Over the last several decades, two of the most significant developments in the U.S. labormarket have been: (1) rising inequality, and (2) growth in both the size and the diversity of immigration flows. Because a large share of new immigrants arrive with very low levels of schooling, English proficiency, and other skills that have become increasingly important determinants of success in the U.S. labormarket, an obvious concern is that such immigrants are a poor fit for the restructured American economy. In this chapter, we evaluate this concern by discussing evidence for the United States on three relevant topics: the labormarket integration of immigrants, the socioeconomic attainment of the U.S.-born descendants of immigrants, and the impact of immigration on the wages and employment opportunities of native workers. We show that low-skilledimmigrants have little trouble finding paid employment and that the wages they earn are commensurate with their skills. Overall, the U.S.-born second generation has achieved economic parity with mainstream society; for some Hispanic groups, however, this is not the case. Finally, we survey the pertinent academic literature and conclude that, on the whole, immigration to the United States has not had large adverse consequences for the labormarket opportunities of native workers.
To empirically distinguish between education and skill, we separately estimate the model among low- and high-educated individuals (those without any college education and those with some college education or more, respectively). We find stark differences in the relative importance of prejudice and skill differences across races to explain differences in wages and job finding rates. For those without a college degree, we estimate that 57% of potential employers are prejudiced and that the utility cost of employing a black worker is about 7 dollars per hour. In terms of skill, blacks and whites are estimated to be quite homogeneous, with the average white worker being 1.4% more skilled than his black counterpart. In contrast, among those with some college education, prejudice is much more concentrated (only 31% of potential employers are prejudiced) and milder (the disutility of employing a black worker is $1.4), whereas these workers are considerably more heterogeneous in terms of their productive abilities (both within and between races). The average black worker is close to 14% less productive than the equivalent white worker. We use the estimated model to gauge the effectiveness of alternative policy approaches to improve labormarket outcomes of black vis-a-vis white workers. In our counterfactual experiments, we consider two types of policies: enforcement of the equal treatment prin- ciple and programs aimed at reducing the premarket skill racial gap. Since our model features free entry of jobs, the effects of different policies are analyzed taking labor de- mand adjustment into account. Our results suggest policies can improve labormarket outcomes of black workers without large decreases in social welfare.
Securing employment is, of course, not the sole measure of successful integration into the labormarket. Immigrants who find work but become stuck in low-paid, insecure jobs remain at risk of marginalization and exclusion. It is therefore also important to ask whether immigrants are able to make their way into more secure, higher skilled jobs after several years in the labormarket. To explore this, this section first analyzes the proportion of mi- grants in vulnerable positions, such as those out-of-employment (including both unemploy- ment and out of the labor force) or those in the lowest skilled jobs. Second, it studies how the proportion of immigrants working in jobs requiring different types of skills (low, medium and high) or not working varies with years since arrival. Finally, we consider the impact of immigrants ’ individual characteristics on their occupational trajectories and evaluate the extent to which the pathways into middle-skilled work for immigrants hold up for those who lack high-level educational qualifications. The analysis is done by cohort of arrival.
Lawyers, physicians and women with Ph.D.’s are the main categories represented in the group of women with professional degrees (see Appendix 2). In both …elds, having a successful career requires the workers to have long hours of work. Doing so is specially challenging for women, who are usually responsible for household work and the care of children. Being able to buy from the market housekeeping services and, specially, child care services at unusual hours allows women with a professional degree or Ph.D. to compete with their male coun- terparts. Table 9 shows how low-skilled immigration has helped professional women increase their probability of working more than 50 and 60 hours (both unconditionally and conditional on working). The fact that the e¤ect is increasing in education level and especially large for women with a professional degree or Ph.D. suggests that the mechanism through which low- skilled immigration is a¤ecting the probability of working long hours is likely to be through a reduction in the prices of market substitutes for household production. The magnitude of the e¤ect is economically signi…cant: the low-skilled immigration ‡ow of the 1990s increased by 2 percentage points, a 7.4 percent increase in the 1990 probability, the probability that a working woman in these groups of the population reported working more than 50 hours a week, and by 0.6 percentage points the probability of working at least 60 hours, a 5 percent increase.
the relevant elasticity of substitution is greater than 1, this will be a lower bound on the elasticity of demand for low-skilledlabor. Thus, a 1% increase in the supply of low-skilledlabor can, at most, cause a 1% decrease in low-skilled wages. The flow of immigrants into America’s labor force has simply not been large enough to explain a 40% deficiency in median wages, compounded over three decades.
