Evaluation of Policies on Inappropriate Treatment of Dead Hogs from the Perspective of Loss Aversion
Abstract
:1. Introduction
2. Literature Review
3. Theoretical and Research Framework
3.1. Theoretical Framework
3.2. Research Framework
4. Data Source and Sample Characteristics
4.1. Data Source
4.2. Variable Description and Sample Characteristics
4.3. Ordered Probit Model
5. Experimental Design
6. Empirical Analysis
6.1. Current Status of Farmers’ Evaluation of Punishment Policies for the Inappropriate Treatment of Dead Hogs
6.2. Results and Discussion
6.3. Policy Recommendations
7. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Variable | Variable Assignment | Frequency (Mean) | Frequency (Standard Deviation) | |
---|---|---|---|---|
Individual Characteristics (IC) | Sex | Female = 0 | 120 | 29.7% |
Male = 1 | 284 | 70.3% | ||
Age | Years | (49.33) | (8.68) | |
Education | Schooling years | (7.34) | (4.39) | |
Marital Status | Unmarried = 0 | 16 | 4% | |
Married = 1 | 388 | 96% | ||
Family Characteristics (FC) | Income | 0%–20% = 1 | 73 | 18.1% |
21%–40% = 2 | 110 | 27.2% | ||
41%–60% = 3 | 86 | 21.3% | ||
61%–80% = 4 | 72 | 17.8% | ||
81%–100% = 5 | 63 | 15.6% | ||
Labor | 0%–20% = 1 | 102 | 25.2% | |
21%–40% = 2 | 154 | 38.1% | ||
41%–60% = 3 | 98 | 24.3% | ||
61%–80% = 4 | 44 | 10.9% | ||
81%–100% = 5 | 6 | 1.5% | ||
Breeding Characteristics (BC) | Experience | Numerical Value | (12.72) | (9.22) |
Scale | 0–51 = 1 | 216 | 53.5% | |
51–500 = 2 | 169 | 41.8% | ||
501–3000 = 3 | 14 | 3.5% | ||
3001–10,000 = 4 | 4 | 1.0% | ||
10,001 and above = 5 | 1 | 0.2% | ||
Dummy Variable (DV) | Reg | Cities below the second tier = 0 | 343 | 84.9% |
Second-tier cities and above = 1 | 61 | 15.1% | ||
External Environment (EE) | Disposal | No = 0 | 181 | 44.8% |
Yes = 1 | 223 | 55.2% | ||
Cooperative | No = 0 | 278 | 68.8% | |
Yes = 1 | 126 | 31.2% |
Question Number | The First Case | The Second Case |
---|---|---|
1 | 50% may be found and a fine 1000 yuan, and 50% may not be found | 100% found and a fine of 500 yuan |
Question Number | The First Case | The Second Case |
---|---|---|
1 | 100% found and a fine of 50 yuan | 50% may be found and a fine of 500 yuan |
2 | 100% found and a fine of 100 yuan | 50% may be found and a fine of 500 yuan |
3 | 100% found and a fine of 150 yuan | 50% may be found and a fine of 500 yuan |
4 | 100% found and a fine of 200 yuan | 50% may be found and a fine of 500 yuan |
5 | 100% found and a fine of 250 yuan | 50% may be found and a fine of 500 yuan |
6 | 100% found and a fine of 300 yuan | 50% may be found and a fine of 500 yuan |
7 | 100% found and a fine of 350 yuan | 50% may be found and a fine of 500 yuan |
8 | 100% found and a fine of 400 yuan | 50% may be found and a fine of 500 yuan |
9 | 100% found and a fine of 450 yuan | 50% may be found and a fine of 500 yuan |
10 | 100% found and a fine of 500 yuan | 50% may be found and a fine of 500 yuan |
Question Number | The First Case | The Second Case |
---|---|---|
1 | 100% found and detention for 1 h | 50% may be found and detention for 10 h |
