DOCUMENT DE TREBALL XREAP2017-03 What drives migration moves across urban areas in Spain? Evidence from the Great Recession Celia Melguizo (AQR-IREA, XREAP) Vicente Royuela (AQR-IREA, XREAP) 3 Abstract In Spain, economic disparities between regions have traditionally played a relevant role in migration. Nevertheless, during the previous high-instability period, analyses provided conflicting results about the effect of these variables. In this work, we aim to determine the role that labour market factors play in internal migration during the Great Recession, paying special attention to the migration response of the heterogeneous population groups. To do so, we resort to an extended gravity model and we consider as a territorial unit the 45 Spanish Functional Urban Areas. Our results point to real wages as having a significant influence on migration motivations. JEL Classification: C23, J61, R23. Keywords: Migration, Spanish urban areas, Labour market factors.. Celia Melguizo Cháfer. AQR Research Group-IREA. Department of Econometrics, University of Barcelona, Av. Diagonal 690, 08034 Barcelona, Spain. E-mail: cmelguizo@ub.edu Vicente Royuela: AQR Research Group-IREA. University of Barcelona, Av. Diagonal 690, 08034 Barcelona, Spain. Email: vroyuela@ub.edu. Acknowledgements We gratefully acknowledge helpful comments and suggestions from Raul Ramos and Bianca Biagi. We are also grateful to the participants of the 28th ERSA & ERES Summer School (Vienna, Austria), 56th ERSA Congress (Vienna, Austria), XLII Reunión de Estudios Regionales (Santiago, Spain) and XII Jornadas de Economia Laboral (Valladolid, Spain). We acknowledge financial support from the Catalan government. All the remaining errors are ours. 4 1. Introduction Migration flows have traditionally occurred as a result of the pursuit of personal improvement. Achieving better personal and environmental conditions has motivated individuals to move from one place to another over short and long distances. For developed economies, literature has generally acknowledged the influence of economic and labour market differentials to explain migratory flows. In Spain, internal migratory flows respond to similar patterns as those observed for most countries of the European Union: during decades of economic prosperity, regional disparities in economies and employment opportunities have motivated migration flows. However, regional disparities in Spain were not the drivers of population flows in the 1980s and early 1990s, when several periods of economic instability took place. Inconclusive results were obtained: Bentolila and Blanchard (1990), Bentolila and Dolado (1991), Antolín and Bover (1997), Bentolila (1997), Ródenas (1994), and De La Fuente (1999) found small or insignificant responses to labour market variables (several times even with the wrong sign). De La Fuente (1999) acknowledges that a decline in migration occurred due to the reduction of interregional income disparities and the adverse effect of a generalized increase in unemployment combined with the growth of unemployment benefits. Economists debated the underlying causes of the unresponsiveness to traditional explanatory variables, with Mulhern and Watson (2010) labelling it an enigma. The Great Recession had a severe impact in Spain. Economic activity shrank by 15.5 percentage points in just six years, unemployment rate increased by 18 p.p., and real wages declined by 7.2 p.p. In this context, international emigration skyrocketed, resulting in a net loss of population.1 Although in 2013 it reached the highest volume of outflows registered for decades, it accounted for just 1.1% of the population. Internal movements have declined since the start of the crisis, as Figure 1 shows, despite the persistence of internal differences in economic and labour market terms (Cuadrado-Roura and Maroto, 2016; Melguizo, 2017). As in the past, the generalized recession in the country affected all cities and regions, constraining migration decisions. 1 Figure A.1 in the appendix displays the evolution of international net migration and emigration for recent years. 5 Figure 1: Evolution of migration among Spain’s Functional Urban Areas This paper investigates the causes of migration flows in Spain during the Great Recession to determine if the Spanish migration enigma during crisis periods remains. We perform the analysis by considering an extended gravity model of migration. Our work is innovative in several ways: we investigate factors driving migration flows in the 2008 to 2014 period in Spain, one of the countries more severely affected by the Great Recession; we consider economically consistent spatial units of analysis, 45 Spanish Functional Urban Areas, improving upon previous work that uses provincial or regional data; we perform our estimates considering consistent estimation methods for count data; we take advantage of the panel dimension of our data set to control for multilateral resistance to migration by means of wide structures of fixed effects; and, we develop our analysis for different population groups, including nationals and foreigners, returned migrants and different age cohorts. Our results point to a high influence of wages on migration. Real wages are significantly associated with migration flows between urban areas, especially in the case of foreigners, for which wages are also a retention factor. Our results for this recessive period only show the influence of employment rate on nationals’ moves. The rest of the paper is organized as follows. Section two reviews the migration literature and the theories explaining these moves. Sections three and four describe the methodology and data, respectively. In section five, we present our main results, and section six concludes. 100150200250Migration AUF (in thousands)1995 2000 2005 2010 2015year 6 2. Literature Review 2.1. A general overview of migration theories Migration and its main motivations have been the focus of extensive discussion in economics literature. Authors have frequently resorted to economic differentials to explain migratory flows: Ravenstein’s pioneering works (1885, 1889) acknowledge the importance of economic disparities in understanding people movements; Hicks (1932) and Bartel (1979) point out that wage differentials motivate people to move to areas with higher salaries; Greenwood (1975, 1985) argues that migration is mainly due to the job seeking process; and Jackman and Savoury (1992) consider migration as a mechanism to improve job-matching between employers and workers. These analyses fall within the disequilibrium theories, which assume that economic differentials among territories tend to level off in the long run. Migration flows and other mobility factors foster the equilibrium among areas. However, rigidities in the labour and housing markets may complicate the adjustment process and determine the speed at which the equilibrium is reached. Nevertheless, this disequilibrium approach is called into question as a consequence of a number of studies reporting un-hypothesized signs for unemployment and real wages. The studies of Graves (1979, 1980), Marston (1985), and Knapp and Graves (1986) highlight the importance of spatial equilibrium. The equilibrium approach establishes that economic differentials among territories may occur in the long term due to other kinds of factors, such as climatic conditions and natural and social endowments, encouraging people to stay in areas where economic and labour market conditions are relatively worse. Thus, economic disparities in equilibrium are a result of constant utility across areas, where amenities and non-economic factors play a relevant role in individual preferences. Equilibrium and disequilibrium approaches were seen as competitors throughout the 1980s and most of the 1990s. However, recent economic literature has been able to reconcile both views around the utility maximization principle, which assumes that migration flows are not only due to the specific attributes of the areas, but also to the value that individuals give to these attributes, which in turn depends on the needs and preferences of individuals and households. 