The role of unofficial farmer’s cooperatives on rule public goods provision

Abstract

Public goods provisions are of extreme disparity between the urban and rural areas in China.

Preferential policies lead public goods shrinking in a rural area. Some farmer cooperatives

and collective behaviors at village level can effectively overcome the "free rider” problem

and enhance the rural public goods provision. This study investigated the rural provision

using survey data from six provinces in China. These results suggest that villages with farmer

cooperatives are likely to provide more public goods related to agricultural production, about

20%-30% on average more than villages without these cooperatives. While those positive

impacts on public goods related to daily life and environmental improvement are relatively

weak. Heterogeneities are found both on types of public goods and regions.

Keywords: Rural Development; Farmer Cooperatives, Public Good, China

JEL codes: H41; Q5; Q12; Q21; R53

1

Introduction

Public goods play a significant role in rural areas for farms’ production and daily life and are

closely interrelated to sustainable development and poverty reduction (Darja et al. 2004;

Beekman et al. 2014). However, public goods are often undersupplied in nature, affecting

economic development, threatening economic stability and prosperity (Besley and Ghatak

2006; Westhoek et al 2013). Inadequate supply of public goods, such as poor rural

transportation, insufficient supply of electricity, drinking water and education, largely limits

regional communications with devolved regions and cause lower efficiency in agricultural

production and economic development (Unger 2003).

Public goods provision in the rural region has been considered to be a primary responsibility

of government, especially from higher levels of governments since the seminal work of

Musgrave (1939) and Samuelson (1954). Local governments’ participation might also

improve the efficiency of public goods provision (Tiebout 1956). However, Kunicová and

Rose-Ackerman (2005) argued that both in theoretical and empirical studies and suggested

that an efficient public good provision system only run better in the government with a strong

democratic and bureaucratic institution. Public goods are provided by the government

actually cannot completely solve the under-provision issues because good government is

itself a public good (Xu et al. 2015).

In China, most villages heavily had relied on financial support from the up-level government

for a long time. Those fiscal resources are considerably collected from farmers’ payment on

taxes and fees (Tsai 2010). However, since the 1990s, Tax for Free Form (TFFF) changed the

rural financial systems and the village had to take more responsibilities on rural public goods.

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Both local governments and farmers have no incentives to provide since “free riders” nature

of the public property. Environmental issues and shortages of public property are becoming

serious in rural areas. Tsai (2010) proposed a model of informal governmental accountability

or solidary groups to provide public goods and argued that in places with weak formal

institutions of accountability, localities with encompassing and embedding solidary groups

are likely to have better public goods provision from local government. Incorporating the

official organization and farmer cooperatives, members have the incentive to offer public

goods responsibly. Otherwise, if the public funds are in the hands of official government or

private individual, none of them can make sure that these funds can be used to invest in local

public projects.

Those of farmer’s cooperatives, as member-owned, and controlled self-help organization in

rural China could be traced to the 1940s and have demonstrated a significant contribution to

social and rural development. Numerous rural cooperative associations were formed mainly

for enterprises, marketing, fund collection and access to new technology. Those cooperatives

are initiated by a group of farmers, with looser networks, organizing beyond village

boundaries. Since the economic reform, the provision of essential public goods and services

has been decentralized into a primary responsibility to local governments. Those associations

mainly have a role to play as an assistant to organize, supervise and maintain the projects.

New cooperative organizations emerged in most regions of China since the 1980s. Up to

2004, the number of new cooperative organizations reached more than 150,000 (Green Book

of China’s Rural Economy, 2004, p.157). The new cooperative organizations take different

forms. The comprehensive agricultural reform at the end of the 1990s further boosted the

cooperatives. Reforms in the election, tax, and fiscal systems are decentralized to village

3

institutions. Farmers have more authorities to obtain and organize their production and daily

life.

In recent years, numerous voluntary farmer cooperatives such as Village Temples, Churches

Lineage Groups, Service for Weddings and Funeral, and Agricultural Mutual Aid Team have

been emerging in the village to provide growing demands. A critical role in those

cooperatives is to promote public goods provision in the village. They attempt to increase the

supply to meet demand through negotiation or unofficial contracting. The cooperatives seem

more efficient than the privacy provisions and can avoid overlapping investments by each

without collaboration.

