State Estimates of Uninsured Children, January 1998. Final Report.. D. Estimates of the Uninsured

05/17/2000

Table I.1 reports the March 1998 CPS sample sizes for all children and uninsured children by state and compares the model-based estimates of the uninsured rate with the direct sample estimates. The sample sizes underscore why it is necessary to employ procedures of the kind used here to construct state-level tabulations of uninsured children. Most state samples include fewer than 100 uninsured children, and 15 states have fewer than 50. Spreading such small numbers of observations over a table with 24 cells (the number of cells in each of the state tables in Part II) cannot yield much information of value.

TABLE I.1
COMPARISON OF DIRECT SAMPLE AND MODEL-BASED ESTIMATES OF THE PERCENTAGE OF CHILDREN UNDER 19 WHO WERE UNINSURED IN 1997

State CPS Sample Sizes Direct Sample
Percent Uninsured
Model-Based
Percent Uninsured
Difference:
Model-Based
Minus Direct
All Children Uninsured Children
U.S. Total 38,461 6,101 15.2 15.2 0.0
Alabama 430 67 15.4 16.4 1.0
Alaska 490 63 11.3 10.9 -0.4
Arizona 761 211 26.2 21.8 -4.4
Arkansas 540 147 28.0 22.8 -5.2
California 4,277 931 18.8 19.4 0.6
Colorado 533 80 14.1 14.0 -0.1
Connecticut 382 48 11.8 9.7 -2.1
Delaware 377 60 14.3 11.2 -3.1
District of Columbia 260 39 14.3 15.6 1.3
Florida 1,509 300 20.3 20.2 -0.1
Georgia 645 103 16.8 14.6 -2.2
Hawaii 328 16 5.5 8.9 3.4
Idaho 719 142 18.2 18.1 -0.1
Illinois 1,653 215 11.1 11.7 0.6
Indiana 498 64 12.7 11.5 -1.2
Iowa 474 50 11.2 10.1 -1.1
Kansas 476 51 10.2 11.3 1.1
Kentucky 446 61 14.1 14.0 -0.1
Louisiana 472 107 22.9 21.3 -1.6
Maine 318 47 14.9 11.6 -3.3
Maryland 376 39 10.4 11.6 1.2
Massachusetts 753 72 9.2 9.1 -0.1
Michigan 1,244 106 8.6 9.3 0.7
Minnesota 573 43 7.4 6.9 -0.5
Mississippi 465 86 19.1 19.9 0.8
Missouri 417 54 13.1 12.4 -0.7
Montana 552 82 13.8 16.0 2.2
Nebraska 492 49 9.7 10.2 0.5
Nevada 478 101 19.4 17.6 -1.8
New Hampshire 368 37 10.3 7.6 -2.7
New Jersey 1,078 180 15.9 14.9 -1.0
New Mexico 780 140 17.1 18.6 1.5
New York 2,449 418 15.8 15.4 -0.4
North Carolina 858 158 18.3 15.2 -3.1
North Dakota 468 64 13.6 11.5 -2.1
Ohio 1,356 153 10.6 11.3 0.7
Oklahoma 570 89 15.5 18.6 3.1
Oregon 452 54 11.2 14.4 3.2
Pennsylvania 1,370 120 8.5 9.4 0.9
Rhode Island 296 24 8.1 7.3 -0.8
South Carolina 415 76 18.8 16.2 -2.6
South Dakota 488 33 6.5 9.1 2.6
Tennessee 459 49 10.9 9.7 -1.2
Texas 2,603 700 25.2 24.7 -0.5
Utah 710 100 12.7 13.2 0.5
Vermont 350 23 6.6 5.9 -0.7
Virginia 517 62 11.9 12.9 1.0
Washington 507 40 7.9 9.5 1.6
West Virginia 369 45 12.6 11.8 -0.8
Wisconsin 487 24 4.9 7.1 2.2
Wyoming 573 78 13.7 14.1 0.4
SOURCE: Mathematica Policy Research, from the March 1998 CPS and other sources.

Differences between the model-based and direct sample estimates vary from 0.1 to 5.2 percentage points (plus or minus). The two estimates are within one percentage point for 25 states but differ by three percentage points or more for 8 states. Sample size is clearly relevant but not the sole factor affecting the size of the difference. For example, all nine states with CPS sample sizes of more than 1,000 children have differences of 1 percentage point or less, but the two largest differences occur in states with above average sample sizes (Arizona and Arkansas). Perhaps more importantly, the model-based estimates show the impact of shrinkage toward the mean. Generally the most extreme rates--which are probably too extreme--are pulled toward the center. We see this in the states of Arizona, Arkansas, Louisiana, and Texas, where the model-based estimates are lower than the (high) direct sample estimates, and in Hawaii, South Dakota, Washington, and Wisconsin, where the model-based estimates are greater than the (low) direct sample estimates. This result is not universal, however. There are states with low direct sample uninsured rates (for example, Vermont and Minnesota) that get assigned even lower rates by the model-based procedure. As low as the direct sample estimates were in these states, the regression model predicted even lower rates. Thus the model did not indiscriminantly eliminate high and low rates.

Table I.2 reports uninsured rates by poverty level for the model-based estimates. It is quite clear from an examination of these rates that the model-based procedure does not generate homogenous uninsured rates across the states. Rather, there are distinctly different patterns in the rates for groups of states.

