Do Farming Scale and Mechanization Affect Moral Hazard in Rice Harvest Outsourcing Service in China?

Abstract

The purchase of agricultural machinery outsourcing services creates a principal–agent relationship between farmers and service providers, where farmers are principals, and service providers are agents. Inconsistent goals and information asymmetry between two parties may induce moral hazard on the part of the agent. Based on survey data from 1106 rice farmers in China, this study uses Logit models to estimate the effect of agricultural machinery outsourcing services on harvesting operators’ work attitudes. The results are as follows. In general, work attitudes of outsourcing service operators are not as serious as those of farmers. After controlling for other factors, we find that purchasing harvest outsourcing services negatively affects the operators’ work attitudes. The results of the grouping estimation indicate that for large-scale farms, using combine harvesters increases the probability of agents displaying serious work attitudes. Propensity score matching analysis also proves the robustness of service providers’ less serious work attitudes. This study provides empirical evidence that moral hazard exists in agricultural machinery outsourcing services. Policies such as standardization of the outsourcing service market, scale management, and use of combine harvesters should be adopted to mitigate moral hazard.

1. Introduction

For many years, agricultural mechanization has been given a high priority in China’s agricultural development. However, due to the decentralized management of smallholders in China, the application of agricultural machinery faces big challenges [1]. Small-scale farmers are reluctant to invest in specialized agricultural machinery [2]. In Southeast Asian countries, it has always been accepted that only by forming large contiguous fields through land consolidation can the adoption of combine harvesters improve rice harvesting prospects [3]. Investment in agricultural machinery for large-scale farmers will benefit them until their average farm size increases to three hectares or more [4].

Since the enactment of the Rural Land Contracting Law in 2003, China has introduced a series of measures to encourage land transfer, including the establishment of land transfer service centers and the confirmation of land use rights. However, the average farm size has hardly changed [5]. Though efforts are still needed to achieve scale management through land consolidation to create conducive conditions for the use of machinery [1,6], China has made considerable progress in agricultural mechanization [7]; from 1978 to 2018, the number of combine harvesters in China increased from 18,980 to 2.06 million, an increase of 107 times [8]. In 2020 it reached 2.20 million.

Agricultural machinery outsourcing services play a significant role in China’s agricultural mechanization [6]. Although small-scale farmers may not be able to purchase agricultural machinery, they can afford harvest outsourcing services. Thus, it can be profitable for both individuals and organizations to purchase agricultural machinery to respond to the demand for mechanical outsourcing services.

Many studies have examined the background of agricultural machinery outsourcing services [7,9], their impacts on agricultural production [10,11,12], and farmers’ willingness to outsource services [13,14,15,16,17]; however, few studies have tested the relationship between farmers and service providers, and most of these studies have been limited to theoretical analysis. Based on game theory and principal–agent theory, Cai and Liu [18] and Huan and Hou [19] argued that service providers may implement extensive operations and reduce service quality to pursue profit maximization. Therefore, suitable intermediaries and perfect contracts need to be introduced [20]. In an empirical study, Qu et al. [21] found service providers’ reduced effort level in both part-time farms and commercial farms.

Since the principal–agent problem is common in outsourcing services in secondary and tertiary industries, we cannot ignore the possibility that it exists in agricultural outsourcing services as well [11]. In harvest outsourcing services, farmers entrust the harvesting operations to harvesting service providers and pay corresponding service fees. In this case farmers are the principals, and the service providers are the agents that are mainly professional machinery companies or household farms. The service fees are normally based on the serviced area. The principals hope all the crops to be harvested efficiently (i.e., they need to reduce harvest losses). However, due to the non-standardized outsourcing market and lack of written contracts [18], the principals are the less informed party in regard to the agent’s skills and the maintenance of the machinery. Therefore, due to inconsistent goals and information asymmetry, the agents, as rational economic decision makers, may use their information advantage to maximize their self-utility through lower effort levels or other moral hazard behaviors (i.e., increasing forward speed of machine), thereby harming the interests of the principals [21,22]. In addition, the natural characteristics of agricultural production, such as long production cycles and vulnerability to the natural environment, make agricultural supervision difficult to implement, increasing the likelihood of moral hazard [18].

