Does Participation in Aquaculture Cooperatives Increase Farmers′ Profit and Output?

Abstract

Individual aquaculture farmers in developing countries play an important role in ensuring food security. This study uses survey data from aquaculture households in Rongcheng and Xiangshan cities in China to explore the impact of cooperative participation on the benefits to the aquaculture households. The empirical results show that the participation of aquaculture farmers in cooperatives has effectively increased their net profit and output per unit area. On average, participating in cooperatives increased the net profit and output per unit area of farmers by approximately 15.55% and 11.47%, respectively. The test results of the mechanism show that the information services, technical training, and product sales guidance provided by the cooperatives have increased the net profit of the farmers. At the same time, the information services and product sales guidance provided by cooperatives are important reasons for the increase in the output per unit area.

1. Introduction

Aquaculture plays a vital role in ensuring food security worldwide. Internationally, China accounts for more than 30% of the world′s total aquatic product output, making it the world’s largest aquatic product producer. However, aquaculture in China is dominated by self-employed individual farmers. It has been argued that individual farmers are often in a disadvantageous position in the market and lack the ability to bargain in comparison with agribusiness [1,2]. In addition, farmers often face many risks in agricultural production, such as disease risks, production risks, market risks, and climate risks [3,4]. It is often difficult for individual farmers to cope with such risks. In fact, there are already some measures to reduce farmers’ risk; the key issue is how to promote the adoption of these measures by farmers [5,6]. Agricultural credit [7,8,9], agricultural extension [10,11], cooperatives [12], and other factors are considered to influence farmers’ adoption of risk prevention and control measures. Among these, cooperatives play an important role in alleviating farmers’ risks and increasing their incomes.

Agricultural cooperatives are an important force in the agricultural sector, helping farmers alleviate poverty through high production and profitability [13,14,15,16]. Cooperatives make it possible for farmers to enhance the collective action by which they can acquire a greater voice in the factor and product markets, which cannot be acquired individually [15]. Collective action can also generate economies of scale that reduce information asymmetry and transaction costs [17]. A large number of studies have empirically verified the impact of participation in cooperatives on agricultural productivity or income [18,19,20,21]. The conclusions of these studies tend to show that cooperative participation can significantly improve the productivity or income of farmers. However, the size of this positive promoting effect may be related to the scale of farmers′ production. In general, small-scale farmers gain more from participating in cooperatives than large-scale farmers [18]. In addition, some studies have examined the heterogeneity of the economic impact of cooperative participation from the perspectives of engagement in off-farm work or lack of it, household income, market proximity, education level, age, and so on [21,22].

There have been various studies on the effect of agricultural cooperatives on farm productivity, price and household welfare [22,23,24,25], farm inputs [26], and farmers’ adoption of new technologies [27] in developing countries. However, existing research focuses on crop farmers rather than aquaculture farmers. Moreover, prior studies on the effect of cooperatives on aquaculture are mostly qualitative analyses [28], which could not objectively measure the impact of cooperative participation on aquaculture farmers. Therefore, the primary objective of this study is to explore the impact of cooperative membership on farmers’ income and production. The empirical analysis of this study is based on the fish farm survey data collected from two regions in China. This paper contributes to the existing literature in two aspects. First, to the best of the authors’ knowledge, this is the first study to explore the quantitative impact of cooperative membership on aquaculture farmers’ income and production using data from China. Prior studies mostly focus on the cooperative membership effect on crop farmers or farm households. Second, this research explores the mechanism of the influence of cooperative participation on the income and output of aquaculture farmers from three dimensions: information supply, technical training, and product sales guidance.

