Does Water Rights Trading Improve Agricultural Water Use Efficiency? Evidence from a Quasi-Natural Experiment
Abstract
1. Introduction
- (1)
- This paper focuses on agriculture, with particular attention to the impact of water rights trading on agricultural sector’s water use. By doing this, it expands our understanding of agricultural water policy.
- (2)
- This study uses a new indicator, “water consumption per unit of agricultural output value,” which represents the amount of water required per unit of agricultural output. This gives a direct reflection of economic value of water use and, when paired with a DID framework, allows for a precise assessment of policy impacts. This approach also avoids potential biases associated with efficiency measurement methods like DEA and SFA, which depend on model specifications and weight selection.
- (3)
- This paper thoroughly explores the spatial spillover effects of water rights trading policies using spatial econometric methods. Currently, there are few studies that have considered the spatial spillover effects of water rights trading policies, so investigating the spatial spillover effects will help fill this research gap.
- (4)
- This paper not only examines the effects of such trading on agricultural water efficiency, but also offers an exploration of the underlying mechanisms. In doing so, it addresses the gaps in mechanism identification and lays a stronger foundation for evaluating the effectiveness of water rights trading policies.
2. Research Hypotheses
2.1. Water Rights Trading and Agricultural Water Use Efficiency
2.2. Mechanisms of Water Rights Trading Affect Agricultural Water Use Efficiency
3. Data and Methods
3.1. Empirical Model
3.1.1. DID Model Design
3.1.2. Impact Mechanism Testing Design
3.1.3. Spatial Econometric Model Design
3.2. Variable Descriptions
3.2.1. Core Explanatory Variable: Water Rights Trading (WRT)
3.2.2. Dependent Variable: Agricultural Water Use Efficiency (AWE)
3.2.3. Control Variables
3.3. Data Resource
4. Empirical Results and Analysis
4.1. Parallel Trend Test
4.2. Baseline Regression Results
4.3. Robustness Tests
4.3.1. PSM + DID
4.3.2. Excluding the Influence of the Water Resource Tax Reform
4.3.3. Excluding Certain Samples
4.3.4. One-Period Lag
4.3.5. Placebo Test
4.4. Heterogeneity Analysis
4.4.1. Water Resource Allocation
4.4.2. Agricultural Reliance
4.4.3. Grain-Producing Regions
4.4.4. Level of Economic Development
5. Further Analysis
5.1. Mechanism Analysis
5.2. Spatial Spillover Effects Analysis
5.2.1. Spatial Autocorrelation Test
5.2.2. Diagnostic Tests for Spatial Models
5.2.3. Spatial Regression Analysis
6. Conclusions and Policy Implication
6.1. Conclusions
6.2. Policy Implication
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Variables | Variable Name | Variable Descriptions | Data Resource |
---|---|---|---|
Dependent Variable | Agricultural Water Use Efficiency | Agricultural Water Use Efficiency: Agricultural water consumption (100 million cubic meters)/Total agricultural output value (100 million RMB) | China Rural Statistical Yearbook |
