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AbstractThis study documents the COVID-19 disease-control measures enacted in rural China and examines the economic and social impacts of these measures. We conducted two rounds of surveys with 726 randomly selected village informants across seven provinces. Strict disease-control measures have been universally enforced and appear to have been successful in limiting disease transmission in rural communities. The infection rate in our sample was 0.001 per cent, a rate that is near the national average outside of Hubei province. None of the villages reported any COVID-19-related deaths. For a full month during the quarantine, the rate of employment of rural workers was essentially zero. Even after the quarantine measures were lifted, nearly 70 per cent of the villagers still were unable to work owing to workplace closures. Although action has been taken to mitigate the potential negative effects, these disease-control measures might have accelerated the inequality between rural and urban households in China.

This study documents the COVID-19 disease-control measures enacted in rural China and examines the economic and social impacts of these measures. We conducted two rounds of surveys with 726 randomly selected village informants across seven provinces. Strict disease-control measures have been universally enforced and appear to have been successful in limiting disease transmission in rural communities. The infection rate in our sample was 0.001 per cent, a rate that is near the national average outside of Hubei province. None of the villages reported any COVID-19-related deaths. For a full month during the quarantine, the rate of employment of rural workers was essentially zero. Even after the quarantine measures were lifted, nearly 70 per cent of the villagers still were unable to work owing to workplace closures. Although action has been taken to mitigate the potential negative effects, these disease-control measures might have accelerated the inequality between rural and urban households in China. (China Q/GIGA)

AbstractThe Poisson ridge estimator (PRE) is a commonly used parameter estimation method to address multicollinearity in Poisson regression (PR). However, PRE shrinks the parameters toward zero, contradicting the real association. In such cases, PRE tends to become an insufficient solution for multicollinearity. In this work, we proposed a new estimator called the Poisson average maximum likelihood‐centered penalized estimator (PAMLPE), which shrinks the parameters toward the weighted average of the maximum likelihood estimators. We conducted a simulation study and case study to compare PAMLPE with existing estimators in terms of mean squared error (MSE) and predictive mean squared error (PMSE). These results suggest that PAMLPE can obtain smaller MSE and PMSE (i.e., more accurate estimates) than the Poisson ridge estimator, Poisson Liu estimator, and Poisson K‐L estimator when the true s have the same sign and small variation. Therefore, we recommend using PAMLPE to address multicollinearity in PR when the signs of the true s are known to be identical in advance.