Transcript Slide 1

Multilateral Attractiveness, Migration
Networks and Destination Choices of
International Migrants to the Madrid
Metropolitan Area
Ludo Peeters
Hasselt University, Belgium
Coro Chasco
Universidad Autónoma de Madrid, Spain
11th International Workshop Spatial Econometrics and Statistics,
Centre INRA, 15-16 novembre 2012, Avignon - France
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1. Multilateral attractiveness – Introduction
• International migration has become an important driver of
social and economic change; it takes place primarily in
cities and, particularly, in large metropolitan areas.
– This is the case for the Madrid metropolitan area in Spain.
– Spain received about 1.3 million new immigrants from all over the
world (2009), of which about 157,000 in the Madrid metro area
(12% of the total number).
(0.5% of Spanish extension)
• To get a better understanding of the local determinants of
international migrants, we choose a modeling strategy that
controls for the possible dependence between the number
of migrant arrivals in a given location and the possibly
(unobserved) attractiveness of all other potential
locations in the metro area.
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1. Multilateral attractiveness – Introduction (ii)
• The basic intuition behind the notion of multilateral
attractiveness (MA):
– Multiple destinations within a narrowly defined spatial choice set
(e.g. a metro-area) are close substitutes for each other (because
they are geographically and/or functionally similar destinations).
– The number of migrant arrivals in a particular destination does not
depend only on the attractiveness of that destination but also on
the attractiveness of all other destinations in a narrowly defined
choice set.
• The attractiveness exerted by each destination is
observable, whereas the MA is unobservable.
– Not controlling for MA gives rise to omitted-variable biases if it is
not properly accounted for.
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2. Related literature to MA
• Multilateral resistance to trade (Anderson and van
Wincoop, 2003)  “Trade between two countries depends
on the bilateral trade “barriers” (customs tariffs, taxes…)
between them relative to average barriers that both countries
face with all their trading partners”.
• Multilateral resistance to migrate (Bertoli and
Fernández-Huertas Moraga, 2011, 2012)  “Bilateral
aggregate flows between two countries depend on the
opportunities to migrate (legal restrictions,
visa…=“cliffs”) to other destinies”.
• Multilateral unattractiveness of trade/migration destinations.
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2. Related literature to MA (ii)
Multilateral attractiveness of migration destinations
• First the migrants have to overcome the national/regional
barriers (e.g. UE Schengen, different language…).
• The decision to migrate to a country is assumed to precede the
choice of a particular destination within the metro area.
• Municipalities=attractors (not barriers) and close substitutes
competing for migrants in the choice set (metro area): they share
a common political, economic and cultural background
(Neubecker et al. 2012).
• When dealing with small geographical units, neglected site
characteristics can more easily extend their influence beyond the
boundaries of the considered spatial units (Guimarães et al.
2004).
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2. Related literature to MA (iii)
MA=The decision to migrate from i to j depends not only
on the attractiveness of j but also on the (unobserved)
attractiveness of other potential destination k ≠ j.
Unobserved site characteristics may induce correlation
across choices and therefore a violation of the IIA
assumption.
• IIA is too restrictive: unobserved shocks influencing a decision
maker’s attitude toward one alternative have no effect in his
attitude toward the other alternatives.
• IIA assumes that the errors are i.i.d.
• If present, the estimation results will be typically biased due to
omitted variables (Hanson, 2010).
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2. Related literature to MA (iv)
Given the discrete nature of destination choices, the
latter are usually modeled within the Conditional Logit
(CL) framework (Davies et al. 2001, Scott et al. 2005).
• The appeal of CL= formal link between the theoretical
objective function of a representative utility-seeking agent
and the likelihood function of the empirical model.
Recent work of Guimarães et al. (2004) and
Schmidheiny and Brülhart (2011).
– Equivalence between CL and Poisson count estimators.
– Consistency with the Random Utility Maximization (RUM)
framework (McFadden, 1974) of both CL and Poisson.
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2. Related literature to MA (v)
The main contribution of this paper:
– Estimation of a Poisson model that controls for the influence of
MA on immigrants’ destination choices.
– Estimate the real impact of the size of “migrant stocks” (ethnic
migrant communities at the destinations) on new immigrants’
destination choices.
– When there are multiple destinations in a relatively small
geographical area, immigrants do not have to be so spatially
clustered (social networks…): hetero-local settlement patterns
(Zelinsky and Lee, 1998) will prevail.
– Ethnic group members can stay closely “connected” through
recent advances in information and communication technology,
improved transportation facilities, etc.