In our model, we assume that people migrate to places providing higher real returns for their labor. However, in order to use their human capital effectively, migrating agents should adapt to the new environment. Clearly, a migrant would be disadvantaged against the native of the same skill type, since the native is more accustomed to and knows the ways of the environment they are to work in. This is called the efficiency loss of migration. Hence, in the same skill group, immigrants earn lower wages than natives. But this wage gap decreases over time (Borjas, 1985; Chiswick, 1978). One possible explanation for this is that migrants get integrated into the labormarket of the host country and as a result they start using their human capital more efficiently. Decreasing the corresponding efficiency losses, therefore, would give rise to higher return to human capital.
chine operators are among the ten occupations with the highest expected gains. A smaller group of occupations that gain is occupations with high cognitive ability intensity and low communications intensity, including aerospace engineering (largest gain), astronomers and physicists (21st) and dietitians (22nd). Workers in these occupations face pressure from high-ability immigrants from developed countries. The results for preventing only unautho- rized immigration is similar, except that cognitive ability-intensive occupations no longer gain. One striking fact stands out: the fraction of an occupation’s labor force that is foreign born is only weakly correlated with large wage gains, because of the potential for realloca- tion. Hence, some occupations with over a quarter of the work force foreign born still see wage effects of less than half a percent for ψ = 10, including diverse occupations such as taxi drivers, chefs, and economists.
where β indicates the estimates of the various labormarket outcomes. Using Equation (7), we can build the fourth block of Table 6. In this block, we first report the contribution to the change in payroll taxes using the estimates of the policy on the labormarket. We can compute this for each of the skill groups, using the average employment variables reported in the summary statistics block of Table 6. These computations suggest that, as a result of the labormarket effect, low-skilled natives contributed 2,113 fewer euros per newly legalized immigrant. This reflects the negative effects that the legalization had on employment outcomes of low-skilled natives. Similarly, we estimate that the policy change increased high-skilled natives’ contributions by 1,231 euros, decreased low-skilledimmigrants’ contributions by 3,283 euros, and increased high-skilledimmigrants’ contributions by 1,764 euros. Importantly, these numbers only reflect the effect of the policy on payroll-tax contributions through the effect on the labormarket. To obtain the total effect, we need to take into account that the newly legalized immigrants started to contribute to payroll taxes. These computations are displayed in the second row of the fourth block of Table 6. Remarkably, the negative contribution of low-skilledimmigrants now becomes positive since we add the direct effect of the policy. This also increases the size of the contribution of high-skilledimmigrants. In total, the estimates from the labor-market data suggest that payroll taxes should have increased by 2,330 euros per newly legalized immigrant at the local level. This contrasts with the direct estimates that we obtained using payroll-tax revenue data. With these data, we obtained that, on average, locations gained 4,189 euros per newly legalized immigrant.
In Charts 5 and 6, the darkest curve (labeled “Wage 97 + TFP + SS,” where SS is an abbreviation for Stolper-Samuelson) subtracts from “Wage 97 + TFP” the computed adverse impact of terms-of- trade changes on the wages of those with low skills in our baseline case. In other words, these darkest curves illustrate what wages for the low-skilled, blue-collar worker and the average blue-collar worker would have been had TFP and the U.S. terms of trade vis-à- vis non-industrialized countries been the only factors affecting wages. As is evident in Charts 5 and 6, the negative relative-price effect almost cancels the positive effect of TFP on real wages, so these two forces together imply that the low-skill wage should have been approximately constant. The gap between the “Wage 97 + TFP + SS” curve and the path of real wages is the part of the wage experi- ence that cannot be explained by changes in TFP and changes in the terms of trade. In our model, this residual reflects the labor-supply effect and the productivity effect (but not the relative-price effect) of increased offshoring by American firms.
