Does Food Insecurity in Early Life Make People More Depressed?—Evidence from CHARLS

Abstract

Based on the China Health and Retirement Longitudinal Study (CHARLS) data, this paper estimates the long-term association between food insecurity and later adult health and health behaviors with the Probit model. The results show that food insecurity in early life significantly increases adults’ depression likelihood (measured by the CES-D scale). The food insecurity experience is also negatively and significantly associated with individual self-rated health status, memory, sleep quality, and life satisfaction. The negative association between food insecurity and cognitive ability and sleep hours is larger for females.

1. Introduction

The economic development level has been greatly improved in recent years; however, the phenomenon of food insecurity is still widespread. According to the State of Food Security and Nutrition in the World, more than 820 million people still suffer from hunger. Moreover, with the outbreak of the COVID-19 pandemic, the number of people suffering from hunger worldwide is increasing, particularly in developing countries and areas [1]. Food insecurity during childhood is not only related to individuals’ health status, but also their educational attainment and labor market performances [2].

Individual health status is a cumulative process over the life course, and childhood adversities may have lasting consequences on adults’ health status. The negative childhood experiences are associated with adults’ health through education levels, occupational choices, and life satisfaction [3,4] or intergenerational transmission [5]. Therefore, it is necessary to study the long-term relationship between early childhood experiences and adults’ health status, contributing to understanding the human capital accumulation with the life course model [6].

Extensive literature has examined the relationship between hunger experience during childhood and one’s health status, such as height, obesity, and hypertension [5,7,8]. Few empirical studies, however, have examined the long-run consequences of food insecurity experience on individual mental health [9,10], especially in developing countries [11,12,13].

The paper explores this issue by using the China Health and Retirement Longitudinal Study (CHARLS), a large nationally representative survey of the middle-aged and elderly population aged 45 years old or above. The survey is unique in the following two aspects. On one hand, it contains self-reported information on food insecurity, which is usually missing in other Chinese household surveys. On the other hand, it provides rich information on health indicators, enabling us to fully explore the health consequences of suffering from food insecurity during childhood.

The results show that food insecurity in early life is positively and significantly associated with one’s depression likelihood (measured by the CES-D scale). Childhood food insecurity is also related to poor self-rated health, memory and sleep quality, and low life satisfaction as adults. The negative association between food insecurity and cognitive ability and sleep hours is larger for females.

The contributions of the paper are as follows. First, while the literature mostly focused on other health outcomes, the paper explores the long-run relationship between food insecurity experience in early life and adults’ mental health and health behaviors in China, the largest developing county. Second, it contributes to a better understanding of the relationship between negative experiences during childhood and later life well-being by exploring the long-run health consequences of food insecurity [14].

2. Theoretical Framework and Literature Review

2.1. Theoretical Framework

Previous studies suggested that food insecurity was associated with depression, chronic diseases, cognitive impairment, and sleep disorders [15,16,17,18]. Food insecurity may invoke biological changes that have long-run health consequences or disrupt children’s environments [17,19].

On the one hand, food insecurity may result in nutritional deficiencies or poor diet quality, impairing one’s immune functioning and brain development [17,19]. For instance, people may intake inadequate essential vitamins and high-level of saturated fat and sugar due to food deprivation, triggering neuroinflammatory and damaging memory [17,20]. The impaired neurodevelopmental processes and brain functions may increase the risk of depression and have long-lasting consequences on children’s socio-emotional and cognitive development [21], decreasing the level of life satisfaction. In addition, food insecurity may also be related to sleep disorders through poor mental health or impaired immune functions [22]. Health behaviors and lifestyle-related behaviors, such as sleep disorders and physical inactivity, were also associated with chronic diseases in the long run [22].

On the other hand, food insecurity may also be connected with child development indirectly through parents’ well-being and parenting [19,23]. Parents may suffer from emotional distress and nutritional deficiencies due to household food insecurity, increasing parents’ depressive symptoms and decreasing parenting quality [24]. For instance, parents with emotional distress may reduce their investments and interactions with children, which impedes children’s cognitive development and is related to behavior problems [23].

The negative health consequences may be more prominent for vulnerable groups who are unable to cope with the adverse health shock, such as children from low-income or resources-constrained households [23]. For instance, based on the resource dilution theory [25], the number of siblings is negatively associated with the amount of parental resources a child receives from the family, such as money, food, and time [26]. Therefore, the negative consequences of food insecurity on health status may be larger for those with more siblings. In addition, compared with urban children, children in rural areas are at a higher risk of food insecurity and more vulnerable to negative health shocks due to limited financial resources and medical healthcare services [27]. The literature also suggested that women were especially vulnerable to food insecurity and socio-economic processes [28]. According to the theories of life-cycle skill formation [29], early investments not only have a large potential return itself, but also enhance the productivity of later investments. To cope with the negative consequences of food insufficiency, one (or her family) may direct resources away from other (or later) investments [29].