Farms of different planting scales have widely different resource endowments. Large-scale farms are mainly commercial and have a higher demand for agricultural technology [23], which makes them more risk-averse [24]. Small-scale farms lack access to decent inputs [25], and their goal is primarily to meet their own food demand. Although outsourcing services make mechanical harvesting available to farms of all scales, service providers may have higher effort levels when serving large-scale farms because the large and concentrated farmland is an attractive service target for them [26]. However, large farmland may make supervision and management difficult and allow the opportunity for moral hazard behavior. Therefore, it is necessary to take into account farming scales when studying moral hazard.

Rice is one of the most important staple foods in China with 30.75 million hectares planted in 2016, 85% of which is harvested by machinery [27,28]. In 2021, the planted area was 29.92 million hectares, 93.73 percent of which was harvested by machinery. This shows the fast development of machinery harvesting, and makes rice harvest a good candidate for research. The aim of the presented research is to examine moral hazard in harvest outsourcing services by studying whether the purchase of outsourcing services negatively affects operators’ effort levels. Different from the part-time farming perspective by Qu et al. [21], this study contributes to the literature on the outsourcing of agricultural production by providing one of the few empirical examinations of moral hazard in outsourcing services from a farming scale perspective.

2. Materials and Methods

2.1. Data

The data used here are derived from the dataset of a national survey conducted in 2016 by China Agricultural University (CAU) in collaboration with the Research Center for Rural Economy (RCRE) of the Ministry of Agriculture and Rural Affairs of China. The research team conducted this survey using the Rural Fixed Observation Point (RFOP) survey system of RCRE, which is a typical rural socio-economic survey system established to understand the production and operation of farm households. The investigation was conducted in the top ten provinces (municipalities and autonomous zones, hereinafter referred to as province) of rice production, which accounted for 81.80% of the total national rice production in 2015 [8]. The stratified sampling method was used to choose two counties in each province, two towns in each county, and two villages in each town. The final investigated households were randomly selected by the professional investigators from RFOP according to the household distribution of each village. In the actual investigation, farmer households who grew rice were also collected from other provinces when investigating other grain and oil varieties. These households were also taken into account. Professional investigators from RFOP collected information on the effort levels of rice harvesting operators, rice production and harvesting conditions, and basic respondent characteristics. In addition, the RFOP office provided regular survey data in 2015 for use. Using “province code”, “village code”, and “household code” as the identification code, the data obtained from the above survey were merged with the regular survey data available at the RFOP office. After excluding some missing data according to the research content of this study, there were 1106 households. These 1106 households covered 19 provinces, which accounted for 95.45% of China’s rice production in 2015 [8].

2.2. Key Variables

The moral hazard of service providers is reflected in their reduced effort levels. However, we have no idea about their original effort level. Based on the view of Adam Smith that the agent’s efforts are inferior to those of principals [29], this study uses the effort level of farmers as a criterion. Service providers’ reduced effort level is studied by comparing their efforts with those of farmers. Work attitudes of harvesting operators are used to measure effort levels [21]. There are three degrees (fine, general, rough) for operators’ work attitudes when harvesting. Operators could be service providers and farmers. Farmers evaluated operators’ work attitudes based on their observations (i.e., forward speed of machine and harvest losses) during the harvest. To simplify it, we consider two levels of work attitudes: “Serious” and “Not Serious”. The dummy variable “WA” is used to denote work attitude, which means “whether the service provider or the farmer was serious about harvesting or not”. When the farmer’s estimate was “fine”, it means that the operator’s work attitude was serious and WA equals 1. When the farmer’s estimation was “general” or “rough”, it means that the operator’s work attitude was not serious and WA equals 0.

To compare the work attitudes of service providers and farmers, we indicate the key independent variable of harvest outsourcing services (Ser). If the farmer used harvest outsourcing services, the operator was the service provider; if the farmer did not use harvest outsourcing services, the operator was the farmer.

2.3. Model Specification

Considering that the dependent variable is a 0–1 dummy variable, a multivariate Logit regression model is established to study the moral hazard in harvest outsourcing services under the control of other factors.

