Child Care Provider Turnover and its Relation to Devolution of Subsidy Policy in Texas Daniel Schroeder Laura Lein Deanna Schexnayder Julie Beausoleil Ying Tang

What is Devolution ? Devolution refers to the transfer of responsibility from a more centralized to a less centralized authority. In this study, devolution is the transfer of responsibility for setting certain child care subsidy policies from the Texas Workforce Commission to local workforce development boards. Authority to set some of these policies was transferred to local boards between September 1999 and January 2000.

Project Overview This research project examines the Texas subsidized child care program from Fiscal Years (FYs) 1998 through 2003 This time period begins two years before policies were devolved to the local level and ends four years after this change in authority. Its purposes are: –to describe the processes by which local boards develop child care policies, and –to determine the extent to which these local policy changes are associated with changes in subsidy participation patterns (subsidy dynamics), family economic outcomes, and child care markets in these local areas.

Research Questions from the overall project: Econometric modeling questions are highlighted 1.How do local child care policies in Texas vary following the devolution of responsibilities for child care policies to the local workforce boards? 2.What is the process by which local policy changes governing the provision of publicly subsidized child care are decided upon and implemented? 3.Which changes in local child care markets are statistically associated with local policy variations? 4.Which changes in the patterns of child care use and family outcomes are statistically associated with local policy variations?

Scope of Econometric Modeling: Question addressed here is highlighted 1.Child Care Subsidy Use and Dynamics: Exit from Subsidy Share of Care Provided by Texas Rising Star Providers Use of Center-Based Care 2.Family Economic Outcomes Earnings of Subsidy Recipients Exit from Employment among Subsidy Recipients Entry or Re-Entry to TANF 3.Child Care Market Outcomes Providers receiving subsidy as share of total formal market Share of Market to which Child Care Subsidy Provides Access Factors Associated with Provider Turnover

Provider Turnover: Rationale Excessive turnover among child care arrangements can prevent children from building lasting bonds with their caregivers, which can have negative implications for their healthy development. Some of the turnover in child care arrangements is caused by providers quitting the business of child care. Policies of the child care subsidy system could have a significant role in provider turnover. With devolution of child care subsidy policy making to the local level in Texas, a natural experiment has emerged with 28 different sets of local policies.

Provider Turnover: How long do providers survive in the child care business? Typical “lifetimes” of child care providers are estimated using registration (or licensing) and de-registration dates in state provider registry. Event history regression analysis is used to determine factors associated with provider turnover. Provider spells are analyzed using proportional hazards regression with time-varying covariates. Independent variables include a combination of local child care policy, local child care market rate data, economic environment measures, and local child care board governance, composition, and dynamics.

Local Policy and Local Board Measures that Vary over Time Maximum reimbursement rates, as measured relative to market rates (see next slide). Co-payment policies measured in terms of percent of income. Some groups do not pay co-payments. Basic income eligibility limits vary from 50% to 85% of State Median Income. Local board composition and dynamics measures: –Whether the local board has a child care committee –Whether the local board has changed child care contractors –For-profit status of child care contractor

Maximum Reimbursement Rates The ratio of maximum reimbursement rates to the 75 th percentile of market rates for care varies by region and over time.

Control Variables Provider history – the longer they are in business, the less likely they are to quit. Provider type – Centers, Registered Homes, Licensed Homes County unemployment and employment growth rates County size and commute patterns: –Large metropolitan core county (> 1 million) –Large metropolitan commuter county –Small metropolitan core county (< 1 million) –Small metropolitan commuter county –Micropolitan county –Rural county

Results across Facility Types Variable descriptionHazard ratio Months provider in business, log0.805 ** Basic income eligibility limit, percent of SMI0.997 ** Co-payment for one child, percent of income0.967 Co-payment for two children, percent of income1.051 Local board changed CC contractor0.964 Local board has CC advisory committee0.897 ** Ratio of max. reimbursement to 75th percentile of market rate ** Local board contractor is for-profit1.028 Local board contractor for-profit status unknown0.987 County unemployment rate, percent1.010 ** County employment growth rate, percent1.006 County commutes to large metro area1.022 County is core of small metro area1.125 ** County commutes to small metro area1.246 ** County is micropolitan0.998 County is rural1.179 ** Licensed home-based facility1.766 ** Registered home-based facility2.339 ** *=p<.05, **=p<.01. Omitted: Centers, Large metro core Highlights Higher reimbursement rates = lesser turnover Higher eligibility limits = lesser turnover Board has CC advisory committee = lesser turnover Most counties outside large metro areas = greater turnover Home-based facilities = greater turnover

Results by Facility Type Variable descriptionCentersHome-based Months provider in business, log0.726**0.833** Basic income eligibility limit, percent of SMI0.992**0.999 Co-payment for one child, percent of income Co-payment for two children, percent of income Local board changed CC contractor Local board has CC advisory committee1.268**0.820** Ratio of max. reimbursement to 75th percentile of market rate 0.471**0.691** Local board contractor is for-profit1.202*0.990 Local board contractor for-profit status unknown County unemployment rate, percent ** County employment growth rate, percent0.983*1.012** County commutes to large metro area County is core of small metro area1.136*1.115** County commutes to small metro area ** County is micropolitan County is rural ** Licensed home-based facility.0.746** *=p<.05, **=p<.01. Omitted: Large metro core Highlights Board has CC advisory committee = lesser turnover among home- based, greater turnover among centers Employment growth = greater turnover among home-based, lesser turnover among centers

Interpretation and Conclusions Maximum reimbursement rates are the single biggest factor in provider turnover. Estimates suggest that increasing the reimbursement rates to the 75 th percentile of market rates would be associated with about a 7% decline in the odds of providers leaving business. The relationship is even stronger when looking only at centers, for which an increase to the 75 th percentile would be associated with a 10.6% decline in odds of providers quitting.

Interpretation and Conclusions Whether the local board has a child care advisory committee is also important, but its effect depends on the type of facility. Among home-based facilities, odds of providers quitting the business are reduced by 18% when boards have an advisory committee. However, among centers the odds of providers quitting are increased by 27% when an advisory committee is present.

Interpretation and Conclusions Employment growth is associated with reduced turnover among centers, but with increased turnover among home-based facilities. This could be an indication of the attractiveness of outside employment to home-based providers when the economy is growing. In general, provider turnover is lowest in large metro areas.

For More Information Questions about the overall project: –Deanna Schexnayder at –Laura Lein at Questions about this paper or statistical methods of overall project: –Daniel Schroeder at 512- On the web: –