the logic of letting go: family and individual migration from rural bangladesh
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Under review, Population Studes. It is now understood that voluntary and forced migration constitute a continuum, yet this paper is among the first studies to develop a theoretical test of these connections. Processes of family migration that drive the global growth of informal settlements are driven by structural factors such as environmental degradation and considerable individual selectivity. This paper introduces a three-outcome model in which individual and family migration constitute distinct family livelihood strategies: individual migration supplementation rural livelihoods while family migration replaces a rural livelihood with an urban one. I test this model using 14 years of migration data from the rural Matlab area of Bangladesh. Family migration becomes more likely than individual migration for men with very low household land holdings. This effect is exacerbated during the period following a catastrophic flood, when the likelihood of family migration rose, particularly for landless men.TRANSCRIPT
The Logic of Letting Go: Family and Individual Migration from Rural
Bangladesh
Running Head: Family and Individual Migration from Rural Bangladesh
Randall S. Kuhn*
Josef Korbel School of International Studies
University of Denver
October 2010
* The author gratefully acknowledges the support of the International Pre-Dissertation
Fellowship of the Social Science Research Council (supported by Ford Foundation and
American Council of Learned Societies); the J. William Fulbright Scholarship (funded by United
States Information Agency, administered by Institute for International Education); the Population
Council Dissertation Fellowship in the Social Sciences; the Mellon Fund for Research in
Population in Developing Countries; and the University of Colorado Population Aging Center
(funded by National Institute on Aging). Many provided valuable commentary on the paper,
including Jane Menken, Douglas Massey, Leah Vanwey, Robert Retherford, Jeroen van
Ginneken, Lynn Karoly, Erin Trapp, Julie DaVanzo, Richard Rogers, Fernando Riosmena, Tania
Barham, and Linda Mamoun. Most of all, the author wishes to thank the research and technical
staff of the Matlab Health and Demographic Surveillance Unit at ICDDR,B: International Centre
for Health and Population Research, and the patient and thoughtful citizens of Matlab.
Author’s Contact Information
Randall Kuhn
Josef Korbel School of International Studies
2201 S. Gaylord St.
Denver, CO 80208
Kuhn
Abstract
It is now understood that voluntary and forced migration constitute a continuum, yet this paper is
among the first studies to develop a theoretical test of these connections. Processes of family
migration that drive the global growth of informal settlements are driven by structural factors
such as environmental degradation and considerable individual selectivity. This paper introduces
a three-outcome model in which individual and family migration constitute distinct family
livelihood strategies: individual migration supplementation rural livelihoods while family
migration replaces a rural livelihood with an urban one. I test this model using 14 years of
migration data from the rural Matlab area of Bangladesh. Family migration becomes more likely
than individual migration for men with very low household land holdings. This effect is
exacerbated during the period following a catastrophic flood, when the likelihood of family
migration rose, particularly for landless men.
Keywords: Rural-Urban Migration, Family Migration, Bangladesh, Developing Countries
Introduction
Recent years have witnessed increasing concern about the growth of informal settlements or
slums in the megacities and rapidly emerging towns of the developing world. Informal
settlements pose many inherent challenges relating to the lack of property rights or tenure,
inaccessibility to markets and public services, and overcrowding (UN-Habitat 2003; Davis
2006). Yet the roots of many of these challenges lie in the underlying forces of economic
deprivation, social exclusion, and environmental degradation -- so called “push factors” -- that
drive rural-urban migration in the first place. Efforts to address the needs and harness the
capabilities of rural-urban migrants thus require an indepth understanding of the rural
vulnerabilities and selectivity behind the migration process.
Social scientists have developed an acute understanding of the heterogeneity in the
motivations for migration, particularly in relation to family livelihoods (Lindstrom and Lauster,
2001; Massey and Espinosa, 1997). The New Economics of Labour Migration has moved
migration theory beyond the simple idea of the lone migrant in search of personal advancement
by accounting for the role of migration in ensuring the collective security and livelihood of a
family unit living in two places at once (Stark 1991; VanWey, 2001). Contextual models account
for the role of ecological resources and other entitlements in individual- and family-level
decisions (Hunter 1998, 2005; Ezra and Kiros 2001; Henry et al. 2004; Massey et al. 2009).
Despite progress, few studies have addressed the distinct livelihood functions of different
modes of migration and their underlying forces of selectivity. One distinguishing characteristic
of many informal settlement dwellers is the movement of an entire family unit to the city, or
family migration, as opposed to the movement of only those family members most likely to find
employment in the city, or individual migration. Family migration poses numerous challenges
for migrants and for planners, including residential overcrowding, the need to provide social and
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health services to new population groups, and the risk that children will opt out of school in
favour of labour market activity. Few demographic studies have modelled the movement of an
entire family, whether in a separate model or as a direct alternative to individual migration.
Instead, a separate literature on forced migration has emerged primarily in the fields of political
geography, refugee studies, and ecology. While many studies explore welfare outcomes among
forced migrants, few address the determinants of such moves beyond the occurrence of
precipitating crises such as conflict or disaster (Keely, Reed, and Waldman 2001).
Such crisis events constitute neither sufficient nor necessary condition for family
migration (Castles 2006). First, many family migration episodes would be traced not to a
particular crisis but to a gradual deterioration of social, economic, and ecological position. More
importantly, the vast majority of families move in or near their original location except in cases
of total catastrophe, while only a small share will make it to a city. As population growth and
climate change increase the occurrence of precipitating events, the magnitude and nature of
forced migration flows would depend greatly on the positive and negative forces of selectivity
that drive families to move to the city.
This paper develops a rational choice model of family and individual migration and tests
it using data from rural Bangladesh. The typical mover-stayer utility maximization, in which
potential urban income is compared to current rural income, is reframed as a three-outcome
maximization. Rural income is instead compared to two potential utility streams, a rural-urban
utility in which the best outcome involves working in the city but raising family in the rural area
(implying individual migration) and a strictly-urban utility in which the best outcome involves
moving the entire family to the city (and thus undertaking family migration). The model thereby
draws a theoretical connection between the total loss of livelihood underlying forced migration
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and the livelihood diversification motives behind the New Economics of Labour Migration.
This model is employed to explore the determinants of family and individual migration
by married men in the rural Matlab area of Bangladesh between 1982 and 1996. Data come from
the Matlab Health and Demographic Surveillance System (HDSS), a unique data archive that is
ideally suited to the prospective modelling of migration behaviour in a representative population.
The primary finding shows that family migration is far more likely than individual migration
among men with minimal land holdings. This effect is exacerbated in the years following a
flood, when the majority of excess family migration occurred among men with little land.
Migration, Livelihoods, and Family
Familial Models of Migration Decision-Making
Existing theory highlights the unique role of migration as a labour market, housing, and
livelihood decision. Neo-classical economists initially conceived migration as a simple labour
market response to an unequal spatial distribution in wages or earnings, suggesting that the
likelihood of migration will increase either with a decline in origin-area wages or a rise in the
expected wage in the destination (Lewis, 1954; Ranis & Fei, 1961; Sjaastad 1962; Todaro, 1970;
Harris & Todaro, 1969; Massey and Espinosa 1997). The typical wage differential model (hence
referred to as WD) predicts migration decision (M) for individual i in terms of the utility (U) to
be derived moving to the urban area (u) versus remaining in the rural area (r).
r u,k ),Max(U k)P(M ki (1)
Mincer (1978) incorporated the role of residential decision-making by extending the WD model
to the utility of an entire migrant family rather than a single labourer. In order for migration to
occur, the benefits accruing to a single working member (usually a husband) would have to be
sufficient enough to compensate any potential “tied movers” for the costs of relocation, social
dislocation, and other psychic costs. In other words, for a family j, the migration of all members
Kuhn 4
(i) depended on the joint utility that all family members could achieve in the urban or rural area:
r u,k ),Max(U k)P(M k*ij , (2)
This model spawned an extensive literature on family migration, largely focused on developed
countries, in which a family unit collectively chose family migration or no migration or, failing
to resolve a joint solution, the family dissolved via divorce.
