Marie-Benoît Magrini
email: mbmagrini@toulouse.inra.fr

Philippe Lemistre
email: plemistr@univ-tlse2.fr

< C3 -Econometric Methods: Multiple/Simultaneous Equation Models < C -Mathematical and Quantitative Methods, J24 -Human Capital|Skills|Occupational Choice|Labor Productivity < J2 -Time

Keywords: C34 -Truncated and Censored Models local labour markets, spatial mobility, migration wage return, selfselection bias, distance, labour-market entry local labour markets, spatial mobility, migration wage return, self-selection bias, distance, labour-market entry marché local du travail, mobilité spatiale, rendement salarial de la migration, biais de sélection, distance, insertion professionnelle

Most research studies on migration see the latter as a move from one geographic area to another. Taking into account distance in our analysis of young French workers' migration from one local labour market to another, enables us to evaluate more precisely the migration decision, viewed as a cost-benefit trade-off based on the distance travelled and some territorial characteristics. We find that the wage returns to migration are nil for the most highly-skilled ones, leading us to consider that these workers conduct their job search on a national scale.

In contrast, the less-skilled workers get a positive return to migration.

framework, and for a particular category: young workers, the most mobile group of the total population [START_REF] Long | Migration and Residential Mobility in the United States[END_REF]. However, our aim here is not so much to use distance in isolation but rather to jointly consider the distance travelled and the migration between local labour markets. Indeed, we make the hypothesis that the decision to migrate and the decision to travel a given distance are interrelated. Models that evaluate returns to migration -and see the latter as no more than a move from one territory to another -make the implicit hypothesis that the costs of migration are uniform, regardless of the distance travelled. These models generally estimate returns to migration by calculating an average migration cost on the basis of a change of territory alone (i.e. without considering the distance travelled). On the contrary, the previous discussion suggests that the choice to relocate from one territory to another is simultaneously linked to the choice of a distance. In other words, the decision to migrate and the choice of a specific distance to cover are two intrinsically interrelated elements that are difficult to separate from each other. This is the reason why we propose to integrate distance into our migration variable. By doing so we aim to account for migration costs, and we then propose a new point of view on the microeconomic study of spatial mobility.

We also wish to be more specific about an important hypothesis, which is that migration behaviour varies with education levels. The interactions between the returns to education and the amplitude of migration have recently been examined (using French data that are comparable to the data exploited here) by associating the distance travelled and the number of years of study [START_REF] Lemistre | Spatial mobility and returns of education: some evidence from a sample of French youth[END_REF]. The essential result is that the returns to initial education are quite clearly influenced by migration. Nevertheless, the education level in this study is represented by a unique continuous variable: this rests on the hypothesis that young people with different education levels make their decisions in the same way -relatively to the other variables of the model. For this reason, we intend to divide the sample into groups of different education levels to highlight, on the one hand, the different returns to migration, and on the other, the various effects of individuals' and territories' characteristics on migration decision. A first attempt along these lines has already been made using French data (see [START_REF] Détang-Dessendre | The Impact of Migration on Wages: Empirical Evidence from French Youth[END_REF]), but the education levels selected were fewer, the geographic areas considered were larger (the French départements) and the territorial characteristics were not considered. The authors of this study concluded that for the least qualified workers, migration had no impact on wages. We demonstrate here that taking into account the distance travelled and using smaller geographic areas invalidates this result.

Moreover, we show that the territories' characteristics can also have strong effects.

For this purpose we use the data from the French "Generation 98 Survey", which enables us to observe the spatial mobility of young people during the first three years of their working life. Internal migrations within the French territory are considered on the basis of a spatial division of the national territory into areas that reflect local labour markets. The French local labour markets involved are called "Zones d'Emploi", translated here as "Employment Zones" and abbreviated EZ. They are the equivalent of the "Labour Market Areas" of the United-States, the "Travel to Work Areas" of the United Kingdom, or the "Local Labour System" of Italy. Each EZ is defined as a geographic space in which inhabitants usually find work and in which local business units find the labour needed to fill the vacancies they provide i . We then observe that almost half of the young workers find employment in zones different from those in which they finished their studies. And by considering the distance travelled by migrants between EZs, we find that the less-skilled workers sometimes migrate long distances away, even though fewer of them do move. This article is organized as follows. In the first section, we reinvestigate the theoretical framework of the income-distance trade-off model. This is an important question to consider when studying the role played by distance in a "spatialized" job-search model. Its empirical specification is the subject of section two. We explain how and why using the distance travelled makes it possible to better address the self-selection bias problem in migration studies; we then introduce our empirical specification. In the third section, we present data and some statistics on individuals' propensity to migrate, with the aim of describing migration behaviour using groups of education levels. We present our results in the following section and examine some observable determinants of migration by considering characteristics of individuals and territories. We also evaluate diverse returns to migration according to the identified determinants of the migration decision based on migration distance travelled, and controlling for the effects of unobservable characteristics (self-selection bias). To conclude, we outline some consequences of these results on public policy, and in particular that related to the government subsidies encouraging workers to find employment by moving.

1. The income-distance trade-off in migration decision: a review of the theoretical framework

Spatial Job-Search Process

The value a person attributes to each job offer v is a function of its individual characteristics X and those of the job in question Y (the employment variables should be understood in a broad sense, and often include geographic specificities). The introduction of space into the job-search model leads to taking into consideration that some individuals can access job offers in a labour market that is external to their market of origin. Even so, it is difficult to determine which individuals widen their field of search. Indeed, individuals whose search in their local area can still have access to job offers coming from outside their local market, through various information channels. Similarly, an individual who has accepted 2004) consider an overall job offer distribution including the home market and the external labour markets taken together, and therefore, with one single reserve job value, regardless of the spatial job-search strategy. This hypothesis therefore leads us to consider a general distribution of job offers that includes external labour markets as well as the home market, with a single employment reserve value V g * , whatever the spatial job-search strategy used.

The effect of the costs of job searching remains indeterminate. However, when only based on the job reserve value, the costs of migration play a determinant role because they clearly govern the decision to migrate. Indeed, in the human capital theory, it is above all the tradeoff between these costs and the income benefits associated with the relocation that determines the decision to migrate or not [START_REF] Sjaastad | The Costs and Returns of Human Migration[END_REF]. The distance travelled is then partly related to the cost of migration, and migration occurs if and only if the gain in terms of income exceeds this cost [START_REF] Falaris | Migration and wage of young men[END_REF][START_REF] Magrini | Migration and Self-Selection: Measured Earnings and Latent Characteristics[END_REF]. Let us denote m i * the individual latent propensity to migrate. An individual will migrate ( m i * >0) only if he can recover the costs of migration. From previous considerations, we assume that he accepts the offer of employment v gi * (originating from the global distribution) only if it exceeds his reserve employment value and exceeds his migration costs c i :

m i * > 0 if v gi * > V gi * + c i (0)
The surplus utility v i * that is derived from migration alone is written:

v i * = v gi * -V gi * and v i * = c i + s i with s i > 0 (1)
That is, the individual migrates only if he obtains a net benefit s i . ii

If it is supposed that the total supplemental utility can be expressed entirely in terms of salary ceteris paribus, the employment reserve salary w i * associated with the overall value

w i * = W (X i ,Y i ,v i *) (2)
The migration costs are associated with certain individual variables x i , which may not explain the salary (some, but not all, x are in common with X i ). The costs are also associated with certain geographic variables z i , which may be characteristics of the zone of departure that induce the individual to migrate (high local unemployment rate, few qualified jobs, few amenities, etc.) or differences between certain characteristics of the desired and original zones [START_REF] Nakosteen | Migration and Income: The Question of Self-Selection[END_REF]. These are what are called push and pull effects [START_REF] Greenwood | Internal Migration in Developed Countries in Handbook of Population and Family Economics[END_REF].

For net surplus s i , many arguments justify this dependence on individual x i and geographic z i variables. For instance, [START_REF] Gibbs | The Information Effects of Origin on Migrants'Job Search Behavior[END_REF] showed that employees from rural areas are less demanding than those from urban areas. As for the individual variables, the participation of women in the labour market cannot be explained in the same way as that of men. This dissimilarity can cause differences in remuneration, which affects the profitability of migration [START_REF] Keith | The Returns to Mobility and Job Search by Gender[END_REF].

