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P O V E R T Y &

E C O N O M I C P O L I C Y R E S E A R C H N E T W O R K

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Siem Reap, Cambodia December 3-9, 2011

The Effects of Priority Forestry Programs on China’s Rural

Household Income

Can Liu

Katrina Mullan

Hao Liu

Wenqing Zhu

November 10, 2011

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The Effects of Priority Forestry Programs on China’s Rural 1

Household Income 2

By Can Liu 3

Katrina Mullan 4

Hao Liu 5

Wenqing Zhu 6

(10-RG-12095-Liu ) 7

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November 10， 2011 20

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The Effects of Priority Forestry Programs on China’s Rural 25

Household Income 26

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Abstract: the paper use 2070 balanced panel dataset, and cluster-specific fixed effect model is 28

used. We find the PFPs have mixed effects on China’s rural household income, and also these 29

impacts of the PFPs on rural household total income, land-based income and off-farm income 30

have been changing with the year. 31

Key words: Priority Forestry Programs, rural household, income, impact, China 32

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1. Introduction 34

With an eightfold increase of per capita gross domestic product (GDP) since 1978, China has 35

witnessed tremendous economic growth over the past three decades (Zheng et al. 20081). 36

Accordingly, Chinese population living in absolute poverty (below a per capita annual income of 37

637 Yuan in 1995 real price) was over 250 million in 1978, but it reduced to 21.5 million by the 38

1 Zheng, JH, Bigsten A, Hu AG (2008) Can China’s growth be sustained? A productivity perspective. World

Development 37(4): 874~888

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end of 2006 (China National Statistics Bureau 2 , or CNSB, 2007). Despite these great 39

achievements, poverty, especially in the rural areas of western China, remains a troublesome 40

phenomenon. Residents living below the World Bank poverty line of $1 per capita per day 41

amounted to 135 million in 2004 (Chen and Ravillion 20043). In its “Human Development Report 42

2005,” the United Nations Development Program (UNDP4) noted that the pace of poverty 43

alleviation in China has slowed down markedly over the past decade (UNDP 2005). Obviously, 44

how to further reduce rural poverty and increase farmer’s income still is a top policy priority to the 45

Government of China. 46

Forests often play a crucial role in the lives of many poor people. Worldwide, almost 70 million 47

people – many indigenous – live in remote areas of closed tropical forests and another 735 million 48

rural people live in or near tropical forests and savannas (FAO 20065, World Bank 20006). In 49

China, most of its 592 poverty counties are found in areas that are far away from urban centers 50

and have poor traffic access; meanwhile, they tend to possess relatively plentiful forests (State 51

Forestry Administration, 20037). In numerous impoverished places, forestry has indeed been a 52

main source of income for farmers (Liu and Lv 20088). 53

2 China National Bureau of Statistics (2008) China Statistical Yearbook 2002~2007. Beijing: China Statistics

Press (in Chinese) 3 Chen SH, Ravillion M (2004) How have the world’s poorest fared since the early 1980s. World Bank Discussion Paper, WPS 3341 4 UNDP (2005) Human Development Report 2005. New York: UNDP 5 FAO (2006) Global Forest Resources Assessment 2005. Rome: FAO. 6 World Bank (2000) Towards a Revised Forest Strategy for the World Bank Group. Washington, DC: The World

Bank 7 State Forestry Administration (2003) China Forestry Development Report. Beijing: China Forestry Publishing

House (in Chinese) 8 Liu C, Lü JZ (2008) The Effects of the Priority Forestry Programs on Farmers’ Income during 1998-2006: A

National Prospective. China Forestry Economics and Development Research Center (Working Paper)

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A crisis-response model offers a more credible explanation, the perception of crises, for example, 54

wood scarcity in the early 19th and Alpine floods in mid-century in France, the occurrence of 55

floods and avalanches in mid-19th century in Switzerland, floods and mudslides in Thailand in 56

1988, and the devastating floods in 1991 in Philippines (Mather et al. 19999; Hirsch and Lohmann 57

198910) was significant active factors with favorable circumstances, policies of these countries 58

have changed, some of them have increased forest resources and some of them are still in the stage 59

of deforestation. Cultural and political climates were highly significant in facilitating effective 60

intervention. The crisis resulting from destructive exploitation was the stimulus for the 61

introduction of new regimes of resource management (Friedrich 1904)11. The serious natural 62

disasters that China suffered in 1997 and 1998 spurred new efforts to protect the country‘s fragile 63

and fragmented environment. Due to severe droughts in 1997, the lower reaches of the Yellow 64

River dried up for 267 days, putting industrial, agricultural and residential water uses in the north 65

plains in great jeopardy (Xu and Cao 2001)12. In 1998, massive floods along the Yangtze River, 66

Songhua River and Nei River claimed that the lives of over 3000 people and led to more than 67

US$ 12 billion in property damages and production loss (State Forestry Administration 2001)13. 68

Serious soil erosion and sandstorm occurred in the later 1990s in the northern China (Liu and 69

Zhang 200614). In the meantime, Chinese grain production reached to historical high, grain price 70

9 Mather, A. S., J. Fairbairn and C. L. Needle (1999) the Course and Drivers of the Forest Transition: the Case of France, Journal of Rural Studies Vol. 15. No. 1 65-90. 10 Hirsch, O. and Lohmann (1989) Contemporary Politics of Environment in Thailand. Asian Survey 29, 439-451. 11 Friedrich, E. (1904) Wesen und Geographische Verbreitung der Raubwirtschaft. Petermann Mitteilungen 50, 68-70 and 92-95. 12 Xu, J.T., Cao, Y.Y., (2001) Converting Steep Cropland to Forest and Grassland: Efficiency and Prospects of sustainability. International Economic Review (Chinese) 2, 56-60 13 State Forestry Administration. (2003) China Forestry Development Report, China Forestry Publish Houses (in Chinese). 14 Liu C, Zhang W (2006) The impact of environmental policy on household income and activity choice: evidence from the Sand Control Program for Areas in the Vicinity of Beijing and Tianjin. Journal of Economics 1: 273～290

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has reached a low level (Liu and Wu 201015). The Southeast Asian crisis affected Chinese 71

economic development; the Government of China took measures to stimulus economic recovery 72

with its abundant financial revenue. Under these circumstances, the Government of China 73

launched six Forestry Priority programs (PFPs) for environmental restoration and pushing 74

economic growth and adjustment of rural economic structures and poverty reduction since 1998. 75

These contexts triggered the government to initiate the Natural Forest Protection Program (NFPP) 76

in 1998 and the Sloping Land Conversion Program (SLCP) in 1999. The U.S Department of 77

Agriculture’s Conservation Reserve Program has many features in common with the SLCP 78

(Uchida et al. 200516; Johnson and Maxwell 200117; Wang and Maclaren 201118). And some forest 79

transition countries have abandon their least productive lands which then revert to forest 80

(Meyfrodt Rudel and Lambin 2010)19 Following successful experimenting during 1998-1999, the 81

NFPP was formally launched in 2000 with an initial investment of 96.4 billion Yuan for the 82

decade (This is equivalent to roughly US$14.1 billion given the current exchange of $1 = 6.85 83

yuan in 2000). A key component of the NFPP was commercially logging bans over 30 million 84

hectares of natural forests in the upper reaches of the Yangtze River and the upper/middle 85

reaches of the Yellow River. In other areas (such as the Northeast, northwest of China and Hainan 86

Province), harvest restrictions were tightly imposed. The SLCP was piloted in Sichuan, Shaanxi, 87

and Gansu provinces in 1999 and 2000 (also known as the ‘Grain for Green’ program in the 88

15 Liu, C., Wu, B.(2010) ‘Grain for Green Programme in China: Policy Making and Implementation? Brief Series—Issue 60, University of Nottingham China Policy Institution( April) 16 Uchida, E., Xu, J., and Rozelle, S., (2005). Cost-effectiveness and sustainability of China’s conservation Set-aside Program, Land Economics 8(2): 247-264. 17 Johnson, J., and Maxwell, B., (2001). The role of the Conservation Reserve Program in controlling rural residential development. Journal of Rural Studies 17: 323-332. 18 Wang, C., Maclaren, V.,(2011). Evaluation of Economic and Social Impacts of the Sloping Land Conversion Program: A Case Study in Dunhua County, China, Forest Policy and Economics. Doi: 10.1016/j.forpol.2011.06.02 19 Meyfroidt, P., Rudel, T., and Lambin, E. F., (2010). Forest transitions, trade, and the global displacement of land use, PNAS Vol. 107, No. 49: 20917-20922.

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international literature). The SLCP was formally implemented in 2001. Its primary goal was to 89

convert 14.6 million hetares of sloping and desertified farmland into forest and grass coverage 90

from 2001 to 2010. When it was formally launched, the SLCP extended to and covered 25 91

provinces, with a budget of 225 billion Yuan. In addition to the above two mega programs, a 92

number of other efforts of ecological restoration and forest expansion have been consolidated into 93

the following four programs: the Desertification Combating Program around Beijing and Tianjin 94

(DCBT), the Shelterbelt Development Program in the Three-Norths (the northwestern, 95

north-central, and northeastern regions of China) and the Yangtze River Basin (SBDP), the 96

Wildlife Conservation and Nature Reserve Program (WCNR), and the Industrial Timber 97

Plantation Program (ITPP) (See table 1). Together with the NFPP and the SLCP, these programs 98

comprise the six PFPs (SFA, 2005). From 1998 to 2009, the afforestation area of NFPP, SLCP, 99

DCBP, SBFP and ITDP is 7957.3 thousand hectares, 21633.4 thousand hectares, 4160.3 thousand 100

hectares thousand hectares, 13872.6 thousand hectares and 846.3 thousand hectares (State Forestry 101

Administration 201020). Rural households have been involved in the implementation of these PFPs, 102

for instance, by the end of 2008, 26.84 million rural households have been involved in SLCP in 25 103

provinces, and 2.52 million rural households have been involved in DCBT in 75 program counties 104

of 5 provinces (Beijing, Tianjin, Hebei, Shanxi and Inner Mongolia). Household forestlands in 105

NFPP area have been coved for NFPP, SBDP, ITPP and WCNR; in the meantime, some of rural 106

households have inputted their labors and capitals in SBDP, ITPP and WCNR. 107

[Insert table 1 here] 108

20 State Forestry Administration (2010) China Forestry Development Report. Beijing: China Forestry Publishing House (in Chinese)

