# The Impact of Rural Labor Force Feminizing Fluctuation on Grain Production and its Regional Differences: Evidence from China

Authors: Haifeng Wang; Guangsi Li; Yunzhi Hu
Journal: Mobile Information Systems
 (2022)

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## Abstract

Based on panel data from 2002 to 2018, this study establishes a variable coefficient model of the interaction term between the proportion of rural female labor force and the ratio of mechanization. The influence of the increase in the rural female labor force ratio on grain production is discussed. The results show that the feminization of the rural labor force has a significant negative impact on grain-planting area and proportion, leading to an adverse impact on grain planting. Conversely, in the plain, which is relatively easy to mechanize with massive production, the adverse effects of feminization of labor on grain production will be weakened. The regional analysis found that feminization of the workforce reduces the area and proportion of food acreage planted in the eastern region, machinery use completely counteracts the negative impact of labor feminization in the eastern and western regions, with a positive effect, whereas in the central region, the impact of machinery use is not significant. Therefore, in the process of increasing the proportion of rural women, we should pay attention to its adverse effects on food cultivation and regional differences and take targeted measures to stabilize the food supply.


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## Body

## 1. Introduction

With the rapid development of industrialization and urbanization in developing countries, a large number of rural laborers have moved to cities, resulting in significant changes in the structure of rural laborers. With economic development, the increase in employment opportunities and the improvement in income returns have attracted a growing number of male laborers to work in cities. Conversely, the left-behind female laborers are engaged in agricultural production, and a “male working and female farming” model has emerged [1, 2]. According to the sample survey data of 931 villages in China in 2011, the proportion of women engaged in agricultural production in rural areas was as high as 69.89% [3]. Men's opportunities and wages for working outside the home have been higher than those of women, whereas women traditionally perform housework and home care, which had led to more women engaging in agriculture. Therefore, how this condition has affected grain-planting warrants investigation. The agricultural production base in developing countries is weak, its land management is fragmented, and the level of technology application is not high. Therefore, the changes in the number or structure of the labor force will affect planting policy decisions. Questions such as whether the increase in the proportion of women engaging in agricultural labor would affect agricultural planting and whether this growth would have an impact on food supply security are issues that require attention and careful research.The feminization of the agricultural labor force (some scholars call it agricultural feminization) is an unbalanced phenomenon of the gender structure of the agricultural labor force gradually formed with the transfer of the agricultural labor force to nonagricultural industries after the implementation of the contract responsibility system with joint output in rural China. The extent of this phenomenon and its substantive impact on agricultural production are very worthy of study. From the existing literature, the research on this issue is mostly focused on the degree of feminization of agricultural labor, the causes, and the impact on the economy and society.However, the existing research on the feminization of the agricultural labor force still has the following deficiencies: first, there is no consensus on the definition of the feminization of the agricultural labor force; second, the macrodata of its distribution and degree are lack of mining and analysis; third, due to the lack of the support of the above research, most of the existing research on the causes of feminization is based on the assumption that the agricultural labor force has been feminized, which is limited to the research on the reasons why the female labor force lags behind the men in the nonagricultural transfer of agricultural labor force and ignores the research on the characteristics of the gender allocation of the labor force in agricultural production; the fourth is the research on the impact of the feminization of agricultural labor force. Although there is no lack of empirical research, most of them are empirical studies on the data of regions and local villages, which are lack of representativeness. At the same time, the research on the impact of feminization of the agricultural labor force on agricultural production is relatively rare. This paper will be committed to making some breakthroughs in the above aspects. It will analyze the regional distribution and land scale distribution of the feminization of the agricultural labor force with macro statistical data, so as to accurately judge the actual degree of the feminization of the agricultural labor force in China, and on this basis, further analyze the reasons for the formation, and finally make an empirical analysis of the impact of the feminization of agricultural labor force on agricultural production.This study has the following policy implications.The key to stabilize food production and ensure food security lies in improving production efficiency, reducing labor costs, and realizing the scale effect. First, it is important to develop agricultural machinery suitable for different terrain, especially small machinery for mountainous and hilly areas, to improve agricultural machinery subsidies, and to reduce labor costs. Second, it is essential to guide the orderly transfer of rural land and to encourage the formation of large-scale business entities, thereby family farms and large planting households could become the backbone of food production and gradually could expand the scale of operation to form a scale effect. Third, it is vital to encourage the development of socialized service organizations for food production and to promote the diversified development of service organizations, involving all aspects of grain production, such as grain production, agricultural machinery services, and formula fertilization to realize the scale effect of all aspects of grain production. Through the replacement of labor by machinery, technology and services would further reduce labor costs, improve labor production efficiency, stabilize grain production, and ensure food security.


## 2. Literature Review

Many scholars have conducted fruitful research on the influence of the change of the rural labor structure on planting production. The influence of the feminization of the labor force on agriculture can be seen from the following two perspectives: (1) the female labor force is engaged in production as an agricultural producer; and (2) the female labor force manages production as agricultural managers.


### 2.1. The Influence of the Female Labor Force as Agricultural Producers

At present, a consensus has not been reached on the research of the feminization of the rural labor force. Some scholars believe that the feminization of the agricultural labor force has not had an adverse impact. When female laborers enjoyed equal rights to use key materials in production and were not more constrained compared with male laborers, the feminization of rural labor does not necessarily contribute to the reducing production efficiency [4]. After controlling the level of human capital [5], investment [6], irrigation intensity, and crop types [7], the efficiency of the rural female labor force in agricultural production was not significantly different from that of the male labor force. If the same levels of agricultural incentives were granted to the women as to their male counterparts, there would be an increase in the overall productivity of the entire farming populace [8]. The rural women in Sub-Sahara Africa, while providing the bulk of farm labor, were discriminated against with respect to the ownership of farm resources compared with the male farmers. Neither significant differences exist in crop productivity between male farmers and their female counterparts, nor were there any significant differences in the input production elasticities [9].Other scholars hold different views, however. Kilic et al. [10] quantified the effect of the impact of gender differences on agricultural productivity and found that women are 22–37% less productive than men in agriculture and that this difference is due mostly to the innate gender difference, and this difference is magnified by child rearing, male off-farm employment, and agricultural fertilizer abuse. Owusu et al. [11] employed a met frontier approach to analyze the differences in the efficiency of male and female farmers. In China, scholars use both microdata and macrodata to conduct relevant research. In terms of microscopic data, Song et al. [12] found that the female labor force faces more difficulties in physical strength, technology, credit, and market information utilization, and their agricultural income was also low. Song and Vernooy [13] found that the female labor force is not dominant in terms of physical strength and capital acquisition, and the agricultural productivity of rural women is much lower than that of men. Cai et al. [14] used 2073 valid samples from Anhui Province to compare the gender differences of farmers’ willingness through a logistics model. Their studies have shown that women farmers’ willingness to farm is obviously lower than that of men, and their sensitivity to agricultural production costs and agricultural production methods also is considerably lower than that of men. Furthermore, in terms of macro data, Yang et al. [15] studied the influence of the female labor force on planting and found that areas with a higher proportion of female labor force would reduce food yield instead of planting cash crops with higher economic benefits.


