BLS WORKING PAPERS

U.S. DEPARTMENT OF LABOR 

Bureau of Labor Statistics 

OFFICE OF PRICES AND LIVING

CONDITIONS 

 

Personal Assessments of Minimum Income and Expenses: What Do They Tell Us about 

‘Minimum Living’ Thresholds and Equivalence Scales? 

Thesia I. Garner, U.S. Bureau of Labor Statistics 

Kathleen Short, U.S. Census Bureau 

 Working Paper 379

 March 2005 

All views expressed in this paper are those of the authors and do not necessarily reflect the views or policies of the U.S. 

Bureau of Labor Statistics.
1

Personal Assessments of Minimum Income and Expenses: What Do They Tell Us about 

‘Minimum Living’ Thresholds and Equivalence Scales? 

By 

Thesia I. Garner 

Division of Price and Index Number 

Research 

U.S. Department of Labor 

And 

Kathleen S. Short 

Housing and Household Economics 

Statistics Division 

U.S. Census Bureau 

February 4, 2003 

Published in John A. Bishop and Yoram Amiel, eds., Inequality, Welfare and Poverty: 

Theory and Measurement, Research on Economic Inequality, Vol. 9, Oxford, UK: 

Elsevier Science, 2003, pp.191-243. 

JEL Classifications: 

D12 Consumer Economics: Empirical Analysis 

I31 General Welfare, Basic Needs, Living Standards, Quality of Life 

I32 Measurement and Analysis of Poverty 

Key Words: well-being, sufficiency, poverty, expenditures, SIPP 

Preliminary results from this study were presented at the 1998 Southern Economics Association Conference in 

Baltimore, Maryland. We thank our conference paper discussant, Denton Vaughan, and others attending the session 

for their comments and suggestions during and after the conference. To Larry Radbill we extend our sincere 

gratitude for his diligence and insight regarding the creation of the Basic Needs Module of the Survey of Income and 

Program Participation. To Gordon Fisher we extend our thanks for loaning us parts of his library, encouraging us 

constantly along the way, and providing comments on drafts of the paper. We also thank Arie Kapteyn and Klaas de 

Vos for their comments on our work and guidance and support throughout the project as we followed the methods of 

the Leyden group and the generations that follow. To Susan Poulin we owe special thanks for discussions 

concerning the Statistics Canada study in which the same basic methods as ours are used. We thank Linda Stinson, 

Clyde Tucker, and others within the Bureau of Labor Statistics for their guidance in exploring the “whys” and 

“hows” of minimum income and spending. Thanks also go to Teague Ruder for research assistance, and David 

Johnson, Chuck Nelson, Daniel Weinberg, and an anonymous referee for excellent comments and suggestions. 

This paper reports the results of research and analysis undertaken by U.S. Bureau of Labor Statistics and U.S. 

Census Bureau staff. This work has gone through internal review within our respective organizations and now is 

released to inform interested parties of research and to encourage discussion. All views expressed in this paper are 

ours and do not reflect the views or policies of their respective agencies or the views of other staff therein. We 

authors accept responsibility for all errors. 
2

Abstract 

Subjective minimum income (MIQ) and minimum spending (MSQ) are the study focus. Basic 

Needs Module (1995) data from the U.S. Survey of Income and Program Participation are 

analyzed. A regression intersection approach is used to estimate household thresholds. MIQ 

thresholds are higher than MSQ thresholds. Both are higher than U.S. official poverty thresholds, 

and thresholds based on a National Academy of Sciences (NAS) methodology. Subjective 

threshold based equivalence scales imply greater economies of scale than those in the other two 

measures but are similar to behavioral scales. This finding suggests that families make trade-offs 

to meet their minimum needs. 
3

1. Introduction 

Economic well-being or welfare can be defined in terms of inequality, poverty, 

sufficiency or insufficiency, or social exclusion or inclusion along some continuum. Each of 

these concepts can in turn be operationalized in various ways using different measurement tools. 

Most often income, expenditures, net worth, and the possession of and access to certain 

commodities within a society are studied. Gordon and colleagues (2000), among others (e.g., 

Callan and Nolan 1991), have identified and discussed major approaches used to measure 

poverty and related concepts. These include: 

1. Consensual/ social indicators 

2. Social exclusion 

3. Subjective measures 

4. Income thresholds 

5. Budget standards 

Often researchers and policymakers use these measures as complements rather than as 

substitutes, as each may present a somewhat different but equally important dimension of one’s 

situation in society. 

Societal norms on what constitutes a minimum standard or level of living can be reflected 

through personal assessments using household survey data. For example, household survey 

respondents answer questions with respect to their own household, a household similar to theirs, 

or a specific household (e.g., a family of two adults and two children). Such measures are often 

referred to as subjective. The literature in which different subjective-based measures of 

economic well-being appear has grown dramatically from their early beginnings in the 1960’s 

(e.g., Van Praag 1968). Recent (since the mid-1990’s) citations in the economics and social 

policy literature include applications or discussions of subjective-based measures that focus on: 

consumption adequacy (e.g., Andrews et al. 2001; Bickel et al. 2000; Gundersen and Oliveira 
4

2001; Pradhan and Ravallion 2000); and poverty, income sufficiency, and social exclusion (e.g., 

Bellido et al. 1999; Middleton 2000; De Vos and Garner 1991; Garner and De Vos 1995; Gordon 

and Pantazis 1997; Gordon et al. 2000; Kot 2000; Townsend et al. 1997; Van den Bosch 2000; 

Vaughan 1996; Waldegrave and Frater 1996). Many other references appear in the literature but 

are not included here, as the list is quite exhaustive (see Gordon and Pantazis, 1997, and Gordon 

et al. 2000, for additional references).1 

Different subjective approaches have been used to produce thresholds to identify those 

less well-off using measures. Thresholds used in this way are assumed to reflect of the level of 

economic resources that families of different sizes need to attain the same level of well-being. 

Gordon et al. (2000) suggest that the “simplest and arguably most democratic” approach to 

produce a subjective poverty threshold is to average responses to a minimum income question 

from the surveyed sample (p. 73). This approach has been followed for Britain (Townsend and 

Gordon 1991; 1996; Townsend et al. 1997) and Australia (Saunders and Matheson 1992). For 

several countries in Western Europe, Deleeck et al. (1992; as reported by Van den Bosch 1993) 

identified the poor using the responses to a minimum income question and an income evaluation 

question in combination. The data of households with a respondent reporting having some 

difficulty making ends meet with their incomes were used to derive the poverty line. Then a 

comparison of actual incomes and reported minimum incomes was used to derive poverty lines 

for different household types. 

Other methods to estimate subjective thresholds have been proposed and used 

extensively. Early work was conducted by a group of Dutch researchers2

 (e.g., Goedhart et al. 

1977; Hagenaars 1986; Hagenaars and De Vos 1988; Hagenaars and Van Praag 1985; Kapteyn et 

 

1

 Some of these are also discussed, for example, in Fisher (2001) and Veit-Wilson (1998) in general discussions of 

setting minimum adequacy or budget standards. 
5

al. 1988; Van Praag 1968). Two separate approaches are presented in the literature. One is based 

on responses to a minimum income question and the other on personal evaluations of current 

income. Both approaches are model-based in the sense that a model is used to explain the interhousehold variation in the responses to survey questions; individual responses alone are not used 

to determine the poverty line directly.3

 Because survey questions are used to elicit responses and 

because the thresholds are model-based, Kapteyn et al. (1988) have pointed out the importance 

of correct specification and robustness. 

The approach examined in this study, the minimum income approach, is considered the 

more basic of the two approaches proposed by the Dutch. The underlying assumption of this 

approach is that only people who have incomes that are at the minimum know what the true 

minimum is. The estimation model reflects this assumption as a regression intersection method is 

used (outlined in the Section 3. Data and Methods). Subjective thresholds using this approach 

have been produced previously for the U.S. by Colasanto et al. (1984), using the 1981 Wisconsin 

Basic Needs Study (BNS), Danziger et al. (1984), using the 1979 Income Survey Development 

Program (ISDP), and De Vos and Garner (1991; Garner and De Vos 1995) using the 1982 

Consumer Expenditure Survey (CE). 

Over the years, concerns about subjective measures of well-being such as these have been 

voiced (e.g., Bradbury 1989; Callan and Nolan 1991; Citro and Michael 1995). Levels of 

estimated thresholds and the equivalence scales implicit in the thresholds have been at issue. 

Generally higher threshold levels than those based on expert budgets, for example, or those used 

 2

 The original group of researchers is known as the “Leyden” group. 

3

 See Van den Bosch (1993) for a brief review of the two model based approaches and the Deleeck et al. (1992) 

approach. 
6

to determine eligibility for social transfers have been reported. (For U.S. examples, see e.g., 

Colasanto et al. 1984; Danziger et al. 1984; De Vos and Garner 1991; Garner and De Vos 1995). 

Equivalence scales implicit in these thresholds suggest greater economies of scale than those 

reported for other measures (for scale comparisons , see: Atkinson et al. 1995; Buhmann et al. 

1988; Citro and Michael 1995). 

Another concern that has been raised is the previously reported wide variation in 

subjective thresholds across different studies. (Regarding estimates for the U.S., see Citro and 

Michael 1995). Given these reported differences, it is clear that systematic evaluations of 

subjective questions are needed. Morisette and Poulin (1991) conducted such a study using 

Canadian data collected from individuals participating in the Survey of Consumer Finances 

(SCF) in 1983, 1986, 1987, and 1988. They found that when the same question wording for 

assessing subjective minimum income was used over time in the SCF, there were no notable 

changes in the answers and resulting thresholds. “Minimum incomes were remarkably stable 

throughout the four years of the study” (p. 36). Also tested in the Canadian study were 

variations in question wording. 

This study builds upon the Canadian work by examining alternative question wordings, 

controlling for questionnaire and time period effects. Two different versions of the minimum 

income question were included in a special Basic Needs Module of the U.S. Survey of Income 

and Program Participation (SIPP). Data were collected in 1995, during wave 9 of the 1993 panel 

(see Section 3. Data and Methods for details). The first question asks for the minimum income 

that the respondent thinks a family like his or hers needs to make ends meet (hereafter referred to 

as minimum or MIQ). The second asks for a minimum amount that the respondent would have 

to spend to provide for the basic necessities of his/her family (hereafter referred to as the 
7

minimum spending questions MSQ). For this question only, basic necessities are defined as 

barely adequate food, shelter, clothing, and other essential items required for daily living. These 

are the latest data available from a federal household survey in the U.S. that includes such 

questions. Subjective thresholds are produced using the methods of Goedhart et al (1977) and 

Kapteyn et al (1988). The same basic approaches were used in whole or in part in the earlier U.S. 

studies (Colasanto et al. 1984; Danziger et al (1984); De Vos and Garner 1991; Garner and De 

Vos 1995).

Thresholds and implicit equivalence scales based on the MIQ and MSQ are compared to 

those based on other approaches, such as the U.S. official poverty thresholds, an approach 

recommended by the National Academy of Sciences Panel on Poverty Measurement and Family 

Assistance, hereafter referred to as the NAS thresholds (Citro and Michael 1995), and relative 

thresholds using the OECD Social Indicators scales (Jenkins and O’Higgins 1987). Subjective 

threshold equivalence scales are relatively flatter than those in official poverty thresholds but are 

in the range of those reported in the literature using econometric modeling of consumption and 

expenditure behavior. Comparing the results from the MIQ- and MSQ- based measures leads us 

to conclude that question wording matters. Asking about “income before taxes needed for 

necessary expenses,” not surprisingly, results in thresholds that are higher than when asking 

about “barely adequate spending needs.” Consistent with the finding of Morisette and Poulin 

(1991), who analyzed essentially the same questions for Canada, the more specific question 

wording of the MSQ results in thresholds that are substantially lower than those based on the 

alternative MIQ. The thresholds based on the personal assessments of respondents result in 

greater percentages of households below their respective thresholds of well-being when their 

incomes are evaluated. Those most worse-off are single parent households. These results are 
8

consistent with those observed using the official poverty thresholds and NAS measure, although 

the incidence is higher for the MIQ and MSQ measures. 