East European immigration has a longer history in North America. The first Polish immigrants were skilled artisans who arrived in Jamestown in 1608. Russian fur traders arrived in Alaska in the mid 1700s, and established posts as far south as Fort Ross (just north of San Francisco) by 1812. The first major wave of immigration from Eastern Europe occurred between the 1880s and the 1920s. With the turmoil of World War I and the Russian Revolution, more than 5.6 million East Europeans arrived in the US for economic, political, and religious reasons. There was little immigration during the Great Depression and World War II with some recovery in the post war period. The second major wave of immigration occurred after the collapse of the Soviet Union in 1990. With the relaxation of emigration restrictions, more than three quarters of a million East Europeans arrived in the U.S. in the decade which followed. Eastern Europe generated over a million refugees every year from 1992 to 2004, peaking with over 2 million refugees in 1996 due to the Balkan conflict 6 .
Hence, the model predicts that the e¤ect of the network size is unambiguous for job- to-job transitions: there is going to be more clustering in network jobs as the network size increases. However, the result only holds under certain conditions for unemployment-to- job transitions, where in addition to the superiority of formal jobs, we also need that the reservation wage is a decreasing function of the network size, or that the arrival rate from network jobs is higher when employed than when unemployed (following Claim 1). In the case of low-skilledimmigrants, especially those who recently arrived, it seems sensible to assume that the job o¤er arrival rate from networks when employed is higher than the o¤er arrival rate from networks when unemployed. The intuition behind this assumption is that when immigrants start working both their knowledge on the host country’s labormarket and their network expand, so that overall, they receive more valuable information per connection than an unemployed worker. Hence, in the case of low-skilled workers, our model reaches a result consistent with one of the …ndings in Patel and Vella (2007). They …nd that recent immigrants locate in the same occupations as their countrymen within regional labor markets, which is consistent with Claim 3. Their other …nding states that recent immigrants enjoy higher wages in common network jobs. We would only be able to explain concurrent higher wages and occupational clustering if e n (N ) <
Hillman (2003) have presented circumstances under which immigration can be pareto- improving. Chapman and Cobb-Clark (1999) develop a comparative static theoretical model to illustrate the effect of immigration on the job prospects of Australian natives. The authors, using feasible Australian values for immigrant spending and the labor force participation rate, conclude that immigration increases the overall employment prospects of unemployed natives. The vast empirical literature for the U.S. finds that employment effects of immigration are negligible, while there may be some negative wage effects for recent immigrants. However, recent research by Borjas (1999, 2003) led to a more negative picture of U.S. immigration. For the U.K., Dustmann, Fabbai, and Preston (2005) find no strong evidence of immigration on aggregate employment. In the case of Germany, Winkelmann and Zimmermann (1993) and De New and Zimmermann (1994) find detrimental effects while Pischke and Velling’s (1997) findings indicate no such effect of migration on employment. Zorlu and Hartog (2005) find very small effects on native wages and no dominant robust patterns of substitution and complementarity for the Netherlands, U.K and Norway. For Australia, Addison and Worwick (2002) find no significant effect of recent immigrant on Australian-born. However, Parasnis, Fausten and Smyth (2006) obtain a significant positive effect on labormarket outcomes for native workers. Roy (1987) finds displacement effects of immigration on Canadian-born while Akbari and DeVoretz (1992) find no displacement effect. Roy (1997), however, finds that foreign-born workers are neither substitutes nor complement with Canadian-born workers. Islam (2007), using time series data, detects a long-run positive relationship between the immigration rate and real wages in Canada.
