Large-and-strong or Small-and-beautiful? Exploring the Relationship Between Organization Size and Performance of Farmer Cooperatives
Qiao Liang China Academy for Rural Development, School of Public Affairs, Zhejiang University, China Email:[email protected]
Rongrong Bai China Academy for Rural Development, School of Public Affairs, Zhejiang University, China Email:[email protected]
Selected Paper prepared for presentation at the 2021 Agricultural & Applied Economics Association Annual Meeting, Austin, TX, August 1 – August 3
Copyright 2021 by Qiao Liang and Rongrong Bai. All rights reserved. Readers may make verbatim copies of this document for non-commercial purposes by any means, provided that this copyright notice appears on all such copies. Large-and-strong or Small-and-beautiful? Exploring the Relationship Between Organization Size and Performance of Farmer Cooperatives
Abstract: This paper explores the relationship between the size and performance of cooperatives. Panel data containing the census of farmer cooperatives from 2014 to 2019 are applied in the empirical analyses. The membership size, as well as its evolution, of cooperatives is mapped. Then a dynamic panel data model is constructed to study the impact of membership size on the performance of cooperatives which is indicated by profit per member. The results show that the effects of membership size on profit per member displays an inverted “U” shape. And the optimal membership size is 19 members, which is “Small and Beautiful”. The effects are heterogeneous across ages, subsidies and geographical regions. The robustness of these results is confirmed by both alternative key explanatory variable indicators and estimation methods.
Key Words: Farmer cooperative; organization size; performance; dynamic panel data model; SYS-GMM
1 Introduction Among all of the organizations in the agricultural sectors, farmer cooperatives have a dominant position in many countries. Farmer cooperatives were initiated in China in the late 1980s. The development of cooperatives gained momentum after 2007 in response to the promulgation of the National Cooperative Law in China. The population of farmer cooperatives increased from 74,219 in the end of 2007 to 1.94 million in 2019.1 Particularly, they help farmers to enhance technology, obtain higher prices, and improve quality of products (Liang and Wang, 2020). In spite of the essential functions, cooperatives in China have some distinctive features compared with cooperatives in Western countries, which have led to some debates on the justification and competitiveness for cooperatives (Liang et al., 2015). Particularly, cooperatives in China are mainly small and local. Most cooperatives
1 Data source: China Rural Cooperative Economy Statistical Yearbook (2019). 1 have a membership of less than 100 and the average membership size is as small as 15 in 2019. In order to further enhance the market competitiveness of cooperatives, both central and local governments have proposed the development goal of “larger and stronger” of cooperatives since 2012. However, some scholars argue that that the expansion of membership size may cause high coordination costs within organizations and therefore result in low efficiency (Nilsson et al., 2012). This is especially true for Chinese cooperatives due to the low capacity of management. This paper seeks to map the evolution of the population and membership size of Chinese farmer cooperatives, and investigate the causal association between cooperative size and performance. The specific research questions are: First, how membership size has been evolving in the past decade? Second, does membership size affect the performance of cooperatives? And if yes, does the effect linear or non-linear? Third, what are the mechanisms of the effect? Are the effects heterogeneous across different cooperative life cycles, and across geographical regions which differ in economic, education, and technique levels? This study aims to make a couple of contributions. First, this paper is one of the first empirical studies on the complex effect of farmer cooperative size on its performance and calculating the optimal membership size, while most existing studies focus on the finance companies and industrial enterprises. The empirical results show that the effect of farmer cooperative size on its performance shows a non-linear inverted U-shaped curve relationship and the boundary of membership size at the top of the curve is 19. The results demonstrate that small membership size is desirable for farmer cooperatives currently in China, which also provides implications for other countries. The optimal membership size varies for cooperatives with different endowments and at different development stages. Second, a dataset comprised of 8,788,123 cooperative observations during 2014 and 2019 is utilized in the empirical analyses, which addresses sample-selection bias and inaccurate estimates caused by small samples. Most studies on the association between organization size and performance of farmer cooperatives are based on qualitative analysis or relatively small samples. The sample used in the current paper is the largest and most extensive one, as far as we know. The remainder of the paper is organized as follows. Section 2 analyses the
2 theoretical mechanism of the relationship between organization size and performance, and provides a literature review on related research. Section 3 introduces the model, estimation methods and data. Section 4 maps the development of Chinese farmer cooperatives and membership size. Section 5 presents the empirical results and discussions. The last section presents the conclusions, related policy implications, and limitations of this study. 2 Theories and relevant researches This section reviews the theories and relevant research regarding the association between organization size and performance. 2.1 Theories The association between organization size and performance can be discussed by various theories, e.g., traditional and neo-classical economics and transaction cost theory. Smith (2010) emphasizes in
