DOCUMENT DE TREBALL XREAP2011-16

The impact of agglomeration effects and accessibility on wages
Anna Matas (GEAP) José Luís Raymond (GEAP) José Luís Roig (GEAP)

The impact of agglomeration effects and accessibility on wages

Authors: Anna Matas Prat, Universitat Autònoma de Barcelona, Dpt. Economia Aplicada, Fac. Ciències Econòmiques, Campus de Bellaterra, 08193 Bellaterra, Barcelona. Tel. 93.581.1578; Fax: 93.581.2292. anna.matas@uab.cat José Luis Raymond Bara, Universitat Autònoma de Barcelona, Dpt. Economia i Historia Econòmica, Fac. Ciències Econòmiques, Campus de Bellaterra, 08193 Bellaterra, Barcelona. Tel. 93.581.1711; Fax: 93.581.2292 josep.raymond@uab.cat. José Luis Roig Sabaté, Universitat Autònoma de Barcelona, Dpt. Economia Aplicada, Fac. Ciències Econòmiques, Campus de Bellaterra, 08193 Bellaterra, Barcelona, Tel. 93.581.1788; Fax: 93.581.2292joseplluis.roig@uab.cat

1. Introduction The benefits from improved accessibility to input and output markets has been given a theoretical basis thanks to the new economic geography literature (Fujita et al., 1999). One of the implications derived from this literature is the new insights provided to the evaluation of transport investment appraisal. In the presence of market failure, conventional user benefit measures give an incomplete picture of the benefits derived from the new infrastructure. Venables (2007) develops a model of urban economics where additional benefits from transport investments derives from the fact that infrastructure increases the size of agglomeration by improving accessibility. The presence of increasing returns to the size of agglomeration in such scenario allows for additional benefits due to additional productivity gains that, it is claimed, should be included in the appraisal exercise. As Graham (2007) puts it, the estimation of the effects of agglomeration and accessibility to agglomeration on productivity should become an input for transport appraisal studies. This paper contributes to the empirical literature on the effects of agglomeration and accessibility on productivity. Specifically, our purpose is to contribute to the debate about the way in which accessibility affects the performance of firms by looking at the case of Spain. In the last three decades, several ambitious road investment programs have been implemented in Spain. Our analysis starts in 1995 when the first motorway plan, started in 1985, was completed and Spain reached a good level of road infrastructures by European standards. Nonetheless, the investments in motorways continued over the following years at a similar rate, which maintained the pace of the previous years. Our aim is to assess whether the improvements in accessibility as a result of these road transport programmes have had any effect on the productivity of firms. Additionally, we also look at the impact of agglomeration effects, measured by employment density, on productivity. Preliminary estimations showed that this impact was non linear and we explore this relationship in the paper. We approach productivity indirectly by using individual wages allocated at the NUTS III level that correspond to provinces in Spain. Therefore, we estimate a wage equation with worker level data. We use a repeated cross-section of individual micro-data for the years 1995, 2002 and 2006. The availability of interprovincial travel time data for each

of the three years allows controlling for transport improvements over the period, by using a market potential variable. The fact that our spatial unit of analysis is the province provides a lower variability of market potential than in the case of using municipalities but, on the other hand, provinces approach much better than municipalities the boundaries of existing local labour markets. This paper is organised in six sections. The next section reviews relevant literature and explains the strategy followed. In section three, the characteristics of the dataset used are explained as well as the changes in accessibility that take place during the study period. Sections four and five show the estimated equations and the derived results. A final section draws the main conclusions.

2. Related literature and selected strategy The effect of economies of agglomeration on productivity has been the subject of a large empirical literature as reviewed in Rosenthal and Strange (2004). However, it was not until the last decade that a theoretical body was developed to explain the microfoundations of the economies of agglomeration beyond the Marshallian triad (Duranton and Puga, 2004). The classification of these microfoundations as “sharing”, “matching” and “learning” has become widely accepted. Additionally, the seminal work by Krugman (1991) that set the basis for the “new economic geography”, established the relation between economies of scale, transport costs and factor mobility that determines the concentration of activity. This line of work offers new insights into the relationship between accessibility and the location of activity. A basic framework to establish the relationship between productivity and agglomeration economies is provided by Combes et al (2008). We consider a firm j producing in region r and sector s that uses labour l and a composite of other inputs k with a technology described by the following Cobb-Douglas production function. Aj is a Hicksneutral factor augmenting technological level, and sj is a labour efficiency index.

y j  Aj  s j l j  k 1  j


(1)