Then the surplus utility ( v i * = c i + s i ) may be written as :

v i * = β 0 + β 1 y i + β 2 x i + φ i (3)
And the associated job reservation wage as:

w i * = δ 0 +δ 1 X i + δ 2 Y i +δ 3 v i * +ε i (4)

An Income-Distance Trade-Off Empirical Model

Neither the overall job value, the reservation wage, nor the surplus utility related to migration are observable. Only the ex-post salary w i is observed. This model is usually [START_REF] Raphaël | Geographic Mobility, Race, and Wage Differentials[END_REF][START_REF] Gabriel | Favorable Self-Selection and the Internal Migration of Young White Males in the United States[END_REF][START_REF] Falaris | Migration and wage of young men[END_REF][START_REF] Magrini | Migration and Self-Selection: Measured Earnings and Latent Characteristics[END_REF]. An important limitation of these estimates is that they only take into account an average effect of migration with two unique states -migrants versus nonmigrants:

m i = 1 if m i * > 0 m i = 0 if m i * ≤ 0 (5a)
where m i is the observed migration variable of a change in territory, assuming that its effect is similar regardless of the amplitude of migration. In other words, a migrant covering 20 km and another covering 1,000 km would be seen as identical.

Many research studies have tried to circumvent this difficulty by evaluating the impact of migration and its determinants between different zones of mobility and by limiting the frontier effects. Having data on the actual distances travelled enables us to specify the model by taking into account the fact that decision to migrate and the distance travelled when migrating are intrinsically interrelated. Therefore we propose to estimate another observed migration variable m i ' expressed by the distance travelled d i considered as a proxy for the total surplus v i , keeping in mind that it will not be possible to disassociate its two components: migration costs and net surplus.

m i ' = d i with d i > 0 if m i * > 0 m i ' = 0 if m i * ≤ 0 (5b)
To simplify, we write the empirical model as follows :

d i > 0 if m i * > 0 d i = 0 if m i * ≤ 0 (5c)
As distance remains closely related to migration costs and as the marginal cost is assumed to be decreasing, we suppose that the marginal returns to migration decrease with distance: (1988) who recommended using the logarithm of the distance. As the log of zero does not exist, the non-migrants are assumed to have moved a minimal distance of 1 km.

Therefore, the model to be estimated is derived from equations (3) and (5c) becomes:

ln d i = β 0 + β 1 x i + β 2 z i +φ i (6)
and the earnings function from equation (4) is :

ln w i = δ 0 +δ 1 X i +δ 2 Y i +δ 3 ln d i +ε i (7)
where the wage is usually expressed in log form, particularly because this specification allows for a direct reading of the returns.

1. Empirical Specification and Data

Specification and migrants' selection bias

The decision to relocate and to migrate a given distance is made if the utility of this decision is positive, that is to say if there exists a linear combination threshold of x i and z i beyond which the decision to migrate dominates the decision to remain sedentary: let us call k i γ that linear combination. This decision rule is not deterministic and we attempt to jointly estimate the probability of relocating and of migrating a given distance. We only observe the distance travelled by individual migrants. Then we propose to use a censored Tobit model adapted to the truncated nature of the migration variable d i . A migration is actually seen as the distance travelled when changing territories ( d i > 1 or ln d i > 0) and the censoring point corresponds to sedentary individuals ( d i = 1 or ln d i = 0). The log-likelihood of this Tobit model is: with N 1 designating the number of observations for which ln d i > 0. So as to ensure that the function in the optimization process is concave, a numerical optimization procedure is used, through the reformulation of the log-likelihood function according to OLSEN's method (1978).

ln L(k,γ ,σ θ ) = ln 1-Φ( k i γ σ φ )         - N 1 2 ln(2πσ φ i:d i =1 ∑ - 1 2σ φ i:d i >1 ∑ ln d i -k i γ ( ) (8)
The estimated earnings function is equation ( 7) in which it is appropriate to deal with the endogeneity bias of the variable ln d i . This endogeneity bias results in a correlation between the error terms of the Tobit estimation of the distance φ (equation 6 estimated via equation 8)

and the ones in the estimation of the earnings function ε [START_REF] Maddala | Limited-dependent and qualitative variables in econometrics[END_REF]. Here, the correlation signifies that the individual non-observed variables in k i are correlated with the non-observed variables in X i . In their model, DÉTANG-DESSENDRE et al. ( 2004) actually do point out that wage returns to migration may be biased because of these unobservable factors influencing both wages and migration decision. The consequence of this is the emergence of an self-selection effect [START_REF] Magrini | Migration and Self-Selection: Measured Earnings and Latent Characteristics[END_REF][START_REF] Yankow | Migration, Job change, and wage growth: a new perspective on the pecuniary return to geographic mobility[END_REF]. Migrants may in fact be characterized by some favourable unobservable intrinsic factors (not represented among the X) that facilitate the migration decision, such as greater motivation or a higher ability to process information, risk aversion, etc.. And these qualities could also positively influence wages, and thus create positive self-selection for the migrants, compared to similar people who do not migrate, when based on observable characteristics. On the other hand, it is possible to imagine that the migrants might be characterized by unobservable negative intrinsic factors that reduce their wages, leading them to widen their search field to find a better job. Relative to observable factors, the self-selection of the migrants appears here to be negative in comparison to similar non-migrants.

Therefore, the question is to determine whether the wage returns to migration are the consequence of unobservable characteristics or whether they are due to the migration process alone (the migration cost-benefit trade-off). Indeed, if a migrant benefits from a wage surplus, it may be either linked to unobservable favourable quality effects, or it might simply be justified by compensation for migration costs or finally, it may be explained by both these reasons at once. However, these two effects may be opposed. In this case, the average return may be nil and/or statistically insignificant [START_REF] Krieg | Occupational change, employer change, internal migration, and earnings[END_REF][START_REF] Falaris | Migration and wage of young men[END_REF], while in reality there is a negative effect of unobservable characteristics and a positive effect of migration costs.

Migration costs can therefore explain the diversity of findings in the empirical literature on migration. We argue that taking into account distance as a proxy for migration costs enables us to distinguish much more clearly between wage variations due to the unobservable characteristics and wage differences that are part of returns to migration iii . It therefore seems important to us to jointly reconsider the estimates of the selection bias and the returns to mobility in our sample by integrating a variable that is more representative of the decision to migrate: the migration distance.

When the correlation between the error terms of the Tobit estimation and the error terms of the estimation of the salary equation is negative, the geographically mobile workers have unobserved characteristics that have a negative effect on salary. They may not, for example, be the "best" ceteris paribus (X i and Y i ). In this case, the return to the endogenous distance is superior to the return estimated by OLS, which captures the negative effect of the "unobservable" variables. In this way, we obtain the effects that stem specifically from migration. The treatment of this endogeneity bias is controlled for using NELSON and OLSEN's method (1978). To do this, we apply the principle of instrumental-variable estimation. Let us remember that because of the specificity of the Tobit model (used to estimate the migration equation), the instrumental-variable procedure does not directly correspond to the two-stage least squares (2SLS), which applies to continuous quantitative variables, but to the method

Sample Selection and Descriptive Statistics

We have exploited data from the Céreq's v "Generation 98" survey in which 55,000 youths who left the French education system with an initial education in 1998 are observed over a three-year period. They are representative of the whole generation of individuals leaving school (700,000). The spatial mobility of young people is seen as a move away from their local labour market (the "Employment Zone", abbreviated "EZ"), occurring between the end of their studies in 1998 and their being employed in 2001, three years after leaving the French education system. This sample is composed of 44,327 young men and women who were As mentioned earlier, the level of education strongly influences both migration and earnings. So we have performed several estimates, per education level. In a first stage, the estimations were carried out for each of the seven levels. Then, the first three and the following three were grouped because the results were adjacent insofar as the determinants of migration were concerned as well as for its impact on salary. Findings are therefore presented for three levels: i) young people with no qualifications or with a diploma equal to or below the French Baccalauréat (denoted "Bac" vii ) ; ii) the Bac graduates that studied a further 2 years ("Bac +2") or a further 4 years ("Bac+4") since their Bachelor's degree; iii) the ones that We observe that the number of employment related relocations greatly increases when we use a partition into EZ rather than into regions, which are more frequently used in studies on the subject. While relocations from EZs to Ezs concern more than half of the higher (EZ moves: 65% vs. regional moves: 38%) and intermediate levels (54% as opposed to 24%), 36%

of the lower-skilled workers also moved from the EZ in which they had studied. When approaching migration using a regional scale, the mobility of the least qualified individuals is much lower. When we use the EZ scale, the propensity to migrate of the low-skilled workers can no longer go overlooked. The Euclidean distance viii reveals some remarkable differences in mobility behaviour. In particular, including distance means that previous results must be evaluated by comparing them to one another, because relocations between EZs can correspond to different distances travelled, and consequently to different migration efforts.

For instance, one individual may be considered as a migrant even though he has only crossed a border, unlike another who may have travelled a distance ten times as great. These differences are obvious in this reduced spatial scale as shown in Table 1.