In much of the developing world, individual migration offers another option (Rogaly,
2003; Rogaly and Rafique, 2003; Lauby and Stark, 1988). If migration improves an individual’s
labour market position, but not his family’s residential position, he may move alone, often for
long durations, while other family members remain in the area of origin, before eventually
returning to the origin area. In other words, a family j chooses whether to send a member i to the
city by maximizing on the income to be derived from remaining in the rural area versus the
income to be derived from a multi-sectoral existence (m) in which a member moves to the city:
m r,k ),Max(U k)P(M kij , (3)
While the value of the multisectoral utility ( mU ) is difficult to quantify explicitly, the New
Economics of Labour Migration (hence referred to as "NELM") provides a systematic
framework for understanding the potential effects of individual migration that would cause a
family to value mU over rU (Stark, 1982). Most notably, the migration of an individual can
substantially improve the welfare of those left behind by migration through the consumption and
investment of remittances, or financial transfers, sent by the migrant (Taylor & Wyatt, 1996; de
Haan, 1999; Kanaiaupuni & Donato, 1999; de Haan and Rogaly 2002; Kuhn 2005). Remittances
may both diversify the sources of family livelihood, thereby offering protection against crisis,
and generate liquidity for rural economic activities (Lauby and Stark, 1988; Durand et al., 1996;
Ellis, 1998; Winters et al., 2002; Chen et al., 2003; Sana 2005; Massey et al., 1999).
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NELM highlights important non-linearities in the land-migration relationship, most
notably that households with small landholdings may benefit more from individual or temporary
migration since they would have more to gain from income diversification since families with
large land holdings might not need either extra income or diversification (VanWey 2003; Taylor
& Wyatt 1996). VanWey (2003) points to the distinct role of temporary migration for those with
moderate land holdings, where migration might contribute to a strategy of investment and
expansion, and for those with smaller land holdings, who might simply be combining rural and
urban wages to achieve a minimum livelihood target. By extension, below a certain land-holding
threshold the rural contribution to wages could become so minimal that rural-urban income
would drop below strictly-urban income, making family migration more likely.
By ignoring the migration of an entire family, equation (3) implicitly assumes that the
joint utility resulting from all family members moving to the city, uU , is necessarily lower than
mU or rU , or that an entire family would never migrate. There are strong empirical reasons for
ignoring this third alternative. Legal barriers (as in modern China or Apartheid-era South
Africa), high urban living costs, low wages, and limited job-related benefits may all encourage
migrants to raise children and retire in rural areas (de Janvry & Garammon, 1977; Gereffi &
Korzeniewicz, 1994; Mackenzie 2002; Brueckner & Kim 2001). Yet continued increases in the
urban youth dependency ratios point to the importance of family migration flows. While some
migrants may have achieved sufficient income to move their families to the city, the world's
informal settlements include a great many families living in poverty. At present, such moves are
addressed, if at all, through the literature on forced migration, which tends to address structural
and political factors rather than individual heterogeneity and agency (Castles 2006).
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Distress, and other sources of family migration
Traditionally, forced migration has been understood both as resulting from collective instigators
(e.g., armed groups) and as affecting collective classes of victims requiring institutional redress,
typically connected to conflict. A gradual relaxing of the collective nature of both cause and
affected group has opened up new space for understanding forced migration as part of a common
continuum with voluntary migration, and thus as a process that might be understood in terms of
within-group heterogeneity (Castles 2006).
First, the forced migrant category has grown to include a broader range of political,
environmental and even economic causes. This includes development-driven displacement
resulting from government public works projects (e.g., roads and dams) and environmental
displacement due to population pressure or poor resource management (Scudder 1993; Cernea
1995, 2006). The increasingly apparent displacement risks posed by climate change have driven
interest in these processes (Myers 2002).
Second, the literature has gradually moved its focus from specific precipitating events
such as disasters or conflicts to the long-term, structural precursors of forced migration,
particularly in fragile political or ecological systems (Curran 2002; Hunter 2005). Systematic
political neglect or abuse may lie at the heart of modern cycles of displacement and discrete
events may precipitate moves, but these are often connected via complex mezo chains of
causation that blur the distinction between voluntary and involuntary migration and introduce a
role for individual agency (Castles 2006). Even a half-century ago, Peterson (1958) distinguished
between forced migration and “impelled migration”, in which migrants retain some agency to
stay or go. The question remains how to operationalize this continuum into a testable framework.
Only recently have a few demographic studies modelled individual migration decisions in
response to a crisis (Engel and Ibañez 2007; Ibañez and Velez 2008; Alvarado and Massey
Kuhn 7
2009). None has yet accounted for heterogeneity in the decision of whether to move, though
Bohra-Mishra and Massey (2010) address migrant destination choice in the context of the Nepali
civil war. A recent World Bank report on climate change and displacement highlights the role of
rural environmental degradation and environmental crises in precipitating rural-urban migration
and urbanization (Raleigh et al. 2008, referring to Pederson 1995; Findley 1994; Ezra 2001). In
keeping with the New Economics literature the report emphasizes the role of the “migration of
certain family members to urban areas” during periods of crisis and as a means of mitigating
future crises, emphasizing that most moves are circular, not permanent (Ezra and Kiros 2001;
Caldwell et al. 1986; Paul 1995; Perch-Nielsen 2004). The authors’ exploration of “distress
migration” emphasizes that such displacements are usually temporary and quite often local, with
most of the distressed travelling fewer than 3km from the origin area, partly in order to protect
their assets and interests in the origin area (Hutton and Haque 2004; Zaman 1991).
A smaller number of studies draw a more direct association between structural change,
family livelihoods, and migration (Bilsborrow 1991, 1992, 2002; Hugo 2008). The mode,
permanence, and destination of crisis migration may be determined both by so-called pull
factors, such as having the connections and skills to earn incomes in other environments, and
push factors, such as the household’s level of attachment to rural livelihoods. Bilsborrow and
DeLargy (1990) begin to identify a continuum of labor market responses to diminishing
agricultural holdings within a peasant “household survival strategy”, proceeding from changes in
crop mix, to local off-farm labor market opportunities; to individual; circular; and seasonal
migration; and finally to family migration.
This continuum of migration responses points to a clear gap in the mover-stayer model of
migration. WD implies a monotonic relationship between land holdings and migration, while
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NELM suggests that households with small land holdings might actually draw greater benefit
from migration than those with no land holdings. These factors may each carry weight for the
decision to migrate in general, but may further explain distinct family and individual migration
decisions, particularly at the margins of economic sustainability. If individual migration
represents an effort to diversify between rural and urban livelihood resources and family
migration represents a departure from rural livelihoods, then family migration should be more
strongly responsive to a lack of land holdings than individual migration. Similarly, the period
immediately following an environmental catastrophe might push a large number of households,
particularly those with constrained livelihood options, below a threshold of rural viability and
into family migration. Before testing a formal model of family and individual migration, I
describe the context of migration. This exploration is not meant to exceptionalize the Bangladesh
experience, but merely to illustrate how a particular livelihood context might set the stage for
family migration.