7 2.2. Recent evidence on migration processes and the case of Spain The utility maximization principle justifies the heterogeneity in results obtained for Europe and the US regarding their internal migration processes. In the US, people tend to be much more mobile than in Europe (Rupansigha et al., 2015). Economic disparities between these territories add to significant cultural and social heterogeneity among regions in Europe. Besides, the main motivations driving migration in the US also differ from those observed in Europe. American works like Partridge et al. (2008), Partridge (2010), and Faggian et al. (2012) find that natural amenities highly influence people movements and attribute to employment opportunities a secondary role. In Europe, economic and labour market differences among regions are key determinants of migration. Biagi et al. (2011) and Etzo (2011) find evidence for Italy: unemployment rate and per capita GDP differentials are relevant factors to explain migration from poorer southern regions to richer regions in the North. For Germany, Hunt (2006) highlights the influence of wage differentials in attracting young skilled workers from eastern to western regions. Détang-Dessendre et al. (2016) analyse 88 French labour market areas and find evidence of a significant influence of employment opportunities on people moves and commuting flows. In Spain, internal migration shows similar patterns to those observed for Europe. Economic disparities between regions leading to disequilibrium factors have traditionally played a relevant role as determinants of population movements throughout the territory. During the 1960s and 1970s, massive movements took place from the poorer regions to Madrid, Catalonia, and Basque Country, the most developed regions, driven by wages and employment opportunities (Santillana, 1981). During the period of high economic instability that took place in Spain in the 1980s and early 1990s, migratory flows declined while poorer regions that had previously been net outmigration areas became net immigration regions, and the opposite occurred for richer regions. In these years, the more important flows were those observed within regions due to the increase of employment in services, which prompted moves towards larger towns. Devillanova and García Fontés (2004) report the existence of the Lowry Effect: relatively large gross flows can generate small net flows, which take place especially for workers in the same education category. In addition, foreign immigration became an important phenomenon in those years, resulting in an important change in internal migration patterns. As Recaño and Roig (2006) explain that migration patterns of 8 foreigners are significantly different from those of the native population—foreigners are about three times more mobile. The first consequence is an increase in aggregate internal flows: about 3.4 p.p. in 2012, which contrasts to the 0.7% in 1960 (Minondo et al., 2013), and about 80% of recent flows had urban areas as a destination. Still, recent interregional migration flows (0.43% in 2002-11) were below the 1960s figures (0.77%) (Recaño et al., 2014). As can be expected, territorial disparities are a major reason for migration and the large interregional flows in the 1960s and 1970s. According to Ródenas (1994), the increase in unemployment due to the economic crisis in the 1980s was resulted in the decrease in migration flows. De la Fuente (1999) notes that the reduction of regional disparities as well as factors related to quality of life caused this decline. In addition, researchers find un-hypothesized signs and, in some cases, lack of significance for both economic and labour market variables, which has attracted the attention of many economists. Later works analysing more recent flows, such as Maza and Villaverde (2004) and Maza (2006), acknowledge the influence of regional income in the decision to move. In addition, Juárez (2000) and Mulhern and Watson (2009, 2010) obtain that unemployment differentials are also relevant factors, whereas Clemente et al. (2016) observe that labour market factors play a substantial role if the economic situation in the origin region is relatively unfavourable. Works focused on micro data, such as Antolín and Bover (1997), include a variety of personal characteristics in the analysis. They find small effect of unemployment rates for the non-registered unemployed and inconclusive results on the effect of wage differentials. The recent literature analysing migration flows also considers heterogeneous groups. Maza et al. (2013) and Clemente et al. (2016), among others, analyse flows of Spanish born versus foreigners. Overall, the selectivity of migrants and the heterogeneity of flows have been labelled as a key factor in explaining population flows. Regarding the technical approach, most academic literature focused on Spain has analysed aggregate migration flows at the provincial or regional level. Some of these works consider a panel structure, and only few of them use origin and destination fixed effects to control for unobserved heterogeneity (such as Martínez Torres, 2007). Although some articles consider count models using the number of migrants between origin and destination (Devillanova and García Fontés, 2004; Reher and Silvestre, 2009; Faggian and Royuela, 2010), most of the literature considers linear models in which the 9 dependent variable is the migration rate or the log of migrants (recently, Clemente et al., 2016). Other works use micro data, analysing the propensity to migrate (Bover and Arellano, 2002; Reher and Silvestre, 2009). Despite the large body of literature on the topic, there is a need to study migration flows during the Great Recession, the most significant crisis experienced in the country since the Civil War in 1936. Besides, there is space for a better analysis of population flows considering economically consistent spatial units, such as FUAs, rather than administrative definitions like province and region, together with differentiated flows, considering Spanish born versus foreigners, returned migrants and different age cohorts. Finally, the literature lacks studies using count data models together with wide structures of fixed effects controlling for multilateral resistance to migration effects. 3. Methodology 3.1. Theoretical approach Based on the maximization utility principle, migrants decide where to go based on the relative area factor endowments and their individual preferences for these factors. The utility (U) that the i-th area reports to the k-th individual is a function of economic and amenity endowments of the area (𝑍𝑖,) and individual idiosyncratic tastes (Ɛ𝑖𝑘): 𝑈𝑖𝑘 = 𝑢(𝑍𝑖,) + Ɛ𝑖𝑘 (1) The deterministic part is “common” to all individuals and is a function of a vector of economic factors and amenities. Given this utility function and following Faggian and Royuela (2010), k-th individual decides to move if the expected utility of a destination area j is higher than the expected reported utility of the origin area i plus the costs of moving, frequently proxied in the literature by the distance between i and j locations: E(Ukj) − c(Dij ) > E(Uki) (2) We aggregate individual decisions at a macro level following the works of Santos, Silva, and Tenreyro (2006) and Miguélez and Moreno (2014), and we define a dummy variable 𝑦𝑖𝑗𝑡𝑘 that takes the value 1 when equation (2) is met at period t and 0, otherwise. The sum of all individual decisions is represented by 𝑦𝑖𝑗𝑡 , which captures the number of flows registered between every pair of spatial units i and j at period t. Thereby, we can 10 write an extensive form of the gravity model including 𝑦𝑖𝑗𝑡 as the endogenous variable and migration potential motivations as independent variables in addition to the origin and destination population size and the distance between the aforementioned origin and destination areas. The gravity equation of our baseline specification is as follows: yijt = eβ0(𝐷𝑖𝑗)𝛽𝑘 ∏ FilλilLl=1 ∏ Fjt−1lλjlLl=1 ∏ 𝑒𝜃𝑡𝑑𝑡𝑇𝑡=1 ∏ 𝑒𝜃𝑖𝑠𝑑𝑖𝑠𝑆𝑠=1 ∏ 𝑒𝜃𝑗𝑠𝑑𝑗𝑠𝑆𝑠=1 Ɛ𝑖𝑗𝑡 (3) where 𝑦𝑖𝑗𝑡 depends multiplicatively on L push (𝐹𝑖) and pull (𝐹𝑗)) factors. An endogeneity problem may arise due to the reverse causality problem, as migration may affect labour market variables. However, in the Spanish case, gross internal migration flows represent a small percentage of the national population, casting doubts on such impact. In Table A5 of Appendix, we show for all FUAs the percentages that net migration flows of people older than 18 represent on total and working age population for 2009 and 2014 respectively. To avoid such potential impact, we lag all right hand variables in equation (3) by one year. Our empirical model also incorporates S dummy variables, 𝑑𝑠 for every origin and destination and one fixed effect for every time period, 𝑑𝑡. 𝐷𝑖𝑗 represents the travel distance between every pair of locations, 𝑒𝛽0is the constant term, and Ɛ𝑖𝑗𝑡 is the idiosyncratic error. 3.2. Estimation strategy The most common practice in empirical migration analyses has been to transform the multiplicative gravity equation by taking natural logarithms and estimating the model using Ordinary Least Squares. However, the log-linear transformation of the model entails several problems. The first problem relates to the presence of zero migration flows between pairs of areas, which becomes particularly relevant when we focus on specific population groups. Since the logarithm of zero is not defined, truncating and censuring these zero migration flows or transforming the data are two common procedures that may be accompanied by efficiency reductions due to the loss of information and/or to by estimation and sample selection bias (Westerlund and Wilhelmsson, 2009). Another problem emerges in the presence of heteroscedasticity, which frequently occurs with migration data. The OLS estimation is based on the homoscedasticity assumption. This implies that the expected value of the error term is a function of the regressors and the estimation variance is biased, affecting the model’s inference. These failures have led to the use of mixed models and nonlinear methods to 11 estimate the gravity equation. Among them, the Poisson Pseudo Maximum Likelihood (PPML) technique proposed by Santos Silva and Tenreyro (2006) has become the workhorse in gravity analyses. PPML, as a count data model, deals in a natural way with the presence of zero migration flows. In addition, it does not make any assumption about the form of heteroscedasticity, thus it is applicable under different heteroscedasticity patterns. These characteristics make PPML the appropriate method for our analysis. In order to carry out the PPML estimation, we resort to the property establishing that the conditional expectation of 𝑦𝑖𝑗𝑡 given the set of regressor 𝑥𝑖𝑗𝑡 =(1, 𝐷𝑖𝑗 , 𝐹𝑖𝑡−1𝑘 , 𝐹𝑗𝑡−1𝑘 , 𝑑𝑡 , 𝑑𝑖 , 𝑑𝑗), as in the following exponential function: 𝐸 (𝑦𝑖𝑗𝑡|𝑥𝑖𝑗𝑡) = 𝑒𝑥𝑝[𝛽0 + 𝛽𝑘 ln(𝐷𝑖𝑗) + ∑ 𝜆𝑖𝑘𝐾𝑘=1 ln𝐹𝑖𝑡−1𝑘 + ∑ 𝜆𝑗𝑘𝐾𝑘=1 ln𝐹𝑗𝑡−1𝑘 + ∑ 𝜃𝑡𝑑𝑡 + ∑ 𝜃𝑖𝑠𝑑𝑖𝑠 + ∑ 𝜃𝑗𝑠𝑑𝑗𝑠𝑆𝑠=1𝑆𝑠=1𝑇𝑡=1 ] (4) Therefore, we can estimate equation (4) without making the log-lineal transformation that OLS methodology requires. 