give up a large proportion of any cash transfer

receiving such as Supplemental Security Income (SSI).

income they might be
For example, SSI bene-
fits are reduced to $25 per month after the first two months of institutiona-
lization. Furthermore, any additional income from Social Security, pensions,

Table 7. Medicaid Institutional Care Expenditures for Fiscal Year 1979
{Amounts in billions of dollars)
Average
Blind or | expenditure
Type of institution Total Elderly disabled per HCFA
expenditures recipients |recipients | recipient
{
|
Total Medicaid |
expenditures..eseccceee $ 20.474 $ 7.647 § 6.226 | (X)
Total institutional
Medicaid expenditures... 9.432 5.873 3.307 (Xx)
Mental hospital......... 786 363 277 $ 10,018
ICF2/mentally retarded. 1.506 035 1.427 13,097
SNES... ccccccececereceee 3.369 2.640 716 | 5,623
ICFe. cc eeeeeseeneveaees 3.771 2.834 887 4,961
. Institutional expendi-
tures as a percent of
total expenditures..... 46.1 76.8 53.1 (X}

X Not applicable.

1Total exceeds elderly plus blind or disabled.

benefits.

21GF-refers-to-intermediate-care-facilitiess ~~ -—--~

3SNF refers to skilled nursi ng facilities.

Excludes “State-only”

Source: Unpublished HCFA State-specific data summaries for FY 1979.

ne 7 eae



Following the analysis of these initial results, work was directed
toward determining the reasons for the negative subsidies and developing an
improved predictor equation. An examination of the characteristics of subsi-
dized and unsubsidized housing units revealed some important systematic dif-
ferences. Nonsubsidized units were more likely to lack central heating,
running water, indoor toilet facilities, etc. Because of these differences,
the MR equation was re-estimated excluding nonsubsidized units which had one
or more of these "shortcomings." This new equation resulted in about the
same proportion of negative subsidy values as the first equation (24
percent).

Further examination of the data indicated that estimating separate equa-
tions for housing units with and without utilities included in rent signi fi-
cantly reduced the proportion of negative subsidy values. It was found that
while about two-thirds of the subsidized units paid rents which included
heating expenses -only or all utility expenses, only about one-third of the
nonsubsidized units paid rents which included these expenses. This differ-
ence pointed to the development of separate equations for those with and
without heat or other utilities included in rent. The results based on
these equations are shown in table D-1.

The net result of the use of separate equations was to reduce the total
proportion of subsidized units with negative subsidies from the original 26
percent to about 18 percent. In addition, the proportion of units with nega-
tive subsidies between $-1 and -$50 was reduced from 10 percent to 6 percent.

Over one half (55 percent) of the remaining 91 housing units with nega-
tive subsidies of greater than $50 per month were located in what appear to
be public housing projects located in large, northern metropolitan areas,
most of these within the central city. Assuming that the regression equa-
tions predict market rents accurately, the results imply that some public
housing residents are worse off than if they had rented in the private mar-
ket. Because this study assumes that public housing subsidies have non-
negative values, the negative subsidy values were changed to $0 when com-
puting average subsidy values to be assigned to the CPS public housing units.



It is not proper, however, to make a similar comparison for those paying
the full-established price. The USDA peak monthly number of children paying
the full price was only 15.541 million, far short of the 22.281 million esti-
mate from the CPS. Part of the difference results because the CPS counts
children eating hot lunches in both private and public schools. - Thus,
private-school children eating unsubsidized (not part of FNS lunch program)
hot lunches are counted by the CPS. More importantly, according to FNS,
there is a substantial monthly turnover among those children who do pay full-
established price for their lunches. While there is an economic incentive
for schools and parents to maximize the number of free and/or reduced-price
lunches, no such incentive exists for those who pay the established price.
This contributes to a high turnover among children who pay full price and,
hence, eat school lunches on a sporadic basis. Unfortunately, the extent of
this turnover is not known and it is, therefore, not possible to apply a
turnover rate to’ the USDA peak monthly enrollment figure and derive an alter-
native estimate for those who paid full-established price.

PUBLIC OR OTHER SUBSIDIZED HOUSING

There are numerous programs designed to “remedy the unsafe and unsani-
tary housing conditions and the acute shortage of decent, safe, and sanitary
dwellings for low-income families" (U.S. Housing Act of 1937, declaration of
policy). Several Federal, State, and local agencies administer these pro-
grams. Some are funded by USDA (for rural families) or largely by State-
local agencies but most are administered by the Department of Housing and
Urban Development (HUD). Among the most important HUD rental housing pro-
grams are Low Rent Public Housing and Sections 8, 236, and 101 (rent supple-
ments) of the U.S, Housing Act. :

Low Rent Public Housing projects are owned, managed, and administered by
a local housing authority. Partial financing may be provided by the State or
HUD. Participation in public housing is determined by two factors: program
eligibility and the availability of housing. Income standards for initial
and, continuing occupancy vary by local housing authority, although the limits
are constrained by Federal guidelines. Rental charges, which, in turn,
define net benefits, are set by a Federal statute not to exceed 25 percent
of net monthly money income. A recipient household can either be a family
of two or more related persons or an individual who is handicapped, elderly,
or displaced by urban renewal or natural disaster. Other HUD programs pro-
vide similar types of housing assistance to low-income families and
individuals.

Two of the more common types of programs in which Federal, State, and
local funds are. used to subsidize private sector rental housing are rent
supplement and interest reduction plans. Under a rent supplement plan (e.g.,

eo + Sections-8—and-101).,-the- difference--between--the—fair-market"--rent—and -the-

rent charged to the tenant is paid to the owner by a government agency.
Under an interest reduction program (e.g., Section 236), the amount of inter-
est paid on the mortgage by the owner is reduced so that subsequent savings
can be passed along to low-income tenants in the form of lower rent charges.



Table G-3. Standard Errors of Estimated Percentages
of Persons Below the Poverty Level:

1979
Base of Estimated percentage
estimated
percentage
(thousands) | 2 or }|5 or | 10 or 25 or
98 95 90 75 50
4.6 7.1 9.8 14.1 | 16.3
3.9 6.1 8.5 12.2} 14.1
2.5 3.9 5.3 7.7 8.9
1.8 2.7 3.8 5.5 6.3
1.2 1.9 2.7 3.9 4.5
0.8 1.2 1.7 2.4 2.8
0.6 0.9 1.2 1.7 2.0
10,000..... 0.4 0.6 0.8 1.2 1.4
25,000. 0.2 0.4 0.5 0.8 0.9
50,000..... 0.2 0.3 0.4 0.5 0.6
100,000.... 0.12 | 0.2 0.3 0.4 0.4
an



Table F-1. Poverty Status in 1979 of Househotds Using Money Income and Altern:

Benefits, by Selected Characteristics—Continued

MOUSENOLOS AS OF MARCH 1900,
SYMBOLS, SEE TEXT

MOUSEHOLOS ARE CLASSIFIED ACCORDING 70 THE POVERTY STATUS OF THE FAMILY OR THE NONFAMILY HOUSEHOLDER,

ative Methods of Valuing Noncash

FOR MEANING OF

BELOW 100 PERCENT OF THE POVERTY LEVEL BELOW 125 PERCENT OF THE POVERTY LEVEL
RECIPLENT AECIPIENT
cunaent anovon casn | poverty | cunnenr ano/on case | Poyerry
money INCOME mAgKeT vaLuc| EQUIVALENT. lounGeT suaRes| wowey INCOME | MARKET vatue| EeusvaceeT” (Bubete Canes
SELECTED cnanacTERISTICS aytht __conceer | “concent” "| “concerr” |“"¢oncert Concerr | “concert” | ‘-concerr’ |" concest
noyse-
MoLOS | wumaen | pov-| wuMBER] eov-] NUMBER pov. | wumaER] Pov-| wumaeR] Pov-| unser] Pov-| numeca] Pov-] Nunazn] pave
ERTY ERIy ERTY Eary eaty eRty ERTY eety
{TmoUs) |eTHOUS) | RATE |t THOUS) | RATE [erHous> | RATE [ctHoUS) | RATE [crHous> | RATE [cTHOUS) | RATE LeTHoUS:| EATYTereousy | SATE
T
VARYING Fo, wousiNG, Ang
a
tiuncuaesanty
TOTAL ee ee ee ess] 79 108) 2 S49] 12.3) 5551] 7.0] 7403] 6] 7 752 13 71] 17.3] 9 631] 12.2] a4 597] 19,7] 42 079] 15.0
TYPE OF RESIOENCE
IWsipE METROPOLITAN aneas. . .| 94 081| 3 72] 10.9) 3 302 ao351 oa] 4 8 2 7
Instoe centnay ciries.”. + | 33 Tos! 3 Se ist] it ans 2 606 1ic3| 2 5 5 4
oursioe cenrta cities: : :| 30 3ue| 2 1975 i oi2| f 3 2 2
OUTSIOE METROPOLITAN AREAS: :| 25 ast | 3 2 be 2 oan] asa] 3 3 5 5
REGION
nontveasts ee far ae] a8 : 1 2 90s) 18.3] 1 702] 9.0] 2 sui] 13,2 2 552 | 194
fontecentaiu; 22222221] 40 993) $ 1a i i 5 201] 13:3] 2 225] voce] 2 283] 12:3] 2 $a | Ut:
SUM eee ect t | a8 gas] 5 2 for 3 3 3 201) 20.7| § o77| isco] & seal ter3| 4 490 t9s2
msm DD DDD i | i bal f one i i 2 300] 15-7] 1 27] 1029] 4 $82] fecal 2 8rd
RACE ANO_SPANISH ORIGIN
OF MOUSEHOLOER®
WHE es . 49 a50| 6 49 5380] 7.7] 9 620 19 s
: 405 | 2 i 1 ara azi3! 3 939 5 2
Seantaw ofan 221 t 730 608] ter * Go i
TYPE OF HOUSEHOLD
Fantey,Mousenouos «| 98 92 218s 4112] 7,0 7 300| 13.0/ 5 528] 95] 6 a7e/ ana] 6 gaa! ane
ARRIEDMCOUPLE FAAICIES. | 48 180] 3 1 682 toe ac 8 080] “0:3 2 909) 6:2) 3 Sas] “7021 3 $131 44:3
MALE HOUSEHOLDER
iFe’ PRES wee] 106 128 139] 61 208 23] 12s] 237] a0] 247] 10.5
FEMALE HOUSERSL GEA. no
HUSBAND PRESENT»... . | 6 suo] 2 4908] 19.3] 1 977] a3 320 2 323) 27.2] 2 194] 32.7] 2 o18| 34.
NowaniLy wousenouos 22222] 20 gaa! & 2 3a] ita] 3 2ar| i328 et © os] 19:5] 5 120] aera] 3 s97| 3
MALE HouseMaLoeR. » 1.1178 Seu] 379) 10:2] o80| 12:3, i 1 350| 15:7 1 438] Tet] 2 eso] i
Fence wouseMouoeR | 2 1] 2 ose) 3 1 4a7| 1213] 2 251] 1878 4333 2 780! 22:0| 3 863] 2015/3 Sur] 3203
ASE OF HOUSEHOLDER
43 to 20 vans oo... . «| 9 300! 1 est ies! — oe 1398 1
23 To 5a veans 22:22:22 2/17 900] f 803] ios] a gue 2 2 2
33 ro an veans 2222225 2/13 son| tage] ese] + 38 i i
3 to sa veans 22222252) 12 ser] i d33| Brel ae i i
33 To 6a veans 22251) a2 ioe] ad sao| att] 935 i i
95 veaRS ano Oven: 22225 2/18 iae] 2 o26| fatt| 998 Hy 2 3
SIZE OF HouSEHOLO
PERSON ee... eee ef ar aie! 5 toms|isa] 2 3 3 s00{ 30.9] 3 4 5
2 Pensons: 221 34 7a] 2 tier] sta] if i 3 086] 12:3] 2 2 2
5 persons: TS aos] a i eie| ina| i
a Penson 12 aro] 3:8 1 aos| izto| 1 i i
3 reneok 3 O96 t 938| 15.6
6 Pensa DIDDIY 2a 3 522 | 20:
F peRSons oR ote 222221 7 RS $ 550 51:7
ABSENCE OF cxLOREN
30 8
2OM, ee ee el az ane] a aor] a7! a 2 aut| 11.3] 1 600| 12.9] 1084 43.
zemeoken tft) eee oo] 833, 1063] 1178] 1 220| Sur] 1 231 | 43°
Semone SDT 1) 3 Be | 38 es] ina] * Fea] tats] * Gaal d
@enttonen flit] te 226 | 175 323] 20:6] 3su| a9ta| daa | 29s
5 cHELOREN Ga‘wiee 22222) * 28 190 | 27:5, 207/588] 283] 725] 2ee
WORK EXPERIENCE IN 1979 -
‘OF MOUSEOLDER
WORKED. see apap to] a zee] aol aan! ss| > ‘ 3079] oe! 5 102] ae
0 mek Ga'wéaé 22222 2} 30 315) i 2863] 2:8) ¢ 500} 3a! f 2 2632} sca] 2 78} B13
27 TO 30 weeKs's 2 222. 2) "3 Sop 37] aisop * aut] 122 sa] 19t2] * ose | a9:
ig To 26 wens | 2 2522/ 2 38 33a} tots]. eas 28:3 sui] 53:3] Sei | 3ace
23 weeks on Less | 2221] 3 oor Sra] ara] ere | Sara 860] ica _ asi | acy
oid NOT ORK SLIT aR SeeE 2 690) 13:8] 9 2] at:3| 4 . 6 453/321] 7 asa Ses
Tenune
omnencoccueteo ..... . «| 53 030) 6 2672 5.0] 3437] oe] 3 6 956] 12.0 2
RENTER-OCCUPIED” rip 23 ats] ¢ 2405) r0r8] 5 s20 asi3/ 3 8 720] 28:2 3
@ccuPIER PATO nO CASN'RENT : 3] “1 820 avs tei] Sha 23:7 350] 57:5
"PERSONS OF "SPANISH ORIGIN WAY BE OF ANY Race’: Tee _ -



Technical Paper 50

Issued March 1982

Alternative Methods
for Valuing Selected
In-Kind Transfer Benefits
and Measuring Their
Effect on Poverty

Timothy M.Smeeding

ASA/Census Research Fellow
and University of Utah

U.S, Department of Commerce
Malcolm Baldrige, Secretary
Joseph R. Wright, Jr., Deputy Secretary
Robert G. Dederick, Assistant Secretary for
Economic Affairs

BUREAU OF THE CENSUS
Bruce Chapman,
Director


A comparison of those receiving and not receiving cash public assistance
groups in table C-2 indicates wide differences between CPS and alternate
estimates. Part of these differences may be due to cash assistance house-
holds who do not report such payments in the CPS, but do report Medicaid
coverage. More likely the difference is due to the fact that CPS and HCFA
have different criteria for classifying public assistance recipients. In
many cases, a person must first qualify for public assistance before quali-
fying for Medicaid. Some of these persons never actually receive any cash
public assistance benefits. HCFA, however, would count such persons among
cash assistance Medicaid beneficiaries, while the CPS would not.

To examine these data in more detail, the alternate and CPS estimates
were divided into four subgroups. A comparison of these data are shown in
table C-3, While this comparison helps clarify matters to some extent, esti-
mates for the aged and disabled still differ considerably. The CPS estimate
of aged Medicaid beneficiaries is 29 percent greater than the alternate,
while the CPS estimate of disabled is about the same percent below the alter-
nate. There are two main reasons for the CPS estimate of aged Medicaid
beneficiaries to be “high.” First, the elderly may misidentify Medicare as
Medicaid, however, the extent of this type of error is probably small. A
second factor concerns the HCFA classification of Medicaid beneficiaries by
disability status. If a person was initially covered by Medicaid as a dis-
abled beneficiary (e.g., a 62-year-old SSI beneficiary) and contiaued in the
program at age 65 without losing SSI status, the person is still classified
both by SSI and HCFA as a disabled beneficiary. The juxtaposition of 29 per-
cent too many elderly and the same percent too few disabled may therefore be
due in large part to this classification problem. All persons covered by
Medicaid and age 65 or over on the CPS were classified as aged beneficiaries,

As previously noted, the CPS does not count children whose Parents or
guardians are not covered by Medicaid. While the CPS estimate covers 93.6
Percent of the alternate estimate for adults, a much lower percentage results
for children, 77.4 percent. A large part of this discrepancy can probably be
pxplained by covered children whose parents or guardians did not qualify for
ledicaid.

wen ae -  d4 -



Table 10. Benefit Weights for Food Transfers in 1979

Household money

Food transfer program recipient groups

income in 1979 All food } Food stamps ; Food stamps | School
transfers only and school Tunch

Tunch only

Total....eoee 95.7 96.9 94.0 98.0
Under $1,250....... 82.8 85.6 70.7 79.5
$1,250 to $2,499... 89.5 95.0 81.9 84.6
$2,500 to $3,749... 92.9 98.0 88.8 84.9
$3,750 to $4,999... 94.4 97.9 93.3 86.2
$5,000 to $6,249... 97.4 97.5 98.2 88.3
$6,250 to $7,499... 97.0 100.0 96.7 93.5
$7,500 to $8,749... 97.8 98.7 97.5 98.0
$8,750 to $9,999... 98.1 97.2 98.2 98.4
$10,000 to $11,249, 98.1 97.4 98.7 97.3
$11,250 to $12,499, 98.1 99.8 97.0 99.0
$12,500 to $13,749. 98.4 98.5 98.0 98.7
$13,750 to $14,999. 99.2 100.0 99.1 99.0
$15,000 or more.... 99.5 100.0 98.3 99.8

NOTE: The benefit weight expresses the recipient-cash equivalent
value (RV-CE) as a percent of market value (MV).

63



Table G-1. Standard Errors of Estimated Numbers
Below the Poverty Level for Total or

White:

1979

(Numbers in thousands)

Standard error
for persons

Size of estimate Standard
error for
65 years | households
Total | and over
100..... 28 17 14
45 27 22
63 38 31
89 54 44
140 82 72
197 108 106
276 128 162
423 (X) 307
566 X) 532
100,000......... 696 X) 973

X Not applicable.

147


(1979), oniy about 5 to 8 percent of Americans had no health insurance cov-
erage (or pudlic program coverage: e.g., Medicaid or Medicare) in 1976.
Some of the uninsured would probably rely on public health clinics or
Medicaid to reduce their financial burden of medical care expenses.

The CEX surveys indicate that the elderly (families with a householder
65 and over) spent a lesser percentage of their incomes on medical care or
medical insurance in 1960-61 (10 to 11 percent) than they did in 1972-73 (12
to 13 percent). The proportion of income spent on medical care by nonelderly
poor persons was 5 to 9 percent in 1960-61 and 4 to 7 percent in 1972-73,
These data suggest that the elderly spent slightly more on medical care after
the introduction of Medicare and Medicaid while the nonelderly poor spent
slightly less.38 The surveys also indicate that 23 percent of all persons
and 17 percent of all poor persons reported medical care as an item received
without ggpense in 1960-61 compared with 13 and 14 percent, respectively, in
1972-73, ,

These small changes in expenditure patterns for the poor do not reflect
the enormous growth in the market value of Medicare and Medicaid insurance,
In 1972, the average value of Medicare was about $400 and the average value
of Medicaid was $609 for the elderly, indicating a combined value of $1,000
for the low income elderly. These values are much larger than the average
medical expenditures ($250) made by low-income elderly persons in 1972-73,
By 1979, the average insurance value of Medicare and Medicaid had increased
to $2,98149 for each elderly person. Inclusion of market value for medical

‘ insurance of this magnitude in the measurement of poverty would almost elimi-
nate the elderly poor. If normal expenditures for medical care are substan-
tially less than this insurance value (as indicated by earlier surveys), the
inclusion of this value may distort estimates of the size and composition of
the poverty population. Use of recipient value rather than market value
could provide a better measure of the “true” value of Medicare. and Medicaid
to the elderly, however, a good measure of recipient value requires health
care expenditure data for elderly persons not covered by Medicare and

380f course some of this difference can be explained by the “normal income
effect" of higher real 1972-73 incomes on medical expenses, and also
higher medical care prices. Between 1960-61 and 1972-73 medical care
expenses as a percentage of GNP increased from 5 to 7 percent, or by
more than 42 percent.

39The reader should note that respondents were asked to exclude payments

made on their behalf by employer-provided health insurance.
40Growth in medical care is affected by changes in the price, quantity,
and quality of the care. While the poor and elderly have experienced
improved access to medical care (Davis and Schoen, 1978), these programs
have, themselves, contributed to the increased cost of medical care.

74



subsidies are excluded. Although only about one-third of the total food,
housing, and medical care in-kind benefits were accounted for, 88 percent of
the value of in-kind benefits received from these means-tested government
transfer programs were covered in developing the estimates in this report.
Since means-tested benefits are provided to those with low incomes, most of
the food, housing, and inedical in-kind transfers to the poor have been
included.

Even though almost all of the means-tested benefits have been included,
most nonmeans-tested benefits (some of which are received by the poor) have
not, because the necessary data are not available. For example, the value
of public or subsidized housing benefits covered in this report are rela-

Table 2. Comparison of Major Public In-Kind Transfer Programs with Total
Private and Public Income In-Kind in 1980

(Current market value in billions)

Other in-kind income
Major Public Total Major as
in-kind | value of | a percent
Type of benefit transfer benefits | of "total"
benefits Tax | Private
Total sub-
sidies
Total benefits........ $72.5 | $59.8} $32.0 | $ 83.5! $ 215.8 33.6
FOOd. cc vevessenceees 126} 1.2{ (NA) | 16.6 ; 3004 41.4
HOUSINGs oe eeseeeeeee 5.4] 32.7] 28.8 14.8!) 52.9 10.2
Medical Care......e. { 54.5} 25.9 3.2 52.1 132.5 41.1
Total means-tested
Denefits.csecccescece 42.4 3.6 (NA) 2.4 48.4 87.6
Total nonmeans-tested
benefits...ccccseceee 30.1; 56.2 32.0 81.1 167.4 18.0

NA Not available.

Sources: For a detailed explanation of the income elements contained in each
cell and the source of those estimates, see appendix B.



Medicaid. The insurance value of Medicaid was derived using a procedure
Similar to that used for Medicare. Separate equations were developed for
persons receiving Medicaid only and receiving both Medicaid and Medicare.

Persons receiving Medicaid only

MVij = EXij x Aj (3)
Cij
Persons receiving Medicaid and Medicare
MWVi 5 = EXij x Aj + SMIP (4)
Cij
where:
Wij = Medicaid insurance value for beneficiary type i (elderly,
disabled, adult, or child) in State (or State group) j
(j = 1,2,....36)4
EXyj = Total vendor payments including institutional care expendi-
tures for beneficiary type i in State j for FY 1979.
Cij = The larger number of either HCFA recipients or CPS covered
population (plus ‘institutionalized and decedents.) for bene-
ficiary type i in State j.
Aj = Administrative and processing markup in State j for 1978

(Health Care Finance Administration, 1980)
SMIP = SMI Part B premium paid on behalf of Medicare beneficiaries
by Medicaid.

The estimates derived using these formulae are shown in table D-3.

Relatively small populations were combined with other States, which
reduced the number of separate groups to 36. (See table D-3.)

123



Housing transfers. The regression equation used to estimate the normal ex-
penditures for housing required a subset of the 1979 AHS data file restricted
to unsubsidized’ units with incomes of less than $20,000 (later analysis indi-
cated that excluding renters with incomes above $20,000 did not affect the
results of the RV-CE determination process). Separate equations were
developed for the elderly (age 62 and over) and nonelderly. The dependent
variable was the annualized market rent (monthly rent x 12). As with the
subsidized units, some of these unsubsidized units had heat included in rent.
The independent variables for the equation-for the nonelderly included number
of children, number of adults, family size, sex of householder, level of pre-
tax money income, and geographical location. The variable for the elderly
included marital status and sex of housenolder, level of pre-tax income, and
geographical location.

The results of these regressions were less satisfactory than the regres-
sion results for food expenditures (R¢ = .26 for the elderly and .18 for the
nonelderly). Several of the independent variables had the expected sign and
were significantly different from zero, but many more did not perform as
expected. .

Given the relatively poor fit indicated by these results for housing
(and even poorer results for medical transfers as discussed later) the cell
matching procedure was used in favor of the regression approach. Average
market rents (normal expenditures) were tabulated for cells defined by family
_ size, age of householder, and level of pre-tax income. (See table D-6.) A
second tabulation further subdivided by geographical location was made in
order to provide indexes for adjusting rents for geographical differences.
(See table D-7.) These market rents were compared to the market value of the
housing subsidy plus the subsidized rent paid by public housing -residents in
order to arrive at average RV-CE amounts as described in chapter 5.

Medical transfers. The regression equations used to estimate normal expendi-
tures for medical care were developed from the 1972-73 CEX. Separate equa-
tions were developed for consumer units with householders over and under 65
years of age. Consumer units whose inedical bills were paid in full by an
employer or union, Medicaid recipients, and most other public health care
transfer beneficiaries (e.g., Veteran's medical beneficiaries) were excluded
from the data base. As noted in chapter 5, Medicare recipients were not
excluded. The dependent variable was annual medical expenses in 1972-73
adjusted to 1979 price levels. For the under 65 year old group the independ-
ent variables were family size, sex of. householder, level of family income,
presence of children, and geographical location. The independent variables
for the over 65 year old group excluded the presence of children variable but
all others mentioned for the under 65 year old group were used.

The results of the regressions for medical care expenditures were unsat-
isfactory (R2 = .07 for the under 65 year old group and .08 for the over 65
year old group). Based on these poor results, a cell matching procedure was
used to estimate normal expenditures for medical care. (See table D-8.)
Adjustments were made to account for geographical differences in the price of

129



recipient values which vary significantly from market values. For medical
care, the choice of valuation techniques is expected to have significantly
different effects on the estimated number of poor. As noted, however, the
estimates of the RV-CE of medical care are difficult to interpret because of
severe problems in constructing a counterfactual group.

POVERTY BUDGET SHARE VALUE

The final valuation approach investigated in this study involves esti-
mating poverty budget shares (P8S) or equivalently, estimating funds released
at the poverty line income level as a result of the receipt of in-kind bene-
fits. In order to derive values for in-kind benefits under the PBS approach,
the budget shares at the poverty level for food, housing, and medical care
must be determined. There were two major decisions which had to be made
before the estimation could proceed. The data base, either 1960-61 or 1972-
73 CEX, had to be chosen and either consumption levels or income Tevels had
to be selected for determining the budget shares. A discussion of the issues
surrounding these decisions is included with the details concerning the PBS
estimation for each type of benefit.

Food transfers. A simple approach was chosen for the food budget share. The
food budget share of .333 used to derive the original poverty levels was
selected.3! The 1960-61 CEX indicates a much smaller food budget share
(about 22 to 28 percent depending on family size) at the poverty level than
333. However, it is unlikely that a family could consistently meet the
calorie requirements of the USDA Economy Food Budget even with food purchases
amounting to one-third of the poverty line. The food budget share of .333
was selected mainly as-a “mid-range” value.

Housing transfers. The estimates of the poverty budget shares for. housing
were based on the 1960-61 CEX. The budget shares were defined to include
both rent and utilities.32 An average of the budget shares based on

3lin the original formulation of poverty levels, one-third was the food
budget share for families of two or more (Orshansky, 1965). Poverty
Jevels for single persons were set at 80 percent of the levels for two
or more persons. Thus the food budget share for one-person units was
never directly estimated in the Orshansky study.

32Heat was included in the rent of roughly 65 percent of the AHS units
which were in public or subsidized housing in 1979. Roughly 30 percent
had all utilities included in their rent. Under some housing programs
tenants are reimbursed for utilities even if they are not included in
rent. Maintenance and management fees are also included in some Section
8 units but not in others. Thus, it is difficult to decide which
utilities to include in either PBS or RV-CE calculations. This treat-
ment differs from the treatment of housing in the RV-CE calculations
where in some cases only heat was included in rent, because light and
water bills could not be separated from other utilities in the 196U-61
CEX. Most analysts would include utilities with housing as part of the
“shelter” which the family consumes, which suggests that the PBS value
for housing should include these items.

69



Overall, in-kind transfer reporting on the CPS can be viewed as fairly
successful. The in-kind data collected in the CPS appear to be as good as
many CPS cash income estimates relative to their administrative data-based

