Despite the large number of possible definitions for the marginally attached, we focus on those individuals who were considered economically in active in the reference week may or may not have searching for employment or even worked in the reference period of 1 year , but had the desire for work. Individuals classified as N are those who do not look for a job neither want to work, i.
Labor market dynamicsis represented by a 4 x 4 transition matrix P, where P ij is the probability of an individual being in state j in the subsequent period given that she is currently in state i , i. This matrix can be represented by:. Longitudinal data is required to calculate the transition rates.
According to these authors, by conveniently controlling the characteristics of individuals, if the transition rate from state x to z is identical to the transition rate from state y to z, the origin state x or y should be considered irrelevant interms of determination of the transition rate of individuals to state z.
Based on this formulation, the necessary and sufficient condition for the marginally attached and the non-attached individuals to have the same behavior is that the transition probability from M to E equals to that from N to E and that the transition probability from M to U equals to that from N to U, i.
Under these circumstances, the four-state Markov model becomes a threestate E, U and O model, The desire to work does not differentiate individuals as the job search criterion does. Alternatively, the conventional job search requirement for characterizing unemployment is likely to be quite restrictive, and those individuals regarded as marginally attached may have a similar behavior than those unemployed, i.
In this case, unemployment assessment should be based on the desire to work and not on job search only. The desire to work itself can distinguish unemployed from those inactive, and the job search criteria would not yield any additional information. Here, the four-state model for the labor market turns out to be the most appropriate one.
Labor Market Outcomes and Reforms in China
Marginally attached individuals should not be included in the non-participating or unemployed groups, for their behavior differs from the latter. These individuals should be placed in a new category. Therefore, it would be rational for statistical agencies to regularly provide statistics with such category. The empirical analysis developed herein consists in testing the restrictions identified above for Brazil, thus building a more realistic depiction of labor market dynamics in this country.
The analysis is based on likelihood ratio tests. The probabilities above are calculated unconditionally and conditional on observable characteristics, because observable characteristics such as age, gender and schooling levels should not be independent of the labor market states. The conditional probabilities are calculated using a multinomial logit model.
Although statistically different from other states, the transition rate of the marginally inactive to employment was similar to the one from unemployment, and clearly higher than those non-participating. These results support the adoption of one more labor market state, given the high probability of future participation in the labor market of individuals who want to work although they do not look for a job. In addition, the transition NE non-participation to employment is close to 3.
The analysis starts with the estimation of mean transition rates between the four labor market states E, U, M, N and the behavior of transition probabilities throughout the analyzed period. The PME data is collected through interviews with all household dwellers aged 10 years or older. The sample of household units is distributed according to the four reference weeks of the month. Monthly results are obtained by the average of these four reference weeks. The data collection follows a method in which each selected household unit is surveyed during four consecutive months, ignored for eight months and then surveyed again for four months, and finally eliminated from the sample.
If during the period 16 months in which the household unit remains in the sample, the family moves away and another family moves in, the information will be obtained from the new family during the remaining period.
Long-run Trends in China’s Urban Unemployment and Labor Force Participation
The PME is subdivided into eight rotation groups. In addition to measuring employment, unemployment and non-participation, the survey allows identifying marginally attached individuals. This is done through a combination of several questions from the questionnaire survey to identify if the individual considered inactive in the reference week would get a job and be available for this. The study was conducted for individuals classified as household head and the transition rates calculated month-to-month from January to December Altogether 1,, observations were selected, which represents million individuals in and million in The short time span for dynamics is not restrictive, as turn over rates in Brazil are quite high Gonzaga Longer spans four or twelve months would reduce and likely bias the sample significantly, given the high attrition rates.
The composition of the working-age household heads in metropolitan areas by labor market states considering the period average is It is interesting to underscore, however, that the marginally attached account for approximately 5.
Table 1 shows the mean transition rate estimates for the period. We observe that the strongest labor market state persistence rate is found for employment EE , followed by out-of-the-labor-market activity NN. The persistence of the marginally attached MM is the lowest. Note that this group is more likely to move to any labor market state, a markedly different pattern from others, perhaps except for the unemployed, who are also more likely to leave the state UE, UN, UM than to stay in it UU.
