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Quality Rating Improvement Systems (QRIS) have emerged as a central strategy in states' efforts to improve the quality of early care and education programs and the capacity of these programs to promote positive outcomes for children. Currently, 23 states have a statewide QRIS. At the heart of each QRIS is a set of standards that describe the requirements a center-based program or home-based early care and education setting must meet in order to qualify for a QRIS rating. Because children's school readiness is a key goal of QRIS, requirements concerning practices that can promote children's learning and development are of special interest. These requirements are typically found in standards' descriptions of acceptable curricula or learning activities, including methods for promoting the learning of children with special needs and children who are English language learners, as well as practices related to child assessment and parent involvement.

A major goal of CCDF is to provide low-income children with access to high-quality care that supports their health and safety. Research underscores the role of health and safety in child care in supporting children's physical, cognitive, and social-emotional development, all of which contribute to their school readiness. Regulations vary widely across states, and more needs to be learned about how regulations, enforcement of regulations, and supports could best improve child outcomes.

Data collected by state and local agencies on young children and the programs serving them have enormous potential value. Families, service providers, policymakers, researchers, advocates and others can use these data to better understand children's needs, improve access to services, strengthen services, enhance the efficiency of services, and understand the short- and long-term impacts of services. In New York, as in most other states, this potential has gone largely unrealized. Early childhood data have typically been maintained in silos by agency and service — and sometimes by region — frustrating efforts to draw comprehensive, point-in-time pictures and comparisons. Usually, too, data have not been available longitudinally, hampering ability to follow children and services over time. All too frequently, data systems are under developed and fail to collect all the data that is needed for research and policy and program decisions. Recently, however, momentum to link early childhood data across agencies and across time is gathering in the states. A few pioneers — including Connecticut, Maryland, and Pennsylvania — have begun to integrate data systems for their state-funded prekindergarten and child care subsidy systems — linking child, program, and personnel databases. These states are also looking to develop linkages with other systems, notably health. Earlier, several states — including Wisconsin, South Carolina — built "data warehouses" to store, link, and provide access to historical data on services to children and adults. Federal actions are stimulating this movement. State Early Childhood Advisory Councils, established under the Head Start Reauthorization Act of 2008, are charged with developing recommendations for "establishing or improving core elements of the State early childhood system, such as a statewide unified data collection system." Statewide Longitudinal Data System grants, begun in 2001, at first focused on elementary and secondary education, but more recent grants, like New York's that began in 2010, include links to preschool data. Further, as a condition of receiving State Fiscal Stabilization Funds through the American Recovery and Reinvestment Act, all state education agencies have committed to build statewide longitudinal data systems to follow individual students from pre-school, through K-12 and postsecondary education, and into the workforce. Also, states successful in the Race to the Top education grant competition, including New York in 2010, had to show "significant progress" toward development of longitudinal P-20 data systems. A consortium of national organizations, the Early Childhood Data Collaborative, has convened to help states build, link, and use early childhood data systems. One of the Collaborative's first efforts has been to articulate a set of key policy questions that a well-crafted early childhood data system can help answer. Their initial questions focus on early care and education data, while anticipating subsequent links to data on other services. This report represents an important first step to help New York answer these questions through a coordinated early childhood data system.

When alarming statistics on America's poverty are reported — like those in the 2010 Census — policy makers need sound, reliable, analytical thinking to make sound, reliable, effective policy. Through research and analysis, we at the National Center for Children in Poverty have worked every day since we began in 1989 to change public thinking and government responses to the very real struggle 44 million Americans living in poverty now endure every single day. We trust this report underscores why the work we do has never been more critical.

This guide has been developed to assist researchers in using public and non-public use U.S. Census Bureau data as well as public and non-public use state administrative data—both separately and in combinations—to analyze state-specific child care subsidy take-up and employment of child care subsidy recipients.

This guide has been developed to assist researchers in using public and non-public use U.S. Census Bureau data as well as public and non-public use state administrative data—both separately and in combinations—to analyze state-specific child care subsidy take-up and employment of child care subsidy recipients.