TABLE I.2
UNINSURED RATES BY POVERTY LEVEL: MODEL-BASED ESTIMATES

State Federal Poverty Level Based on 1997 Annual Family Income
Under 50% 50% to < 100% 100% to < 150% 150% to < 200% 200% to < 350% 350% or More
U.S.Total 25.9 24.5 27.5 21.0 11.5 5.9
Alabama 27.4 21.9 33.2 25.4 9.7 5.4
Alaska 31.2 20.0 22.9 18.2 8.7 4.9
Arizona 39.7 37.0 33.6 26.1 14.6 7.2
Arkansas 39.9 31.3 34.7 26.2 13.3 7.3
California 23.4 25.9 35.9 27.4 16.1 7.7
Colorado 28.4 25.4 26.2 20.3 12.1 6.2
Connecticut 21.2 19.0 17.2 13.8 10.3 5.1
Delaware 22.3 17.0 25.0 18.5 8.6 4.9
District of Columbia 13.3 15.5 36.1 33.0 11.5 5.1
Florida 37.3 34.8 31.2 24.4 12.7 6.6
Georgia 26.6 22.2 29.6 21.4 8.6 4.9
Hawaii 8.6 6.2 13.9 11.4 10.9 5.8
Idaho 45.3 35.1 27.8 21.6 11.1 6.2
Illinois 22.4 20.7 26.7 21.2 6.5 3.4
Indiana 32.7 23.1 20.3 16.5 8.0 4.7
Iowa 16.2 9.6 18.4 15.2 9.3 5.2
Kansas 25.6 18.3 21.7 17.4 8.8 4.8
Kentucky 25.5 16.9 22.7 18.3 11.0 6.1
Louisiana 36.1 32.0 31.8 24.4 14.3 7.5
Maine 23.8 14.8 17.4 14.2 10.5 5.7
Maryland 30.1 25.9 26.8 21.3 8.0 4.1
Massachusetts 15.1 12.2 19.0 15.3 9.5 4.9
Michigan 18.5 13.9 18.9 15.1 7.2 3.9
Minnesota 8.4 4.8 13.9 11.6 7.5 4.2
Mississippi 29.4 26.1 35.4 23.0 11.6 6.7
Missouri 23.6 16.4 21.2 16.8 10.3 5.6
Montana 32.2 20.7 25.6 20.1 11.8 6.5
Nebraska 13.9 8.3 19.4 15.7 10.1 5.5
Nevada 38.9 36.8 33.0 25.0 11.4 6.1
New Hampshire 17.8 10.7 9.7 8.1 9.0 4.9
New Jersey 27.6 26.1 31.5 24.4 13.7 7.1
New Mexico 24.7 27.5 23.1 16.8 17.1 8.2
New York 19.1 18.6 33.1 26.4 12.7 6.2
North Carolina 30.6 24.0 27.0 21.3 10.6 5.6
North Dakota 20.4 11.7 19.7 16.1 10.6 5.9
Ohio 25.3 18.2 18.4 14.8 9.3 5.1
Oklahoma 38.7 28.9 26.9 21.0 13.0 7.1
Oregon 38.2 28.9 25.0 19.8 8.2 4.5
Pennsylvania 21.0 15.2 11.9 9.7 9.3 5.0
Rhode Island 14.5 11.9 12.4 10.0 6.7 3.4
South Carolina 32.0 26.3 29.6 19.3 10.6 6.5
South Dakota 13.8 8.0 16.7 13.8 8.1 4.7
Tennessee 10.3 7.1 16.3 13.1 10.0 5.6
Texas 35.2 37.4 38.1 28.8 19.2 9.2
Utah 35.8 25.0 21.4 17.3 10.0 5.8
Vermont 4.1 2.2 6.7 5.7 8.4 4.7
Virginia 26.6 21.4 23.5 18.8 10.8 5.6
Washington 24.5 18.5 14.5 11.8 8.0 4.3
West Virginia 19.0 12.4 16.1 13.4 10.4 5.8
Wisconsin 6.9 3.9 16.7 13.5 7.3 4.1
Wyoming 32.6 23.0 20.7 16.3 11.2 6.1
SOURCE: Mathematica Policy Research, from the March 1998 CPS and other sources.

For example, there is a general tendency for children who are between 100 percent and 150 percent of poverty to have the highest uninsured rates. Children below 100 percent of poverty often have access to Medicaid while children above 150 percent of poverty are more likely to have employer-sponsored or other private insurance. But despite these tendencies we do see a number of states in which the uninsured rates are highest among children under 50 percent of poverty and then decline with each succeeding higher income level. Arizona, Florida, Idaho, Louisiana, Nevada, Oklahoma, Oregon, Utah, and Wyoming are among the states that fit this pattern. It is likely that these states have low participation in Medicaid since most children under 50 percent of poverty will be covered by Medicaid. In general, the states that fit this pattern have large Hispanic populations or western locations. The high uninsured rates of Hispanic children are well-documented. For the western states, the high uninsured rates at low income levels may reflect low participation in safety net programs generally. Whatever the reason, there is a clear pattern that the model-based estimates are able to identify.

Four states--Hawaii, Minnesota, Vermont, and Wisconsin--have single-digit uninsured rates in the two lowest poverty classes. The first three of these are noted for their broad Medicaid coverage expansions, and we would guess that Medicaid participation is very high among the eligible populations. That the model-based estimates can differentiate between these states and the rest provides additional face validity.