Research on the Influence of Economic Development Quality on Regional Employment Quality: Evidence from the Provincial Panel Data in China
Abstract
:1. Introduction
2. Theoretical Framework and Research Hypotheses
2.1. Economic Development and Employment Quality
2.2. Regional Heterogeneity
2.3. Economic Growth
2.4. Sharing Economy
2.5. Economic Structure
3. Research Design
3.1. Comprehensive Evaluation Model Settings
3.2. Panel Regression Model Settings
3.3. Variable Selection
3.3.1. Explained Variable
3.3.2. Explanatory Variables
3.3.3. Control Variables
3.4. Data Sources
4. Empirical Results and Discussions
4.1. Employment Quality Index
4.2. Economic Development Quality Index
4.3. Descriptive Statistics
4.4. Multicollinearity Test Results
4.5. Analysis of the Regression Results
4.5.1. Unit-Root Test and Cointegration Test
4.5.2. Analysis of the Model Regression Results
4.5.3. Influence of Different Dimensions on Employment Quality
4.5.4. Regional Heterogeneity
4.5.5. Robustness
- (1)
- Replace control variables
- (2)
- Change the regression model
- (3)
- Delete outlier data
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Target Layer | System Layer | Indicator Layer | Unit | Attribute |
---|---|---|---|---|
Employment Quality | Labor Wages | Average employee salary (WAGE) | Yuan | + |
Wage growth rate (DWAGE) | % | + | ||
Social Security | Social insurance ratio (INSURANCE) | % | + | |
Ratio of the minimum wage to the average wage (MINWAGE) | % | + | ||
Employment Structure | The proportion of the urban employed population (TOWN) | % | + | |
The proportion of the employed population in the tertiary industry (THIRD-INDUSTRY) | % | + | ||
The manufacturing employment rate (MANUFACTURE) | % | + | ||
Work Availability | Labor participation rate (LABOR) | % | + | |
Urban registered unemployment rate (UNEMP) | % | − |
Target Layer | System Layer | Indicator Layer | Unit | Attribute |
---|---|---|---|---|
Economic Development Quality | Economic Growth | Gross regional product (GDP) | 108 Yuan | + |
GDP per capita (RGDP) | Yuan/Person | + | ||
Sharing Economy | Per capita disposable income (PCDI) | Yuan | + | |
Consumer expenditure per capita (CPP) | Yuan | + | ||
Economic Structure | Share of primary industry in GDP (FGDP) | % | − | |
Share of the tertiary industry in GDP (SGDP) | % | + | ||
Share of fiscal expenditure in GDP (MGDP) | % | + |
Type | Variables | Specific Explanation |
---|---|---|
Explained Variable | Employment Quality Index (EQI) | It is measured by the comprehensive calculation of the above evaluation index system of employment quality. |
Explanatory Variables | Economic Development Quality Index (EDQI) | It is measured according to the comprehensive calculation of the above economic development quality index system. |
