Does Environmental Regulation Help Mitigate Factor Misallocation?—Theoretical Simulations Based on a Dynamic General Equilibrium Model and the Perspective of TFP
Abstract
:1. Introduction
2. Literature Review
2.1. The Sources of Factor Misallocation
2.2. The Effects of Environmental Regulation
2.3. The Brief Comments
3. The Model
3.1. Product Market Equilibrium
3.2. Factor Misallocation and TFP Losses Shocked by Environmental Regulation
4. Numerical Simulation
4.1. Basic Assumptions and Parameter Settings
- The elasticity of substitution between firm value-added is set to . Estimates of the substitutability among competing firms typically range from 3–10 in the vast literature on productivity studies [72,73]. Broda and Weinstein (2006) argued that lower elasticities for more differentiated goods, so we made this choice for conservatively [72]. Besides, we will also use a relatively moderate elasticity and a more extreme elasticity in simulations as robustness analysis. Table A1, Table A2, Table A3 and Table A4 in Appendix A show these results.
- As mentioned, we supposed that firm 1 in our model faced misallocation but not for firm 2. It is well known that misallocation leads to a loss of firm productivity, and thus we have good reasons to assume that the physical productivity of firm 1 is lower than firm 2. Based on this, we standardized the physical total factor productivity of the two firms as and .
- Environmental regulation is an external policy shock, thus, government tends to differentiate policies according to the wide heterogeneity of firms so that the extent of the shock may be different for different firms [76]. Firstly, we set a benchmark for the environmental regulatory shock—firm 1 and firm 2 face a unit shock at the same time, i.e., . Secondly, we distinguish two alternative options: (1) firm 1 is subjected to a weaker environmental regulatory shock, defined as half the intensity of the benchmark; (2) firm 1 is subjected to a stronger environmental regulatory shock, defined as two times the intensity of the benchmark. Finally, assume that the industry-level environmental regulatory shock () is the geometric mean at the firm level.
- Similar to the parameterization of environmental regulatory shock, we first consider a base case where firm1 and firm 2 have the same overhead labor share, set as . Next, let the overhead labor share of firm 1 be half of the base case as one simulation option, and let the overhead labor share of firm 2 be half of the base case as another simulation option. In addition, the industry-level overhead labor share is also assumed as the firm-level geometric mean.