3 which is used to explain the existence and the boundaries of firms. A firm emerges to save market transaction costs, while as the size of the firm expands, the marginal intra-firm transaction costs increase. The optimal size of the firm depends on the marginal trade-off between internal transaction costs (also called coordination costs) and market transaction costs. Most current analyses on the organization size and boundaries of cooperatives are based on transaction costs theory (Ma et al., 2019). This is because cooperatives are a special firm based on “people” and characterized by collective ownership and democratic decision-making, which lead to high intra-organization transaction costs. Collective action theory is also broadly applied in the analyses on size and boundaries of cooperatives. The key factors that determine the success of collective action are the size of collectivity, governance structure and mechanisms (Qin, 2018). Olsen (1995) puts forward collective action theory in
4 as the Zipf’ Law. It is confirmed by many studies based on samples of firms from different countries and industries (Fujiwara et al., 2004; Luttmer, 2007). However, some scholars find the size distribution of Chinese firms deviate from Zipf’ Law due to governmental intervene (Fang and Nie, 2010). (to be done) There are abundant researches on the associations between firm size and various dimensions of performance, e.g., tax rates, access to financial support, and political behaviors, etc. However, the results regarding the relationship between firm size and performance are not consistent. Most studies find that there is a positive relationship between firm size and performance (Babalola, 2013; Vijayakumar and Tamizhselvan, 2010; Tang and Song, 2008), while there are also studies arguing that firm size has a negative impact on performance (Amato and Burson, 2007; Ammar et al., 2003). In addition, some research demonstrates that the impact of firm size on performance displays an inverted “U” shape, i.e., the increase in firm size positively improves the performance and when the size reaches the boundary, the increase in size begins to negatively influence performance (Wing and Yiu, 1997). Similarly, a few scholars state that the relationship between firm size and performance is complex, rather than a simply positive or negative relationship (Bolarinwa and Obembe, 2017; Niresh and Velnampy, 2014; Olaniyi et al., 2017; Sun and Wang, 2014). On the one hand, the increase in firm size contributes to the improvement of operating efficiency and market competitiveness. On the other hand, firm size expansion is accompanied with higher coordination costs, rigidity and inertia within the firm, which therefore lowers firm performance. The membership size of cooperatives also has two-side effects on the performance, in a similar manner to that of investor owned firms. On the one hand, the enlarging of membership size reduces average costs and improves the negotiation ability of cooperatives in transactions with other market entities. At the same time, it may also obtain more market opportunities, thereby increasing sales volume and output efficiency. On the other hand, the large number of members may result in excessively high organizational costs, which lead to the dilemma of collective action (Xi and Zhang, 2010). In addition, the expansion of membership size may cause excessive supply of products and cooperatives face difficulties in product marketing,
5 which damages the overall output efficiency of cooperatives (Skevas and Grashuis, 2020). Many scholars mention the importance of cooperative organization size, but studies focusing on the relationship of organization size and performance are very limited and the results are not consistent. Instead, empirical studies on cooperative performance often use organization size as a control variable. Cazzuffi and Moradi (2012) conduct an in-depth analysis on the relationship between the size and performance of cooperatives based on the panel data of cooperatives in Ghana in the 1930s. The results show inverted “U” relationships between the organization size and the financial capital per member, between the size and member loyalty, and between the size and cooperative exit. On the contrary, there is a significant and monotonous positive relationship between the size and performance of cooperatives. This may be due to that these Ghanaian cooperatives were still at the early stage of development and mostly had small size. The benefits gained from the expansion of membership size exceed the extra internal coordination costs. As the size further increases, the benefits were offset by the high costs of collective actions. Lerman and Parliament (1991) find that cooperatives with larger sizes are of higher efficient in the conversion of assets into sales, but they perform worse than small size cooperatives in terms of profitability, based on analyses on a panel data of 43 cooperatives in the United States from 1970 to 1987. Arcas et al. (2011) reported that the membership size of cooperatives has no significant effect on the performance. Similarly, Zhou et al. (2015) investigated the impact of organization size on the costs and benefits of agricultural machinery cooperatives and the results show that cooperatives with large sizes do not necessarily benefits from scale economy. Yang et al. (2014) also pointed out that there is no direct relationship between the group size and the performance of cooperatives, based on profound case study on two farmer cooperatives in China. In summary, the research on the impact of cooperative size on performance has the following characteristics. First, many scholars mention the importance of organization size of cooperatives, but empirical tests are very limited. Empirical studies on cooperative performance often use membership size as a control variable. Second, empirical analysis on the impact of cooperative size on performance are