2 | 100% found and detention for 2 h | 50% may be found and detention for 10 h |
3 | 100% found and detention for 3 h | 50% may be found and detention for 10 h |
4 | 100% found and detention for 4 h | 50% may be found and detention for 10 h |
5 | 100% found and detention for 5 h | 50% may be found and detention for 10 h |
6 | 100% found and detention for 6 h | 50% may be found and detention for 10 h |
7 | 100% found and detention for 7 h | 50% may be found and detention for 10 h |
8 | 100% found and detention for 8 h | 50% may be found and detention for 10 h |
9 | 100% found and detention for 9 h | 50% may be found and detention for 10 h |
10 | 100% found and detention for 10 h | 50% may be found and detention for 10 h |
Variable | Variable Assignment | Frequency | Rate of Recurrence | |
---|---|---|---|---|
Policy evaluation | How necessary do you think the punishment policy is for the inappropriate treatment of dead hogs? (PN) | Totally unnecessary = 1 | 12 | 3.00% |
Unnecessary = 2 | 31 | 7.70% | ||
General = 3 | 73 | 18.10% | ||
Necessary = 4 | 144 | 35.60% | ||
Very necessary = 5 | 144 | 35.60% | ||
How well do you think the local government is implementing the punishment policy for the inappropriate treatment of dead hogs? (PI) | Not executed at all = 1 | 27 | 6.70% | |
Basically not Executed = 2 | 72 | 17.80% | ||
General = 3 | 108 | 26.70% | ||
Mostly executed = 4 | 109 | 27.00% | ||
Fully executed = 5 | 88 | 21.80% | ||
How effective do you think the current policy is for the inappropriate treatment of dead hogs? (PE) | Totally ineffective = 1 | 21 | 5.20% | |
No effect = 2 | 38 | 9.40% | ||
General = 3 | 103 | 25.50% | ||
Mostly effective = 4 | 139 | 34.40% | ||
Very effective = 5 | 103 | 25.50% |
Variable | Model I | |||||
---|---|---|---|---|---|---|
Coefficients (DV = PN) | Margin (1) | Margin (2) | Margin (3) | Margin (4) | Margin (5) | |
Sex | −0.010 (0.118) | 0.002 | 0.003 | 0.001 | −0.002 | −0.004 |
Age | 0.005 (0.007) | −0.001 | −0.001 | −0.000 | −0.000 | 0.001 |
Education | 0.016 (0.276) | 0.001 | 0.001 | –0.000 | –0.001 | –0.002 |
Marital Status | 0.015 (0.019) | –0.002 | –0.002 | –0.000 | 0.001 | 0.003 |
Income | 0.156 *** (0.054) | –0.024 | –0.028 | –0.006 | 0.017 | 0.041 |
Labor | 0.048 (0.062) | –0.006 | –0.007 | –0.001 | 0.004 | 0.010 |
Experience | –0.016 ** (0.007) | 0.002 | 0.003 | 0.001 | –0.002 | –0.004 |
Scale | 0.204 * (0.106) | –0.037 | –0.042 | –0.009 | 0.026 | 0.062 |
Reg | 0.197 (0.173) | –0.026 | –0.030 | –0.007 | 0.019 | 0.044 |
Disposal | 0.391 *** (0.117) | –0.060 | –0.069 | –0.015 | 0.043 | 0.101 |
Cooperative | 0.241 ** (0.116) | –0.036 | –0.041 | –0.009 | 0.025 | 0.060 |
LA1 | 0.001 (0.021) | –0.009 | –0.010 | –0.002 | 0.006 | 0.015 |
LA2 | ||||||
Observations | 404 | |||||
Log likelihood | –203.751 | |||||
Prob > chi2 | 0.000 | |||||
Pseudo R2 | 0.159 |
Variable | Model II | |||||
---|---|---|---|---|---|---|
Coefficients (DV = PN) | Margin (1) | Margin (2) | Margin (3) | Margin (4) | Margin (5) | |
Sex | –0.015 (0.118) | 0.002 | 0.003 | 0.001 | –0.002 | –0.004 |