TABLE (1) HERE

Indeed 42% of the relocations from one Employment zone to another are associated with distances of over 100 km, that is twice the distance travelled in the case of migration between regions. As a result, the variability of the distances travelled when we consider migration from EZ to EZ is greater than if we consider migration between regions. Thus, the smaller the spatial scale, the more important it is to take into account this criterion, because the diversity of mobility behaviours is greater. ix Finally, it is important to note that among the migrants, the lesser-skilled workers also migrate over great distances. For instance, the proportion of migrants covering over 300 km is almost the same for both average-and low-skilled workers, whether we consider migration from EZ to EZ or migration from region to region. Thus, including the actual distances travelled in the analysis enables us to re-assess some widely-held views, such as the unwillingness of low-skilled workers to migrate. Nevertheless, we observe that only 3% of the relocations between regions involve distances of less than 20 km, unlike the percentage rises to more than 20% for migrations between EZs. Thus, since travelling distances of less than 20km is much more likely to be considered as commuting rather than migrating, we shall not count those moves as migration x .

3. Results

The Determinants of the Decision to Migrate

In a first step, we examine the different effects of the individual and territorial determinants of the choice to relocate between EZs and to travel over a given distance, according to the education level of the individuals (Table 2).

TABLE (2) HERE

With regard to individual-related determinants, the results and the variables used are identical to those of many other studies and will therefore not be commented xi . These individual variables are the ones that are commonly used in studies on this subject and using this kind of sample, particularly in French studies (see [START_REF] Détang-Dessendre | The Impact of Migration on Wages: Empirical Evidence from French Youth[END_REF]). Those variables are also discussed in the review of the literature by [START_REF] Greenwood | Internal Migration in Developed Countries in Handbook of Population and Family Economics[END_REF]. Territorial characteristics can also influence the decision to migrate. First, young people from predominantly rural areas are more prone to mobility. The low demographic density associated with this type of area reduces the probability of finding employment and increases [START_REF] Nakosteen | Migration and Income: The Question of Self-Selection[END_REF], we took into consideration the differences between the EZs of arrival and the Ezs of departure, so as to take into account that fact that the actor's decision-making process was founded on a comparison between territories (Table 3, T2SLS-2).

Since the population density also reflects the job offers in the local labour market, it is not surprising that in the case of job-related relocations, the migrants mostly move to zones with high population densities, which a priori offer more employment or re-employment opportunities. This finding is reinforced by the observation that young workers are less inclined to migrate to EZs whose unemployment rates are higher than those of the zone they come from.

Finally, since this analysis is performed per education level, it seemed interesting to integrate a characteristic reflecting the territorial level of human capital: we chose the proportion of the population over 15 years of age currently studying. The correlation with this indicator turns out to be positive for youth with education levels lower than the Bac, and unattractive for young people with an education level above the Bac. For the latter, this finding may be fairly surprising, but it can be explained by the fact that many of these young people were trained in an EZ whose youth study ratio was among the highest (particularly in the Paris area), and seemingly, the concentration of training tracks above the Bac exceeds the offer of employment corresponding to the EZ and leads them to migrate. Consequently, the divergence of this indicator between the EZ of arrival and departure being generally negative, the observed relation is negative for the superior education levels. The individual trade-off leading to these relocations remain to be elucidated. The determinant examined here is the existence of a possible salary gain -supposedly reflecting the trade-off between the costs and benefits of migrations -analysed by means of these observable determinants.

Wage Returns to Spatial Mobility

The characterization of the wage equation corresponds to the determinants usually selected (equation 7) in the literature on the subject. And to evaluate the influence of migration in this wage equation, we estimate three wage equations.

The first one does not take into account the selection bias and only measures an average ceteris paribus effect of distance on wages. This model corresponds to the ordinary least squares (OLS) estimate of the coefficient of the migration variable in the earnings function.

Then, we estimate two earnings functions for which the selection bias (equation 7 with equation 6) is taken into account according to two versions of the migration equation ( 6) xiii .

The aim is to capture the various effects on wages of the different determinants of the distance travelled when migrating from an EZ to another. One version refers to the hypothesis that the determinants of mobility are above all linked to the individual's characteristics with respect to his initial situation: the migration equation only includes the individuals' characteristics

T2SLS1 (equation 6 vector x -INDIVIDUAL'S CHARACTERISTICS Table 2). The other version

integrates the territorial characteristics in migration equation T2SLS2 (equation 6 vector x and z -INDIVIDUAL'S CHARACTERISTICS and TERRITORIAL CHARACTERISTICS Table 2).

Comparing the wage equation results generated by these two estimates will enable us, later on, to better understand the cost-benefit trade off mechanism at play in the decision to migrate. The results of these three wage equations for the whole sample and for the subgroups of education levels are presented in Table 3. Only coefficients of the distance 3. For several distances, the wage returns calculated on the basis of these coefficients is also included in Table 3. Annex 1 summarizes the results of one estimation for all the variables xiv .

TABLE (3) HERE

The OLS results for the various distances travelled when relocating seem to be in keeping with the common assumption, according to which the migration effort should be rewarded (positive yields), and all the more so when the distance travelled is great. Thus, as a function of the distance travelled, the lowest yield to migration appears to be 1.1% for the young workers with a Bac+5 education level who travelled 50 km; whereas, the individuals with intermediate education levels who travelled over 900 km obtained the highest yields of 6.3%.

The highly trained individuals are those who benefit the least from the mobility effort. The fact that the relative cost of migration is lower for highly trained workers may explain this finding, although it might also be a reflection of the fact that the group must confront a national labour market. Along these lines, for these highly-trained individuals, geographic mobility is an expected consequence that does not require compensation for the costs attached to relocation. Nevertheless, this finding must be considered in relation to other results because we are dealing, here, with yields rather than absolute increases in earnings. The comparison of the absolute values for the different education levels might reveal a reversed trend.

As previously explained, unobserved factors exist that can both influence the decision to migrate and the salary, contributing to the endogeneity of the migration variable. This phenomenon is confirmed by endogeneity tests that are significant for all education levels, regardless of the migration equation employed. Part of these returns are therefore a priori erroneously attributed to the role of migration alone, which is why the earnings function was estimated by instrumenting the migration variable, as described previously. Two major results stand out from these instrumental estimates. Firstly, the effect of unobserved characteristics varies according to the education level. Secondly, the different returns to relocations obtained using the two migration equations demonstrate the existence of a "territorial trade-off" within the cost-benefit trade-off in the migration decision.

Indeed, for young people with a Bac+5 education level, the coefficient of the migration distance is considerably overestimated since it becomes negative (seen by comparing the OLS and T2SLS1 columns). In other words, the remuneration obtained through the relocation only corresponds to the effect of unobserved characteristics. This result confirms the existence of a positive self-selection bias for the Bac+5 migrants. For the other education levels, on the contrary, the returns to relocation prove to be underestimated. Thus, even though these individuals suffer from unfavourable characteristics that probably compel them to broaden the spatial field of their job search, we can suppose that they would accept a job outside their home area only if it fully or partly compensates for the cost of relocation . The migrants with education level Bac+2, +3 or +4 obtain higher returns to relocation than those with a level below the Bac. These differences reflect not only variations in migration costs between these education levels, but also the small gap between the remunerations of workers with a low education level.

These rates of return are far from negligible. For individuals with a Bac+2, +3 or +4 level, relocating more than 600 km away generates a return that corresponds or exceeds that of an additional year of study. For young workers with an education level below the Bac, the returns to moving such distances are equivalent to the returns to two additional years of study.

The rates of return we have just interpreted were obtained with respect to observable factors explaining the migration variable: the determinants in the migration equation (i.e. the instruments) and marginal determinants in the earnings function. These yields refer to the trade-off between the costs and the benefits of migration. More precisely, a variable that reinforces migration reduces its returns because it makes the relocation less costly. Conversely, a variable that inhibits the decision to migrate makes this decision more costly.

Therefore the salary offered must be significantly higher if the individual is to decide to migrate. Consequently, the returns to migration vary according to the identifiable determinants, and so the cost-benefit trade-off of migration varies.