Independent Variable | Water Rights Trading | Water Rights Trading Pilot: Assigned a value of 1 if the province is a pilot area for water rights trading each year; otherwise, 0 | Ministry of Water Resources of the People’s Republic of China |
Mediating Variables | Cropping Structure | Share of Non-Grain Crop Area: Proportion of sown area devoted to non-grain crops | China Rural Statistical Yearbook |
Water-Saving Irrigation Area | Water-Saving Irrigation Rate: (Area under water-saving irrigation/Irrigated arable land area) × 100% | ||
Agricultural Mechanization | Mechanization Level (Log): Logarithm of total agricultural machinery power per capita (kW/person) | ||
Environmental Regulation | Industrial Pollution Control Investment Intensity: Investment in industrial pollution control/Industrial value added | China Environmental Statistics Yearbook | |
Control Variables | Level of Economic Development | Per Capita GDP (Log): Logarithm of regional GDP per capita | |
Industrial Structure | Tertiary-to-Secondary Industry Ratio: Value added of the tertiary sector/Value added of the secondary sector | ||
Degree of Openness | Trade Openness: Total import and export volume/GDP | ||
Government Fiscal Expenditure | Fiscal Expenditure Ratio: Government expenditure/GDP | ||
R&D Intensity | Science and Technology Spending Share: Science and technology expenditure/ Government expenditure | The Provincial Statistical yearbooks | |
Urbanization Level | Urbanization Rate: Urban population/ Total population | ||
Transportation Infrastructure | Highway Mileage (Log): Logarithm of total highway mileage |
Variable Name | Variable Symbol | Mean | SD | Min | Max |
---|---|---|---|---|---|
Agricultural Water Use Efficiency | awe | 0.086 | 0.075 | 0.006 | 0.477 |
Water Rights Trading | art | 0.169 | 0.376 | 0 | 1 |
Cropping Structure | cropstru | 0.354 | 0.141 | 0.034 | 0.629 |
Water-Saving Irrigation Area | irriagte | 0.437 | 0.257 | 0 | 1 |
Agricultural Mechanization | agriMech | 1.627 | 1.101 | 0.17 | 6.187 |
Environmental Regulation | er | 0.009 | 0.003 | 0 | 0.016 |
Level of Economic Development | pgdp | 10.896 | 0.452 | 9.889 | 12.065 |
Industrial Structure | stru | 1.265 | 0.68 | 0.572 | 4.525 |
Degree of Openness | open | 0.259 | 0.28 | 0.011 | 1.366 |
Government Fiscal Expenditure | gov | 0.277 | 0.189 | 0.118 | 1.216 |
R&D Intensity | rd | 0.017 | 0.012 | 0.002 | 0.063 |
Urbanization Level | urban | 0.584 | 0.122 | 0.262 | 0.893 |
Transportation Infrastructure | road | 11.691 | 0.839 | 9.466 | 12.728 |
(1) | (2) | (3) | (4) | |
---|---|---|---|---|
awe | awe | awe | awe | |
wrt | −0.072 *** | −0.051 ** | −0.038 ** | −0.045 *** |
(0.017) | (0.019) | (0.015) | (0.015) | |
pgdp | −0.007 | 0.046 | ||
(0.023) | (0.032) | |||
stru | 0.035 ** | 0.053 *** | ||
(0.014) | (0.017) | |||
open | 0.048 | 0.069 * | ||
(0.039) | (0.039) | |||
gov | −0.194 *** | −0.124 | ||
(0.066) | (0.079) | |||
rd | 1.644 | 3.912 ** | ||
(1.696) | (1.826) | |||
urban | −0.138 | −0.054 | ||
(0.093) | (0.067) | |||
road | −0.183 *** | −0.168 *** | ||
(0.062) | (0.056) | |||
_cons | 0.098 *** | 0.119 *** | 2.365 *** | 1.520 ** |
(0.003) | (0.007) | (0.635) | (0.601) | |
Year FE | Yes | Yes | Yes | Yes |
