Digital Literacy and the Livelihood Resilience of Livestock Farmers: Empirical Evidence from the Old Revolutionary Base Areas in Northwest China
Abstract
:1. Introduction
2. Concept Definition and Theoretical Foundation
2.1. Concept Definition
2.2. The Direct and Indirect Effects of Digital Literacy on the Livelihood Resilience of Farmers
3. Data and Methodology
3.1. Data Source
3.2. Variable Selection
3.2.1. The Dependent Variable: Livelihood Resilience
3.2.2. The Explanatory Variable: Digital Literacy
3.2.3. Control Variables
3.2.4. Mediating Variables
3.3. Econometrics Model
4. Empirical Results
4.1. Benchmark Regression Results
4.2. Endogeneity Tests
4.3. Robustness Tests
- Replace the calculation method of the dependent variable. Considering the different calculation logic and weights of different calculation methods, this paper uses Principal Component Analysis (PCA) (The results of Cronbach’s Alpha reliability test for the dimensions of livelihood resilience in this paper are 0.7094, which is greater than 0.7, indicating good reliability; the KMO and Bartlett’s test results are 0.715, which is also greater than 0.7, indicating good validity) and equal weighting methods to recalculate the livelihood resilience level of livestock farmers and perform regression. The results are shown in columns (1) and (2) of Table 7. It can be seen that digital literacy positively affects the livelihood resilience of livestock farmers at the 1% significance level;
- Replace the dependent variable. The existing sustainable livelihood framework finds it difficult to comprehensively grasp the full picture of livelihood resilience. This paper draws on the theory of livelihood resilience’s counterpart–vulnerability research to include the vulnerability variables of farmers into the livelihood resilience index system (The resilience of farmers is closely related to vulnerability. In this section, the vulnerability variables are the degree of the farmers’ production reduction due to natural disasters and the degree of land fragmentation. The former is obtained through the question “What is the proportion of your production reduction due to natural disasters in the previous year?”, and the latter is calculated by dividing the total cultivated land area by the total number of cultivated land plots. These are included in the evaluation value system of livelihood resilience, and the final level of livelihood resilience is calculated using the entropy method), re-measure the level of livelihood resilience [55], and conduct regression analysis. The results can be seen in column (3) of Table 7. It can be observed that digital literacy still positively affects the livelihood resilience of farmers at the 1% level of significance;
- Winsorizing treatment: Considering the potential outliers of the dependent variable and their adverse effects on regression, this paper performs 1% and 5% winsorizing treatment on the dependent variable and performs regression. The results are shown in columns (4) and (5) of Table 7. It can be seen that digital literacy positively affects the livelihood resilience of livestock farmers at the 1% significance level.
4.4. Heterogeneity Tests
4.5. Mechanisms Analysis
- The disruption of the “differential mode of association”: Columns (1) and (2) of Table 11, respectively, present the regression results of digital literacy on daily life and agricultural production “differential mode of associations”, showing that digital literacy significantly negatively affects both types of “differential mode of associations”. On one hand, livestock farmers can use digital communication software to break through spatial and temporal limitations, expanding their social circles beyond blood relations, thereby disrupting the “differential mode of association” at the level of daily life. On the other hand, livestock farmers rely on cloud platforms to build online agricultural product sales markets and broaden the channels for purchasing agricultural raw materials, thus disrupting the “differential mode of association” at the level of production. Further, existing studies have proven that disrupting the “differential mode of association” can significantly enhance livestock farmers’ life happiness [60], expand social networks, and improve land use efficiency, effectively strengthening the buffering capacity and self-organizing capacity within livestock farmers’ livelihood resilience. Therefore, the mechanism of action is confirmed to exist;
- Expansion of learning channels: Column (3) of Table 11 presents the regression results of digital literacy on learning channels, showing that digital literacy significantly positively affects information channels. This means that livestock farmers, relying on the rapid development of various application softwares such as social media, news apps, and short video platforms, can obtain knowledge from a wide range of information sources that is several times greater than that from traditional media. This, in turn, broadens their self-learning channels and enhances the efficiency of education. The benefits to livestock farmers from the aforementioned results include reduced educational expenses and increased cultural levels, which significantly improve the learning capacity within livestock farmers’ livelihood resilience. Therefore, the mechanism of action is confirmed to exist;
- Enrichment of income types: Column (4) of Table 11 displays the regression results of digital literacy on income types, indicating that digital literacy significantly positively impacts income types. This means that livestock farmers, by leveraging various online information channels to obtain employment information or expand their professional skills, can enrich their income types and increase their sources of income. For example, some livestock farmers can use short video software to learn about industries such as food delivery, sales, and education; they can also access job information online, which in the past could only be obtained by entering the city or relying on traditional media; some livestock farmers have even started their own businesses through digital means. Scholars both domestically and internationally have pointed out that an increase in income types and sources can significantly increase the various types of household income and total income, which in turn strengthens the buffering capacity and learning capacity of livelihood resilience [61]. Therefore, the mechanism of action is confirmed to exist.
