The Impact of Granting of Forest Certificates on Farmers’ Income—Intermediation Effects Based on Forestland Lease
Abstract
:1. Introduction
2. Analytical Framework and Assumptions
2.1. Granting Forest Certificates and Forestland Leases
2.2. Granting Forest Certificates Contributes to Forestry Income through Forestland Lease In
2.3. Granting Forest Certificates Contributes to Total Household Income through Forestland Lease Out
3. Research Design
3.1. Data Sources
3.2. Selection of the Model’s Variables
- (1)
- The dependent variable in this study is farm household income. It is important to mention that the research sample in this article comprises forest farmers, meaning that the overall income of farming households includes money from forestry activities as well as income from off-farm employment. The main sources of income from forestry were fuelwood extraction and the collection of non-timber forest products (NTFPs) [52]. In the model for the separate regression, the particular variables of total household income and forestry income were incorporated, as referenced in earlier works [17]. The term “total household income” is employed to capture the variations in overall income resulting from the increase in forestland lease and off-farm income generated by the leasing of farmers’ forestland. On the other hand, “forestry income” is utilized to represent the alterations in income derived from the management of forestland subsequent to the leasing of farmers’ land. To minimize the impact of outliers on the empirical results, the data were transformed using logarithms.
- (2)
- The core independent variable in this study is granting forest certificates. Granting forest certificates refers to the process of granting forest titles to farmers. Hence, it is logical to quantify the granting of forest certificates based on the possession of forest titles (data from http://theory.people.com.cn/n/2014/0402/c40531-24802635.html. China’s strategy of economic reform through a dual route. 2 April 2014). In this paper, granting forest certificates is defined as the possession of forestland certificates, as determined by a questionnaire. The questionnaire included a specific question asking whether the individual responding has forestland certificates. Granting forest certificates was treated as a dichotomous variable, with a value of 1 assigned to farmers who responded yes and a value of 0 assigned to those who responded no.
- (3)
- The different grouping variables tested are the area of forestland ownership and business mode. The varying scale and manner of forestland management would significantly influence the business conduct of farming households, resulting in notable disparities among households of different scales and forms of operation. This would also lead to a substantial income difference among farming households [53,54]. The titling of forestland can significantly affect the level of impact on the income of farm households. Thus, this paper categorized farm households based on the characteristics of forestland ownership and business style. The forestland region was classified into three groups based on a field study using the “50 mu” and “100 mu” standards for grouping. These groupings were labeled “1”, “2”, and “3”. The focal variable in this research is forestland lease, and the forestland lease variables encompass the magnitude of forestland leases in and out of the area. The implementation of granting forest certificates has the potential to influence farmers’ willingness to lease forestland. Leasing forestland can generate income for farmers. Therefore, this article incorporates the forestland lease in the regression model to investigate the mediating effect of forestland leases on farmer income when examining the impact of granting forest certificates.
- (4)
- In order to enhance the stability of the regression results and minimize the impact of omitted variables, this paper incorporates a comprehensive set of control variables that may influence the income of farm households. These control variables are classified into four dimensions as follows: the head of the household’s individual characteristics, family characteristics, forestland characteristics, and village-level characteristics. This approach is based on a relevant study [55]. The primary decision-maker for a household’s entire production and operations is typically the head of the household. To account for the influence of the head’s individual characteristics, such as gender, age, education level, occupation, and whether they hold a position as a village cadre, these factors are taken into consideration. On the other hand, the business activities of the farm household are influenced by the household’s characteristics. To control for this, factors such as the total population of the household, the number of laborers, the percentage of off-farm employment, and whether the household is a member of the farmers’ forestry professional cooperatives are considered. Forestland characteristics play a significant role in forestry management. Forestland fragmentation hinders large-scale management and increases transaction and management costs. This study focuses on controlling the impact of forestland fragmentation. Woodland fragmentation is quantified by dividing the total area of woodland owned by households by the number of individual woodland plots. A higher value indicates a lower degree of woodland fragmentation. Furthermore, village-level characteristics encompass the village-level labor force transfer ratio and the village-level forestry income ratio. The labor force transfer ratio at the village level contributes to a decrease in the rural labor force, thereby impacting the labor input of rural residents. Similarly, the village-level forestry income ratio influences the overall forestry income of farmers. Hence, this study selects these two variables to mitigate the effects of village-level characteristics.