A growing body of research is starting to shed light on the ways child care subsidies are achieving one of their central goals—supporting employment for low-income parents. Second in the series Reviews of Research on Child Care Subsidies, this research brief summarizes the Research Connections literature review of the same title, Parent Employment and the Use of Child Care Subsidies, which examines recent research addressing the basic question: What parent employment outcomes are associated with the use of child care subsidies? That is, how do employment decisions and patterns for low-income parents with subsidies tend to differ from those of low-income parents without them? For which subgroups of these parents—such as, those with or without high school diplomas, with or without cash assistance histories—do child care subsidies appear to make more difference? What factors in addition to subsidies influence parents' employment decisions? While these questions are posed broadly in terms of "parents," the research to date focuses on "mothers."

A table summarizing the research questions, groups studied, methods, validity and reliability issues, and findings of studies discussed in an accompanying literature review examining parent employment outcomes associated with the use of child care subsidies

Family, friend, and neighbor (FFN) child care is a widely used form of care for young children in the United States, particularly for children birth through age 2. It accounts for 46 percent of the hours these youngest children spend in nonparental care. Thirty-three experts from a range of research, policy, and practice organizations came together for a symposium on FFN care on November 2, 2005 entitled: Improving Family, Friend, and Neighbor Care: Toward a National Strategy. (See Appendix B for a participant list.) This symposium report outlines the picture of current FFN research, practice, and policy that emerged and identifies next steps to strengthen all three areas. A major step that would support practice, policy, and research alike is to increase public awareness of the widespread use of FFN care by families of all economic levels and ethnicities.

Family, friend, and neighbor (FFN) child care is a widely used form of care for young children in the United States, particularly for children birth through age 2. It accounts for 46 percent of the hours these youngest children spend in nonparental care. Thirty-three experts from a range of research, policy, and practice organizations came together for a symposium on FFN care on November 2, 2005 entitled: Improving Family, Friend, and Neighbor Care: Toward a National Strategy. (See Appendix B for a participant list.) This symposium report outlines the picture of current FFN research, practice, and policy that emerged and identifies next steps to strengthen all three areas. A major step that would support practice, policy, and research alike is to increase public awareness of the widespread use of FFN care by families of all economic levels and ethnicities. The goals of the symposium were to: Review research, policy, and program issues related to improving the quality of family, friend, and neighbor care for children from infancy through school-age. Develop a set of recommendations for state and federal action, and foundation and other private sector initiatives to improve policies, expand research, and improve programming for young children and their families using FFN care. Supported by the Annie E. Casey Foundation, the A. L. Mailman Family Foundation, and the Rauch Foundation, the symposium was organized by the National Center for Children in Poverty (NCCP) of the Mailman School of Public Health, Columbia University and held in Baltimore, Maryland. Welcomes were extended by Ruth Mayden (Casey Foundation), Luba Lynch (Mailman Foundation), Daphny Leveille (Rauch Foundation), and Lee Kreader (NCCP).

A review of research studies examining parent employment outcomes associated with the use of child care subsidies

School-age children spend time in an array of care arrangements. The most common nonparental after-school arrangements are center- or school-based programs, relative care, and self-care.

With over half the nation's infants and toddlers in regular, nonparental child care, the quality of that care is a priority concern for policymakers. Many studies show that high-quality child care supports the positive social, emotional, and cognitive development of young children. The research summarized in this policy brief identifies factors that tend to predict higher quality within arrangement types—family child care, center care, and relative care—and describes the range of quality found in each type. Although different studies measure child care quality in different ways, many researchers group quality measures into two categories, structural and process. Both examine factors that support the responsive and reliable relationships with caregivers so essential for the healthy development of infants and toddlers. Structural measures—child-staff ratio and group size, caregivers' general education and specialized training, their tenure and income—look at aspects of arrangements that support positive child-adult relationships and child development. Process measures directly examine children's experiences, including caregivers' interactions with the children—their attention, warmth, and responsiveness. Though less direct, structural factors are less costly for researchers to study than process factors, which require direct observations. Unlike process factors, structural factors can be regulated by policymakers. Many instruments are used to measure quality in infant and toddler child care arrangements (see the Resources Section for a list of frequently use ones). Most are designed to examine the global quality of child care arrangements—producing a composite rating based on observations of routines, practices, facilities, and equipment—and utilize both process and structural measures. Some are more exclusively composed of process measures. Researchers continue to develop new measures and instruments in this young field of inquiry.

