| Literature DB >> 35627663 |
Yongqiang Chu1,2, Shuguang Shen3.
Abstract
(1) Background: The housing environment is crucial to the health of older Chinese people and is becoming an urgent policy initiative. This study explores factors that facilitate or impede the adoption of policy innovation on major housing adaptation (HA) by Chinese provincial governments using the framework of policy innovation and diffusion theory. (2)Entities:
Keywords: aging in place; elevator retrofit; housing adaptation; piecewise constant exponential (PCE) model; policy innovation
Mesh:
Year: 2022 PMID: 35627663 PMCID: PMC9141314 DOI: 10.3390/ijerph19106124
Source DB: PubMed Journal: Int J Environ Res Public Health ISSN: 1660-4601 Impact factor: 4.614
Figure 1The adoption of the EMDER policy by Chinese provincial governments (2008–2019).
Measurements of Variables.
| Variables | Description of Measurements | Data Source |
|---|---|---|
| Policy adoption | Whether a provincial government adopts the EMDER policy in the current year (1) or not (0). | Collected by author |
| Issue salience | The proportion of households living in medium high-rise dwellings within the total number of households of a province in the year 2000. | Population Census of China 2010 |
| Fiscal dependency | The fiscal dependency of province | China Statistical Yearbook (2008–2019) |
| Central policy signal | Before (0) or during and after the year 2016 (1), when the 24 central ministries jointly promulgated national guidance to promote age-friendly cities and communities. | Collected by author |
| City adoption | Cumulative percentage of cities that had adopted the EMDER policy of a province in the previous year. | Author’s calculation |
| Neighboring adoption | Cumulative percentage of provinces that had adopted the EMDER policy of China in the previous year. | Author’s calculation |
| Population aging | The proportion of the population aged 65 and over to the total population of a province in the previous year | China Statistical Yearbook (2008–2019) |
| Consumption level | Consumption expenditure of urban residents of a province in the previous year. |
Descriptive statistics of variables.
| Variables | 2008–2019 | 2008–2016 | 2017–2019 | ||||||
|---|---|---|---|---|---|---|---|---|---|
| N | Mean | SD | N | Mean | SD | N | Mean | SD | |
| Policy adoption | 280 | 16.00 | 5.71 | 225 | 6.00 | 2.67 | 55 | 10.00 | 18.18 |
| Issue salience | 280 | 0.05 | 0.04 | 225 | 0.05 | 0.04 | 55 | 0.04 | 0.04 |
| Fiscal dependency | 280 | 0.56 | 0.17 | 225 | 0.55 | 0.17 | 55 | 0.59 | 0.15 |
| Neighboring adoption | 280 | 0.15 | 0.12 | 225 | 0.10 | 0.06 | 55 | 0.35 | 0.10 |
| Central policy signal | 280 | 55.00 | 19.64 | 225 | 0.00 | 0.00 | 55 | 55.00 | 100.00 |
| City adoption | 280 | 0.02 | 0.08 | 225 | 0.00 | 0.02 | 55 | 0.09 | 0.16 |
| Population aging | 280 | 0.09 | 0.02 | 225 | 0.09 | 0.02 | 55 | 0.10 | 0.02 |
| Consumption level | 280 | 18,039.61 | 6818.05 | 225 | 16,087.87 | 5358.19 | 55 | 26,024.04 | 6346.57 |
Event history analysis (EHA) models for EMDER policy adoption by provincial governments in China.
| Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | |
|---|---|---|---|---|---|
| Central policy signal | 3.23 (none) | 2.37 (none) | 3.16 (none) | 3.37 (none) | |
| City adoption | −1.56 (2.70) | −1.56(2.70) * | |||
| Neighboring adoption | 2.09 (none) | 1.96 (none) | |||
| Issue salience | 9.29 (5.86) | 9.29 (5.86) | 9.09 (5.76) | 9.29 (5.86) | 9.09 (5.76) |
| Financial dependency | −1.20 (2.89) | −1.20 (2.89) | −1.23 (2.87) | −1.2 (2.89) | −1.23 (2.87) |
| Population aging | 9.60 (17.81) | 9.60 (17.81) | 11.08 (17.60) | 9.60 (17.81) | 11.08 (17.60) |
| Consumption level | −0.00004 (0.00007) | 0.00004 (0.00007) | 0.−00002 (0.00008) | −0.00004 (0.00007) | −0.00002 (0.00008) |
| N | 280 | 280 | 280 | 280 | 280 |
| Log-likelihood | −45.84 | −45.84 | −45.66 | −45.84 | −45.66 |
| LR chi-squared | 4.65 | 4.65 | 5.00 | 4.65 | 5.00 |
The convention is *** p < 0.001, ** p < 0.05, and * p < 0.10.
Piecewise constant exponential models for EMDER policy adoption by provincial governments in China.
| Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | |
|---|---|---|---|---|---|
| Central policy signal | 4.11 (1.16) *** | 2.08 (1.25) | 2.36 (1.61) | 0.22 (1.31) | |
| City adoption | −7.84 (2.96) ** | −8.26 (2.62) ** | |||
| Neighboring adoption | 6.37 (3.32) * | 7.86 (2.92) * | |||
| Issue salience | 11.69 (5.78) * | 9.50 (5.90) | 7.61 (5.80) | 11.15 (5.87) | 8.31 (5.69) |
| Financial dependency | 12.38 (2.28) *** | 6.20 (2.69) * | 7.47 (2.50) ** | 5.33 (2.77) * | 5.58 (2.49) * |
| Population aging | 53.91 (26.27) * | 25.98 (20.98) | 42.65 (21.68) * | 21.55 (18.93) | 44.32 (18.51) * |
| Consumption level | 0.0003 (0.00004) *** | 0.0002 (0.00005) ** | 0.0003 (0.00006) *** | 0.0001 (0.00005) * | 0.00002 (0.00005) *** |
| _cons | −24.52 (3.04) *** | −17.06 (3.03) *** | −20.53 (3.07) *** | −15.94 (3.01) *** | −19.21 (2.75) *** |
| N | 280 | 280 | 280 | 280 | 280 |
| Log-likelihood | 35.46 | 41.36 | 45.33 | 43.26 | 48.85 |
| LR chi-squared | 119.68 | 131.49 | 139.42 | 135.28 | 146.46 |
The convention is *** p < 0.001, ** p < 0.05, and * p < 0.10.