4.6. Further Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Conflicts of Interest
References
- Herrero, M.; Grace, D.; Njuki, J.; Johnson, N.; Enahoro, D.; Silvestri, S.; Rufino, M.C. The roles of livestock in developing countries. Animal 2013, 7, 3–18. [Google Scholar] [CrossRef] [PubMed]
- Phillips, C.A.; Caldas, A.; Cleetus, R.; Dahl, K.A.; Declet-Barreto, J.; Licker, R.; Merner, L.D.; Ortiz-Partida, J.P.; Phelan, A.L.; Spanger-Siegfried, E.; et al. Compound climate risks in the COVID-19 pandemic. Nat. Clim. Chang. 2020, 10, 586–588. [Google Scholar] [CrossRef]
- Wen, H.; Jiang, L. Promoting sustainable development in less developed regions: An empirical study of old revolutionary base areas in China. Environ. Dev. Sustain. 2024, 26, 12283–12308. [Google Scholar] [CrossRef]
- Piao, S.; Jin, X.; Hu, S.; Lee, J.-Y. The Impact of African Swine Fever on the Efficiency of China’s Pig Farming Industry. Sustainability 2024, 16, 7819. [Google Scholar] [CrossRef]
- Li, J.; Li, Q.; Liu, L. Carbon emissions from smallholder pig production in China: A precise account based on farmers’ survey. Environ. Sci. Pollut. Res. 2022, 29, 25651–25664. [Google Scholar] [CrossRef] [PubMed]
- Nzeyimana, J.B.; Butore, J.; Ndayishimiye, L.; Butoyi, M. Impact of COVID-19 on livestock production chain and its consequences on food security: A review. Agric. Sci. Dig.-A Res. J. 2022, 42, 196–202. [Google Scholar] [CrossRef]
- Lecina-Diaz, J.; Martínez-Vilalta, J.; Lloret, F.; Seidl, R. Resilience and vulnerability: Distinct concepts to address global change in forest. Trends Ecol. Evol. 2024, 39, 706–715. [Google Scholar] [CrossRef]
- Urruty, N.; Tailliez-Lefebvre, D.; Huyghe, C. Stability, robustness, vulnerability and resilience of agricultural systems. A review. Agron. Sustain. Dev. 2016, 36, 15. [Google Scholar] [CrossRef]
- Ackerl, T.; Weldemariam, L.F.; Nyasimi, M.; Ayanlade, A. Climate change risk, resilience, and adaptation among rural farmers in East Africa: A literature review. Reg. Sustain. 2023, 4, 185–193. [Google Scholar] [CrossRef]
- Cissé, J.D.; Barrett, C.B. Estimating development resilience: A conditional moments-based approach. J. Dev. Econ. 2018, 135, 272–284. [Google Scholar] [CrossRef]
- Barrett, C.B.; Ghezzi-Kopel, K.; Hoddinott, J.; Homami, N.; Tennant, E.; Upton, J.; Wu, T. A Scoping review of the development resilience literature: Theory, methods and evidence. World Dev. 2021, 146, 105612. [Google Scholar] [CrossRef]
- Speranza, C.I.; Wiesmann, U.; Rist, S. An indicator framework for assessing livelihood resilience in the context of social–ecological dynamics. Glob. Environ. Chang. 2014, 28, 109–119. [Google Scholar] [CrossRef]
- Miao, Y.; Li, Z. The poverty alleviation effect of transfer payments: Evidence from China. Humanit. Soc. Sci. Commun. 2023, 10, 910. [Google Scholar] [CrossRef]
- Ma, L.; Zhang, Y.; Li, T.; Zhao, S.; Yi, J. Livelihood capitals and livelihood resilience: Understanding the linkages in China’s government-led poverty alleviation resettlement. Habitat Int. 2024, 147, 103057. [Google Scholar] [CrossRef]
- Cheng, X.; Yu, Z.; Gao, J.; Liu, Y.; Dai, Y.; Chen, J.; Liu, G.; Xie, Z. How to restore the livelihood resilience of the rural vulnerable? Evidence from 1, 356 households in rural China. Environ. Dev. Sustain. 2024, 1–28. [Google Scholar] [CrossRef]
- Bao, H.X.H.; Jiang, Y.; Wang, Z.; Feng, L. Social capital and the effectiveness of land use policies: Evidence from rural China. Land Use Policy 2024, 139, 107069. [Google Scholar] [CrossRef]
- Chirwa, T.G.; Odhiambo, N.M. Exogenous and endogenous growth models: A critical review. Comp. Econ. Res. Cent. East. Eur. 2018, 21, 63–84. [Google Scholar] [CrossRef]