In a similar vein, the introduction of gap variables for territorial characteristics significantly modifies the returns to migration (T2SLS2 columns). Unquestionably, when they are introduced into the migration equation, the returns to all education levels change (compare the columns T2SLS1 and T2SLS2 in Table 3). More precisely, for individuals with an education level below the Bac and with a Bac+2, +3 or +4 level, the returns diminish; and

people with a Bac+5 level, the negative returns approach nullity. These results can be explained by the migrants' internalization of the "comparative advantage" associated to the new territory, which partly compensates for migration costs, as it is revealed by the migration equation with only individual's characteristics, and therefore reduces the returns required for relocation when estimating the migration equation T2SLS2. In particular, the act of moving to a territory with a higher population density and a lower unemployment rate than the area of departure indicates that the probability of finding another job is higher in the event of a mismatched job found through migrating: the risk incurred by accepting a job in a distant location (for which the risk of evaluation error is greater) is therefore lower and de facto less remunerated. Such territorial characteristics probably also allow for professional advancement, and thus, partly compensate for the cost of relocating to a distant area. Hence, territorial characteristics contribute considerably to defining the distance-income trade-off in the migration decision.

Lastly, let us add that the returns to moves from one EZ to another alone have been evaluated for the purpose of comparison. Its estimation produces an average effect that is much higher than the effect evaluated with the migration distance. On the one hand, this average effect overestimates the returns to relocating short distances away. On the other hand, it does not account for the decreasing marginal cost of migration, which reduces the returns to long distances. In other words, a dichotomous variable hardly reflects the diversity of migration costs, contrary to a distance variable. The double finding of the variability of the returns to migration as a function of the distance travelled and the unmasking of unobservable effects by using precise distances thus reinforces the choice of an estimation method based on the distance travelled for evaluating the returns to spatial mobility.

Conclusion

Geographic mobility is often considered to be an attribute of the most qualified workers.

Yet, conducting an analysis at a sub-regional scale reveals that less qualified individuals can also be quite mobile. Moreover, taking into account the distance travelled when migrating allows for a considerably more accurate evaluation of the impact of geographic mobility on wages. It makes it possible to better understand the cost-benefit trade-off of the migration decision, while at the same time controlling for the effect of unobservable characteristics in the process of job-related migration of the whole set of young workers, including those with a low level of education.

Among the determinants of the cost of migration from an Employment Zone to anothercaptured by using the distance between the place of residence on completion of individuals' studies in 1998 and the place of employment in 2001 -are the education level of the individual, the education level of the wife or of the husband, the number of children, the social origin and characteristics of the territories, which have proven significant for all individualswith non-negligible variations from one education level to another.

Concerning the returns to migration, the results of our investigation did not permit a total dissipation of the imprecision between the costs of the job search and the ones linked to the unobservable though the available variables -have either a positive or negative effect on the salary, according to the education level considered. The main finding is that this effect of unobservable characteristics varies among the individuals in the Bac+2, +3 or +4 education level group, while other research studies on migration showed that this effect was positive for the whole group. Thus, all else held constant, if the salary reflects the contribution to production, migrants are not necessarily the best workers, even for the education levels superior to the Bac. Nevertheless, these young workers receive a positive return to migration, and we can therefore think that those who migrate are not the "best" individuals but those of "average value" who are not able to exploit the local opportunities. This lower ability may have increased the opportunity costs of the relocation, and therefore driven these individuals to accept jobs with relatively lower salaries ceteris paribus, but which still compensate for the costs of migration.

In the end, the estimates per education level reveal clearly differentiated results, which lead us to assume the existence of different scales for the labour market according to education levels. The returns to migration for the most qualified individuals (in France, Bac+5 and higher) in fact only correspond to the remuneration of favourable unobservable qualities. We can therefore infer that these young workers are confronted with a national labour market, and that consequently they do not require to be compensated for the costs of their mobility, beyond the remuneration of their intrinsic qualities. Individuals with other education levels obtain, on the contrary, positive returns to migration in spite of the unfavourable effects of unobservable characteristics. The returns obtained are relatively highfor those with low education levels too. Unlike the most qualified individuals, these young workers are confronted with a multitude of local labour markets (the EZs), and the move from one to another necessitates that the migration costs be remunerated, taking into account the distance We have made these findings by using a sub-regional scale more representative of local labour markets than the regional scale usually used, and by integrating the distances travelled, which remains a key concept in economic theories accounting for the role of space. These results open the way to further research on the subject and call into question the public policies in the field of mobility incentives. In France, since the years 2000, a flat allowance encouraging geographical mobility exists. However this allowance is only granted when the individual relocates more than 200 km away from his/her place of origin. xv While such allowances can be praised, the results obtained call for criticism on several points. First, this allowance should be a function of the worker's skills as the returns to migration are not the same among these young workers, and all the more so, as these returns are lower for the least skilled workers due to the narrow remuneration gaps. Moreover, this is all the more the case that three quarters of young migrants cover a distance of less than 200 km. Finally, the source of financing could differ according to the education level: national for the most skilled, regional (or by territory of arrival) for the least skilled. iii "agents facing unknown higher migration costs have lower migration probabilities, and they are prepared to accept lower wages to stay at home. In other words, differences in migration costs should produce negative auto-selection effects for non-migrants" (DÉTANG-DESSENDRE et al., 2004, p. 671).

iv Note here that another bias can come from the sample selection of the youths occupying a job in 2001.

But when integrating the Mill's ratio of sample selection (according to the Heckman method) for the different groups in the earnings function, the results are not modified. Therefore we have reported the estimations without it, as done by DETANG et al. (2004) for instance. vii More precisely, the French Baccalauréat, designated "Bac", is the national diploma sanctioning the studies completed in the French national secondary school system (Lycée and Collège), which corresponds to an American high school diploma + a variable amount of American university credits obtainable via "Advanced Placement" examinations.

viii

The distance travelled between the EZ of studies in 1998 and the EZ of work in 2001 has been calculated "as the crow flies" between the centroids of the towns of departure and the arrival. In (x,y) space representing the geographic coordinates of points, the distance between two points A and B is:

d(A, B) = (x b -x a ) + (y b -y a
) .

ix

The reader could refer to Briant, Combes and Lafourcade (2010) for a discussion on the impact of the size and the shape of areal unit on some economic migration effects.

x This control of the distance allows us, in a sense, to control for the "Modifiable Areal Unit Problem'

(that is variation in the spatial units used for aggregation can cause variation in statistical results), as here choosing a smaller areal unit will count very small migrations that are out of concern in our analysis. xiii Note here that the validity of instruments is significant for most of the variables employed in the migration equation that are not included in the earnings function, according to the Sargan test. When this test did turn out to be significant, the variable was nevertheless selected as an instrument if its effect was much more significant for migration than for salary. The choice of instruments according to the "inclusion-exclusion" principle is always delicate. This is particularly true for certain variables such as the spouse's education level, the number of children, even some terms characterizing the youths' parents, which also seem to be significant in the gains function for certain groups. However, the combination of these significant terms differs from one group to another under consideration, which did not allow us to pass judgment on a notable significant effect of these variables for the set of all youths. Therefore, this weak level of influence, very unequally characterized from one group to another, led us to retain these variables in the selected equation in which they are clearly more significant. As for the gap variables of the territorial characteristics, they seem to be good instruments for all groups. This discussion demonstrates the difficulty in finding the "true" instrumental variables since the interactions among variables in a same process may be multiple, as PUHANI (2000) exposed.

xiv Annex 1 reports one estimation for all the variables from the T2SLS procedure in which the migration variable has been instrumented by both the individual's and territorial characteristics. The two other estimations are not reproduced because if the coefficient of distance varies greatly between each of the three estimates, the other parameters vary only marginally.

xv

The "Professional Mobility" allowance makes it thus possible to benefit from a tax credit of 1,500 €.

F

  home labour market may have explored other markets. From this perspective, the effect of the costs of the job search on the reserve job value remains indeterminate. For this reason,DÉTANG-DESSENDRE et al. (

  , with characteristics Y, anticipated by an individual with characteristics X, may then be written as:

  as a proxy for the utility of migration, a latent variable estimated via the change of territory alone

  and OLSEN. According to the terminology given in[START_REF] Maddala | Limited-dependent and qualitative variables in econometrics[END_REF], this instrumentation technique based on the Tobit model is noted as T2SLS. As a result of this instrumentation, we obtain the effect that is specific to migration, independently from the unobservables in the earnings function. iv

  employed in 2001. The location of the job is more determining in the migration effort than the location of previous residence. Therefore, we have opted to observe the migration in relation to the place of employment in 2001. vi

  5 years ("Bac +5" which corresponds to a Master degree) or more (to obtain a PhD for instance) since their Bachelor's degree.

  it actually turned out that the unobservable individual aptitudes -at least

  characteristics of the individual and his family. However, this wage surplus is more or less compensated by the "comparative advantage" associated with the territory of arrival in comparison with the territory of departure, which causes the returns to migration to vary according to the territorial characteristics.

v

  Céreq: the French Center for Research on Education, Training and Employment. vi Anyway, the EZ partition used is representative of the local labor markets. Therefore taking the location of residence or the location of job occupied in 2001 will do not significantly change the distance travelled for EZ move from the location occupied in 1998 to the one in 2001. Indeed, EZ are defined up to commuting distance inferior to 20km.

  of Statistics and Economic Studies (INSEE).