Control Variables | Consumer Price Index (CPI) | It is measured by the natural logarithm of the consumer price index. |
Urban–rural Gap (URG) | It is measured by the natural logarithm of the proportion of the urban population to the rural population. | |
Education Expenditure (EE) | It is measured by the natural logarithm of the proportion of education expenditure in the total fiscal expenditure. |
System Layer | Weight | Indicator Layer | Entropy Value | Weight |
---|---|---|---|---|
Labor Wages | 0.20 | WAGE | 0.96 | 0.16 |
DWAGE | 0.99 | 0.04 | ||
Social Security | 0.24 | INSURANCE | 0.95 | 0.19 |
MINWAGE | 0.99 | 0.05 | ||
Employment Structure | 0.42 | TOWN | 0.95 | 0.17 |
THIRD-INDUSTRY | 0.98 | 0.08 | ||
MANUFACTURE | 0.95 | 0.17 | ||
Work Availability | 0.14 | LABOR | 0.98 | 0.07 |
UNEMP | 0.98 | 0.07 |
Regions | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 |
---|---|---|---|---|---|---|---|---|---|---|
Beijing | 60.79 | 62.77 | 66.79 | 67.86 | 69.63 | 62.38 | 72.89 | 75.65 | 77.94 | 81.05 |
Tianjin | 39.47 | 45.69 | 48.71 | 47.53 | 48.19 | 39.13 | 48.24 | 47.93 | 47.95 | 50.10 |
Hebei | 20.06 | 21.70 | 23.13 | 23.67 | 25.06 | 15.35 | 25.23 | 25.08 | 25.65 | 26.76 |
Shanxi | 26.06 | 27.35 | 29.41 | 29.62 | 30.07 | 20.99 | 29.33 | 30.66 | 31.33 | 32.46 |
Nei Mongol | 23.32 | 24.28 | 25.18 | 26.77 | 28.42 | 19.30 | 29.29 | 29.68 | 30.11 | 31.27 |
Liaoning | 31.53 | 33.86 | 34.71 | 36.75 | 37.44 | 27.64 | 35.64 | 35.85 | 36.34 | 37.29 |
Jilin | 25.70 | 27.79 | 28.28 | 31.45 | 32.27 | 23.84 | 33.20 | 34.32 | 34.07 | 34.48 |
Heilongjiang | 26.15 | 26.93 | 27.73 | 27.75 | 28.64 | 19.49 | 28.89 | 29.67 | 30.06 | 30.74 |
Shanghai | 51.09 | 57.12 | 61.22 | 60.30 | 62.08 | 53.78 | 65.34 | 66.76 | 68.27 | 70.87 |
Jiangsu | 29.89 | 31.73 | 33.10 | 42.65 | 45.28 | 35.25 | 45.22 | 46.17 | 46.71 | 48.14 |
Zhejiang | 39.35 | 41.95 | 44.03 | 45.10 | 46.44 | 35.01 | 46.15 | 47.69 | 48.24 | 50.23 |
Anhui | 19.78 | 22.47 | 23.40 | 24.50 | 25.50 | 13.43 | 26.50 | 28.47 | 30.74 | 31.33 |
Fujian | 33.75 | 37.14 | 38.63 | 37.63 | 38.41 | 26.41 | 39.68 | 40.47 | 41.25 | 40.73 |
Jiangxi | 20.28 | 21.92 | 23.55 | 24.80 | 27.44 | 17.85 | 28.94 | 28.91 | 28.60 | 29.84 |
Shandong | 26.20 | 28.43 | 29.95 | 32.19 | 33.27 | 21.88 | 33.27 | 34.01 | 34.41 | 35.48 |
Henan | 19.74 | 21.95 | 23.13 | 26.43 | 28.33 | 15.90 | 29.74 | 30.84 | 29.56 | 29.79 |
Hubei | 21.87 | 24.00 | 24.94 | 26.97 | 29.38 | 18.95 | 30.68 | 31.52 | 31.53 | 32.48 |
Hunan | 21.55 | 22.86 | 23.37 | 23.49 | 24.80 | 14.55 | 24.79 | 25.74 | 26.63 | 28.47 |
Guangdong | 33.72 | 36.29 | 37.62 | 48.64 | 50.71 | 41.61 | 51.06 | 51.52 | 52.44 | 53.65 |
Guangxi | 18.02 | 19.76 | 20.55 | 21.79 | 22.76 | 13.91 | 24.48 | 25.31 | 26.00 | 27.07 |