Moral, pride, and respect of the rural play crucial roles in the small communities and can

efficiently solve the “free rider” issues on public provision. When a community is becoming

more heterogeneous, the traditional moral might play a less important role in connecting

people and generating an incentive to provide public goods. In contrast, government support

and self-provision are two dominative forms in providing public services. Farmer

cooperatives may encourage contribution to public goods provision. Those incentives are not

only on the local farmers but also on formal village committees, as well as higher level of

administrations. Our hypothesis is that the farmer cooperatives in the rural area are likely to

provide higher local public goods provision than localities without this institution.

Data Sources

The data used in this paper to examine the relationship between public goods provision and

farmer cooperatives were conducted by the survey team of the Center for Chinese

4

Agricultural Policy, Chinese Academy of Sciences, leading by Professor Linxiu Zhang of the

Center for Chinese Agricultural Policy, Chinese Academy of Sciences, Professor Scott

Rozelle of the University of California at Davis, and Professor Loren Brandt of the

University of Toronto. The dataset was collected by village survey in 2003 across rural China.

It is now available at China Survey Data Network (CSDN,

http://www.chinasurveycenter.org). Information about village affairs is included, for example,

basic characteristics of villages, the information on public goods investment in the village,

the association development in rural areas and village affair management. Data collection

effort in six provinces, 36 counties, 216 townships, and 2,459 villages and the final datasets

can be considered presenting nationally.

The six provinces for the survey—– Jiangsu, Gansu, Sichuan, Shanxi, Jilin, and Hebei—–

were chosen to reflect differences in levels of economic development as well as regional

differences between north and south China in terrain, institutional history, and social

organization. Gansu is located in the interior and is the lowest developed region in all survey

areas. Shanxi and Hebei are neighboring provinces in the north, but Shanxi is in the interior

and less developed. Jiangsu is a developed region and locale in the coastal. Jilin, in the north

and Sichuan, in the southwest both are less developed but have abundant natural

endowments.

Public goods projects were recorded since 1998 in the survey. Approximate 17 types of

projects were included in this survey such as road or bridge, drinking water, irrigation system,

drainage system, school, clinic, electricity, telephone. Environmental protection projects

including watershed management, grain for green, were also included. Except for basic

information about those projects, the survey paid more attention in details like ‘the source of

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funding’, ‘who benefit from the projects’ and ‘who carried out the project’.

Farmer cooperatives information is gathered by directly asking if there are some farmer

cooperative actions or behaviors in the village. Detailed information also relates to the

structure of the institutions, ‘how the association formed,' ‘how the association operation ’,

and ‘does the associations earn benefits for the community development. The purpose is to

reveal whether the association plays a role in these communities.

The primary results indicated that from 1998 to 2003, approximately 9,200 public projects

were constructed in the sample regions, nearly four projects per village. Over 80% of the

villages have at least one new project. 78% of the total investment is concentrated on the

projects related to the production, such as road or bridge’, ‘drinking water,' ‘irrigation system’

accounting for 30% of the total amount. Most investment projects are on road and bridge,

about 1514 and the largest size of investment is on electricity about 27.08 thousand Yuan.

The average size of land improvement program is also remarkably large although the number

of projects is small. Besides, investments in ‘school’ and ‘grain for green’ are covered 75% of

the sample villages (TABLE 1).

[TABLE 1]

The public goods were supplied by either higher level governments or local villages. In either

case, local village and farmers have replaced the official in decision making. In the survey

regions, almost 55% of projects are provided by the local village. The heterogeneities were

found at a different level of development. More high-level sources are likely to distribute and

transfer into the under or less developed regimes, like Gansu and Shanxi province.

6

The public goods provision was grouped into three types:

Type 1: Direct production activities such as irrigation system, drainage system;

Type 2: Residence’s daily life such as road and bridges, school, clinic, electricity, and

telephone;

Type 3: Environmental protection projects, for example, watershed management, and grain

for green.

Over 68% of projects were invested in the first type of public goods and usually only serve

the village. The main source was from local farmers or village governments (43%) and

reflected the magnitude of demand and supply from local farmers. Type 2 public goods are

often cover large areas beyond the village geographic boundary. Those projects always

cannot be accomplished in the village and even county alone. Supports from the center and

provincial governments are important to those projects (19%). The type 3 projects mainly

provide environmental services, proving positive externalities to the whole society (Figure 1).

The majority funding source came from self-funding, which stand for 55% of the total, of

78% (43% of total) were invested in Type 1 public goods and of 22% (12% of total) were

invested in type 2. Of totally 30% villages having farmer cooperatives, provided 42% of total

public investment: of 66% (28% of total) of the fund be invested in Type 1, and of 30% (13%

of total) were invested in Type 2. In the rest villages (59%) without farmer cooperatives, 51%

(30% of total) and 49% (28% of total) of the total fund invested in Type 1 and Type 2,

respectively. The following sector will introduce empirical models to isolate the impacts of

farmer cooperatives on the public goods provision.