### 2.2. The Influence of Female Labor Force as Agricultural Managers

Scholars also have paid attention to whether female-dominated families have the same agricultural production efficiency as male-dominated families. After men migrate to nonagricultural industries, women will dominate decision-making on farmland management [16]. Women cannot allocate too much time to agricultural land management because of the heavy family affairs and responsibilities [17]. Because of the unequal treatment of women in marriage, inheritance, and collective distribution, women usually do not have equal ownership of land rights and other means of production [18]. Women may be marginalized in the land leasing market [19]. A significant difference exists in agricultural efficiency between men and women when the manager of cultivated land is a woman [20]. In Niger, on average, plots managed by women produce 19 percent less per hectare than plots managed by men [12] The farmland run by women in rural China, however, is as efficient as that run by men, which mainly due to the highly competitive and efficient market and the wide application of modern agricultural technology and mechanization [21].In summary, a wealth of literature has conducted the exploration of the impact of labor force shifts and structural changes on food production, but some deficiencies remain. First, some literature has studied the impact of the feminization of the rural labor force on food production, but the empirical analysis is limited and consistent conclusions are lacking. Second, the existing literature discusses the impact of the feminization of rural labor on grain production but has ignored topographic conditions, which cannot be generalized given China’s vast territory and variable topography. Third, the existing literature has considered the impact of feminization of rural labor on grain production at the national level without taking into account of regional differences.The contributions of this study are as follows: first, based on the provincial panel data, we analyze the influence of feminization of the labor force on food planting from a macro perspective and discuss the weakening effect of agricultural mechanization on gender differences of the labor force in combination with topographic conditions; and, second, we analyze whether regional differences exist between the influence of feminization of labor force on grain planting and the weakening effect of mechanization.


## 2.1. The Influence of the Female Labor Force as Agricultural Producers

At present, a consensus has not been reached on the research of the feminization of the rural labor force. Some scholars believe that the feminization of the agricultural labor force has not had an adverse impact. When female laborers enjoyed equal rights to use key materials in production and were not more constrained compared with male laborers, the feminization of rural labor does not necessarily contribute to the reducing production efficiency [4]. After controlling the level of human capital [5], investment [6], irrigation intensity, and crop types [7], the efficiency of the rural female labor force in agricultural production was not significantly different from that of the male labor force. If the same levels of agricultural incentives were granted to the women as to their male counterparts, there would be an increase in the overall productivity of the entire farming populace [8]. The rural women in Sub-Sahara Africa, while providing the bulk of farm labor, were discriminated against with respect to the ownership of farm resources compared with the male farmers. Neither significant differences exist in crop productivity between male farmers and their female counterparts, nor were there any significant differences in the input production elasticities [9].Other scholars hold different views, however. Kilic et al. [10] quantified the effect of the impact of gender differences on agricultural productivity and found that women are 22–37% less productive than men in agriculture and that this difference is due mostly to the innate gender difference, and this difference is magnified by child rearing, male off-farm employment, and agricultural fertilizer abuse. Owusu et al. [11] employed a met frontier approach to analyze the differences in the efficiency of male and female farmers. In China, scholars use both microdata and macrodata to conduct relevant research. In terms of microscopic data, Song et al. [12] found that the female labor force faces more difficulties in physical strength, technology, credit, and market information utilization, and their agricultural income was also low. Song and Vernooy [13] found that the female labor force is not dominant in terms of physical strength and capital acquisition, and the agricultural productivity of rural women is much lower than that of men. Cai et al. [14] used 2073 valid samples from Anhui Province to compare the gender differences of farmers’ willingness through a logistics model. Their studies have shown that women farmers’ willingness to farm is obviously lower than that of men, and their sensitivity to agricultural production costs and agricultural production methods also is considerably lower than that of men. Furthermore, in terms of macro data, Yang et al. [15] studied the influence of the female labor force on planting and found that areas with a higher proportion of female labor force would reduce food yield instead of planting cash crops with higher economic benefits.


## 2.2. The Influence of Female Labor Force as Agricultural Managers

Scholars also have paid attention to whether female-dominated families have the same agricultural production efficiency as male-dominated families. After men migrate to nonagricultural industries, women will dominate decision-making on farmland management [16]. Women cannot allocate too much time to agricultural land management because of the heavy family affairs and responsibilities [17]. Because of the unequal treatment of women in marriage, inheritance, and collective distribution, women usually do not have equal ownership of land rights and other means of production [18]. Women may be marginalized in the land leasing market [19]. A significant difference exists in agricultural efficiency between men and women when the manager of cultivated land is a woman [20]. In Niger, on average, plots managed by women produce 19 percent less per hectare than plots managed by men [12] The farmland run by women in rural China, however, is as efficient as that run by men, which mainly due to the highly competitive and efficient market and the wide application of modern agricultural technology and mechanization [21].In summary, a wealth of literature has conducted the exploration of the impact of labor force shifts and structural changes on food production, but some deficiencies remain. First, some literature has studied the impact of the feminization of the rural labor force on food production, but the empirical analysis is limited and consistent conclusions are lacking. Second, the existing literature discusses the impact of the feminization of rural labor on grain production but has ignored topographic conditions, which cannot be generalized given China’s vast territory and variable topography. Third, the existing literature has considered the impact of feminization of rural labor on grain production at the national level without taking into account of regional differences.The contributions of this study are as follows: first, based on the provincial panel data, we analyze the influence of feminization of the labor force on food planting from a macro perspective and discuss the weakening effect of agricultural mechanization on gender differences of the labor force in combination with topographic conditions; and, second, we analyze whether regional differences exist between the influence of feminization of labor force on grain planting and the weakening effect of mechanization.


