The estimation of long term impacts of China's key priority forestry programs on rural household incomes

Can Liu, Katrina Mullan, Hao Liu, Wenqing Zhu, Qingjiao Rong

Research output: Contribution to journalArticlepeer-review

24 Scopus citations


We use a large unique household panel dataset spanning 16 years to estimate the impacts of three Key Priority Forestry Programs (the KPFPs) in China on household incomes. The programs are the most significant of China's forest policies namely the Sloping Land Conversion Program (the SLCP), the Natural Forest Protection Program (the NFPP), and the Desertification Combating Program around Beijing and Tianjin (the DCBT). A fixed effect model with clustered standard errors is used to identify programs' impacts based on variation in participation across households and time. In addition to estimating the total impacts of these programs, individually and in combination, we disaggregate the effects by income source, stage of policy implementation, and duration of participation. Overall, the impacts of the KPFPs on rural households' income vary with time of enrollment and policy stage. We observe that the KPFPs in their initial stages of implementation, and for the early years of household participation, had negative, or at best neutral impacts on household incomes, in particular incomes from land. However, the later stages of the SLCP and the DCBT have tended to raise land-based incomes, and the NFPP has ceased to have a negative effect. This is likely to be in part the result of adjustments made by rural households over time in response to changes in the programs, as well as in market and environmental conditions.

Original languageEnglish
Pages (from-to)267-285
Number of pages19
JournalJournal of Forest Economics
Issue number3
StatePublished - Aug 1 2014


  • China
  • Ecological restoration
  • Forest economics
  • Priority forestry programs
  • Rural development
  • Rural households' income


Dive into the research topics of 'The estimation of long term impacts of China's key priority forestry programs on rural household incomes'. Together they form a unique fingerprint.

Cite this