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Ecosystem services are increasingly recognized by governments as natural capital assets that 109

provide benefits to society (Millennium Ecosystem Assessment 2005). The U.S Department of 110

Agriculture’s Conservation Reserve Program has many features in common with the SLCP 111

(Uchida et al. 200521; Johnson and Maxwell 200122). And some forest transition countries have 112

abandoned their least productive lands which then revert to forest (Rudel and Lambin 2010)23. At 113

a time when ecological restoration has become a common cause and payment for ecosystem 114

services has been widely promoted in pursuant of sustainable development (FAO 200924, 115

Millennium Ecosystem Assessment 2005, Li, Feldman, Li and Daily 201125), Thailand, Indonesia 116

and other countries have launched Logging Ban programs similar to NFPP (FAO 2009), and Wild 117

life and natural reserve programs, timber plantation programs have been widely adopted 118

worldwide, scrutinizing China’s recent experience in general and its implementation of the PFPs 119

in particular is interesting. This is because evaluating the program impacts on participating 120

households’ welfare is essential to determine the directions toward which public funding and 121

policy should be mobilized. Lessons learned from China can thus benefit many other countries, 122

especially developing counties that face challenges of both environmental protection and poverty 123

reduction. 124

From the perspective of rural households, the direct effects of these PFPs are reflected mainly in: 125

(1) the government subsidies they receive for retiring and converting the sloping and desertified 126

21 Uchida, E., Xu, J., and Rozelle, S., (2005). Cost-effectiveness and sustainability of China’s conservation Set-aside Program, Land Economics 8(2): 247-264. 22 Johnson, J., and Maxwell, B., (2001). The role of the Conservation Reserve Program in controlling rural residential development. Journal of Rural Studies 17: 323-332. 23 Rudel, P. M., , T., and Lambin, E. F., (2010). Forest transitions, trade, and the global displacement of land use, PNAS Vol. 107, No. 49: 20917-20922. 24 FAO (2009). The State of the World’s Forests 2009. Rome: FAO (pp. 6-15) 25 Li, J., Feldman, M. W., Li, S., and Daily, G. C., (2011). Rural household income and inequality under the Sloping Land Conversion Program in western China, PNAS Vol. 108, No. 19: 7721-7726.

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cropland or rehabilitating grassland (under the SLCP or the DPBT); (2) the government 127

restrictions imposed on their logging, collecting, and even managing practices in case their forests 128

are put under protection for providing more important ecosystems services (under the NFPP or the 129

WCNR); and (3) the government incentives offered for them to engage in plantation and 130

shelterbelt establishment and other related activities (under the ITPP or the SBDP) (see table 1). 131

There exist numerous tradeoffs, most of which can result in changed patterns of land use and 132

production. Induced by the land reallocation and production shift, farmers have to intensify 133

farming and commercial forestry activities on their remaining lands, switch animal husbandry 134

from open gazing to pen raising, or search for off-farm jobs and others in order to sustain their 135

income growth. Therefore, it is expected that following their participation in the PFPs, farmers’ 136

income sources and employment structure, production technology will undergo major 137

transformation. To be sure, in addition to farmers’ own initiatives, efforts, and inputs, the extent 138

and trend of their income and employment changes depend critically on the availability and 139

effectiveness of technical, financial, and personnel assistances provided by the local public 140

agencies. Finally, after the implementation of PFPs, the local ecological conditions have changed 141

which would or might benefit for local production, and further affect rural households’ income and 142

poverty reduction. 143

So far, implementing these PFPs has substantially altered the land use patterns in many upland 144

regions, where both a significant portion of the country’s primary forest ecosystems and a high 145

rate of poverty incidence are found. As a result, a large amount of sloping cropland has been 146

converted to forest and grass coverage and many existing forests, including quite some managed 147

ones, have been subject to strict regulation for commercial use. Thus, a question of broad interest 148

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and major relevance is: How has implementing the PFPs affected the farmers’ income? And 149

what and how has affected on rural poverty reduction by these PFPs? And what and how has the 150

rural poor been affected by these PFPs? The key objective of the paper is to address this question 151

empirically. In the meantime, these PFPs will continue to implement in the 12th-Five-Year 152

National Plan (2011-2015) and the 13th-Five Year National Plan (2016-2020), based on our 153

empirical results, shall we find the alternatives for balancing rural household livelihood and 154

ecosystem restoration or not? 155

The impacts of the SLCP on farmers’ income and livelihood have received a great deal of attention 156

in the literature. Yao, Guo and Huo(2010) use panel dataset of 600 household in three counties in 157

the Loess Plateau region and find that the effect of SLCP on rural households’ crop production 158

income, animal husbandry income and off-farm income varied substantially26. With sample data 159

collected from four counties in Shaanxi Province and Qinghai Province, Xie and et al (2006) 160

demonstrate that SLCP could provide increased household net profits and claim that their result is 161

robust under a range of discount rate and output prices27. Liu and et al (2010) use the dataset of 162

10-year and 15 county-household in 6 province and their results show that the SLCP has 163

significant contribution to rural household income 28 . In addition to examining its cost 164

effectiveness and sustainability, Uchida et al. (2005) 29and Uchida et al. (2007) 30analyze its 165

26 Yao,S.B., GuoY. J., and Huo, X. X. (2010). An Empirical Analysis of the Effects of China’s Land Conversion Program on Farmers’ Income Growth and Labor Transfer, Environmental Management 45: 502-512 27 Xie,C., Zhao, J., Liang D., Bennett, J., Zhang, L., Dai, G., Wang, X.H., (2006). Livelihood Impacts of the Conversion of Cropland to Forest and Grassland Program. Journal of Environmental Planning and Management 49: 555-570. 28 Liu, C., Lv, J.Z., and Yin, R. S., (2010). An Estimation of the Effects of China’s Forestry Programs on Farmers’ Income, Environmental Management 45: 526-540. 29 Uchida E, Xu JT, Rozelle S (2005) Grain for Green: cost-effectiveness and sustainability of China's

conservation set-aside program. Land Economics 81(2): 247-264 30 Uchida E, Xu JT, Xu ZG et al (2007) Are the poor benefiting from China’s land conservation program?

Environment and Development Economics 12(4): 593-620

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influence on eradicating poverty in the countryside. They find that the program has been 166

successful in poverty alleviation, even though poor households may not have benefited the most. 167

Further, their evidence suggests that households participating in the program have already begun 168

transferring their labor to non-farming sectors more rapidly than those not participating in the 169

program. In contrast, using data collected from Sichuan, Shaanxi, and Gansu for the first few 170

years of the program, Xu and Qin (2004)31 show that it made little difference in affecting farmers’ 171

income between. Their conclusion thus implies that the SLCP’s role in relieving poverty is limited; 172

the reduction of poverty is more likely driven by the overall economic development, which 173

provides greater opportunities to farmers, rather than the direct subsidies of the land conversion. 174

Wang and Maclaren (2011) uses a dichotomous logistic regression analysis and find that “the 175

farmers may be forced, by economic necessity, to return to their former practices when a project 176

such as SLCP does not provides them with ability to find a new way of life after the compensation 177

ends” in Dunhua County, China. 178

Observers also point out that in some cases, the goal of the program is not well understood by 179

farmers; and it may even be inconsistent with their aspirations, which have affected their 180

enthusiasm for participation (Du 200432). According to Xu (200333), the main reasons that some 181

farmers lack interest in the program are partly because the subsidies are not delivered on time and 182

in full, and partly because no appropriate remedies were put in place to address the restrictions on 183

31 Xu JT, Qin P (2004) Case Studies of the Socioeconomic Influence of the Converting Cropland to Forest and

Grassland Program and the Nature Forest Protection Program. Beijing: China Forestry Publishing House (in

Chinese) 32 Du SH (2004) Participatory Management and the Protection of Farmer’s Rights and Interests. Beijing: China

Economic Press 33 Xu Q (2003) The development system of community forestry and the study of converting cropland to forests – Shanxi province and Shun country as examples. Forestry Economics 2003 (supplement) (In Chinese)

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intercropping in the forested fields and gathering fuelwood. These factors have led to an adverse 184

effect on the livelihoods of the farmers, who not only rely heavily on forest resources but also tend 185

to be among the poorest rural population. Based on case studies in the upper watersheds of the 186

Mekong and Salween Rivers in north-western Yunnan, Weyerhaeuser et al. (2005)34 also reveal 187

certain negative impacts of the SLCP and the NFPP on the livelihoods of highland communities. 188

The study by Zhi and Shao (2001) 35claims that the income of farm households would be 189

significantly improved during the time period of government subsidies. If the subsidies are 190

terminated after the program expires in 5-8 years and farmers are not allowed to utilize their 191

retired lands for economic purposes, they could suffer a loss. In their opinion, the policy that 192

mandates that the proportion of economic forest be no more than 20% has failed to consider the 193

regional disparity and the basic fact that the country has a large rural population but a relatively 194

small amount of cropland. Thus, the government must take steps to improve farmland quality and 195

increase farming productivity in order to address the issue of food supply following the land 196

conversion. In addition, Xu et al (2010) find that “SLCP has indeed induced a restructuring of 197

agricultural production, whereby participants have shifted relatively more of their inputs out of 198

cropping and into animal husbandry”.36 199

Compared to the SLCP, there have been fewer studies of the socioeconomic impacts of the NFPP 200

and other programs. Xu and et al (2002) found that NFPP caused income losses for households 201

dependent on the state and collective forest sectors37 . Shen and et al (2006) carried out 202

34 Weyerhaeuser H, Wilkes A, Kahrl F (2005) Local impacts and responses to regional forest conservation and rehabilitation programs in China’s northwest Yunnan Province, Agricultural Systems 85: 234~253 35 Zhi L, Shao AY (2001) Practical considerations of converting cropland to forest and grassland. Forestry Economics 3: 43~46 36 Xu, J.,T., Tao, R., Xu, Z.,and Bennett, M. T., (2010). China’s Sloping Land Conversion Program: Does Expansion Equal Success. Land Economics 86(2): 219-244. 37 Xu, J. Katsigris, E., White, T. A., (eds).(2002). Implementing the Natural Forest Protection Program and the

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input-output analysis model and found that that combined impacts of logging ban and the 203

afforestation activities would likely increase output and employment in the economy as a whole38. 204