- Bosworth, G.; Annibal, I.; Carroll, T.; Price, L.; Sellick, J.; Shepherd, J. Empowering Local Action through Neo-Endogenous Development; The Case of LEADER in England. Sociol. Rural. 2016, 56, 427–449. [Google Scholar] [CrossRef]
- Ward, N. Rural development and the economies of rural areas. In A New Rural Agenda; Institute for Public Policy Research: Newcastle upon Tyne, UK, 2006; pp. 46–67. [Google Scholar]
- Yang, N.; Wang, Y.; Jin, H.; Qi, Q.; Yang, Y. Impacts of Internet Information Literacy on Farmers’ Relative Poverty Vulnerability: Evidence from CGSS Survey Data in China. Soc. Indic. Res. 2024, 1–32. [Google Scholar] [CrossRef]
- Nugroho, A.D. Comparing the effects of information globalization on agricultural producer prices in developing and developed countries. Agris-Line Pap. Econ. Dev. Ctries. 2024, 16, 93–107. [Google Scholar] [CrossRef]
- Malik, P.K.; Singh, R.; Gehlot, A.; Akram, S.V.; Das, P.K. Village 4.0: Digitalization of village with smart internet of things technologies. Comput. Ind. Eng. 2022, 165, 107938. [Google Scholar] [CrossRef]
- Chambers, R.; Conway, G. Sustainable Rural Livelihoods: Practical Concepts for the 21st Century; Institute of Development Studies: Brighton, UK, 1992. [Google Scholar]
- Ellis, F. Rural Livelihoods and Diversity in Developing Countries; Oxford University Press: Oxford, UK, 2000. [Google Scholar]
- Obrist, B.; Pfeiffer, C.; Henley, R. Multi-layered social resilience: A new approach in mitigation research. Prog. Dev. Stud. 2010, 10, 283–293. [Google Scholar] [CrossRef]
- Simmie, J.; Martin, R. The economic resilience of regions: Towards an evolutionary approach. Camb. J. Reg. Econ. Soc. 2010, 3, 27–43. [Google Scholar] [CrossRef]
- Barrett, C.B.; Constas, M.A. Toward a theory of resilience for international development applications. Proc. Natl. Acad. Sci. USA 2014, 111, 14625–14630. [Google Scholar] [CrossRef]
- Quand, A.; Neufeldt, H.; McCabe, J.T. Building livelihood resilience: What role does agroforestry play? Clim. Dev. 2019, 11, 485–500. [Google Scholar] [CrossRef]
- Sina, D.; Chang-Richards, A.Y.; Wilkinson, S.; Potangaroa, R. A conceptual framework for measuring livelihood resilience: Relocation experience from Aceh, Indonesia. World Dev. 2019, 117, 253–265. [Google Scholar] [CrossRef]
- Liu, W.; Li, J.; Ren, L.; Xu, J.; Li, C.; Li, S. Exploring livelihood resilience and its impact on livelihood strategy in rural China. Soc. Indic. Res. 2020, 150, 977–998. [Google Scholar] [CrossRef]
- Tang, L.; Xu, Y.; Wang, W.; Wang, Y. Impact of livelihood capital and rural site conditions on livelihood resilience of farm households: Evidence from contiguous poverty–stricken areas in China. Environ. Sci. Pollut. Res. 2023, 30, 123808–123826. [Google Scholar] [CrossRef]
- Zhao, X.; Chen, H.; Zhao, H.; Xue, B. Farmer households’ livelihood resilience in ecological-function areas: Case of the Yellow River water source area of China. Environ. Dev. Sustain. 2022, 24, 9665–9686. [Google Scholar] [CrossRef]
- Marschke, M.J.; Berkes, F. Exploring Strategies that Build Livelihood Resilience: A Case from Cambodia. Ecol. Soc. 2006, 11, 709–723. [Google Scholar] [CrossRef]
- He, K.; Li, F.L.; Wang, H.; Ming, R.Y.; Zhang, J.B. A Low-carbon Future for China’s Tech Industry. Science 2022, 377, 1498–1499. [Google Scholar] [CrossRef] [PubMed]
- Goldfarb, A.; Tucker, C. Digital Economics. J. Econ. Lit. 2019, 57, 3–43. [Google Scholar] [CrossRef]
- Hjort, J.; Poulsen, J. The Arrival of Fast Internet and Employment in Africa. Am. Econ. Rev. 2019, 109, 1032–1079. [Google Scholar] [CrossRef]
- Anadozie, C.; Fonkam, M.; Cleron, J.P.; Kah, M.M. The impact of mobile phone use on farmers’ livelihoods in post—Insurgency Northeast Nigeria. Inf. Dev. 2021, 37, 6–20. [Google Scholar] [CrossRef]