To summarize, the long-term relationship between food insecurity during childhood and adult health is expected to be negative. Such a negative relationship is likely to be larger for women and children without sufficient resources to cope with the adverse health shock.

2.2. Food Insecurity and Individual Health

The literature has examined the association between hunger experience or food insecurity during childhood and later life outcomes, including health status, educational attainment, labor market performance, and cognitive ability [5,15,30,31]. The literature suggested that childhood starvation experience was positively associated with low height and increased the likelihood of obesity and hypertension as adults [32]. Children who experienced the Dutch famine during 1944–1945 were likely to suffer from obesity, coronary heart disease, and breast cancer [7,18]. Some scholars pointed out that exposure to the Ningaloo famine during childhood increased the likelihood of cardiovascular disease and death as adults [33].

The literature suggested that early malnutrition reduced not only the likelihood of attending school, but also the learning effectiveness of individuals [34]. Based on the 1941–1942 famine in Greece, the evidence showed that experiencing famine in early life reduced years of schooling and the likelihood of graduating from high schools [27]. Others used the Zimbabwean civil war and drought shocks as proxy variables for malnutrition and suggested that improvements in nutritional status during childhood were beneficial in increasing height and years of schooling in adulthood [35].

In China, some researchers exploited the variations in effects of famine across cohorts and regions to examine the long-run consequences of the 1959–1961 famine on survivors’ health status and economic status. The results showed that famine experience was significantly associated with individuals’ height, work hours, income, and education level [31].

2.3. Food Insecurity Experience and Mental Health

Food insecurity in early life was also positively associated with one’s depressive symptoms, such as loneliness, fear, and poor memories [9,15,16,17,18]. The literature suggested that experiencing famine during fetal life increased the likelihood of congenital neurological abnormalities [9]. Experiencing Dutch famine during the fetal period may increase the risk of schizophrenia and other psychiatric disorders as adults, such as antisocial personality disorder and psychological disorders [10].

Based on data from Wuhu psychiatric Hospital and Liuzhou psychiatric Hospital in China, some researchers have suggested that people exposed to famine during the fetal or infant period were at a higher risk of schizophrenia as adults [11,36]. Studies from other scholars indicated that individuals who experienced famine during childhood were more likely to be depressed in adulthood [6,13].

3. Data

3.1. Data Source

The main datasets used in this paper are the 2015 wave and the 2014 life history survey of China Health and Retirement Longitudinal Study (CHARLS), which is the first nationally representative household survey focusing on the population aged 45 years old or above in China. The survey design is based on similar aging surveys in developed countries, such as the Health and Retirement Study (HRS) in U.S. and the Survey of Health, Aging and Retirement in Europe (SHARE). The sampling of the 2015 wave is based on the 2013 survey, which randomly selected 150 out of 2855 county-level units of 28 provinces (autonomous regions and municipalities) in China in 2013, then chose three communities from each county-level unit, and finally interviewed all age-eligible sample households in each community.

In 2014, the CHARLS team conducted the life history survey, including all live respondents in the first two waves (2011 wave and 2013 wave). The survey collects detailed information on all aspects of life history since birth, including one’s demographic background, education, health, work, migration, and family information. One unique feature of the survey is that it contains one’s hunger experience, which is usually missing in other Chinese household surveys.

In our analysis, we link the food insecurity information in CHARLS 2014 survey to CHARLS 2015 survey. All outcome variables and independent variables in our analysis are from CHARLS 2015 survey. In

Table 1. Descriptive Statistics.