An additional analysis is conducted to assess the stability of the MIQ measure over time. 

For this the coefficients from the earlier CE study are applied to the SIPP sample. Estimated 

thresholds are updated to 1995 dollars. A comparison of results based on the SIPP MIQ and the 

earlier CE MIQ coefficients applied to the SIPP sample are not only consistent but surprisingly 

similar. 

The paper proceeds as follows. Section 2 summarizes the history of the collection of 

these data in the U.S. Section 3 describes the data and the intersection method used to produce 

the minimum living thresholds. Section 4 includes the regression results, estimated thresholds, 

implicit equivalence scales, and percentages of the population with incomes below the estimated 

thresholds. Section 5 includes a discussion of the findings, and recommendations for further 

research. 

2. Background 

This study began as an effort to develop a package of questions to measure personal wellbeing in the context of an ongoing national survey. The U.S. government had previously 

supported the collection of MIQ data with the Census Bureau’s 1979 Research Panel of the 

Income and Survey Development Program (ISDP) and the Bureau of Labor Statistics (BLS) 

1982 Consumer Expenditure Survey (CE). The MIQ had been added to the CE based upon the 

recommendation of the Expert Committee for the BLS Family Budget Revisions (Watts 1980). 

The committee recommended that a major effort be undertaken to evaluate and perfect a survey 

methodology that would permit a paradigm shift away from the notion that official experts can, 
9

and should define what the populace needs in order to get along or prosper. Early efforts were 

met with resistance (Vaughan 1996). 

In 1990, an interagency group of researchers gathered to consider how to develop a set of 

questions that could be used to assess the more general concept of “well-being” for the 

population (see Kominski and Short 1996; Stinson 1997a). By 1991, a formal proposal from the 

team was presented to the Office and Management and Budget (OMB) to include the questions 

in the SIPP, a Census Bureau survey. As part of the process, the OMB requested that the BLS 

and Census Bureau undertake a research plan to test and evaluate minimum income questions. 

The Census Bureau and BLS developed an evaluation program that included analyses of data 

collected using the SIPP and data collected through cognitive interviews. (For a discussion of the 

cognitive aspect of the evaluation, see Steiger et al. 1997; Garner et al. 1997; Stinson 1997a, 

1997b, 1998.) This paper presents results from the evaluation process using the data collected in 

the SIPP. 

3. Data and Methods 

The data for this study are from the Survey of Income and Program Participation (SIPP) 

collected in 1995 from wave 9 of the 1993 panel. (See Section 3.2 for details.) Minimum income 

questions were included in the Topical Module on Basic Needs for this panel. The module 

included three sections with questions concerning the household’s ability to meet expenses for 

specific commodities, perceptions of food adequacy within the household,4

 and evaluation of 

minimum income. Within the minimum income section, each household respondent was asked 

an income evaluation question and a question about either minimum income or minimum 

 

4

 Analyses of data from the first two sections of the module have been conducted by Bauman (1999), Andrews et al. 

(2001), Bickel et al. (2000), and Gunderson (2001), among others. 
10

spending. A split sample design was used to collect the minimum income and spending data: 

half of the SIPP respondents were asked the income question and the other half were asked the 

spending question. 

3.1 Questions of Interest 

We began our study, just as Morisette and Poulin 1991 did, with the basic assumption 

that the MIQ and MSQ are alternative question wordings that refer to some underlying minimum 

level of living as perceived by household respondents. The specific SIPP questions follow: 

The Minimum Income Question (MIQ) 

To meet the expenses you consider necessary, what do you think is the minimum 

income, BEFORE TAX, a family like yours needs, on a yearly basis, to make ends 

meet? (If you are not living with relatives, what are the minimum income needs, 

BEFORE TAX, of an individual like you?) 

The Minimum Spending Question (MSQ) 

In your opinion, how much would you have to SPEND each year in order to 

provide the BASIC necessities for your family? By basic necessities I mean barely 

adequate food, shelter, clothing, and other essential items required for daily 

living. 

As noted earlier, a split sample design was used so that only one half of the respondents would 

be asked each of the questions. Each respondent was allowed to answer the question in terms of 

a weekly, biweekly, or monthly amount, depending upon convenience. One respondent in a 

household provided an answer to reflect his or her perception of the entire family situation.5

Telephone interviewing was used to collect the data, except where the respondent had no 

telephone or requested a personal interview. 

 

5

 While each question refers to “family” income or spending, only one person in each household was asked one of 

these questions. The MIQ and MSQ follow a battery of questions that deal with “household” material well-being. 

For these reasons the analysis presented in this paper refer to household characteristics rather than family. 
11

The MIQ asks for minimum income, given the expenses the respondent considers 

necessary for a family like theirs. The question leaves it up to the respondent to internally define 

“expenses you consider necessary.” In contrast, the MSQ asks for spending for basic necessities 

and then defines those basic necessities as “barely adequate food, shelter, clothing, and other 

essential items required for daily living.” We hypothesized that thresholds based on the MIQ 

would result in thresholds higher than those based on the MSQ, as basic necessities are defined 

specifically for a relatively small set of items. 

The questions asked in the SIPP are basically the same as those asked in a supplement of 

the Canadian 1988 Survey of Consumer Finances (SCF) for a statistics Canada study (Morisette 

and Poulin 1991). Variations in question wording to assess minimum income needs were 

examined with Canadian SCF data also collected in 1983, 1986, and 1987. In addition, questions 

concerning income satisfaction were asked; however, we do not focus on those results here but 

refer the interested reader to Morisette and Poulin (1991). The Canadian study was part of a 

project to evaluate low-income measurement methods in Canada. Morisette and Poulin reported 

that the MSQ resulted in substantially lower thresholds than those based on the MIQ. Thus, they 

concluded that question wording could substantially affect the level of minimum income 

required. 

The major contribution of the Canadian study, with respect to this study, is the detailed 

and systematic analysis of the impact of alternative question wording. The SIPP MIQ is basically 

the same as the one used by Statistics Canada in the 1988 survey. The only difference is that the 

Canadians did not refer to “minimum income needs” in the non-family part of the question; they 

referred to “minimum needs” only. Morisette and Poulin (1991) reported that the data resulting 

from the most similar wording of the MIQ were “remarkably stable over time”. They concluded 
12

that such stability is reassuring and supports the use of the same type of survey instrument and 

question wording. 

The 1988 Canadian question focusing on spending needs, which is the one most like the 

SIPP MSQ, evolved from successive restrictions on question wording. In the 1986 SCF, 

alternative MIQ wording referred to “the smallest yearly income a family the size of yours would 

need to meet absolutely necessary expenses.” Still the focus was on income. However, in the 

1987 and 1988 surveys, minimum income was replaced by spending for basic needs. Morisette 

and Poulin (1991) refer throughout their study to the spending question as an alternative wording 

to ascertain minimum income. 

As noted, the basic wording of the SIPP MSQ is the same as that used by Statistics 

Canada in the 1988 survey, but the difference in wording may be important. The Canadian 

question asks for “how much do you have to spend” rather than the “how much would you have 

to spend”. Respondents could interpret the Canadian MSQ in terms of what the respondent 

“spends for this barely adequate amount now.” The SIPP MSQ asks the respondent to consider 

how much he or she “would have to spend.” Such a difference in wording could result in 

different responses, as one’s assumptions about one’s own experience or some hypothetical 

experience is being asked about (Stinson 1997a). 

Question wording in the 1982 CE MIQ differed slightly from that used in the SIPP MIQ. 

“Living where you do now and meeting the expenses you consider necessary, what would be the 

smallest income (before any deductions) you and your family would need to make ends meet?” 

With this question, the respondent was asked to answer the question in terms of where the 

respondent and his or her family lives now. It was left up to the respondent to define deductions 

while in the SIPP, before-tax income was specified. The respondent could answer the question 
13

for different time periods, while for the ISDP (Danziger et al. 1984) and the Wisconsin BNS 

(Colasanto et al. 1984), monthly amounts were requested. The MIQ in the ISDP was slightly 

different from the CE question. “Very” smallest income was specified and the respondent was 

asked about minimum income without any qualifiers such as deductions or taxes. The Wisconsin 

BNS question is like the ISDP question in the sense that the “very” smallest income was asked, 

but the minimum income was to be “after taxes.” Higher thresholds would be expected when 

based on current living conditions versus allowing the respondent to consider moving or making 

other changes in the family’s expenditure patterns. Resulting equivalence scales would also 

likely reflect more current spending patterns. 

3.2 Survey Design of the Survey of Income and Program Participation 

With the SIPP 1993 panel survey, household members were interviewed at four-month 

intervals over a three-year period for a total of nine interview waves.6 The reference period for 

most items covered in the interview consists of the 4 months immediately before the month of 

the interview. The number of eligible households in wave 1 of the 1993 SIPP panel was 21,823. 

The final sample participating in wave 9, the wave of households that was to be asked questions 

in the Basic Needs Module, includes 17,572 households. Reductions in sample were due to noninterviews that result for other reasons. Non-interviews occurred for known eligible households, 

while others resulted when some or all members of a household left the original household and 

could not be traced or moved further than 100 miles from an existing SIPP PSU and could not be 

interviewed by phone. The final sample includes new households resulting from splits of the 

original households. The actual sample loss rate by wave 9 has been estimated to be about 26.5 

 

6

 A wave is defined as one round of interviews for a panel. 
14

percent of the original eligible households (U.S. Census Bureau 1998), not the 19.5 percent based 

on the sample participating in the survey by wave 9. 

In addition to sample non-response, there is also non-response for the topical module and 

specific item non-response. For households in wave 9, approximately half were assigned to the 

MIQ sub-sample (50.4 percent) and half were assigned to the MSQ sub-sample (49.6 percent). 

However, as seen in Table 1, not all households answered the minimum question assigned to 

their half-sample. Six of the households did not answer either the minimum income or spending 

question resulting in a reduced sample of 17,566. 

 Table 1. Sample Distribution by Assignment (total sample n=17,566a)

Minimum Income 

Sample 

Minimum Spending 

Sample 

Half Sample 8853 8713 

Observations with minimum values 

reported (extreme outliers omitted) 

6353 6311 

Observations for regressionintersection model 6338 6295 

a

 Six households assigned to the Basic Needs Module did not answer the MIQ or MSQ. 

Previous research reveals that individuals who leave the sample in later stages of SIPP 

panels have a certain profile. Huggins and Winters (1995) reported that people who leave the 

SIPP sample over time are more likely to be classified as poor than those who remained in the 

sample for the entire panel period. Thus, the U.S. Census Bureau has concluded that, “sample 

attrition may affect SIPP estimates of income and poverty, and estimates of benefits for some 

means-tested assistance programs” (U.S. Census Bureau 1998, p.51). Tin (1996) reported that 

estimates of food stamps and Supplementary Security Income (SSI) benefits are affected by 
15

attrition, while benefit data for Aid to Families with Dependent Children (AFDC), General 

Assistance, and Special Supplemental Nutrition Program for Women, Infants, and Children 

(WIC) are not. The Census Bureau’s Quality Report goes on to state, “Although weighting 

adjustments are used in an attempt to compensate for attrition, it appears that they are not 

entirely successful” (U.S. Census Bureau 1998, p.51). If the respondents in wave 9 of the 1993 

SIPP panel represent fewer lower income households, as has been the case for earlier panels, the 

implication is that the estimated thresholds will likely result in overestimates of true minimum 

income and spending when the intersection method is used. Total sample attrition bias is not 

addressed in the estimates presented here. The discussion section suggests possible biases that 

result from this omission. 

3.3 The Intersection Method and Corrections 

The regression-based intersection method, upon which the computation of the thresholds 

is based, is presented next. Procedures to account for potentially not considering all sources of 

household income are applied (see Kapteyn et al. 1988; Homan 1988; De Vos and Garner 1991). 

Sample selection due to item non-response to the MIQ and MSQ is addressed but not accounted 

for in this study. In this sense, the complete model varies somewhat from the specification used 

by De Vos and Garner (1991) in which a correction for sample selection was made. 