(1)$\ln \left(\frac{p_i}{1-p_i}\right)={\alpha }_0+{\alpha }_1\left(Se{r}_i\right)+{\displaystyle \sum }_{j=2}^k{\alpha }_j{c}_{ij}$

where $p_i$ denotes the probability of serious work attitudes ($W{A}_i=1$) for household $i$. $p_i/1-p_i$ is called odds. Therefore, the dependent variable is the natural logarithm of the odds (log odds). $Se{r}_i$ is a dummy variable that equals 1 if household $i$ purchased harvest outsourcing services; otherwise, 0. $c_{ji}$ represents other covariates that have impacts on work attitudes, which are listed in the “Covariates”. ${\alpha }_0$ is the intercept. The regression coefficients ${\alpha }_1,{\alpha }_2,\dots ,{\alpha }_j$ are given in units of log odds, which indicate the amount of change expected in the log odds when there is a one-unit change in the predictor variable with all of the other variables in the model held constant.

In China, there are two harvesting methods—combine harvesting and segmented harvesting [30]. Combine harvesting refers to the use of combine harvesters to finish reaping, threshing, and cleaning simultaneously. Although combine harvesters can significantly reduce the labor force engaged in harvesting and shorten the harvesting process, combine harvesters are generally large and costly. Therefore, in some less developed or developing areas with complicated terrain, farmers prefer segmented harvesting. Segmented harvesting means that each stage of a harvesting operation is completed separately. To study whether the effect of outsourcing services on work attitudes is affected by the harvesting methods. We add the cross term of outsourcing services and harvesting methods to get Equation (2):

(2)$\ln \left(\frac{p_i}{1-p_i}\right)={\beta }_0+{\beta }_1\left(Se{r}_i\right)+{\beta }_2\left(Co{m}_i\right)+{\beta }_3\left(Se{r}_i\times Co{m}_i\right)+{\displaystyle \sum }_{j=4}^k{\beta }_j{z}_{ij}$

where $Co{m}_i$ refers to harvesting methods, which takes on 1 if household $i$ used combine harvesting and 0 if farmer $i$ used segmented harvesting. ${\beta }_0$ and $c_{ij}$ are the intercept and the covariates. The meanings of the other variables are the same as those in Equation (1).

Since the cross term of harvest outsourcing services and harvesting methods is added, the base/default group is farmers who used segmented harvesting and did not purchase outsourcing services. Then the marginal effect of $Ser$ measures the difference of serious attitudes probability between outsourcing services using segmented harvesting and self-service using segmented harvesting, while the marginal effect of $Com$ indicates the difference of farmers’ serious attitudes probability between combine harvesting and segmented harvesting. To compare the difference of serious attitudes probability between harvest outsourcing services using combine harvesting and self-service using combine harvesting and the difference of service attitudes probability between combine harvesting using harvest outsourcing services and segmented harvesting using harvest outsourcing services, the STATA command “lincom” is used to calculate the difference and assess statistical significance. The Logit procedure used to obtain the marginal effect estimates is carried out using STATA 15.0 software.

2.4. Covariates

Production and harvesting conditions that may impact operators’ work attitudes include weather conditions (Wea), pest disease conditions (Pest), planting area (Area), land terrain (Flat), the distance from homestead to the nearest paved road (Htor), labor shortages (Labor), food saving consciousness (Sav), and the selling price of rice (Price). Investigators asked farmers to recall the weather types during harvesting, including normal weather, heavy rain, strong wind, and others. Weather condition is set as a 0–1 dummy variable to simplify the analysis. If the weather type is normal weather, Wea equals 0, otherwise 1. Similar to weather conditions, Pest is set to 1, 2, and 3 if farmers estimated no pest, slight or general pest, and serious pest, respectively. Land terrain and labor shortage were also estimated by farmers. Dummy variable Flat takes the value of 1 if farmers reported that the farmland is flat, and 0 otherwise. Dummy variable Labor takes the value of 1 if farmers reported a shortage of labor when harvesting, and 0 otherwise. If farmers gathered the rice left in the fields after harvesting, they were deemed to have food saving awareness (Sav = 1). Sale price of rice refers to the sale price for the first three months after harvesting as recalled by farmers. If no rice was sold in the first three months, the sale price for the first six months after harvesting is used, and so on. Missing values are filled with the average sale price earned by farmers in the same village for the first three months. Although the sale price was formed after harvesting, we believe that farmers are sufficiently rational and knowledgeable to predict the future price with reasonable accuracy [31].

Household and individual characteristics include the gender (Gen), age (Age), and education (Edu) of the head of the household, agricultural training experience (Train), total family income (Tinc), and rice income share (Rincs).