Using household data, Ni et al. (2002)39, Liu et al. (2005)40, Xu and et al (2006)41 demonstrate 205

that the NFPP had a negative effect on the income of farmers living close to the protected natural 206

forests. But some researchers have found the different impacts of NFPP on rural household. 207

Mullan and et al (2010) find that the NFPP has had a negative impact on income from timber 208

harvesting but has actually dad a positive impact on total household incomes from all sources42. 209

Liu and et al (2010) find the impact of NFPP on rural household income have experienced from 210

the negative to the positive. With data from 18 counties in Shanxi, Inner Mongolia, and Hebei, Liu 211

and Zhang (2006) show that the DCBT had a positive effect on farmers’ income, but the effect 212

varied from county to county, due in part to uneven program investments. However, they did not 213

consider all of the relevant factors affecting farmers’ income, including production inputs and 214

household characteristics. 215

To our knowledge, 1) little work has been conducted so far to make an integrated assessment of 216

the impacts of the PFPs except Liu and et al (2010) and Weyerhaeuser et al. (2005). In fact, most 217

of the existing studies have focused on a single PFP, with few studies dealing with two or more. 218

This is unfortunate given the fact that these overlapping PFPs have all somehow affected farmers’ 219

Sloping Land Conversion Program: Lessons and Policy Recommendations. China Council for International Cooperation on Environment and Development, Western China Forests and Grassland. China Forestry Publishing House. Beijing 38 Shen, Y. Q., Liao, X. C., Yin, R.S., (2006). Measuring the Socioeconomics Impacts of China’s Natural Forest Protection Program. Environment and Development Economics 11(6): 769-788. 39 Ni J, Wang YP, Yang ZW (2002) Evaluation of the Natural Forestry Protection Project in Yunnan. Forestry Inventory and Planning 1: 34~38 (in Chinese) 40 Liu C et al (2005) Regional socioeconomic and ecological effects of the NFPP in China. Journal of Ecology 2005(3): 428~434 41 Xu, J.T., Yin, R.S., Li. Z., and Liu, C.,(2006), “China’s ecological rehabilitation: unprecedented efforts, dramatic impacts, and requisite policies”. Ecological Economics 57: 595-607 42 Mullan, K., Kontoleon, A., Swanson, T. M., and Zhang, S.Q., (2010). Evaluation of the Impact of the Natural Forest Protection Program on Rural Household Livelihoods. Environmental Management 45: 513-525.

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income and their impacts may interact (Yin and Yin 200943). 2) Most of these researchers used the 220

data of the early implementation period of these PFPs, and they did not update their data. 3) 221

Policies and institutional arrangements have been changed since the early period of these PFPs, 222

for example, the key and most important policies were issued by the central governmental 223

agencies for SLCP in 2005, 2007. Therefore, the impacts of these new policies and institutional 224

changes should be considered. 4) Rural policies have been changing since the implementation of 225

these PFPs, which have affected rural households’ income and production choices. 5) The reform 226

of the collective forestland tenure has been implemented since 2003, which has impacted farmers’ 227

forestland and forest resource management, and their production choices. And finally, 6) financial 228

crisis has been also affecting the implementation of PFPs and rural households’ production choices 229

and income and income sources generated. In conclusions, there is a great gap in this aspect. We 230

will use our data to analyze the impact of rural households’ income and poverty reduction and 231

narrow the gap. 232

We use the unique balanced panel dataset of 2070 household of 14years in 15 counties of six 233

provinces (Shandong, Jiangxi, Sichuan, Guangxi, Hebei and Shaanxi), with cluster-specific fixed 234

effect model, we estimate the effects of these five PFPs (due to limited samples of WCNR, we do 235

not estimate the effects) on rural household crop production income, forest income, animal 236

husbandry income, land-based income, off-farm income and total income in general, we also 237

estimate these effects of five PFPs on above income sources by year. We find that the effects on 238

these income sources are mixed, and change by year. 239

43 Yin RS, Yin GP (2009) China’s ecological restoration programs: initiation, implementation, and challenges. Michigan State University Department of Forestry working paper

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The following sections for the paper as follow: methodology and data are presented in section 2 240

and 3, section four is the empirical results and final section is conclusions and discussions. 241

2. Methodology 242

We have collected data of 14-year data of 3375 rural household samples in 6 provinces, and will 243

update these samples with the proposed financial aid (partial aid, we will use other research funds). 244

Broader cross sections and longer time series are two unique features of this dataset. That is, every 245

sample county has at least two PFPs and covers a period of 15 years from 1995, before they were 246

initiated or consolidated, to 2009, when their implementation was well underway. This large and 247

comprehensive dataset, allows us to control for many relevant covariates, including production 248

inputs and household and village characteristics.. 249

The objective of this paper is to quantify the impact of the PFPs on household incomes in affected 250

areas. In order to this, we need to deal with an identification problem: if there are two potential 251

outcomes, Y1, the outcome when the individual participates in a programme; and Y0, the outcome 252

when the individual does not participate, then the impact of participating in the programme is 253

given by: 254

01 YY −=Δ (1) 255

However, estimating this requires information on both Y1 and Y0 for each individual, which is not 256

obtainable because we cannot observe the outcome of participation for non-participants or the 257

outcome of non-participation for participants. Therefore, estimation of the causal effect of the 258

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programme is equivalent to solving a missing data problem (Heckman et al. 199744), and requires 259

the use of techniques that allow the identification of the relevant impacts in the absence of the data. 260

In the case of the PFPs, we want to know the difference between the level of household income 261

when a household is in the programme and when they are not, but we only have data on one 262

situation or the other. 263

For any individual, the observed outcome (Y), following Roy (1951)45, Quandt (1972)46 and 264

Rubin (1978)47, is defined as: 265

01 )1( ititititit YDYDY −+= (2) 266

where D denotes participation in the program, and takes the values 1 (if the individual 267

participates/is treated) or 0 (if the individual does not participate/ is not treated). This can be 268

written as a function of observable characteristics of the individual (xit), unobservable, 269

time-invariant individual-specific effects (αi), and individual-specific disturbance terms (uit1, uit0,). 270

00

11

itiit

itiit

uxYuxY

++=+++=

θβθβα

(3) 271

The full model to be estimated becomes: 272

itiitit xDY εθβα +++= 273

44 Heckman J.J., Ichimura H., Todd P.E. (1997) Matching as an Econometric Evaluation Estimator: Evidence from Evaluation a Job Training Programme. The Review of Economic Studies, Vol. 64, No. 4, Special Issue: Evaluation of Training and Other Social Programmes (Oct., 1997), 605-654. 45 Roy A.D. (1951) Some Thoughts on the Distribution of Earnings. Oxford Economic Papers, Volume 3, Issue 2(Jun., 1951), 135-146. 46 Goldfeld S.M. & Quandt R.E. (1972) Nonlinear Methods in Econometrics. North-Holland Pub. Co. 47 Rubin D.B. (1978) Bayesian Inference for Casual Effects: The Role of Randomization. Annals of Statistics, 6, 34-58.

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Where itε = 0itu + 1( ituD - 0itu ) 274

The estimated treatment effects may be biased if the individual-specific effect or the disturbance 275

term are correlated with participation in the program. The former may occur if unobserved 276

characteristics of the individual affect the probability that they will participate in a PFP. This is 277

likely in the case of voluntary (or semi-voluntary) programs such as the SLCP. It is less likely for 278

programs such as the NFPP in which partipation is decided at the county-level rather than by 279

individual households. The latter would be an example of what is referred to as ‘Ashenfelter’s dip’ 280

– if individuals experience unusually low or high incomes immediately prior to entering a program, 281

the estimated returns to treatment will be biased in a context of mean reversion. 282

There are various methods to estimate a counterfactual outcome against which the outcome for 283

treated individuals can be compared (e.g. Heckman and Robb, 1984; Ashenfelter and Card, 198548; 284

Heckman et al., 1997). The available of panel data allows us to use a fixed-effect model to control 285

for individual-specific heterogeneity that could affect the outcomes of partipation as discussed 286

above. We can consistently estimate average treatment effects on the treated as long as the 287

treatment varies over time and is uncorrelated with time-varying unobservables that affect the 288

outcome of interest. It is also necessary to assume that time trends in the outcome variable among 289

program participants and non-participants are the same. We test these assumptions through 290

comparison of trends in income in the treatment and control groups prior to the introduction of the 291

PFPs. 292

We focus on the impacts of participation in a single PFP or more than one PFP on total income and 293 48 Ashenfelter O. & Card D. (1985) Using the Longitudinal Structure of Earnings to Estimate the Effect of Training Programs. The Review of Economics and Statistics. Vol. 67, No. 4 (Nov., 1985), pp. 648-660.

17 / 48

income from different sources. The impacts on total income are expected to differ because of 294

differences in the characteristics of the individual programs. Each of the programs involve 295

changes in the production activities or land uses of farm households. However, some, such as the 296

SLCP, provide compensation for those changes, while others, such as the NFPP, involve 297

restrictions on households’ resource use rights without compensation. As a result, there will be 298

direct and indirect impacts on household incomes, and these may be positive or negative. The 299

PFPs overlap, both spatially and temporally. We therefore include the interaction effects between 300

the most widespread policies in order to determine whether the combined impacts differ from the 301

individual impacts. Participation in a program is measured using a binary variable for program 302

participation, and using a continuous variable representing the area of land enrolled in each 303

program. 304

The extended production function and cluster fixed model is used. The definitions of these 305

variables are listed in table 2. 306


3. Data 308

The strained random sample model was adopted in this study. In light of the distributions of the 309

rural households’ income and the PFPs as well as our discussions with officials of provincial 310

forestry and other departments and local experts, we first selected 15 counties for our surveys. 311

They are Zhangbei, Pingquan and Yixian in Hebei Province; Xiushui, Xingguo and Suichuan in 312

Jiangxi Province; Zhen’an and Yanchang in Shaanxi Province; and Nanbu, Nanjiang, Muchuan 313

18 / 48

and Mabian in Sichuan Province; Pingguo and Huanjiang in Guangxi Zhuang Nationality 314

Autonomous Region, and Pingyi in Shandong (see Fig. 1). Each of these counties has participated 315

in at least two of the PFPs with the exception of Pingyi County in Shandong Province being used 316

as the baseline county for comparison purpose. For instance, Zhangbei County has participated in 317

the DCBT and the SBDP, and Nanbu County has participated in the NFPP, the SLCP and the 318