  16de facto the probability of leaving. Three main territorial characteristics of the Employment Zones were constructed from the data generated by the INSEE's xii General Population Census (1999): the demographic density, the unemployment rate and the level of education. Like

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Table 1 .

 1 Distances travelled by young people during migration between the end of their studies and three years later

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Table 2 .

 2 Reduced-Form Equation for Distance Travelled

	Education level

Notes: Asymptotic standard errors in parentheses. Significance levels of 10%, 5% and 1% are denoted *, ** and *** respectively.

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Table 3 .

 3 Returns to Migration with Distances Travelled Included in the Estimation The log wage equation variables are summarized in tables A. LAMBDA is the Maddala endogeneity test: correlation between Tobit error term for distance φ (equation 8) and Log wage equation error ε (equation 7).

	Whole sample OLS T2SLS-1 T2SLS-2 Ind. Ind.+Terr. 0.0071 0.0022 0.0018 (0.0005) (0.0024) (0.0012) 0.00016 0.00017 (0.0016) (0.0001) 2.5% 0.8% 0.6% 3.2% 1.0% 0.8% 4.1% 1.3% 1.0% 4.6% 1.4% 1.1% 4.9% 1.5% 1.2% 3.3% 1.0% 0.8% 115 km 61% Note: Page Bac+5 Bac+2, 3 or 4 ≤ Bac OLS T2SLS-1 T2SLS-2 OLS T2SLS-1 T2SLS-2 OLS T2SLS-1 T2SLS-2 Variables in migration equation Ind : Individual's Characteristics Terr : Territorial's Characteristics Ind. Ind.+Terr. Ind. Ind.+Terr. Ind. Ind.+Terr. Estimated coefficients from the log wage equation ln (distance EZ98 -EZ2001) 0.0032 -0.0779 -0.0068 0.009 0.0113 0.0071 0.0066 0.0093 0.0056 (0.0015) (0.0093) (0.0027) (0.0008) (0.0045) (0.0019) (0.0008) (0.0022) (0.0015) LAMDA 0.0799 0.0097 -0.0075 -0.0022 -0.0076 -0.0035 (0.0093) (0.0029) (0.0042) (0.0014) (0.0022) (0.0015) Corresponding wage returns in % per distance (km) 50 km 1.1% -23.3% -2.3% 3.1% 3.9% 2.4% 2.3% 3.2% 1.9% 100 km 1.4% -28.9% -2.9% 4.0% 5.1% 3.1% 2.9% 4.1% 2.5% 300 km 1.8% -35.5% -3.8% 5.2% 6.6% 4.1% 3.8% 5.4% 3.2% 600 km 2.0% -39.1% -4.2% 5.9% 7.5% 4.6% 4.3% 6.1% 3.6% 900 km 2.2% -41.1% -4.5% 6.3% 8.0% 4.9% 4.6% 6.5% 3.8% Returns for the median distance 1.7% -33.6% -3.5% 4.1% 5.2% 3.2% 2.8% 4.0% 2.4% Median distance 210 km 110 km 90 km R² 45% 45% 46%

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  1. Log Wage Equation with Tobit Double Least Squares T2SLS-2 (from Migration Equation with Individual's and territorial characteristics) Notes: Asymptotic standard errors in parentheses. Other regressors are sectors, functions, type of employment contract, monthly working hours. Significance levels of 10%, 5% and 1% are denoted *, ** and *** respectively.

		Whole sample		Bac+5		Bac+2, 3 or 4		≤ Bac
	Intercept	7.250***	(0.018)	7.142***	(0.056)	7.039***	(0.032)	6.886***	(0.021)
	Education level
	Unskilled	-0.413***	(0.009)					-0.045***	(0.005)
	First level of professional certification (Cap, Bep)	-0.397***	(0.008)					-0.021***	(0.004)
	Bac	-0.379***	(0.006)					ref.
	Bac+2	-0.258***	(0.005)			-0.038***	(0.005)
	Bac+3	-0.306***	(0.007)			-0.081***	(0.007)
	Bac+4	-0.213***	(0.006)			ref.
	Bac+5	ref.
	Women	-0.074***	(0.003)	-0.078***	(0.008)	-0.060***	(0.005)	-0.079***	(0.004)
	Age in 1998	0.016***	(0.001)	0.023***	(0.002)	0.016***	(0.001)	0.014***	(0.001)
	Number of months unemployed	-0.006***	(0.000)	-0.016***	(0.001)	-0.010***	(0.000)	-0.004***	(0.000)
	Area at end of schooling
	Urban	ref.		ref.		ref.		ref.
	Suburban area	-0.022***	(0.004)	-0.035*	(0.019)	-0.028***	(0.008)	-0.018***	(0.005)
	"Multi-polar" area	-0.015**	(0.007)	0.032	(0.035)	-0.028**	(0.013)	-0.015*	(0.008)
	Rural area	-0.022***	(0.004)	0.012	(0.017)	-0.040***	(0.007)	-0.024***	(0.005)
	Region
	Paris	ref.		ref.		ref.		ref.
	Parisian region	-0.107***	(0.004)	-0.095***	(0.013)	-0.132***	(0.007)	-0.078***	(0.005)
	North	-0.109***	(0.006)	-0.137***	(0.018)	-0.122***	(0.010)	-0.083***	(0.008)
	East	-0.077***	(0.005)	-0.120***	(0.016)	-0.098***	(0.008)	-0.040***	(0.006)
	West	-0.116***	(0.004)	-0.111***	(0.014)	-0.148***	(0.007)	-0.081***	(0.006)
	Southwest	-0.138***	(0.005)	-0.126***	(0.015)	-0.169***	(0.008)	-0.108***	(0.006)
	Centre-East	-0.100***	(0.004)	-0.108***	(0.013)	-0.121***	(0.008)	-0.066***	(0.006)
	Mediterranean	-0.120***	(0.005)	-0.128***	(0.013)	-0.145***	(0.008)	-0.084***	(0.006)
	ln (distance EZ 98 -EZ 01)	0.002	(0.001)	-0.007**	(0.003)	0.007***	(0.002)	0.006***	(0.001)

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Page 29 of 32 http://mc.manuscriptcentral.com/cres Email: regional.studies@fm.ru.nlRegional Studies


Caroline Merten

Pietro Ferrari

Martine Bakker

Alexander Boss

Aine Hearty

Catherine Leclercq

Oliver Lindtner

Christina Tlustos

Philippe Verger

Jean-Luc J L Volatier

Merten

C Ferrari

Tlustos

Davide Arcella
email: davide.arcella@efsa.europa.eu

Methodological characteristics of the national dietary surveys carried out in the European Union as included in the EFSA Comprehensive European food consumption database

Keywords: Dietary survey, exposure assessment, food consumption, 24-hour recall, 33 food records

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

In this paper an overview of the methodologies and protocols employed in the different national dietary surveys is provided. Specifically, details about dietary assessment methods, interview administration, sampling design, portion size estimation, dietary software, evaluation of under-reporting and non dietary information collected are described. This information is crucial to evaluate the level of accuracy of food consumption data and to anticipate and acknowledge utmost important sources of heterogeneity of national databases included in the Comprehensive database. The Comprehensive database constitutes a unique resource for the estimation of consumption figures across the EU and represents a useful tool to assess dietary exposure to hazardous substances and nutrient intake in Europe. Nevertheless, the many substantial methodological differences that characterise the Comprehensive database are acknowledged and critically discussed. to estimate the amount of food consumed. In the British survey, for food eaten outside of 219 home, a ruler and information on household measures and known packaging size were used.

220

In the majority of surveys (19) a combination of 2 or more measurement tools were used and 221 in 16 studies the picture book was used as one of these tools. In all but four (Ireland, Latvia, suitable for the requirements of providing data at a sufficiently detailed level for the purposes 287 of exposure assessment (EFSA, 2009) but differences exist between these two methods. The 288 dietary record method might be a more suitable tool for the elicitation of a greater level of 289 detail but, among others, a potential drawback might be the selection bias due to high 290 illiteracy levels in some minority groups across Europe (Biró et al., 2002). On the other hand, 291 an accurate memorisation of food consumed the preceding day is required for the 24-hours 292 dietary recalls method. This might represent a difficult cognitive task for the respondent, in 293 particular, for the very young or very old study subjects, and could affect the precision in the 294 quantification of the foods consumed (Thompson and Subar, 2001). Six of the countries 295 providing data into the Comprehensive Database collected dietary data on children younger 296 than 10 years. Half of these countries used the 24-hour recall method and the other half used 297 the dietary record method.