	(1) All	(2) People with Food Insecurity	(3) People without Food Insecurity	(2)–(3)
Panel A: Health outcomes
CES-D	8.02	8.50	6.92	1.58 ***
Self-reported health	2.08	2.02	2.22	−0.20 ***
Cognitive ability	6.76	6.64	7.05	−0.40 ***
Memory	1.85	1.81	1.95	−0.14 ***
Sleep hours	6.34	6.26	6.52	−0.26 ***
Life satisfaction	0.92	0.91	0.93	−0.02 **
Panel B: Individual Characteristics
Male (%)	48.22	50.29	43.58	6.71 ***
Age	61.17	61.96	59.41	2.54 ***
Married (%)	86.68	86.60	86.85	0.25
Education Level
Primary school (%)	66.20	70.28	57.00	13.28 ***
Middle school (%)	21.88	19.50	27.23	−7.73 ***
High school or above (%)	11.92	10.22	15.76	−5.54 ***
Panel C: Household Characteristics
Number of siblings	3.97	4.02	3.84	0.18 ***
Low-income household during childhood	0.38	0.45	0.23	−0.22 ***
Parents had a serious deformity (%)	5.44	6.41	3.25	3.16 ***
Mother’s education level
Primary school (%)	98.03	98.80	96.29	2.52 ***
Middle school (%)	1.15	0.71	2.15	−1.44 ***
High school or above (%)	0.82	0.49	1.57	−1.08 ***
Father’s education level
Primary school (%)	92.02	93.67	88.31	5.36 ***
Middle school (%)	4.62	3.66	6.80	−3.13 ***
High school or above (%)	3.36	2.67	4.90	−2.22 ***
Panel D: Community Characteristics
Urban (%)	36.85	34.41	42.37	−7.96 ***
Access to road (%)	93.96	93.64	94.66	−1.02 **
Nearby access to hospital (%)	79.63	80.21	78.32	1.89 **
Per capital net income (1000 yuan)	4.91	4.65	5.49	−0.84 ***
Main terrain
Plain (%)	39.96	37.40	45.50	−8.01 ***
Hill (%)	31.34	32.11	29.60	2.51 **
Mountainous Region (%)	21.14	22.48	18.13	4.35 ***
Plateau (%)	4.73	4.76	4.65	0.11
Basin (%)	2.83	3.15	2.12	1.03 **
N	10,545	7326	3219

Notes: Column 4 shows t-test results for differences between people with food insecurity or without food insecurity. *** significant at 1% level, ** significant at 5% level. Sources: CHARLS 2014 and CHARLS 2015 survey data.

and

Table 2. Food insecurity experience and individual mental health.

	(1)	(2)	(3)	(4)
Food insecurity	0.173 ***	0.138 ***	0.138 ***	0.138 ***
	(0.022)	(0.023)	(0.023)	(0.023)
Individual Characteristics
Male	−0.344 ***	−0.355 ***	−0.359 ***	−0.360 ***
	(0.021)	(0.021)	(0.021)	(0.021)
Married	−0.268 ***	−0.265 ***	−0.267 ***	−0.267 ***
	(0.030)	(0.030)	(0.030)	(0.030)
Education Level
Middle school	−0.184 ***	−0.167 ***	−0.160 ***	−0.160 ***
	(0.026)	(0.026)	(0.025)	(0.025)
High school or above	−0.344 ***	−0.324 ***	−0.310 ***	−0.310 ***
	(0.036)	(0.036)	(0.035)	(0.035)
Household Characteristics
Number of siblings		−0.007	−0.007	−0.007
		(0.006)	(0.006)	(0.006)
Low-income household during childhood		0.156 ***	0.155 ***	0.155 ***
		(0.022)	(0.022)	(0.022)
Parents had a serious deformity		0.189 ***	0.187 ***	0.187 ***
		(0.045)	(0.046)	(0.046)
Mother’s education level
Middle school		−0.035	−0.040	−0.040
		(0.069)	(0.069)	(0.069)
High school or above		0.263 **	0.268 **	0.268 ***
		(0.104)	(0.104)	(0.104)
Father’s education level
Middle school		−0.028	−0.024	−0.024
		(0.049)	(0.049)	(0.049)
High school or above		−0.075	−0.065	−0.065
		(0.063)	(0.063)	(0.063)
Community Characteristics
Urban			−0.115 ***	−0.115 ***
			(0.040)	(0.040)
Access to road			−0.048	−0.049
			(0.070)	(0.071)
Nearby access to hospital			−0.038	−0.038
			(0.044)	(0.044)
Per capital net income			−0.025	−0.025
			(0.023)	(0.023)
Main terrain
Hill			−0.054	−0.054
			(0.063)	(0.063)
Mountainous Region			−0.116	−0.116
			(0.085)	(0.085)
Plateau			−0.195	−0.196
			(0.181)	(0.181)
Basin			−0.014	−0.015
			(0.121)	(0.122)
Cohort dummies	√	√	√	√
County fixed effects	√	√	√	√
County time trends				√
N	10,545	10,545	10,545	10,545

Notes: Robust standard errors clustered at the county level are shown in parentheses. *** significant at 1% level, ** significant at 5% level.

, we keep all observations that have non-missing values in all independent variables and the main outcome variable “depression”, and finally get 10,545 individuals.

Table 3. Food insecurity and depressive symptoms.