6 mostly based on case studies or cross-sectional data from a limited sample, and lack a comprehensive and dynamic analysis of the overall situation of cooperatives. Third, the impact of cooperative organization size on performance has shown different conclusions in different studies. This may be because these studies are based on different samples and have different sample characteristics, or different measures of organization size and performance are used. Research based on more comprehensive data or the population of cooperatives therefore is desirable. 3 Methodology and data This section establishes an empirical model to estimate the effects of membership size on cooperative performance and introduces the data used in the empirical analyses. 3.1 Empirical model The static panel model is widely used in the existing literatures to analyze the impact of organization size on its performance. However, there may be a two-way causality relationship which will lead to the biased estimation of the results, and the static panel model can hardly capture the dynamic hysteresis effect. Hence, a dynamic panel regression model is established to estimate the effects of membership size on cooperative performance. Both linear function model (1) and nonlinear function model (2) are considered:
푃푒푟푓푖푡 = 훼0 + β 푃푒푟푓푖푡−1 + 훼1 푠푖푧푒푖푡 + ∑ 훾푗 퐶푉푗,푖푡 + δ 푌푒푎푟푡 +
θ퐼푛푑푢푠푡푟푦푖 + μ푃푟표푣푖푛푐푒푖 + 휀푖푡 (1)
2 푃푒푟푓푖푡 = 훼0 + β 푃푒푟푓푖푡−1 + 훼1 푠푖푧푒푖푡 + 훼2 푠푖푧푒푖푡 + ∑ 훾푗 퐶푉푗,푖푡 + δ푌푒푎푟푡
+ θ퐼푛푑푢푠푡푟푦푖 + μ푃푟표푣푖푛푐푒푖 + 휀푖푡 (2) where 푃푒푟푓푖푡 represent the performance of cooperative i in year t, 푠푖푧푒푖푡 is the membership size, and 퐶푉푗,푖푡 is a vector of other control variables. 푌푒푎푟푡 ,
퐼푛푑푢푠푡푟푦푖, Provincei, and 휀푖푡 represent the unobservable fixed year effect, industry effect, province effect and the random error term, respectively. The lagged dependent variable, Perfit−1, is included to capture the persistent effect of past performance level of cooperatives. Due to the inclusion of the lagged term of the dependent variable in the model,
7 there may be correlation between independent variable and random error term. To overcome the endogenous problems, the GMM is introduced in this study, which takes the level value of the explanatory variable as the instrumental variable of the first-order difference equation, and at the same time takes the lag term of the first-order difference of the explanatory variable as the instrumental variable of the level equation (Blundell and Bond, 1998). Finally, the following equation is obtained:
푃푒푟푓푖푡 = β푃푒푟푓푖푡−1 + 훼1푠푖푧푒푖푡 + ∑ 훾푗 퐶푉푗,푖푡 + 휀푖푡 （3）
2 푃푒푟푓푖푡 = β푃푒푟푓푖푡−1 + 훼1푠푖푧푒푖푡 + 훼2푠푖푧푒푖푡 + ∑ 훾푗 퐶푉푗,푖푡 + 휀푖푡 （4）
The system GMM method can simultaneously estimate the original estimated equation containing the variable level value and the estimated equation after the first-order difference. 3.2 Data The data used in our analysis are mainly from China Academy for Rural Development-Qiyan China Agribusiness Database (CCAD).2 The data in the CCAD are from multiple sources. Specifically, the data regarding the registration of cooperatives, e.g., time, industry category, registered capital and registered place, are from the Bureau of Industry and Commerce in China. The data on the number of members, profit, financial loan, and government subsidies, ect. are from the National Enterprise Credit Information Publicity System. Merging different databases by cooperative ID, panel data covering in 2014-2019 for 30 provinces in China are constructed, yielding a total of 7,245,876 observations.3 3.3 Variables and descriptive analysis Firm performance can be measured by different indicators like profit margin, sales per capita and profit, ect. (Amato, 1995; Goddar et al., 2006). This paper uses profit per member to represent the farmer cooperatives’ performance. There are different indicators to measure firm size. Usually the amount of assets
2 The sample covers around 80% of all farmer cooperatives in China, some years even up to 98%. (see Figure A1).
Thus, to some extent, the total study samples can represent all cooperatives in China from 2014 to 2019. 3 Multiple steps are gone through to code and clean the data. First, we delete observations with less than five members and less than four farmer members. Second, observations with missing data for the control variables are removed from the sample. Third, cooperatives in the other sectors than agricultural related sectors are deleted, i.e., this study focuses on cooperatives in agriculture, fishery, forest, animal husbandry, and agricultural services. Finally, a sample including 1097850 observations is maintained. 8 is used to indicate the organization size of capital-oriented enterprises, while membership size and transaction volume are broadly utilized to measure the size of organizations such as collective-action groups and cooperatives. Due to the data availability, this paper adopts the number of members as the indicator of the cooperative size. Various potential influencing factors are included as control variables in the model. They are operating years (age), registered capital (regcap), financial loan (loan) and government subsidies (govsub) (Li and Zhu, 2014; Liang et al., 2014; Wan and Zeng, 2020). In order to reduce data fluctuations and eliminate possible heteroscedasticity problems, some variables (total surplus of cooperatives, total surplus per capita, number of members, operating years and registered capital) are processed in logarithm. The descriptive statistics of each variable are reported in Table 1.
Table 1 Definitions of variables and summary statistics Variables Definition Obs. Mean Std.dev.
Perfit Profit per member (million RMB) 1097850 0.0127 0.2390 sizeit Number of members 1097850 17.2303 91.6645 age Age of the cooperative 1097850 4.2554 2.6761 Whether the cooperative obtained loan 1097850 0.0482 0.2143 financial credit: 1=Yes, 0=No
Whether the cooperative obtained govsub 1097850 0.0459 0.2093 governmental subsidy: 1=Yes, 0=No regcap Registration capital (million RMB) 1097850 2.1916 3.6382
Data source: Author’s calculation based on CCAD data.
