Decisions by Chinese households regarding renting in arable land—The impact of tenure security perceptions and trust

China Economic Review xxx (xxxx) xxxx

Contents lists available at ScienceDirect

China Economic Review journal homepage: www.elsevier.com/locate/chieco

Decisions by Chinese households regarding renting in arable land—The impact of tenure security perceptions and trust Xianlei Maa,1, Nico Heerinka,b,2, Ekko van Ierlandb,3, Hairu Langc,4, Xiaoping Shia, a b c

⁎,5

College of Public Administration, Nanjing Agricultural University, Nanjing, China Department of Social Sciences, Wageningen University, Wageningen, the Netherlands Agricultural and Resource Economics, University of California, Davis, United States of America

ARTICLE INFO

ABSTRACT

Keywords: Property rights Land rental market Tenure security Trust China

Policies aimed at strengthening tenure security through the elimination of land reallocations and provision of land certificates have been implemented with different degrees of success in rural China. In this study, we examine the impact of tenure security perceptions and trust on household decisions to rent in land in a region where tenure security is high and in a region where households face much lower land tenure security. Our regression results suggest that when land tenure is less secure, household perceptions of tenure security positively affect decisions to rent in additional land and the size of the rented land, whereas trust is important for the choice between oral and written contracts. When land tenure is relatively secure, household tenure security perceptions are less relevant, and trust becomes more important for land rental decisions. However, tenure security perceptions do seem to play a role in the choice between oral and written contracts in such high tenure security environments.

1. Introduction In the process of rural structural transformation that China and many other developing countries experience, many laborers leave the agricultural sector and become engaged in off-farm employment. According to the National Bureau of Statistics (NBS), the Chinese rural workforce involved in off-farm employment has increased from 252.78 million people at the end of 2011 to 281.71 million people at the end of 2016; this means that 47.77% of the rural population was engaged in off-farm employment at the end of 2016 (NBS, 2012; NBS, 2017). Well-functioning rural land rental markets can play an important role in this process, as they can enhance productivity as well as equity by allowing households with higher agricultural ability to gain access to additional land and permitting participation in the nonfarm economy by those households with lower agricultural ability (Chamberlin & Ricker-Gilbert, 2016; Deininger, 2003a, 2003b; Holden, Utsuka, & Place, 2008; Jin & Deininger, 2009). The number of rural land rental transactions has increased considerably in China since the 1990s. However, important regional

Corresponding author. E-mail addresses: [email protected] (X. Ma), [email protected] (N. Heerink), [email protected] (E. van Ierland), [email protected] (H. Lang), [email protected] (X. Shi). 1 No.6, Tongwei Road, Nanjing, Jiangsu, 210095, China. 2 P.O. Box 8130, 6700 EW Wageningen, The Netherlands. 3 P.O. Box 8130, 6700 EW Wageningen, The Netherlands. 4 517 Oxford Circle Apt#216, La Casa de Flores, Davis, CA, 95616, USA. 5 No.6, Tongwei Road, Nanjing, Jiangsu, 210095, China. ⁎

https://doi.org/10.1016/j.chieco.2019.101328 Received 24 August 2016; Received in revised form 16 July 2019; Accepted 29 July 2019 1043-951X/ © 2019 Elsevier Inc. All rights reserved.

Please cite this article as: Xianlei Ma, et al., China Economic Review, https://doi.org/10.1016/j.chieco.2019.101328

China Economic Review xxx (xxxx) xxxx

X. Ma, et al.

differences can be observed in the degree of land rental market development, while existing markets often tend to be highly segmented with (usually informal) rental transactions restricted to a close circle of relatives outnumbering formal transactions (Jin & Deininger, 2009; Prosterman et al., 2009; Wang, Riedinger, & Jin, 2015). The limited development of the land rental market in some regions and its segmented nature strongly reduce its potential to enhance productivity and equity; moreover, productivity-enhancing land investments may not be undertaken when land rentals are based on informal contracts (Deininger, 2003a, 2003b). What factors can explain the incomplete development of the land rental market in China? The existing international literature stresses the role of transaction costs in causing low levels of land rental market participation and contributing to land market segmentation (Holden et al., 2008; Holden & Yohannes, 2002; Jin & Deininger, 2009). Transaction costs in the land rental markets consist specifically of the costs involved in acquiring information on potential partners and the costs of negotiating and enforcing contracts, including the costs involved in reducing the risk of land loss. Two major factors that frequently contribute to high transaction costs in developing countries are the insecurity of land rights that arise from existing laws and regulations (Deininger, Xia, & Holden, 2017; Deininger & Zegarra, 2003; Holden, Deininger, & Ghebru, 2011) and a lack of trust among partners (Holden & Ghebru, 2005). Since 1998, the Chinese government has implemented a number of land tenure reforms that are meant to improve tenure security and stimulate the transferability of rural land. Relevant laws include the Land Administration Law of 1998, the Rural Land Contract Law of 2002, the Property Law of 2007, and the Mediation and Arbitration of Rural Land Contract Disputes Law of 2009. Although these reforms have contributed to improved legal, or de jure, tenure security, the extent to which they contribute to land rental market development is not clear. What matters for land renting decisions is perceived tenure security, not just legal tenure security (Broegaard, 2005; Jansen & Roquas, 1998; Sjaastad & Bromley, 2000). Perceived tenure security refers to household perceptions of tenure security and generally takes the form of household probability estimates of the chance of eviction from the land (Van Gelder, 2010). Perceived tenure security depends on the way in which land laws and land titling programs are being implemented (i.e., actual tenure security),6 how information about these laws is being distributed among stakeholders, and on other factors, such as customary land tenure systems and other social norms and traditions (Ma, Heerink, Feng, & Shi, 2015). The importance of tenure (in)security perceptions for decision making by tenants is increasingly recognized. Available studies focus in particular on the role of tenure security perceptions in housing improvement in South America (de Souza, 1998; de Souza, 2001; Van Gelder, 2007; Van Gelder, 2009). Other studies have examined the impact of tenure security perceptions on land investments and input use in Ethiopia (Holden & Yohannes, 2002) and China (Jacoby, Li, & Rozelle, 2002). The role played by tenure security perceptions in rural land renting decisions, however, has been neglected so far in the available literature. Regarding the role of trust in land rental transactions, it may be assumed that a high level of kinship trust reduces transaction costs of land rentals that occur among kinship members. High levels of kinship trust relative to non-kinship trust may be an important explanatory factor in land rental market segmentation. In such situations, landlords tend to worry less about losing land when they rent their land to their relatives. Holden and Ghebru (2005) found that access to land by tenants in Northern Ethiopia was less constrained in communities having a high share of kinship contracts in all rental contracts and hypothesize that lower transaction costs due to higher trust among kin are the underlying reason. We are not aware, however, of any empirical studies examining the roles played by different types of trust (e.g., trust toward kin, trust toward known people and trust toward strangers) in land renting decisions. The main objective of this paper is to examine the impact of tenure security perceptions and trust, as two major factors affecting transaction costs, on farm household decisions to rent in land in China. We focus our analysis on the demand side of the land rental market. The supply side is usually underenumerated in rural household surveys, including the survey that we used for this study, because households who are not found at home at the survey time are not interviewed. As a result, households that migrated elsewhere and rented out their land to other households—a common phenomenon in rural China—are excluded from the sample. The focus on the demand side of the rental market will be discussed in more detail in Section 3.1. The paper contributes to the literature on the effects of land tenure security and trust on land rental market development by (i) examining the effects of perceived tenure security on household decisions to rent in land, and (ii) differentiating between the roles of trust toward kin and trust toward known people. In the empirical analysis, we use Probit models to estimate which factors affect participation decisions and (informal vs. formal) contract choice, and a Tobit model to examine the factors affecting participation intensity (i.e., rented land size). A cross-section data set, containing data for 787 households on land market participation, tenure security, trust and other relevant factors, is used to estimate these models. The data were collected in 21 villages in Gansu Province in Northwest China and in 38 villages in Jiangxi Province in southcentral China and cover the years 2009 and 2010, respectively. The paper is organized as follows. Section 2 reviews the relevant literature concerning factors affecting land rental market development and discusses in more detail the existing knowledge gaps that our study addresses. Section 3 discusses how perceived tenure security and trust affect a potential tenant's decision to participate in the land rental market in theory and specifies the models and estimation strategy that we used for the empirical analysis. Section 4 summarizes the data collection and presents the definitions and descriptive statistics of the variables that we used in the empirical analysis. Section 5 reports and discusses the estimation results. Concluding remarks are presented in Section 6. 2. Factors affecting land rental market development Failure to provide sufficient off-farm labor opportunities is a major factor explaining low levels of participation in rural land 6 Holden and Ghebru (2016) claim that when a legal restriction on land rentals has a weak connection to the norms of the people, it becomes more difficult to enforce.

2

China Economic Review xxx (xxxx) xxxx

X. Ma, et al.

markets and land market segmentation (Feng, 2006; Kung, 2002). In the case of China, massive rural-urban labor migration since the economic liberalization and opening-up policy that began at the end of the 1970s has relaxed the constraints of off-farm labor markets on land rental market development. The ratio of rented land to the total land area contracted to rural households increased rapidly from 4.5% in 2006 to 26% in 2013 (Ye, 2015; Ito, Bao, & Ni, 2016) and to 35% in 2016,7 also occurring partly as a result of government policies actively promoting land rentals. However, large regional differences can be observed in the occurrence of land rental transactions. The rented land shares are largest in the suburbs of metropolitan cities (Beijing, Chongqing, and Shanghai) in some coastal provinces, such as Fujian, Guangdong, Jiangsu, and Zhejiang, and in the major grain production areas of Heilongjiang, Henan, and Hunan provinces (Ito et al., 2016). Moreover, most land rentals take place between acquainted households and are often informal. A survey conducted in 29 provinces in 2015 found that more than 40% of the land transactions did not specify the rental price and/or period and that 88% of the land transfers were to traditional farm households rather than new agricultural management bodies (He, Jiang, Guo, & Gan, 2016). Formal land laws and regulations may be an important factor affecting land rental market development, as shown by the studies available for different countries. In Vietnam, the provision of secure and long-term land rights substantially increased the volume of rental transactions benefiting poor but productive households (Deininger & Jin, 2003). In the Dominican Republic, insecure property rights not only reduce the level of activity in the rental market but also induce market segmentation because rentals are restricted to pre-existing social networks (Macours, de Janvry, & Sadoulet, 2010; Macours & Swinnen, 2002). In Nicaragua, insecure tenure is found to reduce participation on the supply side of the land rental market (Deininger, Zegarra, & Lavadenz, 2003). In the case of Ethiopia, land certification has significantly increased the level of participation in the land rental market (Holden et al., 2011; Holden & Ghebru, 2016). Tenure security in China is mostly determined by the frequency and magnitude of village-level land reallocations (Brandt, Rozelle, & Turner, 2004).8 In these reallocations, all or part of the land in a village is taken back from households and redivided among the households in the village in response to demographic changes or for other reasons (Wang, Tong, Su, Wei, & Tao, 2011). Another important aspect of tenure security is the possession of land certificates and land contracts through which governments confirm the land use rights assigned to rural households (Wang et al., 2015). Little empirical research has been done, however, to examine the impact of tenure security on land rentals. An exception is a study by Jin and Deininger (2009) among almost 8000 households in China's nine agriculturally most important provinces. It found that land use regulations that allow village leaders to confiscate land that has not been utilized for one season have a significant positive impact on the renting in and out of land; the possession of land certificates that are meant to protect land use rights, on the other hand, has no significant impact on land rental market participation. The latter finding is consistent with the results of a study based on a survey held among 329 farm households in northeastern Jiangxi Province (Feng, 2008), which found no significant impact of the possession of written land contracts signed with the village committee on land renting decisions. A study by Wang et al. (2015) among more than 1000 rural households in six provinces, however, found that the possession of land rights documents encourages land renting to nonfamily members instead of family members and that the effect was stronger in the year 2008 than in 2000. It also found that a reduction in major land reallocations encourages households to engage in land renting to nonfamily members. In a recent study, Cheng, Xu, Zhou, He, and Zhang (2019) found, based on data from official nationwide surveys conducted by the Ministry of Agriculture in 2010–2015, that the land titling reform implemented since 2009 led to an increase in the land transferred to agricultural enterprises and cooperatives and a decrease in the probability of households renting land out to other households (which were likely to be relatives and friends); it also encouraged households to use written contracts and to charge pecuniary rents, but did not affect the duration of the rental contracts. Concerning the role of trust among landlords and tenants, existing studies on developing countries focus, in particular, on the role of kin relations in contract arrangements. Sadoulet, de Janvry, and Fukui (1997) found evidence that kin landlords in the Philippines help or are expected to help more frequently in case of emergency than other landlords and thereby provide incentives for cooperative behavior in sharecropping contracts among kin. Holden and Ghebru (2005) observed that the trust inherent in kin relations among farm households in northern Ethiopia helps reduce transaction costs in land rental markets, as the costs of acquiring information and negotiating and enforcing contracts tend to be much lower. Access by tenants to land rental markets is therefore less constrained in communities where a large share of contracts consists of kinship contracts. Kinship relations among Ethiopian landlords and tenants have also been found to reduce the threat of eviction, implying that non-kin tenants are more productive on their sharecropped plots with the aim of increasing the probability of contract renewal (Kassie & Holden, 2007). Macours (2014) found that landowners without formal land titles in Guatemala are more likely to restrict their partners to tenants from the same ethnic group and thereby restrict land allocation within ethnic circles of confidence. Although the available literature provides useful insights into factors explaining the level of land rental market development, two main issues have so far received insufficient attention. First, studies examining tenure security mainly focus on the role of formal land rights derived from land laws and land titling and neglect tenure security perceptions of households. In the case of China, where rural land is formally owned by the village collective and allocated over longer periods by the village leader to households residing within the village, the farmers' perceptions of the probability of future land reallocations are likely to play an important role. Because village leaders have the possibility of taking back land that was allocated to a household but has been rented out in order to reallocate its use