3.3. Econometric Model
4. Results
4.1. Descriptive Analysis
4.2. Benchmark Regression: Granting Forest Certificates and Total Rural Household Income
4.3. Impact of Granting Forest Certificates on Farmer Forestry Income
4.4. Mediating Effects of Forestland Lease
4.5. Grouped Regression Results at the Forestland Scale
5. Discussion
6. Conclusions and Policy Implications
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Variable | Variable Symbol | Definition | Expected Impact | Mean | SD | |
---|---|---|---|---|---|---|
Dependent variable | Total household income | lnincome | Total household income (CNY) taken in logarithms | —— | 11.067 | 1.127 |
Forestry income | lnfincome | Total household forestry income/household forestland area (CNY/hectare) | —— | 8.260 | 1.940 | |
Independent variable | Proportion of forestland certificates owned | certificate | Whether the forestland certificates is in your possession (No = 0; Yes = 1) | Positive relationship | 0.951 | 0.217 |
Grouping variable | Forestland area classification | areatype | Area codes: 1 = less than 50 acres, 2 = 50–100 acres, 3 = more than 100 acres | Positive relationship | 1.853 | 0.908 |
Business model | mtype | Business model: 1—single-family, 2—joint-family, 3—family forestry, 4—company forestry, 5—joint-stock cooperation | Positive relationship | 1.516 | 1.167 | |
Intermediary variable | Scale of forestland lease in | rin | The area of lease in | Positive relationship | 28.297 | 228.934 |
Scale of forestland lease out | nout | The area of lease out | Positive relationship | 1.428 | 1.318 | |
Instrumental variable | Proportion of village forestland certificates issued | VRcer | Number of village forestland certificates actually issued/due for issuance | Positive relationship | 0.934 | 0.307 |
Personal characteristics of the head of household | Gender | gender | Sex of head of household (M = 1; F = 0) | Positive relationship | 0.943 | 0.231 |
Age | age | Age of head of household in year of survey (actual years) | Negative relationship | 56.414 | 10.253 | |
Educational level | edu | Educational level of the head of household (elementary school and below = 1; middle school = 2; middle or high school = 3; college or bachelor’s degree or higher = 4 | Positive relationship | 1.892 | 0.826 | |
Occupation | ocp | Farming = 1; farming and part-time work = 2; farming and part-time work = 3; permanent work outside the home = 4; regular wage income = 5; other = 6 | Positive relationship | 2.800 | 1.905 | |
Whether village cadre | cadre | Whether the head of household is a village cadre (Yes = 1; No = 0) | Positive relationship | 0.389 | 0.494 | |
Family characteristics | Total household population | num | Total household population (persons) | Positive relationship | 4.821 | 2.166 |
Number of laborers | numlabor | Number of family laborers (persons) | Positive relationship | 2.800 | 1.429 | |