- Fang, D.; Zhang, X. The protective effect of digital financial inclusion on agricultural supply Chain during the COVID-19 pandemic: Evidence from China. J. Theor. Appl. Electron. Commer. Res. 2021, 16, 3202–3217. [Google Scholar] [CrossRef]
- Fedorova, E.A.; Nekhaenko, V.V.; Dovzhenko, S.E. Impact of Financial Literacy of the Population of the Russian Federation on Behavior on Financial Market: Empirical Evaluation. Stud. Russ. Econ. Dev. 2015, 26, 394–402. [Google Scholar] [CrossRef]
- Barbalet, J. The analysis of Chinese rural society: Fei Xiaotong revisited. Mod. China 2021, 47, 355–382. [Google Scholar] [CrossRef]
- Schultz, T.W. Investment in Human Capital. Am. Econ. Rev. 1961, 51, 1–17. [Google Scholar]
- Liu, B.; Zhou, J. Digital literacy, farmers’ income increase and rural Internal income gap. Sustainability 2023, 15, 11422. [Google Scholar] [CrossRef]
- Tugade, M.M.; Fredrickson, B.L. Resilient individuals use positive emotions to bounce back from negative emotional experiences. J. Personal. Soc. Psychol. 2004, 86, 320–333. [Google Scholar] [CrossRef]
- Lazarus, R.S. From psychological stress to the emotions: A history of changing outlooks. Annu. Rev. Psychol. 1993, 44, 1–22. [Google Scholar] [CrossRef] [PubMed]
- Sun, Y.; Wang, Y.; Huang, C.; Tan, R.; Cai, J. Measuring farmers’ sustainable livelihood resilience in the context of poverty alleviation: A case study from Fugong County, China. Humanit. Soc. Sci. Commun. 2023, 10, 75. [Google Scholar] [CrossRef] [PubMed]
- Chen, Y.; Fan, Z.; Gu, X.; Zhou, L.-A. Arrival of young talent: The send-down movement and rural education in China. Am. Econ. Rev. 2020, 110, 3393–3430. [Google Scholar] [CrossRef]
- Chen, Z.; Cui, R.; Tang, C.; Wang, Z. Can digital literacy improve individuals’ incomes and narrow the income gap? Technol. Forecast. Soc. Chang. 2024, 203, 123332. [Google Scholar] [CrossRef]
- Chen, H.; Chen, C.-P.; Li, Y.; Qin, L.; Qin, M. How internet usage contributes to livelihood resilience of migrant peasant workers? Evidence from China. J. Rural Stud. 2022, 96, 112–120. [Google Scholar] [CrossRef]
- Lu, S.; Sun, Z.; Huang, M. The impact of digital literacy on farmers’ pro-environmental behavior: An analysis with the Theory of Planned Behavior. Front. Sustain. Food Syst. 2024, 8, 1432184. [Google Scholar] [CrossRef]
- Nunn, N.; Qian, N. US food aid and civil conflict. Am. Econ. Rev. 2014, 104, 1630–1666. [Google Scholar] [CrossRef]
- Zhou, D.; Zha, F.; Qiu, W.; Zhang, X. Does digital literacy reduce the risk of returning to poverty? Evidence from China. Telecommun. Policy 2024, 48, 102768. [Google Scholar] [CrossRef]
- Yang, C.; Ji, X.; Cheng, C.; Liao, S.; Obuobi, B.; Zhang, Y. Digital economy empowers sustainable agriculture: Implications for farmers’ adoption of ecological agricultural technologies. Ecol. Indic. 2024, 159, 111723. [Google Scholar] [CrossRef]
- Stock, J.H.; Yogo, M. Testing for Weak Instruments in Linear IV Regression; NBER Working Paper; National Bureau of Economic Research: Cambridge, MA, USA, 2005. [Google Scholar]
- Agresti, A. Categorical Data Analysis; John Wiley & Sons: Hoboken, NJ, USA, 2012. [Google Scholar]
- Moser, C.O. The asset vulnerability framework: Reassessing urban poverty reduction strategies. World Dev. 1998, 26, 1–19. [Google Scholar] [CrossRef]
- Gustafsson, B.; Ding, S. Maping and understanding ethnic disparities in length of schooling: The case of the Hui minority and the Han majority in Ningxia autonomous region, China. Soc. Indic. Res. 2015, 124, 517–535. [Google Scholar] [CrossRef]