The firm’s profit is given by:

 j  p j y j  w j l j  rj k j

(2)

where wj is the wage and rj the price of other inputs. The following expression is obtained for the wage of worker j after applying first order conditions and substituting:
1

w j   1   

1   

 p j Aj s j  1   rj 

   

(3)

It is also possible to derive expressions (4) and (5) for average productivity of labour as well as total factor productivity.
1

pj yj lj

  1   

1   

 p1j  A j s j  1   rj 

   

(4)

pj yj l j rj
 1 

 p j Aj  s j 



(5)

The last three expressions give us a guide to the channels through which productivity is affected by economies of agglomeration. Some of the effects may arise from pure externality effects as Aj and sj capture. Technological and human capital externalities through learning may help to spill over knowledge and skills to enhance worker productivity. But improved matching effects may also be reflected in higher total factor productivity. Other effects show up in the prices of output pj and inputs kj. A higher price of output translates to higher wage which in turn contributes to a higher agglomeration. Regarding inputs, a large number of accessible suppliers may reduce rj. Conversely, an inelastic supply of an input will induce input price increases as the agglomeration of the firms demanding the input grows. The measurement of economies of agglomeration has followed several strategies. The most obvious is the estimation of production functions that directly establish a link between productivity and agglomeration effects. Most of the early work on agglomeration effects used this strategy. The available evidence makes it possible to establish an increase in range of productivity of between 3%-8% when doubling city size (Rosenthal and Strange, 2004). Most of this work used aggregated data and did not control for the two biases found in the estimation of agglomeration economies, simultaneity bias and unobserved heterogeneity. The first study to carefully deal with these endogeneity issues was Ciccone and Hall (1996), who used an instrumental

variables strategy. Instruments are long lagged agglomeration variables under the assumption that the location of agglomeration 100-150 years ago may explain current locations but not current productivity. More recent work with establishment micro data can approach much better the theoretical models and deal with the endogeneity issues. Henderson (2003) and Martin et al. (2011) are two of the best examples. Both studies show that the effect of the size of total nearby employment (urbanization economies) is less important than the size of employment of the own industry (localization economies)1. An interesting point of the work of Martin et al. (2011) is that they find a non-linear effect of agglomeration in line with the work by Henderson and Au (2006) for Chinese cities. Given the difficulties involved in estimating production functions, different estimation strategies have been followed to indirectly estimate the relation between productivity and agglomeration effects as reviewed by Rosenthal and Strange (2004). One of the most explored is based on equation (3). This implies the estimation of a wage equation as follows:

w js     den j   js

(6)

where w js is the wage in region j and industry s, denj is the density of employment of region j (employees per unit of land area), and  ir is a random disturbance term. Estimating wage equations has the advantage that wages are measured more precisely than average labour productivity and total factor productivity. A number of papers have followed this strategy to test the hypothesis that wages are higher in denser areas due to the effects of agglomeration economies. Nonetheless, other explanations can be adduced for the higher productivity of workers in larger markets. The most obvious candidate is the existence of a sorting effect where non observable skill characteristics can be better rewarded in larger markets. In this case, the higher productivity of denser markets is the result of the concentration of highly skilled workers. From the supply side, a higher nominal wage should be needed to compensate workers for the higher living costs in denser areas. But firms will only be willing to offer a higher wage if productivity advantages due to agglomeration justify a higher reward.
Henderson (2003) finds that the relevant agglomeration measure is the number of establishments rather than employees.
1

The available evidence shows a robust positive relation between wage and density2. In the case of American studies, Glaeser and Mare (2001) find that controlling for observable worker characteristics, the urban wage premium using different data sets is about 25%. When introducing worker fixed effects the premium drops to 5%-10% depending on the dataset. Wheeler (2001) estimates an elasticity of 2.7% with respect to MSA population. Yankow (2006) finds a 19% premium for those workers residing in cities of over 1 million, but when unobserved heterogeneity is controlled for by using fixed effects, the large cities premium reduces to 6%. Both Glaeser and Mare (2001) and Yankow (2006) find that the sorting of skilled individuals partly explains the higher wage in large cities, but a premium still persists. In the case of European countries, several recent studies have found evidence of a positive effect of density on wages. Most notably, Combes et al. (2008) use a large panel of French workers residing and working in 346 employment zones. Estimating with instruments and worker fixed effects to control for unobserved heterogeneity, they find an effect of density of 3%. Mion and Naticchioni (2009), for the case of Italian provinces, control for both workers and firm heterogeneity by using an employeremployee matched dataset. They find an elasticity of 2.2% when estimating with OLS, which is low in comparison with previous studies. The authors argue that the low elasticity is related to the centralized Italian wage bargaining system where sectors are subject to nationally binding conditions like minimum wages. When introducing workers fixed effects and firm size to control for firm heterogeneity, the wage elasticity drops to 0.56%. Controlling for endogeneity by using IV estimators, density falls to 0.2%. Finally, Melo and Graham (2009) do not find very different results for Britain using a panel and Travel-To-Work Areas. The elasticity of employment density when estimating by OLS amounts to 2.2%. When introducing workers fixed effects the estimated elasticity is 1% and the IV estimation reduces the elasticity to 0.8%. The main focus of interest of the present paper is the effect of market potential as a way to capture the effects of accessibility to inputs and output markets on productivity and wages. As far as accessibility changes are related to transport improvements, the effect of market potential can provide a measure of the effects of transport improvements on productivity.