7

Source: http://www.tuliu.com/data/nationalcontracted.html (in Chinese) Deininger et al. (2014) argue that land reallocations hardly affect tenure security because the risk of dispossession for a resident cultivator who uses all or part of her land for agricultural purposes is low. This argument neglects demographic changes as a trigger of land reallocations (Tan et al., 2006; Rozelle, Zhang, Jin, Deininger, & Huang, 2010; Wang et al., 2011). 8

3

China Economic Review xxx (xxxx) xxxx

X. Ma, et al.

right to other households within the village,9 the perceived probability of future land reallocations is likely to affect land renting decisions of farm households. The risk exists because formal laws and regulations that almost totally prohibit land reallocations are not enforced effectively; the seemingly vague and ambiguous formulations inherent in these laws and regulations allow flexible interpretation and adaptation to local conditions and changing situations by local actors (Piotrowski, 2009). Another legal rule that is not enforced effectively is the issuance of land certificates to all rural households. According to a survey held in the nine agriculturally most important provinces, approximately 80% of households possessed land certificates in 2004 (Jin & Deininger, 2009). Another survey, carried out in 2005 in 17 provinces, found that only 53% of the farmers possessed a land certificate (Keliang et al., 2006). The results of a survey held in northwest Jiangxi Province in 2011 and one county in Gansu Province in 2010 show that only 30% of the interviewed households in Jiangxi and as much 95% of the interviewed households in Gansu possessed land certificates (Ma et al., 2015). A two-period panel survey held among more than 1000 rural households in six provinces found that the share of households possessing land certificates increased from 33.9% in 2000 to 66.5% in 2008 (Wang et al., 2015). However, even when households do possess land certificates, perceptions about their role and importance in protecting land rights may affect land rental market participation decisions of such households. Second, the fact that different types of trust may have different effects on land rental market development has been neglected so far in the literature on land markets. The presence of trust is recognized as being essential for cooperation within a group and thereby affects resource use and economic performance (e.g., de Vos & Mol, 2010; Parks & Hulbert, 1995). Fukuyama (1995) divides trust into two components: general (non-kinship or generalized) trust and kinship trust. Kinship trust refers to the trust toward persons with kinship relations, and non-kinship trust refers to the trust toward the community more broadly defined. Generally, high kinship trust may indicate that people with kinship relations (a relatively small group) work easily together, whereas high general trust may allow people from the entire society to cooperate easily (Tu, Mol, Zhang, & Ruben, 2011). High levels of kinship trust help reduce the transaction costs of land rentals that occur among kinship members. As argued in the Introduction, the existence of relatively high levels of kinship trust and low levels of non-kinship trust may be an important factor explaining land rental market segmentation. High levels of non-kinship trust may reduce the transaction costs for those transactions that occur among non-kinship members and can thereby stimulate land rental transactions and reduce market segmentation. In China, land leases among kin are usually based on oral contracts, have no fixed term and do not include rent payments (Wang et al., 2015). Such informal contracts cannot ensure that land rental markets lead to optimal outcomes because they tend to reduce short-term efficiency and to increase tenant disincentives for making long-term investments in land quality (Deininger, 2003a, 2003b; Macours, 2014). Most land rental transactions in rural China occur among households living in the same village (see, e.g., Wang et al., 2015). The partner of a landlord is usually either a relative or a neighbor or other familiar person from the same village. We expect kinship trust and trust toward neighbors and familiar people to have different effects on participation in the land rental market. Hence, in this study, we distinguish between two types of trust: (1) trust in parents, children and brothers/sisters (kinship trust) and (2) trust toward neighbors and acquaintances. Trust toward strangers is not included in our empirical analysis because no land rental transactions occurred with strangers in our research areas during the survey periods. 3. Key variables and model specification 3.1. Key variables Land tenure (in)security may affect both the supply side and the demand side of the land rental markets. On the supply side, a higher level of tenure security reduces the transaction costs involved in land rentals for potential landlords. When land tenure is more secure, a landlord runs a lower risk of (1) not getting the rented land back from the tenant and (2) the village or local government taking the land away for reallocation or other purposes; hence, the landlord is more likely to supply land to the land rental market. On the demand side, a higher level of tenure security reduces transaction costs for potential tenants. A tenant may run a risk of losing rented land when (1) the landlord loses land use rights due to a land reallocation or expropriation by village or local governments and (2) the landlord reclaims the rented land before expiration of the rental agreement. Hence, higher tenure security is expected to stimulate the demand for land rentals. Similar to tenure (in)security, trust may also affect both sides of the land rental market. On the supply side, a potential landlord perceives a lower risk of not getting back the land or misuse of the rented land when he rents land to a person he trusts more. A potential landlord with a relatively high level of trust in others is therefore more likely to supply land to the land rental market. On the demand side, a potential tenant perceives a lower risk of losing rented land when he rents land from a partner he trusts more. Therefore, a potential tenant with a relatively high level of trust in others is more likely to demand land through the rental market. As mentioned in the Introduction, the supply side is usually underenumerated in rural household surveys because many migrated landlords cannot be interviewed.10 As a result, a focus on the renting out of land may introduce a major selection bias. We therefore focus our analysis at the demand side of the land rental market. An important feature of land rental markets in rural China is that almost all land rental transactions occur among households living in the same village.11 Observed land rental transactions are 9

See, e.g., Tan et al. (2006) and Wang et al. (2011) for a more detailed discussion of rural land allocation and land reallocations in China. See, e.g., the large gaps between rented-in and rented-out areas reported by Shi (2007): Tables 4A.2-A.4) and Wang et al. (2015): Table 4). 11 In recent years, however, land rentals to agricultural enterprises and cooperatives have become more popular (e.g., Cheng et al., 2019). 10

4

China Economic Review xxx (xxxx) xxxx

X. Ma, et al.

therefore almost entirely the result of supply and demand forces at the village level. Land rentals by a tenant therefore depend not only on choices made by that tenant and the factors affecting those choices but also on land supply decisions taken by households living in the same village and the factors affecting those supply decisions. To capture supply-side factors, we use village-level averages of household tenure security perceptions and trust levels. This approach has two advantages. First, it takes into account the indirect effect (through land supply) of tenure security perceptions and trust on land renting decisions. Second, it avoids potential reverse causality bias between tenure security perceptions & trust and land renting in decisions. We expect that potential tenants are more likely to rent land in villages with relatively high tenure security perceptions and relatively high levels of trust. An important issue in analyzing the impact of tenure security on land rental decisions is the choice of the tenure security concept. Following Van Gelder (2010), land tenure security can be viewed as composite concept comprising three elements: legal (de jure) tenure security, actual (de facto) tenure security and perceived tenure security. Legal tenure security is defined by formal laws and regulations, whereas actual tenure security refers to the actual control of property, regardless of the legal status in which it is held. Perceived tenure security refers to household perceptions of tenure security, which take the form of household probability estimates of the chance of eviction by the government or the landowner and other factors that may cause involuntary relocation. These three elements are related to each other but do not equate in many developing countries (Van Gelder, 2010). In the case of rural China, recent land tenure reforms have significantly improved legal tenure security, but farm households still experience substantial insecurity of actual and perceived land tenure (Ma et al., 2015). The perceived tenure situation forms the basis upon which rural households can be expected to take land-related decisions. Household perceptions of tenure security therefore can be considered as the proximate determinants of these decisions (Jansen & Roquas, 1998; Ma et al., 2015; Sjaastad & Bromley, 2000). Based on these theoretical considerations, we specify the following hypotheses: (1) Perceptions of tenure security are positively related to the following: a. the probability that a household rents additional land, b. the rented land size, and c. the probability of having an informal rental contract. (2) Trust toward kin and known people is positively related to the following: a. the probability that a household rents additional land, b. the rented land size, and c. the probability of having an informal contract. These hypotheses will be tested empirically below. 3.2. Model specification The standard model used in the literature specifies the impact on rented land area of transaction costs related to tenure (in) security, trust and other factors (Holden & Ghebru, 2005). Other studies divide land leasing behavior into two stages. In the first stage, a farmer chooses whether to rent out/in land or not; in the second stage, given the decision to lease out/in land, the farmer either decides to which partner the contract will be offered (Macours et al., 2010) or decides how much land to include in the transaction (Teklu & Lemi, 2004). Transaction costs arising from insecure land rights and low trust can have different effects on each of the stages in these models (Holden et al., 2008). In this study, we assume that a potential tenant follows two sequential decisions to participate in the land rental market. In the first stage, a tenant chooses whether or not to rent in land based on the household's agricultural ability, the size of its land endowment, the off-farm opportunities available, and the fixed transaction costs associated with land rental market participation. In the second stage, the tenant decides the type of contract and the intensity of participation. The tenant decides which landlord will be offered the contract, whether the contract will be written or oral, and the duration of the contract based on the transaction costs associated with alternative contract choices.12 The choices of contract type may be closely related to each other. If a tenant offers the contract to a relative, an oral contract with a short duration or an open-ended duration is more likely than when the contract is offered to a person without blood ties. Since informal and formal contracts differ in flexibility and enforcement mechanisms, and their pros and cons are intensively discussed in China (e.g., Hong & Gong, 2015; Luo, Lin, & Qiu, 2015; Qian, Hong, & Liu, 2015), we focus our analysis of this stage on the choice between formal and informal contracts. A formal contract is a written contract that is signed between a tenant and a landlord, usually without involvement of a third person or institution. In the second stage, the tenant also decides the intensity of participation (i.e., the area of land to be rented in), based on the size of the household land endowment, the off-farm opportunities available, the available land supply in the village, and variable transaction costs. The basic model that we use for estimating the factors affecting each stage in the land renting in decision making is specified as:

Mi = a 0 + a1PSVi + a2TVi + a3RMVi +

j

a 4jXji + a5Xt + ui

(1)

In the market participation equation, Mi is a dummy variable that equals one if a household participates in the land rental market and otherwise equals zero. In the contract choice equation, Mi is a dummy variable that equals one if a tenant chooses an informal 12 In theory, the tenant also chooses to offer a sharecropping contract or a fixed rent contract at this stage. However, in our research areas, sharecropping does not occur.