counterparts. j

25



Table 28. South: Reductions in Poverty Rates Using Alternative Valuation
Techniques and Income Concepts: 1979
Market Recipient Poverty
Income concept value | value - cash | budget share
approach equivalent approach
approach
BELOW POVERTY LEVEL
Money income alone: H
Poverty rate. .ssseseeceseeceeees 14.6 14.6 | \ 146
Money income plus food and housing:
Poverty rate...ccseseeeee 12,3 12.6 12.8
_ Percent reduction}....... -15.7 -13.7 12.5
Money income plus food, housing, and |
medical care (excluding institu-
tional care expenditures):
Poverty Pater sesssseee : 9.5 11.6; 11.8
Percent reduction}, ' -34.9 -20.6 ! -19.1
Money income plus food, housing, and |
medical care (including institu- 1
tional care expenditures): i j
Poverty rate..... | 9.2 11.0: 11.8
Percent reductionl...... i -37.2 -24.4 | ~19.1
BELOW 125 PERCENT OF POVERTY LEVEL :
Money income alone:
Poverty rate.. Se eccccccceees 19.9 19.9 19.9
Money income plus food and housing:
Poverty rate...ccsssccccececcecece | 18.6 18.8 18.8
Percent reductiont.......cceeeeeee - 6.2 - 5.5 - 5.1
Money income plus food, housing, and ' i
medical care (excluding institu- i
tional care expenditures):
Poverty rate....ceseeee 15.8 17.7 18.2
Percent reduction4......... -20.2 -11.1 - 8.5
~~~"Monéy income plus food, housing, and tti(ié‘YSCti(iti‘~S*
medical care (including institu-
tional care expenditures): :
Poverty rates seseye 15.1 17.3 18.2
Percent reduction, -24.0 -12.7 - 8.5
at

_ percent reduction.inthe number of. poor-
based on money income alone.

98

-from-the- current--poverty estimate ~~



the other approaches.9 Second, because the PBS is tied directly to the
Poverty concept, it is not appropriate for use in studies to determine the
effect of in-kind benefits on the income size distribution in general.
Finally, the PBS approach is based on the assumption that the amount of
necessary expenditures implied by the poverty line can be measured by
observed consumer behavior of families at Poverty line income levels.

GOVERNMENT COST

To compare the economic efficiency of public and private sector provi-
sion of in-kind transfers, a measure of the total government cost (GC) of
producing the given benefit is required. The GC includes the MV of benefits
provided and all. of the associated economic costs of production and progran
management, 10 Explicit accounting for capital costs may also become
important where Government decides to actually produce a given good, rather
than provide it by subsidizing private sector production or investment. The
GC is net of recipient contributions to the program, should there be any.
Because, as generally calculated, it includes all direct costs of providing
a given benefit, GC is normally the proper measure to determine net changes
in budget outlays resulting from a given change in program rules and regula-
tions. If overhead costs are fixed (i.e., they do not vary with minor
changes in program parameters), MV may also prove to be a sufficient measure
of budgetary impact.

SOCIAL BENEFIT VALUE

The social benefit value (SBV) of a public transfer program must be at
least as great as the government cost in order to justify the program. The
SBV should. . include . sptllover effects (consumption and production

3The PBS method captures the "substitution" effect but not the "income"
effect of in-kind benefits, both of which are accounted for by the RV-CE
approach. Unless the income elasticity of denand for a subsidized good
is zero, the PBS approach generally results in an underestimate of the
"true" value (an income elasticity of demand equal to zero implies that
consumption of the good does not increase with higher levels of income).
These considerations are discussed further at the end of this chapter,

10The Gc may also be defined to include the dead weight loss or welfare
costs associated with the taxes or public debt needed to fund a given
transfer benefit or other expenditure. Browning (1978) has estimated
the efficiency costs of taxes. Further, if means-tested income transfer
programs reduce beneficiary labor supply, e.g., see Moffit (1981), an
additional cost element arises. However, because the primary purpose
of this study is to estimate various measures of recipient value and
not to compare total program cost to Social value, such measures are
not included, In comparing the value concepts used to gauge the effect
of in-kind transfers on recipient well-being, these costs are constant
across all measures of benefit value since they depend on the programs
under consideration and not the measures of their value per se.



Manser, M. 1981 "Analyzing In-Kind Income Receipt Using the SIPP Panel Data,"
Washington, Mathematica Policy Research, July.

and L. Christiansen. 1976. "Cost of Living Indexes and Price Indexes
for U.S. Meat and Produce, 1947-1971," in Nestor E, Terleckyj, Ed.,
Household Production and Consumption, New York, National Bureau
of Economic Research, Studies in Income and Wealth, Vol. 40, 1976,
pp. 399-446,

Merriam, I. and A. Skolnick. 1968, Social Welfare Expenditures Under Public
Programs in _ the United States: 1929-1966, Research Report #25.
Washington, Social Security Administration.

Minarik, J. 1981. "A Critique," Challenge, January/February, pp. 44-47.

Moffit, R.A, 1981. "The Negative Income Tax: Would it Discourage Work?"
Monthly Labor Review, April, pp. 23-27. :

Moon, M.. and T. Smeeding. 1981. "Medical Care Transfer, Poverty, and the
Aged," Journal of Health Politics, Policy, and Law, Spring.

Murray, M. 1975. "The Distribution of Tenant Benefits in Public Housing,"
Econometrica, Vol. 43, July. ~

1980. "Real versus Monetary Transfers: Lessons from the American
Experience," presented to the International Symposuim in Alternative
Measures for Improving the Efficiency and Effectiveness of Public
Transfer Policy, University of Angsburg, Germany.

Musgrave, R. and P. Musgrave. 1980. Public Finance in Theory and Practice,
Third Edition, New York, McGraw-Hill.

National Center for Health Statistics Research. 1979. National Nursing Home
Survey, Washington, U.S. Government Printing Office.

National Urban League. 1980. The Myth of Income Cushions for Blacks,
Washington, National Urban League.

Orshansky, M. 1965. "Counting the Poor:' Another Look at the Poverty
Profile," Social Security Bulletin.

1977, "Measuring Poverty: A Debate," Public Welfare, Spring.

___ 1981. "The Elements of Measuring Poverty from Survey Data" “ presented
at the American Statistical Association Proceedings, August 11.

Paglin, M. 1980, Poverty and Transfers In-Kind, Stanford, Hoover
Institution Press.

159



The most recent data which can be used to estimate utility functions are
fron the 1972-73 Consumer Expenditure Survey (CEX). Use of the CEX data has
several major problems. First, there are no price data available for speci-
fic locations. Secondly, the sum of expenditures, taxes, and change in net
worth does not equal income for most households. This occurs because the CEX
assumes that durable goods other than hones are entirely “consumed" in one
year. Thus, expenditures for consumption can easily exceed income, especial-
ly for low income persons. In any case, it is not possible to estimate a
complete system of demand equations (or to impose cross demand equation
restrictions) which are necessary for an efficient solution using the utility
function approach (see Manser, 1981). Even the most sophisticated utility
function approach cannot correct for the inadequacies of the consumption data
on which it is based (see Cooper and Katz (1978) and their Variable Elasti-
city of Substitution model). The quality of the CEX data are not worth the
level of sophistication implied by the more complex empirical utility
functions. .

The other method for determining recipient value is the normal expendi-
ture approach. There are two methods for estimating normal expenditures and
assigning recipient value. The first technique as suggested by Smeeding
(1975) is a “cell matching” procedure to link the CEX expenditure data with
the March CPS in-kind benefit data. In this procedure, the CEX is used to
generate average consumption levels for specific commodities. These consump-
tion levels (Cij) for group (i) and consumption yood (j) are tabulated
according to household pre-tax money income level (adjusted for the change in
consumer prices from 1972-73 and 1979), demographic characteristics (e.g.,
size of household and age of householder), and location (which adjusts for
geographic price differentials}. The in-kind transfer recipients on the CPS
are then assigned a recipient value based on the average consumption level
for the cell which defines that household's characteristics.

Alternatively, following Smolensky and Van der Gaag (1980), a-system of
expenditure functions can be estimated using the CEX for food and medical
care, and the AHS for housing. These functions are known as an “extended
linear expenditure system" (ELES) because they are linear in prices and
income, and they do not require consumption plus change in net worth to equal
income, These equations take the following general form:

Cig = ao + ar + agl + a3R + aqY +€

l6This problem breaks the relationship between consumption and income and
probably explains the low income elasticity estimates derived by re-
searchers who use the CEX to estimate expenditure functions (see
Smolensky and Van der Gaag, 1980).

171m the assignment process, the income of CPS units was defined as money
income plus the MV of all in-kind benefits.

59



Table F-1. Poverty Status in 1979 of Households Using Money income and Alternative Methods of Valuing Noncash
Benefits, by Selected Characteristics—Continued

(HOUSEHOLDS AS OF MARCH 1980,
SYMBOLS, SEE TEXT}

HOUSEHOLDS ARE CLASSIFIED ACCORDING To THE POVERTY STATUS OF THE FAMILY OR THE NONFAMILY WOUSEHOLOER,

FOR MEANING OF

ELON 100 PERCENT OF

THE POVERTY LEVEL

BELOM 125 PERCENT OF THE POVERTY LEVEL

_Recnerent
POVERTY /OR CASH | POvERTY
BUDGET SHARES Coutvaent’ [auoser suanes
SELECTED CHARACTERISTICS CONCEPT CONCEPT CONCEPT
HOUSES
HoLOS NUMBER | Pov Pov- Pov
ERTY ERTY ERTY
«THOUS? (THs) | RATE RATE RATE
VALUING F000, HOUSING, AND ALL
“HEDICAL BENEFTTS
TOM eee ee ee | 8 7782) 9.8 143 15.
‘TYPE OF RESIDENCE
INSIDE METROPOLITAN AREAS. . «| 54 5 3 2 4 a 73a] 8.8) 8 5 6 42.7| 7 14.2
INSIDE CENTRAL CITIES, «+ «| 23 3 1 2 2 80a) 1116/5 3 4 Io] 4 1d
OUTSIDE CENTRAL CITIES .  :| 30 2 a ry 1936] 6.4] 3 2 2 v3] 3 40:3
OUTSIDE METROPOLITAN AREAS <<] 25 3 2 2 3ois| 12.0] 5. 5 a ane| 4 193

REGION

NORTHEAST. «
NORTH CENTRAL,
sue et

RACE AND SPANISH ORIGIN
‘OF HOUSEHOLDER"

WHITE. eee
BLACKS
SPANISH ORIGIN

TYPE OF HOUSEHOLD

FaNILy KOUSEHOLOS.
RIED=COUPLE FANILIES,
ALE FOUSENOLDER, NO

NONFAMILY HOUSEHOLDS
MALE HOUSEHOLDER
FEMALE HOUSEMOLOER

AGE OF HOUSEHOLOER

15 To 2a veaRs ,
25 To 34 YEARS |
38 To 40 Years
45 To 54 YEARS
55 To 68

65 YEARS

SIZE OF HOUSEHOLD

1 PERSON.
2 PERSONS,
3 PERSONS.
& PERSONS.
5 PERSONS.
6 PERSONS,
7 PERSONS of

fore

Re

PRESENCE OF CHILDREN
5 TO 18 YEARS OLD

CHILD,
2 entered
3 SuILOREN
& cHtLont nt
3 Gnitonen on'wénd 221

WORK EXPERIENCE IN 1979
(OF HOUSEHOLDER

WORKEO ‘
4 WEEKS On MORE
27 0 39 REEKS .

OID NOT WORK. ss

TENURE

omenaoccurieD, «
RENTER-OCCUP TEI

OccuPteR PATO NO GaSW'RENT |

°PERSONS OF SPANISH ORIGIN MAY BE OF ANY RACE.

7
24
a3
a2

1 0521 16,8
4495] “8.4
1010] 7.3
383| 7.0
1175] 916
2 13a) ane
3 987| 17.3
art} 79
99] 615
893] 7.2
suo] 902
276| 11,0
319] 18:2

ne

250

10.9
TA

13.5

3)
28.3

10.0
22:7

33.9



RECIPIENT OR CASH EQUIVALENT VALUE

Most economists would agree that if in-kind transfers distort consump-
tion patterns, they add less to a recipient's economic well-being than an
equal dollar value cash transfer. If so, they should be discounted to their
recipient value to reflect. this lower value. Recipient value reflects the
program beneficiary's own valuation of the benefit; it is most often measured
by the amount of cash which would make the recipient just as well off as the
in-kind transfer. In economic theory, the recipient value is also known as
the cash equivalent value (RV-CE) and is formally termed the “Hicksian equi-
valent variation" after John Hicks (1943). Most economists agree that RV-CE
is the proper measure for valuing in-kind transfers to evaluate their effect
on the economic well-being of the poor and the income size distribution
(e.g., Smeeding and Moon, 1980). Not all economists are in full agreement on
this issue, however, since many earlier studies of the effect of income in-
kind on poverty have used MV. The Congressional Budget Uffice (1977, p. 19)
and Hoagland (1980) both used MV but have included a statement that the cash
value of in-kind transfers to recipients may be less than the MV.

Two economists who have studied in-kind transfers, Paglin (1980) and
Browning (1979), prefer to use MV rather than RV-CE. Paglin agrees that in-
kind transfers may be worth less than cash transfers of equal dollar value,
but believes that in-kind transfers should be compared with earned income and
not to cash transfers. On this basis, he argues that in-kind transfers are
Preferred to earned income because of the extra leisure they provide as com-
pared with earnings which imply less leisure and require thre added expenses
of work-related costs (e.g., Clothing and transportation). Since earned
income is measured at its dollar value, in-kind transfers must also be
counted at least at their dollar value (or MV) and cannot be valued less than
earnings if they are preferred to earned income. While Paglin's line of
reasoning also implies that cash transfers should be counted at more than
.their WV (since they are preferable even to in-kind benefits), he neither
makes nor recommends any such adjustment. Nor has he considered the value
of in-kind fringe benefits associated with employment (e.g., employer pension
or health insurance Provision) or other job amenities (e.g., free use of
company cars) which might make earned income preferable to in-kind transfers.

Browning (1979) also recognizes the fact that in-kind transfers may be
worth less than an equal cash transfer. He argues that it is wrong to make
an adjustment for this distortion while ignoring other similar distortions
which make certain goods and services worth less than their MV.2 Browning
feels that analysts should make no such adjustments for in-kind transfers
unless adjustments are made for all types of “welfare costs"--from

—

2For instance, certain income tax provisions. produce distortions which
reduce the price of goods to purchasers because of their deductibility.
If the tax deductibility was not available many people would not purchase
as much of certain goods, e.g., housing, as they do. Therefore, persons
value these goods at less than their true market cost. It can be argued
that some adjustment for this type of welfare cost should be made.



The Cjj's were estimated for three different groupings of food transfer
recipients: 1) those receiving food stamps only, 2) those receiving food
stamps and school lunches, and 3) those receiving school lunches only. For
households receiving school lunches only, the Cij "s were adjusted to reflect
only expenditures for school lunches using data available from the USDA. The
Cij's were then inflated to 1979 price levels based on changes in the food
component of the CPI. Average Cij's were computed for each cell of a matrix
defined by 13 income classes, 6 family sizes, ang 2 age of housenolder yroups
(under 35 years old and 35 years old and over). These C;;'s were then com-
pared to MV;j for food transfers in order to derive the estimates of RV-CE4j.

Benefit weights for food transfers are shown in table 10 for each of the
three subgroups of beneficiaries. In general, the benefit weights are high,
indicating that food transfers are nearly equivalent to cash, 24 Thus, for
food transfers, the RV-CEj;'s are roughly equivalent to the WV G'S. Tne
average benefit weight for “all households was 95.7. These values represent
the amount of cash transfer per dollar gf food transfer that keeps the recip-
ient at the same: level of well-being. 2 In other words, families with $1U0
worth of food transfers would realize the same levels of well-being if given
$95.70 in cash. The lowest average benefit weights were found in the lowest
money income classes. While over 90 percent of all recipients had benefit
weights of 100.0, only 2.2 percent of all beneficiaires had benefit weights
below 80.0. These results are quite consistent with those of other- similar
studies reviewed in chapter 4.

23These groups were chosen based on indications of their significance from

the food regression approach. The estimates of normal expenditures for
food for each cell of the matrix are shown in appendix D.

24this statement is not unqualified, however. It ignores the differential
stigma costs which accompany the Food Stamp Program as compared to an
equal cost cash transfer program. See Weisbrod (1970) for a general
discussion of this issue.

cE stimates of C;;'s using a definition of food restricted to grocery
_....Store_ items. produced.benefit_weights-which-were significantly lower-than
those in table 11 (an overall average of 83 percent compared to 96 per-
cent). Other researchers have used a broader definition of food,
similar to that used in this study but including all restaurant meals
(Smeeding (1975), MacDonald (1977), and Smolensky, et. al. (1977)).
Further discussion of this issue can be found in appendix D.



Table 29. Central City Residents: Reductions in Poverty Rates Using Alternative
Valuation Techniques and Income Concepts: 1979

Market Recipient Poverty
Income concept value | value - cash | budget share
approach equivalent approach
‘ approach
BELOW POVERTY LEVEL
Money income alone:
Poverty rate...ccccccccseccecevces 15.0 15.0 15.0
Money income plus food and housing:
Poverty rate.ccccgessecvccsceeceee 12.0 12.3 12.8
Percent reductionl....cccsccceeees -19.9 -18.0 -14.6
Money income plus food, housing, and
medical care (excluding institu-
tional care expenditures):
Poverty Fates vesesesceseserrereses 7.8 11.4 11.6
Percent reduction+....eccccecseece -47.8 -24.5 | -23.0
Money income plus food, housing, and
medical care (including institu-
tional care expenditures):
Poverty Pabesesesgeees 7.6 10.5 11.6
Percent reduction+...secseccsssone -49.3 -30.0 -23.0
BELOW 125 PERCENT OF POVERTY LEVEL
Money income alone:
Poverty rate.ccsecccssccccscvvcecs 20.5 20.5 : 20.5
Money income plus food and housing: .
Poverty rate...ccseeveeece oo 18.7 18.9 19.3
Percent reductiont,....sseseeeeees - 8.5 - 77 - 5.8
Money income plus food, housing, and
medical care (excluding institu-
tional care expenditures):
Poverty rate..cccsccccccrecsccsecs 14.1 17.4 18.4
Percent reduction*..cceccssseveeee -31.1 -15.1 -10.1
Money income plus food, housing, and
medical care (including institu-
tional care expenditures):
Poverty Fates veseree 13.6 16.9 18.4
Percent reductiOn*..scccccessceees -33.6 -17.4 -10.1

percent reduction in the number of poor from the current poverty estimate
based on money income alone.

99


“Money Income and Poverty Status of Families and Persons in The United
States: 1980, Current Population Reports, Series P-60, No. 128,
Washington, U.S. Government Printing Office.

U.S. Congress, Joint Economic Committee. 1973. “How Public Welfare Benefits

are Distributed in Low Income Areas," Studies in Public Welfare
Paper #6 and Appendix, Washington, U.S. Government Printing Office.

U.S. Senate. 1980. Departments of State, Justice, and Commerce, The Judiuary
and Related Agencies Appropriations Bill, > th Congress, 2nd

Session, September 16.

Van Praag, B. M. S. 1968. Individual Welfare Functions and Consumer Behavior,
Amsterdam, North Holland Publishing Co.

Wansbeck, T. ana A. Kapetyn. 1981. "Empirical Evidence on Preference Forma-
tion," presented to the American Statistical Association, Detroit,
August 12.

Watts, H. 1969. "The Iso-Prop Index: An Approach. to the Determination of
Differential Poverty Income Thresholds," Journal of Human Resources,
Winter.

Weinberg,’ D. 1981. "Housing Benefits from the Section 8 Housing Program,"
Evaluation Review, forthcoming.

Weisbrod, B. 1970. “On The Stigma Effect and The Demand for Welfare Programs:

A Theoretical Note," Institute For Research on Poverty Discussion
Paper #82-70, Madison, Wisconsin, Institute for Research on Poverty.

QrU.S. GOVERNMENT PRINTING OFFICE: 1982-370°737/8162

162,



(2b) Non-elderly
MV25 EXgj (1-034) (1.058)
“eas (ie Tr2y

-101

In summary, the HCFA FY 1979 estimates were transformed into CY 1979
estimates, the administrative costs were included, and the out-of-pocket
premium expenses were deducted in arriving at MV for Medicare. The actual
estimates of the MVjj's are presented in table D-2. To estimate the MVjj's
for Medicare excluding institutional care benefits, the mVig's are multiplied
by the ratio of noninstitutional care expenditures to total Medicare expendi-
tures. This ratio was .977 for 1979.

Table D-2. f£stimated Annual Market Value of Medicare as Insurance for Each
Medicare Beneficiary in 1979 Including Institutional Benefits,
by Type of Beneficiary and State

Market value of Medicare

State
Non-
Elderly elderly
Total.sescseees $ go] ° $1,177
Alabama...ececseseees 731 987
Alaska.. - 1,076 1,442
Arizona. . 827 1,106
Arkansas * 660 646
California... . 1,205 1,607
Colorado...esessveeee 868 1,175
ConnectiCut.ccccceees 965 1,407
Delaware...sseseeeeee 895 1,312
District of Columbia. 1,336 2,321
Florida.sssesessevees 976 1,222
GeOrgidseccececcccees 669 936
892 1,583
1,486 812
1,053 1,514
Indiana..ssssessseeee 772 1,113

121



Addendum: A Diagrammatic Analysis of Various

Approaches to Valuing In-Kind
Transfers

Figure 1 provides a summary of the various approaches for valuing non-
cash transfers which have been discussed in this study. Several. of the
footnotes in this section are designed to assist those with only .a rudimen-
tary background in economic theory. Using a standard utility maximizing
Frgqework, assume that there is a pre-in-kind, transfer budget constraint of
ADSTM and a pre-in-kind transfer utility level of U, that are just tangent
to each other. The consumer maximizes his utility af this point, given the
budget constraint, by purchasing OM units of the (about to be) subsidized
good and 0Q units of other goods. This framework can be used to describe the
manner in which the provision of different types of in-kind benefits affect
the budget constraint, and consequently, the recipient's level of. economic
well-being. .

Assume that a fixed quantity of a subsidized good (or service) which has
a MV of AB(DS) is provided to the recipient without cost. In figure 1, this
is represented by an extension of the budget constraint from AD to ABS.
There has been no change in relative prices (which would create a different
tilt in the budget frontier than that observed in figure 1). Several in-kind
benefits are similar in nature to this case, including Medicaid, Medicare
part A, the Free or Reduced-Price School Lunch Program, and the Food Stamp

20The budget constraint specifies the maximum amount of subsidized and
nonsubsidized goods which a consumer can purchase. For instance, a
person facing the budget constraint AD with no preferences for the sub-
sidized good could purchase up to OA units of other goods. Conversely,
a person with preferences for the subsidized good could purchase up to
OD units of the subsidized good, or:any mix of subsidized and nonsubsi-
dized goods along the line AD. In-kind transfers generally expand the
budget constraint, allowing beneficiaries to enjoy larger quantities of
both subsidized and nonsubsidized goods.

lutitity curves such as Uj, U,, etc., in figure 1 indicate all mixes
of subsidized and nonsubsidized goods which keep the individual at the
same level of satisfaction. A utility curve is based on the recipient's

.. tastes. and preferences. for various goods and services. A higher amount
of goods and services indicates a higher level of well-being. For
instance, a person who can reach Us is better off (or enjoys a higher
level of economic well-being) than a person at utility level Uc or
any other utility level below U,. The assumption that consumers maxi-
mize their level of utility produces the tangency between the utility
curve and the budget constraint.

TM oo 38



Table 16. Market Value Approach: Number of Persons Below the Poverty Level and Poverty
Rates, by Income Concept for Selected Population Subgroups: 1979

(Numbers in thousands. Persons as of March 1980)

Income concept

Money income plus | Money income plus
Money income food, housing, food, housing,
Selected Money income. plus food and and medical care and medical care
characteristics alonel housing (excluding (including
institutional institutional
care) care)
nN
T | |

Poverty Poverty ; Poverty Poverty
Number rate | Number rate | Number rate | Number rate

—

All persons. | 23,623|. 11.1] 19,933 9.4; 14,023 6.6 | 13,634 6.4
South...... + {| 10,108 | 14.6 8,526 12.3 i 6,577 9.5 6,343 9.2
Non-South + | 13,515 | 9.5] 11,407 8.0; 7,446 5.2 7,291 5.1
Elderly (65 i

and over)..... 4,097 | 14.7 3,601 12.9; 1,452 5.2 1,251 4.5
Non-elderly.... | 19,526 10.6 | 16,332 | 8.9; 12,571 ; 6.8 | 12,383 6.7
Central city... | 8,748 | 15.0 7,005 | 12.0} 4,563 7.8 4,436 7.6
Suburb.......6. | 5,661 | 6.7 4,972 | 5.9; 3,733 4.4 3,633 4.3
Nonmetropolitan i | !

Pd. ceeee 9,214 | 13.4 7,957: 11.5) 5,728 | 8.3 5,565 8.1
Black.. ee 7,456} 30.4 5,874 | 23.9! 3,872 15.8) 3,712 15.1
Non-Black...... | 16,167 8.6) 14,059 i 7.5! 10,151 5.4! 9,922 5.3

H i i i

In households ' | j '
with female | H i !
householder, | : !

no husband | j i

Present....... | 9,142 34.8 27.5; 4,762: 18.1: 4,625 17.6
All other Bo . : _ | : .
persons....... : 14,481 7.8} 12,703 ' 6.8 i 9,261 i 5.0: 9,009 4.8

i i . =“ i i

lpiffers from official poverty count for 1979 of 25,345 (11.6 percent) because poverty
status was computed on a household basis.



Chapter 1. Introduction

This is the first of several reports to explore the issues, data
requirements, and technical feasibility of measuring selected types of in-
kind income. The purpose of this report is to present several alternative
methodologies for valuing public in-kind (noncash) transfer benefits and to
determine the effects of these valuations on estimates of the size and compo-
sition of the poverty population. In accordance with Directive No. 14
"Definition of Poverty for Statistical Purposes," issued by the Office of
Management and Budget, the current poverty definition is based on money
income alone.! This definition does not include the value of any in-kind
benefits such as food stamps, Medicaid, and public housing. This report
presents three strategies for valuing a selected group of public in-kind
transfers and the resulting effects on the poverty estimates for 1979. In
addition, the strengths and weaknesses of each valuation technique are
examined, but none is recommended .as the most appropriate method for
valuation. It is expected that this report will be the basis for official
discussions concerning the valuation of noncash benefits and the measurement
of poverty.

This study is based on a continuing effort to broaden and improve the
definition of income used by the Census Bureau. and other public agencies.
Consequently, this paper is highly exploratory in nature as compared. with
normal Census Bureau statistical publications. This particular report is
being issued at this time because of the recent availability of annual data
on noncash benefit recipiency and public interest concerning the effect of
major types of in-kind transfers on the poor.

The report focuses only on those major public in-kind transfer benefits
in the form. of health care, food, and housing for which recjpiency data are
available from the March Current Population Survey (CPS). Ideally, the

Money income is before taxes or payroll deductions and includes earnings
from employment, Social Security, public assistance and welfare, property
income, and any other source of regularly received income.

2This research report is guided by requests from Congress and the Office
of Management and Budget (U.S. Senate, 1980; Cutter, 1980) for such an
analysis. See appendix A for the U.S. Senate statement expressing their
concern over this issue.

3The CPS is a national household survey conducted monthly to obtain
employment and unemployment statistics. Each March, additional questions
are asked on money income and noncash benefits. The data, therefore, are
subject to sampling and nonsampling errors.



APPENDI XES--Continued
Page
Source and reliability of the estimates... 1.5.6.2. 145
Definitions and Explanations. .. 1... 56 eee eee eee 151
Poverty thresholds: 1979. ....0 cee even eveoe 153

Bibliography. «2. ew eee eee we we ee we we we ew we ew ~~ 156

re



Appendix G. Source and Reliability of the Estimates

INTRODUCTION

The estimates in this report are based directly on the March 1980 Cur-
rent Population Survey (CPS) or on a combination of data from other surveys,
administrative sources, and the March 1980 CPS. The CPS is a monthly survey
which deals mainly with labor force data for the civilian noninstitutional
population. In March the monthly labor force questions are supplemented with
questions covering money income and noncash benefits received during the pre-
vious calendar year.

The present CPS sample was initially selected from the 1970 census files
with coverage in all 50 States and the District of Columbia. The sample is
continually updated to reflect new construction. The current CPS sample is
located in 629 areas comprising 1,133 counties, independent cities and minor
civil divisions inthe Nation. In this sample, approximately 68,000 occupied
households were eligible for interview. Of this number, about. 2,900 occupied
units were visited but interviews were not obtained because the occupants
were not found at home after repeated calls or were unavailable for some
other reason.

The estimation procedure used in the CPS involved the inflation of the
weighted sample results to independent estimates of the total civilian nonin-
stitutional population of the United States by age, race, and sex. These
independent estimates were based on statistics from the 1970 Census of Popu-
lation; statistics on births, deaths, immigration and emigration; and statis-
tics on the strength of the Armed Forces.

Since the CPS estimates and other estimates derived from survey data are
based on a sample, they may differ somewhat from the figures that would have
been obtained if a complete census had been taken using the same question-
naires, instructions, and enumerators. There are two types of errors pos-
sible in an estimate based on a sample survey--sampling and nonsampling.
Standard errors primarily indicate the magnitude of the sampling error. They
also partially measure the effect of some nonsampling errors in response and
enumeration, but do not measure any systematic biases in the data. The full
extent of nonsampling error is generally unknown. Consequently, particular
care should be exercised in the interpretation of figures based on a rela-
tively small number of cases or on small differences between estimates.

NONSAMPLING ERROR
Nonsampling errors are particularly important in surveys of income and

expenditures, such as those used in this study. Nonsampling errors can be
attributed to many .sources, e.g., inability to obtain information about all

145



approach (Kraft and Olsen, 1977; Weinberg, 1981), 16 These measures vary sub-
stantially, ranging from 48 to 82 percent of MV.!7 The PBS measure varies
between 38 and 42 percent of MV, depending on family size and age structure.

The GC of public and other subsidized housing has been estimated to
average from 117 percent (Murray, 1975) to 140 percent (Weinberg, 1981) of
the MV of program benefits. Rydell, Mulford, and Heloers (1980) nave shown
that in one particular type of housing program (HUD's Section 8 existing
housing), gross rents are 26 percent above their fair market value solely
because of program regulations for setting rents. Costs of code compliance
monitoring, tenant foreclosure, and other overheads are also substantial.
Subsidized housing programs also incur long term costs involved in contracts
which promise to maintain subsidies to the private owners of rental housing
units over 30- to 40-year periods. In fact, the aggregate long-term costs
are estimated to be quite high, probably exceeding the existing budget
authority for many housing programs (U.S. Congressional Budget uffice, 1979).
In evaluating the budgetary effect of a change in the number of public
housing units, it is therefore important to accurately measure the present
and future GC of the units (net of expected tenant rent expense).

Medical transfers. Valuation of medical care transfers presents several
unique problems, as discussed later in this chapter. Authors who have esti-
mated the recipient value of medical transfers’ include Smolensky, et.al.,
1977; Cooper and Katz, 1978; Smeeding, 1975; Smeeding and Moon, 1980. The
value of medical transfers varies by type of program, income, and other
characteristics of the recipient. For instance, Smeeding (1975) estimated
that based on a -normal expenditure approach, RV-CE's in 1972 were 63, 93, and
78 percent of MV for those who received Medicaid, Medicare, or both programs,
respectively. Altogether, normal expenditure and utility based RV-CE values
averaged between 58 and 74 percent of MV. PBS values for medical care bene-
fits have been estimated by Smeeding and Moon (1980); these estimates were in
the 28 to 38 percent range.

16, funds released approach is similar to the normal expenditure approach,
except that the income effect from all in-kind benefits in the former
approach is not taken into account. Thus, funds released only captures
the substitution effect of the in-kind benefit for the current level of
expenditure, while the normal expenditures approach also captures (and
possibly overstates) the income effect from in-kind transfers. Weinberg
(1981) measures funds released as defined here, but calls it the “income
effect" from public housing.

170ne bias in these measures involves the treatment of low rent public
housing. Some authors (e.g., Smeeding, 1975; Kraft and Olsen, 1977)
argue that families constrained to consume less housing in the project
housing unit than they would consume if given an equal amount of cash,
have RV-CE which are less than MV. In estimating RV-CE for all other
programs, Smeeding assumes RV-CE=MV if normal expenditures exceed MV.
Kraft and Olsen, however, only deal with estimates of RV-CE for low
rent public housing and reduce RV-CE if normal expenditures exceed MV.

35



Appendix J. Bibliography

Aaron, H. 1977. “Comments on The Distribution of Benefits -from Public
Housing," in T. duster, ed. The Distribution of Economic Well-
Being, Studies in Income and Wealth, Vol. 41. National Bureau of
Economic Research (Cambridge, Massachusetts. Ballinger) pp. 65-69.

_____ and G. Von Furstenburg. 1971. "The Inefficiency of Transfers In-Kind:
The Case of Housing Assistance," Western Economic Journal, Vol. 9,
dune, pp. 184-191.

Alperovitz, G. and J. Faux. 1981. ."Missing the Point: A Reply," Challenge,
January-February, pp. 48-50.

Anderson, M. 1978. Welfare, Stanford, Hoover Institution Press.
Behrens, J. and £. Smolensky. 1974. "Alternative Definitions of Income

Redistribution," Public Finance/Finances Publiques, Vol. 28, July
1974, pp. 315-332.

Borzilleri, T. 1980. "In-Kind Benefit Programs and Retirement Income," pre-
pared for the President's Commission on Pension Policy, March.
mimeograph. °

1980a. “In-Kind Benefit Programs and Retirement Income Adequacy,"
National Journal Reports, October 25, pp. 1821-1825.

Browning, E. 1974. “A Comment on Distributional Externalities and In-Kind
Transfers," Public Finance Quarterly.

1976. “Tne Trend Toward Equality in the Distribution of Net Income,"
Southern Economic Journal, Vol. 34, July, pp. 912-936.

___. 1978. "The Burden of Taxation," Journal of Political Economy.

1979, "On the Distribution of Net Income: Reply," Southern Economic
Journal, Vol. 45, January, pp. 945-959,

Bowring, J. 1981. “Necessities Inflation and Distributional Impact ," i

Challenge, January/February, pp. 50-52.

Budget of the United States Government: FY1982. 1981. Washington: U.S.
Government Printing Office.

Clarkson, K. 1976. "Welfare Benefits of the Food Stamp Program," Southern
Economic Journal, Vol. 34, July, pp. 864-78.

156



weight rises to 57.5 percent, mainly because only MV (the denominator) has
been adjusted to exclude these expenditures. Weights for the Medicare only
group are the highest, reflecting a greater willingness to pay for medical
care among the relatively higher-income elderly. However, benefit weights
are the lowest for persons receiving both Medicare and Medicaid. This group
consists of the low-income elderly and disabled. The Medicaid only group
consisting mainly of low-income Monaged adults and children had a benefit
weight of 37.3. If institutional care expenses are excluded, the benefit
weignts for the Medicare and Medicaid group rise from 24.9 to 42.1 percent.

The benefit weights developed in this study are substantially below
those found by Smeeding (1975) for 1972, even when institutional. care bene-
fits are included. That study found a benefit ratio of 68.0 for all medical
benefits in 1972. This compares to the benefit weight of 47.3 for medical
benefits including institutional care expenditures shown in table 13. Exclu-
sion of institutional care expenditures, which results in a benefit wetght of
57.5, brings the overall estimate somewhat closer to Saeeding's earlier
results. There are several factors which help explain the difference in
these estimates. First, although Seeding included institutional benefits,
his results were based on the 1960-61 CEX and not the 1972-73 CEX. Second,
institutional care benefits were only 22 percent of all Medicaid expenditures
in 1972 compared to 46 Percent in 1979. Given these two factors, and the
rapid rise in Medicare and Medicaid medical benefits relative to normal
expenditures for medical care (as we have calculated them) from 1972 to 1979,
the estimates in table 12 are not inconsistent with Smeeding's previous
results.

There were relatively few cases (17.5 percent) for which the RV-CE of
medical care equaled the MV. While 24.3 percent of the Medicare only recipi-
ents had benefit weights of 100.0, only 9.1 percent of Medicaid beneficiaries
would have spent as much on medical care as the MV of Medicaid had they been
given the same transfer in cash. Virtually none (less than 0.1 percent) of
the Medicare and Medicaid group had benefit weights of 100.0, which indicates .
that the medical benefit was not "just as good as cash." Excluding institu-
tional care benefits increases the proportion of cases with benefit weights
of 100.0 from 17.5 percent to only 19.7 percent.

In summary, this portion of the report has discussed the procedures for
estimating the RV-CE for food, housing, and medical care transfers for 1979,
It appears that food transfers are largely equivalent to cash, with an over-
all benefit weight equal to nearly 97 percent of MV. The RV-CE for public
housing are worth 81 cents per dollar of housing sudsidy. Medical benefits
are worth only 47 cents per dollar including, and 58 cents per dollar
excluding, institutittonal expenditures. The benefit weignts for food and
housing are comparable to estimates from earlier research (see table 4),
however, the medical transfer benefit weights are below previous work by
Smeeding for the reasons mentioned earlier. Based on- these results,~it
appears that any reduction in the poverty population resulting froa the valu-
ation of food and housing benefits would be quite similar for both the MV and
RV-CE approaches. In contrast, medical benefits, which inake up over 80 per-
cent of the total MV of the in-kind transfers included in this report, have

68



Table 14.

Eligible for Medicare and Medicaid:

1979--Cont inued

Comparisons of Medical Benefits to the Poverty Line for the Elderly Who are

United States New York Mississippi
Row | Item
One Two One Two One Two
person | persons | person | persons | person | persons
RV-CE FOR MEDICAL BENEFITS} |
INCLUDING INSTITUTIONAL
CARE
16 Medicare..cccsscsseccevcees | $ 685 | $1,370 |$ 758; $ 1,516 504! $ 1,008
17 Medicaid..seoeee . 735 | 1,470 1,652 3,304 382 764
18 TOTAL. eeweccvececes . 1,420 2,840 2,410 4,820 886 ' 1,772
19 Medical/pov. line (13 + 4). 40.9% 65.1% 69.4% 110.6% 25.5% i 40.6%
20 Medical/min. inc. (13 = 3). 54.0 | 70.3 65.6 97.5 33.7 43.9
PBS FOR MEDICAL BENEFITS,
INCLUDING INSTITUTIONAL
CARE
2
al Medical benefit TOTAL .....|$ 380 | $ 497 |$ 380] $ 497 380} $ 497
22 Medical/pov. line (16 = 4). 11.0% 11.4% 11.0% 11.4% 11.0% 11.4%
23 | Medical/min. inc. (17 = 3). 14.5 12.3 10.3 10.1 14.5 12.3
i

\the RV-CE for Medicare and Medicaid are calculated by multiplying by the Medicare only

Different results would be obtained if the computa-
_ tions were based on the joint Medicare-Medicaid benefit weight, the overall medical care
benefit weight, or the benefit weights excluding institutional care.

and Medicaid only benefit weights.

2these values differ from those in table 13 because they-are the average value for persons
65 to 74 years old and 75 years old or older.

77



. Technical Paper 50

Alternative Methods
for Valuing Selected
In-Kind Transfer Benefits
and Measuring Their
Effect on Poverty

U.S. Department of Commerce
Bureau of the Census



Chapter 7. Suggestions for Future Research

There are several important issues in the in-kind benefit valuation area
which warrant further research. First, the quantity and quality of data on
in-kind benefits need to be expanded. Second, refined procedures for valuing
in-kind benefits need to be developed using the expanded data set. Third,
procedures should be developed to adjust survey data for underreporting of
both money income and in-kind benefits.

As pointed out earlier in this report, while most means-tested in-kind
benefits (88 percent) have been covered in this study, only 34 percent of all
food, housing, and medical care benefits ‘were included. Discussions of which
in-kind benefits should ultimately be included as income for purposes of
measuring poverty need to be initiated. Additional survey data are needed to
account for these other benefits. Major sources of benefits for which more
data are needed include employer-provided “fringe” benefits, the tax advan-
tages for homeownership, and the implicit net rental value of owner-occupied
housing.

Refining procedures for valuing in-kind benefits requires more recent
expenditure data than are now available from the 1960-61 and 1972-73 Consumer
Expenditure Surveys. Based on our experience with the Medicare coverage
reported in the 1972-73 CEX, the quality of the medical care expenditure data
needs to be improved in this survey. Use of data from other surveys, such as
the National Medical Care Expenditure Survey and Medical Care Expenditure and
Utilization Survey, needs to be explored. Even with these new data sources,
the identification of “counterfactuals" (nonsubsidized population) required
to value Medicare is virtually impossible. In response to this problem,
several strategies could be employed. First, perhaps survey data could be
collected on how recipients value their benefits (no data have as yet been
collected on how much these benefits are worth in cash). Second, following
the work of Van Praag (1968), Wansbeck and Kapetyn (1981), and others, one
could use survey respondents' subjective measures of well-being in conjunc-
tion with money income and in-kind\ benefits data to estimate values.

lUsing this technique, respondents with given levels of money income,
in-kind transfer benefits, -and family size and structure, are asked to
specify a numerical value which represents their relative level of well-
being. Using regression analysis, differences in these levels can be
converted to cash income. Comparing levels of well-being between fami-
lies of similar income and family size and structure, but with different
noncash benefits, permits the derivation of the impact of in-kind bene-
fits on welfare levels.

103



Table D-4, Estimated Annual Market Value of Medicaid as Insurance for Each
Medicaid Beneficiary in 1979, Excluding Institutional Care, by
Type of Beneficiary and State

Elderly! Disabled!
State or State group ~~
Market | Ratio to Market | Ratio to
value} total Mv2{ value | total mv2
TOtAL ose eecescecsvceee | $ 418 «212 | $1,311 520
Maine, New Hampshire, Vermont.... 307 2133 1,121 575
Massachusetts...ccceseseeee . 801 350 1,414 +550
Rhode Istand, Connecticut . 734 209 1,310 447
New Yorksecccecccsevccces . 886 +200 1,732 «450
New Jersey... . 368 124 1,472 »505
Pennsylvania . 205 096 974 374
Ohidwssceees . 266 151 1,265 2452
Indiana... . 273 2139 1,561 «379
Illinois. . $33 «201 1,701 2915
Michigan. . 310 2153 1,822 -504
Wisconsin. . 408 2174 1,506 2356
Minnesota. . 384 2134 1,425 «290
Towa... ee . 258 173 1,126 +296
MiSSOUTI sc cece ee eeeeeeee eee 225 +255 940 2551
North Dakotd, South Dakota....... 196 118 1,295 2351
Nebraska, KansaS.....ccccceccccee 239 «160 1,110 270
Delaware, Maryland, District of
Columbia.......5 . 519 2255 1,325 . +722
Virginia.. o 383 «200 859 2470
West Virginia. . 316 310 858 »885
North Carolina. . 479 +272 948 +597
South Carolina . 214 ~ 164 455 541
Georgia .| . 332 +249 | 1,085 583
Florida . 346 +332 578 +792
Kentucky, Mississippi. oe 273 267 469 602
Tennessee, Alabama... . 231 2239 428 ~599
Arkansas, Oklahoma. . 342 230 837 «340
Louisiana........ . 366 ~ 300 810 398
Texas.. te eecoee 403 212 1,162 2392
Montana, Idaho, Wyoming. . 268 146 1,380 «431
COTOPAdO. se eeeeeesevcene . 286 150 1,134 330
New Mexico, Utah, Nevada . 225 .180 1,362 476
APiZONAsccccsccecccaese . (NA) J (NA) “(NA) (NA)
Washington . 405 211 1,368 - 404
Oregon... . 300 142 686 +248
California.. . 514 «340 1,329 0779
Alaska, HaWwaii.sscsssscccevcccccs 524 182 1,442 »4U8

NA Not applicable.

1No adjustment was made for adult or child beneficiaries.

2Ratio of MV of Medicaid insurance excluding. institutional care expendi- .
tures to MV including institutional care expenditures.

126



Table I-1. Income Thresholds at the Poverty Level in 1979, by Size and’Type of Family, Number of
Related Children Under 18 Years Old, and Farm-Nonfarm Residence

Number of related children under 18 years old

Size of family unit T
6 or
None 1 2 3 4 5 more
NONFARM
Families With Female Householder,
No Husband Present
1 person (unrelated individual):
Under 65 yearSsescevessseeseee | $ 3,619
65 years and OVErssseseseseees 3,469
2 persons:
Householder under 65 years.... 4,519 | $ 4,934
Householder 65 years and over. 4,336 4,934
3 persons... . 5,510 5,249 | $ 5,805 =
4 persons. . 7,208 7,467 7,433 |$ 7,355
5 persons... . 8,651 8,912 8,874 8,801 | $ 8,504
6 persons... 10,094 } 10,278 | 10,205 | 10,130 9,798 |$ 9,500
7 or more. persons.. 12,680 | 12,866 | 12,828 | 12,717 | 12,386 | 12,128 | $11,535
All Other Families
1 person (unrelated individual}:
Under 65 years..... 3,912
65 years and over.. 3,515
2 persons:
Householder under 65 years.... 4,891 5,479
Householder 65 years and over. 4,392 $,479
3 persons... 5,694 5,879 6,213
4 persons. 7,506 7,617 7,355 7,727
5 persons. 9,059 9,168 8,874 8,651 8,837
6 persons... 10,391 | 10,425 | 10,205 9,983 9,687 9,336