2

Here we only refer to studies using microdata.

The effect of transport improvements on productivity have been approached from different perspectives in the existing literature. Since Aschauer’s (1989) contribution, a large number of articles have been published that have sought to disentangle the effects of public capital on aggregate productivity both on a national and regional level. Nonetheless, over the last two decades a new focus has been given to transportation infrastructure effects with the development of the new economic geography (Krugman, 1991; Fujita et al., 1999). While the former approach deals with infrastructure as a production factor, the new theories put the emphasis on the relation between transport costs and economies of scale. This implies that transportation infrastructure is an important ingredient to the extent that it affects transport costs and accessibility to output and input markets. Better accessibility to inputs may reduce the price paid by firms if they cover part of the transport costs. This may have scale effects that have a positive effect on productivity if there are economies of scale. Regarding output, better accessibility means widening the spatial scope of markets, which increases the effects of existing economies of scale. On the other hand, better accessibility exposes firms to more intense competition. In the long run, some firms will exit the market, which will increase market shares and economies of scale for the remaining firms. Despite the fact that the number of firms decreases, the more intense competition due to better accessibility will lead to lower mark-ups and prices. Productivity may also increase due to a selection effect as a result of increased competition. Less productive producers are forced out of the market, giving rise to a selection effect in which only the more efficient firms remain in the market. But changes in accessibility may also affect agglomeration economies. On the one hand, better accessibility may attract new and relocating firms, causing clustering of activities in places that are positively affected by accessibility changes. On the other hand, transport improvements may reduce the cost of benefitting from agglomeration economies generated by widening the spatial scope of agglomeration benefits. Consequently the effects of agglomeration on productivity/wages will be enhanced (Holl, 2006).

Graham (2007) uses a measure of “effective density” that measures accessibility to employment in the remaining areas, including firms’s own area in the estimation of production functions with a micro dataset of firms. He finds an elasticity of 0.129 for the whole economy, and 0.07 for manufacturing, while services are those that most benefited from agglomeration economies with an elasticity of 0.197. Combes et al. (2008) use density as a potential measure, which is weighted by the inverse of great circle distance between areas. They estimate a potential elasticity of 3.5% with fixed effects. The estimation using instrumental variables reduces elasticity by one percentage point. Mion and Naticchioni (2009) use the provincial disposable income weighted by the inverse of distance as a market potential measure. They obtain a market potential elasticity in their equation when controlling for fixed effects and firm size of 4.5% and after instrumenting slightly increases to 4.6%. In comparison with Combes et al. (2008), the market potential effect on wages is more significant and much larger than employment density. Finally, Melo and Graham (2009) market potential variable is total employment of the Travel-To-Work Areas weighted by the distance deducted from latitude and longitude of TTWA centroids. The elasticity from the OLS estimation is as high as 10%. However, the fixed effects estimation decreases the elasticity to 5.4% whereas instrumenting, as in Mion and Naticchioni (2009), increases the elasticity to 5.8%. Again, the effect of market potential is far higher than that of employment density. In the case of Spain, some studies have estimated a positive albeit reduced effect of accessibility on productivity. Most of these studies follow the strategy of indirectly approaching productivity effects from firm births. Holl (2004), using data on new firms by municipality for the 1980-94 period, finds a significant effect of attraction of new firms for those municipalities which experience an increase in accessibility to the newly built motorway network. This effect is reduced with distance from the network. Other authors like Alañón and Arauzo (2008) and Arauzo (2005) find similar results. Albarrán et al. (2009) find that that the firm’s decision to become an exporter is positively affected by accessibility in terms of internal transport, measured as travel time to main export ports. However, as far as we know there has been no study that has estimated the effect of agglomeration and accessibility on productivity using micro-data. Our paper seeks to fill this gap by estimating a wage equation with individual worker data. The choice of the wage equation strategy is selected given the quality of the wage data in comparison

with the data available on firm surveys. The advantages of wage data come from a higher number of control variables and, mainly, a more detailed spatial disaggregation. In order to measure those effects we use an employment density variable in line with the literature reviewed and a market potential variable to approach accessibility. Additionally, a specialization index is introduced to control for intraindustry externalities and an aggregate index of education level is also included to control for possible human capital externalities.