5

China Economic Review xxx (xxxx) xxxx

X. Ma, et al.

contract and otherwise equals zero. Furthermore, in the rented land area equation, Mi denotes the size of the rented in land area. PSVi denotes the village-level perceived land tenure security.13 Household expectations on future land reallocations and opinions on the importance of land certificates are used as indicators of perceived tenure (in)security and are aggregated at the village level. TVi denotes village-level trust14 (kinship trust and trust toward known people), RMVi denotes village-level, rural-urban migration prevalence. Xji is a set of j control variables for household i, including household characteristics and land characteristics. Xt denotes township characteristics, whereas ui is the residual with standard properties. 3.3. Estimation strategy Although the two decision stages are assumed to be sequential in theory, some observed and unobserved factors will influence the three decisions simultaneously. In fact, a selection bias may exist because unobserved characteristics that influence the probability to rent in land may also influence decisions on the choice of contract type and the quantity of land that is rented (Holden & Ghebru, 2005; Teklu & Lemi, 2004). Neglecting this selectivity effect may result in biased coefficient estimates in the contract choice and rented area equations. We used two Heckman selection models,15 models (2) - (3a) and (2) – (3b) respectively, to test for possible selection bias. They are specified as follows:

RDi = b0 + b1 LRVi + b2 LCVi + b3 TKVi + b4 TPVi + b5 MVi + b6 AGEt + b7 EDUt + b8 OFEi + b9 SCi + b10 HWi + b11 LLRi + RCi = c0 + c1 LRVi + c2 LCVi + c3 TKVi + c4 TPVi + c5 MVi + c6 AGEt + c7 EDUt + c8 OFEi + c9 SCi + c10 HWi +

(2) (3a)

i

RAi = d 0 + d1 LRVi + d2 LCVi + d3 TKVi + d4 TPVi + d5 MVi + d 6 AGEt + b7 EDUt + d8 SCi + d9 HWi + d10 LLRi +

i

i

(3b)

The definitions of the variables used in the regression models are presented in Table 1. b0, …, b11, c0, …, c10, and d0, …, d10 are unknown coefficients; εi, ζi, and ηi are random disturbance terms. If the null hypothesis that no selection bias is present is rejected, we apply a Probit model with sample selection for the decision on contract type (which has a dummy variable as the dependent variable) and a Heckman selection model for the decision on the rented land area. If the null hypothesis cannot be rejected, we estimate a Probit model for the contract type decision and a Tobit model for the rented land area decision. For the contract type decision, we use the contracted land-labor ratio (see Table 1 for definitions) as an instrumental variable that is included in the first stage (i.e., renting in decision) and excluded in the second stage (i.e., informal contract choice). Contracted land is defined as the land allocated by the village committee to the household. Tenant land resources are expected to affect renting in decisions because land-scarce households are more likely to rent additional land. Land scarcity is unlikely to affect the choice between formal and informal contracts for land rentals between households living in the same village. For the rented land area decision, it is usually difficult to find an appropriate instrument that affects the probability to rent in but that does not directly affect the size of the rented land area.16 We use off-farm employment experience as an instrument in our model. We assume that once a household decides to rent in land, the off-farm employment of the household head in the past does not affect the rented land area. Arguably, households with heads that were employed off-farm often have accumulated more wealth and therefore are able to rent more land. We control for this potential effect of off-farm employment experience by including household wealth as an explanatory variable in the rented land area equation. 4. Dataset 4.1. Data collection This study uses data from two household surveys held in Gansu Province in Northwest China and in Jiangxi Province in southcentral China. The survey in Gansu Province was conducted in Minle County, Zhangye City, whereas the survey in Jiangxi Province was conducted in Yanshan County in Shangrao City and in Yujiang and Guixi County in Yingtan City. The farm household survey in Gansu Province was held in May 2010. It spanned 315 households and 21 village leaders that lived in 21 administrative villages and ten townships. Topics included in the household survey comprise farm production, off-farm employment, land rental market, and land and water use in the year 2009, as well as tenure security at the time of the survey.17 The survey was the follow-up of a similar survey carried out, using a stratified random sampling technique, in May 2008. In each of the ten townships in Minle County, 10% of the villages were randomly selected for the survey in May 2008. Within each selected village, 13

A household's own perceived tenure security is excluded when calculating the village-level average (see Section 4.2). A household's own trust level is excluded when calculating the village-level average (see Section 4.2). 15 Macours et al. (2010) use a nested Logit model to jointly estimate the participation and contract choice decisions. We cannot apply this approach because our dataset does not include alternative-specific variables that can be used for estimating a nested Logit model. 16 Due to difficulties of finding an appropriate instrument, a Tobit model rather than a Heckman selection model has been widely used for examining the factors affecting rented land area (Deininger & Jin, 2005; Deininger & Zegarra, 2003; Rahman, 2010; Vranken & Swinnen, 2006). 17 During our interview, we also asked households to estimate whether their land tenure security had changed between the years 2009 and 2010. We found land tenure security to be very stable during that period, and thus, we used tenure security at the time of the survey to approximate tenure security in the year 2009 in the empirical analysis. 14

6

China Economic Review xxx (xxxx) xxxx

X. Ma, et al.

Table 1 Definition of variables and expected signs. Variable

Label

Participation in land market variables Renting in dummy RD Informal contract dummy RC Rented land area RA Perceived tenure security variables Village perception on land reallocations Village perception on land certificates

LRV LCV

Expected signsa

Definition

P

I

C

Land reallocation perception (=1 if the household does not expect a reallocation within 5 years, 0 otherwise), mean value of the other sampled households living in the same village as the surveyed household Land certificates perception (= the importance attached to land certificates for protecting land rights on a scale from 1 to 5), mean value of the other sampled households living in the same village as the surveyed household

+

+

+

+

+

+

Trust toward kin (based on comprehensive questions about trust), mean value of the other sampled households living in the same village as the surveyed household Trust toward known people (based on comprehensive questions about trust), mean value of the other sampled households living in the same village as the surveyed household

+

+

+

+

+

+

1 = household rented in land, 0 = otherwise Contract type of renting household: 1 = informal (oral) contract; 0 = formal contract Total area of land rented in by renting household (mu)

Trust variables Village trust toward kin

TKV

Village trust toward known people

TPV

Village characteristics Village migration

MV

Number of migrating household members, mean value of the other sampled households living in the same village as the surveyed household

+

?

+

Household characteristics Age of household head Education of household head Off-farm employment experience Leader or party member Household wealth

AGE EDU OFE SC HW

Age of the household head (years) Years of formal education of the household head (years) 1 = household head has off-farm employment experience in the past, 0 = otherwise 1 = household head is a party member or village leader, 0 = otherwise Value of agricultural devices, livestock, electronic instruments, house, furniture and transportation vehicle (RMB)

? − − ? ?

+ − ? ? ?

? −

Land characteristics Contracted land – labor ratio

LLR

Ratio of land area contracted by the village committee to the household to laborersb in the household (mu)

−

c

c

? ?

?

a P and C denote the probability of participation in the land rental market and intensity of participation, respectively; I denotes probability of choosing an informal contract. b Number of laborers is standardized using household assessments of their labor force members (one person can be either a full laborer, half laborer or no laborer). c These variables are expected to have insignificant effects in Heckman selection models and thus are identified as instruments (see details in Section 3.3).

15 households were randomly chosen to be interviewed.18 If possible, the same households were also interviewed in May 2010. In 50 cases, the same household could not be found and was replaced by another, randomly selected household in the same village (see Ma et al. 2013, 2016 for details on the stratified random sampling technique). Although 265 households were interviewed in both years, we cannot exploit the panel nature for our analysis because questions about subjective perceptions of land tenure were asked in the May 2010 survey only. Three households had missing information on one of the key variables that we use in our analysis. We therefore include 312 households from Minle County with information for the year 2009 in our sample. A similar farm household survey was held in Jiangxi province in August 2011. Included topics were similar to those in the survey held in Gansu province but refer to the year 2010 instead of 2009. The survey covered 526 households living in 11 administrative villages, which include 38 natural villages19 and six townships. For 175 of the interviewed households, the survey was the follow-up of two similar surveys carried out in three administrative villages, covering 15 natural villages, for the years 2000 and 2005. These three villages are considered representative of the diversity of rural conditions that can be found in northeastern Jiangxi Province (see Feng, 2008 for details on the household and village selection procedures for the first survey). The earlier two surveys, however, did not contain information on land tenure security. The other 351 households were interviewed only in 2011. They were randomly selected within eight other administrative villages, covering 23 natural villages, in the same region. These eight villages were selected using criteria similar to the aforementioned two surveys in Jiangxi Province. Local researchers and policy makers were consulted, and several site visits were made as part of this process. Because of missing information for one or more key variables (e.g., land tenure), we dropped 51 observations. As a result, we use information collected

18

In the first two villages, 16 instead of 15 households were interviewed. The Jiangxi research area is located in a hilly region, and households usually reside in scattered locations within an administrative village. Thus an administrative village consists of several natural villages within which households share similar culture, natural resource endowments, and selfgovernance rules (e.g., land reallocation rules). 19

7

China Economic Review xxx (xxxx) xxxx

X. Ma, et al.

Table 2 Socioeconomic indicators for two case study regions and rural China, 2009–2010. Indicator

Household net income per capita (RMB) Share of agricultural income in total income (%) Household land area per capita (mu) Share of migrants among all laborers (%) Share of households in village that completely migrated (%)c Share of interviewed households with one or more migrant (%) a b c

315 households in Gansu, 2009a

4500 70 4.28 22 3 70

526 households in Jiangxi, 2010a

5326 44 1.58 46 4.5 85

Rural China 2009b

2010b

5153 47 2.26 33 – –

5919 46 2.28 36 – –

Source: Calculated from household surveys, except share of migrated households in village. Source: Calculated from NBS (2010a, 2010b), 2011a, 2011b, 2012). Source: Village leaders survey held in same case study region.

