“At three years of age, we can see the future”: Cognitive skills and the life cycle of rural Chinese children

Abstract

Background

While the Chinese education system has seen massive improvements over the past few decades, there still exists large academic achievement gaps between rural and urban areas, which threaten China’s long-term development. Additionally, recent literature has underscored the importance of early childhood development (ECD) in later-life human capital development.

Objectives

We analyze the lifecycle of cognitive development and learning outcomes in rural Chinese children by first examining if ECD outcomes affect cognition levels, then seeing if cognitive delays persist as children grow, and finally exploring connections between cognition and education outcomes.

Methods

We combine data from four recent studies examining different age groups (0-3, 4-5, 10-11, 13-14) to track cognitive outcomes.

Results

First, we find that ECD outcomes for children in rural China are poor, with almost one-in-two children being cognitively delayed. Second, we find that these cognitive delays seem to persist into middle school, with almost 37% of rural junior high school students being cognitively delayed. Finally, we show that cognition has a close relationship to academic achievement.

Conclusion

Our results suggest that urban/rural gaps in academic achievement originate at least in part from differences in ECD outcomes.

Contributions

While many papers have analyzed ECD, human capital, and inequality separately, this is the first paper to explicitly connect and combine these topics to analyze the lifecycle of cognitive development in the context of rural China.

Introduction

While China’s education system has seen significant improvement over the past two decades, large gaps in performance exist between urban and rural children in China due to substantial structural inequalities. These structural inequalities severely limit the potential of rural students. For example, while overall school access has improved significantly (Chung & Mason, 2012), rural schooling facilities are still much worse than urban facilities, despite recent and substantial efforts to address this gap (Wang et al. 2009). Health issues such as anemia (Li et al., 2018), intestinal worms (Liu et al., 2017) and myopia (Nie et al., 2018) continue to plague rural students, dragging down their academic ability and increasing the urban/rural gap.

Although there are many potential reasons why these education gaps exist in China, new research suggests that differences in early childhood development (ECD) may be contributing to these gaps. Indeed, healthy ECD is increasingly known to be a key component to a successful life cycle of human capital accumulation and has been linked to a variety of positive long-term outcomes in health, education, employment, and adult earnings (Attansio et al. 2015; Heckman et al, 2006; Heckman et al. 2010; Currie amp; Almond 2011; Knudsen et al., 2006). Unfortunately, the inverse is true as well: poor ECD—as may be caused by malnutrition or insufficient stimulation—is known to be linked with developmental delays in toddlers and can lead to low levels of human capital accumulation and poorer outcomes later in life (Grantham-McGregor et al., 2007). Differences in these human capital outcomes may perpetuate poverty, leading to social inequities and even slowing economic growth (Yousafzai et al., 2014; Heckman amp; Masterov, 2007; Wang et al., 2018; UNICEF, 2016).

Despite the well-documented economic and social benefits of ECD investments, poor cognitive development remains a significant problem among young children in developing countries. A recent Lancet review paper estimates that 250 million (43%) children under the age of five in low- to middle-income countries are at risk for development delays and reduced cognition, suggesting that poor cognitive development remains an important issue in underdeveloped countries and regions (Black et al., 2017). Recent work in China demonstrates that there are still high rates of cognitive delay in China today (Wang et al., 2019). Such delays in early childhood have implications for education and later life accumulation of human capital because delayed children are more likely to drop out of school and those who remain tend to have lower achievement rates than their peers (Grantham-McGregor et al, 2007; Black et al., 2017).

Given the persistence of poor cognitive outcomes in underdeveloped areas, it is possible that disparities in ECD outcomes perpetuate the substantial education gaps that exist between China’s urban and rural children. A recent study found that almost 15% of students among four rural counties in Shanxi and Shaanxi provinces dropped out of junior high school before completion. Students from poorer families with worse academic performance than their peers were much more likely to drop out (Yi et al., 2012). Unless children receive the necessary quantity (years of schooling) and quality (learning) of education (Wang et al. 2018), large shares of rural students will be unable to develop the human capital needed to participate in the future knowledge-based, high income economy that China hopes will be emerging in the near future.

The overall goal of this paper is to examine the life cycle of learning outcomes and associations of cognitive development levels with these learning outcomes. There is a Chinese proverb that translates to “At three years of age, we can see a child’s future.” Our objective is to test this proverb empirically using data on children’s cognitive development in China. We have three specific objectives. First, we will review the empirical literature on rural China and examine the nature of ECD outcomes and developmental delays in rural China today. Second, we will study if poor cognitive outcomes persist through an individual’s early life cycle beyond age three. Third, we will investigate the relationship between cognition and academic outcomes in schools.