7 or more persons 13,086 | 13,199 | 12,939 | 12,717 12,424 | 11,978 | 11,868
— — =k - _



Contents

Summary 2. 2 ee ew ee we wee we ee eee rere reer eevee

Chapter
l.
2.
3.

4,

7.

Introduction. 2 6 ee ee ee we ete ee eee ween es
Limitations of the study. 2... ee ee ee ee wee we

Program descriptions, data collection, and quality
evaluation. 2 6 ee ee eee ee ee ee ee eee ne

Methodological issues concerning the valuation of in-kind
benefits. 2. 6 ee eee ee ee we ee eee wr eens

Addendum: A diagrammatic analysis of various approaches
to valuing in-kind transfers... 2. + eee

Derivation of the values of in-kind transfers ......

Addendum: The valuation of medical care benefits and th
determiniaton of poverty status... ....

ie

The effect of alternative methodologies for valuing in-kind

benefits on poverty . 6. se eee ee eee ee wenn

Suggestions for future research . . eee ee eee eee

APPENDIXES

U.S. Senate Statement: "Data collection and poverty

level". . 2 ww we ww ete we ewe we eee ee ee ee
Explanation of the data sources for table 2.......
Quality of the CPS in-kind benefit data ..... 2.2

Details of procedures used to estimate values of in-kind
benefits. 2. 2 ee eee eee eee eee eee een

The use of the normal expenditures approach to approximate
cash equivalent value: theoretical discussion. ... »

Detailed poverty status table... 1 ee eee eevee

iii

.

Page

15

26

38
45

73

78
103

105
106
110

116

137
141



Both of these alternatives solve the problem of simultaneously valuing
all types of income in-kind to arrive at accurate measures of recipient
value. Moreover, both methodologies reduce the need to rely on the costly
and complicated exogenous calculations needed to compute RV-CE. On the

grounds of simplicity and ease of calculation alone, such approaches as these
merit future attention.

Two major problems with survey data on money income and in-kind benefits
are underreporting of recipiency and amounts and misreporting of sources.2
Correction of these problems requires both better survey data and improved
administrative statistics which can be used to apply adjustments to the sur-
vey data. For example, administrative data on the number of families (per-
sons) receiving an in-kind benefit at any time during the calendar year would
help improve the estimation procedures. Second, sophisticated microsimula-
tion models are needed to adjust for these deficiencies in the survey data.3

2tn a recent study, Godreau, Oberheu, and Vaughn (1981) found that misre-
porting of AFDC as general assistance or other welfare was more prevalent
than either underreporting dollar amounts of AFDC or failure to report
receipt of public assistance altogether.

w

For instance, Smeeding (1981a) has shown how in-kind benefit recipiency
patterns differ between the March 1980 CPS recipient patterns and the
results of several microsimulation models, which assign recipiency to CPS
units based entirely on program eligibility rules and administrative
data.

104. |



are covered in appendix C, CPS Medicaid estimates may be superior to CPS
estimates of cash public assistance recipients.

MEDICARE

The Medicare Program consists of two separate but complementary health
plans to provide adequate medical care for the aged and disabled. The
Hospital Insurance Plan (Part A) is designed to provide basic protection
against the costs of hospital and related post-hospital services. In addi-
tion to the elderly, this plan also covers virtually all persons under: 65
years old who receive Social Security or Railroad Retirement benefits based
on long-term disability. Part A is financed jointly by employers and
employees through Social Security payroll deductions. Qualified persons 65
years old and over who are not otherwise eligible for Part A benefits may
pay premiums directly to obtain this coverage. The Supplemental Medical
Insurance Plan (Part B) is a voluntary plan which builds upon the hospital
insurance protection provided by the basic plan and is available to all
Medicare Part A beneficiaries. It provides insurance protection covering
physicians' and surgeons’ services and a variety of medical and other health
services received either in hospitals or on an ambulatory basis. It is
financed through monthly premium payments (about $8.50 per month in 1979) by
each enrollee and further subsidized by Federal general revenue funds.

The Medicare question on the March 1980 CPS attempted to identify all
persons 15 years old and over who were covered by Medicare at any time during
1979. The term "covered" means enrolled in the Medicare Program. In order
to be counted, the person did not necessarily have to receive medical care
paid for by Medicare.

Because Medicare is a national program, detailed and reliable adminis-
trative counts -of enrollees and expenditures are readily available. An
independent estimate comparable to the March 1980 CPS universe can be derived
by adjusting these counts to the comparable U.S. noninstitutionalized popula-
tion and accounting for deaths of beneficiaries between January 1, 1979, and
March 1980, The March 1980 CPS includes those assigned coverage who were
legally covered by Medicare. These include elderly Social Security (OASI)
recipiénts who did not report coverage and other elderly who reported
Medicaid coveragel9 but no Medicare coverage. A comparison of these esti-
mates indicates that the CPS estimate of 25.712 million beneficiaries is 98
percent of the comparable administrative data-based estimate.

SUMMARY AND CONCLUSION

The relationship between reported CPS beneficiaries and alternative
estimates (based on administrative data) is summarized in table 3.

10Two explanations can be offered for this latter source of error: either
these individuals confused Medicaid and Medicare or they did not know
that the State or local Medicaid authority had purchased Medicare
coverage for them. .

23



Table 17.

Level and Poverty Rates,

1979

(Numbers in thousands.

Recipient Value-Cash Equivalent Approach:

Persons as of March 1980)

Number of Persons Selow the Poverty
by Income Concept for Selected Population Subgroups:

Income concept

| Money income plus | Money income plus
| + Money income ; food, housing, | food, housing,
Selected Money income plus food and: and medical care { and medical care
characteristics alone housing (excluding | (including
| institutional | institutional
i | care) | care)
Poverty | Poverty © Poverty ; Poverty
Number rate | Number rate , Number rate ; Number rate
| | |
I 1 |
All persons. 23,623 11.1 | 20,218 { 9.5 18,393 8.7 | 17,318 8.2
South..sseseeee | 10,108 14.6 | 8,723 | 12.6 | 8,023 11.6 | 7,646 11.0
Non-South...... 13,515 9.5} 11,495 ! 8.1 10,370 7.3 5 9,672 6.8
: i |
Elderly (65 | } i
and over)..... 4,097 14.7; 3,649 | 13.1 2,601 93! 1,951 7.0
Non-elderly.... 19,526 10.6 | 16,569 ; 9.0 15,792 8.6 | | 15,367 8.3
Central city... 8,748 15.0: 7,172 | 12.3 6,6U7 | 11.4 : 6,123 10.5
Suburb...eeeeee 5,661 6.7: 5,021 5 5.9 4,622 5.4 4,418 5.2
Nonmetropolitan j i :
APCALceeeeeeee 9,214 13.4: 8,026 : 11.6 7,165 10.4 6,777 9.8
: i nan :
Black... 7,456 30.4 | 6,009 i 24.5 5,571 22.7: 5,144 21.0
Non-Black. 16,167 8.6 | 14,209 j 7.6 12,822 6.8 12,174 6.5
ii i
In households . | !
with female |
householder, :
no husband | ‘
present.. . 9,142 34.8 | 7,384: 28.1 6,967 26.5 6,411 | 24.4
All..other wees | os ce .
persons....... 14,481 7.8 } 12,834 6.9 11,426 6.1 10,907 5.9

Ipiffers from official poverty count for 1979 of 25,345 (11.6 percent) because poverty
status was computed on a household basis.

“84



As evidenced by the data in table 15, the choice of the income concept
has an important influence on the effect of in-kind transfers on the esti-
mates of poor. The broadest income definition, which includes food, housing,
and medical care with institutional expenditures, reduces the poverty rate
from 11.1 to 6.4 percent based on market value. The poverty rate for the
total population is very similar whether institutional expenditures are
included or excluded. As indicated in later tables, however, the treatment
of institutional care yields substantially different results for the elderly.
Of the three in-kind benefits, medical care is by far the most important in
lowering the poverty rate. At market value, the value of food and housing
alone lowers the poverty rate from 11.1 percent to 9.4 percent, while the
remainder of the decrease is due to medical care benefits.

The recipient value-cash equivalent (RV-CE) approach substantially
reduces the number of poor although not to the same extent as market value.
The reduction in the poverty rate using RV-CE for food and housing alone is
nearly the same as for market value (9.5 percent for RV-CE and 9.4 percent
for MV). The inclusion of medical care benefits with institutional care
expenditures reduces the poverty rate further to 8.2 percent for the RV-CE
approach. Thus, the marginal effect of medical benefits on the poverty rate
is less than that of the MV approach. It should be notéd that the inclusion
of institutional care expenditures has a relatively larger impact in the
RV-CE approach than in the MV approach. This: occurs because the normal -
expenditures for all in-kind benefits are higher at a total income level

- which includes the market value of the institutional care expenditures (i.e.,
there is a positive income elasticity of demand for commodities).

The poverty budget share value (PBS) approach has a smaller effect on
the number of poor than either the RV-CE or MV approaches. The poverty rate
using PBS for food and housing alone declines from 11.1 percent to 9.8 per-
cent. The marginal effect of medical care benefits is less than either of
the other valuation methods. “The: poverty rate based on the PBS -approach
inctuding medical benefits is the same (8.9 percent) including or excluding
institutional benefits; this result occurs because the market value of medi-
cal care benefits without institutional care is already above the PBS value.

The reduction in the number of persons below 125 percent of the poverty
level has the same pattern as that for the 100 percent level, however, the
magnitude of this reduction is smaller. For example, using market value of
all in-kind benefits, the number of persons below 125 percent of the poverty
level declines by 29 percent, compared with 42 percent for persons below 100
percent. Persons below 100 percent of the poverty level are more likely to
receive in-kind benefits and, in general, receive larger benefits than per-
sons above the poverty level.

The choice of valuation technique and income definition produces signi-
ficantly different estimates of the number of poor. If only food and housing
benefits are considered, only slight differences between the three valuation
techniques can be noted. Widely different resuits emerge, however, when med-
ical care benefits are included. In this case, the market value technique
lowers the number of poor by 42 percent, whereas the poverty budget share

79



It is important to realize that estimates of the value of in-kind trans-
fers presented here are of a limited and exploratory nature. The implica-
tions drawn from these results must be interpreted in this experimental
context. The wide-ranging nature of the various valuation strategies
reflects the general unsettled nature of economic research in this area. In
fact, one of the aims of this report and others that follow is to focus
public debate on these issues and provide direction toward more definitive
measures of the value of in-kind benefits and their effect on economic
status.

This report is divided into seven chapters and several technical appen-
dices. In chapter 2, the most important limitations surrounding this study
are outlined in detail, and.both conceptual problems and data availability
problems are discussed. Chapter 3 describes, in detail, the in-kind benefit
data collected in the March 1980 CPS and examines their quality. The dif-
ferent methodological approaches to defining in-kind transfer benefits and
assigning benefit value are covered in chapter 4. The brief addendum to
chapter 4 presents a diagrammatic analysis of the various theoretical
approaches to valuing in-kind transfer benefits. . Chapter 5 describes the
empirical procedures underlying each of the three techniques used in this
study to value in-kind transfers: market value, recipient (or cash equiva-
lent) value, and poverty budget share value. This chapter also contains a
comparison of the dollar values assigned using each of these three concepts
and discussions of the strengths and shortcomings of each estimation’ proce-
dure. An addendum to chapter 5 discusses the importance of the valuation
of medical care benefits for determining poverty status. A summary of the
results of the valuations of in-kind transfers on the overall level of pov-
erty and on poverty among important subgroups of the poor is presented in
chapter 6. (Appendix F contains several more detailed tables which allow
readers to make further comparisons.) Chapter 7 describes other research
strategies which have a high potential for improving estimates of the value
of in-kind transfers. Several appendices at the end of the paper provide
readers with a more technical and detailed discussion of the issues presented
in the sections of the report outlined above.

Nine different estimates of the effect of in-kind benefits on the
measurement of poverty are presented in this report. Each of the three
valuation methodologies are given equal treatment and are applied to three
different groups of in-kind benefits: 1) food and housing benefits alone,
2) food, housing, and medical benefits excluding institutional care, and 3)
food, housing, and medical benefits including institutional care. These
three groups were used because of the special problems in valuing medical
care benefits, especially institutional care benefits.



second step was to use the regression coefficients from this equation to
estimate the market rent of public housing units identified on the AHS. The
third step was the determination of the rent subsidy (which is MV) for public
housing. This was defined as the difference between the predicted market
rent and the reported amount of rent paid for the public housing unit (the
subsidized rent (SR)). The final step was assignment of the rent subsidies
to the public housing units on the March 1980 CPS data file.

Overall, the regression results were very good (appendix D), allowing
use of the equation to compute the market value of public housing units. The
procedure of subtracting SR from MR to compute the subsidy value (S) for pub-
lic housing units, however, resulted in negative S values (i.e., MR less than
SR) for 18 percent of all public housing units. In about 12 percent of the
cases, the negative S values were less than $50 per month. Most of the units
with negative S$ values greater than $50 per month were in highrise dwellings
of 20 units or more located in large northern metropolitan areas. Although
this result implies that some public housing beneficiaries May be paying more
rent than they would pay for a similar unit in the private market, these
negative values may be due to prediction error in the regression used to
estimate MR,

A cell-matching procedure was used to assign the MV of the public
housing subsidy derived from the AHS to the public housing: units in tne CPS.
Table 5 shows the cells and corresponding monthly subsidies used in the
match. Negative subsidy values were set equal to zero in order to minimize
their influence in the cell averages, In some cases, cells containing a
small number of observations were combined. This procedure has only a minor
effect on the final estimates.

There are two other aspects of this estimation process which should be
noted. First, estimation of the subsidy values was made assuming that the
public housing recipients on the CPS resided in those units for the full year
(i.e., monthly subsidies in table 5 were multiplied by 12 to derive the
annual estimate). Secondly, while S values were calculated separately for
units with and without heat included in rent, the S values assiyned to CPS
units were based on an average for all units. This procedure was used
because there is no significant difference in S values between those with and
without heat included in rent.3 Furthermore, the CPS data do not distinguish
whether heat is included in rent.

3While MR should differ by the treatment of utilities, there is no apparent
reason why MR-SR (the subsidy) should vary by this treatment.

47



Table 23. Composition of the Poverty Population: Percent Distribution of the Poor
with Selected Characteristics, by Selected Combinations of Valuation

Techniques and Income Concepts: 1979
Percent distribution based on--
Recipient | -
Market value | value - cash Poverty budget
Money approach, equivalent | share approach,
Characteristics income including approach, including all
all in-kind including in-kind
transfers all in-kind transfers
. transfers
Below poverty level..ecreeeee 100.0 i 100.0 100.0 100.0
SOUtH. cseceeesceeceeseeeoeees 42.8 | 46.5 44,2 43.4
Elderly (65 and over)........ 17.3 + 9.2 11.3 16.0
Central city....seee. oeeeees 37.0 32.5 35.4 35.7
Suburb. .seseeee seeoee 24.0 26.6 25.5 24.9
Nonmetropolitan area 39.0 ° 40.8 39,1 39.4
Blacks eccccccccecccrccccccees 31.6 | 27.2 29.7 30.1
In households with female
householder, no husband .
PPESENt).ccecccecevcececeece 38.7 33.9 37.0 36.9
Below 125 percent of poverty
TOVEl. cccececccccccececs see 100.0 100.0 100.0 100.0
SOUTH. cee eecececccececccccene 41.2 44.2 42.5 41.5
Elderly (65 and over)......66 20.1 11.2 15.9 18.8
Central City. ..ccccccscccceee 35.6 33.4 34.8 35.3
Suburb.... eens 25.0 26.1 25.1 | 24.9
Nonmetropolitan area. | 39.4 40.5 40.1 | 39.8
Blacksecsecsesscseevesseeesee | 2867 27.9 28.5 | 28.3
In households with female |
householder, no husband | i
PPOSENteccsccccccecccevesees! 33.8 33.7 33.7 33.4
1

91



income and consumption levels was used as the final estimate of the PBS value
for housing. A detailed discussion of the reasons for using this averaying
procedure is contained in the next section.

Medical transfers. The estimation of poverty budget shares for medical care

transfers first required that decisions be made on the two major issues men-
tioned earlier; which of the CEX surveys should be used and on what basis,
consumption or income, should the shares be computed? The 1960-61 CEX was
chosen as the data base for estimation for several reasons. First,. the
1960-61 survey measured consunption and income levels for a tiie period which
is much closer to 1955, the base year for the food consumption budget shares
used to derive the poverty levels. Second, the 1960-61 data precede the
introduction of Medicare and Medicaid, the programs being valued. Third, the
1972-73 CEX data are weak in the medical expenditure area and do a poor job
of identifying the medically subsidized population. The CEX data for 1972-73
indicate, for example, that consumers spent less on health care than in
1960-61 (5.1 percent, compared with 6.1 percent). This trend may seem sur-
prising since average expenditures for health insurance rose from $77 per
family in 1960-61 to $229 per family in 1972-73. One possible reason for
this decline in the ratio of medical expenditures to income is the large
increase in private, employer-provided, and public health insurance over this
period. The poor quality of CEX medical care expenditure data do not allow
the testing of this hypothesis. Finally, use of the 1960-61 data allows an
examination of the expenditures for persons 65 to 74 years separately from
those persons aged 75 and over.

The estimates of budget shares for medical care (and housing) are based
on an average between a consumption-based share and an income-based share.
The “consumption-based share" is the ratio of medical care expenditures to
total expenditure levels for persons at or near the poverty level. The
“income-based share" is the ratio of medical care expenditures to total pre-
tax income. Although the poverty level is based on food consumption needs,
the estimates of the number of poor are based on before-tax Money income
levels. Budget shares at (or near) the poverty Jevel can be computed from
1960-61 CEX data using either Consumption levels or income levels published
by the Bureau of Labor Statistics. Since income is generally less than con-
sumption for the poor and since medical care expenditures increase faster
with the level of income than with the level of consumption, the income-based
budget shares are generally larger than the consumption-based shares. For
each individual, all consumption-based budget shares sum to unity. However,
income-based poverty budget shares would sum to greater. than unity (if all
budget items were included) because consumption generally exceeds income at
poverty line income levels. In general, our estimates indicate that there
is little difference between the consumption- and income-based poverty budget
shares for either medical care or housing and, therefore, the two were
averaged together.

The poverty budget shares shown in table 13 for medical care can be used
for either those covered by Medicaid alone or those covered by Medicaid and
Medicare but. not for the Medicare only population. These values cannot be
used for persons receiving only Medicare because this program does not pay
for all medical expenses. In 1977, about 62 Percent of the health care

70



regulations through taxes. Income tax distortions do exist, but because the
CPS uses pretax money income and the poor pay little income taxes anyway, tax
distortions are not particularly at issue when measuring the incomes of the
poor. Assigning welfare costs of various rules and regulations to individ-
uals would be difficult even if these costs were known. There is much evi-
dence that most in-kind transfers, especially medical transfers, are worth
less to recipients than an equal cash transfer. (See Smolensky, et.
al. (1977); Smeeding (1975); Cooper and Katz (1978).) Moreover, this study
will show that the different valuation methodologies have significantly dif-
ferent effects on the level of poverty. Thus, to ignore this particularly
important welfare cost on the grounds that one cannot adjust for all welfare
costs seems unwise.

In theory, the recipient or cash equivalent value (RV-CE) can be esti-
mated by assigning a utility function to all recipients. The cash equiva-
lent measure is the amount of cash transfer which would leave the recipient
at the same level of well-being or utility as the in-kind transfer. Accurate
estimates of RV-CE necessitate knowledge of all recipients’ differing utility
functions and the prices that they pay. Because utility functions cannot
be observed and measured with any degree of accuracy, and because of diffi-
culties with current consumption data, a simplified measure of recipient
value has been developed as a substitute.

RV-CE estimates in this study are based on survey data showing normal
expenditures at different income levels. These estimates were derived by
assuming that the RV-CE value of an in-kind transfer is equal to the normal
expenditure on that item by unsubsidized consumer units. This procedure
actually involved matching subsidized units to unsubsidized units with simi-
lar characteristics (e.g., income size, location,’ and age). Income of
unsubsidized units was defined as the sum of money income plus the MV of all
in-kind transfers.5

3A utility function is an economic construct which indicates relative
preferences for various goods and services depending on relative prices
and how these goods and services are substituted for one another.

4In calculating RV-CE, one implicitly assumes that there is no difference
between the comparable family and the recipient family. However, if both
units are eligible for a given benefit and only one actually participates
in the program while the other (the comparison unit) does not, one may
incorrectly infer that the expenditures for the given good by the nonpar-
ticipant are equivalent to those of the participant if there was no
Program. But if they were equivalent why didn't the comparable family
also receive the in-kind transfer? This selectivity bias issue is
addressed later in chapter 5 of this paper.

5For example, if a particular CPS family has $5,000 worth of money income,
$2,000 in Medicaid benefits (measured at MV), and $1,000-in food stamps,
the income level at which to select the comparable family in order to
calculate RV-CE for either Medicaid or food stamps is the $8,000 income
level.

29



no other choice and gets nothing in addition to the amount of the good being
transferred once it is accepted. 3

The MV of an in-kind benefit provided. without cost to the recipient can

be illustrated using figure 1. Assume that the recipient receives AB=DS
units of the subsidized good; the MV of the in-kind transfer is the dollar
value of the goods transferred to the recipient. This approach suggests that
the new (post-transfer) budget constraint is GBS (not ABS). This budget con-
“straint implies that the individual can reach utility level Us after
receiving the transfer. However, this utility level could be reached only if
the recipient could sell or trade the right to the in-kind transfer for other
goods or services; only then would the portion of the budget constraint
labelled GB be available to the recipient. Since the right to sell in-kind
transfers is not available to recipients, those accepting the transfer must
consume at least OU units of the subsidized good. With consumer preferences
as shown in figure 1, the consumer could never reach Us. Thus, unless the
recipient prefers to consume more than OJ units of the in-kind transfer
the MV approach overstates the recipient's gain in welfare from the in-kind

transfer.

Figure 1 can also be used to illustrate the cash equivalent (RV-CE)
value of the in-kind transfer. The RV-CE measure is the amount of cash
transfer that enables the recipient to achieve the same level of well-being
provided by the in-kind transfer.24 In terms of figure 1, the cash equiva-
lent value is the smallest cash-income transfer that keeps the individual on
the indifference curve that passes through point B.22 The amount of total
income which keeps the in-kind transfer recipient at the same level of

23This portrayal of public housing ignores the fact that the unit is
only constrained in terms of its structural characteristics and loca-
tion. At the tenant's expense, the quality of the housing unit can be
altered by the purchase of minor alterations, better furniture, etc.
However, beyond these limited choices, housing consumption is fixed once
tenancy in a Low Rent Public Housing unit is accepted.

24In figure 1 a cash transfer expands the recipient's budget constraint
for both types of goods by an equal amount. For instance, a cash trans-
Fer of AP would move the budget constraint from AD to PR, allowing the
unit to purchase larger amounts of each type of good. With such a
transfer the beneficiary's preferences fully determine the amount of
each type of good which it will consume. In the example above, a cash
transfer of DS (equal to the MV of the in-kind transfer DS) would expand
the budget constraint from AD to GS allowing the recipient to reach
utility level Us. But with an in-kind transfer, U, cannot be reached.

258 indicates tangency between the highest utility or indifference curve
(Uc, Uct, or Uc*) and the post-in-kind transfer budget, constraint ABS.

41



MEDICAL CARE TRANSFERS

As mentioned in chapter 3, HCFA estimates of the number of Medicaid recipi-
ents in 1979 must be adjusted in order to make them comparable to the March
1980 CPS estimate of persons covered by Medicaid at any time during 1979,
First, the HCFA estimates were separated into those receiving and not
receiving cash assistance. The assumption was made that the HCFA estimate of
persons receiving Medicaid benefits but not receiving cash public assistance
is the same as the number of these persons covered by Medicaid, i.e., all
such persons covered by Medicaid actually received benefits. This assumption
was made because the vast majority of these persons became covered only
because they needed inedical attention which they could not afford. .A similar
procedure could not be used for the cash assistance group because a signifi-
cant proportion of this group were covered by Medicaid but did not ‘receive
benefits. Instead, the second component of the independent estimate of per~
sons covered by Medicaid was considered to be the number of persons receiving
cash public assistance (AFDC and SSI) during 1979. This procedure was chosen
because all AFDC recipients are automatically covered by Medicaid and nearly
all SSI recipients are covered. Estimates of the number of persons
receiving cash public assistance in 1979 were made using monthly numbers of
recipients in December 1978 and December 1979, estimates of new applicants
and persons leaving the "rolls" in 1979, and estimates of recidivism. Esti-
mates were also adjusted for decedents and the institutionalized population.
The results of these procedures are contained in table C-2. The "ever-
received" HCFA figures are also included for comparison purposes.

Comparisons of the CPS and alternate estimates should be made with cau-
tion for several reasons. First, the alternate estimate probably exctudes
some beneficiaries who were not covered by Federal-State matching provisions,
but were covered by Medicaid entirely at the option of the State. For in-
stance, California covers some illegal aliens and Provides Medicaid financed
abortions solely from State funds. New York has a higher income eligibility
standard than allowed by Title XIX and categorically covers additional groups
who do not receive AFDC or SSI. There are, however, no estimates of the num-
ber of these beneficiaries. Second, the CPS estimates exclude children in
households where no adults were covered (children in a foster care home).
Unfortunately, it is not possible to separate these children from other chil-
dren using the HCFA noncash public assistance beneficiary data. The "State-
-funds only" and the “covered children only" discrepancies work in opposite
directions, but, the net effect is unknown.

In 15 States (Colorado, Connecticut, Hawaii, Illinois, Indiana, Minnesota,
Mississippi, Missouri,--Nebraska,~ New Hampshire, North Carolina, Ohio,
Oklahoma, Utah, and Virginia) SSI recipients are not automatically
covered. In each of these States qualification standards for Medicaid
are more strict than qualifying standards for SSI. (Hawkins and Rigby,
1979; Davidson, 1979), Anywhere from 10 to 40 percent of SSI benefici-
aries may be ineligible for Medicaid in these States.

— vee ne: (ne _ ——



Housing transfers. Estimation of the MV for public housing has presented a
number of problems to researchers (e.g., see Murray, 1975; Kraft and Olsen,
1977; and Aaron and Von Furstenberg, 1971). In theory, estimation of the MV
is straightforward. The MV is equal to the difference between the market
rental value of the housing unit and the rent actually paid by tenants. This
information was not available from the March 1980 CPS and, therefore, two
alternative estimation methodologies were examined. .

The first method examined was the approach developed by the Congressional
Budget Office (CBO) and Mathematica, Inc. for use in the MATH-CBO simulation
of the MV for public housing. ! This approach assigned MV to households
reporting residence in public housing on the March 1980 CPS microdata file.
Households were first assigned to a Particular housing program and then
assigned MV by income level based on data available fron the Department of
Housing and Urban Development (HUD). The HUD estimates of MV for Low Rent
Public Housing did not account for household size and were estimated sepa-
rately within only eight broad income categories (Housing in the Seventies,
1974). Because more than 40 percent of all public housing units are of this
type, results of this procedure must be viewed with caution.

The second method examined involves the use of hedonic regression tech-
niques to estimate the MV of public housing. A hedonic regression estimates
this value based on characteristics which describe the quality of the housing
unit, such as structural characteristics, neighborhood. conditions, location,
and contract conditions, independent of the income and characteristics of the
tenants who occupy the unit. Use of the personal characteristics of the
occupants of the housing unit in the regression can result in a selectivity
bias, as pointed out by Aaron (1977).2 This procedure avoids the need to
assign a specific housing program to each public housing beneficiary and
enables the calculation of MV using survey data. Because of these advan-
tages, the hedonic regression approach was used to estimate the MV of public
housing in this study. The estimation of the MV of public housing based on
the hedonic technique was a multistage process using data from the 1979
Annual Housing Survey (AHS) and the March 1980 CPS, The first step involved
development of a regression equation to estimate market rent (MR) for a
subset of unsubsidized housing units using rents reported on the AHS. The

[MATH is the acronym for the Mathematica micro-simulation model which is
used by CBO to evaluate the distributional impact of particular in-kind
transfer programs and their effect on poverty.

selectivity bias may arise because the tastes, preferences, and demand
for housing services for public housing tenants Probably differ from
those of the non-public housing tenants which were used to develop esti-

..mates of the housing values.--This problem may bias -the results, however,
not all economists agree on this point. Sheldon Danziger suggests that
the goal of this estimation procedure is to maximize the explanatory
Power of the regression and, therefore, personal characteristics should
be used if they help differentiate between different types and quality
of housing units.



Table D-9. Price Adjustment Factors for Adjustment

of Normal Expenditures for Medical Care

Price
Region Adjustment

Factor
Relative price factOr...cccccccccesees 1.103

Regional Price Factors

New England.... eeeee -960
Middle Atlantic... 1,040
East North Central.. 1.080
West North Central +990
South Atlantic.... 1.020
East South Central.. 940
West South Central.. 970
MOUNTAIN. ccceececcevenccecnssesees 950
Pacific, excluding Hawaii and Alaska.. 1.010
Alaska and Hawaiisccssscsessscvecceves H 1.240

133



utility,is OC, OC', or OC" depending upon the contours of the indifference
curves.25 The RV-CE of the in-kind transfer is the difference between the
total income level and OD. If the recipient chose to consume more of the
good provided in-kind than 0J (to. the right of point B), then the RV approach
provides the same estimate as the MV approach. In this case, the transfer
substitutes for some amount of the good which the recipient would have con-
sumed even if presented with an equal cash transfer.

This discussion indicates that the shape and position of the utility
surface is crucial to the determination of the cash-equivalent value of the
in-kind transfer. Estimation of this value is complicated by the possibility
that different groups of beneficiaries have different utility functions. For
instance, Medicaid and Medicare are very important to the elderly, so they
may be best portrayed as having utility curves shaped like Uce In contrast,
younger healthier persons with less interest in medical insurance may have
a_utility surface like Uc". Notwithstanding such differences, virtually all
studies which attempt to estimate RV-CE assign the same utility function to
each individual. ‘

As mentioned earlier, RV-CE can be approximated by the normal expendi-
ture approach, which does not require the specification of utility functions.
This approach requires the determination of the normal consumption level for
the subsidized good at a total income level of OAG (or 00S). If the consumer
could reach utility level Us, utility would be maximized at point Y by con-
suning OL units of the subsidized good. Because OL is less than the amount
of the in-kind benefit (OJ), the RV-CE value of the transfer is OL. This
implies that some portion of the transfer (i.e., LU) is therefore worthless
to the recipient. If Us was tangent to GBS at a point to the right (left)
of point Y, the RV-CE would rise (fall). If the point of tangency was to
the right of B, the RV-CE and MV would be equa? because normal expenditures
would exceed the MV of the transfer.

An important aspect of the estimation of RV-CE is the income level at
which the normal expenditure is calculated. In the above analysis, the
chosen income level (OAG or ODS) equals initial money income (0D) plus the
MV_of the in-kind transfer (OJ). Selection of a lower value for the trans-
fer would result in a lower normal .expenditure level and, hence, a Tower
estimate of RV-CE, This aspect of the RV-CE procedure is most important in
cases where more than one in-kind transfer is received. In this case, the
income level at which normal expenditure is calculated may be far above the
income and utility level which the recipient can actually reach.

26tThe cash equivalent value of the transfer depends on the rate of sub-

the utility function (as determined by the Engel Curve for the subsi-
dized good). An Engel Curve traces out the amount of a particular good
{or set of goods) which a consumer wil] purchase at different income
levels, assuming no change in the price of a good as income rises.

stitution of the Subsidized good for other goods and “on the position of


Addendum : The Valuation of Medical Care
Benefits and the Determination
of Poverty Status

The determination of the effect of in-kind benefits on the size and com-
position of the poverty population is greatly affected by the treatment of
medical care benefits. This addendum discusses some of the special consider-
ations in valuing medical care benefits and reviews some of the arguments for
excluding them altogether from the determination of poverty status.

It can be argued that medical transfers should be treated on an ex post
facto basis, which implies that medical transfer benefits such as vendor
payments are not really income because they do not increase utility. 5 These
benefits can be viewed as replacement costs for maintaining physical capital
(health). Such a treatment might suggest that poverty standards should be
adjusted for greater health needs, so that medical care needs and benefits
cancel each other. The MV of the insurance provided by Medicaid or Medicare
(including institutional benefits) for an elderly or disabled person is four
to five times the MV of the same insurance“policy for younger adults., because
the elderly and disabled have greater health care needs. The determination
of poverty does not account for this differential need, resulting in an
overstatement of the impact of medical transfers on poverty for the elderly.
More generally, the needs of all those who have above (below) average demands
for medical care will be understated (overstated) by the current poverty
matrix.

Another reason for omitting medical in-kind transfers in assessing pov-
erty status is the problem of defining a proper counterfactual.3® The proper
counterfactual for medical care is the yroup of persons . not receiving
Medicaid or Medicare who paid for their own medical expenses. Une problem is
that the percent of elderly who are not covered by Medicare (or a different
insurance policy) is too small a group to estimate their normal expendi-
tures.3/ The proportion of nonelderly persons who do not have medical insur-
ance is also very small. According to the Congressional Budyet Office

34cmolensky has mentioned this argument in previous correspondence.

35This approach ignores the utility derived from the pure insurance bene-
fit of Medicaid or Medicare coverage.

36In this report, a counterfactual refers to persons not receiving a par-
ticular in-kind benefit who are similar in most other respects to per-
sons receiving the in-kind benefit. See Behrens and Smolensky (1974)
for more on the topic of counterfactuals.

37The Congressional Budget Office (1979, table 1) estimates that only
1.0 percent of the elderly were uninsured in 1976.

73


Changes in the poverty population resulting from use of the recipient
value-cash equivalent (RV-CE) approach are shown in table 17. Because RV-CE
is always less than or equal to MV, the effects on the population subgroups
are less than those resulting from MV, but, the trends are generally the
same. The RV-CE approach significantly reduces the value of medical benefits
relative to MV. As a result, the decline in poverty for tne elderly is some-
what less than when MV is used. The RV-CE approach does not lower the
poverty rate for central-city residents below the rate for nonmetropolitan
residents as was the case with market value.

The results of the PBS approach as shown in table 18 indicate the
smallest effect on poverty for the three valuation techniques. Reduction in
the poverty rates using the PBS approach are similar for various subgroups of
the poverty population. Overall, the PBS method reduces the number of poor
by about 20 percent. The reduction was about 19 percent for the South, 24
percent for Blacks and for persons in families maintained by women (no hus-
band present), 23 percent for central-city residents, and 26 percent for the
elderly. The poverty rate for the elderly using the PBS approach was 10.8
percent, more than twice as large as the 4.5 percent rate froin the MV
approach. Except for the elderly it appears that, under this approach, food
and housing benefits account for most of the reduction in poverty.

Tne effects of valuing -in-kind benefits on the number and percent of
selected population subgroups below 125 percent of the poverty level are
shown in tables 19, 20, and 21. Reductions in the number of persons below
the 125-percent level are somewhat less,.proportionately, than reductions at
the 100-percent poverty level. For example, the poverty rate for the elderly
declined 69 percent using all in-kind benefits at market value; the compar-
able decline at the 125-percent poverty level was 60 percent. The relation-
ship between declines at the 100- and 125-percent poverty levels are similar
for other subgroups and these relationships are generally consistent within
the RV-CE and PBS techniques (see taples 20 and 21).

The data in tables 15 through 21 have been rearranged and summarized in
tables 26 through 32 so that the effect of income concepts and valuation
techniques can be compared for each of the selected poverty subgroups. These
tables have been included so that the full range of poverty estimates can be
examined separately for each of these groups. Although these tables are not
discussed in detail, they are shown at the end of this chapter.

The largest and smallest reductions in poverty rates for the selected
poverty subgroups are summarized in table 22. Because of the relative size
of medical benefits and conceptual problems in their treatment, separate
estimates are shown which exclude-the value of medical care. In all cases,
the largest reductions are attributed to market value and the smallest to
poverty budget share values. Overall, there is a substantial difference
between the largest and smallest reductions in poverty, especially when
medical care benefits are included.

An examination of the largest reductions shown in table 22 reveals the
extreme importance of medical care in lowering estimates of the poor. While

83



The estimation of the GC of medical care transfers presents several
interesting issues, most of which have not yet been satisfactorily resolved.
The GC of Medicare and Medicaid may actually be lower than the private MV for
these same benefits because the Government can avoid the selling cost and any
monopolistic profit margin "load" factor which private health insurers may
receive for the same quantity and quality of medical insurance coverage. The
GC may also fall short of private MV because the Government may not be
willing to pay enough to cover the total cost for certain medical services.
For instance, Medicare guidelines often Specify maximum allowable charges
which are less than “usual, customary, and reasonable" charyes, ji.e., the
going market price for certain services. In such cases, however, the GC of a
Medicare insurance policy may only appear to be less than the private MV
because the lower price results in a lesser quality and/or quantity of care.
However, the attendant regulations and overhead costs which Medicare and
Medicaid place on medical care providers are in excess of those imposed by
private insurance carriers with lesser quality control standards. Claims
processing costs involved in government medical care provision are usually no
more than those found in the private sector.

The GC of Medicare and Medicaid benefits has not been estimated because
the net effect of regulation and overhead costs, lower selling costs, and
absence of monopolistic profits has not been determined. The GC of a
Medicare or Medicaid insurance policy -is probably higher than the private
market cost because of regulatory and overhead costs. For illustrative
Purposes, it is assumed that this cost can be anywhere from 0 to 10 percent
of the MV of Medicare and Medicaid. 18

SUMMARY

Table 4 summarizes the relationship between MV and each valuation tech-
nique. The wide range of values observed in table 4 must be interpreted with
caution because the estimates are based on different studies using different
approaches, different years, different data bases, and different MV measures.
The wide range of values underscores the need to carefully specify the con-
cept and measure of in-kind transfer value. For food transfers (in this case
food stamps} the range of estimates is much smaller than for medical care
or housing. 3 The range of RV-CE for food stamps without the purchase
requirement is likely to be even smaller.

\8these costs are already accounted for in MV if they result in higher
patient charges; undoubtedly this is true to some degree. For instance,
Medicare nursing requirements may raise nursing costs above normal
hospital costs because of a greater required quantity and quality of
nursing services. In such cases, higher salaries will be included in
the hospital overheads and, therefore, into Medicare (and. other.

“> a ==——=-insurers) costs, resulting in ~ zero additional GC in excess of MV.

19The low estimate for RV-CE of 83 is taken from Clarkson (1976) who used
a Cobb-Douglas utility function and constrained food consumption to be
no more than the amount of stamps received.



Table D-3. Estimated Annual Market Value of Medic.

Beneficiary in 1979,
Beneficiary and State

aid as Insurance for Each Medicaid
Including Institutional Care Expenditures, by Type of

f

Elderly! Disabled! ; Adult Child

State or State group (age 65 (blind or (age 21-64, | (age less

and over) disabled) nondisabled) - than 21)
TOtaleccccccccccsceees | $ 1,970 $ 2,522 $ 629 $ 329
Maine, New Hampshire, Vermont.. | 2,316 1,950 529 289
MaSSaChuSetts..ccccccccccvccces: 2,288 2,571 274 310
Rhode Island, Connecticut. : 3,515 2,932 714 357

New York...eee i 4,430 (H) 3,849 1,212 (H) 619 (H)
New Jersey....seeee : 2,957 2,918 691 444
