Activity and employment rates for immigrant women in many industrialised countries display a great variability across national groups. The aim of this paper is to assess whether this fact is due to a voluntary decision (i.e. Large reservation wages by immigrants) or to an involuntary process (i.e. Low labor market.

• 2.8k Downloads • Abstract Activity and employment rates for immigrant women in many industrialised countries display a great variability across national groups. The aim of this paper is to assess whether this fact is due to a voluntary decision (i.e. Large reservation wages by immigrants) or to an involuntary process (i.e.

Low labor market evaluation of their skills). This is done by estimating the reservation wage for each individual in a sample of immigrant women in Italy. Our results show that low activity and employment rates for certain national groups are not associated with high reservation wages. This implies that low participation should not be interpreted as a voluntary decision.

Our main data source is a dataset collected by the ISMU in Lombardy (with the financial support of the Lombardy regional government and other private institutions) in the period 2001-2005. Since 2001, the ISMU has conducted a yearly survey of immigrants living in Lombardy.

As for migration studies, the dataset has a strong comparative advantage. Its most important characteristic is that it is also able to collect information for undocumented aliens due to its data-collection process based on the method of aggregation centres developed by Blangiardo ( ). Survey design - Surveys on aggregation centres are specifically designed to collect information on a representative sample of immigrants that include also irregular migrants. Eprompter Downloads Free there. The idea is that even undocumented individuals generally lead a social life by attending, for example, places of religious worship or cultural centres. Blangiardo’s method hinges on those centres and is based on a three-stage design. In the first, the ISMU interviewers allocate the total number of questionnaires (roughly 8,000 each year) across the 11 provinces into which Lombardy is partitioned; this is aimed at obtaining significant estimates at provincial level by having roughly the same sample variability within each province.

In the second stage, the ISMU selects a number of representative municipalities (slightly less than 350, almost 25 per cent of all the towns in Lombardy) in each province according to the social and economic characteristics of the area. In the third stage, the ISMU interviewers visit all possible aggregation centres for immigrants within each municipality and randomly meet the potential interviewees. The aggregation centres usually fall within 11 categories: help and counselling centres for immigrants, Italian language courses for foreigners, places of worship, healthcare centres, cultural centres, phone/money transfer centres, public offices (police stations, town halls, etc.), ethnic restaurants/bars, ethnic shopping centres, other (train stations, etc.) 3. Within each aggregation centre, the ISMU interviews usually have the support of the leading personalities of the centres (priests, shop owners etc.); this ensures that the interviewers are seen as “trusted” people and not public officials.

As for the ability of the survey to truly detect irregular immigrants, the misreporting of legal status seems tiny. In order to provide a comparison between the ISMU estimates and actual data, we compare survey results with the most recent alternative dataset based on applications for regularization under the Bossi-Fini amnesty law of 2002. In that year, 144,369 individuals applied in Lombardy (647,000 in Italy as a whole): this indicates that at least the 31 per cent of the total foreign population in Lombardy was irregular in 2002. ISMU estimates for that year do not appear far off that figure: in the 2002 wave, self-declared illegal aliens represented 27 per cent of the sample.

Dataset description - This study uses five pooled waves from 2001 to 2005. We concentrate on women of working age (i.e. The 15-64 cohorts), which leaves us with roughly 12 thousand individuals. The ISMU data contain information on social and economic characteristics. Table presents the summary statistics for the variables used in the empirical analysis.

Average schooling is quite high (10.7 years) 4 and comparable with the figures for the Italian population. Almost 60 per cent of the interviewed women is married; they have been residing in Italy for a relatively short period of time (5.7 years). Irregular women make up 11.7 per cent of the total. As for religion, more than two thirds of the dataset is Catholic or Muslim. Other Christians (mainly Greek-Orthodox) are slightly more than one fifth of the sample. Table also provides the distribution of immigrants according to the groups of country of origin (see Appendix 1 for the list of countries belonging to each group). Almost 30 per cent of the immigrant women come from European countries; sizeable groups are also constituted by Central and South Americans and people coming from the Near East and North Africa.

Sample mean Std. School 11,852 10.746 4.541 Potential Experience 11,852 16.764 9.295 Years in Italy 11,989 5.734 4.610 Net monthly wage 9,988 606.874 1214.336 Children below 18yrs 12,015 0.696 0.990 Children above 18yrs 12,015 0.527 1.566 Married 12,015 0.569 0.495 Family network 4,851 0.358 0.479 Family reunification visa 9,914 0.226 0.441 Irregular 12,015 0.117 0.321 No. Percentages Religions: Catholic 12,015 38.33 Muslim 12,015 30.89 Other Christians 12,015 18.46 Buddhists 12,015 3.21 Hindu 12,015 1.35 Other 12,015 2.24 No creed 12,015 5.52 Groups of countries of origin: Central and Eastern European Countries 12,015 28.56 Central or Southern America 12,015 19.55 Near East and North Africa 12,015 18.29 Sub-Saharan Africa 12,015 15. Bloodlust Metal Jdr Pdf Download. 59 East Asia 12,015 12.44 Central Asia 12,015 5.57.