Age | 0.004 (0.007) | –0.001 | –0.001 | 0.000 | 0.000 | 0.001 |
Education | 0.008 (0.275) | 0.001 | 0.001 | 0.000 | –0.001 | –0.002 |
Marital Status | 0.012 (0.019) | –0.002 | –0.002 | 0.000 | 0.001 | 0.003 |
Income | 0.164 *** (0.053) | –0.024 | –0.028 | –0.006 | 0.017 | 0.041 |
Labor | 0.039 (0.062) | –0.006 | –0.007 | –0.001 | 0.004 | 0.010 |
Experience | –0.016 ** (0.007) | 0.002 | 0.003 | 0.001 | –0.002 | –0.004 |
Scale | 0.251 ** (0.107) | –0.037 | –0.042 | –0.009 | 0.026 | 0.062 |
Reg | 0.177 (0.173) | –0.026 | –0.030 | –0.007 | 0.019 | 0.044 |
Disposal | 0.408 *** (0.117) | –0.060 | –0.069 | –0.015 | 0.043 | 0.101 |
Cooperative | 0.244 ** (0.116) | –0.036 | –0.041 | –0.009 | 0.025 | 0.060 |
LA1 | ||||||
LA2 | 0.060 *** (0.023) | –0.009 | –0.010 | –0.002 | 0.006 | 0.015 |
Observations | 404 | |||||
Log likelihood | –203.982 | |||||
Prob > chi2 | 0.000 | |||||
Pseudo R2 | 0.158 |
Variable | Model III | |||||
---|---|---|---|---|---|---|
Coefficients (DV = PI) | Margin (1) | Margin (2) | Margin (3) | Margin (4) | Margin (5) | |
Sex | –0.161 (0.119) | 0.039 | 0.013 | –0.011 | –0.023 | –0.018 |
Age | 0.011 (0.007) | –0.003 | –0.001 | 0.001 | 0.002 | 0.001 |
Education | 0.005 (0.019) | –0.021 | –0.007 | 0.006 | 0.012 | 0.010 |
Marital Status | 0.086 (0.288) | –0.001 | 0.000 | 0.000 | 0.001 | 0.001 |
Income | 0.050 (0.054) | –0.012 | –0.004 | 0.004 | 0.007 | 0.006 |
Labor | 0.255 *** (0.064) | –0.062 | –0.021 | 0.018 | 0.037 | 0.029 |
Experience | –0.018 *** (0.007) | 0.004 | 0.002 | –0.001 | –0.003 | –0.002 |
Scale | 0.296 *** (0.108) | –0.072 | –0.025 | 0.021 | 0.042 | 0.034 |
Reg | 0.237 (0.173) | –0.058 | –0.020 | 0.017 | 0.034 | 0.027 |
Disposal | 0.738 *** (0.118) | –0.180 | –0.062 | 0.052 | 0.106 | 0.084 |
Cooperative | 0.119 (0.118) | –0.029 | –0.010 | 0.008 | 0.017 | 0.014 |
LA1 | 0.044 ** (0.021) | –0.011 | –0.004 | 0.003 | 0.006 | 0.005 |
LA2 | ||||||
Observations | 404 | |||||
Log likelihood | –550.397 | |||||
Prob > chi2 | 0.000 | |||||
Pseudo R2 | 0.107 |
Variable | Model IV | |||||
---|---|---|---|---|---|---|
Coefficients (DV = PI) | Margin (1) | Margin (2) | Margin (3) | Margin (4) | Margin (5) | |
Sex | –0.163 (0.119) | 0.040 | 0.014 | –0.012 | –0.024 | –0.019 |
Age | 0.010 (0.007) | –0.002 | –0.001 | 0.001 | 0.001 | 0.001 |
Education | 0.004 (0.019) | –0.006 | –0.002 | 0.002 | 0.004 | 0.003 |
Marital Status | 0.025 (0.286) | –0.001 | 0.000 | 0.000 | 0.001 | 0.000 |
Income | 0.026 (0.053) | –0.006 | –0.002 | 0.002 | 0.004 | 0.003 |
Labor | 0.256 *** (0.064) | –0.063 | –0.022 | 0.018 | 0.037 | 0.029 |
Experience | –0.019 *** (0.007) | 0.005 | 0.002 | –0.001 | –0.003 | –0.002 |
Scale | 0.333 *** (0.109) | –0.081 | –0.029 | 0.024 | 0.048 | 0.038 |
Reg | 0.213 (0.172) | –0.052 | –0.018 | 0.015 | 0.031 | 0.024 |
Disposal | 0.761 *** (0.118) | –0.186 | –0.065 | 0.054 | 0.110 | 0.087 |
Cooperative | 0.127 (0.118) | –0.031 | –0.011 | 0.009 | 0.018 | 0.015 |
LA1 | ||||||
LA2 | 0.016 (0.023) | –0.004 | –0.001 | 0.001 | 0.002 | 0.002 |
Observations | 404 | |||||
Log likelihood | –552.298 | |||||