4 The distribution of farmer cooperatives size in China [to be completed] The population of farmer cooperatives have increased dramatically, since the National Cooperative Law was promulgated in China. Figure 1 displays the number of Chinese farmer cooperatives and membership size during 2014-2019. It demonstrates that at of the end of 2019, there were 1,687,100 farmer cooperatives legally registered in China. Cooperative membership size was small on average. It is
9 worth noting that the membership size has been shown a downward trend year by year, displaying a distinctive feature of “large group number and small group size”. This can be explained by a couple of reasons. First, the minimum number of members required by the Cooperative Law is relatively low, i.e., five members and four farmer members at the least. When a large number of farmer cooperatives in China are still in the initial stage of development and there is no time yet to grow in such a short period of time. Second, the Chinese governments adopted various measures to support the foundation and development of cooperatives in the early stage of development, which results in the rapid growth of cooperative population. However, improper support measures such as direct funding makes some people found cooperatives opportunistically to obtain the funds but do not really consider the growth or development of cooperatives.
2000000 25 1800000 1600000 20 1400000 1200000 15 1000000 800000 10 600000 400000 5 200000 0 0 2014 2015 2016 2017 2018 2019
Number of farmer cooperatives Average membership size
Figure 1 The number of cooperatives in China and membership size of cooperatives (2014～2019) Data source: Author’s calculation based on CCAD data.
As shown in Figure 2, the evolution of the membership size of cooperatives displays that the average membership size in different sectors has been declining over the years. The size differs in various product sectors, with service cooperatives having the largest size and followed by fishery and forest cooperatives. And the difference is
10 shrinking over time.
35
30
25
20
15
10
5 2014 2015 2016 2017 2018 2019 Agricultural cooperative Forest cooperative Animal husbandry cooperative Fishery cooperative Service cooperative Figure 2 Variance of farmer membership size across sectors (2014-2019) Data source: Author’s calculation based on CCAD data.
This paper employs the Pareto Index to map the distribution and evolution of membership size of Chinese farmer cooperatives.(to be completed). 5 The effects of the organization size on performance of farmer cooperatives 5.1 Baseline results Considering the possible endogenous problems caused by the lag dependent variable, the pooled panel OLS and fixed-effects model regression results are biased. So the GMM estimator is introduced to calculate the effect of membership size on the performance of farmer cooperatives. The GMM approach involves the difference GMM (DIF-GMM) estimation method and the system GMM (SYS-GMM) estimation method. The DIF-GMM estimator had been widely used to solve the endogenous problems in the dynamic panel model. However, Blundell and Bond (1998) pointed out that the DIF-GMM estimation method may cause the problem of weak instrument variables. To address this issue, they proposed the SYS-GMM estimation method, which is extra well-organized and more effective than the DIF-GMM. What’s more, Roodman (2009) pointed out that the coefficient of the lagged dependent variable obtained by the OLS regression and that of the fixed-effects estimation method are the
11 upper and lower bounds of its true coefficient respectively, which can be used as a reference range for whether the GMM estimation result is reliable. In the SYS-GMM estimation, it is firstly necessary to judge the endogenous, exogenous and predetermined variables in the model, which may affect the selection of instrumental variables in the estimation process. In this paper, the membership size of cooperatives, financial loans and government subsidies are set as endogenous variables. Operating years and dummy variables of time, region, and industry effects are set as exogenous variables. The lagged terms of the dependent variables are set as pre-determined variables. All lags after the first order of the predetermined variable, all lags after the second order of the endogenous variable, and all exogenous variables are set as instrumental variables. We use the collapse option in the xtabond2 command to compress the number of instrumental variables to avoid the inefficiency of the estimation results that may be caused by too many instrumental variables. The SYS-GMM estimation in the study is a two-step SYS-GMM estimation method. Table 2 reports the overall and general effect of cooperative membership size on performance. The results in columns (1) and (2) denote the SYS-GMM estimation results for Eq.(1) and Eq.(2), respectively. The results for fixed-effect and OLS estimation are reported in column (3) and (4). The probability value of AR (2) is higher than 0.1, indicating the model passes serial correlation test. The probability value of Hansen test is higher than 0.1, suggesting the instrumental variables are valid and the estimation results are reliable. Moreover, the coefficient of the lagged dependent variable is between the OLS and fixed-effect estimation results, which suggest the SYS-GMM estimation results are reliable. As shown in Table 2, the estimates of Equation (1) and Equation (2) suggest that the effect of membership size on the profit per member presents a non-linear U-shaped curve relationship. Specifically, membership size has positive effect on profit per member of cooperatives at the beginning and displays negative effect later on. These results indicate that the increase in the membership size generates scale economy and brings net benefits to members in the condition of relatively small membership size, otherwise the increase in membership size can also cause negative impact on economic benefits for members due to over-production and high intra-organization coordination costs. And through further calculation of the estimated
12 coefficients, the optimal size of cooperative membership is 19.