- Mu, Z.; Qing, L.; Yu, X. Arranged cohabitation among Chinese Muslims. Proc. Natl. Acad. Sci. USA 2024, 121, e2317704121. [Google Scholar] [CrossRef] [PubMed]
- Wei, K.K.; Teo, H.H.; Chan, H.C.; Tan, B.C. Conceptualizing and testing a social cognitive model of the digital divide. Inf. Syst. Res. 2011, 22, 170–187. [Google Scholar] [CrossRef]
- Lei, L.I.; Zhai, S.X.; Bai, J.F. The dynamic impact of income and income distribution on food consumption among adults in rural China. J. Integr. Agric. 2021, 20, 330–342. [Google Scholar] [CrossRef]
- Tang, Y.; Li, Q.; Wu, Y. The impact of the digital divide on rural older People’s mental quality of life: A conditional process analysis. Heliyon 2024, 10, e37109. [Google Scholar] [CrossRef]
- Dettling, L.J. Broadband in the Labor Market: The Impact of Residential High-Speed Internet on Married Women’s Labor Force Participation. ILR Rev. 2017, 70, 451–482. [Google Scholar] [CrossRef]
- Vandeursen, A.; Helsper, E.J. The third-level digital divide: Who benefits most from being online? Stud. Media Commun. 2015, 10, 29–52. [Google Scholar]
Dimension Layer | Indicator Layer | Attribute | Weight |
---|---|---|---|
Buffering Capacity | The ease of seeking help during financial difficulties | + | 0.0280 |
Expenditures on gift money in social interactions | + | 0.0301 | |
Friends and relatives living in the city | + | 0.0500 | |
The number of friends and relatives who are civil servants or have public institution positions | + | 0.0753 | |
Labor force participation rate | + | 0.0082 | |
Per capita annual household income | + | 0.0939 | |
Cultivated land area | + | 0.0206 | |
Scale of breeding | + | 0.0268 | |
Credit difficulty | − | 0.0481 | |
Means of production value | + | 0.0470 | |
Means of subsistence value | + | 0.0240 | |
Per capita medical expenses | - | 0.0003 | |
Life satisfaction | + | 0.0050 | |
Self-organizing Capacity | Whether it is a village for the resource utilization of manure pollution | + | 0.0411 |
Whether to participate in collective public affairs of the village | + | 0.0055 | |
Transparency of collective public affairs in the village | + | 0.0035 | |
The degree of contact among relatives and friends | + | 0.0267 | |
Regular contacts | + | 0.0510 | |
Distance to the county town | − | 0.0034 | |
Transport accessibility | + | 0.0019 | |
Whether to participate in social organizations or e-commerce | + | 0.0942 | |
Subjective social status | + | 0.0047 | |
Attitudes toward social relationships | + | 0.0143 | |
Learning Capacity | Head of household education level | + | 0.0146 |
Annual household education expenditure | + | 0.0453 | |
Whether to participate in breeding training | + | 0.0255 | |
Whether to have participated in the resource utilization of manure pollution | + | 0.0893 | |
The environmental protection capability of manure pollution resource utilization | + | 0.0051 | |
The income-generating capacity of manure pollution resource utilization | + | 0.0069 | |
The number of information acquisition channels when encountering breeding difficulties | + | 0.0363 | |
Income from working outside the home village | + | 0.0646 | |
The ability to discern the impact of participating in the resource utilization of manure pollution on life | + | 0.0087 |
Dimension Layer | Indicator Layer | Attribute | Weight |
---|---|---|---|
Equipment and Software Operation Literacy | Whether you and your family use smartphones | + | 0.0022 |
Whether the family has been connected to broadband, the Internet, or a wireless network | + | 0.0660 | |
Information and Data Literacy | Whether you have accessed agricultural information through the Internet | + | 0.0150 |
Whether you have purchased agricultural supplies through the Internet | + | 0.1111 | |
