Abstract
Owing to the different roles of men and women in the family, women may affect family welfare differently than men. Existing research only analyzes the impact of non-farm employment or male non-farm employment on family welfare, and the impact of female non-farm employment on the family has not been addressed in existing studies. China is a particularly interesting case given its rapid economic development and rising rates of female non-farm employment. We used data from the China Health and Nutrition Survey to analyze the dietary patterns of family members and identify the factors that influence food choices and nutrient intake. The results showed that female non-farm employment reduced household protein and calorie intake levels and increased the frequency of dining out among rural household members.
1. Introduction
Malnutrition is an important challenge faced by the world, especially in developing countries, and according to the FAO (2016), the global malnourished population exceeded 800 million in 2016. Malnutrition is more serious among farm households in less developed regions, which is not only reflected in poverty of material resources but also the widespread lack of healthy human capital (Kubik et al., 2023). In 2020 China achieved a major victory in the battle against poverty, eliminating the absolute poverty that had existed in rural areas for thousands of years. However, the rural poor who have been lifted from poverty still have a high risk of returning to poverty. Therefore, effectively addressing the challenges rural areas in less developed regions face of returning to poverty (especially because of disease) has become a major concern for developing countries in their efforts to consolidate gains from poverty eradication.
Over the past 40 years of China’s reform and opening up, there has been a rapid acceleration in economic and social development, as well as urbanization. The combined effect of market-oriented economy and industrial restructuring, along with the changes in rural institutions, has led to the formation of a significant non-agricultural economy in China. This economy provides a new employment option for the surplus rural labor force amid a backdrop of marginal diminishing returns to labor. Induced by vast income differences between urban and rural areas, the non-farm employment of rural labor has increased significantly. Hundreds of millions of agricultural laborers have gradually shifted from the agricultural sector to the non-farm sector. In 2021, the total number of migrant workers in China was 292.51 million, 6.91 million more than in the previous year, an increase of 2.42% year-on-year. Among all migrant workers, 64.1% were male and 35.9% were female, with the female share increasing by 1.1 percentage points compared to the previous year. Given the rising trajectory of non-farm employment in rural economic development and its plausible influence on household welfare through the redistribution of household resources, investigating the impact of non-farm employment on household nutrient intake assumes paramount significance in research. The persistent rise in the percentage of female non-farm employment could potentially lead to substantial shifts in household resource allocation. Consequently, this study centers on examining the correlation between female non-farm employment and the nutrient intake of household members. Specifically, we explored three questions: (1) What is the effect of female non-farm employment on nutrient intake in rural households? (2) Does female non-farm employment affect family nutrient intake by increasing eating out? (3) Is there any difference in the impact of different non-farm employment types on household nutrient intake? Rural people self-select themselves to participate in non-farm work. Non-farm employment is an endogenous variable (Ma and Zheng, 2021; Ma et al., 2022). Therefore, we used a two-stage least squares model to analyze data from the China Health and Nutrition Survey (CHNS) from 1991 to 2011, focusing on rural households, which are well suited for producing consistent estimates.
This study reviews the literature on non-farm employment by exploring the relationship between female non-farm employment, the nutrient intake of family members, and eating out. This problem is significant for three reasons. First, although studies have documented the positive effect of non-farm employment on food consumption, its effect on nutrient intake has been overlooked, and higher food consumption expenditure does not necessarily lead to higher nutrient intake. Thus, examining the role of female non-farm employment in influencing household nutrient intake could improve our understanding of the overall impact of non-farm employment on household welfare, and the results of this study complement the existing literature. Second, the existing literature on non-farm employment does not distinguish between the roles of male and female households and only explores the impact of non-farm employment on the diversification of household income and consumption. Third, we incorporate family members dining out into the study of the impact of women’s non-farm employment on the nutrient intake of household members, further exploring potential factors that may influence the nutrient intake of household members as a result of women’s non-farm employment.
The remainder of this paper is organized as follows. Section 2 introduces the background of the paper. Section 3 describes the data used in our empirical analysis. Section 4 describes the study’s main empirical methods, and Sections 5 and 6 present the main empirical results and analysis.