Percentage of off-farm payrolls | Rout | Household off-farm employment/total household labor force | Positive relationship | 0.506 | 0.511 | |
Membership in farmer forestry cooperatives | wcooperation | Are you a member of a farmers’ forestry cooperative? (no local cooperative = 0; yes, but not joined = 1; joined = 2) | Positive relationship | 0.215 | 0.411 | |
Woodland characteristics | Woodland fragmentation | frafor | Household woodland area/number of woodland plots | Negative relationship | 25.529 | 48.369 |
Village characteristics | Percentage of labor transfer at the village level | Vmig | Number of permanent migrant workers in the village/total population of the village | Positive relationship | 0.528 | 0.242 |
Percentage of village forestry revenue | Vfincome | Average per capita income from forestry in the village/average total per capita income | Positive relationship | 0.212 | 0.206 |
Variables | Dependent Variable: Lnincome | |||||||
---|---|---|---|---|---|---|---|---|
Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | |||
certificate | 0.003 *** (0.001) | 0.003 *** (0.001) | 0.002 *** (0.001) | 0.002 ** (0.001) | 0.002 ** (0.001) | 0.002 ** (0.001) | ||
gender | 0.079 (0.154) | 0.122(0.153) | 0.121(0.152) | 0.114 (0.152) | 0.107 (0.155) | |||
age | −0.016 *** (0.004) | −0.010 *** (0.003) | −0.010 *** (0.004) | −0.010 ** (0.004) | ||||
edu | 0.208 *** (0.044) | 0.174 *** (0.047) | 0.167 *** (0.047) | |||||
ocp | 0.057 *** (0.020) | 0.062 *** (0.020) | ||||||
carde | −0.188 *** (0.071) | |||||||
constant | 11.048 *** (0.038) | 10.972 *** (0.149) | 11.840 *** (0.267) | 11.132 *** (0.277) | 11.044 *** (0.278) | 11.049 *** (0.280) | ||
R2 | 0.007 | 0.008 | 0.028 | 0.049 | 0.057 | 0.061 | ||
Variables | Dependent Variable: Lnincome | |||||||
Model 7 | Model 8 | Model 9 | Model 10 | Model 11 | Model 12 | Model 13 | ||
certificate | 0.002 ** (0.001) | 0.002 *** (0.001) | 0.002 ** (0.001) | 0.001 ** (0.001) | 0.001 *** (0.001) | 0.001 ** (0.001) | 0.001 ** (0.001) | |
gender | 0.071 (0.144) | 0.092 (0.144) | 0.062 (0.130) | 0.058 (0.129) | 0.038 (0.130) | 0.056 (0.143) | 0.055 (0.143) | |
age | −0.011 *** (0.004) | −0.008 ** (0.004) | −0.010 *** (0.004) | −0.010 ** (0.004) | −0.009 ** (0.004) | −0.009 ** (0.004) | −0.009 ** (0.004) | |
edu | 0.160 *** (0.047) | 0.145 *** (0.046) | 0.148 *** (0.044) | 0.141 *** (0.044) | 0.129 *** (0.043) | 0.099 ** (0.045) | 0.010 ** (0.045) | |
ocp | 0.058 *** (0.019) | 0.060 *** (0.019) | 0.059 *** (0.018) | 0.058 *** (0.019) | 0.054 *** (0.018) | 0.055 *** (0.020) | 0.055 *** (0.020) | |
carde | −0.191 *** (0.069) | −0.205 *** (0.068) | −0.262 *** (0.066) | −0.256 *** (0.066) | −0.256 *** (0.067) | −0.221 *** (0.070) | −0.222 *** (0.071) | |
num | 0.126 *** (0.016) | 0.053 ** (0.022) | 0.026 (0.022) | 0.026 (0.023) | 0.017 (0.022) | 0.006 (0.025) | 0.006 (0.025) | |