299

Food consumption data collected on more than one day per subject are required to assess 300 chronic exposure. At least two independent short-term assessments days are needed to apply 301 statistical modelling to estimate habitual intake (Dodd et al., 2006). The EGFCD (EFSA, prone to correlation between errors in dietary assessments (Kipnis et al., 2003, Day et al., 307 2004). However, in half of the surveys where 24-hour dietary recalls were used, information 308 on only one single day was collected per subject. Such data can nevertheless be useful to 309 estimate acute exposure. The seven surveys that used 2-day 24-hour recall method were 310 conducted on non-consecutive days, with the exception of Finland where a 48-hour dietary 311 recall method was used. In three of these seven surveys the average distance between two 312 interview days is below the recommended two weeks interval.

314

It has been argued that, accuracy of self-reported dietary information may decrease as the 315 number of days increases (Biro et al., 2002, Gersovitz et al., 1978, Moreno et al. 2005, 316 Whybrow et al., 2008). On the other hand, increasing the number of assessment days of a 317 survey affects the distribution of consumption, particularly at the upper tails (EFSA, 2006). In advantage of reducing the effect of study subjects' day-to-day variation, thus leading to an 320 improved estimation of consumption variability (Willett, 1989). As survey duration increases, 321 also the observed percentage of subjects reporting non zero consumption for commonly and 322 rarely eaten foods becomes larger (Nusser et al., 1995), whereas the observed mean and high 323 percentiles consumption, in consumers only, decreases, as also illustrated by Lambe et al. FFQ or a FPQ along with a 24 h recall or dietary record method. In one country (Ireland), the 334 FFQ focused solely on the intake of meat. Data from FFQ or FPQ may be used in acute 335 exposure assessments to assess the proportion of consumers of a given food, and in chronic 336 exposure assessments to determine the frequency distribution of the consumption of rarely 337 consumed foods. Data from FFQ or FPQ was, however, not transmitted to EFSA. , 1992;Lyu et al., 1998;Casey et al., 1999;Tran et al., 2000;Bogle et al., 2001) concluded 345 that the telephone 24-hour dietary recall interview method provides a reliable and cost-346 effective alternative to a face-to-face method, with similar response rates being observed for 347 both methods whereas there are not as many studies in the literature that have examined 348 postal questionnaires as a method of obtaining dietary information. In general it seems that 349 both face-to-face and telephone interviews are more suitable for collecting dietary and health 350 data compared to postal questionnaires (Sibbaldi et al., 1994). The effect of the number of 351 face-to-face meetings is likely to improve the accuracy of dietary estimates as the number of The home visit is considered the most appropriate location to conduct dietary interviews 356 since it offers the advantage of collecting additional information on foods consumed, such as 357 brand level data and food packaging, and could allow for direct weighing of some dietary 358 items (De Henauw et al., 2002). On the other hand, other locations like medical centres are 359 likely to offer better conditions for the collection of additional information, such as biological 360 samples (Riboli et al., 1997). On the other hand, the use of the household as a sampling unit seems to be a convenient 394 choice since an interviewer could collect information from more subjects during the same 395 visit. However, food consumption estimates are likely to be mutually dependent when 396 subjects from the same household are interviewed, thus leading to a reduced variability in 397 terms of dietary pattern observed. In France and UK, only one respondent per household was 398 included in the study. The random selection of subjects from households sampled within the 399 national household budget survey in Poland seems to offer a good choice since it allows the 400 comparability between the two sets of data. A cost reduction of the survey can be obtained by 401 the use of a multistage sampling design where, for example, a random choice of cities, as 402 primary units, of households, as secondary units, and finally individuals within households is 403 adopted. This method can be combined with a stratification for instance on regions or (e.g. food additives). The use of this information for exposure assessment must be decided 523 case by case, based on the availability of information for the food categories of interest.

524

Information on brand name, food packaging and household processing is available for some 525 of the countries that have used a 24 hour recall as well as for some countries that have used 526 food records. This confirmed that this information can be collected by both protocols. 527 However, the three surveys using the 24 hour dietary recall by means of EPIC SOFT program 528 provided a higher number of different descriptors of household processing information.

529

In summary, the use of brand information is limited to certain foods and only available in a 530 restricted number of surveys, information on household processing might be useful in a larger 531 number of countries whereas the available packaging information appears to be of poor use 547 physical activity levels. This strategy was followed in France, where the cut-off points were 548 corrected on the basis of physical activity levels (Black, 2000b). However, this approach 549 finds a natural limitation in the fact that the assessments of physical activity are also prone to 550 measurement errors (Ferrari et al., 2007). The inclusion in the database of surveys with high frequency of under-reporters may lead to 557 an underestimation of mean dietary exposure and of the percentage of consumers of some 558 particular foods, e.g. foods with high fat or sugar content, whose estimation is more prone to 559 under-reporting (Becker, 1999). On the other hand, under-reporting is likely to have less 560 impact on the assessment of high percentiles of consumption.

562

An extensive review from Gorber et al. (2007), evaluating the relationship between self-563 reported and directly measured height and weight, showed trends of under-reporting for 564 weight (ranging from -0.1 kg to -6.5 kg in women and from -0.1 kg to -3.2 kg in men) and 565 over-reporting for height (ranging from +0.6 cm to +7.5 cm. A slight over-estimation of 566 exposure expressed per kg body weight is therefore expected when using data from the 13 567 dietary surveys that collected the weight information through self reporting.

574

Although in some circumstances the effect and direction of potential bias in exposure 575 estimates can be, to a certain extent, anticipated, this is not consistently possible. For 576 example, it is recognised that estimating high percentiles of habitual consumption, when only 577 one record or recall day is available per subject, leads to overestimation of intake. In dietary 578 surveys the degree of such overestimation is often an unknown quantity in the absence of 579 accurate information on true variability of a given food. Similarly, the effect of a non-random 580 selection of survey participants might be difficult to predict in magnitude and direction. The 581 effects of an uneven sampling over days of the week can be, for example, particularly 582 relevant for the consumption of alcoholic drinks and not for bread, whereas the reported 583 consumption of soft drinks and certain types of fruits and vegetables can be significantly 584 affected if the survey design does not capture consumption patterns evenly across the four 585 seasons. With these examples it is intended to stresses the fact that uncertainty is likely to be 586 specific (i) to food or food groups, (ii) to the characteristic of populations in terms of dietary 587 habits, and possibly (iii) to subgroups within a population.

588

Existing guidance documents on uncertainty refer to the uncertainty in exposure assessment 589 (EFSA, 2006 andWHO, 2008) but not to uncertainty of data inputs. Because the uncertainty

  reflect what individuals or population groups consume in terms of study was carried out as part of the feasibility study prior to the 151 main survey. 152 153 Fourteen surveys also included the administration of a Food Frequency Questionnaire (FFQ) 154 or of a Food Propensity Questionnaire (FPQ) whereas in Germany a diet history was In six surveys the target population included children below 10 years old and in about half of

222

  Slovenia, Estonia) of these 16 surveys a validated or tested picture book was used. EPIC-soft

302 2009 )

 2009 recommends the collection of dietary information for two non-consecutive days for 303 both the 24-hour recall and the dietary record methods. Recording days are here considered 304 as non-consecutive if there is an interval of at least two weeks between them. Collecting data 305 on non-consecutive days has the potential advantage of making dietary measurements less 306

  318particular, increasing the number of survey days (for both recalls and records) has the

324(

  2000). Half of the surveys using the record methodology in the Comprehensive Database 325 (Denmark, France, Ireland and United Kingdom) collected information for more than three 326 days per subject.

  foods may become of public health concern, as a result of high 329 level contamination with a given hazardous substance. In order to accurately estimate the 330 usual intake of these foods, additional information on their frequency of consumption could 331 complement 24-hour dietary recalls or dietary records (Tooze et al., 2006; Kipnis et al., 332 2009), as emphasised in the EGFCD (2009) guideline. Twelve countries reported using a 333

  of surveys in the Comprehensive Database used the face-to-face interview 341 method, mainly at the respondent's home. There were also two surveys conducting 24-hour 342 dietary recalls by phone (Germany and the Netherlands), while in Austria one self-343 administered 24-hour dietary recall was collected via postal mail. Numerous studies (Derr et 344 al.

