Add Thesis

Mobility and Youth Unemployment

Does internal migration influence earnings following youth unemployment? Evidence from the Swedish Labor Market

Written by P. af Burén

Paper category

Bachelor Thesis






Thesis: Internal migration Internal migration is defined as movement from one designated area to another (or a certain distance) in the same county (Greenwood 1997: 650). It is convenient to divide the immigration literature into two broad research areas. One branch deals with the determinants of movement (e.g. DiVanzo 1978; Greenwood 1997; Pissarides and Wadsworth 1989; and Gordon 1985), while the other branch deals with the consequences of migration (e.g. Nakosteen and Zimmer 1980; Hunt and Kau 1985; Nakosteen and Wester and Boman 2011. The purpose of migration can be generated by many different factors. Research trying to explain the determinants of migration includes different characteristics of sending and receiving areas and personal and/or family variables. Regional factors can be housing market, taxation, unemployment, public goods The availability of climate and environmental amenities can also play a role in the decision to relocate. Different life cycle aspects are such as marriage, divorce, completion of school, entry into the labor market, birth, home ownership, aging, running away from home, and retirement. Other personal/family characteristics and treatments are also important, such as income, employment status, education, age, gender, etc. One of the best documented correlations is the relationship between age and immigration (Greenwood 1997: 665). The possibility of migration Sex is highest in the 20s and will decline after this age group. What is more controversial is the correlation between education and immigration, but the general trend is to increase with the migration of education. Between distance and migration Another correlation is observed. Migration decreases as the distance from a certain starting point increases. One way to measure this can be to use the "distance elasticity of migration", which is the percentage change in migration from k to l, which is It is caused by a 1% change in the distance between k and l, and other factors remain unchanged. The elasticity is usually between -0.1 and -2.0 (Greenwood 1997: 667). In addition, studies have found that immigration is associated with different employment status (such as individual unemployment). , Regional unemployment, and national unemployment). DaVanzo (1978) was one of the first to use US microdata. And found that the unemployed are more likely to move than the employed. In addition, she concluded that, A higher unemployment rate in the area of ​​residence will lead to a higher tendency for unemployed individuals to emigrate. In contrast, the regional unemployment rate has no effect on the number of employed persons. Economists have difficulty finding the area of ​​the relationship between immigration and the regional unemployment rate. 2.2 Scar effect Generally speaking, evidence indicates that there is a scar effect. However, it is currently unclear how long this effect will last. Changes in the labor market of various countries and the difficulty of controlling individual heterogeneity may be the cause of the ambiguous results. Heckman and Borjas (1980) studied the effects of unemployment very early and pointed out the difficulty of distinguishing between accidental effects and unobserved heterogeneity. For example, if some people are observed to be (un)employed and continue to have (low) high wages over time, this may be because of the scarring effect or because of differences in unobserved characteristics (such as motivation). Assuming that these differences between units continue to be out of control, estimates would be misleading. Due to the importance of controlling for unobserved features, most of the literature on scarring has focused on different methods of dealing with heterogeneity (eg Mroz and Savage 2006; Arulampalam 2001; Gregg and Tominey 2005; and Gregory and Jukes 2001 ). Parallel branch literature. The possibility of future employment after individual youth is unemployed, not the difference in income. The phenomenon here is more structural or state-dependent than a scar effect. Alulan Param et al. (2000) Use the random effects model in the British labor market and find strong evidence of state dependence. Burgess et al. (2003) applied another technique to cohort analysis and found that in cohorts with high unemployment rate among young people entering the labor market, wage losses were significant, especially for unskilled labor. Other studies on the British labor market include Gregg (2001), who used total unemployment as an instrumental variable and looked for evidence of structural dependence for individual unemployment. Elwood (1982) uses data from the National Longitudinal Survey of Youth (NLSY) to study the US labor market. He found that early unemployment had only a slight structural dependence on unemployment, but it had a significant impact on wages. In addition, Heckman and Borges (1980) found little evidence of true state dependence. Mroz and Savage (2006) also used NLSY data to study the long-term effects of youth employment. They concluded that there are scars in the labor market, but they found that there is also a "catch-up" effect on the possibility of employment and future income. Therefore, their findings indicate that the scarring effect will decrease over time. In addition, they found evidence that the "catch-up" effect has a greater impact on the likelihood of finding a new job than it has on future wages. Literature that focuses on income includes Arulampalam (2001). Read Less