	(1)	(2)	(3)	(4)	(5)
	Be Annoyed	Can’t Concentrate	Be Depressed	Have Difficulty in Doing Things	Be Hopeful about the Future
Food insecurity	0.105 ***	0.128 ***	0.118 ***	0.169 ***	−0.032
	(0.027)	(0.030)	(0.026)	(0.030)	(0.026)
N	11,259	11,127	11,251	11,251	10,938
	(6)	(7)	(8)	(9)	(10)
	Be Fearful	Poor Sleep	Be Happy	Feel Lonely	Cannot Get Going
Food insecurity	0.037	0.123 ***	−0.113 ***	0.110 ***	0.101 **
	(0.033)	(0.027)	(0.023)	(0.030)	(0.032)
N	11,327	11,336	11,265	11,282	11,237

Notes: All regressions control for cohort dummies, gender, marital status, education level, number of siblings, parents’ education level, whether parents had a serious deformity, household income status during childhood, urban community, whether they have access to road, whether they have access to hospital care, per capita net income and terrain type of the community, county fixed effects, and county-specific time trends. Robust standard errors clustered at the county level are shown in parentheses. *** significant at 1% level, ** significant at 5% level.

,

Table 4. Food insecurity and other outcomes.

	(1)	(2)	(3)	(4)	(5)
	Self-Rated Health	Cognitive Ability	Memory	Sleep Hours	Life Satisfaction
Food insecurity	−0.133 ***	0.008	−0.084 ***	−0.073 ***	−0.121 ***
	(0.028)	(0.025)	(0.026)	(0.024)	(0.044)
N	11,411	11,816	11,371	11,268	11,313

Notes: All regressions control for cohort dummies, gender, marital status, education level, number of siblings, parents’ education level, whether parents had a serious deformity, household income status during childhood, urban community, whether they have access to road, whether they have access to hospital care, per capita net income and terrain type of the community, county fixed effects, and county-specific time trends. Robust standard errors clustered at the county level are shown in parentheses. *** significant at 1% level.

,

Table 5. Food insecurity at different stages and health outcomes.

	(1) CES-D	(2) Self-Rated Health	(3) Cognitive Ability	(4) Memory	(5) Life Satisfaction	(6) Sleep Hours
Reference group: without food insecurity
Food insecurity at age 0–5	0.112 ***	−0.046 **	−0.051 **	−0.081 ***	−0.113 **	−0.049 **
	(0.024)	(0.023)	(0.022)	(0.027)	(0.050)	(0.023)
Food insecurity at age 6–12	0.062 **	−0.095 ***	0.045	−0.001	−0.065	−0.023
	(0.025)	(0.026)	(0.025)	(0.029)	(0.050)	(0.026)
Food insecurity at age 13–17	0.072 ***	−0.051	−0.024	−0.047	−0.048	−0.020
	(0.022)	(0.026)	(0.026)	(0.027)	(0.042)	(0.023)
N	10,590	11,464	11,880	11,423	11,364	11,319

Notes: All regressions control for cohort dummies, gender, marital status, education level, number of siblings, parents’ education level, whether parents had a serious deformity, household income status during childhood, urban community, whether they have access to road, whether they have access to hospital care, per capita net income and terrain type of the community, county fixed effects, and county-specific time trends. Robust standard errors clustered at the county level are shown in parentheses. *** significant at 1% level, ** significant at 5% level.

and

Table 6. Robustness checks.

	(1)	(2)	(3)	(4)	(5)	(6)
	CES-D	Self-Rated Health	Cognitive Ability	Memory	Sleep Hours	Life Satisfaction
Panel A: logit model
Food insecurity	0.239 ***	−0.235 ***	0.0124	−0.151 ***	−0.117 ***	−0.241 ***
	(0.040)	(0.048)	(0.042)	(0.045)	(0.041)	(0.089)
N	10,545	11,411	11,816	11,371	11,268	11,313
Panel B: including village/community fixed effects
Food insecurity	0.134 ***	−0.110 ***	−0.030	−0.077	−0.061	−0.099 ***
	(0.024)	(0.028)	(0.028)	(0.032)	(0.029)	(0.031)
N	10,545	12,064	12,093	11,302	11,192	11,244
Panel C: including health status during childhood
Food insecurity	0.132 ***	−0.125 ***	0.007	−0.080 ***	−0.073 ***	−0.111 **
	(0.022)	(0.028)	(0.025)	(0.026)	(0.023)	(0.045)
Health status during childhood	−0.275 ***	0.333 ***	0.044	0.218 ***	0.139 ***	0.231 ***
	(0.029)	(0.034)	(0.030)	(0.035)	(0.034)	(0.051)
N	10,525	11,387	11,785	11,347	11,244	11,292
Panel D: excluding migrant sample
Food insecurity	0.134 ***	−0.106 ***	0.028	−0.081 ***	−0.070 ***	−0.140 ***
	(0.025)	(0.032)	(0.028)	(0.029)	(0.025)	(0.051)
N	8795	9530	9846	9499	9413	9452