Standard figures on employment status are reported in Table, 5 which shows, as is usual in many industrialised countries, that there is considerable heterogeneity in labor market outcomes according to the country of origin. The employment rate averages 60 per cent for the entire sample, while the activity rate is much larger (73.4 per cent). The employment rate of the women coming from the Central and Eastern European Countries (CEEC) and Central and South America is roughly two-thirds higher than that for the Near East and North African countries (NENAC) 6. The figure for the Central Asia group is even smaller. Activity rates display the same great variability across national groups.

Source: Authors’ calculations on ISMU dataset. Activity and employment rates are computed on self-declared working status. Averages weighted according to the sample design. Representativeness - A relevant concern for working on non-administrative data relates the representativeness of the dataset. In order to address this issue, we compare the main ISMU data characteristics with those of the labor Force Survey (LFS) for the North of Italy (where Lombardy is located and where most of the immigrants in Italy live, 2006-2008 averages) 7. The main relevant difference between ISMU and LFS is in the country of origin composition: in LFS the share of women coming from CEEC is 54 percent, 20 percentage points greater than the ISMU figure. The difference between the two sources is quite sizeable and this could raise some issues related to the external validity of the analyses.

However, it should be noted that all other population characteristics are quite similar. The average schooling is 10.2 years in the LFS.

The activity rate is slightly lower (65 per cent) probably due to the different definitions of working status between LFS and ISMU. More importantly the labor market access differences among groups are preserved, for example the NENAC group in the LFS survey still maintain a huge gap in the activity rate in comparison with CEEC group (20 percentage points). Empirical approach. How do economists obtain reservation wages? The aim of this paper is to report an econometric estimate of the immigrants’ reservation wages. In job search theory, the reservation wage is the lowest offered wage that an unemployed individual looking for work is prepared to accept (see Blundell and MaCurdy, ) 8.

Although this is a crucial variable in the neoclassical theory of labor supply, there is still an open discussion on the best way to estimate it. Two prevailing methods are usually available in literature. The first is based upon surveys in which unemployed respondents are directly asked what their reservation wage is 9. This information is widely used in many studies: see, among others, Addison et al. ( ) and, for the Italian case, Sestito and Viviano ( ).

The reliability of this information, however, is widely debated. As shown by Burdett and Vishwanath ( ) and Hofler and Murphy ( ), self-reported reservation wages are often biased and they are usually inconsistent with the actual behaviour of a worker.

As Addison et al. ( ) point out, this is mainly due to the fact that respondents usually answer by indicating the prevailing wage on the labor market, rather than their true reservation value. The second method treats reservation wages as unobservables, that must be inferred econometrically by the actual behaviour of a worker.

This was pioneered by Heckman ( ) in his contribution on women’s shadow prices in the labor market and was subsequently developed in further studies (see, Kiefer and Neumann,; Fishe,; Ferber and Green,; Duncan,; Sharpe and Abdel-Ghany, ). The idea is that observed wages for employed individuals are those that succeed in exceeding the individual reservation values.

This implies that, by controlling for the selectivity bias, actual market wages contain enough information to infer workers’ reservation wages 10. In this paper we adopt the second methodology for two main reasons. The first relates upon the above-mentioned reliability problems of self-reported reservation wages – problems that can be greatly aggravated for migrants, whose understanding of the crucial features of the local labor market is more limited.

The second relates to the availability of sufficient observations in the LFS. In Appendix 2 we provide the results for self-reported reservation wages in the Italian LFS. By concentrating on immigrants living in the North, we end up with just under 1,000 individuals, which is one-tenth of our baseline estimation sample. The methodology we use in this paper is based on the Mohanty’s ( ) extension of the Heckman model with frictional unemployment and feedbacks between labor demand and supply. As will soon be clearer, from a technical point of view, the only difference between this methodology and Heckman’s lies in the first stage, which is bivariate-probit rather than probit estimated. This allows us to take into account a double selectivity bias due to involuntary unemployment or feedback effects between demand and supply 11.

The estimate of reservation wages: an econometric approach A woman i decides to participate in the labor market (i.e. To be active) whenever the wage offers she expects to receive are greater than her own reservation wage.

