. For example many school leavers have continued to migrate to the urban areas with the hope of formal employment as per their training and many have had to lower their expectations. And many have also been forced to seek other alternatives and forget their college diplomas, which have not translated into the expected job!
An important motive behind the longer stay at the destination is the entry into the formal sector. Rural-urban migration in the Todaro s model is a two stage process. The migrants do not obtain a formal sector job when they arrive in the areas of destination. Instead they first spend a period of time unemployed or underemployed in informal sector. Educated and skilled immigrants may have more opportunity to find jobs in formal sector, since often their skills are in demand in the urban formal sector (Banerjee, 1983).
Although a basic hypothesis of probabilistic migration models is that informal sector employment is a temporary staging post for new migrants on their way to formal sector employment but Banerjee (1983) in his study has argued that there are no conclusive tests of probabilistic models in empirical migration literature and it has to examine evidence from a sample survey.
The objective of the present study is to investigate the factors which are determining to get job in urban formal sector by a migrant from rural areas which are estimated by a binary model.
CONTEXT OF IRAN
The increasing pressure of population has led to the division and fragmentation of land resulting in small and uneconomic holdings giving low yields. In the absence of other non-agricultural activities available in the area, the population has only the agricultural sector for employment and income avenues. Therefore, as a result of lack of employment opportunities and income source in rural areas, a high majority of population tends to migrate to the urban areas (Ghaffari and Singh, 2000; Mojtahed, 1980; Nasirkhani, 1991; Sarnaghi, 2001; Taherkhani, 2001, 2004).
Majority of studies concur that migrants leave their area of origin primarily because of lack of economic opportunities in the hope of finding better opportunities elsewhere (Cooke and Bélanger, 2006; Safa, 1975; Selvaraj and Rao, 1993; Wintle, 1992). In other word migration is often a response to economic incentives. The classic analysis of rural-urban migration (Harris and Todaro, 1970) attributes migration to the existence of relatively better economic conditions in urban areas. Most of the studies and surveys in Iran support this thinking that aspiration for better job with social security is a significant motivating factor for migration.
The study of consequences of migration, which so far has received little attention especially in Iran, offers another vital area of research for the academics and policymakers. Relatively little has been published in the regional science literature regarding attitude of migrants towards employment sectors. So, this study attempts to highlight some part of economic consequences of migration on migrants by assessing their job situation.
Principally, in absence of government, dividing the economy into two informal and formal sections is meaningless. After the victory of Islamic revolution in Iran, due to high supervision and intervention of the government and also because of institutional and legislative disharmony, many activities led to informal sector. Currently the large size of informal sector lowers the risk of the individual being unemployed once they moved to the cities and encourage more migration (Etminan and Chaker-ol-Hosseini, 2007).
The informal sector broadly refers to a wide range of economic activities including street food or market vendors, small automotive and machine repair shops, small-scale manufacturing such as garments, shoes or handicrafts carried out by single operators outside the regulatory framework of the state.
Although a high degree of convergence exists between small-scale enterprises and the informal sector, a distinction needs to be made between the two. The informal sector is described as consisting of all small-scale activities that are normally semi-organized (Reddy et al., 2003).
Formal sector employment is defined as working for a firm or organization with more than five workers and using power and also firm or organization with more than 20 workers without using power or being a self-employed professional. Keeping in view this feature, a workable definition of formal sector also was considered. It required the fulfilment of the criteria of (a) protected employment and (b) legal minimum wage.
Social security coverage of labour forces working in the formal sector has made this sector as a first priority of employment in urban area for migrants and they are so interested to be employed in this sector. Although recent baby boom and failure of the formal sector in job creation have necessitated considering the informal sector and its changes in Iran.
MATERIALS AND METHODS
Iranian nationwide census report is a major source of secondary data on
migration. But it does not provide the socio-economic characteristics of the
migrant s households and their occupation. Consequently identifying of
attitude of migrants toward different employment section requires more information
which is feasible via primary data. Then it was necessary to generate such primary
data directly by contacting the migrants.
Basically the sample unit of the present study is migrant who is a male and has migrated to urban area between 1996 and 2001 in form of voluntary migration, as well as their households. The migrant should have at least 5 years of urban experience as a resident of urban.
As it was felt to be not feasible to make an in-depth study of all the in-migrants the researcher chose a sample study. A total number of 400 sample migrant s households were interviewed. The actual process of selecting the migrant was carried out stage by stage by following the method of multistage cluster random sampling.
