How has internal migration in Albania affected the receipt of transfers from family and friends? Florian Tomini Maastricht Graduate School of Governance Maastricht University Jessica Hagen-Zanker Overseas Development Institute, UK World Bank Conference on Poverty and Social Inclusion in the Western Balkans Brussels, Belgium, December 14-15, 2010

Maastricht Graduate School of Governance 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

Maastricht Graduate School of Governance 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).

Maastricht Graduate School of Governance Internal migration and Albania Between 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).

Maastricht Graduate School of Governance Internal migration and Albania Source: Based on Albania LSMS Data

Maastricht Graduate School of Governance 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 km 2 within these zones using satellite maps. –3) Randomly select hhs within selected sub-sections

Maastricht Graduate School of Governance Survey Migrant households come from nearly all districts, but especially from the Northern and Central mountainous areas (the darker areas on the map).

Maastricht Graduate School of Governance 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)

Maastricht Graduate School of Governance 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

Maastricht Graduate School of Governance 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).

Maastricht Graduate School of Governance 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

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

Maastricht Graduate School of Governance 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).

Maastricht Graduate School of Governance Results – NBRM

Maastricht Graduate School of Governance Results - Predicted transfers before and after migration

Maastricht Graduate School of Governance 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.)

Maastricht Graduate School of Governance Results – Migration effect on network Differences in coeff. from separate NBRM (before & after migration)

Maastricht Graduate School of Governance 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).

Maastricht Graduate School of Governance 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

Maastricht Graduate School of Governance Thank You!

Maastricht Graduate School of Governance Transfer frequency from different types of kin

Maastricht Graduate School of Governance Characteristics of migrant hh 75% nuclear families Average hh size >5 >50% of hhhs completed primary & secondary school

Maastricht Graduate School of Governance Hh family members/ close friends hhhs are in contact with Total: 1064 family & friends hh is in contact with

Maastricht Graduate School of Governance Reasons for migration