To avoid the effect of unobservable regional differences on the estimation results, we divide the sample provinces into the Yangtze River basin, the Northeast Plain, and the Southeast Coast according to the Regional Layout Planning for Advantageous Agricultural Products issued by the Ministry of Agriculture and Rural Affairs of China [32]. This classification takes into account the natural resources, planting characteristics, and geographical conditions, which is a good representation of rice production status and outsourcing service market in China.

2.5. Farm Scales

There is no unified classification of farm scales in China. Since this study is not a discussion of what size different farms should have, it is simply an analysis of the effect of outsourcing services on work attitudes in different farm scales. Therefore, the farm scale here is a relative concept. Referring to Li et al. [33], this study uses the statistical characteristics of the sample, the median of rice planting area (0.22 ha), to divide the sample into small-scale farms and large-scale farms. This criterion is close to that used by Li et al. [33]. Specifically, farms with rice planting area less than 0.22 ha are classified as small-scale farms, while farms with rice planting area greater than or equal to 0.22 ha are classified as large-scale farms.

3. Results

3.1. Descriptive Results

Table 1 shows the average work attitude of operators. overall, the average work attitude of farmers (0.32) was more serious than that of service providers (0.17). This was also observed in small-scale farms and large-scale farms, with smaller difference in large-scale farms. Service providers were more serious in providing services to large-scale farms (0.21) than to small-scale farms (0.12). This was also observed when the service providers offered combine harvesting services. when using combine harvesters, 14% of service providers were serious on small-scale farms while 26% of service providers had serious work attitudes on large-scale farms.

Using combine harvesters made service providers’ work attitudes more serious, especially in large-scale farms. Interestingly, when service providers provided combine harvesting services to large-scale farms, their average work attitude (0.26) was very close to that of farmers (0.27). For small-scale farmers, using combine harvesting made them less serious. This may be due to the fact that combine harvesters are not suitable for small-scale farmland. Meanwhile, small-scale farmers own combine harvesters mainly for the purpose of providing outsourcing services to other farmers. The benefits from reducing harvest losses on their own fields are much less than that from providing harvesting services to other farmers. Nevertheless, service providers’ average work attitude was still less serious than that of small-scale farmers. In contrast, using combine harvesters made large-scale farmers more serious. For large-scale famers, 42% of them became serious when in regard to their work attitudes when using combine harvesters.

Table 2 presents the definitions and means of variables. Overall, the percentage of serious work attitudes in the sample was not high. In the total sample, more than three-quarters of operators were not serious about harvesting work. Moreover, 59% of the farms purchased harvest outsourcing services. The proportion of large-scale farms buying outsourcing services was 73%, which was 1.6 times that of small-scale farms (47%). Less than half of the farms (46%) used combine harvesting. Of large-scale farms, 57% used combine harvesters, which was 1.5 times that of small-scale farms. The average rice planting area of the large-scale farms was 0.55 ha, 4.6 times that of small-scale farms. Nevertheless, large-scale farms had less of a lack of labor than large-scale farms, which may be the result of using mechanical harvesting, such as a higher proportion of outsourcing services and combine harvesting. The average age of the household heads on large-scale farms was 52.80 years old, which was 2.63 years younger than that of household heads on small-scale farms. There was little difference in the schooling years of household heads between small-scale farms and large-scale farms. Household heads of large-scale farms had slightly more years of education than household heads of small-scale farms.

3.2. Logit Estimation Results

Table 3 shows the estimation results without the cross term. Column (1) presents the marginal effects of factors in the total sample. The marginal effect of outsourcing services is negative and significant, which means using outsourcing services reduces the probability of serious work attitudes and service providers are less serious about the harvesting than farmers. The marginal effect of combine harvesting is positive and significant. This means that using combine harvesting will increase the probability of serious work attitudes.

Moreover, the marginal effects of bad weather, pests, household income, and rice income share are significantly negative, which means that these factors will decrease the probability of serious work attitudes. In contrast, the marginal effects of planting area, flat terrain, distance from homestead to the nearest paved road, food saving consciousness, and age are positive and significant, which means that these factors will increase the probability of serious work attitudes.