Pennsylvania..... H 2,139 2,604 669 289
Ohio...... i 1,756 2,796 699 287
Indiana i 1,959 4,124 822 310
Illinois. i 2,647 3,305 563 379
Michigan. | 2,024 3,616 823 406
Wisconsin ! 2,348 4,229 $26 352
Minnesota i 2,857 4,918 (H) 694 421
Towa... : 1,489 3,801 \ 768 370
Missouri. 882 (L) 1,705 | 487 221
North Dakota, South Dakota . 1,666 3,691 \ 529 356
Nebraska, Kansas....... . 1,494 4,115 i 831 351

Delaware, Maryland, District of | i

Columbia : 2,033 1,834 i 762 451
Virginia. . 1,917 1,828 H 744 334
West Virginia... . 1,018 970 i 255 (L) 271
North Carolina.... . . 1,760 1,588 i 617 274
South Carolina.. ei 1,302 841 : 598 195
Georgia.. . 1,331 1,861 ! 718 284

Florida.. . 1,041 730 : 334 152 (L)
Kentucky, Mississippi.. eee 1,023 7719 i 488 219
Tennessee, Alabama... . 907 715 (L) 528 214
Arkansas, . Oklahoma . 1,486 2,461 567 324
Louisiana....... . 1,220 2,034 394 206
TEXAS eeeeeeeee on . 1,901 2,961 487 198
Montana, Idaho, Wyoming . 1,832 3,204 527 319
Colorado... cceeseccsceceee . 1,909 3,437 846 363
New Mexico, Utah, Nevada. . 1,253 2,864 ‘ 525 376
Arizona’..... . 0 0 : 0 v
Washington. . 1,922 3,385 i 385 324
Oregon..... . 2,117 2,769 i - 460 231
California. 1,513 1,707 622 393
Alaska, Hawaii 2,879 3,530 342 417

a i

H = high estimate.
L = low estimate.

lexcludes SMI part B premium of $101 for Medicare-Medicaid enrollees.

arizona does not have a Medicaid program.

124



Row 2 (Food)

-. Major in-kind transfer benefits include $9.3 billion in food stamps and
$3.3 billion in school lunch benefits. (U.S. Budget, FY 1982)

- Other public includes several U.S, Department of Agriculture programs,
e.g., Women's, Infant's, and Children's Special Food Program (WIC), and
School Milk (U.S. ‘Budget, FY 1982) :

- Other private includes $6.6 billion in food produced and consumed on farms
and food received as pay in 1979 (Survey of Current Business, July 1981,
table 8.8, lines 93 plus 96), plus +0 billion in employer-provided
“business lunches" furnished by proprietors and partnerships (see

Clotfelter, 1981). The estimates do not include the value of corporate
business lunches.

Row 3 (Housing)

- Major in-kind transfer benefits include $5.4 billion in public and subsi-
dized housing for low income families under various public programs
including Low Rent Public Housing and Sections 8, 235, 236, 101 and 202b
of the 1937 Housing Act. (U.S. Budget, FY 1982)

- Other public includes $28.8 billion in tax exemptions for mortgage inter-
est, property tax deductibility, and tax-exempt bonds to finance mort-
gages; includes $3.9 billion in FHA, VA, FHMA mortgage subsidies for 1980
only (i.e., no account taken of Prior years subsidies which are still in
effect). (U.S. Budget, FY 1982)

- Other private includes $14.8 billion in net imputed rental income for
owner-occupied farm ($3.1 billion) and nonfarm ($11.7 billion) housing in
1979, This estimate is gross rental value ($33.9 billion) less property
taxes, depreciation, and other maintenance costs. (Survey of Current
Business, July 1981, Table 8.8, lines 79 and 86; table 8.5, line 4).

Row 4 (Medical Care)

- Major in-kind transfer benefits include $26.2 billion in Medicaid: and
$28.4 billion in Medicare vendor Payments. (Social Security Bulletin,
1981)

- Other public includes $22.7 billion vendor payments for medical services
under several public programs, including CHAMPUS, Veteran's Health Care,
Worker's Compensation, Maternal and Child Health Care, Public Health
Services, etc., in 1979 (Social Security Bulletin, 1981). Also
included are $3.2 billion in. tax expenditures for tax deductible health
‘and medical expenses (U.S. Budget, FY 1982). Estimates do not include
medical research or construction expenditures.

- Other private includes $52.1 billion in employer-provided group health
insurance contributions for private sector and government employees in

a 108



The independent variables chosen for the equation were based on the work
of Struyk, et.al. (1980) and Malpezzi, et.al. (1980) who also developed
hedonic regressions using AHS data. A set of 59 independent variables were
chosen to represent SC, NC, L, and C. SC contains dummy variables repre-
senting 1) the number of bedrooms, 2) the number of bathrooms, 3) the number
of other rooms, 4) the type of structure, 5) presence of air conditioning,
6) length of tenancy, 7) presence of an elevator, 8) age and age squared for
the housing unit, etc. NC contains dummy variables representing 1) the
tenant's assessment of neighborhood conditions, 2) percent of neighborhood
residents who are Black, 3) presence of broken windows in the neighborhood,
etc. L contains dummy variables representing 1) the 5 largest SMSA's, 2) the
suburb or central city of the next 25 largest SMSA's, 3) the next 30 largest
SMSA's as a group by region, 4) the remainder of SMSA's as a group by region,
and 5) nonmetropolitan areas by region. C contains dummy variables indi-
cating whether or not heat or other utilities were included in rent.

A regression equation was estimated using these variables for all non-
subsidized renters with 1979 money incomes of less than $20,000 (a sample of
10,761 households).2 The regression produced an R2 of .58. Of the 59 inde-
pendent variables from the AHS, 57 possessed the theoretically correct signs
and 53 were significantly different from zero at the 95-percent confidence
level.

After the equation was estimated, predicted MR's for each subsidized
“unit were computed. The subsidy (S) was then computed by subtracting. the
rent paid as reported on the AHS from the predicted MR's. An examination of
these initial results showed that the predicted MR's were less than the
actual rent paid for the public housing unit in about 26 percent of the
cases. Only 10 percent of the units had subsidy values which exceeded =$50,

The resulting “negative” subsidies produced by these procedures may have
resulted because the regression equation is a poor predictor of* MR3 or
because the market rent of some public housing units may be less than the
actual rent being paid. In some cases, such as older low-rent public housing
program units (e.g., the Cabrini Project in Chicago)-it may be possible that
the rents being paid exceed the market rent of the unit.

2 Less than 7 percent of CPS subsidized units had 1979 annual money
incomes over $15,000, and none had money incomes above $20,000.
Including these units did not significantly affect the results of the
regression, in any case.

31t would have been useful if information was available on the relation-
ship between the tenant and the landlord. Over and above the length of
tenancy (which has been controlled for), certain tenant-landlord rela-
tionships (relatives, “reciprocal arrangements" such as “apartment mana-
ger", etc.) may reduce predicted MR in the hedonic equations. However,
if "reciprocal arrangements" such as these can be expected for public
housing units should they suddenly be turned into private market rental
units, or should some public housing tenants rely on housing owned by
relatives, the omission of such information is much Jess damaging.

17



Table D-2, Estimated Annual Market Value of Medicare as Insurance for Each
Medicare Beneficiary in 1979 Including Institutional Benefits,
by Type of Beneficiary and State--Continued

Market value of Medicare
State
Non-
Elderly elderly
Towa... . 751 1,079
Kansas. 886 1,261
Kentucky.. . 625 700
Louisiana... 715 763
MAiN@ceccevecccvcceene 810 990
Maryland.... 1,113 1,662
Massachusetts 1,178 1,419
Michigan.... 1,138 1,523
Minnesota. 807 1,199
Mississippi.....eeeaee 642 | 716
MiSSOUFis.cceseeeeeees 892 | 1,055
Montana... 708 879
Nebraska... 747 1,115
Nevada.....0e 1,144 1,637
New Hampshire.....eeee 775 1,010
New JerSey.cscccssceee 932 1,354
New Mexico... 741 857
New York..ee. 1,017 1,203
North Carolina. 630 902
North Dakota..seveeeee 861 1,128
ONi0e cece cceccerecves 867 1,096
Oklahoma 766 858
Oregon.... 808 1,024
Pennsylvania..... 954 1,249
Rhode Island........0. 1,030 1,223
South Carolina........ 571 745
South. Dakota. 658 838
Tennessee. 698 873
TexaS.ccseeee cue 840 1,157) -+-
Utan. ccecccrceeccccene 666 993
Vermont... 775 1,013
Virginia.....seee 739 1,053
Washington..... 732 981
West Virginia.. 664 621
Wisconsin.... 834 1,223
WYOMING. ooo cere ee en == =~ 696 810



cases in the sample, definitional difficulties, differences in the interpre-
tation of questions, inability or unwillingness on the part of the respond-
ents to provide correct information, inability to recall information, errors
made in collection such as in recording or coding the data, errors made in
processing the data, errors made in estimating values for missing data, and
failure to represent all units with the sample (undercoverage).

In the case of income and expenditures underreporting or nonreporting
are the most serious nonsampling errors. A detailed discussion of underre-
porting and nonreporting of money income data can be found in Current Popula-
tion Reports, Series P-60, No. 129, page 292, A discussion of these same
problems for noncash benefit data has already been covered in appendix C of
this report. Details on the nonsampling errors for the 1972-73 Consumer
Expenditure Survey data can be found in Technical Paper No. 45, 1972-73 U.S,
Consumer Expenditure Survey:- A Preliminary Evaluation.

SAMPLING VARIABILITY

Standard errors, such as those shown in the following tables for the
March 1980 CPS, are primarily measures of sampling variability, that is, the
variation that occurred by chance because a sample rather than the entire
Population was surveyed. The sample estimate and its estimated standard
error enable the construction of confidence intervals, ranges that would
include the average result of all possible samples with a known probability.

For example, if all possible samples were selected, each of those surveyed
“under the same conditions, and an estimate and estimated standard error were
calculated from each sample, approximately 68 percent of the intervals from
one standard error below the estimate to one standard error above the esti-
mate would include the average result of all possible samples. A 95 percent
figure could be ‘expected if the confidence interval were increased to two
standard errors. . .

Tables G-1 through G-4 contain standard errors for the estimated numbers
and percentages of persons and households below the poverty level. These
standard errors apply directly to the estimates of Poverty based on money
income alone derived from the March 1980 CPS,

Use of these tables to estimate standard errors on the number and per-
cent of persons and households below the poverty level based on combinations
of money income and the value of in-kind benefits may be less appropriate.
These standard errors should, however, provide some guidance when comparing
the results. While ordinarily the standard errors are an essential element
in analyses of the income and poverty data published by the Bureau, the
exploratory nature of this work, the use of data from many sources, and the
various assumptions made in the valuation methodologies make the standard
errors much less significant in examining the differences noted throughout
the-report. - —- ~ ~ 7 ~ . 7

146



Table G-4, Standard Errors of Estimated Percentages
of Households Below the Poverty Level:

1979
Base of Estimated percentage
estimated :
percentage

(thousands) | 2 or |5 or | 10 or 25 or
98 95 90 75 50
TBs eccccene 2.2 3.4 4.7 6.8 7.9
1.9 3.0 4.1 5.9 6.8
1.2 1.9 2.6 3.8 4.3
0.9 1.3 1.8 2.7 3.1
. 0.6 0.9 1.3 1.9 2.2
2,500. ..... 0.4 0.6 0.8 1.2 1.4
5,000...... 0.3 0.4 0.6 0.8 1.0
10,000..... 0.2 0.3 0.4 0.6 0.7
25,000..... 0.1 0.2 0.3 0.4 0.4
50,000..... 0.09 | 0.1 0.2 0.3 0.3

150



BUREAU OF THE CENSUS
Bruce Chapman, Director
C. L. Kincannon, Deputy Director .
James R. Wetzel, Acting Associate Director
for Demographic Fields

POPULATION DIVISION
_ Roger A. Herriot, Chief

ACKNOWLEDGMENTS

assistance of several individuals. First, and foremost, he would like to thank John Coder,
Gordon Green, and Roger Herriot. of

Shirley Smith of the Census Bureau quickly and accurately typed this report. 'n addition, the
author would like to recognize the Institute for Research on Poverty at the University of
Wisconsin-Madison which has sponsored and inspired Much of his earlier work in-this-area: cee

SUGGESTED CITATION

U.S. Bureau of the Census, Technical Paper No, 50, Alternative Methods for Valuing Selected
In-Kind Transfer Benefits and Measuring Their Effect on Poverty, U.S. Government Printing
Office, Washington, D.C., 1982.

For sale by the Superintendent of Documents, Washington, D. C, 20402,



can bias the estimation of normal expenditures for other benefits as well.
Although the shape of the utility curve which represents recipient's marginal
rate of substitution between health care (or housing) and other goods is
unknown, the income effect from including the MV of medical transfers (or
housing benefits) in total income definitely overstates the "income" level at
which normal expenditures should be calculated. This is particularly true in
cases where medical care benefits are defined to include institutional care
benefits, where it has been shown that RV-CE is quite low relative to MV. In
summary, the normal expenditures method -for estimating RV-CE provides only
an approximation of the true RV-CE, which in turn may be higher or lower
than those found in this report. As noted in chapter 5, however, the RV-CE
values estimated for 1979 are generally consistent with those estimated by
other researchers using different approaches and different data bases.

oo 140



Table 31. Blacks: Reductions in Poverty Rates Using Alternative Valuation
Techniques and Income Concepts: 1979

Market Recipient Poverty
Income concept value | value - cash } budget share
approach equivalent approach
approach
BELOW POVERTY LEVEL
Money income alone:
Poverty rat@.ccccccccssccevccceces 30.4 30.4 30.4
Money income plus food and housing:
Poverty rate.sccegecsccecceccecees 23.9 | 24.5 25.5
Percent reduction+..scccccesevceee -21.2 -19.4 -16.1
Money income plus food, housing, and
medical care (excluding institu-
tional care expenditures):
POVErty Patecrecegeeecscrecscceees 15.8 22.7 23.1
Percent reduction! bee eeceeeeees -48.1 -25.3 -23.9
Money income plus food, housing, and
medical care (including institu-
tional care expenditures):
Poverty rate...eesee 15.1 21.0 23.1
Percent reduction*.. -50.2 -31.0 -23.9
BELOW 125 PERCENT OF POVERTY LEVEL
Money income alone:
Poverty rat@.ccccceccccesecs 39.2 39.2 39.2
Money income plus food and housing:
Poverty rate. .cecscccsscsccscscces 35.7 36.1 36.6
Percent reductiOn+..ercccccscecces - 8.8 - 7.8 - 6.6
Money income plus food, housing, and
medical care (excluding institu-
tional care expenditures):
Poverty es 28.1 33.8 35.1
Percent reduction-.. se eeeeeees -28.3 -13.9 -10.5
Money income plus food, housing, and
medical care (including institu-
tional care expenditures):
Poverty rate...eeseee soccee 26.9 32.9 35.1
Percent reductionl., . -31.3 -16.2 -10.5

percent reduction in the number of poor

based on money income alone.

from the current poverty estimate



is limited in order to obtain reliable data and everyone in the cell is
assumed to spend the same amount. The reliability of the reyression model is
limited by the goodness of fit. A poor fit will generate predicted Cj;j's
which may differ substantially from estimates derived from the cell matching
procedure. In such cases, the constant term (ay) will dominate the regres-
sion, limiting the variance in Cij's- In such cases, the cell match procgs
dure can result in a greater variance in Cj than the regression model.

Based on the generally poor performance of the regression approach (see
appendix D for details), a cell-matching procedure was used, to estimate Cjj
(and thus the RV-CE,;) for food, housing, and medical care. lV These results
are summarized in tables 10 through 12 which present the average ratio of the
normal expenditures based RV-CE to MV for each type of transfer. These
ratios are termed “benefit weights" following the pioneering work of
Smolensky, et. al. (1977).

Food transfers. The cash equivalent values for food are based on normal
expenditure data derived from the 1972-73 Consumer Expenditure Survey. Food
was defined to include 1) grocery store food, 2) school lunches, 3) homegrown
food, and 4) 50 percent of the cost of food purchased in restaurants. The
rough convention used for food purchased in restaurants attempts to separate
the value of restaurant services from the value of the food consumed. Normal
expenditures were computed using’ a subset of households on the CEX which
excluded househo}ds receiving food stamps and participants in USDA food com-
inodity programs. It is assumed that the C;;'s represent the normal expend-
itures that families receiving food benefits would make in the absence of
such transfers. This definition of food implies complete substitutability
between food stamps (and school lunches) and groceries, homegrown food, and
a high degree of substitution between these and restaurant meals.

20, more sophisticated approach, which allows the researcher to restore
the variance in Cy; using regression residuals (or coefficients of var-
jation tn the cell match approach), has recently been developed by
Schwartz (1981), but was not available in time for this study.

21Reasonably good regression estimates were obtained only for food expend-
itures. However, in order to ensure consistency, a cell matching proce-
dure was used for food as well. In any case, the RV-CE for food trans-
fers estimated by the cell match approach were almost identical to those
obtained by the regression model.

22The USDA Surplus Commodities Food Distribution Program provided basic
food stuffs in 24 categories to qualifying families in roughly one-half
of all U.S. counties in 1972-73. Prior to July 1, 1974, U.S. counties
had the option to either nave a Food Stamp or a Surplus Commodities
Program. After that date, Food Stamps became ‘mandatory and were
required in all counties. The USDA Surplus Commodity Food Distribution
Program was then phased out and no longer exists.

61



Chapier 5. Derivation of the Values of In-Kind
Transfers

This section describes the procedures used to estimate the value of in-
kind transfers based on data from the March 1980 CPS and other sources.
Procedures were developed for estimating market value, recipient or cash
equivalent value, and poverty budget share value for food, housing, and
medical care. One especially significant aspect of this process involves
the definition of medical care transfers and estimation of their values.
Appendix D contains a more technical discussion of several aspects of these
valuation procedures.

MARKET VALUE

The market value (MV) is the cost of a good in the private market. The
assignment of market value to in-kind transfers requires identification of
an analogous good in the private market and the determination of its cost.

Food transfers. The market value of food stamps is easily measured by their
face value, which is the market purchasing power. Estimates obtained
directly from the March 1980 CPS indicate the average MV for food stamps in
1979 was $812 per household or $280 per person.

The estimates for the School Lunch Program for 1979 were derived using
administrative data from the Food and Nutrition Service (FNS) and survey data
from the March 1980 CPS. FNS program data indicate a market value of com-
modities ($710 million) and cash subsidies ($2.090 billion) totalling $2.800
billion. Of these benefits, “Section 4" cash payments ($714 million) and
commodity subsidies for all hot lunches are distributed to each of the 32.5
million children in tne School Lunch Program population, producing a subsidy
of $43 per child. Next, “Section 11" cash subsidies ($1.376 billion) for
free and reduced-price meals only are distributed to the 11.607 million
children in this program, producing an additional $120 subsidy for free and
reduced-price beneficiaries. The total school lunch benefit for each of
these children is $163. These benefits do not vary by region or school
district. Benefits were assigned to each school child on the March 1980 CPS
microdata file resulting in a MV of $2.618 billion or 93.5 percent of the
FNS total.

Some analysts might question the approach used here which treats food
stamps and school lunches in a similar fashion. Both of these food benefits
are substitutes for out-of-pocket food expenses and are in this sense the
same. Identical treatment ‘of food stamps and school lunches, however,
assumes that recipients are indifferent between the two. This may not be a
good assumption since food stamps provide a greater range of choice than
school lunch benefits. It might also be argued that school lunch benefits
have significant positive external benefits to taxpayers, so they should not
be counted at all.

45



Table 26. All Persons: Reductions in Poverty Rates Using Alternative Valuation
Techniques and Income Concepts: 1979

Market Recipient Poverty
Income concept H value | value - cash | budget share
i approach equivalent approach
. approach
BELOW POVERTY LEVEL :
Money income alone:
Poverty rate.scercsccecvcssceveses | 11.1 11.1 11.1
Money income plus food and housing:
Poverty rate.....5. 9.4 9.5 9.8
Percent reduction+. “-15.6 -14.4 -12.2
Money income plus food,-housing, and
medical care (excluding institu-
tional care expenditures):
Poverty rates esses 6.6 8.7 8.9
Percent reduction+......ccseeeevee ~40.6 -22.1 -20.1
Money income plus food, housing, and
medical care (including institu-
tional care expenditures):
Poverty rate..sscessseeeee eee 6.4 8.2 89
Percent reductiont.......eee sees 42.3 -26.7 -20.1
BELOW 125 PERCENT OF POVERTY LEVEL
Money income alone: -
POVErtY Pat@rssccccessecccsseeceees 15.8 15.8 | 15.8
!
Money income plus food and housing: H
Poverty rate. 14.7 14.8! 15.0
Percent reductions. ......cceeeeeeee - 6.8 - 6.2} - 5.1
Money income plus food, housing, and
medical care (excluding institu-
tional care expenditures): |
Poverty Paes es sereeseees te eeeeeee 11.6 13.6 | 14.3
Percent reduction se eeeee -26.2 “A372
Money income plus food, housing, and |
medical care (including institu- ;
tional care expenditures): i
Poverty rate.cccsscseee 11.2 13.3; 14.3
Percent reduction!.,......... -29.2 | -15.4 : + = 92

---~-- percent -reductton-in-the-number of poor” from the-current poverty estimate
based on money income alone.

96



There were two questions dealing with public and low-cost rental housing
on the March 1980 CPS supplement questionnaire. The first question identi-
fied residence in a housing unit owned by a public agency; the CPS estimates
1,794 million households living in such housing; the second question identi-
fied beneficiaries who were not living in public housing projects but who
were paying lower rent because of a government subsidy; this question identi-
fied 0.717 million households. In total, the CPS estimated 2.511 million
households residing in public or sudsidized housing. These questions dif-
fered from other in-kind transfer recipiency questions covering noncash
benefits in that they established current recipiency status (as of the third
week in March 1980) rather than recipiency status during 1979.

In all Vikelihood, an administrative data-based estimate of public
housing units which is comparable to the CPS estimate does not currently
exist. HUD and Congressional Budget Office (CBO) estimates for these four
programs indicate that there were 2.688 million public or subsidized housing
beneficiaries in March 1980: 1.131 million in low rent public housing and
the remaining 1.557 million benefiting from the other three programs. If
one could be sure that all CPS recipients were benefiting from one of these
four programs, the March 1980 CPS estimate would be 93 percent of the HUD-CBO
based estimate. There are several reasons why, however, such a comparison
might not be justified:

1. . Assuming that the households which the CPS intended to identify in
the first question were HUD Low Rent Public Housing Program benefi-
ciaries, the CPS overcounted these benefictaries by .663 million
relative to the HUD-CBO estimate and undercounted other subsidized
units (as identified by the second CPS question) by an equally large
amount. It is possible that the total CPS estimate is accurate, but
if so, not all the beneficiaries were residing in these HUD projects
or they were not sure of exactly the type of HUD project they were
residing in.

2. There are several other HUD programs, various State-local public
housing projects (such as Mitchell-Lama housing in New York City),
and other government agency housing programs (e.g., USDA rural
rental housing and prison "controlled release" housing for convicts)
which may have been counted in the CPS. The comparison of the CPS
and HUD-CBO estimates may, therefore, be less than adequate.

3. In some sense, the public or subsidized housing questions asked on
the CPS were too broad for the task at hand. Military housing,
apartments subject to local “rent control," and State university
graduate student and faculty housing can be legitimately interpreted

19



Chapter 3. Program Descriptions, Data Collection,
and Qudlity Evaluation

This chapter contains brief descriptions of each public in-kind transfer
program covered in the March 1980 CPS, a description of the questions used to
collect the data, and an. evaluation of the data quality. The description of
each program begins with a statement of program objectives and is followed by
general comments regarding program characteristics, eligibility, etc. Next
is a review of the survey questions and the limitations associated with the
question wording and design. Last is a comparison of the survey-derived
estimates of noncash benefit recipients with independent estimates developed
from other sources and adjusted to the CPS concept of calendar-year recipi-
ency for the civilian noninstitutional population. In this section, the tern
“ever received" is used to describe households receiving benefits at any time
during the calendar year 1979.

FOOD STAMPS

The Food Stamp Act of 1977 defines this Federally funded program as one
intended to "permit low-income households to obtain a more nutritious diet."
(From title XIII of P.L. 95-113, The Food Stamp Act of 1977, declaration of
policy.) Food purchasing power is Increased by providing eligible households
with coupons which can be used to purchase food. The Food and Nutrition
Service (FNS) of the U.S. Department of Agriculture (USDA) administers the
Food Stamp Program through State and local welfare offices. The Food Stamp
Program is the major national income support program that. provides benefits
to all low-income and low-resource households regardless of household charac-
teristics (e.g., sex, age, and disability). The questions on participation
in the Food Stamp Program in the March 1980 CPS were designed to identify
households in which one or more of the current members received food stamps
during 1979, Once a food stamp household was identified, a question was
asked to determine the number of current household members covered by food
stamps during 1979. Questions were also asked about the number of months
food stamps were received during 1979 and the total face value of all food
stamps received during that period.

The USDA/FNS publishes monthly counts of the number of persons who
received food stamps during the previous month. These FNS monthly figures
cannot be simply summed over the year to arrive at an estimate which is com-
parable to the CPS estimate because persons receiving benefits for more than
one month would be counted more than once. Unfortunately, USDA/FNS does not
have estimates of the number of persons who "ever received" food stamp bene-
fits at any time during 1979. Assuming that the March 1980 CPS accurately
recorded the average number of months that any household participated in’ the
program, the number of persons who ever received benefits during 1979 is
simply the sum of the number of persons receiving benefits each month divided

15



Table 30. Nonmetropolitan Area Residents:

Reductions in Poverty Rates Using

Alternative Valuation Techniques and Income Concepts: 1979
Market Recipient Poverty
Income concept value | value - cash | budget share
approach equivalent approach
approach
BELOW POVERTY LEVEL i
i
Money income alone: H
Poverty rate... svcccccee 13.4 | 13.4 13.4
Money income plus food and housing:
Poverty Pat@..cccccccsscscccccvese 11.5: 11.6 11.9
Percent reductions. .....cseeeeceee. -13.6 | - 12.9 ell.
Money income plus food, housing, and :
medical care (excluding institu- t i
tional care expenditures): i i
Poverty Patecccccsccceccvcccvacece 8.3; 10.4 10.8
Percent reductionl,......ccseseeee -37.8 | 22.2 719.3
“Money income plus food, housing, and
medical care (including institu-
tional care expenditures):
Poverty rate.sssccsrenees 8.1 9.8 10.8
Percent reductiont....... ~39.6 -26.4 19.3
1
BELOW 125 PERCENT OF POVERTY LEVEL f i .
|
Money income alone: i
Poverty rat@.sccssensees . 19.1 19.1 19.1
Money income plus food and housing:
Poverty rate. .scccccsrccveccsccecs 18.0 18,1 18.3
Percent reduction). ...sessceceeees - 5.4 - 49 - 4.2
Money income plus food, housing, and
medical care (excluding institu- |
tional care expenditures): :
Poverty rate..s.scseeee i 14.7 16.8 17.5
Percent reductiond....ccecececeees | -23.0 -12.1 - 8.1
Money income plus food, housing, and; |
medical care (including institu-
tional care expenditures): |
Poverty rat@sssscsescscccvccsveces | 13.9 16.5 17.5
Percent reductiont......... -27.1 -13.7 - 8.1

_ Percent, reducti

based on inoney incone atone.

100

inthe numberof poor from the.current. poverty. estimate -


Clotfelter, C. 1981. "Business Perks and Tax-Induced Distortions: The Case
of Travel and Entertainment," Working Paper. Duke University,
Institute of Policy Scrences and. Public Affairs.

Congressional Budget Office. 1977. Poverty Status of Families Under Alter-
native Definitions of Income. Background Paper #17 Revised
Washington U.S. Government Printing Office, July.

1979. The Long Term Costs of Public Housing Programs. Background
Paper, Washington, U.S. Government Printing Office.

_ 1980. Feeding the Children Federal Child Nutrition Policies in the
1980's, May.

Cooper, C. and A. Katz. 1978, The Cash Equivalent of In-Kind Income
Stamford, Connecticut, Cooper and Company, December.

Cutter, W. B, 1980. Letter to Courtney Slater, December 12.

Danziger, S., R. Haveman, and R. Plotnick. 1981. "How Income Transfers
Affect. Work, Savings, and the Income Distribution," Journal of
Economic Literature, Vol. 59, September, pp. 975-1028.

Davidson, S. 1979. “The Status of Aid to the Medically Needy," Social
Service Review, March.

Davis, K. and C. Schoen. 1978. Health and the War on Poverty, Washington:
Brookings Institution. ,

Doyle, M, D. Edson, N. Pappas, and W. Boulding. 1980. The Creation of 1980
Data Bases from the March 1978 CPS, Vol. I, Final Report, Washington,
D.C., Mathematica Policy Research. .

Fendler, C. and M. Orshansky. 1979. “Improving the Poverty Definition,"
Proceedings of the American Statistical Association, December.

Friedman, R. 1965. Poverty: Definition and Perspective, (Washington,
American Enterprise Institute).

Fuchs, V. 1969. "Comments on Measuring the Low Income Population” in L.
Soltow, ed., Six Papers on the Size Distribution of Wealth and
Income, National Bureau of Economic Research, New York, Columbia
University Press.

Gibson. R. and C. Fisher. 1979. “Age Differences in Health Care Spending,
Fiscal Year 1977," Social Security Bulletin, Vol. 42, pp. 3-16.

Godreau, K., H. Oberheu, and D. Vaughn. 1981. "AFDC Analysis of Special
Frame Data," presented at the American Statistical Association
proceedings, Detroit, August 10.



Peskin, J. 1976. “In-kind Income and the Measurement of Poverty," Technical

Paper VII in The Measure of Poverty, Washington, D.C., U.S.  Govern-
ment Printing Office, October.

Plotnick, R. and F. Skidmore. 1975. Progress Against Poverty, New York,
Academic Press.

_____ and T. Smeeding. 1979. "Poverty and In-Kind Transfers: Past Trends
and Future Prospects," Public Policy, Summer,

Reynolds, M. and E. Smolensky. 1977. Public Expenditures, Taxes, and the
Distribution of Income: The United States, 1950, 1961, 1970,

New York, Academic Press.

Rigby. D. and E. Ponce. 1981. "SSI Recipients in Medicaid Institutions,
December 1979," Social Security Bulletin, April, pp. 32-37.

Rydell, C., od. Mulford, and L. Helbers. 1980. Price Increases Caused B
Housing Programs, Rand Corporation Report, R-2677-HUD, Santa Monica,
Rand Corporation.

Salt Lake Tribune. October 14, 1981.

Schwartz, S. 1981. "A Model of Household Headship: Economic Well-Being
and the Distribution of Income," unpublished Ph.D. Dissertation,
University of Wisconsin at Madison,

Seninger, S. and T. Smeeding. 1981. “Rural Poverty, Human Resources, and
Welfare Policy," in A. Hawley and S. Mazie, eds, Nonmetropolitan
America _in Transition, Chapel Hill, University of North Carolina
Press. :

Smeeding, T. 1975. Measuring the Economic Welfare of Low-Income Households,
and the Anti-Poverty Effectiveness of Cash and Non-Cash Transfer Pro-
grams. Unpublished Ph.D. dissertation, University of Wisconsin,
Madison.

1975a. “Anti-Poverty Effectiveness, Costs, and Participation in Various
Food Stamp Plans," Brunswick, Maine, Bowdoin College, mimeograph.

1977, "The Anti-Poverty Effectiveness of In-Kind Transfers," Journal
of Human Resources, Vol. 52, Summer,

1981. "The Anti-Poverty Effectiveness of In-Kind Transfers: A "Good
Idea" Gone Too Far?" Policy Studies Journal, forthcoming.

1981a. “Estimating In-Kind Income Using the CPS: Survey vs. Pure
Microsimulation Approaches," presented at the American Statistical
Association proceedings, Detroit, August 11.

-160



share value" is the dollar amount of a good consumed. by households with money
income approximately equal to the poverty level. This amount is freed up to
cover other purchases when replaced by an in-kind transfer whose value is
greater than or equal to this poverty budget share value. Amounts of the
in-kind transfer in excess of this amount are assumed to contribute nothing
to the determination of a household's poverty status. 1

MARKET VALUE

The market value (MV) of an in-kind transfer is equal to the private
market purchasing power of benefits received by the individual. That is,
in-kind transfers present beneficiaries with control over some amount of
economic resources which can be bought and thus have been explicitly valued
in the private market. Because MV is intuitively appealing and relatively
easy to compute, it is the measure which is most often used in studies of the
value and distribution of in-kind transfer benefits. All other valuation
methods require a good estimate of the MV of the good or service being trans-
ferred as a starting place. In some cases, e.g., food stamps, the market
value is directly measurable as the dollar value of food coupons in the
market. In other cases, MV is not so easily estimated. Although the aggre-
gate MV of medical care benefits is easily estimated as the sum of vendor
payments plus the cost to the Government for processing claims and program
management, the MV of these benefits to a specific individual is not so
easily computed. (See the discussion in chapter 5.) In the case of public
housing, the conceptual measure of MV is easily defined as the difference
between the private market rental value of the unit and the rent paid by the
tenants. Estimating MV for public housing becomes problematic when the
private market rental value is not known.

The MV concept is most often used for program budgeting by administering
agencies and the Congressional Budget Office when the net budget cost (net
budget savings) of a proposed program change is estimated. In cases where
overhead costs can be assumed fixed (i.e., not to vary with the proposed
program change), changes in MV will be an accurate predictor of the net
change in government or, budgetary cost. However, in cases where government
cost and MV vary significantly, as in the case of public housing, the govern-
ment cost measure should be used in estimating the costs of the transfer
program under consideration. The MV has also been used in studies of the
distributional effect of in-kind programs and their effect on poverty status.
The choice between using MV or any one of the other valuation techniques for
this purpose presents several difficult and disputed issues.

2

\care should be exercised in the assignment of labels to the value of
in-kind benefits. For instance, Borzilleri (1980, 198Ua) refers to esti-
mates of market value as "funds released," “freed income," or "cash
equivalent." Similarly, Smeeding and Moon (1980) used "market value" and
“government cost" for medical benefits interchangeably.

27



Appendix C. Quality of the CPS In-Kind Benefit Data

This appendix contains additional information on comparisons of March
1980 CPS benefit data with alternative administrative based estimates.

FOOD TRANSFERS

Table C-1 compares selected characteristics of food stamp households

. from a USDA survey taken in October 1979 with the March 1980 CPS data

covering calendar year 1979. _The differences between the USDA and CPS esti-

hates are, in most cases, small. Some of these differences may result from

differences in the reference periods between these surveys. The USDA survey

covers only October 1979, whereas the CPS covers food stamp recipients at any
time in 1979.



Table D-7. Price Adjustment Factors for Adjustment of Normal Expenditures for

Housing

; Householder 62
; years old and

rr

Householder under 62 years old

| over
Residence and region \ _ . __
| 7
i | 2 5
1 1 Persons 1 2] 3 and 4} Persons
| Person or more Person Persons | Persons | or more
i |
j=
Inside
Metropolitan Areas
| |
Northeast ..ssseceseee » 1.12 | 1.09 1.00 +99 1.01 | 1,08
North Central. i 693 | +95 +89 90 -90 95
South.....- i; 483 | -80 -89 ~90 -83 79
WeStesseccacccccscececreeee | 1,09 1.09 1,23 1.32 1.32 1.31
Qutside i :
Metropolitan Areas ' i - i
| . i
Northeast....... 1.36 | 1.34 1.12 1.11 1.13} 1.21
North Central. 1.06 | 1.09 95 97 097 | 1.02
South.... 65 | +69 81 +82 76) 70
West... weeeeee 1.20 | 1.20 1.30 1.40 1.40 | 1.38
i

131



eligibility and directly bill the Medicaid agency for expenses. The agency
would add this person to their list of enrolled individuals even though the
person was unaware of the coverage. These persons would not be identified
as covered by Medicaid in the CPS.

A final decision was made to define the universe at risk for Medicaid as
the CPS covered population. This decision was based on the examination of
the strengths and weaknesses of both conceptual and empirical aspects of each
approach, as discussed previously.

Once the universe at risk was defined, procedures were developed to
assign specific insurance values to the covered population. The first step
in this development was examination of data sources from the Health Care
Finance Administration (HCFA) which could be used to estimate total expendi-
tures for. Medicaid by State and within specific risk classes (elderly, dis-
abled, etc.). The next step involved the estimation of the number of persons
covered by State and risk class. These figures were then used to compute
the market value of Medicaid benefits, which were then assigned to the
covered CPS population.

These procedures require the reconciliation of the HCFA and CPS uni-
verses. The HCFA universe is the population actually receiving medical care
in 1979 paid for by Medicaid. This universe includes the institutionalized,
decedents, and those eligibles covered only as the result of hospitalization
(as in the previous example). They exclude between 0.5 and 1.0 million
Medicaid recipients who were not covered by Federal-State matching provisions
(“State-only" beneficiaries). Finally, the HCFA estimates are for fiscal
year 1979 and are acknowledged, in some cases, to be only rough estimates of
the number of recipients. In contrast, the CPS universe is the covered popu-
lation for calendar year 1979. It includes the “State-only" recipients but
excludes institutienalized, decedents, and unknowing Medicaid benefit recipi-
ents. These groups should theoretically be included in the universe of
covered persons for estimating the insurance value.

The estimates of the number of persons covered by Medicaid within State
and risk class were made by combining the CPS and HCFA information. First,
the CPS estimates were adjusted to include the institutionalized and dece-
dents by State. Next the HCFA estimates were adjusted to exclude benefici-
aries in Puerto Rico and the Virgin Islands. The final estimates were
derived by choosing the CPS estimate of covered or the HCFA estimate of
“ever-received," whichever was greater, for each State and risk class. These
results are summarized in table 6 for the United States.

9altogether, 36 different groups of States or separate States were used in
order to assign eligibility to each of the four types of beneficiaries.
Appendix D presents these groupings.

51



Table B-1. Comparison of Major Public In-kind Transfer Programs with Total Private and
Public Income In-Kind in 1980

"Other" in-kind income
Major Public Total Major as
Row Type of benefit in-kind | value of | a percent
transfer , benefits of Total
benefits! Tax Privates
Total sub-
sidies?
1 Totalescesseees $72.5 | $59.8 32.0 $83.5 $ 215.8 33.6
2 FOOd sesccreevecre 12.6 1.2 (NA) 16.6 30.4 41.4
3 HOUSING... ceeeveee 5.4 32.7 28.8 14.8 52.9 10.2
4 Medical care...... 54.5 25.9 3.2 52.1 132.5 “41.1
5 Total means-
tested benefits.. 42.4 3.6 (NA) 2.4 48.4] 7 87.6
6 Total nonmeans-
tested. benefits.. 30.1 56.2 32.0 81.1 167.4 18.0
1 Li. -

NA Not available.
lincludes only those transfer prograus which are valued in this report.

2Tax subsidies (also known as tax expenditures) are subsidies accruing to households
from tax deductibility of mortgage interest, property taxes, etc., and are shown
separately for those who do not classify such benefits as income in-kind.

3Includes some employer-provided benefits, philanthropic transfers, and some sources
“of nonmarket income in-kind, but excludes other private in-kind transfers (e.g., the
medical bills of the elderly which are paid by their children, or the free housing
and food provided for an elderly parent who lives with their children). In 1979
these benefits totalled roughly $20.0 billion (Lampman and Smeeding, 1982). It was
not possible to separate this estimate into food, housing, and medical components.

Sources: Social Security Bulletin (1981), U.S. Budget, FY 1982 (1981);
Survey of Current Business (1981).

107


expenditures for the elderly were covered: by. Medicare or other public pro-
grams; the remaining 38 percent were covered by out-of-pocket expenditures
(including Medicare-SMI premiums) or by private insurance (Gibson and Fisher,
1979). The PBS estimates are based on the 1960-61 CEX, which was before the
inception of the Medicare program. It was assumed that had Medicare ex-
isted in 1960-61, it would have paid 62 percent of the medical care ex-
penses. As a result, the PBS value for Medicare beneficiaries was limited
to 62 percent of the medical care values in table 13.3

Table 13. Poverty Budget Share (PBS) Dollar Values for 1979

Family size and Food j Housing | Medical
age of householder i care
4
|
One person: Hl
Under 65 yearSeccssseececees | $1,258 | $ 1,487 $ 241
65 to 74 years.. 1,160 | 1,246 375
75 years or Olderesseveesace 1,160 1,319 385
Two persons:
Under 65 years.. . 1,619 | 1,584 359
65 to 74 years.. + 1,463 i 1,414 467
75 years or Oldersssccseveee 1,463 1,459 528
TAPE PEPSONS..ecessseccceocoece 1,921 1,602 473
FOUr PEPSONS. cevesssecaceeeeoece 2,462 1,714 547
FiV@ POPSONSscececseeesecenceres 2,912 1,782 524
SUX PEPSONSeecesececesecenceeses 3,283 1,794 552
|
Seven OF MOPe PEPSONS..eereeeaes 4,071 ; 2,104 6384

Note: The values in this table were derived by multiplying
poverty levels by percentages shown in appendix D,
tables 0-10 and 9-11.

33No such adjustment for Medicare only beneficiaries was made in calcu-
Jating RV-CE. This adjustment was not made because the 1972-73 CEX,
which was used to value Medicare in the RV-CE calculations, includes
Medicare recipients. Because there is no way to get around this problem
by observing a sufficient number of non-Medicare recipients age 65 or
older we decided not to make an adjustment when estimating Cjj for
Medicare only recipients, on the grounds that their willingness to pay
for Medicare may already be underestimated due to its presence. (See
the addendum to this chapter for additional discussion of this issue).

71



Medicaid is by far the largest means-tested income transfer program with
benefits in excess of total cash public assistance benefits from Aid to Fami-
lies with Dependent Children (AFDC), Supplemental Security Income (SSI) and
all other forms of cash public assistance combined. (See table 1.) In 1979,
the average market value of Medicaid benefits per household was in excess of
$2,000, Previous analyses of the effect of Medicaid on the poor (Smeeding
and Moon, 1980) have’ shown that, by itself, Medicaid may account for over 50
percent of the total reduction in poverty from in-kind transfers. For these
reasons, an accurate assessment of the quality of the CPS Medicaid data is
crucial to the outcome of this report. Unfortunately, the Federal-State-
local nature of the Medicaid system coupled with its State specific eligibil-
ity vagaries and governing rules also make it a most difficult program to
analyze. The problem is further compounded by the fact that administrative
data are collected on an “ever-received benefits" basis, while CPS data
identifies those who were ‘"ever-covered or enrolled" during 1979. Thus,
particular care needs to be taken in analyzing both the Medicaid recipiency
data and Medicaid benefit valuation.

Medicaid is, for the most part, a categorical program with complex
eligibility rules which vary from State to State. There are two basic groups
of eligible individuals: the categorically eligible and the medically needy.
The major categorically. eligible groups are all Aid to ‘Families with
Dependent Children (AFDC) recipients and most Supplemental Security Income
(SSI) recipients. Other categorically eligible groups are (1) those who meet
basic State cash assistance eligibility rules (the aged, blind, or disabled;
needy single parents with children; and, in some States, needy unemployed
parents with children. who are not currently receiving Money payments) and
(2) needy: persons meeting categorical eligibility standards but are institu-
tionalized for medical reasons (e.g., low-income elderly persons in nursing
homes). However, such institutionalized persons are not included in the CPS
universe and, therefore, are not reflected in the CPS recipiency statistics.

In roughly one-half of the States, Medicaid coverage is also extended to
the medically needy: persons meeting categorical age, sex, or disability
criteria and having money incomes and assets which exceed eligibility levels
for cash assistance ‘but are not sufficient to meet the cost of medical care.
In some States, qualifying income and asset levels are above those set for
cash assistance. Families with large medical expenses relative to their
incomes and assets may also meet medically needy eligibility standards in
