Long-distance commuting and the effect of differentiated salary expectations in the commuters’ place of living on the wage obtained in the place of working


Despite the efficiency produced by long-distance commuting (LDC) as an adjustment mechanism between local labor markets, the impact that it has on the equilibrium of labor markets has not been studied in depth. This paper uses the case of Chile, since in the last two decades the LDC has increased its importance as a strategy of labor mobility for workers in this country. We demonstrate, both theoretically and empirically, that LDC generates wage differences in the labor markets that receive commuters, as a function of the market equilibrium of these workers’ place of origin. These differences are not only related to labor productivity and/or employment, but also to the wage expectations of commuters in their place of origin.

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  1. 1.

    According to Hass and Osland (2014) 'housing discrimination, the relocation of jobs within cities or the lack of means of transport all result in the distance between homes and workplaces becoming a significant impediment for certain minorities in improving their access to job opportunities. In such cases, the spatial adjustment between the area where these workers reside and where they have their potential work may lead to a potential boom in unemployment and poverty (p. 467).' Thanks to improved transportation techniques, commuting has been an element that has facilitated the reduction in these mobility costs, improving the efficiency of the labor market (Eilmsteiner-Saxinger 2011; Rolfe 2013).

  2. 2.

    LDC has increased its importance as a new mode of labor mobility during the last four decades at a global level, especially in countries such as Mexico, Australia, Canada, Sweden, the UK, Russia, Peru, or Chile, all of them characterized by an important participation of extractive industries in their economy (Gramling et al. 1995; Storey and Mark Shrimpton 1988; Houghton 1993; Green et al. 1999; So et al. 2001; Storey 2001; Lundholm et al. 2004; Aroca and Atienza 2008; Sandow and Westin 2010; Eilmsteiner-Saxinger 2011; Manky 2016).

  3. 3.

    This implies considering that each regional labor market operates as a single labor market, with a particular equilibrium, differentiated from the labor market of the rest of the regions.

  4. 4.

    Berdegué et al. (2017) used the light intensity between counties (346 in Chile) measured by satellites at night in 2012 in order to define the functional territories. Light intensity rather than commuting flows is preferred for the construction of LMFA since the former guarantees a continuity of the functional area over time; meanwhile, the latter are less stable in their composition (see the Chilean map of LMFA’s in Fig. 4 in Appendix 1).

  5. 5.

    For the sake of simplicity, we only consider two different labor markets where the individual can look for a wage offer, the source zone or the host zone if he/she decides to practice LDC.

  6. 6.

    To simplify the theoretical model, the possibility that a worker can change the location of their residence, that is, migrating after accepting a job offer, will not be considered.

  7. 7.

    According to Bleeson (1991), the supply of workers is affected by the available amenities in the place where they live, which contributes to differences in returns to schooling, i.e., salary, across regions.

  8. 8.

    Zaretsky and Coughlin (1995) consider that an adequate proxy to define the level of expected income in (a) would be the average wage for each occupation in a certain labor market.

  9. 9.

    In this regard, we include as regressors in the selection equation the following variables: sex, age (and its squared), years of schooling, a dummy identifying the year of the sample, marital status, number of persons at home, household income, and breadwinner status of the individual.

  10. 10.

    Following the correction of Heckman (1979) in the conditioned expectation of \( \left( {w_{i}^{b} } \right) \), it will be expressed as:

    $$ E[\ln (w_{i}^{b} ) | \ln \left( {\hat{w}_{i}^{a} } \right), x_{i} , \lambda_{i} ] $$

    where \( \lambda_{i} \) is the inverse of the Mills ratio:

    $$ \lambda_{i} = \frac{{\phi \left( {Z_{i} } \right)}}{{1 - \varPhi \left( {Z_{i} } \right)}} $$

    In that \( \phi \left( {Z_{i} } \right) \) and Φ \( \varPhi \left( {Z_{i} } \right) \) are the density function and cumulative distribution for a variable with normal distribution, respectively. The expression \( \varPhi \left( { - Z_{i} } \right) = Pr(Z_{i} = 1|s_{i} ) \) is a function of the probability that an individual commutes depending on the covariates represented in the vector \( s_{i} \).

  11. 11.

    A dummy is introduced for each of the 15 regions in the country for identifying the origin of the interregional commuter. The same is done for each of the 135 LMFA defined, which considers the case of LMFA commuting.

  12. 12.

    For more information about the variables included in the analysis and their sources, see Table 6 in Appendix 1.

  13. 13.

    These are the distance travelled to the work place (\( dist_{l,j} \)), if the worker is hired directly by the company or not (\( non\_direct\_emp_{i} \)), and the number of months the individual has been working for the company (\( tenure_{i} \)).

  14. 14.

    Descriptive statistics are available in Appendix 1 (see Table 7).

  15. 15.

    In our analysis, we do not consider those commuters who work or live in extreme zones including Easter Island, Juan Fernández Islands, or Antarctica.

  16. 16.

    The CASEN allows you to identify LDC only for the year 2009, while the NESI allows the analysis for 2010, 2011, 2012, 2013, 2014, 2015, 2016, and 2017.

  17. 17.