Research context
The interplay between ecological context, livelihoods, and migration can be seen in the
unfolding process of urbanization in Bangladesh. Most generally, Bangladesh is one of the most
densely populated countries in the world, with 1,127 persons per square mile. A truer reckoning
of the ecological and economic fragility of the country would also account for the fact that as of
2005, only 26% of this population lived in cities (UNPD 2010). Furthermore, approximately
30% of Bangladesh’s total land area consists of rivers, swamps, or charlands that shift between
habitable and uninhabitable with the changing course of rivers. The southeast monsoon weather
system, which continues to feed most of Bangladesh’s rice production, floods a further 20% of
land for half of each year, and up to two-thirds of all land in extreme flood years (Mirza 2002).
Given these conditions, it is not surprising to find that Bangladesh has been undergoing
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rapid urbanization. Between 1970 and 1995, the proportion of the Bangladeshi population living
in cities grew from 7.6 per cent to 21.7 per cent, yet even the latter figure was the lowest urban
proportion among the ten largest Asian nations (UNPD 2010). The most prominent rural-urban
migration flows involve movement from areas along the Meghna River Basin in southern
Bangladesh, such as Chandpur, Comilla, and Barisal Districts, to large cities such as Dhaka
(Nabi, 1992). Matlab is situated in the heart of this migrant-sending region. Situated near the
highway about halfway between the two largest cities of Dhaka and Chittagong, travel by boat
(in the rainy season) or bus (in the dry season) to either destination is about six hours, close
enough to carry out regular circular migration but not close enough to commute.
As in many places, urbanization in Bangladesh results at least in part from a process of
rural livelihood diversification in which migration serves as a response to persistent risks to
agricultural production (Kuhn 2003). In Matlab, as elsewhere in Bangladesh, households
depending on underwater rice cultivation are exposed to high levels of unmanaged risk from
price fluctuations and severe flooding. Monsoon rice production also results in extreme seasonal
fluctuations in cash flow, nutrition, employment, and prices (Chen et al., 1979). Small
landholders finance production and mitigate the worst seasonal effects of flooding by borrowing
rice at high, pre-harvest grain prices, repaying these loans at the lower, post-harvest price,
resulting in an effective annualized interest rate of between 30 per cent and 400 per cent,
threatening land foreclosure and fragmentation (Kuhn, 1999; Jensen, 1987; Jahangir, 1979; van
Schendel, 1981; Momin, 1992; van Schendel & Faraizi, 1984).
Remittances from migrants can alleviate each of these risks and contribute to economic
progress. Results from the Matlab Health and Socioeconomic Survey (MHSS) in 1996 reveal
that households with out-migrant kin received 27 per cent of total household income from such
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transfers (Kuhn, 2001). Transfers finance agricultural production and growing-season
consumption, thereby reducing the need to incur debt (Kuhn, 1999; Gardner, 1995; Afsar, 1994;
Stark, 1991). They also enable economically secure households to extend credit to indebted
households (Gardner, 1995). In the process, however, remittance-related liquidity may hasten
cumulative processes of economic displacement (Massey et al. 1998).
Individual migration greatly facilities the sending of transfers. Solo movers can minimize
the extent of consumption at the higher urban cost of living by living in low-cost group housing,
living in company housing, or often sleeping on the floors of their workplaces (Kuhn, 2003).
Furthermore, many men are able to return home during the rice harvest, mitigating much of the
impact on rural labour supply. Matlab’s age-sex distribution in 1996 demonstrates the
significance of male individual migration (Mostafa et al., 1998). In 1996, the male-female ratio
for the 10-14 and 15-19 age groups was 1.08, compared to 0.95 at age 20-24, 0.78 for 25-29, and
0.79 for 30-34. Matlab’s relative proximity to major urban destinations (six hours travel
compared to twenty to twenty-five hours for other areas with high levels of outmigration) should
further facilitate temporary and individual migration (Lucas, 2000).
Despite these forces, the practice of family migration is widespread, as quantified below.
While family migration may have ecological roots, qualitative research points to the underlying
role of economic disenfranchisement and social exclusion (Kuhn 2003). Family migrants were
not merely landless, but often those who had been landless for quite a long time. Landlessness
was often amplified by inadequate access to informal social and patronage entitlements including
sharecropping arrangements, off-farm employment on public works projects, and access to
ecological resources such as ponds and fallow resources (Kuhn 2003, Das Gupta, 1987). For
these households, the dwindling benefits of low rural consumption prices and informal rural
Kuhn 11
exchange are outweighed by the costs, both personal and financial, of having one household
member move back and forth between rural and urban areas. Rather than making a gradual
transition to urban economic activity as temporary labourers in support of a rural household,
family migrants make an immediate transition to the city as economic producers, consumers, and
permanent residents (Unnithan-Kumar, 2003; Roy et al., 1992).
The social and economic processes underlying family and individual migration are
further amplified by the fragile, though not entirely unique, ecological conditions of deltaic areas
of Bangladesh. Extreme floods are the most devastating ecological crisis affecting inland areas
such as Matlab, destroying crops, altering the course of rivers, and permanently submerging
land. The October 1988 flood in Bangladesh occurred at the end of the annual growing season,
resulting not from heavy local rains, but from unusual flows of water from rivers draining the
Himalayas. Most of the permanent property loss was sustained by households living on river
banks, where buildings and land were inundated. But a broader segment of households suffered
lasting indirect effects, including disruptions in subsequent planting seasons, broken
communications and transport links, and commodity price distortions. A household’s ability to
sustain the short-term effects of a flood may be determined not merely by ecological factors, but
also by its own ability to manage risk and cope with crises. Quantitative analysis of family and
individual migration will address both the main effects of the flood and interactions with land
holdings. Before addressing patterns and determinants of family migration, it is first critical to
understand the significance of migration by married men more generally.
The Migration of Married Men in Matlab
The Matlab HDSS provides a dynamic, computerized population event history for the Matlab
study site from 1974 to the current era, conferring numerous analytic advantages for the study of
vital events in general and migration in particular. During the study period, HDSS data collection
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assistants made monthly visits to each household to record the timing and circumstances of all
birth, death, migration, marriage, and divorce events occurring to household members. Periodic
censuses in 1974 and 1982 collected additional data on factors such as education, household
living conditions, and land holdings. The large sample size, time span, and precision provide
what amounts to a quantitative history of a population, with the sole limitation that events
occurring entirely outside the HDSS area are not recorded (e.g. marriages of out-migrants,
onward migrations).i This analysis focuses on vital events occurring from July 1982 through
June 1996, a period covering the significant social and ecological changes described above.
A census conducted in July 1982 provides baseline characteristics such as land holdings
and education. The 1982 census captured 186,695 individuals living in 149 study villages. The
1982-1996 followup period saw an additional 94,255 new births and 43,684 new in-migrations
from outside the HDSS area (largely women who married into the study site and men who had
migrated out before the 1982 census and returned after). The master HDSS event file thus tracks
the vital events of 324,634 individuals in total. The demographic events of all of these
individuals were used to construct dynamic, time-varying counts of household size and co-
resident children. This study population of 53,901 married males included 30,956 males who
were married at the time of the census, 12,710 who were unmarried at the time of the census but
subsequently married, and 10,235 men who were newly migrated into the HDSS area after the
census. Education and land data were added for individuals who entered the system via in-
migration. Because censuses occurred in mid-year, the annual monitoring of events for this study
is organized on a mid-year basis (e.g., 1982 refers to events occurring between July 1982 and
July 1983, 1995 refers to events occurring from July 1995 to July 1996. Each man contributed
an observation for each person-year in which he was present at any time during the year, starting
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from the 1982 census, in-migration, or marriage until censoring. Censoring occurred if a man
died or migrated outside the household during the observation year. The 53,901 eligible males
contributed 473,492 person-years to the analysis, or 8.78 person years per person out of a
possible 14 years.
