Entrepreneurial effect of rural return migrants: Evidence from China

Entrepreneurship is an important means of economic development. Rural migrant workers returning home to start their own businesses can promote employment, alleviate poverty, and achieve rural development structural transformation of rural development. The entrepreneurial effect of rural return migrants is important for rural economic development. Using the data of the China Labor Force Dynamics Survey (CLDS thereafter) 2018 and China Household Finance Survey (CHFS thereafter) 2019, we analyze the entrepreneurial effects of return migrants upon their return to their hometowns. We construct a career choice model and build a mathematical model based on it to formulate the hypothesis. Then, we use the Probit regression model to test the hypothesis empirically. Results find that the rural return migrants can promote entrepreneurship among residents. Land circulation, human capital, and physical capital are stimulating factors in promoting the rural entrepreneurial activities of return migrants. We recommend that the government actively guide the rural return migrants to start businesses and provide security for entrepreneurial activities by upgrading various entrepreneurial elements.

Farming experience, personal characteristics, and entrepreneurial decisions of urban residents: Empirical evidence from China

Entrepreneurship is an important way to provide solutions for social employment problems. Using data from the 2016 China Labor Force Dynamic Survey (CLDS), we explore the influence of farming experience on urban residents' entrepreneurial decisions at the theoretical and empirical levels. A Probit model with instrumental variables method was used to analyze the influence of farming experience on urban residents' entrepreneurial decisions, while a mediating effect model was used to test its channels of action. The results show that: (1) farming experience can contribute to the entrepreneurial decision of urban residents relative to those without experience in farming. To overcome possible endogeneity issues, an Eprobit model based on the estimation of instrumental variables was used for testing. (2) Heterogeneity tests based on age, city type, and physical capital found that this effect was more significant in urban residents with non-capital cities, middle-aged groups, and high-material capital. (3) Farming experience indirectly drives entrepreneurial decisions through the mediating role of promoting positive personality traits, such as "optimism" and "mutual aid consciousness." Therefore, the farming experience has a positive effect on urban residents' entrepreneurial decisions and helps to understand the deeper influence of micro-individual characteristics on entrepreneurial decisions in the urbanization process.

Psychometric Properties of the Assessment of Recovery Capital (ARC) Instrument in a Diverse Low-Income Sample

Background: Recovery capital is a theoretical construct elucidating the resources that support recovery from addiction. The 50-item Assessment of Recovery Capital (ARC) instrument and related brief-format versions are the predominant measures of this construct. However, some of the ARC's psychometric properties are not well-established, particularly in racially and economically diverse populations. Objectives: We aimed to determine if the ARC is a valid and reliable measure of recovery capital in a diverse sample. Methods: Paper-and-pencil survey data were collected between March 2017 and May 2018 from a low-income, racially diverse sample of adults in recovery (N = 273). Participants were recruited from nontreatment community settings throughout a mid-sized northeastern U.S. city. They completed the ARC and sociodemographic questions. To determine the ARC's reliability and factor structure, we used item-level analyses and Cronbach's alpha, followed by confirmatory and exploratory factor analyses. Results: Several items performed poorly, having means close to response extremes and problematically small variances. Cronbach's alpha for the full measure was α = .92; however, alphas for the majority of subscales were below .70. The a priori 10-factor model solution failed, preventing interpretation of the confirmatory factor analysis results. Exploratory factor analysis revealed that although the 10-factor model marginally fit the data, items did not load together as proposed. Not once did all five subscale items load highly on the same factor. Conclusions/Importance: The ARC has substantial weaknesses in its theoretical alignment, item performance, and psychometric properties with diverse populations. We recommend the development of a new multidimensional, theory-aligned measure, following a rigorous measurement development protocol.