## 3. Theoretical Foundations

According to Lewis’s dual economic structure theory, against the background of urbanization, the labor force shifts from agriculture to industry and from rural to urban. In terms of labor resource allocation, farmers are rational, and they maximize their own utility value through optimal allocation of resources, and labor factors will flow from sectors with low marginal productivity to sectors with high marginal productivity. The marginal productivity of the urban industrial sector is higher than that of agriculture, which will lead to the transfer of rural labor to the industrial sector until the wages of rural labor gradually align with those of urban labor.According to the Todaro model, as shown in Figure1, the abscissa represents the number of labors, and Oc and Ov denote the origin of urban and rural labors, respectively. The vertical coordinate represents regional wages, with the left side being urban wages and the right side being rural wages. Curves A and B are the demand curves of the urban and rural labor force, respectively. AA0 is the demand curve of the urban formal sector labor, and AA1 is the demand curve of the urban informal sector labor. The transfer of rural labor depends mainly on the expected wage difference between urban and rural areas. On the basis of this flow principle, the flow of rural labor is divided into two stages: the first stage is the transfer of rural surplus labor. A0A1 and curve B intersect at equilibrium point C, and the equilibrium wage level is U1=R1, the number of the urban labor force is OcL1, and the number of the rural labor force is OvL1. Because of the technological progress and planting efficiency, there is a large surplus of rural labor, and the significant difference between urban and rural wages attracts rural laborers to work in cities. In addition, the migrant workers in cities mainly are men, whereas women stay in rural areas and take up planting tasks. Men work in leisure times, the farm in busy times, and maintain a status of half work and half farming; therefore, the proportion of women in the agricultural labor force has increased. The second stage is the migration of the effective rural labor force. With the rapid development of urbanization, the demand for urban labor has risen, and the demand curve of urban labor has risen from A0A1 to A0A2, intersecting with curve B at equilibrium point D, with a balanced wage level of U2=R2. The amount of urban labor is OcL2, and the amount of rural labor is OvL2.Figure 1
Rural labor migration and changes of agricultural planters.As shown in Figure2, the abscissa indicates the grain-planting area, and the ordinate indicates the output and cost of agricultural production. Curve OC1 represents the initial production function of the female labor force, and curve OP represents the cost function. When the marginal cost and the marginal benefit are equal, farmers have the maximum output. That is, the parallel line P1 of the cost function OP and the initial production curve OC1 are tangent to point A, and the planting area is S1. In plain areas, machinery use instead of labor improves the food production efficiency of the female labor force, and the curve OC1 rises to OC2. Curve OC2 is tangent to the parallel line P2 of the cost function OP at points B, and the planting area is S2>S1. In areas suitable for planting high value-added agricultural products, farmers choose cash crops that generate a higher income, require less physical demand, and take a longer time to replace food crops. Because of the variety of planting products, the food production efficiency of the female labor force decreased, and the curve OC1 dropped to OC3. Curve OC3 is tangent to the parallel line P3 of the cost function OP at points C, and the grain-planting area is S3<S1.Figure 2
Production decision diagram of feminization of rural labor force.In summary, the female labor force is engaged in agricultural planting and production, but compared with the male labor force, the female labor force is weaker in physical strength and more dispersed in energy. As a result, they may have more difficulties devoting themselves to the farming process. The feminization of the agricultural labor force will result in two choices (Figure3): first, the structural adjustment of factors and the adoption of machinery to replace the labor force to alleviate the shortage of labor force. Second, in the adjustment of product structure, cash crops that generate a higher income and require less physical demand but that are more time-consuming will be selected to replace food crops to reduce the grain-planting area and increase the planting of cash crops.Figure 3
Theoretical analysis chart of the influence of feminization of labor force on grain production.


## 4. Empirical Model

### 4.1. Model Specification

In this section, we set up the following model:(1)Yit=a0+a1Xit+a2Pit+a3Wit+a4Fit+a5PMit+a6INDit+a7IRit+uit,with(2)uit=vi+εit,where Yit is the sown area of grain or the proportion of the sown area of grain to the total sown area of crops in province i during time period t; Xit is the ratio of the rural female labor force in province i during time period t; Pit denotes the price of grain, which is equal to the average price of rice, wheat, and corn; Wit denotes labor cost, which is the daily wage of the rural labor force; and Fit denotes food per capita, which directly reflects the status of grain production in each province. The higher the per capita grain possession, the more important the grain production in this area. PMit denotes total power of agricultural machinery. In areas with a higher total power of agricultural machinery and richer cultivated land resources, grain production efficiency is also higher, and local farmers are more inclined to expand the grain-planting area. INDit denotes the degree of industrialization, measured by the proportion of industry and service industry in the regional gross domestic product (GDP), and this index can reflect the differences of economic development in various regions to a certain extent. IRit denotes an irrigated area, which measures the level of rural grassroots construction facilities; uit is an error term, where the two components of this error term, the unobserved time-invariant heterogeneity vi∼IID0,δv2 and the idiosyncratic portion εit∼IID0,δε2, are assumed to be independent of one another and among themselves.On the basis of this formula, the interaction term of the female proportion and the mechanized proportion is introduced, and the construction model is as follows:(3)Yit=a0+a1Xit+a2XitRit+a3Rit+a4Pit+a5Wit+a6Fit+a7PMit+a8INDit+a9IRit+uit,with(4)uit=vi+εit,where Rit denotes mechanical convenience, which is measured by the ratio of the tractor-ploughed area to the total cultivated area and represents the topographic conditions [22]. In addition, to reduce the fluctuation range of variance and residuals, every real variable is in logarithmic form.For panel-based models, either the fixed effects model (FE) or the random effects model (RE) can be selected for parameter estimation. These two methods each have advantages and disadvantages. In general, the fixed effects model is helpful to control endogenous problems. When the unobservable factors in the model are related to explanatory variables, the random effect estimator is biased, and the fixed effects estimator is more advantageous. When the unobservables in the model are independent of the explanatory variables, both fixed effects and random effects models yield consistent estimates, but the latter uses a combination of within-group dynamic information and between-group cross-sectional heterogeneity information in the parameter estimation, and thus its parameter estimation is more efficient.


### 4.2. Data Sources and Descriptive Statistics

The data used in this study are panel data consisting of relevant data from 26 provinces and cities in China for the calendar years 2002–2018. The data are obtained from the official statistical yearbooks, including the China Rural Statistical Yearbook, the China Statistical Yearbook, the National Agricultural Product Cost and Benefit Information Compilation, the China Agricultural Machinery Industry Yearbook, and the China Population and Employment Statistical Yearbook. The results of the descriptive statistical analysis of each variable are shown in Table1.Table 1
Descriptive statistics for the total sample, 2002–2018,N = 442.
VariableSymbolMeanSDMinimumMaximumGrain sown area (1000 Ha)Yit4220.522757.64282.4814283.1Ratio female workforce (%)Xit49.650.9946.5752.86Price of grain (RMB)Pit72.6514.5531.59104.41Labor cost (RMB)Wit51.2027.897.5126.82Ratio of mechanization (%)Rit55.9123.883.6098.85Food per capita (kg)Fit468.44315.90101.511992.36Power of machinery (MW)PMit32886.227300.02102.3133530Industrialization (%)INDit87.415.4265.496.6Irrigated area (1000 Ha)IRit2286.261445.81168.36119.57Ratio mechanization (%)Rit55.9123.883.6098.85Sources: authors’ calculations based on data provided by the official statistical yearbook.From 2002 to 2018, the female ratio of the rural labor force in China showed an upward trend. The proportion of women in rural labor increased from 49.41% in 2002 to 50.83% in 2018 (Figure4). To reduce the fluctuations and observe the long-term trend, the data are processed and centralized in a three-year cycle. Clearly, the proportion of female rural laborers continues to increase, which is also consistent with the year-on-year growth of migrant workers in China.Figure 4
Trend chart of rural female labor force ratio in China from 2002 to 2018.The area under grain cultivation in China has increased steadily from 103,890,000 hectares in 2002 to 117,038,000 hectares in 2018 (Figure5). According to the data of the China Statistical Yearbook of previous years, from 2002, this area decreased slightly to 99,410,000 hectares in 2003 and then increased to 119,230,000 hectares in 2016, which was an increase of 19.94%, with an average annual growth of 1.42%. In 2018, this area decreased slightly to 117,038,000 hectares. Overall, from 2002 to 2018, the grain planted area increased by 12.66%, with an annual growth rate of 0.74%.Figure 5
Trend chart of china’s grain sown area and ratio from 2002 to 2018.