Levels of overall migration were substantial as a source of livelihood and a check on
population growth. While the area’s population grew from 186,232 in mid-1982 to 209,843 in
mid-1996, the 1996 population would have been 40,327 higher in the absence of net
outmigration, accounting for about 20% of the population. Although migration was quite
common for women as well as men, women’s moves were primarily for marriage (with an equal
number of moves in as out) or as tied movers with husbands. Overall, 58% of net out-migration
during the study period was accounted for by men. Among adults over age 15, 76% of men’s
moves were to urban or overseas destinations compared to only 48% of women’s moves. Among
those moves, work was the reported reason for migration among 73% of men’s moves compared
to 9% of women’s moves. Because most moves by married women during this period were tied
to the moves of men, this analysis focuses on the migration of men. The analysis focuses on
moves to urban areas, accounting for about 60% of all moves. Although the results hold for
international migration as well, the infrequency of international family migration (except among
Hindus moving to India) made models more difficult to interpret. Migrants to overseas and rural
destinations were censored but not analyzed.
Given the exclusive emphasis on the migration behaviour of married men, it is important
to understand the salience of post-marital migration within men’s lifetime migration histories.
Fortunately HDSS data make it possible to construct such a history. HDSS field assistants were
trained to ensure that migrants returning from outside the study area were always assigned the
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same permanent Registration Identification (RID) number upon returning to the study area, thus
enabling the classification as return migrants those who migrated after 1974. The long time span
of HDSS also makes it possible to go back to an earlier time period to understand the selectivity
of the sample prior to our 1982 study baseline.
Figure 1 depicts the marriage and migration behaviour of a group of unmarried men age
15+ living in the Matlab HDSS area at the time of the 1974 census. These men are a subset of the
larger study population, which also includes men who were already married in 1974, men who
are under 15 in 1974, and men who were already living outside the study area in 1974. In this
stylized comparison, men begin as unmarried non-migrants (segment 1) and can transition to
either being married before migrating (segments 2 and 3, 74% of all men) or migrated before
marriage (segments 4-6, about 24% of the total). Only 2% of men neither married nor migrated
during the study period. These results involve migration to any destination (e.g. urban, rural, or
overseas) to capture the full extent of attrition.
Figure 1 illustrates the salience of migration, and post-marital migration in particular, in
the typical adult male life course. Although the youngest of these men were still only 36 by the
end of followup, a sizeable 37% of them had already migrated to the city at some point
(segments 3-6). About half of these migrants (18% of the total cohort) experienced a move after
marriage (segments 3 and 4). Although the risk of migration was higher for those men who had
also moved prior to marriage, over one-third of all ever-migrants experienced their first move
only after marriage. Among the remaining 24% of men who had migration experience prior to
marriage (segments 4-6), a little less than half (47%), or 11% of the total cohort, never returned
to the HDSS area and thus are excluded from this analysis (segment 6). Although those who
never returned represent a source of attrition from the study population, HDSS data on returned
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migrants reduces the magnitude of attrition and accounts for their past migration experience.
A Model of Family and Individual Migration
The staggering level of mobility among married men, including those with no prior migration
experience, constitutes the starting point for the statistical analysis. Building on the earlier
background discussion, I identify a three-outcome migration decision (Mi,j) whereby a family
chooses between remaining in the rural origin area (r), sending one member to the city (m), or
moving as a family to the urban area (u)
u m, r,k ),Max(U k)P(M kij,
(4).
This three-outcome model offers ready contrast to the simple mover-stayer model identified in
equation (3), which treats family migration as no different from individual. As with any
migration model, we cannot fully operationalize the three potential utility streams ( kU ), but we
can present divergent predictions for family and individual migration with respect to key
correlates of migration such as land holdings. While both individual and family migration should
become more likely at lower levels of land holding, family migration should become the
dominant form of migration at lower levels of land holdings. Modest land might encourage
individual migration, while no land holdings would encourage family migration.
Initial statistical results compare a test of equation (4) using a multinomial logistic
hazards regression model against a test of equation (3) using a binomial logistic hazards model.
For each year t until an event occurred or a respondent was censored, the outcome for man k was
Mkt = j, where j= i (individual), f (family) or n (no) migration in the multinomial models, and
where j= m (migration) or n (no) migration men in the binomial.ii Only rural-urban moves were
modelled; rural-rural and international migrants were censored, with no event recorded.iii
Family
migration was recorded if the migrant’s wife moved on the same day to the same destination,
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individual migration if the man moved alone or with a group that did not include his wife. iv
Both equations fit a vector of predictive coefficients X and a measure of time (t)
according to the following equation:
nfij
X
X
kt n
is
ksj s
n
is
ksj s
e
enfijjM
,,
),,,Pr(
Because estimation could result in any number of solutions, no migration was chosen as a
reference event for which all values of 0n .
Table 1 summarizes key dependent and independent variables at the person and person-
year levels, and compares men who were in the 1982 census against those migrants who returned
after the 1982 census. Family migration is about twice as likely as individual migration.
Although neither mode of migration is particularly likely in any given person-year, the
cumulative proportion of men who migrated is quite high, with 4.3% individual and 8.4% family.
The study size of 473,561 person years for 53,905 men offers sufficient statistical estimation
power. Both individual and family migration are about twice as likely among men who had
previously migrated. Past migrant destination reports make it possible to account for men who
had any previous rural-urban migration experience, 9.2% of all men and those who had moved to
other destinations (6.6%). As noted above, the mean agricultural holdings were quite low at 113
decimals, or 1.13 acres, with a median holding of just over 0.5 acres per household.
[Insert Table 1 about here]
Results
Table 2 presents a base model of the covariates of migration using the multinomial and binomial
specifications. The models also included controls for household headship, total number of
Kuhn 17
household members (to control for membership in an extended family household unit), whether
the respondent’s occupation was fisherman, and whether the respondent’s religion was Hindu
(with Muslim as the reference category). Each of these controls was negatively associated with
any, family, and individual migration.
Models included village-level fixed-effects specified through controls for village of
origin to eliminate the impact of community-level factors such as ecology, market development,
and community-level variation in migration rates.v The fixed effects controls were jointly
significant according to a chi-square test of difference in both the binomial (χ2
= 802.8, d.f. =
143) and multinomial (χ2 = 1284.9, d.f. = 286) specifications. Covariates estimates and
significance were robust to the inclusion of fixed effects, with only a minor reduction in the
magnitude of the Hindu control coefficient owing to the extreme clustering of half the Hindu
populations in just 9 of the 149 study villages, while 60 village had no Hindus at all. This
suggests that the any individual- or household-level heterogeneity in migration pattern is not
simply the result of clustering, for instance the tendency for people with little land to live in areas
with a common ecological predisposition to migration. Subsequent presentation of results
focuses on the fixed effects specifications.
[Insert Table 2 about here]
Finally, all models control for the positive association between prior and subsequent
migration experience, with separate controls for past rural-urban migration and other forms of
migration. All key findings with respects to land holdings, schooling, age, family composition,
and flood effects are robust to the inclusion of exclusion of this control (not shown). All models
described below were tested separately for men with and without prior migration experience (not
shown). The magnitude of most coefficients was lower for men with prior experience,
Kuhn 18
unsurprising since barriers to entry into migration would be lower for past migrants. In no case
did the coefficient direction differ between those with or without past migration experience.