these States by “spending down" (i.e., having high enough medical expenses)
to obtain eligibility.

The Medicaid question on the March 1980 CPS attempted to identify all
persons 15 years old and over who were covered by Medicaid at any time during
1979, The term “covered" means enrolled in the Medicaid program, i.e., had
a Medicaid medical assistance card or incurred medical bills which were paid
for by Medicaid. In order to be counted, the person did not necessarily have
to receive medical care paid for by Medicaid.

After data collection and creation of an initial microdata file, further
refinements were inade to assign Medicaid coverage to children. In this

21



Table 22. Summary of the Largest and Smallest Reductions in Estimates of Poverty for
Selected Population Subgroups: 1979

[ Impact of in-kind benefits on poverty rate
| Money 5
Selected | income | Largest percent reduction! | Smallest percent reduction?
characteristics poverty
rate
Including | Including Including Including
food, food and food, food and
: | housing, and housing housing, and housing
i i medical care only medical care only
i
All personseeeeeee| 11.1 : 42.3 15.6 ° 20.1 12.2
H |
Elderly (65 and over). 14.7 1 69.5 12.1 26.3 6.3
SOuUtNsececeeee wee | 14.6 | 37.2 15.7 19.1 12.5
Central city.. eel 15.0 | 49,3 19.9 23.0 14.6
Nonmetropolitan areas. | 13.4 |; 39.6 13.6 19.3 11.1
BlackS.csccscccccseeee! 30.4 | 50.2 21.2 23.9 16.1
In households with H
female householder, |
no husband present... | 34.8 49.4 20.9 23.8 16.3

lthe largest reductions are always attributable to the MV approach.

2the smallest reductions are always experienced with the PBS approach.

89


value of all types of public and private in-kind income should be included
to assess the full effect of in-kind benefits on poverty and the size distri-
bution of income. Although some information is available on in-kind benefits
other than those shown in this report, such as employer-provided health
insurance and pension plans, additional research is needed to develop ade-
quate valuation methodologies.

The major types of benefits included in this report are health care
benefits (through the Medicare and Medicaid Programs), food benefits (from
the Food Stamp and National School Lunch Programs), and housing benefits
(from several public or subsidized rental housing programs). Over the past
15 years, there has been a tremendous growth in the value of income in-kind
from these government transfer programs. The data shown in part A of table
1 indicate the extent of this increase. From a little over $2.2 billion in
1965, these major public transfers in the form of food, housing, and medical
benefits increased to more than $72.5 billion in 1980 ($27.8 billion in
constant 1965 dollar terms). Except for Medicare and roughly half of school
Junch benefits, the programs shown in part A are "means-tested" or designed
to benefit the low income population.4 The individual must meet certain
income and asset requirements to be eligible for "means-tested" benefits.

Early in the 1970's, the market value of in-kind benefits for the poor
began to exceed the more commonly known cash public assistance or—"wel fare"
expenditures (i.e., Aid to Families with Dependent Children, Supplemental
Security Income, and General Assistance) which are collected annually inthe
March CPS and other income surveys. (See part B of table 1.) By 1980, more
than $2 of every $3 of means-tested government aid to the poor was in the
form of an in-kind food, housing, or medical transfer benefit. Means-tested
in-kind transfers in current dollars continued to grow between 1975 and 1980
from $22.2 billion to $42.4 billion, although in constant (1975) dollars they
grew only from $22.2 billion to $27.7 billion. In contrast, cash public-
assistance transfers grew only slightly in current dollars ($16.3 billion to
$18.9 billion) and actually fell in constant dollar terms. In particular,
medical care transfer benefits increased most dramatically over this period.
Medicaid was the largest means-tested income transfer -program ($26.2 billion)
in 1980, outweighing both all means-tested cash transfers ($18.9 billion) and
all major food and housing in-kind transfers ($18.0 billion).

4yi rtually all children who eat hot school lunches receive a basic subsidy
under this program, even if they pay "“full-established price." This
subsidy accounts for about 51 percent of total school lunch benefits.
Another 49 percent of benefits go to those who either receive the lunch
for free. or pay.a-reduced price-of 10 to 20 cents per-meal. Medicare -
covers all elderly Social Security beneficiaries and a large group of the
nonelderly disabled, who receive Social Security Disability Insurance
benefits. While about 15 percent of Medicare benefits accrue to poor
elderly persons, Medicare is not a means-tested transfer program.



Determination of the PBS value requires information on the MV for each
commodity. For a given household, if the MV for a commodity is less than the
poverty budget share, the PBS value is equal to the MV. If the MV is greater
than the poverty budget share, then the PBS value is equal to the poverty
budget share. Thus, the PBS approach “caps" the value of in-kind benefits at
the levels implied by the poverty budget shares.

Once the PBS values have been computed, the effect of this valuation
technique on estimates of the number of poor can be measured. There are two
equivalent methods for integrating the PBS values into the measure of pov-
erty. The first method computes Poverty status by adding the PBS value for
food, housing, and medical care to money income and then comparing this new
income level to the appropriate poverty level. Alternatively, the PBS value
could be subtracted from the poverty level and then the money income level
can be compared to the adjusted Poverty level. Both methods yield the same
results.

As mentioned earlier, the PBS approach is not a general valuation tech-
nique, but is only applicable for measuring the effect of in-kind benefits
on estimates of poverty. The PBS estimates are based only on implied budget
shares at the poverty level, and therefore do not reflect differing prefer-
ences for various goods of all income levels. The PBS, therefore, cannot be
treated as a substitute for the RV-CE methodology if recipient's preferences
are deemed important. In the case of medical care transfers, the PBS
approach eliminates many. of the complicated valuation problems mentioned
earlier by defining a poverty line which excludes some or all medical care
expenditures for Medicare and Medicaid. The application of the PBS approach
to food and housing transfers is of less significance since more definitive
measures of value (RV-CE and MV) are available.

SUMMARY

This section has examined three methods for valuing food, housing, and
medical care in-kind transfers. The strengths, weaknesses, and problems in
estimation for each method were Presented. The valuation of medical care
transfers presents special problems, both empirically and conceptually.
Because the market value of medical care transfers is about 80 percent of all
in-kind benefits, the treatment of medical care is of particular importance
in the measurement of poverty for the elderly. Table 14 in the addendum
to this chapter provides data which compare the size of medical care trans-
fers for the elderly to the poverty line for the United States and for the
States with the highest and lowest values for medical care.

In keeping with the objectives of this study, no discussion of the most
preferable approach to value in-kind benefits has been presented. It is
expected that the results of this study will serve as background materials
for such_a discussion. —In_this Feyard, -it- should~be~-noted that~it~ is “not
necessary to use the same valuation technique for each different type of in-
kind benefit. For example, one approach would be to use the MV for food,
RV-CE for housing, and PBS for medical care; any other combination is pos-
sible, as well.

72



Chapter 6. The Effect of Alternative Methodologies
for Valuing In-Kind Benefits on Poverty

The purpose of this chapter is to present estimates of the effect of in-
kind food, housing, and medical care benefits on the measurement of poverty.
These effects are shown for each of the three valuation methodologies and by
the inclusion or exclusion of medical care for the institutionalized popula-
tion and the exclusion of medical care benefits altogether. The examination
of the effects of in-kind benefits on estimates of the poverty population is
divided into five basic elements. The first part of the analysis presents a
general overview of the reductions in the number of persons with income below
the poverty level using three definitions of income and the three valuation
techniques (table 15). The: second Part presents effects of the valuation of
in-kind benefits on selected subgroups of the poverty population. . These
results are contained in taples 16 through 21, Tadte 22 summarizes the
largest and smallest reductions in tne poverty rate for selected subyroups
of the poverty population. The third part indicates the changes in the com-
position of the poor for each of the valuation techniques (table 23). The
fourth part of this analysis: compares results of tnis study with those of
previous valuation research studies (tables 24 and 25). The last part. shows
poverty rates for selected subgroups of the Population and the percent reduc-

tion in these rates for each income definition and valuation technique
(tables 26 through 32).

It should be noted that the poverty statistics contained in this report
differ slightly from the official Poverty statistics published by the Bureau
of the Census for 1979. The determination of poverty for this study was on a
household basis, unlike the official procedure which derives poverty status
separately for families and unrelated individuals. This approach was adopted
since much of the data on in-kind benefits collected in the CPS are reported
on a household basis. Secondary individuals are the group most affected by
this procedure because their Poverty status in this study is dependent on
status of the primary family or individual in the household. The net effect
of this modified procedure is small, causing a reduction in the official
poverty rate (11.6 percent) to 11.1 percent. :

OVERVIEW OF RESULTS

An overview of the effect of the valuation of in-kind food, housing, ana
medical benefits on poverty is shown in table 15. The effect of each valua-
~tion technique is shown for three different income definitions which have
been expanded to include various combinations: of the food, housing, ana medi-
cal in-kind benefits. Table 15 shows numbers of poor and poverty rates for
each combination of income definition and valuation method, as well as the
percent reduction in the number of poor based on money income alone.



The inclusion of the value of in-kind benefits in the measure of poverty
reduces the: estimates of poverty significantly, but does not eliminate
poverty altogether. Use of the maximum values for in-kind benefits reduces
the number of poor by as much as 50 percent for some subgroups such as Blacks
and female householders, however, their poverty rates remain higher than
average. For example, the poverty rates for Blacks and female householders
are 15,1 percent and 17.6 percent, respectively, even after the food,
housing, and medical in-kind benefits have been included at full market value
(chapter 6).

It should be emphasized that the procedures and results presented in
this report are intended to serve as a foundation for discussion of the valu-
ation issues and a starting point for further analysis. While there have
been several previous studies that have examined the effect of in-kind
benefits on the poor, this is the first study that has investigated several
_ different valuation techniques and assessed their effect on different sub-
groups of the poverty population. It is hoped that this report will initiate
objective discussion concerning the valuation of in-kind benefits and the
measurement of poverty.



Appendix B. Explanation of the Data Sources for
Table 2

Table 2 presented estimates of the aggregate value of food, housing, and
medical care benefits. This table is repeated below along with a detailed
explanation of the sources used to derive each estimate. All estimates are
for 1980 unless otherwise noted.

106



raise many of the elderly poor above the poverty line. The table shows sub-
stantial variation in these relationships for New York and Mississippi.

Table 14. Comparisons of Medical Benefits to the Poverty Line for the Elderly Who are
Eligible for Medicare and Medicaid: 1979

United States New York Mississippi

Row | Item
One Two One Two One Two
person | persons ; person | persons { person | persons

MINIMUM INCOME GUARANTEE,
EXCLUDING MEDICAL BENEFITS

+ {$8 2,390 | $ 3,580 | $ 3,552 | $ 4,704 1 § 2,390 | § 3,580

1 SSI guarantee..........
2 Food stamp bonus (MV)...... 240 { 460 120 210 240 460
3 Minimum income guarantee
(1 + 2)ec ce cce cee ceseceeee 2,630 4,040 3,672 4,914 25630 4,040
4 Poverty lime...seccecsceees 3,470 4,360 3,470 4,360 3,470 4,360
5 (Deficit: 4 - 3). . 840 320 -202 584 840 320
MV FOR MEDICAL BENEFITS,
INCLUDING INSTITUTIONAL
CARE
6 Medicare $ 1,011 | $ 2,022 }$ 1,118] $2,236]$ 7431 ¢ 1,486
7 Medicaid 1,970 3,940 4,430 8,860 1,023 2,046
8 TOTAL. ceeeeeeeees 2,981 5,962 5,548 11,096 1,766 3,532

9 Medical/pov. line (8 +

= 4) 85.9% 136.7% | 259.9% 254.5% 50.9% 81.0%
10 Medical/min. inc. (8 = 3)

a 113.3 173.3 15h.1 225.8 67.2 87.4

MV FOR MEDICAL BENEFITS,
EXCLUDING INSTITUTIONAL

CARE
11 | Medicare $ 986} $1,972]$ 1,095 | $2,190/3 728] § 1,456
12 | Medicaid 418 836 886 1,772 273 546
13 | TOTAI 1,404 2,808} 1,981 3,962 | 1,001 2,002
1
14 | Medical /pov. line (1324). | 40.4% | 64.4%} 57.19 45.8%. _
18) -Medical/mine ine. (13-2 3)ef"5323° 6955 Pea 9 49.6

76



Table 32, Female Householders, No Husband Present: Reductions in Poverty Rates
Using Alternative Valuation Techniques and Income Concepts: 1979

Market Recipient Poverty
Income concept value ; value - cash | budget share
approach equivalent approach
approach
BELOW POVERTY LEVEL
Money income alone:
Poverty rate.scscccccecccccccsence 34.8 34.8: 34.8
Money income plus food and housing:
Poverty rate...ccscececcccccccvces 27.5 28.1 29.1
Percent reductionl........cesecees -20.9 -19.2 16.3
Money income plus food, housing, and
medical care (excluding institu-
tional care expenditures):
Poverty Pater.scccececccececsscuces 18.1 26.5 26.5
Percent reductionl. sce. .seseeeee ~47.9 -23.8 -23.8
Money income plus food, housing, and
medical care (including institu-
tional care expenditures):
Poverty rate. .cccccscssceccece 17.6 24.4 26.5
Percent reduction!.........066 49.4 -29.9 -23.8
BELOW 125 PERCENT OF POVERTY LEVEL
Money income alone:
Poverty rate...ccecececcccccccevie 43.1 43.1 43.1
Money income plus food and housing:
Poverty rate...ssgsececccccceveces 39.2 39.6 40.0
Percent reductiont......cccesceeee - 90 - 7.9 - 7.0
Money income plus food, housing, and
medical care (excluding institu-
tional care expenditures):
Poverty rate...ccsccccccccccececes 31.1 37.3 38.6
Percent reductions ........ceece eee -27.8 -13.4 -10.4
~~ Money “income~plus” food, Housing, and [~~ nn
medical care (including institu-
tional care expenditures):
Poverty Pate..scccscccccccsceccvece 30.4 36.2 38.6
Percent reductions. ......cceceeeee -29.4 -15.9 -10.4
!
lper t_reduction in the number of poor from..the. current poverty.-estimate

“"“““pased on money income alone.

102



Table 20.

Subgrou

(Numbers in thousands.

ps:

1979

Recipient Value-Cash Equivalent Approach:

Persons as of March 1980)

Number of Persons Below 125 Percent of
the Poverty Level and Poverty Rates, by Income Concept for Selected Population

Income concept

Money income

Money income plus
food, housing,

Money income plus
food, housing,

Selected Money income plus food and and inedical care and medical care
characteristics alone housing | (excluding (including
! institutional institutional
; care) care)
Poverty Poverty Poverty Poverty
Number rate | Number rate | Number rate |~-Number rate
All persons. | 33,445 15.8 | 31,367 i 14.8 28,877 13.6 | 28,284 13.3
South...ceceees | 13,767 19.9 | 13,011 : 18.8 : 12,245 17.7 | 12,020 17.3
Non-South...... | 19,678 13.8 | 18,356 | 12.9 | 16,632 11.6] 16,264 11.4
Elderly (65 ;
and over).. 6,708 | 24.0 6,286 | 22.5 | 4,72 16.9 4,504 16.1
Non-elderly.... | 26,737 ; 14.5 | 25,081 , 13.6 | 24,165 13.1] 23,780 12.9
Central city.. 11,915 | 20.5 | 10,997 ° 18.9 10,119 17.4 9,843 16.9
Suburb. .seeeees 8,369 : 9.9 7,852 | 9.25 7,194 8.5 7,086 8.3
Nonmetropolitan : : i |
APCAseeeeeeeee | 13,161 19.1 | 12,518 | 18.1) 11,564 : 16.8; 11,355 16.5
. : i i
Blackseeeeceeee | 9,614 | 39.2 8,864 36.1 ! 8,281 | 33.8 8,061 32.9
Non-Black...... | 23,831 ; 12.7 | 22,503 12.0 | 20,596 | 11.0] 20,223 10.8
In households |
with female ! i :
householder, i !
no husband : | i
present....... ©11,317 | 43.1 | 10,421 39.6 9,797 | 37.3 9,520 36.2
All other i |
persons.....6. 22,128 | 11.9 | 20,946 11.3 19,080 | 10.3 | 18,764 10.1
| i
! | n
lpiffers from official poverty count for 1979 of 35,592 (16.3 percent) because poverty

status was determined on a household basis.

87



etc., is forfeited entirely to the institution. Medicaid, therefore, pays
only the difference between the total cost of care and the amount of income
received by the institutionatized individual over and above their $25
allowance. Thus, the benefit from institutionalized care may not be worth
anything to the noninstitutionalized population because their income would
be reduced by roughly the same amount. 1

In response to these considerations, this report presents two estimates
of the market value of medical care. One estimate includes all expenditures
for medical care and the other excludes institutional care benefits for the
elderly and the disabled. The Medicaid institutional care adjustment was
accomplished by subtracting institutional care expenditures from total
expenditures for each State and risk cateyory.!¢ (See appendix D for State-
specific estimates.) Aside from the more general problem of inclusion or
exclusion of institutional care expenditures, there is the broader issue of
whether or not to include the value of medical care for the purposes of
defining poverty. A discussion of these issues can be found in the addendum
at the end of this chapter.

Estimates of the market value of Medicaid were made using the procedures
that have been described. The average Medicaid insurance value per enrollee,
including and excluding institutional care expenditures, are shown in table
8 for each risk category. The estimates of insurance value excluding insti-
tutional care expenditures was only 21 percent of the insurance value
including institutional care for the elderly and 52 percent for the disabled.
The treatment of institutional care expenditures, therefore, is a very impor-
tant aspect of the valuation process.

There are several shortcomings to the technique used to estimate the
market value of Medicaid. First, no estimates are available on the duration
of Medicaid coverage during 1979. Thus, the average MV was assigned to all
beneficiaries, a procedure which overstates MV for part-year recipients and
understates MV for full-year recipients.13 Secondly, no adjustments could
be made for State differences in the types of health care covered by
Medicaid. There are wide differences between States in total Medicaid ©
expenditures which are due to price differences and, to some extent, the dif-
ferences in covered health care. (See appendix D.) Finally, some studies

llfurther details concerning this issue are included in the discussion of
recipient value later in this chapter.

12the same adjustment was made for Medicare by multiplying total expendi-
tures by .979.

13Morton Paglin suggests-that poor Medicaid recipients are more likely to
be covered for longer periods of time. If this is true, larger MV's for
Medicaid should be assigned to the poor. Hence, the number of poor
would be reduced to a greater extent than that produced by assigning the
average MV to all recipients. This impact would, however, be somewhat
less using RV-CE, and negligible using PBS.

55



Medical transfers. The estimation of the. market value of in-kind medical care
transfers is of crucial importance to this study because these programs are
much larger than the food and housing programs combined. The difficulty in
estimating the market value of medical benefits requires a detailed examina-
tion of several issues. First, the nature of the medical care benefits being
transferred must be defined. Second, procedures must be developed for the
treatment and valuation of institutional care expenditures.

In-kind medical care transfers can be defined as an insurance policy or
- as direct consumption of medical services. If benefits are treated as an
insurance policy then they are a nontransferable right granted to all "eli-
gible" persons (either those enrolled in the proyram or those eligible but
not enrolled) whether or not they actually consumed medical services. A
choice must be made between these two concepts, medical care.as insurance or
as consumption. The insurance approach assumes that the right to the medical
care transfer has a value greater than zero. The consumption approach
implies that medical care transfer has value greater than zero only when: med-
ical services are used.> The number of medical care beneficiaries will vary
depending on which of these approaches is used.

The consumption (or benefits received) approach requires information
which is difficult to obtain, .Data are needed on specific amounts of health
care consumed by individuals in various circumstances, but recent data on
this subject are not available. Consequently, it is nearly impossible to
identify actual amounts of medical care consumed by recipients. Another
problem is that substantial amounts of medical care are consumed shortly
before death, e.g., during long expensive periods of nospitalization. Thus,
many persons receiving substantial medical benefits were not included in the
March 1980 CPS,

The insurance approach to value benefits requires that the private mar-
ket analogue be identified and that its cost be distributed to individuals at
risk. The market analogue, in this case, is a nonprofit insurance company
which charges beneficiaries premium amounts, which when summed, equal the
total value of benefits provided (total vendor payments and overhead costs).

A major decision in the insurance approach is to determine the popula-
tion at risk. This group can be defined as all persons eligible to

4much of this discussion is based on previous work by Smeeding and Moon
(1980).

5Sthis approach would result in the situation whereby individuals suffering
the most severe health problems and requiring greater medical care would
be assigned a much higher value than a healthy individual. The reader
should note, however, that if the measurement of poverty were based on-
all physical needs as well as income needs, a greater amount of required
health care would have no effect on poverty status. The current poverty
definition is based on only one aspect of physical need, a nutritionally
adequate diet.

49


The right side of the equation illustrates how the Consumer Price Index
is used to make annual adjustments to the original poverty thresholds for
changes in prices. The weights used for each commodity group included in the
CPI are based on the expenditure patterns of the urban population in 1972.
Changes in the price of each commodity group since 1961 are weighted by these
factors to obtain the overall CPI, which is then used to adjust the poverty
thresholds.

The disaggregation of poverty thresholds presented here illustrates why
many analysts argue that there are conceptual problems in determining poverty
status once in-kind benefits have been introduced. Four of these issues are
summarized in the remainder of this chapter. These are 1) adjustments to the
food to income multiplier to account for noncash benefits, 2) revising the
food to income multiplier using more recent consumption data to reflect
changes in food consumption patterns, real income, and so forth, 3) use of
before-tax income to measure poverty, and 4) use of the change in the overall
CPI to annually adjust poverty thresholds.

The food-to-income multiplier. The income concept used to derive estimates
of poverty in this study includes in-kind benefits as income in Yj on the
left side of the equation, however, no adjustment was made to income (Yat)
on the right side of the equation. Both income (Yat) and food expenditures
(F) on the right side of the equation should reflect noncash benefits as well
as cash to be consistent. Although it can be argued that it would be desirable
to incorporate this information into the poverty thresholds, such adjustments
are not easily made. The following discussion illustrates some of the problems
of incorporating information on in-kind benefits into the poverty concept.

The most straightforward procedure to adjust the poverty lines to
account for the value of in-kind benefits is to recompute the food-to-income
expenditure ratio.: This ratio would be adjusted by adding the value of
in-kind food benefits to cash expenditures for food and dividing this amount
by the sum of after-tax income and the value of all noncash benefits. Micro-
data from household surveys would provide the best source of data to make
this adjustment; however, data for many sources of noncash benefits have not
been collected or valued. In the absence of these data, aggregate data from
administrative sources. might be used to approximate the average food-to-
income ratio.

In order to make this adjustment, decisions must be made on 1) the year
on which the estimates are based, 2) the sources of in-kind benefits to be
included, and 3) the method(s) used to value these benefits. Ideally, the
estimate should be based on the same year as the food expenditure data. This
means that 1955 would be the optimum year for recomputing the multiplier
if the 1955 food expenditure data are used. A second decision must be made
on the sources of noncash benefits to be included. The list of noncash food

4there is the further complication that income on the left side of the
equation is tefore-tax money income whereas income on the right side is
after-tax money income. The problems associated with this inconsistency
are discussed later.

11



--Program, 22 In the Food Stamp Program, however, the“recipient can receive
benefits for all or only part of the month (e.g., the recipient may only
receive an amount of food stamps equal to DR rather than DS). In either
case, the valuation problem is theoretically the same.

The analysis is altered slightly if a person has to pay a certain amount
of money to receive an in-kind benefit. For example, persons who receive
optional Supplemental Medical Insurance (SMI) under Part B of the Medicare
program are required to pay a fee each month. The payment of this insurance
fee can be represented by AF in figure 1. The recipient pays (or gives up)
AF units of other goods in order to receive HE units of health care coverage
under SMI. . The new budget constraint is AHES in this case, but the analysis
remains the same as in the earlier case. Other in-kind Programs which
require beneficiaries to pay a fee include the school lunch programs (both
reduced price and full established price) and most housing programs (e.g.,
Section 8).

There are some unique features of public housing programs, which require
further explanation. In most public housing programs, the beneficiary
(tenant) pays a certain amount in rent (15 to 25 percent of their income)
and, in return, receives a housing transfer. For example, the tenant may pay
a charge equal to AF in return for a housing transfer with a market value of
HE. The payment by the tenant (AF) covers FH units of housing and the
housing authority purchases an additional HE units, implying a total of FE

_ units of housing consumed. Because the tenant's contribution for this type
of public housing is fixed at FH, he is constrained in his housing choice
only by the maximum total value of housing which the housing authority is
willing to purchase. Suppose that a prospective tenant found two acceptable
Section 8 housing units, one with a market rent of FT and the other with a
market rent of FE (as shown in figure 1). Since the tenant pays a fixed fee
equal to FH, he will probably take the more expensive apartment, thus raising
the market value of the transfer from HT to HE and increasing the govern-
ment's cost and the MV of the housing subsidy by TE.

Other public housing-programs, such as Low Rent Public Housing projects,
are more restrictive. In this program, a person who gives up AF in other
commodities can either participate in the program (consume FE units of
housing) or not participate (consume FH units). That is, these public
housing projects are transfers of a “take it or leave it" nature, similar to
the Surplus Commodities Food Transfer Program which was replaced by the. Food
Stamp Program. The potential tenant can either take the HE units of housing
offered by the housing authority or refuse it. In effect, the recipient has

22Medicare and Medicaid are treated as insurance policies with a given

~ value, and school lunch subsidies are treated asa fixed transfer with
a given value. Alternatively, one could model ‘these Programs as a price
subsidy inducing beneficiaries to consume more medical care due to a
lower price. Such a model would be appropriate if medical transfers
were measured on a benefits actually received basis.

40



everyone has the same shape utility functi
value have been made using the normal
approach to valuing in-kind transfers will

on, estimates of cash equivalent
expenditures approach. The PBS
generally result in the lowest

value of the benefit, as in this example (ON). -



Medicaid for recent years. Virtually all of fhe elderly population, however,
are covered by one or both of these programs. 1

For these reasons, the Congressional Budget Office (1977) and Hoagland
{1980) have followed the practice of publishing estimates of the effect of
in-kind transfers on poverty which both include and exclude the value of med-
ical care benefits. This report also presents these alternative estimates
of the effect on the poverty population.

Comparing medical benefits to the poverty line for the elderly. Shown in
table 14 is the relative magnitude of medical care transfers for the elderly
as compared to their minimum income guarantee and poverty level. These
comparisons are shown for medical care valued at MV with and without institu-
tional care, RV-CE, and PBS for the U.S. and for the State with the highest
(New York) and lowest (Mississippi) values for medical transfers. These
alternatives effectively express the potential range of medical care
transfers,

The value of in-kind medical benefits received by the elderly varies
widely depending upon the valuation technique which is used. For an elderly
person living alone, the MV for medical benefits including institutional care
was $2,981 in 1979 ($1,404 excluding institutional care), the RV-CE was
$1,420, and the PBS value was $380. As shown in table 14, medical care bene-
fits as a percent of the minimum income guarantee ranged from 113 percent
based on MV including institutional care to 15 percent for the PBS approach.
Medical care benefits as a percent of the poverty line ranged from 86 percent
for MV including institutional care to 11 percent for the PBS value. The
ratios for MV and RV-CE are even higher for a two-person elderly household.
These data suggest that the market value approach to valuing medical care
including institutional care benefits results in values which, by themselves,

4lBrowning suggested an alternative to using the 1972-73 CEX data since
medical expenditures from this source are affected by the existence of
the Medicare and Medicaid programs. His idea is to adjust the 1960-61
CEX data (prior to the introduction of Medicare and Medicaid) for
changes in medical expenditures which occurred between that time and the
year for which estimates are required. He suggests that the normal
expenditures from the 196U-61 CEX be adjusted by changes in the ratio of
aggregate medical expenditures to GNP. Given that this. ratio increased
by nearly 50 percent between 1960-61 and 1972-73, the proportion of
income spent on medical care would increase by 40 to 80 percent
(depending on how these adjustments are made). It should be noted that
this adjustment assumes that consumers would be willing to spend these
additional amounts on medical’ care. Available data indicate that the
proportion of aggregate health expenditures paid by consumers actually
declined from 70 percent in 1960 to 55 percent in 1973 (Health Care
Financing Review, 1980). This assumption does not seem tenable and,
therefore, this proposal was not used to adjust the medical expenditure
data for the 1972-73 CEX.

75



Appendix E. The Use of the Normal Expenditures
Approach to Approximate Cash
Equivalent Value: Theoretical
Discussion

Use of the normal expenditures approach to estimate cash equivalent
value (RV-CE) may result in either an overestimate or an underestimate of the
"true" but unknown RV-CE. Two factors are important in determining the net
effect of the bias caused by the normal expenditures approach: (1) the shape
of the utility function implied by the normal expenditures approach as com-
pared to its “true" but unknown shape; and (2) the process of calculating
normal expenditures at a "total" income level which includes cash income plus
the market value (MV) of all in-kind transfers.

Figure E-1 shows the relationship between the estimated RV-CE based on
normal expenditures and the true RV-CE for one in-kind benefit. Assume that
a household initially is at utility level Ug tangent to the budget constraint
AD: (the money” income -budget constraint) prior to receiving the in-kind trans
fer. Receipt of the in-kind transfer shifts the budget constraint to aps. }
The normal expenditures approach indicates the level of expenditure on the
subsidized good for a household with total cash income (0G), equal to the sum
of money income (0A) and the MV of the in-kind benefit (AG) received by a
person with similar Characteristics. A person with total cash income of 0G
would face budget constraint GBS and would consume OL units of the subsidized
good to attain utility level Us. However, the portion of this budget con-
straint above B (GB) is not applicable to the recipient of the in-kind bene-
fit, since the subsidized good cannot be exchanged for other goods.

Assume that the true (but unknown) utility curve for the household is
Uc, which touches ABS at point B. If the household consumes OL units of the
subsidized good, the amount of cash income which maintains the same level of
utility is determined by the budget constraint which is parallel to GBS,
tangent to Uc, and passes through point P on the AB portion of the budget
frontier. This budget constraint (RC) implies that the RV-CE of the in-kind
transfer is equal to 0C.2 Since this amount is also equal to the level of
normal expenditures (OL=AP), the normal expenditures approach (in this case)
provides an accurate estimate of the RV-CE. This result will only obtain
when the utility curve implied by the normal expenditures approach (Uc) is
equal to the true but unknown utility curve.

this analysis applies to food and medical care benefits. The analysis is
slightly different for public housing benefits because the recipient must
pay some rent. The difference between the two classes of goods, however,
is mainly quantitative.

2Note that the cash equivalent value of the in-kind benefit (DC) is less
than the market value of the benefit (DS) because the household is
assumed to normally consume less of the good (OL) than provided by the
benefit (AB or DS).

137



Table D-6. Normal Expenditures Values for Housing Used in the Computation of

RV-CE by Age of Householder and Size of Household

(Numbers in dollars)

Householder 62
years old and

Householder under 62 years old

over
Total household
income
2 5
1 | Persons 1 2] 3 and 4| Persons
Person | or more | Person | Persons} Persons | or more
Under $1,250..... see 1,559 1,564 2,084 2,179 2,666 2,660
$1,250 to $2,499..... 1,375 1,664 1,769 2,067 2,083 2,083
$2,500 to $3,749..... | 1,578 1,744 1,941 | 2,045 2,136 2,136
$3,750 to $4,999...., 1,906 1,962 1,997 ! 2,233 | 2,281 2,461
$5,000 to $6,249..... 2,238 2,185 ; 2,092 ' 2,231 | 2,271 2,279
$6,250 to $7,499,. 2,204 2,286; 2,114 | 2,296 | 2,426; 2,327
t
$7,500 to $8,749.....} 2,328 2,356! 2,418 2,418 ; 2,473 2,417
$8,750 to $9,999..... 2,543 2,566; 2,511 2,549 | 2,532 2,482
$10,000 to $11,249... 2,427 | 3,115; 2,449 ; 2,673 2,623 2,587
$11,250 to $12,499... 2,802 | 2,858 i 2,493 | 2,645 - 2,663 2,713
$12,500 to $13,749 2,565 2,734 2,639 | 2,729 2,584 ; 2,806
$13,750 to $14,999 3,100 2,934; 2,676 ! 2,761 2,817, 3,111
$15,000 or more...... 3,166 2,934 j 2,790! 2,784 2,947

2,832

130



Chapter 2. Limitations of the Study

The development of procedures for valuing noncash benefits and the sub-
sequent measurement’ of the effect of ‘these values on the poverty population
required many assumptions. Associated with these assumptions are important
conceptual and empirical limitations which affect the final results. Two of
these limitations must be mentioned before valuation procedures and results
are examined in more detail. First, because the purpose of this study is to
examine the effect of noncash benefits on the poor, this study valued only a
select group of noncash benefits, mainly those received by the poor. A more
important limitation of this study concerns the appropriateness of using the
current poverty thresholds based on money income alone to measure poverty
when income has been redefined to include the value of noncash benefits.

COVERAGE OF NONCASH BENEFITS

This study focuses on the valuation of in-kind benefits received pri-
marily by persons in the lower portion of the income distribution, since its
primary purpose is to measure the effect of these benefits on the size and
composition of the poverty population. Valuation is restricted to government
in-kind transfers in the form of goods and services of a private good nature,
which are received without reciprocal quid pro quo provision of goods and
services by the recipient. Ideally, it would have been preferable to esti-
mate the value of all types of in-kind benefits from both public and private
sources to assess their effect on poverty and on the entire . income size
distribution. This would provide a more comprehensive measure of the distri-
bution of economic well-being. Valuation of many in-kind benefits was not
attempted, however, because some of these benefits are conceptually difficult
to define and, in many cases, sufficient information is not available for
valuation.

A short example should serve to illustrate several problems associated
with valuing some types of in-kind benefits. On a conceptual level, it is
not always easy to differentiate between private and public goods or between

lprivate consumption goods provide benefits directly to the recipient
and need not be shared by others. The income measure is limited to
private goods in order to avoid the unsettled issue of allocating nonex-
clusive public goods benefits to individuals. See Reynolds -and Smolensky
(1977) on the allocation of Public good benefits to households. In addi-
tion, the annual income accounting period which is chosen here leads us
to limit the range of goods and services to consumption goods (or con-
sumption opportunities) excluding investment goods and savings, which
necessitate looking at income from a tulti-period or intertemporal
perspective.



Table D-8. Normal Expenditure Values for Medical Care Used in the Calculation

of RV-CE by Age or Disability Status of the Householder and Size of

Household

(Numbers in dollars)

J

Householder age
' 65 years old and

Householder

under 65 years old and not

over or disabled disabled
Total household _ _ ee _ _
income
2 by
1 | Persons 1 2 3 4} Persons
Person | or more | Person | Persons | Persons | Persons | or more
Under $1,250......64. 341 637 99 209 307 380 410
$1,250 to $2,499. . 291 $47 146 219 373 402 430
$2,500 to $3,749. . 385 578 178 290 390 396 421
$3,750 to $4,999. . 443 608 209 311 263 364 393
$5,000 to $6,249. . 488 828 248 336 256 383 414
$6,250 to $7,499..... 646 770 306 520 443 460 497
$7,500 to $8,749..... 610 891 289 549 | 518 419 575
. 642 807 315 576; 572 450 601
> . 684 868 302 | 585 652 637 675
$11,250 to $12,499... 718 862 309: 588 655 662 72
$12,500 to $13,749... 738 1,060 299 | 606 662 583 712
$13,750 to $14,999... 695 1,070 290 ! 601 661 $82 715
$15,000 or more. 753 1,202 375 678 803 867 926

132 -



Table 5. Average Monthly MV of Housing Subsidies for Residents of Publicly
Owned or Subsidized Rental Housing in 1979, by Region, Location,
and Money Income Level

Northeast | North Central South West

Household money
income in 1979 Not Not Not Not
SMSA | SMSA | SMSA | SMSA | SMSA | SMSA | SMSA | SMSA
Overall average.... $72} $77] $921 $80 |$ 89] $ 81] $ 96] §$ 95
Less than $2,500... 91 96 113 113 112 105 104 104
$2,500 to $4,999... 86 37 99 101 101 94 | —106 104
$5,000 to $7,499... 76 76 85 85 89 89 92 92
$7,500 to $9,999... 65 64 58 58 61 61 72 72
$10,000 to $12,499. 30 30 58 58 47 47 72 72
$12,500 to $14,999. 30 30 58 $8 47 47 72 72
$15,000 or more.... 30 30 58 58 47 47 72 72

SMSA - standard metropolitan statistical area.

4g



Summary

This is the first of several reports to explore the issues, data
requirements, and technical feasibility of measuring and valuing in-kind
income. The purpose of this report is to examine several alternative inethod-
ologies for valuing public in-kind (noncash) transfers and to assess their
effect on the size and composition of the official poverty population. The
current definition of poverty used for statistical purposes, as indicated in
Directive No. 14 issued by the Office of Management and Budget, is based on
money income and does do not include the value of in-kind transfers as
income. Between 1965 and 1980, the market value of major in-kind transfers
in the form of food (food stamps and school lunches), housing (publicly owned
or subsidized rental housing), and medical care (Medicare and Medicaid) grew
from $2.2 billion to over $72.5 billion. In-kind transfers intended for the
low-income population currently exceed cash public assistance by more than
two to one. The Government's statistics on poverty have been criticized by
a number of experts because they fail to account for noncash benefits. This
research report, which is very exploratory in nature, was initiated following
concerns expressed by Congress and the Uffice of Management and Budget and
is in keeping with the Bureau's continuing research on problems and methods
of statistical measurement. A report of this nature can only present esti-
mates based on the official definition of poverty even though the perception
of poverty may vary considerably between individuals in our society.

This report focuses on the valuation of in-kind food, housing, and
medical care transfers received by the low-income population. Because of
this limited focus, the analysis concentrates on the effect of the value of
these benefits on estimates of the poverty population. No analysis is pro-
vided on the effect of in-kind benefits on the overall size distribution of
income because this study. did not take account of the full range of benefits
received by all groups in the population (chapter 2). For example, employer-
provided "fringe" benefits and the tax advantages of homeownership have not
been valued. The study, while accounting for only about 34 percent of total
food, housing, and medical in-kind benefits, accounts for 88 percent of the
benefits targeted to the low-income population. Other studies are planned to
examine methodologies for valuing selected in-kind benefits received by those
at higher income tevels.

Some of the limitations of measuring the effect of the value of in-kind
benefits on the official level of poverty are discussed in chapter 2.. Some
analysts have argued that changing the definition of income to include
in-kind benefits requires that the poverty thresholds be adjusted to reflect
both cash and the value of these benefits. When the official poverty
thresholds were derived, in-kind income was not included in the calculations.


Granneman, T. 1980. "Reforming National Health Programs for the Poor," in M.
Pauly, ed. National Health Insurance What Now, What Late, What
Never, Washington, American Enterprise Institute for Public Policy
Research, pp. 104-136.

1981. “Federalism and Medical Care for the Poor," presented to the
Association for Public Policy Analysis and Management, October.

Hawkins, S. and D. Rigby. 1979. "Effects of SSI on Medicaid Caseloads and
Expenditures," Social Security Bulletin, Vol. 42, February,
pp. 3-14

Health Care Finance Administration, Health Care Financing Review, Summer,

0.

‘Hicks, Jd. 1943, "The Four Consumer Surpluses," Review of Economic Studies,
vol. 7.

Hoagland, G W. 1980. “The Effectiveness of Current Transfer Programs in
Reducing Poverty", presented to the Middlebury College Conference
on Economic Issues. April 19.

Housing in the Seventies. 1974. for the Congressional Subcommittee on
Housing. October. .

Kilpatrick, R. 1973. "The Incomé Elasticity of the Poverty Line," Review of
Economics and Statistics, August.

Kraft, J. and E. Olsen. 1977. “The Distribution of Benefits From Public
Housing," in T. duster (ed.), The Distribution of Economic Well-
Being, NBER Studies in Research in Income and Wealth, No. 41.
ambridge, Massachusetts, Ballinger.

Lampman, R. and T. Smeeding. 1982. "Interfamily Transfers as an Alternative
to Government Transfers to Persons," Review of Income and Wealth,
forthcoming.

Leibowitz, A. 1981. "Fringe Benefits in Employee Compensation" presented to
the National Bureau of Economic Research Conference on The Measure-
ment of Labor Cost, Williamsburg, Virginia, December 3.

Lyon, D., et.al. 1976. Multiple Welfare Benefits in New York City, Rand
Corporation Study R-2002, August.

MacDonald, M. 1977. Food Stamps and Income Maintenance, New York, Academic
Press.

Malpezzi, S., L. Ozanne, and T. Thibodeau. 1980. "Characteristics Prices of
Housing in Fifty-Nine Metropolitan Areas," Washington, © Urban
Institute, R-1367-1.

. 158 -



The low overall benefit weight of 47.3 percent for medical benefits
including institutional care reflects low willingness to pay for medical care
among the elderly and the poor. Medical care expenses paid by institution-
alized persons were not collected in the 1972-73 CEX because the survey did
not cover the institutional population. As a result, when medical care bene-
fits are defined to exclude institutional care expenditures, the benefit

Table 12. Benefit Weights for Medical Care Transfers: 1979

Medical care transfer recipient groups

Household money income in Al | Medicare
1979 medical | Medicaid ! and |Medicare
. transfers only {Medicaid only
}
MEDICAL BENEFITS, INCLUDING i
INSTITUTIONAL CARE -_
Totalessccesscvsees 47.3 37.3 24.9 67.8