Explanatory variables account for the fact that families with different characteristics 

require different amounts of money to make ends meet. Even if a variable has a significant effect 

it does not mean that the effect should be used as a differentiating factor for a poverty line; 

however, such variables are included to obtain unbiased estimates of the differentiating effects. 

The intersection method of producing subjective minimum thresholds was first 

introduced by Goedhart and colleagues (1977). The threshold (Y*) is calculated as the 
16

intersection of the relationship: 

= + + + + + + ε n n ln(Y ) a a ln(Y ) a z a z ... a z min 0 1 2 2 3 3 (1) 

with the line Ymin =Y for different values of zn .We assume that the error term, ε , satisfies the 

classical assumptions for simplicity. Ymin represents the answer to a question about the minimum 

income that the respondent thinks is needed for the family to make ends meet, or some variation 

of that question. Previous research indicates that a log-linear model fits data such as these fairly 

well. An underlying assumption for the intersection approach to estimate a minimum incomebased threshold is that only those who have incomes that are at the minimum know what the 

“true” minimum is. Since that minimum is not known for a society a priori, data are collected 

from a sample representing the whole population. The predicted threshold based on equation (1) 

and the intersection of Ymin =Y is: 

⎥

⎦

⎤ ⎢

⎣

⎡

−

+ + + =

1

0 2 2

2 1 a

a a z a z Y z z n n

n

... * ( ... ) exp . (2) 

As the error term, ε , is not observable, there are different possible choices to deal with 

the term. One could produce the mean prediction and include a term to account for Jensen’s 

inequality or one could assume a median prediction as is done for this study.7

 Kapteyn et al. 

(1988) used the median prediction equation (2) as did other researchers (Colasanto et al. 1984; 

Danziger et al. 1984; De Vos and Garner 1991; Garner and De Vos 1995). The median is chosen 

 

7 In email communication on April 1, 2002, Kapteyn said he and his co-authors never referred to the threshold as the median prediction although 

that is what was being produced. He agreed that if one prefers a prediction based on the mean, another specification would be necessary since 

the predicted value of the expectation is not equal to the expectation of the predicted value, based on Jensen’s inequality (see Greene 1997, pp. 

67, 119). When logs are used, the function is concave and the expectation of the function is less than or equal to the function of the expectation: 

E[ ] [] log( y) ≤ log(E y ). If an unbiased prediction of the subjective threshold were preferred, the expectation of the predicted threshold 

would be: 

⎥

⎦

⎤ ⎢

⎣

⎡

− ⎥

⎦

⎤ ⎢

⎣

⎡

−

+ + = 2(1 ) exp 1

.... { * | } exp

1

2

1

0 2 2

a a

a a z a z E Y z n n σ . Thanks are extended to Kapteyn for discussing the issues surrounding this 

point and clarifying the specification. 
17

as the measure of central tendency for the subjective threshold as it is more robust to outliers 

than the mean. This relationship is presented in Figure 1. 

450

Ymin Ymin=Y

Y*min Y

Ymin=f(Y)

Figure 1. The Subjective Line Based on the Regression Intersection Approach Reportedminimum

Actual total income

Although most minimum income data to date are for cross-sections, this approach was originally 

designed for use with panel data (Kapteyn et al. 1988). By using panel data, it would be possible 

to test whether people gravitate toward some true minimum over time. It is expected that 

respondents who are above the true minimum would find that over time they have a better idea 

of what their true minimum is and would respond accordingly. For those below the true 

minimum, over time they would realize that they are actually underestimating their true income 

needs. Alternative thresholds for different family types and for different regions of the country, 

for example, could be derived using the same intersection approach. An example of these is in 

Figure 2. 
18

450

Y1

*

Ymin

Y

I

II

III

Y2

* Y3*

Ymin=Y

I One adult

II Two adults

III Two adults, 

one child

Figure 2. Subjective Thresholds for Different Family Types Reported minimum

Actual total income

Another assumption underlying the approach is that every respondent gives the same 

meaning to the wording used in the MIQ. In other words, the expression 

“necessary...minimum,” for example, is supposed to have the same welfare connotation for all 

respondents. Variations in responses would be expected when households have differing needs, 

for example a family of two adults would be expected to report a lower minimum income than a 

family of three adults. Differences in responses could also result when they face different prices, 

for example, if prices for necessary commodities were lower in the South than in the Northeast. 

Thresholds would increase when the average income of the entire population increases, just as 

with a relative measure. This is in contrast to the approach of estimating the threshold using 

simple average responses to the minimum income question (this is the approach that has been 

used by Saunders and Matheson 1992; Townsend and Gordon 1991; Townsend et al. 1996, 

1997). 
19

3.4 Specification of Model and Explanatory Variables 

In many of the earliest models using the intersection approach, family size was the only 

differentiating variable, aside from income, included to determine variations in the thresholds. 

However the possible role of other household characteristics has been acknowledged and 

examined in later studies (e.g., Colasanto et al. 1984, Danziger et al. 1984, Hagenaars 1986, 

Kapteyn et al. 1988, Kapteyn et al. 1985, De Vos and Garner 1991, and Garner and De Vos 

1995). Several variables expected to influence responses to the MIQ and MSQ are included. The 

variables could reflect true cost differences or may reflect perceived differences based on how 

the respondent sees his or her household’s situation relative to others in his or her reference or 

comparison group. The socio-demographic variables selected for this study are the same as those 

used by De Vos and Garner (1991) for the Consumer Expenditure Survey MIQ analysis. This 

was done to facilitate comparison with these earlier findings. It should be noted that significance 

alone of a characteristic in the model should not be taken to imply that thresholds should be 

differentiated on the basis of the characteristic. However, inclusion of such variables is designed 

to obtain unbiased estimates of other differentiating effects. 

Income is included in natural logarithmic form8

 and enters the model as before-tax 

money income. Rather than including household size as a continuous variable, household size is 

accounted for using several dummy variables represented by number of persons and number of 

earners. The advantage of this approach is that not all differences between households are forced 

into the logarithmic household size function as in earlier studies (e.g., Kapteyn et al. 1988). A 

disadvantage is that the effect of family size is not directly observed and several variables need 

to be considered together. Other household characteristics include the presence of other persons 

 

8

 The same approach could be applied if the model were linear in income. However, as we use the same approach 

and basically the same model as De Vos and Garner (1991), income is included in logarithmic form. 
20

in the household in addition to the main breadwinner and spouse, the maximum age of others in 

the household, and region of residence. Reference person characteristics include working status, 

age, educational attainment, marital status, gender, and whether the person is not working due to 

disabled. 

Perceived minimal needs can differ based on the actual and relative costs of goods and 

services in the area in which a household resides, and the availability of alternatives one might 

use to meet one’s minimal needs. For example, if basic housing in the West were more 

expensive than basic housing in the Midwest, differences in reported minimums from region to 

region would be expected. Differences in urbanization can also influence the costs of meeting 

minimal needs. Urban dwellers may have more housing options than those living in rural9

 areas 

and greater variation in prices might result. Whether a household owns a home outright or has a 

mortgage can also influence the reported minimum economic needs. Renter versus owner status 

can be considered in future models using the SIPP, and whether the household has a mortgage.10

Dutch researchers (e.g., Kapteyn et al. 1988; Homan 1988) have reported that 

respondents only know approximately the level of their actual incomes. Thus, when answering 

the MIQ, respondents are likely to use estimates of their actual household incomes as frames of 

reference. They anchor their answers to an estimate of their actual income. For example, in 

answering the MIQ, a respondent could say, “I would minimally need about half of my current 

 

9

 While there is no official definition of “rural” in the SIPP, rural is defined here to be consistent with the definition 

for the CE as much as possible and is defined using the variable “mst_cbur” in the internal SIPP file. The codes for 

this variable are 1 for central city and 4 for rural. All other areas are groups together and are considered for this 

study to be suburban. 

10 The Basic Needs Module does not contain information concerning whether a household or family has a mortgage 

or not. However, this information is available in another SIPP module and can be used in future analysis. 
21

income.” These researchers have also surmised that respondents often neglect the incomes of 

some of the components of household income when answering the MIQ, for example the income 

of a child or income from welfare or benefit transfers. As noted in the methods section, a 

household’s reported minimum income level is assumed to strongly depend on the level of the 

household’s actual income. If respondents use an estimate of their actual income as a reference 

point for their minimum income, not accounting for all their income could bias downward their 

reported minimum incomes. Previous researchers (Homan 1988, Kapteyn et al. 1988) have 

proposed methods to deal with this concern. The basic tenet underlying these methods is that 

respondents underestimate their minimum incomes by the same percentage as they underestimate 

their actual incomes. To deal with this potential bias, minimum income is adjusted by a 

percentage that is the same as the percentage that an estimate of actual income, or anchor income 

(as referred to by Homan, 1988), is to actual income. The same approach was applied by De Vos 

and Garner (1991; Garner and De Vos 1995). 

Anchor income enters the MIQ equation as the natural log of anchor income (lnYanc). 

Anchor income is defined as the actual household income the respondent has in mind when 

answering an overall income question without reference to individual sources. The method 

proposed allows for a fraction of a household member’s actual income to be counted in anchor 

income. Specifically when an income component, say for a child, represents a small proportion 

of total income, a weight less than one would be estimated for this income component in the 

anchor income estimation. Although member-level income data are available in the SIPP and 

are used to create total household income, the importance of individual member and non-specific 

member incomes is tested by including an estimate of anchor income in the MIQ regression 

model. (See the Appendix.) 
22

For estimation of the MSQ model, we assumed a relationship between the dollar amount 

needed for minimum spending and actual income. However, anchor income is not included in 

this model as we had no a priori reason to believe that the respondent would consider an 

estimate of actual income as his or her reference point when answering the MSQ. However, if a 

respondent anchors minimum spending as a function of what is saved versus spent out of actual 

household income, anchor income might be a useful concept for this estimation as well. It is 

more likely that the anchor respondents consider when answering the MSQ is their actual 

spending. If this is the case, then an adjustment similar to the one for minimum income might 

prove useful.11

In earlier studies (e.g., Kapteyn et al. 1988; De Vos and Garner 1991; Garner and De Vos 

1995), an additional term is included in minimum income models to account for potential sample 

selection bias arising from non-response to the MIQ.12 However for this study, no term to 

account for sample selection is included in the estimation of the MIQ or MSQ models. The SIPP 

sample suffers from sample selectivity due to attrition as well as non-response to the questions 

under study. Any estimation method using a sample selection correction factor and data from 

wave 9 of the 1993 panel would not yield unbiased estimates for the total population. Thus, we 

chose not to correct for the partial sample selectivity due to MIQ and MSQ non-response at this 

time. All results presented are conditional on the fact that positive MIQ and MSQ responses were 

given. Caution is urged in interpreting the results, as the minimum income and spending 

 

11 Various researchers examine responses to total spending questions and sub-aggregate category total spending 

question (e.g., Battistin et al. 2000; Browning et al. 2002; and Winter 2002). These studies reveal severe 

underreporting of household expenditures the more aggregated the spending. These results suggest that accounting 

for anchor total expenditures would be useful in future analyses of the MSQ if total expenditure and MSQ data are 

available from the same survey. This is not the case with the SIPP; total expenditure data are not collected. Whether 

other spending information that is available in the SIPP could be used in the estimation, can be examined in the 

future. 

12 Kapteyn et al. (1988) found sample selection to be important in their analyses of Dutch data, while De Vos and 

Garner (1991) did not in their analysis of minimum income from the CE data. 
23

responses, actual incomes, and demographic characteristics may differ from those for the whole 

population even after applying population weights. 