Columns (2) and (3) in Table 3 present the estimation results for small- and large-scale farms. The marginal effects of outsourcing services for both small- and large-scale farms are significant and negative, implying that service providers are less serious about harvesting than farmers, whether they provide services to small-scale farms or to large-scale farms. The marginal effect of combine harvesting is significantly positive for large-scale farms but not for small-scale farms. This means that combine harvesting only increases the probability of serious work attitudes in large-scale farms. In addition, the marginal effect of bad weather becomes insignificant for large-scale farms. This means that bad weather would not decrease the probability of serious work attitudes on large-scale farms. The marginal effect of sale price of rice becomes significantly negative for large-scale farms, which means that higher rice price reduces the probability of operators’ displaying serious work attitudes on large-scale farms. Higher rice prices increase farmers’ demand for harvest services. However, when the demand for harvesting outsourcing services exceeds the supply, operators are more likely to deliver a rough harvest.

The estimation results with cross term are presented in Table 4. Column (1) shows the marginal effects of factors in the total sample. The marginal effect of outsourcing services is negative and significant, which is in line with above results. The marginal effect of cross term is statistically significant. This means that the effects of outsourcing services depend on the harvesting methods used. In order to compare the work attitudes of service providers using combine harvesting and farmers using combine harvesting, a “lincom” test is performed in STATA software. As shown at the bottom of Table 4, the odds ratio for harvest outsourcing services using combine harvesting relative to self-service using combine harvesting is 0.241, which is smaller than 1 and statistically significant. This implies that service providers using combine harvesting have less odds of a serious attitude than farmers using combine harvesting. The odds ratio for combine harvesting using outsourcing services relative to segmented harvesting using outsourcing services is 4.001, which is larger than 1 and statistically significant. This means that using combine harvesting increases the likelihood of the service providers having serious work attitudes.

Columns (2) and (3) in Table 4 present the marginal effects of factors for small-scale and large-scale farms. As with the previous results, the marginal effects of outsourcing services are significantly negative at the 1% significance level, which means that purchasing outsourcing services reduces the probability of serious work attitudes of service providers using segmented harvesting on both small-scale and large-scale farms. The “lincom” tests show that the odds ratios for harvest outsourcing services using combine harvesting relative to self-service using combine harvesting are 0.340 and 0.377 for small-scale farms and large-scale farms, respectively, but insignificant. The odds ratios for combine harvesting using outsourcing services relative to segmented harvesting using outsourcing services are 1.589 and 6.740 for small-scale farms and large-scale farms, respectively, but it is statistically significant only for large-scale farms. For large-scale farmers, the use of combine harvesters increases the probability that the outsourcing service operators’ will take their work seriously; however, there is no significant effect for small-scale farmers.

3.3. Robustness Test

We study service provider’s reduced effort level by comparing the work attitudes of farmers and service providers. However, farmers with more serious attitudes may choose to harvest by themselves, which means there may be potential self-selection when participating in harvest outsourcing services. Qu et al. [21] provided robustness test using a propensity score matching (PSM) method, which regarded outsourcing services as the treatment group and self-service as the comparison group. The results in this study could also be supported by the robustness test from Qu et al. [21]. Matching is performed by using the psmatch2 program in STATA software. Nearest-neighbor matching (n = 1; caliper = 0.01) without replacement and 500 bootstrap replications are used for matching, which greatly reduces differences between variables. The bias after matching was less than 10% for each variable [34] and no variable is statistically different after matching. After matching, 877 observations remained on the support region. Average treatment effect is −0.186, which is significant at the 10% significance level. Compared to the Logit regression results, both magnitude and significance level decrease, implying that untreated self-selection questions overestimate the effect of outsourcing services on work attitudes. However, service providers’ average serious work attitude is still lower than that of matched farmers. Therefore, PSM results also show that there is moral hazard, which makes service providers less serious about harvesting than farmers.

4. Discussion

Based on data for 1106 rice farms in China, this study focuses on service providers’ moral hazard in harvest outsourcing services. Previous studies have indicated the moral hazard problem in agricultural outsourcing services from a game theory perspective. However, there are few empirical studies on this issue. In this study, service providers’ moral hazard is studied by comparing their work attitudes with farmers’ work attitudes.

First, descriptive results show that service providers’ average work attitude was less serious than that of farmers. Even though the use of combine harvesters made service providers more serious, their work attitudes were still less serious than farmers’ work attitudes. Second, estimation results of Logit regressions also show that purchasing outsourcing services reduces the probability of serious work attitudes, which means that service providers are less serious about harvesting than farmers. Considering that scale production is one of the reasons for the generation of outsourcing services and an essential factor affecting management efficiency, we also study the effects of outsourcing services on work attitudes in different farm scales. Using the median of rice planting area, farms are divided into small-scale farms and large-scale farms. Similar negative effects of outsourcing services on operators’ work attitudes are also found in small-scale farms and large-scale farms.