2. Methods and Data

2.1. Model Settings

The purpose of this study is to explore the effect of cooperative participation on aquaculture farmers’ benefits. We divided these benefits into two categories: one was economic gains, and the other was yield gains. The models used to test the two types of effects are as follows:

$$lnprofi{t}_i={\beta }_0+{\beta }_{1}fishco{o}_i+{\displaystyle \sum }_{j=2}^9{\beta }_{j}Contro{l}_{ij}+{\epsilon }_i$$

(1)

$$lnoutpu{t}_i={\alpha }_0+{\alpha }_{1}fishco{o}_i+{\displaystyle \sum }_{j=2}^9{\alpha }_{j}Contro{l}_{ij}+{ϵ}_i$$

(2)

where both $lnprofi{t}_i$ and $lnprope{r}_i$ are dependent variables, which represent the net profit and output per unit area obtained by Farmer i from engaging in aquaculture, respectively. $fishco{o}_i$ is an independent variable, which represents whether Farmer i participates in a cooperative. When a farmer participates in a cooperative, it takes the value of 1. Otherwise, its value is taken as 0. When ${\beta }_1$ is greater than 0 and statistically significant, it indicates that participating in the cooperative increases the net profit of the farmer engaged in aquaculture. Similarly, when ${\alpha }_1$ is greater than 0 and statistically significant, it means that participation in a cooperative increases the output per unit area engaged in aquaculture. ${\displaystyle \sum _{j=2}^9}{\beta }_{j}Contro{l}_{ij}$ represents the control variable, including sex, nationality, educational background, number of relatives and friends, village cadre status, farming area, farming experience, family size, and age. ${\epsilon }_i$ is an i.i.d disturbance term.

2.2. Data Sources

The data in this study mainly came from questionnaires. The survey was conducted from January to June 2021. The survey samples were collected from aquaculture households in Rongcheng City, Shandong Province, and Xiangshan County, Zhejiang Province. In 2019, aquaculture output in Zhejiang and Shandong accounted for about 16.72 percent of the total national output, and aquaculture output accounted for about 15.86 percent of the total output. Although the output and output value of aquaculture in the two provinces is not high in China, the number of individual aquaculture farmers in the two provinces is at the forefront of the country. Stratified random sampling was adopted to ensure good sample coverage. The specific process was as follows: we randomly selected 5 towns (subdistrict offices) in each area, 6 villages in each town (subdistrict office), and finally 10 aquaculture households in each village. A total of 600 questionnaires were distributed in the whole data research process. Some incomplete or inconsistent data were excluded. In all, 586 valid questionnaires were returned, giving rise to a questionnaire response rate of 97.7%. The survey mainly covered farmers’ basic personal characteristics, family management, fishery cooperative organizations, breeding area, fishery factor inputs, etc.

2.3. Variables Description

The dependent variables in this study were the net profit and output per unit area of farmers engaged in aquaculture, while the independent variable was the status of the aquaculture farmer’s participation in a cooperative. The value of this independent variable was taken as 1 for participation in a cooperative and 0 for no participation in a cooperative. In this study, 39.1% of the surveyed households participated in cooperatives. As for the mechanism study, three mechanism variables, namely information service, technical training, and product sales guidance, were also considered. All three mechanism variables were graded. For example, on a 1–5 scale, information service measures how frequently the farmers participating in the cooperatives obtained information from the cooperatives. In addition, this study also added a series of control variables including sex, educational background, experience, etc., to minimize the bias of omitted variables.

Table 1. Descriptive statistics of all variables.

