JOURNAL OF REGIONAL SCIENCE, VOL. 39, NO. 1, 1999, pp. 103–123 MIGRATION AND CHANGING EMPLOYMENT STATUS: A HAZARD FUNCTION ANALYSIS* Cecile Detang-Dessendre INRA-ESR, Dijon, France. E-mail: detang@enesad.inra.fr Ian Molho Department of Economics, University of Newcastle, U.K. E-mail: Ian.Molho@ncl.ac.uk ABSTRACT. The effects of different employment-status transitions on migration choices are considered from a search-theoretic perspective. A discrete-time hazard function for migration decisions is estimated on data for young males of rural origin in France. Employment-status transitions are handled as endogenous time-varying covariates. The model is estimated by distance of move. The results show that the long-distance migration hazard is significantly related to labor market variables, and, ceteris paribus, is highest among job-gainers compared to the other transition groups. The probability of contracted (long-distance) migration is found to be higher than that of speculative migration for unemployed workers, especially those who are low-educated. Evidence consistent with cumulative inertia is found for long-distance moves. Short-distance migration hazards are found to be unrelated to labor market variables (including employment-status transitions) and to display no systematic pattern of duration dependence. 1. INTRODUCTION In this paper we model the relationship between employment-status shifts and migration. In particular, we are interested in estimating the impact on migration probabilities of being unemployed as against employed. We also seek to discover the extent to which the movement of (initially) unemployed workers is triggered by finding a job in a distant location, as opposed to movement with no immediate job contract lined-up at the point of destination. Our theoretical approach is to model long-distance migration as part of a job-search process. The process may end in finding work and may also result in migration. Those who move with a job already lined-up at the destination are termed contracted migrants; those who move without such a contract are termed speculative migrants. Consider the case of an unemployed worker who takes a job and possibly moves in the process. There are two-way interactions here: the *Cécile Détang-Dessendre was visiting researcher at the Department of Economics, Newcastle, U.K. from January to July 1996. The authors would like to thank three anonymous referees for constructive comments, and colleagues at Newcastle for helpful discussion. All errors remain our own responsibility. Received January 1997; revised August 1997; accepted November 1997. © Blackwell Publishers 1999. Blackwell Publishers, 350 Main Street, Malden, MA 02148, USA and 108 Cowley Road, Oxford, OX4 1JF, UK. 104 JOURNAL OF REGIONAL SCIENCE, VOL. 39, NO. 1, 1999 change in employment status may cause migration, but at the same time migration may be required for the individual to take advantage of the job opportunity. We analyze the choices involved in a framework which captures this endogeneity between migration and employment-status shifts. Empirically, we analyze migration in a duration-model context where movement is seen as terminating an observed residence spell. Duration models allow us to view residence spells in a historical context where individual migration decisions may in part reflect past experience as well as the current situation. Changes in employment status over the course of a residence spell are modeled as endogenous time-varying covariates. We distinguish between short- and long-distance moves in our empirical work because the former type of move may be induced by housing rather than labor market processes. The duration model treats short- and long-distance migration as competing risks. Finally, we look for evidence of cumulative inertia in residence decisions. The empirical analysis relates to a sample of young men from rural areas in France. The data track the location history and labor market experience of these individuals. We model their first migration decision after leaving school. This decision represents an important part of the process of labor market integration for these workers. The econometric results suggest that the probability of long-distance migration is significantly higher for the unemployed who find work compared to those continuously unemployed or continuously employed. The evidence suggests that the probability of contracted movement is generally higher than speculative movement for the unemployed, and especially for those who are low-educated. Local migration is unaffected by employmentstatus shifts. We also find evidence consistent with cumulative inertia for long-distance moves, but not for local moves. In the following section we give an outline of our theoretical approach to modeling migration. The data are described in Section 3. In Section 4 we set out the empirical framework for duration analysis, and present the main results in Section 5. In Section 6 we provide a summary and conclusion. 2. THEORY AND HYPOTHESES In this section we consider search-theoretic perspectives on migration as the basis for our empirical model (see,for example,David,1974;Gordon and Vickerman, 1982; Greenwood, 1985; Molho, 1986). We make three important distinctions: first, between housing-related and job-related moves (Gordon, 1982; Krumm, 1983); second, between speculative and contracted migration (Silvers, 1977); and finally, between “on-the-job” and unemployed search (Mortenson, 1986). Housing moves tend to be localized in nature whereas job-related migration may involve wider search fields. Thus, while the underlying motivation for moving is typically unobserved, in practice it is important to distinguish between long- and short-distance migration. The former is likely to consist purely of job-related movers whereas the latter may contain a mixture of both types. Virtually all housing-related moves are contracted (the exceptions are those who © Blackwell Publishers 1999. DETANG-DESSENDRE & MOLHO: MIGRATION AND EMPLOYMENT STATUS 105 move and “sleep rough”). Job-related moves may be either contracted or speculative, depending on whether or not the individual has work lined-up at the destination at time of move. Speculative migration may be seen to arise from a “move then search” strategy, where migrants move to a suitable base from which to conduct search for an acceptable opportunity. Contracted migration reflects a “search then move” strategy, where migration occurs as the outcome of a search process once the individual has located an acceptable opportunity in a distant region. The “contracted/ speculative” migration distinction relates to whether people are unemployed or employed at point of destination