4. Results 

The MIQ and MSQ sample characteristics are first presented followed by the results of 

the OLS regression models. The regression results are used to produce minimum thresholds, 

equivalence scales (defined later) and the percentages of households below the thresholds (or 

“poverty” rates). Population weighted thresholds and equivalence scales are based on 

households who participated in wave 9 of the SIPP and who provided usable MIQ and MSQ 

data. However, poverty rates are produced for the population represented by all households 

participating in wave 9. Alternative thresholds, equivalence scales, and rates are presented for a 

CE-based measure, a relative measure, and a version of the NAS Panel (Citro and Michal 1995) 

threshold currently being used in experimental poverty measurement research (Short 2001).13 

Alternative thresholds are described in the Thresholds Section (4.3). Implicit equivalence scales 

from this study are compared to other subjective scales reported for the U.S. A comparison to 

those reported for other countries could be made; however, economic systems and government 

assistance in different countries often differ and thus likely influence answers to personal 

subjective assessments regarding financial needs (See De Vos and Garner 1991). However, given 

 

13 The NAS based experimental poverty measure assumes that the threshold and income or resources measure are 

defined consistently. We are not making that assumption in this study and use the threshold to compare it to beforetax money income without the adjustments recommended by the Panel. We follow a similar approach for the other 

thresholds in that we compare before tax money income to each set of estimated and alternative thresholds. Beforetax money income is compared to official poverty thresholds by the Census Bureau using data from the Current 

Population Survey data to produce official poverty statistics. 
24

that the wording for the two SIPP questions are from the Canadian study of Morisette and Poulin 

(1991), limited comparisons to the Canadian findings are presented. 

4.1 Sample Description

Descriptive statistics for the samples upon which the MIQ and MSQ regression models 

are based are presented in Table 2.14,

15 The samples are similar with a few exceptions. 

Statistically significantly higher minimum incomes are reported at $29,313 as compared to 

minimum spending of $21,318. This is expected given the wording of the two questions. There 

are other statistically significant differences between the two samples, but the differences are not 

large. The MIQ sample is statistically more likely to include households represented by single 

not working individuals and singles greater than age 64, households with more than two people 

and no earners, and households living in the Western region of the U.S. The MSQ sample 

includes a statistically greater percentage of households with the oldest other person in the 

household being in the 6 to less than 12 years of age group. Other sample characteristics do not 

differ statistically for the two samples. 

4.2 Regression 

The OLS regression coefficients for the MIQ and MSQ models are presented in Table 3. 

Comparing results from the MIQ and MSQ models tells us about differences in the two 

questions. Both MIQ and MSQ models do a somewhat reasonable job of explaining the variation 

in responses, although the MIQ model is somewhat better. Adjusted R2

’s are 0.325 and 0.235 

respectively. 

 

14 Weighted sample means are present in Appendix Table 2. 

15Examination of 1995 CE data reveal that consumer units with reference persons who are greater than 64 are 

almost four times as likely to be owners as renters. Two-person consumer units are three times as likely to be 

owners versus renters (U.S. BUREAU OF LABOR STATISTICS 1997). 
25

 Table 2. Sample Means and Standard Deviations of Variables Included in Regressions of 

 Ln(Ymin) 

SIPP MIQ SIPP MSQ 

(n=6338) (n=6295) 

 

 

 

 Mean 

Standard 

Deviation Mean 

Standard 

Deviation 

Difference 

in means 

Total Income (Y) $43,980 $54,547 $45,794 $108,077 

Reported minimum (Ymin) $29,313 $20,702 $21,318 $15,337 ** 

Ln (Ymin) 10.049 0.770 9.745 0.722 ** 

Ln (Y) 10.352 0.896 10.366 0.890 

Ln (Yanc) 10.321 0.894 . . 

Single, working 0.137 0.344 0.131 0.337 

Single, not working 0.116 0.320 0.107 0.309 * 

1 parent, working 0.272 0.445 0.277 0.448 

1 parent, not working 0.037 0.189 0.041 0.199 

2 people, 2 earners 0.132 0.339 0.132 0.339 

2 people, 0 earner 0.083 0.275 0.086 0.280 

>2 people, >2 earners 0.049 0.217 0.051 0.220 

>2 people, 2 earners 0.048 0.215 0.050 0.218 

>2 people, 1 earner 0.034 0.181 0.033 0.178 

>2 people, 0 earner 0.013 0.112 0.009 0.096 * 

2 othersa 0.187 0.390 0.191 0.393 

3 othersa 0.083 0.275 0.084 0.277 

4 or more othersa 0.050 0.217 0.048 0.214 

Age of reference person 48.700 17.000 48.500 16.800 

Age2 of reference person 2663.700 1816.600 2632.400 1790.900 

Single >= 65b 0.101 0.301 0.091 0.287 ** 

2 people >= 65b 0.114 0.318 0.115 0.319 

Oldest <6c 0.068 0.252 0.068 0.252 

Oldest 6<12c 0.088 0.284 0.098 0.298 * 

Oldest >=18c 0.261 0.439 0.254 0.435 

Low education 0.075 0.264 0.071 0.257 

High education 0.508 0.500 0.507 0.500 

Black 0.094 0.292 0.091 0.287 

Work disability of reference person 0.047 0.212 0.048 0.213 

Female, married 0.086 0.281 0.089 0.284 

Female, widowed 0.099 0.298 0.092 0.289 

Female, divorced/separated 0.115 0.319 0.110 0.313 

Female, never married 0.073 0.260 0.070 0.256 

Northeast 0.196 0.397 0.196 0.397 

Midwest 0.255 0.436 0.256 0.438 

West 0.219 0.414 0.205 0.404 ** 

City 0.378 0.485 0.373 0.484 

Rural 0.240 0.427 0.244 0.430 aNumber of others in the household other than the head and spouse. bAge of reference person in household. cMaximum age of others in the household other than the head and spouse. 

 **Significantly different (p=0.05). 

 *Significantly different (p=0.10). 

 Source: 1993 Survey of Income and Program Participation, authors' calculations. 
26

 Table 3. Results of Regression (1) with No Correction for the Effect of Selective Non-Response

SIPP MIQ 

(n=6338) 

SIPP MSQ 

(n=6295) 

Coefficient 

Standard 

Error Coefficient 

Standard 

Error 

Intercept 6.762** 0.142 7.005** 0.143 

Log (Y) -0.733** 0.012 0.205** 0.012 

Log (Yanc) 1.000 0.000 

Single, working -0.084** 0.040 -0.084** 0.040 

Single, not working -0.209** 0.061 -0.217** 0.060 

1 parent, working 0.157** 0.043 0.136** 0.043 

1 parent, not working -0.041 0.059 0.090 0.057 

2 persons, 2 earners 0.027 0.038 -0.006 0.038 

2 persons, 0 earner -0.007 0.045 -0.016 0.044 

>2 persons, >2 earners -0.028 0.054 -0.033 0.053 

>2 persons, 2 earners 0.082 0.052 -0.002 0.051 

>2 persons, 1 earner 0.041 0.057 0.034 0.056 

>2 persons, 0 earner -0.036 0.081 -0.068 0.091 

2 othersa

 0.042 0.030 0.048* 0.029 

3 othersa

 0.046 0.037 0.089** 0.037 

4 or more othersa

 0.006 0.044 0.117** 0.045 

Age of reference personb 0.231** 0.035 0.217** 0.035 

Age2

 of reference personb -0.237** 0.037 -0.174** 0.037 

Single >= 65c

 -0.068 0.062 -0.099* 0.061 

2 persons >= 65c

 -0.096** 0.046 -0.136** 0.045 

Oldest <6d -0.032 0.041 -0.011 0.041 

Oldest 6<12d

 -0.017 0.035 0.021 0.034 

Oldest >=18d

 -0.051* 0.027 -0.058** 0.027 

Low education -0.135** 0.033 -0.078** 0.033 

High education 0.184** 0.018 0.141** 0.018 

Black 0.032 0.029 -0.054* 0.029 

Work disability of reference person -0.119** 0.044 -0.042 0.044 

Female, married 0.019 0.030 0.057* 0.030 

Female, widowed -0.097** 0.034 -0.123** 0.037 

Female, divorced/separated -0.135** 0.027 -0.165** 0.028 

Female, never married -0.166** 0.034 -0.095** 0.035 

Northeast 0.170** 0.023 0.156** 0.023 

Midwest -0.031 0.021 -0.014 0.021 

West 0.040* 0.023 0.042* 0.023 

City 0.022 0.018 0.000 0.019 

Rural -0.079** 0.021 -0.101** 0.021 

 

Adjusted R2

 0.325 0.235 a

Number of others in the household other than the head and spouse. 

b

The estimated Age coefficient and standard error have been divided by 10. The estimated Age2 coefficient and standard error have been divided by 1000. cAge of reference person in household dMaximum age of others in the household other than the head and spouse 

 **Significantly different from 0 (p=0.05) 

 *Significantly different from 0 (p=0.10) 

 Source: 1993 Survey of Income and Program Participation, authors' calculations. 
27

As expected, income is a significant variable when one responds to either question. For 

the MIQ model, the coefficient corrected for anchor income is 0.267. This is essentially the same 

as the income coefficient estimate when anchor income is not considered (results not shown). 

Thus, in the case of the SIPP, the anchor income correction added little to the explanatory power 

of the model. This is not surprising given that total household income was computed as the sum 

of member-level and non-member specific household income sources. Although not all of these 

income components received a weight of 1.0 in the anchor income estimation, the effect on 

minimum income was not significant. This is similar to the results of Kapteyn et al. (1988) when 

total income was based on member specific incomes as opposed to estimates of total household 

income.

When comparing the income coefficients it is clear that minimum spending varies less 

with total household income than does minimum income. This could indicate a consensus 

concerning the level of necessary income to meet one’s basic needs for barely adequate food, 

shelter, clothing, and other essential items required for daily living as opposed to a family’s 

general necessary expenses. In addition, the role of income may partially be taken over by other 

variables like the presence of others in the household in the MSQ model to a greater extent than 

in the MIQ model. 

Household size/working variables for singles and lone parents, age of others in the 

household than the reference person and spouse, Northeast, West, (South is the omitted reference 

region) and rural are important household predictors in explaining variations in responses to the 

MIQ. Additional important variables include reference person age, age squared, education, 

disability as a reason for now working, and gender/marital status. Similar patterns are shown in 

the MSQ model with the addition of the presence of two, three, and four or more persons in the 

household, and Black. Simple t-tests (not shown) reveal that the coefficients for all but one of 
28

the variables in the MIQ model are statistically significantly different from those in the MSQ 

model. The one that is not different is for single, working. 

For the coefficients that are statistically significant in both models, the ones that have the 

greatest effect on minimum income as compared to minimum spending are: one parent working, 

age and age squared, low and high education, disabled reference person, female never married, 

and Northeast. Only in the MSQ model are the coefficients for others in the household and race 

statistically significant. Blacks report statistically significantly lower minimum spending needs 

than non-Blacks. 

The variables accounting for age represent similar trends for the MIQ and MSQ samples. 

Minimum income and spending peak and then level off as age increases with minimum income 

decreasing faster than minimum spending with increases in age. Households composed of two 

people with an older reference person report income and spending needs that are lower than 

those of other households. This is not surprising since it is quite likely that these households own 

their own homes outright and have lower income and spending needs for housing per se.16 When 

considering others in the household than the reference person and spouse, significantly lower 

spending amounts are likely for households in which the other person(s) is greater than 17 years 

of age. These individuals may be more independent, like college students living away from 

home, and require less support from the primary household. 

The findings for education are consistent with those reported earlier by De Vos and 

Garner (1991). Households with reference persons who have less education report needing less 

for income and spending than those with higher education. As noted by the earlier authors, this 

 

16 The published CE coefficients are corrected for sample selection. Thus, to compare conditional thresholds using 

the CE with the conditional thresholds produced using the SIPP MIQ, the inverse Mill’s ratio and lambda coefficient 

were included in the estimation of the CE SIPP thresholds. . Thus, the CE SIPP thresholds are expected to differ 

from the earlier CE thresholds due to three reasons, other than price differences. (1) The CE selection factor is 
29

effect is related to reference group effects. Higher educated reference person households may 

have assumed a higher standard of living than those with lower education. This is likely to be 

reflected in their responses for minimal income and spending requirements. 

The role of gender and marital status is consistent across the MIQ and MSQ models. 

Households headed by women who are married report having greater spending needs than do 

male-headed households. In contrast all other female-headed households report needing less than 

male-headed households based on responses to the MIQ or MSQ. 