Table 3 Actual land tenure security and land rental market indicators. Indicator

Gansu case (2009)a

Jiangxi case (2010)a

Six provinces (2000)b

Six provinces (2008)b

Share of households possessing land certificates (%) Share of households that experienced at least one land reallocation since 1998 (%) Share of renting in households (%) Rented land area per renting household (mu) Share of informal contracts (%) Share of rentals among kin (%)

95 6

30 69

34 65c

64 16c

16 9.7 58 85

37 9.3 91 95

17 3.6 96 47

27 7.7 89 46

a b c

Source: Calculated from household surveys. Source: Survey conducted in Hebei, Hubei, Liaoning, Shaanxi, Sichuan and Zhejiang Provinces (Wang et al., 2015). Share of households in villages that had a major or minor land reallocation in the last five years.

among 475 households in Jiangxi Province for the econometric analysis. Table 2 presents background information on the socioeconomic situation in the two case study regions and compares it with the average values for rural China as a whole. We find that household net income per capita in the two study regions is 10–13% lower than the average for rural China. Agriculture plays a relatively important role in the economy of the Gansu case study region. Per capita land resources are relatively large, and the migration rate is relatively low in the Gansu case, whereas land resources are smaller, and the migration rate is higher than the national average in the Jiangxi case study area. Village leaders estimate that, on average, 3% of the households in the surveyed villages in Gansu had migrated away in 2009, and 4.5% of the households had done so in the surveyed villages in the case of Jiangxi in 2010. Most interviewed households, i.e., 70% in the Gansu case and 85% in the Jiangxi case, had one or more migrated members. These migration data are consistent with those presented in other studies on China (e.g., Mullan et al., 2011; Ma, Heerink, Ierland, & Shi, 2016). Table 3 summarizes the information for two indicators of actual tenure security, i.e., possession of land certificates and land reallocation experience, and land rental market development in our sample, subdivided by region. It is compared with similar information provided by Wang et al. (2015) for a sample of more than 1000 households in sixty villages in six Chinese provinces (Hebei, Hubei, Liaoning, Shaanxi, Sichuan and Zhejiang) for the years 2000 and 2008, presented in the last two columns of the table. We find large regional differences in actual tenure security between the two regions that we examine in this study. Actual tenure security was lowest for the Jiangxi sample, as 69% of the households experienced at least one land reallocation since 1998, and only 30% of the households reported having an official land certificate. In the Gansu sample, as many as 95% of the households possessed official land certificates, whereas only 6% of the households had experienced a land reallocation since 1998. In the survey of the six provinces, the share of households possessing land certificates increased from 34% in 2000 to 64% in 2008, while land reallocations during the preceding five years declined from 65% to 16% of the villages during that period. These findings suggest that actual tenure security considerably improved over time in China and that the research area in Jiangxi in our study is still at an early stage of the transition toward improved actual tenure security, whereas the research area in Gansu is at the final stage. The differences between the two case study regions may be explained from investments in land quality improvement made by rural households in the Gansu case study region, the larger per capita land resources and the limited social security provided by off-farm employment in that region (Ma et al., 2015). Large regional differences were also observed in the land rental market indicators.20 Compared to the six provinces sample for the 20 We did not collect information on timing and form of payment. However, according to narratives collected by open-ended interviews with village consultants who assisted us in arranging the interviews, most rents were paid after harvest and were usually in cash, although payments in grain also occurred.

8

China Economic Review xxx (xxxx) xxxx

X. Ma, et al.

year 2008, in which 27% of the interviewed households mentioned that they rented additional land, a larger share of the households (37%) interviewed in Jiangxi indicated that they rented land in the year 2010. A much lower share (16%) of the households interviewed in Gansu reported that they rented additional land in 2009. The average sizes of the rented land did not differ much between the Jiangxi (9.3 mu21) and Gansu (9.7 mu) samples and are larger than observed in the survey of the six provinces (7.7 mu in 2008). Informal contracts were very common in the Jiangxi case (91%) and in the second round of the survey of the six provinces (89%), whereas formal contracts (42%) had become more popular in the Gansu case. A remarkable finding is that land rentals among kin were much more common in the two case study regions where we did our surveys (Jiangxi: 85%, Gansu: 85%) compared to the land rentals that were examined in the survey of the six provinces (46% of the rentals in 2008). Based on the changes over time observed in the survey of the six provinces, Wang et al. (2015) concluded that in the process of rapid growth in land rental market participation in rural China, some of the key contractual arrangements were changing significantly, whereas others were changing very slowly.22 In this study, we examined to what extent land tenure security perceptions and trust play a role in this respect for two regions representing fundamentally different stages in the continuum from low tenure security and predominantly informal rental contracts to high tenure security and a rising prevalence of formal contracts. 4.2. Definitions and descriptive statistics Table 1 presents the definitions of the variables and the signs of the expected effects of the explanatory variables used in the regression analysis; Table 4 presents the descriptive statistics for the full sample and for each of the two research areas. 4.2.1. Participation in the land rental market The three dependent variables in our analysis consist of land renting in dummy, a (formal or informal) contract choice dummy, and the area of the land that is leased. As discussed in Section 4.1, land rentals are more common in the Jiangxi research area than in the Gansu research area, but informal contracts still dominate most rental transactions in the former. 4.2.2. Land tenure security perceptions Perceived tenure security is represented by two variables. The land reallocation perception dummy takes the value 1 if a household does not expect that a land reallocation will take place within the next 5 years, and 0 if the household either expects a reallocation within 5 years or does not have an idea. The land certificates perception variable reflects a household's assessment of the importance of land certificates for protecting land rights. It takes values ranging from 1 (= not important) to 5 (= very important). We focus our analysis on land use rights certificates. Land certificates are typically designed by provincial governments and contain the seal of the county government, whereas land use rights contracts are completed and signed at the village level and contain the seal of the village level government23 (Prosterman et al., 2009; Wang et al., 2015). Contrary to land use rights contracts, which vary in format and content among the different villages, land certificates have consistent content and formats across the same province. During our interviews in the field, households frequently mentioned that land certificates protect their land rights better than land use rights contracts, because land certificates are issued by local governments and can provide effective juridical evidence of a household's land use rights, particularly when the village wants to resign the land contractual relationship. We use the village-level average of perceived land tenure security in our analysis. A potential endogeneity problem arises from the fact that a household's participation in the land rental market may affect its tenure security perceptions and, thus, may also affect average tenure security in the village. To deal with this problem, the household's own tenure security perception is excluded from calculations of the village-level average.24 The village-level average perceived tenure security is therefore defined as the average tenure security perceptions of the other survey respondents living within the same village. On the one hand, this variable serves as a proxy for the household's own perceived tenure security, which reduces the potential simultaneity bias caused by reverse causality or omitted variables. It assumes that tenure security perceptions are very similar for households living in the same village, as a result of past experiences with land reallocations and the past protection provided by land certificates, and that the decision by one household to rent land does not affect the tenure security perceptions of the other (surveyed) households in the village. On the other hand, as discussed in Section 3.1, land rental choices of a tenant partly depend on land-supply decisions made by households living in the same village and the factors affecting those supply decisions. The village-level average of perceived land tenure security is also expected to capture the supply-side effect of tenure security perception in the village. Notably, households that migrated elsewhere and leased their land to other households are excluded from the sample. The villagelevel average may therefore be subject to measurement error. However, we do not expect this will have much impact on our findings 21

Fifteen mu equals one hectare. Based on data collected through a rural household survey held in 29 provinces, He et al. (2016) concluded that a significant increase occurred during this period in the land rental contract duration, from 5.09 years (in 2013) to 6.18 years (in 2015). They also found that the proportion of rental contracts that involved rental payments of rent decreased from 75.0% in 2013 to 57.5% in 2015. They explain this decline as resulting from the increased frequency of rental transactions that occurred between relatives or friends. 23 Under the terms of the 2003 Rural Land Contracting Law (RLCL), the certification of land use rights should be based on the prior issuance of a land use rights contract. Only rural households who have received land use rights contracts in the village should receive a land use rights certificate from the county or provincial government. However, in some regions, the issuance of land use rights certificates is not consistently linked to the prior issuance of land use rights contracts (Wang et al., 2015). 24 A similar approach is used in Mullan et al. (2011) and Ma et al. (2016) to examine the impact of tenure security on migration decisions 22

9

China Economic Review xxx (xxxx) xxxx

X. Ma, et al.

Table 4 Descriptive statistics of variables. Variable

Full sample

Gansu sample

Jiangxi sample

Difference in means

Mean

St. dev.

Mean

St. dev.

Mean

St. dev.

Participation in land market variables Renting in dummy Informal contract dummya Rented landa

0.285 0.839 9.377

0.452 0.368 9.127

0.160 0.580 9.704

0.367 0.499 10.477

0.366 0.914 9.283

0.482 0.281 8.73

−0.206*** −0.334*** 0.421

Perceived tenure security variables Village perception on land reallocations Village perception on land certificates

0.356 3.603

0.208 0.597

0.401 4.173

0.161 0.304

0.327 3.228

0.230 0.419

0.074*** 0.945***

Trust variables Village trust toward kin Village trust toward known people

0.930 0.775

0.029 0.066

0.926 0.818

0.023 0.035

0.932 0.748

0.033 0.067

−0.006** 0.070***

Village characteristics Village migration

1.142

0.477

0.753

0.231

1.398

0.422

−0.645***

Household characteristics Age of household head Education of household head Off-farm employment experience Leader or party member Household wealth

51.02 6.318 0.484 0.200 65,634

10.85 3.296 0.500 0.408 89,848

46.298 7.558 0.561 0.285 62,868

10.109 3.463 0.497 0.452 64,807

54.122 5.504 0.434 0.145 67,450

10.182 2.911 0.496 0.353 103,065

−7.824*** 2.054*** 0.127*** 0.140*** −4581

Land characteristics Contracted land – labor ratio

3.980

4.582

7.524

5.446

1.652

1.290

5.872***

Source: Household survey. ** and ***: The hypothesis that the variable has the same mean for the two study regions is rejected at the 5% and 1% statistical levels, respectively. a The mean is calculated based on the subsample of households that rent in land instead of the full sample.

for two reasons. On the one hand, only a small share of the households in the two research areas (3% in Gansu and 4.5% in Jiangxi; see Table 3) had completely migrated to urban areas and could not be interviewed. On the other hand, specific village characteristics, including the village government and higher-level governments, are prominent in shaping tenure security perceptions, and these characteristics are similar for all households within the same village. The summary statistics presented in Table 4 indicate that perceived tenure security is lower in the Jiangxi case than in the Gansu case.25 Out of the interviewed households in Jiangxi, 32.7% of the households do not expect that a land reallocation will take place within the next five years. In the Gansu research area, 40.1% of the interviewed households hold similar expectations. Furthermore, with regard to land documents, the average importance attached to land certificates in protecting land rights equals 3.2 (on a scale from 1 to 5) for the interviewed households in Jiangxi, compared to 4.2 in the Gansu research area. 4.2.3. Trust The standard question about trust in the World Values Survey (WVS) is “Generally speaking, would you say that most people can be trusted, or that you can't be too careful in dealing with people?” (Knack & Keefer, 1997:1256). This binary choice question is relatively simple, and it does not fully capture the essence of trust, in particular, kinship trust. More comprehensive methods to measure the level of trust include trust games (Berg, Dickhaut, & McCabe, 1995; Bouma, Bulte, & Van Soest, 2008) and comprehensive survey questions about trust (Leonard, Croson, & De Oliveira, 2010; Tu et al., 2011). Our household surveys included comprehensive questions about trust. Apart from the standard WVS trust question, respondents were asked to indicate their trust level to different groups (i.e., parents, brothers/sisters, children, other relatives, neighbors, familiar (known) people, local officials, and strangers), using a scale from 0 (totally distrust) to 1 (fully trust). We use the average scores for trust toward parents, brothers/sisters and children to measure ‘kinship trust’ and the average scores for trust toward neighbors and familiar (known) people to measure ‘trust toward known people’. The observed average levels of kinship trust equal 0.93 in both the Gansu and Jiangxi research areas. Trust toward known people equals 0.82 in the Gansu area but only 0.75 in the Jiangxi region (see Table 4). We use the village-level trust in our analysis, as described in Section 3.1. A potential endogeneity problem also arises from the fact that a household's participation in the land rental market may affect its trust and thus may also affect the average village-level trust, whereas there may also be omitted variables that affect a household's land rental choices and trust levels.26 To deal with this problem, village-level trust is defined as the average trust of the other respondents within the same village. 25 See Ma et al. (2015) for an analysis of major factors affecting regional differences in perceived (and actual) tenure security between the Jiangxi and Gansu research areas. 26 By participating in land markets, fellow villagers may learn about the intentions and behaviour of others, and as a result build up trust (or distrust). Similar arguments can be found in Fischer (2008) and Tu and Bulte (2010).