2. Background
Existing research has found a significant positive impact of non-farm employment on household income and food consumption. Ma et al. (2022) estimated the effects of non-farm employment on household food consumption, dietary structure, nutritional levels, and carbon footprint. The study found that the dietary and nutritional structure in rural areas of China is still unbalanced and there is substantial room for improvement. Non-farm employment will accelerate the adjustment of household dietary structure and improve nutritional levels, especially for low-income families. Farmers’ participation in non-farm employment has increased the income of farmers’ households, improved the consumption levels of rural households, and ensured food security and dietary diversity (Bai et al., 2023; Chang and Mishra, 2008; Duong et al., 2020; Ma et al., 2022; Martey et al., 2022; Rajkhowa and Qaim, 2022). Bai et al (2023) found a positive correlation between non-farm employment and dietary diversity, as well as consumption of meat, fish, and other aquatic animals, fruits, and dairy products, which are rich in protein and micronutrients. Non-farm employment improves household income by enhancing crop diversity, thereby promoting household dietary diversity. However, an increase in income and food expenditure does not necessarily indicate better nutrient intake. Many factors affect nutrient intake, including sex, age, educational level, marital status (Maliszewski et al., 2017), health knowledge (Almulla et al., 2023; Li et al., 2020), dining outside (Castaeda et al., 2019), family structure (Zingwe et al., 2023) and credit constraints (Thanh and Duong, 2017).
Two categories of rural households engaged in non-farm employment: (1) Households where only the male members have non-farm jobs. Non-farm employment contributes to increased income, thereby leading to improved nutrition for family members (Bai et al., 2023; Ma et al., 2022; Martey et al., 2022; Rajkhowa and Qaim, 2022). Male non-farm employment promotes female empowerment, and the empowerment of women is conducive to improving the nutritional status of family members (Jemaneh and Shibeshi, 2023; Zingwe et al., 2023) (2) Women participating in non-farm employment reduce the duration of family care. Family care provided by women has a greater positive impact on the health and nutrient intake of all family members (Kabeer, 1999; Mataka et al., 2023; Quisumbing and Maluccio, 2000). Involvement of women in non-farm employment can have a detrimental effect on nutrient intake. Therefore, there is uncertainty regarding the effects of female non-farm employment on nutrient intake.
Many studies have explored the impact of gender differences on household decision-making in family life and production. Gender differences are reflected in non-farm employment choices (Das and Mahanta, 2023), agricultural machinery use (Ma et al., 2018), and other aspects. Zheng et al. (2022) found that gender variables influence farmers’ joint decision-making on non-farm employment and mechanization service expenditures. In addition, research has also analyzed the impact of male and female non-farm employment on household agricultural production input (Berhe et al., 2023; Ma et al., 2022; Zhou et al., 2020). However, to our knowledge, there has not been any research exploring gender differences in non-farm employment regarding household members’ nutrient intake.
Non-farm employment impacts household nutrition and food consumption by increasing income (Nguyen and Winters, 2011). Karamba et al. (2011) believe that non-farm employment has no significant impact on household per capita food expenditure. Zezza et al. (2011) proposed that compared with the male labor force, the female labor force will have a significant positive impact on the food consumption of rural families. Ma and Zheng (2021) showed that non-farm employment promotes fruit and vegetable consumption by increasing both purchase frequency and expenditure in rural China. Heterogeneous effects of non-farm employment have also been found between males and females, as well as across different geographic locations. Specifically, a higher level of consumption diversity is associated with non-farm employment for female respondents (relative to males) and for females residing in the eastern and western regions of China (relative to the central region) (Ma et al., 2022). Therefore, there are significant differences between male and female non-farm employment in terms of its impact on households. Compared with men, women are more altruistic and see themselves as family members than individuals (Kabeer, 1999). Related studies have found that within a limited household budget, women focus more on spending on nutrition and health than men (Kariuki et al., 2023; Quisumbing and Maluccio, 2000; Wu and Li, 2011). Female preferences are more inclined to ensure diversity in family diets (Argaw et al., 2020). In a study in rural South Asia, Rao et al. (2019) found that female control of agricultural income was more conducive to household nutritional status. While it is true that the non-farm employment of women may have an impact on the nutrient intake of household members, it is important to note that this impact can be either positive or negative. On one hand, increased income from women’s non-farm employment can potentially improve the overall purchasing power of the household, leading to better access to nutritious food. On the other hand, when women face limitations in time and resources for household responsibilities such as meal preparation and family care, it can potentially lead to a deterioration in nutritional quality. Female non-farm employment reduces family caregiving time and may increase the frequency of family members eating out. You and Davis (2010) showed that an increase in maternal wage rates might increase family members’ spending on dining out and, in turn, negatively impact children’s health status. Increased frequency of family meals has also been shown to benefit members’ nutrient intake (Binkley and Liu, 201; Helton et al., 20239; Koszewski et al., 2011; Zhou et al., 2023).