Under $1,250.. 23.8 23.7 16.2 34.8
$1,250 to $2,499. 30.4 31.0 21.2 44.3
$2,500 to $3,749. 32.9 30.4 21.4 55.4
$3,750 to $4,999, 36.6 32.0 22.3 57.1
$5,000 to $6,249, 41.4 35.1 20.9 63.1
$6,250 to $7,499. 45.7 37.3 22.9 62.4
$7,500 to $8,749. 45.8 39.5 i 23.6 60.4
$8,750 to $9,999. 50.4 40.7 28.7 62.5
$10,000 to $11,249, $1.2 41.9 28.2 65.8
$11,250 to $12,499. 57.2 46.8 | 30.4 70.5
$12,500 to $13,749.. 59.1 47.3 | 28.9 77.8
$13,750 to $14,999.... 64.6 52.4 : 32.8 79.9
$15,000 or more....seeeeee 63.7 49.1 \ 32.7 80.9
MEDICAL BENEFITS, EXCLUDING
INSTITUTIONAL CARE
|
TOtalecceccescceeee 57.5 i 44.1 | 42.1 68.9
i i

Note: The benefit weight expresses the recipient-cash equivalent
value (RV-CE) as a percent of market value (MV).



as owned by a public agency and/or subsidized by some branch of
Government. Indeed, the March 1980 CPS identified a small number
of each of these types of subsidized’ beneficiaries.

4. A more narrow set of CPS questions cannot be readily designed
because rarely do public or subsidized renters know the name of the
program which subsidizes their rent.© In some cases where the land-
lord or building owner is subsidized via reduced mortgage, building,
or maintenance costs (as in the Section 236 program), the public
agency does not often, if ever, come in contact with the tenants.
These tenants have Tittle way of knowing that they are public
housing beneficiaries.

Even if the vast majority of CPS recipients were benefiting from one of
the major HUD programs outlined above (as seems likely based on the location,
housing unit characteristics, incomes, and demographic composition of the CPS
respondents when compared with HUD data), it is not necessary to know the
specific HUD (or other public) rental housing program in.order to value bene-
fits. Chapters 4 and 5 demonstrate how the market value of a particular
rental housing subsidy can be determined directly from AHS and CPS data with-
out identifying the specific program.

MEDICAID

The Medicaid program is designed to furnish medical assistance for needy
families with dependent children and for aged, blind, or permanently and
totally disabled individuals whose incomes and resources are insufficient to
meet the costs of necessary medical services. The program is administered
by State agencies through grants from the Health Care Finance Administration
(HCFA) of the Department of Health and Human Services.

5Note that most, if not all, of the families answering the CPS questions
had incomes low enough to qualify for one of the HUD subsidy programs.
Thus, these types. of households (students, military, etc.) could be
legitimately benefiting from a HUD program, despite the appearance of
benefiting from the different types of “public subsidies" cited.

6In fact, these two quéstions are exactly the same as the questions
used on the Census Bureau's Annual Housing Survey (AHS) to identify such
recipients. The AHS is a nationwide sample of over 65,000 households,
weighted to represent National totals (as in the CPS). The 1979 AHS
provided almost the exact same estimate of public or subsidized housing
(2.589 million units).

TTaken from title XIX of the 1965 Amendments to P.L. 89-97, The
Social Security Act, “Grants to States for Medical Assistance Programs,"
declaration of policy.



(1977) 28

In 41.5 perc
weights equal to 100.0.

Table 11.
Housing: 1979

sgt of all cases, public housing benefits had benefit

Estimated Benefit Weights for Public

Household inoney income in
1979

Benefit weight

for public

housing

Total sscecesccceeveees
Under $1,250..
$1,250 to $2,49
$2,500 to $3,749..
$3,750 to $4,999..
$5,000 to $6,249.,
$6,250 to $7,499..
$7,500 to $8,749..

$10,000 to $11,249.
$11,250 to $12,499...
$12,500 to $13,749. tee
$13,750 to $14,999
$15,000 or more.

80.6
60.2
79.5
86.1
91.2
72.9
79.2
74.8
69.5
53.1
68.8
72.9
75.7.
77.9

the benefit weiyht expresses the recipient-
cash equivalent value (RV-CE) asa percent

of market value (MV).

28these studies used a different formula for estimating RV-CE than this

study.

Their procedure reduced the value of public housing benefits

below MV for families who would have consumed more housing than was pro-
vided by the public housing unit had they received the same amount of
subsidy or cash transfer.

29In 1.4 percent of the cases, RV-CE was negative.

This occurs when nor-

mal expenditures are less than the rent paid by public housing residents
(i-e., Cjj less than SRig)5 in these cases RV-CE was set equal to zero.

65


Appendix F. Detailed Poverty Status Table

141


RECIPIENT VALUES

This section describes the procedures and methods employed in this study
to estimate the recipient value for food, housing, and medical in-kind bene-
fits. There are two basic approaches to estimating the recipient value of
in-kind benefits. These are the utility function approach and the nomaal
expenditure approach. Although the utility function approach has been used
by most researchers,14 the normal expenditures approach was chosen for this
study. The normal expenditures approach was selected over the utility
approach on theoretical and pragmatic grounds,

There are several important problems associated with both the specifica-
tion and estimation of the utility functions required to estimate recipient
value (Manser, 1981, and Manser and Christiansen, 1976). A Cobb-Douglas
utility function which does not require product price information is simplis-
tic and yields unsatisfactory results.1 More sophisticated utility func-
tions like the constant elasticity of substitution (CES) require relative
price data across locations. They also require that the systen of demand
equations underlying the utility function be closed, so that expenditures
equal total income plus the change in net worth. The parameters of the
utility function, income and price elasticities, can either be "imposed" from
exogenous sources or derived directly from available consumption data.
Although utility functions can be estimated for different population sub-
groups, it is assumed that all persons within each yroup possess the same
(unknown) utility funtion with the same (unknown) parameters. Other items
which can affect the utility surface, such as health status, are most often
excluded from the analysis.

Another important consideration is the use of utility functions for
valuing medical insurance. Using the standard CES function, the relative
price for medical care will be the market price in a yiven location, not tie
market price of zero for Medicaid or the below market price for Medicare;
these are the prices which actually guide the recipient's consumption choices
and marginal rate of substitution between medical care and other goods.
Theoretically the market price for this group can be determined from data on
the market prices paid by persons with similar characteristics who did not
participate in these programs. It is difficult, however, to obtain reliable
survey data to make these comparisons.

l4see Murray (1975, 1980), Kraft and Olson (1977), Smotensky, et. al.
(1977), and Cooper and Katz (1978).

15The Cobb-Douglas assumes that the share of expenditure on a yiven good
is fixed and independent of price changes and income level. Thus,
income elasticity, own price elasticity, and cross price elasticity
are assumed to be 1, -1, and 0, respectively.

58



‘developed by Orshansky as an absolute measure of poverty.> Poverty levels
based on an absolute measure are not adjusted for changes in the level of
real income, in society. Persons who favor an absolute measure argue that
use of a frequently updated relative measure would mask significant reduc-
tions in: poverty that occur when the level of real income within a society
is increasing or increases in poverty when the level of real income is
declining. (For an example of an absolute approach to defining poverty
levels, see Friedman, 1965.) The choice between an absolute or relative
concept of poverty is based on a value judgment of how poverty should be
measured. .

Using before-tax income to measure poverty. Formula (1) indicates an incon-
sistency between the definition of income used to measure poverty status and
the definition of income that is implicit in the poverty thresholds. Poverty
estimates are derived by comparing CPS money income before taxes (Vij in
formula (1)) with the specified poverty level. The concept on which’ the
poverty multiplier is based, however, is after (income and payroll) tax
income (Yaz in formula (1)) from the 1955 USDA survey. If the poverty con-
cept had been based on the ratio of food expenditures to before-tax income,
the thresholds would have been higher. Basing the levels on before-tax
income assumes that families experience the same increase in well-being from
an.equal amount of taxes paid or income received. Some analysts (Smeeding,
1977) would prefer to compare the after-tax income of the poor to-the’ current
poverty thresholds, thus keeping the income measure consistent with the
original derivation. Although tne March CPS does not collect the tax infor-
mation required to make this adjustment, previous estimates based on micro-
Simulation (e.g., Smeeding, 1975; Hoagland, 1980) indicate that these
adjustments would increase the poverty rate by less than 10 percent.

Adjusting for changes in prices. Use of the CPI to adjust the poverty
thresholds for changes in prices is problematic for persons receiving in-kind
benefits. The ability of the CPI to reflect changes in cost of living for
the average consumer has been criticized repeatedly. The CPI in recent times
has probably overstated the true change in the cost of living for most people
because of the way the price of new homes and mortgage interest rates affect
the housing component of the CPI. Use of a price index which uses a more
relevant measure of changes in housing costs would reduce the recent rates of

50rshansky refers to the poverty measure as "relatively absolute" because
it contains elements from both concepts; relative because it is based on
the average proportion of income spent on food for all families and
absolute because the poverty levels are based on expenditures for food
required to meet minimum nutritional standards. The poverty levels are
updated annually by changes in the CPI but not by any other means, such
as changes in real income or in expenditure patterns.

13



If the recipient normally spends less than the MV of the in-kind benefit
on the subsidized good, the in-kind transfer will cause a change in the
expenditure pattern. This means that the in-kind benefit is worth less to
the individual than an equal amount of cash which would not lead to a change
in spending habits. If the MV exceeds the normal expenditure level, RV-CE
can be approximated by the level of normal expenditures. If normal expendi-
tures exceed MV, the RV-CE equals MV. That is, because the in-kind transfer
recipient would normally spend at least as much as the MV on. the good, it is
not restrictive and therefore the same as a cash transfer equal to MV.

This RV-CE measure captures both the direct “substitution effect"
between the good which is received in-kind and other goods and the “income
effect" of in-kind transfers because normal expenditures are calculated at a
resource level equivalent to money income plus the MV of all types of in-kind
transfers. This procedure produces the maximum income effect from in-kind
benefits and probably overstates the true income effect® because other in-
kind transfers are counted at MV and not RV-CE.

Limiting the value of an in-kind transfer to normal expenditure levels
which are less than MV implies that the value of amounts of the in-kind
transfer above the normal expenditure Jevel is zero. Estimates of RV-CE
based on this technique may in this way understate the true value of the
transfer depending on the shape of the recipient's unknown utility function.
Thus, there are two potentially offsetting effects: the overstated income
effect and the possible bias from assigning zero value to in-kind benefits
in excess of normal expenditures. Based on these comments and further dis-
cussion of the empirical issues in chapter 5, it is evident that, althouyh
the RV-CE has many desirable qualities, actual calculations are not easily
made. The normal expenditures method only provides an approximation of the
true RV-CE.

Most economists would agree that if very good data and estimating
methodologies were available, the RV-CE approach to valuing in-kind benefits
is theoretically preferred. It is based on observing the behavior of indi-
viduals and seeing how they value various amounts of the goods in question.
However, actual data and statistical estimating methods are imperfect and

San accurate estimate of RV-CE would require a simultaneous determination
of the impact of all types of in-kind transfers on well-being. The true
income effect of an in-kind transfer is its cash equivalent value, but,
unless one can simultaneously estimate RV-CE for all types of in-kind
benefits, one cannot determine all RV-CE's at one time. While Smolensky,
et. al. (1977) have approximated this simultaneous determination for dif-
ferent specific combinations of in-kind transfers, only Murray (1980) has
simultaneously estimated. RV-CE. for a limited set. of food and housing
transfers in specific cities. No national estimates currently exist of
simultaneously determined RV-CE's.

Tsee appendix e! and the addendum to this chapter for a more detailed and
technical discussion of this tradeoff.

EE



residence. It was determined from the Department of Agriculture's 1955 sur-
vey of food consumption that families of three or more persons spend approxi-
mately one-third of their income on food; the poverty level for these fami-
lies was, therefore, set at three times the cost of the economy food plan.
For smaller families and persons living alone, the cost of the economy food
plan was multiplied by factors that were slightly higher in order to compen-
sate for relatively larger fixed expenses of these smaller households.
Households are classified according to the poverty status of the fanily or
the nonfamily householder. The poverty thresholds are updated every year to
reflect changes in the annual average Consumer Price Index (CPI). The aver-
age poverty threshold for a nonfarm family of four was $7,412 in 1979. For
further details, see Current Population Reports, Series P-60, No. 130,

Household. A household consists of all persons who occupy a housing unit. A
jouse, an apartment or other group of rooms, or a single room is regarded as
a housing unit when it is occupied or intended for occupancy as separate
living quarters; that is, when the occupants do not live and eat with any
other persons in the structure and there is either (1) direct access from the
outside or through a common hall or (2) a kitchen or cooking equipment for
the exclusive use of the occupants.

A household includes the related family members and all the unrelated
persons, if any, such as lodgers, foster children, wards, or employees who
share the housing unit as partners are also counted as a household. The
count of households excludes group quarters.

152



participate in the program or just those who are enrolled or covered by the
program, © This decision is not important for the Medicare proyran because
virtually all eligible persons are enrolled. For Medicaid, the decision is
not so easily made. If ‘the population at risk is defined as all eligibles,
estimates must be made of persons who meet the income and asset eligibility
requirement, which differ by State.’ These estimates of Medicaid eligibility
are difficult to make from the CPS because sufficient data were not col-
lected. They would also result in the assignment of eligibility to a large
segment of the population who are not categorically eligible but who reside
in the 35 States which provide spend-down coverage to the “medically needy."
In other words, at] elderly, disabled, one-parent families with children, and
even two-parent families in States with AFDC-UF programs would have some
small probability of being eligible for Medicaid.8 The eligibility approach
could theoretically be extended even further because every person is poten-
tially eligible for Medicaid since currently ineligible persons could become
eligible at some time in the future due to changes in their financial situa-
tion, State of residence, marital status, or disability status.

There are also several considerations concerning the use of the enrolled
or covered approach to describe the population at risk. This approach seems
desirable because persons covered by Medicaid nave the tangible benefit of
knowing that their medical care needs will be met by this insurance coverage.
Also, these persons are accurately identified on the CPS. One problem with
this approach, however, is that persons who are cateyorically eligible but
not enrolled are not accounted for even though they can rely on Medicaid to
pay their medical expenses if the need occurs. Some of these persons may
have received Medicaid assistance without knowing it: The problem with this
approach can be illustrated by an example. An eligible but unenrolled indi-
vidual becomes i11 and is hospitalized. The hospital can verify the

SThe reader should be aware of the differences between recipients, enrol-

lees, and eligibles in medical and other transfer programs, The smallest
group is the recipients, those who actually receive medical care. The
enrollees are those covered by the transfer. This is the group which the
CPS attempts to measure. Finally, the third group is all eligibles,
whether or not they are covered or receive benefits.

TMedicaid may be of little value to some of the eligibles assigned in this
manner, These would be persons who would not enroll even if informed of
their eligibility because the expense of registration outweighs their
perception of benefits.

8Fol lowing this approach, the MATH-CBO model estimated that 63.6 million
Persons had some probability of being eligible for Medicaid in 1980, of
which 32.9 million would qualify because they were “medically needy"
(Doyle, et. al., 1980: 220). Others (e.g., Smeeding and Moon, 1980)
have used the eligibles approach but have assigned coverage only to
those who qualify as categorically needy or medically needy without the
spend-down provision. On this basis, they estimated Medicaid eliyibles
to be 32.3 million persons in 1974,

50



by the average number of months food stamps were received.! Based on this
technique, the March 1980 CPS recipient count of 17.55 million recipients is
77.0 percent of the administrative data-based estimate of 22.80 million bene-
ficiaries. The implicit turnover rate between the monthly and yearly number
of persons benefiting is 1,32 (22.80 divided by 17.25 or the average number
of monthly recipients) .2 Separating the CPS and USDA estimates of the number
of food stamp recipients into those who receive cash public assistance and
those who do not indicates that CPS households receiving both cash public
assistance and food stamps were 95 percent of the USDA-based estimate of such
beneficiaries. The CPS estimate of food stamp households not receiving cash
public assistance was only 59 percent of the estimate based on USDA figures.

Based on this analysis,.the survey estimate accounted for 3 of every 4
food stamp recipients. Such a discrepancy might lead one to wonder about
biases in the characteristics of the survey data, i.e., those reporting
benefits may differ from those who did not. Comparisons of the characteris-
tics of food stamp recipients in the March 1980 CPS and those found in a
USDA. Survey of Food Stamp Recipients for October 1979 are generally in agree-
ment. (See appendix C, table C-1.)

The Food Stamp Program is the only in-kind transfer program for which
benefit amounts, in the form of the face value of stamps received, were col-
lected. After adjusting USDA-data for the 11.9 percent of benefits received
by persons in Puerto Rico, Guam, and the Virgin Islands, the $4.731 billion
in the face value of food stamp benefits from the CPS is 75.6 percent of the
USDA adjusted total of $6.260 billion.> In summary, the March 1980 CPS

captures 77.0 percent of recipients and 75.6 percent of the total face value
of benefits for 1979.

SCHOOL LUNCHES

The National School Lunch Program is designed "to help safeguard the
health and well-being of the Nation's children by assisting the States in

lhe reader should note that this procedure is highly dependent on the
CPS reported “average number of months received." If underreporting of
recipiency is greater for households benefits for short periods of time,
as some speculate, the independent estimate may be too low.

2In their microsimulation model, Mathematica (Doyle, et.al., 1980) used a
turnover rate of 1.43 for Food Stamp Program beneficiaries in fiscal year
1980. While these turnover rates are not strictly comparable because of
the time period difference, this larger turnover rate, coupled with the
average monthly number of recipients of 17.25 million for 1979, would

imply a-control total estimate of 24.67 million persons who. received food...

stamps at some time in 1979. If this were the alternate estimate, about
71 percent of total recipients would have reported benefits on the CPS.

3These USDA totals are not adjusted for food stamp fraud, and so the CPS
amounts are at-least 75.6 percent of the correct comparable total.



If the utility curves are shaped differently than Uc, the normal expend-
itures approach will result in a biased estimate of RV-CE. If the utility
curve is shaped like Uc', the normal expenditures approach understates the
true RV-CE which is equal to DC'. However, if the utility curve was shaped
like U,", the normal expenditures approach overstates the true RV-CE which is
equal to DC". Because the true utility curve is unknown, the accuracy of
the normal expenditures approach in estimating the true RV-CE cannot be
determined.

The second factor which may bias the estimation of RV-CE is the calcula-
tion of normal expenditures at an income level which includes the MV of all
in-kind benefits. This factor may be quantitatively more important than the
implied shape of the utility surface, since many poor families receive more
than one in-kind transfer. Of all poor households receiving in-kind transfer
benefits in 1979, 62 percent received more than one type; in fact, 31 percent
received three or more benefits (U.S. Bureau of the Census, 1980). For these
households, there is particular concern about the calculation of normal
expenditures at a "total" income level which includes the MV of all in-kind
transfers. Normal expenditures should be calculated at an income level which
includes the RV-CE of all in-kind transfers. However, because true RV-CE of
all in-kind benefits can only be determined in a simultaneous fashion, it is
not. possible. to. utilize such a-measure. If .RV-CE is less than. the MV of the
transfer, the normal expenditures approach will produce an overestimate of
the income effect from the in-kind benefit, resulting in an overestimate of
the true RV-CE. Moreover, this bias will likely be larger for households
receiving more than one in-kind transfer, since multiple transfers with RV-CE
below the MV will lead to multiple overstatements of the income effect.3

The importance of these potential biases for different groups of goods
is not known. The MV equals RV-CE for most households receiving food trans-
fers, indicating that the normal expenditure levels for food are in excess of
the MV of food transfers (i.e., the normal expenditure levels for food are
somewhere along BS in figure E-1). Thus, if food transfers are one of
several benefits received, inclusion of the MV of food in total income should
not seriously bias the estimate of RV-CE for other in-kind benefits. If the
RV-CE of the in-kind benefit is substantially below the MV, as in the case of
medical care transfers, inclusion of the MV of these benefits in total income

3The final outcome also depends on the shape of the utility curves as
well. For instance, if the shape of the “true" utility curve is similar
to Uc' in figure E-1, the normal expenditure estimate leads to an under-
estimate of the true RV-CE. On the other hand, if this family is a
multiple benefit recipient, calculation of normal expenditures using the
full MV of in-kind benefits leads to an overstatement. The net effect of
these biases cannot: be determined. ©

4Even if normal expenditures are based on money income only, the RV-CE
for food transfers is still very close to the MV. In contrast, normal
expenditures on medical care are very sensitive to the income level at
which they are calculated.

139



Table 18.

Poverty Budget Share Approach:

(Numbers in thousands. Persons as of March 1980)

Number of Persons Below the Poverty Level and
Poverty Rates, by Income Concept for Selected Population Subyroups:

iy7g

|

Income concept

i

Money income

Money income plus
food, housing,

Money income plus
food, housing,

Selected Money income. plus food and and medical care and medical care
characteristics | alone! housing (excluding (including
H institutional institutional
| care) care)
: Poverty : Poverty Poverty Poverty
: Number rat Number rate Number rate Number rate
_ All persons. ‘23,623 11.1. 20,743 9.8 18,866 8.9 18,866 8.9
South.....e + 10,108 14.6 8,841 12.8 8,180 11.8 8,180 11.8
Non-South. : 13,515 9.5° 11,902 8.3 10,686 7.5 10,686 7.5
Elderly (65 ‘ :
and over)..... 4,097 14.7 3,838 13.7 3,019 10.8 3,019 10.8
Non-elderly.... 19,526 10.6. 16,905 9.2 15,847 8.6 16,847 8.6
Central city. 8,748 15.0 7,470 12.8 6,739 11.6 6,739 11.6
Suburb. ..eeeees $,661 6.7 5,079 6.0 4,696 5.5 4,696 5.5
Nonmetropolitan '
APA. ee eeeenee 9,214 13.4 8,194 11.9 7,432 10.8 7,432 | 10.8
Blackeessseeece 7,456 30.4 6,255 25.5 5,677 23.1 5,677 23.1
Non-Black...... 16,167 8.6 14,488 7.7 13,189 H 7.0 13,189 7.0
In households !
with female | i i
householder, : H i |
no husband H i
present....... 9,142 34.8 7,655 29.1 6,969 26.5 6,969 26.5
All other : i
PersonS..se.e. | 14,481 7.8 13,088 7.0 11,897 6.4 | 11,897 \ 6.4
i |

Ipiffers from official poverty count for 1979 of 25,345 (11.6 percent) because

status was determined on a household basis.

85

poverty


The RV-CE measures for medical care and-housing transfers may be signi-
ficantly lower than MV. To some extent, medical transfers enable elderly and
poor recipients to pay for medical care which they either went without or
received for free before these proyrams were created. This low willingness
to pay for medical care manifests itself in the relatively low RV-CE. The
lowest estimates for public housing are based-on studies which consider only
the Low Rent Public-Housing Program. . Estimates of RV-CE for other housing
programs are higher (e.g., Section 8 housing). The wide differences between
RV-CE's for medical and housing transfers indicate the need for more precise
and definitive estimates.

The PBS measures are much lower than the MV or RV~CE values for housing
and medical care but are nearly the same as market value for food benefits.
This is caused by the procedures used to estimate PBS which limit expendi-
tures to the average amounts spent by the poverty population. The PBS esti-
mates for food are nearly the same as market value because the amount of
food stamps received by beneficiaries are based on food budgets similar to
those used to develop the official poverty level.

Table 4. Alternative Relative Values for In-kind Transfers

Relative measure of value

Type of in-kind transfer 7
Recipient
Government | Market or cash | Poverty

cost } value | equivatent | budget
, value2/| shares

Foods... cece eeeeeeeeee 108 100 83-97 94-98
Medical Care....seseeeee 100-110 100 58-74 28-38
HOUSING. eeesecvececeees 117-140 100 48-82 38-42

lincludes only food stamps. Estimates on the recipient value
of school lunch benefits are not available.

2tncludes both normal expenditure and utility based estimates
of RV-CE as explained above.

37



Table 15. Overall Effect of In-Kind Benefit Valuation Techniques on the Number and
Percent of Poor: 1979

(Numbers in thousands. Persons as of March 1980)

Recipient value - Poverty

Market value cash equivalent budget shares
Income concept Below Below Below Below Below Below
100 125 100 125 100 125
percent | percent | percent | percent | percent | percent
of of of of of of
poverty | poverty | poverty | poverty | poverty | poverty
level level level level level level

Money income alone:!
Number Of poor.sssccsceeeee 23,623 33,445 23,623 33,445 23,623 33,445

Poverty rate.sccsceceeccces 11,1 15.8 11.1 15.8 111 15.8
Money income plus food and

housing: |

Number Of pOOr....seseeeeee | 19,933 31,178 20,218 31,367 20,743 31,744

sees 9.4 14,7 9.5 14.8 9.8 15.0

Poverty Pater ssseseee . .
. -15.6 | - 6.8 -14.4 - 6.2 -12.2 - 5.1

Percent reduction“.....

Money income plus food,
housing and medical care
(excluding institutional
care expenditures ,
Number of poor. . 14,023 24,689 18,393 28,877 18,866 30,381
Poverty rate.. eee 6.6 11.6 8.7 13.6 8.9 14.3
Percent reduction®....sseee -40.6 -26.2 -22.1 -13.7 -20.1 - 9.2

Money income plus food,
housing and medical care
(including institutional
care expenditures):
Number of poor. 13,634 23,674 17,318 28,284 18,866 30,381
Poverty rate.. 6.4 11.2 8.2 13.3 8.9 14.3
Percent reduction -42.3 -29.2 -26.7 -15.4 -20.1 - 9.2

ae

lpiffers fromthe official poverty count for 1979 of 25,345 (11.6 percent) because.
poverty status was computed on a household basis; the official estimate of persons
below 125 percent of the poverty level is 35,592 (16.3 percent).

2Percent reduction in the number of poor from the current poverty estimate based on
money income alone.

80



Table 9 summarizes the results of the assignment of market values to the
March 1980 CPS microdata file. The results of these assignments are compared
against administratively derived estimates. The CPS estimates do not equal
administrative totals due to underreporting on the survey. Assignments of
values to the CPS data file accounted for about 81 percent of the in-kind
food benefits and about 89 percent of the value of in-kind medical benefits;
no administrative estimate could be derived for housing benefits. The ratio
of assigned benefits to the administrative estimate was lowest for food
stamps (76 percent) and highest for Medicare (95 percent). The relative size
of the medical in-kind transfers should be noted. Including institutional
benefits, medical transfers are $39.8 billion, about 80 percent of the total
$49.6 billion in in-kind benefits assigned to CPS beneficiaries.

Table 9. Comparisons of the Market Values of In-Kind Benefits Assigned to the
March 1980 CPS with Administrative Estimates

(Billions of dollars)

CPS as a percent
Type of program March 1980 |Administrative |of administrative
CPS estimate estimate
FOOD
Total.. $ 7.349 $ 9.060 81.1
Food stamps..... 4.731 6.260 75.6
SCHOO] TUNCH. .ceceeesereceeeees 2.618 2.800 93.5
HOUSING
Housing total....cccscccesesees 2.462 (NA) (X)
MEDICAL CARE
Excluding Institutional Care
Expenses
Totalecscseccececens 32.140 (NA) (X)
Medicaid....eeee 9.760 (NA) (X)
Medicar@scccscsecccccesessnee 22. 380 (NA) (X)
Including Institutional Care
Expenses
Totalecssscccscsenes 39.790 43.743 91.0
Medicaid... 16.883 19.632 86.0
Medicare. ceccscreccccecereees 22,907 24.141 95.0

NA Not available. X Not applicable.

57



approach lowers the number of poor by about 20 percent. Thus, these two
methods yield poverty estimates which differ by 5.2 million persons. Taking
into account all combinations of income definitions and valuation techniques,
the number of poor range from a low of 13.6 million using the market value
approach (including medical care with institutional expenditures) to a high
of 20.7 million using the PBS approach and excluding medical care benefits.

CHANGES IN POVERTY FOR POPULATION SUBGROUPS

Previous studies have indicated that the valuation of in-kind benefits
can affect various subgroups of the poverty population in different ways.
Tables 16 through 13 show changes in poverty numbers and rates by valuation
approach and income definition for selected characteristics of the popula-
tion. Tables 19 through 21 present similar information for persons below
125 percent of the poverty level. .

Results of the market value approach are summarized in table 16. The
inclusion of the value of all in-kind transfers lowers the poverty in the
South by 37 percent, compared with 46 percent in the non-South. This result
is probably due to greater accessibility of benefits outside the South
(Smeeding, 1981). The importance of the value of medical care benefits is
indicated by their effect on poverty among the elderly. Valuation of all
in-kind benefits lowers poverty for the elderly by 69 percent compared to a
37-percent reduction for the nonelderly. The inclusion of institutional care
expenditures has almost no additional impact on poverty of the nonelderly but
lowers the poverty rate for the elderly from 5.2 percent to 4.5 percent.
These changes in poverty among the elderly are supported by the earlier work
by Smeeding and Moon (1980) and Hoagland (1980). Other data in the table
indicate that poverty is lowered proportionally more for persons in central
cities, for Blacks, and for persons in households maintained by women with no
husband present than for their counterparts. While the poverty rate for res-
idents of central cities (15 percent) was somewhat higher than the rate for
nonmetropolitan residence (13 percent) based on official poverty levels, this
relationship is reversed when in-kind benefits are valued at MV (see also
Seninger and Smeeding, 1981). Use of an income definition restricted to food
and housing does not produce such major differences in the reduction of pov-
erty between subgroups. - For instance, the MV of food and housing reduces the
number of elderly poor by about 12 percent, compared with 16 percent for the
non-elderly. The reduction resulting from food and housiny transfers was
about 14 percent for the South and 16 percent for the non-South. Blacks,
persons in central cities, and persons in families maintained by women still
benefit relatively more from these programs but to a much lesser extent than
when medical benefits are counted.

One important fact which emerges from table 16 is that the inclusion of
the value of in-kind benefits as income does not. lift the income of all fami-
lies above the poverty level even when all benefits are included and assigned
market value. Poverty rates for subgroups such as Blacks and persons in fam-
ilies maintained by women are still substantial (15 percent and 18 percent,
respectively).

81



Appendix D. Details of Procedures Used to Estimate
Values of In-Kind Benefits

A more detailed discussion of the procedures used to estimate the value
of in-kind benefits is contained in this appendix.

MARKET VALUES

Food transfers. The procedures used to assign the market value of food bene-
fits as described in chapter 5 do not require further clarification.

Housing transfers. For public or subsidized housing, the first step in the
estimation of MV was the calculation of the private market rental value (MR )
of public or subsidized housing units. The method used for estimating MR
begins with a hedonic regression where the dependent variable, MR, is ex-
plained by the quantity and quality of the housing unit. The characteristics
of the occupants were not used as explanatory variables. The reyression
equation was hypothesized to have the following functional form:

Ln(MR) = ag + ay SC + ap NC +azL + agC te (1)

where Ln refers to the natural logarithn, ag is the constant term, SC are the
Structural characteristics of the housing unit, NC are neighborhood charac-
teristics, L are geographic location variables, € are contract condition
variables, and € is the error term. In this Tog-linear form, the coeffi-
cients (a1, a2, a3, and aq) can be interpreted as the percentage change in MR
given a unit change in the corresponding independent variable.

The coefficients for this equation were estimated for a‘subset of non-
Subsidized rental units using data reported on the Annual Housing Survey
(AHS) for. 1979. This equation was then used to estimate the MR of public or
subsidized housing on the AHS. The housing subsidy (S), which is the MV, was
derived by subtracting the reported rent of the housing unit (SR) from the
predicted MR. This procedure has some advantage over use of administrative
data available from HUD on the actual market rent and subsidized rent for a
sample of Section 8 housing units. There is some evidence that landlords
inflate the market rent above competitive levels. The housing authority
bears the extra cost since tenants pay a fixed percentage of their income as
rent. oe

Rydell, et. al. (1980) find that Section 8 rental housing units axperi-
enced a 26 percent increase in rents after the introduction of “fair
market" rent regulation. Weinberg {1981) argues that rent is only about
4 percent larger than MR for a small set of Section 8 rental housing
units, but his. findings are disputed by Rydell, et. al.

116



Table D-1.

Tre Distribution of Estimated Subsidy
Other Subsidized Rental Housing in 1979

Values

for Public and

(Unweighted sample cases)
With Without
Heat and | Heat and
Monthly Subsidy Values Total Utilities | Utilities
and Regression Results Included Included
in Rent in Rent
DISTRIBUTION OF SUBSIDY VALUES
Total cccccccscccccvcvscscccecee 1,464 939 525
-$201. or.more... 1 0 1
~$101 to -$200........c econo 32 19 ~ 13
-$51 to -$100. 58 26 32
-$1 to -$50... 168 102 66
$0 to $50... 266 166 100
$51 to $100. 341 221 120
$101 to $200 533 371 162
$201 to $300 o. . 59 34 25
$301 OF MOTE. ccc eccecacacececccneree 6 0 6
REGRESSION RESULTS
Reve ecaneeeees wee : (x) 43 47
Number of observations.. . (Xx) 2,731 5,455
Independent variables... . (X) 59 59
Insignificant variables....eceeseeeeee (X) 6 8

X Not ap

plicable.

119



All of the CPS figures in this table are weighted estimates adjusted to
updated independent population estimates based on the 1970 Census of
Population. The alternative estimates rely on 1979 administrative figures.
Adjusting the CPS estimates in table 3 to results from the 1980 Census of
Population would show the CPS estimates for each of these programs to be 1 to
3 percentage points closer to their administrative benchmark than
indicated.

Table 3. Comparison of March 1980 CPS and Al ternative Estimates of In-kind
Benefit Recipients: 1979

(Numbers in millions)

CPS as percent
Program type March 1980 | Alternative jof alternative
CPS estimate estimate

FOOD

Food stamps...
School lunch:

so eeeeecees 17.549 22.801 77.0

Free and reduced-price....... 10,229 ; 11.607 88.1
FUT price. ..ccccsecveecevees 22.281 | (NA) (NA)
HOUS ING

Public or other subsidized i
NOUSING. . ce eesceccecsceveccvcs 2.511 | (NA) (NA)

MEDICAL CARE

Medicare. csecsccccccccceceesece 25.712 26.257 98.0
MEdiCA1d.. cee ccececececceceeees 18.136 20.794 87.2

NA Not available

lgecause the large majority of the computer tabulations and data adjust-
ments which were prepared for this report were completed prior to the
availability of 1980 census population figures, it was not possible to
use the 1980 census population controls in this report.



Appendix H. Definitions and Explanations

Population coverage. This report includes the civilian noninstitutional pop-
ulation of the United States (the 50 States and the District of Columbia) and
members of the Armed Forces living off post or with their families on post,
but excludes all other members of the Armed Forces.

Money income. Income distributions and income summary. measures (such as
medians and means) shown in this report’ are limited to money income before
payment of Federal, State, local, or Social Security (FICA) taxes and before
any other types of deductions, such as union dues and Medicare premiums.
Total money income is the sum of the amounts received from wages and sala~-
ries, self-employment income (including losses), Social Security, Supplemen-
tal Security income, public assistance, interest, dividends, rent, royalties,
estates or trusts, veterans’ payments, unemployment and workers‘ compensa~
tion,. private and. government retirement and disability pensions, alimony,
child-support; and any other source of money income which was regularly
received. Capital gains (or losses) and lump sum or one-time payments such
as life insurance settlements are excluded.

Underreporting. As in most household surveys, the estimates of the number of
money income recipients and the total amount of money income derived from tne
March CPS are somewhat less than comparable estimates derived from independ-
ent sources, such as the Bureau of Economic Analysis, Social Security’
Administration, and Veterans Administration. The difference between the sur-
vey estimate and the independent estimate is generally termed “underre-
porting." Underreporting tends to be more pronounced for income sources such
as public assistance and welfare, unemployment compensation, and property
income (interest, dividends, and net rental income). Estimates of income
from wages and salaries tend to have Jess underreporting than most incone
types. For 1978 (the latest year for which estimates of underreporting are
available), underreporting of wage and salary income was only about 3 per-
cent, compared with 41 percent for unemployment compensation and 24 percent
for public assistance. For total money income, underreporting was about 10
percent. For further details concerning the reporting of money income, see
Current Population Reports, Series P-60, No. 123.

Poverty (low-income) classification. In this report, households are classi-
fied as being above or below the poverty level using the poverty index devel-
oped at the Social Security Administration in 1964 and revised by a Federal
Interagency Committee in 1969. It should be noted that this index is based
solely on money income and does not reflect the fact that many persons are
receiving noncash benefits such as those described in this report. The ind
is based on the Department of Agriculture's 1961 economy food glaa a
reflects the different consumption requirements of families based on their
size and composition, sex and age of the family householder, and farm-nonfara

151



Table B. The Elderly: Comparison of the Number of Poor and Poverty Rates
Using Alternative Income Concepts and Valuation Techniques: 1979

(Numbers in thousands)

Valuation technique

Income concept Market | Recipient or cash | Poverty budget
value | equivalent value share value
approach approach approach
Money income alone:
Number of poor.. se eeceae 4,097 4,097 4,097
Poverty rate.ccscecesesceee 14,7 14.7 14.7
Money income plus food and
housing:
Number of poor..sesceeseees 3,601 3,649 3,838
Poverty rate.... . 12.9 13.1 13.7
Percent reductionl......... -12.1 -10.9 - 6.3
Money income plus food,
housing, and medical care
(excluding institutional
care expenditures):
Number of ‘poor..: 7 1,452 2,601 ~ 3,019
Poverty rate...... . 5.2 . 9.3 10.8
Percent reduction!......... -64.6 -36.5 -26.3
Money income plus food,
housing, and medical care .
(including institutional
care expenditures):
Number of poor....csccoeees 1,251 25242 3,019
Poverty rate....+s . 4.5 8.0 10.8
Percent reductiont......... -69.5 -45.3 -26.3

\percent reduction in the number of
mate based on money’ income alone.

poor from the current poverty esti-



medical care. (See table D-9.) These values were assigned to units on the
March CPS file in a manner similar to that used for food and housing benefits.

Poverty Budget Shares

The calculation of the poverty budget shares (PBS) requires the estima-
tion of the amount of expenditures for food, housing, and inedical care for
persons at or near the poverty level. These estimates were based on expendi-
ture and pre-tax income data from the 1960-61 CEX. The PBS ratios developed
in this study are shown in tables D-10 and D-11. The poverty thresholds for
each group were multiplied by these ratios to approximate the dollar values
of the poverty budget shares for food, housing, and medical care. The data
on medical care expenditures from the 1972-73 CEX shown in table D-11 were
not used in the determination of PBS values but were included for illustra-
tive purposes.

- wee 134



Table F-1. Poverty Status in 1979 of Households Using Money Income and Alternative Methods of Valuing Noncash
Benefits, by.Selected Characteristics

Heese Ae OU TTARCH 1980. HOUSEHOLOS ARE CLASSIFIED ACCORDING To THE POVERTY STATUS OF THE FAMILY OR THE NONFAMILY HOUSEHOLDER. FOR MEANING OF
‘SYMBOLS, SEE TEXT)

BELOW 100 PERCENT OF THE POVERTY LEVEL SELOW 125 PERCENT OF THE POVERTY LEVEL
RECIPIENT Recipient
curren ano7on'casn | poverty | current ano7oR CASH | Poverry
WOnEy INCOME] Wanner vatue| EQUIVALENT” leuoGet snaReS | MONEY INCONE| HanKeT vaLuc| EOuIVALENT” | auDSes ehanes
SELECTED CHARACTERISTICS au] "concer | “concert” | ‘concer’ |"'"conceerTM | ““eanceer’ | “Concer re“e)/ SAULNaeENT /9U0GET sna
Houses
oL0s| ween] pov-| wumaeR] rov-| wun@en] pov-| wmaer] Pov-| wuneen] pov-| womaen] eov-| wmaen] pov-] nunaen] pave
erty Envy Envy enty ERty erty Erry erty
rmous) |ctnousy| RATE |(THous)| RATE crHoUS)| RATE) (THOUS)| RATE [cTHOUS) | RATE [cTHOUS) | RATE |cTHoUS) | RATELeTHOUS) | RATE
VALUING £000 AND NOUSING ONLY
TOTAL sv sss ses | 79 108) 9 S49) s244! 8 206] 10,5] 8 411] 10,6] © 703] 11.0! 13 r18| 17.3) 12 077] 16.3] 12 048 xesal 13 232 | 46.7
TYPE OF RESIDENCE
Insipe wernopoLiran ancas. . .| 56 051] 5 5 5 200] 9.8] p°ve1| 15.7] 7 078] 19.61 7 921] 14.7] 8
INSIOE cenrnay eirtes: . : <| 23 Yo8| 3 2 3 top] 1306) $ ona] 2is2] 4 gos] tore! 4 o3e| tesa]
SIDE ceMTmAL cltTes + 2) 30 308 2 i 2 150] 7:0] 3 435] f1:8) 5 S95] 19:0] 3 See] tore] 3
outsioe meFRoPocivan anéag | 2| 35 089| 3 5 5 fou! 13:6] 3 233] 20:9] 4 G99] 20r0| 3 de7| dost | 3
REGION
NORTHEAST. «6 6 ee eee © «| 17 447) 2 899] 10,9] 2 a 2 2 618] 15,0) 2727] 15.6
wont ceninies 2222222] 20 953] 2 250] tec] 2 5 5 oa7| aere| 3 io0] i8:0
SoU eS LLL 52] Bs 523} 5 S03] asta] 3 5 3 3 059| 19:8) 3 to2| 2oc0
meses DESDE LED D 1 BB 2bs) 288) foro] 7 i H 2 223] ini) 2 98! istt
ACE AN SPANISH ORIGIN |

OF HOUSEHOLOER"

HTEs ee eae + «| 69 458] 6 $ ¢ 150] 9.9 9 81] 13.9) 9 725| asso] 9 913) 19.3
BLACK, sw ww eee +e} 8 405) 2 2 2 091) 24,9 2950) 35.1] 2 977! 35, +3 064) 36.5
SPANISH oRtGIN SS! rt) 338 : on) 193) i 000] 26:8] T G02) 26:9] i ozo! a9:3
TYPE oF HouseHoLo .

FAMILY HOUSENOLOS, 5 2 + + 320] 9.1] 9 4 7 $96] 13.0| 7 077] a2.1| 7 a2i| 12.2] 7 a96| 203
RRIED-couPLE Faltités: | t 2545] 313] 2 2 4 80 375) “80/3 480] ‘6:1/ 3 sia! “8:

MALE HOUSEROLOER,

NIFE” PRESENT, . 172] 10.1 26s] 15.5] 250/ 19,7/ 250] a4.7| ase | 18.9

FERALE HOUSEMOLBER, "NO

Hussino resent»... . .| 8 suo! 2575) 30.2/ 2 2 3 281] 38.0] 2 952| 38.0| 2 963] 34.9| 3 029/ 35.5
wowPanity wousenovos 7.22 :| 20 ez] § 229] 2osa] 3 H & 120] 29:8] 3 b00| 25:6| § G27) 2er2| 8 o30| 29:8

wate Mousenouoen <> <2) “6 Soa) tase] aase| t 15s] 2018) i see) ics) 1 Sool iesg| 931] aoc

Penice mousenocoek >>: 2] 12 000] 2 S7e| base] 2 H #373] Seca] 4 218) 3826] 4 139) Sata, 8 3a5| 35:8
AGE OF HOUSEHOLOER |
43 To 28 veans . +} og 300] 4 1 1 1 545] 28a fa 1 n2!

25 To 30 YEARS | 2] a8 900] H i 2 ase] i538 2 2 387

33 ro a0 yeas 22222252] 15 $00) t i H i ses| 12%0 \i 1 563

#3 to 50 yeas 222222] 12 Ser] t 1asi| i233 a 1 Sea

85 To oe veans 22212] 12 ta] bt 1 1 1 930] 15:0 pi 1 iu