10

China Economic Review xxx (xxxx) xxxx

X. Ma, et al.

Trust may be related to land tenure security perceptions, as households with high levels of trust may perceive tenure security to be relatively secure, especially in regions where formal institutions are less important in shaping tenure security perceptions. The correlation coefficients between the two trust variables and the two tenure security perception variables range from −0.38 to 0.36.27 Hence, collinearity is not expected to have much impact on the empirical results. 4.2.4. Other explanatory variables The other independent variables consist of village, household, land and regional characteristics. The village characteristic that we include is village migration, which serves as an indicator of the development of the labor market. Migration decisions may be endogenous in models explaining household land rental decisions (Feng & Heerink, 2008). We therefore define the village migration variable as the average number of migrant members28 of the other surveyed households living in the same village. The average number of migrant household members is 1.40 in the Jiangxi case, almost double that of the Gansu case (0.75). We expect that it has a positive effect on the probability that a household rents additional land and on the quantity of land rented. Its impact on contract choice is unclear a priori. The household characteristics include the household head's age, education, off-farm employment experience, village leadership or party membership and household wealth. Older household heads usually have more agricultural management experience and may therefore rent additional land. On the other hand, the productivity of the household heads usually declines as they grow older. The impact of the age of the household head on the probability of renting additional land and on the size of the rented land can therefore be positive as well as negative. Its impact on the use of informal contracts for land rental transactions is expected to be positive because older household heads tend to have more social contacts and may be less familiar with the formal legal system than younger ones. Education of the household head is expected to have a negative effect on the decision to rent in land. Controlling for village-level migration, households with more educated heads are more likely to participate in off-farm employment and therefore are less likely to rent in additional land. Households with more educated heads will usually be more familiar with existing laws and regulations. They are therefore less likely to use informal contracts in their land rental transactions. Off-farm employment experience of household heads is expected to have negative effects on the probability of leasing additional land. Household heads with experience off-farm have a comparative advantage in non-agricultural activities and are therefore less likely to specialize in agriculture by expanding their farm sizes. The impact of such experience on contract choice is unclear a priori. Household heads that are members of the communist party or village leaders are expected to have more knowledge about land transfer regulations and more information on off-farm employment opportunities. Their land renting (participation and intensity) decisions may therefore be affected either positively or negatively. They are also likely to have a better understanding of formal contract enforcement as well as a stronger power to enforce informal rental contracts. Hence, the effect of party membership and village leadership on contract choice is also indeterminate. Household wealth is used as an indicator of the economic and social power of a household within the village. Wealthy households can use their wealth and better credit access not only to rent additional land but also to finance the initial costs of off-farm employment. They are also likely to have more power in enforcing both informal and formal contracts. Hence, the impact of household wealth on all three land rental indicators is indeterminate. We use one land characteristic in the regression analyses, namely, the contracted land area (i.e., the size of the land allocated to the household by the village leader) per laborer. Households with a relatively large land endowment have a lower need to lease additional land. Hence, this variable is expected to have a negative impact on the probability of renting additional land and on the rented land size. Finally, 15 township dummy variables are included in the models explaining land renting (participation and intensity) decisions. They control for major unobserved differences between townships in factors such as land quality and institutional environment, which may affect land renting decisions. Due to the much smaller sample sizes used for estimating the contract choice equation, four county-level dummy variables instead of township dummies are included to control for major unobserved differences between the counties in which the interviewed households live. 5. Estimation results Significant regional differences exist between the two case study regions in the socioeconomic indicators and the land tenure security and land rental market indicators that we report in Table 2 and Table 3. We therefore estimated three sets of models. The first set examines the factors affecting land rental market participation, contract choice and intensity of participation for the whole sample, whereas the other two sets examine these factors for the Gansu and Jiangxi sample separately. This allows us to examine whether overall conclusions that would be drawn from the pooled sample as a whole need to be modified when we take the different stages in the tenure security/contract formalization continuum observed in the two samples into account. As explained in Section 4.1, the data collected among 787 households in 59 villages were used for estimating the two-stage model. We applied cluster-adjusted standard errors, which were adjusted for 59 villages for the full sample, 21 villages for the Gansu sample, and 38 villages for the Jiangxi sample in the econometric models for participation and intensity of participation decisions to account 27 The correlation coefficients are: −0.38 for kinship trust & perception on land reallocation, −0.05 for kinship trust & perception on importance of land certificates, −0.17 for trust toward known people & perception on land reallocation, and 0.41 for trust toward known people & perception on importance of land certificates. 28 We used the share of migrants to all household members between 16 and 65 years of age as an alternative measure of a household's degree of involvement in migration, and we obtained similar results in our empirical analysis.

11

China Economic Review xxx (xxxx) xxxx

X. Ma, et al.

for correlated errors within villages. The type of contract decision model is estimated for the 224 households in the sample that rented in land by using cluster-adjusted standard errors for 55 villages for the full sample, 18 villages for the Gansu sample and 37 villages for the Jiangxi sample. The Heckman selection model test results indicate that the null hypothesis of no selection bias for the contract type decision model cannot be rejected (at 10% significance) for the full sample and two separate samples.29 We therefore report the results of the Probit model for the decision on contract type. For the rented land area model, the statistics show that we cannot reject (at 5% significance) the null hypothesis no selection bias30 occurs, and thus, the regression results for the Tobit model are reported for the rented land area decision. Detailed regression results for the full sample and for the Gansu and Jiangxi samples separately are reported in Tables A1–A3 in the Appendix. Average marginal effects of the land tenure security and trust variables are presented in Table 5. We will focus the discussion of the regression results mainly on these average marginal effects. 5.1. Perceived tenure security We find that tenure security perceptions related to (absence of) future land reallocations positively affect the probability that households rent in land and affect the rented land area in the Jiangxi case and in the full sample but not in the Gansu case. In the Gansu case, on the other hand, it is the choice of contract type that is significantly affected by land reallocation perceptions, with households that expect no future land reallocations revealed to be more likely to use informal contracts. These rather diverse findings seem to be related to the fact that land reallocations were hardly an issue in the Gansu case at the time of the survey but were still relatively common in the Jiangxi case, whereas formal rental contracts were frequently signed in the Gansu case study area but were rarely used in the Jiangxi area (see Table 3). Hence, our results provide support for hypotheses 1a and 1b for the Jiangxi sample (and the full sample) and hypothesis 1c for the Gansu sample. With regard to perceptions on the importance of land certificates for protecting land rights, we find that they have a significant positive effect on the rented land area in the Gansu sample (and the full sample) and on the use of formal rental contracts in the Gansu sample. The lack of a significant relationship for the Jiangxi case suggests that perceptions about the importance of land certificates play a role only when most of the households possess certificates, as is the case with the surveyed households in Gansu (see Table 3). The positive effect of the perceived importance of land certificates on the signing of formal contracts is opposite to the relationship that we postulated (based on lower transaction costs in land rentals) in Section 3.1. This finding suggests that recent policies promoting land certification in rural China also contribute to the formalization of land rental contracts. Hence, our results regarding the perceptions on the importance of land certificates provide support for hypothesis 1b for the Gansu sample (and the full sample), while the finding for hypothesis 1c is opposite to a priori expectations for that sample. 5.2. Trust With respect to the other variables of our main interest, trust in others, we find that kinship trust has a significant positive effect on the likelihood of renting in land in the Gansu case. Trust toward neighbors and familiar (known) people surprisingly has a significant negative effect on land renting in and rented land area in the same sample. Trust levels do not significantly affect the land renting in decision nor the rented land area in the Jiangxi case. The latter finding may have to do with the fact that land rentals are more common and are usually based on informal contracts in the Jiangxi case study region. Tenure security seems to be more important than trust in that region. For the Gansu sample, on the other hand, we find that trust is relatively more important. The finding that trust toward neighbors and familiar people in the Gansu region negatively affects land renting in may have to do with factors outside agriculture, such as a greater probabilities of finding off-farm jobs, but such a determination will require more investigation. Contract choice in land rentals is affected by trust in the Jiangxi case but not in the Gansu case. High levels of kinship trust in a village are positively related to the likelihood of signing an informal contract, whereas high levels of trust toward known people are positively related to the likelihood of signing formal contracts. These findings suggest that trust plays an important role in informal/ formal contract decisions in regions with low actual land tenure security and predominately informal land transactions, as in the Jiangxi case (see Table 3). When actual tenure is more secure, as in the Gansu case, the role of trust in shaping contract choices is taken over by perceptions on the importance of land certificates. These findings provide only very limited support for hypothesis 2, which is formulated in Section 3.1. The hypothesis that higher levels of trust are expected to increase the probability of land renting in (2a) is supported only for the case of kinship trust in the Gansu sample. High levels of trust toward known people may not increase the scale of the land rental market in regions where land rentals among kin are predominant, as is the case in our two case study regions. However, they do seem to increase the likelihood of using formal contracts instead of informal contracts in regions where actual tenure security is low. 29 The Wald test of independent equations has aχ2 statistic of 0.48 (P value = .48) for the full sample, and 0.23 (P value = .63) and 1.12 (P value = .29) for the Gansu sample and Jiangxi sample, respectively. 30 The Wald test of independent equations has aχ2 statistic of 0.76 (P-value = .38) for the full sample and 0.85 (P value = .36) for the Jiangxi sample. The two-step estimates in STATA report an inverse Mills ratio of −95 (P value = .80) for the Gansu sample; the one-step procedure based on maximum likelihood estimation for the Heckman model is not concave for the Gansu sample.

12

China Economic Review xxx (xxxx) xxxx

X. Ma, et al.

Table 5 Average marginal effects for tenure security and trust. Informal contract dummy (probit)a

Rented land area (tobit)b

Std. Err.

Coef.

Std. Err.

Coef.

Std. Err.

Land tenure security perceptions Village perception on land reallocations Full sample 0.303*** Gansu sample 0.093 Jiangxi sample 0.309**

0.110 0.196 0.128

−0.082 0.595** −0.007

0.083 0.243 0.076

3.478*** 0.661 3.718**

1.323 2.304 1.606

Village perception on land certificates Full sample 0.069 Gansu sample 0.089 Jiangxi sample 0.072

0.052 0.089 0.065

−0.148* −0.781** −0.079

0.081 0.319 0.070

1.080 1.972* 0.997

0.743 1.179 0.930

Village Trust Village trust toward kin Full sample Gansu sample Jiangxi sample

0.728 1.005 1.081

2.348** −0.106 2.080*

1.076 2.680 1. 072

8.253 17.384 9.479

8.873 12.224 13.261

0.313 0.734 0.404

−1.031* −0.027 −0.889*

0.594 1.648 0.485

−2.971 −19.744* −0.980

3.982 10.600 4.905

Model: variable

Rent in dummy (probit)a Coef.

1.037 2.328** 1.012

Village trust toward known people Full sample −0.275 Gansu sample −1.939*** Jiangxi sample −0.087

Standard errors are adjusted for clusters (villages). *,** and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively. a In the Probit model, the predictive marginal probabilities of renting in are reported for each variable. b In the Tobit model, the marginal effects on the unconditional expected value of the (censored and uncensored) observed dependent variable are reported for each variable.