Communication and Collaboration Literacy | Whether you have joined social media groups for agricultural production communication | + | 0.0523 |
Whether you have joined a social media group for village affairs in your village | + | 0.0097 | |
Digital Content Creation Literacy | Whether you have sold agricultural products through the Internet | + | 0.1810 |
Whether you have engaged in digital credit through the Internet | + | 0.1871 | |
Problem Solving Literacy | Whether you search the Internet for information to solve problems when encountering difficulties | + | 0.1122 |
Occupation-Related Literacy | Whether cost reduction and efficiency enhancement have been achieved through the use of agricultural ICT technology | + | 0.0466 |
Whether agricultural labor has been released through the use of agricultural ICT technology | + | 0.0936 | |
Whether the ecological environment has been improved through the use of agricultural ICT technology | + | 0.1231 |
Variables | Definition | Mean | Std. Dev. |
---|---|---|---|
Digital literacy | Based on the livelihood resilience index system calculated from Table 1 | 0.162 | 0.069 |
Livelihood Resilience | Based on the digital literacy index system calculated from Table 2 | 0.175 | 0.129 |
Proportion of agricultural income | The percentage of the total agricultural output value to the total family income | 0.581 | 0.336 |
Family consumption | The percentage of family consumption expenditure to the total family income | 0.785 | 2.703 |
Actual family members | The number of members actually living in the household | 4.284 | 1.678 |
Land transfer | Has the land been transferred?: 1 = Yes; 0 = No | 0.523 | 0.500 |
Whether it is a demonstration household for breeding | 1 = Yes; 0 = No | 0.202 | 0.402 |
Whether it is a family farm | 1 = Yes; 0 = No | 0.109 | 0.312 |
Gender of the decision-maker | 1 = Male; 0 = Female | 0.802 | 0.398 |
Age of the decision-maker | Age | 49.585 | 10.004 |
Health status of the decision-maker | 1 = Healthy; 2 = Frail; 3 = Chronic illness; 4 = Serious illness; 5 = Disabled | 1.305 | 0.737 |
Whether the decision-maker is an ethnic minority | 1 = Yes; 0 = No | 0.534 | 0.499 |
Living “differential mode of association” | Whether to mainly rely on relatives for help when encountering economic difficulties in life: 1 = Yes; 0 = No | 0.770 | 0.421 |
Production “differential mode of association” | Whether the purchase of feed materials in production mainly depends on surrounding farmers and merchants: 1 = Yes; 0 = No | 0.387 | 0.487 |
Learning Channels | 1 = Agricultural software; 2 = Short video software; 3 = Social media software; 4 = Shopping software; 5 = Search engine software | 1.615 | 1.215 |
Income Types | 1 = Operating income; 2 = Wage income; 3 = Business and industrial income; 4 = Property income; 5 = Transfer income | 3.105 | 0.933 |
Variable | Livelihood Resilience | Livelihood Resilience |
---|---|---|
(1) | (2) | |
Digital Literacy | 0.145 *** (0.016) | |
Equipment and software operation literacy | 0.122 ** (0.060) | |
Information and data literacy | 0.031 (0.046) | |
Communication and collaboration literacy | 0.595 *** (0.072) | |
Digital content creation literacy | 0.249 *** (0.034) | |
Problem solving literacy | 0.229 *** (0.046) | |
Occupation-related literacy | −0.022 (0.028) | |
Proportion of agricultural income | 0.012 * (0.006) | 0.007 * (0.006) |
Family consumption | −0.001 * (0.001) | −0.001 * (0.001) |
Actual family members | 0.001 (0.001) | 0.001 (0.001) |
Land transfer | 0.001 (0.004) | 0.000 (0.004) |
Whether it is a demonstration household for breeding | 0.016 *** (0.005) | 0.015 *** (0.005) |
Whether it is a family farm | 0.046 *** (0.007) | 0.040 *** (0.007) |