A table of methods and findings on child care subsidy use

What care arrangements do American parents make for their infants and toddlers while they are at work, school, or otherwise unavailable to provide care themselves? Research findings that respond to this question come from two recent nationally representative surveys: the Early Childhood Longitudinal Study, Birth Cohort, and the National Survey of America's Families (NSAF), 2002. Additional research helps answer a second, related question: What factors influence the types of care arrangements made for this country's children under age 3?

Training and education of infant and toddler caregivers is one important factor associated with the quality of child care they provide. In response to research showing that high-quality care supports positive development in young children, policymakers have established training and education requirements for licensed providers and launched numerous initiatives to train and educate caregivers. Caregivers themselves believe training and education to be important, including relative and unregulated family child care providers, many of whom express interest in accessing training although not required by regulation to do so. What approaches to caregiver training and education are the most likely to improve the quality of care for children under age 3 in family child care homes and centers? To help answer this question, this brief describes research findings on a small number of training initiatives targeting infant and toddler caregivers where quality was observed before and after training. While it is too soon to draw firm generalizations from this young and scattered body of research, findings from these studies raise pertinent considerations for policymakers. (See Research-to-Policy Connections No. 2, Infant and Toddler Child Care Quality, for a list of observational instruments frequently used to measure quality.) Other research has focused on increases in provider knowledge and/or self-reported changes in caregiving practices as a result of training and education. It is not clear whether effects found using either of these measures correspond to effects on quality seen with observational measures.

An overview of research on subsidized child care since 1996

In 2004, spending on child care subsides from the main U.S. public funding sources—Child Care and Development Fund and Temporary Assistance for Needy Families—reached more than $11 billion (Field Initiated Child Care Research Projects, 2004). A growing body of research, employing a range of methodologies and data sources, has begun to identify the characteristics and child care arrangements of low-income families and children most likely to participate in subsidy programs. Although child care subsidy research is still a young field, preliminary findings on predictors of child care subsidy use are emerging. This research brief summarizes the Research Connections literature review of the same title, Predictors of Child Care Subsidy Use, which examines recent research addressing the basic question: What family and child care characteristics are associated with the use of child care subsidies? That is, among eligible families, what factors tend to predict which families will actually use assistance to help pay for the care and education their children need while parents work or participate in education and training? Given that the rate of subsidy use remains relatively low, policymakers want to know what distinguishes the families that use these services.

A review of research on factors influencing child care subsidy use among eligible families.

To better understand who is served by child care subsidy systems and what services they receive, research is needed that examines the dynamics of subsidy use across multiple state policy contexts. Such research has been limited by the lack of comparable micro-data at the state level. Child Care Policy Research Partnerships funded by the U.S. Department of Health and Human Services have helped to fill this gap by constructing comparable, analyzable data sets in several states. This study made use of data sets constructed by three of the Child Care Research Partnerships. Consortium members representing Illinois, Maryland, Massachusetts, and Oregon joined together to build a collaborative, cross-state research design. Researchers in Texas, who were already engaged in similar research, agreed to join the project. Researchers from each of the states met together several times over a two-year period to develop comparable longitudinal data sets using administrative data from state child care subsidy systems. The research team included policy experts familiar with each of the five states and analysts familiar with administrative data and analytic methods. State agency partners played a critical role in helping the research partners understand data elements and policies in each state, and provided valuable feedback on the study results and interpretations. This study provides important new information about the characteristics of children and families who received subsidies, the services they received, the length of time they received services, and the stability of children’s care arrangements while in the subsidy system.