SBDP (see Table 3 and Fig.1 ). 319

[Insert Fig. 1 and Table 3 here] 320

Sample villages and households were chosen randomly. Specifically, we chose the villages from 321

the village list of a county and households from the household list of a village. Except for 322

Zhangbei (of 270 sample households, we selected 90 households in each township) and Huanjiang, 323

Pingguo, Yixian, Muchuan and Pingyi (of 135 sample households, we selected 45 households in 324

each township) where three townships were selected, six townships were chosen in all other 325

counties. Overall, 15 households were chosen in each sample village except for Zhangbei County 326

(30 households were chosen). Altogether, we interviewed 3,375 households in 216 villages of 72 327

townships. Our initial survey was conducted in 2004 as part of our program monitoring and 328

assessment efforts supported by the Asian Development Bank and the Chinese Ministry of 329

Finance. To understand the microeconomic shifts over time, we asked interviewees to recall their 330

production activities and other relevant information back to 1995. Then, in 2005, 2006, 2007, 331

2008 and 2009, we repeated our surveys. As such, we were able to assemble a panel dataset 332

covering fourteen years (1995-2008), which has a longer and more continuous coverage than 333

almost any other datasets used by others to assess the impacts of the six PFPs. In order to help 334

19 / 48

interviewees to describe their production and consumption behaviors, we designed the 335

questionnaires in terms of specific production and consumption activities, and asked multiple 336

family members to recall their household activities in each year, and cross-checked the responses 337

by consulting with village resource persons and statistical data and information of case study 338

counties, townships and villages. All these steps served to ensure high quality of the data 339

collected. 340

However, our surveys did not contain complete information from some households. This is 341

because a few of them moved away from the sample villages, errors occurred to some interviews, 342

or certain families failed to clearly and exactly recall what had happened to them in the previous 343

year(s). These factors led to the outcome of a slightly unbalanced panel over time. To measure 344

rural households’ income mobility, we need a balanced panel dataset. We decided to remove those 345

observations with incomplete information and/or incomplete interviews, resulting in a balanced 346

panel of 2070 households for the study. 347

It is evident from Table 3 that, over the study period, more and more households were involved in 348

the PFPs. Since the launching of these PFPs in 1998, the number of households that were not 349

involved with the PFPs declined from 1507 in 1998 to 508 in 2008. While some participated in 350

multiple PFPs, others did not participate in any of them. While 33 households participated in both 351

the NFPP and the SLCP in 1999, the figure increased to 656 households by 2008. Meanwhile, 9 352

households were involved in the SLCP, NFPP and SBDP in 2008 (see Table 3). More specifically, 353

a large number of households took part in the NFPP and the SLCP, but only a few were involved 354

in the SBDP and even fewer in the ITPP. By the end of 2008, the sample households that 355

20 / 48

participated in the NFPP, SLCP, DCBT, ITPP, SBDP, SLCP&NFPP, SLCP&ITPP, DCBT&SBDP, 356

NFPP&SBDP, ITPP&SBDP and SLCP&NFPP&SBDP were 291, 236, 277, 12, 51, 656, 2, 19, 1, 6, 357

0, 2 and 9 household (s) respectively (see table 4). Table 3 also indicates that most of these 358

sampling households participated in the NFPP and the SLCP. Only few households participated in 359

ITPP. There are 508 households that did not participate in any PFPs until 2008; these households 360

are allocated both PFP areas and non-PFP area. 361

[Insert Table 4 here] 362

The NFPP and the SLCP were initiated in Sichuan and Shaanxi provinces in 1998 and 1999, 363

respectively; and Jiangxi Province and the Guangxi Zhuang Autonomous Region were included in 364

the SLCP in 2001 and 2002, respectively. Both the SLCP and the DCBT were launched in Hebei 365

Province in 2000. 366

We could find that these counties are located in the east, west, north and south of China, although 367

rural households’ Total income in some counties is lower that of the national average （such as 368

Zhangbei County and Mabian County, some counties’ rural households’ income is the same as the 369

national average (such as Yi County) or higher than that of the national average (Pingyi County 370

and Muchuan County). It is to say, these samples of rural households are in fact representative of 371

the rural China and these PFP areas (see Fig. 1). 372

Included in the dataset are the following variables: (1) household demographics (household size, 373

educational achievement of the household head, and the like); (2) monetary outputs (total income, 374

off-farm income, and income from land-based enterprises) and inputs (labor, farmland, cash 375

21 / 48

expenditure, etc.) for land-based and off-farm activities; (3) the statuses of the PFP participation; 376

and (4) natural and socioeconomic conditions (annual precipitation and average plot sizes of 377

forestland and farmland). We collected case study provincial and county statistics data for the 378

research. 379

Total income and cash outlay of sample households were deflated and converted to the 1994 380

constant yuan, using the rural consumer price index and rural industrial product price index from 381

the Chinese Statistical Yearbooks, published by the China National Statistical Bureau (2009). 382

Average annual household total income has been increasing since 1995, with the amount being 383

4282.06 yuan, 5045.84 yuan, 7229.39 yuan and 12505.30 yuanin 1995, 1999, 2004 and 2008 384

respectively (see Table 4). The weight of off-farm income was 69.09%, 58.90% and 62.96% in 385

1995, 1999, 2004 and 2008, respectively. The level of production expenditure for land-based 386

activities has been increasing, being 524.37 yuan, 613.85 yuan, 714.33yuan and 1526.62 yuan in 387

1995, 1999, 2004 and 2008, respectively. Among the land-based income included crop production 388

income, forest income and animal husbandry income and other land-based income, crop 389

production income does not change too much, while forest income and animal husbandry income 390

increase rapidly, i.e. from 1995 to 2008, rural household forest income increases from 355.08 yuan 391

to 1098.26 yuan; their animal husbandry income also be up from 947.77yuan to 2196.15 yuan. All 392

these data indicate that rural household income sources have greatly changed since 1995. Rural 393

housed average off-farm labor input has increased from 88.75 person-days in 1995 to 269.35 394

person-days in 2008, while land-based labor input has decreed 245.95 person-days in 1995 to 395

186.77 person-days in 2008. Road condition has somewhat improved especially in 2008 due to the 396

new rural construction in China. 397

22 / 48

With the implementation of the PFPs, rural household land structure has been changing. Farmland 398

area per household decreased from 8.27 mu49 in 1995 down to 7.97 mu in 1999, 5.77 mu in 2004 399

and to 5.66 mu in 2008; meanwhile forestland area per household rose from 9.90 mu in 1995, to 400

10.61 mu in 1999, 14.40 mu in 2004 and 18.64 mu in 2008 respectively. Area of other land and 401

grassland types per household fluctuated during the study period. The household forestland area 402

expand is the PFPs and the reform of the collective forest tenure in China, especially after 2003 403

when the reform was launched, the Government of China decided to allocate collective forestland 404

to individual management. 405

Acreage enrolled in the SLCP and the NFPP is largest among these PFPs; the percentage of 406

household enrolled in the SLCP increased from 15% in 1999, 44% in 2004, and 47% in 2008; 407

during the same period, the area enrolled in the NFPP increased from 27%in 1999 to 44% in 2008; 408

and the weight that enrolled in the DCBT also increased from 0.00% to14% in 2008; the 409

percentage that households have enrolled in the SBDP has been up from 0.00% in 1999 to 4% in 410

2008; and the percentage that enrolled in the ITPP did not show significant change, from 0.00% in 411

1999 to 1% in 2008 (see Table 4). 412

The income of the sample rural households grew gradually and the average income per household 413

was 4,442.23 yuan (in 1994 constant price) in 1995 and then increased to 12,109.45 yuan (in 1994 414

constant price). The structure of income has experienced significant changes. The income from 415

cropland was the main source of income before 2005, in that the ratio of the income from cropland 416

declined from 68.56% in 1995 to 40.79% by 2004; the off-farm income became the main source 417

49 15 mu=1 hectare

23 / 48

of income in 2005 and 2006, with the ratio being 50.3% and 50.01%, respectively. Furthermore, 418

the ratio of subsidy from the PFPs in total income increased, as the ratios rose from 0.3% to 2.9%, 419

8.9% and 8.34% in 1995, 2000, 2005 and 2006, respectively (see Table 5). 420

[Insert Table 5 here] 421

4. Empirical results 422

A couple of technical issues – choice of random-effects vs. fixed-effects estimation technique and 423

potential endogeneity bias – must be resolved before any formal empirical attempt. First, note that 424

equation (2) can be estimated as a fixed-effect or random-effect model. Whether we adopt the 425

random-effects or fixed-effects estimation technique hinges on the outcome of a Hausman test 426

(Woodridge, 1999). To that end, we ran the corresponding regressions of the total income of 427

sample households against the PFP dummies. It is found that in both cases the χ values are 428

greater than the critical values at the 1% confidence level. These results indicate that we should 429

estimate a fixed-effects model, rather than a random-effects one. One advantage of the 430

fixed-effects estimation is its control over unobserved fixed factors that could confound the 431

estimation (Pender 2005). 432

Another key issue is the potential endogeneity bias – whether or not households’ participation in 433

any of the PFPs is endogenously decided. If households have the freedom to select for 434

participation, then their participation becomes endogenous and an assessment of the program 435

impact must be made accordingly. Otherwise, ignoring the endogenous choice by households will 436

lead to biased estimates (Woodridge 199950, Uchida et al. 200751). In the current context, 437

50 Wooldridge, J. M. (1999) Introductory Econometrics: A Modern Approach, South-Western College Publishing

24 / 48

endogeneity is most pertinent to the SLCP, Liu and et al (2010) used the same sample household 438

dataset (1995-2004) and their result has rejected the endogeneity test. The dataset given that the 439

other programs were largely imposed on the rural households, who had little power to determine 440

whether and how they should participate. In the meantime, the SLCP subsidy is much higher than 441

that the net return generated from the converted cropland. Fig 2 and 3 show that net returns 442

generated from unconverted cropland in Yellow River Catchment and the Yangtze River 443

Catchment, which is two or three times higher than that of the converted cropland (State Forestry 444

Administration 199852, Liu and Wu 2010), higher economic returns encourage rural households to 445

participate in the SLCP and also help us to reject the endogeneity test. 446

[Insert Fig. 2 and Fig. 3 here] 447

By use of cluster-specific fixed effect model, we estimate the effects of these five PFPs (due to 448

limited samples of WCNR, we do not estimate the effects) on rural household crop production 449

income, forest income, animal husbandry income, land-based income, off-farm income and total 450

income in general, we also estimate these effects of five PFPs on above income sources by year. 451