The above table obscures the dynamics over the cycle. GDP growth rates varied over the period 1. The following graphs show the quarterly mean transition rates for through for the selected sample of household heads into employment, unemployment and inactivity. Interestingly, the transition rates are relatively stable over time. With regard to exitinto employment Figure 2 , the UE and ME rates are close to each other, but the former is slightly higher than the latter.
Figure 3 shows the transitions into unemployment. As seen in Table 1 , the persistence in unemployment is at a much higher level than the other transitions into unemployment.
The mobility into unemployment of the marginally attached is more volatile, while the other form of non-participation presents itself on a stable and low level in any period. As shown in Figure 4 , most individuals who were previously non-participating stayed as such NN. The proportion of people who came from unemployment is relatively small and constant.willboofoldest.tk
How is technological advancement changing the labor market?
Conversely, the transition MN has a greater variability, increasingat the end of the period. This analysis can also be made for the interviewed population, split according togender.
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One of the reasons is certainly the restriction in the sample, which takes only the household heads into account. Female household heads have participation rates closer to male household heads. Looking at the migration into employment, the high volatility of ME canbe seen for both sexes, as well as its proximity to EU. Men, however, have mobility rates higher than women, regardless of the state of origin.
Persistence into unemployment is more volatile, where the rates for women are higher than those for men. This also occurs on UN and MN transitions. Tables 2 and 3 show the mean transition matrices according to gender. Note that the exit from unemployment differ between men and women: men tend to move into employment whereas women are more likely todrop out of the labor force or become marginally attached.
To illustrate the dynamic properties of the transition matrix, we estimated the limiting distribution of labor market states, implied by the transition matrix above. The Markov assumption implies that:. Table 4 shows the average sample distribution and the limiting distribution for the whole population and for men and women. First, it ispossible to observe that the initial and extreme-value distributions are quite similar, indicating small variability of the transition rates over time, as shown in the graphs above. Secondly, note the low importance of the "marginally attached" M state, as it has the smallest rate in the population.
The authors indicate two equivalence conditionsfor the marginally attached group to be considered similar, from a behavioral stand point, with the unemployed group. The evidence is clear that technological change has reduced the need for routine mechanized work and increased both the demand and pay for high-skilled technical and analytic work. The impact of automation and artificial intelligence is an acceleration of a trend decades in the making.
Switchboard operators have recently been replaced by phone and interactive voice response menus, and many grocery store clerks have been replaced with self-checkout machines. With advances in AI, reports claim that truck drivers , paralegals , and even surgeons might see their occupations upended by changing technology. In this environment, tech jobs could seem like the only occupations with guaranteed job growth. Although there is a growing need for developers and data scientists, jobs in personal care and the medical industry are expanding even faster. The need for more home health aides—as well as growth in other health-related occupations—is driven largely by the aging baby boomer population entering retirement and by technological advances that increase the effectiveness of care.
Research has shown that the need for basic data processing skills and manual labor will decline over the next decade, while cognitive, social, and emotional skills will be more in demand. These skills—such as solving complex problems, working in teams, giving advice, and demonstrating leadership—facilitate more human interaction.
An important difference between the jobs being lost and the ones being gained is the difference in pay. The tech-related jobs replacing those positions are much higher paying, while such jobs as home health aide and personal care aide pay less. Many workers in the occupations that are losing jobs do not have the skills to easily move into those higher-paying jobs.
The difference between declining jobs and growing ones also affects racial or ethnic and gender equity. Women make up nearly 90 percent of word processors and typists—the job that will see the biggest decline over the next decade. When disability is accounted for, the effects of age and TTD diminish. Not many articles validate their approach by comparing properties of different estimation models.
In order to evaluate popular models used in the literature and to gain an understanding of the divergent results of previous studies, an empirical analysis based on a claims data set from Germany is conducted. This analysis generates a number of useful insights.