Additionally, dining out is generally more expensive than eating at home, and the nutritional content of home meals is higher than that of dining out with the same expenditure. The University of Minnesota Dietetic Research Program has shown that family members eating together develop habits that contribute to good eating habits and increase their intake of beneficial nutrients (Burgess-Champoux et al., 2009). Furthermore, the frequency of family meals is negatively associated with substance abuse, sexual activity, depression or suicide, antisocial behavior, violence, school problems, eating disorders, and alcohol abuse (Fisher et al., 2007; Fulkerson et al., 2006; Neumark-Sztainer et al., 2008). Increased frequency of family meals has a positive effect on adolescent grade point averages, academic commitment, positive values, social competence, and positive mindsets (Eisenberg et al., 2004; Fulkerson et al., 2006; Victoria-Montesinos et al., 2023). Many studies have found that the nutritional quality of food eaten out is lower than that of food consumed at home (Binkley, 2006; Auchincloss et al., 2014; Nguyen and Powell, 2014). Therefore, the increased frequency of dining out among family members is detrimental to their nutrient intake and the accumulation of human capital.
Women often make decisions regarding daily household consumption; thus, they largely determine the food consumption and nutrient intake of their family members (Kassie et al., 2020). In rural China, women are often the main bearers of family care and household work. Moreover, women influence the food consumption and nutrient intake of their family members through daily household food consumption decisions. Therefore, if women’s participation in non-farm employment results in an increase in the frequency of family members dining out, the nutrient intake of household members will be negatively affected.
3. Data
3.1 Study area and data collection
This study mainly used data from the CHNS for eight periods from 1991–2011 (1991, 1993, 1997, 2000, 2004, 2006, 2009, 2011).1 The survey was jointly conducted by the Chinese Center for Disease Control and Prevention and the Population Research Center of the University of North Carolina, USA, and is an early and long-running survey covering a large area of China. The survey covered nine provinces that vary substantially in geography, economic development, public resources, and health indicators. A multistage random clustering process was used to draw samples from each province. Counties in the nine provinces were stratified by income (low, middle, and high), and a weighted sampling scheme was used to select four counties in each province randomly. In addition, the provincial capital and a lower-income city were selected when feasible. Other large cities in these two provinces were also selected. Villages and townships within the counties and urban and suburban neighborhoods within the cities were randomly selected. Between 1989 and 1993, 190 primary sampling units were used, and a new province and its sampling units were added in 1997. There were approximately 4400 households in the overall survey, covering 19 000 individuals. Follow-up levels were high; however, families that migrated from one community to another were not followed. Movement within the primary sampling units and some larger urban entities was attempted. The following processing steps were applied to the raw data: First, the individual, household, and community data from CHNS were matched, and only one data entry per family per year was retained. Second, urban samples were removed. Third, missing values were deleted. Ultimately, we obtained 3874 observed samples from 1156 households, constituting an unbalanced panel dataset.
