multilateral attractiveness, migration networks and destination choices of international migrants to...

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Multilateral Attractiveness, Migration Networks and Destination Choices of International Migrants to the Madrid

Metropolitan Area

Ludo PeetersHasselt University, Belgium

Coro ChascoUniversidad 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.

@ Ludo Peeters (UH, Belgium) and Coro Chasco (UAM, Spain), 2012

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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 municipalities are grouped into 5 statistical zones (NUTS4).

The central city of Madrid and 40 surrounding 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).

Choice set:41

municipalities (NUTS5)

3. Data: origin-groups of immigrants

11@ Ludo Peeters (UH, Belgium) and Coro Chasco (UAM, Spain), 2012

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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3. Data: network effects (ii)


Net annual

loss:-2,734

Shift-share analysis

Net annual gain:

+2,734

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3. Data: network effects (iii)


LQ𝑖𝑗=𝑛𝑖𝑗 /POP 𝑗

𝑛𝑖/POPLocation Quotient of new immigrants (nij): for each immigrant group i in location j (specialization index)

Latin-Americans: Highly dispersed in middle-income/low-unemployment areas

Moroccans: Some areas of concentration in in middle-income/low-unemployment areas

EU-25: Some concentration high-income/low-unemployment areas

Chinese: Concentration in low-income/high-unemployment locations

Bulgarians & Romanians: Some concentration in low-income/high-unemployment places

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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, low-quality, 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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• 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):

• Share of individuals that will choose destination . Individual immigrants m from the same origin have identical preferences and derive equal utility from the choice of a destination .

4. Theoretical model: specification


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• Conditional Logit Model (CL): implicitly assumes that the total number of migrants from origin to the metro area as a whole, , 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 choosing destination assuming identical preferences:

4. Theoretical model: specification (ii)


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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• Poisson model with “MA” term:

4. Theoretical Model: specification (iii)


E.g.: an increase in the wage rate, , in destination will redirectsthe number of migrant arrivals from all other destinations to destination (Neubecker et al., 2012, p. 6), which implies an increase in the number of arrivals in destination and a concomitant reduction in the number of arrivals in destination

𝜖 𝑖𝑗𝐵=𝜕 ln𝑛𝑖𝑙/𝜕 ln𝑤𝑙>0

Ignoring the multilateral elasticity of immigration () leads to underestimation of the bilateral response ()

I = “Multilateral-Attractiveness”: the number of migrants nij always depends on the expected utility associated with all the destinations in the choice set.

Multiplicative form:

LOG form:

OBSERVATIONS:

①

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4. Theoretical model: specification (iv)


Since the MA term 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:

②

Weak influence of MA (small size of ) Large + values (given )

③The MA term does not vary across destinations : it only accounts for the deterministic utility components of all potential destinations in the choice set (one cannot differentiate between and ) .

Potential endogeneity problem allowing the MA term to vary across destinations, interacting with a destination-specific effect (Peeters, 2013): Note: either i or j = unobservable.

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• How capturing the unobserved effects induced by the new MA term , in order to control for potential violations of the IIA assumption?

4. Theoretical Model: estimation


Poisson pseudo maximum likelihood (PPML) estimator:- PPML yields the same estimate for as CL.- CL ensures consistency with the underlying RUM that describes the choices

of utility-maximizing agents).Space-time panel data model: potential violations of the IIA assumption can be controlled for by introducing origin-destination fixed effects, Origin-time dummies are included to ensure compatibility with CL.

①

• Estimation of a Conditional Fixed-Effects Poisson model:

②

③

origin-specific local characteristics (e.g., migrant stocks) vector of unknown parameters

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5. Empirical model: specification

. 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


Identify the local (demographic, economic, and social) determinants of international migrant arrivals in the municipalities of the Madrid metro area:

W

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5. Empirical model: specification (ii)

I. Demographic and economic factors:. Population density (): proxy for high-level urban (“man-made”) amenities: .. GDP per capita (): wages expectations at destination: .. Disposable income per capita (): 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: .. Employment-growth (), unemployment rate () : employment expectations.. Square of the employment growth: effects of an acceleration (or slowdown) in job growth depending on the initial employment-growth rate: .


W

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5. Empirical model: specification (iii)

II. Spatial factors:

. Spatial lag of GDP p.c.: for 1st & 2nd order contiguity W.

. Centrality index (j is included to avoid “donut holes”)

Captures the potential disjuncture between workplace (where wages are paid) and place of residence of immigrants. ().

. Public transportation lines (): destination’s connectivity with the city of Madrid.

Local attractiveness, or repulsiveness, of a destination’s relative spatial position within the metropolitan area.

W

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5. Empirical model: specification (iv)

III. Migrant stocks:

. Percentage of the immigrant population from origin of the total population in destination : 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: the Euclidian distance between and . It should allow us to examine whether network externalities extend beyond the boundaries of any given location.


W

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5. Empirical model: specification (v)

IV. Origin-destination fixed effects and other dummies:. two-way origin-destination (O-D) fixed effects: time-invariant location-specific utility components that may be perceived differently by immigrant groups . 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.. : 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).


W

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6. Results – Common local characteristics

O-D O-Y

parameters estimates are strongly sensitive to the choice of model specification

[lnW

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• 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 .

6. Results – Some comments

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6. Results - – Some comments (ii)

More attractive

Less attractive

Commuting?Job-skills mismatches?


NON-LINEARITY:

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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SIZE OF MIGRANT STOCK – LOCAL STOCKS ()

• 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 ()

• 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.

6. Results – Some comments (iii)

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• Raw residuals

• Pearson residuals

• Computing fixed effects (Baltagi, 2009, p. 230)

Analysis of residuals


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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

• 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 (?)


For each i: general effect on j of the rest of destinations

For each j: effect of each of the destinations (k)

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