Table 2 Baseline regression results SYS-GMM linear (1) SYS-GMM non-linear(2) FE (3) OLS (4)
0.1844*** 0.1854*** -0.2092*** 0.2875*** 푃푒푟푓푖푡−1 (0.0303) (0.0305) (0.0360) (0.0215)
-0.0008 0.0164*** -0.0077*** -0.0040*** 푠푖푧푒푖푡 (0.0014) (0.0061) (0.0022) (0.0003)
-0.0028*** 0.0003 0.0002*** 2 푠푖푧푒푖푡 (0.0010) (0.0004) (0.0000)
0.0006** 0.0007** 0.0006 0.0008*** 푎푔푒 (0.0003) (0.0003) (0.0014) (0.0002)
0.0019 0.0022 0.0058*** 0.0095*** 푙표푎푛 (0.0067) (0.0067) (0.0014) (0.0006)
-0.0005 0.0001 0.0062*** 0.0054*** 푔표푣푠푢푏 (0.0069) (0.0069) (0.0013) (0.0007)
0.0016*** 0.0017*** 0.0000 0.0014*** 푟푒푔푐푎푝 (0.0001) (0.0001) (omitted) (0.0001)
0.0075 -0.0131 0.0229*** 0.0137*** _cons (0.0055) (0.0089) (0.0039) (0.0031)
Province YES YES YES YES
Year YES YES YES YES
Industry YES YES YES YES
AR(2) 0.903 0.900
Hansan 0.600 0.617 test
Obs 374526 374526 374526 374526
Notes: ***, **, * denote significance at 1% level, 5% level and 10% level, respectively; robust standard errors are in brackets.
The impact of the cooperative membership size on profit per member presents an inverted “U” shape. In the early stage of the cooperatives, the organization size
13 mainly brought the scale effect, such as the declining average cost of the use of technology and equipment, the transaction costs saved in the purchase and sale of inputs, and the improvement of negotiation power and so on. With the expansion of size and the increase of the heterogeneity degree of members, the negative effect of excessively high internal coordination costs has emerged. Our results tend to be in line with the findings of enterprises which suggest that also inverted “U” shape organization size on its performance (Wing and Yiu, 1997). That is to say, while the cooperative “internalizes” the market transaction costs, it will incur additional management costs (transaction costs within the organization). When this increase in management costs equals a decrease in market transaction costs (savings), the boundary of the cooperative tends to balance. However, as evidenced in the study conducted by Arcas et al. (2011) found a monotonously positive impact of Spanish cooperative size on its performance, and the size was not yet optimal. Despite the Chinese farmer cooperatives was, and still is, at early stage of development, there is an inverted U-shape relationship between cooperative size and its performance. The limitation of the cooperative managers’ capacity is, perhaps, the most main reason. Unlike most cooperatives in Western countries that hire professional managers to be responsible for the management of cooperatives, the management of cooperatives in Chinese mainly relies on farmer members, who are often rural talents with relatively strong financial strength or sales ability but low managerial capacity (Liang et al., 2015). Hence, It can be said that the improvement of the ability of cooperative management personnel may make the inflection point of the impact of cooperative size on performance come later. Simultaneously, the improvement of internal governance can facilitate collective action and weaken the negative impact of the membership size on cooperatives’ performance (Cazzuffi and Moradi, 2012). 5.2 Robustness checks To ensure the robustness of the above estimation, a number of robustness tests are conducted. First, this paper transforms the core explanatory variables by the farmer members. Second, considering that there may be a two-way causality between some dependent variables and the cooperative performance, this study replaces the endogenous variables in the model with a lagging period. Third, this paper adopts
14 one-step SYS-GMM and the forward orthogonal deviations transformation (FOD) based on two-step SYS-GMM estimation methods (Arellano and Bover, 1995) to conduct the robustness analysis. The results are reported in Table 3. It can be seen that all models pass Hansen and AR (2) test, and the sign and significance level of the regression coefficient are consistent with previous section, which indicates that the results are robust in our study.
Table 3 The results of robust test (1) (2) (3) (4)
0.1873*** 0.1950*** 0.1404*** 0.1855*** 푃푒푟푓푖푡−1 (0.0399) (0.0293) (0.0270) (0.0304)
0.0158** 0.0174*** 0.0238** 0.0155*** 푠푖푧푒푖푡 (0.0071) (0.0063) (0.0098) (0.0058)
-0.0026** -0.0031*** -0.0035** -0.0027*** 2 푠푖푧푒푖푡 (0.0012) (0.0010) (0.0016) (0.0010)
0.0006** 0.0008*** -0.0001 0.0007** 푎푔푒 (0.0003) (0.0003) (0.0003) (0.0003)
0.0004 0.0065 0.0148** 0.0026 푙표푎푛 (0.0072) (0.0062) (0.0065) (0.0066)
0.0011 0.0026 -0.0015 0.0002 푔표푣푠푢푏 (0.0072) (0.0063) (0.0070) (0.0069)
0.0016*** 0.0016*** 0.0014*** 0.0016*** 푟푒푔푐푎푝 (0.0002) (0.0001) (0.0001) (0.0001)
-0.0134 -0.0141 -0.0300** -0.0118 _cons (0.0097) (0.0090) (0.0125) (0.0086)
Province YES YES YES YES
Year YES YES YES YES
Industry YES YES YES YES
AR(2) 0.905 0.938 0.699 0.898
Hansan test 0.617 0.586 0.687 0.625
Obs 374526 374526 374526 374526
Notes: ***, **, * denote significance at 1% level, 5% level and 10% level, respectively; robust
15 standard errors are in brackets. The results in columns (1) and (2) denote the estimation results for one-step SYS-GMM and FOD, respectively. The estimation results for transforming the core explanatory variables by the farmer members and replacing the endogenous variables in the model with a lagging period are reported in column (3) and (4).