3.2 Variable settings
Explanatory variables
Female household non-farm employment. The proportion of household female non-farm employment. Variable data related to the main occupation types were obtained from the CHNS questionnaire. The questions classified occupation types into 13 categories, of which Category 5 included farmers, fishermen, and hunters, and the other 12 categories were all non-farm occupations. A binary dummy variable was constructed accordingly: “1” for participation in non-farm employment and “0” for participation in agricultural production. The non-farm employment of females aged 18–60 in each household was counted, and the presence of females in the household was assigned a value of “1” for non-farm employment; otherwise, it was assigned a value of “0”. We then take the number of women in the household with non-farm employment divided by the total number of women in the household.
Dependent variable
Family members’ dining out. Variables regarding the location of the three-day meal preparation were obtained from the CHNS questionnaire. The questions classified meal locations into seven categories, with Category 1 being at home and the other six eating out. A binary dummy variable was constructed: “1” for dining out and “0” for not eating out. The three-day meal frequency for each household member was obtained by dividing the number of three-day meals by the total number. Second, the mean value of the three-day eating-out frequency of the household was calculated to obtain the eating-out variable of the household members.
Nutrient intake of household members. The explanatory variable of this study was the nutrient intake of household members, which was measured by the three-day protein and calorie intake of each standard person in the household. Protein is a nutrient that is one of the basic building blocks of human cells, tissues, and organs. Proteins are made up of amino acids that provide energy and help maintain the body’s physiological functions. Protein can be obtained from animal and plant foods such as meat, fish, legumes, nuts, and cereals. A calorie is a measure of energy. It refers to the amount of energy, that the body can use, contained in food. Various food items possess varying calorie content, and the body requires a specific intake of calories to sustain normal physiological functions. Proteins are nutrients, and calories are a measure of energy in food; eating the right amount of protein and calories is important for maintaining a healthy body.
Variable data were obtained from the CHNS data for three-day protein and calorie intake per person. Because of age and sex differences, each person’s protein and calorie requirements may be different; therefore, this study converted each farm household member into a standard person according to the Chinese Dietary Reference Intake Scale (DRIS)2 and then summed up to obtain the standard number of people in the household. Finally, the three-day protein and three-day calorie intakes of the household were divided by the standard number of people in the household to obtain the three-day protein and three-day calorie intakes of each standard person in the household.
3.2.3 Control variables
In addition to the above key variables, Per capita household income, Average educational level of family members, Average health status of family members, Proportion of agricultural income, Proportion of men and women in families, Ratio of adults to older persons, Number of children aged 0–3, Number of children aged 3–6, Number of children aged 6–12, Number of children aged 12–18, Community dining conditions, Family vegetable gardens, Livestock and poultry breeding, Domestic fuel, Cultivated areas and Ratio of male non-farm employment were controlled for. Again, for computational simplicity and to reduce the variance between variables with excessively large values affecting the regression results, all protein intake, calorie intake, and household per capita income were taken as logarithms (Table 1).
Variable definitions and descriptive statistics.
Citation: International Food and Agribusiness Management Review 27, 2 (2024) ; 10.22434/ifamr2023.0079
4. Empirical strategy
To analyze the effects of female non-farm employment and dining out on the nutrient intake of household members and their mechanisms, the following panel data model was constructed:
Citation: International Food and Agribusiness Management Review 27, 2 (2024) ; 10.22434/ifamr2023.0079
Where carboit denotes the per capita protein intake of families; kcalit denotes the per capita calorie intake of families; FeNaEit denotes female household non-farm employment participation; Mit denotes family members dining out; Xit denotes the control variable; i and t denote households and years, respectively;
The employment of women in non-farm activities within rural households is influenced by various factors, including the health status and nutrient intake of family members. As a result, the model specification may face potential issues related to selection bias and reverse causality. To address these issues, we utilized a synthesized instrumental variable (IV) that adhered to the requirement of the exclusion restriction. The paper by Ma et al. (2022) was consulted for the selection of instrumental variables. The IV measures the proportion of female non-farm workers (excluding respondents) in the total sample of each county. The dataset consists of nine provinces, with four counties surveyed in each province, resulting in a total of 36 counties. However, the specific names of the counties were not disclosed by the CHNS. Significantly, the instrumental variable employed in this study demonstrates a strong correlation with the non-farm employment of women within the household, thereby satisfying the condition of relevance. Additionally, the instrumental variable used in this study is independent of the household’s nutrient intake, thereby satisfying the condition of exogeneity as it is uncorrelated with the error term. In all instrumental variable models employed in this paper, the number of instrumental variables equals the number of endogenous variables, indicating a case of exact identification. Under such circumstances, the estimation methods of generalized method of moments (GMM) and two-stage least squares (2SLS) are completely equivalent.