This report describes changes in the supply of regulated child care after the passage of the Personal Responsibility and Work Opportunity Reconciliation Act of 1996 using Illinois and Maryland as examples. It particularly examines communities with the highest concentrations of lowincome families in these two states. This third publication from the Child Care Research Partnership at the National Center for Children in Poverty at the Joseph L. Mailman School of Public Health of Columbia University looks at differences within and between the states over time. Recognizing that child care supply responds to a complex economic system, the report uses administrative data to answer the following questions: 1) In Illinois and Maryland, how did the statewide supply of regulated child care—center and family care—change between June 1996 and June 1998? Illinois and Maryland saw only 6 percent growth in regulated child care slots per 1,000 children under age 13 during this two-year period. Slots per 1,000 children increased by just eight (from 146 to 154) in Illinois and 14 (from 231 to 245) in Maryland. All of the Maryland growth in capacity and most of the Illinois growth came in center care. The number of family child care providers declined slightly in Illinois and Maryland, although the number of slots grew modestly in Illinois and decreased a bit in Maryland. 2) How did the supply of regulated child care change at the community (zip code) level in each state, particularly in the communities most likely to be affected by welfare reform—those with high concentrations of low-income individuals? Illinois had a greater proportion of zip codes with high and very high concentrations of low-income people than Maryland. In 1996 and again in 1998, in both states, communities with the highest concentrations of low-income people had significantly fewer regulated slots per 1,000 children than communities with the lowest concentrations of low-income individuals. Generally, the areas with higher concentrations of low-income individuals in both states saw very little growth in child care supply. In both states, the greatest growth in child care capacity occurred in areas with lower concentrations of low-income people. In both states, center care increased in all income areas, but generally grew more in more affluent areas. In Illinois, the proportion of the child care supply from family child care was greater in areas with higher concentrations of lowincome people. In Maryland, family child care made up about the same proportion of the supply in all areas. 3) How did the number of Head Start and prekindergarten programs change in each state? Maryland had high growth in the number of Head Start and prekindergarten programs. Both states had greater growth in numbers of prekindergarten than Head Start programs. In both states, the percentage of centers offering prekindergarten programs grew between 1996 and 1998. In Maryland, the percentage of centers offering Head Start programs also grew slightly, while in Illinois this percentage declined slightly. 4) How did the number of regulated child care programs offering care during nontraditional hours change in the communities in each state? The percentages of centers and homes that offered care for extended hours scarcely increased in Maryland and did not increase in Illinois. In both states, larger percentages of centers were open for extended hours in areas with lower concentrations of low-income people. By contrast, in both states, larger percentages of child care homes offered care for extended hours in areas with higher concentrations of low-income people. 5) What portions of the net increase/decrease in capacity at the community level in each state were attributable to opening, closing, and ongoing (existing) providers? What portions of center and family child care slots were offered by new providers each year? Over the two-year period, the number of child care homes closing exceeded the number of homes opening in both states. While in both states most growth in slots was attributable to the opening of new centers rather than the expansion of existing centers, more than one-third of Maryland’s center care growth came in existing centers that increased capacity. In both Illinois and Maryland, the largest absolute numbers of slots lost from centers closing were in areas with very high concentrations of low-income people. Each year in Illinois, the proportion of family child care slots in new homes was greater than the proportion of center slots in new centers. In Maryland, the percentage of slots both in new centers and new homes increased substantially between June 1997 and June 1998.

Conducted for the Administration for Children and Families in the US Department of Health and Human Services, the National Study of Child Care for Low-Income Families by Abt Associates Inc. and the National Center for Children in Poverty, is a five-year research effort in 17 states and 25 communities to provide information on the response of states and communities to the child care needs of low-income families, on the employment and child care choices made by low-income families and on the factors that influence those choices.