4. Data 4.1. Urban areas As reported above, we concentrate our analysis on migration between urban areas. We consider areas to be urban if they meet definition of Functional Urban Areas (FUA) developed by the European Commission and the OECD in 2011 in the Urban Audit project. A FUA is the closest definition of a city, based on population and density criteria and its commuting zone. In Spain, the 45 FUAs included 951 municipalities in 2013.2 Figure 2 maps Spain’s FUAs, which represent about 10% of the national territory and, in 2013, accounted for over 61% of the population and about 68% of employment. Spain’s FUAs have large differences in population size and density, and in economic aspects and labour market performance. Madrid and Barcelona are the biggest urban areas: 137 and 127 municipalities and 6.5 and almost 5 million inhabitants 2 We follow the work of Ruiz and Goerlich (2015) to identify municipality changes in FUAs. We specifically consider the cases of “Villanueva de la Concepción” and “La Canonja” municipalities, which emerged during the considered period due to the disaggregation from “Antequera” and “Tarragona” respectively. We also take into account the case of “Oza-Cesuras,” which emerged from the aggregation of “Oza Dos Rios” and “Cesuras,” which no longer exist. Therefore, the number of municipalities in the considered FUAs has varied over the period. In 2016 Spain had 8,124 municipalities in total. 12 respectively. Nevertheless, the median FUA is quite far from these values, accounting for 13 municipalities and about 300,000 inhabitants. From an economic perspective, we also observe considerable heterogeneity among urban areas. In 2013, the average household income in Madrid, the urban area with the highest value, is 89.7 p.p. higher than that of Marbella, the city with the lowest average level. We can also observe large differences in unemployment rates. In 2013, Donostia, a northern urban area, registered the lowest unemployment rate (13.7%), which contrasts with Almeria, a southern province, differing by more than 30 p.p. Figure 2: Representation of Spain’s Functional Urban Areas Selecting FUAs as the territorial unit of analysis has a number of advantages. They are not mere geographical areas, but territories that are economically and socially integrated and prove to be the best approximation to the concept of local labour markets. These urban areas differ not only in economic and labour factors, but also in amenities and infrastructures, which may affect their attractiveness. Therefore, determining the influencing factors of migration between them implies performing a precise analysis of long distance moves rather than analysing short distance moves and regional or provincial data. Finally, analysing FUAs overcomes the limitations of analyses that just take into account cities and do not consider the suburbanization 13 processes. In our analysis, we remove from our observations the migration moves between FUAs whose travel distance in both directions is less than 120 kilometres. We follow the work of De la Roca (2015) in order to establish the 120 km threshold, which aims to remove from our observations those residential variations that may not imply a migration move, i.e., municipality changes that do not imply a social or a workplace change for the migrant.3 4.2. Data sources The analysis of the determinants of migration between the 45 Spanish FUAs for the 2008 to 2014 period requires the use of disaggregated data at municipality level. The final data involves a list of sources.4 Migration flows are obtained from the Residential Variation Statistics (Estadística de Variaciones Residenciales, EVR). This micro dataset contains information on individual moves that imply a municipality change, and it is compiled on the basis of municipality registration data. EVR exploits information such as the date of the residential variation and the municipalities of departure and arrival. It also accounts for nationality, birth place (either municipality or country of origin), birth date, and gender, which allows us to identify some characteristics of migrants and makes it possible to determine the migration motivations for specific groups that may present heterogeneous behaviour. EVR provides high-quality information due to the application of advanced control and data collection procedures, but also because of the Continuous Register implementation, which updates residential variation information immediately. The potential criticism of use of this data is that it represents only registered moves. However, in Spain, a registration certificate is mandatory to have access to basic social and municipal services and the right to vote, which serves as an incentive for movers to register. The alternative source, the Population Census, may not allow for tracing of the Great Recession and has been criticized in the past for erroneous input methods for nonresponse questions, making the information unreliable (Ródenas and Martí, 2009). Other sources, such as the Labour Force Survey or Social Security Records, are alternatives that are suitable for investigating either aggregate flows or personal characteristics of working people. 3 The number of origin-destination pairs is not 1,980 but 1,910, as we remove moves between the FUAs with travel distances of less than 120 km. 4 Detailed information about the datasets and the components and sources of information are compiled in table A.2, while descriptive statistics are displayed in Table A.3 in the Appendix. 14 As for the explanatory variables of our empirical model, we had to work with municipal data to build FUA consistent variables. Data for population comes from Spain’s Continuous Register, and we measure distance in minutes.5 We resort to Spain’s Social Security records for information on employment. The workers’ affiliation records with Social Security provide data on registered employment at the municipality level, and we obtain municipal working age population data from Spain’s Continuous Register. We use the average provincial wage provided by the Spanish Tax Agency (AEAT), and we use information on local housing costs collected by Idealista, a web-based real estate firm that works at the national level. We deflate nominal variables using provincial (NUTS 3) Consumer Price Indexes (CPI). Finally, we resort to the Spanish Meteorological Agency (AEMET) to obtain information on natural amenities such as temperature and rainfall. 5. Results We estimate the effect that labour market factors exert on migration for people older than 18 years of age to remove the bias that family responsibilities may generate in our results. Later, we disaggregate adult migrants by citizenship and their link with the destination (return/non-return migration). The distinction of the groups6 allows us to determine heterogeneity related to the preferences of internal migrants, which makes it possible to ascertain the role of labour market factors as determinants of internal migration in Spain. As mentioned, Table 1 presents the results of the estimation of internal migration motivations during the recent economic downturn. We consider several fixed effect structures. In column (1) we include time fixed effects, which allows to control for global time-specific events. Column (2) reports our baseline specification, including time and origin and destination fixed effects, controlling for time-invariant characteristics of every FUA. Column (3) considers a dyadic origin-destination fixed effect to control for specific permanent dyadic characteristics, such as common co-official languages that may favour migration, and social networks, as past migration 5 The driving distance indicated on Google Maps is considered for FUAs located in peninsular Spain. For FUAs located on islands, we consider the flight time (minutes) provided by AENA on regular flights between Spanish airports, which we add to the distance to the closest airport, the driving distance between the island airport and the island FUA, and an extra hour to take into account the minimum lapse of time to remain at the airport. 6 Table A4 in the Appendix displays the total number of migrants in each group and the percentage of adult migrants that each group represents. 