The Hausman test is required to determine whether a fixed- or random-effects model should be used when dealing with panel data. The Hausman test results rejected the original hypothesis at the 1% significance level, indicating that the fixed-effects model is superior to the random-effects model.
5. Results
5.1 Impact of female non-farm employment on nutrient intake of household members
The panel instrumental variable regression, as shown in Table 2, reveals that the p-value of the underidentification test (Kleibergen-Paap rk LM statistic) is 0.000, which is less than 0.01. This result indicates the rejection of the hypothesis of underidentification. Furthermore, the value of the first-stage weak identification test (Cragg-Donald Wald F statistic) is 68.07, significantly exceeding the critical value of 16.38 suggested by Stock and Yogo (2005) for a maximal IV size of 10%. As a result, there are no concerns regarding weak instrumental issues, and the instrumental variable estimates are considered valid. The regression results of the first-stage equation in the panel 2SLS estimation, presented in Table 2, demonstrate that the instrumental variable is statistically significant at the 1% level, with positive coefficients.
Impact of female non-farm employment on nutrient intake of household members and family members dining out.
Citation: International Food and Agribusiness Management Review 27, 2 (2024) ; 10.22434/ifamr2023.0079
The results of the regression of Model 1 show that female non-farm employment has a significant negative effect on the protein intake of rural household members, and the coefficient is significant at the 10% level (Table 2). The regression results of Model 2 show that female non-farm employment has a significant negative effect on the calorie intake of rural household members. Although female non-farm employment has a significant negative impact on the nutrient intake of family members, male non-farm employment does not have a significant effect on the calorie and protein intake of family members. As we have analyzed earlier, the time allocation of women in a household may have a greater impact on the nutrition of family members when compared to men, as women tend to invest more time and experience in caring for the family. However, it must be pointed out that excessive or inadequate intake of protein and calories can have adverse effects on human health. The regression results do not necessarily indicate that female non-farm employment leads to a decline in the quality of nutrient intake, but they do suggest that female non-farm employment alters the nutrient intake structure of family members. This change may be related to dietary patterns.
Furthermore, income exhibits a significant positive influence on calorie intake and protein intake. The ratio of men to women within a household, along with the number of children across different age groups (0–3, 3–6, 6–12 and 12–18), negatively impacts both the protein and calorie intake of rural household members. This suggests that households with a greater number of men and children are more susceptible to malnourishment. Importantly, the structure of a family significantly influences the nutrient intake of its members, with larger families requiring a greater supply of nutrients to meet their needs.
5.2 Impact of female non-farm employment on family members dining out
The results of the aforementioned research indicate that non-farm employment among women is associated with a decrease in nutrient intake among family members. This led us to further explore the reasons behind this decline. Therefore, we investigated the impact of women’s non-farm employment on dining out among family members.
The Model 3 results in Table 2 show that female non-farm employment increased the proportion of family members eating out. This shows that part of the reduction in family care time due to female non-farm employment is replaced by income. However, as discussed earlier, dining out may alter the nutrient intake composition of family members, thus female non-farm employment may have an impact on the nutrient intake of family members through dining out. However, as discussed earlier, increased dining out can lead to a decline in nutrient intake; therefore, female non-farm employment may negatively impact family nutrient intake by increasing eating out (Table 3).
Non-farm employment type statistics.