Hainan | 26.32 | 27.72 | 28.69 | 31.19 | 31.56 | 22.18 | 31.94 | 33.04 | 34.88 | 37.06 |
Chongqing | 23.99 | 28.00 | 29.83 | 31.90 | 34.16 | 24.11 | 35.01 | 35.97 | 37.31 | 38.34 |
Sichuan | 21.37 | 23.35 | 24.22 | 27.38 | 27.57 | 17.02 | 27.67 | 28.94 | 31.21 | 32.71 |
Guizhou | 17.38 | 19.79 | 21.08 | 22.10 | 23.18 | 13.88 | 24.84 | 25.89 | 27.14 | 28.42 |
Yunnan | 19.42 | 20.42 | 21.35 | 22.85 | 23.71 | 13.59 | 25.61 | 26.55 | 27.14 | 28.15 |
Xizang | 20.82 | 22.38 | 24.85 | 27.50 | 29.10 | 26.04 | 32.69 | 36.01 | 37.15 | 39.78 |
Shaanxi | 22.64 | 24.43 | 24.24 | 27.04 | 28.34 | 23.19 | 30.88 | 32.38 | 32.34 | 34.03 |
Gansu | 18.90 | 19.82 | 21.25 | 22.83 | 24.29 | 14.97 | 24.89 | 26.55 | 27.57 | 28.21 |
Qinghai | 23.00 | 24.91 | 26.21 | 27.37 | 28.46 | 19.17 | 29.57 | 31.17 | 32.03 | 33.44 |
Ningxia | 23.89 | 25.78 | 27.22 | 28.16 | 29.67 | 20.63 | 30.93 | 31.80 | 32.94 | 34.45 |
Xinjiang | 30.07 | 32.24 | 32.84 | 33.44 | 34.45 | 25.40 | 34.80 | 35.63 | 36.52 | 38.34 |
Year | Mean | Median | SD | Min | Max |
---|---|---|---|---|---|
2019 | 37.91 | 34 | 12.45 | 26.8 | 81.1 |
2018 | 36.57 | 32.3 | 12.01 | 25.6 | 77.9 |
2017 | 35.81 | 31.8 | 11.78 | 25.1 | 75.6 |
2016 | 34.75 | 30.9 | 11.6 | 24.5 | 72.9 |
2015 | 34.41 | 30.2 | 11.71 | 13.4 | 62.4 |
2014 | 33.83 | 29.4 | 11.34 | 22.8 | 69.6 |
2013 | 32.51 | 27.8 | 11.21 | 21.8 | 67.9 |
2012 | 30.62 | 27.2 | 11.18 | 20.6 | 66.8 |
2011 | 29.18 | 25.8 | 10.46 | 19.8 | 62.8 |
2010 | 26.97 | 23.9 | 9.751 | 17.4 | 60.8 |
System Layer | Weight | Indicator Layer | Weight | Entropy Value |
---|---|---|---|---|
Economic Growth | 0.25 | DGDP | 0.06 | 0.94 |
RGDP | 0.20 | 0.96 | ||
Sharing Economy | 0.40 | PCDI | 0.21 | 0.96 |
CPP | 0.19 | 0.96 | ||
Economic Structure | 0.35 | FGDP | 0.06 | 0.99 |
SGDP | 0.14 | 0.97 | ||
MGDP | 0.15 | 0.97 |
Regions | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 |
---|---|---|---|---|---|---|---|---|---|---|
Beijing | 45.18 | 48.24 | 49.41 | 52.98 | 54.46 | 59.21 | 63.03 | 67.00 | 71.27 | 73.07 |
Tianjin | 33.97 | 37.20 | 36.77 | 39.26 | 40.70 | 41.73 | 46.97 | 48.32 | 48.97 | 51.45 |
Hebei | 19.25 | 21.75 | 18.68 | 18.88 | 19.70 | 22.95 | 25.87 | 27.72 | 29.72 | 32.27 |
Shanxi | 24.52 | 24.95 | 20.88 | 20.87 | 21.48 | 23.97 | 26.26 | 35.36 | 32.64 | 32.64 |
Nei Mongol | 27.48 | 30.80 | 30.81 | 31.49 | 32.07 | 34.17 | 35.62 | 37.18 | 38.74 | 39.45 |
Liaoning | 21.45 | 27.94 | 26.95 | 28.93 | 28.51 | 27.27 | 29.72 | 33.41 | 35.86 | 36.23 |
Jilin | 26.49 | 29.77 | 27.57 | 27.83 | 27.78 | 27.49 | 31.8 | 33.40 | 33.73 | 35.32 |
Heilongjiang | 22.47 | 26.15 | 23.80 | 23.32 | 22.51 | 23.83 | 28.31 | 31.35 | 32.77 | 34.85 |
Shanghai | 42.23 | 44.79 | 45.07 | 49.26 | 52.53 | 56.18 | 62.63 | 65.76 | 69.92 | 71.12 |
Jiangsu | 26.58 | 29.01 | 27.86 | 30.44 | 32.02 | 34.84 | 36.45 | 39.89 | 41.48 | 42.98 |