4.2. The Impacts of on ATFP in the Absence/Presence of Environmental Regulatory Shock and Overhead Labor
4.3. The Impacts of on ATFP in the Absence/Presence of Environmental Regulatory Shock and Overhead Labor
5. Conclusions and Implications
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A. Numerical Simulation Results Using Other Alternative Elasticities of Substitution
0 | 0.1 | 0.2 | 0.3 | 0.4 | 0.5 | 0.6 | 0.7 | 0.8 | 0.9 | 1 | |
---|---|---|---|---|---|---|---|---|---|---|---|
Scenario 1. No and | |||||||||||
effective ATFP | 1.015 | 1.015 | 1.015 | 1.015 | 1.015 | 1.015 | 1.015 | 1.015 | 1.015 | 1.015 | 1.015 |
distorted ATFP | 1.015 | 1.014 | 1.011 | 1.008 | 1.005 | 1.003 | 1.001 | 1.000 | 1.000 | 1.000 | 1.000 |
ATFP Loss (%) | 0.000 | 0.116 | 0.384 | 0.701 | 0.997 | 1.231 | 1.389 | 1.478 | 1.517 | 1.526 | 1.527 |
Scenario 2. Keep and as the benchmark | |||||||||||
effective ATFP | 1.015 | 1.015 | 1.015 | 1.015 | 1.015 | 1.015 | 1.015 | 1.015 | 1.015 | 1.015 | 1.015 |
distorted ATFP | 1.015 | 1.014 | 1.011 | 1.008 | 1.005 | 1.003 | 1.001 | 1.000 | 1.000 | 1.000 | 1.000 |
ATFP Loss (%) | 0.000 | 0.116 | 0.384 | 0.701 | 0.997 | 1.231 | 1.389 | 1.478 | 1.517 | 1.526 | 1.527 |
Scenario 3. keeping as the benchmark | |||||||||||
effective ATFP | 0.841 | 0.841 | 0.841 | 0.841 | 0.841 | 0.841 | 0.841 | 0.841 | 0.841 | 0.841 | 0.841 |
distorted ATFP | 0.841 | 0.835 | 0.818 | 0.792 | 0.764 | 0.740 | 0.722 | 0.713 | 0.708 | 0.707 | 0.707 |
ATFP Loss (%) | 0.000 | 0.677 | 2.797 | 6.146 | 10.059 | 13.687 | 16.402 | 18.014 | 18.721 | 18.907 | 18.921 |
Scenario 4. keeping as the benchmark | |||||||||||
effective ATFP | 1.416 | 1.416 | 1.416 | 1.416 | 1.416 | 1.416 | 1.416 | 1.416 | 1.416 | 1.416 | 1.416 |
distorted ATFP | 1.416 | 1.415 | 1.415 | 1.415 | 1.415 | 1.414 | 1.414 | 1.414 | 1.414 | 1.414 | 1.414 |
ATFP Loss (%) | 0.000 | 0.008 | 0.026 | 0.046 | 0.065 | 0.079 | 0.089 | 0.095 | 0.097 | 0.097 | 0.098 |
Scenario 5. keeping as the benchmark | |||||||||||
effective ATFP | 1.028 | 1.028 | 1.028 | 1.028 | 1.028 | 1.028 | 1.028 | 1.028 | 1.028 | 1.028 | 1.028 |
distorted ATFP | 1.028 | 1.028 | 1.026 | 1.023 | 1.020 | 1.018 | 1.016 | 1.015 | 1.015 | 1.015 | 1.015 |
ATFP Loss (%) | 0.000 | 0.030 | 0.240 | 0.521 | 0.796 | 1.018 | 1.171 | 1.258 | 1.296 | 1.306 | 1.307 |
Scenario 6. keeping as the benchmark | |||||||||||
effective ATFP | 0.996 | 0.996 | 0.996 | 0.996 | 0.996 | 0.996 | 0.996 | 0.996 | 0.996 | 0.996 | 0.996 |
distorted ATFP | 0.996 | 0.994 | 0.991 | 0.987 | 0.984 | 0.982 | 0.980 | 0.979 | 0.979 | 0.979 | 0.979 |
ATFP Loss (%) | 0.000 | 0.198 | 0.521 | 0.873 | 1.189 | 1.433 | 1.597 | 1.688 | 1.726 | 1.737 | 1.737 |
0 | 0.1 | 0.2 | 0.3 | 0.4 | 0.5 | 0.6 | 0.7 | 0.8 | 0.9 | 1 | |