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- Outline: DATA & MODEL • Goal
– Model of choice between multiple destinations of migration
– Choice set = 41 municipalities of Madrid metropolitan area
 Choice set covers a relatively small geographical area
 Spatial spillovers possible  potential violation of IIA
– Five broadly defined origin-groups of immigrants
• Assumptions
– Aggregate data
– All individual migrants from same origin-group have identical
preferences
– Total number of immigrants (from each origin-group) to Madrid metro
area is fixed  focus on (zero-sum) allocation across 41 destinations
• Data
– Panel data: two periods (2005 and 2009)
– Within- or time-variation to identify model parameters
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3. Data: Madrid metro area
The central city of Madrid and 40
surrounding municipalities (NUTS5).
The municipalities are grouped into 5
statistical zones (NUTS4).
Choice set:
41
municipalities
(NUTS5)
Geographical size of the study area = relatively small: 2,700 Km2
Population = 5.8 million people (about 3.2 million = 55% in the city of Madrid).
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3. Data: origin-groups of immigrants
There are three reasons:
1. Represent 85% of
immigrants to the metro
area in 2005-2009.
2. Not homogeneous:
different ethnic, religious &
linguistic backgrounds: not
homogeneous.
3. Different settlement histories: Latin Americans, Moroccans
and western Europeans have a long immigration history in Spain;
Bulgarians–Romanians and Chinese are more recent.
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3. Data: network effects
• Shift-share analysis: The city of Madrid looses immigrants in favor of
the rest of the metro area (holding constant the composition of the total
inflow of immigrants to the metro area) MA  2nd decision to migrate.
 The city of Madrid (a=1): first destiny for immigrants before “choosing”
their final home in a metro area municipality (a=2).
 The city of Madrid gains immigrants at the expense of the rest of the
metro area due to the changed composition of the total inflow of migrants
(e.g., relatively more EU25 immigrants in 2009).
 The “origin” gains are not sufficient to offset, or outweigh, the “area”
losses, giving rise to a net loss of 2,734 immigrants to the city of Madrid
on an annual basis.
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Shift-share
analysis
3. Data: network effects (ii)
Net
annual
loss:
-2,734
Net
annual
gain:
+2,734
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3. Data: network effects (iii)
Location Quotient of new immigrants (nij): for each immigrant
group i in location j (specialization index)
𝑛𝑖𝑗 POP𝑗
LQ𝑖𝑗 =
𝑛𝑖 POP
Bulgarians &
Romanians: Some
concentration in
low-income/highunemployment
places
EU-25: Some
concentration
highincome/lowunemployment
areas
Moroccans:
Some areas of
concentration in
in middleincome/lowunemployment
areas
Latin-Americans:
Highly dispersed
in middleincome/lowunemployment
areas
Chinese: Concentration in lowincome/high-unemployment locations
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3. Data: network effects (iv)
• The spatial settlement patterns of new immigrants to the Madrid metro
area do not conform to the image of concentrations in high-density, lowquality, inner-city locations.
• Suburbanization propensity of new immigrants: immigrants tend to bypass
the central city of Madrid because of a “metropolitan de-concentration” 
emergence of “edge cities,” which are characterized by an increasing share
of the metro area’s employment.
• Therefore, hetero-local settlement patterns prevail:
 New immigrants settle in multiple locations throughout the metro area.
 Magnitude (and even sign) of the local migrant stock effect is uncertain.
 Network externalities may actually extend far beyond the boundaries
of a destination  spatial spillovers
 Use a measure of supra-local (external) migrant stock  spatial lag (?)
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4. Theoretical model: specification
• The indirect utility of an individual m who migrates from origin i in
destination j can be adequately approximated by the following linear
Random-Utility Model (RUM):
• Assuming IIA (i.i.d. errors), the probability that an individual migrant m from
origin i chooses destination j rather than any other destination k ≠ j,
(McFadden, 1974):
•
= 1, ∀𝑚  𝑃𝑗|𝑚 = 𝑃𝑗 : Share of individuals that will choose
destination 𝑗. Individual immigrants m from the same origin 𝒊 have identical
preferences and derive equal utility from the choice of a destination 𝑗.
𝑗 𝑃𝑗|𝑚
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4. Theoretical model: specification (ii)
• Conditional Logit Model (CL): implicitly assumes that the total number of
𝐽
migrants from origin 𝒊 to the metro area as a whole, 𝑛𝑖 = 𝑗=1 𝑛𝑖𝑗 , is given
and does not depend on the location-specific attributes (Schmidheiny and
Brülhart, 2011, p. 215). Then, the expected number of migrants from origin 𝑖
choosing destination 𝑗 assuming identical preferences:
stochastic
version
• Poisson model: the ML estimation of 
coincides with the CL estimator
(Schmidheiny and Brülhart, 2011).
• A new element: I = MA variable (to
control for the IIA property).
Multiplicative form:
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4. Theoretical Model: specification (iii)
• Poisson model with “MA” term:
LOG form:
Multiplicative form:
OBSERVATIONS:
① I = “Multilateral-Attractiveness”: the number of migrants nij always depends on the
expected utility associated with all the destinations in the choice set.