We find that the effects on these income sources are mixed, and change by year. The empirical 452

results are presented in table 5 ~8. 453

The paper focuses on the effects of PFPs on rural household income, although other variables are 454

important to explain for rural household income change, we will mainly discuss about these effect 455

51 Uchida E, Xu JT, Xu ZG et al (2007) Are the poor benefiting from China’s land conservation program?

Environment and Development Economics 12(4): 593-620 52 State Forestry Administration (1998) China Forestry Development Report. Beijing: China Forestry Publishing

House (in Chinese)

25 / 48

of PFPs. With regard to rural household total income, from 1995-2008, the effects of the NFPP, 456

the SLCP, the SBDP and the DCBT on household total income are insignificant. The coefficient of 457

ITDP is -0.25 and significant at 0.10 level, the coefficients of interaction of NFPP and SLCP are 458

significant at 0.10 level and the coefficient is 0.08(see table 6). 459

The NFPP and the DCBT are insignificant to impact rural household total income, non-farm 460

income, land-based income, crop production income, forest income and animal husbandry income 461

(see table 6). The coefficient of the SLCP to land-based income, forest income is 0.23 and -1.93, 462

and significant at 0.05 level. The impact of the SLCP on off-farm income, crop production income, 463

animal husbandry and total income is insignificant. The impact of the interaction between the 464

NFPP and the SLCP is significant to total income (0.08 and significant at 0.10 level), land-based 465

income (0.24 and significant at 0.05 level), crop production income (-0.40 and significant at 0.05 466

level) and forest income (2.90 and significant at 0.01 level). The coefficient of the impact of the 467

ITPP on total income is -0.25 and significant at 0.10 level; on other income sources is not 468

significant. The SBDP has contributed to crop production income (0.68 and significant at 0.05 469

level), and has the negative impacts on land-based income (-0.21 and significant at 0.01 level) and 470

animal husbandry income (-3.83 and significant at 0.01 level) (see table 6). 471

Table 6 tells us the effects of the PFPs on rural household income from 1995-2008, the dynamic 472

effects of the PFPs on rural household total income, off-farm income and land-based income are 473

presented in table 7~9. Total income is used as the independent variable, the impacts of both the 474

NFPP and the DCBT on total income are not significant since their implementations. Since the 475

launch of SLCP, it has positively and significantly impacted the total income until 2005, but its 476

26 / 48

significant level has reduced from 0.01 level to 0.05 level, and the parameter has become smaller, 477

i.e. from 0.15 to 0.06; after 2005, the impact of the SLCP on total income is insignificant. The 478

coefficient of the impact of the interaction of both the SLCP and the NFPP on total income is 0.12 479

(from 1995 to 1999), 0.08 (from 1995-2007) and 0.08 (from 1995 to 2008) and significant at 0.10 480

level. The ITPP has a negative effect on total income except from 1995—2003 and significant at 481

0.01 level, 0.05 level or 0.10 level. The impact of SBDP on total income has been negative before 482

2005 and significant at 0.01 level, and it has been insignificant after 2005 (see table 7). 483


With regard off-farm income, the NFPP, the DCBT, SLCP, the interaction of the NFPP and the 485

SLCP, ITDP have not affected rural household off-farm income since their implementations. The 486

impact of the SBDP on off-farm income is mixed (the coefficient is -1.16, -1.60 and 2.94 487

respectively and significant at 0.01 level or insignificant) (see table 8). 488


When we consider the contributions of the PFPs to rural household land-based income, we find 490

that: (1) the effects of both the NFPP and DCBT on rural household land-based income are 491

insignificant since these two programs were launched. (2) The contribution of the SLCP is 492

positive and significant at 0.05 level or 0.01 level; and the coefficient changes from 0.23 to 0.42, 493

the coefficient increases from 0.26 in 1999 to 0.42 in 2001, and then gradually decreases from 494

0.42 to 0.23 in 2008. (3) The impact of the interaction of the NFPP and the SLCP is insignificant 495

before 2004, and after then is significant at 0.10 level or 0.05 level and the coefficient increases 496

27 / 48

from 0.17 to 0.24. (4) The contribution of the ITDP is -0.67 and -0.68 in 2005 and 2006, and 497

significant at 0.01 level, it is insignificant in the rest of years. (5) The impact of the SBDP is 498

negative and significant at 0.01 level, and the coefficient changes from -0.51 to -0.10 (see table 499

9). 500


5. Conclusions and discussions 502

Measuring the impact of these PFPs on rural household total income and different source incomes 503

is not a straightforward matter, mainly because the Chinese economy has undergone huge changes 504

since the programs have been in place. With introduction of the PFPs, as we discussed above, rural 505

household production endowments have been changing by year. Rural households participated in 506

the SLCP, their cropland was converted to forest or grass coverage, their cropland has been 507

decreased, while their forestland has been increased. Under the new context, as the rational 508

economic stakeholder, rural households allocate their production endowment to maximum their 509

benefits. 510

We use the unique balanced 2070 household panel dataset, the time span is 14 years. 511

Cluster-specific fixed effect model is used, observations within a cluster are thought to be 512

correlated as a result of an unobserved cluster effect (Wooldridge 200253). Cluster-specific fixed 513

effect model allows us to exclude coloration within the cluster the effects of PFPs on rural 514

household total income and other income sources are mixed. During the period from 1995 to 2008, 515

53 Wooldridge, J.M. (2002), Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: MIT Press.

28 / 48

With regard to rural household total income, from 1995-2008, the effects of the NFPP, the SLCP, 516

the SBDP and the DCBT on household total income are insignificant. The impacts of ITDP and 517

the coefficients of interaction of NFPP and SLCP on total income are positive. In the Natural 518

Forest Protection Program area, both the SLCP and the NFPP are positive to rural household total 519

income (the contribution of the SLCP or NFPP=the coefficient of the SLCP or the NFPP + 520

the coefficient of the interaction coefficient of the SLCP or the NFPP * 0 or 1). The impacts of 521

PFPs on other sub-income are also mixed. If we consider the contribution(s) of the SLCP and the 522

NFPP to the total income, land-based income and forest income are positive, to crop production 523

income is negative, and to non-farm income and animal husbandry income are insignificant (see 524

table 6). 525

Our empirical results indicate that the impacts of PFPs on total income and sub-income sources 526

have been changing by year. Since the implementation of the SLCP, the contribution of the SLCP 527

to land-based income is positive and changing by year (see table 7) and to total income is positive 528

until 2006. If we consider the impact of the interaction of both of the SLCP and the NFPP, we will 529

find that the impact of the SLCP is positive in NFPP area until 2008 except 1995-2006. 530

As we find that not many sample rural households participate in the ITDP, we observed that rural 531

households rented their forestland to state forest farms or domestic and foreign forest enterprises, 532

and the forestland rent is lower compared to their productivity or profit, therefore, the impact of 533

the ITDP on household income is negative. 534

SBDP areas are allocated national wide, the governmental forest agencies required that these 535

participated households input their labors for afforestation or reforestation, and forest resource 536

29 / 48

management such as forest fire and pesticide control, governmental agencies did not pay subsidy 537

except 100 yuan per mu for afforestation or reforestation, after 2003, governmental agencies 538

annually paid 5 yuan per mu for the state sheltbelt forests. Therefore, the impacts of SBDP on 539

rural household total income and land-based income have experienced from the significantly 540

negative to the insignificant or negative coefficients have become smaller (see table 7 and 9). 541

The DCBT is composed of the SLCP, watershed management, and resettlement from the fragile 542

ecological sites and switch animal husbandry from open gazing to pen raising. The DCBT area is 543

located in the desertified area that covers 75 counties or banners of 5 province and municipalities, 544

in accordance with the policies for the SLCP (see table 1), if rural household conversed one mu 545

sloping or desertified cropland to forest or grass coverage, he or she has to plant one or more mu(s) 546

trees on the barren land, most of these rural households in the DCBT did not have the barren land 547

for afforestation, he or she has to use their desertified cropland for afforestation as the barren land, 548

which would reduce his or her net return after deducting the additional costs. Therefore, the effects 549

of the DCBT on rural household total income and land-based income are insignificant. 550

Among these five PFPs, more sample rural households participated in the NFPP and the SLCP. 551

After their participation, their behaviors have changed. With implementation of the SLCP, 552

cropland has converted to forest or grass coverage, it is easy for us to understand that the effect of 553

the SLCP on crop production income is negative. With strict control and conservation of 554

forestland, the impact of the SLCP on forest income is negative and significant within the period 555

from 1995 to 2008. The SLCP subsidy is much higher than that of net return generated from the 556

converted cropland, which increases land-based income, the SLCP is positive to total income and 557

30 / 48

land-based income of these sample rural households. These households who participated in the 558

SLCP also reallocate their production factors. To find the land-based activity intensity variations, 559

we calculated the indexes of production expenditure intensity and labor input for land-based 560

production intensity (see Table 9), their labor intensity index and the production expenditure 561

intensity index who participated in the SLCP is much higher that of those who did not participate 562

in the SLCP, for example, the labor intensity index increases from 1.00 in 1999 to 1.72 in 2003 563

and reduces to 1.43 for the SLCP participants, while for non-SLCP participants, the index is much 564

lower than that of the SLCP participants; the production expenditure intensity who participated in 565

the SLCP increased from 1.00 in 1999 to 11.54 in 2008, while for those whose did not 566

participate the SLCP, the index increased from 1.00 in 1999 to 4.59 in 2008 (see table 10). With 567

intensive land-based activities, the outputs of land-based activities are higher than that period 568

before the implementation of the SLCP. In accordance with the policy documents, the 569

governmental agencies have strengthened the technical extension, which are benefit to the 570

production transfer from the extensive to the intensive. We find that both off-farm labor weight 571

and off-farm labor index differences between these participants and non-participants are small, the 572

SLCP participants’ and non-participants’ off-farm labor weight increased 30 percentages and 29 573

percentages from 1999 to 2008 respectively; and off-farm labor index increased 1.40 times and 574

1.87 times, which caused the insignificant impact of the SLCP to rural household off-farm income. 575