Citation: International Food and Agribusiness Management Review 27, 2 (2024) ; 10.22434/ifamr2023.0079
It is important to note that our findings suggest a significant positive impact of household income and the proportion of male non-farm employment on dining out behavior among family members. This implies that higher household income and a greater share of male non-farm employment are associated with an increased likelihood of dining out. The results can be interpreted in several ways. First, higher household income provides families with more disposable income, which potentially enables them to afford dining out more frequently. This could be attributed to increased purchasing power and improved financial stability. Secondly, a higher proportion of male non-farm employment within the household may indicate a higher overall family income, as non-farm jobs tend to offer better wages and benefits compared to agricultural or informal sector work. This increased income may contribute to a greater willingness or ability to dine out. In addition, the presence of male non-farm employment may also suggest a more flexible work schedule or reduced time constraints for family members, making it easier to engage in dining out activities. This allows for greater convenience and enjoyment in terms of meals outside the home (Table 4).
Effect of occupation type on nutrient intake and family members dining out.
Citation: International Food and Agribusiness Management Review 27, 2 (2024) ; 10.22434/ifamr2023.0079
5.3 Mechanism analysis
In Section 5.2 we analyzed the impact of female non-agricultural employment on the dining out behavior of family members. In order to further study the role of dining out in the effect of female non-farm employment on the nutrient intake of family members, we separately included the dining out variable in Models (2) and (3) through regression Models (4) and (5). The regression results are shown in Table 2, which indicate that dining out decreases the intake of protein and increases the intake of calories. The results of the study indicate that female non-farm employment may lead to a decrease in protein intake among family members by increasing the frequency of eating out. Although the findings suggest that eating out is not the main reason for the decrease in calorie intake, it appears to contribute to a less favorable nutritional intake structure. Protein is one of the essential nutrients for the human body, playing a crucial role in maintaining muscle quality, bone health, and immune function. If dining out leads to insufficient protein intake, it may result in problems such as decreased muscle mass and impaired immune function. On the other hand, dining out typically involves choosing high-energy foods, such as fried foods, desserts, and sugary drinks. These foods are often high in calories but relatively low in nutritional density. If calorie intake exceeds the body’s needs, it may lead to weight gain, obesity, and related health issues. Therefore, due to female non-agricultural employment, the frequency of family members dining out increases, and the nutrient intake of family members develops in an unhealthy direction.
5.4 Further analysis: The impact of female non-farm employment type on the nutrient intake of family members and family members eating out
There are many types of jobs for female non-farm employment. We further explored whether different occupation types had different effects on the nutrient intake of family members. The CHNS questionnaire classified occupations into 13 types. However, of the 3874 samples, we observed only 10 non-farm occupations. Owing to data limitations, we could only determine whether women aged 18–60 were employed in the family. We set 10 occupation types to “0” and “1” dummy variables, where “1” means that there are women in the family engaged in this non-farm occupation type and “0” means not. Occupation type of the family per capita protein, calorie intake, and family members’ eating out. Table 5 presents the regression results.
Heterogeneity test results.
Citation: International Food and Agribusiness Management Review 27, 2 (2024) ; 10.22434/ifamr2023.0079
The results suggest that the specific types of non-farm employment occupations held by women in a family can have an impact on the nutrient intake and dining-out behaviors of family members. When examining the correlations with protein and calorie intake, we found that the occupation of “Driver” showed a positive association with the protein and calorie intake of family members. This suggests that families with women working as drivers may have a higher consumption of protein and calories, potentially due to the nature of their occupation or the increased financial resources that come with it. On the other hand, occupations such as “Senior professional/technical staff,” “Service industry personnel,” and the category labeled as “Other” were found to have correlations only with calorie intake. “Service industry personnel” showed a positive correlation with calorie intake, indicating that families where women work in the service industry tend to have higher calorie intakes. However, the occupations of “Senior professional/technical staff” and “Other” showed negative correlations with calorie intake, suggesting that families with women working in these occupations may have lower calorie intakes for reasons that could include factors such as job demands, work-life balance, or dietary preferences. Additionally, the occupation types of “Senior professional/technical staff,” “General professional/technical workers,” “General office staff,” and “Soldiers and police officers” were associated with a lower likelihood of eating out. This indicates that when women in the family are employed in these occupations, the family tends to have a higher proportion of meals consumed at home. The reasons behind this trend could be related to the nature of these occupations, such as fixed schedules, commuting patterns, and possibly a greater emphasis on family responsibilities.