Notes: All regressions control for cohort dummies, gender, marital status, education level, number of siblings, parents’ education level, whether parents had a serious deformity, household income status during childhood, urban community, whether they have access to road, whether they have access to hospital care, per capita net income and terrain type of the community, county fixed effects, and county-specific time trends. Robust standard errors clustered at the county level are shown in parentheses. *** significant at 1% level, ** significant at 5% level.

contain different outcomes and, thus, have different numbers of observations.

3.2. Main Variables

We define the key explanatory variable “food insecurity” based on respondents’ answers to the question from the 2014 life history survey: “When you were a child before age 17, was there ever a time when your family did not have enough food to eat?” We construct a dummy variable indicating whether one experienced food insecurity, with 0 for “No” and 1 otherwise. In the analysis, 67.32% of respondents experienced food insecurity in early life. The respondents also report age ranges during which they experienced food insecurity based on the question: “At what age ranges did this (your family had not enough food to eat) happen?” The options range from 1 to 3, representing “age 0–5”, “age 6–12”, and “age 13–17”.

The CHARLS contains a Chinese version of the Center for Epidemiologic Studies Depression Scale (CES-D) questionnaire, including ten questions about depression status on a four-point scale (with zero being the least serious and three being the most serious), such as whether the respondents felt lonely, fearful, or hopeful about the future last week. Following previous literature, we construct the depression variable based on the CES-D scale that ranges from 0 to 30 by aggregating answers to the ten questions to measure individual mental health [37].

The 2015 wave of CHARLS provides rich information on health outcomes and health behaviors. The first one is self-reported general health status on a four-point scale that is assumed to be closely associated with current and future objective health status [38], with 1 being poor and 4 being excellent.

The CHARLS questionnaire assesses one’s cognitive ability with a series of questions: whether they answer the date of the day correctly, whether they correctly calculate the result of subtracting 7 from 100 for five times in a row, whether they accurately recall the ten words the interviewer read, and could draw the graphs presented. The paper generates the cognitive ability variable by aggregating answers to the questions, with a final score of 0 to 11.

The paper measures one’s memory and life satisfaction based on answers to the question: “How would you rate your memory at the present time? Would you say it is excellent, very good, good, fair, or poor.” and “Please think about your life as a whole. How satisfied are you with it?” To measure one’s sleep quality, we construct the “sleep hours” variable based on the question: “In the past month, how many hours did you actually fall asleep for every night on average”. The respondents also reported their sleep quality with the statement:” My sleep was restless”. The options range from 1 to 4, with 1 being “rarely or none of time” and 4 being “most or all of the time”.

Table 1 is summary statistics of the main variables in terms of food insecurity. Panel A suggests that there are significant differences in health status between the two groups. People who experienced food insecurity in early life are more likely to be depressed, have poorer self-rated health, cognitive ability, memory, sleep hours, and lower overall life satisfaction in adulthood. Panels B and C show that people who experienced food insecurity are older, more likely to be male, and have parents with lower education levels. Panel D shows that people with food insecurity reside in villages that are poorer and more likely to be located in hills and mountainous regions, suggesting that food deprivation is more likely to happen in less developed regions.

In summary, people with food insecurity in early life seem to have poorer health status, and their characteristics differ significantly. Next, the paper further examines the association between food insecurity and individual health outcomes within a regression framework.

3.3. Empirical Strategy

We are interested in estimating the long-run association between food insecurity experience and adults’ health status and health behaviors. We start with the following regression model:

$${Health}_i={\beta }_0+{\beta }_{1}food\_{insecurity}_i+{\beta }_2{X}_i+{\epsilon }_i$$

(1)

where the dependent variable, Health, is one’s health status and health behaviors, including depression status, self-rated health, cognitive ability, memory, sleep hours, and overall life satisfaction. The coefficient of interest is ${\beta }_1$ on the key explanatory variable food_insecurity, a dummy variable defined to be one if an individual experienced food insecurity during childhood. The vector X contains the individual-level (cohort dummies, one’s gender, marital status, and education level), household-level (the number of siblings, parents’ education level, household income status during childhood, and whether parents had a serious deformity during childhood), and community-level variables (urban community, access to road, access to hospital care, and per capita net income and terrain type of the community).