Zhejiang | 29.31 | 30.46 | 28.75 | 31.43 | 32.95 | 37.09 | 39.19 | 42.84 | 46.14 | 48.51 |
Anhui | 22.52 | 24.81 | 22.32 | 23.33 | 23.48 | 24.30 | 27.99 | 31.01 | 33.52 | 33.24 |
Fujian | 23.78 | 25.73 | 24.95 | 26.51 | 27.91 | 29.11 | 32.37 | 36.56 | 38.66 | 39.21 |
Jiangxi | 22.25 | 24.69 | 21.10 | 23.25 | 24.09 | 25.55 | 27.92 | 29.96 | 33.33 | 34.02 |
Shandong | 18.75 | 21.24 | 21.16 | 23.23 | 23.71 | 26.71 | 27.44 | 29.68 | 31.22 | 33.36 |
Henan | 17.21 | 18.61 | 17.97 | 19.18 | 20.62 | 21.56 | 23.72 | 26.62 | 29.01 | 29.22 |
Hubei | 22.44 | 24.41 | 21.98 | 23.89 | 25.40 | 26.76 | 29.33 | 31.82 | 34.78 | 35.10 |
Hunan | 22.66 | 24.44 | 22.24 | 23.70 | 24.67 | 26.92 | 28.19 | 30.97 | 32.20 | 34.81 |
Guangdong | 24.93 | 26.95 | 25.32 | 28.39 | 29.99 | 34.15 | 36.61 | 39.74 | 40.42 | 42.99 |
Guangxi | 22.39 | 24.15 | 21.74 | 23.06 | 23.68 | 25.90 | 27.28 | 29.61 | 31.07 | 31.84 |
Hainan | 28.83 | 30.37 | 28.82 | 30.11 | 30.86 | 31.78 | 33.94 | 35.15 | 38.22 | 40.13 |
Chongqing | 26.29 | 32.29 | 28.91 | 28.35 | 29.49 | 30.65 | 33.20 | 34.68 | 34.65 | 37.40 |
Sichuan | 24.03 | 25.00 | 23.00 | 23.59 | 24.38 | 24.66 | 28.20 | 32.16 | 33.73 | 33.15 |
Guizhou | 26.64 | 33.00 | 32.42 | 31.91 | 31.34 | 31.65 | 31.09 | 33.48 | 33.54 | 34.72 |
Yunnan | 24.29 | 28.79 | 27.71 | 28.5 | 26.93 | 27.07 | 29.42 | 32.87 | 33.81 | 34.54 |
Xizang | 14.66 | 24.81 | 26.08 | 25.11 | 26.50 | 29.57 | 32.55 | 30.05 | 32.32 | 32.49 |
Shanxi | 24.94 | 27.32 | 25.26 | 24.96 | 24.96 | 24.44 | 26.78 | 31.07 | 32.26 | 32.45 |
Gansu | 28.01 | 30.24 | 27.46 | 28.73 | 28.86 | 29.87 | 33.55 | 34.88 | 38.95 | 38.42 |
Qinghai | 44.86 | 47.75 | 47.65 | 47.25 | 47.29 | 50.48 | 49.83 | 46.92 | 48.37 | 49.24 |
Ningxia | 32.26 | 33.78 | 31.44 | 31.90 | 32.19 | 34.27 | 37.78 | 41.20 | 39.46 | 38.92 |
Xinjiang | 28.73 | 30.22 | 29.23 | 31.18 | 30.78 | 30.35 | 33.81 | 40.37 | 40.57 | 38.18 |
Year | Mean | Median | SD | Min | Max |
---|---|---|---|---|---|
2019 | 39.40 | 35.32 | 10.04 | 29.22 | 73.07 |
2018 | 38.43 | 34.65 | 9.81 | 29.01 | 71.27 |
2017 | 36.81 | 33.48 | 9.32 | 26.62 | 67.00 |
2016 | 34.09 | 31.80 | 9.88 | 23.72 | 63.03 |
2015 | 31.43 | 29.11 | 9.06 | 21.56 | 59.21 |
2014 | 29.74 | 27.91 | 8.35 | 19.70 | 54.46 |
2013 | 29.06 | 35.21 | 8.06 | 18.88 | 52.98 |
2012 | 27.85 | 26.95 | 7.62 | 17.97 | 49.41 |
2011 | 29.34 | 27.94 | 6.94 | 18.61 | 48.24 |
2010 | 26.43 | 24.94 | 7.03 | 14.66 | 45.18 |
Variables | N | Mean | Median | SD | Min | Max |
---|---|---|---|---|---|---|
EQI | 310 | 32.26 | 29.39 | 11.96 | 13.43 | 81.05 |
EDQI | 310 | 32.26 | 30.41 | 9.700 | 14.66 | 73.06 |
CPI | 310 | 4.631 | 4.629 | 0.0120 | 4.611 | 4.666 |
URG | 310 | 0.313 | 0.224 | 0.643 | −1.228 | 2.152 |
EE | 310 | −2.933 | −3.002 | 0.349 | −3.521 | −1.693 |