To study the effects of outsourcing services on work attitudes in different harvesting methods, the cross term of outsourcing services and harvest methods is included in models. The results show that the negative effects of outsourcing services on the probability of serious work attitudes are significant in segmented harvesting. However, in combine harvesting, service providers’ work attitudes are not less serious than those of small-scale farmers and large-scale farmers. From the principal–agent perspective, moral hazard could be mitigated through income incentives. Combine harvesters are more advanced and expensive than machines used in segmented harvesting, causing the service fee for combine harvesting services to be higher than that for segmented harvesting services [35]. Therefore, service providers using combine harvesting are more likely to have serious work attitudes, while service providers using segmented harvesting are more likely to have stronger needs to harvest a larger area within a certain period of time to increase their income, which causes rough harvesting. This is observed in increased average work attitude of service providers using combine harvesting in the descriptive results and the positive marginal effect of combine harvesting in estimation results. Therefore, the income incentives from the higher service fee of combine harvesting could be the reason for the insignificant effects of outsourcing services in combine harvesting. The same reason makes service providers using combine harvesters more likely to have serious work attitudes than those using segmented harvesting. However, this is only observed in large-scale farms. Service providers of combine harvesters are more willing to provide services to large-scale farms [26,36] because the service fee for large-scale farms, which is proportional to the serviced area, will be higher than that for small-scale farms. Meanwhile, combine harvesters are more suitable for operation on large-scale farmland [37]. In small-scale farms, the small planting area makes the operation of combine harvesters difficult. Therefore, in small-scale farms, service providers’ work attitudes would not be much more serious even when using combine harvesters. This is also reflected in the descriptive results.

Moreover, other factors also affect operators’ work attitudes. Both bad weather and pests make harvesting more difficult and increase harvesting losses. In such cases, operators will increase the speed of their machine to finish harvesting as soon as possible or to avoid greater harvest losses caused by bad weather or pests. Meanwhile, bad weather and pest disease themselves increase harvest losses, making it difficult to determine whether the increased losses are due to the bad weather and pest disease or to moral hazard, thereby increasing the likelihood of harvest operators taking less care with their work. However, large-scale farmers are able to use weather information to make scientific decisions about the timing of harvesting and to avoid adverse effects of weather. They also have the advantage of having timely access to mechanical harvesting services before bad weather arrives. These make it possible for bad weather to have no significant impact on the work attitudes of operators of large-scale farms. For outsourcing service providers, larger areas mean higher service fees, which incentivize serious work attitudes and reduce the probability of laziness. On flat farmland, harvesting operators are more likely to work effectively, because flat terrain facilitates harvesting operations, especially mechanical harvesting operations. The farther the homestead from the nearest paved road, the longer the dirt road to reach the farmland, and the more attention is required in driving the machine. Moreover, farmers who are economically disadvantaged or elderly are more likely to live farther away from paved roads [38,39]. They may be more concerned about food and more serious about harvesting. It can be seen that farmers with a food saving consciousness will pay more attention to harvest operations. Similarly, older farmers usually have a stronger food saving consciousness, and they are reluctant to incur food losses.

5. Conclusions

Most studies on agricultural machinery outsourcing services have focused on outsourcing behavior and production efficiency [10,11,12,13,14,15,16,17]. Agricultural machinery outsourcing services create a principal–agent relationship between farmers (principals) and outsourcing service providers (agents) [18,19,20,21,22]. A few studies have provided theoretical evidence that inconsistent goals and information asymmetry between the farmer and service provider create the moral hazard that provides the latter the motivation and opportunity to conduct inefficient work [18,19,20]. Using a sample of 1106 households in China in 2016, this study conducts Logit regressions to investigate the effect of outsourcing services on the work attitudes of rice harvest operators, which also takes farming scale into account. The estimation results suggest that after controlling for other possible factors, purchasing outsourcing services negatively affects the work attitudes of harvest operators, thereby indicating the existence of moral hazard in mechanical harvesting outsourcing services. A similar result is found using a propensity score matching method. For large-scale farmers, the use of combine harvesters can mitigate moral hazard to a certain extent. Expansion of the planting area also increases the probability of the operator taking their work seriously.