The estimation of the above equation may be biased due to endogeneity concerns of food insecurity experience [27]. Some unobservable factors may be related to both early food insecurity experience and individual current health status. For example, people whose families were poor during childhood are more likely to suffer from hunger and food deprivation, and may also be in poorer health as adults. In this case, the above regression could overestimate the consequences of food insecurity on one’s health status.

In addition to possible omitted variables, the estimation of the food insecurity experience may be subject to measurement errors because our measure of food insecurity experience is self-reported. Respondents may have “recall bias” when recalling whether or not they experienced food insufficiency during childhood, leading to biased estimates. The literature showed that people were less likely to have recall bias when recalling some unique events, such as relocation or famine experiences [5]. Food insufficiency experience is also one of the significant and definitive shocks throughout the life, therefore the measurement error concerns due to recall bias may be minor in the analysis.

4. Estimation Results

4.1. The Long-Run Relationship between Food Insecurity Experience and Individual Health

Table 2 shows estimation results of the relationship between early food insecurity experience and adults’ mental health with the ordered Probit model. All columns include county fixed effects, and sequentially add individual-, household-, community-level characteristics, and county-specific time trends in Columns 1 to 4. The results suggest that food insufficiency experience is negatively and significantly associated with one’s mental health.

In terms of individual characteristics, being male, married, and education level are also negatively significantly associated with depression status. Marriage may provide informal family care and emotional support, reducing the likelihood of being depressed [39]. An increase in education level can improve household income, material living standards, and one’s health awareness, which is beneficial to health status. Families with low income during childhood and parents with severe deformities are negatively connected with one’s mental health.

To further illustrate the association between food insecurity and individual mental health, the paper regresses the ten items of CES-D on food insecurity experience. The results in Table 3 indicate that food insecurity is negatively and significantly associated with almost all mental health outcomes. Individuals who experienced food insecurity in early life are more likely to feel annoyed, lonely, anxious, painful, and have sleep problems [13].

Table 4 presents estimation results with other health outcomes and health behaviors. The results show that food insecurity experience is significantly and negatively associated with one’s self-rated health status, memory, life satisfaction, and sleep hours.

The paper further explores the negative consequences of experiencing food insufficiency at different stages. The results in Table 5 show that such a negative relationship is more prominent for those who experienced food insecurity before age five. We also take the group who experienced food insecurity at age 6–12 as the reference group in

Table A2. Food insecurity at age 0–5 and age 13–17 and health outcomes.

	(1)	(2)	(3)	(4)	(5)	(6)
	CES-D	Self-Rated Health	Cognitive Ability	Memory	Sleep Hours	Life Satisfaction
Reference group: food insecurity at age 6–12
Without food insecurity	−0.046	0.088 ***	−0.056 **	0.033	0.049	0.028
	(0.030)	(0.033)	(0.028)	(0.033)	(0.028)	(0.057)
Food insecurity at age 0–5	0.116 ***	−0.049	−0.058 **	−0.067 **	−0.039	−0.127 **
	(0.027)	(0.025)	(0.024)	(0.030)	(0.025)	(0.052)
Food insecurity at age 13–17	0.072 ***	−0.043	−0.039	−0.036	−0.009	−0.053
	(0.024)	(0.026)	(0.027)	(0.027)	(0.024)	(0.045)
N	10,545	11,411	11,816	11,371	11,268	11,313

Notes: All regressions control for cohort dummies, gender, marital status, education level, number of siblings, parents’ education level, whether parents had a serious deformity, household income status during childhood, urban community, whether they have access to road, whether they have access to hospital care, per capita net income and terrain type of the community, county fixed effects, and county-specific time trends. Robust standard errors clustered at the county level are shown in parentheses. *** significant at 1% level, ** significant at 5% level.

in Appendix A and the results are consistent. The findings are consistent with previous studies, which indicated that the negative consequences of early childhood shocks on health development were the most significant and pronounced [14]. Early life nutrition during the critical period has profound and persistent consequences on neurological development and health [9,14]. Some scholars pointed out that, compared with other age stages, the first two years were the fastest period for postnatal brain development and the most critical for cognitive development and general health [40,41]. Malnutrition, therefore, during the period may be related to permanent deficits in brain and behavioral function [9,14]. The literature also suggested that cognitive abilities, such as IQ, were relatively stable by about ten years old [42], and the negative consequences of food insecurity during the period may be modest.