With regard to the NFPP, table 10 indicates that both land-based intensity index and production 576

expenditure intensity index differences between the NFPP participants and non-NFPP participants 577

are smaller than those differences between the SLCP participants and non-SLCP participants, 578

these both indexes of the NFPP participants are smaller than those non-NFPP participants, for 579

31 / 48

example, the labor intensity index and production expenditure intensity of the NFPP participants 580

are 1.58 and 3.21 in 2008 respectively, these two indexes of non-NFPP participants are 1.93 and 581

4.36 in 2008 respectively (see table 11). These differences of off-farm labor indexes and off-farm 582

labor weights between the NFPP participants and non-NFPP participants are similar to the 583

difference between the SLCP participants and non-SLCP participants, for instance, the off-farm 584

labor weights of the NFPP participants and non-NFPP participants increased 31 percentages and 585

28 percentages from 1998 to 2008 respectively. 586

[Insert table 10 and table 11 here] 587

The impact of the interaction between the NFPP and the SLCP on total income and land-based 588

income has experienced from the insignificant to the significant and positive. The overlap of the 589

SLCP and the NFPP is located in Shaanxi and Sichuan provinces, i.e., Muchuan, Nanbu, Nanjing, 590

Mabian, Yanchang and Zhen’an counties. The government of China decided to permit rural 591

households to cut their trees in the NFPP area in the pilot counties in 2007, rural household could 592

generate some cash income from natural forests. As we discuss above, rural households input their 593

labors and cash for land-based activities. 594

Overall, the contributions of various production inputs and other control variables are in line with 595

our expectations. Notably, labor and production expenditure for land-based activities and off-farm 596

employments have contributed to income growth positively. The government of China has taken 597

some measures to raise rural household income, No. 1 Document of the State Council of China 598

and the Central Committee of Communist of China, to reduce taxation, subsidy and etc., table 5 599

indicates that the dummy variables of the year is positive and significant, and the coefficient has 600

32 / 48

become larger by year. 601

602

33 / 48

Table 1. Key policy measures of the PFPs Program Key Policies

Sloping Land Conversion

Program (SLCP),

covering 25 provinces

during 2001-2010

• Sloping or desertified cropland is converted into ecological and/or economic forest, and grassland; ecological forest should account for 80% of total

converted land.

• The central government subsidizes farmers in the form of seeds or seedlings, grain, and cash.

• Subsidies last 8 years for ecological forest, 5 years for economic forest, and 2 years for grassland. The annual cash subsidy is 300 yuan/ha, and the annual

grain subsidy is 1500 kg/ha in the Yellow River basin and 2250 kg in the Yangtze River basin.

• The central government also makes fiscal transfers to compensate the entailed losses to local fiscal revenues.

• Estimated total investment is 225 billion RMB.

• switch animal husbandry from open gazing to pen raising

• In 2007, the State Council decided that the second round subsidy would be taken, but the subsidy would be cut the half. i.e. the annual subsidy was to 70

yuan per mu in the Yellow Catchment, and 105 yuan per mu. The subsidy period was another five or eight years for the ecological forests or economic forests

respectively. In the meanwhile, the sloping land conversion to forest or grass coverage would be stopped since 2007 and afforestion on the barren forestland

would be continued.

Natural Forest Protection

Program (NFPP),


during

2000-2010

• Complete ban on commercial logging in the upper Yangtze and middle Yellow River basins and sharp reduction in commercial harvests in other program

areas.

• Shutting down of certain processing facilities, compensating logging firms, and disposing displaced workers and equipment.

• Promotion of afforestation and forest management wherever possible.

• Strengthening administration and law enforcement, including forest protection.

• Restricting the forest industry, and improving the efficiency of timber utilization.

• Initial investment commitment is 96.4 billion (US$14.1 billion).

Shelterbelt Development

Program (SBDP),

covering all 31 provinces

during

2001-2010

• Including shelterbelt programs in the Three Norths (northwest, north, and northeast), the Yangtze River basin, the Zhujiang River basin, and the Taihang

Mountain Range.

• Mobilization of public agencies, civil society, individuals to contribute to the shelterbelt development and tree planting.

• Encouraging local government investment and local labor contribution, and adopting new silvicultural techniques.

• Total planned investment is 70 billion yuan (US$10.2 billion).

34 / 48

Desertification

Combating around

Beijing and Tianjing

(DCBT), including Inner

Mongolia, Hebei, Shanxi,

Beijing, and Tianjin

during 2001-2010

• Convert desertified land into forestland and grassland by means of flexible and diversified measures based on the local conditions.

• Changing herding and animal husbandry practices to control overgrazing and rehabilitate degraded grassland.

• Developing irrigation projects, and resettling people away from fragile areas.

• Extension of suitable production technology and energy sources.

• Establishing desertification monitoring and dust storm forecasting systems

• Total projected investment is 57.7 billion yuan (US$8.4 billion)

• switch animal husbandry from open gazing to pen raising

• In 2007, the State Council decided that the second round subsidy would be taken, but the subsidy would be cut the half. i.e. the annual subsidy was to 70

yuan per mu in the Yellow Catchment, and 105 yuan per mu. The subsidy period was another five or eight years for the ecological forests or economic forests

respectively. In the meanwhile, the sloping land conversion to forest or grass coverage would be stopped since 2007 and afforestion on the barren forestland

would be continued.

Industrial Timber

Plantation Development

Program (ITPP),


during 2001-2015

• Market-driven and profit-orientated efforts for increasing domestic timber supply.

• As high as 70% of the investment may come from subsidized National Development Bank loans, with 20% from direct government funding and 10%

from other sources; in addition, tax incentive is provided.

• Encouraging active participation by various enterprises – state or collectively owned, shareholder based, or fully private.

• Planned area of establishment is 4.69 million ha by 2005, 9.2 million ha by 2010, and 13.33 million ha by 2015.

• Projected total investment is 71.8 billion (US$10.5 billion)

35 / 48

Table 2 the definitions of these variables Definition variable

Total income (Yuan) R

Land-based income (Yuan) (incomes directly generated from land) R

Off-farm income (Yuan) R

Crop production income (Yuan) R Forest income (Yuan) R

Animal husbandry income (Yuan) R Headman of village or sub-village (if yes=1; otherwise =0) X

Household size (person) (number of household members) X

The education years the household head received (year) X

Road condition(if hard road surface=1; otherwise=0) X

The distance to the township from the village (kilometer) X

Production expenditure for land-based activities (Yuan) Z

Off-farm employment (person-days) z

Labor for land-based activities (person-days) Z

Farmland area (mu) Z

Forestland area (mu) Z

Grassland area (mu) Z

Other land area (mu) Z

Area enrolled in the NFPP (mu) Y

Area enrolled in the DCBT (mu) Y

Area enrolled in the SLCP(mu) Y

Area enrolled in the ITDP (mu) Y

Area enrolled in the SBDP (mu) Y

36 / 48

NFPP dummy (if yes=1; otherwise 0) Y

DCBT dummy (if yes=1; otherwise 0) Y

SLCP dummy (if yes=1; otherwise 0) Y

ITDP dummy (if yes=1; otherwise 0) Y

SBDP dummy (if yes=1; otherwise 0) Y

Both NFPP and SLCP dummy (if yes=1; otherwise 0) Y

37 / 48

Table 3 the case study counties and PFPs???

province county NFPP SLCP DCBT ITPP SBDP Hebei Zhangbei ○ ○ ★ ○ ★

Pingquan ○ ○ ★ ○ ★ Yi Xian ○ ★ ○ ○ ★

Jiangxi Xiushui ○ ★ ○ ★ ★ Shuichuan ○ ★ ○ ★ ★ Xingguo ○ ★ ○ ○ ★

Shaanxi Yanchang ★ ★ ○ ○ ★ Zhen’an ★ ★ ○ ○ ★

Sichuan Mabian ★ ★ ○ ○ ★ Muchuan ★ ★ ○ ★ ★

Nanbu ★ ★ ○ ★ ★ Nanjiang ★ ★ ○ ○ ★

Guangxi Pingguo ○ ★ ○ ★ ★ Huanjiang ○ ★ ○ ★ ★

Shandong Pingyi ○ ○ ○ ○ ○ Footnote: NFPP, SLCP, DCBT, ITPP and SBDP represent, respectively, the Natural Forest Protection Program, the Sloping Land Conversion Program, the

Desertification Combating Program around Beijing and Tianjing, Industrial Timber Plantation Development Program and Shelterbelt Development Program in the Three-Norths and the Yangtze River Basin; ★indicates that the sample county participates in the PFP; ○ indicates otherwise.

38 / 48

Table 4 The evolution of sample households’ participation in the PFPs

(The total number of sampling household =2070)

Year SLCP NFPP DCBT ITPP SBDP

SLCP

&

NFPP

SLCP

&

ITPP

SLCP

&

SBDP

NFPP

&

SBDP

DCBT

&

SBDP

ITPP

&

SBDP

SLCP

&

NFPP

&

ITPP

SLCP

&

NFPP

&

SBDP

NONE

1995 0 0 0 0 0 0 0 0 0 0 0 0 0 2070

1996 0 0 0 0 0 0 0 0 0 0 0 0 0 2070

1997 0 0 0 0 1 0 0 0 0 0 0 0 0 2069

1998 0 562 0 0 0 0 0 0 1 0 0 0 0 1507

1999 270 529 0 0 0 33 0 0 1 0 0 0 0 1237

2000 38 444 0 0 0 459 0 0 1 0 0 0 0 1128

2001 50 399 0 0 0 504 0 0 1 0 0 0 0 1116

2002 163 330 131 0 0 573 0 0 1 3 0 0 0 869

2003 257 242 246 0 1 660 0 0 1 4 0 1 0 658

2004 276 254 263 0 1 648 0 0 1 6 0 1 0 620

2005 274 231 282 8 39 669 0 10 1 0 0 1 2 553

2006 268 226 283 9 63 673 0 19 1 0 0 2 2 524

2007 298 222 277 13 53 677 2 15 1 4 1 2 2 503

2008 291 236 277 12 51 656 2 19 1 6 0 2 9 508

Note: NFPP, SLCP, DCBT, WCNR, and SBDP represent, respectively, the Natural Forest Protection Program, the Sloping Land Conversion Program, the Desertification Combating Program

around Beijing and Tianjin, the Wildlife Conservation and Nature Reserve Program, and the Shelterbelt Development Program in the Three-Norths and the Yangtze River Basin.