---|---|---|---|---|---|---|---|---|---|---|---|
Scenario 1. No and | |||||||||||
effective ATFP | 1.015 | 1.015 | 1.015 | 1.015 | 1.015 | 1.015 | 1.015 | 1.015 | 1.015 | 1.015 | 1.015 |
distorted ATFP | 1.015 | 1.015 | 1.015 | 1.014 | 1.014 | 1.013 | 1.012 | 1.012 | 1.011 | 1.011 | 1.010 |
ATFP Loss (%) | 0.000 | 0.018 | 0.059 | 0.112 | 0.170 | 0.230 | 0.288 | 0.345 | 0.399 | 0.450 | 0.498 |
Scenario 2. Keep and as the benchmark | |||||||||||
effective ATFP | 1.015 | 1.015 | 1.015 | 1.015 | 1.015 | 1.015 | 1.015 | 1.015 | 1.015 | 1.015 | 1.015 |
distorted ATFP | 1.015 | 1.015 | 1.015 | 1.014 | 1.014 | 1.013 | 1.012 | 1.012 | 1.011 | 1.011 | 1.010 |
ATFP Loss (%) | 0.000 | 0.018 | 0.059 | 0.112 | 0.170 | 0.230 | 0.288 | 0.345 | 0.399 | 0.450 | 0.498 |
Scenario 3. keeping as the benchmark | |||||||||||
effective ATFP | 0.841 | 0.841 | 0.841 | 0.841 | 0.841 | 0.841 | 0.841 | 0.841 | 0.841 | 0.841 | 0.841 |
distorted ATFP | 0.841 | 0.840 | 0.838 | 0.835 | 0.832 | 0.828 | 0.825 | 0.821 | 0.817 | 0.813 | 0.809 |
ATFP Loss (%) | 0.000 | 0.088 | 0.319 | 0.652 | 1.055 | 1.504 | 1.980 | 2.471 | 2.965 | 3.458 | 3.942 |
Scenario 4. keeping as the benchmark | |||||||||||
effective ATFP | 1.416 | 1.416 | 1.416 | 1.416 | 1.416 | 1.416 | 1.416 | 1.416 | 1.416 | 1.416 | 1.416 |
distorted ATFP | 1.416 | 1.416 | 1.416 | 1.415 | 1.415 | 1.415 | 1.415 | 1.415 | 1.415 | 1.415 | 1.415 |
ATFP Loss (%) | 0.000 | 0.001 | 0.004 | 0.008 | 0.011 | 0.015 | 0.019 | 0.023 | 0.027 | 0.030 | 0.033 |
Scenario 5. keeping as the benchmark | |||||||||||
effective ATFP | 1.028 | 1.028 | 1.028 | 1.028 | 1.028 | 1.028 | 1.028 | 1.028 | 1.028 | 1.028 | 1.028 |
distorted ATFP | 1.028 | 1.028 | 1.028 | 1.028 | 1.027 | 1.027 | 1.026 | 1.026 | 1.025 | 1.025 | 1.024 |
ATFP Loss (%) | 0.000 | 0.007 | 0.014 | 0.050 | 0.094 | 0.142 | 0.190 | 0.238 | 0.284 | 0.328 | 0.370 |
Scenario 6. keeping as the benchmark | |||||||||||
effective ATFP | 0.996 | 0.996 | 0.996 | 0.996 | 0.996 | 0.996 | 0.996 | 0.996 | 0.996 | 0.996 | 0.996 |
distorted ATFP | 0.996 | 0.995 | 0.995 | 0.994 | 0.993 | 0.993 | 0.992 | 0.991 | 0.991 | 0.990 | 0.990 |
ATFP Loss (%) | 0.000 | 0.041 | 0.101 | 0.170 | 0.242 | 0.313 | 0.382 | 0.447 | 0.509 | 0.567 | 0.621 |
0 | 0.1 | 0.2 | 0.3 | 0.4 | 0.5 | 0.6 | 0.7 | 0.8 | 0.9 | 1 | |
---|---|---|---|---|---|---|---|---|---|---|---|
Scenario 1. No and | |||||||||||
effective ATFP | 1.000487 | 1.000487 | 1.000487 | 1.000487 | 1.000487 | 1.000487 | 1.000487 | 1.000487 | 1.000487 | 1.000487 | 1.000487 |
distorted ATFP | 1.000487 | 1.000378 | 1.000213 | 1.000096 | 1.000034 | 1.00001 | 1.000002 | 1 | 1 | 1 | 1 |
ATFP Loss (%) | 0 | 0.010944 | 0.027448 | 0.039172 | 0.045299 | 0.047791 | 0.048559 | 0.048724 | 0.048744 | 0.048745 | 0.048745 |