Variable	Description	Observation	Mean	Std.Dev	Min	Max
lnprofit	Logarithm of net profit (CNY)	586	11.341	0.420	9.656	12.222
lnoutput	Logarithm of output per unit area (kg)	586	6.322	0.098	6.168	6.533
fishcoo	Whether farmer i has participated in a cooperative (1 = participation, 0 = participation	586	0.391	0.488	0.000	1.000
inform	Frequency of information services provided by cooperatives	586	2.382	1.591	1.000	5.000
techno	Frequency of technical training provided by cooperatives	586	3.096	1.706	1.000	5.000
pro	Frequency of product sales guidance provided by cooperatives	586	3.171	1.755	1.000	5.000
Sex	1 = male, 0 = female	586	0.176	0.381	0.000	1.000
Ethnicity	1 = Han nationality, 0 = Other ethnic groups	586	0.874	0.332	0.000	1.000
Education	Years of education for farmers	586	2.826	1.480	1.000	8.000
Friends	Number of friends and relatives	586	7.360	3.428	2.000	13.000
Villcad	1 = village cadres, 0 = not village cadres	586	0.138	0.345	0.000	1.000
lnCularea	Logarithm of aquaculture area (0.01 hectares)	586	2.844	0.303	2.197	3.332
Exp	Number of years engaged in aquaculture	586	9.454	5.018	1.000	18.000
Famsize	Total household size	586	5.534	1.703	3.000	8.000
Age	Age of farmer	586	40.036	8.674	21.000	66.000

shows the descriptive statistics for each variable.

3. Results

3.1. Baseline Estimations

Table 2. Estimation results of cooperative participation benefits.

	(1)	(2)	(3)	(4)
	lnoutput	lnoutput	lnprofit	lnprofit
fishcoo	0.1339 ***	0.1555 ***	0.3328 ***	0.1147 ***
	(0.0039)	(0.0030)	(0.0328)	(0.0191)
Sex		−0.0038		0.0313
		(0.0035)		(0.0224)
Ethnicity		0.0001		−0.0393
		(0.0040)		(0.0261)
Education		0.0008		0.0425 ***
		(0.0010)		(0.0066)
Friends		0.0001		−0.0003
		(0.0004)		(0.0025)
Villcad		−0.0055		0.0494 **
		(0.0038)		(0.0247)
lnCularea		−0.1294 ***		1.0585 ***
		(0.0073)		(0.0475)
Exp		0.0010 **		−0.0025
		(0.0004)		(0.0026)
Famsize		0.0006		0.0053
		(0.0008)		(0.0050)
Age		−0.0000		−0.0003
		(0.0002)		(0.0010)
Constant	6.3910 ***	6.7367 ***	11.2114 ***	8.1964 ***
	(0.0024)	(0.0196)	(0.0205)	(0.1266)
N	586	586	586	586
Adj R2	0.67	0.84	0.15	0.76

Note: The standard errors in parentheses; *** and ** after numbers indicate that the significance level of 1% and 5% passed the significance test.

shows the estimation results of the effect of cooperative participation on farmers’ economic benefits. Columns (2) and (4) of Table 2 correspond to the regression results of Models (1) and (2), respectively. For comparison, the regression results of the control variables not considered are shown in Columns (1) and (3).

Column (1) of Table 2 shows that without adding the control variables, the estimated correlation coefficient of fishcoo was 0.1339, which was statistically significant at the 1% level. It indicates that compared with farmers who did not participate in the cooperatives, farmers participating in the farming of the cooperatives had a higher output per unit area. Column (2), with the addition of the control variables, shows that the estimated correlation coefficient of fishcoo increased to 0.1555, still statistically significant at the 1% significance level. It means that relative to farmers who did not participate in the cooperatives, the average output per unit area of farmers participating in the cooperative increased by about 15.55%. This is an estimated result with significant economic significance. According to Column (3), when the control variables were not added, the estimated correlation coefficient of fishcoo was 0.3328 and statistically significant; that is, farmers who participated in the cooperatives had higher net profits. After the control variables were considered, the estimated coefficient dropped to 0.1147, which means that participation in cooperatives increased the net profit of aquaculture households by about 11.47%, which is of significant economic significance.