numlabor | 0.155 *** (0.032) | 0.206 *** (0.033) | 0.204 *** (0.033) | 0.214 *** (0.032) | 0.210 *** (0.034) | 0.210 *** (0.034) | ||
rout | 0.631 *** (0.071) | 0.622 *** (0.070) | 0.631 *** (0.070) | 0.639 *** (0.074) | 0.638 *** (0.074) | |||
wcooperation | 0.121 ** (0.054) | 0.116 ** (0.053) | 0.129 ** (0.060) | 0.130 ** (0.061) | ||||
frafor | 0.002 *** (0.001) | 0.001 (0.001) | 0.001 (0.001) | |||||
vmig | 0.115 (0.138) | 0.112 (0.138) | ||||||
vfincome | −0.028 (0.169) | |||||||
constant | 10.624 *** (0.263) | 10.355 *** (0.264) | 10.187 *** (0.260) | 10.162 *** (0.260) | 10.152 *** (0.261) | 10.127 *** (0.291) | 10.135 *** (0.301) | |
R2 | 0.121 | 0.139 | 0.209 | 0.210 | 0.216 | 0.195 | 0.195 |
Variables | Dependent Variable: lnfincome | |||||||
---|---|---|---|---|---|---|---|---|
Model 14 | Model 15 | Model 16 | Model 17 | Model 18 | Model 19 | |||
certificate | 0.000 (0.001) | −0.000 (0.001) | −0.001 (0.001) | −0.001 (0.001) | −0.001 (0.001) | −0.001 (0.001) | ||
gender | 0.693 ** (0.298) | 0.750 ** (0.292) | 0.749 ** (0.292) | 0.752 ** (0.289) | 0.725 ** (0.295) | |||
age | −0.024 *** (0.009) | −0.023 *** (0.009) | −0.023 ** (0.009) | −0.020 ** (0.009) | ||||
edu | 0.018 (0.107) | 0.036 (0.111) | 0.078 (0.110) | |||||
ocp | −0.026 *** (0.047) | −0.009 (0.046) | ||||||
carde | −0.736 *** (0.157) | |||||||
constant | 8.254 *** (0.088) | 7.608 *** (0.287) | 8.888 *** (0.551) | 8.827 *** (0.639) | 8.840 *** (0.641) | 8.780 *** (0.635) | ||
R2 | 0.000 | 0.007 | 0.022 | 0.022 | 0.022 | 0.053 | ||
Variables | Dependent Variable: Lnfincome | |||||||
Model 20 | Model 21 | Model 22 | Model 23 | Model 24 | Model 25 | Model 26 | ||
certificate | −0.001 (0.001) | −0.001 (0.001) | −0.001 (0.002) | −0.002 (0.001) | −0.001 (0.001) | −0.001 (0.001) | −0.001 (0.001) | |
gender | 0.723 *** (0.295) | 0.727 ** (0.296) | 0.667 ** (0.315) | 0.585 ** (0.295) | 0.461 (0.296) | 0.443 (0.322) | 0.476 (0.331) | |
age | −0.020 ** (0.009) | −0.019 ** (0.009) | −0.020 ** (0.009) | −0.020 ** (0.009) | −0.018 ** (0.008) | −0.019 ** (0.009) | −0.019 ** (0.009) | |
edu | 0.076 (0.110) | 0.075 (0.111) | 0.063 (0.111) | 0.022 (0.109) | 0.050 (0.104) | 0.099 (0.120) | 0.084 (0.119) | |
ocp | −0.010 (0.046) | −0.009 (0.046) | −0.006 (0.047) | −0.025 (0.047) | −0.037 (0.042) | −0.019 (0.048) | −0.016 (0.049) | |
carde | −0.736 *** (0.157) | −0.736 *** (0.157) | −0.708 *** (0.160) | −0.643 *** (0.161) | −0.675 *** (0.157) | −0.495 *** (0.175) | −0.460 *** (0.175) | |
num | −0.006 (0.036) | −0.004 (0.046) | −0.008 (0.048) | −0.017 (0.048) | −0.007 (0.046) | −0.016 (0.053) | −0.024 (0.052) | |
numlabor | 0.022 (0.073) | 0.012 (0.076) | 0.017 (0.048) | 0.050 (0.071) | 0.078 (0.077) | 0.086 (0.077) | ||
rout | −0.142 (0.154) | −0.222 (0.149) | −0.189 (0.149) | −0.192 (0.150) | −0.165 (0.152) | |||