Presentation of results focuses on measures of household land holdings, individual schooling
attainment, age, and the effects of a flood.
Livelihood measures
The principal point of distinction between family and individual migration in the above model
lies in the differential role of rural livelihood resources as both a potential deterrent to migration
(of any form) and as a platform for livelihood diversification (favouring individual migration).
The principal variable of interest is the total land holdings of the man’s household, measured in
HDSS in decimals, or hundredths of an acre. Before presenting the results of a simple logged-
land specification (shown in columns 4-6 of Table 2), Figure 2 explores a detailed categorical
specification that demonstrates the robustness of the logged relationship. A stable categorization
was create by joining rare land values with neighbouring categories until all groups contained at
least 1% of the sample.vi
The resulting land distribution was roughly power law in form, with
low values far more common than higher ones. Landless men were by far the modal category,
accounting for 21%. While 79% had some land, the median holding was only 50 decimals and
87% had less than 200 decimals. As a result, the mean land holding of 114 decimals was
considerably higher than the median. The resulting 23 land holding categories were entered into
fixed effects logistic regression models with the same controls as the models in Table 2.
Coefficient estimates are shown in Table A1. For each land holding category, predicted
probabilities of family and individual migration were derived from these estimations using the
Stata clarify package and plotted in Figure 2.
[Insert Figure 2 about here]
The categorical specification demonstrates the greater value of land as a predictor of
Kuhn 19
family versus individual migration, particularly when delineating between men with no land at
all and men with very small land holdings. For example, relative to men with between 20 and
100 decimals of land (the middle 40% of the sample), men with no land at all were only 12%
more likely to practice individual migration, a difference that was not statistically significant, but
were 45% more likely to practice family migration. A binomial model would split this difference
by predicting a 35% increase in the likelihood of migration. Men with just 10 decimals of land
were only 2% more likely to practice individual migration than men with 20 to 100 decimals, but
27% more likely to practice family migration. While small variations in land ownership have
little effect on individual migration, they have quite large effects on family migration.
The differential effects of low-level land holdings on family and individual migration
also go a long way towards explaining the relative frequency of family migration in this context
(recall that the annual probability of family was 0.89% compared to 0.48% for individual
moves). For the top quartile of landowners (with 150 decimals or more of land), the adjusted
likelihood of family migration is about 50% higher and the difference is only moderately
significant. This gap widens with decreasing land holdings, to about 2-to-1 for men with 20 to
150 decimals, 2.3-to-1 for men with 10 decimals, and 2.6-to-1 for men with no land at all. A
decomposition across the actual distribution of land holdings reveals that two-thirds of the
difference between the overall family and individual migration probability was accounted for by
men holding less than 50 decimals (one-half acre) of land and 40% of the gap was explained by
landless men alone.
Although the magnitude and significance of the land-migration relationship diverges
substantially between family and individual moves, both are well captured by the single variable,
logged-land specification employed in the base regression models in Table 2. The single measure
Kuhn 20
takes the log of land holdings (measured in decimals); landless men are given a value of 0. Since
the lowest non-zero value of land holding is 10 (natural log = 2.3), the transformation generates a
large linear distance between landless households and those with very little land without the use
of an extra landless dummy variable. The categorical results suggest that the likelihood of
individual migration begins to rise at the very highest levels of land holdings, supporting earlier
findings of an economies of scale effect whereby households with very large land holdings may
use migration as part of an accumulation strategy (VanWey 2005). Yet such households
represent just over 2% of men in this land distribution. The cubic land specification used by
VanWey (2005) to capture curvilinear land holding effects was significant only at the 10% level
and could not actually predict the non-monotonic pattern (results not shown).
For both family and individual moves the log-land specification is highly significant
compared to other single variable specifications, and captures the shape of the land-migration
relationship across the distribution, as shown in the comparison of fitted probabilities in Figure 2.
Although the single-variable specification explains both family and individual migration, the
separation of the two streams is nonetheless justified on the basis of the substantial and
significant variations in coefficient magnitude and significance. Coefficient estimates suggest
that a doubling of total household land holdings would be associated with a 8.5% decline in the
relative risk of family migration but only a 3.2% decline in the risk of an individual move. Under
this specification, the relative risks of migration for a man from a landless household compared
to a household with the median landholding of 50 decimals (0.5 acres) would be 64% higher for
family migration (off a larger base) but only 20% higher for individual moves.
To indirectly address some of the environmental aspects of rural livelihoods, the base
model also explores the role of internal migration behaviour as a predictor of family and
Kuhn 21
individual migration. As noted above, family migration outside the district may represent the
culmination of a gradual process of economic and ecological displacement that may include rural
displacement. Flooding, environmental degradation, and the short-term unintended consequences
of the flood control embankment resulted in fairly high levels of internal displacement. The base
model includes two measures of internal migration, one for whether the household moved
internally (to a separate village) in the current or previous year, and another accounting for
internal migration experience in any previous year since 1982. A simple mover-stayer model of
migration, would have suggested a reduced risk of out-migration in the year following internal
migration, followed by a return to baseline risk.
In fact, such a result would mask divergent effects of internal migration on family and
individual migration. Internal migration in the current or previous year was associated with a
substantial reduction in the likelihood of both family and individual migration, with men about
80% less likely to leave the study site if they had recently moved villages. Net of this effect,
however, strikingly different migration patterns emerge for those with less immediate internal
migration experience. Internal migrants had a substantially reduced risk of individual migration
(50% relative risk reduction, significant at the p<0.001 level), but a significantly increased risk
of family migration (44% increased risk, p<0.001). In the case of both land holdings and internal
migration, livelihood vulnerability appears to act in the manner of a “push factor” for family
migration, but less so for individual migration.
Human capital and life course measures
The model of family and individual migration offers less clear predictions for the role of
individual human capital or life course factors on migration. It would be simple to assume that
family migrants are simply more vulnerable in every respect compared to individual migrants.
Yet it is also possible that, after adjusting for the effects of rural livelihood vulnerability, that
Kuhn 22
family migration would be driven by classic pull factors like education. Furthermore, the Mincer
model of tied movers suggests that only those men with high levels of schooling would be better
able to finance the high long-term costs of permanent urban residence associated with family
migration. The base model includes controls for three levels of respondent's schooling
completion: 1-4 years, 5-9 years (lower secondary school), and 10+ years (higher secondary
school), each compared to the reference category of no schooling.
In the case of education, a two-outcome model of migration versus no migration would
generate the statistically correct result that schooling increases the likelihood of migration, but
would miss some salient differences between family and individual moves. Each additional level
of schooling attainment is associated with an increased likelihood of both family and individual
migration, suggesting that forces of positive educational selection operate even in the family
migration decision. Yet for each level of schooling, the coefficient for individual migration is
significantly larger. An interesting pattern emerges across different levels of schooling whereby
each additional level of schooling associated with a relatively large increase in the likelihood of
individual migration (coefficients = +.272, +.576, +.821), whereas family migration responds
primarily to higher levels of schooling (coefficients = +.150, +.229, and +.672). One
interpretation of these effects is that some family migrants move not because their strictly rural
income is so low but rather because their strictly urban income is so high that they can actually
afford to raise their children in the city. Nevertheless, it should be clear that family migration
involves some level of individual agency and responsiveness to individual human capital, not
merely livelihood loss.
