Abstract
This paper tests adverse selection in the market for child care. A unique data set containing quality measures of various characteristics of child care provided by 746 rooms in 400 centers, as well as the evaluation of the same attributes by 3,490 affiliated consumers (parents) in the U.S., is employed. Comparisons of consumer evaluations of quality to actual quality show that after adjusting for scale effects, parents are weakly rational. The hypothesis of strong rationality is rejected, indicating that parents do not utilize all available information in forming their assessment of quality. The results demonstrate the existence of information asymmetry and adverse selection in the market, which provide an explanation for low average quality in the U.S. child care market.
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Notes
Bond (1982) investigated whether or not there was a difference between the maintenance records of pickup trucks that were purchased and used and those that were original-owner trucks. To the extent that maintenance records are proxies of quality, this paper is an exception.
See Blau (2001), Chapter 2 for a detailed overview of the child care market.
See Poterba (1995) for a detailed discussion of the reasons for government intervention in health care and education markets.
Most of the data collectors were individuals who were involved in early childhood education, such as former child care teachers, assistant teachers, or center directors. After a week-long training program, data collectors were required to carry out actual observations and data collection in actual centers. During these practices, inter-rater reliability was evaluated, and site coordinators, who were individuals with experience in administering the survey instruments, provided additional training if the agreement between observers was less than 80%. It should be noted that this is the standard procedure to train data collectors in child development research, and this study arguably provided some of the best training to the data collectors in terms of the duration of the training, emphasis on inter-rater reliability, and providing in-person training (as opposed to training through videos, etc.).
There were no statistically significant differences between centers that participated in the study and centers that declined to participate with respect to such characteristics as the legal capacity of the center, age of the center, auspice type, enrollment, and the age group of children served. Similarly, no systematic state and auspice differences are found in the return rates for parent surveys. Details can be found in Mocan (1997) and Cryer and Burchinal (1997).
Line B is discussed below.
Easy-to-observe aspects for infant-toddler include the following: furniture for routine care; furniture for play and learning; softness in room; room arrangement; arriving and leaving times; keeping children clean and neat; talking with children; small muscle activities; active play activities; chances for children to make friends; teacher’s behavior with children; discipline; and daily schedule. Difficult-to-observe aspects for infant-toddlers include: room decoration; meals and snacks; nap time; diapering and toileting; healthful caring; health rules; safe caring; safety rules; books and pictures activities; art activities for toddlers; music activities; activities with blocks; pretend play; sand and water play; and activities for different cultures. Preschool observable quality includes: arriving and leaving; diapering and toileting; keeping children clean; furniture for routine care; furniture for play and learning; furnishing for relaxation and comfort; room arrangement; room decoration; small muscle activities; space for active play; how teacher supervises active play; pretend play; schedule; how teacher supervises play activities; free-choice play activities; and how pleasant the room feels. Preschool unobservable quality includes: meals and snacks; nap or rest time; helping children to understand talk; helping children learn to talk well; helping children learn to think and reason; teacher’s talking; supervision of small muscle activities; equipment for active play; time for active play; art activities; music activities; block play; sand and water play; space for child to be alone; group times; and activities about different cultures.
The first-stage regressions had explanatory power. The mean R square of all models was 0.29.
When Eq. 1 is estimated by OLS, the estimated coefficients of Q were smaller than the ones obtained from instrumental variables estimation, and the hypothesis of weak rationality was rejected in each case. All of these results are available upon request.
For example, consider the question on napping, which includes aspects of the nap schedule, adult supervision, and the quality of the nap area. Imagine that the accurate rating of this question is a 5 on the scale from 1 to 7, and parents who have the opportunity to observe the center repeatedly give this question a rating of 5 on average. Imagine further, that when the trained observers visited the center, it was a “bad” day for whatever reason, and the center received a rating of 4.
Robust standard errors are adjusted for clustering at the center level.
In the light of the results of the previous section, this formulation conjectures that parents may overestimate by 3, 2, or 1 points. For example, if parents overestimate by 3 points, then, an average parent is going to assign a rating of 7 when actual quality is equal to 4. In this case, when actual quality is 5, the parent is forced to assign 7, despite the fact that she is willing to assign 8. In this example, censoring exists when actual quality is 4 or greater.
The percentage of negative prediction errors was 20 in total quality, 16 in observable quality, and 24 in unobservable quality.
The amount of information parents extract from centers and classrooms may depend on their children’s tenure at the center. However, the data set does not contain information on time spent at the center to analyze this aspect.
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Acknowledgements
This research is supported by a grant from the Smith Richardson Foundation to the NBER (Grant No: 9901-651) and also by an NBER Nonprofit Sector Research Grant. Paul Niemann, Kaj Gittings, and Damba Lkhagvasuren provided outstanding research assistance. I thank Jeffrey Zax, Patrick Emerson, Murat Iyigun and two referees for helpful comments.
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Mocan, N. Can consumers detect lemons? An empirical analysis of information asymmetry in the market for child care. J Popul Econ 20, 743–780 (2007). https://doi.org/10.1007/s00148-006-0087-6
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DOI: https://doi.org/10.1007/s00148-006-0087-6