39 / 48

Table 5 Summary statistics of the household data in 1995, 1999, 2003 and 2008 Year 1995 1999 2004 2008

Variable Mean SD Mean SD Mean SD Mean SD

Total income (Yuan) R0 4282.06 3203.33 5045.84 3691.25 7229.39 5390.89 12505.30 12886.80

Land-based income (Yuan) (incomes directly generated from land) R1 1323.33 2634.21 1875.66 3216.72 3075.84 4737.62 6891.35 9183.56

Off-farm income (Yuan) R 2958.73 1886.66 3170.18 1955.08 4153.55 2742.57 5613.95 9819.57

Crop production income (Yuan) R 1641.65 1299.96 1616.13 1232.68 1779.00 1433.69 1571.10 2329.02

Forest income (Yuan) R 355.08 822.80 395.54 905.51 549.01 1327.89 1098.26 6578.01

Animal husbandry income (Yuan) R 947.77 1121.85 1068.39 1222.37 1357.33 1687.50 2196.15 6544.49

Headman of village or sub-village (if yes=1; otherwise =0) X 0.09 0.29 0.09 0.29 0.09 0.29 0.08 0.27

Household size (person) (number of household members) X 3.56 1.20 3.74 1.24 3.90 1.32 4.15 1.52

The education years the household head received (year) X 6.36 2.79 6.36 2.79 6.36 2.79 7.12 3.56

Road condition(if hard road surface=1; otherwise=0) X 0.41 0.49 0.41 0.49 0.41 0.49 0.55 0.50

The distance to the township from the village (kilometer) X 8.36 6.85 8.36 6.85 8.36 6.85 8.62 8.51

Production expenditure for land-based activities (Yuan) Z 524.37 473.42 613.85 560.32 714.33 708.99 1526.62 5466.36

Off-farm employment (person-days) z 88.75 134.33 119.31 165.65 177.19 212.00 269.35 283.12

Labor for land-based activities (person-days) Z 245.95 172.56 250.14 174.85 236.77 172.79 186.77 153.87

NFPP dummy (if yes=1; otherwise 0) Y 0.00 0.00 0.27 0.44 0.44 0.50 0.44 0.50

DCBT dummy (if yes=1; otherwise 0) Y 0.00 0.00 0.00 0.00 0.12 0.33 0.14 0.34

SLCP dummy (if yes=1; otherwise 0) Y 0.00 0.00 0.15 0.35 0.44 0.50 0.47 0.50

ITDP dummy (if yes=1; otherwise 0) Y 0.00 0.00 0.00 0.00 0.00 0.02 0.01 0.09

SBDP dummy (if yes=1; otherwise 0) Y 0.00 0.00 0.00 0.02 0.00 0.05 0.04 0.20

Both NFPP and SLCP (if yes=1; otherwise 0) Y 0.00 0.00 0.016 0.12 0.31 0.46 0.32 0.47

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Table 6 Empirical result of the impact of Priority Forest Programs on different income sources VARIABLES VARIA

BLES

Total income (Yuan)R

Non-farm income

(Yuan) R

Land-based income

(Yuan) R

crop production

income (Yuan) R

Forest income

(Yuan) R

animal husbandry

income (Yuan) R

Headman of village or sub-village (if

yes=1; otherwise =0) X

0.12***(0.04) 2.24***(0.52) 0.14***(0.04) -0.43**(0.18) -1.10(0.74) 0.79(0.70)

Household size (person) (number of

household members) X

0.30***(0.07) -0.01(0.45) 0.18**(0.08) 0.10(0.14) 0.28(0.46) 1.34***(0.36)

The education years the household

head received (year) X

0.01***(0.00) 0.07**(0.03) 0.00(0.003) -0.01(0.01) -0.03(0.06) 0.01(0.03)

Road condition(if hard road

surface=1; otherwise=0)

X 0.10*(0.05) 1.15***(0.40) -0.03(0.08) -0.03(0.25) -1.05***(0.38) -0.70(0.64)

The distance to the township from

the village (kilometer)

X -0.00(0.01) 0.20(0.15) 0.02(0.02) 0.14**(0.06) -0.32**(0.16) -0.10(0.14)

Production expenditure for

land-based activities (Yuan) Z

0.02**(0.01) 0.00(0.03) 0.17***(0.03) 0.50***(0.06) 0.15***(0.04) 0.43***(0.07)

Off-farm employment (person-days) z 0.02***(0.00) 0.59***(0.03) -0.01**(0.00) -0.01(0.01) -0.00 (0.01) -0.03**(0.01)

Labor for land-based activities

(person-days)

Z -0.01***(0.00) -0.09*(0.05) 0.05(0.03) 0.12*(0.07) 0.08*(0.04) 0.20***(0.06)

Farmland area (mu) Z 0.01**(0.00) 0.06*(0.03) 0.02***(0.01) 0.15**(0.06) 0.00(0.03) 0.11***(0.02)

Forestland area (mu) Z 0.01**(0.00) -0.01(0.03) 0.00(0.00) -0.06***(0.02) 0.18**(0.08) 0.03(0.03)

Grassland area (mu) Z -0.00(0.00) -0.05**(0.02) 0.00(0.00) -0.02(0.02) -0.06***(0.02) 0.04(0.03)

Other land area (mu) Z 0.00(0.00) -0.01(0.03) 0.01***(0.00) 0.03**(0.01) 0.07(0.04) 0.03*(0.02)

NFPP dummy (if yes=1; otherwise 0) Y -0.02(0.05) 0.15(0.61) -0.05(0.08) -0.36(0.22) -0.23(0.56) 0.61(0.51)

41 / 48

DCBT dummy (if yes=1; otherwise

0) Y

-0.08(0.08) -0.24(0.30) -0.12(0.15) 0.33(0.47) -1.90(1.31) 0.95(1.45)

SLCP dummy (if yes=1; otherwise 0) Y 0.02(0.03) -0.03(0.42) 0.23**(0.10) 0.26(0.19) -1.93**(0.97) -0.25(0.40)

Both NFPP and SLCP dummy (if

yes=1; otherwise 0) Y

0.08*(0.04) 0.11(0.75) 0.24**(0.09) -0.40**(0.20) 2.90***(1.10) 0.14(0.57)

ITDP dummy (if yes=1; otherwise 0) Y -0.25*(0.14) 0.76(2.12) -0.09(0.33) 0.67(0.46) -5.90(7.48) -4.09(2.76)

SBDP dummy (if yes=1; otherwise 0) Y 0.03(0.06) -0.52(0.37) -0.21***(0.06) 0.68**(0.29) -0.92(1.31) -3.83***(0.56)

year1996(if yes=1; otherwise =0) t -0.03***(0.01) 0.03(0.07) -0.01(0.02) 0.00(0.03) -0.03(0.03) 0.13*(0.07)

year1997(if yes=1; otherwise =0) t 0.00(0.01) 0.20**(0.09) -0.01(0.02) -0.04(0.03) -0.04(0.05) 0.07(0.08)

year1998(if yes=1; otherwise =0) t 0.07***(0.03) 0.23(0.18) 0.06(0.05) 0.06(0.08) 0.05(0.21) 0.20(0.44)

year1999(if yes=1; otherwise =0) t 0.12***(0.04) 0.59***(0.20) 0.08(0.06) 0.06(0.09) 0.13(0.28) -0.18(0.20)

year2000(if yes=1; otherwise =0) t 0.20***(0.04) 0.84***(0.27) 0.11*(0.06) 0.24*(0.14) -0.10(0.25) 0.27(0.67)

year2001(if yes=1; otherwise =0) t 0.25***(0.04) 1.19***(0.29) 0.12*(0.06) 0.22(0.15) -0.10(0.28) -0.26(0.34)

year2002(if yes=1; otherwise =0) t 0.34***(0.04) 1.56***(0.26) 0.22***(0.07) 0.28**(0.13) 0.22(0.31) -0.14(0.37)

year2003(if yes=1; otherwise =0) t 0.40***(0.04) 1.87***(0.23) 0.27***(0.07) 0.32***(0.12) 0.40(0.38) -0.28(0.32)

year2004(if yes=1; otherwise =0) t 0.49***(0.04) 2.54***(0.38) 0.31***(0.07) 0.14(0.185) 0.63(0.40) 0.35(0.64)

year2005(if yes=1; otherwise =0) t 0.53***(0.05) 3.28***(0.60) 0.31***(0.09) -0.16(0.14) -0.83(1.06) -1.95**(0.81)

year2006(if yes=1; otherwise =0) t 0.64***(0.06) 3.55***(0.62) 0.37***(0.09) -0.18(0.15) -0.74(1.09) -1.59**(0.72)

year2007(if yes=1; otherwise =0) t 0.70***(0.05) 5.08***(0.74) 0.27***(0.10) -1.17***(0.24) -1.61(1.46) -3.1***(0.86)

year2008(if yes=1; otherwise =0) t 0.77***(0.06) 4.86***(0.74) 0.26**(0.13) -2.19***(0.53) -2.70**(1.21) -3.70***(1.05)

Constant α 7.83***(0.08) -2.54**(1.09) 6.35***(0.29) 2.91***(0.67) -6.62***(1.70) -1.84(1.29) R 0.32 0.46 0.25 0.30 0.11 0.12

1. Standard errors are in parentheses, and figures were rounded. 2. *** means significant at the 0.01, level, ** at 0.05 level, and * at 0.10 level.

