The key to advancing urbanization is to promote the urban integration of numerous migrant workers. Two stages of decision making are involved for migrant workers, including residence (staying in cities) and settlement (transferring hukou into cities). The homestead is a necessity for migrant workers to keep their “peasant” status, which will further affect migrant workers’ identification with cities and influence their decision making towards urbanization. This paper uses data from the 2017 China Migrants Dynamic Survey (CMDS), through the coarsened exact matching (CEM) method and the analysis of mediation effects, to estimate how homesteads influence migrant workers’ urbanization intention and how the sense of identity serves as a mediator variable in this mechanism. Empirical results show that the ownership of homesteads is negatively correlated with migrant workers’ urbanization intention. Migrant workers with homesteads are 1.2% less likely to stay and 4.4% less likely to settle down in cities compared with their counterparts who do not have a homestead. In addition, identity plays a mediating role in the influence mechanism of homesteads on migrant workers’ urbanization intention. That is, the homestead has an indirect effect on migrant workers’ willingness to stay and settle down in cites through the sense of identity, aside from its direct effects. The mediation effect accounts for 20.87% of the total effect for willingness to stay and 25.63% of the total effect for willingness to settle down. This paper also represents how these coefficients vary by different regions and migration distances. Therefore, policymakers should provide institutional support for correctly guiding migrant workers to “abandon their land and enter the city” and strengthen their sense of identity to the city.

Although ordinal health outcome values are categories like "poor" health or "moderate" depression, they are often assigned values 1,2,3,… for convenience. We provide results on interpretation of subsequent analysis based on ordinary least squares (OLS) regression. For description, unlike for prediction, the OLS estimand's interpretation does not require that the 1,2,3,… are cardinal values: it is always the "best linear approximation" of a summary of the conditional survival functions. Further, for Blinder-Oaxaca-type decomposition, the OLS-based estimator is numerically equivalent to a certain counterfactual-based decomposition of the survival function, again regardless of any cardinal values. Empirically, with 2022 U.S. data for working-age adults, we estimate a higher incidence of depression in the rural population, and we decompose the rural-urban difference. Including a nonparametric estimator that we describe, estimators agree that 33%-39% of the rural-urban difference is statistically explained by income, education, age, sex, and geographic region. The OLS-based detailed decomposition shows this is mostly from income.

Rolling-window methods are widely used to explore time variation in economic relationships. Testing across overlapping windows induces a multiple-testing problem and can inflate the familywise error rate. Existing corrections are often either overly conservative or computationally intensive. We propose a harmonic mean p-value procedure that provides a global test together with window-level diagnostics. We establish asymptotic familywise error rate control under conditions that allow dependence induced by overlapping windows. Simulations using several standard time-series tests show accurate size, higher power, and substantially lower computational cost than bootstrap-based extreme-statistic procedures.

China’s Environmental Protection Tax (EPT), introduced in 2018, provides a useful setting for examining whether pollution-oriented regulation can also deliver carbon-mitigation benefits and whether such benefits depend on prior low-carbon policy exposure. Using panel data for 285 Chinese prefecture-level cities from 2002 to 2022, this study first estimates a difference-in-differences model exploiting cross-provincial variation in the post-2018 EPT shock and then applies a triple-difference framework to examine whether prior exposure to the Low-Carbon City (LCC) and Carbon Emissions Trading (CET) pilots was associated with stronger EPT effects. The results show that, on average, the EPT reduced PM2.5 concentration, carbon emissions, and carbon intensity by approximately 3.7%, 9.6%, and 10.8%, respectively, although the evidence is stronger for the two carbon-related outcomes than for PM2.5. The clearest and most stable heterogeneous evidence appears for carbon intensity, especially for the LCC-only group; the Dual group shows the largest point estimate, but its external validity is limited. Further analysis suggests that post-EPT changes in industrial structure upgrading and green invention patent grants were more visible in cities with prior low-carbon policy exposure. Overall, the findings indicate that prior low-carbon policy exposure was associated primarily with a stronger EPT effect on carbon intensity.