4.2. Robustness Checks

In order to test the robustness of the estimation results, the paper estimates the association between food insecurity and health status using the logit model, including village/community fixed effects and other possible omitted variables, and exploring the possible self-selection concerns due to migration.

First, Panel A in Table 6 is the estimation results with the logit model and shows that the direction and significance of coefficients change little. To further account for possible time-invariant unobservable factors at the village/community level that affect food insecurity and adult health status, the paper includes the village/community fixed effect in Panel B. The results suggest that the main findings are robust.

Second, individual health status during childhood may affect both the likelihood of experiencing food insecurity and health status as adults. Health status during childhood, however, may be an outcome variable of food insecurity. While we are not sure whether it is an omitted variable or outcome variable, it is interesting to see how the variable inclusion alters estimation coefficients. The paper constructs the “individual health status during childhood” variable based on the question in CHARLS 2014 questionnaire: “How would you like to evaluate your health before you were 15 years old (including 15 years old)” and add the variable in the regression model. Panel C in Table 6 suggests the main findings are robust when controlling for one’s health status during childhood.

Finally, to explore possible self-selection concerns due to migration, the paper examines the relationship between one’s food insecurity experience and her migration possibility. As

Table A1. The correlation between migration and food insecurity experience.

	Migration
Food insecurity experience	−0.008
	(0.008)
R2	0.066
N	10,475

Notes: All regressions control for cohort dummies, gender, marital status, education level, number of siblings, parents’ education level, whether parents had a serious deformity, household income status during childhood, urban community, whether they have access to road, whether they have access to hospital care, per capita net income and terrain type of the community, county fixed effects, and county-specific time trends. Robust standard errors clustered at the county level are shown in parentheses.

of the Appendix A shows, the correlation is very weak (the coefficient is 0.008) and insignificant. To further alleviate the concerns, the paper repeats the main analysis by excluding those whose current residence is not the same as their birthplace. The coefficients in Panel D of Table 6 change little, suggesting the migration concerns may be minor.

4.3. Heterogeneity

We further discuss the long-run health consequences of food insecurity across different groups. In particular, we show differences in terms of gender, areas, and number of siblings using the CHARLS data.

The results in Panel A of

Table 7. Heterogeneity.

	(1)	(2)	(3)	(4)	(5)	(6)
	CES-D	Self-Rated Health	Cognitive Ability	Memory	Sleep Hours	Life Satisfaction
Panel A: in terms of gender
Food insecurity	0.173 ***	−0.142 ***	−0.043	−0.110 ***	−0.129 ***	−0.149 ***
	(0.031)	(0.033)	(0.030)	(0.034)	(0.030)	(0.055)
Male	−0.308 ***	0.182 ***	0.339 ***	0.257 ***	0.090 **	0.152 **
	(0.037)	(0.038)	(0.036)	(0.039)	(0.036)	(0.072)
Male × Food insecurity	−0.074	0.019	0.112 ***	0.055	0.122 ***	0.071
	(0.044)	(0.045)	(0.042)	(0.047)	(0.042)	(0.083)
N	10,545	11,411	11,816	11,371	11,268	11,313
Panel B: in terms of urban community
Food insecurity	0.157 ***	−0.156 ***	−0.021	−0.116 ***	−0.066 **	−0.137 ***
	(0.030)	(0.031)	(0.028)	(0.032)	(0.029)	(0.053)
Urban	−0.081	−0.021	0.158 ***	0.090	−0.026	0.054
	(0.044)	(0.046)	(0.043)	(0.047)	(0.043)	(0.085)
Urban × Food insecurity	−0.049	0.058	0.075	0.080	−0.019	0.048
	(0.045)	(0.047)	(0.044)	(0.048)	(0.044)	(0.087)
N	10,545	11,411	11,816	11,371	11,268	11,313
Panel C: in terms of number of siblings
Food insecurity	0.066	−0.129 ***	0.015	−0.018	−0.058	0.009
	(0.048)	(0.050)	(0.046)	(0.051)	(0.046)	(0.086)
More than three siblings	−0.073	0.001	0.046	0.033	0.035	0.162
	(0.049)	(0.051)	(0.047)	(0.052)	(0.047)	(0.090)
More than three siblings × Food insecurity	0.092	−0.005	−0.009	−0.084	−0.019	−0.170
	(0.053)	(0.055)	(0.051)	(0.056)	(0.051)	(0.096)
N	10,545	11,411	11,816	11,371	11,268	11,313

Notes: All regressions control for cohort dummies, gender, marital status, education level, number of siblings, parents’ education level, whether parents had a serious deformity, household income status during childhood, urban community, whether they have access to road, whether they have access to hospital care, per capita net income and terrain type of the community, county fixed effects, and county-specific time trends. Robust standard errors clustered at the county level are shown in parentheses. *** significant at 1% level, ** significant at 5% level.

show that the association between food insecurity and cognitive ability and sleep hours is larger for females. However, we do not find the significant differences in health consequences of food insecurity for those who come from urban or rural areas or with a different number of siblings in Panels B and C.