Gender of the decision-maker | 0.015 *** (0.005) | 0.013 *** (0.005) |
Age of the decision-maker | −0.000 (0.000) | 0.000 (0.000) |
Health status of the decision-maker | 0.006 ** (0.003) | 0.005 * (0.003) |
Whether the decision-maker is an ethnic minority | −0.016 ** (0.008) | −0.012 * (0.008) |
Xigui County | −0.001 (0.009) | −0.014 (0.009) |
Jingyuan County | −0.001 (0.010) | −0.011 (0.010) |
Pengyang County | −0.023 ** (0.009) | −0.028 *** (0.009) |
Ganzhou District | −0.011 ** (0.006) | −0.014 *** (0.006) |
Constant term | 0.137 *** (0.015) | 0.134 *** (0.015) |
Amount of observed data | 1047 | 1047 |
R-squared | 0.219 | 0.288 |
Variable | Digital Literacy | Livelihood Resilience | Digital Literacy | Livelihood Resilience |
---|---|---|---|---|
(1) | (2) | (3) | (4) | |
Digital literacy | 0.366 *** (0.085) | 0.672 *** (0.160) | ||
Topographical characteristics of the village | −0.014 ** (0.006) | −0.019 *** (0.006) | ||
The average digital literacy of the other farmers in the village | 0.573 *** (0.112) | |||
The collective financial income of the village where the farmer resides | 0.004 ** (0.002) | |||
Control variables | Controlled | Controlled | Controlled | Controlled |
County-level dummy variable | Controlled | Controlled | Controlled | Controlled |
Constant term | 0.144 (0.038) | 0.086 *** (0.025) | 0.250 *** (0.031) | 0.015 (0.043) |
Amount of observed data | 1047 | 1047 | 1047 | 1047 |
R-squared | 0.137 | 0.067 | 0.105 | 0.023 |
Matching Method | ATE | Standard Error | t-Test |
---|---|---|---|
Nearest neighbor matching (k = 1) | 0.034 | 0.006 | 6.04 *** |
Nearest neighbor matching (k = 4) | 0.029 | 0.005 | 6.01 *** |
Radius matching (radius = 0.01) | 0.029 | 0.005 | 6.41 *** |
Kernel matching | 0.03 | 0.004 | 6.59 *** |
Variable | Livelihood Resilience | Livelihood Resilience | Livelihood Resilience (Incorporating Vulnerability) | Livelihood Resilience (1% Winsorized) | Livelihood Resilience (5% Winsorized) |
---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | |
Digital Literacy (Principal Component Analysis Method) | 0.028 *** (0.004) | ||||
Digital Literacy (Equal Weighting Method) | 0.145 *** (0.015) | ||||
Digital Literacy (Entropy Method) | 0.140 *** (0.016) | 0.139 *** (0.015) | 0.125 *** (0.014) | ||
Control Variables | Controlled | Controlled | Controlled | Controlled | Controlled |
County-Level Dummy Variable | Controlled | Controlled | Controlled | Controlled | Controlled |
Constant Term | 0.100 *** (0.019) | 0.097 *** (0.017) | 0.140 *** (0.015) | 0.140 *** (0.015) | 0.142 *** (0.014) |
Amount of Observed Data | 1047 | 1047 | 1047 | 1047 | 1047 |
R-Squared | 0.186 | 0.227 | 0.217 | 0.218 | 0.205 |
Variable | Non-Ethnic Minority Dominated Village | Ethnic Minority Concentrated Village | Decision-Maker Is Not the Hui Ethnicity | Decision-Maker Is the Hui Ethnicity |
---|---|---|---|---|
(1) | (2) | (3) | (4) | |
Digital literacy | 0.136 *** (0.017) | 0.182 *** (0.037) | 0.113 *** (0.024) | 0.164 *** (0.020) |
Control variables | Controlled | Controlled | Controlled | Controlled |
County-level dummy variable | Controlled | Controlled | Controlled | Controlled |
Amount of observed data | 917 | 130 | 759 | 288 |
R-squared | 0.214 | 0.424 | 0.186 | 0.233 |
Variable | Total Family Income | Decision-Maker Is Male | Decision-Maker Is Female | ||
---|---|---|---|---|---|
(1) 0~33% | (2) 33~66% | (3) 66~99% | (4) | (5) | |
Digital literacy | 0.117 *** (0.027) | 0.187 *** (0.027) | 0.107 *** (0.027) | 0.162 *** (0.017) | 0.069 ** (0.038) |
Control variables | Controlled | Controlled | Controlled | Controlled | Controlled |
County-level dummy variable | Controlled | Controlled | Controlled | Controlled | Controlled |