In formulas, this implies that she is active whenever w i 0 – w i r = y 1 i ≥ 0, where w o i is the expected wage offer, w r i is her reservation wage and y 1i represents the (normalised) individual preference for labor market participation. Whenever y 1i is greater than zero, individual i participates to the labor market, whenever it is negative she prefers to stay at home. It immediately follows that reservation wages can be obtained as w i r = w i 0 – y 1 i and can be computed by estimating preferences (y 1i) and wage offers (w o i). Estimates for y 1i and w o i are obtained in two steps. (2)by a bivariate probit with partial observability. The choice of the bivariate probit is particularly useful since it allows us to treat demand and supply components simultaneously.

As mentioned above, this allows us to take into account the existence of feedbacks between the decision to participate and the expected labor market outcome. Operationally, x 1 contains a set of variables aimed at capturing the economic and cultural determinants for the labor supply, while x 2 includes all the possible personal characteristics which are likely to influence the employer’s willingness to hire an individual. As for the supply components, x 1 includes schooling, potential experience, religion dummies and their interaction with marital status, number of children below and above 18 years of age and a set of time dummies. The number of under age children may indicate a greater interest for childcare and housekeeping while the number of children over 18 should have a positive effect on the labor supply since offspring could need financial support from the family of origin.

Religion dummies indicate a cultural attitude toward labor, especially when a woman is married. As will be clear later, religion dummies play a fundamental role as identification variables in the empirical strategy. On the demand side, x 2 contains schooling, years since migration, dummies for country of origin, time, and space 12. Years since migration are expected to enhance the probability for a worker to be employed since during these years the worker is likely to increase his ability to understand the crucial features of the host country’s labor market and local language; 13 country dummies capture the workers’ heterogeneity in terms of the quality of the institutions in their area of origin (for example, educational system, sectoral specialization) while spatial dummies capture time invariant local characteristics that are likely to influence employment levels.

X 1 and x 2 share the schooling variable and time dummies, since education is likely to have an effect on both the demand and supply components and year dummies take into consideration business cycle fluctuations. The latent variable of interest (ŷ 1) is calculated by taking the predicted values (linear prediction) of equation ( ). (3) where u i = ε i − c 13 λ 1 i − c 23 λ 2 i, λ 1 i = φ x 1 i b 1 Φ x 2 i b 2 − ρ x 1 i b 1 1 − ρ 2 F x 1 i b 1, x 2 i b 2, ρ and λ 2 i = φ x 2 i b 2 Φ x 1 i b 1 − ρ x 2 i b 2 1 − ρ 2 F x 1 i b 1, x 2 i b 2, ρ. Φ and Φ represent, respectively, the density and the cumulative function of a univariate standard normal distribution, F denotes the bivariate standard normal distribution, while ρ (rho) is the correlation of the error terms in the bivariate probit. X 3 includes standard variables in migration-augmented mincerian equations: schooling, potential experience and years since migration. The regression includes spatial dummies ( D s) to take into account spatial differences in wage levels; time dummies ( D t) to control for business cycle effects. Country of origin dummies ( D c) control for institutional factors such as educational quality (inserting area dummies deliver very similar results).

W ^ 0 = exp ln w m ^ Where ln w m ^ is the predicted value of equation ( ). W ^ o represents the expected market wage conditional on the individual characteristics and controlling for the selectivity bias due to participation and hiring decisions 14. Reservation wages can now be calculated as w ^ r = w ^ 0 - y ^ 1 for all the individuals in the sample.

By comparing all the variables in x 1, x 2 and x 3, the crucial role of religion dummies and their interaction with marital status as identification variables is now apparent. The idea is that religion is a private matter that affects the individual working decisions but should not affect the labor market evaluation (wages) and the hiring decision by a non-discriminating employer. In other words, the worker’s productivity should be influenced by the country of origin’s institutional setting (school quality, sectoral specialization etc.) but not by the migrant’s private attitudes toward religion (which does, however, influence her decision to work). This implies that by inserting country dummies in equations ( ) and ( ) we estimate a supply effect that is within-country and across religions. This can be done only if we have an imperfect overlap between religions and countries of origin, i.e. When there are different religious creeds within a country. In order to have a suitable sample for this identification we exclude all individuals coming from a country where, according to the ISMU dataset, only one creed is professed.

The list of a diversification indicator of religions (Herfindhal index: HI 15) in each country is provided in Appendix 1; the higher HI the less diverse the country; in all the analyses we exclude all countries with a HI equal to one. A possible challenge to the identification of equations ( ), (2) and (3) is the presence of occupations in which foreigners coming from the same nation tend to cluster (Patel and Vella, ). This would invalidate the cross-country comparison since reservation wages are driven by cross-nation sector of specialization and the mobility is low. Ideally, this would be solved by inserting sector dummies in equation ( ); however, in this case the computation of wage offers for not-employed immigrants would be impossible since predicted values cannot be calculated for unemployed and inactive. It should be noted, however, country dummies in equation ( ) and ( ) take also into account differentiated search networks and their effects on wages, thus limiting the relevance of these concerns.

