对我国粗放型劳动密集产业的理性思考

我国是世界上劳动力资源最丰富而劳动力成本又很低的国家,发展粗放型劳动密集产业符合我国的比较优势。但是,粗放型劳动密集产业又是建立在低附加值和国民的低福利的基础上,其潜在的市场竞争力又是很有限的．况且,以跨国公司为重要载体的市场国际化、经济全球化的时代,劳动力成本低廉对我国经济发展的特殊作用正在明显减弱．我国粗放型劳动密集产业正面临严峻挑战。     

农村劳动力就业空间行为研究——以江苏、山东、安徽三个样本村为例

中国农村劳动力就业行为的特殊性与复杂性引起国内外学者的广泛关注。农村劳动力流动特征、流动原因、产生的影响等是目前研究的传统思路。从行为地理学视角,以就业感知行为、就业决策行为、就业行为、就业体验行为为基本内容,建立了农村劳动力就业空间行为研究的分析框架。以江苏、山东、安徽的三个样本村为典型案例地,通过实地调研、问卷访谈和统计分析后,认为现阶段中国农村劳动力就业空间行为类型主要有本地农业就业型、本地非农就业型、外地流动就业型三种类型,分析了各就业类型的空间行为特征:农村劳动力在就业前对预期就业的环境感知都不强,感知强度与距离和生活环境关联密切;经济因素是影响农村劳动力就业的主要因素,但对不同空间类型劳动力的影响程度不同;我国农村劳动力就业普遍存在兼业现象,且从事的行业、部门也在不断变化;农村劳动力就业满意度以本地非农就业型为最高,其它两类满意度都较低。     

农村劳动力外出就业对个人、家庭及农村社会的影响探析

农村劳动力外出就业给农民个人、家庭和农村社区带来了一系列显性的和隐性的影响,这些影响对农民群体和整个社会都是不容忽视的。     

国外社会升级研究进展及对中国的启示

社会升级研究的兴起源于经济全球化带来的经济升级不能产生相应的社会升级,是对联合国2030可持续发展目标(SDG)之"体面劳动与经济增长"目标的响应.为弥补国内地理学偏重经济升级而忽视社会升级的研究不足,本文系统梳理了国际上社会升级研究的主要进展,对其概念、理论基础和主要研究议题开展了分析.认为当前社会升级研究方兴未艾,...     

青海农村剩余劳动力转移问题研究

文章通过对青海省农村剩余劳动力现状的分析,表明青海农村劳动力资源过剩严重,农村剩余劳动力行业和地域分布不合理,农村劳动力转移仍然处于无组织的自发状态,劳动力整体素质低下,同时,分析了青海农村剩余劳动力转移中存在的主要问题和难点,提出消除制度障碍,调整农业产业结构,大力发展农村第三产业,发挥乡镇企业吸纳农村剩余劳动力的功能,建立城乡统一的劳动力市场体系,大力推进农村城镇化进程,吸引农村剩余劳动力向小城镇聚集,加大教育投入,提高农村劳动者素质,搞好农民工技能鉴定工作等措施。     

Firm-level productivity growth returns of social capital: Evidence from Western Europe

We analyze the firm-level labor productivity growth returns of social capital—defined as a synthetic measure of “generalized trust,” “active participation,” and “social norms”—using a large sample of manufacturing firms in France, Germany, Italy, Portugal, and Spain. We find that firms' labor productivity growth is higher in areas with a better social capital endowment. The positive returns of social capital are, nevertheless, unevenly distributed across firms, with smaller, less productive, less capital-endowed, and low-tech firms benefitting the most from operating in strong social capital ecosystems.     

Social capital determinants and labor market networks

We explore the links between determinants of social capital and labor market networks at the neighborhood level. We harness rich data taken from multiple sources, including matched employer–employee data with which we measure the strength of labor market networks, data on neighborhood homogeneity that has previously been tied to social capital, and new data—not previously used in the study of social capital—on the number and location of nonprofit sector establishments at the neighborhood level. We use a machine learning algorithm to identify the potential determinants of social capital that best predict neighborhood‐level variation in labor market networks. We find evidence suggesting that smaller and less centralized schools, and schools with fewer poor students, foster social capital that builds local labor market networks, as does a larger Republican vote share. The presence of establishments in a number of nonprofit‐oriented industries are identified as predictive of strong labor market networks, likely because they either provide public goods or facilitate social contacts. These industries include, for example, churches and other religious institutions, fire and rescue services including volunteer fire departments, country clubs and golf courses, labor unions, chamber music groups, hobby clubs, and schools.     

The spatial dimension of internal labor markets

We integrate into a unified framework the spatial and the employment dimensions of worker mobility, tracing workers across firms, across establishments, and across regions. Drawing upon the spatial dimension of internal labor markets in firms with multiple establishments in multiple locations, our results indicate that the contemporaneous wage premium to migration is around 3 percentage points. For the case of job switchers, we find that the return to regional migration is due to access to better jobs at the destination. We also document the existence of an urban premium for same‐employer migrants but for employer changes this premium is driven by selection.     

Human capital sorting: The “when” and “who” of the sorting of educated workers to urban regions

The sorting of high‐ability workers is often advanced as one source of spatial disparities in economic outcomes. There are still few papers that analyze when human capital sorting occurs and whom it involves. Using data on 16 cohorts of university graduates in Sweden, we demonstrate significant sorting to urban regions on high school grades and education levels of parents, i.e., two attributes typically associated with latent abilities that are valued in the labor market. A large part of this sorting has already occurred in deciding where to study, because the top universities in Sweden are predominantly located in urban regions. The largest part of directed sorting on ability indicators occurs in the decision of where to study. Even after controlling for sorting prior to labor market entry, the “best and brightest” are still more likely to start working in urban regions. However, this effect appears to be driven by Sweden's main metropolitan region, Stockholm. We find no influence of our ability indicators on the probability of starting to work in urban regions after graduation when Stockholm is excluded. Studies of human capital sorting need to account for selection processes to and from universities, because neglecting mobility prior to labor market entry is likely to lead to an underestimation of the extent of the sorting to urban regions.     

Why do students leave school early in OECD countries? The role of regional labor markets and school policies

This paper examines the determinants of early school leaving (ESL) in a panel of 371 regions of OECD countries observed between 1998 and 2019. The empirical analysis includes both local factors previously emphasized by micro-economic studies and national-level factors such as education policies. We find that labor market opportunities for young people, as captured by the youth unemployment rate or the size of low-skill sectors, can pull students out of school. Conversely, late access to a large number of vocational education tracks, high preprimary enrollment and continuous training for teachers are strongly and negatively correlated with ESL rates.