4.4. Discussion

The findings are consistent with previous research on food insecurity [12,15,18,43]. The systematic review on food insecurity suggested that food insecurity was associated with one’s depression status and stress. Other scholars conducted a meta-analytic investigation and found a strong association between food insecurity and depression, anxiety, and sleep disorders [15]. With a nationally representative sample of students from kindergarten to eighth grade in Canada, the study showed that cumulative and persistent food insecurity was detrimental to health status [18].

Our study is also closely related to the human capital theory, which indicates that human capital formation is a dynamic process and shocks during childhood have persistent consequences on the children’s human capital accumulation and subsequent socioeconomic outcomes [14]. Like other forms of adversity in early life, the negative health consequences of food insecurity can persist into adulthood [44].

With the high prevalence of food insecurity and lack of interventions in China [45,46] and other low-income countries [45], food insecurity remains a persistent problem. Given the effectiveness of the food nutrition assistance programs in reducing food deprivation [46], it is important to ensure that young children and vulnerable families participate in the program. In addition, as the theoretical framework indicates, food insecurity is connected with one’s health through parents’ well-being and emotional status. Thus, the food assistance program should also help parents reduce stress and create favorable environments for child development [19].

In recent years, the Chinese government has promulgated a series of national regulations to promote national nutrition initiatives and healthy eating behaviors, such as the Management Measures of Nutrition Improvement Work issued in 2010 [47], the National Nutrition Plan (2017–2030) in 2017 [48], and Heathy China Initiative (2019–2030) in 2019 [49]. In addition to the regulation, the Chinese government also has implemented food assistance programs. For instance, on 23 November 2011, the General Office of the State Council launched a national school meal program targeting rural students–the Nutrition Improvement Program for Rural Compulsory Education Students (NIPRCES) [50]. Children in primary and middle schools received a 600 CHY subsidy per annum for school meals. With the program, the prevalence of food insecurity has decreased and cognitive function has improved among children [50]. In 2012, the China National Health Commission and other related institutions established the Nutrition Improvement Program [51] to improve nutrition and lifestyles for children in poor regions. The program provided children with a free daily nutrition packet and organized health activities to increase their nutrition awareness. In 2013 and 2019, the Central Finance further increased the investment, and more than 900 million children have benefited from the program, which greatly improved their health status [51].

As our study indicates, food insecurity has long-run associations with one’s mental health. Besides increasing food quantity and quality for children, the food assistance program should provide professional assistance to reduce children’s and parents’ emotional distress due to food insecurity, especially for females. For instance, the nutrition programs could incorporate nutrition education into the school curriculum. In addition, schools also could offer after–school activities or training sessions related to nutrition education to promote parents’ nutritional knowledge and healthy behaviors.

Our study has the following limitations. First, the paper uses a single question from a retrospective survey to measure food insecurity during childhood, which is potentially subject to recall bias. More work can be done if we get more detailed information on food insecurity, such as the severity or length of exposure. Second, without the longitudinal data or exogenous shocks, or other means of definitively establishing causality, our estimates may be better interpreted as correlations between early food insufficiency and adult health though we have done a series of robustness checks to account for obvious confounding factors.

Despite these limitations, we are among the few studies that examined the long-run association between food insecurity and adult health in developing countries. Our study suggests that food insecurity in early life (before 17 years old) is associated with higher levels of depression, poorer general health, memory, and sleep quality, and lower life satisfaction for adults aged 45 or above, almost 30 years after food insecurity. Most of the previous research, however, was focused on the short- or middle-term consequences of food insecurity or used unrepresentative samples [43,52]. For instance, with Canadian National Longitudinal Survey of Children and Youth (NLSCY) data, the evidence showed that children and youth with hunger experiences were more likely to have poor health in youth [43].

5. Conclusions

The paper examines the long-term relationship between food insecurity experience during childhood and individual health status and health behaviors based on the Probit model and China Health and Retirement Longitudinal Survey (CHARLS). The results show that early food insecurity experience significantly increases adults’ depression status measured with CES-D. The food insecurity experience is also negatively and significantly associated with individual self-rated health status, memory, sleep quality, and life satisfaction. The negative association between food insecurity and cognitive ability and sleep hours is larger for females.