Variables | EQI | EDQI | CPI | URG | EE |
---|---|---|---|---|---|
EQI | 1 | ||||
EDQI | 0.772 *** | 1 | |||
CPI | −0.0620 | −0.096 * | 1 | ||
URG | 0.847 *** | 0.715 *** | −0.138 ** | 1 | |
EE | −0.347 *** | −0.000 | −0.00900 | −0.541 *** | 1 |
Variables | VIF | 1/VIF |
---|---|---|
GUR | 5.210 | 0.192 |
Economy | 3.620 | 0.276 |
Edu | 2.530 | 0.396 |
CPI | 1.040 | 0.963 |
VIF mean | 3.100 |
Statistic | EDQI | CPI | GUR | Edu |
---|---|---|---|---|
LLC | −10.3253 *** | −12.9083 *** | −15.4224 *** | −6.1905 *** |
p-Value | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
Smoothness | Data smooth | Data smooth | Data smooth | Data smooth |
Cointegration Test | Specific Test | Statistic | p-Value |
---|---|---|---|
Pedroni | Modified Phillips-Perron t | 8.2808 | 0.0000 |
Phillips-Perron t | −23.1290 | 0.0000 | |
Augmented Dickey–Fuller t | −14.7554 | 0.0000 | |
Westerlund | Variance ratio | 2.0940 | 0.0181 |
Variables | Model (1) | Model (2) | Model (3) | Model (4) | Model (5) | Model (6) | Model (7) |
---|---|---|---|---|---|---|---|
EQI | 0.616 *** (0.041) | 0.610 *** (0.043) | 0.435 *** (0.057) | 0.635 *** (0.043) | 0.416 *** (0.057) | 0.444 *** (0.056) | 0.427 *** (0.057) |
CPI | −8.279 (18.289) | 41.549 ** (20.356) | 37.760 * (20.226) | ||||
URG | 7.283 *** (1.651) | 9.237 *** (1.901) | 8.514 *** (1.701) | 10.224 *** (1.925) | |||
EE | −3.295 (2.672) | −6.985 *** (2.667) | −6.610 ** (2.663) | ||||
_cons | 12.388 *** (1.331) | 50.918 (85.126) | 15.938 *** (1.519) | 2.122 (8.431) | −176.472 * (94.282) | −5.224 (8.219) | −178.953 * (93.418) |
N | 310.000 | 310.000 | 310.000 | 310.000 | 310.000 | 310.000 | 310.000 |
R2 | 0.451 | 0.451 | 0.487 | 0.454 | 0.494 | 0.499 | 0.505 |
R2_a | 0.389 | 0.388 | 0.427 | 0.391 | 0.434 | 0.439 | 0.444 |
Variables | Model (1) | Model (2) | Model (3) | Model (4) |
---|---|---|---|---|
Economic Growth | 0.316 *** | 0.243 *** | ||
(0.035) | (0.081) | |||
Sharing Economy | 0.214 *** | 0.078 | ||
(0.027) | (0.066) | |||
Economic Structure | 0.079 | −0.138 * | ||
(0.074) | (0.079) | |||
CPI | 32.718 * | 81.590 *** | 67.240 *** | 38.961 * |
(19.473) | (19.830) | (22.221) | (22.555) | |
URG | 8.177 *** | 7.290 *** | 18.497 *** | 8.564 *** |
(1.884) | (2.107) | (1.996) | (2.090) | |
EE | −0.594 | −4.781 * | −6.419 ** | 0.670 |
(2.604) | (2.623) | (3.137) | (2.884) | |
_cons | −133.753 | −368.798 *** | −306.268 *** | −154.799 |
(90.369) | (91.921) | (104.114) | (105.450) | |
N | 310.000 | 310.000 | 310.000 | 310.000 |
R2 | 0.542 | 0.518 | 0.407 | 0.548 |
R2_a | 0.486 | 0.458 | 0.334 | 0.488 |
Variables | Central Region | Western Region | Eastern Region |
---|---|---|---|
EDQI | 0.244 (0.207) | 0.237 * (0.136) | 0.536 *** (0.082) |
CPI | 55.388 (43.297) | 66.120 ** (30.840) | 25.337 (38.547) |