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Figure 3 Different Types of Public Goods Relative Funding Sources and Famer Cooperative

Empirical Analysis

An Endogenous Switching Model

Most of the previous studies used a Tobit model to assess the impact of policies or behaviors

at the village level on the public goods provision. Luo (2007) investigated the influences of

elections, fiscal reform on public goods provision in rural China. A dummy variable is

directly added into the model to estimate the effect of public goods provision in villages.

While the possible endogenous issues in the village to choose to have informal institutions or

not were considered. The unobserved factors are affecting the form of the informal institution

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might also contribute to the provision of public goods. Village chooses to have a farmer’s

cooperative actions may be not random but decided by some exogenous factors. Those

decisions will switch the sample into differences groups: villages have informal institutions

and villages have no informal institutions informal. If we ignore those selection problems, it

is possible that the coefficient on the variable will be biased. An efficient method to deal with

this endogenous self-selection problem was solved by Maddala and Nelson (1975) with a

switching model. Lee (1978) and Adamchik and Bedi (2000) developed this method and

applied it into the estimate with limited dependent variables.

In this study, an endogenous switching regression model is specified to estimate the effects of

farmer cooperatives on village public goods provision. A switching equation sorts the sample

over two different groups: have and not having cooperative behaviors. The behavior with two

regression equations, and a criterion function Athat determines which group the agent faces:

(8) Y 1 i=a1+β1 X1 i+ε1 i if Ai=1

Y 2 i=a2+ β2 X2 i+ε2 i if Ai=0

Given Ai={1 if γ Z i+μi

0 if γ Zi+μi

Where Y 1= ji are the dependent variables in the continuous equations, X jivectors of weakly

exogenous variables. To simplify, assumeε 1i, ε 2iand μi have a trivariate normal distribution,

with mean vector zero and covariance matrixσ ij. The covariance betweenε 1i, and ε 2i is not

defined as Y 1 i and Y 1 iare never observed simultaneously. Given the assumption with respect

to the distribution of the disturbance terms, the logarithmic likelihood function for the system

of equations:

9

(9)

lnL=∑ Ai [¿ lnF (γ Z i+ ρi ε1i /ρ1

√1− ρ12

)+ ln( g ( ε1 i

σ1 )σ 1

)]+[ (1−Ai ) lnF (γ Z i+ρi ε2 i / ρ2

√1−ρ22

)+ ln( g( ε2 i

σ2 )σ2

)]¿where g ( ∙ ) and f ( ∙ )are normal probability density functions for each object equation. ρ1is the

coefficient of correlation between ε 1i and μi. Where ρ2 is the coefficient of correlation

between ε 2iandμi.

Based on the data available, three measures are used in each type of public goods provision:

total value (VALUE_TOT), the total number (VALUE_NO), and average public investment by

households (VALUE_PH) from 1998 to 2003. To identify the sole effects of farmer

cooperatives accurately, the analysis control numbers of factors that may also have an effect

on village public goods provision. For example, factors related to geography and

demography, social-economic variables, information about village affair management, as well

as some factors reflecting the level of democratic and fiscal policies. We also incorporate the

region as a fixed effect in the model. The detailed summary and description are presented in

Table 2.

[TABLE 2]

Regressions are estimated in three different types of public goods separately. Each estimator

will include three equations: the villages with cooperatives, the villages without cooperatives,

and the possibility of villages select to organize farmer cooperatives or not. We incorporate

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enough control variables into each group, such as geographic and demographic variables,

social-economic variables, information about village affair management, as well as some

factors reflecting the level of democratic and fiscal policies. We also assume some

characteristic variables, such as village population, household income, agricultural income,

the number of workers in the up-level government and so on, will affect the determining

indicators.

Empirical results

Table 3a-Table 3c present the estimates switching in two groups for three types of public

goods provision when we control for geographic, demographic, economic, and fiscal factors

with three different indicators. They are generated using the fully maximum method. In Table

3a, the results shown in a column titled as FC=0 are the estimates for the group without

farmer cooperatives, which as predicted show that higher household income, total population,

the road passing thru the village, and more households having telephone or cell-phone are

positively and significantly related to the public goods provision.