The three-outcome migration model also generates predictions about the effects of age
and life course transition on migration. In general, older men and men with more children should
Kuhn 23
be less likely to migrate at all, yet there are multiple reasons why migration declines over the life
course. One factor relates to familial obligations, which merely require family unity, whether all
members are living in the rural area or urban area and thus should have less effect on family
migration. Another factor relates to the diminishing returns to long-term investments over the
life-course; older men may have fewer opportunities to acquire further human capital in the
urban area or to see the benefits of investments in long-term livelihood improvements. Both
interpretations suggest that the likelihood of individual migration should decline more rapidly
with age. At the same time, if individual migration represents a relatively short-term livelihood
response, then one might expect that individual moves would become relatively more frequent at
older ages as an opportunity to generate a temporary livelihood supplement without long-term
separation from family. The age effects in the base model indicate that individual migration
experiences a much more rapid age-related decline than family migration. Using an age and age-
squared specification, the likelihood of individual migration declines rapidly with age, before
levelling off slightly at older ages. By contrast, the likelihood of family migration remains
relatively unchanged throughout the 30s before declining more rapidly through the 40s.
To further explore the incentives for family and individual migration across the life
course, Table 3 introduces controls for the effects of children of different ages. Family migration
is not merely more compatible with aging, it is also more compatible with childbearing. The risk
of both family and individual migration is substantially reduced if a man has a child under age 1
(54% reduction for individual, 57% reduction for family, not statistically different from one
another). But whereas the likelihood of individual migration remains substantially reduced for
men with children age 1-4 (56% relative risk reduction), family migration is actually slightly but
significantly more likely (14% increased risk) if a man has a child age 1-4. Less strikingly,
Kuhn 24
having a child age 5-9 results in a moderately decreased risk of individual migration (31%
reduction) but no significant impact on family migration. Children age 10-14 had no effect on
individual migration and slightly reduced the likelihood of family migration, but these effects
were not jointly significant. These results suggest that the presence of small children is a
demonstrable impediment to individual migration, but a less clear impediment to family
migration. While the presence of an infant may create logistical challenges to moving an entire
family, somewhat older children offer no such limitation. One interpretation of this divergence
would suggest that potential individual migrants have a choice not to move given their familial
obligations, whereas potential family migrants do not have such a choice at their disposal and are
forced to migrate irrespective of childrearing responsibilities. The finding of a positive effect of
children age 1-4, however, may actually suggest slightly greater agency. Vulnerable families
may be forced to consider alternatives to rural life, yet they may also take advantage of windows
of opportunity for family resettlement, much as in rich countries (Rogers et al. 19xx). After
controlling for the effects of young children, age patterns of individual and family migration
become remarkably similar.
Variation over time – main effects and interactions
One final factor driving family and individual patterns was variation over time, particularly
relating to ecological conditions related to the construction of a river embankment and the
occurrence of a major flood in 1988. While analysis of the specific interactions between
ecological and socioeconomic vulnerability is beyond the scope of this paper, it is important to
establish that excess migration risks in the post-1988 period are indeed associated with heavy
flooding. To isolate this effect, Figure 3 illustrates spatial variation in the relative risk of family
migration in 1988 in relation to the three major rivers in the area, the primary river Meghna
touching only the southwest corner of the site, the major tributary Gumti touching only the
Kuhn 25
northern tip, and the Dhonnagoda running through the site. The shape and flow of the
Dhonnagoda created a tidal flow on the left bank of the river, thereby exacerbating flood risks
and justifying the placement of the river embankment on that side. Results are based on the
regression models in Table 3, thereby controlling out some of the socioeconomic variations (like
high rates of internal migration) that may also vary spatially. Village-year controls were added to
capture spatial variation. Map A depicts village-level relative risks of family migration adjusted
for baseline variation in risk in all other years.
The pattern of spatial variation demonstrates that the spike in family migration rates
observed following the flood was indeed associated with flooding, though the pattern is perhaps
counterintuitive. After adjusting for baseline risk, the map indicates lower relative risks at the
water’s edge, largely because every year is a flood year for those living right on the river.
Moving away from the immediate river bank, relative risks are highest along the main canals of
the river, particularly on the one major canal on the embankment side. Finding are adjusted for
baseline migration risks, and thus control for lower levels of family migration on the right-hand
side of the river. Panel B instead maps absolute spatial variation in 1988, thereby incorporating
the general tendency towards high levels of family migration in riverine areas. This map
illustrates that overall family migration risk in 1988 was universally higher on the embankment
side of the river, particularly in areas along the major Meghna and Gumti rivers which always
have high family migration risk, and in the major canal areas that saw a temporary spike to
comparable migration rates. While excess family migration risk is not simply a matter of
proximity to water, the story is clearly related to the flood.
The risk of excess family migration was not confined to the flood year, however, but
rather carried on through 1991. Table 4, columns 1 and 2 present the coefficients of models with
Kuhn 26
observation year dummies for each year in the study period (1982 is the reference year). There
was a gradual rise in the family migration rate over time. The relative risk of family migration
peaked in 1988, but remained significantly higher through 1991 before declining. Individual
migration was relatively high in 1988, but actually peaked in 1985 and 1986. Columns 3-6
combine observation year dummies with an interaction between each observation and household
land holdings. Land effects on family migration were more negative for the years 1988 to 1991,
ranging from -0.149 to -0.159, all significantly different from zero. Joint significance tests
further support that the land interaction coefficients for these four years were jointly significantly
different from land interaction coefficients for all other years. Main effects on family migration
from 1988 to 1991 remain jointly significant as well, such that family migration increased during
this period, but especially among households with lower land holdings. As with the main effects,
observation year-land interactions for individual migration were not statistically significant.
Interactions effects of land holdings and time on the risk of family migration are
summarized in Figure 3, which compares the predicted probability of individual and family
migration over time for a landless man and a man with the mean landholding of 115 decimals.
For men with mean landholdings, the probability of family migration between 1988 and 1991
was about 15% higher than in the two years preceding and following this era (1.32% vs. 1.15%).
Landless men experienced a more pronounced four-year peak on top of an already high baseline
family migration risk, rising from 1.51% to 1.93%, or a 28% increase.
Conclusion
The preceding theoretical framework and analysis set out to bridge the divide separating the
literatures on voluntary and forced migration, particularly with respect to the role of family
livelihoods. In this area of rural Bangladesh as in much of the world, rural-urban migration
represents a critical livelihood adaptation strategy even after marriage, yet individual and family
Kuhn 27
migration represent two distinct approaches to migration given the gravity of the decision to raise
children in the urban setting and to forgo many of the social and economic benefits of rural life.
A number of specific differences in the determinants of family and individual migration
would be missed by a two-outcome model. The relative likelihood of family migration is much
higher among men who lack land, the principal economic resource in the area. Family migration
is also more likely among those who had recently experienced internal migration, which is itself
typically associated with the loss of economic livelihoods or housing security in the former
location. Extensive qualitative research carried out in the study site indicate that family migration
patterns could be further understood in the context of deeper forms of socioeconomic
vulnerability such as a long history of landlessness, limited access to patronage resources
including employment and informal resources, and exclusion from social exchange networks.
In spite of the ecological and socioeconomic vulnerabilities, family migration requires
some amount of agency. This is most evident in the positive selectivity of family migration on
schooling, though this selectivity is substantially less than exists for individual migration. While
children are less likely to discourage family migration than they are to discourage individual
migration, the pattern of child age selectivity nevertheless reveals considerable agency. Family
migration is quite unlikely among families with infant children, yet it is somewhat more likely
among families with children age 1 to 4, suggesting that families may be strategic in finding
opportune stages in the life course to carry out this drastic relocation decision.
