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Motivation

Motivation. Family networks  economic, social support etc Networks non-stable  affected by social, economic changes, or physical location What happens to the network when migration is characterized by relocation of households? Will the composition of received transfers change?

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Motivation

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  1. Motivation • Family networks  economic, social support etc • Networks non-stable  affected by social, economic changes, or physical location • What happens to the network when migration is characterized by relocation of households? • Will the composition of received transfers change? • Will the sending relatives be different? • Our study: looks at how family solidarity & networks have been affected by internal migration when entire households move. • Data is scarce  Based on own data collected

  2. Literature • Economic aspects of inter-family transfers (Becker 1974; Chiappori 1988; Cox & Rank 1992). • Contacts and support after migration (Litwak 1960; Jitodai 1963; Wellman et al 1997; Ruan et al 1997). • Solidarity after transition (Cox 1996; Vullnetari & King 2008). • Other views  co-insurance agreements (Stark 1991).

  3. Internal migration and Albania • Between 1945-1990 internal migration in Albania was centrally controlled (international migration not allowed). • The collapse of communist regime in 1990  people migrated either internationally or internally. • Internal migration is not circular and often characterized by relocation of the whole household (De Soto et al, 2002; Cila, 2006) . • Motivation seem to relate mostly to economic reasons (work seeking, etc) (Carletto et al., 2004).

  4. Internal migration and Albania Source: Based on Albania LSMS Data 2002 - 2005

  5. Survey Hh-survey in peri-urban Tirana, April 2008 Recently populated areas with high informality 112 hhs sampled, 26 also qualitative interview Sampling methodology: 1) Define the recently populated areas (5 main zones). 2) Sub-divide SU of 1 km2 within these zones using satellite maps. 3) Randomly select hhs within selected sub-sections

  6. Survey • Migrant households come from nearly all districts, but especially from the Northern and Central mountainous areas (the darker areas on the map).

  7. Data – selection of family members • Members of kinship (including relatives/non-relatives) they have been in contact with both now and in the past. • Total: 1064 kinship members hhs are in contact with. • Next  getting the information on transfers with randomly selected (alphabetical order of given names) relatives/non-relatives: • Parents/ parents in law (1) • Siblings (2) • Children (2) • Other relatives (2) • Non-relatives (friends, neighbours, etc) (1)

  8. Data - transfer questions • Transfers to the household in the past 12 months. • Hh are also asked about transfers in the past. • Transfers in 1991  if they moved before & in 1997 • Transfers in 1997  if they moved in 1998 or after. • “Transfers” included: • Financial transfers • Transfers of goods • Services transfers

  9. Methodology • The transfers occur within a defined limit of time, and probabilities of consecutive transfers are not dependent on each other. • Frequency data shows over-dispersion (variance is greater than mean)  standard Poisson model not suitable • Two may be the causes of this over-dispersion: • 1) idiosyncratic and random bias in receiving transfers (households do not have the same probability for receiving a certain frequency of transfers), and • 2) households do not receiving transfers systematically because of their characteristics or relatives characteristics (i.e. limited contacts in the past 12 months before migration).

  10. Methodology – model testing • Models considered: • PRM (Poisson) • ZIP (Zero Inflated Poisson) • NBRM (Negative Binomial Regression) • ZINB (Zero Inflated Negative Binomial) • Results confirm over-dispersion due to idiosyncratic factors and random bias. • NBRM and ZINB give best results. All transfers combined

  11. Methodology • To account for over-dispersion among the count outcomes we use a “negative binomial regression model”, where: - expected value of the model. - vector of estimated coefficients, - vectors of covariates including characteristics of receiving household and sending relative. is the estimated value of the model dependent on a vectors of covariates, - accounts for the over-dispersion in the data.

  12. Empirical strategy • We pool the data from before and after migration, accounting for when the transfer takes place with the “migration dummy”. • When applicable, the variable is adjusted to the period before migration (i.e. age, number of children etc.). • Models are estimated separately for different types of transfers and for all transfers combined. • In addition, to check for how role of relatives has changed before and after migration we check for differences in coefficients using “seemingly unrelated estimations” (Weesie, 2000).

  13. Results – NBRM

  14. Results - Predicted transfers before and after migration

  15. Results - NBRM • At all ages financial transfers are more frequent after migration, while services and goods are less frequent. • Friends transfer more frequently financial transfers and services than other kinship members (effect not significant), but less goods. (Migration effect is not yet known). • Frequency of financial transfers is higher from old to young hhh, and from male to female headed hh. • Education of hhh influences negatively financial transfers, but positively other transfers. • (Income variable influences transfers negatively but it is not significant.)

  16. Results – Migration effect on network Differences in coeff. from separate NBRM (before & after migration)

  17. Results – migration effect on network • The frequency of financial transfers from siblings and other relatives decreases if compared to the frequency of transfers from friends (same effect for parents but not significant). • Similar trends are confirmed for services. Friends start transferring more frequently than parents, siblings and other relatives. • Transfers from children increase more than form friends for financial transfers and goods (results are to be treated with caution).

  18. Conclusion • Internal migration seems to have a positive effect on the receipt of financial transfers. • Migrants receive less frequently goods (the change in types of goods exchanged), and services (more time spent in employment or job-search activities). • Internal migration has affected the support network (transfers from friends and children have increased more than transfers from siblings and others). • Caveats: • Small-scale household survey in a very specific context • Survey focused exclusively on migrant households

  19. Thank You!

  20. Transfer frequency from different types of kin

  21. Characteristics of migrant hh • 75% nuclear families • Average hh size >5 • >50% of hhhs completed primary & secondary school

  22. 110 61 202 274 417 Hh family members/ close friends hhhs are in contact with Total: 1064 family & friends hh is in contact with

  23. Reasons for migration

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