5.3 Heterogeneity across organization ages Age is often seen as a key factor in the cooperative life cycles. Given different stages with distinct characteristics, the impact of cooperative size on performance may be varying between different life stages. We divide the stage of cooperatives into three groups of age ≤5, 5-10, and age >10, based on the distribution of samples and the legal time of Chinese cooperatives. The average membership size is displayed in Figure 3. And the effect of cooperative size on performance across the three stages was assessed, as can be seen in Table4. The results vary between different groups. The results of Hansen test and AR(2) test show that the three-stage dynamic panel model setting is reasonable. Comparing the significance level, Hansen test and AR(2) test of both the linear and non-linear effect between all the groups, we find there is a non-linear effect in the group of age ≤5 and 5＜age≤10, whereas a significantly negative impact in the third stages (age ＞10). Specially, both the group of age≤5 and5＜age ≤10 show an inverted U-shaped effect of cooperative size on performance. Moreover, the optimal size in the second stage (27) is larger than the first stage (20). The reasons for the above results may be due to: (1) Cooperatives in the first stage (age≤5) suffer from the lack of growth resources, insufficient management experience, uncertain prospects and other major constraints that limit their optimal membership size. (2) Group between the ages of 5 to 10 represents more mature cooperatives. They possess competitive advantages over younger groups, especially in their resources possession and managerial capacity. It gives them a greater market power and more confidence, and allows them to enlarge organization size but do not surpass the collection of existing resources. Thus, the second group (5＜age≤10) were found had a larger optimal size than those (age≤5) in the initial phase of life circle. (3) Cooperatives in the third stage (age＞10) are more likely to be susceptible to the previous growth model, forming organizational inertia, relying entirely on the
16 previous growth model, lacking innovative spirit, and solidifying it as their own competitive advantage, while ignoring the improvement of technological innovation and management capabilities. As the cooperative size continues to expand during this period, which may actually hamper their performance, increase the difficulty of management and even fall into a state of “diminishing returns to scale”.
Table 4 Heterogeneity of the effect of membership size on cooperative performance across ages Age≤5 5＜Age≤10 Age＞10
Linear Non-linear Linear Non-linear Linear Non-linear
*** *** *** *** ** 푃푒푟푓푖푡−1 0.0714 0.0786 0.3117 0.3030 0.1197 0.0900 (0.0209) (0.0212) (0.0641) (0.0623) (0.0579) (0.0755)
* ** *** 푠푖푧푒푖푡 -0.0004 0.0118 0.0017 0.0211 -0.0053 0.0146 (0.0013) (0.0062) (0.0022) (0.0102) (0.0020) (0.0220)
2 * ** 푠푖푧푒푖푡 -0.0019 -0.0032 -0.0022 (0.0010) (0.0016) (0.0032)
0.0016 0.0023 -0.0088 -0.0086 0.0060 -0.0160 푙표푎푛 (0.0073) (0.0072) (0.0115) (0.0114) (0.0047) (0.0164)
0.0031 0.0021 0.0018 0.0022 -0.0019 -0.0117 푔표푣푠푢푏 (0.0073) (0.0073) (0.0118) (0.0114) (0.0047) (0.0182)
0.0019*** 0.0019*** 0.0011*** 0.0021*** 0.0017*** 푟푒푔푐푎푝 (0.0001) (0.0001) (0.0003) (0.0004) (0.0005)
0.0044 -0.0110 0.0030 -0.0188 0.0192*** -0.0137 _cons (0.0062) (0.0098) (0.0092) (0.0149) (0.0064) (0.0299)
Province YES YES YES YES YES YES
Year YES YES YES YES YES YES
Industry YES YES YES YES YES YES
AR(2) 0.134 0.143 0.900 0.917 0.251 0.183
Hansen 0.383 0.238 0.627 0.598 0.553 0.740
Obs 232506 232506 129236 129236 12784 12784
Notes: ***, **, * denote significance at 1% level, 5% level and 10% level, respectively; robust standard errors are in brackets.
17
60
50
40
30
20
10
0 2014 2015 2016 2017 2018 2019 Age≤5 5＜Age≦10 Age＞10
Figure 3 Variance of farmer membership size across government ages (2014-2019) Data source: Author’s calculation based on CCAD data.