Amount of observed data | 345 | 345 | 345 | 840 | 207 |
R-squared | 0.157 | 0.276 | 0.209 | 0.225 | 0.071 |
Variable | Buffering Capacity | Buffering Capacity | Self-Organizing Capacity | Self-Organizing Capacity | Learning Capacity | Learning Capacity |
---|---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | (6) | |
Digital Literacy | 0.009 ** (0.004) | 0.069 *** (0.010) | 0.067 *** (0.010) | |||
Equipment and Software Operation Literacy | 0.028 * (0.017) | 0.070 * (0.038) | 0.024 (0.040) | |||
Information and Data Literacy | 0.006 (0.013) | −0.037 (0.029) | 0.061 * (0.031) | |||
Communication and Collaboration Literacy | −0.006 (0.020) | 0.328 *** (0.046) | 0.274 *** (0.048) | |||
Digital Content Creation Literacy | 0.016 * (0.010) | 0.223 *** (0.022) | 0.010 (0.023) | |||
Problem Solving Literacy | 0.015 (0.013) | −0.006 (0.029) | 0.220 *** (0.031) | |||
Occupation-Related Literacy | 0.001 (0.008) | −0.021 (0.018) | −0.002 (0.019) | |||
Control Variables | Controlled | Controlled | Controlled | Controlled | Controlled | Controlled |
County-Level Dummy Variable | Controlled | Controlled | Controlled | Controlled | Controlled | Controlled |
Constant Term | 0.038 *** (0.004) | 0.039 *** (0.004) | 0.037 *** (0.010) | 0.037 *** (0.009) | 0.063 *** (0.010) | 0.059 *** (0.010) |
Amount of Observed Data | 1047 | 1047 | 1047 | 1047 | 1047 | 1047 |
R-Squared | 0.150 | 0.153 | 0.132 | 0.232 | 0.131 | 0.179 |
Variable | Living ”Differential Mode of Association” | Production “Differential Mode of Association” | Learning Channels | Income Types |
---|---|---|---|---|
(1) | (2) | (3) | (4) | |
Digital Literacy | −1.206 *** (0.341) | −0.819 ** (0.330) | 3.101 *** (0.276) | 1.718 *** (0.276)) |
Control Variables | Controlled | Controlled | Controlled | Controlled |
County-Level Dummy Variable | Controlled | Controlled | Controlled | Controlled |
Amount of Observed Data | 1047 | 1047 | 1047 | 1047 |
R-Squared | 0.040 | 0.025 | 0.211 | 0.115 |
Variable | Objective Hollowing-Out | Subjective of Rural Hollowing-Out | Subjective Indicators of Rural Hollowing-Out | |||
---|---|---|---|---|---|---|
No Caregiving Burden | Only the Elderly Care Burden | Only the Child-Rearing Burden | Dual Caregiving Burden | |||
(1) | (2) | (3) | (4) | (5) | (6) | |
Digital literacy | 0.027 (0.031) | 0.987 ** (0.402) | 1.039 (0.852) | 1.760 * (1.016) | 1.172 * (0.672) | 0.090 (0.981) |
Control variables | Controlled | Controlled | Controlled | Controlled | Controlled | Controlled |
County-level dummy variable | Controlled | Controlled | Controlled | Controlled | Controlled | Controlled |
Constant term | 0.588 *** (0.031) | 0.645 * (0.374) | −0.436 (1.087) | 1.115 (0.936) | 0.339 (0.660) | 0.920 (1.038) |
Amount of observed data | 1047 | 1047 | 278 | 238 | 345 | 186 |
R-squared | 0.144 | 0.239 | 0.083 | 0.102 | 0.052 | 0.249 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Ma, X.; Cheng, L.; Li, Y.; Zhao, M. Digital Literacy and the Livelihood Resilience of Livestock Farmers: Empirical Evidence from the Old Revolutionary Base Areas in Northwest China. Agriculture 2024, 14, 1941. https://doi.org/10.3390/agriculture14111941
Ma X, Cheng L, Li Y, Zhao M. Digital Literacy and the Livelihood Resilience of Livestock Farmers: Empirical Evidence from the Old Revolutionary Base Areas in Northwest China. Agriculture. 2024; 14(11):1941. https://doi.org/10.3390/agriculture14111941
Chicago/Turabian StyleMa, Xuefeng, Liang Cheng, Yahui Li, and Minjuan Zhao. 2024. "Digital Literacy and the Livelihood Resilience of Livestock Farmers: Empirical Evidence from the Old Revolutionary Base Areas in Northwest China" Agriculture 14, no. 11: 1941. https://doi.org/10.3390/agriculture14111941