3. Data

We use micro-data on individual wages from the Spanish Structure of Earnings Survey (SES). The SES is a four-yearly survey carried out in all Member States of the European Union using the same methodology with the objective of providing accurate and harmonised data on wage levels and their distribution. In the case of Spain, the survey is conducted by the National Institute of Statistics and data is available for the years 1995, 2002 and 2006. The main advantages of this database are its size, a large set of variables to control for workers and firm characteristics, information on the industrial sector at two-digit level and the possibility of allocating workplaces on an NUSTS-III level. In order to guarantee data comparability among the three years, we considered workers in firms with at least 10 employees in the manufacturing and service sectors, excluding workers in the health and education sectors given that this information is available only for 2002 and 20063. The final number of observations was 109,596 for 1995, 114,368 for 2002 and 86,456 for 2006. Table 1 reports a summary of the statistics for the relevant individual variables in the dataset. Our variable of analysis is the gross monthly wage, which includes the base wage plus any additional fixed or variable wage payments, except overtime payments, before deducting income tax and worker’s contributions to the public social insurance system4. Workplaces are allocated at NUTS-III level, the smallest geographic unit for which we can have information, which corresponds to the 47 Spanish mainland provinces. Table 1 shows that the mean gross monthly wage decreased in constant prices between 1995 and 2006. This reduction can be explained by the behaviour of some of the main determinants of wages: a decrease in the average value of years of tenure, an increase in the rate of female employment and a significant growth in part time labour. Wage data also reveals a large dispersion among provinces that is highly persistent over time. For instance, the coefficient of variation of the average wages per province is equal to 0.15 for 1995 and 0.14 for 2006.

Workers located in the Canary Islands, Balearic Islands, and in the North-African cities of Ceuta and Melilla have also been excluded. 4 Alternatively, we work with the gross hourly wage. The results proved to be very similar, however we selected monthly wages due to the lack of reliability in the number of working hours reported for some workers in the sample.

3

Table 1. Summary statistics for the main variables in the study (average across all individuals in the sample)
1995 Mean Gross monthly wage (Euros, 2006) Age (years) Tenure (years) Gender Male Female Working time Full time Part time Type of contract Indefinite Fixed term Level of education Primary or less Compulsory secondary Post-compulsory secondary Vocational training University Degree Observations 1620 38.5 10.8 2002 2006 Individual continuous variables Std. Dev. Mean Std. Dev. Mean 1168 1616 1202 1561 10.9 38.0 10.9 38.5 9.9 8.0 9.8 7.5 Individual discrete variables (shares) 66.1% 33.9% 90.1% 9.9% 78.6% 21.4% 27.0% 30.8% 11.2% 16.3% 14.7% 114368 61.1% 38.9% 85.0% 15.0% 77.8% 22.2% 27.5% 28.1% 12.0% 15.2% 17.3% 86456

Std. Dev. 1070 10.9 9.5

74.7% 25.3% 96.0% 4.0% 76.1% 24.0% 32.0% 31.1% 12.4% 13.4% 11.0% 109596

The spatial variables used in the analysis are detailed in Table 2. All these variables are computed at the aggregate NUTS-III level. The use of provinces allows for a sufficient level of territorial disaggregation whereas at the same time providing a rich range of potential explanatory variables. Accessibility is measured according to the traditional market potential based on Harris (1954) definition. The market potential for each province is defined as

 GDPj
j

 tij

;

that is, the sum of GDP over all the provinces j, weighted by travel time between each origin and destination provinces. In line with common practice, after exploring different values for α, our final choice was the simple inverse cost weighting scheme,

  1.