Scenario 2. Keep and as the benchmark | |||||||||||
effective ATFP | 1.000487 | 1.000487 | 1.000487 | 1.000487 | 1.000487 | 1.000487 | 1.000487 | 1.000487 | 1.000487 | 1.000487 | 1.000487 |
distorted ATFP | 1.000487 | 1.000378 | 1.000213 | 1.000096 | 1.000034 | 1.00001 | 1.000002 | 1 | 1 | 1 | 1 |
ATFP Loss (%) | 0 | 0.010944 | 0.027448 | 0.039172 | 0.045299 | 0.047791 | 0.048559 | 0.048724 | 0.048744 | 0.048745 | 0.048745 |
Scenario 3. keeping as the benchmark | |||||||||||
effective ATFP | 0.771105 | 0.771105 | 0.771105 | 0.771105 | 0.771105 | 0.771105 | 0.771105 | 0.771105 | 0.771105 | 0.771105 | 0.771105 |
distorted ATFP | 0.771105 | 0.762412 | 0.742278 | 0.723915 | 0.713294 | 0.708831 | 0.707443 | 0.707145 | 0.707108 | 0.707107 | 0.707107 |
ATFP Loss (%) | 0 | 1.140293 | 3.883673 | 6.518717 | 8.104904 | 8.785585 | 8.998994 | 9.044871 | 9.050515 | 9.050772 | 9.050773 |
Scenario 4. keeping as the benchmark | |||||||||||
effective ATFP | 1.414216 | 1.414216 | 1.414216 | 1.414216 | 1.414216 | 1.414216 | 1.414216 | 1.414216 | 1.414216 | 1.414216 | 1.414216 |
distorted ATFP | 1.414216 | 1.414216 | 1.414215 | 1.414214 | 1.414214 | 1.414214 | 1.414214 | 1.414214 | 1.414214 | 1.414214 | 1.414214 |
ATFP Loss (%) | 0 | 4.29 × 10−5 | 0.000108 | 0.000153 | 0.000177 | 0.000187 | 0.00019 | 0.000191 | 0.000191 | 0.000191 | 0.000191 |
Scenario 5. keeping as the benchmark | |||||||||||
effective ATFP | 1.015151 | 1.015151 | 1.015151 | 1.015151 | 1.015151 | 1.015151 | 1.015151 | 1.015151 | 1.015151 | 1.015151 | 1.015151 |
distorted ATFP | 1.015151 | 1.01513 | 1.014999 | 1.014894 | 1.014837 | 1.014812 | 1.014805 | 1.014803 | 1.014803 | 1.014803 | 1.014803 |
ATFP Loss (%) | 0 | 0.00213 | 0.014976 | 0.025341 | 0.03103 | 0.033403 | 0.034147 | 0.034308 | 0.034328 | 0.034329 | 0.034329 |
Scenario 6. keeping as the benchmark | |||||||||||
effective ATFP | 0.979487 | 0.979487 | 0.979487 | 0.979487 | 0.979487 | 0.979487 | 0.979487 | 0.979487 | 0.979487 | 0.979487 | 0.979487 |
distorted ATFP | 0.979487 | 0.979298 | 0.979102 | 0.978975 | 0.978911 | 0.978886 | 0.978878 | 0.978876 | 0.978876 | 0.978876 | 0.978876 |
ATFP Loss (%) | 0 | 0.019297 | 0.039267 | 0.052279 | 0.058822 | 0.061426 | 0.062217 | 0.062385 | 0.062406 | 0.062407 | 0.062407 |
0 | 0.1 | 0.2 | 0.3 | 0.4 | 0.5 | 0.6 | 0.7 | 0.8 | 0.9 | 1 | |
---|---|---|---|---|---|---|---|---|---|---|---|
Scenario 1. No and | |||||||||||
effective ATFP | 1.000487 | 1.000487 | 1.000487 | 1.000487 | 1.000487 | 1.000487 | 1.000487 | 1.000487 | 1.000487 | 1.000487 | 1.000487 |
distorted ATFP | 1.000487 | 1.00047 | 1.000433 | 1.000391 | 1.00035 | 1.000313 | 1.000279 | 1.000249 | 1.000222 | 1.000199 | 1.000179 |