This empirical study is the first to examine moral hazard in agricultural machinery outsourcing services. Agricultural outsourcing services facilitate the expansion of agricultural production beyond individual households as well as socialized division of labor. Service providers are essentially hired labors, and their productivity is lower than that of family labors [40]. Therefore, the outsourcing service market needs regulation, such as suitable intermediaries and standardized written contracts, to ensure service efficiency. Outsourcing service providers will work more efficiently on large-scale farms when using combine harvesters, which indicates that land scale is critical to service scale. Therefore, even though the service scale has contributed to China’s agricultural modernization, scale management remains the ultimate goal. Although this study provides the empirical evidence on moral hazard in agricultural outsourcing, it does not indicate how it impacts agricultural production. Furthermore, it is unknown whether existing studies have underestimated the productivity of outsourced services when the inefficiency of service providers is considered. These topics warrant further research.

Author Contributions

Conceptualization, X.Q. and D.K.; methodology, X.Q. and D.K.; software, X.Q.; investigation, X.Q. and L.W.; data curation, X.Q.; writing—original draft preparation, X.Q.; writing—review and editing, D.K., L.W. and M.A.; project administration, L.W.; funding acquisition, L.W. and M.A. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Restrictions apply to the availability of these data. Data were obtained from the grain economy research group of “Investigation and evaluation of grain harvest loss” and are available from the corresponding author with the permission of the grain economy research group of “Investigation and evaluation of grain harvest loss”.

Conflicts of Interest

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Tables

Table 1

Average work attitude of operators.

Sample	Full Farms	Small-Scale Farms	Large-Scale Farms
All farms	0.23	0.24	0.22
Self-service (Ser = 0)	0.32	0.34	0.27
Outsourcing service (Ser = 1)	0.17	0.12	0.21
Self-service using combine harvesters (Ser = 0 and Com = 1)	0.33	0.17	0.42
Outsourcing service using combine harvesters (Ser = 1 and Com = 1)	0.21	0.14	0.26

Note: Self-service represents farmers who did not purchase harvest outsourcing services. Samples that used manual reaping and manual threshing were not included when counting large-scale farmers, since large-scale farmers are unlikely to adopt these methods.

Table 2

Sample descriptive statistics.

Variable	Definition	All Farms	Small-Scale Farms	Large-Scale Farms
Dependent variable
WA	1 if operators treat harvesting seriously, 0 otherwise	0.23	0.24	0.22
Core independent variables
Ser	1 if reaping and threshing services purchased, 0 otherwise	0.59	0.47	0.73
Com	1 if combine harvesting services purchased, 0 otherwise	0.46	0.38	0.57
Production and harvesting variables
Wea	Weather condition. 1 if bad weather, 0 if normal	0.16	0.12	0.20
Pest	No pest = 1, slight pests = 2, general or serious pests = 3	1.84	1.79	1.91
Area	Planting area of rice (ha)	0.33	0.12	0.55
Flat	1 if the terrain is flat, 0 otherwise	0.75	0.77	0.76
Htor	Distance from homestead to the nearest paved road	0.34	0.42	0.25
Labor	1 if farmers report a lack of manpower; 0 otherwise	0.28	0.31	0.23
Sav	1 if farmers pick up rice after harvest; 0 otherwise	0.16	0.15	0.17
Price	The price of rice (yuan/kg)	2.98	3.04	2.92
Household and individual variables
Gen	Gender of household head (male = 1, female = 0)	0.84	0.84	0.85
Age	Age of household head	54.12	55.43	52.80
Edu	Schooling of household head (years)	7.01	6.87	7.19
Train	1 if household head obtained agricultural training; 0 otherwise	0.09	0.11	0.08
Tinc	Household income (ten thousand yuan)	7.07	6.31	7.95
Rincs	Rice income as a percentage of total income (%)	15.80	7.82	24.32
N	Number of observations	1106	548	532

Table 3

Marginal effects without the cross term.