Methods

To meet our objectives, we draw on analyses from four different papers. These four papers each examine the cognitive development of one of the four age cohorts that we analyze. The first paper, “Are infant/toddler developmental delays a Problem across China?” (Wang et al., 2019), examines ECD outcomes across a large sample of infants and toddlers, aged 0 to 3, in rural China. The second paper, “The Persistence and Fade-out Paradox: New Evidence on Medium Run Effects of an Early Childhood Development Intervention” (Wang et al., 2019), focuses on young children, ages 4-5. The third manuscript, “Better Cognition, Better School Performance?: Evidence from Primary Schools,” (Zhao et al., 2019) studies the nature of cognition and academic performance in rural Chinese elementary schools among third and fourth graders, ages around 9 to 10. Finally, the fourth paper, “IQ, Grit, and Academic Achievement: Evidence from Rural China,” (He et al., 2019) investigates the effects of cognition, ‘grit’ on academic achievement among rural seventh graders, ages around 13 years old. These four papers all used similar randomized sampling strategies, and were conducted by the same research group.

Results

The Nature of ECD in Rural China

Our analysis of ECD outcomes in rural China shows that infants and toddlers have a high prevalence of developmental delays. In our sample, 49% of infants were cognitively delayed, 52% had language delays and 53% had social emotional delays (Figure 1, first three bars). In other words, in these three key variables nearly half of all those in our sample had measures that were less than −1 standard deviation below the average of international norms. While the level of motor delays was less (31%--Figure 1, fourth bar), more than twice as many infants and toddlers in our sample had motor delays compared with counterparts in healthy populations. While not shown in Figure 1, fully 85% of individual infant/toddlers was delayed in at least one dimension. This overall level of cognitive/non-cognitive delay is many times higher than the 15% rate that we expect to find in a healthy population.

Figure 1: Developmental Delays in 6-30 Month Children.

Data source: Lei Wang, Wilson Liang, Siqi Zhang, Laura Johnson, Mengjie Li, Cordelia Yu, Yongli Sun, Qingrui Ma, Yu Bai, Cody Abbey, Renfu Luo, Ai Yue, Scott Rozzelle., 2019. “Are Infant/Toddler Developmental Delays a Problem across Rural China?” (Rural Education Action Project Working Paper) Stanford CA: Stanford University.

Note 1. Cognitive delay, language delay, social-emotional delay and motor delay measured using the third edition of the Bayley Scales (Bayley-III).

Note 2. Developmental delay is measured by the share of children with cognition scores less than −1 standard deviations

Unfortunately, this conclusion is consistent with other studies that examine cognitive development in rural China. A large (n= 1,442) 2014 survey of rural households in Shaanxi province found that one in two rural toddlers were cognitively delayed (Yue et al., 2017). Such findings are confirmed by other studies (Luo et al., 2017; Dill et al., 2019). Moreover, these rates of cognitive/non-cognitive delays are much higher than those found in urban China. Studies surveying infants and children from urban areas in China find rates of cognitive delays range from 5% to 16% percent—a range that is much lower than found in rural areas, but that is consistent with healthy populations found elsewhere in the world (Dill et al., 2019).

Persistence of Cognitive Delays

The results of studies in China also suggest that these cognitive delays in infants persist, or at least do not significantly improve, as children grow. Table 1 and Figure 2 illustrate this by tracking cognitive scores and the amount of cognitive delays in each age cohort: infants, toddlers, children, and youths. In the case of infants (that we described in the previous section), 49% exhibited cognitive delays. As seen in the table, when these infants and toddlers grow into young children (4-5 years old), we see this situation only modestly improves: 42.7% of children are cognitively delayed. The average cognitive score is only 2.8 points above 85 points, the cutoff value for cognitive delay and far below the mean that would be expected in a healthy population (100 points).

Table 1:

Average Cognition and rates of cognitive delay in each age cohort

	Age cohort
	Infants and Toddlers (2-3 years old)	Young children (4 to 5 years old)	Older Children (10 to 11 years old)	Youth (13 to 14 years old)
Average IQ	--	87.8	87.75	87.4
Percentage of sample with cognitive delays	49%	42.7%	36.8%	37.4%

Data sources:

Column 1: Qiran Zhao, Xiaobing Wang, Scott Rozelle, 2019 “Better Cognition, Better School Performance? Evidence from Primary Schools in China.” (Rural Education Action Project Working Paper) China Economic Review.