## 4.1. Model Specification

In this section, we set up the following model:(1)Yit=a0+a1Xit+a2Pit+a3Wit+a4Fit+a5PMit+a6INDit+a7IRit+uit,with(2)uit=vi+εit,where Yit is the sown area of grain or the proportion of the sown area of grain to the total sown area of crops in province i during time period t; Xit is the ratio of the rural female labor force in province i during time period t; Pit denotes the price of grain, which is equal to the average price of rice, wheat, and corn; Wit denotes labor cost, which is the daily wage of the rural labor force; and Fit denotes food per capita, which directly reflects the status of grain production in each province. The higher the per capita grain possession, the more important the grain production in this area. PMit denotes total power of agricultural machinery. In areas with a higher total power of agricultural machinery and richer cultivated land resources, grain production efficiency is also higher, and local farmers are more inclined to expand the grain-planting area. INDit denotes the degree of industrialization, measured by the proportion of industry and service industry in the regional gross domestic product (GDP), and this index can reflect the differences of economic development in various regions to a certain extent. IRit denotes an irrigated area, which measures the level of rural grassroots construction facilities; uit is an error term, where the two components of this error term, the unobserved time-invariant heterogeneity vi∼IID0,δv2 and the idiosyncratic portion εit∼IID0,δε2, are assumed to be independent of one another and among themselves.On the basis of this formula, the interaction term of the female proportion and the mechanized proportion is introduced, and the construction model is as follows:(3)Yit=a0+a1Xit+a2XitRit+a3Rit+a4Pit+a5Wit+a6Fit+a7PMit+a8INDit+a9IRit+uit,with(4)uit=vi+εit,where Rit denotes mechanical convenience, which is measured by the ratio of the tractor-ploughed area to the total cultivated area and represents the topographic conditions [22]. In addition, to reduce the fluctuation range of variance and residuals, every real variable is in logarithmic form.For panel-based models, either the fixed effects model (FE) or the random effects model (RE) can be selected for parameter estimation. These two methods each have advantages and disadvantages. In general, the fixed effects model is helpful to control endogenous problems. When the unobservable factors in the model are related to explanatory variables, the random effect estimator is biased, and the fixed effects estimator is more advantageous. When the unobservables in the model are independent of the explanatory variables, both fixed effects and random effects models yield consistent estimates, but the latter uses a combination of within-group dynamic information and between-group cross-sectional heterogeneity information in the parameter estimation, and thus its parameter estimation is more efficient.


## 4.2. Data Sources and Descriptive Statistics

The data used in this study are panel data consisting of relevant data from 26 provinces and cities in China for the calendar years 2002–2018. The data are obtained from the official statistical yearbooks, including the China Rural Statistical Yearbook, the China Statistical Yearbook, the National Agricultural Product Cost and Benefit Information Compilation, the China Agricultural Machinery Industry Yearbook, and the China Population and Employment Statistical Yearbook. The results of the descriptive statistical analysis of each variable are shown in Table1.Table 1
Descriptive statistics for the total sample, 2002–2018,N = 442.
VariableSymbolMeanSDMinimumMaximumGrain sown area (1000 Ha)Yit4220.522757.64282.4814283.1Ratio female workforce (%)Xit49.650.9946.5752.86Price of grain (RMB)Pit72.6514.5531.59104.41Labor cost (RMB)Wit51.2027.897.5126.82Ratio of mechanization (%)Rit55.9123.883.6098.85Food per capita (kg)Fit468.44315.90101.511992.36Power of machinery (MW)PMit32886.227300.02102.3133530Industrialization (%)INDit87.415.4265.496.6Irrigated area (1000 Ha)IRit2286.261445.81168.36119.57Ratio mechanization (%)Rit55.9123.883.6098.85Sources: authors’ calculations based on data provided by the official statistical yearbook.From 2002 to 2018, the female ratio of the rural labor force in China showed an upward trend. The proportion of women in rural labor increased from 49.41% in 2002 to 50.83% in 2018 (Figure4). To reduce the fluctuations and observe the long-term trend, the data are processed and centralized in a three-year cycle. Clearly, the proportion of female rural laborers continues to increase, which is also consistent with the year-on-year growth of migrant workers in China.Figure 4
Trend chart of rural female labor force ratio in China from 2002 to 2018.The area under grain cultivation in China has increased steadily from 103,890,000 hectares in 2002 to 117,038,000 hectares in 2018 (Figure5). According to the data of the China Statistical Yearbook of previous years, from 2002, this area decreased slightly to 99,410,000 hectares in 2003 and then increased to 119,230,000 hectares in 2016, which was an increase of 19.94%, with an average annual growth of 1.42%. In 2018, this area decreased slightly to 117,038,000 hectares. Overall, from 2002 to 2018, the grain planted area increased by 12.66%, with an annual growth rate of 0.74%.Figure 5
Trend chart of china’s grain sown area and ratio from 2002 to 2018.


## 5. Results and Discussion

### 5.1. Estimation of the Basic Model

As shown in Table2, the feminization of the labor force has a significant negative impact on the grain-planting area, which indicates that the increase in the feminization of the labor force will lead to a decrease in the grain area. There are two reasons for this. First, compared with the male labor force, the female’s physical strength tends to be weaker, and the energy women expend on planting is less than the energy men expend, which is not conducive to the grain-planting area. Second, female laborers may select less physically demanding and more profitable cash crops as food substitutes and thus increase cash crop cultivation for higher profits.Table 2
Basic model estimation results.
ln grain sown areaRatio grain sown areaFE (1)RE (1)FE (2)RE (2)Xit−0.016∗∗ (−2.14)−0.016∗∗ (−2.16)−0.449 (−0.96)−0.390 (−0.80)Rit0.001 (1.26)0.001∗ (1.81)0.028 (0.90)0.043∗ (1.52)lnPit0.010 (0.26)0.012 (0.30)3.239∗ (1.59)3.330∗ (1.79)lnWit−0.044∗∗∗ (−3.06)−0.053∗∗∗ (−3.75)1.100 (1.13)0.312 (0.35)Fit0.675∗∗∗ (12.84)0.665∗∗∗ (11.63)17.09∗∗∗ (6.88)16.62∗∗∗ (7.05)lnPMit0.037 (1.16)0.049 (1.46)−6.654∗∗∗ (−2.84)−5.635∗∗∗ (−2.78)INDit−0.002 (−0.45)−0.001 (−0.20)0.017 (0.09)0.158 (1.02)lnIRit−0.020 (−0.29)<0.032 (0.56)−2.301 (−0.72)0.104 (0.04)Constant5.023∗∗∗ (8.59)4.547∗∗∗ (8.78)38.67 (1.50)3.610 (0.16)Observations442442442442Number of province26262626Note. Numbers in parentheses are t statistics. Asterisks refer to p values: ∗∗∗p<0.01, p∗∗<0.05, and p∗<0.1.The price of food has a positive effect on the area of crop grown and its proportion. According to economist Schultz, farmers are rational and optimize the allocation of resources to obtain maximum benefits. The supply of food is an important manifestation of rational production, and farmers constantly adjust their decisions based on expected returns, which are based on the market price of food and the cost of cultivation. Food prices positively influence the area of plant cultivation and the proportion of cropland. The higher the food price is, the more farmers expand the area for grain cultivation to pursue more profits. Because of the limited land resources, the expansion of grain cultivation area inevitably will reduce the cultivation area of other crops, resulting in an increase in the proportion of grain cultivation area.Labor costs have a negative effect on the area and its share of food cultivation, suggesting that as labor prices rise, the area and share of food cultivation in China will be reduced. A possible economic explanation lies in the fact that relative factor prices are the key determinant of product structure. To maximize profits, farmers can choose only factor substitution or product substitution. The former does not change the structure of agricultural cultivation, but rather optimizes the combination of production factors to reduce the cost of food production in accordance with the change of relative factor prices. The latter, however, will directly affect the structure of agricultural cultivation. To balance the increase of food production costs, farmers will invest in land, capital, and other agricultural products and also will increase the area planted with cash crops to obtain higher prices and income, which is not conducive to food production.The influence of other variables on the grain-planting area and the proportion of the grain-planting area is basically in line with economic theory. The per capita grain output has a significant positive impact on the grain-planting area and its proportion. The more important the position of grain production is, for example, in the main grain-producing areas, the greater the grain-planting area and its proportion. In addition, the total mechanical power and mechanical convenience both have a positive impact on crop planting. The greater the total mechanical power, the easier it is to work in the area and the larger the grain-planting area. With economic development, it is possible that a large number of machines are developed and put into use. Therefore, it is easier for agricultural producers in the plain areas, where mechanization is relatively easy, to adjust the cropping structure toward food crops and to reduce production costs through factor substitution and large-scale operation to obtain greater returns. Because of the scarcity of land resources, it also is possible that a large amount of land is used for industrial development, which definitely will reduce the grain-planting area and also reduce the cash crop-planting area. The general cash crops, such as vegetables, are planted near the suburbs of cities, so the land-crowding effect is more obvious than the planting and rural food. Thus, the proportion of food cultivation shows a positive effect.