5.3. Other explanatory variables The control variables included in the three-stage model show mixed results for the full sample and the two separate samples (see Tables A1 – A3 in the Appendix). Village migration prevalence has a positive effect on the participation decision in the Jiangxi case, as expected. However, it has a significant negative effect on the participation decision and the intensity of participation in the Gansu case. The relative abundance of agricultural land in the Gansu case (see Table 2) may play a role in this respect. The age of the household head has a significant negative effect on the two land rental decisions in the Jiangxi sample.31 This finding may be related to reduced physical strength, lower ambitions, or other potentially age-related factors. As expected, we also find that age has a positive effect on the likelihood of using informal rental contracts for the same sample. The level of education of the household head has a significant negative effect on the probability that the household will rent additional land (for the Gansu sample and the full sample) and the use of informal contracts (for the full sample). This finding is consistent with a priori expectations (see Section 4.2). We also find that households headed by a party member or village leader and relatively wealthy households are more likely to use formal contracts in the Gansu case. In the Jiangxi case, where rentals among kin dominate the land rental market, these factors do not significantly affect the contract type choices. As expected, the contracted land area per laborer has a significant negative effect on the probability that a household leases additional land in the Jiangxi sample. For the Gansu sample, however, the land resources of the households do have a significant impact. Again, the relative abundance of agricultural land in the Gansu case seems to play a role in explaining this result. 5.4. Robustness tests Perceptions on the importance of land certificates may have a larger effect on land rental decisions of households living in villages where a large share of the households possess land certificates because more households may be willing to rent out land. To test this effect, we introduced the interaction of village perception of land certificates and a dummy indicating village certificate prevalence32 into the regression models. The results are reported for the full sample and for the Jiangxi sample in Tables A4–A6 in the Appendix, and the marginal effect is reported in Table A7. The premise for perceptions on the importance of land certificates having a larger effect on land rental decisions in villages with a high prevalence of land certificates is supported for the full sample but not for the Jiangxi sample. The finding for the whole sample reflects the fact that possession of land certificates is much greater for the Gansu 31 We also tested for potential nonlinearities in this relationship by adding age squared to the models, but we did not find a statistically significant effect for the squared term. 32 Village certificate prevalence is defined as 1 if more than half of the households in a village possessed land certificates, and 0 otherwise. For the Gansu sample, this variable consists of ones only.

13

China Economic Review xxx (xxxx) xxxx

X. Ma, et al.

sample than for the Jiangxi sample (see Table 3). The main conclusions that we drew from the regression results presented in Tables A1–A3 and Table 5 still hold when we add the interaction term. Second, Probit and Tobit models may generate unreliable standard errors in small samples. As a robustness check, we therefore calculated bootstrapped standard errors for the first- and third-stage models.33 The results, based on 400 bootstrap replications, where bootstrapped standard errors account for the cluster characteristics of the data, are reported in Tables A8 and A9 in the Appendix. As expected, bootstrapped standard errors for some variables in the models are larger than the asymptotic standard errors reported in Tables 5 and A7. However, the main conclusions that we drew from the regression results presented in Tables 5 and A7 still hold when we use bootstrapped, instead of asymptotic, standard errors. 6. Conclusions The development of land rental markets may play an important role in enhancing productivity as well as equity among rural households, particularly when major imperfections exist in rural credit and labor markets. Using data collected in two household surveys in Minle County, Zhangye City, Gansu Province in Northwest China in 2011 and in Yanshan County in Shangrao City and in Yujiang County and Guixi County in Yingtan City, Jiangxi Province in southcentral China in 2010, we examined the impact of tenure security perceptions and trust on household decisions to rent in land. The two case study regions present rather diverse cases. Land reallocations were virtually absent, and the possession of land certificates was almost universal in the Gansu case at the time of the survey; reallocations were still a common phenomenon in the Jiangxi case, and only a minority of the interviewed households possessed land certificates. These differences in so-called actual tenure security seem to play an important role in shaping the relationship between land rental decisions and the perceptions of tenure security and the trust levels of households. Our regression results suggest that when actual tenure security is low, as is the case with the Jiangxi sample, household perceptions of tenure security positively affect decisions to rent in additional land and the size of the rented land, whereas trust is important for the choice between oral and written contracts. When land tenure becomes more secure, as in the Gansu case, tenure security perceptions are less relevant, and trust becomes more important for land rental decisions in a market where more and more transactions take place with those other than family members. However, tenure security perceptions do seem to play a role in the choice between oral and written contracts in such markets. These findings have important implications for the allocative efficiency and productivity of China's agriculture. First, provision of secure land tenure environment to households through the elimination of land reallocations and provision of land certificates remains important for land transfers from less efficient to more efficient farmers in regions where tenure security remains low. Second, trust becomes more important for land transfers when the land tenure environment is relatively secure. Our empirical evidence suggests that trust toward kin promotes land transfers, whereas trust toward known people reduces land transfers. More research is needed to explain this counterintuitive result. The focus of our research is on two economically less-developed areas with low degrees of urbanization where most land rental transactions (85–95%) occur between relatives. To examine the extent to which the main conclusions of our paper hold in different settings in rural China, similar studies may be carried out, particularly in coastal regions where land transactions between households and village committees or between households and agricultural enterprises are more common. We expect that land tenure policies and non-kinship trust play more important roles in affecting participation decisions and contractual arrangements in coastal regions where land transactions are not mainly restricted to kinships. The robustness of our main findings may further be examined though the use of panel datasets that would minimize the potential bias caused by omitted variables that are relatively stable over time. When these limitations are taken into account, the results of our study point to a number of potentially important implications for policy making. The recent land tenure policy reforms are expected to have a stronger impact on the development of land rental markets if additional measures would be taken to further enhance the belief among rural households that land certificates do protect their land rights and that land reallocation in response to demographic changes is prohibited in regions with a high migration prevalence (like the region in Jiangxi province that we surveyed). In particular, future amendments of the Land Administration Law, the Rural Land Contract Law and the Property Law and future enforcement of the “Three Rights Separation Policy”34 may further enhance the role of land certificates in protecting land rights; further, the Mediation and Arbitration of Rural Land Contract Disputes Law may assign a larger role to the use of land certificates in the mediation and arbitration of rural land conflicts. At the same time, the rural legislative system may reduce the costs borne by farm households in protecting their land rights through legal channels, such as official meditation and arbitration and going to court. The evidence presented in this study confirms that segmentation and informal rental contracts continue to dominate land rental markets in less-developed regions in rural China. The coexistence of high levels of kinship trust and much lower levels of non-kinship trust seems to play an important role in this respect. Measures that may be taken to improve trust toward non-kin may focus on the enlargement of farmers' social networks, e.g., by provision of more collaboration opportunities among villagers or introduction of participatory or community-based development projects in which farmers are involved in project design and project management. Although our study is limited to two relatively small regions in rural China, the insights gained are likely to be relevant for a wider 33 We failed to calculate bootstrapped standard errors in the second stage model. The possible reason is that only 16% of the tenants in our sample selected a formal contract, causing an unbalanced dataset. 34 The ‘Three Rights Separation Policy’ decomposes land property rights into land ownership, land contract rights and land management rights. This policy is considered as the core of the new rural land tenure reform, and aims to activate the land transfer market and promote the rural economy.

14

China Economic Review xxx (xxxx) xxxx

X. Ma, et al.

range of less-developed regions in countries with similar land tenure systems (e.g., Ethiopia, Vietnam). Although formal tenure security may be enhanced through appropriate land laws and regulations, persistent perceptions of land tenure insecurity and lack of trust among potential partners may limit the development of land rental markets and thereby constrain their potential in promoting productivity and equity. The focus on formal land rights in much of the ongoing research on land rental markets therefore needs reorientation and to be broadened to also include informal institutions, such as tenure security perceptions and trust. Acknowledgements Financial supports for this paper have been gratefully received from the National Natural Science Foundation of China (71773054, 71573134 and 71603121), the National Key R&D Programme of China (the SURE+ project, 2016YFE0103100) and the China's 111 Project (B17024). Appendix Table A1

Regression results for participation decision, Probit. Variable

Full sample

Gansu sample

Jiangxi sample

Coef. (Std. Err.)

Coef. (Std. Err.)

Coef. (Std. Err.)

Land tenure security variables Village perception on land reallocation Village perception on land certificates

0.991***(0.361) 0.225(0.170)

0.425(0.896) 0.406(0.413)

0.872**(0.364) 0.203(0.185)

Trust variables Village trust toward kin Village trust toward known people Village migration prevalence

3.393(2.406) −0.900(1.023) 0.136(0.149)

10.686**(4.720) −8.899**(3.557) −1.255**(0.498)

2.860(3.082) −0.245(1.141) 0.267*(0.159)

Household characteristics Age of household head Education of household head Off-farm employment experience Leader or party member Ln(Household wealth)

−0.021***(0.005) −0.028*(0.016) −0.142(0.116) 0.176(0.140) −0.028(0.038)

−0.016(0.011) −0.049**(0.025) −0.087(0.144) 0.075(0.178) −0.002(0.087)

−0.026***(0.007) −0.022(0.022) −0.142(0.158) 0.240(0.208) −0.051(0.044)

Land characteristics Contracted land – labor ratio

−0.031(0.019)

−0.030(0.020)

−0.120**(0.055)

Regional characteristics Township dummies Observations Mean VIFa Log pseudolikelihood % correct prediction

Yes 787 2.52 −423.908 73.32

Yes 312 2.13 −122.124 84.29

Yes 475 2.11 −294.169 65.68

Fifteen town dummies are included in the model to control town fixed effects, but these are not reported in the table. Standard errors were adjusted for 59 clusters (villages) for the full sample, 21 clusters (villages) for the Gansu sample, and 38 clusters (villages) for the Jiangxi sample. *,**, *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively. a Mean VIF tests the degree of multicollinearity among the independent variables.

Table A2

Regression results for contract choice, Probit. Variable

Full sample

Gansu sample

Jiangxi sample

Coef. (Std. Err.)

Coef. (Std. Err.)

Coef. (Std. Err.)

Land tenure security variables Village perception on land reallocation Village perception on land certificates

−0.463(0.482) −0.830*(0.453)

2.717**(1.339) −3.567**(1.806)

−0.051(0.578) −0.604(0.491)

Trust variables Village trust toward kin Village trust toward known people

13.204**(6.138) −5.795*(3.400)

−0.485(12.237) −0.123(7.532)

15.835**(7.729) −6.766*(3.755)

Village characteristics

(continued on next page)

15

China Economic Review xxx (xxxx) xxxx

X. Ma, et al.

Table A2 (continued) Variable

Full sample

Gansu sample

Jiangxi sample

Coef. (Std. Err.)

Coef. (Std. Err.)

Coef. (Std. Err.)

Village migration prevalence

0.317(0.266)

1.120(0.834)

0.284(0.275)

Household characteristics Age of household head Education of household head Off-farm employment experience Leader or party member Ln(Household wealth)

−0.002(0.016) −0.109**(0.045) 0.098(0.265) 0.059(0.292) −0.113(0.112)

−0.057(0.042) −0.176(0.125) 0.087(0.515) −0.852*(0.457) −0.765**(0.343)

0.027*(0.017) −0.063(0.053) 0.069(0.318) 0.643(0.553) 0.071(0.087)

Regional characteristics County dummies Observations Mean VIFa Log pseudolikelihood % correct prediction

Yes 224 2.01 −71.851 87.05

Yes 50 1.82 −19.164 82.00

Yes 174 1.50 −41.167 91.95

Four town dummies were included in the model to control county fixed effects, but these are not reported in the table. Standard errors were adjusted for 55 clusters (villages) for the full sample, 18 clusters (villages) for the Gansu sample, and 37 clusters (villages) for the Jiangxi sample. *,**, *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively. a Mean VIF tests the degree of multicollinearity among the independent variables.

Table A3

Regression results for intensity of participation, Tobit. Variable

Full sample

Gansu sample

Jiangxi sample

Coef. (Std. Err.)

Coef. (Std. Err.)

Coef. (Std. Err.)