after migration. Our third and final distinction concerns whether people are employed or unemployed before migration, at point of origin. Job-related moves may arise for people who are originally employed as the outcome of an on-the-job search process, and for people who are originally unemployed. Putting the latter two distinctions together, we can conceive of the following classes of job-related migration. First, there are movers who are continuously unemployed over the move; second, there are those who are continuously employed; third, there are those who shift from employment to unemployment over the move; finally, there are those who shift from unemployment to employment over the move. Our analysis is principally concerned with the relationship between migration and these differing employment-status transitions. We take the employment status of the individual at start of period as exogenous to migration decisions over the period. However, the employment status at end of period is clearly endogenous in that the decision to accept a distant job offer, for example, will result in migration and also affect employment status. Our empirical framework attempts to allow for this endogeneity in estimating the impact of employment-status shifts on migration. Hypotheses Employment-status effects. Employment-status shifts are relevant only to job-related migration and not to housing moves. Therefore one would expect short-distance moves to be less strongly affected than long-distance ones by employment status shifts, in so far as they are more likely to contain housingrelated migrants. Turning to job-related migration, it is commonly perceived that in developed economies people rarely move speculatively in search of work because of the risks involved in such a strategy. Hence, ceteris paribus we would expect an individual who ended an observation period in unemployment to have relatively low long-distance migration probabilities. However for individuals employed at the end of an observation period, we expect those who gained work over the period to be more likely to move long distances than the continuously employed, ceteris paribus, because the latter must have received and accepted a new job offer, whereas the former may have stayed at their original job. Duration-dependence effects. There are good reasons for expecting duration dependence in migration behavior. The marginal costs and benefits of search © Blackwell Publishers 1999. 106 JOURNAL OF REGIONAL SCIENCE, VOL. 39, NO. 1, 1999 may change over the residence spell, especially in relation to move costs. Individuals may form attachments to their home or area of residence which may grow over time, for example as their local knowledge and social and economic ties develop. This may lead to a process of cumulative inertia. In particular, Shaw (1991) points to the growth of local human capital over time. Processes of this kind would lead one to expect falling migration probabilities over the course of a residence spell. Alternatively, the stochastic evolution of individuals’ preferences or local area attributes may cause individuals to increasingly outgrow their original habitat, potentially leading to higher migration probabilities over time (Gordon and Molho, 1995). 3. THE DATA The model was estimated using individual-level panel data from a 1993 survey including 551 young, French, male workers of rural origin (Dessendre, 1994). Seven rural districts were chosen on economic, demographic, and geographical criteria, to cover a broad spectrum of French rural areas. Within these districts specific villages were then selected for analysis. All these villages were far away from the nearest major town.1 The sampling frame included all people aged 21–22 or 26–27 in 1993 who had lived in the district at any time up to age 10, and it was constructed using local population records. Extensive efforts were made to track down all individuals who had since moved, as reflected in the high response rates of 83 percent overall, and 75 percent for migrants. Information was collected using an interview questionnaire. This included recall questions that allowed us to build a monthly record of employment status and residence for each individual. In constructing this data set the intention was to investigate the labor-market-integration process of young workers from rural backgrounds. The period under analysis (up to 1993) is of interest because it covered a period of apparent slowdown in the decline of rural populations in France (Cavailhes et al., 1994). Long-distance migration is defined as moves over 60 kilometers (37 miles). This definition allows a reasonable sample of long-distance movers, without too serious a risk that someone would move that far and continue to commute to their original district. Two versions of short-distance migration were tried, moves less than 60 kilometers and moves less than 25 kilometers (excluding moves within the same village). The latter are likely to contain the highest proportion of purely housing-related moves. We analyzed the residence duration among males up to the first migration away from the district of origin after leaving full-time education. Individuals left full-time education at different ages, so the maximum potential residence 1 The areas chosen were villages around the following areas (distance to the nearest town with more than 100,000 inhabitants given in brackets): Dole (50 km) and Louhans (70 km), Burgundy; Morlaix (50 km) and Redow (75 km), Brittany; Saint-Dié-des-Vosges (75 km) in the East; Le Luc (100 km) in the South; and Saint-Jean-de-Maurienne (70 km) in the Alpes. © Blackwell Publishers 1999. DETANG-DESSENDRE & MOLHO: MIGRATION AND EMPLOYMENT STATUS 107 duration up to first migration varied accordingly. Table 1 gives an indication of the distribution of complete residence durations in our data, using various definitions of migration according to distance of move. We see from this table that migration of some description is observed for nearly half the sample, and nearly a quarter moved long-distance. The time pattern shows that the rate of movement decreases with duration (some explanations for this are discussed later). Three quarters of long-distance moves and a third of short-distance moves (less than 25 kilometers) take place in the first year. Turning to the issue of employment-status effects, people tended to record related job and residence changes as happening at the same time, rather than distinguishing the exact timing of the moves. This is probably an advantage from our point of view as it makes clear the relation between the decisions. We do not have to hunt out employment-status changes that may have happened soon after the residence