The results displayed in a column titled as FC=1 are the estimates for the group with farmer

cooperatives. Similar consequences are predicted in the group without farmer cooperatives,

but the marginal effects in those variables are more significant in the group with farmer

cooperatives. The indexes used to calculate the farmer cooperatives like the variables of

FC_MEB, FC_FEE, FC_MEET, and FC_CHAT are positively significantly impact on the

investments. Those imply the villages with farmer cooperatives are likely to get more public

goods provision than villages without these groups. This result is consistent with Tsai (2007)

and Luo et al. (2007)’s estimators. On one hand, farmers have enough incentives to promote

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these production projects such as irrigation system and drainage system; on another hand,

collaboration might largely reduce the individual cost.

The results are shown in a column titled as select are the estimates for the village’s selection

to have farmer cooperatives. Household income and total population are two key variables to

make villages select having farmer cooperatives. Regional diversities also significantly affect

the villages choose to have cooperatives or not. Type 2 and Type 3 public goods provision

indicate relatively weak correlations with farmer cooperatives and control variables. In the

case of projects related to resident’s daily life, such as road and bridges, school, the indexes

used to estimate the farmer cooperatives only have a statistically significant impact (at a 95%

confidence level) on investment per household. In the case of projects related to

environmental protection, the negative impacts are noted in total investments and some

projects. However, the level of uncertainty in these estimates as very high and the magnitude

of the effect was subtle. The interpretation is that the public goods need huge resources from

both up-level and local village, like road, school, and power line. A local institution, like the

village committee, is less affordable. An example is a national project “road to every village”

which had induced the central government to invest in road building projects during the past

several years. The local village might have some supports. However, incentives and impacts

are limited.

The heterogeneities among regions are far more complex. In the production activities (Type

1), no surprise, the relatively undeveloped regions (Gansu, Shangxi, and Sichuan) have fewer

provisions. An interesting result is that, unlike the other developed region having a high level

of public goods provision. Jiangsu invited less both in total investment and the number of

projects. However, a positive sign is found in the investment per household in Jiangsu, which

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suggests that self-provision and lower level village provision in the developed region is a

more important source of supporting productive public goods.

Parallel results are represented in Type 2. A difference is that likely developed region, the

coefficient of investment per household in undeveloped regions is positive. However, this

does not mean that the sources of those public goods (Road, School, Telephone) are collected

from self-provision and lower level village provision in the undeveloped region. This might

be a different reason in the developed region. Relatively lower population density and more

investment from the central government (e.g. National road net, electricity net, and telephone

net) is two main factors support the results. Moreover, Regional heterogeneities in type 3

provision case are more significant. The largest diversity of provision compared to the base

region is Shangxi province, where the government invests an enormous number of the fund

on avoiding deforestation and soil erosion.

[Table 3]

Based on estimations of the switching model, the public goods in the villages having

cooperative or not are predicted (see Table 4). In general, villages with cooperative are

associated with the much higher provision, not only on total investment but also on the

investment per household and number of projects. Aggregate investment in villages with

cooperative might be higher by 41% than those without. The gap in per household and the

numbers of projects are 6% and 57%. In the villages without cooperative, total investment

could be enhanced by 47% if farmer cooperatives were established. The improvement in per

household and the numbers of projects will be 45% and 52%. The gap in providing type 2

and type 3 projects exceeds those provision in type 1 projects. On average, the difference in

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provision type 1 goods is below 30%, no matter with or without cooperatives. While the

average magnitudes in provision type 2 and type, three goods are over 40%.

[Table 4]

Discussion and Conclusion

This study analyzes the potential contribution of local farmer cooperatives to a public goods

provision in rural China. Empirical results strongly support the hypothesis that local farmer

cooperatives as an informal institution are likely to promote public goods provision related to

agriculture production. These findings are much more robust in effective public goods

provision. The average contribution of the farmer cooperative to public goods supply can be

20%-30%. While the impact of farmer cooperatives in providing public goods related to daily

life and environmental enhancement is not as strong since those kinds of projects usually not

serve the local needs and require funding from up-level funding.

China governments have been actively investing billions Yuan annually in urban public goods

to meet and sustain the increasing demand (Fan et al. 2002; Zhang and Kanbur 2005).

Numbers of roads, schools, and parks are established to meet the growing population

particularly the immigration from rural areas. However, public goods in rural area shrunk in

the last decade. The broad reform on policy and society in rural fiscal management and rural

democratization make farmers and a local village undertake the duty of providing public

goods. The public goods provisions in rural China indicated a declining trend, particularly in

maintaining roads, village school, deteriorating environment and irrigation systems. The

farmer cooperatives are organized in the rural area initially for marketing exchange (Peng

2001; Xiao et.al. 2008; Martinez-Bravo, et al 2014). In fact, they might play a significant role

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in rural public goods provision: collective action in the institution can enforce all the

membership provision, in particular, public projects. Group demands funding is most likely

met from the higher-level government.