15 episodes between pairs of FUAs may generate a stock of migrants with strong personal links, which are usually difficult to capture. Finally, we consider two additional structures of fixed effects: a model with dyadic destination-time and monodic origin fixed effects in column (4), and a model with dyadic origin-time and monodic destination fixed effects in column (5). These specifications allow us to proxy different sources of multilateral resistance to migration7 and, therefore, help us to deal with another potential source of endogeneity. Destination-time fixed effects take into account any shock that may occur and modify the preferences for the different destinations, whereas origin-time fixed effects consider the changes that modify migration preferences by origin. Our estimates use the PPML method, avoiding problem of omitting variables by considering different structures of fixed effects. Column (1) includes time fixed effects plus fixed amenities variables for origin and destination, which is clearly insufficient but allows us to find the basic estimates of a gravity equation, where the parameters for population are close to one: larger flows come from and to larger cities. This model uses both between and within information for all variables. In our case, there are considerable differences in the size of the FUAs, thus between differences are significant. Column (2) introduces origin and destination fixed effects in line with most of the empirical literature applied to the Spanish case. This model captures permanent elements of every FUA by means of a list of dummies; consequently, the parameters of the control variables exploit only within information, which is a small portion of the overall variation. Still, with this strategy we are able to capture fixed non-observables that can bias the estimation. In this specification, FUAs with more population attract large flows of migrants. The estimates of the parameters for employment opportunities and wages behave as expected, while high housing costs allow for emigration and at the same time act as a pull factor, potentially signalling a higher quality of life. Distance is significant and negatively affecting migration flows, as expected. Column (3) 7 Multilateral resistance to migration refers to the influence that third area characteristics may exert on the migration flows between two given areas. Not considering the potential sources of multilateral resistance to migration may bias the results and lead to endogeneity (Hanson, 2010), as the omission of relevant information generates regressors correlated with the error term, which is in turn also spatially and serially correlated. The Common Correlated Estimator (CCE) (Pesaran, 2006) performs correctly when the longitudinal and cross-sectional dimensions of the panel are large enough, which is not our case. In addition, this estimator exhibits the same problems as the OLS estimator in the presence of zero flows and heteroscedasticity. For all these reasons, we opt for the fixed effects structures. 16 introduces dyadic origin-destination fixed effects. The result is an increase in the adjustment of the model, which calls for specificities in migration costs between pairs of origins and destinations.8 Still, the parameters for the control variables hardly change, and consequently these specificities are not correlated with our covariates. Table 1: PPML Estimation results for total migrants (≥18) Notes: 11,460 observations. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 The last two specifications widen the fixed effects structure and allow for a similar interpretation as column 2, but controlling for all time varying effects at the destination (columns 4) and origin (column 5). Most parameters in these new and preferred specifications experience a decrease in the magnitude and in the standard errors. The latter effect is responsible for having significant and positive parameters for employment opportunities in the origin, an unexpected result. On the contrary, the 8 Table A.6 in the Appendix displays the basic results considering alternative measures of distance, such as physical distance (km) and straight line distance. As in Poot et al. (2016), straight-line distance reports the lower parameter, while in our case time distances yield lower parameters than distance in km. Migration flows (1) (2) (3) (4) (5) log Population O 0.972*** 0.488 0.475 0.564 (0.0197) (0.393) (0.392) (0.353) log Population D 0.992*** 1.341*** 1.319*** 1.198*** (0.0186) (0.355) (0.353) (0.258) log Distance (time) -0.985*** -1.056*** -1.056*** -1.056*** (0.0352) (0.0268) (0.0268) (0.0268) Emp. Rate O -0.0328 0.233 0.225 0.226* (0.280) (0.155) (0.153) (0.131) Emp. Rate D -0.315 0.291** 0.287** 0.285** (0.325) (0.142) (0.141) (0.130) log Real Wage O -0.612*** -0.680*** -0.689*** -0.603*** (0.202) (0.202) (0.199) (0.158) log Real Wage D -0.0844 0.638*** 0.643*** 0.665*** (0.200) (0.240) (0.240) (0.171) log Housing Costs O 0.0935 0.0887** 0.0851* 0.114*** (0.113) (0.0451) (0.0446) (0.0413) log Housing Costs D 0.194 0.0807* 0.0809* 0.0506 (0.122) (0.0481) (0.0481) (0.0458) Amenities O yes no no no no Amenities D yes no no no no T FE yes yes yes no no O FE no yes no yes no D FE no yes no no yes OD FE no no yes no no OT FE no no no no yes DT FE no no no yes no R-squared 0.949 0.978 0.995 0.980 0.979 17 positive parameter for housing costs in the destination stops being significant, casting doubt on the role of housing prices as quality of life signal. The literature analysing migration in Spain reports that some of the conflicting results of aggregate models are due to specificities of individuals, as heterogeneous groups respond differently to push and pull factors. We first perform separate analyses depending of the place of birth of movers, considering those who move back to their origin areas. Table 2 reports the estimates of our preferred specification for all nationals, returned nationals, and foreigners. Our preferred specifications include controls for all destination-time or origin-time specific events, allowing for concentration on the parameters in the origin and destination respectively. Table 2: Results by place of birth Nationals Returned Nationals Foreigners (1) (2) (3) (4) (5) (6) log Population O 1.706*** 2.162*** 0.417 (0.362) (0.677) (0.634) log Population D 1.662*** 0.831 1.501*** (0.305) (0.664) (0.488) log Distance (time) -1.116*** -1.116*** -1.119*** -1.119*** -0.853*** -0.853*** (0.0321) (0.0321) (0.0437) (0.0437) (0.0224) (0.0224) Emp. Rate O 0.0869 0.960** 0.902** (0.0945) (0.433) (0.378) Emp. Rate D 0.304*** 0.0102 0.399 (0.109) (0.197) (0.34) log Real Wage O 0.469*** 0.48 -2.635*** (0.18) (0.319) (0.313) log Real Wage D 0.450** 0.603* 0.515* (0.197) (0.316) (0.303) log Housing Costs O -0.0146 0.072 0.0591 (0.0394) (0.0644) (0.0768) log Housing Costs D 0.0306 -0.274*** -0.139* (0.0442) (0.0785) (0.0836) O FE yes no yes no yes no D FE no yes no yes no yes OT FE no yes no yes no yes DT FE yes no yes no yes no R-squared 0.962 0.961 0.921 0.915 0.988 0.986 Notes: 11,460 observations. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 With respect to the effect of the traditional gravity variables, for Spanish national migrants, population in origin and destination display significant and positive parameters. Besides, returned nationals leave areas with higher populations, whereas foreigners behave in the opposite way, as they are attracted by FUAs with higher populations. Distance reports the expected negative and significant parameter for all 18 groups. Its magnitude is lower for foreigners: previous moving experiences decrease the negative impact of distance in migration decisions. As for the economic variables, we see that wages have a greater effect on the foreigners group, whereas employment has a counterintuitive positive effect at origin. For Spanish national migrants, employment at destination exerts the expected influence. On the contrary, for returned national migrants, we observe a positive sign of employment at origin. This significant parameter is close to that observed for foreigners and is far from what one would expect. Finally, housing costs at destination have a negative influence on the moves of foreigners and returned migrants, which clearly shows the preference of these groups for cheaper housing. We finally analyse migration by age cohort. As in Clemente et al. (2016), we differentiate three cohorts: 18 to 30, 30 to 60, and above 60. Table 3 displays the results of these new estimates. As above with national returned migrants, we see that the above 60 group is least affected by push and pull economic factors, as it is by far the group with the lowest adjustment models. On the contrary, younger migrants are the ones with models reporting higher R2 statistics. Younger migrants are more affected by wage differentials, followed by migrants between 30 and 60. For younger migrants, high real wages in the origin discourage leaving their city, while better wage prospects in the destination encourage them to move. Employment rates do not display any significant parameters in the origin or destination. As in the previous crisis in Spain, we interpret this result as related to the almost non-existent employment opportunities in Spain during the Great Recession. Moving from an urban area with 25% unemployment to another city with about 20% unemployment presents the potential migrant with a low margin for improvement. It is difficult to interpret the parameters associated with housing costs, as they arise as positively related with