### 5.2. Estimation of Model with Cross-Term

To study the superposition effect of the feminization of the labor force and the use of machinery, the cross-term of both is introduced. According to the regression results (Table3), we found that the intersection of female proportion and mechanization convenience has a positive impact on grain-planting area and planting area proportion. This result shows that in areas where agricultural production mechanization is easier after the proportion of female rural labor force rises, agricultural producers will replace factors and adopt agricultural machinery to replace the labor force; in contrast, the family labor force will choose to maintain or even expand the area and proportion of grain sown to reduce production costs through a large-scale operation. This conclusion is basically in line with reality. In plain areas where mechanization is relatively easy, agricultural producers are more likely to adjust their planting structure toward food crops and to reduce production costs through factor substitution and scale operations to offset the negative impact of the feminization of the rural labor force on food production. In mountainous and hilly areas where mechanization is more difficult; however, it is challenging for machinery to replace labor to alleviate the impact of the labor shortage.Table 3
Estimate result of a model with cross-term.
ln grain sown areaRatio grain sown areaFE (1)RE (1)FE (2)RE (2)Xit−0.041∗∗∗ (−3.35)−0.041∗∗∗ (−3.27)−2.360∗∗∗ (−2.79)−2.343∗∗∗ (−2.70)Xit×Rit0.041∗∗ (2.09)0.042∗∗ (2.02)3.125∗∗∗ (3.32)3.191∗∗∗ (3.16)Rit0.021∗∗ (2.20)0.022∗∗ (2.13)1.580∗∗∗ (3.38)1.625∗∗∗ (3.25)lnPit0.013 (0.35)0.015 (0.40)3.469∗ (1.70)3.608∗ (1.95)lnWit−0.046∗∗∗ (−3.28)−0.054∗∗∗ (−3.87)0.984 (1.01)0.215 (0.23)Fit0.667∗∗∗ (13.49)0.657∗∗∗ (12.47)16.46∗∗∗ (7.37)16.15∗∗∗ (7.85)lnPMit0.041 (1.33)0.052∗ (1.60)−6.339∗∗∗ (−2.76)−5.469∗∗∗ (−2.74)INDit−0.002 (−0.42)−0.001 (−0.15)0.040 (0.24)0.173 (1.25)lnIRit−0.020 (−0.30)0.029 (0.51)−2.257 (−0.85)0.082 (0.04)Constant6.235∗∗∗ (7.29)5.824∗∗∗ (7.55)131.8∗∗∗ (3.45)99.93∗∗∗ (2.59)Observations442442442442Number of province26262626Note. Numbers in parentheses are t statistics. Asterisks refer to p-values: ∗∗∗p<0.01, p∗∗<0.05, and p∗<0.1.


### 5.3. Robustness Checks

To check the robustness of the results, we removed about a quarter of the provinces and selected the top 20 provinces in China's plain area for regression analysis. The regression results are shown in Table4, and both the significance and sign of the explanatory variables are relatively close to the results in Tables 2 and 3, indicating that the model regression results are relatively robust.Table 4
Estimate result of the robustness test.
ln grain sown areaRatio grain sown areaFE (1)RE (1)FE (2)RE (2)Xit−0.0413∗∗∗ (−4.23)−0.0418∗∗∗ (−3.81)−2.572∗∗∗ (−4.33)−2.448∗∗∗ (−3.66)Xit×Rit0.0474∗∗ (2.48)0.0482∗∗ (2.34)3.730∗∗∗ (5.32)3.843∗∗∗ (4.01)Rit0.0239∗∗ (2.58)0.0245∗∗ (2.44)1.877∗∗∗ (5.32)1.949∗∗∗ (4.10)lnPit0.0345 (0.80)0.0386 (0.90)4.148∗ (1.74)5.106∗∗∗ (2.69)lnWit−0.0419∗∗∗ (−2.95)−0.0510∗∗∗ (−3.65)1.709 (1.72)−0.216 (−0.22)Fit0.647∗∗∗ (12.57)0.644∗∗∗ (11.43)14.81∗∗∗ (6.76)15.87∗∗∗ (7.20)lnPMit0.0247 (0.75)0.0320 (0.95)−5.355∗∗ (−2.51)−4.229∗∗ (−2.11)INDit0.001 (0.10)0.002 (0.49)−0.033 (−0.20)0.330∗∗ (2.33)lnIRit−0.003 (−0.04)0.037 (0.63)−4.070 (−1.71)−1.232 (−0.45)Constant6.158∗∗∗ (6.80)5.739∗∗∗ (7.88)159.8∗∗∗ (5.67)89.17∗∗∗ (2.97)Observations340340340340Number of provinces20202020Note. Numbers in parentheses are t statistics. Asterisks refer to p-values: ∗∗∗p<0.01, p∗∗<0.05, and p∗<0.1.