These findings point to the need for further expansion and integration of existing models
of the role of livelihoods and involuntary migration. For the literature on labour migration,
including the New Economics of Labor Migration, the spectrum of livelihood-based migration
options could be broadened to incorporate the movement of an entire family. The existing model
Kuhn 28
of livelihood diversification through temporary and individual migration works well enough
when modest livelihoods can be supplemented by migration, but more adverse conditions may
imply a point to the largely rational response of replacing rural livelihoods entirely with urban
ones. Such conditions may be more common in rural Bangladesh than in places with a broader
base of rural capital, but they may surely play out in a variety of settings. The adverse conditions
underlying family migration also raise the continued need to inject a stratification perspective
into the study of migration.
For forced migration studies, these results allow us to broaden our conception of
involuntary migration to include moves that are non-coercive and uncoordinated, yet occur over
long distances and durations. It also adds depth to our existing understanding of the motives
behind such migration, moving beyond the specific crisis and a general sense of vulnerability to
a specific set of predisposing risks for family migration.
Ultimately, the significance of this study and of an integrated model of voluntary and
involuntary migration derive from the need for behavioural models of population displacement
and welfare in anticipation of the long-term effects of global climate change. Episodes like the
1988 flood illustrates both the significance of family migration under such crisis conditions, the
duration of such effects, and the importance of livelihood effects rather than simple ecological
effects. The likelihood of family migration rose substantially in the years immediately following
a catastrophic flood, far more so than the likelihood of individual migration. The family
migration peak lasted not one but four years, with little difference between 1988 and the three
subsequent years. The immediate spike in family migration risk came not in the most flood-
prone areas, which see flooding and high rates of family migration every year, but in areas of
moderate flood risk. Most importantly, the relative risk of family migration in the entire four
Kuhn 29
year post-flood period were higher for men from households with smaller land holdings, on top
of their already greater risk of family migration. Post-flood main effects and land interactions
were robust to the inclusion of village fixed-effects, suggesting that excess family migration was
less the result of the rising waters themselves than of the ensuing social and economic
disruptions. Future research should explore the specific interactions between exposure to
environmental risk, generalized economic disruption, and household livelihoods in greater depth.
Extrapolating from this one small study area to the broader coastal ecosystem, this post-
flood dislocation of population could have had substantial impacts on rural-urban migration.
Over a four-year period, about 1,400 married left the study site with their families, or about 250
more than would have left in a normal year. Roughly 30% of these men were landless, compared
to 21% of the total sample. Each of these men travelled with a wife and an average of 2 children
under age 15. In a study area population of 200,000, between 5,000 and 6,000 moved during a
four-year period under highly adverse conditions, half of them children, about 1,000 of whom
would not have moved in the absence of a flood. Simply extrapolating this level of displacement
to the quarter of Bangladesh’s population living in deltaic areas, about 30 million at the time,
would imply the displacement of approximately 750,000 people in a four-year period, about
150,000 of whom would not have moved had there not been a flood. While these migrants do not
constitute the flood of displaced individuals that might be associated with a worst-case scenario
climate event, they point a more relevant and equally challenging form of displacement,
sustained and highly selective on livelihood vulnerability, with occasional spikes that dip ever
more deeply into the pool of vulnerable households.
The forces of vulnerability and agency at play in the family migration decision raise
questions about the welfare of family migrants and appropriate policy measures. Even in arguing
Kuhn 30
that family migration is rational and thus perhaps not easily or sensibly discouraged, it may
nonetheless be sub-optimal or avoidable under some conditions. Periods of ecological disaster
offers periods of potential risk and opportunity. Some displacement is unavoidable, yet continued
excess displacement over a period of four years places considerable burdens on urban areas,
thereby justifying efforts to promote rural population retention. The duration of risk and its
concentration among socioeconomically vulnerable families suggests that medium-term
livelihood support (such as employment on reconstruction) would at least mitigate the migration
flow. For the subset of families who could not and should not be discouraged from abandoning
rural life, urban transition programs targeting the needs of socially excluded and economically
vulnerable families could carry further benefits. While forced migration is normally attributed to
acts of God, politics, or both, this paper proceeded to understand the role of household
vulnerability and agency in driving the process of family migration. More family migration can
be expected in the future. Much more can be done to understand it, and to provide families and
societies with the tools they need to make it both avoidable and tolerable.
Kuhn 31
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Table 1: Means of key dependent and independent Variables
Person Level Person-
Year Level
Migration Outcomes
Individual Migration 4.3%
0.49%
Family Migration 8.4%
0.96%
Past Migration Experience
Rural-Urban 9.2%
8.5%
Other External 6.6%
3.6%
Internal n/a
7.1%
Any in current year
2.8%
Independent Variables
Household Land 112.9
(168.8)
113.4 (170.3)
Years of Schooling 3.1 (3.8) 3.1 (3.7)
1-4 Years 23%
22%
5-9 Years 21%
22%
10+ Years 10%
9%
Age 37.5 (12.4)
43.1 (12.2)
Any child age 0-1 18%
13%
Any child age 1-4 39%
46%
Any child age 5-9 34%
48%
Any child age 10-14 28%
42%
Household head 51%
60%
Household Size 7.8 (3.6)
8.4 (4.1)
Fisherman 5%
5%
Hindu 15%
15%
Cases 45,382 473,561
Notes: * - First entry to sample is in 1982 for cases in census, and on first return post-1982 for others. Standard deviations in parenthesis where applicable.