move, but which in reality may have been a “cause” of the move in the search process; the fact that the two are so closely linked in peoples’ minds that they date them together tells us that they are determined simultaneously. Likewise, differing definitions of the time over which employment-status changes took place tended not to affect the results substantially. However, it does highlight the importance of treating employment-status effects as endogenous in the econometric modeling. Table 2 indicates the number of migrants by distance of move and changes in employment status over the three-month period prior to the move. This table shows only eight cases where a continuously unemployed individual moved, even though there were 589 spells of unemployment for 551 men in our data. These raw data seemingly points to low levels of speculative migration, though at this stage we have not controlled for other influences on migration or allowed for endogeneity between employment transitions and mobility. Most of the moves in Table 2 are concentrated among the continuously employed or those gaining TABLE 1: Residence Durations by Distance of Move [1 month–6 months] [7 months–12 months] [13 months–18 months] [19 months–24 months] [25 months–30 months] [31 months–36 months] year 4 year 5 year 6 year 7 year 8 more than 8 years Total All Migration Migration ≥ 60 km Migration < 60 km Migration < 25 km 108 47 25 15 7 11 14 13 13 2 2 4 261 69 22 9 8 3 3 4 2 2 0 0 2 124 39 25 16 7 4 9 10 11 11 2 2 2 137 13 13 11 5 2 4 9 9 9 2 2 2 81 Notes: The Full Sample consisted of 551 men; censored (Incomplete) cases not shown. © Blackwell Publishers 1999. 108 JOURNAL OF REGIONAL SCIENCE, VOL. 39, NO. 1, 1999 employment. Of those gaining employment 53 percent of moves were longdistance compared to 44 percent for the continuously employed, suggesting that moves associated with finding employment tend to be job-related as one would expect. These would appear to be contracted moves, in the sense that the individual moved their place of residence over the period and ended up gaining a job in the process. Information was also available on the temporary or permanent nature of employment contracts. These data showed 90 cases of people who changed from temporary to permanent work (in continuous employment); nine percent of these changes were associated with migration, but these were primarily local moves, with only two percent of the 90 combined with long-distance migration. Of the transitions from unemployment to permanent work, eight percent were associated with a long distance move. These figures are consistent with the view that temporary workers search locally for permanent jobs rather than more widely, perhaps reflecting the development of links and contacts in the area. 4. THE EMPIRICAL FRAMEWORK In this paper we analyze the data using a duration model that may be seen to emerge as the outcome of a search process (see Kiefer, 1988) for a discussion of the derivation of duration models from search theory). It is convenient to model durations in terms of the “hazard,” that is the probability of leaving a state (in this case a residence) in some period t, conditional on not having left in the period up until t. We model this hazard using a discrete-time logit specification (1) λkt = [ 1 + exp(–θt – Xkβ – Yktγ )]–1 where λkt is the hazard for individual k at time t; θt is the “baseline hazard” that captures changes in the average hazard for all individuals over the course of the residence spell; X is a vector of explanatory variables or “covariates” that are fixed over time but vary over individuals, such as gender or qualifications at time of leaving school; and Y is a vector of ‘time-varying covariates’, i.e., explanatory variables that vary across time and individuals, such as changing employment status over the spell. β and γ are conformable vectors of coefficients. This model involves organizing the data so that each observed time period becomes a separate observation. Thus for example, say that a ten-period residence TABLE 2: Completed Residence Duration by Employment-Status Change Stay Unemployed All migration Migration ≥ 60 km Migration < 60 km Migration < 25 km 8 6 2 1 Find Job 85 45 40 18 Lose Job 7 2 5 1 Stay Job Total 161 71 92 63 261 124 137 83 Notes: Number of unemployment spells in data equals 589; number of employment spells in data equals 768. © Blackwell Publishers 1999. DETANG-DESSENDRE & MOLHO: MIGRATION AND EMPLOYMENT STATUS 109 spell ending in migration is observed for some individual. Such a case would involve nine separate observations of zero for the dependent variable, followed by an observation of a one. Some residence spells are likely to be incomplete in the sense that the spell was still ongoing and migration had not yet occured by the time of interview. Such spells are termed “censored” and enter the data as a series of zeroes with no one at the end. Standard binary logit models may be used to estimate the model (see for example, Allison, 1982; Jenkins, 1995). The model is in discrete-time in the sense that it specifically allows for only positive integer values of durations. For example, a residence spell may last one period, or two periods, or three periods, etc. This contrasts with continuous-time models such as the Cox Proportional Hazards Model, where the spell may end at any positive time t (see Cox and Oakes, 1984).2 The discrete-time formulation is appropriate to our problem because this is the format in which our duration data are observed (this is typically the case in most duration studies). It has the further advantage that time-varying covariates are easily handled, and ‘ties’ in the data (cases where multiple individuals have exactly the same observed residence duration) present no special estimation problems. An interesting aspect of duration analysis concerns the issue of duration dependence, where migration probabilities may causally depend on the length of the spell to date. Such effects show up in the baseline hazard. A decreasing baseline over time would, for example, be consistent with cumulative inertia. In order to model empirically these effects, it is important that the form of the baseline hazard should be as flexible as possible. One attraction of the logit model is that it does not require prior restrictions on the shape of the baseline hazard. Free estimation is allowed via coefficients on a sequence of appropriately defined time-dummies. The Cox Proportional Hazards Model shares this great flexibility in that it too does not pre-specify the shape of the baseline hazard. The advantage of the logit model over the Cox model is that it allows us to estimate the baseline explicitly, whereas in the Cox model it is treated