5.4 Heterogeneity across availability of government subsidies The development of cooperatives that receive government subsidies and those that do not receive government subsidies showed serious differences and imbalances in China. In order to investigate the impact of cooperative size on performance in more depth, this paper further grouped estimates according to whether the cooperatives obtain the government’s subsidies using SYS-GMM. Our results (see Table 5) show that the cooperatives that obtain government subsidies conform to the linear function relationship model, and the estimated coefficient is about -0.0183, which is significant at the 1% level; the cooperatives that not received government subsidies conform to the quadratic function relationship model, and the coefficient of the first term and quadratic terms are both significant at the 5% level. This suggests that the size of cooperatives that receive government subsidies has a significant negative impact on its performance, while the size of cooperatives that do not obtain government subsidies has an inverted U-shaped effect on performance. It appears that cooperatives that receive government subsidies are likely to be relatively well-developed demonstration cooperatives, with relatively good operating
18 conditions, mature management experience and advanced production technology, and the optimal size boundary of the cooperative is relatively large in China. However, our results is not entirely consistent with expectations, probably due to the cooperative size with government subsidies is relatively larger than no subsidies (see Figure 4). And many of them had reached the optimal turning point, and were already in a state of diminishing returns to size. In this respect, the development model of “small and beautiful” is currently more suitable for Chinese farmer cooperatives.
Table 5 Heterogeneity of the effect of membership size on cooperative performance across subsidies Subsidies No subsidies
Linear Non-linear Linear Non- linear
0.1199* 0.1106* 0.1871*** 0.1826*** 푃푒푟푓푖푡−1 (0.0637) (0.0624) (0.0311) (0.0313)
-0.0116** 0.0032 -0.0003 0.0152** 푠푖푧푒푖푡 (0.0052) (0.0168) (0.0014) (0.0066)
-0.0021 -0.0025** 2 푠푖푧푒푖푡 (0.0026) (0.0011)
0.0098*** 0.0092*** 0.0002 0.0003 푎푔푒 (0.0031) (0.0030) (0.0003) (0.0003)
0.0457** 0.0471*** -0.0064 -0.0045 푙표푎푛 (0.0201) (0.0177) (0.0067) (0.0067)
0.0027*** 0.0026*** 0.0015*** 0.0015*** 푟푒푔푐푎푝 (0.0009) (0.0008) (0.0001) (0.0001)
0.0092 -0.0123 0.0058 -0.0117 _cons (0.0126) (0.0242) (0.0057) (0.0095)
Province YES YES YES YES
Year YES YES YES YES
Industry YES YES YES YES
AR(2) 0.247 0.253 0.831 0.809
Hansan test 0.742 0.895 0.908 0.333
Obs 14719 14719 359807 359807
19
Notes: ***, **, * denote significance at 1% level, 5% level and 10% level, respectively; robust standard errors are in brackets.
60
50
40
30
20
10
0 2014 2015 2016 2017 2018 2019 Government subsidies No government subsidies
Figure 4 Variance of farmer membership size across government subsidies (2014-2019) Data source: Author’s calculation based on CCAD data.
5.5 Heterogeneity across geographical regions Given that the development level of farmer cooperatives varies from region to region, we assess the impact of cooperative size on performance by comparing the groups in the eastern China and in the central and western China.4 There is some evidence that the two regions have varying levels of education and technology. Generally, the economic development level of the eastern region is higher than the central and western area. Table 6 reports the estimated results, which shows that there is a negative effect between the membership size and the cooperatives’ performance in the central and western China, while an inverted U-shape in the eastern China. The negative effect of
4 Considering the significant differences in the level of economic development and resource endowments across various regions in China, the 30 provinces in China are divided into two regions. The eastern region includes the province of Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Beijing, Tianjin, Fujian, Shandong, Guangdong, and Hainan. Nineteen provinces are included in the central and western region: Inner Mongolia, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Gansu, Qinghai, Shanxi, Xinjiang, Ningxia, Hunan, Henan, Hubei, Jiangxi, Shaanxi, Heilongjiang, Anhui, and Jilin. 20 cooperative size on performance in the central and western area is, perhaps, due to many of them have exceeded the boundary of optimal size. The average membership size of farmer cooperatives in the central and western China is smaller than the eastern China (see Figure 5). Findings suggest that the optimal cooperative size in the Eastern area is larger than in the central and western region. One reason why the eastern cooperatives might perform better than the central and western ones could be because the group in eastern region with a good base of resources. Especially they likely have more advanced technology and chairmen with higher education and managerial capacity, which generate competitive advantages for the cooperatives in the area, facilitate collective action and allow them to enlarge size.