Sampling Design and Data Collection
There was no ready-made list of migrant households from secondary sources.
So, it was decided to go ahead first personally for identifying the migrant
household and then selecting a few of them for interview at a later stage. Migrant
households are to be found in almost all the parts of the city. According to
the census there were 25 Provinces in Iran. Out of them three province were
selected by simple random sampling.
In the next stage one district of each province were selected by simple random sampling and then by the same method one city and in each city three residential areas were selected. In order to give equal representation to all areas 45 samples were collected from each residential area, at least a total of 400 samples households were interviewed, consisting of 133 migrant households.
With the help of the pre-tested interview schedule, a field of the study was conducted and the primary data were collected. The interviews were carried out with a semi-structured, open-ended questionnaire. The interviews were conducted from January to July of 2007.
RESULTS AND DISCUSSION
Job Expectation and Current Job in the City
Out of the 400 migrant respondents, biggest percentage (25.8%) of the migrants
expected job in the formal sector as a labor and the next high percentage (21%)
belong to non-manual jobs expectation, which these two tendencies indicates
that many migrants who move to cities would like to obtain a manual job and
non-manual jobs in the formal sector. Nearly 19% of migrants expected job in
the informal sector and 13.3% did not expect any particular tape of job, 8%
expected work in building construction and 13.3% expected self employment in
city. Thirty percent of those, who were student or unemployed at the time of
migration had come to the city with specific intention of pursuing job in the
formal sector whereas only 25.5% willing to be accepted as a laborer in the
informal sector. Near 70% of migrants that were employed before migration in
their own agricultural activities had prospect to get job in formal sector in
different employment section such as manufacturing and trade (self-employed),
non-manual works and laborer.
A large percentage (31.3%) of those, who employed as agricultural labor before migration expected to employed as a labor in the formal sector, 16.7% expected to work in building constructions, 18.8% to get job in any informal sector undertaking and 18.8% desired to get employment in non-manual works. Out of 92 migrants employed in the informal sector before migration, 22.8% wanted to be employed in the same job whereas 38% desired to joint in the formal sector as a labor and 16.3% expected to get non-manual job.
The overall assessment of the expectation of migrants reveals that the largest percentage of the migrants had migrated with the job expectation to get job in the formal sector as a laborer. The next more expected works was non-manual work in private, public and government sector which usually their employment security and income are higher than other works. Except those who had been employed in non- manual works before migration, the expectation of migrants was to be employed in occupations different from their earlier occupation. This means that they were not satisfied with their jobs at the time of migration.
In the second stage this research had an inquiry about current job of migrants and their expectation at the time of migration. The results of our research show that out of the total sample migrants, 13% got employment in the formal sector as a laborer whereas 25.8% had come with the expectation to get the formal sector employment. 23.3% got employment in building construction, but only 8% had come with expectation to work in this field. Biggest percentages (27%) of migrants were employed in the informal sector employment, whereas only 18.8% had come with such expectation and 8.3% were employed in non-manual works in private and government sector, whereas 21% had expected to find job in this field. Nearly 29% were self-employed but only 13.3% had come with such expectations, among self-employed migrants all those who had expectation to get job in manufacturing and trade sector obtained their expected job.
In overall the survey data suggest that not all migrants job at time of survey had coincidence with their expectation that can be caused by high rare of unemployment in Iran.
The Role of Migrant Networks in Finding Job
Earlier studies show that previous contacts to urban area, friends and relatives
in the place of destination play an important role in the supplying information
about urban job opportunities and also help the migrant to get a job. Cook and
Belanger (2006) believe as much as kin and family can be an influence that attracts
to a destination, so a lake of personal ties make a move less likely or difficult.
Consequently for finding job the presence or absence of networks may ultimately
be more significant than individual human capital or certain community characteristics.
The survey data reveals that 61.5% of migrant had come to city with pre-arrange
job, suggestion from friends and relatives or both of them and surprisingly
the same percentage of migrant got job within one month which supports the role
of urban based contacts in getting job. Whereas out of 154 migrants, those who
had no idea about job finding nearly 64% waited for job more than one month.
The role of migrant networks in internal migration has been extensively documented. Zhao (2000) reports that more than 75.6% of the migrants were assisted by relatives and friends during their first trip out of the villages. Similar results are found in a sample survey of 1500 migrants in Shandong Province of China, where more than 70% of sample migrants had prearranged jobs before migration (Meng, 2000).