  352

  be ideally conducted by nutritionists or dieticians. In alternative 363 adequately trained staff could be employed. In the Comprehensive Database, over half of the 364 surveys were conducted by an interviewer with a nutritional background. includes data from the most recent national dietary surveys 368 carried out in 20 MSs of the European Union. Some surveys (Estonia, Ireland, Spain and 369 Sweden) started before 2000, thus limiting the accuracy of consumption estimates of dietary 370 items recently introduced into the market. 371 372 Sample representativeness is a crucial aspect for the evaluation of the food consumption data 373 gathered in the Comprehensive Database. Representativeness is a qualitative parameter 374 related to the sampling design of a dietary survey. Significant biases can arise from a survey 375 sample that does not represent the population at national level.

  individuals or households) were selected randomly in all dietary surveys, but 378 differences in the sampling design are observed. Concerning the sampling frames used, 379 National population registers, address registers, postcode address files, general population 380 census, consumer panels and electoral lists (only for the adult population) can all be 381 considered as optimal sampling frames as far as these lists are regularly updated and each 382 subject within the national population register is likely to be selected. The national register of 383 the general practitioner's practices used in Bulgaria seems to be a good sampling frame for 384 children since, due to the national legislation, all children must be listed there. In Italy the use 385 of telephone books has the potential of introducing unwanted bias, as non-telephone 386 numbers issued after the publication of the telephone directory, and 387 unlisted numbers will be automatically excluded. In Spain, the use of universities, health 388 centres and pharmacies to randomly recruit subjects is likely to constitute a potential source 389 of bias. In Slovakia, the study population cannot be considered representative of the general 390 population since subjects were only selected among employees of confectionary and bakery 391 manufactures.392 393

  404rural/urban areas to reduce sampling bias because the part of the variability of the studied 405 indicator explained by the strata is controlled(Cochran, 1977). All surveys in the 406 Comprehensive Database were stratified for gender and age groups with the exception of 407 Austria. The number of subjects participating in the dietary survey varied sizeably from 408 survey to survey. The adult population group (from 18 to 64 years of age) was the only 409 represented age group in all 20 MSs with a number of study subjects varying from 400 410 (Slovenia) to 10,419 (Germany) where the study population was sampled at a very fine 411 geographical level (Federal State level). A large sample gives the opportunity to collect more 412 information on rarely consumed foods. 413 414 The target population of a national dietary survey includes all people living in the country at 415 the time of the study. However, institutionalised subjects, such as the elderly in retirement 416 homes or people residing in hospitals, prisons or military barracks, have not been included in 417 the large majority of dietary surveys. Their exclusion is often considered necessary to 418 simplify sampling procedures, as outlined in the recommendation of the EGFCD (EFSA, 419 2009) for a future pan-European dietary survey. breastfeeding women being specifically excluded only in 7 surveys, 421 where included their number is overall rather low, i.e. from 0 (Spain I) to 59 (Denmark) 422 breastfeeding women and from 0 (Spain II) to 52 (Germany) pregnant women. Therefore 423 dietary estimates of these important subgroups should be treated cautiously. 424 425 One important aspect of food consumption data is their representativeness over the different 426 seasons. Ideally, 24-hours dietary recalls and dietary records should be collected uniformly 427 over the four seasons. This was achieved in the majority of surveys in the Comprehensive 428 Database, with the exception of Bulgaria I, Bulgaria II, Estonia, Finland, Hungary, Latvia, 429 Netherlands, Poland and Spain II where an uneven distribution of recording days over 430seasons was reported. This issue is particularly relevant when using food consumption data to 431 assess exposure to hazardous chemicals mainly present in seasonal foods. In addition, it has 432 been suggested that a proportionate collection of dietary estimates in week versus weekend 433 days should be adopted, at population level, in the sampling phase(Lyhne et al., 2005). 434 Weekend days were particularly under represented in Austria, Bulgaria and Slovakia and 435 over represented in Bulgaria II. The effects of uneven sampling fractions over days of the 436 week are potentially relevant for foods that exhibit specific consumption patterns related to 437 weekend consumption, e.g. alcoholic drinks. 438 439 It has been argued that the response rates of dietary surveys should be high enough to ensure 440 that individual dietary estimates can be generalised to the general population, thus avoiding 441 under-sampling of specific population subgroups. Response rates were relatively low in 442 Hungary (27%), Spain II (28%), Italy (33%), Belgium (41%), Netherlands (42%) and 443 Germany (42% for the total study). The high response rate in Slovakia (98%) is likely due to 444 the very convenient sampling frame used. No information was collected on type of incentives 445 for the study subjects. The use of incentives is a common method for increasing survey 446 response but can also introduce bias in dietary estimates by inadvertently drawing individuals 447 from selected population subgroups or by influencing respondents' responses (Singer & Weighing is considered to be the most precise method for measuring food alone or in combination with other methods, in the United Kingdom, Ireland and Spain only.

457

  As an alternative to weighing all food items eaten, consumption can be measured based on 458 the subjects' estimates of portion size. The parallel use of different Portion Size Measurement 459 Aids (PSMAs), such as photographs, household measures, rulers etc, was considered the most 460 convenient option by the EGFCD (EFSA, 2009) in order to obtain best estimates for different 461 foods. Indeed the majority of surveys (in 19) used a combination of two or more 462 measurement tools, with 16 using the picture book as one of the tools and all but four of these 463 used a validated or tested picture book. Measuring children's dietary intake is challenging.

  Cautious interpretation of consumption data is advisable in almost half of the surveys which 489 did not use yield factors to disaggregate composite foods from cooked into their raw 490 ingredients. Hence, the cooked foods are considered as consumed as such and consumption 491 estimates of these particular food groups might differ from those in countries where yield 492 factors were used to disaggregate the recipes into the ingredients as purchased.493Overestimation of exposure might result from these five surveys in certain foods such as 494 pasta or rice (the cooked weight of one portion is far higher than its raw weight) whereas 495 underestimation may result in other foods such as meat or fish (their weight decrease when 496 cooked since they lose water).

  were not disaggregated into ingredients. As a result, an underestimation of

  532for exposure assessment. In this respect, the EGFCD (EFSA, 2009) recommended the 533 collection of information on brand name, physical characteristics of the packaging and 534 household cooking procedures. However, other data sources, like market share data from 535 marketing research studies, could be linked to the food consumption data in order to fill the 536 information gap for exposure assessments.

  was assessed in a number, but not all, surveys by comparison of individual 540 energy intake estimates with Goldberg cut-off points. In the evaluated surveys a varying 541 proportion of under-reporters was identified, i.e. between 12% to 37%. It was assessed that 542 the Goldberg's cut-offs have moderately low sensitivity in identifying under-reporters (Black, 543 2000b), thus suggesting that, although most study participants identified as extreme under-544 reporters are likely to have truly underestimated energy intake, a proportion of study-subjects 545 identified as normal-reporters are likely to be under-reporters. One effective way to improve 546 the identification of under-reporters is to use complementary information on individuals'

  document of the EGFCD (EFSA, 2009) it was suggested that individual 553 measurements should not be excluded on the basis of estimated under-or over-reporting

  to exposure estimates based on the Comprehensive Database is 571 presented and discussed for a number of methodological parameters characterising each 572 dietary survey. It has been highlighted that different survey methodologies have a potential to 573 induce uncertainties on exposure, especially for the comparison of results between countries.

  Hankin JH, Liu LQ, Wilkens LR, Lee JH, Goodman MT, Kolonel LN. 1998. 787 Telephone vs face-to-face interviews for quantitative food frequency assessment. J Am Diet

^

  Questionnaire administered only to subjects between 15 and 79 years old § Information on physical activity collected by means of questionnaires within all surveys.

Table 1 -

 1 Basic information on the dietary surveys included in the "Comprehensive European Food Consumption Database"

	Country	Name of the dietary survey (Acronym)	Institution providing the data	Reference publication
	Austria	Austrian Study On Nutritional Status (ASNS)	Institute of Nutritional Sciences -University of Vienna	Elmadfa et al., 2009.