3.2. Heterogeneity Test

The impact of cooperative participation on the net profit and output per unit area of aquaculture farmers may vary among farmers with different characteristics. The survey data in this study came from Rongcheng and Xiangshan in China. These two cities have significant differences in geographical location and economic development. Because of these differences, the effect of cooperative participation on the net profit and output per unit area of aquaculture farmers may also be different. For example, Xiangshan belongs to the East China Sea, while Rongcheng belongs to the Yellow Sea. Water pollution in the East China Sea is more severe than that in the Yellow Sea, which may reduce aquaculture production and product quality in the East China Sea, thus affecting economic returns Therefore, this study constructed a regional dummy variable region. When the investigated farmer belonged to Xiangshan, this variable was set to 1, otherwise, it was set to 0. The estimated results of fishcoo and region cross-product coefficients are respectively shown in Columns (1) and (2) of

Table 3. Heterogeneity test results based on region and village cadre status.

	(1)	(2)	(3)	(4)
	lnprofit	lnoutput	lnprofit	lnoutput
fishcoo	0.0989 ***	0.1600 ***	0.1204 ***	0.1550 ***
	(0.0254)	(0.0039)	(0.0205)	(0.0032)
fishcoo*region	0.0297	−0.0095 *
	(0.0349)	(0.0054)
fishcoo*Villcad			−0.0387	0.0037
			(0.0496)	(0.0077)
Sex	0.0294	−0.0038	0.0305	−0.0037
	(0.0224)	(0.0035)	(0.0224)	(0.0035)
Ethnicity	−0.0408	−0.0002	−0.0391	0.0001
	(0.0261)	(0.0040)	(0.0261)	(0.0040)
Education	0.0429 ***	0.0007	0.0423 ***	0.0008
	(0.0066)	(0.0010)	(0.0066)	(0.0010)
Friends	−0.0000	0.0001	−0.0004	0.0001
	(0.0025)	(0.0004)	(0.0025)	(0.0004)
Villcad	0.0484 *	−0.0054	0.0675 **	−0.0072
	(0.0246)	(0.0038)	(0.0339)	(0.0052)
lnCularea	1.0618 ***	−0.1295 ***	1.0590 ***	−0.1295 ***
	(0.0475)	(0.0073)	(0.0475)	(0.0074)
Exp	−0.0026	0.0010 **	−0.0026	0.0010 **
	(0.0026)	(0.0004)	(0.0026)	(0.0004)
Famsize	0.0061	0.0006	0.0053	0.0006
	(0.0051)	(0.0008)	(0.0050)	(0.0008)
Age	−0.0002	−0.0000	−0.0003	−0.0000
	(0.0010)	(0.0002)	(0.0010)	(0.0002)
Constant	8.1696 ***	6.7356 ***	8.1963 ***	6.7367 ***
	(0.1275)	(0.0197)	(0.1266)	(0.0196)
N	586	586	586	586
Adj R2	0.76	0.84	0.76	0.84

Note: The standard errors in parentheses; ***, **, and * after numbers indicate that the significance level of 1%, 5% and 10% passed the significance test.

. The results show that the coefficient of fishcoo*region was positive in Column (1) and statistically insignificant. Therefore, the effect of cooperative participation on net profit was not significantly different between Rongcheng and Xiangshan. In Column (2), the estimated coefficient of the fishcoo*region was significantly negative, which means that participation in cooperatives had a greater promotional effect on the output per unit area in Rongcheng than in Xiangshan.

According to Table 1, about 13.8% of the aquaculture farmers were village cadres. Such cadre status may enable farmers to obtain more information in the cooperatives and thus additional benefits. Therefore, this study constructed a cross-product term for cooperative participation and village cadre status and used the estimated coefficient of the cross-product term to reflect the difference between village cadre status and non-village cadre status. The estimation results are shown in Columns (3) and (4) of Table 3. The results show that the estimated coefficients of the cross-product term fishcoo*Villcad were not significant. Therefore, it can be concluded that the aquaculture farmers who are village cadres have not obtained higher net profit and output per unit area than the non-cadre aquaculture farmers.

In addition, this study also explored whether there were significant differences in the effect of cooperative participation on the net profit and output per unit area among farmers of different ages, education levels, and sexes, respectively.

Table 4. Heterogeneity test results based on age, education, and gender.