We next argue that our model, though simple, has the capability of being applied to real world data, although a de…nitive answer must wait for more serious empirical work. In fact, the linking of imports of new foreign varieties—the extensive margin— to wage inequality is compatible with available empirical evidence. The correlation between the growth in the extensive margin and the growth in the relative wage of high-skilledlabor was high, over 0.93, in both U.S. and Mexican manufacturing industries during the period 1980-2000. The variety-skill complementarity appears to be a plausible assumption as shown by the facts in regards to U.S. production organization. The movements of the relative price of high-skill to low-skill intensive goods and the relative wage of high-skilled to low-skilledlabor are also consistent with the observations in the U.S., so our model does not require the Stolper-Samuelson price-wage mechanism.
The final experiment considers a version of turbulence along the lines of Ljungqvist and Sargent (2008) and similarly Wasmer (2006) to study the role of worker and firm specific human capital. We assume that skills are firm specific in Germany and are lost after a separation. Concretely, we assume that highly skilled workers (good types) lose their skills and become a normal type upon separation, while workers with normal skill levels become bad types. That is, a large fraction of the people in the work force lose 10% of their skill levels upon separation. This assumption transforms skills that are attached to the worker in the U.S. to skills that are more firm-specific in Germany. 43 The higher risk of losing skills increases the surplus for medium- and high-skilled workers in Ger- many. As a result the average EU rates decline for these groups. For low-tenured workers, the decline is not as pronounced as observed in the data. Two effects are at work. Making skills more match-specific in Germany tends to increase the average surplus and therefore the average UE rate because it is more attractive for firms to post vacancies. However, the composition of the unemployment pool changes, too. There are more bad types in the search pool. This effect tends to make it less attractive to post vacancies. In our calibration, there are 44% bad types in the unemployment pool for the U.S., while in Germany, due to the skill losses, the number increases
In this paper, we combine linguistic data with U.S. census data. We directly assign languages a value of one if they incorporate sex-based distinctions in the grammatical structure of their language. This could arise in the form of distinctions between male and female pronouns or grammatical genders for example (Corbett 2013). We assign this indicator variable to each language spoken among a sample of migrants to the U.S. We draw this sample from each decennial census from 1910 to the present, and augment this sample with the ACS for the period 2000-2014 (Ruggles et al. 2015). In order to fo- cus solely on women who are most likely to engage with the labormarket, we restrict the analysis to migrants aged 16 to 65. Moreover, we focus on in- dividuals engaged in the formal labormarket, so we exclude those working in farming occupations. We also exclude migrants who report speaking Eng- lish in the home, since these individuals are likely to have a far different set of occupational choices, and we cannot precisely specify their mother tongue 4 . For the pooled sample, this yields roughly five million individuals
JEPE, 3(4), S. Chemsripong, p.767-781. Since, Thailand has joined ASEAN Economic Community (AEC) in 2015 lead to free labor movement in 5 subjects. Apart from barriers to professional and skilled worker, fewer move than anticipated due to language and training difference and lack of MRAs. To sum up three characteristic of Thai labormarket has been register: First, the labor movement to high skills labor in 7 subjects including foreign labor, business and investment from foreign investors. Most of them are multinational corporation in Thailand which important to economic development and labor employment in Thailand. Second, unemployment of labor supply in Thailand register as a few numbers, only one percent, this means labor supply in Thailand will use. Thailand does not have any excess supply of labor lead cause a shortage of workers in the manufacturing sector. The labor movement to semi-low skills according to the open market exchange negotiations between the benefits a chance to increase the workforce of Thailand can still go to work abroad. Third, manufacturing sector indicated that capacity technology. There are still remains. It cannot find workers to work with the machine. The problem of labor shortage in the manufacturing sector, it will be more. It can be seen from the industries of Thailand today with the use of foreign labor in almost every industry. The use of foreign labor is not cheap, but because there is not enough. Therefore, Thailand labor policy, it's a very important labor. The strategic must be positioned to supply enough workers to accommodate up to attendance and AEC requires cooperation from all sectors including want strict immigration story of narrowing the illegal eavesdropping and the secret to working without permission. Potential effects of further labormarket of Thailand from opening the market to move people between countries.