Prob > chi2 | 0.000 | |||||
Pseudo R2 | 0.104 |
Variable | Model V | |||||
---|---|---|---|---|---|---|
Coefficients (DV = PE) | Margin (1) | Margin (2) | Margin (3) | Margin (4) | Margin (5) | |
Sex | –0.096 (0.119) | 0.026 | 0.006 | –0.013 | –0.010 | –0.009 |
Age | 0.006 (0.007) | –0.002 | 0.000 | 0.001 | 0.001 | 0.001 |
Education | 0.022 (0.019) | 0.034 | 0.008 | –0.017 | –0.013 | –0.012 |
Marital Status | –0.125 (0.282) | –0.006 | –0.001 | 0.003 | 0.002 | 0.002 |
Income | 0.002 (0.054) | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
Labor | 0.253 *** (0.064) | –0.068 | –0.016 | 0.034 | 0.027 | 0.024 |
Experience | –0.009 (0.007) | 0.002 | 0.001 | –0.001 | –0.001 | –0.001 |
Scale | 0.559 *** (0.112) | –0.151 | –0.035 | 0.075 | 0.059 | 0.052 |
Reg | 0.169 (0.174) | –0.046 | –0.011 | 0.023 | 0.018 | 0.016 |
Disposal | 0.289 ** (0.118) | –0.078 | –0.018 | 0.039 | 0.031 | 0.027 |
Cooperative | 0.259 ** (0.119) | –0.070 | –0.016 | 0.035 | 0.027 | 0.024 |
LA1 | 0.022 (0.022) | –0.006 | –0.001 | 0.003 | 0.002 | 0.002 |
LA2 | ||||||
Observations | 404 | |||||
Log likelihood | –527.046 | |||||
Prob > chi2 | 0.000 | |||||
Pseudo R2 | 0.094 |
Variable | Model VI | |||||
---|---|---|---|---|---|---|
Coefficients (DV = PE) | Margin (1) | Margin (2) | Margin (3) | Margin (4) | Margin (5) | |
Sex | –0.091 (0.120) | 0.024 | 0.006 | –0.012 | –0.009 | –0.008 |
Age | 0.006 (0.007) | –0.002 | 0.000 | 0.001 | 0.001 | 0.001 |
Education | 0.024 (0.019) | 0.037 | 0.009 | −0.018 | −0.014 | −0.013 |
Marital Status | −0.137 (0.282) | −0.006 | −0.001 | 0.003 | 0.002 | 0.002 |
Income | −0.017 (0.053) | 0.004 | 0.001 | −0.002 | −0.002 | −0.002 |
Labor | 0.263 *** (0.064) | −0.071 | −0.017 | 0.035 | 0.028 | 0.025 |
Experience | −0.010 (0.007) | 0.003 | 0.001 | −0.001 | −0.001 | −0.001 |
Scale | 0.549 *** (0.113) | −0.147 | −0.035 | 0.073 | 0.057 | 0.051 |
Reg | 0.179 (0.174) | −0.048 | −0.011 | 0.024 | 0.019 | 0.017 |
Disposal | 0.292 ** (0.118) | −0.078 | −0.018 | 0.039 | 0.031 | 0.027 |
Cooperative | 0. 264 ** (0.119) | −0.071 | −0.017 | 0.035 | 0.028 | 0.025 |
LA1 | ||||||
LA2 | −0.042 * (0.023) | 0.011 | 0.003 | −0.006 | −0.004 | −0.004 |
Observations | 404 | |||||
Log likelihood | −525.860 | |||||
Prob > chi2 | 0.000 | |||||
Pseudo R2 | 0.096 |
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Yang, C.; Wang, J. Evaluation of Policies on Inappropriate Treatment of Dead Hogs from the Perspective of Loss Aversion. Int. J. Environ. Res. Public Health 2019, 16, 2938. https://doi.org/10.3390/ijerph16162938
Yang C, Wang J. Evaluation of Policies on Inappropriate Treatment of Dead Hogs from the Perspective of Loss Aversion. International Journal of Environmental Research and Public Health. 2019; 16(16):2938. https://doi.org/10.3390/ijerph16162938
Chicago/Turabian StyleYang, Chenchen, and Jianhua Wang. 2019. "Evaluation of Policies on Inappropriate Treatment of Dead Hogs from the Perspective of Loss Aversion" International Journal of Environmental Research and Public Health 16, no. 16: 2938. https://doi.org/10.3390/ijerph16162938