	(1)	(2)	(3)	(4)	(5)	(6)
	lnprofit	lnoutput	lnprofit	lnoutput	lnprofit	lnoutput
fishcoo	0.1107 *	0.1761 ***	0.2853 ***	0.1201 ***	0.1121 ***	0.1553 ***
	(0.0617)	(0.0095)	(0.0400)	(0.0061)	(0.0209)	(0.0032)
fishcoo*Age	0.0011	−0.0094 **
	(0.0268)	(0.0041)
fishcoo*Edu			−0.0571 ***	0.0119 ***
			(0.0118)	(0.0018)
fishcoo*Sex					0.0139	0.0015
					(0.0464)	(0.0072)
Sex	0.0295	−0.0044	0.0245	−0.0023	0.0261	−0.0043
	(0.0224)	(0.0035)	(0.0220)	(0.0033)	(0.0283)	(0.0044)
Ethnicity	−0.0418	0.0001	−0.0385	−0.0000	−0.0392	0.0002
	(0.0261)	(0.0040)	(0.0256)	(0.0039)	(0.0261)	(0.0040)
Education	0.0426 ***	0.0009	0.0735 ***	−0.0056 ***	0.0427 ***	0.0008
	(0.0066)	(0.0010)	(0.0091)	(0.0014)	(0.0066)	(0.0010)
Friends	−0.0000	0.0001	−0.0004	0.0001	−0.0003	0.0001
	(0.0025)	(0.0004)	(0.0025)	(0.0004)	(0.0025)	(0.0004)
Villcad	0.0486 **	−0.0058	0.0445 *	−0.0044	0.0497 **	−0.0054
	(0.0247)	(0.0038)	(0.0242)	(0.0037)	(0.0247)	(0.0038)
lnCularea	1.0614 ***	−0.1295 ***	1.0230 ***	−0.1221 ***	1.0584 ***	−0.1294 ***
	(0.0475)	(0.0073)	(0.0472)	(0.0072)	(0.0475)	(0.0074)
Exp	−0.0026	0.0010 **	−0.0016	0.0008 **	−0.0025	0.0010 **
	(0.0026)	(0.0004)	(0.0025)	(0.0004)	(0.0026)	(0.0004)
Famsize	0.0061	0.0006	0.0064	0.0004	0.0054	0.0006
	(0.0051)	(0.0008)	(0.0050)	(0.0008)	(0.0051)	(0.0008)
Age	−0.0002	0.0002	−0.0003	0.0000	−0.0003	−0.0000
	(0.0012)	(0.0002)	(0.0010)	(0.0001)	(0.0010)	(0.0002)
Constant	8.1687 ***	6.7266 ***	8.2050 ***	6.7349 ***	8.1964 ***	6.7367 ***
	(0.1305)	(0.0202)	(0.1242)	(0.0189)	(0.1267)	(0.0196)
N	586	586	586	586	586	586
r2_a	0.76	0.84	0.77	0.85	0.76	0.84

Note: The standard errors in parentheses; ***, **, and * after numbers indicate that the significance level of 1%, 5% and 10% passed the significance test.

shows the result of whether the economic benefits brought by cooperative participation changed with the changes in farmers’ age, education level, and sex. According to Table 4, the coefficient of the cross term between cooperative participation and age was positive and negative in Columns (1) and (2) respectively, but it was statistically significant only in the case of Column (2). It shows that there was no significant difference in the effect of cooperative participation on the net profit among farmers of different ages. Meanwhile, the older the farmers, the weaker the promotional effect of cooperative participation on output per unit area. The coefficient of the cross term between cooperative participation and education was significantly negative and positive in Columns (3) and (4), respectively. For those farmers with a higher educational level, cooperative participation had a weaker effect on net profit but a stronger effect on output per unit area. The estimated coefficients of cooperative participation and sex were both positive and statistically insignificant, indicating that there was no significant difference in the promotional effect of cooperative participation on net profit and output per unit area between male and female farmers.

