Abstract
This study evaluates the labor response of rural households participating in the Grain for Green program in China, the largest payments for ecosystem services program in the developing world. Using a panel data set that we designed and implemented, we find that the participating households are increasingly shifting their labor endowment from on-farm work to the off-farm labor market. However, the effects vary depending on the initial level of human and physical capital. The results support the view that one reason why the participants are more likely to find off-farm employment is because the program is relaxing households’ liquidity constraints.
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Notes
- 1.
The program was officially implemented in 2001. Pilot projects for the program got under way in 1999 in selected provinces. The Grain for Green program is also known as the Sloped Land Conversion Program.
- 2.
The study by Groom et al. (2006) uses a household survey implemented in 2004 and collected 1999 pre-program data on a recall basis.
- 3.
However, this limitation in fact may strengthen our finding. In a sense, we are doing a stricter test of how many new individuals are working off the farm due to the program. If we indeed find that there are additional new individuals in the off-farm labor market, we conjecture that we would find an increase in the duration as well.
- 4.
The rural nonfarm sectors consist essentially of Township and Village Enterprises (TVEs) and the rural private economy.
- 5.
For an excellent overview of Grain for Green, see Xu et al. (2004).
- 6.
Due to recent controversies over fiscal pressures, hikes in grain prices, and delivery of program compensation, the government scaled back expansion of the program in 2005 (Xu et al., 2006). However, in 2007, the government announced that it will extend the program until 2021 with the same goal of converting 15 million ha in total (Guowuyuan, 2007).
- 7.
The annual average official exchange rate in 2001 was 8.28 Chinese yuan to one U.S. dollar. The purchasing-power parity conversion factor in 2001 was 1.9 yuan to the dollar (World Bank, 2003).
- 8.
According to Xu et al. (2006), actual compensation received by farm households in our sample fell short of the compensation standards set in the program guidelines for a fraction of the households. Among the households that did not voluntarily participate, they received, on average, only 46% of their promised compensation in 2002, compared with 62% for those who participated voluntarily. There are two plausible reasons for this shortfall in receiving payment. First, based on informal interviews during our field work we found some farmers whose payments were lagging due to logistical reasons. Program expansion had been so fast that local government agencies responsible for program supervision did not have sufficient manpower to check whether the converted land satisfied government-stipulated requirements (such as tree types and survival rates). Second, some case studies have found that local governments retained some compensation to make up for expenditure shortfalls and tax arrears. In other cases government kept some of the funds to compensate themselves for expenditures on plant seedlings and other costs.
- 9.
Based on our sampling strategy, the sample is representative of households in participating counties in the three provinces. For example, when we compare the provincial means of household size and area of cultivated land in 1999, we find them comparable to the statistics published by National Bureau of Statistics of China (1999).
- 10.
In this study off-farm labor includes any labor that is not on a farm. We define an individual to have an off-farm occupation if the person engages in wage-earning activities in an off-farm firm or in nonfarm self employment for at least seven days in a given year.
- 11.
The reliability of the DID estimator lies in the identification assumption that there are no omitted time-varying effects that are correlated with program participation. For example, the identification assumption might be violated if other local governmental programs existed that both affected labor allocation and were correlated with participation in Grain for Green. Unfortunately, we did not have information to control for other governmental programs and thus the reader needs to interpret all results with this caveat in mind.
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Acknowledgements
An earlier version of this manuscript has appeared in the American Journal of Agricultural Economics (2009, 91(1): 70–86). The authors are grateful for the assistance in survey design, data collection and entry by Michael Bennett, Yazhen Gong, Zhigang Xu, Fujin Yi, and other members of the Center for Chinese Agricultural Policy, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences. We thank Stephen Vosti, James Wilen, Amelia Blanke, Susan Richter, Aslihan Arslan, the Co-editor and three anonymous referees for their comments and suggestions. The authors acknowledge the support from the Ford Foundation, National Science Foundation of China (70024001) and the Agricultural Extension Service of Rhode Island (AES contribution no. 5202) for financial assistance.
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Appendix: Assessing Recall Bias
Appendix: Assessing Recall Bias
The study relies on information for 1999 that was collected in 2003, and we acknowledge the potential for problems inherent in recall data, especially regarding the pre-program period. Long-term recall data are potentially inaccurate, although this issue continues to be debated in the literature. Unfortunately, the Chinese government's quick decision to implement Grain for Green and lack of transparency in the details of its implementation precluded interviews with potential participants at the program's onset. We also endeavor to deal with the recall bias by reestimating all of the analyses using a sample of individuals from only 67 households—the 27 households that switched from nonparticipant to participant status between the two surveys and the 40 nonparticipating households. With this subset, while the sample is smaller, the data are true panel data and are not subject to errors due to recall. With this subsample, we compare the changes in off-farm labor between 2002 and 2005 to avoid having to rely on the recall data for 1999. If the results from the analysis using the subsample are consistent with the results from the analysis using the full sample, it would suggest that that recall bias is limited.
Overall, the findings from the smaller subset are consistent with those from the full sample (Tables 9.7 and 9.8). The DID estimates for the subset are slightly larger than the estimates for the full sample. This consistency between samples suggests that recall bias in 1999 was limited and/or that the DID approach was able to control for the bias that existed in both groups.
9.1.1 Heterogeneous Program Effect on Off-Farm Labor Using Zeldes’ Rule
We found consistent results when we split the households using Zeldes’ rule into liquidity-constrained and -unconstrained groups and compared the DID estimates (Zeldes, 1989). The DID estimates for the constrained group was positive and statistically significant both at the household and individual levels. The DID estimates for the unconstrained group were insignificant. The number of participating households that were liquidity-constrained and unconstrained were 170 and 55, respectively, and for non-participating households 32 and 8. At the individual level, the number of participating individuals that were liquidity constrained and unconstrained were 1,316 and 478, respectively, and for non-participating individuals 226 and 72, respectively.
The DID estimates for liquidity-constrained and unconstrained individuals were 0.180 (z = 2.46) and –0.049 (z = 0.31), respectively. The findings are consistent with the results from the quartile approach.
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Uchida, E., Rozelle, S., Xu, J. (2009). Conservation Payments, Liquidity Constraints and Off-Farm Labor: Impact of the Grain for Green Program on Rural Households in China. In: Yin, R. (eds) An Integrated Assessment of China's Ecological Restoration Programs. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-2655-2_9
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