Geographic variations in income needs are reflected in the coefficients for the region and 

urbanization variables. The signs of the relationships between the geographic variables and 

reported income minimums are the same for both models. Households living in the Northeastern 

and Western regions of the U.S. reported higher minimum income and spending than those in 

other areas. The comparison group is the South. These results are consistent with the finding that 

interarea prices for shelter faced by households living in the Northeast and West are higher than 

those faced by households living in the South and Midwest (see Kokoski et al. 1994). 

The reported minimum income and spending of households in rural areas are lower, 

holding all else constant, than those in more urbanized areas. Again this may reflect price and 

current expenditure differences, as well as differences in income. Housing often accounts for the 

largest expenditure for households and could easily result in different reported minimum 

monetary needs for people living in urban versus and rural areas. Households in urban areas 

spent an average of one-third of their total expenditures on housing in 1995, while those in rural 

areas spent less at a little over one-fourth of their total spending (U.S. Bureau of Labor 

Statistics, 1997). Households living in rural areas are four times as likely to be owners as 

 

included in the estimation of the thresholds so conditional thresholds result. (2) The SIPP population differs as 

reflected in the 1995 sample weights. (3) And general economic conditions differ. 
30

opposed to renters, while those living in more urban areas are only 1.5 times as likely to be 

owners. In addition, homeowners living in rural areas are less likely to have a mortgage, leading 

to lower average out-of-pocket spending for housing (U.S. Bureau of Labor Statistics 1997). 

Other factors could include the fact that some households in rural areas can produce their own 

food, thus lowering what they might need for food spending, for example. Bartering, particularly 

for food, may also be more of an option for these households thereby reducing monetary income 

and spending needs. 

The results from the MIQ model are fairly similar to those from the earlier U.S. study (De 

Vos and Garner 1991) although the fit of the earlier model was better than for the SIPP-based 

models. Morissette and Poulin (1991) also report a better overall fit of their model specification 

for the MIQ as opposed to the MSQ. 

Next, estimated thresholds based on the MIQ and MSQ are presented along with implicit 

equivalence scales, and percentages of households below the estimated thresholds. These are 

compared to statistics based on the U.S. official poverty threshold, a relative threshold, and a 

threshold currently being used in experimental poverty measurement research at the BLS and 

Census Bureau, as noted earlier. 

4.3 Thresholds 

In this section, household-population weighted thresholds for 1995, based on the MIQ and MSQ 

models, are presented. In each case, the estimated thresholds use the positive income and 

spending reports of participants in wave 9 of the 1993 SIPP panel. To produce sample 

thresholds, the average sample characteristics are used along with the estimated coefficients. 

These sample thresholds are then weighted to produce conditional (based on positive minimum 

income or spending responses) thresholds for the population. In this regard, the resulting 
31

thresholds are more comparable to those presented by the Morissette and Poulin (1991) for 

Canada and Danziger et al. (1984) and Colasanto et al. 1984) for the U.S., than those presented 

by De Vos and Garner (1991). The first three sets of researchers did not correct for possible 

sample selection bias but the latter did. Once the correction was made by De Vos and Garner 

(1991), the remaining coefficients were used in the estimation of the thresholds. In that study, 

correcting for sample selection versus not resulted in little difference in the total population and 

conditional thresholds as the coefficient on the correction factor was not statistically significant. 

An alternative set of MIQ thresholds are produced using the coefficients from the 

previously reported CE coefficients for the U.S. (De Vos and Garner 1991) but using the SIPP 

MIQ sample and population weights. Using the earlier CE MIQ coefficients provides a way to 

examine the effects of using basically the same methodology,17 a different survey instrument and 

sample, and an earlier time period. The CE thresholds are based on coefficients derived from an 

estimation of the MIQ using data collected in 1982 but updated to 1995. 18 The thresholds are 

referred to as CE MIQ SIPP. It is likely that the 1995 CE MIQ thresholds will differ from the 

MIQ SIPP not only because respondents are conditioned differently in the CE and SIPP,19 but 

also because the economic situation differed during the two survey periods. Such a direct 

comparison has not previously been conducted. 

Other alternative thresholds include those used for official poverty measurement, a 

relative poverty measure, and a NAS based measure. In the SIPP data file, official poverty 

thresholds are assigned to all households. For this study, average official thresholds are presented 

 

17 The predicted thresholds based on the 1982 CE coefficients and 1995 SIPP sample are price updated using the All 

Items Consumer Price Index-Urban for 1995 (Index value is 152.4 with 1982-84=100.0). 

18 CE respondents are conditioned to think more about expenditures while SIPP participants are conditioned to think 

more about income. 

19 These are referred to as OECD Social Indicators or OECE poverty threshold scales (see Atkinson et al. 1995 and 

Buhmann et al. 1988 with reference to Jenkins and O’Higgins 1987). 
32

for the total sample population and for differing household types. These will differ from those 

presented in tables of official poverty thresholds due to population weighting in the SIPP. 

The relative household threshold computed and compared is based on one-half the 

median of before-tax monetary income per adult equivalent. The scale used in early OECD 

publications20 is applied with weights of 1.0 for the first adult, 0.7 for each additional adult, and 

0.5 for each child. This person-weighting is used for this study as it is the same as that used in 

the De Vos and Garner (1991) study. More recently, the square root of family size has been used 

often in OECD publications (See Atkinson et al. 1995). For this study, household relative 

thresholds are obtained by multiplying the number of equivalent adults in each household by 

median household adult equivalent income times 0.50. 

The NAS-based threshold that we use was produced as part of a joint BLS-Census Bureau 

experimental poverty measurement project. We refer to these thresholds as “NAS-3” since we 

use a three-parameter equivalence scale to account for differences between adults and children 

and single parents and others (see Johnson et al. 1997; Short et al. 1999; Short 2001). In contrast, 

the NAS Panel recommended a two-parameter scale ((Citro and Michael 1995). 

 

20 Income statistics are shown for the MIQ weighted sample only. They differ only slightly for the MSQ weighted 

sample. 
33

Table 4. Weighted Means of Household Income and Alternative Thresholds by Household Size for 

Selected Sample: 1995

 (SIPP Sample Household Population Weighted)

 SIPP MIQ SIPP MSQ

Household Size Sample Size Threshold Confidence Intervalb Sample Size Threshold Confiden

1 person 1,604 $16,738 $15,987 $17,489 1,495 $11,503 $11,001

 1 person, >= 65a 640 11,715 10,918 12,510 571 9,363 8,741

 1 person, < 65a 964 19,634 18,550 20,717 924 12,629 11,953

2 people 2,013 22,103 21,213 22,994 2,027 14,458 13,829

 2 people, >= 65a 597 16,969 15,820 18,118 598 13,070 12,227

 2 people, < 65a 1,416 24,116 23,033 25,198 1,429 14,995 14,272

3 people 1,050 25,785 24,611 26,958 1,066 15,796 15,035

4 people 988 29,469 28,092 30,845 1,024 18,250 17,366

5 people 448 27,926 26,044 29,807 443 18,821 17,576

6 people or more 235 25,389 22,894 27,884 240 18,125 16,434

 

 Total 6,338 $22,930 6,295 $14,989 

 

Household Size 

Actual Before 

Tax Income Officiald

 NAS-3 Relativee 

1 person $23,377 $7,595 $7,190 $8,438 

 1 person, >= 65a 16,686 7,340 7,190 $8,438 

 1 person, < 65a

 27,235 7,741 7,190 $8,438 

2 people 43,917 9,793 10,197 14,205 

 2 people, >= 65a

 35,583 9,219 10,138 14,345 

 2 people, < 65a

 47,184 10,018 10,220 14,151 

3 people 49,922 11,837 14,354 19,076 

4 people 55,621 15,223 16,282 23,465 

5 people 57,815 17,925 18,544 28,243 

6 people or more 51,893 22,121 22,736 37,083 

 

 Total $42,530 $11,391 $12,055 $16,714 

a Age of reference person

b 90 % confidence intervals. c Based on SIPP MIQ sample characteristics, 1982 coefficients from the De Vos and Garner 1991 study with correction in prediction equation, and All Items CPI-U f

1995 (Index value 152.4 with 

982-84=100). 

d

 Official poverty thresholds are for families but are used for households in this study. 

e Based on one-half the median income per adult equivalent (adult equivalents based on 1.0 for first adult, 0.7 for each additional adult, and 0.5 

for each child) in MIQ sample 

Source: 1993 Survey of Income and Program Participation, authors' calculations. 
34

According to the NAS Panel, the experimental threshold should represent a dollar amount 

for a basic set of commodities that includes food, clothing, shelter, and utilities, and a small 

additional amount to allow for other needs (e.g., household supplies, personal care, non-workrelated transportation). To produce a set of thresholds for the population, a basic threshold is first 

developed for a reference family composed of two adults and two children. Data from the CE are 

used. Thresholds for different family types are derived from the base threshold using parameters 

that are to account for differences in economies of scale and the number of adults and children in 

the household. 

In Tables 4 and 5, estimated MIQ and MSQ thresholds are shown along with the 

alternative thresholds specified to measure poverty in this study. The upper panels in each table 

include results for the MIQ and MSQ thresholds, while the lower panels include average actual 

before-tax money income for households and the alternative thresholds. Before-tax money 

income is used as it is the official income for poverty measurement in the U.S. Table 4 includes 

thresholds and incomes for households differentiated by household size and if the reference 

person is greater than 64 years of age or not. All households are represented in Table 4. Table 5 

presents thresholds for selected household composition types. The household composition 

groupings in Table 5 represent 88 percent of the weighted sample.21

As seen in Table 4, the 1995 MIQ thresholds for households, both the SIPP MIQ and CE 

SIPP, are on average relatively high compared to the alternative thresholds that have been used 

for poverty measurement in the U.S. (those in the lower panel). The average MIQ thresholds are 

 

21 Income statistics are shown for the MIQ weighted sample only. They differ only slightly for the MSQ weighted 

Table 5. Weighted Means of Household Income and Alternative Thresholds by Household Composition for Selected Sample: 1

 (SIPP Sample Household Population Weighted)

 SIPP MIQ SIPP MSQ 

Household Type 

Sample 

Size Threshold Confidence Intervalb

Sample 

Size Threshold 

Confidence 

Intervalb

One adult, no children, head>=65a 640 $11,715 $10,918 $12,510 571 $9,363 $8,741 $9One adult, no children, head <65a 964 19,634 18,551 20,717 924 12,629 11,953 13

Two adults, no children, head>=65a 597 16,969 15,820 18,119 597 13,069 12,226 13

Two adults, no children head<65a 1,262 24,048 22,851 25,245 1,295 15,017 14,236 15

Three adults, no children 451 23,082 21,512 24,651 447 14,374 13,381 15

One adult, one child 154 24,633 22,778 26,489 135 14,790 13,712 15

One adult, two children 118 24,997 23,202 26,791 134 15,410 14,402 16

Two adults, one child 481 28,583 26,965 30,202 485 17,283 16,278 18

Two adults, two children 630 31,511 29,822 33,200 661 19,217 18,196 20

Two adults, three children 260 30,407 28,079 32,735 265 19,710 18,278 21

 Total 5,557 5,514 

 Poverty 

 Thresholds 

Household Type 

Actual 

Before Tax 

Income Officiald NAS-3 Relativee

One adult, no children, head>=65a $16,686 $7,340 $7,190 $8,438 One adult, no children, head <65a 27,235 7,741 7,190 8,438 

Two adults, no children, head>=65a 35,583 9,219 10,138 14,345 

Two adults, no children head<65a 50,280 10,001 10,138 14,345 Three adults, no children 59,687 11,789 15,514 20,252 One adult, one child 23,459 10,150 10,850 12,658 One adult, two children 20,763 11,466 12,881 16,877 Two adults, one child 48,693 11,985 13,655 18,565 Two adults, two children 55,766 15,101 15,514 22,784 Two adults, three children 61,336 17,692 17,282 27,003 

a Age of reference personb 90 % confidence intervalsc Based on SIPP MIQ sample characteristics, 1982 coefficients from the De Vos and Garner 1991 study with correction in prediction equation, and All Items CPI-U for 1d Official poverty thresholds are for families but are used for households in this study. e Based on one-half the median income per adult equivalent (adult equivalents based on 1.0 for first adult, 0.7 for each additional adult, and 0.5 for each child) in MIQ sa

Source: 1993 Survey of Income and Program Participation, authors' calculations. 
37

approximately twice as large as the average household threshold based on the current official 

measure. The SIPP MIQ threshold is 137 percent of the relative threshold and the CE SIPP 

threshold is 135 percent of the relative. In contrast, the MSQ threshold is 90 percent of the 

average total relative threshold, but it is 132 times as large as the average official poverty line for 

the U.S. The average NAS-3 threshold is only slightly higher than the average official threshold 

so the relationship to the MIQ and MSQ thresholds is similar to that reported for the official 

threshold. 