migration flows both in the origin and destination. In this case, we relate the negative side of the story: urban areas with a greater decrease in housing prices leave residents with fewer opportunities to sell their property and subsequently to leave their city. As other works indicate, housing ownership (particularly high in Spain) acts as a brake on population moves. Finally, we comment on the factors influencing flows of older people. In this case, we see a positive and significant parameter for real wages at the origin, an aspect that is difficult to explain in more aggregate models or in the analysis of flows not considering the life cycle (non-returned nationals). Interestingly this group is the one more seriously 19 affected by migration costs (distance), reporting a strong role of social networks achieved over the years. Table 3: Results by age cohort [18-30) [30-60) ≥ 60 (1) (2) (3) (4) (5) (6) log Population O 1.067* 0.142 0.488 (0.566) (0.354) (0.559) log Population D 1.686*** 0.740*** 1.522*** (0.449) (0.285) (0.541) log Distance (time) -0.991*** -0.991*** -1.083*** -1.083*** -1.118*** -1.118*** (0.0261) (0.0261) (0.0262) (0.0262) (0.0475) (0.0476) Emp. Rate O 0.305 0.203 -0.106 (0.227) (0.130) (0.254) Emp. Rate D 0.385 0.189 0.483 (0.265) (0.124) (0.330) log Real Wage O -0.930*** -0.703*** 0.655** (0.252) (0.176) (0.331) log Real Wage D 0.859*** 0.480*** 1.687*** (0.258) (0.182) (0.387) log Housing Costs O 0.174*** 0.0829* 0.0532 (0.0676) (0.0466) (0.0762) log Housing Costs D 0.153** 0.0161 -0.0367 (0.0660) (0.0522) (0.0733) O FE yes no yes no yes no D FE no yes no yes no yes OT FE no yes no yes no yes DT FE yes no yes no yes no R-squared 0.978 0.978 0.980 0.980 0.870 0.869 Notes: 11,460 observations. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 6. Concluding remarks The analysis on the role of labour market factors in internal migration decisions during the Great Recession in Spain is interesting for several reasons. First, this recent economic downturn has had a strong effect on wages and, more importantly, on unemployment rates, which have greatly absorbed the economic shock. In this context, we observe a decline in internal migration, which led us to wonder to about the extent to which migration has been motivated by economic and labour market factors. Secondly, the results for migration determinants obtained in a previous period of instability in Spain—the 1980s and early 1990s—cast doubt on the influence of labour market influences on migration during the current economic crisis. 20 This work aims to determine the role of economic and labour market factors in migration flows between Spain’s FUAs during the recent economic crisis. The results highlight that labour market factors exert a significant influence on internal migration decisions. The influence of real average wages is relevant, especially for foreigners and returned nationals. On the contrary, the effect of employment rate on migration flows is less clear, as we only observe a significant positive effect of employment rate at the destination for nationals. An unexpected positive effect of employment rate at the origin is found for returned nationals and foreigners. Conflicting results are also reported for housing costs. These results are in line with the previous literature, which considers the phenomenon of migration in Spain an enigma. Still, when disaggregating migration flows by age cohort, most results are in line with the theory: real wages report significant parameters in line with the theory (negative in the origin and positive in the destination) for working age groups, while employment rates are not relevant, as unemployment rates are so high in this period that they discourage moves for finding a job. Our results can be seen as a proof of the role of the life cycle in explaining migration decisions. The obtained results show a strong role of population flows in driving spatial equilibrium on wages. 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(1946) “The P1 P2/D hypothesis: On the intercity movement of persons” American Sociological Review, 11, 677–686. 25 Appendix Figure A.1: Evolution of external migration flows Net External Migration External Emigration -2000200400600800Net External Migration (in thousands)2000 2005 2010 2015Year0100200300400500Emigration (in thousands)2000 2005 2010 2015Year 26 Table A.2: Variables definition and information sources Variable Proxy Source Migration flows Counts of people flows over 18 years old flows that take place between FUAs which are located at a minimum distance of 120 km. EVR Population Total population Continuous Register (INE) Geographical distance Driving distance in time (minutes) Google maps and own calculations Flight distance in time (minutes) if origin and/or destination FUAs are located in an island Employment Rate Registered employment in the municipalities that integer the FUAs divided by the FUAs working age population Public State Employment (Social Security Database) Real Wage Nominal Wage deflated by CPI (NUTS 3 level). For the Basque country and Navarra the data is obtained from the Wage Structure Survey. The final average income of these FUAs takes into account the relationship between NUTS 2 regional average income provided by the Tax Agency and the EES wage. AEAT Housing costs Average cost of housing per sq-m of the municipalities within every FUAs with at least 50 sale advertisements on the webpage. We calculate the average housing costs for every FUAs as a by weighted average in terms of local population. Idealista Temperature January average temperature AEMET. Guía resumida del clima en España 1981-2010 Raining Yearly average precipitation AEMET. Guía resumida del clima en España 1981-2010 Coast 1: if the FUA has a coast; 0: otherwise Google maps 27 Table A.3: Descriptive Statistics Variable Mean Std. Dev. Min Max Perc. of zero mig. flows (over 1) Overall between within Total migration flowsa 77.11623 240.2902 239.597 18.91341 0 5182 0.0093 National mig. flowsa 52.89014 150.1369 149.4347 14.83595 0 2815 0.0255 Foreigners mig. flowsa 24.22609 98.16614 96.53414 17.93921 0 2881 0.1051 Non-returned mig. flowsa 64.84921 208.2827 207.5543 17.93638 0 4633 0.0140 Returned mig. flowsa 12.26702 35.797 35.39043 5.430352 0 612 0.2178 log Population 12.80887 0.846215 0.846305 0.012611 11.70911 15.69795 - log Distance (time) b1 5.743414 0.453164 0.453263 0 4.313703 6.451628 - log Distance (km) b2 6.442407 0.6149484 0.6150826 0 4.788857 7.826089 - Euclidean distance 6.206696 0 .6376079 0 .6377471 0 4.476022 7.697176 - Employment Rate 0.470909 0.082319 0.073947 0.036204 0.061432 0.711543 - log Real Wages 9.824285 0.164877 0.155951 0.053612 9.374074 10.21438 - log Housing Costs 7.664878 0.293618 0.239011 0.170618 6.902994 8.496229 - log Temperature 2.138842 0.401992 0.40208 0 1.131402 2.901422 - log Raining 6.245654 0.531201 0.531316 0 5.019265 7.490362 Coast 0.487958 0.499877 0.499986 0 0 1 - Notes: (i) N = 11460; n = 1910; T = 6 (ii) a We consider migration flows of people equal or older than 18 years (iii) b1 Travel distance in time considering flight travel time in the case of islands b2 Travel distance in km Table A.4: Percentage of adult migrants that each group represents Migration groups Total Number of Migrants Percentage (%) Older than 18 883,752 100.00% Nationals 606,121 68.58% Return nationals 140,580 23.19% Foreigners 277,631 31.42% Aged 18-30 294,511 33.32% Aged 30-60 512,779 58.02% Aged older than 60 76,200 8,62% 28 Table A.5: Percentage of net flows on total and working age population FUAs Percentage of net flows (%) 2009 2014 Total Population Working age Popa Total Population Working age Popa Albacete -0.054 -0.066 -0.257 -0.310 Algeciras -0.145 -0.178 -0.007 -0.008 Alacant/Alicante -0.103 -0.123 -0.042 -0.050 Almería -0.044 -0.053 -0.016 -0.020 Badajoz -0.052 -0.063 -0.209 -0.253 Barcelona 0.031 0.037 0.065 0.077 Bilbao 0.063 0.072 0.005 0.006 Burgos -0.170 -0.199 -0.220 -0.260 Cádiz -0.114 -0.135 -0.223 -0.265 Cartagena -0.018 -0.022 -0.071 -0.088 Castellón -0.164 -0.196 -0.093 -0.111 Córdoba -0.191 -0.230 -0.189 -0.227 A Coruña 0.020 0.023 -0.100 -0.116 Donostia -0.009 -0.011 0.098 0.115 Elche/Elx -0.044 -0.053 -0.020 -0.024 Gijon -0.063 -0.071 -0.090 -0.102 Granada -0.070 -0.084 -0.136 -0.165 Huelva -0.011 -0.013 -0.105 -0.128 Jaén -0.184 -0.224 -0.282 -0.340 Jérez de la Frontera -0.009 -0.011 -0.139 -0.171 Palmas Gran Canaria -0.063 -0.076 -0.068 -0.081 León -0.149 -0.171 -0.285 -0.329 Lleida -0.106 -0.126 -0.133 -0.160 Logroño -0.103 -0.122 -0.020 -0.024 Madrid 0.065 0.077 0.118 0.141 Málaga 0.084 0.101 0.097 0.118 Marbella 0.092 0.111 0.167 0.202 Murcia -0.014 -0.017 -0.080 -0.099 Ourense 0.038 0.043 -0.148 -0.169 Oviedo -0.072 -0.082 -0.216 -0.247 Palma de Mallorca -0.023 -0.027 0.087 0.105 Pamplona/Iruña 0.111 0.133 -0.022 -0.027 Reus -0.083 -0.101 -0.039 -0.048 Salamanca -0.273 -0.319 -0.349 -0.410 Santander -0.061 -0.071 -0.070 -0.081 S. Compostela 0.075 0.086 -0.097 -0.112 Sevilla 0.063 0.077 -0.097 -0.118 Sta. Cruz de Tenerife -0.070 -0.082 -0.045 -0.053 Tarragona -0.087 -0.104 -0.045 -0.055 Toledo -0.010 -0.012 -0.064 -0.079 Valencia -0.041 -0.048 -0.041 -0.049 Valladolid -0.103 -0.119 -0.169 -0.198 Vigo -0.015 -0.017 -0.066 -0.077 Vitoria-Gasteiz 0.053 0.061 0.139 0.164 