as a nuisance term and is not explicitly estimated. We compared the performance of the logit model against various alternatives. The Cox Proportional Hazards Model specification proved problematic. A variety of tests suggested that the proportionality assumption embedded in this model was inappropriate to our data.3 First, we looked at Kaplan–Meier plots of the survivor function. In principle we would expect these plots to be parallel if the proportionality assumption held (see Cox and Oakes, 1984). Inspection of the plots in Figure 1 indicates that proportionality does not hold for our data. Second, we applied tests of the form suggested by Blossfeld, Hamerle, and Mayer (1989). This involved construction of variables such as z=xln(t), where t is the The Cox Proportional Hazard Model is specified as λkt = θtexp(Xkβ + Yktγ). The Cox model (specified in note 2) implies that the ratio of the hazards for two individuals with different X covariates is independent of the baseline hazard. The logit model involves no such assumption. 2 3 © Blackwell Publishers 1999. 110 JOURNAL OF REGIONAL SCIENCE, VOL. 39, NO. 1, 1999 FIGURE 1: Kaplan–Meier Survival Estimates by Strata. observed duration to date and x is a covariate of the original specification (in particular we tried educational qualification). If the proportionality assumption held we would expect variables such as z to attract insignificant coefficients in the estimated Cox model. In fact, they turned out to be highly significant.4 In view of these empirical findings and given the advantages of the logit specification outlined above, we decided to reject the Cox model. Finally, we compared the logit model with two alternative discrete-time specifications of the hazard function: the probit and the complementary log-log (or extreme-value-distribution) models.5 The latter results are presented in 4 For example, the variable ‘(educational level of baccalaureat or above) × [log(duration)]’ had a t-statistic of 7.3 in our analysis. We also tried variables of this kind in the logit model and found them to be completely insignificant, providing further support for the logit model. 5 The extreme-value distribution model may be derived as a discrete-time analogue to the Cox Proportional Hazards Model (see Narendranathan and Stewart, 1993). Note however, that this model does not itself imply proportionality in the hazards; indeed, proportionality is inconsistent with discrete-time analysis where the hazard is bounded above by unity. The term ‘extreme value’ is applied to this distribution because it specifies the hazard as negative double exponential: λkt = 1 – exp{–exp[θ(t) + Xkβ + Yktγ]}. © Blackwell Publishers 1999. DETANG-DESSENDRE & MOLHO: MIGRATION AND EMPLOYMENT STATUS 111 Appendix 1; the findings proved very similar to those obtained with the logit model. We chose to concentrate on the logit specification because it is perhaps the most familiar and easiest to interpret. Differentiating Equation (1) we find ∂λ/∂x = βλ(1 – λ) (2) where x is an explanatory variable in Equation (1) and β is the associated coefficient. Note in particular that a positive β means that an increase in the value of x increases the hazard, thus implying a greater risk of migration and a shorter likely residence duration (and vice versa for a negative coefficient). Employment status is included in the Y covariates in our model. Changes in employment status enter as time-varying covariates because by definition they capture changes in individual circumstances over the residence spell. We argued in the previous section that changes in employment status are likely to be endogenous to the migration decision. Therefore, we instrumented this variable in our main estimations using lagged values of employment status, information on educational field, father’s dwelling, and siblings as our instruments (see Appendix 2 for details). We argued above that housing moves are likely to differ from job moves. The model is one of competing risks in the sense that there are two alternative exits from a residence spell: local- or long-distance migration. Therefore we estimated different models according to the distance of move. We estimated hazard functions for long moves (over 60 kilometers), treating short-distance movers (less than 60 kilometers) and nonmovers as censored; and we estimated hazard functions for short moves, treating long-distance movers and nonmovers as censored.6 This procedure amounts to conditional maximum-likelihood estimation of a discrete-time competing-risks model, as discussed in Allison (1982).7 For comparison purposes we also estimated models for all moves, and for shortdistance moves of less than 25 kilometers. 5. RESULTS Estimated logit equations for the hazard functions for migration are presented in Table 3. The sample consists of 22,223 cases because in this framework each month of observation counts as a separate case for each of the 551 individuals. We interpret the results in terms of the migration hazard (the probability that a move will occur in the current period, given that no move occured up to that time). 6 Recall that moves within the same village are not counted. The econometrics of competing-risk models in a discrete-time framework are not well developed. Allison (1982) argues that the procedure adopted here is consistent but not fully efficient. 7 © Blackwell Publishers 1999. 112 © Blackwell Publishers 1999. TABLE 3: Migration Hazards, The Logit Model All Migration † Educational level : 0.81 Reference –0.66*** 0.58 1.04*** 0.40 Reference –0.43*** 0.04 –0.98*** –0.29 0.53 1.22*** 1.10*** Reference –0.61** –0.83*** Reference 0.17 –0.07 –0.06 –0.68** –1.12*** –1.86*** –1.31*** –1.48*** –1.61*** (0.14) Migration > 60 km *** (0.26) (0.27) 1.28 Reference –0.57 1.35*** 1.13*** –0.42 Reference –0.21 –0.01 –0.99*** –0.34 1.80*** 2.42*** 1.41*** Reference –1.67*** –1.88*** (0.29) (0.30) (0.30) (0.30) (0.36) (0.44) (0.38) (0.42) (0.47) Reference 0.48 –0.13 0.20 –1.06** –1.17** –2.07*** –1.94*** –1.69*** –2.58*** (0.24) (0.37) (0.15) (0.32) (0.16) (0.19) (0.23) (0.22) (0.56) (0.30) (0.31) (0.19) Migration < 60 km (0.50) (0.43) 0.29 Reference –0.69** –0.43 0.90*** 0.66* Reference –0.61*** 0.02 –0.93*** –0.27 –0.59 0.37 0.70 Reference –0.06 –0.04 (0.40) (0.44) (0.42) (0.48) (0.50) (0.67) (0.68) (0.67) (1.06) Reference –0.04 0.12 –0.07 –0.26 –0.98* –1.52** –0.76 –1.14** –0.99* (0.40) (0.44) (0.21) (0.74) (0.23) (0.28) (0.37) (0.32) (0.69) (0.47) (0.47) (0.21) Migration < 25 km (0.29) (0.33) 0.29 Reference –0.25 –0.70 0.92*** 0.53* Reference –0.68** 0.25 –0.93** –0.20 –0.67 0.22 0.72 Reference 0.26 0.19 (0.42) (0.41) (0.43) (0.40) (0.52) (0.60) (0.48) (0.55) (0.56) Reference 0.31 0.59 0.48 0.57 0.05 –1.01 –0.27 