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Table 1 Number and Size of Public Goods Projects in Rural China, 1998–2003

Number of projects

Average Size($1000)

Total value(1000 Yuan)

Percentage of Total value

Electricity 1299 27.08 35176.88 27.14%Build road or bridge 1514 15.48 23443.44 18.08%Build school 971 17.77 17258.03 13.31%Telephone line 713 17.47 12458.54 9.61%Drinking water 750 11.99 8995.94 6.94%Grain for green 968 7.96 7707.91 5.95%Irrigation system 713 10.45 7453.80 5.75%Radio/TV cable 505 8.87 4480.00 3.46%Watershed management 181 15.12 2737.22 2.11%Terracing 214 9.12 1951.23 1.51%Drainage system 229 8.43 1931.21 1.49%Land improvement 76 21.58 1640.12 1.27%Leisure & recreation space 270 4.84 1306.65 1.01%Downtown planning and improvement 175 5.07 887.31 0.68%

Logging ban and foresting 299 2.76 826.65 0.64%Build clinic 200 2.85 570.53 0.44%Eco-forest 77 5.45 419.48 0.32%Building pasture 23 17.02 391.51 0.30%Total 9177 209.35 129636.45 1.00

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Table 2. The Description of Variables Using in the Estimation

Variable Mean S. D.

VALUE_TOT The total value of investments in log form (1000 yuan) 11.84 2.18

VALUE_PH Average value of investments by household in log form (1000 yuan) 3.85 2.30

VALUE_NO Total number of public projects 4.60 2.05

FC Are the village have any farmer’s cooperatives? 1=yes; 0=no 0.30 0.30

FC_MEB No. of households are members of this Farmer’s cooperatives? 12.45 72.88

FC_FEE Do this Farmer’s cooperatives need to pay members fee? 1=yes; 0=no 0.04 0.28

FC_MEET Do this Farmer’s cooperatives hold meeting every year?? 1=yes; 0=no 0.10 0.30

FC_CHAT Do this Farmer’s cooperatives have any constitution? 1=yes; 0=no 0.09 0.28

FC_TRAIN Numbers of technical training was organized in your village. 4.69 5.77

INCOM_HOUS Household income in log form 14.31 1.20

INCOM_AG Agricultural income in log form 61.31 24.89

POP Number of the population in log form. 7.01 0.76

POP_OUTWORK Number of the population out of the village in log form 4.73 1.25

POP_HIGHSCOL Number of Senior high and above graduates 3.86 1.27

NO_FIRM Number of firms in the village 2.07 7.50

ROADPASS Any tarred road passing thru your village? 1=yes; 0=no 0.47 0.50

DIS_COMMITTEE The distance from the village committee seat to the nearest tarred road 4.73 11.66

DIS_WATER Distance from village committee seat to the major drinking water source 0.33 0.94

DEBT Is the village committee in debt? 1=yes 2=no 0.76 0.43

GOVN_WORKER How many are fellow villagers working at the up-level government? 1.51 0.99

ACC_ELE How many households have access to electricity? 5.67 0.82

ACC_WATER How many households have access to tap water? 5.25 1.25

ACC_TELE How many households have telephone or cell-phone? 4.49 1.46

RE_JIANGSU The village is in JIANGSU province. 1=yes; 0=no 0.19 0.39

RE_GANSU The village is in GANSU province. 1=yes; 0=no 0.13 0.34

RE_ SICHUAN The village is in SICHUAN province. 1=yes; 0=no 0.15 0.36

RE_SHANXI The village is in SHANXI province. 1=yes; 0=no 0.15 0.36

RE5_JILIN The village is in JILIN province. 1=yes; 0=no 0.15 0.36

RE5_HEBEI The village is in HEBEI province. 1=yes; 0=no 0.23 0.42

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Table 3a Switching Model Analysis of the Impact of Associations on Type 1 Public Provision in Rural China, 1998–2003

Dep. Var. = VALUE_TOT Dep. Var. = VALUE_PH Dep. Var. = VALUE_NO VARIABLES FC=0 FC=1 Select FC=0 FC=1 Select FC=0 FC=1 Select

FO_MEB 0.0011 0.004 0.0043***(0.00496) (0.0086) (0.000491)