Zaragoza -0.133 -0.156 -0.044 -0.052 Notes: a Working age population is the population older than 16 29 Table A.6: Checking the results by considering different distance measures Variables Dep. Vble: Counts of migration moves (1) (2) (3) (4) (1) (2) (3) (4) Population O (nl) 0.892*** 0.488 0.608* 0.897*** 0.489 0.621* (0.0231) (0.393) (0.357) (0.0234) (0.393) (0.358) Population D (nl) 0.907*** 1.350*** 1.201*** 0.912*** 1.347*** 1.160*** (0.0220) (0.356) (0.255) (0.0221) (0.355) (0.254) Travel Time Distance Km (nl) -0.624*** -0.912*** -0.912*** -0.912*** (0.0388) (0.0302) (0.0302) (0.0302) Euclidean Distance Km (nl) -0.604*** -0.902*** -0.902*** -0.902*** (0.0387) (0.0306) (0.0306) (0.0306) Emp. Rate O -0.0473 0.232 0.234* -0.0306 0.232 0.237* (0.290) (0.155) (0.134) (0.291) (0.155) (0.134) Emp. Rate D -0.350 0.291** 0.280** -0.336 0.291** 0.281** (0.318) (0.142) (0.129) (0.316) (0.142) (0.129) Real Wage O (nl) 0.122 -0.680*** -0.611*** 0.167 -0.679*** -0.604*** (0.237) (0.203) (0.160) (0.239) (0.203) (0.160) Real Wage D (nl) 0.668*** 0.631*** 0.659*** 0.723*** 0.632*** 0.646*** (0.224) (0.240) (0.170) (0.224) (0.240) (0.169) Housing Costs O(nl) 0.0616 0.0900** 0.118*** 0.0242 0.0900** 0.120*** (0.116) (0.0450) (0.0412) (0.118) (0.0451) (0.0413) Housing Costs D(nl) 0.165 0.0820* 0.0512 0.130 0.0820* 0.0518 (0.122) (0.0482) (0.0464) (0.122) (0.0483) (0.0465) Constant -21.67*** -13.85 8.023 -13.12*** -22.23*** -14.08 7.502 -12.70*** (2.779) (9.526) (6.083) (3.751) (2.763) (9.522) (6.106) (3.717) Amenities O yes no no no yes no no no Amenities D yes no no no yes no no no T FE yes yes no no yes yes no no O FE no yes yes no no yes yes no D FE no yes no yes no yes no yes OD FE no no no no no no no no OT FE no no no yes no no no yes DT FE no no yes no no no yes no Observations a 11,460 11,460 11,460 11,460 11,460 11,460 11,460 11,460 R-squared 0.935 0.976 0.977 0.977 0.935 0.975 0.977 0.977 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 SÈRIE DE DOCUMENTS DE TREBALL DE LA XREAP 2006 CREAP2006-01 Matas, A. (GEAP); Raymond, J.Ll. (GEAP) "Economic development and changes in car ownership patterns" (Juny 2006) CREAP2006-02 Trillas, F. (IEB); Montolio, D. (IEB); Duch, N. (IEB) "Productive efficiency and regulatory reform: The case of Vehicle Inspection Services" (Setembre 2006) CREAP2006-03 Bel, G. (PPRE-IREA); Fageda, X. (PPRE-IREA) "Factors explaining local privatization: A meta-regression analysis" (Octubre 2006) CREAP2006-04 Fernàndez-Villadangos, L. (PPRE-IREA) "Are two-part tariffs efficient when consumers plan ahead?: An empirical study" (Octubre 2006) CREAP2006-05 Artís, M. (AQR-IREA); Ramos, R. (AQR-IREA); Suriñach, J. (AQR-IREA) "Job losses, outsourcing and relocation: Empirical evidence using microdata" (Octubre 2006) CREAP2006-06 Alcañiz, M. (RISC-IREA); Costa, A.; Guillén, M. (RISC-IREA); Luna, C.; Rovira, C. "Calculation of the variance in surveys of the economic climate” (Novembre 2006) CREAP2006-07 Albalate, D. (PPRE-IREA) "Lowering blood alcohol content levels to save lives: The European Experience” (Desembre 2006) CREAP2006-08 Garrido, A. (IEB); Arqué, P. (IEB) “The choice of banking firm: Are the interest rate a significant criteria?” (Desembre 2006) CREAP2006-09 Segarra, A. (GRIT); Teruel-Carrizosa, M. (GRIT) "Productivity growth and competition in spanish manufacturing firms: What has happened in recent years?” (Desembre 2006) CREAP2006-10 Andonova, V.; Díaz-Serrano, Luis. (CREB) "Political institutions and the development of telecommunications” (Desembre 2006) CREAP2006-11 Raymond, J.L.(GEAP); Roig, J.L.. (GEAP) "Capital humano: un análisis comparativo Catalunya-España” (Desembre 2006) CREAP2006-12 Rodríguez, M.(CREB); Stoyanova, A. (CREB) "Changes in the demand for private medical insurance following a shift in tax incentives” (Desembre 2006) CREAP2006-13 Royuela, V. (AQR-IREA); Lambiri, D.; Biagi, B. "Economía urbana y calidad de vida. Una revisión del estado del conocimiento en España” (Desembre 2006) CREAP2006-14 SÈRIE DE DOCUMENTS DE TREBALL DE LA XREAP Camarero, M.; Carrion-i-Silvestre, J.LL. (AQR-IREA).;Tamarit, C. "New evidence of the real interest rate parity for OECD countries using panel unit root tests with breaks” (Desembre 2006) CREAP2006-15 Karanassou, M.; Sala, H. (GEAP).;Snower , D. J. "The macroeconomics of the labor market: Three fundamental views” (Desembre 2006) 2007 XREAP2007-01 Castany, L (AQR-IREA); López-Bazo, E. (AQR-IREA).;Moreno , R. (AQR-IREA) "Decomposing differences in total factor productivity across firm size” (Març 2007) XREAP2007-02 Raymond, J. Ll. (GEAP); Roig, J. Ll. (GEAP) “Una propuesta de evaluación de las externalidades de capital humano en la empresa" (Abril 2007) XREAP2007-03 Durán, J. M. (IEB); Esteller, A. (IEB) “An empirical analysis of wealth taxation: Equity vs. Tax compliance” (Juny 2007) XREAP2007-04 Matas, A. (GEAP); Raymond, J.Ll. (GEAP) “Cross-section data, disequilibrium situations and estimated coefficients: evidence from car ownership demand” (Juny 2007) XREAP2007-05 Jofre-Montseny, J. (IEB); Solé-Ollé, A. (IEB) “Tax differentials and agglomeration economies in intraregional firm location” (Juny 2007) XREAP2007-06 Álvarez-Albelo, C. (CREB); Hernández-Martín, R. “Explaining high economic growth in small tourism countries with a dynamic general equilibrium model” (Juliol 2007) XREAP2007-07 Duch, N. (IEB); Montolio, D. (IEB); Mediavilla, M. “Evaluating the impact of public subsidies on a firm’s performance: a quasi-experimental approach” (Juliol 2007) XREAP2007-08 Segarra-Blasco, A. (GRIT) “Innovation sources and productivity: a quantile regression analysis” (Octubre 2007) XREAP2007-09 Albalate, D. (PPRE-IREA) “Shifting death to their Alternatives: The case of Toll Motorways” (Octubre 2007) XREAP2007-10 Segarra-Blasco, A. (GRIT); Garcia-Quevedo, J. (IEB); Teruel-Carrizosa, M. (GRIT) “Barriers to innovation and public policy in catalonia” (Novembre 2007) XREAP2007-11 Bel, G. (PPRE-IREA); Foote, J. “Comparison of recent toll road concession transactions in the United States and France” (Novembre 2007) XREAP2007-12 Segarra-Blasco, A. (GRIT); “Innovation, R&D spillovers and productivity: the role of knowledge-intensive services” (Novembre 2007) SÈRIE DE DOCUMENTS DE TREBALL DE LA XREAP XREAP2007-13 Bermúdez Morata, Ll. (RFA-IREA); Guillén Estany, M. (RFA-IREA), Solé Auró, A. (RFA-IREA) “Impacto de la inmigración sobre la esperanza de vida en salud y en discapacidad de la población española” (Novembre 2007) XREAP2007-14 Calaeys, P. (AQR-IREA); Ramos, R. (AQR-IREA), Suriñach, J. (AQR-IREA) “Fiscal sustainability across government tiers” (Desembre 2007) XREAP2007-15 Sánchez Hugalbe, A. (IEB) “Influencia de la inmigración en la elección escolar” (Desembre 2007) 2008 XREAP2008-01 Durán Weitkamp, C. (GRIT); Martín Bofarull, M. (GRIT) ; Pablo Martí, F. “Economic effects of road accessibility in the Pyrenees: User perspective” (Gener 2008) XREAP2008-02 Díaz-Serrano, L.; Stoyanova, A. P. (CREB) “The Causal Relationship between Individual’s Choice Behavior and Self-Reported Satisfaction: the Case of Residential Mobility in the EU” (Març 2008) XREAP2008-03 Matas, A. (GEAP); Raymond, J. L. (GEAP); Roig, J. L. (GEAP) “Car ownership and access to jobs in Spain” (Abril 2008) XREAP2008-04 Bel, G. (PPRE-IREA) ; Fageda, X. (PPRE-IREA) “Privatization and competition in the delivery of local services: An empirical examination of the dual market hypothesis” (Abril 2008) XREAP2008-05 Matas, A. (GEAP); Raymond, J. L. (GEAP); Roig, J. L. (GEAP) “Job accessibility and employment probability” (Maig 2008) XREAP2008-06 Basher, S. A.; Carrión, J. Ll. (AQR-IREA) Deconstructing Shocks and Persistence in OECD Real Exchange Rates (Juny 2008) XREAP2008-07 Sanromá, E. (IEB); Ramos, R. (AQR-IREA); Simón, H. Portabilidad del capital humano y asimilación de los inmigrantes. Evidencia para España (Juliol 2008) XREAP2008-08 Basher, S. A.; Carrión, J. Ll. (AQR-IREA) Price level convergence, purchasing power parity and multiple structural breaks: An application to US cities (Juliol 2008) XREAP2008-09 Bermúdez, Ll. (RFA-IREA) A priori ratemaking using bivariate poisson regression models (Juliol 2008) SÈRIE DE DOCUMENTS DE TREBALL DE LA XREAP XREAP2008-10 Solé-Ollé, A. (IEB), Hortas Rico, M. (IEB) Does urban sprawl increase the costs of providing local public services? Evidence from Spanish municipalities (Novembre 2008) XREAP2008-11 Teruel-Carrizosa, M. (GRIT), Segarra-Blasco, A. (GRIT) Immigration and Firm Growth: Evidence from Spanish cities (Novembre 2008) XREAP2008-12 Duch-Brown, N. (IEB), García-Quevedo, J. (IEB), Montolio, D. (IEB) Assessing the assignation of public subsidies: Do the experts choose the most efficient R&D projects? (Novembre 2008) XREAP2008-13 Bilotkach, V., Fageda, X. (PPRE-IREA), Flores-Fillol, R. Scheduled service versus personal transportation: the role of distance (Desembre 2008) XREAP2008-14 Albalate, D. (PPRE-IREA), Gel, G. (PPRE-IREA) Tourism and urban transport: Holding demand pressure under supply constraints (Desembre 2008) 2009 XREAP2009-01 Calonge, S. (CREB); Tejada, O. “A theoretical and practical study on linear reforms of dual taxes” (Febrer 2009) XREAP2009-02 Albalate, D. (PPRE-IREA); Fernández-Villadangos, L. (PPRE-IREA) “Exploring Determinants of Urban Motorcycle Accident Severity: The Case of