SPATIAL ISSUES RELATED TO MY RESEARCH:

Agglomeration, migrations and the role of human

capital.An analysis for the Spanish

Provinces.Rosa Sanchis-Guarner Herrero

Grup d’Anàlisi Quantitativa Regional

Institut d’Economia Aplicada

Regional i Pública

19th Advanced Summer School in Regional Science

GIS and Spatial EconometricsUniversity of Groningen 4-12 July 2006

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1. MY RESEARCH: What? In my research I try to analyse

MIGRATORY INTER-PROVINCIAL MOVEMENTS

Under a NEG framework (forward linkage) Considering the role played by HUMAN CAPITAL

I do it for the Spanish provinces in the period 1988-2002

NEG predicts the formation of agglomerations through two mechanisms (Krugman 91, 92): Backward linkage: predicts movements of firms. Forward linkage: predicts movements –

migrations- of workers towards economic agglomerations attracted by higher real wages → I focus in this mechanism.

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1. MY RESEARCH: Why? There exists little literature on NEG empirics and

even less focused on the verification of the forward linkage: Crozet (2004), Poncet (2006) and Tirado et al (WP 2006).

Besides this, the migration literature has highlighted the importance of human capital on the migratory decision.

I follow the theoretical model of Crozet (2004), extending it to take into account the effect of human capital endowments of the regions.

Crozet (2004) derives a migration equation that relates migrants to market potentials combining a migration model (migratory decision) and a standard NEG model (price indexes).

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2. THE THEORETICAL EQUATIONS: Model with human capital R regions; 3 sectors (traditional, manufactures and

services); 2 factors of production (mobile and immobile workers)

The migration decision given by the maximization of:

The new reduced equation including human capital is:

The migrations proxy is explained by: The manufactures and services price indexes (NEG elements) Expected nominal wage, migratory cost (distance & borders) and

hk endowments

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3. THE EMPIRICAL APPLICATION: Data and gravity estimable equations We will estimate several linear gravity equations

that include a proxy for agglomerations (MP) and for human capital endowments: General model with human capital

Our dependent variable is the share of migrants from j to i in t : we construct it from the flows of migrants from j to i in t for the 47 Spanish peninsular provinces.

Our proxy for human capital endowments is the average years of education for the employed workers (IVIE)

We use DATA from INE and IVIE.

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3. THE EMPIRICAL APPLICATION: Estimation issues and general results As our dependent variable is in logs and we have

several flows that are 0 we reformulate the model as a SAMPLE SELECTION MODEL

We use the Heckman two-step estimator to obtain the coefficients.

To quantitatively assess the effects we calculate the conditional marginal effects (on the selected sample)

Despite some data problems (autocorrelations), in some of the specifications used we find evidence that support our model.

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4. SOME SPATIAL ISSUES: The spatial issues in variables We can have problems of spatial

dependence and/or spatial heterogeneity in:

The dependent variable: share of migrants from j to i in t

For instance we can have different behaviour on specific groups of regions (coast or the south)

The regressors, specially on: wages and employment probability employed workers

spatial depend

ence

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4. SOME SPATIAL ISSUES: The spatial issues in variables

Averaged share 2001 Human capital endowment 2000

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4. SOME SPATIAL ISSUES: The spatial issues in variables

Employment rates in 2000

Nominal wages in 2000

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4. SOME SPATIAL ISSUES: Panel structure of the dataset I analyse migration for a panel of:

47 provinces15 years (1988-2002)

Is the spatial dependence structure stable during the entire period of analysis?

For a short period this could hold, but we can’t be sure for a long period of analysis

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4. SOME SPATIAL ISSUES : Migration variable constructed from flows My dependent variable share of migrants

is constructed from migration flows:

We can have spatial dependence on: Host regions (i): from a specific province j

migrants move to provinces i which are close in the space

Home regions (j): from a group of provinces j that are close in the space migrants move to a specific province i

More complicated relationships: from a group of provinces j that are close in the space migrants move to provinces i which are close in the space

flows from i (home region) to j (host

region)

THANK YOU FOR YOUR ATTENTION

Agglomeration, migrations and the role of human capital.

An analysis for the Spanish Provinces.

Rosa Sanchis-Guarner HerreroGrup d’Anàlisi Quantitativa

RegionalInstitut d’Economia

AplicadaRegional i Pública

19th Advanced Summer School in Regional Science

GIS and Spatial EconometricsUniversity of Groningen 4-12 July 2006