This variable picks up both changes in GDP and changes in interurban travel times derived from improvements in the road network5. Table 2. Summary statistics for the spatial variables in the study (average across all individuals in the sample)
1995 Market potential Mean Std. Dev. Min Max Employment density Mean Std. Dev. Min Max Specialisation Mean Std. Dev. Min Max Human capital Mean Std. Dev. Min Max 2002 2006 2002/ 1995 31.6% 2006/ 2002 13.4% 2006/ 1995 49.2%

3195 1001 1630 4879 91.3 85.0 3.1 212.2 1.35 1.28 0.05 13.19 16.0 4.5 8.0 24.8

4205 1300 2324 6405 121.8 116.9 3.4 310.5 1.36 1.39 0.10 14.84 21.0 4.9 13.0 30.2

4766 1478 2581 7276 147.5 141.2 3.9 370.9 1.30 1.26 0.12 14.73 23.4 4.8 14.1 31.5

33.4%

21.1%

61.6%

1.1%

-4.6%

-3.5%

31.2%

11.4%

46.3%

Secondly, agglomeration economies are measured through employment density, i.e., workers per square kilometre. Thirdly, we include an index of the degree of industrial specialization of each province computed as the employment share of sector r in the total employment of province j, sectors being defined at two-digit levels. So, this variable depends on both the province and the sector. Finally, we measure the level of human capital as the share of working population with a tertiary degree. Alternatively, similar results are obtained when the measure used is the average years of education6. All the spatial variables analysed present a high level of variability among provinces. This is particularly true for the variables accounting for urbanisation and location economies. Moreover, dispersion is approximately constant over time, except for human capital, which shows a continuous reduction. According to this data, the different road programmes implemented have not led to a more homogeneous spatial
5

Travel time matrices have been computed for each year in the sample using a GIS based on average speeds for each type of road. 6 Data on employment and education of the labour force at the province level come from the Instituto Valenciano de Investigaciones Económicas (IVIE) database.

distribution of market potential. With respect to the evolution over time, employment density, market potential and human capital have grown at a very high rate throughout the period. It is interesting to note that market potential and human capital have followed a very similar pattern. On the contrary, the index of industrial specialisation presents a slight reduction between 2002 and 2006. Figure 1 presents the map of market potential for the Spanish provinces for 2006. The map shows a wide degree of variation between provinces, with higher market potential for those provinces located along the Mediterranean coast, the Basque Country in the north, and Madrid and its surrounding provinces. Map 2 displays the percentage change in travel time between the final and the initial year of the sample for each province computed as the sum of travel time from province j to the rest of provinces. The road infrastructure programs implemented from 1995 to 2006 have significantly reduced travel times for those provinces located in the north-west corner and, in general, in the western part of Spain and to a lesser extent for provinces located in the centre and south-east of the country along the Mediterranean coast. In general, the areas that have most benefited from road investment correspond to areas with a low level of market potential. The pattern of market potential over time has followed very closely that of changes in travel time (Figure 3). This relation is confirmed by a correlation coefficient of -0.82 between change in travel time and change in market potential. Figure 1. Market potential for 2006

Figure 2. Percentage change in travel time

Figure 3. Percentage change in market potential

4. Estimated equations As has been explained, the estimation approach selected is the wage equation. The dependent variable is the log of gross monthly wage in Euros deflated by the Spanish consumer price index and the base year is 2006. The selected regressors are employment density, market potential, specialization and human capital. We also include straight-line distance to the French border to account for the geographical location of each province. Indirectly, this variable is used as a proxy for accessibility to European markets. The use of worker’s level data from the SES makes it possible to account for individual heterogeneity through a large number of employee and workplace variables. We control for the following individual characteristics: age, gender, level of education (9 levels) and tenure, which are among the main determinants of individual wage. We also control for a wide range of characteristics of the workplace: working time (full/part time), type of contract (indefinite/fixed term), industrial disaggregation (two digits) and type of occupation (12 categories). Finally, we include time dummy variables. In our view, controlling for educational level and other individual features highly contributes to alleviating the frequently faced econometric problem of omitted variable bias when estimating the impact of spatial externalities. The equation is estimated for all sectors in the sample and for manufacturing and service sectors in order to allow for different impacts between industrial groups. As is standard, we estimate the equation in logs, so we can interpret the estimated coefficients as elasticities. The first estimates are obtained by applying ordinary least squares (OLS). Preliminary work showed that the effect of employment density on wages might not be linear. This result is in line with Martin et al (2011). Although in the standard wage equation specification the relation with density is linear, we propose a quadratic form based on the econometric evidence7. The covariance matrix has been obtained by the White method robust to heteroskedasticity. The estimated coefficients for individual and workplace characteristics all have the expected sign and are statistically significant. The full
7

A more flexible functional form using splines was first tried suggesting that a parsimonious quadratic form describes well the behaviour of the density effect on wages.