8 veans AnDovéRr? 222222] 18 dae) 2 2 i $938) 29:3 Ia 4 G34) 2ei8
\

SIZE oF HoustHoLo {

1 PERSON. wy fee 3 3 5500] 30.9] 5 180! 29.1 5
2 PERSONS. | SS tee 2 1 3 086/ 12.5) 2959! 12:0] 2
3 PERSONS. 2: | tae 1 4616] 11:7] 1 gau! 10:7) 4
4 PERSONS. 2. > pit 1 , 1499} 1200] 2 389) a1ca) 2
5 PERSONS. |. tae It 938] 15.6] 70| 14:5

& PERSONS. ¢ ; us 522| 20.9]  4a2| 19.3

7 PERSONS OR HORE mit

PRESENCE OF CHILOREN
5 TO 18 YEARS OLD

1 CHILO.

see eee ee! az aus] a 326] 10.7] 2138) oa} a ase] 9.3 1 1657 1 665] 13.4) 1 693! 13.6
genomes Str 2] 4B Sh) § Hg) tess] 4 gee) ga) 2 age] ga H 1s 1983) 305) 1 S021 3:6
Seaton DELI IT) Sa] gaps] gk) ea) Saba e 3 #2] 8:3] 1 28 as
@ GuteDREN Sse TT rt 2] 7 3es] $55) $89] $88] azs8) $83] a wn 35) decal 3or | 28:3
BS EMICDREN Sn“wGné S222] 1 288) 288) SEk] 8B) aed] 3g) 38:7 Bi w/a) Ba

WORK EXPERIENCE IN 1979
OF HOUSEHOLDER

WORKED~ 5a°749] 3 5

40 WEEKS OR “MORE 50 515] 1 2

27 TO 39 WEEKS , 3 369

19 TO 26 WEEKS | 2770

13 WEEKS OR LESS 087
010 NOT WORK... 19599] 5 7
TENURE
ONNER-OCCUPIED . . . . . + + «| 53 830! 4 305] 8.0] 4 063] 7.5| 4 064 6458 6311] 11.7] 6 6 aun] 43.
RENTER-OCCUPIED, 2... + «| 23 849! 4 50! 20.3] 3 a51| 16:1| 3975) 16.7| 4 6 720, 6 oi} 25:3] 6 6 395| 26:8
OCCUPIER PAID NO CASH RENT . >| “1 429] 304] 27,6] — 371] 26:01 ~ 372] 2620 536] 3705] 525] 36:7 526! 36:6

*PERSONS OF SPANISH ORIGIN MAY BE OF ANY RACE.



Table G-2. Standard Errors of Estimated Numbers
Below the Poverty Level for Blacks:
1979

(Numbers in thousands)

Standard error
, for persons
Size of estimate Standard
error for
65 years | households
Total | and over

28 7 | 14
44 25 22
62 33 31
88 37 44
135 tH) 72
182 x) 106
229 (X)
242 x) (x)

X Not applicable.

148



Figure 1.
Approaches to Valuing In-Kind Transfers

Other goods

39



Table A. All Persons: Comparison of the Number of Poor and Poverty Rates
Using Alternative Income Concepts and Valuation Techniques: 1979

(Numbers in thousands)

Valuation technique

Income concept Market j Recipient or cash | Poverty budget
. value | equivalent value share value
approach approach | ° approach

Money income alone:

Number Of POOr..sseccveeece 23,623 23,623 23,623
Poverty rate...csceccsececs 11.1 11.1 11.1
Money income plus food and
housing:
Number of poor... 19,933 20,218 20,743
Poverty Pateseresres 9.4 . 9.5 — 9.8
Percent redyction*+...sseeee -15.6 -14.4 -12.2
Money income plus food,
housing, and medical care
(excluding institutional i
care expenditures): |
|
Number Of POOr...ssccecscece 14,023 | 18,393 18,866
Poverty rate...eeeee . 6.6 | 8.7 8.9
Percent reduction!......... 40.6 | -22.1 -20.1
|
Money income plus food, |
housing, and medical care |
(including institutional
care expenditures): |
|
Number of poorssesesseeeeee | 13,634 | 17,318 18,866
Poverty rate..... +. 6.4 | 8.2 8.9
Percent reductionl......... 42,3 | -26.7 -20.1

Ipercent reduction in the number of poor from the current poverty estimate
based on money income alone.

ix



externalities) which accrue to taxpayers who finance the program as well as
net benefits to the program recipient. Social benefit values have not been
presented in this study because of difficulties in assigning values to indi-
vidual recipients.

SUMMARY OF VALUATION CONCEPTS

This discussion has shown that each of the in-kind valuation concepts
has its own strengths and weaknesses, depending on the estimation procedure
used. The market value approach is preferred if value is considered to be
the purchasing power of benefits in the private market place. The cash
equivalent approach is preferred if value is considered to be the individ-
ual's assessment of the worth of the goods received. However, estimating
cash. equivalent value can be quite problematic given the available expendi-
ture data and lack of a good counterfactual for valuing medical benefits.
The poverty budget shares approach can only be used in assessing the effect
of in-kind transfers on poverty; it is preferred if value is considered to
be no more than the amount of expenditures for the specified good assumed to
be included in the current poverty level budget. Because of difficulties in
estimating cash equivalent value, particularly medical benefits, PBS provides
an additional limited alternative to the market value approach.

Market value ‘has been the ‘most widely used of these three methods to
value in-kind transfers. Researchers who prefer the cash-equivalént approach
have argued that market value overstates the gain in the recipient's economic
well-being since cash equivalent values are. most often less than market
value. In such cases, recipients of an in-kind benefit would be willing to
accept an amount of cash which is less than the market value because their
normal consumption pattern has been altered. Those who have favored using
MV in studies of poverty and income distribution (Browning, . 1979;
Congressional Budget Office, 1977; Anderson, 1978; Hoagland, 1980; and
Paglin, 1980) either implicitly or explicitly suggest that in-kind transfers
have exactly the same value to recipients as cash transfers of an equal
dollar amount, despite what some may feel is an overstatement of the value of
in-kind transfers.

PREVIOUS ESTIMATES OF THE VALUE OF IN-KIND TRANSFERS

The comparison of the results of valuation techniques is complicated by
the existence of several procedures which provide differing estimates of cash
equivalent value. The comparisons are further complicated by the fact that
the ratio of cash equivalent to market value increases with the level of
income since the consumption of most goods increases with the level of
income. Comparisons which follow are based only on the average cash equiva-
lent value at all income levels. Because cash equivalent valuation is more
problematic than other valuation techniques, a brief review of previous
work follows prior to the summary comparison of results.

llFor other reviews of this literature, see Cooper and Katz (1978);
Danziger, Haveman, Plotnick (1981); Manser (1981); and Peskin (1976).

33


Table C-3. Medicaid Coverage in 1979: CPS Compared to Alternate Estimate,
by Type of Beneficiary

(Numbers in millions)

CPS as a

Recipient category March | Alternate | percent of
1980 CPS alternate

Total ercccsscccceccrsseece 18.136 20.794 87.2

L(t eee er 3.424 2.6051 129.2
Blind or disabled...ssseeeeseee 1.757 2.503 70.2
AdUItS.esccsesecceeeerescceeeee | 44915 5.247 93.7
Children. ...ccscecccvvcevevcces 8.039 10.393 77.4

115



benefits would include homegrown food, employer-provided “business Tunches"
and subsidized cafeterias, charitable "soup kitchens," and the minute amount
of government in-kind transfers available in 1955 (i.e., a relatively small
amount of school lunch subsidies and surplus commodities, but no food
stamps). The value of these benefits would be added to cash food expendi-
tures. The income side of the ratio would include these food benefits and a
large number of other noncash benefits that existed in 1955 as well. Some of
these benefits are implicit rent for owner-occupied housing, capital gains,
interhousehold transfers in-kind (e.g., free rent and medical care expenses
paid by others), employer-provided health insurance and other fringe bene-
fits, benefits from charities, and various benefits from Government such as
mortgage interest subsidies, medical care transfers and subsidized housing.
Neither precise nor complete estimates are available on the aggregate value
of these benefits in 1955. Had data on in-kind income. been. available in
1955, it is likely that the poverty line would have been raised somewhat from
its current level, although the amount is uncertain. The third decision con-
cerns the technique chosen to value these benefits. As discussed later, the
valuation technique selected can have a substantial: effect on the value of
benefits which in turn could affect the food-to-income ratio.

Revising the food consumption data and conceptual implications. Aside from
the issue of the effect of in-kind benefits on the food-to-income ratio,
formula (1) also reveals that the current poverty thresholds are based on
fixed period food consumption data (1955) and food plans (1961). Some
analysts have argued that poverty levels should be updated periodically to
reflect changes in the proportion of cash income spent on food. Fendler and
Orshansky (1979) have recalculated the poverty levels based on data from the
1965 USDA Household Food Consumption Survey. The ratio of food expenditures
to after-tax income from the 1965 survey was .290 compared with the .333 used
to derive the current levels. This increases the food budget multiplier by
15 percent, from 3.00 to 3.45. Changes in food requirements in the USDA
Thrifty Food Plan for different size and structure families were also incor-
porated in their study. Based on their work, the poverty line for a four-
person family was 21 percent above the current poverty line. This new
poverty line was 43 percent of the median income of a four-person family (as
compared to 44 percent in 1963). Using these new poverty lines, Fendler and
Orshansky found a 52-percent increase in the number of poor in 1977 relative
to the official poverty count. Both the use of the 1965 expenditure data and
the introduction of data from the Thrifty Food Plan increased the number of
poor.

It should be recognized that updating the food-to-income ratio would
implicitly move the poverty measure closer to a relative concept. A relative
measure of poverty is based on the overall level of well-being in a society.
A poverty line based on a relative measure would change as real income levels

or expenditure patterns for_the whole society change. Periodic updating.of
current poverty levels based on changes in food-to-income ratios since 1955

would have produced a relative poverty measure since the proportion of income
spent on food has declined. Many analysts view the official poverty Tevels



investment and consumption yoods. First, some argue (Musgrave and Musgrave,
1980) that items such as primary and secondary education are largely a public
good, i.e., all citizens benefit because the population is literate; there-
fore, no specific value should be assigned to individuals. Second, is educa-
tion to be treated as an investment benefit to the recipient in terms of
future earnings or as a consumption benefit to the family in the form of day
care and other services provided at little or no direct cost? On an empiri-
cal level, no data exist. with which to assiyn values for Veteran's Adminis-
tration (VA) and Federal Housing Administration (FHA) insured mortgages and
many employer-provided "fringe" benefits. For other benefits such as
employer-provided group health and pensions, some data are available, how-
ever, more detailed data are needed before adequate valuation procedures can
be developed.

The in-kind benefits included in this study can be placed in proper per-
spective by comparing them with total in-kind food, housing, and medical
benefits. These comparisons are shown in table 2. The first column of table
2 shows the total market value of the in-kind benefits covered in this study.
The next three columns of the table provide estimates of the total market
value of in-kind benefits which have not been covered. The benefits which
have not been accounted for include those from both the public and private
sectors.

These estimates cover benefits which are received free or below market
price as well as those received as tax subsidies. Including tax subsidies
as in-kind income, however, is controversial, since not everyone would agree
that the amount of an individual's earnings which is not taxed because of
specific allowed deductions is equivalent to an in-kind transfer. For those
who do not agree with the inclusion of tax subsidies, colun 3 can be
excluded from table 2.2 The values in the table exclude some types of
in-kind benefits such as interfamily transfers of food and housing and free
private use of employer-provided vehicles. The benefits which are included
and the source of the estimated values are detailed in appendix B.

While the estimated values for in-kind benefits in table 2 can be con-
sidered rough, they provide the reader with a general appreciation of the
limited set of in-kind transfers included in this study. In total, the
market value of in-kind transfers analyzed in this report is only slightly
more than one-third of the total value of all food, housing, and medical
in-kind benefits. This proportion is’ somewhat higher (39 percent) if tax

2Indirect income tax subsidization of a particular type of expenditure
whether by exclusion from tax, deduction from the tax base, or tax credit
can be regarded as no different than a direct subsidy of the same amount
for the same expenditure (see Musgrave and Musgrave, 1980). These subsi-
dies are also known as "tax expenditures" to highlight the fact that a
certain amount of foregone tax revenue has the same effect on the govern-
ment budget as an equal amount of direct expenditures. However, some
may not feel that tax subsidization is an in-kind benefit in the same
sense as those valued in this report. As noted, those who do not choose
to count tax subsidies as in-kind benefits can exclude them from table 2.



This report presents several different approaches to valuing in-kind
benefits and describes the policy-relevant aspects of each approach. Special
attention was given to three valuation techniques that are appropriate for
the purpose of measuring poverty. These approaches are 1) market value,
2) recipient or cash equivalent value, and 3) poverty budget share value.

1, The market value is equal to the purchase price in the private
market of the goods received by the recipient, e.g., the face value
of food stamps.

2. The recipient or cash equivalent value is the amount of cash that
would make the recipient just as well off as the in-kind transfer;
it, therefore, reflects the recipient's own valuation of the bene-
fit. The recipient or cash equivalent value is usually less than
and never more than the market value. Even though cash equivalent
value is the theoretically preferred measure, it is quite difficult
to estimate, especially for medical care.

3. The poverty budget share value, which is tied to the current poverty
concept, limits the value of food, housing, or medical transfers to
the proportions spent on these items by persons at or near the
poverty Tine in 1960-61, when in-kind transfers were minimal. It
assumes that in-kind transfers in excess of these amounts are not
relevant for determining poverty status because an excess of one
type of good (e.g., housing) does not compensate for a deficiency
in another good (e.g., medical care). Because the value of in-kind
transfers are limited in this way, the poverty budget share approach
assigns the lowest average values to in-kind transfers of the three
methods used.

A major portion of the work in this study involved the development of
procedures to estimate the value of in-kind benefits. Various administrative
and survey data were used to derive estimates of these values which were then
assigned to persons reporting recipiency of in-kind benefits on the March
1980 Current Population Survey (CPS) data file; the March CPS data file is
used annually as the source of poverty estimates. Of particular importance
are the conceptual and empirical approaches used to value medical care
benefits. Of the five transfer programs covered in this report, medical
transfers constitute over 80 -percent of the total market value of in-kind
benefits.

Because of the importance of medical benefits, and because of the prob-
lematic nature of valuing these benefits, several alternative definitions of
in-kind benefits to be included as income were investigated; these defini-
tions were food and housing alone; food, housing, and medical care excluding

institutional care benefits; and food, housing, and medical care including
institutional care (chapter 5).~

A summary of the results of the valuations of in-kind transfers on the

overall level of poverty and on poverty among important subgroups of the poor
is presented in chapter 6. Combinations of the three different valuation



RECIPIENT VALUES

The estimates of recipient value shown in this report were derived using
the normal expenditures approach. Both the 1979 AHS and 1972-73 CEX were
used as sources of expenditure data because the CPS does not collect these
data. Two procedures for estimating normal expenditures were examined.
These were a regression approach and a cell-matching approach. In the re-
gression approach, the first step is to develop an equation which estimates
the normal expenditure based on characteristics of the consumer unit using
the AHS or CEX. The independent variables were restricted to those available
from the CPS. The second step is to use this equation to estimate the level
of normal expenditures on the CPS. In the cell-matching approach average
expenditures are tabulated within cells of a matrix which defined unique
cross-classifications of consumer units from the AHS and CEX. The socio-
economic variables used to define these cells were also restricted to those
available from the CPS. These average values are assigned to each CPS unit
from the cell defined by that unit's unique set of characteristics. Both
regression and cell-matching approaches were used in this study to estimate
normal expenditures for food, housing, and medical care. Because the regres-
sion approaches proved to be unsatisfactory for housing and medical expendi-
tures, the cell-matching approach was used to assign recipient values to the
CPS file for food, housing, and medical benefits in order to be consistent.

Food transfers. The regression equation used to estimate normal expenditures
‘olTowed the work of Smolensky and Van Der Gaag (1980). The depend-

ent variable was defined to include all food for home consumption, the value ©

of homegrown food, and one-half of restaurant expenditures. The independ-
ent variables included 1) the number and age of children, 2) age and sex of
the householder, 3) household size, and 4) level of money income. Households
receiving food stamps or food commodities were excluded from the data base.
The 1972-73 food expenditure and income data were adjusted to reflect 1979
prices.

The results of the regression for food expenditures were satisfactory
“(R2 = .45), with the coefficients for most variables having the expected sign
and being significant at the 95-percent confidence level. The RV-CE benefit
weights based on this equation were, on average, about 98 percent of the
market value. Even though the regression results were satisfactory, the cell
matching approach was chosen to provide the final RV-CE estimates in order to
be consistent with the procedures used for medical and housing. (See table
D-5.) The overall RV-CE benefit weight from the cell matching was 96 percent
compared to the 98 percent for the regression method.

5/

~The inclusion of all restaurant expenses assumes perfect substitution
between food stamps and these expenditures. The exclusion of restaurant
expenditures altogether assumes no substitution. In theory, the income
and substitution effects between food stamps and restaurant meals move in
opposite directions. Food stamps reduce the price of food consumed at
home (implying lower restaurant expenditures) but increase total income
which should increase restaurant expenditures (assuming that restaurant
meals are a normal good).

127



Appendix A. U.S. Senate Statement. “Data -
Collection and Poverty Level”

Official poverty statistics published by the Bureau of the Census cur-
rently ignore billions of dollars: of Government in-kind benefits, such as
food stamps, housing subsidies and medical care. The Congressional Budget
Office has shown that including in-kind benefits in the income statistics
would cause the number of people in poverty to decline to about ¥ million as
compared to official statistics showing nearly 25 million people in poverty.
The official statistics show no significant reduction in recent years in the
incidence of poverty, although in-kind benefit programs have expanded
greatly.

The Committee considers it essential that official poverty statistics
reflect, at the earliest possible date, the effects of in-kind benefits.
Without such information Congress and the Executive Branch cannot be certain
that Government transfer programs are properly targeted. a

The Census Bureau has recognized the need for better data on in-kind
benefits. The most recent March Current Population Survey has collected data
on some types of in-kind program benefits. In addition, Census has underway
an experimental survey~-known as the Survey of Income and Program Participa-
tion--which collects more extensive data. However, Census has not yet pub-
lished the data collected thus far and has no current plans for integrating
such data with cash income data now reported routinely.

The Committee has inscribed language in the bill directiny the Secretary
of Commerce to expedite the program of collecting, through surveys, data on
benefits received and data on participation in federally funded, in-kind
benefit programs. Programs on which data to be reported include, but are not
necessarily limited to, food stamps, medicaid, medicare and subsidies in
areas such as housing, nutrition, child care and transportation. The
Secretary of Commerce is further directed to continue research and testing of
techniques for assigning monetary values to in-kind benefits, and for calcu-
lating the impact of such benefits on income and poverty estimates. The
Secretary of Commerce is also directed to include in survey reports beginning
no later than October. 1, 1981, appropriate sumartes of data on in-kind bene-
fits and estimates of the effect of in-kind benefits on tne. number of fami-
lies and individuals below the poverty level.

Department of State, Justice, and Commerce, The Judiciary and Related
Agencies Appropriation Bill, 1981. U.S. Senate, Yoth Congress, 2nd
Session. September 16, 1980: 33-34,

105



Housing transfers. The cash equivalent values for publicly owned or other
subsidized housing are based on data from the 1979 Annual Housing Survey.
The first step was the calculation of the Cij's, which are the average normal
rental expenditure for unsubsidized renters, for each cell of .a matrix
defined by selected household characteristics.2© These tabulations were
restricted to households with annual incomes of less than $20,000. The cells
were defined by age (under 62 years old and 62 years old and over), household
size, and household income levels. The average rental value for each of
these cells was assigned to “matching" CPS households residing in public
housing units. These rental values were adjusted to reflect yeographical
differences in the price of rental housing based on differences in average
rental values by region and metropolitan location as tabulated from the AHS.
(See appendix D for the subsidy values assigned under the normal expendi -
tures approach.)

The final determination of the RV-CEyj;'s requires comparison of the
Cij's with the MRyj's (market rents). As noted earlier, the RV-CEjj is equal
to Cj; less the rent (SRij) paid by the subsidized household when Cij is less
than AR; 5. Tf the Cj; 1s greater than MRj; then RV-CEjj is equal to MRyj
less the subsidized rent paid (SRjj);in this ‘case, RV-CE;;° equals the marked
value of the housing subsidy. nN calculating the RV-C ij» the SRyj was
assigned to CPS households based on estimates obtained from the AHS. "These
subsidized rents are identical to those used in determination of the MV;j's.

The results of estimating RV-CE for public housing benefits are shown in
table 11, Overall, benefit weights averaged 80.6 percent for all households
receiving benefits and varied irregularly by income class with a low value of
53.1 percent for the "$10,000 to $11,249" category and a high of 91.2 per-
cent of the "$3,750 to $4,999" category.¢/. These estimates are quite similar
to those found by Murray (1975, 1980) and Smolensky, et. al. (1977) but,
somewhat greater than those found by Smeeding (1975) and Kraft and Olsen

26some Nonsubsidized units had heat included in their rent while others
did not. This fact was ignored in estimating the Cj;'s. This is the
same as the procedure used in determining the MV of Public and subsi-
dized housing.

27While benefit weights tend to rise with income for most types of in-kind
transfers, this is not the case for public housing benefits. The subsi-

 -dized_rent. which ..public-housing-tenants— -pay~is- calculated as-a~percent

of their income and thus rises monotonically with income, while the
Cij's also rise with income. Thus, the RV-CE estimate, which equals
normal housing expenditures less the subsidized rent in cases where the
normal housing expenditures are less than the market rent, does not
necessarily increase with income.

a ene



providing an adequate supply of foods" (P.L. 79-396, the National School
Lunch Act_of 1946) for all children at moderate cost. Additional assistance
Ts provided for children determined by local school officials to be unable
to pay the "full established" price for lunches. Like the Food Stamp
Program, the School Lunch Program is administered by the Food and Nutrition
Service of the U.S. Department of Agriculture through State educational
agencies or through regional USDA nutrition services for nonprofit private
schools.

All students eating lunches prepared at participating schools pay less
than the total cost of the lunches. Some students pay the “full established"
price for lunch (which itself is subsidized), while others pay a “reduced"
price for lunch, and still others receive a “free” lunch. Program regula-
tions require students receiving free lunches to live in households with
incomes below 125 percent of the official poverty level. Those students
receiving a reduced-price school lunch (10 to 20 cents per meal) live in
households with incomes between 125 percent and 195 percent of the official
poverty level. Over one-half of the children benefiting from this program
during 1979 paid full price. Most of the other children received free
lunches; only about 6 percent. of all children benefiting from the School
Lunch Program paid a reduced price (U.S. Congressional Budget Office, 1980).
The data in this report do not distinguish between recipiency. of. free and
reduced-price school lunches, but because of the low number of reduced-price
lunches and the low price itself, this distinction is practically insignifi-
cant for valuation purposes.

The questions on the March 1980 CPS provide a limited amount of data for
the School Lunch Program. Questions concerning the program were designed to
identify the number of household members 5 to 18 years old who "usually" ate
a hot lunch in 1979. This defined the universe of household members
receiving this noncash benefit. This approach was necessary because the
majority of children benefit indirectly, i.e., they pay full-established
price but are not aware that these lunches are subsidized. A second question
identified the number of members receiving free or reduced-price lunches.
The number paying the full-established price was estimated using the differ-
ence between the number who usually ate hot lunches and the number who ate
free or reduced-price lunches.

Because the school population is fairly stable over the school year, and
the free or reduced-price School Lunch Program operates on an annual
accounting period, the USDA 1979 peak monthly average number of lunches
served (11.607 million) is a fairly good alternative estimate to compare with
the CPS estimate. The number of children who were reported as free or
reduced-price school lunch beneficiaries on the CPS was 88.1 percent of the
USDA peak monthly estimate.

4There may be some turnover in this USDA population which is not captured.
The CPS income period covers all-of 1979 and thus scans two "half" school
years. Students entering (September 1979) and leaving (June 1979) public
schools with School Lunch Programs are mutually exclusive and are not
both counted in a peak monthly estimate.

17



Figure E-1
Use of Normal Expenditures Approach
to Measure Cash Equivalent Value

Subsidized good (medical care or food)

138



Table 1. Major In-Kind Transfer Benefits: 1965-80

(Current and constant (1965) dollar market value of benefits in billions)

_ T
Type of benefit 1965, 1970 1975 | 1980
A, MAJOR IN-KIND TRANSFERS (MEANS-TESTED and
NONMEANS-TESTEDL
Total food, housing, and medical care..... $ 2.166 | $15.014 | $36.685 | $72. 527
In constant 1965 dollars.....eeeeeee oe 2.166 12.200 | 21.505 | 27.771
448 1.656 6.412 | 12.580
.033 1.119 4.386 , 9.247
415 537 2.026 3.333
Housing: |
Public housing? rere errr teecceee sees 351 1.640 2.263 | 5.402
Medical Care:

Total ecsesecsessevece ere eee ry 1.367 11.718 | 28,010 | 54.545
Medicaid... . 1.3673 5.606 | 14.555 | 26.154
Medicare...... Pee c cece ee anenccssescccees (NA) 6.112 | 13.455 ; 28,391

i
B. MAJOR MEANS-TESTED TRANSFER BENEFITS ONLY \
i
Total means-tested benefits. 5.979 17.492 | 38.509 | 61.299
In constant 1965 dollars 5.979 14.214 | 22.574 | 23.471
Cash public assigtance........ see eeeneeees 4.025 8.864 | 16.312 | 18.863
In-kind benefits’. ..sccecsssesscsceccseces 1.954 8.628 | 22.197 | 42.436
Percent of total means-tested benefits
which are: ‘
In-Kind. ...eeeee oesee 32.7 49.3 57.5 69.2
Medicaid alOne....cccceceseesccsncvecece 22.9 32.0 37.8 42.7

NA Not available.

IMeans-tested income transfer programs are those which benefit only families with
low enough incomes and resources (assets) to qualify. Nonmeans-tested benefits
have no income or resource test.

2"Public housing" includes public and subsidized housing for low income families
under various public programs including: Low Rent Public Housing, and Sections 8,
235, 236, 101, and 202b of the 1937 Housing Act.

3prior to the inception of Medicaid, various public assistance programs provided
medical assistance benefits to low income persons. The 1965 figure is for vendor
payments under these programs. See: Social Security Bulletin, June 1981.

4Excludes “paid" School Lunch benefits and Medicare.

3



Table D-11. Poverty Budget Shares for Medical Care: 1960-61 and 1972-73 CEX

1960-61 CEX 1972-73 CEX
Family size and age
myl |) ce wt} wc?
PL PL Average PL L Average
Totalescccccseee | 564 6.7 6.1 3.6 5.1 4.4
| Gel | 6.7 6.4 | 5.4 | 3.4 4.4
TB years and oversi. ins fie | ue /173] 102 | 114
2 persons:
Under 65 years....... 8.8 6.3 7.4 8.0 3.9 6.0
7B years and overse, asd 17 | 1gth| 239] 10.2 | r03
3 PErsOnS.cesscseseseeee | 8.9 7.5 8.2 5.7 4.8 5.3
4 persons..seesreseesees | Bl | 6.6 7.4] 5.4 | 3.4 4.4
5 PErsOnS.eccecsecseceee | 648 5.1 6.0 5.7 5.0 5.4
6 persons or more4......; 5.9 | 5.3 5.6 5.0 4.5 4.8
i ! H

IMedical care expenditures (MY) (other than Medicare premiums in 1972-73)
relative to the pre-tax income level closest to the poverty line.

2Medical care expenditures (MC) as above, but relative to the level of
current consumption expenditures closest to the poverty line.

3The estimates for 1972-73 are for those. 65 or older.

4calculated at the simple average poverty line for families of six and
—- >> families of-seven ‘or-more. nn

136



procedure, al] children under 21 years old in families were assumed to be
covered by Medicaid if either the householder or spouse reported being
covered by Medicaid.® All adult AFDC recipients and their children and SSI
recipients living in the 36 States which legally require Medicaid coverage
of all SSI recipients were also assigned coverage. The data shown in this
report exclude children covered by Medicaid in households in which no adult
member was covered by Medicaid. However, because there are no administrative

data which separately identify these recipients, the extent of the bias is
unknown,

The Health Care Finance Administration (HCFA) annually compiles State-
specific data for Medicaid beneficiaries. These data include State-specific
estimates of the aggregate number of persons who “ever received" Medicaid
(i.e., a once-only count), broken down by cash public assistance status and
four recipient groups: aged (65 or older), blind or disabled, adults, and
children. Additional data are available on State-specific expenditures for
different types of medical care and on the length of stay in skilled nursing
facilities and intermediate care facilities by category of recipient. These
data are not without error and present substantial difficulties in developing
an administrative data-based estimate of Medicaid coverage comparable to the
cPS.9 Most importantly, HCFA does not make an annual estimate of the “ever-
enrolled" Medicaid population (which could be compared with the CPS ques-
tion). Because some Medicaid coverees do not actually incur._Medicaid-
financed medical expenses, CPS and HCFA data are not directly comparable.
In addition, the usual adjustments for non-CPS population Medicaid recipients
(death, institutionalization, non-U.S. State population) also need to be
made. This process is outlined in appendix D. The results of these compari-
sons indicate that the CPS ever-covered beneficiary estimate is 87 percent
of the HCFA data-based estimate. The 18.136 million CPS estimate compares
quite well with alternative estimates of Medicaid coverage from the 1977
National Medical Care Expenditure Survey (NMCES) and Department of. Health
and Human Services (HHS) estimates based on the Income Survey Development
Program (ISDP) data for 1979. Moreover, it seems superior to Medicaid cover-
age estimates from the 1976 Survey of Income and Education (SIE) and the 1976
Health Interview Survey (HIS). Subject to a few minor qualifications which

8this procedure was required mainly because the Medicaid coverage question
was asked only for persons 15 years old and over.

9In compiling the HCFA estimates, each State bears the responsibility for
estimating the ever-received number, and thus, avoiding double counting
of recipients. Most State estimates are, in turn, aggregates of county,
city, or other local Medicaid authorities’ estimates. The possibilities
for double counting persons are quite high, particularly if they have
moved..during the year or..if.they were on and off the Medicaid. roles. twice
or more during the year. Not all States report each year and some which
do report aggregate totals do not report all of the specific detailed
breakdowns into recipient groups, expenditures types, etc. As a result,
some of the data are estimated from previous years' reports or from
other sources.



1981b. “The Size Distribution of Wage and Nonwage Compensation:
Employer Cost vs. Employee Value," presented to the National Bureau
of Economic Research Conference on Measurement of Labor Cost,
Williamsburg, Virginia, December 3. ~~

and M. Moon 1930. Valuing Government Expenditures: The Case of Medical
Transfers and Poverty," Review of Income and Wealth, September,

Smolensky, E., L. Stiefel, M. Schmundt, and R. Plotnick. 1977. “Adding In-

Kind Transfers to the Personal Income and Outlay Account: Implica-

tions for the Size Distribution of Income," in T. Juster (ed.), The

Distribution of Economic Well-being, National Bureau of Economic

Research Studies on Income and Wealth, No. 41, Cambridge,
Massachusetts, Ballantine.

and J. Van der Gaag, 1980."Income, Consumption, True Equivalence Scales
and Characteristics of the Poor," Madison, Wisconsin, Institute for
Research on Poverty, mimeograph.

| Social Security Bulletin. 1981, June.

1981, Annual Statistical Supplement: 1979

Struyk, R., L. Malpezzi, and F. Wann. 1980. "The PCE Deflator Factor:
Initial Analysis of Alternatives," Washington, Urban Institute,
Working Paper 1436-01, August.

Survey of Current Business. 1981, July.

Triplett, J. 1981. “Reconciling the CPI and the PCE Deflator," Monthly Labor
Review, September, pp. 3-15.

U.S. Bureau of the Census, 198la. "Money Income of Families and Persons in
the United States: 1979, Current Population Reports, Series P-60,
No. 129, Washington, U.S. Government Printing Office.

1980. "Money Income and Poverty Status of Families and Persons in the
United States: 1979° (Advance Report)," Current Population Reports,
Series P-60, No. 125, Washington, U.S. Government Printing Office.

1981. “Characteristics of Households and Persons Receiving Noncash
Benefits: 1979, Current Population Reports, Series P-23, No. 110,
Washington, U.S. Government Printing Office.

1981a. “Characteristics of Households Receiving Noncash Benefits:
1980, Current Population Reports, Series P-60, No. 128, Washington,
U.S. Government Printing Office.

161



Table 24. Comparison of Results from Previous Studies Using the Market Value
of In-Kind Transfers

Poverty rate |
i Percent
Study Data set and Prior to After reduction
income year in-kind in-kind | in poverty
transfers | transfers |
A. FOOD, HOUSING,
AND MEDICAL
TRANSFERS
ae
This report). ...eseee CPS; 1979 11.1 6.4 42.3
Hoagland/CB02........'| Aged CPS; 1974
aged to 1976 10.0 5.3 47.0
Hoagland...sssseeeeee | Aged CPS; 1978
aged to 1980 8.6 3.9 54.6
Paglin3....seceeseeee | Grouped CPS; 10.0 3.6 64.0
1975
B. FOOD AND HOUSING
TRANSFERS ONLY
This report! .......06 CPS; 1979 11.1 9.4 15.6
Hoagland/CB02........ Aged CPS; 1974
aged to 1976 10.0 7.4 26.0
Hoagland.....seeeeeee | Aged CPS; 1978 |
i aged to 1980 8.6 5.9 | 31.3
| |

lin this report, CPS money income data are not adjusted for income under-
reporting, as in the other studies.

2Hoagland uses the same data base and estimation techniques as the CBO
study, but makes estimates for persons rather than households. Thus, the
results are labeled "Hoagland/CBO."

3Both of Paglin's estimates include some adjustment for taxes paid and for
household income sharing.

93


Table D-10. Poverty Budget Shares for Food and Housing: 1960-61 CEX

!
Food Housing
Family size and age

T
ey! | nyt | wc2
i Average
j
1

1 person: j
Under 65 yearS..sssescessesese | 33.3 | 40.0 38.7 39.4
65 to 74 years..... . 3 22: .
75 years and older.....sseeeee | 33.3 | 42.1 33.8 38.0

2 persons:
Under 65 years... 33.3 | 35.6 29.5 32.6
65 to 74 years... » {33.3 | 35.7. 29.1 32.4
75 years and older....sseeeeee | 33.3 |. 37.0. 29.0 33.0

3 POrSONnS...cesseseeee ecccscesee | 3303 | 30.7 24.8 27.8
4 PErsONS...sccesesevese sceceees | 33.3] 23.7 22.6 23.2

5 Persons. ...csccveeeee esecseees | 33.3 | 20.8 20.2 | 20.4

6 persons or More. .ssececceeees | 3303 | 17.3 17.0 : 17.2
|

lgood (FY) and housing (HY) expenses calculated relative to the
pre-tax money income level closest to the poverty line (PL).

2Housing (HC) expenses calculated relative to the level of cur-
rent consumption expenditures closest to the poverty line.

3calculated at the simple average poverty line for families of
six and families of seven or more persons.

135



Table 19,

Market Value Approach:
and Poverty Rates, by Income Concept for Selected Population Supgroups:

Number of Persons Below 125 Percent of the Poverty Level

(Numbers in thousands. Persons as of March 1980)

1979

T

Income

concept.

Money income

Money income plus
food, housing,

Money income plus
food, housing,

Selected Money income Plus food and| and medical care and medical care
characteristics alone housing | (excluding (including
j institutional institutional
. | care) care)
i
t t
Poverty |} Poverty © Poverty Poverty
Number rate | Number rate Number rate Number rate
t °
All persons. | 33,445 15.8 { 31,178 14.7; 24,689 11.6 23,674 11.2
i i
13,767 19.9 i 12,918 18.6; 10,982 15.8 10,461 15.1
19,678 13.8 j 18,260 12.8: 13,707 9.6 13,213 9.3
Elderly (65 | : -
and over)..... 6,708 | 24.0 | 6,262 22.4 3,269 11.7 2,650 9.5
Non-elderly.... | 26,737 : 14.5 \ 24,916 13.5 21,420 11.6 21,024 11.4
Central city... | 11,915 20.5 10,903 18.7 8,205 14.1 7,911 13.6
Suburb.. 8,369 9.9 7,823 9.2 6,346 7.5 6,170 7.3
Nonmetropolitan
APCAcseeeseeee | 13,161 19.1 | 12,452 18.0 10,139 14.7 9,593 13.9
9,614 © 39.2 | 8,764 35.7 6,896 28.1 6,607 26.9
23,831 12.7 | 22,414 11.9 17,793 9.5 17,067 91
In households
with female |
householder, |
no husband :
present....... | 11,317 43.1 ; 10,302 39.2 8,172 31.1 7,988 30.4
All other i
persons..... 22,128 11.9 | 20,876 11.2 16,517 8.9 15,686 8.4

Differs from official poverty
status was determined on a household basis.

count for 1979 of 35,592 (16.3 percent) because

poverty



The period between 1965 and 1979 saw Sharply declining poverty rates
during the first 5 years followed by a 10-year stretch in which poverty
declined only slightly. In 1965, the Poverty rate was 17.3 percent but
declined to 12.6 percent in 1970, Between 1970 and 1979 the poverty rate
declined only slightly (11.6 percent) using the present definition of income.

Given these trends, widespread public interest concerning the disposi-
tion of these transfer payments and their effect on the poor. has arisen
during the last decade. Although much is known about the growth and magni-
tude of outlays for these programs, there was very little comparable
information on the number of recipients and their economic and demographic
circumstances until recently. In an attempt to provide limited information
on this subject, the Bureau of the Census began in 1979 the necessary
planning to supplement the usual collection of annual money income data in
the CPS with questions designed to collect information on the selected group
of noncash benefits shown in table 1.

Underlying this study are data collected in the March 1980 CPS which
provide the first nationwide picture of in-kind transfer recipiency for the
1979 calendar year. The March CPS measures household -money income and ~
provides estimates of the extent of Poverty and the characteristics of the
poor. This information was used in conjunction with in-kind benefit data to
estimate the effect of such benefits on the poor. -It is expected that future
surveys will continue to collect this information and provide the source for
annual updating and year-to-year comparison of such estimates.

5Information on CHAMPUS (Civilian Health and Medical Program of the United
States) and military health care Programs was also collected. However,
this report presents no data or analysis of the value of CHAMPUS medical
benefits. Because CHAMPUS provides medical care to members of the Armed
Forces and their families and the CPS only includes military personnel
if they live off base or with their civilian families on base, the CPS
sample of CHAMPUS coverees excludes nonmarried military living on base.
Moreover, in 1978, only 35,000 of the 5.3 million poor families (U.7 per-
cent of all poor families) were headed by a person in the Armed Forces.
Based on these figures, the effect of CHAMPUS on poverty should be very
small in any case. Finally, there is some fear that persons covered by
different military health plans, e.g., CHAMPVA (Civilian Health and
Medical Program of the Veteran's Administration) reported CHAMPUS cover-
age. No specific information on these other military or veteran health
plans was collected. For these reasons, no estimates are Presented on
the value of CHAMPUS.

SPrevious efforts to value in-kind transfers and measure their effect on
poverty (Smeeding, 1975; U.S. Congressional Budget Office, 1977;
Hoagland, 1980; and Paglin, 1980) have not been able to rely on survey-
based estimates of in-kind benefit recipiency. Other studies of multiple
benefit recipiency concentrated on a small number of areas (U.S. Joint
Economic Committee, 1973; Lyon, et. al. 1976) or on Blacks only (National
Urban League, 1980).



COMPARISONS WITH OTHER STUDIES

There have been a number of previous studies which have attempted to
estimate the effect of in-kind transfers on the estimates of the number of
poor. Most of these studies have used the market value approach. Table 24
compares the results of these studies with the market value estimates pre-
sented in this report. All of these estimates are based on the CPS and value
nearly the same set of in-kind benefits; however, there are some differences
which should be noted. First, the "Hoagland" and “Hoagland/CBO" estimates
are based on aged -(projected) March.CPS microdata files which were also
adjusted for income underreporting. These data bases, which were prepared by
Mathematica, Inc., used a simulation model to assign recipiency of in-kind
benefits (Doyle, et. al., 1980). Paglin's estimates were derived from pub-
lished March CPS data and also include adjustments for taxes (which increases
the number of poor) and for income sharing between unrelated household mem-
bers (which decreases the number of poor). Early research by Smeeding
(1975) indicates that accounting for both taxes and ncome sharing has little
net effect on the number of poor. While absolute levels of poverty are not
directly comparable between these studies, the relative effects of in-kind
transfers can be compared.

The relative effect of the value of in-kind transfers on poverty in this
study is less than that for each of the other studies. Several factors may
help explain these differences. First of all, adjusting for underreporting
increases money income levels and tends to reduce poverty independent of the
effect of in-kind transfers. Second, this study made no adjustment for
underreporting of in-kind transfers (about 11 percent of total market value
of benefits). This was not a problem for the other studies since they used
information derived from administrative sources. Third, the simulation model
used to derive the Hoagland data base assigned higher rates of multiple bene-
fit recipiency than observed in the March CPS (Smeeding, 198la). The CPS
data indicate that about 23 percent of the poor received three or more of the
five in-kind transfers covered in this report and that 28 percent received
none of these in-kind benefits. In contrast, the data based used by Hoagland
indicates that 47 percent of the poor received benefits from three or more
Programs and only 7 percent received no benefits ‘at all. In general, this
apparent overassignment of in-kind benefits in the Hoagland study resulted in
a lower estimate of the poor. Fourth, the Paglin study assigned a greater
proportion of the value of in-kind benefits to the poor than is indicated by
the CPS in-kind recipiency data. For example, Paglin assigned about 76 per-
cent of the value of food stamps and 70 percent of the value of Medicaid to
the poor, whereas, in this study the comparable proportions were 60 percent
and 48 percent, respectively. Fifth, the Hoagland/CBO study assigned about
$5.2 billion in housing benefits to the poor compared to only $2.5 billion
assigned in this study. Finally, the Hoagland study used a data base aged to

~ 1980,-and, therefore, reflects higher food stamp outlays in 1980.
The data in table 24 show that the Paglin estimate in the reduction in

poverty was the largest, 64 percent, and this study's estimate the smallest,
42 percent, when food, housing, and medical care are counted. Results based



Table 27. Elderly: Reductions in Poverty Rates Using Alternative Valuation
Techniques and Income Concepts: 1979

Market. | Recipient | Poverty
Income concept value {| value - cash» budget share
approach } equivalent | approach
H approach H
i H
i T
BELOW POVERTY LEVEL :
Money income, alone: : :
Poverty ratessscccscceccscseeceece 14.7? 14.7 14.7
Money income plus food and housing: H
Poverty rate... sees oe 12.9 13.1 13.7
Percent reduction*...seccecceecers -12.1 -10.9 - 6.3