Column 2: Lei Wang, Yiwei Qian, Scott Rozelle, Chuyu Song, Nele Warrinnier and Sean Sylvia., 2019. “The Persistence and Fade-out Paradox :New Evidence on Medium-Run Effects of an Early Childhood Development Intervention.” (Rural Education Action Project Working Paper) Stanford CA: Stanford University.

Column 3: Qiran Zhao, Xiaobing Wang, Scott Rozelle, 2019 “Better Cognition, Better School Performance? Evidence from Primary Schools in China.” (Rural Education Action Project Working Paper) China Economic Review.

Column 4: Xinyue He, Fang Chang, Huan Wang, Sarah-Eve Dill, Scott Rozelle, Prashant Loyalka.,“IQ, Grit, and Academic Achievement: Evidence from Rural China.” (Rural Education Action Program Working Paper). Stanford, CA: Stanford University.

Note 1: Cognition scores are measured by Bayley’s III for column 1, Weschler Preschool and Primary Scale of Intelligence (WPPSI) for column 2, Raven Intelligence Quality (IQ) scale for column 3, and a weighted average of Wechsler Intelligence Scale for Children (WISC IQ) and Raven’s Standard Progressive Matrices (Raven IQ) for column 4.

Note 2: For all columns, Cognitive delay is measured by the share of young children with cognition scores less than −1 standard deviations (at 85 IQ cutoff).

Figure 2: Percentage of Children With Cognitive Delays in Each Age Group in Rural china.

Data source: Authors’ review.

Note: We integrate the statistics of cognitive delays for each age group, 49% for the group of 0-3 years old comes from Figure 1 (Developmental Delays in 6-30 Month Children);42.7% for the group of 4-5 years old comes from Figure 2 (Cognitive Delays in Rural China 4-5 Years Old); 36.8% for the group of 10-11 years old comes from Figure 3 (Cognitive Development (IQ) among Rural China Primary-School Students); 37.4% for the group of 13-14 years old is a weighted average of 40% and 37% from Figure 4 (Cognitive Development among Rural China Middle School Students).

Moving on to the next age group (elementary school-aged children), our results show that older children have an average Raven’s cognition score of 87.7 points, and 36.8% are cognitively delayed. The average score, 87.7 points, once again, is just above the cutoff value for cognitive delay and far from what one would see with a healthy population. Similarly, although 36.8% (the rate of delay of elementary school-aged children in our sample) is better than 42.7% (that rate of delay of pre-school aged children in our sample), this still means that more than one in three rural Chinese children are cognitively delayed.

This situation does not appear to improve when these elementary school-aged children grow into youth (junior high school-aged children). According to the data, these children have a weighted average of Raven’s and WISC cognition scores that reveals an average cognition score of 87.4 and a 37.4% prevalence of cognitive delays. This suggests that cognitive delays in infancy persist as children mature, as one in three youth are still cognitively delayed. In fact, given the nature of this study, we actually can causally demonstrate this persistence. This persistence is supported by the literature: cognitive scores change very little over time from 3 to 18 years of age (Brooks-Gunn et al. 2006, as referenced in Heckman, 2013.

Cognition and Academic Performance

To address our final objective, in this section we explore the relationship between cognition and academic achievement. As can be seen in Table 2, IQ and academic performance of older children who are in rural elementary schools are highly correlated regardless of the exact specification (row 1). When controlling for individual and family effects (only), a one-point increase in IQ is associated with an increase of math scores by 0.037 standard deviations (SDs) (column 1). When we add additional controls for school characteristics (by adding school fixed effects), a one point increase in IQ predicts a 0.034 SD increase in math scores (column 2). Similar results are found when adding class fixed effects (column 3).

Table 2:

Multivariate Analysis of Correlates of IQ Scores and Standardized Math Scores (Dependent Variable) for Primary School Students in Henan and Anhui provinces Using Regression Models Without and With School Fixed Effects.

Variable Namesa	Without School Fixed Effect (1)	With School Fixed effect (2)	With Class Fixed Effect (3)
IQ scores	0.037 (p<0.01) (0.001)	0.034 (p<0.01) (0.001)	0.034 (p<0.01) (0.001)
School fixed effects	no	yes	yes
Class fixed effects	no	no	yes
Family characteristics a	yes	yes	yes
Individual characteristics b	yes	yes	yes
Baseline math scores	no	no	no
Observations	3,109	3,109	3,109
R-squared	0.348	0.433	0.463

Data Source: Author’s survey from Zhao et al. 2019 “Better Cognition, Better School Performance: Evidence from Primary Schools in China.”