Barcelona” (Març 2009) XREAP2009-03 Borrell, J. R. (PPRE-IREA); Fernández-Villadangos, L. (PPRE-IREA) “Assessing excess profits from different entry regulations” (Abril 2009) XREAP2009-04 Sanromá, E. (IEB); Ramos, R. (AQR-IREA), Simon, H. “Los salarios de los inmigrantes en el mercado de trabajo español. ¿Importa el origen del capital humano?” (Abril 2009) XREAP2009-05 Jiménez, J. L.; Perdiguero, J. (PPRE-IREA) “(No)competition in the Spanish retailing gasoline market: a variance filter approach” (Maig 2009) XREAP2009-06 Álvarez-Albelo,C. D. (CREB), Manresa, A. (CREB), Pigem-Vigo, M. (CREB) “International trade as the sole engine of growth for an economy” (Juny 2009) XREAP2009-07 Callejón, M. (PPRE-IREA), Ortún V, M. “The Black Box of Business Dynamics” (Setembre 2009) XREAP2009-08 Lucena, A. (CREB) “The antecedents and innovation consequences of organizational search: empirical evidence for Spain” (Octubre 2009) SÈRIE DE DOCUMENTS DE TREBALL DE LA XREAP XREAP2009-09 Domènech Campmajó, L. (PPRE-IREA) “Competition between TV Platforms” (Octubre 2009) XREAP2009-10 Solé-Auró, A. (RFA-IREA),Guillén, M. (RFA-IREA), Crimmins, E. M. “Health care utilization among immigrants and native-born populations in 11 European countries. Results from the Survey of Health, Ageing and Retirement in Europe” (Octubre 2009) XREAP2009-11 Segarra, A. (GRIT), Teruel, M. (GRIT) “Small firms, growth and financial constraints” (Octubre 2009) XREAP2009-12 Matas, A. (GEAP), Raymond, J.Ll. (GEAP), Ruiz, A. (GEAP) “Traffic forecasts under uncertainty and capacity constraints” (Novembre 2009) XREAP2009-13 Sole-Ollé, A. (IEB) “Inter-regional redistribution through infrastructure investment: tactical or programmatic?” (Novembre 2009) XREAP2009-14 Del Barrio-Castro, T., García-Quevedo, J. (IEB) “The determinants of university patenting: Do incentives matter?” (Novembre 2009) XREAP2009-15 Ramos, R. (AQR-IREA), Suriñach, J. (AQR-IREA), Artís, M. (AQR-IREA) “Human capital spillovers, productivity and regional convergence in Spain” (Novembre 2009) XREAP2009-16 Álvarez-Albelo, C. D. (CREB), Hernández-Martín, R. “The commons and anti-commons problems in the tourism economy” (Desembre 2009) 2010 XREAP2010-01 García-López, M. A. (GEAP) “The Accessibility City. When Transport Infrastructure Matters in Urban Spatial Structure” (Febrer 2010) XREAP2010-02 García-Quevedo, J. (IEB), Mas-Verdú, F. (IEB), Polo-Otero, J. (IEB) “Which firms want PhDs? The effect of the university-industry relationship on the PhD labour market” (Març 2010) XREAP2010-03 Pitt, D., Guillén, M. (RFA-IREA) “An introduction to parametric and non-parametric models for bivariate positive insurance claim severity distributions” (Març 2010) XREAP2010-04 Bermúdez, Ll. (RFA-IREA), Karlis, D. “Modelling dependence in a ratemaking procedure with multivariate Poisson regression models” (Abril 2010) XREAP2010-05 Di Paolo, A. (IEB) “Parental education and family characteristics: educational opportunities across cohorts in Italy and Spain” (Maig 2010) XREAP2010-06 Simón, H. (IEB), Ramos, R. (AQR-IREA), Sanromá, E. (IEB) SÈRIE DE DOCUMENTS DE TREBALL DE LA XREAP “Movilidad ocupacional de los inmigrantes en una economía de bajas cualificaciones. El caso de España” (Juny 2010) XREAP2010-07 Di Paolo, A. (GEAP & IEB), Raymond, J. Ll. (GEAP & IEB) “Language knowledge and earnings in Catalonia” (Juliol 2010) XREAP2010-08 Bolancé, C. (RFA-IREA), Alemany, R. (RFA-IREA), Guillén, M. (RFA-IREA) “Prediction of the economic cost of individual long-term care in the Spanish population” (Setembre 2010) XREAP2010-09 Di Paolo, A. (GEAP & IEB) “Knowledge of catalan, public/private sector choice and earnings: Evidence from a double sample selection model” (Setembre 2010) XREAP2010-10 Coad, A., Segarra, A. (GRIT), Teruel, M. (GRIT) “Like milk or wine: Does firm performance improve with age?” (Setembre 2010) XREAP2010-11 Di Paolo, A. (GEAP & IEB), Raymond, J. Ll. (GEAP & IEB), Calero, J. (IEB) “Exploring educational mobility in Europe” (Octubre 2010) XREAP2010-12 Borrell, A. (GiM-IREA), Fernández-Villadangos, L. (GiM-IREA) “Clustering or scattering: the underlying reason for regulating distance among retail outlets” (Desembre 2010) XREAP2010-13 Di Paolo, A. (GEAP & IEB) “School composition effects in Spain” (Desembre 2010) XREAP2010-14 Fageda, X. (GiM-IREA), Flores-Fillol, R. “Technology, Business Models and Network Structure in the Airline Industry” (Desembre 2010) XREAP2010-15 Albalate, D. (GiM-IREA), Bel, G. (GiM-IREA), Fageda, X. (GiM-IREA) “Is it Redistribution or Centralization? On the Determinants of Government Investment in Infrastructure” (Desembre 2010) XREAP2010-16 Oppedisano, V., Turati, G. “What are the causes of educational inequalities and of their evolution over time in Europe? Evidence from PISA” (Desembre 2010) XREAP2010-17 Canova, L., Vaglio, A. “Why do educated mothers matter? A model of parental help” (Desembre 2010) 2011 XREAP2011-01 Fageda, X. (GiM-IREA), Perdiguero, J. (GiM-IREA) “An empirical analysis of a merger between a network and low-cost airlines” (Maig 2011) SÈRIE DE DOCUMENTS DE TREBALL DE LA XREAP XREAP2011-02 Moreno-Torres, I. (ACCO, CRES & GiM-IREA) “What if there was a stronger pharmaceutical price competition in Spain? When regulation has a similar effect to collusion” (Maig 2011) XREAP2011-03 Miguélez, E. (AQR-IREA); Gómez-Miguélez, I. “Singling out individual inventors from patent data” (Maig 2011) XREAP2011-04 Moreno-Torres, I. (ACCO, CRES & GiM-IREA) “Generic drugs in Spain: price competition vs. moral hazard” (Maig 2011) XREAP2011-05 Nieto, S. (AQR-IREA), Ramos, R. (AQR-IREA) “¿Afecta la sobreeducación de los padres al rendimiento académico de sus hijos?” (Maig 2011) XREAP2011-06 Pitt, D., Guillén, M. (RFA-IREA), Bolancé, C. (RFA-IREA) “Estimation of Parametric and Nonparametric Models for Univariate Claim Severity Distributions - an approach using R” (Juny 2011) XREAP2011-07 Guillén, M. (RFA-IREA), Comas-Herrera, A. “How much risk is mitigated by LTC Insurance? A case study of the public system in Spain” (Juny 2011) XREAP2011-08 Ayuso, M. (RFA-IREA), Guillén, M. (RFA-IREA), Bolancé, C. (RFA-IREA) “Loss risk through fraud in car insurance” (Juny 2011) XREAP2011-09 Duch-Brown, N. (IEB), García-Quevedo, J. (IEB), Montolio, D. (IEB) “The link between public support and private R&D effort: What is the optimal subsidy?” (Juny 2011) XREAP2011-10 Bermúdez, Ll. (RFA-IREA), Karlis, D. “Mixture of bivariate Poisson regression models with an application to insurance” (Juliol 2011) XREAP2011-11 Varela-Irimia, X-L. (GRIT) “Age effects, unobserved characteristics and hedonic price indexes: The Spanish car market in the 1990s” (Agost 2011) XREAP2011-12 Bermúdez, Ll. (RFA-IREA), Ferri, A. (RFA-IREA), Guillén, M. (RFA-IREA) “A correlation sensitivity analysis of non-life underwriting risk in solvency capital requirement estimation” (Setembre 2011) XREAP2011-13 Guillén, M. (RFA-IREA), Pérez-Marín, A. (RFA-IREA), Alcañiz, M. (RFA-IREA) “A logistic regression approach to estimating customer profit loss due to lapses in insurance” (Octubre 2011) XREAP2011-14 Jiménez, J. L., Perdiguero, J. (GiM-IREA), García, C. “Evaluation of subsidies programs to sell green cars: Impact on prices, quantities and efficiency” (Octubre 2011) SÈRIE DE DOCUMENTS DE TREBALL DE LA XREAP XREAP2011-15 Arespa, M. (CREB) “A New Open Economy Macroeconomic Model with Endogenous Portfolio Diversification and Firms Entry” (Octubre 2011) XREAP2011-16 Matas, A. (GEAP), Raymond, J. L. (GEAP), Roig, J.L. (GEAP) “The impact of agglomeration effects and accessibility on wages” (Novembre 2011) XREAP2011-17 Segarra, A. (GRIT) “R&D cooperation between Spanish firms and scientific partners: what is the role of tertiary education?” (Novembre 2011) XREAP2011-18 García-Pérez, J. I.; Hidalgo-Hidalgo, M.; Robles-Zurita, J. A. “Does grade retention affect achievement? Some evidence from PISA” (Novembre 2011) XREAP2011-19 Arespa, M. (CREB) “Macroeconomics of extensive margins: a simple model” (Novembre 2011) XREAP2011-20 García-Quevedo, J. (IEB), Pellegrino, G. (IEB), Vivarelli, M. “The determinants of YICs’ R&D activity” (Desembre 2011) XREAP2011-21 González-Val, R. (IEB), Olmo, J. “Growth in a Cross-Section of Cities: Location, Increasing Returns or Random Growth?” (Desembre 2011) XREAP2011-22 Gombau, V. (GRIT), Segarra, A. (GRIT) “The Innovation and Imitation Dichotomy in Spanish firms: do absorptive capacity and the technological frontier matter?” (Desembre 2011) 2012 XREAP2012-01 Borrell, J. R. (GiM-IREA), Jiménez, J. L., García, C. “Evaluating Antitrust Leniency Programs” (Gener 2012) XREAP2012-02 Ferri, A. (RFA-IREA), Guillén, M. (RFA-IREA), Bermúdez, Ll. (RFA-IREA) “Solvency capital estimation and risk measures” (Gener 2012) XREAP2012-03 Ferri, A. (RFA-IREA), Bermúdez, Ll. (RFA-IREA), Guillén, M. (RFA-IREA) “How to use the standard model with own data” (Febrer 2012) XREAP2012-04 Perdiguero, J. (GiM-IREA), Borrell, J.R. (GiM-IREA) “Driving competition in local gasoline markets” (Març 2012) XREAP2012-05 D’Amico, G., Guillen, M. (RFA-IREA), Manca, R. “Discrete time Non-homogeneous Semi-Markov Processes applied to Models for Disability Insurance” (Març 2012) SÈRIE DE DOCUMENTS DE TREBALL DE LA XREAP XREAP2012-06 Bové-Sans, M. A. (GRIT), Laguado-Ramírez, R. “Quantitative analysis of image