ATFP Loss (%) | 0 | 0.001767 | 0.005425 | 0.0096 | 0.013697 | 0.017479 | 0.02087 | 0.023865 | 0.026493 | 0.028792 | 0.030802 |
Scenario 2. Keep and as the benchmark | |||||||||||
effective ATFP | 1.000487 | 1.000487 | 1.000487 | 1.000487 | 1.000487 | 1.000487 | 1.000487 | 1.000487 | 1.000487 | 1.000487 | 1.000487 |
distorted ATFP | 1.000487 | 1.00047 | 1.000433 | 1.000391 | 1.00035 | 1.000313 | 1.000279 | 1.000249 | 1.000222 | 1.000199 | 1.000179 |
ATFP Loss (%) | 0 | 0.001767 | 0.005425 | 0.0096 | 0.013697 | 0.017479 | 0.02087 | 0.023865 | 0.026493 | 0.028792 | 0.030802 |
Scenario 3. keeping as the benchmark | |||||||||||
effective ATFP | 0.771105 | 0.771105 | 0.771105 | 0.771105 | 0.771105 | 0.771105 | 0.771105 | 0.771105 | 0.771105 | 0.771105 | 0.771105 |
distorted ATFP | 0.771105 | 0.770048 | 0.767357 | 0.763685 | 0.759542 | 0.75528 | 0.751123 | 0.747199 | 0.743571 | 0.740261 | 0.737266 |
ATFP Loss (%) | 0 | 0.137301 | 0.488497 | 0.971617 | 1.522417 | 2.095265 | 2.660278 | 3.199436 | 3.703012 | 4.166756 | 4.589863 |
Scenario 4. keeping as the benchmark | |||||||||||
effective ATFP | 1.414216 | 1.414216 | 1.414216 | 1.414216 | 1.414216 | 1.414216 | 1.414216 | 1.414216 | 1.414216 | 1.414216 | 1.414216 |
distorted ATFP | 1.414216 | 1.414216 | 1.414216 | 1.414216 | 1.414215 | 1.414215 | 1.414215 | 1.414215 | 1.414215 | 1.414215 | 1.414215 |
ATFP Loss (%) | 0 | 6.95 × 10−6 | 2.13 × 10−5 | 3.77 × 10−5 | 5.37 × 10−5 | 6.85 × 10−5 | 8.18 × 10−5 | 9.35 × 10−5 | 1.04 × 10−4 | 1.13 × 10−4 | 1.21 × 10−4 |
Scenario 5. keeping as the benchmark | |||||||||||
effective ATFP | 1.015151 | 1.015151 | 1.015151 | 1.015151 | 1.015151 | 1.015151 | 1.015151 | 1.015151 | 1.015151 | 1.015151 | 1.015151 |
distorted ATFP | 1.015151 | 1.015166 | 1.015153 | 1.015127 | 1.015099 | 1.015071 | 1.015044 | 1.01502 | 1.014998 | 1.014979 | 1.014962 |
ATFP Loss (%) | 0 | 0.000109 | 0.001457 | 0.002362 | 0.005175 | 0.007969 | 0.010585 | 0.012965 | 0.015096 | 0.01699 | 0.018666 |
Scenario 6. keeping as the benchmark | |||||||||||
effective ATFP | 0.979487 | 0.979487 | 0.979487 | 0.979487 | 0.979487 | 0.979487 | 0.979487 | 0.979487 | 0.979487 | 0.979487 | 0.979487 |
distorted ATFP | 0.979487 | 0.97944 | 0.979382 | 0.979326 | 0.979274 | 0.979228 | 0.979187 | 0.979152 | 0.979122 | 0.979096 | 0.979073 |
ATFP Loss (%) | 0 | 0.004822 | 0.010671 | 0.016459 | 0.021774 | 0.026493 | 0.030617 | 0.034196 | 0.037294 | 0.039976 | 0.042303 |
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0 | 0.1 | 0.2 | 0.3 | 0.4 | 0.5 | 0.6 | 0.7 | 0.8 | 0.9 | 1 | |
---|---|---|---|---|---|---|---|---|---|---|---|
Scenario 1. No and | |||||||||||