Money income plus food, housing, and —
medical care (excluding institu-
tional care expenditures):
Poverty ratescsccgeevcccsccvcecere | 5.2 9.3 10.8
Percent reductionl......sscececees | ~64.6 | -36.5 ~26.3
Money income plus food, housing, and ! i
medical care (including institu- i |
tional care expenditures): {
Poverty rate. .ccgeveeseeee { 4.5: 7.0 10.8
Percent reduction!......... | -69.5 | -52.4 -26.3
i
BELOW 125 PERCENT OF POVERTY LEVEL |! : ~
Money income alone:
Poverty rat@ssccsccecscevcecrecees | 24.0 24.0 24.0
Money income plus food and housing: :
Poverty rate... 22.4 | 22.5 23.3
Percent reductio - 6.6, - 6.3 - 3.1
Money income plus food, housing, and : :
medical care (excluding institu- ! i
tional care expenditures): : Hl
Poverty rate.ccccsecccccseees : 11.7 | 16.9 20.4
Percent reduction+...scsecceececee | 751.3 -29.8 -14.9
Money income plus food, housing, and :
medical care (including institu- '
tional care expenditures):
Poverty rat@.scecsvcceveccccecsece 9.5 16.1 20.4
Percent reductionl...csscssceeees 60.5 -32.9 “1419

lpercent reduction in the number of poor from the current poverty estimate

based on money income alone.

97



Table C-2. Comparisons of CPS and Alternative Estimates of Persons Covered by
Medicaid in 1979

(Numbers in millions)

CPS as a “Ever
Alternate percent of | received"
Type of recipient CPS estimate alternate HCFA?
Totalesssoee 18.136 20.794 87.2 21.540
No cash public assistance
PECEIVEdssseeseeseeeeceeee | 4,917 3.6101 ,4 136.2 5.575
Received cash public
ASSISTANCE. eceeeeceeeeees 13.219 17.184 76.9 15.960
AFDC adult..cccccccccceee 3.710 4.3452 85.4 3.875
AFDC child.... . 6.718 8.9972 74.6 7.924
SSI aged. sssscceeseceeees 1.606 1.7863, 4 89.9 2.071
SSI blind or disabled...... | 1.185]  2.0563,4 57.6 2.091

HCFA estimate adjusted for decedents and institutionalization and for
1.269 million beneficiaries in Puerto Rico and the Virgin Islands.

2aFDC “ever-received" estimates from Mathematica MATH Model for FY 1980
(Doyle, et.al., 1980) adjusted to CY 1979,

3ss1 “ever-received" estimates, obtained from Jack Schmulowitz, SSA. Basis:
December 1978 caseload, plus new applicants, minus decedents and institu-
tionalized, minus one-third of SSI recipients in "209b" States which do not
provide categorical Medicaid coverage to SSI beneficiaries.

4ndjustment for institutionalization: all institutionalized Medicaid recip-
ients living in mental hospitals or homes for the mentally retarded are
subtracted. In addition, all aged, blind, or disabled recipients who
reside in skilled nursing homes (SNF) or intermediate care facilities (ICF)
for 9 months (270 days) or more during 1979 are subtracted. Further ad-
justments for SNF (ICF) recipients who (1) die, (2) move from one type of
institution to another, (3) temporarily move to a hospital and then die, or
(4) move to a hospital and then move back to an SNF or an ICF are made for
Medicaid recipients residing in an SNF (ICF) for less than 9 months. These
adjustments are used to adjust the administrative estimate of those who
“ever-received" SSI and the elderly, blind, and disabled Medicaid recipi-
ents receiving no cash assistance. About 62 percent of all institution-
alized aged Medicaid recipients and 71 percent of all institutionalized
blind or disabled Medicaid recipients were judged to have been excluded
from the March 1980 CPS because of either being in an institution at the
time of the survey, or because of dying prior to the March 1980 survey
date. These estimates are based on the 1977 National Nursing Home Survey
(1979: table 28) and on Rigby and Ponce (1981).

Stincludes institutionalized, decedents, and residents of Puerto Rico and
Virgin Islands.

113



where consumption expenditures (Ci) by any unit (i) ona given good (j) are
a function of family characteristics, (F), location (L}, region (R), and
pre-tax income (Y), plus an error term (€). Again, consumption expenditures
and income are adjusted for consumer price index changes between 1972-73 and
1979 prior to estimating Cije As in the cell matching approach the location
and region variables capturé inter-area price differences. In this case, CPS
in-kind benefit recipients’ values for F, L, R, and Y along with the regres-
sion coefficients are used to assign a predicted Cjj to CPS in-kind benefit
recipients.18 Both the cell matching and regression approaches risk "selec-
tivity bias" in that some consistent but unknown difference exists between
the group used to derive the consumption levels and the beneficiary group to
which these levels are assigned.

Use of either the cell matching or regression approach requires the
adjustment of consumption levels (Cij's) to 1979 prices since they are based
on the 1972-73 CEX (housing estimates were estimated using the 1979 AHS and,
therefore, did not require adjustment).

As mentioned earlier, all valuation techniques require a good estimate
of MV.as a starting place. The final -step in estimating cash equivalent
values (RV-CEjj's) is to compare the Cij's with the market values (MV; 5's).
The RV-CE;; is equal to the Cij if Cjj iS less than MV; j and equal to Wig if
Cij is greater than or equal to MVij- In the case of housing, this procedure
is modified slightly because the Fecipient must pay some rent. For these
benefits, the Cij's must be compared to the market rent (the market value of
housing services’ consumed). The market rent (MRjj) is actually the sum of
the rent paid by the tenant and the rent subsidy. “If Cjj is less than MRij>
RV-CEjj is equal to the Cj; less the rent paid. If ij iS greater than
MRij> then RV-CE;j is equal to MRyj less the rent paid dy the subsidized
household. The procedure assumes that if normal expenditures exceed market
value, the benefit is equivalent to cash (normal expenditures greater than
market value are valued at $0). When market value exceeds normal expendi-
tures, the cash equivalent value equals normal expenditures.

Although both the cell matching and regression approaches would be
expected to yield similar results, each procedure has its own shortcomings.
In a cell match model, differences in expenditures are accounted for by the
various recipient characteristics used to define the cells. ‘In the regres-
sion model, the coefficients control for differences in the characteristics
of recipients. A limitation of the cell approach is that the variance of
normal expenditures is suppressed.19 This occurs because the number of cells

18smolensky and Van Der Gaag (1980) use the predicted Cj; from this equa-
tion along with a Stone-Geary utility function to estimate relative
no ee EQUIV AT ONCE SCALOS on oe ee ee

19The regression helps direct the construction of the cells by indicating
which demographic characteristics are most significant in explaining the
variance in each type of consumer expenditure.

60



Medical transfers. The RV-CE estimates of medical transfers were derived
using the expenditure data from the 1972-73 CEX, As with food ‘stamps and
housing, the first step was the selection of the universes from which nonnal
expenditures were computed. However, unlike with food and housing, there are
fundamental problems in deriving a relevant counter-factual group for valuing
in-kind medical care expenses by the RV-CE Concept. This is because most
individuals who are not covered by a public in-kind program are covered by a
private in-kind program, either in part or full. Among individuals under 65
who are not covered by Medicaid, employer group policies are widespread. And
for the over 65 group, the problem is even more acute--almost everyone over
65 is covered by either Medicare, Medicaid, or both,

Ideally, the counter-factual group should contain only individuals who

obtain all their medical Care dealing directly with insurance companies, :

doctors, and hospitals. All the premiums, co-payments, etc. would be paid
directly by the individual. It was not possible to obtain sufficiently large
groups like this in the estimation Procedure. For the under 65 group, indi-
viduals were included who were covered for part of their care by employer
contributions. Since only data on their outlays were available and not the
employers', this probably leads to a downward bias in the estimate of the
RV-CE value of Medicaid. For the over 65 group, it was necessary, unfortu-
nately, to include all the individuals who were covered by Medicare only.
This makes the estimates of value for Medicare also very difficult to inter-
pret. They may be on balance upward biased or downward biased so they are
subject to a high degree of unreliability. After the adjustments, the
total sample size available for estimation was. about 9,000 observations.

The next step was the tabulation of expenditures (Cij's) for medical
services and health insurance by income class, family size, and age (under
65 years old and 65 years old and over). Both income and expenditures were
adjusted to 1979 prices prior to tabulation (see appendix D for the nonnal
expenditure values used to calculate the RV-CE for medical care). In addi-
tion, an adjustment was made to account for geographical differences in
prices prior to assigning RV-CE values to Medicare and Medicaid benefici-
aries on the March CPS data file. Table 12 contains the results of the
RV-CE estimations for medical benefits. Estimates including and excluding
institutional care benefits are shown separately.

ee

1972-73 CEX data did not allow that these persons be excluded. It would
appear, therefore, that these estimates of normal expenditures on medi-
cal care are baised downward. Studies have shown (Gibson and Fisher,
~--~-—~--1979),,.however,,--that-Hedi care~covers~ only “about” 62 percent of the total
health care expenses of the elderly. This study has assumed that 100
percent of the normal expenditures for medical care substitutes for
Medicare, a procedure which biases the estimates upward. The net effect
of these downward and upward biases is unknown.



tively small compared to other in-kind housing subsidies, such as tax
subsidies in the form of mortgage interest deductions and mortgage interest
subsidies to owner-occupied homes, which were not included. While most of
the $167.4 billion in nonmeans-tested benefits is. received by the middle and
upper income groups, small proportions received by the poor may be signifi-
cant because the total amount is so large. This is an important limitation
which should be noted. It is hoped that further research will allow the
valuation of all of these benefits so that their effect on the estimated
number of poor and on the well-being of the entire population can be
determined. ,

IN-KIND BENEFITS AND THE MEASUREMENT OF. POVERTY

Incluston of the value of in-kind benefits as income for the purpose of
measuring poverty creates some conceptual problems. The official poverty
thresholds, which were developed by Mollie Orshansky of the Social Security
Administration in 1965, are based on relationships between food requirements,
cash expenditures for food, and cash income. Orshansky (1981) and. others
have stated that changing the income measure used to determine poverty by
adding the value of in-kind benefits requires changing the poverty line as
well, so that it reflects both cash and in-kind benefits. These analysts feel
that revision of the poverty level is necessary from a conceptual standpoint
and that the exclusion of in-kind benefits from the current definition can be
attributed only to the lack of information available on the subject at the
time the poverty definition was developed.

These issues can be seen more clearly by first reviewing the conceptual
underpinnings of the official poverty concept. The discussion will be
facilitated by a symbolic representation of the manner in which poverty
status is currently determined. The ith family (or unrelated individual) is
designated to be in poverty in the jth year if its level of total money
income is less than its corresponding poverty threshold:

is
(income) less (original poverty x (adjustment for changes (1)
than thresholds) in prices)
Y PL. EXP
Vij < EFP., 3 s =f \ ae
55 K k61 72



Table I-1. Income Thresholds at the Poverty Level in 1979, by Size and Type of Family, Number of
Related Children Under 18 Years Old, and Farm-Nenfarm Residence--Continued

Number of related children under 18 years old

Size of family unit

6 or
None 1 2 3 4 5 more

FARM

Families With Female Householder
No Husband Present

1 person (unrelated individual):

Under 65 years....ee $ 3,076 j
65 years and over. 2,948
2 persons:
Householder under 65 years.... 3,841 | $ 4,194
Householder 65 years and over. 3,686 4,194
3 persons.. oe 4,682 4,462 | $ 4,935
4 persons. 6,129 6,346 6,318 |$ 6,251
5 persons... 7,353 7,575 7,542 7,480 | $ 7,229 \
6 persons., 8,581 8,737 8,675 8,612 8,329 1$ 8,076
7 or more persons..... o 10,776 | 10,938 | 10,905} 10,811 | 10,528] 10,308 | $ 9,805

All Other Families

person (unrelated individual):
Under 65 years.... . 3,324
65 years and over... . 2,988
2 persons:

Householder under 65 years.... 4,156 4,656
Householder 65 years and over. 3,732 4,656
3 persons. 4,840 4,997 5,281
4 persons. 6,382 6,476 6,251 6,568
5 persons. 7,701 7,794 7,542 7,353 7,511
6 persons... 8,832 8,861 8,675 8,486 8,234 8,360
7 or more persons.. 11,126 | 11,220 | 11,000 | 10,811 | 10,561 | 10,182; 10,089



on food and housing benefits only (not available from the Paglin study) show
a substantially larger reduction in poverty in the Hoagland studies than in
this study. These results reflect the larger assignment of in-kind benefits
to the poor in both the Hoagland and Paglin studies.

Results from this study and from earlier work by Smeeding (1981) are
shown in table 25 for in-kind benefits valued using the cash equivalent
approach. The earlier estimates are based on a simulation model which was
quite similar to the model used by the Hoagland/CBO study (Smeeding, 1975).
This model simulated in-kind benefit recipiency and benefit levels in a simi-
lar manner but did not “age" the file (Doyle, et. al., 1980). In this study
and in the earlier study, Smeeding used the normal expenditure approach to
estimate cash equivalent value. The Smeeding results for 1972 and 1974 indi-
cate significantly larger reductions in the number of poor, compared with
this study (47 and 38 percent as compared with 27 percent). Some of the dif-
ferences in these estimates are due to adjustments for underreporting of
income, smaller numbers of in-kind benefit recipients, higher benefit weights
for medical care, and over-assignment of multiple benefit recipiency in the
earlier studies. As far as is known there are no other national estimates
of the effect of in-kind benefits on estimates of the poor using the cash
equivalent approach. !

Although not contained in tables 24 or 25, Smeeding and Moon (1980) used
a modified poverty budget share (PBS) approach to value medical care trans-
fers in conjunction with cash equivalent values for food and housing. This
methodology, which also involved adjustments for income underreporting and
use of after-tax income, reduced the number of poor by about 26 percent, com-
pared with a reduction of 20 percent in this study using the PBS approach.

lother earlier studies by Smeeding (1977) and Smeeding and Moon (1980) did
not value school lunch benefits and the latter study also excluded the
value of public housing.



change in CPI and thus reduce estimates of the poverty population.© As shown
in formula (1), the poverty thresholds are adjusted using the overall CPI,
which reflects changes in the prices of commodities faced by persons of vari-
ous income levels. Many feel that updating of poverty levels should be based
on a price index for low income individuals. This CPI for the poor would re-
flect their expenditure patterns rather than those of the entire population--
i.e., higher budget shares for food, housing, and energy.. There is disagree-
ment on procedures to compute a price index for the poor and it is not clear
how such an index would affect the poverty line.7

STriplett (1981) estimates that if the housing component of the CPI was
based on changes in the price of rental housing since 1972, the CPI for
1980 would have been about 12 percent lower. This adjustment would
lower the poverty thresholds by 12 percent, reducing the number of poor
by about 18 percent. Such revisions to the CPI have been officially
. Proposed and are scheduled to take effect in 1983.

7IMinarik (1981) presents ‘a "necessities index" which rose an averagé of
7.1 percent per year during the 1970's, compared to 7.4 percent for the
CPI. Alperovitz and Faux (1981) present a necessities index which rose
by 8.6’ percent per year over this period. Bowring (1981) readjusted
both of these indexes and found that necessities prices rose faster than
the CPI regardless of which necessities index was used.



The foregoing analysis suggests that the use of the normal expenditures
approach to approximate cash equivalent value may have two associated biases.
First, RV-CE may be underestimated because in-kind transfers in excess of
normal expenditures are valued at zero. Second, RV-CE may be overestimated
because normal expenditures are calculated at an income level which includes
the MV of all in-kind transfers, since we cannot consistently or simultane-
ously estimate the RV-CE. The extent of each of these biases is unknown.
The rationale for using the normal expenditure approach instead of a utility-
based approach is explained further in the next section. A detailed diagram-
matic explanation of the normal expenditures technique for estimating RV-CE
is shown fin appendix E.

A modified funds-released approach called poverty budget shares (PBS)
can also be used to value in-kind benefits, as demonstrated in figure 1. As
mentioned earlier, this approach is tied to the current poverty concept, and
only has justification as an approach to measure the impact of in-kind bene-
fits on poverty. Assume that the official government poverty threshold for
a particular type of family is OR(=0P). The value of the subsidized good
(say medical care) that families at this income level would consume is ON.
This level of expenditure (ON) could be subtracted from the poverty threshold
(OR) for persons receiving the in-kind transfer, thus creating a "new"
poverty line (00). The logic of this adjustment implies that since recipi-
ents of medical transfers no-longer need to make any payments for medical
care, their cash income need only be above tne "new" poverty line (UD) to
escape poverty. In the case of medical care, this procedure prevents large
medical transfers from raising individuals (or families) above the poverty
thresholds. The PBS approach enables the in-kind transfer recipient to move
to utility level Up, because adjustment of the poverty line leaves them with
just enough other income (0A) to escape poverty. But note that U, is below
Uc, Uct, and Ucu--the cash equivalent value measures, indicating that, in
this case, it provides a lower bound estimate of the value of in-kind bene-
fits. In fact, only if the recipient had right angle indifference curves
(which included the line segment KB) would the PBS value equal the cash
equivalent value. Such a utility curve indicates that in-kind benefits in
excess of ON (i.e., KB in figure 1) are completely worthless to the recipi-
ent; this is not the case for the utility curves shown in this diagram.
Thus, in studies of income distribution the PBS value would understate the
true recipient value. .

This discussion has illustrated the wide range of methods that can be
used to value in-kind transfers. It has been shown that the MV approach
assigns the highest possible value to the in-kind transfer (OJ=DS). On the
one hand, because the recipient can never reach utility level U, on budget
constraint GB, the MV will overstate the true recipient value. On the other
hand, the true estimate of recipient value is heavily dependent on the shape
of an individual's utility function. It was shown that three different
alternatives for utility curves (Uc, Uci, and Uc") produce three different
estimates of RV-CE (DC, DC', and DC"). Because of difficulties associated
with estimation of utility functions, and the unrealistic assumption that

43


the total number of poor is reduced 42 percent when medical care is included,
it is reduced by only 16 percent when medical care is excluded, When inedical
care is counted, groups with above average reductions in poverty are the
elderly (70 percent), central-city residents (49 percent), Blacks (50 per-
cent), and persons in families maintained by women, no husband present (49
percent). When medical care benefits are not counted, all of these groups
except the elderly have above average reductions in poverty. In fact, the
elderly have the lowest reduction (12 percent) of any of these subgroups.

An. examination of the smallest reductions indicated a diminished impor-
tance of medical care in lowering the estimates of the poor. A comparison of
the smallest reductions including and excluding medical care indicate a
reduction of 20 percent when medical care is included and 12 percent when it
is excluded. Comparisons of the reductions for the subgroups show a similar
pattern to that described for the largest. reductions. The elderly still have
the largest reduction including medical care (26 percent) and the smallest
reduction excluding medical care (6 percent). ‘

Of all of the groups discussed, the elderly experienced the smallest
relative reduction in poverty when only food and housing were counted and the
largest when medical care was also counted. The elderly poor benefit less
from food and housing transfers than the non-elderly, This probably occurs
because many of the elderly poor (based on cash income) have assets. which
are larger than those allowed in the Food Stamp Program and because many (67
percent) own their own homes.

CHANGES IN THE COMPOSITION OF THE POOR

Given the differential impact of in-kind benefits on various subgroups
of the poor, it might be expected: that changes in the overall composition of
the poverty population would occur. Table 23 shows the composition of the
poor based on the current poverty definition and on three selected combina-
tions of valuation techniques and income concepts which were chosen to high-
light the largest and smallest changes. The market value approach valuing
all in-kind benefits results in a significant change in the composition of
the poor. Under this approach, the elderly comprise only about 9 percent of
the poor as compared with 17 percent under the money income definition. How-
ever, the proportion of the poor living in the South increased from 43 to 47
percent. These changes again reflect the importance of medical care benefits
in the determination of poverty for the elderly and the lower provision of
medical care benefits in’ the South. Under the market value approach
excluding medical care, the composition of the poor shows very little change.
There is also very little change in composition noted for the PBS approach
even when medical benefits are counted.

... Only slightly different results were noted for -persons below 125 percent—
of the poverty level. In general the changes in composition are less pro-
nounced than for the 10U-percent poverty level.



Chapter 4. Methodological Issues Concerning the
Valuation of In-Kind Benefits

The collection of data on the receipt of noncash benefits in the CPS was
an important and essential step toward any kind of valuation research. How-
ever, collection of these recipiency data is a routine Operation compared
with the assignment of values to these benefits. Researchers realize that
no single measure of the value of in-kind benefits is adequate for all uses,
since different strategies for valuing in-kind income depend on the purposes
for which the data will be used. Unfortunately, most research studies have
not established the conceptual basis for their approach to valuing in-kind
transfers, and the use of inappropriate measures of the value of noncash
benefits may lead to incorrect policy decisions.

The purpose of. this chapter is to illustrate the wide range of values
that can be estimated for in-kind transfers. The conceptual basis for each
measure is explained, and the range of values between the measures is shown
for food, housing, and medical care. First, an explanation of the three
major concepts of in-kind transfer value is provided. These concepts are
market value, recipient or cash equivalent value (as measured by normal
expenditures), and poverty budget share value (an application of the funds-
released principle). A diagrammatic analysis of these three measures can be
found in the addendum at the end of this chapter. Next, government .cost and
social benefit concepts of value are briefly reviewed. While values based
on these concepts were not assigned to recipients in this report because
they are not directly relevant or applicable to poverty measurement, these
concepts provide a- useful comparison to the other three valuation methods.

The final part of this chapter contains a comparison of results from
Previous valuation studies using the three major conceptual approaches and
the government cost concept. A rough index number is assigned to the food,
housing, and medical in-kind transfers; the market value of benefits is
assigned an index value of 100. The results of this comparison are shown in
table 4 at the end of this section.

Before examining each valuation technique in detail, it is useful to
understand the major conceptual differences between them and their general
relationship to one another. "Market value" is the private market cost of
the goods and services transferred to the recipient. "Government cost" is
the total delivery cost of these goods, which are generally acquired by gov-
ernment at their market value. In general, the social benefits to recipients
and taxpayers must be at least as large as the government cost if the provi-
sion of a given benefit is to be efficient in an economic sense. "Recipient
or cash equivalent value" is the cash amount for which recipients would be
willing to trade their right to the in-kind transfer given their current
incomes (including cash and other in-kind transfers). The “poverty budget

26



Table D-5.

Householder and Size of Household for 1979

(Numbers in dollars)

Normal Expenditure Values for Food Used in the Com

putation of RV-CE by Age of

Householder age less
than 35 years old

| Householder age 35 years old and over

Total Household | |
income | ! 3 5
1 ; 2 | Persons 1 2 3 4 | Persons
Person | Persons | or more | Person | Persons |Persons | Persons | or more

: i , 1
Under $1,250......... 363 | 747 | 847 | 835 | 1,514] 2,529; 2,760} 3,398
$1,250 to $2,499, 423 | 528 | 738 i 956 1,591 1,691 | 2,658 3,160
$2,500 to $3,749, 672 833 | 933 | 984} 1,474 2,226: 2,516 3,722
$3,750 to $4,999, 775} 392 i 925 | 1,123; 1,956 2,651 | 2,624 3,400
$5,000 to $6,249, : 723 | 1,316! .1,082! 1,159 } 1,974 2,947! 3,162 3,561
$6,250 to $7,499..... : 716 1,270) 1,679. 1,262: 1,875 2,558! 3,156 3,337
| ! i :
$7,500 to $8,749... 701 | 1,552! 1,834. 1,534 1,975 2,752: 2,836 2,431
$8,750 to $9,999..... 733.: 1,401; 2,215 1,494 | 1,925 2,287 © 2,992 3,670
$10,000 to $11,249... 859° 1,012; 2,389 1,463 | 2,281 2,325 | 2,340 3,614
$11,250 to $12,499... 959 1,611} 2,486 1,347 : 2,408 2,543 + 3,360 4,165
$12,500 to $13,749... 1,626! 1,505! 2,355 1,388 2,191 2,514 3,011 3,796
$13,750 to $14,999... 1,201 | 1,641; 2,095 1,405 2,235 3,198 3,412 3,586
$15,000 or more. 1,293 1,919 i 2,878 1,757; 2,693 3,436 5 4,002 4,681
Spon come an -_ 128



where

Yay = level of before-tax total money income.

EFP., = the 1961 Economy Food Plan.
Yat = the reciprocal of the ratio of food expenditures to after-tax
= income for all families, as determined by the 1955 Survey of

55 Food Consumption Expenditures.

k = specific commodities used in computing the Consumer Price Index,

Pai = the ratio of the price of commodity k in year j to 1961.

Peet

EXP, 25 = the ratio of expenditures on commodity k in 1972 to total expen-

EP. ditures in 1972, (The weights used to calculate the Consumer
72 Price Index have been updated based on results from the 1972-73

Consumer Expenditure Survey. )

The right side of the expression separately identifies the original
poverty thresholds and the Consumer Price Index (CPI) which is used to adjust
them for changes in prices over time.

The original poverty levels were first computed for the year 1963 based
on U.S. Department of Agriculture (USDA) data from the 1961 Economy Food Plan
and the 1955 Survey of Food Consumption. The Economy Food Plan was the Teast
costly of four USOA food plans which provided a nutritionally adequate diet.
This food plan could be used as a standard for food, but there were no stand-
ards for other necessities, such as housing and medical care. One solution
to this problem of how much money to assign to cover the cost of nonfood
necessities was to look at the Proportion of income spent by the average fam-
ily on such items. For example, if the average family spent one-third of its
after-tax income on food and two-thirds on other items, then it might be rea-
sonable to suppose that, with proper budgeting, the poor could do the same.
The 1955 Survey of Food Consumption provided estimates of the proportion of
after-tax money income that the average family spent for food. The recipro-
cal of this ratio (Yat/F)55, which is often called "the multiplier," was
multiplied by the Economy Food Plan (EFP¢,) to derive the original poverty
thresholds. The multiplier determined from the 1955 survey was about three
for families of three or more persons, indicating that they spent about one-
third of their income on food. For smaller families and persons living
alone, the cost of the economy food plan was multiplied by factors that were
slightly higher in order to compensate for the -relatively larger fixed
expenses of these smaller households. Thus, formula (1) is a simplification
of reality, sipce there are separate poverty thresholds for each household
configuration.

3The complete matrix of poverty thresholds can be found in appendix I.

— ee wee LQ cee — _—



have found large differences in the insurance value of Medicaid by race and
metropolitan-nonmetropolitan residence (Smeeding and Moon, 1980; Davis and
Schoen, 1978). Earlier research (Smeeding, 1975) has indicated that the net
impact of adjustments to account for these differences would be small.

The market value of Medicare was computed by dividing the total Medicare
expenditures less the SHI premiums by the number of persons covered by
Medicare. The insurance value of Medicare including institutional care bene-
fits was estimated to be $1,243 in 1979;. the insurance value net of institu-
tional care expenditures was $1,215.

Table 8. The Average Market Value or Insurance Value for
Medicaid, by Risk. Category: 1979

Average Medicaid insurance

value per enrollee
Type of beneficiary Including Excluding
institutional | institutional
care care
Elderly! (age 65 or over)....... $ 1,970 $ 418
Disabled! (blind or disabled)... 2,522 1,311
Adult (age 21-64, nondisabled).. 629 629
Child (age less than 21)........ 329 329

-——-- +L. - 1

lexcludes SMI premium.



Table D-4 presents an estimate of the value of Medicaid for the elderly
and disabled which excludes institutional care benefits. These estimates
were derived by subtracting the expenditures for institutional care from
total vendor payments in equations (3) and (4). The percent of the total
value of Medicaid insurance which is for noninstitutional care is also shown
for each State or State group.

125



1979; excludes other health-related employer benefits such as sickness and
accident insurance, and employer-provided private disability insurance.
(Survey of Current Business, July 1981, table 8.4, line 10)

Row 5 (Total Means-Tested Benefits)

Major in-kind transfer benefits exclude "paid" school lunch benefits and
Medicare. Virtually all children who eat hot school lunches receive a
basic subsidy under this program, even if they pay "full established
price". This subsidy accounts for about 51 percent of total school lunch
benefits. The other 49 percent of benefits go to those who either receive
the lunch for free or pay a reduced price of 10 to 2U cents per meal.
Medicare covers all elderly Social Security beneficiaries and a large
group of the nonaged disabled, primarily those receiving Social Security
benefits. About 15 percent of Medicare benefits accrue to poor elderly
persons, however, Medicare is not a means-tested transfer program.

Other public includes expenditures for USDA income-tested programs such
as WIC, expenditures for maternal and child health care, and one-half of
mortgage interest guarantees (which are income-tested only in the broadest
sense yecause they accrue mainly to households with incomes above the
median).

Other private includes health care vendor payments made by charitable
organizations ($2.0 billion) and charitable food and housing expenditures
($0.4 billion) (Social Security Bulletin, 1981). Because these payments
are generally for the benefit of the indigent, they were included under
means-tested benefits.

109



Table C-l. Characteristics of Food Stamp Households: October 1979
USDA Survey and March 1980 CPS

October March
Selected Characteristics 1979 USDA | 1980 CPS
RACE AND SPANISH ORIGIN OF HOUSEHOLDER!

Total..e.ee see eee cece et eteceene 100.0 100.0
White 58.5 63.0
Black.. 41.5 35.3
Spanish orig 13.1 10.5
SEX OF HOUSEHOLDER
Malessccseccccnveee se ececcccvcece sees 31.0 42.0
Female. scececcccccccccees se seeecenece 69.0 58.0
AGE OF HOUSEHOLDER
15 to 19 years........5 tee eeewoevees . 4.0 1.9
20 to 34 years.. 41.0 39.1
35 to 44 years.. 15.7 17.4
45 to 54 years.. 10.5 13.1
55 to 59 years...... 4.9 5.4
60 to 64 years..... 5.9 “6.6
65 years or older...... veeee 18.0 17.3
SIZE OF HOUSEHOLD
One person....... ooo ecccsvccccses sees 31.1 28.4
Two persons... 21.2 20.7
Three persons. 19.0 18.5
Four persons..... 12.5 14.2
Five PersonS...cccsceee 7.8 8.6
Six persons..... . 4.2 4.5
Seven persons...... eeee oe 1.9 5.0
Eight persons or more...seccccsececes 2.3 °
Received cash public assistance2..... 42.2 49.1

lpersons of Spanish origin may be of any race.

2Public Assistance includes AFDC, emergency assistance and general
assistance, but excludes SSI.

111



approaches and three different income concepts result in the presentation of
nine alternative estimates of poverty. Table A presents a comparison of
these estimates with the official poverty estimate based on before-tax money
income alone. (The poverty statistics in this report differ slightly from
the official poverty statistics published by the Bureau of the Census, since
poverty was determined on a household basis rather than for families and
unrelated individuals. See chapter 6 for further details.)

The broadest income definition, which includes food, housing, and medi-
cal care with institutional benefits, reduces the poverty rate from 11.1
percent to 6.4 percent based on the iwnarket value approach. Of the three
in-kind benefits, medical care is. by far the most important for lowering the
poverty rate. At market value, for example, the value of food and housing
alone lower the poverty rate from 11.1 percent to 9.4 percent, while the
remainder of the decrease is due to medical care benefits.

The recipient or cash equivalent value approach substantially reduces
the number of poor although not to the same extent as market value. The
reduction in the poverty rate using this approach for food and housing alone
is nearly the same as for market value (9.5 percent remained poor compared
to 9.4 percent for market value). The inclusion of medical care benefits

. With institutional care expenditures reduces the poverty rate further to 8.2

percent for the recipient or cash equivalent method. Thus, tne marginal
effect of medical benefits on the poverty rate is significantly less than
that of the market value approach.

The poverty budget shares approach has a smaller effect on the number of
poor than either of the other approaches. The poverty rate using the poverty
budget share method for food and housing alone declines from 11.1 percent to
9.8 percent. The marginal effect of medical care benefits is less than
either of the other valuation methods as well. The poverty rate based on the
poverty budget share approach including medical benefits is 8.9 percent.

The choice of valuation technique and income concept produces a wide
range of estimates when the value of in-kind benefits is included in the
determination of poverty using the current poverty definition. The reduction
in the estimated number of poor ranges from a high of 42 percent to a low
of about 12 percent. Overall, the estimated number of poor in 1979 was 23.6
million. This number declines to 13.6 million using the procedure producing
the 42-percent reduction and 20.7 million under the procedure producing the
12-percent reduction.

In no subgroup of the population is the effect of the value of medical
benefits greater than on the elderly. (See table 8.) The official poverty
rate for the elderly was 14.7 percent in 1979. Food and housing benefits at
market value reduce this poverty rate to 12.9 percent. The addition of the
value of medical benefits with institutional care included reduces the
poverty rate for this group dramatically to 4.5 percent. Other combinations
of valuation techniques and income concepts have smaller effects.

vii


Table 21.

Poverty Budget. Share Approach:

Number of Persons Below 125 Percent of the

Poverty Level and Poverty Rates, by Income Concept for Selected Population

Subgroups:

(Numbers in thousands.

1979

Persons as of March 1980)

|

Income concept

! Money income plus | Money income plus
Money income food, housing, food, housing,
Selected Money income plus food and and medical care and medical care
characteristics’ alone! | housing (excluding (including
institutional institutional
care) care)
Poverty Poverty Poverty Poverty
Number rate Number rate Number rate Number rate
All persons. 33,445 15.8 31,744 15.0 30,381 14.3 i 30,381 14.3
South..... 13,767 19.9 13,061 18.8 12,596 18.2 i 12,596 18.2
Non-South. 19,678 13.8 18,683 13.1 17,785 | 12.5 |; 17,785 12.5
i
Elderly (65 |
and over)..... 6,708 24.0 6,497 23.3 5,708 | 20.4 | 5,708 20.4
Non-elderly.... 26,737 14.5 25,247 13.7 24,673 | 13.4) 24,673 13.4
Central city... 11,915 20.5 11,227 19.3 10,712 18.4 i 10,712 18.4
Suburb.......e. 8,369 9.9 7,915 9.3 7,574 | 8.9: 7,574 8.9
Nonmetropolitan | i
ArCa.seeeeeees 13,161 19.1 = 12,603 18.3 12,096 | 17.5 | 12,096 17.5
Blackseceeceeee 9,614 39.2 8,980 36.6 8,603 35.1 | 8,603 35.1
Non-Black...... 23,831 12.7 22,764 12.1 21,778 11.6: 21,778 11.6
i | 1 i
In households i | i
with female : | |
householder, | |
no husband : ' : :
present....... . 11,317 43.1 10,520 | 40.0; 10,142 | 38.6 10,142 38.6
All other ; es ee
“persons 22,128 W.9° 21,2245 11.45 ~ 20,239 | 10.9 , 20,239 i 10.9
' { i 4 !

lpiffers from official poverty count for 1979 of 35,592 (16.3 percent) because: poverty
status was determined on a household basis.

--88....



some types of goods (e.g., large amounts of medical: care) may be signifi-
cantly upward biased in their valuations using RV-CE. For tnis reason (and
other reasons outlined below), we now introduce a method that imposes a limit
on the amount that each in-kind good can contribute to a family's economic
well-being for purposes of measuring poverty.

POVERTY BUDGET SHARE VALUE

The third valuation method examined in this study is poverty budget
shares (PBS). The PBS approach is a different and less general valuation
technique which links the value of in-kind benefits directly to the current
concept of poverty. The PBS approach assumes that, for the purpose of meas-
uring poverty, the value of an in-kind benefit can be no more than that
implied by observed consumption levels for people near the poverty level.
The derivation of the current poverty level, which is based on well-specified
food needs, implies certain levels of need for other commodities as well
(i.e., some amount of expenditure for each good is budgeted into the poverty
level) even though no well-specified tevels of minimum housing or medical
care needs exist. It can be argued that the value of these benefits for
Purposes of measuring poverty should be limited to no more than the amounts
which are assumed to be included in the poverty levels. To assign a larger
value to those benefits assumes that they can substitute for other commod-
ities, which is not the case.

A short example should illustrate the rationale for the PBS approach.
The market value of Medicaid coverage in the State of New York for an elderly
person was estimated in this study to be $4,430 (see appendix D, table D-3)
for 1979. The poverty threshold for an elderly person living alone in 1979
was $3,472. Medicaid benefits valued at $4,430 are far in excess of the
imptied share of the $3,472 poverty level required for medical care. for
purposes of measuring poverty, the person's medical needs have been met.
Under the PBS approach, the excess value of medical care are not counted
because these excess benefits cannot be substituted for other needs at the
poverty level such as food and housing. The PBS approach is particularly
useful for. assessing the effect of medical care benefits on estimates of
poverty because of the problematic nature of assigning values to these
benefits.

There are several important aspects of the PBS approach which should be
mentioned. First, because the procedure limits the value of in-kind bene-
fits, especially that of medical care, it has less effect on poverty than

80rshansky (1977) suggests that the proper value of in-kind benefits
is equal to the expenditure on the given good prior to the receipt of
the in-kind benefits, i.e., the amount of funds released for additional
consumption. The PBS approach is similar to this measure in that it
measures funds released, but only at poverty line income levels.

31



Appendix |. Poverty Thresholds: 1979

153


Medicare. Computation of estimated market values (MV) for Medicare benefits
required data from a number of sources. The basic equation for estimating MV
for Medicare requires administrative data from the Department of Health and
Human Services and survey data from the Health Care Finance Administration
and the Bureau of the Census. The equation used was

where:
Wij

EXij

CBj

SMIP

Wij = EXij x Ax CV

JN ee - SMIP , (2)
Cig x CBy

MV of Medicare including institutional care for beneficiary
type i (i = elderly, non-elderly) in State j.

Total vendor payments including institutional care expendi-
tures for beneficiary type i in State j for FY 1979.

The 3.4 percent administrative claims Processing markup in
FY 1979,

The change in vendor payments between FY 1979 and calendar
year (CY) 1979, about 5.8 percent.

The total number of Medicare beneficiaries, including institu-
tionalized and decedents of beneficiary type i in State j
during FY 1979 as provided by HCFA.

The change in number of beneficiaries between FY 1979 and
CY 1979 for beneficiary type i, an increase of 11.2 percent
for the non-elderly, and an increase of 5.7 percent, for the
elderly.

The $101 for Medicare part B insurance premium,

Substituting these values into equation (2) yields the following formu-
lae for elderly and non-elderly beneficiaries:

(2a) Elderly

M15 EX)j (1.034) (1.058)

~ typ (07)

“-101

120

wq


Table 25. Comparison of Results from Previous Studies Using the Cash
Equivatent Value of In-Kind Transfers

Poverty rate
Percent
Study Data set and Prior to After reduction
income year in-kind in-kind | in poverty
transfers | transfers
A. FOOD, HOUSING, |
AND MEDICAL j
TRANSFERS |
This report). ...cceeee cPs, 1979 11.1 8.2 | 26.7
Smeeding (1981)2...... cps, 1974 11.6 7.2) 37.9
i
CPS, 1972 11.9 6.2 | 47.0
B. FOOD AND HOUSING | '
ONLY '
This reportl.......ee5 cps, 1979 | M1 9.5. 14,4
Smeeding (1981)2...... cps, 1974 | 11.6 | 9.0 | 22.4
cS, 1972 11.9 8.3 30.2

lin this report, CPS money income data are not adjusted for income under-
reporting, as in the other studies.

2smeeding (1981) adjusts Smeeding (1977)--the source of the 1972
estimates--and Smeeding-Moon (1980)--the source of the 1974 estimates,
for omission of housing transfers in 1972, and school lunch benefits in
both years. ‘

95


Food transfers. There are several estimates of the cash equivalent value of
food stamps, some based on empirical utility function approaches (Cooper and
Katz, 1978; Clarkson, 1976; Smolensky, et.al., 1977; Murray, 1980) and others
based on the normal expenditure approach (MacDonald, 1977; Smeeding, 1975;
Plotnick and Smeeding, 1979). The studies based on food stamp data prior to
January 1, 1979, provided estimates of RV-CE which were between 83 and 96
percent of my.12 Smeeding (1975a) estimated RV-CE for a food stamp program
with no purchase requirement, all else constant, and found an average RV-CE
of 97 percent of MV. Based on these estimates, food stamps are, on the
average, for all practical purposes as good as cash. The PBS values for
food transfers vary with family size (as do the poverty lines themselves),
In general, PBS values range between 94 and 98 percent of the market value.

The GC for food programs (food stamps and school lunches) is generally
larger than MV because of ovérhead costs. MacDonald (1977) reports that
such food stamp costs are 8 to 9 cents per dollar of food stamps provided. 15
It is difficult to estimate the GC of the School Lunch Program because
little information on program overhead is available and conceptual problems
abound. For instance, the commodities procured, processed, and distributed
as part of the National School Lunch Program cost $860. million in 1979.
However, agricultural’ price supports and purchasing policies would likely
have necessitated the same Procurement, processing, and distribution costs
regardless of whether or not these commodities were used for the School
Lunch Program. To charge all of these costs to the School Lunch Program is
clearly inappropriate.

Housing transfers. Several authors have estimated RV-CE for different types
of public housing using a utility function approach (Smolensky, et. al.,
1977; Murray, 1975, 1980; Cooper and Katz, 1978). Other researchers have
used the normal expenditures approach (Smeeding, 1975) and the funds released

12The Food Stamp Program prior to this time required the recipient to
pay a fixed price for a larger amount of stamps. For example, in order
to receive $150 in stamps, a family might have paid $50 prior to 1979,
After 1979, the purchase requirement was removed and this same- family
received $100 worth of stamps without cost. The MV remained the same,
but the total required food budget share declined, thus affecting the
level of food expenditures and the relationship between RV-CE and MV.

13the estimates of RV-CE may vary significantly at different income
levels. In particular, lower income families may have much lower values
of RV-CE--possibly as low as 60 percent of MV.

14The pBs values may average more than the RV-CE values because very low
income families may have RV-CE less than PBS,

\5these costs are lessened to some extent if one counts State and local
government food sales taxes paid on purcnases with food stamps as a
savings. The cost figures are better termed Federal Government cost
for food stamps.