0.02 –0.04 (0.31) (0.72) (0.22) (0.35) (0.22) (0.26) (0.30) (0.28) (0.98) (0.42) (0.48) (0.30) (0.34) (1.01) (0.31) (0.33) (0.31) (0.33) (0.39) (0.36) (1.21) (0.59) (0.50) (0.32) (0.41) (0.67) (0.64) (0.66) (0.61) (0.69) (0.88) (0.73) (0.70) (0.74) JOURNAL OF REGIONAL SCIENCE, VOL. 39, NO. 1, 1999 Baccalaureat and above Technical college Low level Distance from school more than 100 km National Service done before t Not single at t Original Region : Burgundy Brittany Alps South East P(lost a job between t-3 and t) †† P(found a job between t-3 and t) †† P(stayed unemployed between t-3 and t ) †† P(stayed employed between t-3 and t) †† Found a permanent job in the last year ††† Found a temporary job in the last 6 months ††† Baseline Hazard [1–3 months] [4–6 months] [7–9 months] [10–12 months] [13–18 months] [19–24 months] [25–30 months] [31–36 months] [37–42 months] [43–48 months] *** [49–60 months] (5th year) [61–72 months] (6th year) more than 72 months (after 6 years) intercept N –2logL *** –1.46 –1.21*** –2.51*** –4.48*** 22223 2437.93 (0.37) (0.37) (0.44) (0.28) Migration > 60 km *** –2.45 –2.18*** –2.98*** –5.71*** 22223 1173.21 (0.79) (0.79) (0.79) (0.43) Migration < 60 km * –0.84 –0.61 –2.02** –4.98*** 22223 1550.91 (0.46) (0.46) (0.54) (0.33) Migration < 25 km 0.15 0.39 –0.82 –6.23*** 22223 1026.74 (0.64) (0.65) (0.69) (0.58) Notes: Standard errors in brackets. *** indicates significant at 1% level; ** indicates significant at 5% level; * indicates significant at 10 percent level –2logL : –2 times the value of the maximized log likelihood function. † Baccalaureat and above; technical college: diploma from technical college and drop out from general college without baccalaureat; low level: drop out from technical college without diploma and before general college. †† These variables have been instrumented. ††† Defined as 1 if the individual entered into a permanent (temporary) contract in the year (6 months) up to t-3. DETANG-DESSENDRE & MOLHO: NEED SHORTER RUNNING HEAD © Blackwell Publishers 1999. All Migration 113 114 JOURNAL OF REGIONAL SCIENCE, VOL. 39, NO. 1, 1999 The Control Variables Ceteris paribus the estimated equations show that the hazard for longdistance migration is significantly higher for highly educated workers (baccalaureat or higher) than for the less-educated. This result may reflect a greater willingness to move among these workers; it also fits in with the notion that young workers from rural backgrounds must move if their aim is a high-level career. The results show that the hazard increases significantly for individuals who were educated in a location over 100 kilometers away from their district of origin, all else being equal. This variable is intended to capture an artefact of the data whereby people who studied away from home may choose to stay at their place of study and hence count as migrants on entry to the labor market. It may also reflect “broader horizons” for such workers. Interestingly, these education variables are not significant for local moves of less than 25 kilometers and are only marginally so for moves of less than 60 kilometers. At the time of the survey, young men in France were required to do National Service (unless they had some form of exemption—true for 21 percent of our sample). This episode of a young man’s career (and any associated migration) is clearly a special circumstance, and hence such periods have been dropped in our data. However, it is still arguable that the periods before and after National Service may be different with respect to migration. The results show that ceteris paribus the migration hazard after National Service is significantly higher than before. This is consistent with the idea that young men delay important decisions of this nature until after National Service is complete. Some regional effects are also in evidence, with low migration hazards most evident in the south.8 Being single as opposed to married or living together appears to exert little effect on migration, even over short distances. This result appears surprising at first because one would expect housing moves to be closely related to family structure. However, it is consistent with a common perception in these rural areas that, upon marrying it is usually the woman who moves to join the man. The Baseline Hazard No obvious time pattern for local moves is detectable in the baseline hazard (at least for moves less than 25 kilometers), indicating an absence of durationdependence effects. However, for long-distance moves there is a clear and pronounced tendency for the hazard to fall over time (after controlling for other observed influences on migration), consistent with the presence of cumulative inertia. These results imply that attachments to the immediate residence do not depend on time, but ties to the general area do.9 It appears that once people 8 The two districts in Burgundy were amalgamated into one, as were those in Brittany, leaving five regions from the original seven districts. 9 The declining baseline long-distance migration hazard may also be interpreted in other ways, e.g., in terms of aging of the worker, or falling job offer rates over time. © Blackwell Publishers 1999. DETANG-DESSENDRE & MOLHO: MIGRATION AND EMPLOYMENT STATUS 115 organize their career and private life in a geographical space, they may change their specific location within that space but find it increasingly difficult to move away altogether. A trend-fitting exercise yielded the following results for the baseline hazard for long-distance moves θ$ = 0.85− 0.419 t (2.70) b –7.73g with R2 = 0.844; F = 59.7; n = 13 t-statistics in brackets (time measured at mid-points). The predicted trend falls at a decreasing rate. Labor Market and Employment-Status Effects The first and most obvious point to note is that labor market employmentstatus effects are only significant for long-distance moves, in keeping with the view that long-distance migration is more likely to be job-related. Lagged experience of finding either permanent or temporary employment significantly reduces the hazard for long-distance migration; an individual who finds a job may move at the time, but afterwards is unlikely to move.10 Note that in this case finding employment includes people who (for example) switched from temporary to permanent work. Finally we turn to the main focus of our study: the current employmentstatus transition variables. These variables capture contemporaneous effects of employment transition on migration and have been instrumented in the manner described in Appendix 1. Staying in employment is treated as the reference category. The continuously unemployed are found to be significantly more likely to move long distance, all else held constant.11 We discuss this result more fully below. Loss of employment also attracts significant positive coefficients, perhaps reflecting forced migration. However, the sample of movers in this category is small so this result should be interpreted tentatively. More interestingly, the results suggest that finding employment significantly raises the migration hazard compared to that for the continuously employed and the continuously unemployed. The former effect is reflected in the t-statistic on this coefficient which is larger than 5; evidence of the latter effect may be seen in the fact that the estimated coefficient of 2.42 for job-finders is more than 2 standard errors above that of 1.41 for the unemployed. This result suggests a powerful long-distance migration response results from finding employment. 