Land tenure security variables Village perception on land reallocation Village perception on land certificates

12.444**(4.867) 3.867(2.707)

4.216(14.898) 12.583(8.168)

10.251**(4.428) 2.748(2.615)

Trust variables Village trust toward kin Village trust toward known people

29.539(31.913) −10.633(14.294)

110.954(74.474) −126.017*(64.069)

26.131(36.633) −2.702(13.530)

Village characteristics Village migration prevalence

1.574(1.886)

−17.763**(7.209)

3.056(1.953)

Household characteristics Age of household head Education of household head Leader or party member Ln(Household wealth)

−0.293***(0.077) −0.328(0.239) 3.061(2.104) 0.120(0.476)

−0.181(0.158) −0.557(0.430) 0.379(3.505) 1.606(1.589)

−0.353***(0.094) −0.328(0.301) 4.665*(2.764) −0.362(0.483)

Land characteristics Contracted land – labor ratio

−0.078(0.314)

−0.138(0.374)

−0.882(0.655)

Regional characteristics Township dummies Observations Mean VIFa Log pseudolikelihood

Yes 787 2.56 −1159.689

Yes 312 2.14 −289.327

Yes 475 2.17 −859.538

Fifteen town dummies are included in the model to control town fixed effects, but these are not reported in the table. Standard errors are adjusted for 59 clusters (villages) for the full sample, 21 clusters (villages) for the Gansu sample and 38 clusters (villages) for the Jiangxi sample. *,**, *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively. a Mean VIF tests the degree of multicollinearity among the independent variables.

16

China Economic Review xxx (xxxx) xxxx

X. Ma, et al.

Table A4

Regression results for participation decision with interaction term, Probit. Variable

Full sample

Jiangxi sample

Coef. (Std. Err.)

Coef. (Std. Err.)

Land tenure security variables Village perception on land reallocation Village perception on land certificates Village certificate prevalence Village perception on land certificates × Village certificate prevalence

0.972***(0.365) 0.255(0.233) −1.043(1.334) 0.202(0.392)

0.847**(0.396) 0.272(0.233) 0.306(1.711) −0.162(0.487)

Trust variables Village trust toward kin Village trust toward known people

3.074(2.440) −0.746(1.079)

2.534(3.225) −0.237(1.164)

Village characteristics Village migration prevalence

0.121(0.148)

0.250(0.159)

Household characteristics Age of household head Education of household head Off-farm employment experience Leader or party member Ln(Household wealth)

−0.022***(0.005) −0.030*(0.016) −0.149(0.116) 0.190(0.141) −0.028(0.038)

−0.026***(0.007) −0.022(0.022) −0.148(0.158) 0.255(0.213) −0.052(0.044)

Land characteristics Contracted land – labor ratio

−0.031(0.019)

−0.117**(0.055)

Regional characteristics Township dummies Observations Mean VIFa Log pseudolikelihood % correct prediction

Yes 787 19.47 −423.181 73.57

Yes 475 14.77 −293.794 66.53

Fifteen town dummies are included in the model to control town fixed effects, but these not reported in the table. Standard errors are adjusted for 59 clusters (villages) for the full sample and 38 clusters (villages) for the Jiangxi sample. *,**, *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively. a Mean VIF tests the degree of multicollinearity among the independent variables, including all interactions.

Table A5

Regression results for contract choice with interaction term, Probit. Variable

Full sample

Jiangxi sample

Coef. (Std. Err.)

Coef. (Std. Err.)

Land tenure security variables Village perception on land reallocation Village perception on land certificates Village certificates prevalence Village perception on land certificates × Village certificates prevalence

−0.592(0.483) −0.461(0.533) 1.428(3.736) −0.576(1.037)

−0.205(0.544) −0.516(0.608) −5.212(4.081) 1.226(1.162)

Trust variables Village trust toward kin Village trust toward known people

11.901*(6.116) −6.257*(3.283)

14.933*(7.872) −7.461*(3.818)

Village characteristics Village migration prevalence

0.294(0.257)

0.220(0.272)

Household characteristics Age of household head Education of household head Off-farm employment experience Leader or party member Ln(Household wealth)

−0.004(0.016) −0.105**(0.047) 0.070(0.266) 0.082(0.304) −0.111(0.114)

0.026(0.018) −0.072(0.058) 0.007(0.324) 0.674(0.591) 0.076(0.089)

Regional characteristics County dummies Observations

Yes 224

Yes 174

17

(continued on next page)

China Economic Review xxx (xxxx) xxxx

X. Ma, et al.

Table A5 (continued) Variable

Mean VIFa Log pseudolikelihood % correct prediction

Full sample

Jiangxi sample

Coef. (Std. Err.)

Coef. (Std. Err.)

28.32 −71.296 87.95

14.13 −40.959 91.95

Four town dummies are included in the model to control county fixed effects, but these are not reported in the table. Standard errors are adjusted for 55 clusters (villages) for the full sample and 37 clusters (villages) for the Jiangxi sample. *,**, *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively. a Mean VIF tests the degree of multicollinearity among the independent variables, including all interactions.

Table A6

Regression results for intensity of participation with interaction term, Tobit. Variable

Full sample

Jiangxi sample

Coef. (Std. Err.)

Coef. (Std. Err.)

Land tenure security variables Village perception on land reallocation Village perception on land certificates Village certificates prevalence Village perception on land certificates × Village certificates prevalence

12.344***(4.695) 2.209(3.197) −23.879(20.276) 7.259(6.068)

10.298**(4.564) 2.104(2.951) 4.203(32.130) −0.355(9.283)

Trust variables Village trust toward kin Village trust toward known people

30.575(31.516) −7.916(14.615)

29.018(36.911) −3.426(13.868)

Village characteristics Village migration prevalence

1.708(1.877)

3.216(1.962)

Household characteristics Age of household head Education of household head Leader or party member Ln(Household wealth)

−0.290***(0.076) −0.352(0.243) 3.080(2.061) 0.140(0.479)

−0.349***(0.092) −0.323(0.302) 4.498(2.730) −0.359(0.481)

Land characteristics Contracted land–labor ratio

−0.087(0.317)

−0.930(0.675)

Regional characteristics Township dummies Observations Mean VIFa Log pseudolikelihood

Yes 787 20.13 −1158.920

Yes 475 15.56 −859.238

Fifteen town dummies are included in the model to control town fixed effects, but these are not reported in the table. Standard errors are adjusted for 59 clusters (villages) for the full sample and for 38 clusters (villages) for the Jiangxi sample. *,**, *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively. a Mean VIF tests the degree of multicollinearity among the independent variables, including all interactions.

Table A7

Average marginal effects for tenure security and trust with interaction term. Rent in dummy (Probit)a

Informal contract dummy (Probit)a

Rented land area (Tobit)b

Coef.

Std. Err.

Coef.

Std. Err.

Coef.

Std. Err.

Land tenure security perceptions Village perception on land reallocations Full sample Jiangxi sample

0.296*** 0.299**

0.111 0.139

−0.104 −0.026

0.082 0.068

3.454*** 3.742**

1.281 1.661

Village perception on land certificates Full sample (Village certificates prevalence = 1)

0.122*

0.073

−0.239

0.138

0.186**

0.094

Model: variable

18

(continued on next page)

China Economic Review xxx (xxxx) xxxx

X. Ma, et al.

Table A7 (continued) Model: variable

Full sample (Village certificates prevalence = 0) Jiangxi sample (Village certificates prevalence = 1) Jiangxi sample (Village certificates prevalence = 0) Village trust Village trust toward kin Full sample Jiangxi sample Village trust toward known people Full sample Jiangxi sample

Rent in dummy (Probit)a

Informal contract dummy (Probit)a

Rented land area (Tobit)b

Coef.

Std. Err.

Coef.

Std. Err.

Coef.

Std. Err.

0.082 0.036 0.096

0.082 0.115 0.082

−0.065 0.222 −0.063

0.094 0.320 0.080

0.039 0.047 0.053

0.060 0.225 0.741

0.937 0.895

0.739 1.132

2.100** 1.920*

1.072 1.035

8.555 10.543

8.790 13.379

−0.227 −0.084

0.329 0.411

−1.103* −0.960*

0.576 0.494

−2.215 −1.245

4.089 5.036

Standard errors are adjusted for clusters (villages). *,** and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively. a In the Probit model, the predictive marginal probabilities of renting in are reported for each variable. b In the Tobit model, the marginal effects on the unconditional expected value of the (censored and uncensored) observed dependent variable are reported for each variable.

Table A8

Bootstrap regression results for participation decision. Full sample

Gansu sample

Jiangxi sample

Variable

Coef. (Std. Err.)

Coef. (Std. Err.)

Coef. (Std. Err.)

Land tenure security variables Village perception on land reallocation Village perception on land certificates

0.991***(0.337) 0.225(0.166)

0.425(1.569) 0.406(0.535)

0.872**(0.398) 0.203(0.191)

Trust variables Village trust toward kin Village trust toward known people

3.393(2.512) −0.900(1.302)

10.686(8.712) −8.899*(5.424)

2.860(2.805) −0.245(1.433)

Village characteristics Village migration prevalence

0.136(0.196)

−1.255(0.818)

0.267(0.185)

Household characteristics Age of household head Education of household head Off-farm employment experience Leader or party member Ln(Household wealth)

−0.021***(0.005) −0.028(0.018) −0.142(0.112) 0.176(0.137) −0.028(0.040)

−0.016(0.012) −0.049*(0.029) −0.087(0.225) 0.075(0.232) −0.002(0.112)

−0.026***(0.007) −0.022(0.023) −0.142(0.144) 0.240(0.186) −0.051(0.047)

Land characteristics Contracted land–labor ratio

−0.031(0.023)

−0.030(0.028)

−0.120**(0.054)

Regional characteristics Township dummies Observations Mean VIFa Log pseudolikelihood % correct prediction

Yes 787 2.52 −423.908 73.32

Yes 312 2.13 −122.124 84.29

Yes 475 2.11 −294.169 65.68

Fifteen town dummies are included in the model to control town fixed effects, but these are not reported in the table. Standard errors are adjusted for 59 clusters (villages) for the full sample, 21 clusters (villages) for the Gansu sample, and 38 clusters (villages) for the Jiangxi sample. *,**, *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively. a Mean VIF tests the degree of multicollinearity among the independent variables.

19

China Economic Review xxx (xxxx) xxxx

X. Ma, et al.

Table A9

Bootstrap regression results for intensity of participation. Full sample

Gansu sample

Jiangxi sample

Variable

Coef. (Std. Err.)

Coef. (Std. Err.)

Coef. (Std. Err.)

Land tenure security variables Village perception on land reallocation Village perception on land certificates

12.444***(4.416) 3.867(2.556)

4.216(23.915) 12.583(10.214)

10.251**(4.746) 2.748(2.664)

Trust variables Village trust toward kin Village trust toward known people

29.539(35.106) −10.633(16.932)

110.954(148.830) −126.017(86.458)

26.131(35.671) −2.702(16.127)

Village characteristics Village migration prevalence

1.574(2.468)

−17.763(13.743)

3.056(2.325)

Household characteristics Age of household head Education of household head Leader or party member Ln(Household wealth)

−0.293***(0.073) −0.328(0.225) 3.061(1.922) 0.120(0.492)

−0.181(0.176) −0.557(0.486) 0.379(4.029) 1.606(1.899)

−0.353***(0.081) −0.328(0.271) 4.665**(2.342) −0.362(0.487)

Land characteristics Contracted land–labor ratio

−0.078(0.391)

−0.138(0.500)

−0.882(0.676)

Regional characteristics Township dummies Observations Mean VIFa Log pseudolikelihood

Yes 787 2.56 −1159.689

Yes 312 2.14 −289.327

Yes 475 2.17 −859.538

Fifteen town dummies are included in the model to control town fixed effects, but these are not reported in the table. Standard errors are adjusted for 59 clusters (villages) for the full sample, 21 clusters (villages) for the Gansu sample and 38 clusters (villages) for the Jiangxi sample. *,**, *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively. a Mean VIF tests the degree of multicollinearity among the independent variable.