    The outcomes obtained in one-to-one PSM are available upon request. For testing the adequacy of these estimates, we performed a two-sample equality of mean vector test when the variances are not known for either LDC worker definitions: interregional commuter and LMFA commuter (James 1954). The test is applied for the different regressors defined in PSM analysis before and after applying said PSM. The outcomes show that regressors are significantly different for LDC workers and residents in their source zone. However, there is no significant statistical difference between them after applying the PSM. This proves the adequacy of the matching applied. The outcomes obtained for the test applied are available upon request.

  18. 18.

    The analysis for the rest of the regions of the country is carried out in the same way. The results can be obtained upon request to the authors.

  19. 19.

    The LMFA 77 is composed of the following municipalities: Santiago, Cerrillos, Cerro Navia, Conchalí, El Bosque, Estación Central, Huechuraba, Independencia, La Cisterna, La Florida, La Granja, La Pintana, La Reina, Las Condes, Lo Barnechea, Lo Espejo, Lo Prado, Macul, Maipú, Ñuñoa, Pedro Aguirre Cerda, Peñalolén, Providencia, Pudahuel, Quilicura, Quinta Normal, Recoleta, Renca, San Joaquín, San Miguel, San Ramón, Vitacura, Puente Alto, Pirque, San José de Maipo, Colina, Lampa, Tiltil, San Bernardo, Buin, Calera de Tango, Paine, Curacaví, Talagante, El Monte, Isla de Maipo, Padre Hurtado, and Peñaflor.

  20. 20.

    The LMFA 100 is composed of the following municipalities: Antofagasta, Mejillones, and Sierra Gorda.

  21. 21.

    See Tables 8 (for the estimates of wage premium, Eq. 12) and 9 (for the commuted distance, Eq. 13) in Appendix 1.


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The authors acknowledge the financial support from Chilean Fondecyt grant 1191162 ‘Do mining tax windfalls crowd-out other local revenues? Evidence from Chile’.

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Correspondence to Manuel Pérez-Trujillo.

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Appendix 1

See Fig. 4.

Fig. 4

Source: Self-elaborated

Maps for Chile identifying the regions of the country and the 135 LMFA’s defined by Berdegué et al. (2017).

Appendix 2

When considering the following Bellman equation—already considered in Eq. (1):

$$ w_{r}^{a} = b - c^{a} \left( {s^{a} } \right) - c^{b} \left( {s^{b} } \right) + s^{a} \cdot\frac{{\lambda^{a} \left( {s^{a} } \right)}}{r}\cdot\mathop \smallint \limits_{{w_{r} }}^{\infty } \left[ {w^{a} - w_{r}^{a} } \right] \cdot {\text{d}}F\left( {w^{a} } \right) + s^{b} \cdot\frac{{\lambda^{b} \left( {s^{b} } \right)}}{r}\cdot\mathop \smallint \limits_{{w_{r} }}^{\infty } \left[ {w^{b} - w_{r}^{b} } \right] \cdot {\text{d}}F\left( {w^{b} } \right) $$

The optimal effort in the search for employment in the host region (b) is obtained as a solution to the following problem:

$$ \mathop {argmax}\limits_{{s^{b} }} w_{r}^{a} $$

Upon solving:

$$ \frac{{\partial c^{b} \left( {s^{b} } \right)}}{{\partial s^{b} }} = \frac{{\left( {\lambda^{b} \left( {s^{b} } \right) + s^{b} \cdot\frac{{\partial \lambda^{b} \left( {s^{b} } \right)}}{{\partial s^{b} }}} \right)}}{r}\cdot\mathop \smallint \limits_{{w_{r} }}^{\infty } \left[ {w^{b} - w_{r}^{b} } \right] \cdot {\text{d}}F\left( {w^{b} } \right) $$

Using this equation, we can calculate the optimal wage that would be obtained in the optimal search effort applied on the host region (b) which is:

$$ w^{*b} = \frac{{\frac{{\partial c^{b} \left( {s^{b} } \right)}}{{\partial s^{b} }} \cdot r}}{{\lambda^{b} \left( {s^{b} } \right) + s^{b} \cdot\frac{{\partial \lambda^{b} \left( {s^{b} } \right)}}{{\partial s^{b} }}}}\cdot\frac{1}{{\left( {1 - F\left( {w_{r}^{b} } \right)} \right)}} + w_{r}^{b} $$

When considering that the reservation wage in the host region (\( w_{r}^{b} \)) is dependent on the sum of the reservation wage in the source region (\( w_{r}^{a} \)) and the transport costs (\( c_{t} \left( {d_{a,b} } \right) \)):

$$ w_{r}^{b} = w_{r}^{a} + c_{t} \left( {d_{a,b} } \right) $$

Then, we can define Eq. (17) as:

$$ w^{b} = \frac{{\frac{{\partial c^{b} \left( {s^{b} } \right)}}{{\partial s^{b} }} \cdot r}}{{\lambda^{b} \left( {s^{b} } \right) + s^{b} \cdot\frac{{\partial \lambda^{b} \left( {s^{b} } \right)}}{{\partial s^{b} }}}}\cdot\frac{1}{{\left( {1 - F\left( {w_{r}^{b} } \right)} \right)}} + w_{r}^{a} + c_{t} \left( {d_{a,b} } \right). $$

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Pérez-Trujillo, M., Oyarzo Aguilar, M. & Paredes Araya, D. Long-distance commuting and the effect of differentiated salary expectations in the commuters’ place of living on the wage obtained in the place of working. Ann Reg Sci 65, 459–489 (2020). https://doi.org/10.1007/s00168-020-00991-7

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