I analyze the evolution of the U.S. labormarket from 2002 to 2014. The Great Reces- sion’s employment declines fell disproportionately on groups with low levels of observ- able skills. Compositional changes lead averages to obscure downward movement in real wages over this time period. Traditional measures of wage inequality similarly tend to understate the relative decline in low-skilled individuals’ labormarket opportunities. To understand the low-skilledlabor market’s deterioration, I construct wage and em- ployment counterfactuals that capture the distinctive predictions of leading institutions- and markets-centric viewpoints. Institutions-centric counterfactuals, which emphasize weaknesses in workers’ bargaining positions, predict that this period’s minimum wage increases would have significantly increased the number of low-skilled individuals with wage rates near or below the minimum wage. The data are inconsistent with this pre- diction. By contrast, counterfactuals that emphasize the effects of trade, technology, and other competitive market forces are able to match long-run employment changes. My framework highlights that the minimum wage’s effects evolve with labormarket condi- tions. In addition to their relatively direct effects, labor replacing developments in trade and technology exacerbate the minimum wage’s effects on employment. Importantly, this observation holds whether labor markets are competitive or subject to significant bargaining frictions at baseline.
low-skilled, low-paid individuals’ (Nannestad, 2004). Low-paid jobs typically do not pay wages sufficient to cover monthly expenses, therefore workers in these jobs may decide to live on costs of the welfare state instead of working. The immigrant population suffering from underrepresentation in employment and overrepresentation in low-paid jobs is therefore vulnerable to live on welfare state benefits. Additionally living on benefits weakens ‘immigrant’s incentive to invest in acquiring the necessary preconditions for labormarket participation’ (Nannestad, 2004), such as language skills, which then results in a low integration process. ALMPs, if carried out effectively, are able to obviate and solve these problems. Time limits of recipiency and benefit reductions in case of non- satisfying job search behavior of immigrants, encourages people already living on benefit to keep on searching for jobs. Support in terms of placement services, job searching programs, counselling or trainings however try to integrate immigrants into the labormarket once they entered the labormarket and prevent them from claiming welfare benefits.
In macroeconomic models dynamic e¤ects of shocks are simulated by imposing the ad hoc assump- tion of homoscedasticity of the underlying stochastic processes. However, recent research has shown that various macroceconomic time series exhibit a strong time-varying variance with clustering of periods with high and low volatility. Sims and Zha (2006) use a structural vector autoregression model allowing for Markov regime switching and show that the best …t is obtained with a model that features time-varying variances of structural disturbances. More recently, Fernandez-Villaverde and Rubio-Ramirez (2010) estimate the stochastic volatility present in aggregate time series and the estimation by Justiniano and Primiceri (2008) show a strong stochastic volatility of shocks in the United States that vary considerably across types of shocks.
Identifying the causal impact of occupational recognition is not straightforward due to self-selection on the part of the immigrants. Presumably, those immigrants who obtain occupational recognition would also perform comparatively well in the labormarket if they had not received it, even conditional on other observable characteristics. This is because having obtained recognition reflects a specific set of skills that is likely to be generally valued in the labormarket, both in the regulated and unregulated segment. In addition, immigrants who decide to go through the costly application process are likely to differ from those who do not in terms of unobservable characteristics such as ambition and motivation, factors that on their own would be associated with better labormarket outcomes. We deal with these issues by exploiting a novel German data set that links detailed survey information on the exact timing of the application process for recognition with comprehensive social security data on the respondents’ entire work histories in Germany. Taking advantage of the longitudinal dimension of our data, we estimate both static and dynamic difference-in-differences specifications, comparing the labormarket outcomes of immigrants who obtain full recognition to those of immigrants who either never apply or have not yet received full recognition themselves. While the estimates from the static models allow us to assess the average effects of occupational recognition on labormarket outcomes in our sample, the estimates from the dynamic specifications provide information on the precise evolution of the employment and wage effects over time.