3.3. Mechanism Analysis

In the above empirical results, we verified that cooperative participation can significantly increase the net profit and output per unit area of farmers engaged in aquaculture. The further question that needs to be answered is how cooperative participation brings these benefits to farmers. That is, what is the mechanism by which cooperative participation affects the farmers’ benefits? To answer this question, we designed three questions in the questionnaire, involving the frequency of information services that aquaculture farmers obtained from cooperatives, the frequency of technical training, and the frequency of product sales guidance. By examining the effect of these three variables on net profit and output per unit area, the underlying mechanism could be revealed.

Table 5. Test results of the sources of benefits from cooperative participation.

	(1)	(2)	(3)	(4)	(5)	(6)
	lnprofit	lnoutput	lnprofit	lnoutput	lnprofit	lnoutput
inform	0.0133 **	0.0292 ***
	(0.0064)	(0.0020)
techno			0.0535 ***	−0.0105
			(0.0062)	(0.0065)
pro					0.0518 ***	0.0660 ***
					(0.0059)	(0.0008)
Sex	0.0304	−0.0028	0.0220	−0.0165	0.0222	−0.0016
	(0.0230)	(0.0071)	(0.0217)	(0.0229)	(0.0217)	(0.0064)
Ethnicity	−0.0503 *	−0.0113	−0.0498 **	−0.0627 **	−0.0501 **	−0.0045
	(0.0267)	(0.0082)	(0.0251)	(0.0266)	(0.0251)	(0.0075)
Education	0.0452 ***	0.0025	0.0325 ***	0.0241 ***	0.0335 ***	0.0031
	(0.0068)	(0.0021)	(0.0066)	(0.0070)	(0.0065)	(0.0019)
Friends	−0.0002	0.0000	−0.0004	0.0012	−0.0009	0.0002
	(0.0026)	(0.0008)	(0.0024)	(0.0026)	(0.0024)	(0.0007)
Villcad	0.0541 **	−0.0016	0.0430 *	0.0280	0.0370	−0.0091
	(0.0253)	(0.0078)	(0.0239)	(0.0253)	(0.0240)	(0.0071)
lnCularea	1.1143 ***	−0.0834 ***	1.0071 ***	0.2698 ***	0.9940 ***	−0.1086 ***
	(0.0492)	(0.0152)	(0.0466)	(0.0493)	(0.0470)	(0.0136)
Exp	−0.0062 **	−0.0035 ***	−0.0067 ***	−0.0160 ***	−0.0052 **	0.0005
	(0.0026)	(0.0008)	(0.0024)	(0.0026)	(0.0024)	(0.0007)
Famsize	0.0057	0.0012	0.0053	0.0012	0.0045	−0.0003
	(0.0052)	(0.0016)	(0.0049)	(0.0052)	(0.0049)	(0.0014)
Age	0.0000	0.0004	0.0000	0.0013	−0.0001	0.0001
	(0.0010)	(0.0003)	(0.0010)	(0.0010)	(0.0010)	(0.0003)
Constant	8.0723 ***	6.6247 ***	8.2897 ***	5.9011 ***	8.3254 ***	6.8874 ***
	(0.1299)	(0.0401)	(0.1232)	(0.1302)	(0.1241)	(0.0368)
N	586	586	586	586	586	586
Adj R2	0.75	0.34	0.78	0.11	0.78	0.93

Note: The standard errors in parentheses; ***, ** and * after numbers indicate that the significance level of 1%, 5% and 10% passed the significance test.

shows the test results of the mechanism of the effect of cooperative participation on the economic benefits of aquaculture farmers. Columns (1) and (2) of Table 5 give the estimated results of the effect of information services on net profit and output per unit area, and Columns (3) and (4) give the estimated results of the effect of technical training on the net profit and output per unit area. Column (5) gives the estimated results of the effect of product sales guidance on net profit.