In a robustness check, we calculate reservation wages for employed individuals by inserting sector dummies with results much in line with the one presented here 16. Cross-country differences and robustness After computing the reservation wages, we test whether they systematically differ across groups of nationalities. We calculate the percentage differences between each group and a reference cluster. Our first choice would have been native women. However since the ISMU dataset only concentrates on immigrants, we chose the CEEC (Central and Eastern European Countries) group.

The choice of CEEC as a benchmark relies on the fact that they share similar institutions with the host country and are generally seen as a reference group in the assimilation patterns in most countries 17. We focus on the differences between the CEEC group and two nationalities that display the lowest activity and employment rates: NENAC and Central Asia. If the reservation wages for those groups were higher, the observed low labor market participation would be interpreted as voluntary: the value NENAC and Central Asian women attach to their time spent at home is so high that they are not attracted by the local labor market. Conversely, if their reservation wages were comparable or lower to that of our reference group, their inactive status would be interpreted as involuntary: their reservation values are not particularly high but they remain unemployed because the arrival rate of job offers is quite low. We further check the robustness of these estimates along four lines. The first check is based on Italian migration law according to which it is necessary to have a job in order to obtain a visa.

As an exception to this rule, immigrants can enter Italy to join their family and obtain a visa based on family reunification. For those cases, migrants’ true shadow values should be revealed since they do not need to work to obtain a residence permit.

We check this issue by restricting the analysis to those women who migrated to Italy with a family reunification visa. This information is available for all years except for 2004: migrants entering the country for with a family reunification visa amount to 2,408. The previous scheme obviously implies that the immigrants have a good knowledge of the Italian migration laws. We can generalise it by focussing on migrants with a blood relative already residing in the country at migration time. Again, for those individuals their shadow values could be higher, since they can rely on the family's financial support while looking for work. We check this issue by restricting the analysis to women who entered the country when a next of kin was already residing here. This leaves us with 1,753 individuals.

The third check is based on the analysis of the irregular migrants. Undocumented aliens have very weak bargaining power with respect to their employers as they cannot join a union and must work off the books.

This implies that wage offers are usually quite low (Accetturo and Infante, ) and, therefore, they may be quite close to the reservation wages. Moreover, illegal aliens’ incentives to work are particularly strong since their only chance of being regularised under one of the recurrent amnesties is strictly linked to evidence that they are employed on Italian soil. In other words, they are more likely to accept the jobs they are offered. In the ISMU dataset, irregular women are surveyed each year and they amount to 1,303 individuals.

The fourth check is based on the relationship between religions and countries. As we said before, we already discard all the observations coming from one-religion countries. However, in some countries a religion could prevail but not be the only one (for example, Islam in Arab countries or Roman Catholicism in Central and South America); this implies that from the employer’s point of view religions and countries of origin are quite indistinguishable thus invalidating our identification structure. We cope with this problem by restricting our analysis to a group of countries that can be considered truly multi-religious. This is done by discarding all observations coming from countries whose HI based on religions exceeds 0.75: this leaves us with 8,305 individuals. Table shows the results of both the bivariate probit estimates (columns [1] and [2]) and the wage equation (column [3]).

As expected, schooling positively affects the probability to be active and to be employed in the labor market. Moreover, potential experience increases the probability to supply labor and the years spent in Italy increases the likelihood to be employed. The number of children below (above) 18 years old reasonably raise (decrease) the probability to be active. Estimates also show that the correlation among error terms (rho) is quite high 18. Source: Authors’ calculations on ISMU dataset. Robust standard errors in parenthesis. Wage equation standard error take into account the two-step procedure.

Stars show significance levels, *** up to 1 per cent, ** between 1 per cent and 5 per cent, * between 5 per cent and 10 per cent. All regressions are weighted according to the sample design. In the second step, we estimate a classical wage equation for all working individuals and we take into account the possible selection bias by plugging the computed λ 1 and λ 2 into the equation. Results are displayed in column [3]. All variables have the expected sign and are significant.

The highly significant coefficients of λ 1 and λ 2 imply that selection is at work. The use of estimated values of columns [1] and [3] allows us to compute reservation wages. Before starting with the comparison across national groups it is worth assessing whether estimated reservation wages are consistent with the predictions of the standard theory of labor supply (Table ). As Blundell and MaCurdy ( ) point out, individuals with a higher inter-temporal elasticity of substitution should have lower reservation wages: this is confirmed in our estimates since younger individuals attach a lower economic value to time spent at home compared with older cohorts. Even education plays an important role in setting the individual's reservation value.

Consistently with the standard theory, we find that more educated individuals usually set higher reservation wages. Finally we check whether individuals residing in a city 19 have a lower reservation value because of lower job-search costs thanks to better access to the transportation network. This is confirmed by the bottom panel, in which we show reservation wages for workers in a city by educational group.

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