References Berg, J., Dickhaut, J., & McCabe, K. (1995). Trust, reciprocity, and social history. Games and Economic Behavior, 10(1), 122–142. Bouma, J., Bulte, E., & Van Soest, D. (2008). Trust and cooperation: Social capital and community resource management. Journal of Environmental Economics and Management, 56(2), 155–166. Brandt, L., Rozelle, S., & Turner, M. A. (2004). Local government behavior and property right formation in rural China. Journal of Institutional and Theoretical Economics, 160(4), 627–662. Broegaard, R. J. (2005). Land tenure insecurity and inequality in Nicaragua. Development and Change, 36(5), 845–864. Chamberlin, J., & Ricker-Gilbert, J. (2016). Participation in rural land rental markets in sub-Saharan Africa: Who benefits and by how much? Evidence from Malawi and Zambia. American Journal of Agricultural Economics, 98(5), 1507–1528. Cheng, W., Xu, Y., Zhou, N., He, Z., & Zhang, L. (2019). How did land titling affect China's rural land rental market? Size, composition and efficiency. Land Use Policy, 82, 609–619. Deininger, K. (2003a). Land markets in developing and transition economies: Impact of liberalization and implications for future reform. American Journal of Agricultural Economics, 85(5), 1217–1222. Deininger, K., & Jin, S. (2003). Land sales and rental markets in transition: Evidence from rural Vietnam. World Bank Policy Research Working Paper No. 3013. World Bank. Deininger, K., & Jin, S. (2005). The potential of land rental markets in the process of economic development: Evidence from China. Journal of Development Economics, 78(1), 241–270. Deininger, K., Xia, F., & Holden, S. T. (2017). Gender-differentiated impacts of tenure insecurity on agricultural performance in Malawi's customary tenure systems. World Bank Policy Research Working Paper. World Bank. Deininger, K., & Zegarra, E. (2003). Determinants and impacts of rural land market activity: Evidence from Nicaragua. World Development, 31(8), 1385–1404. Deininger, K., Zegarra, E., & Lavadenz, I. (2003). Determinants and impacts of rural land market activity: Evidence from Nicaragua. World Development, 31(8), 1385–1404. Deininger, K. W. (2003b). Land policies for growth and poverty reduction. Washington, D.C.: World Bank. Feng, S. (2006). Land rental market and off-farm employment-rural households in Jiangxi Province. P.R. China: Wageningen University Ph.D. Feng, S. (2008). Land rental, off-farm employment and technical efficiency of farm households in Jiangxi Province, China. NJAS-Wageningen Journal of Life Sciences, 55(4), 363–378. Feng, S., & Heerink, N. (2008). Are farm households' land renting and migration decisions inter-related in rural China? NJAS-Wageningen Journal of Life Sciences, 55(4), 345–362. Fischer, J. A. V. (2008). Is competition good for trust? Cross-country evidence using micro-data. Economics Letters, 100(1), 56–59. Fukuyama, F. (1995). Trust: The social virtues and the creation of prosperity. New York: Free Press. He, X., Jiang, T., Guo, L., & Gan, L. (2016). China's arable land transfer market development and households transfer behavior - evidence from household survey in 29 provinces from 2013-2015. Management World, 6, 13–23 (in Chinese). Holden, S., & Ghebru, H. (2005). Kinship, transaction costs and land rental market participation. Discussion paperDepartment of Economics and Management, Norwegian University of Life Scienceshttp://www.umb.no/statisk/dre-projects/data/discussion_paper_files/kinship_and_land_rental_market_participation3.pdf. Holden, S., & Yohannes, H. (2002). Land redistribution, tenure insecurity, and intensity of production: A study of farm households in southern Ethiopia. Land Economics, 78(4), 573.

20

China Economic Review xxx (xxxx) xxxx

X. Ma, et al.

Holden, S. T., Deininger, K., & Ghebru, H. (2011). Tenure insecurity, gender, low-cost land certification and land rental market participation in Ethiopia. The Journal of Development Studies, 47(1), 31–47. Holden, S. T., & Ghebru, H. (2016). Land rental market legal restrictions in northern Ethiopia. Land Use Policy, 55, 212–221. Holden, S. T., Utsuka, K., & Place, F. M. (2008). The Emergence of Land Markets in Africa: Impacts on Poverty and Efficiency. Washington DC. USA: Resources for the Future. Hong, M., & Gong, L. (2015). Empirical research on the rural land transfer oral contracts self-enforcing mechanism. Agricultural Economic Problems, 8, 13–20 (in Chinese). Ito, J., Bao, Z., & Ni, J. (2016). Land rental development via institutional innovation in rural Jiangsu, China. Food Policy, 59, 1–11. Jacoby, H. G., Li, G., & Rozelle, S. (2002). Hazards of expropriation: Tenure insecurity and investment in rural China. American Economic Review, 92(5), 1420–1447. Jansen, K., & Roquas, E. (1998). Modernizing insecurity: The land titling project in Honduras. Development and Change, 29(1), 81–106. Jin, S., & Deininger, K. (2009). Land rental markets in the process of rural structural transformation: Productivity and equity impacts from China. Journal of Comparative Economics, 37(4), 629–646. Kassie, M., & Holden, S. (2007). Sharecropping efficiency in Ethiopia: Threats of eviction and kinship. Agricultural Economics, 37(2–3), 179–185. Keliang, Z., Prosterman, R., Jianping, Y., Ping, L., Riedinger, J., & Yiwen, O. (2006). The rural land question in China: Analysis and recommendation based on a seventeen-province survey. New York University Journal of International Law and Politics, 38, 761–839. Knack, S., & Keefer, P. (1997). Does social capital have an economic payoff? A cross-country investigation. Quarterly Journal of Economics, 112(4), 1251–1288. Kung, J. K. (2002). Off-farm labor markets and the emergence of land rental markets in rural China. Journal of Comparative Economics, 30(2), 395–414. Leonard, T., Croson, R. T. A., & De Oliveira, A. (2010). Social capital and public goods. Journal of Socio-Economics, 39(4), 474–481. Luo, B., Lin, W., & Qiu, Z. (2015). Farmland land rental contract and partner choice: Evidence from rural household survey. Journal of Agrotechnical Economics, 9, 4–16 (in Chinese). Ma, X., Heerink, N., Feng, S., & Shi, X. (2015). Farmland tenure in China: Comparing legal, actual and perceived security. Land Use Policy, 42, 293–306. Ma, X., Heerink, N., Ierland, E. V., & Shi, X. (2016). Land tenure insecurity and rural-urban migration in rural China. Papers in Regional Science, 95(2), 383–406. Macours, K. (2014). Ethnic divisions, contract choice, and search costs in the Guatemalan land rental market. Journal of Comparative Economics, 42(1), 1–18. Macours, K., de Janvry, A., & Sadoulet, E. (2010). Insecurity of property rights and social matching in the tenancy market. European Economic Review, 54, 880–899. Macours, K., & Swinnen, J. F. M. (2002). Patterns of agrarian transition. Economic Development and Cultural Change, 50(2), 365–394. Mullan, K., Grosjean, P., & Kontoleon, A. (2011). Land tenure arrangements and rural-urban migration in China. World Development, 39, 123–133. National Bureau of Statistics (NBS)) (2017, 2017-02-27). "National economic and social development statistical bulletin of the People's Republic of China in 2016." (in Chinese), http://finance.people.com.cn/n1/2017/0228/c1004-29113654.html National Bureau of Statistics (NBS) (2010a). China statistical yearbook. Beijing: China Statistical Publishing House. National Bureau of Statistics (NBS) (2010b). China rural statistical yearbook. Beijing: China Statistical Publishing House (in Chinese). National Bureau of Statistics (NBS) (2011a). China statistical yearbook. Beijing: China Statistical Publishing House. National Bureau of Statistics (NBS) (2011b). China rural statistical yearbook. Beijing: China Statistical Publishing House (in Chinese). National Bureau of Statistics (NBS) (2012). Report on the situation of rural migrant workers in 2011. Beijing: China Statistical Publishing House (in Chinese). Parks, C. D., & Hulbert, L. G. (1995). High and low trusters' responses to fear in a payoff matrix. Journal of Conflict Resolution, 39(4), 718–730. Piotrowski, S. (2009). Land property rights and natural resource use: An analysis of household behavior in rural China. Frankfurt, Germany: Peter Lang. Prosterman, R., Zhu, K., Ye, J., Riedinger, J., Li, P., & Yadav, V. (2009). Secure land rights as a foundation for broad-based rural development in China: Results and recommendations from a seventeen-province survey. (NBR Special Report). Qian, L., Hong, M., & Liu, H. (2015). Selection of agricultural land transfer contract from the perspective of pattern of difference sequence. Journal of Northwest A&F University, 4, 47–54 (in Chinese). Rahman, S. (2010). Determinants of agricultural land rental market transactions in Bangladesh. Land Use Policy, 27(3), 957–964. Rozelle, S., Zhang, L., Jin, S., Deininger, K., & Huang, J. (2010). Rights and rental: Is rural cultivated land policy and management constraining or facilitating China's modernization? In J. C. Oi, S. Rozelle, & X. Zhou (Eds.). Growing pains: Tensions and opportunity in China's transformation. Shorenstein APARC: Stanford, CA. Sadoulet, E., de Janvry, A., & Fukui, S. (1997). The meaning of kinship in sharecropping contracts. American Journal of Agricultural Economics, 79(2), 394–406. Shi, X. (2007). Away off the farm - The impact of off-farm employment on farm production, factor market development and sustainable land use in Jiangxi Province, P. R. China. The Netherlands: Shaker Publishing BV. Sjaastad, E., & Bromley, D. W. (2000). The prejudices of property rights: On individualism, specificity, and security in property regimes. Development Policy Review, 18(4), 365–389. de Souza, F. A. M. (1998). Land tenure security and housing improvements in Recife, Brazil. Habitat International, 23(1), 19–33. de Souza, F. A. M. (2001). Perceived security of land tenure in Recife, Brazil. Habitat International, 25(2), 175–190. Tan, S., Heerink, N., & Qu, F. (2006). Land fragmentation and its driving forces in China. Land Use Policy, 23(3), 272–285. Teklu, T., & Lemi, A. (2004). Factors affecting entry and intensity in informal rental land markets in southern Ethiopian highlands. Agricultural Economics, 30(2), 117–128. Tu, Q., & Bulte, E. (2010). Trust, market participation and economic outcomes: Evidence from rural China. World Development, 38(8), 1179–1190. Tu, Q., Mol, A. P. J., Zhang, L., & Ruben, R. (2011). How do trust and property security influence household contributions to public goods?: The case of the sloping land conversion program in China. China Economic Review, 22(4), 499–511. Van Gelder, J. L. (2007). Feeling and thinking: quantifying the relationship between perceived tenure security and housing improvement in an informal neighbourhood in Buenos Aires. Habitat International, 31(2), 219–231. Van Gelder, J. L. (2009). Legal tenure security, perceived tenure security and housing improvement in Buenos Aires: An attempt towards integration. International Journal of Urban and Regional Research, 33(1), 126–146. Van Gelder, J. L. (2010). What tenure security? The case for a tripartite view. Land Use Policy, 27(2), 449–456. de Vos, B. I., & Mol, A. P. J. (2010). Changing trust relations within the Dutch fishing industry: The case of National Study Groups. Marine Policy, 34(5), 887–895. Vranken, L., & Swinnen, J. (2006). Land rental markets in transition: Theory and evidence from Hungary. World Development, 34(3), 481–500. Wang, H., Riedinger, J., & Jin, S. (2015). Land documents, tenure security and land rental development: Panel evidence from China. China Economic Review, 36, 220–235. Wang, H., Tong, J., Su, F., Wei, G., & Tao, R. (2011). To reallocate or not: Reconsidering the dilemma in China's agricultural land tenure policy. Land Use Policy, 28(4), 805–814. Ye, J. (2015). Land transfer and the pursuit of agricultural modernization in China. Journal of Agrarian Change, 15(3), 314–337.

21