E.g.: an increase in the wage rate, 𝑤, in destination 𝑙 will redirectsthe number of migrant
arrivals from all other destinations 𝑗 ≠ 𝑙 to destination 𝑙 (Neubecker et al., 2012, p. 6),
which implies an increase in the number of arrivals in destination 𝑙 and a concomitant
reduction in the number of arrivals in destination 𝑗
𝑀
𝜖𝑖𝑗
= 𝜕 ln 𝑛𝑖𝑗 𝜕 ln 𝑤𝑙 < 0
𝐵
𝜖𝑖𝑗
= 𝜕 ln 𝑛𝑖𝑙 𝜕 ln 𝑤𝑙 > 0
𝑀
Ignoring the multilateral elasticity of immigration (𝜖𝑖𝑗
) leads to underestimation of
𝐵
the bilateral response (𝜖𝑖𝑗
)
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4. Theoretical model: specification (iv)
② Since the MA term φ𝑖 is unobserved, it ends up in the error term of the
model if not properly accounted for, leading to a Poisson (multiplicative)
model without the “MA” term:
instead of:
Weak influence of MA (small size of φ𝑖 )  Large + values 𝑣𝑖𝑗 (given 𝑢𝑖𝑗 )
③ The MA term 𝝋𝒊 does not vary across destinations 𝑗: it only accounts for the
deterministic utility components of all potential destinations in the choice set
(one cannot differentiate between α𝑖 and φ𝑖 )  𝑬(𝒖𝒊𝒋 |𝒚𝒋 ; 𝜶𝒊 , 𝝋𝒊 ) ≠ 𝟎.
Potential endogeneity problem  allowing the MA term to vary across
destinations, interacting φ𝑖 with a destination-specific effect (Peeters, 2013):
ζ𝑗  𝝋𝒊 𝜻𝒋 = 𝝓𝒊𝒋
Note: either i or j = unobservable.
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4. Theoretical Model: estimation
• How capturing the unobserved effects induced by the new MA term 𝝓𝒊𝒋 ,
in order to control for potential violations of the IIA assumption?
① Poisson pseudo maximum likelihood (PPML) estimator:
- PPML yields the same estimate for 𝛃 as CL.
- CL ensures consistency with the underlying RUM that describes the choices
of utility-maximizing agents).
② Space-time panel data model: potential violations of the IIA assumption can
be controlled for by introducing origin-destination fixed effects, 𝜸𝒊𝒋 = −∅𝒊𝒋
③ Origin-time dummies are included to ensure compatibility with CL.
• Estimation of a Conditional Fixed-Effects Poisson model:
𝒙𝒊𝒋 : origin-specific local characteristics
(e.g., migrant stocks)
𝛃𝟐 vector of unknown parameters
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5. Empirical model: specification
Identify the local (demographic, economic, and social) determinants of
international migrant arrivals in the municipalities of the Madrid metro area:
W
. Unit of observation: O-D-Year (indexed i,j,t); immigration data: 2005 and 2009
. Explanatory variables:
1) Lagged one year (2004 and 2008), to mitigate potential simultaneity biases.
2) In natural-log: numbers and monetary units enter in natural-log form.
3) Monetary values: in real terms (constant 2008 prices, CPI deflated).
4) Other: percentages
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5. Empirical model: specification (ii)
W
I. Demographic and economic factors:
. Population density (PD𝒋 ): proxy for high-level urban (“man-made”) amenities: β1 > 0.
pc
. GDP per capita (GDP𝒋 ): wages expectations at destination: β2 > 0.
pc
. Disposable income per capita (INC𝒋 ): higher-level amenities (schools, health-care…).
. Square of the income variable: to examine whether housing costs offset, or outweigh,
the benefits of improved living conditions: β5 < 0.
. Employment-growth (ΔEMP𝒋 ), unemployment rate (UR𝑗 ) : employment expectations.
. Square of the employment growth: effects of an acceleration (or slowdown) in job
growth depending on the initial employment-growth rate: β7 > 0.
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5. Empirical model: specification (iii)
W
II. Spatial factors:
. Public transportation lines (𝒍𝒏 PTL𝒋 ): destination’s connectivity with the city of Madrid.
. Spatial lag of GDP p.c.: for 1st & 2nd order contiguity W.
Captures the potential disjuncture between
workplace (where wages are paid) and place of
residence of immigrants. (β3 > 0).
. Centrality index (j is included to avoid “donut holes”)
Local attractiveness, or repulsiveness, of a destination’s
relative spatial position within the metropolitan area.
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5. Empirical model: specification (iv)
W
III. Migrant stocks:
. Percentage of the immigrant population from origin 𝒊 of the total population
in destination 𝒋: If hetero-local settlement patterns prevail, it is not clear a
priori the sign and strength of its coefficient.