equation is presented in Appendix 1, whereas Table 3 in the text offers the coefficients estimated for the variables of interest to our analysis. This Table shows that market potential, specialization and human capital all have a positive effect on productivity with elasticity values that are roughly in line with empirical evidence for OLS equations. As for employment density, we obtain a positive sign for the first coefficient and a negative one for its square, which are both highly statistically significant. It has to be noted that the coefficients of interest are highly stable to the selection of control variables. Table 3. Estimation results for the OLS equation   ln (market potential) ln (employment density) (ln (employment density))^2 ln (specialization) ln (human capital) Distance to French border R square All sectors  0.104726 (25.77) 0.121010 (27.52) -0.01221 (20.86) 0.041588 (35.75) 0.067284 (17.543) -0.000067 (17.16) 0.5928 Manufacturing   0.093585 (19.08) 0.079249 (15.05) -0.00637 (9.08) 0.040170 (31.67) 0.081762 (17.04) -0.000117 (23.11) 0.5476 Services  0.133138 (19.41) 0.171342 (23.43) -0.01929 (19.65) 0.054697 (11.18) 0.046082 (7.19) -0.000019 (2.86) 0.6206

However, as has been largely documented, the OLS estimates can be flawed due to possible endogeneity problems. As explained in Section 2, in our context the potential sources of endogeneity can not be neglected8. In accordance with the literature, we consider that the troublesome variables are: employment density, market potential, specialisation and human capital stock. We deal with endogeneity by means of instrumental variable estimates (IV) following the standard practice of using lagged values of endogenous variables as instruments under the hypothesis that these variables are correlated with the endogenous explanatory variable but, at the same time, are independent from the random error term. Market potential is instrumented by the same variable computed for 1980, the first year travel time between provinces are
8

Combes et al (2011) discuss the main sources of bias in the identification of agglomeration effects.

available. Employment density is instrumented by population density computed for 1860 and specialization with data of this variable in 1980. Finally, following Dalmazzo and de Blasio (2007) we used the demographic structure of the population to instrument human capital. Specifically, we used the difference between the share of population between 15 and 19 years of age and the share of those between 5 and 9 years of age9. The results are presented in Table 4. Table 4. Estimation results for the 2SLS equation ln (market potential) ln (employment density) (ln (employment density))^2 ln (specialization) ln (human capital) Distance to French border R square All sectors  0.079132 (12.58) 0.085643 (11.72) -0.00693 (7.08) 0.045020 (32.52) 0.045728 (7.51) -0.000076 (19.24) 0.5926 Manufacturing   0.085238 (12.15) 0.061729 (7.14) -0.00374 (3.24) 0.040690 (26.66) 0.058212 (7.94) -0.000123 (23.60) 0.5475 Services   0.085729 (7.91) 0.111801 (9.50) -0.01044 (6.50) 0.039886 (5.53) 0.033145 (3.14) -0.000025 (3.35) 0.6203

When dealing with estimation by instrumental variables, the problem of weak instruments emerges as a well documented issue in the econometric literature. Weak correlation between the instrument and the endogenous variable can make estimation by IV far worse than estimation by OLS. In our case, the hypothesis of weak instruments was tested by using the partial R-square. As Table 5 shows, in all cases the partial R-square is high enough to reject weak instruments and, therefore, the IV estimates can be considered valid. The Hausman test rejects the null hypothesis of exogeneity10.

The underlying hypothesis for this instrument is that those between 5 and 9 years of age in 1991 will be 15 and 19 years of age ten years of later. They could not be employed ten years later with a university degree. Conversely, the population between 15 and 19 years of age in 1991 can be employed ten years later with a university degree. Therefore, the larger the share of population between 15 and 19 in 1991, the larger the percentage of working population with a tertiary degree ten years later, and the larger the share of population between 5 and 9 years of age in 1991, the lower this percentage ten years later. The results of testing the null hypothesis that variables are exogenous are: All sectors: Robust score chi2(5) = 500.27 (p=0.0000); Robust regression F(5,3103) = 101.22 (p = 0.0000).
10

9

Table 5. Shea’s Adjusted Partial R-square ln (market potential) ln (employment density) (ln (employment density))^2 ln (specialization) ln (human capital) All sectors 0.4198 0.3548 0.3517 0.7205 0.3825 Manufacturing 0.4858 0.378 0.3764 0.7077 0.3832 Services 0.4066 0.3631 0.3556 0.4558 0.3647

Comparing the coefficients estimated by OLS and IV reveals that endogeneity can affect elasticities. Overall, estimating by IV reduces the estimated coefficients for the spatial variables with a larger effect on service industries in comparison with manufacturing sectors. We find that human capital presents the largest endogeneity bias. The coefficient for market potential slightly decreases for the manufacturing sector whereas for the service sector the reduction is much more pronounced. The effect on the specialisation coefficient is almost unnoticeable for manufacturing and again stronger for service industries. As for density, the effect of endogeneity is relatively small when the compound effect of the two coefficients is computed.