FO_FEE 0.0686** 0.0809 0.0106(0.027) (0.271) (0.591)

FO_MEET 0.220 0.155*** 2.007***(0.301) (0.021) (0.728)

FP_CHAT 0.144* 0.104 0.213(0.096) (0.392) (0.711)

FO_TRAIN 0.00535** 0.00167** 0.00624**(0.0029) (0.0008) (0.0059)

POP 0.551 0.819* 0.155* 0.156 0.484** 0.153* 0.489 0.0214 0.0162(0.280) (0.453) (0.0905) (0.891) (0.178) (0.096) (9.589) (0.894) (0.186)

POP_OUTWORK 0.0390 -0.0912 0.0373 -0.106 0.0554 0.347*(0.109) (0.0593) (0.0384) (0.105) (0.818) (0.189)

INCOM_HOUS 0.328** 0.562*** 0.0902 0.353 0.583** 0.0909 1.290 1.330*** 0.0586(0.155) (0.187) (0.0682) (0.219) (0.281) (0.0601) (2.220) (0.326) (0.0836)

NO_FIRM -0.00353 0.00514 -0.00383 0.00591 -0.0192 0.0594**(0.0109) (0.00442) (0.00515) (0.0110) (0.152) (0.0288)

ROADPASS 0.289* 0.0243** 0.308*** 0.0236** 0.256 0.257*(0.156) (0.013) (0.0847) (0.070) (3.331) (0.125)

DIS_COMMITTEE -0.00300 -0.0145** -0.00334 -0.0156 -0.0532 -0.0700(0.0111) (0.00656) (0.0125) (0.0108) (0.162) (0.0607)

DIS_WATER -0.0280 -0.0202 -0.0251 -0.0296 0.0974 -0.492***(0.0673) (0.0805) (0.0528) (0.0990) (1.452) (0.103)

DEBT 0.103 -0.0135 0.105 -0.0239 -0.282 0.610*(0.164) (0.104) (0.198) (0.0737) (3.509) (0.321)

ACC_TELE 0.277*** 0.251*** 0.262** 0.257 0.823 -0.153(0.105) (0.0873) (0.127) (0.177) (1.398) (0.224)

RE_Jiangsu -0.452 -0.778* -0.0459 -0.534*** -0.839 -0.0657 0.0739 -2.191*** -0.0468(0.283) (0.426) (0.148) (0.154) (0.804) (0.217) (5.634) (0.801) (0.176)

RE_Gansu -1.132*** -1.702*** -0.0222 -1.081*** -1.641*** -0.0298 0.0298 3.106** -0.046421

(0.300) (0.361) (0.165) (0.345) (0.256) (0.272) (5.091) (1.563) (0.168)RE_ Sichuan -1.210*** -1.647*** -0.409*** -1.281*** -1.652*** -0.416** -0.207 1.780*** -0.0165

(0.265) (0.348) (0.142) (0.137) (0.226) (0.208) (4.384) (0.660) (0.112)RE_Shangxi 1.214*** -0.0300 0.0981 1.235*** 0.0617 0.101*** 0.228 1.972 -0.00162

(0.311) (0.363) (0.118) (0.177) (0.553) (0.0326) (4.540) (1.653) (0.153)RE5_Jilin 1.466*** 1.597*** -0.480*** 1.428*** 1.628*** -0.476*** -0.121 5.177*** 0.00664

(0.332) (0.357) (0.133) (0.0668) (0.145) (0.127) (10.47) (1.282) (0.429)Constant 12.35*** 16.91*** 0.0585 10.63*** 15.17*** 0.0564 0.0483 19.84 -0.615

(1.662) (2.028) (0.702) (2.218) (1.824) (1.378) (41.89) (0) (0.727)Observations 1,266 1,266 1,266 1,266 1,266 1,266 883 883 883

Robust standard errors in parentheses;*** p<0.01, ** p<0.05, * p<0.1

22

Table 3b Switching Model Analysis of the Impact of Associations on Type 2 Public Provision in Rural China, 1998–2003

Dep. Var. = VALUE_TOT Dep. Var. = VALUE_PH Dep. Var. = VALUE_NO VARIABLES FC=0 FC=1 Select FC=0 FC=1 Select FC=0 FC=1 SelectFO_MEB 0.00262 0.00257 0.00619**