It is important to consider that these interpretations are based on observed correlations and further research is required to establish causality and understand the underlying mechanisms. Additionally, the lack of detailed information regarding specific occupation types within the “Other” category may limit the clarity of the results.
5.5 Heterogeneity analysis
To further understand the differences in the effects of female non-farm employment on the nutrient intake of rural households with different characteristics, farm households were grouped according to their Average educational level of family members and dining conditions in the community where the household is located. The grouping was performed according to the mean value of each indicator, with higher groups being above the mean value and lower groups being below the mean value. The results of the group regression based on the 2SLS are presented in Table 5.
The impact of female non-farm employment on the nutrient intake of family members varies across households with different levels of education. We divided the sample into two groups based on the mean level of education among family members and conducted regressions. The results indicate that there is no significant difference in the impact of female non-farm employment on protein intake across households with different levels of education. However, households with higher levels of education exhibit a greater suppression effect on the calorie intake of family members compared to households with lower levels of education. The possible reason for this difference is that people with higher levels of education are more likely to obtain relevant health knowledge and information, and are also more likely to accept the concept of healthy eating. Therefore, the calorie intake of women in non-farm employment may be relatively healthier after their income increases. On the other hand, people with lower levels of education may be more influenced by social and economic factors, making it more difficult for them to obtain knowledge and information about healthy eating.
The effect of female non-farm employment on household members’ nutrient intake varied among farming households with different community dining conditions. In China, household cooking is generally undertaken by women, and their participation in non-farm employment leads to less time for household cooking, while the increase in income from non-farm employment may prompt household members to eat away from home. Currently, eating-out conditions in rural China are far different from those in urban areas, and there are also large disparities between villages owing to different economic development conditions, so there may be cases where off-farm employment increases the willingness of farmers to eat out, but local conditions do not allow it. On the contrary, a large body of evidence suggests that increased eating-out consumption has a negative impact on dietary health (Binkley, 2008). We obtained the community dining condition variable by summing the number of stores where items are available in the farm household’s community and dividing them into high and low subgroups for subgroup regressions based on means. The results showed that there was no significant difference in the effect of female non-farm employment on protein intake among households with different levels of community dining and that the suppressive effect on calorie intake of household members was greater in farmers with higher levels of community dining than in those with low levels of community dining. Non-farm employment may be accompanied by the provision of healthy dietary knowledge and promotion in the workplace or community catering. Rural women who have access to high-quality community catering services may be more likely to come into contact with information about healthy eating, have a higher awareness and knowledge of balanced and low-calorie diets, and be more inclined to provide healthy dietary choices for their family members. Rural women with high-quality community catering services may have more opportunities to come into contact with a variety of dietary choices and low-calorie dishes in their work or community restaurants. This guiding effect of the catering environment may lead them to provide healthier, low-calorie dietary choices for their family members when they return home.
5.6 Robustness testing
By changing the variable characterization of female non-farm employment, whether there were females in the household involved in non-farm employment instead of the proportion of women involved in non-farm employment in the household was used. The same model was used to estimate Equations 1–3 separately, and the estimated results (Table 6) were consistent with the baseline regression. Thus, female non-farm employment negatively affects household members’ protein and calorie intake. Female non-farm employment increases the frequency of eating out, and the regression results are robust.
Robustness test results.
Citation: International Food and Agribusiness Management Review 27, 2 (2024) ; 10.22434/ifamr2023.0079
In Section 5.3, we have examined the significance of dining out in relation to the influence of women’s non-farm employment on the nutrient intake of family members. To strengthen the reliability of this mechanism, we conducted a separate investigation into the impact of dining out on the nutrient intake of family members. The regression results, presented in Table 7, demonstrate that dining out significantly reduces protein intake for family members, while increasing calorie intake. This finding further corroborates the robustness of our analysis in Section 5.3.
Mechanism robustness test results.