URG | 13.369 ** (6.098) | 16.601 *** (3.118) | 1.900 (4.220) |
EE | −10.491 * (5.365) | −8.389 ** (3.580) | 1.431 (5.118) |
_cons | −269.799 (196.42) | −307.909 ** (139.862) | −91.787 (181.268) |
N | 80.000 | 120.000 | 110.000 |
R2 | 0.453 | 0.599 | 0.501 |
R2_a | 0.365 | 0.541 | 0.427 |
Variables | Model (1) | Model (2) | Model (3) | Model (4) | Model (5) | Model (6) | Model (7) |
---|---|---|---|---|---|---|---|
EDQI | 0.606 *** (0.041) | 0.605 *** (0.044) | 0.467 *** (0.059) | 0.629 *** (0.044) | 0.453 *** (0.059) | 0.475 *** (0.058) | 0.463 *** (0.063) |
CPI | −1.481 (19.277) | 35.916 * (21.368) | 52.743 * (28.092) | ||||
URG | 5.811 *** (1.776) | 7.415 *** (2.011) | 7.060 *** (1.823) | 10.452 *** (1.147) | |||
EE | −4.391 (2.833) | −7.347 ** (2.860) | −1.482 (1.471) | ||||
_cons | 13.179 *** (1.343) | 20.068 (89.693) | 15.397 *** (1.482) | −0.720 (9.067) | −151.085 (99.060) | −7.383 (8.990) | −234.543 * (129.50) |
N | 310.000 | 310.000 | 310.000 | 310.000 | 310.000 | 310.000 | 310.000 |
R2 | 0.457 | 0.457 | 0.480 | 0.463 | 0.486 | 0.493 | 0.778 |
R2_a | 0.397 | 0.394 | 0.419 | 0.400 | 0.424 | 0.432 | 0.775 |
Variables | Model (1) | Model (2) | Model (3) | Model (4) | Model (5) | Model (6) | Model (7) |
---|---|---|---|---|---|---|---|
EDQI | 0.952 *** (0.045) | 0.953 *** (0.045) | 0.421 *** (0.048) | 0.952 *** (0.037) | 0.421 *** (0.048) | 0.474 *** (0.063) | 0.462 *** (0.059) |
CPI | 13.151 (37.196) | 56.563 ** (27.836) | 31.328 (21.244) | ||||
URG | 11.205 *** (0.721) | 11.351 *** (0.721) | 10.093 *** (1.135) | 8.396 *** (2.031) | |||
EE | −11.907 *** (1.040) | −1.855 (1.463) | −6.972 ** (2.865) | ||||
_cons | 1.551 (1.503) | −59.401 (172.398) | 15.165 *** (1.427) | −33.375 *** (3.302) | −246.811 * (128.932) | 8.373 (5.544) | −151.438 (98.096) |
N | 310.000 | 310.000 | 310.000 | 310.000 | 310.000 | 310.000 | 280.000 |
R2 | 0.596 | 0.597 | 0.774 | 0.717 | 0.777 | 0.775 | 0.498 |
R2_a | 0.595 | 0.594 | 0.773 | 0.715 | 0.775 | 0.773 | 0.435 |
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Wang, Q.; Shao, J. Research on the Influence of Economic Development Quality on Regional Employment Quality: Evidence from the Provincial Panel Data in China. Sustainability 2022, 14, 10760. https://doi.org/10.3390/su141710760
Wang Q, Shao J. Research on the Influence of Economic Development Quality on Regional Employment Quality: Evidence from the Provincial Panel Data in China. Sustainability. 2022; 14(17):10760. https://doi.org/10.3390/su141710760
Chicago/Turabian StyleWang, Qiong, and Jiahui Shao. 2022. "Research on the Influence of Economic Development Quality on Regional Employment Quality: Evidence from the Provincial Panel Data in China" Sustainability 14, no. 17: 10760. https://doi.org/10.3390/su141710760