According to Columns (1) and (2) of Table 5, the estimated coefficients of the extent to which cooperatives provided information services were 0.0133 and 0.0292, respectively, and they were statistically significant. This means that the higher the frequency of information services provided by cooperatives, the greater the net profit and output per unit area of aquaculture. The estimated coefficients of the frequency of technical training provided by cooperatives were 0.0535 and −0.0105 respectively, but they were only statistically significant in the case of Column (3). This indicates that the higher the frequency of technical training that the farmers obtain from the cooperative, the higher the net profit. This may be because the technical training that farmers receive is mainly to improve the quality of aquatic products, and the improvement in quality has prompted them to sell at higher prices in the market, thereby obtaining more profits. According to Columns (5) and (6) of Table 5, the estimated coefficients of the information service variable were both positive and statistically significant. This indicates that the higher the frequency of product sales guidance provided by cooperatives, the higher the net profit and output per unit area. Cooperative participation improves net profits by enabling farmers to obtain more information, technical training, and product sales guidance services. At the same time, cooperative participation increases output per unit area by giving farmers access to more information and product sales guidance services.

4. Discussion

4.1. The Effect of Cooperative Participation on Farmers’ Profit and Output

The results of the baseline regression showed that farmers’ participation in cooperatives could significantly improve their net profits and output per unit area. Numerous studies on agriculture have reached similar conclusions [22,29,30,31]. The reasons behind this are well documented. First, cooperatives can promote collective action and reduce transaction costs [32,33]. For example, cooperatives can provide farmers with unified agricultural supplies, unified management, unified sales, and other services, giving full play to the advantages of economies of scale and reducing costs. Second, participation in cooperatives enhance farmers’ bargaining power in the market [34]. Third, cooperatives provide production and sales information [29]. The information comes either from word of mouth among cooperative members or from collective action. Fourth, participation in cooperatives expands political influence, enabling farmers to gain more policy support [35]. For example, in China, the Law on Specialized Farmer Cooperatives stipulates that the central and local finance departments shall allocate funds, respectively, to provide support for specialized farmer cooperatives to carry out services such as information, training, agricultural infrastructure construction, and technology extension. At the same time, professional cooperatives also receive tax benefits.

In the mechanism analysis part, this study empirically explored the mechanism behind cooperative participation on the improvement of net profit and unit area yield from three aspects: information, technical training, and product sales guidance. The results showed that the information, technical training, and product sales guidance provided by cooperatives significantly increased the net profit of farmers. Meanwhile, only the information and product sales guidance significantly improved the output per unit area, while the technical training did not significantly affect the output per unit area. The increase in the frequency of technical training services provided by cooperatives significantly improved net profits but had no significant impact on output per unit area. The reason behind this may be that the technical training provided by cooperatives focuses on improving the quality of aquatic products rather than on increasing yields. The Chinese government is vigorously promoting training on green breeding technology, which can improve the quality of aquatic products, although it cannot increase the output in the short term.

4.2. The Heterogeneity in Effect of Cooperative Participation on Farmers’ Profit and Output

Several important conclusions were drawn from the heterogeneity test. First, cooperative participation promoted the output per unit area of Rongcheng farmers more than that of Xiangshan farmers. Rongcheng, a county-level city in Shandong Province, China, has been known as the top city in aquatic product output for 39 consecutive years and won the title of “National Demonstration Zone of Healthy Aquaculture and Ecological Aquaculture” in 2021. Rongcheng outperforms Xiangshan in aquaculture and the development of aquaculture cooperatives. Second, there was no significant difference in the effect of cooperative participation on net profit and output per unit area between the sampled farmers who were village cadres and those who were not. The reason for this result may be that village leadership may bring a double effect. On the one hand, village cadre status requires them to undertake routine work in the daily operation of cooperatives, which will take up the time they invest in aquacultural activities. Thus, cooperative participation will reduce the benefits of farmers who are village cadres. On the other hand, compared with non- cadre village farmers, farmers who are village cadres can obtain more information and benefits when participating in cooperatives.

