Benefits of high quality child care persist 30 years later
Back in the 1970s, some 111 infants from low-income families, of whom 98% were African-American, took part in an early childhood education program called the Abecedarian Project. From infancy until they entered kindergarten, the children attended a full-time child care facility that operated year-round. The program provided educational activities designed to support their language, cognitive, social and emotional development.
The latest data from that project, following up the participants at age 30, has found that these people had significantly more years of education than peers who were part of a control group (13.5 years vs 12.3), and were four times more likely to have earned college degrees (23% vs 6%).
They were also significantly more likely to have been consistently employed (75% had worked full time for at least 16 of the previous 24 months, compared to 53% of the control group) and less likely to have used public assistance (only 4% received benefits for at least 10% of the previous seven years, compared to 20% of the control group). However, income-to-needs ratios (income taken into account household size) didn’t vary significantly between the groups (mainly because of the wide variability; on the face of it, the means are very different, but the standard deviation is huge), and neither did criminal involvement (27% vs 28%).
See their website for more about this project.
Evidence that more time at school raises IQ
It would be interesting to see what the IQs of those groups are, particularly given that maternal IQ was around 85 for both treatment and control groups. A recent report analyzed the results of a natural experiment that occurred in Norway when compulsory schooling was increased from seven to nine years in the 1960s, meaning that students couldn’t leave until 16 rather than 14. Because all men eligible for the draft were given an IQ test at age 19, statisticians were able to look back and see what effect the increased schooling had on IQ.
They found that it had a substantial effect, with each additional year raising the average IQ by 3.7 points.
While we can’t be sure how far these results extend to other circumstances, they are clear evidence that it is possible to improve IQ through education.
Why children of higher-income parents start school with an advantage
Of course the driving idea behind improved child-care in the early years is all about the importance of getting off to a good start, and you’d expect that providing such care to children would have a greater long-term effect on IQ than simply extending time at school. Most such interventions have looked at the most deprived strata of society. An overlooked area is that of low to middle income families, who are far from having the risk factors of less fortunate families.
A British study involving 15,000 five-year-olds has found that, at the start of school, children from low to middle income families are five months behind children from higher income families in terms of vocabulary skills and have more behavior problems (they were also 8 months ahead of their lowest income peers in vocabulary).
Low-middle income (LMI) households are defined by the Resolution Foundation (who funded this research) as members of the working-age population in income deciles 2-5 who receive less than one-fifth of their gross household income from means-tested benefits (see their website for more detail on this).
Now the difference in home environment between LMI and higher income households is often not that great — particularly when you consider that it is often a difference rooted in timing. LMI households are more common in this group of families with children under five, because the parents are usually at an early stage of life. So what brings about this measurable difference in language and behavior development?
This is a tricky thing to derive from the data, and the findings must be taken with a grain of salt. And as always, interpretation is even trickier. But with this caveat, let’s see what we have. Let’s look at demographics first.
The first thing is the importance of parental education. Income plus education accounted for some 70-80% of the differences in development, with education more important for language development and income more important for behavior development. Maternal age then accounted for a further 10%. Parents in the higher-income group tended to be older and have better education (e.g., 18% of LMI mothers were under 25 at the child’s birth, compared to 6% of higher-income mothers; 30% of LMI parents had a degree compared to 67% of higher-income parents).
Interestingly, family size was equally important for language development (10%), but much less important for behavior development (in fact this was a little better in larger families). Differences in ethnicity, language, or immigration status accounted for only a small fraction of the vocabulary gap, and none of the behavior gap.
Now for the more interesting but much trickier analysis of environmental variables. The most important factor was home learning environment, accounting for around 20% of the difference. Here the researchers point to higher-income parents providing more stimulation. For example, higher-income parents were more likely to read to their 3-year-olds every day (75% vs 62%; 48% for the lowest-income group), to take them to the library at least once a month (42% vs 35% vs 26%), to take their 5-year-old to a play or concert (86% vs 75% vs 60%), to a museum/gallery (67% vs 48% vs 36%), to a sporting activity at least once a week (76% vs 57% vs 35%). Higher-income parents were also much less likely to allow their 3-year-olds to watch more than 3 hours of TV a day (7% vs 17% vs 25%). (I know the thrust of this research is the comparison between LMI and higher income, but I’ve thrown in the lowest-income figures to help provide context.)
Interestingly, the most important factor for vocabulary learning was being taken to a museum/gallery at age 5 (but remember, these correlations could go either way: it might well be that parents are more likely to take an articulate 5-year-old to such a place), with the second most important factor being reading to 3-year-old every day. These two factors accounted for most of the effects of home environment. For behavior, the most important factor was regular sport, followed by being to a play/concert, and being taken to a museum/gallery. Watching more than 3 hours of TV at age 3 did have a significant effect on both vocabulary and behavior development (a negative effect on vocabulary and a positive effect on behavior), while the same amount of TV at age 5 did not.
Differences in parenting style explained 10% of the vocabulary gap and 14% of the behavior gap, although such differences were generally small. The biggest contributors to the vocabulary gap were mother-child interaction score at age 3 and regular bedtimes at age 3. The biggest contributors to the behavior gap were regular bedtimes at age 5, regular mealtimes at age 3, child smacked at least once a month at age 5 (this factor also had a small but significant negative effect on vocabulary), and child put in timeout at least once a month at age 5.
Maternal well-being accounted for over a quarter of the behavior gap, but only a small proportion of the vocabulary gap (2% — almost all of this relates to social support score at 9 months). Half of the maternal well-being component of the behavior gap was down to psychological distress at age 5 (very much larger than the effect of psychological distress at age 3). Similarly, child and maternal health were important for behavior (18% in total), but not for vocabulary.
Material possessions, on the other hand, accounted for some 9% of the vocabulary gap, but none of the behavior gap. The most important factors here were no internet at home at age 5 (22% of LMIs vs 8% of higher-incomes), and no access to a car at age 3 (5% of LMIs had no car vs 1% of higher incomes).
As I’ve intimated, it’s hard to believe we can disentangle individual variables in the environment in an observational study, but the researchers believe the number of variables in the mix (158) and the different time points (many variables are assessed at two or more points) provided a good base for analysis.