# Presentation: 543067

## Understanding Recent Changes in Child Poverty

- Austin Nichols, Urban Institute
- <anichols AT ui.urban.org>
**Tenth Annual ACF/OPRE Welfare Research and Evaluation Conference**
- June 6, 2007

## What Happened to Child Poverty?

- Black child poverty fell dramatically in the 1990’s.

## Why does child poverty change?

- Demographic Structure
- Marital status of parents, number of parents present
- Lerman (1996): increase in single parents, 1971 to 1989
- Number of children per family
- Age of parents
- Education of workers in the family
- Earnings and Employment
- Unemployment rates
- Blank and Blinder (1986); Hoynes, Page, and Stevens (2005)
- Government Policies: taxes and credits, welfare, etc.
- Meyer and Rosenbaum (2001): EITC spurred LFP in 90s

## Cycles in Child Poverty and Unemployment

## Other Explanatory Factors

## Other Recent Research

- Lichter and Crowley (2004), using the CPS, attribute half the decline in poverty among children living with single mothers between 1996 and 2000 (and 69% of the decline for Black children) to increased maternal employment.
- Lichter, Qian, and Crowley (2005), using the PUMS, conclude employment explains 27% of decreased poverty for children of White ever-married single mothers, and 18% of decreased child poverty for children of Black ever-married single mothers, between 1990 and 2000.
- Both use standardization: multiply partitioned child poverty rate at time 2 by time 1 category weights—counterfactual is: what would poverty have been if sizes of populations had not changed?

## Options for Methods to Explain Changes in Poverty Rates

- Panel regression
- Shift-share analysis or standardization
- Categorical explanatory variables only
- Gomulka and Stern (1990)
- use data from time 1 and coefficients from time 2 to partition change among coefficients and characteristics (not detailed decompositions); also reweight proportions in categories to base year (see Table 5), similar to Dinardo, Fortin, & Lemieux (1996)
- Yun (2003)
- Blinder-Oaxaca-style decomposition generalized to nonlinear models
- Fairlie (2005)
- “matching” decomposition requires equal samples, which in practice requires repeatedly drawing samples from the larger group (more recent survey year in the case of year-to-year differences measured in the CPS)

## Data

- I use the same data used in computing the official poverty rate, microdata from the March CPS, survey years 1981 to 2006. The March CPS for 1981 refers to income earned in 1980, but measures family structure in early 1981.
- Highest prevailing minimum wages are measured at the state level, and deflated by the national CPI research series.
- State unemployment rates are from the DoL BLS Local Area Unemployment Statistics (LAUS) program.

## Results

- Panel regressions
- Logit (probit produces same results)
- State and year fixed effects
- Only changes within a state can explain changes in poverty rates.
- Coefficients scaled by mean of f(X)*100 so interpretation of ‘effect’ is in percentage points
- Mean is 6 for White children, and 11 for Black children
- “Decomposition” explains proportion of changes in poverty rates due to changes in explanatory factors, and their influence on poverty (“coefficients”)

## Slide 10

## Slide 11

## Year Pairs for Decomposition: 1993-2000, 2000-2005

## Explaining Change in Rates

## Conclusions

- Both panel regression and various decompositions indicate that families of Black children are more “exposed” to risk of macroeconomic shocks.
- Panel regression shows parent’s work attachment, education, and family structure are strong predictors of child poverty, and state unemployment rates and real minimum wages are not.
- Decompositions are sensitive to assumptions and techniques, but state unemployment rates and minimum wages appear much more important.
- Decreasing state unemployment rates are associated with lower poverty for children in families with no education beyond high-school in the earlier period (1993-2000), and rising unemployment and falling real minimum wages are associated with higher child poverty in the latter (2000-2005).

## More conclusions

- The panel regressions are really repeated cross-sections, so they measure predictors of child poverty at a point in time—work and education of parents, and number of parents.
- Oaxaca-style decompositions measure whether observed changes in poverty rates can be “explained” by observed changes in other factors, but cannot hope to identify causes of changes in either poverty rates or explanatory factors.

## Speculation

- Would like to know—did welfare reform or the EITC expansion drive changes in labor force participation and employment rates? Timing appears coincident with EITC (but no causal connection established).
- Or was the employment boom of the 1990’s driven entirely by exogenous shifts in labor demand?  Labor demand for low-skill workers seems to have a small positive slope, or to be nearly perfectly elastic, possibly as an artifact of wage controls, so it’s implausible.

## Open Questions

- What are the impacts of policy on explanatory factors, and poverty?  Needs a better research design than any extant work. Regression “horse races” unconvincing on EITC vs. welfare reform.
- Are the later consequences (including intergenerational impacts) of poverty spells the same for children who are poor in high-poverty years, as for children who are poor in low-poverty years?
- Is a child always better off with working parents, if he would otherwise be poor?