Table 2 -

 2 Information on the dietary method used within the dietary surveys

	Country	Method	Number of replicates	Days between non consecutive replicates* Mean (5 th th -95 percentile)	Additional food frequency (FFQ) or propensity (FPQ) questionnaire^
	Austria	24 h dietary recall	1	Not applicable	No
	Belgium	24 h dietary recall	2	23 (12 -42)	Yes
	Bulgaria I	24 h dietary recall	1	Not applicable	Yes
	Bulgaria II	24 h dietary recall	2	3 (2 -5)	Yes
	Czech Republic	24 h dietary recall	2	79 (43 -141)	Yes
	Denmark	Food record	7	Consecutive days	No
	Estonia	24 h dietary recall	1	Not applicable	Yes
	Finland	48 h dietary recall	1	Not applicable	Yes
	France	Food record	7	Consecutive days	No
	Germany	24 h dietary recall	2	16 (7 -39)	Diet history
	Hungary	Food record	3	2 consecutive days and 1 non consecutive day §	No
	Ireland	Food record	7	Consecutive days	Yes, only focused on meat
	Italy	Food record	3	Consecutive days	No
	Latvia	24 h dietary recall	2	68 (27 -106)	Yes
	Netherlands	24 h dietary recall	2	11 (8 -17)	Yes
	Poland	24 h dietary recall	1	Not applicable	No
	Slovakia	24 h dietary recall	1	Not applicable	No
	Slovenia	24 h dietary recall	1	Not applicable	Yes
	Spain I	Food record	3	Consecutive days	Yes
	Spain II	24 h dietary recall	2	3 (1 -16)	Yes
	Sweden	Food record	7	Consecutive days	No
	United Kingdom Food record	7	Consecutive days	Yes
	* Information extracted from the "Comprehensive European Food Consumption Database".
	^ Information collected by means of FFQ or FPQ was not transmitted to EFSA
	§ 2 consecutive weekdays and one weekend day

Table 3 -

 3 Administration of the interview

		Number of	Method of	Place of interview	Interviewer with
	Country	meetings	administration		nutritional
					background
	Austria	0	Post	Not applicable

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Table 7 -

 7 Percentage of record or recall days according to the day of the week and season %

of record or recall days according to the day of the week* % of record or recall days according to the season*

	Country	Week days	Week end days	Unclassified	Spring 21.03-21.06	Summer 22.06-22.09	Fall 23.09-22.12	Winter 23.12-20.03	Unclassified
	Austria	49			14		37	21	26	25	27	1
	Belgium	76			24		0	26	25	27	23	0
	Bulgaria I	92			8		0	100	0	0	0	0
	Bulgaria II	54			46		0	60	40	0	0	0
	Czech Republic	74			26		0	34	23	12	31	0
	Denmark	72			28		0	25	26	39	10	0
	Estonia	73			27		0	0	100	0	0	0
	Finland	67			33		0	9	0	0	91	0
	France Germany Hungary Ireland Italy	71 75 67 71 78 F § o r		29 25 33 § 29 22		0 0 0 0 0	20 20 0 26 26	17 27 0 28 24	24 40 0 27 25	39 13 100 18 25	0 0 0 0 0
	Latvia	72			28		0	0	49	50	0	0
	Netherlands Poland	71 77	P	29 23		0 0	0 0	0 31	100 69	0 0	0 0
	Slovakia Slovenia Spain I Spain II Sweden	78 76 43 73 71	5 24 e 30 26 29 e r	17 0 27 0 0	23 11 28 75 0	19 14 7 19 0	29 56 25 0 0	7 19 22 6 0	23 0 17 0 100
	United Kingdom * Information extracted from the "Comprehensive European Food Consumption Database". 71 29 0 31 § Percentages reported by the national data provider. R	24	22	23	0
						e
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								O n l
								y

Table 8 -

 8 Exclusion criteria

	Country	Institutionalised subjects excluded	Pregnant and breastfeeding women excluded	Number of breastfeeding women*	Number of pregnant women*	Over sampled population groups
	Austria	Yes	No	Not available	Not available
						Subjects from
	Belgium	Yes	No	7	9	15 to 18 and older than 75
						years
	Bulgaria I	Yes	Yes	Not applicable Not applicable	No

Table 10 -

 10 Dietary software and database used

	Country	Dietary software	Database used before providing the data to EFSA: Portion size Standard recipe Food Yield factors composition
	Austria	MS Access 2003 based on German nutrient data base BLS II.3	Yes	Yes	Yes, from raw to cooked foods	Yes
	Belgium	EPIC SOFT Program	Yes	Yes	Yes, from raw to cooked foods*	No
	Bulgaria I	NUTRICALC	Yes	Yes	to raw foods* Yes, from cooked	Yes
	Bulgaria II	NUTRICALC	Yes	Yes	to raw foods* Yes, from cooked	Yes
	Czech Republic	Paradox for Windows	Yes	Yes	to raw foods* Yes, from cooked	Yes
	Denmark	GIES	Yes	Yes	to raw foods* Yes, from cooked	Yes
	Estonia	Finnish micro nutrica nutritional analysis program	Yes	Yes	to raw foods* Yes, from cooked §	Yes
	Finland	Finessi	Yes	Yes	Yes, from cooked to raw foods* §	Yes
	France	MS Access	Yes	Yes	No	Yes
	Germany	EPIC SOFT Program	Yes	Yes	cooked foods* Yes, from raw to	Yes
	Hungary	NutriCompEtrend	Yes	Yes	to raw foods Yes, from cooked	Yes
	Ireland	WISP-DES	Yes	Yes	to raw foods* Yes, from cooked	Yes
	Italy	INRAN-DIARIO 3.1	Yes	Yes	Yes, from cooked to raw foods* §	Yes
	Latvia	PGAIS	No	Yes	cooked foods Yes, from raw to	Yes
	Netherlands	EPIC SOFT Program	Yes	Yes	Yes, from cooked to raw foods*	No
	Poland	Dieta FAO	Yes	Yes	to raw foods* Yes, from cooked	Yes
	Slovakia	Alimenta 4.3	No	Yes	Yes, from cooked to raw foods §	Yes
	Slovenia	Blaise 4.7	No	Yes	Yes, from raw to cooked foods

Table 11 -

 11 Additional information on food coding

			Specific information collected on:
	Country	Brand*	Household	Packaging*
		(% of food	processing*	(% of food
		records)	(% of food records)	records)
	Austria	3	1	0
	Belgium	19	22	24

Table 12 -

 12 Method of identification of under-reporters, cut-off values and percentage of underreporters

				Under-	Under-
	Country	Method	Cut off value	reporters	reporters
				excluded	(%)

Table 13 -

 13 Number of subjects according to the different self-reported eating patterns * In Germany information on further special diets, like halal, was also collected but here these are considered as unspecified.

	Country	Normal diet*	Vegetarian diet*	Diet related to	health condition*	Slimming diet*	Vegetarian and	slimming diet*	Unspecified*
	Austria	2123							0
	Belgium	2642	1	331				271
	Bulgaria I	863	1	70	116			154
	Bulgaria II								1723
	Czech Republic	1572	9	86	66			18
	Denmark								4118
	Estonia								1866
	Finland	1377	29	584	26	22	0
	France	3167	19	314	181	1	397
	Germany	10839	287	2106	141	1	552**
	Hungary	1360							0
	Ireland	764	9	77	70			38
	Italy	3124		80	76			43
	Latvia								2070
	Netherlands	691	12	8	24			15
	Poland								4134
	Slovakia	83							2678
	Slovenia	410							0
	Spain I	1051		10	1			6
	Spain II	398		4	16			0
	Sweden	1188	18	2				2
	United Kingdom	1333	66			314	11	0

* Information extracted from the "Comprehensive European Food Consumption Database" *

Table 14 -

 14 Non dietary information

		Body	Information available on
	Country	weight and height	Physical activity* § (% of subjects)	Education level* (% of subjects)	Ethnicity* (% of subjects)
	Austria	Estimated	100	100	14
	Belgium	Estimated	100	100	0
	Bulgaria I	Measured	0	100	100
	Bulgaria II	Measured	0	100	100
	Czech Republic	Estimated	100	100	0
	Denmark	Estimated	0	0	0
	Estonia	Estimated	100	100	100
	Finland	Measured	0	99	0
	France	Measured	74 ^	100	0
	Germany	Measured	0	100	0
	Hungary	Estimated	0	0	0
	Ireland	Measured	100	100	100
	Italy	Estimated	0**	83	0
	Latvia	Estimated	0	100	0
	Netherlands	Estimated	100	100	100
	Poland	Measured	0	100	0
	Slovakia	Estimated	3	3	6
	Slovenia	Estimated	100	99	0
	Spain I	Estimated	80	28	0
	Spain II	Measured	100	99	0
	Sweden	Estimated	0	100	0
	United Kingdom	Measured	96	100	100
	* Information extracted from the "Comprehensive European Food Consumption Database"

** Data not transmitted to EFSA

http://mc.manuscriptcentral.com/tfac Email: fac@tandf.co.ukFood Additives and Contaminants

Gersovitz M, Madden JP, Smiciklas-Wright H. 1978.Validity of the 24-dietary recall and

Availablefrom :

ACKNOWLEDGMENTS:

seven day record for group comparisons. J Am Diet Assoc.73:48-55.

722

Goldberg GR, Black AE, Jebb SA, Cole TJ, Murgatroyd PR, Coward WA, Prentice AM. 1991. 723 Critical evaluation of energy intake data using fundamental principles of energy physiology: 724 Derivation of cut-off limits to identify under-recording. Eur J Clin Nutr. 45: 569-81.

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