5. Results The estimated equations confirm that, after controlling for individual and firm characteristics, all the spatial variables included in the equation have a significant effect on firm productivity, indirectly approached by wages. Firstly, we find a positive and significant effect of road accessibility, measured through market potential, on wages with a similar impact on manufacturing and service industries. Secondly, market size positively influences productivity, although the effect is non linear and follows a different pattern for manufacturing and services. Thirdly, there is evidence of positive human capital externalities even after controlling for individual educational level. Fourthly, significant benefits are derived from economies of localisation and, finally, the

Manufacturing: Robust score chi2(5) = 136.95 (p=0.0000); Robust regression F(5,1648) = =27.70 (p = 0.0000) Services: Robust score chi2(5) = 287.79 (p = 0.0000); Robust regression F(5,1454) = 58.18 (p= 0.0000)

distance to the French border is significant for manufacturing but only marginally for services. Table 6 shows the elasticities of wages with respect to the spatial variables in the equation. Market potential shows the highest effect on wages with an elasticity value of around 8.5%. Although this value is higher than the value of 4.6% reported by Mion and Naticchioni (2008) and that of 2.4% by Combes et al. (2009), it has proven highly robust to the different specification we have explored and is clearly in line with a mean elasticity of 10.1% and a median elasticity of 7.6% reported in the meta-analysis by Melo et al. (2009). An elasticity value of around 4% for the specialization index provides evidence of significant intraindustry externalities in the case of Spanish provinces. As for the education level (share of working population with tertiary degree) we found an elasticity value of 5.8% for manufacturing and 3.3% for service industries. It is important to remark that the estimated equation already includes the individual educational level; therefore, in our context, the level of education of the province can be interpreted as an approximation of human capital externalities. Table 6. Wage elasticities Market potential Specialization Human capital (semi-elasticity) Density (minimum) Density (mean) Density (maximum) All sectors 0.0791 0.0450 0.0547 0.0700 0.0193 0.0036 Manufacturing 0.0852 0.0407 0.0582 0.0533 0.0260 0.0174 Services 0.0857 0.0399 0.0331 0.0882 0.0122 -0.0116

Given that we have specified a non-linear function for employment density, the elasticity is not constant. We have computed the effect of density on wages for a range of values that comprises all the observed values in the sample. Figure 5 shows that the relationship between density and wages is clearly non-linear for low levels of density and almost becomes flat after density has reached a certain threshold.

Figure 5. Relationship between employment density and wages
1.4 Services 1.3 All sectors Manufacturing Wages 1.2

1.1

1.0 50 100 150 200 250 300 350 400

Employment density

As for elasticities, Figure 6 shows a different pattern for manufacturing and service industries: for an employment density of under 50 workplaces per square kilometre, elasticity is higher for service industries; however, for higher values the effect of density is reversed. In the service sector very high employment density leads to negative values for elasticity Figure 6. Elasticity wage-employment density
.12 .10 .08 Wage elasticity .06 .04 .02 .00 Services=214 -.02 50 100 150 200 250 300 350 400 Manufacturing All sectors

Employment density

Table 6 reports the elasticity for the minimum, mean and maximum values of employment density in the sample. The computed elasticities show that those areas with low employment density can obtain substantial benefits from urban agglomeration, whereas the benefits are almost null in those provinces with high density levels. For the service sector, congestion costs appear to offset the benefits from agglomeration after

a value of 214 workplaces per square kilometre. The size of elasticity for the mean sample value is around 2%, in line with the most recent estimates.

6. Conclusions We estimate a wage equation with individual data from Spanish workers in order to provide evidence for the effect of spatial externalities and, particularly, the impact of agglomeration and road accessibility on the performance of firms. We control for a large set of individual and firm variables and for the main spatial (local) characteristics that are known to contribute to the explanation of productivity differences. From an econometric viewpoint, accounting for endogeneity by using the instrumental variables approach results in some changes in the estimated coefficients of the spatial variables. Increasing market size gives rise to productivity gains. However, the effect is non-linear with stronger externalities for low density provinces, albeit with a different pattern for the manufacturing and service sectors. Localisation economies also play a role in explaining wage differences. Interurban accessibility plays the most important role in explaining wage (productivity) disparities. Our results support previous empirical findings that accessibility improvements positively affect firms’ productivity (Graham, 2007). Therefore, road investments that increase market potential through reductions in travel time are likely to have a positive effect on productivity. Such external effects should be added to the benefits measured in the traditional CBA as a part of what is usually called wider economic benefits. Nonetheless, we want to stress that the elasticity of productivity with respect to accessibility is small and its effects are subject to effective reductions in travel time. The additional external benefits will depend on the characteristics of the investment project. This paper measures the direct effects of accessibility on wages through market potential. However, in order to approximate the final effect of accessibility it would be necessary to take the feedback effects into account: Increasing accessibility increases productivity and, as a consequence, market potential. In a second round this increase in market potential further increases productivity and so on. In this context, the final elasticity wage-market potential would be higher than the first round effect, which is the elasticity of 0.08 estimated in the paper.