Citation: International Food and Agribusiness Management Review 27, 2 (2024) ; 10.22434/ifamr2023.0079
6. Discussion and policy implications
Malnutrition remains a significant concern in rural households across many developing countries. With a growing number of rural females entering non-farm employment, as the rural labor force transitions to more lucrative opportunities, understanding the impact of their labor allocation is crucial. Not only does it affect the welfare of these women, but also that of other family members due to their vital role in caregiving. To examine the influence of female non-farm employment on the nutrient intake of family members and the underlying mechanisms, this study utilizes panel data from the CHNS 1991–2011, focusing on rural areas. The effects of female non-farm employment and dining out on household members’ nutrient intake are empirically analyzed using two-stage least squares (2SLS) regression. Additionally, the study explores the role of dining out in the relationship between female non-farm employment and household members’ nutrient intake.
The results showed that female non-farm employment reduced household protein and calorie intake levels and increased the frequency of dining out among rural household members. The increase in income from non-farm employment has increased the frequency of family members dining out, leading to a shift in the structure of nutrient intake towards an unfavorable direction. The different occupation types of female non-farm employment will have different effects on nutrient intake and dining out. In addition, the impact of women’s non-farm employment on household members’ nutrient intake varies across families with different levels of education and communities with different dining environments.
The Chinese government has implemented “egg and milk projects” in many places to improve the nutrient intake by rural youth, but the intake of high-quality nutrients, such as protein by adults, should also be a concern. Owing to limited employment conditions in rural areas, rural women must leave their homes to participate in non-farm employment, which leads to a decrease in their role in family care. Therefore, while rural revitalization is underway, the development of small and micro enterprises in rural areas should be encouraged to provide women with employment opportunities near their homes. In addition, efforts should be made to challenge and change gender norms that place the burden of food preparation solely upon women. Encouraging men to share household responsibilities, including cooking and nutrition-related tasks, can help ensure a more balanced and nutritious diet for the family. Additionally, providing support for working women in the form of accessible and affordable child-care services can assist in managing their dual roles of being breadwinners and caregivers. This can alleviate some of the time constraints that are faced by women, enabling them to dedicate more time and attention to food preparation and nutrition planning. Improving the availability and affordability of nutritious food options within rural communities is another significant factor. Enhancing local food production and distribution systems can contribute to a more diverse and balanced dietary. This could involve supporting small-scale farmers and promoting agricultural practices that prioritize nutritionally-rich crops. Creating community gardens or supporting farmers’ markets can also increase access to fresh and affordable foods. Furthermore, nutrition education plays a crucial role in equipping individuals and families with the knowledge and skills to make informed dietary choices. Implementing nutrition education programs that are tailored to the specific needs and contexts of rural communities can empower individuals to make healthier food choices, even in the face of limited access to resources.
The findings of our study provide evidence regarding the impact of female non-farm employment on household nutrient intake, a topic that has not been thoroughly addressed in previous research on nutrient intake and food consumption. While this study fills a small gap in the field of rural nutrition research, it is important to acknowledge its limitations. Firstly, due to data availability, we only analyzed two basic nutrients, namely protein and calories, and did not explore the effects of other micronutrients and nutrients on female non-farm employment. Additionally, our observations indicate that the different types of female non-farm employment have varying impacts on the caloric and protein intake of family members. Moreover, heterogeneity analysis has revealed disparities in caloric and protein consumption outcomes. However, the underlying causes of these disparities were not investigated in this study. To gain a comprehensive understanding of these phenomena, future research should aim to explore the factors contributing to such variations and examine their implications.
Acknowledgements
We would like to thank Editage (www.editage.cn) for English language editing. This research was funded by The National Natural Science Foundation of China (grant number 71973105). There is no conflict of interest.
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Corresponding author
There are data on nutrient intake, which the CHNS began investigating in 1991. Therefore, we discarded the 1989 data.
A standard person refers to an 18-year-old male who has a light physical activity level. The average daily protein requirement (EAR) of each standard person is 60 g. The average caloric requirement (EAR) per standard person per day is 1800 kcal. Due to space limitations, the detailed calculation process of the standard number of people has not been explained in the text.
