Welfare Participation and Time Use in China

Abstract

Using the newly available China Family Panel Studies (CFPS) 2010 survey data and a propensity score matching method with tobit regressions, this study examines the possible influence of participation in China's primary welfare program, Dibao, on household head's time use pattern in both urban and rural areas. We find that Dibao lowered recipients time spent on various leisure and social activities and increased their idle time for both urban and rural residents. Urban Dibao recipients also tended to spend less time on education activities, while rural Dibao recipients spent more time on personal care and household activities such as housekeeping and sleeping or resting. Urban Dibao recipients spent more time on work-related activities on typical non-work days, while rural Dibao recipients spent less time on work activities on typical work days. These findings provide pioneering evidence on the possible link between welfare participation and time use patterns in the global welfare literature. As Dibao is constantly expanding, the empirical evidence from this article suggests that Dibao might have some unintended adverse effects on recipients, including less time spent on leisure and social activities, reduced work and education activities or increased underground employment, and more time spent on being idle. Future reforms of Dibao and its supplementary policies and programs need to consider how best to promote both the economic and holistic well-being of the participants.

Not Only Health: Environmental Pollution Disasters and Political Trust

“…(Neimenggu, Xinjiang, Xizang, Hainan, Ningxia, and Qinghai province are not included in the survey objects of the CFPS.) CFPS has been widely applied in academic studies in both science and social science fields [43][44][45][46][47][48], which confirm the reliability of this survey database.…”

Financial Literacy, Portfolio Choice and Financial Well-Being

“…Designed by an interdisciplinary group of scholars, the survey collected a sample representative of the national population. The survey covers 1% of the nation's total population, selected by a stratified multi-stageclustering sampling method from the Population Census, with the strata set at the provincial level and the ultimate sampling unit set at the residential community level (Gao, Wu & Zhai 2014;Li & Wu 2014)…”

Rural minimum living standard guarantee (rural Dibao) program boosts children's education outcomes in rural China

“…Beyond the current income effect, however, researchers disagree on how the program affects household welfare more broadly. Gao, Wu, and Zhai (2015) claim that Rural Dibao has a negative impact on household welfare by showing that Rural Dibao participation leads to less time spent on work, leisure, and social activities and more time spent on being idle, resulting in reduced household welfare. Han, Gao, and Xu (2016) support this argument by showing that participation leads to less expenditure on social activities.…”

“…In other studies, for example, Golan et al (2017) and Westmore (2018), the authors have panel data but do not exploit the over-time characteristics of their samples to identify causality. The only three papers that claim to address the endogeneity problems that exist in studying the program's impacts on welfare all rely on propensity score matching with their cross-sectional data (Gao et al, 2015;Han et al, 2016;Wang et al, 2019).…”

“…Almost all of the previous studies produce empirical findings that are based on correlations and do not produce estimates of the true causal relationship between Rural Dibao participation and household welfare. For example, many previous studies (e.g., Gao et al , 2015; Han et al , 2016; Kakwani et al , 2019; Li and Sicular, 2014; Wang et al , 2019a, b; Zhao et al , 2017) have used only a single set of cross-sectional data in their analyses. In other studies, for example, Golan et al (2017) and Westmore (2018), the authors have panel data but do not exploit the over-time characteristics of their samples to identify causality.…”