(0.00208) (0.00173) (0.00250)FO_FEE 0.520 0.556*** 0.375

(0.361) (0.0351) (0.233)FO_MEET 0.968** 1.103*** 0.372

(0.470) (0.218) (0.820)FP_CHAT 0.930*** 0.886*** 0.249

(0.328) (0.152) (0.738)FO_TRAIN 0.00609 0.0261** 0.0112*

(0.0259) (0.0132) (0.0145)POP 0.599 0.0134* 0.131* 0.292 0.722* 0.0540 0.549 0.130* 0.301

(0.393) (0.083) (0.027) (0.226) (0.408) (0.136) (0.473) (0.062) (0.209)POP_OUTWORK -0.166** -0.108 -0.199 -0.0832 -0.239 0.0217

(0.0683) (0.200) (0.127) (0.0610) (0.170) (0.213)INCOM_HOUS 0.242 0.291 0.0872 0.307 0.151 0.0944 0.0673 0.480 0.270*

(0.365) (0.306) (0.101) (0.228) (0.266) (0.0816) (0.306) (0.341) (0.151)NO_FIRM -0.029*** 0.00884 -0.014*** 0.0218 -0.013** 0.0196***

(0.000524) (0.0330) (0.00319) (0.0551) (0.00621) (0.00761)ROADPASS 0.509*** 0.616*** 0.744*** 0.518*** 0.655** 0.334

(0.0809) (0.0899) (0.146) (0.0992) (0.287) (0.328)DIS_COMMITTEE 0.00594 -0.039*** 0.0228 -0.033*** 0.0184 -0.0489

(0.00570) (0.0101) (0.0151) (0.00848) (0.0164) (0.0332)DIS_WATER -0.066*** -0.094*** 0.142 -0.143*** -0.0917 -0.00502

(0.0141) (0.0206) (0.121) (0.0246) (0.228) (0.102)DEBT 0.140 0.272* 0.494 0.320** 0.211 0.0376

(0.148) (0.159) (0.365) (0.132) (0.302) (0.311)ACC_ELE 0.166 -0.156 0.0120 -0.280*** -0.356*

(0.257) (0.526) (0.231) (0.0893) (0.206)ACC_TELE -0.0706 0.339** 0.164* 0.271*** 0.248 -0.114

23

(0.116) (0.167) (0.0899) (0.0800) (0.186) (0.197)RE_Jiangsu 0.590 -1.977*** 0.254 0.231 -1.847*** 0.222 0.879 2.025*** 0.278

(0.676) (0.730) (0.215) (0.282) (0.413) (0.174) (0.537) (0.597) (0.246)RE_Gansu -1.482*** -3.556*** 0.481*** -0.575 -2.542*** 0.109* 0.361 1.869*** 0.662**

(0.484) (0.646) (0.158) (0.467) (0.196) (0.0614) (0.640) (0.657) (0.283)RE_ Sichuan -1.676*** -2.645*** 0.451** -0.998*** -1.745*** 0.0480 0.447 1.590* 0.0770

(0.622) (0.778) (0.193) (0.254) (0.399) (0.265) (0.614) (0.910) (0.287)RE_Shangxi 1.016** -1.077** 0.294* 1.568*** -0.206 0.0370 0.635 1.666*** 0.223

(0.516) (0.493) (0.167) (0.193) (0.203) (0.0566) (0.425) (0.482) (0.212)RE5_Jilin 0.784* -0.391 0.110 2.532*** 1.240*** -0.51*** 1.665*** 2.213*** -0.184

(0.414) (0.409) (0.134) (0.487) (0.166) (0.0570) (0.613) (0.775) (0.316)Constant 13.39*** 18.03*** 0.479 9.640*** 13.59*** 1.133 2.198 7.228** 1.369

(3.892) (3.571) (1.093) (3.551) (3.516) (1.120) (2.846) (3.576) (1.441)Observations 422 422 422 422 422 422 277 277 277

Robust standard errors in parentheses;*** p<0.01, ** p<0.05, * p<0.1

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Table 3c Switching Model Analysis of the Impact of Associations on Type 3 Public Provision in Rural China, 1998–2003Dep. Var. = VALUE_TOT Dep. Var. = VALUE_PH Dep. Var. = VALUE_NO

VARIABLES FC=0 FC=1 Select FC=0 FC=1 Select FC=0 FC=1 SelectFO_MEB 0.00153 0.00187* 0.00182*