Third, the older the farmer, the smaller the profits and output benefits brought by cooperative participation. Some literature has verified that the increasing age of farmers affects the probability of their participation in cooperatives to a certain extent [30,36]. Older farmers are less able to learn than younger farmers, which prevents them from taking full advantage of the benefits offered by cooperatives. Fourth, with the improvement in farmers’ educational level, the promotional effect of cooperative participation on net profit was weakened, but the promotional effect on output per unit area was strengthened. In fact, most of China′s aquaculture cooperatives focus on the production end and offer little help to farmers at the sales end. The production end mostly depends on technical factors, which is more favorable for farmers with a higher educational level. At the sales end, some farmers have a low educational level, but they may have more breeding experience and are relatively familiar with the market. At the same time, they can obtain more market information through cooperatives to promote aquatic product sales and increase net profits. Finally, there was no significant difference between male and female farmers in the promotional effect of cooperative participation on net profit and output per unit area. This may be mainly because most aquaculture activities in China are family-based, and even if the farmers are female, their husbands are still involved. Therefore, sex did not affect the profits and output benefits brought by cooperative participation.

4.3. The Limitations and Research Prospect

There are still some issues to be solved in this study. On the one hand, when individual farmers become organized, their premium ability in the market can be enhanced, thus increasing their net profits. Due to data availability, this study was unable to explore this mechanism. At the next step, market factors will be considered in the questionnaire to improve the mechanism analysis of the effect of cooperative participation on farmers’ income. On the other hand, the heterogeneity of cooperatives was not adequately considered. For example, breeding cooperatives can be either government-driven or spontaneously organized by farmers. These two types of cooperatives have significant differences in external support, which may cause heterogeneity in the impact on members’ benefits. In future studies, the heterogeneity of cooperatives will be further considered, so as to more comprehensively examine the impact of cooperative participation on farmers’ benefits. In addition, there may be a time gap between cooperative participation and economic effects. This time gap was not considered in this study. While the existence of this time gap does not alter the results of significant economic gains from cooperative participation, it may underestimate the economic gains. In future research, we will add an option for the duration of cooperative participation in the questionnaire design to more accurately estimate the economic impact of cooperative participation.

5. Conclusions and Policy Implications

This study used survey data from aquaculture households in Rongcheng and Xiangshan, cities in China, to explore the effect of cooperative participation on aquaculture farmers’ net profit and output per unit area. Our study showed that the participation of aquaculture farmers in cooperatives significantly increased their net profit and output per unit area. On average, participating in cooperatives increased the net profit and output per unit area of farmers by approximately 15.55% and 11.47%, respectively. Cooperative participation promoted the output per unit area of Rongcheng farmers more than that of Xiangshan farmers. The older the farmers, the smaller the profits and output benefits brought by cooperative participation. With the improvement in farmers’ educational level, the promotional effect of cooperative participation on net profit was weakened, but the promotional effect on output per unit area was strengthened. Cooperative participation improved net profits by enabling farmers to obtain more information, technical training, and product sales guidance services. At the same time, cooperative participation increased output per unit area by giving farmers access to more information and product sales guidance services.

The policy implications of our results are as follows. Firstly, in the current context of rural revitalization, the Chinese government should take various policy measures to encourage farmers to participate in professional farming cooperatives, so as to help farmers increase their income. This is especially important for China’s large aquaculture provinces such as Zhejiang and Shandong. Secondly, cooperatives should increase support for older farmers, and at the same time, establish a mutual aid mechanism between experienced older farmers and well-educated young farmers, so as to give full play to their respective advantages and maximize the economic benefits. Thirdly, the government should establish a normalized assistance mechanism for cooperatives and provide support in aquaculture information, technical training, and market information, such as regularly sharing the latest aquaculture technology and market information with cooperatives and conducting regular technical training.