The average total MIQ threshold, $22,930, is about 54 percent of average household 

income in 1995. The average MSQ threshold is $14,989, that is 35 percent of household income. 

The MIQ thresholds increase as household size increases from one to four people, while 

the MSQ thresholds increase as household size increases from one to five. The decrease in 

threshold levels for larger households is likely due to the relatively smaller number of 

households upon whom these thresholds are based. A similar pattern was reported by Garner 

and DeVos (1995) using the 1982 CE data. The difference in the maximum threshold level for 

the MIQ relative to the MSQ thresholds is perhaps related to the specificity of the MSQ question. 

The relationship between the MIQ threshold and the official threshold is similar to the 

comparison between the “Get-Along” threshold and the official threshold for a family of four 

(husband, wife and two children). 22 Vaughan (1993) reported that the 1989 “Get-Along” 

threshold for this family is about 173 percent of the official poverty threshold for a family of 

four. Citro and Michael (1995, p. 139, Table 2-4) reported that the 1992 “Get-Along” threshold 

is about 176 percent of the official threshold. For this study, the MIQ SIPP two adults with two 

 

22 The Gallup Poll asked samples of adults the following question for most years between 1946 and 1989: “What is 

the smallest amount of money a family of four (husband, wife and two children) needs each week to get along in this 

community?” For most years, weekly amounts were annualized on the basis of a 52-week year. Then thresholds 

were produced. The NAS Panel requested that Gallup ask the “Get-Along” question in their August 1992 poll. 
38

children threshold is 209 percent of the official poverty threshold (Table 5). In contrast, the MSQ 

based threshold is only 127 percent of the official threshold. 

To compare the relative magnitudes of average MSQ thresholds to MIQ thresholds, ratios 

are computed. These are presented in Table 6. As evidenced from the previous tables, MIQ 

thresholds are consistently higher than those based on the MSQ. For one-person households, the 

MSQ threshold is approximately 69% of the MIQ threshold. The ratio is lowest for households 

composed of three people (61.3 %) and highest for six-person households (71.9%). Morisette and 

Poulin (1991) report ratios of the MIQ to MSQ thresholds that range from a low of 58 % for a 

one-person family to 64.5 % for a family with six members. In contrast to the SIPP ratios 

thresholds, the MIQ to MSQ ratios for Canadian families consistently increase as household 

(family) size increases. 

Table 6. Ratios of MSQ to MIQ Thresholds: Canada and U.S. 

 Canadac

 U.S.c

Household Sizeb 1988 SCF 1995 SIPP 

1 person 0.583 0.687 

2 persons 0.606 0.654 

3 persons 0.620 0.613 

4 persons 0.631 0.619 

5 persons 0.639 0.674 

6 persons 0.645 0.719 a Thresholds based on the intersection method. Final thresholds produced for population of reporters. b Thresholds for families were produced for Canada while those for households were produced for the U.S. c Source: Canadian Survey of Consumer Finances, Morisette and Poulin 1991. d Source: 1993 Survey of Income and Program Participation, and authors' own calculations. 

After comparing the MIQ and MSQ thresholds to those found in earlier work, we 

conclude that question wording matters as well as does context. Requesting information about 

income before -vs. after-tax can have an effect on the estimates and question specificity. It is not 
39

surprising that asking about spending for barely adequate commodities would result in lower 

thresholds than when less restrictive question wording, like for minimum income, is asked. 

Rather than the MSQ being considered as providing just a different question wording for the 

MIQ, minimum spending and minimum income are likely to be different concepts as are actual 

income and total expenditures. Both are related, but the underlying concepts differ. With regard 

to context, when questions appear in an income survey, like the SIPP, or an expenditure survey, 

like the CE, responses are likely to be conditioned regarding the focus of the survey instrument.

4.4 Equivalence Scales 

Comparing the implicit equivalence scales from the MIQ and MSQ thresholds with 

others allows us to evaluate the set with regard to how households of varying sizes and 

compositions account for their needs. Equivalence scales are generally presented as currency 

amounts, or ratios of amounts, needed by families or households of different size and/or 

structure.23 In the MIQ case, income amounts for different family or household types are used 

and in the MSQ case, spending amounts are used. 

To understand how equivalence scales are interpreted, an example based on the scale 

used to produce the relative thresholds presented in Table 4 is considered. For example, a oneperson household would need one unit of income or spending to maintain a given level of living 

compared to a two-person family composed of two adults who would need 1.7 times as much as 

a single adult, and a three-person family with two adults and one child who would need 2.2 times 

as much as a single adult. All equivalence scales for this study are computed relative to the 

thresholds for one-person households. 

 

23 Currency units are usually used for developed countries. However, for developing countries, quantity units could 

serve as the basis of the scale. 
40

Table 7 presents the equivalence scales implicit in the thresholds using data from the 

1995 SIPP and compares them to earlier studies (Colasanto et al. 1984; Danziger et al. 1984; 

Garner and De Vos 1995) using the intersection approach and U.S. Also presented are the scales 

implicit in the CE MIQ SIPP thresholds, official poverty thresholds, NAS-3 thresholds, and 

relative thresholds. Scales are presented for one person through six people except for those 

strictly based on the CE, where the top group includes six or more people. The top group for the 

CE MIQ SIPP scale is composed of only six people. For several of the scales, results are 

presented by age of the reference person, less than age 65 years or greater than or equal to 65 

years, when the household is composed of one or two people. 
Table 7. Average Equivalence Scales by Household Size Compared to Earlier U.S. Studies Using MIQ Th

 This Study: 

Selected Sample 

Earlier Studies 

Using MIQ Additional This Study 

SIPP 

MIQ 

SIPP 

MSQ 

CE 

MIQ 

SIPPb

1982 

CE 

MIQc

1982 CE 

MIQ 

with 

Expensec

1979 

MIQ 

ISDPd

1981 

MIQ 

BNSe Official 

NAS3 Relative

1 person 1.00 1.00 1.00 1.00 1.00 . 1.00 1.00 1.00 1.00  1 person, >= 65a 0.70 0.81 0.78 0.86 0.76 0.65 0.97 1.00 1.00  1 person, < 65a 1.17 1.10 1.12 1.06 1.10 1.00 1.02 1.00 1.00 2 people 1.32 1.26 1.43 1.44 1.31 . 1.35 1.29 1.42 1.68  2 people, >= 65a 1.01 1.14 1.32 1.37 1.25 0.80 1.21 1.41 1.70  2 people, < 65a 1.44 1.30 1.48 1.47 1.34 1.25 1.32 1.42 1.68 3 people 1.54 1.37 1.65 1.73 1.91 1.40 1.61 1.56 2.00 2.26 4 people 1.76 1.59 1.78 1.96 1.89 1.54 1.82 2.00 2.26 2.78 5 people 1.67 1.64 1.83 2.12 1.95 1.65 2.01 2.36 2.58 3.35 6 people 1.52 1.59 1.71 1.95 1.89 1.75 2.19 2.68 2.94 3.97 Approximate scale elasticity based on house-hold size 1 to 6 0.28 0.28 0.32 0.41 0.39 0.31 0.44 0.56 0.61 0.76 

a Age of reference person in householdb Based on SIPP MIQ sample characteristics, 1982 coefficients from the De Vos and Garner 1991 study with correction in prediction equation, and All Items CPI-U for 

1995 (Index value 152.4 with 1982-84=100).

c Garner and De Vos 1995 and unpublished results using sample selection correction; 'six persons' refer to households of six or more people. d Scales based on one person being male, households with two people with husband and wife, and households with more than three people including a husband and wife 

with children. Danziger et al. 1984. 

e Colansanto et al. 1984. fBased on one-half the median income per adult equivalent (adult equivalents based on 1.0 for first adult, 0.7 for each additional adult, and 0.5 for each child) in MIQ 

sample. Source: 1993 Survey of Income and Program Participation, authors' calculations. .
42
43

The approximate scale elasticity is calculated using the procedure described in Buhmann 

et al. (1988) and Atkinson et al. (1995) where the logarithm of the scale is a function of a 

constant and the logarithm of family size. Only household sizes of one to six people are 

included. Atkinson et al. (1995) have noted that this approximation summarizes well the relation 

between need and size, although alternative scales with the same elasticity may generate 

different results. Earlier researchers (e.g., Buhmann et al. 1988; Atkinson et al. 1995) have 

reported that equivalence scales based on subjective measures tend to reflect greater economies 

of scale than expert-based measures (e.g., OECD Social Indicators, U.S. Official Poverty 

Measure, NAS-3), and behavioral models. However, scales based on behavioral models are in 

the range of those based on subjective measures. Behavioral scales are derived from studies that 

aim to measure utility or welfare indirectly through revealed preferences of consumer spending 

constrained by some measure of income. 

Looking at household sizes of one to six people, the scale elasticity for the SIPP MIQ and 

MSQ measures in Table 7 are both 0.28.24 When the CE MIQ coefficients are applied to the 

SIPP MIQ sample, the elasticity is slightly higher (0.32) reflecting smaller economies of scale 

than the two SIPP measures. The CE measures produce even greater elasticities (0.41 and 0.39). 

The scale elasticity based on the 1979 ISDP data (Danziger et al 1984) is the same as for the 

SIPP MSQ threshold (0.31). In contrast, the 1981 Wisconsin BNS-based thresholds (Colasanto et 

al. 1984) result in scale economies (0.44) more similar to those based on behavioral studies using 

multivariate analysis of household expenditures with data from the U.S. CE Survey. Reported 

elasticities from these studies range from 0.40 to 0.48 (see Merz et al. 1993; Phipps and Garner 

1994; Johnson and Garner 1994/95). However, scale elasticities as low as 0.23 have been 

 

24 If the elasticity is based on households composed of one to five people, the elasticities differ and reflect fewer 

economics of scale: 0.35 and .31 for MIQ and MSQ respectively. 
44

reported for the U.S. using econometric modeling (see Nelson 1993), as high as 0.66 for pricedependent scales (Johnson and Garner 1994/95), and even higher for other scales (e.g., Jorgensen 

and Slesnick 1987, Johnson 1998). 

Expert-based scales tend to reflect smaller economies of scale and thus greater scale 

elasticities. For example, the experimental NAS-3 scale has an equivalence scale elasticity of 

approximately 0.61. The official poverty thresholds reflect similar economies of scale 

(elasticity=0.56). The OECD Social Indicator-based scale results in a relatively large elasticity 

indicating the least economies of scale among those examined in this study (0.76). 

Equivalence scales for selected household types are presented in Table 8. As found for 

the different household sizes, the MIQ and MSQ thresholds result in equivalence scales that are 

rather flat compared to the expert scales. For example, the additional income or spending needed 

for one more child or person is less for the subjective measures than for the other scales 

presented in Table 8. 