### 5.4. Regional Analysis

The main grain production areas in China are divided into three parts: first, Eastern China, which includes Liaoning, Jilin, Heilongjiang, Hebei, Shandong, Jiangsu, Zhejiang, Fujian, Guangdong, and Hainan provinces; second, the Central China, which includes Shanxi, Henan, Hubei, Hunan, Jiangxi, and Anhui provinces; and, third, Western China, which includes Chongqing, Sichuan, Guizhou, Yunnan, Guangxi, Shaanxi, Gansu, Ningxia, Inner Mongolia, and Xinjiang.According to the noted division, we further analyzed the influence of the increasing female ratio in Eastern China, Central China, and Western China on the sown area and its proportion of grain. In this part, we conducted a Hausman test on the model to select a relatively suitable estimation method for quantitative analysis.As shown in Table5, the substitution effect of machinery on the labor force varies across regions. In the eastern region, an increase in the proportion of females decreases the area and proportion of grain cultivation. When combined with the convenience of machinery, both the area and proportion of grain cultivation are significantly increased. Moreover, in the eastern regions, because of the flat terrain and extensive use of machinery, the substitution effect of factors is greater than the debilitating nature of the female labor force, making the area and proportion of grain cultivation increase instead of decrease. For example, Heilongjiang, Jiangsu, and Shandong are located in the eastern regions, and the terrain is mainly plain with relatively flat arable land. Thus, when the rural labor force is feminized, the mechanical replacement of labor can be more smoothly realized, and agricultural producers may devote more resources to food production, thus weakening the negative impact of the rising proportion of female rural labor force. For example, during the period from 2001 to 2016, the sown areas of grain in the three provinces increased from 8,291,200 hectares, 4,822,600 hectares, and 6,912,600 hectares to 14,214,500 hectares, 5,475,900 hectares, and 8,404,800 hectares, respectively.Table 5
Regional analysis results.
ln grain sown areaRatio grain sown areaEastern ChinaCentral ChinaWestern ChinaEastern ChinaCentral ChinaWestern ChinaXit−0.037∗ (−1.86)−0.016 (−1.18)−0.060∗∗ (−1.79)−3.950∗∗∗(−2.36)−1.072 (−1.25)−2.015 (−1.07)Xit×Rit0.039∗ (2.07)0.020 (1.02)0.0782∗ (1.69)3.169∗ (1.89)2.405∗ (1.54)2.835 (1.00)Rit0.019∗ (1.93)0.011 (1.13)0.040∗ (1.77)1.575∗ (1.89)1.246∗ (1.58)1.454 (1.06)lnPit−0.015 (−0.36)0.018 (0.36)0.044 (0.75)5.816∗ (1.53)−4.980 (−1.79)7.419∗ (1.85)lnWit−0.018 (−1.24)0.006 (0.25)−0.064∗∗∗ (−4.10)−1.165 (−0.61)3.553∗ (2.51)−2.980∗∗ (−2.28)Fit0.710∗∗∗ (14.60)0.225∗ (2.13)0.635∗∗∗ (13.39)18.230∗∗∗ (12.99)7.597 (0.95)21.69∗∗∗ (3.80)lnPMit0.006 (0.09)0.035 (1.25)0.128∗ (1.79)−5.104 (−1.57)<0.767 (0.73)−6.584 (−1.67)INDit−0.008∗ (−1.83)0.009∗∗∗ (5.52)-0.013∗∗∗ (−3.67)0.662∗∗∗ (2.73)−0.113 (−0.53)0.255 (0.85)lnIRit−0.163∗∗ (−2.48)0.161∗ (1.80)0.205∗ (1.68)4.687 (1.32)12.910∗∗ (2.95)−5.652 (−1.39)Constant5.591∗∗ (3.30)−0.367 (−0.83)5.885∗∗∗ (5.32)84.18 (1.38)−12.23 (−0.18)85.08 (0.91)Observations170102170170102170Number of province1061010610Note. Numbers in parentheses are t statistics. Asterisks refer to p-values: ∗∗∗p<0.01, p∗∗<0.05, and p∗<0.1.In the central regions, the feminization of the labor force and the cross-term of both do not significantly affect the grain-planting area, but the cross-term significantly and positively affects the proportion of the grain-planting area. This result indicates that the feminization of the labor force, including the superposition effect of the two factors will not lead to a decrease in the grain-planting area, but it will lead to a decrease in the cash crop-planting area and will make the proportion of area under grain cultivation higher. The possible reason for this may be that the central region belongs to the traditional grain-growing region, and its economy is not as developed as that of the eastern region. Thus, the demand for cash crops, such as vegetables, is not as high, and the planting takes longer. As a result, farmers prefer to grow grain. During the period from 2002 to 2018, the sown areas of peanuts, cotton, and melons in Shanxi Province decreased from 18,800 hectares, 72,300 hectares, and 46,300 hectares to 5,400 hectares, 2,300 hectares, and 16,200 hectares, respectively; and the sown areas of peanuts, cotton, and melons in Anhui Province decreased from 284,600 hectares, 321,200 hectares, and 211,400 hectares to 144,200 hectares, 86,300 hectares, and 8,000 hectares, respectively [22–24].In the western region, the proportion of females has a significant and negative impact on the grain-planting area, whereas the cross-term has a positive impact on the grain-planting area, but both do not have a significant impact on the proportion of the grain-planting area. This result shows that after the proportion of the female labor force increased in the western region, the area planted with grain and cash crops decreased at the same time. Therefore, the proportion of planted area did not change significantly. After the use of machinery, however, the negative effect of the feminization of the labor force is weakened, increasing both the grain-planting area and the cash crop area. Because Sichuan and Guizhou are typical labor-exporting cities, a large number of male laborers go out to work, leaving women to engage in planting, and the loss of effective labor is unfavorable to agricultural cultivation. Because of the massive use of machinery, the originally barren land in the west was developed and planted, which increased the planting area. During 2002–2018, the sown areas of grain, cotton, vegetables, and fruits in Xinjiang increased from 1,514,600 hectares, 943,900 hectares, 164,200 hectares, and 83,000 hectares to 2,219,600 hectares, 2,491,300 hectares, 273,300 hectares, and 111,300 hectares, respectively [25].The rising proportion of the rural labor force females mainly affects food production in eastern and western China. This effect may be due to the differences in socioeconomic conditions and natural geographic conditions of each province in China. In addition, the cultivation of food crops and cash crops in each region is bound to show different changes in accordance with economic development and income growth.