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Table 2: Covariates of Migration Using Two-Outcome and Three-Outcome Models
No Village Fixed Effects Village Fixed Effects
Binomial Logit -
Any Migration
Multinomial Logit Binomial Logit -
Any Migration
Multinomial Logit
Variables Individual Migration
Family Migration
Individual Migration
Family Migration
Household Land Holdings (logged)
-0.101*** -0.048*** -0.128*** -0.101*** -0.048*** -0.127***
(0.007) (0.012) (0.008) (0.007) (0.012) (0.008)
Internal Migrant in Previous Year
-1.428*** -1.489* -1.415*** -1.427*** -1.504* -1.404***
(0.240) (0.591) (0.262) (0.240) (0.591) (0.263)
Any Internal Migration History
0.194* -0.538** 0.415*** 0.114 -0.684*** 0.366***
(0.077) (0.186) (0.085) (0.081) (0.192) (0.090)
1-4 Years+ 0.228*** 0.321*** 0.181*** 0.193*** 0.272*** 0.150***
(0.035) (0.060) (0.043) (0.036) (0.061) (0.044)
5-9 Years 0.379*** 0.628*** 0.232*** 0.359*** 0.576*** 0.229***
(0.036) (0.059) (0.044) (0.036) (0.059) (0.045)
10+ Years 0.724*** 0.843*** 0.664*** 0.723*** 0.821*** 0.672***
(0.044) (0.074) (0.054) (0.045) (0.075) (0.056)
Age -0.044*** -0.106*** -0.007 -0.044*** -0.113*** -0.004
(0.010) (0.015) (0.013) (0.010) (0.015) (0.013)
Age squared 0.000 0.001*** -0.000** 0.000 0.001*** -0.000**
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Any Rural-Urban Migration Experience
0.964*** 0.601*** 1.155*** 0.913*** 0.504*** 1.127***
(0.034) (0.058) (0.041) (0.035) (0.059) (0.042)
Any Other External Migration Experience
0.637*** 0.59*** 0.782*** 0.596*** 0.308*** 0.747***
(0.041) (0.071) (0.049) (0.041) (0.072) (0.049)
Household head -0.537*** -0.935*** -0.331*** -0.540*** -0.924*** -0.344***
(0.035) (0.061) (0.042) (0.036) (0.062) (0.043)
Household Size -0.053*** -0.084*** -0.035*** -0.051*** -0.081*** -0.035***
(0.004) (0.007) (0.005) (0.004) (0.007) (0.005)
Fisherman -0.864*** -1.107*** -0.806*** -0.929*** -1.157*** -0.869***
(0.125) (0.270) (0.141) (0.129) (0.290) (0.144)
Hindu -0.757*** -1.029*** -0.628*** -0.591*** -0.767*** -0.509***
(0.056) (0.104) (0.067) (0.070) (0.125) (0.084)
Constant -1.908*** -1.502*** -3.214*** -2.140*** -1.421*** -3.597***
(0.192) (0.303) (0.248) (0.238) (0.381) (0.304)
Observations 458448 458448 458448 458448
Log Likelihood: 5048.640 5195.511 5851.483 6480.410
DF 14 28 157 314
RSquared 0.0753 0.0717 0.0846 0.0850
Notes: Robust standard errors in parentheses; *** p<0.001, ** p<0.01, * p<0.05
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Table 3: Covariates of Multinomial Logistic Regression on Three-Outcome Migration Model: Young Child Effects
Variables Individual Family
Has child 0-1 Year Old -0.766*** -0.835***
(0.070) (0.053)
Has child 1-4 Year Old -0.827*** 0.141***
(0.048) (0.037)
Has child 5-9 Year Old -0.372*** -0.076
(0.055) (0.040)
Has child 10-14 Year Old -0.093 -0.144**
(0.068) (0.046)
Age 0.002 0.009
(0.019) (0.015)
Age squared -0.001** -0.001***
(0.000) (0.000)
Observations 458448
Log Likelihood: 7292.9
DF 328
RSquared 0.0979
Notes: Robust standard errors in parentheses; *** p<0.001, ** p<0.01, * p<0.05
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Table 4: Annual Variation in Individual and Family Migration, Main Effects and Land Interactions
Year Effects Only Year Effects Land Interactions
Individual Family Individual Family Individual Family
1982 (Omitted) -- -- -- -- -0.151*** -0.266***
(0.046) (0.033)
1983 -0.083 -0.373*** -0.360 -0.720*** -0.068 -0.130***
(0.141) (0.111) (0.260) (0.171) (0.048) (0.038)
1984 0.171 0.052 -0.072 -0.348* -0.078 -0.112***
(0.133) (0.100) (0.243) (0.157) (0.042) (0.033)
1985 0.550*** 0.327*** 0.161 -0.156 -0.037 -0.087**
(0.123) (0.094) (0.225) (0.147) (0.033) (0.028)
1986 0.509*** 0.227* 0.166 -0.179 -0.050 -0.112***
(0.123) (0.095) (0.226) (0.145) (0.035) (0.027)
1987 0.210 0.364*** -0.054 -0.002 -0.072 -0.125***
(0.130) (0.092) (0.237) (0.138) (0.039) (0.025)
1988 0.413*** 0.472*** 0.089 0.175 -0.056 -0.149***
(0.125) (0.091) (0.228) (0.134) (0.035) (0.024)
1989 0.304* 0.404*** 0.053 0.117 -0.077* -0.152***
(0.127) (0.092) (0.232) (0.136) (0.038) (0.024)
1990 0.148 0.237* -0.343 -0.030 -0.012 -0.159***
(0.131) (0.095) (0.254) (0.141) (0.044) (0.026)
1991 0.251* 0.358*** -0.026 0.072 -0.070 -0.153***
(0.127) (0.092) (0.230) (0.136) (0.037) (0.024)
1992 0.189 0.053 -0.330 -0.446** -0.004 -0.085**
(0.128) (0.097) (0.239) (0.152) (0.038) (0.028)
1993 0.004 0.291** -0.223 -0.135 -0.085* -0.107***
(0.131) (0.093) (0.234) (0.141) (0.038) (0.025)
1994 -0.074 0.275** -0.228 -0.216 -0.108** -0.088***
(0.132) (0.093) (0.230) (0.142) (0.037) (0.025)
1995 -0.215 0.044 -0.513* -0.371* -0.066 -0.112***
(0.135) (0.096) (0.243) (0.145) (0.042) (0.026)
Observations 458448 458448
Log Likelihood 7657.267 7751.8
DF 366 386
RSquared 0.101 0.101
Notes: Robust standard errors in parentheses; *** p<0.001, ** p<0.01, * p<0.05
Kuhn 41
Source: ICDDR,B (1996)
Married, never migrated
Unmarried, never migrated
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995
Figure 1: First Migration and Marriage Status by Year,1974 Census Cohort of unmarried men age 18+
Migrated beforemarriage, returned
Migrated before marriage, never returned
Migrated aftermarriage
Kuhn 42
Figure 2: Predicted probability of any, family, or individual migration, by land holdings, categorical and linear logged specifications
Source: ICDDR,B (1996). Categorical specification from Appendix Table 1. Linear specification from Table 2, columns 4-6.
Kuhn 43
Figure 3: Spatial variation in relative risk of family migration in 1988, with and without adjustment for baseline risk
Adjusted for baseline Risk Unadjusted for baseline risk
Source: ICDDR,B (1996). Models not shown
Kuhn 44
Figure 4: Annual predicted probabilities of family Migration, for landless and median landholding households (with confidence intervals shaded)
Source: Model shown in Table 4, columns 3-6
i Migration in and out of the study site and household was recorded only after a period of six months, excluding
most seasonal or circular migration episodes, business trips, or vacations.
ii Multinomial models must account for the “Independence of Irrelevant Alternatives” (IIA) assumption that the
relative choice between outcomes 1 and 2 would not be affected by elimination of outcome 3. Hausmann
specification tests compare coefficients and standard errors of possible two-outcome models with those of the
chosen model. All tests show no significant differences between the two-outcome models and the chosen model (at
the p<=0.01 level).
Kuhn 45
iii Rural-rural moves, which typically involve individual seasonal migration episodes or nuclear family resettlement,
require a separate analytic model that includes destination-specific resources that are relevant in the rural context
(land purchase opportunities, local labor opportunities). International moves are difficult to model because income
expectations depend more on labor relations and social connections than on education, and because international
family migration is a rare event.
iv All models were also tested using a definition of family migration as involving a husband, wife, and children (if
any were alive). Cases in which a husband and wife moved without their children were rare, and the models
produced statistically similar results. Separate models also relaxed the requirement that wives move on the same day
as husbands, instead defining family migration as husbands and wives moving in the same week or month. It was
also quite rare for wives to move in the same month if they did not move on the same day, and thus results were
unchanged.
v While estimating a fixed-effects multinomial logistic regression is problematic because of the categorical nature of
the dependent variable, the inclusion of village-level controls in a standard model results in no bias as long as the
number of observations per cell (village) approaches infinity. An average cell contains 1,800 married male person-
years and 600 unmarried ones.
vi These groupings build on already-observed clumping patterns. Between 100 and 200 decimals, values are grouped
to the nearest 20 decimal unit (so 110 decimals becomes 100, 130 becomes 120). Above 200 decimals, values are
group to the nearest 50 decimal unit.