Province FE | No | Yes | No | Yes |
N | 372 | 372 | 372 | 372 |
Unmatched | Mean | %Reduct | t-Test | ||||
---|---|---|---|---|---|---|---|
Variable | Matched | Treated | Control | %Bias | |Bias| | t | p > |t| |
pgdp | U | 10.824 | 10.917 | −21.8 | −1.68 | 0.094 | |
M | 10.824 | 10.854 | −7.1 | 67.3 | −0.5 | 0.616 | |
stru | U | 1.036 | 1.332 | −52.6 | −3.56 | 0.000 | |
M | 1.036 | 1.015 | 3.8 | 92.8 | 0.52 | 0.603 | |
open | U | 0.200 | 0.276 | −27.6 | −2.2 | 0.028 | |
M | 0.200 | 0.206 | −2.2 | 91.9 | −0.18 | 0.858 | |
gov | U | 0.254 | 0.283 | −17.9 | −1.24 | 0.217 | |
M | 0.254 | 0.245 | 5.7 | 68.2 | 0.61 | 0.544 | |
rd | U | 0.015 | 0.018 | −27.8 | −1.95 | 0.052 | |
M | 0.015 | 0.015 | 0.3 | 99 | 0.03 | 0.979 | |
urban | U | 0.575 | 0.587 | −10.6 | −0.79 | 0.431 | |
M | 0.575 | 0.584 | −7.7 | 27.5 | −0.6 | 0.551 |
(1) | (2) | (3) | (4) | (5) | |
---|---|---|---|---|---|
awe | awe | awe | awe | awe | |
wrt | −0.045 *** | −0.046 *** | −0.042 ** | −0.041 *** | |
(0.014) | (0.015) | (0.016) | (0.015) | ||
L.wrt | −0.036 *** | ||||
(0.012) | |||||
wft | 0.012 | ||||
(0.011) | |||||
pgdp | 0.126 *** | 0.054 | 0.029 | 0.050 * | −0.018 |
(0.040) | (0.032) | (0.036) | (0.030) | (0.034) | |
stru | 0.069 ** | 0.054 *** | 0.043 ** | 0.054 *** | 0.037 ** |
(0.026) | (0.017) | (0.019) | (0.017) | (0.017) | |
open | 0.254 *** | 0.068 * | 0.152 *** | 0.068 * | 0.013 |
(0.059) | (0.036) | (0.047) | (0.040) | (0.038) | |
gov | −0.160 | −0.104 | −0.148 * | −0.090 | −0.377 *** |
(0.124) | (0.078) | (0.081) | (0.078) | (0.124) | |
rd | 1.930 | 4.131 ** | 4.226 * | 3.356 * | 3.907 ** |
(1.917) | (1.795) | (2.084) | (1.773) | (1.688) | |
urban | −1.177 *** | −0.071 | −0.110 | −0.042 | −0.023 |
(0.271) | (0.067) | (0.152) | (0.066) | (0.082) | |
road | −0.210 *** | −0.157 ** | −0.176 *** | −0.161 ** | −0.150 ** |
(0.073) | (0.059) | (0.061) | (0.060) | (0.057) | |
_cons | 1.781 ** | 1.302 * | 1.869 *** | 1.367 ** | 2.052 *** |
(0.761) | (0.640) | (0.658) | (0.663) | (0.692) | |
Year FE | Yes | Yes | Yes | Yes | Yes |
Province FE | Yes | Yes | Yes | Yes | Yes |
N | 372 | 372 | 324 | 341 | 279 |
Water-Abundant Regions | Water-Scarce Regions | |
---|---|---|
awe | awe | |
wrt | −0.044 * | −0.068 *** |
(0.022) | (0.020) | |
pgdp | −0.015 | 0.108 ** |
(0.032) | (0.044) | |
stru | 0.045 *** | 0.050 ** |
(0.015) | (0.022) | |
open | 0.106 * | 0.077 * |
(0.053) | (0.042) | |
gov | −0.220 ** | 0.169 |
(0.084) | (0.186) | |
rd | 7.639 *** | 0.661 |
(1.928) | (1.361) | |
urban | 0.020 | −0.075 |
(0.109) | (0.068) | |
road | −0.089 | −0.137 * |
(0.055) | (0.073) | |
_cons | 1.272 * | 0.421 |
(0.615) | (0.743) | |
Province/Year | Yes | Yes |
N | 180 | 192 |
High Agricultural Dependence | Low Agricultural Dependence | |
---|---|---|
awe | awe | |
wrt | −0.053 *** | −0.035 |
(0.015) | (0.021) | |
pgdp | 0.033 | 0.051 |
(0.035) | (0.030) | |
stru | 0.068 *** | 0.010 |
(0.021) | (0.010) | |
open | 0.107 ** | −0.048 ** |
(0.045) | (0.018) | |
gov | −0.157 * | 0.030 |
(0.084) | (0.099) | |
rd | 1.781 | 4.829 ** |
(2.633) | (1.871) | |
urban | −0.104 | −0.042 |
(0.120) | (0.053) | |
road | −0.234 *** | −0.047 |
(0.066) | (0.035) | |