Table 6 Heterogeneity of the effect of membership size on cooperative performance across areas Central and western China Eastern China
Linear Non-linear Linear Non- linear
0.1843*** 0.1909*** 0.0988* 0.0975* 푃푒푟푓푖푡−1 (0.0318) (0.0318) (0.0520) (0.0569)
-0.0063*** 0.0046 -0.0007 0.0386*** 푠푖푧푒푖푡 (0.0006) (0.0059) (0.0015) (0.0117)
-0.0012 -0.0062*** 2 푠푖푧푒푖푡 (0.0009) (0.0019)
0.0009*** 0.0005* 0.0009 0.0012 푎푔푒 (0.0002) (0.0002) (0.0008) (0.0008)
0.0047*** 0.0022 0.0049 0.0035 푙표푎푛 (0.0013) (0.0074) (0.0135) (0.0127)
0.0059*** 0.0034 -0.0087 -0.0043 푔표푣푠푢푏 (0.0013) (0.0093) (0.0056) (0.0059)
0.0019*** 0.0018*** 0.0013*** 0.0019*** 푟푒푔푐푎푝 (0.0001) (0.0001) (0.0003) (0.0003)
0.0178*** 0.0024 0.0081*** -0.0416*** _cons (0.0011) (0.0076) (0.0025) (0.0148)
21
Year YES YES YES YES
Industry YES YES YES YES
AR(2) 0.782 0.805 0.558 0.632
Hansan test 0.134 0.038 0.203 0.797
Obs 308181 308181 66345 66345
Notes: ***, **, * denote significance at 1% level, 5% level and 10% level, respectively; robust standard errors are in brackets.
50 45 40 35 30 25 20 15 10 5 0 2014 2015 2016 2017 2018 2019
Central and western China Eastern China
Figure 5 Variance of farmer membership size across geographic regions (2014-2019) Data source: Author’s calculation based on CCAD data.
6 Conclusions and policy implications Recently, there has been a surge of interests in the cooperatives. Meanwhile, further enhancing the vitality and realizing the objectives of “big and strong” are the important challenges that cooperatives faced. Thus, this paper employs the Pareto Index to map the distribution and evolution of membership size of Chinese farmer cooperatives. Furthermore, summarizing the theoretical mechanism of the relationship between organization size and performance, we examine the impact of cooperative size on its performance in China. The following results emerged from the study. (1) The average membership size of Chinese farmer cooperatives is relatively small, and has been declining over the years. (2) The membership size has a
22 significant inverted U-shaped impact on the profit per member of cooperatives, and the optimal size of cooperative is small (19 members) in practice. (3) The effects of membership size on cooperative size are heterogeneous across cooperative ages, i.e., both the group (age≤5) and the group (5＜age≤10) show a quadratic function, and optimal size of the latter (27) is larger than the former (20). Simultaneously, there is a negative impact in the group of age＞10. (4) The effect of membership size displays an inverted U-shape in the cooperatives that receive subsidies, whereas a negative impact in the cooperatives that have no subsidies. (5) The empirical results between different geographical regions demonstrate that the membership size has a negative impact on cooperative size in the central and western China, meanwhile, there is an inverted U-shape effect in the eastern China. Based on the results above, several policy implications can be drawn as follow. First, considering the distribution of cooperative size is severely distorted, the Chinese government should play a supporting role. On the one hand, the government should take the improvement of the cooperatives’ internal governance as the starting point, continuously optimize the support methods, and realize the benign interaction with the cooperatives. On the other hand, it is necessary for the government to reduce inclusive support for cooperatives appropriately, meanwhile, encourage and guide inefficient cooperative to logout or merge with dynamic cooperatives to reduce the waste of public resources and improve the allocation efficiency of resources. Second, the cooperatives ought to put great emphasis on the two opposite life mechanisms of both the “growth rigidity” (growth inertia) and the “growth experience accumulation”. And it is crucial for the cooperatives to use the growth experience fully, meanwhile, innovate constantly. Third, in the game between the government and the cooperatives, as the government currently uses the size of the cooperative as one of the criteria of issuing subsidies, this will send a signal that the larger the cooperative, the higher the probability of receiving government subsidies. To a certain extent, this induces some cooperatives to blindly expand the size without considering their own management capabilities, internal governance and technical level, ect., finally beyond the optimal size boundary, and even fall into the “scale expansion trap”. Fourth, as the resource endowments differ among cooperatives, it is ill-advised
23 to follow a unified standard of cooperative size. Face with the evidence that the membership size effect the cooperative performance, the cooperatives should expand size carefully in the future. And particularly, it is quite necessary for the cooperatives to improve internal motivation, increase dynamics, and thereby drive more farmers to increase their income. Furtherly, it must be pointed out that there are still some limitations in this paper, hereby providing directions for future research. There are a couple of limitations in this study. First, this paper has limitations in measuring the performance of cooperatives, and defines cooperative performance as the economic benefits of cooperatives, although this measurement is used by a majority of previous literature, the content of cooperative performance is relatively broad, with multiple dimensions,, and we only focus on the economic side, which may not entirely reflect the farmer cooperatives’ performance. So it is expected to be widely discussed from other dimensions in future research. Second, awaring of the significance of cooperative’s external technical development level, internal governance arrangements and cooperative manager’s capabilities, but we do not have data on this, hence, we are unable to explore their role in the effect of organization size on performance.
24
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Appendix A
4000000 100.00% 3500000 95.00% 3000000 90.00% 2500000 2000000 85.00% 1500000 80.00% 1000000 75.00% 500000 0 70.00% 2014 2015 2016 2017 2018 2019 Total number of Chinese coopperatives in official stastics Total sample in the study Percentage Figure A1 The number of Chinese cooperatives (2014-2019) Data source: China Rural Operating Management Statistical Yearbook (2014-2018), China
Rural Cooperative Economy Statistical Yearbook (2019) and CCAD data.
28