10 The variable for permanent work is defined for the year up to t-3; for temporary work it is for the 6 months up to t-3. We found that finding temporary work prior to this had no significant effect, while finding permanent work had the same effect in the first six months as in the last. 11 Lagged unemployment-experience variables had insignificant coefficients and have been deleted. © Blackwell Publishers 1999. 116 JOURNAL OF REGIONAL SCIENCE, VOL. 39, NO. 1, 1999 The data in Table 2 shows 45 long-distance migrants who found work, and 71 who stayed in work. However, the model predicts a higher migration hazard among the former group than the latter. These findings partly reflect the larger sample of persons who are at risk of migration among the latter group, the fact that the model controls for other effects to give the impact of employment-status shifts for an otherwise identical individual, and the fact that current employment status is treated as endogenous. Table 4 evaluates the predicted hazard for the explanatory variables at the sample means. An individual who was continuously employed but conformed to the sample average in all other respects is predicted to have a very low long-distance migration hazard of 0.08 percent; alternatively if the individual were continuously unemployed this hazard would increase to 0.3 percent; finally, if he were unemployed then found work the hazard would rise further to 0.9 percent. Table 4 also gives some illustrative calculations for other case-types. These show important interactions in the effects of the covariates. For example, gaining employment raises the long-distance migration hazard for a highly educated individual in the first year of his residence spell from 3.9 to 12.3 percent, whereas for an average individual the effect is much smaller. Speculative and Contracted Migration Our model allows us to estimate the probability of an unemployed worker undertaking long-distance contracted migration as compared to long-distance speculative migration. The former refers to changes of address where the individual has work lined up at the destination, and so is observed in employment immediately on arrival at their new location at time t; the latter refers to the opposite case. Consider an unemployed worker with characteristics (education, location, length of residence, etc.) given by the vector Z. The probability of the worker finding employment found using our intrumentation of employment TABLE 4: Predicted Migration Hazards (Percentage) All migration Average individual (AI) Average individual (AI) continuously employed AI continuously employed and in first year of spell AI, gains employment AI, in first year of spell AI, highly educated AI, gains employment and in first year of spell AI, gains employment, in first year of spell and highly educated AI continuously unemployed 0.6 0.4 1.4 1.4 1.79 1.31 4.5 9.9 1.3 Migration > 60 km 0.12 0.08 0.36 0.86 0.56 0.42 3.87 12.28 0.32 Notes: Figures based on the logit models in Tables 3. Predictions calculated by setting all explanatory variables to their sample mean values (including the baseline hazard). The hazard for those continuously employed was then calculated by setting P (stay in employment) = 1, P (stay unemployed) = 0, etc. Similar calculations were done for those finding employment, etc. © Blackwell Publishers 1999. DETANG-DESSENDRE & MOLHO: MIGRATION AND EMPLOYMENT STATUS 117 status (described in Appendix 1) is given as p(Z). We evaluate the long-distance migration hazard given that he finds work as λ(Zp(Z)=1), and calculate the probability of contracted migration as λ(Zp(Z)=1) × p(Z). Similarly, the probability of the same unemployed worker undertaking speculative migration is calculated as λ(Zp(Z)=0) × (1–p(Z)). Table 5 presents the results of such an analysis for various types of workers, fixing the values of Z to their sample average for the unemployed unless otherwise stated. The probability of contracted migration is higher than that for speculative migration for all groups, despite the low probability of finding work. This qualitative result accords with prior expectations. The only cases where the difference is small tend to be those where the migration probability is very low; these cases are likely to be heavily influenced by individual idiosyncracies in the data. The ratio of contracted to speculative migration tends to be higher for low—as opposed to high—educated workers, perhaps reflecting the greater chance of the latter group to find work after moving. Overall, the prevalence of speculative migration appears somewhat higher in Table 5 than we had originally anticipated in view of the small number of continuously unemployed movers in the raw data (see Table 2). However, this result should be treated as tentative because the numbers of continuously unemployed movers are small. Employment Status: Exogenous or Endogenous? In Table 6 we report the results of fitting the long-distance migration hazard function using actual values for employment-status transitions, as opposed to the instrumented values.12 The “continuously unemployed effect” is negative in this model, and the difference in coefficients between the unemployed who gain work and those who do not is around 3.9. This difference falls to around 1 after instrumenting for the employment-transition variables. This finding is consistent with the view that some unemployed individuals move because they find work whereas others find work because they are prepared to move. The estimated impact of finding work (compared to continuous unemployment) on migration is therefore weaker once we have purged the data of this latter effect. These results may be compared to the findings of Van Dijk et al. (1988). They used a binary logit framework applied to pure cross-section data on one-year migration probabilities in the Netherlands, and five-year migration probabilities in the northeastern States of the U.S. They treated employment-status shifts as exogenous. For the Netherlands they found that the unemployed who found jobs were significantly more likely to migrate than the employed, but that ceteris paribus, the continuously unemployed were not. In contrast, for the U.S. they found that all (initially) unemployed individuals were more likely to move than the employed, regardless of whether they found work. They attributed the 12 We found the estimated hazard coefficients of the model using instrumented employmentstatus variables to be more stable with respect to inclusion and exclusion of variables than the results using the raw variables. This finding seems to support our use of an instrumented variables approach. © Blackwell Publishers 1999. 