	(1) All Farms		(2) Small-Scale Farms		(3) Large-Scale Farms
Ser	−0.342 ***	(0.042)	−0.283 ***	(0.062)	−0.338 ***	(0.058)
Com	0.144 ***	(0.041)	0.011	(0.063)	0.179 ***	(0.048)
Production and harvesting variables
Wea	−0.094 **	(0.041)	−0.419 ***	(0.135)	0.073	(0.059)
Pest = 2	−0.108 ***	(0.028)	−0.123 ***	(0.038)	−0.095 **	(0.040)
Pest = 3	−0.115 ***	(0.034)	−0.102 **	(0.046)	−0.150 ***	(0.044)
Area	0.221 ***	(0.049)	0.044	(0.377)	0.125 ***	(0.048)
Flat	0.105 ***	(0.033)	0.231 ***	(0.044)	−0.028	(0.044)
Htor	0.048 ***	(0.015)	0.033 *	(0.019)	0.104 ***	(0.026)
Labor	−0.023	(0.025)	0.009	(0.032)	−0.066	(0.045)
Sav	0.057 *	(0.033)	0.095 **	(0.047)	0.028	(0.048)
Price	0.030	(0.036)	0.057	(0.040)	−0.290 ***	(0.105)
Household and individual variables
Gender	0.014	(0.035)	−0.018	(0.048)	0.054	(0.050)
Age	0.002 *	(0.001)	0.003 *	(0.002)	0.002	(0.002)
Edu	0.006	(0.005)	0.010	(0.006)	0.001	(0.007)
Train	−0.025	(0.046)	0.009	(0.058)	0.011	(0.068)
Tinc	−0.005 *	(0.003)	−0.012	(0.008)	−0.003	(0.003)
Rincs	−0.002 **	(0.001)	−0.007 ***	(0.002)	−0.001	(0.001)
Region	Yes		Yes		Yes
N	1106		548		532
pseudo R2	0.147		0.286		0.157

Note: Robust standard errors are in parentheses; ***, **, and * indicate 1%, 5%, and 10% significance levels, respectively.

Table 4

Marginal effects with the cross term.

	(1) All Farms		(2) Small-Scale Farms		(3) Large-Scale Farms
Ser	−0.407 ***	(0.062)	−0.328 ***	(0.085)	−0.457 ***	(0.086)
Com	0.011	(0.081)	−0.137	(0.155)	−0.050	(0.105)
$Ser\times Com$	0.195 *	(0.103)	0.194	(0.176)	0.319 **	(0.132)
Production and harvesting variables
Wea	−0.093 **	(0.041)	−0.421 ***	(0.134)	0.090	(0.059)
Pest = 2	−0.106 ***	(0.028)	−0.122 ***	(0.037)	−0.094 **	(0.040)
Pest = 3	−0.110 ***	(0.034)	−0.105 **	(0.047)	−0.141 ***	(0.044)
Area	0.221 ***	(0.049)	0.046	(0.376)	0.119 **	(0.048)
Flat	0.103 ***	(0.033)	0.234 ***	(0.044)	−0.039	(0.043)
Htor	0.046 ***	(0.015)	0.032 *	(0.019)	0.105 ***	(0.026)
Labor	−0.025	(0.025)	0.007	(0.032)	−0.070	(0.045)
Sav	0.065 **	(0.033)	0.095 **	(0.047)	0.049	(0.048)
Price	0.023	(0.037)	0.052	(0.040)	−0.344 ***	(0.112)
Household and individual variables
Gender	0.009	(0.034)	−0.025	(0.048)	0.044	(0.046)
Age	0.002 *	(0.001)	0.003 *	(0.002)	0.002	(0.002)
Edu	0.007	(0.005)	0.010 *	(0.006)	0.002	(0.007)
Train	−0.027	(0.045)	0.007	(0.058)	0.015	(0.068)
Tinc	−0.005 *	(0.003)	−0.012	(0.008)	−0.003	(0.003)
Rincs	−0.002 **	(0.001)	−0.007 ***	(0.002)	−0.001	(0.001)
Region	Yes		Yes		Yes
N	1106		548		532
pseudo R2	0.150		0.288		0.169
lincom test	Odds ratio		Odds ratio		Odds ratio
The effect of harvest outsourcing service among combine harvesting
	0.241 ***	(0.134)	0.340	(0.428)	0.377	(0.257)
The effect of combine harvesting among harvest outsourcing service
	4.001 ***	(1.667)	1.589	(1.107)	6.740 ***	(3.560)

Note: Robust standard errors are in parentheses; ***, **, and * indicate 1%, 5%, and 10% significance levels, respectively.

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