(0.00325) (0.00092) (0.00072)FO_FEE -0.322 -0.343 0.0324

(0.709) (0.281) (1.054)FO_MEET 1.770 1.840 -0.114

(1.291) (1.581) (1.851)FP_CHAT -0.911 -0.954 0.0866

(1.307) (1.669) (1.845)FO_TRAIN 0.0442** 0.0453** 0.0789

(0.0277) (0.0205) (0.261)POP 1.617* 1.883 0.703*** 1.370 1.584 0.682*** 0.278 1.005 0.330

(0.874) (1.533) (0.206) (1.039) (2.500) (0.248) (0.560) (4.801) (0.423)POP_OUTWORK 0.230*** -0.500* 0.223 -0.505** -0.0204 -0.0771

(0.00661) (0.264) (0.147) (0.215) (0.205) (0.243)INCOM_HOUS -1.017** -0.0836 -0.419** -0.690 -0.211 -0.323 -0.283 -0.692 -0.328

(0.415) (0.487) (0.176) (0.718) (0.621) (0.209) (0.482) (2.239) (0.378)NO_FIRM -0.0217*** -0.0304 -0.0203 -0.0374 0.0353 0.111

(0.00362) (0.0886) (0.0320) (0.112) (0.0550) (0.101)ROADPASS 0.147 -0.315 0.0845 -0.349 -0.121 0.274

(0.210) (0.507) (0.272) (0.732) (0.293) (0.815)DIS_COMMITTEE -0.00706** 0.0141 -0.00900 0.0148 -0.00550 0.00107

(0.00328) (0.0240) (0.0190) (0.0187) (0.0198) (0.0221)DIS_WATER 0.0906*** -0.199 0.0590 -0.146 0.0811 0.0858

(0.0100) (0.368) (0.116) (0.208) (0.212) (0.377)DEBT -0.362*** 1.338* -0.331 1.406* -0.166 0.748

(0.0715) (0.707) (0.257) (0.803) (0.274) (1.632)ACC_ELE -0.320 -1.670 -1.148 -2.147 0.142

(0.832) (1.706) (2.321) (2.964) (0.291)25

ACC_TELE 0.370*** 0.473* 0.347 0.464 0.0946 -0.203(0.0876) (0.258) (0.323) (0.322) (0.224) (0.396)

RE_Jiangsu -0.438 -1.854 0.723 -0.895 -1.867*** 0.623* 0.0856 -0.460 0.0772(1.186) (2.113) (0.445) (0.847) (0.689) (0.334) (1.117) (1.182) (1.053)

RE_Gansu -2.825*** -2.227** 0.277 -2.799*** -2.118*** 0.254* 0.116 1.245 -0.541(0.708) (1.030) (0.292) (0.817) (0.529) (0.132) (0.879) (3.987) (0.693)

RE_ Sichuan -2.290*** -2.744*** 0.420 -2.543*** -2.854*** 0.361 0.158 -0.657 -0.132(0.797) (1.055) (0.269) (0.634) (0.581) (0.220) (0.752) (0) (0.662)

RE_Shangxi 1.423* -1.398 0.304 1.456*** -1.399** 0.263 0.453 -0.881 -0.467(0.746) (1.148) (0.317) (0.564) (0.629) (0.273) (0.670) (0) (0.521)

RE5_Jilin 2.223*** 0.948 0.788*** 2.045*** 1.067** 0.734** 0.622 0.780 -0.0877(0.733) (0.846) (0.286) (0.219) (0.434) (0.288) (1.316) (1.724) (1.282)

Constant 15.83*** 11.10*** 0.873 10.15** 9.131*** -0.245 2.789 5.547 2.137(3.586) (4.029) (1.622) (4.116) (2.542) (1.672) (5.729) (0) (4.666)

Observations 233 233 233 233 233 233 127 127 127Robust standard errors in parentheses;*** p<0.01, ** p<0.05, * p<0.1

26

27

Table 4 Predicted the Reductions/Increases of Public Goods Provision in Villages’ With/Without-Cooperative If Without/With-Cooperative

Total investments(1000 Yuan)

Investments per household(Yuan)

No. of Investments(100)

Reductions of investments in group with-cooperative if without- cooperative

Type 1 PGWith-Cooperative 243 126 6If without-Cooperative 178 113 3

Type 2 PGWith-Cooperative 454 294 3If without-Cooperative 409 287 1

Type 3 PGWith-Cooperative 57 9 6If without-Cooperative 49 5 1

Increases of investments in group without-cooperative if with-cooperative

Type 1 PGWithout-Cooperative 197 113 2If With- Cooperative 223 126 3

Type 2 PGWithout-Cooperative 301 241 1If With- Cooperative 338 294 3

Type 3 PGWithout-Cooperative 12 5 1If With- Cooperative 13 9 1

28