After controlling for individual level of education, there is also evidence of positive human capital externalities. References Alañón, A and J.M. Arauzo (2008) Accesibilidad y localización industrial. Una aplicación a las regiones españolas fronterizas con Francia. Revista de Estudios Regionales 82: 71-103 Albarrán, P., R. Carrasco and A. Holl (2009) Domestic transport cost reductions and firms’ export behaviour, Documento de Trabajo 2009-13. FEDEA. Arauzo, J.M. (2005) Determinants of industrial location, Papers in Regional Science 84: 105-120. Au, Ch-Ch. y J.V. Henderson (2006) “Are Chinese cities too small?”, Review of Economic Studies, 73: 549-576 Ciccone, A. (2002) Agglomeration effects in Europe. European Economic Review 46: 213-227. Ciccone, A. y R.E. Hall (1996) “Productivity and the density of economic activity”, American Economic Review 86: 54-70. Combes, P.P., Mayer, T. y Thisse, J. (2008) Economic geography. The integration of regions and nations. Princeton University Press. Combes, P.P., Duranton, G. and Gobillon, L. (2008) Spatial wage disparities: Sorting matters. Journal of Urban Economics 63: 723-742. Combes, P.P., Duranton, G. and Gobillon, L. (2011) The identification of agglomeration economies. Journal of Economic Geography 11: 256-266. Dalmazzo, A. and G. de Blasio (2007) Production and consumption externalities of human capital: an empirical study for Italy. Journal of Population Economics 20: 359382. Duranton, G. y D. Puga (2004) “Micro-foundations of urban agglomeration economies” en J.V. Henderson y J.F. Thisse eds. Handbook of Urban and Regional economics, Vol. 4, Noth-Holland. Fujita, M., P. Krugman and A. Venables (1999) The spatial economy. MIT press Glaeser, E. and D.C. Mare (2001) Cities and skills, Journal of Labor Economics 19: 316-342 Graham, D.J. (2007) Agglomeration, productivity and transport investment. Journal of Transport Economics and Policy 41: 317-343. Holl, A.(2004) “Manufacturing location and impacts of road transport infrastructure: empirical evidence from Spain”, Regional Science and Urban Economics 34: 341-363.

Holl, A. (2006) A review of the firm-level role of transport infrastructure with implications for transport project evaluation, Journal of Planning Literature, 21: 3-14. Henderson (2003) Marshall’s scale economies, Journal of Urban Economics 53: 1-28 Krugman, P. (1991) Increasing returns and Economic Geography, Journal of Political Economy 99: 438-499. Martin, P., T. Mayer and F. Mayneris (2011) Spatial concentration and plantlevel productivity in France, Journal of Urban Economics, 69: 182-195. Melo, P.C., D.J. Graham and R.B. Noland (2009) A meta-analysis of estimates of urban agglomeration economies. Regional Science and Urban Economics 39: 332342. Melo, P.C. and D.J. Graham (2009) Agglomeration economies and labour productivity: evidence from longitudinal worker data for GB’s Travel-to-Work Areas, SERC Discussion Paper 31, LSE. Mion, G. and P. Naticchioni (2009) The spatial sorting and matching of skills and firms. Canadian Journal of Economics 42: 28-55. Rosenthal, S.S. y W.C. Strange (2004) “Evidence on the nature and sources of agglomeration economies” en J.V. Henderson y J.F. Thisse eds. Handbook of Urban and Regional economics, Vol. 4, Noth-Holland Venables, A. (2007) Evaluating urban transport investments. Cost-Benefit Analysis in the presence of agglomeration and income taxation. Journal of Transport Economics and Policy 41: 173-188. Wheeler, C.H. (2001) Search, sorting and urban agglomeration, Journal of Labor Economics, 19: 879-899. Yankow, J.J. (2006) Why do cities pay more?. An empirical examination of some competing theories of the urban wage premium, Journal of Urban Economics, 60: 139-161.

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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)

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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 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)

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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)

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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)

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)

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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)

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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)

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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) XREAP2009-09 Domènech Campmajó, L. (PPRE-IREA) “Competition between TV Platforms” (Octubre 2009)

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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)

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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) “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)

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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)

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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) 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)

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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) 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)

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