factors in a cultural heritage tourist destination” (Abril 2012) XREAP2012-07 Tello, C. (AQR-IREA), Ramos, R. (AQR-IREA), Artís, M. (AQR-IREA) “Changes in wage structure in Mexico going beyond the mean: An analysis of differences in distribution, 1987-2008” (Maig 2012) XREAP2012-08 Jofre-Monseny, J. (IEB), Marín-López, R. (IEB), Viladecans-Marsal, E. (IEB) “What underlies localization and urbanization economies? Evidence from the location of new firms” (Maig 2012) XREAP2012-09 Muñiz, I. (GEAP), Calatayud, D., Dobaño, R. “Los límites de la compacidad urbana como instrumento a favor de la sostenibilidad. La hipótesis de la compensación en Barcelona medida a través de la huella ecológica de la movilidad y la vivienda” (Maig 2012) XREAP2012-10 Arqué-Castells, P. (GEAP), Mohnen, P. “Sunk costs, extensive R&D subsidies and permanent inducement effects” (Maig 2012) XREAP2012-11 Boj, E. (CREB), Delicado, P., Fortiana, J., Esteve, A., Caballé, A. “Local Distance-Based Generalized Linear Models using the dbstats package for R” (Maig 2012) XREAP2012-12 Royuela, V. (AQR-IREA) “What about people in European Regional Science?” (Maig 2012) XREAP2012-13 Osorio A. M. (RFA-IREA), Bolancé, C. (RFA-IREA), Madise, N. “Intermediary and structural determinants of early childhood health in Colombia: exploring the role of communities” (Juny 2012) XREAP2012-14 Miguelez. E. (AQR-IREA), Moreno, R. (AQR-IREA) “Do labour mobility and networks foster geographical knowledge diffusion? The case of European regions” (Juliol 2012) XREAP2012-15 Teixidó-Figueras, J. (GRIT), Duró, J. A. (GRIT) “Ecological Footprint Inequality: A methodological review and some results” (Setembre 2012) XREAP2012-16 Varela-Irimia, X-L. (GRIT) “Profitability, uncertainty and multi-product firm product proliferation: The Spanish car industry” (Setembre 2012) XREAP2012-17 Duró, J. A. (GRIT), Teixidó-Figueras, J. (GRIT) “Ecological Footprint Inequality across countries: the role of environment intensity, income and interaction effects” (Octubre 2012) XREAP2012-18 Manresa, A. (CREB), Sancho, F. “Leontief versus Ghosh: two faces of the same coin” (Octubre 2012) SÈRIE DE DOCUMENTS DE TREBALL DE LA XREAP XREAP2012-19 Alemany, R. (RFA-IREA), Bolancé, C. (RFA-IREA), Guillén, M. (RFA-IREA) “Nonparametric estimation of Value-at-Risk” (Octubre 2012) XREAP2012-20 Herrera-Idárraga, P. (AQR-IREA), López-Bazo, E. (AQR-IREA), Motellón, E. (AQR-IREA) “Informality and overeducation in the labor market of a developing country” (Novembre 2012) XREAP2012-21 Di Paolo, A. (AQR-IREA) “(Endogenous) occupational choices and job satisfaction among recent PhD recipients: evidence from Catalonia” (Desembre 2012) 2013 XREAP2013-01 Segarra, A. (GRIT), García-Quevedo, J. (IEB), Teruel, M. (GRIT) “Financial constraints and the failure of innovation projects” (Març 2013) XREAP2013-02 Osorio, A. M. (RFA-IREA), Bolancé, C. (RFA-IREA), Madise, N., Rathmann, K. “Social Determinants of Child Health in Colombia: Can Community Education Moderate the Effect of Family Characteristics?” (Març 2013) XREAP2013-03 Teixidó-Figueras, J. (GRIT), Duró, J. A. (GRIT) “The building blocks of international ecological footprint inequality: a regression-based decomposition” (Abril 2013) XREAP2013-04 Salcedo-Sanz, S., Carro-Calvo, L., Claramunt, M. (CREB), Castañer, A. (CREB), Marmol, M. (CREB) “An Analysis of Black-box Optimization Problems in Reinsurance: Evolutionary-based Approaches” (Maig 2013) XREAP2013-05 Alcañiz, M. (RFA), Guillén, M. (RFA), Sánchez-Moscona, D. (RFA), Santolino, M. (RFA), Llatje, O., Ramon, Ll. “Prevalence of alcohol-impaired drivers based on random breath tests in a roadside survey” (Juliol 2013) XREAP2013-06 Matas, A. (GEAP & IEB), Raymond, J. Ll. (GEAP & IEB), Roig, J. L. (GEAP) “How market access shapes human capital investment in a peripheral country” (Octubre 2013) XREAP2013-07 Di Paolo, A. (AQR-IREA), Tansel, A. “Returns to Foreign Language Skills in a Developing Country: The Case of Turkey” (Novembre 2013) XREAP2013-08 Fernández Gual, V. (GRIT), Segarra, A. (GRIT) “The Impact of Cooperation on R&D, Innovation andProductivity: an Analysis of Spanish Manufacturing and Services Firms” (Novembre 2013) XREAP2013-09 Bahraoui, Z. (RFA); Bolancé, C. (RFA); Pérez-Marín. A. M. (RFA) “Testing extreme value copulas to estimate the quantile” (Novembre 2013) 2014 XREAP2014-01 Solé-Auró, A. (RFA), Alcañiz, M. (RFA) “Are we living longer but less healthy? Trends in mortality and morbidity in Catalonia (Spain), 1994-2011” (Gener 2014) XREAP2014-02 SÈRIE DE DOCUMENTS DE TREBALL DE LA XREAP Teixidó-Figueres, J. (GRIT), Duro, J. A. (GRIT) “Spatial Polarization of the Ecological Footprint distribution” (Febrer 2014) XREAP2014-03 Cristobal-Cebolla, A.; Gil Lafuente, A. M. (RFA), Merigó Lindhal, J. M. (RFA) “La importancia del control de los costes de la no-calidad en la empresa” (Febrer 2014) XREAP2014-04 Castañer, A. (CREB); Claramunt, M.M. (CREB) “Optimal stop-loss reinsurance: a dependence analysis” (Abril 2014) XREAP2014-05 Di Paolo, A. (AQR-IREA); Matas, A. (GEAP); Raymond, J. Ll. (GEAP) “Job accessibility, employment and job-education mismatch in the metropolitan area of Barcelona” (Maig 2014) XREAP2014-06 Di Paolo, A. (AQR-IREA); Mañé, F. “Are we wasting our talent? Overqualification and overskilling among PhD graduates” (Juny 2014) XREAP2014-07 Segarra, A. (GRIT); Teruel, M. (GRIT); Bové, M. A. (GRIT) “A territorial approach to R&D subsidies: Empirical evidence for Catalonian firms” (Setembre 2014) XREAP2014-08 Ramos, R. (AQR-IREA); Sanromá, E. (IEB); Simón, H. “Public-private sector wage differentials by type of contract: evidence from Spain” (Octubre 2014) XREAP2014-09 Bel, G. (GiM-IREA); Bolancé, C. (Riskcenter-IREA); Guillén, M. (Riskcenter-IREA); Rosell, J. (GiM-IREA) “The environmental effects of changing speed limits: a quantile regression approach” (Desembre 2014) 2015 XREAP2015-01 Bolance, C. (Riskcenter-IREA); Bahraoui, Z. (Riskcenter-IREA), Alemany, R. (Risckcenter-IREA) “Estimating extreme value cumulative distribution functions using bias-corrected kernel approaches” (Gener 2015) XREAP2015-02 Ramos, R. (AQR-IREA); Sanromá, E. (IEB), Simón, H. “An analysis of wage differentials between full- and part-time workers in Spain” (Agost 2015) XREAP2015-03 Cappellari, L.; Di Paolo, A. (AQR-IREA) “Bilingual Schooling and Earnings: Evidence from a Language-in-Education Reform” (Setembre 2015) XREAP2015-04 Álvarez-Albelo, C. D., Manresa, A. (CREB), Pigem-Vigo, M. (CREB) “Growing through trade: The role of foreign growth and domestic tariffs” (Novembre 2015) XREAP2015-05 Caminal, R., Di Paolo, A. (AQR-IREA) Your language or mine? (Novembre 2015) XREAP2015-06 Choi, H. (AQR-IREA), Choi, A. (IEB) When one door closes: the impact of the hagwon curfew on the consumption of private tutoring in the Republic of Korea SÈRIE DE DOCUMENTS DE TREBALL DE LA XREAP (Novembre 2015) 2016 XREAP2016-01 Castañer, A. (CREB, XREAP); Claramunt, M M. (CREB, XREAP), Tadeo, A., Varea, J. (CREB, XREAP) Modelización de la dependencia del número de siniestros. Aplicación a Solvencia II (Setembre 2016) XREAP2016-02 García-Quevedo, J. (IEB, XREAP); Segarra-Blasco, A. (GRIT, XREAP), Teruel, M. (GRIT, XREAP) Financial constraints and the failure of innovation projects (Setembre 2016) XREAP2016-03 Jové-Llopis, E. (GRIT, XREAP); Segarra-Blasco, A. (GRIT, XREAP) What is the role of innovation strategies? Evidence from Spanish firms (Setembre 2016) XREAP2016-04 Albalate, D. (GiM-IREA, XREAP); Rosell, J. (GiM-IREA, XREAP) Persistent and transient efficiency on the stochastic production and cost frontiers – an application to the motorway sector (Octubre 2016) XREAP2016-05 Jofre-Monseny, J. (IEB, XREAP), Silva, J. I., Vázquez-Grenno, J. (IEB, XREAP) Local labor market effects of public employment (Novembre 2016) XREAP2016-06 Garcia-López, M. A. (IEB, XREAP), Hemet, C., Viladecans-Marsal, E. (IEB, XREAP) Next train to the polycentric city: The effect of railroads on subcenter formation (Novembre 2016) XREAP2016-07 Vayá, E. (AQR-IREA, XREAP), García, J. R. (AQR-IREA, XREAP), Murillo, J. (AQR-IREA, XREAP), Romaní, J. (AQR-IREA, XREAP), Suriñach, J. (AQR-IREA, XREAP), Economic impact of cruise activity: the port of Barcelona (Desembre 2016) XREAP2016-08 Ayuso, M. (Riskcenter, XREAP), Guillen, M. (Riskcenter, XREAP), Nielsen, J. P. Improving automobile insurance ratemaking using telematics: incorporating mileage and driver behaviour data (Desembre 2016) XREAP2016-09 Ruíz, A. (GEAP, XREAP), Matas, A. (GEAP, XREAP), Raymond, J. Ll. How do road infrastructure investments affect the regional economy? Evidence from Spain (Desembre 2016) 2017 XREAP2017-01 Bernardo, V. (GiM-IREA, XREAP); Fageda, X. (GiM-IREA, XREAP) Globalization, long-haul flights and inter-city connections (Octubre 2017) XREAP2017-02 Di Paolo, A. (AQR-IREA, XREAP); Tansel, A. Analyzing Wage Differentials by Fields of Study: Evidence from Turkey (Octubre 2017) XREAP2017-03 Melguizo, C. (AQR-IREA, XREAP); Royuela, V. (AQR-IREA, XREAP) What drives migration moves across urban areas in Spain? Evidence from the great recession (Octubre 2017) xarxa.xreap@gmail.com

migrations in Spain

Abstract

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migrations in Spain

Abstract

Extracted data

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