_cons | 2.490 *** | 0.048 |
(0.794) | (0.483) | |
Year FE | Yes | Yes |
Province FE | Yes | Yes |
N | 180 | 192 |
Major Grain-Producing Regions | Non-Major Grain-Producing Regions | |
---|---|---|
awe | awe | |
wrt | −0.037 | −0.062 *** |
(0.021) | (0.011) | |
pgdp | −0.037 | 0.122 *** |
(0.054) | (0.033) | |
stru | −0.006 | 0.082 *** |
(0.015) | (0.013) | |
open | −0.014 | 0.085 ** |
(0.063) | (0.030) | |
gov | −0.216 | −0.085 |
(0.152) | (0.060) | |
rd | 0.773 | 4.378 ** |
(1.980) | (1.875) | |
urban | 0.353 | −0.026 |
(0.520) | (0.038) | |
road | −0.050 | −0.212 *** |
(0.052) | (0.059) | |
_cons | 0.934 | 1.093 * |
(0.799) | (0.565) | |
Year FE | Yes | Yes |
Province FE | Yes | Yes |
N | 144 | 228 |
High Level | Low Level | |
---|---|---|
awe | awe | |
wrt | −0.047 *** | −0.061 *** |
(0.010) | (0.019) | |
pgdp | 0.103 *** | 0.080 |
(0.025) | (0.050) | |
stru | 0.069 *** | 0.059 *** |
(0.016) | (0.018) | |
open | 0.057 ** | 0.247 *** |
(0.025) | (0.063) | |
gov | 0.164 | −0.054 |
(0.096) | (0.073) | |
rd | −0.737 | 4.653 ** |
(0.918) | (2.152) | |
urban | −0.042 | 0.336 |
(0.024) | (0.285) | |
road | −0.037 | −0.166 ** |
(0.044) | (0.059) | |
_cons | −0.694 | 1.031 |
(0.451) | (0.722) | |
Year FE | Yes | Yes |
Province FE | Yes | Yes |
N | 180 | 192 |
(1) | (2) | (3) | (4) | |
---|---|---|---|---|
cropstru | awe | irriagte | awe | |
wrt | 0.040 *** | −0.041 ** | 0.116 ** | −0.042 ** |
(0.014) | (0.015) | (0.046) | (0.016) | |
cropstru | −0.100 ** | |||
(0.047) | ||||
irriagte | −0.025 * | |||
(0.014) | ||||
pgdp | 0.091 * | 0.055 | −0.087 | 0.043 |
(0.050) | (0.032) | (0.107) | (0.032) | |
stru | 0.023 | 0.055 *** | −0.002 | 0.053 *** |
(0.017) | (0.017) | (0.058) | (0.017) | |
open | −0.166 *** | 0.053 | 0.206 | 0.074 * |
(0.043) | (0.036) | (0.133) | (0.039) | |
gov | −0.017 | −0.126 | 0.083 | −0.122 |
(0.104) | (0.078) | (0.356) | (0.075) | |
rd | −1.147 | 3.797 ** | 1.883 | 3.958 ** |
(1.685) | (1.753) | (4.742) | (1.795) | |
urban | 0.219 | −0.032 | −0.186 | −0.059 |
(0.129) | (0.060) | (0.593) | (0.064) | |
road | 0.076 | −0.161 *** | 0.127 | −0.165 *** |
(0.045) | (0.056) | (0.103) | (0.057) | |
_cons | −1.548 * | 1.365 ** | −0.126 | 1.516 ** |
(0.848) | (0.614) | (1.557) | (0.600) | |
Year FE | Yes | Yes | Yes | Yes |
Province FE | Yes | Yes | Yes | Yes |
N | 372 | 372 | 372 | 372 |
(1) | (2) | (3) | (4) | |
---|---|---|---|---|
agrimech | awe | er | awe | |
wrt | 0.844 ** | −0.039 ** | 0.003 *** | −0.039 ** |
(0.323) | (0.016) | (0.001) | (0.016) | |
agrimech | −0.006 * | |||
(0.004) | ||||
er | −2.192 ** | |||
(0.895) | ||||
pgdp | 0.242 | 0.047 | −0.002 | 0.041 |
(0.875) | (0.032) | (0.002) | (0.031) | |
stru | 0.077 | 0.053 *** | −0.001 | 0.050 *** |
(0.281) | (0.017) | (0.001) | (0.017) | |
open | 0.583 | 0.073 * | −0.001 | 0.068 * |
(0.798) | (0.036) | (0.002) | (0.038) | |
gov | 7.258 * | −0.078 | 0.002 | −0.120 |
(4.144) | (0.087) | (0.006) | (0.076) | |
rd | −21.786 | 3.772 ** | −0.189 * | 3.498 * |
(44.012) | (1.729) | (0.099) | (1.783) | |
urban | 0.707 | −0.050 | −0.003 | −0.060 |
(1.727) | (0.064) | (0.005) | (0.065) | |
road | 0.118 | −0.167 *** | 0.003 | −0.162 *** |