Table 8. Average Equivalence Scales by Household Composition: 1995 

Household Type SIPP MIQ 

CE MIQ 

SIPP MSQ SIPPb Official NAS-3 Relativec

One adult, no children, head>=65a 0.60 0.74 0.70 0.95 1.00 1.00 

One adult, no children, head <65a 1.00 1.00 1.00 1.00 1.00 1.00 

Two adults, no children, head>=65a 0.86 1.03 1.17 1.19 1.41 1.70 

Two adults, no children head<65a 1.22 1.19 1.33 1.29 1.41 1.70 

Three adults, no children 1.18 1.14 1.59 1.52 2.16 2.40 

One adult, one child 1.25 1.17 1.24 1.31 1.51 1.50 

One adult, two children 1.27 1.22 1.34 1.48 1.79 2.00 

Two adults, one child 1.46 1.37 1.39 1.55 1.90 2.20 

Two adults, two children 1.60 1.52 1.53 1.95 2.16 2.70 

Two adults, three children 1.55 1.56 1.61 2.29 2.40 3.20 

a

Age of reference person

b

Based on SIPP MIQ sample characteristics, 1982 coefficients from the De Vos and Garner 1991 study with correction in prediction equation. 

and All Items CPI-U for 1995 (Index value 152.4 with 1982-84=100). 

c

Based on one-half the median income per adult equivalent (adult equivalents based on 1.0 for first adult, 0.7 for each additional adult, and 0.5 for 

each child) in MIQ sample. 

Source: 1993 Survey of Income and Program Participation, authors' calculations.
45

Expert scales are likely more appropriate for situations in which commodities with few 

economies of scale, for example, food, account for a relatively larger share of total spending. 

Such scales are also more appropriate in situations in which intra-household allocations do not 

reflect the preferences of policy makers.25 Expert scales are less likely to assume that a 

household makes substitutions across commodities. For example, underlying the U.S. official 

poverty thresholds is the assumption that families spend about one-third of their incomes on 

food. In contrast, behavioral and subjective scales reflect households’ substituting across 

commodities explicitly or in their responses to their personal assessments of how to best meet the 

needs of their families. For example, the respondent to the MSQ could determine that his or her 

household could get by spending less on food and clothing in order to spend more on housing. 

The respondent would accomplish this for his or her household by eating at home more or 

sharing clothing among children. Alternatively, less expensive alternatives for some 

commodities available to individual families free up resources that could be spent on other 

commodities. 

The question, “Which type of scale is more appropriate?” remains to be answered.26 It 

may depend on the overall level of well-being in a given society or the location of the threshold 

along the income distribution. When one’s focus is on the least well-off in society, the expert 

scale might be more useful. When one’s focus is the entire distribution, household survey based 

scales, behavioral and subjective, might be more appropriate. 

Regardless of choice of equivalence scale, results from analyses of household survey data 

through the use of behavioral and subjective models suggest that perhaps it is time to re-examine 

 

25 See Lind (2001) for a discussion of differences in the assumptions of social planners versus households in the 

derivation of equivalence scales. 

26 See Bradbury (1989) for a discussion of the use of subjective measures in the production of equivalence scales 

and potential biases for scales used for public policy. 
46

the underlying assumptions of the OECD and similar expert scales.27 Apart from providing for 

commodities with smaller economies of scale, expert scales can overcompensate for the cost of 

commodities such as shelter and utilities for households and families in highly developed 

countries. 

4.5 Percentages of Households with Incomes Below Various Thresholds 

Tables 9 and 10 include percentages of the household population with actual before-tax 

incomes below various thresholds. The rates for the three SIPP minimum thresholds are 

presented and compared to those based on the official poverty thresholds, the NAS 3-parameter 

thresholds, and relative threshold. Table 9 includes the rates by different household sizes while 

Table 10 presents rates for households with and without children. The percentages of households 

with incomes below a given threshold are highest, on average, when based on the MIQ, either 

the SIPP MIQ or CE MIQ SIPP-. Of all households, 27.9 percent have total household incomes 

below the CE MIQ SIPP thresholds followed by the SIPP MIQ, 27.8 percent. The next highest 

percentage is for the relative poverty threshold (17.6 percent), followed by the SIPP MSQ 

threshold (15.0 percent), and the NAS 3-parameter threshold (10.0 percent). The measure using 

official poverty thresholds results in rates that are the lowest (9.8% overall).28

For all household size groups but two, the rates based on the official poverty line and 

those based on the NAS 3-parameter thresholds are lower than for the other definitions of wellbeing. The exceptions are for households composed of five and six people. For five people, the 

lowest rates result when the SIPP MSQ thresholds are used with 9.2 percent of households 

having before-tax money income below the corresponding household-size threshold. For these 

 

27 See Van den Bosch et al (1993) for more on this issue. 

28 The official poverty rate, based on the Current Population Survey, for 1995 was 13.8 percent. 
47

Table 9. Percentage of Households with Income Below Various Thresholds by Household 

 Size: 1995 (SIPP Sample Household Population Weighted)

Household Size SIPP MIQ SIPP MSQ 

CE MIQ 

SIPPb Official NAS-3 Relativec

1 person 40.0 25.1 38.9 14.6 13.4 18.4 

 1 person, >= 65a 38.3 30.3 44.3 16.1 14.7 23.5 

 1 person, < 65a 41.0 22.4 35.8 13.8 12.7 15.5 

2 people 21.5 11.0 24.8 6.3 6.8 12.4 

 2 people, >= 65a 18.1 9.9 29.2 4.9 6.7 14.4 

 2 people, < 65a 22.8 11.5 23.0 6.8 6.8 11.6 

3 people 26.2 13.4 24.2 6.2 8.7 15.9 

4 people 23.8 11.6 21.0 8.4 9.0 18.8 

5 people 23.4 9.2 25.1 12.4 11.4 26.6 

6 people or more 24.5 14.6 27.1 22.2 20.7 41.0 

 

 Total 27.8 15.0 27.9 9.8 10.0 17.6 a

Age of reference person

b

Based on SIPP MIQ sample characteristics, 1982 coefficients from the De Vos and Garner 1991 study with correction in prediction equation, 

and All Items CPI-U for 1995 (Index value 152.4 with 1982-84=100).

c

Based on one-half the median income per adult equivalent (adult equivalents based on 1.0 for first adult, 0.7 for each additional adult, and 0.5 for 

each child) in MIQ sample

Source: 1993 Survey of Income and Program Participation, authors' calculations.

same measures, rates are also the lowest for households composed of six people (14.6 of all 

households are in this group). 

The rates in Table 10 distinguish between households with and without children 

specifically, and distinguish smaller households according to the age of the reference person. 

When elderly and non-elderly one- and two-person households are compared by age, the elderly 

are worse off than the non-elderly when rates are based on the official measure of poverty, the 

NAS-3 and relative thresholds. The pattern is not consistent when the three subjective measures 

are used. The SIPP MIQ and CE MIQ SIPP result in relatively higher poverty rates for nonelderly singles than for elderly singles. Rates for non-elderly singles range from 41 percent to 
48

Table 10. Percentage of Households with Income Below Various Thresholds by Household 

Composition: 1995 (SIPP Sample Household Population Weighted) 

Household Type SIPP MIQ SIPP MSQ 

CE MIQ 

SIPPb Official NAS-3 Relativec

One adult, no children, head>=65 38.3 30.3 35.8 16.1 14.7 23.5 

One adult, no children, head <65 41.0 22.4 44.3 13.8 12.7 15.5 

Two adults, no children, head>=65 18.1 9.9 18.1 4.9 6.7 14.4 

Two adults, no children head<65 17.3 8.0 29.2 4.5 4.2 8.4 

Three adults, no children 12.9 5.5 16.3 3.1 5.3 10.9 

One adult, one child 65.4 41.6 60.8 23.9 26.2 36.3 

One adult, two children 75.3 48.5 70.4 25.0 31.3 47.0 

Two adults, one child 25.3 9.4 18.9 3.8 5.7 12.0 

Two adults, two children 24.7 10.5 18.3 6.1 6.8 16.1 

Two adults, three children 24.1 7.6 22.4 9.0 5.8 22.9 

a

Age of reference person 

b

Based on SIPP MIQ sample characteristics, 1982 coefficients from the De Vos and Garner 1991 study with correction in prediction equation,. 

and All Items CPI-U for 1995 (Index value 152.4 with 1982-84=100). 

c

Based on one-half the median income per adult equivalent (adult equivalents based on 1.0 for first adult, 0.7 for each additional adult, and 0.5 for 

each child) in MIQ sample 

Source: 1993 Survey of Income and Program Participation, authors' calculations. 

44.3 percent. Twenty-nine percent of non-elderly couples without children are below the CE 

MIQ SIPP threshold. 

When considering the relative economic position of households with children, Table 10 

shows that MIQ thresholds yield the highest rates of those produced. The highest rates are for 

single-parent households. Single parents with one or two children are the least well-off, 

regardless of the measure. The greatest need results when the SIPP MIQ and CE MIQ SIPP 

measures are used. Up to 75 percent of all single-parent families with two children did not have 

enough income to meet minimum needs as measures by the MIQ-income intersection approach. 

When the SIPP MSQ is used, 48.5 percent did not have enough income to meet the minimum 

spending needs estimated by the MSQ-income intersection for the population. The household is 

also the worse off when the expert-based thresholds are compared to household income. For 
49

example, about 25 percent of these households are below the official poverty line. A striking 

result from this table is that households with children are substantially worse off when the MIQ 

measures are used compared to other measures. Households most well-off, as distinguished by 

low rates, include those composed of three adults and no children. This result applies to all 

measures except the NAS-3. 

In reviewing the percentages of households below the thresholds produced for this study, 

several issues with regard to the MIQ and MSQ arise. The MIQ is designed to relate the current 

income needs of a household to its current income. The question asked is about income to make 

ends meet. It could be that making ends meet is assuming one’s current level of living and 

income. Although originally designed to allow household respondents to adjust their minimum 

incomes over time, most often the question is not answered again and the resulting threshold is 

based on a single point in time when the respondent answered. Consequently, the minimum 

income-based thresholds have been referred to as ‘income sufficiency’ thresholds (e.g., Garner 

and De Vos 1995). In contrast, the MSQ requests that household respondents think of a 

minimum spending amount for basic necessities and then defines those. Thus, it could be that 

respondents have some basic level or standard of need in mind when answering the MSQ rather 

than the MIQ. 

 With regard to the MIQ in the CE and SIPP, results presented in this study reveal that the 

MIQ thresholds are not only consistent but also similar for the SIPP population. This suggests 

that respondents using different survey instruments (i.e., an expenditure survey or an income 

survey) during different time periods interpret the question similarly. We conclude that the MIQ 

is less sensitive to context than we expected a priori. 

5.0 Discussion and Conclusions
50

This research supports previous research in finding that asking respondents about their 

minimum income and spending needs results in significantly higher thresholds than those based 

on expert-based approaches. The minimum income- and spending- based thresholds produced in 

this study are higher than the U.S. official poverty thresholds and an NAS-based measure except 

in one instance. Further, equivalence scales calculated from personal assessment-based 

measures like the MIQ and MSQ result in greater economies of scale than the NAS-based scales, 

OECD scales, and those implicit in the official poverty measure. However, they are similar to 

scales derived from behavioral models of household expenditure survey data. This finding 

suggests that respondents may be better able to make tradeoffs to meet their needs, as reflected in 

their perceptions as well as actual expenditure behavior, than is expected or desired by experts or 

social policy makers. Another reason for the flatter equivalence scales derived from personal 

assessment and behavioral models is that respondents consider children to be consumption 

goods. Thus, the addition of a child or children does not add that much more to the needs of the 

household. The household instead spends more on the children and less on other previously 

purchased commodities.29 No attempt is made to suggest which approach or measure is better. 

Each has a place in policy and economic analysis. 

Another conclusion of this study is that question wording matters for personal assessment 

questions. The minimum income question results in higher thresholds than the minimum 

spending question. This is consistent with the findings of others (e.g., Morisette and Poulin 1991) 

that have asked differently worded questions of similar samples of households. The minimum 

income questions asked in the SIPP and CE allow respondents to define what the phrase ‘to make 

ends meet’ means. It is likely that a larger bundle of commodities is the reference for 

 

29 This explanation has been suggested by various researchers (e.g., Vaughan 2002). 
51

respondents, as opposed to the specific list provided in the minimum spending question. Thus, 

higher thresholds would be expected with the MIQ. 