Factors Which Determine to Get Job in Formal Sector
A binary model has adopted to examine the effect of independent variables
on getting job in the formal sector. The dependent variable is measured dichotomously,
(1 if migrant had been employed in the formal sector, 0 otherwise). The binary
logistic regression analysis and computation of the log odds that a particular
outcome (hear; getting job in formal sector) will occur (Table
Table 1 reports the results of this simple prediction. As most of migrants are not occupied in formal sector, the predicted outcome for all has been set to occupied in formal sector. This crude method results in an accurate prediction for 78.8 of cases. Hopefully our logistic regression will do better than this.
Omnibus tests (Table 2) are general tests of how well the model performs. In a report we can use either this or the Hosmer-Lemeshow test shown on the Table 4. Table 3 gives useful statistics that are equivalent to R2 in multiple regressions. It is not possible to compute an exact R2 in logistic regression, but these two statistics are useful approximations. Here, present model accounts for between 11.8 and 18.4% of the variance.
This following Table 4 gives the results of the Hosmer-Lemeshow test that we requested. This test gives a measure of the agreement between the observed outcomes and the predicted outcomes. This statistic is a test of the null hypothesis that the model is good, hence a good model is indicated by a high p-value: as in this model where p = 0.260. If the p-value is less than 0.05 then the model does not adequately fit the data.
||Classification of dependent variable
|(a) Constant is included in the model and (b) The cut value is 500|
||Omnibus tests of model coefficients
||Hosmer and lemeshow test
||Classification of observed cases and prediction results
cut value is 500
||Variables in the equation
|Variable(s) entered on step 1: Age, DumSec, DumDeg, Dumprearr, Dumsug, URBEX|
Next Table 5 summarizes the results of our prediction and
should be compared to the equivalent table in Block 0. Our model correctly predicts
the outcome for 79.5%. Although this is not much better than the situation reported
in Block 0, we are now correctly predicting 21.2% of the migrants, who are employed
in formal sector.
Table 6 gives the coefficient for each predictor variable in the model. The Exp (B) column gives an indication of the change in the predicted odds of getting job in formal sector for each unit change in the predictor variable and the Wald statistic indicates how useful each predictor variable is.
The negative coefficient of age indicates that the odds of getting job in formal sector declines significantly with increasing age and both secondary and degree education also influence on getting job in formal sector significantly. The coefficients reveal that an increase of 1 year of age is associated with a decrease in the odds of getting job in formal sector by a factor of 0.053 and that each unit increase in secondary education and degree education is associated with increase in the odds of getting job in formal sector by factors of 1.348 and 1.320, respectively.
Although our results confirms the influence of urban base contacts in waiting period for getting the first job in the city and also on migrants earning but there is no significant effect of variables (Dumprearr and Dumsug) which are representative of urban contacts, on odds of migrant s employment in formal sector.
The URBEX coefficient of 0.365 reveals that with other variables held constant; if duration of urban residence increases by one year; on average the estimated logit increases by about 3.65 units suggesting a positive relationship between two variables. This results support the two stage process of rural-urban migration contributed by Todaro s model.
Although in binary model we are not much concern about model fit or R2, in overall the full model was significantly reliable (Chi-square = 50.429, df = 6, p<0.001) and the model accounted for between 11.8 and 18.4% of the variance in job s sector status.
Findings underscore that more than 55% of the migrant in the sample expected to join the formal sector as a labourer or non-manual workers or self-employed, but a small fraction entered this sector.
And a substantial proportion of migrants were attracted to urban area by opportunities in the extended informal sector. Except those who had been employed in non- manual works before migration, the expectation of migrants was to be employed in occupations different from their earlier occupation. This means that they were not satisfied with their jobs at the time of migration.
Although the results of this survey confirms the influence of urban base contacts in waiting period for getting the first job in the city and also on migrants earning but they highlight that there is no significant effect of migrant network on odds of migrant s employment in formal sector.
The factors that contributed to getting job in formal sector were estimated by a binary model. In conclusion, this model indicates that odds of obtain job in formal sector declines significantly with increasing age and each unit increase in secondary education and degree education is associated with increase in the odds of getting job in formal sector by factors of 1.348 and 1.320, respectively. As might be expected, the likelihood of getting job in formal sector increases with duration of urban residence.