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The results indicate that the choice of estimation method makes little difference and if they differ, ordinary least squares regression tends to perform better than the alternatives. When validating the methods out of sample and out of period, there is no evidence that including TTD leads to better predictions of aggregate future HCE. It appears that the literature might benefit from focusing on the predictive power of the estimators instead of their actual fit to the data within the sample. Two interrelated advances in genetics have occurred which have ushered in the growing field of genoeconomics.
The first is a rapid expansion of so-called big data featuring genetic information collected from large population—based samples. The second is enhancements to computational and predictive power to aggregate small genetic effects across the genome into single summary measures called polygenic scores PGSs. Together, these advances will be incorporated broadly with economic research, with strong possibilities for new insights and methodological techniques.
The health of an individual is intertwined with practically every economic decision including education, marriage, fertility, labor market, and investments.
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These outcomes in turn affect income and wealth and hence have implications for intergenerational transfer of economic advantage or disadvantage. A rich body of theoretical and empirical work considers the role of the family in health production over the life cycle and the role of health in household economic decisions. This literature starts by considering family inputs regarding health at birth, then moves through adolescence and midlife, where relationship decisions affect health. After midlife, health, particularly the health of family members, becomes an input into retirement and investment decisions.
There is a rich economics literature on the direct benefits of caregiving, including allowing the care recipient to remain at home for longer than if there was no informal care provided. There is also a growing literature outlining the associated costs of care provision. Although informal care helps individuals with disabilities to remain at home and is rewarding to many carers, there are often negative effects such as depression and lost labor market earnings that may offset some of these rewards.
Economists have taken several approaches to quantify the net societal benefit of informal care that consider the degree of choice in caregiving decisions and all direct and indirect benefits and costs of informal care. In the past century, many developing countries have experienced rapid economic development, which is usually associated with a process of structural transformation and urbanization. At the early stage of economic development, an economy usually relies on labor-intensive industries for growth.
Rural—urban migrants thus provide the necessary labor force to urban production.
Les marchés urbains du travail en Afrique subsaharienne
Since they are more productive in industrial sectors than in agricultural sectors, aggregate output increases and economic growth accelerates. In addition, abundant migrants affect the rates of return to capital by changing the capital—labor ratio. They also change the skill composition of the urban labor force and hence the relative wage of skilled to unskilled workers. Therefore, rural—urban migration has wide impacts on growth and income distribution of the macroeconomy. What are the forces that drive rural—urban migration?
It is well understood that cities attract rural migrants because of better job opportunities, better career prospects, and higher wages. Moreover, enjoying better social benefits such as better medical care in cities is another pull factor that initiates rural—urban migration. Finally, agricultural land scarcity in the countryside plays an important role on the push side for moving labor to cities.
The aforementioned driving forces of rural—urban migration are work-based. However, rural—urban migration could be education-based, which is rarely discussed in the literature. In the past decade, it has been proposed that cities are the places for accumulating human capital in work.
It is also well established that most of the high-quality education institutions including universities and specialized schools for art and music are located in urban areas. A youth may first move to the city to attend college and then stay there for work after graduation.
From this point of view, work-based migration does not paint the whole picture of rural—urban migration. In this article, we propose a balanced view that both the work-based and education-based channels are important to rural—urban migration. The migration story could be misleading if any of them is ignored. Criminal law consists of substantive and procedural parts. Substantive law is the set of rules defining conduct that violates the law. Procedural criminal law is the set of rules regulating the process of punishment.
Substantive rules apply mostly to individual actors, and procedural rules apply to public enforcement agencies and adjudicators. Economic theory of criminal law consists of normative and positive parts. Normative economic theory, which began with writings by Beccaria and Bentham, aims to recommend an ideal criminal punishment scheme.
Positive economic theory, which appeared later in writings by Holmes and Posner, aims to justify and to better understand the criminal law rules that exist. Since the purpose of criminal law is to deter socially undesirable conduct, economic theory, which emphasizes incentives, would appear to be an important perspective from which to examine criminal law.