Note: Standard errors in parentheses below coefficients

^a:Family characteristics are controlled by variables that measure number of siblings a respondent has, household assets, mother’s and father’s age and education, and father’s drinking habits and smoking habits

^b:Individual characteristics are controlled variables that indicate gender and if respondent has attended preschool

Assuming the relationship between IQ and math scores is linear, the difference in math scores between a student with an IQ of 85 (the cutoff value for cognitive delay) and a student with an IQ of 100 (the mean value in a healthy population) is approximately 0.5 SDs. Similarly, there is a difference of 0.44 SDs when comparing the average IQ score of our sample (87.04 points) with the average IQ score of a healthy population (100 points). For perspective, a difference of 0.5 SDs represents approximately one semester to one year’s worth of learning (Dill et al., 2019); this means that the average elementary student in rural China is about one semester to one year behind.

We also examine the correlations of cognition and academic achievement among rural Chinese youth who are attending junior high school. As with 4th and 5th graders in elementary school, there is a strong correlation between cognition and academic performance among rural youth. The dependent variable in Table 3 is standardized achievement scores on math tests administered at the end of the seventh grade. Two different measures of IQ are used in these regressions depending on the sample: WISC-IQ from the WISC test and SPM-IQ from the Ravens test. These regressions control for baseline math ability (as collected from math scales administered by the research team at the beginning of the year), as well as controlling for family, individual, and class characteristics.

Table 3:

Value-added relationship between cognitive IQ and academic achievement for entire sample of junior high school students in Gansu and Shaanxi provinces.

Independent Variable names	Dependent variable: Standardized Math Scores
WISC-IQ scores	0.03 (p<0.01) (0.00)
SPM-IQ scores		0.01 (p<0.01) (0.00)
Class Fixed Effects	Yes	Yes
Family characteristics a	Yes	Yes
Individual characteristics b	Yes	Yes
Baseline math scores	Yes	Yes
Observations	472	2507
R-Squared	0.685	0.548

Data Source: Table 3 in He et al, 2019 “IQ, Grit, and Academic Achievement: Evidence from Rural China”

Note: Standard errors are in parentheses below coefficients

^a:Family effects are controlled by variables that measure parental education, household assets, and if one or both parents have migrated for work

^b:Individual effects are controlled by gender and if they are boarding at their school

Our results find that both measures of IQ are strongly correlated with increased academic achievement in the case of rural youth who are attending junior high schools (Table 3). When using WISC-IQ as our cognition measure, we find that a one point increase in WISC-IQ predicts a standardized math achievement score increase of 0.03 points (column 1). Using Raven’s (SPM-IQ), a one point increase in Raven’s IQ leads to a 0.01 SD increase in math score (column 2). The results from both tables therefore suggest that cognition is a predictor for academic achievement in rural schools.

Conclusion

China has made huge strides in expanding and improving its education system; however, despite these major improvements, substantial gaps in academic achievement still exist between rural and urban students. Our study seeks to explain this urban/rural achievement gap by tracing it back to differences in ECD outcomes. To do so, we use data that were used in four recent papers to explore the life cycle of cognitive development and learning achievement.

Our results show that high rates of cognitive delay exist among rural infants and toddlers, persist as children age, and that cognition is a strong predictor of academic achievement into junior high school. During early childhood, around 50% of rural children are found to be cognitively delayed. As children age into elementary school and then into junior high, rates of delay continue to be around 40%. In both elementary school and junior high, IQ is very strongly correlated with academic achievement. Academic achievement in junior high has profound consequences for children in China as standardized tests at this age determine access to further education. In other words, our findings suggest that the traditional proverb, “at three years of age we can see a child’s future” remains relevant in China today.

The findings in this paper may also explain the origins of the rural/urban academic gap in China. Rates of early childhood cognitive delay are substantially higher among rural children than their urban counterparts. If this cognition gap persists at older ages and cognition has a causal effect on academic achievement, intervening to improve ECD outcomes among rural children may be a promising approach for reducing future inequality in China.

Beyond exacerbating inequality, poor levels of human capital in rural China may directly threaten China’s economic future. Over two-thirds of China’s children (and nearly three quarters of infants and toddlers) come from rural areas in China (Wang et al., 2018). This means that a majority of China’s future labor force will have been born and spend their early years in rural areas. Given the persistence of cognitive outcomes, unless something is done soon to address cognitive delay among rural children, a significant portion of China’s future workforce will be cognitively delayed. China’s future growth will depend on industrial upgrading and transitioning to an innovation-based economy. Given high rates of cognitive delay, it is unclear if China’s labor force will have the skills to support this transition (Wang et al., 2018).