## 5.1. Estimation of the Basic Model

As shown in Table2, the feminization of the labor force has a significant negative impact on the grain-planting area, which indicates that the increase in the feminization of the labor force will lead to a decrease in the grain area. There are two reasons for this. First, compared with the male labor force, the female’s physical strength tends to be weaker, and the energy women expend on planting is less than the energy men expend, which is not conducive to the grain-planting area. Second, female laborers may select less physically demanding and more profitable cash crops as food substitutes and thus increase cash crop cultivation for higher profits.Table 2
Basic model estimation results.
ln grain sown areaRatio grain sown areaFE (1)RE (1)FE (2)RE (2)Xit−0.016∗∗ (−2.14)−0.016∗∗ (−2.16)−0.449 (−0.96)−0.390 (−0.80)Rit0.001 (1.26)0.001∗ (1.81)0.028 (0.90)0.043∗ (1.52)lnPit0.010 (0.26)0.012 (0.30)3.239∗ (1.59)3.330∗ (1.79)lnWit−0.044∗∗∗ (−3.06)−0.053∗∗∗ (−3.75)1.100 (1.13)0.312 (0.35)Fit0.675∗∗∗ (12.84)0.665∗∗∗ (11.63)17.09∗∗∗ (6.88)16.62∗∗∗ (7.05)lnPMit0.037 (1.16)0.049 (1.46)−6.654∗∗∗ (−2.84)−5.635∗∗∗ (−2.78)INDit−0.002 (−0.45)−0.001 (−0.20)0.017 (0.09)0.158 (1.02)lnIRit−0.020 (−0.29)<0.032 (0.56)−2.301 (−0.72)0.104 (0.04)Constant5.023∗∗∗ (8.59)4.547∗∗∗ (8.78)38.67 (1.50)3.610 (0.16)Observations442442442442Number of province26262626Note. Numbers in parentheses are t statistics. Asterisks refer to p values: ∗∗∗p<0.01, p∗∗<0.05, and p∗<0.1.The price of food has a positive effect on the area of crop grown and its proportion. According to economist Schultz, farmers are rational and optimize the allocation of resources to obtain maximum benefits. The supply of food is an important manifestation of rational production, and farmers constantly adjust their decisions based on expected returns, which are based on the market price of food and the cost of cultivation. Food prices positively influence the area of plant cultivation and the proportion of cropland. The higher the food price is, the more farmers expand the area for grain cultivation to pursue more profits. Because of the limited land resources, the expansion of grain cultivation area inevitably will reduce the cultivation area of other crops, resulting in an increase in the proportion of grain cultivation area.Labor costs have a negative effect on the area and its share of food cultivation, suggesting that as labor prices rise, the area and share of food cultivation in China will be reduced. A possible economic explanation lies in the fact that relative factor prices are the key determinant of product structure. To maximize profits, farmers can choose only factor substitution or product substitution. The former does not change the structure of agricultural cultivation, but rather optimizes the combination of production factors to reduce the cost of food production in accordance with the change of relative factor prices. The latter, however, will directly affect the structure of agricultural cultivation. To balance the increase of food production costs, farmers will invest in land, capital, and other agricultural products and also will increase the area planted with cash crops to obtain higher prices and income, which is not conducive to food production.The influence of other variables on the grain-planting area and the proportion of the grain-planting area is basically in line with economic theory. The per capita grain output has a significant positive impact on the grain-planting area and its proportion. The more important the position of grain production is, for example, in the main grain-producing areas, the greater the grain-planting area and its proportion. In addition, the total mechanical power and mechanical convenience both have a positive impact on crop planting. The greater the total mechanical power, the easier it is to work in the area and the larger the grain-planting area. With economic development, it is possible that a large number of machines are developed and put into use. Therefore, it is easier for agricultural producers in the plain areas, where mechanization is relatively easy, to adjust the cropping structure toward food crops and to reduce production costs through factor substitution and large-scale operation to obtain greater returns. Because of the scarcity of land resources, it also is possible that a large amount of land is used for industrial development, which definitely will reduce the grain-planting area and also reduce the cash crop-planting area. The general cash crops, such as vegetables, are planted near the suburbs of cities, so the land-crowding effect is more obvious than the planting and rural food. Thus, the proportion of food cultivation shows a positive effect.


## 5.2. Estimation of Model with Cross-Term

To study the superposition effect of the feminization of the labor force and the use of machinery, the cross-term of both is introduced. According to the regression results (Table3), we found that the intersection of female proportion and mechanization convenience has a positive impact on grain-planting area and planting area proportion. This result shows that in areas where agricultural production mechanization is easier after the proportion of female rural labor force rises, agricultural producers will replace factors and adopt agricultural machinery to replace the labor force; in contrast, the family labor force will choose to maintain or even expand the area and proportion of grain sown to reduce production costs through a large-scale operation. This conclusion is basically in line with reality. In plain areas where mechanization is relatively easy, agricultural producers are more likely to adjust their planting structure toward food crops and to reduce production costs through factor substitution and scale operations to offset the negative impact of the feminization of the rural labor force on food production. In mountainous and hilly areas where mechanization is more difficult; however, it is challenging for machinery to replace labor to alleviate the impact of the labor shortage.Table 3
Estimate result of a model with cross-term.
ln grain sown areaRatio grain sown areaFE (1)RE (1)FE (2)RE (2)Xit−0.041∗∗∗ (−3.35)−0.041∗∗∗ (−3.27)−2.360∗∗∗ (−2.79)−2.343∗∗∗ (−2.70)Xit×Rit0.041∗∗ (2.09)0.042∗∗ (2.02)3.125∗∗∗ (3.32)3.191∗∗∗ (3.16)Rit0.021∗∗ (2.20)0.022∗∗ (2.13)1.580∗∗∗ (3.38)1.625∗∗∗ (3.25)lnPit0.013 (0.35)0.015 (0.40)3.469∗ (1.70)3.608∗ (1.95)lnWit−0.046∗∗∗ (−3.28)−0.054∗∗∗ (−3.87)0.984 (1.01)0.215 (0.23)Fit0.667∗∗∗ (13.49)0.657∗∗∗ (12.47)16.46∗∗∗ (7.37)16.15∗∗∗ (7.85)lnPMit0.041 (1.33)0.052∗ (1.60)−6.339∗∗∗ (−2.76)−5.469∗∗∗ (−2.74)INDit−0.002 (−0.42)−0.001 (−0.15)0.040 (0.24)0.173 (1.25)lnIRit−0.020 (−0.30)0.029 (0.51)−2.257 (−0.85)0.082 (0.04)Constant6.235∗∗∗ (7.29)5.824∗∗∗ (7.55)131.8∗∗∗ (3.45)99.93∗∗∗ (2.59)Observations442442442442Number of province26262626Note. Numbers in parentheses are t statistics. Asterisks refer to p-values: ∗∗∗p<0.01, p∗∗<0.05, and p∗<0.1.


## 5.3. Robustness Checks

To check the robustness of the results, we removed about a quarter of the provinces and selected the top 20 provinces in China's plain area for regression analysis. The regression results are shown in Table4, and both the significance and sign of the explanatory variables are relatively close to the results in Tables 2 and 3, indicating that the model regression results are relatively robust.Table 4
Estimate result of the robustness test.
ln grain sown areaRatio grain sown areaFE (1)RE (1)FE (2)RE (2)Xit−0.0413∗∗∗ (−4.23)−0.0418∗∗∗ (−3.81)−2.572∗∗∗ (−4.33)−2.448∗∗∗ (−3.66)Xit×Rit0.0474∗∗ (2.48)0.0482∗∗ (2.34)3.730∗∗∗ (5.32)3.843∗∗∗ (4.01)Rit0.0239∗∗ (2.58)0.0245∗∗ (2.44)1.877∗∗∗ (5.32)1.949∗∗∗ (4.10)lnPit0.0345 (0.80)0.0386 (0.90)4.148∗ (1.74)5.106∗∗∗ (2.69)lnWit−0.0419∗∗∗ (−2.95)−0.0510∗∗∗ (−3.65)1.709 (1.72)−0.216 (−0.22)Fit0.647∗∗∗ (12.57)0.644∗∗∗ (11.43)14.81∗∗∗ (6.76)15.87∗∗∗ (7.20)lnPMit0.0247 (0.75)0.0320 (0.95)−5.355∗∗ (−2.51)−4.229∗∗ (−2.11)INDit0.001 (0.10)0.002 (0.49)−0.033 (−0.20)0.330∗∗ (2.33)lnIRit−0.003 (−0.04)0.037 (0.63)−4.070 (−1.71)−1.232 (−0.45)Constant6.158∗∗∗ (6.80)5.739∗∗∗ (7.88)159.8∗∗∗ (5.67)89.17∗∗∗ (2.97)Observations340340340340Number of provinces20202020Note. Numbers in parentheses are t statistics. Asterisks refer to p-values: ∗∗∗p<0.01, p∗∗<0.05, and p∗<0.1.