effective ATFP | 1.118 | 1.118 | 1.118 | 1.118 | 1.118 | 1.118 | 1.118 | 1.118 | 1.118 | 1.118 | 1.118 |
distorted ATFP | 1.118 | 1.115 | 1.108 | 1.095 | 1.080 | 1.062 | 1.044 | 1.027 | 1.013 | 1.004 | 1.000 |
ATFP Loss (%) | 0.000 | 0.239 | 0.943 | 2.072 | 3.551 | 5.275 | 7.102 | 8.863 | 10.367 | 11.413 | 11.803 |
Scenario 2. Keep and as the benchmark | |||||||||||
effective ATFP | 1.118 | 1.118 | 1.118 | 1.118 | 1.118 | 1.118 | 1.118 | 1.118 | 1.118 | 1.118 | 1.118 |
distorted ATFP | 1.118 | 1.115 | 1.108 | 1.095 | 1.080 | 1.062 | 1.044 | 1.027 | 1.013 | 1.004 | 1.000 |
ATFP Loss (%) | 0.000 | 0.239 | 0.943 | 2.072 | 3.551 | 5.275 | 7.102 | 8.863 | 10.367 | 11.413 | 11.803 |
Scenario 3. keeping as the benchmark | |||||||||||
effective ATFP | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 |
distorted ATFP | 1.000 | 0.996 | 0.982 | 0.958 | 0.922 | 0.878 | 0.830 | 0.784 | 0.744 | 0.717 | 0.707 |
ATFP Loss (%) | 0.000 | 0.414 | 1.812 | 4.427 | 8.428 | 13.842 | 20.440 | 27.628 | 34.408 | 39.466 | 41.421 |
Scenario 4. keeping as the benchmark | |||||||||||
effective ATFP | 1.458 | 1.458 | 1.458 | 1.458 | 1.458 | 1.458 | 1.458 | 1.458 | 1.458 | 1.458 | 1.458 |
distorted ATFP | 1.458 | 1.457 | 1.453 | 1.449 | 1.443 | 1.436 | 1.430 | 1.424 | 1.419 | 1.415 | 1.414 |
ATFP Loss (%) | 0.000 | 0.079 | 0.298 | 0.629 | 1.040 | 1.495 | 1.957 | 2.386 | 2.744 | 2.988 | 3.078 |
Scenario 5. keeping as the benchmark | |||||||||||
effective ATFP | 1.126 | 1.126 | 1.126 | 1.126 | 1.126 | 1.126 | 1.126 | 1.126 | 1.126 | 1.126 | 1.126 |
distorted ATFP | 1.126 | 1.125 | 1.119 | 1.108 | 1.094 | 1.077 | 1.059 | 1.042 | 1.028 | 1.018 | 1.015 |
ATFP Loss (%) | 0.000 | 0.071 | 0.623 | 1.619 | 2.986 | 4.620 | 6.378 | 8.091 | 9.565 | 10.597 | 10.983 |
Scenario 6. keeping as the benchmark | |||||||||||
effective ATFP | 1.102 | 1.102 | 1.102 | 1.102 | 1.102 | 1.102 | 1.102 | 1.102 | 1.102 | 1.102 | 1.102 |
distorted ATFP | 1.102 | 1.098 | 1.089 | 1.075 | 1.059 | 1.041 | 1.022 | 1.006 | 0.992 | 0.982 | 0.979 |
ATFP Loss (%) | 0.000 | 0.402 | 1.253 | 2.509 | 4.097 | 5.908 | 7.801 | 9.608 | 11.141 | 12.201 | 12.595 |
0 | 0.1 | 0.2 | 0.3 | 0.4 | 0.5 | 0.6 | 0.7 | 0.8 | 0.9 | 1 | |
---|---|---|---|---|---|---|---|---|---|---|---|
Scenario 1. No and | |||||||||||
effective ATFP | 1.118 | 1.118 | 1.118 | 1.118 | 1.118 | 1.118 | 1.118 | 1.118 | 1.118 | 1.118 | 1.118 |
distorted ATFP | 1.118 | 1.118 | 1.117 | 1.115 | 1.113 | 1.111 | 1.109 | 1.107 | 1.105 | 1.103 | 1.101 |
ATFP Loss (%) | 0.000 | 0.039 | 0.135 | 0.269 | 0.427 | 0.599 | 0.778 | 0.962 | 1.145 | 1.328 | 1.507 |
Scenario 2. Keep and as the benchmark | |||||||||||
effective ATFP | 1.118 | 1.118 | 1.118 | 1.118 | 1.118 | 1.118 | 1.118 | 1.118 | 1.118 | 1.118 | 1.118 |