. Size of “external” migrant stocks (yet sufficiently close) to any given location:
loc
MS𝑖𝑗ext = 𝑘≠𝑗 𝑒 −D𝑗𝑘 MS𝑖𝑘
; D𝑗𝑘 the Euclidian distance between 𝑗 and 𝑘. It
should allow us to examine whether network externalities extend beyond the
boundaries of any given location.
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5. Empirical model: specification (v)
W
IV. Origin-destination fixed effects and other dummies:
. 𝜸𝒊𝒋 = −𝝓𝒊𝒋 , two-way origin-destination (O-D) fixed effects: time-invariant locationspecific utility components that may be perceived differently by immigrant groups
. 𝜶𝒊𝒕 , origin-year (O-Y) dummies: to ensure compatibility of Poisson with CL. In addition,
those dummies can absorb origin-specific immigration policies in Spain and unobserved
utility components.
. 𝜽𝒊𝑴𝒕 : origin-Madrid-year (O-M-Y) dummies: time-varying effects to capture the
“idiosyncratic nature” of the city of Madrid—even if only because of the scale effect (the
city of Madrid attracts, on average, about 50% of all immigrants to the metro area).
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6. Results – Common local characteristics
parameters estimates are strongly sensitive
to the choice of model specification
O-D

O-Y
[lnW
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6. Results – Some comments
• Parameters estimates are strongly sensitive to the choice of model
specification.
• Biases induced by not controlling for MA are generally in the expected
direction: giving rise to unexpected (wrong) signs in some instances.
• The dramatic changes in the estimates suggest that destination choices
are strongly influenced by the destinations’ MA.
• GDP per capita coeff. is statistically insignificant; in contrast, GDP per
capita in the 1st&2nd order neighborhoood is positive and significant:
immigrants have a preference for settling in locations close to major
economic activity places because they find them too costly.
NON-LINEARITY:
• The coefficients on gross disposable income per capita and its square,
positive and negative, respectively, suggest that locations become
increasingly less attractive with increasing income levels—eventually
turning into a negative (repulsive) effect at very high income levels .
• Attractiveness decreases with greater accessibility with the city of
Madrid: transportation encourages sub-urbanization .
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6. Results - – Some comments (ii)
NON-LINEARITY:
More attractive
Commuting?
Job-skills
mismatches?
Less attractive
Employm growth coeff < 0:
When a given location is initially
experiencing a relatively low
growth rate (relatively
unfavorable prospects for job
opportunities), speeding up its
local growth turns out not to be
sufficient to “gain” additional
immigrants; at best, that
location is able to cut back on
its “loss” of new migrant
arrivals.
[Employm. growth coeff]2 > 0: new immigrants tend to be particularly attracted by
growing job opportunities only in those places where there was already a high growth in
employment before.
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Results – Local/external migrant stocks
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6. Results – Some comments (iii)
SIZE OF MIGRANT STOCK – LOCAL STOCKS (μ𝑖 )
•
•
Positive (and strongly significant) only for Chinese immigrants.
Many recent immigrants from China settle and concentrate in the municipality
of Parla, located in the southern part of the metropolitan area and adjacent
to Fuenlabrada, where Chinese immigrants are heavily involved in the
wholesale business (Cobo Calleja industrial park, which is one of the biggest
Chinese industrial sites in Europe).
SIZE OF MIGRANT STOCK – LOCAL STOCKS (υ𝑖 )
•
Positive for Moroccan immigrants (significant at the 1% level) and EU
immigrants (albeit only marginally significant). The message returned here is
that Moroccans seem to be particularly attracted by locations close to those
that have a large established stock of co-nationals. Thus, the proximity of a
sizeable ethnic community is important, because of cultural-religious motives
(e.g., easy access to mosques, halal food, etc.), but new immigrants may face
competition from their co-national in the local labor and/or housing market
and, hence, prefer settling in more dispersed locations.
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Analysis of residuals
• Raw residuals
• Pearson residuals
• Computing fixed effects (Baltagi, 2009, p. 230)


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Analysis of residuals – Spatial autocorrelation
Controlling for MA (fixed effects)
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Analysis of residuals – Spatial autocorrelation
NOT controlling for MA (no fixed effects)
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Sensitivity analysis
 Destination choice is not “gender
neutral”
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Conclusion: question
• Identifying cross effects  not possible when using FEs
For each j: effect of each of the
destinations (k)
For each i: general effect on j
of the rest of destinations
• How to improve (extend) the model by using spatial econometric
approach?
 How to impose parametric structure to MA term?
 How to estimate (asymmetric) spatial cross effects?
• pos. shock in k vs. l and k
• pos. shock in k and j vs. h
 How to account for time-varying MA = not possible using FEs
• Using a simple model with spatial lags of location characteristics likely to
be insufficient or inappropriate (?)
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