(1.010) | (0.057) | (0.002) | (0.057) | |
_cons | −5.094 | 1.487 ** | 0.003 | 1.525 ** |
(14.130) | (0.646) | (0.029) | (0.606) | |
Year FE | Yes | Yes | Yes | Yes |
Province FE | Yes | Yes | Yes | Yes |
N | 372 | 372 | 372 | 372 |
Year | I | z | p-Value |
---|---|---|---|
2011 | 0.306 | 3.349 | 0.001 |
2012 | 0.314 | 3.461 | 0.001 |
2013 | 0.284 | 3.194 | 0.001 |
2014 | 0.267 | 3.122 | 0.002 |
2015 | 0.300 | 3.381 | 0.001 |
2016 | 0.229 | 3.231 | 0.001 |
2017 | 0.271 | 3.000 | 0.003 |
2018 | 0.270 | 2.919 | 0.004 |
2019 | 0.242 | 2.586 | 0.010 |
2020 | 0.250 | 2.685 | 0.007 |
2021 | 0.232 | 2.470 | 0.013 |
2022 | 0.257 | 2.748 | 0.006 |
Test | Category | Test Statistic | p-Value | |
---|---|---|---|---|
LM Test | Spatial Error Model (SEM) | Lagrange Multiplier | 200.700 | 0.000 |
Robust Lagrange Multiplier | 57.063 | 0.000 | ||
Spatial Autoregressive Model (SAR) | Lagrange Multiplier | 144.021 | 0.000 | |
Robust Lagrange Multiplier | 0.384 | 0.536 | ||
LR Test | Spatial Autoregressive Model (SAR) vs. Spatial Durbin Model (SDM) | 29.37 | 0.000 | |
Spatial Error Model (SEM) vs. Spatial Durbin Model (SDM) | 45.94 | 0.000 | ||
Wald Test | SARSDM can be simplified to SAR | 29.85 | 0.000 | |
SEMSDM can be simplified to SEM | 42.47 | 0.000 | ||
Hausman | Random Effects | 168.00 | 0.000 |
(1) | (2) | (3) | ||
---|---|---|---|---|
SAR | SDM | Wx | SEM | |
Main | ||||
wrt | −0.043 *** | −0.041 *** | −0.024 ** | −0.041 *** |
(−7.607) | (−7.160) | (−2.111) | (−6.966) | |
pgdp | 0.048 *** | 0.034 ** | −0.061 ** | 0.055 *** |
(3.279) | (2.016) | (−2.052) | (3.437) | |
stru | 0.050 *** | 0.040 *** | −0.041 *** | 0.051 *** |
(7.738) | (5.935) | (−2.687) | (7.995) | |
open | 0.087 *** | 0.099 *** | 0.015 | 0.082 *** |
(5.255) | (5.531) | (0.470) | (4.803) | |
gov | −0.114 ** | −0.160 *** | −0.186 * | −0.108 ** |
(−2.518) | (−3.530) | (−1.945) | (−2.357) | |
rd | 3.004 *** | 2.312 *** | 3.654 ** | 3.281 *** |
(4.275) | (3.104) | (2.521) | (4.320) | |
urban | −0.136 *** | −0.117 ** | −0.124 | −0.140 *** |
(−2.663) | (−2.292) | (−1.124) | (−2.719) | |
road | −0.149 *** | −0.154 *** | −0.073 | −0.150 *** |
(−8.246) | (−8.404) | (−1.536) | (−8.057) | |
0.336 *** | 0.210 *** | |||
(6.146) | (3.065) | |||
λ | 0.308 *** | |||
(4.419) | ||||
N | 372 | 372 | 372 | 372 |
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Liu, H.; He, B.; Chen, W. Does Water Rights Trading Improve Agricultural Water Use Efficiency? Evidence from a Quasi-Natural Experiment. Water 2025, 17, 2414. https://doi.org/10.3390/w17162414
Liu H, He B, Chen W. Does Water Rights Trading Improve Agricultural Water Use Efficiency? Evidence from a Quasi-Natural Experiment. Water. 2025; 17(16):2414. https://doi.org/10.3390/w17162414
Chicago/Turabian StyleLiu, Hengyi, Bing He, and Wei Chen. 2025. "Does Water Rights Trading Improve Agricultural Water Use Efficiency? Evidence from a Quasi-Natural Experiment" Water 17, no. 16: 2414. https://doi.org/10.3390/w17162414
APA StyleLiu, H., He, B., & Chen, W. (2025). Does Water Rights Trading Improve Agricultural Water Use Efficiency? Evidence from a Quasi-Natural Experiment. Water, 17(16), 2414. https://doi.org/10.3390/w17162414