Context (i.e., the type of survey in which the questions are asked) appears to play less of 

a role than we thought originally. It was hypothesized that a minimum income-based threshold 

using data collected in the U.S. Consumer Expenditure Survey (CE) would result in thresholds 

that were closer in value to the MSQ-based thresholds. Since respondents to the CE had been 

asked detailed expenditure questions up to four times over 12 months, they would be more 

focused on spending, both actual spent and adjustments that would be possible to reach a 

minimum. However, the resulting estimated CE-based thresholds, equivalence scales, and 

“poverty” rates are closer to the SIPP MIQ thresholds than they are to the MSQ thresholds. The 

conclusion is that consistent and similar minimum income questions result in consistent results 

over time.30 

An examination of the CE MIQ thresholds (from Garner and De Vos 1995) yields an 

alternative interpretation of the effect of survey context and prior questions in the survey 

instrument. CE respondents were asked detailed expenditure questions several times, as noted 

previously, prior to being asked the MIQ. SIPP respondents were asked detailed income 

questions up to eight times in the survey and details about certain expenditures in prior modules. 

Also, in the SIPP, prior to being asked the MIQ and MSQ, respondents were asked about their 

ability to meet specific expenses. We suggest that the greater the detail asked concerning actual 

expenditures and the less specific the MIQ question wording, the higher the amount reported. 

This explanation is consistent with the work of researchers examining the relationship between 

 

30 Citro and Michael (1995) stated the following in their review and evaluation of subjective-based poverty 

thresholds and questions upon which these thresholds are based. “If such responses were available over time on a 

consistent basis, however, they could provide useful information with which to evaluate the official methodology for 

updating the thresholds” (p. 136-137. 
52

asking about total expenditures using one question versus asking about detailed expenditures 

with multiple questions (e.g., Winter 2002). The greater the detail asked, the higher the 

expenditure reported. The same relationship has been observed for income. More detailed data 

collected in the SIPP has consistently resulted in higher values than less detail using the Current 

Population Survey (e.g., Coder and Scoon-Rogers 1996). 

Cognitive work suggests that improvements in question wording are possible. In 

concluding the cognitive work for the SIPP minimum income and spending questions, Stinson 

(1997a) suggests that perhaps the questions as worded place too great a cognitive burden on the 

respondent. She recommended questions asking the respondent what he or she considers a basic 

need and then asking further questions about each of these. One person’s necessity could be 

another’s luxury. For example, do the aged who lived through the Great Depression and World 

War II have a need for fewer necessities and are they better able to meet their basic needs than 

younger generations? Cognitively, with regard to the spending question, each commodity group 

could be asked about in turn, for example, one would ask, “In your opinion, how much would 

you have to SPEND each year in order to provide barely adequate food for your family?” This 

would be followed by questions for shelter, clothing, and other necessities. In the end, the analyst 

would add the parts together. 

While the results presented in this study shed light on the issues raised above, additional 

modeling work would be fruitful. The model used in this paper was selected to maintain 

consistency with the earlier work of De Vos and Garner (1991), but possible improvements are 

clear. Some could only be made with additional questions being asked, while others can be made 

with the data that are currently available in the SIPP. For example, if data were available on total 

expenditures in the SIPP, a different MSQ model than the one used for this study could better 
53

explain the variation in responses. Rather than intersecting actual income with income for 

minimum spending, intersecting actual total spending might be more appropriate. The resulting 

new MSQ thresholds would reflect spending on some basic bundle of commodities rather than 

on income needs per se. 

Other model improvements would take account of the rich detail of information available 

in the SIPP that was not available to earlier researchers. For example, data were collected in the 

SIPP Basic Needs Module in 1995 to reflect a household’s ability to meet expenses for specific 

commodities. These data could be used as explanatory variables in both the MIQ and MSQ 

equations. Other questions refer to a household’s ability to meet expenses and whether the 

household has been evicted from the home or apartment for not paying rent or mortgage. 

Questions that are most related to basic needs, as defined in the minimum spending question, 

would be most useful for the MSQ model. Fixed expenditures have been reported to be 

important in models of minimum income (e.g., De Vos and Garner 1991), as well as particular 

expenditures (Garner and De Vos 1995). In addition, Dutch analysis reveals that households 

who have recently suffered a considerable decrease in their incomes report significantly higher 

minimum incomes than do household with stable incomes (De Vos and Garner 1991). Panel 

data from the SIPP would be ideal in modeling this situation. 

Although marital status of female-headed households is included in the model, 

information about the length of time since widowhood or divorce/separation began might help 

explain the negative coefficient on these variables. The longer the time period, the more likely 

the household is able to adjust spending to meet their needs. 

To better understand the life circumstances of the aged (those age 65 years or older), 

additional information would be useful. For example, a health status variable that reflects more 
54

than work disability could add explanatory power to the model. The SIPP includes general 

health status questions as well as those specifically focusing on activities of daily living. Such 

variables could inform the discussion concerning the additional demands of poorer health on 

minimum economic needs. 

Differences between the aged and non-aged might also be better explained by including 

variables that indicate whether household own their homes and their monthly out-of-pocket 

expenses, for example, for mortgages, property taxes, house insurance, and basic maintenance 

and repairs. 

Other refinements to this work involve taking account of sample attrition and selectivity 

issues in the SIPP. Preliminary attempts to correct for sample selection, not reported here, were 

not sufficient to explain observed patterns. It is important to deal with these issues as differential 

sample attrition from the SIPP has been documented. People leaving the sample have been 

shown to be more likely to rent rather than own their homes, live in large metropolitan areas, 

have very low income and few assets, be Black or Hispanic, be between the ages of 15 and 24, 

and never married (U.S. Census Bureau 1998; McArthur and Short 1985; Jabine, King, and 

Petroni 1990 as reported in Citro and Michael 1995). Further, some welfare participants are more 

likely to remain in the sample over the panels (Tin 1996). 

Another recommendation would be to use the term “family” and “household” more 

carefully in administering the MIQ and MSQ in the SIPP or similar surveys. The SIPP questions 

are embedded in a module about “household” well-being but they specifically refer to “family” 

situations. It is difficult to predict how the respondents interpreted these questions when there 

were multiple families in a household. More attention needs to be paid to this particular issue. 
55

While much work remains, nevertheless, the findings presented here support those of 

Morisette and Poulin (1991) that reports of minimum incomes can be “remarkably stable” over 

time, and more specific wording of questions result in important differences in results. We 

suggest that this is true if questions and the surveys in which the questions are contained are 

themselves stable over time. Given this, the task of measuring well-being could benefit greatly 

from this information. As recognized by others, for example Citro and Michael (1995), these 

measures could provide useful information with which to evaluate other measures of well-being. 
56

Appendix 

The anchor income concept can be explained as follows. First, the relationship in 

equation (1) is assumed to exist between actual Ymin and actual Y. Second, there exists an anchor 

income, Yanc, that underestimates actual income, Y, by a certain percentage. And third, reported 

minimally necessary income, Yminr, underestimates Ymin by the same percentage that Yanc

underestimates actual income, Y. Thus 

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ =

anc

r Y

Y Ymin Ymin

* . (1A) 

Substituting equation (1A) into (1) results in equation (2A) which is used for the MIQ 

intersection model: 

= + − + + + + + + ε anc n n ln(Y ) a (a )ln(Y ) ln(Y ) a z a z ... a z minr 0 1 1 (2A) 2 2 3 3

Anchor income, Yanc, is an estimate of the income the respondent has in mind when 

answering the MIQ. Anchor income is estimated as a weighted sum of income components, in 

this case, of household members’ and household non-member specific income. An assumption is 

made that only the income of the person accounting for the greatest share of total household 

income is fully taken into account. The weights of all other sources of income can be between 

zero and one, inclusive. The estimate of anchor income enters equation (2A) with a coefficient of 

one. See Homan 1988 and De Vos and Garner 1991 for the explicit model specification. 

In the SIPP, detailed income data are collected for each member of the household and for 

the household as a whole when income cannot be assigned to an individual such as for food 

stamps. When an individual does not provide the data, a proxy respondent is asked to provide 

the household member’s income. Member and household data are used to create member and 
57

household component incomes that are included in a regression model to estimate anchor income 

for each household. 

The estimated weights are produced from the regression for each of the following subcomponents of household income according to who receives the income: spouse, children, other 

household members, and food stamps received at the household level. The results of this 

estimation produce an anchor income in which the income of the spouse is fully taken into 

account; the incomes of children, other household members, and food stamps are not fully 

counted (see Appendix Table A1). When estimating equation (2A), the weights from the anchor 

income equation are fixed when the other variables (e.g., household composition, age, education) 

are added to the model. De Vos and Garner (1991) note that this procedure may not seem 

satisfactory from a methodological perspective, yet this approach is followed in this study since 

one of the study objectives is to compare to earlier work in which similar methods were used 

(i.e., De Vos and Garner 1991; Garner and De Vos 1995). 

 Appendix Table A1. The Estimated Coefficients of Anchor 

 Income

 SIPP MIQ 

weight 

a symptotic standard 

error (a..s.e.) 

Income of main 

breadwinner 1.000 

Income of spouse 1.000 0.038 

Income of children 0.513 0.047 

Income of other household 

members 0.581 0.042 

Food stamps 0.308 0.098 aFixed at 1. 

 *Significantly different from 1 (p = 0.05) based on t-test=(1-weight)/a.s.e. 

 Source: 1993 Survey of Income and Program Participation, authors' calculations. 

Empirically, when anchor income is included in the regression equation of minimum 

income, the impact of actual income on minimum income is obtained by adding one, the 

coefficient of anchor income, to the reporting income coefficient based on the estimation. 
58

The predicted threshold with anchor income accounted for in the MIQ equation would be 

estimated as in equation (3A). 

⎥

⎦

⎤ ⎢

⎣

⎡

−

+ + + =

1

0 2 2

2

a

a a z a z Y z z n n

n

... * ( ... ) exp (3A). 

 Appendix Table A2. Weighted Sample Means of Variables Included in 

 Regressions of Ln (Ymin) 

SIPP MIQ 

(n=6338) 

SIPP MSQ 

(M=6295) 

Mean Mean 

Total Income (Y) $42,530 $43,968 

Reported minimum (Ymin) $28,813 $21,034 

Ln (Ymin) 10.036 9.732 

Ln (Y) 10.317 10.328 

Ln (Yanc) 10.286 . 

Single, working 0.148 0.142 

Single, not working 0.114 0.104 

1 parent, working 0.265 0.270 

1 parent, not working 0.041 0.047 

2 people, 2 earners 0.132 0.131 

2 people, 0 earner 0.078 0.081 

>2 people, >2 earners 0.050 0.051 

>2 people, 2 earners 0.047 0.048 

>2 people, 1 earner 0.033 0.032 

>2 people, 0 earner 0.012 0.011 

2 othersa

 0.181 0.186 

3 othersa

 0.081 0.084 

4 or more othersa

 0.051 0.049 

Age of reference person 48.114 47.811 

Age2 of reference person 2600.350 2565.330 

Single >= 65b

 0.096 0.085 

2 people >= 65b

 0.106 0.107 

Oldest <6c

 0.074 0.072 

Oldest 6<12c

 0.088 0.097 

Oldest >=18c

 0.268 0.260 

Low education 0.078 0.072 

High education 0.497 0.496 

Black 0.121 0.120 

Work disability of reference person 0.049 0.049 

Female, married 0.081 0.083 

Female, widowed 0.095 0.088 

Female, divorced/separated 0.123 0.118 
59

 Appendix Table A2. CONTINUED 

SIPP MIQ 

(n=6338) 

SIPP MSQ 

(M=6295) 

Mean Mean 

Female, never married 0.084 0.083 

Northeast 0.200 0.201 

Midwest 0.242 0.249 

West 0.226 0.206 

City 0.393 0.387 

Rural 0.235 0.239 

 aNumber of others in the household other than the head and spouse. bAge of reference person in household cMaximum age of others in the household other than the head and spouse. 

 Source: 1993 Survey of Income and Program Participation, authors' calculations. 
60

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