42 / 48

Table 7 the empirical result of the effects of Priority Forest Programs on rural household total income by year

Period 1995~98 1995~99 1995~2000 1995~2001 1995~2002 1995~2003 1995~2004 1995~2005 1995~2006 1995~2007 1995~2008

NFPP dummy (if


-0.00

(0.03)

0.02

(0.04)

0.02

(0.05)

0.02

(0.05)

0.01

(0.05)

0.02

(0.05)

0.01

(0.05)

-0.01

(0.05)

-0.01

(0.05)

-0.03

(0.05)

-0.02

(0.05)

DCBT dummy (if


n.a n.a n.a n.a -0.04

(0.10)

-0.03

(0.08)

-0.03

(0.09)

-0.05

(0.10)

-0.06

(0.10)

-0.07

(0.08)

-0.08

(0.08)

SLCP dummy (if


n.a 0.13***

(0.04)

0.13***

(0.04)

0.15***

(0.04)

0.11***

(0.04)

0.11***

(0.03)

0.10***

(0.03)

0.06**

(0.03)

0.04

(0.03)

0.03

(0.03)

0.02

(0.04)

Both SLCP and

NFPP dummy (if

yes=1; otherwise 0)

Y

n.a 0.12*

(0.06)

0.01

(0.05)

-0.03

(0.05)

-0.00

(0.06)

-0.01

(0.05)

0.017

(0.05)

0.04

(0.05)

0.06

(0.04)

0.08*

(0.04)

0.08*

(0.05)

ITDP dummy (if


n.a n.a n.a n.a n.a 0.06***

(0.01)

-0.02**

(0.01)

-0.44***

(0.14)

-0.45***

(0.10)

-0.32***

(0.12)

-0.25*

(0.14)

SBDP dummy (if


-0.13***

(0.01)

-0.18***

(0.02)

-0.21***

(0.02)

-0.23***

(0.03)

-0.18***

(0.05)

-0.35***

(0.06)

-0.46***

(0.08)

-0.02

(0.18)

0.06

(0.11)

0.07

(0.08)

0.03

(0.06)

3. Standard errors are in parentheses, and figures were rounded. 4. *** Means significant at the 0.01, level, ** at 0.05 level, and * at 0.10 level.

43 / 48

Table 8 the empirical result of the effects of Priority Forest Programs on rural household off- income by year

Period 1995~98 1995~99 1995~2000 1995~2001 1995~2002 1995~2003 1995~2004 1995~2005 1995~2006 1995~2007 1995~2008

NFPP dummy (if


1995~98 1995~99 1995~2000 1995~2001 1995~2002 1995~2003 1995~2004 1995~2005 1995~2006 1995~2007 1995~2008

DCBT dummy (if


0.07

(0.18)

0.11

(0.19)

0.11

(0.24)

0.13

(0.27)

0.18

(0.29)

0.20

(0.29)

0.22

(0.34)

0.25

(0.41)

0.29

(0.47)

0.19

(0.55)

0.15

(0.61)

SLCP dummy (if


n.a n.a n.a n.a -0.07

(0.73)

0.33

(0.45)

0.33

(0.48)

0.11

(0.39)

-0.05

(0.35)

-0.14

(0.35)

-0.24

(0.30)

Both SLCP and

NFPP dummy (if

yes=1; otherwise 0)

Y

n.a 0.17

(0.30)

0.30

(0.323)

0.35

(0.35)

0.35

(0.30)

0.38

(0.25)

0.31

(0.31)

0.20

(0.34)

0.17

(0.39)

0.03

(0.45)

-0.04

(0.42)

ITDP dummy (if


n.a 0.84

(0.54)

-0.22

(0.28)

-0.35

(0.35)

-0.37

(0.36)

-0.31

(0.32)

-0.16

(0.36)

-0.17

(0.47)

-0.19

(0.60)

0.03

(0.67)

0.12

(0.75)

SBDP dummy (if


n.a n.a n.a n.a n.a -1.16***

(0.23)

-1.60***

(0.29)

1.95

(1.39)

2.94***

(0.47)

0.90

(1.70)

0.76

(2.12)

1. Standard errors are in parentheses, and figures were rounded. 2. *** means significant at the 0.01, level, ** at 0.05 level, and * at 0.10 level.

44 / 48

Table 9 the empirical result of the effects of Priority Forest Programs on rural household land-based income by year

Period 1995~98 1995~99 1995~2000 1995~2001 1995~2002 1995~2003 1995~2004 1995~2005 1995~2006 1995~2007 1995~2008

NFPP dummy (if

yes=1; otherwise

0)

Y

0.01

(0.04)

0.06

(0.05)

0.04

(0.06)

0.03

(0.06)

0.02

(0.05)

0.02

(0.06)

-0.03

(0.08)

-0.03

(0.08)

-0.04

(0.08)

-0.05

(0.08)

-0.05

(0.08)

DCBT dummy (if

yes=1; otherwise

0)

Y

n.a n.a n.a n.a 0.06

(0.18)

0.00

(0.15)

0.05

(0.16)

-0.08

(0.13)

-0.13

(0.15)

-0.14

(0.15)

-0.12

(0.15)

SLCP dummy (if

yes=1; otherwise

0)

Y

n.a 0.26***

(0.08)

0.35**

(0.18)

0.42*

(0.22)

0.38**

(0.18)

0.34**

(0.14)

0.34**

(0.14)

0.29**

(0.12)

0.26**

(0.12)

0.24**

(0.11)

0.23**

(0.10)

Both SLCP and

NFPP dummy (if

yes=1; otherwise

0)

Y

n.a -0.11

(0.15)

0.03

(0.10)

0.00

(0.12)

0.20

(0.10)

0.06

(0.10)

0.13

(0.10)

0.17*

(0.10)

0.20**

(0.10)

0.23**

(0.10)

0.24**

(0.09)

ITDP dummy (if

yes=1; otherwise

0)

Y

n.a n.a n.a n.a n.a -0.03

(0.05)

-0.08

(0.05)

-0.67***

(0.20)

-0.68***

(0.18)

-0.34

(0.24)

-0.09

(0.33)

SBDP dummy (if

yes=1; otherwise

0)

Y

-0.13***

(0.02)

-0.20***

(0.02)

-0.21***

(0.02)

-0.20***

(0.02)

-0.45**

(0.21)

-0.51***

(0.17)

-0.46***

(0.15)

-0.22**

(0.10)

-0.20***

(0.07)

-0.10***

(0.05)

-0.21***

(0.06)

1. Standard errors are in parentheses, and figures were rounded. 2. *** Means significant at the 0.01, level, ** at 0.05 level, and * at 0.10 level.

45 / 48

Table 10 land-based input and output index comparison between the SCLP and non-SLCP

cropland land-based labor off-farm labor weight off-farm labor production expenditure labor intensity index production expenditure intensity index

year non-SLCP

(1)

SLCP

(2)

non-SLCP

(3)

SLCP

(4)

non-SLCP

(5)

SLCP

(6)

SLCP

(7)

non-SLCP

(8)

SLCP

(9)

non-SLCP

(10)

non-SLCP

(11)=(3)/(1)

SLCP

(12)=(4)/(2)

non-SLCP

(13)=(9)/(1)

SLCP

(14)=(10)/(

2)

1999 1.00 1.00 1.00 1.00 0.33 0.27 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

2000 0.94 0.84 0.98 1.02 0.37 0.32 1.16 1.25 1.17 1.36 1.04 1.21 1.24 1.61

2001 0.93 0.79 0.98 1.00 0.39 0.35 1.29 1.41 1.25 1.49 1.06 1.26 1.35 1.88

2002 0.84 0.67 1.02 0.92 0.42 0.39 1.49 1.56 1.51 1.97 1.21 1.39 1.79 2.96

2003 0.75 0.57 1.02 0.95 0.44 0.42 1.63 1.81 1.78 2.55 1.37 1.66 2.39 4.46

2004 0.73 0.54 1.04 0.93 0.44 0.43 1.69 1.86 1.77 2.66 1.44 1.72 2.43 4.90

2005 0.65 0.58 0.98 0.97 0.49 0.44 1.90 2.00 1.80 2.73 1.50 1.68 2.76 4.72

2006 0.65 0.59 1.00 0.99 0.49 0.45 1.99 2.13 2.07 2.86 1.53 1.69 3.17 4.88

2007 0.71 0.58 0.92 0.94 0.54 0.52 2.19 2.73 3.05 5.38 1.31 1.63 4.31 9.28

2008 0.70 0.57 0.71 0.82 0.62 0.57 2.40 2.87 3.22 6.63 1.02 1.43 4.59 11.54

46 / 48

Table11 land-based input and output index comparison between the NFPP and non-NFPP

cropland labor input off-farm labor weight Off-farm labor production expenditure labor intensity index production expenditure intensity index

year non-NFPP (1) NFPP

(2)

Non-NFPP

(3)

NFPP

(4)

non-NFPP

(5)

NFPP

(6)

Non-NFPP

(7)

NFPP

(8)

Non-NFPP

(9)

NFPP

(10)

Non-NFPP

(11)=(3)/(1)

NFPP

(12)=(4)/(2)

Non-NFPP

(13)=(9)/(1)

NFPP

(14)=(10)/(2)

1998 1.00 1.00 1.00 1.00 0.32 0.26 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

1999 0.95 0.97 1.03 1.02 0.34 0.28 1.10 1.09 1.09 1.09 1.09 1.05 1.14 1.12

2000 0.83 1.04 1.13 0.89 0.41 0.29 1.42 1.00 1.29 0.96 1.37 0.86 1.56 0.92

2001 0.82 0.99 1.18 0.91 0.43 0.32 1.59 1.10 1.34 1.00 1.44 0.91 1.63 1.01

2002 0.74 0.89 1.23 0.91 0.46 0.34 1.76 1.18 1.41 1.01 1.67 1.02 1.92 1.13

2003 0.66 0.76 1.28 0.92 0.49 0.35 1.92 1.23 1.42 1.03 1.92 1.21 2.14 1.36

2004 0.63 0.74 1.28 0.94 0.49 0.36 1.96 1.29 1.40 0.98 2.03 1.28 2.21 1.33

2005 0.66 0.70 1.40 0.90 0.51 0.39 2.19 1.35 1.48 0.90 2.12 1.30 2.24 1.29

2006 0.66 0.71 1.44 0.94 0.51 0.41 2.27 1.47 1.65 0.95 2.17 1.34 2.49 1.34

2007 0.71 0.68 1.48 1.05 0.57 0.47 2.59 1.90 2.61 1.95 2.07 1.55 3.66 2.88

2008 0.71 0.66 1.37 1.05 0.63 0.54 2.14 3.11 3.11 2.12 1.93 1.58 4.36 3.21

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Fig. 1 Sample Household income distribution

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Fig. 2 the cropland net profit and the SLCP subsidy in Yellow River Catchment

Fig. 3 the cropland net profit and the SLCP subsidy in Yangtze River Catchment

0

50

100

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200

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

yixian yanchang SLCP subsidy

0

100

200

300

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

nanbu nanjiang mabian muchuan xiushuixingguo suichuan yixian zhenan yanchangHuanjiang Pingguo SLCP subsidy

POVERTY & ECONOMIC POLICY...IDRC photo: N. McKee POVERTY & ECONOMIC POLICY R E S E A R C H N E T W O...

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