## 5.4. Regional Analysis

The main grain production areas in China are divided into three parts: first, Eastern China, which includes Liaoning, Jilin, Heilongjiang, Hebei, Shandong, Jiangsu, Zhejiang, Fujian, Guangdong, and Hainan provinces; second, the Central China, which includes Shanxi, Henan, Hubei, Hunan, Jiangxi, and Anhui provinces; and, third, Western China, which includes Chongqing, Sichuan, Guizhou, Yunnan, Guangxi, Shaanxi, Gansu, Ningxia, Inner Mongolia, and Xinjiang.According to the noted division, we further analyzed the influence of the increasing female ratio in Eastern China, Central China, and Western China on the sown area and its proportion of grain. In this part, we conducted a Hausman test on the model to select a relatively suitable estimation method for quantitative analysis.As shown in Table5, the substitution effect of machinery on the labor force varies across regions. In the eastern region, an increase in the proportion of females decreases the area and proportion of grain cultivation. When combined with the convenience of machinery, both the area and proportion of grain cultivation are significantly increased. Moreover, in the eastern regions, because of the flat terrain and extensive use of machinery, the substitution effect of factors is greater than the debilitating nature of the female labor force, making the area and proportion of grain cultivation increase instead of decrease. For example, Heilongjiang, Jiangsu, and Shandong are located in the eastern regions, and the terrain is mainly plain with relatively flat arable land. Thus, when the rural labor force is feminized, the mechanical replacement of labor can be more smoothly realized, and agricultural producers may devote more resources to food production, thus weakening the negative impact of the rising proportion of female rural labor force. For example, during the period from 2001 to 2016, the sown areas of grain in the three provinces increased from 8,291,200 hectares, 4,822,600 hectares, and 6,912,600 hectares to 14,214,500 hectares, 5,475,900 hectares, and 8,404,800 hectares, respectively.Table 5
Regional analysis results.
ln grain sown areaRatio grain sown areaEastern ChinaCentral ChinaWestern ChinaEastern ChinaCentral ChinaWestern ChinaXit−0.037∗ (−1.86)−0.016 (−1.18)−0.060∗∗ (−1.79)−3.950∗∗∗(−2.36)−1.072 (−1.25)−2.015 (−1.07)Xit×Rit0.039∗ (2.07)0.020 (1.02)0.0782∗ (1.69)3.169∗ (1.89)2.405∗ (1.54)2.835 (1.00)Rit0.019∗ (1.93)0.011 (1.13)0.040∗ (1.77)1.575∗ (1.89)1.246∗ (1.58)1.454 (1.06)lnPit−0.015 (−0.36)0.018 (0.36)0.044 (0.75)5.816∗ (1.53)−4.980 (−1.79)7.419∗ (1.85)lnWit−0.018 (−1.24)0.006 (0.25)−0.064∗∗∗ (−4.10)−1.165 (−0.61)3.553∗ (2.51)−2.980∗∗ (−2.28)Fit0.710∗∗∗ (14.60)0.225∗ (2.13)0.635∗∗∗ (13.39)18.230∗∗∗ (12.99)7.597 (0.95)21.69∗∗∗ (3.80)lnPMit0.006 (0.09)0.035 (1.25)0.128∗ (1.79)−5.104 (−1.57)<0.767 (0.73)−6.584 (−1.67)INDit−0.008∗ (−1.83)0.009∗∗∗ (5.52)-0.013∗∗∗ (−3.67)0.662∗∗∗ (2.73)−0.113 (−0.53)0.255 (0.85)lnIRit−0.163∗∗ (−2.48)0.161∗ (1.80)0.205∗ (1.68)4.687 (1.32)12.910∗∗ (2.95)−5.652 (−1.39)Constant5.591∗∗ (3.30)−0.367 (−0.83)5.885∗∗∗ (5.32)84.18 (1.38)−12.23 (−0.18)85.08 (0.91)Observations170102170170102170Number of province1061010610Note. Numbers in parentheses are t statistics. Asterisks refer to p-values: ∗∗∗p<0.01, p∗∗<0.05, and p∗<0.1.In the central regions, the feminization of the labor force and the cross-term of both do not significantly affect the grain-planting area, but the cross-term significantly and positively affects the proportion of the grain-planting area. This result indicates that the feminization of the labor force, including the superposition effect of the two factors will not lead to a decrease in the grain-planting area, but it will lead to a decrease in the cash crop-planting area and will make the proportion of area under grain cultivation higher. The possible reason for this may be that the central region belongs to the traditional grain-growing region, and its economy is not as developed as that of the eastern region. Thus, the demand for cash crops, such as vegetables, is not as high, and the planting takes longer. As a result, farmers prefer to grow grain. During the period from 2002 to 2018, the sown areas of peanuts, cotton, and melons in Shanxi Province decreased from 18,800 hectares, 72,300 hectares, and 46,300 hectares to 5,400 hectares, 2,300 hectares, and 16,200 hectares, respectively; and the sown areas of peanuts, cotton, and melons in Anhui Province decreased from 284,600 hectares, 321,200 hectares, and 211,400 hectares to 144,200 hectares, 86,300 hectares, and 8,000 hectares, respectively [22–24].In the western region, the proportion of females has a significant and negative impact on the grain-planting area, whereas the cross-term has a positive impact on the grain-planting area, but both do not have a significant impact on the proportion of the grain-planting area. This result shows that after the proportion of the female labor force increased in the western region, the area planted with grain and cash crops decreased at the same time. Therefore, the proportion of planted area did not change significantly. After the use of machinery, however, the negative effect of the feminization of the labor force is weakened, increasing both the grain-planting area and the cash crop area. Because Sichuan and Guizhou are typical labor-exporting cities, a large number of male laborers go out to work, leaving women to engage in planting, and the loss of effective labor is unfavorable to agricultural cultivation. Because of the massive use of machinery, the originally barren land in the west was developed and planted, which increased the planting area. During 2002–2018, the sown areas of grain, cotton, vegetables, and fruits in Xinjiang increased from 1,514,600 hectares, 943,900 hectares, 164,200 hectares, and 83,000 hectares to 2,219,600 hectares, 2,491,300 hectares, 273,300 hectares, and 111,300 hectares, respectively [25].The rising proportion of the rural labor force females mainly affects food production in eastern and western China. This effect may be due to the differences in socioeconomic conditions and natural geographic conditions of each province in China. In addition, the cultivation of food crops and cash crops in each region is bound to show different changes in accordance with economic development and income growth.


## 6. Conclusions

Based on the panel data of 26 provinces from 2002 to 2018, this study has analyzed the impact of the feminization of the rural labor force on grain planting and regional differences. Our results indicate the following: (1) the feminization of the rural labor force significantly negatively affects the grain-planting area and its ratio and has an adverse impact on grain planting. (2) The impact of the feminization of the labor force on grain planting is affected by topography. In flat areas where machinery is easier to operate, machinery will weaken the adverse impact of the feminization of the labor force on grain planting. (3) The influence of the feminization of the labor force on grain planting has significant regional differences. In Eastern China and Western China, machinery completely offsets the negative effects of the feminization of the labor force, showing positive effects. In Central China, however, machinery has increased the proportion of the grain-planting area, which has no significant impact on the grain-planting area. [26].



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*Source: 2004465-2022-08-19.xml*