118 © Blackwell Publishers 1999. TABLE 5: Predicted Contracted and Speculative Long-Distance Migration (Percentage) Category (# in sample) prob (finding job) hazard (migfind job) hazard (migstay unemp) prob (contracted migration) prob (speculative migration) 36.5 32.9 6.9 1.2 2.6 0.4 2.5 0.4 1.6 0.2 Well-educated + first year (745) Well-educated + not in first year (287) Low-educated + first year (2197) Low-educated + not in first year (2079) 44.6 19.3 50.1 18.8 9.4 3.0 2.4 0.6 3.6 1.0 0.9 0.2 4.2 0.6 1.2 0.1 2.0 0.8 0.4 0.1 Well-educated before NS (628) Well-educated after NS (404) Low-educated before NS (1815) Low-educated after NS (2461) 30.4 46.9 35.1 31.4 6.1 8.2 1.2 1.2 2.3 3.1 0.4 0.4 1.8 3.8 0.4 0.4 1.6 1.6 0.2 0.3 Notes: Prob (find job) and migration hazard respectively constructed from the model described in Appendix 1 and from Table 3 (long-migration hazard), setting all variables to their sample average for the unemployed unless otherwise stated. Prob (contracted migration) is given as column 1 multiplied by column 2; prob(speculative migration) given as (1-column 1) multiplied by column 3. JOURNAL OF REGIONAL SCIENCE, VOL. 39, NO. 1, 1999 Well-educated people (1032) Low-educated people (4276) DETANG-DESSENDRE & MOLHO: MIGRATION AND EMPLOYMENT STATUS 119 TABLE 6: Employment Status Effects in the Long Migration Hazard (Percentage) Observed variables Gain employment Stay unemployed Lose employment 2.68 –1.21 –3.47 (0.28) (0.46) (0.72) Instrumented variables 2.42 1.41 1.80 (0.47) (0.47) (0.69) Notes: The table reports coefficients (standard errors in brackets) on the employment transition variables in the long migration hazard function, based on observed employment transitions compared with the instrumented variables. Results for control variables not reported. difference between the two countries to higher levels of speculative migration in the U.S. Our results suggest that their method may underestimate the prevalence of speculative migration because the continuously unemployed appear more likely to migrate once we have accounted for the simultaneity between migration and employment-status shifts. Alternative Distribution Functions Finally, we compared estimates of the long-distance migration hazard function based on the logit model with those derived using the probit and the extreme value (complementary log-log) models. Full results are reported in Appendix 1. In terms of signs and significance of estimated coefficients the models are very similar. The logit and extreme value models appear to fit the data somewhat better than the probit, judging by the maximized log likelihood values. Table 7 reports predictions of the long-distance migration hazard evaluated at the sample means, using the different models. The predicted effects of the various employment-status transitions are very similar in the extreme value and logit models. This finding reflects the fact that the migration hazards are low, and hence lie in the tail of the distribution where the logit and extreme value models are quite similar. The probit model tends to produce rather larger effects for employment gainers compared to other transition groups, though not dramatically so. TABLE 7: Predicted Migration Hazards with Alternative Distribution Functions Average Individual (AI) AI continuously employed AI gains employment AI continuously unemployed Notes: See Table 4. © Blackwell Publishers 1999. Logit Long-migration Probit Extreme Value 0.12 0.08 0.86 0.32 0.18 0.1 1.19 0.57 0.12 0.08 0.86 0.32 120 6. JOURNAL OF REGIONAL SCIENCE, VOL. 39, NO. 1, 1999 CONCLUSIONS In this paper we analyze the effects of employment-status transitions on migration. The issue was considered from a search-theoretic perspective. A discrete-time hazard function model was fitted to duration data on a sample of young men completing school in rural locations in France. This econometric approach was used partly because it handles durations as discrete, which is the form in which such data are invariably collected, and partly because we found that the more traditional proportional hazard specification was inappropriate for our data. The discrete-time model allows a flexible baseline hazard to be explicitly estimated. Employment-status transitions were treated as endogenous in the estimated model. We also distinguished between short- and long-distance moves, in a competing-risks framework. Our main results may be summarized as follows: (a) Labor market variables were found to significantly affect long-distance migration but not short-distance moves. This result is consistent with the view that long-distance moves are primarily job-related whereas many local moves are primarily housing-related. (b) We found that unemployed individuals who gain employment are significantly more likely to move long-distances than those who are continuously employed or unemployed, all else held constant. (c) Unemployed individuals are more likely to undertake contracted as opposed to speculative long-distance migration, this is especially so among the lesseducated. (d) The baseline hazard for local moves was found to display no apparent duration-dependence effects, but for long-distance moves there was significant evidence of a decreasing hazard over time, consistent with cumulative inertia. Rural populations in France are historically in decline, although recent evidence suggests this trend may be faltering (Cavailhes et al., 1994). Our results suggest that the out-migration of young men from such areas is at least in part motivated by labor market considerations. In future research it would be interesting to investigate the permanence of such outward moves and, in particular, whether there is evidence of return migration in later life. REFERENCES Allison, Paul. 1982. “Discrete-time Methods for the Analysis of Event Histories,” in S. Leinhardt (ed.), Sociological Methodology, San Francisco: Jossey-Bass Publishers. Blossfeld, Hans-Peter, Alfred Hamerle, and Karl Mayer. 