distorted ATFP | 1.118 | 1.118 | 1.117 | 1.115 | 1.113 | 1.111 | 1.109 | 1.107 | 1.105 | 1.103 | 1.101 |
ATFP Loss (%) | 0.000 | 0.039 | 0.135 | 0.269 | 0.427 | 0.599 | 0.778 | 0.962 | 1.145 | 1.328 | 1.507 |
Scenario 3. keeping as the benchmark | |||||||||||
effective ATFP | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 |
distorted ATFP | 1.000 | 0.999 | 0.998 | 0.995 | 0.992 | 0.989 | 0.985 | 0.982 | 0.978 | 0.974 | 0.970 |
ATFP Loss (%) | 0.000 | 0.063 | 0.230 | 0.473 | 0.773 | 1.115 | 1.486 | 1.879 | 2.285 | 2.700 | 3.119 |
Scenario 4. keeping as the benchmark | |||||||||||
effective ATFP | 1.458 | 1.458 | 1.458 | 1.458 | 1.458 | 1.458 | 1.458 | 1.458 | 1.458 | 1.458 | 1.458 |
distorted ATFP | 1.458 | 1.458 | 1.457 | 1.456 | 1.456 | 1.455 | 1.454 | 1.453 | 1.453 | 1.452 | 1.451 |
ATFP Loss (%) | 0.000 | 0.013 | 0.045 | 0.089 | 0.138 | 0.192 | 0.247 | 0.303 | 0.358 | 0.411 | 0.464 |
Scenario 5. keeping as the benchmark | |||||||||||
effective ATFP | 1.126 | 1.126 | 1.126 | 1.126 | 1.126 | 1.126 | 1.126 | 1.126 | 1.126 | 1.126 | 1.126 |
distorted ATFP | 1.126 | 1.126 | 1.126 | 1.124 | 1.123 | 1.121 | 1.119 | 1.118 | 1.116 | 1.114 | 1.112 |
ATFP Loss (%) | 0.000 | 0.002 | 0.067 | 0.172 | 0.304 | 0.452 | 0.610 | 0.774 | 0.940 | 1.105 | 1.269 |
Scenario 6. keeping as the benchmark | |||||||||||
effective ATFP | 1.102 | 1.102 | 1.102 | 1.102 | 1.102 | 1.102 | 1.102 | 1.102 | 1.102 | 1.102 | 1.102 |
distorted ATFP | 1.102 | 1.101 | 1.100 | 1.098 | 1.096 | 1.094 | 1.092 | 1.090 | 1.088 | 1.085 | 1.083 |
ATFP Loss (%) | 0.000 | 0.074 | 0.202 | 0.364 | 0.546 | 0.741 | 0.941 | 1.143 | 1.344 | 1.543 | 1.737 |
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Dong, X.; Yang, Y.; Zhuang, Q.; Xie, W.; Zhao, X. Does Environmental Regulation Help Mitigate Factor Misallocation?—Theoretical Simulations Based on a Dynamic General Equilibrium Model and the Perspective of TFP. Int. J. Environ. Res. Public Health 2022, 19, 3642. https://doi.org/10.3390/ijerph19063642
Dong X, Yang Y, Zhuang Q, Xie W, Zhao X. Does Environmental Regulation Help Mitigate Factor Misallocation?—Theoretical Simulations Based on a Dynamic General Equilibrium Model and the Perspective of TFP. International Journal of Environmental Research and Public Health. 2022; 19(6):3642. https://doi.org/10.3390/ijerph19063642
Chicago/Turabian StyleDong, Xu, Yali Yang, Qinqin Zhuang, Weili Xie, and Xiaomeng Zhao. 2022. "Does Environmental Regulation Help Mitigate Factor Misallocation?—Theoretical Simulations Based on a Dynamic General Equilibrium Model and the Perspective of TFP" International Journal of Environmental Research and Public Health 19, no. 6: 3642. https://doi.org/10.3390/ijerph19063642