wcooperation | 0.659 *** (0.133) | 0.627 *** (0.128) | 0.677 *** (0.143) | 0.650 *** (0.141) | ||||
frafor | 0.010 *** (0.002) | 0.010 *** (0.003) | 0.010 *** (0.003) | |||||
vmig | 0.049 (0.365) | 0.136 (0.366) | ||||||
vfincome | 0.993 *** (0.358) | |||||||
constant | 8.762 *** (0.651) | 8.717 *** (0.663) | 8.883 *** (0.673) | 8.784 *** (0.649) | 8.691 *** (0.622) | 8.722 *** (0.710) | 8.356 *** (0.725) | |
R2 | 0.054 | 0.054 | 0.053 | 0.110 | 0.172 | 0.148 | 0.160 |
Model 27 | Model 28 | Model 29 | |
---|---|---|---|
Variables | Nout | Lnincome | Lnincome |
certificate | 0.0018 | 0.0020 *** | |
(1.5963) | (2.7842) | ||
nout | 0.3438 *** | 0.3413 *** | |
(13.0132) | (13.0118) | ||
Constant | 1.4159 *** | 10.5858 *** | 10.5747 *** |
(30.6321) | (179.8228) | (178.5880) | |
Observations | 991 | 983 | 983 |
R-squared | 0.1614 | 0.1658 | |
Number of id | 530 |
Dependent Variable: Lnincome | |||
---|---|---|---|
Variables | Areatype = 3 | Areatype = 2 | Areatype = 1 |
certificate | −0.001 (0.003) | 0.017 (0.013) | 0.002 ** (0.001) |
gender | −0.197 (0.454) | −0.040 (0.399) | 0.161 (0.161) |
age | −0.015 (0.012) | −0.005 (0.014) | −0.009 * (0.005) |
edu | 0.096 (0.139) | 0.190 (0.141) | 0.090 * (0.052) |
ocp | 0.077 (0.075) | 0.127 ** (0.062) | 0.043 ** (0.022) |
carde | −0.436 * (0.226) | −0.370 (0.276) | −0.179 ** (0.082) |
num | −0.105 (0.118) | 0.126 (0.078) | 0.001 (0.027) |
numlabor | 0.417 ** (0.165) | 0.184 (0.121) | 0.195 *** (0.037) |
rout | 1.016 *** (0.269) | 0.544 *** (0.178) | 0.628 *** (0.088) |
wcooperation | 0.183 (0.265) | −0.018 (0.677) | 0.053 (0.125) |
frafor | 0.008 * (0.004) | 0.003 (0.003) | 0.001 (0.001) |
vmig | 0.514 (0.436) | −0.657 (0.490) | 0.131 (0.143) |
vfincome | −0.243 (0.449) | −0.221 (0.561) | 0.010 (0.200) |
constant | 10.206 *** (0.869) | 9.590 *** (1.153) | 10.111 *** (0.345) |
R2 | 0.300 | 0.322 | 0.182 |
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Li, L.; Liu, M.; Yang, Y.; Xie, F.; Liu, X. The Impact of Granting of Forest Certificates on Farmers’ Income—Intermediation Effects Based on Forestland Lease. Forests 2024, 15, 888. https://doi.org/10.3390/f15050888
Li L, Liu M, Yang Y, Xie F, Liu X. The Impact of Granting of Forest Certificates on Farmers’ Income—Intermediation Effects Based on Forestland Lease. Forests. 2024; 15(5):888. https://doi.org/10.3390/f15050888
Chicago/Turabian StyleLi, Lishan, Meifang Liu, Yuchao Yang, Fangting Xie, and Xiaojin Liu. 2024. "The Impact of Granting of Forest Certificates on Farmers’ Income—Intermediation Effects Based on Forestland Lease" Forests 15, no. 5: 888. https://doi.org/10.3390/f15050888
APA StyleLi, L., Liu, M., Yang, Y., Xie, F., & Liu, X. (2024). The Impact of Granting of Forest Certificates on Farmers’ Income—Intermediation Effects Based on Forestland Lease. Forests, 15(5), 888. https://doi.org/10.3390/f15050888