1989. Event History Analysis. New Jersey: Lawrence Erlbaum Associates. Cavailhes, Jean, Cécile Dessendre, Florence Goffette-Nagot, and Bertrand Schmitt. 1994. “Change in the French Countryside: Some Analytical Propositions,” European Review of Agricultural Economics, 21, 429–449. 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Gordon, Ian and Ian Molho. 1995. “Duration Dependence in Migration Behaviour: Cumulative Inertia versus Stochastic Change,” Environment and Planning A, 27, 1961–1975. Jenkins, Stephen. 1995. “Easy Estimation Methods for Discrete-Time Duration Models,” Oxford Bulletin of Economics and Statistics, 57, 129–137. Kiefer, Nicholas. 1988. “Economic Duration Data and Hazard Functions,” Journal of Economic Literature, 26, 646–679. Krumm, Ronald. 1983. “Regional Labor Markets and the Household Migration Decision,” Journal of Regional Science, 23, 361–376. Molho, Ian. 1986. “Theories of Migration: A Review,” Scottish Journal of Political Economy, 33, 396–419. Mortensen, Dale. 1986. “Job Search and Labor Market Analysis,” in O. Ashenfelter and R. Layard (eds.), Handbook of Labor Economics, Vol. 2, Amsterdam: North-Holland. Narendranathan, W. and M. Stewart. 1993. “How Does the Benefit Effect Vary as Unemployment Spells Lengthen?” Journal of Applied Econometrics, 8, 361–381. Shaw, Kathryn. 1991. “The Influence of Human Capital Investment on Migration and Industry Change,” Journal of Regional Science, 31, 337–416. Silvers, Arthur L. 1977. “Probabilistic Income Maximizing Behavior in Income Maximizing Migration,” International Regional Science Review, 2, 29–40. Van Dijk, Jouke, Alan Schlottman, Hendrik Folmer, and Henry Herzog Jr. 1988. “Efficiency of Job Matching Mechanizms: A Cross National Comparison,” Papers of the Regional Science Association, 64, 79–94. © Blackwell Publishers 1999. 122 JOURNAL OF REGIONAL SCIENCE, VOL. 39, NO. 1, 1999 APPENDIX 1: Results for Long-Distance Migration Based on Alternative Discrete-Time Hazard Models Logit Educational level † baccalaureat and above technical college low level Distance from school more than 100 km National Service done before t Not single at t Original region Burgundy Brittany Alps South East P(lost a job between t-3 and t) †† P(found a job between t-3 and t) †† P(stayed unemployed between t-3 and t ) †† P(stayed employed between t-3 and t) †† Found a permanent job in the last year ††† Found a temporary job in the last 6 months ††† Baseline Hazard [1–3 months] [4–6 months] [7–9 months] [10–12 months] [13–18 months] [19–24 months] [25–30 months] [31–36 months] [37–42 months] [43–48 months] [49–60 months] (5th year) [61–72 months] (6th year) more than 72 months (after 6 years) intercept N –2logL Notes: see Table 3. © Blackwell Publishers 1999. Probit Extreme Value 1.28*** (0.19) Reference –0.57 (0.40) 0.49*** (0.08) Reference –0.18 (0.14) 1.26*** (0.19) Reference –0.57 (0.40) 1.35*** (0.44) 1.13*** (0.21) –0.42 (0.74) 0.48*** 0.46*** –0.19 (0.19) (0.09) (0.28) 1.35*** 1.10*** –0.43 (0.42) (0.21) (0.73) Reference –0.21 (0.23) –0.01 (0.28) –0.99*** (0.37) –0.34 (0.32) 1.80*** (0.69) 2.42*** (0.47) Reference –0.05 0.04 –0.33*** –0.11 0.62*** 0.82*** (0.09) (0.11) (0.13) (0.13) (0.25) (0.18) Reference –0.22 –0.04 –0.98*** –0.34 1.80*** 2.43*** (0.23) (0.27) (0.37) (0.31) (0.69) (0.46) 1.41*** (0.47) 0.56*** (0.18) 1.38*** (0.47) Reference Reference Reference –1.67*** (0.50) –0.54*** (0.17) –1.69*** (0.49) –1.88*** (0.43) –0.63*** (0.16) –1.89*** (0.43) Reference 0.48 (0.40) –0.13 (0.44) 0.20 (0.42) –1.06** (0.48) –1.17** (0.50) –2.07*** (0.67) –1.94*** (0.68) –1.69*** (0.67) –2.58*** (1.06) –2.45*** (0.79) –2.18*** (0.79) –2.98*** (0.79) –5.71*** (0.43) 22223 1173.21 Reference 0.16 –0.09 0.03 –0.43** –0.46** –0.76*** –0.78*** –0.72*** –0.95*** –0.90*** –0.80*** –1.05*** –2.70*** 22223 1185.18 (0.16) (0.17) (0.17) (0.18) (0.20) (0.23) (0.24) (0.25) (0.34) (0.26) (0.26) (0.25) (0.16) Reference 0.49 –0.13 0.21 –1.03** –1.14*** –2.05*** –1.91*** –1.65*** –2.54*** –2.43*** –2.15*** –2.95*** –5.71*** 22223 1171.89 (0.40) (0.44) (0.41) (0.47) (0.49) (0.66) (0.67) (0.67) (1.06) (0.78) (0.79) (0.79) (0.42) DETANG-DESSENDRE & MOLHO: MIGRATION AND EMPLOYMENT STATUS 123 APPENDIX 2: Instruments for Employment Status Our aim here was to estimate a reduced-form model with which to predict the current probability of employment. We modeled the probability of employment in the current spell pt using logit equations pt = prob(jobt = 1) = logit(Zt ; jobt-m) where jobt takes the value 0 if the individual is currently unemployed, and 1 if he is employed; Z includes independent variables from the main analysis of migration, plus other variables such as educational field (industrial, service, agricultural, scientific or general), the employment of the father, parental housing type, the number of siblings, and year dummies. We treat the lagged employment status variables, jobt-m, as pre-determined. We estimated equations of this form separately by employment status in t-1, and by sex and educational qualification. We also treated individuals who had just left school separately because lagged employment values were unavailable in these cases. One may think of these as discrete-time duration models, where 1-pt is the hazard of leaving employment for an employed individual (jobt-3 = 1), and pt is the hazard for someone originally unemployed (jobt-3 = 0). Accordingly, we also included a baseline hazard in Z to capture duration dependence effects in employment and unemployment. The correlation coefficient between the actual jobt and predicted probabilities pt* generated by this exercise had a value of 0.878. We predicted variables for changes in employment status over a 3 month period using pt* as |RS0 if employed in t − 3 |Tb1 − p *g if unemployed in t − 3 0 if employed in t − 3 p(gain employment) = RS T p * if unemployed in t − 3 R|0 if unemployed in t − 3 p(lose employment) = S |Tb1 − p *g if employed in t − 3 p(stay unemployed)t = t t t t t again treating employment status in t-3 as predetermined. These predicted transition variables correlated with the actual transitions as follows: stay unemployed r = 0.84; find job r = 0.60; lose job r = 0.5. The predicted transition variables were used in the explanation of residence durations, treating “stay in employment” as the reference category. For individuals who had just left school we initially included variables for the transitions “school to employment” and “school to unemployment” in the migration equations; likelihood-ratio tests suggested that we could treat the school period as unemployment, and hence for the remainder of the analysis we adopted that treatment in the construction of the above variables. © Blackwell Publishers 1999.